Understanding the Compound Flood Risk along the Coast of the Contiguous United States
Abstract. Compound flooding is a type of flood events caused by multiple flood drivers. The associated risk has usually been assessed using data-based statistical analyses or physics-based numerical models. This study proposes a compound flood (CF) risk assessment (CFRA) framework for coastal regions in the contiguous United States (CONUS). In this framework, a large-scale river model is coupled with a global ocean reanalysis dataset to (a) evaluate the CF exposure risk related to the coastal backwater effects on river basins, and (b) generate spatially distributed data for analyzing the CF hazard risk using a bivariate statistical model of river discharge and storm surge. The two kinds of risk are also combined to achieve a holistic understanding of the continental-scale CF comprehensive risk. The estimated CF risk shows remarkable inter- and intra-basin variabilities along the CONUS coast with more variabilities in the CF hazard risk over the US West and Gulf coastal basins. Different risk assessment methods present significantly different patterns in a few key regions, such as San Francisco Bay area, lower Mississippi River and Puget Sound. Our results highlight the needs to weigh different CF risk measures and avoid using single data-based or physics-based CFRAs. Uncertainty sources in these CFRAs include the use of gauge observations, which cannot account for the flow physics or resolve the spatial variability of risks, and underestimations of the flood extremes and the dependence of CF drivers in large-scale models, highlighting the importance of understanding the CF risks for developing a more robust CFRA.
Dongyu Feng et al.
Status: open (until 30 Mar 2023)
- RC1: 'Comment on hess-2023-31', Anonymous Referee #1, 07 Mar 2023 reply
Dongyu Feng et al.
Dongyu Feng et al.
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In this manuscript, the authors apply a compound flood risk assessment (CFRA) to the coastal regions of the contiguous United States. In contrast to several previous studies, they do not only perform a bivariate extremes analysis between storm surge and river streamflow (referred to in their manuscript as data-based CFRA), but they couple their statistical analysis to a large-scale river-routing model and population exposure information (referred to in their manuscript as physics-based CFRA). Their main finding appears to be that their metrics, such as the marginal and joint exceedance probabilities and Kendall’s rank correlation, differ substantially depending on the type of CFRA. The authors use their results to argue that ‘data-based’ CFRA alone does not provide a holistic view of CFRA and provides a biased view, hence different types of CFRA need to be considered. Although my experience in this field is limited, I think that this contribution is welcome and useful, and I also found it well written. I do have several comments, though, that I hope will help to improve the manuscript.
-‘Data-based CFRA’: I am not sure if this is a commonly used term, but it is ambiguous to me because ‘physics-based CFRA’ is also based on data. Would it be possible to find terminology that more clearly distinguished the two types of CFRA? Perhaps ‘statistics-based’ and ‘dynamics-based’ or so?
-Figures: for the maps, color schemes are used that I doubt are color-blind friendly/perceptually uniform. Could the authors please check and modify their figures where necessary?
L64-L92: I am missing an introduction of existing large-scale compound flood risk studies with similar aims, such as https://nhess.copernicus.org/articles/23/823/2023/. I would encourage the authors to include a paragraph on such studies and explain the similarities and differences with their own manuscript. The same comment applies to the Discussion section.
L144: Kendall’s rank correlation is computed, but it is not clear to me if/what lag is allowed between the two variables. Could the authors please also discuss how the samples for which the rank correlation is computed are conditioned, i.e., are cases in which one of the variates but not necessarily both variables are extreme (two-sided) also considered or not, and why (not)?
Section 2.1: information about declustering of the peak events seems to be missing, although declustering is necessary to avoid dependence in the extremes. Could the authors please explain if/how their data was declustered?
L318-332: this paragraph discusses figures that are not part of the main manuscript. I would either include both the discussion and those figures in the manuscript or put both in the supplements, and only briefly refer to them. Its current form is inconvenient to read in my opinion.
Section 3.2.1: A comparison with the results of previous (‘data-based’) studies in this region (e.g., Wahl et al., 2015; Nasr et al., 2021) would provide useful context, but is missing. Could this be added?
L400-402: while I appreciate this remark, data that covers a longer period cannot simply be obtained. Could the authors please explain/discuss how they imagine a dataset such as the GTSM reanalysis being extended back in time with reasonable confidence? In the absence of such data, how can we get a sense of how uncertain the results of the present study are given the relatively short period used? This seems especially relevant given the underrepresentation of tropical cyclones in the observations/reanalysis data. And what about the influence of temporal variability on the dependence between the two variates examined? (e.g., Wahl et al., 2015)
L440-445: The open access to the code used for the paper is commendable. I strongly recommend doing the same for the output data instead of sharing it ‘upon request’.
L55: ‘across rivers and estuaries’: I suggest changing this to ‘different rivers and estuaries’, unless variation within a river or estuary is meant.
L103 could the authors please comment on using a river routing model v.s. using a hydrological model that also includes groundwater? I am not an expert, but I can imagine that changes in groundwater also affect the propagation time between upstream and the coastal interface?
L114: It only becomes clear later why the 2nd boundary condition is used (i.e., in eq. 6). It would be helpful to briefly motivate the 2nd boundary condition here already. Besides, it is not clear where this fixed level is derived from – is it the mean level in GTSM?
L119: ‘statistical model’ please specify what this refers to.
L175: ‘using an R-package’ please specify which package.
L192: is this necessary? Can the authors not just change the scale of the relevant plots?
L200: to avoid confusing this section for a results section, I suggest replacing ‘investigate’ with ‘review’ or ‘discuss’. Additionally, I found this section not as well connected to the rest of the manuscript. Perhaps some sentences could be added as to why these uncertainty sources are introduced here.
L206-207: would be more consistent to define the different types of uncertainty in the same order as they are introduced.
L252: ‘shift’ is this the same as ‘lag’? If so, I think that would be a clearer term.
L258: I suggest to change +/- 5 days to: a window of 10 days around the extreme, if this is indeed the intended meaning.
Section 2.2.4: please consider putting this in a table, for a clearer overview.
L279: ‘elevation gradient’ – would it be worth plotting the gradient instead of the elevation itself in figure?
L286-287: could use some more explanation, in my opinion.
L298: I think this needs to be ‘Section 2.2.4 at 24 river basins’, correct?