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Article

Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method

1
University Institute for Water and Environment, University of Murcia, 30100 Murcia, Spain
2
Euro-Mediterranean Water Institute (IEA), 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2021, 13(2), 222; https://doi.org/10.3390/w13020222
Received: 5 December 2020 / Revised: 9 January 2021 / Accepted: 13 January 2021 / Published: 18 January 2021
(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling)
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. View Full-Text
Keywords: random forest regression; reference evapotranspiration; multi-model ensembles; Climate Change; fifth assessment report; random forest regression kriging; Kling–Gupta efficiency random forest regression; reference evapotranspiration; multi-model ensembles; Climate Change; fifth assessment report; random forest regression kriging; Kling–Gupta efficiency
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MDPI and ACS Style

Ruiz-Aĺvarez, M.; Gomariz-Castillo, F.; Alonso-Sarría, F. Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method. Water 2021, 13, 222. https://doi.org/10.3390/w13020222

AMA Style

Ruiz-Aĺvarez M, Gomariz-Castillo F, Alonso-Sarría F. Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method. Water. 2021; 13(2):222. https://doi.org/10.3390/w13020222

Chicago/Turabian Style

Ruiz-Aĺvarez, Marcos; Gomariz-Castillo, Francisco; Alonso-Sarría, Francisco. 2021. "Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method" Water 13, no. 2: 222. https://doi.org/10.3390/w13020222

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