Preprints
https://doi.org/10.5194/hess-2021-460
https://doi.org/10.5194/hess-2021-460

  08 Sep 2021

08 Sep 2021

Review status: this preprint is currently under review for the journal HESS.

SMAP retrieval assimilation improves soil moisture estimation across irrigated areas in South Asia

Jawairia A. Ahmad1, Barton A. Forman1, and Sujay V. Kumar2 Jawairia A. Ahmad et al.
  • 1Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USA
  • 2Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

Abstract. A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and GPM Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF) corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF-matching is implemented to map the statistical moments of the SMAP soil moisture retrievals to the land surface model climatology. Comparison of assimilated and model-only soil moisture estimates with publicly available in-situ measurements highlight the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 % even though assimilation only occurred during less than 10 % of the study period due to frozen soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. SMAP retrieval assimilation corrected biases associated with unmodeled hydrologic phenomenon (e.g., anthropogenic influences due to irrigation). The highest influence of assimilation was observed across croplands. Improvements in soil moisture translated into improved spatiotemporal patterns of modeled evapotranspiration, yet limited influence of assimilation was observed on states included within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data scarce regions.

Jawairia A. Ahmad et al.

Status: open (until 03 Nov 2021)

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Jawairia A. Ahmad et al.

Jawairia A. Ahmad et al.

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Short summary
Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data scarce regions.