Estimating soil moisture in gullies from adjacent upland measurements through different observation operators

SummarySoil moisture datasets in large gullies are rare due to the difficulty of direct sampling in such landform. This study attempted to estimate spatial soil moisture averages in gullies from measurements of adjacent uplands by using observation operators, based on three-year soil moisture datasets in a gully catchment of the Loess Plateau. Soil moisture datasets in 2010 and 2011 were used for developing observation operators and those in 2012 were used for validation. Several nonlinear and linear methods including cumulative distribution function (CDF) matching method, linear regression (LRG) method, mean relative difference (MRD) method and linear rescaling (LRS) method were used to define observation operators. The results showed observation operators significantly improved the predictions compared to when using spatial averages of uplands as the direct surrogates for gullies. Among different methods, the CDF matching method performed best in estimating soil moisture in gullies followed by the LRG, LRS and MRD methods. Validation analysis showed that the linear observation operators such as LRS, MRD and LRG had better temporal transferability than the nonlinear operators. The MRD observation operators for various layers could successfully transfer in time whereas temporal transferability only succeeds to a limited extent for other observation operators. Furthermore, the MRD, LRG and LRS methods exhibited better vertical transferability than the CDF matching method. However, the transferability of observation operators across the whole root zone layers was not successful.

► Four linear and nonlinear methods are used to define observation operators.
► Spatial averages of gullies are estimated accurately from hillslope soil moisture.
► Nonlinear operators perform better in estimation of spatial averages in gullies.
► Linear operators show better spatial-temporal transferability than nonlinear ones.
► A larger number of parameters shows better estimation but worse transferability.