A comparative evaluation of the continuous and event-based modelling approaches for identifying critical source areas for sediment and phosphorus losses.

Proper identification of critical source areas (CSAs) is important for economic viability of any best management practices (BMPs) aimed at reducing sediment and phosphorus loads to receiving water bodies. Both continuous and event-based hydrologic and water quality models are widely used to identify and assess CSAs, however, their comparative assessment is lacking. In this study, we have used continuous Soil and Water Assessment Tool (SWAT) and event-based Agriculture Non-Point Source (AGNPS) pollution models to identify CSAs for sediment and phosphorus in a watershed in Ontario, Canada. Along with their original version, both models were re-conceptualized to incorporate saturation excess mechanism of runoff generation, which is also refereed as variable source area (VSA) integration. The models were set-up using high resolution spatial, crop- and land-management, and meteorological dataset; and calibrated with reasonable accuracy against streamflow, sediment and phosphorus concentration data at multiple locations. Threshold value (t-value) approach was used to identify CSA areas in the watershed. Results showed that both models were in agreement (up to 96% of fields) that summer season did not constitute hot-moments (<6% of the watershed area as CSAs) for both sediment and phosphorus. SWAT models identified winter (50% of watershed area as CSA) and AGNPS models identified early spring (50% of watershed areas as CSAs) season as the hot-moment for both sediment and phosphorus. Contrasting result, as indicated by low (1%) matching in field CSA potential, was observed in autumn season. In the same season, VSA integrated SWAT and AGNPS models showed better matching (43% for sediment and 31% for phosphorus), highlighting the importance of VSA integration in the models. Qualitative validation of model-based CSA potential with oblique aerial-photograph-based CSA potential in two soil moisture conditions (wetter and drier) indicated slightly better performance of the SWAT models, and over-prediction of the AGNPS models. However, a more comprehensive analysis based on more detailed field observations is needed to further confirm the results.