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基于 PLUS 和 RUSLE 模型的黄河流域土壤侵蚀特征及情景分析。

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models.

机构信息

College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.

Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China.

出版信息

Int J Environ Res Public Health. 2023 Jan 10;20(2):1222. doi: 10.3390/ijerph20021222.

DOI:10.3390/ijerph20021222
PMID:36673979
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9858744/
Abstract

Soil erosion is an important global environmental issue that severely affects regional ecological environment and socio-economic development. The Yellow River (YR) is China's second largest river and the fifth largest one worldwide. Its watershed is key to China's economic growth and environmental security. In this study, six impact factors, including rainfall erosivity (R), soil erosivity (K), slope length (L), slope steepness (S), cover management (C), and protective measures (P), were used. Based on the revised universal soil loss equation (RUSLE) model, and combined with a geographic information system (GIS), the temporal and spatial distribution of soil erosion (SE) in the YR from 2000 to 2020 was estimated. The patch-generating land use simulation (PLUS) model was used to simulate the land-use and land-cover change (LUCC) under two scenarios (natural development and ecological protection) in 2040; the RUSLE factor P was found to be associated with LUCC in 2040, and soil erosion in the Yellow River Basin (YRB) in 2040 under the two scenarios were predicted and evaluated. This method has great advantages in land-use simulation, but soil erosion is greatly affected by rainfall and slope, and it only focuses on the link between land-usage alteration and SE. Therefore, this method has certain limitations in assessing soil erosion by simulating and predicting land-use change. We found that there is generally slight soil erosivity in the YRB, with the most serious soil erosion occurring in 2000. Areas with serious SE are predominantly situated in the upper reaches (URs), followed by the middle reaches (MRs), and soil erosion is less severe in the lower reaches. Soil erosion in the YRB decreased 11.92% from 2000 to 2020; thus, soil erosion has gradually reduced in this area over time. Based on the GIS statistics, land-use change strongly influences SE, while an increase in woodland area has an important positive effect in reducing soil erosion. By predicting land-use changes in 2040, compared to the natural development scenario, woodland and grassland under the ecological protection scenario can be increased by 1978 km and 2407 km, respectively. Soil erosion can be decreased by 6.24%, indicating the implementation of woodland and grassland protection will help reduce soil erosion. Policies such as forest protection and grassland restoration should be further developed and implemented on the MRs and URs of the YR. Our research results possess important trend-setting significance for soil erosion control protocols and ecological environmental protection in other large river basins worldwide.

摘要

土壤侵蚀是一个重要的全球性环境问题,严重影响区域生态环境和社会经济发展。黄河是中国的第二大河,也是世界上第五大河流。其流域是中国经济增长和环境安全的关键。本研究采用了降雨侵蚀力(R)、土壤可蚀性(K)、坡长(L)、坡度(S)、植被覆盖和管理(C)、保护措施(P)等 6 个影响因子,基于修正的通用土壤流失方程(RUSLE)模型,结合地理信息系统(GIS),对 2000 年至 2020 年黄河流域土壤侵蚀(SE)的时空分布进行了估算。利用斑块生成土地利用模拟(PLUS)模型,对 2040 年自然发展和生态保护两种情景下的土地利用/土地覆被变化(LUCC)进行了模拟;发现 RUSLE 因子 P 与 2040 年的 LUCC 有关,并对两种情景下 2040 年黄河流域的土壤侵蚀进行了预测和评价。该方法在土地利用模拟方面具有很大的优势,但土壤侵蚀受降雨和坡度的影响很大,而且只关注土地利用变化与 SE 之间的联系。因此,该方法在模拟和预测土地利用变化对土壤侵蚀的评估方面存在一定的局限性。我们发现黄河流域的土壤侵蚀总体上较弱,最严重的土壤侵蚀发生在 2000 年。严重土壤侵蚀区主要分布在上游(URs),其次是中游(MRs),下游土壤侵蚀较轻。2000 年至 2020 年,黄河流域土壤侵蚀减少了 11.92%;因此,该地区的土壤侵蚀随时间逐渐减少。根据 GIS 统计,土地利用变化对土壤侵蚀有很大影响,而林地面积的增加对减少土壤侵蚀有重要的积极作用。通过预测 2040 年的土地利用变化,与自然发展情景相比,生态保护情景下的林地和草地面积将分别增加 1978km 和 2407km,土壤侵蚀可减少 6.24%,表明实施林地和草地保护将有助于减少土壤侵蚀。应在黄河中游和上游进一步制定和实施森林保护和草原恢复等政策。本研究结果对世界其他大河流域的土壤侵蚀控制方案和生态环境保护具有重要的指导意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/61a4ea1bdb3f/ijerph-20-01222-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/cb84fb089e9d/ijerph-20-01222-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/4b21a328425e/ijerph-20-01222-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/ec388bfdbd13/ijerph-20-01222-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/85245785a65c/ijerph-20-01222-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/393210b79122/ijerph-20-01222-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/631389ba52eb/ijerph-20-01222-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/61a4ea1bdb3f/ijerph-20-01222-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/cb84fb089e9d/ijerph-20-01222-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/4b21a328425e/ijerph-20-01222-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/ec388bfdbd13/ijerph-20-01222-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/85245785a65c/ijerph-20-01222-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/393210b79122/ijerph-20-01222-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/631389ba52eb/ijerph-20-01222-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5856/9858744/61a4ea1bdb3f/ijerph-20-01222-g007.jpg

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