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The impacts of floods on environmental assets are often not assessed. In this communication, we reflect on this issue by analysing the reported environmental consequences of the 2023 Emilia-Romagna floods. The information on the environmental impacts is constructed by collecting data from reports, press releases, and interviews in the aftermath of the events. The most frequently reported damage involves water resources and water-related ecosystems, with cultural and supporting ecosystem services particularly affected. Indirect effects in time and space, intrinsic recovery capacity, cascade impacts on socio-economic systems, and the lack of established monitoring activities appear to be the most challenging aspects for future research. [ABSTRACT FROM AUTHOR]

The impacts of floods to environmental assets are often not assessed. This communication reflects on this issue by analysing the reported environmental consequences of the 2023 Emilia-Romagna flood. The information on the environmental impacts is constructed by collecting data from reports, press releases and interviews in the aftermath of the event. The most frequently reported damages involve water resources and water-related ecosystems, with cultural and supporting ecosystem services particularly affected. Indirect effects in time and space, intrinsic recovery capacity, cascade impacts on socio-economic systems and lack of established monitoring activities appear as the most challenging aspects for future research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Flood risk management generally relies on economic assessments performed using flood loss models of different complexity, ranging from simple univariable to more complex multivariable models. These latter accounts for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. In this paper we collected a comprehensive dataset 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), buildings characteristics (size, type, quality, economic value) as well as 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 performance of four literature flood damage models of different nature and complexity are compared with the performance of univariable, bivariable and multivariable models empirically developed for Italy and tested at the micro scale based upon observed records. The uni- and bivariable models are produced testing linear, logarithmic and square root regression while multivariable models are based on two machine learning techniques, namely Random Forest and Artificial Neural Networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. [ABSTRACT FROM AUTHOR]

Flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data, which is often rooted in a lack of protocols and reference procedures for compiling loss datasets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia River flood event of January 2014, when a sudden levee breach caused the inundation of nearly 52 km² in northern Italy. After this event local authorities collected a comprehensive flood loss dataset of affected private households including building footprints and structures and damages to buildings and contents. The dataset was enriched with further information compiled by us, including economic building values, maximum water depths, velocities and flood durations for each building. By analyzing this dataset we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multivariable loss models for residential buildings and contents. The accuracy of the proposed models is compared with that of several flood damage models reported in the literature, providing additional insights into the transferability of the models among different contexts. Our results show that (1) even simple univariable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multivariable models that consider several explanatory variables outperform univariable models, which use only water depth. However, multivariable models can only be effectively developed and applied if sufficient and detailed information is available. [ABSTRACT FROM AUTHOR]

Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 52 km2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth. However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available. [ABSTRACT FROM AUTHOR]

The assessment of flood risk is affected by various uncertainties along the entire flood risk modelling chain, including the simulation of hydrological and hydraulic phenomena, the failure modelling of flood defence systems and the assessment of economic damage. Consequently, deciding on where and how to invest in flood risk reduction measures is not a trivial task. We explore the effects of uncertainties on the choice of alternative flood defence heightening schemes for the lower stretch of the Po River, from the city of Borgoforte to Pontelagoscuro. Uncertainties taken into account relate to the (1) flood peak and flood wave shape in generating the design event, (2) width of the breaches and (3) damage modelling. We do so by integrating into a Monte Carlo framework both a HEC-RAS model previously developed for the area under study and a damage model recently proposed for a nearby area. The analysis involves three steps. First, a global sensitivity analysis is carried out to understand which of the input factors drives the uncertain outcomes. Second, a Monte Carlo analysis is performed for three alternative flood defence heightening schemes. This step aims at understanding whether any of the schemes clearly outperforms the others under uncertainty in terms of risk reduction. Third, we comment on the robustness of the evidence provided by the analysis in a sensitivity auditing fashion. This last step is of paramount importance for effectively communicating policy outcomes and supporting flood risk management decisions. [ABSTRACT FROM AUTHOR]

A Flow Duration Curve (FDC) exemplifies the relationship between the frequency and magnitude of streamflow at given location and it is of interest for many hydrological problems (e.g., water management and supply, human and irrigation purposes, etc.). However, relying on historical streamflow records, FDCs are constrained to gauged stations and, thus, typically available for a small portion of the world's rivers. The SWOT (Surface Water and Ocean Topography) satellite mission (in orbit from 2021) will provide unprecedented bi-dimensional observations of rivers wider than 100m, providing water surface heights, width and slope, enabling the estimation of river discharge at global scale and in ungauged basins. This work considers the mission lifetime (three years) and the three satellite orbits (i.e., 211, 489, 560) that will monitor the Po River (Northern Italy). The aim is to test the ability to observe the river hydrological regime, which is, for this test case, synthetically reproduced by means of a quasi-2D hydraulic model.For each satellite overpass, discharge estimation is pursued considering different river stretches (5, 10 and 20 km) and using synthetic remotely sensed measurements derived from a numerical hydraulic model corrupted with minimal observational errors. FDCs obtained with synthetic observations are compared with those defined referring to daily discharge values recorded at four gauging stations along the studied reach. Discharge assessment is performed using the Manning equation, under the assumption of a trapezoidal section, known bathymetry, and roughness coefficient.Results highlight the potential of the mission to provide a realistic reconstruction of the flow regimes at different locations. Higher errors are obtained at the FDC tails, where very low or high flows have lower likelihood of being observed or might not occur during the mission lifetime period. Among the tested discretizations, 20 km stretches provided the best performances, with root mean absolute errors, on average, lower than 13.3%. [ABSTRACT FROM AUTHOR]