Keywords
integrated environmental modelling, exploratory modelling, agricultural actors, water futures
Start Date
15-9-2020 8:00 AM
End Date
15-9-2020 8:20 AM
Abstract
Optimization of watershed best management practice (BMP) scenarios based on watershed modeling and intelligent optimization algorithms provides an effective way for watershed management to achieving optimal environmental and economic effectiveness. Existing studies commonly conduct the BMP scenario optimization by allocating diverse BMPs on boundary-fixed spatial units (referred to as BMP configuration units; e.g., subbasins, hydrologic response units, farms). This means the shapes and sizes of each BMP configuration unit cannot be changed and optimized once they have been created through spatial discretization before the optimization process. This sort of “boundary-fixed” method entirely ignores a dimension of solution space search, i.e., that of adjusting boundaries of BMP configuration units, which can induce changes in the areas of configured BMPs as well as the possibilities of occupying critical positions for environmental management. As a result, the optimization in a boundary-fixed manner may produce less effective and practical BMP scenarios. In this study, we propose a new optimization framework of BMP scenarios in a boundary-adaptive manner. The proposed optimization framework adopts slope positions (basic landform units along hillslope inherently related to physical hillslope processes) as BMP configuration units and dynamically adjusts their boundaries by using the quantitative information on their spatial gradation (i.e., fuzzy slope positions) during the optimization. The experiment with a case study of the proposed optimization framework, compared with that using boundary-fixed slope position units, showed that the proposed optimization framework can significantly enlarge the search space for optimal solutions and obtain optimal BMP scenarios with better cost-effectiveness and higher optimization efficiency. The proposed optimization framework for spatial optimization of BMP scenarios can be easily adapted for other watershed models, intelligent optimization algorithms, and spatial unit types available for boundary adjustment.
Developing Agtor: An agricultural management model for the integrated exploration of water futures
Optimization of watershed best management practice (BMP) scenarios based on watershed modeling and intelligent optimization algorithms provides an effective way for watershed management to achieving optimal environmental and economic effectiveness. Existing studies commonly conduct the BMP scenario optimization by allocating diverse BMPs on boundary-fixed spatial units (referred to as BMP configuration units; e.g., subbasins, hydrologic response units, farms). This means the shapes and sizes of each BMP configuration unit cannot be changed and optimized once they have been created through spatial discretization before the optimization process. This sort of “boundary-fixed” method entirely ignores a dimension of solution space search, i.e., that of adjusting boundaries of BMP configuration units, which can induce changes in the areas of configured BMPs as well as the possibilities of occupying critical positions for environmental management. As a result, the optimization in a boundary-fixed manner may produce less effective and practical BMP scenarios. In this study, we propose a new optimization framework of BMP scenarios in a boundary-adaptive manner. The proposed optimization framework adopts slope positions (basic landform units along hillslope inherently related to physical hillslope processes) as BMP configuration units and dynamically adjusts their boundaries by using the quantitative information on their spatial gradation (i.e., fuzzy slope positions) during the optimization. The experiment with a case study of the proposed optimization framework, compared with that using boundary-fixed slope position units, showed that the proposed optimization framework can significantly enlarge the search space for optimal solutions and obtain optimal BMP scenarios with better cost-effectiveness and higher optimization efficiency. The proposed optimization framework for spatial optimization of BMP scenarios can be easily adapted for other watershed models, intelligent optimization algorithms, and spatial unit types available for boundary adjustment.
Stream and Session
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