Abstract

For lowering sediment, nitrogen (N) and phosphorus (P) pollution of surface water bodies at the catchment scale, environmental legislation require programs of pollution abatement measures. To be able to ensure the cost-effectiveness of such programs we first need to identify high risk areas, which give rise to increased pollutant runoff. Process-based GIS models provide the opportunity to identify such critical areas and hence better target diffuse pollution abatement actions. However, these models are data intensive and their spatially-distributed parameterization in poorly monitored catchments is not feasible without extensive input data pre-processing and significant simplifying assumptions. This study implements the widely-used SWAT river basin model (Soil Water Assessment Tool) to study a medium-sized Greek catchment with the typical data limitations met at the national level, in order to identify critical diffuse pollution source areas that may serve as the key areas for meeting the objective of ‘good ecological status’ of water bodies set by the European Water Framework Directive (WFD). Model parameterization and evaluation are presented along with the decisions made to overcome problems related to data representation in the catchment, in an effort to provide guidance on SWAT modeling in areas with similar characteristics. The results show that sediments and nutrients could be adequately reproduced in large time steps (monthly or seasonal) and that even with the current data limitations, the seasonal variation and the most critical areas of pollutant losses to waters could be adequately identified. The study proposes a transparent modeling approach under data limitations without neglecting possible deficiencies; however, it maintains that the SWAT model, if appropriately parameterized with respect to the land-use and soil differentiation within a limited-gauged catchment, can still facilitate the selection and placement of suitable practices across the landscape for a cost-effective diffused pollution management.

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