Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections
Over the next century, coastal regions are under threat from projected rising sea levels and the potential emergence of groundwater at the land surface (groundwater inundation). The potential economic and social damages of this largely unseen, and often poorly characterised natural hazard are substa...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-03-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1111065/full |
| _version_ | 1797868247549214720 |
|---|---|
| author | Lee A. Chambers Brioch Hemmings Simon C. Cox Catherine Moore Matthew J. Knowling Kevin Hayley Jens Rekker Frédérique M. Mourot Phil Glassey Richard Levy |
| author_facet | Lee A. Chambers Brioch Hemmings Simon C. Cox Catherine Moore Matthew J. Knowling Kevin Hayley Jens Rekker Frédérique M. Mourot Phil Glassey Richard Levy |
| author_sort | Lee A. Chambers |
| collection | DOAJ |
| description | Over the next century, coastal regions are under threat from projected rising sea levels and the potential emergence of groundwater at the land surface (groundwater inundation). The potential economic and social damages of this largely unseen, and often poorly characterised natural hazard are substantial. To support risk-based decision making in response to this emerging hazard, we present a Bayesian modelling framework (or workflow), which maps the spatial distribution of groundwater level uncertainty and inundation under Intergovernmental Panel on Climate Change (IPCC) projections of Sea Level Rise (SLR). Such probabilistic mapping assessments, which explicitly acknowledge the spatial uncertainty of groundwater flow model predictions, and the deep uncertainty of the IPCC-SLR projections themselves, remains challenging for coastal groundwater systems. Our study, therefore, presents a generalisable workflow to support decision makers, that we demonstrate for a case study of a low-lying coastal region in Aotearoa New Zealand. Our results provide posterior predictive distributions of groundwater levels to map susceptibility to the groundwater inundation hazard, according to exceedance of specified model top elevations. We also explore the value of history matching (model calibration) in the context of reducing predictive uncertainty, and the benefits of predicting changes (rather than absolute values) in relation to a decision threshold. The latter may have profound implications for the many at-risk coastal communities and ecosystems, which are typically data poor. We conclude that history matching can indeed increase the spatial confidence of posterior groundwater inundation predictions for the 2030-2050 timeframe. |
| first_indexed | 2024-04-09T23:52:59Z |
| format | Article |
| id | doaj.art-d95a7f1c58854322987de32d270ef784 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-09T23:52:59Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-d95a7f1c58854322987de32d270ef7842023-03-17T05:22:25ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-03-011110.3389/feart.2023.11110651111065Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projectionsLee A. Chambers0Brioch Hemmings1Simon C. Cox2Catherine Moore3Matthew J. Knowling4Kevin Hayley5Jens Rekker6Frédérique M. Mourot7Phil Glassey8Richard Levy9GNS Science, Lower Hutt, New ZealandWairakei Research Centre, GNS Science, Taupō, New ZealandGNS Science, Lower Hutt, New ZealandGNS Science, Lower Hutt, New ZealandSchool of Civil, Environmental and Mining Engineering, Faculty of Engineering, Computer and Mathematical Sciences, The University of Adelaide, Melbourne, VIC, AustraliaGroundwater Solutions Pty., Ltd., Melbourne, VIC, AustraliaKōmanawa Solutions Ltd., Dunedin, Otago, New ZealandWairakei Research Centre, GNS Science, Taupō, New ZealandGNS Science, Lower Hutt, New ZealandGNS Science, Lower Hutt, New ZealandOver the next century, coastal regions are under threat from projected rising sea levels and the potential emergence of groundwater at the land surface (groundwater inundation). The potential economic and social damages of this largely unseen, and often poorly characterised natural hazard are substantial. To support risk-based decision making in response to this emerging hazard, we present a Bayesian modelling framework (or workflow), which maps the spatial distribution of groundwater level uncertainty and inundation under Intergovernmental Panel on Climate Change (IPCC) projections of Sea Level Rise (SLR). Such probabilistic mapping assessments, which explicitly acknowledge the spatial uncertainty of groundwater flow model predictions, and the deep uncertainty of the IPCC-SLR projections themselves, remains challenging for coastal groundwater systems. Our study, therefore, presents a generalisable workflow to support decision makers, that we demonstrate for a case study of a low-lying coastal region in Aotearoa New Zealand. Our results provide posterior predictive distributions of groundwater levels to map susceptibility to the groundwater inundation hazard, according to exceedance of specified model top elevations. We also explore the value of history matching (model calibration) in the context of reducing predictive uncertainty, and the benefits of predicting changes (rather than absolute values) in relation to a decision threshold. The latter may have profound implications for the many at-risk coastal communities and ecosystems, which are typically data poor. We conclude that history matching can indeed increase the spatial confidence of posterior groundwater inundation predictions for the 2030-2050 timeframe.https://www.frontiersin.org/articles/10.3389/feart.2023.1111065/fullsea level risegroundwater inundationMODFLOWpredictive uncertaintyiterative ensemble smootherPEST++ |
| spellingShingle | Lee A. Chambers Brioch Hemmings Simon C. Cox Catherine Moore Matthew J. Knowling Kevin Hayley Jens Rekker Frédérique M. Mourot Phil Glassey Richard Levy Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections Frontiers in Earth Science sea level rise groundwater inundation MODFLOW predictive uncertainty iterative ensemble smoother PEST++ |
| title | Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections |
| title_full | Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections |
| title_fullStr | Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections |
| title_full_unstemmed | Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections |
| title_short | Quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections |
| title_sort | quantifying uncertainty in the temporal disposition of groundwater inundation under sea level rise projections |
| topic | sea level rise groundwater inundation MODFLOW predictive uncertainty iterative ensemble smoother PEST++ |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1111065/full |
| work_keys_str_mv | AT leeachambers quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT briochhemmings quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT simonccox quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT catherinemoore quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT matthewjknowling quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT kevinhayley quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT jensrekker quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT frederiquemmourot quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT philglassey quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections AT richardlevy quantifyinguncertaintyinthetemporaldispositionofgroundwaterinundationundersealevelriseprojections |