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MODELING REFERENCE EVAPOTRANSPIRATION WITH MINIMAL INPUT DATA FOR CLIMATE CHANGE IMPACT STUDIES
Abstract
Impact assessments of climate change commonly use either synthetic or downscaled meteorological data. Many meteorological variables are missing or uncertain in such an approach. In this paper, it is shown that in such studies, this limits the use of classical reference ET0 models, e.g., the FAO-56 Penman Monteith model, which require several climatic data inputs. The objective of this study was to assess the capability of the minimal-input approach to predict ET0 and irrigation water requirements solely from temperature. Two methodologies are considered: machine learning and optimized empirical models. The models examined are calibrated and validated using daily data from the presented case study in Slovakia. Model's performance assessment is evaluated by R , RMSE, and bias (PBIAS), with particular attention to predictive robustness under input restrictions. The results show that both proposed methods can provide a reliable estimate of ET0 when, e.g., radiation, wind speed and humidity are unavailable. The machine learning model performs slightly better than the optimized empirical equation in terms of generalization. The estimated ET0 is used to assess irrigation water requirements, demonstrating the potential of data-limited modelling for regional climate change impact assessments.
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