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In many agricultural regions, the human use of water for irrigation is oftenignored or poorly represented in land surface models (LSMs) and operationalforecasts. Because irrigation increases soil moisture, feedback onthe surface energy balance, rainfall recycling, and atmospheric dynamics is notrepresented and may lead to reduced model skill. In this work, we describefour plausible and relatively simple irrigation routines that can be coupledto the next generation of hyper-resolution LSMs operating at scales of 1 kmor less. The irrigation output from the four routines (crop model,precipitation delayed, evapotranspiration replacement, and vadose zone model) is compared against a historical field-scale irrigationdatabase (2008–2014) from a 35 km2 study area under maize production and center pivot irrigation in western Nebraska (USA). We find that the most yield-conservative irrigation routine (crop model) produces seasonal totals of irrigation that compare well against the observed irrigation amountsacross a range of wet and dry years but with a low bias of 80 mm yr−1.The most aggressive irrigation saving routine (vadose zonemodel) indicates a potential irrigation savings of 120 mm yr−1 andyield losses of less than 3 % against the crop model benchmark andhistorical averages. The results of the various irrigation routines andassociated yield penalties will be valuable for future consideration bylocal water managers to be informed about the potential value of irrigationsaving technologies and irrigation practices. Moreover, the routines offerthe hyper-resolution LSM community a range of irrigation routines to betterconstrain irrigation decision-making at critical temporal (daily) andspatial scales (< 1 km)

hydrology and earth system sciences

A case study of field-scale maize irrigation patterns in western Nebraska: implications for water managers and recommendations for hyper-resolution land surface modeling