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Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data

Joanna Kuczynska-Siehien ORCID logo, Dimitrios Piretzidis ORCID logo, Michael G. Sideris, Tomasz Olszak and Viktor Szabó

Abstract

This study deals with the analysis of temporal gravity variations in central Poland, deduced from multiple sources and covering the period from 2002–2016. The gravity data sets used comprise GRACE monthly solutions, GLDAS land surface models and absolute gravimeter measurements from the FG-5 gravimeter located in Józefosław, Poland. All data are corrected using standard processing methods in order to include the same gravity effects. After removing the annual and semi-annual components from all data using least-squares spectral analysis and seasonal-trend decomposition, the deseasoned time series are derived and examined for signatures of extreme hydrological events. The signatures of several severe drought and flood conditions affecting Poland and central Europe are identified. A complementary correlation analysis is performed to assess the level of agreement between different data sources. A higher correlation is shown when the analysis is restricted in the 2009–2012 period that includes the 2010 extreme flood and 2011 increased precipitation events, both affecting Poland.

Funding source: Narodowe Centrum Nauki

Award Identifier / Grant number: UMO-2015/17/B/ST10/03927

Funding statement: Financial support for this research was provided to the first author by the National Science Center, Poland, agreement number UMO-2015/17/B/ST10/03927 from 16th March 2016; and to the third author by Natural Sciences and Engineering Research Council of Canada.

Acknowledgment

The two anonymous reviewers are thanked for their valuable comments and suggestions. Dr. Don Chambers and Dr. Jennifer Bonin are thanked for kindly providing geocenter time series consistent with RL06 GRACE data. NASA’s PODAAC and GES DISC services are thanked for making freely available the GRACE Level 2 data and the GLDAS models. Figure 1 is produced using the M_Map mapping toolbox [72].

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Received: 2019-01-15
Accepted: 2019-03-28
Published Online: 2019-04-20
Published in Print: 2019-07-26

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