Bangor University

DOCTOR OF PHILOSOPHY
Climate change and Welsh catchments: Implications for hydrological regime, water
quality and water abstraction

Dallison, RichardAward date:
2021
Awarding institution:
Bangor University
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Bangor University

DOCTOR OF PHILOSOPHY
Climate change and Welsh catchments: Implications for hydrological regime, water
quality and water abstraction

Dallison, RichardAward date:
2021
Awarding institution:
Bangor University
Link to publication

General rights
Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners
and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.
• You may not further distribute the material or use it for any profit-making activity or commercial gain
• You may freely distribute the URL identifying the publication in the public portal ?
Take down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately
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Download date: 11. Mar. 2021

Climate change and Welsh catchments: Implications forhydrological regime, water quality and water abstraction
A thesis submitted to Bangor University by
Richard John Harvey Dallison
In candidature for the degree ofDoctor of Philosophy
Supervised by
Dr. Sopan Patil & Dr. Prysor Williams
School of Natural Sciences, Bangor University
Submitted: 27th October 2020 | Defended: 10th December 2020
Corrected: 8th March 2021 | Accepted: 10th March 2021

Research completed as part of the Dŵr Uisce Project
Funded by the European Regional Development Fund (ERDF) through the Interreg
Ireland-Wales Co-operation Programme 2014-2020.

ACKNOWLEDGEMENTS
Dedicated to the memory of my sister, Rachael Dallison“Time flies. Suns rise and shadows fall.
Let time go by. Love is forever”
Firstly, I would like to thank all of the staff in the School of Natural Sciences who have helped
to shape myself and the research contained within this thesis, during my time as a student at
Bangor. I am particularly deeply indebted to both Dr Sopan Patil and Dr Prysor Williams fortheir continued supervision, support, guidance, and patience during the PhD process. Sopan,
firstly thank you for encouraging me to undertake this opportunity to begin with, and for yourcontinued encouragement throughout. Without your expertise in hydrology and hydrologicalmodelling, the thesis quite simply would not be complete. Furthermore, your championing
and support of opportunities such as attendance and participation at conferences andworkshops, as well as paper submissions to journals, has benefitted both me and the researchimmensely. Prysor, your valuable advice, constructive feedback, continued encouragement,
and especially keen eye for detail, have all helped to improve the research completed andcommunicated hugely. Thank you both especially for your time in reading the steady streamof paper and chapter drafts in the final few months of the PhD process, your feedback reallyhas been invaluable. Additionally, without the efforts of Mr Ian Harris in sourcing further
computing power, this research would not have been completed on time. I am therefore very
grateful for both this and you support, advice and expertise during all of my time at Bangor.
The whole Dŵr Uisce team, both past and present, in Ireland and in Wales, have all also had
a hand in shaping this work, and I am grateful for every comment, question and discussion
over the course of the research, I look forward to continuing to work with you all. I must also
acknowledge the European Regional Development Fund through the Interreg Ireland-Wales
Co-operation Programme for the funding of this research. I am also very appreciative of Dŵr
Cymru Welsh Water for the provision of both data and knowledge for this research, as well
as all other organisation that have provided data.
The PhD process would also have been very different without the support, kindness, laughter,and camaraderie of many fantastic friends, to all of whom I am very grateful. A special thankyou though, must go especially to Rebecca Jones, Rob Brown, Luke Prosser, and Luke Tyler,
without whom the PhD process would have been very lonely. Rebecca, without you inparticular I most likely would not have reached this stage, so I will be eternally grateful to you.
Finally, thank you to my Mum who has always encouraged me to follow my ambitions andaspirations and believed in me to achieve anything. Your continued love and support has kept
me going through difficult times and always helps me to find the best route forward. I would
not be where I am today without you and your unwavering belief in me.
iv

EXECUTIVE SUMMARY
Historical and future climate change is likely to be one of the key drivers of alterations seen
in catchments globally. The implications of changing precipitation patterns in particular range
from increased seasonality of flows, to changing frequency, duration, and severity of extreme
events, to variations in pollutant sourcing and transport. These changes display regional
differences, but it is also reasonable to expect catchment level variation. Factors such as
topography, land use/land cover and soil type all cause variation of regional trends. Suchalterations could have consequences for all water users, but flow regime changes in particularwill directly impact those abstracting water from rivers. In this work, hydrological regimechange is investigated for five catchments in Wales (Clwyd, Conwy, Dyfi, Teifi, Tywi), both
historically and under a future worst-case scenario of global greenhouse gas emissions. For
the future period water quality is also considered. Subsequently, impacts on abstraction for
public water supply (PWS) and hydroelectric power (HEP) are also quantified.
The first study investigates historical trends in average and extreme precipitation, airtemperature and streamflow, before correlating these factors with actual total abstraction
data for PWS (as a proxy for overall water demand). A strong warming trend in autumn
average temperatures in all catchments is observed (Sen’s slope range: 0.38-0.41, p <0.05),
with a north-south divide detected in streamflow and extreme temperature trends. A positive
correlation between abstraction volume and temperature in four catchments is established(Spearman’s ρ range: 0.094-0.403, p <0.01; Pearson’s r range 0.073-0.369, p <0.01). The study
provides new insight into the relationship between hydroclimatic factors and total PWS
demand and highlights local variation in broader regional changes.
The second study uses the Soil and Water Assessment Tool (SWAT) to model future average
and extreme streamflow and water quality. The 12-model ensemble UK Climate Projections
2018 dataset, based on Representative Concentration Pathway 8.5 (RCP8.5) conditions, isused for future daily weather input (2021-2080). A small decline in mean annual flow isobserved (-4% to -13%), with much larger variations seasonally. Spring and autumn
streamflows are most affected, increasing and decreasing by up to 41% and 52%, respectively;
these changes corresponded with increased frequency of high flow events in spring, and low
flow events in autumn. Water quality generally declines in all catchments and seasons, withincreased concentrations of suspended sediment, nitrogen, and phosphorous, and decreased
dissolved oxygen. Results suggest a double threat for water abstractors, especially for PWS,
of both declining flows and water quality, in summer and autumn in particular.
The implications of projected hydrological regime changes are investigated in the third study,
with a focus on PWS and HEP. A key PWS abstraction in the Tywi is studied using the Water
Evaluation And Planning (WEAP) system. The temperature-demand relationship establishedin the first study is used to project future total water demand under RCP8.5 conditions; static
and reducing demand scenarios are also considered. Under all scenarios there is an increasein occurrences of insufficient streamflow to satisfy demand. Alterations in HEP operations areanalysed for sixteen sites in the Conwy, and nine in the Tywi. Results suggest a decrease intotal annual abstraction volume, resulting in a loss of generation potential, this is especiallypronounced up to the mid-2050s. Further work is needed to quantify the financial andenvironmental implications of observed trends; additional work on water quality changes
would also strengthen PWS planning.

v

CONTENTS
DECLARATION AND CONSENT ........................................................................................ i
ACKNOWLEDGEMENTS ................................................................................................. iv
EXECUTIVE SUMMARY ................................................................................................... v
LIST OF FIGURES ............................................................................................................ ix
LIST OF TABLES ............................................................................................................. xii
ABBREVIATIONS ...........................................................................................................xiv
CHAPTER 1: INTRODUCTION ........................................................................................ 1
1.1

Study context ............................................................................................................... 2

1.2

Study justification ........................................................................................................ 5

1.3

Research aim and objectives ....................................................................................... 8

1.4

Thesis structure ........................................................................................................... 8

1.5

The Dŵr Uisce Project................................................................................................ 10

CHAPTER 2: LITERATURE REVIEW .............................................................................. 11
2.1

Global warming and climatic change......................................................................... 12

2.1.1

Historical changes ......................................................................................................... 12

2.1.2

Future projections ......................................................................................................... 17

2.2

Climatic trends in the UK and Wales ......................................................................... 20

2.2.1

Historical changes ......................................................................................................... 20

2.2.2

Future projections ......................................................................................................... 22

2.3

Changing hydrology in the UK and Wales ................................................................. 25

2.3.1

Historical hydrological trends ....................................................................................... 25

2.3.2

Future projections under climate change ..................................................................... 27

2.4

Water quality implications of hydroclimatic change ................................................. 31

2.5

Future water resource use implications for Wales ................................................... 36

2.6

Summary .................................................................................................................... 40

CHAPTER 3: METHODOLOGICAL OVERVIEW ............................................................. 41
3.1

Overarching methodology ......................................................................................... 42

3.2

Study catchments ...................................................................................................... 43

3.3

Trend analysis ............................................................................................................ 51

3.4

Study 1: Historical analysis ........................................................................................ 54

3.4.1

Hydroclimatic data ........................................................................................................ 54

3.4.2

Hydroclimatic analysis................................................................................................... 55

3.4.3

Water abstraction relationships ................................................................................... 57

3.5

Study 2: Hydrological modelling ................................................................................ 58vi

3.5.1Model selection............................................................................................................. 58

3.5.2

Model setup, calibration and validation ....................................................................... 61

3.5.3

Future model runs......................................................................................................... 65

3.5.4

Data analysis ................................................................................................................. 68

3.6

Study 3: Future water availability analysis ................................................................ 69

3.6.1

Study area and analysis framework .............................................................................. 69

3.6.2

Hydroelectric power ..................................................................................................... 70

3.6.3

Public water supply ....................................................................................................... 71

CHAPTER 4: STUDY I. Influence of historical climate patterns on streamflow andwater demand in Wales, UK ........................................................................................ 74
4.1

Abstract...................................................................................................................... 75

4.2

Introduction ............................................................................................................... 76

4.3

Data and methods ..................................................................................................... 79

4.3.1

Study catchments.......................................................................................................... 79

4.3.2

Data ............................................................................................................................ 81

4.3.3

Trend analysis ............................................................................................................... 82

4.3.4

Correlation analysis of hydroclimatic and abstraction data ......................................... 83

4.4

Results........................................................................................................................ 84

4.4.1

Trend and breakpoint analysis ...................................................................................... 84

4.4.2

Correlation analysis of hydroclimatic and abstraction data ......................................... 89

4.5

Discussion .................................................................................................................. 92

4.5.1

Hydroclimatic trends ..................................................................................................... 92

4.5.2

Weather and demand for water ................................................................................... 96

4.5.3

Study implications ......................................................................................................... 97

4.6

Conclusions ................................................................................................................ 98

CHAPTER 5: STUDY II. Modelling the impact of future climate change on streamflow
and water quality in Wales, UK ................................................................................. 100
5.1

Abstract.................................................................................................................... 101

5.2

Introduction ............................................................................................................. 102

5.3

Data and methods ................................................................................................... 106

5.3.1

Study catchments........................................................................................................ 106

5.3.2

Future climate change data ........................................................................................ 109

5.3.3

Hydrological modelling ............................................................................................... 111

5.3.4

Trend analysis ............................................................................................................. 115

5.4

Results...................................................................................................................... 116

5.4.1

Climate projections ..................................................................................................... 116

5.4.2

Streamflow .................................................................................................................. 118

5.4.3

Water quality .............................................................................................................. 122

vii

5.5

Discussion ................................................................................................................ 123

5.5.1

Streamflow trends ...................................................................................................... 123

5.5.2

Water quality trends ................................................................................................... 126

5.5.3

Study implications ....................................................................................................... 128

5.6

Conclusions .............................................................................................................. 130

CHAPTER 6: STUDY III. Impacts of climate change on future water availability forhydropower and public water supply in Wales, UK ................................................. 131
6.1

Abstract.................................................................................................................... 132

6.2

Introduction ............................................................................................................. 133

6.3

Data and methods ................................................................................................... 135

6.3.1

Study catchments........................................................................................................ 135

6.3.2

Future streamflow and climate projections................................................................ 137

6.3.3

Hydroelectric power assessment ................................................................................ 139

6.3.4

Public water supply assessment ................................................................................. 141

6.3.5

Trend analysis ............................................................................................................. 143

6.4

Results...................................................................................................................... 144

6.4.1

Hydroelectric power ................................................................................................... 144

6.4.2

Public water supply ..................................................................................................... 150

6.5

Discussion ................................................................................................................ 152

6.5.1

Hydroelectric power ................................................................................................... 152

6.5.2

Public water supply ..................................................................................................... 154

6.5.3

Study implications ....................................................................................................... 155

6.6

Conclusions .............................................................................................................. 156

CHAPTER 7: DISCUSSION .......................................................................................... 158
7.1

Hydroclimatic trends and implications .................................................................... 159

7.2

Future water quality implications for abstractors ................................................... 162

7.3

Summary .................................................................................................................. 165

CHAPTER 8: CONCLUSIONS ...................................................................................... 167
8.1

Final conclusions ...................................................................................................... 168

8.2

Recommendations for further work ........................................................................ 169

8.3

Final summary.......................................................................................................... 170

REFERENCES .............................................................................................................. 172
APPENDICES .............................................................................................................. 201
Appendix 1: SWAT model calibration charts ..................................................................... 202
Appendix 2: Ensemble projections for seasonal and annual streamflow ......................... 204

viii

LIST OF FIGURES
Figure 1.1. UK annual average rainfall for the period 1981-2010 (Met Office, 2020). ............. 6
Figure 1.2. Thesis study catchments ......................................................................................... 7
Figure 2.1. Historical atmospheric concentrations of CO2, CH4 and N2O (IPCC, 2013) ........... 12
Figure 2.2. Observed and projected (2017-2100) global mean surface temperature. Adaptedfrom IPCC (2018) ...................................................................................................................... 13
Figure 2.3. Historical over-land annual precipitation anomalies (IPCC, 2013)........................ 14
Figure 2.4. CO2 projetions for each Representative Concentration Pathway (IPCC, 2013) .... 18
Figure 2.5. Projections of future global average surface temperature (IPCC, 2013) .............. 19
Figure 2.6. Projections of future UK average temperature (Lowe et al., 2018) ...................... 24
Figure 2.7. Projections of future UK average precipitation (Lowe et al., 2018)...................... 24
Figure 2.8. UK abstraction demand in the 2050s and 2080s as a percentage of available
discharge during low flow conditions (ASC, 2016b) ................................................................ 38
Figure 3.1. Thesis study catchments displaying streamflow gauges used in all studies; thelocation of pseudo-meteorological stations and water quality monitoring locations used inStudy 2, and the hydroelectric power abstractions locations examined in Study 3 ............... 44
Figure 3.2. Abstraction locations in each catchment for all purposes .................................... 50
Figure 3.3. Representation of the processes modelled in the land phase of SWAT (Neitsch etal., 2011). ................................................................................................................................. 60
Figure 3.4. Representation of the processes modelled in the in-stream phase of SWAT
(Neitsch et al., 2011). ............................................................................................................... 60
Figure 3.5. Pseudo-meteorological stations used in SWAT, with elevation............................ 67
Figure 4.1. Study I catchments showing, (A) stream order and gauging station locations, (B)elevation, and (C) land use/land cover .................................................................................... 80
Figure 4.2. Warming trend in historical average autumn temperatures in all catchments.... 85
Figure 4.3. Declining histrocal trend in the frequency of the hotest days annually for the Dyfi,
Teifi and Tywi ........................................................................................................................... 87
Figure 4.4. Declining histrocal trend in the frequency of the coldest summer days for the Dyfi,
Teifi and Tywi ........................................................................................................................... 87
Figure 4.5. Historcal annual average streamflows for all study catchments .......................... 88
Figure 4.6. Normalised daily hydroclimatic data compared with normalised daily water supply
abstraction volume for January 2012 to September 2015 ...................................................... 91ix

Figure 4.7. Normalised monthly hydroclimatic data compared with normalised monthly watersupply abstraction volume for January 2012 to September 2015 .......................................... 92
Figure 4.8. Annual North Atlantic Oscillation index 1825-2019 relative to the 1951-1980
average index value, adapted from Osborn (2011) ................................................................. 95
Figure 5.1. Study II catchments showing, (A) stream order, gauging stations and water quality
monitoring locations, and (B) catchment elevation .............................................................. 107
Figure 5.2. Study II catchments showing, (A) land use/land cover, and (B) soil types .......... 108
Figure 5.3. Pseudo-meteorological stations used in SWAT future weather input ................ 110
Figure 5.4. Future projections (2021-2080) for seasonal and annual daily mean precipitationand temperature .................................................................................................................... 117
Figure 5.5. Seasonal and annual average percentage change from 1990-2010 baseline, for a
5-year moving average of future (2021-2080) streamflow ................................................... 119
Figure 5.6. Overview of the direction, magnitude and significance of annual and seasonal
trends in projected future (2021-2080) average streamflow ............................................... 120
Figure 5.7. Overview of the direction, magnitude and significance of annual and seasonal
trends in projected future (2021-2080) extreme streamflows ............................................. 120
Figure 5.8. Flow duration curve comparison of 1990-2010 and 2060-2080 for, (A) Conwy andDyfi catchments and, (B) Clwyd, Teifi and Tywi catchment .................................................. 122
Figure 5.9. Overview of the direction, magnitude and significance of annual and seasonal
trends in projected future (2021-2080) water quality concentrations ................................. 123
Figure 6.1. Study III catchments, showing land use/land cover classification ...................... 136
Figure 6.2. Projections for future seasonal and annual mean temperature, precipitation andstreamflow for the Conwy and Tywi, as well as percentage change from 1990-2010 baseline
for a 5-year moving average of future streamflow ............................................................... 139
Figure 6.3. Studied abstraction locations for hydroelectric power and public water supply;
stream order and catchment elevation also shown .............................................................. 141
Figure 6.4. Future monthly total water demand at the Tywi public water supply abstraction
under three future water demand scenarios: increased, static and decreased ................... 143
Figure 6.5. Overview map of the direction, magnitude and significance of annual trends innumber of days Astart achieved for the periods 2021-2079 and 2021-2054 .......................... 145
Figure 6.6. Overview map of the direction, magnitude and significance of annual trends innumber of days Amax reached for the periods 2021-2079 and 2021-2054 ........................... 146
Figure 6.7. Overview map of the direction, magnitude and significance of annual trends inmean Adaily for the periods 2021-2079 and 2021-2054 ......................................................... 147
Figure 6.8. Overview map of the direction, magnitude and significance of annual trends intotal abstraction for the periods 2021-2079 and 2021-2054 ................................................ 148

xFigure 6.9. Comparison of the direction, magnitude and significance of annual and seasonal
trends in future hydroelectric power characteristics for the period 2021-2079. Based on the
average of all Zone 2 (Z2) and Zone 3 (Z3) abstractions in each catchment ......................... 149
Figure 6.10. Comparison of the direction, magnitude and significance of annual and seasonal
trends in future hydroelectric power characteristics for the period 2021-2054. Based on the
average of all Zone 2 (Z2) and Zone 3 (Z3) abstractions in each catchment ......................... 150
Figure 6.11. Comparison of the direction, magnitude and significance of annual and seasonal
trends in future public water supply unmet demand characteristics for the periods 2021-2079
and 2021-2054 ....................................................................................................................... 151
Figure A1.1. Observed and simulated streamflow for Clwyd catchment calibration ........... 202
Figure A1.2. Observed and simulated streamflow for Conwy catchment calibration .......... 202
Figure A1.3. Observed and simulated streamflow for Dyfi catchment calibration .............. 202
Figure A1.4. Observed and simulated streamflow for Teifi catchment calibration .............. 203
Figure A1.5. Observed and simulated streamflow for Tywi catchment calibration ............. 203
Figure A2.1. Ensemble projections of seasonal and annual streamflow for the Clwyd........ 204
Figure A2.2. Ensemble projections of seasonal and annual streamflow for the Conwy ...... 205
Figure A2.3. Ensemble projections of seasonal and annual streamflow for the Dyfi ........... 206
Figure A2.4. Ensemble projections of seasonal and annual streamflow for the Teifi .......... 207
Figure A2.5. Ensemble projections of seasonal and annual streamflow for the Tywi .......... 208

xi

LIST OF TABLES
Table 2.1. Overview of the characteristics of the four Representative Concentration Pathways(RCPs), adapted from Moss et al. (2010) ................................................................................. 17
Table 2.2. Breakdown of reasons for Welsh streams not achieving good ecological status in
the Water Framework Directive classifcations (NRW, 2019a) ................................................ 33
Table 3.1. Summary of comparative study catchment details ................................................ 46
Table 3.2. Number of abstraction locations per use descriptor for each catchment ............. 50
Table 3.3. Details of gauging stations used throughout the thesis (NRFA, 2020). .................. 55
Table 3.4. SWAT calibration parameters ................................................................................. 62
Table 3.5. Kling-Gupta efficiency calibration and validation statistics for streamflow in eachcatchment ................................................................................................................................ 64
Table 3.6. Water quality sample details for SWAT validation ................................................. 65
Table 3.7. Catchment SWAT model characteristics and run period information ................... 68
Table 4.1. Key Study I catchments details ............................................................................... 81
Table 4.2. Mann-Kendall trend analysis and Sen’s slope indicator results for average autumn
air temperatures trends (1981-2015) ...................................................................................... 86
Table 4.3. Pettitt and Standard Normal Homogeneity Test breakpoint analysis statistics foraverage autumn temperatures ................................................................................................ 86
Table 4.4. Signifcant Mann-Kendall trend analysis and Sen’s slope indicator results for
extreme temperature events (1982-2015) .............................................................................. 87
Table 4.5. Signifcant Mann-Kendall trend analysis and Sen’s slope indicator results for
extreme streamflow events (1982-2015) ................................................................................ 89
Table 4.6. Pearson’s and Spearman’s rank correlation coefficient results for daily and monthly
abstraction data with hydroclimatic factors (2012-2015) ....................................................... 90
Table 5.1. Key Study II catchments details ............................................................................ 109
Table 5.2. Catchment SWAT model details, with calibration and validation statistics ......... 113
Table 5.3. SWAT calibration parameters ............................................................................... 113
Table 5.4. Pearson's correlation values between observed and modelled water quality
concentrations ....................................................................................................................... 114
Table 5.5. Pearson's correlation values between modelled water quality concentrations andstreamflow ............................................................................................................................. 114

xii

Table 6.1. Key Study III catchments details ........................................................................... 137
Table 6.2. Abstraction conditions as defined by NRW guidelines for Zone 2 and Zone 3 sites(NRW, 2020b)......................................................................................................................... 141
Table 6.3. Percentage change in annual hydroelectric power characteristics when comparingfar (2070-2079) and near (2045-2054) decadal averages with 2021-2030 baseline ............ 150
Table 6.4. Percentage change in annual public water supply unmet demand when comparingfar (2070-2079) and near (2045-2054) decadal averages with 2021-2030 baseline ............ 151

xiii

ABBREVIATIONS
AR5

Fifth Assessment Report (bythe IPCC)

NIC

National Infrastructure
Committee

ASC

Adaptation Sub-Committee(of the CCC)

NRFA

National River Flow Archive

NRW

Natural Resources Wales

CCC

Committee on Climate
Change

PPE

Perturbed parameter
ensemble

CEH

Centre for Ecology &Hydrology

PSO

Particle Swarm Optimisation

CHESS

Climate, Hydrology & Ecology
research Support System

PWS

Public water supply

RCM

Regional climate model

CMIP5

5th phase Coupled Model
Intercomparison Project

RCP

Representative Concentration
Pathway

DCWW

Dŵr Cymru Welsh Water

SDGs

DEM

Digital elevation model

Sustainable Development
Goals (from the UN)

DO

Dissolved oxygen

SNHTStandard Normal
Homogeneity Test

EEA

European Environment
AgencySS

Suspended sediment

EU

European Union

SWAT

Soil and Water Assessment
Tool (hydrological model)

FDC

Flow duration curve

SWAT-CUP

FFH

Future Flows Hydrology(dataset)

SWAT Calibration andUncertainty ProgrammeTN

Total nitrogen

FiT

Feed-in-Tariff

TP

Total phosphorus

GCM

Global climate model

UK

United Kingdom

GHG

Greenhouse gas

UKCIP02

HEP

Hydroelectric power

UK Climate Impacts
Programme 2002

HoF

Hands-off-flow

UKCP09UK Climate Projections 2009

HRU

Hydrological Response Unit

UKCP18UK Climate Projections 2018

IPCC

Intergovernmental Panel on
Climate ChangeUN

United Nations

WEAP

KGE

Kling-Gupta efficiency

Water Evaluation And
Planning system

LULC

Land use/land cover

WMO

MK

Mann-Kendall (trend analysis)

World MeteorologicalOrganisation

MOHC

Met Office Hadley Centre

WSP

Water service provider

NAO

North Atlantic Oscillation

Z2 / Z3

Zone 2 / Zone 3 (HEP sites)xiv

CHAPTER 1: INTRODUCTION

1

Introduction

1.1 Study context
Global climatic change, chiefly caused by anthropogenic inputs of greenhouse gases (GHGs)
to the atmosphere (IPCC, 2013), has fundamentally altered the hydrological cycle in various
ways (IPCC, 2014a). Changing precipitation patterns, altered snowmelt timing and speed, andincreases in extreme weather events have all caused variation in the hydrological regime ofriver systems (IPCC, 2014a). Such alterations have had numerous impacts on river systems
across the globe, such as changes to ecology/river health (Durance & Ormerod, 2007; Lynch
et al., 2016; Stagl & Hattermann, 2016; Bussi et al., 2018), variation in flood characteristics(Hattermann et al., 2013; Kundzewicz et al., 2018; Langhammer & Bernsteinová, 2020;
Tramblay et al., 2020), thermal regime changes (Isaak et al., 2012; Arora et al., 2016; Islam etal., 2019; Jin et al., 2020), and riverbed morphology alteration (Rumsby & Macklin, 1994;
Ashmore & Church, 2001; Slater & Singer, 2013; Slater et al., 2019). In terms of humansystems, one of the most important consequences is for change in available water resource(Vörösmarty, 2000; Arnell, 2004a; Alcamo et al., 2007; Konapala et al., 2020), which can be
abstracted for uses such as public water supply (PWS), hydroelectric power (HEP), agriculture,and heavy industry. Water resource availability globally is undoubtably under pressure fromvarious factors, including urbanisation (Astaraie-Imani et al., 2012; McGrane, 2016),
agricultural intensification (Watts et al., 2015b; Holden et al., 2017), and population increase(Vörösmarty et al., 2010; Staddon & Scott, 2018). However, climate change and the impacts
that increased global temperatures and changing precipitation patterns will have on water
availability is arguably one of the most challenging.
Climate change not only has the ability to impact on the supply of water, but is also likely tohave an impact on demand for water. Domestically, increased air temperature, humidity andsunshine hours can increase water use in the UK, especially for purposes such as bathing andgarden watering (Downing et al., 2003; Goodchild, 2003; Green & Weatherhead, 2014;
Xenochristou et al., 2020). Furthermore, likely increases in temperature and decreases inrainfall have the potential to generate much greater demand for water from the agricultural
sector, for irrigation of crops and water supply for animals (Downing et al., 2003; Knox et al.,2010; Rio et al., 2018; Hess et al., 2020). This creates a two-part threat to future water security
and water resource availability.

2Introduction
Another concern for catchments under future climate change is the impact on water quality,
which could have far-reaching consequences for PWS (Wilby et al., 2006; Whitehead et al.,2009b; Mo et al., 2016). Water quality is expected to decline because of alterations in flow
regime, in particular extreme flows, as well due to changing precipitation patterns (Watts etal., 2015a; Sayers et al., 2016; Collet et al., 2018; Rau et al., 2020). Increasing discharge
volumes cause both greater erosion of the riverbed and banks, as well as increasing thetransport capacity of the flow (Delpla et al., 2009; Mortazavi‐Naeini et al., 2019).
Furthermore, intensification of extreme precipitation and flood events also allows for greater
washing of pollutants from the land surface to streams, increasing pollutant concentrations(Delpla et al., 2009; Whitehead et al., 2009b; Ockenden et al., 2017; Hashempour et al., 2020).
In addition, increases in the intensity and frequency of low flow events gives less opportunity
for dilution of pollutants; this is particularly problematic downstream of point-sources ofpollution (Delpla et al., 2009; Watts & Anderson, 2016; Miller & Hutchins, 2017).
In Wales, it is expected that future climate change will take the form of warmer temperatures
year-round, with exaggerated seasonality of precipitation when compared to current
conditions, meaning drier summers and wetter winters (Welsh Government, 2013; ASC,
2016a; Lowe et al., 2018). A greater number of extreme weather events are projected, withmore frequent and intense precipitation events (Welsh Government, 2013; ASC, 2016a; Loweet al., 2018). These changes have the potential to substantially impact on the availability ofsurface water resources in the country, a resource that is highly relied upon in Wales forseveral sectors, in particular PWS (DCWW, 2019a). Furthermore, changes in water quality
could also have significant impacts on PWS as well as create further challenges for many
abstractors. Changes in quality of raw water entering drinking water treatment plants could
prove costly, both financially and in terms of energy use, with water potentially requiring
additional treatment (Mo et al., 2016; Valdivia-Garcia et al., 2019). As has been
demonstrated, it is therefore important to have a thorough understanding of futurehydrological regime characteristics under a changing climate for Wales, in order to best plan
and adapt for changing water resource availability and quality.
Such climate change adaptation planning is in fact mandated in Wales, with the Climate
Change Act (2008) requiring Welsh Ministers to regularly produce a report on their objectives,
actions, and priorities in terms of the impact of climate change for Wales. At the time of its

3

Introduction
enactment, the Act set a target of an 80% reduction in GHG emissions compared to 1990
levels by 2050, however, in 2019 a new, more ambitious net zero emissions target was set.
The Act also established the Committee on Climate Change (CCC), a statuary body with the
purpose of advising the UK and devolved governments on progress towards emission targets
and climate change adaptation (CCC, 2020). The Adaptation Sub-Committee (ASC) of the CCC
fulfils the requirement of the Climate Change Act (2008) to prepare a climate change risk
assessment every five years, the next to be released in June 2021 (Welsh Government,
2019a). Additionally, the Environment Act (Wales) 2016 aims to position Wales as ready toadapt to climate change as part of a low carbon economy. Parts 1 and 2 of the Act specificallyrelate to sustainable natural resources management, and adaptation to climate change
respectively.
The Water Industry Act (1991) identifies the main duties for water service providers in the UK
and provides the framework for water services regulation, a role currently performed by
Ofwat. Furthermore, the Water Resources Act (1991) developed the environmental
regulation and oversight relating to water supply, currently under the jurisdiction of Natural
Resources Wales. Additionally, the Water Resources Management Plan Regulations (2007)
amended the Water Industry Act (1991) to make it a statuary obligation for water providers
in England and Wales to produce a water resource management plan every five years, thelatest being released in 2019. Any measures undertaken must ensure compliance with theEuropean Union’s (EU) Water Framework Directive in terms of surface water quality (although
the future of this Directive is uncertain given the UKs exit from the EU) and the Water Supply(Water Quality) Regulations (Wales) 2018, in terms of drinking water supply standards.
Furthermore, the Wellbeing of Future Generations (Wales) Act 2015 requires the Welsh
Government and public bodies in Wales to incorporate long-term thinking into decision
making, particularly around seven wellbeing goals set out in the Act. This thesis directly
addresses three of these goals, in particular, (1) a prosperous Wales, (2) a resilient Wales, and(3) a globally responsible Wales. In addition, of global relevance to this research are theUnited Nations’ (UN) seventeen Sustainable Development Goals (SDGs; UN, 2015). No lessthan nine bear relevance to this work (SDGs 3, 6, 7, 8, 9, 11, 12, 13, 14); with Goal 6 (ensure
availability and sustainable management of water and sanitation for all), and Goal 13 (takeurgent action to combat climate change and its impacts) being the most relevant.

4

Introduction

1.2 Study justification
Wales is often viewed as a country with an abundant supply of water and higher than average
precipitation when compared to the rest of the UK, indeed for some areas this is true, annual
rainfall volumes in the north of the country being some of the highest in the UK (Figure 1.1;
MET Office, 2020). However, in terms of PWS, the main water supplier Dŵr Cymru Welsh
Water (DCWW) states that there are already regions at risk of seasonal water deficit in Wales(DCWW, 2019a), as well as areas that will face seasonal water scarcity problems in the future
unless measures are put in place soon to ensure continued supply (DCWW, 2019b). Thisvulnerability comes from the heavy reliance on surface waters in the region, with little
abstraction from groundwater as a back-up. Importantly, Wales is also an exporter of waterto other areas of the UK, for example, up to 360 million litres of water per day are piped 117
km from the Elan Valley reservoirs in mid Wales to supply the Birmingham metropolitan area
(Warren & Holman, 2012; DCWW, 2016a). In addition, Lake Vyrnwy in north Powys, alongwith the River Dee, supply over 230 million litres of water per day to Merseyside and Cheshire(United Utilities, 2018). Lake Vyrnwy is also used to regulated flow in the River Severn in timesof drought, ensuring supplies for several water companies in the midlands and west of
England (United Utilities, 2018). Recent plans also include the possibility of diverting waterfrom the lake via the Severn system to supply water to London and the southeast of England,by the latter half of this century (Atkins et al., 2016). Although these two key systems do not
fall within the catchments studied in this thesis, the results of this work are still relevant tothem and the supplies of water to the reservoirs. This current and potentially even greater
future reliance of millions of people on the catchments of Wales for drinking water supply
alone, makes the country an important region to study. In addition to the heavy reliance of
PWS on streamflow, the rivers of Wales also support various other sectors from the important
tourism and leisure industry, to heavy industry, to agriculture and to both large- and smallscale HEP generation. HEP in particular, although relatively small in the electricity generation
portfolio of Wales, is a sector that has been growing in recent years with over 300 new
schemes implemented since 2002, from a starting point of approximately 40 (Welsh
Government, 2019b). Despite this substantial increase in the number of new schemes, theinstalled capacity has only risen by 20 MW from a 160 MW baseline in 2002 (Welsh
Government, 2019b). This disparity points to the nature of the schemes developed, the5Introduction
majority being small-scale; these types of schemes are considerably more vulnerable tochanging streamflow regimes. While changes to Feed-in-Tariffs (the rate paid for electricity
generated through renewable technologies) and business rates in the last two years haveseen a decline in in the installation of new schemes (Welsh Government, 2019b), it is still
important to understand the performance of installed schemes under future streamflow
conditions to enable robust planning in the energy sector to ensure a stable electrical supply.

Figure 1.1. UK annual average rainfall for the period 1981-2010 (Met Office, 2020).

As a result of the reliance on surface waters by various sectors in Wales, as well as the impact
of individual catchment characteristics on the response of a watershed to precipitation,
understanding future changes in the timing, quantity and quality of streamflows at acatchment scale is crucial. Five catchments in Wales have been selected for study, The Clwyd,
Conwy, Dyfi, Teifi, and Tywi (Figure 1.2). These five have specifically been chosen to reflect
the wide variety of catchment characteristics displayed across Wales, allowing for6

Introduction
comparisons of responses to different conditions to be made. The catchments differ in termsof topography, land use/land cover proportions, size, and location. All of these factors havethe potential to influence how future climate change will impact of streamflows and water
quality. Furthermore, these catchments provide water for a population in excess of 975,000
people, as well as agricultural, industrial, and commercial purposes, and more than 70 HEP
schemes. The characteristics of each catchment are described in greater detail in the overall
methodology (Chapter 3) and study chapters (Chapters 4, 5 and 6). The research presented inthis thesis has therefore been undertaken in order to strengthen the knowledge base of
future hydrological regime for the region, with the results being useful in future water
resource allocation planning, as well as ensuring PWS security and efficient HEP generation.

Figure 1.2. Study catchments; streams greater than second order shown, as defined by Strahler method.

7

Introduction

1.3 Research aim and objectives
The overarching aim of this thesis is an assessment of the impact of future climate change on
the streamflow characteristics of catchments in Wales and an evaluation of the effect of suchchanges on the abstractable water resource. To achieve this, the thesis will address thefollowing research objectives:
I.

Assess and analyse historical changes in hydroclimatic factors at a catchmentscale for Wales;

II.

Quantify the relationship between weather conditions and total demand forwater from surface sources in Wales;

III.

Construct hydrological models for each study catchment and apply future
weather data under a worst-case scenario for future climate change;IV.

Measure and analyse future hydrological regime and water quality changes,
given future climate forcing;V.

Calculate catchment level change in water availability for public water supply
and hydroelectric power generation.

1.4 Thesis structureThis thesis is comprised of eight chapters. The first (this chapter), provides context to thework, details the justification for the study, and provides a brief introduction to the chosen
study area and catchments. The overarching aim and objectives are also stated.
Chapter 2 presents a review of literature relevant to the thesis. Firstly, context is given to theglobal climate change picture both historically and for the future. The latest report by the
IPCC (AR5) is used as a basis for discussion of global change, with recent relevant peerreviewed work being included to provide further scientific grounding. A review of the changes
observed and projected for the UK and Wales is then undertaken. Projected changes inclimate for both nations are discussed in the context or UK Climate Projections by the MET
Office as well as peer-reviewed work. Literature detailing the impact of the discussed futureclimatic changes on hydrological regime and quality of surface waters in Wales are thenintroduced, before the potential implications for water resource use in Wales are conferred.

8

Introduction
Chapter 3 details the data, analytical methods, and modelling processes used in the thesis,
justifying the reason for their use and presenting the implications that arise from their use.
Detailed descriptions of the five catchments studied, as well as an explanation for their use
and discussion on the applicability of the results gained outside of Wales, are also provided.
Chapter 4 explores historical hydroclimatic change for the five study catchments, conducting
trend analysis for 34 years of temperature, precipitation, and streamflow data. Daily actual
abstraction volume data for PWS in each catchment is correlated with the hydroclimatic
factors in order to explore the relationship between weather and total water demand.
Chapter 5 considers the impact of climate change on the hydrological regime and water
quality of the study catchments, under a high future GHG emissions scenario (Representative
Concentration Pathway 8.5), using the latest UK Climate Projections 2018 data. Future
streamflow projections are generated using the Soil and Water Assessment Tool (SWAT) as
well as projections for four water quality factors. Analysis of changes in average and extreme
flows, as well as the average concentration of the water quality factors is presented for theperiod 2021-2079.
Chapter 6 uses the streamflow data generated in Chapter 5 to quantify the impact of climatechange on the generation potential of HEP schemes in the Conwy and the Tywi. Furthermore,
the temperature-water demand relationship established in Chapter 4 is implemented in orderto project water demand under increasing future temperatures and relate this to available
streamflow at a key PWS abstraction location. This chapter therefore provides an impact
assessment of future climate change on two key streamflow-dependent sectors in Wales.
Chapter 7 provides an overall discussion of the findings of the thesis and places them in thecontext of previously published research. The chapter also provides the opportunity to discuss
the implications of the results and conclusions of each study chapter upon each other,
providing fresh insight into the implications of future climate change on a variety of sectors.
Chapter 8 addresses how the aims set out in Chapter 1 have been met, as well as delivers
recommendations for future research to further advance knowledge in the field. Finally, an
overall summary of the conclusions is drawn, highlighting the novel study areas andknowledge gaps addressed and detailing the implications of the findings.

9Introduction
A list of figures and a list of tables have been included after the contents page for navigational
aid; a list of abbreviations follows this. Two appendices are included at the end of the thesis,presenting, for each catchment, (1) calibration charts comparing modelled and observed
streamflow and, (2) ensemble projections of future seasonal and annual streamflow.
The study chapters of this thesis (Chapters 4, 5 and 6) have been submitted in full for
publication with peer-reviewed journals. Chapter 4 has been published in Water (Dallison etal., 2020), while Chapters 5 and 6 have been submitted to the Hydrological Sciences Journal
and the Journal of Hydrology: Regional Studies, respectively. Conceptualisation of each of thestudies has been the work of Richard Dallison and Sopan Patil. Implementation andinvestigation of each of the studies has been conducted by Richard Dallison, along withmodelling, data analysis and curation, visualisation, and original draft writing and editing.
Reviewing and editing of drafts, as well as supervision, has being undertaken by Sopan Patiland Prysor Williams for all studies, as well as Ian Harris for the second study (Chapter 5), who
also assisted with resource procurement for this piece of research.

1.5 The Dŵr Uisce Project
The work contained within this thesis has been undertaken as part of the Dŵr Uisce Project,which has the overarching aim of improving the efficiency of water distribution in Ireland andWales. In practice the project has four key themes, the first investigates new smart and lowcarbon technologies, such as pumps-as-turbines, drain water heat recovery and smart
network controls, to enable energy savings in future water distribution and end use. The
second theme is policy support and guidance, work here encompasses benchmarking andenergy auditing the water sector, as well as completing life cycle assessments on thetechnologies developed under the first theme, in order to improve efficiency in the sector.
Third is dissemination and collaboration, through a smart specialisation cluster set up by the
project, allowing the communication of the project outputs, as well as learning opportunities
through four demonstration sites, using the technologies developed. Finally, climate change,
this theme is over-arching to the whole project and is the area in which the research in thisthesis has been undertaken. The aim of this section of the project is to assess the impacts offuture climate change on (1) water supply and water users in the Dŵr Uisce region, and (2)the technologies developed in theme 1 of the project; this thesis has a focus on the former.
10CHAPTER 2: LITERATURE REVIEW

11

Literature review

2.1 Global warming and climatic change
2.1.1 Historical changes
The relationship between atmospheric greenhouse gases (GHGs) and global temperatures has
been studied for well over a century, with Arrhenius (1896) first suggesting the idea of theGreenhouse Effect and the link between GHGs and temperature. The seminal work by
Callendar (1938) then made the connection between rising carbon dioxide (CO2) andincreasing temperatures, due to absorption and re-emittance of ‘sky-radiation’. It is now
widely accepted by the scientific community (Cook et al., 2013, 2016; Oreskes, 2018), thatclimate change is unequivocally occurring and has been/is being exacerbated substantially by
the anthropogenic input of GHGs to the atmosphere (IPCC, 2013). The Fifth Assessment
Report (AR5) published by the Intergovernmental Panel on Climate Change (IPCC) states thatby 2011, atmospheric concentrations of CO2, methane (CH4) and nitrous oxide (N2O) had
increased by 40%, 150% and 20% respectively, compared to 1850 levels (Figure 2.1; IPCC,
2014a).

Figure 2.1. Atmospheric concentrations of CO2, CH4 and N2O from both direct atmospheric measurement (lines)
and indirect analysis of ice cores (dots; IPCC, 2013).

These GHG concentrations are higher than any seen in the last 800,000 years and roughly 40%
of GHG emissions from 1750 to 2011 are estimated to have been released since 1970 (IPCC,
2013). Concurrently, warming of the global climate system has occurred at an unprecedented
rate, with it being extremely likely to have been predominantly caused by GHG concentrations
and other anthropogenic drivers (IPCC, 2014b). Global average temperatures reachedapproximately 1.0°C warmer, when compared to pre-industrial levels in 2017, with global
warming likely to reach 1.5°C between 2030 and 2052 (Figure 2.2; IPCC, 2018).

12

Literature review

Figure 2.2. Observed (grey line to 2017) and projected (2017-2100) global mean surface temperature. Solidorange line represents estimated anthropogenic warming, dashed continuation and orange horizontal error bar
represent central estimate of time range when 1.5°C increase is likely to be reached. Future projections are
based on net zero global CO2 emissions by 2055 (grey plume) and 2040 (blue plume), both with declining nonCO2 radiative forcing after 2030. Purple plume represents net zero global CO2 emissions by 2055 combined withno decline in net non-CO2 forcing after 2030. Adapted from IPCC (2018).

With regard to global precipitation changes, AR5 states that there is low confidence inchanges in average precipitation since 1901, with a small increase detected (IPCC, 2013).
However, when studying latitudinal zones (Figure 2.3), it is clear that more defined regionalscale changes have occurred over the last century. The most substantial change is seen in the
mid-latitudes of the Northern Hemisphere (30°N to 60°N), with a trend towards increasing
precipitation observed from 1901 to 2008. Further analysis reveals that this increasing trend
is most pronounced in the period pre-1951 (IPCC, 2013). In contrast, evidence for change inthe mid-latitudes of the Southern Hemisphere (60°S to 30°S) is less clear, with a slight increase
seen to the 1980s and a small decline after this. Precipitation over the tropics (30°N to 30°S)
is largely stable, up to the 1980s, whereby a small decline is seen, peaking in the mid-1990s,
before returning to levels in line with the rest of the dataset (IPCC, 2013).

13

Literature review

Figure 2.3. Historical over-land annual precipitation anomalies compared to the 1981-2000 mean, as detectedby five global precipitation datasets. Timeseries for four latitudinal bands as well as globally shown (IPCC, 2013).

Trends in precipitation reported by the IPCC are consistent with those found in the literature
prior to its publication; for example Dore (2005) reports that for the latitudinal zone 30°N to
85°N, an increase of between 7% and 12% has occurred over the 20 th century. The change inthe south (0°S to 55°S) is much less pronounced at around 2%, reflecting the difference in the
distribution of landmasses for the two zones (Dore, 2005). Similarly, Trenberth (2011)
identifies historical trends of dry areas (the sub-tropics) becoming drier, and wet areas (midto high-latitudes in the north) becoming wetter, with little change in the global average.
Additionally, using fourteen climate models compared to observed changes which correspondwith those suggested by the IPCC, Zhang et al. (2007) show that anthropogenic forcing has
had a detectable impact on changes in precipitation between 1925-1999 and 1950-1999.
Since the publication of AR5, further evidence has been published on historical global
14

Literature review
precipitation change, this also largely supports the reported trends. Gu & Adler (2015) for
example, showed how global precipitation change occurred concurrently with surface
warming, especially the increases observed between 1901-2010 over high latitudes of thenorthern hemisphere. Further regional variations are also described, with increases in annual
precipitation seen in Australia and north America, while declines are observed for tropical
Africa and the Indian-Tibetan region (Gu & Adler, 2015). Furthermore Adler et al. (2017) study
precipitation trends during the ‘satellite era’ (1979-2014) using the Global Precipitation
Climatology Project (GPCP) monthly dataset (Adler et al., 2003; Huffman et al., 2009). The
results of this shorter-term study suggest a near-zero trend in global precipitation change, but
do highlight the same regional trends seen in the longer-term studies (Adler et al., 2017).
Examining change in extreme weather and climate events, AR5 reveals that at a highconfidence level, globally there has been an increase in the value of both maximum andminimum temperatures (IPCC, 2013). The rate of change of increasing minimum
temperatures is also shown to be greater than the rate of change in maximum temperatures.
The Asia region is most effected by increasing minimum temperatures, while Africa is more
greatly affected by increases in maximum temperature (IPCC, 2013). Similar regionality isdisplayed for changes in the frequency of heatwaves, with Europe, Asia and Australia all being
subject to a great number of heatwaves through the 20 th century. As with trends inprecipitation, changes in extreme temperatures presented by the IPCC agree with studies
published more recently. Horton et al. (2015) demonstrate for the northern hemisphere thatduring the satellite era, minimum temperature have increased for all regions except western
North America, especially in summer and autumn; maximum temperature increases are more
observed across Eurasia, in all seasons except winter. Work by Papalexiou et al. (2018) also
speaks to an increase of global maximum temperatures over the period 1966-2015; Mishra
et al. (2015) also demonstrate that the increase in extreme temperatures are pronounced forurban areas globally. Global increases in the number of heatwaves are also demonstrated inwork by Liu et al. (2017), Chapman et al. (2019) and Perkins-Kirkpatrick & Lewis (2020), withgeneral consensus also being for an increase in intensity. While changes in average
precipitation volume were small across the 20th century, trends in extreme precipitation
events have been more extensive, with heavy precipitation events increasing
disproportionality compared to the mean (IPCC, 2013). This trend is seen globally, but regional

15

Literature review
and seasonal variation are detected, with the most consistent increases in heavy rain event
severity observed in Europe and North America (IPCC, 2013). Once again, recent research by
the likes of Asadieh & Krakauer (2015), Alexander (2016), and Papalexiou & Montanari (2019)supports these findings.
Regardless of location and regional differences in climate change impacts, all areas will see
consequences due to future climate change. Indeed, all global regions have alreadyexperienced impacts, both on natural and human systems, such as agriculture and foodproduction, poverty and livelihoods, ocean systems, and freshwater resources (IPCC, 2014a,
2014c). Freshwater systems are some the most effected however, due particularly tochanging precipitation patterns as well as alterations in snowmelt characteristics which have
had a major impact on the global hydrological cycle (IPCC, 2014a). Changes in hydrologicalregime have already caused important consequences in various regions for issues such as
water resource allocation, fluvial ecosystems, and flood and drought management, as
outlined in Chapter 1. The sustainability of water supply and increasing water scarcity are ofparticular concern, this is true not only for arid countries, but also more generally, especiallyin areas of dense population. England, for example, is not a country classed as arid, it does
however have areas of water stress, in the south of the country in particular. This area alreadyexperiences water shortages (Cook, 2016), a situation which is likely to occur more frequently
under future climate change. Dobson et al. (2020), for example, projects an increase in theprobability of water use restriction implementation doubling by 2050 and quadrupling by
2100.
In recent years, changes in Earth’s climate have been particularly stark, with 2019 being thesecond warmest year on record, after 2016 (WMO, 2020) and July 2019 being the hottest
month on record (Blunden & Arndt, 2020). Averaged across Europe, 2019 was the warmest
year on record, with central Europe experiencing drought (European Commission, 2020).
Globally, the hottest 5-year period on record was also set for 2015-2019, as was the hottest
decade (2010-2019), with each decade since the 1980s being warmer than the last (WMO,
2020). Records were also set in 2019 for highest global mean sea level and ocean heat
content, while May, June and July saw record low sea-ice extents (WMO, 2020). Additionally,
the American Meteorological Society reported that global atmospheric CO2 concentration
reached a record high in 2019 of 409.8 ppm, while the CO2 uptake of global oceans also16

Literature review
increased to 33% higher than the 1997-2017 average (Blunden & Arndt, 2020). All of thesechanges point to an apparent worsening of the impacts of climate change in recent years.

2.1.2 Future projections
Projections for global climate in the future generally show a continuation of the trends alreadyobserved over the 20th century and first two decades of the 21st. However, the extent to which
this change will continue is unknown, due to its dependence on future atmospheric GHG
concentrations and the degree of climate forcing and global warming that this causes. Since
2010, the context of future climate change and its impacts has been based on four
Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5), developed by
the scientific community for the IPCC in advance of AR5 (Moss et al., 2010; van Vuuren et al.,2011). The RCPs represent a trajectory of atmospheric GHG concentrations under which a
specific degree of radiative forcing would be reached. The designation of each RCP therefore
relates to this degree of radiative forcing reached by 2100 (Table 2.1), with RCP2.6 forexample, relating to 2.6 W m-2 by 2100 (this scenario peaking at 3 W m-2 before 2100 andthen declining).
Table 2.1. Overview of the characteristics of the four Representative Concentration Pathways (RCPs), adaptedfrom Moss et al. (2010).

RCP
RCP2.6
RCP4.5
RCP6.0
RCP8.5

Radiative forcing
Peak at 3 W m-2, decline to
2.6 W m-2 by 2100
Rising to reach 4.5 W m-2
peak by 2100
Rising to reach 6 W m-2 by
2100, peak soon after
Rising to reach 8.5 W m-2 by
2100, peak in 2200s

CO2 equivalent concentration (ppm)

Pathway

Peak at 490 before 2100, then declines

Peak and decline

650 at stabilisation in 2100

Stabilisation

850 at stabilisation after 2100

Stabilisation>1,370 in 2100

RisingWhile RCP2.6 denotes a peaking and declining pathway associated with early GHG mitigation,
RCP4.5 and RCP6.0 describe stabilising pathways due to GHG emissions reduction after 2050
(Figure 2.4), with RCP4.5 peaking in terms of radiative forcing at 2100 and RCP6.0 before 2150
(van Vuuren et al., 2011). RCP8.5 represents a worst-case scenario of future global emissions(Figure 2.4), the 90th percentile of the no-policy baseline scenarios, with increasing high levelsof atmospheric GHG concentrations, leading to a scenario of radiative forcing still being insignificant increase by 2100 (van Vuuren et al., 2011).
17

Literature review

Figure 2.4. CO2 emissions under each Representative Concentration Pathway (RCP; coloured lines) andhistorically (black line). Coloured plumes show range of scenarios relating to atmospheric CO 2 concentrations as
described in the panel on the figure (ppm; 5% to 95% range shown for each category; IPCC, 2013).

The IPCC used the fifth phase of the Coupled Model Intercomparison Project (CMIP5) in AR5for the analysis of future climate change under the forcing of each RCP (IPCC, 2013). The
CMIP5 ensemble comprises 42 global climate models (GCMs) at different spatial resolutions(IPCC, 2013; Ahmed et al., 2019) and is used to give both a multi-model mean, and range ofmodel outcomes. Projections by the IPCC under all RCPs suggest a continuation of global
warming, at least in the short-term, with temperatures stabilising under RCP2.6 before 2050
at 1°C (±0.4°C [1 standard deviation]) higher than the 1986-2005 mean (IPCC, 2013). Under
RCP8.5, mean temperatures for 2081-2100 are projected to rise by 3.7°C (±0.7°C), whencompared to the same mean, with projections for RCP4.5 and RCP6.0 being 1.8°C (±0.5°C) and2.2°C (±0.5°C) respectively (Figure 2.5; IPCC, 2013). These results relate to an assessment ofhigh confidence in likely exceedance of 1.5°C above 1850-1900 temperatures by 2081-2100
for RCP4.5, RCP6.0 and RCP8.5; this is also true for exceedance of 2°C by RCP6.0 and RCP8.5(IPCC, 2013). It is also noted by the report that it is as likely as not (medium confidence) that
global temperatures will exceed 4°C under RCP8.5, occurring in more than half of the CMIP5
ensemble models.

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Literature review

Figure 2.5. Global average surface temperature (relative to 1986-2005 mean) for 2006-2100 under
Representative Concentration Pathways. Multi-model mean shown for RCP2.6 (blue line) and RCP8.5 (red line),
5% to 95% range of CMIP5 model outcomes shown by bands. Multi-model mean for mean 2081-2100
temperature for all RCPs also shown on right hand side, with model ranges (IPCC, 2013).

Global precipitation levels also display an increase to 2100, although much more gradual thanglobal temperature change; a 2% to 5% increase over the period is observed under RCP2.6
and RCP8.5 respectively (IPCC, 2013). Interestingly, little relationship is seen between changes
in total precipitation and extreme precipitation (IPCC, 2013), with the intensity of extreme
precipitation events projected to increase. These increases are expected both for short-term
events (individual storms) and longer periods, with a 5% (RCP4.5) to 20% (RCP8.5) increase in
5-day maximum precipitation accumulation (IPCC, 2013). Return periods of large precipitation
events are also shown to be impacted, extreme precipitation events being very likely to
become more severe and frequent, a 10% to 20% reduction in return period is observed per
1°C warming; for tropical regions this value is even higher (IPCC, 2013). With regards totemperature extremes, it is stated that it is virtually certain that fewer cold extremes will be
experienced, but a greater number of hot extremes, in line with global warming. Warmest
daily maximum temperature is shown to increase between 1.5°C (RCP2.6) and 5.5°C (RCP8.5)
by 2100, with coldest daily minimum increasing by an even larger margin, between 2°C(RCP2.6) and 7°C (RCP8.5). Heatwaves are also projected to very likely occur more frequently
and for longer; in the same manner as for precipitation, the magnitude of events with a 20year return period is set to increase substantially for minimum and maximum temperatures(IPCC, 2013).

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2.2 Climatic trends in the UK and Wales
2.2.1 Historical changes
The climate of Wales has altered in line with general trends seen globally and for the wider
UK, that being over the course of the 20th century an increase in mean temperature, with daily
temperatures increasing in all seasons, while annual precipitation has seen little change, butlarge seasonal variation. Estimations of the degree and nature of changes vary between
studies, dependent on the time frame under review. Farrar & Vaze (2000), for example, reportthat for the period 1901 to 1998 annual temperatures warmed slightly, with more substantialchange observed in autumn and winter, but little change in the summer. Similarly, the Welsh
Government (2013) detailed how over the course of 1914-2006 mean annual temperaturerose 0.7°C, with the greatest increase in autumn (0.99°C) and least in winter (0.47°C). While
the two reports agree about increased seasonality of precipitation, decreases in summer andautumn, increases in winter and spring, there is a contrast in the annual observations. Farrar& Vaze (2000) report a ~3% increase in annual precipitation, while Welsh Government (2013)
suggest a 0.9% decrease over their longer and more recent study period. These difference
seem to be driven by different magnitudes or seasonal change reported, especially insummer, with Welsh Government (2013) reporting a 24% decrease, and Farrar & Vaze (2000)
a smaller 15% decrease. The majority of works align with either the latter view, a small decline
in annual precipitation historically, or suggest no trend at all (Perry, 2006; Jenkins et al., 2008;
ASC, 2016b; de Leeuw et al., 2016).
In terms of extreme temperature and precipitation events, a report by the MET Office Hadley
Centre (MOHC) on UK climate extremes states that for the period 1961-2017 annual
maximum temperatures increased across the UK, including Wales specifically (MOHC, 2018a).
The number of warm spell days (days associated with spells of six or greater consecutive days
where temperatures exceed the daily climatology 90th percentile) also increased
substantially, more than doubling for Wales when comparing the 1961-1990 and 2008-2017
averages (MOHC, 2018a). Extreme events associated with cold weather also display warming,with fewer icing days (days where maximum temperature is less than 0°C) and lowest
minimum temperatures increasing (MOHC, 2018a). For Wales specifically, average lowest
minimum temperature increases from -7.8°C for 1961-1990 to -6.6°C for 2008-2017; icing

20Literature review
days reduce from 4.2 to 2.7 (MOHC, 2018a). This non-standard record period of 2008-2017
does have the potential to miss-represent long-term trends, as the time period is more likely
to be influenced by decadal time-scale variation, nonetheless it does provide a usefulcomparison of the current state of change in the climate. Precipitation trends reported by
MOHC (2018a) display an increase for highest 5-day precipitation total from 1960 to 2000,
rising 4%, with this being more pronounced in the west of the UK. Additionally, 1-day extreme
rainfall (greater than 99th percentile) totals also increases by 17% nationally, with the change
being more pronounced for Wales than the national average (MOHC, 2018a). Little change inthe length of wet spells (consecutive rainfall days) is observed.
Further literature (Perry, 2006; Jenkins et al., 2008; Burt & Ferranti, 2012; Jones et al., 2013)
studying the nature of change in extreme temperature and precipitation events, agrees withthe broad findings of the MOHC report. Perry (2006), for example, corroborates the findings
relating to annual maximum temperature, with observations of a 0.59°C increase for northWales (p <0.01) and a 0.55°C increase for south Wales (p <0.05), for the period of 1914-2004.
An increase in summer and annual minimum temperature is also reported at a significancelevel of p <0.01 for both north Wales (+0.88°C and +0.84°C respectively) and south Wales(+0.56°C and +0.55°C); once again in agreement with MOHC (2018a). Furthermore, Jones etal. (2013) brings agreement to the precipitation trends shown, reporting an increase between
1961-2009 in 1-, 5- and 10-day precipitation totals for north and south Wales, albeit only
statistically significant for 5- and 10-day events. Within these trends, statistically significant
increases in spring 2-, 5- and 10-day totals are observed, while the same is true for summer
declines, this also being significant for 1-year events. Winter totals for all four event lengths
rise significantly (Jones et al., 2013). The literature also supports observations of increasing
seasonality in extreme precipitation events, with a greater proportion of winter precipitation
falling during intense events (Osborn & Hulme, 2002; Jenkins et al., 2008; Burt & Ferranti,
2012; Jones et al., 2013).
Studying the most recent complete year, 2019, in isolation, Kendon et al. (2020) report it tobe the 12th warmest on record for the UK overall, with national records set for all time highest
recorded temperature (38.7°C) and winter high (21.2°C). For Wales, annual averagetemperature was 0.5°C higher than the 1981-2010 average, mainly due to the winter average
being 1.6°C higher, one of the top 10 on record (Kendon et al., 2020). A below average number

21

Literature review
of frost days also occurred for the sixth straight year, while rainfall was above both the 19812010 and 1961-1990 averages, at 107% and 112% respectively (Kendon et al., 2020). The work
conducted in Chapter 4 further examines the changes seen in temperature and precipitation
for Wales for the period 1982-2015, investigating changes in annual and seasonal averages
and frequency of extreme events, as well as breakpoints of marked change during the period.

2.2.2 Future projections
For the UK the key point of reference on future climate is the MOHC’s UK Climate Projections(UKCP). This climate analysis tool provides the most up-to-date assessments of future climate
outlook for the nation, aiding with planning future risk management and adaptation plans.
Recent projections were published in late 2018 (hereafter UKCP18), succeeding the 2009
iteration (UKCP09). The UKCP09 projections have been widely used in literature for impact
assessments on various sectors, indeed 107 impact assessment studies using UKCP09 data
are indexed by Web of Science at time of writing (6th October 2020). To date however, little
peer-reviewed literature has yet been published using the UKCP18 projections, with only two
impact study results appearing in Web of Science searches (6th October 2020) for theseprojections (Kay et al., 2020; Mohammadi et al., 2020). A further three studies have been
published assessing the methods and performance of the projections (Murphy et al., 2019;
Fosser et al., 2020; Rostron et al., 2020). The research by Kay et al. (2020) in fact both assess
the impact of climate change on river flows in ten UK catchments, and compares the UKCP09
and UKCP18 outputs, demonstrating that for the catchments studied, the results of bothprojections are similar and comparable. Therefore, the results of previous work using UKCP09
projections remain valid and relevant, allowing for future works to assess new research areas.
The UKCP18 data contain three ‘strands’, probabilistic (strand 1), global (strand 2) andregional (strand 3). Strand 1 provides probabilistic projections at a 25 km grid resolution
derived from 350 climate model simulations, expressing a wide range of potential outcomes(Murphy et al., 2018). This dataset is important for exploring uncertainty in future impact
assessments, giving the most complete view of outcomes under all four RCP scenarios
assessed in AR5 (Murphy et al., 2018). Strand 2 uses 28 climate models; a 15-member
perturbed parameter ensemble (PPE) from the MOHC’s HadGEM3-GC3.05 (hereafter GC3.05)
model, and a 13-member ensemble from the CMIP5 coupled ocean-atmosphere model.

22

Literature review
These thirteen CMIP5 models were selected from the 42 used by the IPCC in AR5, based on
their suitability in terms of data coverage and performance criteria (Gohar et al., 2018). These
globally based projections under RCP8.5 are particularly useful for exploring the impact of
spatially large-scale drivers of change, for example global circulation, and are provided at 60km grid spacing to the year 2100 (Murphy et al., 2018). Finally, strand 3 projections use twelveof the fifteen GC3.05 PPE members used for strand 2, downscaled to a regional projection
and driven by global simulations for the period 1980-2080 under RCP8.5 conditions (Murphy
et al., 2018). Projections are presented as a 12 km grid for the British Isles, therefore taking
account of regional-scale features such as mountains, coastlines, and large waterbodies,
allowing for local level analysis (Murphy et al., 2018). It is these regional projections that havebeen used in this thesis for future streamflow estimation (Chapter 5) and subsequent impact
assessment for hydroelectric power (HEP) and public water supply (PWS; Chapter 6).
The UKCP18 overview report describes projected future UK climate to 2100 using the strand
1 probabilistic projections detailed above. Under RCP2.6 conditions, the median annual
average temperature for 2080-2099 is expected to reach 1.4°C higher than a 1981-2000
baseline, under RCP8.5 this figure is 3.9°C (Figure 2.6; Lowe et al., 2018), further analysis
suggest that temperature increases will be larger in summer than winter (MOHC, 2019a). For
precipitation the same seasonal variation is seen, with reductions in median summer average(RCP2.6 -15%; RCP8.5 -29%) being much more pronounced than increases for winter (RCP2.6+6%; RCP8.5 +18%; Figure 2.7; Lowe et al., 2018). Additionally, regional variation is alsopresent, with southern and central England seeing larger winter increases, and the south
bigger summer reductions, than the UK average (MOHC, 2019b).

23

Literature review

Figure 2.6. Change in UK average temperature compared to the 1981-2000 mean, under RCP2.6 (blue) and
RCP8.5 (red). Shading shows percentile boundaries, with solid line indicating the median; black lines areobserved temperatures made by the National Climate Information Centre (Lowe et al., 2018).

Figure 2.7. Percentage change in UK average precipitation compared to the 1981-2000 mean, under RCP2.6(blue) and RCP8.5 (red) for winter (top) and summer (bottom). Shading shows percentile boundaries, with solidline indicating the median; black lines are 5-year mean of observed precipitation made by the National Climate
Information Centre (Lowe et al., 2018).

24

Literature review
With respect to trends in future climate projected for Wales specifically, the UK Climate
Change Risk Assessment for Wales, by the Adaptation Sub Committee (ASC) of the Committeeon Climate Change, is the most recent publication to report (ASC, 2016b). This report was
published prior to the release of UKCP18 datasets, so assesses trends under the former
UKCP09 projections, with an update using the new projections expected by the end of 2020
(Welsh Government, 2019a). As aforementioned, these results are still valid, being shown by
Kay et al. (2020) to be similar and comparable to the most recent projections. With that inmind, Murphy et al. (2009) present trends for Wales, based on a high-medium future GHG
emissions scenario (SRES A1B, equivalent to between RCP6.0 and RCP8.5). In Wales, climate
is set to follow general UK trends of warmer wetter winters and hotter drier summers, withan increase in occurrence of extreme events (Murphy et al., 2009). Mean winter temperaturefor example is projected to increase by 2°C (median probability; range of 1.1°C to 3.1°C;
representing 10% and 90% probability level) for Wales by 2080 (compared to 1961-1990
levels); mean summer temperatures increase by a greater amount 2.5°C (1.2°C to 4.1°C;
Murphy et al., 2009). Summer maximum and minimum temperatures are both expected toincrease, maximum by 3.4°C (1.3°C to 6.1°C) and minimum by 2.6°C (1.1°C to 4.6°C; Murphy
et al., 2009). Annual precipitation is projected to be stable at a median probability level under
SRES A1B to 2080, with the 10% to 90% probability range being -4% to +5%. This masks
seasonal variation however, with winter precipitation expected to increase by 14% (+2% to+30%) and summer to decrease by 16% (-36% to +6%; Murphy et al., 2009).
Work conducted in Chapter 5 characterises alterations observed under future UKCP18 climate
projections for Wales, investigating changes in annual and seasonal averages up to 2080,
representing an update on the UKCP09 trends presented by the ASC (2016b) and Murphy etal. (2009) and discussed above for Wales.

2.3 Changing hydrology in the UK and Wales
2.3.1 Historical hydrological trends
As previously discussed, hydrological systems have been some of the worst effected systems
globally by climate change historically (IPCC, 2014a), with notable variation in impact
dependent on regional characteristics. The UK is no exception, with Harrigan et al. (2018)25

Literature review
demonstrating a 6.8% (interquartile range: 0.1% to 13.8%) increase in annual mean flow
between 1965-2014 when studying 146 near natural catchments across the UK. The same
study also analysed seasonal trends over the same period, with winter, summer and autumn
mean flows increasing by 12.7% (4.1% to 26.2%), 1.4% (-11.2% to 18.5%), and 16.7% (5.5% to25.5%) respectively; spring flow is shown to have declined by 10.7% (-19.0% to 1.6%; Harrigan
et al., 2018). Interestingly, mapped results for the UK reveal that annual mean streamflow
trends display regionality in Wales, with catchments in the north of the country having
generally positive trends, and those in the south being negative (Harrigan et al., 2018).
Variations in trend characteristics (magnitude and significance) are also observed across theUK, with a northwest-southeast divide being particularly prominent. For example, seasonal
analysis conducted by Hannaford & Buys (2012) found that for the period 1969 to 2008, allseasons showed predominantly upwards trends for mean streamflow in catchments in
Scotland and the northwest. In the south and east however, the picture was much morevaried, with downward trends dominating for spring, upwards for autumn, and mixedchanges seen in winter and summer (Hannaford & Buys, 2012).
With regards to extreme hydrological events, Harrigan et al. (2018), for the same study
conditions as above, demonstrate a 13.5% (7.9% to 23.8%) increase in Q5 volume (flowvolume exceeded 5% of the time), with agreement of trend direction in 88.4% of catchments.
The same level of agreement is not achieved for low flows, with 52.4% of catchments
displaying an increase in volume for this metric, and 47.6% showing a decline (Harrigan et al.,2018). Additionally, work by Hannaford & Marsh (2008), was some of the first to identify thenorthwest-southeast divide in large streamflow event trends. Results of the work
demonstrate an increase in annual flow maxima across all of the UK, but with a marked
increase in magnitude in Wales and northwest England (generally upland areas with amaritime climate) compared to the east and south. For trends in 10- and 30-day maximum
flows, the frequency of large flows, and flood magnitudes, significant increases are seen again
for the west and north, but little evidence is seen in the south and east (Hannaford & Marsh,2008). The increasing trends seen for the westerly catchments are supported by the resultsof Dixon et al. (2006), who studied 56 gauging stages in Wales and the Midlands of Englandfor the period 1962-2001. Even here, the vast majority of trends display an east-west gradientin terms of both statistical significance and magnitude of change, being true for increases in26

Literature review
annual maximum flow, high flows (Q10), Qmean, low flows (Q90), and annual minimum flows(Dixon et al., 2006). Furthermore, Hannaford and Buys (2012) identify that for low flows, thenorthwest-southeast divide only occurs in summer (northwest increasing, southwest
decreasing), although different regional variations are still seen in other seasons. In winter for
example, low flows show a decline in Scotland, Northern Ireland and northeast England, while
the midlands and the south are mainly positive. The divide is still present in the high flow
analysis, albeit generally less defined. In winter for example, strong positive trends are seen
for Scotland, Northern Ireland and the northwest; while positive trends are also seen in thesouth, but are substantially weaker and generally confined to only smaller catchments(Hannaford & Buys, 2012). Furthermore, in both spring and summer a positive trend in highflows is seen in the northwest, while results are more mixed in the southeast; for autumn
rises are seen across the country (Hannaford & Buys, 2012). Work by Laizé & Hannah (2010),
to further characterise the spatial variation of seasonal streamflow, suggests that spatial
patterns show inter-season variability, meaning the divide is not clear cut. More generally the
paper suggests that it is the physical characteristics of basins which drive differences, withelevation and permeability being particularly important. In the UK, this corresponds to thecharacteristics of the areas in the southeast and northwest, as well as to the greater exposure
to prevailing westerly winds that the northwest receives (Laizé & Hannah, 2010).
Evidence reviews completed by Hannaford (2015), Watts et al. (2015a) and Garner et al.
(2017), using published literature on historical UK hydrological change, all identify similar key
trends for average and extreme flows, in line with those detailed above. The overarching
agreement in the literature being for little change in summer flows, with small declines seen
in some areas, an increase in annual and winter flows, and an increase annually in high andlow flow event frequency and magnitude.

2.3.2 Future projections under climate changeWith regard to future impacts for the hydrology of the UK and Wales, continued changes inthe timing, quantity and quality of streamflow are likely. Several studies in recent years havesought to suggest future UK streamflow scenarios for both average (Christierson et al., 2012;
Prudhomme et al., 2012; Sanderson et al., 2012; Kay et al., 2020) and extreme flows(Prudhomme et al., 2013a, 2013c; Kay et al., 2014a, 2014b, 2020). Several more have studied

27

Literature review
smaller areas, Charlton & Arnell (2014) for example for England only; Remesan et al. (2014)
for the River Derwent; Cloke et al. (2013) for the upper Severn; and Bell et al. (2012) for theThames. General consensus among these studies is for a largely stable annual average
streamflow, but with large variations seasonally, in particular increases in winter anddecrease in summer. These changes are in line with a continuation of historical trends
discussed above, and with future climate projections for the nation.
The seasonal average picture for future streamflows is for greater winter and spring flow,
with reductions in summer and autumn; as with historical flows, this is particularly applicable
to north-western impermeable upland catchments. This trend was demonstrated for thenear-future (end of 2020s) by Christierson et al. (2012), who reported, using UKCP09 data, a
reduction in summer flows across the country. Furthermore, a small increase in winter flows
was observed for western areas, with annual mean flow decreasing for all of the UK. Longerterm research by Prudhomme et al. (2012) to 2050 explored national level changes undereach of the eleven ensemble members that form the HadRM3-PPE-UK model that drives
UKCP09 (Murphy et al., 2007). Comparing mean seasonal flows for 2040-2069 with those forthe baseline 1961-1990 period highlights a decrease in summer mean flow that is particularly
pronounced in the west; autumn flows are more variable with a larger proportion of the 11
models showing a decline, more so for England and Wales than Scotland (Prudhomme et al.,2012). Winter is characterised by increasing flows, showing spatial variability between the 11
models for areas of greatest magnitude of change; generally, however, England and Wales
are more effected than Scotland. Spring flows are the most variable, with little agreement inmagnitude or trend direction displayed (Prudhomme et al., 2012). Sanderson et al. (2012)
project future streamflow even further, to the 2080s, in their study of the UK using the same
11-member ensemble, under the SRES A1B future emissions scenario. Once again winterstreamflows display an increase across the country by the 2080s, with agreement for almost
the whole country on this direction of change. Coastal regions of west Wales are projected tosee a particularly large increase, between 15% and 25%, with all 11 models in agreement. For
spring flows all but the north of Scotland is reported to see no change or a small decrease,
however there is little agreement on this between the models (Sanderson et al., 2012).
Summer and autumn flows show a greater degree of agreement for trend direction between
the models, with decreases for the whole of the UK in summer and all but the northwest of28

Literature review
Scotland in autumn. In summer, similar to winter, coastal regions in the north, west and southof Wales are projected to see large (-15% to -25%) decreases in mean flow, with agreement
in all 11 models; autumn mean flow shows a more moderate decline (-5% to -15%; Sanderson
et al., 2012). Finally, median annual flow for the period 2040-2069 was shown by Kay et al.
(2020) to decrease for 10 catchments studied throughout the UK, under both RCP4.5 and
RCP8.5 conditions; the magnitude of this reduction only being marginally greater under
RCP8.5, but with greater uncertainty.
Kay et al. (2020) also analysed changes in high (Q5) and low (Q95) flows under the same
conditions as for average flows above. Trends in Q5 displayed variation in line with the eastwest divide aforementioned; those catchments in the west exhibiting small increases in futurehigh flows, and those in the east presenting little or no change. Low flows were much moreconsistent, all catchments’ Q95 projected to decline under both RCP4.5 and RCP8.5 (Kay et al.,2020). The majority of research on future extreme streamflows is framed within the context
of changing flood and drought risk. This is true of Collet et al. (2018) who’s findings concurwith those of Kay et al. (2020). The study used the Future Flows Hydrology (FFH) dataset(Prudhomme et al., 2013b) to compare 2080s hydrological hazards with the 1970s. The
median trend of the FFH, is for increases in frequency, magnitude and duration of both floods
and droughts almost exclusively in the west of the UK only, especially concentrated in thesouth west of England and Wales (Collet et al., 2018). The magnitude and duration of droughts
is projected to show a larger percentage change than the magnitude of floods, but changes
in frequency are broadly the same (Collet et al., 2018). Interestingly, the paper also assess thechange in seasonality of events, showing that floods are much more likely to occur in winterin the future, while droughts are less seasonally bound, but still more likely to occur in theautumn, and to a lesser extent, the summer (Collet et al., 2018).
A considerable amount of work has been completed in the last decade for the whole of the
UK and regions/catchments thereof, in characterising future high flow and flood regimes
under changing climate (e.g. Cloke et al., 2013; Lavers et al., 2013; Kay et al., 2014a, 2014b;
Sayers et al., 2016; Jenkins et al., 2017). Kay et al. (2014a, 2014b) comprehensively reviewed
probabilistic impacts on flood magnitude in particular, for England, Wales and Scotland. Using
the UKCP09 projections, it is shown for England and Wales that under the SRES A1B emissions
scenario, peak flows in 20-year return period floods will increase by between 20% to 25% in29Literature review
the east and by 25% to 35% in the west (Kay et al., 2014a). Under a high A1F1 SRES emissions
scenario (equivalent to RCP8.5), this trend remains, with magnitudes of change becoming
larger, the east rising to between 25% to 35%, and the west to between 35% to 45% (Kay etal., 2014a). For Scotland a similar trend is observed in terms of spatial distribution, the west
being more greatly affected than the east, but the magnitude of change is larger; the very
north west for example, seeing a 45% to 60% increase under SRES A1F1 scenario by the 2080s(Kay et al., 2014b). Sayers et al. (2015) also suggest a 13% increase by 2080 under a 2°C
warming scenario, for peak streamflows in Wales, increasing to 42% in east Wales, and 36%
in west Wales under 4°C scenario. These results have large implications for return periodchanges; for example, under the 2°C scenario, the return period for an event currently at 100
years, would reduce to ~40 years; under the 4°C scenario this would be reduced to less than
11 years in east Wales, and ~13 years in the west of the country (Sayers et al., 2015).
While future hydrological drought trends have not received the same level of attention as
flood events in the literature, a few studies have characterised projected changes through the
21st century. As is common with many of the studies presented in this section, the HadRM3PPE-UK 11-model ensemble has been used by Rahiz & New (2013) to comprehensively assess
the severity of future droughts in the 2020s, 2050s and 2080s; the results for the 2080s are
discussed here. The intensity of modest droughts is to increase substantially for England andWales, while the increase in intensity of the most severe droughts is set to be less, although
the most pronounced area in the UK is shown to be Wales (Rahiz & New, 2013). Additionally,
the frequency of drought occurrence shows the same spatial pattern for the UK as intensity,
but the rate of change for severe and moderate events is broadly the same, both increasing
in England and Wales (Rahiz & New, 2013). Average drought length however is shown todecrease, with little change in the number of 12- and 10-month droughts projected, but anincrease in 6- and 3-month events, especially the latter and for both severe and moderate
events (Rahiz & New, 2013). Overall for Wales, therefore, the study projects increased
intensity and frequency events, which will very likely be shorter in length than at present. The
results of Rudd et al. (2019) are broadly in agreement with Rahiz & New (2013), with similar
spatial patterns for intensity and drought duration shown for the 2080s. Studying low flow
characteristic changes, Kay et al. (2018) compared low flow frequency curves, to quantify thepercentage change in 2- and 20-year return period events between the 1975-2004 average

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and the 2070-2099 average. As with the previous worked based on drought, results show adecrease in the volume of 2- and 20-year low flows, suggesting an increase in drought
severity; again this is especially pronounced across England and Wales (Kay et al., 2018).
In addition to changes in precipitation and temperature projected up to 2080, Chapter 5 also
analyses trends seen in annual and seasonal average streamflow, as well as the intensity andfrequency of extreme high and low streamflow events.

2.4 Water quality implications of hydroclimatic change
Changes in precipitation patterns and hydrological regime will impact and influence the instream environment. One of the key change areas will be river water quality, with key
processes such as run-off, overland flow, stream power, dilution potential and carrying
capacity all likely to be altered. Globally, the effect of changing precipitation extremes andstreamflow patterns on water quality factors has been well studied, often at a regional orcatchment-scale (e.g. Johnson et al., 2009, 2015; Tu, 2009; Whitehead et al., 2009b; Rehana& Mujumdar, 2012; Crossman et al., 2013; Michalak, 2016; Wang et al., 2018; Guo et al.,2019). General consensus is that increasing extreme precipitation events will likely lead tolarger overland flows with greater capacity for eroding and flushing of non-point-source
pollutants from the land surface into river systems (Nilsson & Malm Renöfält, 2008;
Whitehead et al., 2008; Guo et al., 2019). In addition, large discharge events are likely to cause
greater in-stream erosion of bed and banks, causing increased turbidity (Nilsson & Malm
Renöfält, 2008; Bussi et al., 2016). Paradoxically, an increased likelihood of low flow events is
also expected to decrease water quality, with less water available for dilution, this is clearly alarger issue downstream of point-sources of pollution, for example industrial and wastewater
treatment outfalls (Nilsson & Malm Renöfält, 2008; Whitehead et al., 2008, 2009b). Work by
Gau et al. (2019) for 102 water quality monitoring stations in southeast Australia identifies
that same day streamflows are the largest driver of declining water quality, also suggestingthat reduced flow and lack of dilution is less important; this is of course highly location
dependent however.
In light of this body of research, the previously discussed projected increases in magnitudeand frequency of larger streamflow and precipitation events, as well as decreasing summer

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Literature review
flow and increased magnitude, frequency and duration of droughts is invariably expected to
impact negatively on water quality in the UK and Wales. In terms of water quality changes,
less work has been published in the UK on this topic than for hydroclimatic changes, with evenfewer publications focusing on or featuring Wales specifically. Nonetheless a consensus in theliterature is for a general decrease in surface water quality under future climate change in the
UK (Johnson et al., 2009; Whitehead et al., 2009b; Ritson et al., 2014; Watts et al., 2015a;
Hutchins et al., 2016). The nature of that change is not likely to be uniform, with catchment
characteristics such as basin permeability, soil types, land cover proportions, topography, andpollution sources all contributing to variation (Whitehead et al., 2009a). Key fundamental
process will remain largely similar across the UK however, and the implications of climatechange on these key processes is discussed below.
Natural Resources Wales (NRW), in its latest assessment of the ecological status of rivers for
the Water Framework Directive (WFD), identified a total 1,352 reasons why sections of riverin Wales were not achieving good ecological status under the WFD classifications (NRW,
2019a). When investigating the causes behind these failures, it has been found that 47.6% ofreasons for not achieving a good status is due to diffuse pollution sources, with 30.3% being
due to point sources (NRW, 2019a). Looking at the west Wales river basin district, which
encompasses all of the catchments studied in this thesis, 733 reasons are listed for river
sections not achieving good ecological status. Both diffuse and point sources of pollution
cause a greater proportion of total failure reasoning for this region than the full Wales
dataset, at 48.7% and 34.1% respectively (NRW, 2019a). Changes to flow regime (e.g. water
impoundment or abstraction) have a similar proportion of impact for both regions, as donatural causes, such as natural barriers to fish migration or natural streamflow conditions.
Physical stream modification, for the purpose of river navigation, for example, or due toconstruction, has a larger impact when looking at the whole of Wales, than for the west Wales
region, potentially reflecting the greater level of urbanisation represented in the all Wales
dataset. Table 2.2 provides an overview for all of Wales and the west Wales river basin districton the breakdown of reason for stream sections not achieving good ecological status.

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Table 2.2. Categorisation of reasons for stream sections not achieving good ecological status in the Water
Framework Directive classifcations, for all of Wales, and the west Wales river basin district (NRW, 2019a).

ReasonDiffuse source
Point source
Physical modification
Flow
NaturalOther

All of WalesWest Wales

Number

Percent

Number

Percent

644
409
176
52
32
39

47.6%
30.3%
13.0%
3.8%
2.4%
2.9%

357
250
57
22
18
29

48.7%
34.1%
7.8%
3.0%
2.5%
3.9%

Jin et al. (2012) found that for the River Thames, nitrate concentrations increased
considerably in the winter months when modelling the period 2070-2099 compared toobservations for 1961-1990, with a decrease shown in summer. This is commensurate withprevious research that suggests larger winter flows will cause greater flushing of nitrate fromland to river systems (Whitehead et al., 2009a). Furthermore, there is evidence to suggeststhat longer residency times and high water temperature will cause greater loss of nitrate
through de-nitrification in rivers (Whitehead & Williams, 1982), explaining the lower summer
concentrations. A similar trend was found by Conlan et al. (2007) for nitrate and ammonia in
the River Wharfe catchment, with levels falling under drier summer conditions. There is therisk, however, that after prolonged periods of drought (greater than a year), the effect of the
return of rainfall could result in exceptionally high peaks of nitrogen in rivers systems, as
projected for the River Kennet by Wilby et al. (2006). Hutchins et al. (2016) review changes inriver quality for the UK also, they suggest that the use of combined sewer overflow systems,
which are common in the UK and especially in Wales, with over 3,000 (DCWW, 2015), arelikely to cause an increase in particulate phosphorus, especially during times of high flow, as
well as increases in sediment transport. Increases in the concentration of future suspended
sediments is supported by Bussi et al. (2016), who showed that under extreme precipitation
events, suspended sediment yields in the River Thames increased substantially, due to greatersplash erosion, greater overland flow, and larger channel flow. Additionally, Jennings et al.
(2009) showed for the River Flesk (Ireland), independent of factors such as combined sewer
overflows, that phosphorous levels are to substantially increase in the winter months, linked
to higher precipitation levels causing greater delivery of contaminants to the river system.
These trends fit with international literature such as Crossman et al. (2013), who found for
the Black River in Canada, that total phosphorous loads in winter were greatly increased33

Literature review
under future climate projections, caused by larger precipitation volumes enabling greatertransfer into streams. Nilawar & Waiker (2019) found that for the large Purna river basin in
India, under future RCP8.5 conditions, sediment concentrations were set to increase fivefold
when comparing 2054-2075 with a baseline period of 1980-2005; 50% of this rise wasattributed to greater stream power causing riverbank erosion.
In general, increased SS loads have implications for other water quality factors, as pollutants
such as phosphorous and bacteria adsorb to the grains, meaning that projected increases in
SS concentrations will likely lead to higher levels of other pollutants (Dearmont et al., 1998;
Loperfido, 2014). However, when looking at faecal indicator bacteria, such as coliforms, as anindicator of pathogen organism presence in freshwater, work has shown that the situation
may be more nuanced. Dakhlalla & Parajuli (2020) show for the Big Sunflower River, that while
increased rainfall causes greater washing of bacteria into rivers, increases in temperaturecause faster die-off rates. These results are in agreement with other established research,such as Cho et al. (2010), Hofstra (2011), and Wilkes et al. (2011), which have investigatedthis relationship in various regions and catchment types globally. In addition, Bussi et al.
(2017) show for the Conwy (also studied in this thesis) a decline in future faecal indicator
bacteria even under an intensified agricultural scenario. Their research compares observed
1960-1990 bacteria levels with modelled data for 2060-2090 using the INCA-Pathogens
model, under the SRES A1B scenario. The declines observed are also attributed to combined
temperature increases, causing faster bacterial die-off, and precipitation declines, especiallyin summer, causing a reduction in runoff and therefore contaminant routing (Bussi et al.,2017). It should be noted however, that Bussi et al. (2017) assess monthly average changes,
and therefore projected increases in the magnitude and frequency of large streamflow events(Kay et al., 2014a), which could cause a greater number of spikes in bacterial concentrations,
are not accounted for. None of the studies mentioned explicitly account for combined sewer
overflows either, these could be especially important for the study region of this thesis, when
considering the large number in operation, and the potentially contribution to contaminationfrom bacteria in times of high flows (Astaraie-Imani et al., 2012; Abdellatif et al., 2015).
Therefore, while average pathogen concentrations are likely to decline under future climatechange, it is also likely that larger spike will be seen, this could prove problematic for water
treatment works, as well as in-stream ecology, and sectors such as the shellfish industry.

34

Literature review
A further consideration for future water quality is the impact of changing (mainly increasing)
air temperatures on stream water temperature and associated effects on water quality
factors such as dissolved oxygen (DO). The relationship between air and river watertemperature is well established and has been studied globally (Crisp & Howson, 1982;
Preud’homme & Stefan, 1992; Erickson & Stefan, 2000; Morrill et al., 2005; Harvey et al.,2011). Morrill et al. (2005) estimated a 0.6°C to 0.8°C rise in water temperature for every 1°C
rise in air temperature when studying 43 rivers in 13 countries, they did however note that
an S-shaped relationship better captures changes at air temperatures close to zero and above
25°C. This S-shaped relationship is also suggested as the best fit by Harvey et al. (2011) who
studied four sample sites in Newfoundland. Erickosn & Stefan (2000) also concluded that therelationship between the two factors depart from linearity after 25°C, for two catchments in
the US; the relationship is also closer when studied as a weekly or monthly average, rather
than daily (Erickson & Stefan, 2000; Webb et al., 2003; Morrill et al., 2005). The effect of watertemperature on DO has also been well researched, with increased temperature causing adecrease in DO concentrations (Morrill et al., 2005; Nilsson & Malm Renöfält, 2008; Cox &Whitehead, 2009; Harvey et al., 2011). Future warming of summer temperatures in particular
could therefore cause DO concentration to fall to critically low levels, potentially impacting
in-stream ecology, threatening various aquatic life (Morrill et al., 2005; Cox & Whitehead,
2009; Harvey et al., 2011). Eutrophication also has the potential to be altered, especially whencombined with increasing nutrient supply, as is expected due to increased overland flow andassociated flushing of nutrients from soils into river systems (Nilsson & Malm Renöfält, 2008;
Watts & Anderson, 2016). With increased nutrient supply, and chemical reactions runningmore quickly at warmer temperatures, there is the potential for a large increase in the sizeand development speed of algal blooms in the UK (Whitehead et al., 2008, 2009a, 2009b; Cox& Whitehead, 2009; Watts & Anderson, 2016). This increase in nutrient supply, temperature,
and algal volume is likely to increase the biological oxygen demand of river systems, putting
yet further pressure on DO levels (Whitehead et al., 2008). These problems could be further
exacerbated by the potential for long periods without rain in the summer in future, followed
by intense precipitation events, further increasing spikes in poor water quality (Delpla et al.,2009; Whitehead et al., 2009a; Watts & Anderson, 2016).

35

Literature review
It is therefore likely that river systems in Wales will see marked changes in water quality under
future hydroclimatic change. It is crucial to understand the nature of this change in order toplan for and mitigate its effects. Therefore, Chapter 5 assesses future seasonal changes in theconcentrations of four water quality parameters: suspended sediment, total phosphorous,
total nitrogen, and dissolved oxygen.

2.5 Future water resource use implications for Wales
Water resources in Wales are overseen and regulated by NRW on behalf of the Welsh
Government. NRW manages and monitors abstraction, impoundment and discharge licences
for water use in the country as well as being tasked with monitoring aspects such as water
quality and pollution (NRW, 2020a). The largest exploitation of natural water resource inWales is for PWS, which for Wales is mainly under the jurisdiction of Dŵr Cymru Welsh Water(DCWW). In the past two decades the amount of water supplied by DCWW has reduced byabout a fifth, to ~800 million litres per day, down from ~1000; approximately half of thisreduction is due to a lowering in the amount of leakage in the system; the rest because offalling demand from heavy industry (DCWW, 2019a). This reduction is despite an increase inaverage consumption per capita from 110 litres per day in the 1970s to 157 litres per day in2016 (DCWW, 2016b). Wales is highly reliant on surface waters for PWS, in fact roughly 95%
of supply sources used by DCWW are taken from surface waters (DCWW, 2016c), mostly from65 impounding reservoirs owned and operated by the company in Wales (DCWW, 2019a).
While only 3% of water falling as rain is abstracted for PWS in Wales, compared to figuresnearer 50% in south and east of England, the country still faces potential water supply issues.
This heavy reliance on surface waters and lack of back-up ground water supplies, makes Wales
more vulnerable in times of prolonged severe drought. Indeed, there are already regions at
risk of water deficit in Wales during dry summer months, a situation compounded by a relativelack of transfer capabilities between water supply zones (DCWW, 2019b).
Currently the industry regulator for England and Wales, Ofwat, and the Water Act 2003,
require water companies to develop a water resources management plan, in which
companies plan the supply of water into the future (HM Government, 2017). The most recent
for DCWW covers the period 2020-2045, but it should be noted that this is a snapshot of
thinking at the time, and that actual planning is continuous (DCWW, 2019a). The latest water36

Literature review
resources management plan for DCWW states an expected drop in the amount of water
available for abstraction, both due to reductions in abstraction license amounts by NRW andclimate change. In addition, total water demand is predicted to fall by DCWW, based ontargets for reductions in leakage; individual large customer usage predictions anddevelopment forecasts for Wales (DCWW, 2019a). With the majority of water being takenfrom large storage reservoirs for the most of Wales, and given the trends previously
mentioned of increasing winter precipitation and declining summer precipitation, combinedwith longer and more intense droughts, means that the water supply systems could still comeunder strain. Increasing precipitation in winter, for example, will do little to combat shortages
in the summer and autumn due to reduced filling, if reservoirs are already at maximum
capacity in the proceeding winter.
Climate change not only has the ability to impact on the supply of water, but also water
demand. Despite projections for an overall decline in demand by DCWW, it is likely that, as istrue for precipitation, demand patterns will become more exacerbated. Domestically,
increasing summer temperatures are likely to lead to a greater amount of water usage for
personal bathing and gardening, with Downing et al. (2003) showing, under 2.3°C warming to2050, a 7% (winter) to 21% (summer) increase in domestic demand. Goodchild (2003)
demonstrated a 3.3 litre per person increase (range 1.3-5.7) in summer domestic water
demand, equating to 2.1% increase, when studying the Essex water supply zone under future
increased temperatures. It was also noted by Goodchild (2003) that peak water demand also
increases, with a 7-day domestic water demand average of 183 litres per person occurring
10% of the time under future scenarios, compared to 5% at the time of study. Xenochristou
et al. (2020) found that sunshine duration and air temperature are the two most important
weather based controls on domestic water demand. During week days, a one hour increase
in daily sunshine duration related to a 6 litre per person increase in water use; for daily air
temperature, a rise of between 2.5 and 7.5 litres per person was seen per 1°C warming after
15°C, depending on the affluence of the water user (more affluent increasing the greatest;
Xenochristou et al., 2020). Furthermore, likely increases in temperature and decreases inrainfall have the potential to generate much greater demand for water from the agricultural
sector in the UK, for irrigation of crops and water supply for animals. Downing et al. (2003)
for example showed an average 84% increase in water demand for potato production in the37

Literature review
UK between by 2050 under a high future emissions scenario, or 74% under a medium
scenario; with potatoes being one of the most water intensive crops to grow.
Looking more widely at general water use and abstraction by all sectors, it is clear that here
too there are substantial implications in terms of water availability, especially in summer
months. Although very much a worst-case scenario, Figure 2.8 highlights two potential future
problems for catchments and water users in Wales when looking at total abstraction demand.
The scenario presented is set during low flow conditions under RCP8.5 levels of future
emissions, with an increased population (ASC, 2016b). First, future demand is set to outpace
supply significantly in this scenario, with substantially less water available than is needed
across the UK but especially so in Wales. Second, and more important environmentally, is thelack of adequate streamflow to satisfy even minimum environmental flows. These flow levels
are often assigned to water abstractors, to ensure a sufficient flow is maintained to protect
the ecological and morphological status of the river. Insufficient flow to maintain these levels
could have serious implications for the health of the river and its ecology.

Figure 2.8. UK abstraction demand in the 2050s (left) and 2080s (right) as a percentage of available dischargeduring low flow conditions, under high population growth and RCP8.5 equivalent future scenario. Maroon colour
denotes areas of negative available resource, i.e. those areas where the available flow is not sufficient to satisfy
required environmental flow, meaning no abstraction is possible (ASC, 2016b).

38

Literature review
Additional pressure is also potentially faced by public water supplies in the form of changing
water quality, as previously discussed. Nitrate peaks as projected by Wilby et al. (2006) for
the River Kennet, for example, would be greater than the legal limit acceptable by the UK for
drinking water supply, so would require additional treatment methods to be in place. This isa trend likely to be seen across the country; with decreasing water quality there is anincreased environmental and financial cost in order to maintain drinking water standards. Ifhigher levels of water pollution were seen consistently, such changes may even require theintroduction of new technologies or drinking water treatment plant upgrades, once again thiswould be potentially highly costly.
Planning of future PWS resources has been under consideration for some time in the UK,
Arnell (2004b) for example, assessed that, given a projected 30-50% reduction in Q95 by 2050
and higher pressures on the supply-demand balance due to both higher consumer demand,and reduced supply availability (lowered abstraction license levels due to Water Framework
Directive), a significant difficulty could be posed for the industry. In addition, Arnell and
Delaney (2006) suggested that a potential 20% increase in water demand for irrigation could
have the most severe knock on effects for supply sustainability; Downing et al. (2003),
however, suggests peak domestic demand has the potential to be the most important factor
in future water supply planning. However, work is still ongoing and needed, with ensuring
future water supply often touted as one of the key challenges facing Wales and the UK undera changing climate. The Adaptation Sub-Committee (ASC) of the Committee on Climate
Change, for example states that ‘more action is needed’ to tackle and understand the risks topublic water supplies in Wales, the highest level of urgency it gives (ASC, 2016b).
Furthermore, the Welsh Government’s climate change adaptation plan (Welsh Government,
2019a) states that risks to public water supplies from drought and low flows is one of four key
risk areas in relation to climate change. Finally, DCWW in their 2050 plan (DCWW, 2018) statethat reduction in available water and water quality change are two key future trends that
require planning and adaptation, to safeguard clean drinking water for all.
Work reported in Chapter 5 of this thesis addresses potential changes in streamflow andwater quality, which could be important for all water users in the future. Furthermore,
Chapter 6 address future water resource availability in Wales for both PWS and HEP, analysing
trends in unmet demand for PWS and generation potential for HEP.

39

Literature review

2.6 Summary
As can be seen by the research presented, changes in the quantity and timing of water supply
historically and for the future have been relatively well studied for the UK as a whole, but lesswell for Wales specifically. Wales is however an important area to study, with evidence inmany of the papers cited in this section detailing how the west of the UK and Wales is more
likely to see a greater impact of future changes in both average and extreme hydroclimaticconditions. Furthermore, the impacts of projected climate change on water quality
parameters for Wales has seen even less research, as has future water resource availability.
In the light of this, as well as global, UK and Welsh level policy, and the heavy reliance onwater resources in Wales, as outlined in Chapter 1, it is important to have a goodunderstanding of how climate in Wales will change and the impacts that such changes will
have on hydrology. Furthermore, given the dependence on surface waters, greater
knowledge of how industries that are heavily reliant on water will be affected is crucial. Whilewater resource management plans published by DCWW do provide important insight into theimpacts on water supply, there is scope for further research assessing the influence ofchanging climate on water supply and demand relationships, the bearing of extreme events,
and the impact of changing water quality. Likewise, the impact on other large water users has
seen limited research, especially considering the consequences that changes in theseasonality and quantity of streamflow could cause. It is these knowledge gaps that this thesis
aims to address, as laid out in the aim and objectives section in Chapter 1. It is hoped that byaddressing the gaps relating to (1) water demand links to prevailing weather conditions, (2)future streamflow and water quality characteristics, and (3) the impacts on water abstraction
locations, that more informed future planning decisions may be made.

40

CHAPTER 3: METHODOLOGICAL OVERVIEW

41

Methodological overview

3.1 Overarching methodology
The research conducted in this thesis is based on a process of results, with the findings of theearlier studies informing the research direction and methods of latter studies. While theobjectives of the research were established at the project outset (Chapter 1), the overarching
and individual study methodologies have been continuously developed based on the findings.
This approach has been taken in order to ensure that the most appropriate methods are used
given the output data, and that the results are therefore meaningful, reliable, and useful. The
same study area and five catchments (details in Section 3.2) have been maintained
throughout the thesis, allowing a full picture of change within each to be established.
The first study investigates historical trends in temperature, precipitation, and streamflow foreach catchment, in order to establish the current direction and magnitude of change in thevariables. In addition, the relationship between the variables and total water demand, using
total public water supply (PWS) abstraction volume as a proxy, is also completed. This study
was undertaken in order to characterise for the first time the historical trends in both average
and extreme streamflow, precipitation, and temperature for several of the catchments. This
characterisation is also then used to inform the methodology of the future hydrologicalmodelling completed in Study 2, as well as to provide context and comparison with the future
observed trends. In addition, the establishment of the relationship between weather andtotal PWS demand has not been made for Wales previously, with little work of this kind being
published for the UK more widely. This work is important for the assessment of potential
future PWS demand generally, with the results, for temperature in particular, also important
for the future water resource availability analysis completed in Study 3.
Study 2 analyses the impact of future climate change on the streamflow regime and water
quality of the five catchments. Projected future climate input is based on a worst-casescenario of future global greenhouse gas (GHG) emissions, in the form of Representative
Concentration Pathway 8.5 (RCP8.5) from the Intergovernmental Panel on Climate Change(IPCC). RCP8.5 represents one of the worst emissions outcomes of a no-policy scenario, withhigh population growth, continued coal use, and no downturn in global GHG emissions (Riahi
et al., 2011; van Vuuren et al., 2011), leading to a scenario of radiative forcing still rising by
2100, and not peaking until the 2200s (Moss et al., 2010; van Vuuren et al., 2011). This is an42

Methodological overview
extreme future scenario, in the 90th percentile of future modelled outcomes of no climatechange mitigation (van Vuuren et al., 2011) and has been used in this thesis despite the higher
levels of uncertainty associated with it, due to the critical nature of the study subject, inparticular PWS. To ensure an uninterrupted future PWS it is vital to take account of such a
scenario, to allow for robust planning. Furthermore, the use of this scenario is important for
all potential water users, hydroelectric power (HEP), for example. In a future society whererenewable energy is likely to make up a larger part of the overall energy mix, understanding
the potential fluctuation in HEP generation caused by alteration in streamflows under climatechange is highly important. The same is also true for purposes such as abstraction for
agricultural irrigation, also likely to become more important under a warmer and drier futuresummer climate in the Wales. The results of Study 2 not only inform Study 3, but also, as
aforementioned, have significance to any abstractors or users of, surface waters in Wales.
Changes in streamflow and water quality will impact on all water users, as well as aquatic life,
the results of this work therefore have wide reaching consequences, providing context to thedecision and policy making of various organisations.
The third and final study uses the daily future streamflow output modelled in Study 2 toinvestigate the impact of climate change on available water resource for two of the key water
abstracting sectors in Wales, HEP and PWS. These two sectors were chosen based on theiryear-round water use, streamflow allowing (as opposed to the more seasonal nature of
agricultural demand, for example), as well as their importance to society, for water supply
and energy generation. The study was undertaken in order to provide a degree ofquantification of the potential societal, environmental, and economic impacts of climatechange induced hydrological regime change in the future, as well as to provide context tofuture decision making in terms of preparation and adaptation.

3.2 Study catchments
Five catchments in Wales have been chosen for study, the Clwyd, Conwy, Dyfi, Teifi and Tywi
(Figure 3.1); with each exhibiting varied characteristics such as topography, hydrometry, land
cover, and climate. They are all also used extensively for water abstraction for a variety of
sectors, such as PWS, HEP, agriculture and industry. The rationale behind the selection ofWales as the study area, as well as these five particular catchments, is provided in Section 1.2.
43

Methodological overview

Figure 3.1. Study catchments displaying streams larger than third order and catchment elevation. The locationof streamflow gauges used in all three studies are shown (green circles), as well as the location of pseudometeorological stations (orange octagons) and water quality monitoring locations (pink triangles) used in Study
2. The 25 hydroelectric power abstractions locations examined in Study 3 are also shown (blue stars).

44

Methodological overview
The following section provides detailed descriptions of each catchment and a comparison ofcharacteristics between catchments. Reported details and figures are based on the catchment
areas as modelled for Study 2. Morphometric calculations are based on data derived from the
OS Terrain 5 digital elevation model (DEM) from Ordnance Survey (Ordnance Survey, 2020);
land use/land cover (LULC) proportions are calculated from 2012 CORINE Land Cover datafrom the European Environment Agency (EEA, 2012). Gauged streamflow data (NRFA, 2020),
and precipitation data from the Climate, Hydrology and Ecology research Support System(CHESS) dataset (Robinson et al., 2017), for the hydrological years 1982 to 2015, has beenused in the calculation of hydrometric signatures and precipitation statistics. In relation tohydrometric signatures, mean streamflow (𝑄) and precipitation (𝑃) for the period 1982-2015
have been used to calculate runoff ratio (𝑅𝑄𝑃 ), as per Equation 3.1. Values closer to 1 denotea streamflow dominated catchment, and nearer to 0 signals evapotranspiration is foremost:

𝑅𝑄𝑃 =

𝑄
𝑃

(Eq. 3.1)

Baseflow index was calculated by the digital filter method (Sawicz et al., 2011) to give an
indication of the long-term contribution of baseflow to total streamflow. Direct runoff at agiven time-step (𝑄𝐷𝑡 ) is first computed, as per Equation 3.2:

𝑄𝐷𝑡 = 𝑐𝑄𝐷𝑡−1 +1+𝑐(𝑄𝑡 − 𝑄𝑡−1 )2(Eq. 3.2)

where 𝑐 is a constant with value 0.925, 𝑄𝐷𝑡−1 is direct runoff at the previous time-step (at𝑡 = 0, 𝑄𝐷 is assumed to be 0) and 𝑄𝑡 and 𝑄𝑡−1 are streamflow at this and the previous timestep respectively. Baseflow (𝑄𝐵𝑡 ) for the time-step is then calculated using Equation 3.3:
𝑄𝐵𝑡 = 𝑄𝑡 − 𝑄𝐷𝑡(Eq. 3.3)

Baseflow index (𝐼𝐵𝐹 ) can then be computed by Equation 3.4, summing all time-step values,
where 𝐼𝐵𝐹 values closer to 1 infer catchments with higher baseflow contribution:

𝐼𝐵𝐹 = ∑

𝑄𝐵
𝑄

(Eq. 3.4)

Finally, streamflow elasticity (𝐸𝑄𝑃 ) has been calculated as a measure of the sensitivity of eachcatchment to precipitation, as established by Schaake (1990) and Dooge (1992), and modified

45

Methodological overview
by Sankarasubramanian et al. (2001), which compares the percentage change in precipitation
to the percentage change in streamflow, normalised by the runoff ratio, as in Equation 3.5:

𝐸𝑄𝑃 = 𝑚𝑒𝑑𝑖𝑎𝑛 (

𝑑𝑄 𝑃
× )
𝑑𝑃 𝑄

(Eq. 3.5)

where 𝑑𝑄 and 𝑑𝑃 represent the difference between the previous year’s and current year’s
mean annual streamflow and precipitation respectively, and 𝑃 and 𝑄 are as above. Values
greater than 1 represent elastic catchments which are sensitive to precipitation changes, withvalues less than 1 denoting the opposite, inelastic catchments. A summary of catchment
characteristics is made in Table 3.1, with detailed descriptions for each catchment after.

Catchment land use/land cover (%)

Precipitation

Morphometricand hydrometricparameters

Table 3.1. Summary of comparative study catchment details based on the catchments as delineated in Study 2.
Morphometric parameters derived from the 5 m resolution OS Terrain 5 DEM from Ordnance Survey (Ordnance
Survey, 2020) with hydrometric signatures based on streamflow for the hydrological years 1982-2015 (NRFA,
2020). Precipitation statistics calculated from CHESS data (Robinson et al., 2017); land use/land cover derived
from 2012 CORINE Land Cover data (EEA, 2012).

Clwyd

Conwy

Dyfi

Teifi

Tywi

Mean elevation (m)Maximum elevation (m)Mean slope (%)
Runoff ratio
Baseflow index
Streamflow elasticity
Mean annual rainfall (mm)

750.1
210.4
557
12.5
0.52
0.59
1.28
977

541.8
309.8
1062
19.7
0.93
0.28
1.21
1968

507.2
271.4
903
25.3
0.89
0.38
1.06
1803

995.3
198.1
591
11.6
0.76
0.54
1.09
1398

1364.6
212.5
801
16.6
0.80
0.48
1.17
1596

Winter proportion (%)
Spring proportion (%)
Summer proportion (%)
Autumn proportion (%)

28.4
20.1
21.0
30.5

32.1
19.2
18.2
30.5

31.2
18.8
20.1
29.9

30.2
18.8
19.8
31.2

30.8
19.1
19.7
30.4

48.3
2.4
80.9

44.1
0.7
30.6

52.1
0.3
30.0

55.7
0.8
83.0

49.5
0.7
64.6

9.3
71.6

0.1
30.5

0.4
29.6

1.8
81.2

0.7
63.9

7.3

13.7

21.5

5.5

16.0

1.6
4.5
1.2

2.7
5.5
5.5

1.8
18.9
0.8

1.2
3.2
1.1

1.0
10.1
4.9

9.4

42.1

45.3

9.7

17.1

Natural grassland
Moors and heathland
Transitional woodland scrub

2.3
6.6
0.5

16.6
23.6
1.9

30.4
11.3
3.6

7.2
1.2
1.3

8.4
6.5
2.2

Peat bog
Sparsely vegetated areas

0.0
0.0

8.4
4.1

2.6
0.2

0.9
0.0

1.3
0.0

Catchment area (km2)

Mean rain free days per year
Urban
Agriculture
ArablePastureForestBroadleaf
Coniferous
Mixed

Scrub

46

Methodological overview
The mostly easterly of the catchments, the Clwyd, is the third largest in terms of overall
catchment area, with its headwaters in the Clocaenog forest and outlet to the Irish Sea at
Liverpool Bay. It is largely agriculturally dominated (80.9%), with the majority of this being
pasture (as is common in all catchments) at 71.6%; the largest proportion of land classed asurban (2.4%) is also found here. The Clwyd has the lowest maximum elevation of all of thecatchments at 557 m, this is almost half the height of the same measurement in theneighbouring Conwy catchment; and is also the second least sloping on average, at 12.5%. In
terms of hydrometric signatures, out of all of the catchments, the Clwyd has the lowest runoff
ration (0.52), and conversely the highest baseflow index (0.59) and streamflow elasticity(1.28). These results show that the Clwyd is the most baseflow dominated of the catchments
and has the largest proportion of total incoming precipitation exiting by evapotranspiration.
The catchment is, however, still sensitive to changes in precipitation, of which it receives theleast of all the catchments, the annual average being 977 mm. This precipitation has the mosteven split between seasons of all catchments, with autumn seeing the most (30.5%) andspring the least (20.1%).
The Conwy also flows into the Irish Sea on the north Wales coast, from its source in
Snowdonia; indeed, the Snowdonia National Park area covers some 70% of the catchment.
This mountainous terrain gives the Conwy the second largest mean slope value at 19.7% andthe highest maximum catchment elevation, at 1062 m. This is also one of the highest rainfall
areas in Wales, with the catchment receiving 1968 mm of precipitation per year on average,
with only 44.1 rain free days per year, the lowest of those studied. The Conwy has the largest
divergence in seasonal rainfall distribution, with 32.1% occurring in winter, compared to
18.2% in summer. Both the Conwy and the Dyfi differ from the other three catchments in thatthe main LULC is not agriculture. For the Conwy, scrub is the largest category (42.1%), withthis being mainly moors and heathland (23.6%), and to a lesser extent, natural grasslands(16.6%). The Conwy also features the largest area of peat bog (8.4%), and, due to themountainous terrain, 4.1% of land is sparsely vegetated. When looking at the catchmenthydrometrics, it can be seen that these correspond with the morphometric, LULC andprecipitation characteristics, giving the Conwy a flashy response. Runoff ratio is the highestof all catchments (0.93) and baseflow index the lowest (0.28), with streamflow elasticity thesecond highest, at 1.21, meaning the catchment is sensitive to changes in precipitation.

47

Methodological overview
The Dyfi is the smallest catchment to be studied (507 km2) and is situated in west Wales,
flowing from the Aran Mountains in the east, to the Irish Sea. It is similar to the Conwy inmany regards, especially in terms of LULC proportions, precipitation volume and regime, andmorphometric parameters. In terms of LULC, the Dyfi has the lowest amount of urban (0.3%)
and agricultural (30.0%) land of any of the study catchments, but the highest amount offorested land, at 21.5% (1.8% broadleaf, 18.9% coniferous, 0.8% mixed). Scrub land is thelargest proportion (45.3%), but in contrast to the Conwy, a larger part of this is natural
grassland (30.4%) than moors and heathland (11.3%). The Dyfi has the second highest single
catchment elevation point in its headwaters, at 903 m as well as the steepest average slope(25.3%). Runoff ration is second only to the Conwy, at 0.89, with baseflow index being second
lowest (0.38), the streamflow elasticity score is the lowest of all catchments (1.06), but is still
greater than 1, suggesting that for a given change in precipitation, a greater change is seen instreamflow. This gives the catchment a flashy nature, with little baseflow contribution, little
loss to evapotranspiration, and being small and steeply sloping. Annual mean precipitation
total is 1803 mm, the second largest, with 61.1% of this falling in winter and autumn; the Dyfi
also has the second largest number of rain free days per year, 52.1.
The Teifi, in south Wales, flows into Cardigan Bay from its source at Llyn Teifi, in the Cambrian
Mountains. The catchment is the second largest of those studied (995 km2), but also has thelowest maximum elevation, at 591 m, as well as the least steep mean catchment slope angle,
11.6%. The Teifi has the highest proportion of land classified as agricultural (83.0%) andconversely the lowest proportion of forest cover (5.5%) and scrub land (9.7%). These
characteristics are similar to those seen in the Clwyd; it therefore follows that the hydrometricsignatures of the Teifi are also similar to those seen in the Clwyd. Runoff ratio is 0.76 andstreamflow elasticity is 1.09, both the second lowest of all five catchments; baseflow index is0.54, the second highest in the study. As with the Clwyd, the characteristics of the catchment
lead it to being less flashy in nature, in particular than the Conwy and Dyfi, but withstreamflow still sensitive to changes in precipitation. The Teifi has the most rain free days of
all catchments annually on average (55.7), with largest proportion of rainfall falling in autumn(31.2%) and least in spring (18.8%).
The Tywi, with its source roughly 15 km south of the Teifi’s, is the largest catchment to bestudied (1365 km2); flowing from its source in the Cambrian Mountains to Carmarthen Bay,48

Methodological overview
roughly 80 km of its length is designated as a special area of conservation. The river enters
Llyn Brianne reservoir 10 km from its source; constructed in the 1970s the reservoir regulates
the river in times of low flow, to ensure sufficient water for abstraction for PWS downstream.
The majority of the Tywi is used as pasture (63.9%), with scrub and forested land being thenext largest proportion of LULC (17.1% and 16.0% respectively). The catchment receives 1596
mm of rainfall annually on average, with 30.8% of this being in winter and 19.1% in spring; amean 49.5 rain free days are experienced per year. The highest elevation in the catchment is
801 m, while the average slope is 16.6%, making it both the third highest and steepest
catchment studied. Runoff ratio (0.80), baseflow index (0.48), and streamflow elasticity (1.17)
values are all also mid-range when comparing the five catchments.
The bedrock geology of the catchments has been analysed using the British Geological Survey(BGS) Geology 625k (DiGMapGB-625) data set (BGS, 2019), with the majority in all catchments
found to be mudstone, siltstone and sandstone in nature, ranging from 60% and 64% in the
Clwyd and Conwy, to 87% in the Dyfi and Teifi, and 73% in the Tywi. The remaining bedrockin the Teifi and Tywi is made up of interbedded sandstone and conglomerate (13% and 27%
respectively); this also makes up the second largest proportion of remaining bedrock in theother three catchments (Clwyd, 18%; Conwy, 17%; Dyfi, 12%). The majority of that remaining
in the Dyfi and Conwy catchments is Felsic Tuff, at 1% and 12% respectively, while thatremaining in the Clwyd is limestone with subordinate sandstone and argillaceous rock (16%),and siltstone and sandstone with subordinate mudstone (6%). While the bedrock of the
Conwy, Dyfi, Teifi and Tywi has a low permeability, the limestone and sandstone deposits
found under the Clwyd catchment are much more permeable (BGS, 2019).
As can be seen, the five catchments studied offer a variety of different catchment
characteristics (morphometric parameters, hydrometric signature, LULC) which influence
their hydrological regime, water quality, and response to precipitation. These characteristics
and individual catchment response profiles could be important when considering futurechanges in climate and weather events, and the potential knock impacts this may have forcatchments and water abstractors. As previously mentioned, each catchment is abstracted
from for a variety of uses, these abstractions, except PWS, are shown in Figure 3.2 (PWS notshown due to data licence conditions prohibiting the identification of individual abstractions);
with a breakdown in numbers by sector provided in Table 3.2.

49

Methodological overview

Figure 3.2. Abstraction locations for the purposes of agriculture, amenity (e.g. land and pond throughflow),environment (e.g. wetland support), industrial/commercial, and energy production. Public water supply
abstractions not shown due to dataset licence restrictions.
Table 3.2. Number of abstraction locations per sector for each catchment; ‘water supply’ includes both public
and private water undertakings; ‘energy production’ accounts for both hydroelectric power and water used inother energy generation processes.

Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Agriculture

Amenity

Environment

7
2
4
4
2

1
2

1

9
3

50

Industry/commercial

Energyproduction

Water
supply

Total

4
5
4
2
4

7
20
23
14
10

5
5
11
3
3

24
35
42
32
22

Methodological overview

3.3 Trend analysis
Long-term trend analysis of annual and seasonal datasets is a common thread throughout thisthesis. In each of the three study chapters Mann-Kendall (MK) trend analysis (Mann, 1945;
Kendall, 1975) has been used to detect the direction and statistical significance of trends inthe meteorological, hydrological, water quality and water abstraction capability datasets
studied. MK analysis is nonparametric and tests for a positive or negative monotonic trend ina time series based on the ranks of individual observations (Helsel & Hirsch, 2002). For a timeseries 𝑋 = [𝑥1 , 𝑥2 , . . . , 𝑥𝑛 ], MK is calculated following Equations 3.6 to 3.10. Firstly, the test
statistic, 𝑆, is calculated by Equation 3.6:
𝑆 = ∑ 𝑎𝑖𝑗

(Eq. 3.6)𝑖<𝑗where 𝑎𝑖𝑗 is calculated as per Equation 3.7:
1
𝑎𝑖𝑗 = 𝑠𝑔𝑛(𝑥𝑗 − 𝑥𝑖 ) = 𝑠𝑔𝑛(𝑅𝑗 − 𝑅𝑖 ) = { 0−1𝑖𝑓 𝑥𝑖 < 𝑥𝑗
𝑖𝑓 𝑥𝑖 = 𝑥𝑗
𝑖𝑓 𝑥𝑖 > 𝑥𝑗

(Eq. 3.7)

where 𝑅𝑗 and 𝑅𝑖 are the ranks of observations 𝑥𝑗 and 𝑥𝑖 . Under the assumption that thedataset is independent and randomly ordered, Mann (1945) showed that 𝑆 tends towards
normality when 𝑛 ≥ 8, with the mean and variance of 𝑆 being calculated as per Equations 3.8
and 3.9 respectively (Kendall, 1975):
𝐸(𝑆) = 0(Eq. 3.8)𝑛(𝑛 − 1)(2𝑛 + 5)
18

(Eq. 3.9)

𝑉𝑎𝑟(𝑆) =where 𝑛 is the length of the time series. By comparing the standardised variable 𝑢 with thestandard normal variate at signficnace 𝛼, it is possible to test the significance of the trends,
as in Equation 3.10:(𝑆 − 1)⁄√𝑉𝑎𝑟(𝑆)𝑢={ 0(𝑆 + 1)⁄√𝑉𝑎𝑟(𝑆)51

𝑓𝑜𝑟 𝑆 > 0
𝑓𝑜𝑟 𝑆 = 0
𝑓𝑜𝑟 𝑆 < 0(Eq. 3.10)

Methodological overview
However, the original MK test is vulnerable to false trend detection when dealing with
autocorrelated data; that being data which displays a correlation with a lagged version of
itself over a given time period (Hamed & Rao, 1998). This is common in hydrological andmeteorological data due to the seasonality of the datasets. A modified MK test has therefore
been performed, as per the Hamed and Rao autocorrelation correction method, thecalculations for which are show in Equations 3.11 to 3.14 (Hamed & Rao, 1998). The varianceof 𝑆 is first recalculated as 𝑉 ∗ (𝑆), as shown in Equation 3.11:

𝑉 ∗ (𝑆) = 𝑉𝑎𝑟(𝑆) ×𝑛𝑛(𝑛 − 1)(2𝑛 + 5) 𝑛
=
× ∗
∗
𝑛
18
𝑛

(Eq. 3.11)

where 𝑛⁄𝑛∗ is a correction to 𝑉𝑎𝑟(𝑆) (Equation 3.9) accounting for autocorrelations in thedata and is calculated using Equation 3.12:
𝑛−1

𝑛
2
=1+
× ∑(𝑛 − 𝑖)(𝑛 − 𝑖 − 1)(𝑛 − 𝑖 − 2)𝜌𝑠 (𝑖)∗𝑛𝑛(𝑛 − 1)(𝑛 − 2)

(Eq. 3.12)

𝑖=1where 𝜌𝑠 (𝑖) is the autocorrelation coefficient of the ranked data, expressed in Equation 3.13:

𝜌𝑠 (𝑖) =∑𝑁−𝑗𝑖=1 [(𝑥𝑖 − 𝑋)(𝑥𝑖+𝑗 − 𝑋)]∑𝑁
𝑖=1(𝑥𝑖 − 𝑋)

2

(Eq. 3.13)

where 𝑁 is the number of ranks of observations and 𝑋 is the dataset mean. The result ofEquation 3.11 allows for a modification of Equation 3.10, enabling significance testing whichreflects the autocorrelation correction (Equation 3.14):(𝑆 − 1)⁄√𝑉 ∗ (𝑆)𝑢 ={ 0(𝑆 + 1)⁄√𝑉 ∗ (𝑆)∗𝑓𝑜𝑟 𝑆 > 0
𝑓𝑜𝑟 𝑆 = 0
𝑓𝑜𝑟 𝑆 < 0(Eq. 3.14)

In addition, in order to better quantify the magnitude of the detected trends, Sen’s
nonparametric slope estimator (Sen, 1968) has also been applied. This procedure estimates
the slope of a linear trend in a sample of 𝑛 pairs of data. Firstly, the slopes of the entire timeseries of data points, 𝑄𝑖 , are calculated using Equation 3.15:

𝑄𝑖 =𝑋𝑗 − 𝑋𝑘𝑓𝑜𝑟 𝑖 = 1,2, . . . , 𝑛𝑗−𝑘52(Eq. 3.15)

Methodological overview
where 𝑋𝑗 and 𝑋𝑘 are data values at time-steps 𝑗 and 𝑘. All 𝑄𝑖 values are then ranked smallest
to largest, with median slope, 𝑄𝑚𝑒𝑑 , then being calculated as Equation 3.16:

𝑄𝑚𝑒𝑑 = {𝑄[(𝑛+1)/2](𝑄[𝑛/2] + 𝑄[(𝑛+2)/2] )/2

𝑖𝑓 𝑛 𝑖𝑠 𝑜𝑑𝑑
𝑖𝑓 𝑛 𝑖𝑠 𝑒𝑣𝑒𝑛(Eq. 3.16)

A negative 𝑄𝑚𝑒𝑑 result indicates a negative trend, with the opposite true for a positive trend;
the value of the result indicates the steepness of the trend, the median absolute value change
per time-step. To calculate if the median slope is statistically significantly different from zero,
Equation 3.17 is used:
𝐶𝛼 = 𝑍1−𝛼/2 √𝑉 ∗ (𝑆)(Eq. 3.17)

where 𝑍1−𝛼/2 is obtained from a standard normal distribution table and 𝑉 ∗ (𝑆) is as definedin Equation 3.11.
These methods for trend analysis and slope detection were chosen because of theirnonparametric nature, allowing for the detection of trends in datasets which are nonnormally distributed and that display seasonality, unlike trend analysis methods such as linear
regression (Jaiswal et al., 2015). This is important when dealing with hydrological andmeteorological data which often displays the characteristics aforementioned (Kundzewics &Robson, 2004). This combination of modified MK trend analysis alongside Sen’s slope
estimator, is a method that has been used extensively for similar studies as well as moregenerally for the analysis of trends in hydrological, meteorological and water resources data
globally (Gocic & Trajkovic, 2013; Murphy et al., 2013; da Silva et al., 2015; Atta & Dawood,2017); it was therefore deemed suitable for use in this work.
For all three studies, trend analysis has been conducted on mean values based on hydrologicalyears (1st October to 30th September) and each individual season. Results for any given year,
2021 for example, therefore are based on data from the following time periods; annual:
October 2020 to September 2021; winter: December 2020 to February 2021; spring: March
to May 2021; summer: June to August 2021; and autumn: September to November 2021. As
can be observed, due to the nature of hydrological years ending in the middle of autumn,
there is a mismatch between seasons and annual data, this has no impact however on themeasurement and detection of trends seasonally or annually.

53

Methodological overview

3.4 Study 1: Historical analysis
3.4.1 Hydroclimatic data
For the first study (Chapter 4), a 34-year period (1982-2015) of daily air temperature, total
precipitation, and streamflow volume data has been analysed for each of the five study
catchments. This time frame corresponds with the most recent period of complete andreliable data available for all three datasets across all catchments, at the time of analysis.
Thirty years is classically considered as the minimum required dataset length in order to carryout climatic analysis (IPCC, 2007), not allowing smaller timeframe variations and cycles to
impact on observed trends. This is also the period suggested for use by the World
Meteorological Organisation (WMO) in the assessment of climate norms (WMO, 2017). Given
this, the parity of study period timing for each catchment was prioritised over additional study
period length for any given catchment or dataset. This decision was made to ensure crosscomparability of results for all catchments, enabling interpretation of the impact ofcatchment location and characteristics on the observed trends.
Daily climatic data studied (air temperature and total precipitation) was obtained from the
CHESS dataset (Robinson et al., 2017) from the UK Centre for Ecology and Hydrology (CEH).
This gridded dataset was chosen due to its complete coverage across Wales, especially whencompared to data from individual meteorological stations, of which there are few with long,complete, and consistent records in the study region, leaving some catchments
underrepresented. CHESS precipitation data is based on the CEH 1 km Gridded Estimates ofAreal Rainfall (GEAR) dataset, which is an interpolated dataset based on meteorologicalstation rainfall observations adjusted for topography, to provide full UK coverage (Keller etal., 2015). Temperature data for CHESS is downscaled to a 1 km grid, also taking account of
topographical data, from the 0.5 degree gridded Climate Research Unit Time Series, version
3.21 dataset (CRU TS3.21), which is also based on meteorological station observations (Harris
et al., 2014). In order to provide a single dataset of each climatic variable for each catchment,
the mean value of all grid cells contained with a catchment was calculated for each day of thestudy period; it is this value, after inspection for anomalies, that has been used in the analysis
described in the following section.

54

Methodological overview
Daily streamflow data has been obtained from the UK National River Flow Archive (NRFA) fora single velocity-area gauging station in each catchment, as shown in Figure 3.1 (NRFA, 2020).
The stations used were those with as close to a natural flow regime as possible, as well as along record length, to allow for a more robust attribution of streamflow changes to external
factors, as opposed to regulation or abstraction. For each station, a review of the station
information, hydrometric description, and flow record and regime descriptions wasundertaken, to ensure the record was appropriate and check for any required correction to
the data. This investigation showed that no correction or action was needed for any of theflow records used, details of each gauging station are provided in Table 3.3.
Table 3.3. Details of gauging stations used throughout the thesis (NRFA, 2020).

Clwyd
Conwy
Dyfi
Teifi
Tywi

StationID

Upstream
area (km2)

Stationelevation (m)StationopenedNRFA brief flow regime description

66001
66011
64001
62001
60006

404
345
471
894
130

15.3
11.6
5.9
5.2
7.5

1959
1964
1962
1959
1968

Low flows augmented by groundwater
Largely natural
Natural to within 10% at Q95
Largely natural
Natural to within 10% at Q95

3.4.2 Hydroclimatic analysis
In order to best characterise any changes in the climatic and streamflow data, analysis was
performed annually and seasonally (as described in Section 3.3) for both average and extreme
temperatures, precipitation volumes and flows. Analysed time series are based on the WMO
standard methods for annual, seasonal and multi-month calculations (WMO, 2017). For eachseason and year, mean flow, temperature and precipitation has been calculated for the
analysis of averages. For the extreme analysis, 1-day maximum and minimum flow andtemperature, as well as maximum precipitation total, for each season and year has been
extracted; this characteristic was chosen to give an indication of the change in magnitude ofextremes during the study period. Additionally, the number of days above and below thedataset 95th and 5th percentiles respectively has been calculated for streamflow andtemperature, with analysis of precipitation only taking account of the former. This analyticalmethod was chosen to give an understanding of the change in frequency of extremes.
The above extracted time series were first analysed using the aforementioned MK analysis
methods. Subsequently, a breakpoint analysis has been performed on each of the historical55

Methodological overview
average and extreme hydroclimatic times series, once again both seasonally and annually, todetect any abrupt changes during the study period. This analysis was performed in order toassist in the attribution of changes to known events that may have occurred in one or morecatchments (e.g. damn construction or major LULC change or climate driver alteration). Two
nonparametric methods of breakpoint analysis have been employed, Pettitt’s test (Pettitt,1979) and the Standard Normal Homogeneity Test (SNHT) method (Alexandersson, 1986;
Alexandersson & Moberg, 1997). Pettitt’s test has been shown to perform better at detectingchanges in the middle of a time series, whereas SNHT performs more favourable at the startand end of a time series (Hawkins, 1977; Jaiswal et al., 2015; Hänsel et al., 2016). It wastherefore decided to perform both tests to ensure the best coverage of potential breakpoints.
Both methods detect changes in the value of the dataset mean and are location specific,
therefore identifying the point of change within the time series, (Hänsel et al., 2016).
Pettitt’s test, for a time series 𝑥1 , 𝑥2 , . . . , 𝑥𝑛 , calculates the distribution function for time series
data both before (𝐹1 (𝑥)) and after (𝐹2 (𝑥)) a breakpoint, 𝑡. The nonparametric test statistic,
𝑈𝑡 , is calculated as per Equation 3.18:
𝑡𝑛

𝑈𝑡 = ∑ ∑ 𝑠𝑔𝑛(𝑥𝑡 − 𝑥𝑗 )(Eq. 3.18)

𝑖=1 𝑗=𝑡+1

Where 𝑛 is the time series length and 𝑠𝑔𝑛(𝑥𝑡 − 𝑥𝑗 ) is calculated by Equation 3.19:
1𝑠𝑔𝑛(𝑥𝑡 − 𝑥𝑗 ) = { 0−1𝑖𝑓 𝑥𝑖 < 𝑥𝑗
𝑖𝑓 𝑥𝑖 = 𝑥𝑗
𝑖𝑓 𝑥𝑖 > 𝑥𝑗

(Eq. 3.19)

Equation 3.20 calculates the test statistic 𝐾, which is subsequently used in the calculation of
the confidence level (𝜌) for the given 𝑛, as in Equation 3.21, itself a term in the calculation of
the approximate significance probability (𝑝) of a breakpoint, in Equation 3.22:
𝐾 = 𝑀𝑎𝑥|𝑈𝑡 |(Eq. 3.20)−𝐾𝜌 = 𝑒𝑥𝑝 ( 2)𝑛 + 𝑛3(Eq. 3.21)

𝑝 =1−𝜌

(Eq. 3.22)

56

Methodological overview
The value of 𝐾 is compared to a critical value based on the time series length, to determine
whether the change is statistically significant (Jaiswal et al., 2015).
SNHT compares the mean of the first 𝑛 observations with the mean of the remaining
observations (𝑛 − 𝑘), in the calculation of the test statistic 𝑇𝑘 , as shown in Equation 3.23:
𝑇𝑘 = 𝑘𝑍1 2 + (𝑛 − 𝑘)𝑍2 2(Eq. 3.23)

where 𝑍1 and 𝑍2 are calculated by Equations 3.24 and 3.25 respectively:𝑘(𝑥𝑖 − 𝑥)1𝑍1 = ∑𝑘𝜎𝑥(Eq. 3.24)𝑖=1𝑛(𝑥𝑖 − 𝑥)
1
𝑍2 =
∑
𝑛−𝑘
𝜎𝑥

(Eq. 3.25)

𝑖=𝑘+1

where 𝑥 is the mean and 𝜎𝑥 is the standard deviation, of the series. 𝑇𝑘 is the point at which
there is maximum difference in value of the means of the proceeding and succeeding timeseries points. The value of the test statistic is compared to a critical value based on the timeseries length, to determine whether the change is statistically significant (Jaiswal et al., 2015).

3.4.3 Water abstraction relationships
In addition to the analysis performed on the three hydroclimatic factors, an investigation of
the relationship between these factors and total water demand has also been performed. The
water demand data to enable this analysis has been obtained from Dŵr Cymru Welsh Water(DCWW) in the form of total daily abstraction for 22 abstraction points within the five study
catchments. This daily total has been used as a proxy for overall PWS demand across all
sectors and also takes account of leakage. The data was provided for just under five calendar
years, from 1st January 2012 to 30th September 2016, however, due to the length of record
available for the hydroclimatic data, analysis was only completed up to the 30th September
2015. The relationship between the three hydroclimatic factors and total abstraction has
been characterised by both Pearson’s & Spearman’s rank correlation coefficients. This
analysis has been conducted for both daily data, as well as monthly mean. Pearson’s
coefficient value (r) was computed using Equation 3.26:

57

Methodological overview

𝑟=∑𝑛𝑖=1(ℎ𝑐𝑖 − ̅̅̅ℎ𝑐)(𝑎𝑏𝑠𝑖 − ̅̅̅̅̅
𝑎𝑏𝑠)
2
2√(∑𝑛𝑖=1(ℎ𝑐𝑖 − ̅̅̅ℎ𝑐) ) (∑𝑛𝑖=1(𝑎𝑏𝑠𝑖 − ̅̅̅̅̅
𝑎𝑏𝑠) )(Eq. 3.26)

where ℎ𝑐𝑖 refers to the daily/monthly average hydroclimatic variables studied, 𝑎𝑏𝑠𝑖 refers to̅̅̅ & 𝑎𝑏𝑠̅̅̅̅̅ represent the mean of the entire
daily/monthly total abstraction volume, and ℎ𝑐
respective datasets. The nonparametric Spearman’s correlation coefficient (ρ) was calculatedusing Equation 3.27, a modified version of Pearson’s correlation coefficient which calculates
correlation between ranks, as opposed to raw data:𝜌=∑𝑛𝑖=1(𝑅(ℎ𝑐𝑖 ) − ̅̅̅̅̅̅̅̅
𝑅(ℎ𝑐)) (𝑅(𝑎𝑏𝑠𝑖 ) − ̅̅̅̅̅̅̅̅̅
𝑅(𝑎𝑏𝑠))
2
2√(∑𝑛𝑖=1(𝑅(ℎ𝑐𝑖 ) − ̅̅̅̅̅̅̅̅
𝑅(ℎ𝑐)) ) (∑𝑛𝑖=1(𝑅(𝑎𝑏𝑠𝑖 ) − ̅̅̅̅̅̅̅̅̅
𝑅(𝑎𝑏𝑠)) )(Eq. 3.27)

where 𝑅(ℎ𝑐𝑖 ) refers to the rank of the daily/monthly average hydroclimatic variables studied,𝑅(𝑎𝑏𝑠𝑖 ) refers to the rank of the daily/monthly total abstraction volume, and 𝑅(ℎ𝑐) &𝑅(𝑎𝑏𝑠) represent the mean rank of the entire respective datasets. These two complementary
methods have been used to test for a linear trend, in the case of Pearson’s, and a monotonic
trend in the case of Spearman’s rank. It was important to characterise the trend between
abstraction and temperature in particular at this stage, in order for the relationship to be usedin the research conducted in Study 3 (Chapter 6) which required the projection of future water
demand based on climatic conditions.

3.5 Study 2: Hydrological modelling
3.5.1 Model selection
For the second study, future hydrological modelling of streamflow regime and water quality
is the key focus. When selecting the hydrological model used for the study, several factors
were taken into consideration while comparing multiple options. First, due to the size of thecatchments to be studied, it was decided that a semi-distributed model was required,balancing greater computational efficiency, when compared to fully distributed models, withmaintained representation of catchment characteristics, when compared with a lumped
model (Suliman et al., 2015). Second, the model needed to be operable at a continuous daily
time-step in order to make use of the future climate input data, as well as enable analysis of58

Methodological overview
extreme streamflows and events. It was also important for the model to be physically based,with both land and in-stream processes being modelled, and the ability to handle both water
quantity and quality data. Several well-established hydrological models were considered,including, but not limited to, APEX (Williams & Izaurralde, 2010), MIKE-SHE (Refsgaard &Storm, 1995), SWAT (Arnold et al., 1998), TOPMODEL (Beven, 1997), and VIC (Liang et al.,1994). The decision was made to proceed with the Soil and Water Assessment Tool (SWAT)
model, using the interface with QGIS for catchment delineation and model setup. SWAT waschosen after a review of several studies comparing the performance of different hydrologicalmodels (Yang et al., 2000; El-Nasr et al., 2005; Golmohammadi et al., 2014; Suliman et al.,2015; Kauffeldt et al., 2016), with SWAT performing favourably in these, especially for low
flows which are important to the latter stages of the research, where future water availability
is assessed. Additionally, SWAT is well supported by the model development team, offering
trouble-shooting support as well as training workshops. This, coupled with the user-friendly
nature of the model interface which eliminates the need for high level computer coding, wasimportant, as the research was not being undertaken by an experienced modeller.
As was required by the model selection process, SWAT is a physically based, semi-distributed,
continuous time-step model, which operates at a catchment scale. The model makes use of
sub-basins, which are further sub-divided into hydrological response units (HRUs), these are
lumped areas of unique LULC, soil, and slope configuration. The output from each HRU isrouted to the corresponding sub-basin, and eventually the reach outlet (Neitsch et al., 2011).
Hydrological processes modelled by SWAT are precipitation, evapotranspiration, infiltration,
plant uptake, soil moisture redistribution, surface runoff, lateral flow, percolation to andrevap from the shallow aquifer, return flow and recharge of the deep aquifer, these are shownin Figure 3.3 (Neitsch et al., 2011). In terms of in-stream processes related to water quality,
the model allows for point sources of pollution to be added, as well modelling diffuse sources
based on the LULC data provided. In addition, dissolved and particle transport is modelled,
along with bed/bank erosion, sorption of pollutants onto sediments, biogradation andtransformation, and dilution, diffusion, deposition, accumulation and resuspension ofpollutants, as shown in Figure 3.4 (Neitsch et al., 2011). Owing to the extensive number ofmodelled processes, the continued development of the model over more than two decades,
and its ability to operate in ungagged basins, SWAT has been used extensively for the59Methodological overview
assessment of climate change impacts on water quantity (Qiao et al., 2014; Coffey et al., 2016;
Perra et al., 2018; Sultana & Choi, 2018; Yuan et al., 2019) and quality (Nerantzaki et al., 2016;
Yang et al., 2017; Pesce et al., 2018; Jilo et al., 2019) globally.

Figure 3.3. Representation of the processes modelled in the land phase of SWAT (Neitsch et al., 2011).

Figure 3.4. Representation of the processes modelled in the in-stream phase of SWAT (Neitsch et al., 2011).

60

Methodological overview

3.5.2 Model setup, calibration and validation
SWAT requires three key data inputs for initial model setup, these being elevation, LULC, andsoil. In terms of elevation data, the OS Terrain 5 DEM from Ordnance Survey (Ordnance
Survey, 2020) has been used. This high-resolution raster dataset, with a 5-metre post spacing(Ordnance Survey, 2017), was chosen in order to provide the most accurate catchment
delineation and water routing as possible, as well as maintaining computational efficiency forthe size of catchments studied. In terms of LULC, data from the CORINE Land Cover 2012
dataset (Copernicus Land Monitoring Data; EEA, 2012) was chosen due to the close match of
LULC categories between it and the pre-exiting SWAT LULC categories, allowing for easy input
to, and interpretation by, the model. The CORINE dataset is accurate spatially to within 25
metres, with a minimum mapping unit of 25 hectares for areal features and width of 100
metres for linear features; the accuracy of identified LULC type is greater than 85% (EEA,
2017). The dataset is generated by computer assisted photointerpretation of IRS P6 LISS III
and RapidEye satellite data (EEA, 2017). Soil data was obtained from the EU soil database
version 2.0, specifically the Soil Geographical Database of Eurasia, a 1:1,000,000 scale vector
dataset (European Commission, 2004). The calculation of SWAT soil parameters wascompleted using the Pedo Transfer Function developed by Saxton & Rawls (2006), based on
the characteristics associated with the given World Reference Base for Soil Resources (FAO,
1998) classification of each soil type. This dataset was used based on research and thesuggestion of the SWAT model developers, having accurate and full coverage for the study
area, being in a format that is easily transferable to SWAT, and having been used in the model
successfully previously.
Initial setup of the SWAT model starts with catchment delineation from a DEM, this process
first develops the stream network, based on the standard procedure (Martz & Garbrecht,
1992) of filling sinks in the DEM, calculation of flow direction using the D8 algorithm for eachpixel, and calculation of flow accumulation; a stream formation threshold of 1 km2 was usedat this stage. The user then adds any additional inputs of water, reservoirs, pollution point
sources and watershed outlets. For the purposes of this research, reservoirs have been
ignored, partly due to the gauging stations used for model calibration being located on
streams that are largely unaffected by upstream regulation, and partly due to the complex
nature of reservoir operation in SWAT, for which insufficient data was available to properly

61

Methodological overview
model. Outlets can operate as water abstraction locations, aside from the furthest outlet
downstream, which is classified as the final watershed outlet, for this reason, outlets wereadded at all PWS abstraction and HEP inlet locations in each catchment, to later enable
analysis of future water abstraction potential at each location. Watershed boundaries arethen delineated by SWAT, followed by subbasins, and then by HRUs therewithin, which aredefined based on homogenous areas of LULC, soil and slope.
Historical weather data was added to the model to enable an initial SWAT run, this was thenfollowed by calibration. The historical daily temperature and precipitation data used was thesame as that for Study 1, from CHESS, here used for the period 1982 to 1998, with a 3-year
model warm-up period. Weather data input for the model was based on single daily values oftemperature and precipitation for each catchment, based on the mean of the gridded data,
as before. Calibration was completed for each catchment individually, for streamflow only,
using NRFA historical daily streamflow, also as in Study 1. The particle swarm optimisation(PSO) method was used to perform a multi-parameter calibration in the SWAT calibration anduncertainty programme 2012 (SWAT-CUP). A total of 17 parameters (Table 3.4) were
calibrated simultaneously, these were selected based on a literature review of studies
operating in similar catchment types, as well as on the advice of SWAT model developers.
Table 3.4. SWAT parameters calibrated through SWAT-CUP using the particle swarm optimisation method.

Input filelocation

Parameter

Description

ESCO
EPCO
SURLAG
GW_Delay
Alpha_BF
GWQMIN
RCHRG_DPREVAPMN
GW_REVAP
ALPHA_BF_D
CANMX
CN2
CH_N2
CH_K2
SOL_AWC
SOL_K
SOL_Z

Soil evaporation compensation factor
Plant uptake compensation factor
Surface runoff lag time
Groundwater delay
Baseflow alpha factor
Threshold depth of water in shallow aquifer for return flow to occur
Deep aquifer percolation fraction
Threshold depth of water in shallow aquifer for "revap" to occur
Groundwater "revap" coefficient
Baseflow alpha factor for deep aquifer
Maximum canopy storage
SCS runoff curve number for moisture condition 2
Manning's "n" value for the main channel
Effective hydraulic conductivity in main channel alluvium
Available water capacity of the soil layer
Saturated hydraulic conductivity
Depth from soil surface to bottom of layer

62

.bsn
.bsn
.bsn
.gw
.gw
.gw
.gw
.gw
.gw
.gw
.hru
.mgt
.rte
.rte
.sol
.sol
.sol

Methodological overview
PSO is a complex but highly efficient method of calibration, capitalising on swarm intelligence
which takes inspiration from flocking birds (Kennedy & Eberhart, 1995). In this example, aflock of birds is searching a set area for a single food source, while no bird knows the exact
location of the food, they are all aware of their individual distance from it, as well as theclosest themselves and the group as a whole has come to it. The optimal solution for efficient
identification of the food source is to learn from the group, travelling in the direction of thelocation identified as closest to the food (Lee et al., 2008; Abbaspour, 2014). This theory canbe applied to parameter calibration, with birds being substituted for particles searching forthe optimal parameter value within a given range (search space), to satisfy a goodness-of-fit
metric (Qi et al., 2015). The group is initialised with each particle in the swarm randomly
assigned a position and velocity, the initial locations are evaluated for model fit, and these
automatically become each particles’ current best solution. The group’s best solution is set atthe value of the particle with the best fit of all particles. The individual particles theniteratively work towards, with a random function, a combination of their own and the groups
best known position (Qi et al., 2015). For example, for a given particle, 𝑎, at a given iteration,𝑖, the velocity (𝑣) for the next iteration 𝑣𝑎𝑖+1 is updated by Equation 3.28 (Lee et al., 2008):
𝑣𝑎𝑖+1 = 𝑤𝑣𝑎𝑖 + 𝑐1 𝑟1 (𝑝𝑎𝑖 − 𝑥𝑎𝑖 ) + 𝑐2 𝑟2 (𝑝𝑔𝑖 − 𝑥𝑎𝑖 )(Eq. 3.28)

where 𝑤 is an inertia factor for control of movement; 𝑐1 and 𝑐2 are acceleration coefficients
which attract the particle in the direction of the local and global best positions respectively;
and 𝑟1 and 𝑟2 are randomly generated, uniformly distributed variables between 0 and 1. The
current velocity and position of particle 𝑎 is represented by 𝑣𝑎𝑖 and 𝑥𝑎𝑖 , with 𝑝𝑎𝑖 and 𝑝𝑔𝑖
representing the location of the current best found position of 𝑎 and the group (𝑔),respectively. The position of 𝑎 is then updated by Equation 3.29 (Lee et al., 2008):
𝑥𝑎𝑖+1 = 𝑥𝑎𝑖 + 𝑣𝑎𝑖+1(Eq. 3.29)

This cycle continues until either the objective function value is met, or the maximum numberof iterations is reached. The method is highly efficient due to swarm intelligence, whichenables a complex search to take place, with individual particles undertaking only simple
search patterns (Xia et al., 2018). This approach allows for faster centring upon the optimal
solution, when compared with other calibration methods (Abbaspour, 2014) this was one of63Methodological overview
the key reasons for selecting this method. A total of 100 model runs were undertaken per
catchment for this research, with ten particles undergoing ten iterations; in all cases asatisfactory objective function value had been reached at this point. The objective functionused in this work was the Kling-Gupta efficiency (KGE; Gupta et al., 2009), with the parametervalues from the PSO iteration with the highest KGE being taken forward for implementation
into SWAT. Equation 3.30 details the calculation of KGE:
𝐾𝐺𝐸 = 1 − √(𝑟 − 1)2 + (𝑎 − 1)2 + (𝛽 − 1)2(Eq. 3.30)

where 𝑟 is the linear correlation between observations and simulations; 𝛼 refers to the ratio
between the standard deviation in the simulated flow and the standard deviation in theobserved flow; and 𝛽 is the ratio between the mean simulated and mean observed flow. Oncecalibrated parameter values had been finalised, these were checked to ensure that they werestill within a range of what would be expected and acceptable for the catchment, so as toensure that no over-parametrisation had occurred. These values were then updated within
the SWAT databases and the newly calibrated model was validated against observed daily
streamflow for the period 1999 to 2014, again with a 3-year model warm-up period.
Calibration and validation KGE results for each catchment are shown in Table 3.5.
Table 3.5. Kling-Gupta efficiency (KGE) calibration and validation values for comparison of gauged and modelleddaily mean streamflow for each catchment.

Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Calibration KGE

Validation KGE

0.810
0.770
0.788
0.851
0.841

0.788
0.718
0.728
0.723
0.717

In terms of water quality, four factors have been studied, suspended sediment, total nitrogen,total phosphorous and dissolved oxygen. These factors were chosen based on the available
and reliable outputs achievable in SWAT, observation data availability for output validation,
as well as their importance to overall stream water quality, aquatic life, and water abstractors.
While the inclusion of other factors, such as coliforms, persistent bacteria, and dissolved
organic carbon, was considered, these ultimately fell short of one or more of the selection
criteria, so were deemed unsuitable. Due to a lack of sufficient historical data for any of thefour factors, it was not possible to calibrate the model for water quality. A validation has been64

Methodological overview
carried out however, with model output being compared with data taken from the Natural
Resources Wales (NRW) water quality data archive held in WISKI (NRW, 2019b). This dataset
provides occasional, and often temporally randomly distributed, data points on each of thefour factors, for the period 2000-2010. Sampling frequency varies from monthly to as
infrequently as once per year, with sampling rate generally increasing towards the end of thetime period in all catchments, Table 3.6 summaries the number of samples available for eachfactor and catchment. The samples were mostly collected by NRW to check compliance with
EU directives, so there is a high confidence in the dataset reliability and consistency; for thisreason, and following an inspection of data for obvious outliers, no correction or alteration
was made to the data. A single sampling location for each catchment was chosen (shown in
Figure 3.1), with the aim of maximising the number of data points and, where possible,
ensuring coverage for all four factors. Unfortunately, the latter aim was only fully achievablein three of the five catchments, with no observations recorded for total nitrogen at the Conwy
or Dyfi sampling locations. Validation, where possible, took the form of the calculation of
Person’s correlation coefficient values between simulated and observed data points, to givean indication of the agreement in the datasets.
Table 3.6. Total samples used in the validation of studied water quality factors in SWAT.

Number of samples
Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Suspended
sediment

Total
phosphorous

Total nitrogen

Dissolved
oxygen

85
118
105
82
23

63
83
85
74
19

63
0
0
75
79

84
106
104
82
22

3.5.3 Future model runs
With calibration and validation successfully completed, future daily weather data could be
implemented into the catchment models. Section 3.1 details the rationale behind theselection of RCP8.5 as the future global GHG emissions scenario under which the future
streamflow and water quality assessment was conducted. Future daily temperature andprecipitation data, generated under RCP8.5 conditions, was obtained from the Met Office
Hadley Centre’s (MOHC) UK Climate Projections 2018 (UKCP18) dataset, specifically the65Methodological overview‘Regional Projections on a 12 km grid over the UK for 1980-2080’ (MOHC, 2018b). UKCP18
data was selected for use as the latest and most comprehensive projections for the UK. The
regional projections in particular were chosen as the dataset with the finest spatial scale, atthe time of implementation into SWAT, which takes account of regional-scale landscapefeatures, allowing for a local level analysis (Murphy et al., 2018). This dataset is an update to
the previous version (UKCP09), which incorporates improved representation of extreme
events as well as greater spatial resolution (MOHC, 2018b), both important to the analysis in
Studies 2 and 3. The dataset consists of an ensemble of twelve member models, each of which
has been dynamically downscaled from the 60 km gridded HadGEM3-GC3.05 global coupledmodel perturbed parameter ensemble (Murphy et al., 2018). Each of the ensemble members
differs due to natural climate variability as well as uncertainty in the projections (Kendon etal., 2019). Input weather data was inspected for obvious outliers or anomalous entries before
use in SWAT, with no other corrections or alterations undertaken on the data.
To enable the implementation of the UKCP18 data into SWAT, it was necessary to convert thegridded data to points, with a point at the centre of each 12 km grid being assigned that grid’s
daily climate variable values, and termed a pseudo-meteorological station. A total of 28 gridsquares for which the central point lay within one of the five catchments were identified; asubset of thirteen of these were taken forward to be used as pseudo-meteorological stations(Figure 3.5). This selection was required because of the substantial time and computational
requirement of extracting daily weather data values from the supplied NetCDF format, toallow for implementation in SWAT. Station selection was completed with the aim of ensuring
a good coverage both spatially, as well as in terms of elevation, which is particularly important
in SWAT for temperature and precipitation variation based on elevation and topography.
While narrowing down the number of stations may limit the representation of variability due
to elevation, this was mitigated in four of five catchments, those which share a boundary(Clwyd with Conwy, and Teifi with Tywi), as pseudo-meteorological station data from both
catchments was made available to each individual model, increasing data coverage while
maintaining computer resource efficiency. As the Dyfi has no neighbouring catchments thathave been modelled in this study, this catchment has the highest density of stations, to ensure
the best coverage possible (Figure 3.5).

66

Methodological overview

Figure 3.5. Pseudo-meteorological stations used in the implementation of UKCP18 future weather data into
SWAT, elevation in metres shown below station name.

Future modelling at a daily time-step was completed for the hydrological years 2021-2080,
with each of the five catchments being modelled for each of the twelve ensemble members.
This approach has been taken to account for some of the uncertainty in the projections,allowing for an envelope of potential future streamflow and water quality changes under
RCP8.5 conditions to be established, as well as an ensemble mean. Due to the size of thecatchments studied, and limits set upon the size of Microsoft Access databases, which areused to store the model output, model runs for the 60-year period had to be split in to
multiple periods; as many as four, 15-year periods for the largest catchments (Table 3.7). To
67

Methodological overview
counteract as far as possible any discontinuity in the output at these breaks, a 7-year model
warm up period was used before the start of each period. In order to further mitigate
disjointedness in model outputs, as few breaks as possible were used in each catchment,
therefore smaller catchments are split into fewer, longer periods (Table 3.7), in this way
individual dataset continuity was prioritised above overall approach consistency.
Table 3.7. Catchment SWAT model characteristics and run period information.

Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Total subbasins

Total HRUs

Number ofmodel periods

Model period
lengthModel period
break points

302
202
227
425
496

697
770
662
1333
1697

3
2
2
4
4

20 years
30 years
30 years
15 years
15 years

2041, 2061
2051
2051
2036, 2051 2066
2036, 2051 2066

3.5.4 Data analysis
Trend analysis has been conducted for seasonal and annual average streamflow and water
quality factor output, using the mean of the outputs of all 12 model runs. The method used
for analysis is the same as for Study 1, MK trend analysis, as detailed in Section 3.3. The
method used to characterise low flows also remains as conducted in Study 1; the analysis ofhigh flows, however, has been modified. The characterisation of extreme high flows for Study
2 takes account of discrete high flow events, to give a better understanding of how such
events are changing in magnitude and frequency, as well as duration. In the previous analysis,change in the number of days per year/season when streamflow exceeded the dataset 95 th
percentile was analysed. In this study, it is the number of events per year/season with at least
one day of flow above the 60-year 95th percentile, which are analysed, where a single event
could have several days above the 95th percentile. This method helps to mitigate the impactthat multi-day high flow events could have on the analysis, which could potentially give theimpression that high flow events are becoming more frequent, as opposed to longer induration and magnitude. By identifying individual events, the analysis of individual event
duration and frequency is also therefore possible. Individual events were classified as startingand ending when streamflow was equal to baseflow; this method was selected as it enabledmulti-peak events to be easily identified. Baseflow was calculated by the digital filter method
as defined by Sawicz et al. (2011) and presented in Equations 3.2 and 3.3 in Section 3.2.

68

Methodological overview

3.6 Study 3: Future water availability analysis
3.6.1 Study area and analysis framework
The third study uses the Conwy and Tywi only, in the analysis of the impact of modelledstreamflow changes from Study 2 on PWS and HEP. These two catchments were chosen due
to their contrasting physical characteristics (Section 3.2, Table 3.1), especially in terms of
LULC, slope, and catchment area, enabling investigation of the impact of these differences oncatchment water resources. In addition, both catchments host several non-impoundment
run-of-river HEP schemes, suitable for the analysis undertaken in this study, sixteen in the
Conwy and nine in the Tywi (NRW, 2019c). Furthermore, the Tywi also contains the single
largest river-based PWS abstraction location found in any of the five catchments. Indeed, it
supplies the largest water treatment works in all of DCWW’s network, producing water forover 400,000 consumers, therefore a highly important strategic asset (DCWW, 2019a).
Daily streamflow data for the analysis has been taken from Study 2, with the mean of all 12model runs dataset for each catchment being used. The mean dataset has been used forcomputational efficiency, especially considering the scenario analysis conducted for PWS(details below) and the number of HEP locations studied. While this method does remove theability to give a range of potential future outcomes, the results are still able to provide a
sufficient picture of the future situation and the challenges likely to be faced.
For both HEP and PWS, MK trend analysis of the studied characteristics (described below) was
undertaken for both a medium-term and long-term scenario. The medium-term scenario
spanned the years 2021-2054, a useful study period for both industries. For HEP, this is theregular life span of small-scale systems, such as those studied here, that have been installed
recently or are to be installed in the near future (Hatata et al., 2019; Killingtveit, 2019). For
PWS, the timeframe covers the period of focus of DCWW’s most recently published water
resource management plan (DCWW, 2019a), as well as their vision document to 2050
(DCWW, 2018). The long-term scenario (2021-2079) gives an indication of the far-futurechallenges faced by both industries and the potential future adaptation needs.

69

Methodological overview

3.6.2 Hydroelectric power
Calculation of the ability of future streamflows to power run-of-river HEP installations in the
Conwy and Tywi was undertaken manually, using new methods developed for this study.
Abstraction conditions for the total 25 schemes identified from a dataset of licensed
abstractions, provided by NRW (NRW, 2019c), were first set as per the general licensing
guidelines for HEP schemes, set out by NRW (NRW, 2020b). These guidelines were used due
to a lack details pertaining to the actual abstraction conditions placed on the individual
schemes studied. They are, however, a good proxy, as these are commonly applied, aside
from in situations where specific environmental, ecological or other concerns require more
stringent abstraction conditions (NRW, 2020b).
The guidelines set out several conditions relevant to the analysis of this study, these being
the hands-off-flow (HoF; streamflow volume below which abstraction is not permitted),
maximum daily abstraction volume (Amax), and percentage take (Qtake; proportion of flow
between HoF and Amax permitted for abstraction). Two parallel sets of guidelines exist,
depending on the gradient of the stream section between which water is abstracted, andreturned (NRW, 2020b). While HoF volume is the same for both (set at Q95; streamflow
volume exceeded 95% of the time), Amax and Qtake differ. Schemes with a depleted reachwhere the slope is less than 10% are classed as ‘Zone 2’ (Z2), while those with a depleted
reach slope greater than 10% are classed as ‘Zone 3’ (Z3). Z2 schemes have an Amax volume of
1.3 times mean annual streamflow, whereas for Z3 schemes, this is less, being set at the meanannual streamflow. Conversely, Qtake is lower in Z2 schemes than Z3, being 50% and 70%
respectively (NRW, 2020b). Due to the fact that individual characteristics of the schemesstudied were not known, the depleted reach slope has been estimated for each scheme,
based on the slope of the subbasin immediately downstream of the abstraction location.
While not a perfect approximation, this method does give a good indication of the situationof each scheme, allowing for the best decision to be made upon zone classification.
With the scheme classification and associated abstraction conditions of each scheme in mind,
the calculation of daily abstraction (𝐴𝑑𝑎𝑖𝑙𝑦 ) for each scheme was executed by Equations 3.31
and 3.32, develop for this study. These calculations operate under the assumption that each
HEP scheme abstracts the maximum amount of flow allowable:

70

Methodological overview

𝑄𝑠𝑢𝑟𝑝𝑙𝑢𝑠 = 𝑄 − 𝐻𝑜𝐹(Eq. 3.31)

where 𝑄𝑠𝑢𝑟𝑝𝑙𝑢𝑠 is the amount of water available for abstraction, 𝑄 is daily streamflow, and𝐻𝑜𝐹 represents the hands-off-flow; the result is used in the calculation of 𝐴𝑑𝑎𝑖𝑙𝑦 :𝑖𝑓 𝐴𝑑𝑎𝑖𝑙𝑦 < 𝐴𝑠𝑡𝑎𝑟𝑡0,
𝐴
𝐴𝑑𝑎𝑖𝑙𝑦 = 𝑄𝑠𝑢𝑟𝑝𝑙𝑢𝑠 × 𝑄𝑡𝑎𝑘𝑒 { 𝑚𝑎𝑥, 𝑖𝑓 𝐴𝑑𝑎𝑖𝑙𝑦 > 𝐴𝑚𝑎𝑥
𝐴𝑑𝑎𝑖𝑙𝑦, 𝑖𝑓 𝐴𝑠𝑡𝑎𝑟𝑡 < 𝐴𝑑𝑎𝑖𝑙𝑦 > 𝐴𝑚𝑎𝑥

(Eq. 3.32)

where 𝑄𝑡𝑎𝑘𝑒 is the proportion of flow available for abstraction as per the zone conditions,𝐴𝑠𝑡𝑎𝑟𝑡 refers to the minimum abstraction volume required to start, and for efficient operationof, the turbine, and 𝐴𝑚𝑎𝑥 represents the maximum permitted abstraction volume. It has been
assumed that an impulse turbine is in use at each site, as is common with small-scale HEP
schemes in upper catchment reaches, such as those analysed in this study (Lilienthal et al.,
2004; Cobb & Sharp, 2013; Židonis et al., 2015). Impulse turbines have largely high and stableefficiency after approximately 10% of designed flow is achieved (Paish, 2002; Novara &McNabola, 2018; Chitrakar et al., 2020), making them ideal for settings with variable daily
abstraction (Cobb & Sharp, 2013). For this reason, 𝐴𝑠𝑡𝑎𝑟𝑡 was set at 10% of 𝐴𝑚𝑎𝑥 for eachscheme, which has been assumed as the designed flow volume.
Four factors have been studied in the analysis of the impact of future streamflow changes on
HEP operations, these being, the number of days Astart is achieved, the number of days Amax isreached, mean daily abstraction on days Astart is achieved, and total abstraction. These factors
have been calculated annually and seasonally for each scheme. The mean of all combined Z2
and Z3 schemes in each catchment has then been calculated separately, with MK analysis
conducted on these mean datasets, allowing for a comparison of the response both between
catchments, as well as between scheme classifications. The four factors chosen for analysis
were selected in order to provide as complete a picture as possible in terms of the potentialchange in future HEP scheme operation and power generation.

3.6.3 Public water supply
Assessment of the ability of future streamflows to satisfy PWS demand at the previously
referenced abstraction location in the Tywi has been undertaken using the Water Evaluation
And Planning (WEAP) system (Yates et al., 2005). This programme was chosen due to its ability

71

Methodological overview
to easily implement different scenarios and compare the results; this was important as theassessment of three future water demand scenarios was being undertaken. The three
scenarios studied are based on an increasing, static, and decreasing future PWS demand, withdemand data being derived from the 5-year PWS abstraction dataset provided by DCWW andused in Study 1. This data has been taken as a proxy for total water demand and leakage.
Future daily water demand under the increasing demand scenario was based on the linear
relationship established in Study 1 between daily temperature and daily total abstraction
from the abstraction location studied on the Teifi. This relationship has then been
extrapolated and applied to the mean daily temperature of the three pseudo-meteorologicalstations positioned within the Tywi for the SWAT modelling. The daily temperature
relationship was chosen as the predictor of future demand because of the difficulty thatwould be encountered if using the daily precipitation relationship, due to the presence ofdays without rainfall, which is likely to skew the data. This scenario represents a worst-casein terms of future demand, with no effort made to reduce demand or combat leakage. The
static scenario represents a control dataset and presumes abstraction rates stay constant atpresent levels. Present levels were established by calculating mean demand on each day of
the year from the 5-year dataset provided by DCWW. This year of mean daily demand wasthen repeated yearly for the full period of study. Finally, the decreasing demand scenario isbased on a projection by DCWW for a 20% decline in total water demand from the Tywi
catchment by the end of the study period, due to a decline in domestic demand and reductionof network leakage (DCWW, 2019a). The decreased demand scenario starts with the samebase year as is used and repeated in the static scenario, with the 20% decline being appliedlinearly across the full 60-year study period and represents an optimistic scenario of
substantial demand reduction and therefore abstraction requirement. The three scenarios
chosen represent a wide range of future demand levels, enabling a broad assessment of
future climate change impacts on abstraction capability.
The daily ability of future streamflow to satisfy the volume of demand set out in the three
scenarios, has been assessed based on the abstraction license conditions under which theabstraction operates. The main condition in the license is a compensatory HoF, set at 681.91
million litres per day (SWWRA, 1965); this volume was incorporated into WEAP and total daily
unmet demand (𝐷𝑢𝑛𝑚𝑒𝑡 ) was then calculated for each scenario, using Equation 3.33:
72

Methodological overview
𝐷𝑢𝑛𝑚𝑒𝑡 = 𝑄 − 𝐻𝑜𝐹 − 𝐷(Eq. 3.33)

where 𝐷 is daily total water demand; and 𝑄 and 𝐻𝑜𝐹 are defined as Equation 3.31. At timesof low flow, the abstraction licence allows for releases to be made from Llyn Brianne reservoir,in the upper reaches of the catchment, which can subsequently be abstracted downstream,
up to a given limit. Due to this mechanism, 𝐷𝑢𝑛𝑚𝑒𝑡 is therefore also assumed to be equal to
the total daily required reservoir release. MK trend analysis for PWS has been undertaken
seasonally and annually on three characterises of 𝐷𝑢𝑛𝑚𝑒𝑡 for each future demand scenario
and for the medium- and long-term. Total volume of unmet demand per season/year, numberof days per season/year when demand is unmet, and mean annual/seasonal unmet demand
have all been calculated. These three characteristics have been chosen as they provide a good
overview of the changes and challenges faced in the future supply of water, in the catchment.
They indicate, amongst others, the potential future volumes of water required to be released
from Llyn Brianne, the frequency with which release may be required, and the mean volumeof release per day when compensation of flows is needed. All of these have important
implications for water resource and reservoir management.

73

CHAPTER 4: STUDY I
Influence of historical climate patterns on streamflow andwater demand in Wales, UK
R.J.H. Dallison, S.D. Patil and A.P. Williams
School of Natural Sciences, Bangor University

This chapter has been published in full, in Water, 2020, 12(6): 1684.
DOI: 10.3390/w12061684.

Author contributions
Richard Dallison: conceptualisation, methodology, formal analysis, investigation, data
curation, writing – original draft, writing – review & editing, visualisation
Sopan Patil: conceptualisation, writing – review & editing, supervision
Prysor Williams: writing – review & editing, supervision74

Dallison, Patil and Williams. Study I

4.1 Abstract
Ensuring reliable drinking water supplies is anticipated to be a key future challenge facing
water service providers due to fluctuations in rainfall patterns and water demand caused byclimate change. This study investigates historical trends and relationships between
precipitation, air temperature and streamflow in five catchments in Wales, before correlatingthese with actual total abstraction data provided by the water company, to give insight into
the supply-demand balance. Changes in seasonal and annual averages, as well as extreme
events, are assessed for a 34-year period (1982-2015) and a breakpoint analysis is performed
to better understand how climate has already changed and what this might mean for the
future of water supply. Results show a north-south divide in changes in extreme temperatureand streamflow; a strong warming trend in autumn average temperatures across Wales (Sen’sslope range: 0.38-0.41, p <0.05), but little change in precipitation. Abstraction, as a proxy foroverall water demand, is shown to be positively correlated to temperature (Spearman’s ρvalue range: 0.094-0.403, p <0.01; Pearson’s r value range 0.073-0.369, p <0.01) in four of fivecatchments. Our study provides new insight into the relationship between abstraction volume
and hydroclimatic factors and highlights the need for catchment-scale water resource
planning that accounts for hydroclimatic variations over small spatial distances, as thesenuances can be vital.
Keywords: Abstraction; Breakpoint analysis; Climate change; Hydroclimatic change; Trend
analysis; Water demand75Dallison, Patil and Williams. Study I

4.2 Introduction
Water service providers (WSPs) face a vast array of challenges and uncertainties whenplanning their future operations and services. Brown et al. (2010) compiled a list of 94 priority
research questions for the UK water sector, in which the impact of climate change on water
quantity was ranked as the second most important question. Their work also highlighted theneed to better understand the drivers of water demand, both domestic and commercial, inorder to improve future demand forecasting. Previous work on the characterisation ofhydroclimatic trends in the UK suggests that precipitation and streamflows have become
more seasonal, a pattern that is expected to continue (Whitehead et al., 2009b; Suggitt et al.,2015; Garner et al., 2017). A study by Christiersen et al. (2012) showed, using UK Climate
Predictions 2009 (UKCP09) data, that by the late 2020s, increases in winter precipitation levels
are likely to be more prominent in northern and western parts of the UK, while decreases insummer flows will be seen more generally across the whole country. Similarly, a hydrologicalmodelling study by Prudhomme et al. (2012) showed that summer precipitation andstreamflows will decrease across the UK by varying amounts for the period 2040-2069;
whereas future winter precipitation and streamflows showed an upward trend, especially for
Wales. Extreme precipitation events are also projected to become more seasonal in the UK,with longer duration and more intense rainfall events in winter becoming more common(Suggitt et al., 2015). Mayes (2000) suggested that these anticipated changes will not be
uniform across the UK, and current rainfall gradients are likely to be accentuated, i.e., thesouth getting drier in summer and the north getting wetter in winter.
Regional-scale understanding of water resource provision in the UK is particularly importantbecause water supply is under the control of individual water companies that serve separate
regions of varying sizes, populations, and physical characteristics. For instance, in south-east
England, which is already a water stressed area, studies on future hydroclimatic trends
suggest that summer streamflow levels will continue longer into autumn, with overallsummer flow levels declining also. Furthermore, winter streamflows will increase andcontinue longer into spring, leading to accentuated seasonality in terms of season longevity
and flow volumes (Diaz-Nieto & Wilby, 2005; Wilby & Harris, 2006; Wilby et al., 2006; Clokeet al., 2010; Arnell, 2011). However, an increase in winter precipitation will do little to combat
summer shortages if no further storage capacity is developed soon (Whitehead et al., 2013).

76

Dallison, Patil and Williams. Study I
Borgomeo et al. (2014) suggested that, due to the combined effect of climate change andsignificant predicted population growth, the London water supply zone urgently required
both supply and demand-side interventions if the current standard of water provision is tocontinue. In Scotland, although winter precipitation is predicted to increase in the future, alower percentage of it will fall as snow (Capell et al., 2013, 2014). This will make catchments
more responsive to winter precipitation and increase the pressure on water managers to deal
with larger discharge events (Baggaley et al., 2009). For summer precipitation andstreamflows, Blenkinsop & Fowler (2007) noted that Scotland has a limited amount ofgroundwater storage capacity, which heightens the drought risk from any reduction in nonwinter precipitation. In Wales, studies suggest that winter and summer season characteristics,
e.g. wet winters and dry summers, will be exacerbated, especially in winter (Dixon et al., 2006;
Fowler & Wilby, 2010; Thompson et al., 2017).
In this study, we use Wales as a case study region, a country often viewed as abundant inwater resources, receiving some of the highest average annual rainfall totals in the UK (Met
Office, 2020), but which in reality has zones of water deficit (DCWW, 2016a). Wales is also
important due to its role as an exporter of water to major metropolitan areas in England. Dŵr
Cymru Welsh Water (DCWW), the major WSP for Wales, has over 20 bulk water trades, thelargest of which supplies 360 million litres per year to Severn Trent Water for distribution
around Birmingham (Warren & Holman, 2012; DCWW, 2016a).
Past studies on water resources in Wales have predominantly been conducted either as partof UK-wide research (Prudhomme et al., 2003; Whitehead et al., 2009b; Fowler & Wilby, 2010;
Hannaford, 2015; Suggitt et al., 2015; Watts et al., 2015a; Burt et al., 2016), or with a focus
on the combined England and Wales region (Arnell, 2011; Henriques et al., 2015). Whenfocusing specifically on the area covering Wales, Fowler & Wilby (2010) projected a muchlarger magnitude of increase in winter flows from the 1960-1990 average to 30-year averages
centred on 2025, 2055 and 2085, than the corresponding decreases in summer precipitation.
Dixon et al. (2006) showed a significant upward trend across 56 Welsh and West Midlands
catchments between 1962-2001 for winter high flow values, but no significant changes in themean annual values. Conflictingly, Macdonald et al. (2010), demonstrated that during theperiod 1973-2002, there was no significant change in the seasonality of rainfall across 30
catchments in Wales, which they proved to have a significant link to streamflows; however,77Dallison, Patil and Williams. Study I
they did show that the frequency of occurrence of extreme precipitation events in Wales had
increased during the study period. These contrasting results highlight the need for carefulconsideration when selecting study period length and timeframe due to the potential impact
on trend analysis results and projections.
Up until recently, supply-side measures to tackle water scarcity and to manage water
resources have traditionally been the main path towards a reliable sustainable future water
network. However, it has increasingly been recognised over the past decade that demandside interventions should also play a role as an adaptation measure (NIC, 2018). For this to bea viable option, further work is needed to understand the relationship between prevailing andantecedent weather conditions, and demand for water in the UK. Several studies have lookedat the general interplay between the two in the UK (Herrington, 1996; Downing et al., 2003;
Butler & Memon, 2006; Browne et al., 2013), and abroad (Gutzler & Nims, 2005; Balling &Gober, 2007; Chang et al., 2014). This interplay has been investigated since at least as far backas the 1990s, with Herrington (1996) stating at the time, that up to 40% of total consumption
in summer can be due to garden watering, which is obviously highly affected by the prevailingweather conditions. Goodchild (2003) used summer daily domestic water demand data (55%of UK piped water supply at the time) from 41 domestic properties and daily meteorological
data to develop a demand prediction model. The model included ten weather variables toaccount for current and antecedent conditions; evapotranspiration, days since rain, andtemperature were all important functions. This modelling work projected a 2.1% increase inaverage summer 7-day household demand by the 2020s. More recently, Parker & Wilby(2013) reviewed domestic water demand in the UK and noted the lack of studies on weather
and climate. It is also important to look not only at domestic demand, but also industrial,
agricultural and non-revenue water use (e.g. leakage), as they all influence the long-termsustainability of water supply.
In this study, we look at the implications of past trends in hydroclimatic data on two of the
problems identified by Brown et al. (2010): (1) impact of climate variations on water
availability and (2) understanding the factors affecting water demand. The first problem has
been addressed by assessing trends in seasonal and annual average climate and streamflow
data as well extreme event frequency and magnitude, using Mann-Kendall trend analysis andbreakpoint analysis. We have addressed the second problem by investigating the historical78

Dallison, Patil and Williams. Study I
links between hydroclimatic factors and total water demand, using actual abstraction data
provided by DCWW as a proxy for demand. To our knowledge, this is one of the first studies
conducted independently of a WSP to use actual abstraction data provided by a WSP in thismanner in the United Kingdom. The reliance on Wales for water supply in other regions,combined with a potentially inaccurate assumption of national water abundance in Wales,
makes the region a crucial area of study in terms of water management and water supply
availability. Our research has therefore been undertaken in order to provide information for
future water resource planning and policy decisions, as well as future research. It is hopedthat this will be achieved by providing evidence of the long-term trends and links between
prevailing weather and flow conditions, as well as total demand for water in Wales.

4.3 Data and methods
4.3.1 Study catchments
Our study focusses on the rivers Clwyd and Conwy in the north, the Dyfi in the west, and theTeifi and Tywi in the south of Wales (Figure 4.1). We selected these catchments as a result ofthem being among the largest systems within the region, encompassing a range of land
use/land cover (LULC), and exhibiting a variety of different catchment characteristics.
Furthermore, the catchments are also mostly encompassed within DCWW’s water supply
zone (Figure 4.1), and all have multiple surface water abstraction locations licensed to DCWW
for use for public water supply. Brief catchments descriptions have been provided below andkey comparative details about each are given in Table 4.11. Figures for LULC in the catchments
were calculated from 2012 CORINE Land Cover data (Copernicus Land Monitoring Data), while
the catchment size, longest stream length, elevation and slope values were calculated using
a 5 m digital elevation model provided by Ordnance Survey.

1Stated catchment details/characteristics, such as land cover proportions and catchment area, differ for thisstudy from those shown in Chapters 3, 5 and 6 due to variations in delineated catchment area for this study.
Catchment areas defined in this study have been delineated manually in ArcMap using the Hydrology toolset of
the Spatial Analyst toolbox; other chapters use the area delineated during catchment setup for SWAT modelling.

79

Dallison, Patil and Williams. Study I

Figure 4.1. (A) Catchments; streams (formation threshold of 1 km2); stream order (derived by Strahler method);and gauging station locations. (B) Catchment elevation. (C) Catchment land use/land cover derived from CORINELand Cover data.

80Dallison, Patil and Williams. Study I
Table 4.1. Key study catchments details. Catchment area, main channel length, and slope data derived from
Ordnance Survey provided 5 m digital elevation model; land use/land cover data derived from 2012 CORINELand Cover data.

River

Catchment
area (km2)Mainchannellength(km)

Meancatchment
slope(degrees)

Catchment land use/land cover (%)
Urban

Agricultural

ForestScrub*

Wetland

Clwyd
803
35
7.1
3.7
80.3
7.2
8.8
0.0
Conwy
564
43
10.7
1.6
32.3
13.2
40.7
7.9
Dyfi
676
42
14.2
0.4
30.6
20.1
43.2
5.1
Teifi
1011
80
6.6
0.9
83.1
5.5
9.5
1.1
Tywi
1363
109
9.2
0.7
64.6
16.0
17.1
1.3
* Scrub-designated land includes land cover such as natural grasslands, transitional woodland-shrub, moors,and heathlands.

In terms of LULC, the Clwyd, Teifi, and Tywi are dominated by agricultural land use (Figure4.1); while the largest proportion of LULC in Conwy and Dyfi is scrubland (mainly in the formof moors and heathland in Conwy, and natural grasslands in Dyfi). Agricultural land in all study
catchments is mostly pastureland, however, Clwyd does have a larger proportion of its total
LULC as arable land (10.4%). Forested land in all catchments is mainly coniferous, except inConwy where there is an equal proportion of coniferous and mixed forest (5.1% LULC each)and 3% broadleaf forest. The bedrock geology is predominantly mudstone, siltstone, andsandstone in all five catchments, ranging from about 60% in Conwy and Clwyd, 73% in Tywi,
and over 85% in Dyfi and Teifi. The catchments range in area covered from 1363 km 2 at the
Tywi to less than half of that for the Conwy (564 km2). The steepest catchment is the Dyfi,
with an average slope of 14.2 degrees, being largely in the south of the Snowdonia region,
while the Teifi in the south is the least steep catchment, with an average slope of 6.6 degrees.

4.3.2 Data
Hydrological and meteorological data for all five study catchments was obtained for the
longest complete period possible for all datasets, that being the 34-year period from 1st
October 1981 to 30th September 2015 (except flow data for the Dyfi, which was only available
to 5th May 2014). Daily precipitation and air temperature data were obtained from the Centre
for Ecology and Hydrology’s (CEH) Climate, Hydrology and Ecology research Support System(CHESS) dataset. The mean daily value across all the 1 km grids contained within eachcatchment was calculated for both precipitation and temperature, giving a daily average value

81

Dallison, Patil and Williams. Study I
for each variable across each catchment. Daily streamflow data for the catchments was
obtained from CEH’s National River Flow Archive dataset; gauging station locations are shownin Figure 4.1.
Daily water abstraction volumes were obtained from DCWW, which is the main water supply
company for this region. However, these data were only available for a 5-year period from 1st
January 2012 to 30th September 2016. Therefore, comparison of water abstraction data withhydroclimatic data could only be completed for overlapping dates between the datasets, 1 st
January 2012 to 30th September 2015. The daily water abstraction data was provided for 22
abstraction locations within the five study catchments and has been used as a proxy foroverall demand across the network for all consumers, as well as leakage.

4.3.3 Trend analysis
We selected the nonparametric Mann-Kendall (MK) test (Mann, 1945; Kendall, 1975) todetect any consistent trends in the hydroclimatic data over time. The decision to use a
nonparametric test was taken due to the nature of both climate and hydrology data generally
being non-normally distributed and displaying seasonality, which goes against the assumptionof constant distribution (Kundzewics & Robson, 2004). Moreover, hydrology data often
displays auto-correlation, making it unsuitable for parametric testing (Hamed & Rao, 1998).
The MK test maintains the sequential order of the data and calculates Kendall’s tau (ameasure of association between two samples) between each value and all values proceedingit, to test for a monotonic increase or decrease relationship in the data over time (Helsel &Hirsch, 2002). MK has also been successfully applied to similar hydroclimatic data in various
locations globally (Murphy et al., 2013; Basarin et al., 2016; Mwangi et al., 2016; Hajani et al.,2017) so was deemed suitable for use in this study. Details on the specific equations relating
to the MK test can be found in Jaiswal et al. (2015).
Long-term average trends in the datasets were examined by analysing seasonal and annual
averages for each catchment; where winter is December to February; spring is March to May;
summer is June to August; autumn is September to November; and where, for example, the1982 hydrological year runs from 1st October 1981 to 30th September 1982. For theabstraction data, we have taken the sum of abstractions in each catchment, with trend
analysis being performed on this value, in order that the results are comparable to the82Dallison, Patil and Williams. Study I
hydroclimatic factors analysed. We also examined trends in the frequency and magnitude ofextreme events relating to hydroclimatic factors. This was achieved firstly by analysis oftrends in maximum and minimum temperatures; maximum 1-day precipitation total andcumulative rainfall totals; and maximum and minimum average 1-day streamflow volumes.
Second, we undertook an analysis of “events over threshold”, in order to establish whether
there are generally more or less extreme weather events per year/season over the study
period. This was completed by taking the 5th and 95th percentile values of a whole dataset(seasonally and annually) and analysing the number of times in each year and season thatthat value is surpassed (95th percentile) or not reached (5th percentile) for temperature andstreamflow, and surpassing the 95th percentile only for precipitation. The MK test was appliedwith Sen’s slope estimator (Sen, 1968) in order to estimate the size and direction of trends;
Hamed and Rao’s method of auto-correlation correction was also applied in order to remove
any apparent trend which the data exhibits with itself over time (Hamed & Rao, 1998).
In order to detect any sudden changes in the hydroclimatic data, we undertook a breakpoint
analysis on the seasonal/annual average and extreme event data. This analysis was completedusing two well-established methods, the Standard Normal Homogeneity Test (SNHT) method(Alexandersson, 1986; Alexandersson & Moberg, 1997) and the Pettitt method (Pettitt, 1979).
These two methods were used to ensure the widest possible detection of breaks in the data;with the SNHT being more accurate at detecting breaks at the start and end of time series,
and the Pettitt method being more reliable in the middle (Hawkins, 1977; Hänsel et al., 2016).
If annual values of the variable being tested are identically distributed and independent, bothmethods accept the null hypothesis. The alternative hypothesis (that there is a change pointin the series) will be accepted however, if a shift in the value of the mean has occurred (Hänsel
et al., 2016). Both methods are also location specific, so will identify the year at which themean changed within the time series. For further information on the equations that drive thetwo methods, we suggest Hänsel et al. (2016) and Jaiswal et al. (2015).

4.3.4 Correlation analysis of hydroclimatic and abstraction data
In order to investigate the relationship between the three hydroclimatic factors and water
abstraction we calculated both Pearson’s & Spearman’s rank correlation coefficients. The
analysis was performed for each catchment with daily and monthly average temperature,83

Dallison, Patil and Williams. Study I
precipitation and streamflow all being compared separately to daily and monthly total
abstraction volume. We calculated Pearson’s coefficient values (r) using Equation 4.1:𝑟=

∑𝑛𝑖=1(ℎ𝑐𝑖 − ̅̅̅ℎ𝑐)(𝑎𝑏𝑠𝑖 − ̅̅̅̅̅
𝑎𝑏𝑠)(Eq. 4.1)

2
2√(∑𝑛𝑖=1(ℎ𝑐𝑖 − ̅̅̅ℎ𝑐) ) (∑𝑛𝑖=1(𝑎𝑏𝑠𝑖 − ̅̅̅̅̅
𝑎𝑏𝑠) )

where ℎ𝑐𝑖 refers to the daily average hydroclimatic variables studied, 𝑎𝑏𝑠𝑖 refers to daily total
̅̅̅ & 𝑎𝑏𝑠̅̅̅̅̅ represent the mean of the entire respective datasets. The
abstraction volume, and ℎ𝑐
nonparametric Spearman’s correlation coefficient (ρ) was calculated using Equation 4.2, amodified version of Pearson’s correlation coefficient which calculates correlation between
ranks, as opposed to raw data:̅̅̅̅̅̅̅̅̅)∑𝑛𝑖=1(𝑅(ℎ𝑐𝑖 ) − ̅̅̅̅̅̅̅̅
𝑅(ℎ𝑐)) (𝑅(𝑎𝑏𝑠𝑖 ) − 𝑅(𝑎𝑏𝑠)

𝜌=

2

2

̅̅̅̅̅̅̅̅̅) )√(∑𝑛𝑖=1(𝑅(ℎ𝑐𝑖 ) − ̅̅̅̅̅̅̅̅
𝑅(ℎ𝑐)) ) (∑𝑛𝑖=1(𝑅(𝑎𝑏𝑠𝑖 ) − 𝑅(𝑎𝑏𝑠)(Eq. 4.2)

where 𝑅(ℎ𝑐𝑖 ) refers to the rank of the daily average hydroclimatic variables studied, 𝑅(𝑎𝑏𝑠𝑖 )refers to the rank of the daily total abstraction volume, and 𝑅(ℎ𝑐) & 𝑅(𝑎𝑏𝑠) represent themean rank of the entire respective datasets.

4.4 Results
Our results indicate a north-south divide when looking at extreme temperature andstreamflow changes. Additionally, a strong warming trend in average autumn temperatures
across Wales is observed, however little change in precipitation is seen. Abstraction, as aproxy for overall water demand, is strongly positively correlated to temperature in allcatchments and negatively with streamflow and precipitation in all catchments except the
Dyfi. Below we provide a detailed breakdown and explanation of these results.

4.4.1 Trend and breakpoint analysis
4.4.1.1 Precipitation
We found no significant trends during MK analysis or breakpoint analysis of annual andseasonal precipitation averages in any of the five catchments for the study period; but Sen’sslope indicator values do suggest a slight decrease in average spring precipitation across all84

Dallison, Patil and Williams. Study I
catchments. In addition, only two significant trends were present in terms of extreme
precipitation events; the first is a decrease in total cumulative spring precipitation in the
Clwyd catchment throughout the period (Tau = −0.184, p = 0.040; Sen’s Slope = −1.196). The
second is a decrease in the number of extreme wet autumn days in the Teifi catchment (Tau= −0.593, p = 0.003; Sen’s Slope = −0.138). Although both of these trends are mirrored in theother catchments, those trends are not statistically significant, highlighting the need to study
individual catchments to account for varying characteristics. Additionally, the Pettitt
breakpoint analysis showed a marked increase in winter cumulative precipitation in the
Conwy after 1989 (p = 0.040), the mean for the post 1989 period increasing by 13.4%
compared to the pre-1989 period.
4.4.1.2 Temperature
Unlike precipitation, air temperature data does display significant trends in Wales over thecourse of the study period. In all catchments, we observe a warming trend in average autumn
temperature (Figure 4.2); the rate of increase is marginally higher in the two north Wales
catchments compared to the two in the south of the country (Table 4.2).

Figure 4.2. Warming trend in average autumn temperatures displayed in all catchments. Linear trend for the
Dyfi catchment shown with red dashed line to exemplify the linear trend in all catchments.

85

Dallison, Patil and Williams. Study I
Table 4.2. Mann-Kendall trend analysis and Sen’s slope indicator results for average autumn air temperatures
trends (1981-2015) in each of the five study catchments. Values for Kendall’s Tau underlined are significant at p
<0.05, and those in bold are significant at p <0.01.

River

Kendall’s tau

Sen’s slope

Clwyd
Conwy
Dyfi
Teifi
Tywi

0.337
0.348
0.344
0.337
0.344

0.041
0.040
0.038
0.038
0.038

These findings correlate with both the Pettitt and SNHT breakpoint analysis in all catchments,
which shows a step increase in autumn temperatures in 1994, with the percentage change inpre- and post-1994 mean temperatures being larger in the north (Table 4.3). We found no
significant trends in the MK analysis of annual, winter, spring, or summer datasets, however
when looking at the breakpoint analysis, further changes are seen. In the two most northerly
catchments, winter temperatures show a break and increase under SNHT analysis in 1987 (p= 0.042 & 0.048 for the Clwyd and Conwy respectively). All catchments also show a breakpoint
increase in average spring temperatures in 1987 and annual average temperatures in 1988,
both under SNHT analysis.
Table 4.3. Pettitt and Standard Normal Homogeneity Test (SNHT) method breakpoint analysis statistics forshown stepped increase in annual average autumn temperatures since 1994 in all study catchments.

River

Pettitt p value

SNHT p value% change in 1994-2015 mean from 1982-1993 mean

Clwyd
Conwy
Dyfi
Teifi
Tywi

0.001
0.002
0.003
0.002
0.003

0.009
0.008
0.011
0.010
0.010

+12.2%+12.7%+11.8%+10.3%+10.6%

When looking at extreme temperature events, the three most southerly catchments, Dyfi,
Teifi and Tywi, display both a decrease in the number of hottest days annually (days above
the whole dataset 95th percentile; Figure 4.3), as well as a decrease in the number of coldest
summer days (days below the summer dataset 5th percentile; Figure 4.4); suggesting anarrowing of temperature ranges, especially in summer. The Conwy catchment in the northalso displays the latter trend of fewer of the coldest summer days (Table 4.4). This narrowingof temperature ranges has however, not been abrupt enough to cause a breakpoint in thedata, with no significant changes seen.

86

Dallison, Patil and Williams. Study I

Figure 4.3. Number of occurrences annually where daily average temperature is greater than the 95 th percentile
temperature value for the full 34-year dataset. Linear trend for the Teifi catchment shown with red dashed line
to exemplify the linear trend in all three catchments.

Figure 4.4. Number of occurrences during summer where daily average temperature is less than the 5 th
percentile temperature value for the full 34-year summer dataset. Linear trend for the Teifi catchment shownwith red dashed line to exemplify the linear trend in all three catchments.
Table 4.4. Statistically significant Mann-Kendall trend analysis and Sen’s slope indicator results for extreme
temperature events (1982-2015) for all catchments. No significant trends were found in the Clwyd catchment.
Values for Kendall’s Tau underlined are significant at p <0.05.

River

FactorKendall’s
tau

Sen’s
slope

Conwy

Number of summer days below temperature dataset 5th percentile
Number of days annually above temperature dataset 95th percentile
Number of summer days below temperature dataset 5th percentile
Number of days annually above temperature dataset 95th percentile
Number of summer days below temperature dataset 5th percentile
Number of days annually above temperature dataset 95th percentile
Number of summer days below temperature dataset 5th percentile−0.271−0.165−0.260−0.173−0.269−0.191−0.266−0.125−0.250−0.133−0.250−0.143−0.286−0.136

Dyfi
Teifi
Tywi

87

Dallison, Patil and Williams. Study I
4.4.1.3 Streamflow
We observe only one statistically significant trend in seasonal and annual average streamflow
data, this being in annual flows in the Teifi; here a significant increase in average annual flow
(Figure 4.5) is detected over the study period (Tau = 0.240; Sen’s slope = 0.204; p = 0.044).
The Teifi also shows the only observed breakpoint in the average flow data, that being a stepincrease in winter flow in 2013 (p = 0.019) the mean for the post 2013 period increasing by
63.0% compared to the pre-2013 period.

Figure 4.5. Annual average streamflows for study catchments, with linear trend lines also shown (dashed lines).
The only statistically significant trend, an increase in streamflow in the Teifi catchment, is highlighted in red.

We found various trends in terms of extreme events in streamflow in Wales (Table 4.5); once
again the three most southerly catchments show similar trends, in this instance, an increasein volume of the annual and summer 1-day minimum flow volume. The Conwy shows anincrease in winter minimum flow volume, along with an increase in the maximum summer
flow, while the Clwyd displays an increase in annual maximum flow volume.

88

Dallison, Patil and Williams. Study I
Table 4.5. Mann-Kendall trend analysis and Sen’s slope indicator results for statistically significant trends inextreme streamflow events (1982-2015). Values for Kendall’s Tau underlined are significant at p <0.05, and thosein bold are significant at p <0.01.

River

FactorClwyd

Annual 1-day maximum
Summer 1-day maximum
Winter 1-day minimum
Summer 1-day maximum
Annual 1-day maximum
Spring 1-day minimum
Summer 1-day minimum
Annual 1-day minimum
Winter 1-day maximum
Spring 1-day minimum
Summer 1-day minimum
Annual 1-day minimum
Autumn 1-day minimum
Summer 1-day minimum
Annual 1-day minimum

Conwy

Dyfi

Teifi

Tywi

Kendall’s tau

Sen’s slope

0.239
0.252
0.193
0.222
0.184
0.339
0.317
0.355
0.269
0.237
0.237
0.254
0.239
0.348
0.320

0.406
1.882
0.030
1.341
1.238
0.065
0.048
0.045
2.037
0.071
0.059
0.056
0.019
0.012
0.011

4.4.2 Correlation analysis of hydroclimatic and abstraction data
When looking at the daily actual abstraction data (1st January 2012 to 30th September 2015)
under both correlation coefficient tests, all catchments display a positive relationshipbetween air temperature and volume of water abstracted (Table 4.6), except for the Dyfi,
which shows the reverse trend. All catchments, except the Dyfi, also show a statisticallysignificant negative correlation between precipitation and volume of water abstracted, andconsequently a negative relationship between streamflow and abstraction volume (Table
4.6). In order to investigate the situation for Wales as a whole, we have also included in Table
4.6 the correlation results for total daily and monthly abstraction volume in all fivecatchments with average daily/monthly temperature, total daily/monthly rainfall volume,and total daily/monthly streamflow volume, for all five catchments combined. The
correlations seen in this dataset are consistent with those seen in most individual catchments.
In addition, all catchments also display a positive relationship between streamflow andprecipitation, and a negative one between streamflow and air temperature.

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Dallison, Patil and Williams. Study I
Table 4.6. Pearson’s and Spearman’s rank correlation coefficient results for daily and monthly abstraction datawith hydroclimatic factors (2012-2015). Statistics relating to a combined analysis of total daily/monthly
abstraction in all catchments with average daily/monthly temperature, total streamflow volume and total
precipitation volume across all catchments are also provided. Values for r and ρ underlined are significant at p
<0.05, and those in bold are significant at p <0.01.

River

Clwyd

Conwy

Dyfi

Teifi

Tywi

Combined

Hydroclimatic
variable
Temperature
Precipitation
Streamflow
Temperature
Precipitation
Streamflow
Temperature
Precipitation
Streamflow
Temperature
Precipitation
Streamflow
Temperature
Precipitation
Streamflow
Temperature
Precipitation
Streamflow

Pearson’s r+0.369−0.181−0.615+0.197−0.111−0.169−0.398−0.054+0.012+0.073−0.073−0.117+0.177−0.165−0.509+0.207−0.190−0.547

Daily
Spearman’s ρ+0.403−0.225−0.553+0.197−0.182−0.298−0.404−0.140+0.018+0.094−0.082−0.235+0.218−0.202−0.559+0.252−0.223−0.581

Pearson’s r+0.619−0.509−0.868+0.279−0.415−0.389−0.372-0.171+0.0550.094−0.147−0.148+0.485−0.768−0.942+0.504−0.755−0.925Monthly
Spearman’s ρ+0.665−0.485−0.808+0.248−0.381−0.429−0.250−0.250−0.014+0.125−0.256−0.374+0.482−0.724−0.961+0.489−0.729−0.943Figure 4.6 shows the aforementioned combined data at a daily time-step from all catchments
which has been normalised to each factor's maximum and minimum dataset value. Clear
relationships between the hydroclimatic factors and actual abstraction data are observed.
When looking at temperature, a positive correlation can be seen in the scatter plot, while thetime series plot clearly also shows a large amount of consistency between the averagetemperature and total water abstraction volume. Arguably the clearest trend seen in the
scatter plots is total streamflow volume and total actual abstraction volume, showing anegative correlation. This can also be seen in the time series plot that clearly shows drops inabstraction volume at times of largest flow volumes. Generally, the weakest correlations
shown in Table 4.6 relate to precipitation and actual abstraction, the same is true for thegraphs presented in Figure 4.6. Although peaks in precipitation do tend to coincide with adrop in abstraction, there is much more noise in the precipitation dataset, which makes these
relationships more difficult to pinpoint.

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Dallison, Patil and Williams. Study I

Figure 4.6. Normalised daily hydroclimatic data compared with normalised daily abstraction data for the period
1st January 2012 to 30th September 2015, for all study catchments combined. (A1, B1, C1) Paired comparison,with black dashed lines representing trend direction; (A2, B2, C2) time series comparison. Combined data isbased on an average across all catchments for temperature, while precipitation, streamflow and abstraction
volumes are all total volumes summed from all catchments.

The correlations and relationships seen in the daily data are made more evident when looking
at the combined normalised monthly data, as shown in Figure 4.7. Clear negative trends canbe seen between total abstraction volume with both total precipitation and total streamflow
volume, when looking at scatter and time series line graphs. Clear peaks in abstraction can beseen at times of lowest streamflow and precipitation levels, and vice versa. Temperature andtotal abstraction are shown to also be broadly in-line when looking at the time series plot,
with a clear positive trend shown when looking at the scatter data.

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Dallison, Patil and Williams. Study I

Figure 4.7. Normalised monthly hydroclimatic data compared with normalised monthly abstraction data for theperiod 1st January 2012 to 30th September 2015, for all study catchments combined. (A1, B1, C1) Paired
comparison, with black dashed lines representing trend direction; (A2, B2, C2) time series comparison.
Combined data is based on an average across all catchments for temperature, while precipitation, streamflow
and abstraction volumes are all total volumes summed from all catchments.

4.5 Discussion
4.5.1 Hydroclimatic trends
The results of the trend, breakpoint, and correlation analyses show a selection of spatiallyvarying changes across the five catchments over the 34-year study period. Wales has amaritime climate which is strongly influenced by the North Atlantic Oscillation (NAO), a major
cause of atmospheric circulation variability in the north Atlantic region (Dixon et al., 2006;92

Dallison, Patil and Williams. Study I
Beranová & Huth, 2007). This is important to consider when analysing changes that haveoccurred across all catchments. NAO displays year-to-year and longer-term variability,
therefore breakpoints in temperature, precipitation, and streamflow data that occur acrossall five catchments and likely to be rooted in changes in NAO. However, when explainingchanges that affect a subset of the catchments, it is likely that catchment characteristics interms of topography, LULC and surrounding landscapes have had an impact by interactingwith these large-scale meteorological processes (West et al., 2019). The breakpoint analysis
is a prime example of this interplay, a combination of NAO, climate change and other nonclimate/weather related changes at a catchment and at a national level are likely to have
impacted on the climatology breaks shown. No single factor accounts for all of breaks shown
across the catchments. While a change in NAO is likely to have been a key driver, other factors
such as LULC change and climate variations may well have had an impact on spreading the
breakpoints out over the range of approximately seven years shown (1987-1994).
When looking at both temperature and streamflows, a north-south divide in results can often
be seen. Taking the extreme events results for streamflows as an example, it can be seen thatin the two northerly catchments, the Clwyd and Conwy, the largest annual 1-day discharge
events have become larger over the study period. Meanwhile the two most southerly
catchments, the Teifi and Tywi, have seen the smallest 1-day flow values becoming larger, inparticular when looking at annual and summer flows. These results are consistent with thefindings of Dixon et al. (2006) who showed that in the period 1977-2001, summer minimum
flow volume values had increased significantly in south Wales catchments, but not thenorth—mainly due to the more mountainous terrain of north Wales (Figure 4.1).
Furthermore, increases in the lowest summer flow values were also seen in Osborn and
Hulme (2002), and were linked to increased light summer rainfall over the period. The Dyfi
catchment in mid-Wales shows a combination of these two patterns with maximum andminimum 1-day flows increasing in the summer and annually, showing a shift to generally
wetter summers over the period, again corresponding to the reasoning and results put
forward in Dixon et al. (2006) for mid-Wales. These results are also consistent with the meancatchment slope data shown earlier (Table 4.2) with the Dyfi and Conwy in particular being
the steepest catchments, and arguably the flashiest, of the five studied. This factor may wellcontribute to the increase in summer maximum flows observed over the study period.

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Dallison, Patil and Williams. Study I
It is interesting to note that the aforementioned changes in streamflows occur despite a lackof corresponding statistically significant change in annual or seasonal average precipitation,
an observation also made by Macdonald et al. (2010). This mismatch of significant trends
between precipitation volumes and streamflows is surprising, as the two factors arecorrelated significantly in all catchments, suggesting that flows in the catchments are highly
sensitive to small changes in precipitation. Similarly, the only statistically significant trend
seen in extreme precipitation events, a decreasing trend in cumulative spring rainfall in the
Clwyd, does not relate to a corresponding change in average or extreme spring streamflows
during the study period. Although not analysed in this study, it is also possible that changing
LULC during the study period could have had an effect on streamflows, independent ofprecipitation. Changes in agricultural land use in particular could have a large impact in the
study catchments, affecting processes such as water infiltration and runoff (Wheater & Evans,2009). Between the early 1980s and the late 2000s there was an increase in the amount ofagricultural land classified as permanent grassland and rough grazing, despite overall
agricultural land area remaining relatively stable (Welsh Government, 2019c). From the late
2000s to present, the total amount of agricultural land has grown to its highest level sinceWorld War II, with the vast majority of this being for grazing (Welsh Government, 2019c).
Furthermore, forest cover has been continually increased throughout the study period, withgreater planting taking place at the start of the study period and the rate steadily declining
throughout (Welsh Government, 2015a). Forested land also impacts on the connectivity ofriver flows and precipitation, with processes such as evapotranspiration and interception
storage diverting water from, or delaying water reaching, rivers (Wheater & Evans, 2009). This
could once again explain some of the mismatch between significant precipitation andstreamflow trends.
Changes in seasonal and annual averages, as well as extreme events are most observed intemperature, for the factors studied. Once again, a north-south divide can be seen in parts ofthese results, with the three southernmost catchments displaying a decrease in both theannual number of days that are hotter than the dataset 95th percentile, and the number ofsummer days that are colder than the dataset 5th percentile. These two changes suggest anarrowing of the temperature ranges in these catchments, with less extreme hot days
annually and less extreme cold days in the summer. These findings are in line with wider UK

94

Dallison, Patil and Williams. Study I
research such as Dessai & Hulme (2008) who showed, via the Central England Temperaturerecord, that annual average summer temperatures increased between 1960-2007 whencompared to a 1961-1990 mean. Nesbitt et al. (2016) also presented a trend for summer days
becoming warmer over the period 1954 to 2012 for south-east and south-central UK, as did
Luterbacher et al. (2004) for the period 1977 to 2003. Annual NAO index has been largely
negative on average throughout the study period, especially so in the latter two thirds (Figure4.8). Negative NAO has been linked to colder maximum temperatures (Beranová & Huth,
2007), while climate change has been causing increasing summer temperatures on average.
These two factors combined could be contributing to the narrowing temperature range
observed, with NAO bringing down the maximum, and climate change bringing up theminimum. Furthermore, in all catchments, an increase in average autumn temperatures
across the study period has been observed, with this being slightly more pronounced in thetwo north Wales catchments. All of the observed trends also fit with general UK observationsof a greater degree of warming in the south of the country than the north, and theexacerbation of the temperature gradient between them. Furthermore, the north-south
divide also fits when considering the surrounding geography of the catchments, themountainous Snowdonia region lies just to the south of the two most northerly catchments,
heavily influencing the climate here and causing local variation in the weather that is broughtin from the Atlantic (Dixon et al., 2006).

Figure 4.8. Annual North Atlantic Oscillation (NAO) index 1825-2019 relative to the 1951-1980 average index
value (0 on this plot). Our study period has been highlighted in yellow; updated and adapted from Osborn (2011).

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Dallison, Patil and Williams. Study I

4.5.2 Weather and demand for water
When looking at the correlations between daily and monthly total abstraction data andhydroclimatic factors, it is clear that both average temperature and precipitation volume playa crucial role in influencing the total amount of water abstracted; volumes abstracted
increasing in higher temperatures and decreasing on wetter days. Given that the data takes
account for total abstraction volume, these relationships are not surprising, as it is likely thaton hotter days there will be both more domestic demand (more showers, car washing, garden
watering etc.), as well as higher demand from agriculture, in particular for water for livestockin the catchments studied. The opposite is true for wetter days, which reduce the agricultural
demand from public water supplies and reduce domestic demand, especially for water use
on external areas of a property. These overall relationships do fit with other work that has
sought to quantify the relationship between climate variables and domestic demand, both in
the UK and abroad. Slavíková et al. (2013) showed that in the Czech Republic, air temperature
relative to the season average, accounted for most variability in residential water
consumption. Similarly in the UK, Goodchild (2003) showed that domestic water demand in
41 houses in an Essex case study correlated with an R2 value of 0.44 with days when maximum
temperatures were over 25 °C. Additionally, total daily sunshine hours were shown to have astronger correlation with demand, having an R2 value of 0.53 (Goodchild, 2003). Work by
Xenochristou et al. (2020) also found that sunshine hours and air temperature were the mostinfluential weather variables on domestic demand, along with humidity.
However, using total abstraction volumes as a proxy for demand does present somechallenges. For example, factors such as the usage of water internally, within drinking water
treatment plants, as well as network water leakage, may mask or alter observed trends in thedataset. In addition, it is difficult to apportion the total abstraction volume to different usergroups, such as domestic, industrial, and agricultural on a daily basis. Furthermore, different
user groups may have different relationships with weather conditions, for example Massoud
et al. (2018) showed for California’s Central Valley, that while agricultural demand increasedin drier years, precipitation volume had little impact on urban water demand. Additionally,
we recognise that the comparison period between the hydroclimatic factors and abstraction
data is relatively short in this study, at four years. This does not give the opportunity toinvestigate relationships such as the effect of prolonged drought on water use, or other96

Dallison, Patil and Williams. Study I
longer-term patterns. Nevertheless, the overall abstraction data does give a baseline
relationship to work with when considering the impact of future climate change on total
water demand.

4.5.3 Study implications
Our study has shown that the climate of Wales has changed since the early 1980s, and that
this will have contributed to both the supply and demand of water in the region. On the basisof the results found, it is clear that these changes have been more keenly felt in terms of
average air temperatures than precipitation volumes; this applies to both annual and seasonal
averages as well as extreme events. When looking at the impact that these climatic changes
have had on streamflows it can be seen that seasonal and annual average flows haveremained largely unchanged, instead it is extreme flow events that have been more greatly
affected. These changes occur with a north-south divide, with the largest annual flow eventsgetting larger in the north, and the south becoming less dry in the summer when looking at
the lowest flows. These changes could have important impacts if they are continued into the
future, with implications not only for water supply, but also in terms of water resource
management, to prevent flooding and other related natural disasters. Furthermore, large
industrial users as well as applications such as hydroelectric developments could be impacted
by changes in river regime. In particular the viability of some small-scale hydroelectric
installations could be called in to question in some areas due to changing streamflow
characteristics and flow duration curves, further emphasising the importance of a solid
understanding of the relationship between, and emerging trends in, hydroclimatic factors.
Climate change induced alterations in the future timing, quantity, and quality of water
available for supply, as well as policy relating to adaptation and management methods tocope with these predicted changes, needs to be further researched for Wales. In particular,hydrological modelling studies comparing current baseline streamflow (such as that
presented in this study) to future streamflow under various climate change scenarios, could
prove to be particularly useful. This work is crucial to better inform future water supplydemand dynamic assessments, water resource management, and adaptation planning. Thisis especially true if projected increases in the reoccurrence, duration, and intensity of extreme
events under future climate change, as suggested in other research (Slingo, 2014; King &97

Dallison, Patil and Williams. Study I
Karoly, 2017; Betts et al., 2018; Met Office, 2019; Harkness et al., 2020), are correct. Thissuggested research would also go some way to addressing some of the aforementioned
priority research questions laid out in Brown et al. (2010); but must however also keep inmind the cost and practicality of adaptation measures, in order to ensure both a continued
unbroken water supply service, and affordable water for all. More broadly, this research has
shown the need to research and understand historical trends and future projections ofhydroclimatic factors at local, catchment levels. This is clear when looking at the presenteddifferences in observed trends seen over small spatial distances, due to changing landcharacteristics, and it is these nuances that are vital to incorporate into future planning for
any industry that relies on surface water abstraction.

4.6 Conclusions
This research has highlighted the potential for water scarcity problems even in a relatively
water-rich region such as Wales. For example, with observed trends such as warmer average
autumn temperatures providing for potentially greater water use in the season, the pressureson summer water supply could in the future extend further into the autumn. Althoughpotentially increased demand could be countered by a trend of the largest discharge events
becoming larger in the north of Wales, and summers becoming less dry in the south of Wales,
any increase in flow is of little use if the capacity to store this additional water is not sufficient
to make use of it.
Finally, we suggest that further research should focus on how future climate change will affectthe relationship between weather factors, streamflow, and water demand, both in Wales andglobally. For example, research concerning trigger temperatures for significant increases inwater use, or the effect of long-term higher than averages temperatures on water demand,
would aid understanding of the finer detail of the dynamic between hydroclimatic factors andtotal water abstracted. We also hope that this study will set a frame onto which future climatechange research focusing on surface waters, and the future provision of water services canbe built; being one of the first steps in securing the long-term sustainability of water supply
services in the region and further afield.

98Dallison, Patil and Williams. Study I
Acknowledgements: This study was conducted as part of the Dŵr Uisce project, which aims
at improving the long-term sustainability of water supply, treatment and end-use in Irelandand Wales. The project has been supported by the European Regional Development Fund(ERDF) through the Interreg Ireland-Wales Co-operation Programme 2014-2020. The work
has been used as a case study for Dŵr Uisce Work Package 7 “Climate Change”, and forms
part of a study assessing the impact of climate change on water resource exploitation in
Wales. The authors would like to thank Dŵr Cymru Welsh Water for the provision of actual
abstraction volume data for the catchments studied. We also thank two anonymous
reviewers for their comments and helpful suggestions, which have improved the paper.

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CHAPTER 5: STUDY II
Modelling the impact of future climate change on streamflow
and water quality in Wales, UK
R.J.H. Dallison, A.P. Williams, I.M. Harris and S.D. Patil
School of Natural Sciences, Bangor University

This chapter has been submitted in full for publication in the Hydrological Sciences Journal
and was undergoing peer-review at the time of submission of this thesis.

Author contributions
Richard Dallison: conceptualisation, methodology, software, validation, formal analysis,
investigation, data curation, writing – original draft, writing – review & editing, visualisation
Prysor Williams: writing – review & editing, supervision
Ian Harris: resources, writing – review & editing
Sopan Patil: conceptualisation, software, writing – review & editing, supervision100Dallison, Williams, Harris and Patil. Study II

5.1 Abstract
Climate change is likely to have a major impact on future hydrological regimes, impacting
numerous sectors reliant on surface waters. We use the Soil and Water Assessment Tool
(SWAT) to model future (2021-2080) streamflow and water quality variables (nitrogen,phosphorus, suspended sediment, and dissolved oxygen), in five catchments in Wales, undera worst-case scenario of future greenhouse gas concentrations (RCP8.5). Results show adecline in annual average flows (-4% to -13%) but larger changes seasonally (spring, up to 41%
increase; autumn, up to 52% reduction). The magnitude and frequency of high flow events
increases in spring (magnitude: Sen’s slope range 0.165-0.589, p <0.01), with more low flows
in autumn (Sen’s slope range 0.064-0.090, p <0.01). Water quality declines, with higher
nitrogen, phosphorus, and sediment concentrations and lower dissolved oxygen levels. The
findings have economic and environmental implications for abstractors, as water resources
will become more unreliable, seasonal, and polluted.

Keywords: Hydrological modelling; Particle swarm optimisation; Trend analysis; UKCP18;
Water resource management; Water supply

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Dallison, Williams, Harris and Patil. Study II

5.2 Introduction
Globally, river catchments are likely to face significant changes due to a variety of factors,
including climate change, land use/land cover change, increasing urbanisation, increased
population, and greater demand for water (Rockström et al., 2009; Gleick & Palaniappan,
2010; Heathwaite, 2010; Cosgrove & Loucks, 2015; Bijl et al., 2016). The Intergovernmental
Panel on Climate Change (IPCC), in their fifth assessment report (AR5), project a rise in both
surface temperatures and the number and intensity of extreme precipitation events under all
future greenhouse gas emissions scenarios throughout the 21st century (IPCC, 2014b). For theUK, a small increase is projected in average precipitation by 2100, compared to the 1986-2005
baseline; average temperatures are also expected to increase (IPCC, 2014b). Furthermore,
the UK Climate Projections 2018 (UKCP18) Science Overview Report suggests a move towards
warmer, wetter winters, and hotter, drier summers, as well as an increase in extreme weather
events (Lowe et al., 2018). These changes will have implications for catchments in terms ofannual and seasonal average flows, high and low flows, and water quality.
When studying past trends, various studies have shown that alterations in streamflow
patterns have already occurred due to climate change, as well as other factors. Hannaford(2015), conducted a review of climate-induced changes specifically on UK streamflows, using
the 130 benchmark network gauging stations with near-natural flow regimes. The results
show an increase in annual outflows, albeit only statistically significantly in Scotland, withgreater seasonal variability. Winter and autumn average flows are shown to have increased
between 1961-2010 across the UK, with spring and summer flows increasing in Scotland andWales, and decreasing in England (Hannaford, 2015). High flows are also shown to haveincreased in Scotland and Wales significantly (p <0.05), rising 22.4% and 26.5% respectively
over the 50-year period. These results are consistent with studies such as Harrigan et al.
(2018), which shows similar trends in UK catchments over the period 1965-2014; for example
a UK-wide increase in annual and winter mean flows. In contrast to Hannaford (2015)
however, large decreases in spring flow were seen across Britain (Harrigan et al., 2018).
Additionally, Dixon et al. (2006) found for catchments in western Britain, that there was anincrease in annual, autumn and winter maximum flows; but little change in annual average

102

Dallison, Williams, Harris and Patil. Study IIflows. Furthermore, Dallison et al. (2020)2 analysed 34 years of historical streamflow andclimate data for five catchments in Wales and also found little change in annual average flows.
However, low flow volumes were shown to have increased in the summer in the three mostsoutherly catchments, while annual maximum flow increased in the northerly catchments.
Several studies have analysed future seasonal and annual average streamflows for the UK,
mostly using UK Climate Projections data (UKCIP02, UKCP09 & UKCP18). While little work has
been published using the new UKCP18 data, Kay et al. (2020) compared it with the UKCP09
projections across ten catchments in the UK and found the estimates of change under bothmodels were similar, although uncertainty was higher in the most recent version. A study
using UKCP09 data by Watts et al. (2015a) suggests that although little change is seen in
annual average rainfall, seasonal changes are more pronounced. Precipitation increases in
winter are likely to be greatest along the western coast of the UK, while the largest summer
decreases in precipitation are expected in southern England (Watts et al., 2015a).
Investigating the impact of these changes on river flows in the short and long-term
respectively, Christiersen et al. (2012) and Prudhomme et al. (2012) both projected annual
averages flows to remain relatively stable. Both studies also project a reduction in springflows, with small increase in the winter. However, the most consistent trend seen is adecrease in summer flows across the UK, especially in the north and west (Christierson et al.,2012; Prudhomme et al., 2012). These results are consistent with the findings of Chun et al.
(2009) and Charlton & Arnell (2014) which both state that, despite variations due to different
catchment characteristics, UK annual average streamflows will decline, with the magnitudeof change less than that seen seasonally.
For extreme events in the UK, such as large discharge events/flooding and long-term drought,
it is likely that climate change with alter their frequency, duration, and severity (IPCC, 2014b;
Watts et al., 2015a; Sayers et al., 2016; Miller & Hutchins, 2017). Extreme streamflow changes
are mainly driven by changes in precipitation regime. Fowler & Ekström (2009), when
examining extreme rainfall for example, forecast increases in winter, spring and autumn to
the 2080s, with this being especially pronounced in the west and Wales. Furthermore, Rau etal. (2020) found that extreme hourly precipitation is set to increase under a high future2

As presented in Chapter 4.

103

Dallison, Williams, Harris and Patil. Study IIemission scenario, especially in the long-term (2070-2100), up to 112% in some areas. Collet
et al. (2018) demonstrated using the future flows hydrology database (Prudhomme et al.,2013b), that for western areas of Britain especially, an increase in the frequency, durationand magnitude of future flood events is likely when comparing the period 1961-1990 to 20692098. A large, but less likely increase in the duration and magnitude of droughts is also shown(Collet et al., 2018). Similarly, Charlton & Arnell (2014) showed an increase in high flows anddecrease in low flows in six study catchments using the UKCP09 data. These changes wereparticularly linked to changes in winter precipitation for high flows, and summer temperatureand precipitation for low flows (Charlton & Arnell, 2014).
In terms of changes to water quality, it is likely that the aforementioned projected alterations
in average and extreme streamflows will have a deleterious impact in many instances.
However, individual catchment characteristics will have a large impact on the magnitude ofthese changes. Whitehead et al. (2009a), for example, showed across six UK catchments thatpatterns in future water quality varied greatly based on the geology, topography,
urbanisation, agricultural land use and point source inputs of each catchment. Furthermore,their research also showed that variation is also possible within catchments. For example, theRiver Tweed showed a trend of decreasing nitrate levels in the upland headwaters in summer,
but increasing levels in the lower reaches (Whitehead et al., 2009a). A further study byWhitehead et al. (2009b) discussed the varied future drivers of water quality changes. These
drivers include variations in flow influencing stream power and therefore the erosion andtransport of sediment. Streamflow regime variations also impact the dilution and movementof pollutants; in addition, changing water temperature can alter in-stream chemical process.
Delpla et al. (2009) also reviewed the impact of both flood and low flow events on water
quality, finding that increases in the magnitude of rain events, as well as changing droughtrewetting cycles, are likely to increase the amount of organic matter reaching streams. Thesefindings correspond with those shown by Ockenden et al. (2017), who showed a clear positive
trend between total phosphorous load and annual rainfall, and therefore streamflow.
Furthermore, Mortazavi-Naeini et al. (2019) demonstrated a positive relationship between
suspended sediment concentrations and extreme winter floods, as well as an increase in total
phosphorus levels during low flow events. Finally, Watts & Anderson (2016) suggest thatgenerally lower summer flow will increase the likelihood of algal blooms and lower dissolved104

Dallison, Williams, Harris and Patil. Study II
oxygen levels, while subsequent summer flood events will increase wash of pollutants suchas nutrients into river systems.
This work aims, via hydrological modelling, to investigate alterations to streamflow and water
quality brought about by climate change in five catchments in Wales, UK. This is arguably anunder-studied region of the UK in this context, especially when compared to drier regions,such as southeast England. It is a key area to study however, due to the importance of theriver networks to the region. An obvious example of this is public water supply, 95% of which
is taken from surface water sources (DCWW, 2019b). With some water supply zones alreadybeing in deficit in the region (DCWW, 2019b), future water supply sustainability is crucial tounderstand. Furthermore, Wales exports large quantities of water to metropolitan areas incentral and northern England (Warren & Holman, 2012; DCWW, 2016a), meaning thatalterations in future flow regime could have impacts beyond the country’s borders. In
addition, understanding future flow characteristics is important to sectors such ashydroelectricity generation, which is abundant, and growing, in Wales. Over 360 hydroelectric
projects operate on rivers in Wales, ranging from small (<1 MW) to large (>70 MW) with a
total estimated generation off 389 GWh, accounting for approximately 1.3% of total
electricity generation in Wales in 2018 (Welsh Government, 2019b). Surface waters are also
an important part of the tourism economy, with 96 million day visits to Wales in 2018 (Welsh
Government, 2020), many attracted to the region for its natural beauty, and often visiting
rivers, lakes and coast. The industry contributed £3,064 million to the Welsh economy interms of gross value added in 2016 (Welsh Government, 2020), therefore understanding
changes that may affect the natural landscape and resources of the region, and its continued
appeal to tourists, is crucial. Finally, any changes in flow characteristics or water quality inparticular, could have important implications for river ecology and aquatic life in both rivers
and coastal waters. To ensure sufficient protection, it is important to understand potential
future changes and challenges now, and to plan mitigation measures.

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5.3 Data and methods
5.3.1 Study catchments
Our study focusses on five catchments in Wales: the Clwyd, Conwy, Dyfi, Teifi, and Tywi
(Figure 5.1). These catchments have been selected for study due to the variety of different
characteristics displayed, in factors such as topography (Figure 5.1), land use/land cover(LULC), and soil types (Figure 5.2). These are also among the largest catchments contained,
source to sink, entirely within Wales; key details relating to the characteristics of eachcatchment are provided in Table 5.1. In addition, the catchments are important economically,socially and environmentally, in line with the reasons for selecting Wales as a study region.
Water from these catchments, for example, directly contributes to the supplies of over
975,000 people (derived from DCWW, 2019b), as well as over 70 hydroelectricity scheme
abstractions (derived from NRW, 2019a). Furthermore, these catchments are also importantenvironmentally, with various land protections and designations covering areas of thecatchments, such as National Parks, Sites of Special Scientific Interest, National Nature
Reserves, and Special Areas of Conservation. Finally, the catchments also flow into watersthat are spawning and/or nursery grounds for a variety of fish species such as cod, sole, plaice,spotted ray and sandeel (Ellis et al., 2012).

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Dallison, Williams, Harris and Patil. Study II

Figure 5.1. Study catchments as delineated in SWAT. (A) Streams and stream orders (derived by Strahler method), gauging stations and water quality
monitoring locations. (B) Catchment elevation and streams (third order and larger).

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Dallison, Williams, Harris and Patil. Study II

Figure 5.2. (A) Catchment land use/land cover categorisation derived from CORINE Land Cover data (EEA, 2012). (B) Catchment soil classification derived fromthe EU Soil Database (European Commission, 2004).

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Dallison, Williams, Harris and Patil. Study II
Table 5.1. Key study catchments details. Catchment area refers to area modelled in SWAT. Elevation data derived
from 5 m resolution OS Terrain 5 DEM from Ordnance Survey; land use/land cover data derived from 2012
CORINE Land Cover data (EEA, 2012).

2

Catchment land use/land cover (%)

Catchment area (km )Maximum catchment elevation (m)Mean catchment elevation (m)UrbanAgriculture
ArablePastureForestBroadleaf
Coniferous
Mixed
Scrub
Natural grassland
Moors and heathland
Transitional woodland scrub
Peat bog
Sparsely vegetated areas

Clwyd

Conwy

Dyfi

Teifi

Tywi

750.1
557
210.4
2.4
80.9
9.3
71.6
7.3
1.6
4.5
1.2
9.4
2.3
6.6
0.5
0.0
0.0

541.8
1062
309.8
0.7
30.6
0.1
30.5
13.7
2.7
5.5
5.5
42.1
16.6
23.6
1.9
8.4
4.1

507.2
903
271.4
0.3
30.0
0.4
29.6
21.5
1.8
18.9
0.8
45.3
30.4
11.3
3.6
2.6
0.2

995.3
591
198.1
0.8
83.0
1.8
81.2
5.5
1.2
3.2
1.1
9.7
7.2
1.2
1.3
0.9
0.0

1364.6
801
212.5
0.7
64.6
0.7
63.9
16.0
1.0
10.1
4.9
17.1
8.4
6.5
2.2
1.3
0.0

The catchments range in size from Dyfi at 507 km2 to Tywi at over two and half times the size
(1365 km2). In terms of LULC, agriculture and scrubland are the two main land cover types
across the five catchments, with Clwyd, Teifi, and Tywi being heavily agriculture based (83%to 65%), and Conwy and Dyfi having higher propositions of scrubland (42% and 45%,
respectively) such as natural grasslands, moors and heathlands. Most of the agricultural landin all catchments is pasture used for grazing sheep and cattle, with Clwyd having the highest
proportion used for growing crops. Urban areas account for little land area in all our study
catchments. Where urban areas are present, they tend to be located in the coastal regions.
Forested land is the third largest land cover in all catchments, with this mostly being
coniferous and often located in mid to high elevations. In terms of underlying soil
classification, the vast majority in all catchments is young Cambisol (brown soil), while Podzols
are most commonly found in higher altitude areas.

5.3.2 Future climate change data
UKCP18 data were used as future weather input for hydrological modelling, specifically the‘Regional Projections on a 12 km grid over the UK for 1980-2080’ dataset (MOHC, 2018b). This
provided an ensemble of twelve regional climate model (RCM) projections derived and,109

Dallison, Williams, Harris and Patil. Study II
dynamically downscaled, from the 60 km HadGEM3-GC3.05 global coupled model (GCM)
perturbed parameter ensemble (Murphy et al., 2018). The 12 RCM ensemble members aremodelled on the basis of no downturn in greenhouse gas emissions, using the Representative
Concentration Pathway 8.5 (RCP8.5), therefore taking a worst-case scenario approach in asimilar vein to studies such as Ficklin & Barnhart (2014) and Lee et al. (2018). Uncertainty in
the GCM, as well as the natural climate variability cause the 12 ensemble members to differ(Kendon et al., 2019). The 12 km gridded UKCP18 data were extracted to a single point at thecentre of each grid square, and these points were treated as pseudo-meteorological stations.
Out of the total 28 grid squares that have the centre point lying within one of the fivecatchments, 13 were selected as pseudo-meteorological stations (Figure 5.3).

Figure 5.3. Pseudo-meteorological stations used in SWAT future weather input, those selected for use in the
study are highlighted in green and labelled.

110Dallison, Williams, Harris and Patil. Study II
These selections were made to ensure good coverage across all catchments, considering thepotential changes in precipitation and temperature based on elevation, and to manage thecomputational resource limitations in the process of extracting grid values to the point
locations. Where catchments share a boundary (Clwyd and Conwy; Teifi and Tywi), all pseudometeorological stations in both catchments were made available as inputs to each individual
catchment model to provide additional weather data coverage.

5.3.3 Hydrological modelling
In this study, we have used the Soil and Water Assessment Tool (SWAT), a physically based,semi-distributed hydrological model developed by the US Department of Agriculture’s
Agricultural Research Service (Arnold et al., 1998). SWAT has been used internationally andfor a variety of purposes due to the wide range of catchment processes that the model
considers (Gassman et al., 2014). In recent years, SWAT has been used for purposes such as
ecological impact assessments (Chambers et al., 2017; Kakouei et al., 2018), crop/agriculturalplanning studies (Fereidoon & Koch, 2018; Yang et al., 2018), hydropower assessment (Park& Kim, 2014; Haguma et al., 2017; Abera et al., 2018), and future flood flow characterisation(Singh & Goyal, 2017; Xu et al., 2019). The model has also been widely used for investigatingchanges in the quantity and timing of streamflows under climate change in a variety ofcatchment types; for example, Coffey et al. (2016) for two catchments in Ireland, Perra et al.
(2018) for a Mediterranean catchment, Sultana & Choi (2018) for a snow-dominatedcatchment in northern California, and Yuan et al. (2019) for the Yangtze River in China. Water
quality impacts under future climate change have also been studied using SWAT, with papers
such as Nerantzaki et al. (2016) and Jilo et al. (2019) studying the sediment yield in catchments
in Greece and Ethiopia, respectively; Pesce et al. (2018) inspecting nitrogen, phosphorous anddissolved oxygen in a catchment in Italy, and Yang et al. (2017) investigating total nitrogenchanges in a river in northeast China.
SWAT operates at a daily time-step and is designed for river basin-scale use. Being semidistributed, SWAT makes use of sub-basins, which are further divided into Hydrological
Response Units (HRUs) of areas with similar soil type, LULC and topography. The model iscomputationally efficient due to calculations being performed for each HRU and latersummed together at sub-basin outlets (Bailey et al., 2016). Full and detailed information on

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Dallison, Williams, Harris and Patil. Study II
the equations, inputs and processes that govern the behaviour of the model, as well as theoutputs, are provided in the SWAT theoretical documentation (Neitsch et al., 2011) and the
input/output documentation (Arnold et al., 2012).
Here, SWAT has been used to simulate daily streamflow and four water quality parameters,
total nitrogen (TN), total phosphorus (TP), suspended sediment (SS), and dissolved oxygen(DO). Each study catchment was set-up, calibrated, and validated separately. The key inputs
to the SWAT model are elevation, LULC, soil characteristics, and weather data. The 5 m
resolution OS Terrain 5 DEM from Ordnance Survey was used alongside soil data from the EU
soil database (European Commission, 2004) and land cover data from the CORINE Land Coverdataset (Copernicus Land Monitoring Data; EEA, 2012). Historical daily observed airtemperature and precipitation data were obtained from the Centre for Ecology andHydrology’s (CEH) Climate, Hydrology and Ecology research Support System (CHESS) dataset(Robinson et al., 2017). Historical streamflow data was obtained from CEH’s National River
Flow Archive dataset (NRFA, 2020).
Calibration and validation was completed with the SWAT Calibration and Uncertainty
Programme 2012 (SWAT-CUP), specifically using the Particle Swarm Optimisation (PSO)
method (Kennedy & Eberhart, 1995). The PSO method iteratively improves the model
simulation proficiency, taking into consideration the previous best-known calibration point,
and allows for a quicker and more efficient calibration process (Clerc & Kennedy, 2002; Coelloet al., 2004). SWAT was calibrated at each study catchment against observed daily average
streamflow only. Calibration was undertaken for the period 1985 to 1998, with a 3-year model
warm-up period occurring beforehand and ten simulations with ten iterations per simulation
were completed for each catchment calibration. Kling-Gupta efficiency (KGE; Equation 5.1)
was used as the goodness-of-fit metric for model calibration (Gupta et al., 2009):
𝐾𝐺𝐸 = 1 − √(𝑟 − 1)2 + (𝑎 − 1)2 + (𝛽 − 1)2(Eq. 5.1)

where 𝑟 is the linear correlation between observations and simulations; 𝛼 refers to the ratio
between the standard deviation in the simulated flow and the standard deviation in theobserved flow; and 𝛽 is the ratio between the mean simulated and mean observed flow.

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Dallison, Williams, Harris and Patil. Study II
The calibrated SWAT model was then used for a validation run for the period 1999 to 2014,
once again with a 3-year warm-up period beforehand. Calibration and validation KGE values
for each catchment are shown in Table 5.23. A total of 17 parameters were used in thecalibration, the names and details of these are provided in Table 5.3. These parameters wereselected based on a literature review of similar catchments, and after consultation withexperienced SWAT modellers.
Table 5.2. Number of hydrological response units (HRUs) and sub-basins per catchment model. Number of subbasin containing gauging station also show, along with Kling-Gupta efficiency (KGE) values for the calibrationand validation periods.

Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Total HRUs

Total subbasins

Gauge subbasin (outfall)

Calibration
KGE

Validation
KGE

697
770
662
1333
1697

302
202
227
425
496

297
194
223
420
459

0.810
0.770
0.788
0.851
0.841

0.788
0.718
0.728
0.723
0.717

Table 5.3. SWAT parameters calibrated through SWAT-CUP programme using particle swarm optimisation
method.

Input filelocation

Parameter

Description

ESCO
EPCO
SURLAG
GW_Delay
Alpha_BF
GWQMIN
RCHRG_DPREVAPMN
GW_REVAP
ALPHA_BF_D
CANMX
CN2
CH_N2
CH_K2
SOL_AWC
SOL_K
SOL_Z

Soil evaporation compensation factor
Plant uptake compensation factor
Surface runoff lag time
Groundwater delay
Baseflow alpha factor
Threshold depth of water in shallow aquifer for return flow to occur
Deep aquifer percolation fraction
Threshold depth of water in shallow aquifer for "revap" to occur
Groundwater "revap" coefficient
Baseflow alpha factor for deep aquifer
Maximum canopy storage
SCS runoff curve number for moisture condition 2
Manning's "n" value for the main channel
Effective hydraulic conductivity in main channel alluvium
Available water capacity of the soil layer
Saturated hydraulic conductivity
Depth from soil surface to bottom of layer

3

.bsn
.bsn
.bsn
.gw
.gw
.gw
.gw
.gw
.gw
.gw
.hru
.mgt
.rte
.rte
.sol
.sol
.sol

Charts comparing observed and simulated streamflow for each catchment during the calibration period can befound in Appendix 1, Figures A1.1 to A1.5.

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Dallison, Williams, Harris and Patil. Study II
Due to the lack of sufficient historical sampling data, water quality parameters were notcalibrated. However, a validation of the water quality output was attempted, by comparing
model output to the limited measured water quality data available from the Natural
Resources Wales (NRW) water quality data archive (NRW, 2019b). While the water quality
validation showed a good degree of correlation for DO in most catchments, the correlations
for the other factors were less agreeable (Table 5.4). The modelled TP, TN and SS output doall correlate well with modelled streamflow however (Table 5.5), suggesting that this is the
key driver of change in the uncalibrated model.
Table 5.4. Pearson’s correlation values between observed (where available) and modelled water quality
concentrations. Underlined values indicate a correlation significant at p <0.05, those shown in bold are
significant at p <0.01.

Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Suspended
sediment

Total
phosphorous-0.0330.200-0.139
0.099
0.788

0.174
-0.026-0.0900.024-0.172

Total nitrogen
0.109

0.075
0.116

Dissolved
oxygen
0.646
0.022
0.060
0.806
0.788

Table 5.5. Pearson’s correlation values between modelled water quality concentrations and streamflow.
Underlined values indicate a correlation significant at p <0.05, those shown in bold are significant at p <0.01.

Catchment
Clwyd
Conwy
Dyfi
Teifi
Tywi

Suspended
sediment

Total
phosphorous

Total nitrogen

Dissolved
oxygen

0.033
0.654
0.923
-0.022
0.091

0.035
0.626
0.602
-0.024-0.093

0.063
0.707
0.595
0.361
0.442

0.185
-0.539-0.5880.204
0.226

Future runs of the calibrated and validated SWAT model were completed for the hydrologicalyears 2021 to 2080, where a hydrological year runs from 1st October to the following year’s
30th September and is named after the later year. Some discontinuity in the model output iscaused by the need to run the models in sections due to database size restrictions on the
output files. This was mitigated as far as possible by including a 7-year warm-up period at thestart of all model run sections. Model run sections were as follows, Conwy and Dyfi: 20202050, 2051-2080; Clwyd: 2020-2040, 2041-2060, 2061-2080; Teifi and Tywi: 2020-2035, 20362050, 2051-2065, 2066-2080.

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Dallison, Williams, Harris and Patil. Study II

5.3.4 Trend analysis
To assess any trends displayed in the future climatic, streamflow, and water quality data, theMann-Kendall (MK) test was performed (Mann, 1945; Kendall, 1975). MK tests for amonotonic positive or negative change in a time series by calculating Kendall’s tau(association test) between each value and all of the proceeding values (Helsel & Hirsch, 2002).
The nature of hydrological data lends itself to this nonparametric method as it is generally
non-normally distributed and displays seasonality, going against a constant distribution
assumption (Kundzewics & Robson, 2004). Additionally, auto-correlation is often also
displayed in the data, making a parametric test further unsuitable; it is for this reason, thatduring the MK test the Hamed & Rao method of auto-correlation correction was also applied(Hamed & Rao, 1998). Sen’s slope estimator (Sen, 1968) was also used in order to better
estimate the size and direction of any detected trends.
Trend analysis for both streamflow volumes and water quality factors were conducted forannual (hydrological years) and seasonal (winter, December to February; spring, March toMay; summer, June to August; autumn, September to November) averages. Streamflow
extremes were also considered, testing seasonally and annually for trends in 1-day maximum
flow, 1-day minimum flow, and number of low flow days (defined as days were streamflow isless than the 5th percentile value for the full 60-year dataset). High flow events have also been
analysed, with the number of independent events per year with at least one streamflow peakabove the 60-year 95th percentile value being calculated. Change in event length was also
analysed, being defined as the period either side of any peak flow where streamflow was
greater than baseflow; at the start and end of each event streamflow is therefore equal tobaseflow. The digital filter method defined by Sawicz et al. (2011) has been used to calculate
direct runoff, as per Equation 5.2, to subsequently enable the calculation of baseflow:

𝑄𝐷𝑡 = 𝑐𝑄𝐷𝑡−1 +1+𝑐(𝑄𝑡 − 𝑄𝑡−1 )2(Eq. 5.2)

where 𝑄𝐷𝑡 is direct runoff at a given time-step, 𝑐 is a constant with value 0.925, 𝑄𝐷𝑡−1 is direct
runoff at the previous time-step (at time-step t = 0, 𝑄𝐷 is assumed to be 0) and 𝑄 is total
streamflow. Baseflow (𝑄𝐵𝑡 ) for the same time-step was then calculated using Equation 5.3:
𝑄𝐵𝑡 = 𝑄𝑡 − 𝑄𝐷𝑡

115(Eq. 5.3)

Dallison, Williams, Harris and Patil. Study II
An event containing multiple peaks above the dataset 95 th percentile is classified as a single
event. In addition, a trend analysis was also performed on the input future temperature andprecipitation data to identify any annual or seasonal changes occurring.

5.4 Results
Our results show a large decrease in summer and autumn average streamflows, with acorresponding increase in spring and winter flows. Overall, these seasonal changes result in asmall decrease in annual average flows. Trends in extreme streamflows show much greater
variability between catchments and seasons. However, all catchments display a trend for an
increasing number of extreme low flow days, as well as lower 1-day minimum flow volumes.
Water quality appears to be highly linked to streamflow, especially changes in extreme highand low flows. Summer, in particular, shows lower water quality standards across allcatchments and variables. To provide further context to the streamflow and water quality
results, we first include a brief trend analysis of seasonal and annual average mean
temperatures and precipitation. As with all of the trend analysis subsequently presented, thishas been completed using the MK trend test. Due to the lack of calibration of water quality
parameters, only long-term seasonal and annual average trends have been presented in thisstudy, unlike streamflows, where extreme flows have also been analysed.

5.4.1 Climate projections
Future air temperature shows a significant (p <0.01) increase for all stations in all seasons, aswell as annually, as can be seen in the statistics reported in Figure 5.4 relating to the mean
temperature of all stations. For precipitation trends, winter volumes increase across theboard (p <0.01), while summer volumes decrease (p <0.01). Although autumn precipitation
volumes all decrease across the future study period, at only six of the thirteen stations is thishighly significant (p <0.01), a further two are significant at p <0.05, and the remaining five are
not significant; the average across all is significant at p <0.05. For spring, there is combination
between upward and downward trends, but none are significant.

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Dallison, Williams, Harris and Patil. Study II

Figure 5.4. Future projections (2021-2080) for seasonal and annual daily mean precipitation (left) andtemperature (right). Thin coloured lines represent the 13 pseudo-meteorological stations in the catchments;
thick black line denotes the average across all stations. Sen’s slope statistics for the thirteen station mean also
show, bold p values are significant at <0.05.

117Dallison, Williams, Harris and Patil. Study II

5.4.2 Streamflow
5.4.2.1 Long-term averages
Data relating to the average of all twelve model outputs4 for future streamflows display longterm trends both annually and seasonally, however, the direction and statistical significanceof these varies between catchments. Using the 1990-2010 period average as a baseline, it canbe seen that in most cases there is consistency between catchments (Figure 5.5). Where there
are exceptions to this, these mainly relate to the Dyfi catchment, which generally displays asmaller magnitude of change, especially in the spring, autumn, and annual average. Anothernotable outlier is the Clwyd catchment in winter, which shows a clear decrease in the latter
half of the study period, which is not observed in the other catchments. These relationships
are further highlighted in the MK trend analysis results, which are summarised in Figure 5.6.
A statistically significant increase in spring flows is observed in all catchments (Clwyd & Dyfi p<0.05; Conwy, Teifi & Tywi p <0.01), while a corresponding highly significant (p <0.01)
decrease in autumn flows is identified in all catchments except the Dyfi (p = 0.530). Summer
streamflows also decrease in all catchments, but only statistically significantly in the Conwy(p <0.05) and Dyfi (p <0.01). Winter streamflows are the most varied, with the Clwyd and Teifi
decreasing, and the Conwy, Dyfi and Tywi increasing; these trends are only significant for thethree most northerly catchments (p <0.01). Finally, when looking at annual average
streamflows, all catchments except the Dyfi display declining trends, albeit only significantlyin the Clwyd (p <0.01); the Dyfi increases significantly (p <0.01).

4

Projected seasonal and annual average streamflow for each individual ensemble member and catchment canbe found in Appendix 2, Figures A2.1 to A2.5.

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Dallison, Williams, Harris and Patil. Study II

Figure 5.5. Percentage change from the 1990-2010 baseline average (y-axis) for a 5-year moving average of2021-2080 future streamflow data (x-axis). Seasonal and annual averages shown for each catchment. Please
note that the y-axis scale on the annual average graph (bottom) is a factor of two smaller than the seasonal
average graphs.

119Dallison, Williams, Harris and Patil. Study II

Figure 5.6. Overview of the direction, magnitude and significance of annual and seasonal average trends inprojected future (2021-2080) average streamflow, as detected by Mann-Kendall trend analysis, based on the
average of all 12 RCM model outputs.

5.4.2.2 Extreme streamflows
Firstly, we can see that across all catchments, maximum 1-day flows are increasing
significantly (p <0.01) in spring, while a decrease is shown in autumn in all catchments except
the Dyfi (Figure 5.7). Furthermore, the Conwy and Dyfi display a significant change (p <0.01)
in maximum 1-day flow volume in winter and summer, an increase and decrease respectively.
Minimum 1-day flows decrease in summer, autumn and annually in all catchments, with thistrend being statistically significant in autumn in all catchments (Figure 5.7).

Figure 5.7. Overview of the direction, magnitude and significance of annual and seasonal trends in projected
future (2021-2080) extreme streamflows, as detected by Mann-Kendall trend analysis, based on the average ofall 12 RCM model outputs.

In terms of number of extreme low flow days and high flow events, a decrease in autumn isseen for high flows, alongside an increase in the number of low flow days (Figure 5.7),
suggesting a general reduction in streamflows in autumn corresponding with the average
autumn flow data. Similarly, for spring in all catchments except the Dyfi, there is a statisticallysignificant (p <0.01) increase in the number of extreme high flow events, once again
corresponding to an increase in average spring flows too. Annually, a statistically significant

120

Dallison, Williams, Harris and Patil. Study II(p <0.01) increase in the number of extreme low flow days is observed in all catchments(Figure 5.7), suggesting generally drier conditions in the catchments on average.
When 1-day maximum flow volume and number of extreme high flow events are compared,
the trends observed are almost identical in all catchments and seasons (Figure 5.7). In somecases, winter and spring in particular, this combination suggests an increase in both themagnitude and frequency of the largest flows. When looking at the mean high flow event
duration, it can be seen that change is projected to be smaller, with a general trend towards
shorter events in summer and autumn and longer events in the spring. This leads to a pictureof fewer, shorter flooding events in summer and autumn across the country, with spring highflow events becoming more frequent and generally longer. Similarities can also be seen in the
1-day minimum flow volume and number of extreme low flow days; the vast majority ofsignificant increases or decreases in minimum flows are mirrored by a decrease or increase
respectively in the number of extreme low flow days. This suggests that in summer andautumn, as well as annually on average, catchments will see a greater number of low flow
days, which are also more severe.
5.4.2.3 Annual flow exceedance characteristics
It is clear that significant alterations are projected to take place in future streamflow in termsof both seasonal/annual averages, and extreme flows. These changes in flow regimes of thecatchments are also clear when comparing the flow duration curves (FDC) of streamflow forthe baseline period (1990-2010) with late-future period streamflow (2060-2080; Figure 5.8).
As can be seen, in all catchments, the water volumes that are most regularly exceeded (>75%of the time) are projected to become lower. This decrease is particularly prominent in thelowest flow volumes, those exceeded more than 95% of the time; in keeping with the extreme
flows MK trend analysis which also shows minimum flow levels decreasing and more extreme
low flow days. High flows vary between catchments, once again in line with the extreme flows
trend analysis. The Conwy and Dyfi, for example, both display an increase in discharge
volumes exceeded 0 to 25% of the time in the 2060-2080 period compared to the 1990-2010
period. This once again corresponds with the observed trends of larger maximum flows, anda greater number of large flow events per year, both seen in the MK trend analysis. The Teifi& Tywi display little change in high flows between the two periods in the FDCs, which reflects121

Dallison, Williams, Harris and Patil. Study II
the lack of statistically significant trends seen in annual maximum flow volume and numberof extreme high flow events seen in the MK trend analysis. The Clwyd displays the only
statistically significant negative trend, in the MK analysis of annual maximum flow andnumber of extreme high flow events; this is also evident in the FDC, which shows future flow
volumes being consistently lower than the 1990-2010 baseline.

Figure 5.8. Flow duration curves for each catchment; darker of each colour pair represents daily 1990-2010 data;
lighter colour represents daily 2060-2080 data. (A) Conwy and Dyfi catchments, showing accentuation of both
high and low flows. (B) Clwyd, Teifi and Tywi catchments, showing accentuation of lower flows.

5.4.3 Water quality
As can be seen in Figure 5.9, in the majority of cases trends seen in each water quality factor
are consistent across all catchments and seasons. In terms of SS and TP concentrations
specifically, 88% of all of the trends are increasing, with the Dyfi being the only catchment
showing negative trends in long-term TP concentrations, in winter, spring and annually.
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Dallison, Williams, Harris and Patil. Study II
Summer, in terms of SS concentrations, is the most spatially variable, with the Conwy and Dyfi
both displaying a statistically significant decrease (p <0.01). The Tywi is also set slightly apartfrom the other catchments in terms of SS, in that it has the fewest significant trends, and also
shows a significant decrease in autumn SS concentrations (p <0.01), the only catchment to doso. TN concentrations are also mostly consistent spatially, with the Dyfi once again being themain exception, with a decline in concentrations in line with those displayed for thecatchment in TP. Additionally, TN levels are shown to be decreasing in winter in the Teifi also,
albeit not statistically significantly. All catchments present a statistically significant (p <0.01)
increase in summer TN concentrations, while the Conwy displays the same in all seasons andannually. DO levels are generally decreasing over the study period with autumn and annual
trends in particular seeing a statistically significant (p <0.01) decrease in all catchment except
the Teifi. In winter too, DO is decreasing in all catchment except the Teifi, while in spring the
Clwyd, Conwy and Teifi all display an increasing trend. An increasing trend is also observed in
the Dyfi for DO in summer, the opposite of that seen in the Clwyd, Teifi and Tywi.

Figure 5.9. Overview of the direction, magnitude and significance of annual and seasonal average trends inprojected future (2021-2080) water quality concentrations, as detected by Mann-Kendall trend analysis, basedon the average of all 12 RCM model outputs.

5.5 Discussion
5.5.1 Streamflow trends
The results of the MK trend analysis show a selection of spatially varying changes across thefive catchments, however, when studying summer and autumn average flows, there is acommon pattern in all catchments of a general decrease. For the summer season, this trend123

Dallison, Williams, Harris and Patil. Study II
is only statistically significant in the Conwy and Dyfi, and in autumn, it is significant in allcatchments except the Dyfi. The projected general decline in summer and autumn streamflow
is also observed in other research studying mean flows, such as UK CEH (2012) and Fowler &Kilsby (2007). The only trend seen at a statistically significant level in all study catchments, anincrease in spring average streamflow, is also observed in these pieces of research. Annual
average flows only change significantly in the Clwyd (decrease) and Dyfi (increase), with allother catchments displaying a non-significant decline. These findings are consistent withthose of Kay et al. (2020), who showed a decreasing trend in mean annual streamflow for four
catchments in the west of England and east Wales, as we observed in all catchments except
the Dyfi. The same can be said for projections made by the UK Centre for Ecology andHydrology (CEH), who also show a small decline in mean annual streamflow for Wales up to
the 2050s (UK CEH, 2012). This change is primarily driven in all catchments except the Dyfi,
by declines in summer and autumn average flow being greater in magnitude than increases
in winter and spring.
Arguably, in terms of the magnitude of large streamflows, the Conwy and the Dyfi catchments
once again show more similarity with each other, while the Clwyd, Teifi and Tywi are all moreclosely connected. This can be seen for the annual data, when looking at Figure 5.8 inparticular, with the Conwy and Dyfi displaying an accentuation of both the highest and lowest
flows, with greater accentuation in low flows than high. The remaining three catchments only
show an accentuation of low flows, this being particularly prominent in the Clwyd and Tywi.
The Dyfi and Conwy are the most mountainous, steeply sloping, and smallest in area of thecatchments, which makes them highly responsive to precipitation, and flashy. It is these
specific catchment characteristics that partly foster the observed statistically significant
trends of increasing winter and annual maximum streamflow, as well as number of high flow
events. Such physical catchment characteristics are known to cause high responsiveness, withstudies such as Dunne (1978), Weiler & McDonnell (2006) and Hornberger et al. (2014) linking
smaller catchment area and greater steepness to a quicker catchment response and larger
streamflow events. These responses are further exacerbated by the soil characteristics of thecatchments, with the Conwy and the Dyfi in particular being dominated in their upper reaches
by peaty and gleysol soils, with the majority of this also lacking underlying groundwater oraquifer storage (Boorman et al., 1995; European Commission, 2004). This once again causes

124Dallison, Williams, Harris and Patil. Study II
greater overland and subsurface flow, rather than infiltration and storage, leading to a flashiercatchment regime (Boorman et al., 1995).
It is notable however, when comparing the Conwy and the Dyfi, that accentuation of future
low flows is much greater for the Conwy than the Dyfi (Figure 5.8), with this likely due to the
Dyfi displaying the opposite trend for winter minimum flow and number of low flow days,
than the Conwy. In fact, the Dyfi is the only catchment to show a significant reduction in thenumber of low flow days in winter, as well as an increase in winter minimum flow volume.
Such changes can be attributed to LULC characteristics, in this case the larger proportion ofconiferous and mixed forestry present in the Dyfi. A study by Robinson et al. (2003)
demonstrated that for western Europe, forest areas, in particularly those that are coniferousdominated, help to maintain higher baseflow conditions. This relationship also aids in
explaining the non-significant increase seen in the winter low flows for the Tywi, which has
the second largest proportion by some margin of LULC designated as coniferous and mixed
forestry. Furthermore, the statistically significant increase in annual average flows in the Dyfi
is driven by increases seen in winter and spring streamflows, outweighing decreases insummer and autumn. This difference from the other catchments is also likely due to the large
proportion of forested land in the Dyfi; Teutschbein et al. (2015), for example, showed that
increased forest cover led to increased annual average flow in their study of sixteen small
catchments in Sweden.
Annual low flows in all catchments show an accentuation of presently seen low flows, into
the 2060-2080 period (Figure 5.8); this is likely due to the statistically significant declines seenin summer and autumn average flows into the future (Figure 5.6). These seasons traditionallyhave seen the lowest flows, so the fact that they are generally getting drier and warmer,
would explain this decline in minimum flow volume. This trend observed in low flows acrossthe catchments is in line with trends described in Kay et al. (2020), who showed for ten
catchments in the UK, under RCP8.5 conditions, low flows are declining in volume. This isparticularly seen in the more westerly of the catchments studied (Kay et al., 2020), those
closest to this study’s catchments.
Our results correspond with other research conducted recently in the UK in terms of seasonalchanges in high and low flows, especially when considering the decrease in low summer and125

Dallison, Williams, Harris and Patil. Study II
autumn flows, and the increase in high winter and spring flows. Visser-Quinn et al. (2019), forexample, detailed an increase in the magnitude, duration and frequency of both high and low
flow events for Wales in the far future (2090s) under RCP8.5. Our results also show anincrease in the frequency and magnitude of such events, however, in terms of event duration
we find the opposite trend, a decline in most seasons as well as annually. Our results also
show an increasing number of extreme high winter flow events, and extreme low autumn
flows specifically, also corresponding with the results of Collet et al. (2018). When studying
the UK and Wales as part of wider studies, Marx et al. (2018) also showed a decline in meanannual low flows in Atlantic influenced western Europe, albeit the smallest change of all
regions across Europe. Additionally, Thober et al. (2018) found an increase in high flows andflooding in the UK under future climate scenarios representing RCPs 2.6, 6.0 and 8.5.

5.5.2 Water quality trends
Unlike the streamflow trends discussed, the results of the MK trend analysis of water quality
factors is much more homogenous across the study region. The exceptions to this are the Dyfi
catchment in terms of TP and TN in particular, and the Teifi in terms of DO; these outliers canbe explained by studying the catchment characteristics. The Dyfi catchment, for example, has
the lowest proportion of land designated as agricultural of all of the catchments, and thelargest proportion designated as both forest and scrub (Table 5.1). These characteristics givethe Dyfi catchment a significantly different LULC make-up than the Clwyd, Teifi and Tywi inparticular. Agricultural land is well documented to be one of the highest contributors to TP
and TN levels in river systems (Edwards et al., 2000; Burt et al., 2011; Mockler et al., 2017;
Weigelhofer et al., 2018). Burt et al. (2011) stated that on average in England and Wales, 60%of nitrogen in surface waters originates from agricultural land, but this can be as high as 90%
in agriculturally-dominated catchments. Similarly, for phosphorus, the House of Commons
Environmental Audit Committee (2018) reported that run-off from agricultural land was oneof the main sources of TP in surface waters, especially at times of highest water levels, i.e.
winter and spring in this study’s catchments. These relationships, and the significantly lower
proportion of agricultural land in the Dyfi catchment, go some way to explain this marked
departure from the trends seen in all other study catchments. This LULC with TP and TN
relationship also explains the trends seen in all catchments except the Dyfi, for example
increases in winter TP in the more agriculturally-dominated catchments. Furthermore, the126Dallison, Williams, Harris and Patil. Study II
increases seen in summer TP and TN in all catchments are also indicative of the dominance of
in-stream processes on their generation during warmer conditions (Edwards et al., 2000).
Moreover, given the dependence of TP and TN concentrations on streamflow, as
aforementioned and demonstrated in Table 5.5, it can also be observed that the declines in
winter and spring TP and TN in the Dyfi relate to the unique streamflow trends observed inthis catchment. Specifically, the Dyfi displays the only statistically significant increase in low
volumes in winter, as explained previously. Additionally, decreases in TP and TN observed
annually relate to the only statistically significant increase in annual average flows displayedin any of the five catchments. These correlations also help to explain the departure from thegenerally congruous trends in streamflow and water quality displayed between the Conwy
and Dyfi, with the only major differences observed in their streamflow responses being inannual average and winter low flows.
The SS concentration results show a consistent rise across almost all catchments and seasons.
The general uplift is likely due to a variety of reasons depending on the season. Certainly, inthe spring and winter, increasing trends observed in the number and magnitude of high flow
events is likely the cause, along with an increase generally in average flows. These larger flows
not only increase debris washed into river systems by overland flow, but also increase bankand bed erosion, as well as bed transport (Zeiger & Hubbart, 2016; Janes et al., 2017;
Vercruysse & Grabowski, 2019), causing an increase in total SS concentrations. In summer
and autumn, with temperatures increasing and precipitation decreasing, there is an increased
likelihood of drought, as well as fewer rainy days. This means that when rain does arrive, there
is much more dust and debris to be washed from the land surface into river systems,
especially as dry land also has lower infiltration rates (Clark et al., 2017; Pulley & Collins,2019). Combined with the fact that lower streamflows have less dilution potential, this is thelikely cause of the SS increases seen in these seasons (Whitehead et al., 2009b). Negative
trend shown in summer Conwy and Dyfi SS concentrations, are likely caused by statisticallysignificant declines in maximum flow (both p <0.01) and average flow (Conwy p <0.05; Dyfi p<0.01) volumes during the season across the study period, not seen in the other catchments
The observed downward trends in DO seen for most seasons in the majority of catchments
are likely linked to the rise in air temperatures seen in all seasons and annually (Figure 5.4).
Raised air temperatures have been shown to correlate positively with average water127

Dallison, Williams, Harris and Patil. Study II
temperatures, albeit this relationship can be masked or affected by factors such as discharge
volume, upstream dams and point source inputs (Webb et al., 2003; Caissie, 2006; Hannah &Garner, 2015; Beaufort et al., 2020; Daniels & Danner, 2020). The known negative relationshipbetween stream water temperature and DO concentration due to the decreased carrying
capacity of warmer water (Ravansalar et al., 2016; Chaturvedi & Misra, 2020), is therefore
likely to be the cause of the declines seen in the study catchments. Furthermore, the broadly
increasing concentrations of TP and TN shown in all catchments will likely also have an impact,
causing an increase in aquatic plant growth during warmer temperatures, reducing oxygen
levels in waters.
When studying water quality results as a collective, a clear decrease in water quality acrossall catchments and seasons can be observed. There is mostly agreement in terms of trend
direction, in spring and autumn, however, in terms of statistically significant trends, more ofthese are present in summer, winter and annually. As previously mentioned, the key outlier
is the Dyfi catchment, mainly due to its unique LULC characteristics compared to the othercatchments, however, here too there are still significant water quality challenges, especiallyin the autumn.

5.5.3 Study implications
This research has been undertaken to provide a backdrop on which future planning,
adaptation, and mitigation measures can be based for various industries. It is some of the firstto make use of the new UKCP18 future climate projections and is certainly the first toinvestigate the impact of the projections for streamflow and water quality in Wales. It is
hoped that by providing future projections of average and extreme high and low flows, aswell as water quality changes, for a large area of Wales, that the results will be applicable tostakeholders in a variety of industries.
This study has shown how, under a worst-case scenario of future emissions, key river systems
in Wales will be impacted by climate change in various ways in terms of both water quantityand quality. It is clear that the effects of climate change on streamflows will be most keenly
felt seasonally, with annual average flows staying relatively stable in comparison. It is likelythat seasonal streamflow patterns currently seen will be exacerbated in the future, withmodelling results showing increases in winter and spring average flows and decline in summer
128

Dallison, Williams, Harris and Patil. Study II
and autumn flows. Trends in extreme events will also intensify, with large streamflow events(floods) generally increasing in magnitude and frequency in winter and spring, and low flows(droughts) increasing in frequency and magnitude in summer and autumn in particular. These
streamflow trends could have important implications in terms of river ecology, flooding,droughts, and for a variety of stakeholders, not least of all water service providers (WSPs).
Given that ~95% of water abstracted for public water supply in Wales is taken from surface
sources (DCWW, 2019b), the trends seen could impact on the ability of WSPs to continue tomeet demand, especially in summer months, during peak demand (Dallison et al., 2020).
Although annual average flows are relatively stable, if declining slightly in most catchments,
it is the fact that there will be considerably less water in summer and autumn that could prove
problematic from ecological and economic perspectives. Additionally, any increases in winterstreamflow will do little to combat summer shortages if there is insufficient capacity to store
it for use later in the year; this is particularly true for the Dyfi and Teifi, which are more reliant
on river abstraction, compared to impoundment reservoirs used in the other catchments.
Furthermore, the quality of incoming water could also cause problems at drinking water
treatment plants, with SS, TP, and TN in particular potentially requiring plants to provide moretreatment, negatively impacting on energy use, operation and maintenance costs, chemicaluse, and potentially plant capacity.
The changes seen in annual flow exceedance probabilities could have implications for all large
water abstractors, with abstraction licences often stipulating conditions, for example
minimum ‘compensation flow’ volumes. With lower flows expected to occur for a greater
proportion of the time annually in the future, this could impact on the amount of water
available for users such as industry, agriculture, and hydroelectric power generation. UKhydroelectric power generation schemes, both large and small, warrant the attention of
future work in particular, due to the large potential impacts of changes in streamflow, andlow flows especially. The timing, season length and total generation capacity of hydroelectric
generation schemes could be impacted badly by changes in streamflow, potentially affecting
the future electricity grid-mix in the UK.
Finally, the changes seen in water quality factors could have an array of impacts on the aquatic
ecosystem supported by surface waters. Increases in TP and TN, for example could lead toincreases in eutrophication in lakes and rivers. Additionally, the declining DO levels shown

129

Dallison, Williams, Harris and Patil. Study II
could seriously impact on fish species within catchments, with lower levels being well
documented to decrease fish survival rates and badly impact spawning and development.

5.6 Conclusions
This research has presented the key changes in future seasonal and annual stream quantityand quality parameters in Wales. The results highlight the need for adaptation now, in orderto prepare infrastructure and systems to combat changes, this is especially true for watersupply, despite Wales being seen as a water rich region. Planning and mitigating now will help
to ensure the continued supply and clean drinking water into the future, especially in thesummer and autumn when lower flows and water quality will challenge WSPs.
Finally, we suggest a variety of areas for future research focus. Firstly, adaptation andmitigation measures required for all large abstractors impacted by future changes in riverwater quantity and quality need to be investigated, to ensure a robust defence against
climate-induced changes. Relating to this, further hydrological modelling, using more
optimistic projections for future emissions, such as RCPs 2.6, 4.5 and 6.0 would be useful, toenable a broader range of potential future impacts to be compiled, and more informed
decisions to be made. This is particularly relevant to WSPs, to enable a continued high-quality
future water supply, and to plan the future provision of drinking water treatment plants.
Furthermore, investigation of the impact of streamflow changes on the total output
generation potential of hydroelectric systems is needed at a national level, to enable robust
planning across the country, to ensure stability in the national electricity grid. Lastly, research
on climate change links to total water demand is also required, in order to ensure future watersupply-demand budgets are achievable under future conditions, and to put in place
management measures where necessary.
Acknowledgements: This study was conducted as part of the Dŵr Uisce project, which aims
at improving the long-term sustainability of water supply, treatment and end-use in Irelandand Wales. The project has been supported by the European Regional Development Fund(ERDF) through the Interreg Ireland-Wales Co-operation Programme 2014-2020. The work
has been used as a case study for Dŵr Uisce Work Package 7 “climate change”, and forms partof a study assessing the impact of climate change on water resource exploitation in Wales.

130

CHAPTER 6: STUDY III
Impacts of climate change on future water availability forhydropower and public water supply in Wales, UK
R.J.H. Dallison, S.D. Patil and A.P. Williams
School of Natural Sciences, Bangor University

This chapter has been submitted in full for publication in the Journal of Hydrology: Regional
Studies and was undergoing peer-review at the time of submission of this thesis.

Author contributions
Richard Dallison: conceptualisation, methodology, software, formal analysis, investigation,
data curation, writing – original draft, writing – review & editing, visualisation
Sopan Patil: conceptualisation, writing – review & editing, supervision
Prysor Williams: writing – review & editing, supervision131

Dallison, Patil and Williams. Study III

6.1 Abstract
Climate change is predicted to have a large impact on the hydrological regimes of Welsh
rivers. However, its influence on the abstraction capability of key sectors, such as public watersupply (PWS) and hydroelectric power (HEP), is not yet fully understood. We use the Soil andWater Assessment Tool (SWAT) to generate future (2021-2079) streamflows under a worstcase scenario of greenhouse gas emissions (Representative Concentration Pathway 8.5) at
two catchments in Wales, the Conwy and Tywi. SWAT streamflow output is used to estimate
total unmet demand for PWS and changes in generation characteristics for HEP. PWS unmet
demand is assessed using the Water Evaluation And Planning (WEAP) system underincreasing, static, and declining demand scenarios. Mann-Kendall analysis is performed todetect and characterise trends. Under all future demand scenarios, there is increased
occurrence of insufficient streamflow to satisfy PWS demand. For HEP, a decrease in annual
abstraction volume results in a loss of generation potential, despite an increasing number ofdays that maximum abstraction is reached. Changes in HEP generation and PWS availability
are most pronounced in the medium-term (2021-2054), with rate of change slowing after
2060. We provide a novel perspective on future water resource availability in Wales, giving
context to management planning to ensure future PWS sustainability and HEP generation
efficiency.

Keywords: Hydroclimatic change; Hydroelectric power; Hydrological modelling; Trend
analysis; Water resource management; Water supply-demand balance132

Dallison, Patil and Williams. Study III

6.2 Introduction
Climate change is a key driver of hydrological regime alteration globally, with prevailingweather conditions being inextricably linked with streamflow. Given the dependence ofsociety on river systems for everyday life, it is of great importance to understand how climatechange will impact water resource availability. In the UK, it is widely accepted that climatechange will lead to hotter, drier summers and warmer, wetter winters, as well as an increasein the magnitude, frequency and duration of extreme weather events (ASC, 2016a; Lowe etal., 2018). In terms of streamflow, an exacerbation of low and high flows is expected, as well
as a reduction in average summer and autumn flows and an increase in winter and spring
average flows; these opposing trends should leave annual averages relatively stable(Christierson et al., 2012; Prudhomme et al., 2012; Watts et al., 2015a; Kay et al., 2020).
Climate change and streamflow responses in Wales specifically are projected to be in line withthe trends suggested for the rest of the UK, with summer mean and maximum temperatures,
as well as winter total and extreme precipitation volumes, predicted to increase (ASC, 2016b;
Kay et al., 2020). These changes have the potential to cause a large impact on major water
consuming sectors such as agriculture, heavy industries, public water supply (PWS), andhydroelectric power (HEP) generation. The Adaptation Sub-Committee (ASC) of theCommittee on Climate Change, for example, suggests that PWS and agricultural water
demand for irrigation could be two of the most pressurised sectors in the UK under futureclimate and streamflow changes, with demand potentially outstripping available water
resources (ASC, 2016a). Furthermore, Arnell & Delaney (2006) detailed how, even at that timeof publication, PWS companies in England and Wales would need to adapt to ensure sufficient
raw water resources due to changes to low flow regimes. In Wales, Carless & Whitehead(2013) demonstrate decreasing summer and autumn streamflows with corresponding
increases in winter and spring. In the given context of a theoretical 99 kW micro-HEP
installation, this causes an accentuation of seasonality in terms of energy generation, withextra supply in wetter seasons compensating for lower supply in drier seasons, leading to anannually stable situation (Carless & Whitehead, 2013). However, these changes are likely to
be catchment-specific, depending on the individual catchment’s topographic and land covercharacteristics as well as the prevailing weather patterns.

133Dallison, Patil and Williams. Study III
In 2018, renewable energy in Wales accounted for 25% of the total electricity generated, with
HEP making up 5.2% of this, and therefore 1.3% of overall Welsh electricity generation (Welsh
Government, 2019b). A large increase in the number of small-scale HEP schemes has occurredin the last eight years due to financial incentives in the form of Feed-in-Tariffs (FiT; Welsh
Government, 2019), and also given that most opportunities for large-scale schemes havebeen exhausted (Carless & Whitehead, 2013). However, due to the curtailment of the FiT
scheme in 2019, the number of new developments has slowed, with further schemes only
likely to be commissioned where conditions are optimal (Welsh Government, 2019b). Thesetypes of schemes, usually run-of-river in design, generally have little or no water
impoundment, making them particularly vulnerable to changes in hydrological conditions.
Run-of-river HEP schemes are usually designed and optimised on the basis of historical flow,
with flow duration curves used to set abstraction conditions. While this method is a goodstarting point for designing the most efficient system to maximise power output, it neglects
to account for future streamflows. Climate change has the potential to cause major alteration
to the hydrological characteristics of river systems, modifying the timing and quantity ofavailable water. High and low flows are particularly likely to be affected (Sayers et al., 2015;
Watts et al., 2015a; ASC, 2016b; Kay et al., 2020), and these extreme flows are often
important in terms of HEP scheme design. In 2017, the Welsh Government set a target for
70% of electricity consumed in Wales to be generated by renewables by 2030 (Welsh
Government, 2019b). HEP plays a small but important role in reaching this target, especiallyin the winter and spring seasons when electricity generation from other sources, such as solarPV, is lower. Therefore, understanding the nature of change in the abstractable flow for HEP
schemes under future climate change is crucial. This will allow for more robust planning of
the future energy mix in Wales as well as maximisation of resource use efficiency andelectricity generation.
PWS also relies heavily on surface waters in Wales, such as rivers and lakes, with over 95% of
the supplied water originating from these sources in the country (DCWW, 2019b). In Wales,
PWS is largely under the authority of Dŵr Cymru Welsh Water (DCWW), who provide an
average of 800 million litres of water per day to over three million people (DCWW, 2019a).
While the majority of surface water used is from lakes and reservoirs, a substantial proportionis taken from lowland river abstractions, especially for rivers in south Wales, such as the Wye,134

Dallison, Patil and Williams. Study III
Usk and Tywi, which are often supported by upstream reservoirs in times of low flow (DCWW,
2019a). While the water supply system in Wales is largely resilient to climate change, due tothe amount of water stored, changes in climate still do have the ability to change the required
water management and places greater pressure on the system (ASC, 2016b). The ASC report
a 12% supply-demand surplus for PWS in Wales, however, this is set to decline under
projected population growth and climate change, with three of DCWW’s water resourcezones expected to be in deficit by the 2080s (ASC, 2016b; DCWW, 2019b).
Projected increases in the occurrence of low flows will impact how often PWS and HEP
operators are permitted to abstract water from rivers in the future, with stringent hands-offflow (HoF) regulations applied to abstractions to ensure the protection of low flows
downstream. In addition, a greater occurrence of large flow events will potentially be of little
use to abstractors to compensate for less abstraction potential due to low flows, if systemcapabilities, such as turbine size for HEP, do not allow for it. It is therefore important to study
these future changes now, to successfully plan for systems that are resilient to climate change
and make the most of available future flows.
In this research, we study the impact of climate change on two catchments in Wales, UK,
Conwy and Tywi, especially in terms of future water availability for small-scale HEP generation
and PWS. This is a less-studied region of the UK in this regard, due to the perceived abundanceof water resources in Wales. However, owing to both the reliance on surface waters for PWS
and the large recent increase in small-scale HEP projects, this is an important area in which tounderstand future water resource changes.

6.3 Data and methods
6.3.1 Study catchments
Our two study catchments, Conwy and Tywi (Figure 6.1), were selected due to theircontrasting physical characteristics, especially in terms of catchment land use/land cover(LULC), mean catchment slope, and catchment area (Table 6.1); all potentially giving rise todifferences in hydrological regime. These catchments are also exploited for their water
resources in the form of HEP and PWS, with Natural Resources Wales (NRW) providing
licenses for sixteen HEP abstractions for non-impoundment run-of-river schemes in Conwy,

135

Dallison, Patil and Williams. Study III
and nine in Tywi (NRW, 2019). Both catchments are also used for PWS, however, only the
Tywi catchment has an abstraction taken directly from the river, with PWS in Conwy sourced
from reservoirs only (DCWW, 2019a). The downstream river-based abstraction in Tywi is
supported in times of low flow by upstream releases from the Llyn Brianne reservoir (DCWW,
2019b). This abstraction supplies the largest drinking water treatment plant in DCWW’s
network, serving a population of ~400,000 in Swansea, Neath, Bridgend and the Vale of
Glamorgan (DCWW, 2019a), making the abstraction an important location to study in termsof future water supply-demand balance.

Figure 6.1. Study catchments, displaying CORINE Land Cover classification (EEA, 2012). Inset, location ofcatchments within Wales, and Wales within the UK.

The Conwy catchment is more mountainous in topography, with a steeper mean slope, andgreater maximum elevation, peaking 261 m higher than the Tywi, at 1062 m, near the summitof Snowdon. The Tywi catchment, however, has more than double the drainage area ofConwy, at 1365 km2 to Conwy’s 541.8 km2 (Table 6.1). The catchments are also contrasting interms of LULC (Figure 6.1), with the Tywi being dominated by agriculture, pasture specifically(63.9%), which is well-distributed throughout all but the highest elevations in the catchment.

136

Dallison, Patil and Williams. Study III
The Conwy catchment, on the other hand, has a larger proportion of scrubland (42.1%), in
particular moors/heathland (23.6%) and natural grassland (16.6%), mostly to the west of themain channel, with the eastern side being more pasture-dominated (Figure 6.1). The Conwy
also features a large peat bog in the south of the catchment, accounting for 8.4% of total
catchment LULC; this is significantly more than is seen in the Tywi (Table 6.1). Forests cover aslightly larger area of the Tywi (16.0%) than the Conwy (13.7%), but the mix of forestry types
is different, with notably more coniferous forest in the Tywi (10.1%), while the Conwy has
equal proportions of coniferous and mixed forest (5.5%; Table 6.1).
Table 6.1. Key study catchments details. Catchment area, elevation, and slope data derived from 5 m resolution
OS Terrain 5 DEM from Ordnance Survey; land use/land cover data derived from 2012 CORINE Land Cover data
(EEA, 2012).

Catchment area (km2)

Catchment land use/land cover (%)

Maximum catchment elevation (m)Mean catchment slope (%)
Urban
Agriculture
ArablePastureForestBroadleaf
Coniferous
Mixed
Scrub
Natural grassland
Moors and heathland
Transitional woodland scrub
Peat bog
Sparsely vegetated areas

Conwy

Tywi

541.8
1062
19.7
0.7
30.6
0.1
30.5
13.7
2.7
5.5
5.5
42.1
16.6
23.6
1.9
8.4
4.1

1364.6
801
16.6
0.7
64.6
0.7
63.9
16.0
1.0
10.1
4.9
17.1
8.4
6.5
2.2
1.3
0.0

6.3.2 Future streamflow and climate projections
Future streamflows for both catchments were modelled using the Soil and Water Assessment
Tool (SWAT) at a daily time-step (Arnold et al., 1998, 2012; Neitsch et al., 2011) from 2021 to
2079. SWAT has been used extensively in the context of future hydrological regime change
and water resources assessment globally (Coffey et al., 2016; Perra et al., 2018; Sultana &Choi, 2018; Yuan et al., 2019; Khan et al., 2020), with publications focussing on both HEP (Park& Kim, 2014; Shrestha et al., 2016; Haguma et al., 2017; Abera et al., 2018) and water137

Dallison, Patil and Williams. Study III
availability (Sharma et al., 2016; Veettil & Mishra, 2016; Bessa Santos et al., 2019; RivasTabares et al., 2019).
Input data for SWAT were obtained from Ordnance Survey (5 m resolution OS Terrain 5 DEM;
Ordnance Survey, 2020), the EU soil database (European Commission, 2004) and the CORINELand Cover dataset (Copernicus Land Monitoring Data; EEA, 2012). Additionally, historicaldaily weather data (air temperature and precipitation) and streamflow data, both used formodel calibration and validation, was sourced from the Centre for Ecology and Hydrology’s(CEH) Climate, Hydrology and Ecology research Support System (CHESS) dataset (Robinson etal., 2017) and the National River Flow Archive dataset (NRFA, 2020), respectively. Streamflow
data was taken from gauges with as close to natural flow as possible, station 66011 for Conwy,
and 60006 for Tywi. The catchments were calibrated individually with the streamflow data,
for a 14-year period of 1985-1998. We used the Particle Swarm Optimisation method(Kennedy & Eberhart, 1995) for model calibration, with the Kling-Gupta efficiency (KGE) as
the goodness-of-fit metric (Gupta et al., 2009). The calibration KGE values were 0.770 for
Conwy and 0.841 for Tywi. Following the calibration of model parameters, we validated themodel for the 1999-2014 period and obtained the KGE values of 0.718 for Conwy and 0.717
for Tywi.
Future streamflows were projected based on the weather input derived from the UK Climate
Projections 2018 dataset, ‘Regional Projections on a 12 km grid over the UK for 1980-2080’(MOHC, 2018b). A worst-case scenario approach was taken, using an ensemble of twelve
regional climate models downscaled from the 60 km HadGEM3-GC3.05 global coupled model(Murphy et al., 2018), under Representative Concentration Pathway 8.5 (RCP8.5) conditions.
RCP8.5 represents a scenario of high population growth, continued coal use, and no downturnin global greenhouse gas emissions. This approach allows planners the ability to take accountof the most extreme adaptation/mitigation potentially need. A summary of futuretemperature, precipitation and streamflow is shown in Figure 6.2. Temperatures areprojected to increase in all seasons, while precipitation and streamflow both increase in thewinter and spring, but decrease in the summer and autumn, leading to a small overall annual
reduction.

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Dallison, Patil and Williams. Study III

Figure 6.2. Projections for seasonal and annual mean temperature, precipitation and streamflow for Conwy (left)
and Tywi (right). Bottom panels shows percentage change from 1990-2010 baseline average for a 5-year moving
average of seasonal and annual mean streamflow, at the gauging station locations identified in Figure 6.3.

6.3.3 Hydroelectric power assessment
Assessment of changes in the abstraction regime for HEP locations was undertaken for nonimpoundment, run-of-river based schemes only, of which there are a total of sixteen in
Conwy, and nine in Tywi. Abstraction locations were obtained from NRW. Due to lack of
specific information pertaining to each HEP scheme, such as abstraction licence conditions,139

Dallison, Patil and Williams. Study III
scheme layout, generation capacity etc., abstraction conditions were based on generallicensing guidelines laid out by NRW (NRW, 2020b). Guidance from the organisation statesthat for schemes creating a depleted reach (i.e. run-of-river schemes) and that do not operate
on rivers supporting salmon spawning or protected species, there are two types of abstraction
permitted, Zone 2 (Z2) and Zone 3 (Z3; NRW, 2020). It was assumed for the purposes of thisstudy that all schemes operate under Z2 or Z3; a summary of the conditions placed on
abstraction rates of these zones is made in Table 6.2. Schemes are categorised into eitherzone based on the gradient of the depleted reach, with those below 10% gradient being Z2,
and those above being Z3. As information on the actual depleted reach was not available, theaverage slope of the sub-basin immediately downstream of the abstraction location was
taken, and the corresponding zone type applied (Figure 6.3). Under the assumption that each
HEP site abstracts the maximum amount of flow available to it, and given daily average
streamflow (𝑄), Equations 6.1 and 6.2 were used to calculate daily average abstraction
volume (𝐴𝑑𝑎𝑖𝑙𝑦 ) at each location in line with abstraction rates dependent on the site zone:
𝑄𝑠𝑢𝑟𝑝𝑙𝑢𝑠 = 𝑄 − 𝐻𝑜𝐹(Eq. 6.1)

where 𝐻𝑜𝐹 represents the compensation hands-off-flow release required to protect low
flows and 𝑄𝑠𝑢𝑟𝑝𝑙𝑢𝑠 is the amount of water available for abstraction, used to calculate 𝐴𝑑𝑎𝑖𝑙𝑦 :𝑖𝑓 𝐴𝑑𝑎𝑖𝑙𝑦 < 𝐴𝑠𝑡𝑎𝑟𝑡0,
𝐴𝑑𝑎𝑖𝑙𝑦 = 𝑄𝑠𝑢𝑟𝑝𝑙𝑢𝑠 × 𝑄𝑡𝑎𝑘𝑒 { 𝐴𝑚𝑎𝑥, 𝑖𝑓 𝐴𝑑𝑎𝑖𝑙𝑦 > 𝐴𝑚𝑎𝑥
𝐴𝑑𝑎𝑖𝑙𝑦, 𝑖𝑓 𝐴𝑠𝑡𝑎𝑟𝑡 < 𝐴𝑑𝑎𝑖𝑙𝑦 > 𝐴𝑚𝑎𝑥

(Eq. 6.2)

where 𝑄𝑡𝑎𝑘𝑒 is the proportion of flow available for abstraction as per the zone conditions,𝐴𝑠𝑡𝑎𝑟𝑡 refers to the minimum abstraction volume required to start, and for efficient operationof, the turbine, and 𝐴𝑚𝑎𝑥 represents the maximum permitted abstraction volume. A further
assumption was that an impulse turbine is in use at each site, as is common with small-scale
HEP schemes in upper catchment reaches, such as those analysed in this study (Lilienthal etal., 2004; Cobb & Sharp, 2013; Židonis et al., 2015). Impulse turbines have largely high andstable efficiency after approximately 10% of designed flow is achieved (Paish, 2002; Novara& McNabola, 2018; Chitrakar et al., 2020), making them ideal for settings with variable Adaily(Cobb & Sharp, 2013). For this reason, Astart was set at 10% of Amax for each scheme, which isthe assumed design flow volume.

140

Dallison, Patil and Williams. Study III
Table 6.2. Abstraction conditions as defined by NRW guidelines for Zone 2 and Zone 3 sites (NRW, 2020b).

Condition

Definition

Gradient of the stream between
abstraction point and return flow
Streamflow rate below which
Hands-off-flow (HoF)
abstraction is not permitted
Maximum abstraction
Maximum rate of abstraction, above
volume (Amax)
which no additional flow can be taken
Proportion of flow between HoF andPercentage take (Qtake)
Amax permitted for abstractionaStreamflow volume exceeded 95% of the timeb
Mean annual streamflow volume
Depleted reach gradient

Zone 2

Zone 3

<10%

>10%

Q95 a

Q95 a

1.3 x Qmean b

Qmean b50%

70%

Figure 6.3. Studied abstraction locations for hydroelectric power (HEP) and public water supply (PWS); HEP
locations categorised by abstraction regime type. The ‘>’ and ‘<’ markers denote the river section of the PWS
abstraction, as to identify the specific location would violate data licence conditions. River network, with stream
orders defined by Strahler method, and catchment elevation, based on 5 m resolution OS Terrain 5 DEM from
Ordnance Survey, also shown.

6.3.4 Public water supply assessment
Assessment of the impact of future hydrological regime change on PWS was undertaken in
the Tywi catchment only, due to the lack of river-based PWS abstraction in the Conwy
catchment. The supply-demand balance at the aforementioned single major abstraction141

Dallison, Patil and Williams. Study III
location (Figure 6.3) was calculated using the Water Evaluation And Planning (WEAP) system(Raskin et al., 1992; Yates et al., 2005). WEAP is an integrated water resource management
model that assimilates the demand and infrastructure management with physicalhydrological processes and allows for multiple scenario analysis and comparison (Raskin etal., 1992; Yates et al., 2005). The WEAP model has been used extensively for scenario analysis
related to water resource planning and allocation in a variety of contexts, such as the impactsof climate and land use change (Purkey et al., 2008; Joyce et al., 2011; Ashofteh et al., 2013;
Esteve et al., 2015; Tena et al., 2019), reservoir and dam operation planning (McCartney &Menker Girma, 2012; Demertzi et al., 2014; Vonk et al., 2014; Azari et al., 2018), ecosystem
requirements and environmental protection (Thompson et al., 2012; Fatemi et al., 2013;
Flores-López et al., 2016; Momblanch et al., 2020), and population increase and urbanisation
impacts (Höllermann et al., 2010; Kumar et al., 2017; Toure et al., 2017; Alamanos et al.,2020). However, WEAP is not designed for detailed optimisation studies, such as that requiredfor HEP (Yates et al., 2005). For this reason, the model has only been used in the PWS analysis
for this study.
Future total daily water demand was calculated for three scenarios (Figure 6.4), representing
increased, static, and declining abstraction requirement. The increased demand scenario was
based on the linear relationship between historical daily temperature and total water
abstraction from the location, as presented by Dallison et al. (2020). The water abstraction
data was provided by DCWW for January 2012 to December 2016 period. The static demand
scenario was based on the same 2012-2016 data, with a mean for each day being taken acrossthe 5-year dataset; this year of mean values was then applied every year for the future period.
The decreased demand scenario used the same starting base as the static scenario, withdemand decreasing linearly by 20% across the period. This decrease is in line with thatprojected in DCWW’s latest water resource management plan (DCWW, 2019a), based on an
extensive leakage reduction programme and decreasing domestic water usage, despite aprojected increase in the population served. A compensatory HoF was also implemented in
WEAP, set at 681.91 million litres per day as laid out in the abstraction license for this location(SWWRA, 1965). Total daily unmet demand (𝐷𝑢𝑛𝑚𝑒𝑡 ) was then calculated under eachscenario using Equation 6.3:
𝐷𝑢𝑛𝑚𝑒𝑡 = 𝑄 − 𝐻𝑜𝐹 − 𝐷

142(Eq. 6.3)

Dallison, Patil and Williams. Study III
where 𝐷 is daily total water demand; 𝑄 and 𝐻𝑜𝐹 are defined as Equation 6.1. Due to theaforementioned system of reservoir low flow support by Llyn Brianne, Dunmet is therefore also
assumed to be equal to the total daily required reservoir release.

Figure 6.4. Future monthly total water demand at the Tywi public water supply abstraction under the three
future water demand scenarios: increased, static, and decreased. Thick dashed lines represent the linear trendof each scenario.

6.3.5 Trend analysis
Trends in the output data from the HEP and PWS calculations were analysed using MannKendall (MK) trend analysis (Mann, 1945; Kendall, 1975), in line with other studies that haveanalysed data relating to hydrological regime change (Murphy et al., 2013; Mwangi et al.,2016; Mudbhatkal & Amai, 2018; Dallison et al., 2020; Jin et al., 2020). The nonparametric
test was deemed suitable due to the nature of hydrological data, which is non-normally
distributed and exhibits seasonality. The Hamed & Rao method of auto-correlation correction(Hamed & Rao, 1998) was applied, along with Sen’s slope estimator (Sen, 1968), to evaluate
the direction and size of detected trends. Trends for all factors were analysed based on
seasonal (winter, December to February; spring, March to May; summer, June to August;autumn, September to November) and annual averages (hydrological years), or totals,
dependent on the factor. HEP trends for each catchment were analysed separately for the
average of all Z2 and all Z3 abstractions. The factors analysed were: (1) number of days where
Adaily is greater than Astart, i.e. number of days generation is possible, (2) number of days Amax
reached, (3) mean Adaily on days generation possible, and (4) total seasonal/annual
abstraction. For PWS, total unmet demand, number of days demand unmet, and mean unmet
demand, were all analysed under each demand scenario. For both HEP and PWS, MK analysis
143

Dallison, Patil and Williams. Study III
was applied to the full 2021-2079 (long-term) period, as well as to the end of a medium-term
period, 2021-2054. This approach is useful for HEP, as the near future analysis is more in linewith the life span of recently installed, and soon to be installed small-scale systems (Hatata etal., 2019; Killingtveit, 2019). For PWS, the near future analysis is a similar period as is currently
being planned for, 2050 being the end of the planning period for recently published water
resource management plans (e.g. DCWW, 2019). For Wales specifically, DCWW also recently
published a vision document to 2050 (DCWW, 2018). HEP and PWS baseline (2021-2030), near
future (2045-2054) and far future (2070-2079) decadal averages were also taken seasonallyand annually for the same factors as the MK analysis, to enable further visualisation of apotential medium- and long-term planning needs for both industries.

6.4 Results
6.4.1 Hydroelectric power
With regards to change in the number of days annually when Astart is achievable, allabstraction locations display a decrease in both the medium-term (2021-2054) and long-term(2021-2079), although these trends are statistically significant for more HEP abstractions in
the medium-term analysis than in the long-term (Figure 6.5). Trends in the number of days
when Amax is reached are more variable, especially in the long-term, with differences seen
both within and between the two catchments. The Conwy in this period, for example, exhibits
statistically significant increases for all abstractions in the west of the catchment, while thoseon the eastern side vary, with a statistically significant decrease being present for one location(Figure 6.6). In the medium-term, there is more agreement between abstraction locations,with all but one Z3 abstraction in each catchment seeing an increase in the number of days
when maximum abstraction is achieved. The combination of these two broad trends (fewerdays of abstraction, but larger abstraction volumes available) causes mean Adaily on days whenabstraction is possible, to increase for the vast majority of locations in both time periods, but
especially so in the medium-term (Figure 6.7). However, when observing the change in total
volume abstracted per year, a decrease is displayed in all locations, for both time periods(Figure 6.8). Notably in the medium-term trends for total abstraction, all Z3 abstractions had
statistically significant (p <0.05) decreases in volume, while all Z2 locations decline without
statistical significance (Figure 6.8), the only factor which showed such a split.
144

Dallison, Patil and Williams. Study III

Figure 6.5. Overview of the direction, magnitude and significance of annual trends in number of days Astartachieved for the periods 2021-2079 (top) and 2021-2054 (bottom). Trends detected by Mann-Kendall trend
analysis for individual hydroelectric power abstraction locations.

145

Dallison, Patil and Williams. Study III

Figure 6.6. Overview of the direction, magnitude and significance of annual trends in number of days Amax
reached for the periods 2021-2079 (top) and 2021-2054 (bottom). Trends detected by Mann-Kendall trend
analysis for individual hydroelectric power abstraction locations.

146

Dallison, Patil and Williams. Study III

Figure 6.7. Overview of the direction, magnitude and significance of annual trends in mean Adaily for the periods
2021-2079 (top) and 2021-2054 (bottom). Trends detected by Mann-Kendall trend analysis for individualhydroelectric power abstraction locations.

147

Dallison, Patil and Williams. Study III

Figure 6.8. Overview of the direction, magnitude and significance of annual trends in total abstraction for theperiods 2021-2079 (top) and 2021-2054 (bottom). Trends detected by Mann-Kendall trend analysis for individualhydroelectric power abstraction locations.

148

Dallison, Patil and Williams. Study III
Figures 6.9 and 6.10 provide more details on the seasonal trends observed in the four factors
studied, for long-term and medium-term respectively. Examining the trends taking place inthe 2021-2079 period, it can be seen that the most significant change generally occurs in thespring and autumn, with small declines in annual total abstraction and number of days Astartis achieved. In the medium-term, a greater number of statistically significant trends are seen
in winter and summer, with increases in annual mean Adaily also being statistically significant.
Total abstraction also varies considerably between the two periods, with significant increases
seen across both catchments and abstraction zones in winter and spring, and decreases insummer and autumn, leading to an overall decrease annually at the Z3 abstractions in themedium-term analysis. Little difference can be seen between the corresponding trends forthe two catchments, with trend direction agreeing in all but three occasions, and few
occasions of varying magnitude of change. In terms of difference between the two abstraction
types, once again little variation is seen. However, when studying the percentage change fromthe baseline (2021-2030) annual average, with the near and far future decadal annual
averages (Table 6.3), large differences in the number of days Amax is reached are shown
between the zones. In the far-future average, the Z3 abstractions display a 9% decline in Tywi
and stay stable in Conwy. Nonetheless, the Z2 abstractions increase quite significantly in both
catchments, 32% and 18% respectively. Few differences are seen between the zones,
catchments or time periods for the other factors studied. With the exception of total
abstraction, the magnitude of change from the baseline is greater for the near-future than
far-future.

Figure 6.9. Overview of the direction, magnitude and significance of annual and seasonal trends in futurehydroelectric power characteristics for the period 2021-2079, as detected by Mann-Kendall trend analysis. Basedon the average of all Zone 2 (Z2) and Zone 3 (Z3) abstractions in each catchment with the exception of total
abstraction, which is based on the sum of the total abstraction of all locations in each respective zone.

149Dallison, Patil and Williams. Study III

Figure 6.10. Overview of the direction, magnitude and significance of annual and seasonal trends in futurehydroelectric power characteristics for the period 2021-2054, as detected by Mann-Kendall trend analysis. Basedon the average of all Zone 2 (Z2) and Zone 3 (Z3) abstractions in each catchment with the exception of total
abstraction, which is based on the sum of the total abstraction of all locations in each respective zone.
Table 6.3. Percentage change in annual hydroelectric power characteristics when comparing far (2070-2079)
and near (2045-2054) decadal averages with a baseline decade of 2021-2030.

Conwy
Z2
Tywi
Conwy
Z3
Tywi

2070-2079 average
2045-2054 average
2070-2079 average
2045-2054 average
2070-2079 average
2045-2054 average
2070-2079 average
2045-2054 average

Astartachieved

Amax
reachedMean Adaily

Total
abstraction

-13%
-15%
-9%
-11%
-11%
-12%
-7%
-13%

+18%
+54%
+32%
+47%
0%
+11%
-9%
+11%

+1%
+12%
+1%
+8%
+3%
+5%
-1%
+5%

-11%
-5%
-7%
-2%
-9%
-8%
-8%
-7%

6.4.2 Public water supply
Under all three demand scenarios, in both the medium- and long-term, we observe an
increase in the number of days annually when streamflow is not sufficient to satisfy demand,
as well as an increase in the mean and total unmet demand volume (Figure 6.11). These
trends are larger in magnitude for the 2021-2054 period and are also statistically significant
for this study period (p <0.05), with the exception of mean unmet demand in the decreased
demand scenario (Figure 6.11). No unmet demand is recorded at any point in the wintermonths under any scenarios, whereas Kendall’s Tau results suggest a decrease in unmet
demand in spring, especially in the medium-term analysis, although this is not statisticallysignificant. Autumn displays the most consistency in trends across both time periods and thethree demand scenarios, with large magnitudes of change seen in total unmet demand andnumber of days demand unmet.
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Dallison, Patil and Williams. Study III

Figure 6.11. Overview of the direction, magnitude and significance of annual and seasonal trends in future public
water supply unmet demand characteristics for the periods 2021-2079 and 2021-2054, as detected by MannKendall trend analysis. No unmet demand is recorded in winter throughout the future period; therefore, no
trends are displayed.

Further analysis (Table 6.4) reveals that for all factors considered, and under all demand
scenarios, the degree of change between the annual averages of the baseline and near future,
is much greater than that between the baseline and far future. For example, under theincreased demand scenario, total unmet demand is 167% higher than the baseline in the near
future, whereas the far future period is only 84% higher. Similarly, for the number of days
demand is not met in the static demand scenario, the near future average is 151% higher thanthe baseline at 43.4 days per year, compared to only 30.7 days per year in the far future
period, a 77% increase on the baseline of 17.2 days (Table 6.4). The same is true for mean
unmet demand in the decreased demand scenario, with the near future period being 49%
higher than the baseline, and the far future being only 10% greater.
Table 6.4. Percentage change in annual public water supply unmet demand characteristics when comparing near(2045-2054) and far (2070-2079) future decadal averages with a baseline decade of 2021-2030.

Increase demand
Value
Total unmet
demand(million m3)
Days demand
unmet
Mean unmet
demand (m3)2021-2030
2045-2054
2070-2079
2021-2030
2045-2054
2070-2079
2021-2030
2045-2054
2070-2079

1.55
4.12
2.84
17.2
43.9
31.4
57,015
93,16273,506% changefrombaseline
+167%
+84%
+155%
+83%
+63%
+29%

151

Static demand
Value
1.53
3.95
2.58
17.3
43.4
30.7
55,89390,232
68,721% changefrombaseline
+159%
+69%
+151%
+77%
+61%
+23%

Decreased demand
Value
1.51
3.60
2.11
17.2
42.8
28.6
55,63983,14061,351% changefrombaseline
+138%
+40%
+149%
+66%
+49%
+10%

Dallison, Patil and Williams. Study III

6.5 Discussion
6.5.1 Hydroelectric power
The results of the MK trend analysis conducted on HEP abstraction characteristics display arange of changes, with most variation seen between the seasons and time periods studied,rather than between catchments or abstraction zones. These patterns are driven by thestreamflow changes shown in Figure 6.2. An example of this are the declines seen in springand summer flows in Conwy and Tywi up to the late 2050s, followed by an increase in summer
flows to the end of the study period for Tywi and a plateau for Conwy. Autumn flows plateau
after this point for both catchments. These seasonal streamflow changes lead to thedifferences in trends observed in the MK analysis, with larger and more statistically significant
results being seen in the medium-term than long for declines in mean abstraction, number ofdays Astart achieved, and number of days Amax reached. Changes in these factors in summer inparticular are only significant in the medium-term analysis, due to the levelling off (Conwy)
and increase (Tywi) seen in streamflow after this period. This observed shift towards less daysand volume of abstraction in the summer and autumn, and an increase in the winter andspring is in line with previously published research for Wales and the UK. To our knowledge,
the only previous study that has investigated climate change impacts on hydropower in Wales
specifically is the aforementioned work of Carless & Whitehead (2013). Their researchpresents trends in streamflow and potential HEP output for streams in the upper Severncatchment, similar in nature to the abstraction locations studied in the present research. A
baseline, 2020, and 2050 scenarios are considered, with a marked decline in flow and power
output in summer and early autumn months displayed in 2050 (Carless & Whitehead, 2013).
Winter and early spring months show a smaller magnitude increase for the same period,
leading to a stable situation annually (Carless & Whitehead, 2013). These results align wellwith that of our study’s medium-term trend analysis for total abstraction. While no further
studies use Wales as a region of focus specifically, other research based on the UK, or regions
thereof, seems to be consistent with our results; Scotland for example, which is comparable
to Wales due to the similar nature of catchments and also due to the high uptake of HEP in
the country. Sample et al. (2015) summarises the research on the potential impacts of climatechange on Scotland’s HEP. It is suggested that run-off, which is highly sensitive to climatechange, is closely linked to generation potential. This relationship, in line with the results of152

Dallison, Patil and Williams. Study III
this study, is projected to cause a decline in summer generation potential; although this is
likely to be offset in the winter, HEP schemes may be unable to make use of the higher flows
due to design limitations (Sample et al., 2015). Further work in Scotland by Thompson (2012)
on the impacts of climate change on streamflow also lend weight to the findings of thisresearch, once again suggesting an accentuation of low summer flows and high winter flows
in the future. These trends are also echoed on a UK-scale by research such as UK CEH (2012)
and Kay et al. (2020) both corroborating the seasonal and annual driving streamflow trends
observed in the study catchments of this research.
Furthermore, the trends observed of mean abstraction volume increasing, while total
abstraction volume is decreasing, suggests that there will be fewer days of abstraction in the
future, but with a greater abstraction per day. This trend also fits with the observed decreasein the number of days Astart is reached, and the increases seen in the number of days Amax isachieved. Once again, this is particularly pronounced in the medium-term analysis and is
supported by research on low and high flows in Wales. Collet et al. (2018), Marx et al. (2018),
Thober et al. (2018) and Visser-Quinn et al. (2019) all suggest that for Wales, in the future,
the magnitude, duration and frequency of extreme high and low streamflows will increase.
In terms of observed differences between Z2 and Z3 abstraction types, a smaller magnitudeof decline is observed for change in total abstraction in Z2 abstractions compared to Z3 in themedium- and long-term, as well as lack of statistical significance. This suggests that Z2
abstractions are potentially more resilient to climate-induced streamflow change in terms ofannual total generation potential. This is possibly caused by the greater volume of water that
it is allowable to abstract at these locations, being 1.3 times Qmean, as opposed to Qmean onlyin Z3 abstractions. This difference allows for Z2 abstractions to make greater use of larger
magnitude flows and the observed increases in number of days Amax is reached annually. Thisis further exemplified in the fact that Z2 abstractions on average in both the medium- andlong-term have a larger increase in the number of days Amax is reached, than Z3 abstractions.
While differences are seen for certain aspects between the two abstraction conditions zone
types, little difference in observed trends is seen when comparing the two catchments. This
lack of variation is likely due to the fact that the vast majority of the currently operating
schemes, and therefore those studied, are located in the uppermost reaches of the153Dallison, Patil and Williams. Study III
catchments, predominantly on first order streams. This positioning leaves little time for LULC
differences to cause impact to run-off generation and streamflow significantly between
catchments. The same is true for topographical characteristics, with the majority of
abstractions being located in topographically homogenous areas across the two catchments.
While all four of the factors studied are important for HEP scheme design, perhaps the mostimportant in terms of future profitability is total abstraction. For all time periods, catchments
and abstraction zones, a decline is observed, suggesting less generation output in the future.

6.5.2 Public water supply
When viewing the unmet demand trend analysis results with the streamflow and demand
trends, it can be seen that streamflow is the major factor, rather than water demand. In
particular, large declines observed in summer and autumn streamflow lead to continued
shortages of water, even in the reduced demand scenario. Similarly to HEP, the characteristics
of future streamflow change also correspond with the difference seen between the mediumand long-term trend analysis conducted for PWS unmet demand. Spring streamflows increase
considerably from the mid-2040s, while winter flows display a small increase; annual
streamflow volume remains relatively stable throughout. Increases in winter and springstreamflows however do not factor into annual mean and total unmet demand, as streamflow
is continually sufficient during these seasons. This means there is no offset for the increased
demand and decreased streamflow seen in summer and autumn, leading to an increase
annually in the number of days and volume of unmet demand under all scenarios. This
problem is particularly pronounced in the medium-term, with both summer and autumn
streamflow declining most steeply up to the mid-2050s. These results agree with publishedliterature, with studies such as Sanderson et al. (2012), Henriques et al. (2015), and Afzal &Ragab (2019) projecting large decrease in water availability by the 2050s and beyond for theUK, and Wales specifically. Furthermore, the findings are commensurate with reports fromorganisation such as the ASC (2016a, 2016b), stating more action is needed to tackle futurewater supply security; Welsh Government (2015b), detailing the necessary steps for resiliencein Welsh water supply; and, for catchments reliant on river-based abstraction such as Tywyn
Aberdyfi and Pembrokeshire, DCWW (2019a). Imbalances observed could lead to problems
for water supply, especially in times of prolonged drought, as increases in winter and springflow will do little to combat summer and autumn shortages if reservoirs are already full.
154

Dallison, Patil and Williams. Study III

6.5.3 Study implications
With regards to HEP abstractions, due to lack of scheme specific data, the results represent
an indication of future change in generation potential and timing for the study catchments,and more broadly for catchments across Wales, and the 364 HEP projects there within (Welsh
Government, 2019b). For example, seasonal shifts in streamflow, and therefore generation
capacity, will likely still impact on specific scheme layouts in a similar way to that that has
been assumed in this study. The results are therefore helpful in planning future schemes andmore generally, alteration in UK energy mix. Significantly less generation is expected to bepossible in the summer and autumn in the future, which will be important to consider whenplanning the future resilience of the energy network. Overall, the results show that in themedium-term (2021-2054), abstraction characteristics and potential at installed HEP
generation sites is likely to change greatly compared to the baseline; this is significant as thisperiod falls at the end of the general lifespan of small hydroelectric projects (~40 years) being
built in the last and upcoming five years (Hatata et al., 2019; Killingtveit, 2019). For those sites
under development in the next five years, it may be beneficial to review scheme design inview of future flow conditions, for example by installing a larger turbine to make more use ofincreases in high flows, thus maximising generation potential over the lifetime of the project.
In the longer-term, the magnitude of observed changes is smaller for all factors except total
abstraction, than the medium-term. Therefore, when site redevelopment and replacement
opportunities arise in the future, it will be important to once again looking at future projectedchange to best determine the most efficient scheme upgrades and alterations.
In terms of PWS, while the specific abstraction studied may be relatively resilient to futurechanges in streamflow, due to the supporting flow provided by the large upstream reservoir,
the results of this work could have important implications for other, non-supported, directriver abstractions. The changes in streamflow and results on unmet demand demonstrated inthis study, if applied to other catchments in Wales and the UK, could have major implications
for future water supply sustainability. The increases in unmet demand that have been
observed, even under a decreased demand scenario, clearly suggests that the pace of change
in reduction of water demand by processes such as leakage reduction and domestic water
use education and awareness raising, needs to be implemented as soon as possible. These155

Dallison, Patil and Williams. Study III
measures should certainly be the first step in ensuring future water supply security, rather
than large infrastructure projects to increase supply, in all but the most pressing of cases.
The results and implications of this work can also be paralleled to other large water
abstractors in the region which will be operating under the same streamflow regime changes.
Industrial and agricultural users in particular could face challenges, causing alterations inoperation practices and timing, due to lack of available water, especially for those abstracting
directly from rivers and streams. The abstraction location and use data used to identify runof-river HEP schemes for this study details over 217 surface water-based abstractions used
for industrial and commercial purposes, and a further 444 used for agriculture across Wales.
These numbers highlight the potential scale of impact of future hydrological regime changes,
and the need to act now to ensure resilience to such changes.

6.6 Conclusions
This research has demonstrated clear trends in future availability of water for the HEP
locations studied in the Conwy and Tywi catchments, as well as the major PWS abstraction in
the Tywi. Our results highlight that spring and autumn are the key change seasons long-termin terms of water availability, while winter and summer are more impactful in the mediumterm. The results also demonstrate the need for action now, especially in terms of theplanning and design of HEP installations. Schemes and turbines must be designed with future
streamflow patterns in mind, in particular increasing winter and spring flows, rather thandecreasing summer and autumn flows, thus allowing for the maximisation of power
generation over the course of a year. Additionally, regardless of future demand decline,
increased pressure is likely to be placed on PWS due to large declines seen in future
streamflows, therefore careful planning is also required here in order to ensure continued
water supply.
Finally, we suggest avenues of future research. Firstly, the impact of different climatechange/emissions scenarios on future HEP output and PWS availability is required at anational-scale. While a worst-case scenario approach has been taken in this study, it isparamount to consider the range of potential outcomes under different emissions pathways,
to ensure that the most proportionate future planning and adaptation measure are156

Dallison, Patil and Williams. Study III
undertaken. Furthermore, research on specific HEP schemes, with historical generation data,
would be highly beneficial as this would allow future climate change scenarios to be tested
after calibration with actual data, giving a better understanding of how future hydrologicalregime change will impact on generation potential. Secondly, further research taking a holistic
view of all catchment abstractions is needed, including industrial, commercial, andagricultural purposes, as well as PWS and HEP. This approach would give greater insight to
how total catchment water demand will impact on individual water users, as well as thecatchment as a whole. In addition to this, the inclusion of impacts of other climate change
mitigation measures, such as upland management for flood control, would give a complete
picture of future potential alterations. This holistic catchment level approach would give thebest chance of ensuring fair and equitable distribution of changing future water resources.
Acknowledgements: This study was conducted as part of the Dŵr Uisce project, which aims
at improving the long-term sustainability of water supply, treatment and end-use in Irelandand Wales. The project has been supported by the European Regional Development Fund(ERDF) through the Interreg Ireland-Wales Co-operation Programme 2014-2020. The work
has been used as a case study for Dŵr Uisce Work Package 7 “climate change”, and forms partof a study assessing the impact of climate change on water resource exploitation in Wales.
The authors would like to thank Natural Resources Wales for the provision of abstraction
location and type/use data, and Dŵr Cymru Welsh Water for the provision of actual
abstraction volume data.

157

CHAPTER 7: DISCUSSION

158

Discussion

7.1 Hydroclimatic trends and implications
While a discussion on the findings of each study is found in each of the study chapters, here
we will discuss the results in relation to each other, and their applicability at a larger scale.
When the results of all three studies (Chapters 4, 5, and 6) undertaken in thesis are viewed
together, or as a subset thereof, several aspects become clear. First, it is evident thatobserved historical changes in hydroclimatology are indicative of the beginning of what could
be much larger change for Wales in the future. For example, in the analysis of the 34 years ofhistorical data, only small change is seen for the most part in seasonal and extreme
streamflow, however, much greater change is projected for the future, as increasing global
greenhouse gas concentrations continue to take greater effect on the global climate system(IPCC, 2013). It is also notable that those statistically significant trends seen in the historical
data continue into the future; the observed trend of increased average autumn temperatures,for example, continues for all catchments to 2080 (Section 5.4.1). This trend also follows the
expected acceleration of global temperature changes over the next 60 years (IPCC, 2018),
with the average rate of change across all five catchments between 1982-2015 being 0.039°C
per year, compared to 0.062°C per year between 2021-2080. Additionally, trends observed inboth Study 1 and Study 2 demonstrate a greater degree of change in extreme events thanseasonal and annual averages; historically, this change is in agreement with work for the UK
such as Hannaford & Buys (2012), Hannaford (2015) and Watts et al. (2015a). Accentuationof streamflow seasonality is most pronounced in the future average and extreme trends
presented in Study 2 (Section 5.4.2), with winter and spring set to get wetter, and summer
and autumn drier, in line with accepted projections for the UK (Prudhomme et al., 2012;
Suggitt et al., 2015; Garner et al., 2017; Lowe et al., 2018).
The results shown in Studies 1 and 2 are also indicative of the results found at a European
scale. Stahl et al. (2010), for example, when studying 441 near-natural catchments acrossEurope for the period 1962-2004, presented similar trends to those found in Study 1 for
Wales. This similarity in changes shown for Wales is especially true for trends in seasonality,with these in turn being very similar to the magnitude of change presented for other Atlanticinfluenced western European countries (Stahl et al., 2010). In addition, Forzieri et al. (2014)
found similar future seasonal shifts for non-snow influenced catchments in Europe as arepresented in Study 2 of this thesis. The 7-day average streamflow of a control period (1961-

159

Discussion1990) was compared with projected streamflow in the 2080s under the SRES A1B scenario(equivalent to between Representative Concentration Pathways (RCP) 6.0 and 8.5) from thefourth assessment report of the Intergovernmental Panel on Climate Change (IPCC). Of thetwelve catchments studied, those in western and central continental Europe in particular(especially France, Italy, and the Czech Republic) show the same accentuation of winter highflows and summer low flows (Forzieri et al., 2014) as projected in the results of this research(Section 5.4.2.3). In addition, the work of Schneider et al. (2013) shows trends for decreases
in annual 1-day minimum flow, and increases in 1-day maximum flow in temperate oceanic
Europe, when comparing a baseline period of 1971-2000 with modelled data for 2041-2070.
Their trend observations clearly correlate with those shown in Section 5.4.2.2 of this thesis,
being similar in magnitude as well as direction.
The benefit of conducting climate change evaluation studies and impact assessments at acatchment scale is highlighted in particular in Studies 1 and 2. The results of both chaptersdemonstrate how factors such as physical geographic location, as well as catchment
characteristics can impact on the influences of nationwide climatic forcing. The streamflow
results presented in Study 1 (Section 4.4.1.3), for example, show how the catchments in thenorth of Wales display different trends to those in the south, due to the impact of the North
Atlantic Oscillation, as evidenced in earlier research by the likes of Dixon et al. (2006),
Beranová & Huth (2007), and Osborn (2011). Furthermore, Study 2 describes how different
and individual catchment characteristics such as underlying soil type, topography, and land
cover can influence the responses of a catchment to long-term changes in climate. This isparticularly well demonstrated by the Conwy and the Dyfi, which display similar changes instreamflow characteristics, aside from low flows in winter and spring, as well as annual
average flow (Section 5.4.2). These differences are chiefly caused by variation in land covercharacteristics, in particular a greater proportion of coniferous forest in the Dyfi which has
been shown by Robinson et al. (2003) to help maintain baseflow conditions. This differenceis the key driver of the distinct opposition of trends seen in terms of future total phosphorous(TP) and total nitrogen (TN) concentrations in winter and annually in particular, with the Dyfi
showing a decline, and the Conwy an increase (Section 5.4.3). Both TP and TN are shown tocorrelate with streamflow statistically significantly in the modelling, explaining thediscrepancies between the two catchments, despite the broadly similar hydrological response160

Discussion
overall. These findings correspond with the findings of Dearmont et al. (1998) and Price &Heberling (2018) who detail decreases in overall water quality, including nutrients, withincreased streamflow. Furthermore, a study by Whitehead et al. (2009b) also reports a
decline in water quality linked with climate change influenced alterations in streamflow.
Dissolved oxygen (DO) and TP concentrations in particular are shown to be highly sensitive to
streamflow (Whitehead et al., 2009b), once again demonstrating that a catchment approach,taking account of local streamflow conditions, is key for robust projections of future water
quality changes.
Moreover, results presented in Study 3 show how even within catchments, hydrologicalresponses can vary (Section 6.4.1). The Conwy catchment in particular displays significant
trends only in the most westerly hydroelectric power (HEP) abstraction sites for long-term(2021-2079) changes in the number of days maximum abstraction volume is achievable, andthe mean annual abstraction of water (Section 6.4.1). This finding of intra-catchment variation
corresponds with those of Coulthard et al. (2005), who linked within catchment variation inerosion and deposition phases with local discharge distinction in the River Ouse, Yorkshire.
Similarly, Tetzlaff et al. (2007) showed that differences in soil type was the key driver of intracatchment variability in streamflow for a 223 km2 case study catchment in Scotland.
Additionally, changes in HEP generation characteristics seen in this thesis, such as a decreasein the number of days per year when generation is achievable, link well with alterations in
extreme streamflow characteristics detailed in Study 2 (Section 5.4.2.2). In this case a decline
in minimum flow volumes and an increase in number of low flow days is the driving cause;
consistent with the findings of Carless & Whitehead (2013) for the River Severn. Furthermore,nuances in the few differences between the HEP results for each catchment also link tovariation in extreme streamflows. Taking the number of days maximum abstraction isachievable in summer for Zone 3 abstractions in the long-term, as an example, it can be seenthat the opposing trends (decrease in Conwy, increase in Tywi) correlate with thecorresponding opposing trends for both catchments in 1-day maximum flow, and to a lesser
extent, number of high flow days. This high sensitivity of HEP schemes to small changes instreamflow trends is mirrored by the findings of Sample et al. (2015), for Scotland, who foundgeneration potential, and therefore profit margin to be highly sensitive to changes inprecipitation and streamflow. Furthermore, one of the first papers to look at climate change

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Discussion
impact on HEP in the UK, Harrison (2006), found that decreases in generation potential insummer months are likely to outweigh the increases in winter months, due to design
limitations of installed infrastructure. Relatively little work has been completed either
nationally, regionally, or globally on the impact of climate change on HEP generation, thefindings presented here go some way to starting to address this for small scale HEP, with theresults being more widely applicable than the area studied. Small-scale HEP technology isbroadly standard globally, and while clearly different countries will have different regulationsin terms of abstraction licence conditions, the results presented here can still be generalised
to similar catchments in other regions. Indeed, with both the historical and future streamflow
patterns presented in this thesis corresponding well with the trends seen in northern andcentral Europe in other work, as discussed earlier, it is reasonable to assume that HEP resultspresented in Study 3 are also indicative of likely change in abstraction and generation
potential for those areas.
Study 3 also highlights the importance of study length when conducting impact assessments,
with considerable differences being observed in the trends displayed for medium-term (20212054) and long-term (2021-2079) analysis periods (Section 6.4). Therefore, it is crucial that
impact assessment studies take account of this, to ensure that projected future trends, andconsequently their impact, are realistic and useful to the industry being studied. In thisinstance, the medium-term scenario is particularly applicable to HEP as it falls within the initialexpected generation lifespans of schemes installed recently, giving important insight into thepotential changes in generation regime. For public water supply (PWS), both future periods
studied are useful, allowing for medium- and long-term planning to be implemented.

7.2 Future water quality implications for abstractors
One key relationship that has not previously been discussed in this thesis is the impact of thewater quality findings presented in Study 2 (Section 5.4.3), in relation to the results of Study
3 (Section 6.4) for both PWS and HEP. For HEP, changes in suspended sediment (SS) loads arelikely to have a deleterious impact on infrastructure, in particular the turbine blades andcasing. Changes in SS loads are expected to cause additional abrasion, scouring and pitting,especially when considering that some of the largest increases in SS loads are seen in the
periods of heaviest streamflows, and therefore greatest throughput of water and sediment162

Discussion
through the HEP system. Indeed, Padhy & Saini (2008) conclude that sediment concentration,
flow velocity, grain size, and grain hardness are the key determinants of abrasion potential ofsuspended sediments on HEP turbines. Work by Naidu (1999) supports this, describing how a
10% increase in water velocity (analogous to sediment velocity here) increases erosion
capability by 27%, while Neopane (2010) describes a directly proportional relationshipbetween erosion potential and particle concentration. Under the future projections made in
Studies 2 and 3, both of these factors increase, with greater concentrations of SS shown to be
likely during the main generation seasons, as well a larger proportion of time that HEP
schemes are running at maximum capacity, and mean abstraction on generation days
becoming larger. In addition, while we can assume that grain hardness remains constant overthe period of study, larger flow velocities such as those projected in this work also have theability to suspend larger particles, thereby further increasing the abrasion potential. The
findings of Worster & Denny (1955) demonstrate that when considering erosion caused bysliding and grazing, particle size is directly proportional to amount of wear. Although, further
findings by Padhy & Saini (2011) showed that fine sediments can also be highly damaging toturbines, especially in systems with high head.
The multi-faceted change in abrasion potential clearly have implications for the maintenanceand life-span of HEP schemes, even despite the projected overall decrease in future total
abstraction potential. These changes could impact on their viability, productivity, and overall
output of schemes, further confounding pressures already faced by small-scale HEP such as
business rate increases and the curtailment of feed-in-tariffs, as detailed in Section 1.2.
Careful consideration should therefore be given to this area during the design, construction,and operation phases of new HEP schemes, in order to ensure that either greater protections
are made (e.g. finer screens on intake weirs, or sediment bypass systems) or more regularservicing of parts is completed. For schemes that are already in operation, greater monitoringshould be undertaken, to guard against component failure or system damage, and in somecases, upgrading of parts or retrofitting of systems may be needed (Hauer et al., 2018).
In addition, the impact of the studied water quality parameters on PWS is likely to be large,
not just due to the four factors themselves, but also due to knock-on impacts, such as higher
TN and TP levels giving rise to greater eutrophication. These alterations are likely to lead toan increase in required treatment to maintain drinking water standards, this has economic

163

Discussion
and operational efficiency impacts, and consequently an environmental toll. The link between
raw water quality and treatment cost has long been established, with Dearmont et al. (1998),
for example, showing that for twelve water treatment works in Texas, a 1% increase in SS
concentrations increases chemical costs by 0.25%. This increase in cost is not just related to
the removal of sediments however, as SS can be seen as an indicator of wider water quality,
which also requires greater treatment (Dearmont et al., 1998). Indeed, SS is an importanttransport medium for various pollutants which can become adsorbed to sediment grains(Dearmont et al., 1998), including phosphorus (House et al., 1998), heavy metals, and bacteria(Loperfido, 2014). This clearly makes SS a highly influential water quality parameter, withincreases in concentrations bringing a variety of challenges for water treatment facilities,
likely increasing costs and chemical requirements, and lowering treatment capacity.
A review by Price & Heberling (2018) investigated the impacted of changes in water quality
parameters on the operation and maintenance (O&M) cost of water treatment plants. Alongwith sediment, nutrients and pesticides (TP, TN and nitrates) were found to be among the key
drivers of O&M cost increases induced by lower raw water quality (Price & Heberling, 2018).
Various studies support this conclusion, Forster & Murray (2007), for example, found a 0.08%
increase in O&M cost per 1% increase in total pesticide load, when studying the agricultural
Maumee Catchment in Ohio. These results correspond with the results of Murray (2001) and
Fiquepron et al. (2013) who found 0.12% and 0.01% increases in O&M costs per 1% increase
in total pesticide load, in water taken from the Great Lakes and catchments across France
respectively. Also looking at nitrogen loads individually, Fiquepron et al. (2013) demonstrated
a 0.05% O&M cost increase, while Mosheim & Ribaudo (2017) found the value to be 0.06%,
both results are relative to 1% increase in total nitrogen concentrations. For phosphorous
individually, the results are similar, McDonald & Shemie (2014) reporting a 0.19% increase
per 1% concentration increase, and Heberling et al. (2015) suggesting a value of 0.06%.
When considering the wastewater treatment side of the water supply process, Astaraie-Imaniet al. (2012) found that future climate change will reduce water quality downstream ofwastewater treatment works in the UK. This is caused by more frequent releases from storm
tanks and combined sewer overflows due to increases in the frequency and magnitude ofextreme precipitation events (Astaraie-Imani et al., 2012), as also demonstrated in Section
5.4.2.2. These projections are supported by the work of Abdellatif et al. (2015) for the164Discussion
northwest of England, who project a 37% increase in total combined sewer overflow spill
volume and a 32% increase in total spill duration, when comparing 1961-1990 with 2070-2099
under a future emission scenario equivalent to RCP8.5. These results are congruous withthose of Tait et al. (2008) who indicate a combined 40% increase in sewer overflow and floodtank spill volume by 2080 for an urban catchment in the UK. The most prominent water
quality implications of these changes are declines in dissolved oxygen (DO) concentrations,increases in nutrient levels and the release of harmful bacteria to waterways (Astaraie-Imaniet al., 2012). These changes have important implications for downstream ecology, as well
impact on bathing waters and shellfish harvesting. Given that combined sewer systems arecommon in Wales and the UK, and that coastal waters support a variety of industries, notleast of all tourism and fishing, this is clearly an area of concern for the region, and nation.
In light of the findings presented above, and given the degree of projected water qualitychange in Study 2, it is clear that water service providers could face substantial challenges inmaintaining both acceptable future water supply quality and water quality downstream ofwastewater treatment facilities. In terms of drink water supply, the first steps in trying toensure output quality could be to lengthen retention times in the different treatment
processes or altering the dosing of chemicals used, followed by changing treatment processes
if needed. Both of these options are likely to decrease the volume of water that is possible totreat in a given timeframe, therefore effecting plant efficiency, expenditure per litre treated,
and the environmental impact in terms of additional chemical use. It is also possible that suchchanges would not be sufficient to maintain drinking water standards, therefore investment
in both new treatment infrastructure, as well as new water sources may be necessary toensure the safety and continuity of supplies (Jones et al., 2007; Hanson et al., 2016).

7.3 Summary
Overall, this work has shown that catchments in Wales have already undergone alterations
due to climate change, and that these alterations will continue into the future, likely at an
expedited rate compared to that already seen. Such changes in average and extreme
streamflows, demonstrated in this thesis, will have wide-reaching consequences. Firstly, river
systems themselves will alter due to changing flow regime as a consequence of alteredprecipitation input. This will be especially felt in terms of decreased water quality and likely
165Discussion
changes in aquatic life and geomorphological characteristics. Furthermore, catchments as awater resource, it has been shown, will alter, with changing timing, quantity, and quality ofwater available for abstraction or use. Given the reliance on surface waters in Wales, this
could have significant impacts in terms of agricultural productivity, energy generation,
manufacturing activity, the leisure and tourism industry, and PWS; consequently, thesechanges could have a large impact on the economy of the nation.
In addition, the trends observed in this research appear also to be relevant to a wider region
than that focussed on in this thesis. Several studies that have examined the whole of Europe,and that have been presented in this discussion (Stahl et al., 2010; Schneider et al., 2013;
Forzieri et al., 2014; Papadimitriou et al., 2016), have shown that the magnitude and directionof trends seen in Wales, generally corresponds with other northern and western European
areas where the climate is also influenced by the Atlantic. The results of this research cantherefore be seen as also adding value to the knowledge base of this wider region, and notjust those catchments and the area specifically studied. It should also be kept in mind, thatthe projections shown in this work offer a worst-case scenario of the future, based on highfuture global greenhouse gas emissions. Therefore, while planning for the future impacts ofclimate change is highly important, and these results do offer crucial insight, it is likely thatthe magnitude of changes seen, and therefore impacts, will be reduced. Nonetheless, robust
planning for future water resource allocation is critical, therefore, the new findings andconclusions drawn from this thesis and the research within are important and useful.

166

CHAPTER 8: CONCLUSIONS

167

Conclusions

8.1 Final conclusions
The research presented in this thesis has been undertaken to further our knowledge in thefield of catchment level hydroclimatic changes in Wales, and the impacts of such changes on
the exploitation of surface water resources. Through worst-case future emission scenariohydrological modelling it has been shown that Wales could potentially face substantial
challenges in the future in terms of supply-demand balance, not just for public water supply(PWS), but for all sectors that make use of surface water resources. Increased futurehydrological seasonality has been shown, with spring and autumn being the most susceptible
to change both in terms of average flows and extreme events. Furthermore, significant
potential future challenges in terms of water quality have been identified, which will impact
on those tasked with managing surface waters in Wales, as well as any users or abstractors of
the resource in the region. Two sectors have been explored in terms of the changes identified,
PWS and small-scale hydroelectric power (HEP). Both sectors face the need for adaptation inorder to cope with changes in the timing, quantity, and quality of surface water resources in
the medium- and long-term.
Overall, this thesis has been successful in achieving its aim of an assessment of the impact of
future climate change on the streamflow characteristics of catchments in Wales, and anevaluation of the effect of such changes on the abstractable water resource (as laid out in
Section 1.3). Historical changes in daily temperature, precipitation and streamflow have all
been analysed for the period 1982-2015, as well as corelated with total water demand for
2012-2015, giving new insight into water use under changing weather conditions in theregion, and addressing objectives I and II of this thesis. Objectives III and IV were achieved
with five induvial hydrological models being set-up, calibrated, validated, manipulated, andimplemented with future worst-case emissions scenario climate data, allowing for an analysis
of future hydrological regime and water quality change. Finally, objective V has beenaddressed in terms of a projection of future unmet demand for a key river-based PWS
abstraction location under three demand scenarios, as well as future abstraction potential for
25 HEP installations in two catchments.
The future hydroclimatic picture for Wales is complex and potentially faces major change, butthat is not to say it is without hope, especially with studies such as those presented in this168

Conclusions
thesis providing valuable insight into the future planning, management and adaptation need.
A significant proportion of future Welsh prosperity and development could be linked to theimportant surface water resource. Therefore, further study focussing on Wales specificallyand the challenges it faces, in the manor outlined above, is essential. As aforementioned, thestudy has also highlighted the importance of local-scale study, as well as national level
analysis, with each scale important for different purposes and a combination of the two
approaches required to best plan for the future management and use of water resources.

8.2 Recommendations for further work
While the findings of this research have addressed knowledge gaps surrounding the impactsof climate change on the hydrology of Wales, and water resource use thereafter, further
research is still required, especially around impact assessment. It is clear from the results ofthis work for example, that extreme high and low streamflow events are increasing in termsof frequency and magnitude (Chapters 4 and 5). While Chapter 6 goes someway to address
the implications of these changes on PWS and HEP, more work is required to fully understandthe nature of these changes, especially the duration of drought periods, which could havepotentially large consequences for all water users, but PWS especially. In terms of extreme
high flow events, this work provides background for further study on areas such as fluvial
flooding, with modelling at a finer temporal and spatial resolution required to develop themost informed future planning strategies. Furthermore, the relationship established in
Chapter 4 between prevailing weather conditions and overall water demand is an importantfirst step in better managing the future supply-demand balance in Wales. This work could becontinued further by the implementation of a component analysis, to give a better
understanding of how different user groups (agriculture, domestic, industry, etc.) respond tochanges in factors such as air temperature. Analysis of this kind would enable a more rounded
picture of future water demand to be achieved, enabling robust water resource planning.
The results presented in Chapter 6 represent an overview of the likely impacts ofhydroclimatic change on the two industries studied. While PWS impacts have been studied
and shared by Dŵr Cymru Welsh Water (DCWW) in the form of their water resource
management plan (DCWW, 2019a) and drought plan documents (DCWW, 2019b), little work
has been completed for other industries, especially with a focus on Wales. HEP may play a169Conclusions
relatively small role in the overall energy portfolio of Wales, but the projected changes in theseasonality and performance of the sector could have significant implications for HEP
schemes, both locally and nationally. It is therefore crucial that further research be completed
for the whole of Wales, and more broadly the UK, to fully understand the significance of suchchanges for the energy provision mix of the nation. On a more localised level, it is importantthat research be carried out on individual HEP schemes, to ensure optimised operation for
the future. Looking more broadly than those industries studied in this research, the impact ofprojected hydroclimatic changes on sectors such as heavy industry and agriculture are also
important to understand, especially for the latter, given the increasing pressure on global andlocal food systems. Clearly, further work is needed to fully understand the risks posed by
future hydroclimatic changes on the overall exploitation of water resources in Wales, and thepotential economic and environmental impacts of any changes.
Moreover, Chapter 5 indicates the potential changes in four water quality variables, linked tochanging hydroclimatic factors, with substantial alterations likely for all four as well as water
quality more generally. Once again, the implications of these changes in Wales has received
little attention thus far, clearly making this a key area of future research. Various elements
would benefit from further work in this regard: in-stream ecology risks; eutrophication
changes; the impact on drinking water treatment plant processes, efficiency, and finances;suspended sediment load changes and implications for HEP; and future compliance withenvironmental regulations, to name just a few. Furthermore, it has been shown that changes
in water temperature are very likely given projected changes in air temperature and the
known relationship between the two (Crisp & Howson, 1982; Erickson & Stefan, 2000; Morrill
et al., 2005; Hannah & Garner, 2015). It is therefore suggested that an area for future researchshould be the impact of increased water temperatures for Wales, which has the potential toimpact not only in-stream processes, and therefore overall water quality, but also processes
and chemical reactions in environments such as drinking water treatment plants.

8.3 Final summary
This thesis has provided a grounding knowledge of spatial and temporal changes in Welshhydroclimatology, giving insight into the future environment that abstractors, water resource
managers, and water users will be operating in. The thesis also presents the need for greater
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Conclusions
work at a catchment level, with Chapters 4 and 5 in particular demonstrating how significant
change can occur between catchments even over small distances, due to factors such the
topographical characteristics of the catchment and its surrounds. Indeed, Chapter 6 highlightsthat variation in streamflow response can be found even within catchments. This underscores
the need for awareness of small-scale studies and local management plans in the future,especially when working to address or mitigate against the impacts of future climate change.
Several knowledge gaps have been addressed, as well as novel research methods used,through the course of the thesis. First, a daily and monthly relationship between total water
abstraction and three hydroclimatic factors has been established for the first time for Wales,
allowing, as conducted in Study 3, for projections of future abstraction requirements for PWS
to be made. In terms of Study 2, the research conducted is some of the first to make use of
the latest UK climate projections (UKCP18), with little peer-reviewed literature having been
published using this data at the time of writing. This is especially true when considering boththe study area and research topic, with no articles having been published using the data for
Wales, or in reference to future water resource use. Furthermore, the use of the Soil andWater Assessment Tool (SWAT) for modelling of streamflow and water quality is one of thefirst for Wales, with the future water quality analysis itself being previously sparsely publishedon for the region specifically. Finally, Study 3 develops for the first time a method for assessing
the impacts of streamflow changes on the abstraction potential of multiple individual HEP
schemes, where the abstraction licences conditions are unknown. This represents amethodological advancement, allowing for large-scale assessments of the impact of climatechange on HEP generation potential, this has previously not been available.
Overall, the research conducted has been successful in addressing the objectives of the thesis
and, therefore, achieving the overarching aim laid out at the start of project. Furthermore,
the work presented represents an advancement of knowledge for the field, as set-out above,by addressing knowledge gaps in relation to the hydroclimatic character of catchments within
Wales and implications for water resource use. It is hoped that the findings of this research
will be used to inform future multi-sector water resources management planning, as well as
being useful to the specific sectors studied and more, with the results being applicable to avariety of stakeholders. In this manor it is hoped that the findings of the thesis will help
towards ensuring long-term sustainability of water abstraction and use in Wales, and beyond.

171

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APPENDICES

201

Appendices

Appendix 1: SWAT model calibration charts

Figure A1.1. Observed streamflow (blue) compared to the best simulation (red) for the Clwyd catchment,
following calibration for the period 1st January 1985 to 31st December 1998; KGE = 0.810.

Figure A1.2. Observed streamflow (blue) compared to the best simulation (red) for the Conwy catchment,
following calibration for the period 1st January 1985 to 31st December 1998; KGE = 0.770.

Figure A1.3. Observed streamflow (blue) compared to the best simulation (red) for the Dyfi catchment, following
calibration for the period 1st January 1985 to 31st December 1998; KGE = 0.788.

202

AppendicesFigure A1.4. Observed streamflow (blue) compared to the best simulation (red) for the Teifi catchment,
following calibration for the period 1st January 1985 to 31st December 1998; KGE = 0.851.

Figure A1.5. Observed streamflow (blue) compared to the best simulation (red) for the Tywi catchment,
following calibration for the period 1st January 1985 to 31st December 1998; KGE = 0.841.

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Appendices

Appendix 2: Ensemble projections for seasonal and annual streamflow

Figure A2.6. Seasonal and annual future streamflow projections for the Clwyd based on a 5-year moving mean;
coloured lines represent individual model runs from the 12-member ensemble, black line is the ensemble mean.

204

AppendicesFigure A2.7. Seasonal and annual future streamflow projections for the Conwy based on a 5-year moving mean;
coloured lines represent individual model runs from the 12-member ensemble, black line is the ensemble mean.

205

AppendicesFigure A2.8. Seasonal and annual future streamflow projections for the Dyfi based on a 5-year moving mean;
coloured lines represent individual model runs from the 12-member ensemble, black line is the ensemble mean.

206

AppendicesFigure A2.9. Seasonal and annual future streamflow projections for the Teifi based on a 5-year moving mean;
coloured lines represent individual model runs from the 12-member ensemble, black line is the ensemble mean.

207

AppendicesFigure A2.10. Seasonal and annual future streamflow projections for the Tywi based on a 5-year moving mean;
coloured lines represent individual model runs from the 12-member ensemble, black line is the ensemble mean.

208