Freshwaterhack Project: Data integration for multi-hazard risk assessment

Resource type: Collection Resource
Storage: The size of this resource is 1.7 KB
Created: Oct 26, 2016 at 11:21 p.m.
Last updated: Sep 13, 2017 at 4:04 p.m.
Citation: See how to cite this resource
Sharing Status: Public
Views: 1088
Downloads: 72
+1 Votes: 1 other +1 this
Comments: 1 comment


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.

Subject Keywords

Deleting all keywords will set the resource sharing status to private.

Resource Level Coverage


Coordinate System/Geographic Projection:
WGS84 EPSG:4326
Coordinate Units:
['Decimal degrees']
North Latitude
East Longitude
South Latitude
West Longitude

Collection Contents

Add Title Type Owners Sharing Status My Permission Remove
CONUS digital elevation model of 1/16 degree grid cells CompositeResource Christina Bandaragoda Public & Shareable Open Access
Workflow for landslide models in Island County, WA CompositeResource Christina Bandaragoda Public & Shareable Open Access
LiDAR derived Bare Earth DEM 30ft grid (ASCII) CompositeResource Victoria Nelson Public & Shareable Open Access
Landlab Landslide Component Explained CompositeResource Ronda Strauch Public & Shareable Open Access
Root Cohesion Table CompositeResource Ronda Strauch Public & Shareable Open Access
Island County Contributing Area from 30ft Lidar Dinf CompositeResource RECEP CAKIR Private & Shareable None
Island County Slope from 30ft Lidar Dinf CompositeResource RECEP CAKIR Private & Shareable None
chelan_watershed_boundary CompositeResource Jeffrey Keck Public & Shareable Open Access


This resource belongs to the following collections:
Title Owners Sharing Status My Permission
Freshwaterhack of UW Geohackweek Christina Bandaragoda · Anthony Arendt · Nicoleta Cristea  Public &  Shareable Open Access

How to Cite

CAKIR, R., J. Keck, C. Bandaragoda, R. Strauch, E. Istanbulluoglu, Y. Zou, V. Nelson, S. S. Nudurupati (2017). Freshwaterhack Project: Data integration for multi-hazard risk assessment, HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


Victoria Nelson 4 years, 11 months ago

Here are a couple of more resources for learning how to use Landlab:

+1 Votes: Be the first one to 

New Comment