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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2016-617
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-2016-617
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 Nov 2016

30 Nov 2016

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This preprint has been retracted.

Upscaling of soil moisture content from surface to profile: multi-station testing of observation operators

Xiaodong Gao1,2,3, Xining Zhao1,2,3, Luca Brocca4, Ting Lv3, Gaopeng Huo3, and Pute Wu1,2,3 Xiaodong Gao et al.
  • 1Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi Province, China
  • 2Institute of Soil and Water Conservation, CAS and MWR, Yangling, Shaanxi Province, China
  • 3National Engineering Research Center for Water Saving Irrigation at Yangling, Yangling, Shaanxi, China
  • 4Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy

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 upscale surface observations to profile soil moisture based on multi-station in situ measurements 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; prediction errors decreased as data length increased from half year (non-growing season) to two years, but accuracy did not further improve at longer interval. 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 generally performed better than exponential filter method in both calibration and validation periods. This suggests that observation operators are a robust statistical tool for upscaling soil moisture from surface to profile. The findings here may be applied in the prediction of profile soil moisture from surface measurements obtained via various means, including remote sensing techniques.

This preprint has been retracted.

Xiaodong Gao et al.

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Xiaodong Gao et al.

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Short summary
We built observation operators by the CDF matching method. Two-year duration was identified as the optimal data length in prediction accuracy. Application in different climates in USA showed these operators are a robust statistical tool for upscaling soil moisture from surface to profile by using exponential filter as a reference method. The findings here may be applied in the prediction of profile soil moisture from surface measurements via remote sensing techniques.
We built observation operators by the CDF matching method. Two-year duration was identified as...
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