Assessment of climate change impact on the stream-flow in a typical debris flow watershed of Jianzhuangcuan catchment in Shaanxi Province, China

The study on the stream-flow change associated with future climate change scenarios has a practical significance for local socio-economic development and eco-environmental protection. A study on the Jianzhuangcuan catchments was carried out to quantify the expected impact of climate change on the stream-flow using a multi-model ensemble approach. Climate change scenarios were developed by ensemble four Global Climate Models, which showed good performance for Jianzhuangcuan catchment. Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, was used to investigate the impacts on stream-flow under climate change scenarios. The model was calibrated and validated using daily stream-flow records. The calibration and validation results showed that the SWAT model was able to simulate the daily stream-flow well, with Nash–Sutcliffe efficiency >0.83 for Yaoping Long station, for calibration and validation at daily and monthly scales. Their difference in simulating the stream-flow under future climate scenarios was also investigated. The results indicate a 0.6–0.9 °C increase in annual temperature and changes of 12.6–18.9 mm in seasonal precipitation corresponded to a change in stream-flow of about 0.62–3.67 for 2020 and 2030 scenarios. The impact of the climate change increased in both scenarios.     

Assessing climate change impacts on water balance in the Mount Makiling forest, Philippines

A statistical downscaling known for producing station-scale climate information from GCM output was preferred to evaluate the impacts of climate change within the Mount Makiling forest watershed, Philippines. The lumped hydrologic BROOK90 model was utilized for the water balance assessment of climate change impacts based on two scenarios (A1B and A2) from CGCM3 experiment. The annual precipitation change was estimated to be 0.1–9.3% increase for A1B scenario, and ?3.3 to 3.3% decrease/increase for the A2 scenario. Difference in the mean temperature between the present and the 2080s were predicted to be 0.6–2.2°C and 0.6–3.0°C under A1B and A2 scenarios, respectively. The water balance showed that 42% of precipitation is converted into evaporation, 48% into streamflow, and 10% into deep seepage loss. The impacts of climate change on water balance reflected dramatic fluctuations in hydrologic events leading to high evaporation losses, and decrease in streamflow, while groundwater flow appeared unaffected. A study on the changes in monthly water balance provided insights into the hydrologic changes within the forest watershed system which can be used in mitigating the effects of climate change.     

Assessing climate change impacts on river hydrology – A case study in the Western Ghats of India

The objective of this study is to evaluate the hydrological impacts of climate change on rainfall, temperature and streamflow in a west flowing river originating in the Western Ghats of India. The long-term trend analysis for 110 yr of meteorological variables (rainfall and temperature) was carried out using the modified Mann–Kendall trend test and the magnitude of the trend was quantified using the Sen’s slope estimator. The Regional Climate Model (RCM), COordinated Regional climate Downscaling EXperiment (CORDEX) simulated daily weather data of baseline (1951–2005) and future RCP 4.5 scenarios (2006–2060) were used to run the hydrological model, Soil and Water Assessment Tool (SWAT), in order to evaluate the effect of climate change on rainfall, temperature and streamflow. Significant changes were observed with regard to rainfall, which have shown decreasing trend at the rate of 2.63 mm per year for the historical and 8.85 mm per year for RCP 4.5 future scenarios. The average temperature was found to be increasing at \(0.10\,^{\circ }\hbox {C}\) per decade for both historical and future scenarios. The impact of climate change on the annual streamflow yielded a decreasing trend at the rate of \(1.2\,\hbox {Mm}^{3}\) per year and 2.56 \(\hbox {Mm}^{3},\) respectively for the past and future scenarios. The present work also investigates the capability of SWAT to simulate the groundwater flow. The simulated results are compared with the recession limb of the hydrograph and were found to be reasonably accurate.     

Impact of water demand on hydrological regime under climate and LULC change scenarios

The present study focuses on an assessment of the impact of future water demand on the hydrological regime under land use/land cover (LULC) and climate change scenarios. The impact has been quantified in terms of streamflow and groundwater recharge in the Gandherswari River basin, West Bengal, India. dynamic conversion of land use and its effects (Dyna-CLUE) and statistical downscaling model (SDSM) are used for quantifying the future LULC and climate change scenarios, respectively. Physical-based semi-distributed model Soil and Water Assessment Tool (SWAT) is used for estimating future streamflow and spatiotemporally distributed groundwater recharge. Model calibration and validation have been performed using discharge data (1990–2016). The impacts of LULC and climate change on hydrological variables are evaluated with three scenarios (for the years 2030, 2050 and 2080). Temperature Vegetation Dyrness Index (TVDI) and evapotranspiration (ET) are considered for estimation of water-deficit conditions in the river basin. Exceedance probability and recurrence interval representation are considered for uncertainty analysis. The results show increased discharge in case of monsoon season and decreased discharge in case of the non-monsoon season for the years 2030 and 2050. However, a reverse trend is obtained for the year 2080. The overall increase in groundwater recharge is visible for all the years. This analysis provides valuable information for the irrigation water management framework.     

应用统计降尺度方法预估江淮流域未来降水

统计降尺度方法广泛应用于弥补大气环流模式(GCM)模拟区域气候变化能力较弱的不足。利用1960~2009年的NCEP/NCAR再分析资料和江淮流域52个站点降水观测资料,通过敏感性分析,针对4个季节分别选择10个大尺度预测因子,采用主成分分析(PCA)和支持向量机(SVM)相结合的方法,建立了江淮流域降水统计降尺度模型。检验结果表明,该模型获取的江淮流域降水的偏差显著减小,能够描述降水在月、年尺度的变化,适用于HadCM3输出的大尺度气候场,具有预测未来降水变化的能力。将统计降尺度模型应用于HadCM3在A2情景下输出的2020~2099年大尺度预测因子,分3个时段:2020~2039年,2050~2069年和2080~2099年,从年和季节两个时间尺度分析江淮流域未来降水变化。结果表明,相对1960~1999年,未来3个时段的降水有小幅增加,其中2080~2099年增幅最大,为3.6 mm;在未来3个时段的不同季节,降水变化呈现出不同特征。     

Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.     

Modelling climate-change impacts on groundwater recharge in the Murray-Darling Basin, Australia

A methodology is presented for assessing the average changes in groundwater recharge under a future climate. The method is applied to the 1,060,000 km² Murray-Darling Basin (MDB) in Australia. Climate sequences were developed based upon three scenarios for a 2030 climate relative to a 1990 climate from the outputs of 15 global climate models. Dryland diffuse groundwater recharge was modelled in WAVES using these 45 climate scenarios and fitted to a Pearson Type III probability distribution to condense the 45 scenarios down to three: a wet future, a median future and a dry future. The use of a probability distribution allowed the significance of any change in recharge to be assessed. This study found that for the median future, climate recharge is projected to increase on average by 5% across the MDB but this is not spatially uniform. In the wet and dry future scenarios the recharge is projected to increase by 32% and decrease by 12% on average across the MDB, respectively. The differences between the climate sequences generated by the 15 different global climate models makes it difficult to project the direction of the change in recharge for a 2030 climate, let alone the magnitude.     

Understanding the past to interpret the future: comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data

In evaluating potential impacts of climate change on water resources, water managers seek to understand how future conditions may differ from the recent past. Studies of climate impacts on groundwater recharge often compare simulated recharge from future and historical time periods on an average monthly or overall average annual basis, or compare average recharge from future decades to that from a single recent decade. Baseline historical recharge estimates, which are compared with future conditions, are often from simulations using observed historical climate data. Comparison of average monthly results, average annual results, or even averaging over selected historical decades, may mask the true variability in historical results and lead to misinterpretation of future conditions. Comparison of future recharge results simulated using general circulation model (GCM) climate data to recharge results simulated using actual historical climate data may also result in an incomplete understanding of the likelihood of future changes. In this study, groundwater recharge is estimated in the upper Colorado River basin, USA, using a distributed-parameter soil-water balance groundwater recharge model for the period 1951–2010. Recharge simulations are performed using precipitation, maximum temperature, and minimum temperature data from observed climate data and from 97 CMIP5 (Coupled Model Intercomparison Project, phase 5) projections. Results indicate that average monthly and average annual simulated recharge are similar using observed and GCM climate data. However, 10-year moving-average recharge results show substantial differences between observed and simulated climate data, particularly during period 1970–2000, with much greater variability seen for results using observed climate data.     

Reservoir operation using system dynamics under climate change impacts: a case study of Yamchi reservoir,Iran

This paper proposes a decision support system for Yamchi reservoir operation in semi-arid region of Iran. The paper consists of the following steps: Firstly, the potential impacts of climate change on the streamflow are predicted. The study then presents the projections of future changes in temperature and precipitation under A2 scenario using the LARS-WG downscaling model and under RCP2.6, RCP4.5, and RCP8.5 using the statistical downscaling model (SDSM) in the northwestern of Iran. To do so, a general circulation model of HadCM3 is downscaled by using the LARS-WG model. As a result, the average temperature, for the horizon 2030 (2011–2030), will increase by 0.77 °C and precipitation will decrease by 11 mm. Secondly, the downscaled variables are used as input to the artificial neural network to investigate the possible impact of climate change on the runoffs. Thirdly, the system dynamics model is employed to model different scenarios for reservoir operation using the Vensim software. System dynamics is an effective approach for understanding the behavior of complex systems. Simulation results demonstrate that the water shortage in different sectors (including agriculture, domestic, industry, and environmental users) will be enormously increased in the case of business-as-usual strategy. In this research, by providing innovative management strategies, including deficit irrigation, the vulnerability of reservoir operation is reduced. The methodology is evaluated by using different modeling tests which then motivates using the methodology for other arid/semi-arid regions.     

Response of streamflow to projected climate change scenarios in an eastern Himalayan catchment of India

Snowmelt run-off model (SRM) based on degree-day approach has been employed to evaluate the change in snow-cover depletion and corresponding streamflow under different projected climatic scenarios for an eastern Himalayan catchment in India. Nuranang catchment located at Tawang district of Arunachal Pradesh with an area of 52 km² is selected for the present study with an elevation range of 3143–4946 m above mean sea level. Satellite images from October to June of the selected hydrological year 2006–2007 were procured from National Remote Sensing Centre, Hyderabad. Snow cover mapping is done using NDSI method. Based on long term meteorological data, temperature and precipitation data of selected hydrological year are normalized to represent present climatic condition. The projected temperature and precipitation data are downloaded from NCAR’s GIS data portal for different emission scenarios (SRES), viz., A1B, A2, B1; and IPCC commitment (non-SRES) scenario for different future years (2020, 2030, 2040 and 2050). Projected temperature and precipitation data are obtained at desired location by spatially interpolating the gridded data and then by statistical downscaling using linear regression. Snow depletion curves for all projected scenarios are generated for the study area and compared with conventional depletion curve for present climatic condition. Changes in cumulative snowmelt depth for different future years are highest under A1B and lowest under IPCC commitment, whereas A2 and B1 values are in-between A1B and IPCC commitment. Percentage increase in streamflow for different future years follows almost the same trend as change in precipitation from present climate under all projected climatic scenarios. Hence, it was concluded that for small catchments having seasonal snow cover, the total streamflow under projected climatic scenarios in future years will be primarily governed by the change in precipitation and not by change in snowmelt depth. Advancing of depletion curves for different future years are highest under A1B and lowest under IPCC commitment. A2 and B1 values are in-between A1B and IPCC commitment.     

Impact of climate change on the wheat-growing season over Iran

The objective of this paper is to derive and analyze the present and future climate projections over the region of wheat production over Iran. In addition, the projected future climate fluctuation results will be used to assist the maximum performance of wheat and to be used as the main basis for planning changes in the farming calendar in Iran. Observed climate (temperature and degree day) changes during the period (1951–2009) will be discussed. Projected future changes up to 2100 based on the MAGICC/SCENGEN 5.3 compound model was utilized. Furthermore, 18 scenarios were used to derive a single GCM model referred to as the United Kingdom Hadley Center Global Environment Model, which will be used to select the worst, best, and average scenario.     

Simulating climate change impact on soil erosion using RUSLE model <Emphasis Type="BoldItalic">−</Emphasis> A case study in a watershed of mid-Himalayan landscape

Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011–2040, 2041–2070, and 2071–2099, at large scale. Rainfall erosivity (R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data – IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility (K) factor map of the watershed. Topographic factors, slope length (L) and steepness (S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985–2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers.     

基于CMIP6气候模式的新疆积雪深度时空格局研究

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式,通过对比新疆地区1979—2014年积雪深度长时间序列数据集,评估了气候模式在新疆地区模拟积雪深度的模拟能力,接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响,因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上,其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征,气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升,其中气温的增幅相对比较明显,达0.43 ℃·（10a）^-1,而降水的增幅为0.63 mm·（10a）^-1,新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时期相对基准期均呈增加的趋势。SSP1-1.9情景下,21世纪近期、中期和末期北部大部分地区的积雪深度将会有所增加;SSP1-2.6情景下,北部阿尔泰山地区的积雪深度在21世纪近期有所减小,但中期和末期将会有所增加;SSP2-4.5情景下,21世纪不同时期东部地区的积雪深度将会有所增加,北部和中部大部分地区在不同时期积雪深度将会变小;SSP3-7.0情景下,21世纪不同时期北部和西南地区的积雪深度将会普遍变小,东部地区的积雪深度将普遍增加;SSP4-3.4和SSP4-6.0情景下,21世纪不同时期西南昆仑山地区的积雪深度将会普遍变小,东部地区的积雪深度将普遍增加;SSP5-8.5情景下,北部阿尔泰山地区和东部地区的积雪深度将普遍增加。     

Episodic recharge and climate change in the Murray-Darling Basin, Australia

In semi-arid areas, episodic recharge can form a significant part of overall recharge, dependant upon infrequent rainfall events. With climate change projections suggesting changes in future rainfall magnitude and intensity, groundwater recharge in semi-arid areas is likely to be affected disproportionately by climate change. This study sought to investigate projected changes in episodic recharge in arid areas of the Murray-Darling Basin, Australia, using three global warming scenarios from 15 different global climate models (GCMs) for a 2030 climate. Two metrics were used to investigate episodic recharge: at the annual scale the coefficient of variation was used, and at the daily scale the proportion of recharge in the highest 1% of daily recharge. The metrics were proportional to each other but were inconclusive as to whether episodic recharge was to increase or decrease in this environment; this is not a surprising result considering the spread in recharge projections from the 45 scenarios. The results showed that the change in the low probability of exceedance rainfall events was a better predictor of the change in total recharge than the change in total rainfall, which has implications for the selection of GCMs used in impact studies and the way GCM results are downscaled.     

气候变化情景下北江飞来峡水库极端入库洪水预估

以北江飞来峡水库上游为研究对象,构建了网格分辨率为0.25°×0.25°的VIC(Variable Infiltration Capacity)水文模型,应用CMIP5多模式输出的降尺度结果与VIC模型耦合,对RCP2.6、RCP4.5和RCP8.5情景下未来时期(2020-2050年)飞来峡水库的入库洪水进行预估,并根据IPCC第5次评估报告处理和表达不确定性的方法来描述预估结论的可信度。结果表明,2020-2050年飞来峡水库年最大洪峰流量和年最大7日、15日洪量在RCP2.6情景下"大约可能"呈增加趋势,在RCP4.5和RCP8.5情景下"较为可能"呈增加趋势,水库防洪安全风险增大。与历史时期(1970-2000年)相比,未来水库极端入库洪水增加的可能性从大到小依次为RCP4.5、RCP2.6和RCP8.5情景,其中设计洪水100年、50年和20年一遇的洪峰流量在3种排放情景下均呈上升趋势,100年、50年和20年一遇的最大7日、15日洪量在RCP4.5情景下以上升为主,而在RCP2.6和RCP8.5情景下则主要呈减少态势。     

Future climate change impact evaluation on hydrologic processes in the Bharalu and Basistha basins using SWAT model

Urbanisation and climate change can have adverse effects on the streamflow and water balance components in river basins. This study focuses on the understanding of different hydrologic responses to climate change between urban and rural basins. The comprehensive semi-distributed hydrologic model, SWAT (Soil and Water Assessment Tool), is used to evaluate how the streamflow and water balance components vary under future climate change on Bharalu (urban basin) and Basistha (rural basin) River basins near the Brahmaputra River in India based on precipitation, temperature and geospatial data. Based on data collected in 1990–2012, it is found that 98.78% of the water yield generated for the urban Bharalu River basin is by surface runoff, comparing to 75% of that for the rural Basistha basin. Comparison of various hydrologic processes (e.g. precipitation, discharge, water yield, surface runoff, actual evapotranspiration and potential evapotranspiration) based on predicted climate change scenarios is evaluated. The urban Bharalu basin shows a decrease in streamflow, water yield, surface runoff, actual evapotranspiration in contrast to the rural Basistha basin, for the 2050s and 2090s decades. The average annual discharge will increase a maximum 1.43 and 2.20 m³/s from the base period for representative concentration pathways (RCPs) such as 2.6 and 8.5 pathways in Basistha River and it will decrease a maximum 0.67 and 0.46 m³/s for Bharalu River, respectively. This paper also discusses the influence of sensitive parameters on hydrologic processes, future issues and challenges in the rural and urban basins.     

气候变化对黄河流域生态径流影响预估

人类活动和气候变化严重改变了黄河水文情势和生态径流,分析未来气候变化对河流生态的影响对流域水资源管理和长期规划意义重大。本文对第六次国际耦合模式比较计划（CMIP6）的13个全球气候模式数据进行偏差订正,驱动水文模型进行径流模拟,应用流量历时曲线方法分析SSP1-2.6、SSP2-4.5、SSP5-8.5情景下2026年至21世纪末年、季节尺度的花园口生态径流变化。结果表明:订正能明显降低降水、气温模拟偏差;人类活动严重影响了1986-2010年花园口生态径流;2026-2100年年均气温和年降水量增加趋势显著,低排放情景增速慢,高排放情景增速快;气候变化可在一定程度上缓解水库调控、水土保持等人类活动对生态径流的负面影响,SSP5-8.5情景缓解程度最高,冬季缓解程度最高,夏、秋季最低。     

气候自然变异在气候变化对水资源影响 评价中的贡献分析——Ⅱ.未来期应用分析

基于模型率定期(基准期)气候自然变异的模拟方法及气候自然变异引起的径流变化的可能情况分析,此部分研究未来期(2021~2051年,2061~2091年)气候变化下径流变化情况及气候自然变异的影响。基于CSIRO、NCAR、MPI三种气候模式及A1B、A2、B1三种排放方式共7种未来气候情景,应用和基准期相同的水文模型和研究流域,引入基准期模型率定出的参数,考虑气候自然变异的影响,对未来气候变化对水资源的影响进行分析。为消除气候模式本身的系统误差,采用δ差值方法得到各模式各排放情景下的未来气候情景。该项研究主要说明如何在气候变化的影响评价中将气候自然变异的贡献分离出来,从而实现更客观的气候变化的影响评价。研究结果表明,气候变异的影响在整个气候变化进程中的贡献随时间的推移将有所不同。未来2021~2051年期间,气候自然变异的影响相对较大;未来2061~2091年期间,由温室气体引起的气候变化的影响占主导。     

A method for predicting the impact of climate change on slope stability

 A major effect of man-induced climate change could be a generally higher frequency and magnitude of extreme climatological events in Europe. Consequently, the frequency of rainfall-triggered landslides could increase. However, assessment of the impact of climate change on landsliding is difficult, because on a regional scale, climate change will vary strongly, and even the sign of change can be opposite. Furthermore, different types of landslides are triggered by different mechanisms. A potential method for predicting climate change impact on landsliding is to link slope models to climate scenarios obtained through downscaling General Circulation Models (GCM). Methodologies, possibilities and problems are discussed, as well as some tentative results for a test site in South-East France. Received: 25 October 1997 · Accepted: 25 June 1997     

Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China

Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5.