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ARS Home » Pacific West Area » Davis, California » Crops Pathology and Genetics Research » Research » Publications at this Location » Publication #394376

Research Project: Resilient, Sustainable Production Strategies for Low-Input Environments

Location: Crops Pathology and Genetics Research

Title: Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS

item DOHERTY, CONOR - Stanford University
item JOHNSON, LEE - California State University
item VOLK, JOHN - National Aeronautics And Space Administration (NASA) - Johnson Space Center
item MAUTER, MEAGAN - Desert Research Institute
item BAMBACH, NICOLAS - University Of California, Davis
item McElrone, Andrew
item Alfieri, Joseph
item HIPPS, LAWRENCE - Utah State University
item Prueger, John
item CASTRO, SEBASTIAN - University Of California, Davis
item ALSINA, MARIA MAR - E & J Gallo Winery
item Kustas, William - Bill
item MELTON, FORREST - California State University

Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/27/2022
Publication Date: 8/31/2022
Citation: Doherty, C.T., Johnson, L.F., Volk, J., Mauter, M.S., Bambach, N.E., McElrone, A.J., Alfieri, J.G., Hipps, L.E., Prueger, J.H., Castro, S.J., Alsina, M., Kustas, W.P., Melton, F.S. 2022. Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS. Irrigation Science. 40:515-530.

Interpretive Summary:

Technical Abstract: Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017-2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time-of-year. Overall meteorological inputs introduced RMSE of 0.33-0.65 mm/day (7-27%) across sites while SIMS introduced RMSE of 0.55-0.83 mm/day (19-24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET.