The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors
Open accessAbstract
Impacts of floods on human society have been drawing increasing human concerns in recent years. In this study, flood observations from EM-DAT (Emergency Events Database) and DFO (Dartmouth Flood Observatory) datasets were analyzed to investigate frequency and intensity of floods, and flood-induced mortality, flood-affected population as well during 1975-2016 across the globe. Results indicated that: (1) occurrence rate of floods, flood-induced mortality and flood-affected population were generally increasing globally. However, flood-induced mortality and flood-affected people per flood event were in slight decrease, indicating that flood-induced mortality and flood-affected people due to increased floods exceeded those by individual flood event; (2) annual variation of mortality per flood event is highly related to floods with higher intensity. Specifically, the flood frequency and flood-induced mortality are the largest in Asia, specifically in China, India, Indonesia and Philippine; while significantly increased flood-affected population and mean annual mortality was detected in China, USA and Australia; (3) tropical cyclones (TC) are closely related to flood-induced mortality in parts of the countries along the western coast of the oceans. The frequency of channel floods in these regions is the largest and large proportion of flood-induced deaths and the highest flood-induced mortality can be attributed to TC-induced flash floods; (4) Population density and GDP per unit area are in significantly positive correlation with the number of flood-related victims per unit area, number of deaths and economic losses with exception of low-income countries. However, the flood-affected population and flood-induced mortality increase with decrease of per capita GDP; while the per capita economic loss increases with the increase of per capita GDP, indicating that the higher the population density and GDP per unit for a region, the higher sensitivity of this area to flood hazards.
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
Nonparametric Tests Against Trend
Mann, H. B. (1945) · Econometrica · doi:10.2307/1907187 · Cited by 17,144
Estimates of the Regression Coefficient Based on Kendall's Tau
Sen, P. K. (1968) · Journal of the American Statistical Association · doi:10.1080/01621459.1968.10480934 · Cited by 14,517
Griggs, D. J., & Noguer, M. (2002) · Weather · doi:10.1256/004316502320517344 · Cited by 5,754
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J. (2010) · Bulletin of the American Meteorological Society · doi:10.1175/2009bams2755.1 · Cited by 3,237
Changes in tropical cyclone number, duration, and intensity in a warming environment
Webster, P. J., Holland, G. J., Curry, J. A., & Chang, H.-R. (2005) · Science · doi:10.1126/science.1116448 · Cited by 3,237
Tropical cyclones and climate change
Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea, C., Held, I., Kossin, J. P., Srivastava, A. K., & Sugi, M. (2010) · Nature Geoscience · doi:10.1038/ngeo779 · Cited by 3,194
Global flood risk under climate change
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., & Kanae, S. (2013) · Nature Climate Change · doi:10.1038/nclimate1911 · Cited by 3,013
A Rank-Invariant Method of Linear and Polynomial Regression Analysis
Theil, H. (1992) · Advanced Studies in Theoretical and Applied Econometrics · doi:10.1007/978-94-011-2546-8_20 · Cited by 2,517
Future flood losses in major coastal cities
Hallegatte, S., Green, C., Nicholls, R. J., & Corfee-Morlot, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1979 · Cited by 2,503
The influence of autocorrelation on the ability to detect trend in hydrological series
Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) · Hydrological Processes · doi:10.1002/hyp.1095 · Cited by 2,111
Cited by
Causes, impacts and patterns of disastrous river floods
Merz, B., Blöschl, G., Vorogushyn, S., Dottori, F., Aerts, J. C. J. H., Bates, P., Bertola, M., Kemter, M., Kreibich, H., Lall, U., et al. (2021) · Nature Reviews Earth & Environment · doi:10.1038/s43017-021-00195-3 · Cited by 732
EM-DAT: the Emergency Events Database
Delforge, D., Wathelet, V., Below, R., Sofia, C. L., Tonnelier, M., van Loenhout, J. A. F., & Speybroeck, N. (2025) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2025.105509 · Cited by 373
Hamel, P., & Tan, L. (2022) · Environmental Management · doi:10.1007/s00267-021-01467-w · Cited by 225
Matthews, V., Longman, J., Berry, H. L., Passey, M., Bennett-Levy, J., Morgan, G. G., Pit, S., Rolfe, M., & Bailie, R. S. (2019) · Frontiers in Public Health · doi:10.3389/fpubh.2019.00367 · Cited by 75
Impacts of water depth on pedestrian speed, gait, and stability: results from an experimental study
Li, X., Song, W., Zhang, J., & Bode, N. W. F. (2026) · Acta Polytechnica CTU Proceedings · doi:10.14311/app.2026.57.0226 · Cited by 1
Metadata
- Authors
- Hu, P., Zhang, Q., Shi, P., Chen, B., & Fang, J.
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 643
- Pages
- 171-182
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W2809099638
- Cited by
- 309
- References
- 37
- FWCI
- 11.37
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors. https://app.cochat.ai/papers/10.1016/j.scitotenv.2018.06.197
- The paper
- Hu, P., Zhang, Q., Shi, P., Chen, B., & Fang, J. (2018). Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2018.06.197
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.