[1]TONG Shan,CAO Guangchao,YAN Xin,et al.Quantitative Study on Soil Erosion and Its Influencing Factors on the South Slope of Qilian Mountain[J].Research of Soil and Water Conservation,2022,29(05):100-107,116.
Copy
Quantitative Study on Soil Erosion and Its Influencing Factors on the South Slope of Qilian Mountain
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
29
Number of periods:
2022 05
Page number:
100-107,116
Column:
Public date:
2022-08-20
- Title:
- Quantitative Study on Soil Erosion and Its Influencing Factors on the South Slope of Qilian Mountain
- Keywords:
- soil erosion; soil erosion dynamic change; soil erosion prediction; analysis of influencing factors
- CLC:
- S157.1
- DOI:
- -
- Abstract:
- Soil erosion is one of the main causes of soil degradation and soil fertility decline, which directly affects the sustainable development of regional ecological economy. Based on RUSLE model, CA Markov model and LMDI model, we studied the spatial change and prediction of soil erosion on the south slope of Qilian Mountain from 2000 to 2019, and the quantitatively analyzed the soil erosion change characteristics and influencing factors under different topographic conditions, so as to provide reference for soil and water conservation and control in the study area. The results show that:(1)the soil erosion modulus decreased from northwest to southeast, and the overall increase rate was 0.064 5/a;(2)the change of soil erosion could be divided into two stages: 2000—2005 was the aggravation stage of soil erosion, and 2005—2019 was the mitigation stage of soil erosion;(3)from 2019 to 2027, although soil erosion tended to reduce, it was also necessary to prevent the transformation from low-level to high-level erosion below extreme intensity;(4)the soil erosion modulus increased with the increase of altitude and slope; the soil erosion modulus reached to the maximum in the area with altitude of 4 700～5 200 m and slope>30°, which were 10 460.72 and 7 256.32 t/(km2·a), respectively; the amount of soil erosion decreased with the increase of slope, and the order of soil erosion in different slope directions was west>north>south>east>horizontal direction;(5)the impact of vegetation on soil erosion must be positive, while rainfall does not necessarily aggravate soil erosion, and soil erosion is less affected by vegetation and rainfall, which is mainly distributed in Menyuan County. In conclusion, soil erosion on the southern slope of Qilian Mountains has been well controlled in recent years, and soil erosion can be classified and controlled according to specific environmental conditions.
- References:
-
[1] Lda B, Tsa B, Kai F, et al. Effects of erosion degree, rainfall intensity and slope gradient on runoff and sediment yield for the bare soils from the weathered granite slopes of SE China[J]. Geomorphology, 2019,352:106997.
[2] 史东梅,金慧芳,蒋光毅.土壤侵蚀对坡耕地耕层质量退化作用及其评价趋势展望[J].农业工程学报,2019,35(18):118-126.
[3] Lal R. Soil erosion and the global carbon budget [J]. Environ—Ment International, 2003,29(4):437-450.
[4] 胡光印,董治宝,逯军峰,等.近30 a来长江源区沙滚化时空演变过程及成因分析[J].干旱区地理,2011,34(2):300-308.
[5] 康琳琦.青藏高原1980—2009年土壤侵蚀量的估算与比较[D].成都:四川农业大学,2017.
[6] 林慧龙,郑舒婷,王雪璐.基于RUSLE模型的三江源高寒草地土壤侵蚀评价[J].草业学报,2017,26(7):11-22.
[7] 陈豪.基于USLE模型的祁连山国家公园土壤水力侵蚀动态分析[D].兰州:兰州大学,2020.
[8] 孙定钊,梁友嘉.基于改进Markov-CA模型的黄土高原土地利用多情景模拟[J].地球信息科学学报,2021,23(5):825-836.
[9] 玛丽艳姑丽·阿西穆,塔西甫拉提·特依拜,买买提·沙吾提,等.基于Markov模型的植被覆盖动态变化预测研究[J].水土保持研究,2013,20(1):121-125.
[10] 邹艳,张绍良,闵扬海.城市人口规模及空间分布模拟与预测[J].城市问题,2020(6):64-72.
[11] 武国胜,林惠花,朱鹤健.基于马尔柯夫模型的福建长汀土壤侵蚀动态预测[J].福建师范大学学报:自然科学版,2011,27(1):93-98.
[12] 赵博轩.基于RUSLE和CA-Markov的土壤侵蚀变化监测及预测研究[D].北京:中国地质大学,2020.
[13] 赵明松,李德成,张甘霖.1980—2010年安徽省土壤侵蚀动态演变及预测[J].土壤,2016,48(3):588-596.
[14] 贺倩,戴晓爱.基于LMDI模型的三江源区植被对土壤侵蚀变化影响的定量分析[J].长江科学院院报,2020,37(7):61-67.
[15] 贾磊,姚顺波,邓元杰,等.渭河流域土壤侵蚀时空特征及其地理探测[J].生态与农村环境学报,2021,37(3):305-314.
[16] 樊登星,余新晓,贾国栋,等.北京山区灌草坡面水土流失特征及其影响因素[J].中国水土保持科学,2014,12(2):24-28.
[17] 马驼,于兴修,刘前进,等.沂蒙山区土壤侵蚀空间分布及其影响因素动态变化[J].中国农业科学,2010,43(22):4652-4662.
[18] 何琪琳,李斌斌,张风宝,等.东北黑土区坡耕地土壤侵蚀对影响因素响应的定量分析[J].地理学报,2021,76(5):1218-1230.
[19] 马贤磊,唐亮,孙萍丽,等.城镇土地生态环境效应的影响因素研究:基于LMDI分解模型[J].南京农业大学学报:社会科学版,2018,18(2):117-128.
[20] Ang B W. The LMDI approach to decomposition analysis: A practical guide [J]. Energy Policy, 2005,33(7):867-871.
[21] Qian H E, Dai N, Chen N. Assessing the effects of vegetation and precipitation on soil erosion in the Three-River Headwaters Region of the Qinghai-Tibet Plateau, China[J].Journal of Arid Land,2020,12(5):865-886.
[22] 钱大文,曹广民,杜岩功,等.2000—2015年祁连山南坡生态系统服务价值时空变化[J].生态学报,2020,40(4):1392-1404.
[23] 刘贤德,张学龙.祁连山天然林—河西走廊生命线[J].森林与人类,2004,24(4):16-19.
[24] 杨荣荣,曹广超,曹生奎,等.祁连山南坡表层土壤有机质含量反演[J].生态科学,2020,39(5):57-63.
[25] 虞敏,曹广超,曹生奎,等.近30年祁连山南坡降水量变化特征分析[J].水土保持研究,2019,26(2):241-248.
[26] 付建新,曹广超,郭文炯.1980—2018年祁连山南坡土地利用变化及其驱动力[J].应用生态学报,2020,31(8):2699-2709.
[27] 魏健美,李常斌,武磊,等.基于USLE的甘南川西北土壤侵蚀研究[J].水土保持学报,2021,35(2):31-37,46.
[28] 林锦阔.基于RUSLE模型沂河流域土壤侵蚀研究[D].济南:山东师范大学,2017.
[29] 刘斌涛,陶和平,史展,等.青藏高原土壤可蚀性K值的空间分布特征[J].水土保持通报,2014,34(4):11-16.
[30] 符素华,刘宝元,周贵云,等.坡长坡度因子计算工具[J].中国水土保持科学,2015,13(5):105-110.
[31] Sun I, Cheng G W, Li W P, et al. On the Variation of NDVI with the principal climatic elements in the Tibetan Plateau [J].Remote Sensing,2013,5(4):1894-1911.
[32] 于涛,沈浩,仲嘉亮.基于CA-Markov模型的新疆克州土地利用动态模拟研究[J].新疆环境保护,2008,30(1):11-14.
[33] 赵博轩.基于RUSLE和CA-Markov的土壤侵蚀变化监测及预测研究[D].北京:中国地质大学,2020.
[34] Hong Q L, Xu Y, Hai Y D, et al. Analysis of Energy consumption influence factors in Japan based on LMDI model[J]. Applied Mechanics and Materials, 2011,71-78:2416-2419.
[35] 付建新,曹广超,郭文炯.1998—2017年祁连山南坡不同海拔、坡度和坡向生长季NDVI变化及其与气象因子的关系[J].应用生态学报,2020,31(4):1203-1212.
[36] 崔晓临,自红英,王涛.秦岭地区植被NDVI海拨梯度差异及其气温响应[J].资源科学,2013,35(3):618-626.
[37] 张静静,郑辉,朱连奇,等.豫西山地植被NDV1及其气候响应的多维变化[J].地理研究,2017,36(4):765-778.
[38] 陈红,江旭聪,任磊,等.基于RUSLE模型的淮河流域土壤侵蚀定量评价[J].土壤通报,2021,52(1):165-176.
[39] 陈豪.基于USLE模型的祁连山国家公园土壤水力侵蚀动态分析[D].兰州:兰州大学,2020.
- Similar References:
Memo
-
Last Update:
2022-08-20