DOI: 10.57760/sciencedb.agriculture.00023
title: 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
title: 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

description: 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 load 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. Based on the above method, the spatial distribution dataset of grassland-type NPS pollution loading to rivers in the Kherlen River Basin is produced, including the releases of total phosphorus and total nitrogen NPS pollution in the basin and hydro-fluctuation zone. The distribution estimation method of grassland-type NPS pollution load used in the dataset is capable of simulating NPS pollution with the raster cell as the smallest unit, which can be combined with remote sensing image to improve the simulation accuracy of NPS pollution in accordance with local conditions. This dataset helps to provide support for the division of spatial control unit of NPS pollution in the Kherlen River Basin, as well as providing data support for related resources and ecosystem researches in the basin.
description: 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 load 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. Based on the above method, the spatial distribution dataset of grassland-type NPS pollution loading to rivers in the Kherlen River Basin is produced, including the releases of total phosphorus and total nitrogen NPS pollution in the basin and hydro-fluctuation zone. The distribution estimation method of grassland-type NPS pollution load used in the dataset is capable of simulating NPS pollution with the raster cell as the smallest unit, which can be combined with remote sensing image to improve the simulation accuracy of NPS pollution in accordance with local conditions. This dataset helps to provide support for the division of spatial control unit of NPS pollution in the Kherlen River Basin, as well as providing data support for related resources and ecosystem researches in the basin.