Abstract
Abstract. Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site.
Highlights
Being prepared with the necessary tools and information for dealing with an emergency situation is important
Our database is currently focused on providing support to Water Erosion Prediction Project (WEPP) based models, but our data inputs have been used in other hydrology models
The soil data are based on the SSURGO or STATSGO Natural Resources Conservation Service (NRCS) soil databases (Soil Survey Staff, 2011; U.S Department of Agriculture (USDA), 1991); the digital elevation model (DEM) is from the U.S Geological Survey (USGS) (Gesch et al, 2002; Gesch, 2007), and land cover is derived from LANDFIRE existing vegetation type data (Rollins, 2009; LANDFIRE, 2010)
Summary
Being prepared with the necessary tools and information for dealing with an emergency situation is important. BAER teams must determine if treatments to minimize erosion and runoff are needed and prioritize their spatial application in order to protect watersheds and downstream values at risk including life and property (Parsons et al, 2010). The complexities and uncertainties of erosion processes following wildfires and the high cost of mitigation (up to $5,000 per ha) require managers to make tough decisions when it comes to addressing post-fire effects. The database includes spatial tools to rapidly update input layers with user supplied post-fire earth observations of burn severity. We are building an online database to provide end-users (BAER team specialists, land managers and researchers) with the basic tools and spatial data needed to incorporate remotely sensed earth observations into processbased erosion models. Improving the accessibility of both modeling capabilities and the required data sets will lead to better assessment tools for forest managers, researchers and BAER teams
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