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.

Highlights

  • 20 Soil moisture (SM) is an important variable in geophysical science

  • 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

  • 5 Conclusions Soil moisture estimation across South Asia was implemented in this study by assimilating SMAP soil moisture retrievals into a land surface model

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Summary

Introduction

20 Soil moisture (SM) is an important variable in geophysical science. In land surface models, soil moisture primarily influences the energy cycle by controlling latent heat flux and soil temperature (Al-Kayssi et al, 1990), and the water cycle via evapotranspiration, soil infiltration capacity, and runoff (Penna et al, 2011). Various techniques have been used in soil moisture estimation such as in situ station networks, physical modeling, and remote sensing (Seneviratne et al, 2010; Hauser et al, 2017; Reichle et al, 2021). Several studies have attempted to improve water budget estimation by assimilating soil moisture observations into land surface model (LSM) estimates. Huang et al (2008) assimilated in situ surface soil moisture measurements and low-frequency PMW remote sensing data into the Simple Biosphere Model (SiB2) and produced improvements in surface soil moisture estimates. An inverse technique of estimating the amount of groundwater pumped could potentially be developed if accurate soil moisture estimates are available (apart from the other water budget contributing variables). Soil moisture records may be able to provide the much needed information about the extent and amount of groundwater pumping across the whole of South Asia.

Study domain
Noah-MP land surface model
SMAP Level3 soil moisture for assimilation
In situ soil moisture measurements for model evaluation
ALEXI evapotranspiration for model evaluation
GOME-2 fluorescence for model evaluation
Nominal replicate (NR)
Data assimilation (DA)
Experimental results
Evaluation using in situ measurements
Timeseries evaluation
Statistical analysis
Irrigation impact
Conclusions
Full Text
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