Download Invited Commentary A simple framework for evaluating regional

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
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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
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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
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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
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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)
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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)
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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)
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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)
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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)
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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
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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)
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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)
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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.
This will not necessarily be true in other regions of the
world, such as in arid and semi-arid climates where relatively small changes in rainfall can result in much greater
changes in flow and so may impart greater impacts on
river-fed wetlands.
ACKNOWLEDGEMENTS
Research for this paper was funded by the UK Natural Environment Research Council and the International
Convention on Wetlands (Ramsar). The authors are grateful to Nick Davidson, Heather Mackay, Max Finlayson
and other members of the Convention’s Science and
Technical Review Panel for their encouragement and constructive comments. Two anonymous referees and Prof
Andrew Baird provided very useful, constructive reviews,
which strengthened the paper. The National Institute of
Water and Atmospheric Research, New Zealand, supported Doug Booker to complete his input to the study.
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