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 (ET${}_0$) 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. ET${}_0$ 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 ET${}_0$. 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 ET${}_0$ increase in headwaters and a smaller increase in the coast.

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R² = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

A partir de un total de 92 series de precipitación mensual de la base de datos de la Agencia Estatal de Meteorología (AEMET) se ha realizado un análisis exhaustivo de sequías en el marco espacial de la Demarcación Hidrográfica del Segura (DHS) y Mancomunidad de los Canales del Taibilla (MCT), organismos encargados de la planificación de los recursos hídricos, y del abastecimiento de agua potable, respectivamente, de gran parte del sureste de España. El objetivo principal de este estudio es la regionalización de los patrones temporales de sequía a partir del Standarized Precipitation Index (SPI), con escala de 12 meses (SPI12), para evaluar las sequías hidrológicas. El periodo seleccionado es 1968–2017. La regionalización se ha realizado a partir del Análisis de Componentes Principales (ACP), a través del cual se han obtenido 4 regiones homogéneas. A partir de estas regiones se ha identificado el porcentaje de área afectada por sequías de diferente severidad, y la frecuencia de ocurrencia de sequías (basado en el método Kernel Ocurrence Rate Estimation-KORE). Se observa un incremento de la frecuencia de ocurrencia de sequías y del área afectada por las mismas en prácticamente todas las regiones, lo que conlleva importantes repercusiones territoriales, sociales y económicas en un ámbito territorial semiárido que presenta un déficit hídrico destacado.

Se propone una metodología para realizar proyecciones espacio-temporales de la evapo- transplraclón de referencia (ET₀) y el posible impacto de cambio climático en cuencas se- miáridas. El estudio se desarrolla en la Demarcación Hidrográfica del Segura, utilizando para estimar la ET₀ el modelo de Hargreaves calibrado mensualmente mediante el método de Allen. Se parte de una regionalización de temperaturas máximas y mínimas a partir de los modelos globales MPEH5 y MPEH5C y los escenarios de emisión SRESA2, SRESA1B, SRESB1 y E1. Se ha evaluado la tendencia temporal y la distribución espacial de ET₀ mediante Theil-Sen,