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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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Input data for trial run of landslide probability component of landlab

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Chelan county watershed

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Example data for TauDEM

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

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Taudem is awesome!

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Data and scripts used to prepare forcing data for PREEVENTS project

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Data for PNNL WRF data extraction

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

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

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This hydroshare resource contains data needed to reproduce results in Keck et al., 2021, Bedload response to precipitation variability across a mountainous channel network.

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This resource contains the DEM, regolith depth map, DEM-of-Difference and the location polygon of the landslide source area of each field site used to evaluate calibrated MassWastingRunout model performance. MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive details typical of runout models used for landslide inundation hazard mapping. An initial Digital Elevation Model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition.