Abstract

Best management practices (BMPs) for controlling non-point sources (NPS) nutrient loss at the watershed scale has become a global hotspot due to the severe eutrophication worldwide. Identifying priority management areas (PMAs) is particularly important for designing BMPs. However, such an endeavor is extremely challenging due to random spatio-temporal processes, particularly in lowland. In this study, a new model-based method was proposed to identify sensitive areas with the largest contribution to lake eutrophication. This method integrated three indices derived from three models to assess the risk due to nutrient dynamics in loss processes including sources, sinks and transformation of nutrients within the polders (lowland artificial watersheds) around Lake Gehu, eastern China and nutrient transport in the river and lake areas. A total of 67 polders in a 2 km wide buffer of Lake Gehu, as assessment units, were identified into four-level PMAs based on the integration. Results showed that 77% polders were Level II PMAs, and none were Level IV PMAs. The ratios of Level I and Level III PMAs was 18% and 5%, respectively. These values indicated that nutrient transport in rivers or lakes is vital for lake eutrophication and significantly influences the PMA map. This study demonstrated that the process-based model for risk assessment in identifying PMAs is useful in guiding decision-making for controlling lake eutrophication.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.