TY - JOUR
T1 - Combining Remotely Sensed Evapotranspiration and an Agroecosystem Model to Estimate Center-Pivot Irrigation Water Use at High Spatio-Temporal Resolution
AU - Zhang, Jingwen
AU - Guan, Kaiyu
AU - Zhou, Wang
AU - Jiang, Chongya
AU - Peng, Bin
AU - Pan, Ming
AU - Grant, Robert F.
AU - Franz, Trenton E.
AU - Suyker, Andrew
AU - Yang, Yi
AU - Chen, Xiaohong
AU - Lin, Kairong
AU - Ma, Zewei
N1 - Funding Information:
The authors acknowledge the support from USDA National Institute of Food and Agriculture Foundational Program Cyber-physical systems (2019-67021-29312) and NSF CAREER award (1847334) managed through the NSF Environmental Sustainability Program. Access to field sites and data sets in Western Nebraska was provided by The Nature Conservancy, the Western Nebraska Irrigation Project, and the South Platte Natural Resources District. Funding for the AmeriFlux core sites was provided by the U.S. Department of Energy's Office of Science. This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. T.E.F. acknowledges the financial support of the USDA National Institute of Food and Agriculture, Hatch project (1020768).
Funding Information:
The authors acknowledge the support from USDA National Institute of Food and Agriculture Foundational Program Cyber‐physical systems (2019‐67021‐29312) and NSF CAREER award (1847334) managed through the NSF Environmental Sustainability Program. Access to field sites and data sets in Western Nebraska was provided by The Nature Conservancy, the Western Nebraska Irrigation Project, and the South Platte Natural Resources District. Funding for the AmeriFlux core sites was provided by the U.S. Department of Energy's Office of Science. This research was a contribution from the Long‐Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. T.E.F. acknowledges the financial support of the USDA National Institute of Food and Agriculture, Hatch project (1020768).
Publisher Copyright:
© 2023. American Geophysical Union. All Rights Reserved.
PY - 2023/3
Y1 - 2023/3
N2 - 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, and r around 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.
AB - 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, and r around 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.
KW - agroecosystem model
KW - data assimilation
KW - evapotranspiration
KW - irrigation water use
KW - model-data fusion
UR - http://www.scopus.com/inward/record.url?scp=85152579172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85152579172&partnerID=8YFLogxK
U2 - 10.1029/2022WR032967
DO - 10.1029/2022WR032967
M3 - Article
AN - SCOPUS:85152579172
SN - 0043-1397
VL - 59
JO - Water Resources Research
JF - Water Resources Research
IS - 3
M1 - e2022WR032967
ER -