Abstract
Multivariate statistics are commonly used to identify the factors that control the dynamics of runoff or sediment yields during hydrological processes. However, one issue with the use of conventional statistical methods to address relationships between variables and runoff or sediment yield is multicollinearity. The main objectives of this study were to apply a method for effectively identifying runoff and sediment control factors during hydrological processes and apply that method to a case study. The method combines the clustering approach and partial least squares regression (PLSR) models. The case study was conducted in a mountainous watershed in the Three Gorges Area. A total of 29 flood events in three hydrological years in areas with different land uses were obtained. In total, fourteen related variables were separated from hydrographs using the classical hydrograph separation method. Twenty-nine rainfall events were classified into two rainfall regimes (heavy Rainfall Regime I and moderate Rainfall Regime II) based on rainfall characteristics and K-means clustering. Four separate PLSR models were constructed to identify the main variables that control runoff and sediment yield for the two rainfall regimes. For Rainfall Regime I, the dominant first-order factors affecting the changes in sediment yield in our study were all of the four rainfall-related variables, flood peak discharge, maximum flood suspended sediment concentration, runoff, and the percentages of forest and farmland. For Rainfall Regime II, antecedent condition-related variables have more effects on both runoff and sediment yield than in Rainfall Regime I. The results suggest that the different control factors of the two rainfall regimes are determined by the rainfall characteristics and thus different runoff mechanisms.
Highlights
Soil erosion affects soil productivity and sustainable agriculture
Many studies have demonstrated that land use type can change runoff and sediment yield [29]; the percentages of forest and farmland during the study years were included in the partial least squares regression (PLSR) models
In a PLSR model, the importance of a predictor for both the independent and dependent variables is given by the variable importance for the projection (VIP) [10,12]
Summary
Soil erosion affects soil productivity and sustainable agriculture. Erosion by water strips the fertile topsoil on site, degrades water quality, and clogs streams, rivers, and reservoirs by transporting sediments off site [1]. Analysis of the relationships between sediment transport, rainfall, and runoff characteristics can facilitate the elucidation of the factors and processes determining sediment responses [3]. Many recent studies have evaluated factors that control hydrological and sediment responses at an inter-event scale. Oeurng et al [4] use a Pearson correlation matrix and factorial analysis to assess the relationships between precipitation, discharge, and suspended sediment transport to explain the hydrological and sedimentological responses in a catchment of France. López-Tarazón et al [5] use multiple regression equations derived from Pearson correlation analysis to describe the relationships between rainfall, runoff, and sediment transport in a mountainous catchment. Wine et al [6] apply stepwise regression to determine control factors of runoff in watersheds of the Southern Great Plains
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