Comparative study of model prediction of diffuse nutrient losses in response to changes in agricultural practices

Abstract

This article presents a comparative study of modelled changes in nutrient losses from two European catchments caused by modifications in agricultural practices. The purpose was not to compare the actual models used, but rather to assess the uncertainties a manager may be faced with after receiving decision support from consultants using different models. Seven modelling teams were given the same data about two catchments and their management characteristics and were asked to model the same changes in management practices using the model of their own choice. This can potentially cause accumulated 'errors' due to differences in the modelling teams' interpretation of relevant processes and definitions of boundary conditions (inputs). The study was carried out within the framework of the EUROHARP project, which aimed at harmonising procedures for quantifying diffuse losses of nitrogen and phosphorus from agriculture. Models are important for assessing river basin management plans (RBMPs) as required e.g. under the EC Water Framework Directive and Action Plans under the EC Nitrates Directive. This article illustrates some challenges with respect to interpreting such modelling results. The selected management scenarios include changes in fertiliser application levels, changes in livestock numbers and changes in land-use and crop rotation systems. Seven models were applied for the same scenarios in the Enza catchment in Italy and the Zelivka catchment in the Czech Republic. All models had been calibrated and validated with respect to historical data of climatic conditions, water quality and discharge measurements. The modelling results reveal a variation in predicted effects of the management scenarios, causing different conclusions with respect to choice of best management practice for reducing nutrient losses. The study demonstrates that it is important that care is taken by modellers and involved decision makers throughout the entire modelling process, both with regard to a common understanding of the problem definition, understanding of boundary conditions, and uncertainty of outputs and interpretation of results.