Statistical downscaling of extreme daily precipitation,evaporation, and temperature and construction of future scenarios

Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularly for the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation, and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlying driving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensively addressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme daily temperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the Dongjiang River basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emission scenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature. For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2 and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitation and evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extreme precipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitation process over the Dongjiang River basin. In pre‐flood seasons (April to June), the mixing of the dry and cold air originated from northern China and the moist warm air releases excessive rainstorms to this basin, while in post‐flood seasons (July to October), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristics collectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessment of future precipitation indices in the Shikoku region using a statistical downscaling model

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.     

中国平均降水和极端降水对气候变暖的响应:CMIP5模式模拟评估和预估

基于24个CMIP5全球耦合模式模拟结果,分析了中国区域年平均降水和ETCCDI强降水量(R95p)、极端强降水量(R99p)对增暖的响应.定量分析结果显示,CMIP5集合模拟的当代中国区域平均降水对增温的响应较观测偏弱,而极端降水的响应则偏强.对各子区域气温与平均降水、极端降水的关系均有一定的模拟能力,并且极端降水的模拟好于平均降水.RCP4.5和RCP8.5情景下,随着气温的升高,中国区域平均降水和极端降水均呈现一致增加的趋势,中国区域平均气温每升高1℃,平均降水增加的百分率分别为3.5%和2.4%,R95p增加百分率为11.9%和11.0%,R99p更加敏感,分别增加21.6%和22.4%.就各分区来看,当代的区域性差异较大,未来则普遍增强,并且区域性差异减小,在持续增暖背景下,中国及各分区极端降水对增暖的响应比平均降水更强,并且越强的极端降水敏感性越大.未来北方地区平均降水对增暖的响应比南方地区的要大,青藏高原和西南地区的R95p和R99p增加最显著,表明未来这些区域发生暴雨和洪涝的风险将增大.     

Relationships between the North Atlantic Oscillation index and temperatures in Europe in global climate models

The study evaluates relationships between the North Atlantic Oscillation (NAO) index and winter temperatures (including indices of extremes) over Europe in an ensemble of transient simulations of current global climate models (GCMs). We focus on identification of areas in which the NAO index is linked to winter temperatures and temperature extremes in simulations of the recent climate (1961–2000), and evaluate how these relationships change in climate change scenarios for the late 21st century (2071–2100). Most GCMs are able to reproduce main features of the observed links. The NAO index is more important for cold than warm extremes, which is also reproduced by the GCMs. However, all GCMs underestimate the magnitude of the NAO influence on cold extremes when averaged over northern and western Europe. For future scenarios, the links between the NAO and temperatures are mostly analogous to those in the recent climate, except for one GCM (CM3) in which the influence of the NAO on temperature almost disappears over whole Europe. This suggests that future scenarios from this particular GCM should be evaluated with caution. The NAO index is found to represent a useful covariate that explains an important fraction of variability of cold extremes in winter, and its incorporation into extreme value models for daily temperatures (and their possible changes under climate change) may improve performance of these models and reliability of estimates of extremes and their uncertainty.     

MM5V3模式对IPCCA1B情景下中国地区极端事件的模拟和预估

利用MM5V3区域气候模式单向嵌套ECHAM5全球环流模式,对中国地区1978-2000年及IPCC A1B情景下2038-2070年气候分别进行了水平分辨率为50 km的模拟试验.文章首先检验了模式模拟的当代极端气候结果,在此基础上对6个极端温度指数和6个极端降水指数的未来变化进行了预估.检验结果表明:MM5V3模式对中国地区当代日最高、最低温度及强降水(大雨和暴雨)日数的空间分布和概率特征均具有一定的模拟能力,但模拟的日最高温度在大部分地区偏低,日最低温度在南方地区偏低、西北地区偏高.概率统计结果显示日最高温度向低值频段偏移,日最低温度在0℃的峰值附近明显偏高.模式对大雨和暴雨年平均日数的模拟在东部地区偏多,概率统计结果则为一致偏大.未来中国地区极端气候预估结果表明:极端高温、极端低温和相对高温在全国范围内都将升高,且线性趋势均为上升;霜日日数则为减少,并具有下降趋势;暖日日数和相对低温在青藏高原和新疆部分地区有所减少、其它地区均为增加,且线性趋势暖日日数为上升,相对低温不明显.极端降水指数的变化具有区域特征,其中单日最大降水、连续五日最大降水、最长无雨期、强降水日数、简单降水强度和极端降水总量均在江淮、华南及西南地区有所增多,而在东北及内蒙古地区有所减少,未来中国南方地区降水的极端化趋势将更加显著.极端降水指数的线性趋势除最长无雨期外其它均为上升.     

区域气候模式RegCM3对华北地区未来气候变化的数值模拟

采用高水平分辨率区域气候模式进行区域未来气候变化预估,对理解全球增暖对区域气候的潜在影响和科学评估区域气候变化有很好的参考价值.这里对国家气候中心使用25 km高水平分辨率区域气候模式RegCM3单向嵌套全球模式MIROC3.2_hires在观测温室气体(1951—2000)和IPCC A1B温室气体排放情景下(2001—2100)进行的共计150年长时间模拟结果,进行华北地区未来气温、降水和极端气候事件变化的分析.模式检验结果表明:模式对当代(1981—2000)气温以及和气温有关的极端气候事件(霜冻日数、生长季长度)的空间分布和数值模拟较好;对降水及和降水有关的极端气候事件(强降水日期、降水强度、五日最大降水量)能够模拟出它们各自的主要空间分布特征,但在模拟数值上存在偏大、偏强的误差.和全球模式驱动场相比,区域模式模拟的气温、降水和极端气候事件有明显的改进.2010—2100年华北地区随时间区域平均气温升高幅度逐渐增大,随之霜冻日数逐渐减少,生长季长度逐渐增多;同时随温室效应的不断加剧,未来降水呈增加的趋势,强降水日期和五日最大降水量逐渐增多、降水强度逐渐增大.从空间分布看,21世纪末期(2081—2100)气温、降水以及有关的极端气候事件变化比21世纪中期(2041—2060)更加明显.     

Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling

ABSTRACT

Numerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended.     

A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes

In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile‐mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100‐year return level runoffs. Results show an increase in the 100‐year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer. Copyright © 2013 John Wiley & Sons, Ltd.     

Flows and sediment dynamics in the Ganga River under present and future climate scenarios

Identification of factors controlling sediment dynamics under natural flow regimes can establish a baseline for quantifying effects of present day hydrological alteration and future climate change on sediment delivery and associated flooding. The process-based INCA-Sediment model was used to simulate Ganga River sediment transport under baseline conditions and to quantify possible future changes using three contrasting climate scenarios. Construction of barrages and canals has significantly altered natural flow regimes, with profound consequences for sediment transport. Projected increases in future monsoonal precipitation will lead to higher peak flows, increasing flood frequency and greater water availability. Increased groundwater recharge during monsoon periods and greater rates of evaporation due to increased temperature complicate projections of water availability in non-monsoon periods. Rainfall and land surface interaction in high-relief areas drive uncertainties in Upper Ganga sediment loads. However, higher monsoonal peak flows will increase erosion and sediment delivery in western and lower reaches.     

Physically based maximum precipitation estimation under future climate change conditions

Estimation of the extreme precipitation over a target watershed under a changing climate would be necessary to design safe large hydraulic structures. For this purpose, the maximum precipitation (MP) estimation approach was applied to the American River Watershed (ARW) in Northern California under several future climate conditions over 90 water years (2010–2099). These future climate conditions were obtained using 13 future climate projections from two general circulation models (ECHAM5 and CCSM3) based on four future climate scenarios (Special Report on Emissions Scenarios: A1B, A1FI, A2, and B1). A total of 1,170 future projected severe storm events (90 years × 13 projections) were selected with respect to the 72‐hr basin‐average precipitation over the ARW. The 72‐hr basin‐average precipitation for each of the selected severe storm events was maximized over the ARW by horizontally shifting the atmospheric boundary conditions of a regional atmospheric model in order to optimize the path of the storm system that corresponded to the particular event. After maximization, the MP estimates, which are the largest precipitation depths among the maximized results, were obtained as 836.7 mm for the early half‐century period (2010–2054) and 1,056.5 mm for the late half‐century period (2055–2099).     

Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project

This paper presents the background, objectives, and preliminary outcomes from the first year of activities of the Polish–Norwegian project CHIHE (Climate Change Impact on Hydrological Extremes). The project aims to estimate the influence of climate changes on extreme river flows (low and high) and to evaluate the impact on the frequency of occurrence of hydrological extremes. Eight “twinned” catchments in Poland and Norway serve as case studies. We present the procedures of the catchment selection applied in Norway and Poland and a database consisting of near-natural ten Polish and eight Norwegian catchments constructed for the purpose of climate impact assessment. Climate projections for selected catchments are described and compared with observations of temperature and precipitation available for the reference period. Future changes based on those projections are analysed and assessed for two periods, the near future (2021–2050) and the far-future (2071–2100). The results indicate increases in precipitation and temperature in the periods and regions studied both in Poland and Norway.     

Assessment and evaluation of potential climate change impact on monsoon flows using machine learning technique over Wainganga River basin,India

In this study, classification- and regression-based statistical downscaling is used to project the monthly monsoon streamflow over the Wainganga basin, India, using 40 global climate model (GCM) outputs and four representative concentration pathways (RCP) scenarios. Support vector machine (SVM) and relevance vector machine (RVM) are considered to perform downscaling. The RVM outperforms SVM and is used to simulate future projections of monsoon flows for different periods. In addition, variability in water availability with uncertainty and change point (CP) detection are accomplished by flow–duration curve and Bayesian analysis, respectively. It is observed from the results that the upper extremes of monsoon flows are highly sensitive to increases in temperature and show a continuous decreasing trend. Medium and low flows are increasing in future projections for all the scenarios, and high uncertainty is noticed in the case of low flows. An early CP is detected in the case of high emissions scenarios.     

Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed

Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.     

Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River Basin,USA

An appropriate, rapid and effective response to extreme precipitation and any potential flood disaster is essential. Providing an accurate estimate of future changes to such extreme events due to climate change are crucial for responsible decision making in flood risk management given the predictive uncertainties. The objective of this article is to provide a comparison of dynamically downscaled climate models simulations from multiple model including 12 different combinations of General Circulation Model (GCM)–regional climate model (RCM), which offers an abundance of additional data sets. The three major aspects of this study include the bias correction of RCM scenarios, the application of a newly developed performance metric and the extreme value analysis of future precipitation. The dynamically downscaled data sets reveal a positive overall bias that is removed through quantile mapping bias correction method. The added value index was calculated to evaluate the models' simulations. Results from this metric reveal that not all of the RCMs outperform their host GCMs in terms of correlation skill. Extreme value theory was applied to both historic, 1980–1998, and future, 2038–2069, daily data sets to provide estimates of changes to 2‐ and 25‐year return level precipitation events. The generalized Pareto distribution was used for this purpose. The Willamette River basin was selected as the study region for analysis because of its topographical variability and tendency for significant precipitation. The extreme value analysis results showed significant differences between model runs for both historical and future periods with considerable spatial variability in precipitation extremes. Copyright © 2012 John Wiley & Sons, Ltd.     

Projected changes in flood indices in selected catchments in Poland in the 21st century

The aim of this study is to estimate likely changes in flood indices under a future climate and to assess the uncertainty in these estimates for selected catchments in Poland. Precipitation and temperature time series from climate simulations from the EURO-CORDEX initiative for the periods 1971–2000, 2021–2050 and 2071–2100 following the RCP4.5 and RCP8.5 emission scenarios have been used to produce hydrological simulations based on the HBV hydrological model. As the climate model outputs for Poland are highly biased, post processing in the form of bias correction was first performed so that the climate time series could be applied in hydrological simulations at a catchment-scale. The results indicate that bias correction significantly improves flow simulations and estimated flood indices based on comparisons with simulations from observed climate data for the control period. The estimated changes in the mean annual flood and in flood quantiles under a future climate indicate a large spread in the estimates both within and between the catchments. An ANOVA analysis was used to assess the relative contributions of the 2 emission scenarios, the 7 climate models and the 4 bias correction methods to the total spread in the projected changes in extreme river flow indices for each catchment. The analysis indicates that the differences between climate models generally make the largest contribution to the spread in the ensemble of the three factors considered. The results for bias corrected data show small differences between the four bias correction methods considered, and, in contrast with the results for uncorrected simulations, project increases in flood indices for most catchments under a future climate.     

Climate change and uncertainty assessment over a hydroclimatic transect of Michigan

Predictions of a warmer climate over the Great Lakes region due to global change generally agree on the magnitude of temperature changes, but precipitation projections exhibit dependence on which General Circulation Models and emission scenarios are chosen. To minimize model- and scenario-specific biases, we combined information provided by the 3rd phase of the Coupled Model Intercomparison Project database. Specifically, the results of 12 GCMs for three emission scenarios B1, A1B, and A2 were analyzed for mid- (2046–2065) and end-century (2081–2100) intervals, for six locations of a hydroclimatic transect of Michigan. As a result of Bayesian Weighted Averaging, total annual precipitation averaged over all locations and the three emission scenarios increases by 7 % (mid-)–10 % (end-century), as compared to the control period (1961–1990). The projected changes across seasons are non-uniform and precipitation decreases by 3 % (mid-)–5 % (end-) for the months of August and September are likely. Further, average temperature is very likely to increase by 2.02–2.85 °C by the mid-century and 2.58–4.73 °C by the end-century. Three types of non-additive uncertainty sources due to climate models, anthropogenic forcings, and climate internal variability are addressed. When compared to the emission uncertainty, the relative magnitudes of the uncertainty types for climate model ensemble and internal variability are 149 and 225 % for mean monthly precipitation, and they are respectively 127 and 123 % for mean monthly temperature. A decreasing trend of the frost days and an increasing trend of the growing season length are identified. Also, a significant increase in the magnitude and frequency of heavy rainfall events is projected, with relatively more pronounced changes for heavy hourly rainfall as compared to daily events. Quantifying the inherent natural uncertainty and projecting hourly-based extremes, the study results deliver useful information for water resource stakeholders interested in impacts of climate change on hydro-morphological processes.     

Stormwater quantity and quality response to climate change using artificial neural networks

This research investigates the potential impacts of climate change on stormwater quantity and quality generated by urban residential areas on an event basis in the rainy season. An urban residential stormwater drainage area in southeast Calgary, Alberta, Canada is the focus of future climate projections from general circulation models (GCMs). A regression‐based statistical downscaling tool was employed to conduct spatial downscaling of daily precipitation and daily mean temperature using projection outputs from the coupled GCM. Projected changes in precipitation and temperature were applied to current climate scenarios to generate future climate scenarios. Artificial neural networks (ANNs) developed for modelling stormwater runoff quantity and quality used projected climate scenarios as network inputs. The hydrological response to climate change was investigated through stormwater runoff volume and peak flow, while the water quality responses were investigated through the event mean value (EMV) of five parameters: turbidity, conductivity, water temperature, dissolved oxygen (DO) and pH. First flush (FF) effects were also noted. Under future climate scenarios, the EMVs of turbidity increased in all storms except for three events of short duration. The EMVs of conductivity were found to decline in small and frequent storms (return period < 5 years); but conductivity EMVs were observed to increase in intensive events (return period ≥ 5 years). In general, an increasing EMV was observed for water temperature, whereas a decreasing trend was found for DO EMV. No clear trend was found in the EMV of pH. In addition, projected future climate scenarios do not produce a stronger FF effect on dissolved solids and suspended solids compared to that produced by the current climate scenario. Copyright © 2010 John Wiley & Sons, Ltd.     

Nonstationary extreme value analysis of temperature extremes in China

In a changing climate, the common assumption of stationarity of climate extremes has been increasingly challenged, raising the need to incorporate non-stationarity in extreme value modeling. In this study, quantile regression is used to identify the trends of annual temperature extremes and their correlations with two large climate patterns, the western Pacific subtropical high (WPSH) and the Arctic Oscillation (AO) at 357 stations in China. Statistical significant positive trends and correlations between warm (or cold) temperature extremes and WPSH (or AO) have been detected at most stations. The influence of WPSH on warm extremes is significant in southern China, while the AO mainly affects the cold extremes in northern and eastern China. Then, annual temperature extremes are fitted to generalized extreme value (GEV) distributions with time-varying parameters. The summer (or winter) mean daily maximum (or minimum) temperatures and two climate indices, the WPSH index and the AO index, are chosen as covariates. In total, 16 candidate GEV distribution models are constructed, and the best fitting model with the lowest Bayesian information criterion (BIC) is selected. The 20-year return levels of annual warm (or cold) extremes in the period 1961–1980 and 1991–2010 are computed and compared. The changes of 20-year return levels of annual warm and cold extremes are jointly determined by trend and distributional changes of annual temperature extremes. Analysis of large scale atmospheric circulation changes indicate that a strengthening anticyclonic circulation and increasing geopotential height in recent decades may have contributed to the changes in temperature extremes in China.     

Climate change impacts on surface water resources in the Rheraya catchment (High Atlas,Morocco)

This study aimed to quantify possible climate change impacts on runoff for the Rheraya catchment (225 km²) located in the High Atlas Mountains of Morocco, south of Marrakech city. Two monthly water balance models, including a snow module, were considered to reproduce the monthly surface runoff for the period 1989?2009. Additionally, an ensemble of five regional climate models from the Med-CORDEX initiative was considered to evaluate future changes in precipitation and temperature, according to the two emissions scenarios RCP4.5 and RCP8.5. The future projections for the period 2049?2065 under the two scenarios indicate higher temperatures (+1.4°C to +2.6°C) and a decrease in total precipitation (?22% to ?31%). The hydrological projections under these climate scenarios indicate a significant decrease in surface runoff (?19% to ?63%, depending on the scenario and hydrological model) mainly caused by a significant decline in snow amounts, related to reduced precipitation and increased temperature. Changes in potential evapotranspiration were not considered here, since its estimation over long periods remains a challenge in such data-sparse mountainous catchments. Further work is required to compare the results obtained with different downscaling methods and different hydrological model structures, to better reproduce the hydro-climatic behaviour of the catchment.

EDITOR M.C. Acreman

ASSOCIATE EDITOR R. Hirsch     

Assessment of future groundwater recharge in semi‐arid regions under climate change scenarios (Serral‐Salinas aquifer,SE Spain). Could increased rainfall variability increase the recharge rate?

The projected impact of climate change on groundwater recharge is a challenge in hydrogeological research because substantial doubts still remain, particularly in arid and semi‐arid zones. We present a methodology to generate future groundwater recharge scenarios using available information about regional climate change projections developed in European Projects. It involves an analysis of regional climate model (RCM) simulations and a proposal for ensemble models to assess the impacts of climate change. Future rainfall and temperature series are generated by modifying the mean and standard deviation of the historical series in accordance with estimates of their change provoked by climate change. Future recharge series will be obtained by simulating these new series within a continuous balance model of the aquifer. The proposed method is applied to the Serral‐Salinas aquifer, located in a semi‐arid zone of south‐east Spain. The results show important differences depending on the RCM used. Differences are also observed between the series generated by imposing only the changes in means or also in standard deviations. An increase in rainfall variability, as expected under future scenarios, could increase recharge rates for a given mean rainfall because the number of extreme events increases. For some RCMs, the simulations predict total recharge increases over the historical values, even though climate change would produce a reduction in the mean rainfall and an increased mean temperature. A method based on a multi‐objective analysis is proposed to provide ensemble predictions that give more value to the information obtained from the best calibrated models. The ensemble of predictions estimates a reduction in mean annual recharge of 14% for scenario A2 and 58% for scenario A1B. Lower values of future recharge are obtained if only the change in the mean is imposed. Copyright © 2014 John Wiley & Sons, Ltd.