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A New Spatiotemporal Estimator to Downscale GRACE Gravity Models for Terrestrial and Groundwater Storage Variations Estimation
Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, (CAN).
University West, Department of Engineering Science, Division of Mathematics, Computer and Surveying Engineering. Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, (CAN).ORCID iD: 0000-0003-0067-8631
Département de Géomatique Appliquée, Université de Sherbrooke, Sherbrooke, (CAN).
Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, (CAN).
2022 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 23Article in journal (Refereed) Published
Abstract [en]

This study proposes a new mathematical approach to downscale monthly terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) and estimates groundwater storage anomalies (GWSA) at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, simultaneously. The method combines monthly 3° GRACE gravity models and daily 0.25° hydrological model outputs and their uncertainties in the spectral domain by minimizing the mean-square error (MSE) of their estimator to enhance the quality of both low and high frequency signals in the estimated TWSA and GWSA. The Global Land Data Assimilation System (GLDAS) was the hydrological model considered in this study. The estimator was tested over Alberta, Saskatchewan, and Manitoba (Canada), especially over the Province of Alberta, using data from 65 in-situ piezometric wells for 2003. Daily minimum and maximum GWS varied from 14 mm to 32 mm across the study area. A comparison of the estimated GWSA with the corresponding in-situ wells showed significant and consistent correlations in most cases, with r = 0.43–0.92 (mean r = 0.73). Correlations were >0.70 for approximately 70% of the wells, with root mean square errors <24 mm. These results provide evidence for using the proposed spectral combination estimator in downscaling GRACE data on a daily basis at a spatial scale of 0.25° × 0.25°.  

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 14, no 23
Keywords [en]
Climate models; Frequency estimation; Geodetic satellites; Groundwater; Mean square error; Uncertainty analysis; Daily spatiotemporal downscaling; Down-scaling; Gravity modeling; Gravity recovery and climate experiment satellites; Gravity recovery and climate experiments; Groundwater storage; Groundwater storage change; Spectral combination; Storage changes; Terrestrial water storage; Digital storage
National Category
Climate Science
Identifiers
URN: urn:nbn:se:hv:diva-19504DOI: 10.3390/rs14235991ISI: 000897372500001Scopus ID: 2-s2.0-85143797146OAI: oai:DiVA.org:hv-19504DiVA, id: diva2:1730030
Note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Available from: 2023-01-23 Created: 2023-01-23 Last updated: 2025-02-07

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Eshagh, Mehdi

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