means and were frequently greater than the means, particularly in the sandy soil. The stochastic simulation results offered the possibility of ranking cropping systems into classes of probability of exceeding a given value of leaching
and the possibility of deriving suggestions for improved crop management. © 2000 Elsevier Science B.V. All rights reserved.
Keywords
:
Nitrate leaching; Stochastic model; Cropping systems; Breakthrough probability
1. Introduction
Several methods for assessing the risk of non- point-contamination of surface water and ground-
water resources have been proposed. These differ in scale and complexity.
Simpler methods are based on indices. Such methods require a small number of input parame-
ters and describe the system combining such in- dices in a final assessment of pollution risk or
groundwater vulnerability. Some indices have been developed specifically for nitrogen pollution
risk Giardini and Giupponi, 1993; Bockstaller et al., 1997 and are also used as a technical support
to apply regional policy rules Regione Emilia- Romagna, 1993.
A more fundamental approach is based on the application of water and nutrient balance calcula-
tions, coupled with solute transport models, at different spatial and temporal scales Donatelli et
al., 1994; Corwin and Wagenet, 1996; Donatelli et al., 1999. Input parameters are more detailed and
difficult to measure, but the results are numerical estimates, which quantify the polluting mass flow.
It is now widely understood that the use of numerical models offer some very important and
well-known advantages an increase in the level of understanding of the physical processes, an in-
crease in the possibility of synthesizing the current level of knowledge, etc., however the uncertainty
of the final results must be carefully considered. Models are imperfect tools because of errors due
to the simplification of physical processes, errors in parameter values, and numerical errors. More-
over, most models fail to account for naturally existing heterogeneity Wu et al., 1997 and for
the implication of both spatial and temporal variability.
Nevertheless, farmers or environmentalists can- not wait for the setting up of perfect models
Stockdale et al., 1997 and models are often proposed as important tools for forecasting infor-
mation in decision-making for environmental management Hoogenboom et al., 1994; Ceccon
et al., 1995; Jame and Cutforth, 1996; Girondel and Arondel, 1997. Some information on spatial
and temporal variability associated to model re- sults is therefore strongly needed Loague and
Corwin, 1996.
Overall variability is generated by: a soil spa- tial variability in particular for hydrological
parameters and the type and quantity of organic matter; b climatic temporal variability over the
years evapotranspiration vs. precipitation bal- ance is often considered as the driving variable;
and c anthropic management through different cropping systems crop type, fertilization, irriga-
tion, soil tillage, etc.. Several works have been carried out at a regional or basin scale that take
the spatial variability between soils into account Connolly et al., 1997; Richter et al., 1998, but
not that within a soil, which might be very impor- tant Warrick, 1990; Schulz and Huwe, 1997.
The scale at which these models are used has changed greatly over the last 40 years Addiscott
and Tuck, 1996. Greater effort has been made more recently, in order to enhance modelling,
towards the so-called ‘upscaling’, both in time and in space, and produce instruments that are useful
to assess the risk of non-point pollution from agricultural systems Wu et al., 1997.
The present study is a methodology proposal. A stochastic-mechanistic approach to predict the
N-NO
3
leaching probability, for pedological ‘ho- mogenous’ areas, has been set up and applied. As
indicated by Addiscott et al. 1991 and Heath- waite et al. 1993, nitrogen leaching loss is chosen
as a suitable indicator for non-point source pollu- tion risk assessment. Temporal and spatial scale
variability is incorporated into a classical deter- ministic model, through the repeated use of such a
model, as proposed by Go¨rres and Gold 1996.
The model that was chosen is LEACHN Hutson and Wagenet, 1992. The main soil parameters
hydraulic conductivity, retention curve and me- teorological data are considered as stochastic vari-
ables. The scaling factor approach Miller and Miller, 1956 has been used to insert the stochastic
variability of soil hydrological parameters into the model. This approach allows one to include the
hydrological variability in a model through only one parameter, the scaling factor. The scaling
theory has been extensively used since long Sim- mons et al., 1979; Russo and Bresler, 1980; Rao et
al., 1983; Warrick 1990: several works used the scaling factor for modelling Peck et al., 1977;
Warrick and Amoozegar-Fard, 1979; Hopmans and Stricker, 1989 or to insert stochastic compo-
nents into deterministic models Boulier and Vau- clin, 1986; Vachaud et al., 1988; Braud et al.,
1995.
The objective of this paper was to calculate and to discuss the predicted probability to exceed a
given level of NO
3
leaching loss for several crop- ping systems.
2. Methods
