the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Nov 2025
Indirect costs of floods: a case study of highways road users
Abstract. Hydrometeorological events have a significant impact on road infrastructure and traffic flow. Floods can lead to the collapse or weakening of bridges, compromise river protection, and inundate riverine roads, thereby restricting or halting the traffic. These events incur additional user costs owing to the increased travel times resulting from the rerouting and reduced speeds on the affected roads. The economic impact of such natural events on road networks is typically quantified in terms of the infrastructure recovery costs. However, the expected road user cost (EUC) associated with driving on damaged roads is often neglected. This study aimed to estimate the flood risk to road networks by integrating a hydrological-hydraulic model with road and bridge vulnerability assessments and traffic assignment models to calculate the EUC. The procedure was applied to the "Aconcagua Bajo" Watershed in central Chile, considering floods with return periods ranging from 2 to 100 years, the vulnerability of bridges to scour, road waterlogging, travel times, and fuel consumption costs incurred by the road users.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4016', Anonymous Referee #1, 01 Dec 2025
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RC2: 'Comment on egusphere-2025-4016', Anonymous Referee #2, 19 Mar 2026
This study presents a method/procedure that integrates hydrologic and hydraulic flood modeling with road and bridge vulnerability analysis and traffic modeling to estimate user-related costs. The framework is applied to the Aconcagua Bajo watershed in Chile, analyzing floods of varying exceedance probabilities (2-100 year return periods) and their effects on bridges, roads, and overall traffic efficiency. Overall, this study is comprehensive, and the findings are meaningful for accurately estimating indirect costs due to floods. However, I still have several comments and suggestions for improving the current work.
1) Line 17: “EUC” stands for expected road user cost or expected user cost? Please make sure it is consistent throughout the manuscript, and write the full name for its first appearance and use the acronym afterwards, e.g., “EAD” in Lines 94 and 109, why “HMH” stands for hazard modeling, and so on. Also, it is suggested to add a list of all the acronyms to the appendix part.
2) Line 25: Floods can be also caused by rapid snowmelt.
3) Equation (3): The second term on the right-hand side of the equation should be P(h_j+1) instead of PD(h_j+1).
4) Equation (4): Please make it clear that T_1>T_2>T_3.
5) Sections 5.1 and 5.2: What is the spatial resolution of the DEM data used in hydrological and hydraulic modeling? How were the model parameters, such as the Manning’s roughness coefficients, calibrated in this study?
6) Section 5.2: It should be noted that the uncertainty in the hydraulic modeling process determines the accuracy of the model outputs. Thus, it is suggested to employ the probabilistic flood inundation maps instead of the deterministic maps for the future analysis if possible. Please refer to the paper below.
Reference: “Uncertainty analysis and quantification in flood insurance rate maps using Bayesian model averaging and hierarchical BMA” (https://doi.org/10.1061/JHYEFF.HEENG-5851)
7) Table 5: What do the terms, H_av and L, stand for?
8) Figures 3 and 4: Please add a scale bar to the maps. For some reason, the inundation extents under different flood scenarios in Figure 4 look quite similar.
9) Section 7.4: It would be helpful to add a conceptual sketch of the mechanism of the traffic assignment model.
10) Figure 6: To avoid confusion, it is suggested to change the horizontal axis to the exceedance probability of return periods in years.
11) Lines 400-406: It is also important to note that the uncertainty in model calibration/evaluation should not be ignored, given the sampling uncertainty over limited space and time. Please refer to the paper below for more information about the limitations of some commonly used evaluation metrics in flood modeling.
Reference: “Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis” (https://doi.org/10.1111/jfr3.12982)
12) Lines 450-457: This paragraph is similar to the paragraph above and thus redundant. It is suggested to remove it.
13) Line 497: The terrain data are “digital terrain models” or “digital elevation models”?Citation: https://doi.org/10.5194/egusphere-2025-4016-RC2
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Review of the manuscript egusphere-2025-4016: “Indirect Costs of Floods: A Case Study of Highways Road Users”
The manuscript presents an integrated framework to estimate Expected User Costs (EUC) for highway networks affected by flooding. The approach combines hydrological and hydraulic modelling, bridge and road vulnerability models, and static traffic assignment to quantify indirect impacts on users in terms of increased travel time and fuel consumption. The method is applied to the “Aconcagua Bajo” watershed in Chile, and EUC is computed across return periods from 2 to 100 years. The topic is timely and important, particularly because quantifying indirect economic impacts on road users remains insufficiently addressed in flood-risk literature. The explicit inclusion of fuel consumption in the EUC metric is a notable and useful contribution.
The manuscript is scientifically relevant and addresses an important gap by estimating user-based indirect flood losses. However, several aspects of the structure, clarity, and methodological transparency require improvement before publication.
Overall, I recommend major revision to strengthen structure, clarity, and methodological completeness.
Specific Comments:
Conceptual clarity & definitions
The definition is unclear. Please clarify what is meant by risk-receptor consequences within this framework, or provide a reference.
State of the art & motivation
The statement “Engineers face challenges in estimating traffic reassignment…” requires a supporting reference. Several works in the hybrid flood-risk modelling literature could be cited.
Data and replicability
“The traffic assignment model uses road and traffic data obtained from national road and traffic surveys…”
Please specify the exact datasets, source years, and URLs if publicly available, to ensure replicability.
Road-flood overlay and hydraulic outputs
When overlaying flood maps with the road network, each segment may experience varying flood depths. Please clarify which water-depth value is used for computing vulnerability and speed reduction (e.g., maximum depth along the segment? average? depth at centroid?).
Vulnerability models
Equations (5) and (6) provide the calibrated probability of interruption, but a brief qualitative description of the underlying model is needed for readers unfamiliar with the reference. For example, explain in a sentence whether it is an empirical fit derived from controlled experiments, traffic data, or physical modelling.
Traffic modelling
The sentence mentions speed, traffic volume, and travel time, but not fuel consumption. Since fuel consumption is essential for EUC, clarify how FCC is computed, linking clearly to Section 7.5.
Hydraulic modelling
The DEM resolution of 12.5 m is used in hydrological modelling; confirm explicitly whether the same resolution is used in HEC-RAS 2D hydraulic simulations.
Also clarify whether road elevations (crest level, crown elevation) are known or assumed based on DEM.
Traffic interruption thresholds
“The limit values of Pint-light and Pint-heavy of 15% were defined.”
This choice is unclear. Please explain why 15% was selected, whether this threshold is standard in the literature, and how sensitive results are to this assumption.
Structure and flow
(i) Methodology (all models, equations, thresholds),
(ii) Case-study description (all input data and site characteristics),
(iii) Results (hydraulic outputs, exposure, vulnerability, network costs),
(iv) Discussion and limitations.
This would significantly improve readability.