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ECOHYDROLOGY Ecohydrol. 2, 1–17 (2009) Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/eco.37 Invited Commentary A simple framework for evaluating regional wetland ecohydrological response to climate change with case studies from Great Britain M. C. Acreman,* J. R. Blake, D. J. Booker,† R. J. Harding, N. Reynard, J. O. Mountford and C. J. Stratford Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK ABSTRACT It is widely accepted that the Earth’s climate is changing more rapidly than it has in the past and that over the next 100 years temperatures will rise and patterns of precipitation will be altered. These predictions for the future have important implications for all ecosystems, particularly those, such as wetlands, whose ecological character is very dependent on its hydrological regime. The potential impacts of climate change on wetland hydrology are of interest to a wide range of stakeholders from wetland managers to international policy makers. Ecohydrological models that combine climate changes, hydrological processes and ecological response provide a means of estimating what might happen to some wetland functions and species in the future. This paper presents a framework that can be used for combining models and available data at a regional scale and is appropriate for different wetlands, in different countries and for different levels of data availability. The simple models are based on broad conceptual understanding of wetland hydrology and are intended to describe basic ecohydrological processes within the constraints of data availability; they are thus fit for the purpose of general assessment and do not pretend to provide precise results for specific wetland sites. Data from Great Britain (GB) have been used to demonstrate each step in the framework for two temperate wetland types: rain-fed wetlands (wet heaths or degraded raised mires) and floodplain margins. Whilst GB may be considered to be relatively data rich, we believe that sufficient information is available in many countries to apply this framework for the regional assessment of climate change impacts on wetlands. Although the models successfully represent the baseline conditions, it is not possible to test whether they accurately predict the future vulnerability of the selected areas. Results for GB suggest that predictions of reduced summer rainfall and increased summer evaporation will put stress on wetland plant communities in late summer and autumn with greater impacts in the south and east of GB. In addition, impacts on rain-fed wetlands will be greater than on those dominated by river inflows. Copyright 2009 John Wiley & Sons, Ltd. KEY WORDS wetlands; climate change; ecohydrology; assessment framework; Great Britain Received 14 May 2008; Accepted 7 October 2008 INTRODUCTION It is widely recognized that the Earth’s climate is now changing more rapidly than in the past because of anthropogenically increased emissions and atmospheric concentrations of greenhouse gases since the industrial revolution (Lashof and Ahuja, 1990). Indeed the latest report from the Intergovernmental Panel on Climate Change (IPCC) indicates that global warming is now unequivocal, with an increase of nearly 0Ð8 ° C in global average temperature since the late 19th century. Latest projections suggest that this warming trend will continue, resulting in an increase in global temperatures of between about 1 and 6 ° C by the end of this century (IPCC, 2007) * Correspondence to: M. C. Acreman, Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK. E-mail: [email protected] † Now at: National Institute of Water and Atmospheric Research, Riccarton, Christchurch, New Zealand. Copyright 2009 John Wiley & Sons, Ltd. and significant changes in rainfall. Resultant ongoing changes in local climate have major implications for many aspects of our lives and those of natural ecosystems (Anderson et al., 2008). The spatial distribution of the world’s non-ocean ecosystems is largely determined by two environmental variables: temperature and precipitation (Walter and Breckle, 1985). Thus climate change is likely to lead to changes in the broad distribution of, for example, forests, savannahs and deserts. The hydrology of wetlands (which is controlled largely by precipitation and evaporation), particularly periodic saturated conditions, creates the physicochemical conditions that make them different from well-drained terrestrial or fully aquatic deepwater systems (Mitsch and Gosselink, 1993). Consequently, wetland ecosystems are likely to be altered because climate changes will have direct impacts on precipitation and indirect impacts on evaporation (through changes to temperature and other variables, such as radiation 2 M. C. ACREMAN ET AL. and wind-speed). Winter (2000) undertook a hypothetical assessment of wetlands in different hydrological and landscape settings and concluded that wetlands whose hydrology is dependent on precipitation are more vulnerable to climate change than those fed by groundwater. Burkett and Kusler (2000) recognized that not only is climate change likely to lead to loss of wetlands, such as tundra, marshes and wet meadows underlain by permafrost, but also wetlands that are dried can become net sources of carbon dioxide (but with possible reduction of methane) serving as a positive feedback to global warming. Parish et al. (2007) came to similar conclusions for peatlands. Clément and Aidoud (2007) reported that oligotrophic habitats were the most sensitive palustrian wetlands to climate change. Turetsky et al. (2007) found that organic matter accumulation was generally greater in unfrozen bogs compared with permafrost landforms, inferring that surface permafrost inhibits peat accumulation. The diversity of reported response of wetlands to climate change illustrates that such a response is in reality a result of a balance between changes in water table, temperature, nutrient cycling, physiological acclimation and community reorganization (Oechel et al., 2000). Kont et al. (2007) reported climatically induced changes in water levels in an Estonian inland bog over a 47-year period. Groundwater levels were found to be rising in a ridge-pool microtope, but falling in a ridge-hollow microtope, demonstrating the potential complexity of wetland response to climate change. Johnson et al. (2005) modelled water table levels and vegetation in Prairie wetlands and found that climate change would result in a shift in the productive habitat for breeding waterfowl. Dawson et al. (2003) calculated that under United Kingdom Climate Impacts Programme (UKCIP) 1998 climate change scenarios, North West Scotland could have a small increase in water availability in the summer, whereas Southern England would experience a decrease and associated lowering of wetland water tables. Under the definition of the International Convention on Wetlands (Ramsar), wetlands cover a wide range of habitat types including floodplains, marshes, rivers, estuaries and near-shore coastal zones (Davis, 1994). Wetland ecosystems cover around 6% of the land surface of the Earth (OECD, 1996) and support over 10 000 species of fish and over 4000 species of amphibians (Bergkamp et al., 1998). In the United Kingdom alone, over 3500 species of invertebrates, 150 species of aquatic plant, 22 species of duck and 33 species of wader have been identified living in wetlands, whilst all 6 native species of amphibian depend on wetlands for breeding (Merritt, 1994). Each species has particular requirements for physical habitat conditions, such as temperature and water regime. These wetland conditions are likely to change under future climates. Therefore, climatically induced loss of wetland habitats could have significant implications for UK biodiversity (Acreman and José, 2000). In particular, climate change will put large numbers of bird species at risk of extinction (Thomas et al., 2004); for example, critical habitats for migratory birds Copyright 2009 John Wiley & Sons, Ltd. in Mediterranean coastal wetlands could be completely destroyed with a 1Ð5–4Ð2 ° C of warming (Böhning-Gaese and Lemoine, 2004). Additionally, wetlands have a significant role in the hydrological cycle of whole catchments (Bullock and Acreman, 2003), which means that the area over which they have an influence is substantial. This paper presents a generic, but quantitative, method for assessing the likely impacts of climate change on wetlands at a regional scale. Steps in the method are illustrated with an example application to two wetland types found in Great Britain (GB). The focus of the work is on how hydrological alterations caused by climate change will have an impact on wetlands, but the method is readily applicable to direct impacts of temperature or other implications of climate change. In coastal and estuarine wetlands, there may be additional threats arising from, for example, sea-level rise. In addition, indirect impacts arising from human interventions to try and mitigate climate change effects could in some places have a much larger impact on wetlands, such as increased withdrawals from surface and/or groundwater for irrigation. WETLAND ASSESSMENT APPROACHES There is a need to be able to anticipate the implications for wetlands of future changes in climates, particularly for agencies responsible for implementing wetland conservation through policy development or local action, such as reserve owners, national government agencies and the International Convention on Wetlands in the 131 signatory countries around the globe. Whilst all wetlands have their own unique ecohydrological character, which requires some distinctive local considerations, it is possible to envisage a generic approach on how assessments of climate change implications can be made at a regional level. The UKCIP has produced generic guidelines for developing a risk-uncertainty framework for incorporating climate change in decision-making (Willows and Connell, 2003). This framework describes a tiered approach to considering climate change, enabling decision-makers to recognize and evaluate the risks posed by climate. Tier 1 : This is termed ‘risk screening’ and is generally a qualitative assessment of potential risks posed by a changing climate. A good ecohydrological example is the work of Winter (2000) that considers the impact of climate change on wetlands using conceptual qualitative understanding of processes in different landscape locations. Tier 2 : The second tier approach involves a generic, quantitative assessment to identify potential consequences of change. In this tier, models need to include important processes, but can have generalized form and parameters that are broadly applicable to areas or regions, as they are not intended to represent details of specific sites. There is a paucity of methods and studies of this type. Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 3 A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS Tier 3 : This third tier is a more detailed quantitative assessment to establish the magnitude and probability of the consequences of a changing climate and hence future risk for particular locations. This level of approach requires models that represent all relevant processes and parameter values at the site of interest, such as the coupled surface-groundwater modelling approach used by Thompson et al. (2004) and Thompson (2008) for assessing the impacts of climate change on the North Kent Marshes, GB. There are numerous examples of similar detailed approaches, including models of groundwater and cypress ponds in the Coastal Plain forest region, USA (Mansell et al., 2000), Prairie wetlands in USA (Gerla and Matheney, 1996), floodplain wetlands in GB (Bradley, 2002), cutover peatlands (Kennedy and Price, 2004) and blanket peat (Lapen et al., 2005) in Canada. The drawback with the Tier 3 approach is that considerable site data are required and as such it cannot be readily applied on a regional basis, hence the need for the intermediate Tier 2 approach as detailed in this paper. Acreman and Miller (2007) developed a similar riskbased framework for wetland assessment, which proposed a sequential approach starting with simple general screening-level models and moving to more complex models if simple models cannot provide sufficiently detailed or certain results. This study presents a simple framework for evaluating regional wetland ecohydrological response to climate change, i.e. a tier 2 approach. This provides an intermediate level of assessment that is appropriate at the regional scale to highlight broad issues and can be implemented with few data and a general conceptual understanding of wetland processes. ASSESSMENT FRAMEWORK METHODOLOGY As discussed above, the Tier 2 approach does not attempt to model all wetland processes at a detailed level. It identifies key processes that are important for a given wetland type within a defined region. It therefore recognizes that the results will not be very certain for any specific location, but rather give a general indication of the direction of change in ecohydrology of wetlands in that region. The proposed method has 6 steps that are shown as a flow chart in Figure 1 and described in the following text. Step 1. Define objectives of study, wetland types and scenarios This step involves defining the boundaries of the assessment and what it aims to achieve. This will include the types of wetland to be examined, particular species to be included, especially if the wetland is vital for one or more species/communities or ecosystem service. It also includes the definition of the climate change scenarios to be analysed, e.g. timescale 2050 s, 2080 s, ‘business as usual’ and the baseline against which these scenarios are to be compared, e.g. the current situation or the recent past. Step 2. Define species requirements for wetland variables The interaction of physical conditions, temperature, light, water, nutrients, salinity, chemistry, soils and rocks provides the habitat for the species/communities that inhabit wetlands. As these conditions change, they may become more or less favourable for different communities, leading to exclusion of some species and providing the opportunity for new species to become established. As driving forces change, hydrological and chemical pathways and processes may change, possibly generating very different conditions. The second step in the assessment framework involves defining the habitat requirements of the species/communities. These could be hydrological conditions, water table level, duration, frequency or related conditions, such as soil penetrability (for birds to probe for food). 1. Define objectives of study, wetland types and scenarios 2. Define species requirements for wetland variables 3. Select appropriate climate model and variables 4. Develop models to convert climate variables to wetland variables 5. Run models to define relevant wetland variables 6. Assess degree of likely change in wetland species/communities Figure 1. Flow diagram of method. Copyright 2009 John Wiley & Sons, Ltd. Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 4 M. C. ACREMAN ET AL. Step 3. Select appropriate climate model and variables The next step is to assess the climate models that are available to give predictions in the study area and to specify which variables are available as output from these models. Many models are available; these may be global or regional, may provide information on different scenarios, may be applied at different resolutions and may use different variables. In many cases, down-scaling is required, i.e. the climate models may provide average conditions for large areas that need to be processed further to give results for specific regions or local areas. Ideally, the climate model outputs directly reflect the wetland species/community needs to be defined in Step 2, such as temperature or rainfall. The outputs can be used indirectly to calculate habitat variables, e.g. rainfall and wind-speed are used in water balance calculations to provide soil moisture or water table level (Step 4). The variables available directly from climate models can be intrinsically useful for highlighting generic, nonspecific to species, implications e.g. the changing relative magnitudes of rainfall and evaporation indicate bulk changes in water availability. Step 4. Develop models to convert climate variables to wetland variables Many of the physical variables that determine habitat conditions in wetlands are not produced directly by climate models. As a result, a further step is required in the assessment framework to derive these variables. Water table regime is a key wetland variable that determines ecological character. Its evaluation requires a conceptual understanding of the wetland function to be developed, including water inputs, outputs and internal processes (Acreman and Miller, 2007) such as that shown in Figure 2 for a floodplain wetland. This is then formulated as the simplest computational model that gives adequate results (modelled hydrological time-series that correspond sufficiently well with observed patterns for the purpose of the study). Models can vary from simple spreadsheets that calculate water balance to complex finite difference models that simulate processes of water movement through the soil and rocks (e.g. MODFLOW, Bradley, 2002; Bradford and Acreman, 2003) and open channels (e.g. MIKE 11, Thompson et al., 2004). Step 5. Run models to define relevant wetland variables In this step, the various elements defined in steps 1–4 are assembled and the wetland hydrology model parameters are calibrated for the baseline (e.g. current or recent) climate; the models are then re-run using the climate change driving data to predict the response of the wetland variables to the future climate scenario. Step 6. Assess degree of likely change in wetland species/communities In this step, the results of the model runs (Step 5) are used to assess the degree to which the selected climate change scenarios (Step 1) will have an impact on the Copyright 2009 John Wiley & Sons, Ltd. Figure 2. Conceptual cross-section diagram of a floodplain wetland (green) in direct contact with underlying aquifer (brick) and river (blue). Hydrological inputs are: precipitation (P), over-bank flow (OB), runoff (R), lateral inflow (L) and, when the water table is high, groundwater discharge (GD). Outputs are: evaporation (E), surface outflow (OF), drainage (D) and, when the water table is low, groundwater recharge (GR). After Acreman and Miller (2007). habitat requirements of the species/communities (Step 2) that characterize the wetland (Step 1). APPLICATION OF THE FRAMEWORK TO GREAT BRITAIN In this section, the assessment framework is applied to two wetland types in GB to assess likely broad, regional scale impacts. Although these are temperate wetlands, the method has potential for application to other regions including tropical and semi-arid wetlands. Step 1. Define objectives of study, wetland types and scenarios Wetlands typically comprise a complex mosaic of vegetation communities that result from the physiochemical processes (interaction of precipitation, evaporation, runoff, groundwater, water chemistry, nutrient status and microtopography) and plant competition and ecohydrological feedbacks between the vegetation and the physical environment (Belyea and Baird, 2006). However, for explanatory purposes it is convenient to use simpler representations. One type of wetland, where hydrology is controlled largely by rainfall and evaporation, is wet heath or degraded raised/blanket bog. Another type is the river floodplain margin, in which hydrology is controlled primarily by river level (Duranel et al., 2007). It should be emphasized that the current application is concerned with the floodplain margin (i.e. near the river), rather than the entire floodplain. Wet heath (or degraded raised/blanket bog) is often associated with the Erica tetralix–Sphagnum compactum community which is classified under the UK national vegetation classification (NVC) community as M16 (Rodwell, 1991). M16 occurs on suitable soils throughout Britain, though absent in the more Oceanic west part of Scotland and in the English Midlands. River floodplain margins are commonly associated with Cynosurus cristatus-Caltha palustris grassland, classified as NVC community MG8 (Rodwell, 1992). MG8 was not mapped by Rodwell (1992), though it is suggested that this vegetation is widespread, but local in the British lowlands with significant stands in Southern England. Subsequent work has shown this Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 5 A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS community not only to be more diverse (with a variant especially typical of grazing marshes in Somerset) but also more extensive than suggested when first described (Gowing et al., 2002). These communities were selected as they currently have a wide distribution (Elkington et al., 1991; Wheeler et al., 2004), allowing potential climate change effects on these two wetland types to be evaluated in a standardized manner for different locations throughout GB. The occurrence of M16 varies according to geographical location: in Southern and Eastern England it is associated with wet heaths: in Northern England, Wales and Scotland it occurs on thin ombrogenous degraded blanket bog at higher altitudes (Elkington et al., 2001; Mountford et al., 2005). In both cases, the unifying feature is a periodically waterlogged soil. M16 wet heath usually occurs on oligotrophic, shallow (<0Ð5 m), acidic peats or on acid, sandy mineral soils (JNCC, 2004b, 2006). When M16 occurs on deeper peats, it may indicate degraded blanket bog or degraded raised mire still capable of natural regeneration (English Nature, 2001; JNCC, 2004c, 2006). The occurrence of MG8 also varies according to location. In Southern England it generally occurs in hydrologically managed meadow areas with ditch networks, whilst in Northern England it occurs in more natural floodplain meadows (JNCC, 2004a; Wheeler et al., 2004). In both cases the underlying soils are highly permeable, allowing significant lateral water movement. They are typically either well-structured alluvial soils over gravel or chalk, or high porosity organic soils (Wheeler et al., 2004). Typical saturated hydraulic conductivity ranges are: 1 ð 102 1 ð 107 m/s for alluvial soils, 1 ð 102 1 ð 106 m/s for sandy mineral soils (Freeze and Cherry, 1979) and from over 3Ð8 ð 104 to 4Ð5 ð 108 m/s for peat (Boelter, 1975), although even higher values have been reported in the acrotelm, i.e. the variably saturated upper peat layer (typically 1 ð 102 m/s, Ingram, 1983). Peat permeability varies according to the degree of humification/decomposition, compaction, and is lower in the catotelm, i.e. the saturated lower layer (Boelter, 1975; Gilman, 1994). The assessment framework was applied to six representative locations in GB to evaluate geographical variations in the impact of climate change by 2080. The sites were selected according to availability of data on precipitation, evaporation and river discharge from a regional climate model. in Wheeler et al. (2004), the green area shows preferred conditions, the amber area denotes ‘tolerable’ conditions that are not ideal, but which the plants can withstand for short periods whilst the red area marks conditions which the vegetation cannot tolerate. Wet heath/degraded raised mires (M16) require periodically waterlogged soils (Elkington et al., 2001) particularly in the winter and this plant community cannot tolerate extended dry conditions (water table more than 0Ð2 m below the surface). Floodplain margins (with vegetation such as MG8) are characterized by shallow water tables (Wheeler et al., 2004) with wetter conditions in the winter and drier conditions in the summer. Whilst there is detailed data for MG8 vegetation (Wheeler et al., 2004), data for M16 is scarce and the values used in Figure 3 are ‘best estimates’ derived from Mountford et al. (2005). Step 3. Select appropriate climate model and variables In 2002, the UKCIP produced a benchmark set of climate change scenarios for the United Kingdom (Hulme et al., 2002). Using a suite of global and regional climate models, the United Kindom Hadley Centre generated scenarios, known as the UKCIP02, on a 50-km grid across the United Kingdom, representative of four future global emissions pathways; termed low, medium–low, medium–high and high emissions. For the United Kingdom these scenarios suggest: Step 2. Define species requirements for wetland variables It is widely accepted that soil water table level regime is a dominant control on plant communities in wetlands (Silvertown et al., 1999). Various studies have collated water level and water quality requirements of wetland species and vegetation in GB (Newbold and Mountford, 1997; Elkington et al., 2001; Gowing et al., 2002; Wheeler et al., 2004). Figure 3 shows a ‘traffic light-based’ water level regime zones diagram that depicts the mean water table requirements for each month of the year. As defined Copyright 2009 John Wiley & Sons, Ltd. Figure 3. Water level requirements for MG8 floodplain margins (after Wheeler et al., 2004) and M16 wet heath (after Mountford et al., 2005). Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 6 M. C. ACREMAN ET AL. ž average annual temperatures may rise by 2–3Ð5 ° C by the 2080s, with associated increases in evaporation; ž winters are likely to become wetter and summers drier, with greatest changes in the South and East; ž summer soils are likely to become drier for longer. In this paper, the UKCIP02 medium–high scenario has been used. It is recognized that a full assessment of the potential impacts of climate change on a specific wetland should assess the uncertainties in climate change projections by comparing a range of different scenarios from a number of global or regional climate models. Changes in rainfall are directly available from the climate models, but changes in potential evaporation (required for the subsequent discharge modelling) are estimated from changes in temperature, wind-speed, radiation and humidity by use of standard methods, such as the Penman–Monteith equation (Monteith, 1965). Figure 4 shows baseline (1961–1990) and future (2071–2100) monthly rainfall and potential evaporation for the six sample sites across GB. Figure 4 shows that under climate change, the period during which evaporation exceeds rainfall is extended, so that there is a general implication that conditions will be drier in the summer months (May–September), with greater differences in the south of GB than in the north. Step 4. Develop models to convert climate variables to wetland variables The two wetland types selected in Step 1 were: (i) rainfed wetlands with M16 wet heath vegetation and (ii) river floodplain margin wetlands with MG8 wet grassland vegetation. Conceptual understanding has been developed for each (see below) and coded as individual computational models. The simple models adopt a water balance approach to simulate the wetland water level. The analyses exclude horizontal dimensions to allow comparison between sample sites without having to specify a (hypothetical) wetland extent for each. The wetland surface is taken as the vertical datum. This was considered an acceptable approach for regional analysis. Overall, the approach can be summarized as ‘if this type of wetland occurred at different locations across GB, then . . .’, rather than trying to model the unique processes that might occur at different sites. Water level in the rain-fed wetland is driven by precipitation (P) as the input and evaporation (E; incorporating transpiration) as an output (Figure 5). Using a daily time-step, the water level is updated by adding Figure 4. Monthly rainfall (black) and potential evaporation (red/grey), in mm/day, at the six sample sites across GB. The solid lines represent baseline (1961– 1990) climate, the dashed lines show the projected values for the 2080s (2071– 2100). Copyright 2009 John Wiley & Sons, Ltd. Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS Figure 5. Conceptual understanding of rain-fed wetland hydrology. any precipitation and subtracting the actual evaporation. If the new water level is higher than a defined maximum water level, any excess water leaves the wetland as surface outflow (OF, i.e. saturation overland flow), capping the new water level at the maximum value. For British upland blanket peat rain-fed wetlands, research has shown that storm runoff is dominated by saturation overland flow, along with near-surface through flow in the acrotelm caused by impeded percolation through the saturated catotelm (Evans et al., 1999; Holden and Burt, 2002, 2003a). Infiltration-excess overland flow was not in evidence, although macropores and pipes within the deeper peat were also found to contribute to runoff (Holden and Burt, 2003a). The predominantly saturated conditions, along with the high hydraulic conductivity of acrotelm (typically 1 ð 102 m/s, Ingram, 1983), explain the absence of infiltration-excess flows, even though peat hydraulic conductivity at the studied site is low (typically 1 ð 107 m/s below a depth of 40 cm, Holden and Burt, 2003b). Whilst the maximum water level may correspond with the wetland surface, the conceptualization also allows it to be below the surface to account for potential micro-topographic effects or free drainage from a highly permeable surface horizon (such as the acrotelm for peat substrates). Having shown that runoff from M16 vegetation over peat substrate is likely to be dominated by saturation, as opposed to infiltration-excess, overland flow, one must examine whether the same mechanism applies to M16 wet heath vegetation over the sandy mineral soil substrate. In this case, the substrate hydraulic conductivity will be between 1 ð 102 and 1 ð 106 m/s (Freeze and Cherry, 1979). The record UK 24-h rainfall total is currently 279 mm (i.e. averaging 3Ð2 ð 106 m/s; Met Office, 2008). Therefore, at the daily time-step of the model, infiltration-excess flows are unlikely to be generated for this combination of substrate hydraulic conductivity and rainfall intensity. The record UK rainfall intensity over a 5-min period is 1Ð07 ð 104 m/s (32 mm in 5 min) occurred in Lancashire in 1893 (Met Office, 2008), which is at about the mid-point of the logarithmic hydraulic conductivity range. It is therefore possible that infiltration-excess runoff might occur over short timescales, particularly under climate change scenarios of increased rainfall intensity (during winter, see Hulme et al., 2002), although such processes cannot be included Copyright 2009 John Wiley & Sons, Ltd. 7 in a model with a daily time-step. Notwithstanding this, it is our assertion that the adopted conceptualization is appropriate for a simplified Tier 2 approach as the vast majority of events during the modelled periods are unlikely to cause infiltration-excess overland flow. Thus, it is reasonable to assume that runoff from the high permeability sandy mineral soil substrate will also be dominated by saturation overland flow. As there are no data for baseline period runoff from the rain-fed wetlands, the generated overland flows cannot be compared against measured values. However, this does not detract from the current application as, in the next step, the modelled wetland water levels are calibrated to the baseline conditions. Subsequent Tier 3 modelling could incorporate testing against generated overland flows, provided such data were available. Changes in water level account for the specific yield of the substrate, i.e. the volume of water released from storage by gravity per unit surface area per unit water table decline (Freeze and Cherry, 1979). For example, if the substrate specific yield is 0Ð40, a 0Ð010-m water input will cause the water level to rise by 0Ð025 m. As a first approximation, the specific yield is assumed invariant with depth, although as shown by Gilman (1994) for a peat substrate it may sometimes decrease with depth. If necessary, and if data were available for parameterization, subsequent Tier 3 modelling could address this issue. Whilst the specific yield for the sandy mineral soil is likely to be in the range 0Ð10–0Ð35 (Johnson, 1967), the specific yield of peat can be highly variable, dependent on the degree of decomposition. For example, Boelter (1965) gives the following ranges: surficial undecomposed moss peat (0Ð52–0Ð79), woody peat and deeper undecomposed moss peat (0Ð19–0Ð33) and dense, decomposed herbaceous peat (0Ð10–0Ð15). Boelter (1975) gives: sphagnum moss peat (0Ð23–0Ð86), woody peat (0Ð19–0Ð27), herbaceous peat (0Ð13–0Ð57) and decomposed peat (0Ð08). Finally, Boelter (1964 and 1969, in Gilman, 1994) gives: undecomposed peat near the surface of acid mires (>0Ð5), fen peat and humified peat deeper in acid mires (0Ð1–0Ð2), slightly humified fibric peats (over 0Ð42), moderately humified hemi or mesic peats (0Ð15–0Ð42) and highly humified sapric peats (<0Ð15). Price and Schlotzhauer (1999) have shown that the total storativity in compressible peat substrates should be taken as the sum of the specific yield and storativity due to peat compression. However, they also note that where compressibility is low, total storativity can be adequately represented using only specific yield, and suggest that shallow (<0Ð5 m) peat generally possesses low compressibility. Therefore, since M16 wet heath vegetation generally occurs on peat less than 0Ð5 m thick (JNCC, 2004b, 2006), the use of specific yield to represent total storativity would seem appropriate. The simple model also assumes that the ground surface position is fixed, although for compressible substrates such as peat, the ground surface moves in response to seasonal and long-term variations in the water table and this ‘acts to reduce the impact of water level variation Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 8 M. C. ACREMAN ET AL. Table I. Processes, parameters and data sources used in example GB wetland modelling application. Rain-fed wetland Controlling hydrological processes User parameters Balance between evaporation and precipitation, with runoff via saturation overland flow Maximum water level, substrate specific yield, extinction top, extinction bottom. Driving data, baseline period MORECSa daily time-series of precipitation and potential evaporation Driving data, climate change scenario MORECSa potential evaporation and rainfall data multiplied by percentage change as predicted by climate model UKCIP02 medium-high scenario (Kay et al., 2006). Wet grassland evaporation ratios as an approximation for M16 vegetation Ratio for adjusting potential evaporation to account for wetland vegetation type a MORECS, Floodplain margin wetland Difference in water levels between river and floodplain margin wetland Depth to wetland, interaction rate and stage-discharge relationship parameters a, b and c. Discharge records 1961–1990 (Calver et al., 2005), mean annual flood data is taken from the UK Hydrometric Register (Institute of Hydrology, 1993) Discharge records 1961–1990 multiplied by percentage change, hydrological model and climate model using UKCIP02 medium-high scenario (Kay et al., 2006). n/a Meteorological Office Rainfall and Evaporation Calculating System (Field, 1983). on the wetland plant community’ (Gilman, 1994, p. 38). Price (2003) has shown that peat may posses a self-preservation mechanism during periods of water deficit, whereby its compressibility allows the degree of saturation to remain higher than if the peat were rigid. Precipitation is taken directly from the baseline and climate change daily time-series datasets (Table I). Actual evaporation is calculated from the daily potential evaporation data; the potential evaporation value is initially multiplied by a ‘relative evaporation’ coefficient to adjust for the wetland vegetation type (Acreman, 2004; GascaTucker et al., 2007). As few data exist on evaporation from M16 wet heath vegetation (Mountford et al., 2005), monthly wet grassland and sedge fen evaporation ratios (from Mountford et al., 2002) were compared in separate trial model runs to approximate the response of wet heaths. The modelled mean water levels were fairly insensitive to the evaporation ratio, justifying the use of wet grassland ratios (Figure 6) as an approximation. Figure 6. Evaporation ratios for wet grassland (after Mountford et al., 2002). Copyright 2009 John Wiley & Sons, Ltd. Figure 7. Example variation of evaporation extinction coefficient against water level. The potential evaporation value is also multiplied by an evaporation extinction coefficient, which accounts for reduced evaporation as the depth to water table increases (Figure 7). When the water level is above the ‘extinction top’ (point A) there is no reduction in evaporation (the extinction coefficient is 1Ð0; this allows for varying vegetation rooting depths or a tension-saturated capillary fringe above the water table) and when the water level is below the ‘extinction bottom’ (point B) the evaporation is reduced to zero (coefficient equals 0Ð0). Between these two points, the extinction coefficient is assumed to decline linearly. This linear simplification is appropriate for the current application to a temperate region, although alternative assumptions, such as a square root function (as used by Granberg et al. (1999) in a boreal application), are also plausible. Any application to arid regions should consider that, at steady state, the evaporative flux will be an inverse power law function of the depth to water table (Coudrain-Ribstein et al., 1998) Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 9 A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS The spatial variability of hydraulic conductivity and specific yield, along with peat compressibility, have not been incorporated in the modelling as we wish to implement a consistent simplified approach across the different substrates for our Tier 2 application. The M16 rain-fed wetland vegetation may be found on either (compressible) peat or (rigid) mineral soils (JNCC, 2004b, 2006) and although the exclusion of peat compressibility, and its effect on hydrological response, is rather simplistic (Whittington and Price, 2006), as discussed above, compressibility is likely to be less significant for the thin peat substrates being modelled. Subsequent Tier 3 applications could address such issues (see Section on Discussion). Conceptual understanding of the river floodplain margin wetland adjacent to the river (Figure 8) is that the water table level is mainly controlled by river level (stage). This was demonstrated as the dominant process for floodplain margin wetlands on the River Thames (Duranel et al., 2007). Further from the river, water table levels will have other influences such as rainfall and evaporation; whilst upslope runoff, groundwater discharge and recharge could also be significant. The wetland surface may be below the river bankfull level to account for natural levees. This difference in level is termed ‘depth to wetland’ in the model. The hydrological inputs are lateral inflow (L) and over-bank flow (OB) whilst the outputs are drainage (D) and surface outflow (OF). Using a daily time-step, the model updates the wetland water level according to the relative positions of the previous wetland water level, the current river stage and the fixed bankfull level. This gives three cases: 1. The previous wetland water level and the river stage are both below bankfull. In this case the change in the wetland water level, due to groundwater flux, is approximated as being in proportion to the height (i.e. head) difference between the two levels (in our simplified model we term the constant of proportionality the ‘interaction rate’). If the river stage is higher than the previous wetland water level, then the wetland level increases (lateral inflow) and if the stage is lower then the level decreases (drainage outflow). 2. The river stage is above bankfull. In this case, the wetland water level is set to the river stage value (stage rising above bankfull indicates that an over-bank input is filling the wetland; falling stage—whilst above bankfull—represents surface outflow from the wetland back into the river). Since this analysis excludes horizontal dimensions, it is assumed that the over-bank flows are of sufficient volume to fill the wetland within the daily time-step and that wetland surface outflow is unimpeded. 3. The previous wetland water level was above bankfull, but the river stage has now fallen back below bankfull. In this case the wetland water level is set to the bankfull level (surface outflow from the wetland ceases once the wetland water level falls to the bankfull level). The above processes are driven by the river stage, which is itself calculated from the daily river discharge given in the climate change dataset. The stage-discharge (SQ) relationship for each site takes the form: 1 Q c SD a b 1 where a, b and c are parameters to be specified or fitted. The bankfull level is determined using the stagedischarge relationship and the bankfull discharge. In this application, for each of the sample sites, the bankfull discharge is approximated as the mean annual flood discharge. This implies that, on average, the wetland sites should be flooded once every 2Ð33 years (Richards, 1982). The relevant values are taken from the Hydrometric Register for the period ending 1990, which coincides with the baseline period (Institute of Hydrology, 1993). It should be noted that the bankfull discharges given in the Hydrometric Register cannot be used directly as the anthropogenically modified channel conditions at the gauging stations are unlikely to reflect the more natural conditions coincident with the hypothesized floodplain margin wetlands. Table I summaries the hydrological processes considered in the models, the model parameters and the data sources used to estimate the impacts of climate change. The discharge data are shown in Figure 9. The river discharge data sets are intended to be typical examples of flow regimes from the regions of GB since the response of individual catchments will vary according to factors such as size, shape, drainage network, topography, soils and geology. Step 5. Run models to define relevant wetland variables Figure 8. Conceptual understanding of river floodplain margin wetland hydrology. Copyright 2009 John Wiley & Sons, Ltd. The computational models, representing the selected wetland and vegetation types, were initially run for the baseline climate data. Since the amber zone of the water level requirements diagram (e.g. Figure 3) is ‘only tolerable for limited periods’ (Wheeler et al., 2004, p.24), the monthly means for the baseline time period should ideally remain within the green zone. The model user parameters were therefore calibrated accordingly. This involved a manual optimization, which attempted to ensure that the parameters were given realistic values based on published data (see below for values tested). Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 10 M. C. ACREMAN ET AL. Figure 9. Monthly mean discharge time-series, in m3 s1 , at the six sample sites across GB (after Calver et al., 2005). The solid lines represent baseline (1961– 1990) climate, the dashed lines show the projected values for the 2080s (2071– 2100). Of course, other parameters-sets may be equally good, if not better, as the models are likely to suffer from equifinality (e.g. Beven, 1989, 2001). However, in this application, the aim was to select a reasonable parameter set representative of general GB conditions, rather than attempt to identify a supposedly ‘optimal’ set for each wetland site. The same calibrated parameters were then used in the model runs for the future climate scenarios, as the processes they describe are assumed to stay the same. For the rain-fed wetland, the maximum water level was set to 0Ð05 m (tested: 0Ð00 m or 0Ð05 m), the substrate specific yield to 0Ð4 (tested: 0Ð3, 0Ð35, 0Ð4, 0Ð5), the extinction bottom to 0Ð5 m (tested: 0Ð5, 1Ð0, 1Ð5 m) and the extinction top to 0Ð1 m (tested: 0Ð2, 0Ð1, 0Ð0 m). The extinction bottom coincides with maximum peat depth for M16 wet heath vegetation, i.e. 0Ð5 m (JNCC, 2006). The extinction top relates to a 7 cm mean capillary fringe thickness for sandy soils Copyright 2009 John Wiley & Sons, Ltd. (Carsel and Parrish, 1988). The use of a maximum water level slightly below the mean surface height reflects the ‘rapid generation of near-surface or surface runoff, which is characteristic of blanket peat catchments, and occurs when water levels. . . are within 5 cm of the surface (i.e. within the acrotelm)’ (Evans et al., 1999, p. 159). The specific yield value is similar to measurements for ‘typical’ peat (0Ð44, Morris and Johnson, 1967), ‘typical’ peat (0Ð3–0Ð5, Acreman and Miller, 2007), Sphagnum moss peat (0Ð23–0Ð86, Boelter, 1975), herbaceous peat (0Ð13–0Ð57, Boelter, 1975) and moderately humified hemi or mesic peat (0Ð15–0Ð42, Boelter, 1964 and 1969, in Gilman, 1994). It is slightly above the maximum for sand (0Ð10–0Ð35, Johnson, 1967). It thus represents a compromise between the values for the two different potential substrates: peat and sandy mineral soil. For the river floodplain margin wetland, the ‘interaction rate’ was set to 0Ð1-m wetland water level change per metre of head difference per day (fixed), the depth Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 11 A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS to wetland was set to 0Ð5 m, the stage-discharge parameter a was set to 0Ð0 and the parameter c was set to 2Ð0. These parameters represent typical values (e.g. Freeze and Cherry, 1979; Institute of Hydrology, 1993 and Duranel et al., 2007) and were held constant at all of the modelled sample sites. Individual values of the stage-discharge relationship parameter b were specified at each of the sample sites. This allowed the discharge data, which varied in magnitude according to the catchment type and scale, to be fitted to the vegetation water level requirements. The following values were used: 170 (Northern Scotland), 45 (Southern Scotland), 65 (Wales), 50 (Northern England), 7 (South WestEngland) and 21 (South East England). The initial conditions (i.e. the starting wetland water level) for the models were specified as follows: for the rain-fed wetland, the initial water level was set to 0Ð05 m. This represents the optimal end of year value for M16 vegetation (Figure 3). For the river floodplain margin wetland, the initial water level was set to the river stage for the first day. Both models were rather insensitive to their initial conditions, which is unsurprising given that the model output is monthly averages for a 30-year period. The model results for the baseline and future climates, for the rain-fed and the river floodplain margin wetland types, are given in Figures 10 and 11. Although both models calculate daily changes in the wetland water level for the 30-year period (baseline and future), the output has been summarized using mean monthly values for each time period under consideration. Step 6. Assess degree of likely change in wetland species/communities Under both baseline and future climates, South West England and Wales exhibit a seasonal rainfall pattern, Figure 10. Monthly mean water table levels (m) for the rain-fed wetland type, superimposed on the M16 wet heath water level requirements zone graphs, at the six sample sites across GB. The solid lines represent baseline (1961– 1990) climate, the dashed lines show the projected values for the 2080s (2071– 2100). Copyright 2009 John Wiley & Sons, Ltd. Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 12 M. C. ACREMAN ET AL. Figure 11. Monthly mean water table levels (m) for the river floodplain margin wetland type, superimposed on the MG8 grassland water level requirements zone graphs, at the six sample sites across GB. The solid lines represent baseline (1961– 1990) climate, the dashed lines show the projected values for the 2080s (2071– 2100). with higher monthly mean rainfall in the winter. In Scotland, Northern and South East England, monthly mean rainfall for the baseline is evenly distributed through the year; in the future this pattern remains in Northern Scotland, but in Southern Scotland, Northern and South East England summers become drier and winters wetter. In all regions, the period over which monthly mean potential evaporation exceeds monthly mean rainfall increases by a few weeks in spring and autumn. For the future scenario, these periods range from June–August in Northern Scotland to April–October in South East England. Under the baseline climate, monthly mean river flow is lower in summer than in winter across all of GB. Under the future climate, summer river flows reduce in all regions, whilst winter river discharge increase in all regions except for Northern England. In general, reduced summer rainfall and increased summer evaporation may lead to a northerly shift in Copyright 2009 John Wiley & Sons, Ltd. wetland vegetation types. For example, England may become too dry for some rain-fed communities, such as wet heath and degraded raised mire (M16) and conditions may become marginal in Wales. In addition, summer conditions may become too dry in Southern England for MG8 floodplain margin vegetation. The results are consistent with the hypothetical assessment of Winter (2000), who considered that wetlands fed directly by rainfall would be the most vulnerable to climate change. However, it should be noted that the sitespecific ecohydrological response of compressive peat substrates will be more complex than for rigid mineral substrates, with feedbacks between water table positions, peat subsidence and decomposition (Whittington and Price, 2006). The model outputs are monthly mean water levels calculated for the time periods being considered and therefore do not show the variability for each individual year. Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS As such, extreme events, such as significant floods or droughts, are not depicted. These could potentially cause hydrological thresholds, critical for wetland vegetation existence, to be exceeded with no possibility of recovery. The model outputs therefore represent the general suitability for particular wetland vegetation communities, but do not purport to elucidate the timing of any changes in these communities. Furthermore, the 2071–2100 forecast does not include any uncertainty effects. Such effects could be incorporated by considering alternative climate change scenarios (for example, the remaining UKCIP02 Low, Medium–Low and High emissions) although this is outside the scope of the current example application as defined in Step 1. Uncertainty is implicitly included in the water requirement specifications (e.g. Figure 3) through the use of different zones as opposed to a single ‘optimal’ line. Table II summarizes the likely change in vegetation communities for both rain-fed wet heath/degraded raised mire and river-fed floodplain margin wetlands under the Medium–High UKCIP02 climate change emissions scenario. It should be noted that the results, whilst providing general regional implications for the effects of climate change, are specific to the modelled sites and their hydrological conceptualization. For example, in terms of localized climate effects, those rain-fed wetlands in mountainous areas that occur on windward sites may experience greater rainfall than leeward sites (which lie in the rainshadow) meaning that they could potentially still support the wetland vegetation type, whilst the neighbouring leeward site could not. The hydrological functioning, and hence necessary conceptual understanding, of similar wetland sites can also vary significantly according to local factors such as morphology, geology and topography (Devito et al., 1996; Ferone and Devito, 2004). These generalized results are therefore applicable to other wetlands, providing that they are also controlled by the processes included in the model conceptual understanding. DISCUSSION AND CONCLUSIONS We used simple water balance models to represent our conceptual understanding of how two different wetland types work. These models are classed as ‘tier 2’ (Willows and Connell, 2003), between qualitative conceptual modelling and detailed site studies. Models in Tier 2 do not represent all hydrological processes present within the studied wetlands as would be the case with the physically based distributed two-or three-dimensional models in Tier 3, but provide more quantitative estimates than can be derived from the qualitative Tier 1 approach. The aim was to investigate the potential effects of climate change on wetland communities across regions and not to simulate an exact time-series of hydrological conditions or spatial representations of wetland conditions. Water balance hydrological models were an appropiate choice given the level of information on future climatic conditions and the amount of data required for parameterising and setting boundary conditions of multidimensional Copyright 2009 John Wiley & Sons, Ltd. 13 models for simulation of wetland hydrology. We were able to calibrate the models to reproduce water table level regimes appropriate for the expected plant communities under the baseline climate conditions. The models were limited to assessing whether baseline plant communities are likely to be sustained under hydrological alterations caused by future climate change. We did not attempt to model possible changes in plant communities or feedback mechanisms, such as alterations to evaporation rates that any new plant communities may cause. Predictions of future global and regional climate change are inherently uncertain, depending, as they do, on social and economic scenarios, which are impossible to predict with any certainty. Even for a given scenario, regional predictions, particularly of rainfall (and extremes), remain very uncertain. Nevertheless, we propose that frameworks and models based on best scientific understanding provide an acceptable estimate of the vulnerability of wetlands to future hydrological alterations. Whilst GB may be considered to be relatively data rich, we believe that sufficient information is available in many countries to apply this framework for the assessment of climate change impacts on wetlands. In other regions of the world, different processes may be more important, for example, infiltration is a key process in sandy soils of some floodplains in India (Nielsen et al., 1991), groundwater discharge is dominant in dambos of Southern Africa (McCartney, 2000), salinity (Jolly et al., 2008) and evaporation (Coudrain-Ribstein et al., 1998) are major factors in arid/semi-arid wetlands and the seasonal freeze-thaw cycle can control the ecohydrological functioning of peat bogs in Canada (Montemayor et al., 2008). The climate change scenario used for this vulnerabilitytype analysis has been kept deliberately simple, applying monthly percentage changes to rainfall and potential evaporation to the driving climate data of the hydrological model. While this does not provide any assessment of the changes in river flow due to changes in sub-monthly rainfall, rainfall intensity or rainfall variability, it does allow this type of scenario data to be derived relatively easily for anywhere in the world, for example, from the IPCC 4th Assessment Report scenario set (Meehl et al., 2007). Having evaluated, using a Tier 2 approach, the regional wetland ecohydrological response to climate change, those wetland types and locations identified as being more sensitive to climate change could be analysed in greater detail using a Tier 3 modelling approach. The proposed methodology can thus also be used as a screening tool. The Tier 3 modelling could include: (i) field measurements and baseline monitoring: to identify critical ecohydrological processes and feedbacks to be incorporated (e.g. trajectories of vegetation community change, and their feedbacks, in response to ecohydrological change), provide site-specific model parameterization, provide data for calibration and validation (including Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco Copyright 2009 John Wiley & Sons, Ltd. January–December, mean water level ‘green’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ Wales Northern England South West England South East England Southern Scotland January–July, November–December, mean water level ‘green’ August–October, mean water level ‘amber’ January–December, mean water level ‘green’ Northern Scotland 1961–1990 2071–2100 June–November, mean water level ‘amber’ January–May, December, mean water level ‘green’ January–July, October–December, mean water level ‘green’ Aug-Sep, mean water level ‘amber’ January–June, October–December, mean water level ‘green’ Jul-Sep, mean water level ‘amber’ January–May, December, mean water level ‘green’ June–November, mean water level ‘amber’ January–May, November–December, mean water level ‘green’ June–October, mean water level ‘amber’ January–December, mean water level ‘green’ Rain-fed wetlands January–December, mean water level ‘green’ March–August, November–December, mean water level ‘green’ Januaru–February, September–October, mean water level ‘amber’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ 1961–1990 2071–2100 January–February, mean water level ‘red’ January–August, November–December, mean water level ‘green’ September–October, mean water level ‘amber’ April–July, November–December, mean water level ‘green’ March, August–October, mean water level ‘amber’ January–August, October–December, mean water level ‘green’ September, mean water level ‘amber’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ January–December, mean water level ‘green’ River-fed wetlands Table II. Summary of climate change impacts on rain-fed and river-fed wetlands (green D wetlands will be maintained; amber D wetlands under threat; red D wetlands likely to be lost). 14 M. C. ACREMAN ET AL. Ecohydrol. 2, 1–17 (2009) DOI: 10.1002/eco 15 A FRAMEWORK TO ASSESS CLIMATE CHANGE IMPACTS ON WETLANDS validation of internal model processes, e.g. the generated overland flows from rain-fed wetlands in the above GB application); (ii) using higher temporal resolution data, which would permit the shorter model time-step necessary to evaluate the potentially increased significance of short duration events under climate change (e.g. infiltration-excess flows could become more significant under increasing rainfall intensities for the GB rain-fed wetland application); (iii) adding model dimensionality to capture spatial effects such as wetland topography, morphology, variable substrate depths and size; and (iv) addressing spatial heterogeneity of physical parameters such as specific yield, and possible anisotropic hydraulic conductivities. For the particular case of M16 wet heath vegetation on peat (as opposed to mineral soil) substrate, more advanced Tier 3 modelling could include: (i) peat compressibility effects on wetland hydrology (e.g. Price and Schlotzhauer, 1999; Kennedy and Price, 2005) and its feedback on dewatering which ameliorates the effect of water level variation on wetland vegetation (Gilman, 1994; Price, 2003); (ii) an advanced representation of the runoff-generating process in peat, which will be needed to capture spatio-temporal variations in the ecohydrological functioning of blanket mires (Holden and Burt, 2003a); (iii) the role of vertical flow reversals in compressible peatland caused by water deficit and water table drawdown (Devito et al., 1997); (iv) erosion and regeneration processes in peat, as climate change induced rainfall increases may increase erosion in upland areas but promote peat regeneration in lowland areas (Heathwaite, 1993); and (v) the variability of peat hydraulic conductivity and specific yield in space, for example, according to the degree of humification and compaction (Gilman, 1994). The high range of peat specific yield and hydraulic conductivity values reported in the previous section indicate that this may well be essential for detailed studies, although Holden and Burt (2003c) found that hydraulic conductivity measurements made in blanket peat using compressible soil theory might not vary significantly with depth (between 10 and 80 cm below surface). The potential impacts of future climate change on wetland hydrology are of interest to a wide range of stakeholders from site managers to international policy makers. Ecohydrological models that combine climate changes, hydrological processes and ecological response provide a means of estimating what might happen to some characteristics of wetlands in the future. The framework presented in this paper offers a step-by-step method that can be used for combining models and available data at a regional scale and is appropriate for different wetlands, in different countries, with different data availability. The simple models are based on broad conceptual understanding and are intended to describe basic wetland hydrological processes within the constraints of data availability; they are thus fit for the purpose of general assessment and do not pretend to provide precise results for specific wetland sites. Although the models successfully represent the baseline conditions, it is not possible to test Copyright 2009 John Wiley & Sons, Ltd. whether they accurately predict the future vulnerability of the selected areas. Data from GB have been used to demonstrate each step in the framework and results suggest that reduced summer rainfall and increased summer evaporation will put stress on wetland plant communities in late summer and autumn with conditions becoming too dry in the south of England for some rain-fed communities. In addition, these impacts are likely to be less for wetlands fed by river flows than for rain-fed wetlands. 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