Source trancing analysis of nitrogen in the upper reach of Xin’an River Basin based on SWAT model

IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Ai Wang , Jiangyu Wang , Benjie Luan , Siru Wang , Zhiguo Huo
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Abstract

Non-point source nitrogen (N) pollution has presented significant challenges to the management of water resources and threatened environmental sustainability. The Xin’an River Basin is a crucial strategic water source in the Yangtze River Delta, and total nitrogen (TN) is the main pollution factor threatening the water quality in this region. This paper simulated the migration and transformation process of non-point source N pollution in the upper reaches of Xin’an River Basin based on SWAT model, and identified the main sources of TN through scenario analysis. The results indicated that the SWAT model accurately captured the rainfall-runoff processes and TN transport and transformation dynamics in the study area, with Ens and R2 values exceeding 0.7 during both the calibration and validation periods. The annual TN load from hillslope into the rivers ranged from 7427.68 t to 16854.39 t during 2003–2018. The temporal variation of TN load was consistent with precipitation, while the spatial distribution was mainly affected by land use. Scenario simulation results revealed that atmospheric N deposition, fertilizer N application, and livestock N input represented the primary N sources in the area. It is noteworthy that, although atmospheric N deposition contributed a smaller input quantity in the model, its conversion rate (10.57 %) surpassed that of N fertilizer application (4.11 %) and livestock N input (9.41 %). Meanwhile, the reduction of atmospheric N deposition has a more significant effect on TN load mitigation and TN concentration at the outlet section than the reduction of fertilizer N application, especially during normal water years. The analysis highlighted that the trend of TN concentration variation at the outlet under different scenarios and hydrological conditions further underscored the importance of optimizing N management strategies based on hydrological years. These findings offer critical insights and a robust scientific basis for the effective management of non-point source N pollution in the upper reaches of the Xin’an River.
基于SWAT模型的新安河上游氮源转换分析
非点源氮污染对水资源管理提出了重大挑战,并威胁到环境的可持续性。新安河流域是长三角地区重要的战略水源,总氮是威胁该地区水质的主要污染因子。本文基于SWAT模型模拟了新安河流域上游非点源氮污染的迁移转化过程,并通过情景分析确定了TN的主要来源。结果表明,SWAT模型准确地捕捉了研究区降雨径流过程和全氮输运转化动态,在定标和验证期间Ens和R2值均超过0.7。2003-2018年坡面向河流的总氮年负荷在7427.68 ~ 16854.39 t之间。全氮负荷的时间变化与降水基本一致,空间分布主要受土地利用的影响。情景模拟结果表明,大气N沉降、肥料N施用和牲畜N输入是该地区主要的N源。值得注意的是,虽然大气氮沉降在模型中贡献的输入量较小,但其转化率(10.57%)超过了施氮(4.11%)和牲畜氮(9.41%)。同时,减少大气N沉降对出口段TN负荷减缓和TN浓度的影响比减少施氮量更显著,特别是在正常水龄。分析表明,不同情景和水文条件下的出水口TN浓度变化趋势进一步凸显了基于水文年优化N管理策略的重要性。这些发现为新安江上游非点源氮污染的有效治理提供了重要的见解和坚实的科学依据。
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来源期刊
Ecological Indicators
Ecological Indicators 环境科学-环境科学
CiteScore
11.80
自引率
8.70%
发文量
1163
审稿时长
78 days
期刊介绍: The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.
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