Tracing sediment sources in a plain river network area by using optimized experimental design and reflectance spectroscopy

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2023-12-19 DOI:10.1016/j.watres.2023.121041

Junfeng Xiong , Chen Lin , Ronghua Ma , Zhipeng Wu , Lei Chen

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Abstract

Soil erosion in a plain river network area with dense rivers, fertile land, and agricultural development is easily causes river siltation, agricultural non-point source pollution, and water eutrophication. Therefore, the negative impact of the sediment on the environment cannot be underestimated. Most traditional sediment fingerprint tracing studies have focused on mountain basins and lack a scheme suitable for plain river network sediment tracing. Here, a typical plain river network in the Taihu Basin was selected as the study area. The flow structure and characteristics were analysed, and a sampling scheme for the stream segment and a two-step model of sediment tracing in a plain river network were proposed to quantitatively distinguish the types of sediment sources. The results indicated that the traditional discriminant function analysis adequately distinguishes the contribution rate of basin soil and has a good validation accuracy (R² = 0.96, root mean square error of calibration = 5.91 %), whereas Random Forest obtains better discrimination results by mining non-linear information in the soil spectra of different land types, with R² values of 0.89, 0.83, and 0.80 for farmland, forest, and grassland, respectively. The average proportion of soil in the sediment in the watershed was 23 %, and the proportion of soil in the watershed increased from upstream to downstream. The sediment sources of the Caoqiao, Yincun, and Shaoxiang Rivers mainly came from grassland (44 %), forest (39 %), and farmland (42 %), respectively. Land-use distribution, water conservation facilities, and soil particle size were the main factors affecting these sources. Each river adopts measures to remove the corresponding pollutants, optimise water and soil conservation measures for riverbank green belts and forest, and regularly clean up silt in water conservancy ditches and rivers, which can reduce the pollution impact caused by sediment.

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利用优化实验设计和反射光谱法追踪平原河网地区的泥沙来源

在河流密集、土地肥沃、农业发达的平原河网地区，水土流失很容易造成河流淤积、农业非点源污染和水体富营养化。因此，泥沙对环境的负面影响不容小觑。传统的泥沙指纹追踪研究大多集中在山区盆地，缺乏适合平原河网泥沙追踪的方案。本文选择太湖流域典型的平原河网作为研究区域。分析了其水流结构和特征，提出了河段取样方案和平原河网泥沙溯源两步法模型，对泥沙来源类型进行了定量区分。结果表明，传统的判别函数分析能充分区分流域土壤的贡献率，具有较好的验证精度（R2=0.96，校正均方根误差=5.91%），而随机森林通过挖掘不同土地类型土壤光谱中的非线性信息，获得了较好的判别效果，农田、森林和草地的 R2 值分别为 0.89、0.83 和 0.80。流域沉积物中土壤的平均比例为 23%，且土壤比例从上游向下游递增。草桥河、殷村河和少乡河的泥沙来源主要来自草地（44%）、森林（39%）和农田（42%）。土地利用分布、水利设施和土壤颗粒大小是影响这些水源的主要因素。各河流采取相应的污染物清除措施，优化河岸绿化带和森林的水土保持措施，定期清理水利沟渠和河道中的淤泥，可减少泥沙造成的污染影响。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.