ABSTRACT:

This example runs the 'landslide' component of Landlab and is designed for undergraduate and graduate students interested in learning more about Landlab and landslide modeling. Landlab is a Python-based landscape modeling environment and the landslide component is one of many components available for users to access and link together to build their own landscape model. For more information about Landlab, see http://landlab.github.io/#/. Data needed for the example are spatial data on landscape characteristics for Thunder Creek watershed in North Cascade mountains of Washington. They include soil, geology, vegetation, topography, and landform data that can be used for earth surface analyses such as landslides and hydrology. Thus, the data can be used for more than this landslide example. Elevation was acquired from STRM at 30 m grid scale; the other datasets are matched to in scale and location. Slope was derived from the elevation file and represents dimensionless "tan theta". Specific contributing area represents the 'upstream' area draining to each cell divided by the cell's width (so minimum value is 30 m). Landform data was developed by Jon Riedel of National Park Service. Landslides were extracted from these data as "mass wasting" events. 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. Washington State Department of Natural Resources (WADNR) provides the source of lithology in its surface geology maps that displays rocks and deposits as geologic map units. These were aggregated into eight classes based on similarities in origin and generally increasing strength by Dr. Riedel. Cohesion represent root cohesion based on the LULC ; soils are assumed to be primarily cohesionless, lacking “true cohesion” because of their low clay content in this mountain terrain. Soil depth comes from NRCS soil survey depth-to-restricted layer (weighted-average aggregation) within each soil map unit. Transmissivity was derived from the soil survey saturated hydraulic conductivity (depth averaged) multiplied by depth-to-restricted layer for each soil map unit. All soils within this watershed are sandy loam or loamy sand; therefore, soil surface texture was used as an indicator of internal angle of friction (phi). A header file is also provided to understand the spatial details of the ASCII files and to facilitate capability with GIS. Projection for raster mapping is NAD_1983_UTM_Zone_10N.

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:

This resource is a Powerpoint presentation that explains the Landlab Landslide Component. It includes a diagram depicting the model and a flowchart describing the data source and needs, model input and calculations, output, and potential stakeholders whom could benefit from the analyses. An example map produced by the component is provided, as well as a description of how the component works.

ABSTRACT:

This resource provides a table of root cohesion values (kPa) to use when reclassifying land use/land cover (LULC) rasters to cohesion rasters. LULC can be acquired from USGS National Land Cover Data (NLCD) based on 2011 Landsat satellite data (USGS 2014b; Jin 2013) and should be grouped into eight generalized categories: water, wetland, snow/ice, barren, herbaceous, shrub/scrub, forest, and developed. The root cohesion rasters can then be used in landslide modeling as parameters needed to create triangle distributions. The distributions will be right skewed, which is typically found in field data (Hammond et al. 1992). Spatially distributed values for root cohesion based on LULC were determined from Schmidt et al. (2001) and other literature, except for barren and developed classifications that were assumed to have few roots and thus, small root cohesion.

ABSTRACT:

The NOCA landslide observatory host the driver code and data files needed to run Landlab's landslide component, which models annual landslide probability for North Cascades National Park Complex.

ABSTRACT:

This NOCA landslide data repository host the driver code and data files needed to run Landlab's LandslideProbability component, which models annual shallow landslide probability in a steep mountainous region in northern Washington, U.S.A. The model application covers North Cascade National Park Complex (NOCA), using 30-m grid resolution over 2,700 km2. The model use the classic infinite slope, limited equilibrium model driven by contemporary climatology from the Variable Infiltration Capacity (VIC) macroscale hydrology model. Readily available topographic, geophysical, and land cover data are provided to calculate the factor-of-safety stability index in a Monte Carlo simulation, which explicitly accounts for parameter uncertainty.

Data used for this analysis are spatial data on landscape characteristics for NOCA. They include soil, geology, vegetation, topography, and landform data that can be used for quantitative landslides hazard assessment. Elevation was acquired from National Elevation Dataset (NED) at 30 m grid scale; other datasets are matched to scale and location. Slope was derived from the elevation file as "tan theta". Specific contributing area represents the 'upstream' area draining to each cell divided by the cell's width (so minimum value is 30 m). Landform data was developed by Jon Riedel of National Park Service. Landslides were extracted from these data identified as "mass wasting" events. 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. Cohesion represent total cohesion, which is equivalent to root cohesion in this application; soils are assumed to be primarily cohesionless, lacking “true cohesion” because of their low clay content in this mountain terrain. Root cohesion is based on the LULC referenced to a look-up table within this resource: (https://www.hydroshare.org/resource/a771ba9bbae24ed8b4673c945fc321a3/). Soil depth comes from Soil Survey Geographic Database (SSURGO) maintained by NRCS processed as soil survey depth-to-restricted layer (weighted-average aggregation) within each soil map unit. An alternative modeled soil depth (SD) described in the accompany paper is also provided, but revisions in the driver notebook would be required to reference this file to see adjusted results. Transmissivity was derived from the soil survey saturated hydraulic conductivity (depth averaged) multiplied by depth-to-restricted layer for each soil map unit; another T file based on the model soil depth is also provided. However, the model can be run using hydraulic conductivity using data file provided to calculate T. All soils within this watershed are sandy loam or loamy sand; therefore, soil surface texture was used as an indicator of internal angle of friction (phi). A header file is provided to understand the spatial details of the ASCII files and to facilitate capability with GIS. Spatial reference for raster mapping is NAD_1983, Albers conical equal area projection.

The model run archived in this resource runs with Landlab version 1.1.0 . The component code (landslide_probability9Jun17.py) is provided as an archive to run a notebook that replicates results in Strauch et al., (in review) . As Landlab is developed with newer versions, the notebook and/or provided component code may need updating to run properly. To run the notebook to replicate results, use the resource "Regional Landslide Hazard Using Landlab - NOCA Observatory", HydroShare resource: https://www.hydroshare.org/resource/07a4ed3b9a984a2fa98901dcb6751954/

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:

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:

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:

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:

This resource supports the work published in Strauch et al., (2018) "A hydroclimatological approach to predicting regional landslide probability using Landlab", Earth Surf. Dynam., 6, 1-26 . It demonstrates a hydroclimatological approach to modeling of regional shallow landslide initiation based on the infinite slope stability model coupled with a steady-state subsurface flow representation. The model component is available as the LandslideProbability component in Landlab, an open-source, Python-based landscape earth systems modeling environment described in Hobley et al. (2017, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017). The model operates on a digital elevation model (DEM) grid to which local field parameters, such as cohesion and soil depth, are attached. A Monte Carlo approach is used to account for parameter uncertainty and calculate probability of shallow landsliding as well as the probability of soil saturation based on annual maximum recharge. The model is demonstrated in a steep mountainous region in northern Washington, U.S.A., using 30-m grid resolution over 2,700 km2.

This resource contains a 1) User Manual that describes the Landlab LandslideProbability Component design, parameters, and step-by-step guidance on using the component in a model, and 2) two Landlab driver codes (notebooks) and customized component code to run Landlab's LandslideProbability component for 2a) synthetic recharge and 2b) modeled recharge published in Strauch et al., (2018). The Jupyter Notebooks use HydroShare code libraries to import data located at this resource: https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/.

The Synthetic Recharge Jupyter Notebook <Synthetic_recharge_LandlabLandslide.ipynb> demonstrates the use of the Landlab LandslideProbability Component on a synthetic grid with synthetic data with four options for parameterizing recharge. This notebook was used to verify and validated the theoretical application and digital representation of Landslide processes.

The Modeled Recharge Jupyter Notebook <NOCA_runPaper_LandlabLandslide.ipynb> models annual landslide probability in the North Cascades National Park Complex, and was used to verify that model results in Strauch et al., (2018) could be reproduced online.

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:

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:

This project investigates the impacts on residential power demand during warm summers when air quality is compromised by smoke from wildfires. We hypothesize that the energy use increases when the air is smoky because of additional purchase and use of air conditioners and air purifiers when temperatures are warm and the air is smoky from wildfires because windows must be kept closed, eliminating the evening cooling ability practiced by homeowners. We'll focus our analysis in the Seattle area using Seattle City Light energy use data and SeaTac weather station data. U.S. Air Quality Index (AQI), EPA’s index for reporting air quality ranging from 0 to 500, will be used for air quality data. The timeframe will initially focus on June through August during 2015 through 2018.

ABSTRACT:

https://www.hydroshare.org/resource/6d8c3c46f4c8422796f28584eb9bdfaa/

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:

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:

!!! This is a fork from https://www.hydroshare.org/resource/5b964154ebf945848087bdc772cc921e/ with some minor modifications for CyberGIS-Jupyer for Water (CJW) platform !!!
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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:

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.