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

Conor T Doherty; Lee F Johnson; John Volk; Meagan S Mauter; Nicolas Bambach; Andrew J McElrone; Joseph G Alfieri; Lawrence E Hipps; John H Prueger; Sebastian J Castro; Maria Mar Alsina; William P Kustas; Forrest S Melton

doi:10.1007/s00271-022-00808-9

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

Irrig Sci. 2022;40(4-5):515-530. doi: 10.1007/s00271-022-00808-9. Epub 2022 Aug 13.

Authors

Conor T Doherty¹, Lee F Johnson^{2

3}, John Volk⁴, Meagan S Mauter¹, Nicolas Bambach⁵, Andrew J McElrone^{6

7}, Joseph G Alfieri⁸, Lawrence E Hipps⁹, John H Prueger¹⁰, Sebastian J Castro⁷, Maria Mar Alsina¹¹, William P Kustas⁸, Forrest S Melton^{2

3}

Affiliations

¹ Department of Civil and Environmental Engineering, Stanford University, Stanford, CA USA.
² Division of Science & Environmental Policy, California State University, Monterey Bay, Seaside, CA USA.
³ Earth Science Division, NASA Ames Research Center, Moffett Field, CA USA.
⁴ Hydrologic Sciences Division, Desert Research Institute, Reno, NV USA.
⁵ Department of Land, Air and Water Resources, University of California, Davis, CA USA.
⁶ Crops Pathology and Genetics Research Unit, USDA-ARS, Davis, CA USA.
⁷ Department of Viticulture and Enology, University of California, Davis, CA USA.
⁸ Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD USA.
⁹ Department of Plants Soils and Climate, Utah State University, Logan, UT USA.
¹⁰ National Laboratory for Agriculture and the Environment, USDA-ARS, Ames, IA USA.
¹¹ E and J Gallo Winegrowing Research, Modesto, CA USA.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s00271-022-00808-9.