the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the spatial correlation of potential compound flooding in the United States
Abstract. When coastal and river floods occur concurrently or in close succession, they can cause a compound flood with significantly higher impacts. While our understanding of compound flooding has improved over the past decade, no studies to date have assessed the spatial correlation of compound flooding. To address this gap, we develop a framework that captures dependence between coastal total water level and river discharge across a set of locations along the U.S. coastline. Using 41 years of observed data from 41 station combinations, we stochastically model 10,000 years of spatially-joint events of extreme sea level and river discharge based on their dependence structure and cooccurrence rate. We define potential compound flooding as events in which both drivers exceed their respective 99th percentile thresholds. Results based on our simulated large event set show that the U.S. West coast shows high spatial correlation of potential compound flooding. Among all three coasts, the West coast has the highest frequency of widespread potential compound flooding, with around 50 % of compound events arising at multiple locations simultaneously. We identify two clusters with mutually high joint occurrence rates of simultaneous compound events on this coast, namely 1) Charleston – Cresent City – North Spit, and 2) Santa Monica – Los Angeles – La Jolla. Widespread compound events are less frequent on the East coast where approximately 30 % of potential compound flooding may affect multiple locations. Moderate spatial dependence is observed in the central region and weaker spatial dependence for the remaining locations on this coast. In contrast, the Gulf coast shows the weakest spatial correlation, where over 82 % of compound events only affect single locations. Our findings highlight the importance of accounting for spatial dependence in compound flood assessments. Our large set of stochastic spatially-joint events can be used as boundary conditions for the hydrologic-hydraulic models to simulate the surface inundation and further assess risks of compound flooding in low-lying coastal and estuarine areas.
Competing interests: One of the (co-)authors is a member of the editorial board of Natural Hazards and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-2993', Anonymous Referee #1, 05 Aug 2025
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RC2: 'Comment on egusphere-2025-2993', Anonymous Referee #2, 11 Oct 2025
The study proposes a statistical framework for evaluating the spatial correlation of compound flooding, demonstrating its use for some coastal locations in the USA. While the methodology appears potentially valuable for flood risk assessments in coastal cities, the manuscript currently lacks sufficient detail to fully convey its approach and practical implementation. This limits the reader’s ability to understand and apply the proposed framework. Furthermore, the accompanying scripts intended to reproduce the analysis are incomplete.
I offer the following comments to help improve the article.
- L108: Why did the authors use daily data and not instantaneous (e.g., hourly or 15-minute) data?
- L120-139: Instead of filling up data gaps based on information from nearby stations, would have been possible to perform all the analyses only considering time periods and storms unaffected by these gaps? Please discuss pros and cons of the two alternative approaches.
- L161-186, as well as Section 2.4, provide a description of critical methodological steps of this work; however, they lack a rigorous mathematical framework. The authors should include suitable mathematical formulations, possibly using subscripts for referring to generic locations or events, when explaining the proposed approach.
- While addressing the previous comment, the authors should clarify if and how they account for mixed-population effects within each water-level or flow time series. In several parts of the manuscript (e.g., L288, L355, L368, L415), the authors observe that there are different possible generating mechanisms for these extremes, such as tropical cyclones and extra tropical cyclones affecting storm surges, or snowmelt and convective events affecting riverine flooding. Could the coexistence of different parent distributions limit the applicability of the proposed methodology? The authors should discuss these aspects in detail.
- L402: it is expected that larger storms will have a greater spatial footprint and affect a larger number of stations. The authors should therefore rephrase the sentence at L402. For example: “However, these relative occurrence rates become significantly higher with increasing thresholds, reflecting the fact that bigger storms can affect more locations”
- The code provided to reproduce the analyses (assets for the review process) apparently lacks several parts, including a routine for automated threshold selection (L154), and subroutines that are called within the provided scripts. Those subroutines contain functions that are used in the shared scripts; without those files, it is impossible to run the provided codes. E.g., in the R script “Cal_dependence_autmoated_thres.R”, calls to the following missing scripts are included: “transFun.HT04.R”, “predict.mex.conditioned.R”, “u2gpd.R”, “revTransform.R”, “Migpd_Fit.R”, “mexTransform.R”.
Minor comments
- L49-50: add references supporting this statement regarding the effects of TCs and ETCs on extreme and moderate, more frequent events, respectively.
- Fig 2b, L293-296: How were the 5th-95th-percentile confidence intervals obtained?
- Fig 4 is unclear. Does the y-axis show the percentage of events affecting 1, 2, …, etc. distinct locations over the 10,000-simulation period? If yes, please consider using a more informative label for the y-axis.
Citation: https://doi.org/10.5194/egusphere-2025-2993-RC2
Data sets
10,000 years of spatially joint events of extreme sea levels and river discharges in the U.S. Huazhi Li https://zenodo.org/records/15728000?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjZlYzM1YWIxLTI0MTUtNGJmZS04ZGY1LTg0NzI1ZmJmNGM5MyIsImRhdGEiOnt9LCJyYW5kb20iOiJlMDY1NjA3MGZjZTZhMWE2NTAwZWNjNWQ4OWIzMGE0OCJ9.m0Ci0FpG-EMsBic4SKae6LbjAheAMdRFa-l61mo0zS3fhinjeZPBmJDOif8w02FngH-RhE6NRvoRv-sHjQom5g
Model code and software
Scripts for 'Assessing the spatial correlation of potential compound flooding in the United States' Huazhi Li https://zenodo.org/records/15728083?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjNiYTZmOTFiLWVjYzUtNGE0Yy05Mjc1LWE4NTFlM2E4M2M4ZiIsImRhdGEiOnt9LCJyYW5kb20iOiJmNTI3OGRkZDcxNGRkOTM2MDA5ZGUwM2U2MTVlMzg3MCJ9.e70SxuVFOxTlI_Mnc1FP3rCI2_8lVmirO4vTSGXhLvb8XhLFAXDyOjGFW52R4WHgIowENcAJEJhthW0N7z4p9Q
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This paper presents a well-structured study with a solid methodological approach to assessing the spatial correlation of potential compound flooding along the U.S. coastlines. The work represents an appreciable scientific contribution by addressing a relevant and previously underexplored aspect of compound flooding.
I recommend acceptance subject to technical corrections. In particular, there is a typographical error in line 138, where “Santa Monita” should be corrected to “Santa Monica.” There may be other similar typographical issues worth checking.
No further technical issues were identified.