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Modelling the effects of climate and land-use change on the hydrochemistry and ecology of the River Wye (Wales)

Modelling Climate and Land-use
Change Impacts on Hydrology, Nitrate
and Aquatic Biota in the River Wye
Gianbattista Bussi1*, Paul G. Whitehead1, Cayetano Gutiérrez-Cánovas2,3, José L. J. Ledesma4,
Steve J. Ormerod2, Raoul-Marie Couture5,6
1 School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1
3QY (UK)
2 Catchment Research Group, Cardiff University, School of Biosciences, The Sir Martin Evans
Building, Museum Avenue, Cardiff CF10 3AX (UK)
3 Freshwater Ecology and Management group, Department of Evolutionary Biology, Ecology and
Environmental Sciences - Institut de Recerca de la Biodiversitat (IRBio), University of Barcelona,
Avinguda Diagonal, 643, 08028 Barcelona (Spain)
4 Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences,
Lennart Hjelms väg 9, 750 07 Uppsala (Sweden)
5 Norwegian Institute for Water Research, Gaustadalléen 21, Oslo, 0349 (Norway)
6 Ecohydrology Group, Department of Earth and Environmental Sciences, University of Waterloo,
Waterloo, Canada, G1S1W2
*Corresponding author:
Dear Editor,
Climate and Land-use Change Impacts on
Hydrology, Nitrate and Aquatic Biota in the River Wye (Wales)
results of a modelling framework to evaluate the impacts of socio-economic scenarios, including
climate change and land-use change, on the nutrient concentration and aquatic biota of the River
Wye, in Wales. We believe that this paper should be of interest for your journal, from a
methodological point of view, since the approach we present, coupling climate, hydrological, water
quality and ecological models, has not been employed before and from a policy point of view, since it
indicates possible future outcomes of ecological diversity response in aquatic communities, pointing
out possible mitigation strategies. This paper cover a topic that has implications the hydrosphere, the
biosphere and the anthroposhpere.
Thank you for your consideration of this manuscript.
The authors
Cover Letter
Modelling Climate and Land-use
Change Impacts on Hydrology, Nitrate
and Aquatic Biota in the River Wye
Gianbattista Bussi1*, Paul G. Whitehead1, Cayetano Gutiérrez-Cánovas2,3, José L. J. Ledesma4,
Steve J. Ormerod2, Raoul-Marie Couture5,6
1 School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1
3QY (UK)
2 Catchment Research Group, Cardiff University, School of Biosciences, The Sir Martin Evans
Building, Museum Avenue, Cardiff CF10 3AX (UK)
3 Freshwater Ecology and Management group, Department of Evolutionary Biology, Ecology and
Environmental Sciences - Institut de Recerca de la Biodiversitat (IRBio), University of Barcelona,
Avinguda Diagonal, 643, 08028 Barcelona (Spain)
4 Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences,
Lennart Hjelms väg 9, 750 07 Uppsala (Sweden)
5 Norwegian Institute for Water Research, Gaustadalléen 21, Oslo, 0349 (Norway)
6 Ecohydrology Group, Department of Earth and Environmental Sciences, University of Waterloo,
Waterloo, Canada, G1S1W2
*Corresponding author:
*Graphical Abstract
Modelling Climate and Land-use
Change Impacts on Hydrology, Nitrate
and Aquatic Biota in the River Wye
Gianbattista Bussi1*, Paul G. Whitehead1, Cayetano Gutiérrez-Cánovas2,3, José L. J. Ledesma4,
Steve J. Ormerod2, Raoul-Marie Couture5,6
1 School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1
3QY (UK)
2 Catchment Research Group, Cardiff University, School of Biosciences, The Sir Martin Evans
Building, Museum Avenue, Cardiff CF10 3AX (UK)
3 Freshwater Ecology and Management group, Department of Evolutionary Biology, Ecology and
Environmental Sciences - Institut de Recerca de la Biodiversitat (IRBio), University of Barcelona,
Avinguda Diagonal, 643, 08028 Barcelona (Spain)
4 Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences,
Lennart Hjelms väg 9, 750 07 Uppsala (Sweden)
5 Norwegian Institute for Water Research, Gaustadalléen 21, Oslo, 0349 (Norway)
6 Ecohydrology Group, Department of Earth and Environmental Sciences, University of Waterloo,
Waterloo, Canada, G1S1W2
*Corresponding author:
Socio-economic scenarios used to assess future changes in river nutrients and biota
Climate change expected to cause nutrient enrichment
Longitudinal position along the river mediates ecological response
Land-use change plays critical role in mitigation of climate change
*Highlights (for review)
Modelling Climate and Land-use
Change Impacts on Hydrology, Nitrate
and Aquatic Biota in the River Wye
Gianbattista Bussi1*, Paul G. Whitehead1, Cayetano Gutiérrez-Cánovas2,3, José L. J. Ledesma4, 5
Steve J. Ormerod2, Raoul-Marie Couture5,6 6
1 School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 7
3QY (UK) 8
2 Catchment Research Group, Cardiff University, School of Biosciences, The Sir Martin Evans 9
Building, Museum Avenue, Cardiff CF10 3AX (UK) 10
3 Freshwater Ecology and Management group, Department of Evolutionary Biology, Ecology and 11
Environmental Sciences - Institut de Recerca de la Biodiversitat (IRBio), University of Barcelona, 12
Avinguda Diagonal, 643, 08028 Barcelona (Spain) 13
4 Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, 14
Lennart Hjelms väg 9, 750 07 Uppsala (Sweden) 15
5 Norwegian Institute for Water Research, Gaustadalléen 21, Oslo, 0349 (Norway) 16
6 Ecohydrology Group, Department of Earth and Environmental Sciences, University of Waterloo, 17
Waterloo, Canada, G1S1W2 18
*Corresponding author: 19
Interactions between climate change and land use change might have substantial effects on aquatic 21
ecosystems, but are still poorly understood. Using the Welsh River Wye as a case study, we linked 22
models of water quality (Integrated Catchment - INCA) and climate (GFDL - Geophysical Fluid 23
Dynamics Laboratory and IPSL - Institut Pierre Simon Laplace) under greenhouse gas scenarios 24
(RCP4.5 and RCP8.5) to drive a bespoke ecosystem model that simulated the responses of aquatic 25
biota. The potential effects of economic and social development were also investigated using 26
scenarios from the EU MARS project (Managing Aquatic Ecosystems and Water Resources under 27
Multiple Stress). Longitudinal position along the river mediated response to increasing anthropogenic 28
pressures. Upland locations appeared particularly sensitive to nutrient enrichment or potential re-29
acidification compared to lowland environments which are already eutrophic. These results provide 30
some ideas on how to mitigate future impacts and reiterate the need for sensitive land management in 31
upland, temperate environments which are likely to become increasingly important to water supply 32
and biodiversity conservation as the effects of climate change intensify. 33
Keywords: Climate Change, Water quality, River Wye, Nitrogen, Ecology 35
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With the Paris Agreement and the Intergovernmental Panel on Climate Change (IPCC) Reports 38
(Pachauri et al., 2014), there is no doubt about the global significance of climate change driven by 39
anthropogenic sources of carbon dioxide. The positive and negative impacts of climate change on the 40
natural environment and people across the globe are still being considered and debated, but the 41
potential changes in precipitation, temperature and sea level rise over the next century are likely to be 42
significant, with important impacts on hydrology, water quality and ecology (Whitehead et al., 2009). 43
The IPCC report considers the impacts of socioeconomic change superimposed on climate change. 44
Thus Shared Socio-economic Pathways (SSPs) will interact with climate change to generate a 45
combined impact on people and livelihoods. This provides an integrated framework for addressing 46
issues of change for national, regional and local governments and organizations to consider. 47
The IPCC report and the EU Water Framework Directive (Chave, 2001) provide a backdrop to a major 48
new research project entitled MARS (Managing Aquatic Ecosystems and Water Resources under 49
Multiple Stress) funded by the European Union under the 7th Framework Programme (Hering et al., 50
2015). This project aims to address two target groups, namely water managers, assessing and 51
restoring rivers and lakes, and policy makers, drafting and implementing policies related to water. In 52
any such study there is a need to understand the effects of multiple stressors on surface waters and 53
groundwaters, their biota, and the ecosystem services they provide to people. River ecosystems are 54
very likely to be affected by land-use or climate changes (Strayer and Dudgeon, 2010). Several 55
studies have observed a reduction in diversity or abundance of river organisms in response to land-56
use (e.g., Gutiérrez-Cánovas et al., 2013), climate change (e.g., Durance and Ormerod, 2007) or 57
anthropogenic disturbances (Ruhí et al., 2015). The reduction in river biodiversity is likely to reduce 58
the capacity of these ecosystems to provide essential goods and services (Hooper et al., 2005), such 59
as clean water, flood prevention or timber. The knowledge from the EU MARS project can be 60
translated into advice on river basin management, assessing how to restore multiply stressed rivers 61
and lakes and providing new measures or programmes to meet the EU Water Framework Directives. 62
As part of the MARS project, upland Wales has been investigated as a Northern Region that has 63
been subject to much environmental change over the past 50 years (Durance and Ormerod, 2007; 64
Whitehead et al., 2009, 1998a). Many of the upland headwaters in mid and southern Wales drain into 65
large river systems and one of those is the River Wye (Figure 1). In this study we evaluate the River 66
Wye in terms of its hydrology, water quality and ecology and how these might change under a 67
changing climate and changing socio-economic pressures. We utilise the INCA suite of models 68
(Wade et al., 2002; Whitehead et al., 1998a) to quantify the change and use the model to simulate 69
new future approaches to manage the environment. 70
The River Wye catchment is located in the Western Regions of the UK, in South and Mid- Wales, as 72
shown in Figure 1. It flows from Mid-Wales towards South-East Wales, reaching the River Severn 73
estuary and the Bristol Channel at the town of Chepstow. Its catchment area is 4131 km2. The Rivers 74
Lugg and the Monnow are its main tributaries, flowing into the main River Wye reach downstream of 75
Hereford (Jarvie et al., 2005). The main land use is agriculture with livestock farming predominating in 76
the north and west and more intensive arable farming in the south and east of the catchment. There is 77
some industry based around the major towns (e.g. Monmouth and Chepstow). The upland areas of 78
the catchment are generally used for rough grazing, while lowland areas support mixed and dairy-79
farming and horticulture (Oborne et al., 1980). The water quality of the River Wye is characterised by 80
patterns of high winter concentrations of nitrate and low summer concentrations (Oborne et al., 1980), 81
mainly related to agriculture and fertiliser usage. 82
The Wye catchment is rich in wildlife and habitats and this is recognised in the designation of the Wye 83
and several tributaries as a riverine Special Area of Conservation. The area offers many opportunities 84
for water-based recreation. The River Wye is a well-established and nationally significant salmon and 85
brown trout rod fishery and also a locally important coarse fish fishery. Elver fishing also takes place 86
within the tidal reaches of the Wye. The Elan Valley system of reservoirs (North-Western part of the 87
catchment) is vital in providing water for Birmingham, Gloucestershire and South Wales. The local 88
economy is moderately dependent on businesses requiring water abstraction, primarily agricultural, 89
where trickle and spray irrigation is frequently used. 90
Figure 1. The River Wye catchment and the INCA model sub-catchments.
Daily water discharge time series have been retrieved from the National River Flow Archive (NRFA, 93 Several stream gauges can be found within the River Wye 94
catchment (see Table 1). Nitrate and Ammonium data were obtained from the Centre for Ecology and 95
Hydrology for eight locations within the River Wye catchment, collected from 2004 to 2009 with a 96
monthly to fortnightly frequency, and used for model calibration. Data collected by the Environment 97
Agency of England and Wales was also used for three locations, spanning from 1974 to 2012, and 98
used for model validation (Simpson, 1980). 99
Table 1. Data used for the INCA model set-up and calibration.
Variable Data source Use
Precipitation and
temperature Met Office Input 1960-2015 All
Elevation Ordnance Survey Parameter - All
Land use
Land Cover Map 2007, Centre of
Ecology (Smith et al., 2007) Parameter 2007 All
deposition Donald and Stoner, (1989) Parameter 1989 All
Sewage treatment
plants European Environment Agency Parameter 2014
Reaches 11, 13,
14, 17, 18
Flow National River Flow Archive
Calibration and
validation 1960-2015
Reaches 2
, 8, 9,
10, 14, 15, 17,
21, 28
Nitrate Centre for Ecology and Hydrology Calibration 2004-2009 Reaches
2, 9,
12, 15, 27, 28, 29
Environment Agency of England and
Wales Validation 1974-2012 Reaches
2, 14,
The INCA model is a process-based model which simulates the main processes related with rainfall-103
runoff transformation and the cycle and fate of several compounds, such as nitrate, ammonium, 104
carbon and phosphorus. The INCA Model has been developed over several years as a result of 105
several research projects and is a dynamic computer model that predicts water quantity and quality in 106
rivers and catchments. The primary aim of INCA is to provide a process-based representation of the 107
factors and processes controlling flow and water quality dynamics in both the land and in-stream 108
components of river catchments, whilst minimising data requirements and model structural complexity 109
(Whitehead et al., 1998a, 1998b). As such, the INCA model produces daily estimates of discharge, 110
Also, the model is semi-distributed, so that spatial variations in land use and management can be 112
taken into account. The hydrological and nutrient fluxes from different land use classes and sub-113
catchment boundaries are modelled simultaneously and information fed sequentially into a multi-114
reach river model. The INCA model was originally tested on 20 catchments in the UK for a variety of 115
purposes (Crossman et al., 2013; Lu et al., 2017, 2016; Nizzetto et al., 2016; Whitehead et al., 2016), 116
including catchments in Wales (Bussi et al., 2017b), and over 20 catchments across the EU and now 117
30 catchments around the world. The INCA model has also been tested for land-use and climate 118
change impact assessment applications (Bussi et al., 2016a, 2016b). 119
Figure 2. The land component of nitrogen cycle, from Whitehead et al. (1998a) and Wade et al. (2002a).
The INCA model requires time series of Hydrological Effective Rainfall (HER) and Soil Moisture Deficit 122
(SMD) as inputs, and these have to be produced by an independent hydrological model, which takes 123
into account soil water retention and evapotranspiration. In this study, the PERSiST model was used 124
(Futter et al., 2014), a simple and flexible hydrological model especially created to produce inputs for 125
the INCA family of models. Precipitation and temperature data were taken from Met Office stations. 126
Several stations exist within the Wye catchment, measuring daily meteorological variables. Given the 127
topography of the catchment, with steep slopes and relatively large difference in altitude from the 128
uplands to the lowlands, and the natural spatial variability of rainfall and temperature, a single station 129
cannot provide exhaustive information about the precipitation falling on the catchment and the 130
temperature over the whole catchment. For this reason, the average precipitation falling on the 131
catchment and the average catchment temperature were determined, using information from several 132
rain gauges spread all over the catchment. The mean temperature was calculated as the average 133
between minimum and maximum temperature. 134
Spatially distributed information is required to estimate some of the INCA model parameters. The 135
Ordnance Survey (OS) Terrain 50 was used as a digital elevation model. The digital elevation model 136
was used to define the sub-catchment boundaries, and to calculate their areas and mean reach slope. 137
The Land Cover Map 2007, released by the Centre of Ecology and Hydrology in 2011, was used to 138
characterise the land uses in the catchment (Smith et al., 2007). The land cover categories were 139
aggregated to six classes of land use: forest, short vegetation (ungrazed), short vegetation (grazed, 140
non-fertilised), short vegetation (fertilised), arable and urban, following Jin et al. (2012). The 141
proportion of land use for each sub-catchment is required by the INCA model. The River Wye 142
catchment was divided into several sub-catchments (Table 2.). For each catchment, catchment area, 143
reach length and land uses were defined. 144
The INCA model was calibrated over the time period 1/1/2004 31/10/2009 and validated over the 145
time period 1960-2015, with a daily time step. The model parameters were manually adjusted to 146
reproduce observed values of hydrological and water quality variables. In particular, observed values 147
of water discharge were used to calibrate the hydrological model parameters (direct runoff residence 148
time, soil water residence time, ground water residence time, threshold soil zone flow, rainfall excess 149
proportion, maximum infiltration rate, discharge/velocity relationship coefficient and exponent) and 150
observed values of nitrate concentration were used to calibrate the INCA model parameters 151
(denitrification rate in soil and river, nitrification rate in soil and river, mineralisation rate in soil, 152
immobilisation rate in soil, fertiliser addition rate in soil, plant uptake). Manual calibration has been 153
proved as a robust method for obtaining acceptable simulations with the INCA family of models 154
(Ledesma et al., 2012). 155
Table 2. INCA model sub-catchments and reaches and their correspondent land uses.
h (km)
Forest (%)
(%) Urban (%)
1 3.84 1.73 0 33 67 0 0 0
2 6.70 3.24 0 0 100 0 0 0
3 11.75 5.79 25 10 65 0 0 0
4 5.22 2.04 16 7 77 0 0 0
5 41.28 11.12 26 7 40 26 1 0
6 103.92
13.4 6 12 42 39 1 1
7 183.95
22.4 3 43 51 0 0 4
8 358.81
44.7 6 5 23 62 3 1
9 323.74
20.08 4 13 20 56 7 1
10 244.31
33.5 19 14 24 37 6 0
11 892.50
72.2 7 3 3 43 42 2
12 131.92
22 8 12 12 57 11 0
13 162.73
25.1 7 1 4 52 35 1
14 186.06
25.5 4 0 0 14 79 3
15 130.81
15.5 3 6 6 70 15 2
16 166.74
26 1 0 0 40 58 1
17 72.59 11.9 6 0 0 10 57 27
18 30.02 16.7 21 0 0 8 71 0
19 43.93 9.8 13 0 0 4 75 8
20 153.44
22.9 7 0 1 22 69 0
22 40.36 3.7 6 0 3 36 55 1
23 161.29
27.5 2 18 8 49 23 0
24 118.94
17.6 4 0 2 9 84 1
25 91.14 27.3 2 0 0 23 76 0
26 67.75 16.7 6 1 2 41 49 1
27 142.83
25.6 7 1 2 49 40 1
28 118.47
29.1 29 0 0 17 49 5
29 18.91 7 21 0 0 29 34 16
In order to forecast the response of the biological communities of the River Wye to land-use and 159
climate change scenarios, ecological models were built using biological and environmental data from 160
78 locations placed in Mid and North Wales. In those rivers, aquatic invertebrates were collected in 161
spring (March-April 2012-13) using kick-sampling (2-minutes just in riffles) to characterise the 162
biological communities (see Supplementary Material Table 1 for the sampling details and biological 163
metric estimation). Invertebrate richness (number of invertebrate taxa) and response diversity (variety 164
and range of biological attributes to cope with disturbance based on functional richness, Villéger et al., 165
2008) were derived to quantify the response of these aquatic communities. Response traits included 166
fuzzy coded traits such as number of generations per year, lifespan, reproduction mode, respiration 167
type, resistance form and dispersal capacity (Tachet et al., 2010). The main gradients of 168
anthropogenic or natural environmental variation were also characterised, including annual pH, total 169
oxidised nitrogen (TON), altitude (alt), precipitation of the wettest month (prec_max), geographical 170
latitude (lat) and longitude (lon). TON was log-transformed to reduce distribution skewness. All the 171
predictors were standardised to mean=0 and SD=1 to allow for within-model coefficient comparison in 172
form of Standardised Effect Sizes (SES). As global models, generalised linear models with a 173
Gaussian (response diversity) or Quasipoisson (species richness) error distributions were fitted for 174
each of the biotic indicators. pH, TON and the interaction pH x TON were included as stressors, and 175
altitude, precipitation of the wettest month, latitude, longitude, and the interaction pH x latitude as 176
descriptors of natural variability 177
A multi-model inference procedure (Feld et al., 2016; Grueber et al., 2011) was used to produce the 178
final statistical multiple regression relationship, SES and the significance for each predictor. Briefly, 179
this procedure consists in producing the models resulting from all possible predictor combinations. 180
Then they were ranked using Akaike Information Criterion (AIC), i.e. the model ranking first was the 181
one minimising the AIC value. The top models that differed in two AIC units or less from the first 182
were retained, along with their model weights (probability of being the best model). 183
Final models were obtained by using a model averaging approach, where we derived a weighted 184
Burnham & Anderson, 2002). The variances explained by fixed terms (r2marginal) and by the 186
combination of fixed plus random terms (r2conditional) were estimated using the method provided by 187
Nakagawa and Schielzeth (2013). Also, for each of the top models, we checked residuals to assess 188
the normality and homoscedasticity of their distributions. Details of the ecological modelling are given 189
by Feld et al. (2016). 190
Socio-economic scenarios
Future socio-economic scenarios were implemented within the INCA model to simulate the impact of 193
human-induced changes such as land-use change, population growth and agriculture intensification. 194
These scenarios were based on the Shared Socioeconomic Pathways (SSPs) from Moss et al. 195
(2010), van Vuuren et al. (2014), (2014) and Kriegler et al. (2014). The scenarios were 196
downscaled in the MARS project in concert with the stakeholders, such that they represent likely and 197
achievable futures (Cremona et al., 2017). Three scenarios were considered: 198
1. Consensus (called SSP2 in the MARS project). In this world, trends typical of recent decades 199
continue, with some progress towards achieving development goals, reductions in resource 200
and energy intensity at historic rates, and slowly decreasing fossil fuel dependency. 201
2. Fragmentation, or Fragmented World (called SSP3 in the MARS project). The world is 202
separated into regions characterised by extreme poverty, pockets of moderate wealth and a 203
bulk of countries that struggle to maintain living standards for a strongly growing population. 204
Changes in pH were also considered, as very intense use of fossil energy (coal and 205
unconventional sources) with absence of flue-gas desulphurisation technologies to reduce 206
costs is foreseen. This would lead to a pervasive and large acid sulphur deposition, which 207
decreases pH (2030: -0.50 pH units / 2060: -0.75 pH units). 208
3. Technoworld (called SSP5 in the MARS project). This world stresses conventional 209
development oriented toward economic growth as the solution to social and economic 210
problems through the pursuit of enlightened self-interest. The preference for rapid 211
conventional development leads to an energy system dominated by fossil fuels, resulting in 212
high GHG emissions and challenges to mitigation. Lower socio-environmental challenges to 213
adaptation result from attainment of human development goals, robust economic growth, 214
highly engineered infrastructure with redundancy to minimize disruptions from extreme 215
events, and highly managed ecosystems. 216
The socio-economic scenarios were implemented by changing some of the INCA model parameters 217
in order to reproduce the impact of the different storylines, as indicated in Table 3.
Table 3. Land-use change scenarios implemented in INCA.
Scenario Consensus Fragmentation Technoworld
Forest land
10% of the forest area turned into
arable land
5% of the forest area turned into
10% of the forest area turned into
arable land
Arable land
30% of arable
land was into
15% of grassland turned into
arable land
30% of grassland was turned
arable land
Fertiliser use
Nitrogen fertiliser application
decreased by 50%
Nitrogen fertiliser application
increased by 15%
Nitrogen fertiliser appli
increased by 30%
Growing season extended two
months due to climate change
Growing season extended two
months due to climate change
Growing season extended two
months due to climate change
Effluent flows
were increased by
30% due to population growth
Effluent flows were increased by
30% due to population growth
Effluent flows were increased by
30% due to population growth
Climate change scenarios
In order to model the impact of climate change, future scenarios of precipitation and temperature are 221
needed. These were obtained using two different global circulation models (GCMs): 222
1. The GFDL (Geophysical Fluid Dynamics Laboratory) model, developed by the National 223
Oceanic and Atmospheric Administration (NOAA, US) (Donner et al., 2011), 224
2. The IPSL (Institut Pierre Simon Laplace) model, developed by the IPSL Climate Modelling 225
Centre (France) (Dufresne et al., 2013). 226
Daily precipitation and temperature, spatially averaged over the Wye catchments, were obtained from 227
these two models forced by two different Representative Concentration Pathways (RCPs), or 228
greenhouse gas concentration trajectories (Moss et al., 2008), the RCP4.5 and the RCP8.5. RCP4.5 229
describes a mean global warming of 1.4 (0.9 to 2.0) °C for 2046-2065, while RCP8.5 presents a 230
global warming of 2.0 (1.4 to 2.6) °C for the same time period. 231
The climate model data were corrected in order to remove the model bias in reproducing past 232
precipitation and temperature. In particular, a Delta Change approach was used to correct the bias of 233
RCM (Regional climate models) scenarios to generate local climate scenarios. The approach is based 234
upon transferring the monthly average change signal between RCM (regional climate model) control 235
(2006-2010 in this case) and RCM scenario period to an observed time series (2006-2010 in this 236
case). Three data series were used in the bias correction: (1) GCM control periods (2006-2010), (2) 237
Baseline periods (2006-2010), and (3) GCM scenario period (2011-2099). 238
The scenario daily temperature ( ) was derived by adding the absolute monthly change signals 239
to the observed time series. In the case of precipitation, the observed data were scaled with the 240
relative change signals given by the RCM: 241
Where and are observed daily temperature and precipitation, and 244
are monthly average RCM temperature and precipitation of the control period, and and 245
are monthly average RCM temperature and precipitation of the scenario period. The 246
resulting time series of climate change-affected precipitation and temperature are summarised in 247
Figure 3. 248
Figure 3. Changes in precipitation and temperature forecasted by the climate models.
Combinations of climate change scenarios and socio-economic scenarios were used to drive the 251
INCA model and the ecology model, to obtain a wide range of possible future pathways. This 252
approach has been used widely in the past (Herrero et al., 2017; Prudhomme and Davies, 2009; 253
Ruiz-Villanueva et al., 2015), and although its limitations have also been clearly pointed out (Singh et 254
al., 2014), it still provides meaningful results for impact and mitigation analysis. 255
Flow and nitrate
The INCA model was calibrated and validated to reproduce both the observed flow and nitrate 259
concentration at the stations indicated in Table 1. The results are shown in Table 4, with calibration 260
and validation performances shown in terms of Nash-Sutcliffe Efficiency (NSE, Nash and Sutcliffe, 261
1970) for the daily flow and in term of percent bias (PBIAS) for the nitrate concentration. Reproduction 262
of observed flows is in general good or very good, apart from the station 2, located in the headwaters 263
(catchment area around 10 km2), where the model is able to reproduce the low flows but is 264
underestimating the flood peaks (not shown). In terms of nitrate concentration, again the results were 265
satisfactory (|PBIAS| < 20%) apart from station 2, where the calibration PBIAS was -53%. 266
Calibration results are also shown in Figure 4, Figure 5 and Figure 6, where the daily time series of 267
the modelled flows and nitrate concentrations are shown against the corresponding observed values 268
for selected subcatchments. While it is clear that the model is reproducing the observed flow, some 269
errors in the simulation of the nitrate concentration can be observed, although the general seasonal 270
patterns are reproduced. Nevertheless, given the uncertainty in both the processes and the nitrate 271
measurements, the generic behaviour and the catchment response to climate and land-use changes, 272
the performance of the model can be considered satisfactory. 273
Table 4. INCA model calibration and validation results
Stream Station NSE Flow PBIAS Nitrate NSE
Flow PBIAS Nitrate
Wye 2 0.22 -53% 0.19 -50%
Wye 6 - - 0.60 -
Wye 9 0.77 -15% 0.79 -
Wye 12 - -14% - -
Wye 14 0.82 - 0.73 -66%
Wye 17 0.61 - - -
Wye 27 - 19% - -
Wye 28 0.66 13% 0.79 -2%
Wye 29 - 18% - -
Llynfi 15 0.59 17% 0.56 -
Ieithon 8 0.60 - 0.64 -
Irfon 10 0.58 - 0.61 -
Mynwy 21 0.43 - 0.52 -
Figure 4. INCA model results and observed flow at six sites along the Wye (2004-2009). Note that the stream gauge for
the River Lugg does not measure flows above 35 m3 s-1. Red lines: model results. Black lines: measurements.
Figure 5. INCA model results for Nitrate-N, 2004-2009). Red lines: model results. Black dots: measurements.
Figure 6. INCA model results for nitrate loads, 2004-2009. Red lines: model results. Black dots: measurements.
Flow and nitrate
The impacts of climate change on the flows of the River Wye for the time period 2050-2079 (hereafter 286
2060s) are shown in Figure 7. It is shown that the different combinations of climate model and RCP 287
provide slightly different results. For example, the model GFDL, coupled with the RCP 8.5 indicates 288
that little changes in hydrology should occur, in agreement with the results in Figure 3, i.e. little 289
changes in precipitation and temperature. Meanwhile, the model IPSL coupled with the RCP 4.5 290
forecasts an increase of 50-100% in autumn-early winter flows. The impacts of climate change on the 291
nitrate concentration of the River Wye for 2060s are shown in Figure 8. Nitrogen concentrations are 292
expected to decrease in the consensus world for the middle and low Wye. However, for the 293
fragmented and technoworld scenarios, models predicted a strong increase in nitrogen concentration 294
for the upper and low Wye. 295
Figure 7. Impacts of climate change on flow.
Figure 8. Impacts of climate change and land-use change on nitrate.
Ecological Predictions
The results of the ecological modelling showed that pH was the most important stressor, with a strong 301
latitudinal interaction (Figure 9). pH was positively related with ecological metrics, with no evident 302
interaction with TON. At lower latitudes (red: minimum latitude, yellow: latitude Q10 and green: 303
latitude Q50) acidity had a much lower effect on biotic metrics, compared to higher latitudes in North 304
Wales (blue: latitude Q90, violet: maximum latitude). Increased TON values reduced only response 305
diversity (Supplementary Material, Table 1, Fig. 1), which was the most responsive variable. Spatial 306
and environmental descriptors were also generally important. In particular, rainfall (prec_max) and 307
elevation, in a lesser extent, had a general negative influence in biotic variables. 308
The future predictions for the ecological metrics reflected different patterns. Invertebrate richness 309
showed little variation in the future scenarios respect to baseline conditions. Only in the Fragmented 310
scenario invertebrate richness showed a slight reduction respect to baseline. On the other hand, 311
response diversity displayed different responses in relation with the longitudinal position in the river 312
and future scenario considered ( 313
Figure 9). The upper Wye site seems to reflect a substantial reduction in response diversity under the 315
Techno and Fragmented world scenarios for both 2030s and 2060s periods, which is greater in the 316
latter scenario. However, the middle site shows a similar but less pronounced pattern of response 317
diversity decline under Techno and Fragmented world scenarios, and an increase under the 318
consensus world scenario for both periods. The lower Wye is expected to change little respect to 319
current conditions under any of the scenarios considered. Despite the low magnitude of the changes 320
in the lower Wye section, the increase in response diversity under any of the scenarios considered is 321
remarkable. 322
Figure 9. Projected changes for invertebrate response diversity (RD) in the upper, middle and low Wye catchment for
the baseline period (base), 2030 and 2060, and for each of the climatic models (GFDL, IPSL) and scenarios (cons:
consensus world SSP2, tech: technoworld SSP5 and frag: fragmented world - SSP3).
As shown in Figure 4, the INCA model provides very good results in terms of reproduction of the daily 328
flow (Moriasi et al., 2007). The model results are more accurate for the lower stretches of the River 329
Wye (e.g. reach 28 in Figure 4) than for the upper reaches (e.g. reach 9 in Figure 4), where the 330
largest flow peaks are slightly underestimated. This is because the upper Wye is located in a 331
mountainous area where the precipitation is potentially locally very large, especially at high 332
elevations, but the spatial representation of the precipitation patterns through the available raingauges 333
in these areas is poor, mainly because the majority of the measuring stations are located at low or 334
middle elevations. Despite this, the results of the hydrological model are satisfactory. 335
Figure 5 and Figure 6 show the results in terms of nitrate concentration and load. It can be seen that 336
the model is reproducing well the average concentrations of nitrogen at all reaches and the 337
seasonality of the nitrogen concentration, with lower concentrations in summer and higher 338
concentrations in winter. This behaviour is typical of the diffuse source pollutants such as nitrogen, 339
which enters the systems via fertilisers in the agricultural areas (Whitehead et al., 1998b). This is 340
confirmed by the average concentrations, which are relatively low in the upper reaches and higher in 341
the middle and lower reaches, where the agricultural fraction of the catchment is higher (Table 2). The 342
model is also clearly showing some errors in predicting the daily concentration of nitrogen. However, 343
this is unlikely to produce any bias in the results of this study, given that these are provided in terms 344
of long-term averages or seasonal averages, and the day-by-day variations of the nitrogen 345
concentration are not analysed. The results in terms of nitrogen loads are clearly better than the 346
concentration ones, due to the good results of the model in predicting the flow. 347
The predicted impacts of climate change on the climate of the Wye catchment are fairly similar for all 348
models and RCPs scenarios, especially in terms of temperature. A slight increase in winter 349
temperature is foreseen for the 2060s, although no large variations are predicted for summer 350
temperatures. The precipitation is forecasted to decrease slightly in summer, apart from the model 351
IPSL under RCP 4.5, which is predicting an increase in precipitation, especially in winter. Similar 352
patterns have been identified in other catchments in the same climatic area (Bussi et al., 2017b), 353
although in the case of the River Wye the climate change signal on precipitation and temperature is 354
rather weak. The effect of climate change on the flow of the river Wye is shown in Figure 7. It shows 355
that the flow is forecasted to increase based on the IPSL model under RCP 4.5, while the other 356
combinations of climate model and RCP do not show very significant alterations in the flow, in 357
agreement with the predictions shown in terms of precipitation and temperature. These results are 358
similar to other catchments of the area (Bussi et al., 2017b), but differ from other catchments located 359
in Southern or Eastern England, where summer flows are predicted to decrease more significantly 360
(Bussi et al., 2016a, 2016b; Guillod et al., 2017). 361
The changes in nitrate concentrations due to the combined climate and land-use change are shown in 362
Figure 8. The key messages are: (i) Climate change is unlikely to cause large variations in the upper 363
reaches, but could lead to significant increases in nitrate concentrations in the lower reaches, due to 364
increased runoff from agricultural areas; (ii) Land-use change plays a very important role in enhancing 365
or controlling the impact of climate changes; and (iii) The consensus scenario is the most effective in 366
controlling the increase of nitrate caused by climate change. By contrast, the technoworld scenario 367
causes an enhancement of the nitrate concentration increase due to climate change. The fragmented 368
scenario seems not to cause large variations of nitrate concentrations for the upper and middle 369
reaches, but it shows an increase of nitrogen in the lower reaches. No other studies were conducted 370
on the impact of climate change on the nitrate concentration of the River Wye or rivers in the same 371
It is important to bear in mind that socio-economic scenarios were represented as static scenarios, i.e. 373
they do not change along with changes in climate. This is a limitation in the representation of future 374
changes, as climate affects land use and socio-economic development (Bussi et al., 2017a). 375
However, this was compensated by simulating a large number of combinations between climate 376
outcomes, land-use scenarios and socio-economic scenarios, which all span in a relatively narrow 377
range of future conditions. 378
Our ecological models showed that pH is still the main anthropogenic driver of change in the Welsh 379
uplands (Ormerod and Durance, 2009), as occur in other poor base areas exposed to past sulphur 380
deposition (Petrin et al., 2008). Nutrients were also important, causing a reduction in response 381
diversity. Predicted changes under future scenarios in nutrient concentration, pH, flow and climate 382
seem to have more pronounced effects on the upper and middle parts of the River Wye, at least for 383
response trait diversity. Uplands could be more sensitive to nutrient enrichment or potential 384
acidification because they have more diverse communities composed of many pollution-intolerant 385
species, while lowland sections are inhabited by generalist and tolerant species (e.g., Sánchez-386
Montoya et al. 2009). For the upper part of the Wye, our models predicted a 4-fold or 8-fold increase 387
in nitrogen concentration under fragmented world scenario. The middle section could be also severely 388
affected. The combination of increased nutrients and reduced pH (fragmented world) seems to affect 389
more dramatically the diversity of response traits of the invertebrates than their taxonomic diversity. 390
This result suggests that pollution-tolerant species could be replacing sensitive species at locations 391
affected by high nutrient concentrations, which is reflected by changes in trait diversity. A reduced 392
diversity of response traits could result in a less stable and resilient community (Hooper et al., 2005) 393
and have indirect effects on the ecosystem functions and services provided by rivers (Suding et al., 394
2008; Woodward et al., 2012). On the other hand, the restorative land-use changes simulated for the 395
consensus world seems to have little impact respect to the biological baseline conditions, despite the 396
projected reductions in nitrogen. These results might advocate for more ambitious measures if we aim 397
to produce a real recovery of the sites severely impacted by nutrient enrichment.
We are grateful to the MARS project (Managing Aquatic ecosystems and water Resources under 400
multiple Stress) funded under the EU Seventh Framework Programme, Theme 6 (Environment 401
including Climate Change), Contract No.: 603378 (, and to the MaRIUS 402
project (Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity), funded by 403
NERC, under the UK Droughts and Water Scarcity Programme (Grant NE/L010364/1). The 404
meteorological data (precipitation and temperature) were provided by the UK Met Office. The river 405
flow data were provided by the National River Flow Archive. The nitrate concentration data were 406
provided by the Environment Agency of England and Wales and by the Centre for Ecology and 407
Hydrology. C.G- -2015-408
25785). 409
Bussi, G., Dadson, S.J., Whitehead, P.G., Prudhomme, C., 2016a. Modelling the future impacts of climate and
land-use change on suspended sediment transport in the River Thames (UK). J. Hydrol. 542, 357 372.
Bussi, G., Janes, V., Whitehead, P.G., Dadson, S.J., Holman, I.P., 2017a. Dynamic response of land use and
river nutrient concentration to long-term climatic changes. Sci. Total Environ. 590 591, 818 831.
Bussi, G., Whitehead, P.G., Bowes, M.J., Read, D.S., Prudhomme, C., Dadson, S.J., 2016b. Impacts of climate
change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK). Sci. Total
Environ. 572, 1507 1519. doi:10.1016/j.scitotenv.2016.02.109
Bussi, G., Whitehead, P.G., Thomas, A.R.C., Masante, D., Jones, L., Cosby, B.J., Emmet, B.A., Malham, S.K.,
Prudhomme, C., Prosser, H., 2017b. Climate and land-use change impact on faecal indicator bacteria in a
temperate maritime catchment (the River Conwy, Wales). J. Hydrol. In press.
Chave, P., 2001. The EU water framework directive. IWA publishing,.
Cremona, F., Vilbaste, S., Couture, R.-M., Nõges, P., Nõges, T., 2017. Is the future of large shallow lakes blue-
green? Comparing the response of a catchment-lake model chain to climate predictions. Clim. Change
141, 347 361. doi:10.1007/s10584-016-1894-8
Crossman, J., Futter, M.N., Oni, S.K., Whitehead, P.G., Jin, L., Butterfield, D., Baulch, H.M., Dillon, P.J., 2013.
Impacts of climate change on hydrology and water quality: Future proofing management strategies in the
Lake Simcoe watershed, Canada. J. Great Lakes Res. 39, 19 32. doi:10.1016/j.jglr.2012.11.003
Donald, A.P., Stoner, J.H., 1989. The quality of atmospheric deposition in Wales. Arch. Environ. Contam. Toxicol.
18, 109 119. doi:10.1007/BF01056195
Donner, L.J., Wyman, B.L., Hemler, R.S., Horowitz, L.W., Ming, Y., Zhao, M., Golaz, J.C., Ginoux, P., Lin, S.J.,
Schwarzkopf, M.D., Austin, J., Alaka, G., Cooke, W.F., Delworth, T.L., Freidenreich, S.M., Gordon, C.T.,
Griffies, S.M., Held, I.M., Hurlin, W.J., Klein, S.A., Knutson, T.R., Langenhorst, A.R., Lee, H.C., Lin, Y.,
Magi, B.I., Malyshev, S.L., Milly, P.C.D., Naik, V., Nath, M.J., Pincus, R., Ploshay, J.J., Ramaswamy, V.,
Seman, C.J., Shevliakova, E., Sirutis, J.J., Stern, W.F., Stouffer, R.J., Wilson, R.J., Winton, M., Wittenberg,
A.T., Zeng, F., 2011. The dynamical core, physical parameterizations, and basic simulation characteristics
of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Clim. 24, 3484 3519.
Dufresne, J.L., Foujols, M.A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H.,
Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., de Noblet, N., Duvel, J.P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P.,
Grandpeix, J.Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M.P., Lefevre, F., Levy,
C., Li, Z.X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot,
J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray,
P., Viovy, N., Vuichard, N., 2013. Climate change projections using the IPSL-CM5 Earth System Model:
From CMIP3 to CMIP5. Clim. Dyn. 40, 2123 2165. doi:10.1007/s00382-012-1636-1
Durance, I., Ormerod, S.J., 2007. Climate change effects on upland stream macroinvertebrates over a 25-year
period. Glob. Chang. Biol. 13, 942 957. doi:10.1111/j.1365-2486.2007.01340.x
Feld, C.K., Segurado, P., Gutiérrez-Cánovas, C., 2016. Analysing the impact of multiple stressors in aquatic
Futter, M.N., Erlandsson, M.A., Butterfield, D., Whitehead, P.G., Oni, S.K., Wade, A.J., 2014. PERSiST: a flexible
rainfall-runoff modelling toolkit for use with the INCA family of models. Hydrol. Earth Syst. Sci. 18, 855 873.
Grueber, C.E., Nakagawa, S., Laws, R.J., Jamieson, I.G., 2011. Multimodel inference in ecology and evolution:
Challenges and solutions. J. Evol. Biol. 24, 699 711. doi:10.1111/j.1420-9101.2010.02210.x
Guillod, B.P., Jones, R.G., Dadson, S.J., Coxon, G., Bussi, G., Freer, J., Kay, A.L., Massey, N.R., Sparrow, S.N.,
Wallom, D.C.H., Allen, M.R., Hall, J.W., 2017. A large set of potential past, present and future hydro-
meteorological time series for the UK. Hydrol. Earth Syst. Sci. Discuss. 1 39. doi:10.5194/hess-2017-246
Gutiérrez-Cánovas, C., Millán, A., Velasco, J., Vaughan, I.P., Ormerod, S.J., 2013. Contrasting effects of natural
and anthropogenic stressors on beta diversity in river organisms. Glob. Ecol. Biogeogr. 22, 796 805.
Hering, D., Carvalho, L., Argillier, C., Beklioglu, M., Borja, A., Cardoso, A.C., Duel, H., Ferreira, T., Globevnik, L.,
Y., Schmutz, S., Venohr, M., Birk, S., 2015. Managing aquatic ecosystems and water resources under
multiple stress An introduction to the MARS project. Sci. Total Environ. 503 504, 10 21.
Herrero, A., Buendía, C., Bussi, G., Sabater, S., Vericat, D., Palau, A., Batalla, R.J., 2017. Modeling the
sedimentary response of a large Pyrenean basin to global change. J. Soils Sediments.
Hooper, D.U., Chapin, F.S., Ewel, J.J., Hector, A., Inchausti, P., Lavorel, S., Lawton, J.H., Lodge, D.M., Loreau,
M., Naeem, S., Schmid, B., Setälä, H., Symstad, A.J., Vandermeer, J., Wardle, D.A., 2005. Effects of
biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3 35.
Jarvie, H.P., Jürgens, M.D., Williams, R.J., Neal, C., Davies, J.J.L., Barrett, C., White, J., 2005. Role of river bed
sediments as sources and sinks of phosphorus across two major eutrophic UK river basins: The Hampshire
Avon and Herefordshire Wye. J. Hydrol. 304, 51 74. doi:10.1016/j.jhydrol.2004.10.002
Jin, L., Whitehead, P.G., Futter, M.N., Lu, Z., 2012. Modelling the impacts of climate change on flow and nitrate in
the River Thames: assessing potential adaptation strategies. Hydrol. Res. 43, 902 916.
Kriegler, E., Edmonds, J., Hallegatte, S., Ebi, K.L., Kram, T., Riahi, K., Winkler, H., van Vuuren, D.P., 2014. A
new scenario framework for climate change research: The concept of shared climate policy assumptions.
Clim. Change 122, 401 414. doi:10.1007/s10584-013-0971-5
Ledesma, J.L.J., Köhler, S.J., Futter, M.N., 2012. Long-term dynamics of dissolved organic carbon: Implications
for drinking water supply. Sci. Total Environ. 432, 1 11. doi:10.1016/j.scitotenv.2012.05.071
Lu, Q., Futter, M.N., Nizzetto, L., Bussi, G., Jürgens, M.D., Whitehead, P.., 2016. Fate and Transport of
Polychlorinated Biphenyls (PCBs) in the River Thames Catchment Insights from a Coupled Multimedia
Fate and Hydrobiogeochemical Transport Model. Sci. Total Environ. 572, 1461 1470.
Lu, Q., Whitehead, P.G., Bussi, G., Futter, M.N., Nizzetto, L., 2017. Modelling metaldehyde in catchments: a
River Thames case-study. Environ. Sci. Process. Impacts 19, 586 595. doi:10.1039/C6EM00527F
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harme, R.D., Veith, T.L., 2007. Model evaluation
guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASAE 50, 885 900.
Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S.,
Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J.,
Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change
research and assessment. Nature 463, 747 56. doi:10.1038/nature08823
Change, Impacts, and Response Strategies. IPCC, Geneva.
Nakagawa, S., Schielzeth, H., 2013. A general and simple method for obtaining R2 from generalized linear
mixed-effects models. Methods Ecol. Evol. 4, 133 142. doi:10.1111/j.2041-210x.2012.00261.x
Nash, J.E., Sutcliffe, J.V., 1970. River flow forecastin through conceptual models - Part 1 - A discussion of
principles. J. Hydrol. 10, 282 290. doi:10.1016/0022-1694(70)90255-6
Nizzetto, L., Bussi, G., Futter, M.N., Butterfield, D., Whitehead, P.G., 2016. A theoretical assessment of
microplastic transport in river catchments and their retention by soils and river sediments. Environ. Sci.
Process. Impacts 18, 1050 1059. doi:10.1039/C6EM00206D
uuren, D.P., 2014. A
new scenario framework for climate change research: the concept of shared socioeconomic pathways. Jt.
Comm. J. Qual. Patient Saf. 36, 387 400. doi:10.1007/s10584-013-0905-2
Oborne, A.C., Brooker, M.P., Edwards, R.W., 1980. The chemistry of the River Wye. J. Hydrol. 45, 233 252.
Ormerod, S.J., Durance, I., 2009. Restoration and recovery from acidification in upland Welsh streams over 25
years. J. Appl. Ecol. 46, 164 174. doi:10.1111/j.1365-2664.2008.01587.x
Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., Church, J.A., Clarke, L., Dahe, Q.,
Dubash, N.K., Edenhofer, O., Elgizouli, I., Field, C.B., Forster, P., Friedlingstein, P., Fuglestvedt, J.,
Gomez-Echeverri, L., Hallegatte, S., Hegerl, G., Howden, M., Jiang, K., Kattsov, V., Lee, H., Ach, K.J.,
J.J., Pichs-Madruga, R., Plattner, G.K., Power, S.B., Preston, B., Ravindranath, N.H., Reisinger, A., Riahi,
K., Rusticucci, M., Scholes, R., Seyboth, K., Sokona, Y., Stavins, R., Stocker, T.F., Tschakert, P., van
Vuuren, D., van Ypserle, J.P., 2014. Climate change 2014: synthesis report. Contribution of Working
Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC.
Petrin, Z., Englund, G., Malmqvist, B., 2008. Contrasting effects of anthropogenic and natural acidity in streams:
a meta-analysis. Proc. Biol. Sci. 275, 1143 1148. doi:10.1098/rspb.2008.0023
Prudhomme, C., Davies, H., 2009. Assessing uncertainties in climate change impact analyses on the river flow
regimes in the UK. Part 2: Future climate. Clim. Change 93, 197 222. doi:10.1007/s10584-008-9461-6
Ruhí, A., Muñoz, I., Tornés, E., Batalla, R.J., Vericat, D., Ponsatí, L., Acuña, V., von Schiller, D., Marcé, R.,
Bussi, G., Francés, F., Sabater, S., 2015. Omnivory mechanisms mediate food chain length responses to
environmental variation in a Mediterranean river network. Freshw. Biol. 61, 1536 1549.
Ruiz-Villanueva, V., Stoffel, M., Bussi, G., Francés, F., Bréthaut, C., 2015. Climate change impacts on discharges
of the Rhone River in Lyon by the end of the twenty-first century: model results and implications. Reg.
Environ. Chang. 15, 505 515. doi:10.1007/s10113-014-0707-8
Simpson, E.A., 1980. The harmonization of the monitoring of the quality of rivers in the United Kingdom /
Contrôle harmonisé de la qualité des rivières au Royaume Uni. Hydrol. Sci. Bull. 25, 13 23.
Singh, R., Wagener, T., Crane, R., Mann, M.E., Ning, L., 2014. A vulnerability driven approach to identify adverse
climate and land use change combinations for critical hydrologic indicator thresholds: Application to a
watershed in Pennsylvania, USA. Water Resour. Res. 50, 3409 3427. doi:10.1002/2013WR014988
Smith, G., Beare, M., Boyd, M., Downs, T., Gregory, M., Morton, D., Brown, N., Thomson, A.G., 2007. UK Land
Cover Map Production Through the Generalisation of OS MasterMap®. Cartogr. J. 44, 276 283.
Strayer, D.L., Dudgeon, D., 2010. Freshwater biodiversity conservation: recent progress and future challenges. J.
North Am. Benthol. Soc. 29, 344 358. doi:10.1899/08-171.1
Suding, K.N., Lavorel, S., Chapin, F.S., Cornelissen, J.H.C., Diaz, S., Garnier, E., Goldberg, D., Hooper, D.U.,
Jackson, S.T., Navas, M.-L., 2008. Scaling environmental change through the community-level: a trait-
based response-and-effect framework for plants. Glob. Chang. Biol. 14, 1125 1140. doi:10.1111/j.1365-
Tachet, H., Richoux, P., Bournaud, M., Usseglio-
biologie, écologie (Vol. 15). CNRS editions, Paris.
eill, B.C., Ebi, K.L., Riahi, K., Carter, T.R., Edmonds, J., Hallegatte, S., Kram,
T., Mathur, R., Winkler, H., 2014. A new scenario framework for Climate Change Research: Scenario
matrix architecture. Clim. Change 122, 373 386. doi:10.1007/s10584-013-0906-1
Villéger, S., Mason, N.W.H., Mouillot, D., 2008. New multidimensional functional diversity indices for a
multifaceted framework in functional ecology. Ecology 89, 2290 2301. doi:10.1890/07-1206.1
Wade, A.J., Durand, P., Beaujouan, V., Wessel, W.., Raat, K.J., Whitehead, P.G., Butterfield, D., Rankinen, K.,
Lepisto, A., 2002. A nitrogen model for European catchments: INCA, new model structure and equations.
Hydrol. Earth Syst. Sci. 6, 559 582. doi:10.5194/hess-6-559-2002
Whitehead, P.G., Leckie, H., Rankinen, K., Butterfield, D., Futter, M.N., Bussi, G., 2016. An INCA model for
pathogens in rivers and catchments: Model structure, sensitivity analysis and application to the River
Thames catchment, UK. Sci. Total Environ. 572, 1601 1610. doi:10.1016/j.scitotenv.2016.01.128
Whitehead, P.G., Wilby, R.L., Battarbee, R.W., Kernan, M., Wade, A.J., 2009. A review of the potential impacts of
climate change on surface water quality. Hydrol. Sci. Journal/Journal des Sci. Hydrol. 54, 101 123.
Whitehead, P.G., Wilson, E., Butterfield, D., 1998a. A semi-distributed integrated nitrogen model for multiple
source assessment in catchments (INCA): Part I model structure and process equations. Sci. Total
Environ. 210 211, 547 558. doi:10.1016/S0048-9697(98)00037-0
Whitehead, P.G., Wilson, E., Butterfield, D., Seed, K., 1998b. A semi-distributed integrated flow and nitrogen
model for multiple source assessment in catchments (INCA): Part II application to large river basins in
south Wales and eastern England. Sci. Total Environ. 210 211, 559 583. doi:10.1016/S0048-
Woodward, G., Gessner, M.O., Giller, P.S., Gulis, V., Hladyz, S., Lecerf, A., Malmqvist, B., McKie, B.G., Tiegs,
S.D., Cariss, H., Dobson, M., Elosegi, A., Ferreira, V., Graca, M.A.S., Fleituch, T., Lacoursiere, J.O.,
Nistorescu, M., Pozo, J., Risnoveanu, G., Schindler, M., Vadineanu, A., Vought, L.B.-M., Chauvet, E.,
2012. Continental-Scale Effects of Nutrient Pollution on Stream Ecosystem Functioning. Science (80-. ).
336, 1438 1440. doi:10.1126/science.1219534
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... However, the chemical composition of groundwater is easily affected by utilization activities such as irrigation [8], water diversion canals [9], and pumping in multilayer wells [10]. Therefore, it is crucial to analyze the influence of control mechanisms such as human activities, climate, and geochemistry [11][12][13] on hydrochemistry. The study of hydrochemistry can help us understand the groundwater cycle and provide a basis for water quality evaluation, sustainable utilization, and ecological protection. ...
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Phreatic groundwater hydrochemistry is important for sustainable water utilization and ecological stability in arid regions. Based on the test data from collected water samples, this study explored the phreatic groundwater recharge, hydrochemical evolution, and quality of the Sugan Lake Basin using hydrogeochemical, isotopic, and multivariate statistical analyses. The stable isotopic results showed that the phreatic groundwater in the alluvial fan, plain, and wetland areas of the basin generally originated from modern water, and the phreatic groundwater in the piedmont was mainly recharged by paleowater under low-temperature conditions. Carbonate is the dominant mineral in the regional rock weathering process. Phreatic groundwater in the piedmont is controlled by mineral dissolution and cation exchange; however, phreatic groundwater in other areas of the basin is significantly affected by river infiltration. This indicates that the hydrochemical regime of phreatic groundwater is sensitive to natural river flow without disturbing human activity. Class I–V groundwater samples accounted for 2.86%, 25.71%, 34.29%, 14.29%, and 22.86%, respectively. Total dissolved solids, total hardness, sulfate, chloride, nitrite, Na, Fe, Hg, and Cr VI are important factors that determine groundwater quality. This study deepens the understanding of phreatic groundwater hydrochemical characteristics and hydrologic cycles in the Sugan Lake Basin and provides background values of hydrochemistry without human interference for further study in arid inland basins.
... Governments have funded research on groundwater and ecology, leading to a sharp increase in the number of published articles in this field globally. At present, international studies mainly focus on (1) the action of microorganisms (Flynn et al. 2008;Maamar et al. 2015;Beganskas et al. 2018;Fillinger et al. 2021;Griebler et al. 2022); (2) groundwater-surface water interaction in the hyporheic zone (Sophocleous, 2002;Hayashi and Rosenberry, 2002;Kalbus et al. 2006;Conant et al. 2019); (3) biodiversity (Griebler and Lueders, 2009;Danielopol et al. 2000;Boulton et al. 2008); (4) the response of groundwater and ecosystem in changing environments (Dams, 2012;Crosbie et al. 2013;Bussi et al. 2018;Epting et al. 2022); (5) groundwater resource evaluation based on ecological consideration and (6) groundwater-dependent ecosystem (Griebler et al. 2010;Korbel and Hose, 2011;Bertrand et al. 2012;Glanville et al. 2016;Voisin et al. 2020;Majola et al. 2022). In China, research in this field mainly focus on solving practical problem, including (1) regional water cycle evolution (Huan et al. 2011;Zhu et al. 2014;Yin et al. 2021;Chen et al. 2022); (2) surface watergroundwater environment interaction (Wu et al. 2005;Wang et al. 2007;Ling et al. 2011;Ma et al. 2013;Zhu et al. 2017); (3) groundwater ecological effects (Ma et al. 2002;Xu et al. 2016;Wang et al. 2018;Wang et al. 2019); (4) the control and prevention of groundwater pollution (Luo, 2008;Xue and Zhang, 2009;Teng et al. 2012;Zhang et al. 2014;Xi et al. 2018); (5) ecological remediation of groundwater pollution (Zhang et al. 2004;Zhou et al. 2021;Li et al. 2021;Fei et al. 2022); (6) groundwater microbial effects He and Chen. ...
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Groundwater, as a critical component of the hydrological cycle, is essential for sustainable ecosystem development. To clarify the current status of domestic and overseas research, and to identify hotspots, frontier and future trends of groundwater and ecology research, this study utilizes bibliometric methods and CiteSpace software to examine relevant published articles in the Web of Science (WOS) and CNKI databases from 1978 to 2022. Specifically, this study analyzes (1) the annual number of published papers; (2) research institutions; (3) keywords; and (4) evolution of research hotspots. The findings reveal that the United States, China, and Germany are the top three countries in groundwater and ecology research. International research hotspots mainly focus on microbial ecology, climate change, groundwater-surface water interactions in the hyporheic zone, biodiversity, and submarine groundwater discharge, while domestic research hotspots mainly focus on ecological water conveyance, ecological flow, groundwater development and utilization, groundwater pollution, and groundwater and ecological protection. Both domestic and international research hotspots exhibit interdisciplinary features with diverse research objects and assessment methods. Future research in this area is expected to focus on topics such as contamination, groundwater quality, framework, mechanism, spatial distribution, and dissolved organic matter. Additionally, the study of ecological recharge, ecological flow, ecological protection, water intake and use will continue to be the hot topics domestically.
... Table 1 shows that previous LCA studies vary in their scope and system boundaries. Some studies include anaerobic digestion within the system boundaries (Vo et al., 2018a;Collet et al., 2017;Gerloff, 2021), others consider biogas (Zhang et al., 2020;Reiter and Lindorfer, 2015) or CO 2 Bussi et al., 2018) burden free, and they may (Zhang et al., 2020;Vo et al., 2018a;Reiter and Lindorfer, 2015;Collet et al., 2017) or may not (Reiter and Lindorfer, 2015;Parra et al., 2017) include the upgrading process. The detail in which other processes are considered in the P2M pathway varies as well. ...
Power-to-Methane (P2M) pathways are proposed as an innovative solution to utilise surplus renewable electricity for long-term and long-distance storage. This electricity can produce hydrogen using electrolysis and, with the input of CO2 from biogas, be further used for the production of synthetic methane. The methanation reaction can be done with a biocatalyst or nickel catalyst, each with a different pathway of pre- and post-treatment steps. To date, only a limited number of studies have analysed the environmental impact of P2M pathways using life cycle assessment, and no study has directly compared the biological and catalytic P2M pathways. The goal of this research is to close this knowledge gap by quantifying the environmental impact of synthetic methane production and identifying differences between both pathways. Mass and heat balances of both pathways were simulated with AspenPlus and used as basis for a thorough life cycle inventory of the material and energy demand. The global warming potential per MWh synthetic CH4 is similar for the biological and catalytic pathways, but the impact is differently distributed between the processes. The catalytic pathway requires more sulfur removal, compression power and cooling demand. In the biological pathway, the bioreactor has a large impact due to its electricity and nutrient demand, whereas the catalytic reactor's impact is almost negligible.
... The ecological modeling methods for environmental management are improving. Integrated hydrological and ecological modeling improves our understanding of the status of aquatic biodiversity and opens new opportunities to apply methods such as diagnostic tools for river ecosystem management (Bussi et al., 2018;Schuwirth et al., 2019). Various statistical approaches in combination with different spatial scale can be applied to develop better relationship between land use and bioindicator for better river basin management (Schäfer and Piggott, 2018;Escalas et al., 2019). ...
Given the many threats to freshwater biodiversity, we need to be able to resolve which of the multiple stressors present in rivers are most important in driving change. Phytoplankton are a key component of the aquatic ecosystem, their abundance, species richness and functional richness are important indicators of ecosystem health. In this study, spatial variables, physiochemical conditions, water flow alterations and land use patterns were considered as the joint stressors from a lowland rural catchment. A modeling approach combining an ecohydrological model with machine learning was applied. The results implied that land use and flow regime, rather than nutrients, were most important in explaining differences in the phytoplankton community. In particular, the percentage of water body area and medium level residential urban area were key to driving the rising phytoplankton abundance in this rural catchment. The proportion of forest and pasture area were the leading factors controlling the variations of species richness. In this case deciduous forest cover affected the species richness in a positive way, while, pasture share had a negative effect. Indicators of hydrological alteration were found to be the best predictors for the differences in functional richness. This integrated model framework was found to be suitable for analysis of complex environmental conditions in river basin management. A key message would be the significance of forest area preservation and ecohydrological restoration in maintaining both phytoplankton richness and their functional role in river ecosystems.
... Based on the field survey and collected data, we comprehensively analyzed the rocky desertification classification system, land degradation evaluation standard, environmental quality evaluation standard, and soil erosion intensity classification standard proposed by researchers (Cai, 1995;Wei, 1996;Wang et al., 2002;Bussi et al., 2018). Then, referring to the soil erosion classification standard of China, the grading standard for soil erosion intensity in the karst mountainous areas of Guizhou, which was combined with the unique geographical environment characteristics of the Guizhou karst mountainous area, was determined. ...
The ecological environment of karst mountainous areas is fragile, and soil erosion has become an important factor restricting the social and economic development of these regions. How can the degree of soil erosion be scientifically assessed with a reasonable evaluation method? What is effect of the ecological environmental control measures? This study used extenics to assess the degree of soil erosion in karst mountainous areas. The typical karst mountainous area of Guizhou Province was used as the research object. In this study, a soil erosion monitoring indicator system for karst mountainous areas was developed, an evaluation model based on extenics was established, and an index correlation function and comprehensive correlation function were designed to assess the soil erosion of the karst mountainous areas. The key results were as follows. In the soil erosion monitoring index system, the soil nutrient indexes, soil erosion modulus, rock exposure, diversity index, flood season rainfall, population density and natural population growth rate had significant effects on soil erosion in the karst mountainous area. Additionally, we determined the grading standards for soil erosion intensity in typical karst mountains. Finally, the evaluation model was applied to a case study. The status of water and soil conservation was as follows: XW > ML > SB > ZS. These results provide new insights into the evaluation method, namely, extenics, which is helpful for conveniently, quickly and scientifically assessing soil erosion.
... As a result of global warming and associated climate change impacts, both temperature and precipitation changes should be considered for hydrological impacts through General Circulation Models (GCMs) for at least the next 30 to 50 years. On this line, Bao et al. (2019), Scheepers et al. (2018) and Bussi et al. (2018) stipulated an increase in precipitation and runoff in many dry land drainage areas. On the other hand, some authors have stated that annual runoff will decrease in some regions (Nerantzaki et al. ...
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Arid region water reservoirs have different characteristics and solutions from humid regions with the most water shortage in the world socio-economically. This paper outlines possible implementation methodologies, procedures and guidance for water storage in natural and artificial reservoirs for better operation and management rules taking into account the impacts of climate change. The literature is full of methodological applications regarding the impact of climate change on the hydro-meteorological records, but the same is not available in reservoirs (surface and underground), which is the scope of this paper. In addition, reservoir structures offer the necessary mitigation and adaptation activities against the effects of climate change to design, construct, maintain, operate or increase their existing capacity. To increase groundwater reservoir capacity in local aquifers, precipitation, associated flooding and flash flooding should be diverted to artificial groundwater recharges through precipitation and surface runoff harvesting activities. Definitions of fully or partially penetrating underground dams are also explained. The real groundwater feeding application is offered from the Kingdom of Saudi Arabia as arid region representative. Finally, a series of recommendations are presented for the future design and management of reservoirs.
... Small streams with a small volume of water also have only a limited ability to dilute pollutants such as nutrients from agriculture (Kristensen & Globevnik, 2014). Small tributary streams have appeared to be particularly sensitive to nutrient enrichment (Bussi et al., 2018). The impact of human activities is therefore potentially greater on small water bodies than on larger ones (Kristensen & Globevnik, 2014). ...
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Species–environment relationships were studied between the occurrence of 13 fish and lamprey species and 9 mainly map-based environmental variables of Finnish boreal small streams. A self-organizing map (SOM) analysis showed strong relationships between the fish species and environmental variables in a single model (explained variance 55.9%). Besides basic environmental variables such as altitude, catchment size, and mean temperature, land cover variables were also explored. A logistic regression analysis indicated that the occurrence probability of brown trout, Salmo trutta L., decreased with an increasing percentage of peatland ditch drainage in the upper catchment. Ninespine stickleback, Pungitius pungitius (L.), and three-spined stickleback, Gasterosteus aculeatus L., seemed to benefit from urban areas in the upper catchment. Discovered relationships between fish species occurrence and land-use attributes are encouraging for the development of fish-based bioassessment for small streams. The presented ordination of the fish species in the mean temperature gradient will help in predicting fish community responses to climate change.
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It is of great value to explore the evolution and dynamic mechanism of land use classification systems, such as improving the current classification system and providing guidance for scientific land use methodologies. In this study, the evolution process of land use classification systems in China is analyzed, the characteristics of which in terms of content-form, the urban–rural land use relationship and land functionality are compared, and the evolution mechanism of which is evaluated. The findings reveal that: (1) The land use classification systems in China have undergone a three-stage evolution process, comprising an initial "exploration stage", followed by an "improvement stage", culminating in a "maturity stage"; (2) The content and form of these systems exhibit distinct characteristics, marked by the refinement of construction land, stability in hierarchy, and an increase in the number of classifications. The urban–rural land use relationships have transitioned from a state of "urban–rural separation" to "urban–rural coordination", and ultimately to "urban–rural integration". Moreover, land functions have evolved from single to comprehensive; (3) The evolution of land use systems is primarily driven by national policies, socioeconomic development, and resource endowments, and in essence, it is constrained by the man-land relationship. To meet the needs of global village development, future land classification systems should strive to establish universal international standards.
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Based on the perspective of ecological security constraints, this research takes panel data of 42 counties (cities) in the urban agglomeration around Poyang Lake in China from 2000 to 2020 and uses a spatial econometric model to investigate the impact of transportation accessibility on industrial investment. The findings herein present an obvious spatial relationship between industrial investment among cities under ecological security constraints and reveal how transportation accessibility has a significant spatial effect on industrial investment in this area. Transportation accessibility has promoted industrial investment in the local region but restrained industrial investment in the surrounding areas. A series of endogenous and robustness tests strengthen this conclusion. Lastly, the effect of transportation accessibility on industrial investment in the UAAPYL is influenced by the lake’s circle structure and shows obvious heterogeneity.
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Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of risk-based approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline (1900–2006), (ii) five near-future scenarios (2020–2049) and (iii) five far-future scenarios (2070–2099). The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period ( > 3 months) and shorter-duration high precipitation (1–30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK.
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Abstract The combined indirect and direct impacts of land use change and climate change on river water quality were assessed. A land use allocation model was used to evaluate the response of the catchment land use to long-term climatic changes. Its results were used to drive a water quality model and assess the impact of climatic alterations on freshwater nitrate and phosphorus concentrations. Climatic projections were employed to estimate the likelihood of such response. The River Thames catchment (UK) was used as a case-study. If land use is considered as static parameter, according to the model results, climate change alone should reduce the average nitrate concentration, although just by a small amount, by the 2050s in the Lower Thames, due to reduced runoff (and lower export of nitrate from agricultural soils) and increased instream denitrification, and should increase the average phosphorus concentration by 12% by the 2050s in the Lower Thames, due to a reduction of the effluent dilution capacity of the river flow. However, the results of this study also show that these long-term climatic alterations are likely to lead to a reduction in the arable land in the Thames, replaced by improved grassland, due to a decrease in agriculture profitability in the UK. Taking into account the dynamic co-evolution of land use with climate, the average nitrate concentration is expected to be decreased by around 6% by the 2050s in both the upper and the lower Thames, following the model results, and the average phosphorus concentration increased by 13% in the upper Thames and 5% in the lower Thames. On the long term (2080s), nitrate is expected to decrease by 9% and 8% (upper and lower Thames respectively) and phosphorus not to change in the upper thames and increase by 5% in the lower Thames.
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PurposeErosion processes at the catchment scale control a basin’s morphology and sediment patterns in the river network. Eroded sediments are transported and deposited downstream and may cause environmental problems and relevant effects on water storage and hydropower infrastructures. Quantification of water and sediment yield is complex due to the physical processes involved and their temporal and spatial variability, especially at the light of current global change. Materials and methodsNumerical models that use spatially distributed information constitute a useful tool for these estimates, when sufficient input data are available. In this study, we applied the hydrological and sedimentological TETIS model to determine the patterns of water and sediment yield in a large mountain catchment. Flow discharge data obtained from two gauged stations were used for calibration and validation of the hydrological sub-model. Data from two reservoir bathymetries at the outlet of the study area were used for calibration of the sedimentological sub-model. After model calibration, several scenarios of climate and land use change were simulated. Results and discussionClimate scenarios show a general decrease in average annual precipitation and an increase in temperature, associated with an increase in extreme rainfall events. Global change scenarios lead to a counteracting effect between the increase in sediment transport during extreme events and the decrease in sediment erosion associated with afforestation following the abandonment of agricultural land. In the case of the most extreme climate scenario combined with total catchment deforestation, the model indicates a complete siltation of the reservoir by 2050. Conclusions Model performance emphasizes its potential as a tool for evaluating water and sediment yield for large catchments, as well as of its usefulness for water and sediment management in light of future climate and land use change scenarios.
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We constructed a model chain into which regional climate-related variables (air temperature, precipitation) and a lake’s main tributary hydrological indicators (river flow, dissolved inorganic carbon) were employed for predicting the evolution of planktonic blue-green algae (cyanobacteria) and zooplankton (rotifer) biomass in that lake for the mid-21st century. Simulations were based on the future climate predicted under both the Representative Concentration Pathways 4.5 and 8.5 scenarios which, combined with three realistic policy-making and basin land-use evolution lead to six scenarios for future water quality. Model outputs revealed that mean annual river flow is expected to decline between 3 to 20%, depending of the scenario. Concentration of river dissolved inorganic carbon is predicted to follow the opposite trend and might soar up to twice the 2005-2014 average concentration. Lake planktonic primary producers will display quantitative changes in the future decades whereas zooplankters will not. A 2 to 10% increase in mean cyanobacteria biomass is accompanied by a stagnation (-3 to +2%) of rotifer biomass. Changes in cyanobacteria and rotifer phenology are expected: a surge of cyanobacteria biomass in winter and a shortening of the rotifer biomass spring peak. The expected quantitative changes on the biota were magnified in those scenarios where forested area conversion to cropland and water abstraction were the greatest.
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The effects of climate change and variability on river flows have been widely studied. However the impacts of such changes on sediment transport have received comparatively little attention. In part this is because modelling sediment production and transport processes introduces additional uncertainty, but it also results from the fact that, alongside the climate change signal, there have been and are projected to be significant changes in land cover which strongly affect sediment-related processes. Here we assess the impact of a range of climatic variations and land covers on the River Thames catchment (UK). We first calculate a response of the system to climatic stressors (average precipitation, average temperature and increase in extreme precipitation) and land-cover stressors (change in the extent of arable land). To do this we use an ensemble of INCA hydrological and sediment behavioural models. The resulting system response, which reveals the nature of interactions between the driving factors, is then compared with climate projections originating from the UKCP09 assessment (UK Climate Projections 2009) to evaluate the likelihood of the range of projected outcomes. The results show that climate and land cover each exert an individual control on sediment transport. Their effects vary depending on the land use and on the level of projected climate change. The suspended sediment yield of the River Thames in its lowermost reach is expected to change by −4% (−16% to +13%, confidence interval, p = 0.95) under the A1FI emission scenario for the 2030s, although these figures could be substantially altered by an increase in extreme precipitation, which could raise the suspended sediment yield up to an additional +10%. A 70% increase in the extension of the arable land is projected to increase sediment yield by around 12% in the lowland reaches. A 50% reduction is projected to decrease sediment yield by around 13%.
The application of metaldehyde to catchment agricultural areas to control slugs and snails has caused severe problems for drinking water supply in recent years. In the River Thames catchment, metaldehyde has been detected at levels well above the EU and UK drinking water standards of 0.1[small mu ]g/l at many sites across the catchment between 2008 and 2015. Metaldehyde is applied in autumn and winter, leading to increased concentrations in surface waters. It is shown that a processed-based hydro-biogeochemical transport model (INCA-contaminants) can be used to simulate metaldehyde transport in catchments from areas of application to the aquatic environment. Simulations indicate that high concentrations in the river system are a direct consequence of excessive application rates. A simple application control strategy for metaldehyde in the Thames Catchment based on model results is presented.
Water resources globally are affected by a complex mixture of stressors resulting from a range of drivers, including urban and agricultural land use, hydropower generation and climate change. Understanding how stressors interfere and impact upon ecological status and ecosystem services is essential for developing effective River Basin Management Plans and shaping future environmental policy. This paper details the nature of these problems for Europe's water resources and the need to find solutions at a range of spatial scales. In terms of the latter, we describe the aims and approaches of the EU-funded project MARS (Managing Aquatic ecosystems and water Resources under multiple Stress) and the conceptual and analytical framework that it is adopting to provide this knowledge, understanding and tools needed to address multiple stressors. MARS is operating at three scales: At the water body scale, the mechanistic understanding of stressor interactions and their impact upon water resources, ecological status and ecosystem services will be examined through multi-factorial experiments and the analysis of long time-series. At the river basin scale, modelling and empirical approaches will be adopted to characterise relationships between multiple stressors and ecological responses, functions, services and water resources. The effects of future land use and mitigation scenarios in 16 European river basins will be assessed. At the European scale, large-scale spatial analysis will be carried out to identify the relationships amongst stress intensity, ecological status and service provision, with a special focus on large transboundary rivers, lakes and fish. The project will support managers and policy makers in the practical implementation of the Water Framework Directive (WFD), of related legislation and of the Blueprint to Safeguard Europe's Water Resources by advising the 3rd River Basin Management Planning cycle, the revision of the WFD and by developing new tools for diagnosing and predicting multiple stressors.