Present-day and future precipitation in the Baltic Sea region as simulated in a suite of regional climate models

Here we investigate simulated changes in the precipitation climate over the Baltic Sea and surrounding land areas for the period 2071–2100 as compared to 1961–1990. We analyze precipitation in 10 regional climate models taking part in the European PRUDENCE project. Forced by the same global driving climate model, the mean of the regional climate model simulations captures the observed climatological precipitation over the Baltic Sea runoff land area to within 15% in each month, while single regional models have errors up to 25%. In the future climate, the precipitation is projected to increase in the Baltic Sea area, especially during winter. During summer increased precipitation in the north is contrasted with a decrease in the south of this region. Over the Baltic Sea itself the future change in the seasonal cycle of precipitation is markedly different in the regional climate model simulations. We show that the sea surface temperatures have a profound impact on the simulated hydrological cycle over the Baltic Sea. The driving global climate model used in the common experiment projects a very strong regional increase in summertime sea surface temperature, leading to a significant increase in precipitation. In addition to the common experiment some regional models have been forced by either a different set of Baltic Sea surface temperatures, lateral boundary conditions from another global climate model, a different emission scenario, or different initial conditions. We make use of the large number of experiments in the PRUDENCE project, providing an ensemble consisting of more than 25 realizations of climate change, to illustrate sources of uncertainties in climate change projections.     

On interpreting hydrological change from regional climate models

Although representation of hydrology is included in all regional climate models (RCMs), the utility of hydrological results from RCMs varies considerably from model to model. Studies to evaluate and compare the hydrological components of a suite of RCMs and their use in assessing hydrological impacts from future climate change were carried out over Europe. This included using different methods to transfer RCM runoff directly to river discharge and coupling different RCMs to offline hydrological models using different methods to transfer the climate change signal between models. The work focused on drainage areas to the Baltic Basin, the Bothnian Bay Basin and the Rhine Basin. A total of 20 anthropogenic climate change scenario simulations from 11 different RCMs were used. One conclusion is that choice of GCM (global climate model) has a larger impact on projected hydrological change than either selection of emissions scenario or RCM used for downscaling.     

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

全球1.5℃和2.0℃升温下潮白河流域气候和径流量变化预估

潮白河流域为北京主要供水源,其水资源量对北京用水保障至关重要,因此开展该流域在全球1.5℃和2.0℃升温下的径流预估研究具有现实意义。利用1961—2001年WATCH数据对SWAT水文模型进行率定和验证,在此基础上,应用第五次耦合模式比较计划(CMIP5)中5个全球气候模式在典型浓度路径(RCP4.5、RCP6.0和RCP8.5)下预估的全球1.5℃和2.0℃升温下的数据驱动SWAT模型,开展了潮白河流域气温、降水及径流量的变化预估研究,并量化评估由气候模式和RCPs导致的水文效应的不确定性。结果表明：(1) SWAT模型基本能较好地模拟潮白河流域的月径流特征,应用该模型进行气候变化对径流量的影响评估是可行的。(2)在全球1.5℃和2.0℃升温下,潮白河流域年平均温度较基准期(1976—2005年)分别增加1.5℃和2.2℃,年平均降水量也增加4.9%和7.0%。预估的年径流量在全球1.5℃升温下总体略有增加,盛夏和秋初的径流量占全年的比例也有所增加;在全球2.0℃升温下,年径流量增幅达30%以上,但夏季径流量占全年的比例明显减少。(3)在全球2.0℃升温下,潮白河流域极端丰水流量明显增加,洪涝发生风险增大。(4)未来气温、降水量和径流量的预估都存在一定的不确定性,在全球2.0℃升温下不确定性更大;相对而言,径流量的不确定性要远大于降水量的不确定性;无论是全球1.5℃升温下还是2.0℃升温下,预估不确定性主要来源于全球气候模式。     

环境变化对流域水文水资源的影响评估及不确定性研究进展

综述了区分气候变化和人类活动对水文要素影响的研究方法,提出当前研究中应将流域水文过程变化的环境因素来源分为气候自然变异、人为气候变化和人类活动三个方面,并给出了方法体系;基于环境变化下流域水文过程的复杂不确定性,详细剖析了环境变化下流域水文过程的不确定性来源,总结了常用的不确定性评估方法,并介绍了针对定性不确定性（奈特不确定性）评估的信息差距理论。指出为提高减缓和适应环境变化能力,未来应加强以气候自然变异、人为气候变化和人类活动三源分解的环境变化影响研究;不确定性分析应成为环境模拟的固有组成部分,在影响评价中应注重不确定性的评估,并应加强不确定性信息在流域管理决策中的应用研究以及流域风险管理研究。     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

Improved regional scale processes reflected in projected hydrological changes over large European catchments

For the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the recent version of the coupled atmosphere/ocean general circulation model (GCM) of the Max Planck Institute for Meteorology has been used to conduct an ensemble of transient climate simulations These simulations comprise three control simulations for the past century covering the period 1860–2000, and nine simulations for the future climate (2001–2100) using greenhouse gas (GHG) and aerosol concentrations according to the three IPCC scenarios B1, A1B and A2. For each scenario three simulations were performed. The global simulations were dynamically downscaled over Europe using the regional climate model (RCM) REMO at 0.44° horizontal resolution (about 50 km), whereas the physics packages of the GCM and RCM largely agree. The regional simulations comprise the three control simulations (1950–2000), the three A1B simulations and one simulation for B1 as well as for A2 (2001–2100). In our study we concentrate on the climate change signals in the hydrological cycle and the 2 m temperature by comparing the mean projected climate at the end of the twenty-first century (2071–2100) to a control period representing current climate (1961–1990). The robustness of the climate change signal projected by the GCM and RCM is analysed focussing on the large European catchments of Baltic Sea (land only), Danube and Rhine. In this respect, a robust climate change signal designates a projected change that sticks out of the noise of natural climate variability. Catchments and seasons are identified where the climate change signal in the components of the hydrological cycle is robust, and where this signal has a larger uncertainty. Notable differences in the robustness of the climate change signals between the GCM and RCM simulations are related to a stronger warming projected by the GCM in the winter over the Baltic Sea catchment and in the summer over the Danube and Rhine catchments. Our results indicate that the main explanation for these differences is that the finer resolution of the RCM leads to a better representation of local scale processes at the surface that feed back to the atmosphere, i.e. an improved representation of the land sea contrast and related moisture transport processes over the Baltic Sea catchment, and an improved representation of soil moisture feedbacks to the atmosphere over the Danube and Rhine catchments.     

Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia

Future climate projections and impact analyses are pivotal to evaluate the potential change in crop yield under climate change. Impact assessment of climate change is also essential to prepare and implement adaptation measures for farmers and policymakers. However, there are uncertainties associated with climate change impact assessment when combining crop models and climate models under different emission scenarios. This study quantifies the various sources of uncertainty associated with future climate change effects on wheat productivity at six representative sites covering dry and wet environments in Australia based on 12 soil types and 12 nitrogen application rates using one crop model driven by 28 global climate models (GCMs) under two representative concentration pathways (RCPs) at near future period 2021–2060 and far future period 2061–2100. We used the analysis of variance (ANOVA) to quantify the sources of uncertainty in wheat yield change. Our results indicated that GCM uncertainty largely dominated over RCPs, nitrogen rates, and soils for the projections of wheat yield at drier locations. However, at wetter sites, the largest share of uncertainty was nitrogen, followed by GCMs, soils, and RCPs. In addition, the soil types at two northern sites in the study area had greater effects on yield change uncertainty probably due to the interaction effect of seasonal rainfall and soil water storage capacity. We concluded that the relative contributions of different uncertainty sources are dependent on climatic location. Understanding the share of uncertainty in climate impact assessment is important for model choice and will provide a basis for producing more reliable impact assessment.     

River discharge and freshwater runoff to the Barents Sea under present and future climate conditions

River discharge forms a major freshwater input into the Arctic Ocean, and as such it has the potential to influence the oceanic circulation. As the hydrology of Arctic river basins is dominated by cryospheric processes such as snow accumulation and snowmelt, it may also be highly sensitive to a change in climate. Estimating the water balance of these river basins is therefore important, but it is complicated by the sparseness of observations and the large uncertainties related to the measurement of snowfalls. This study aims at simulating the water balance of the Barents Sea drainage basin in Northern Europe under present and future climate conditions. We used a regional climate model to drive a large-scale hydrological model of the area. Using simulated precipitation derived from a climate model led to an overestimation of the annual discharge in most river basins, but not in all. Under the B2 scenario of climate change, the model simulated a 25% increase in freshwater runoff, which is proportionally larger than the projected precipitation increase. As the snow season is 30–50 day shorter, the spring discharge peak is shifted by about 2–3 weeks, but the hydrological regime of the rivers remains dominated by snowmelt.     

Water balance modelling in the Baltic Sea drainage basin – analysis of meteorological and hydrological approaches

Summary Efforts to understand and simulate the global climate in numerical models have led to regional studies of the energy and water balance. The Baltic Basin provides a continental scale test basin where meteorology, oceanography and hydrology all can meet. Using a simple conceptual approach, a large-scale hydrological model of the water balance of the total Baltic Sea Drainage Basin (HBV-Baltic) was used to simulate the basinwide water balance components for the present climate and to evaluate the land surface components of atmospheric climate models. It has been used extensively in co-operative BALTEX (The Baltic Sea Experiment) research and within SWECLIM (Swedish Regional Climate Modelling Programme) to support continued regional climate model development. This helps to identify inconsistencies in both meteorological and hydrological models. One result is that compensating errors are evident in the snow routines of the atmospheric models studied. The use of HBV-Baltic has greatly improved the dialogue between hydrological and meteorological modellers within the Baltic Basin research community. It is concluded that conceptual hydrological models, although far from being complete, play an important role in the realm of continental scale hydrological modelling. Atmospheric models benefit from the experience of hydrological modellers in developing simpler, yet more effective land surface parameterisations. This basic modelling tool for simulating the large-scale water balance of the Baltic Sea drainage basin is the only existing hydrological model that covers the entire basin and will continue to be used until more detailed models can be successfully applied at this scale. Received November 24, 2000 Revised April 4, 2001     

Reducing Uncertainties in Climate Projections with Emergent Constraints:Concepts,Examples and Prospects

Models disagree on a significant number of responses to climate change,such as climate feedback,regional changes,or the strength of equilibrium climate sensitivity.Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections.In this paper,the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties,and a review of emergent constraints found in the last two decades.Potential links between highlighted predictors are explored,especially those targeting uncertainty reductions in climate sensitivity,cloud feedback,and changes of the hydrological cycle.Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed.This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship.This would also permit more efficient model evaluation and better targeted model development.In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected,which call for robust statistical inferences that allow cross-validation of more likely estimates.     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

On the tropical origin of uncertainties in the global land precipitation response to global warming

Understanding the response of the global hydrological cycle to recent and future anthropogenic emissions of greenhouse gases and aerosols is a major challenge for the climate modelling community. Recent climate scenarios produced for the fourth assessment report of the Intergovernmental Panel on Climate Change are analysed here to explore the geographical origin of, and the possible reasons for, uncertainties in the hydrological model response to global warming. Using the twentieth century simulations and the SRES-A2 scenarios from eight different coupled ocean–atmosphere models, it is shown that the main uncertainties originate from the tropics, where even the sign of the zonal mean precipitation change remains uncertain over land. Given the large interannual fluctuations of tropical precipitation, it is then suggested that the El Niño Southern Ocillation (ENSO) variability can be used as a surrogate of climate change to better constrain the model reponse. While the simulated sensitivity of global land precipitation to global mean surface temperature indeed shows a remarkable similarity between the interannual and climate change timescales respectively, the model ability to capture the ENSO-precipitation relationship is not a major constraint on the global hydrological projections. Only the model that exhibits the highest precipitation sensitivity clearly appears as an outlier. Besides deficiencies in the simulation of the ENSO-tropical rainfall teleconnections, the study indicates that uncertainties in the twenty-first century evolution of these teleconnections represent an important contribution to the model spread, thus emphasizing the need for improving the simulation of the tropical Pacific variability to provide more reliable scenarios of the global hydrological cycle. It also suggests that validating the mean present-day climate is not sufficient to assess the reliability of climate projections, and that interannual variability is another suitable and possibly more useful candidate for constraining the model response. Finally, it is shown that uncertainties in precipitation change are, like precipitation itself, very unevenly distributed over the globe, the most vulnerable countries sometimes being those where the anticipated precipitation changes are the most uncertain.     

A Demonstration of the Uncertainty in Projections of UK Climate Change Resulting from Regional Model Formulation

Regional climate models (RCMs) are now commonly used to downscale climate change projections provided by global coupled models to resolutions that can be utilised at national and finer scales. Although this extra tier of complexity adds significant value, it inevitably contributes a further source of uncertainty, due to the regional modelling uncertainties involved. Here, an initial attempt is made to estimate the uncertainty that arises from typical variations in RCM formulation, focussing on changes in UK surface air temperature (SAT) and precipitation projected for the late twenty-first century. Data are provided by a relatively large suite of RCM and global model integrations with widely varying formulations. It is found that uncertainty in the formulation of the RCM has a relatively small, but non-negligible, impact on the range of possible outcomes of future UK seasonal mean climate. This uncertainty is largest in the summer season. It is also similar in magnitude to that of large-scale internal variations of the coupled climate system, and for SAT, it is less than the uncertainty due to the emissions scenario, whereas for precipitation it is probably larger. The largest source of uncertainty, for both variables and in all seasons, is the formulation of the global coupled model. The scale-dependency of uncertainty due to RCM formulation is also explored by considering its impact on projections of the difference in climate change between the north and south of the UK. Finally, the implications for the reliability of UK seasonal mean climate change projections are discussed.     

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.     

Climate change signal and uncertainty in projections of ocean wave heights

In this study, projections of seasonal means and extremes of ocean wave heights were made using projections of sea level pressure fields conducted with three global climate models for three forcing-scenarios. For each forcing-scenario, the three climate models’ projections were combined to estimate the multi-model mean projection of climate change. The relative importance of the variability in the projected wave heights that is due to the forcing prescribed in a forcing-scenario was assessed on the basis of ensemble simulations conducted with the Canadian coupled climate model CGCM2. The uncertainties in the projections of wave heights that are due to differences among the climate models and/or among the forcing-scenarios were characterized. The results show that the multi-model mean projection of climate change has patterns similar to those derived from using the CGCM2 projections alone, but the magnitudes of changes are generally smaller in the boreal oceans but larger in the region nearby the Antarctic coastal zone. The forcing-induced variance (as simulated by CGCM2) was identified to be of substantial magnitude in some areas in all seasons. The uncertainty due to differences among the forcing-scenarios is much smaller than that due to differences among the climate models, although it was identified to be statistically significant in most areas of the oceans (this indicates that different forcing conditions do make notable differences in the wave height climate change projection). The sum of the model and forcing-scenario uncertainties is smaller in the JFM and AMJ seasons than in other seasons, and it is generally small in the mid-high latitudes and large in the tropics. In particular, some areas in the northern oceans were projected to have large changes by all the three climate models.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

Quantification of uncertainty sources in a probabilistic climate change assessment of future water shortages

As the incorporation of probabilistic climate change information into UK water resource management gathers apace, understanding the relative scales of the uncertainty sources in projections of future water shortage metrics is necessary for the resultant information to be understood and used effectively. Utilising modified UKCP09 weather generator data and a multi-model approach, this paper represents a first attempt at extending an uncertainty assessment of future stream flows under forced climates to consider metrics of water shortage based on the triggering of reservoir control curves. It is found that the perturbed physics ensemble uncertainty, which describes climate model parameter error uncertainty, is the cause of a far greater proportion of both the overall flow and water shortage per year probability uncertainty than that caused by SRES emissions scenario choice in the 2080s. The methodology for producing metrics of future water shortage risk from UKCP09 weather generator information described here acts as the basis of a robustness analysis of the North Staffordshire WRZ to climate change, which provides an alternative approach for making decisions despite large uncertainties, which will follow.     

Hydrological response to climate change in a glacierized catchment in the Himalayas

The analysis of climate change impact on the hydrology of high altitude glacierized catchments in the Himalayas is complex due to the high variability in climate, lack of data, large uncertainties in climate change projection and uncertainty about the response of glaciers. Therefore a high resolution combined cryospheric hydrological model was developed and calibrated that explicitly simulates glacier evolution and all major hydrological processes. The model was used to assess the future development of the glaciers and the runoff using an ensemble of downscaled climate model data in the Langtang catchment in Nepal. The analysis shows that both temperature and precipitation are projected to increase which results in a steady decline of the glacier area. The river flow is projected to increase significantly due to the increased precipitation and ice melt and the transition towards a rain river. Rain runoff and base flow will increase at the expense of glacier runoff. However, as the melt water peak coincides with the monsoon peak, no shifts in the hydrograph are expected.