Preprints
https://doi.org/10.5194/hess-2023-309
https://doi.org/10.5194/hess-2023-309
15 Jan 2024
 | 15 Jan 2024
Status: this preprint is currently under review for the journal HESS.

Quantifying the potential of using SMAP soil moisture variability to predict subsurface water dynamics

Aruna Kumar Nayak, Xiaoyong Xu, Steven K. Frey, Omar Khader, Andre R. Erler, David R. Lapen, Hazen A. J. Russell, and Edward A. Sudicky

Abstract. Advances in satellite Earth observation have opened up new opportunities for a global monitoring of soil moisture (SM) at fine to medium resolution, but satellite remote sensing can only measure the near-surface soil moisture (SSM). As such, it is critically important to examine the potential of satellite SSM measurements to derive the water resource variations in deeper subsurface. This study compares the SSM variability captured by the Soil Moisture Active and Passive (SMAP) satellite and the Soil Water Index (SWI) derived from SMAP SSM with subsurface SM and groundwater (GW) dynamics simulated by a high resolution fully-integrated surface water - groundwater model over an agriculturally-dominated watershed in eastern Canada across two spatial scales, namely SMAP product grid (9 km) and watershed (~4000 km2). SMAP measurements compare well with the hydrologic simulations in terms of SSM variability at both scales. Simulated subsurface SM and GW storage show lagged and smoother characteristics relative to SMAP SSM variability with an optimal delay of ~1 days for the 25‒50 cm SM, ~6 days for the 50‒100 cm SM, and ~11 days for the GW storage for both scales. Modelled subsurface SM dynamics agree well with the SWI derived from SMAP SSM using the classic characteristic time lengths (15 days for the 0‒25 cm layer and 20 days for the 0‒100 cm layer). The simulated GW storage showed a slightly delayed variation relative to the derived SWI. The quantified optimal characteristic time length Topt for SWI estimation (by matching the variations in SMAP-derived SWI and modeled root zone SM) is comparable to Topt obtained in other agricultural regions around the world. This work demonstrates SMAP SM measurements as a potentially useful aid when predicting root zone SM and GW dynamics and validating fully integrated hydrologic models across different spatial scales. This study also provides insights into the dynamics of near surface–subsurface water interaction and the capabilities and approaches of satellite-based SM monitoring and high resolution fully-integrated hydrologic modelling.

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Aruna Kumar Nayak, Xiaoyong Xu, Steven K. Frey, Omar Khader, Andre R. Erler, David R. Lapen, Hazen A. J. Russell, and Edward A. Sudicky

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-309', Anonymous Referee #1, 13 Feb 2024
    • AC1: 'Reply on RC1', Xiaoyong Xu, 09 Apr 2024
  • RC2: 'Comment on hess-2023-309', Anonymous Referee #2, 26 Feb 2024
    • AC2: 'Reply on RC2', Xiaoyong Xu, 09 Apr 2024
Aruna Kumar Nayak, Xiaoyong Xu, Steven K. Frey, Omar Khader, Andre R. Erler, David R. Lapen, Hazen A. J. Russell, and Edward A. Sudicky

Data sets

Quantifying the linkage between SMAP soil moisture and fully-integrated hydrologic simulations A. K. Nayak et al. https://doi.org/10.5281/zenodo.8145252

Aruna Kumar Nayak, Xiaoyong Xu, Steven K. Frey, Omar Khader, Andre R. Erler, David R. Lapen, Hazen A. J. Russell, and Edward A. Sudicky

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Short summary
Satellite remote sensing measures only the near-surface soil water content. This study demonstrates that satellite-based near-surface soil water variability is a strong reflection of deeper subsurface water fluctuation and quantifies the response time differences between dynamics of satellite near-surface soil water and water in deeper subsurface. Result support the use of satellite near-surface soil water measurements as indicators and/or predictors of water resources in deeper subsurface.