地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (11): 1755-1767.doi: 10.12082/dqxxkx.2019.180447

• 地理空间分析综合应用 • 上一篇    下一篇

六盘水市土壤侵蚀时空特征及影响因素分析

牛丽楠1,2, 邵全琴1,*(), 刘国波1,2, 唐玉芝1,2   

  1. 1. 中国科学院地理科学与资源研究所 陆地表层格局与模拟重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2018-09-05 修回日期:2019-09-25 出版日期:2019-10-25 发布日期:2019-12-11
  • 通讯作者: 邵全琴 E-mail:shaoqq@igsnrr.ac.cn
  • 作者简介:牛丽楠(1996-),女,内蒙古赤峰人,博士生,研究方向为地图学与地理信息系统专业。E-mail: niuln.18b@igsnrr.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项项目(XDA23100203);国家重点研发计划课题(No.2017YFC0506501)

Analysis on Spatiotemporal Characteristics and Factors of Soil Erosion in Liupanshui City

NIU Li'nan1,2, SHAO Quanqin1,*(), LIU Guobo1,2, TANG Yuzhi1,2   

  1. 1. Key Laboratory of Terrestrial Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-09-05 Revised:2019-09-25 Online:2019-10-25 Published:2019-12-11
  • Contact: SHAO Quanqin E-mail:shaoqq@igsnrr.ac.cn
  • Supported by:
    CAS Strategic Priority Research Program(XDA23100203);National Key Research and Development Program of China(No.2017YFC0506501)

摘要:

六盘水市是我国生态地位极其重要,水土流失又较为严重的城市。近些年,六盘水市实施了一系列生态工程,为了定量分析六盘水市土壤侵蚀状况及其影响因素,本文基于RUSLE模型,利用降雨数据、遥感影像数据、土地利用数据等,对贵州省六盘水市1990-2015年土壤侵蚀模数和土壤侵蚀量进行定量模拟,分析其时空动态变化,利用地理探测器定量分析影响因素,并进行空间相关性分析,结果表明: ① 六盘水市土壤侵蚀以微度和中度侵蚀为主。土壤侵蚀严重地区主要分布在北盘江流域与南盘江流域交界处以及喀斯特山地地区,煤矿开采使植被覆盖等抑制土壤侵蚀因子减少作用,使局部地区土壤侵蚀程度加剧。② 1990-2010年平均土壤侵蚀模数整体为下降趋势,2010-2015年为上升趋势。其中2000年平均土壤侵蚀模数最大,2010年平均土壤侵蚀模数最小。该变化由降雨可蚀性因子和植被覆盖度因子综合影响所致。③ 植被覆盖度因子和多年平均降雨量因子是影响区域土壤侵蚀空间分布的重要因素。未利用土地、植被覆盖度小于0.3、坡度在25°以上和降雨量在1543~1593 mm之间的地区为高风险侵蚀区域。④ 植被覆盖度与土壤侵蚀在空间上全部呈负相关性,降雨因子与土壤侵蚀在空间上存在负相关性和正相关性。⑤ 土壤侵蚀改善区域大多分布在生态工程区域内,生态工程建设能够改善土壤侵蚀情况,不同生态工程保护侧重点不同导致土壤侵蚀改善程度不同。退耕还林还草工程对六盘水市土壤侵蚀的改善具有重要意义,六盘水市更宜退耕还林。

关键词: 土壤侵蚀, 时空变化, RUSLE模型, 地理探测器, 六盘水市

Abstract:

Liupanshui is a city with very important ecological status and serious soil erosion in China. In recent years, Liupanshui has adopted a series of ecological construction projects, therefore, it is very important to quantitatively analyze soil erosion and its influencing factors. Based on the Revised Universal Soil Loss Equation (RUSLE) model and geographical detector method, we calculated the average soil erosion modulus of Liupanshui city during 1990-2015. We analyzed the changes of spatiotemporal patterns, and explored the quantitative analysis of the influencing factors of geographic detector, and spatial correlation analysis. Results show that: (1) The Average Soil Erosion Modulus (ASEM)of Liupanshui was 23.50 t·hm-2·a-1, with an average soil erosion amount of 1578.42×104 t·a-1. The micro and moderate erosion were the dominant erosion types, occupying 83.49% of the total area, while the strong and violent erosion accounted for only 5.3%. The strong erosion area in Liupanshui was mainly located at the junction of Beipanjiang River Basin and Nanpanjiang River Basin as well as the Karst areas with fragile eco-environment. (2) The ASEM was the largest in 2000, which increased by 5.50% compared with that in 1990. The ASEM in 2005 decreased by 18.2% compared with that in 2000. The ASEM in 2010 was the smallest, 11.4% lower than that in 2005. The ASEM in 2015 increased compared with that in 2010. The soil erosion intensity in Liupanshui city in 2000-2015 was weaker than that in 1990-2000. The area of violent erosion decreased, and the strong erosion shifted to micro, light and moderate erosion. (3) The vegetation coverage factor and the perennial average rainfall factor are important factors affecting the spatial distribution of regional soil erosion. Moreover, unused land, vegetation coverage less than 0.3, slope above 25° and rainfall between 1543~1593 mm are high-risk erosion areas. (4) Vegetation coverage and soil erosion have negative correlation in space, while rainfall factors have negative correlation and positive correlation in space. (5) Soil erosion improvement areas are mostly distributed in ecological engineering areas, so ecological engineering construction can improve soil erosion. Different ecological engineering protection priorities lead to different degree of soil erosion improvement. By simulating rainfall to calculate the soil erosion modulus and soil erosion amount before and after Grain-for-Green, it could be seen that the soil erosion situation in Liupanshui had improved after Grain-for-Green. Compared with cultivated land-forest land and cultivated land-grassland land use change area, Liupanshui city is more suitable to return cultivated land to forest. Implementation of the Grain-for-Green Project should be continued in Liupanshui city, and focus more on areas with complex topography and fragile eco-environment.

Key words: soil erosion, spatiotemporal variations, RUSLE model, Geodetector, Liupanshui