A new flood risk assessment framework for evaluating the effectiveness of policies to improve urban flood resilience

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.To better understand the impacts of flooding such that authorities can plan for adapting measures to cope with future scenarios, we have developed a modified Drivers-Pressures-State-Impact-Response (DPSIR) framework to allow policy makers to evaluate strategies for improving flood resilience in cities. We showed that this framework proved an effective approach to assessing and improving urban flood resilience, albeit with some limitations. This framework has difficulties in capturing all the important relationships in cities, especially with regards to feedbacks. There is therefore a need to develop improved techniques for understanding components and their relationships. While this research showed that risk assessment is possible even at the mega-city scale, new techniques will support advances in this field. Finally, a chain of models engenders uncertainties. However, the resilience approach promoted in this research, is an effective manner to work with uncertainty by providing the capacity to cope and respond to multiple scenariosResearch on the CORFU (Collaborative research on flood resilience in urban areas) project was funded by the European Commission through Framework Programme 7, Grant Number 244047. The work in this paper was partially funded by the PEARL (Preparing for Extreme And Rare events in coastaL regions) project, supported by the European Union's Seventh Framework Programme under Grant Agreement No 603663

On the Design of an Intelligent Sensor Network for Flash Flood Monitoring, Diagnosis and Management in Urban Areas

We propose an intelligent sensor system based on a new sensing methodology, relying also on 3D map reconstruction techniques, for computing with high precision, in real-time and without human intervention the parameters needed for stream-flow computation: water levels, morphology of the streams of all potentially flooded areas by each controlled stream. The collected data will be continuously transmitted, through a communication infrastructure, to software agents designed to compute the stream-flow and to quantify the spatial distribution of flood risk for each controlled watershed. The computed risks, together with other data coming from other sources (barometric sensors, camera operators of public organizations, emergency agencies, private citizens), will be analyzed by a diagnostic decision system implementing a risk-alert scheduling strategy. This system will be able to diagnose the health state of the controlled environment and to define specialized alarm levels for each potentially interested area that will be used to alert all citizens (ubiquity) locally present (alerting locality).© 2014 Published by Elsevier B.V. Open access under CC BY-NC-ND licenseAncona, M.; Corradi, N.; Dellacasa, A.; Delzanno, G.; Dugelay, J.; Federici, B.; Gourbesville, P.... (2014). On the Design of an Intelligent Sensor Network for Flash Flood Monitoring, Diagnosis and Management in Urban Areas. Procedia Computer Science. 32:941-946. doi:10.1016/j.procs.2014.05.515S9419463

Modelling to bridge many boundaries: the Colorado and Murray-Darling River basins

Increasing pressure on shared water resources has often been a driver for the development and utilisation of water resource models (WRMs) to inform planning and management decisions. With an increasing emphasis on regional decision-making among competing actors as opposed to top-down and authoritative directives, the need for integrated knowledge and water diplomacy efforts across federal and international rivers provides a test bed for the ability of WRMs to operate within complex historical, social, environmental, institutional and political contexts. This paper draws on theories of sustainability science to examine the role of WRMs to inform transboundary water resource governance in large river basins. We survey designers and users of WRMs in the Colorado River Basin in North America and the Murray-Darling Basin in southeastern Australia. Water governance in such federal rivers challenges inter-governmental and multi-level coordination and we explore these dynamics through the application of WRMs. The development pathways of WRMs are found to influence their uptake and acceptance as decision support tools. Furthermore, we find evidence that WRMs are used as boundary objects and perform the functions of ‘boundary work’ between scientists, decision-makers and stakeholders in the midst of regional environmental changes

Integrated Groundwater Resources Management: Spatially-Nested Modelling Approach for Water Cycle Simulation

International audienc

Simple-Yet-Effective SRTM DEM improvement scheme for dense urban cities using ANN and remote sensing data: Application to flood modeling

10.3390/w12030816Water (Switzerland)123Jan-1

Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information

Current topographic survey technology provides high-resolution (HR) datasets for urban environments. Incorporating this HR information in models aiming to provide flood risk assessment is desirable because the flood wave propagation is depending on the urban topographic features, i.e. buildings, bridges and street networks. Conceptual, numerical and practical challenges arise from the application of shallow water models to HR urban flood modeling. For instance, numerical challenges are occurrence of wet-dry fronts, geometric discontinuities in the urban environment and discontinuous solutions, i.e. shock waves. These challenges can be overcome by using a Godunov-type scheme. However, the computational cost of this type of schemes is high, such that HR two-dimensional shallow water simulations with practical relevance have to be run on supercomputers. The porous shallow water model is an alternative approach that aims to reduce computational cost by using a coarse resolution and accounting for unresolved processes by means of the porosity terms. Usually, a speedup between two and three orders of magnitude in comparison to HR simulations can be obtained. This study reports preliminary results of a practical test case concerning pluvial flooding in a district of the city of Nice, France, caused by the intense rainfall event on October 3rd, 2015. HR topography data set on a 1 m resolution is available for the district, whereby street features of infra-metric dimensions have been included. A reference solution is calculated by a HR shallow water model on a 1 m by 1 m structured computational grid. The porous shallow water model is run on a 10 m by 10 m grid and the influence of the drag source term is studied. The model results show a large deviation, which is caused by the poor meshing strategy of the porous shallow water (AP) model. The study also summarizes practical challenges that arise during the application of the AP and HR models to a large urban catchment. The main difficulty is to obtain a good mesh. In smaller scale investigations, the mesh is currently constructed by hand such that the cell edges align with buildings. This approach is not feasible for large scale urban catchments with a large number of buildings. Future steps that have to be taken, such as a strategy for automatic mesh generation, are reported on

Towards district scale flood simulations using conventional and anisotropic porosity shallow water models with high-resolution topographic information

Current topographic survey technology provides high-resolution (HR) datasets for urban environments. Incorporating this HR information in models aiming to provide flood risk assessment is desirable because the flood wave propagation is depending on the urban topographic features, i.e. buildings, bridges and street networks. Conceptual, numerical and practical challenges arise from the application of shallow water models to HR urban flood modeling. For instance, numerical challenges are occurrence of wet-dry fronts, geometric discontinuities in the urban environment and discontinuous solutions, i.e. shock waves. These challenges can be overcome by using a Godunov-type scheme. However, the computational cost of this type of schemes is high, such that HR two-dimensional shallow water simulations with practical relevance have to be run on supercomputers. The porous shallow water model is an alternative approach that aims to reduce computational cost by using a coarse resolution and accounting for unresolved processes by means of the porosity terms. Usually, a speedup between two and three orders of magnitude in comparison to HR simulations can be obtained. This study reports preliminary results of a practical test case concerning pluvial flooding in a district of the city of Nice, France, caused by the intense rainfall event on October 3rd, 2015. HR topography data set on a 1 m resolution is available for the district, whereby street features of infra-metric dimensions have been included. A reference solution is calculated by a HR shallow water model on a 1 m by 1 m structured computational grid. The porous shallow water model is run on a 10 m by 10 m grid and the influence of the drag source term is studied. The model results show a large deviation, which is caused by the poor meshing strategy of the porous shallow water (AP) model. The study also summarizes practical challenges that arise during the application of the AP and HR models to a large urban catchment. The main difficulty is to obtain a good mesh. In smaller scale investigations, the mesh is currently constructed by hand such that the cell edges align with buildings. This approach is not feasible for large scale urban catchments with a large number of buildings. Future steps that have to be taken, such as a strategy for automatic mesh generation, are reported on

Flood warning systems and ubiquitous computing

10.1051/lhb/2012034Houille Blanche611-1

Coastal current prediction using CMA evolution strategies

We propose a data-driven evolutionary approach to the modeling of marine currents in the Bay of Monaco. The CMA (Covariance Matrix Adaptation) evolution strategy is used to optimize the parameters of a predictive model that may be used as a surrogate of expensive and time-consuming finite-element simulations. The models obtained are reasonably accurate and easy to interpret