Climate-sensitive Derived Flood Frequency Analysis Based on Flood Events Characteristics

Cafiero, Luigi; Bertola, Miriam; Valent, Peter; Laio, Francesco; Blöschl, Günter; Viglione, Alberto

doi:10.5194/egusphere-2026-2808

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https://doi.org/10.5194/egusphere-2026-2808

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11 Jun 2026

| 11 Jun 2026

Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

Climate-sensitive Derived Flood Frequency Analysis Based on Flood Events Characteristics

Luigi Cafiero, Miriam Bertola, Peter Valent, Francesco Laio, Günter Blöschl, and Alberto Viglione

Abstract. Understanding how flood frequency changes under non-stationary hydro-climatic conditions remains a key challenge in hydrology. This study presents a Bayesian process-based framework for flood frequency analysis that explicitly accounts for the seasonal dependence of rainfall–runoff processes and their sensitivity to climate change. The approach links an event-based rainfall–runoff model with probabilistic representations of storm, soil moisture, and catchment response, allowing the joint propagation of uncertainty from climate drivers to flood quantiles. The process-based structure of the framework also enables the disentangling of individual flood drivers, such as the upward shift of the zero-degree isotherm, long-term changes in soil moisture regimes, and variations in precipitation intensity. The framework is implemented in Austrian hotspots, i.e. groups of similar catchments, using long-term hydrometeorological records and regional climate projections (EURO-CORDEX). Results show that (i) changes in flood frequency are primarily driven by projected increases in precipitation intensity, while temperature and soil moisture act as modulators or amplifiers of this signal; (ii) precipitation changes have larger but more uncertain impacts on floods than temperature and soil moisture variations; (iii) the expected reduction in soil moisture tends to mitigate frequent floods but has mores limited influence on rare events. The proposed methodology provides a transferable tool for assessing climate-sensitive flood hazards in non-stationary environments.

Received: 16 May 2026 – Discussion started: 11 Jun 2026

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Luigi Cafiero, Miriam Bertola, Peter Valent, Francesco Laio, Günter Blöschl, and Alberto Viglione

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RC1:
'Comment on egusphere-2026-2808', Anonymous Referee #1, 30 Jul 2026 reply
The manuscript presents an interesting and timely contribution to climate-sensitive flood frequency analysis. The proposed framework is original in the way it combines event-based derived flood frequency analysis, Bayesian inference, and climate-change perturbations of key flood-generating processes. In my opinion, the work is scientifically sound, well structured, and potentially suitable for publication. The modelling assumptions are generally well motivated and supported by previous literature, and the paper provides useful insights into the relative role of precipitation, temperature-related processes, and antecedent catchment conditions in shaping future flood frequency.
I therefore recommend publication after minor revisions. My comments mainly concern clarifications of some modelling choices, additional discussion of the implications of the adopted framework, and a few suggestions to improve the presentation of the results.
The modelling assumptions are justified by previous literature and, overall, appear reasonable. At the same time, these assumptions largely determine the outcome, since they directly contribute to the simulated catchment response through a simplified rainfall-runoff transformation. I suggest that the authors briefly recall the main literature references supporting each modelling choice, to strengthen the consistency and transparency of the simulation framework.

Antecedent wetness is estimated by considering precipitation and evapotranspiration, through temperature, over the 100 days preceding each event. Is this duration equally justified for all the catchments considered, regardless of catchment size and response time? Would a continuous simulation framework have provided a stronger basis for representing antecedent conditions? I am not asking the authors to make additional numerical comparisons, but I think it would be useful to include a short discussion comparing the adopted framework with the state of the art. In particular, the authors could briefly discuss the advantages and limitations of a Monte Carlo simulation based on multivariate event forcing, as opposed to a continuous simulation based on precipitation and temperature time series, with rainfall-runoff transformation parameters treated as random variables.

What is the expected role of model structure in overall uncertainty? The adopted rainfall-runoff model strongly simplifies the transformation from rainfall to runoff, and this may affect the variability of the simulated flood response, possibly reducing the output variability. I encourage the authors to add a comment on this point in the discussion.

The model includes a relatively large number of parameters, 25 according to Table B1, which need to be inferred from the selected rainfall and discharge events. Although the authors correctly state that the parameters are estimated from event characteristics rather than directly from flood peaks, the number of parameters remains high. This may result in substantial uncertainty in the model response. I suggest that the authors comment more explicitly on this aspect in the discussion section.

To what extent do the adopted priors influence the final model output? A short discussion, or a sensitivity-oriented comment, would be helpful to clarify whether the results are robust with respect to the prior assumptions.

Would it be possible to quantify or show, for example in an appendix, the variability of the fitted distributions within each hotspot? It would also be useful to discuss whether this variability is comparable across the different hotspots or whether some hotspots show a substantially larger internal heterogeneity.

In Figure 5, I suggest using the same axis limits for each variable across all seasons. This would make the comparison among seasons easier and would improve the readability of the figure.

Since no explicit bias correction is applied, I suggest adding one sentence to clarify how the variables enter the framework in a differential or relative-change form, except for temperature, for which absolute changes are considered.

The maps are useful for showing spatial variability, especially in relation to orography. However, they do not always facilitate the interpretation of the results across the different components, seasons, and return periods. I suggest adding a complementary figure, similar in spirit to Figure 12, showing the relative contributions of temperature, precipitation, and antecedent wetness conditions. This could be repeated for the four seasons for one representative hotspot, or alternatively shown at the annual scale. Such a figure would help the reader better understand the relative role of the different drivers and would complement the spatial information provided by the maps.

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Citation: https://doi.org/10.5194/egusphere-2026-2808-RC1

Luigi Cafiero, Miriam Bertola, Peter Valent, Francesco Laio, Günter Blöschl, and Alberto Viglione

Supplement

https://doi.org/10.5194/egusphere-2026-2808-supplement

Data sets

Supplementary material - Climate-sensitive Derived Flood Frequency Analysis Based on Flood Events Characteristics Luigi Cafiero https://doi.org/10.5281/zenodo.20051452

Model code and software

Supplementary material - Climate-sensitive Derived Flood Frequency Analysis Based on Flood Events Characteristics Luigi Cafiero https://doi.org/10.5281/zenodo.20051452

Luigi Cafiero, Miriam Bertola, Peter Valent, Francesco Laio, Günter Blöschl, and Alberto Viglione

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Short summary

Floods are expected to become more frequent and intense under climate change, but their future evolution remains uncertain. We developed a process-based framework to separate key flood drivers in Austria. Using an event-based rainfall–runoff model with climate projections, we found that increasing precipitation intensity is the main driver of future flood changes, while drier soils tend to reduce moderate floods.


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