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General enquiries on this form should be made to: Defra, Science Directorate, Management Support and Finance Team, Telephone No. 020 7238 1612 E-mail: [email protected] SID 5 Research Project Final Report Note In line with the Freedom of Information Act 2000, Defra aims to place the results of its completed research projects in the public domain wherever possible. The SID 5 (Research Project Final Report) is designed to capture the information on the results and outputs of Defra-funded research in a format that is easily publishable through the Defra website. A SID 5 must be completed for all projects. A SID 5A form must be completed where a project is paid on a monthly basis or against quarterly invoices. No SID 5A is required where payments are made at milestone points. When a SID 5A is required, no SID 5 form will be accepted without the accompanying SID 5A. This form is in Word format and the boxes may be expanded or reduced, as appropriate. ACCESS TO INFORMATION The information collected on this form will be stored electronically and may be sent to any part of Defra, or to individual researchers or organisations outside Defra for the purposes of reviewing the project. Defra may also disclose the information to any outside organisation acting as an agent authorised by Defra to process final research reports on its behalf. Defra intends to publish this form on its website, unless there are strong reasons not to, which fully comply with exemptions under the Environmental Information Regulations or the Freedom of Information Act 2000. Defra may be required to release information, including personal data and commercial information, on request under the Environmental Information Regulations or the Freedom of Information Act 2000. However, Defra will not permit any unwarranted breach of confidentiality or act in contravention of its obligations under the Data Protection Act 1998. Defra or its appointed agents may use the name, address or other details on your form to contact you in connection with occasional customer research aimed at improving the processes through which Defra works with its contractors. SID 5 (2/05) Project identification 1. Defra Project code 2. Project title CC0362 Development of a metamodel tool for regional integrated climate change management 3. Contractor organisation(s) Institute of Water and Environment, Cranfield University 54. Total Defra project costs 5. Project: Page 1 of 23 £ 316,416.36 start date ................ 01 September 2003 end date ................. 31 August 2005 6. It is Defra’s intention to publish this form. Please confirm your agreement to do so. ................................................................................... YES NO (a) When preparing SID 5s contractors should bear in mind that Defra intends that they be made public. They should be written in a clear and concise manner and represent a full account of the research project which someone not closely associated with the project can follow. Defra recognises that in a small minority of cases there may be information, such as intellectual property or commercially confidential data, used in or generated by the research project, which should not be disclosed. In these cases, such information should be detailed in a separate annex (not to be published) so that the SID 5 can be placed in the public domain. Where it is impossible to complete the Final Report without including references to any sensitive or confidential data, the information should be included and section (b) completed. NB: only in exceptional circumstances will Defra expect contractors to give a "No" answer. In all cases, reasons for withholding information must be fully in line with exemptions under the Environmental Information Regulations or the Freedom of Information Act 2000. (b) If you have answered NO, please explain why the Final report should not be released into public domain Executive Summary 7. The executive summary must not exceed 2 sides in total of A4 and should be understandable to the intelligent non-scientist. It should cover the main objectives, methods and findings of the research, together with any other significant events and options for new work. ABSTRACT There is widespread acceptance in the UK that the climate is changing due to human emissions of greenhouse gases. Such changes in climate will affect all sectors of society and the environment at all scales, ranging from the national to the local. It is also recognised that, whilst the climate changes, not everything else stands still, so that the socio-economic and political changes which influence the impact of climate change also need to be considered in a full assessment of the effects of our changing climate. The ‘Regional Climate Change Impact and Response Studies in East Anglia and North West England’ (RegIS) study developed and applied a methodology for stakeholder-led, regional Integrated Assessment (IA). Key needs for future stakeholder-led Integrated Assessments identified at a UKCIP-hosted stakeholder workshop at the end of RegIS were: Results from far greater numbers of scenario (climate and socio-economic) combinations; Better understanding of the effects of scenario uncertainty on the impacts; sensitivity analyses of the integrated models to be able to better understand the effects of multiple interactions on impacts. Greater degree of model linkage and interaction and more feedbacks and adaptive responses. However, most existing models for the sectors represented have unacceptably long runtimes for allowing rapid simulation and interactive engagement with the IA. It was from this that the two-year ‘Development of a metamodel tool for regional integrated climate change management’ (RegIS2) project was born. Its aim being to investigate and develop the use of computationally simpler modelling techniques, so called ‘metamodels’ or ‘reduced form models’, within a user friendly interface (the ‘Regional Impact Simulator’) to make the RegIS methodology available to the wider stakeholder community. The RegIS2 project has further built upon the ‘Drivers-Pressure-State-Impact-Response’ (DPSIR) framework which was used successfully in RegIS. By incorporating the widely used DPSIR framework within the RegIS2 Integrated Assessment Methodology, the methodology is transportable to other regions of the UK, thereby providing a framework for regional versions of the Regional Impact Simulator or indeed a national version. In addition, the two contrasting regions (East Anglia and North West England) provide good ‘test-beds’ for the diversity of the problems and approaches needed for a tool to support regional Integrated Assessment in England and the remainder of Great Britain. SID 5 (2/05) Page 2 of 23 The main output from the RegIS2 project is the Regional Impact Simulator, a user friendly tool which allows the impacts and interactions of future climate or socio-economic change to be assessed by stakeholders across the agricultural, hydrological, biodiversity and coastal sectors. The Regional Impact Simulator contains the: Metamodels – a range of calibrated and validated metamodels simulating fluvial and coastal flooding; rural landuse change and agricultural production; water resources, supply and demand; habitat change and species suitability; Scenarios- baseline scenarios plus the UKCIP02 climate scenarios for 2020s Low, 2020s High, 2050s Low and 2050s High and the four RegIS regional socio-economic scenarios (Global Sustainability, Global Markets, Regional Stewardship and Regional Enterprise), and Datasets necessary to run the metamodels. The philosophy behind the Regional Impact Simulator was to make the interface as intuitive as possible, such that an interested individual could use it with minimal recourse to help files and without need for training. The design of the interface was therefore crucial to the success of the project, and the Regional Impact Simulator has been through multiple iterations with stakeholders. The final version contains four main screens of differing purpose, and increasing levels of interaction with the model variables: The first, About screen introduces the software (purpose of the screens, project team etc.); The second, Predefined Scenario Futures, screen allows the user to rapidly identify relative sensitivity of an Indicator(s) to the different climate and/or socio-economic scenarios. The Regional Impact Simulator can be run after only clicking on a climate scenario, a region and (optionally) a socio-economic scenario; The third, Exploratory Analysis, screen lets the user explore a scenario in greater detail by changing the scenario pressure variables which act as input parameter values in the models. This screen can be used to carry out sensitivity analyses under the baseline conditions or guided uncertainty analyses of futures; The fourth, Influencing the Impacts, screen allows the user to test regional adaptation responses to the impacts previously identified. The Regional Impact Simulator was built using a modular Object Oriented approach, in which the modules (or meta-models) were written in various programming languages and compiled in libraries of executable functions (DLLs) or executables (EXEs). The modular approach to model integration has the advantage of allowing independent model development, so that the models can be developed and tested as stand-alone versions before being integrated with the other models. The meta-models are run as serial processes (sequentially in the order: High River Flow, Flood, Agriculture, Water Resources, Biodiversity) with the inclusion of parallel processes where possible. The interface development played an important role in minimising the run-times of the coupled meta-models. On a typical new PC (2.8 GHz processor with 1Gb RAM), the Regional Impact Simulator will complete a Predefined Scenario Futures integrated assessment for East Anglia in less than 15 seconds for one habitat, or 28 seconds for all habitats (though speed of computer has a large impact). Stakeholder evaluation of the Regional Impact Simulator suggests that it has attained an acceptable level of intuitiveness and user-friendliness in design and run-time. The rapid run-time enables impacts to be studied in far more detail than in a normal impact study using complex models, though this ironically leads to more questions as to why a response happens. This should however be viewed in a positive light as, where normally the answers were simply a black box to be taken on trust by Stakeholders, it is now possible for a user to study what is happening in response to changes in some detail. It is important that the fast run-times do not affect the perceived credibility of the Regional Impact Simulator. The multiple facets that determine the credibility of an IAM in the eyes of stakeholders dictate that it has been approached from a variety of perspectives: Basing the metamodels on widely accepted pre-existing models; The slider bars with which the User changes the Pressure or Responses show the numerical values, so that the magnitude of changes being made are clear; The fast runtimes enable Users to carry out many simulations to rapidly investigate the sensitivity of system under baseline conditions, and to compare this, where appropriate, with their own knowledge; In the Influencing the Future screen, the user is automatically guided to the Responses that that affect the chosen Indicator. It is therefore transparent to the user which choices will have greatest effect; A hierarchy of User support - tooltips (for immediate provision of a short definition or explanation); concise higher level Help files which provide basic background understanding for the vast proportion of Users, and full documentation within the Help files for technical users; Explicitly communicating that the very model inputs themselves which derive from the scenarios SID 5 (2/05) Page 3 of 23 are uncertain. The colour coding approach used in Regional Impact Simulator, and the actual breadth of the coloured uncertainty bands on the slider bars conveys clearly that different values (and the assumptions behind them) are likely to give rise to differing outcomes which the User can rapidly explore due to the rapid run times Integrated, regional assessment represents a powerful tool in environmental change impact and adaptation studies that has many advantages over ‘traditional’ sectoral approaches. IA models such as RegIS allow the relationships between sectors to be evaluated and thus the consequences of changes in one sector to be assessed with respect to another sector. Single sector studies are clearly limited by their inability to treat all of the processes that may be important in affecting a sector. It becomes clear when undertaking assessments in this way that the various regional-scale sectors are interdependent with changes and potential adaptation options for one sector sometimes having profound effects on other sectors. During the evaluation process, it was asked whether it would be useful to develop the Regional Impact Simulator further. There were no negative responses, with most respondents being highly positive. The evaluation process showed that there is demand for such assessment tools as the Regional Impact Simulator, but that there are always ways of improving on their development, not least with respect to the interface design and functional capacity. A number of developments would enhance the capacity and utility of the system: a) application to new regions and/or coupling regional and national scale applications- this was suggested by most respondents in the evaluation process, b) refinements to the user interface, c) refinement to existing models, d) addition of new meta-models (e.g. urban development, forestry), e) new assessment concepts (risk, valuation, visualisation). The Regional Impact Simulator addresses both autonomous and societal adaptation strategies. However, whilst autonomous adaptation is dealt with explicitly by the modelling approaches, the Regional Impact Simulator does not indicate the policy mechanisms that would be needed to implement such societal adaptation strategies in practice. The need remains for stakeholders to make decisions themselves about what is or is not feasible, achievable and/or desirable in terms of adaptation responses, as they are best placed to do this, informed by using the Regional Impact Simulator to explore the effect of acting on the pressure variables through regional responses. In such a way, the Regional Impact Simulator should promote better informed decision makers and decision-making. Project Report to Defra 8. As a guide this report should be no longer than 20 sides of A4. This report is to provide Defra with details of the outputs of the research project for internal purposes; to meet the terms of the contract; and to allow Defra to publish details of the outputs to meet Environmental Information Regulation or Freedom of Information obligations. This short report to Defra does not preclude contractors from also seeking to publish a full, formal scientific report/paper in an appropriate scientific or other journal/publication. Indeed, Defra actively encourages such publications as part of the contract terms. The report to Defra should include: the scientific objectives as set out in the contract; the extent to which the objectives set out in the contract have been met; details of methods used and the results obtained, including statistical analysis (if appropriate); a discussion of the results and their reliability; the main implications of the findings; possible future work; and any action resulting from the research (e.g. IP, Knowledge Transfer). SID 5 (2/05) Page 4 of 23 Introduction The ‘Regional Climate Change Impact and Response Studies in East Anglia and North West England’ (RegIS) project (CC0337) (Holman et al., 2001; Holman and Loveland 2001) represented the first attempt at quantitatively modelling the cross-sectoral impacts of climate change within an integrated framework at a regional scale within the UK. The RegIS project demonstrated that an evaluation of the regional impacts of climate change, through an integrated methodology, on the agriculture, hydrology, biodiversity, and coastal sectors is possible, but not straightforward. Although the ‘Drivers-Pressure-State-Impacts-Responses’ modelling framework used was successful, the project was not able to fully integrate the cross-sectoral climate change impacts because of runtime and data problems with some of the detailed mechanistic research models used. The complexity of these models hindered the wider applicability of the RegIS methodology. Although the development of the methodology was stakeholder-led, the application of the methodology was not. At the end of the project, the UK Climate Impacts Programme (UKCIP) hosted a technical workshop for stakeholders to discuss the project results and to reflect on the lessons learnt. Key issues for future stakeholderled Integrated Assessments came out of this workshop, as stakeholders wanted: Results from far greater numbers of scenario (climate and socio-economic) combinations - RegIS simulated the effects of two climate-only and two climate-socio-economic scenario futures for the 2050s timeslice only; Better understanding of the effects of uncertainty in the scenarios of future climate (e.g. Hulme et al. 2002) and socio-economic conditions on the impacts; The ability to conduct sensitivity analysis to be able to better understand the effects of multiple interactions on impacts. Greater degree of model linkage and interaction and more feedbacks and adaptive repsonses. This current RegIS2 project proposed to investigate and develop the use of computationally simpler modelling techniques, so called ‘meta models’ within a user friendly interface (the ‘Regional Impact Simulator’) to make the RegIS methodology available to the wider stakeholder community. Objectives The objectives of the ‘Development of a metamodel tool for regional integrated climate change management’ (RegIS2) project were to: 1. To refine the RegIS risk assessment methodology for conducting climate change impact assessments on agriculture, hydrology, biodiversity and coasts into a form usable by Stakeholders, that explicitly addresses local and regional scale impacts, interactions between sectors, the use and linkage of metamodels and stakeholder concerns and responses. 2. To develop regional scenarios of the pressure of climate change, and state-impact characteristics which influence social and economic sensitivity to climatic change. 3. To identify, with stakeholders in the North West and East Anglian regions, critical impacts, interactions and adaptive responses, and suitable interface design. 4. To develop a robust and user friendly software tool, containing an encrypted version of the RegIS geographically-referenced database for the North West and East Anglian regions of relevant environmental data and scenarios of climate and socio-economic change, the metamodels and the input data needed for Stakeholders to analyse climate change impacts, their interactions and adaptive responses using the metamodels. 5. To develop, calibrate and validate metamodels based upon simulations using existing impact models for the North West and East Anglian regions for four sectors: agriculture, water, biodiversity, and coastal zones. 6. To investigate the effects of climate change on critical impacts and interactions using the linked metamodels and scientific expertise. Specifically, to identify significant driving forces, impacts and interactions, analyse the relative sensitivity of impacts and interactions, and evaluate changes in impacts and interactions over short, medium and long-term time scales. 7. To investigate adaptive responses to climate change using linked metamodels and scientific and stakeholder expertise. Specifically, to analyse adaptive mechanisms for minimising negative and maximising positive impacts/interactions, define appropriate adaptive responses for implementation over short, medium and long-term time scales, and simulate the effect of different policy scenarios on adaptive strategies. SID 5 (2/05) Page 5 of 23 Selection of the study areas The Regional Impact Simulator aims to improve the understanding of local/regional impacts of global change in a wide range of stakeholder audiences, and thereby to aid public discussion. In a climatically, physiographically and economically diverse country such as Great Britain, this was felt to be best supported by allowing the User to explore consequences in two contrasting regions. The Regional Impact Simulator was developed to be used in two contrasting regions of England – the North West of England and East Anglia (Table 1)- these were the two regions used in RegIS, thereby allowing this project to gain value-added benefit from the earlier research project, socio-economic scenario development and modelling. By capturing a significant range of the heterogeneity of the country, it was hoped that this would allow users to extrapolate the findings and understanding to other regions of the UK. Table 1 Characteristics of the study areas Region Boundaries Development North West Cheshire, Greater Manchester, Merseyside, Lancashire and Cumbria Extensive urban development within Manchester/Merseyside conurbations Flooding Flood-prone areas in narrow river valleys and coastal fringes Agriculture Mostly livestock farming systems, extensive in uplands and hills but more intensive in lowlands Mostly provided from surface water resources, with significant imports Important upland habitats (e.g. upland hay meadows, artic alpines) Water resources Biodiversity East Anglia Norfolk, Suffolk and Cambridgeshire Low level of urbanization, with the exception of key urban centres such as Cambridge and Norwich Significant low lying or below sea level areas (e.g. Fens) prone to coastal flooding Intensively cultivated, with an emphasis on arable agriculture. Irrigation important. Greater dependence on groundwater resources Important lowland (arable field margins, fens) and coastal (e.g. coastal grazing marsh, saltmarsh) habitats The North West is the larger of the two regions and is dominated by the higher (up to about 900 masl) land in the north and along its eastern boundary, although lower land is found in the west and south of the region. The proximity of the North Atlantic provides a moderating influence on the climate, producing a generally milder and wetter climate (average annual rainfall = 650 - 3200 mm), allowing for the effects of altitude. The climate of East Anglia is influenced by its low relief (most land is below 60 m above sea level (masl)) and its proximity to the continent. It is the region least affected by the moderating influence of the sea, so rainfall is lower and there is a greater daily or monthly temperature range than elsewhere. Rainfall (average annual = 550 - 750 mm) is more or less equally distributed throughout the year. Methodological context Fundamental to delivering a software interface (the ‘Regional Impact Simulator’) containing computationally simpler ‘metamodels’ to the wider stakeholder community, was to build upon the successful stakeholder integration within RegIS (described in Holman et al 2005a). In doing so, it was necessary to agree with stakeholder representatives: • definitions of the components of the RegIS Integrated Assessment Methodology (IAM) • the fundamental guiding design principles for the Interface; • useful output indicators for each of the metamodels; • appropriate regional adaptive responses to incorporate within the Interface; • appropriate Interface functionality. SID 5 (2/05) Page 6 of 23 RegIS2 Integrated Assessment Methodology The assessment framework was based on the Driver-Pressure-State-Impact-Response (or DPSIR) framework, which was first proposed by the OECD in the early 1990s (OECD, 1993) and has been widely used in environmental management for the last 10 years, (e.g., Turner et al., 1998; La Jeunesse et al., 2003; Holman et al., 2005a; Holman et al., 2005b). As a conceptual model it has proved useful for promoting dialogue between the different disciplines who must work together to solve complex environmental problems and it has been adopted by relevant government agencies such as the England and Wales Environmental Agency and the European Environment Agency (EEA, 2003). However, even when used in a conceptual role, the authors are aware of important differences of view as to what is a driver, pressure, etc. This lack of clarity hinders more widespread application of the DPSIR framework. There is also an increasing demand for more integrated assessment of environmental change, which translates into a need to develop formal integrated assessment models. When the DPSIR framework is being used as the basis for such models the differences of interpretation have to be resolved and a consensus developed across the model developers and users, and ideally much more widely. The foci and methodological approaches of the DPSIR domains used in RegIS2 for conducting an integrated climate change impact assessment are given below. Drivers: Definition: the underlying exogenous (to the region) causes of environmental change, e.g. climate and socioeconomic change, national and international (EU) policy. Method: identify and describe qualitative, narrative storylines, e.g. based on the IPCC-SRES framework. Pressure: Definition: the variables that quantify drivers within the region, e.g. regional population, regional GDP, crop prices or gross margins, sea level, standard of protection, temperature, precipitation, water availability, etc. Method: develop regional, quantitative scenarios. State: Definition: the variables that represent the sensitivity of the system/sector to the pressure variables, e.g. river flows, land use areas, species distributions. Method: use meta-models to derive sectorally-specific state variables that include autonomous (spontaneous) adaptation Impact: Definition: a measure of whether the changes in the state variables have a negative or positive effect on individuals, society and/or environmental resources Method: derive impact indicators (summary statistics) and translate these into impact classes (good, bad, OK) reflecting value judgements. Impact indicators are based on whether the state variables reach a certain threshold determined by expert knowledge/ judgement, stakeholder views and historic analogues. Examples of impact indicators include the number of flood days, species sensitivity index, etc. Response: Definition: planned (societal level) adaptation that aims to minimise negative impacts (or maximise positive impacts / benefits) by acting on the socio-economic pressure variables – a response may include several policy measures, e.g. changing water consumption, restricted housing development, conservation plans, etc. Methods: identify adaptation options with stakeholders; use RegIS2 interface/models to evaluate ways of minimising impacts by modifying the socio-economic pressure variables. Responses cannot act on the drivers (e.g. climate change mitigation policy) nor physical pressure variables (e.g. climate variables and sea levels). Figure 1 shows the linkages within the modelling within the DPSIR framework. The Drivers are in all cases qualitative descriptions of future environmental issues. They bound the problem that subsequent analyses will address. The pressures are the quantitative representation of these drivers that provide the inputs to the RegIS2 meta-models. Note that the pressures from one sector are sometimes derived from the states of another sector: urbanisation and land cover being notable examples. The states and impacts are then the central outcomes of the RegIS2 meta-models. Finally, the responses then act on the pressure variables. The pressures can be modified in ways that reflect, for example, alternative policy strategies. It is important to note that there is no feedback of the responses to the drivers, i.e. climate mitigation policy is not considered to be an option at the scale of the study regions. Furthermore, the responses are based on generic options, without detailed specification of how these options could be implemented in practice. Such implementation strategies are based on socio-political and commercial processes which the stakeholders using the Regional impact Simulator are better able to assess. SID 5 (2/05) Page 7 of 23 Figure 1 Schematic diagram of the model linkage within the DPSRI framework of the RegIS IAM [Haxagons are RegIS outputs; parallelograms are internal model outputs] RegIS2 ‘Regional Impact Simulator’ design Initial concepts The philosophy behind the Regional Impact Simulator was to make the interface as intuitive as possible, such that an interested individual could use it with minimal recourse to help files and without need for training. The design of the interface was therefore crucial to the success of the project, a process requiring a participatory design approach (van Asselt and Rijkens-Klomp 2002) with the help of stakeholder who formed the project’s Steering Committee. They represented key groups including national and regional Government, non-government organizations and industry. There are few existing examples of participatory IA software, the principle exception being GB-QUEST (Carmichael et al 2004) which has a different purpose - to engage stakeholders in a debate about what would constitute a desirable future. The initial concepts agreed with the Steering Committee were that the: Design o layout should be informed by the DPSIR components on the RegIS2 IAM o Slider bars were the chosen means of varying input parameters in order to increase the transparency of the model/scenario assumptions; o Guidance must be given as to ‘realistic’ ranges of values associated with the uncertainty attached to that parameter within the scenario; o The user should be able to concurrently view and compare output from more than one sector; o since different users or audiences prioritise or value issues differently, they will wish to explore topics in varying degrees of depth or detail, which the design should allow; Speed o the user should be able to rapidly get outputs from the system, without having to go through an extensive or prolonged model set-up; o the run times should be as short as possible to prevent Users ‘switching off’; SID 5 (2/05) Page 8 of 23 Scale o The principle output should be regionally aggregated to prevent the system being used for detailed site analysis, but that the user should be able to view more detailed mapped output; The user should be able to vary certain input parameters to look at consequences of changes (Response) but, because the tool is not intended for intervention specific policy analysis, the responses available should be given as regional outcomes rather than the policies that would accomplish those outcomes. For example, a possible Response to insufficient water supply might be to reduce domestic per capita water consumption. Users may consider outside of the Regional Impact Simulator which policies or strategies (such as water pricing, metering etc) might be appropriate to accomplish this outcome. o Based upon the initial underlying concepts for the Regional Impact Simulator, the first agreed ‘mock-up’ design is shown in Figure 2. Scenario selection Climate scenario 2020 Low Socio-economic scenario 2020 High 2050 Low 2050 High ? ? ? GS ? WM ? ? RS RE Brief description of scenario ? ? Region East Anglia North West ? ? Coasts Aggregated indicators Area flooded = 100km2 Loss of Coastal GM = 10km2 Gain of saltmarch = 20km2 User selected mapped indicator River flows Agriculture GDP Population Subsidy Input value selection RUN Scenario ‘best guess’ uncertainty limit Slider Scenario default value Figure 2 Initial concept design of the RegIS2 interface Final design The Regional Impact Simulator went through multiple subsequent iterations within the project team and with the Steering Committee as the design evolved. Important additions or evolutions to the initial design were: The Interface developed from the original concept of a single screen to the final version which contains three screens of differing purpose, and increasing levels of interaction with the model variables. It was felt that the single Interface screen was becoming too complex and would be off-putting to some nontechnical stakeholders because of the apparent multitude of choices. The three screens provide an ordered hierarchy of interaction: o The first, Predefined Scenario Futures, screen (Fig. 3)- allows the user to rapidly identify relative sensitivity of an Indicator(s) to the different climate and/or socio-economic scenarios. For the chosen climate scenario (baseline or future changed climate for the 2020s or 2050s) and socio-economic scenarios (baseline or future), a default set of values are used, based upon preexisting data (e.g. from the climate scenarios- Hulme et al., 2002) or the expert judgement of the RegIS2 team. o The second, Exploratory Analysis, screen (Fig. 4)- it is widely acknowledged that there is uncertainty in any scenario, which cannot be defined by a single set of unique values, so this screen lets the user explore a scenario in greater detail by changing the scenario pressure variables which act as input parameter values used in the models. o The third, Influencing the Impacts, screen (Fig 5)- allows the user to test regional adaptation responses to the impacts previously identified. After an impact indicator is selected, a list of SID 5 (2/05) Page 9 of 23 relevant response parameters is presented which are known to affect the selected indicator and which one may be able to act upon. It does not include climate change variables (e.g. temperature) as one cannot directly act upon them. Changing any of the sliders and re-running the model changes the impact indicator and allows the user to identify regional adaptation responses which reduce or minimise the impact. Extensive use of tooltips were implemented so that by hovering the mouse over a component of the screen (scenario name, Indicator etc) a short definition or explanation appears allowing the user to gain a basic understanding of the concept without having to resort to the Help Files; Explicit communication of scenario uncertainty was given by a traffic light-based system of colour coding of the slider bars which differentiates between uncertainty which is “credible” within the context of a scenario storyline (green for ‘go’) and more “extreme” uncertainty which may be possible but is outside of the considered wisdom for the scenario (yellow for ‘caution’). For obvious reasons, the ability for the user to go into the ‘red’ was not given; The ability to compare spatial Indicator results with those from a previous run; A library of plant species photographs was included, so that the user could see the species being modelled; It was decided to separate Pressures and Responses (which act upon Pressures) within the Interface, because the effects of climate mitigation (e.g. Responses acting upon the climate Pressures) are not appropriate at the regional scale. Figure 3 The Predefined Scenario Futures screen, for investigating default scenario combinations SID 5 (2/05) Page 10 of 23 Figure 4 The Exploratory Analysis for conducting sensitivity analysis and exploring scenario uncertainty and interactions Figure 5 The Influencing the Impacts screen for testing regional adaptation responses to identified impacts SID 5 (2/05) Page 11 of 23 Scenarios Climate Four climate change scenarios were included - the 2020s Low emissions, 2020s High emissions, 2050s Low emissions and 2050s High emissions. They are all based on scenarios developed on behalf of the UK Climate Impacts Programme (UKCIP), known as the UKCIP02 scenarios (Hulme et al., 2002). The Low and High emissions scenarios were selected in order to capture the effects of uncertainties in future greenhouse gas emissions on UK climate, while the two timeslices allow investigation of the evolution of change. In addition, these four scenarios are all used within the MONARCH biodiversity project (Berry et al. 2005), allowing comparison. All the UKCIP02 scenarios are based on the high-resolution regional climate model (HadRM3) from the Hadley Centre for Climate Prediction and Research. As they are based on a single climate model, they do not cover uncertainties in how the climate system will respond to these emissions (scientific or modelling uncertainty). The credible uncertainty range on the climate pressure slider bars indicate the range of predictions of regional climates over the UK simulated by other global climate models based on Hulme et al. (2002). Increases in mean annual temperature, relative to 1961-90, range from 0.3 to 1.1oC by the 2020s and 0.5 to 2.6oC by the 2050s. In all seasons, and for all scenarios, there is a northwest to southeast gradient in the magnitude of the climate warming over the UK, the southeast consistently warming by at least several tenths of a degree Celsius more than the northwest. Changes in total annual precipitation, relative to 1961-90, range from – 4 to +3% by the 2020s and –9 to +7% by the 2050s. In winter precipitation increases in all regions with the largest changes of up to 23% occurring in the east. Conversely, in summer precipitation decreases across virtually all of the UK with the largest changes of up to –32% occurring in the south. Socio-economic Future socio-economic change can be as significant, if not more significant, than climate change for some sectors (Holman et al., 2005b), so that the widespread practice of studying the impacts of future climate changes as if they were imposed on today’s society is becoming increasingly untenable. Socio-economic scenarios are, to a certain extent, based on assumptions that are known to be tenuous (Gaffin et al 2004). These scenarios are not intended to predict the future, but are tools for helping to evaluate societal responses to major environmental changes such as global climate change, assuming that (Morris et al 2004): 1. The future is unlike the past, and is shaped by human choice and action; 2. The future cannot be foreseen, but exploring the future can inform present decisions; 3. There are many possible futures, so scenarios map ‘possibility space’; 4. Scenario development involves a mix of rational analysis and subjective judgement. Within RegIS2, the original socio-economic scenarios as used in RegIS (Holman et al., 2005a) were refined, updated and an improved spatial resolution implemented. The regional descriptive storylines of alternative futures of the RegIS scenarios (Shackley & Deanwood, 2003) were derived from an iterative procedure between researchers and stakeholders with interests in the sectors under consideration at national, regional and local levels and are broadly similar to the related UK national (Berkhout et al. 2002) and global narratives (Nakicenovic and Swart, 2000; Arnell et al., 2004). In accordance with the original preferences of the RegIS funders and the UKCIP, formally independent socio-economic scenarios were developed, also referred to as ‘non-climate change’ scenarios, as they do not permit interaction between climate and socio-economic changes, such as occurs in coevolutionary socio-economic scenarios (Lorenzoni et al., 2000). Using non-climate change socio-economic scenarios has the benefit that it permits a clearer distinction between the effects of physical climate change, and socio-economic changes (and hence identifies more clearly the role of response to the impacts identified in the 2050s). The feedbacks between climate change and socio-economic change within co-evolutionary socio-economic scenarios (e.g. Lorenzoni et al., 2000) make the relative impact of physical climate change, socio-economic change and socio-economic/political responses difficult to untangle. From the qualitative scenarios for the region, projections for a number of quantitative non-spatial and spatial indicators have been developed for use as inputs to the numerical models. These indicators are: population, Gross Value Added (GVA), persons per household and population density. The quantification has been based on the qualitative storylines and informed by regional trends and Government projections. Spatial indicators are urbanization, forest and protected areas. SID 5 (2/05) Page 12 of 23 Scenario linkage –to link or not to link global and regional scenarios Because formally independent socio-economic scenarios were used, it was necessary to decide how to link the climate and socio-economic (non-climate) scenarios. Two alternative options were possible: 1) To formally link the climate and socio-economic (non-climate) scenarios through their associated emissions scenarios to create internally consistent visions of the future. This was the approach taken in RegIS (Holman et al. 2005a) and was based on the rationale that a strong market-driven SE scenario implies higher emissions of carbon, whilst a community-based SE scenario implies lower emissions of carbon. In this way only futures in which the emissions of the regional socio-economic scenario were consistent with the global emissions scenario behind the climate change scenario could be simulated; 2) To provide no formal linkage between the two sets of scenarios, but to leave the choice to the user. This approach allows regions to develop along divergent socioeconomic paths from those associated with the emissions scenario behind the climate change scenario. The second approach was chosen because of the greater flexibility afforded to the User. Because the climate change scenarios are dependent upon global emissions of carbon, but the SE scenarios operate at a range of scales from the local to the global, allocation of relationships between the SE scenarios and the climate change scenarios assumes that the same local to national socio-economic strategy is underway everywhere in the world. At the scale of the RegIS2 regions this is a highly uncertain assumption, and so the ability to investigate alternative trajectories of regional socio-economic development that are not necessarily consistent with global developments was allowed. Guidance is given to the User regarding the consistency of climate and socioeconomic scenario linkages. Metamodels Description of the sectoral metamodels Because the focus of the RegIS2 project was on the development of the IA software, the environmental compartments modelled (agriculture, biodiversity, coasts and floodplains and water resource management) were those as used in RegIS. These were originally selected as a result of stakeholder concerns about environmental change impacts in the study areas (Science and Policy Associates & ESYS, 1996). However, the scope of the models, range of indicators and linkages between models were greatly expanded from those described in Holman et al (2005a). Flooding (Fluvial and coastal environments) Climate change is predicted to change the frequency of flooding in both the fluvial and coastal flood plains. It will also affect valuable ecosystems such as saltmarsh and coastal and fluvial grazing marsh. The flood metamodel considers the impacts of both sea-level rise and changing flood flows in rivers, and the effects on the distribution of these ecosystems. The present location and standard of flood defences for both coastal and fluvial flood plains are determined from the National Flood and Coastal Defence Database (NFCDD) and the National Flood Risk Assessment (NaFRA). Where data on the standard of defence is unavailable it has been calculated based on the land use and ODPM Guidance (PPG 25). The flooding methodology uses the 2003 Indicative Floodplain Map (Environment Agency) to identify the flood hazard zones for the 200 year and 100 year events for coastal and fluvial floods, respectively. The effect of relative sea level rise on coastal flooding is estimated by raising the surge heights by the magnitude of relative sea-level rise (i.e. including regional land uplift/subsidence), leading to a reduction in the defence standards. A similar approach is used to determine the effect of changing the flood flow on flood defence standards of protection. The High Flows metamodel is based upon the Catchment Descriptors method (Bayliss, 1999) in the Flood Estimation Handbook, and simulates the QMED or the median annual flood Indicator (Robson et al., 1999). An increase in peak river flow is translated into an increase in the risk of flooding, based on a relationship between the change in flow, defence standard and change in defence standard. The impact on coastal habitats is modelled based on the capacity for saltmarshes to respond vertically and horizontally to sea-level rise. In the vertical domain, losses of saltmarsh are assumed to occur once the critical value of the rate of sea-level rise as a function of tidal range has been exceeded. Hence saltmarshes in areas with a large tidal range are less vulnerable than those in areas with low tidal range. In the horizontal domain, saltmarsh gains may occur due to managed realignment or unplanned coastal abandonment. Saltmarsh increase due to migration will often be at the expense of areas that are presently coastal grazing marsh, resulting in a SID 5 (2/05) Page 13 of 23 decline in these areas, although the creation of compensatopry inland fluvial grazing marsh is explored by the model. Agriculture A metamodel approach was developed which rapidly simulates the results of the full agricultural land use model described in Rounsevell et al. (2002). The areas of each grid available for agriculture are initially modified according to urban and forest extent (using the Land Cover Map 2000 for the baseline and the socio-economic scenarios for the future), and flooding (from the flood metamodel). The method combines a metamodel which predicts the yield of a wide range of crops as a function of climate and soil type, with a farm model which determines the optimum cropping given crop gross margins, labour and machinery costs and soil workability. The yield metamodel used a neural network which was fitted to spatial modelled yield data from Holman et al. (2005), derived using the ACCESS model, using soil data (rooting depth and availability of water), weather parameters, latitude, carbon dioxide level and amount of irrigation. For the farm model metamodel which calculates the area of cropping which maximises profit, the data was characterised by the crop gross margin of each crop, the soil type and workable hours in the winter, the maturity date of each crop, spring and autumn evaporation and rainfall, and the change in labour and machinery costs. A regression approach was used to predict the percentage of the farm in a given crop and the profit. The results of the agricultural metamodel then give the level of production of each commodity in each region. The user specifies the level of production change in each scenario and the level of irrigation available. The model thus iterates, altering the prices of crops and water, to obtain the correct level of production and irrigation. Based on the final level of farm profit, land was also classified as either cropped, extensive or abandoned, from which levels of production, water use and nitrate leaching are calculated. Water resources management The water resources management metamodel is divided into a number of modules operating at different spatial scales. For every sub-catchment and catchment in the two regions, the flow duration curve (FDC) for each is simulated based upon the Low Flow Estimation methodology, according to the distribution of soil types, urbanization (which comes from the socio-economic scenarios), climate, cropping and land use classes (from the Agricultural metamodel). From the flow duration curve, the availability of water for abstraction in each subcatchment is calculated, after allowing for the environmental flow requirements of the river. Of the many methodologies available for calculating environmental flow allocations (e.g. reviews by Acreman and Dunbar, 2004; Tharme, 2003), the approach chosen is based upon that used in the Environment Agency's Resource Assessment Management (or RAM) methodology. This limits abstraction to protect low flows and flow variability within the flow duration curve, whilst also recognising that abstraction is not unlimited at the high flow end of the FDC due to water quality and practicality considerations (e.g. turbidity, faecal contamination etc). The regional water supply availability is derived from the water availability in the catchments, allowing for water imports/exports and reservoir storage. The regional water demand is the sum of the agricultural water demand (from the agricultural metamodel), domestic, industrial and commercial demand (based upon the socio-economic scenarios) and leakage from that proportion of the water demand supplied by main water. Finally a supplydemand balance for the regional provides a measure of the robustness of the supply-demand system to the pressures exerted in the scenario. Biodiversity The SPECIES model (Spatial Estimator of the Climate Impacts on the Envelope of Species; Pearson et al., 2002; Harrison et al., in press) is used to simulate the impacts of climate change on the potential geographical distribution of 46 species associated with 8 habitats, chosen to interact with the agricultural, coastal and water environments. SPECIES uses an artificial neural network to characterise the currently suitable climate space for a species and to estimate likely changes in its spatial distribution. This way it is possible to see how the climate space for individual species could contract, expand or shift as the climate changes. Five bioclimatic variables are used within the model: growing degree days > 5°C; absolute minimum temperature expected over a 20-year period; annual maximum temperature; accumulated annual soil water deficit; and accumulated annual soil water surplus. The model is trained using existing empirical data on the European distributions of species to enable the full climate space of a species to be characterised and to capture their response to climatic conditions that might be expected under future scenarios. Tests of model performance show good agreement between observed and simulated distributions for all 46 species. Specifically, all modelled species have statistics based on the Receiver Operating Characteristics curve (ROC) greater than 0.9, indicating very good discrimination ability, and 37 SID 5 (2/05) Page 14 of 23 species have Kappa statistics of similarity greater than 0.7 indicating very good agreement whilst 11 have Kappa statistics between 0.4 and 0.7 indicating reasonable agreement between observed and simulated distributions. Predictions of potential climate space for each species are combined with data on the distribution of habitats, protected sites and outputs from the other sectoral models to simulate the impacts of climate and socio-economic changes on species’ suitability at the regional scale. For the coastal species, model outputs of changes in the area of salt marsh and coastal and floodplain grazing marsh are used. For fens and bog species, outputs from the water resources model of indicators of low and high flows are related to species’ preferences for dry and wet conditions, respectively. For lowland heath and upland hay meadows, output from the agricultural meta-model on land use distribution and from the socio-economic scenarios of the distribution of protected sites is used to assess habitat loss. Habitat re-creation is simulated according to rules associated with historic habitat distributions, relevant soil properties (pH for lowland heath and brown earths for upland hay meadows), output from the agricultural meta-model on land use distribution and proximity to existing habitat. Finally, for cereal field margins, outputs from the agricultural meta-model on nitrogen inputs and the distribution of arable land use are related to species’ tolerances to nitrogen stress for plants, whilst output on switches from spring to autumn sowing is related to the availability of over-winter food supplies for higher organisms. Results The value of the Regional Impact Simulator is not so much in the results of the simulations carried out by the RegIS2 Team, but in the ability it gives Stakeholders to explore future scenarios and therefore in their simulation results, interpretations and actions. However, example results from using the Regional Impact Simulator are given below from simulations of the following common scenario futures: Baseline; 2050s Low climate + Global Markets future; 2050s High climate + Global Markets future; 2050s High climate + Regional Stewardship future. Flooding under future climate change and socio-economic scenarios in East Anglia and North West England The Median Annual Flood (QMED) increases under all climate scenarios, with the increases being greatest in the 2050s and for the North West. Under the Regional Stewardship scenario, urbanization decreases so that the increase of QMED due to climate change is slightly diminished to increases of 5-14% in QMED. The impacts of climate change on QMED are generally exacerbated in both regions by the Global Markets scenario, in which urbanization increases, for both the 2050s Low and High scenarios, with QMED increasing by 7-16% and 1021%, respectively. Without adaptation, climate change will lead to the area at risk of flooding and the economic damage for both regions increasing with the increase in the climate pressures. For the 1 in 75 year event, the area at risk of flooding will maximise under the 2050s High scenario and the Global Market scenario to reach 297215 and 109649 hectares in East Anglia and North West, respectively. The estimated economic damages will be £8606 and £ 6600 million in East Anglia and North West respectively. The number of people affected by flooding has a different pattern. The highest figures of people affected will be reached under the 2050s High and Regional Stewardship scenarios; this is anticipated because the number of household occupancy under the Regional Stewardship scenario would be 3 while it would be 2 under the Global Market scenario. Given that the increase in sea level and river peak flow can cause a decline in the standard of protection of the sea and river defences respectively, responding to climate change by raising the tidal and fluvial defences will result in increasing their standard of protection, and lead to a great reduction in the areas at risk of flooding and a decline in economic damages despite the climate change pressures. For example, the adaptation measures under the 2050s High and Global Market scenario in East Anglia will decrease all impacts of flooding: the total areas at risk of flooding will decrease from 95605 to 46788 hectares for the 1 in 10 year event, the economic damages will go down from £ 2304 to £1414 million, and the number of people affected will decrease from 54090 to 30232. Upgrading flood defences to existing Defra guidance and maximising the creation of habitats under the Regional Stewardship combined with the climate pressures under the 2050s High scenario suggest that the areas at risk of flooding and the estimated damages are less than those experienced today in both regions. This indicates that although climate change is the biggest factor in increasing areas at risk and consequently damages, our adaptive responses can generate a significant decrease in areas at risk and damages. However, under this scenario there SID 5 (2/05) Page 15 of 23 is a cost of displacing people as the tidal defences are retreated to allow the saltmarsh in the coastal flood plain to migrate inland in order to maximise stocks. This is not quantified in the flood metamodel and the inundated areas are not considered at risk of flooding. Impact of future socio-economic and climate changes on regional agricultural land use and the consequences for the wider environment The results of the study show that in East Anglia cropping remains quite stable, though grassland is eliminated in the 2050s High scenarios – almost certainly due to the low yield in the drier conditions. Sunflowers are only significant in the 2050High + Regional Stewardship scenario, which is probably related to the high flat rate subsidy in the Regional Stewardship scenario. Nitrate leaching increases as fertiliser use increases with the large increases in crop yields in the Global Markets scenarios, because fertiliser requirement is proportional to expected yield. In the North West there is a very much greater range of outcomes though all future scenarios suggest a reduction in grassland with the greatest in the 2050s High climate scenario combined with the Regional Stewardship socio-economic scenario. In the Regional Stewardship scenario, the low level of demand for milk products, which can easily be supplied from a smaller area, and the flat subsidy structure encourages the widest range of break cropping. None of the six cereal field margin species modelled lose climate space in East Anglia, but they all gain some climate space in North West England ranging from 2% for Papaver rhoeas to 62% for Legousia hybrida under the 2050s High scenario. The socio-economic scenarios show little difference in the arable area. However, the effects of nitrogen fertiliser use on species suitability show much greater differences between the socio-economic scenarios. Species’ suitability is lowest in East Anglia under the Regional Enterprise scenario where between 19 and 27% of grid cells are classified as being marginal due to nitrogen stress, whilst between 3 and 6% are classified as unsuitable. This is followed by the Global Markets scenario, where between 10 and 19% are classified as marginal due to nitrogen stress, whilst between 0.5 and 2% are unsuitable. Finally, the Global Sustainability and Regional Stewardship scenarios show all grid cells to be suitable. Species’ suitability is slightly worse under the 2050s Low climate scenario combined with the different socio-economic scenarios than the 2050s High scenario, because there are slightly more N input in the former case. For the North West region under the 2050s Low and Regional Enterprise scenario. Only 2% of grid cells are classified as marginal due to nitrogen stress for the most tolerant species, Galium aparine. For Papaver rhoeas (medium tolerance), 13% are marginal and 2% become unsuitable due to nitrogen increases. The least tolerant species, Legousia hybrida, shows 12% of grid cells becoming marginal and 15% becoming unsuitable due to nitrogen stress. In order to reduce the impacts on species with low and medium tolerances to nitrogen, a possible adaptation option would be to restrict chemical inputs, although the adoption of such a policy though would depend on the socio-economic scenario under consideration, being less likely under Global Markets. Linking water resource availability and agricultural irrigation demand Climate change impacts the naturalised flows on which the abstraction availability is based, with the reduced annual rainfall in the 2020s and 2050s being reflected in reduced river flows. By the 2050s under a High climate change scenario, abstraction availability in East Anglia is reduced by over 300 Ml/d, a reduction of around 30% compared to current availability. Similar reductions in abstraction availability are seen in the North West region, although abstraction availability is much higher in the North West compared to East Anglia. In parallel with the reduction in abstraction availability, there are variable changes to irrigation demand in both region. The greatest increases are in East Anglia under a 2050s High climate (40% increase in demand compared to the baseline) and in the North West under 2050s Low and High climates where demand increases by between 200 and 250 %. The larger percentage rise in the North West reflects the very low baseline irrigation demand in the region. The net result of the decrease in abstraction availability coupled by the increase in irrigation demand differs between the two regions. Even under a 2050s climate, with its resultant decrease in abstraction availability, there is still a positive supply-demand balance in the North West, indicating that increased irrigation demand can be met from the available resource. However, in East Anglia, the supply-demand balance under baseline socioeconomic conditions shows a progressively increasing supply deficit. With changing futures, the profitability of irrigation changes, due to changes in water costs, demand for produce, gross margins etc. The extent to which the environment will be protected is a function of the socio-economic context, and differs greatly between the socio-economic futures. Under the GM and RE scenarios the Regional Environmental Priority is low but the interaction of supply with demand differs. Under the GM scenario and sufficient water can be abstracted to satisfy demand, giving a positive supply-demand balance. Under GS and RS SID 5 (2/05) Page 16 of 23 scenarios, the Regional Environmental Priority is high and the necessary water is allocated to protect the river ecology at the expense of abstraction availability. The net result is that despite reduced water demand in these futures, there is a significant water resources deficit which is likely to lead to further restrictions on water demand, particularly in drier than average years. The adaptive capacity of the water sector in these futures is, however, fairly high, although this is for contrasting reasons. Under the GM and RE scenarios, the focus of water management is on supply, which means there is potential adaptive capacity in demand management. Under the RS and GS scenarios, demand management has very effectively lowered demand so that there is little further capacity to adapt. However, increasing supply remains an option if more water were to be abstracted from the environmental allocation of water. The efficacy of pricing to control water demand also varies according to the socio-economic conditions prevailing within a future. Under futures where local sustainable agricultural production is a priority, total irrigation water costs need to rise significantly before significant reductions in irrigation abstraction were observed. In such socioeconomic futures, constraints on irrigation demand are best managed through volumetric restrictions on the abstraction licences, and trigger limits on summer abstraction. Wetland habitat changes from an integrated assessment linking water resources and biodiversity Changes to the high and low flows within each catchment are a consequence of the changing climate, the changing landscape (both urban and rural) and the changing non- consumptive water demand patterns of society. However, the relative sensitivity of a catchment’s flows to climate and socio-economic changes depend largely upon changes in urbanisation and the components of water demand (especially per capita water demand) which are closely linked to the socio-economic scenario chosen. In catchments where there are no dramatic changes in urbanisation, the flows are more sensitive to climate change rather than to the socio-economic changes. The results therefore show the significant influence of future urbanization, population water requirements and regional environmental allocation of water on the hydrological responses of catchments to future change. In general, climate change in the 2050s will produce decreases in naturalized flows, the decreases being greater under a 2050s High climate change scenario and in East Anglia. Whether the effects of the socio-economic scenario moderate or amplify these reductions depends on the characteristics of the scenario and the region: In those catchments with low levels of urbanization (in much of the northern and central parts of the North West and much of East Anglia to a greater extent) and under GS and RS scenarios with low water demand (especially per capita water consumption), flows will be little affected by these socio-economic factors, and real flows will be determined primarily by climate change. In those catchments with low levels of urbanization and under GM and RE scenarios in which water demand is high (especially per capita water consumption), increased abstraction will lead to the reductions in naturalized flows caused by the impacts of climate change being amplified; Finally, in those catchments with significant urbanized areas, the influence of urbanization and population water requirements can be highly significant. Decreases in low flows, as given by the daily naturalised Q95, will be partially or in some cases completely moderated by the effects of effluent returns. High flows, as given by the Q5 may increase to a lesser extent than Q95 due to the increased returns, leading to potential increases in waterlogging stress. Three of the six fen species modelled show virtually no change in climate space in East Anglia (P. australis, V. moulinsiana and G. maxima). The other three species show no gains in climate space, but significant loses of 28% (E. palustris), 40% (V. dioica) and 56% (R. scleratus) centred in the west and south of the region under the 2050s High scenario. In all cases this is likely to be due to increased summer dryness. Four of the 11 bog species show only small changes in climate space in the North West (E. vaginatum, M. gale, R. alba and C. vulgaris). The other seven bog species show either no or small gains in climate space combined with larger losses, ranging from 11% for B. pendula to 86% for C. tullia, under the 2050s High scenario. The impacts of high and low flows on species’ suitability under the climate and socio-economic scenarios depends on the species’ sensitivities to drought and waterlogging stress. As fens are a very restricted habitat, the habitat mask led to only 14% of the squares being suitable for either fen species. P. australis, a low sensitivity species, shows little change under the scenarios, while R. scleratus, a medium sensitivity species, shows that 2% of grid cells become unsuitable under the 2050s Low scenario, with 0.5% becoming marginal due to dryness under GM and one square marginal due to wetness under RS. Under the 2050s High and GM scenario, a further 4% of cells become unsuitable due to loss of climate space or habitat, 3.9% of grid cells become unsuitable due to dryness and 1.1% become marginal, for RS these figures are 4%, 0% and 1.2% respectively. In the North West, species with a high drought tolerance show very little loss under any of the scenarios, E. vaginatum, for example, only loses 4% of grid cells under the 2050s High GM scenario in the Lake District. For those with a medium drought tolerance, such as L. cordata, 14.5 % of grid cells become unsuitable under the SID 5 (2/05) Page 17 of 23 2050s Low GM and RE scenarios. A further 8% are lost under 2050s High for these scenarios, mostly from the southern part of the region. Under 2050s High GM an additional 4.4% became marginal due to dryness along the Eden valley. For S. cuspidatum, under the 2050s Low GM and RE scenarios between 14 and 17% of cells become marginal due to dryness in northern Lancashire and Cumbria and under the GM 2% of cells in the Eden valley become unsuitable due to dryness. This pattern continues for the 2050s High GM and RE scenarios with 6% of grid cells becoming unsuitable due to climate, and 4% under GM due to dryness, with the consequence that the number of marginal cells decreases. The possibilities for adaptation are limited and being based on increasing water demand and supply that could be difficult to meet, especially in East Anglia. Regional scale assessment of the impacts of climate change on coastal and fluvial ecosystems and the scope for adaptation The results for the two regions demonstrates the differences that the critical value of relative sea level rise has on the viability of coastal habitats under future sea level rise. The North West has a generally higher sediment and biomass availability than East Anglia, which is reflected in the current (baseline) condition of less threatened habitats, and lower predictions of future losses. Within East Anglia, Suffolk experiences increased future losses of saltmarsh in comparison to Norfolk, for the same reasons of lower sediment and biomass availability, demonstrated by a lower critical value. The socio-economic choices can have more effect on habitats and their recreation than the future changes in climate. Currently, there are insufficient areas for the recreation of habitats that are predicted to be lost under future sea level rise, however, this can be mitigated by the provision of more suitable areas. Specific examples include subjecting agricultural land and low density urban areas to increased flooding frequencies through removal or abandonment of defences, or relocation of properties. The conversion of both these areas to coastal and fluvial grazing marsh through an increase in flooding frequency is subject to the adaptation choice that these areas will not be protected to the same degree as dense urban areas. Under the Global Markets scenario it would be more evident that arable land would not be protected, as in the global context the retention and protection of these areas would be uneconomic. There is intense competition between saltmarsh and coastal grazing marsh for space around the intertidal zone, due to coastal squeeze limiting the area available to saltmarsh and coastal grazing marsh limited to the extent of the floodplain and the available land within that. Competition for space ultimately leads to the loss of grazing marsh over the 2050s timescale due to the difficulties of maintaining coastal grazing marsh in-situ. An adaptation option is the creation of fluvial grazing marsh as a replacement for coastal grazing marsh. The creation of both coastal and fluvial grazing marsh does presume that management will be undertaken to preserve these habitats, through the provision of some defences or protection and active grazing management. These aspects are not included in the metamodel and under which socio-economic scenarios this kind of management is likely has not been investigated. Discussion Use of the Regional Impact Simulator It was never intended that the Regional Impact Simulator would be a Decision Support System, but rather it would have a wider educational / information role. It was hoped that, through allowing the user to explore the uncertain future and consequently improve their understanding of the issues and the implications of the multiple interactions between the sectors driving landscape change, it would inform better decision making and policy development. In considering the use of the Regional Impact Simulator within adaptation frameworks, a broad distinction can be made between its use in adaptive capacity building and its use in informing actions that implement operational adaptation decisions. The wider use of the Regional Impact Simulator can support elements of adaptive capacity building by communicating climate change information and building awareness of potential impacts at an appropriate regional level. Implementing adaptation decisions focuses on avoiding anticipated or reducing cumulative impacts of climate change, ensuring that distribution impacts of adaptation are minimised and ensuring that adaptive measures taken by one organization or sector do not adversely impact upon others (Adger et al., 2005). The Regional Impact Simulator can inform many of these issues, through spatially displaying the impacts and consequences of regional adaptation responses. However, it is its use to inform the integration of adaptation actions and policies across sectors, such that unintentional adaptation resulting from actions in one sector does not reduce the SID 5 (2/05) Page 18 of 23 effectiveness of purposeful adaptation in another sector (which remains a key challenge to achieve affective adaptation -Adger et al., 2005) which may be the most significant use of the Regional Impact Simulator. Assessing the success of the Regional Impact Simulator in delivering the needs of stakeholders From a scientific perspective, RegIS2 was successful in developing the use of computationally simpler modelling techniques for IA, and has produced new understandings which are the subject of the main report. However, the question needs to be asked as to whether a user friendly interface was successfully developed, and whether the stakeholder community will make use of the Regional Impact Simulator and the RegIS methodology. The latter question will only be answered in due course, after the Regional Impact Simulator is made freely available to the wider Stakeholder community and time to judge its use has been elapsed. However, a beta prototype of the Regional Impact Simulator was released to a group of approximately 60 volunteer stakeholders for external evaluation. These stakeholders came from national Government (8), regional Government (5), local Government (6), NGO’s (18, ranging from National Park authorities and environmental regulators to sustainability fora), charities (5), commercial organizations (11, ranging from water and power utilities to consultancies) and academic/research institutions (6) from within the two regions, from elsewhere in the UK and from outside the UK. The evaluation process was intended to get independent feedback on the design and functionality of the Regional Impact Simulator, rather than on the numerical values of the output itself. Questions addressed the design and operation of the Interface, the usefulness of the model output and potential future developments. Users were asked to score their answers between 1 (Low) and 5 (High) or to identify strengths and weaknesses. The results of the feedback provide an important metric for whether the Regional Impact Simulator achieved its aims Key determinands of the success of the interface were: 1. Whether it was deemed to be easy to use and intuitive; 2. Whether the runtime was considered acceptable; 3. Whether the output indicators were useful. Ease of use and intuitiveness All bar one of the evaluators considered that the ease of use/intuitiveness of the Interface was adequate or better, although none rated it as ‘very easy’. The intuitive feel of the Interface was not something that the evaluators could assess prior to installing and running the software. Therefore we do not believe that the respondents were positively self-selecting with regard to this metric. There was an approximately even distribution of responses to how long it took to understand how to use the Regional Impact Simulator, with no relationship between their assessment of this and the intuitiveness. There was an even split between those who needed to use the help tutorial and those who didn’t. All those who required more than 1 hour to understand the interface used the Tutorial, as did some who required half an hour or less. It is not possible to provide a substantive assessment of whether the design fully met it’s objective. However, we believe that the generally positive assessment of its intuitiveness when considered with the even distribution of times taken and need for the tutorial, which suggests that the stakeholders had a broad range of backgrounds and technical ability, provides an affirmation for the interface design. Whilst a single design will never fully meet the diverse needs of all stakeholders, the hierarchical nature of the three screens within the Regional Impact Simulator appears to have satisfied most respondants. Run times The run times for the Regional Impact Simulator are longer than was initially hoped for, prior to model development. This partly relates to the finer spatial resolution adopted by the flooding metamodel (1km) and partly to the reading and writing of files between the metamodels. The runtime is also computer-dependent, as can be seen from Table 2 which shows the run-times for 2 contrasting PCs representing an old PC and a current typical PC. Despite this unavoidable variability in performance, all but 2 of the respondents felt that the runtime was adequate or better. Although the ‘typical’ new PC had a run time of around 20 seconds, this is reduced to around 10 seconds when run without the biodiversity models- the last in the cascade of models. Usefulness of outputs All of the evaluations rated both the usefulness of the indicators and model results as adequate or better SID 5 (2/05) Page 19 of 23 Table 2 System performance of the Regional Impact Simulator PC ‘type’ Processor RAM Windows East Anglia* Operating runtime (secs) system ‘old’ (4 years Pentium 4 512Mb 2000 291 [54]2 old) 1.6 GHz ‘Typical’ (1 Pentium 4 1Gb XP 14 [28] year old) 2.8 GHz * 2050s High climate + Regional Enterprise scenarios- all settings at default 1 Only saltmarsh habitat selected 2 All habitats selected 3 Only blanket/raised bog habitat selected North West* runtime (secs) 363 [65] 2 19 [31] Credibility of using reduced form or metamodels within the Regional Impact Simulator It is important that the fast run-times above do not affect the perceived credibility of the Regional Impact Simulator. The multiple facets that determine the credibility of an IAM in the eyes of stakeholders dictate that it has been approached from a variety of perspectives: Basing the metamodels on widely accepted pre-existing models; All of the metamodels have either been validated against results from their widely accepted pre-existing model or against observed data; The slider bars with which the User changes the Pressures or Responses show the numerical values, so that the magnitude of changes being made are clear; The fast runtimes enable Users to carry out many simulations to rapidly investigate the sensitivity of system under baseline conditions, and to compare this, where appropriate, with their own knowledge; In the Influencing the Future screen, the user is automatically guided to the Responses that that affect the chosen Indicator. It is therefore transparent to the user which choices will have greatest effect; A hierarchy of User support - tooltips (for immediate provision of a short definition or explanation); concise higher level Help files which provide basic background understanding for the majority of Users, and full documentation within the Help files for technical users; Explicitly communicating that the very model inputs themselves which derive from the scenarios are uncertain. The colour coding approach used in Regional Impact Simulator, and the actual breadth of the coloured uncertainty bands on the slider bars conveys clearly that different values (and the assumptions behind them) are likely to give rise to differing outcomes which the User can rapidly explore due to the rapid run times. Future Developments During the evaluation process, it was also asked whether it would be useful to develop the Regional Impact Simulator further. There were no negative responses, with most being highly positive (scoring 4 or 5). It was then asked whether the development should be in expanding or changing the range of sectors, expanding or changing the regions modelled or extending the simulation to provide national coverage. Although a small number of respondents felt that the scope of any further development should be determined by a wider consultation exercise, most respondents selected “more or different regions” and “national coverage”. Two issues are evident from this. Firstly that there is a wider demand for regional integrated assessment- many of the respondents were in regional or national organizations and could see the benefits of such a tool for their own area. Secondly, although there is a perception that the regional scale is the most appropriate for decision making with regard to climate change impacts and adaptation, respondents felt that national coverage was required. This may reflect the small size of the UK and the importance of national organizations such as the regulatory organizations (e.g. Environment Agency, English Nature). A number of developments would enhance the capacity and utility of the system: a) application to new regions and/or coupling regional and national scale applications- this was suggested by most respondents in the evaluation process, b) refinements to the user interface- e.g. different ways of visualising and classifying the output data, such as enhanced GIS and data classification tools. It would also be interesting to explore the potential of allowing users to control/modify the spatial input data within the interface (e.g. adding zones of environmental protection, planning restrictions, new housing development, etc.), rather than SID 5 (2/05) Page 20 of 23 c) d) e) implementing aggregate effect on the whole of the region. Allowing the user to do this is clearly of relevance given the importance of location for some model outputs; refinement to existing models- e.g. changing spatial resolution, improving the temporal resolution by running dynamic simulations. Dynamic simulations would better account for the path dependency of socio-economic futures; addition of new meta-models (e.g. urban development, forestry); new assessment concepts (risk, valuation, visualisation). Conclusions The RegIS2 project has developed a user-friendly tool (the Regional Impact Simulator) to allow stakeholders, for the first time, to carry out their own impact and adaptation studies. The major advantage of the metamodel approach used in the Regional Impact Simulator is the large number of analyses that can be carried out in a short space of time. A single run of the whole system typically takes about 20 seconds, though speed of computer has a large impact, as does the number of habitats selected for analysis. This enables impacts to be studied in far more detail, though this ironically leads to as many questions as to why a response happens. This should however be viewed in a positive light as, where previously the answers were simply a black box to be taken on trust, it is now possible for a user to study what is happening in response to changes in some detail. Integrated, regional assessment represents a powerful tool in environmental change impact and adaptation studies that has many advantages over ‘traditional’ sectoral approaches. IA models such as RegIS allow the relationships between sectors to be evaluated and thus the consequences of changes in one sector to be assessed with respect to another sector. Single sector studies are clearly limited by their inability to treat all of the processes that may be important in affecting a sector. It becomes clear when undertaking assessments in this way that the various regional-scale sectors are interdependent with changes and potential adaptation options for one sector sometimes having profound effects on other sectors. The use of the Regional Impact Simulator to inform the integration of adaptation actions and policies across sectors, such that unintentional adaptation resulting from actions in one sector does not reduce the effectiveness of purposeful adaptation in another sector, may be the most significant use of the Regional Impact Simulator. The Regional Impact Simulator addresses both autonomous and societal adaptation strategies. However, whilst autonomous adaptation is dealt with explicitly by the modelling approaches, the Regional Impact Simulator does not indicate the policy mechanisms that would be needed to implement such societal adaptation strategies in practice. The need remains for stakeholders to make decisions themselves about what is or is not feasible, achievable and/or desirable in terms of adaptation responses, as they are best placed to do this, informed by using the Regional Impact Simulator to explore the effect of acting on the pressure variables through regional responses. In such a way, the Regional Impact Simulator should promote better informed decision makers and decision-making. References Acreman, M. and Dunbar, J. (2004). Defining environmental river flow requirements- a review. Hydrology and Earth System Sciences, 8(5), 861-876. Adger, W.N., Arnell, N.W. and Tompkins, E.L. (2005). Successful adaptation to climate change across scales. Global Environmental Change, 15, 77-86. Arnell, N.W., Livermore, M.J.L., Kovats, S. Levy, P., Nicholls, R., Parry, M.L. and Gaffin, S.R. (2004). Climate and socio-economic scenarios for global-scale climate change impacts assessments: characterising the SRES storylines. 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Downscaling and geo-spatial gridding of socioeconomic projections from the IPCC Special Report n Emissions Scenarios (SRES). Global Environmental Change 14, 105-123. Holman I.P., Rounsevell M.D.A., Shackley S., Harrison P.A., Nicholls R.J., Berry P.M. and Audsley E. (2005a). A regional, multi-sectoral and integrated assessment of the impacts of climate and socio-economic change in the UK: I Methodology. Climatic Change, 71, 9-41. Holman, I.P. Nicholls, R.J. Berry, P.M. Harrison, P.A. Audsley, E. Shackley, S. and Rounsevell, M.D.A. (2005b). A regional, multi-sectoral and integrated assessment of the impacts of climate and socio-economic change in the UK: II Results. Climatic Change, 71, 43-73. Holman, I.P., Loveland, P.J., Nicholls, R.J., Shackley, S., Berry, P.M., Rounsevell, M.D.A., Audsley, E., Harrison, P.A., and Wood, R. (2001). RegIS – Regional Climate Change Impact Response Studies in East Anglia and North West England. DEFRA, London, p.20, (available from www.ukcip.org.uk). Holman, I.P. and Loveland, P.J. (eds.) (2001). Regional Climate Change Impacts in East Anglia and the North West (the RegIS project). Final report of MAFF Project No. CC0337 (available from www.ukcip.org.uk). Hulme, M., Jenkins, G.J., Lu, X., Turnpenny, J. R., Mitchell, T.D., Jones, R.G., Lowe, J., Murphy, J.M., Hassell, D., Boorman P., McDonald, R. & Hill, S. (2002). Climate Change Scenarios for the United Kingdom: The UKCIP02 Scientific Report. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK, 120 pp. La Jeunesse I. M., Rounsevell, M., Vanclooster, M. and Romanowicz, A.A. (2003). ‘Delivering a Decision Support System tool to a river contract : a way to implement the participatory approach principle at the catchment scale?’ Physics and Chemistry of the Earth, 28, 547-554 Lorenzoni, I., Jordan, A., Hulme, M., Turner, R.K., and O'Riordan, T. (2000). A co-evolutionary approach to climate change impact assessment: Part I. Integrating socio-economic and climate change scenarios. Global Environmental Change, 10, 57-68. Morris J., Gomez M., Vasileiou K. And Berbel J. (2004). WADI scenario definition. In Vecino J.B. and Martin C.G. (eds). Sustainability of European Irrigated Agriculture under Water Framework Directive and Agenda 2000. Final report of 5th Framework project EVK1-2000-00057, ISBN 92-894-8005-X. Nakicenovic, N. and Swart, R. (eds.) (2000). Special Report on Emissions Scenarios. A Special report of Working Group III of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change, Cambridge University Press Cambridge, p. 599. Organization of Economic Co-operation and Development (1993). OECD Core set of indicators for environmental performance review. Environmental Monograph No. 83, OECD, Paris. Pearson, R.G., Dawson, T.P., Berry, P.M. and Harrison, P.A. (2002). SPECIES: A spatial valuation of climate impact on the envelope of species. Ecological Modelling 154(3), 289-300. Rounsevell, M.D.A. Annetts, J.E., Audsley, E. Mayr, T. and Reginster, I. (2003). Modelling the spatial distribution of agricultural land use at the regional scale. Agriculture, Ecosystems and Environment, 95(2-3), 465-479 Science and Policy Associates Inc and ESYS: 1996. Proposal for a UK Integrated Climate Change Impacts Assessment. Report prepared for the Department of the Environment and the Environment Agency. Department of the Environment, London, p. 28. Shackley, S. & Deanwood, R. (2003), Constructing social futures for climate-change impacts and response studies: building qualitative and quantitative scenarios with the participation of stakeholders. Climate Research, 24 (1), 71-90. Tharme, R.E. (2003). A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Research and Applications, 19, 397-441. Turner, R.K. et al. (1998) Coastal management and environmental economics: analysing environmental and socio-economic change on the British coast. The Geographical Journal, 164, 269-281. van Asselt MBA and Rijkens-Klomp N (2002). A look in the mirror: reflection on participation in integrated assessment from a methodological perspective. Global Environmental Change 12, 167-184 References to published material SID 5 (2/05) Page 22 of 23 9. This section should be used to record links (hypertext links where possible) or references to other published material generated by, or relating to this project. SID 5 (2/05) Page 23 of 23