Assessing the effects of the climate change on land cover changes in different time periods

The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.     

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。     

Impacts of climate change on stream flow and water quality in a drinking water source area,Northern China

Understanding the impacts of climate change on water quality and stream flow is important for management of water resources and environment. Miyun Reservoir is the only surface drinking water source in Beijing, which is currently experiencing a serious water shortage. Therefore, it is vital to identify the impacts of climate change on water quality and quantity of the Miyun Reservoir watershed. Based on long-time-series data of meteorological observation, future climate change scenarios for this study area were predicted using global climate models (GCMs), the statistical downscaling model (SDSM), and the National Climate Centre/Gothenburg University—Weather Generator (NWG). Future trends of nonpoint source pollution load were estimated and the response of nonpoint pollution to climate change was determined using the Soil and Water Assessment Tool (SWAT) model. Results showed that the simulation results of SWAT model were reasonable in this study area. The comparative analysis of precipitation and air temperature simulated using the SDSM and NWG separately showed that both tools have similar results, but the former had a larger variability of simulation results than the latter. With respect to simulation variance, the NWG has certain advantages in the numerical simulation of precipitation, but the SDSM is superior in simulating precipitation and air temperature changes. The changes in future precipitation and air temperature under different climate scenarios occur basically in the same way, that is, an overall increase is estimated. Particularly, future precipitation will increase significantly as predicted. Due to the influence of climate change, discharge, total nitrogen (TN) and total phosphorus (TP) loads from the study area will increase over the next 30 years by model evaluation. Compared to average value of 1961?~?1990, discharge will experience the highest increase (15%), whereas TN and TP loads will experience a smaller increase with a greater range of annual fluctuations of 2021 ~ 2050.     

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.     

Impacts of climate change on the hydro-climatology of the upper Ishikari river basin,Japan

Evidence for climate change impacts on the hydro-climatology of Japan is plentiful. The objective of the present study was to evaluate the impacts of possible future climate change scenarios on the hydro-climatology of the upper Ishikari River basin, Hokkaido, Japan. The Soil and Water Assessment Tool was set up, calibrated, and validated for the hydrological modeling of the study area. The Statistical DownScaling Model version 4.2 was used to downscale the large-scale Hadley Centre Climate Model 3 Global Circulation Model A2 and B2 scenarios data into finer scale resolution. After model calibration and testing of the downscaling procedure, the SDSM-downscaled climate outputs were used as an input to run the calibrated SWAT model for the three future periods: 2030s (2020–2039), 2060s (2050–2069), and 2090s (2080–2099). The period 1981–2000 was taken as the baseline period against which comparison was made. Results showed that the average annual maximum temperature might increase by 1.80 and 2.01, 3.41 and 3.12, and 5.69 and 3.76 °C, the average annual minimum temperature might increase by 1.41 and 1.49, 2.60 and 2.34, and 4.20 and 2.93 °C, and the average annual precipitation might decrease by 5.78 and 8.08, 10.18 and 12.89, and 17.92 and 11.23% in 2030s, 2060s, and 2090s for A2a and B2a emission scenarios, respectively. The annual mean streamflow may increase for the all three future periods except the 2090s under the A2a scenario. Among them, the largest increase is possibly observed in the 2030s for A2a scenario, up to approximately 7.56%. Uncertainties were found within the GCM, the downscaling method, and the hydrological model itself, which were probably enlarged because only one single GCM (HaDCM3) was used in this study.     

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.     

Application of a hydrometeorological model chain to investigate the effect of global boundaries and downscaling on simulated river discharge

In the current study, two regional climate models (MM5 and REMO) driven by different global boundary conditions (the ERA40 reanalysis and the ECHAM5 model) are one-way coupled to the uncalibrated hydrological process model PROMET to analyze the impact of global boundary conditions, dynamical regionalization and subsequent statistical downscaling (bilinear interpolation, correction of subgrid-scale variability and combined correction of subgrid-scale variability and bias) on river discharge simulation. The results of 12 one-way coupled model runs, set up for the catchment of the Upper Danube (Central Europe) over the historical period 1971–2000, prove the expectation that the global boundaries applied to force the RCMs strongly influence the accuracy of simulated river discharge. It is, however, noteworthy that all efficiency criteria in case of bias corrected MM5 simulations indicate better performance under ERA40 boundaries, whereas REMO-driven hydrological simulations better correspond to measured discharge under ECHAM5 boundaries. Comparing the hydrological results achievable with MM5 and REMO, the application of bias-corrected MM5 simulations turned out to allow for a more accurate simulation of discharge, while the variance in simulated discharge in most cases was better reflected in case of REMO forcings. The correction of subgrid-scale variability within the downscaling of RCM simulations compared to a bilinear interpolation allows for a more accurate simulation of discharge for all model configurations and all discharge criteria considered (mean monthly discharge, mean monthly low-flow and peak-flow discharge). Further improvements in the hydrological simulations could be achieved by eliminating the biases (in terms of deviations from observed meteorological conditions) inherent in the driving RCM simulations, regardless of the global boundary conditions or the RCM applied. In spite of all downscaling and bias correction efforts described, the RCM-driven hydrological simulations remain less accurate than those achievable with spatially distributed meteorological observations.     

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.     

基于SDSM的珠江中上游气候模拟及未来情景预估

预估喀斯特生态脆弱区的未来气候变化对于区域资源的合理开发利用及生态环境保护具有重要参考价值,而目前应用降尺度方法模拟喀斯特地区的未来气候情景仍存在较大的探讨空间。本文依据珠江流域红柳江区13个气象站1961-2001年的实测日气温、日降水量资料和全球大气NCEP再分析资料,采用SDSM模型预测流域在HadCM3模式SRES A2和B2两种排放情景下未来年份气温和降水的变化趋势。结果表明:（1）SDSM模型可以较为准确地模拟研究区的气温和降水变化,确定性系数分别可达99%和65%左右;（2）A2、B2两种情景下,21世纪气温和降水均表现出明显的上升趋势,且随时间推移增幅逐渐增大。截至21世纪末,A2、B2两种情景下的年平均气温变化分别为+3.39 ℃和+2.49 ℃,日均降水将分别增加117.30 %和80.90 %;（3）未来的气温上升以秋季和春季变化最为明显,降水则表现为夏季降水增幅最大。分析成果可为喀斯特区的气候变化影响评价与应对决策提供数据基础和理论依据。      

Simulation Study on Precipitation Recycling Ratio in the Tibetan Plateau from 1982 to 2005

A dynamical downscaling approach using a regional climate model WRF (Weather Research and Forecasting Model Vision 3.5) driven by a global climate model CCSM4 (The Community Climate System Model Version 4) was adopted, and the downscaling results for the historical period (1982-2005) were evaluated for annual mean precipitation rate and evaporation rate over the Tibetan Plateau (TP). Furthermore, the spatial distribution and seasonal variation characteristics of Precipitation Recycling Ratio (PRR) simulated by CCSM4 and WRF were analyzed with the QIBT (Quasi-isentropic Back-trajectory method). The results show that the historical spatial distributions of annual mean precipitation rate and evaporation rate over the TP were found to better reproduce in the dynamical downscaling modeling compared to its coarse-resolution forcing. The PRR of the TP is 32% simulated by WRF, with a higher PRR in the wet season and a lower PRR in the dry season for the river basins in the northern TP, but the opposite seasonal variation was found for the river basins in the southern TP. In addition, the different land covers over the TP are more precisely represented in the WRF model, the PRR of grassland, shrubland and sparsely vegetation is higher than that of other land cover types.     

Simulating climate change impact on soil carbon sequestration in agro-ecosystem of mid-Himalayan landscape using CENTURY model

Soils play significant roles in global carbon cycle. The increase in atmospheric CO₂ due to climate change may have a significant impact on both soil organic carbon storage and management practices to sequester organic carbon in agricultural areas. The aim of the study was to simulate climate change impact on soil carbon sequestration using CENTURY model. The statistical downscaling model (SDSM) was used to downscale the climate variables (temperature and rainfall) under two scenarios A2 and B2 for three periods: 2020 (2011–2040), 2050 (2041–2070) and 2080 (2071–2099). Downscaling was better in case of temperature than precipitation, which was evident from coefficient of correlation for temperature (r ² = 0.91–0.99) and precipitation (r ² = 0.71–0.80). Downscaling of climate data revealed that the temperature may increase for the years 2020, 2050 and 2080 periods, whereas precipitation may increase till 2020 and then it may reduce in 2050 and 2080 as compared to 2020 in the study area. For CENTURY model, the input parameters were obtained through soil sampling and interviewing the farmers as well, whereas the climatic variables (maximum temperature, minimum temperature and precipitation) were taken from the SDSM output. The historical data of soils were collected from the literature, and six agricultural sites were selected for estimating soil carbon sequestration. After soil sampling of the same sites, it was found that the organic carbon had increased two times than historical data might be due to the addition of high organic matter in the form of farm yard manure. Therefore, the model was calibrated, considering more organic carbon in the area, and was validated using random points in the study area. Determination coefficient (r ² = 0.95) and RMSE (538 g c/m²) were computed to assess the accuracy of the model. The organic carbon was predicted from 2011 to 2099 and was compared with the 2011 predicted data. The study revealed that the amount of soil organic carbon in Bhaitan, Kanatal, Kotdwar, Malas, Pata and Thangdhar sites may reduce by 11.6, 15.8, 17.19, 13.54, 19.2 and 12.7%, respectively, for A2 scenario and by 9.62, 15.6, 15.72, 11.45, 16.96 and 13.36% for B2 scenario up to 2099. The study provides comprehensive possible future scenarios of soil carbon sequestration in the mid-Himalaya for scientists and policy makers.     

青海都兰过去2000年来的气候重建及其变迁

根据青海都兰地区树木年轮样本建立了目前我国最长的年轮年表序列。通过年表与气候要素间相关函数、响应函数及响应面分析,选择了可被重建的气候因子,建立了重建方程,恢复了青海都兰地区历史时期的平均温度;并分析讨论了近2 000年来该地区的气候变化。阐述了这个地区的冷暖交替及周期循环。对一些重大气候事件,如中世纪暖期、小冰期和近一个世纪以来的升温等,逐个事件进行了剖析。并与全球温度变化进行了对照。都兰温度曲线为青藏高原东部地区提供了一个较好的气候变化信息表。     

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TRMM卫星降水的太行山区降水时空分布格局

基于TRMM 3B42V7数据,综合采用多元线性回归、偏最小二乘回归和地理加权回归3种方法,建立了太行山区卫星降水产品的降尺度校正模型,将遥感降水信息从0.25°×0.25°降尺度到0.05°×0.05°。在结果评估和优选的基础上,分析了"像元-集水区-全区"年、月降水的多时空尺度干湿季节分布和垂向分布特征,并从机理方面论证了研究的合理性。结果表明:①地理加权回归校正效果最优,可明显降低校正降水与实测降水系列的均方根误差和平均相对偏差且提高决定系数;偏最小二乘回归可降低两项误差,但对决定系数无提升;多元线性回归最差,各项指标均无改善。②处于夏季风迎风侧的东坡和南坡降水量普遍高于500 mm,背风侧的西坡和北坡降水量较低,最大年降水量位于东南坡海拔1 300~1 500 m的地带。③研究区7-9月降水量占全年的58.7%,干湿季节降水量之比为1:18,各集水区的变化范围为1:13~1:25。④季风风向影响降水中心的移动路径,各月降水量沿高程变化梯度区间为-5.2~6.7 mm/hm,且迎风坡降水的垂向分布更复杂。     

黑河流域气候平均降水的精细化分布及总量计算

利用黑河流域气象观测站降水资料和DEM资料,分析了气候平均年和月降水量与地理地形参数的关系.结果显示,黑河流域气候平均降水量与测站的海拔、纬度、坡度显著相关,据此建立了降水量与地理地形参数的关系模型;拟合分析表明,年降水量拟合值与实测值的相关系数达0.94,二者在大部分地区分布特征基本一致,拟合值稍大;逐月降水量拟合相对误差在上游和中游都很小.基于降水量与地理地形参数的关系模型,利用高分辨率DEM资料,扩展得到了黑河流域上中游100m×100m精细化分布的气候平均年降水量和各月降水量.结果表明,精细化分布的降水量场能够表现出更多与地形和地势有关的细节,这是只利用气象测站资料的分析结果所不能反映的.在黑河流域气候平均降水量空间精细化分布基础上,按照黑河流域上中游面积5.08×104 km2计算,其气候平均年降水总量约为150.6×108 m3,降水主要集中在5-9月.     

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km². The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds.     

统计降尺度法对未来区域气候变化情景预估的研究进展

由于迄今为止大部分的海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了3种常用的降尺度法：动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括：统计降尺度法的基本假设,统计降尺度法的优缺点，以及常用的3种统计降尺度法；还论述了用统计降尺度法预估未来气候情景的一般步骤，以及方差放大技术在统计降尺度中的应用；同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性；最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。     

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.     

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.