The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Nonstationary Precipitation Intensity-Duration-Frequency Curves for Infrastructure Design in a Changing Climate
Open accessAbstract
Extreme climatic events are growing more severe and frequent, calling into question how prepared our infrastructure is to deal with these changes. Current infrastructure design is primarily based on precipitation Intensity-Duration-Frequency (IDF) curves with the so-called stationary assumption, meaning extremes will not vary significantly over time. However, climate change is expected to alter climatic extremes, a concept termed nonstationarity. Here we show that given nonstationarity, current IDF curves can substantially underestimate precipitation extremes and thus, they may not be suitable for infrastructure design in a changing climate. We show that a stationary climate assumption may lead to underestimation of extreme precipitation by as much as 60%, which increases the flood risk and failure risk in infrastructure systems. We then present a generalized framework for estimating nonstationary IDF curves and their uncertainties using Bayesian inference. The methodology can potentially be integrated in future design concepts.
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 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
Increase in hourly precipitation extremes beyond expectations from temperature changes
Lenderink, G., & van Meijgaard, E. (2008) · Nature Geoscience · doi:10.1038/ngeo262 · Cited by 1,072
Monitoring and Understanding Trends in Extreme Storms: State of Knowledge
Kunkel, K. E., Karl, T. R., Brooks, H., Kossin, J., Lawrimore, J. H., Arndt, D., Bosart, L., Changnon, D., Cutter, S. L., Doesken, N., et al. (2013) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00262.1 · Cited by 536
Cited by
Landslides in a changing climate
Gariano, S. L., & Guzzetti, F. (2016) · Earth-Science Reviews · doi:10.1016/j.earscirev.2016.08.011 · Cited by 1,737
Climate Extremes and Compound Hazards in a Warming World
AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., & Sadegh, M. (2020) · Annual Review of Earth and Planetary Sciences · doi:10.1146/annurev-earth-071719-055228 · Cited by 1,103
Self-citationMiller, J. D., & Hutchins, M. (2017) · Journal of Hydrology: Regional Studies · doi:10.1016/j.ejrh.2017.06.006 · Cited by 685
Zhou, Q., Leng, G., Su, J., & Ren, Y. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.12.184 · Cited by 454
Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T., et al. (2021) · Hydrology and Earth System Sciences · doi:10.5194/hess-25-3897-2021 · Cited by 357
Metadata
- Authors
- Cheng, L., & AghaKouchak, A.
- Venue
- Scientific Reports
- Published
- 2014-11-18
- Volume
- 4
- Issue
- 1
- Pages
- 7093-7093
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2027939963
- Cited by
- 518
- References
- 4
- FWCI
- 17.10
- 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
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.
