Evaluating Watershed Response Using WEPPcloud—EU for Wildfire Burned Areas in Portugal

Abstract

WEPPcloud—European Union (EU) is an online interface of the Water Erosion Prediction Project (WEPP) model designed to predict streamflow and sediments in European forested watersheds. Although designed to be run in any location in Europe, its performance has not yet been evaluated against observed data. This study evaluates WEPPcloud—EU's performance in simulating streamflow and suspended sediment yield for two burned catchments, Ermida and Serra de Cima, in continental Portugal during the first year after the fire. The default parameters significantly underestimated streamflow and suspended sediment yield at the outlet of both catchments, suggesting the need for catchment-specific parameterization. Selected model input parameters (bulk density, sand, clay, organic matter and rock content) were changed based on existing information on local conditions at the beginning of an autocalibration procedure. This combined re-parameterization and autocalibration procedure (based on saturated hydraulic conductivity, anisotropy and baseflow coefficient) improved the performance of WEPPcloud—EU model, yielding satisfactory results for both streamflow and suspended sediment yield for both catchments. The autocalibration improved model accuracy, with predicted average streamflow from the default 3.3 to 6.3 mm day⁻¹ (observed 6.9 mm day⁻¹) and suspended sediment yield from 0.5 to 8.2 kg ha⁻¹ day⁻¹ (observed 11.7 kg ha⁻¹ day⁻¹) in the Ermida catchment. In Serra de Cima, predicted average streamflow improved from 1.8 to 1.9 mm day⁻¹ (observed 1.8 mm day⁻¹) and suspended sediment yield from 1.4 to 35.4 kg ha⁻¹ day⁻¹ (observed 28.5 kg ha⁻¹ day⁻¹). This study demonstrated WEPPcloud—EU potential as a decision-support tool for post-fire emergency stabilization in catchments in Europe. Future studies in different eco-regions in Europe are needed for a broader assessment of the model's potential, including for emergency stabilization scenarios and with an emphasis on the importance of existing local information for achieving satisfactory predictions.