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Spatiotemporal Characteristics of Soilwater Storage Along Regional Transect on the Loess Plateau, China
Author(s) -
Jia Xiaoxu,
Shao Ming'an,
Zhao Chunlei,
Zhang Chencheng
Publication year - 2017
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201600328
Subject(s) - transect , soil science , arid , soil water , environmental science , vegetation (pathology) , elevation (ballistics) , plateau (mathematics) , loess , loess plateau , geology , spatial variability , soil carbon , soil horizon , precipitation , hydrology (agriculture) , geomorphology , geography , mathematical analysis , oceanography , mathematics , medicine , paleontology , statistics , geometry , geotechnical engineering , pathology , meteorology
Knowledge of the spatiotemporal variability of soil water storage (SWS) is important for vegetation restoration and water resources optimization in semi‐arid areas. This study investigated the spatiotemporal characteristics of SWS in the 0–5 m profile and identified representative sites for reliably estimating the mean SWS along regional transect of the Loess Plateau. Mean relative difference and the associated standard deviation (SDRD) were used to assess temporal stability (TS) of profile SWS. The time‐averaged mean SWS in the 5‐m profile generally decreased from south to north following the decreasing rainfall gradient, with a mean value of 704 mm. Spatiotemporal analysis showed that temporal variations in SWS decreased with increasing soil depth, while the spatial variations increased. Comparisons of SDRD and the number of time‐stable locations with SDRD <5% among various depths indicated that TS increased with increasing soil depth. The representative sites identified for the transect well reflected mean SWS of the five soil layers, and the accuracy of estimation increased with increasing soil depth. The TS of SWS patterns was controlled by precipitation, temperature, clay content, field capacity, soil organic carbon, elevation, slope gradient, and normalized difference vegetation index in the study area. This study demonstrates the feasibility of directly estimating the patterns of SWS using TS analysis on a large scale, which might be comprehensively controlled by the combined effects of climate, soil, topography, and vegetation.