the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Mar 2026
Integrating total water level data and visual evidence to assess coastal flooding in San Diego County
Abstract. Coastal flooding in Southern California poses a growing threat to communities and infrastructure, exacerbated by climate change and sea level rise. Total water level (TWL), the combination of sea level, tides, and wave runup, is increasingly used to forecast coastal flooding, but validating the thresholds at which flood impacts occur remains a challenge. This study examines the relationship between modeled TWL and photographic or video evidence of flooding in San Diego County from 2010 to 2024. We integrate model output from the Coastal Data Information Program with visual records from community monitoring programs to assess spatial and seasonal variations in flood occurrence. We also evaluate the influence of atmospheric rivers and El Niño conditions. Atmospheric river days were associated with an increase in the likelihood of observed flooding, and El Niño winters showed a positive but weaker correlation. Overall results demonstrate a robust but imprecise correlation between modeled TWL and observed flood impacts, with uncertainty driven largely by convenience sampling in the visual dataset. Despite these limitations, modeled TWL is shown to be a useful proxy for flood risk. Our findings underscore the need for systematic flood impact documentation to refine threshold estimates and improve flood forecasting and coastal management.
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Status: open (until 17 Apr 2026)
- RC1: 'Comment on egusphere-2026-820', Anonymous Referee #1, 24 Mar 2026 reply
Data sets
Integrating Total Water Level with Photo Flooding Evidence Dataset Authors/Creators Denali Pinto and Tom Corringham https://doi.org/10.5281/zenodo.18729177
Model code and software
Integrating Total Water Level with Photo Flooding Evidence Code Denali Pinto and Tom Corringham https://doi.org/10.5281/zenodo.18729150
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- 1
GENERAL COMMENTS
The manuscript “Integrating total water level data and visual evidence to assess coastal flooding in San Diego County” aims to link modeled total water levels (TWLs) with photographic and video evidence of coastal flooding to estimate impact thresholds across locations in San Diego County. The authors use logistic regression to quantify the probability of flood occurrence as a function of TWL and explore temporal patterns, spatial variability, and the potential influence of atmospheric rivers (ARs) and ENSO on coastal flooding impacts. The study addresses an important and timely problem: translating modeled TWLs into meaningful, impact-based thresholds by integrating TWL estimates with visual observations. This approach is valuable and potentially impactful. However, in its current form, the manuscript has several major limitations that hinder its scientific robustness and reproducibility.
The most critical issues are: (1) the absence of clear, consistent, and physically grounded definitions for key concepts throughout the text, such as ‘coastal flooding’, ‘flood impact’, and ‘impact threshold’; (2) a Methods section that is incomplete and does not provide sufficient detail to allow reproducibility, with several statistical analyses introduced for the first time in the Results; (3) a paper structure in which the boundaries between Methods, Results, and Discussion are frequently violated; (4) a tendency to overstate conclusions, especially regarding the physical interpretation of the lagged relationship between ARs and the occurrence of flood-impact photos; and (5) figures that do not effectively communicate the specific results cited in the accompanying text.
Below, I provide detailed comments outlining where improvements are needed.
SPECIFIC COMMENTS
1. Lack of clear and consistent definitions of key concepts - the manuscript does not provide rigorous, consistent definitions of “coastal flooding”, “coastal flood impact”, and “impact threshold”. These concepts are used interchangeably throughout the manuscript or introduced without clear explanation, making it difficult to interpret the results. For instance, it remains unclear whether “coastal flooding” is identified on photos/videos based on water levels reaching a physical morphological feature (e.g., dune) or infrastructure (e.g., seawall) or based on observed impacts such as the need for a cleanup response (as suggested in line 91). This distinction is critical, as it directly affects how photographic evidence is classified and how thresholds are derived. The lack of clarity limits the interpretation of the full manuscript and reduces confidence in the reported results. Clear, physically meaningful definitions should be established early in the manuscript and applied consistently throughout.
2. Methods section is insufficient for reproducibility - a key NHESS criterion is that methods are described with enough detail to allow replication, which is not fully met in its current form. Several critical elements are unclear or missing: the beach slope definition used in the Stockdon runup model is not clearly specified (is it foreshore beach slope, mean beach slope, or satellite-derived tidal datum-referenced slope?); the TWL calculation procedure is not fully described; the social media search strategy is incomplete – what other terms were searched besides San Diego coastal flooding and king tides? Are these use together or individually?; what are flood observations and how different they are from flooding events listed on section 1.1?; the handling of duplicate or ambiguous photo submissions is unclear (line 95 mentions consolidation without specifying how this process is done); and several statistical methods (linear regression, GLMM, and Bayesian logistic regression) are introduced for the first time in the Results section without prior description in the Methods. All methodological steps should be clearly described in the Methods section.
3. Paper structure - the separation between Methods, Results, and Discussion is frequently unclear. Methodological descriptions appear in the Results (e.g., lines 267–272, 273, 306), while interpretation and discussion of mechanisms or prior literature are also included in the Results (e.g., lines 161–162, 194–198, 261–265, 294–295, 330–335). In contrast, the Discussion reads more like a Conclusions section, lacking a summary and a deeper interpretation of findings and research contribution. Reorganizing these sections would improve clarity and ensure a more consistent structure.
4. Overclaiming and overstating results - several conclusions are stated with greater certainty than supported by the analysis. For instance, the repeated claim that modeled TWL and photographic evidence “validate” each other is not fully supported by the analysis – this is explicitly mentioned from lines 337 - 339; the analysis demonstrates correlation, not validation. TWL-max-daytime is stated to outperform daily maximum TWL, but no quantitative comparison is presented among the different regressors used in the logistic regression models, and Figures 7 and 8 show nearly identical time series between these two variables.
5. Ambiguity and redundancy in TWL predictor definitions and temporal aggregation - the distinction among daily maximum TWL, daytime maximum TWL, and lagged TWL variables (e.g., TWL-max, TWL-max-daytime) is unclear and appears, in some cases, redundant. It is not evident how different daytime maximum TWL is from daily maximum TWL, nor why it should be a more appropriate predictor of flooding. If the rationale is that photos are predominantly taken during daylight hours, this should be explicitly stated and justified. Even in that case, a more direct and physically meaningful approach would be to match each photo to the closest hourly TWL value, or to a short time window around the photo (e.g., ±3 hours), rather than relying on aggregated daytime maxima.
Additionally, the definitions of the predictor variables are ambiguous—for example, TWL-daytime and TWL-max-daytime may represent the same quantity depending on how they are constructed. This lack of clarity makes it challenging to interpret the logistic regression results and assess whether the different predictors provide independent information. This issue is further compounded by the lack of clarity on how flooding is identified in the photos (i.e., based on observed impacts vs. water level extent on land), which directly affects how these TWL metrics should be interpreted.
Furthermore, the authors note (lines 203–204) that photo evidence and TWL do not always align. This mismatch may stem from the definition of flooding used. If flooding is identified based on impacts (e.g., cleanup), rather than actual water levels, then timing mismatches between peak water levels and photo capture are expected. Conversely, if flooding is defined based on observed water levels in the photos, then discrepancies with TWL magnitude likely reflect uncertainty in the modeled TWL. This raises additional questions regarding how images are georeferenced and whether a consistent vertical datum is used to relate observed water levels to TWL estimates. Without a clear framework linking observed water levels, model estimates, and thresholds, it is difficult to interpret these mismatches.
A clearer definition of all variables, along with a quantitative comparison of their performance (e.g., daily max vs daytime max vs time-of-photo TWL), is needed.
6. Physical interpretation of the AR–photo relationship is not supported - the manuscript’s TWL formulation (not explicitly stated, but TWLs are generally = tides + nontidal residual + wave runup) could, in principle, include AR-driven sea level anomalies within the nontidal residual component, though the authors do not decompose or formally demonstrate this. More importantly, the main AR result, a lagged GLMM indicating increased odds of photo occurrence following AR events, relates ARs to the likelihood of a photo being taken, not to the physical occurrence of flooding. These are not equivalent. The observed lagged relationship is likely influenced by behavioral and observational biases from researchers and the community (e.g., increased documentation during and after major storms), rather than a direct physical mechanism. This distinction should be clearly acknowledged, and the results should be interpreted with caution rather than as evidence of a causal AR–flooding relationship.
7. Figures do not effectively support or communicate the results - several figures present the data in ways that introduce bias or do not align with the claims in the text, specifically:
a. Figure 3 shows absolute photo counts by month without normalizing for total photo availability, which can bias the interpretation of seasonal flood frequency. It is unclear how many photos were taken each month (e.g., December vs April) and what proportion of those actually show flooding. Without this context, the counts alone are difficult to interpret and may reflect sampling effort rather than true variability in flooding occurrence.
b. Figure 6 presents absolute counts per transect rather than proportions, similarly to Figure 3, making spatial comparisons sensitive to uneven sample size.
c. Figures 7 and 8 show nearly identical time series, yet the text claims one predictor outperforms the other without quantitative support.
d. Figure 9 presents thresholds only for the preferred predictor (TWL-max-daytime), while claims of improved uncertainty relative to other variables are not supported by comparable results.
e. Figure 12 does not clearly convey that the odds ratios represent the probability of a flood photo being taken, rather than the physical probability of flooding.
8. Wave direction is not considered - given the physical importance of wave direction in determining whether wave runup produces impacts at a coastline with a given orientation, the absence of wave direction as a potential explanatory variable for spatial patterns of flooding represents an important gap. Beach orientation relative to the dominant wave approach can be critical for whether runup reaches and exceeds impact thresholds.
CONCLUSION
Overall, as currently presented, the study does not yet quantify physically meaningful flood thresholds, but rather statistical relationships between modeled TWL estimates and the occurrence of photographic evidence. While the approach is promising, the current implementation is limited by unclear definitions, insufficient methodological transparency, and a lack of physical grounding linking TWLs to observed impacts. Addressing these issues is necessary before the results can be interpreted as robust or transferable.