ABSTRACT:

This report describes the Bertrand Creek update of the surface water quantity hydrologic model, TOPNET, which was originally developed as part of the WRIA1 Watershed Management Project. The model update has increased the spatial discretization of model elements, which has resulted in an improved representation of the spatial variability of precipitation, as well as land use and soil based parameters in both the U.S. and Canadian portions of the watershed. After updating the hydrologic model, we conducted a new calibration to the observed streamflow measurements at Bertrand Creek watershed outlet at Rathbone Road, which began in 2003. Nash-Sutcliffe efficiency statistics greater than E= 0.85 show that the updated model and calibration are a good representation of the hydrologic system, given the current drainage and groundwater pumping practices. Using a historic climate record of 60 years, the water balance presented illustrates the mean monthly relationships between precipitation, baseflow, soil storage, surface runoff, and irrigation demand. Exceedence probability flow results show that there is a low probability of sustaining both irrigation water use and instream flows without significant water management activities. The updated model developed in this work can be used to understand the potential effects of proposed management solutions with for scenario testing and sensitivity analysis in future work.

ABSTRACT:

Advanced Hydrology Climate Datasets, Scripts, and Analysis.
Testing HydroShare Collections...testing, testing.

ABSTRACT:

This is sample Matlab script for postprocessing of DHSVM bias and low flow corrected data using Integrated Scenarios Project CMIP5 climate forcing data to model future projected streamflow in the Skagit River Basin. Testing HydroShare Collections...testing, testing.

ABSTRACT:

The Nooksack River Basin HydroShare Observatory is designed to be used by individual researchers or research or classroom groups collaborating on hydrologic research in the transboundary Nooksack River Basin, State of Washington, USA and Province of British Columbia, Canada. The goal is to increase data and model discovery, computing, and publication functions provided by HydroShare. All resources available in this Collection are publicly available, and contributed by individual authors using HydroShare. The intent of this observatory is to play a cumulative role in maximizing the scientific return on individual investments of contributors conducting research in the Nooksack.

Observatories depend on collections of data and groups of people. In HydroShare, the Nooksack Observatory is a resource type called a 'Collection', and we anticipate the addition of 'Groups' when that specific software functionality is completed (expected for 2016). In short, a collection is an organization of HydroShare data, while a group is an organization of HydroShare users . Collections and Groups are designed to provide efficiency in research by enhancing individual capabilities to progress their research by sharing and using information with the group, and may be organized by geography, discipline, or research topic. If successful, this Observatory will help researchers avoid ‘reinventing the wheel’ and progressing individual and collaborative research studies using shared resources.

ABSTRACT:

The Thunder Creek HydroShare Observatory is designed to be used by individual researchers or research or classroom groups collaborating on hydrologic research in the Thunder Creek, Skagit River Basin, State of Washington, USA. The goal is to increase data and model discovery, computing, and publication functions provided by HydroShare. All resources available in this Collection are publicly available, and contributed by individual authors using HydroShare. The intent of this observatory is to play a cumulative role in maximizing the scientific return on individual investments of contributors conducting research in the Thunder Creek drainage.

Observatories depend on collections of data and groups of people. In HydroShare, the Nooksack Observatory is a resource type called a 'Collection', and we anticipate the addition of 'Groups' when that specific software functionality is completed (expected for 2016). In short, a collection is an organization of HydroShare data, while a group is an organization of HydroShare users . Collections and Groups are designed to provide efficiency in research by enhancing individual capabilities to progress their research by sharing and using information with the group, and may be organized by geography, discipline, or research topic. If successful, this Observatory will help researchers avoid ‘reinventing the wheel’ and progressing individual and collaborative research studies using shared resources.

ABSTRACT:

CEE 474 Hydraulics of Sediment Transport (3) A. HORNER-DEVINE
Introduction to sediment transport in steady flows with emphasis on physical principles governing the motion of sediment particles. Topics include sediment characteristics, initiation of particle motion, particle suspension, bedforms, streambed roughness analysis, sediment discharge formulae, and modeling of scour and deposition in rivers and channels. Prerequisite: CEE 347. Offered: Sp.

ABSTRACT:

On May 20, 2016, the UW Civil and Environmental Engineering Sediment Transport class (CEE 474) ventured to the far reaches of the Nooksack watershed in search of a watershed perspective. This movie trailer highlights a field trip to visit the locations of data used in a class projects to explore suspended sediment, turbidity and streamflow data collected by the USGS and Nooksack Indian Tribe. This work was supported by the UW Mountain to Sea Initiative, a collaborative effort of the College of Engineering, College of the Environment, and UW Tacoma.

ABSTRACT:

To Do: Climate Impacts Group

ABSTRACT:

This ArcGIS point shapefile contains 229 points at a 1/16 degree spatial resolution (~6km) used for gridded climate forcing datasets in the Chehalis Basin.

ABSTRACT:

This ArcGIS point shapefile contains 8 points selected from 229 grid cells at a 1/16 degree spatial resolution (~6km) used for gridded climate forcing datasets in the Chehalis Basin. The eight grid cells correspond to the grid cell closest to the centroid of each of the following basins: Wynochee, Satsop, Cloquallum, Black, South Fork Chehalis, Skookumchuck, Newaukum, and Elk. The meteorological forcing data from the 1/16 degree (~6 km) resolution is interpolated to finer DHSVM hydrologic model resolutions (150m) for the Chehalis Basin. .

ABSTRACT:

DHSVM configurations require spatial information for each point included as an input time series: elevation (m), latitude (m; UTM), longitude (m, UTM). The steps required to calculate this information are outlined in this HydroShare resource which includes sample scripts to export the point shapefile table from ArcGIS, calculate the UTM values, generate tables of latitude, longitude, and elevation, and convert the tables into DHSVM text format in a list for the model configuration file. The point shapefiles for the 8 Chehalis subbasins are also compressed and available in this resource.

To run the model, the input file lists each grid cell in the following format (example for 1 point in a list).
Station Name 1 = data_46.53125_-123.28125
North Coordinate 1 = 5153114.279000
East Coordinate 1 = 478431.528300
Elevation 1 = 290
Station File 1 = /civil/shared/ecohydrology/christina/forcs_dhsvm/data_46.53125_-123.28125

ABSTRACT:

This is a Digital Elevation Model of the continental United States giving the elevation average for 1/16 degree grid cells commonly used in hydrologic modeling and downscaling of climate data.
See Maurer, E.P., D.P. Lettenmaier, and N. J. Mantua, 2004, Variability and predictability of North American runoff, Water Resour. Res. 40(9), W09306 doi:10.1029/2003WR002789

ABSTRACT:

A new paradigm in hydrologic and earth system modeling is emerging where complex systems once coded in Fortran, C++ and cryptic scripts developed for research are being reconfigured in Open Source Python component based systems. In this workshop, we will introduce participants to one example of how this new type of modeling system, Landlab, can be linked to CUAHSI resources, such as the Water Data Center for time series data and HydroShare to run models online, access model inputs, and publish outputs for research collaboration and public dissemination.

Participants will learn how Landlab, a Python-based modeling environment, allows building numerical landscape models of earth surface dynamics such as geomorphology, hydrology, ecohydrology, glaciology, and stratigraphy, and a rapidly expanding network of open source collaborators.

ABSTRACT:

DHSVM was configured to cover the Upper and Lower Chehalis basins (Hydrologic Unit Codes, or HUCs 17100103 and 17100104). These do not include areas draining to the estuary downstream of Aberdeen. The area extends into the Olympic National Forest to the North, the Willapa Hills to the South, and Onalaska to the East with streamflow output corresponding to locations of interest draining 55 sub-basin areas. In DHSVM, the watershed is subdivided into a uniform square grid of cells, or model elements, with spatial resolution generally ranging between 10 m and 150 m; for this study we use a resolution of 150 m. The spatial distribution of the soil and vegetation characteristics of the watershed are captured at the scale of the 150 m Digital Elevation Model (DEM) used as a primary input to DHSVM. As discussed below, some features of the stream network were difficult to resolve, even at this resolution, specifically due to (1) inconsistencies in the average downstream directions when elevations were averaged to a 150 m grid, and (2) the close vicinity of headwater gridcells at sub-watershed boundaries. Terrain analysis methods for digital streamflow network and watershed delineation (available in ArcGIS) were used to resolve the spatial distribution of elevation characteristics at the 150 m scale using flow pathways developed at the 30 m scale.

ABSTRACT:

to do

ABSTRACT:

This Landlab tutorial example using radiation and potential evapotranspiration (PET) components.

It uses DEM as input. Outputs are fields of: PET and solar radiation (incoming, net, and relative). Relative radiation is the ratio of shortwave radiation on a sloped surface to shortwave radiation on a flat surface for a given DOY and latitude. This ratio is used to scale net radiation on a flat surface across a watershed as well as to scale PET across the domain. The component offers four different ways of setting PET on flat surface. Options: PET for flat surface can be set using one of the four methods, presented with the same name used in the instantiation of the component.

Note: To use this tutorial, you should have Landlab (version 1.0) installed on your computer. To install Landlab, please follow the instructions @ https://landlab.github.io/#/#install

To run this tutorial, use the HydroShare 'Open With' function and select 'JupyterHub'. For instructions on how to run an interactive iPython notebook, click here: https://github.com/landlab/tutorials/blob/master/README.md
For more Landlab tutorials, click here: https://github.com/landlab/landlab/wiki/Tutorials

ABSTRACT:

all the files to run with 2015 plus nested Sauk and Sauk improved calibration.

plus code

ABSTRACT:

Context: This HydroShare resource is a prototype Help page. Since HydroShare is being designed to facilitate social, model and data organization, this resource is a snapshot in time of a Use Case for how HydroShare was used to advance science, education and outreach. Please use the comments below and let me know if this resource was useful to you as an example of how to use HydroShare to run a model.

Steps used to run a Landlab model in HydroShare at the CUAHSI 2016 Landlab Workshop:
1. Go to Collaborate on the HydroShare dashboard
2.'Ask to Join' the Landlab group.
3. Interactive step-- Owners of the Landlab group accepted requests to join the group.
4.Click on the Landlab Group, second tab 'Resources'
5.Scan list of search for Landlab CUAHSI Colloqium 2016 Workshop
6.You are now in what is called a 'collection resource' - scroll down to the Collection Contents and click on Thunder Creek Landlab Landslide Example
7.You are now in what is called a 'generic resource' which contain multiple files - all of Ronda's inputs a Jupyter notebook example. Click on Open With - a blue button on the upper right of the screen.
8.Select Open With Jupyter-HUB NCSA. This takes you to a landing page that is Python code for loading your landlab python notebook.
9.Skip down to the first code cell and run. The output will request your HydroShare user password. This is needed for security because you are running code from a server (currently a development server at USU, in a few days on the NCSA super computer at U of Illinois).
10.Run the next code block which results in generating a link to open your notebook from the HydroShare server.
11. Click on the hyperlink to your notebook to launch. landslide_driver.ipynb
12. Run the model.

ABSTRACT:

This is a User Story of how the 2016 National Water Center Innovators used HydroShare to organize their data, run their models, and publish their research fom the summer institute.

Steps for research team members to get started with the Group:
1. Go to Collaborate in the Dashboard
2. Ask to Join the National Water Center Innovators Group. The group owner will be emailed your request, and you will be emailed a confirmation that your invitation has been accepted.

Steps for project team leaders to get started with their team Collection:
3. Go to My Resources in the Dashboard
4. New Resource - Select Collection
5. Add details with each team member as an Author
6. Invite team members to add their resources to the Team Collection.
7. Go to settings for your resource, the lock icon in the top right, and share your resource by clicking on Group, select the NWC Innvoators Group.
Now your work can be added to Collections by Adnan (summer institute data ' owner').

Steps for team members to get started with their individual resources:
8. Go to My Resources in the Dashboard
9. Create new Resources -- Upload Data - see Help on Dashboard for details on Resource Types and Settings.
10. You have a HydroShare Profile! Fill it out! Click on the top right empty icon and it will take you to a profile form where you can put your avatar or headshot.
11. Work with your team to upload their data and populate your Hydroshare Collection. Consider naming structures, list of authors and consistent descriptions that will be useful for future work. Metadata is a gift to the future!

e.g. Template for Project Organization
All summer institute 2016 Keywords: NWC Innovators, flood modeling, inundation mapping, forecasting error, emergency response; e.g. FloodHippo project keywords: FloodHIPPO, DisasterZoo, geofencing
Author order options: Summer institute recommendation - use report author order for the Collection Resource. For individual resources, list by contribution, other options include alphabetical, or listing only single owners as author.
Abstract: Consider repeating parts of the Abstract from the Collection folder with additional details on each resource included. Instructions for one line to repeat in 2016 summer project and one line to repeat with project title. (This step may become obsolete depending on hierarchy viewing functions added to the HS).
Contributors: this should be any person or institute that has given time or resources that supported the project or resource; e.g. USACE, U of Alabama.
Credits: List NSF funding for NWC.

ABSTRACT:

The Lower Nooksack Water Budget provides an estimate of the land components of the water cycle in the Lower Nooksack Subbasin’s sixteen drainages, as they vary seasonally throughout the year. It is intended to provide a common body of factual information to support water resources professionals and their salmon recovery partners working with the WRIA 1 Joint Board, in water supply planning and instream flow negotiations. This Collection links to a list of Collections relating to each Chapter in the Lower Nooksack Water Budget report.

ABSTRACT:

This overview introduces and summarizes the Lower Nooksack Water Budget, full technical report, which can be accessed at the Water Resource Inventory Area 1 (WRIA 1) Watershed Management website document library (http://wria1project.whatcomcounty.org/Home/Water-Budget/97.aspx).

This resource is a subset of the LNWB Ch01 Public Processes Collection Resource.

ABSTRACT:

Overview:
Topnet-WM refers to the Water Management version of Topnet developed as a work product for the Utah State University WRIA 1 Watershed Management Project (Tarboton, 2007). This version of the model evolved from the Topnet Model developed in a collaboration between NIWA New Zealand and Utah State University (Bandaragoda et al., 2004; Ibbitt and Woods, 2004) that combines TOPMODEL concepts (Beven and Kirkby, 1979; Beven et al., 1995a) for the simulation of relatively small drainages combined with channel routing. This approach provides a modeling system that can be applied over large watersheds using smaller drainages within the large watershed as model elements.

In Topnet-WM, spatial variability is represented by subdividing the watershed domain into model elements at the scale of drainages. Within drainages, the modeling is essentially lumped but includes parameterization of some subgrid variability, notably (1) the wetness index, used to parameterize the variability of soil moisture, (2) a depletion curve, used to parameterize the variability of snow water equivalent, (3) the fraction of area that is irrigated, and (4) areas with artificial drainage. Surface runoff and baseflow can be designated as model outputs at multiple nodes within a drainage. The model may thus be classified as semi-distributed.

Topnet-WM includes many enhancements beyond the original Beven and Kirkby TOPMODEL, such as: (1) calculation of reference evapotranspiration using the ASCE standardized Penman-Monteith method (Allen et al., 2005; Jensen et al., 1990); (2) calculation of snowmelt using the Utah Energy Balance Snowmelt model (Tarboton et al., 1995a); (3) the partition of model elements into separate components representing irrigated and non-irrigated areas; (4) artificial drainage to represent the effect of ditch and tile drained areas on the runoff response; (5) the partition of the model elements into pervious and impervious areas to allow representation of urbanization; (6) options for the diversion and storage of water under different management options; and (7) components to calculate water use and implement water right rules.

The Lower Nooksack Water Budget will be estimated based on the distributed hydrologic model, Topnet-WM. The Lower Nooksack Water Budget included updating the data inputs and model calibration, which requires a thorough understanding of how the model represents physical hydrologic processes. In order to guide the development of model inputs and analysis of model outputs, the project team has edited and reviewed portions of the WRIA 1 Water Management Project Phase III Task 4.1 report (Tarboton, 2007) to include in the general description of the Topnet-WM model that follows. This chapter provides reference to the details of the model processes used by Topnet-WM to convert data inputs into model outputs.

This resource is a subset of the LNWB Ch02 Model Processes Collection Resource.

ABSTRACT:

Overview:
The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose:
This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data:
1. Standards for metadata and data formats
2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security)
3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants)
4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed)
5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive)
6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the LNWB Ch03 Data Processes Collection Resource.

ABSTRACT:

Overview:
The model of watershed hydrology and water management used for the Lower Nooksack Water Budget is Topnet-WM, developed for Water Resources Inventory Area 1 (WRIA 1) in an effort led by researchers from Utah State University, as reported in peer-reviewed publications (Bandaragoda et al., 2004; Ibbitt and Woods, 2004; Tarboton, 2007). The model has also been applied, at finer spatial resolution, to the Fishtrap Creek and Bertrand Creek watersheds (Bandaragoda, 2008; Bandaragoda and Greenberg, 2009). The model processes of Topnet-WM are described in detail in Chapter 2 Model Processes. The daily meteorological variables required by Topnet-WM are precipitation, temperature (minimum and maximum), and wind speed.

Prior to the Lower Nooksack Water Budget project, WRIA 1 Topnet-WM used interpolated climate data (1946-2006) from 19 weather stations located within or near the WRIA 1 boundary. A significant component of the Lower Nooksack Water Budget Project was to update Topnet-WM to use the high resolution (1/8 lat/long degree; approximately one data point every 8 miles) gridded climate dataset that is updated and distributed, on an ongoing basis, by the University of Washington (UW) Land Surface Hydrology Research Group1 , following methods described in Maurer et. al. (2002) and Hamlet and Lettenmaier (2005). This dataset includes daily precipitation, wind speed, and daily maximum and minimum temperatures over the 1915 through 2011 water years (October 1 through September 30).

Figure 1 shows the distribution of the updated mean annual precipitation distribution derived from the Lower Nooksack Water Budget Topnet-WM gridded climate data for the 172 drainages (black dots) in WRIA 1. The lowest annual precipitation values are around Lummi Island and Bellingham (31-38 inches per year) and the highest precipitation values are near Mount Baker (121-207 inches per year). The increase in annual precipitation follows a gradient of increase from the west coast of the watershed to the eastern mountains, reflecting the role of orographic uplift of moist oceanic air masses in generating precipitation in this region.

Purpose:
The purpose for updating climate data used for watershed model inputs is to use the most current and up to date datasets. For the Lower Nooksack Water Budget Topnet-WM model, this includes new Snotel stations, an additional 8 years of daily climate data, and a higher resolution data product, compared to the initially developed Topnet-WM (Tarboton, 2007), which was populated with climate data ending in 2004. Updated climate data helps build our knowledge of the watershed system, since we have more information about when and where water is input to the system as rain and/or snow.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

TOPNET Water Management Model Inputs : Climate and Precipitation data inputs.

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

ABSTRACT:

Matlab code to process Climate data.

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

ABSTRACT:

Example Topnet-WM model climate output text files

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

ABSTRACT:

Geodatabase of GIS files of grid points and drainage centroids for WRIA 1

This resource is a subset of the LNWB Ch04 Climate Data Collection Resource.

ABSTRACT:

Overview:
Land cover mapping represents the coverage of vegetation, bare, wet and built surfaces (developed and natural surfaces) at a given point in time. The existing land cover map was developed by Whatcom County Planning and Development Services (PDS) during spring of 2012 for the Lower Nooksack Water Budget. The dataset represents ground conditions between 2006 and 2010. The project team created the existing condition land cover dataset by combining local and regional datasets to get the most accurate and current data for the U.S. and Canadian portions of WRIA 1. The development of the existing land cover map includes 14 land cover categories; each has a unique impact on the water balance. The agricultural land cover class was further classified into crop types.

Land cover and crop types influence evapotranspiration and infiltration, playing an important role in determining the watershed’s water balance. Land cover data provides information used to parameterize the water movement through the vegetation canopy and water demand of plant evapotranspiration in the estimation of the water budget by the hydrology model.

Land cover changes over time, as exemplified by comparing the existing and historic land cover data in WRIA 1, displayed in Figure 1 and Figure 2. Historic land cover mapping developed by Utah State University (Winkelaar, 2004) as part of the WRIA 1 Watershed Management Project was used to represent land cover/land use for the undepleted flow simulations. This work was done using a suite of studies and ancillary datasets, including turn of the century GLO maps and NRCS soils data. Methods and sources more thoroughly described in Mapping Methodology and Data Sources for Historic Conditions Landuse/ Landcover Within Water Resource Inventory Area 1 (WRIA1) Washington, U.S.A. The historic land cover map includes 10 land cover classes.

Purpose:
Within the Topnet-WM hydrologic model used to estimate the Lower Nooksack Water Budget, the local land cover type is used to parameterize the water movement through the vegetation canopy and water demand for plant evapotranspiration, as described in detail in Chapter 2: Water Budget Model. Water input to the canopy comes from rainfall, snowmelt, and irrigation. The process of some water retention by the canopy is known as interception. Potential evapotranspiration is first satisfied from the canopy interception storage. Water that passes through the canopy to the soil becomes input to the vadose zone soil storage. The vadose zone is the unsaturated soil region above the water table. Potential evapotranspiration not satisfied from the interception storage becomes potential evapotranspiration from the vadose zone soil storage. The model calculates crop evapotranspiration using the Penman-Monteith method. Irrigation requirements are calculated using potential crop evapotranspiration and irrigation efficiency. Land cover mapping also identifies impervious surfaces where water directly runs off, as well as lakes and wetlands where water is stored and evaporates.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Matlab code to convert raster, lookup tables, and shapefile data to area averaged parameter values.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

ABSTRACT:

This resource contains two files to recognize Geospatial organizations. Lulccharts_wria1.xlsx contains the spatial extents, land cover codes, tables, and charts for the WRIA1 region. Lulc_charts_lowerNookonly.xlsx contains only the spatial extents for the Lower Nooksack Subbasin of the WRIA1 region.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

ABSTRACT:

The GIS data contains Whatcom County, Washington Agricultural land cover analysis and land cover shapes.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

ABSTRACT:

wria1_lulc_water_budget.mdb is an ArcGIS geodatabase meant to generate estimate land cover model inputs, and all other layer files (.lyr) are meant to provide land-use classifications in the Whatcom County, Washington region.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

ABSTRACT:

Overview:
Water, in its many forms is one of Whatcom County’s signature features from snow-capped mountains,to our rainy climate, salmon-bearing streams, wetlands, lakes, marine waters, and marine shorelines. Five distinct hydrologic components control the storage and movement of water through the canopy and soils: canopy interception store (green trees), upper soil zone (vadose zone, brown soil fill) store, groundwater saturated zone (gray soil fill), channel flow (blue), and artificial drainage (blue line from agriculture to channel). Surface water inputs from direct precipitation, throughfall through the vegetation canopy, and irrigation are taken as input to the unsaturated, or vadose zone soil store. The unsaturated portion of the upper soil layer (brown), or vadose zone, is shown with recharge water (blue downward line) infiltrating the surface layer of soils, draining through the unsaturated zone (brown), to recharge the saturated zone (gray). The thickness of the vadose zone changes as the water table level (hashed gray and brown interface) shifts up and down, depending on the water held in the saturated zone. Based on the input and storage in the vadose zone, recharge to groundwater (gray, saturated zone) and surface water runoff is calculated. The vadose zone soil store is decreased by artificial drainage, representing ditch and tile drains that remove water directly from the vadose zone soil store to channels. The vadose zone soil store calculation also accounts for potential upwelling from groundwater where the water table is shallow. The groundwater saturated zone calculations account for recharge, upwelling and groundwater pumping and produce baseflow as an output. In the Lower Nooksack Water Budget, baseflow is defined as the outflow from the saturated zone and referred to as groundwater contribution; and baseflow and surface runoff are combined to calculate channel flow.

Purpose:
The baseflow in streams is supported by the gradual drainage of groundwater in shallow aquifer systems. The rate of this drainage depends on the amount of water stored in shallow aquifers (depth to water table) and the hydraulic properties of the aquifer, specifically the lateral hydraulic conductivity, or its depth integral, transmissivity. The amount of water stored depends on recharge, the vertical movement of water through unsaturated soils from the surface into the shallow groundwater. The rate of recharge is determined by the supply of water above. This is a function of whether surface water input is retained in the soil zone where it is taken up by plant roots and becomes evapotranspiration, or whether it infiltrates beyond the root zone and percolates to aquifers. These processes depend on the properties of the soils, such as porosity, field capacity, and hydraulic conductivity. The representation of the hydrologic processes of recharge and drainage to baseflow on a drainage scale is done using estimates based on measured data at point locations, as well as soil texture information. As more data is collected, information about subsurface processes can be incorporated into the model representation.

For the Lower Nooksack Water Budget soils parameters, soils data was compiled from both local and federal datasets. Using data available from the Natural Resource Conversation Service (NRCS – formerly the Soil Conservation Service) soils databases (NRCS; SSURGO and STATSGO (www.soilsdatamart.gov)), we have used estimates of averaged soils parameter values over each drainage area as data inputs for the hydrology model compiled in previous work (Tarboton, 2007). These soil parameters include plant available soil moisture, soil depth, hydraulic conductivity, and wetting front suction. Earlier calibrations of Topnet-WM showed that the most sensitive and therefore important soil parameters controlling baseflow movement are saturated soil store sensitivity (f) and soil profile lateral conductivity or transmissivity (To). The Lower Nooksack transmissivity parameters were derived from aquifer hydraulic conductivity values for specific wells, completed within shallow near surface aquifers, as described in the U.S. Geological Survey (USGS) Lynden-Everson-Nooksack-Sumas (LENS) Study (Cox and Kahle, 1999). Although the variability in well data is high given the heterogeneity of glacial and alluvial deposits, interpolating available well data to derive drainage average values captures the drainage level heterogeneity. Here changes in average depth to water table described in the Department of Ecology Study, Nooksack Watershed Surficial Aquifer Characterization (Tooley and Erickson, 1996), were used. Water movement through the surficial aquifer is assumed to decrease exponentially as the depth to the water table increases based on the Topmodel algorithm (Beven, et al., 1995a).

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Soil data from the NRCS lower resolution State Soil Geographic (STATSGO) Database.

The SSURGO data are generally not available in uninhabited landscapes with dense vegetation, such as National Forest lands. SSURGO data are available everywhere in the Lower Nooksack Subbasin. Where SSURGO data are not available in WRIA 1, STATSGO soils datasets were merged to parameterize Topnet-WM (see Tarboton, 2007, this same data layer will be used in this Lower Nooksack Water Budget project. The soils data generally pertains to the upper 80 inches of surficial material, with data in WRIA 1 ranging from 0.03 inches to 2 feet. Soils parameterization in the 2012 work is derived using information from the soils database using data extraction and depth averaging of publicly available soils data accessible through the USDA-NRCS Soils Data Mart1 . A search for soils data for Canada was completed during earlier work (Tarboton, 2007), but adequate data available in electronic form was not found at that time

This resource is a subset of the LNWB Ch06 Soil Processes and Inputs Collection Resource.

ABSTRACT:

GIS raster grids of soils layers for WRIA 1 and Bertrand Creek and Fishtrap Creek spatial extent, including intermediate files.

This resource is a subset of the LNWB Ch06 Soil Processes and Inputs Collection Resource.

ABSTRACT:

Soil data from the NRCS high resolution Soil Survey Geographic (SSURGO) Database.

The SSURGO data are generally not available in uninhabited landscapes with dense vegetation, such as National Forest lands. SSURGO data are available everywhere in the Lower Nooksack Subbasin. Where SSURGO data are not available in WRIA 1, STATSGO soils datasets were merged to parameterize Topnet-WM (see Tarboton, 2007, this same data layer will be used in this Lower Nooksack Water Budget project. The soils data generally pertains to the upper 80 inches of surficial material, with data in WRIA 1 ranging from 0.03 inches to 2 feet. Soils parameterization in the 2012 work is derived using information from the soils database using data extraction and depth averaging of publicly available soils data accessible through the USDA-NRCS Soils Data Mart1 . A search for soils data for Canada was completed during earlier work (Tarboton, 2007), but adequate data available in electronic form was not found at that time

This resource is a subset of the LNWB Ch06 Soil Processes and Inputs Collection Resource.

ABSTRACT:

This resource contains parameter grids (Ascii files) and two Excel spreadsheets which are the Land Cover Model Parameter Lookup Tables (i.e., lulc_existing.xls and lulc_historic.xls). The lulcExisting.xls lookup table separates the monthly crop coefficients according to WRIA1 land cover class. lulcHistoric.xls contains some historic land cover classes that were not used within the 2012 Lower Nooksack Water Budget.

This resource is a subset of the LNWB Ch05 Land Cover Collection Resource.

ABSTRACT:

The Lower Nooksack Subbasin is comprised of a variety of land and water uses both agricultural and nonagricultural. The water uses, described in this chapter include municipal, industrial, residential, and commercial. The following analysis of nonagricultural water use is divided into three sections: municipal/industrial, residential, and commercial/industrial.

The utilities that serve municipal/industrial customers in the Lower Nooksack Subbasin include the City of Bellingham, the City of Everson, the City of Ferndale, the City of Lynden, and the PUD #1 of Whatcom County. For these utilities, large municipal user water system records were obtained for years 2007 through 2011. Averages for this five year period were used in the model for water use, return flows, and interbasin transfers. The average annual diversion by the large utilities municipalities in our analysis totals nearly 25 cfs, 82% of which are transferred out of the Nooksack basin. The majority of that water serves Cherry Point industries; a small amount serves out-of-basin irrigators.

Residential, or domestic, water use was estimated according to population. Population and per capita water use rates are the foundation for calculating an average daily residential water use for each drainage in the Lower Nooksack Subbasin. 2010 Census geospatial census data were used to calculate the population for each drainage. The 2010 population for the Lower Nooksack Subbasin was 46,204, up 8,345 since the 2000 census. A per capita demand rate of 88 gallons per capita per day was agreed upon by the WRIA 1 Joint Board Staff Team to use as input along with the new population numbers. Seasonal demand factors were also determined to distribute monthly water use such that maximum demand occurred in the summer months and minimum demand in the winter.

Businesses and other operations that are not supplied water by a municipality are either served by a smaller public water system or are self-supplied. For commercial users not within the service area of the municipalities listed above, water use was estimated based on average use rates agreed on in 2003 with the WRIA 1 Water Quantity Technical Team. Commercial use was based on the type of operation identified in the Whatcom County Assessor records.

This resource is a subset of the LNWB Ch08 Water Management - industrial, residential, and commercial water use Collection Resource.

ABSTRACT:

Overview:
Artificial drainage is used throughout WRIA 1 to aid in the flow of water on top of or through the soil, sometimes to slow it down, other times to direct it to a specific location at any given depth of the landscape. Some of these systems are critical to the farm operations that make Whatcom County one of Washington’s top agricultural producers, others help riverside and lowland communities alleviate the impacts of high flowing rivers and streams. In high precipitation events, increased numbers of flow pathways provided by artificial drains may increase the peak stormwater quantities and contribute to flood impacts. Some artificial drains may even offer opportunities to improve low instream flows in the late season.

Purpose:
The model feature that represents artificial drainage has been incorporated into WRIA 1 Topnet Water Management (Topnet-WM) because of the assumption that agricultural drainage installed during development of agriculture in WRIA 1 has altered the runoff processes to a large enough degree that these alterations should be part of the simulation. Calculating how the ditches and tiles influence the drainage of the soils was done based on a drainage coefficient from NRCS technical guides. The Lower Nooksack Water Budget project team used previously existing (2007 Topnet-WM model) compiled information, data and maps of the many ditches and tile drains that exist in the Lower Nooksack study area in order to develop the artificial drainage inputs for the 2012 work conducted on the Lower Nooksack Water Budget.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Correspondence between David Tarboton and Becky Peterson on drainage procedures, and two memos describing PhaseIII Task4 work.

Within the Rainfall-Runoff Transformation of Topnet-WM there are five subcomponents: canopy interception store, vadose zone soil store, groundwater saturated zone, channel flow, and artificial drainage. Surface water input to the canopy interception store comprises rainfall and snow as well as sprinkler irrigation. Throughfall is computed based upon the canopy interception capacity, surface water input, and water in canopy storage and is taken as input to the vadose zone soil store. Potential evapotranspiration not satisfied from the interception store becomes potential evapotranspiration from the vadose zone soil store. Drip irrigation is also an input to the vadose zone soil store. Based on the input and storage in the vadose zone soil storage, recharge to groundwater and surface runoff is calculated. The vadose zone soil storage is depleted for areas with artificial drainage, representing ditch and tile drains that remove water directly from the vadose zone soil storage to stream channels. The vadose zone soil store calculation also accounts for potential upwelling from groundwater where the water table is shallow. The groundwater saturated zone calculations account for recharge, upwelling and groundwater pumping and produce baseflow as an output. Baseflow and surface runoff from the vadose zone soil store are combined to calculate channel flow.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Geodatabase of Lower Nooksack and WRIA 1 ditches and tile drain areas.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Overview:
Streamflow is part of the dynamic hydrologic system that supports a range of water dependent activities in Whatcom County, including farming, fishing and recreation. The relationship between streamflow, groundwater recharge and groundwater discharge, precipitation, fish habitat and crop production is critical for understanding how best to manage water to meet those needs. Streamflow is the best characterized, and most easily measured, component of the dynamic hydrologic system, and as such, is the primary metric used in modeling the water budget. For example, facilitating development of water management options that improve streamflow in the late summer is one of the reasons for developing the Lower Nooksack Water Budget.

The Lower Nooksack Water Budget project included a review of available streamflow measurements made available since WRIA 1 Water Management Project Phase III Task 1 (2002). For the streamflow database update (Lower Nooksack Water Budget Project, Task 2), the project team compiled available information, data, and maps of the stream data collected in the Lower Nooksack study area, as well as WRIA 1 upstream boundary conditions, and updated the database of measured streamflow from 1999 to 2011. The updated streamflow database will be used to calibrate and validate the Topnet-WM hydrologic model, and to calculate water budgets for each of the Lower Nooksack drainages; data will be available in an ASCII format for all other drainages but will not be formally summarized.

The following section begins with the list of streamflow gages in WRIA 1, followed by an explanation of how the data were used for model inputs. This information was used along with other Lower Nooksack Water Budget technical components to calculate the hydrologic model outputs working with the WRIA 1 Joint Board’s existing hydrologic model and supporting technical tools.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Matlab code for processing stream flow data into model inputs.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

ABSTRACT:

Geodatabase of streamflow data.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

ABSTRACT:

Access Database of streamflow records.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

ABSTRACT:

Analysis and charts with boundary flow relationship development and data outputs.

This resource is a subset of the LNWB Ch10 Stream Flow Collection Resource.

ABSTRACT:

Overview:
The Lower Nooksack Water Budget is calculated using a numerical simulation model called Topnet-WM. This hydrologic model uses data distributed in space and time to determine the flow of water between various locations and points in time on a daily time step. The modeling of the watershed is limited by the representation of hydrologic processes built into the model, the spatial data used to parameterize the model, and the climate time series data which provides the daily water inputs to the model. To address the limits of our data and knowledge of the system, parameters are used to control the relationships among hydrologic processes and the data used to represent them. In model calibration, parameters are changed within a range of expected values so that the model representation results in modeled streamflow that closely matches observed streamflow. The calibration parameters used include: saturated soil store sensitivity, hydraulic conductivity, overland flow velocity, transmissivity, and impervious surface fraction. The saturated soil store sensitivity, or f parameter, is the most sensitive parameter in this model. It is a measure of the sensitivity of lateral groundwater flow to changes in groundwater level.

The process of model calibration is complex because of limitations in models, input and output data, mathematical structure of the models, and quantitative methods used to fit the model to the data, as well as imperfect knowledge of basin characteristics (Schaake, 2003). In a world of perfect understanding of hydrologic processes, perfect input data, and no scale discrepancy between modeled and measured data, it might be possible to avoid hydrologic model calibration. An important result from the National Weather Service Distributed Model Intercomparison Project (DMIP; Smith et al., 2004a) experiment was the acknowledgement that uncalibrated models do not have the benefit of accounting for the known biases in the rainfall archives over the calibration period. Only in the absence of precipitation and other data input biases, might uncalibrated models be able to outperform calibrated models (Reed et al., 2004). Errors in input data cannot be ignored (Gupta et al., 1998), and therefore model calibration cannot be avoided.

Past work by the Lower Nooksack Water Budget Project Team has examined ways to improve the use of streamflow data that are available within a watershed and that can be used for model calibration, especially to improve the model performance where streamflow data are not available (Bandaragoda et al., 2004; Bandaragoda, 2007; Bandaragoda and Greenberg, 2009; Bandaragoda, 2008; Bandaragoda and Nielson, 2011, Neilson et al., 2010, Tarboton, 2007). The primary calibration locations in this project focused on Fishtrap Creek, Bertrand Creek and Tenmile Creek, with verification at Nooksack River locations at Cedarville and Ferndale.

In hydrologic model calibration, streamflow prediction statistics can be used as a measure of model performance, but the calibration must also address issues relevant to understanding the heterogeneity of the hydrologic system and the unique locations that are modeled. Implemented carefully, automatic calibration techniques that employ multiple objectives and estimates of distributions of watershed parameters may be a step towards both improving models and understanding hydrologic processes.

As calibration is used to conduct diagnostic model analysis and interactive learning about watersheds, our understanding of how to best model the movement of water can increase, and lead to an improvement in our existing models. As the existing models develop, the reliance on calibration will decrease, development of new models will increase, and our predictions of streamflow will improve.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Matlab code for reading model outputs and plotting figures.

This resource is a subset of the LNWB Ch11 Model Calibration Collection Resource.

ABSTRACT:

Plotted figures for the Topnet-WM model calibration outputs.

This resource is a subset of the LNWB Ch11 Model Calibration Collection Resource.

ABSTRACT:

Overview:
The availability of updated climate data, streamflow data, updated water use estimates and the incorporation of the Topnet Water Management (Topnet-WM) components provides the opportunity to build watershed knowledge by better understanding the climate, watershed hydrology and water budget.

Purpose:
The Lower Nooksack Water Budget project analysis focused on the 16 drainages of the Lower Nooksack Subbasin and for each drainage considers precipitation, evapotranspiration, storage, streamflow and user withdrawals. Storage includes canopy storage, unsaturated soil storage, and subsurface storage. Total streamflow includes baseflow, surface runoff, and artificial drainage. User withdrawals include irrigation, dairy, municipal/industrial water supply, and residential and commercial water use served by small public water systems or private wells. The sum of user withdrawals for each drainage is partitioned into groundwater and surface water withdrawals. In addition to streamflow prediction at each drainage, streamflow is calculated at multiple locations of interest (nodes) within each drainage. Total streamflow at nodes is partitioned into surface runoff and baseflow.

Model calculations are conducted on a daily timestep from 1952-2011. In future work, further summaries of daily information and other various components can be done at multiple time scales (daily, monthly, seasonal, annual) and multiple spatial scales (WRIA 1, Upper or Lower Nooksack, individual drainage).

This resource is a subset of the LNWB Ch12-13 Existing and Historic Model Outputs Collection Resource.

ABSTRACT:

This resource is a subset of the LNWB Ch11 Model Calibration Collection Resource.

ABSTRACT:

Overview:
Agricultural water use includes the irrigation of croplands and the water needs of dairy farms. Irrigated agriculture is an important component of the Lower Nooksack Water Budget due to the high demand for water during the relatively dry summer seasons. Along with out-of-basin industrial water use, irrigation is the largest use of water in the Lower Nooksack Subbasin and has a commensurate effect on the water budget. The highest demand for irrigation water occurs during the month of July when streamflows are low. Dairy water use equals the amount of water the cows drink plus the water used for washdown. The dairy demand is small relative to irrigation.

Since measured diversion or withdrawal records are not available, an estimate of crop irrigation requirements was developed using an empirically derived calculation of water demand. Drainage-wide irrigation estimates are based on the acres irrigated, type of crop, method of irrigation, and soil types.

Recent crop data from Washington Department of Agriculture and previous studies are summarized by surface water drainage area. The U.S. portion of the Lower Nooksack Subbasin contains approximately 54,044 acres of which 28,140 acres are irrigated or approximately 80% of the countywide irrigation total. Major crops include grass hay, pastureland, field corn, raspberries, and potatoes which comprise 97% of the crops grown in the Subbasin.

Within the 16 Lower Nooksack Subbasin drainages, the irrigated agriculture areas have the highest percent of total drainage area in Scott, Fourmile, Kamm, and Wiser Lake/Cougar Creek. Bertrand and Fishtrap Creek percentages include the irrigated area on both the US and Canadian sides. By volume alone, Bertrand and Fishtrap Creek drainages use the highest amount of irrigation water. Irrigation water use rates for each drainage can be found in Chapter 11 Existing Scenario, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated internally in the Topnet Water Management (Topnet-WM) model. The Topnet model uses the Penman Monteith method (adopted and standardized by the American Society of Civil Engineers (2005)) for calculating evapotranspiration of a reference crop [ETr] (short cut grass or tall alfalfa). Tall alfalfa is the reference crop integrated in the WRIA 1 Topnet model. The difference between potential and actual evapotranspiration is the amount of crop water demand that must be satisfied by irrigation.

Purpose:
This section defines the model inputs necessary for calculating the irrigation demand outputs. Results summarizing the irrigation demand on a monthly basis can be found in Chapter 11 Existing Conditions, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated, along with the other components of the water budget. The inputs defined in this chapter include crop type, number of acres, monthly crop coefficients, type of irrigation application (drip or spray) and irrigation efficiencies. Inputs were developed for the 16 Lower Nooksack Subbasin drainages only.

This resource is a subset of the LNWB Ch07 Water Management - Agricultural water use Collection Resource.

ABSTRACT:

The Lower Nooksack Water Budget provides an estimate of the water cycle components in the Lower Nooksack Subbasin’s 16 drainages, as they vary seasonally throughout the year. It is intended to provide a common body of factual information to support water resource professionals and their salmon recovery partners working with the WRIA 1 Joint Board on water supply planning and instream flow negotiations. This overview introduces and summarizes the Lower Nooksack Water Budget, full technical report, which can be accessed at the Water Resource Inventory Area 1 (WRIA 1) Watershed Management website document library (http://wria1project.whatcomcounty.org/Home/Water-Budget/97.aspx).

The work was produced in three stages, for three audiences: technical work, usability testing and public accessibility, with the review audience widening with each stage.

Stage 1 - WRIA 1 Joint Board Watershed Management Team and related Technical Teams reviewed and commented on technical products (data inputs, outputs, and model analysis).

Stage 2 - WRIA 1 Joint Board Management Team, Water Resource and Salmon Recovery Staff Teams and respective constituents provided feedback on the usability of the technical work.

Stage 3 - The draft final report, public overview and work products were then presented to the WRIA 1 Joint Board’s policy and technical bodies, their stakeholders and public audiences in a series of 2012 presentations at public meetings; with work also posted on the WRIA 1 Project public website.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
Topnet-WM refers to the Water Management version of Topnet developed as a work product for the Utah State University WRIA 1 Watershed Management Project (Tarboton, 2007). This version of the model evolved from the Topnet Model developed in a collaboration between NIWA New Zealand and Utah State University (Bandaragoda et al., 2004; Ibbitt and Woods, 2004) that combines TOPMODEL concepts (Beven and Kirkby, 1979; Beven et al., 1995a) for the simulation of relatively small drainages combined with channel routing. This approach provides a modeling system that can be applied over large watersheds using smaller drainages within the large watershed as model elements.

In Topnet-WM, spatial variability is represented by subdividing the watershed domain into model elements at the scale of drainages. Within drainages, the modeling is essentially lumped but includes parameterization of some subgrid variability, notably (1) the wetness index, used to parameterize the variability of soil moisture, (2) a depletion curve, used to parameterize the variability of snow water equivalent, (3) the fraction of area that is irrigated, and (4) areas with artificial drainage. Surface runoff and baseflow can be designated as model outputs at multiple nodes within a drainage. The model may thus be classified as semi-distributed.

Topnet-WM includes many enhancements beyond the original Beven and Kirkby TOPMODEL, such as: (1) calculation of reference evapotranspiration using the ASCE standardized Penman-Monteith method (Allen et al., 2005; Jensen et al., 1990); (2) calculation of snowmelt using the Utah Energy Balance Snowmelt model (Tarboton et al., 1995a); (3) the partition of model elements into separate components representing irrigated and non-irrigated areas; (4) artificial drainage to represent the effect of ditch and tile drained areas on the runoff response; (5) the partition of the model elements into pervious and impervious areas to allow representation of urbanization; (6) options for the diversion and storage of water under different management options; and (7) components to calculate water use and implement water right rules.

The Lower Nooksack Water Budget will be estimated based on the distributed hydrologic model, Topnet-WM. The Lower Nooksack Water Budget included updating the data inputs and model calibration, which requires a thorough understanding of how the model represents physical hydrologic processes. In order to guide the development of model inputs and analysis of model outputs, the project team has edited and reviewed portions of the WRIA 1 Water Management Project Phase III Task 4.1 report (Tarboton, 2007) to include in the general description of the Topnet-WM model that follows. This chapter provides reference to the details of the model processes used by Topnet-WM to convert data inputs into model outputs.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
The Lower Nooksack Water Budget Project involved assembling a wide range of existing data related to WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. This Data Management Plan provides an overview of the data sets, formats and collaboration environment that was used to develop the project. Use of a plan during development of the technical work products provided a forum for the data development and management to be conducted with transparent methods and processes. At project completion, the Data Management Plan provides an accessible archive of the data resources used and supporting information on the data storage, intended access, sharing and re-use guidelines.

One goal of the Lower Nooksack Water Budget project is to make this “usable technical information” as accessible as possible across technical, policy and general public users. The project data, analyses and documents will be made available through the WRIA 1 Watershed Management Project website http://wria1project.org. This information is intended for use by the WRIA 1 Joint Board and partners working to achieve the adopted goals and priorities of the WRIA 1 Watershed Management Plan.

Model outputs for the Lower Nooksack Water Budget are summarized by sub-watersheds (drainages) and point locations (nodes). In general, due to changes in land use over time and changes to available streamflow and climate data, the water budget for any watershed needs to be updated periodically. Further detailed information about data sources is provided in review packets developed for specific technical components including climate, streamflow and groundwater level, soils and land cover, and water use.

Purpose:
This project involves assembling a wide range of existing data related to the WRIA 1 and specifically the Lower Nooksack Subbasin, updating existing data sets and generating new data sets. Data will be used as input to various hydrologic, climatic and geomorphic components of the Topnet-Water Management (WM) model, but will also be available to support other modeling efforts in WRIA 1. Much of the data used as input to the Topnet model is publicly available and maintained by others, (i.e., USGS DEMs and streamflow data, SSURGO soils data, University of Washington gridded meteorological data). Pre-processing is performed to convert these existing data into a format that can be used as input to the Topnet model. Post-processing of Topnet model ASCII-text file outputs is subsequently combined with spatial data to generate GIS data that can be used to create maps and illustrations of the spatial distribution of water information. Other products generated during this project will include documentation of methods, input by WRIA 1 Joint Board Staff Team during review and comment periods, communication tools developed for public engagement and public comment on the project.

In order to maintain an organized system of developing and distributing data, Lower Nooksack Water Budget project collaborators should be familiar with standards for data management described in this document, and the following issues related to generating and distributing data:
1. Standards for metadata and data formats
2. Plans for short-term storage and data management (i.e., file formats, local storage and back up procedures and security)
3. Legal and ethical issues (i.e., intellectual property, confidentiality of study participants)
4. Access policies and provisions (i.e., how the data will be made available to others, any restrictions needed)
5. Provisions for long-term archiving and preservation (i.e., establishment of a new data archive or utilization of an existing archive)
6. Assigned data management responsibilities (i.e., persons responsible for ensuring data Management, monitoring compliance with the Data Management Plan)

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
Agricultural water use includes the irrigation of croplands and the water needs of dairy farms. Irrigated agriculture is an important component of the Lower Nooksack Water Budget due to the high demand for water during the relatively dry summer seasons. Along with out-of-basin industrial water use, irrigation is the largest use of water in the Lower Nooksack Subbasin and has a commensurate effect on the water budget. The highest demand for irrigation water occurs during the month of July when streamflows are low. Dairy water use equals the amount of water the cows drink plus the water used for washdown. The dairy demand is small relative to irrigation.

Since measured diversion or withdrawal records are not available, an estimate of crop irrigation requirements was developed using an empirically derived calculation of water demand. Drainage-wide irrigation estimates are based on the acres irrigated, type of crop, method of irrigation, and soil types.

Recent crop data from Washington Department of Agriculture and previous studies are summarized by surface water drainage area. The U.S. portion of the Lower Nooksack Subbasin contains approximately 54,044 acres of which 28,140 acres are irrigated or approximately 80% of the countywide irrigation total. Major crops include grass hay, pastureland, field corn, raspberries, and potatoes which comprise 97% of the crops grown in the Subbasin.

Within the 16 Lower Nooksack Subbasin drainages, the irrigated agriculture areas have the highest percent of total drainage area in Scott, Fourmile, Kamm, and Wiser Lake/Cougar Creek. Bertrand and Fishtrap Creek percentages include the irrigated area on both the US and Canadian sides. By volume alone, Bertrand and Fishtrap Creek drainages use the highest amount of irrigation water. Irrigation water use rates for each drainage can be found in Chapter 11 Existing Scenario, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated internally in the Topnet Water Management (Topnet-WM) model. The Topnet model uses the Penman Monteith method (adopted and standardized by the American Society of Civil Engineers (2005)) for calculating evapotranspiration of a reference crop [ETr] (short cut grass or tall alfalfa). Tall alfalfa is the reference crop integrated in the WRIA 1 Topnet model. The difference between potential and actual evapotranspiration is the amount of crop water demand that must be satisfied by irrigation.

Purpose:
This section defines the model inputs necessary for calculating the irrigation demand outputs. Results summarizing the irrigation demand on a monthly basis can be found in Chapter 11 Existing Conditions, Water Budget Model Outputs for Lower Nooksack Drainages.

Crop evapotranspiration is an integral component of the hydrologic cycle and is calculated, along with the other components of the water budget. The inputs defined in this chapter include crop type, number of acres, monthly crop coefficients, type of irrigation application (drip or spray) and irrigation efficiencies. Inputs were developed for the 16 Lower Nooksack Subbasin drainages only.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

The Lower Nooksack Subbasin is comprised of a variety of land and water uses both agricultural and nonagricultural. The water uses, described in this chapter include municipal, industrial, residential, and commercial. The following analysis of nonagricultural water use is divided into three sections: municipal/industrial, residential, and commercial/industrial.

The utilities that serve municipal/industrial customers in the Lower Nooksack Subbasin include the City of Bellingham, the City of Everson, the City of Ferndale, the City of Lynden, and the PUD #1 of Whatcom County. For these utilities, large municipal user water system records were obtained for years 2007 through 2011. Averages for this five year period were used in the model for water use, return flows, and interbasin transfers. The average annual diversion by the large utilities municipalities in our analysis totals nearly 25 cfs, 82% of which are transferred out of the Nooksack basin. The majority of that water serves Cherry Point industries; a small amount serves out-of-basin irrigators.

Residential, or domestic, water use was estimated according to population. Population and per capita water use rates are the foundation for calculating an average daily residential water use for each drainage in the Lower Nooksack Subbasin. 2010 Census geospatial census data were used to calculate the population for each drainage. The 2010 population for the Lower Nooksack Subbasin was 46,204, up 8,345 since the 2000 census. A per capita demand rate of 88 gallons per capita per day was agreed upon by the WRIA 1 Joint Board Staff Team to use as input along with the new population numbers. Seasonal demand factors were also determined to distribute monthly water use such that maximum demand occurred in the summer months and minimum demand in the winter.

Businesses and other operations that are not supplied water by a municipality are either served by a smaller public water system or are self-supplied. For commercial users not within the service area of the municipalities listed above, water use was estimated based on average use rates agreed on in 2003 with the WRIA 1 Water Quantity Technical Team. Commercial use was based on the type of operation identified in the Whatcom County Assessor records.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Overview:
The availability of updated climate data, streamflow data, updated water use estimates and the incorporation of the Topnet Water Management (Topnet-WM) components provides the opportunity to build watershed knowledge by better understanding the climate, watershed hydrology and water budget.

Purpose:
The Lower Nooksack Water Budget project analysis focused on the 16 drainages of the Lower Nooksack Subbasin and for each drainage considers precipitation, evapotranspiration, storage, streamflow and user withdrawals. Storage includes canopy storage, unsaturated soil storage, and subsurface storage. Total streamflow includes baseflow, surface runoff, and artificial drainage. User withdrawals include irrigation, dairy, municipal/industrial water supply, and residential and commercial water use served by small public water systems or private wells. The sum of user withdrawals for each drainage is partitioned into groundwater and surface water withdrawals. In addition to streamflow prediction at each drainage, streamflow is calculated at multiple locations of interest (nodes) within each drainage. Total streamflow at nodes is partitioned into surface runoff and baseflow.

Model calculations are conducted on a daily timestep from 1952-2011. In future work, further summaries of daily information and other various components can be done at multiple time scales (daily, monthly, seasonal, annual) and multiple spatial scales (WRIA 1, Upper or Lower Nooksack, individual drainage).

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

Compressed GIS files used in PhaseIIITask4 to create ditch and tile drain layers.

Spatial data files specifying the areas with ditch and tile drainage were developed in 2004 by the USDA, Natural Resources Conservation Service (NRCS; Resource Conservationist, John Gillies, and Agricultural Engineer, Dean Renner). Drainage coefficients from NRCS technical guides are shown in Table 2. The drained areas were estimated using a geographic information system (GIS) to identify hydric soils on agricultural land use areas, based on the assumption that if a hydric soil was cleared and in agricultural use, then there was a drainage system in place to manage water using sub-surface (tile) and surface (open ditch) practices. For the Lower Nooksack Water Budget these are areas used to determine the tile drained and ditch drained fraction of each drainage model element as well as to assign drainage coefficients.

This resource is a subset of the LNWB Ch09 Artificial Drainage Collection Resource.

ABSTRACT:

The 2016 NOAA-National Water Center (NWC) Innovators Program hosted 34 graduate student research fellows from 21 universities across the country from June 6 to July 20, 2016. The intent was to create an innovation incubator where students from many universities can exchange ideas and advance concepts towards the effective functioning of the National Water Model across the continental United States. During the 7-week program, the resident research fellows worked collaboratively on 12 projects specifically focusing on flood inundation and disaster response. The projects were thematically categorized in four domains: flood modeling, inundation mapping, forecast errors, and emergency response. This HydroShare collection presents a featured project with a view to aid aggregation of project outcomes and dissemination of scientific findings in similar future initiatives.

ABSTRACT:

Geohackweek is a 5-day workshop (November 14-18, 2016) held at the University of Washington eScience Institute. Participants came to the program with experience with Python programming and analysis of geospatial data (e.g. remote sensing analysis, vector mapping, environmental modeling, etc,) and learn more about open source technologies used to analyze geospatial datasets. The Freshwaterhack includes a subset of the geohack projects that are related to hydrology, hydrologic modeling, and water resources in order to support open source tool development and data sharing and catalyze water research that can be translated to national and global scales. The Freshwaterhack is facilitated by a collaborative network of Freshwater Science and Engineering coordinated by the Mountain to Sea Strategic Research Initiative, supported by UW College of Engineering, UW College of the Environment, and UW Tacoma.

Visit the Github respoitory at https://github.com/geohackweek/geohackweek.github.io for more information.

ABSTRACT:

A common challenge in interpreting and validating remote sensing data is in comparing these data to direct observations on the ground. Often remotely sensed data will cover large regions and have different temporal and spatial sampling frequency than point observations derived in the field. This kind of analysis requires geospatial tools to enable resampling, assessment of spatial statistics and extrapolation of point data to broader regions. The integration of satellite missions (GRACE) with hydrology models for determining drought indicators and water levels has been done in the United States (Houborg, et al., 2012; Zaitchick et al., 2008) using data assimilation from sophisticated observatory networks that are not available, for example, in sub-saharan Africa. However, there are studies that have analyzed operational, technical, institutional, financial, and environmental predictors of functionality for groundwater access (well) data collected from over 25,000 community-managed handpumps in Liberia, Sierra Leone, and Uganda (Foster, 2013). Lahren and Cook (2016) code and analyze the reasons for failure in 250,000 water points in 25 countries and found that 30% of boreholes are not functioning, either for "technical or mechanical” reasons, or for "low quantity". According to the World Bank, water supply failure in Africa is estimated to “represent a lost investment in excess of $1.2 billion” (Bonsor et. al. 2015). Women and girls continue to be the world’s water collectors, spending a significant fraction of their time and energy on the task (Sorenson et al 2011, Graham et al 2016, Cook et al 2016). Can a planetary scale observational tool be used to understand groundwater access and vulnerability for domestic use in rural households? If so, we can further investigate and develop the GRACE for Girls project.

Research Questions

How much is hydrological scarcity contributing to handpump failure in Africa?

In areas where domestic water access is primarily through wells, are areas with non-functional wells because of low quantity observable with remote sensing data?
What spatial statistics can be used to understand the reasons for well failure using geolocated water points and falling groundwater levels?

Sample data

Point data: The Water Point Data Exchange (WPDx) is a global platfrom for sharing water point data to understand water services with 240,000 + water points in the dataset with the quantity of data varying between government support for complete datasets (all 101,000 water points in Uganda) as well as data in other countries with known GRACE observable groundwater levels (India).

The WPDx data downloaded in February 2016, and coded for well failure due to water resources issues (Lahren and Cook (2016)), is provided on Hydroshare. Go to Collaborate. Ask to Join Freshwater Group. Click on link for Freshwaterhack of UWGeohackweek. Go to Collection Contents. Click on Freshwaterhack Project: Groundwater Resources and GRACE

Remote sensing data: The Gravity Recovery and Climate Experiment (GRACE, a joint mission of NASA and the German Aerospace Center) measures the Earth's gravity anomalies to study how mass is distributed around the planet and used for studying Earth's eceans, geology, and climate.

GRACE land are available at http://grace.jpl.nasa.gov, supported by the NASA MEaSUREs Program.
D.N. Wiese. 2015. GRACE monthly global water mass grids NETCDF RELEASE 5.0. Ver. 5.0. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at http://dx.doi.org/10.5067/TEMSC-OCL05.

Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015), Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res. Solid Earth, 120, doi:10.1002/2014JB011547.

ABSTRACT:

Geospatial tools and visualization is needed to develop a data and model integration pipeline for assessing landslide hazards.  This project is one component of multi-hazard (earthquake, flood, tsunami) assessment in watersheds spanning mountain peaks to coastal shores.  A common challenge in interpreting and validating distributed models is in comparing these data to direct observations on the ground. Modeling data of landslides for regional planning intentionally cover large regions and many landslides, incorporating different temporal and spatial sampling frequency and stochastic processes than observations derived from landslide inventories developed in the field. This kind of analysis requires geospatial tools to enable visualization, assessment of spatial statistics and extrapolation of spatial data linked to hierarchical relationships, such as downstream hydrologic impacts.  
Landslide geohazards can be identified through numerous methods, which are generally grouped into quantitative (e.g., Hammond et al. 1992; Wu and Sidle 1995) and qualitative (e.g., Gupta and Joshi 1990; Carrara et al. 1991; Lee et al. 2007) approaches. Mechanistic process-based models based on limited equilibrium analysis can quantify the roles of topography, soils, vegetation, and hydrology (when coupled with a hydrologic model) in landsliding in quantitative forms (Montgomery and Dietrich 1994; Miller 1995; Pack et al. 1998).  Processed-based models are good for predicting the initiation of landslides even where landslide inventories are lacking, but missed predictions likely stem from parameter uncertainty and unrepresented processes in model structure implicitly captured in qualitative approaches. A common qualitative approach develops landslide susceptibility based on experts rating multiple landscape attributes.  These approaches provide general indices rather than quantified probabilities of relative landslide susceptibility applicable to the study location and cannot represent causal factors or triggering conditions that change in time (van Western et al. 2006). Both approaches rarely provide a probabilistic hazard in space and time, requisite for landslide risk assessments beneficial for planning and decision making (Smith 2013).
This project will start the groundwork to integrate numerical modeling developed by University of Washington  with qualitative assessments of landslide susceptibility performed by Washington Department of Natural Resources.

ABSTRACT:

Data
Data
To do

ABSTRACT:

There is an important gap in flood modeling: flood models do not sufficiently resolve sediment dynamics in river networks and related consequences for channel conveyance. Especially in tectonically active regions, changes in channel capacity due to geomorphic processes may sometimes be as or more important than the frequency of high discharge events in determining flood risks.

Alex Bryk et al. at UC Berkeley have been using Earth Engine to study geomorphology. Specifically, this animation (https://docs.google.com/presentation/d/16U1vGD1ewGyBBVh_UHslYRkf5FNLZHmsLiD_1fcaAzY/edit SLIDE 67) shows patterns of erosion and deposition over time. Because they can scale their algorithms to run anywhere, their findings are challenging conventional wisdom for how rivers form and evolve.

How to can use remotely sensed data, together with tools such as GEE, to quantify changes in the sediment flux through river systems?
How can we use these tools to improve our understanding of sediment and flooding in the Pacific Northwest?

Geohackweek questions to explore:
(1) Which watersheds have the greatest upland river dynamics?
(2) Which watersheds have the greatest lowland and esturay dynamics?
(3) In which watersheds should we extend our observational networks and modeling studies to best understand those areas most affected by flooding uncertainty related to sediment?

ABSTRACT:

Downloads from data.waterpointdata.org/dataset/Water-Point-Data-Exchange-Complete-Dataset/

ABSTRACT:

National Hydrography Dataset (wbdhu12_a_17110006)

ABSTRACT:

National Hydrography Dataset
Upper White Chuck River =171100060104
Headwaters Suiattle River = 171100060201
Miners Creek - Suiattle River=171100060202
Milk Creek -Suiattle River =171100060205

ABSTRACT:

Table of climate centroids for gridded 1/16 degree forcing datasets used in Sauk-Suiattle watershed models.

ABSTRACT:

The coordinates for grid center points have been converted into an ESRI point shapefile, with numbering consistent for downloading of this data set:
Livneh B., T.J. Bohn, D.S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and L.D. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013, Nature Scientific Data, 2, 150042, doi:10.1038/sdata.2015.42.

Available at: ftp://livnehpublicstorage.colorado.edu/public/Livneh.2015.NAmer.Dataset/

ABSTRACT:

The coordinates for grid center points have been converted into an ESRI point shapefile, with numbering consistent for downloading of this data set:

Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.

Available for download at: ftp://livnehpublicstorage.colorado.edu/public/Livneh.2013.CONUS.Dataset/

ABSTRACT:

The coordinates for grid center points have been converted into an ESRI point shapefile, with numbering consistent for downloading of this data set:

Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.

Available for download at:
ftp://livnehpublicstorage.colorado.edu/public/Livneh.2013.CONUS.Dataset/
and
ftp://ftp.hydro.washington.edu/pub/blivneh/CONUS/
and
http://www.cses.washington.edu/rocinante/Livneh/Livneh_WWA_2013

ABSTRACT:

Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic Unit Maps: U.S. Geological Survey Water-Supply Paper 2294, 63 p.

https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset/
The Watershed Boundary Dataset (WBD) - is a nationally consistent watershed dataset that is subdivided into 6 levels (12-digit hucs) and is available from the USGS and USDA-NRCS-National Cartographic and Geospatial Center's (NCGC). The new 8-digit WBD (130 megabytes) and the new 12-digit WBD (980 megabytes) are available as geodatabases for download, along with the metadata. The WBD contains the most current, the highest resolution and the most detailed delineation of the watershed boundaries.

ABSTRACT:

This original dataset was downloaded from the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global Dataset (https://lta.cr.usgs.gov/SRTM1Arc). The DEM was projected to UTM and resampled to 150m grid cell. Terrain analysis steps included pit filling and a subtraction of elevation along the stream network to 'burn in' the flow pathway to allow flow directions to drain low elevation areas to the outlet at the Sauk River confluence with the Skagit River.

ABSTRACT:

Testing
Testing

[Modified in JupyterHub on 2017-02-10 01:56:05.915793]
This is a demo of the HydroShare Landlab Watershed Dynamics Notebook

ABSTRACT:

Watershed Dynamics Model Demo #1

ABSTRACT:

This demo was developed as an example of how to save a Landlab model output back to HydroShare, with an example Python script ported from a Synthetic Results Script to reproduce results from:
J.M. Adams, N.M Gasparini, D.E.J. Hobley, G.E. Tucker, E.W.H. Hutton, S.S. Nudurupati, and E. Istanbulluoglu. (in prep) The Landlab OverlandFlow component: a Python library for computing shallow-water flow across watersheds. Planned for submission to Geoscientific Model Development.
The manuscript is based on Landlab version 1.0.1:
https://github.com/landlab/landlab/tree/v1.0.1
Installation instructions and documentation for Landlab are provided at:
http://landlab.github.io/#/
and
http://landlab.readthedocs.org

Source: https://github.com/landlab/pub_adams_etal_gmd

ABSTRACT:

This tutorial demonstrates implementation of the Cellular Automaton Tree-GRass-Shrub Simulator (CATGRaSS) [Zhou et al., 2013] on a flat domain. This model is built using components from the Landlab component library. CATGRaSS is spatially explicit model of plant coexistence. It simulates local ecohydrologic dynamics (soil moisture, transpiration, biomass) and spatial evolution of tree, grass, and shrub Plant Functional Types (PFT) driven by rainfall and solar radiation.

Each cell in the model grid can hold a single PFT or remain empty. Tree and shrub plants disperse seeds to their neighbors. Grass seeds are assumed to be available at each cell. Establishment of plants in empty cells is determined probabilistically based on water stress of each PFT. Plants with lower water stress have higher probability of establishment. Plant mortality is simulated probabilistically as a result of aging and drought stress. Fires and grazing will be added to this model soon.

This model (driver) contains:
- A local vegetation dynamics model that simulates storm and inter-storm water balance and ecohydrologic fluxes (ET, runoff), and plant biomass dynamics by coupling the following components:
- PrecipitationDistribution
- Radiation
- PotentialEvapotranspiration
- SoilMoisture
- Vegetation

- A spatially explicit probabilistic cellular automaton component that simulates plant competition by tracking establishment and mortality of plants based on soil moisture stress:
- VegCA

To run this Jupyter notebook, please make sure that the following files are in the same folder:
- cellular_automaton_vegetation_flat_domain.ipynb (this notebook)
- Inputs_Vegetation_CA.txt (Input parameters for the model)
- Ecohyd_functions_flat.py (Utility functions)

[Ref: Zhou, X, E. Istanbulluoglu, and E.R. Vivoni. "Modeling the ecohydrological role of aspect-controlled radiation on tree-grass-shrub coexistence in a semiarid climate." Water Resources Research 49.5 (2013): 2872-2895]

ABSTRACT:

This a download of VIC fluxesw data and vizualization processing results from the Daily_VIC_1915_2011 (Livneh et al. 2013); Livneh B., E.A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions, Journal of Climate, 26, 9384–9392.

ABSTRACT:

This package is an add-on to the hydrodynamic model ANUGA. These operators are used to simulate the erosion, transport, and deposition of sediment across the domain, and the effects of vegetation drag on the flow. Download ANUGA from https://github.com/GeoscienceAustralia/anuga_core.

Visit https://github.com/mperignon/anugaSed to learn more.

Running this module on JupyterHub requires launching a new terminal, following installation commands and importing animation libraries using: pip install git+https://github.com/jakevdp/JSAnimation.git

Launch Operators.ipynb from the Open With button (top left), select the JupyterHub server at NCSA. This Notebook varies from the operator.ipynb on Github in that is that it has two examples of plotting concentation and elevation change.

ABSTRACT:

to do

ABSTRACT:

This is a flood modeling project that was conducted during the 2016 NOAA-National Water Center Innovator Program.

Various low-complexity flood inundation mapping tools have been developed recently as part of large-scale high resolution hydrologic prediction initiatives. However, there remains a knowledge-gap regarding the ability of these tools to capture inundation extent and depth under different scenarios of floodplain features and flood magnitudes. The objective of this study is to fill the gap by comparing two of such new generation low-complexity tools, AutoRoute and Height Above the Nearest Drainage (HAND), with a two-dimensional hydrodynamic model (Hydrologic Engineering Center-River Analysis System, HEC-RAS 2D).

This collection has a graphical abstract, all the required input data, brief outline of methodology featuring necessary pre-processing steps, sample outputs, and an instructional video tutorial.

ABSTRACT:

The NOCA landslide observatory host the driver code and customized component code as well as locates the data files needed to run Landlab's LandslideProbability component. It contains multiple Jupyter notebooks to demonstrate this component:

1) Synthetic recharge LandlabLandslide - Used to demonstrate the component on a synthetic grid with synthetic data with 4 options for parameterizing recharge.
2) NOCA_run_eSurfpaper_LandlabLandslide - models annual landslide probability for North Cascades National Park Complex, designed to replicate a portion of the modeling and results in Strauch et al., (2017) A hydro-climatological approach to predicting regional landslide probability using Landlab, eSurf: XX-XX. Data for this notebook are located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/

ABSTRACT:

Observatory

ABSTRACT:

This is a poster developed for the EarthCube All Hands Meeting: https://www.earthcube.org/2017-all-hands-meeting; Seattle WA, USA June 7-9, 2017 “Making Connections & Moving Forward”.

Authors:
Christina J. Bandaragoda1, Anthony Castronova2, Jimmy Phuong3, Ronda Strauch1, Erkan Istanbulluoglu1, Sai Siddhartha Nudurupati1, David Tarboton4, Dandong Yin5, Shaowen Wang5, Katherine Barnhart6, Gregory E. Tucker6, Eric W. H. Hutton7, Daniel E. J. Hobley8, Nicole M. Gasparini9, Jordan M. Adams9

Affiliations:
1 Department of Civil and Environmental Engineering, University of Washington, Seattle, USA; 2 Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI), USA;
3 Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, USA; 4 Department of Civil & Environmental Engineering, Utah State University, Logan, USA; 5 National Center for Supercomputing Applications (NCSA), University of Illinois, Urbana-Champagne, USA;
6 Department of Geological Sciences, University of Colorado, Boulder, USA; 7 Community Surface Dynamics Modeling System (CSDMS), University of Colorado, Boulder, USA; 8 Cardiff University, Cardiff, UK; 9 Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA.

Abstract:
The ability to test hypotheses about surface processes coupled to both subsurface and atmospheric regimes is invaluable to research in the Earth and planetary sciences; ,to swiftly develop experiments using community resources is extraordinary. However, creating a new model can demand a large investment of time, expert software skills, and can be constrained to adapting existing models with limited flexibility to address new questions. Advancing the state of knowledge includes not only experimentation and publication, but also communication and distribution of large, and complex models and datasets. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for sharing data and models. Together, Landlab on HydroShare accelerates the development of new process models by providing (1) a set of tools for regular and irregular grid structures, data manipulation and visualization to make it faster and easier to develop new physical process components, (2) a suite of modular and interoperable process components that can be combined to create an integrated model; (3) cyber infrastructure that provides collaboration functions with multiple levels of sharing and privacy settings, Creative Commons license options, and DOI publishing, and 4) cloud access with high-speed processing from the CyberGIS HydroShare JupyterHub server at the National Center for Supercomputing Applications. New users can run models from a web browser, while advanced users can execute and develop models from command line terminals. Landlab on HydroShare supports the modeling continuum from fully developed modelling applications, prototyping new science tools, hands on research demonstrations, and classroom applications. The HydroShare-Landlab building block in EarthCube is a model of technology collaboration and tool exchange in the geoscience modeling community.

ABSTRACT:

This is a collection of step by step demonstrations on how to use HydroShare Apps.

ABSTRACT:

This is a step-by-step demonstration of how to Add Images, PDFs, and Videos to digital maps using the HydroShare GIS App using an example from this related HydroShare resource: Ames, D. (2016). Algae Growth in Utah Lake Time-lapse, HydroShare, http://www.hydroshare.org/resource/4c8ecb05a72647339df0df6e9a87718f

ABSTRACT:

This is a step-by-step demonstration of how to view and download forecasts from any stream in the National Hydrography Dataset with the National Water Model App.

ABSTRACT:

Daily Weather Research and Forecasting (WRF) hydrometeorology files for the Sauk-Suiattle watershed.

Daily Weather Research and Forecasting (WRF) for the Pacific Northwest is made available by the Climate Impacts Group at the University of Washington.

Please cite:
Salathé, EP, AF Hamlet, CF Mass M Stumbaugh, S-Y Lee, R Steed: 2017. Estimates of 21st Century Flood Risk in the Pacific Northwest Based on Regional Scale Climate Model Simulations. J. Hydrometeorology. DOI: 10.1175/JHM-D-13-0137.1

ABSTRACT:

The NOCA landslide observatory host the driver code and customized component code as well as locates the data files needed to run Landlab's LandslideProbability component. It contains multiple Jupyter notebooks to demonstrate this component:

1) Synthetic recharge LandlabLandslide - Used to demonstrate the component on a synthetic grid with synthetic data with 4 options for parameterizing recharge.
2) NOCA_run_eSurfpaper_LandlabLandslide - models annual landslide probability for North Cascades National Park Complex, designed to replicate a portion of the modeling and results in Strauch et al., (2018) A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynamo. 6: 49-75. Data for this notebook are located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/

ABSTRACT:

The NOCA landslide observatory host the driver code and customized component code as well as locates the data files needed to run Landlab's LandslideProbability component. It contains multiple Jupyter notebooks to demonstrate this component:

1) Synthetic_recharge_LandlabLandslide - Used to demonstrate the component on a synthetic grid with synthetic data with 4 options for parameterizing recharge.
2) NOCA_run_eSurfpaper_LandlabLandslide - models annual landslide probability for North Cascades National Park Complex, designed to replicate a portion of the modeling and results in Strauch et al., (2018) A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam. 6: 49-75. Data for this notebook are located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/
3) ThunderCreek_LandlabLandslide - Model of landslide probability for the Thunder Creek portion of North Cascades National Park using a 'lognormal spatial' distribution for recharge.

ABSTRACT:

Thunder Creek, Skagit River Basin, State of Washington, USA.

ABSTRACT:

Hydroinformatics is numbers
slowly dripping
down a pipeline
of vintage equations
explosive datasets
and exhaustive agendas,
wet with the sweat of
dedicated scientists
transient moments of clarity
and flowcharts.

Hydroinformatics is water converted
by our creativity and innovation
into a reservoir of 0's and 1's
assimilated into a pillow
that soften our edges with collaboration
and smooths our future consequences
with heroic feats of knowledge building
on where the water has been
and where the water is going.

ABSTRACT:

This resource was developed as a HydroShare workshop demonstration for the CUAHSI Hydroinformatics conference, July 25-27, 2017, Tuscaloosa, AL.

When you open this resource with the CUAHSI JupyterHub server (upper right, click on Open With, Select JupyterHub NCSA), you will launch a Welcome Notebook that will connect you to the CyberGIS virtual machine on the ROGER super computer at the University of Illinois, Urbana-Champagne. When you execute (Run Step 1 and Step 2 only) in the Jupyter Notebook cells on the Welcome Notebook, you will download related data and two Notebooks designed to explore hydrologic research problem solving using data and model integration in HydroShare . Skip Step 3 "Welcome" tutorial steps unless you want to explore how to do work and Save back to HydroShare.

Click on the hyperlink to ThunderCreek_DataIntegration_Beginner.ipynb. The beginner notebook is an Introduction for new HydroShare users who may have limited experience with Python code and Jupyter Notebooks. The advanced notebook explores how to combine watershed data with hydrology models (e.g. DHSVM) and the Landlab modeling framework (e.g. landsliding).

The problem: Researchers need a modeling workflow that is flexible for developing their own code, with easy access to distributed datasets, shared on a common platform for coupling multiple models, usable by science colleagues, with easy publication of data, code, and scientific studies.

The emerging solution: Collaborate with the CUAHSI HydroShare community to use and contribute to water data software and hardware tools, so that you can focus on your science, be efficient with your time and resources, and build on existing research in multiple domains of water science.

Beginner Notebook (time savings ~ 9 months)
Download water data from CUAHSI HIS
Develop your own utilites (e.g. download hydrometeorology)
Save your results on HydroShare for your colleagues

Advanced Notebook (time savings ~2.5 years)
Run a preconfigured hydrology model installed on the CUAHSI JupyterHub server
Run a published Landlab landslide model
Publish your results and get a DOI

This is a Watershed Dynamics Model developed by the Watershed Dynamics Research Group in the Civil and Environmental Engineering Department at the University of Washington for the Thunder Creek basin in the Skagit Watershed, WA, USA in collaboration with CUAHSI.

The resource was originally derived from a reproducible demonstration of the landslide modeling results from: Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2017-39, in review, 2017.

How are you using HydroShare?
https://docs.google.com/forms/d/e/1FAIpQLSeD4K9faWoHjy_ZwZhz3zHWxYH2vIhBFsvz5uhVbMvsXNuoeA/viewform

ABSTRACT:

The coordinates for grid center points have been converted into an ESRI point shapefile, with numbering consistent for downloading of this data set:
Livneh B., T.J. Bohn, D.S. Pierce, F. Munoz-Ariola, B. Nijssen, R. Vose, D. Cayan, and L.D. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013, Nature Scientific Data, 2, 150042, doi:10.1038/sdata.2015.42.

Available at: ftp://livnehpublicstorage.colorado.edu/public/Livneh.2015.NAmer.Dataset/

ABSTRACT:

GIS-Based Mapping of Soil Distribution in Thunder Creek Watershed
Traditionally, information about soil distribution has been acquired through intensive fieldwork. Although ideal, this technique is not feasible in the National Parks and wilderness areas in Washington where hiking trails provide the only access to many hectares of land. With the increasing capabilities of Geographic Information Systems (GIS) and remote sensing software, it is possible to model soil-landscape relationships via digital topographic and environmental data and satellite imagery as proxies for the soil-forming factors, combined with a reduced amount of fieldwork. In this study, a quantitative model of soil distribution in the 30,000 hectare Thunder Creek Watershed in North Cascades National Park (NOCA) is being created based on information acquired from digital data and field sampling.

ABSTRACT:

This resource contains a HydroShare Map Project file created using the HydroShare GIS web app. The Map Project file is in JSON format and contains data regarding the state of the project upon creating this resource.

ABSTRACT:

Node ID

ABSTRACT:

This dataset contains a shapefile and corresponding attribute table listing a Landlab rastermodel GridCodeID as a point in the centroid of each 30 m grid cell within the North Cascades National Park study domain.

ABSTRACT:

Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic Unit Maps: U.S. Geological Survey Water-Supply Paper 2294, 63 p.

https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset/
The Watershed Boundary Dataset (WBD) - is a nationally consistent watershed dataset that is subdivided into 6 levels (12-digit hucs) and is available from the USGS and USDA-NRCS-National Cartographic and Geospatial Center's (NCGC). The new 8-digit WBD (130 megabytes) and the new 12-digit WBD (980 megabytes) are available as geodatabases for download, along with the metadata. The WBD contains the most current, the highest resolution and the most detailed delineation of the watershed boundaries.

ABSTRACT:

Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic Unit Maps: U.S. Geological Survey Water-Supply Paper 2294, 63 p.

https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset/
The Watershed Boundary Dataset (WBD) - is a nationally consistent watershed dataset that is subdivided into 6 levels (12-digit hucs) and is available from the USGS and USDA-NRCS-National Cartographic and Geospatial Center's (NCGC). The new 8-digit WBD (130 megabytes) and the new 12-digit WBD (980 megabytes) are available as geodatabases for download, along with the metadata. The WBD contains the most current, the highest resolution and the most detailed delineation of the watershed boundaries.

ABSTRACT:

Upper Rio Salado Watershed Boundary

ABSTRACT:

This resource contains ESRI GIS point shapefiles with centroid locations of the 1/16th degree hydrometeorology gridded dataset. Each GIS file attribute table includes the latitude, longitude, and elevation of each centroid.
There are two GIS files:
1. NAgridpoint_lessthan250m_Skagit_25kmbuffer.shp -- This is a subset of the North America gridded points within a 25km buffer of the Skagit basin boundary, only including those points below 250m elevation. There are 54 points.
2. NAgridpoint_lessthan550m_Skagit_15kmbuffer.shp -- This is a subset of the North America gridded points within a 15km buffer of the Skagit basin boundary, only including those points below 550m elevation. There are 75 points.

This data was intended as a test for spatial averaging of a low elevation dependent correction of WRF gridded climate data to Livneh et al., 2015.

ABSTRACT:

This iPython notebook demonstrates the workflow for obtaining and processing gridded meteorology data files with the Observatory for Gridded Hydrometeorology Python library.

Using the Sauk-Suiattle, Elwha, and Upper Rio Salado watersheds as the study sites of interest, each Jupyter notebook will guide the user through assembling the datasets and analyses from each of seven gridded data product.

In Usecase 1, users may inspect their study site of interest given in the form of summary spatial visualizations. The treatgeoself() function will yield a mapping file per study site, which reduces the gridded cell centroids to the subset that intersects with the study area (i.e., within the watershed). Within treatgeoself(), the user may determine the amount of buffer space to include outside of the study site (default is 0.06-degree buffer region).

In Usecase 2, each of the mapping files are used to guide data retrieval from each of the gridded data products. A series of _get_ functions then downloads the files to designated subfolders. The resulting file paths are cataloged into the mapping file, which can be summarized for data availability according to the elevation gradient using the mappingfileSummary() function. These downloaded files are compressed into tar.gz files, then migrated with their respective mapping files as content files within a new HydroShare resource, for ease of collaborative use.

In Usecase 3, the downloaded files from Usecase 2 are processed in to spatial and temporal summary statistics. The gridclim_dict() function compiles and computes daily, monthly, annual, and monthly-yearly average values for each variable described in the gridded data product metadata (e.g., the ogh_meta class dictionary). Monthly averages are then visualized as time-series plots, while spatial averages are visualized as spatial heatmaps. Finally, the dictionary of dataframes (the product of the spatial-temporal analyses) is saved into a json file and migrated out as a content file within a new HydroShare resource.

ABSTRACT:

Data exploration using GRACE remotely derived groundwater levels and well point datasets

The problem: If the well is dry, is the problem due to hydrology or humans? Kenya, Uganda, and Tanzania are three countries with an extensive well dataset. What are the spatial statistics of failures? Are functioning/non-functioning wells scattered randomly, or does failure follow a hydrologic pattern?

A common challenge in interpreting and validating remote sensing data is in comparing these data to direct observations on the ground. Often remotely sensed data will cover large regions and have different temporal and spatial sampling frequency than point observations derived in the field. This kind of analysis requires geospatial tools to enable resampling, assessment of spatial statistics and extrapolation of point data to broader regions. In Geohackweek 2016, our project team ('Oh Well') left code to explore this problem in this HydroShare resource.

Please see the attached project presentation slide show for an introduction to the team.

Source of the Notebook:
<a href="http://nbviewer.jupyter.org/github/amrhein/freshwaterhack/blob/master/grace_wells.ipynb" rel="nofollow">http://nbviewer.jupyter.org/github/amrhein/freshwaterhack/blob/master/grace_wells.ipynb</a>

Google Earth Engine Resources:
Here is a script that selects a single CHIRPS precipitation image from the collection:
<a href="https://code.earthengine.google.com/2870eedb36d247bc25d95c9cc2c4ac50" rel="nofollow">https://code.earthengine.google.com/2870eedb36d247bc25d95c9cc2c4ac50</a>

Here is a script to get mean CHIRPS data:
<a href="https://code.earthengine.google.com/3a09aaa437f327c392ac7798df1e2c09" rel="nofollow">https://code.earthengine.google.com/3a09aaa437f327c392ac7798df1e2c09</a>

ABSTRACT:

Input data for trial run of landslide probability component of landlab

ABSTRACT:

Administrative Units for three African countries with WPDx data coverage from http://www.gadm.org

ABSTRACT:

Resource for ongoing access to JupyterHub server for using Geohackweek 2017 tutorials.

ABSTRACT:

This resource contains data files used in Use Case 2 for Lowering the barriers to computational modeling of Earth’s surface: coupling Jupyter Notebooks with Landlab, HydroShare, and CyberGIS for research and education.

ABSTRACT:

Hydroinformatics is teal
the color of new ideas
innovation
creativity
and the chaos of change
spinning up complex
relationships between
data
models
human impacts
human scientists
and the institutions that govern
and don't govern
our water.

Hydroinformatics is water everywhere
in our hopes and dreams
in our code and publications
compressed into tar balls
heavy with responsibility
and light with hope
for the future.

ABSTRACT:

This output is a bias correction test to generate a hybrid gridded meteorology product. Each observation based hydrometeorology grid cell is corrected to each WRF modeled grid cell. Further, the bias of WRF is corrected near observations by correcting all grid cells by the low elevation spatially averaged monthly mean temperature and precipitation. This resource contains five sets of forcing files: 1) raw Livneh, 2) raw WRF, 3) Livneh corrected to WRF at each grid cell, 4) #3 plus a global correction based on low elevation bias for Precip, Tmax and Tmin, and 5) #3 plus a global correction based on low elevation bias for Precip, and Tmax and Tmin each corrected to the Tavg bias. This dataset was generated September 25, 2017 using Observatory code from <a href="https://github.com/ChristinaB/Observatory" rel="nofollow">https://github.com/ChristinaB/Observatory</a>.

ABSTRACT:

Taudem is wonderful. This example is for the Sauk watershed.

ABSTRACT:

This resource is a subset of the resource below and provides a demonstration of running a landslide model using Landlab for Thunder Creek watershed within North Cascades National Park Complex (NOCA). It allows the adjustment of model input to explore effects on landslide probability, such as fire. The notebook takes 23 min to run straight through.

Bandaragoda, C., A. M. Castronova, J. Phuong, E. Istanbulluoglu, S. S. Nudurupati, R. Strauch, N. Gasparini, E. Hutton, G. Tucker, D. Hobley, K. Barnhart, J. Adams, D. Tarboton, S. Wang, D. Yin (2017). Lowering the barriers to computational modeling of Earth’s surface: coupling Jupyter Notebooks with Landlab, HydroShare, and CyberGIS for research and education, HydroShare, http://www.hydroshare.org/resource/70b977e22af544f8a7e5a803935c329c.

When you open this resource with the CUAHSI JupyterHub server (upper right, click on Open With, Select JupyterHub NCSA), you will launch a Welcome Notebook that will connect you to the CyberGIS virtual machine on the ROGER super computer at the University of Illinois, Urbana-Champagne. When you execute (Run Step 1 and Step 2 only) in the Jupyter Notebook cells on the Welcome Notebook, you will download related data and Notebooks designed to explore hydrologic research problem solving using data and model integration in HydroShare . Skip Step 3 "Welcome" tutorial steps unless you want to explore how to do work and Save back to HydroShare.

The problem: Researchers need a modeling workflow that is flexible for developing their own code, with easy access to distributed datasets, shared on a common platform for coupling multiple models, usable by science colleagues, with easy publication of data, code, and scientific studies.

The emerging solution: Collaborate with the CUAHSI HydroShare community to use and contribute to water data software and hardware tools, so that you can focus on your science, be efficient with your time and resources, and build on existing research in multiple domains of water science.

This is a Watershed Dynamics Model developed by the Watershed Dynamics Research Group in the Civil and Environmental Engineering Department at the University of Washington for the Thunder Creek basin in the Skagit Watershed, WA, USA in collaboration with CUAHSI.

The landslide model was originally derived from a reproducible demonstration of the landslide modeling results from: Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydro-climatological approach to predicting regional landslide probability using Landlab, Earth Surf. Dynam. 6, 49-75, https://doi.org/10.5194/esurf-6-49-2018, 2018.

ABSTRACT:

Taudem is awesome!

ABSTRACT:

Presentations created by participants of the GSA 2017 meeting short course, Landlab Earth Surface Modeling Toolkit: Building and Applying Models of Coupled Earth Surface Processes.

Participants selected a tutorial group to join in the second part of the course. Throughout the afternoon, groups explored the topic they chose with a Landlab developer. At the end of the day groups shared what they did with Landlab using these presentations.

ABSTRACT:

Air temperature, ground temperature and relative humidity data was collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 - 1800 m above sea level. Data was collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Selected sites included relative humidity sensors to indicate whether precipitation was occuring. Data was collected every 3-4 hours from May 2015 to Sept 2018 (with ongoing collection). Code for processing daily mean, minimum, maximum, and rates of temperature changes with elevation is available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org.

ABSTRACT:

Hydroinformatics is code
wandering through your mind
while walking in the woods
while breastfeeding at 2 am
while listening to the drone of endless conference calls.

Each problem solved
unpacking a treasure chest
of new issues to consider.
Each impenetrable ceiling shattered
revealing more questions
on how to maintain resilience
on how to choose which path to follow
on which pattern to use to lace up your boots.

Hydroinformatics is the grit of unwielding commitment
to make a contribution in a world
that is constantly changing,
is the beauty of building boats that float
through exhilaration rapids
through icy conditions
through the ungraspable vapors that carry you
through existing in multiple forms.

ABSTRACT:

The ability to test hypotheses about surface processes coupled to both subsurface and atmospheric regimes is invaluable to research in the Earth and planetary sciences; to swiftly develop experiments using community resources is extraordinary.  However, creating a new model can demand a large investment of time, expert software skills, and can be constrained to adapting existing models with limited flexibility to address new questions.  Advancing the state of knowledge includes not only experimentation and publication, but also communication and distribution of large, and complex models and datasets.  Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models.  HydroShare is an online collaborative environment for sharing data and models.  Together, Landlab on HydroShare accelerates the development of new process models by providing (1) a set of tools for regular and irregular grid structures, data manipulation and visualization to make it faster and easier to develop new physical process components, (2) a suite of modular and interoperable process components that can be combined to create an integrated model; (3) cyber infrastructure that provides collaboration functions with multiple levels of sharing and privacy settings, Creative Commons license options, and DOI publishing, and 4) cloud access with high-speed processing from the CyberGIS HydroShare JupyterHub server at the National Center for Supercomputing Applications.  New users can run models from a web browser, while advanced users can execute and develop models from command line terminals.  Landlab on HydroShare supports the modeling continuum from fully developed modelling applications, prototyping new science tools, hands on research demonstrations, and classroom applications.  The HydroShare-Landlab-CyberGIS interoperability is a model of technology collaboration and tool exchange in the hydrologic modeling community.

ABSTRACT:

The Lower Nooksack Water Budget carries out the work described in Section 2 of the WRIA 1 Watershed Management Plan, and is a tier 1 action identified in the Watershed Management Detailed Implementation Plan. The work was funded through the Watershed Management Joint Board, Lower Nooksack Strategy and Funding Plan. Project documents are available from Whatcom County at http://wria1project.whatcomcounty.org/Home/Water-Budget/97.aspx This resource contains graphic design used in the Lower Nooksack Water Budget by asmallpercent (http://www.asmallpercent.com/).

ABSTRACT:

Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We aim to advance open-source software infrastructure to support scientific investigation and data-driven decision making with a prototype system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. The widespread disruption of water treatment processes and uncertain drinking water quality within distribution systems in Puerto Rico poses risk to human health. However, there is no existing digital infrastructure to scientifically determine the impacts of the hurricane. After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. This project will archive and make accessible data on environmental variables unique to Puerto Rico, damage caused by Hurricane Maria, and will begin to address time sensitive needs of citizens. The initial focus is to work directly with public utilities to collect and archive samples of biological and inorganic drinking water quality. Our goal is to advance understanding of how the severity of a hazard to human health (e.g., no access to safe culinary water) is related to the sophistication, connectivity, and operations of the physical and related digital infrastructure systems. By rapidly collecting data in the early stages of recovery, we will test the design of an integrated cyberinfrastructure system to for usability of environmental and health data to understand the impacts from natural disasters. We will test and stress the CUAHSI HydroShare data publication mechanisms and capabilities to (1) assess the spatial and temporal presence of waterborne pathogens in public water systems impacted by a natural disaster, (2) demonstrate usability of HydroShare as a clearinghouse to centralize selected datasets related to Hurricane Maria, and (3) develop a prototype cyberinfrastructure to assess environmental conditions and public health impacted by natural disasters. The project thus serves to not only document post-disaster conditions, but develops a process to track the impact of recovery over time, as monitored through health, power availability and water quality.

PLAIN LANGUAGE SUMMARY
There is an urgent need to understand the impacts of infrastructure damage on public health after natural disasters. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. We aim to expand access to diverse datasets useful for understanding disaster related environmental conditions, with a focus on drinking water quality information. The research products will be made publicly available using a collaborative, online sharing platform – HydroShare. Curating a central repository of assembled data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve the monitoring of the recovery process. We will prototype this system with an assessment of drinking water, environmental, and public health concerns unique to Puerto Rico in the aftermath of Hurricane Maria. By working directly with public water utilities, we intend to characterize and map the severity of impaired water resources and distribution systems in Puerto Rico. Developing cyber and social infrastructure to understand the dynamics of drinking water contamination after natural disasters will improve disaster preparedness and response, and contribute to efforts across the nation and the world to build for a resilient future.

Poster presented at AGU Fall Meeting New Orleans Ernest N. Morial Convention Center
Session: NH23E Late-Breaking Research Related to the 2017 Hurricane Season in the Americas (Harvey, Irma, Jose, Maria): Poster Contributions
Program: Natural Hazards
Day: Tuesday, 12 December 2017

ABSTRACT:

Overview: There is urgent need to characterize the severely impaired water resources and distribution systems in Puerto Rico and inform the community about how they can protect themselves against hazards in their water. The situation is also an important opportunity to engage the public in collecting samples and create a rich dataset to not only better understand the impacts of Hurricane Maria, but build preparedness towards future water crises. Hurricane Maria may be one of the most complex disasters in human history - we need to have all available data strategically archived and integrated for use in further research. In this moment in time, in this one special place, the uncertainties and stress of where to find clean drinking water and how to restore basic services is beyond human comprehension. The current situation has been generated by a unique culmination of pre-existing conditions, natural disaster, disaster response, and lack of infrastructure. The current widespread disruption of drinking water distribution systems in Puerto Rico may pose risks to human health, but there is no existing digital infrastructure to scientifically determine the impacts of baseline environmental conditions, the hurricane event, and response to the crisis within a framework of understanding impacts to population health. We propose to provide drinking water test kits and analyze for biological, inorganic chemicals, and organic compounds. One month after Hurricane Maria, elementary questions on how to provide needed water quantity and quality and how to support basic human health care cannot be answered. With this project funding, we can soon archive and make accessible data on environmental variables unique to Puerto Rico and Hurricane Maria, unique damage caused by the storm (lack of electricity, blocked transportation corridors), and begin to address time sensitive needs of victims limited by the natural water resources of the island.

Intellectual Merit: Hurricane, environmental, water quality and health data integrated in one infrastructure system will be a resource for researchers to examine all aspects of how natural-human coupled systems respond to extreme weather events. We will have a unique dataset to allow us to generate testable hypotheses on how the severity of a hazard to human health and well-being is related to the sophistication, connectivity, and operations of the physical and digital infrastructure systems. In the short term, we plan to test the design of an integrated cyberinfrastructure system to increase the accessibility of environmental and health data for understanding the impacts from hurricane-related natural disasters. Conceptually, it is well understood that the severity of the disaster is a function of the sophistication of the physical and digital infrastructure. This work will develop a prototype of a synthesized system to advance our understanding of how infrastructure and data-driven information can reduce the impacts of natural disaster, and serve as a platform for future research.

Broader Impacts: Hurricanes Maria, Irma and Harvey are high profile events that have had catastrophic societal impacts. This will be a community-led activity coordinated through CUAHSI to ensure that the data are assembled to be broadly accessible to the research community. Research that deepens our understanding of these events, which will be greatly facilitated by the assembled data, will have broad impact in not only the affected areas but also in other parts of the country subject to hurricane flooding. CUAHSI membership includes over includes over 130 institutions and having this information centrally available through CUAHSI data services would provide a common point of access, in a consistent and documented format, with tools already developed. This will facilitate readiness in advance of disasters, to prepare to collect post-disaster data, as well as facilitate broad and unanticipated use of this data when it is available and easily accessible for research on HydroShare. This RAPID grant targeted at Maria, will expand our capacity to understand and support communities around the world who need to develop information collection and sharing infrastructure towards fostering self-resiliency.

ABSTRACT:

This work improves the Skagit Climate Science Consortium to model the Skagit (SC2DHSVM2015) calibration in the Sauk-Suiattle Basin. We use the spatially-distributed DHSVM glacio-hydrology model (Frans, 2015; Naz et al. 2014) for predicting
hydrologic states (glaciers, snow, soil moisture, streamflow) within modeled basins for historical and future climate conditions.  This work builds on the DHSVM-glacier model supported by a 2014-2015 collaboration managed by the Skagit Climate Science Consortium to model the Skagit (SC2DHSVM2015).

This work is intended to by used as an input for streamflow temperatures that are modeled and mapped at the scale of stream links using the DHSVM- RBM glacier model, and a sediment module driven by hydrology using the concept of sediment transport capacity, and/or rating curves to give a sediment load associated with each flood.

Use this resource to launch Jupyter notebooks that use the latest versions of OGH library (current version included).

ABSTRACT:

This output is a bias correction test to generate a hybrid gridded meteorology product. This dataset was generated December 21, 2017 using Observatory code from https://github.com/ChristinaB/Observatory.

ABSTRACT:

This output is a bias correction test to generate a hybrid gridded meteorology product. This dataset was generated December 22, 2017 using Observatory code from https://github.com/ChristinaB/Observatory.

ABSTRACT:

Testing ogh and pyDHSVM for the Sauk Watershed.

ABSTRACT:

This is a reference resource generated to direct interested researchers to a presentation giving an overview of water system impacts of Hurricane Maria in Puerto Rico for the WATER SCIENCE AND TECHNOLOGY ISSUES FOR THE NATION
WSTB- 35th Anniversary Meeting – December 5, 2017
National Academy of Sciences, Washington DC

This presentation was originally made available by the National Academy of Sciences, Division of Earth and Life Sciences.
Please see:
http://dels.nas.edu/resources/static-assets/wstb/miscellaneous/2017/06%20-%20Ramirez-Toro,%20Graciela%20-%20Water%20System%20Resilience%20in%20Disasters%20-%20Puerto%20Rico.pdf

ABSTRACT:

Observed streamflow from Sauk near Sauk (12189500) and Sauk above White Chuck(12186000). This dataset was generated January 16, 2018.

ABSTRACT:

Data and scripts used to prepare forcing data for PREEVENTS project

ABSTRACT:

Observed streamflow from Sauk near Sauk (12189500) from 1911 - 2020, updated for Python 3.7 runs on CUAHSI JupyterHub, with Ulmo installation to CyberGIS-Water. Right click on a Jupyter Notebook (ipynb) after joining a group with access to that server.

Observed streamflow from Sauk near Sauk (12189500) and Sauk above White Chuck(12186000) was originally generated January 16, 2018.

ABSTRACT:

This is a DHSVM model instance for the Sauk watershed uploaded for use in the University of Washington eScience Winter Incubator.

ABSTRACT:

This is a DHSVM model instance for the Sauk watershed uploaded for use in the University of Washington eScience Winter Incubator.

ABSTRACT:

This shapefile describes the Census 2010 published population estimates by US County-equivalent boundaries for the United States Territory of Puerto Rico.

The original Census 2010 County-equivalent shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data:
http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip

Other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal:
https://www.census.gov/geo/maps-data/data/tiger-data.html

ABSTRACT:

A study of landslide probability in Skagit Basin as a collaboration (MOA) between University of Washington and Seattle City Light (SCL). The project's objective is to better understand landslides in the watersheds containing the electrical transmission lines and facilities of SCL's Skagit Hydroelectric Project. A recently completed landslide model (Strauch et al. 2018) will be run using subsurface flow derived from a basin calibrated hydrologic model (Distributed Hydrology Soil and Vegetation Model - DHSVM) at 150-m grid resolution. The modeling will estimate contemporary and future probability of landslide initiation and create landslide hazard maps at a 30-m resolution. Future hydrology will be generated from running DHSVM with future climatology from two different Global Climate Models (GCMs) with two different representative concentration pathways (RCPs) emission scenarios for two future time periods. The analysis will also evaluate the sensitivity of the landslide model to subsurface flow and reduced cohesion simulating a fire.

ABSTRACT:

Adapted from Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic Unit Maps: U.S. Geological Survey Water-Supply Paper 2294, 63 p. Updated information not from this source is enclosed in square brackets below. A copy of USGS Water-Supply Paper 2294 may be ordered from USGS Information Services

The United States is divided and sub-divided into successively smaller hydrologic units which are classified into four levels: regions, sub-regions, accounting units, and cataloging units. The hydrologic units are arranged or nested within each other, from the largest geographic area (regions) to the smallest geographic area (cataloging units). Each hydrologic unit is identified by a unique hydrologic unit code (HUC) consisting of two to eight digits based on the four levels of classification in the hydrologic unit system.

ABSTRACT:

Adapted from Seaber, P.R., Kapinos, F.P., and Knapp, G.L., 1987, Hydrologic Unit Maps: U.S. Geological Survey Water-Supply Paper 2294, 63 p. Updated information not from this source is enclosed in square brackets below. A copy of USGS Water-Supply Paper 2294 may be ordered from USGS Information Services

The United States is divided and sub-divided into successively smaller hydrologic units which are classified into four levels: regions, sub-regions, accounting units, and cataloging units. The hydrologic units are arranged or nested within each other, from the largest geographic area (regions) to the smallest geographic area (cataloging units). Each hydrologic unit is identified by a unique hydrologic unit code (HUC) consisting of two to eight digits based on the four levels of classification in the hydrologic unit system.

ABSTRACT:

To Do

ABSTRACT:

This is a Collection of public health summaries and documents related to Puerto Rico. The Henry J. Kaiser Family Foundation (KRR) has led a series of population health interviews and analyses to describe Puerto Rico and US Virgin Islands' population health status before and after Hurricane Maria. This includes information from 2016-2017 ( before Hurricane Maria) and reports through 2018 (following Hurricane Maria).

ABSTRACT:

Nooksack River Water Budget, Electronic Appendix, 2012

ABSTRACT:

Studies of earth surface and environmental systems are becoming increasingly complex with integration of knowledge across multiple domains, enabled by technological advances to provide the collection of massive quantities of data, but requiring data science advances to improve usability of the largest of these datasets - spatially distributed time series of precipitation, temperature, and related atmospheric forcing data (hydrometeorology) used to drive hydrologic processes in models. Increasing the efficiency of using gridded, hydrometeorology data by scientists can be achieved by 1) increasing access to the latest research products such that 2) there is a decrease in effort spent on data processing and 3) an increase in time spent analyzing spatial and temporal characteristics which impact earth surface and environmental modeling experiments. The development of digital land-based Observatories supports ongoing improvements of knowledge at the watershed scale, critical for local decision-making, policy development, and natural disaster planning. The Observatory Gridded Hydrometerology (OGH) python library is designed as an open source software tool for environmental scientists and modelers to easily download and access time series from within regional, continental or global scale hydrometeorology products. This is especially useful for scientists and modelers who are not trained to select the most appropriate climate forcing data for their modeling study, do not have software tools for downloading and processing large datasets for watershed scale applications, and want to publish and run models in a cloud environment. We demonstrate the the use of this library with examples from three publicly available climate research products generated from interpolated gridded observations, hydrologic model and atmospheric model generated climate forcings. Our use cases include download, subset, and generation of statistics useful for for hydrologic and geomorphology modelers who study processes requiring time series of precipitation and temperature data for long term (+50 year) modeling studies. The OGH library is available on publicly accessible Github repository to encourage use in model research studies, and to expand the number of hydrometeorology products supported by this software with future contributions by researchers and software developers.

ABSTRACT:

This is the root collection resource for management of all weather, hydrologic and related population health and drinking water baseline data collected before and after Hurricane Maria in Puerto Rico. This collection holds numerous Collections and composite resources comprising 1) Environmental data related to Hurricane Maria, including meteorological data, precipitation, stream sensor and chemistry data, soil sensor data, and cloud monitoring data (ceilometer and cloud camera data) – as available given that some sensors were damaged or destroyed during the hurricane; 2) Data Sharing Agreements for the use of private and confidential water resources and health data for conducting research in a HIPAA-compliant manner, 3) Drinking Water Sample Data including analytical results from water quality samples collected after Hurricane Maria in Puerto Rico from public drinking water sources. Analytical results include laboratory analyses for waterborne pathogens and inorganics parameters. All derived data, including analytical results, metadata, and the methods employed to collect the data adhere to standard methods for water analysis and the standards outlined by the Genomic Standards Consortium and/or Standard Methods for Examination of Water and Wastewater; 4) Geospatial Data includes roads and road closure information, Safe Drinking Water Information System point locations of Community and Public Water Systems, locations of 69 hospitals in Puerto Rico, mudslide locations and landslide hazards obtained from the USGS, and storm deforestation rasters generated from satellite data. The data providers for this collection include the Luquillo Critical Zone Observatory, NOAA National Weather Service, NOAA National Water Center, FEMA, Department of Homeland Security, Kaiser Permanente, and many others.

There are separate collections for Hurricanes Harvey (https://www.hydroshare.org/resource/544f1afd7c0e42a49b8e59737a660bfd/) and Irma (https://www.hydroshare.org/resource/f9635d1c216d4c63b303ab1c655986e8/). Resources from 2017 US Hurricanes may also be shared with The CUAHSI 2017 Hurricane Data Community group (https://www.hydroshare.org/group/41) to make them accessible to interested researchers, and anyone may join this group. Resources related to 2017 Hurricane Maria impacts and ongoing drinking water studies may be shared with the Puerto Rico Water Studies Group (https://www.hydroshare.org/group/43) to make them accessible to interested researchers, and anyone may join this group.

This collection has been produced by a Collaborative Research grant by the US National Science Foundation RAPID Award "Building Infrastructure to Prevent Disasters Like Hurricane Maria" (ID 1810647) in collaboration with the RAPID Award "Archiving and Enabling Community Access to Data from Recent US Hurricanes" (ID 1761673).

ABSTRACT:

This resource contains a data-table from the Center for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) survey program Selected Metropolitan Area Risk Trends (SMART) MMSA prevalence data. The data were retrieved in 2018 after the 2017 sampling data had been released.

See source data access portal for the BRFSS SMART MMSA Prevalence data (2011 to 2017):
https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factors-Selected-Metropolitan-Area/j32a-sa6u/data

ABSTRACT:

This resource contains historic and future climate forcings for the Sauk DHSVM model.
Included are computed summaries for the historical meteorology data from Livneh et al. 2013 and the WRF data from Salathe et al. 2014.

ABSTRACT:

This the output from the vias correction notebook. Results for historic and future models are included

ABSTRACT:

The CUAHSI-SCOPE team conducted user-based research to evaluate and design an improved user experience for HydroShare. The user-oriented project focused on identifying key users and workflows, defining current limitations of the system, and developing a comprehensive document of design recommendations.

ABSTRACT:

After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. There is no existing digital infrastructure to scientifically determine the hurricane impact on drinking water quality, the severity of a hazard to human health, or baseline data on the sophistication, connectivity, and operations of the distributed physical and related digital infrastructure systems. We test data publication mechanisms after Hurricane Maria in Puerto Rico to understand risk to human health by (1) assessing the spatial and temporal presence of waterborne pathogens in multiple types of systems, (2) demonstrate usability of CUAHSI HydroShare as a clearinghouse for data related to Hurricane Maria, Harvey and Irma and (3) and develop a prototype cyberinfrastructure to assess environmental and public health impacts. Our resulting archive and research software engineering practices provide a prototype cyberinfrastructure system for researchers to study natural disasters.

How can data sharing and archiving capabilities be enhanced to ensure the greatest impact? Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We advance open-source software infrastructure to support scientific investigation and data-driven decision making with a data sharing system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. Research products are made Findable, Accessible, Interoperable, and Reproducible (FAIR) using a collaborative, online sharing platform – HydroShare. Curating a central repository of assembled research data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve planning, preparedness, and monitoring of the recovery process.

This workshop focuses on the presentation of preliminary data for the purpose of collaborative design that ensures the research products are delivered based on the preferences of future users. Participants answered the questions 1 ) What information about water do people need after a disaster? 2) How is information about water most effectively shared? 3) What are the difficulties faced when trying to communicate this type of information? Results were grouped to understand the information needs of academic water data researchers, federal drinking water regulators, local utilities (PRASA and community system operators, health researchers, and household data owners.

The National Science Foundation funded Collaborative RAPID: Building Infrastructure for Preventing Drinking Water Disasters project policies support data sharing mechanisms informed by federal and project guidelines. The workshop was hosted by Inter American University of Puerto Rico, Center for Environmental Education, Conservation and Research (CECIA-IAUPR) and the National Science Foundation Collaborative RAPID Project Team (NSF 1810647 ) . Participants include scientists and professionals from University of Puerto Rico San Juan, Region 2 Caribbean Environmental Protection Division (CEPD) Region 2 US EPA , Western Hemisphere Association of Sanitary and Environmental Engineers and Scientists, US Department of Health Potable Water Program, Puerto Rico Aqueduct and Sewer Authority (PRASA), and Patillas Community Water Systems.

Links to online data resources:
Hurricane Maria 2017 StoryMaps at https://arcg.is/00f1ij
Collaborative RAPID project Wiki: https://github.com/hydroshare/PuertoRicoWaterStudies/wiki
CUAHSI Community Project Landing page: https://www.cuahsi.org/projects/maria2017
HydroShare Puerto Rico Water Studies Group Resources: https://www.hydroshare.org/group/43
Collaborative RAPID Project Team: https://github.com/hydroshare/PuertoRicoWaterStudies/wiki/Collaborators

Workshop Outcomes:
1. User driven data priorities by scenario card sorting
2. Recruitment for Design Interviews for population health data
3. Collaborative Design for Information Distribution
4. RAPID project refined personas
5. Puerto Rico Water Studies Group collaborative authorship experiment (this resource)

ABSTRACT:

This assessment of blocked roads in the days after Hurricane Maria made landfall was conducted and published by FEMA. The extent of the blocked roads assessment included the affected zones in Puerto Rico and the US Virgin Islands.

Please refer to the FEMA data services for the original data files (published 2017-09-26) and methodology: [https://data.femadata.com/NationalDisasters/HurricaneMaria/Data/Transportation/]

ABSTRACT:

Christina Bandaragoda 1, Miguel Leon 2, Jimmy Phuong 3, Graciela Ramirez-Toro 4, Melitza Crespo Medina 4, Kelsey Pieper 5, William Rhoads 5, Tim Ferguson-Sauder 6, Jeffery S Horsburgh 7, Jerad Bales 8, Martin Seul 8, Emily Clark 8, Sean Mooney 3, Kari Stephens 3, Erkan Istanbulluoglu 1, Julia Hart 1, Marc Edwards 5, Amy Pruden 5, Virginia Riquelme 5, Ishi Keenum 5, Ben Davis 5, Matthew Blair 5, Greg House 5, David G Tarboton 7, Amber Spackman Jones 7, Eric Hutton 9,10,11, Gregory E Tucker 9,10,11, Lynn McCready 9, Scott Dale Peckham 11, W. Christopher Lenhardt 13, Ray Idaszak 13, William G McDowell 13 David Arctur 14

(1)University of Washington, Seattle, WA, United States, (2) University of Pennsylvania, Earth & Environmental Science, Philadelphia, PA (3) University of Washington Seattle Campus, Biomedical and Health Informatics, Seattle, WA, United States, (4) Center for Environmental Education Conservation and Research of Inter American University of Puerto Rico, (5) Virginia Tech, Blacksburg, VA, United States, (6) Olin College, Needham, MA (7) Utah State University, Logan, UT, (8) Consortium of Universities for the Advancement of Hydrological Science, Boston, MA (9) Community Surface Dynamics Modeling System, Boulder, CO, United States, (10) Cooperative Institute for Research in Environmental Sciences, Boulder, CO (11) University of Colorado, Boulder, CO, United States, (12) Renaissance Computing Institute, Chapel Hill, NC, United States, (13) University of New Hampshire. (14) University of Texas

Building research software infrastructure to prevent disasters like Hurricane Maria

After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. There is no existing digital infrastructure to scientifically determine the hurricane impact on drinking water quality, the severity of a hazard to human health, or baseline data on the sophistication, connectivity, and operations of the distributed physical and related digital infrastructure systems. We test data publication mechanisms after Hurricane Maria in Puerto Rico to understand risk to human health by (1) assessing the spatial and temporal presence of waterborne pathogens in multiple types of systems, (2) demonstrate usability of CUAHSI HydroShare as a clearinghouse for data related to Hurricane Maria, Harvey and Irma and (3) and develop a prototype cyberinfrastructure to assess environmental and public health impacts. Our resulting archive and research software engineering practices provide a prototype cyberinfrastructure system for researchers to study natural disasters.

How can data sharing and archiving capabilities be enhanced to ensure the greatest scientific impact?
Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We advance open-source software infrastructure to support scientific investigation and data-driven decision making with a data sharing system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. Research products are made Findable, Accessible, Interoperable, and Reproducible (FAIR) using a collaborative, online sharing platform – HydroShare. Curating a central repository of assembled research data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve planning, preparedness, and monitoring of the recovery process.

ABSTRACT:

Subsurface recharge is a surface water model (Topnet-WM) output that can be used as an independent constraint on the groundwater recharge that is specified for the groundwater model (MODFLOW). This will help to ensure that the surface water and groundwater models are internally consistent. The surface water modeling will help to identify a longterm period of representative climatic conditions and resulting groundwater recharge, which can serve as input over a representative time period and for the calibration of a steadystate groundwater model. The surface water modeling will also provide important insights about stream baseflows for calibrating the groundwater model. The analysis of surface water and groundwater conditions is conducted over a large model domain with progressively increasing resolution (e.g. with increased focus in Bertrand) where we are addressing specific questions of drainage impacts and groundwater withdrawal impacts from wells on surface water. The current model domain of the surface water flow model, Topnet-WM, includes the entire WRIA 1 watershed, but calibration and water use inputs have only been refined for the Lower Nooksack Subbasin portion of the basin with additional model development was previously completed in the Bertrand Creek and Fishtrap Creek drainages resulting in the surface model resolution being increased from a single watershed average to 46 sub-drainages per watershed. The following steps estimate groundwater recharge in the conceptual/numerical groundwater model:
 A distribution of recharge will be developed for the entire conceptual/numerical model domainin Bertrand Creek. Model simulations for GW model test coupling with outputs for Water Management On.
 The greater resolution groundwater recharge estimates for Bertrand Creek sub-drainages developed from the 2016 Topnet-WM model are also used as input to the groundwater model .

Initial work:
 Jupyter Notebooks contain code that can be used to run the Topnet model with R code for processing outputs to develop Annual estimates of water budget quantities per drainage.
Resigned work:
The original Topnet-WM model written in Fortran was recoded in C++ and new model output files were designed for groundwater model coupling.

ABSTRACT:

Hydroinformatics is a hurricane
filling my eyes with
glimmering
determination and fear
a swirl of chaos and community
growing on the energy of
unmet expectations.

Hydroinformatics is power
empathic knowledge
sensitive infrastructure
networked
in a mesh of human dignity
ingenuity and vulnerability indexed
by anger [close eyes, inhale]
by sadness [open eyes, exhale]
and gratefulness to be seen
smoke signals in the middle of an ocean.

Hydroinformatics es un huracán
llenando mis ojos con
resplandeciente
determinación y miedo
un remolino de caos y comunidad
creciendo en la energía de
expectativas incumplidas

La hidroinformática es poder
conocimiento empático
infraestructura sensible
en red
en una malla de dignidad humana
ingenio y vulnerabilidad indexados
por enojo [ojos cerrados, inhalar]
por tristeza [ojos abiertos, exhalación]
y agradecimiento para ser visto
señales de humo en el medio de un océano.

ABSTRACT:

The spatially-distributed DHSVM glacio-hydrology model (Frans, 2015; Naz et al. 2014) is a tool for predicting hydrologic states (glaciers, snow, soil moisture, streamflow) within modeled basins for current and future climate conditions. The DHSVM glacio-hydrology model for the Skagit Basin was developed over several years and under three separate agreements with the University of Washington Department of Civil and Environmental Engineering, one with support from Seattle City Light (2013-2014), another with support from the Seattle City Light, the Skagit Climate Science Consortium (SC2), and the Swinomish Indian Tribal Community (2014-2015), and a third with support from the Sauk-Suiattle Indian Tribe (2016-2017).This work builds on the DHSVM-glacier model supported by a 2014-2015 collaboration (managed by the SC2 to model the Skagit (SC2DHSVM2015), and DHSVM-glacier model inputs supported by a 2016-2017 collaboration with the Sauk-Suiattle Indian Tribe managed by SC2. Development of this dataset required use of the current DHSVM glacio-hydrology model and all data produced with the model are a culmination of the three agreements and belong to all five parties. The parties to the original agreements are not responsible for any use of the model or data produced by the model.

The data generated in this work includes:

1.1 Analysis of current streamflow predictions: daily, monthly average, monthly exceedance probabilities, low flows and peak flows, using time periods consistent with data in use for visualization by Skagit Climate Consortium collaborators (1961-2010). Both the daily streamflow and summary statistics are provided.
1.2 Analysis of future streamflow predictions: daily, monthly average, monthly exceedance probabilities, low flows and peak flows, using time periods consistent with data in use for visualization by Skagit Climate Consortium collaborators (2000-2049, 2045-2074, and 2050-2099). Both the daily streamflow and summary statistics will be provided. Both RCP 4.5 and RCP 8.5 are included.

Additional specified locations based on Upper Skagit Indian Tribe planned locations of interest for restoration or further study available on request.

A Google Map of links in the DHSVM digital network selected for streamflow output are available at:
https://www.google.com/maps/d/viewer?mid=13-UUJ47RPVMrBjPlFvDSALjM9qS_5p8l&ll=48.61346235731046%2C-121.49554813369826&z=9

ABSTRACT:

After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. There is no existing digital infrastructure to scientifically determine the hurricane impact on drinking water quality, the severity of a hazard to human health, or baseline data on the sophistication, connectivity, and operations of the distributed physical and related digital infrastructure systems. We test data publication mechanisms after Hurricane Maria in Puerto Rico to understand risks to human health by assessing the spatial and temporal presence of waterborne pathogens in multiple types of systems, demonstrating usability of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare system as a clearinghouse for data related to Hurricanes Maria, Harvey, and Irma, and developing a prototype cyberinfrastructure to assess environmental and public health impacts. Our resulting archive and research software engineering practices provide a prototype cyberinfrastructure system for researchers to study natural disasters.

Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We advance open-source software infrastructure to support scientific investigation and data-driven decision making with a data sharing system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. Research products are made Findable, Accessible, Interoperable, and Reproducible (FAIR) using HydroShare, a collaborative online sharing platform. Curating a central repository of assembled research data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve planning, preparedness, and monitoring of the recovery process.

Almost Like Maria Team Members: Christina Bandaragoda, University of Washington Miguel Leon, University of Pennsylvania Jim Phuong, University of Washington Graciela Ramirez-Toro, Inter American University of Puerto Rico Kelsey Pieper, Virginia Tech William Rhoads, Virginia Tech Tim Ferguson-Sauder, Olin College Jeffery Horsburgh, Utah State University Jerad Bales, Consortium of Universities for the Advancement of Hydrological Science Sean Mooney, University of Washington Martin Seul, Consortium of Universities for the Advancement of Hydrological Science Kari Stephens, University of Washington Erkan Istanbulluoglu, University of Washington Julia Hart, University of Washington Marc Edwards, Virginia Tech Amy Pruden, Virginia Tech Virginia Riquelme, Virginia Tech Ishi Keenum, Virginia Tech Ben Davis, Virginia Tech Emily Lipscomb, Virginia Tech David Tarboton, Utah State University Amber Spackman Jones, Utah State University Eric Hutton, Cooperative Institute for Research in Environmental Sciences Gregory Tucker, University of Colorado Boulder Scott Peckham, University of Colorado Boulder Christopher Lenhardt, Renaissance Computing Institute William McDowell, University of New Hampshire David Arctur, University of Texas at Austin

ABSTRACT:

A beta version of a computational network-based sediment model was developed in order to connect processes of sediment supply on hillslopes, routing in streams, and deposition in reservoirs. The sediment model is developed in a framework called Landlab and driven by a physically-based, distributed hydrology model called DHSVM. The coupled sediment-hydrology model is designed to integrate relevant temporal and spatial scales of hillslope geomorphology, hydroclimatology and river network processes along with answering questions that are relevant to engineering application. The coupled model framework is designed to be applicable in other global watersheds, and could be useful for predicting sediment budgets particularly in the face of environmental and land use/land cover changes.

This model was developed for the Elwha Watershed, in the State of Washington. The information, data, or work presented herein was funded in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy, under Award Number DE-EE0006506 and the Hydro Research Foundation. Neither the United States Government nor any agency thereof, nor any of their employees, makes and warranty, express or implied, or assumes and legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

ABSTRACT:

After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. There is no existing digital infrastructure to scientifically determine the hurricane impact on drinking water quality, the severity of a hazard to human health, or baseline data on the sophistication, connectivity, and operations of the distributed physical and related digital infrastructure systems. We test data publication mechanisms after Hurricane Maria in Puerto Rico to understand risks to human health by assessing the spatial and temporal presence of waterborne pathogens in multiple types of systems, demonstrating usability of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare system as a clearinghouse for data related to Hurricanes Maria, Harvey, and Irma, and developing a prototype cyberinfrastructure to assess environmental and public health impacts. Our resulting archive and research software engineering practices provide a prototype cyberinfrastructure system for researchers to study natural disasters.

Discussion Prompt: How can data sharing and archiving capabilities be enhanced to ensure the greatest scientific impact?

Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We advance open-source software infrastructure to support scientific investigation and data-driven decision making with a data sharing system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. Research products are made Findable, Accessible, Interoperable, and Reproducible (FAIR) using HydroShare, a collaborative online sharing platform. Curating a central repository of assembled research data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve planning, preparedness, and monitoring of the recovery process.

ABSTRACT:

Subsurface recharge is a surface water model (Topnet-WM) output that can be used as an independent constraint on the groundwater recharge that is specified for the groundwater model (MODFLOW). This will help to ensure that the surface water and groundwater models are internally consistent. The surface water modeling will help to identify a longterm period of representative climatic conditions and resulting groundwater recharge, which can serve as input over a representative time period and for the calibration of a steadystate groundwater model. The surface water modeling will also provide important insights about stream baseflows for calibrating the groundwater model. The analysis of surface water and groundwater conditions is conducted over a large model domain with progressively increasing resolution (e.g. with increased focus in Bertrand, Fishtrap, Kamm, Schneider, Wiser/Cougar, Scott, Fourmile, Tenmile and Deer Creek drainage areas) where we are addressing specific questions of drainage impacts and groundwater withdrawal impacts from wells on surface water. The current model domain of the surface water flow model, Topnet-WM, includes the entire WRIA 1 watershed, but calibration and water use inputs have only been refined for the Lower Nooksack Subbasin portion of the basin with additional model development was previously completed in the Bertrand Creek and Fishtrap Creek drainages resulting in the surface model resolution being increased from a single watershed average to 46 sub-drainages per watershed. The following steps estimate groundwater recharge in the conceptual/numerical groundwater model:
 A coarse distribution of recharge will be developed for the entire conceptual/numerical model domain (38 of 172 basins in WRIA1 within GW model domain). Model simulations for GW model test coupling with outputs for Water Management On and Off are in the compressed file <modelruns_1952_WRIA1_081418.gz>
 For the Lower Nooksack Subbasin, watershed-scale average groundwater recharge values developed by the 2012 Topnet-WM surface water model will be specified as initial inputs to the groundwater model.
 The greater resolution groundwater recharge estimates for Bertrand Creek sub-drainages developed from the 2016 Topnet-WM model are also used as input to the groundwater model .

Initial work:
 Jupyter Notebooks contain code that can be used to run the Topnet model with R code for processing outputs to develop Annual estimates of water budget quantities per drainage.
Resigned work:
The original Topnet-WM model written in Fortran was recoded in C++ and new model output files were designed for groundwater model coupling.

ABSTRACT:

In the DHSVM-glacier model update, we incorporated RBM, a spatially distributed semi-Lagrangian stream temperature model (Yearsley 2009, 2012). At each time step and each stream segment, DHSVM now provides to RBM the hydrologic and meteorological inputs including air temperature, downward short wave and long wave radiation, vapor pressure, wind speed, and reach inflows and outflows. A proof of concept Source-to-Sink Network Routing Model was developed to capture the sediment transport given capacity generated from the largest annual storms, mass wasting events, and networked for 439 stream links in the Sauk watershed. The simulation results are for streamflow in each stream link from running the DHSVM-Glacier-RBM model for sediment network modeling. For developing scenarios and testing the proof-of-concept sediment network modeling, this includes 40 year time intervals from one historic (1960-2010) and three future climate scenarios (2060-2099).

ABSTRACT:

Water data is all colors
lecturing on repeat interrupted by silent contributions
it swings in highs and lows, blown by the
shifting hue of smokey sunsets on purple mountains
majestic along the x-axis of chaos.

Water data is all genders, exhausted by trying
to synthesize heterogeneous variables into a box
that does not yet exist
to hold the size of innovation we need now.

Water data is all discplines, escaping the corner
of language, behavior and standards;
it pulses like a community protecting
what we drink, serve our families, and share with friends.

Water data, black hole of massive minutia
searching for the glimmer that will spark
the Age of Aquarius to control the fires,
when the headscarf to beard ratio is m=1.
and where Y = scientific progress; X = participation, and the intercept=0.

ABSTRACT:

This a reproducible demonstration of the landslide modeling results from eSurf paper: Strauch et al. (2018). It is a minor modification to a tutorial simulation created for the broader Thunder Creek watershed in the North Cascades [include link here].

ABSTRACT:

The HydroShare Web App provides easy access to a containerized version of SUMMA as part of the NSF-funded Pangeo project. Pangeo uses docker images that contain SUMMA and pysumma and that allow SUMMA to be run from within Jupyter notebooks. The Pangeo instance enables SUMMA to be used in commercial cloud environments as well as for graduate education. [ Link to snow modeling course taught by Dr. Jessica Lundquist at the University of Washington as part of CUAHSI’s Virtual University in (Fall 2018; Fall 2019: Snow Hydrology and Modeling). Link to graduate course taught by Bart Nijssen at the University of Washington in Spring 2019 (CEWA 564 Advanced Hydrology).]

ABSTRACT:

Surface-groundwater interactions are simulated using a loose coupling (3 steps) of the Topnet-WM surface water model and the MODFLOW groundwater model. Data included here are draft results of the surface model and final results of the coupled surface-groundwater model. Draft surface model outputs (recharge) was used as a model drivers for the groundwater model (Step 1). The groundwater model was run, informed by surface water management, to generate depth to water table outputs (Step 2). The depth to groundwater from Step 2 was used to update the surface model subsurface state variable twice per year. The steady state groundwater level per drainage with no irrigation was used to update the model each March 1, to improve saturated winter subsurface conditions. The steady state groundwater model with irrigation was used to update the surface water model subsurface state variable each October 1. Final model results are available for streamflow and water budget components on a daily timestep from 1955-2010 as part of the project “Development of a Numerical Groundwater Model for the Lynden/Everson/Nooksack/Sumas (LENS) Area of Whatcom County” for Phase 4 – Numerical Model Development for the WRIA 1 Watershed Management Project Joint Board (Joint Board).

This work was developed to provide updated model code and recharge estimates of Bertrand and WRIA Drainage from Surface Water Modeling with Water Management for coupling to the MODFLOW groundwater model. Subsurface recharge is a surface water model (Topnet-WM) output that can be used as an independent constraint on the groundwater recharge that is specified for the groundwater model (MODFLOW). This will help to ensure that the surface water and groundwater models are internally consistent. The surface water modeling will help to identify a longterm period of representative climatic conditions and resulting groundwater recharge, which can serve as input over a representative time period and for the calibration of a steadystate groundwater model. The surface water modeling also provide important insights about stream baseflows for calibrating the groundwater model. The analysis of surface water and groundwater conditions is conducted over a large model domain with progressively increasing resolution (e.g. with increased focus in Bertrand) where we are addressing specific questions of drainage impacts and groundwater withdrawal impacts from wells on surface water. The current model domain of the surface water flow model, Topnet-WM, includes the entire WRIA 1 watershed, but calibration and water use inputs have only been refined for the Lower Nooksack Subbasin portion of the basin with additional model development was previously completed in the Bertrand Creek drainages resulting in the surface model resolution being increased from a single watershed average to 46 sub-drainages per watershed. The following steps estimate groundwater recharge in the conceptual/numerical groundwater model:

A distribution of recharge was developed for the entire conceptual/numerical model domain WRIA1, inclusive of the LENS domain. Model simulations for GW model test coupling with outputs for Water Management On. Of the 172 modeled Nooksack drainages, those with water management data available are primarily in the Lower Nooksack.

A distribution of recharge was developed for the entire conceptual/numerical model domain in Bertrand Creek. Model simulations for GW model test coupling with outputs for Water Management On. The greater resolution groundwater recharge estimates for Bertrand Creek sub-drainages developed from the 2016 Topnet-WM model are also used as input to the groundwater model.

For developers: the original Topnet-WM model written in Fortran was recoded in C++ and new model output files were designed for groundwater model coupling. Model software code and related files are available at https://github.com/ChristinaB/Topnet-WM.

ABSTRACT:

The overall goal of the WRIA 1 Watershed Management Project is to have water of sufficient quantity and quality to meet the needs of current and future human generations. This goal includes the restoration of salmon, steelhead, and trout populations to healthy and harvestable levels, and the improvement of the habitat upon which fish rely. Since the inception of the Project in 1998, steps have been taken to achieve this goal, including the development of the technical information necessary to evaluate instream and out-of-stream needs. The goal of integrating hydraulic, hydrology and fish habitat models is to describe the relationship between weighted usable area and streamflow for species and life stage utilization throughout the year. In 1999, the Instream Flow Methods Conference achieved agreement among the invited experts on the most appropriate method(s) for estimating an accurate relationship between streamflow and fish habitat quantity and quality in Water Resources Inventory Area 1 (WRIA 1). Over the 2000 through 2004 period, sites where field data were to be collected were identified by consensus of the Instream Flow and Fish Habitat technical team members based on a number of factors including representativeness of the site, the availability of fish utilization data (e.g., spawner surveys, smolt traps), locations where instream flows were established by Ecology in 1985 (WAC 173-501), and management issues or where known conflicts over water use existed. The field data collection and analyses of the collected field data were conducted to implement the agreed upon most appropriate methods for estimating the relation between streamflow and fish habitat quantity and quality at 37 sites within WRIA 1 (Figure 1). After collecting hydraulic and fish habitat characteristics of these locations, Intensive and Rapid Assessment sites were incorporated into the hydrology model node structure used in the first implementation of the WRIA 1 Watershed Modeling using Topnet-WM (Tarboton 2007) as well as the second implementation focused on estimating the Lower Nooksack Water Budget (Bandaragoda et al. 2012). This work, Data Integration of WRIA 1 Hydraulic, Fish Habitat, and Hydrology Models, is intended to increase the usability of existing data and to help achieve the overall goals of the Water Resources Inventory Area No. 1 (WRIA 1) Watershed Management Project. No new modeling was performed as part of this technical project.

ABSTRACT:

The focus of our study on the Hydrology, Stream Temperature and Sediment Impacts of Climate Change in the Skagit River Basin is to improve our understanding of the Skagit River earth processes systems using a coupled glacio-hydrology model and future climate projections. Our modeling work focused on the Sauk sub-watershed, but also include results for naturalized streamflow at Skagit River Hydroelectric Project reservoir locations (Ross, Diablo, Gorge) and at sixteen tributaries using future climate change scenarios. This project utilized data products from the Integrated Scenarios of the Future Northwest Environment project, which identified a core set of 10 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5; Mote et al., 2015) as the best performing models based on comparisons of observed 20th century climate of the Pacific Northwest. To simulate streamflow, we used the Distributed Hydrology Soil Vegetation Model (DHSVM) – a coupled glacio-hydrology model, and advanced the model code to include RBM stream temperature model, and new advances in sediment transport modeling. The model domain included the entire Skagit River basin at 150m digital elevation model (DEM) resolution, with nested models of 50m resolution of selected subbasins (Thunder Creek and Cascade Creek) that have the major glacier ice cover at their high elevations. The modeling steps included: (a) hydrometeorology bias correction, (b) spin up of the glacier model to develop realistic glacier cover in the glaciated uplands prior to watershed hydrology and streamflow predictions; (c) calibration of DHSVM using select model parameters and climate forcing bias correction in select subbasins; (c) model validation using historical streamflow observations; (d) projections of streamflow into the future using CMIP5 models; (e) bias-corrections of modeled streamflow to match observations based on monthly mean, (f) streamflow temperature modeling, and (g) suspended sediment modeling. Results include modeled streamflow for 90% exceedance probability flows in summer months. Validation and corrections to the glacio-hydrology model were conducted using empirical data (collected by North Cascades National Park), naturalized flows at reservoir locations (three reservoirs), and observed stream gauges (where and when available at 16 Skagit River tributaries). Future projections were calculated using GCMs for multiple overlapping fifty year periods starting from 2010 to 2099.

In glaciated high elevation basins, the current conditions of approximately 100 km2 of glacier ice are projected to decrease to less than 50 km2 by 2050. If global emissions stop increasing by the 2050s, it is likely that the highest elevation glaciers will continue to store pockets of ice and provide some glacier melt in the summer months. By the 2050s, the low flows will show a wide range of change conditioned on elevation. Low summer flows (10 year low flows; 7Q10) are projected to decrease 10-20% in low-elevation rain-dominated tributaries, (e.g., Rinker, Clear Creek), and ~30% in mid-elevation mixed rain and snow tributaries (e.g., South Fork Sauk). High flows and peak event are projected to slightly increase by 2050, with statistically significant peak events (100 year flood) expected to increase 20-30% for a conservative climate change scenario (RCP 4.5 Ensemble), and +40% for other estimates (RCP 8.5 Ensemble). Historic Maximum Daily Maximum Temperature during summers were 17.8 oC, 19.2 oC and 17.4 oC at the Sauk R. above Suiattle, the Sauk River near Darrington, and the White Chuck River, respectively, and projected to increase by 2-3 oC for all scenarios and sites; the Sauk River near Darrington shows the largest increase in MDMT and the White Chuck River the least. Historical suspended sediment load is ~ 40% lower than the 5-year mean annual historic yield. In two future scenarios, using data from 10 future models, suspended sediment load increases over time due to changes in hydrology, with moderate climate models giving up to threefold increases in suspended load.

ABSTRACT:

Scope - GIS inputs, products, and final outputs for 1km source areas in PR. Jupyter Notebooks with Landlab code. This is a Discoverable resource with private geo locations. Users must request access and sign the responsible use agreement managed by Graciela Ramirez-Toro.

Overall project Objective: Develop a prototype cyberinfrastructure to assess conditions of environmental resources (including drinking water quality and landscape conditions) and population health impacted by natural disasters like Hurricane Maria. We have adapted existing cyberinfrastructure components to foster streamlined disaster preparedness, recovery, and population health research.

Community Surface Dynamics Modeling System (CSDMS)
Eric Hutton is the Senior Software Engineer with experience in multi-language codes, sediment transport and geophysical model development, and model coupling. Eric is overseeing and coordinating the software development necessary to ingest and use the identified data into the Landlab framework. Greg Tucker is the Director of CSDMS and PI on the NSF Landlab project. Lynn McCready is supporting the organization of planning and user testing workshops.

Original types of data - some is in other resources
Geospatial Data includes roads and road closure information, Safe Drinking Water Information System point locations of Community and Public Water Systems, locations of 69 hospitals in Puerto Rico, mudslide locations and landslide hazards obtained from the USGS, and storm deforestation rasters generated from satellite data. An effort will be taken to acquire information about electrical power availability in relation to hospitals and drinking water treatment and distribution systems, it is unknown at this time if these data may become available. Population health data publicly available from the Institute for Health Metrics and Evaluation (IHME) will be used at the highest resolution available (5 x 5 km) to map the Local Burden of Disease for Puerto Rico.

ABSTRACT:

Template

ABSTRACT:

This folder contains files for each grid point from the PNNL WRF 2018 gridded meteorology dataset that is within the Sauk watershed. The PNNL WRF 2018 precipitation is aggregated as 1 hr mean (i.e. each hour is mean hourly precipitation rate). No further aggregation was done to precipitation or any other of the variables. Each file is formatted as forcing data for DHSVM. Variables wind and relative humidty are derived from the PNNL WRF 2018 data.

ABSTRACT:

This folder contains files for each grid point from the PNNL WRF 2018 gridded meteorology dataset that is within the Sauk watershed. Precipitation is aggregated to 24 hr mean (i.e. each hour is the mean 24 hour precipitation rate). No aggregation was done to any other of the variables. Each file is formatted as forcing data for DHSVM. Variables wind and relative humidty are derived from the PNNL WRF 2018 data.

ABSTRACT:

Days after Hurricane Maria made landfall in Puerto Rico on September 20, 2017, the Federal Emergency Management Agency (FEMA) responded by assessing the extent of flooding hazard in Puerto Rico and the US Virgin Islands. Two mapping systems for remote sensing were used, Copernicus EMS and the NASA NASA MSFC SPoRT. The resulting raster images for flooding extent represented central and eastern Puerto Rico, but did not include the western segment of the island which was Hurricane Maria's exit trajectory.

The imagery files were taken from the FEMA data services. Please refer to the FEMA website for the original files and inference methods:
https://data.femadata.com/NationalDisasters/HurricaneMaria/Data/RemoteSensing/FEMA_FloodDetectionMaps/

ABSTRACT:

In the wake of Hurricane Maria, the Federal Emergency Management Agency (FEMA) was called in to conduct damage assessments. The resulting data collection was a series of geodatabases for sections of the affected zone surveyed. Here, the geodatabases have been transformed into ESRI shape files for ease of use.

The original data files can be found at the FEMA data services webportal. Please refer inquiries about the survey data and the data collection methods therein to FEMA:
https://data.femadata.com/NationalDisasters/HurricaneMaria/Data/DamageAssessments/Visual/

ABSTRACT:

Studies of water and environmental systems are becoming increasingly complex and require the integration of knowledge across multiple domains. At the same time, technological advances have enabled the collection of massive quantities of data for studying earth system changes. Fully leveraging these datasets and software tools requires fundamentally new approaches in the way researchers store, access and process data. Waterhackweek, supported by the National Science Foundation Cybertraining program, serves the national interest by motivating a culture shift within the hydrologic and more broadly earth science communities toward open and reproducible software practices that will enhance interdisciplinary collaboration and increase capacity for addressing complex science challenges around the availability, risks and use of water. This cyberseminar series consists of presentations from the Cybertraining investigators and research software developers, each focusing on a specific water-related use cases, tool, or library. Topics will consist of both introductory and advanced concepts that are relevant to a wide range of water and informatics use-cases, e.g. publishing large datasets, running numerical models, organizing collaborative research projects, and meeting journal requirements by following open data standards. The goal of the 2019 series is to prepare the incoming Waterhackweek (March 25-29, 2019) participants for the in-person capstone event in which their skills and creativity will be used to address natural hazards, however, these topics and technologies are also relevant to the broader water science community. We welcome all undergraduate, graduate, and early career scientists to join us in this public cyberseminar series.

ABSTRACT:

Hydrological and meteorological information can help inform the conditions and risk factors related to the environment and their inhabitants. Due to the limitations of observation sampling, gridded data sets provide the modeled information for areas where data collection are infeasible using observations collected and known process relations. Although available, data users are faced with barriers to use, challenges like how to access, acquire, then analyze data for small watershed areas, when these datasets were produced for large, continental scale processes. In this tutorial, we introduce Observatory for Gridded Hydrometeorology (OGH) to resolve such hurdles in a use-case that incorporates NetCDF gridded data sets processes developed to interpret the findings and apply secondary modeling frameworks (landlab).

LEARNING OBJECTIVES
- Familiarize with data management, metadata management, and analyses with gridded data
- Inspecting and problem solving with Python libraries
- Explore data architecture and processes
- Learn about OGH Python Library
- Discuss conceptual data engineering and science operations

Use-case operations:
1. Prepare computing environment
2. Get list of grid cells
3. NetCDF retrieval and clipping to a spatial extent
4. Extract NetCDF metadata and convert NetCDFs to 1D ASCII time-series files
5. Visualize the average monthly total precipitations
6. Apply summary values as modeling inputs
7. Visualize modeling outputs
8. Save results in a new HydroShare resource

For inquiries, issues, or contribute to the developments, please refer to https://github.com/freshwater-initiative/Observatory

ABSTRACT:

HydroShare data sharing instructions for Waterhackweek presenters.

ABSTRACT:

Do water borehole failures, in Uganda, correlate with geographic details, population, or political reasons? Is there a trend based on which orgnization oversaw the installation or raised the capital? Can we create an ML model to determine if correlations exist?

ABSTRACT:

Water Resource Inventory Area 1 (WRIA1)

ABSTRACT:

Hydroinformatics is strength in numbers.
Evidence aligned on the meridian
of purposeful investigation.
Deep understanding that comes
from the back breaking work
of decades
at desks that are not ergonomic
at screens that are never big enough
at windows waiting to be cleaned.

Hydroinformatics is a commitment
to discovering the physical process
that matters most
rejection of inefficient filters
disgust at uncertain methods
and a passion to learn more.

Hydroinformatics is hard won proof
legitimized by microscopic
attention to macroscopic details
to uncover the Earth's secrets
as they are whispered
on softly falling snowflakes
uncovered from layers of ice
and released in a digital avalanche of knowledge.

ABSTRACT:

Hydroinformatics is digital machinations
of ideas converted
into a pile of niche shaped
tools made of human cogs.
A community-shaped machine
that is a discontinuous
expanding puzzle,
an evolutionary masterpiece.

Hydroinformatics is fantastical dreams
of how the Earth works
natural philoshophies on
sharing optimistic futures
and kindness.
The deep kindness that flows as
quiet listening.
Converging. Diverging. Converging.

Flag = 0 Wrenching pleas unspoken.
Flag = 1 "Please believe. The earth is round
and it is in our hands. Let me show you."

for each i in n dreamers
{
01010001110111110111111;
}
in a loop we creatively compute
fiercely share
and gently protect.

ABSTRACT:

The ability to test hypotheses about hydrology, geomorphology, and atmospheric processes is invaluable to research in the Earth and planetary sciences. To swiftly develop experiments using community resources is an extraordinary emerging opportunity to accelerate the rate of scientific advancement. Knowledge infrastructure is an intellectual framework to understand how people are creating, sharing, and distributing knowledge -- which has dramatically changed and is continually transformed by Internet technologies. We are actively designing a knowledge infrastructure system for earth surface investigations. In this paper, we illustrate how this infrastructure can be utilized to lower common barriers to reproducing modeling experiments. These barriers include: developing education and training materials for classroom use, publishing research that can be replicated by reviewers and readers, and advancing collaborative research by re-using earth surface models in new locations or in new applications. We outline six critical elements to this infrastructure, 1) design of workflows for ease of use by new users; 2) a community-supported collaborative web platform that supports publishing and privacy; 3) data storage that may be distributed to different locations; 4) a software environment; 5) a personalized cloud-based high performance computing (HPC) platform; and 6) a standardized modeling framework that is growing with open source contributions. Our methodology uses the following tools to meet the above functional requirements. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. The Consortium of Universities Allied for Hydrologic Science (CUAHSI) supports the development and maintenance of a JupyterHub server that provides the software environment for the system. Data storage and web access are provided by HydroShare, an online collaborative environment for sharing data and models. The knowledge infrastructure system accelerates knowledge development by providing a suite of modular and interoperable process components that can be combined to create an integrated model. Online collaboration functions provide multiple levels of sharing and privacy settings, open source license options, and DOI publishing, and cloud access to high-speed processing. This allows students, domain experts, collaborators, researcher, and sponsors to interactively execute and explore shared data and modeling resources. Our system is designed to support the user experiences on the continuum from fully developed modeling applications to prototyping new science tools. We have provided three computational narratives for readers to interact with hands-on, problem-based research demonstrations - these are publicly available Jupyter Notebooks available on HydroShare.

To interactively compute with these Notebooks, please see the ReadMe below.
To develop these Notebooks, go to Github: https://github.com/ChristinaB/pub_bandaragoda_etal_ems or https://zenodo.org/badge/latestdoi/187289993

ABSTRACT:

CUAHSI’s Water Data Services are community developed, open access, and available to everyone. Workshops are used to share and learn how these services can help researchers and teams on a variety of research tasks. We include an overview of how to develop data management plans, which are increasingly required by most funders. Materials describe how to discover and find a broad array of water data-time series, samples, spatial coverages, published datasets, and case study workflows. CUAHSI apps and tools are introduced for expediting and documenting workflows. We have provided interactive curriculum and tutorials with examples of how toShare your data within a group and publish your data with a DOI. Future training opportunities and funding opportunities for graduate students are listed.

This workshop was a featured event at the 2019 UCOWR Annual Water Resources Conference, Tuesday, June 11 from 1:00 p.m. – 3:50 p.m., White Pine Meeting Room, Cliff Lodge Snowbird, Utah

ABSTRACT:

Hydroinformatics is exapanding and contracting with the seasons.
Firmly planted, mind traversing
the Universe of possibilities on how
to bring order to the chaos
in tidy rows and columns
with so many labels
it makes you wonder.

YO - hey mama, sweet metadata.
DA - go daddy, design me an experience.

Hydroinformatics is for reproducing.
Hydroinformatics is for the reproducers.

Deep breathe.

Extreme minority status is a mere
snapshot in a dynamic time series
accurately logged with extensive commenting
used to reform the template
of what it means to be relevant
of how to carry the weight
of the human race
into the Age of Aquarius.

Fluid transport of natural history
tenderly nurtured
transforming attention to detail into a
raging series of stochastic events that
we all know will happen
because you make the data talk to us in a way we can see.

Hydroinformatics is creating a future, that otherwise would not exist.

ABSTRACT:

So much metadata. So little time.

IN the world of met data, gridded datasets at continental or regional scales which need to be corrected to seasonal averages based on observations.
e.g. match average 30 January precipitation and compare that to your gridded product at that location.
If temperature is not a good fit, add or subtract the monthly difference to shift the monthly average values to the monthly average of the dataset you trust.
It addresses scale mismatch issues of downscaling general circuluatoin models (100 km) to a local monthly average at a 5 km grid.
This structure of the bias correction can be applied to future datasets downscaled with the same structure as the historic gridded products.

ABSTRACT:

This HydroShare resource is intended to serve as the evolving publication list for HydroShare. All HydroShare team members should have edit access to this resource so everyone on the team can update this resource with new publications over time.

ABSTRACT:

Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 to 1800 m above sea level. Data were collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Select sites included relative humidity sensors to indicate whether precipitation was occurring. Data were collected every 3-4 h from December 2015 to Sept 2018 (with ongoing collection). Code for analysis of daily mean, minimum, maximum, and temperature change with elevation (lapse rates) are available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare at (http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d) with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org. Hydrologic models are generally structured with a single annual average lapse rate parameter which assumes a linear temperature gradient with elevation. The daily data (2016-2018) is used as part of ongoing studies on the non-linear dynamics and temporal variability of temperature with elevation to improve assessments of watershed function and salmon habitat.

Please see Related Resources Section and Readme.md for additional citation information related to this resource.

Land Acknowledgement: The Coast Salish people are the indigenous inhabitants of Western Washington. The Nooksack Watershed, from the peak of Mount Baker to the Bellingham Bay, is the unceded ancestral land of the Nooksack Tribe and Lummi Nation. They are still here, continuing to honor and bring to light their ancient heritage. The University of Washington acknowledges the Coast Salish peoples of this land, the land which touches the shared waters of all tribes and bands within the Suquamish, Tulalip and Muckleshoot nations.

ABSTRACT:

[ to do ]

ABSTRACT:

You are invited to learn a new online tool for exploring streamflow in the Skagit River watershed. The tool provides historical and future streamflows based on hydrologic modeling by University of Washington (UW). The visualization and streamflow data can be used in long-term planning as well as in designs for long-lived infrastructure and resource projects. This training includes slides for a presentation and interactive run exercises using the visualization tool and explore how to use the tool to discover interesting patterns based on CMIP5 climate changes.

As the climate warms, people want information on what to consider as they plan for potential changes in streamflows. The following visualizations show a large set of outputs from a modeling study conducted by researchers at the University of Washington Civil and Environmental Engineering Department and supported by several organizations with a common interest in understanding a potential range of future conditions (Seattle City Light, Swinomish Indian Tribal Community, and the Sauk-Suiattle Indian Tribe in partnership with the Skagit Climate Science Consortium). The study is available at: https://www.hydroshare.org/resource/e5ad2935979647d6af5f1a9f6bdecdea/. The study modeled projected changes in streamflows at 20 locations in the Skagit River Watershed.

Specific locations modeled include: Red Cabin Creek, Finney Creek, Jackman Creek, Illabot Creek, Cascade River, Jordan Creek, Bacon Creek, Marblemount to Newhalem, Gorge, Diablo,Thunder Creek, Ross, Sauk River near Sauk, Big Creek, Sauk River at Darrington, Sauk River above Clear Creek, Sauk River above White Chuck, White Chuck, North Fork Sauk River, South Fork Sauk River,

Visualizations include Monthly Averages and Extremes within multiple dashboard page viewers with embedded maps, charts, and figures, with a tab on Definitions & Documentation used in the visualizations also provided.

Direct link to the tool - http://www.skagitclimatescience.org/projected-changes-in-streamflow/

Time: 1.5 hours

These files were originally developed for the Skagit Streamflow Visualization Online Tool Training on February 13, 2020 with Seattle City Light staff.

Attached files include: Help Guide, Training slideshow (with links to more data/info), Exercise with answers

ABSTRACT:

Studies of water and environmental systems are becoming increasingly complex and require integration of knowledge across multiple domains. At the same time, technological advances have enabled the collection of massive quantities of data for studying earth system changes. Fully leveraging these datasets and software tools requires fundamentally new approaches in the way researchers store, access and process data. The project serves the national interest by motivating a culture shift within the hydrologic and more broadly earth science communities toward open and reproducible software practices that will enhance interdisciplinary collaboration and increase capacity for addressing complex science challenges around the availability, risks and use of water. Project's CyberTraining approach provides virtual learning experiences throughout an academic year, with online learning modules oriented around a one-week in-person workshop (WaterHackWeek) that will focus on hands-on real-world research projects. These research projects are designed to serve the national interest by preparing for natural hazards such as floods, hurricanes and climate change, and to advance the nation's health by making tools and data accessible to health researchers, local governments, and citizens.

New cyberinfrastructure that emphasizes data sharing and open, reproducible software practices is currently in development, but requires a mode of knowledge transfer, or CyberTraining, that extends beyond currently available university curriculum. Project's aim is to ensure successful use of community cyberinfrastructure to 1) publish large datasets, 2) run numerical models, 3) organize collaborative research projects, and 4) meet journal requirements to follow open data standards. The activities take advantage of HydroShare, a National Science Foundation funded cyberinfrastructure platform, operated by the Consortium of Universities Allied for Hydrologic Sciences (CUAHSI), for sharing hydrologic data and models. The short-term goals are to develop new CyberTraining modules; the long-term goals are to have an annually recurring WaterHackWeek, to distribute curriculum CUAHSI to more than 130 member universities, and advance cyberinfrastructure education for the broader geoscience community. The use of the hackweek educational model extends the use of cyberinfrastructure to promote the progress of science by including a specific emphasis on graduate student training as instructors, training coordinators, and building research networks with data providers who are stakeholders outside of academia. For example, case studies include data and resource management by Native American tribal governments, Hurricane Maria data archive for research in Puerto Rico, improving flood forecasting, and tool-building using complex numerical models such as the National Water Model. This project allows to test the educational model in the water research community, in addition to connecting team's research and curriculum to annually recurring hackweeks in neuro, astro, ocean, and geo sciences. The team of researchers is actively engaged in experimenting with this new model, and in testing its efficacy through robust evaluation metrics. The proposed activities encourage collaboration and support for use of cyberinfrastructure at all stages of the educational pipeline and provides participants with opportunities for networking, career development, community building and design of open-source software tools.

ABSTRACT:

The ability to test hypotheses about hydrology, geomorphology, and atmospheric processes is invaluable to research in the Earth and planetary sciences. To swiftly develop experiments using community resources is an extraordinary emerging opportunity to accelerate the rate of scientific advancement. Knowledge infrastructure is an intellectual framework to understand how people are creating, sharing, and distributing knowledge -- which has dramatically changed and is continually transformed by Internet technologies. We are actively designing a knowledge infrastructure system for earth surface investigations. In this paper, we illustrate how this infrastructure can be utilized to lower common barriers to reproducing modeling experiments. These barriers include: developing education and training materials for classroom use, publishing research that can be replicated by reviewers and readers, and advancing collaborative research by re-using earth surface models in new locations or in new applications. We outline six critical elements to this infrastructure, 1) design of workflows for ease of use by new users; 2) a community-supported collaborative web platform that supports publishing and privacy; 3) data storage that may be distributed to different locations; 4) a software environment; 5) a personalized cloud-based high performance computing (HPC) platform; and 6) a standardized modeling framework that is growing with open source contributions. Our methodology uses the following tools to meet the above functional requirements. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. The Consortium of Universities Allied for Hydrologic Science (CUAHSI) supports the development and maintenance of a JupyterHub server that provides the software environment for the system. Data storage and web access are provided by HydroShare, an online collaborative environment for sharing data and models. The knowledge infrastructure system accelerates knowledge development by providing a suite of modular and interoperable process components that can be combined to create an integrated model. Online collaboration functions provide multiple levels of sharing and privacy settings, open source license options, and DOI publishing, and cloud access to high-speed processing. This allows students, domain experts, collaborators, researcher, and sponsors to interactively execute and explore shared data and modeling resources. Our system is designed to support the user experiences on the continuum from fully developed modeling applications to prototyping new science tools. We have provided three computational narratives for readers to interact with hands-on, problem-based research demonstrations - these are publicly available Jupyter Notebooks available on HydroShare.

To interactively compute with these Notebooks, please see the ReadMe below.
To develop these Notebooks, go to Github: https://github.com/ChristinaB/pub_bandaragoda_etal_ems or https://zenodo.org/badge/latestdoi/187289993

ABSTRACT:

Evaluating the future change in the hydrologic response of rivers is typically carried out using a complex sequence of linked numerical models starting with climate projections by general circulations models (GCMs) with various assumptions about emissions scenarios on radiation (representative concentration pathways (RCPs), downscaling using statistical methods, or dynamic downscaling using nested regional climate models (RCMs), bias correction of selected downscaled hydrometeorological variables of interest using observed data sets (e.g. precipitation and temperature from gridded station data), and finally use of the bias corrected meteorological forcing as inputs to hydrologic models to simulate hydrologic change and various impacts. To address the issues encountered in the Pacific Northwest, Skagit and Nooksack basin studies, and mountain environments in general we have developed a hybrid approach which bias-corrects and combines simulated data from high-resolution regional climate models (RCMs) with long-term gridded interpolations of in situ data from weather stations. This is achieved by focusing on two primary objectives: 1) removing bias in the atmospheric model, while preserving the temperature and precipitation gradients in the physically based simulations, and 2) preserving the spatio-temporal correlations and time series characteristics of the gridded meteorological records based on station observations. The computational methods employed are intended to be flexible and (where the supporting data sets are available) can be broadly applied in support of hydrologic modeling in mountain environments. Substantial improvements in streamflow simulations in the Skagit case study provide proof of concept that temperature and precipitation bias at moderate to high elevation is effectively reduced by the new hybrid data processing approach and greatly improved model predictions, but the size of the dataset is massively large for investigating the details of the methods. This dataset and code is designed for tutorial and illustrations using the Skookum Creek watershed.

!! Disclaimer: Work in progress. !!
Sample dataset for demonstrating the methods in the upscaling-downscaling paper by
View this Google Map: https://www.google.com/maps/d/edit?mid=1MwvcMk2UvEO8K4NGTM_3jGzXGkxOL-81&ll=48.67006706749427%2C-122.2752676685605&z=10

ABSTRACT:

!!! This is a fork from https://www.hydroshare.org/resource/5b964154ebf945848087bdc772cc921e/ with some minor modifications for CyberGIS-Jupyer for Water (CJW) platform !!!
----------------------------------------------------------------------------------------------------
The ability to test hypotheses about hydrology, geomorphology, and atmospheric processes is invaluable to research in the Earth and planetary sciences. To swiftly develop experiments using community resources is an extraordinary emerging opportunity to accelerate the rate of scientific advancement. Knowledge infrastructure is an intellectual framework to understand how people are creating, sharing, and distributing knowledge -- which has dramatically changed and is continually transformed by Internet technologies. We are actively designing a knowledge infrastructure system for earth surface investigations. In this paper, we illustrate how this infrastructure can be utilized to lower common barriers to reproducing modeling experiments. These barriers include: developing education and training materials for classroom use, publishing research that can be replicated by reviewers and readers, and advancing collaborative research by re-using earth surface models in new locations or in new applications. We outline six critical elements to this infrastructure, 1) design of workflows for ease of use by new users; 2) a community-supported collaborative web platform that supports publishing and privacy; 3) data storage that may be distributed to different locations; 4) a software environment; 5) a personalized cloud-based high performance computing (HPC) platform; and 6) a standardized modeling framework that is growing with open source contributions. Our methodology uses the following tools to meet the above functional requirements. Landlab is an open-source modeling toolkit for building, coupling, and exploring two-dimensional numerical models. The Consortium of Universities Allied for Hydrologic Science (CUAHSI) supports the development and maintenance of a JupyterHub server that provides the software environment for the system. Data storage and web access are provided by HydroShare, an online collaborative environment for sharing data and models. The knowledge infrastructure system accelerates knowledge development by providing a suite of modular and interoperable process components that can be combined to create an integrated model. Online collaboration functions provide multiple levels of sharing and privacy settings, open source license options, and DOI publishing, and cloud access to high-speed processing. This allows students, domain experts, collaborators, researcher, and sponsors to interactively execute and explore shared data and modeling resources. Our system is designed to support the user experiences on the continuum from fully developed modeling applications to prototyping new science tools. We have provided three computational narratives for readers to interact with hands-on, problem-based research demonstrations - these are publicly available Jupyter Notebooks available on HydroShare.

To interactively compute with these Notebooks, please see the ReadMe below.
To develop these Notebooks, go to Github: https://github.com/ChristinaB/pub_bandaragoda_etal_ems or https://zenodo.org/badge/latestdoi/187289993

ABSTRACT:

The structure of our course is designed to mirror the structure of the interactive learning in a Jupyter Notebook. It’s just like a chemistry lab, we squeeze the Earth into a web browser shaped beaker, and turn on the team science bunsen burner. Each Section in the module is based on one experiment (or computational workflow) which replicates results available in a published journal article in collaboration with publication coauthors. Sub-sections have various themes, research question motivations, datasets, models, but each have one assessment focusing on skills to address Nested Learning Objectives identified by each Summary question. Sub-sections include: Introduction, Theoretical Background, Methods, Results, Discussion, Conclusion.
In the Experiments, we distinguish between cyberinfrastructure, data science, and geoscience domain methods. We also introduce Team Science (Convergence, Diversity, Inclusion, Equity) and Information Science elements in each Section (Findable, Accessible, Interoperable, Accessible).

ABSTRACT:

Work in progress.

ABSTRACT:

[to complete by team]
Samples were collected from PRASA, Non-PRASA, and improvised systems all over the Island
[to complete by team]

ABSTRACT:

We developed a new approach for mapping landslide hazard combining probabilities of landslide impact derived from a data-driven statistical approach applied to three different landslide datasets and a physically-based model of shallow landsliding. This data includes the site characteristics used in the empirical approach to derive a susceptibility index (SI) and a probability of failure, and the physically based probability derived from a previous regional study (see Related Resources). These probabilities are integrated into a weighting term that is used to adjust the physical model of landslide initiation to account for empirical evidence not captured by the infinite slope stability model alone. The data and modeling are for a 30 meter grid resolution study domain in the North Cascades National Park Complex, Washington, U.S.A (see Resource Coverage).

The data are provided as Esri ArcGIS shapefiles and rasters, as well as an example ASCII files for one raster and the header for conversion of ASCII to raster. Spatial reference for raster mapping is NAD_1983, Albers conical equal area projection. Elevation was acquired from National Elevation Dataset (NED) at 30 m grid scale; other datasets are matched to scale and location. Curvature, slope (tan theta), and aspect are derived from elevation. A wetness index, divided into five categories, is derived from elevation calculated as the natural log of the ratio of the specific catchment area to the sine of the local slope. Land use and land cover (LULC) data were acquired from USGS National Land Cover Data (NLCD) based on 2011 Landsat satellite data and grouped into eight general categories. Mapped landslides were provided by the National Park Service (NPS) from a landform mapping inventory. Source areas used to define initiation zones were identified as the upper 20% of debris avalanche landslide types. Lithology is provided by Washington State Department of Natural Resources surface geology maps and is grouped into seven categories. Other layers include the boundary of the national park used to demonstrate the model, the area included in the analysis (i.e., excluding high-elevation areas covered by glaciers, permanent snowfields, and exposed bedrock, wetlands and other water surfaces, and slopes less than 17 degrees), the empirical based SI, the calculated weight, and the probabilities of landslide activity for the empirical, physical, and weight-adjusted physical models. Additional data and information that supports this research or facilitates future research is available in Supplementary Information (See Related Resources).

This repository holds the data used in the paper: A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA, published in Natural Hazards and Earth System Sciences 19, 1-19, 2019.

ABSTRACT:

ESIP Lab Incubator Projects (2018) Unite!
Watermesh: civil digital infrastructure for real-world health impacts made possible by information flow, data sharing, and data security that enables clean water for everyone.
Geoweaver: a web system to allow users to easily compose and execute full-stack Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) workflows in web browsers by taking advantage of the online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries.

ABSTRACT:

Digital Water: Emerging Data Science and Research Software
Course Number: CEE 599 B,D
Christina Bandaragoda, University of Washington
Learn how to use digital infrastructure to publish, manage, and operate software to translate your research between science
domains for research, policy development, and decision-making using observations and models. Discover data, generate and
test hypotheses, and ethically cooperate on reproducible research with online platforms, modeling frameworks, and open
source software for interactive science focusing on water data and translation tools.
Prerequisites: None. Additional resources will be provided for those not familiar with Python. Students new to programming
should anticipate extra time for completing assignments.

Class resources
1. Make a folder with your name on it for your work; this will be private until consent forms are finished.
2. Upload a Notebook draft and materials for 5 minute presentation OR readme with links to Github.

Add MyBinder: https://www.hydroshare.org/resource/51188b5303514b20b1b092a24c6620e9/

ABSTRACT:

Panel Presentation on February 17, 2023; San Juan Puerto Rico
14th CECIA-IAUPR Biennial Symposium on Potable Water Issues in Puerto Rico: Science, Technology and Regulation

Presenters:
Dr. Christina Norton, University of Washington
Christopher Lenhardt, RENCI, University of North Carolina at Chapel Hill
Dr. Elaine Faustman, University of Washington
Jill Falman, University of Washington