A FRAMEWORK FOR THE PREDICTION OF SOIL MOISTURE

Through its influence on the mobility of troops and materiel, the interaction between weather and landscape is of primary importance to the effectiveness and timeliness of Army operations. More specifically, knowledge of the spatial and temporal variability in soil moisture over large areas, at the scale of tactical operations (∼100 m), has the potential to dramatically improve trafficability assessments. The majority of Army operations are conducted in regions where field observations of soil moisture are sparse in space and/or time or completely unavailable. However, remotely sensed information about the factors that affect the spatial variability in soil moisture over a range of spatial scales are available. We present here a framework by which we can fuse these remotely sensed data representing the various factors affecting soil moisture through the existing tRIBS hydrologic model to produce forecasts of the spatial distribution of soil moisture. Using data assimilation techniques these forecasts can be dynamically updated when remotely sensed observations of soil moisture using become available. When used in conjunction with tactical decision aids, such as IWEDA, the proposed fusion of data through tRIBS has the potential to improve trafficability assessments and other soil moisture dependent Army operations.