Harnessing SMAP Satellite Soil Moisture Product to Optimize Soil Properties to Improve Water Resource Management for Agriculture
Abstract
Proper estimates of soil physical and hydraulic properties are of prime importance for management of water resources in agriculture dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using 6 years (2015-2021) of satellite soil moisture observations from NASA’s SMAP mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that controls model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the study area. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4% and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9%. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional reports. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR Basin and beyond.
