Download evaluating the impacts of climate change on catchment

EVALUATING THE IMPACTS OF CLIMATE CHANGE ON
CATCHMENT NITROGEN TRANSFER: A MODELLING STUDY ON
THE RIVER WENSUM, UK.
By
Andrew Todd
Thesis presented in part-fulfilment of the degree of Master of Science in accordance with the
regulations of the University of East Anglia
School of Environmental Sciences
University of East Anglia
University Plain
Norwich
NR4 7TJ
© 2015 Andrew Todd
This copy of the dissertation has been supplied on condition that anyone who consults it is
understood to recognise that its copyright rests with the author and that no quotation
from the dissertation, nor any information derived there from, may be published without
the author’s prior written consent. Moreover, it is supplied on the understanding that
it represents an internal University document and that neither the University nor the author
are responsible for the factual or interpretative correctness of the dissertation.
1
Contents
Abstract ................................................................................................................................................... 3
1.
Introduction .................................................................................................................................... 4
1.1 Pollution types .............................................................................................................................. 4
1.2 Nitrogen transport ........................................................................................................................ 4
1.3 Significance of Climate Change ..................................................................................................... 5
1.4 Relevant policy .............................................................................................................................. 6
1.5 Aims and objectives ...................................................................................................................... 7
2.
Literature Review ............................................................................................................................ 7
2.1 Water quality studies in the Wensum ..................................................................................... 8
2.2 Similar studies in other catchments.............................................................................................. 9
2.3 Use of climate models and hydrological models ........................................................................ 11
3.
Methodology................................................................................................................................. 12
3.1 Study Site ................................................................................................................................... 12
3.1.1 Climate ................................................................................................................................. 13
3.2 Model description - Soil and Water Assessment Tool ................................................................ 13
3.3 Model Inputs ............................................................................................................................. 15
3.4 Model Calibration ....................................................................................................................... 15
3.5 Incorporating climate change ..................................................................................................... 16
4.
Results ........................................................................................................................................... 18
5.Discussion .......................................................................................................................................... 21
5.1 Temporal variation ...................................................................................................................... 21
5.1.1 Increasing winter TN levels .................................................................................................. 21
5.1.2. Decreasing summer TN levels ............................................................................................. 22
5.3 Limitations................................................................................................................................... 24
5.3.1 Data limitations .................................................................................................................... 24
5.3.2 Assumptions within model .................................................................................................. 24
5.3.3. Variables not considered .................................................................................................... 24
6.
Conclusion ..................................................................................................................................... 25
Acknowledgments................................................................................................................................. 26
References ............................................................................................................................................ 26
2
Abstract
This study assessed the impact of potential climate change on the nitrogen loads to surface
and sub-surface waters and was conducted using the Soil and Water Assessment Tool
(SWAT) model. The study focused on the 20km2 Blackwater sub catchment located in the
main 593 km2 River Wensum catchment, Norfolk. The SWAT model was calibrated and
validated using sets of measurements of nitrate concentrations and water flow from the
Wensum Demonstration Test Catchment (DTC). The SWAT model was then run using a
baseline scenario corresponding to an actual measured time series, and a UKCP09 climate
change scenario was applied using a ‘mid-term’ (2050) and ‘long-term’ (2050) scenario
assuming a medium emissions scenario (A1B) . Both time frames saw increased
temperatures and a significant increase in seasonality of rainfall towards a pronounced
winter maxima. Overall it was found that there was a 14.3% and 20.7% decrease in total
nitrogen loading in 2050 and 2080 respectively.
Keywords: Nitrogen, Climate, SWAT, Wensum.
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1. Introduction
Climate change can result in significant changes in the variables and processes that affect
water quality. One of such changes is an increase in the concentration of nutrients
transferred to aquatic environments, which has a wide range of impacts on human wellbeing,
economic services and flora and fauna, all of which depend on clean water directly or
indirectly (Shiklomanov 2000, Solheim et al. 2010, UNEP 2012).
1.1 Pollution types
Sources of pollution can be split into two groups; point source and non-point source. Point
sources discharge from single points, e.g. sewage treatment plants, and are therefore
relatively straightforward to manage. Non point sources tend to pose difficulties to manage
successfully as pollutant release can be over a larger area and often irregular (Carpenter et
al. 1998). Identifying both sources of pollution is important to ensure effective management
(Niraula et al. 2013). Non point sources are responsible for the majority of water pollution in
the United Kingdom (Dunn et al. 2012). Agricultural practices have long been recognised as
significant sources of non-point source water pollution (Johnes 1996, Dupas et al. 2015).
Significant quantities of nitrogen and phosphorous are transmitted to water bodies as a
result of intensive agricultural practices, including excessive applications of inorganic
fertilizer and increased sediment mobilization (Chaubey et al. 2010, Silgram et al. 2010). The
leakage of these nutrients to watercourses can cause eutrophication which poses a serious
threat to the freshwater, estuarine, and marine environments (Donner et al. 2002, Diaz and
Rosenburg 2008, Vörösmarty et al. 2010).
1.2 Nitrogen transport
Knowledge of the nitrogen cycle and its perturbations is crucial for successful modelling
studies. The human alteration of the nitrogen cycle is immense (Vitousek et al. 1997). The
creation of reactive nitrogen increases every year predominantly due to agricultural activities
and demands (Galloway et al. 2008). Naturally nitrogen is fixed by Rhizobium associated
with legumes, and sometimes by lightening, although the industrial Haber-Bosch process
now produces the majority of reactive nitrogen for agriculture (Galloway et al. 2004,
Galloway et al. 2008). The most commonly applied nitrogen species in fertilizer are, nitrate
and ammonium as they are most readily available for crop uptake. Nitrate is usually
4
the focus of nitrogen based water quality studies as it is highly soluble and can therefore be
transported by regular hydrological processes such as leaching, throughflow or percolation
(see figure 1) (Mehdi et al. 2015). Ammonium, in contrast, is not very mobile as when soil
temperatures exceed 5◦C it is readily adsorbed onto clay minerals or used by microorganism
and plants. Excess nitrogen that is not taken up by crops either becomes a source for
transfer to aquatic environments, or is removed from the soil to the atmosphere via
denitrification or volatilization (Gassman et al. 2007, Neitsch et al. 2011).
Figure 1: Inputs, outputs and transport processes of N and P from agricultural land (Source: Carpenter et al 1998)
1.3 Significance of Climate Change
In the northern hemisphere the period between 1982-2012 represented the warmest thirty
year period for 1400 years (IPCC 2013). With global greenhouse gas emissions continuing
to rise these trends seem set to continue (IPCC 213). This warming results in increasing
precipitation and evapotranspiration, due to roughly exponential increase in water holding
capacity of the atmosphere with temperature rise (Min et al. 2011). Furthermore the warming
will increase winter rainfall because a greater proportion of precipitation will fall as rain
instead of snow.
The observed global temperature increase over several decades has been linked to
significant changes in the hydrological cycle and associated impacts on water quality (Bates
et al. 2008). Changes to nutrient loads are correlated with surface runoff and sediment
transport rates (McElroy et al 1976), both of which are impacted by climate change (Marshall
and Randhir 2008, Tong et al. 2007). Therefore, whilst the processes involved in nitrogen
pollution and transmission to waterbodies are understood, the response of water quality to
future climate changes remains uncertain, and spatially variable (Wilson and Wong 2011).
The use of climate model scenarios provide the best information to address these
5
uncertainties associated with climate change and investigate the impact on hydrology and
water quality at regional scales (Whitehead et al. 2009).
1.4 Relevant policy
Since the Second World War there has been a shift towards increasingly mechanised
agriculture, the changing agricultural practices have potential for adverse impacts on
waterbodies (O’connell et al. 2007).
Nitrogen, especially in its reactive nitrate form, is a key pollutant in aquatic ecosystems.
Increased concentrations in watercourses can be damaging to human and environmental
wellbeing and have financial implications terms of water management and treatment costs
(Ahmadi et al. 2014).There is existing legislation in place such as the EUs Nitrogen directive
and Water Framework Directive (European Parliament 2000) which set maximum
acceptable levels for nitrate concentration in water bodies. Regulation is necessary as
agricultural sources provide the largest transport of excess N to waterways (Foley et al.
2005). The degradation of both surface and groundwater is a documented problem
throughout Europe with nutrient enrichment causing waterbodies to breach EU regulation
(Outram et al. 2014). In England and Wales 17% of the 7,300 river monitoring points
exceeded the 50 mg/l nitrate drinking water limit at least once during the winter months of
2005 (Defra 2008).
Quantifying potential future changes to nitrate pollution in the Wensum catchment should be
of importance to various stakeholders within the catchment and could prove useful to the
long term management plans of the Wensum river basin. Many of the issues discussed
above are exacerbated by the fact that the Wensum catchment has a significant amount
agricultural land use. The dominance of high intensity agriculture is associated with
decreased water quality (Green et al. 2014, Mehdi et al. 2015). The main channel is
adjudged to be of poor ecological status according to water framework directive indictors.
Therefore the impact of climate change in this region is of particular concern. The small
scale nature of the study offers the opportunity to reveal more detail than regional or national
analysis of water quality.
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1.5 Aims and objectives
Aim:
To model the impacts of climate change on nitrate load in the Blackwater sub-catchment and
compare results with the baseline data.
Objectives:
o
Evaluate the accuracy and uncertainties involved in both hydrological and
climate models.
o
Quantify the export of nitrogen into the River Wensum from the Blackwater
sub-catchment.
o
Investigate the processes involved in nitrogen pollution and how they are
impacted by climate change.
2. Literature Review
The impact of climate change on the hydrological cycle has been a heavily researched topic
however much of the effort has concentrated on direct impacts such as water resource
availability and flooding (Dunn et al. 2012, Charlton and Arnell 2014). Relatively less
attention has been paid to the indirect effects of climate change on water quality (Whitehead
et al. 2009, Dunn et al. 2012). It is understood that changes in rainfall and temperature, and
increases in extreme event frequency associated with climate change will have impacts on
the hydrological cycle (Whitehead et al. 2009, Charlton and Arnell 2014). Changes to the
hydrological cycle have the potential to damage water quality standards and have
ramifications for human and ecosystem health (Murdoch et al 2000, Wilby et al. 2006,
Ahmadi et al. 2014).
However despite awareness of possible impacts to water quality there is debate about the
processes involved and whether nutrient loading will increase or decrease. A crucial cause
of this uncertainty is due to the challenge involved in predicting changing rainfall patterns in
future climate scenarios . The UKCP09 (United Kingdom Climate Projections 2009)
projections suggest an increase in winter precipitation whilst a decrease in summer
precipitation for south east England (Defra 2009) with the frequency of extreme events also
predicted to rise (Whitehead et al. 2009). These changing patterns of precipitation are linked
to river flow and thus have associated impacts on nutrient loads and water quality (Arnell
2003, Whitehead et al. 2009).
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Higher precipitation and associated increases in runoff will be linked with greater nitrate
pollution. A study by Donner et al. (2002) attributed ~25% of the nitrate export to the Gulf of
Mexico to increased runoff alone. Observational studies have also shown an increase in
nitrate pollution during wetter years with greater nitrogen retention in dry years (Kaushal et
al. 2008). The processes by which runoff increases nitrate pollution is partly caused by the
increased erosion and mobilization of fine sediments and an increase in the flushing of
diffuse pollution into the river channel and along the river system (Wilby et al. 2006,
Whitehead et al. 2009, Solheim et al. 2010). In contrast, there will likely to be periods of
lower river flows in the future, predicted during dryer summer months (Defra 2009). The
higher temperature increases potential evapo- transpiration, which may lead to decreased
soil moisture and runoff (Stone et al., 2001, Jeppesen et al., 2009). The decrease in
precipitation may lead to lower diffuse pollution due to reduced runoff, and therefore a
decrease in the quantity of sediments transmitted from the soil profile to rivers. On the other
hand the lower flows would reduce the volume of water available for the dilution of pollutants
and thus lead to higher concentrations (Murdoch et al. 2000, Whitehead et al 2009).
Increased temperatures could also lead to increased soil nitrogen mineralization leading to
higher nitrate concentrations when the build up of nitrogen is washed out during storm
events (Wilby et al. 2006, Whitehead et al. 2009). Another factor to consider with higher
temperatures is that dissolved nitrates are more likely to cause eutrophication as lower
dissolved oxygen levels are associated with warmer air temperatures (Murdoch et al. 2000).
The lack of consensus on the pattern of future nitrogen pollution amongst several studies is
due to the differences in methods adopted between them. There are a range of variables
including the choice of: study area, hydrological model, water quality variable and climate
scenario (Dunn et al. 2012, El-Khoury et al. 2015). This lack of agreement highlights the
importance of further research, especially on smaller scales as there is no larger uniform
pattern to changes in nitrogen transport.
2.1 Water quality studies in the Wensum
Several other water quality studies have been undertaken in the Wensum catchment. A
range of pollutants have been studied including Nitrogen (Evans 2012, Outram et al. 2014),
Phosphorous (Demars et al. 2005, Outram et al. 2014, Whitehead et al. 2015), and fine
grained sediment (Collins et al 2013).
Concentrating on the studies concerning nitrogen. Acute storm induced N transfer was
highlighted in the blackwater sub catchment. The 2011/12 drought caused desiccation and
soil wetting resulting in increased rates of mineralization. The outcome led to high nitrate
8
concentrations in the Wensum (Outram et al 2014).In addition it has been shown that there
is abundant nitrate supply in the Blackwater sub catchment and that supply was transport
limited rather than supply limited (Outram et al. 2014). Evans (2014) has also carried out a
less detailed reconnaissance survey to identify sources of diffuse pollution. Features such as
eroded fields and tracks were recorded in parts of the Wensum catchment to identify hig risk
areas.
However despite these investigations into water quality that have occurred in the Wensum,
no study appears to have considered the impact that climate change could have on water
quality in the region. In other regions in the world, however, there are several other studies.
Several use the same SWAT model although other modelling methods are used. Relevant
information from these studies is discussed below.
2.2 Similar studies in other catchments
Several SWAT based modelling studies have been carried out. The use of the hydrological
model has often been paired with climate models or land use models (Tong et al 2012).
There are authors investigating the impact of climate change on water quality in other
catchments around the world using SWAT: Bouraoui et al (2002) modelled nutrient loading
in the Rver Ouse Catchment and predicted an increase in N loss due to processes of soil
mineralization and increased leaching. Ahmadi et al. (2014) undertook a similar study in
midwestern United States in an agricultural catchment. Marhsall and Randhir (2008) paired
SWAT with IPCC high and low scenarios climate in New England. There is also a wide
range of literature in which other hydrological models and transport models have been used
other than SWAT. For example Jeppsen et al (2011)’s approach investigating N transport in
cultivated catchments in Northern Europe for different climate scenarios used a combination
of three different models; a statistical N-model, the MIKE11-TRANS model and the INCA-N
model. As another example Dupas et al (2015) used a mass balance model, Nutting, to
estimate N loss.
Whilst the majority show that N dynamics are impacted either directly or indirectly by
changes to climate the magnitude and direction of change show differences. The majority of
studies using a climate scenario which predicts increased rainfall show an increase in
nutrient transfer to surface water (Bouraoui et al. 2002, Jeppsen et al. 2011, Tong et al.
2012, El-Khoury et al. 2015). For example, Tong et al. (2012) modelled the wettest climate
scenario predicted N to increase by 11% compared to driest scenario. However, Chang
(2004) found most catchments forecast to have decreased N loads under future climate
scenarios. Denitrification and mineralization processes can be directly impacted by
9
temperature rises. Weyhenmeyer et al. (2007) found warmer water increases the rate of
denitrification processes, thus reducing the N concentration in warmer months. Enhanced
biological uptake of nitrogen in a warmer climate is also a documented process (George et
al. 2004). Nitrate uptake from the soil is undertaken by two mechanisms; Nitrate uptake by
vascular plants dominates in summer months whereas bacterial denitrification occurs when
soil temperatures are low. Therefore a range of studies have found that depending on the
temperature to increased nutrient uptake by crops can lead to reduced N transport to water
courses or reduced mineralization my microbes (Fan and Shibata 2015). It is the poorly
drained, wet soils that are likely to have high denitrification potential due to prevalence of low
oxygen conditions (Galloway et al. 2004).
Several studies have found that there is not just a change in total N transfer to the river
network but also changes in timing, in terms of seasonal distribution. Fin and Shibata (2015)
recorded increased surface runoff and nutrient runoff in winter months, driven by
precipitation changes but exacerbated by a decrease snowfall and increase rainfall in winter
months due to the higher predicted temperatures. Maximum monthly sediment yield shifted
from May to April, and therefore these months saw greater proportions of total annual
nutrient load under the future climate scenarios (Fan and Shibata 2015). Tu (2009)
Investigated the impact of both climate and land use change on streamflow and nitrogen in
Eastern Massachusetts. In this case it was found that the total annual volume of streamflow
and nitrogen loads were not significantly altered, rather, the timing of both were significantly
affected by climate change. Nitrogen loading and streamflow again increased in winter
months and fell in summer months due to differences in temperature and precipitation under
climate scenarios.
Land use change introduces more complex factors into joint studies of climate change and
nutrient loss which are harder to predict and account for as there are a wide range of direct
and indirect impacts that climate change has on land use and vice-versa (Vitousek et al.
1997). In China, Wu et al. (2012) found that climate change had the greater impact on
increasing N loads compared to changing land use to increase livestock densities. Tong et al
(2012) also found that climate change had more impact on N concentration than land use
change, which alone was responsible for just a 3% in N concentrations. While El-Khoury et
al. (2015) determined that the increase in organic N levels was both a consequence of
climate change and land use change to similar degrees. In any case it is clear there have
been a variety of studies investigating the complex interaction of land use and climate
change and find different relative importance of each of them on nutrient pollution of
watercourses.
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2.3 Use of climate models and hydrological models
There are a range of both climate and hydrological models used in the literature. Global or
regional climate models can be applied directly but a more common approach when applying
climate impacts to hydrological models is to downscale, global climate models to an
appropriate scale for the river basin, using either statistical to dynamic method (Dunn et al.
2012, Fiseha et al. 2014, El-Khoury et al. 2015). For example Marshall and Randhir (2008)
and Jeppsen et al. (2011) both used IPCC based global climate models in conjunction with
SWAT in their studies of water quality in the Connecticut River and European catchments
respectively. Dunn et al. 2012 used the UKCP09 projections in their investigation into future
water quality in Scotland. In this study a projections the UKCP09 database will also be
applied to investigate possible future changes to water quality caused by climate change.
The hydrological models used in the literature range from water balance models to more
complex catchment models that can model water flow and simulate point source and diffuse
pollution, at regional and national scales (El-Khoury et al. 2015). The SWAT model used in
this project has been used extensively in the literature (Gassman 2007), often for similar
studies investigating climate change and nutrient transport (Bouraoui et al. 2002, Donner et
al. 2002, El-Khoury et al. 2015). It is important to consider how each model represents
nutrient transport and how key hydrological, ecological and bio-geochemical processes
interact (Bouwman et al. 2013). This is difficult as key concepts of nutrient delivery, supply
and transport are not fully understood and can only be reduced to simplified equations.
Attempting to include ecology into the system is a current goal to attempt to better simulate
hydrological process to closer match reality. However, it adds another layer of complexity,
especially considering the differences in spatial and temporal scales operating between the
terrestrial and aquatic ecosystems. Other models use different approaches of describing
nutrient transport, often in response to different spatial scales of study location (Bouwman et
al. 2013). For example; regression based models, with nutrient inputs and measured river
outputs, are often used for extrapolation when there are limited measurements of nutrient
export or over large river basin scale studies (Maybeck 1982, Mayorga et al., 2010, Wilby et
al. 2006). As mentioned earlier SWAT has occasionally been used in conjunction with the
UKCP09 database. More detail on the two tools will be provided in the Methods section.
11
3. Methodology
3.1 Study Site
Figure 2 Study location. The Blackwater sub-catchment of the River Wensum, Norfolk, showing the land cover of minicatchments A–F (Source: Cooper et al. 2015a)
The River Wensum is a 78 km long, enriched, lowland calcareous river in Norfolk, East
Anglia. The Wensum drains a 593 km2 area containing Site of Special scientific Interest
(SSSI) and European Special Area of Conservation (SAC) designated sites. The entire
catchment is underlain by Cretaceous Chalk with the eastern side overlain by Pleistocene
Crag sands and gravel while the western side has glacial till overlain by chalky boulder clay
(Outram et al. 2014, Wensum alliance 2015). The Wensum Catchment is monitored as part
of the Demonstration Test Catchment project (DTC) which aims to investigate how to costeffectively control diffuse agricultural diffuse pollution in order to improve water quality in
rural river catchments (Demonstrating Catchment Management, 2015). Of the 20 subcatchments in the Wensum it is the 20km2 Blackwater sub catchment which is intensively
monitored as part of the DTC project. The Blackwater sub-catchment is further subdivided in
6 mini-catchments. Data recorded including pH, turbidity, temperature, stage, flow,
ammonium, chlorophyll, dissolved oxygen and electrical conductivity at 30-minute resolution.
Nitrate and Phosphorous measurements are also recorded (Cooper et al.2015b).
12
Predominantly subsurface N transport pathways are more common due to the permeable
nature of the Blackwater sub-catchment.
Intensive arable farming is the dominant land use in the Blackwater sub-catchment with N
fertiliser application rates of around 220 kgNha−1 on cereal crops. It is therefore expected for
water quality to be lower in catchments because of the dominant agricultural land use.
(Green et al 2014). Indeed, the ecological status of the Blackwater tributary is compromised,
with 99.4% of its protected habitat in an unfavourable and declining state, largely because of
high rural N, P and sediment inputs. (Sear et al. 2006, Outram et al. 2014, Wensum Alliance
2015). Furthermore, the Blackwater sub-catchment has long recession periods after storm
events, the nitrate concentration on the descending limb of a storm can stay elevated for
several days during periods of high concentration. Although the very low relief and flat
topography of the Wensum catchment may offer some natural protection from pollution as
nitrogen lost through runoff should be lower in comparison to catchments with greater relief
(Liu et al. 2014)
3.1.1 Climate
The Wensum catchment has a temperate maritime climate with a mean annual temperature
of 10.1 °C and mean annual precipitation total of 674 mm over the 1981–2010 period.
(Cooper et al 2015b)
3.2 Model description - Soil and Water Assessment Tool
The water quality modelling will be carried out using SWAT software (Arnold et al. 1998), a
sophisticated, process-based hydrological and water quality model with a range of soil,
hydrological, weather and nutrient components (Santhi et al. 2001). SWAT has been
chosen, in preference to other software such as Farmscoper or SPARROW, as it is suited to
this study for a number of reasons. Firstly, it is a freely available ArcGis extension, it is able
to simulate water yield and quality across long timescales and is suited to agricultural river
basins (Borak and Bera 2003, Tong et al. 2007). SWAT divides the catchment into
Hydrological Response Units (HRUs) based on land use, soil type and elevation differences
that influence hydrological conditions. The flexible nature of SWAT allows simulation of
fertilizer application rate and timing with simple parameter changes. Furthermore, SWAT and
similar models offer improvements on other tools by linking ecology, biogeochemistry and
the physical habitat with water quality (Bouwman et al. 2013).
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The way SWAT tracks the movement and transformation of several forms of Nitrogen is
described. Surface runoff volume is used by the Modified Universal Soil Loss Equation
(MUSLE) to calculate soil erosion and sediment yield (Marshall and Randhir 2008,
Panagopoulos et al. 2011) .The assimilation of nitrogen by plants is calculated using a
supply and demand approach. The amount of nitrate removal from soil in runoff, lateral flow
and percolation are estimated in terms of water volume and average nitrogen concentration
in the soil layer. (Neitsch et al. 2011). Organic N transport is calculated using a loading
function adapted from McElroy et al. (1976) which is based on organic N in the top soil layer
and sediment yield. Denitrification rates are calculated in relation to nitrate levels, water
content, temperature and carbon source presence (Neitsch et al. 2011). The potential
evapotranspiration rate is calculated using the Hargreaves equation which is a function of air
temperature and maximum solar radiation (Neitsch et al. 2011). Therefore, to summarize,
the model considers the removal of nitrogen via the following processes; plant uptake,
leaching, denitrification, volatilization, runoff and soil erosion (Ahmadi et al. 2014). See
figure 3. For a schematic.
Figure 3: Diagram showing how nitrogen is represented in SWAT. (Source: Neitsch et al. 2011).
. SWAT is considered a robust, interdisciplinary tool with hundreds of related papers
published (e.g. Jha et al. 2004, Santhi et al. 2006, Gassman et al. 2007, Panagopoulos et al,
2011, Niraula et al. 2013, El-Khoury et al. 2015, Fan and Shibata 2015, Mehdi et al. 2015).
It has therefore been demonstrated on numerous occasions that the SWAT model is capable
of carrying out this study. Although it is worth recognizing that the complexity of
deterministic models, such as SWAT, often creates intensive data and calibration
requirements, which generally limits their application in large watersheds (Bouwman et al.
2013). In this case SWAT was used with the ARCGIS 10.2 interface.
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3.3 Model Inputs
Data requirements are high due to SWAT being a process based model. Much of the data is
available on the Wensum Alliance database containing data from the DTC. Nitrogen data is
collected every 30minutes within the Blackwater sub catchment.
o
Meteorological data – Wensum Alliance Server
o
N and P loads – Wensum Alliance Server
o
River Discharge - Wensum Alliance Server
o
Water abstraction – Environment Agency
o
Soil data - Wensum Alliance Server
o
Land Use – Wensum Alliance Server
o
Topography – LandMap database
To setup the model hydrological response units were delineated according to elevation, soil
data, and land use, along with defining stream network and inputs and outputs. The SWAT
model was calibrated using data collected by the Wensum Alliance since January 2009 to
the present day. SWAT-CUP is a computer program that was calibration of the model. There
is lots more information on input files available from Neitsch et al. (2005).
Climate change simulations were based on the UKCP 09 simulations as explained later.
Once the model has been run with the climate simulation the differences in nitrogen
concentration can be compared with the baseline data.
3.4 Model Calibration
The model was calibrated until an acceptable Nash Sutchcliffe efficieny (ENS) was reached.
The model was run for 5 year 6month time period from 1st January 2009 to April 30th 2014.
Changes were made to relevant parameters based on SWAT guidance (Arnold et al. 2011)
and other studies discussing SWAT calibration (Santhi et al. 2001, Reungsang et al. 2005).
The main amendments were to the runoff curve parameter.
The results achieved for the calibration for daily flow and total N are shown below. Both ENS
and R2 values.
15
Table 1: SWAT calibration statistics comparing observed values and baseline predictions
Regression
statistics
Daily Flow
Total N
Calibration
score
ENS
0.53
R2
0.52
ENS
0.52
R2
0.51
Both the ENS and R2 were above the minimum statistical requirements, so the baseline simiulation
was accepted. It should be noted that it is far from optimal.
3.5 Incorporating climate change
The climate change applied to model were based on techniques utilized by Bouraoui et al.
(2004), Tong et al. (2007) and Dunn et al. (2012). The aim of this study is not to accurately
predict future climate change but rather to assess the hydrological and nitrogen transport
response to possible future climate change. Therefore the scenarios used reflect a range of
possible conditions, they will give an indication of a possible set of impacts that will provide
decision makers with information to make adaptive and flexible choices (Tong et al. 2007). In
order to make the study more manageable the simplified assumption was made that the type
and range of crops did not change over time and that the same management processes
were applied. This study did was not focusing on changing land use.
The climate change scenarios in this study are based on the UKCP09 climate projections.
The new 2009 UK Climate Projections provide a more complete picture for climate change
assessments than previous scenarios because they provide future data based upon a more
systematic characterisation of uncertainty across several climate models and different
emission estimates (Street et al. 2009, Dunn et al. 2012). ‘Mid-term change’ was set for the
year 2050 and ‘long term change’ for 2080, both under the A1B (medium) emissions
scenario (Nakicenovic et al. 2000). Precipitation and temperature were the variables
adjusted; both of them are important variables in the SWAT model as they are included in
several equations involved in the simulation of nutrient transport to rivers including,
evapotranspiration, surface runoff, baseflow and soil erosion (Panagopoulos et al. 2011).
The annual average changes in temperature and precipitation are shown in the table below
based on the central estimates for each scenario. The climate change variables are shown
16
as percentage change for precipitation and degrees Celsius for temperature. To calculate
the deviation from the baseline the parameters were edited using a simple method employed
by Bouraoui et al. (2002). The projected change in temperature (oC) for each month was
added to the baseline temperature for each day of the corresponding month. The estimated
percentage change in precipitation for each month was multiplied with the daily baseline
values for that month to calculate the precipitation amendments.
Table 2: Change of precipitation (%) and temperature (°C) from baseline as predicted for each scneario.
P (%)
T Change (oC)
2050 Scenario
Winter mean
6%
2.2
Summer mean
-8%
2.8
Annual Average
2%
2.5
winter mean
22%
3
Summer mean
-23%
2.9
Annual Average
4.45%
2.95
2080 Scenario
17
4. Results
Table 3: Changes to temperature (oC), precipitation (mm) and total nitrogen (kg/ha) for UKCP09 2050 and 2080 central estimates assuming a medium emissions scenario (A1B)
Month
T (oC)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual Average
Percentage
change
Baseline Scenario
P (mm)
TN (kg/ha)
T (oC)
Mid-term 2050
P (mm)
TN (kg/ha)
T (oC)
Long-term 2080
P (mm)
TN (kg/ha)
4.5
5
7
8.5
11.5
14
16.5
17
14.5
11
7.5
5
43.2
41.2
45.7
40.5
35.6
53.1
47.1
53
66.3
60.4
67.8
62.1
5.43
3.26
3.36
1.98
1.25
1.34
2.3
0.65
0.35
2.03
3.96
4.23
6.7
7.5
9.5
11
14
16.8
19.3
19.8
17
13.5
9.7
7.2
45.792
40.376
44.786
39.69
34.888
48.852
43.332
48.76
64.974
59.192
71.868
65.826
6.43
2.80
1.45
1.63
1.58
0.21
0.12
0.15
0.90
2.03
3.98
4.48
7.5
7.9
10
11.4
14.45
17
19.4
19.9
17.4
14
10.5
8
51.8
45.3
37.4
36
28.4
38.7
34.3
38.1
72.9
50.1
81.3
74.5
6.97
2.89
0.23
1.61
1.31
0.12
0.08
0.09
0.89
1.02
4.02
4.65
10.2
616
2.51
12.7
603.6
2.15
13.15
588.6
1.99
-
-
-
19.7
2.01
14.34
22.43
4.45
20.7
18
The table above shows the results of the models for the years 2050 and 2080. The annual
average changes from the baseline of TN are shown as well as monthly variation
The result show a 14.3% decrease in N in response to a 2% increase in precipitation and a
2.5oC increase average annual in temperature for the 2050 simulation. The 2080 simulation
showed a 20.7% decrease in N from the baseline, a 4.45% increase in precipitation and a
2.95oC increase in average annual temperature. In future climates, according to the UKCP09
projections, the pronounced rainfall maximum in winter combined with higher summer
temperatures and evaporation will lead to high winter flows and low river flows, amplifying
the existing flow regime.
25
Baseline
2050
2080
Temperature (oC)
20
15
10
5
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
Figure 4: Graph showing average monthly temperature (oC) for the baseline, 2050 and 2080 time frames
19
90
Baseline
2050
2080
80
Precipitation (mm)
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
Figure 5: Graph showing average monthly precipitation (mm) for the baseline, 2050 and 2080 time frames.
8
Baseline
Total Nitrogen (kg/ha)
7
2050
2080
6
5
4
3
2
1
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
Figure 6: Graph showing average Total Nitrogen (kg/ha) for the baseline, 2050 and 2080 time frames
The decrease in precipitation was not massive in terms of changes to annual totals but there
was significant alteration in terms of seasonal variability. As displayed in figure 5 winter
rainfall increased while summer rainfall fell markedly. The decreases in precipitation led to
increases in both annual water yield and surface runoff despite increases from the baseline
in the winter months.
20
The temperature increase was fairly even across seasons in both mid and long-term
simulations, with an average temperature increase of 2.95oC compared to the baseline in
2080.
Both the models outputs showed increase in TN annually. The monthly trend also has
similarities. In both cases the overall TN to increases during the winter months and fall
drastically during the summer months. January exhibits the greatest N concentration in all
scenarios with TN values of 6.97 kg/ha as an average for January 2080. In every month
although there are differences in magnitude of changes to TN between time frames, the
direction of changes from the baseline are always identical.
5.Discussion
The results of this study suggest that nitrogen loading to the River Wensum will decrease in the
Blackwater sub-catchment, under future climate conditions predicted by UKCP09 assuming a
medium emissions scenario. Although there are bound to be several drivers of changes to nutrient
loading it is clear that changes to precipitation, both intensity and duration, and temperature have
significant impacts on the timing and magnitude of nitrogen transport. Several trend in the results
can be explained.
5.1 Temporal variation
5.1.1 Increasing winter TN levels
The winter months of November, December and predominantly January all showed elevated
TN concentration in both long-term and mid-term simulations. This corresponds with the
expected trend as reported in the literature. The main driver of the higher nitrogen
concentrations would seem to be the increase in winter precipitation. As explained
previously, an increase in number and intensity of precipitation events brings an associated
increase in surface runoff, N leaching and erosion all of which increase the loading of
nitrogen to both surface water and groundwater (Jeppsen et al. 2009). Indeed
Panagopoulos et al. (2011) attributed 40% of total annual N loss to processes in winter
months and numerous other studies show that show that nitrate pollution is more
pronounced during wet years, especially in intensively farmed catchments (Kaushal et al.
2008). Freezing soils in the winter months also have an impact as a change flow pathways
from subsurface to surface flow dominant has potential to increase N transport
(Panagopoulos et al 2011). This would probably only be an relevant during the baseline
21
period as it was the only scenario that had several days with subzero temperatures, but
could contribute in colder periods.
Another explanation for the high, and increasing, winter nitrogen loads is due to flushing
nitrate from soils. A number of factors are at play in this process. The greater summer
temperatures, combined with the decreased summer rainfall, results in less transport of
nitrogen in the summer months also due to accelerated mineralization processes there is
higher concentrations remaining in the soil before the winter. The more frequent winter
rainfalls then wash out the nitrogen that has accumulated over the summer. This flushing
process would help contribute to the high winter nitrogen levels. Whitehead et al., 2006;
Wilby et al., 2006).
What makes these results slightly unusual is that in most studies that predict an increase in
rainfall under future climate scenarios, an increase rather than a decrease in N loading is
observed. (E.g. Bouraoui et al. 2002, Jeppsen et al. 2011, Tong et al. 2012, El-Khoury et al.
2015). The next section offers further suggestions on why this has not been the case in the
Blackwater sub-catchment.
5.1.2. Decreasing summer TN levels
Whilst the increases in nitrogen levels in the winter is notable it is clear the severe decrease
in N loading in the summer months outweighs the winter increase and is responsible for the
overall fall in N concentration.
One might expect the lower summer rainfall and lower daily flow to lead to an increase in
nitrogen concentration due less water available in the river to dilute the nitrates. (Whitehead
et al 2009b). Especially in agricultural catchments in the lower course of a river, like the
Wenusm, as here there is extra nitrate input from fertilizer application. Therefore, the
reduced dilution effect in summer would be more pronounced. This scenario would explain
why the baseline model had higher TN levels that both future scenarios. To understand why
the TN levels fell in the summer compared to the baseline the effect of temperature on the
rate of denitrification should be examined. The rate of denitrification has been shown to
increase under certain climate change scenarios (Marshall and Rhandir 2008). Warmer
summers can be linked to accelerated denitrification, and removal of N from the soil to the
atmosphere, thereby reducing the potential source to waterbodies. A study focusing on the
River Tamar by Whitehead et al. (2009b) found that by 2050 the denitrification effect began
having a greater impact on nitrate concertation compared to the lack of dilution explaining,
thus lowering the nitrate concentration. This shift therefore could explain why summer TN
22
levels decreased so drastically in the summer months after 2050 and highlight that while the
rates of both processes are accelerated by increasing temperature, it would seem that the
denitrification process increases at a faster rate relative to the impact caused by less dilution
of nitrates. It has already been shown how marked the impact of this denitrification effect in
low summer flows can be, in a study by Whitehead and Williams (1982 ) the River Thames
lost up 70% of nitrate through the process. In agricultural catchments a typical amount of
denitrification is expected to account for between 10-20% of fertilization rate (Neitsch et al
2005). A study on Connecticut River Watershed for the period 2060-2100 predicted an
average annual decrease in nitrogen. The decrease was not only related to increased
denitrification as described above but also reduced annual runoff due to increased
evapotranspiration (Marshall and Randhir 2008).
An example of a response of the agricultural ecosystem to climate change that has indirect
effects on the flows of nitrogen to freshwater is the increased growth rate of crops (Patil et al.
2010). An increase in CO2 and temperature is associated with processes that lead to more
N efficient crops, which in turn means reduced transpiration and increases N uptake and
thus removal from the soil (Jeppesen et al. 2011). This therefore leads to a decreased risk
of N leaching. Since the highest rates of plant growth occur during the growing season in the
spring and summer this would agree with the lower predicted N levels during these periods
in the Blackwater sub-catchment. Indeed it is another variable that has been shown to offset
an increase in N concentration caused by to increased rainfall (Patil et al. 2010). This
phenomenon would also account for the seasonal variation that shows August is consistently
the month with the lowest N concentration. Faster crop growth in the previous months would
have taken up and depleted the nutrients in the soil profile (Bouraioui et al.2002). Increases
in the rates of decomposition and mineralization also occur with increases in temperature
(Ahmadi et al. 2014, Fan and Shibata 2015). These alter the ratio of nitrogen species as
mineralization is shown to decrese the amount of organic nitrogen in the soil but is also
linked with increasing nitrate levels. (Wilby et al. 2006, Whitehead et al. 2009, Ahmadi et al.
2014)
23
5.3 Limitations
5.3.1 Data limitations
In every modelling study there will always be inherent epistemic uncertainty involved in the input
data (Apel et al. 2009). In this case there can always be finer elevation grids or improved
delineations of land use, improved resolution of flow or nitrate measurements; all of which would
improve the precision of the model. There must be some trade of in terms of accuracy to allow time
to carry out the study with the available computational power.
Another inherent flaw with the data is that the results obtained will only be accurate under climate
projection and emissions scenario chosen, In this case the central estimates of the UKCP09 climate
projection and A1B emissions scenario. A broad spectrum of future climate projections would ensure
the best results, (Nakicenovic et al. 2000) however there were time limitations to consider in this
study.
5.3.2 Assumptions within model
As with all modelling studies these results represent a simplification of the reality of a highly
complex catchment system (El-Khoury et al. 2015, Whitehead et al. 2015). In this study the
incorporation of climate change was simplified down to two parameters; precipitation and
temperature. In reality solar radiation, humidity and a range of other variables would also have had
an impact (El-khoury et al. 2015).
Simplifying of the climate change applied to the model because only precipitation and temperature
parameters where changed to simulate climate change impacts. In reality both solar radiation and
humidity parameters would have also been impacted (El-Khoury et al. 2015). Prediction of regional
precipitation is notoriously difficult, especially intensity, and represents a major uncertainty in this
study as storm events represent one of the major controls on nitrogen mobilization (Rozemeijer and
Broers 2007, Jepssen et al. 2011). Thus, any change in future precipitation could lead to rather
different results, especially as change to rainfall tend to be amplified in runoff projections (Chiew
and McMhan 2002). Additionally SWAT only considers certain variables that may be impacted by
climate, certain biological and ecological variables are neglected in order to focus on physical and
chemical changes.
5.3.3. Variables not considered
Land use change is one of the major variables that is likely to change with climate, either directly or
indirectly. For example the diversification or expansion of crops is likely to see warm season crops
such as maize and sunflower encroach on areas currently used for small-grained cereals, etc. (Olsen
24
and Bindi 2002). This in itself will impact nitrogen dynamics but the larger change will come from
how farmers change their farming practices in response to climate change. Changing management
practices not taken into account including those that would have been capable of being modelled by
SWAT, such as planting, tillage, irrigation and fertilization practices (Panagopoulos et al. 2011).
Changes to the duration of farming or timings of fertilizer application will all have major impacts on
the transfer of nutrients to waterbodies, Increased period of bare soil in Autumn will occur if crops
are harvested earlier and winter crop planted later, increase the risk of N loss especially under
wetter climates (Olsen et al. 2004, Patil et al. 2004), but were out of the scope of this study. This is
justified due the complexity that cchanges in land use introduce; There are direct human changes
which impact biological, chemical and physical process as well as possible subsequent feedbacks
involved with these human alterations (Murdoch et al.2000).
6. Conclusion
Climate change can potentially have important consequences on regional water resources. This
study evaluates the impacts of UKCP09 climate change predictions on nitrogen dynamics in the
Blackwater sub-catchment of the River Wensum catchment, Norwich. Evaluation at this regional
scale is important to help understand changes in hydrology (Marshal and Randhir 2008).
Although this study is based on hypothetical scenarios that are far from certain, changes in runoff
and nutrient loading are shown to be a distinct possibility in the future that will require a
management response (Tong et al. 2007). The results although insight to be gained concerning the
relative importance of different processes involved in the nitrogen cycle under different climate
pressures. For example, in this case increasing temperatures and increasing seasonality of rainfall
towards a pronounced winter maxima seem to increase nitrogen loss through plant uptake and
denitrification so that it outweighs the nitrate increase through the processes of surface runoff and
leaching. The overall 14.3% and 20.7% decrease in total nitrogen loading in 2050 and 2080
respectively in the Blackwater sub-catchment is attributed the processes impacted by increased
temperature more so than increased precipitation.
In summary, climate change had significant impacts on water quantity and quality in the Blackwater
sub catchment of the River Wensum. The simulated impacts on nitrogen transport varied between
seasons and time frames and could have important implications on future water management in the
Wensum Catchment.
25
Acknowledgments
Thank you to Dr Helen He and Sam Taylor who have provided advice and helped get the
project off the ground. Additionally, I would like to acknowledge the assistance of the
Environment Agency for access to river flow and abstraction data. I would also like to thank
my parents who helped fund this course and made studying this Masters possible.
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