scale appropriate modelling: representing cause-and-effect relationships in nitrate pollution at the...

Scale appropriate modelling: representing cause-and-effect

relationships in nitrate pollution at the catchment scale

for the purpose of catchment scale planning

Paul Quinn

Water Resources Systems Research Laboratory, School of Civil Engineering and Geosciences,

University of Newcastle, Newcastle NE1 7RU, UK

Accepted 23 December 2003

Abstract

Laboratory and small-scale field observations have yielded fundamental insights into the causes of nitrate pollution.

Detection of downstream, off-site impacts of nitrate pollution reveals its effects. However, there is a gulf in our knowledge and

practice that prevents the expertise gained at the small-scale from contributing to a sound scientific basis for planning at the

catchment scale. Many modellers may thus rely on simple empirical models when simulating nitrate pollution at the catchment

scale as these models can reflect their judgments and uncertainties. Other modellers struggle to apply physically-based,

distributed models within complex, three-dimensional heterogeneous landscapes, inducing equifinality and predictive

uncertainty problems. One way for planners and scientists to advance is to create scale appropriate modelling techniques, which

can call upon a range of model types [including complex physically-based, quasi-physical, semi-distributed models and lumped

Minimum Information Requirement (MIR) models]. This paper argues that the modeller must use the appropriate model type, at

the appropriate scale, in order to best understand nitrate losses observed at that scale. When simulating at the catchment scale,

the modeller must accept that there are processes that are not fully understood and cannot be modelled with accuracy, yet the

modeller must still produce decision support tools that are capable of solving real world problems, despite inherent model

uncertainty. Thus, this paper will show, through a fully worked example, how hydrological flow paths and nitrate pollution

sources can be simulated at the catchment scale by first, reflecting our understanding of the physical world and second paying

full respect to catchment scale issues and uncertainty problems. The River Great Ouse (1400 km2) case study is a typical intense

arable region of the UK, where the available data sources are by no means perfect, but where nitrate policy must still be

implemented. Thus, physically-based model simulations, simple MIR models, GIS data sources and ‘expert’ knowledge are

brought together to create a simple, applied modelling toolkit to simulate nitrate pollution and support catchment policies that

reduce the loss of nitrate to rivers.

q 2004 Elsevier B.V. All rights reserved.

Keywords: Nitrate pollution; Catchment hydrology; Decision support

1. Introduction

This paper is a follow up to a discussion paper that

appeared in Quinn (2002), where the problems of

0022-1694/$ - see front matter q 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.jhydrol.2003.12.040

Journal of Hydrology 291 (2004) 197–217

www.elsevier.com/locate/jhydrol

E-mail address: [email protected] (P. Quinn).

http://www.elsevier.com/locate/jhydrol

scaling and process representation were made. Here a

similar discussion is made, but it is followed by a fully

worked example of scale appropriate modeling

applied to nitrate pollution. The paper will address

the nitrate pollution problem at the catchment scale,

and will argue that a detailed, complex physical model

is not necessary at this scale. However, the simple

catchment models presented here are underpinned by

a sound, physical understanding of the real world;

utilizes the outcome of any research evidence and

suggests key measurements to be made in the field. By

prioritizing the goals of the models a priori, a clear

design of the final minimalist modelling structure can

be made, that is not too ambitious, but is fit for

purpose. Physically-based models, quasi-physical

hillslope scale models and simple lumped Minimum

Information Requirement models (MIR models) are

all required to satisfy the ultimate model goal. The

key aspect of the proposed strategy is to use models in

relation to scale: physical models are used at the plot

scale (1 £ 1 m–5 £ 5 m), quasi-physical models are

used at the hillslope and small catchment scale. MIR

models then mimic the output time series created by

physical models of nitrate loss (EPIC, Williams et al.,

1990 and SLIM, Addiscott and Whitmore, 1991) and

also the quasi-physical runoff model (TOPMODEL,

Quinn and Beven, 1993). The need to predict at a

range of catchment scales effects and to represent

uncertainty, are addressed by tailoring the model

structure to solving the catchment scale nitrate

problem only and by demonstrating that the simu-

lations are appropriate for underpinning sensible

decision making. The paper does not use scaling-up

theory in the classical sense (Bloschl, 1997), but will

demonstrate that the fluxes of flow and nitrate, that are

generated at the plot scale, can be routed downstream

and mixed with other flow components (such as

groundwater or baseflow) to give a realistic catchment

scale estimate of nitrate pollution.

There is an abundance of sound scientific knowl-

edge about good land management practice that can

be built into our catchment scale models that should

allow us to solve the nitrate pollution problem without

being stifled by uncertainty anxiety. Equally, when

changing scale, from a point to a catchment, the

processes that can be measured and simulated change

radically. Thus, a thorough understanding of the

issues of scale that relate to measurements, processes,

model parameters and data allows a simple but robust

simulation methodology to be used. An overall

modeling framework is then developed which will

show how scale appropriate simulations allow us to

synthesize our expertise into a simple catchment scale

model and a simple visual decision making toolkit.

Thus, a key output of the paper is to send clear

message to catchment planners that as modellers, we

understand both the local controls of nitrate losses and

how they impact downstream. It may seem obvious

from many studies that a reduction in nitrate fertiliser

application rate, will obviously reduce nitrate pol-

lution. However, reduction in fertilizer applications

may also start to impact upon farmer incomes,

therefore policy makers need to know the correct

level of fertilizer application to give adequate crop

growth, whilst staying within a legal catchment

requirement, such as the EC Nitrate Directive. Current

work on sustainable off-takes of nitrogen within crops

(DEFRA, 2000), has gone a long way to reducing

large nitrate surpluses in soil columns, but the policy

maker still needs re-assurance that these new

application rates will attain the legal limit set by the

EC directive. Equally, policy makers need to know the

impact of buffer strips, wetlands (Muscatt et al.,

1993), river denitrification rates (as represented in

models such as INCA, Wade et al., 2002) and flow

mixing effects on the overall catchment scale nitrate

level. This can only be achieved by understanding

both plot and hillslope scale nitrate flux processes.

Thus, the message for catchment policy makers that

arises from the current study is ‘to identifify land use

and management options that reduce the connection

of nitrate sources to the receiving waters, whilst also

maintaining farmer incomes’. Hence, a balanced

approach to fertilizer application rates, crop choice,

tillage regime, and creating buffer strips and wetlands

can protect receiving waters and thus create confi-

dence that land management will drive down nitrate

levels to a safe level, be that a legal or ecological

requirement.

It is widely recognized that environmental

measurements cannot be scaled-up directly (Beven,

1989). The types of measurements taken at a point

(1 m2), may differ radically from measurements made

at the hillslope scale (1 ha), in small catchments

(1 km2) or in large catchments (1000 km2). However,

some environmental measurements can be made

P. Quinn / Journal of Hydrology 291 (2004) 197–217198

accurately at all scales, for instance the water balance

and nitrate balance, and as such they can form the

basis of a combined monitoring and modelling

strategy for addressing scale issues. In principle,

synchronous determinations of the water and nitrate

fluxes made at the point, plot, hillslope, catchment and

basin scales, offers the best hope of understanding

scale dependent effects and determining modelling

strategies appropriate to specific scales of application.

In this paper it is suggested that the scaling up of

cause-and-effect relationships can be achieved by

combining the use of the outputs from physically-

based models applied at the ’local’ scale (i.e. the plot

scale and field scales) with quasi-physical models at

the hillslope/small catchment scale (which can reflect

buffers and wetland effects) and a simple MIR model

that routes and mixes flow downstream so that

simulations can be made at any catchment scale. A

MIR model can be defined as the simplest model

structure required that satisfies the modelling needs of

the policy maker, whilst still ensuring that the model

parameters retain physical significance (Quinn et al.,

1999). The sources of data needed for the MIR model

should be readily available, for example from a GIS of

land use and soil and a range of crucial field

measurements (such as rainfall and flow). In this

study two MIR models work together to give the final

catchment modeling tool, the first in a nitrate (N),

leaching model which emulates outputs of the

physically-based models EPIC (Williams et al.,

1990) and SLIM (Addiscott and Whitmore, 1991).

The physically-based models can produce numerous

output time series of flow and nitrate at the plot scale.

A simple mathematical function is then determined

that can mimick the output of the model for the greater

majority of the simulations (this is the nitrate MIR,

TOPCAT-N). The physically-based models can be set

up for many agric/meteorological scenarios, this

includes a long time series (8 years of daily data),

differing crops and soil types plus, different appli-

cation rates and fertilizer timings. In all the cases

simulated (see Quinn et al., 1999; Dayawansa, 2002) a

common pattern of N loss was determined, that in

essence, requires only an estimate of the pre-winter

nitrate level and the soil type in order to simulate daily

nitrate loss. Whilst there are many ongoing processes

(such as mineralization and crop nitrogen extraction)

and varying fertilizer rates and timings, the major

impact of these processes is in their net effect on the

pre-winter nitrate levels that are ready for mobiliz-

ation. Thus, TOPCAT-N can produce leaching and N

loss rate per unit area, that can be routed downstream

and mixed with other flow components such, runoff

with lower N leachate, denitrified baseflow or low N

overland flow, to give a final catchment scale nitrate

simulation. The catchment flow MIR (TOPCAT) is a

simple routing and mixing model, that is sensitive to

catchment size and the statistical distribution of

available N sources within the catchment.

Physically-based models can be used effectively at

the plot scale (sometimes referred to as the point

scale) and within field scale experiments where the

acquisition of data is appropriate to the structure of the

model. Problems of parameterizing physical models

at the catchment scale, due to heterogeneity and

uncertainty, are reported elsewhere (Franks et al.,

1997; Beven, 1993). At the hillslope scale or small

catchment scale, many modellers may decide that

quasi-physical, semi-distributed models are more

appropriate (Beven et al., 1995), but even quasi-

physical models may not be applicable at the larger

catchment scale. As a result, modellers may choose to

use a simple, parsimonious model structure for use at

the catchment scale (a black box model, a meta-model

or neural network model). In the MIR approach the

simplest model structure is sought which satisfies the

condition that the chosen MIR must first, be able to

mimic the output of whichever physical or quasi-

physical models have been used at the plot/field scale

or hillslope scale and route this flow to the catchment

scale. Thus, suitable respect has been paid to the

physical factors that influence nitrate pollution, but

only the MIR models are used for policy making and

communicating key catchment scale effects.

2. Processes, monitoring and modelling:

how do they change with scale?

Process representation is arguably the most funda-

mental problem of scaling issues. As scale increases,

processes integrate to yield responses, which require

data sets and simulation strategies, which differ

markedly from those appropriate to smaller scales.

By showing the processes at each scale, it is possible

to look at some problems of process simulation and

P. Quinn / Journal of Hydrology 291 (2004) 197–217 199

measurement, thus paving the way for the modeling

strategy presented here and reinforcing the need for

multi-scale catchment measurements. A number of

good quality research experiments are underway in

many locations, with many models and measurements

being studied at many scales (SteenVoorden et al.,

2002; Haygarth and Jarvis, 2002). The result is an

abundance of basic knowledge on nitrate fluxes and

agricultural runoff, though few of them have any true

scaling up component. The recent work of Haygarth

and Jarvis (2002), does report that progress is being

made into joining basic agronomic concepts with

hydrological process understanding, though they do

suggest that the subject area is still in its early stages.

Fig. 1, is an attempt to demonstrate how hydro-

logical processes change with scale, which allows us

to identify key factors that influence nitrate losses. It is

then possible to envisage which models are appro-

priate at which scale. Thus, complex physically-based

nitrate and soil hydrological processes are best suited

to the plot/point scale. At the point scale (Fig. 1A) soil

type, crop type, nitrogen cycling processes and

leaching processes are dominant. In Fig. 1A, 1 m2

of soil is assumed to be the plot scale or ‘point’ scale.

Fig. 1. Processes and scale (A) A typical 1 m2 soil column where N losses are dominated by soil conductivity, roots and macroporosity. (B) A

typical hillslope section where flow connectivity is the key pollutant factor (C) A small catchment where a distribution of Critical Source Areas

and Variable Source Areas dominate pollutant losses. (D) A large catchment where dominant land use, topography and rainfall gradients

dominate pollutant processes.


In Fig. 1A, the soil, roots and macropores are shown,

all of which control the soil moisture and nitrate

regime. Given the localized research scale or plot

scale nature of the measurements, fully physically-

based models can reasonably be developed to test

basic agronomic ideas and physical relationships.

However, the bulk of the knowledge currently

archived in physical-based models relate to one-

dimensional fluxes only and perhaps should only be

used at this scale.

At the hillslope scale (1–5 ha), we are faced with a

greater problem, as many differing macro scale

processes are in operation. Fig. 1B, shows the

hydrological processes anticipated in a typical UK

hillslope scenario, (i.e. with some land drainage). In

general, hydrological processes tend to vary greatly

between the catchment divide and the main channel,

reflecting the change in landscape. The dynamics of

both the unsaturated and saturated flow processes are

spatially and temporarily complex. Along with leach-

ing, a key cause of nitrate mobilization is related to the

existence of source areas that have a high nitrate

transport capacity. Surface and near surface source

areas for nitrate and phosphate loss have been referred

to as Critical Source Areas (CSAs). Whilst subsurface

controlled CSAs are probably synonymous with runoff

related to Variable Source Areas (VSAs). The role of

topography, soil and human influences are at their

greatest at the hillslope scale. The temporal dynamics

of source area operation and the flow connectivity of

the source areas to the receiving waters need to be

addressed. This is probably best achieved using

simple, functional quasi-physical models that reflect

the likely functioning of the CSAs and VSAs

(Heathwaite et al., 2000). It should be noted that

VSAs can also provide good nitrate uptake, buffering

and denitrification capacity. Attempts to proactively

disconnect CSAs can also be considered, by

(i) avoiding nitrate application on CSAs and by

(ii) disconnecting nitrate rich runoff (for example in

land drains or small ditch networks), from the main

receiving waters. Strategic hillslope scale land man-

agement options using wetlands, hedgerows and

buffer zones are often underused in nitrate manage-

ment. Our catchment models should reflect the impact

of the dominant hillslope flow path, which in many

cases connects nutrient rich flows to the channel, and

the impact of any buffers and wetlands that reduce

the amount of the total available nitrate that reaching

the receiving water. Thus the proposed methodology

uses an MIR model for point scale model coupled to a

quasi-physical hydrological flow path model. The goal

of hillslope scale modelling is to reflect how much of

the available nitrate on the hillslope is in direct

connection to the receiving waters.

Experiments at the catchment scale (1–10 km2)

should encompass a range of typical hillslopes and

source areas that exist within the region. In Fig. 1C,

the likely operation of VSAs, which are controlled by

the topography, can be simulated. Equally, some

attempt must be made to quantify the prevalence and

hydrological activity of CSAs in the area, which are

related to husbandry. CSA activity can only be

estimated from knowledge of agronomic practices,

by field inspection and knowing whether or not best

management practices are in use. In Fig. 1D, the scale

is increased once again from a single catchment to the

basin scale (1000–10000 km2). The key influence of

nitrate release at this scale is the large-scale variability

of land use, rainfall regime and topography. Any

model or measurement should try to reflect this

variability. At the basin scale a broad re-classification

of the landscape is needed to reflect the differing

regimes of nitrate inputs, the gross variability of the

rainfall/evaporation regime and the hydrological

potential for the transport of nitrates in each zone

(including both natural and man made factors).

Hence, the model becomes a spatial index of nitrate

availability and an index of nitrate transport potential

(Heathwaite et al., 2000) as modified by best

management practice and by known buffer zones

and wetlands. This type of model can be coupled to a

GIS so that the overall model structure can be a simple

catchment hydrological MIR model fed by statistical

distributions of land use characteristics (Quinn et al.,

1999). The model assumes a per unit area production

of leached flow and N loss, i.e there is no

representation of where the N land units are within

the catchment, and simulates a ‘lumped’, routed and

mixed nitrate value per unit time.

3. An a priori determination of the model required

A review of the underlying drivers that control

nitrate pollution should be carried out before any


model is developed. First, the complex natural

processes of nitrate pollution need to be prioritized

within the context of farmer behaviour and the end

user needs of the catchment planner. This a priori

analysis goes a long way to simplifying the final

model structure and input information needed to

represent the key processes underlying pollution

problems. The first level of analysis relates to the

climate inputs of the model, which are themselves

complex and have a large data input requirement.

However, if the principle assumption at the catchment

scale is that ‘it will rain’ and ‘it will probably rain

heavily, on ploughed soils, loaded with nitrates in the

early winter period’, then we require an input time

series that has such a range of typical winter storm

events. Thus, if we can simulate and manage this

typical situation of nitrate loss, then we have satisfied

a major goal of the catchment management problem.

The role of socio-economics in the behaviour of

farmers and their land management decisions (the so

called ‘actor behaviour’), is also very complex and

requires extensive and detailed study, much of which

is still not adequately understood by the environmen-

tal modelling community. Thus, it is argued here, that

this component too can be left out of the analysis, if

the astute assumption is made that all farmers must

make a profit and will always maximize their

livestock and crop production. Put simply, farming

practices will remain intense and that any proposed

solutions will have to work with the farmers directly

and not reduce their profit margins.

If a full study of the factors that influence nitrate

losses are made, it is clear that the situation is complex

it terms of physical, chemical and land use manage-

ment options. Thus, an attempt is made to prioritize

the underlying, generic components of the complex

system that will be retained and described in simple

terms in the catchment model. It is possible, to express

the numerous input components of the model into

more general terms that are grouped into integrated,

effective parameters. For example, all the forms of

fertilizer application, its incorporation into the soil,

the uptake by crops and the effects of the tillage

regime, can be expressed as the ‘total nitrate available

for transport at the onset of the winter drainage

period’. This deduction is based on both the

physically-based models (shown below) and from

knowledge gained from agronomic experts. One must,

however, accept the large amount of uncertainty

associated with using effective model parameters. The

MIR model relies on this nitrate availability par-

ameter, as does the decision support toolkit. Equally,

all the soil related terms can be grouped together as a

single, effective ‘soil type’ parameter. Soil texture

(clay, sand, silt), soil structure, soil management (such

as mole drains and land drains) and the impact of soil

on the physical and chemical processes can all be

expressed as a vulnerability to nitrate loss (see below).

Finally, any aspects of local enhanced nitrate losses or

any evidence of landscape related dentrification

features, for example riparian buffers and wetlands,

can also be described in terms their efficiency to

enhance or reduce the overall nitrate export to the

receiving waters. It is no surprise (see below), that

during the production of the TOPCAT-N model, that a

nitrate availability term and a single, soil parameter

terms were determined as our minimum information

requirement.

The hydrological model contains a number of key

flow paths, all of which will have a nitrate

concentration associated with them. It is by under-

standing how these flow components mix together as

we change catchment size that allows us to predict

nitrate concentrations at any scale, for a typical range

of summer and winter storm events.

4. The hydrological MIR model—TOPCAT

TOPCAT is a simplification of the model TOP-

MODEL (Beven and Kirkby, 1979; Quinn and Beven,

1993; Beven et al., 1995), and as such, uses identical

soil moisture stores and subsurface flow equations.

TOPCAT does not, however use a topographic

distribution function and thus does not allow the

representation of topographically controlled variable

source areas. The model TOPCAT also contains an

extra baseflow/dry weather flow component and two

overland flow components that are caused by intense

agricultural management practices.

The TOPCAT hydrological model (seen in Fig. 2),

uses a simple moisture root zone store to receive

inputs of rainfall and potential evaporation per unit

time (usually on a daily time step). The moisture

content of the root zone store can fluctuate between

SRMIN and SRMAX (which are both expressed in


units of depth). SRMIN is the minimum amount of

moisture retained in the root zone, which represents

the permanent wilting point of the soil. In real terms

when the root zone reaches SRMIN the moisture is

empty, hence the actual evaporation falls to zero at

this point. SRMAX is the maximum soil moisture

holding capacity of the root zone. SRMAX is a

function of both the soil field capacity and the actual

rooting depth of the vegetation cover. The root zone

must be full before any excess rainfall is allowed to

percolate deeper into the soil. This excess percolating

flow is referred to here as Hydrologically Effective

Rainfall (HER).

Three moisture stores are used in the hydrological

model: the unsaturated zone root zone store, the

saturated ‘event’ subsurface store and the ‘old’

subsurface store (or the background flow store).

Excess HER is assumed to move vertically into the

subsurface stores within one timestep (i.e within one

day). A proportion of the HER can bypass the event

subsurface store and enter the old subsurface store.

The old subsurface store is conceptualised as having a

large moisture storage capacity and will generate a

constant background flow rate (Qback). The par-

ameter SPLIT controls the fraction of the HER that

enters the event subsurface store. For catchments that

are dominated by surface runoff the SPLIT value

should be set to 1 (i.e. 100% of the flow enters the

event subsurface store) and logically the background

flow rate should be set to zero. A catchment with a

distinct base flow component requires the value of

Qback to be set as an input parameter, this can be

based on either observations of flow (taken during an

extended low flow period) or from direct calibration.

The SPLIT parameter can be estimated using a water

balance approach or can be calibrated directly. Quinn

et al. (1999); Anthony et al. (1996) also coneptualised

the background flow to include ‘dry weather flow’

which arises from urban sources and sewage treatment

works (which influences the final nitrate estimates).

The rate of subsurface flow leaving the event

subsurface store is approximated to by an exponential

function taken directly from TOPMODEL (Quinn and

Beven, 1993; Beven et al., 1995). The current

moisture status in the event subsurface store is

described as SBAR, which is expressed as a positive

soil moisture deficit value. The rate at which moisture

is lost from the store per unit time is given by:

Qb ¼ Q0 £ expð2SBAR=mÞ ð1Þ

Where Qb is event sub surface flow and m is the

recession rate parameter. The recession parameter can

be approximated to by either studying recession rates

in observed storm events or from calibration directly.

The term Q0 represents the discharge of the catchment

when the soil moisture deficit is at its lowest and Q0

be determined directly from TOPMODEL theory

Fig. 2. A schematic diagram showing the flow components and parameters used in the TOPCAT hydrological model (after Van Herpe

et al., 2002).


(Beven et al., 1995):

Q0 ¼ expð2gÞ ð2Þ

g is the mean of soils/topographic index as defined

and used in TOPMODEL and but this term can be set

to a constant and value of 6 in all TOPCAT

simulations.

Within each time step the total amount of water in

the subsurface store is determined by calculating the

vertical flow entering the event subsurface store

(HER £ SPLIT) and the amount leaving ðQbÞ:

SBARðtÞ ¼ SBARðt21Þ 2 ðHER £ SPLITÞ þ Qb ð3Þ

Where SBARðtÞ and SBARðt21Þ are the current time

step and the previous time step for the catchment

storage deficits, respectively.

Quick flow in TOPCAT is assumed to be

predominately overland flow but may include any

fast flow response associated with a surface flow

source. As such, two components of quick flow are

represented to reflect intense agricultural systems and

quick flow is always assumed to reach the channel

within one time step. First, the Quick parameter

determines the fraction of rainfall in one time step that

converts directly into quick surface runoff, however,

this type of flow can only be generated when the root

zone reaches field capacity (SRMAX). This com-

ponent of quick flow attempts to approximate to large

overland flow ‘wash off’ events that are commonly

observed in intense arable systems; particularly in

winter. Quick overland flow only occurs after the

SRMAX value is reached and thus stops large

amounts of overland flow being generated in every

storm. Thus the first component of quick flow is:

ROQuickðtÞ ¼ RðtÞ £ Quick ð4Þ

Where ROQuickðtÞ is the quick flow surface runoff, R

is rainfall at time step t:

In order to reflect a key nitrate runoff related flow

path, a second component of overland flow is allowed.

This is referred to as the CSA quick flow. Basically,

within intense agricultural zones some nitrate rich

land parcels can be intersected by active hydrological

flow paths that have a direct connection to the

receiving channels. For example, areas close to the

channel (including variable source areas), imperme-

able roads and their associated ditches, farm buildings

or fields that are cross cut by tire tracks could all give

rise to quick flow that reaches the channel. Quick CSA

runoff can be generated irrespective of the root zone

soil moisture content, and as such can operate in all

storms. Nitrate rich areas close to land drains can be

considered as CSA’s as they connect surface overland

flow sources of nitrate and sediment directly to the

channels. Although usually associated with high

losses of phosphate (especially in particulate form)

high nitrate losses can be lost in preferential flow paths

to local field drains under certain circumstances (such

as hard standings and exposed compost heaps). These

areas are seen as small, potent areas of nitrate loss that

operate in all storms that give rise to chronic nitrate

pollution problems. A full discussion CSA concepts

are discussed elsewhere (Preedy et al., 2001; Endreny

and Wood, 1999; Gburek et al., 2000; Heathwaite

et al., 2000; Quinn, 2002). The inclusion of the quick

CSA flow component, even though it is usually small

in magnitude, can build up to give a significant

component of nitrate loss over time. Thus the quick

CSA is estimated using the QuickCSA parameter.

ROCSAðtÞ ¼ RðtÞ £ QuickCSA ð5Þ

Where ROCSA is the runoff generating from CSA’s

in each time step and QuickCSA is a fraction.

The total discharge from the catchment is the sum

of all the flow components generated within one

timestep:

QðtÞ ¼ Qb þ ROQuick þ ROCSA þ Qback ð6Þ

Where QðtÞ is the total stream flow at time step t: It is

the mixing of these flow components that allow a

sensible representation of the nitrate losses to be made

at the catchment scale.

In TOPCAT, the effect of changing catchment size

is represented by the manipulation of recession

parameter m and the baseflow term Qback. If it is

assumed that plot and hillslope scale response is rapid

when compared to a large catchment and that the size

of the catchment is correlated to the attenuation of the

runoff response, then simple rules can be followed for

adjusting m and Qback. As the recession parameter

contains both the hillslope and the channel routing

effects, the m parameter value is assumed to be

proportional to the catchment size. The parameter m

should ideally, be determined from field observation,

thus an average response time of the catchment can be

determined at least one or more catchment locations.


Any increase or in decrease the catchment size, will

have a corresponding change in the m value. The

opportunity to measure flow and nutrients as part of

the study catchment should not be missed and should

be seen as worthy activity in terms of cost benefit

analysis within modeling studies.

The value of the Qback baseflow term is also

assumed to increase as the size of the catchment

increases (this may be a natural fluvio-geomorpholo-

gical phenomena, but often it is related to dry weather

flow from urban sources that exist in larger catch-

ments). The effects of increasing Qback is also vital to

the final estimate of nitrate concentration, as typically

nitrate levels in the Qback term are generally lower

that event runoff nitrate levels from intense agricul-

ture. Denitrified groundwater sources or dry weather

flow from good quality treatment plants is commonly

observed, as is proven by studying nitrate concen-

trations in the summer dry periods. Fig. 3 shows the

typical scaling effects caused by m and Qback

alteration as represented in TOPCAT. Even though

there is an uncertainty component associated with this

approach, it is a suitable and simple methodology for

representing the flow dynamics and flow mixing at the

catchment scale.

5. The nitrate transport MIR model (TOPCAT-N)

The Nitrate (N) component of TOPCAT-N, first

estimates the amount of N leached from the root zone

by the HER and then routes this flow through the

event subsurface flow store before it is mixed with the

other flow components in the channel. A significant

proportion of the N that builds up in the soil during

the season is assumed to be available to leaching. The

bulk of the leaching is assumed to occur during the

main rainy season i.e. winter, therefore an estimate of

the total N available to leaching before the main

drainage period is vital to the model. In Europe, the

main drainage season also occurs just after the main

harvest (in autumn), when nitrates levels have been

raised to their highest. The total amount of N loss is a

function of N available to leaching, the leaching

efficiency of the soil and the total amount of HER.

The N availability (Ninitial) term describes the

mass of N in the root zone prior to the start the

leaching season, at the beginning of each yearly crop

cycle (usually taken as September in Europe). The

value of Ninitial must be obtained either through field

sampling or through existing soil crop N cycle models

(Anthony et al., 1996). Generally, (in European

conditions) a surplus of nitrates will build up in

arable soils due to high fertiliser usage and manure

application (Owens et al., 2000; Goulding, 2000).

This surplus undergoes various physical, chemical

and biological phenomena within the soil of which a

proportion will be susceptible to leaching. The N

available to leaching flow is determined largely by the

balance between the N applied, N in the crop uptake

but also from residual N levels and mineralization

(all of which are represented in the EPIC model).

Fig. 3. The effect of scaling up flow in TOPCAT using recession and baseflow terms.


If the result is a surplus, then the bulk of this surplus is

assumed to be available for leaching. Typically, the N

surplus is estimated from an understanding of farmer

practice within a region assuming that the ‘average’

farmer tends to create similar N status of their soil for

crop production. Nintial is set to a maximum value

just before the onset of winter drainage, thus for multi-

year simulations, the value is automatically reset to

this maximum value every 365 days.

As discussed in the introduction, physically-based

models are used to study the primary N mobilization

mechanisms for a wide range of circumstances. This

creates an abundance of output time series than can be

analysed and thus mimicked with a more simple MIR

function. A simple MIR model can be shown to fit to

the physical model dynamics, whilst retaining the

important parameters that have some physical inter-

pretable meaning. In the original development of

TOPCAT (Quinn et al., 1999) a simple N loss function

based on the model SLIM was developed (Addiscott

and Whitmore, 1991). During the development of

TOPCAT-N in this study, the work was repeated

using the EPIC model (2002).

The analysis of the many EPIC model simulations

reinforced the findings of the original Quinn et al.

(1999) study using the SLIM model, where it was

concluded: that the total N loading was the most

important single factor; that N leaching was the

dominant N loss mechanism; that a good estimate of

total HER was vital and that a direct relationship

existed between N loss pattern and the HER. Fig. 4

shows an example EPIC output of the annual N export

for an 8 year simulation, run at a daily time step, for a

typical UK circumstance (Dayawansa, 2002). The

figure shows the fundamental relationship between

HER and the estimate of N loss. Quinn et al. (1999)

went on to show that this pattern is produced for a

range of soil types, until a point where the total N in

the root zone becomes depleted. Fig. 5 attempts to

show the basic operation of TOPCAT-N MIR model.

Fig. 5A, shows the N loss over time, for one drainage

season, for three different Ninitial values (20, 40 and

60 Kg/ha) for a fixed soil type (Clay Loam). This

reflects the sensitivity of the model output to Ninitial

loading term. Fig. 5B shows the rate and pattern of N

loss caused by the generation of HER in the model for

a fixed Ninitial input. In Fig. 5C we show the

cumulative N loss pattern for a range of soil types

(as defined by there water holding capacity fÞ: Finally

in Fig. 5D, we show that if the cumulative N loss is

converted to a fraction of the Ninitial term, then a

single unique N loss pattern can be determined from

the Ninitial term and a term described as the drainage

efficiency (defined as cumulative HER/f) Thus a

single unique N leaching efficiency function of

TOPCAT-N can be expressed as:

1 ¼ HER=f ð7Þ

Fig. 4. An EPIC simulations show a clear relationship between annual nitrate loss and annual HER loss, for 8 years of simulation.


f ¼ 1:11112 0:203ð1Þ3; where ð1 # 1:34Þ ð8Þ

f ¼ 1; where ð1 . 1:34Þ ð9Þ

where f is the cumulative proportion of the original

Ninitial leached. The ðHER=fÞ term is referred to as

the drainage efficiency. f is the water holding

capacity of the soil and is derived from standard

tables for the soil water holding capacity of a soil type.

f is usually expressed as a fraction of the water that

can be held in 1 m of soil—where sand is approxi-

mately 0.18 and clay is approximately 0.42.

The N lost within event subsurface flow at any

given time step is given by the difference in the

proportion of the N lost between the last time step and

the current time step. This is converted to a

concentration by introducing:

Nactive ¼ ððfðt21Þ £ NinitialÞ2 ðfðtÞ £ NinitialÞÞ ð10Þ

Nactive is the amount of N lost in any one time step.

This value is converted to a concentration by knowing

the current value of Qb and converting the units to mg/l.

In the current model we assume that the N in the

quick flow surface runoff is negligible (0 mg/l),

though as stressed above this is not always true. For

the circumstances being studied here, large winter

overland flow events typically have low N concen-

tration when compared to subsurface N leaching

events and commonly acts to dilute N loss as opposed

to enriching it. The background N concentration

(Nback) is the concentration of N present in the ‘old’

subsurface flow (or background flow). Usually, the

background concentrations are reached during the dry

periods (the dry weather flow situation), when there is

no influence from recent rainfall events. Several grab

samples of N taken during the extended dry periods

should be sufficient to give a good estimate of the

background N concentration. The ‘old’ N concen-

tration in Qback may reflect both the old denitrified

flow from groundwater sources, urban N sources and

sewage treatment plants.

The N losses from different flow components

combined together to produce the final stream NO3

Fig. 5. The operation of the TOPCAT-N model, showing how nitrate fluxes can be normalized into a single mathematical MIR function.


concentration. The mixed load ðLðmÞÞ is calculated on

simple mass balance basis. The mixed load is divided

by total flow to obtain the concentration of N in the

stream in mg/l.

LðmÞ ¼ðNactive£QbÞþ ð0£ ðROQuickþROCSAÞÞ

þ ðNback£QbackÞ ð11Þ

N ¼ LðmÞ=ðQb þðROQuickþROCSAÞþQbackÞ ð12Þ

6. Calibration of TOPCAT

For many years there has been debate on the ‘best’

calibration technique to produce the ‘best model’

(Beven, 1989, 1993; Klemes, 1986; Sorooshian,

1991). Equally, quantitative uncertainty analysis has

revealed many problems of model calibration and

predictive uncertainty (Freer et al., 1996). In a recent

study of Van Herpe et al. (2002), the GLUE

(Generalized, Likelihood Uncertainty Estimate) pro-

cedure has been used to both determine the problems

of equifinality observed at the catchment scale and

also to justify the assumption of using parsimonious

modelling approaches. The Van Herpe et al. (2002),

study suggests that the acquisition of key field

measurements such as flow and nitrate levels will

improve model construction (especially for m; Qback

and Nback). In most circumstances (in northern

European climates) there is a strong rainfall–runoff

relationship that can be approximated by a simple

hydrological model, such as TOCPAT, through

altering the hydrological parameters, m SRMAX,

Qback and SPLIT. This is achieved by following an

interactive, hydrological evaluation approach that fits

the simulated flow to the various flow components

seen in the observed time series. As the TOPCAT

model instantaneously updates as the parameter

buttons are altered (using an Excel interface), the

ability to interactively fit to the observed data is

relatively quick. In practice the following series of

steps are required:

† First Qback is fixed by fitting the simulated

hydrograph to the observed low flows in the

summer periods.

† The m parameter is fitted to a series of suitable

large storms with long, continuous recession limbs,

these are typically large storms observed in the

middle of winter, and not those which are unduly

ifluenced by antecedent drying effects. Fitting m to

the recession limb has a strong impact on the peak

flows.

† Usually, a fit can be made to both peaks and

recessions by manipulating m; but if this is not

possible there is probably water balance problem.

For example, if there are large amounts of flow

being lost to the groundwater, then both m and the

SPLIT parameter must be altered together to gain a

realistic fit.

† Finally, SRMAX is altered to show the impact of

the soil root zone drying, as driven by the potential

evaporation rate, in longer dry periods.

† Any visual evidence of overland flow peaks can

be fitted using the quickflow parameters

A goodness of fit criteria is also calculated for

the period of calibration, which in this case is the

Nash and Sutcliffe r2 efficiency term. Together the

goodness of fit and the visual assessment of

hydrological response are used to give the final

chosen model. The selected model chosen does not

always have the best r2 value, but has the best

visual fit whilst retaining a reasonable model

efficiency. This approach of using both hard

quantitative criteria (such as r2) and softer

information (such as visual goodness of fit) is

reported in Seibert and McDonnell (2002).

The final model chosen is used as a ‘benchmark’

simulation, against which the sensitivity and uncer-

tainty levels and the impacts of catchment scale

management options can be shown. One must accept

the benchmark simulation as a reasonable estimate of

nitrate flux (plus some uncertainty bounds), and then

from this starting position, try to demonstrate the

reduction the nitrate losses overall.

7. An application the river great Ouse UK

The River Great Ouse is a surface water catchment

supplying a water supply intake at the Clapham flow

gauge (1400 km2). The area is intensely agricultural

and contains some large urban areas. The Great Ouse

has been designated a Nitrate Vulnerable Zone (NVZ)

and will thus be subject to land use regulations in


order to reduce N inputs from agriculture. Fig. 6

shows The River Great Ouse catchment, the river

network and a series of numbered subcatchments.

It must be stressed that the River Ouse is not a

research catchment and it is being used here to

demonstrate a typical catchment scale modelling

exercise some input data problems exist. However,

as the outfall of this catchment coincides with a water

intakes works the EC Nitrate Directive, relating to

potable drinking water, must be strictly observed. As

such, the frequency of failure of the river abstraction

point in terms of nitrate levels is a problem. Hence,

the local water authority has measured nitrate levels

and flows on a daily basis over a 4 year period. This

allows a study to investigate both the flow and nitrate

levels at the catchment scale and to consider future

land use options. The study also allows us to

recommend of a range of policies that will benefit

the catchment despite the uncertainty of the key

model parameters and the data errors that exist. Again

some common sense evaluation is needed to create

confidence that the processes being represented are

realistic but should also estimate our uncertainty so

that it can considered by the catchment manager when

setting policy.

Soils maps for the area allow an estimate of the

effective soil parameter ðfÞ for the whole catchment

to be made (MAFF (now DEFRA), 1984). Despite

some soil variability seen in the soil map of the area,

confidence that the whole area is generally of a clay or

clay loam class is a realistic estimate (a value f ¼

0:41 is used here).

The value of effective average water holding

capacity estimated for the whole catchment is

equivalent to Clay loam, although the catchment is

dominantly clay. However, we know from expert

knowledge of the area, that cultivated clay soils will

contain land drains and mole drains and the effective

field capacity is probably lower than that suggested by

the GIS. So, a lower water holding capacity is

probably more realistic. Therefore, it is quite

important to deduce a reasonable value for effective

soil type based on both soil class and on local

husbandry expertise. We should also appreciate that

Fig. 6. A map of the River Great Ouse (1400 km2) and its subcatchments.


this incurs uncertainty and should be added to our

final predictions. The GIS does contain a large amount

of information, but it should only be used as a guide to

the deduction of a final effective input parameters.

Fig. 7 shows an example layer taken from the

national GIS database of agricultural statistics show-

ing the arable land (which is mainly winter wheat and

winter barley). Here the assumption is made that the

land cover maps can be used as the nitrate source

index map. As such, each land unit is given an Ninitial

value based on local expertise and measurements

(Anthony et al., 1996). Given the local knowledge of

the area, it is assumed that little or no best manage-

ment practice strategies are in operation (i.e. little

denitrification potential was in operation at that time).

Another important assumption of the model is that

the subcatchments are agriculturally similar. Hence,

the flow and its associated N loss, is assumed to be

produced equally throughout the catchment. If this

assumption holds true then a simple, lumped statisti-

cal representation of land use patterns is justified. To

demonstrate this assumption the catchment statistics

were derived for Fig. 7, and for other layers of the

GIS, including the Urban areas. Table 1 shows the

results of the land class distributions derived.

Table 1, shows the distribution of land use for the

Ouse subcatchments. The total remaining area is made

up of woodland and water covered areas. The Ninitial

values were taken from Anthony et al., 1996.)

In the simulations shown below we have simulated

three land classes:

(1) The arable class-which has been approximated to

by a winter wheat (sown on October 1st and

harvested in September 1st). The estimated

Ninitial value for arable land is determined to

be 45 kg/ha in Anthony et al., 1996, where

detailed studies of N losses from agricultural

plots where reported.

(2) A perennial grass class, which is assumed to have

full crop cover all year round. A component of

the N total includes the effects of animal kept on

that land. A value of 55 kg/ha for grass cover was

used in the Anthony et al., 1996 study.

Fig. 7. A map of the percentage of each 1 km grid cell occupied by wheat.


(3) An urban class with low effective rooting depth

(Ninitial is taken as 0 kg/ha for urban areas) the

impact of urban N sources is represented by a

constant Nback concentration associated with a

background dry weather flow rate Qback.

NB by just using the information in Table 1, the

aggregate Ninitial value is estimated as 40 kg/ha.

The meteorological inputs are generated as a single

typical time series for a position representing the

centre of the catchment. A series of Meteorological

weather stations run by the UK Meteorological Office,

supply data for 60 weather stations across the UK, of

which three stations are close the River Great Ouse

though none of them are actually in the catchment

itself. As the catchment of 1400 km2 large some

worry that local storms and areal averaging may affect

the input time series, especially the storm intensities.

A set of hydrological parameters have been

calibrated against the observed flow using a balance

of a good visual hydrograph fit and the calculation of

the r2 value. Fig. 8, shows the observed and simulated

flows and the Nitrate levels estimated by the model.

An r2 value of 78% was determined for the full period

of 1248 days. It is possible to get a small increase in r2

value with more calibration, but only at the expense of

a poorer fit to storm peaks. In Fig. 8, clearly the

baseflow component (Qback ¼ 0.1 mm/day) and

the recession rates (m ¼ 0.012 m/day) can be fitted

by the model, though not all the peaks are matched.

The effects of antecedent soil drying effects gave rise a

value of SRMAX ¼ 0.1 m. The loss of HER from the

soil to the groundwater is 24% (i.e SPLIT ¼ 76%).

In order to fit the N time series a Nback value

of 1.7 mg/l is used and the Ninitial value for

the catchment is optimised to 34 kg/ha. This gives

a reasonable fit to the winter N levels, the summer

levels and the recession/mixing N pattern in the flow

after storm events. However, this is lower than the

aggregate value determined from Table 1. This lower

value may be due to some denitrifying effects taking

place within the channel. In this version of the model

the effect of within river denitrification and all

denitrification effects should be captured in the

Ninitial value.

The simulated results also show that the model is

very sensitive to changes in the Ninitial and the soil

texture term. The Ninitial term and to a lesser degree

the soil texture term are highly uncertain at the

catchment scale. Any land use planner would do well

to set future land use change plans in the light of the

uncertainty. The research problem remains, that an

improved estimate of the catchment scale Ninitial

values and a better soil texture terms are needed

within our models. Thus the catchment planner should

accept the existence of the uncertainty in the nitrate

model at the catchment scale. This does not mean that

the nitrate problem is not understood and cannot be

addressed through the nitrate model. The catchment

planner can still study the land management options

open to them, through the model, but their decisions

should be tempered by the knowledge that the

quantitative uncertainty. However, the likely impacts

of any land use management can be expressed at the

catchment scale by manipulating the flow and nitrate

model parameters. The impact of any change can be

shown relative to the benchmark simulation, which is

assumed to sit somewhere within the uncertainty

envelope.

All land use management options that lower the

total amount of Ninitial to the runoff process can be

Table 1

A land use classification for the Ousecatchment. The similarity of the Land use across the catchment is reflected in the land users observed

within the numbered subcatchments as seen in Fig. 6

Catchment area Arable %

Ninitial ¼ 45 kg/ha

Grass %

Ninitial ¼ 55 kg/ha

Urban %

Ninitial ¼ 0 kg/ha

Total % area remaining

Ninitial ¼ 0 kg/ha

1 44.07 35.22 1.59 19.12

2 43.36 42.81 2.32 11.51

3a þ 3b 49.53 35.36 7.47 7.64

1 þ 2 þ 4 44.60 38.00 4.17 13.23

3a þ 3b þ 5 45.62 34.31 10.56 9.51

1 þ 2 þ 3a þ 3b þ 4 þ 5 þ 6a þ 6b þ 6c 48.07 34.59 5.69 11.65


reflected as a reduction in this parameter value. The

catchment planner can demonstrate to other policy

makers and maybe to farmers directly, that certain

land use change options should be taken up (such as

the better use of fertilizer types and volume and some

land cover changes). Any land management change

can be represented by altering the Ninitial term, per

land use and then estimating the Ninitial in the

whole catchment for any given land use change

percentage.

It is also possible for other strategic/topographi-

cally related land use management options to be

reflected in the catchment nitrate model. If, in the

Great Ouse, it is known that farming practice is

intense, that all land drains and ditches are operating

efficiently and that there are little or no best manage-

ment practices are in operation (especially in the study

period), then we can argue that the current simulation

reflects a landscape with a near maximum nitrate

loss. For the Great Ouse, it would be possible to

re-establish denitrification capacity by setting up

buffer zones, wetlands and by targeting low order

ditches for ponding and infiltrating nitrate rich runoff.

Also, the planting of hedgerows and utilization of

other land uses (for example coppice woodland grown

in riparian areas), could all reduce the total amount of

Ninitial that is reaching the larger river system. In

essence, the implementation of a strategic denitrifica-

tion strategy can be reflected by reducing the Ninitial

term.

Thus, let us assume a future scenario where the

land use management options for the River Great Ouse

Fig. 8. The calibrated flow ðr2 ¼ 78%Þ and the nitrate benchmark simulation for the outfall of the Great Ouse.


are proposed to be:

(1) The loss of 5% of land to coppice woodland with

no fertilizer applied in those zones. This should

give a direct reduction of Ninitial of about 5%.

(2) Buffer strips (3 m wide) are implemented

throughout the catchment protecting all rivers.

These areas are assumed to have no fertilizer

application and should lower the Ninitial by a

value approximating to the area that these

features occupy. Here we will estimate that the

total area constitutes about 5% of the total farm

land.

(3) A rough estimate of the denitrifying rate of the

buffer strips is also needed. Here we will assume

the features are capable of lowering the Ninitial

by between 10–15%.

(4) Ponding and infiltration of nitrate rich flows are

assumed to denitrify the flow by an extra 10%.

(5) Forms of slow release fertilizer are used that

retain nitrate in a form unavailable to leaching.

We will assume that this can reduce Ninitial

by about 10%

Points 1 and 2 reduce the total N applications

rates and thus lower Ninitial directly. Points 3, 4

and 5 are more subjective but research into the

operation of these features clearly shows that they

lower the amount of N entering the receiving waters

(Johnes and Heathwaite, 1997). This would assume

a lowering of Ninitial by about 40%. However,

the exact amount by which Ninitial should be

altered to reflect each option and the estimate of

Ninitial for the whole catchment is difficult to assess

with accuracy, but it can be argued with some

certainty, that the nitrate level in the Great Ouse

will fall. Hence, let us assume that a 30% decrease

in Ninitial occurs (i.e it changes from 34–23.8 kg/

ha), then the routed and mixed flows are simulated

at the catchment scale demonstrating the impact on

the resultant nitrate concentration (Fig. 9). The

model is clearly sensitive to the change in Ninitial.

Even with uncertainty, confidence is built that the

implementation of good land use management has

the desired effect at the catchment scale. In essence

the end user is being encouraged to ask ‘what if?’

questions, based on the key concept of how much of

the total N in the landscape is reaching the outfall of

the catchment. Equally, the changes may not be

seen as being too draconian and may be acceptable

to the farmers.

A similar type of modelling approach that controls

both the total N applied and the ability to disconnect

nutrient rich flows from the receiving water was

carried out by Johnes and Heathwaite (1997). In their

study they showed the positive benefits of implement-

ing land use change options and government schemes

and strategic N management options. Even though

their results are also subject to great uncertainty,

confidence is raised that positive small changes in

land use practices can have great environmental

Fig. 9. The effect of lowering Ninitial by 30% if a number of land use management options are implemented together.


benefits. Johnes and Heathwaite showed an average

reduction of 15–20% in N concentration for each of

the schemes they proposed.

8. A catchment scale decision making toolkit

TOPCAT and the efficient use of GIS data can be

seen as visualization tools that reflect our expertise

and understanding of the spatial and temporal

operation of hydrological flow path at a number of

catchment sizes. The models are built to reflect the

needs of the land use planner and it can be envisaged

that such planners could use the models directly to

underpin their decision-making. The catchment scale

model can reflect the implementation of a range of

best management practices and can thus play a role in

Fig. 10. (A) The three-dimensional DSM for nitrate loss. (B) The likely impacts of land use change and how they are mapped onto the DSM to

produce a net lowering of nitrate loss.


the outreach phase of the land management planning.

To reinforce these decisions even further, a Decision

Support Matrix (DSM) has been developed that

allows us to conceptualize our local process under-

standing, reflect our uncertainty in our estimates and

also visualize the likely impact of land management

option.

If our knowledge is synthesised into a minimum

amount of information required to visualize nitrate

losses at the catchment scale then a three-dimensional

matrix will suffice (Fig. 10A). The three axis of the

DSM are related to (i) Ninitial, in the soil, or how

much of the nitrate being applied and is vulnerable to

leaching over winter (ii) the soil texture and structure

that impact the nitrate leaching (the effective leaching

efficiency) and (iii) a strategic denitrification potential

of the landscape, i.e. the use of topographically related

buffers and wetlands to target nitrate rich flow.

The axis are expressed in term of risk of

pollution and may or may not have absolute values

associated with them. It is more important that the

user realizes whether or not their current farm

practice, set with the local soil and topographical

regime, is vulnerable to nitrate loss. By using

carefully posed questions relating fertilizer usage

and visual evidence of observed runoff regime from

the land, both the land use manager and the farmer

can estimate (even in general terms) how much of

the applied nitrate is lost and how it is lost. The

position chosen on the matrix may differ between

the planner and farmer and also between farmers,

however, despite the uncertainty in the final chosen

location on the matrix, all the estimates tend to fall

within a local zone on the matrix. The philosophy of

the matrix is that ‘no matter where you plot your

land unit within the matrix now, you should be able

to move to a lower pollution risk level’. Fig. 10B

shows two faces of the DSM that are designed to

reflect that lower pollution levels can be achieved

by altering the land management. It is now the role

of the land use planner (or scientist) to demonstrate

that switching land use, switching nitrate load and

fertilizer type, that the per unit area mobilization of

nitrate has changed (see Fig. 10B). Equally, there

may be strategic locations within the landscape

where VSA/CSAs can be targeted for management

and the hydrological flow paths altered to lower

nitrate loss. The estimated impact of these land

management options can be visualized on the

matrix. Whilst the exact impacts of the chosen

options are difficult to estimate, due to uncertainty

and scaling effects, it can be argued with certainty

that the measures will reduce nitrate loss. The

methods by which land management reduces nitrate

loss can also be demonstrated in physical models at

the plot scale, in quasi-physical models at the

hillslope scale and their net effect simulated with

some confidence at the catchment scale.

We can conclude that the role of physical-based

models is to underpin the basic knowledge of nitrate

mobilization. The hydrological flow path model

reflects how nitrates are probably reaching the

receiving waters and how flow propagates through

the landscape. Together the knowledge and experience

gathered can underpin a simple visual DSM that can be

used to communicate and encourage land use change

whilst minimizing the economic impact on the farmer.

9. Conclusion

The role of models in reflecting our understanding

of nitrate loss is important to the final establishment of

best management practice in nitrate management.

Experiments and trials at the plot scale have given us

the fundamental insight into the physics of nitrate

loss. As such, a series of physical nitrate loss models

have been created. It has been argued here, that the

physical measurements and models needed to create

physical models restrict their suitability to the plot

scale. Hence MIR models are created that mimic the

output time series of the one-dimensional physically-

based model outputs. The parameters used in the MIR

models should have some meaning of to the end user

and should be based directly on the physically-based

model (i.e they have some physical basis). In practice

the effective values of the input parameters are given

for the whole catchment and are usually based on GIS

datasets and the local knowledge of land use practice.

As such, high uncertainty is expected in the input

parameters. Given that the final model structure is

quite simple, a clear reflection of our model

uncertainty can be made and communicated to the

end user.

Calibration is carried out for the purposes of

creating a best visual fit with the highest possible


r2 value. The calibrated simulation chosen, is used a

benchmark time series against which a series of land

management options can be tested and their impact

shown visually. All land use change options can be

represented in the catchment model parameters

(mainly through management of the Ninitial term),

and even though it is impossible to estimate the

absolute change in Ninitial for any management

option, an adequate estimate of the value should be

possible. Finally, all the knowledge gained about

nitrate losses, catchment hydrology and management

from plot to catchment scale can be captured in a

DSM, which allows land planners and farmers to

estimate whether or not their land is vulnerable to

high nitrate loss. Equally, the matrix can show

visually the likely impact of any particular land

management option. Therefore a series of land

management options can be depicted at one time to

show how high nitrate polluting land units can, in

general terms, move to a lower nitrate pollution risk.

The DSM is based on all the expertise gathered

from studying nitrate and runoff across scale, the

expertise is gathered by running a range of scale

appropriate models, prioritizing which factors need

to be retained in the catchment model and then

visualizing both our understanding and our uncer-

tainty in an unambiguous way.

Producing simple MIR models is not an easy task,

the user must be able to: use complex physical-based

models; analyse large datasets; determine simple

functional models capable of mimicking the output

of the physical model; collaborate closely with

farmers, end users and agronomic scientists and

finally produce tools that are suitable for addressing

the nitrate problem whilst understanding scaling and

uncertainty problems.

It is important that fundamental hydrological and

nitrate processes are communicated to land man-

agers, so that they have confidence that certain land

management options are improving environmental

standards. This study has highlighted key concepts

related to plot scale nitrate losses and demonstrated

how the nitrate pollution propagates downstream. A

key message to policy makers as results of the

study, is that there are a number of agronomic

options (such as a better use of fertilisers) and the

impact of strategic landscape features (such as

wetlands) that can disconnect and lower nitrate

sources, so that they have less impact at the

catchment scale.

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