Multifactor analysis and simulation of the surface runoff and soil infiltration at different slope gradients

3rd International Conference on Water Resource and Environment

地球科学;生态环境科学

Huang, J.^1,2 ; Kang, Q.^1,2 ; Yang, J.X.^1,2 ; Jin, P.W.^1,2

Soil and Water Conservation Monitoring Center of Pearl River Basin, Guangzhou

510611, China^1

Pearl River Hydraulic Research Institute, Guangzhou

510611, China^2

关键词: Antecedent soil moisture;    Multifactor analysis;    Multiple nonlinear regression;    Nash-Sutcliffe coefficient;    Natural rainfalls;    Positive correlations;    Root mean square errors;    Soil infiltration;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/82/1/012019/pdf
DOI  :  10.1088/1755-1315/82/1/012019

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The surface runoff and soil infiltration exert significant influence on soil erosion. The effects of slope gradient/length (SG/SL), individual rainfall amount/intensity (IRA/IRI), vegetation cover (VC) and antecedent soil moisture (ASM) on the runoff depth (RD) and soil infiltration (INF) were evaluated in a series of natural rainfall experiments in the South of China. RD is found to correlate positively with IRA, IRI, and ASM factors and negatively with SG and VC. RD decreased followed by its increase with SG and ASM, it increased with a further decrease with SL, exhibited a linear growth with IRA and IRI, and exponential drop with VC. Meanwhile, INF exhibits a positive correlation with SL, IRA and IRI and VC, and a negative one with SG and ASM. INF was going up and then down with SG, linearly rising with SL, IRA and IRI, increasing by a logit function with VC, and linearly falling with ASM. The VC level above 60% can effectively lower the surface runoff and significantly enhance soil infiltration. Two RD and INF prediction models, accounting for the above six factors, were constructed using the multiple nonlinear regression method. The verification of those models disclosed a high Nash-Sutcliffe coefficient and low root-mean-square error, demonstrating good predictability of both models.