Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units

Abstract

Spatial optimization of watershed best management practice (BMP) scenarios based on watershed modeling is an effective decision support tool for watershed management. During such optimization, existing types of BMP configuration units for configuring BMPs (or BMP configuration units, e.g. subbasins, hydrologic response units, farms) remain fixed boundaries once they have been created through spatial discretization prior to BMP scenario optimization. This sort of “boundary-fixed” method does not allow for adjustments to the spatial characteristics of BMP configuration units. Hence, it runs the risk of missing superior BMP scenarios that could have been obtained by adjusting unit boundaries and may produce less effective spatial optimization. In this article, we propose a new approach to the spatial optimization of BMP scenarios based on boundary-adaptive configuration units. The proposed optimization approach adopts slope positions (basic landform units along hillslopes inherently related to physical hillslope processes) as BMP configuration units and dynamically adjusts their boundaries by using quantitative information about their spatial gradation (i.e. fuzzy slope positions) during the optimization. A case study conducted in the Youwuzhen watershed in Fujian, China, showed that the proposed optimization approach can significantly enlarge the search space and obtain optimal BMP scenarios with better cost-effectiveness and higher optimization efficiency than with boundary-fixed units. The proposed optimization approach provides a new alternative framework for spatial optimization of BMP scenarios, in which other watershed models, intelligent optimization algorithms, and BMP configuration units available for boundary adjustment can be applied to BMP scenario optimization in a boundary-adaptive manner. This study also exemplifies the potential for transforming qualitative, vague, and empirical geographical knowledge about slope position units related to physical hillslope processes and BMPs into quantitative, explicit, and automated geospatial algorithms for effectively resolving environmental management problems in a more geographically meaningful way.

Optimizing the Implementation Plan of Watershed Best Management Practices with Time-varying Effectiveness under Stepwise Investment

“…The second type of optimization method is an intelligent optimization algorithm-based method that simplifies, formulates, and solves the complex optimization problem of selecting and locating BMPs by incorporating watershed modeling (Chen et al, 2016;Srivastava et al, 2002;Veith et al, 2003;Zhu et al, 2021). The optimization problem formulation comprises objectives, geographic decision variables, and constraining conditions Zhu et al, 2021). Optimization objectives are often related to multiple and potentially conflicting objectives, including eco-environmental effectiveness and economic investment.…”

“…A set of decisions determined for all spatial units constitutes a BMP scenario. The constraining conditions refer to the restrictive situations that enable better representation and solving of the optimization problem, including spatial constraints (e.g., suitable spatial locations for implementing BMPs and spatial relationships among BMPs) and nonspatial constraints (e.g., limited budgets) (Zhu et al, 2021).…”

“…In the literature, the spatial units are classified into five types with different levels in the watershed : subbasins (Liu et al, 2019), slope position units (Qin et al, 2018), hydrologically connected fields (Wu et al, 2018), farms and hydrologic response units (HRUs) (explicitly referring to HRUs in the SWAT [Soil and Water Assessment Tool]) (Gitau et al, 2004;Kalcic et al, 2015), and grid cells (Gaddis et al, 2014). The second perspective introduces diverse spatial constraints to ensure manuscript submitted to Water Resources Research that the optimization results have meaningful geographic interpretations and practicability (Kreig et al, 2019;Wu et al, 2018;Zhu et al, 2021). Existing studies have considered three types of spatial constraints: spatial relationships between BMPs and locations, spatial relationships among adjacent BMPs, and spatial characteristic adjustment of spatial units (e.g., unit boundary; Zhu et al, 2021).…”

“…The second perspective introduces diverse spatial constraints to ensure manuscript submitted to Water Resources Research that the optimization results have meaningful geographic interpretations and practicability (Kreig et al, 2019;Wu et al, 2018;Zhu et al, 2021). Existing studies have considered three types of spatial constraints: spatial relationships between BMPs and locations, spatial relationships among adjacent BMPs, and spatial characteristic adjustment of spatial units (e.g., unit boundary; Zhu et al, 2021). These studies have significantly improved the reasonability, practicability, and efficiency of optimization methods for watershed BMP scenarios.…”

“…In this study, we proposed a new simulation-optimization framework for the implementation plan of BMPs considering two important, realistic factors: stepwise investment and time-varying BMP effectiveness. This framework extended the existing spatial optimization framework of BMP scenarios Maringanti et al, 2011;Qin et al, 2018;Zhu et al, 2021)…”