Articles | Volume 21, issue 2
https://doi.org/10.5194/hess-21-1051-2017
https://doi.org/10.5194/hess-21-1051-2017
Research article
 | 
20 Feb 2017
Research article |  | 20 Feb 2017

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

Justin Gibson, Trenton E. Franz, Tiejun Wang, John Gates, Patricio Grassini, Haishun Yang, and Dean Eisenhauer

Abstract. In many agricultural regions, the human use of water for irrigation is often ignored or poorly represented in land surface models (LSMs) and operational forecasts. Because irrigation increases soil moisture, feedback on the surface energy balance, rainfall recycling, and atmospheric dynamics is not represented and may lead to reduced model skill. In this work, we describe four plausible and relatively simple irrigation routines that can be coupled to the next generation of hyper-resolution LSMs operating at scales of 1 km or 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 irrigation database (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 amounts across a range of wet and dry years but with a low bias of 80 mm yr−1. The most aggressive irrigation saving routine (vadose zone model) indicates a potential irrigation savings of 120 mm yr−1 and yield losses of less than 3 % against the crop model benchmark and historical averages. The results of the various irrigation routines and associated yield penalties will be valuable for future consideration by local water managers to be informed about the potential value of irrigation saving technologies and irrigation practices. Moreover, the routines offer the hyper-resolution LSM community a range of irrigation routines to better constrain irrigation decision-making at critical temporal (daily) and spatial scales (< 1 km).

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Short summary
The human use of water for irrigation is often ignored in models and operational forecasts. We describe four plausible and relatively simple irrigation routines that can be coupled to the next generation of models. The routines are tested against a unique irrigation dataset from western Nebraska. The most aggressive water-saving irrigation routine indicates a potential irrigation savings of 120 mm yr−1 and yield losses of less than 3 % against the crop model benchmark and historical averages.