Various scales of input data exist to parameterise diffuse pollution models for the UK. For screening methodologies such as the phosphorus indicators tool—PIT [Heathwaite, A.L., Sharpley, A.N., Bechmann, M., 2003a. The conceptual basis for a decision support framework to assess the risk of phosphorus loss at the field scale across Europe. Journal of Plant Nutrition and Soil Science 166, 1–12; Heathwaite, A.L., Burke, S., Quinn, P.F., 2003b. The nutrient export risk matrix (the NERM) for strategic application of biosolids to agricultural land. International Association for Hydrological Sciences Publication 285, 1–9], which is applied throughout England and Wales, some assessment of the implications of using input data derived at different scales must be made. This work is further driven by practical issues such as licensing costs and data availability, which mean that not all data are readily accessible for all end users. This paper represents a first step towards quantifying the ‘value-added’ to model predictions by using input data derived at three different scales: 50×50 m, 1×1 km and 5×5 km. Model runs using PIT were carried out against observed phosphorus water quality data from the River Start and River Gara, which are the main sub-catchments of Slapton Ley, a grade 1 National Nature Reserve in southwest England. Model runs for the main 46 km2 Slapton catchment were also undertaken. The results show that some improvement in the ability of the model to capture the observed water quality behaviour may be made by using higher resolution DEM data, though these improvements may be outweighed by the extra data processing and computational time. Conversely, model runs driven by the 5 km data demonstrate consistent under-prediction for all three test catchments, which is perhaps not surprising given the greater degree of averaging underlying datasets at this scale. Results from the 1 km datasets provide the best agreement with observed water quality data, and appear to provide the ‘best available’ means of parameterising PIT at the national scale.