Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Ehsan Forootan; Nooshin Mehrnegar; Maike Schumacher; Leire Anne Retegui Schiettekatte; Thomas Jagdhuber; Saeed Farzaneh; Albert I J M van Dijk; Mohammad Shamsudduha; C K Shum

doi:10.1016/j.scitotenv.2023.169476

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Sci Total Environ. 2024 Feb 20:912:169476. doi: 10.1016/j.scitotenv.2023.169476. Epub 2023 Dec 23.

Authors

Ehsan Forootan¹, Nooshin Mehrnegar², Maike Schumacher¹, Leire Anne Retegui Schiettekatte¹, Thomas Jagdhuber³, Saeed Farzaneh⁴, Albert I J M van Dijk⁵, Mohammad Shamsudduha⁶, C K Shum⁷

Affiliations

¹ Geodesy Group, Department of Sustainability and Planning, Aalborg University, Rendburggade 14, Aalborg 9000, Denmark.
² Geodesy Group, Department of Sustainability and Planning, Aalborg University, Rendburggade 14, Aalborg 9000, Denmark. Electronic address: Nooshinm@plan.aau.dk.
³ Microwaves and Radar Institute, German Aerospace Center, 82234 Wessling, Germany; Institute of Geography, University of Augsburg, 86159 Ausburg, Germany.
⁴ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
⁵ Fenner School of Environment & Society, College of Science, Australian National University, Canberra, Australia.
⁶ Institute for Risk and Disaster Reduction, University College London (UCL), Gower Street, London WC1E 6BT, United Kingdom.
⁷ Division of Geodetic Science, School of Earth Sciences, Ohio State University, Columbus, OH, USA.

PMID: 38145671
DOI: 10.1016/j.scitotenv.2023.169476

Abstract

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5^∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003-2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

Keywords: Bayesian merging; Data assimilation; Drought indices; GRACE/GRACE-FO; Groundwater droughts; Large scale hydrology; MCMC merger.