- Email: [email protected]
Editorial: Climate Change and Variability, Uncertainty and Decision Making
Journal of Environmental Management (1997) 49, 1–6
Editorial: Climate Change and Variability, Uncertainty and DecisionMaking Greg Paoli∗ and Brad Bass† ∗Institute for Risk Research, University of Waterloo, Waterloo, Ontario, U.S.A. and †Environmental Adaptation Research Group, Atmospheric Environment Service, Downsview, Ontario, U.S.A.
An evolving and important trend in the provision of scientific advice is the explicit statement of uncertainty. When confronted with uncertainty, policy makers often tend to ignore it. Instead, they may consider decision parameters to be single-valued and deterministic or solicit expert opinion and trust it implicitly as the best estimate. The uncertainty, which is fundamental to the problem, is often suppressed as an annoying and confusing aspect. The inherent variability of the weather at different spatial and temporal scales ensures that there will always be unpredictable, extreme weather events. These deviations from what might locally be called normal weather can be quite pleasant, such as an abnormally warm winter day, or disastrous. Recent disasters associated with extreme atmospheric events have placed extreme weather events in the fore of the world’s policy arenas. Decison-makers in all economic and political sectors are wondering if there are identifiable trends in these extreme weather events. Decision areas that rely upon weather variables include: • • • • • • • • • • • • • • • •
water resource management telecommunication and energy distribution structural engineering construction project management emergency response planning crop insurance general insurance recreation area siting shipping and transportation energy demand forecasting offshore oil drilling nuclear installation operation forest fire prevention commodities market analysis air and water quality fisheries management
0301–4797/97/010001+06 $25.00/0/ev960111
1997 Academic Press Limited
2
Editorial
as well as a wide variety of individual business and personal decisions. To a large degree, current scientific opinion points towards associating the recent barrage of extreme climate events with global climate change caused by anthropogenic greenhouse gas (GHG) emissions. In order to determine the risk of climate change, it is useful to clearly define it. The atmosphere and oceans are a dynamic, coupled system subject to certain boundary and initial conditions. Climate is the averaged state of the weather and variability. If climate does not change, then the frequency of a particular set of conditions in the atmosphere and ocean should be stable in the long term. Climate change reflects a change in this mean state and/or the variability of variables such as precipitation and temperature. The prediction of these changes and their impacts are difficult due to the uncertainties associated with generating future climate scenarios. While not entirely convincing to all, a consensus exists that significant study of these uncertainties is warranted, considering the consequences. The incorporation of uncertainty into decisions is a fundamental aspect in risk analysis. The official Canadian standard, Risk Analysis Requirements and Guidelines, by the Canadian Standards Association states that: ‘‘Risk estimates shall be expressed in understandable terms, the strengths and limitations of different risk measures used should be explained, and the uncertainties surrounding estimates of risk should be set out in straightforward language.’’ (CAN/CSA-Q634-91, Clause 5.5) The treatment of uncertainty can be time-consuming and difficult, but the decision may have attributes which make a systematic treatment of uncertainty worthwhile and necessary (Morgan and Henrion, 1990). (1) The loss function is highly asymmetric in an uncertain quantity. Equal deviations from the mean in either direction produce entirely different results to different stakeholders. (2) An important, uncertain qantity has a highly asymmetric distribution. Meteorological variables often have asymmetric distributions. The quantity of the asymmetry imparts greater sensitivity to uncertain quantities. (3) Thorough examination of the uncertainty may change the ‘‘best estimate’’. In the case of asymmetric distributions, there can be a great difference between the mean and median values, and an analysis of the uncertainty may reveal which statistic is more appropriate. (4) Consideration of uncertainties can be a guide for model refinement. Considering a decision’s sensitivity to uncertain parameters can bring about a refinement in a model which is used to guide the decision. For example, expressing the parameters as fuzzy or interval numbers allows the decision-maker to assess the impact of the uncertainty in meeting the various objectives. (5) Consideration of uncertainties provides a standard with which to assess different analyses. There are a variety of tools available to analyse the results of any decision. A careful treatment of uncertainty allows the decision-maker to weigh the evidence accordingly. (6) Policy analysts have a responsibility to be clear about the limitations of their analyses. Some adaption strategies may involve huge capital outlays of public money and concern human, wildlife and environmental safety. Experts may be tempted to provide pronouncements with no uncertainty, but this is unrealistic, unethical and irresponsible.
G. Paoli and B. Bass
3
In February of 1994, the Water Resources and Marine Adaptation Division of Environment Canada (since April 1994 these activities have been transferred to the Environmental Adaptation Research Group of the Atmospheric Environment Service, Environment Canada), the Institute for Risk Research (IRR) at the University of Waterloo, and the International Geosphere-Biosphere Programme—Biospheric Aspects of the Hydrological Cycle (IGBP-BAHC) organized a two-day workshop to explore new techniques for estimating the frequency of weather extremes, with special attention being given to incorporating the uncertainty associated with climate change. The workshop was also supported by the Water Issues Branch of Environment Canada, the Canadian Global Change Program, the Canadian National Committee of the International Decade for Natural Disaster Reduction and Emergency Preparedness Canada. The goal of the workshop was to bring two distinct scientific communities—the BAHC community, specifically BAHC-Focus 4 or the Weather Generator Project, and a community applying methods of decision analysis to account for uncertainty—together with Canadian government policy makers and scientists from both government and academia. While similarities were evident among the participants, there were also quite divergent views of the usefulness of climate change information in its present, highly uncertain, form. To some degree, the divergence can be explained by the scope of each participant’s decision area. For government policy, climate change information, even in rudimentary form, is required in determining mitigation and adaptation strategies. For those who must plan and implement climate-sensitive systems—buildings, reservoirs, farms, waste management—the current level of climate change uncertainty is probably too large to be useful. Nevertheless, climate variability and uncertainty must be included in their models and designs, specifically in regard to extreme events. BAHC scientists are investigating the connections between the biota, at different scales, and the hydrologic cycle in regards to climate change. Uncertainty in the meteorological inputs to simulation models is used to evaluate the model sensitivity to variety of climatic data sets. The two communities, BAHC-Focus 4 and decision analysis, address different aspects of the uncertainty associated with climate change. BAHC-Focus 4 is primarily concerned with the spatial uncertainties associated with general circulation models (GCMs) when modelling ecological and hydrological processes. The output of a GCM is in the order of 300 km, meaning that each value represents a grid cell of 300 km×300 km. Sampling theory suggests that the minimum resolution of a climate signal is 600 km. Using this output to model processes or make decisions at a daily time step, at scales below 100 km, adds a tremendous amount of uncertainty. In addition, several of the surface variables are not considered reliable (see Russo and Zack, this volume). In order to address this problem, BAHC-Focus 4 is using a wide variety of approaches to improve the representation of surface weather variables at what are considered sub-grid scale resolutions in the GCM models. In this volume, the papers of Ba´rdossy, Russo and Zack, and Guenni offer three different aspects of ‘‘downscaling’’ GCM output, and other climate data sets at large resolutions, to resolutions of 10 and even 1 km. Ba´rdossy (‘‘Downscaling from GCMs to Local Climate through Stochastic Linkages’’) links GCM pressure anomalies in the free atmosphere to local precipitation. This downscaling method includes a fuzzy rule-based technique for classifying pressure anomalies into circulation patterns and a multivariate stochastic model linking circulation patterns to daily precipitation in small catchments. This linkage is based on
4
Editorial
the historical statistical correspondence between circulation patterns and precipitation which is assumed to be stable under climate change. Changes in precipitation under climate change are assumed to be driven by changes in the frequency of circulation patterns. If this assumption is valid, and the frequency of circulation patterns is well described by GCM simulations, the methodology can be expected to give useful precipitation scenarios for present and future climates. Russo and Zack (‘‘Downscaling GCM Output with a Mesoscale Model’’) use a dynamic, physically-based, model that simulates weather at scales between 100 and 1 km. The method uses the GCM output of the free atmosphere, which is considered to be more reliable than the surface output at spatial scales of hundreds of kilometres, as input to the mesoscale model. The mesoscale model accounts for local geographic features and the physical processes important in determining the weather. The paper provides examples of the mesoscale output for the Great Lakes Basin, and the authors describe how such a model might be used to generate explanatory mechanisms for extreme weather events. This type of modelling is useful in generating regional scenarios, based on prevailing climate patterns and future scenarios. Guenni (‘‘Spatial Interpolation of Stochastic Weather Parameters’) describes an approach to spatial interpolation for providing weather model data at locations without historical records. The interpolated parameters are not meteorological observations. Instead a stochastic model for rainfall is generated at a point with meteorological observations. The stochastic parameters are represented as coefficients of annual periodic functions to account for seasonality. These parameters are considered to be regional variables, which can be interpolated to locations between stations. In this way, a simulated weather record can be generated at any point using interpolated parameters. The next six papers are representative of different engineering approaches to managing the decision process. Of these, the first three papers—Lind, Hobbs, and Caselton and Luo—are concerned with the provision and end use of probabilistic assessments. These papers are written from the perspective of statistical and decision analysis. The next three papers offer three different modelling approaches covering mathematical programming, simulation modelling and grey system theory. Bass, Xia and Huang offer an interval analysis approach to the prediction of weather variables under uncertainty. Bass and Huang use an interval parameter hop, skip and jump approach to incorporate climatic uncertainty into decision making. Venema et al. use a combination of simulation and dynamic optimization to evaluate different food production strategies in the Senegal River Basin under climate change. Lind (‘‘Three Informational–Theoretical Methods to Estimate a Random Variable’’) focuses on the problem of choosing a distribution when the underlying process is poorly understood and the data base is very weak. The choice of distribution may be critical in cases where extreme event probabilities are important in facility design. Lind uses an information–theoretical approach to producing distributions which satisfy the sample rule, a constraint that is particularly relevant where data are sparse. The information–theoretical approach provides a more objective probability distribution for sparse data sets which require more effort than the simple fitting of a distribution of common form. Hobbs (‘‘Bayesian Methods for Analysing Climate Change and Water Resource Uncertainties’’) recommends the use of Bayesian analysis to manage the decisionmaking process in the face of the inherent uncertainty in water resource planning and the additional uncertainty of climate change. Bayesian analysis provides a nearly complete decision-making package which is strongly grounded in probability theory.
G. Paoli and B. Bass
5
It provides a means for combining expert knowledge with empirical observations, measuring the sensitivity of decisions, measuring the cost of uncertainty, and choosing among alternatives to minimize the expected loss. Hobbs provides an overview of Bayesian analysis and several examples, while addressing some of the criticisms of Bayesian analysis. One such criticism is described in the following paper. Caselton and Luo (‘‘Using Dempster–Shafer Theory to Represent Climate Change Uncertainties’’) advocate an alternate statistical inference mechanism known as Dempster–Shafer (D–S) Theory. D–S Theory is widely used in artificial intelligence representations of decision-making. Similar to Bayesian analysis, it also grounded in probability theory, but the authors suggest that it is a more general inference approach of which Bayes theorem is a subset. D–S Theory is applied in situations involving very sparse knowledge or near-ignorance. The D–S inference scheme does not provide any more information than can be correctly inferred from the data. This possible indeterminacy may present difficulties for decisions regarding immediate actions, but D–S Theory does inform the decision-maker of the magnitude and effect of uncertainty. This paper provides insight into the nature of knowledge representation and decision making under climatic events. Bass, Xia and Huang (‘‘Incorporation of Differentiated Prediction Approach and Interval Analysis for the Prediction of Weather Variables under Uncertainty’’) present an empirical approach to the prediction of meteorological variables. The method combines a differential prediction model with uncertainty represented as an interval or a grey number. A differentiated prediction model (DPM) is based on the mathematics of differential equations. It is used in applications that may also be amenable to prediction with time series, but DPMs rely on fewer data requirements and assumptions. An interval representation of uncertainty is simpler than representations based on probability or fuzzy sets. A case study is presented for the prediction of monthly temperature and precipitation in Wuhan, China. Bass and Huang (‘‘Incorporating Climate Change into Risk Assessment: Quantifying Climatic Uncertainty in Grey Programming Models’’) use an interval representation of various uncertainties to evaluate agricultural and forestry expansion in the Mackenzie River Basin under a climate change scenario. The particular mathematical programming model is an interval parameter hop, skip and jump model. The approach is generalized to a multiobjective grey programming model. This paper links with BAHC-Focus 4 in that Bass and Huang illustrate how the interval uncertainty that is used in the Weather Generator Project can be explicitly included in the constraint equations when the model is applied to decisions that are required on a much shorter time step. Venema, Schiller and Adamowski (‘‘A Water Resources Planning Response to Climate Change in the Senegal River Basin’’) compare a state-imposed rice production scheme, based on large plantations to an alternative based on village scale irrigation and agro-forestry. A time series decomposition of stream flow in the Senegal River is used to illustrate that the assumption of stationarity, used in the rice-production scheme, may not be a valid for policy in the Senegal River Basin. A simulation model is used to compare three alternative hydrological scenarios and principles of dynamic programming are applied to reservoir management to optimize the allocation of water under each scenario. This paper provides a clear example of how a variety of approaches can be combined to evaluate an actual policy alternatives under climate change. During the workshop, the recurrent issues all dealt with mismatches of varying type and scope. One mismatch is between the past few years of extreme weather and the climate based on previous 30 or 40 years. Are we seeing a signal of climate change? A
6
Editorial
second mismatch is between the reliability of any GCM climate change scenario and the needs of decision-makers. The level of uncertainty which is warranted with respect to climate change is much larger than the acceptable level of uncertainty in most climatically-sensitive decisions. Of a related nature is the mismatch in scales between GCM output and the scale of many current decisions. This collection of papers may provide some insight in how to cope with these mismatches both in scientific applications of the International Geosphere-Biosphere Programme and in the policy questions that governments will face in the next century. We would like to thank the various sponsors of the workshop, but several individuals helped make this project a reality: Paul Louie, the chief of the Water Resources and Marine Adaptation Division, Hans Bolle and Ephrat Lahmer-Naim, formerly of the BAHC Core Project Office who provided ongoing support and arranged for funding at crucial points during the organization of the workshop, Pavel Kabat, the chair of BAHC who encouraged us to pursue this special issue of the Journal of Environmental Management, the contributors to this issue, and in particular Ben Hobbes, without whose urging this special issue would not have been possible.
References Morgan, M. G. and Henrion, M. (1990). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. New York: Cambridge University Press.
Editorial: Climate Change and Variability, Uncertainty and DecisionMaking Greg Paoli∗ and Brad Bass† ∗Institute for Risk Research, University of Waterloo, Waterloo, Ontario, U.S.A. and †Environmental Adaptation Research Group, Atmospheric Environment Service, Downsview, Ontario, U.S.A.
An evolving and important trend in the provision of scientific advice is the explicit statement of uncertainty. When confronted with uncertainty, policy makers often tend to ignore it. Instead, they may consider decision parameters to be single-valued and deterministic or solicit expert opinion and trust it implicitly as the best estimate. The uncertainty, which is fundamental to the problem, is often suppressed as an annoying and confusing aspect. The inherent variability of the weather at different spatial and temporal scales ensures that there will always be unpredictable, extreme weather events. These deviations from what might locally be called normal weather can be quite pleasant, such as an abnormally warm winter day, or disastrous. Recent disasters associated with extreme atmospheric events have placed extreme weather events in the fore of the world’s policy arenas. Decison-makers in all economic and political sectors are wondering if there are identifiable trends in these extreme weather events. Decision areas that rely upon weather variables include: • • • • • • • • • • • • • • • •
water resource management telecommunication and energy distribution structural engineering construction project management emergency response planning crop insurance general insurance recreation area siting shipping and transportation energy demand forecasting offshore oil drilling nuclear installation operation forest fire prevention commodities market analysis air and water quality fisheries management
0301–4797/97/010001+06 $25.00/0/ev960111
1997 Academic Press Limited
2
Editorial
as well as a wide variety of individual business and personal decisions. To a large degree, current scientific opinion points towards associating the recent barrage of extreme climate events with global climate change caused by anthropogenic greenhouse gas (GHG) emissions. In order to determine the risk of climate change, it is useful to clearly define it. The atmosphere and oceans are a dynamic, coupled system subject to certain boundary and initial conditions. Climate is the averaged state of the weather and variability. If climate does not change, then the frequency of a particular set of conditions in the atmosphere and ocean should be stable in the long term. Climate change reflects a change in this mean state and/or the variability of variables such as precipitation and temperature. The prediction of these changes and their impacts are difficult due to the uncertainties associated with generating future climate scenarios. While not entirely convincing to all, a consensus exists that significant study of these uncertainties is warranted, considering the consequences. The incorporation of uncertainty into decisions is a fundamental aspect in risk analysis. The official Canadian standard, Risk Analysis Requirements and Guidelines, by the Canadian Standards Association states that: ‘‘Risk estimates shall be expressed in understandable terms, the strengths and limitations of different risk measures used should be explained, and the uncertainties surrounding estimates of risk should be set out in straightforward language.’’ (CAN/CSA-Q634-91, Clause 5.5) The treatment of uncertainty can be time-consuming and difficult, but the decision may have attributes which make a systematic treatment of uncertainty worthwhile and necessary (Morgan and Henrion, 1990). (1) The loss function is highly asymmetric in an uncertain quantity. Equal deviations from the mean in either direction produce entirely different results to different stakeholders. (2) An important, uncertain qantity has a highly asymmetric distribution. Meteorological variables often have asymmetric distributions. The quantity of the asymmetry imparts greater sensitivity to uncertain quantities. (3) Thorough examination of the uncertainty may change the ‘‘best estimate’’. In the case of asymmetric distributions, there can be a great difference between the mean and median values, and an analysis of the uncertainty may reveal which statistic is more appropriate. (4) Consideration of uncertainties can be a guide for model refinement. Considering a decision’s sensitivity to uncertain parameters can bring about a refinement in a model which is used to guide the decision. For example, expressing the parameters as fuzzy or interval numbers allows the decision-maker to assess the impact of the uncertainty in meeting the various objectives. (5) Consideration of uncertainties provides a standard with which to assess different analyses. There are a variety of tools available to analyse the results of any decision. A careful treatment of uncertainty allows the decision-maker to weigh the evidence accordingly. (6) Policy analysts have a responsibility to be clear about the limitations of their analyses. Some adaption strategies may involve huge capital outlays of public money and concern human, wildlife and environmental safety. Experts may be tempted to provide pronouncements with no uncertainty, but this is unrealistic, unethical and irresponsible.
G. Paoli and B. Bass
3
In February of 1994, the Water Resources and Marine Adaptation Division of Environment Canada (since April 1994 these activities have been transferred to the Environmental Adaptation Research Group of the Atmospheric Environment Service, Environment Canada), the Institute for Risk Research (IRR) at the University of Waterloo, and the International Geosphere-Biosphere Programme—Biospheric Aspects of the Hydrological Cycle (IGBP-BAHC) organized a two-day workshop to explore new techniques for estimating the frequency of weather extremes, with special attention being given to incorporating the uncertainty associated with climate change. The workshop was also supported by the Water Issues Branch of Environment Canada, the Canadian Global Change Program, the Canadian National Committee of the International Decade for Natural Disaster Reduction and Emergency Preparedness Canada. The goal of the workshop was to bring two distinct scientific communities—the BAHC community, specifically BAHC-Focus 4 or the Weather Generator Project, and a community applying methods of decision analysis to account for uncertainty—together with Canadian government policy makers and scientists from both government and academia. While similarities were evident among the participants, there were also quite divergent views of the usefulness of climate change information in its present, highly uncertain, form. To some degree, the divergence can be explained by the scope of each participant’s decision area. For government policy, climate change information, even in rudimentary form, is required in determining mitigation and adaptation strategies. For those who must plan and implement climate-sensitive systems—buildings, reservoirs, farms, waste management—the current level of climate change uncertainty is probably too large to be useful. Nevertheless, climate variability and uncertainty must be included in their models and designs, specifically in regard to extreme events. BAHC scientists are investigating the connections between the biota, at different scales, and the hydrologic cycle in regards to climate change. Uncertainty in the meteorological inputs to simulation models is used to evaluate the model sensitivity to variety of climatic data sets. The two communities, BAHC-Focus 4 and decision analysis, address different aspects of the uncertainty associated with climate change. BAHC-Focus 4 is primarily concerned with the spatial uncertainties associated with general circulation models (GCMs) when modelling ecological and hydrological processes. The output of a GCM is in the order of 300 km, meaning that each value represents a grid cell of 300 km×300 km. Sampling theory suggests that the minimum resolution of a climate signal is 600 km. Using this output to model processes or make decisions at a daily time step, at scales below 100 km, adds a tremendous amount of uncertainty. In addition, several of the surface variables are not considered reliable (see Russo and Zack, this volume). In order to address this problem, BAHC-Focus 4 is using a wide variety of approaches to improve the representation of surface weather variables at what are considered sub-grid scale resolutions in the GCM models. In this volume, the papers of Ba´rdossy, Russo and Zack, and Guenni offer three different aspects of ‘‘downscaling’’ GCM output, and other climate data sets at large resolutions, to resolutions of 10 and even 1 km. Ba´rdossy (‘‘Downscaling from GCMs to Local Climate through Stochastic Linkages’’) links GCM pressure anomalies in the free atmosphere to local precipitation. This downscaling method includes a fuzzy rule-based technique for classifying pressure anomalies into circulation patterns and a multivariate stochastic model linking circulation patterns to daily precipitation in small catchments. This linkage is based on
4
Editorial
the historical statistical correspondence between circulation patterns and precipitation which is assumed to be stable under climate change. Changes in precipitation under climate change are assumed to be driven by changes in the frequency of circulation patterns. If this assumption is valid, and the frequency of circulation patterns is well described by GCM simulations, the methodology can be expected to give useful precipitation scenarios for present and future climates. Russo and Zack (‘‘Downscaling GCM Output with a Mesoscale Model’’) use a dynamic, physically-based, model that simulates weather at scales between 100 and 1 km. The method uses the GCM output of the free atmosphere, which is considered to be more reliable than the surface output at spatial scales of hundreds of kilometres, as input to the mesoscale model. The mesoscale model accounts for local geographic features and the physical processes important in determining the weather. The paper provides examples of the mesoscale output for the Great Lakes Basin, and the authors describe how such a model might be used to generate explanatory mechanisms for extreme weather events. This type of modelling is useful in generating regional scenarios, based on prevailing climate patterns and future scenarios. Guenni (‘‘Spatial Interpolation of Stochastic Weather Parameters’) describes an approach to spatial interpolation for providing weather model data at locations without historical records. The interpolated parameters are not meteorological observations. Instead a stochastic model for rainfall is generated at a point with meteorological observations. The stochastic parameters are represented as coefficients of annual periodic functions to account for seasonality. These parameters are considered to be regional variables, which can be interpolated to locations between stations. In this way, a simulated weather record can be generated at any point using interpolated parameters. The next six papers are representative of different engineering approaches to managing the decision process. Of these, the first three papers—Lind, Hobbs, and Caselton and Luo—are concerned with the provision and end use of probabilistic assessments. These papers are written from the perspective of statistical and decision analysis. The next three papers offer three different modelling approaches covering mathematical programming, simulation modelling and grey system theory. Bass, Xia and Huang offer an interval analysis approach to the prediction of weather variables under uncertainty. Bass and Huang use an interval parameter hop, skip and jump approach to incorporate climatic uncertainty into decision making. Venema et al. use a combination of simulation and dynamic optimization to evaluate different food production strategies in the Senegal River Basin under climate change. Lind (‘‘Three Informational–Theoretical Methods to Estimate a Random Variable’’) focuses on the problem of choosing a distribution when the underlying process is poorly understood and the data base is very weak. The choice of distribution may be critical in cases where extreme event probabilities are important in facility design. Lind uses an information–theoretical approach to producing distributions which satisfy the sample rule, a constraint that is particularly relevant where data are sparse. The information–theoretical approach provides a more objective probability distribution for sparse data sets which require more effort than the simple fitting of a distribution of common form. Hobbs (‘‘Bayesian Methods for Analysing Climate Change and Water Resource Uncertainties’’) recommends the use of Bayesian analysis to manage the decisionmaking process in the face of the inherent uncertainty in water resource planning and the additional uncertainty of climate change. Bayesian analysis provides a nearly complete decision-making package which is strongly grounded in probability theory.
G. Paoli and B. Bass
5
It provides a means for combining expert knowledge with empirical observations, measuring the sensitivity of decisions, measuring the cost of uncertainty, and choosing among alternatives to minimize the expected loss. Hobbs provides an overview of Bayesian analysis and several examples, while addressing some of the criticisms of Bayesian analysis. One such criticism is described in the following paper. Caselton and Luo (‘‘Using Dempster–Shafer Theory to Represent Climate Change Uncertainties’’) advocate an alternate statistical inference mechanism known as Dempster–Shafer (D–S) Theory. D–S Theory is widely used in artificial intelligence representations of decision-making. Similar to Bayesian analysis, it also grounded in probability theory, but the authors suggest that it is a more general inference approach of which Bayes theorem is a subset. D–S Theory is applied in situations involving very sparse knowledge or near-ignorance. The D–S inference scheme does not provide any more information than can be correctly inferred from the data. This possible indeterminacy may present difficulties for decisions regarding immediate actions, but D–S Theory does inform the decision-maker of the magnitude and effect of uncertainty. This paper provides insight into the nature of knowledge representation and decision making under climatic events. Bass, Xia and Huang (‘‘Incorporation of Differentiated Prediction Approach and Interval Analysis for the Prediction of Weather Variables under Uncertainty’’) present an empirical approach to the prediction of meteorological variables. The method combines a differential prediction model with uncertainty represented as an interval or a grey number. A differentiated prediction model (DPM) is based on the mathematics of differential equations. It is used in applications that may also be amenable to prediction with time series, but DPMs rely on fewer data requirements and assumptions. An interval representation of uncertainty is simpler than representations based on probability or fuzzy sets. A case study is presented for the prediction of monthly temperature and precipitation in Wuhan, China. Bass and Huang (‘‘Incorporating Climate Change into Risk Assessment: Quantifying Climatic Uncertainty in Grey Programming Models’’) use an interval representation of various uncertainties to evaluate agricultural and forestry expansion in the Mackenzie River Basin under a climate change scenario. The particular mathematical programming model is an interval parameter hop, skip and jump model. The approach is generalized to a multiobjective grey programming model. This paper links with BAHC-Focus 4 in that Bass and Huang illustrate how the interval uncertainty that is used in the Weather Generator Project can be explicitly included in the constraint equations when the model is applied to decisions that are required on a much shorter time step. Venema, Schiller and Adamowski (‘‘A Water Resources Planning Response to Climate Change in the Senegal River Basin’’) compare a state-imposed rice production scheme, based on large plantations to an alternative based on village scale irrigation and agro-forestry. A time series decomposition of stream flow in the Senegal River is used to illustrate that the assumption of stationarity, used in the rice-production scheme, may not be a valid for policy in the Senegal River Basin. A simulation model is used to compare three alternative hydrological scenarios and principles of dynamic programming are applied to reservoir management to optimize the allocation of water under each scenario. This paper provides a clear example of how a variety of approaches can be combined to evaluate an actual policy alternatives under climate change. During the workshop, the recurrent issues all dealt with mismatches of varying type and scope. One mismatch is between the past few years of extreme weather and the climate based on previous 30 or 40 years. Are we seeing a signal of climate change? A
6
Editorial
second mismatch is between the reliability of any GCM climate change scenario and the needs of decision-makers. The level of uncertainty which is warranted with respect to climate change is much larger than the acceptable level of uncertainty in most climatically-sensitive decisions. Of a related nature is the mismatch in scales between GCM output and the scale of many current decisions. This collection of papers may provide some insight in how to cope with these mismatches both in scientific applications of the International Geosphere-Biosphere Programme and in the policy questions that governments will face in the next century. We would like to thank the various sponsors of the workshop, but several individuals helped make this project a reality: Paul Louie, the chief of the Water Resources and Marine Adaptation Division, Hans Bolle and Ephrat Lahmer-Naim, formerly of the BAHC Core Project Office who provided ongoing support and arranged for funding at crucial points during the organization of the workshop, Pavel Kabat, the chair of BAHC who encouraged us to pursue this special issue of the Journal of Environmental Management, the contributors to this issue, and in particular Ben Hobbes, without whose urging this special issue would not have been possible.
References Morgan, M. G. and Henrion, M. (1990). Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. New York: Cambridge University Press.