Landscape Water Storage and Subsurface Correlation from Satellite Surface Soil Moisture and Precipitation Observations
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ShortGianotti_WRR_2019.pdf
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Author(s)
Short Gianotti, Daniel J.
Salvucci, Guido D.
Akbar, Ruzbeh
McColl, Kaighin A.
Cuenca, Richard
Entekhabi, Dara
Date Issued
November 2019
Journal
Water Resources Research
Publisher
American Geophysical Union
Citation
Short Gianotti, D. J., Salvucci, G. D., Akbar, R., McColl, K. A., Cuenca, R., & Entekhabi, D. (2019). Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resources Research, 55, 9111–9132.
Version
Author's final manuscript
Abstract
Surface soil moisture measurements are typically correlated to some degree with changes in subsurface soil moisture. We calculate a hydrologic length scale, λ, which represents (1) the mean-state estimator of total column water changes from surface observations, (2) an e-folding length scale for subsurface soil moisture profile covariance fall-off, and (3) the best second-moment mass-conserving surface layer thickness for a simple bucket model, defined by the data streams of satellite soil moisture and precipitation retrievals. Calculations are simple, based on three variables: the autocorrelation and variance of surface soil moisture and the variance of the net flux into the column (precipitation minus estimated losses), which can be estimated directly from the soil moisture and precipitation time series. We develop a method to calculate the lag-one autocorrelation for irregularly observed time series and show global surface soil moisture autocorrelation. λ is driven in part by local hydroclimate conditions and is generally larger than the 50-mm nominal radiometric length scale for the soil moisture retrievals, suggesting broad subsurface correlation due to moisture drainage. In all but the most arid regions, radiometric soil moisture retrievals provide more information about ecosystem-relevant water fluxes than satellite radiometers can explicitly “see”; lower-frequency radiometers are expected to provide still more statistical information about subsurface water dynamics.
MIT Department
Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2019wr025332
