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Ronda Strauch

University of Washington | PhD Research Scientist

Subject Areas: Landslides, climate change, transportation

 Recent activity

ABSTRACT:

Landslide probability modeling can be used to better understand landslides in the watersheds containing the electrical transmission lines and facilities. A recently published landslide model (Strauch et al. 2018) updated to use spatially distributed saturation (depth to water table) derived from a basin calibrated hydrologic model (Distributed Hydrology Soil and Vegetation Model - DHSVM) at 150-m grid resolution. Contemporary and future probability of landslide initiation is used to create landslide hazard maps at a 30-m resolution. Our case study of the Skagit Hydroelectric Project evaluates the sensitivity of the landslide model to subsurface saturation and reduced cohesion of a simulated a fire. We compare historic landslide probability to two future time periods using two scenarios (RCP 4.5 and RCP 8.5) and a representative distribution of global climate models (GCMs).

This resource is an updated copy of 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.

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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.

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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.

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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.

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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.

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 Contact

Resources
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Collection 0
Composite Resource 0
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Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
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MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Generic Generic
Thunder Creek Landlab Landslide Example
Created: June 9, 2016, 10:34 p.m.
Authors: Ronda Strauch · Erkan Istanbulluoglu · Sai Nudurupati · Christina Bandaragoda · Jon Riedel

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.

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Collection Resource Collection Resource

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.

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Generic Generic
Landlab Landslide Component Explained
Created: Nov. 9, 2016, 5:27 p.m.
Authors: Ronda Strauch · Erkan Istanbulluoglu

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.

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Generic Generic
Root Cohesion Table
Created: Nov. 15, 2016, 5:42 p.m.
Authors: Ronda Strauch · Erkan Istanbulluoglu

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.

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Generic Generic

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.

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Generic Generic

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/

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Generic Generic

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/

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Generic Generic

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/

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Generic Generic

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.

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Generic Generic

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.

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Generic Generic
End-of-course tutorial presentations
Created: Nov. 1, 2017, 4:14 p.m.
Authors: Nathan Lyons

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.

Show More
Generic Generic

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.

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Composite Resource Composite Resource

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.

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Composite Resource Composite Resource
Energy Use When Warm and Smoky
Created: Nov. 6, 2018, 8:38 p.m.
Authors: Ronda Strauch · Joseph Contreras · Joe McEwen · John Rudolph

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.

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Generic Generic

ABSTRACT:

Landslide probability modeling can be used to better understand landslides in the watersheds containing the electrical transmission lines and facilities. A recently published landslide model (Strauch et al. 2018) updated to use spatially distributed saturation (depth to water table) derived from a basin calibrated hydrologic model (Distributed Hydrology Soil and Vegetation Model - DHSVM) at 150-m grid resolution. Contemporary and future probability of landslide initiation is used to create landslide hazard maps at a 30-m resolution. Our case study of the Skagit Hydroelectric Project evaluates the sensitivity of the landslide model to subsurface saturation and reduced cohesion of a simulated a fire. We compare historic landslide probability to two future time periods using two scenarios (RCP 4.5 and RCP 8.5) and a representative distribution of global climate models (GCMs).

This resource is an updated copy of 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.

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