Methodologies to estimate economic flood damages\ud are increasingly important for flood risk assessment and\ud management. In this work, we present a new synthetic flood\ud damage model based on a component-by-component analysis\ud of physical damage to buildings. The damage functions\ud are designed using an expert-based approach with the support\ud of existing scientific and technical literature, loss adjustment\ud studies, and damage surveys carried out for past flood events\ud in Italy. The model structure is designed to be transparent\ud and flexible, and therefore it can be applied in different geographical\ud contexts and adapted to the actual knowledge of\ud hazard and vulnerability variables.\ud The model has been tested in a recent flood event in northern\ud Italy. Validation results provided good estimates of postevent\ud damages, with similar or superior performances when\ud compared with other damage models available in the literature.\ud In addition, a local sensitivity analysis was performed\ud in order to identify the hazard variables that have more influence\ud on damage assessment results

Flood damage modelling is becoming an essential component in flood risk management. However damage assessments are affected by large uncertainty, mainly related to the use of depth–damage functions. In some countries, where no site‐specific curves are available, a transfer of damage models developed from other areas is required, adding extra uncertainty in the modelling process. This paper discusses the transferability in space of damage curves from literature, with a focus on ‘function uncertainty’, pointing out, especially for mesoscale ones, the lack of detailed information in terms of flood and/or building characteristics that can allow to identify the conditions of applicability of the models. New site‐specific depth–damage functions are then developed for the residential sector, at meso‐ and microscale, based on damage data from the 2010 flood in Veneto, Italy. The application of the new curves reveals a better performance of the mesoscale model compared with the more detailed microscale one, probably due to the small extent of the inundated area.

Abstract. Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.

Abstract. Methodologies to estimate economic flood damages are increasingly important for flood risk assessment and management. In this work, we present a new synthetic flood damage model based on a component-by-component analysis of physical damage to buildings. The damage functions are designed using an expert-based approach with the support of existing scientific and technical literature, and have been calibrated with loss adjustment studies and damage surveys carried out for past flood events in Italy. The model structure is designed to be transparent and flexible, and therefore it can be applied in different geographical contexts and adapted to the actual knowledge of hazard and vulnerability variables. The model has been tested in a recent flood event in Northern Italy. Validation results provided good estimates of post-event damages, with better performances than most damage models available in the literature. In addition, a local sensitivity analysis has been performed, in order to identify the hazard variables that have more influence on damage assessment results.

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This study provides an assessment of recent changes in near‐surface air temperature over the central Adriatic region of Italy, focusing on extreme events at annual and seasonal scales. It investigates trends in selected indices calculated from quality controlled and homogenized daily temperature data recorded from 1980 to 2012 at 34 meteorological stations distributed over the territory. The results reveal overall warming tendencies, particularly pronounced in spring and summer, with significant increases in the duration of warm spells (WSDI) and in the frequency of warm days (TX90p) and warm nights (TN90p), summer days (SU) and tropical nights (TR). Moreover, cold‐related extremes (cold spell duration (CSDI), cool days (TX10p) and cool nights (TN10p), frost days (FD) and icing days (ID)) show significant reductions, although of lower magnitudes, thus confirming the recent warming over the study domain. Prevalent increasing tendencies are also observed for absolute extreme temperature indices (highest and lowest daily values of maximum and minimum temperatures), but with more mixed and less uniform spatial patterns. Finally, the influence of large‐scale circulation modes on temperature extremes is examined. The results highlight the presence of significant correlations between most of the selected extreme temperature indices and the East‐Atlantic pattern, in particular for the warm season.

IN-depth SYnthetic Model for Flood Damage Estimation (INSYDE)is a model for the estimation of flood damage to residential buildings at the micro-scale. This study investigates the sensitivity of INSYDE to the accuracy of input data. Starting from the knowledge of input parameters at the scale of individual buildings for a case study, the level of detail of input data is progressively downgraded until the condition in which a representative value is defined for all inputs at the census block scale. The analysis reveals that two conditions are required to limit the errors in damage estimation: the representativeness of representatives values with respect to micro-scale values and the local knowledge of the footprint area of the buildings, being the latter the main extensive variable adopted by INSYDE. Such a result allows for extending the usability of the model at the meso-scale, also in different countries, depending on the availability of aggregated building data.

Abstract. Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterized by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight international research groups, the objective of this study was to compare, in a blind-validation test, the performances of different models for the assessment of the direct flood damage to the residential sector at the building level (i.e. microscale). The test consisted of a common flood case study characterized by high availability of hazard and building data but with undisclosed information on observed losses in the implementation stage of the models. The nine selected models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the modelling approaches, and heterogeneity of the original calibration context in relation to both hazard and vulnerability features. By avoiding possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the reliability of their estimations, especially regarding the potentials of local and multivariable models. From another perspective, the exercise allowed us to increase awareness of strengths and limits of flood damage modelling, which are summarized in the paper in the form of take-home messages from a modeller's perspective.