Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework
Chesapeake Research Consortium1, United States Environmental Protection Agency2, United States Geological Survey3, Pennsylvania State University4, University of Maryland Center for Environmental Science5, Silver Spring Networks6, Johns Hopkins University7, Virginia Tech8, Research Triangle Park9, Swarthmore College10
30 Sep 2021-Environmental Modelling and Software (RTI International. P.O. Box 12194, Research Triangle Park, NC 27709-2194. Tel: 919-541-6000; e-mail: publications@rit.org; Web site: http://www.rti.org)-Vol. 144, pp 105141
TL;DR: This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States' largest estuary and reflects the framework's current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.
Abstract: 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 pollutant load control for the United States' largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >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 tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.
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01 Jul 2015
TL;DR: The software, interfaces, and architecture of WRESTore are described, scenarios for implementing the WRESTORE tool in a watershed community's planning process are provided, and considerations for future developments are discussed.
Abstract: WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation-optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments. A participatory design tool that uses interactive and human-guided approaches to simulation-optimization.Tool can be used to engage stakeholders in assist with planning of conservation practices.Users can be engaged to view and evaluate designs based on quantifiable and un-quantifiable criteria.The software is web-based and can be used for engagement with individual users or multiple users.
5 citations
TL;DR: In this article , the authors integrated the entropy weight method with the calibrated Soil and Water Assessment Tool (SWAT) model to comprehensively evaluate and screen the efficacy and cost-effectiveness of six selected BMPs.
5 citations
18 Jul 2022
TL;DR: This paper introduces the first implementation of a hybrid and customized evolutionary multi-objective (EMO) algorithm to improve the Chesapeake Bay Watershed's (CBW) water quality.
Abstract: The careful selection of Best Management Practices (BMPs) to reduce loading, such as nitrogen, phosphorus, and sediments, can substantially improve the water quality of water-sheds. This paper introduces the first implementation of a hybrid and customized evolutionary multi-objective (EMO) algorithm to improve the Chesapeake Bay Watershed's (CBW) water quality. To make the algorithm scalable, we inject a few solutions obtained using an integer programming algorithm (IPOPT) in the initial population of EMO. Also, a repair operator is applied to satisfy every equality constraint. Combining these approaches can find a set of non-dominated trade-off solutions from 1,012 variables (Tucker county in West Virginia) to a staggering 153,818 variable problem (the whole state of West Virginia). Furthermore, a pre-liminary analysis of obtained trade-off solutions finds interesting properties of BMP allocations, providing an optimistic picture of applying the proposed customized optimization algorithm in addressing other bigger states leading to the whole Chesapeake Bay watershed.
2 citations
TL;DR: In this article , the problem of selecting a subset of practices that minimizes pollutant load subject to budgetary and environmental constraints is formulated as a continuous optimization model with a linear multiplicative objective function and linear constraints.
1 citations
31 May 2023
TL;DR: In this paper , the authors used the Dynamic World near real-time global LULC dataset to compare how geospatial environmental models of water quality and hydrology respond to growing vs. non-growing season LULC for temperate watersheds of the eastern United States.
Abstract: Abstract Most readily available landuse/landcover (LULC) data are developed using growing season remote sensing images often at annual time steps. We used the Dynamic World near real-time global LULC dataset to compare how geospatial environmental models of water quality and hydrology respond to growing vs. non-growing season LULC for temperate watersheds of the eastern United States. Non-growing season LULC had more built area and less tree cover than growing season data due to seasonal impacts on classifications rather than actual LULC changes (e.g., quick construction or succession). In mixed-LULC watersheds, seasonal LULC classification inconsistencies could lead to differences in model outputs depending on the LULC season used, such as an increase in watershed nitrogen yields simulated by the Soil and Water Assessment Tool. Within reason, using separate calibration for each season may compensate for these inconsistencies, but lead to different model parameter optimizations. Our findings provide guidelines on the use of near real-time and high temporal resolution LULC in geospatial models.
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TL;DR: In this article, an integrated synthesis with timelines and evaluations of ecological responses to eutrophi- cation in Chesapeake Bay, the largest estuary in the USA, are provided.
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