Abstract

Irrigated agriculture is the largest consumer of freshwater globally. Effective water management is crucial to support ongoing agricultural intensification to meet increasing demand for food, fuel, and fiber production. Knowledge of where and when irrigation occurs is needed for management and hydrological modeling, yet data on patterns of irrigation through time are not common. Here, we produced annual, high resolution (30 m) irrigation maps across the greater Republican River Basin region of the High Plains Aquifer for 1999-2016 by combining all available Landsat satellite imagery with climate and soil covariables in Google Earth Engine. Random forest classification had accuracies from 92-100% and generally agreed with county statistics (r2 = 0.88-0.96). Two novel indices we developed to integrate plant greenness and moisture information scored highest on variable importance metrics, suggesting they may improve satellite classification of irrigation in other regions. We found considerable interannual variability in irrigation location and extent, including fields added and/or removed from irrigation, that correspond to sub-regional differences in regulations and water source. Statistical modeling suggested precipitation and commodity price interact to influence irrigated extent through time. High prices incentivized expansion to increase crop yield and profit, but dry years required greater irrigation intensity, thus reducing area in this supply-limited region. We then combined our satellite-derived estimates of irrigated area with existing volumetric datasets to examine trends in irrigation depth over time. Datasets produced with this approach can provide a basis for enhancing water sustainability by providing consistent, spatially explicit tracking of irrigation dynamics over time.