[1]YAO Zhi-hong,YANG Qing-ke,WANG Feng-ping,et al.Quantitative Marking of Non-climatic Factors Influencing Soil Moisture in Loess Hilly Area[J].Research of Soil and Water Conservation,2012,19(05):23-28.
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Quantitative Marking of Non-climatic Factors Influencing Soil Moisture in Loess Hilly Area
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
19
Number of periods:
2012 05
Page number:
23-28
Column:
Public date:
2012-10-20
- Title:
- Quantitative Marking of Non-climatic Factors Influencing Soil Moisture in Loess Hilly Area
- CLC:
- S152.7
- DOI:
- -
- Abstract:
- Soil moisture is one of the most important factors influencing soil erosion modeling and prediction. A lot of research results have contributed to evaluation of climatic factors such as rainfall. The impacts of non-climatic factors, such as land-use, slope degree, slope aspects and slope position, on soil moisture were analyzed based on the field observation data in Loess hilly region. The influence coefficient on soil water of each factor and calculation method were proposed. And soil moisture simulation equation of each month from May to October was developed. The main results were:(1) The average soil water content of all land use types from May to October followed the same trend as a reversed parabola: decreasing firstly and increasing late, and the lowest point was in August from high to low level, the soil water contents were in the sequence of terraced land>cropping slope land>waste grassland>orchard land>Robinia pseudoacacia land>Caragana korshivskii land;(2) Based on the effects of slope degree, land use, slope aspect and slope position on vertical distribution of soil water contents,the influence coefficient on soil water of each factor was calculated by standardization algorithm;(3) Taking monthly soil water contents as the dependent variable and slope degree, standardized slope aspect and land-use type, relative slope position coefficient as the independent variables, the regression equations for each month from May to October were fitted. The regression result showed that the R2 ranged from 0.54 and 0.64 and P valuables are far less than 0.05. And it was meaningful for soil water simulation in the similar areas.
- References:
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