The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Bayesian Spatial Modeling of Extreme Precipitation Return Levels
Abstract
Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply it to a region in Colorado. Separate hierarchical models are constructed for the intensity and the frequency of extreme precipitation events. For intensity, we model daily precipitation above a high threshold at 56 weather stations with the generalized Pareto distribution. For frequency, we model the number of exceedances at the stations as binomial random variables. Both models assume that the regional extreme precipitation is driven by a latent spatial process characterized by geographical and climatological covariates. Effects not fully described by the covariates are captured by spatial structure in the hierarchies. Spatial methods were improved by working in a space with climatological coordinates. Inference is provided by a Markov chain Monte Carlo algorithm and spatial interpolation method, which provide a natural method for estimating uncertainty.
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
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
Cited by
Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P., Johnson, F., Kendon, E. J., Lenderink, G., & Roberts, N. M. (2014) · Reviews of Geophysics · doi:10.1002/2014rg000464 · Cited by 1,533
A compound event framework for understanding extreme impacts
Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., Risbey, J., Schuster, S., Jakob, D., & Stafford‐Smith, M. (2014) · WIREs Climate Change · doi:10.1002/wcc.252 · Cited by 869
Statistical Modeling of Spatial Extremes
Davison, A. C., Padoan, S. A., & Ribatet, M. (2012) · Statistical Science · doi:10.1214/11-sts376 · Cited by 578
Non-stationary extreme value analysis in a changing climate
Cheng, L., AghaKouchak, A., Gilleland, E., & Katz, R. W. (2014) · Climatic Change · doi:10.1007/s10584-014-1254-5 · Cited by 537
Risser, M. D., & Wehner, M. F. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075888 · Cited by 386
Metadata
- Authors
- Cooley, D., Nychka, D., & Naveau, P.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2007-09
- Volume
- 102
- Issue
- 479
- Pages
- 824-840
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2071769383
- Cited by
- 524
- References
- 2
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cooley, D., Nychka, D., & Naveau, P. (2007). Bayesian Spatial Modeling of Extreme Precipitation Return Levels. Journal of the American Statistical Association. https://doi.org/10.1198/016214506000000780
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