Temporal Upscaling of Agricultural Evapotranspiration with an Improved Evaporative Fraction Method

Le résumé fourni par la source

Evapotranspiration (ET) is a crucial parameter for agricultural management and the hydrologic cycle, and instantaneous satellite images are the primary data source for regional ET. The constant evaporative fraction method (EFO) is a common approach for converting short-time ET (ETst) to daily ET (ETday). However, EFO has some limitations due to simple assumptions, including the following: the short-time evaporative fraction (EFst) equals the daily evaporative fraction (EFday). This study proposed an improved evaporative fraction method (EFI) through theoretical derivation and data analysis without additional data requirements, enabling the accurate upscaling of ETst to ETday. The vapor pressure deficit and available energy were considered in EFI to describe the main effect factor and estimate the deviation between EFst and EFday, defining the deviation coefficient and potential deviation between EFst and EFday. EFI was tested through four aspects: different agricultural systems, various sites, two growth stages, and different sources of EFst, comparing estimated ETday from EFI and measured ETday. EFI reduced the mean absolute percentage error (MAPE) of ETday estimation from 23% to 16% when EFst is derived from measured data compared to EFO. Similarly, the MAPE of ETday estimation reduced from 38% to 31% when EFst is derived from a remote sensing model (Surface Energy Balance Algorithm for Land, SEBAL). EFI performs better during the growing period than the fallow season, providing critical information for irrigation practices. Crop type is not a main control factor for the relationship between η (ratio between VPD and Rn-G) and EFst, and EFI is adaptable to various agricultural systems. The encouraging results of EFI in different scenarios demonstrate its accuracy and robustness. Therefore, EFI is anticipated to upscale EFst to EFday, generating a more accurate ET on a regional scale through remote sensing technology.

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