多源信息集成的2022年克鲁伦河流域草原型流域面源污染入河负荷空间分布数据集研制

doi:10.19788/j.issn.2096-6369.100027

农业大数据学报 ›› 2025, Vol. 7 ›› Issue (1): 31-42.doi: 10.19788/j.issn.2096-6369.100027

• “一带一路”沿线农业资源环境监测与分析专栏 • 上一篇下一篇

多源信息集成的2022年克鲁伦河流域草原型流域面源污染入河负荷空间分布数据集研制

谢成玉¹(), 王辰怡², 黄莉¹, 高秉博², 尹文杰¹, SUKHBAATAR Chinzorig³, 王庆涛¹, 陈华杰¹, 冯权泷², 李淑华⁴, 冯爱萍¹^,^*()

1.生态环境部卫星环境应用中心，北京 100094，中国
2.中国农业大学土地科学与技术学院，北京 100083，中国
3.蒙古科学院地理与生态地质研究所，乌兰巴托 15170，蒙古
4.北京市农林科学院信息技术研究中心，北京 100097，中国

收稿日期:2024-04-03 接受日期:2024-06-03 出版日期:2025-03-26 发布日期:2025-02-05
通讯作者: 冯爱萍，E-mail：fengaiping1108@126.com。
作者简介:谢成玉，E-mail：xiecy_123@163.com。
基金资助:
国家重点研发计划项目(2021YFE0102300)

Research of Spatial Distribution Dataset of Grassland-type Non- point Sources Pollution Loading to Rivers in the Kherlen River Basin in 2022 Integrated by Multi-source Information

XIE ChengYu¹(), WANG ChenYi², HUANG Li¹, GAO BingBo², YIN WenJie¹, SUKHBAATAR Chinzorig³, WANG QingTao¹, CHEN HuaJie¹, FENG QuanLong², LI ShuHua⁴, FENG AiPing¹^,^*()

1. Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100094, China
2. College of land science and technology, China Agricultural University, Beijing, 100083, China
3. Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia
4. Research Center of Information Technology, Academy of Agriculture and Forestry Sciences, Beijing, 100097, China

Received:2024-04-03 Accepted:2024-06-03 Published:2025-03-26 Online:2025-02-05

摘要/Abstract

摘要：

克鲁伦河流域位于“一带一路”沿线，共建绿色“一带一路”是“一带一路”顶层设计中的重要内容，保护流域的生态安全是中蒙两国面临的共同挑战和共同责任，因此理清克鲁伦河流域草原型流域面源（Non-point sources, NPS）污染入河负荷的空间分布，对于划分流域最优NPS污染空间管控单元至关重要。基于遥感分布式污染估算（DPeRS）模型，结合草原地表径流型和消落带型NPS污染特点研发草原型流域NPS污染入河负荷分布估算方法。该方法以遥感数据为驱动，实现了逐月尺度像元级NPS污染入河负荷分布估算，相较于以往NPS污染模拟模型，综合考虑了消落带型NPS污染对河流的影响。草原型流域NPS污染入河负荷由草原地表径流型NPS污染入河负荷和草原消落带型NPS污染入河负荷两部分组成。草原地表径流型NPS污染入河负荷分溶解态和颗粒态分别进行估算，主要基于克鲁伦河流域干湿沉降、土壤、草原利用强度等地面数据及遥感数据核算草地氮磷平衡，结合流域降水、土壤氮磷含量等草原型NPS污染连续型参数的空间分布特征，耦合定量遥感反演模型与NPS污染地面模型开展空间负荷估算；草原消落带型NPS污染入河负荷则是基于2019—2022年4—10月逐月哨兵2号影像提取的流域消落带范围，结合消落带不同地类土柱淹没释放模拟实验获取的NPS污染总氮和总磷释放速率，估算草原消落带型NPS污染负荷量。基于以上方法最终得到克鲁伦河流域草原型流域NPS污染入河负荷空间分布数据集，并统计得出2022年流域NPS污染总氮和总磷入河量分别为3542.5 t/yr和1559.9 t/yr，其中地表径流型NPS污染总氮和总磷入河量分别为3105.0 t/yr和1387.1 t/yr，消落带型NPS污染总氮和总磷入河量分别为437.5 t/yr和172.8 t/yr。本数据集为实现高精度的流域NPS污染管控单元划分技术提供了有力支撑，对于中蒙两国维护“一带一路”沿线的资源生态安全具有重要的参考意义。

数据摘要：

项目	描述
数据库（集）名称	多源信息集成的2022年克鲁伦河流域草原型流域面源污染入河负荷空间分布数据集
所属学科	土地资源与信息技术
研究主题	克鲁伦河流域草原型流域面源污染入河负荷模拟
数据时间范围	2022年
时间分辨率	无
数据地理空间覆盖	克鲁伦河流域
空间分辨率	30 m
数据类型与技术格式	30 m草原型流域面源污染总磷入河负荷（TIF格式） 30 m草原型流域面源污染总氮入河负荷（TIF格式）
数据库（集）组成	数据集为2022年克鲁伦河流域30m空间分辨率草原型流域面源污染总氮总磷入河负荷量
数据量	2.73 GB
主要数据指标	克鲁伦河流域草原型流域面源污染总磷入河负荷量、克鲁伦河流域草原型流域面源污染总氮入河负荷量
数据可用性	https://cstr.cn/17058.11.sciencedb.agriculture.00114 https://doi.org/10.57760/sciencedb.agriculture.00114
经费支持	国家重点研发计划项目克鲁伦河流域面源污染遥感监测与评估技术研发（2021YFE0102300）

关键词: 克鲁伦河流域, 草原型流域面源污染, 入河负荷, 地表径流型面源污染, 消落带型面源污染

Abstract:

The Kherlen River Basin is located along the Silk Road, and jointly building a green road is an important part of the top-level design of the Silk Road. China and Mongolia face the common challenge and responsibility of protecting the ecological security of the basin. Therefore, it is important to clarify the spatial distribution of grassland-type load estimation of non-point sources (NPS) pollution into the Kherlen River Basin, which is essential for the division of the optimal spatial control unit of NPS pollution in the basin. On the basis of Chinese self-developed DPeRS (Diffuse Pollution estimation with Remote Sensing) model, this paper has developed a method for estimating the grassland-type load distribution of NPS pollution into river in the basin, by combining the NPS pollution characteristics of surface runoff on grassland and incorporating the spatial distribution of NPS pollution loads in hydro-fluctuation zone. The method is driven by remote sensing data, and it can realize the distribution of NPS pollution load estimation into river at the pixel level month by month. Compared to the previous NPS pollution simulation models, this method comprehensively takes into account the impact of hydro-fluctuation zone NPS pollution on rivers. The grassland-type load estimation of NPS pollution is composed of two parts: the NPS pollution of surface runoff on grassland and the NPS pollution of hydro-fluctuation zone on grassland. The NPS pollution load of surface runoff on grassland into the river is mainly estimated from the dissolved state and erosion particle state. Firstly, the nitrogen and phosphorus balance of grassland was calculated based on ground data (such as wet and dry deposition data, soil data, grassland utilization intensity) and remote sensing data in the Kherlen River Basin. Then, space load estimation is carried out by coupling a quantitative remote sensing inversion model with the ground model of NPS pollution, by combining the spatial distribution characteristics of continuous parameters in estimating the grassland-type NPS pollution, such as precipitation in the watershed, soil nitrogen and phosphorus content. Grassland NPS pollution loads in hydro-fluctuation zone is estimated based on the extent of hydro-fluctuation zone extracted from month-by-month Sentinel 2 imagery from April-October, 2019-2022. And the volume of grassland-type NPS pollution loads in the hydro-fluctuation zone is calculated by the release rates of NPS pollution total nitrogen and total phosphorus obtained from submerged release simulation experiments of soil columns in different land use type in the hydro-fluctuation zone. Based on above methods, the spatial distribution dataset of grassland-type NPS pollution load into river is finally obtained. And the NPS pollution load of total nitrogen and total phosphorus into river is 3542.5 t/yr and 1559.9 t/yr in 2022, respectively. The total nitrogen and phosphorus of surface runoff type NPS into the river were 3105.0 t/yr and 1387.1 t/yr, respectively. The total nitrogen and phosphorus of hydro-fluctuation zone type NPS into the river were 437.5 t/yr and 172.8 t/yr, respectively. This dataset provides a strong support for the realization of high-precision division technology of NPS pollution control unit, which is of great reference significance for China and Mongolia to maintain the resource and ecological security along the Silk Road.

Data summary:

Item	Description
Dataset name	Research of Spatial Distribution Dataset of Grassland-type Non-point Sources Pollution Loading to Rivers in the Kherlen River Basin in 2022 Integrated by Multi-source Information
Specific subject area	Land resources and information technology
Research topic	Grassland-type (NPS) pollution loading to rivers in the Kherlen River Basin
Time range	2022 year
Temporal resolution	No
Geographical scope	Kherlen River basin
Spatial resolution	30 meter
Data types and technical formats	Grassland-type NPS pollution of total phosphorus loading to rivers in the Kherlen River Basin with 30 m resolution (TIF format) Grassland-type NPS pollution of total nitrogen loading to rivers in the Kherlen River Basin with 30 m resolution (TIF format)
Dataset structure	The dataset is 2022 grassland-type NPS pollution of total phosphorus and total nitrogen loading to rivers in the Kherlen River Basin with 30 m resolution
Volume of dataset	2.73 GB
Key index in dataset	Grassland-type NPS pollution of total phosphorus loading to rivers in the Kherlen River Basin Grassland-type NPS pollution of total nitrogen loading to rivers in the Kherlen River Basin
Data accessibility	https://cstr.cn/17058.11.sciencedb.agriculture.00114 https://doi.org/10.57760/sciencedb.agriculture.00114
Financial support	Research and development on remote sensing monitoring and assessment technology of non-point source pollution in Kherlen River Basin under the national key research and development program（2021YFE0102300）

Key words: Kherlen River Basin, grassland-type NPS pollution of basin, loads into river, NPS pollution of surface runoff, NPS pollution of hydro-fluctuation zone

谢成玉, 王辰怡, 黄莉, 高秉博, 尹文杰, SUKHBAATAR Chinzorig, 王庆涛, 陈华杰, 冯权泷, 李淑华, 冯爱萍. 多源信息集成的2022年克鲁伦河流域草原型流域面源污染入河负荷空间分布数据集研制[J]. 农业大数据学报, 2025, 7(1): 31-42.

XIE ChengYu, WANG ChenYi, HUANG Li, GAO BingBo, YIN WenJie, SUKHBAATAR Chinzorig, WANG QingTao, CHEN HuaJie, FENG QuanLong, LI ShuHua, FENG AiPing. Research of Spatial Distribution Dataset of Grassland-type Non- point Sources Pollution Loading to Rivers in the Kherlen River Basin in 2022 Integrated by Multi-source Information[J]. Journal of Agricultural Big Data, 2025, 7(1): 31-42.

图/表 12

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参考文献 14

[1]	王辰怡, 高秉博, Chinzorig S, 等. 2022年克鲁伦河流域土壤全氮含量与土壤全磷含量数据集. 农业大数据学报, 2023, 5(3): 104-111. doi: 10.19788/j.issn.2096-6369.230314
[2]	FANG H. Effect of soil conservation measures and slope on runoff, soil, TN, and TP losses from cultivated lands in northern China. Ecological Indicators, 2021, 126: 107677. DOI: 10.1016/j.ecolind.2021.107677.
[3]	SHEN W, ZHANG L, LI S, et al. A framework for evaluating county-level non-point source pollution: joint use of monitoring and model assessment. Science of the Total Environment, 2020, 722: 137956. DOI:10.1016/j.scitotenv.2020.137956.
[4]	PENG K, LI J, ZHOU X, et al. Simulation and control of non-point source pollution based on MIKE model: A case study of Danjiang river basin, China. Ecohydrology & Hydrobiology, 2023, 23(4): 554-568. DOI:10.1016/j.ecohyd.2023.08.001.
[5]	冯爱萍, 吴传庆, 王雪蕾, 等. 海河流域氮磷面源污染空间特征遥感解析. 中国环境科学, 2019, 39(7): 2999-3008.
[6]	ADU J T, KUMARASAMY M V. Assessing non-point source pollution models: a review. Polish Journal of Environmental Studies, 2018, 27(5). DOI: 10.15244/pjoes/76497.
[7]	丁建华, 沈旭, 常文婷, 等. 基于SWAT模型的南苕溪流域面源污染时空格局研究. 环境污染与防治, 2023, 45(12): 1654-1659.
[8]	LIU J, ZHANG L, ZHANG Y, et al. Validation of an agricultural non-point source (AGNPS) pollution model for a catchment in the Jiulong River watershed, China. Journal of Environmental Sciences, 2008, 20(5): 599-606. DOI:10.1016/S1001-0742(08)62100-2. pmid: 18575114
[9]	赵串串, 冯倩, 侯文涛, 等. 基于AnnAGNPS模型的灞河流域非点源污染模拟研究. 环境污染与防治, 2019, 41(3):317-322.
[10]	尹京晨, 丁晗, 李泽利, 等. 基于SPARROW模型的面源污染模拟研究进展. 环境工程, 2022, 40(6):253-260+294.
[11]	XUE J, WANG Q, ZHANG M. A review of non-point source water pollution modeling for the urban-rural transitional areas of China: Research status and prospect. Science of the Total Environment, 2022, 826: 154146. DOI:10.1016/j.scitotenv.2022.154146.
[12]	张秀红, 李任革, 赵月帅, 等. 基于DPeRS模型的西安市农业空间面源污染特征解析. 环境工程, 2023, 41(12):142-149.
[13]	成立新, 岳林芳, 郑重, 等. 内蒙古自治区畜禽养殖粪污的调研情况. 当代畜禽养殖业, 2019, (6):59-64.
[14]	冯爱萍, 黄莉, 王雪蕾, 等. 浦阳江流域(浦江县段)面源污染模型估算及河流生态缓冲带重点区域识别. 环境工程学报, 2022, 16(1): 73-84.

数据名称	单位	数据来源
流域消落带区域	——	提取2019—2022年4—10月有效哨兵2影像
30 m土地利用数据	——	https://data.casearth.cn/sdo/detail/5fbc7904819aec1ea2dd7061
30 m克鲁伦河流域土壤类型	——	http://globalchange.bnu.edu.cn/research/soil
30 m克鲁伦河流域坡度坡长	°	https://wist.echo.nasa.gov/api//
30 m克鲁伦河流域植被覆盖度	%	https://ladsweb.nascom.nasa.gov/search
30 m克鲁伦河流域草地氮磷平衡量	t/yr	基于草地氮磷平衡量计算得到
克鲁伦河流域氮磷干湿沉降数据	kg·m²/(ha·yr)	流域草地面积乘以氮、磷沉降通量参数
消落带淹没面源释放速率	mg/(m²·d)（裸土/植被覆盖地类）； mg/(g·d)（粪便覆盖地类）	基于现场采样的消落带淹没释放模拟实验
淹没概率	--	克鲁伦河月度30 m空间分辨率的水体分布数据
消落带地类覆盖度	%	10 m×10 m样方调查
草原牲畜量（羊）	只	统计年鉴和草原蒙古包数据
羊粪便排泄系数	kg/（只·年）	参考文献中获取^[13]

	总氮释放速率			总磷释放速率
土地类型	裸土 [mg/(m²·d)]	植被覆盖 [mg/(m²·d)]	粪便覆盖 [g/(m²·d)]	裸土 [mg/(m²·d)]	植被覆盖 [mg/(m²·d)]	粪便覆盖 [g/(m²·d)]
H4	9.75	9.67	0.12	4.06	11.60	0.01
H7	9.75	8.65	0.01	1.20	3.41	0.01
M1	0.01	0.12	0.07	0.51	0.01	0.02
M2	0.05	0.10	0.07	0.02	0.08	0.01
M3	0.13	0.01	0.01	0.41	0.02	0.01
平均值	3.94	3.71	0.06	1.24	3.02	0.01

NPS污染	消落带覆盖类别	消落带内NPS污染释放量（t/年）
NPS污染	消落带覆盖类别	全流域	中国境内	蒙古境内
总氮	裸土	114.2	39.4	74.9
	植被	119.5	41.2	78.3
	粪便	203.8	28.7	175.1
总磷	裸土	35.9	12.4	23.6
	植被	97.3	33.5	63.8
	粪便	39.6	5.4	34.2

多源信息集成的2022年克鲁伦河流域草原型流域面源污染入河负荷空间分布数据集研制

Research of Spatial Distribution Dataset of Grassland-type Non- point Sources Pollution Loading to Rivers in the Kherlen River Basin in 2022 Integrated by Multi-source Information

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