Climate Change and Floodplain Management in the United States

Federal agencies use flood frequency estimates to delineate flood risk, manage the National Flood Insurance Program, and ensure that Federal programs are economically efficient. The assumption behind traditional flood risk analysis is that climate is stationary, but anthropogenic climate change and better knowledge of interdecadal climate variability challenge the validity of the assumption. This paper reviews several alternative statistical models for flood risk estimation that do not assume climate stationarity. Some models require subjective judgement or presuppose an understanding of the causes of the underlying non-stationarity, which is problematic given our current knowledge of the interaction of climate and floods. Although currently out of favor, hydrometeorological models have been used for engineering design as alternatives to statistical models and could be adapted to different climate conditions. Floodplain managers should recognize the potentially greater uncertainty in flood risk estimation due to climate change and variability and try to incorporate the uncertainties into floodplain management decision-making and regulation.     

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.     

Adaptive and risk-based approaches to climate change and the management of uncertainty and institutional risk: The case of future flooding in England

This paper focuses on how scientific uncertainties about future peak flood flows and sea level rises are accounted for in long term strategic planning processes to adapt inland and coastal flood risk management in England to climate change. Combining key informant interviews (n = 18) with documentary analysis, it explores the institutional tensions between adaptive management approaches emphasising openness to uncertainty and to alternative policy options on the one hand and risk-based ones that close them down by transforming uncertainties into calculable risks whose management can be rationalized through cost-benefit analysis and nationally consistent, risk-based priority setting on the other hand. These alternative approaches to managing uncertainty about the first-order risks to society from future flooding are shaped by institutional concerns with managing the second-order, ‘institutional’ risks of criticism and blame arising from accountability for discharging those first-order risk management responsibilities. In the case of river flooding the poorly understood impacts of future climate change were represented with a simplistic adjustment to peak flow estimates, which proved robust in overcoming institutional resistance to making precautionary allowances for climate change in risk-based flood management, at least in part because its scientific limitations were acknowledged only partially. By contrast in the case of coastal flood risk management, greater scientific confidence led to successively more elaborate guidance on how to represent the science, which in turn led to inconsistency in implementation and increased the institutional risks involved in taking the uncertain effects of future sea level rise into account in adaptation planning and flood risk management. Comparative analysis of these two cases then informs some wider reflections about the tensions between adaptive and risk-based approaches, the role of institutional risk in climate change adaptation, and the importance of such institutional dynamics in shaping the framing uncertainties and policy responses to scientific knowledge about them.     

River discharge to the Baltic Sea in a future climate

This study reports on new projections of discharge to the Baltic Sea given possible realisations of future climate and uncertainties regarding these projections. A high-resolution, pan-Baltic application of the Hydrological Predictions for the Environment (HYPE) model was used to make transient simulations of discharge to the Baltic Sea for a mini-ensemble of climate projections representing two high emissions scenarios. The biases in precipitation and temperature adherent to climate models were adjusted using a Distribution Based Scaling (DBS) approach. As well as the climate projection uncertainty, this study considers uncertainties in the bias-correction and hydrological modelling. While the results indicate that the cumulative discharge to the Baltic Sea for 2071 to 2100, as compared to 1971 to 2000, is likely to increase, the uncertainties quantified from the hydrological model and the bias-correction method show that even with a state-of-the-art methodology, the combined uncertainties from the climate model, bias-correction and impact model make it difficult to draw conclusions about the magnitude of change. It is therefore urged that as well as climate model and scenario uncertainty, the uncertainties in the bias-correction methodology and the impact model are also taken into account when conducting climate change impact studies.     

Learning about climate change and implications for near-term policy

Climate change is an issue of risk management. The most important causes for concern are not the median projections of future climate change, but the low-probability, high-consequence impacts. Because the policy question is one of sequential decision making under uncertainty, we need not decide today what to do in the future. We need only to decide what to do today, and future decisions can be revised as we learn more. In this study, we use a stochastic version of the DICE-99 model (Nordhaus WD, Boyer J (2000) Warming the world: economic models of global warming. MIT Press, Cambridge, MA, USA) to explore the effect of different rates of learning on the appropriate level of near-term policy. We show that the effect of learning depends strongly on whether one chooses efficiency (balancing costs and benefits) or cost-effectiveness (stabilizing at a given temperature change target) as the criterion for policy design. Then, we model endogenous learning by calculating posterior distributions of climate sensitivity from Bayesian updating, based on temperature changes that would be observed for a given true climate sensitivity and assumptions about errors, prior distributions, and the presence of additional uncertainties. We show that reducing uncertainty in climate uncertainty takes longer when there is also uncertainty in the rate of heat uptake by the ocean, unless additional observations are used, such as sea level rise.     

RECOGNISING THE UNCERTAINTY IN THE QUANTIFICATION OF THE EFFECTS OF CLIMATE CHANGE ON HYDROLOGICAL RESPONSE

A widely used method of evaluating effects of climate change on flow regime is to perturb the climate inputs to a rainfall–runoff model and examine the effect on a statistic of the modelled flows. Such studies require four elements: a method of perturbing the climate, a rainfall–runoff model, a study catchment and a flow index. In practice the direction and magnitude of the estimated effects depend on each of the four elements, leading to concern over the usefulness and generality of the results. To investigate these uncertainties two climate scenarios and eight climate sensitivity tests have been applied to three UK catchments using two conceptual rainfall–runoff models in order to quantify effects of climate change on three flow indices representing mean runoff, flood magnitudes and low flows. The sensitivity tests were found to be useful to assess the suitability of the models to simulate flows outside the conditions experienced in their calibration. Both models gave internally consistent results but, on close examination, one model was found inappropriate for this application. Results show that the effect of climate change on flow varies between catchments and that different flow response indices can change in opposite directions, e.g. floods increased in magnitude while low flows reduced. Contrasting results were obtained from the two climate scenarios.     

Assessing Climate Change Implications for Water Resources Planning

Numerous recent studies have shown that existing water supply systems are sensitive to climate change. One apparent implication is that water resources planning methods should be modified accordingly. Few of these studies, however, have attempted to account for either the chain of uncertainty in projecting water resources system vulnerability to climate change, or the adaptability of system operation resulting from existing planning strategies. Major uncertainties in water resources climate change assessments lie in a) climate modeling skill; b) errors in regional downscaling of climate model predictions; and c) uncertainties in future water demands. A simulation study was designed to provide insight into some aspects of these uncertainties. Specifically, the question that is addressed is whether a different decision would be made in a reservoir reallocation decision if knowledge about future climate were incorporated (i.e., would planning based on climate change information be justified?). The case study is possible reallocation of flood storage to conservation (municipal water supply) on the Green River, WA. We conclude that, for the case study, reservoir reallocation decisions and system performance would not differ significantly if climate change information were incorporated in the planning process.     

2C or not 2C?

Political attention has increasingly focused on limiting warming to 2 °C. However, there is no consensus on both questions “Is the 2 °C target achievable?” and “What should be done with this target that becomes increasingly difficult to achieve?”. This paper aims at disentangling the points of deep uncertainty underlying this absence on consensus. It first gives simple visualizations of the challenge posed by the 2 °C target and shows how key assumptions (on the points of deep uncertainty) influence the answer to the target achievability question. It then proposes an “uncertainties and decisions tree”, linking different beliefs on climate change, the achievability of different policies, and current international policy dynamics to various options to move forward on climate change.     

Climate-related uncertainties in projections of the twenty-first century terrestrial carbon budget: off-line model experiments using IPCC greenhouse-gas scenarios and AOGCM climate projections

A terrestrial ecosystem model (Sim-CYCLE) was driven by multiple climate projections to investigate uncertainties in predicting the interactions between global environmental change and the terrestrial carbon cycle. Sim-CYCLE has a spatial resolution of 0.5°, and mechanistically evaluates photosynthetic and respiratory CO₂ exchange. Six scenarios for atmospheric-CO₂ concentrations in the twenty-first century, proposed by the Intergovernmental Panel on Climate Change, were considered. For each scenario, climate projections by a coupled atmosphere–ocean general circulation model (AOGCM) were used to assess the uncertainty due to socio-economic predictions. Under a single CO₂ scenario, climate projections with seven AOGCMs were used to investigate the uncertainty stemming from uncertainty in the climate simulations. Increases in global photosynthesis and carbon storage differed considerably among scenarios, ranging from 23 to 37% and from 24 to 81 Pg C, respectively. Among the AOGCM projections, increases ranged from 26 to 33% and from 48 to 289 Pg C, respectively. There were regional heterogeneities in both climatic change and carbon budget response, and different carbon-cycle components often responded differently to a given environmental change. Photosynthetic CO₂ fixation was more sensitive to atmospheric CO₂, whereas soil carbon storage was more sensitive to temperature. Consequently, uncertainties in the CO₂ scenarios and climatic projections may create additional uncertainties in projecting atmospheric-CO₂ concentrations and climates through the interactive feedbacks between the atmosphere and the terrestrial ecosystem.     

Estimating the economic impacts of climate change on infectious diseases: a case study on dengue fever in Taiwan

Researchers of climate change have suggested that climate change and variability has a significant influence on the epidemiology of infectious diseases, particularly vector-borne diseases. The purpose of this study is to explore how climate conditions and the dengue fever epidemic in Taiwan are related and to estimate the economic impact of climate change on infectious diseases. To achieve these objectives, two different methods, one involving the Panel data model and the other the Contingent Valuation Method (CVM), are applied in this study. At first, we use the Panel data model to assess the relationship between climate conditions and the number of people infected by dengue fever during the period from January 2000 to February 2006 in 308 cities and townships in the Taiwan. The results of the empirical estimation indicate that climate conditions have an increasingly significant impact on the probability of people being infected by dengue fever. The probability of being infected by dengue fever due to climate change is then calculated and is found to range from 12% to 43% to 87% which represent low, mid, and high probabilities of infection caused by climate change when the temperature is increased by 1.8°C. The respondent’s willingness to pay (WTP) is also investigated in the survey using the single-bounded dichotomous choice (SBDC) approach, and the results show that people would pay NT$724, NT$3,223 and NT$5,114 per year in order to avoid the increased probabilities of 12%, 43%, and 87%, respectively, of their being infected with dengue fever.     

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.     

Agricultural risk management policies under climate uncertainty

Climate change is forecasted to increase the variability of weather conditions and the frequency of extreme events. Due to potential adverse impacts on crop yields it will have implications for demand of agricultural risk management instruments and farmers’ adaptation strategies. Evidence on climate change impacts on crop yield variability and estimates of production risk from farm surveys in Australia, Canada and Spain, are used to analyse the policy choice between three different types of insurance (individual, area-yield and weather index) and ex post payments. The results are found to be subject to strong uncertainties and depend on the risk profile of different farmers and locations; the paper provides several insights on how to analyse these complexities. In general, area yield performs best more often across our countries and scenarios, in particular for the baseline and marginal climate change (without increases in extreme events). However, area yield can be very expensive if farmers have limited information on how climate change affects yields (misalignment in expectations), and particularly so under extreme climate change scenarios. In these more challenging cases, ex post payments perform well to increase low incomes when the risk is systemic like in Australia; Weather index performs well to reduce the welfare costs of risks when the correlation between yields and index is increased by the extreme events. The paper also analyses the robustness of different instruments in the face of limited knowledge of the probabilities of different climate change scenarios; highlighting that this added layer of uncertainty could be overcome to provide sound policy advice under uncertainties introduced by climate change. The role of providing information to farmers on impacts of climate change emerges as a crucial result of this paper as indicated by the significantly higher budgetary expenditures occurring across all instruments when farmers’ expectations are misaligned relative to actual impacts of climate change.     

Institutional design propositions for the governance of adaptation to climate change in the water sector

This paper provides an evidence-based contribution to understanding processes of climate change adaptation in water governance systems in the Netherlands, Australia and South Africa. It builds upon the work of Ostrom on institutional design principles for local common pool resources systems. We argue that for dealing with complexities and uncertainties related to climate change impacts (e.g. increased frequency and intensity of floods or droughts) additional or adjusted institutional design propositions are necessary that facilitate learning processes. This is especially the case for dealing with complex, cross-boundary and large-scale resource systems, such as river basins and delta areas in the Netherlands and South Africa or groundwater systems in Western Australia. In this paper we provide empirical support for a set of eight refined and extended institutional design propositions for the governance of adaptation to climate change in the water sector. Together they capture structural, agency and learning dimensions of the adaptation challenge and they provide a strong initial framework to explore key institutional issues in the governance of adaptation to climate change. These institutional design propositions support a “management as learning” approach to dealing with complexity and uncertainty. They do not specify blueprints, but encourage adaptation tuned to the specific features of local geography, ecology, economies and cultures.     

Uncertainties linked to land-surface processes in climate change simulations

 The impact of climate change on the hydrology of continental surfaces is critical for human activities but the response of the surface to this perturbation may also affect the sensitivity of the climate. This complex feedback is simulated in general circulation models (GCMs) used for climate change predictions by their land-surface schemes. The present study attempts to quantify the uncertainty associated with these schemes and what impact it has on our confidence in the simulated climate anomalies. Four GCMs, each coupled to two different land-surface schemes, are used to explore the spectrum of uncertainties. It is shown that, in this sample, surface processes have a significant contribution to our ability to predict surface temperature changes and perturbations of the hydrological cycle in an environment with doubled greenhouse gas concentration. The results reveal that the uncertainty introduced by land-surface processes in the simulated climate is different from its impact on the sensitivity of GCMs to climate change, indeed an alteration of the surface parametrization with little impact on model climate can affect sensitivity significantly. This result leads us to believe that the validation of land-surface schemes should not be limited to the current climate but should also cover their sensitivity to variations in climatic forcing. Received: 24 June 1999 / Accepted: 20 April 2000     

Agreeing to disagree: uncertainty management in assessing climate change, impacts and responses by the IPCC

Dealing consistently with risk and uncertainty across the IPCC reports is a difficult challenge. Huge practical difficulties arise from the Panel’s scale and interdisciplinary context, the complexity of the climate change issue and its political context. The key question of this paper is if the observed differences in the handling of uncertainties by the three IPCC Working Groups can be clarified. To address this question, the paper reviews a few key issues on the foundations of uncertainty analysis, and summarizes the history of the treatment of uncertainty by the IPCC. One of the key findings is that there is reason to agree to disagree: the fundamental differences between the issues covered by the IPCC’s three interdisciplinary Working Groups, between the type of information available, and between the dominant paradigms of the practitioners, legitimately lead to different approaches. We argue that properly using the IPCC’s Guidance Notes for Lead Authors for addressing uncertainty, adding a pedigree analysis for key findings, and particularly communicating the diverse nature of uncertainty to the users of the assessment would increase the quality of the assessment. This approach would provide information about the nature of the uncertainties in addition to their magnitude and the confidence assessors have in their findings.     

Future change of extreme temperature climate indices over East Asia with uncertainties estimation in the CMIP5

How well the climate models simulate extreme temperature over East Asia and how the extreme indices would change under anthropogenic global warming are investigated. The indices studied include hot days (HD), tropical nights (TN), growing degree days (GDD), and cooling degree days (CDD) in summer and heating degree days (HDD) and frost days (FD) in winter. The representative concentration pathway 4.5 (RCP 4.5) experiments for the period of 2075–2099 are compared with historical simulations for the period of 1979–2005 from 15 coupled models that are participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). To optimally estimate future change and its uncertainty, groups of best models are selected based on Taylor diagrams, relative entropy, and probability density function (PDF) methods previously suggested. Overall, the best models’ multi-model ensemble based on Taylor diagrams has the lowest errors in reproducing temperature extremes in the present climate among three methods. Selected best models in three methods tend to project considerably different changes in the extreme indices from each other, indicating that the selection of reliable models are of critical importance to reduce uncertainties. Three groups of best models show significant increase of summerbased indices but decrease of the winter-based indices. Over East Asia, the most significant increase is seen in the HD (336 ± 23.4% of current climate) and the most significant decrease is appeared in the HDD (82 ± 4.2%). It is suggested that the larger future change in the HD is found over in the Southeastern China region, probably due to a higher local maximum temperature in the present climate. All of the indices show the largest uncertainty over Southeastern China, particularly in the TN (~3.9 times as large as uncertainty over East Asia) and in the HD (~2.4). It is further noted that the TN reveals the largest uncertainty over three East Asian countries (~1.7 and 1.4 over Korea and Japan, respectively). These future changes in extreme temperature events have an important implication for energy-saving applications and human molarity in the future.     

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.     

Probabilistic evaluation of climate change impacts on paddy rice productivity in Japan

Projecting the impacts of climate change includes various uncertainties from physical, biophysical, and socioeconomic processes. Providing a more comprehensive impact projection that better represents the uncertainties is a priority research issue. We used an ensemble-based projection approach that accounts for the uncertainties in climate projections associated with general circulation models (GCMs) and biophysical and empirical parameter values in a crop model. We applied the approach to address the paddy rice yield change in Japan in the 2050s (2046–2065) and 2090s (2081–2100) relative to the 1990s (1981–2000). Seventeen climate projections, nine (eight) climate projections performed by seven (six) GCMs conditional on the Special Report on Emission Scenarios (SRES) A1B (A2), were included in this projection. In addition, 50 sets of biophysical and empirical parameter values of a large-scale process-based crop model for irrigated paddy rice were included to represent the uncertainties of crop parameter values. The planting windows, cultivation practices, and crop cultivars in the future were assumed to be the same as the level in the baseline period (1990s). The resulting probability density functions conditioned on SRES A1B and A2 indicate projected median yield changes of +?17.2% and +?26.9% in Hokkaido, the northern part of Japan, in the 2050s and 2090s with 90% probability intervals of (??5.2%, +?40.3%) and (+?6.3%, +?51.2%), relative to the 1990s mean yield, respectively. The corresponding values in Aichi, on the Pacific side of Western Japan, are 2.2% and ??0.8%, with 90% probability intervals of (??15.0%, +?14.9%) and (??33.4%, +?17.9%), respectively. We also provided geographical maps of the probability that the future 20-year mean yield will decrease and that the future standard deviation of yield for 20 years will increase. Finally, we investigated the relative contributions of the climate projection and crop parameter values to the uncertainty in projecting yield change in the 2090s. The choice of GCM yielded a relatively larger spread of projected yield changes than that of the other factors. The choice of crop parameter values could be more important than that of GCM in a specific prefecture.     

Accounting for uncertainties and time preference in economic analysis of tackling climate change through forestry and selected policy implications for Scotland and Ukraine

The paper discusses the development of economic techniques for dealing with uncertainties in economic analysis of planting trees to mitigate climatic change. In consideration of uncertainty, time preference and intergenerational equity, the traditional cost-benefit analysis framework is challenged with regard to the discounting/non-discounting of carbon uptake benefits, and because it usually uses a constant and positive discount rate. We investigate the influence of various discounting protocols on the outputs of economic analysis. The idea of using the declining discount rate is also considered. Several numerical examples dealing with the analysis of afforestation for carbon sequestration in Scotland and Ukraine are provided. We show that the choice of discounting protocols have a considerable influence on the results of economic analysis, and therefore, on the decision-making processes related to climate change mitigation strategies. The paper concludes with some innovative insights on accounting for uncertainties and time preference in tackling climate change through forestry, several climate policy implications of dealing with uncertainties, and a brief discussion of what the use of different discounting protocols might imply for decision making.