Restricted water allocations: landscape-scale energy balance simulations and adjustments in agricultural water applications

Authors: DHUNGEL, RAMESH - Kansas State University; AIKEN, ROBERT - Kansas State University; LIN, XIAOMAO - Kansas State University; KENYON, SHANNON - Kansas Groundwater Management District #4; Colaizzi, Paul; LUHMAN, RAY - Kansas Groundwater Management District #4; Baumhardt, Roland - Louis; O'BRIEN, DAN - Kansas State University; KUTIKOFF, SETH - Kansas State University; Brauer, David

Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/8/2019
Publication Date: 10/21/2019
Publication URL: https://handle.nal.usda.gov/10113/6730610
Citation: Dhungel, R., Aiken, R., Lin, X., Kenyon, S., Colaizzi, P.D., Luhman, R., Baumhardt, R.L., O'Brien, D., Kutikoff, S., Brauer, D.K. 2019. Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2019.105854.
DOI: https://doi.org/10.1016/j.agwat.2019.105854

Interpretive Summary: The fresh water supply for irrigation is decreasing because of dwindling supplies and increased competition for other uses. In some cases, more irrigation water is applied than what some crops actually need, resulting in further decreases in water supply, increased cost, and reduced crop yields. Models can predict actual water needs for different crops using satellite and weather data. Using these models, irrigation water planners and landowners can determine the most equitable and profitable distribution of available irrigation water. An advanced crop water use model was shown to accurately predict water use of corn in small experimental fields, but the model has yet to be tested on large commercial fields over several seasons. Therefore, scientists at USDA-ARS (Bushland, Texas) and Kansas State University tested the model over a large irrigated area in Northwest Kansas over five years. The model matched irrigation and precipitation data obtained from farmers’ fields during the irrigation season, but the model over-predicted irrigation needs after crop irrigations had stopped and crops started to senesce. The model nonetheless identified crops that received more water than needed, and hence identified opportunities for water conservation. This is crucial for mitigating unsustainable water use for irrigation, enhancing food security, and sustaining rural economies that depend on irrigated crop production.

Technical Abstract: Research that incorporates information from satellites into conventional biophysical models has great importance and interest. Comprehensive crop water algorithms can help track crop stress, schedule irrigation (Irr), and acquire water right information for effective water management and increased productivity in semi-arid and arid environments. Overall objective was to utilize the automated biophysical surface energy balance model BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) to understand some critical water management issues in the agriculture sector. BAITSSS served as a digital crop water tracker and irrigation scheduler to simulate hourly landscape evapotranspiration (ET) at 30 m spatial resolution. North American Land Data Assimilation System (NLDAS) weather data and Landsat-based vegetation indices were inputs of BAITSSS to simulate surface energy balance components along with Irr. Two agricultural-dominated groundwater regions of northwest Kansas, USA were studied during the five-year period (2013-2017). We compared model-simulated Irr to reported Irr in the field within water right management units (WRMU). The sum of reported Irr and precipitation, representing in-season water supply, was also compared to model simulated ET as an indicator of well-watered ET. The model was able to simulate reasonable ET values, Irr quantities, and to differentiate various distribution patterns of crops within WRMU. However, unknown water management, within WRMU, constrained explicit inference of actual ET and Irr amounts. The model appears suitable for quantifying the upper bound of in-season water supply (Irr plus precipitation) expected for well-watered crops in the U.S. Central High Plains. A WRMU exhibiting significantly different in-season water supply than the simulated ET may present opportunities to modify Irr rates or to gain inference about deficit Irr.