Preprints
https://doi.org/10.5194/hess-2017-292
https://doi.org/10.5194/hess-2017-292
12 Jul 2017
 | 12 Jul 2017
Status: this preprint has been retracted.

Depth scaling of soil moisture content from surface to profile: multistation testing of observation operators

Xiaodong Gao, Xining Zhao, Luca Brocca, Gaopeng Huo, Ting Lv, and Pute Wu

Abstract. The accurate assessment of profile soil moisture for spatial domains is usually difficult due to the associated costs, strong spatial-temporal variability, and nonlinear relationship between surface and profile moisture. Here we attempted to use observation operators built by Cumulative Distribution Frequency (CDF) matching method to directly predict profile soil moisture from surface measurements based on multi-station in situ observations from the Soil and Climate Analysis Network (SCAN). We first analyzed the effects of temporal resolution (hourly, daily and weekly) and data length (half year in non-growing season, half year in growing season, one year, two years and four years) on the performance of observation operators. The results showed that temporal resolution had a negligible influence on the performance of observation operators. However, data length significantly changed the prediction accuracy of observation operators, and a two-year interval was identified as the optimal data length in building observation operators. A dataset with a two-year duration was therefore used to test the robustness of observation operators in three primary climates (humid continental, humid subtropical and semiarid) of the continental USA, with the popular exponential filter employed as a reference approach. The results indicated that observation operators reliably predicted profile soil moisture for the majority of stations in both calibration and validation periods and performed almost equally well with the exponential filter method. This suggests that observation operators are a feasible statistical tool for depth scaling of soil moisture. The findings here have the potential to be applied in profile soil moisture prediction from surface measurements at a range of environments if the target site has long enough (two years) soil moisture observations even with coarse temporal resolutions.

This preprint has been retracted.

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Xiaodong Gao, Xining Zhao, Luca Brocca, Gaopeng Huo, Ting Lv, and Pute Wu

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Xiaodong Gao, Xining Zhao, Luca Brocca, Gaopeng Huo, Ting Lv, and Pute Wu
Xiaodong Gao, Xining Zhao, Luca Brocca, Gaopeng Huo, Ting Lv, and Pute Wu

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Latest update: 17 May 2024
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This preprint has been retracted.

Short summary
Profile soil moisture is key state variable in the Critical Zone ecology and hydrology. This paper sucessfully used a simple statistical method, the cumulative distribution frequency (CDF) matching method for the first time, to predict profile soil moisture (0–100 cm) from surface measurement (5 cm). The findings here can provide insights into profile soil moisture estimation from remote sensing moisture products.