| Study on a forecasting model of precipitation penetration depth in the middle arid zone of Ningxia |
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| DOI：10.7606/j.issn.1000-7601.2016.06.21 |
| Key Words: precipitation penetration depth forecasting model soil texture natural grassland Ningxia |
| Author Name | Affiliation | | LI Hong-ying | Ningxia Key Lab for Meteorological Disaster Prevention and Reduction, Yinchuan, Ningxia 750002, China
Ningxia Meteorological Science Institute, Yinchuan, Ningxia 750002, China | | MA Guo-fei | Ningxia Key Lab for Meteorological Disaster Prevention and Reduction, Yinchuan, Ningxia 750002, China
Ningxia Meteorological Science Institute, Yinchuan, Ningxia 750002, China | | JIN Fei | Wuzhong Meteorological Service, Wuzhong, Ningxia 751100, China | | DUAN Xiao-feng | Ningxia Key Lab for Meteorological Disaster Prevention and Reduction, Yinchuan, Ningxia 750002, China
Ningxia Meteorological Science Institute, Yinchuan, Ningxia 750002, China | | WANG Jing | Ningxia Key Lab for Meteorological Disaster Prevention and Reduction, Yinchuan, Ningxia 750002, China
Ningxia Meteorological Science Institute, Yinchuan, Ningxia 750002, China | | MA Li-wen | Ningxia Key Lab for Meteorological Disaster Prevention and Reduction, Yinchuan, Ningxia 750002, China
Ningxia Meteorological Science Institute, Yinchuan, Ningxia 750002, China | | ZHANG Xiao-yu | Ningxia Key Lab for Meteorological Disaster Prevention and Reduction, Yinchuan, Ningxia 750002, China
Ningxia Meteorological Science Institute, Yinchuan, Ningxia 750002, China |
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| Abstract: |
| In order to understand the penetration of precipitation in the soil at natural grassland of central arid zone in Ningxia, the penetration was calculated based on the increment of soil water content, and the statistical model for predicting the penetration depth of different soil texture was studied by statistical methods including regression, stepwise regression and correlation analysis. The results showed that the regression models for penetration depth prediction were more notable than the stepwise regression models. R2 of the regression models was 0.60～0.67, while R2 of the stepwise regression models was 0.49～0.58. The coefficients of precipitation or precipitation days in two models all had statistically significant relationships with penetration depth. Through the comparison between the predicted results and the measured values by correlation analysis method, the correlation coefficient between them was over 0.70. In particular, the prediction results by two models were significantly correlated (the correlation coefficient was 0.88～0.93). From the usage of the prediction model, the stepwise regression model was selected for penetration depth prediction. In addition, the prediction model performed better for the sand loam soil in Xingren than others and the relative errors of 81%～100% samples were about 30% or lower. The model for the loam soil in Tongxin was acceptable and the relative errors of 55%～60% samples were about 30 percent or lower. The model for the sand soil was not very good and the relative errors of half of samples were about 30 percent or lower. Furthermore, the influence of soil texture on the penetration depth was verified by the stepwise regression model. Under the same precipitation process, the penetration depth was in the following order: sand soil＞sandy loam soil＞loam soli. |
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