Source appointment at large-scale and ungauged catchment using physically-based model and dynamic export coefficient

Data scarcity has caused enormous problems in non-point pollution predictions and the related source appor-tionment. In this study, a new framework was developed to undertake the source apportionment at a large-scale and ungauged catchment, by integrating the physically-based model and a surrogate model. The improvements were made, in terms of the application of a physically-based model in an ungauged area for the transfer process and the parametric transplantation process. The new framework was then tested in the Chaohu Lake basin, China. The result suggested that there has been a good match between simulated and observed data. Although the planting industry was the largest emission source with 48.16% of nitrogen (N), itonly contributed 12.61% of N flux to the Chaohu Lake. The ungauged catchments surrounding the Chaohu Lake were identified as non -negligible sources with 8.46% of phosphorus (P) contribution. The rainfall conditions could have great im-pacts on source apportionment results; e.g., the planting industry contributed from 68.17t of P in dry year to 436.02t in wet year. The new framework could be extended to other large-scale watersheds for source appor-tionment with data limitations.

Automated summary

A new framework was developed in this study to undertake source apportionment at a large-scale and ungauged catchment by integrating physically-based and surrogate models. The framework was tested in the Chaohu Lake basin, China, and achieved good matching between simulated and observed data. The study identified the significant contributions of the planting industry and ungauged catchments to pollution flux, and highlighted the influence of rainfall conditions on source apportionment results.