Development of irrigation water management model for reducing drought severity using remotely sensed soil moisture footprints

With an increase of population, agriculture, and industry, the demand for water has increased gradually across the world. Currently, agricultural crops have been damaged by drought severity due to climate changes that contribute to water scarcity. Policy/decision makers need to be prepared for reducing damages to crops due to severe droughts. For this reason, a genetic algorithm (GA)-based irrigation water management model (IWMM) adapting a hydrological model [soil water atmosphere plant (SWAP)] was developed. This approach is linked with a noisy Monte Carlo genetic algorithm (NMCGA) that can estimate effective soil hydraulic properties from in situ/remotely sensed (RS) soil moisture data. Based on the estimated soil parameters, vegetation information, and historical weather forcings, long-term root zone soil moisture (SM) and evapotranspiration (ET) dynamics were reproduced at fields using SWAP in a forward mode. This approach incorporates a soil moisture deficit index (SMDI) that can estimate the weekly drought severity using the daily estimated soil moisture dynamics. The irrigation schedules, intervals, and amounts were determined by the degree of drought based on the SMDI values (below 0 indicating drought). The Lubbock and Walnut Creek (WC) 11/14 sites in Texas and Iowa were selected for testing the applicability of the studied approach using in situ (point scale) and RS (airborne sensing scale) soil moisture products. As this approach irrigates the appropriate/minimum water amounts (yearly average 65.5-136.1 mm) to the agricultural fields, one could prevent the drought-driven crop damages with the positive SMDI values. Thus, the newly developed model could be helpful for improving agricultural water management and reducing drought severity efficiently in irrigated agriculture.