28 Jul 2025

Predicting Frozen Ground and Thaw Risk from Standard Land Model Output: Data, Algorithms, and GeoWATCH Implementation

Abstract: The Geospatial Weather Affected Terrain Conditions and Hazards (GeoWATCH) tool provides real-time mobility predictions at 30 m resolution on demand for any location on the globe. This tool combines dynamic weather data provided by the Air Force 557 Weather Wing (557WW) with static terrain data to downscale soil moisture from global and regional scales to resolutions better suited for terrain analysis applications. Frozen and thawing ground data layers were recently incorporated into the GeoWATCH framework to better support terrain assessment for Warfighter functions in cold regions. This report documents our approach for diagnosing the frozen and thawing ground data layers and provides examples. First, using data from controlled land-surface model simulations, we established simple curve-fitting formulas relating soil temperature to frozen water content. We then added the new formulas to the GeoWATCH code so that end users can generate frozen soil products on demand. Finally, GeoWATCH uses the resultant frozen soil product with a series of soil layers to determine the risk of actively thawing soil and springtime mud conditions. While the new overlays are not integrated into the GeoWATCH mobility diagnostic calculations, they provide insight into soil state conditions critical for operations and weather-based risk assessment in cold regions.

16 Sep 2021

An Investigation of the Feasibility of Assimilating COSMOS Soil Moisture into GeoWATCH

Abstract: This project objective evaluated the potential of improving linked weather-and-mobility model predictions by blending soil moisture observations from a Cosmic-ray Soil Moisture Observing System (COSMOS) sensor with weather-informed predictions of soil moisture and soil strength from the Geospatial Weather-Affected Terrain Conditions and Hazards (GeoWATCH). Assimilating vehicle-borne COSMOS observations that measure local effects model predictions of soil moisture offered potential to produce more accurate soil strength and vehicle mobility forecast was the hypothesis. This project compared soil moisture observations from a COSMOS mobile sensor driven around an area near Iowa Falls, IA, with both GeoWATCH soil moisture predictions and in situ probe observations. The evaluation of the COSMOS rover data finds that the soil moisture measurements contain a low measurement bias while the GeoWATCH estimates more closely matched the in situ data. The COSMOS rover captured a larger dynamic range of soil moisture conditions as compared to GeoWATCH, capturing both very wet and very dry soil conditions, which may better flag areas of high risk for mobility considerations. Overall, more study of the COSMOS rover is needed to better understand sensor performance in a variety of soil conditions to determine the feasibility of assimilating the COSMOS rover estimates into GeoWATCH.