Development of an integrated modeling approach for identifying multilevel non-point-source priority management areas at the watershed scale

Abstract

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. Key Points Multilevel PMAs are proposed in the case of natural randomness The river migration process can affect the distribution of PMAs globally The impacts of climate forces are characterized by the multilevel PMAs © 2014. American Geophysical Union. All Rights Reserved.