The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change
Open accessAbstract
Abstract. A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29–31° N, 85–95° W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (CI): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
An Introduction to Statistical Modeling of Extreme Values
Coles, S. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4471-3675-0 · Cited by 7,352
Global surface temperature change
Hansen, J., Ruedy, R., Sato, M., & Lo, K. (2010) · Reviews of Geophysics · doi:10.1029/2010rg000345 · Cited by 3,081
An Overview of the Global Historical Climatology Network-Daily Database
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., & Houston, T. G. (2012) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-11-00103.1 · Cited by 1,993
Precipitation Characteristics in Eighteen Coupled Climate Models
Dai, A. (2006) · Journal of Climate · doi:10.1175/jcli3884.1 · Cited by 1,098
Precipitation Extremes Under Climate Change
O’Gorman, P. A. (2015) · Current Climate Change Reports · doi:10.1007/s40641-015-0009-3 · Cited by 881 · cited 3×
“…precipitation extremes are expected to increase due to the greater moisture content of a warmer atmosphere following Clausius– Clapeyron scaling (O’Gorman, 2015; Lenderink and Attema, 2015; Scherrer et al., 2016): e.g., “Disasters like Louisiana floods will worsen as planet warms, scientists…”
BackgroundInfluential
Cited by
Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Beagley, J., Belesova, K., Boykoff, M., Byass, P., Cai, W., Campbell-Lendrum, D., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(20)32290-x · Cited by 1,943
Extreme weather impacts of climate change: an attribution perspective
Clarke, B., Otto, F., Stuart-Smith, R., & Harrington, L. (2022) · Environmental Research: Climate · doi:10.1088/2752-5295/ac6e7d · Cited by 573
Attribution of extreme rainfall from Hurricane Harvey, August 2017
van Oldenborgh, G. J., van der Wiel, K., Sebastian, A., Singh, R., Arrighi, J., Otto, F., Haustein, K., Li, S., Vecchi, G., & Cullen, H. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa9ef2 · Cited by 481
Self-citationRisser, M. D., & Wehner, M. F. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075888 · Cited by 386
A protocol for probabilistic extreme event attribution analyses
Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., et al. (2020) · Advances in Statistical Climatology, Meteorology and Oceanography · doi:10.5194/ascmo-6-177-2020 · Cited by 328
Self-citation
Metadata
- Authors
- van der Wiel, K., Kapnick, S. B., van Oldenborgh, G. J., Whan, K., Philip, S., Vecchi, G. A., Singh, R. K., Arrighi, J., & Cullen, H.
- Venue
- Hydrology and Earth System Sciences
- Publisher
- Copernicus GmbH
- Published
- 2017-02-14
- Volume
- 21
- Issue
- 2
- Pages
- 897-921
- ISSN
- 1027-5606 (ISSN-L)
- 1607-7938
- OpenAlex
- W2519647569
- Cited by
- 202
- References
- 6
- FWCI
- 18.68
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change. https://app.cochat.ai/papers/10.5194/hess-21-897-2017
- The paper
- van der Wiel, K., Kapnick, S. B., van Oldenborgh, G. J., Whan, K., Philip, S., Vecchi, G. A., Singh, R. K., Arrighi, J., & Cullen, H. (2017). Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-21-897-2017
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.