Abstract
Abstract. Profile soil moisture (SM) in mountainous areas is important for water resource management and ecohydrological studies of downstream arid watersheds. Satellite products are useful for providing spatially distributed SM information but only have limited penetration depth (e.g., top 5 cm). In contrast, in situ observations can provide measurements at several depths, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using multiple methods. This study evaluates methods to calculate subsurface SM from surface SM and its application to satellite SM products, based on a SM observation network in the Qilian Mountains (China) that has operated since 2013. Three different methods were tested to estimate subsurface SM at 10 to 20, 20 to 30, 30 to 50, and 50 to 70 cm, and, in a profile of 0 to 70 cm, from in situ surface SM (0 to 10 cm): the exponential filter (ExpF), the artificial neural network (ANN), and the cumulative distribution function (CDF) matching methods. The ANN method had the lowest estimation errors (RSR), while the ExpF method best captured the temporal variation of subsurface soil moisture; the CDF method is not recommended for the estimation. Meanwhile the ExpF method was able to provide accurate estimates of subsurface soil moisture at 10 to 20 cm and for the profile of 0 to 70 cm using surface (0 to 10 cm) soil moisture only. Furthermore, it was shown that the estimation of profile SM was not significantly worse when an area-generalized optimum characteristic time (Topt) was used instead of station-specific Topt for the Qilian Mountains. The ExpF method was applied to obtain profile SM from the SMAP_L3 surface soil moisture product, and the resulting profile SM was compared with in situ observations. The ExpF method was able to estimate profile SM from SMAP_L3 surface data with reasonable accuracy (median R of 0.65). Also, the combination of the ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in mountainous areas compared to the SMAP_L4 root zone product. The ExpF method is useful and has potential for estimating profile SM from SMAP surface products in the Qilian Mountains.
Highlights
Soil moisture (SM) is considered to be an essential climate variable (Bojinski et al, 2014) because of its critical role in the water, energy (Jung et al, 2010), and carbon cycles (Green et al, 2019)
We focus on the Qilian Mountains, which is a water source for several key inland rivers with terminal lakes in Northwest cm depth in the Qilian Mountains (China), including the Heihe, Shiyang, and Shule rivers (He et al, 2018)
The exponential filter (ExpF) method estimates subsurface SM based on soil water index (SWI), while the artificial neural network (ANN) and cumulative distribution function (CDF) methods are based on volumetric soil moisture
Summary
Soil moisture (SM) is considered to be an essential climate variable (Bojinski et al, 2014) because of its critical role in the water, energy (Jung et al, 2010), and carbon cycles (Green et al, 2019). Knowledge of profile SM is important for runoff modeling (Brocca et al, 2010), water resource management (Gao et al, 2018), drought assessment (Jakobi et al, 2018), and climate analysis (Seneviratne et al, 2010). Methods for SM measurements include ground-based measurements and satellite-based measurements (Dobriyal et al, 2012). Most ground-based methods enable the determination of SM changes with high temporal resolution at different depths but with limited spatial coverage (Jonard et al, 2018).
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