The considerable increase in flood damages in Europe in recent decades has shifted attention from flood protection to flood risk management. Assessments of expected damage provide critical information for flood risk management efforts. The evaluation of potential damages under different flood scenarios through quantification of their ability to provide relative short-, medium- and long-term risk reduction, supports decision-makers in discriminating among several alternative mitigation actions. End-users should be aware of, and knowledgeable about, the limitations and uncertainties of such analyses, as well-informed decisions regarding efficient and sustainable flood risk management will become increasingly relevant under future climate and socio-economic changes. In this context, a method was developed to identify and quantify the role of the input parameters in the uncertainty of the potential flood economic damage assessment in urban areas with low sloping/flat terrain and complex topography using a GIS-based, free and open-source software called Floodrisk. Sets of plausible input parameters for the model’s two flood loss modelling subroutines (hydraulic modelling and damage estimation) were dynamically combined to quantify the contribution of their inner parameters to the total damage assessment uncertainty. To estimate the contributions of each input to overall model uncertainty, the combination of input parameters that minimized the error in the spatial distribution assessment of the extensive damages affecting (downtown) Albenga (Italy), enumerated after the historical Centa River flood of November 5, 1994, was taken as a reference. In this specific case, a high epistemic uncertainty for the damage estimation module was noted for the specific type and form of the damage functions used. In the absence of region-specific depth-damage functions, the vulnerability curves were adapted from a range of geographic and socio-economic studies. Given the strong dependence of model uncertainty and sensitivity to local characteristics, the epistemic uncertainty associated with the risk estimate was reduced by introducing additional information into the risk analysis. Implementing newly developed site-specific curves and a more detailed classification of the construction typology of the buildings at risk, led to a substantial decrease in modelling uncertainty, along with a decrease in the sensitivity of the flood loss estimation to the uncertainty in the depth-damage function input parameter. These findings indicated the need to produce and openly disseminate data in order to develop micro-scale risk analysis through site-specific vulnerability curves. Moreover, this study highlighted the urgent need for research on the development and implementation of methods and models for the assimilation of uncertainties in decision-making processes.

近几十年来，欧洲洪水灾害的大幅增加已将人们的注意力从洪水防护转移到洪水风险管理上。对预期损害的评估为洪水风险管理工作提供了关键信息。通过量化其相对降低短期，中期和长期风险的能力，评估不同洪水情景下的潜在损失，可帮助决策者区分几种缓解措施。最终用户应了解并了解此类分析的局限性和不确定性，因为在未来的气候和社会经济变化下，关于有效和可持续的洪水风险管理的明智决策将变得越来越重要。在这种情况下，洪水风险。动态组合了模型的两个洪水损失建模子例程（液压建模和损害估计）的合理输入参数集，以量化其内部参数对总损害评估不确定性的贡献。为了估算每个输入对整体模型不确定性的贡献，在11月5日发生历史性的中原河洪水之后，对输入参数进行了组合，这些输入参数将对影响（市区）阿尔本加（意大利）的广泛破坏的空间分布评估中的误差最小化。 1994年，被作为参考。在此特定情况下，对于所使用的损坏函数的特定类型和形式，损坏估计模块存在较高的认知不确定性。在没有特定于区域的深度破坏功能的情况下，脆弱性曲线是根据一系列地理和社会经济研究改编而成的。考虑到模型不确定性和对局部特征的敏感性的强烈依赖性，通过在风险分析中引入其他信息来减少与风险估计有关的认知不确定性。实施新开发的针对特定地点的曲线以及对有风险的建筑物的建筑类型进行更详细的分类，导致建模不确定性大大降低，洪水损失估算对深度不确定性的敏感性也随之降低。损坏功能输入参数。这些发现表明，有必要产生和公开传播数据，以便通过针对特定地点的脆弱性曲线开展微观风险分析。而且，