Effects of Different Spatial Configuration Units for the Spatial Optimization of Watershed Best Management Practice Scenarios

Abstract

Different spatial configurations (or scenarios) of multiple best management practices (BMPs) at the watershed scale may have significantly different environmental effectiveness, economic efficiency, and practicality for integrated watershed management. Several types of spatial configuration units, which have resulted from the spatial discretization of a watershed at different levels and used to allocate BMPs spatially to form an individual BMP scenario, have been proposed for BMP scenarios optimization, such as the hydrologic response unit (HRU) etc. However, a comparison among the main types of spatial configuration units for BMP scenarios optimization based on the same one watershed model for an area is still lacking. This paper investigated and compared the effects of four main types of spatial configuration units for BMP scenarios optimization, i.e., HRUs, spatially explicit HRUs, hydrologically connected fields, and slope position units (i.e., landform positions at hillslope scale). The BMP scenarios optimization was conducted based on a fully distributed watershed modeling framework named the Spatially Explicit Integrated Modeling System (SEIMS) and an intelligent optimization algorithm (i.e., NSGA-II, short for Non-dominated Sorting Genetic Algorithm II). Different kinds of expert knowledge were considered during the BMP scenarios optimization, including without any knowledge used, using knowledge on suitable landuse types/slope positions of individual BMPs, knowledge of upstream-downstream relationships, and knowledge on the spatial relationships between BMPs and spatial positions along the hillslope. The results showed that the more expert knowledge considered, the better the comprehensive cost-effectiveness and practicality of the optimized BMP scenarios, and the better the optimizing efficiency. Thus, the spatial configuration units that support the representation of expert knowledge on the spatial relationships between BMPs and spatial positions (i.e., hydrologically connected fields and slope position units) are considered to be the most effective spatial configuration units for BMP scenarios optimization, especially when slope position units are adopted together with knowledge on the spatial relationships between BMPs and slope positions along a hillslope.

Assessing Climate Change Impacts on Streamflow, Sediment and Nutrient Loadings of the Minija River (Lithuania): A Hillslope Watershed Discretization Application with High-Resolution Spatial Inputs

“…A recent research comparing different watershed configurations (HRUs, spatially explicit HRUs, hydrologically connected fields, and slope position units.) by Zhu et al (2018) [35] also states that using the slope position units as BMP configuration units with the hillslope based discretization yielded the best results.…”

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

“…which type of BMP should be applied, if any). The decisions made with regard to all spatial units (hereafter referred to as BMP configuration units) constitute a spatial configuration of BMPs in the watershed—the so-called BMP scenario (Zhu et al, 2019a). Constraining conditions that need to be satisfied during the optimization process are associated with the optimization problem under study and generally include non-spatial constraints (e.g.…”

“…BMPs or land uses A and B must not be close to each other, Wu et al, 2018; Xiao and Murray, 2019). Spatial constraints are closely related to the type of BMP configuration unit selected and taking them into consideration is vitally important in ensuring that the optimization problem and its solutions have meaningful geographical interpretations; the solutions need to be reasonable and practical (Cova and Church, 2000; Qin et al, 2018; Yao et al, 2018; Zhu et al, 2019a).…”

“…The second type of spatial constraint is concerned with the spatial relationships among adjacent BMPs such as the compatibility of adjacent land use (Ligmann-Zielinska et al, 2008) and the interactions BMPs have with BMP configuration units that have upstream–downstream relationships (Qin et al, 2018; Wu et al, 2018). To consider such spatial constraints, BMP configuration units should, inherently, have adjacency relationships such as farms (Arabi et al, 2006; Gitau et al, 2004), or the spatial topology that relates each unit to other units, for example, hydrologically connected fields (Wu et al, 2018) and slope position units (Qin et al, 2018; Zhu et al, 2019a).…”

“…These BMP configuration units all support the representation of spatial relationships between BMPs and spatial locations, although HRUs may not match with spatial locations in a one-to-one style explicitly (Arnold et al, 2010). Except for HRUs, all other types of BMP configuration units can represent spatial interactions among adjacent BMPs, especially those with inherent upstream–downstream relationships on different scales such as subbasins and hydrologically connected fields at the watershed scale and slope position units at the hillslope scale (Zhu et al, 2019a).…”