77 DASSP: A System for High-Resolution, Global Prediction of Soil Moisture Content and Soil Strength

Tuesday, 12 January 2016
Room 242 ( New Orleans Ernest N. Morial Convention Center)
Jerry Bieszczad, Creare LLC, Hanover, NH; and M. P. Ueckermann, C. A. Brooks, R. Chambers, W. E. Audette, and J. D. Walthour

Knowledge of soil moisture content and soil strength at high resolution can provide critical information to support applications in agriculture, forestry, construction, recreation, and defense. This presentation describes DASSP, a hydrometeorological modeling approach and cloud-based computing architecture for generating high-resolution estimates of soil moisture content and soil strength. These ground state conditions are produced with global coverage at spatial scales of tens of meters, and the hydrometeorological modeling approach is applicable to either climatological, current, or forecast weather conditions. DASSP's ground state predictions are generated via a physics-based downscaling approach that fuses weather-scale (1/4 degree spatial scale) land surface model estimates of soil moisture and land surface water and energy fluxes, with terabytes of geospatial data including topography, land cover, soil classification, and vegetation information at high resolution (1 to 3 arc-second spatial scale). A two-stage physics-based hydrological model is then applied to downscale the weather-scale data to the resolution of the highest-resolution data sets. The first downscaling stage computes steady-state soil moisture redistribution due to topography and soil texture effects using a TOPMODEL-based formulation (e.g., moisture from the water table is allocated toward regions with a high topographic wetness index). The second downscaling stage accounts for dynamic weather-driven effects by computing water balances that disaggregate water fluxes from the weather-scale land surface model based on the high-resolution geospatial data (e.g., transpiration water fluxes are allocated toward regions with greater vegetative coverage). The results of these two downscaling stages are then combined, yielding high-resolution soil moisture estimates. These soil moisture estimates are then combined with soil texture classification data as inputs to soil strength prediction algorithms (e.g., estimation of rating cone index). DASSP's algorithms have been implemented in a high-performance, cloud-based computing architecture that provides interactive access to data using a slippy map user interface accessible from any modern web browser, an OGC-compliant WMS server for GIS integration, and quantitative download of data in GeoTIFF format. Integration of DASSP has been demonstrated with NASA's NLDAS and GLDAS, as well as Air Force Weather's LIS weather and land surface data products. DASSP's high-resolution soil moisture predictions are being validated through comparison to in situ sensor data from soil moisture monitoring networks (e.g., USDA SCAN), as well as remotely–sensed spatial data (e.g., NASA AirMOSS) for various biome types. In this presentation, we describe the underlying DASSP soil moisture downscaling approach, its high-performance computing architecture, initial validation results, and envisioned private and public sector applications. We also provide a live demonstration of its usage through a web-based user interface for near-real-time, global soil moisture prediction.

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