Application of a hillslope-scale soil moisture data assimilation system to military trafficability assessment

Abstract

Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a \ soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active-Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of "slow go" conditions in approximately two-thirds of the watershed, and an increase in the risk of "no go" conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability.Highlights: 1. We apply a soil moisture data assimilation system (DAS) to military trafficability 2. Assimilation improves prediction of impaired trafficability 3. Ensemble-based assimilation supports quantification of trafficability risk

A comparison of ground vehicle mobility analysis based on soil moisture time series datasets from WindSat, LIS, and in situ sensors

“…The US Army has been cooperating with other agencies to understand how satellitederived soil moisture can be incorporated into existing terrain characterization models (Baylot et al, 2013;Frankenstein et al, 2015). Flores et al (2014) demonstrated the usefulness of soil moisture data similar to that expected from the NASA Soil Moisture Active Passive (SMAP) mission. They simplified their analysis by considering only mobility limitations caused by soil strength, which, as Flores et al (2014) noted, may overemphasize soil strength impacts.…”

Full-featured ground vehicle mobility analysis using different soil moisture sources

“…Flores et al (2014) demonstrated the usefulness of soil moisture data similar to that expected from the NASA Soil Moisture Active Passive (SMAP) mission. They simplified their analysis by considering only mobility limitations caused by soil strength, which, as Flores et al (2014) noted, may overemphasise soil strength impacts.…”

Full-featured ground vehicle mobility analysis using different soil moisture sources

“…Flores et al (2014) demonstrated the usefulness of soil moisture data similar to that expected from the NASA Soil Moisture Active Passive (SMAP) mission. They simplified their analysis by considering only mobility limitations caused by soil strength, which, as Flores et al (2014) noted, may overemphasise soil strength impacts. Stevens et al (2016) employed a simplified time series approach based upon only soil moisture time series and compared mobility assessment using three different soil moisture sources.…”