砒砂岩地区降雨与植被耦合关系对侵蚀产沙的影响

doi:10.11821/dlyj201603010

摘要/Abstract

摘要：

研究降雨--植被耦合关系对侵蚀产沙的影响,为侵蚀治理提供数据支持.基于TRMM降雨数据和MODIS 250 m NDVI,分析降雨和植被的年内分布模式和匹配关系,并构建降雨--植被耦合指数RV,用于反映侵蚀产沙状况.进一步利用年降雨量,NDVI以及其分布参数(峰度系数和偏斜度)与输沙量进行相关分析和多元回归分析.结果显示:降雨的集中,偏斜程度和波动性要比植被更为明显;RV与实际输沙量的相关系数为0.84,可以很好地反映侵蚀产沙的相对大小;年输沙量与降雨的分布参数相关性最高,达到0.94和0.87,对提高回归模型的拟合程度影响也最大;考虑降雨量,NDVI及其分布参数的模型的拟合程度最好,R_a²达到0.9232.因此,降雨与植被的年内匹配模式对侵蚀产沙具有重要影响.

关键词: 降雨, 植被, 耦合关系, 侵蚀产沙, 砒砂岩

Abstract:

Pisha sandstone area is a major source of coarse sediment, as well as a region with the most intense soil erosion in the Loess Plateau. Its erosion is difficult to control and manage in the eco-environmental construction. This paper reveals the influence of coupling relationship between precipitation and vegetation on sediment yield by analyzing the relationship among precipitation, vegetation and sediment yield. MODIS 250-m NDVI data and TRMM rainfall data are used to research the distribution patterns and matching relations between rainfall and vegetation during the year. Based on the analytical results, a rainfall and vegetation coupling index RV is constructed to reflect the situation of erosion and sediment yield. The index RV is validated by the measured data of Longmen control hydrological station. This paper further analyzes the impact of rainfall and vegetation matching pattern on erosion and sediment yield in different years. Correlation analysis and multiple regression analysis are carried out between sediment yield and the different combinations of annual rainfall, cumulative NDVI and their temporal distribution parameters (kurtosis and skewness). The results show that, (1) The concentration degree and skew degree of rainfall during the year, and its inter-annual instability are more obvious than NDVI. (2) The coupling index RV is not sensitive for the erosion produced by little precipitation, but can effectively reflect the relative size of the sediment yield in the annual period, especially for the time of the maximum erosion. And the correlation coefficient is 0.84. (3) Rainfall kurtosis and skewness have the highest degree of correlation with annual sediment yield, with correlation coefficient reaching 0.94 and 0.87 respectively, which significantly affect erosion and sediment yield. (4) By comparing the several regression models, we found that rainfall kurtosis and skewness are most important for promoting the model fitting degree of model, indicating its impact on erosion and sediment yield. (5) If rainfall, NDVI and their distributed statistical parameters are simultaneously considered in the modeling process, the model correction determination coefficient can reach 0.9232, having the best fitting degree for the sediment yield. Therefore, the matching pattern of rainfall and vegetation during the year is the dominant factor impacting erosion and sediment yield. And the same annual rainfall will cause significant differences in erosion and sediment yield due to their different distribution patterns during the year. When heavy and continuous rainfall occurs, the weaker the protective effect of vegetation is, the greater the amount of erosion and sediment is. Moreover, during the growth period when vegetation lost its protection capability, even a small rainfall event would produce a greater erosion and sediment yield.

Key words: precipitation, vegetation, coupling relationship, sediment yield, Pisha sandstone

张喜旺, 秦奋. 砒砂岩地区降雨与植被耦合关系对侵蚀产沙的影响[J]. 地理研究, 2016, 35(3): 513-524.

Xiwang ZHANG, Fen QIN. Coupling relationship of precipitation and vegetation and its influence for sediment yield in Pisha sandstone area[J]. GEOGRAPHICAL RESEARCH, 2016, 35(3): 513-524.

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变量	y	x₁	x₂	x₃	x₄	x₅	x₆
y	1.00
x₁	0.43	1.00
x₂	-0.14	0.67	1.00
x₃	0.94	0.38	-0.21	1.00
x₄	0.87	0.34	-0.11	0.94	1.00
x₅	0.33	-0.46	-0.60	0.40	0.54	1.00
x₆	0.21	0.44	0.46	0.39	0.52	0.09	1.00

因变量	自变量	回归模型	R²	R_a²	F	Sig
y	x₁, x₂	y = 1.433x₁-461.656x₂ + 693.651	0.5234	0.3872	3.8433	0.0748
y	x₁, x₂, x₃, x₄	y = 0.068x₁+ 23.712x₂+ 64.157x₃-34.229x₄+ 5.246	0.8998	0.8196	11.2248	0.0103
y	x₁, x₂, x₅, x₆	y = 1.528x₁-296.450x₂+ 176.245x₅-63.748x₆+ 501.673	0.6688	0.4039	2.5244	0.1687
y	x₁, x₂, x₃, x₄, x₅, x₆	y = -0.038x₁+ 284.335x₂+ 85.566x₃-80.435x₄+ 90.794x₅-528.622x₆-100.283	0.9744	0.9232	19.0241	0.0174