Open AccessFactors Controlling Temporal Stability of Surface Soil Moisture: A Watershed‐Scale Modeling Study
Author(s) -
Wei Lingna,
Dong Jianzhi,
Gao Man,
Chen Xi
Publication year - 2017
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.2136/vzj2016.12.0132
Subject(s) - watershed , environmental science , sampling (signal processing) , water content , soil science , scale (ratio) , moisture , hydrology (agriculture) , sampling design , spatial variability , vegetation (pathology) , mathematics , statistics , geography , geology , meteorology , computer science , medicine , population , geotechnical engineering , cartography , demography , filter (signal processing) , pathology , machine learning , sociology , computer vision
Core Ideas Soil moisture data at a watershed scale can be derived from distributed land surface modeling. Factors controlling soil moisture temporal stability can be studied using model simulations. Soil moisture estimation accuracy is improved when dominant factors are considered a priori. The minimum number of sampling points and the shortest sampling periods were investigated. The spatial variability of soil moisture makes it difficult to represent watershed‐scale soil moisture using traditional point‐scale soil moisture sensors. In the temporal stability method, the spatial pattern of soil moisture is assumed to persist with time. Hence, measurements at a representative point can be used to represent the mean soil moisture. We investigated the factors that determine temporal stability and attempted to locate these representative points. Long‐term simulated high‐resolution soil moisture data, at a watershed scale, were used. Results showed that locations with a dominant vegetation cover and a low topographic wetness index (TI) can provide reasonable mean soil moisture estimates. Using vegetation cover and TI information, we minimized the number of the sampling locations needed for identifying the best estimate of the true watershed‐scale mean. The sampling period duration is also a key factor. Using random combination tests, the minimum number of required sampling points and shortest sampling time were estimated. When 10 sampling points were used, a sampling period of 10 mo was required for accurately determining the representative point. Our study results will help apply the temporal stability method to the estimation of areal soil moisture and the calibration and validation of remote sensing data.