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.     

Food security in the face of climate change: Adaptive capacity of small-scale social-ecological systems to environmental variability

Improving the adaptive capacity of small-scale irrigation systems to the impacts of climate change is crucial for food security in Asia. This study analyzes the capacity of small-scale irrigation systems dependent on the Asian monsoon to adapt to variability in river discharge caused by climate change. Our study is motivated by the Pumpa irrigation system, a small-scale irrigation system located in Nepal that is a model for this type of system. We developed an agent-based model in which we simulated the decisions farmers make about the irrigation strategy to use according to available water flow. Given the uncertainty associated with how climate change may affect the Asian monsoon, we simulated the performance of the system under different projections of climate change in the region (increase and decrease in rainfall, reduction and expansion of the monsoon season, and changes in the timing of the onset of the monsoon). Accordingly to our simulations, farmers might need to adapt to rainfall intensification and a late onset in the monsoon season. The demands for collective action among farmers (e.g. infrastructure repair, meetings, decisions, etc.) might increase considerably due to climate change. Although our model suggests that investment in new infrastructure might increase the performance of the system under some climate change scenarios, the high inequality among farmers when water availability is reduced might hinder the efficiency of these measures due to a reduction of farmers’ willingness to cooperate. Our modeling exercise helps to hypothesize about the most sensitive climate change scenarios for smallscale irrigation farming in Nepal and helps to frame a discussion of some possible solutions and fundamental trade-offs in the process of adaptation to improve for food and water security under climate change.     

Multiple adaptation types with mitigation: A framework for policy analysis

Effective climate policy will consist of mitigation and adaptation implemented simultaneously in a policy portfolio to reduce the risks of climate change. Previous studies of the tradeoffs between mitigation and adaptation have implicitly framed the problem deterministically, choosing the optimal paths for all time. Because climate change is a long-term problem with significant uncertainties and opportunities to learn and revise, critical tradeoffs between mitigation and adaptation in the near-term have not been considered. We propose a new framework for considering the portfolio of mitigation and adaptation that explicitly treats the problem as a multi-stage decision under uncertainty. In this context, there are additional benefits to near-term investments if they reduce uncertainty and lead to improved future decisions. Two particular features are fundamental to understanding the relevant tradeoffs between mitigation and adaptation: (1) strategy dynamics over time in reducing climate damages, and (2) strategy dynamics under uncertainty and potential for learning. Our framework strengthens the argument for disaggregating adaption as has been proposed by others. We present three stylized classes of adaptation investment types as a conceptual framework: short-lived “flow” spending, committed “stock” investment, and lower capacity “option” stock with the capability of future upgrading. In the context of sequential decision under uncertainty, these subtypes of adaptation have important tradeoffs among them and with mitigation. We argue that given the large policy uncertainty that we face currently, explicitly considering adaptation “option” investments is a valuable component of a near-term policy response that can balance between the flexible flow and committed stock approaches, as it allows for the delay of costly stock investments while at the same time allowing for lower-cost risk management of future damages.     

Drought in Northeast Brazil—past,present, and future

This study provides an overview of the drought situation in Northeast Brazil for the past, present, and future. Droughts affect more people than any other natural hazard owing to their large scale and long-lasting nature. They are recurrent in the region and while some measures have been taken by the governments to mitigate their impacts, there is still a perception that residents, mainly in rural areas, are not yet adapted to these hazards. The drought affecting the Northeast from 2012 to 2015, however, has had an intensity and impact not seen in several decades and has already destroyed large swaths of cropland, affecting hundreds of cities and towns across the region, and leaving ranchers struggling to feed and water cattle. Future climate projections for the area show large temperature increases and rainfall reductions, which, together with a tendency for longer periods with consecutive dry days, suggest the occurrence of more frequent/intense dry spells and droughts and a tendency toward aridification in the region. All these conditions lead to an increase in evaporation from reservoirs and lakes, affecting irrigation and agriculture as well as key water uses including hydropower and industry, and thus, the welfare of the residents. Integrating drought monitoring and seasonal forecasting provides efficient means of assessing impacts of climate variability and change, identifying vulnerabilities, and allowing for better adaptation measures not only for medium- and long-term climate change but also for extremes of the interannual climate variability, particularly droughts.

     

Integrating top–down and bottom–up approaches to design global change adaptation at the river basin scale

The high uncertainty associated with the effect of global change on water resource systems calls for a better combination of conventional top–down and bottom–up approaches, in order to design robust adaptation plans at the local scale. The methodological framework presented in this article introduces “bottom–up meets top–down” integrated approach to support the selection of adaptation measures at the river basin level by comprehensively integrating the goals of economic efficiency, social acceptability, environmental sustainability and adaptation robustness. The top–down approach relies on the use of a chain of models to assess the impact of global change on water resources and its adaptive management over a range of climate projections. Future demand scenarios and locally prioritised adaptation measures are identified following a bottom–up approach through a participatory process with the relevant stakeholders and experts. The optimal combinations of adaptation measures are then selected using a hydro-economic model at basin scale for each climate projection. The resulting adaptation portfolios are, finally, climate checked to define a robust least-regret programme of measures based on trade-offs between adaptation costs and the reliability of supply for agricultural demands.This innovative approach has been applied to a Mediterranean basin, the Orb river basin (France). Mid-term climate projections, downscaled from 9 General Climate Models, are used to assess the uncertainty associated with climate projections. Demand evolution scenarios are developed to project agricultural and urban water demands on the 2030 time horizon. The results derived from the integration of the bottom–up and top–down approaches illustrate the sensitivity of the adaptation strategies to the climate projections, and provide an assessment of the trade-offs between the performance of the water resource system and the cost of the adaptation plan to inform local decision-making. The article contributes new methodological elements for the development of an integrated framework for decision-making under climate change uncertainty, advocating an interdisciplinary approach that bridges the gap between bottom–up and top–down approaches.     

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.     

Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the East of England

Projections of future climate change are plagued with uncertainties, causing difficulties for planners taking decisions on adaptation measures. This paper presents an assessment framework that allows the identification of adaptation strategies that are robust (i.e. insensitive) to climate change uncertainties. The framework is applied to a case study of water resources management in the East of England, more specifically to the Anglian Water Services’ 25 year Water Resource Plan (WRP). The paper presents a local sensitivity analysis (a ‘one-at-a-time’ experiment) of the various elements of the modelling framework (e.g., emissions of greenhouse gases, climate sensitivity and global climate models) in order to determine whether or not a decision to adapt to climate change is sensitive to uncertainty in those elements.Water resources are found to be sensitive to uncertainties in regional climate response (from general circulation models and dynamical downscaling), in climate sensitivity and in climate impacts. Aerosol forcing and greenhouse gas emissions uncertainties are also important, whereas uncertainties from ocean mixing and the carbon cycle are not. Despite these large uncertainties, Anglian Water Services’ WRP remains robust to the climate change uncertainties sampled because of the adaptation options being considered (e.g. extension of water treatment works), because the climate model used for their planning (HadCM3) predicts drier conditions than other models, and because ‘one-at-a-time’ experiments do not sample the combination of different extremes in the uncertainty range of parameters. This research raises the question of how much certainty is required in climate change projections to justify investment in adaptation measures, and whether such certainty can be delivered.     

The AVOID programme’s new simulations of the global benefits of stringent climate change mitigation

Quantitative simulations of the global-scale benefits of climate change mitigation are presented, using a harmonised, self-consistent approach based on a single set of climate change scenarios. The approach draws on a synthesis of output from both physically-based and economics-based models, and incorporates uncertainty analyses. Previous studies have projected global and regional climate change and its impacts over the 21st century but have generally focused on analysis of business-as-usual scenarios, with no explicit mitigation policy included. This study finds that both the economics-based and physically-based models indicate that early, stringent mitigation would avoid a large proportion of the impacts of climate change projected for the 2080s. However, it also shows that not all the impacts can now be avoided, so that adaptation would also therefore be needed to avoid some of the potential damage. Delay in mitigation substantially reduces the percentage of impacts that can be avoided, providing strong new quantitative evidence for the need for stringent and prompt global mitigation action on greenhouse gas emissions, combined with effective adaptation, if large, widespread climate change impacts are to be avoided. Energy technology models suggest that such stringent and prompt mitigation action is technologically feasible, although the estimated costs vary depending on the specific modelling approach and assumptions.     

Optimal growth with adaptation to climate change

We find that approximately a quarter of the world’s productive capital could be sensitive to climate; therefore, this capital faces the risk of accelerated obsolescence in a world warming by an average of 0.2 °C per decade. We examine the question of optimal adaptation to climate change in a vintage capital growth model without uncertainty. Along the optimal pathway, adaptation is proactive with an anticipation period of approximately twenty years. While there is additional investment in this scenario compared with a no-climate-change baseline, the overall cost to adapt is low relative to the potential losses from maladaptation. Over-investment in protection capital allows the economy to be consistently well-adapted to climate; thus, such a policy prevents transient maladaptation costs. Sensitivity analysis with an integrated assessment model suggests that costs could be ten times larger if adaptation only begins after vulnerable sectors are impacted.     

Learning to Adapt: Organisational Adaptation to Climate Change Impacts

Analysis of human adaptation to climate change should be based on realistic models of adaptive behaviour at the level of organisations and individuals. The paper sets out a framework for analysing adaptation to the direct and indirect impacts of climate change in business organisations with new evidence presented from empirical research into adaptation in nine case-study companies. It argues that adaptation to climate change has many similarities with processes of organisational learning. The paper suggests that business organisations face a number of obstacles in learning how to adapt to climate change impacts, especially in relation to the weakness and ambiguity of signals about climate change and the uncertainty about benefits flowing from adaptation measures. Organisations rarely adapt ‘autonomously’, since their adaptive behaviour is influenced by policy and market conditions, and draws on resources external to the organisation. The paper identifies four adaptation strategies that pattern organisational adaptive behaviour.     

Climate response of rainfed versus irrigated farms: the bias of farm heterogeneity in irrigation

Researchers who do not take into account farm heterogeneity in implementing specific climate change adaptation options might significantly bias their findings. To prove this point, this paper focusses on irrigation as an adaptation option to climate change and highlights the fact that there is no such thing as “irrigation.” Instead, different farms consider water management options across a spectrum that ranges from purely rainfed farms to purely irrigated farms with in between the extreme practices such as supplemental irrigation, water conservation practices, and different irrigation techniques. Accounting for such differences is necessary, yet difficult due to a lack of farm-specific data on water management and irrigation. This paper uses unique Farm Accountancy Data Network data of Western European farmers on the proportion of farmland that each farm irrigates. Unlike previous work, this allows taking into account some within-irrigation heterogeneity instead of simply categorizing farms as being “irrigated.” We estimate and compare climate response models based on the Ricardian cross-sectional method for a large range of irrigation categories. The results give insights into how the farm irrigation climate response can be significantly different depending on how irrigation is defined. This proves that ignoring within-adaptation differences when comparing non-adaptation with adaptation (in this case, rainfed versus irrigated agriculture) might lead to biased conclusions with regard to effectiveness of adaptation strategies. We therefore argue that it might be more relevant to understand at which point and under which circumstances irrigated agriculture is more or less beneficial than rainfed agriculture.     

Localized climate change scenarios of mean temperature and precipitation over Switzerland

There is a growing need of the climate change impact modeling and adaptation community to have more localized climate change scenario information available over complex topography such as in Switzerland. A gridded dataset of expected future climate change signals for seasonal averages of daily mean temperature and precipitation in Switzerland is presented. The basic scenarios are taken from the CH2011 initiative. In CH2011, a Bayesian framework was applied to obtain probabilistic scenarios for three regions within Switzerland. Here, the results for two additional Alpine sub-regions are presented. The regional estimates have then been downscaled onto a regular latitude-longitude grid with a resolution of 0.02° or roughly 2 km. The downscaling procedure is based on the spatial structure of the climate change signals as simulated by the underlying regional climate models and relies on a Kriging with external drift using height as auxiliary predictor. The considered emission scenarios are A1B, A2 and the mitigation scenario RCP3PD. The new dataset shows an expected warming of about 1 to 6 °C until the end of the 21st century, strongly depending on the scenario and the lead time. Owing to a large vertical gradient, the warming is about 1 °C stronger in the Alps than in the Swiss lowlands. In case of precipitation, the projection uncertainty is large and in most seasons precipitation can increase or decrease. In summer a distinct decrease of precipitation can be found, again strongly depending on the emission scenario.     

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.     

Mass-cargo-affine industries and climate change

The impact of low water periods on inland navigation and companies is well known by ship-operators and companies that rely on this mode of transport but it is rarely a topic of climate impact research. As climate change might affect the frequency and intensity of low water periods, quantifying the impact of climate change on companies and the effects of possible adaptation measures is vital. In this study, we present a model for quantifying the impact of low water events on companies which rely on inland navigation and apply that model to three anonymous iron and steel companies along the River Rhine. The deviation of optimal storage, the storage level that evens out risk vs. fixed capital, is used in the model to measure the vulnerability of companies. The results show that, depending on the climate scenario, the companies might have to deal with either one or five additional days of empty storage in the near future (2021–2050) and up to nine more days by the 2071–2100 period. Seasonal analysis shows that, consistent with the change in the river discharge, the biggest deviations from optimal storage level occur in the late summer/early autumn. Analysis of adaptation options shows that companies would need to increase storage capacity by 2.5 % for the 2021–2050 period, and by 25 % by the 2071–2100 period. A reduction of ship sizes is not an adaptation option for the three companies in this study, because these companies already use relatively small vessels. This is however an efficient adaptation option for companies which employ larger vessels for transport. Another adaptation option would be to reduce the share of transportation via inland waters, but the feasibility of this option depends on the availability and cost of other modes of transport.     

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。     

The impact of future climate change on West African crop yields: What does the recent literature say?

In West Africa, agriculture, mainly rainfed, is a major economic sector and the one most vulnerable to climate change. A meta-database of future crop yields, built up from 16 recent studies, is used to provide an overall assessment of the potential impact of climate change on yields, and to analyze sources of uncertainty.Despite a large dispersion of yield changes ranging from −50% to +90%, the median is a yield loss near −11%. This negative impact is assessed by both empirical and process-based crop models whereas the Ricardian approach gives very contrasted results, even within a single study. The predicted impact is larger in northern West Africa (Sudano-Sahelian countries, −18% median response) than in southern West Africa (Guinean countries, −13%) which is likely due to drier and warmer projections in the northern part of West Africa. Moreover, negative impacts on crop productivity increase in severity as warming intensifies, with a median yield loss near −15% with most intense warming, highlighting the importance of global warming mitigation.The consistently negative impact of climate change results mainly from the temperature whose increase projected by climate models is much larger relative to precipitation change. However, rainfall changes, still uncertain in climate projections, have the potential to exacerbate or mitigate this impact depending on whether rainfall decreases or increases. Finally, results highlight the pivotal role that the carbon fertilization effect may have on the sign and amplitude of change in crop yields. This effect is particularly strong for a high carbon dioxide concentration scenario and for C3 crops (e.g. soybean, cassava). As staple crops are mainly C4 (e.g. maize, millet, sorghum) in WA, this positive effect is less significant for the region.     

Ecosystem Evaluation, Climate Change and Water Resources Planning

This paper considers ecosystem evaluation under conditions of climate change in the context both of the U.S. Water Resources Council's Principles and Guidelines (P&G) and the more general Federal regulations governing environmental evaluation. Federal water agencies have responsibilities for protecting aquatic ecosystems through their regulatory programs and operations and planning missions. The primary concern of water resources and aquatic ecosystems planning in the United States is on the riparian or floodplain corridors of river systems. In the context of climate change, planning for these systems focusses on adaptation options both for current climate variability and for that engendered by potential climate change.Ecosystems appear to be highly vulnerable to climate change, as described in IPCC reports. Aquatic ecosystems are likely to be doubly affected, first by thermally induced changes of global warming and second by changes in the hydrologic regime. Perhaps as much as any of the issues dealt with in this issue, the evaluation of ecosystems is linked to fundamental questions of criteria as well as to the details of the Federal environmental planning system. That system is a densely woven, interlocking system of environmental protection legislation, criteria and regulations that includes a self-contained evaluation system driven by the National Environmental Policy Act (NEPA) procedural guidelines (United States Council on Environmental Quality, 1978) and Environmental Impact Statement (EIS) requirements. The Corps of Engineers must use both the P&G and the NEPA/EIS system in discharging its responsibilities.If U.S. Federal agencies are to take the lead in formulating and evaluating adaptation options, there needs to be a reexamination of existing evaluation approaches. Among the elements of the P&G that may require rethinking in view of the prospects of global climate change are those relating to risk and uncertainty, nonstationarity, interest rates, and multiple objectives. Within the government planning process, efforts must be made to resolve inconsistencies and constraints in order to permit the optimal evaluation of water-based ecosystems under global climate change. The interrelationships of the two systems are described in this paper, and alternative ways of viewing the planning process are discussed. Strategic planning and management at the watershed level provides an effective approach to many of the issues. Current NEPA/EIS impact analysis does not provide a suitable framework for environmental impact analysis under climate uncertainty, and site-specific water resources evaluation relating to climate change appears difficult at current levels of knowledge about climate change. The IPCC Technical Guidelines, however, provide a useful beginning for assessing the impacts of future climate states.     

The Vulnerability of the Australian Beef Industry to Impacts of the Cattle Tick (Boophilus microplus) under Climate Change

An integrated assessment is presented of the potential impacts of the cattle tick (Boophilus microplus Canestrini) on the Australian beefindustry under climate change. The project was carried out as a case study to test an impact assessment approach that was designed to integrate biological, production and socio-economic impacts on managed and natural systems. A climate-driven, tick population model was run for European, zebu and crossbred cattle breeds having different levels of resistance to cattle ticks. A geographical information system (GIS) was used to organise spatial data on climate scenarios and industry statistics and to undertake regional analyses.A comparison was made of the two available approaches to conducting impact assessments, namely a bottom-up approach using sensitivity analysis and a top-down approach using climate change scenarios from a global circulation model (GCM) (CSIRO, 1996). The output, in terms of the abundance of tick populations and reductions in cattle productivity for each breed showed significant expansions in potential geographical impacts. In the absence of any adaptation measures, the results indicated changes in the losses in live weight gain of cattle tick ranging from 7780 tonnes per year by 2030 to 21637 tonnes per year by 2100, in comparison with estimates for current losses of 6594 tonnes per year.The principal adaptation options available to the beef industry are to switch to breeds that are more resistant to cattle ticks, or to increase the frequency of treatments with various tick control products. In this paper we focus on switching breeds as an adaptive measure when appropriate damage thresholds are triggered under the climate change scenarios. When adaptation measures were put in place, the losses ranged from 4962 tonnes in 2030 to 5619 tonnes in 2100 compared with 2636 tonnes at present if all producers adopted the optimal breed structure. Optimal breed structure was defined as one that would prevent tick numbers per animal exceeding 100 ticks per animal for European and 700 ticks per animal for crossbred breeds of cattle in any week of the year under a tick control strategy that was suitable for present climatic conditions. The lower threshold for European breeds reflects their vulnerability to explosive increases in numbers because of their low resistance to ticks. The results of the analyses using the GCM scenarios were used in an economic model to calculate costs of lost live-weight gain for 2030, 2070 and 2100. The greatest increases in costs were incurred in the southern parts of the current distribution in Queensland and potentially in northern New South Wales if the present quarantine barrier failed.Given the great uncertainty of the nature of possible regional changes in climate, analyses of the sensitivity of losses in live weight gain to changes in climatic variables were also undertaken. The analyses included a measure of likely impacts of cattle tick on the beef cattle industry, in the absence of adaptation measures, as a baseline measure of sensitivity. The likely impacts on crossbred cattle were insensitive to the climatic variables.When adaptive breed changes were allowed, the economic impacts on the industry were insensitive to the GCM scenarios. This suggests that, at least in this instance, reducing the uncertainties in climate change scenarios is not a priority if the adaptation strategies can be implemented in a cost-effective manner. Finally we made a qualitative assessment of the sustainability and robustness of alternative approaches to adaptation and assessed regional vulnerability to cattle tick under climate change. The conclusions were so strongly dependent on assumptions about the future of other global changes, in particular the ability to maintain quarantine barriers and to retain effective acaricides at comparable costs to the present, that we strongly recommend that risk assessments of climate change extend to all relevant variables in involved in global change where possible.     

气候变化对密云水库水资源的影响及其适应性管理对策

密云水库近30 a入库水资源量日益减少,严重影响城市供水和可持续发展,其中气候变化对水资源的影响成为最受关注的问题之一。以海河流域密云水库的水资源供应为例,研究了气候变化对入库水资源的影响。结果表明：除SRES A2情景下在2025年入库流量减少外,其他情景均表现为入库流量增加。对入库流量增加的情景,采用"零调整方案",即不采取调整措施是可以的,但由于未来北京水资源压力较大,有必要采取一些综合对策。通过多目标条件分析,为解决北京的饮用水供应问题,建议采用开源（跨河流调水）、节流（水田改旱地）及污水治理三管齐下的方案。