Report and papers with guidelines on calibration of urban flood models

Computer modelling offers a sound scientific framework for well-structured analysis and management of urban drainage systems and flooding. Computer models are tools that are expected to simulate the behaviour of the modelled real system with a reasonable level of accuracy. Assurance of accurate representation of reality by a model is obtained through the model calibration. Model calibration is an essential step in modelling. This report present concepts and procedures for calibration and verification of urban flood models. The various stages in the calibration process are presented sequentially. For each stage, a discussion of general concepts is followed by descriptions of process elements. Finally, examples and experiences regarding application of the procedures in the CORFU Barcelona Case Study are presented. Calibration involves not only the adjustment of model parameters but also other activities such as model structural and functional validation, data checking and preparation, sensitivity analysis and model verification, that support and fortify the calibration process as a whole. The objective in calibration is the minimization of differences between model simulated results and observed measurements. This is normally achieved through a manual iterative parameter adjustment process but automatic calibration routines are also available, and combination parameter adjustment methods also exist. The focus of a model calibration exercise is not the same for all types of models. But regardless of the model type, good modelling practice should involve thorough model verification before application. A well-calibrated model can give the assurance that, at least for a range of tested conditions, the model behaves like the real system, and that the model is an accurate and reliable tool that may be used for further analysis. However, calibration could also reveal that the model cannot be calibrated and that the correctness of the model and its suitability as a tool for analysis and management of real-world systems could not be proven. The conceptualisation and simplification of real-world systems and associated processes in modelling inevitably lead to errors and uncertainty. Various modelling components introduce errors such as the input parameters, the model concept, scheme and corresponding model output, and the observed response measurements. Ultimately, the quality of the model as quantified by how much it deviates from reality is an aggregate of the errors that have been brought into it during the modelling process. Thus, it is important to identify the different error sources in a model and also account for and quantify them as part of the modelling.The work described in this publication was supported by the European Community’s Seventh Framework Programme through the grant to the budget of CORFU Collaborative Research on Flood Resilience in Urban Areas, Contract 244047

A review of modelling methodologies for flood source area (FSA) identification

Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises

A multi-period shelter location-allocation model with evacuation orders for flood disasters

Floods are a significant threat for several countries, endangering the safety and the well-being of populations. Civil protection authorities are in charge of flood emergency evacuation, providing means to help the evacuation and ensuring that people have comfortable and safe places to stay. This work presents a multi-period location-allocation approach that identifies where and when to open a predefined number of shelters, when to send evacuation orders, and how to assign evacuees to shelters over time. The objective is to minimize the overall network distances that evacuees have to travel to reach the shelters. The multi-period optimization model takes into account that the travel times vary over time depending on the road conditions. People’s reaction to the flood evolution is also considered to be dynamic. We also assume that shelters become available in different time periods and have a limited capacity. We present a mathematical formulation for this model which can be solved using an off-the-shelf commercial optimization solver, but only for small instances. For real size problems, given the dynamic characteristics of the problem, obtaining an optimal solution can take several hours of computing time. Thus, a simulated annealing heuristic is proposed. The efficiency of the heuristic is demonstrated with a comparison between the heuristic and the solver solutions for a set of random problems. The applicability of the multi-period model and of the heuristic is illustrated using a case study which highlights the importance and the benefits of adopting a dynamic approach for optimizing emergency response operations