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,Tiejun Wang,Dean Eisenhauer,Patricio Grassini,Haishun Yang,John Gates,Trenton E Franz

doi:10.5194/hess-21-1051-2017

Justin Gibson, Tiejun Wang + Show 5 more

Open Access

https://doi.org/10.5194/hess-21-1051-2017

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Abstract

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).

Highlights

Regional land surface models (LSMs) often ignore or do a poor job of representing irrigation physics (Kumar et al, 2015)
Significant gauge-to-gauge variability was observed within the seven rain gauge time series within each growing season with a mean of 320 mm and a coefficient of variation (CV) of 35 % (Fig. 3)
In terms of growing season ETc, the HPRCC reported an average of 815 mm and was within 10 % of the county-level values estimated by Sharma and Irmak (2012)

Summary

Introduction

Regional land surface models (LSMs) often ignore or do a poor job of representing irrigation physics (Kumar et al, 2015). The USDA Farm and Ranch Irrigation Survey (USDA, 2014) contains survey data on the county level; data are only reported every 5 years and irrigation data are given on a pumping volume basis instead of depth per irrigated area as needed by LSMs (Siebert et al, 2010). Another well-known irrigation database, AQUASTAT (FAO, 2008), contains irrigation data on a spatial scale too coarse for investigating important feedback, like land–atmospheric coupling, and lacks information for Europe and North America. Gibson et al.: A case study of field-scale maize irrigation patterns in western Nebraska gation databases ( Grassini et al, 2011, 2014, 2015), mostly focusing on benchmarking on-farm irrigation in relation to crop production

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