南方花岗岩区典型崩岗小流域悬浮泥沙来源研究

发布时间：2018-03-03 15:56

本文选题：崩岗　切入点：小流域　出处：《福建农林大学》2017年硕士论文　论文类型：学位论文

【摘要】：为研究南方花岗岩区典型崩岗小流域内悬浮泥沙来源,本文以安溪县龙门镇崩岗侵蚀小流域为研究对象,在4种土地利用类型(侵蚀林地、茶园、耕地和崩岗侵蚀区)中共采集85个泥沙源地土样,同时在河道布设采样器收集降雨后的侵蚀悬浮泥沙。通过分析样品中的34种指纹因子,运用组合指纹法筛选出最佳指纹因子组合,并计算出各泥沙源地的泥沙运移规律。结果表明:(1)利用无参数Kruskal-Wallis检验对所有因子进行无参数检验,筛选出显著差异性(P0.05)的指纹识别因子。各子流域中符合条件的指纹因子有:子流域A(Li、Co、Ni、Sb、Ca共5种),子流域B(Li、Ti、Cr、Mn、Co、Ni、Cu、Zn、Sr、Cd、Sn、Pb、Ca、Al、V、As、P、石英和高岭土共19种),子流域C(Li、B、Ti、Cr、Mn、Co、Ni、Zn、Sr、Cd、Sn、Ba、Tl、Pb、Bi、Ca、Mg、V、Ga、As、P、K、石英、高岭土、辉石和微斜长石共26种)和子流域D(Li、Ti、Cr、Mn、Co、Ni、Cu、Zn、Sr、Cd、Sn、Ba、Tl、Pb、Bi、Fe、Ca、Mg、Al、V、Ga、As、P、K、石英、高岭土、辉石和微斜长石共28种)。(2)对通过无参数检验的指纹因子进行多元判别回归分析得出各子流域的最佳指纹因子组合。子流域A的最佳指纹因子组合是Li、Sb和Ca 3种指纹因子,子流域B的最佳指纹因子组合是Ti、Li、Sn和Ca 4种指纹因子,子流域C的最佳指纹因子组合是Ca、Li、Sn和Mn 4种指纹因子和子流域D的最佳指纹因子组合是Ca、Li、Sn、K和Ba这5种指纹因子。各子流域的累积贡献率都达到90%以上,符合分析要求。通过多元判别效果检验得各子流域中不同泥沙源地的正确判别率都在80%以上,说明所建立判别模型的可靠性。(3)利用多元混合模型定量得出各子流域悬浮泥沙的贡献率。子流域A中各泥沙源地的相对泥沙贡献率均值分别为侵蚀林地(55.26%),茶园(21.44%)和耕地(23.30%);子流域B中茶园(21.51%),耕地(20.07%)和崩岗侵蚀区(58.42%);子流域C中侵蚀林地(17.08%),茶园(42.18%),耕地(19.31%)和崩岗侵蚀区(21.43%);子流域D中侵蚀林地(21.76%),茶园(24.55%),耕地(29.99%)和崩岗侵蚀区(23.70%)。拟合优度检验值均大于0.8,再次证明该混合模型得出的结果可以接受。(4)泥沙源地悬浮泥沙贡献率与降雨之间的耦合关系:子流域A中茶园和耕地的泥沙贡献率均随降雨数据130增大而增大。子流域B中茶园泥沙贡献百分比与降雨雨量和平均降雨强度均呈正相关性,耕地的贡献值随降雨平均降雨强度的增大而增大;崩岗侵蚀区的悬浮泥沙贡献值与降雨雨量、历时和平均强度都呈极显著相关性,且为正比例相关。在子流域C中侵蚀林地的泥沙贡献百分比与降雨的平均降雨强度呈正相关,茶园贡献值与降雨雨量呈正相关,崩岗侵蚀区的贡献值也仅与降雨历时呈正相关。在子流域D中茶园的泥沙贡献值分别随降雨量和降雨历时呈显著性正相关。
[Abstract]:In order to study the source of suspended sediment in the typical gully valley of granite region in southern China, this paper takes Longmen town, Anxi County as the research object, and uses four kinds of land use types (eroding woodland, tea garden, etc.). A total of 85 soil samples from sediment sources were collected, and a sampler was installed in the river to collect erosion suspended sediment after rainfall. Through the analysis of 34 fingerprint factors in the samples, The best combination of fingerprint factors was screened by the combined fingerprint method, and the sediment transport law of each sediment source was calculated. The results show that the non-parametric Kruskal-Wallis test is used to test all the factors. The fingerprint identification factors of significant difference (P0.05) have been screened out. The suitable fingerprint factors in each subwatershed are as follows: AliCoCoNiNiSbCa-Ca in subwatershed, BLiTiCr-MnCr-MnCoNiNiCuPe, PbCaAlVAsPe, quartz and kaolin, 19 species of quartz and kaolin. A total of 26 species of pyroxene and microplagioclase) and in the subbasin of China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China, China. Twenty-eight species of pyroxene and microplagioclase were obtained by multivariate discriminant regression analysis of fingerprint factors without parameter test. The optimum combination of fingerprint factors of sub-watershed A was Li-Sb and Ca. The best fingerprint factor combination of subbasin B is Tiangliang Sn and Ca four fingerprint factors. The best fingerprint factor combination of C is four kinds of fingerprint factors of Caanliang Sn and mn, and the best combination of fingerprint factors of D is five kinds of fingerprint factors. The cumulative contribution rate of each subwatershed is above 90%. According to the requirement of analysis, the correct discriminant rate of different sediment sources in each subbasin is above 80% by multivariate discriminant effect test. It shows that the reliability of the discriminant model is established. (3) the contribution rate of suspended sediment in each sub-watershed is quantitatively obtained by using the multivariate mixed model. The mean relative sediment contribution rates of each sediment source in sub-watershed A are 55.26% and 21.44%, respectively, for eroded forest land and tea garden. In subbasin B, the tea garden is 21.51U, the cultivated land is 20.07) and the erosion area is 58.42U; in the subbasin C, the eroded woodland is 17.08m, the tea garden is 42.18U, the cultivated land is 19.31) and the erosion area is 21.4343; in the subbasin D, the woodland is 21.761U, the tea garden is 24.55m, the cultivated land is 29.9999m) and the collapse erosion area is 23.70m. The results obtained by the mixed model are all more than 0.8, which proves the coupling relationship between the contribution rate of suspended sediment and rainfall in sediment source area: the contribution rate of tea garden and cultivated land in sub-watershed A is both with rainfall. The percentage of tea garden sediment contribution in sub-watershed B was positively correlated with rainfall and average rainfall intensity. The contribution of cultivated land increases with the increase of the average rainfall intensity, and the suspended sediment contribution in the erosion area is significantly correlated with rainfall, duration and average intensity. The percentage of sediment contribution of eroded forest land in sub-watershed C was positively correlated with the average rainfall intensity of rainfall, and the value of tea garden contribution was positively correlated with rainfall rainfall. The contribution of tea garden in subbasin D was positively correlated with rainfall and rainfall duration.
【学位授予单位】：福建农林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：S157

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