Forootan, Ehsan;
Mehrnegar, Nooshin;
Schumacher, Maike;
Schiettekatte, Leire Anne Retegui;
Jagdhuber, Thomas;
Farzaneh, Saeed;
van Dijk, Albert IJM;
... Shum, CK; + view all
(2023)
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.
Science of The Total Environment
, 912
, Article 169476. 10.1016/j.scitotenv.2023.169476.
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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.
| Type: | Article |
|---|---|
| Title: | 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 |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.scitotenv.2023.169476 |
| Publisher version: | http://dx.doi.org/10.1016/j.scitotenv.2023.169476 |
| Language: | English |
| Additional information: | © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Bayesian merging, Data assimilation, Drought indices, GRACE/GRACE-FO, Groundwater droughts, Large scale hydrology, MCMC merger |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10184969 |
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