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

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.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

“…Incorporation of high-frequency and high-resolution visible and nearinfrared data into the remote sensing models may improve their ability to capture phenological shifts particularly in arid/semi-arid regions, and agricultural systems in general 48,49 . Improvement of gridded meteorological model inputs 50,51 , land cover classification data and soils data 52 may also lead to improved model performance in both natural ecosystems and in croplands. In particular, datasets compiled from agricultural weather stations and used to compute bias correction surfaces for reference ET could be re-evaluated to ensure reference surface compliance with the assumptions of the American Society of Civil Engineers Penman-Monteith equation 53 .…”

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

“…Furthermore, PT-JPL integrates some physically based functions that serve a wide range of hydro-meteorological conditions that are not specific to agroecosystems [70]. The good performance of the SIMS model might be due to its basic features, as it is a reflectance-based model implementing parts of the FAO-56 dual crop coefficient model [7] and combining remotely sensed vegetation parameters and spatially resolved crop type information [71], and it has been shown to be useful in producing accurate evapotranspiration estimates for irrigated agriculture in the Western United States [31,53]. Srivastava et al [72] evaluated the moderate resolution imaging spectroradiometer (MODIS) satellite-based remote-sensing techniques, and the water-budget approach built into the semidistributed variable infiltration capacity landsurface model, against the two-step approach in the Kangsabati River Basin in eastern India.…”