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Journal of Data Science


Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 221–238
Pub. online: 24 May 2024      Type: Statistical Data Science      Open accessOpen Access
Received
31 July 2023
Accepted
14 April 2024
Published
24 May 2024

Abstract

One measurement modality for rainfall is a fixed location rain gauge. However, extreme rainfall, flooding, and other climate extremes often occur at larger spatial scales and affect more than one location in a community. For example, in 2017 Hurricane Harvey impacted all of Houston and the surrounding region causing widespread flooding. Flood risk modeling requires understanding of rainfall for hydrologic regions, which may contain one or more rain gauges. Further, policy changes to address the risks and damages of natural hazards such as severe flooding are usually made at the community/neighborhood level or higher geo-spatial scale. Therefore, spatial-temporal methods which convert results from one spatial scale to another are especially useful in applications for evolving environmental extremes. We develop a point-to-area random effects (PARE) modeling strategy for understanding spatial-temporal extreme values at the areal level, when the core information are time series at point locations distributed over the region.

Supplementary material

 Supplementary Material
A pdf file containing the Supplemental Tables is included in the Supplemental Materials. Code to reproduce the PARE analysis for the three windows considered in this paper is provided as a Quarto project available for download on Github (https://github.com/carly-fagnant/Spatial_Extreme_Value_Modeling).

References

 
ACECHouston (2019). Recommendation for: Rainfall Depths and Intensities in Harris County, Technical Report, ACEC Houston.
 
Besag J (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B, Methodological, 36(2): 192–236. Publisher: [Royal Statistical Society, Wiley].
 
Bivand RS, Pebesma E, Gómez-Rubio V (2013). Applied Spatial Data Analysis with R, 2nd edition. UseR! Series. Springer.
 
Cooley D, Cisewski J, Erhardt RJ, Mannshardt E, Jeon S, Omolo BO, et al. (2012). A survey of spatial extremes: Measuring spatial dependence and modeling spatial effects. REVSTAT Statistical Journal, 10(1): 135–165. Number: 1.
 
Craigmile PF (2014). Spatial change-of-support and misalignment problems.
 
Cressie N (2006). Block Kriging for lognormal spatial processes. Mathematical Geology, 38(4): 413–443. https://doi.org/10.1007/s11004-005-9022-8
 
Cressie N, Wikle CK (2011). Statistics for Spatio-Temporal Data. John D. Wiley & Sons, Inc., Hoboken, NJ.
 
Fagnant C (2021). Spatiotemporal Extreme Value Modeling with Environmental Applications, Thesis, Rice University. Accepted: 2021-10-06T16:17:51Z.
 
Fagnant C, Gori A, Sebastian A, Bedient PB, Ensor KB (2020). Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas. Natural Hazards, 104(2): 1597–1621. https://doi.org/10.1007/s11069-020-04235-x
 
Gelfand AE, Zhu L, Carlin BP (2001). On the change of support problem for spatio-temporal data. Biostatistics, 2(1): 31–45. https://doi.org/10.1093/biostatistics/2.1.31
 
Getis A (2009). Spatial weights matrices. Geographical Analysis, 41(4): 404–410. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.2009.00768.x. https://doi.org/10.1111/j.1538-4632.2009.00768.x
 
Getis A, Aldstadt J (2004). Constructing the spatial weights matrix using a local statistic. Geographical Analysis, 36(2): 90–104. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.2004.tb01127.x https://doi.org/10.1111/j.1538-4632.2004.tb01127.x
 
Gilleland E, Katz RW (2016). extRemes 2.0: An extreme value analysis package in R. Journal of Statistical Software, 72(8): 1–39. https://doi.org/10.18637/jss.v072.i08
 
Gotway CA, Young LJ (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97(458): 632–648. Publisher: [American Statistical Association, Taylor & Francis, Ltd.].
 
Hosking JR, Wallis JR (1987). Parameter and quantile estimation for the generalized Pareto distribution. Technometrics, 29(3): 339–349. https://doi.org/10.1080/00401706.1987.10488243
 
Menne MJ, Durre I, Korzeniewski B, McNeal S, Thomas K, Yin X, et al. (2018). Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.
 
Miranda ML, Callender R, Canales JM, Craft E, Ensor KB, Grossman M, et al. (2021). The Texas flood registry: A flexible tool for environmental and public health practitioners and researchers. Journal of Exposure Science and Environmental Epidemiology, 31(5): 823–831. Number: 5. Publisher: Nature Publishing Group.
 
Pebesma EJ (2004). Multivariable geostatistics in S: The gstat package. Computers & Geosciences, 30(7): 683–691. https://doi.org/10.1016/j.cageo.2004.03.012
 
Perica S, St Laurent M, Trypaluk C, Unruh D, Wilhite O (2018). Precipitation-Frequency Atlas of the United States. Volume 11, Version 2.0. Texas. NOAA Atlas. Publisher: United States, National Weather Service.
 
Risser MD, Wehner MF (2017). Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophysical Research Letters, 44(24): 12,457–12,464. https://doi.org/10.1002/2017GL075888
 
Schedler JC (2020a). https://github.com/juliaSchedler/hausdorff.
 
Schedler JC (2020b). Advances in the Analysis of Spatially Aggregated Data. Thesis, Rice University. Accepted: 2020-04-27T19:14:17Z.
 
Sebastian A, Gori A, Blessing RB, Van Der Wiel K, Bass B (2019). Disentangling the impacts of human and environmental change on catchment response during Hurricane Harvey. Environmental Research Letters, 14(12): 124023. https://doi.org/10.1088/1748-9326/ab5234
 
Storey A, Talbott M (2009). Harris County Flood Control District Hydrology and Hydraulics Guidance Manual, Technical Report, Harris County Flood Control District.
 
United States National Oceanic And Atmospheric Administration, Fagnant C, Gori A (2020). Southeast Texas Precipitation.
 
Van Der Wiel K, Kapnick SB, Van Oldenborgh GJ, Whan K Philip S, Vecchi GA, et al. (2017). Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change. Hydrology and Earth System Sciences, 21(2): 897–921. https://doi.org/10.5194/hess-21-897-2017
 
Van Oldenborgh GJ, Van Der Wiel K, Sebastian A, Singh R, Arrighi J, Otto F, et al. (2017). Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environmental Research Letters, 12(12): 124009. https://doi.org/10.1088/1748-9326/aa9ef2
 
Wang SYS, Zhao L, Yoon JH, Klotzbach P, Gillies RR (2018). Quantitative attribution of climate effects on Hurricane Harvey’s extreme rainfall in Texas. Environmental Research Letters, 13(5): 054014. https://doi.org/10.1088/1748-9326/aabb85
 
Zhang W, Villarini G, Vecchi GA, Smith JA (2018). Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature, 563(7731): 384–388. https://doi.org/10.1038/s41586-018-0676-z

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Copyright
2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
CAR model change-of-support extended-Hausdorff distance metric geospatial modeling spatial-temporal extremes

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