State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
College of Water Sciences, Beijing Normal University, Beijing 100875, China.
Int J Environ Res Public Health. 2018 Sep 23;15(10):2090. doi: 10.3390/ijerph15102090.
Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status within a catchment and develop a targeted planning strategy for river restoration. To address this need, coupling hydrological and machine learning models were constructed to identify priority zones for river restoration based on a dataset of aquatic organisms (i.e., algae, macroinvertebrates, and fish) and physicochemical indicators that were collected from 130 sites in September 2014 in the Taizi River, northern China. A process-based model soil and water assessment tool (SWAT) was developed to model the temporal-spatial variations in environmental indicators. A support vector machine (SVM) model was applied to explore the relationships between aquatic organisms and environmental indicators. Biological indices among different hydrological periods were simulated by coupling SWAT and SVM models. Results indicated that aquatic biological indices and physicochemical indicators exhibited apparent temporal and spatial patterns, and those patterns were more evident in the upper reaches compared to the lower reaches. The ecological status of the Taizi River was better in the flood season than that in the dry season. Priority zones were identified for different hydrological seasons by setting the target values for ecological restoration based on biota organisms, and the results suggest that hydrological conditions significantly influenced restoration prioritization over other environmental parameters. Our approach could be applied in other seasonal river ecosystems to provide important preferences for river restoration.
确定河流修复的优先区域对于生物多样性保护和集水区管理至关重要。然而，由于野外采集的难度，数据有限，这导致了人们需要更好地了解集水区内的生态状况，并制定有针对性的河流修复规划策略。为了满足这一需求，研究人员构建了水文和机器学习模型，以根据 2014 年 9 月从中国北方太子河流域 130 个地点采集的水生生物（藻类、大型无脊椎动物和鱼类）和理化指标数据集，确定河流修复的优先区域。研究开发了一个基于过程的土壤和水评估工具 (SWAT) 模型，以模拟环境指标的时空变化。支持向量机 (SVM) 模型被应用于探索水生生物与环境指标之间的关系。通过耦合 SWAT 和 SVM 模型，模拟了不同水文期的生物指数。结果表明，水生生物指数和理化指标表现出明显的时空格局，且在上游比下游更为明显。太子河的生态状况在丰水期优于枯水期。通过基于生物群的目标值来设置生态恢复的目标值，确定了不同水文季节的优先区域，结果表明，水文条件对其他环境参数的恢复优先级有显著影响。我们的方法可以应用于其他季节性河流生态系统，为河流修复提供重要的参考。
