Introduction

The pollution of fresh and marine waters has a long history. The first reports of deteriorated water quality, often with hygienic problems, but also about eutrophication symptoms, can be traced back to the latter half of the 19th century. Water pollution has probably started even earlier, at least in certain regions in Europe; this has been concluded by the population and production amounts connected with the information about the old production technology [START_REF] Billen | Estimates of early industrial inputs of nutrients to river systems: implication of coastal eutrophication[END_REF]. The growing concern of water pollution launched mitigation against loading to a lesser degree already in the first half of the 20th century, but in growing extent from the 1970's onwards. Success stories have been reported particularly concerning large fresh water lakes in temperate region of Europe, e.g. Lake Geneva and Lake Constance [START_REF] Anneville | Recovery of Lake Geneva from eutrophication: quantitative response of phytoplankton[END_REF][START_REF] Hase | Phytoplankton response to reoligotrophication in large and deep Lake Constance: Photosynthetic rates and chlophyll concentrations[END_REF], as well as lakes in the boreal zone (e.g. (Rekolainen, S., et al., 2001)). The development has not been as successful in marine waters, examples are the Baltic Sea, the North Sea and the Adriatic Sea [START_REF] Löfgren | Agricultural development and nutrient flows in the Baltic states and Sweden after 1988[END_REF], and the deterioration has not ceased in many freshwater lakes either. Thus, the general conclusion has been made in Europe as well as in the U.S.: Eutrophication is still considered as one of the major environmental problem (European Environment Agency, 1999, U.S. Environment Protection Agency, 1995).

The recent reductions in point source loads, municipal and industrial sewage waters, have emphasised the contribution of agricultural losses (Rronvagn, B., et al., 1995[START_REF] Rekolainen | Phosphorus losses from agriculture to surface waters in the Nordic countries[END_REF][START_REF] Van Der Molen | Agricultural nutrient losses to surface water in the Netherlands : impact, strategies and perspectives[END_REF]. In contrast to the municipal and industrial sources, diffuse losses are by far more difficult to quantify. Moreover, the assessment of best management practices, i.e. what is the optimal set of cost-effective abatement measures to reduce these losses, is not an easy task. This has been pointed out in several international contexts, e.g. in reports produced by the OSPARCOM and HELCOM, whose task is to assess the total loads and also contributions of various human activities on these loads (Helcom, 1998[START_REF] Sft | Development of HARP guidelines -Harmonised quantification and reporting procedure for nutrients[END_REF]. Under the OPSPAR umbrella, a scientific working group has proposed a harmonised reporting guidelines, but so far no agreement has been achieved about the most suitable methodologies for estimating nutrient losses from diffuse sources [START_REF] Sft | Development of HARP guidelines -Harmonised quantification and reporting procedure for nutrients[END_REF].

Agricultural losses of nutrients are of a diffuse nature, having both temporal and spatial dimensions. Thus, these losses can only be assessed by studying the whole watershed over time. Long-term monitoring of the losses at the watershed outlet as well as necessary data of watershed properties, variables and management operations, are required to achieve a sufficient accuracy and precision of estimation of the losses, its possible trends and to understand the factors affecting the losses. However, monitoring requires plenty of time and resources. Integrated management, including the loss assessments over several watersheds, which often differ much in terms of natural characteristics and human activities, is usually possible only using appropriate models.

Several models have been developed in order to estimate diffuse nutrient losses from a watershed. They vary very much in terms of complexity, structure and validation status. These models may be classified in several ways, however, two main categories can be identified: conceptual and physically-based models. Conceptual models are based on observations, and do not usually simulate the processes. Physically-based models contain descriptions of relevant physical, chemical and biological processes affecting nutrient transport from soil to water. The choice of model depends on the initial objectives of the task, and also on the available data.

Within the framework of the international conventions, such as OSPARCOM, HELCOM, and also in the implementation of the EU Water Framework Directive, the criteria for model selection is an ongoing process. The objective of this study was to test, how a few existing models, which differ much from each other conceptually, and in terms of complexity, can fulfill the requirements for the OSPARCOM harmonised reporting system. In date: 6 December 2001 doing this, we used observed data from a few small well-monitored river basins in order to see how these models can simulate spatial and temporal variations in watersheds, where data is relatively frequent and sufficient for parameterisation of complicated models.

Methods

study areas 2.1.1. Location and characteristics

Field measurements done on three European watersheds have been used as a database to conduct this study. One in Finland and the two others in France. The Savijoki watershed belongs to the National monitoring network of small drainage basins and it is located in south-western Finland, about 20 km Northeast from the city of Turku (22.5E, 60.5N). It is a tributary of the River Aurajoki that discharges to the Baltic Sea at the city of Turku (Figure 1). The Coët Dan and Cétrais watersheds are located in the western part of France. Both streams are tributaries of the Blavet and the Don (itself tributary of La Vilaine), respectively, which flow into the Atlantic Ocean (Figure 2). The following developments use data and numbers summarised in Table 1. All watersheds are comparable in size, the Cétrais watershed being about 2 to 3 times as big as the other two. While agricultural lands cover most of the French watersheds, the Finnish one is mostly covered by forests. Agricultural land uses and practices are available for all three watersheds. Practices at the field scale have been recorded on the Cétrais watersheds nearly on a yearly basis, on the Coët Dan watershed four enquiries have been performed (1988, 1991, 1994 and 1999) and three on the Savijoki watershed (1987, 1999 and 2000).

Missing data has been extrapolated from precise agricultural surveys performed several times in the last decade or so. Field crops are stored on a GIS for the French watersheds. In Savijoki, interview information on crop distribution and agricultural practices was available only for three years. In Finland, agricultural statistics are usually available only for administrative units, and the data is not geo-referenced. As a result, extrapolation of missing data was not possible in Savijoki.

Agricultural land use is very different from one watershed to the other and generally reflects agricultural intensity. In France, it seems to be highly correlated with livestock intensity on the watersheds. Spring cereals are mostly grown on the Savijoki watershed, in correlation to low livestock density. The Cétrais watershed is clearly a cattle breeding area with relatively medium intensity compared to the French and European average. As a result more than 50% of the area is covered by grasslands while cereal and com are grown mostly as animal feed. The Naizin watershed is clearly the most intensively farmed watershed because of the high animal population raised on it. Swine, broilers and cattle are raised on the watershed, swine population accounting for most of livestock units produced. 85% of the crops produced on the watershed serve as animal feed.

Grasslands are therefore less important in size than for the Cétrais watershed, while cereal and corn account for most of the difference.

Table 1 : Physical characteristics for the three watersheds

Characteristics of the watersheds Area(km J ) A clear differentiation between the French and the Finnish watersheds can be made on soils. Finnish soils are the result of the aftermath of glacier retreat and can be divided into three main categories: moraines, clay and peat. Clay soils can be very deep in southern Finland, reaching as much as 28m in depth. Artificial drainage is needed to provide good conditions for management practices, especially after snowmelt in spring.

Soil covering the French watersheds are the result of alteration of granite and schist mostly and can be described as brown soils. They are usually very thin compared to the Finnish ones, rarely deeper than 1 m. They tend to naturally drain relatively well, although local topography requires artificial drainage for the Cétrais watershed. Data on soil texture and organic matter content is available for all three watersheds. Local rainfall data have usually been measured directly on the watershed sites. Data for the Naizin and Cétrais watersheds are not significantly different and will be assumed to be the same. In both regions, climate can be described as temperate, although there is a clear Nordic influence in Finland. With a yearly average temperature of 4.8 °C in Saviyoki, the climate is 6.7°C colder than in Brittany over the year. The average temperature is actually greatly lower due to winter months while temperatures are comparable in summer. Rainfall pattern are almost opposite with more rain or precipitation in late fall and winter in Brittany, while the precipitation peak occurs during the summer months in south-western Finland. It tends to rain more in Brittany than in Finland. Nearly a third of the total precipitation falls in the form of snow in Finland while snowfall is exceptional in Brittany.

Climatic characteristics

date: 6 December 2001

Discharge measurements and water quality sampling

Discharge is measured continously on all stations by a V-notch overfall weir and a limnigraph for the Saviyoki and Naizin watersheds, while a Doppler velocity meter does the same job for the Cétrais watershed. Since the mid 1990s, water samples are mostly taken by automatic samplers often enough so that nutrient loads can be best evaluated. Grab sampling is also done as a complement.

Nutrients analysed always include NO3-N, NH4-N, Tot-P and PO4-P in all watersheds. Total Suspended Solids are not analysed in Cétrais but are elsewhere. In Finland, many more analysis have been performed, which include Tot-N, Ca, Mg, K, Na, SO4, CI, TOC pH, conductivity, alkalinity and turbidity.

Mean phosphorus concentrations and loads are quite similar between the Saviyoki and the Naizin watersheds. These values are relatively low in both cases. Load and concentration values for nitrogen are very different and seem to be correlated to agricultural intensity. Total nitrogen concentrations reported for the French watersheds actually correspond to flow weighted average nitrate concentrations. While nitrogen exports from the Saviyoki watershed are rather low (around 8 kg N/ha/yr), values for the Cétrais and the Naizin watershed are medium to high (around 22 and 45 kg N/ha/yr, respectively).

Simple methods

Nutrient losses from land to surface waters depend on meteorological forces, soils, topography and land use. The most simple approaches to estimate nutrient losses are based on regression equations, in which one or several of the dominant factors (e.g. field percentage (FP) within the watershed) have been used to calculate the losses. Two regression models have been chosen for testing in the MicroHARP framework: Regression model [START_REF] Rekolainen | Phosphorus and nitrogen load from forest and agricultural areas in Finland[END_REF] for total nitrogen (TotN) and total phosphorus (TotP), and Ekholm model [START_REF] Ekholm | Relationship between catchment characteristics and nutrient concentrations in an agricultural river system[END_REF] for TotP.

Another method to estimate nutrient losses is based on export coefficients. These simple [START_REF] Rekolainen | Phosphorus and nitrogen load from forest and agricultural areas in Finland[END_REF] studied diffuse nutrient loading from 23 agricultural and forest areas belonging to a network of small hydrological drainage basins in Finland. Savijoki watershed was one of the areas in the study. The network represents different climate and land-use conditions of the country. There are no lakes in the study basins and, based on the land-use, the basins were divided into three classes: forest basins (FP < 8%), mixed basins (FP 8 -35%) and agricultural basins (FP > 35%). Both loading models based on loading functions provide a quick method for estimating nutrient losses. Through these functions the pollutant load can be related to parameters such as climate, slope, soil type, land-use, management practices etc. Collection of the relevant export coefficients for each watershed has been started during the first year of the project. Two simple loading models have been chosen for testing: the Cemagref export coefficient model [START_REF] Bioteau | Testing of the Harp guidelines #6 and #9, first report : methods[END_REF] for TotN and the Johnes model [START_REF] Johnes | Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters: the export coefficient modelling approach[END_REF] for TotP. In the following the regression models and the simple loading models are described in more detail.

Regression models

phosphorus and nitrogen loads were found to be highly dependent on the proportion of agricultural land in the basins. In the study period 1981-1985 the phosphorus load from the forest basins varied from 5.9 to 16 kg km" 2 a" 1 and nitrogen load from 200 to 310 kg km' 2 a" 1 . The load from cultivated land varied from 66 to 160 kg km' 2 a" 1 for phosphorus and from 640 to 1400 kg km' 2 a" 1 for nitrogen. Based on this data, [START_REF] Rekolainen | Phosphorus and nitrogen load from forest and agricultural areas in Finland[END_REF] They examined the effects of watershed characteristics and riverine processes on the concentration of nutrients and total suspended solids using data from outlets of 12 tributaries (9-139 km ) and 22 main channel sites of a river draining an intensively cropped area of 1088 km 2 . Their appraoch is not only for total P (TotP) but also for dissolved reactive P (DRP), and takes into account the land use, the topography and the total size of the watershed. Using this approach the TotP loss was calculated as:

Pi = q,a (6)
where q â = the total runoff volume for the year i [I] q = the mean concentration of TotP calculated as: For the equation 7 the coefficient of determination (r 2 ) was 0.89 and for the equation 8, it was 0.69.The mean slope of the fields can be estimated e.g. using a Digital Terrain Model and calculate the mean slope of every single (agricultural) grid from the elevation difference to all neighbouring grids.The parameters of these models (equations 7 and 8) have to be calibrated or preferably derived from the local data.

date : 6 December 2001

Simple loading models

The export coefficient model for N load developed at Cemagref [START_REF] Bioteau | Testing of the Harp guidelines #6 and #9, first report : methods[END_REF] is based on the idea that nutrient losses from a watershed or field can be related to parameters such as climate, soil type, land-use and management practices. Two agricultural soil units with similar physical and chemical characteristics, and located in same climatic conditions are assumed to behave similarly in terms of runoff and nutrient leaching. The first step in the modelling procedure is collecting measured N export coefficients representing similar climatic and land-use conditions to those in the study area. The bibliographic references are grouped in a matrix containing many parameters of the reported sites: landuse, location, pedological data, climatic data, system of production, management data etc.

For selection of the export coefficients, the order of parameter dominance should be: landuse, place, soil type, geology, system of production, precipitation, drainage flow, management practices. The second step of the modelling procedure is linking the actual fields within the study area to the export coefficient matrix with GIS. The nitrogen losses (in Kg N per year) can then be calculated for each field unit by multiplying the selected export coefficient by the area of the unit.

The export coefficient model developed by [START_REF] Johnes | Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters: the export coefficient modelling approach[END_REF]

Mid-range models

The extension services in France have developed a method to help the farmers to adapt their fertilisation. A balance has been built on a yearly basis (Comifer, 1995) to assess the amount of inorganic nitrogen (X) to apply : For the following year, the initial amount of nitrogen is estimated from this Rf and simple leaching models. Here, Burns model [START_REF] Burns | An equation to predict the leaching of surface-applied nitrate[END_REF] has been used.

X =

-physically based models : 2.4.1. ICECREAM

The selected model, ICECREAM [START_REF] Äijö | Viljelyalueiden valumavesien hallintamalli (In English: Management model for arable lands)[END_REF], is a field-scale mathematical simulation model predicting water, soil, phosphorus (P) and nitrogen (N) losses at the edge of fields and out of the root zone. It is an extension of the CREAMS/GLEAMS models [START_REF] Knisel | Representing management practices in GLEAM[END_REF], Knisei, W. G. E., 1980) originally developed in the U.S. to assess and compare the impact of different management practices on soil and nutrient losses.The hydrology, crop growth, and partly also the erosion calculations have been further developed by [START_REF] Rekolainen | Adapting the CREAMS model for Finnish conditions[END_REF].

The hydrology component of ICECREAM simulates daily runoff using a modification of the SCS Curve Number method (Wischmeier, W. H. et Smith, D. D., 1978), which relates to soil texture and structure, land use and management practice. The matrix flow in soil is described by a simple 'tipping bucket' system using the user-defined hydraulic conductivity and pF-curve values for porosity, field capacity and wilting point. Evaporation is calculated by a model presented by [START_REF] Ritchie | Model for predicting evaporation from a row crop with incomplete cover[END_REF]. Erosion is computed using the modified Universal Soil Loss Equation, MUSLE (Foster, G.R.,etal., 1981).

The submodels for P and N are mainly taken from the GLEAMS model with a few adaptations to achieve a better fit to local conditions. The P cycle in the soil is described by three inorganic and two organic pools, and flows between these and the biomass. The most active inorganic pool is the plant available P, which has been defined as anion exchange extractable P (Sharpley, A. N., et al., 1994). Chemical fertiliser P is added to plant available P at the day of fertilisation, and crops are assumed to take up P from this pool only.

The loss of soluble P with surface runoff also originates only from this pool, whilst the loss of particulate P (attached to soil particles) takes place from all soil P pools.

The nitrogen submodel includes the significant pools and flows of the nitrogen cycle in soil. Nitrate (NOj") and ammonium (NHt*) constitute the plant-available inorganic nitrogen. Plant uptake of nitrogen is calculated on the basis of biomass demand and supply.

The model considers two sources of mineralisation from organic N pools: fastcycling fresh organic N pool (litter N), associated with crop residues and microbial biomass, and the more stable organic N pool, associated with the soil humus. Organic N associated with humus is further divided into two pools, active and stable.

The nitrogen pools are connected by reactions such as mineralisation and immobilisation.

Most of the reactions are controlled by moisture content and temperature of soil layers. Inorganic or organic fertilisers and the atmospheric deposition constitute the input of nitrogen to the model. Nitrogen losses take place from the topsoil by erosion and in soluble form by surface runoff. Nitrate and ammonium are also percolated from the lowest simulated soil layer. Soil nitrate can be reduced to nitrogen gases by denitrification. Volatilisation, the loss of ammonium N to the atmosphere, is estimated simultaneously with nitrification.

BMP1-GLEAMS

GLEAMS v.2.10 (Groundwater Loading Effects of Agricultural Management Systems) is a mathematical model to simulate the complex climate-soil-management interactions for fields-size area. GLEAMS operates in a daily time step. It does not simulate movement at groundwater level. The main aim of BMP 1-GLEAMS is to use GLEAMS for all homogeneous surface units coming from GIS (see method 2) and coupled it with a hydrologie model. BMP 1-GLEAMS simulates in detail most of nitrogen movements in soil and particularly flows between both organic and mineral nitrogen pools.

A detailed and complete description of the model is given in the user manual provided with the model [START_REF] Knisel | GLEAMS : Grounwater Loading Effects of Agricultural Managements Systems, Version 2.10[END_REF]. Knisel also proposes a summarised description and model modifications. GLEAMS v. 2.10 is composed of four components operating simultaneously: hydrology, erosion/sediment yield, pesticides and plant nutrients. However the model can operate without using all modules and the module pesticides can be disconnected particularly.

SWAT

SWAT is a spatially distributed model developed to predict the effects of management (Climate and vegetative changes, reservoir management, groundwater withdrawals, water transfer) on water sediment and chemical yields on large river basins. SWAT can analyse watersheds and river basins of 100 square miles by subdividing the area into homogenous units. The model simulates hydrology, pesticide and nutrient cycling, erosion and sediment transport. Water, nutrients and sediment quantities predicted at the field scale are routed in the hydraulic network. Information required on the inputs can be obtained from existing databases. A linkage will be done using data already available in the existing GIS on the Cétrais and Coët Dan watersheds and SWAT.

Description provided herein is mostly taken from available descriptions of SWAT, especially that available at http://www.wiz.unikassel.de/model_db/models.html. The model was developed by modifying the SWRRB, (Arnold et al, 1990) and ROTO (Arnold et al., 1990) models for application to large, complex rural basins. SWRRB is a distributed version of CREAMS, which can be applied to a basin with a maximum of 10 subbasins, and SWAT is an extended and improved version of SWRRB, running simultaneously in several hundred subbasins.

The SWAT hydrology model is based on the water balance equation. A SCS curve number is generated for the computation of overland flow runoff volume, given by the standard SCS runoff equation (USDA, 1972). A soil database is used to obtain information on soil type, texture, depth, and hydrologie classification. In SWAT, soil profiles can be divided into ten layers. Infiltration is defined in SWAT as precipitation minus runoff. Infiltration moves into the soil profile where it is routed through the soil layers. A storage routing flow coefficient is used to predict flow through each soil layer, with flow occurring when soil moisture in a layer exceeds field capacity. When water percolates past the bottom layer, it enters the shallow aquifer zone [START_REF] Arnold | A comprehensive surface-groundwater flow model[END_REF]. Channel transmission loss and pond/reservoir seepage replenishes the shallow aquifer while the shallow aquifer interacts directly with the stream. Flow to the deep aquifer system is effectively lost and cannot return to the stream [START_REF] Arnold | A comprehensive surface-groundwater flow model[END_REF]. Based on surface runoff calculated using the SCS runoff equation, excess surface runoff not lost to other functions makes its way to the channels where it is routed downstream. Sediment yield used for in-stream transport is determined from the Modified Universal Soil Loss Equation (MUSLE) [START_REF] Arnold | Spatial Scale Variability in Model Development and Parameterization[END_REF]. For sediment routing in SWAT, deposition calculation is based on fall velocities of various sediment sizes. Rates of channel degradation are determined from Bagnold's (1977) stream power equation. Sediment size is estimated from the primary particle size distribution (Foster and others, 1980) for soils the SWAT model obtains from the STATSGO (USDA 1992) database. Stream power also is accounted for in the sediment routing routine, and is used for calculation of re-entrainment of loose and deposited material in the system until all of the material has been removed.

Statistics

One component of a model evaluation exercise is to define criteria by which the agreement between observation and prediction can be assessed. As part of this research programme, various statistical indices were either taken from the literature or identified in discussions with statisticians. A description of the indices selected is given below. They were not all used in the subsequent modelling exercises, but they are listed here to give an indication of the types of index that are available.

The simplest way to assess model performance is to plot the predicted and observed values on a suitable diagram; this method represents an essential starting point for any model evaluation exercise. The visual method, although useful, needs to be backed up by some appropriate statistical evaluation so that relative model performance can be assessed in different situations. What is required is a system that enables us to estimate the fit (or the lack of fit) that is expressed graphically in the diagrams.

The criteria used in selection of the statistical indices were purposely chosen in relation to our specific context.

Many alternatives were available, but only those with a simple and straightforward application were selected.

The Set of indices

An index calculated from observed and predicted data can be used to express an overall fit of the model simulation or the fit of

Notation

The evaluation process in the current research project deals mainly with the prediction of annual nutrient loads in rivers. The adopted notation is as follows:

Given a sample of N annual observations and their N corresponding annual predictions O¡ Observed nutrient loss in the i-th year i=l,.

Vi

For all values (i = 1,...,N)

We will refer to the graph {(i,P¡)} as the Predicted Curve (the plot of predicted values against time), and similarly the Observed Curve refers to the plot of observed data against time.

comparison between different modelling approaches.

Properties

Two properties follow by the definition of TE: The numerator of the above ratio represents the total discrepancy between predicted and observed values. The reason for dividing by the TE=0 i¿ total of the observed values is to scale the total error in relation to the size of the experiment. This enables us to use the same index (1) for

Scaled Root Mean Squared Error

N ( 15
)
This quantity (2) is a measure of the Spread around the ideal case ofP¡ = O¡ Vi: With a perfect fit, the value (P¡-0¡) would be zero. The left-hand factor in ( 2) is included in order to scale the Root Mean Square Error in a similar way to that used for Index ( 14).

date : 6 December 2001

Properties

Again, from the definition of ( 15) it follows that:

SRMSE>0 SRMSE = 0 iff Vi (/>, = O,).

Model Efficiency

Y/Or O/ ( 16
)
This quantity is widely used in Model Evaluation exercises and is based on more sophisticated considerations than the previous indices.

In order to understand its behaviour, ME can be rearranged as follows (ME in this form is known as Sutton-Rathcliffe's Coefficient):

N 1- YjOrO/ (16a)
ME e ]-oo; +1] (ME has no lower bound and its upper bound is 1)

MEXOiff

The third property suggests that when ME becomes negative the fit is unacceptably poor. Under these conditions, the statistical sample variance of (PrO¡) is less than (or equal to) the sample variance of (O r ). Therefore accepting the model predictions is no better than simply using the mean of the observed data.

Properties

The following observations can be made:

Coefficient of Determination

T/OrO/

CD = (17)

This is the ratio between the spread of the observed values around their mean and the spread of the predicted values around the observed mean.

Properties

CD>0 then CD =1

In general, the closer CD is to 1, the better the fit. When CD = 1, the statistical variance does not change if we use the predicted rather than the observed values. There are therefore reasons to believe that there is good agreement between observed and predicted data.

Coefficient of Shape

CS = s (18)

This quantity (similar to CD) represents the ratio between the spread around their respective means of both the predicted and the observed values (i.e. ratio between the sample variance of the two sets). In graphical terms, this is reflected by a similarity in the shape of the predicted and observed curves.

Properties Same properties as CD

CS = 0 iff V constant K > 0 so that Vi (P¡ = O,. V K)
The second property means that when the predicted amounts differ from the observed amounts by a certain constant K, CS does not vary. This behaviour will be characterised visually as a predicted curve similar to the observed but shifted upwards or downwards by a constant factor. In this situation, the simulation is likely to be responsible for the overall lack of fit. NOTE: As any index calculation is carried out on an equal number N (number of years) of observations O¡ against their predictions /*,-, the condition of good fit for loss (EP¡ . EO¡) renders CD . CS. With these conditions, the information given by CS will be identical to that from CD.

Results :

3.1. simple methods

application of the simple methods for nutrient loads estimation 3.1.1.1. Regression models on the Savijoki watershed

In the first phase of the modelling procedure the simple regression model by [START_REF] Rekolainen | Phosphorus and nitrogen load from forest and agricultural areas in Finland[END_REF] was applied at Savijoki watershed. The TotN and TotP loads were calculated with equations 1 and 2. The only parameter needed is field percentage, which for Savijoki is 39% during the study period 1981-2000. The TotP export from the Savijoki watershed calculated by the equation ( 1) is 64 kgkm-2a-l which is very close to the measured mean annual TotP load for the period 1981-1997 (61 kgkm-2a-l).

The TotN export calculated by the equation ( 2) is 685 kgkm-2a-l, which is somewhat lower than the measured mean value 819 kgkm-2a-l.

To be able to model specific annual load values, the impact of precipitation was taken into account by using a multiplication factor that relates the specific annual rainfall to longterm mean annual rainfall (equations 3-5). Measured precipitation values were corrected to account for measuring errors. The measured and modelled annual TotP and TotN loads are presented in Fig. l. For TotP the model either over-or underestimated the load while for TotN it was more common that the model underestimated the load (80% of the cases).

The mean modelled loads for the period 1981-2000 were 64 kgkm-2a-l for TotP and 685 kgkm-2a-l for TotN. In the year 1984 both TotP and TotN loads were highly underestimated. This is probably due to the high amount of precipitation (946 mm) in 1984.

Next, TotP was estimated with the model developed by [START_REF] Ekholm | Relationship between catchment characteristics and nutrient concentrations in an agricultural river system[END_REF]

• J 1 1 J s H ¡i s s lili 1 m 1 1 1 isa il Si l|; •v •!' 1 I IB Ä B 1 ||| it-; BJë F ! 1 S ! I s Meas • Mod. • • i i " i i ! i ili il

Regression models on the Cétrais watershed

The simple regression model [START_REF] Rekolainen | Phosphorus and nitrogen load from forest and agricultural areas in Finland[END_REF] was applied on the Cetrais watershed, and each sub-watershed. The tot N and tot P loads were calculated with equations I and 2.

The only parameter needed is the field percentage, which changes slightly from one year to the other and more from one subswatershed to the other (see Figure 6). Only nitrogen loads have been measured on the Cétrais sub-watersheds. Total N loads estimated with regression model are highly underestimated : the tot N export calculated with equation 1 is 10,7 kg N/ha/year (mean for every points and every years, u = 0.7) and the measured value is 22,7 kg N/ha/year (a = 7.5).

We have tried to calibrate this regression model, with data from 1997 and 1998, because we have measurements for these years on all the stations. The Figure 7 shows Nexp measured (mean 97-98) on each station including Naizin watershed, compared to the field percentage. western part of France, one can observe very different ranges of animal density (and thus of nutrient losses) from one area to the other : the field percent is not enough to estimate the loads.

For improving annual loads values around the range, we considered the impact of annual precipitation compared to long-term mean annual rainfall (data coming from Météo France). For tot N the model underestimates the loads as soon as they are greater than 10 kg/ha (see Figure 8). 65 70 75 80

We do not have enough points for calibrating this regression equation (correlation between these two variables is very bad). In fact, in the

Regression models on the Coe't Dan watershed

The simple regression model [START_REF] Rekolainen | Phosphorus and nitrogen load from forest and agricultural areas in Finland[END_REF]) was applied on the Naizin watershed.

The field percentage varies slightly on this watershed from 82 to 85% on the 1993-2000 period.

As observed on the Cétrais watershed, the tot Nexp are greatly underestimated (see Figure 9) : the tot Nexp mean is 15.9 tons and the mean measured value is 63.2 tons.

On the contrary, this model overestimates tot P loads (see Figure 10). Taking into account the precipitation for annual estimation gives no improvement.

On Naizin watershed, [START_REF] Ekholm | Relationship between catchment characteristics and nutrient concentrations in an agricultural river system[END_REF] model was applied for totP loads. This model overestimates the measured loads too : the mean totP load estimated with Ekholm model on Naizin watershed is 5.9 tons / year and the mean measured tot P load is 0.5 ton / year.

Application of simple loading methods

Application of the N export coefficient model in the Savijoki watershed

The first step in applying the N export coefficient model was evaluating the representativity of available measured export coefficients in climatic and land-use conditions similar to those in Savijoki. In Finland, there are only few experimental fields where the effects of agricultural practices on nutrient leaching are studied. Moreover, the study period for testing different practices is usually rather short (only a few years). As a result, it is often difficult to distinguish between the effects of long-term variability in hydrologie conditions and the differences in actual management practices. Due to lack of data on actual measured export coefficients, a data base built for a decision-making system VIHTA [START_REF] Äijö | Viljelyalueiden valumavesien hallintamalli (In English: Management model for arable lands)[END_REF]Tartari 2000, Puustinen 2001) was utilised for deriving the export coefficients for nitrogen. The VIHTA-model was developed to help selecting the best agrienvironmental protective measures to reduce nutrient loads to surface waters. The model utilises estimated values of nutrient loads for a set of field properties, e.g. soil type, slope and vegetation (altogether 96 different categories). In VIHTA-model the vegetation is classified as covering (grass) or not covering (cereals etc.). The load estimates for different field property categories are based on measurements, modelling and experts' assessments.

In Savijoki, interview data on crop distribution and management practices was available only for the years 1987, 1999 and 2000. The dominating soil type on the fields is silty clay and the mean slope of the fields is 0.5 %. The N export value chosen for these conditions was 15 kg/ha/a for covering crops and 18 kg/ha/a for non covering crops. The proportion of the different crop types in the fields have remained rather similar during the study years. The total area of each crop type was calculated for each of the years and the total N load for the agricultural area was calculated by multiplying the export values by the area occupied by the crop. Estimates for N leaching from forests, scattered settlement and the natural background leaching were the same that have been used in the nutrient source assessment system VEPS for Savijoki conditions. VEPS interface was developed at SYKE for assessing nutrient sources and loads at Finnish watersheds. The system provides information on the magnitude of nutrient losses to surface water systems, source apportionment of loads and the variation of loads in time. In VEPS, the estimation of nutrient load from forestry is based on load functions for different forestry practices and statistical information about these practices collected from forestry board districts. The estimates of natural background losses are based on research results from representative basins. For scattered settlement, the estimates of export coefficients for nutrient leaching are based on experimental results for different types of sewage systems and information about population density. Figure 11 shows the calculated totN load for Savijoki watershed. The modelled totN load is of the same order as the measured one. Differences in measured values reflect the variation in annual discharge, which for the test years was lowest in 1987 (344 mm) and highest in 2000 (442 mm). The export coefficient model is not able to take into account the varying hydrological conditions during the study years.

20000

Application of export coefficient model on the Cétrais and Naizin watersheds

For Naizin watershed, the crops are quite the same from one year to the other (see point 2.1. for details). So, the estimated N loads are roughly the same from 1993 to 2000.

Unfortunately the measured loads are very different from one y can to the other : for dry years, the export coefficient model overestimates the measured loads, and for wet years it underestimates them (see Figure 12).

A mean N load for the 1993-2000 period on Naizin watershed as an estimation from the export coefficient model is 75.4 kg N/ha, with a standard deviation of 2.6. The mean measured N load of 63.2 kg N/ha is different from the estimated value.

For Cétrais watershed, the export coefficient model overestimates the measured N loads every time but for 2000 which is a very wet year (see Figure 12).

Application ofJohnes model on the Savijoki watershed

Input data for Johnes model (Eq. 9) consist of spatial distribution of land-use, fertilizers applied, number of livestock and human population and atmospheric deposition. Phosphorus export coefficients are needed for each identifiable source (e.g combinations of crop and fertilizer). Due to lack of data on actual, measured export coefficients for Savijoki conditions, the same procedure was applied here as when calculating the totN load by the N export coefficient model. The relevant values for P export where chosen from the VIHTA-database, and the overall TotP load from the field area was calculated by multiplying the export values by the area occupied by the crop. The effect of phosphorus deposition is included in the VIHTA values as a default. Estimates for P load from forests and the natural background leaching were the same that have been used in VEPS. The TotP load from scattered settlement was also calculated based on VEPS estimates (kg P/km2 of scattered settlement). The data on crop distribution was available only for the years 1987, 1999 and 2000. Fig. 1 shows the calculated TotP load for the Savijoki watershed. As can be seen, the modelled TotP values fit rather well the measured values in the years 1987 and 2000, but are very much higher than the measured ones in 1999. The measured TotP load for the year 1999 (647 kg) was among the lowest ones for the whole study period 1981-2000. The low measured value can be partly explained by the difficulties related to sampling strategy: some of the peaks in nutrient leaching may not be detected.

Application of Johnes model on the Cétrais and Naizin watersheds

The Johnes export coefficient model seems to predict the general average of the Phosphorus load at the outlet of the Coët Dan watershed. However, this model is very sensitive to the export coefficient used for com. Indeed, the value used in this study was 0.65 kg P/ha/yr, which was taken from the "cerals value" reported by Johnes, since no specific com export values were defined in the article. Other others in the littérature propose much higher values for com comprised between 1.0 and 5.4 kg P/ha/yr. Applying such values in the model gives much higher exported loads since com covers an important part of the Coët Dan watershed. 1981-1986 and 1988-1998. All the loads are expressed in kg per km2 of total area.The calculated indexes for goodness and fît are shown in Table 2. In terms of the scaled total error (TE), the results by the original regression model and the regression model modified by precipitation were reasonable. This was also the case for the N export

Cétrais and Coët Dan

As noticed before simple methods do not fit very well the nitrogen loads, neither on the Cétrais (see When applying the simple models, the most time-consuming phase was interpretation of the interview data from the years 1987, 1999 and 2000. Only part of the interview data from the year 1987 was stored in a data base, and the crop distribution was not geo-referenced.

During the interview for the years 1999 and 2000, detailed information was collected on crops and management practices. The interview was carried out by the Southwest Finland Regional Environment Centre. Applying the regression models was very fast and easy, as only a few parameters were needed. Applying the export coefficient models is also easy, provided that relevant coefficients are available. In the Savijoki case, due to lack of measured export coefficients, estimated values for nutrient export included in two decision-making systems (VIHTA and VEPS) were used in load calculations. This method was also easy and fast to apply, but the discretization scheme for different land-use and management conditions was rather simple. Table 5 summarises the time required to perform simple methods.

Cétrais and Coët Dan watersheds

Basic data acquisition is common for all the methods. The watersheds have been delineated on a DEM basis, which is quite simple. But there has been need of more precise date: 6 December 2001 delineation, especially for flat areas (north part of Naizin watershed, most of Cétrais one) : field measurements took one day for Naizin and 2 for Cétrais (because the watershed is larger). Some data have been bought : DEM, rain. For field percentage, we used one field records, but we could have bought some Corine Land cover maps.

Table 5 : time needed

The simple methods are very easy to perform as soon as the required data are introduced in the GIS. It has been a little bit longer on Cétrais watershed because we used the methods on several subwatersheds.

Mid-range models 3.2.1. Parameters preparation from GIS

G.I.S. Georeferenced

Database

Non georeferenced Database -Agricultural practices

-soils, water analysis -datafrom experts climatic data

Results

From overlays

Homogeneous areas (pedology, topography, Land cover)

+ agricultural practices

Agrihydrological Modelling

Nutriment losses assessment FigurelS : GIS and databases organisation for NT and PB model and BMP1-GLEAMS

Collecting and organising data in a GIS is the first step before one can model nutriments losses at the watershed scale (see Figure 15).

Input and update procedures on GIS include three steps: the structural block, the agricultural field, the elementary unit

First and second entities: the agricultural field and the structural block

The agricultural field is an homogeneous unit where management practices are steady for at least one agricultural cycle. At this scale, fertilisation practices, soil work or crop yields are homogeneous. However, limits of such fields are subject to changes from year to year. Because of this, another unit of larger size must be found, for which the boundaries do not change from year to year. Such units have been called structural blocks. Practically, it corresponds to the land defined by tree hedges, streams, roads and private property's limits.

The structural block is also farmed by only one farmer, and can be divided into several agricultural fields (which boundaries can vary from year to year). Thus, watershed boundary, roads, hydrographie network, tree hedges and private property limits have been digitised and serve as the basis for agricultural fields.

Third entity: the elementary unit

This unit is obtained from overlaying agricultural field units with the soil coverage in the GIS. This unit is homogeneous for land use, fertilisation practices, soil type and climate for the whole period of study.

Construction of the structural blocks

Watershed boundary

This datum is essential to implement the three methods in the project. Therefore, it is important to explain in details how we managed to define watershed boundaries as precisely as we could.

Watershed boundaries were first delineated from the interpretation of contours from the 1/25000 scale topographic map.

Watershed boundaries were first delineated from the interpretation of contours from the 1/25000 scale topographic map. date: 6 December 2001

Boundary determination using the DEM

The DEM represent landscape's morphology. Actually, land is represented as a series of pixels of defined dimensions and to which is assigned an altitude.

Both watershed boundaries were verified using the Data Elevation Model. In particular, Cétrais watershed boundary was redefined using the DEM compared to the first assessment from the contour analysis.

The boundaries obtained were then compared with field verification. Some of the boundaries had to be changed especially in the flat areas. Indeed, anthropogenic drain ways like ditches, roads, urban areas can significantly alter natural flow direction.

Roads, hydrographie network, tree hedges

Those data have been digitised using both the 1999 digital aerial photo and the 1/25000 scale topographic map. More details were obtained from field verifications Most of the time tree hedges, woods, road or walk ways define property limits. However, only electric enclosures or even no obvious mark sometimes are used as property limits. This information is necessary to deduce the structural block limits. It may also be the most difficult data to collect because farmers may retire or exchange their lands. Those changes have to be accounted for.

Construction of the agricultural fields

Structural blocks on both watersheds are used as a basis for the construction of the agricultural fields. The agricultural field is the geographical unit that may be observed when on the field. The difficulty, as already expressed above, is to be able to satisfactorily represent the crop and practices changes, as well as the possible changes in the field limits from crop cycle to the next. Three main methods are used to obtain this type of information. The first one results from surveys conducted near farmers, the second one involves direct field observations and the third method derives from the use of aerial photographs. This last technique combined photos available from the National Geographic Institute, as well as pictures taken during ultra-light flights at a low altitude of 300 meters. It is a technique which is used more and more frequently nowadays [Scott, 1999 #91].

Illustration of the fields and blocks construction

An example of the changes in the crop coverage from Coët Dan watershed is illustrated below. For the purpose of illustration, four obvious land units can be clearly defined. They correspond to structural blocks defined earlier (numbered 288, 322, 324 and 325). One can notice on the maps that the geographical limits of the units 288 and 325 do not change from 1995 to 1997 (see figure 26). The same cannot be said for units 322 and 324, which are divided into smaller units with varying boundaries from one year to the next.

After several years a structural block such as 322 could be subdivided into several "cultural units". To take into account effects of previous fertilisation practices, we have to keep these limits one year to the next.

These units form the basis for the agronomic analysis conducted from year to year and are named agricultural fields defined earlier and illustrated below:

Parameters estimation.

Mid-range models need much more parameters than simple models. Climatic data, such as daily rain, temperature and PET come from Météo-France. Soil water capacity is estimated from a soil map and Gleams reference tables.

On Cétrais watershed, only N.Turpin and P.Bordenave model was applied (then was no need of improving export coefficient models as every required reference has been found in literature). For some fields there where soil analysis : for these fields, parameters have been estimated according to the soil analysis values. For any other field, local references have been used for the estimation of soil N supply.

The description of agricultural practices come from 3 enquiries :

The first enquiry was performed in 1997. It dealt with the description of practices, field per field, from 1992. Some farmers have signed a contract : they engaged in 1998 to improve their fertilisation practices and they register what they are doing.

The farmers who did not signed this contract have been enquired at the beginning of year 2001 : they have been asked what they have changed in their practices for the last 5 years, have and why.

All the reference tables required for estimating the model parameters from these enquired data are described in a previous report [START_REF] Bioteau | Testing of the Harp guidelines #6 and #9, first report : methods[END_REF]. The sub watersheds appear to be quite different PZ4 has a much more intensive use than the others : crops yields and fertilisation are much higher than on the other sub watersheds.

There appear to be a slight extensification from 1998 : the farmers who have signed a contract reduce fertilisations and sometime expected yields. Unfortunately, only 20% of the farmers signed such a contract and among the others many use more fertilisers nowadays.

calibration.

No calibration has been required for mid-range model on Cétrais watershed.

validation

The model has been validated with some field measurements : measured autumn in organic nitrogen is compared with the estimation of the model (see Figure 20). The estimation is accurate for predicting the N content for fields with corn and cereals. There is still a problem for predicting the N content of soil under grassland : the measurement show high contents of ammoniacal N, even in the deep soil layers. These high contents have still no valid explanation yet but they are measured everywhere in the western part of France. For nitrate N content of the soil, the model realises acute predictions.

goodness and fit

On Cétrais watershed, NT model fits quite well the measurements (see Figure 22), for all subwatersheds and all years (even for wet date : 6 December 2001 years such as [2000][2001]. One exception is point PZ7 for years 98-99, with estimation twice the measurements. This point is particular in the watershed, because there is an old tin mine : extraction has been performed with sulphuric acid, and the pH in the river are very low (2.5). There is a need of more investigations to be sure one can rely on the measurements performed.

On Coët Dan watershed, the model slightly overestimated the measured loads. The survey of the practices is not as precise as it has been performed on Cétrais watershed. The loads are much higher. Some questions are remaining on the effective rate of volatilisation after slurry spreading (depending on the soil temperature and on the wind speed immediately after spreading operations). The model parametrisation and simulations were done before the new fanner interview at Savijoki was carried out (spring 2001). Thus, data on crop distribution was based on the interview from 1987. The new interview revealed only minor changes in crop distribution during 1999-2000, as compared to the situation in 1987 (Figure 23). ICECREAM model was run for the period 1980-2000 (1980 being a warmup year for the model) for barley, spring wheat and oats, which were the dominating crop types in 1987. The management practices where kept the same through the whole period. Silty clay is the dominant soil type on field parcels, and was chosen as the only soil type for model calculations. The parameters controlling crop growth and water and nutrient flow in soil were selected based on earlier model experiments carried out by ICECREAM in Finland (e.g. [START_REF] Granlund | Modelling of nitrate leaching on a heavy clay soil in Southern Finland[END_REF][START_REF] Rankinen | Modelling of vegetative filter strips in catchment scale erosion control[END_REF], Tartari et al. 2001). The model predicts the surface runoff and nutrient load at the edge of the field and percolation of water and nutrients below the root zone. The VIHTA export coefficients were used to calculate load estimates for grass area and for other crops that were not modelled (VIHTA crop type: not covering) The total potential nutrient load from the field area was then calculated by summing up the modelled loads from different crop type areas for each year. The potential loads from forested area and scattered settlement were estimated following the same method as when applying simple loading models. 3.3.12. BMP 1-GLEAMS GLEAMS [START_REF] Knisel | GLEAMS : Grounwater Loading Effects of Agricultural Managements Systems, Version 2.10[END_REF] is "Groundwater Loading Effects of Agricultural Management Systems" model. Principles are described in Appendix 2. GLEAMS (v.2.10) was used and partially calibrated on several fields located on two small watershed in Brittany: Saint Leger watershed (Ille and Vilaine) and Ploudiry watershed (Finistère), and also on Solepur pilot (Finistère). This pilot is a 3000 m2 drained experimental plot (comparable to a macrolysimetric case) which has received massive pig slurry spreading. Initially, we tested the model without modification using data measured on sites (given climatic, soil physicochemical characteristics and agricultural practices... ). Comparisons between predicted values and measured were carried out on PET Penman values, amount of mineral nitrogen present in soil, crop production and on nitrate and ammonia losses under rooting zone. On each parameter, important deviations were observed between not modified model estimates and real values. This led us to replace calculate PET by values calculated by Meteo France. This first modification involved a clear improvement of percolation value, but not of nitrate losses. The soil mineral nitrogen was very underestimated for several periods due to general mineralisation underevaluation (mostly ammonification process).

Time needed to perform mid-range models

Crop distribution (%of field area)

Crop production was also very lower than measured values. So we have to modify mineralisation, reorganisation, and denitrification modules to find values close to those measured in the soil.

We slightly corrected the LAI curve for two principal crops (com forage and winter cereal) simultaneously, in order to get production estimates closer to reality [START_REF] Mainguy | Etude du modèle GLEAMS pour estimer les flux d'azote à l'échelle parcellaire : travail réalisé sur le bassin versant de la Fontaine du Theil à Saint Léger (35[END_REF]. Lastly, GLEAMS has been coupled with a simplified hydrological model fixed during the year previous the test. The modified model well reproduced water flows and nitrogen yearly concentration " flows " on each watershed without modifying parameters. Deviations between modelled and measured values are 17 % and 10.6 % for Saint-Léger watershed and Ploudiry watershed respectively [START_REF] Peltier | Utilisation de GLEAMS pour estimer les flux d'azote à l'échelle parcellaire : travail réalisé sur les bassins versants de la Fontaine du Theil à Saint Léger (35) et de du Kerouallon à Ploudiry -Loc-Eguiner (29[END_REF].

parameter estimation for SWAT on the Cétraîs and the Coët Dan watersheds

Four types of data were required to run the SWAT model.

The first type of data is the Digital Elevation Model (DEM). The ones used for both watersheds have been derived from original 50-m resolution sold by the National Geographic Institute (IGN) described in the previous report. The new 5-m resolution was obtained using the Universal 2 Krigging method (Duros, 2001). Soil map and data make up for the second important type of data in SWAT. Available soil maps have been described in the previous report. Textural properties were associated to each soil unit using textural data obtained from soil pits. Pits were not dug for each soil type initially described. Similar soil unit were put together to finally create a map containing 7 and 9 soil units for the Cétrais and the Naizin Watersheds, respectively. All other rotations were associated to those major types, depending on how close or similar rotations were from one to another. The simulations performed with mid-range models have shown the importance of defereffects of manure spreading operations during the four years before the estimation of the load. So it has been decided to work with crop rotations, and not one year only. The fertilisation practices and yields on the 45 types of crop rotations on Cétrais watershed has been defined as a mean of all the similar rotations on the watershed. There may be a bias due to this summary of agricultural practices. To have an idea of this bias, we performed method 2 with the 71 rotations' practices instead of each field surveyed practices. The Figure 24 shows that while doing this, we actually introduce a bias : the loads will be underestimated for all the point (excepted point PZ7). But this bias is smaller that the bias we could obtain when taking into consideration one year only (and thus neglecting defer effect of previous manure spreading).

Climatic data (fourth type of data) were obtained through local official weather stations described in the basin characteristics.

calibration, 3.3.2.1. ICECREAM

ICECREAM was not calibrated or validated in the present study as no measurements on nutrient leaching or nutrient contents in field soils were available from Savijoki fields. The

BMP1-GLEAMS

Results obtained on Solepur pilot are related to calibration of denitrification and of volatilization because measured values were available [START_REF] Martinez | Etude du suivi expérimental d'un dispositif expérimental (biologique et physico-chimique) de sur-épandage de lisiers de porc[END_REF][START_REF] Martinez | Solepur : a soil treatment process for pig slurry with subsequent denitrification of drainage water[END_REF]. Denitrification was predicted correctly but volatilization is underestimated. After four years spreading (1992 to 1995) corresponding to 23,250 nitrogen kg.ha-1 on the whole period, the model simulates a faster nitrogen concentration decrease in draining water than that measured. Evolution of amount of total soil nitrogen is well predicted, but not quality: The model predicts an accumulation of mineral date : 6 December 2001 parametrisation was based on earlier applications of the model in similar conditions. nitrogen whereas it is an accumulation under organic form actually. This preliminary step of calibration enabled us to identify modules, which must be improved for a use on numerous fields of a watershed. The animal waste module must be improved concerning solid manure because it is mineralized too fast. In fact, there are not sufficient back effects and nitrogen losses by leaching are over-estimated during the year of spreading. The nitrogen flow between two soil nitrogen pool requires also improvement. Neither flow nor nitrogen dynamic seems correct. Lastly, the case of grazing meadows requires specific module, which will be written. In spite these reserves, GLEAMS modified use coupled to a hydrological module BMP 1-Gleams model appears relevant to us a priori, to carry out simulations of management practices impact on nitrogen flow in water. Two years of monitoring (1992)(1993)(1994) were used to calibrate flows for the BMP 1-Gleams model. Results obtained for six non-calibrated years show a good correlation between the simulated and measured cumulated nitrogen fluxes at the outlet of the Coèt Dan (Naizin) watershed. Overall, 301 tonnes of nitrogen were simulated to leave the watershed after six years, while 334 tonnes were measured. A 10% underestimation was thus made using this model for this criteria.

SWAT on Cétrais and Coët Dan

It was decided that there should not be any deep aquifers considered in the SWAT simulations for the French watersheds because of the rocky and impermeable substrate below the 1-m thick soils. Three parameters were used as calibration factors:

The curve number, which by default was too high for the French watersheds

The Alpha base flow was calibrated for adjusting shallow aquifer after-evetn discharge

The groundwater delay parameter as also tested although it had little effect on the model results.

Three methods were used for calibration. 1) Comparison between measured daily flows to estimated ones at both watershed outlets; 2) Comparison between cumulated flows; 3) Visual comparison was essential for calibrating the Alpha base flow.

Results for flow calibration:

All default curve numbers were eventually reduced by 20 points for all crops to better fit local French conditions. Curve numbers were not changed for roads and impermeable surfaces.

Calibration procedure is summarised in the table below.

Results and observations for modelled nitrogen exports

The 1999 version of the SWAT model was first used. In this version, leached nitrogen computed at the bottom of the soil profile is not routed to the shallow aquifer. As a consequence, N in the surface water leaving the watershed did not include computed leached N but only N included in surface runoff and lateral flow.

The 2000 version of SWAT was thus further used since it was reputed having corrected that problem. Yet, using the version directly downloadable through the SWAT Internet site, it is our understanding that leached nitrate computed at the bottom of the soil profile is still not tracked through the shallow aquifer to 38 date: 6December2001

be further delivered at the outlet. Instead, an optional value of initial nitrate concentration per HRU in the shallow aquifer has been added in the latest version. The nitrate concentration in the shallow aquifer is therefore unrelated to the nitrate loads computed at the bottom of the soil profile. It is our understanding that using the available versions of SWAT, there is little hope of linking the N cycle computed in the soil and nitrogen leaving at the outlet of the watershed.

It is our opinion that nitrates computed at the bottom of the soil profile should eventually be tracked and routed through the shallow aquifer. However, time required to make this modification in the source code was not taken for this study. Instead, a simpler modification in the source code was made. "Nitrate leached from the soil profile" (referred to as NO3L among the variable names) was added to "nitrate transported into the main channel in the groundwater loading form the HRU"

(referred to as N03GW). This modification is imperfect because there is not buffering that should occur in the shallow aquifer.

In summary, the available version allows the user to set an initial nitrate concentration into the aquifer. It is likely that good results on the nitrogen fluxes could be obtained if indeed, actual nitrate concentrations in a particular HRU were to be pretty much constant, and, if good flows were computed. However, computations done for the nitrogen cycle within the soil profile and at its bottom are unlinked or unrelated to nitrogen fluxes computed at the watershed outlets. For HRU delineation, we chose a multiple hydrologie response unit calculation with 1% land use over subbasin area, and 1% soil class over land use area. Duros (2001) built a script to affect each HRU to its spatial localisation, so that maps can be drawn (see Figure 27).

validation 3.3.3.1. ICECREAM

The modelled total load from the catchment area represents the potential total load to the river and it does not take into account any retention. Figure 30

BMP1-GLEAMS and SWAT on French watersheds

Two years of monitoring (1992)(1993)(1994)

5

A.

X X

Ay X ,X p' x • • • X • *'" ' ä.x » « A x X • • * • X , •' x • _. "à

Retention

Testing of the HARP Guideline #9

One of the targets of the MicroHARP project was, whenever appropriate, to quantify the retention processes of nutrients within the study catchments for testing the HARP Guideline #9. Ackording to the Guideline #9 (Reference number: 2000-12), retention of nitrogen and phosphorus is defined as permanent removal of phosphorus and nitrogen in the surface waters of the river systems. Parameters influencing nitrogen and phosphorus retention are, inter alia, renewal date : 6 December 2001 time in lakes, input of nitrogen and phosphorus to freshwater systems, trophic level, oxygen condition, volumes of lakes, temperature, nitrogen fixation, general water chemistry, water vegetation and human activity in the catchment. The Guideline #9 classifies the different methods for estimating retention into three categories: Models of nitrogen and phosphorus retention based on the mass balances of river Systems (including both rivers and lakes), cf. example in Annex 1 of the Guideline #9

Models of nitrogen and phosphorus retention based on mass balances of lakes and transformation of these findings related to the whole river system, cf. example in Annex 2 in the Guideline #9

In-situ measurements or other types of measurements that provide retention coefficients for nitrogen removal in streams and rivers.

In the MicroHARP catchments, only the first method for estimating retention has been tested, since there are no lakes in the catchments, and no In-situ measurements on retention coefficients were available. The method described in Annex 1 of the Guidelines (Nitrogen and phosphorus mass balance models for river systems, a German approach) is based on an analysis that has been carried out with data on the discharges/losses and riverine loads of nitrogen and phosphorus in 100 different rivers, located in different parts of Europe. River catchments smaller than 100 km 2 were not considered. The original reference given in the Guideline (Behrend & Opitz 1999. The fate of point and diffuse nutrient emissions into river systems -results of an input-output analysis. Hydrobiologia (in print)) is probably a draft title, as it was not found in a literature review. Instead, a paper by [START_REF] Behrend | Retention of nutrients in river systems: dependence on specific runoff and hydraulic load[END_REF] describes the method proposed by Guideline #9 in details. The problem is that the notation in the Guidelines and the original paper [START_REF] Behrend | Retention of nutrients in river systems: dependence on specific runoff and hydraulic load[END_REF] are somewhat different. After some comparison of the method descriptions it was found out that the retention of nutrients can be calculated ackording to the equations given by Guidelines #9 after some assumptions made on the units in the equations. The method used in the Guidelines is described below.

Calculation method

In the following, it is assumed that retention processes are the main reasons for the difference between the observed load (L) and total discarge/losses (D): R abs = retention expressed in absolute units (e.g. kg a-1) D N,P = sum of all losses/discharges within a river basin upstream from the river mouth monitoring station, and LN,P = N or P load at the river mouth monitoring station.

In order to eliminate the influence of catchment area when comparing river basins, it is essential to use normalized values for retention. [START_REF] Behrend | Retention of nutrients in river systems: dependence on specific runoff and hydraulic load[END_REF] define load weighed nutrient retention RL as Rabs divided by LN,P.

The model presented by the Guidelines for quantification retention requires the following parameters: a.

The catchment area (A in km 2 ); b.

The water-flow (Q in m 3 s -1); and c.

The area of surface waters within the river catchment (As in km 2 ).

The area of the surface waters in the catchment (AS) can be calculated from detailed statistics on land use or by using the surface area of the lakes and reservoirs (ALake), on the basis of land use maps (e.g. CORINE Land-cover) and the river surface according to the following equation: The second part of the sum is derived from the analysis of different river systems according to stream order (e.g. [START_REF] Billen | Global change in nutrient transfer from land to sea: biogeochemical processes in river systems[END_REF] and measurements in rivers of different size (cf. also Behrendt & Opitz 2000). The parameters in this equation should be developed specifically for the region/catchment under consideration. Behrendt & Opitz (2000) found that the specific nitrogen and phosphorus retention (load weighed retention RL) of river systems can be described by the following statistical model where x is the driving force and a and b are the coefficients of the model. The analysis was carried out based on data from 100 European catchments, the size of the catchments varying from 121 to 194 000 km2. The most important driving forces were the hydraulic load and/or specific runoff. Hydraulic load (HL) is defined as the annual runoff (Q) divided by the water surface area (As) of the river basin. Specific runoff (q, in ls-1 km-2) is defined as the runoff divided by the area of the river basin. The unit for HL is not mentioned in the Guideline, but in Savijoki Q in m3a-l and As in m2 were tested. The coefficients a and b are given in the Guidelines for both q and HL (HL3 in the Guidelines).

Results from Savijoki

First, the area of surface waters (As) was calculated for Savijoki. The total catchment area is 15.4 km2 and equation ( 2) then gives As = 0.026 km2 (ALAKE = 0). For Savijoki, HL was 218.68 ma-1 and q 11.7 ls-1 km-2 (mean for the period 1981-2000). The coefficients a and b were given in the Table 1 (Guideline #9) for totN and totP and for both HL and q. Thus, two estimates were calculated forRL:

For nitrogen: RL,N(q) =0.46

For phosphorus: RL,P(q) = 0.40 RL,N(HL) =0.10 and RL,P(HL) = 0.09 and Rabs for totN and totP can then be calculated by multiplying the measured load by RL,N and by RL,P, respectively. The estimates for RL,N and RL,P based on HL seem reasonable, suggesting that approximately ten per cent of the measured load is retained. The method based on q gives much higher values for specific retention. The reason for this is possibly that the specific runoff q does not directly take into account water residence time in the catchments. The hydraulic load HL takes into account the surface area of the channels and better describes the time-lag in runoff. However, since no comprehensive analysis on the variation of q and HL in Finnish catchment is available so far, no further conclusions on these properties can be drawn from the Savijoki results. Moreover, as there are no lakes in Savijoki and artificial drainage is widely used in the fields, the residence time in the catchment is short. As aresult, Savijoki is not a representative area for testing the proposed method.

Conclusion

Physically based models have been applied on schist soils having very shallow groundwater table. Those pedoclimatic conditions therefore seemed quite ideal to apply the models used. Physically based models are rather appealing because they simulate water fluxes as well as nutrient cycles and fluxes. In addition, available interfaces make their use easier than ever before. However, at one point or another in the models, routines are eventually based on empirical equations. In addition, many parameters are introduced as a result of modelling a large number of processes. Many not to say most of those parameters are nearly impossible to measure individually. For instance, the percentage of the root zone, which mineralises is practically impossible to measure on a field by field basis. Similarly, denitrification coefficients, curve numbers or initial groundwater levels on a field by field basis are very difficult to quantify. Daily variations of parameters such as groundwater levels cannot be verified on all fields either. date : 6 December 2001 Parameters used in physically based models are therefore calibrated by comparing measured crop exports, inorganic nitrogen pool in the soil or nutrient loads at the outlets of the watersheds. Those measured parameters are not input parameters in the models but are results of the models themselves.

An important calibration effort is therefore necessary as shown earlier in this report. Calibration time can be very long (at least one month for hydrology with the model used). Among that time, one should take into account the time necessary to familiarise oneself with the model and its subtleties. For instance, it was necessary to modify the source code for BMP 1-Gleams. Even while testing the model on small watersheds, it was necessary to make some modifications to account for environmental context different from the conditions for which the model was originally written.

It is our opinion that for physically based models, and more so for this type of model, the couple model/modeller is crucial. In other words, for this type of models, only very experienced personnel can effectively use the models. In addition, knowing the difficulties encountered while using and calibrating those models in relatively small watersheds (less than 3500 ha), we are doubtful on their applicability on large watersheds. Similarly, we have shown that those models do not manage to properly represent and simulate existing agricultural practices (see results on SWAT simulations). We are thus doubting the ability of these models, with the same expertise acquired in the course of this study, to satisfactorily predict water quality changes after improvements in agricultural practices.

In fact the choice of a particular model is derived from the objectives: for diagnostic purposes, models used in method 2 seem quite satisfactory. However, these models cannot be used to simulate nutrient fluxes at the outlet of the watersheds. It seems that this method works rather well because in the studied basins, there is a more or less constant correlation between water and nutrient fluxes. This implies that nutrient sources are nearly unlimiting. There is no evidence that the nutrient sources would still be unlimiting and the current relation between water and nutrient fluxes would still hold after changes in fertilisation practices for instance. Problems unresolved for method 2 can be summarised as such: nitrogen budget is open. Fate of the organic nitrogen pool, nor the amount of nitrogen volatilised are not accounted for in the current version.

In conclusion, it is our opinion that the choice of a model must be made according to the objectives, the means available to attain those objectives and the expertise available to do the job.

Figure

  Figure 1 : Location of the Savijoki watershed in south western Finland

Figure

  Figure 3 : climatic variable for the study watersheds

  for the Finnish watershed from 1961-1990 and for the French watersheds from 1978-2000.

  calculated regression models using only land use (share date : 6 December 2001 of agricultural land) to predict the TotP and TotN losses. A high positive correlation (r = 0.96) was found for the following equations: export [kgkm'V] FP = share of all agricultural land expressed as percentage of the total drainage basin area Since these equation are derived from data covering several years, they do not take into account the effect of variation in rainfall. To be able to estimate specific annual values, the regression model was modified by taking into account the impact of rainfall amounts by using a multiplication factor that relates the specific annual rainfall to long-term mean rainfall: annual TotP loss for the calendar year i, [kgkmV] N, = the annual TotN loss for the calendat year i, [kgkmV] f¡ = multiplication factor calculated as: precipitation for the year i [mm] Pn, = mean annual precipitation over a long period (preferably 30 years) [mm][START_REF] Ekholm | Relationship between catchment characteristics and nutrient concentrations in an agricultural river system[END_REF] developed a more complicated regresssion model to estimate P losses based on results obtained from one agriculturally watershed in southern Finland.

  of all agricultural land expressed as percentage of the total drainage basin area SI = mean slope of the fields [%area of the above watershed [km 2 ]

date : 6

 6 [START_REF] Arnold | Spatial Scale Variability in Model Development and Parameterization[END_REF]

Figure 4 :

 4 Figure 4 : regression model results for 1981-200 on Savijoki watershed

Figure 5 :

 5 Figure 5 : Measured and modelled annual total phosphorus load (TotP) in the Savijoki catchment. Model: EkhoJm et al. (2000).

Figure

  Figure 6 : field percentage on Cétrais subwatersheds

Figure 7 :

 7 Figure 7 : Neip measured (mean 1997-1998) as a function of field percent

Figure 8 :

 8 Figure 8 : estimated N loads compared to measured ones -regression models on Cétrais watershed

Figure 9 :Figure 10 :

 910 Figure 9 : estimated N loads compared to measured ones -regression models on Naizin watershed estimated losses (N in t) 120

Figure 11 :

 11 Figure 11 : Measured and modelled annual total nitrogen load (TotN) in the Savijoki watershed.Model: Nitrogen export coefficient model.

Figure 12 :

 12 Figure 12 : estimated N loads compared to measured ones -export coefficient model

Figure 13 :

 13 Figure 13 : Measured and modelled annual total phosphorus load (TotP) in the Savijoki watershed. Model: Johnes model.

Figure 14 :

 14 Figure 14 : Measured and modeUed annual total phosphorus load (TotP) in the Coët Dan watershed. Model: Johnes model

  Figure 16 : DEM definition

  Figure 17 : structural blocks

Figure

  Figure 20 : estimated and measured inorganic nitrogen in some fields on Cétrais and Coët Dan watersheds

Figure 22 :

 22 Figure 22 : estimated N loads compared to measured ones -mid-range model on Cétrais and Coët Dan watersheds

Figure 23 :

 23 Figure 23 : Agricultural crop distribution (% of field area) in the Savijoki watershed. Data from fanner interviews in 1987,1999 and 2000.

Figure 24 :

 24 Figure 24 : use of mid-range model with management practices introduced in SWAT

Figure 26 :Figure 29 :

 2629 Figure 26 : estimated and measures flows on Cétra is watershed cumulative flow (m 3 )

Figure 30 :

 30 Figure 30 : Modelled and measured TotP (a) and TotN (b) loads in the Savijoid watershed.Model: ICECREAM for cereals, other land-uses as described in text.

Figure 31 :

 31 Figure 31 : Comparison between daily simulated and measured cumulated Nitrogen fluxes (Mg)

Figure

  Figure 33 : yearly N loads from 1994-95 to 1999-2000 on Coët Dan watershed -model : BMP1-GLEAMS

Comparison of estimated and measured Phosphorus loads at the Coët Dan outlet

		1400.00
		1200.00 1000.00 -800.00		A / \
	*	600.00
		400.00
		200.00
		0.00
		1992	1993	1994	1995	1996 year 1997	1998	1999	2000	2001
				• kg/yr measured	-kg/yr estimated
		3.1.3. Goodness and fit
		3.1.3.1. Saviioki
	For two of the simple methods (N export
	coefficient model and Johnes model) model
	estimates were available only for the years
	1987, 1999 and 2000 due to lack of date. In
	order to be able to compare the goodness and
	fit for all the simple methods for the whole
	study period 1981-2000, the modelled load for
	1987 was used as an estimate for the years

Table 4

 4

	), nor on the Naizin (see
	Table3) watershed:
	. for P especially, scaled total error and scaled
	root mean square errors are extremely high
	regarding the mean estimated values,
	. for N, model efficiency is negative for any
	tested model (excepted Regression model with

On Cétrais watershed, the comparisons have been made with per hectare loads, to be able to compare subwatershed of different size. date : 6 December 2001

Table 2 : Goodness and fit of simple methods on Savijoki catchment. Loads per km2 of total area.

 2

	year
	1981,0
	1982,0
	1983,0
	1984,0
	1985.0
	1986,0
	1987,0
	1988,0
	1989,0
	1990.0
	1991,0
	1992.0
	1993,0
	1994.0
	1995.0
	1996,0
	1997,0
	1998.0
	1999.0
	2000.0
	mean
	number of

years : N Indexas Scaled total error Scaled root mean square error Sutton-Rathdiffe's coefficient Coefficient of Determination Coefficient of shape Maximum error Coefficient or residual mass observed loss

							Predieted loss : method 1
	N	744,9	TP	45,1	Regression model N TP 685.0	: ; 64,0 i	Regression model with rain ; N 732,3 TP ! 68,4;	Ekholm et al. | model ! TP j 99,1;	Export coefficient I ; mortal * niuum ; N : 838.6;
		823.0		48.5	685.0	64,0J	626.6	58.5!	73.4!	838.6!
		605,3		30,6	685,0	64,0j	624,0	53.3Í	47,6!	838,6!
	1308,3		111.8	685,0	64.0Í	827,0	77.3 [	127,6!	838.6!
		683.8		64,0	685.0	64.0Ì	578.1	54,0:	48.6;	838.6J
	1075.3		68,6	685.0	64.0!	727.4	68,0!	98.6!	836,6!
		682.8		66.6	685.0	64,0j	690,3	64.5J	71.1!	838,6!
		782,3		82.1	685,0	64.0 !	773.3	72,3!	86,4!	838.6!
		744.2		55.3	685,0	64.0Í	693,5	64.8Í	74.4Í	838.6Í
		762.5		51.8	685,0	64.0;	708,5	66,2j	76.3J	638.6!
		827,3		95,7	685.0	64.0J	710.6	66,4!	86,4!	838.6!
		648.7		55.9	685,0	64,0;	630,4	58,9;	75,1!	838.6;
		608,7		53,9	685.0	64,0 i	614,4	57,41	53.4!	838.6J
		676.3		56.8	68S.0	64.0Î	662,4	61.9!	66,0J	838.6J
	1207,5		54,2	685.0	64.0J	717.4	67,0;	85,2!	838.6!
		743,0		60.8	685.0	64.0;	611.7	57,2j	59.11	838,6;
		633.0		36,7	685,0	64.0!	653.8	61.13	59.8!	838.6!
		755.0		60.4	685.0	64,0:	691.6	64.6J	73.2!	836.6!
		951.0		42,0	665.0	64.0;	649,6	60,7;	75.1 !	923.2!
	1126,0		72.5	685,0	64.0J	776.0	72.5J	91.4J	950.5!
		819.4		60.7	685.0	64.0;	685.0	64.0-	76,4;	848,5;
		20.0		20.0
						:		:	:
		TE			02	0.2Í	0.2	0.2!	0.31	0,2!
		SRMSE		0,3	0.3I	0.3	0.3;	0.4!	02\
		ME			-0.4	o,oj	-0.1	0,3;	-0.4!	0.1;
		CO CS			22	31.4.= : :	1,8 10.4	7,8j 10.4!	0.6! 1.0]	23,3! 45.3Ì
		MaxE			623.3	47.8)	490,1	34,5!	54,0:	469,7!
		CRM			0,2	-0,11	0.2	-0.1!	-0,3!	o,o|

Table 3 : Goodness and fit on Naizin -loads per ha of total area

 3

	observed loss
							¡
				Regression model	j
		0	0
	year	N	TP	N		TP	i
	1993	60,7	0,5		11.8		1,3:
	1994	72,0	0,3		12.1		1.3!
	1995	39,6	0.9		12.1		1.3!
	1996	13.9	0,3		12.0		1.3j
	1997	38,5	0,2		12.0		1.3!
	1998	53,8	0,3		11.8		1.2!
	1999	59,4	0,5		21,5		2.4|
	2000	67.9			12.0		1.3Í
	mean	61.32	0,50		15.93		1.71J
	number of ye Indexes	8	7				; i
	Seated total error	TE		:V0.74	136.08!
	Scaled root mean square er SRMSE		<*68	2,22!
	Sutton-Rathdrffe's coeffic ME		-• •-:•	*98	•23,43!
	Coefficient of Determination CD			0.19	0,12!
	Coefficient of shape	CS		32,59	0,25!
	Maximum error	MaxE		59,83	70.67Ì
	Coefficient or residual mass CRM			0.74	-2,91!

Predicted loss : method 1

	Regression model with rain	i	Ekholm j model j	export coefficient ! ; model ;	Johnes model
		N	TP		TP	|	N	j	TP
		12.0	1.3			4,6;		59.7:	0,472
		17.1	1.8			6,o;		60.9;	0,468
		14.8	1.6			7.9Î		61.5!	0.459
		10.7	1,1			3.8!		64,4!	0,462
		10.7	1.1			1.7a		64.3!	0.464
		14,6	1,5			3.3!		59.9!	0.440
		11.9	1.4			6.0!		63.0!	0,457
						5.5!		65.2!	0.462
		15,88	1.70			5,90;	75,41j	0.46
						: :		t :
			113.18		126.53!		138,72
	:•	Ü.S9 4,06	2,05 •19,80		9,39f. •436,04-i	vim	0.42 0,11
		G>1	0,14			0.14J	J «3.26!	0,12
		72.08	0.78			0,01!	78540!	471,18
		54.83	70,14		65,95!	50.50-I	71,49
		0,77	-2,40		-12.46!	-O.23Í	-0.27

24

date : 6 December 2001

Table 4 :

 4 Goodness and fit on Cétrais -loads per ha of total area

	observed loss		Predicted loss : method 1
	Regression model	Regression model with rain
	PZ1-97
	PZ1-98
	PZ1-99
	PZ1-2000
	PZ3-97
	PZ3-98
	PZ3-99
	PZ3-2000
	PZ7-97
	Z7-98
	PZÑ-97
	PZN-96
	number of points : N
	ndexes
	Scaled total error	91.13	90.61	84,91	94,04
	Scaled root mean square error	30.14	34.08	82.83	15.64
	Sutton-Rathcliffe's coefficient	-1.97	•2.80	-21,42	0,20
	Coefficient of Determination	0,04;	0.03	0,01	17,00
	Coefficient of shape	5736,05	18,67	0.50	1085.34
	Maximum error	35.38	35,18	32,41	36.22
	Coefficient or residual mass	-2,87	-3.40	-9,09	0.03
	3.1.4. Time needed to perform simple models
	3.1.4.1. Savijoki watershed
	Most of the water quality and quantity data (in
	terms of measured concentrations and water
	flow at the catchment outlet) are stored in the
	databases of SYKE. The GIS-data for soils,
	catchment boundary, field parcels and land-use
	were also available at SYKE. Precipitation
	data was bought from the Finnish
	Meteorological Institute. For the MicroHARP
	project, the annual loads of TotN and TotP
	were calculated for the years 1998-2000. The
	loads for earlier years (1981-1997) were
	already available (Vuorenmaa et al. 2001).

Table 5

 5

		summarises time required to perform simple
		methods. Building the reference matrix for
		export coefficient model was for the longer
		operation.
		for simple methods
	Operation	Cétrais	Time (days)
	Basic date acquisition (DEM, rain, Corine Land
	cover maps)
	Command, reception and GIS integration
	Watershed delineation (field measurements and
	digitalisation)
	calculation of nutrient loads for years 1998-
	2000
	Regression models
	• Data acquisition from GIS
	• overlay of digital land-use and soil
	maps and digitised fields parcels by Arc
	View 3.2
	• Models programmation and run (Excel)
	• Goodnessand fit
	Expert coefficient models :
	• overlay of digital land-use and soil
	maps and digitised fields parcels by Arc
	View 3.2
	• soils maps : field measurements and
	digitalisation
	• field maps : digitalisation
	• crop maps : field data acquisition and
	digitalisation (per year)
	• interviews at Savijokd catchment
	• analysing interview data from 3 years
	• reference matrix for N (some for both
	watersheds)
	• review of relevant nutrient load from
	VIHTA
	• reference matrix for P (some for both
	watersheds)
	• data acquisition from GIS
	• running VEPS for Savijoki conditions
	• models programmation and run (excel)
	• goodness and fit
	Total for regression models
	Total for export coefficient models
	• l m watershed
	• each next watershed

Table 6 : goodness and fit for method 2 on Cétrais watershed observed loss observed NI TP Predicted loss : method 1 Rsgrosslon modol : Regression modoi wfth rain • TP TP Ekhokn ; model I TP j export coefficient model N Johnes model TP Method 2 NT and PB model PZ1-97 PZ1-98 PZ1-S9 PZ1-2000

 6

		21.9		10.5	1.1	10,2			26.0	0.25	22.6
		22,8		10.6	1.1	11.7	1.2!	2.2;	22.8	0.30	23.2
		26.6		10.6	1.1	13.6	1.4=	3.0Í	26.2:	0,27	25.8
	PZ3-97	36,5 20.3	1.1	JM.	1.1	13.3	1.3!		26,2J	0,27	34,8 "23Ï8
	PZ3-98	18.2									23,6
	PZ4-96 PZSHif PZ3-99 PZ3T2000	-""".. 18,7 23.1 Ji 5 .. 19,4					>	-i		26,4 3.2.8 "25,5 '"¿ays 2.7.0
	PZ5r98 PZ6-96 PZ7-S7 PZ7-98_ PZN'^97 PZN-98	_21i?.. 33.9 5¡6 Ï7,1" 11¡9 Td,8" 17,8								23.0 "34Ï4 34,5 "22Ï8 2.4.0 ••"8,6 15.2
	mean	22.7	0.1	10,7		11,9		2,9;	25.5!		25.0
	number of points :N	18
	Indexes Scaled total era Scaled root mean square error Sutton-RathclmVs coefficient	TE SRMSE ME	0.53 0,61 -2.66	91.13; 30.141 -W)	0,48 0.56 -2.08	: 90.61: 34.08!	84.91! 82.83: -21.42J	8; tí.33|	94.04 15.64 0^0	:.-.*&
	Coefficient of Determination		CO	! 0.38	0.04:	0^7	0.03;	0.01 j		17.00
	Coefficient of shape		CS	109,26	5736.05)	2467	18.67!	0.50Í	5.65Í	1085.34
	Maximum error		MaxE	25,84	35,38!	23,47	35,18:	32.41 i	13.34:	36,22	12,04
	Coefficient or residual mass		CRM	0,53	-2,87;	0.48	-3.40!	-9,09;	\	0.03

Table 7 : goodness and fit observed loss

 7

	0	0
	N	TP
	BO,/	U,ö
	72,0	0,3
	39,6	0,9
	13.9	0,3
	38,5	0.2
	53.8	0.3
	59.4	0.5
	67,9
	61,32	U.50
	e	i
	Indexes
	Scaled total error	TE
	Scaled root mean square er SRMSE
	Sutton-RathclmVs cocffic ME
	Coefficient of Determination CO
	Coefficient of shape	CS
	Maximum error	MaxE
	Coefficient or residual mass CRM

on Coët Dan watershed (per ha of total area) Predicted loss : method 1

	Regression model	! Regression model with rain j • :	Ekholm ! model |	export coefficient ; j model !
	N	TP	j	N	TP	j	TP	!	N	j
	11,8	1,3:	12.U	1,3:	4,6;	öa,/:
	12.1	1.3;	17.1	1.8!	6.0:	60.9Î
	12.1	1.3!	14.8	1.6Ì	7.9=	61,5!
	12.0	1.3Ì		10,7	i.ii		3.8;	64,4!
	12,0	I.3;		10,7	1.1j	1.7!	64,3!
	11,8 21.5	U! 2.4]		14.6 11.9	1.5; 1,4;	3,3; 6,0;	59.9: 63,0|
	12,0	1.3;					5.5j	65,2;
		i./i;		lb.ttti			t).W:	'0,41 j
					•		:	:
	0.74	136.08Ì	0.61	113.18J	126.53J	A30J
	0.68	2,22!		0.59	2.05Ì	9.39Í	. 0,36!
	-2.W	-23.43;	,2,0«	-19.80;	-«36,04;	<0,t2;
	0,19	0,12!		0,21	0.14Ì		0,14;	«3.26Ì
	32,59	0.25J	72,08	0.78J	0.01	-	*8.4Oj
	59,83	70.67;		54.83	70,14!	65,95		50.50!
	0,74	-2,911	Q.77	-2.40!		-12.46		••.-• -0Í3J

Table 8 : time needed for mid-range models

 8

			Operation	Time Cétrais	(days) CoëtDan	Who can do it
	Basic data
	Watershed delineation
	Parameters :
	•	soil map	40	25	Student
	•	fields	map	10	5	Student
	•	crop map (per year -at least 3 years)	8	5	Student
	•	fertilisation practices (per year -at least
		3 years)		40	35	Student
	• •	on Naizin, interpolation between surveys local references for the equation	10 15	30 10 15	Permanent Permanent Permanent
	•	coherence verification between all the
		data
	Model understanding	1
	1 simulation		20 minutes	20 minutes
	Validation (usually dead-line for the report)	5	5	permanent
						33
	date : 6 December 2001

Table 9 : Swat calibration for the Cétrais watershed.

 9

		mom	default values	10	points	20	points	20	points	20	points
				dropped	dropped		dropped		dropped
	Brapa baso (low		0.03		0.03		0.1		0.18		0.038
	^PpdWaler delay i		1		1		0.05		0.05		1
	R*J?7		0.65		0.73		0.74		0.8		0.65
	R 2 98		0.36		0.45		0.67		0.67		0.49
			0.63		0.63		0.63		0.7		0.72
	mm&^ . -•-	54902 m 3	0.44 99407		0.56 87601	0.65 78272	0.67 72040		0.54 67695

  Figure 32 : nitrate leached under fields -model BMP1-GLEAMS

	no3l
	O
	7.243 -46.03
	46.03 -88.051
	86.051 -137.871
	137.871-300
	>300
			Cemagref
	500	500 Meters	GIS Lab
			edition 29/11/01
	44		date : 6 December 2001

• • --' * x A Regression model Regression model with rain e»port coefficient model xB^P1-Gleams • NT and PB model measured loss (kg N per ha) 15 25 35 45 55 65 75 Figure 34 : measured and estimated N loads all models on Cétrais and Coët Dan watershed 45

		3.3.4. goodness and fit
		observed loss
		0	0
	year	N	TP
	isy'j	tSU./
	1994	72.0
	1995	39.6
	1996	13.9
	1997	38,5
	1998	53.8
	1999	59,4
	2000	67.9
	mean
	rumbero! ye
	date: 6 December 2001

bl.J¿ 8 Indexes Scaled total error TE Scaled root mean square er SRMSE Sutton-Rathclrrre's coefflc ME Coefficient of Determination CD Coefficient of shape CS Maximum error MaxE Coefficient or residual mass CRM

	Regression model	•
	N	TP	:
		11.»	1.3:
	0.3	12.1	1,3!
	0.9	12,1	1.3J
	0,3	12.0	1.3]
	02	12.0	1.3]
	0,3	11.8	1.2:
	0.5	21.5	2.4
		12.0
	J.bU
	1
		0.74
		0.63
		•2,9*
		0.19
	32,59
	59,83
		0,74

] 1.3: l.M; 136.06 ! Ì22Ì -23,43: 0.12] 0,25: 70.67Ì -2,91] Predicted loss : method 1 Regression model MWI rain]

				Sheen ] model ]	export : coefficient ; model ;
	N	TP	:	TP	]	N	:
	12,0		1,3;		4,b:	by,/:
	17.1		1,8;		6,0i	60,9:
	14,8		1.6!		7.9:	6I.5]
	10,7		1,1]		3.8]	64.4]
	10,7		1.1Ì		1,7:	64,3]
	14.6		1,51		3.3Ì	59,9
	11.9		1.4;		6,0]
					5,5]
	lS.Uti		l.'u:		o,au;
	0.61	113.18:	126.53!
	0,59		2.05Ì		9,39]
	; -XJtß	-19.W:	-436,04]
	0,21		0,14]		0.14]
	72,08		0.78:		0,01:
	54.83	70.14!	65.95]
	0,77	-2,40]	-12,46]

: 63.0; 65.2] /0.41] 0.30! 0,36] •«.12; 833: 78.40] 50.50: -0^3]

		Method 2		Method 3
	Johnes	NT and PB	BMP1-;
	modol	model	Gleams ]
	TP	N	N	:
	U.4/¡¿
	0.468
	0.459			36,39]
	0.462			21.51]
	0.464	56,2	45,49!
	0.440	74,4		51.28!
	0.457	74.4		52,94]
	0.462
	U,4b	B¿,b/	bJ.b/;
	138.72	0,13		0.07!
	0.42 0,11	aia 0.72
	0.12	9,34
	471.18	11.59
	71,49	20.67
	-0.27	0,49

. time needed to perform physically based models

	3.3.5time needed for physically based models
			Operation	Time GLEAMS	(days) SWAT	Who can do it
	Basic data
	Watershed delineation
	Parameters :
	•	soil map	40	40	Student
	•	fields	map	10	10	Student
	•	crop map (per year -at least 3 years)	8	8	Student
	•	fertilisation practices (per year -at least
		3 years)		40	40	Student
	• •	on Naizin, interpolation between coherence verification between all the surveys data	5 15	15 5 15	Permanent Permanent
	•	building a database for all the data
	•	building SWAT input files
	Model understanding	10	30
	Calibration :					SWAT
		• soil data measurements	2 years	2 years		N
	• water measurements • calibration for water fit 1 simulation	3 years 10 10 hours	3 years 10 2 hours		64.85 88.75 47.15 81.31 62.37
	Validation (usually dead-line for the report) goodness and fit	5	10	permanent	nays
							045
						•••'••'	0,07]	0.43
							<M>5]	•0,59
							1,32]	:	2.53
							3,83:	2^7
							7.59;	49,16
							0,49;	O.15

date : 6 December 2001

date: 6 December 2001

date: 6December2001

date : 6 December 2001

date : 6 December 2001

date : 6 December 2001

date : 6 December 2001

date : 6 December 2001

date: 6 December 2001