农业大数据学报2025，Vol.7Issue(1)：31-42,12.DOI:10.19788/j.issn.2096-6369.100027
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
摘要
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.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引用本文复制引用
谢成玉,李淑华,冯爱萍,王辰怡,黄莉,高秉博,尹文杰,SUKHBAATAR Chinzorig,王庆涛,陈华杰,冯权泷..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):31-42,12.基金项目
国家重点研发计划项目(2021YFE0102300). （2021YFE0102300）
