Description:
In a Nutshell
Researchers in Colorado State University's Department of Civil and Environmental Engineering have developed a new method to increase resolution of soil moisture maps by incorporating fine-resolution information on topography, vegetation, and soil. Estimated soil moisture patterns are becoming more readily available at coarse (9 km - 50 km) to intermediate (500 m - 1 km) spatial resolutions. However, many applications, such as water management and agricultural production, require finer resolutions (10 - 100 m). It is now possible to estimate fine-scale variations in soil moisture with CSU's EMT + VS model.
Advantages
* Provides both estimated soil moisture patterns (most accurate values possible) and simulated soil moisture patterns (most realistic statistics possible)
* Applicable to any selected date or hypothetical conditions
* Rapid generation of results (no model spin-up)
* Little specialized expertise required for use
* Produces fine spatial resolutions (grid cells with 10 - 30 m linear dimension)
* Large spatial extents (100 km by 100 km regions)
* Applicable for data-limited environments (performs well without calibration to local observations)
* Can accept additional data if data are abundant
* Can reproduce time-varying soil moisture structures
Applications
* Land Management
* Water Management
* Forestry
* Infectious Disease
* Military Tactics and Logistics
* Mobility Assessments
* Flood Forecasting
* Landslide Prediction
The figure shows an example application of the EMT + VS model to the Reynolds Creek Watershed in Idaho, USA. Part (a) shows the watershed elevation, and part (b) shows the vegetation cover (represented by the soil adjusted vegetation indexx or SAVI), which are both inputs to the model. Part (c) shows the soil moisture pattern that is generated by the model using a single spatial average soil moisture for the whole watershed as the key input (along with the elevation and vegetation data).
