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, 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 similar or superior performances when compared with other damage models available in the literature. In addition, a local sensitivity analysis was performed in order to identify the hazard variables that have more influence on damage assessment results.
Flood damage evaluation today is a crucial component of any strategy for flood risk mitigation and management (Messner and Meyer, 2006; Messner et al., 2007; Merz et al., 2010). In particular, models and methodologies for estimating economic damages are key for evaluating and comparing flood mitigation measures and for defining flood risk management plans (Bouwer et al., 2013; Schröter et al., 2014).
Available damage models can be classified into two main classes: empirical and synthetic models (Smith, 1994; Merz et al., 2010). Empirical models use damage datasets collected from past flood events to link vulnerability and hazard variables to damage (data-driven approaches), while synthetic models adopt a more conceptual expert-based approach using hypotheses and assumptions about damage mechanisms (what-if analysis). Empirical and synthetic damage models can be employed for a variety of applications (e.g. Papathoma-Köhle et al., 2015), such as the derivation of damage functions for different types of assets, post-event damage estimation, and analysis of uncertainty sources in damage assessments.
Despite their growing importance, there are still relevant issues in the application of flood damage models (Handmer, 2003; Meyer et al., 2013). First, the relative scarcity of observed damage data is often a relevant obstacle in developing and improving existing models. Models based on data-driven approaches are especially prone to this issue because they require specific calibration to be applied in different contexts (Merz et al., 2010; Bubeck and Kreibich, 2011). Synthetic models, adopting expert-based assumptions of hazard–damage relationships, are less dependent on datasets for model derivation, though they still require additional data for calibration and validation (Smith, 1994; Merz et al., 2010).
Second, even when reliable and comprehensive datasets are available, it is generally not possible to extrapolate adequate damage functions due to the well-known complexity of damage mechanisms (Andrè et al., 2013; Cammerer et al., 2013; Scorzini and Frank, 2015). Damage computation methods based on probabilistic approaches might offer a solution to this issue (Schröter et al., 2014), yet this research topic is still relatively unanswered in literature.
Third, the evaluation of flood mitigation measures requires methodologies for estimating economic damages at both the micro-scale (e.g. building-scale strategies for vulnerability reduction) and the mesoscale (e.g. spatial planning strategies) (Schröter et al., 2014). When micro-scale strategies are considered, empirical models are less suitable because the model structure generally considers few explicative variables. For residential buildings these typically include the water depth, the building structure, and the number of floors (Messner and Meyer, 2006; Schröter et al., 2014); as a consequence, it is not possible to evaluate the effect of the full range of mitigation strategies available, such as the use of permeable materials, the moving of vulnerable components, etc. Synthetic models can overcome this limitation since their level of complexity can be designed to adapt to the required detail. Still, subjectivity in what-if analyses may result in uncertain damage estimates (Gissing and Blong, 2004). In addition, these models are often affected by a lack of transparency, which limits their applicability and transferability, as well as possible improvements (Scorzini and Frank, 2015). Indeed, in many cases the rationale behind model development (e.g. assumptions, mechanisms considered, built-in parameters) is not clearly presented and relevant variables to be used are not well explained.
Given this framework, in this paper we propose a probabilistic methodology to derive synthetic damage curves for residential buildings called INSYDE (In-depth Synthetic Model for Flood Damage Estimation). The method is based on an explicit component-by-component analysis of physical damages to buildings, which takes into account available knowledge on damage mechanisms. INSYDE is transparent and can be applied in different contexts. Implemented functions and values are clearly explained so that they can be totally or partly modified according to the physical context in which the model is applied. Conversely, the methodology allows for different levels of detail in the analysis, hence the damage model can be adapted to the actual knowledge of relevant hazard and vulnerability variables.
The damage functions composing the model have been designed using an expert-based approach with the support of existing scientific and technical literature, loss adjustment studies, and damage surveys carried out for past flood events in Italy. It is important to note that the current version presented in this paper is limited to residential building damage estimation. The general methodology, however, can be extended to other types of assets, such as commercial or industrial buildings.
Subsequently, the model was validated against loss data collected for a recent flood event in northern Italy and compared with the results provided by several damage models in the literature. Finally, we performed a sensitivity analysis of the model hazard parameters in order to explore in more detail the model behaviour and quantify the influence of each hazard parameter. The results and relevant findings are discussed in order to highlight strengths and weaknesses of the proposed model.
INSYDE adopts a synthetic approach consisting of the simulated, step-by-step inundation of residential buildings, and in the evaluation of the corresponding damage based on building and hazard features. Such a methodology can also be referred to as a what-if analysis.
Damages are first modelled on a component-by-component basis using physically
based mathematical functions and are then converted into monetary terms using
full replacement costs derived from reference price lists. The overall
economic damage to each building
Damage components and subcomponents considered in INSYDE, and relationships with event features and building characteristics parameters.
Event features parameters considered in INSYDE.
For each subcomponent, a mathematical function describing the damage
mechanism and associated cost is formulated, considering expert-based
knowledge as well as available technical and scientific documentation. The
general formulation can be described as follows:
The cost for each subcomponent is determined by the unit price (up) and the
extension (ext). The latter is the measure of the physical dimension of the
damage (e.g.
Tables 2 and 3 describe in detail the event features and building characteristics parameters, their unit of measurement, their range, and the default values in case no information is supplied to the model. When some of the input data are missing, another option available in the model is to sample these parameters from distributions, which also allows uncertainty analysis in input data. The damage functions and the general assumptions for all the damage subcomponents are reported in the Supplement, while Table 1 synthesizes the event features and building characteristics considered for each subcomponent function. The variables listed in Tables 2 and 3 can directly affect damage estimation in terms of extension or indirectly by influencing other variables. An example of the latter case is YY (year of construction), which, as shown in Table 1, has no direct impact on the damage mechanisms of the different building subcomponents but indirectly influences the selection of other variables such as PD (heating system distribution) and PT (heating system type). Another example is NF (number of floors), which only directly affects soil consolidation despite indirectly influencing many other building components because damage on upper floors can only occur if the floors actually exist in the building. The number of flooded floors is then calculated as a function of inundation depth and interfloor height of the building (IH). Please refer to the Supplement for more details on assumptions and a description of considered damage mechanisms.
Building characteristics parameters considered in INSYDE.
Another important aspect of the proposed approach is that several of the damage mechanisms are modelled using probabilistic functions rather than deterministic ones. The choice of the type of function is based on the authors' knowledge, consistent with the expert-based approach of INSYDE, and on the availability of information in literature. Some damage processes are well understood and, in our opinion, do not require a probabilistic treatment. For instance, we suppose that if a building is flooded then the basement will always be flooded, under the assumption that flood-proof measures to prevent this are rarely implemented. Other damage mechanisms can also be well explained, even though there is a degree of uncertainty due to building characteristics. For instance, electrical systems are considered to be damaged if some of their components are reached by flood water, but the height of these components may vary depending on the building. In this case however, we decided to overlook this uncertainty and use deterministic functions since the variability of the height of these components is usually small.
Finally, there are damage mechanisms on which the influence of hazard and
building parameters cannot be determined a priori, and these mechanisms were modelled adopting a probabilistic approach. The motivation is that, even
if the damage mechanism itself is known, it is impossible to
deterministically define, for certain hazard variables, a threshold below
which no damage occurs and above which it does. For instance, it is known
that plaster is usually not damaged for short duration flood events, while
replacement might be necessary in case of a long duration flood
(Penning-Rowsell et al., 2005). However, it is not possible to define a
deterministic threshold for the variable “flood duration” because it
depends on variables like the type of plaster, the season in which the flood
occurs, and so on. In practice, these types of variables are usually not
obtainable, or if they are, it is not possible to have a clear understanding
of how they affect the damage mechanism. One could assume, for example, that
the threshold value for plaster replacement is 18
To account for these uncertainties, the model considers that for some of the
building components, given a certain flood hazard intensity measure IM, there
are two possible damage states DS, not damaged (ds
Within this probabilistic framework, the INSYDE model can be used to estimate
building damage in two ways depending on the user's requirements. The first
consists of simply calculating the expected loss of each component according
to Eq. (4), using the expected damage ratio
It should be mentioned that the distribution of
The fragility functions adopted in INSYDE can be found in the Supplement. An example is shown in Fig. 1.
Fragility function for internal plaster. Here, the intensity measure of flood hazard IM is given by flood duration.
In addition to expert knowledge and technical papers, the set-up of the damage
functions was supported by an observational method, which helped to identify
the most influential variables on damage occurrence for the different
building subcomponents. In particular, an analysis has been carried out on
the relation between observed damages and the damage explicative parameters
(hazard and vulnerability parameters) considered by INSYDE, using highly
detailed damage data for about 60 affected buildings during the November 2012
flood in the Umbria region in central Italy (Molinari et al., 2014). Chi-square
hypothesis tests were performed on contingency tables based on available
data, in order to analyse the possibility of any correlation between certain
event and building variables and damage mechanisms on building elements. A
higher correlation was found for water depth, in particular for the damage to
electrical systems (significance level
It is important to underline that during the model design, observed damage data were essentially used to analyse the relations between hazard parameters and damage mechanisms in order to improve physical damage functions. For instance, the functions for structural damage found in literature were implemented in the model after some modifications, as they were not in line with the observed damages. Such a usage is consistent with an expert-based approach because observed data were first interpreted and then used to modify parts of the model structure rather than applied to calibrate the parameters of existing functions.
Figures 2 and 3 provide an example of damage functions developed for a
default building in the case of a flood with a duration
Example of INSYDE damage
functions for all building subcomponents, considering the following event
variables: flow velocity
Example of INSYDE damage functions considering the following event
variables: flow velocity
To complete the INSYDE methodology, the absolute damage figures computed can be converted into relative value by dividing them by the replacement value of the building. This value is given as a function of the building type and structure and year of construction of the building based on existing literature and official studies (Cresme-Cineas-Ania, 2014).
The model was validated using loss data of the 2010 flood collected by the municipality of Caldogno in the Veneto region in northeastern Italy. Available building loss data, related to about 300 affected buildings, were based on the “quantification of damage” forms sent out by the authorities, in the frame of the loss compensation process by the state. These data consisted of actual restoration costs, certified by original receipts and invoices. The total reported loss was estimated to be approximately EUR 7.5 million.
Aside from registered losses, the following event and building information was available (Scorzini and Frank, 2015):
external water depth ( sediment load ( floor area (FA) and number of floors (NF) of damaged buildings; structural type of damaged buildings (BS): almost equally distributed among
reinforced concrete and masonry buildings; typology of damaged buildings (BT): 151 detached houses, 70 semi-detached
houses, and 75 apartment buildings. A further distinction between elements
with and without basements was available. In addition, a finishing level (FL)
was attributed to each single building based on its quality; year of construction (YY) of the buildings.
The first part of the validation exercise consisted of applying INSYDE
deterministically (i.e. without considering any source of uncertainty) with
the previous data as input, assuming the default values in Tables 2 and 3
for missing variables. Calculated total loss was equal to EUR 7.42 million, with a relative error of
Figure 3, showing estimated losses against observed ones, provides a more in-depth analysis of the results. The model tended to overestimate low damages and to underestimate high ones (Fig. 3a), with a root mean square error (RMSE) equal to EUR 28 996. Nevertheless, there was a high degree of agreement between the two distributions (Fig. 3b).
The results presented above were compared with those obtained by applying other deterministic micro-scale damage functions from the literature (Scorzini and Frank, 2015). These included: Debo (1982), Dutta et al. (2003), FLEMOps (Thieken et al., 2008), and others specifically applied in damage assessment studies in Italy, i.e. Oliveri and Santoro (2000), Luino et al. (2009), and Arrighi et al. (2013).
Table 4 summarizes total loss estimates and RMSE calculated by using the
selected micro-scale models and INSYDE. The output from these functions
ranged from EUR 5.8 to 13 million, resulting in a maximum relative error
from the reported building losses (EUR 7.5 million) of about 73.6 %
(RMSE
Comparison of loss estimates produced by INSYDE and other models in literature.
Notwithstanding, the second part of the validation exercise consisted of performing a building-by-building comparison of observed and modelled losses, with the objective of determining the degree of agreement between them after considering in a simplified manner some of the potential sources of uncertainty. This was done by estimating loss ranges – both observed and modelled – for each building, and then computing the hit rate (HR) as the fraction between the number of cases in which there was an overlap between them and the total number of buildings.
In order to reflect the uncertainty in damage data, the observed loss range
for each building was assumed to be
Given these assumptions, the hit rate for the 2010 flood event was 56 %. This is a reasonably good result, considering that only a limited number of sources of uncertainty were considered and that their quantification was done in a simplified and rather conservative manner. In addition, it should be mentioned that this part of the validation exercise is extremely demanding in the sense that a simplified model of reality is used to estimate losses on a building-by-building basis. Even with its detailed, probabilistic structure, INSYDE simply cannot account for some of the building-to-building variability. There is a level of detail past which uncertainties become aleatory, meaning it is not reasonably possible to reduce them using damage models such as this (Der Kiureghian and Ditlevsen, 2009), and those uncertainties are not accounted for.
To conclude, the obtained validation results unequivocally show that INSYDE is able to produce accurate damage estimations and explain reasonably well a part of the uncertainty that inherently exists in flood damage estimations.
Distributions adopted for the input variables in the validation exercise.
The damage dataset used to validate the model in Sect. 3 did not allow full investigation of the model behaviour. The limited total flood extent and the slow flow processes that occurred in the study area resulted in low values of hazard variables like water depth, flow velocity, and sediment load. Therefore, the test did not allow assessment of the influence of these parameters in determining the damage, that is the sensitivity of the model structure to high values of the hazard variables. To further explore the importance of each of these parameters, we performed a local sensitivity analysis. In this application, the damage was computed by varying alternately each hazard parameter while the others were kept constant. The building characteristics variables have not been analysed at this stage, nor has the uncertainty related with the probabilistic modelling of some of the damage mechanisms (see Sect. 5 for a more detailed discussion).
Comparison of observed against modelled loss for the buildings
affected by the 2010 flood in Caldogno.
Results of the local sensitivity analysis in case of low velocity, long duration flood.
Two different flood conditions have been considered to explore the model
behaviour in different conditions: a low velocity, long duration flood and,
conversely, a high velocity, short duration flood event. For the first case,
the fixed values of depth, velocity, duration, and sediment load were
respectively
Computations were performed considering a standard reinforced concrete
building with two floors and a basement, 100
Figures 5 and 6 summarize the results of the local sensitivity analysis in
the two chosen flood conditions, showing the relative influence of each
hazard variable in determining the total economic damage. As expected, water
depth is the most influential parameter since all the damage functions
directly depend on it. Relative changes in flood duration have much more
impact in low velocity, long duration events, while the relevance of velocity
is more evident at higher values, when structural damages can become
important. In both scenarios sediment load has a relatively marginal
importance. The influence of water quality
Results of the local sensitivity analysis in the case of high velocity, short duration flood.
Review of existing synthetic damage models.
As introduced in Sect. 2, the approach followed in INSYDE was derived from a detailed analysis of present state-of-the-art synthetic flood damage modelling for the residential sector, as depicted in Table 6. The table reports, for the main models found in the literature: considered hazard and vulnerability parameters, the estimated types of damage, the approach for the monetary evaluation of damage, whether or not models have been validated, and whether or not a sensitivity analysis has been performed. Starting from this analysis, the main strengths of existing models were identified and incorporated in INSYDE. Likewise, INSYDE tries to overcome the limitations of available approaches.
As far as hazard and vulnerability are concerned, similar to the model developed within the FloodProBE project (Walliman et al., 2013), INSYDE allows consideration of all the hazard parameters that were found to be significant in the literature, namely water depth, velocity, sediment and contaminant loads, and flood duration (Kelman and Spence, 2004; Thieken et al., 2005; Kreibich et al., 2009; Merz et al., 2010). Moreover, the vulnerability features of any specific building can be defined by means of a set of parameters (such as building size, type, structure, finishing level, maintenance level, etc.), allowing for an in-depth analysis of vulnerability (see the FloodProBE and the MCM models). This helps overcome the problem of representing the entire building stock by means of a set of predefined building types presently characterizing the majority of models. Conversely, in practical flood damage assessments, collecting all the required information on event and building features necessary for defining the input parameters used in INSYDE (Tables 2, 3) may not always be straightforward. Nevertheless, when some of these data are missing, the flexibility of the model allows the user to choose between two different options. The first, which can be suitable for rapid flood damage estimations, consists of applying the default values for the different parameters listed in Tables 2 and 3. This approach, however, may result in estimation errors and does not allow the treatment of uncertainties related to input data. These problems can be overcome by using the more detailed option of sampling these parameters from distributions instead (defined beforehand on a case-by-case basis), taking advantage of the probabilistic structure implemented in the model.
In regard to estimated damages, INSYDE presents two main strengths. First, like the FloodProBE and MCM (Penning-Rowsell et al., 2005) models, damage functions are derived component by component, which allows an in-depth analysis and description of damage mechanisms (Sect. 2 and Supplement). Second, not only losses to the building fabric and functions (e.g. systems) are modelled, but also costs related to cleaning the building, removing water and waste, and drying, which can represent an important share of the total economic damage in some cases. Damage to inventories is not considered at the moment because inventories present a higher variability than the building fabric, requiring a mixed empirical–synthetic approach. It is also important to note that in the current version the model considers only the potential damage, while factors that can affect damage, such as flood warning, preparedness, and precautionary measures, are not incorporated. Additional corrective coefficients should be used in order to adjust potential for actual losses (Smith, 1994; Thieken et al., 2005; Messner and Meyer, 2006; Poussin et al., 2015).
Regarding the monetary estimation of damage, INSYDE first estimates damages in physical terms. This is an important feature because physical measures are undisputable, while associated monetary values depend on the estimation method, underlying assumptions, stakeholders, etc. The analysis of damage in physical units supplies unambiguous estimates that can be used as the base for different economic evaluations. In INSYDE, the monetary translation is carried out by using building price books that can be updated and adapted to the region of implementation of the model. This way, the model can be easily applied to different geographical regions.
Another important feature of INSYDE regards the possible treatment of uncertainty embodied in the model structure. While the contribution of hazard components of risk to total damage uncertainty has been highlighted in several research works (Merz and Thieken, 2009; Merz et al., 2010; de Moel and Aerts, 2011; Thieken et al., 2014), relatively few studies present methods to explicitly account for uncertainty in damage estimations. Egorova et al. (2008) assessed uncertainties in the value of elements at risk and developed a methodology for incorporating uncertainties in depth–damage curves. Schröter et al. (2014) applied eight flood damage models with different levels of complexity to predict relative building damage in residential sectors for five historic flood events in Germany. The authors observed that the use of additional explanatory variables aside from the water depth improved the predictive capability of models, especially in applications to different regions and different flood events. In addition, models based on probabilistic structure (e.g. Bayesian networks) were more reliable than deterministic models.
In such a context, the main findings from the literature were taken into account in the development of the INSYDE model structure and respective R program, which enables the explicit analysis of input data and damage mechanism uncertainties, as previously described. It should be noted that it is the first time that uncertainty in damage mechanisms is included in a synthetic damage model. From this point of view, the probabilistic approach adopted in the model is an innovation in these types of models.
In this article, we have included a local sensitivity analysis of individual hazard parameters with the aim of illustrating the behaviour of the model and performing a “sanity check” on model results. An in-depth analysis of model uncertainty was not performed since we felt it would be unpractical and beyond the scope of this paper, which is focused on model structure. Indeed, for a comprehensive analysis of all possible sources of uncertainty we should take into account physical damage mechanisms together with the other model components such as economic damage functions, influence of hazard and vulnerability parameters, and probabilistic functions. Therefore, an in-depth analysis of model sensitivity and uncertainty is planned as a follow-up to the present research work.
In this paper, we present a new synthetic damage model called INSYDE. The model incorporates the latest developments in flood damage modelling and has been designed to be a flexible and transparent methodology, suitable for a variety of applications regarding damage assessment, vulnerability analysis of buildings, and analysis of uncertainty sources. In particular, the adopted probabilistic approach represents a first attempt to address the uncertainty issues regarding model damage mechanisms and parameters.
Model validation in a test case in Italy showed that INSYDE can provide good estimates of post-event flood losses, with similar or superior performances when compared with other damage models available in the literature. The validation exercise also showed that the model is able to explain a part of the uncertainty that inherently exists in flood loss estimations reasonably well.
Despite having been developed and tested with Italian case studies, the flexibility of the model structures allows for easy modification of both the model structure (i.e. damage functions) and the model parameters (such as building characteristics and unitary prices) for application in other countries. For the same reason, the structure of INSYDE makes it adaptable, with appropriate modifications, for flood damage assessment of other types of assets, such as building contents or commercial and industrial buildings.
In order to increase the transparency and reproducibility of the methodology,
the model functions are available for download as R open source code,
currently hosted on GitHub (
The authors gratefully acknowledge the region of Umbria for providing damage data for the 2012 flood. Edited by: B. Merz Reviewed by: S. Fuchs and one anonymous referee