A modular and parallelized watershed modeling framework

Zhu, Liang‐Jun; Liu, Junzhi; Qin, Cheng‐Zhi; Zhu, A‐Xing

doi:10.1016/j.envsoft.2019.104526

Cited by 28 publications

(16 citation statements)

References 43 publications

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“…To compare the effects of these four types of BMP configuration units for watershed BMP scenarios optimization, a widely used spatial optimization framework of BMP scenarios based on watershed modeling coupled with intelligent optimization algorithms [2,10,12] was adopted in this study. As shown in Figure 2, the spatial optimization framework of watershed BMP scenarios mainly consists of four components: (1) BMP configuration units for allocating BMPs within the watershed; (2) a BMP knowledge base together with BMP configuration units as inputs to generate and evaluate BMP scenarios; (3) models for evaluating each watershed BMP scenario, including a distributed watershed model that can simulate spatial interactions between spatially explicitly distributed BMPs and effectively assess the environmental effectiveness of each BMP scenario [2,5,33], and a BMP scenario cost model for estimating the economic efficiency of each BMP scenario; (4) a multi-objective optimization component based on an intelligent optimization algorithm such as NSGA-II (Non-dominated Sorting Genetic Algorithm II) [34], which includes initializing BMP scenarios based on BMP configuration units and a BMP knowledge base, and generating new BMP scenarios or proposing optimal ones based on the evaluation results of all current BMP scenarios. These components are elaborated in four subsections (Sections 2.3-2.6) followed by the subsection of the study area and dataset (Section 2.2).…”

Section: Methodsmentioning

confidence: 99%

“…To simulate the spatial interactions between spatially explicitly distributed BMPs effectively, a fully distributed watershed model was constructed based on an open-source, modular, and parallelized watershed modeling framework, Spatially Explicit Integrated Modeling System (SEIMS, https://github.com/lreis2415/SEIMS; [33]) in this study. With the flexible modular structure and the parallel-computing middleware [33,43], SEIMS allows users to add their own algorithms in a nearly serial programming manner and to customize parallelized watershed models according to the characteristics of the study area and the application requirements. SEIMS also supports model-level parallel computation for applications which need numerous model runs, such as BMP scenarios optimization in this study.…”

Section: Watershed Model and Bmp Scenario Cost Modelmentioning

confidence: 99%

“…After conducting the parameter sensitivity analysis using the Morris screening method [45], the SEIMS-based watershed model in the study area was calibrated by an auto-calibration procedure based on the NSGA-II algorithm (a multi-objective optimization algorithm extended from the Genetic Algorithm; [34]) provided in SEIMS [33]. The Morris screening method is a so-called one-step-at-a-time (OAT) global sensitivity analysis method, which was used to qualitatively identify important parameters for the simulation of streamflow and sediment export at the outlet in this study.…”

Section: Watershed Model and Bmp Scenario Cost Modelmentioning

confidence: 99%

“…The Morris screening method is a so-called one-step-at-a-time (OAT) global sensitivity analysis method, which was used to qualitatively identify important parameters for the simulation of streamflow and sediment export at the outlet in this study. Then a small number of sensitivity parameters (such as the baseflow exponent and the baseflow recession coefficient for groundwater and soil water capacity) were selected for auto-calibration in this study [33]. The NSGA-II was also applied to BMP scenarios optimization in this study, and is described in detail in Section 2.6.…”

Section: Watershed Model and Bmp Scenario Cost Modelmentioning

confidence: 99%

“…where f reduction-rate (X) is the reduction rate of soil erosion under the BMP scenario X compared to that under the baseline scenario; v(0) and v(X) are the total amount of soil erosion (kg) under the baseline scenario and the X scenario, respectively. The NSGA-II [33], which has been successfully applied to many similar studies [2,[16][17][18], was selected as the intelligent optimization algorithm in this study. As shown in Figure 2, the NSGA-II algorithm first initializes the initial BMP scenarios (called "population" in NSGA-II) based on one type of BMP configuration unit and one available BMP configuration strategy as described in Sections 2.3 and 2.4.…”

Section: Multi-objective Optimization By An Intelligent Optimization mentioning

confidence: 99%

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

Zhu

Qin

Zhu

et al. 2019

Water

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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.

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