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首页> 外文期刊>Agricultural Water Management >A framework for priority non-point source area identification and load estimation integrated with APPI and PLOAD model in Fujiang Watershed, China.
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A framework for priority non-point source area identification and load estimation integrated with APPI and PLOAD model in Fujiang Watershed, China.

机译:结合福建流域APPI和PLOAD模型的优先面源识别和负荷估算框架。

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摘要

The non-point source (NPS) pollution is difficult to manage and control due to its complicated generation and formation. In large scale watersheds, the priority sources areas (PSAs) identification is an important and necessary process for efficient aquatic environmental management. Here, a framework for the PSAs identification and pollution load estimation in PSAs screened for Best Management Practices (BMPs) is proposed. Fujiang Watershed, a branch of Jialingjiang, the upper reach of Yangtze River, was chosen for evaluation of the method proposed here. The entire Fujiang Watershed was divided into 21 subbasins, after which the Agricultural Pollution Potential Index (APPI) was used to identify the PSAs, and a modified runoff coefficient was introduced to mitigate the impact of the rainfall heterogeneity in the process. Next, the identified PSAs were further divided into 34 subbasins, after which quantification of the pollution load was conducted using the Pollution Load (PLOAD) model. The results indicated that there are five subbasins have much higher NPS pollution load intensities, with an average value of 6.05 t/km2/year for TN and 0.31 t/km2/year for TP. According to the cluster analysis on land use structure, these five subbasins were featured by higher proportion of agricultural land, suggesting a need for better fertilizer application management. The method developed here provided a helpful framework for conducting NPS pollution management in a large watershed.
机译:非点源(NPS)污染由于其复杂的产生和形成而难以管理和控制。在大规模流域中,优先源区(PSA)的识别是有效进行水生环境管理的重要且必要的过程。在此,提出了针对PSA进行PSA识别和污染负荷估算的框架,以筛选出最佳管理实践(BMP)。选择长江上游嘉陵江支流福建流域,对此处提出的方法进行评估。将整个福建流域划分为21个子流域,然后使用农业污染潜力指数(APPI)识别PSA,并引入修正后的径流系数以减轻过程中降雨非均质性的影响。接下来,将识别出的PSA进一步划分为34个子盆地,然后使用污染负荷(PLOAD)模型对污染负荷进行量化。结果表明,有五个子流域的NPS污染负荷强度更高,TN的平均值为6.05 t / km 2 /年,平均值为0.31 t / km 2 /年(TP)。根据土地利用结构的聚类分析,这五个子流域的农业用地比例较高,表明需要更好的肥料施用管理。这里开发的方法为在大流域进行NPS污染管理提供了有用的框架。

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