ABSTRACT:

This HydroShare resource archives a reproducible geospatial data processing pipeline designed for hydrologic and geomorphic analysis and modeling. The workflow standardizes heterogeneous raster datasets from sources such as OpenTopography, USDA soil databases, NLCD, MTBS, and satellite platforms including Landsat and MODIS into spatially aligned, modeling-ready grids. Input layers include digital elevation models, soil properties, burn severity, land cover, and satellite-derived ecohydrologic indicators. The pipeline performs coordinate reference system harmonization, resolution enforcement, raster resampling, grid alignment, and export to analysis-ready formats suitable for hydrologic and geomorphic modeling, feature engineering, and machine learning workflows. While example outputs demonstrate integration with terrain–hydrology simulations and landslide probability assessment, the primary contribution of this resource is the reproducible raster harmonization framework. The workflow is broadly applicable to watershed analysis, landscape evolution studies, hydro-geomorphic process modeling, and other geospatial preprocessing tasks requiring consistent spatial alignment and physically interpretable feature derivation.

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This resource contains some MATLAB scripts used to teach statistical hydrology section of Physical Hydrology class at the University of Washington, Seattle, WA.

ABSTRACT:

This resource provide materials for running snow 17 in a catchment in Washington state for a class application. The model is coded by Dr. Mark Raleigh

ABSTRACT:

This resource presents basic attributes of the Landlab earth surface modeling toolkit and several examples. This resources is presented at the University of Washington Waterhackweek, September 3 2020.

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.