Browsing by Subject "Reference evapotranspiration"

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    Evapotranspiration response to climate change in semi-arid areas: using random forest as multi-model ensemble method
    (MDPI, 2021-01-18) Ruiz-Álvarez, Marcos; Gomariz Castillo, Francisco; Alonso Sarria, Francisco; Geografía
    Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) 𝑀=0.903, 𝑆𝐷=0.034 for KGE and 𝑀=3.17, 𝑆𝐷=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.
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    Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale
    (Elsevier, 2025-03-12) Ye, Yu; Zamora Izquierdo, Miguel Ángel; Skarmeta Gómez, Antonio; González Vidal, Aurora; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Accurate estimation and forecasting of Reference Evapotranspiration () is critical for almost all agricultural activities and water resource management. However, the most commonly used Penman-Monteith model (FAO56-PM) requires a large amount of input data and it is difficult to compute for general users. Machine Learning (ML) techniques can be used to address this shortcoming. Nevertheless, most studies are site-specific and lack generalizability. This study compares standard ML and Deep Learning (DL) algorithms for estimating and forecasting daily at different spatial scales in Spain. While Transfer Learning (TL) is a well-established ML technique, its application in computation remains largely unexplored. We applied TL in a novel approach to retrain DL models, enabling adaptation to diverse local climatic conditions, which is particularly important in this domain. All possible combinations of FAO56-PM inputs were evaluated. The results showed that with three or more climatic variables, the TL process can consistently reduce errors by using an appropriate amount of new data to retrain the models. In estimation, with 20% (120 days) of new data, TL models can provide the same performance as if they were trained with local data, both regionally and nationally (improvement of MAE from 26.4% to 99.5%). During forecasting, we used predicted weather data as input, and despite inherent biases in some variables, the TL models successfully adapted using 9-36 days of new data, significantly improving predictive performance (reducing MAE from -1.1% to 134.3%). Thus, the TL process is highly recommended as a promising methodology for increasing the generalization capability of DL models in both daily estimation and forecasting under diverse climatic conditions with limited local data.