ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services. The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, srad0.nc, tmin0.nc, tmax0.nc, prcp0.nc. The model parameter files include: inputcontrol.dat, param.dat, control.dat, outputcontrol.dat, siteinitial.dat. This model instance resource is complete for model simulation.

ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services. The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, srad0.nc, tmin0.nc, tmax0.nc, prcp0.nc. The model parameter files include: inputcontrol.dat, param.dat, control.dat, outputcontrol.dat, siteinitial.dat. This model instance resource is complete for model simulation.

ABSTRACT:

This is prepared by running the ueb_setup.py file without using the logan watershed raster tif file

ABSTRACT:

This is created using the user raster of logan watershed. The ueb_setup.py file is modified which does not include the watershed delineation.

ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services. The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, tmin0.nc, tmax0.nc, srad0.nc, prcp0.nc, ueb_setup.py, hydrogate.py. The model parameter files include: inputcontrol.dat, control.dat, param.dat, outputcontrol.dat, siteinitial.dat. This model instance resource is complete for model simulation.

ABSTRACT:

This is a demo of makeing plot for UEB output data

ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services (https://github.com/CI-WATER/Hydro-DS). The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, tmin0.nc, tmax0.nc, srad0.nc, prcp0.nc, ueb_setup.py, hydrogate.py. The model parameter files include: control.dat, param.dat, inputcontrol.dat, outputcontrol.dat, siteinitial.dat. This model instance resource is complete for model simulation and the corresponding model output files are also included.

ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services. The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, tmin0.nc, tmax0.nc, srad0.nc, prcp0.nc, ueb_setup.py, hydrogate.py. The model parameter files include: control.dat, param.dat, inputcontrol.dat, outputcontrol.dat, siteinitial.dat. This model instance resource is complete for model simulation.

ABSTRACT:

This is an example to demonstrate how to use NetCDF Operator (NCO) software and OPeNDAP service to access and analyze the Multidimensional (NetCDF) resource https://www.hydroshare.org/resource/f3f947be65ca4b258e88b600141b85f3/ .

ABSTRACT:

This sciunit object has two executions.
- e1 is using UEB output "SWIT.nc" to prepare rain-plus-melt input xmrg files for RDHM sac model. Results saved in "SAC_input" folder.
- e2 is using the xmrg files to run RDHM sac model to generate time series output of basin discharge. Results saved in "SAC_out" folder.

ABSTRACT:

This is testing running RDHM ueb+sac with sciunit

ABSTRACT:

It was created using HydroShare UEB model inputs preparation application which utilized the HydroDS modeling web services. The model inputs data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, tmin0.nc, tmax0.nc, srad0.nc, prcp0.nc, ueb_setup.py, hydrogate.py. The model parameter files include: control.dat, param.dat, siteinitial.dat, inputcontrol.dat, outputcontrol.dat. This model instance resource is complete for model simulation. The corresponding model output is also included in the resource. Use the HydroShare user account and the link to access the UEB web app (https://appsdev.hydroshare.org/apps/ueb-app/)

ABSTRACT:

This is the model simulation of snow water equivalent in Logan River watershed from 2008 to 2009. The model used is the Utah Energy Balance model which is a snowmelt model. The simulation result is used as the input data for SAC-SMA model to simulate the stream flow of the watershed. asdfasd

ABSTRACT:

This resource collects all the data and code to reproduce the hydrologic modeling research, which coupled Utah Energy Balance (UEB) snowmelt model with the Sacramento Soil Moisture Accounting (SAC-SMA) run-off model to simulate the basin snowmelt process and discharge for the Dolores River watershed. This research is aimed to evaluate the UEB model performance using the Daymet input for water supply forecast in the study watershed. It is also aimed to demonstrate how to use different web based apps and software to support reproducible hydrologic modeling research.

This resource collects 3 resources:
resource 1: includes the model input/output files for UEB model simulation which was created using the UEB web application.
resource 2: includes the Sciunit object to help reproduce rain plus melt input processing (based on UEB model output) and SAC-SMA model simulation process.
resource 3: includes the observational data, model output files, and python analysis code for model results analysis.

ABSTRACT:

This resource stores the Sciunit object to help repeat the model input preparation and model execution for basin discharge simulation in Dolores River watershed using the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model. The rain plus melt input for SAC-SMA model was created from the Utah Energy Balance snowmelt model output.

This Sciunit object is a container that enables reproduction of the modeling process without software installation. The Jupyter Notebook app in HydroShare can help execute the Sciunit object.

ABSTRACT:

This resource includes the simulation and observation discharge data and corresponding data analysis code for Dolores River watershed. The observation data is from USGS gage station. The basin discharge simulation was created by coupling Utah Energy Balance snowmelt model and Sacramento Soil Moisture Accounting (SAC-SMA) runoff model.
This resource is aimed to demonstrate how to use the Jupyter Notebook to repeat the data analysis process. To test please use the link to access the app (https://jupyter.cuahsi.org/hub/login)

ABSTRACT:

This resource was created using the HydroShare UEB model input preparation web application (UEB web app) that utilizes the HydroDS modeling web services. The model input data files include: watershed.nc, aspect.nc, slope.nc, cc.nc, hcan.nc, lai.nc, vp0.nc, tmin0.nc, tmax0.nc, srad0.nc, prcp0.nc, ueb_setup.py, hydrogate.py. The model parameter files include: control.dat, param.dat, siteinitial.dat, inputcontrol.dat, outputcontrol.dat. This model instance resource is complete for model simulation. The corresponding model output is also included in the resource. Use a HydroShare user account and the link to access the UEB web app (https://appsdev.hydroshare.org/apps/ueb-app/) to run and reproduce this model instance.

ABSTRACT:

This resource collects all the data and code to illustrate reproducibility of hydrologic modeling research, which coupled Utah Energy Balance (UEB) snowmelt model with the Sacramento Soil Moisture Accounting (SAC-SMA) run-off model as part of the National Weather Service Research Distributed Hydrologic Modeling Framework to simulate the basin snowmelt process and discharge for the Dolores River watershed. This research is aimed to evaluate the Utah Energy Balance (UEB) model performance using Daymet input for water supply forecast in the study watershed. It is also aimed to demonstrate how to use different web based apps and software to support reproducible hydrologic modeling research.

ABSTRACT:

This resource stores the Sciunit object to help repeat the model input preparation and model execution for basin discharge simulation in Dolores River watershed using the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model. The rain plus melt input for SAC-SMA model was created from the Utah Energy Balance snowmelt model output (https://www.hydroshare.org/resource/8f9320123baa4ef1b4a304f0ce20ab08/)

This Sciunit object is a container that enables reproduction of the modeling process without software installation. The JupyterHub web app in HydroShare can help execute the Sciunit object.

In this resource, there are 2 Sciunit objects:
earthcube_use_case.zip: a Sciunit object which supports SAC-SMA model simulation for a water year
use_case.zip: a demo Sciunit object which supports SAC-SMA model simulation for 1 month

ABSTRACT:

This resource includes the simulation and observation discharge data and corresponding data analysis code for Dolores River watershed. The observation data is from USGS gage station. The basin discharge simulation was created by coupling Utah Energy Balance snowmelt model and Sacramento Soil Moisture Accounting (SAC-SMA) runoff model.
This resource is aimed to demonstrate how to use the Jupyter Notebook to repeat the data analysis process. To test please use the link to access the app (https://jupyter.cuahsi.org/hub/login)

ABSTRACT:

This resource includes the data analysis code and results using a subset of the model simulation of snow water equivalent for the watershed of Dolores River above McPhee reservoir in the Colorado River Basin from 1988 to 2010. The model used is the Utah Energy Balance model which is a physically based snow melt model.

The data analysis code used NetCDF Operator commands (http://nco.sourceforge.net). It first subsets the data from January to May, 2009 to identify the maximum snow water equivalent for each grid cell within this period and write the result to a new NetCDF file (max.nc). It then subsets the data for April 1st and 15th, 2009 (april_1.nc, april_15.nc) and evaluates the snow water equivalent difference between the two dates to create a new NetCDF file (diff.nc). This provides the analysis result for accumulation (increase) or ablation (decrease) during this period. Water managers often track such snow water equivalent changes in water supply forecasts

ABSTRACT:

This resource includes tutorial Jupyter Notebooks for Python Modeling Toolkit (pymt https://pymt.readthedocs.io). Pymt was developed by the Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/), that provides the tools needed for coupling models that expose the Basic Model Interface (BMI https://bmi.readthedocs.io/).

HydroShare users can test and run those Jupyter Notebooks directly through the "CUAHSI JupyterHub" web app with the following steps:
- click on the "Open with" button. (on the top right corner of the page)
- select "CUAHSI JupyterHub". (Need to join the CUAHSI JupyterHub group for the first time web app user.)
- select "CSDMS Workbench" server option. (Make sure to select the right server option.)

For more details, please check the "Instruction" PDF file listed in this resource. If you have trouble running those notebooks, please create a GitHub issue at the CSDMS Help Desk: https://github.com/csdms/help-desk/issues

ABSTRACT:

This resource includes a bunch of Jupyter Notebooks for Landlab (https://landlab.github.io), which is an open-source Python-language package for numerical modeling of Earth surface dynamics. There are two groups of Jupyter Notebooks in this resource: 1) "teaching": for people who are an educator looking for tutorials to use in the classroom. 2) "tutorial": for people who are interested in teaching themselves Landlab.

HydroShare users can test and run those Jupyter Notebooks directly through the "CUAHSI JupyterHub" web app with the following steps:
- click on the "Open with" button. (on the top right corner of the page)
- select "CUAHSI JupyterHub". (Need to join the CUAHSI JupyterHub group for the first time web app user.)
- select "CSDMS Workbench" server option. (Make sure to select the right server option.)

For more details, please check the "Instruction" PDF file listed in this resource. If you have trouble running those notebooks, please create a GitHub issue at the CSDMS Help Desk: https://github.com/csdms/help-desk/issues

ABSTRACT:

This resource includes two Jupyter Notebooks as a quick start tutorial for the soilgrids Data Component of the PyMT modeling framework (https://pymt.readthedocs.io/en/latest/).

The soilgrids Python package provides a set of functions that allows downloading of the global gridded soil information from SoilGrids https://www.isric.org/explore/soilgrids, a system for global digital soil mapping to map the spatial distribution of soil properties across the globe. The soilgrids package also includes a Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) that can be used for data/model coupling under the PyMT modeling framework.

The pymt_soilgrids Python package uses the BMI of the soilgrids package to convert it into a reusable, plug-and-play data component for PyMT modeling framework. This allows the SoilGrids datasets to be easily coupled with other datasets or models that expose a BMI.

HydroShare users can test and run the Jupyter Notebooks (soilgrids.ipynb, pymt_soilgrids.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If there is any question or suggestion about the soilgrids data component, please create a github issue at https://github.com/gantian127/soilgrids/issues

ABSTRACT:

This resource includes two Jupyter Notebooks as a quick start tutorial for the NWIS Data Component of the PyMT modeling framework (https://pymt.readthedocs.io/) developed by Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/).

The bmi_nwis package is an implementation of the Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) for the USGS NWIS dataset (https://waterdata.usgs.gov/nwis). This package uses the dataretrieval package (https://github.com/USGS-python/dataretrieval) to download the NWIS dataset and wraps the dataset with BMI for data control and query. This package is not implemented for people to use but is the key element to convert the NWIS dataset into a data component for the PyMT modeling framework.

The pymt_nwis package is implemented for people to use as a reusable, plug-and-play NWIS data component for the PyMT modeling framework. This package uses the BMI implementation from the bmi_nwis package and allows the NWIS datasets to be easily coupled with other datasets or models that expose a BMI.

HydroShare users can test and run the Jupyter Notebooks (bmi_nwis.ipynb, pymt_nwis.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub".
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If there is any question or suggestion about the NWIS data component, please create a github issue at https://github.com/gantian127/bmi_nwis/issues

ABSTRACT:

This collection includes several resources for the example data and model components for the PyMT modeling framework (https://pymt.readthedocs.io/). PyMT is an Open Source Python package, developed by the Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/), that provides the tools needed to run and couple models that expose the Basic Model Interface (BMI https://bmi.readthedocs.io/).

Each resource in this collection includes tutorial Jupyter Notebooks demonstrating how to use the data and model components. HydroShare users can test and run those notebooks using the CUAHSI JupyterHub web app without the need for software installation and data download to their local computers.

Please click on the link for each resource from the "Collection Content" section and get more details about how to run the corresponding Jupyter Notebooks through HydroShare.

ABSTRACT:

This report presents results from an online survey of members of the Community Surface Dynamics Modeling System (CSDMS) conducted in 2021. A total of 135 responses were received from community members. Demographics indicate the same lack of diversity that applies across the US geosciences. The survey indicates strong interest in CSDMS' community-building activities, and suggests that CSDMS has succeeded in lowering the barrier to code sharing and access. Continuing technical barriers relate in part to developing and debugging codes for modeling and model-data analysis, and to learning and using software created by colleagues. There is a strong need for cyber-learning opportunities, with desired training modes including multi-day in-person courses and self-paced online materials. Interest is growing in CSDMS products such as Landlab, and services such as research software consulting. Collectively, the survey highlights continuing needs for community engagement on a variety of levels: more training opportunities; networking and interaction; technical support and assistance; barrier-bridging technologies; and proactive outreach to broaden access to and participation in the Earth-surface process community.

ABSTRACT:

This resource includes two Jupyter Notebooks as a quick start tutorial for the ERA5 Data Component of the PyMT modeling framework (https://pymt.readthedocs.io/) developed by Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/).

The bmi_era5 package is an implementation of the Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) for the ERA5 dataset (https://confluence.ecmwf.int/display/CKB/ERA5). This package uses the cdsapi (https://cds.climate.copernicus.eu/api-how-to) to download the ERA5 dataset and wraps the dataset with BMI for data control and query (currently support 3 dimensional ERA5 dataset). This package is not implemented for people to use and is the key element to help convert the ERA5 dataset into a data component for the PyMT modeling framework.

The pymt_era5 package is implemented for people to use as a reusable, plug-and-play ERA5 data component for the PyMT modeling framework. This package uses the BMI implementation from the bmi_era5 package and allows the ERA5 datasets to be easily coupled with other datasets or models that expose a BMI.

HydroShare users can test and run the Jupyter Notebooks (bmi_era5.ipynb, pymt_era5.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub".
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If there is any question or suggestion about the ERA5 data component, please create a github issue at https://github.com/gantian127/bmi_era5/issues

ABSTRACT:

This resource includes a Jupyter Notebook to demonstrate how to use several CSDMS Data Components (https://csdms.colorado.edu/wiki/DataComponents) to download topography and soil datasets to calculate the hourly landslide susceptibility for a study area in Puerto Rico when Hurricane Maria hit the island on September 20th, 2017.

HydroShare users can run the Jupyter Notebook (landslide_puertorico.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If you encounter "Kernel Restarting" error when running this notebook on CUAHSI JupyterHub, select "Kernel" -> “Shut Down All Kernels" -> "Restart Kernel and Clear All Outputs" and rerun this notebook.

Please go to https://github.com/gantian127/landslide_usecase to learn how to run this notebook on local PC or CSDMS JupyterHub.

ABSTRACT:

This resource includes a Jupyter notebook to demonstrate how to use the CSDMS Data Component (https://csdms.colorado.edu/wiki/DataComponents) to download the topography dataset and use the Landlab component (https://landlab.readthedocs.io/en/master/) to delineate the watershed and simulate the overland flow for a study area in the Boulder County.

HydroShare users can run the Jupyter Notebook (overland_flow.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If you encounter "Kernel Restarting" error when running this notebook on CUAHSI JupyterHub, select "Kernel" -> “Shut Down All Kernels" -> "Restart Kernel and Clear All Outputs" and rerun this notebook.

Please go to https://github.com/gantian127/overlandflow_usecase to learn how to run this notebook on local PC or CSDMS JupyterHub.

ABSTRACT:

This resource includes a Jupyter Notebook to demonstrate how to use the CSDMS Data Component (https://csdms.colorado.edu/wiki/DataComponents) to download surface wave properties from the WAVEWATCH III model output for a given time period, interpolate it to a specific location, and calculate the wave power over time at that point.

HydroShare users can run the Jupyter Notebook (wavepower_usecase.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If you encounter "Kernel Restarting" error when running this notebook on CUAHSI JupyterHub, select "Kernel" -> “Shut Down All Kernels" -> "Restart Kernel and Clear All Outputs" and rerun this notebook.

Please go to https://github.com/bundzis/wavewatch3_usecase to learn how to run this notebook on local PC or CSDMS JupyterHub.

ABSTRACT:

This resource includes a Jupyter Notebook to demonstrate how to use several CSDMS data components (https://csdms.colorado.edu/wiki/DataComponents) to download topography, snow, and temperature data to calculate the permafrost active layer thickness and simulate the hillslope diffusion process for a study area in Alaska.

HydroShare users can test and run the Jupyter Notebook (permafrost_alaska.ipynb) directly through the "CUAHSI JupyterHub" web app with the following steps:
- For the new user of the CUAHSI JupyterHub, please first make a request to join the "CUAHSI Could Computing Group" (https://www.hydroshare.org/group/156). After approval, the user will gain access to launch the CUAHSI JupyterHub.
- Click on the "Open with" button. (on the top right corner of the page)
- Select "CUAHSI JupyterHub"
- Select "CSDMS Workbench" server option. (Make sure to select the right server option. Otherwise, the notebook won't run correctly.)

If you encounter "Kernel Restarting" error when running this notebook on CUAHSI JupyterHub, select "Kernel" -> “Shut Down All Kernels" -> "Restart Kernel and Clear All Outputs" and rerun this notebook.

Please go to https://github.com/gantian127/permafrost_usecase to learn how to run this notebook on local PC.

ABSTRACT:

This collection includes HydroShare resources for the use case Jupyter Notebooks of the CSDMS Data Components (https://csdms.colorado.edu/wiki/DataComponents ). These use cases cover a variety of topics, including landslide susceptibility mapping, modeling of overland flow in a wildfire-impacted catchment, permafrost landscape processes, and wave power. Each use case is designed to demonstrate the application and the capabilities of the CSDMS Data Components.

Please click on each HydroShare resource link in this collection to access the corresponding Jupyter Notebook and learn how to run it on the CUAHSI JupyterHub.

ABSTRACT:

This resource includes two Jupyter Notebooks as a quick start tutorial for the ROMS data component of the PyMT modeling framework (https://pymt.readthedocs.io/) developed by Community Surface Dynamics Modeling System (CSDMS https://csdms.colorado.edu/).

bmi_roms package is an implementation of the Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) for the ROMS model (https://www.myroms.org/) datasets. This package downloads the datasets and wraps them with BMI for data control and query. This package is not implemented for people to use but is the key element to convert the ROMS model output dataset into a data component for the PyMT modeling framework.

The pymt_roms package is implemented for people to use as a reusable, plug-and-play ROMS data component for the PyMT modeling framework. This package uses the BMI implementation from the bmi_roms package and allows the ROMS datasets to be easily coupled with other datasets or models that expose a BMI.

If there is any question or suggestion about the ROMS data component, please create a github issue at https://github.com/gantian127/bmi_roms/issues

ABSTRACT:

bmi_dbseabed provides a set of functions that allow downloading of the dataset from dbSEABED (https://instaar.colorado.edu/~jenkinsc/dbseabed/), a system for marine substrates datasets across the globe. bmi_dbseabed also includes a Basic Model Interface (BMI https://bmi.readthedocs.io/en/latest/) that can be used for data/model coupling under the PyMT modeling framework.

pymt_dbseabed is a package that uses the bmi_dbseabed pacakge to convert dbSEABED datasets into a reusable, plug-and-play data component for PyMT modeling framework. This allows dbSEABED datasets to be easily coupled with other datasets or models that expose a Basic Model Interface.

If there is any question or suggestion about the dbSEABED data component, please create a github issue at https://github.com/gantian127/bmi_dbseabed/issues