Analysis of Sediment Source in Small Watershed of Karst Depression Based on Composite Fingerprint Method

CHEN Ying; WEI Xingping; ZHANG Aiguo; LEI Shan

doi:10.13870/j.cnki.stbcxb.2020.05.019

您当前的位置：

首页 >

文章列表页 >

Analysis of Sediment Source in Small Watershed of Karst Depression Based on Composite Fingerprint Method

更新时间：2025-03-12

- Analysis of Sediment Source in Small Watershed of Karst Depression Based on Composite Fingerprint Method
- Vol. 34, Issue 5, Pages: 131-136(2020)
- 作者机构：
  
  重庆师范大学地理与旅游学院,重庆,401331
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2020.05.019
  CLC： S157.1
- Published：2020
- 稿件说明：
移动端阅览
CHEN Ying, WEI Xingping, ZHANG Aiguo, et al. Analysis of Sediment Source in Small Watershed of Karst Depression Based on Composite Fingerprint Method[J]. 2020, 34(5): 131-136.
DOI：

CHEN Ying, WEI Xingping, ZHANG Aiguo, et al. Analysis of Sediment Source in Small Watershed of Karst Depression Based on Composite Fingerprint Method[J]. 2020, 34(5): 131-136. DOI： 10.13870/j.cnki.stbcxb.2020.05.019.

摘要

岩溶洼地是重庆岩溶地区常见的一种地貌类型，洼地小流域是人们从事农事活动的重要场所，研究流域不同土地利用侵蚀产沙特征，对认识流域侵蚀产沙规律和治理水土流失具有重要意义。选取了1个建有水库的岩溶洼地小流域，分析源地和泥沙样品的39个地球化学特征，利用复合指纹和多元混合模型计算各源地侵蚀泥沙贡献率。结果表明：流域筛选出的最佳指纹因子组合为U、χlf、Rb、Li、137Cs、χ、Mn，正确判别累积率为99.93%，6种泥沙源地的总正确判别率为91.49%，符合复合指纹法应用的条件。碳酸盐岩耕地、碳酸盐岩林地、碳酸盐岩草地、碎屑岩林地、碎屑岩草地和沟道/裂隙对流域沉积泥沙的相对贡献分别为16.29%，41.16%，13.03%，16.67%，4.48%，8.37%，拟合优度为93.57%，相对误差平均值为8.26%，表明模型判别结果可信。各泥沙源地中碳酸盐岩耕地单位面积泥沙贡献值远高于其他泥沙源地，是碳酸盐岩林地的1.98倍，碳酸盐岩草地的2.07倍，碎屑岩林地的2.13倍，碎屑岩草地的2.21倍，即单位面积碳酸盐岩耕地土壤抗蚀能力最弱。

Abstract

Karst depression is a common landform type in karst areas of Chongqing. Small depression watershed is an important place for agricultural activities. Studying the characteristics of erosion and sediment yield of different land uses in the watershed is of great significance for understanding the laws of erosion and sediment production and controlling soil and water loss. A small karst depression basin with a reservoir was selected

and 39 geochemical properties of the sediment source as well as sediment samples were analyzed. The composite fingerprint and multivariate mixed model were used to calculate the erosion and sediment contribution rate of each sediment source. The results showed that the best fingerprint factor combination screened by the catchment was U

χlf

137Cs

χ and Mn. The cumulative rate of correct discrimination was 99.93%

and the total correct discrimination rate of the six sediment sources was 91.49%

which accorded to the application conditions of compound fingerprint method. The relative contributions to sediments in the basin of carbonate arable land

carbonate woodland

carbonate grassland

clastic rock forest

clastic grassland and trench/fissure soil were 16.29%

41.16%

13.03%

16.67%

4.48% and 8.37%

respectively

the goodness of fit of the model was 93.57%

and the average relative error was 8.26%

indicating that the model result was credible. Among all sediment source areas

the sediment contribution per unit area of carbonate rock farmland was far higher than that of other sediment source areas

which was 1.98 times that of carbonate forest land

2.07 times that of carbonate grassland

2.13 times that of clastic rock forest land

and 2.21 times that of clastic rock grassland. The soil corrosion resistance of carbonate rock farmland per unit area was the weakest.

关键词

Keywords

references

周慧平,常维娜,张龙江.基于泥沙指纹识别的小流域颗粒态磷来源解析[J].农业工程学报,2015,31(13):251-256.

周曼,林嘉辉,黄炎和,等.复合指纹法分析红壤区强度开发小流域泥沙来源[J].水土保持学报,2019,33(1):20-24.

宓莹.云南大石坝水库流域土壤侵蚀与沉积泥沙来源的关系研究[D].南京:南京师范大学,2015.

陈方鑫.利用生物标志物和复合指纹分析法识别小流域泥沙来源[D].武汉:华中农业大学,2017.

Collins A, Walling D, Leeks G J L. Composite fingerprinting of the spatial source of fluvial suspended sediment:A case study of the Exe and Severn River basins,United Kingdom[J].Géomorphologie:Relief, Processus, Environnement,1996,2(2):41-53.

Collins A L, Naden P S, Sear D A, et al. Sediment targets for informing river catchment management:International experience and prospects[J].Hydrological Processes,2011,25(13):2112-2129.

Lamba J, Thompson A M, Karthikeyan K G, et al. Sources of fine sediment stored in agricultural lowland streams, Midwest, USA[J].Geomorphology,2015,236(1):44-53.

张信宝,贺秀斌,文安邦,等.川中丘陵区小流域泥沙来源的

137

Cs和

210

Pb双同位素法研究[J].科学通报,2004(15):1537-1541.

郭进,文安邦,严冬春,等.复合指纹识别技术定量示踪流域泥沙来源[J].农业工程学报,2014,30(2):94-104.

汪南.基于复合指纹分析法的元江干热河谷区小流域泥沙来源研究[D].昆明:云南大学,2018.

杨明义,徐龙江.黄土高原小流域泥沙来源的复合指纹识别法分析[J].水土保持学报,2010,24(2):30-34.

赵恬茵.复合指纹识别法研究黄土高原小流域泥沙来源[D].陕西杨凌:西北农林科技大学,2017.

林金石,黄炎和,张旭斌,等.南方花岗岩区典型崩岗侵蚀产沙来源分析[J].水土保持学报,2012,26(3):53-57.

常维娜,周慧平,高燕.基于复合指纹法的九乡河小流域泥沙来源解析[J].水土保持学报,2014,28(6):106-110,154.

郑红雷.重庆南川石漠化地区可持续发展模式研究[D].重庆:西南大学,2010.

郑必昭.土壤分析技术指南[M].北京:中国农业出版社,2013.

周慧平,陈玉东,常维娜.指纹技术识别泥沙来源:进展与展望[J].水土保持学报,2018,32(5):1-7.

Wilkinson S N, Hancock G J, Bartley R, et al. Using sediment tracing to assess processes and spatial patterns of erosion in grazed rangelands, Burdekin River basin, Australia[J].Agriculture,Ecosystems & Environment,2013,180:90-102.

Franz C, Makeschin F, Weiß H, et al. Sediments in urban river basins:Identification of sediment sources within the Lago Paranoá catchment, Brasilia DF,Brazil-using the fingerprint approach[J].Science of the Total Environment,2014,466:513-523.

Motha J A, Wallbrink P J, Hairsine P B, et al. Determining the sources of suspended sediment in a forested catchment in southeastern Australia[J].Water Resources Research,2003,39(3):1056-1069.

Walling D E. Tracing suspended sediment sources in catchments and river systems[J].Science of the Total Environment,2005,344(1/3):159-184.

张果,胡雪峰,吴小红,等.上海城市土壤磁化率的垂向分布特征及环境指示意义[J].土壤学报,2011,48(2):429-434.

程倩云,彭韬,张信宝,等.西南喀斯特小流域地表、地下河流细粒泥沙来源的

137

Cs和磁化率双指纹示踪研究[J].水土保持学报,2019,33(2):140-145,154.

李安定,李苇洁,彭熙,等.贵州喀斯特区野生葛藤群落主要种群生态位[J].浙江农林大学学报,2012,29(4):491-497.

Views

1031

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Tracing Fine Sediment Sources in the Surface and Subsurface Rivers of a Karst Watershed Using Compound Fingerprinting with 137Cs and Magnetic Susceptibility in Southwest China

Using Composite Fingerprints to Qualify Sediment Source in Watershed with Intensive Exploitation on Red Soil Region

Inversion of Black Soil Layer Thickness Based on Multi-Temporal High-Resolution Remote Sensing Data and Machine Learning

Small Watershed Erosion Model （SWEM）

Spatiotemporal Variation of Soil Erosion Intensity in Mollisols of A Small Watershed—A Case Study of Guangrong Small Watershed

Related Author

王世杰

曹乐

张信宝

彭韬

程倩云

LIN Jinshi

ZHANG Liting

GE Hongli

Related Institution

中国科学院水利部成都山地灾害与环境研究所

中国科学院地球化学研究所, 环境地球化学国家重点实验室

中国科学院大学

中国科学院普定喀斯特生态系统观测研究站, 贵州普定

College of Resource and Environment Science, Fujian Agriculture and Forestry University

⁰