A case study of field-scale maize irrigation patterns in western Nebraska: implications for water managers and recommendations for hyper-resolution land surface modeling
2016
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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