Gary W. Shenk Gopal Bhatt Kevin Asplen Olivia H. Devereux Jessica Rigelman J. Hugh Ellis Benjamin F Hobbs Darrell J Bosch George L Van Houtven Arthur E McGarity Lewis C. Linker William P. Ball Daniel E Kaufman 2021 <div id="abs0010" class="abstract author" lang="en"><div id="abssec0010"><p id="abspara0010"><span>Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program's (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of&nbsp;pollutant load&nbsp;control for the United States' largest&nbsp;</span>estuary<span>. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of &gt;30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework's current utility for investigating cost-effective&nbsp;tradeoffs&nbsp;and its usefulness as a foundation for future analysis of restoration strategies.</span></p></div></div> application/pdf 10.1016/j.envsoft.2021.105141 en Elsevier Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework article