Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations

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dc.authoridUz, Metehan/0000-0002-4533-7681
dc.authoridAtman, Kazim Gokhan/0000-0001-8800-9736
dc.authoridZhang, Yu/0000-0001-5706-8290
dc.contributor.authorUz, Metehan
dc.contributor.authorAkyilmaz, Orhan
dc.contributor.authorShum, C. K.
dc.contributor.authorKeles, Merve
dc.contributor.authorAy, Tugce
dc.contributor.authorTandogdu, Bihter
dc.contributor.authorZhang, Yu
dc.date.accessioned2025-01-27T21:04:08Z
dc.date.available2025-01-27T21:04:08Z
dc.date.issued2022
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe monthly high-resolution terrestrial water storage anomalies (TWSA) during the 11-months of gap between GRACE (Gravity Recovery And Climate Experiment) and its successor GRACE-FO (-Follow On) missions are missing. The continuity of the GRACE-like TWSA series with commensurate accuracy is of great importance for the improvement of hydrologic models both at global and regional scales. While previous efforts to bridge this gap, though without achieving GRACE-like spatial resolutions and/or accuracy have been performed, high-quality TWSA simulations at global scale are still lacking. Here, we use a suite of deep learning (DL) architectures, convolutional neural networks (CNN), deep convolutional autoencoders (DCAE), and Bayesian convolutional neural networks (BCNN), with training datasets including GRACE/-FO mascon and Swarm gravimetry, ECMWF Reanalysis-5 data, normalized time tag information to reconstruct global land TWSA maps, at a much higher resolution (100 km full wavelength) than that of GRACE/FO, and effectively bridge the 11-month data gap globally. Contrary to previous studies, we applied no prior detrending or de-seasoning to avoid biasing/aliasing the simulations induced by interannual or longer climate signals and extreme weather episodes. We show the contribution of Swarm and time inputs which significantly improved the TWSA simulations in particular for correct prediction of the trend component. Our results also show that external validation with independent data when filling large data gaps within spatio-temporal time series of geophysical signals is mandatory to maintain the robustness of the simulation results. The results and comparisons with previous studies and the adopted DL methods demonstrate the superior performance of DCAE. Validations of our DCAE-based TWSA simulations with independent datasets, including in situ groundwater level, Interferometric Synthetic Aperture Radar measured land subsidence rate (e.g. Central Valley), occurrence/timing of severe flash flood (e.g. South Asian Floods) , drought (e.g. Northern Great Plain, North America) events occurred within the gap, reveal excellent agreements.
dc.description.sponsorshipScientific and Technological Research Council of Turkey -TUBITAK [119Y176]; United States National Science Foundation (NSF) Partnerships for Innovation Program [2044704]; United States Agency for International Development (USAID) project [72038621CA00002]; NASA Earth Surface Interior Program [80NSSC20K0494]; Dir for Tech, Innovation, & Partnerships; Translational Impacts [2044704] Funding Source: National Science Foundation
dc.description.sponsorshipThis work is partially supported by Scientific and Technological Research Council of Turkey -TUBITAK (119Y176) and also is a part of the first author's dissertation. We acknowledge partial supports from the United States National Science Foundation (NSF) Partnerships for Innovation Program(2044704), the United States Agency for International Development (USAID) project (72038621CA00002), and the NASA Earth Surface Interior Program (80NSSC20K0494). We thank Dr. Shaoxing Mo from Nanjing University for helpful discussions. CSR RL06 Mascon solutions are available in http://www2.csr.utexas.edu/grace.Swarm Level-2 data products are downloaded from International Center for Global Earth Models (ICGEM -http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm).ERA5-Land (ERA5L) datasets are available on European Centre for Medium-Range Weather Forecast website (ECMWF -https://cds.climate.copernicus.eu).GLDAS Noah Land Surface Model is available in https://disc.gsfc.nasa.gov/datasets/.Annual subsidence rate data are obtained from https://data.cnra.ca.gov/.The developed codes and predicted TWSA data set are available fromcorresponding author upon reasonable request. The handling editor and four anonymous reviewers are gratefully acknowledged for their constructive comments which significantly improved the manuscript.
dc.identifier.doi10.1016/j.scitotenv.2022.154701
dc.identifier.issn0048-9697
dc.identifier.issn1879-1026
dc.identifier.pmid35337878
dc.identifier.scopus2-s2.0-85127169620
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.scitotenv.2022.154701
dc.identifier.urihttps://hdl.handle.net/20.500.12428/27561
dc.identifier.volume830
dc.identifier.wosWOS:000790510400003
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofScience of The Total Environment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectGRACE
dc.subjectGRACE-FO
dc.subjectSwarm
dc.subjectDeep learning neural networks
dc.subjectTerrestrial water storage anomaly
dc.subjectGroundwater storage
dc.titleBridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations
dc.typeArticle

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