Climate Change, Weather Insurance Design and Hedging Effectiveness

The insurance industry has so far relied on historical data to develop and price weather insurance contracts. In light of climate change, we examine the effects of this practice in terms of the hedging effectiveness and profitability of insurance contracts. We use simulated crop and weather data for today’s and future climatic conditions to derive optimal weather insurance contracts. We assess the hedging effectiveness and profits of adjusted contracts that are designed with data that accounts for the changing distribution of weather and yields due to climate change. We find that, with climate change, the benefits from hedging with adjusted contracts almost triple and expected profits increase by about 240%. Furthermore, we investigate the effect on risk reduction (for the insured) and profits (for the insurer) from hedging future weather risks with non-adjusted contracts, which are based on historical weather and yield data. When offering non-adjusted insurance contracts, we find that insurers either face substantial losses, or generate profits that are significantly smaller than profits from offering adjusted insurance products. Non-adjusted insurance contracts that create profits in excess of the profits from adjusted contracts cause at the same time negative hedging benefits for the insured. We observe that non-adjusted contracts exist that create simultaneously positive profits and hedging benefits, however at a much larger uncertainty compared to the corresponding adjusted contracts.weather insurance design, climate change, non-stationarity, hedging effectiveness

Berechnung der Bewässerungsbedürfnisse unter aktuellen und zukünftigen Bedingungen in der Schweiz

<p>With climate change, rising temperatures and decreasing summer precipitation are likely to induce increasing water demands for irrigation in the future. Although Switzerland as a whole is a country with abundant water supplies, temporary regional water shortages are already occurring under current climatic conditions. Hydrological projections suggest that the availability of water from rivers for irrigation will decrease even further as climate change progresses. Information on expected changes in irrigation needs is of great importance for the long-term planning of irrigation infrastructure projects.</p><p>Against this background, this paper utilizes the FAO method for estimating crop-specific irrigation needs and applies it on the basis of climate projection data until the end of the century for potato, carrot, sweetcorn, apple, lettuce, strawberry, clover, ryegrass and grazing crops at selected sites on the Swiss Central Plateau (Aigle, Basel, Bern, Changins, Güttingen, Payerne, Reckenholz) and for different soil types (sand, sandy loam, loam, silt, silty clay loam, silty loam, loamy sand, silty clay, clay).</p><p>The projected changes are analysed by emission scenario, site, and projection horizon. The changes for a field crop (potatoes), a vegetable crop with multiple harvests (lettuce) and ryegrass are discussed as examples. The results for all other crops are given in the Appendix.</p><p>Estimates of the changes in irrigation-water needs differ substantially, depending on the assumptions regarding fu-ture global emissions of greenhouse gases (Emission path RCP2.6: Effective climate-change mitigation measures gain traction and lead to a reduction in CO2 concentration in the atmosphere to around 400 ppm by the end of the century; Emission path RCP8.5: Without climate-protection measures, the CO2 content in the atmosphere rises con-tinuously to almost 1000 ppm by the year 2100): with RCP2.6, a slight increase in irrigation for all crops is projected up to the middle of the century, followed by a slight decrease; with RCP8.5, irrigation needs for all crops rise steadily up to the end of the century.</p><p>The percentage changes in irrigation needs differ only slightly between the sites on the Central Plateau and according to soil type. In addition, only minor differences in terms of the change signals were noted between the crop types studied with regard to the reference timeframe of 1981–2010: by the time horizon of 2045−2074, RCP8.5 projections (i.e. without climate-change mitigation) suggest an average increase in irrigation needs of 26% for potatoes and lettuce and 28% for ryegrass. According to this emission pathway, increases of 48% (potatoes), 49% (lettuce) and 55% (ryegrass) would be expected by the end of the century (2070−2099).</p><p>The different seasonal changes in temperature and precipitation levels also have a varying impact on the additional irrigation needs according to growing season. Major changes in irrigation levels over the century are to be expected in summer and autumn, regardless of site and crop. Minimal changes in irrigation are to be expected in spring. With all these estimates, however, the climate-projection uncertainties that continue to increase along with the advancing time horizon must be taken into account.</p><p>The method operationalised here provides a basis for estimating the irrigation needs of different crops on the Swiss Central Plateau under current and future climatic conditions, depending on important soil parameters. Reviews of the model parameters and validations of estimated values for different climate regions, soils and crops are of crucial importance for the future, in order to reduce existing model insecurities to the greatest extent possible.</p&gt

Bringing diverse knowledge sources together:a meta-model for supporting integrated catchment management

Integrated catchment management (ICM), as promoted by recent legislation such as the European Water Framework Directive, presents difficult challenges to planners and decision-makers. To support decision-making in the face of high complexity and uncertainty, tools are required that can integrate the evidence base required to evaluate alternative management scenarios and promote communication and social learning. In this paper we present a pragmatic approach for developing an integrated decision-support tool, where the available sources of information are very diverse and a tight model coupling is not possible. In the first instance, a loosely coupled model is developed which includes numerical sub-models and knowledge-based sub-models. However, such a model is not easy for decision-makers and stakeholders to operate without modelling skills. Therefore, we derive from it a meta-model based on a Bayesian Network approach which is a decision-support tool tailored to the needs of the decision-makers and is fast and easy to operate. The meta-model can be derived at different levels of detail and complexity according to the requirements of the decision-makers. In our case, the meta-model was designed for high-level decisionmakers to explore conflicts and synergies between management actions at the catchment scale. As prediction uncertainties are propagated and explicitly represented in the model outcomes, important knowledge gaps can be identified and an evidence base for robust decision-making is provided. The framework seeks to promote the development of modelling tools that can support ICM both by providing an integrated scientific evidence base and by facilitating communication and learning processes

Challenges in developing an integrated catchment management model

Directives and policies increasingly call for more integrated management of land and water. Frameworks such as integrated catchment management may address these calls, and yet their implementation requires decisions to be taken under conditions of extreme complexity and uncertainty. This paper summarises a participatory framework through which decision-makers, experts and system modellers can collaboratively develop an integrated model to support such decisions. A feasibility study showed the potential of the framework. An operational version of the model, able to analyse the effects of 50 management options on 20 indicators, is estimated to require in the order of 225 man-months from system modellers, alongside substantial inputs from stakeholders. The significant technical challenges confronting such an exercise may be overshadowed by the institutional challenges, including the fundamental question of whether organisations are truly committed. However, the reward for overcoming such challenges is the opportunity to achieve genuine improvements in the social, economic and environmental quality of our catchments

Bayesian challenges in integrated catchment modelling

Bayesian Networks (BNs) are increasingly being used as decision support tools to aid the management of the complex and uncertain domains of natural systems. They are particularly useful for addressing problems of natural resource management by complex data analysis and incorporation of expert knowledge. BNs are useful for clearly articulating both the assumptions and evidence behind the understanding of a problem, and approaches for managing a problem. For example they can effectively articulate the cause effect relationships between human interventions and ecosystem functioning, which is a major difficulty faced by planners and environment managers. The flexible architecture and graphical representation make BNs attractive tools for integrated modelling. The robust statistical basis of BNs provides a mathematically coherent framework for model development, and explicitly represents the uncertainties in model predictions. However, there are also a number of challenges in their use. Examples include i) the need to express conditional probabilities in discrete form for analytical solution, which adds another layer of uncertainty; ii) belief updating in very large Bayesian networks; iii) difficulties associated with knowledge elicitation such as the range of questions to be answered by experts, especially for large networks; iv) the inability to incorporate feedback loops and v) inconsistency associated with incomplete training data. In this paper we discuss some of the key research problems associated with the use of BNs as decision-support tools for environmental management. We provide some real-life examples from a current project (Macro Ecological Model) dealing with the development of a BN-based decision support tool for Integrated Catchment Management to illustrate these challenges. We also discuss the pros and cons of some existing solutions. For example, belief updating in very large BNs cannot be effectively addressed by exact methods (NP hard problem), therefore approximate inference schemes may often be the only computationally feasible alternative. We will also discuss the discretisation problem for continuous variables, solutions to the problem of missing data, and the implementation of a knowledge elicitation framework

Bayesian networks for a multi-objective evaluation of River Basin Management Plans

The European Water Framework Directive (WFD) sets out an integrated perspective to water management in river catchments and river basin districts and is a key driver in the movement towards Integrated River Basin Management. Integrated river basin management must deliver objectives related to the WFD in the wider context of various other stakeholder interests, for example related to flooding, water resources, employment and cost. In managing such complex systems, a specific objective can be achieved through different management actions. Likewise, a specific management action can have implications for multiple objectives. Synergies or conflicts between specific objectives and between specific actions are likely to occur, and need careful consideration in order to increase the efficiency of planned management actions. However, such integrated decision making is a very difficult and highly complex task, which cannot easily be accomplished by either single or groups of planners. Integrated modelling tools to facilitate and enhance communication within a group of decision-makers and inform a more objective and evidence-based multi-criteria decision-making process are required. The scope for the development of such an integrated tool is being tested by the Catchment Science Centre (CSC) at The University of Sheffield. The CSC and the Environment Agency are jointly developing a tool termed the Macro-Ecological Model (MEM). The MEM is developed as a consistent framework for the integration of knowledge and information about environmental, social and economic processes and process-interactions that are affected by management actions and have impacts on multiple management objectives. The MEM enables knowledge from various different resources to be integrated, including empirical data, model results and even expert knowledge using a Bayesian Belief Network (BBN) approach. BBNs have the advantage of representing system understanding in an intuitive, graphical format. Furthermore, the approach provides the ability to explicitly account for uncertainties in model predictions. Therefore, the model framework provides a good tool for visualising system understanding and communicating uncertainties. Applied in a participatory process, it can support robust decision making in river basin management. The conceptual model framework is illustrated with examples from the prototyping study. The prototype model captures the process interactions affecting the management objectives "Ecological Status" (composed of both Biological Quality and Physico-chemical Quality) and "Flood Risk". It is planned to be later extended to incorporate further environmental, and also social and economic objectives

A consistent framework for knowledge integration to support Integrated Catchment Management

The European Water Framework Directive (WFD) sets out an integrated perspective to water management in river catchments and river basin districts and is a key driver in the movement towards Integrated River Basin Management. Integrated river basin management must deliver objectives related to the WFD in the wider context of various other stakeholder interests, for example related to flooding, water resources, employment and cost. In managing such complex systems, a specific objective can be achieved through different management actions. Likewise, a specific management action can have implications for multiple objectives. Synergies or conflicts between specific objectives and between specific actions are likely to occur, and need careful consideration in order to increase the efficiency of planned management actions. However, such integrated decision making is a very difficult and highly complex task, which cannot easily be accomplished by either single or groups of planners. Integrated modelling tools to facilitate and enhance communication within a group of decision-makers and inform a more objective and evidence-based multi-criteria decision-making process are required. The scope for the development of such an integrated tool is being tested by the Catchment Science Centre (CSC) at The University of Sheffield. The CSC and the Environment Agency are jointly developing a tool termed the Macro-Ecological Model (MEM). The MEM is developed as a consistent framework for the integration of knowledge and information about environmental, social and economic processes and process-interactions that are affected by management actions and have impacts on multiple management objectives. The MEM enables knowledge from various different resources to be integrated, including empirical data, model results and even expert knowledge using a Bayesian Belief Network (BBN) approach. BBNs have the advantage of representing system understanding in an intuitive, graphical format. Furthermore, the approach provides the ability to explicitly account for uncertainties in model predictions. Therefore, the model framework provides a good tool for visualising system understanding and communicating uncertainties. Applied in a participatory process, it can support robust decision making in river basin management. The conceptual model framework is illustrated with examples from the prototyping study. The prototype model captures the process interactions affecting the management objectives “Ecological Status” (composed of both Biological Quality and Physico-chemical Quality) and “Flood Risk”. It is planned to be later extended to incorporate further environmental, and also social and economic objectives