Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

Zhang, Jingwen; Guan, Kaiyu; Zhou, Wang; Jiang, Chongya; Peng, Bin; Pan, Ming; Grant, Robert F.; Franz, Trenton E.; Suyker, Andrew; Yang, Yi; Chen, Xiaohong; Lin, Kairong; Ma, Zewei

doi:10.1029/2022wr032967

Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center‐Pivot Irrigation Water Use at High Spatio‐Temporal Resolution

Journal Article · 23 February 2023 · Water Resources Research

DOI:https://doi.org/10.1029/2022wr032967· OSTI ID:2422766

^[1]; ^[2]; Zhou, Wang ^[3]; Jiang, Chongya ^[3]; ^[2]; Pan, Ming ^[4]; ^[5]; ^[6]; Suyker, Andrew ^[6]; ^[3]; ^[7]; ^[7]; ^[3]

School of Civil Engineering Center of Water Resources and Environment Sun Yat‐sen University Zhuhai China; Agroecosystem Sustainability Center Institute for Sustainability, Energy, and Environment University of Illinois at Urbana Champaign Urbana IL USA; Department of Natural Resources and Environmental Sciences College of Agricultural, Consumer and Environmental Sciences University of Illinois at Urbana Champaign Urbana IL USA
Agroecosystem Sustainability Center Institute for Sustainability, Energy, and Environment University of Illinois at Urbana Champaign Urbana IL USA; Department of Natural Resources and Environmental Sciences College of Agricultural, Consumer and Environmental Sciences University of Illinois at Urbana Champaign Urbana IL USA; National Center for Supercomputing Applications University of Illinois at Urbana Champaign Urbana IL USA
Agroecosystem Sustainability Center Institute for Sustainability, Energy, and Environment University of Illinois at Urbana Champaign Urbana IL USA; Department of Natural Resources and Environmental Sciences College of Agricultural, Consumer and Environmental Sciences University of Illinois at Urbana Champaign Urbana IL USA
Center for Western Weather and Water Extremes Scripps Institution of Oceanography University of California San Diego La Jolla CA USA
Department of Renewable Resources University of Alberta Edmonton AB Canada
School of Natural Resources University of Nebraska‐Lincoln Lincoln NE USA
School of Civil Engineering Center of Water Resources and Environment Sun Yat‐sen University Zhuhai China

Abstract

Estimating irrigation water use accurately is critical for sustainable irrigation and studying terrestrial water cycle in irrigated croplands. However, irrigation is not monitored in most places, and current estimations of irrigation water use has coarse spatial and/or temporal resolutions. This study aims to estimate irrigation water use at the daily and field scale through the proposed model‐data fusion framework, which is achieved by particle filtering with two configurations (concurrent, CON, and sequential, SEQ) by assimilating satellite‐based evapotranspiration (ET) observations into an advanced agroecosystem model,ecosys. Two types of experiments using synthetic and real ET observations were conducted to study the efficacy of the proposed framework for estimating irrigation water use at the irrigated fields in eastern and western Nebraska, United States. The experiments using synthetic ET observations indicated that, for two major sources of uncertainties of ET difference between observations and model simulations, which are bias and noise, noise had larger impacts on degrading the estimation performance of irrigation water use than bias. For the experiments using real ET observations, monthly and annual estimations of irrigation water use matched well with farmer irrigation records, with Pearson correlation coefficient (r) around 0.80 and 0.50, respectively. Although detecting daily irrigation records was very challenging, our method still gave a good performance with RMSE, BIAS, andraround 2.90, 0.03, and 0.4 mm/d, respectively. Our proposed model‐data fusion framework for estimating irrigation water use at high spatio‐temporal resolution could contribute to regional water management, sustainable irrigation, and better tracking terrestrial water cycle.

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). AmeriFlux

Sponsoring Organization:: USDOE

DOE Contract Number:: AC02-05CH11231

OSTI ID:: 2422766

Journal Information:: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 3 Vol. 59; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)

Country of Publication:: United States

Language:: English

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