Abstract
All flood hazard and risk assessment suffer from a certain degree of uncertainty due to multiple factors, such as flood frequency analysis, hydrodynamic model calibration, or flood damage (magnitude–damage functions) models. The uncertainty linked to the flood frequency analysis is one of the most important factors (previous and present estimation point to 40%). Flood frequency analysis uncertainty has been approached from different points of view, such as the application of complex statistical models, the regionalization processes of peak flows, or the inclusion of non-systematic data. Here, we present an achievable approach to defining the uncertainty linked to flood frequency analysis by using the Monte Carlo method. Using the city of Zamora as the study site, the uncertainty is delimited by confidence intervals of a peak flow quantile of a 500-year return period. Probabilistic maps are derived from hydrodynamic results, and further analysis include flood hazard maps for human loss of stability and vehicle damage. Although the effect of this uncertainty is conditioned by the shape of the terrain, the results obtained may allow managers to achieve more consistent land-use planning. All those Zamora city results point out the probable underestimation of flood hazard (the higher hazard areas increase around 20%) and risk when the uncertainty analysis is not considered, thus limiting the efficiency of flood risk management tasks.
Highlights
Floods are probably the most frequently recurring natural phenomenon affecting society in terms of space and time, regardless of their geographical location or socioeconomic development as shown by the data collected by the InternationalDisasters Database for the period 1900–2018 [1]
The first time, the improved and extended maximum annual peak flow record for gs2121 gauge station at Zamora city was used for Flood Frequency Analysis (FFA) by using generalized extreme value (GEV) probability distribution with a Maximum Likelihood (ML) estimator for parameters (Figure 3b)
Parameters were used to transform the 500 series of probability values into series of instantaneous maximum annual flow values. For each of these 500 series of peak flows, a GEV distribution function was fitted again to obtain the maximum instantaneous peak flow values associated with the different return periods considered in the study, as well as the confidence intervals of this estimated value
Summary
Floods are probably the most frequently recurring natural phenomenon affecting society (human and goods) in terms of space and time, regardless of their geographical location or socioeconomic development as shown by the data collected by the InternationalDisasters Database for the period 1900–2018 [1]. Even if there are data series of maximum annual flow with lengths greater than 30 records, a value considered sufficiently long by Aronica et al [5] for the application of the cumulative probability distribution function (CDF) to the annual maxima series, the representativeness of this time series with respect to the global behaviour of the peak flow variable is limited This lack of knowledge causes the existence of the so-called epistemic uncertainty, resulting from imperfect knowledge of the system (e.g., [6,7,8]), or from simplifications associated with the selected modelling approach and parametrizations
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