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Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

We present an application and validation of the SAFRAN meteorological analysis system for north-east Spain. SAFRAN is also compared to the SPAN analysis system and the meteorological model HIRLAM-HNR, both operational at AEMET. This application of SAFRAN is intended for hydrological studies. This is the first study that shows an application of SAFRAN outside of France and that compares it with SPAN. This is also the first article validating SPAN's rainfall values. Using one year of observational data, the results show that both SAFRAN and SPAN have a similar performance, which is also similar to SAFRAN's performance in France. Thus, SAFRAN and SPAN are both good tools to force land surface models at high resolution in the area of SAFRAN works under the assumption of the existence of climatically homogeneous zones. Two different sets of zones were tested, one based on the AEMET meteorological warning zones and another one based on hydrological catchments. Better results were obtained when using meteorological warning zones. However, the difference is small. In north-east Spain, SAFRAN has the same limitations that were previously shown in France: the spatial structure of the fields is not realistic enough and wind speed is underestimated. As expected, both SAFRAN and SPAN work better in flat areas than over areas of steep relief. This can be a problem in hydrological studies, especially for the Ebro river basin, where most of the runoff is generated in the Pyrenees. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to project the possible evolution of river flood damages due to climate change, and applies it to mainland France. Its main contributions are (i) to demonstrate a methodology to investigate the full causal chain from global climate change to local economic flood losses; (ii) to show that future flood losses may change in a very significant manner over France; (iii) to show that a very large uncertainty arises from the climate downscaling technique, since two techniques with comparable skills at reproducing reference river flows give very different estimates of future flows, and thus of future local losses. The main conclusion is thus that estimating future flood losses is still out of reach, especially at local scale, but that future national-scale losses may change significantly over this century, requiring policy changes in terms of risk management and land-use planning. [ABSTRACT FROM AUTHOR]

In order to evaluate the uncertainty associated with the impact model in climate change studies, a CO2 responsive version of the land surface model ISBA (ISBA-A-gs) is compared with its standard version in a climate impact assessment study. The study is performed over the French Mediterranean basin using the Safran-Isba-Modcou chain. A downscaled A2 regional climate scenario is used to force both versions of ISBA, and the results of the two land surface models are compared for the present climate and for that at the end of the century. Reasonable agreement is found between models and with discharge observations. However, ISBA-A-gs has a lower mean evapotranspiration and a higher discharge than ISBA-Standard. Results for the impact of climate change are coherent on a yearly basis for evapotranspiration, total runoff, and discharge. However, the two versions of ISBA present contrasting seasonal variations. ISBA-A-gs develops a different vegetation cycle. The growth of the vegetation begins earlier and reaches a slightly lower maximum than in the present climate. This maximum is followed by a rapid decrease in summertime. In consequence, the springtime evapotranspiration is significantly increased when compared to ISBA-Standard, while the autumn evapotranspiration is lower. On average, discharge changes are more significant at the regional scale with ISBA-A-gs. [ABSTRACT FROM AUTHOR]

In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter., (Copyright © 2017 Elsevier B.V. All rights reserved.)

In this study we explore the seasonal predictability of water resources in a Mediterranean environment (the Boadella reservoir, in north-eastern Spain). Its utility for end-users is assessed through the analysis of economic value curve areas (EVA). Firstly, we have built monthly multiple linear regression (MLR) models for the in-flow, out-flow and volume anomalies by identifying the underlying relationships between these predictands and their potential predictors, both meteorological and human influenced: rainfall, maximum and minimum temperatures, reservoir volume and discharge. Subsequently, we have forecast the monthly anomalies with these models for the period 1981-2010 (up to seven months ahead). We have tested the aforementioned models with four strategies in a leave-one-out cross-validation procedure (LOOCV): a) Climatology (Clim.), b) persistence (Pers.), c) antecedent observations+climatology (A+Clim.), d) antecedent observations+European Centre for Medium-range Weather Forecasts (ECMWF) System 4 anomalies (A+S4). Climatology is the operational strategy against which the other approximations are compared. The second and third approaches only use observations as input data. Finally, the last one combines both observations and ECMWF System 4 forecasts. The LOOCV revealed that reservoir volume is the variable best described by the MLR models, followed by in-flow and out-flow anomalies. In the case of volume anomalies, the predictability displayed provides added value with respect to climatology with a minimum of four months in advance. For in-flow and out-flow this is true at one month ahead, and regarding the latter variable we encounter enhanced predictability also at longer horizons for the summer months, when water demands peak (a valuable result for end-users). Hence, there is a window of opportunity to develop future operational frameworks that could outperform the use of climatology for these variables and forecast horizons., (Copyright © 2016. Published by Elsevier B.V.)