FULLTEXT01- Rainfall Runoff Modelling

7/29/2019 FULLTEXT01- Rainfall Runoff Modelling

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Examensarbete vid Institutionen fr geovetenskaper

ISSN 1650-6553 Nr 228

Rainfall-runoff Model Application in

Ungauged Catchments in Scotland

Alexander Peter Anthony Fionda


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Abstract

Rainfall-runoff model application for ungauged catchments in Scotland

Alexander Peter Anthony FiondaDepartment of Earth Sciences, Uppsala University

Villavgen 16, SE-752 36 Uppsala, Sweden.

The conceptual rainfall-runoff model Hysim is used to estimate the flow in ungauged

catchments in Scotland by Scottish Water. However, there are non-quantified uncertainties

associated with the outcomes of the modelling strategy used. In order to identify and quantify

these uncertainties it was necessary to use the framework of proxy-basin validation in order

to evaluate the performance of different modelling strategies.

The proxy-basin validation test requires hydrologically analogous catchments for the

evaluation of models, a Region Of Influence regionalisation method was used in order group

selected catchments by Q95(%MF). Four groups of four catchments were established, which

covered Q95(%MF) 5-7%, 7-9%, 9-11% and 11-13%.

The allocation of donor catchment and target catchment for each Q95(%MF) group

was accomplished through discussion with Scottish Water with respect to existing Scottish

Water modelled catchments. A single donor catchment and three target catchments were

therefore indicated for each group.

Two modelling strategies were developed by the study; the first full transposition method

used the entire optimised parameter-set from the donor catchment with the exception of the

target catchments catchment area parameter. The second partial transposition method usedthe entire optimal parameter-set with the exception of the target catchments interception

storage, time to peak, rooting depth and catchment area parameters.

It was found that the full transposition method had the least uncertainty associated its use

for flow estimation when the parameter-set was derived from a donor catchment calibration

that was excellent. Contrarily, it was found that the partial transposition model method had

the least uncertainty associated with flow estimation for parameter-sets that were derived

from a relatively poor donor catchment calibration.

Encouraged by this testing framework, this study has suggested the use of catalogue of

donor parameter-sets that can be used to estimate flow for catchments that are hydrologicallysimilar. This strategy of hydrological modelling has been recommended to improve existing

Scottish Water Hysim methodology.

Keywords

ungauged catchment, proxy-basin validation, region of interest, transposition method, hysim,

rainfall-runoff model, sepa, scottish water, scotland.


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Referat

Anvndning av en avrinningsmodell i ett skotskt avrinningsomrde utan

vattenfringsmtningar

Alexander Peter Anthony Fionda

Institutionen fr geovetenskaper, Uppsala universitet

Villavgen 16, 752 36 UPPSALA.

Scottish Water anvnder den begreppsmssiga avrinningsmodellen Hysim fr att uppskatta

vattenfringen i skotska avrinningsomrden utan vattenfringsdata. Den valda

modelleringsstrategin har emellertid resulterat i icke-kvantifierade oskerheter i berknade

vattenfringar. Fr att identifiera och kvantifiera de oskerheter som r frbundna med olika

modelleringsstrategier var det ndvndigt att anvnda sig av information frn likartadeavrinningsomrden.

Den valda regionaliseringsmetoden anvnde hydrologiskt analoga avrinningsomrden som

definition p likhet. Analogin grundades p inflytanderegion (Region of Influence) som

erhlls genom att gruppera utvalda avrinningsomrden utefter Q95 (% medelflde). Fyra

grupper med fyra avrinningsomrden valdes ut grundat p fljande Q95-grnser (%

medelflde): 5-7%, 7-9%, 9-11% and 11-13%.

Frdelningen av analoga avrinningsomrden (omrden med vattenfringsmtningar vars

parametervrdesuppsttningar skulle verflyttas) och mlomrden (utan mtningar) fr varje

Q95-grupp erhlls efter diskussion med Scottish Water frn omrden dr Scottish Water

modellerat vattenfringen. Ett analogt omrde och tre mlomrden valdes ut fr varje grupp.

Studien anvnde tv modelleringsstrategier. Den frsta metoden, total verflyttning,

anvnde hela parametervrdesuppsttningen frn det analoga omrdet med undantag av

mlomrdets area. Den andra metoden, partiell verflyttning, anvnde hela

parametervrdesuppsttningen med undantag fr mlomrdets interceptionslager, tid till

hgflde, rotdjup och area.

Den totala verflyttningsmetoden hade lgst oskerhet nr parametervrdesuppsttningen

hrleddes frn ett omrde med utmrkt kalibrering. Den partiella verflyttningsmetoden hade,

andra sidan, lgst oskerhet nr parametervrdesuppsttningen hrleddes frn ett omrde

med dlig kalibrering.

Efter att ha provat de tv metoderna utmynnade studien i ett frslag till en katalog med

parametervrdesuppsttningar fr omrden som kan bedmas som hydrologiskt lika. Denna

strategi fr hydrologisk modellering har rekommenderats som frbttring av befintlig Hysim-

metodik hos Scottish Water.

Nyckelord

Avrinningsomrde utan vattenfringsdata, validering mot likartade omrden,

inflytanderegion, verflyttningsmetod, hysim, avrinningsmodell, SEPA, Scottish Water,

Skottland.


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Contents

1. Introduction ................................................................................................................. 1

1.1 Research objectives ................................................................................................. 1

1.1.2 Scottish Water and its resource systems .......................................................... 2

1.1.2 The role of hydrologic modelling in Scottish Water ........................................ 5

1.2 The use of Hysim rainfall-runoff modelling by Scottish Water............................ 12

1.2.1 Quantifying the uncertainty associated with parameterisation ...................... 13

1.2.2 Modelling flow in ungauged catchments ....................................................... 15

1.3 Key questions and summary of methods ............................................................... 16

2 Materials and Methods................................................................................................. 18

2.1 Analogue and target site selection from SEPA catchments .................................. 18

2.2 The Hysim conceptual rainfall-runoff model ........................................................ 21

2.3 Derivation of inputs ............................................................................................... 24

2.4 Hysim model calibration ....................................................................................... 26

2.5 Development of parameter transposition methods ................................................ 27

2.6 Evaluating model performance using the proxy-basin test ................................... 28

3 Results.......................................................................................................................... 29

3.1 Hydrological statistics of mega-zones and SEPA catchments .............................. 29

3.2 Calibration quality of donor catchments ............................................................... 31

3.3 Evaluating model performance with transposition method chosen....................... 39

3.4 Evaluating model performance with selection of target catchment ...................... 44

4 Discussion .................................................................................................................... 47

4.1 Hydrological statistics of mega-zones and SEPA catchments .............................. 474.2 Calibration quality of donor catchments ............................................................... 48

4.3 Uncertainty identified with selection of target catchment..................................... 53

4.4 A more pragmatic methodology for estimations of flow ...................................... 54

5 Conclusion ................................................................................................................... 56

6 Acknowledgements...................................................................................................... 58

7 References.................................................................................................................... 59

8 Appendices................................................................................................................... 63

Appendix A: Hysim operational notes ........................................................................... 63


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Appendix B: Parameter-set references ........................................................................... 64

Appendix C: Results of validation ................................................................................. 65

Definition of terms

MF - the mean flow.

Q95 - the 95th

percentile of mean flow; the flow exceeded or equalled 95 % of the time.

Q95(%MF) - the 95th

percentile of mean flow as a percentage of mean flow.

Source catchment - a catchment containing source of water, which is utilised by Scottish Water.

Donor catchment - the catchment for which an optimal parameter-set is achieved through calibration.

Target catchment/analogue - a catchment chosen through a method of regionalisation to be similar

in character to the donor catchment.

Model a software based representation of a physical system. Model software consists of a

programmed framework, into which physical data and estimated parameters are placed, in order to

represent a physical system. This study evaluates model performance, where a model consists of the

programming, input data and parameters as a whole. This status is stored by Hysim the model

programming- as a single project file, which is referred to as a model in its own right.

Parameter-set - a set of estimated parameter values that may be adjusted in order to manipulate the

outcomes of a model.

Optimal parameter-set - a set of parameter values that provide the best estimation of flow,

commonly achieved through the calibration of a model.

Transposition the process of transferring parameter values from a donor catchment optimal

parameter-set to a target catchment parameter-set.

Full Transposition Method (FTM) - a method describing the transposition of every parameter fromthe donor catchments optimal parameter-set to the target catchment parameter-set. The catchment

area of the target is maintained as a parameter for the target catchment parameter-set.

Partial Transposition Method (PTM) - a method describing the transposition of part of the donor

catchments optimal parameter-set to the target catchment parameter-set. catchment area, time to

peak, rooting depth and interception storage of the target are maintained as parameters for the target

catchment parameter-set.

Uncertainty - A state of having limited knowledge where it is impossible to exactly describe existing

state or future outcome; in this study this is quantified by evaluating model performance, using

accuracy between estimated and recorded flow.


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1.IntroductionThe benign human curiosity in the future drifts in and out of focus in society. It can enthral as the

subject of films and can spell boon or doom in the media. As a species capable of producing much of

what we utilise in our day to day existence it is our privilege to be able to successfully predict the

outcomes of what we create and control. In order to do so, we rely on the continual development of

the mathematical model. However, when we attempt to utilise the environment around us, there is the

desire, and often assumption, that a similar level of prediction is available. We necessitate accurate

environmental prediction, whether it may concern the local weather next week or global climate in the

next century. Unfortunately, the natural world is almost infinite in its scale of complexity and cannot

be represented in its entirety by any model. As such, the outcomes of mathematical models that

attempt to tell us more about the future is discussed more as a form of prophecy than prediction

(Beven, 1993).

Hydrological variables are but one aspect of the natural world. Mathematical models, especiallyconceptual rainfall-runoff models, are a capable means of narrowing down future states of

hydrological variables for a given area. For water management companies this is essential, as

predictions of the likely states of variables are invaluable in resource planning. It is within the realms

of prediction that rainfall-runoff models, capable of simulating flow in areas that are ungauged, are

best suited.

Models of hydrological systems have been progressing for the best part of three decades. One

branch of development of modelling tools leads to the prediction rainfall and consequential runoff in a

hydrological system. Conceptual rainfall-runoff models are among the most ubiquitously used tools in

hydrology. Input data is more readily available for their application unlike their counterparts: the

complex, physically based, distributed models. Conceptual models are often comparatively simpleand easy to use, that said, the drawbacks of model parameters being inter-correlated or over-

parameterised is not uncommon. It is the case that some model parameters will have a physical bias

that ties directly to variations on the catchment scale. Due to the fact that such variations are virtually

unquantifiable in the field, calibration is an essential step in representing real runoff calculations. This

leads to the pursuit of the optimal parameter-set that produces the greatest closeness to reality and a

process of parameter alteration that inevitably brings about multiple solutions with different sets of

parameters. Uncertainty therefore arises in modelling, it is discussed as the confusion as to which set

of parameters to choose for application by Beck, 1987. This study aims to elaborate upon the

uncertainty associated with parameter selection by testing parameter-sets that have been derived by

various methods. It will then be possible to quantify this uncertainty by the comparison of the

accuracy of these methods.

1.1 Research objectivesThis study is undertaken in cooperation with Scottish Water -the publically owned water authority for

Scotland, who expressed considerable interest in improving the efficiency of their rainfall-runoff

modelling strategy for various operations. This study aims to evaluate model performance, where a

model consists of the programming, input data and parameters as a whole. In doing so, the focus of

evaluation will be on changes made to the parameter-set and potentially data. In literature surrounding

model evaluation, the model software itself is usually under scrutiny and described as the model;

such analysis is not the focus of this study.A review of the current internal and external publications on Scottish Waters modelling strategy


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reveals non-quantified uncertainties in the input, parameterisation and calibration of their modelling

scheme that require addressing. This paper attempts to identify and quantify the uncertainty

surrounding parameterisation by testing the accuracy of various methods of parameter-set derivation.

Furthermore, this uncertainty evaluation may then be used to infer an improved, more pragmatic

method for modelling the flow in ungauged catchments.

Using the framework of proxy-basin validation to evaluate the uncertainties associated with flow

estimations in ungauged catchments requires the following aims to be fulfilled:

i. Select catchments for experimentation that are both approved by a monitoring agency interms of quality and that represent typical Scottish Water source catchments. Use a method of

regionalisation to group hydrologically analogous catchments in compliance with the proxy-

basin framework

ii. Identify catchments that are suitable for deriving parameter-sets and those that are suitable asthe target of the evaluation process; so called donor and target catchments. Update theinput data and data selection periods and improve the calibration of existing Scottish Water

models for those catchments identified as donor catchments.

iii. Develop two methods of parameter transposition and test parameter-sets upon targetcatchments in order to evaluate accuracy and quantify uncertainty associated with parameter-

set selection. Interpret whether uncertainties are quantified enough for the recommendation of

using a single method of parameter-set derivation for the estimation of flow in all

hydrological analogous, ungauged catchments.

In completing these objectives, it is possible to identify a single donor parameter-set for eachhydrologically similar group that can be used to estimate flow in ungauged catchments with

hydrological similarity to a quantified level of accuracy. A library of models would then exist that

would each represent a range of hydrological similarity that could be used whenever flow was needed

to be estimated in an ungauged catchment. This builds upon suggestions by Jacobs (2010); the ability

to approve this as an outcome would recommend a more pragmatic Hysim methodology for Scottish

Waters estimation of flow in ungauged catchments.

For the objectives of this report to be upheld it is important to address some additional vulnerability

within the current scheme of Hysim modelling that Scottish Water employs. A detailed method for the

calibration of Hysim models must be documented and made consistent with Scottish Water

guidelines; however the method should be seen to improve existing modelling procedure in order to

assist with future Hysim modelling studies. Where there are pre-existing calibrations models for

catchments, it is an aim of this to update or improve these models where possible. This may be

achieved through taking advantage of the improved rating and record of evapotranspiration or

precipitation data records or by alterations in the model construction process.

1.1.2 Scottish Water and its resource systemsScotland, with respect to global water availability, is a water rich country. In terms of actual water

availability Northern Europe has 34.6 x 103m3 per year per capita average for the past 60 years; when

compared to the average for the entire of Europe (4.9 x 103m3) it is clear that there is a uneven

geographic distribution of available water throughout Europe (Gleik, 1993). It is important not to


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construe this data as a reflection of unlimited water resource capability; there are problems with water

resources in relation to the public supply of water. A wide variability exists in the ability for the water

authority, Scottish Water, to maintain water supply during peak demands and during droughts.

In 2002, Scottish Water was crated by the merger of three water authorities in accordance with the

Water Industry [Scotland] Act 2002. Scottish Water is accountable to the Scottish Parliament through

the Scottish Ministers, it is publically owned. It remains a product of the amalgamation of 210 water

boards and local councils since 1968. This unification provides the authority with a unified, consistent

and strategic approach to Water Resource Planning that strengthens the operations it defines from its

Water Resource Plan (WRP). The WRP (Scottish Water, 2009) is a regulatory document that has been

developed in collaboration with the Scottish Environmental Protection Agency (SEPA). Its aims are

to:

Define Scottish Waters long term water resources strategy to ensure the consistent supply ofdrinking water to protect public health and facilitate economic growth, while abstracting and

using water in a sustainable way to provide a value for money service for customers.

Provide a twenty five year assessment of the Supply Demand Balance across Scotland at azone-level that is consistent with good practice in the UK.

Justify investment to restore deficits in the Supply Demand Balance in a prioritised waterresource zones during the next investment period and beyond.

The WRP therefore represents the interests of: environmental and water resource regulation,

economic regulation, customer interests and consumer quality respectively (Scottish Water, 2009).

The Water Resource Plan is subject to the model of planning guidance SEPA provides. As such,

Scottish Water is requested to produce data for all Water Resource Zones (WRZs) defined within

Scotland. WRZs are defined as the largest possible zone in which all customers experience the

same risk of supply failure from a resource shortfall (Scottish Water, 2009). For the 2007/2008

period, 230 water resource zones exist across Scotland. Due to the low population density in Scotland,there is a large variation in the distribution of WRZs. A large quantity of WRZs are located in the

Highlands and Islands, which supply isolated communities; contrasting with the eleven centrally

located WRZs that supply almost half the population of Scotland (Scottish Water, 2009). Such an

extensive collation of WRZs is unfamiliar to the majority of water management authorities; in

England and Wales companies usually have one to ten WRZs. Therefore, the environmental agency

guidelines that request data on all WRZs seems a task implicated with difficulties on a number of

levels: specifically the collation of data for 230 WRZs and their constituent water sources.


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Avera ge Dem and (Ml/d)

Argyll and Bute 46

Ayrshire and Inverclyde 66

Central Scotland 110

Dumfries and Galloway 31

East Lothian and Borders 25

Fife 26

Fort William 21

Grampian 36

Inverness and Central Highlands 28

North West Coast 21

Orkney 18

Shetland 23

Skye and Lochalsh 32

Tayside and Rural Forth Valley 18

Western Isles 23

Wick 8

Scotland Total 2009/10 481

13.9 28.7 7 7

2,044 5,035 220 278

127 372.6 13 16

13.4 26.8 22 22

10.7 22 13 14

6.7 14.6 28 28

3.3 7.5 19 19

8.7 19.6 10 11

148.5 420.2 11 17

86.2 201.6 20 24

143 357.3 1 11

8.9 17.9 19 19

78.3 131.1 5 18

59.5 145.3 11 17

255.8 440.4 8 14

1,265.40 2,712.20 11 30

Avera ge

Demand (ml/d)

Population

(000s)

Number

of WRZs

Number of

WTWs

Number of

Sources

41.8 65.9 32 33

FIGURE 1.2.1:SCOTTISH WATER MEGA-ZONE REGIONAL GROUPING WITH ALLOCATED WATERRESOURCE ZONES(WRZS).IMAGES USED WITH PERMISSION (SCOTTISH WATER,2009).

Water resource zones are grouped geographically into sixteen mega-zones, shown in figure 1.2.1. The

disparity of population density across Scotland is notably significant, elucidating the need for an

additional WRZs for every small pocket of population across a large area; these are classified as

standalone zones. In studying the population given in thousands it is a frequent trend that a smaller

population per mega-zone have a greater number of WRZs i.e. the population of central Scotland:

2,712,200, which is supplied by 11 WRZs whereas Argyle and Bute have a population of 41,800 and

are supplied by 32 WRZs. However, this is not a rule as such; some low populations also have a low

number of WRZs i.e. Wick, a population of 28,700 and 7 WRZs (Scottish Water, 2009).

Water resource zones are supplied by Water Treatment Works (WTW), the distribution of which is

directly influenced by the occurance of standalone zones. Each standalone zone is supplied directly by

a single WTW, making 202 WTW zones that are supplied by a sole WTW across Scotland. The

remaining 28 WRZs have more than one WTW. The Central Scotland mega-zone incorporates the

cities of Edinburgh and Glasgow; the 11 WRZs within Central Scotland contain 30 WTW and serve

54% of the household population of Scotland (Scottish Water, 2009). The interconnectivity provided

between these zones reduces the risk of supply failure within the mega-zone; although, there is a

difference in risk between certain zones over others (Scottish Water, 2009). The risk of supply failure

is considerably greater in the standalone zones as there is limited or no connectivity between the

WTW. Efforts are being made by Scottish Water to further plans that would ensure a greater

interconnectivity between standalone zones and reduce the risk of supply failure amongst these areas.


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The supply demand balance from raw water sources to the water treatment works output for each

water resource zone is essential for effective water management across Scotland. A suppy system

incorporates the assets of collection, storage, transfer and treatment up to the output of the water

treatment works (Scottish Water, 2009). It is the part of the supply system concerning collection that

is of greatest interest for defining water sources in Scotland. Scottish Water Report that for 2007/2008

there were 532 sources providing water for supply to the population of Scotland; see figure 1.2.2.

Large population centres in Central Scotland, such as Edinburgh, Glasgow, Dundee and Stirling are

supplied by a small number of large reservoirs, whereas isolated communities in remote parts of

Scotland rely upon numerous small reservoirs. This follows the trends identified from the disparate

state of WRZ distribution across Scotland.

FIGURE 1.2.2:SURFACE WATER SOURCES UTILISED BY SCOTTISH WATER.IMAGE USED WITH PERMISSION

(SCOTTISH WATER,2009).

The distributions of raw water sources across Scotland are illustrated by map of WTW localities

across the country; see figure 1.2.3. The majority of the 59 loch sources are located in the northwest

of Scotland. Groundwater sources are found throughout Scotland; there are 42 spring sources and 54

borehole systems that make up 96 in total. 207 river sources are divided into: 103 indirect sources,which feed reservoirs and 104 pure river sources, which are generally larger in the east and smaller in

the west. Impounding reservoirs, of which there are 170, and their contributing feeder river sources

provide 82% of raw water to water treatment works in Scotland. Direct river sources provide 10% of

raw water, whilst lochs and groundwater each provide 4% and 4% respectively (Scottish Water,

2009).

1.1.2 The role of hydrologic modelling in Scottish WaterHydrological assessment occurs on a variety of levels dependent upon the project at hand. Scottish

Water (2009) identified various scenarios where hydrological assessment is required for a watermanagement authority. There is a division highlighted between internal projects i.e. a Scottish Water

capital project with water quality or growth considerations and Scottish Water capital projects with

environmental consideration. This study will focus on hydrological assessment associated with the

eventual calculation of yield; a requisite for the supply-demand balance for all Scottish Water capital

projects including Scottish Waters Water Resource Plan (Scottish Water, 2009).

Yield is expressed in terms of the maximum continuous output that can be supplied in drought severe

enough that on average its occurrence would cause a failure of supply one in forty years (Scottish

Water, 2009). The use of conceptual rainfall-runoff models, such as Hysim, for estimation of stream

flows is universal in water management authorities. This flow data requires some method of

transformation before yield can be calculated. The estimation of yield requires either the estimation of


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a natural flow duration curve (FDC) or its 95 flow percentile (Q95). Software such as Hysim-Aquator

is capable of yield estimates directly from Hysim simulation data, whilst the Report 108 based

Method (Institute of Hydrology, 1992) may also be used to estimate yield using one regression

equation (Gustard et al., 1992). In addition, Scottish Government Directions on Environmental

Standards (SGES) determine an allowance of abstraction given as a percentage of the FDC (Scottish

Water, 2009). There is therefore a great necessity to represent catchment flow data in its Q95 and FDC

form. Techniques from which an FDC may be obtained are: gauged records, Hysim modelling and

Low Flows Enterprise calculation.

A long term record of gauged flow for the focus catchment is undoubtedly the most accurate, reliable

and practical method of FDC production. Empirical observations will always be of greatest value to

the hydrologist, yet lengthy continuous gauged flow data for Scotland, and indeed much of the world,

is not available. Furthermore, in the context of individual water management authorities such as

Scottish Water, their abstraction sites are not close enough to long term gauges for representative

FDCs to be derived. Where funding and time permits, it is beneficial to initialise flow gauging for

sites (Mott Macdonald, 2010). It is suggested that there is suitability in short term direct flow gaugingif enough analysis into finding a suitable analogue is undertaken. For the implementation of flow

gauging to be effective in a project there must be a local, long term analogue. If such an analogue

cannot be found then the gauging period for the catchment in focus must be greater than four years,

which may extend beyond practical means for the project. If a local, long term analogue can be found

and gauged data is provided that is over three years in length then transposition will be used between

catchments and allow a revised FDC that better represents the focus catchments. Methods of

transposition between catchments are detailed by Jacobs (2010); however there is no comparison

between the efficacies of this procedure compared to the representation of the focus catchment by a

rainfall-runoff model. A modelling strategy would inevitably require, and use, the same proximal,

long term target catchment. Comparison between the resulting FDC would illustrate the value ofinitiating flow gauging at a focus site.

Low Flows Enterprise (LFE), developed by Centre for Ecology and Hydrology (CEH), is a software

package that is used to estimate the flow duration curve at ungauged sites. Wallingford

Hydrosolutions currently maintain this software. The Scottish Environmental Protection Agency use

LFE as the elected method for FDC derivation at ungauged sites in Scotland. Scottish Water has

purchased LFE and is capable of providing LFE estimates on request. LFE obtains FDC through the

selection of 5 Region-Of-Influence (ROI) gauged catchment sites, which must be determined to be

similar to the donor catchments hydrological statistics. These five ROI provide an individual FDC,

which is rescaled by the mean flow for the subject site; this is calculated by a separate model within

the LFE software.

Hysim-Aquator permits the transfer of flow data and its derivative FDC or Q95 to calculate a yield.

Aquator achieves this through the simulation of daily transfers and abstractions for a given WRZ and

represents this as a one in forty yield. The Hysim-Aquator method for yield calculation was developed

in 2001, as a Scotland and Northern Ireland Forum for Environmental Research (SNIFFER) project. It

is a combined software package comprising of the hydrological rainfall-runoff simulation model

Hysim and Aquator, which is a water resource system model.

Hysim as stand-alone software is a daily rainfall-runoff model. Its intended use is to simulate a

historic daily river flow series based on historic daily rainfall and potential evapotranspiration; whilst

taking into account artificial influences such as: groundwater abstractions, river abstractions or river

discharges (Manley, 1978).


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Aquator was developed especially for the aforementioned SNIFFER project. It uses output from the

Hysim model as an input for the simulation of a water resource system; Hysim was also specially

adapted for this project. The daily storage of a reservoir or loch may be simulated based upon a

balance of inputs and outputs in terms of demands, compensation and freshets. Aquator is capable of

modelling a number of demand centres as well as the key components of a resource system, such as:

pumping stations, water treatment works, pipelines, hydro-generators, river abstractions and

groundwater abstractions (Manley, 1978). The application and accuracy of Hysim-Aquator is limited

by the availability of good quality input variables and parameters; guidance is provided by Scottish

Water on the processing of input data.

Background of Scottish Waters Hysim models

31 individual Hysim rainfall-runoff models are currently in use for 70 WRZs. These models are

consequently responsible for covering 250 source catchments, which in turn feed 90 WTWs

For the 31 independent donor catchments there are three catchments that provide gauged data for the

implementation of 40% of the Hysim models; these are: Green Burn located at Loch Dee, data is usedfor 29 catchments and 8 WRZ models, River Creed located at Creed Bridge, data is used for 18

catchments and 12 WRZ models, River Calder located at Muirshiel, data is used for 35 catchments

and 6 WRZ models. Other source catchments are used for Hysim model calibration; however these

catchments have been applied to two or three models only (Scottish Water, 2009).

The 31 Hysim models were developed as part of larger studies than the models themselves; in these

studies it was thought pragmatic to apply a single calibrated model to a range of catchments, despite

more representative catchments being available for calibration. The models that use Green Burn and

River Calder for calibration amongst others- have not been critically reviewed in order to assess the

on-going validity of these calibrations since there original development in 2001 and 2002. However,the necessity of applying a single model to a number of hydrologically different catchments such as

Creed Bridge illustrates the lack of alternative gauged catchments available on the Western Isles and

Northern Isles.

To continue the discussion of validity, the data quality upon which the models are based is also in

question. The River Calder gauging station at Muirshiel is noted to be downstream of the River Calder

abstraction intake and is therefore artificially impacted; the catchment is also identified by SEPA as

unsuitable for use as an analogue. This issue is not brought to attention in the 2001 report by

Camphill, from which the River Calder calibration is derived. There is significant reason to question

the validity and revisit the calibration considering the wide scope of its application.

Short term gauges have been used for the calibration of Hysim models: using one year of gauged data,

the Geimisgarve and Clibh catchments are applied. These short term gauges were developed

specifically for the Water Framework Directive (WFD) WR1 SR06 project (Scottish Water, 2006),

which required models for a large number of remote islands in Scotland. These short term gauges

were used as alternative calibrations for comparison with an adopted Creed Bridge model. The

calibration for Clibh was accepted in three models and Geimisgarve was accepted for a single model.

This position highlights the difficulty in establishing good Hysim donor parameter-sets for the large

number of remote sites in the North Western Isle, the Western Isles and the Northern Isles. It has

meant that the normal practise for Hysim calibration cannot always be followed i.e. the recommended

record length would usually require at least 5 years of representative, gauged data.


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A program of additional Scottish Water flow gauging sites was implemented since 2006 as a direct

result of the conclusions drawn by the WFD WR1 SR06 project. This aided the confirmation of river

flows at key project sites, which was not previously possible and helped strengthen observations made

in those catchments. The resulting flow records cannot be used for direct calibration of Hysim models

until the representative record length exceeds 5 years; ideally 7 years. However, the flow records may

be used for indirect validation of existing Hysim flow records in order to help agreement upon a FDC

for specific water sources during consultation with SEPA. SEPA will use the LFE instead of this FDC

unless flow gauging can provide a high level of confidence to the Hysim modelled flow.

Hysim-Aquator models are developed for reservoir or loch multiple-source system and generally not

used for WRZs that are only supplied by river intakes. The criteria for their disuse is a system for

which there is no storage available; exceptions do exist, such as the River Dee sources, which are used

to extend gauged flow records. The rationale for excluding rivers is that river sources are generally

smaller with low yields and therefore lower priority. It has been a concern that Hysim models do not

perform well around the 99th

percentile of flow (Q99). This issue is not as critical in systems with low

storage as it is normally the combined impact of the whole flow regime and storage capacity availablethat determines the system yield. In contrast, a river with no storage has a yield that is determined

based on the lowest daily flow values from the driest 3 or 4 years within the flow record. Therefore,

any poor model performance at these very lowest flows can have a significant impact on yield

sensitivity for river-only systems (Scottish Water, 2009).


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FIGURE 1.2.3:THE DISTRIBUTION OF SCOTTISH WATER CALIBRATION GAUGES AND MODELLED WATERTREATMENT WORKS THROUGHOUT MAINLAND SCOTLAND AND ISLANDS.IMAGE TAKEN FROM JACOBS (2010).


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Region of influence: identifying hydrological similarity without geographical constraints

SEPA quality approved catchments are presented on figure 1.2.4. These catchments are categorised

by their LFE derived Q95(%MF) flow descriptor, which is used to identify LFE ROI groupings across

Scotland. The purpose of using Q95(%MF) is to eliminate the requisite for regional boundary grouping

and can allow a number of catchments to be regionally grouped without boundaries. This is important

due to the number of isolated source catchments in Scotland, such as islands, which would be

unaccounted for if boundary regionalisation of catchments was pursued. LFE ROI catchments are

mapped based upon Q95(%MF) values, which reflect regional variation in hydrological regimes. Five

main regional groups are established, grouped by Hydrometric Area (HA) boundaries. Such a

simplification of grouping causes a few stations to be in the wrong grouping such as Killing and

Cultybraggan (Scottish Water, 2009). These stations have more hydrological similarity to stations in

the North West region, yet are included in the central region due to the HA being of the Tay. In

addition, Alness and Diriebught House stations have a better hydrological fit with the North East

Region (Scottish Water, 2009).

Further elaborations on using ROI as an alternative for regionalisation are discussed in subsequentchapters that discuss the literature surrounding catchment selection for parameter derivation and

application. Scottish Water adopts this scheme of LFE ROI groups across Scotland in order to identify

suitable catchments for use in the validation of optimal parameter-sets. If the desire is to estimate

flows for an ungauged catchment with a Q95(%MF) of 6% it is possible to refer to the 5% 10% Q95(%MF)

group and establish a number of target catchments for validation. This is useful tool as there is a

reliable potential analogue gauge available that represents natural flow regimes that are mostly

checked for hydrometric quality. In this LFE approach for obtaining suitable catchments, a distance

factor is neglected unlike the SEPA analogue selection tool as it was developed to be reliant on

proximity between catchments.


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FIGURE 1.2.4:LOW FLOWS ENTERPRISE (LFE)REGION OF INFLUENCE (ROI) STATIONS AND SUGGESTED

ANALOGUES BY THE SCOTTISH ENVIRONMENTALPROTECTION AGENCY (SEPA).95th

PERCENTILE OF FLOW AS A

PERCENTAGE OF MEAN FLOW (Q95(%MF)) IS ILLUSTRATED BY THE COLOUR AND SIZE KEY.TAKEN FROM JACOBS(2010).


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1.2 The use of Hysim rainfall-runoff modelling by Scottish WaterScottish Water rely upon the use of a conceptual hydrological rainfall-runoff that is calibrated to

nearby hydrologically analogous catchments in order to produce yield estimations for the majority of

surface water supply systems in Scotland. Yield is defined as the maximum continuous output for

given surface water source that can be supplied during a dry period of a stated severity. Yield

estimations require flow data and, due to the lack of long term site specific flow gauging within a

reasonable proximity to abstraction sites, representative target catchments are required for flow

estimation in ungauged rivers. Unlike other locations in Britain such as England, there is not the same

length or level of detail to historical flow records that affords the direct use of flow gauging records

for yield estimation. These direct flow gauging installations are usually restricted to timescales under

three years and are not suitable for direct application in model calibration. A requisite for model

calibration is a good record of at least seven years of gauged flow data (Scottish Water, 2009).

Therefore, direct flow gauge installations are usually used exclusively to provide validation of the

optimal parameter-set for the catchment.

The conceptual rainfall-runoff hydrological simulation modelling software used by Scottish Water is

Hysim, which is continuously developed by Water Resource Associates. Hysim can be integrated

within Oxford Scientifics water resource system model Aquator in order to produce estimations of

yield for a given catchment. It is the case that uncertainties in Hysim modelling strategy and

procedures have the potential to significantly undermine the confidence in Scottish Waters yield

estimates. This has the implication of making any planning or investment schemes, based on the

estimation of yield, less reliable. The Hysim-Aquator yield modelling process has been used by

Scottish Water for over 10 years and it is understood that there is a lack of repeatability in some of

their Hysim models. It is assumed that this is due to the number of times certain models have been

updated or even the lack of a consistent guidance framework for application, which is often protracted

by the use of different consultants.The data input, parameterisation and calibration processes for Hysim are aimed to be as objective and

consistent as possible, yet these uncertainties are still apparent. The uncertainty and related sensitivity

associated with these three key processes of modelling are not quantified. It would seem pertinent to

quantify uncertainties and sensitivities within these processes in order to strengthen the reliability and

confidence in model flow estimations and thus gain a more accurate yield estimate.

Scottish Water identifies potential uncertainties sourced from inconsistencies in modelling procedure

that are related to the project specific circumstances of the model genesis (Scottish Water, 2009). For

instance, when genesis lies in large projects, the focus of the project can lay beyond the scope of

detailed flow estimation and appraisal of models. Such projects often produce models that have lessimportance placed upon the quality of the model calibration and input data. It is also the case that

Hysim models created and adapted by different companies that offer different approaches to the

construction of models and weight internal protocol over guidance available from Scottish Water.

Neuman (2003), states that the bias and uncertainty that result from an inadequate conceptual

mathematical model are typically larger than those introduced through an inadequate choice of

parameter values; it is essential to choose the correct donor catchment and target catchment for model

calibration and transposition respectively. In light of this, there is a strong need to create a clear and

pragmatic methodology for the selection of a parameter-set for use on a target catchment that is

ungauged. In order to avoid the aforementioned caveats of model construction it would be beneficial

to construct a library of Scottish Water acknowledged Hysim models that could be applied to


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ungauged catchments with a good degree of confidence; as outlined in the closing notes of Jacobs

(2010).

There is considerable justification for an improvement in the method in which a model is calibrated

and applied to an ungauged catchment using an analogue catchment. Without improvement, any

future work where Hysim models are updated or new models developed will continue to provide

inaccurate estimations of flow for a given catchment. A common recommendation, based on a poor

correlation between calibration and direct gauged flows, is to scale the estimated Hysim flow series to

agree with SEPAs low flows enterprise flow duration curve (Scottish Water, 2011). LFE estimates

may also be uncertain and lead to misleading yield estimates and be an inadequate result for the

estimation of yield by Scottish Water.

1.2.1 Quantifying the uncertainty associated with parameterisationA number of literature sources discuss methods of validation for models in order to evaluate the

uncertainty that exists in a particular model. This paper requires the evaluation of uncertainty

associated with parameterisation. Seibert (1999a) gives a thorough review of the meaning and

application of the term validation in a hydrological modelling context. A series of applications

incorporating all current methods of validation with specific outcomes is detailed. A method for

gaining a measure of model parameter uncertainty in between hydrologically similar, gauged

catchments is identified by Seibert (1999a) as the proxy-basin test. Calibration takes place on a single

catchment and validation of the optimal parameter-set is achieved by the transposition of these

parameters to another gauged catchment. Seibert et al. (1999b) used a conceptual rainfall-runoff

model, the Hydrologiska Byrns model (HBV), to calibrate a single catchment and validate this

calibration on a further two catchments of similar character in the Black Forest, Germany. An

expression of model efficiency was studied for every application of the calibrated parameter-set. In

the optimisation of one parameter-set and application on the similar two catchments the average

measure of efficiency was 0.76 (1 corresponding to a perfect fit). When calibrated in thehydrologically analogous catchments and parameters were applied to the original catchment the

measure of efficiency was 0.84. These steps are characteristic of the proxy basin method and elucidate

that there is less uncertainty associated with the model with 0.84 thus quantifying uncertainties

associated with parameter choices.

The proxy-basin test of validation provides a significant solution to the main objective of this

investigation: to quantify the uncertainty associated with parameterisation when estimating flow in

ungauged catchments. It is essential to use the framework of the proxy-basin test in order to evaluate

the uncertainty associated with the parameter-set construction methods that are proposed. As indicated

by the aforementioned study by Seibert et al. (2009b) a measure of model efficiency according to theNash-Sutcliffe Efficiency Criterion is the preferred method of evaluating the performance of a model.

In previous studies commissioned by Scottish Water i.e. Jacobs (2010), the model efficiency for

Hysim is not used to calibrate or evaluate the performance of the model; instead the FDC and

associated flow descriptor statistics (Q95, MF, Q95(%MF)) are used as a measure of accuracy and

therefore an evaluation of the performance of the model with a specific parameter-set. A concern in

this approach is highlighted through conference in this study due to the fact that FDCs, unlike model

efficiency neglect the temporal aspect of model performance. Additionally, it is possible for an

estimated FDC to exactly match a recorded FDC whilst the model efficiency is very poor. However,

in studies by Westerberg (2011), which involved the analysis of FDC calibrations in 23 basins, the

FDC calibration method was found to have potential for calibration to regionalised FDCs for

ungauged basins; reducing the initial model uncertainty by approximately 70% (Westerberg, 2011).


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Therefore the use of FDC in calibration and as a comparative measure of accuracy is used throughout

this report.

1.2.2 ROI as a method of regionalisationUsing the framework of proxy-basin validation in order to evaluate the accuracy of parameter-sets -in

accordance with the scheme outlined by Seibert et al (1999b)- requires a method of regionalisation

for the application of parameter-sets. The process of transferring information from neighbouring

catchments to the catchment of interest is generally referred to as hydrological regionalisation

(Blschl and Sivapalan, 1995). It is used to make predictions about hydrological quantities at sites

where data are absent or inadequate, frequently for design purposes (Beran, 1990). Three

regionalisation methods are used to identify suitable gauged catchments, from which the optimised

parameter values are used to estimate flow for the target ungauged catchment:

i. The regression method establishes a relationship between the optimised parameter values andcatchment climate and physical attributes. Parameter values are then estimated for the

ungauged catchment from its attributes and the identified relationship.

ii. The spatial proximity method uses parameter values from the geographically closest gaugedcatchment because neighbouring catchments are expected to behave similarly due to shared

physical and climatic characteristics

iii. The physical similarity method transfers the entire set of parameter values from a physicallysimilar catchment.

Varying the method by which parameters are transferred from the optimal parameter-set of a donor

catchment to the target catchment is the source of the full parameter and partial transposition methodsthat are evaluated for associated uncertainty in this study. Therefore a degree of regionalisation must

be factored into the choice of donor and ungauged catchments. The spatial proximity method, where

the geographically closest gauged catchment has its parameters transferred to the target catchment

would be somewhat adequate for application in Scotland. However, this is unlikely due to the high

variation in catchment character across Scotland, owing to underlying geologies and marine

landforms for which there are Scottish Water source catchments assigned.

Scottish Water utilise ROI as an approach to regionalisation in order to categorise suitable donor

catchments and target catchments for parameter transfer. Acreman & Wiltshire, 1987 first suggest this

approach with the premise that the technique allows each donor catchment to have a unique set of

target catchments, which inclusively constitute the region for that catchment. Thus, there are no

boundaries indicating a specific variable and donor catchments within a specific area do not need to

have the same target catchments. According to Feaster and Tasker (2002) the ROI is defined as a set

containing the n closest stations. The ROI is defined as the set of all stations closer than a distance R

(in predictor variable or geographic space) from the site or, if the number of stations in that set is

smaller than some minimum allowable number n, the n closest stations. Scottish Water use predictor

variables such as: location, SAAR and BFI to identify donor and target catchments. In order to test the

validity of Q95(%MF) in such a role, Q95(%MF) is used to help select the catchments for parameter transfer.

ROI in application is seen on figure 1.2.4, using gauged values of Q95(%MF). As such, Q95(%MF) ROI will

be used as a proxy for regionalisation methods in the allocation of donor and target for the provided

SEPA catchments in this study.


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1.2.2 Modelling flow in ungauged catchments, a calibration scheme compatiblewith Scottish Water and Scotland

A previous study for Scottish Water by Jacobs Engineering UK Limited (Jacobs, 2010) investigated

approaches to Hysim rainfall-runoff modelling and the resultant impact on yield sensitivity. The

particular focus of this study was to investigate the sensitivity of Hysim models to the choice of target

catchment as well as the impact of using different calibration periods (record length and

representativeness). The resulting variable Hysim model outputs were then tested in Aquator water

resource system models using different catchment sizes and reservoir storage volume to assess the

impact in terms of yield sensitivity and uncertainty. As well as presenting conclusions on model

sensitivity and consequential yield sensitivity -the former of which will contribute to the discussion of

this paper- the study provides a tailored procedure for flow estimation in ungauged catchments using

Hysim for Scottish Water.

As this study is interested in isolating the uncertainty associated with parameterisation it is imperative

to adhere to a standard method for input data selection and calibration procedure whilst updating these

existing processes where improvements can be made without perturbing the uncertainty in parameter

choice. The Jacobs (2010) study is therefore used as the reference of a data processing, selection and

model calibration procedure that suits Scottish Water. Using this approved calibration procedure as a

framework will allow this paper to take advantage of the outcomes of the Jacobs study and further

develop the standard calibration method to suit the objectives of this paper. As there is no method for

evaluating the choice of parameter-set in ungauged catchments provided by the Jacobs study it would

be useful to extend this calibration procedure to formalise a standard method for testing a donor

parameter-sets ability to estimate flows in an ungauged catchment.

The total process accounted for in the Jacobs (2010) study covered: donor catchment selection, targetcatchment selection, data acquisition, data quality control, calculation of catchment statistics,

calculation of catchment parameters (catchment area, time to peak, rooting depth and interception

storage), the calibration of the donor catchment using Hysim, comparison of estimated flow to

recorded flow and final calculation of flow estimation descriptors (Jacobs, 2010). The procedures

outlined by Jacobs (2010) serve as a foundation for the development of this studys methodology due

to the bespoke nature of their outcomes to suit Scottish Water guidelines.

Jacobs (2010) aimed to ensure consistency and repeatability in the Hysim calibration procedure by

removing the degree of user subjectivity from the process i.e. eliminating the manual adjustment of

parameters. It was suggested in the study that an increase in user subjectivity would exist between thecalibrations of multiple catchments. Also identified was the trade-off between subjectivity and level

of detail, time spent, user experience and quality of the calibration. The calibration process was

designed to enable relative differences in resulting yields to be discussed with the same procedure

followed in the calibration process. This is important as subjectivity was identified as a key cause of

sensitivity in the use of Hysim by Scottish Water (2009). In the calibration methodology for this

report it is necessary to achieve the best possible calibration and so manual calibration is essential for

some calibrations. However, manual adaptation of parameters beyond the standard calibration

procedure must be limited to a number of attempts for best fit between estimated and recorded flow;

thus, limiting the subjectivity. In addition, the Jacobs (2010) report identified that the uncertainty

associated with different catchment choice appears to be slightly larger than the uncertainty associatedwith choice of record length and found that an eight year calibration offered the most reliable


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estimations of flow. Selected catchments for this study therefore have an eight year period of good

quality recorded data in order to eliminate the influence of other uncertainties upon the observations

of this studys aims.

Additionally, Jacobs (2010) suggested that there was an increase in yield sensitivity with a reduced

flashiness of catchment. It should be noted that, due to the small sample size involved in the study,

these conclusions were considered provisional within the report itself. It would be useful to explore

these provisional conclusions in this papers discussion of uncertainties associated with the character

of target of catchments chosen.

A final remark of the Jacobs (2010) study was the suggestion that collating a library of pre-

calibrated Hysim project files would be an adequate solution to limit uncertainty and reduce the

labour involved with detailed calibration for each application. Producing a library of well calibrated

Hysim projects, each with a quantified uncertainty and clear construction method would allow

Scottish Water or external consultants to use a model where justified. Essentially, this study evaluates

the proxy basin methodology for estimating flows in ungauged catchments. If uncertainty is reduced

due to the use of single method of parameterisation for hydrologically analogous catchments then thissingle method can be used to produce a number of calibration parameter-sets that could each be used

on a large number of hydrologically analogous catchments with a known level of uncertainty; thus

creating a pragmatic and cost effective estimation of flow in ungauged catchments.

1.3 Key questions and summary of methodsIn this thesis three key questions are addressed upon completion of the stated objectives of the study:

i. Is it possible to use the proxy-basin test framework to quantify the uncertainties associated withparameter transposition?

ii. Is there a method of parameter transposition that has a lower uncertainty associated with itsapplication?

iii. How can this information be used to create a more pragmatic model application scheme withinScottish Water?

In order to support these hypotheses, the objectives of the report were accomplished with the

following procedural methodology:

i. Selection of 16 gauged catchments that are approved by SEPA and are representative ofcatchments that are utilised by Scottish Water. This is accomplished through the comparison of

hydrological statistics between catchments and mega-zones, and supplemented by discussion with

Scottish Water.

ii. Use of ROI as a regionalisation method for grouping potential donor and target catchmentsaccording to Q95(%MF) flow descriptor. Allocation of donor and target catchments according to

availability and quality of data as well referring to existing use within Scottish Water.

iii. Update and improve existing Scottish Water catchment calibration models if encountered. Updatedata used in projects where possible and choose a different time period where beneficial.


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iv. Development of two parameter transposition method identified as full transposition and partialtransposition methods. Evaluation of the performance of these parameter-sets on each group of

target catchments using one calibrated donor model according to the proxy-basin test framework;

elucidating uncertainty associated with these parameter-sets.

v. Comparison of catchment characteristics in relation to parameter-set performance in order toexpand upon conclusions made about uncertainty in target catchment selection mad by previous

studies.


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2 Materials and Methods2.1 Analogue and target site selection from SEPA catchmentsHydrological representativeness

There are 207 Scottish Water river sources within Scotland, of which 103 feed reservoirs and 104 are

standard river sources. The standard river sources are directly applicable for the investigation of

runoff and approximation of yield for a water source; therefore, 104 rivers distributed throughout

Scotland are suitable candidates for flow estimation studies. In total, 24 catchments referred to as

analogue catchments by SEPA- were refined from those selected by SEPAs analogue selection tool

and evaluation expertise at Scottish Water. Data for these 24 catchments were obtained from the

respective parties and covered the entire recorded period for flow, precipitation andevapotranspiration; the specific derivation of which is covered in later chapters.

In order to represent the range of water resource catchments that water authorities in Scotland utilise

for water supply in Scotland it was essential to compare the hydrological statistics of catchments to

the average statistics of Scottish Water mega-zones that are identified on figure 1.2.1. The

hydrological variables that were studied included:

A value of catchment area was referenced from the UK Hydrimetric Register (UKHR) deliveredby the Centre for Ecology and Hydrology (CEH) (2008).

Standard annual average rainfall (SAAR) was referenced from the UKHR. Base flow index (BFI); a value derived from gauged daily flow data. This represents the

contribution of the slow flow or groundwater flow in the total measured runoff at the catchment

outlet , giving a degree of flashiness i.e. the frequency and rapidity of short term changes in daily

runoff values (Deetris & Lital, 2008). This was referenced from the UKHR.

Base flow index (BFI HOST SCOT); a base flow value that is derived from Low Flows Enterpriseresults.

95th percentile flow value as a percentage of the mean flow (Q95(%MF)); a value derived fromgauged daily flow data where available,else Low Flow Enterprise modelling was used. This value

is a commonly used measure of flashiness and other runoff characteristics; it illustrates the flow

that is exceeded 95% of the time as a percentage of mean flow.

It should be noted that Polloch, Skeabost and all mega-zones use the calculated BFI hydrology of soil

types Scotland (HOST SCOT) value, which is obtained from LFE results. BFI HOST SCOT is not a

substitution for gauged BFI and has been flagged as producing inadequate results in uses by Jacobs

(2010); however, this does not directly affect the choices made to exclude specific catchments.

Previous studies by Jacobs (2010) critically assessed catchments using factors of: artificial

influence, standing water area, record length, Institute of Hydrology grading quality and any further

information that would be influential to the suitability of the gauged data for a catchment. This

revealed features that could perturb the natural flow of the river and cause error in the evaluation

phase of the experiment and were taken into account when selecting catchments for experimentation.


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Confirmation of ROI grouping

Using ROI for Q95(%MF) catchments were grouped into four Q95(%) groups. The plot of aforementioned

catchment statistics were observed in order to interpret the suitability of Q95(%MF) for grouping

catchments of hydrological similarity. Each group was then elected a donor catchment, chosen for its

reliability as a presently used model and representativeness of typical flow per Q95(%MF) group. The

remaining catchments within each group would then be denoted as target catchments. In total there

were sixteen catchments identified for use in the study: four donor catchments and twelve catchment

analogues; these are displayed on table 2.1.1. The distribution of these chosen catchment analogues

across Scotland in relation to their Q95(%MF) group is illustrated on figure 2.1.2.

Of the twenty four catchments that refined from SEPA provided analogues there were eight omitted

from the study. These eight catchments represented Q95(%MF) groups that were below 5% and above

13%. These catchments were not used for the evaluation of transposition performance; however, they

were included in observations of catchment representativeness (see figures 3.1.1 to 3.1.3).

TABLE 2.1.1: DESIGNATION OF DONOR AND TARGET CATCHMENTS.DONOR CATCHMENTS ARE INDICATED IN BOLD,ALL OTHER CATCHMENTS ARE CATCHMENT ANALOGUES.

Group

Station

Name

Area

(km)

SAAR

(mm)

BFI

(gauged)

BFI-HOST

(SCOT)

Q95

(%MF)Braevallich 24.10 2745 0.22 0.22 6.5%Glen Strae 36.62 2772 0.26 0.21 5.2%Polloch 8.05 2650 0.23 0.23 5.5%Deephope 30.99 1486 0.32 0.26 6.1%Durkadale 19.60 1145 0.28 0.42 8.8%Barsolus 32.83 1150 0.35 0.38 9.0%Inverlochy 47.09 2946 0.26 0.24 7.1%Skeabost 80.55 2218 0.26 0.26 7.9%Luss 35.47 2296 0.35 0.28 9.4%Candermill 25.50 1034 0.40 0.31 9.2%Creed Bridge 44.83 1462 0.25 0.44 9.3%Dargall Lane 2.07 2439 0.21 0.28 9.8%Lathro 24.60 1164 0.54 0.43 11.0%Brockhoperig 38.59 1732 0.37 0.34 11.4%Kinross 33.60 1266 0.56 0.42 12.1%Whitburn 31.95 1032 0.32 0.30 11.5%1

1-13%

9-11%

7-9%

5-7%

Selection of time periods

It was essential to make sure that each chosen catchment had a period of gauged data that was at least

ten years in length and that this was of good quality. Ten years was considered the calibration period

length for previous studies by Jacobs (2010). Ten years allowed for two years for model warm-up and

the eight years of calibration data. Scottish water recommends a minimum of seven years of datarecord; therefore this is more than satisfactory. The data to be used was: rainfall, evapotranspiration

and flow data; making a total of 48 sets of data that would be subject to scrutiny.

The selection of time periods of data was dictated by the availability, quality and representativeness of

rainfall, flow and potential evapotranspiration input data. It was chosen that there would be one time

period for each Q95(%MF) group, making four time periods in total. Selection was achived by comparing

the data between the four catchments in each group then deciding which time period is most complete

and which is most respresentative of each individual catchment. It was thought best to keep the time

series the same across each four catchments in each Q95(%MF) group in order to similarise climate limits

upon inputs across Scotland for the Q95(%MF) group and therefore enable a fair test. If climate


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conditions across Scotland differed from the average for a given day, month or year then these trends

would impact all catchments in direct comparison with each other.

In some instances Scottish Water calibrations existed for catchments that this study had allocated

donor catchments. It was seen as useful to improve these models by updating existing data where

improved data was available and selecting or extending time periods where possible.


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FIGURE 2.1.2:CHOSEN CATCHMENTS FROM SEPA PROVIDED CATCHMENTS, AN INDICATION OF Q95(%MF)ROI

GROUPING IS PROVIDED.SUPPLIED BY SCOTTISH WATER ON REQUEST.

2.2 The Hysim conceptual rainfall-runoff model


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The conceptual rainfall-runoff model employed for this study is Hysim, a Hydrological Simulation

model developed by the Water Resource Associates (WRA). WRA are a network of consultants in

water resources, water quality, hydrology, groundwater hydrology and flooding. Their clients include

some of the most important water management bodies in Europe i.e. the European Union, United

Kingdom Environmental Agency, National Power, French government, British Waterways and

SNIFFER. SNIFFER is a research and development company that works cooperatively with Oxford

Scientific Software to develop catchment rainfall-runoff models as well as models for water resource

system simulations such as the estimation of yield (Entec UK, 2003). The development of Aquator-

Hysim was undertaken by SNIFFER on behest of Scottish Water for the surface water yield and

operational reliability project. The combined program is considered the best practice methodology for

estimation of yield (Scottish Water, 2009) and improves previous estimations of yield. Consequently

an integration of the Aquator with the rainfall-runoff model in use for England, the Hydrologic

Resource Centre reservoir resource Simulator (HEC-ResSim), is currently in development (US Army

Corps of Engineers, 2011).

Hysim is founded upon two IBM Mathematical Formula Translating System (FORTRAN)subroutines. Initially the model processes both the hydrology and hydraulics of a catchment

separately. The hydrology routine is based on seven reservoirs within the catchment. These are

illustrated on figure 2.2.1.

FIGURE

2.2.1:F

LOW CHART OFH

YSIM HYDROLOGY CALCULATION ROUTINE.T

AKEN FROMM

ANLEY(2006).

In the model it is parameters that determine the capacity of and the rate of transfer between each

storage as well as the equations that determine transfer processes. Parameters are designated through

calibration of the model, they are constant throughout time. Variables in the model describe the

volumes in each storage and rates of transfer, they vary with time.


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Parameters alterations are split into three sections within Hysim, these are data, basic and advanced.

In accordance with the Jacobs (2010) standard calibration methodology, which is based upon

guidance in the Hysim User Manual (Manley, 2006), the data specific and advanced hydraulic

parameters remain at their default values for this study. The basic hydrological parameters that are

changed within this report are illustrated at their default values on figure 2.2.2.

FIGURE 2.2.2:BASIC PARAMETER VALUES FOR ALTERATION DURING THE CALIBRATION PROCESS.SCREENSHOT

FROM HYSIM V5.00(MARCH 2010 BUILD).

Data requirements for Hysim are practically obtainable in the field, they are: potential

evapotranspiration, potential snowmelt, precipitation, the net value of discharges and abstractions,

groundwater abstractions. Input formats are monthly, daily, hourly; though daily is usually sufficient

and is the format used for all data in this study. However, it worth noting that data used for input isnot required to be in the same time-step format for either hydrological or hydraulic subroutines.

Spatially, the data can be distributed amongst user specified sub-catchments. This can be

advantageous in reflecting heterogeneity in the hydrology or meteorology across the catchment area;

however, this is not required for the study at hand.

In the current version of the model: Hysim v5.00 (March 2010 build), there are in excess of six

different output data. The most critical of these output data are the daily mean simulated flows,

recorded flows as well as the basic statistical analysis and monthly summaries. If sub-catchments or

channels are setup there is a generation of flow for every time increment. There is also an output for

recorded and simulated daily flow for each reach. Actual evapotranspiration is also available as anoutput of the model should it be required.

A measure of efficiency or accuracy of the simulations is required in order to evaluate the success of a

model and adjust parameters with the goal of achieving an optimal parameter-set. Hysim provides a

packaged graphics tool for instant hydrograph comparison between the recorded and simulated flow.

Hysim also provides a summary of the daily and monthly difference statistics. Additionally Manley

(2006) indicates that efficiency, the percentage of explained variance, can be calculated:

(2)

In this case Qm represents mean daily flow, Qo is observed daily flow, and Qs is simulated daily flow.


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2.3 Derivation of inputsFlow rainfall and evapotranspiration data

Historic flow data is provided by SEPA for each of the candidate catchments. Time series data of

historical flow measurements is compared to simulated flow in order to evaluate the accuracy of the

simulation. The quality of recorded flow data varied between catchments; data could be of a poor

standard or missing for days, weeks, months or years in an otherwise complete record. A solution of

infilling the missing data was necessary in order to provide a complete record of flow for a small

number of the 16 catchments. Two approaches were found to be successful for flow infill. The most

accurate infilling of missing data requires the construction, calibration and simulation of a model

based on the catchment with the poor flow record. This process follows the same process of model

development as outlined in this study. Simulated values may then be substituted for the

corresponding missing values in the recorded flow record. This method required good quality and

availability of other input data as well as a complete parameter-set for the catchment. This method

was therefore only suitable for the donor catchments as these were the only complete, fully calibrated

models. In the case of infilling missing data for the analogous catchments, an average of ten years of

values was obtained, taking the five years preceding and superseding the missing value in the flow

record. Both methods provided successful representations of recorded flow that was otherwise

unavailable and allowed more accurate statistical comparisons to be drawn between simulated and

recorded flow for catchments.

In previous studies by Scottish Water, rainfall was obtained from daily measurement gauges local to

the catchment. This procedure was prior to the first Water Resource Plan; it involved the

identification of suitable rainfall gauges and infilling gaps or extending records in order to identify

rain gauging weights, completed externally from Hysim. Importing this data into Hysim allowed the

generation of daily, aerial, weighted rainfall for a catchment. 37.5% of all Hysim models used thismethod in order to compute rainfall until recent revisions (Scottish Water, 2009).

Discussed in Scottish Waters Hydrology Guidance (2010), the Met Office has recently revised their

method for providing gridded rainfall data across the United Kingdom. Data is currently available for

a 5km2

gridded data set, from the start of 1958 up to the end of 2009. The updated Met Office gridded

rainfall is used for input into Hysim for the eight catchments.

The derivation of a suitable potential evapotranspiration (PET) series depends upon data availability

for specific catchments. Typically, daily PET series can be generated by Hysim for the period 1918 to

1998 using a tool developed as part of the SNIFFER project (SNIFFER, 2001). This method is used

for calculations of PET for the eight catchments; however, data will only be available to the end of1998. Methods for extending PET beyond 1998 exist; Scottish Waters preference for PET extension

is the acquisition of MORECS PE weekly 40km2

data grids, which are superimposed onto existing

data from 1995 to 1998; providing a prediction beyond 1998. Due to the implicated costs of acquiring

and updating MORECS data, this is not be used for PET estimation in this study. The preferred

method in this study is the application of an average annual PET value from the calculated Hysim PE

data series. A monthly average is applied from the last 10 year time period of study and extended to

fill the remaining time period. Where this is not possible it is recommended by Jacobs (2010) to scale

yearly averages of near-catchment PET data provided by the Centre of Hydrology according to

relationships derived from overlapping periods of data. Daily values of PET were obtained from the

average of preceding Hysim data. In some cases this was an essential step due to the lack of availabledata for internal Hysim PET calculations. Although PET input data was more uncertain, the accuracy


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of rainfall data is seen as more critical for modelling. This is reflected in the single parameter

optimisation method outlined in appendix 8.1. It was found that PET factor could be adjusted in

recognition of its unreliability in order to improve estimations.

Parameters

Initial parameters were estimated by referring to hydrological data available for the catchments. These

data are illustrated in figure 3.2.1; manually calculated parameters were: catchment area (km2), time

to peak (hrs), interception storage (mm) and rooting depth (mm). These parameter values are

approximations based upon methods of calculation according to the Hysim User Manual, Manley

(2006), which use ordnance survey map observations of area and forest cover. The remaining

parameters of the Hysim model were kept at their default values shown on figure 2.2.2. It was

important to use these specific parameters for alteration as they are those historically observed in

previous evaluations of Hysim performance by Jacobs (2006).

Catchment area was obtained from the NRFA Hydrometric register from the Centre of Ecology and

Hydrology (CEH) (Centre for Ecology and Hydrology, 2008).

Time to peak controls the simulation of the response of minor channels within the catchment; both the

Hysim User Manual (Manley, 2006) and the The UK Flood Estimation Handbook (Institute of

Hydrology, 1999) give the equation for calculation as:

(2)

L is the length of the stream, S is slope of the stream in m/km and Tp is time to peak in hours.

Interception storage represents the storage of moisture with flora; with moisture being added to this

storage from rainfall. It is therefore an approximation of the proportion of the vegetation density ofsurfaces for a catchment. A value of 2.0mm is normal for grassland and urban areas and up to 10.0mm

for woodland (Manley, 2006). Soil rooting depth is also dependent upon studying vegetation

coverage, typically it is between 500 and 1000; woodlands may be as high as 5000mm.

Time to peak, interception storage and soil rooting depth were calculated through observations of

1:10000, 1:25000 and 1:50000 Ordnance Survey (OS) maps. This was made available by the OS

Openspace Application Programming Interface (API) as an overlay for Google Earth (Brock, 2009).

Accessing OS maps via Google Earth allowed the plot of river courses and presentation of elevation

transects across a rivers course. It also allowed accurate calculations of vegetation coverage areas,

where the proportion of grassland/moorland to woodland was required for interception storage andsoil rooting depth.


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2.4 Hysim model calibrationOnce satisfied with the input data, as well as the initial input parameters for the proposed modelling

scenario, the recommended modelling strategy for Hysim was approached. This strategy has been

consistently developed alongside Hysim, for this study it follows the guidance manual by Manley in

2006. Due to the extensive version of history and continual maintenance of Hysim by developers, it

was of high priority to check that the current version of Hysim in use was fully up to date before

beginning modelling.

The strategy suggests selecting discharge data that is representative of the natural flow regime, of

good quality and of suitable length. A period of data of ten years length is used in the previous studies

by Jacobs (2010). This accounts for two years of at the beginning of the record that represents the

model warm-up period and an additional eight years of accurately simulated flow. Model warm-up is

a key process of runoff modelling, the two years signify part of the simulation period but not part of

the analysis period (Manley, 2006). A period of ten years was therefore chosen for each of the

catchments, of which two years would be considered warm-up and was not included in analysis.

The input data: flow, evapotranspiration and precipitation are stored in individual files, with a

separate file for the parameter-set. Hysim references these individual files using a single project file.

Once a project file was created, a standard calibration methodology was applied; this is detailed in

appendix 8.1. The goal of calibration is to select an optimum set of parameters that achieve simulation

values that are the closest to recorded values.

1. Initial parameters estimated from calculated derivations of catchment area, time to peak,interception storage and rooting depth. All other parameters left as default.

2. Single parameter optimisation for PET3. Extremes of Error Estimate (EEE) for horizon boundary permeability, base horizon

permeability, upper interflow and lower interflow.

4. Manual alteration of parameters according to known relationships between parameter andflow estimations.

Single parameter optimisation is an automatic calibration process within Hysim for a single

parameter. As potential evapotranspiration (PET) is the most uncertain parameter this is used in the

single parameter mode run, so as to provide the best estimation for this parameter. In previous

calibrations by Scottish Water this parameter choice for single parameter run was rainfall factor; this

does not correspond with the guidance provided by the Hysim User Manual (Manley, 2006). If thereis good coverage, with a sufficient density and spread -as with improvements made to the quality of

Met Office provided 5km2 gridded rainfall- then the use of PET for single parameter optimisation is

suitable. In some cases, using single parameter optimisation for PET produced unrealistic parameter

values and dubious flow estimations. In these instances it was more beneficial to use the precipitation

factor for single parameter optimisation. EEE is also an automatic calibration process within Hysim

for multiple parameter estimation. Hysim simultaneously optimises four parameters; these are:

horizon boundary permeability, base horizon permeability, upper interflow and lower interflow.

Manual estimation was a necessary step in achieving an optimal parameter-set. This was done

according to noted relationships between parameter and flow estimations, which was only possibleafter considerably experience of using Hysim. As such, this was extensively time consuming. Once


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enough expertise was acquired, it seemed valuable to make no more than ten attempts of manual

calibration, otherwise the model would become over-parameterised; over-parameterisation greatly

increases subjectivity, which should be avoided in models. By manually altering parameter values it is

possible to combine the requirements of a manual calibration with the advantages of automatic

calibration in order to provide a closer fit between simulations and observations in accordance with

Boyle et al. (2000).

2.5 Development of parameter transposition methodsIt was important to quantify the uncertainties associated with the choice of different parameter-sets.

This was accomplished by developing two different methods of parameter transposition. Th

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