Spatial variability and forecast of soil water in the ultra-deep loess profile across a south-north transect of the Chinese Loess Plateau

被引:0
作者
Gong, Tiexiong [1 ,7 ]
Zhu, Yuanjun [2 ,3 ,4 ,5 ]
Qiao, Jiangbo [3 ,4 ]
Shao, Ming'an [2 ,3 ,4 ,5 ,6 ]
机构
[1] Gansu Agr Univ, Coll Resources & Environm, Lanzhou, Peoples R China
[2] Northwest A&F Univ, Coll Resources & Environm, Yangling, Peoples R China
[3] Northwest A&F Univ, Key Lab Soil Eros & Dryland Farming Loess Plateau, Yangling, Peoples R China
[4] Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling, Peoples R China
[5] Univ Chinese Acad Sci, Beijing, Peoples R China
[6] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
[7] Gansu Agr Univ, Coll Resources & Environm, Lanzhou 730070, Peoples R China
基金
中国国家自然科学基金;
关键词
artificial neural networks; Chinese Loess Plateau; soil water; ultra-deep loess profile; ARTIFICIAL NEURAL-NETWORK; TEMPORAL STABILITY; MOISTURE; STORAGE; DYNAMICS; MODEL; SIMULATION; PATTERNS; IMPACTS; ZONE;
D O I
10.1002/hyp.15108
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Knowledge of the spatial variability and forecast of soil water in the ultra-deep (>21 m) loess profile are important for understanding the chemical, physical, and biological processes in the CZ. In this study, we regularly monitor soil water content (SWC) in deep soil profile along regional transect on the Loess Plateau. Descriptive statistical analysis found that the coefficient of variation (CV) of mean SWC in Ansai and Shenmu were 16.036% and 13.606%, respectively, indicating moderate variability. The CV of mean SWC in Yangling, Changwu, and Fuxian were 4.111%, 7.951%, and 6.117%, respectively, showing low variability. Geo-statistical analysis indicated that mean SWC showed strong spatial dependence. Wavelet analysis showed that the approximative trend of mean SWC in five sampling sites showed an increased trend along depth series. In addition, a good fit line equation (R2 = 0.329) was established by using observed values and ANN-forecast of three sites (Yangling, Fuxian, and Shenmu). And the RMSE values of five sample sites, respectively, were 1.302, 4.546, 2.662, 6.231, and 4.293. This study fills the gap in research' ultra-deep (>21 m) soil water changes and forecast, evidence from soil borehole data. At the same time, our research provides valuable information for the vegetation restoration and modelling of deep soil water.
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页数:12
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