Supporting decision-making in agricultural water management under data scarcity using global datasets – chances, limits and potential improvements

Abstract

Assessing alternative agricultural water management strategies requires long-term field trials or vast data collection for model calibration and simulation.This work aims to assess whether an uncalibrated agro-hydrological model using global input datasets for climate, soil and crop information can serve as a decision-support tool for crop water management under data scarcity.This study employs the Cool Farm Tool Water (CFTW) at eight eddy covariance sites of the FLUXNET2015 dataset. CFTW is tested using global (CFTWglobal) and local (CFTWlocal) input datasets under current and alternative management scenarios.Results show that the use of global datasets for estimating daily evapotranspiration had little effect on the median Root Mean Square Error (RMSE) (CFTWglobal: 1.70mm, CFTWlocal: 1.79mm), while, however, the median model bias is much greater (CFTWglobal: -18.6%, CFTWlocal: -4.3%). Furthermore, the periods of water stress were little affected by the use of local or global data (median accuracy: 0.84), whereas the use of global data inputs led to a significant underestimation of irrigation water requirements (median difference: 110mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local soil and reference evapotranspiration for some European growing seasons.We identify model outputs that can support decision-making when relying on global data, such as periods of water stress and the daily dynamics of water use. However, our findings also emphasize the difficulty of overcoming data scarcity in decision-making in agricultural water management. Furthermore, we provide recommendations for enhancing model performance and thus may increase the accessibility of reliable decision support tools in the future.