Numerical simulation of flooding from multiple sources using adaptive anisotropic unstructured meshes and machine learning methods

Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this thesis, a 2D control-volume and finite-element (DCV-FEM) flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. This adaptive unstructured mesh technique can dynamically modify (both, coarsening and refining the mesh) and adapt the mesh to achieve a desired precision, thus better capturing transient and complex flow dynamics as the flow evolves. A flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost. The above adaptive mesh flooding model (named as Floodity) has been further developed by introducing (1) an anisotropic dynamic mesh optimization technique (anisotropic-DMO); (2) multiple flooding sources (extreme rainfall and sea-level events); and (3) a unique combination of anisotropic-DMO and high-resolution Digital Terrain Model (DTM) data. It has been applied to a densely urbanized area within Greve, Denmark. Results from MIKE 21 FM are utilized to validate our model. To assess uncertainties in model predictions, sensitivity of flooding results to extreme sea levels, rainfall and mesh resolution has been undertaken. The use of anisotropic-DMO enables us to capture high resolution topographic features (buildings, rivers and streets) only where and when is needed, thus providing improved accurate flooding prediction while reducing the computational cost. It also allows us to better capture the evolving flow features (wetting-drying fronts). To provide real-time spatio-temporal flood predictions, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time flood prediction and inform the public in a timely manner, reducing injuries and fatalities. Additionally, data-driven optimal sensing for reconstruction (DOSR) and data assimilation (DA) have been further introduced to LSTM-ROM. This linkage between modelling and experimental data/observations allows us to minimize model errors and determine uncertainties, thus improving the accuracy of modelling. It should be noting that after we introduced the DA approach, the prediction errors are significantly reduced at time levels when an assimilation procedure is conducted, which illustrates the ability of DOSR-LSTM-DA to significantly improve the model performance. By using DOSR-LSTM-DA, the predictive horizon can be extended by 3 times of the initial horizon. More importantly, the online CPU cost of using DOSR-LSTM-DA is only 1/3 of the cost required by running the full model.Open Acces

A GPU-Accelerated Shallow-Water Scheme for Surface Runoff Simulations

The capability of a GPU-parallelized numerical scheme to perform accurate and fast simulations of surface runo in watersheds, exploiting high-resolution digital elevation models (DEMs), was investigated. The numerical computations were carried out by using an explicit finite volume numerical scheme and adopting a recent type of grid called Block-Uniform Quadtree (BUQ), capable of exploiting the computational power of GPUs with negligible overhead. Moreover, stability and zero mass error were ensured, even in the presence of very shallow water depth, by introducing a proper reconstruction of conserved variables at cell interfaces, a specific formulation of the slope source term and an explicit discretization of the friction source term. The 2D shallow water model was tested against two dierent literature tests and a real event that recently occurred in Italy for which field data is available. The influence of the spatial resolution adopted in dierent portions of the domain was also investigated for the last test. The achieved low ratio of simulation to physical times, in some cases less than 1:20, opens new perspectives for flood management strategies. Based on the result of such models, emergency plans can be designed in order to achieve a significant reduction in the economic losses generated by flood events

Groß-skalige 2D-hydraulische Modellierung: Verbesserung der Analyse der Flutdynamik mit remote sensing und freien geographischen Informationen

This work investigates the integration of hydro-geomorphic models, traditional data (static stage gages) and novel data sources, such as remotely sensed images and Crowdsourced data (Volunteering Geographic Information or VGI), for observation-driven improvements of hydro-modelling tools. The Tiber river basin, was selected as case study with a focus domain on the approximately 120 km channel upstream of Rome for its strategic importance in the protection of the historical city centre and the coastal urbanized zone. A parsimonious hydrological modelling algorithm was implemented, calibrated and validated for calculating the flow hydrographs of the ungauged small basins contributing to the study area. Furthermore, to delineate the boundaries computational domain of the hydraulic model for the Data Assimilation application, a DEM-based hydro-geomorphic floodplain delineation algorithm adapted from literature was tested with different DEMs and considering also its parametrization varying the stream orders. The adopted DA methodology is the Ensemble Kalman Filter (EnKF) that requires multiple simulations for representing the uncertainties related to the model and the observations errors. New approaches were proposed for integrating, as observations in the DA method, traditional static sensors, and simultaneously remotely sensed images and VGI data. Despite the static sensor have already been adopted in literature as observations in a DA framework, some new technical measures were necessary for integrating them in Quasi-2D hydraulic model. The assimilation of satellite images resulted to be effective, since the whole computational domain is interested by the water levels correction, although the improvement of the model performance persisted for only some hours of simulation. The usefulness of VGI have been investigated considering the uncertainties related to their reliability mostly in terms of accuracy and time allocation. Results show the potential of new data for improving the performance of the flood model, partially overcoming the limitations and the potential scarce availability of the traditional sensors. Finally, the simultaneous integration of all the three types of observations gave promising results, improving the performance of the model compared to the ones obtained assimilating only Satellite images or VGI observations.Diese Arbeit untersucht die Integration von hydro-geomorphen Modellen, traditionellen Daten (statische Stufenpegeln) und neuartigen Datenquellen wie Remote-Sensing-Bildern und Crowdsourced-Daten (volunteering Geographic Information oder VGI), um beobachtungsorientierte Verbesserungen von Hydromodellierungswerkzeugen zu erreichen. Das Tiber-Flusseinzugsgebiet wurde als Fallstudie mit einem Schwerpunkt auf dem etwa 120 km stromaufwärts von Rom gelegenen Kanal ausgewählt und zwar wegen seiner strategischen Bedeutung für den Schutz des historischen Stadtzentrums und der urbanisierten Küstenregion. Ein sparsamer hydrologischer Modellierungsalgorithmus wurde implementiert, kalibriert und validiert, um die Fluss-Hydrographen der durch Pegel nicht erfassten kleinen Becken zu berechnen, die zum Untersuchungsgebiet beitragen. Um die Grenzen des rechnerischen Bereichs des Hydraulikmodells für die Data-Assimilation-Anwendung abzugrenzen, wurde außerdem ein DEM-basierter, aus der Literatur angepasster Algorithmus zur Abgrenzung von hydrogeomorphen Flutebenen mit verschiedenen DEMs getestet, wobei auch die Parametrisierung der Stream-Reihenfolge berücksichtigt wurde. Die angenommene DA-Methode ist der Ensemble Kalman Filter (EnKF), der mehrere Simulationen zur Darstellung der mit dem Modell verbundenen Unsicherheiten und Beobachtungsfehler erfordert. Es wurden neue Ansätze für die Integration herkömmlicher statischer Sensoren, von Fernerkundungs-Bildern und von VGI-Daten als Beobachtungen für das DA-Verfahren vorgeschlagen. Obwohl die statischen Sensoren bereits in der Literatur als Beobachtungen in einem DA-Rahmen verwendet wurden, waren einige technische Maßnahmen erforderlich, um sie in das Quasi-2D-Hydraulikmodell zu integrieren. Die Assimilation von Satellitenbildern erwies sich als effektiv, da der gesamte rechnerische Bereich an der Korrektur des Wasserstandes interessiert ist; die Verbesserung der Modellleistung dauerte allerdings nur einige Stunden in der Simulation an. Die Nützlichkeit von VGI wurde unter Berücksichtigung der mit ihrer Zuverlässigkeit verbundenen Unsicherheiten hauptsächlich hinsichtlich Genauigkeit und Zeitzuordnung untersucht. Die Ergebnisse zeigen das Potenzial neuer Daten zur Verbesserung der Leistung des Flutmodells, wobei teilweise die Einschränkungen und die oftmals knappe Verfügbarkeit herkömmlicher Sensoren überwunden werden. Schließlich ergab die gleichzeitige Integration aller drei Arten von Beobachtungen vielversprechende Ergebnisse und verbesserte die Leistung des Modells im Vergleich zur Nutzung nur von Satellitenbilder oder VGI-Beobachtungen

Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model

Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes

Shallow Water Equations in Hydraulics: Modeling, Numerics and Applications

This Special Issue aims to provide a forum for the latest advances in hydraulic modeling based on the use of shallow water and related models as well as their novel application in practical engineering. Original contributions, including those in but not limited to the following areas, will be considered for publication: new conceptual models and applications, flood inundation and routing, sediment transport and morphodynamic modelling, pollutant transport in water, irrigation and drainage modeling, numerical simulation in hydraulics, novel numerical methods for the shallow water equations and extended models, case studies, and high-performance computing

Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic