Addressing the spatial disconnect between national-scale total maximum daily loads and localized land management...

Area of Science:

• Environmental science
• Water resource management
• Ecological modeling

Background:

• Regulatory programs for watershed mitigation often rely on widespread adoption of best management practices (BMPs) to meet Total Maximum Daily Load (TMDL) goals.
• The Chesapeake Bay Total Maximum Daily Load (TMDL) requires jurisdictions to develop Watershed Implementation Plans (WIPs) for BMP implementation.
• Current bay-level models lack the spatial resolution to account for local conditions, potentially impacting BMP effectiveness and cost-efficiency.

Purpose of the Study:

• To evaluate if fine-scale spatial heterogeneity in BMP placement can achieve equivalent or superior nutrient and sediment reduction compared to state-level WIP recommendations at a lower cost.
• To test the efficacy of a modified Soil and Water Assessment Tool (SWAT), Topo-SWAT, for simulating BMP scenarios at a finer resolution.

Main Methods:

• Utilized Topo-SWAT, a modified version of the Soil and Water Assessment Tool (SWAT), to simulate BMP adoption scenarios in the Spring Creek watershed.
• Initialized Topo-SWAT with detailed land use and management data, followed by systematic calibration and validation against 12 years of observed data.
• Determined individual BMP cost-effectiveness and ranked them to design a cost-effective scenario.

Main Results:

• A cost-effective BMP adoption scenario achieved equal or greater load reduction than the WIP scenario at 74% of the cost.
• The optimized scenario utilized eight management-based BMPs, including no-till, manure injection, cover cropping, riparian buffers, land retirement, manure application timing, wetland restoration, and nitrogen management (15% less N input).

Conclusions:

• Leveraging fine-scale watershed models can significantly improve the cost-effectiveness of BMP implementation for meeting TMDL goals.
• The study demonstrates the potential for watershed models with finer inference scales to optimize BMP recommendations under regulatory frameworks like the Chesapeake Bay TMDL.