Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

Abstract

Abstract. Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depthintegrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas

“…Previous studies have shown that deeper SM is often related to surface SM using cross-correlation analysis (Mahmood and Hubbard, 2007;Mahmood et al, 2012;Wang et al, 2017). However, correlation strength depends on location and climate (Ford et al, 2014;Carranza et al, 2018). Mahmood and Hubbard (2007) found that the correlation coefficient between surface (0.5 cm) and subsurface SM from 0 to 60 cm depth ranged from 0.21 to 0.69 for different locations and land covers.…”

“…Mahmood and Hubbard (2007) found that the correlation coefficient between surface (0.5 cm) and subsurface SM from 0 to 60 cm depth ranged from 0.21 to 0.69 for different locations and land covers. Carranza et al (2018) reported that the coupling strength also depends strongly on SM itself.…”

Application of soil moisture information for operational water management

“…Groundwater Carranza et al (2018) showed that surface soil moisture shows high correlations with soil moisture at greater depths during wet conditions in the Twente study area. They refer to the correlated period as coupled conditions, while periods with low correlation are referred to as decoupled conditions.…”

“…Several studies show that statistical methods (e.g. Albergel et al, 2008;Carranza et al, 2018) or hydrological modelling (e.g. Sabater et al, 2007;Renzullo et al, 2014;Dumedah et al, 2015;Blyverket et al, 2019) can help in extrapolating surface soil information to deeper layers.…”

“…In addition, the data assimilation approach enables to translate surface soil moisture information to root zone soil moisture information, extending purely statistical methods (e.g. Albergel et al, 2008;Carranza et al, 2018). Furthermore, the approach integrated the remote sensing data, which are available every 2-3 days for the Twente study area, to daily information using the hydrological model.…”