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

• Published in: Agricultural water management Vol. 296; p. 108803
• Main Authors: Kayatz, Benjamin, Baroni, Gabriele, Hillier, Jon, Lüdtke, Stefan, Freese, Dirk, Wattenbach, Martin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2024; Elsevier
• Subjects: climate; Cool farm tool; Crop water requirement; Crop water use; data collection; decision making; decision support systems; eddy covariance; Evapotranspiration; FAO56; farms; Irrigation; irrigation water; model validation; soil; water management; water stress; Irrigation; Cool farm tool; Crop water use; Crop water requirement; FAO56; Evapotranspiration
• DOI: 10.1016/j.agwat.2024.108803
• ISSN: 0378-3774

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: 1.70 mm, CFTWlocal: 1.79 mm), 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 overestimation of irrigation water requirements (median difference: 110 mm). The model performance improves predominantly through the use of more representative local precipitation data, followed by local reference evapotranspiration and soil 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. [Display omitted] •We tested an uncalibrated agro-hydrological model driven with global input data to overcome issues of data scarcity limiting informed decision-making in agricultural water management.•The model framework is able to reproduce the daily dynamics of crop water use and identify water-stressed periods.•Use of global data as opposed to local data increases the bias for daily water use and thereby overestimates irrigation requirements.•Use of local precipitation data is the most important means by which the model performance can be improved.•Results using global and local input data for various irrigation interventions agree in relative but not in absolute terms.