Dataset Information

Can Surface Soil Moisture Information Identify Evapotranspiration Regime Transitions?

ABSTRACT: The transition of evapotranspiration between energy- and water-limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro-meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil profile, over which water uptake is dominated by plant root distribution. We use flux tower, surface (θ _s ; observations at 5 cm), and vertically integrated in situ soil moisture ( θv ; 0-50 cm) observations to address the question: Can surface soil moisture robustly identify evapotranspiration regime transitions? Results demonstrate that θ _s and θ _v are hydraulically linked and have synchronized evapotranspiration regime transitions. As such, θ _s and θ _v capture comparable statistics of evapotranspiration regime prevalence, which supports the utility of remote-sensing θ _s for large-scale land-atmosphere exchange analysis.

SUBMITTER: Dong J

PROVIDER: S-EPMC9286566 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Can Surface Soil Moisture Information Identify Evapotranspiration Regime Transitions?

Dong Jianzhi J Akbar Ruzbeh R Short Gianotti Daniel J DJ Feldman Andrew F AF Crow Wade T WT Entekhabi Dara D

Geophysical research letters 20220405 7

The transition of evapotranspiration between energy- and water-limitation regimes also denotes a nonlinear change in surface water and energy coupling strength. The regime transitions are primarily dominated by available moisture in the soil, although other micro-meteorological factors also play a role. Remotely sensed soil moisture is frequently used for detecting evapotranspiration regime transitions during inter storm dry downs. However, its sampling depth does not include the entire soil pro ...[more]

PMID: 35865657

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Project description:This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m-3 and 0.001 m3 m-3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m-3, but increased the root zone bias by 0.014 m3 m-3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables.

Dataset Information

Can Surface Soil Moisture Information Identify Evapotranspiration Regime Transitions?

Publications

Can Surface Soil Moisture Information Identify Evapotranspiration Regime Transitions?

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