Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 21, issue 2
Hydrol. Earth Syst. Sci., 21, 1051-1062, 2017
https://doi.org/10.5194/hess-21-1051-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Observations and modeling of land surface water and energy...

Hydrol. Earth Syst. Sci., 21, 1051-1062, 2017
https://doi.org/10.5194/hess-21-1051-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 Gibson1, Trenton E. Franz1, Tiejun Wang1,2, John Gates3, Patricio Grassini4, Haishun Yang4, and Dean Eisenhauer5 Justin Gibson et al.
  • 1School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA
  • 2Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, P. R. China
  • 3The Climate Corporation, San Francisco, CA, USA
  • 4Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
  • 5Biological Systems Engineering Department, University of Nebraska-Lincoln, Lincoln, NE, USA

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 1km 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 35km2 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 80mmyr−1. The most aggressive irrigation saving routine (vadose zone model) indicates a potential irrigation savings of 120mmyr−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 (<1km).

Publications Copernicus
Special issue
Download
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.
The human use of water for irrigation is often ignored in models and operational forecasts. We...
Citation
Share