Estimating irrigation demand with geospatial and in-situ data: Application to the high plains aquifer, Kansas, USA

Abstract

Unsustainable groundwater mining threatens the economic stability of several regions around the world. Developing sustainable water-use policies and integrated water-management plans requires reliably predicting water demand under variable weather and land-use conditions. Here, a water-budget model capable of estimating spatial and temporal variations in daily irrigation demand under variable weather and land-use scenarios is modified to consider irrigation management and groundwater pumping limitations. This model uses a combination of geospatial and in-situ measured data, including land-use and land-cover maps, crop-specific evapotranspiration data, and weather data. A coupled sub-model limits pumping rates based on pump capacity and spatial and temporal variations in the saturated thickness of the aquifer. The ability of this water-budget model to accurately estimate irrigation demand is demonstrated using the High Plains aquifer region of Kansas, USA, an aquifer that has undergone, and continues to undergo, significant depletion due to decades of irrigation. The model was calibrated with reported and measured water use for more than 1200 fields, with ratios of simulated annual irrigation demand to actual reported water use of 1.10, 0.78, 0.75, 0.93, and 1.40 for corn, sorghum, soybean, winter wheat, and alfalfa, respectively. Further simulations at a variety of scales, from field to multi-county levels, demonstrate that the developed water-budget model is capable of simulating the spatial and temporal variability of irrigation demand.