The identification of priority management areas (PMAs) at the large-basin scale is notably complex because of the random nature of watershed processes, which complicates the application of traditional deterministic PMAs. In this study, a multilevel PMA (ML-PMA) framework is designed as a new tool to pinpoint these sensitive areas, within a basin, that contribute the most to water quality deterioration. The main advantage of the ML-PMA framework is the wide availability of its supplementary tools and its complete framework, which integrates both watershed and river processes in addressing PMAs at the watershed scale. The watershed model, stream model, and a Markov chain approach are integrated to depict the dynamics of watershed processes and various water quality statutes. Based on the results of this study, the river migration process is vital for water quality degradation in the river network and significantly influenced the final PMA map. In addition, the proposed ML-PMA framework considers the impact of climatic conditions and hydrological properties and allows for a more cost-effective allocation of PMAs among different years. In the authors' view, the connectivity of PMAs in terms of flux distribution and propagation downstream on which the ML-PMA is based makes the ML-PMA framework particularly interesting for watershed non-point-source pollution control.

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The assessment of peak flow rate, total runoff volume, and pollutant loads during rainfall process are very important for the watershed management and the ecological restoration of aquatic environment. Real-time measurements of rainfall-runoff and pollutant loads are always the most reliable approach but are difficult to carry out at all desired location in the watersheds considering the large consumption of material and financial resources. An integrated environmental modeling approach for the estimation of flash streamflow that combines the various hydrological and quality processes during rainstorms within the agricultural watersheds is essential to develop targeted management strategies for the endangered drinking water. This study applied the Hydrological Simulation Program-Fortran (HSPF) to simulate the spatial and temporal variation in hydrological processes and pollutant transport processes during rainstorm events in the Miyun Reservoir watershed, a drinking water resource area in Beijing. The model performance indicators ensured the acceptable applicability of the HSPF model to simulate flow and pollutant loads in the studied watershed and to establish a relationship between land use and the parameter values. The proportion of soil and land use was then identified as the influencing factors of the pollution intensities. The results indicated that the flush concentrations were much higher than those observed during normal flow periods and considerably exceeded the limits of Class III Environmental Quality Standards for Surface Water (GB3838-2002) for the secondary protection zones of the drinking water resource in China. Agricultural land and leached cinnamon soils were identified as the key sources of sediment, nutrients, and fecal coliforms. Precipitation volume was identified as a driving factor that determined the amount of runoff and pollutant loads during rainfall processes. These results are useful to improve the streamflow predictions, provide useful information for the identification of highly polluted areas, and aid the development of integrated watershed management system in the drinking water resource area.

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