This chapter aims to describe the latest innovative approaches for integrating heterogeneous observations from static social sensors within hydrological and hydrodynamic modelling to improve flood prediction. The distinctive characteristic of such sensors, with respect to the traditional ones, is their varying lifespan and space-time coverage as well as their spatial distribution. The main part of the chapter is dedicated to the optimal assimilation of heterogeneous intermittent data within hydrological and hydraulic models. These approaches are designed to account for the intrinsic uncertainty contained into hydrological observations and model structure, states and parameters. Two case studies, the Brue and Bacchiglione catchments, are considered. Finally, the evaluation of the developed methods is provided. This study demonstrates that networks of low-cost static and dynamic social sensors can complement traditional networks of static physical sensors, for the purpose of improving flood forecasting accuracy. This can be a potential application of recent efforts to build citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication but also can help improve models and increase flood resilience.

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Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.@en

Floods are the natural hazards that are causing the most deaths worldwide. Flood early warning systems are one of the most cost-efficient methods to reduce death rates, triggering decisions about the evacuation of exposed population. Although previous studies have investigated the effect of human behaviours on evacuation processes, studies analysing a combination of behaviours, flood onset and warning timing are limited. Our objective is to explore how changes on the aforementioned factors can affect casualties. This is done within a modelling framework that includes an agent-based model, a hydraulic model, and a traffic model, which is implemented for the case study of Orvieto (Italy). The results show that the number of casualties is most impacted by people’s behaviour. Besides, we found that a delay of 30 min in releasing the warning can boost the number of casualties up to six times. These results may help managers to propose effective emergency plans.

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This study aims at proposing novel approaches for integrating qualitative flow observations in a lumped hydrologic routing model and assessing their usefulness for improving flood estimation. Routing is based on a three-parameter Muskingum model used to propagate streamflow in five different rivers in the United States. Qualitative flow observations, synthetically generated from observed flow, are converted into fuzzy observations using flow characteristic for defining fuzzy classes. A model states updating method and a model output correction technique are implemented. An innovative application of Interacting Multiple Models, which use was previously demonstrated on tracking in ballistic missile applications, is proposed as state updating method, together with the traditional Kalman filter. The output corrector approach is based on the fuzzy error corrector, which was previously used for robots navigation. This study demonstrates the usefulness of integrating qualitative flow observations for improving flood estimation. In particular, state updating methods outperform the output correction approach in terms of average improvement of model performances, while the latter is found to be less sensitive to biased observations and to the definition of fuzzy sets used to represent qualitative observations.@en

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study is applied in the Brue catchment, located in UK. The first methodological step is to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model are implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge are generated as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location are assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that different model structures can provide different improvements of model performances, and, different optimal location of the sensors. @en