2022年克鲁伦河流域草原面源污染管控分区数据集

doi:10.19788/j.issn.2096-6369.100034

Abstract

Abstract:

The ecological and environmental safety of the Kherlen River Basin is directly related to the sustainable development of both China and Mongolia. Scientific delineation of non-point source pollution control units is crucial for precise implementation of water environment policies and efficient management in the basin. However, currently, there is a lack of effective zoning data to guide specific measures in pollution control in this region. Traditional methods of dividing pollution control units struggle to accurately reflect the differences in grassland non-point source pollution, thereby affecting management effectiveness to some extent. Grassland non-point source pollution is influenced by multiple factors, exhibiting both attribute repetition and spatial continuity. To capture these characteristics more accurately, a clustering method that balances attribute repetition and spatial continuity is required. In this study, focusing on the Kherlen River Basin and targeting the influencing factors of grassland non-point source pollution, we comprehensively considered key continuous data such as annual average precipitation, temperature, digital elevation, grassland carrying capacity, and soil total nitrogen and phosphorus content. Utilizing the Spatial Toeplitz Inverse Covariance Clustering (STICC) method, which effectively handles attribute dependencies and spatial consistency strategies, we conducted clustering analysis and constructed a 2022 dataset for non-point source pollution control zoning in the Kherlen River Basin. To validate the accuracy of this dataset, we compared the zoning effects using the DUNN clustering accuracy evaluation index with other traditional zoning results. The results showed that the STICC method outperforms methods like K-Means, Spectral K-Means, GMM, and Repeated Bisection in clustering accuracy. It can more effectively identify heterogeneous pollution areas, significantly enhancing the precision of management. Additionally, this study preserved the original continuity of the data, resulting in a more accurate depiction of pollution characteristics. Compared to traditional methods, the zoning data provided in this study improves detail presentation by more than 50%. This dataset not only offers strong support for in-depth studies on non-point source pollution characteristics in the Kherlen River Basin but also provides a solid data foundation for related control decisions.

Data summary:

Item	Description
Dataset name	Dataset on Grassland Non-Point Source Pollution Management and Control Zones for the Kherlen River Basin in 2022
Specific subject area	Land resources and information technology
Research topic	Non-Point Source Pollution Management and Control Zones
Time range	2022
Geographical scope	Kherlen River Basin
Spatial resolution	1 km
Data types and technical formats	.shp
Dataset structure	The dataset includes a special map and basic dataset for the control zones of non-point source pollution in the Kherlen River basin in 2022. The special map consists of two maps of primary and secondary control zones, and the basic dataset consists of six key indicator data files for zoning.
Volume of dataset	163.8 MB
Key index in dataset	Primary control zone for non-point source pollution, secondary control zone for non-point source pollution
Data accessibility	https://doi.org/10.57760/sciencedb.08471 https://cstr.cn/31253.11.sciencedb.08471
Financial support	Research on Monitoring and Assessment Technology of Non-point Pollution of Kherlen River Based on Remote Sensing（2021YFE0102300）

Key words: Kherlen River basin, non-point source pollution, STICC, management and control zones, spatial clustering

LI ShuHua, LI XiaoLan, LIU Yu, GAO BingBo, Sukhbaatar Chinzorig, FENG AiPing, LI CunJun, REN YanMin. Dataset on Grassland Non-Point Source Pollution Management and Control Zones for the Kherlen River Basin in 2022[J].Journal of Agricultural Big Data, 2025, 7(1): 43-50.

Figures/Tables 8

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References 15

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数据名称	数据类型	数据来源
数字高程	连续型	https://www.gscloud.cn
草地载畜强度	连续型	https://www.scidb.cn/detail?dataSetId=4160057f68ad47d389f7a01ad7fb895a&version=V1
土壤全氮含量	连续型	https://agri.scidb.cn/en/detail?dataSetId=4160057f68ad47d389f7a01ad7fb895a
土壤全磷含量	连续型	https://agri.scidb.cn/en/detail?dataSetId=4160057f68ad47d389f7a01ad7fb895a
年平均降水	连续型	https://www.worldclim.org/
年平均气温	连续型	https://www.worldclim.org/

分区编号	年平均气温	年平均降水	土壤全氮含量	土壤全磷含量	数字高程	草原载畜强度
I	2708.358	3879.900	0.209	1.317	1605.541	58.092
II	2723.685	3735.346	0.331	1.226	1460.833	206.898
III	2732.018	2816.983	0.411	2.026	1400.272	82.381
IV	2741.175	3263.390	0.593	1.396	1180.195	132.350
V	2750.028	2126.999	0.431	4.010	1219.633	116.361
VI	2751.309	2365.984	0.457	1.270	1015.388	91.391
VII	2753.952	2625.266	0.362	0.717	759.713	65.023

聚类方法	Dunn指数
STICC	0.0002760
K-Means	0.0000896
Spectral K-Means	0.0001290
GMM	0.0000185
Repeated Bisection	0.0001600

Dataset on Grassland Non-Point Source Pollution Management and Control Zones for the Kherlen River Basin in 2022

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