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.     

High resolution modeling of the regional impacts of climate change on irrigation water demand

In the Arkansas River Basin in southeastern Colorado, surface irrigation provides most of the water required for agriculture. Consequently, the region’s future could be significantly affected if climate change impacts the amount of water available for irrigation. A methodology to model the expected impacts of climate change on irrigation water demand in the region is described. The Integrated Decision Support Consumptive Use model, which accounts for spatial and temporal variability in evapotranspiration and precipitation, is used in conjunction with two climate scenarios from the Vegetation-Ecosystem Modeling and Analysis Project. The two scenarios were extracted and scaled down from two general circulation models (GCMs), the HAD from the Hadley Centre for Climate Prediction and Research and the CCC from the Canadian Climate Centre. The results show significant changes in the water demands of crops due to climate change. The HAD and CCC climate change scenarios both predict an increase in water demand. However, the projections of the two GCMs concerning the water available for irrigation differ significantly, reflecting the large degree of uncertainty concerning what the future impacts of climate change might be in the study region. As new or updated predictions become available, the methodology described here can be used to estimate the impacts of climate change.     

A method to study the impact of climate change on variability of river flow: an example from the Guadalupe River in Texas

This work introduced a method to study river flow variability in response to climate change by using remote sensing precipitation data, downscaled climate model outputs with bias corrections, and a land surface model. A meteorological forcing dataset representing future climate was constructed via the delta change method in which the modeled change was added to the present-day conditions. The delta change was conducted at a fine spatial and temporal scale to contain the signals of weather events, which exhibit substantial responses to climate change. An empirical transformation technique was further applied to the constructed forcing to ensure a realistic range. The meteorological forcing was then used to drive the land surface model to simulate the future river flow. The results show that preserving fine-scale processes in response to climate change is a necessity to assess climatic impacts on the variability of river flow events.     

Identifying future climate change and drought detection using CanESM2 in the upper Siem Reap River,Cambodia

Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site).     

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.     

A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom

Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.     

Global streamflow and thermal habitats of freshwater fishes under climate change

Climate change will affect future flow and thermal regimes of rivers. This will directly affect freshwater habitats and ecosystem health. In particular fish species, which are strongly adapted to a certain level of flow variability will be sensitive to future changes in flow regime. In addition, all freshwater fish species are exotherms, and increasing water temperatures will therefore directly affect fishes’ biochemical reaction rates and physiology. To assess climate change impacts on large-scale freshwater fish habitats we used a physically-based hydrological and water temperature modelling framework forced with an ensemble of climate model output. Future projections on global river flow and water temperature were used in combination with current spatial distributions of several fish species and their maximum thermal tolerances to explore impacts on fish habitats in different regions around the world. Results indicate that climate change will affect seasonal flow amplitudes, magnitude and timing of high and low flow events for large fractions of the global land surface area. Also, significant increases in both the frequency and magnitude of exceeding maximum temperature tolerances for selected fish species are found. Although the adaptive capacity of fish species to changing hydrologic regimes and rising water temperatures could be variable, our global results show that fish habitats are likely to change in the near future, and this is expected to affect species distributions.     

Comparison of the use of alternative UKCP09 products for modelling the impacts of climate change on flood frequency

Climate change could have significant impacts on hydrology. This paper uses UK Climate Projections 09 (UKCP09) products to assess the impacts on flood frequency in Britain. The main UKCP09 product comprises conditional probabilistic information on changes in a number of climate variables on a 25?×?25?km grid across the UK (the Sampled Data change factors). A second product is a Weather Generator which produces time-series of current weather variables and future weather variables based on the Sampled Data and consistent with the change factors. A third product comprises time-series from a Regional Climate Model (RCM) ensemble which were used to downscale Global Climate Models (GCMs) on which the projections are based and whose outputs were used in the production of the Sampled Data. This paper compares the use of Sampled Data change factors, Weather Generator time-series, RCM-derived change factors and RCM time-series. Each is used to provide hydrological model inputs for nine catchments, to assess impacts for the 2080s (A1B emissions). The results show relatively good agreement between methods for most catchments, with the four median values for a catchment generally being within 10% of each other. There are also some clear differences, with the use of time-series generally leading to a greater uncertainty range than the use of change factors because the latter do not allow for the effects of, or changes in, natural variability. Also, the use of Weather Generator time-series leads to much greater impacts than the other methods for one catchment. The results suggest that climate impact studies should not necessarily rely on the application of just one UKCP09 product, as each has different strengths and weaknesses.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

Modeling the impacts of climate change on nitrogen retention in a 4th order stream

Climate induced changes of temperature, discharge and nitrogen concentration may change natural denitrification processes in river systems. Until now seasonal variation of N-retention by denitrification under different climate scenarios and the impact of river morphology on denitrification have not been thoroughly investigated. In this study climate scenarios (dry, medium and wet) have been used to characterize changing climatic and flow conditions for the period 2050–2054 in the 4th order stream Weiße Elster, Germany. Present and future periods of nitrogen turnover were simulated with the WASP5 river water quality model. Results revealed that, for a dry climate scenario, the mean denitrification rate was 71% higher in summer (low flow period between 2050 and 2054) and 51% higher in winter (high flow period) compared to the reference period. For the medium and wet climate scenarios, denitrification was slightly higher in summer (3% and 4%) and lower in winter (9% and 3% for medium and wet scenarios, respectively). Additionally, the variability of denitrification rates was higher in summer compared to winter conditions. For a natural river section, denitrification was a factor of 1.22 higher than for a canalized river reach. Besides, weirs along the river decrease the denitrification rate by 16% in July for dry scenario conditions. In the 42 km study reach, N-retention through denitrification amounted to 5.1% of the upper boundary N load during summer low flow conditions in the reference period. For the future dry climate scenario this value increased up to 10.2% and for the medium climate scenario up to 5.4%. In our case study the investigated climate scenarios showed that future discharge changes may have a larger impact on denitrification rates than future temperature changes. Overall results of the study revealed the significance of climate change in regulating the magnitude, seasonal pattern and variability of nitrogen retention. The results provide guidance for managing nitrogen related environmental problems for present and future climate conditions.     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

Global-scale climate impact functions: the relationship between climate forcing and impact

Although there is a strong policy interest in the impacts of climate change corresponding to different degrees of climate change, there is so far little consistent empirical evidence of the relationship between climate forcing and impact. This is because the vast majority of impact assessments use emissions-based scenarios with associated socio-economic assumptions, and it is not feasible to infer impacts at other temperature changes by interpolation. This paper presents an assessment of the global-scale impacts of climate change in 2050 corresponding to defined increases in global mean temperature, using spatially-explicit impacts models representing impacts in the water resources, river flooding, coastal, agriculture, ecosystem and built environment sectors. Pattern-scaling is used to construct climate scenarios associated with specific changes in global mean surface temperature, and a relationship between temperature and sea level used to construct sea level rise scenarios. Climate scenarios are constructed from 21 climate models to give an indication of the uncertainty between forcing and response. The analysis shows that there is considerable uncertainty in the impacts associated with a given increase in global mean temperature, due largely to uncertainty in the projected regional change in precipitation. This has important policy implications. There is evidence for some sectors of a non-linear relationship between global mean temperature change and impact, due to the changing relative importance of temperature and precipitation change. In the socio-economic sectors considered here, the relationships are reasonably consistent between socio-economic scenarios if impacts are expressed in proportional terms, but there can be large differences in absolute terms. There are a number of caveats with the approach, including the use of pattern-scaling to construct scenarios, the use of one impacts model per sector, and the sensitivity of the shape of the relationships between forcing and response to the definition of the impact indicator.     

Bottom-up climate risk assessment of infrastructure investment in the Niger River Basin

The Niger River is the third largest river in the African continent. Nine riparian countries share its basin, which rank all among the world’s thirty poorest. Existing challenges in West Africa, including endemic poverty, inadequate infrastructure and weak adaptive capacity to climate variability, make the region vulnerable to climate change. In this study, a risk-based methodology is introduced and demonstrated for the analysis of climate change impacts on planned infrastructure investments in water resources systems in the Upper and Middle Niger River Basin. The methodology focuses on identifying the vulnerability of the Basin’s socio-economic system to climate change, and subsequently assessing the likelihood of climate risks by using climate information from a multi-run, multi-GCM ensemble of climate projections. System vulnerabilities are analyzed in terms of performance metrics of hydroelectricity production, navigation, dry and rainy season irrigated agriculture, flooding in the Inner Delta of the Niger and the sustenance of environmental flows. The study reveals low to moderate risks in terms of stakeholder-defined threshold levels for most metrics in the 21st Century. The highest risk levels were observed for environmental flow targets. The findings indicate that the range of projected changes in an ensemble of CMIP3 GCM projections imply only relatively low risks of unacceptable climate change impacts on the present large-scale infrastructure investment plan for the Basin.     

Impact of reservoir operation and climate change on the hydrological regime of the Sesan and Srepok Rivers in the Lower Mekong Basin

The transboundary Sesan and Srepok sub-basins (2S) are the “hot-spot” areas for reservoir development in the Lower Mekong region, with 12 reservoirs built in the Vietnam territory. This study examines the impacts of reservoir operations in Vietnam and projected climate change on the downstream hydrologic regime of the 2S Rivers by jointly applying the Soil Water Assessment Tool (SWAT) and Water Evaluation and Planning (WEAP) models. Different scenarios of reservoir operation are considered and simulated to assess their impact on annual, seasonal, and monthly flow regimes under maximum hydropower capacity generation with and without taking into account the minimum flow requirement downstream near the Vietnam border with Cambodia. The precipitation and temperature projections from the high-resolution regional climate model HadGEM3-RA under two Representative Concentration Pathways, 4.5 and 8.5, of HadGEM2-AO are used as future climate change scenarios for the impact assessment. The study results show that reservoir operation leads to an increase in the dry season stream flows and a decrease in the wet season stream flows. The monthly flow regime exhibits considerable changes for both the Sesan and Srepok Rivers but with different magnitudes and patterns of increase and decrease. Climate change is likely to induce considerable changes in stream flows, though these changes are comparatively lower than those caused by reservoir operation. Climate change is likely to have both counterbalancing and reinforcing effects over the impact of reservoir operation, reducing changes during dry season but increasing changes in most of the other months.     

Modelling the impact of climate extremes: an overview of the MICE project

This paper provides an overview of the aims, objectives, research activities undertaken, and a selection of results generated in the European Commission-funded project entitled “Modelling the Impact of Climate Extremes” (MICE) – a pan-European end-to-end assessment, from climate model to impact model, of the potential impacts of climate change on a range of economic sectors important to the region. MICE focussed on changes in temperature, precipitation and wind extremes. The research programme had three main themes – the evaluation of climate model performance, an assessment of the potential future changes in the occurrence of extremes, and an examination of the impacts of changes in extremes on six activity sectors using a blend of quantitative modelling and expert judgement techniques. MICE culminated in a large stakeholder-orientated workshop, the aim of which was not only to disseminate project results but also to develop new stakeholder networks, whose expertise can be drawn on in future projects such as ENSEMBLES. MICE is part of a cluster of three projects, all related to European climate change and its impacts. The other projects in the cluster are PRUDENCE (Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) and STARDEX (Statistical and Regional Dynamical Downscaling of Extremes for European Regions).     

气候变化对跨境水资源影响的适应性评估与管理框架

气候变化增加了国际河流冲突的可能性,加强跨境水资源适应性管理是流域国可持续发展的必然选择。梳理了适应性相关研究的国内外最新进展,认识到适应性管理的关键问题是要发展一套科学评估未来气候变化影响及适应性策略的程序。通过论述气候变化下跨境水资源的适应性评估与管理框架,提出一个气候变化影响决策评估工具,包括信息收集、需求分析、对策分析、综合评估以及实施与调控5个阶段。该项研究将适应性管理与气候变化、定量化脆弱性及适应能力关联评价、成本效益分析、多目标优化决策和动态调控等有机结合,为从跨界层面制定具有针对性的适应性管理对策提供了思路与方法,有利于促进国际河流流域可持续发展。     

Climate-change effects on extreme precipitation in central Europe: uncertainties of scenarios based on regional climate models

Observations as well as most climate model simulations are generally in accord with the hypothesis that the hydrologic cycle should intensify and become highly volatile with the greenhouse-gas-induced climate change, although uncertainties of these projections as well as the spatial and seasonal variability of the changes are much larger than for temperature extremes. In this study, we examine scenarios of changes in extreme precipitation events in 24 future climate runs of ten regional climate models, focusing on a specific area of the Czech Republic (central Europe) where complex orography and an interaction of other factors governing the occurrence of heavy precipitation events result in patterns that cannot be captured by global models. The peaks-over-threshold analysis with increasing threshold censoring is applied to estimate multi-year return levels of daily rainfall amounts. Uncertainties in scenarios of changes for the late 21st century related to the inter-model and within-ensemble variability and the use of the SRES-A2 and SRES-B2 greenhouse gas emission scenarios are evaluated. The results show that heavy precipitation events are likely to increase in severity in winter and (with less agreement among models) also in summer. The inter-model and intra-model variability and related uncertainties in the pattern and magnitude of the change is large, but the scenarios tend to agree with precipitation trends recently observed in the area, which may strengthen their credibility. In most scenario runs, the projected change in extreme precipitation in summer is of the opposite sign than a change in mean seasonal totals, the latter pointing towards generally drier conditions in summer. A combination of enhanced heavy precipitation amounts and reduced water infiltration capabilities of a dry soil may severely increase peak river discharges and flood-related risks in this region.     

A maximum entropy method for combining AOGCMs for regional intra-year climate change assessment

This paper deals with different responses from various Atmosphere-Ocean Global Climate Models (AOGCMs) at the regional scale. What can be the best use of AOGCMs for assessing the climate change in a particular region? The question is complicated by the consideration of intra-year month-to-month variability of a particular climate variable such as precipitation or temperature in a specific region. A maximum entropy method (MEM), which combines limited information with empirical perspectives, is applied to assessing the probability-weighted multimodel ensemble average of a climate variable at the region scale. The method is compared to and coupled with other two methods: the root mean square error minimization method and the simple multimodel ensemble average method. A mechanism is developed to handle a comprehensive range of model uncertainties and to identify the best combination of AOGCMs based on a balance of two rules: depending equally on all models versus giving higher priority to models more strongly verified by the historical observation. As a case study, the method is applied to a central US region to compute the probability-based average changes in monthly precipitation and temperature projected for 2055, based on outputs from a set of AOGCMs. Using the AOGCM data prepared by international climate change study groups and local climate observation data, one can apply the MEM to precipitation or temperature for a particular region to generate an annual cycle, which includes the effects from both global climate change and local intra-year climate variability.     

Risk management tools for sustainable fisheries management under changing climate: a sea cucumber example

Sustainable fisheries management into the future will require both understanding of and adaptation to climate change. A risk management approach is appropriate due to uncertainty in climate projections and the responses of target species. Management strategy evaluation (MSE) can underpin and support effective risk management. Climate change impacts are likely to differ by species and spatially. We use a spatial MSE applied to a multi-species data-poor sea cucumber/béche-de-mer fishery to demonstrate the utility of MSE to test the performance of alternative harvest strategies in meeting fishery objectives; this includes the ability to manage through climate variability and change, and meeting management objectives pertaining to resource status and fishery economic performance. The impacts of fishing relative to the impacts of climate change are distinguished by comparing future projection distributions relative to equivalent no-fishing no-climate-change trials. The 8 modelled species exhibit different responses to environmental variability and have different economic value. Status quo management would result in half the species falling below target levels, moderate risks of overall and local depletion, and significant changes in species composition. The three simple strategies with no monitoring (spatial rotation, closed areas, multi-species composition) were all successful in reducing these risks, but with fairly substantial decreases in the average profit. Higher profits (for the same risk levels) could only be achieved with strategies that included monitoring and hence adaptive management. Spatial management approaches based on adaptive feedback performed best overall.     

气候变化和水的最新科学认知

政府间气候变化专门委员会（IPCC）于2008年4月8日正式通过了"气候变化和水"技术报告。该报告建立在IPCC 3个工作组第四次评估报告的基础上,客观、全面而审慎地评估了与水有关的气候变化以及对水的过去、现在和未来的认知。最重要的进展是：过去几十年观测到全球变暖已经与大尺度水文循环的大规模变化联系在一起;气候模型对21世纪的模拟结果一致显示出降水在高纬和部分热带地区将增加,而在部分亚热带和中低纬地区将减少的结果;预计到21世纪中期,河流年平均径流和水量可能会因为高纬和部分湿润热带地区的气候变化而增加,而在中低纬和干旱热带将可能减少;许多地方降水强度和变率的增加将使洪旱危险性上升;预计冰雪储藏的水的补给将在本世纪减少;预计较高的水温和极端变化,包括洪旱等,将影响水质并加剧水污染;对全球而言,气候变化对淡水系统负面影响将超过收益;预计由于气候变化导致的水量-水质变化将影响食物的产量、稳定性、流通和利用;气候变化影响现有水的基础设施的功能和运行,包括水电、防洪、排水、灌溉系统,同时影响到水的管理;目前的水管理措施不足以应对气候变化的影响;气候变化挑战"过去水文上的经验能得到未来的情况"的传统说法;为保障平水和干旱情况所设计的适应选择,必须综合需水和供水双方的战略;减缓措施可以降低升温对全球水资源的影响程度,进而减低适应的需求;水资源管理明显地影响到很多其他政策领域。