A Bayesian Spatial Hierarchical Framework for Process-Informed Nonstationary Analysis of Precipitation Frequencies

James Doss-Gollin

Rice University

Yuchen Lu

Rice University

Benjamin Seiyon Lee

George Mason University

November 10, 2023

Discussion

Brand new results – validation ongoing and feedback appreciated
Is Atlas 14 too high in SE TX (Nielsen-Gammon, 2020)?
- Small “region of influence” neglects that Harvey could have hit a wide range
- Stationary model, short-record stations, sampling only from recent years (Missing at Random)
Are our results too low in SE TX?
- Possible over-smoothing ( $ρ$ large)
- Daily maxima $\neq$ 24 hour maxima
- Separate trends for $μ$ and $σ$

References

Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Scientific Reports, 4(1), 7093. https://doi.org/10.1038/srep07093

Fagnant, C., Gori, A., Sebastian, A., Bedient, P. B., & Ensor, K. B. (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

Fauer, F. S., Ulrich, J., Jurado, O. E., & Rust, H. W. (2021). Flexible and consistent quantile estimation for intensitydurationfrequency curves. Hydrology and Earth System Sciences, 25(12), 6479–6494. https://doi.org/10.5194/hess-25-6479-2021

Koutsoyiannis, D., Kozonis, D., & Manetas, A. (1998). A mathematical framework for studying rainfall intensity-duration-frequency relationships. Journal of Hydrology, 206(1), 118–135. https://doi.org/10.1016/S0022-1694(98)00097-3

Nielsen-Gammon, J. W. (2020). Observation-based estimates of present-day and future climate change impacts on heavy rainfall in Harris County.

Salas, J. D., Obeysekera, J., & Vogel, R. M. (2018). Techniques for assessing water infrastructure for nonstationary extreme events: A review. Hydrological Sciences Journal, 63(3), 325–352. https://doi.org/10.1080/02626667.2018.1426858

Schlef, K. E., Kunkel, K. E., Brown, C., Demissie, Y., Lettenmaier, D. P., Wagner, A., et al. (2023). Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves. Journal of Hydrology, 616, 128757. https://doi.org/10.1016/j.jhydrol.2022.128757

Serinaldi, F., & Kilsby, C. G. (2015). Stationarity is undead: Uncertainty dominates the distribution of extremes. Advances in Water Resources, 77, 17–36. https://doi.org/10.1016/j.advwatres.2014.12.013

Ulrich, J., Jurado, O. E., Peter, M., Scheibel, M., & Rust, H. W. (2020). Estimating IDF Curves Consistently over Durations with Spatial Covariates. Water, 12(11), 3119. https://doi.org/10.3390/w12113119

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A Bayesian Spatial Hierarchical Framework for Process-Informed Nonstationary Analysis of Precipitation Frequencies James Doss-Gollin Rice University Yuchen Lu Rice University Benjamin Seiyon Lee George Mason University November 10, 2023

A Bayesian Spatial Hierarchical Framework for Process-Informed Nonstationary Analysis of Precipitation Frequencies

Teamwork Makes the Dream Work

Motivation

Heavy rainfall: Texas and 🌎

Existing guidance leaves gaps

Yet addressing nonstationarity is hard

Conceptual framework

Nonstationary extreme value models

Estimation uncertainty is an Achilles heel

Case study

Data

Spatially Varying Covariates

Shifting and widening of the distribution

Estimated return levels}

Trends and comparison

Well-calibrated inferences

Discussion

Ongoing work

More stations (ongoing)

Parametric duration dependence (ongoing)

Conclusions

Summary

References