A statistical method for source apportionment of riverine

nitrogen loads

B. Grizzettia,*, F. Bouraouia, G. de Marsilyb, G. Bidoglioa

aJoint Research Centre of the European Commission, Soil and Waste Unit, Institute for Environment and Sustainability,

TP 460, I-21020 Ispra (VA), ItalybLaboratory of Applied Geology, UMR CNRS ‘Sisyphe’, University Paris VI, France

Received 30 November 2003; revised 1 May 2004; accepted 1 July 2004

Abstract

This study describes a source apportionment methodology for riverine nitrogen transport. A statistical model was developed

relating stream nitrogen load to spatially referenced nitrogen sources and basin characteristics. It is able to estimate the nitrogen

load on the basis of only three parameters. It considers applied fertiliser, atmospheric deposition and point discharges as sources

and rainfall, topography and water flow as basin characteristics. The model was calibrated on three European watersheds, the

Wash (UK), the Zelivka (Czech Republic) and the Vilaine (France) river basins in order to represent a range of different

climatic characteristics and nitrogen input pressures. Based on model predictions, the spatial and temporal variations of diffuse

nitrogen emission were described, identifying critical areas more prone to nitrogen loss. The model makes it possible to

estimate the contribution of each nitrogen sources (point and diffuse) to the total river load. Finally, scenarios of different

nitrogen input pressure were analysed. The model was shown to be a suitable tool for understanding the fate of the actual

nitrogen input pressure and evaluating the impact of alternative management strategies with a limited data requirement.

q 2004 Elsevier B.V. All rights reserved.

Keywords: Nitrogen; Modelling; Management; Load estimation

1. Introduction

The Water Framework Directive (Commission of

the European Communities, 2000) introduces new

criteria and measures for managing Europe’s waters

under an integrating ecosystem-based approach

(Kallis and Butler, 2001). The Directive aims at good

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

doi:10.1016/j.jhydrol.2004.07.036

* Corresponding author. Tel.: C39 332 789 079; fax: C39 332

785 601.

E-mail address: bruna.grizzetti@jrc.it (B. Grizzetti).

ecological and chemical quality status for all waters

(surface, underground and coastal) by means of

pollution-control measures that have to be planned

at the hydrologic river basin level. The implemen-

tation of the Water Framework Directive poses a

variety of challenges, including a demanding time-

table, the practical implementation of the new

measures, and the financial costs to the Member

States (Murray et al., 2002). With regard to point and

diffuse sources of pollution, the Water Framework

Directive requires Member States to identify

Journal of Hydrology 304 (2005) 302–315

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B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315 303

significant human pressures and evaluate their

impacts on water quality. The pollution of surface

and sub-surface water by nitrate is controlled

specifically by the Nitrate Directive (Council of the

European Communities, 1991). Although the Nitrate

Directive came into force more than a decade ago, the

large amount of nitrate in the water is still a major

concern for European countries, since the designation

of vulnerable areas and the enforcement of standards

have been a very slow process (Kallis and Butler,

2001). The characterisation of water status, the

description of pollution sources impact, the establish-

ment of monitoring programs and the implementation

of river basin management plans require an analysis

of the current basin status and estimates of the

relative significance of the different sources of

pollution. Considering the necessity for integrated

management and the scale of planned application

(national or transboundary), models are indispensable

tools. Models enhance the planning process providing

a forecast of the impact on water quality, contributing

to the understanding the processes responsible for

pollution and detecting areas of concern (Novotny

and Olem, 1994).

A variety of methods have been used to model the

transfer of nitrogen in river basins. Conceptual and

physically based models (Conan et al., 2003; Billen

and Garnier, 2000; Arheimer and Brandt, 1998;

Arnold et al., 1998; Whitehead et al., 1998; Wittgren

and Arheimer, 1996) describe the processes respon-

sible for nitrogen wash-off into surface water and

leaching to groundwater in large heterogeneous

basins. They allow forecasting and a better under-

standing of processes; however, calibration and data

requirements are important in large-scale applications

aimed at policy support. Other simplified models

addressing the problem of water quality are proposed

in the literature and based on the export-coefficient

approach (Hetling et al., 1999; Johnes, 1996; Grimvall

and Stalnacke, 1996), mass balance GIS-based

method (Pieterse et al., 2003; Skop and Srensen,

1998; Adamus and Bergman, 1995) and statistical

regression (Seitzinger et al., 2002; De Wit, 2000,

2001; Behrendt and Optiz, 2000; Smith et al., 1997).

Besides the objectives required by a comprehen-

sive basin analysis of nitrogen fate, two fundamental

aspects have to be considered in model selection: the

spatial and temporal scales of the study and the data

availability at that scale. The use of physically based

models at the regional scale poses a variety of

problems as they rely on a fine resolution parameter-

isation (Beven, 2001). The choice of model complex-

ity depends on the information available, on the

natural variability and a fine resolution is useless

when data required by the model to describe this

variability are not available (Bloschl and Grayson,

2002). Thus, the choice of an appropriate model for a

particular application results in a trade-off between

model complexity and uncertainty of input par-

ameters. In general, the appropriate level of model

complexity decreases with the size of the area to be

modelled (Addiscott and Mirza, 1998). Statistical

models have often been used to study nitrogen fate at

large scales (Caracao and Cole, 1999; Howarth et al.,

1996; Seitzinger and Kroeze, 1998; Beusen et al.,

1995), but models with more detailed descriptions of

nitrogen sources and attenuation processes represent

more accurately the predictions of stream export

(Alexander et al., 2002a).

The objective of this study is to present the

development of a model to assess the fate of the actual

nitrogen input pressures and evaluate the impact of

management strategies on water quality at medium and

large basin scales. In particular, the model endeavours

to quantify the relative importance of different nitrogen

sources (point and diffuse) in the total export and to

identify the areas subjected to higher pollution

pressures where priority actions should be undertaken.

Several important aspects were considered in the

model development: the parsimony of the model

parameters and the data requirement, the availability

of data and the spatial variation of watershed proper-

ties. The model presented in this study employs a

statistical approach to estimate the nitrogen river loads,

considering spatially referenced physical and climatic

basin characteristics.

2. Methodology

2.1. The model

The model is inspired by the spatially referenced

regressions on watershed attributes (SPARROW)

methodology (Smith et al., 1997), where the river

nitrogen load is modelled by a non-linear regression

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315304

equation on nitrogen sources and basin attributes. The

SPARROW methodology was applied in large areas

of the United States (Alexander et al., 2000; Preston

and Brakebill, 1999; Smith et al., 1997), and in New

Zealand (Alexander et al., 2002b).

The modelling approach proposed in this study

employs a simplified conceptual model, which

distinguishes between two different pathways in

nitrogen transfer from sources to the river outlet:

nitrogen passing through land before reaching the

stream, and nitrogen delivered directly to the water

body. The first pathway concerns the diffuse sources

while the latter is typical of point sources. The model

considers applied fertiliser (artificial and manure),

atmospheric deposition, scattered dwellings as diffuse

nitrogen sources, and discharges from sewers, waste

water treatment plants and industries as point sources.

It was decided to use nitrogen application instead of

nitrogen surplus as input to avoid the introduction of

new uncertainty with estimates of crop uptake and to

allow scenario analysis without considering the

impact of changes in the fertiliser application rate

on the crop uptake, which is difficult to evaluate.

The region of study is subdivided into a number of

sub-basins according to data availability. In each sub-

basin, the river nitrogen load is related to the sum of

the different nitrogen sources reduced by the retention

processes occurring in soils and water. The model

considers a routing structure for the nitrogen load

along the watershed, according to the river network.

The nitrogen load estimated at the outlet of a sub-

basin is entered as an additional point source to the

downstream sub-basin. The load at the outlet of sub-

basin i is expressed as

log10 Li Z log10ðxDS;iBiRi CxPS;iRiÞCEi (1)

Li is the annual nitrogen load (t N/year); Bi, basin

reducing factor (dimensionless); Ri, river reducing

factor (dimensionless); xDS,i, sum of diffuse sources

(t N/year); xPS,i, sum of point sources (t N/year) and Ei

is the error term.

The nitrogen removal occurring in soils and in the

river is represented by the basin and the river reducing

factors, respectively. A variety of factors can

influence the rate of nitrogen export from catchments,

including natural basin characteristics, hydrological

and meteorological conditions, land use and

management practices (Lepisto, 1995; Howarth et

al., 1996). The relationship between nitrogen export

and different basin characteristics (annual rainfall, wet

season rainfall, temperature, slope, topography wet-

ness index (TWI), river length, drainage density,

annual flow, low flow) were examined. The rainfall

during the rainy season, the TWI (Beven and Kirkby,

1979) and the low flow were retained as they better

described the nitrogen export from catchments,

according to a preliminary analysis based on Pearson

correlation coefficient and a principal component

analysis.

Rainfall enhances the nitrogen loss by leaching or

surface and sub-surface runoff. These processes

increase when soils are saturated. We found that

rainfall occurring during the rainy season, when the

soils are almost saturated, was more significant for

describing the nitrogen export than the annual rainfall.

The TWI measures the degree of accumulation of

water at a site. High values of this index are typical of

saturated areas, where the conditions are more

suitable for denitrification. The retention of nitrogen

in surface waters is determined by the residence time

and area of contact at the sediment–water interface

(Haag and Kaupenjohann, 2001). The residence time

increases in low flow conditions, enhancing the

denitrification process (Kronvang et al., 1999).

Nitrogen removal decreases in deeper channels as

the contact surface between sediment and stream

water is reduced (Alexander et al., 2000), and the

stream depth generally decreases in low flow

conditions. Therefore, the in-stream retention pro-

cesses were related to the low water flow in the model.

In the model, the basin reducing factor is assumed

to be a function of the rainfall amount in the rainy

season and of the TWI. This factor includes all

nitrogen losses due to soil denitrification, volatilis-

ation, plants consumption and soil storage. The river

reducing factor is related to water low flow and it

accounts for in-stream denitrification, volatilisation,

aquatic plant consumption and net sedimentation. The

diffuse sources are reduced by both factors, while

point sources are affected only by the river reduction

factor, as they reach the stream directly. Both basin

and river reducing factors are parameterised as

exponential decreasing functions, as follows

Bi Z expðKðaPxP;i CaBxB;iÞÞ (2)

Fig. 1. Location of the three watersheds.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315 305

Ri Z expðKðaQxQ;iÞÞ (3)

xP,i is the normalised rainfall of rainy season

(dimensionless); xB,i, normalised topography wetness

index (dimensionless); xQ,i, normalised water dis-

charge of low flow period (dimensionless); aP,

rainfall coefficient (dimensionless); aB topography

coefficient (dimensionless); aQ water flow coefficient

(dimensionless); i is the sub-basin number.

Since the variables representing the basin charac-

teristics, i.e. the rainfall, the TWI and the low flow, are

expressed by different measurement scales, their

values were scaled before being entered in the

model in order to compare the different parameters

and the model coefficients. The rainfall and water low

flow variables, unlike the TWI, have a positive

influence on nitrogen losses, thus they were taken in

the reciprocal form before scaling. In this way all a

coefficients, in Eqs. 2 and 3, are expected to be

positive. The variables were reduced to the same scale

by maximum scaling (Frank and Todeschini, 1994), as

expressed by the following equation

xj;i Z x0j;i=Uj ðj Z P; j Z B; j Z QÞ

Uj Z max ðx0j;iÞ for i Z 1;.; n

((4)

In this study, the maximum scaling method was

used because it avoids negative values. The error

term, in Eq. 1, is assumed to be multiplicative, and the

estimation algorithm is applied when both sides of the

regression equation have been converted into logar-

ithmic form. Thus discrepancies between observed

and predicted nitrogen loads can be minimised

independently of differences in magnitude.

2.2. The studied areas

The model was first developed in a large area

draining into The Wash in the United Kingdom, and

then calibrated on two other European basins Zelivka

(Czech Republic) and Vilaine (France), in order to

represent different climate characteristics and nitro-

gen input pressures (Fig. 1). Selected characteristics

of the three watersheds are given in Table 1.

The region of study draining towards The Wash in

the UK has a surface area of about 8900 km2 and

partially covers the river basins of Great Ouse, Nene,

Welland and Witham, in East Anglia. The average air

temperature is 4 8C in winter and 17 8C in summer,

with rainfall spatially ranging between 528 and

695 mm/year and increasing with altitude. The rivers

have low natural base flow except in the south-eastern

part where the Chalk aquifer crops out and the rivers

are fed mainly by base flow. The average flow of the

Great Ouse at the Offord gauging station is 15 m3/s,

with flow peaks during the winter season. The area is

generally low-lying, with elevations not exceeding

250 m a.s.l. The coastal plain, called the Fenland, was

not included in the study as no data were available.

The western and central parts are characterised by rich

clay soils, while sandy soils dominate in the eastern

part. Livestock and cereals are farmed intensively

throughout the region, creating potential diffuse

sources of both ground and surface-water pollution.

Grassland is dominant in the western part, where the

density of cattle and sheep is high. In the studied area,

the average nitrogen input by fertiliser is about

147 kg N/ha year, but the western part tends to have

higher nutrient inputs, reflecting the higher livestock

density. The population density is about 230 inhabi-

tants per km2, and more than 95% of the population is

connected to the sewerage system.

The Zelivka catchment (1186 km2) is situated

south–east of Prague in the Czech Republic and

belongs to the Elbe basin. The downstream part of

the river is dammed and constitutes the Zelivka

drinking water reservoir. The mean annual

Table 1

Characteristics of the three watersheds

Watershed Area (km2) Rainfalla (mm) Average water flow

(m3/s)

Arable land (%) Fertiliser appli-

cation (kg N/

ha year)

Wash 8913 528–695 15 74 147

Zelivka 1186 640–720 6 52 42

Vilaine 10,530 630–1000 70 74 125

a The rainfall values refer to the highest and lowest average annual precipitation among the available rainfall gauging stations.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315306

precipitation is around 670 mm and the typical range

of annual temperature is 5–8 8C. The mean annual

discharge at the basin outlet is 6.3 m3/s, with peaks in

March and April due to snowmelt. The region is

characterised by silty loam soils, especially Dystric

Cambisol, but near the riverbeds Eutric Gleysols and

Gleyic Fluvisols prevail. Arable lands cover 52% of

the watershed, permanent grass lands 13% and forest

30%. Nowadays the livestock and agricultural

productions are of medium intensity. Nevertheless

the reduction in fertiliser application on arable land,

which started in 1989 as a result of the change in the

state economy, has not yet been followed by a

decrease of the in-stream nitrate concentration (Holas

et al., 1999). The number of inhabitants is about

53,000. Half of them live in towns and villages,

where waste water is connected to water treatment

plants, while the rural population disposes sewage in

septic tanks.

The Vilaine catchment is situated in the Brittany

region, in the north-western of France. It covers

10,530 km2 and discharges into the Atlantic Ocean.

The climate of the region is oceanic, with an average

annual temperature of 12 8C. The average annual

precipitation is 773 mm, but it reaches 1000 mm in

the western part of the basin. The average discharge at

the downstream gauging station is 70 m3/s, with

higher values during the winter season. The entire

region is characterised by clay and silty soils.

Agricultural lands occupy about 70% of the catch-

ment area and cereal production and husbandry are

intensive throughout. Half of the French pig farms are

concentrated in the Brittany region. The population

density is about 80 inhabitants/km2 and more than

60% of the population is connected to sewage and

waste water treatment plants.

2.3. Model parameterisation

During the model parameterisation, each region of

study was subdivided into a number of sub-basins

according to data availability, so that each sub-basin

drains towards a water quality gauging station. During

the selection of the stations, careful attention was

given to the homogeneity of sub-basin size. When the

sub-basins map was lacking, the digital elevation

model (DEM) was processed to delineate the areas

draining to the gauging stations. For the Vilaine

catchment, annual values of nitrate load were

available only for four gauging stations, including

the basin outlet. Nevertheless the watershed was

subdivided into a larger number of catchments, to

increase the spatial resolution, although calibration

was done on the four available points. For each

watershed, the period of calibration was established

according to the available data time-series. The

watershed subdivision and the calibration period are

summarised in Table 2. Once the watershed discre-

tisation was established, the nitrogen input from

different sources was computed for each sub-basin.

The data source varies according to the watershed, but

for fertiliser application, point discharges, and number

of people not connected to the sewer system, the

information was provided on an administrative basis.

Data on atmospheric nitrogen deposition were derived

from the EMEP data (EMEP, 2003) or direct

measurements. A rainfall gauge was assigned to

each sub-basin by the Thiessen polygon technique and

the average value of the TWI was computed based on

the DEM. Finally, the average value of low flow was

calculated at the outlet of each sub-basin. In the case

of the Vilaine, ten stations were available for water

discharge records, and the low flow values for

Table 2

Characteristics of model parameterisation in the three watersheds

Calibration period Number of sub-

basins

Mean sub-basin area

(km2)

Number of water

quality stations

Total number of

observations

Wash 1995–1999 50 178 50 248

Zelivka 1996–2000 13 91 13 65

Vilaine 1992–1997 112 94 4 24

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315 307

the ungauged sub-basins were obtained by regression

between low flow and drained river length (r2 Z0.99).

3. Results and discussion

3.1. Model calibration and validation

The calibration objective is to estimate the optimal

values for the model parameters aP, aB and aQ, which

are unique for each watershed under study. Then,

based on these optimal parameters and the watershed

attributes, different values of basin and river reducing

factor (Eqs. (2) and (3)) are determined for each sub-

basin of the watershed. The parameter calibration was

performed separately in the three basins under study,

by means of the optimisation model UCODE (Poeter

and Hill, 1998), which minimises the sum of squared

residuals by the modified Gauss–Newton method. The

agreement between the measures and the model

predictions was evaluated by the Nash–Sutcliffe

coefficient, which takes values between 1 and KN,

with 1 indicating a perfect agreement. The Nash–

Sutcliffe coefficient E (Nash and Sutcliffe, 1970) is

expressed as

E Z 1 K

PniZ1ðOi KPiÞ

2PniZ1ðOi KmOÞ

2(5)

where Oi and Pi correspond, respectively, to the

measured and predicted nitrogen loads for the sub-

basin i, and mO is the average measured nitrogen load.

In all cases the model predictions are in reasonable

agreement with the measured values, as indicated by

the Nash–Sutcliffe coefficients of 0.88, 0.96 and 0.90

for the Wash, Zelivka and Vilaine basins, respectively.

According to Willmott (1984) the root mean

squared error (RMSE) can be divided into a

systematic and an unsystematic portion. The first

indicates a systematic tendency of the model to over-

or under-estimate, while the latter expresses the model

inaccuracy due essentially to measurement errors.

Thus a low portion of systematic root mean squared

error (RMSEs) can indicate the model robustness. In

the present study, the systematic part of RMSE is

below 10% for all basins. Discrepancies between

predicted and measured values occur in some cases

but the mean absolute error obtained in all calibrations

is always lower than 30% of the average measured

load. This uncertainty is connected to measurement

and model input uncertainty, besides model structure.

The calibration results for the Wash, the Zelivka and

the Vilaine basins are shown in Fig. 2.

The model was validated with observations that

had not been used for calibration, in the Wash (from

1990 to 1994) and in the Zelivka basins (for the year

2000), with an efficiency of 0.96 in both cases, which

confirm the model robustness (Fig. 3). No validation

was done for the Vilaine as all the data were used for

calibrating the model. The satisfactory results of

model calibration and validation show the flexibility

of the model and its applicability to basins character-

ised by different activities and climates.

3.2. Nitrogen emission pattern

Once calibrated and validated, the model was used

to quantify the amount of nitrogen discharged into the

stream (diffuse emission) and to locate the critical

areas with the largest contribution to the total nitrogen

load. The maps of diffuse emissions of nitrogen in the

studied watersheds are shown in Fig. 4.

In the Wash basin, diffuse emissions are higher in

the western part, where cattle farming is intense. In

this area, the nitrogen retention processes are reduced,

as precipitations are higher than in the sub-basins

situated in the south. Moreover, the southern sub-

basins lie partially over the Chalk aquifer outcrop,

where water can infiltrate and nitrate can be

Fig. 2. Predicted values versus the measured values of nitrogen load for the calibration of the Wash, the Zelivka and the Vilaine basins.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315308

temporary stored or removed by denitrification

(Edmunds et al., 1984). In the Zelivka basin, the

land use characteristics account for the spatial

variability of the diffuse emissions, which are directly

correlated to the surface area of arable land (rZ0.95)

and inversely correlated to forest area (rZK0.90). In

the Vilaine catchment, the low values of diffuse

emissions in the central part correspond to the areas

around the city of Rennes, where there is little arable

land and waste water is collected. Elsewhere, low

values are found in flat or forested areas. The intense

diffuse emissions estimated in the north-western part

of the basin can be explained by the steeper slopes and

higher precipitation, which increase the nitrate

leaching and washout. The estimated average nitrogen

loss to surface waters, referred to the agricultural area,

are 21 and 35 kg N/ha year for the Zelivka and the

Vilaine basins, respectively. These values are in

agreement with those reported by Kronvang et al.

(2003), i.e. 28 and 37 kg N/ha year.

Maps of diffuse emissions can be produced for the

different years taken into account in the model. The

diffuse emissions can change from year to year,

increasing during the rainy years (level of significance

!0.01), but in general the pattern remains the

same, as it is strongly influenced by nitrogen input,

which do not vary much in time. An example is

given in Fig. 5, which shows the estimated diffuse

emissions in the Vilaine basin for three consecutive

years (1992–1994).

Fig. 3. Predicted values versus the measured values for the validation of the Wash and the Zelivka basins.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315 309

3.3. Source apportionment

Once calibrated, the model can estimate the relative

contribution to the river export by each nitrogen

source by considering the different pathways of

nitrogen transfer. The comparison between the nitro-

gen source apportionment of the basin input and of the

river load, according to model predictions, can give an

insight into basin behaviour and help to identify the

sources that most affect the water quality. It is essential

to understand which factors have an impact on the

water quality in order to provide effective programs to

remedy pollution (Puckett, 1995). The source appor-

tionment estimate for the principal water gauging

stations in the three basins is shown in Fig. 6 and

Table 3. In all three watersheds, the input of nitrogen is

dominated by agricultural sources except the Czech

basin, where forests cover 30% of the area and

fertiliser applications have decreased in the last

decade. The nitrogen load in the Vilaine River

depends mainly on agricultural inputs due to the

intensive cropping and breeding. Furthermore, the

contribution of point sources is small, as more than

60% of the population is connected to waste water

treatment plants. For the English basin, point sources

contribute 32% to the total load (Table 3). The values

refer to the upstream part of the Great Ouse catchment,

also characterised by a great diffuse emission rate.

The contribution of the different sources to the

river total load depends on the inputs and on

the ability of basin to remove nitrogen during its

transport from land to water. The model can estimate

the average nitrogen removal in the studied basins

(Table 4). The total removal values (Table 4) are

rather high as they include the crop uptake, besides the

losses by volatilisation and denitrification. However,

an estimate of the basin nitrogen retention by

denitrification, volatilisation and soil storage, can

easily be computed as the difference between the total

basin retention estimated by the model and the

nitrogen removed by crops. In Central and Northern

Europe, up to 80% of the nitrogen input is retained in

watersheds (Caracao and Cole, 1999). In southern

Sweden, it was estimated that 48% of the nitrogen

losses from arable land is removed during the

transport to surface waters (Arheimer and Brandt,

2000). Hetling et al. (1999) observed that the loss of

nitrogen in fertilisers, once they had been spread on

the field, by storage, denitrification and volatilisation

could be about 10–30% of the agricultural input. In

the Zelivka basin, the lower value of nitrogen removal

during the transport through the soil may indicate the

saturation of the nitrogen retention capacity of soils.

In fact in this region, the nitrate concentration in the

rivers has not decreased even after the reduction of

fertiliser input (Holas et al., 1999), which means that

soils are releasing the previously stored nitrogen.

The nitrogen retention by the rivers increases with

the water residence time, which occurs where the area

is mostly flat, as in the Vilaine basin, or partly

Fig. 4. Diffuse nitrogen emission (kg N/ha year) estimated for the Wash, the Zelivka and the Vilaine basins, during the studied periods.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315310

occupied by a lake, as in the Zelivka basin. In Europe,

the river retention is estimated around 10–20% (Haag

and Kaupenjohann, 2001; Howarth et al., 1996), but

higher values of water retention are observed in lakes

(Howarth et al., 1996). In general the calibrated

retention rates found in this study fall within the range

given in the literature.

3.4. Scenario analysis

Once the areas of high nitrogen pressure have been

identified, and the contribution of each nitrogen

source to the water pollution has been estimated, the

model can be used to test the impact of alternative

management strategies. In the Vilaine watershed, the

effect of changing nitrogen input was investigated in

the two basins draining into the Oust (O041) and the

Vilaine (V042) gauging stations, respectively (Fig. 7).

These basins are characterised by similar source

contributions but differ in the diffuse emissions and in

the source apportionment of the river load (Fig. 7).

The average diffuse emission rate in the O041 basin is

37 kg N/ha year compared to the 20 kg N/ha year

estimated for the V042 basin. This is partly due to

the nitrogen application, around 140 kg N/ha year in

the O041 and 130 kg N/ha year in the V042, but also

to the physical and climate characteristics. In fact, in

the O041 basin, the precipitation and the slope are

Fig. 5. Nitrogen diffuse emission (kg N/ha year) estimated for the Vilaine basin for the years 1992, 1993 and 1994, with annual precipitations of

702, 795 and 1000 mm, respectively.

Fig. 6. Pie charts of the nitrogen source apportionment of input (on

the left) and of the river transport (on the right) for the three studied

watersheds.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315 311

higher than in the V042, contributing to stronger

nitrate leaching and wash out. The greater extent of

the urban area in the V042 may explain the major

contribution by point sources to the river load in this

basin. The impact of a decrease in the diffuse and

point sources was tested in the two basins. The

variation of the predicted nitrate load at the two

outlets when the fertiliser and point discharges were

reduced by 5, 10 and 20% was investigated. The

results of the different scenarios are shown in Fig. 7.

The reduction of fertiliser application of 10%

produces a decrease of more than 5% in the in-stream

nitrate load. The model can predict the overall annual

variation, but differences in seasonal values may be

expected.

In the O041 basin, the model was also used to

evaluate the potential impact of installing new pig

farms for a total increase of 10,000 pigs. Three

Table 3

Nitrogen source apportionment of the river transport for the three

studied watersheds

Fertiliser

input (%)

Atmospheric

deposition

(%)

Point sources

(%)

Washa 64 4 32

Zelivka 70 16 14

Vilaine 91 3 6

a Estimated on the upstream basin of the Great Ouse river.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315312

scenarios were tested corresponding to different

locations of the farms (Fig. 8): in scenarios 1 and 2,

the new source is placed in the upstream part of the

basin, while in scenario 3 the farms are set close to the

basin outlet. The hypothetic farms produce about

140 t N/year, which are added as manure to the sub-

basin where the farms are placed. The same change in

the basin input leads to different variations on in-

stream nitrate load predicted at the basin outlet in the

three scenarios (Fig. 8). The increments of

the nitrogen load differ according to the location of

the sub-basin concerned by the new farms, and

especially by its typical retention rate and its distance

from the outlet. In fact the higher impact occurs in

scenario 1, where the farms are placed in a sub-basin

with a low retention capacity (71%), while the lower

variation takes place in scenario 2, where the farms

are situated in a sub-basin with a higher retention

capacity (83%). A comparison of the impact gener-

ated by scenarios 2 and 3, which involve sub-basins of

similar retention, 83 and 81%, seems to show that the

longer the path (scenario 2), the more nitrogen is

removed during the transport.

These applications show that the model is suitable

for a general evaluation of management strategies.

Nevertheless, the scenario analysis results should be

Table 4

Nitrogen total removal with respect to the gross load

Total removal

(%)

Land removal

(%)

River removal

(%)

Washa 91 93 2

Zelivka 73 74 6

Vilaine 83 81 17

Land and river retention percentages refer to their input.a Estimated on the upstream basin of the Great Ouse river.

interpreted with some caution, as the model does not

consider in detail the physical processes involved in

nitrogen transport and transformation, e.g. the release

of nitrogen previously stored in the soils. Moreover,

the basin response to variation in nitrogen input does

not necessarily occur immediately but can involve a

lag time (Stalnacke et al., 2003). A long transfer time

of water through the soil and the aquifer, the soil

capacity to store excessive amount of nitrogen or, on

the contrary, the mineralisation of previously accu-

mulated organic nitrogen can retard the variation of

in-stream nitrogen concentration due to changes in the

nitrogen input. Stalnacke et al. (2003) observed that in

Latvia the strong decrease in agricultural intensity

during the 1990 s has caused a only small change in

river nitrogen concentration. The same was found in

the Zelivka catchment by Holas et al. (1999).

Considering this peculiar condition, an analysis was

made in this study of the Zelivka watershed to

investigate the reduction in the river nitrogen load that

would be expected as a result of the decrease of the

nitrogen fertiliser application of the 1990s, if no

release of nitrogen previously stored in the basin

occurred. The nitrogen input and the weather

characteristics of the period 1996–2000 form the

present scenario. Then the model was calibrated while

the fertiliser application was increased by 35%, which

corresponds to the typical amount of the 1980s, thus, a

new set of model coefficient aP, aT and aQ, was

obtained for precipitation, topography and water flow,

respectively, using the present measured water quality

series but with the underlying assumption that the

nitrogen concentrations measured during 1996–2000

are still conditioned by past fertiliser application.

Finally, the new coefficients were entered in the

model considering the nitrogen input and the weather

conditions of the present scenarios in order to predict

the nitrogen loads that would have been expected over

the period 1996–2000 if there had been no reduction

in agricultural intensity. The comparison between the

nitrogen load predictions obtained with the coeffi-

cients calibrated in the two different conditions shows

that a reduction of 29% in the nitrogen load may be

expected at the basin outlet as a result of the input

changes, once the surplus of nitrogen already

accumulated in the soil or stored in the groundwater

has been depleted.

Fig. 7. Scenarios analysis of decreasing nitrogen input in the basins drained by the stations Oust 04199200 and Vilaine 04213000. (a) Nitrogen

source apportionment of input and of the river transport. (b) Nitrate load variation according to the different scenarios.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315 313

4. Conclusion

In this study, a statistical model was developed

relating in-stream nitrogen load to spatially refer-

enced nitrogen sources and basin characteristics.

Fig. 8. Scenarios analysis of placing new pig farms in the basin drained b

scenarios 1, 2 and 3. The location of the farm in the three scenarios is ind

The model was successfully calibrated and validated

in three European basins, representing different

climate characteristics and land uses. The model

was shown to be able to describe the spatial

and temporal variability of the nitrogen pressure on

y the stations Oust 04199200. Variation of nitrate load according to

icated by the map.

B. Grizzetti et al. / Journal of Hydrology 304 (2005) 302–315314

the river system, identifying the areas subject to

higher nitrogen losses. Moreover, the model made it

possible to estimate the relative contribution by each

nitrogen source to the river export and understand the

basin behaviour and the role of each source. The

model is rather flexible and can be applied to basins

characterised by different activities and climate.

Since the model structure considers the spatial

distribution of the data and includes physical and

climatic parameters for the basin description, the model

can also be used in a predictive mode and for the

evaluation of different management strategies. In

particular, the impact of variations in nitrogen input

was studied as well as the contribution by nitrogen

stored in soils. Nevertheless, the scenario results should

be considered with caution as the model does not

consider the physical processes involved in nitrogen

transport and transformation. All these model appli-

cations are achieved with parsimony of parameters

(only three parameters) and data requirement.

The model was shown to be suitable for screening

studies of medium and large basins. Model applica-

bility at a finer scale has to be investigated in more

detail. In fact, the analysis of the nitrogen pressure,

and in general of the model results, was made using

the sub-basins as unit of evaluation; the size of sub-

basins depends on the detail of the spatially referenced

data of basin characteristics and nitrogen sources, as

well as on weather and water gauging stations density,

DEM resolution, river network information, and

naturally the sources description. It is important to

note that these data are usually available at the scale

we used in this study for the whole Europe. The

applicability of the model to larger European basins is

under investigation.

Acknowledgements

This work was carried out partially using the data

collected in the frame of EUROHARP, a research

project founded by the European Commission under

the Fifth Framework Program (contract no EVK1-CT-

2001-00096). The authors would like to thank the

Hydrobiological Institute (HBI-AS CR) for providing

the data of the Zelivka catchment, and the Institut

Francais de l’Environment (IFEN) for providing the

data of the Vilaine catchment.

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