Abstract:Abstract. Climate change and its possible effects on water resources has become an increasingly near threat. Therefore, the study of these impacts in highly regulated systems and those suffering extreme events is essential to deal with them effectively. This study responds to the need for an effective method to integrate climate change projections into water planning and management analysis in order to guide the decision-making, taking into account drought risk assessments. Therefore, this document presents a … Show more
“…In this work, the ANTILOPE quantitative precipitation estimates (QPEs) are used for precipitation estimation (Champeaux et al, 2009). The ANTILOPE QPEs are based on a fusion between the radar data provided by the operational radar network ARAMIS (Tabary, 2007) and the measurements at rain gauges, spatialized by the kriging method. ANTILOPE QPEs are available at hourly time step and 1 km × 1 km resolution.…”
Section: The Studied Eventsmentioning
confidence: 99%
“…The risk associated with flash flood events is of growing importance, in particular in the Mediterranean area (Payrastre et al, 2011;Ruin et al, 2014;Suárez-Almiñana et al, 2020). Extreme precipitation events are expected to increase both in frequency and amplitude in the context of a changing climate (IPCC, 2014).…”
Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.
“…In this work, the ANTILOPE quantitative precipitation estimates (QPEs) are used for precipitation estimation (Champeaux et al, 2009). The ANTILOPE QPEs are based on a fusion between the radar data provided by the operational radar network ARAMIS (Tabary, 2007) and the measurements at rain gauges, spatialized by the kriging method. ANTILOPE QPEs are available at hourly time step and 1 km × 1 km resolution.…”
Section: The Studied Eventsmentioning
confidence: 99%
“…The risk associated with flash flood events is of growing importance, in particular in the Mediterranean area (Payrastre et al, 2011;Ruin et al, 2014;Suárez-Almiñana et al, 2020). Extreme precipitation events are expected to increase both in frequency and amplitude in the context of a changing climate (IPCC, 2014).…”
Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.
“…Para obtener las aportaciones futuras, en este caso se utilizaron las tasas medias de cambio de aportaciones obtenidas por Suárez-Almiñana et al, (2020b), procedentes del desarrollo de una metodología de integración de proyecciones climáticas para la planificación hidrológica. En el citado estudio, se trabajó con la media del ensamblado compuesto por 9 Modelos Climáticos Regionales pertenecientes a las Sendas Representativas de Concentración (SRC) 4.5 y 8.5, ya que según lo acordado en la cumbre de cambio climático de París de 2015, la SRC más probable es un escenario intermedio entre estos dos, la SRC 6.0 (Barranco et al, 2018;Suárez-Almiñana et al, 2020a;2020b). Por lo tanto, estas tasas medias proceden de la corrección del sesgo de las aportaciones futuras (procedentes de un modelo hidrológico) y su comparación con las del periodo histórico , extrayendo así las tasas medias de cambio para cada horizonte.…”
Section: Datos De Entrada Del Modelo Rreaunclassified
“…provenientes de proyecciones climáticas relacionadas con los recursos hídricos a nivel europeo. Estos datos también se presentan en tasas de cambio por horizonte futuro (diferenciando entre la media del ensamblado o SRC) y comparten origen con las proyecciones climáticas utilizadas para la extracción de las tasas de cambio aplicadas a las aportaciones en este estudio (Suárez-Almiñana et al, 2020b).…”
Section: Datos De Entrada Del Modelo Rreaunclassified
“…Sin embargo, hay que tener en cuenta que en este estudio se han utilizado aportaciones producto de la aplicación de tasas medias de cambio a un periodo histórico, y aunque han sido obtenidas por medio de proyecciones climáticas, estos datos tienen mucha incertidumbre asociada, que además va aumentando en la cadena de modelos (Suárez-Almiñana, et al, 2020b). También, hemos utilizado un modelo simplificado en el que los porcentajes de incumplimientos corresponden a los promedios de la serie histórica y de los horizontes futuros, que quizás estén condicionadas por los periodos de sequía y los meses de verano, en los que algunos cauces carecen de agua.…”
Section: Simulaciones De Cambio Climáticounclassified
<p>En este estudio se analiza el efecto del cambio climático en la calidad del agua de la cuenca del Júcar a partir de estimaciones futuras de aportaciones hidrológicas y temperatura del agua (Ta). Para ello, se utilizó un modelo de calidad de aguas a escala de cuenca con el que se estimó el estado ecológico de todas las masas de agua, basándose en las concentraciones de DBO<sub>5</sub>, P, NH<sub>4</sub><sup>+</sup> y NO<sub>3</sub><sup>- </sup>para los horizontes futuros 2020, 2050 y 2080. De este análisis se obtuvo un incremento del número de masas con altos niveles de contaminación (80-100% incumplimientos) en los horizontes 2050 y 2080, localizadas sobre todo en la parte media y baja de la cuenca. Además, la degradación de la DBO<sub>5</sub> y el NH<sub>4</sub><sup>+ </sup>es muy dependiente de la temperatura del agua, poniendo de manifiesto la importancia de considerar esta variable en el modelo.</p>
The Mediterranean is one of the most vulnerable regions to climate change impacts. Climate change scenarios predict that water temperature will increase up to 2.2–2.9ºC by the end of the century in Mediterranean rivers. This will cause an impact on water quality (oxygen dissolved reduction), reduce the available habitat of cold-water fish species and affect macroinvertebrates. Risk assessment methodology develops indicators that integrate hazard, exposure and vulnerability. Risk maps are key tools to prioritize the areas in which adaptation measures should be implemented in order to improve the adaptive capacity of ecosystems. The risk of habitat loss and ecosystem damage is very high in Mediterranean rivers. For RCP8.5, the 80% of the waterbodies that currently have brown trout presence are in High Risk (HR) or Very High Risk (VHR) of disappearing in the long term future (2070–2100) and the 35% in the short term (2010–2040). It will affect the middle sections first and the headwaters of the rivers later. The 92% of the waterbodies are in HR-VHR of macroinvertebrate family’s affection (2070–2100) and dissolved oxygen may be reduced by 0.5–0.75 mgO2/l (2070–2100). The restoration of the riverside vegetation is the main adaptation measure. This reduces significantly the stream temperature. Other measures are the groundwater protection and cold-water discharge from the reservoirs.
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