A Web GIS Based Simulation Tool For Coastal Urban Flood Prediction

Flooding in urban areas due to heavy rainfall coupled with high tides is a major concern affecting development of coastal cities all over the world. There is a spectrum of models such as 2D distributed flood models to simplified storage cell models using analytical expressions. All such models demand a high level of skill to handle geospatial data making it difficult for decision makers. Thus development of web GIS based hydrological application becomes essential. Traditionally, most web GIS based applications have used conceptual model because of low data requirements and parameter calibrations. In this paper web GIS based integrated flood model has been presented. Both the web GIS server and the associated hydrological model have been indigenously built. The web GIS server has been built using Java, Java Servlet Page, JQuery, HTML and XML technologies while the associated hydrological model has been built in MATLAB language and both are stored on the server side. The data input to the model is from the client-side through web browser. The model is capable of simulation 1D overland flow using mass balance approach, 1D diffusion wave based channel flow model and quasi 2D raster based floodplain model. The study presents a web GIS based urban flood simulation tool for a coastal urban catchment of Navi Mumbai, India. The three main outputs from the tool are a) generation discharge and stage hydrographs at any point along the channel; b) Water level profile plot at any hour of the simulation and c) Flood map animation in case of flooding in channel. The results of the model application indicate that the model can be used as an effective coastal urban flood simulation tool

Modelling Hydrodynamic and Sedimentation Processes in Large Lowland Rivers: An Application to the Paraná River (Argentina)

In the last decade 1D, 2D and 3D numerical models have been extensively used to simulate river-floodplain hydraulics and sediment deposition processes in floodplains. Large river-floodplain ecosystems in lowland areas show characteristic reach lengths of the order of hundred of kilometers, floodplain widths of the order of tens of kilometers and river widths of the order of a few kilometers. The floodplain itself shows also a very complex geomorphology. Computationally intensive water flow and sediment transport models cannot take into account these peculiarities, and particularly the large time and space scales involved. On one hand, 1D models are not appropriated because the one-dimensional flow description is not representative of the complex flow pattern; on the other hand, higher dimensionality models, even if they can provide the necessary level of processes representation at small spatial scales, cannot be applied over large time and space scales due to the computational demands. An alternative to high resolution models is the implementation of quasi-2D models which can capture the fundamental characteristic of water flow and sediment dynamics in those situations. Thus, a compromise between computational costs and processes representation can be achieved. In this work a quasi-2D model, suitable for the time-dependent water and sediment transport processes simulation in large lowland river systems, including their floodplain, is presented. Water flow and sediment equations are represented by means of the interconnected irregular cells scheme. Different simplifications of 1D Saint Venant equations are used to represent the discharge laws between fluvial cells. Spatially-distributed transport and deposition of fine sediments throughout the river-floodplain system are simulated. The model is applied over a 208 km reach of the Paraná River between the cities of Diamante and Ramallo (Argentina) and involving a river-floodplain area of 8100 km². After calibration and validation, the model is applied to predict water and sediment dynamics during synthetically generated extraordinary floods of 100, 1000 and 10000 years return period. The potential impact of a 56 km long road embankment constructed across the entire floodplain was simulated. Results with and without the road embankment shows that upstream water levels, inundation extent, flow duration and sediment deposition increases in the presence of the embankment.National University of Rosario. Rosario, Argentina.University of Newcastle, Newcastle, Australia

Impact‐based forecasting for pluvial floods

Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof-of-concept for an impact-based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network-based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio-temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact-based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact-based forecast could be used to disseminate impact-based early warnings

Two-dimensional hydrodynamic modelling of the lower Parana river

En este trabajo se presenta la implementación del modelo matemático bidimensional CCHE2D para caracterizar la hidrodinámica del Río Paraná Inferior, en el tramo comprendido entre el km 410 y el km 452 de la ruta de navegación Santa Fe-Océano. La batimetría del cauce se definió a partir de relevamientos realizados por la Dirección Nacional de Vías Navegables-Distrito Paraná Inferior (DNVN-DPI) entre los años 2010 y 2012. Asimismo, para definir los límites del cauce se utilizaron imágenes satelitales y cartas náuticas del Servicio de Hidrografía Naval (SHN). Se realizaron simulaciones, en régimen permanente e impermanente, utilizando respectivamente caudales aforados y caudales generados mediante transformación de niveles (zw) en caudales (Q), con la curva zw-Q del km 448. Los caudales simulados variaron entre 11640 m3/s y 28600 m3/s, a los cuales le corresponden tiempos de excedencia de 97,7 % y 0,3 % respectivamente. De esta manera, se representaron condiciones de aguas bajas, medias y altas del río. Los resultados del modelo hidrodinámico fueron contrastados con mediciones de caudales realizadas mediante ADCP (Acoustic Doppler Current Profiler) por FICH (2004-2006) y con niveles hidrométricos diarios registrados en Rosario (km 416) y en Puerto San Martín (km 448) por la Prefectura Naval Argentina (PNA). Los valores del coeficiente de rugosidad de Manning, obtenidos en el proceso de calibración del modelo, variaron entre 0,024 s/m1/3 y 0,025 s/m1/3, mientras que, la viscosidad turbulenta y las tensiones adicionales de Reynolds se estimaron a partir del modelo de dos ecuaciones k-e. Los resultados obtenidos indican que el modelo hidrodinámico simula satisfactoriamente la dinámica del flujo en el tramo estudiado, tanto en régimen permanente como impermanente; ya sea en lo que concierne a la reproducción de niveles hidrométricos, pendientes hidráulicas, profundidades, distribución de velocidades y caudales específicos en secciones transversales; como así también, en lo que respecta a los caudales derivados en las bifurcaciones del cauce.Fil: Basile, Pedro Abel. Universidad Nacional de Rosario; Argentina

Selection of optimal escape routes in a flood-prone area based on 2D hydrodynamic modelling

Optimizing escape routes during an extreme flood event is an effective way to mitigate casualties. In this study, a model for selecting optimal escape routes in a flood-prone area has been proposed, which includes a module for predicting the two-dimensional hydrodynamics and modules for assessing the hazard degree for evacuees, calculation of evacuation times and determination of different escape routes. In the module for determining escape routes, two evacuation schemes were used: Scheme A to find optimal escape routes based on established road networks, and Scheme B to design a new optimal evacuation route. Extreme overbank floods occurred in the Lower Yellow River (LYR) in July 1958 (‘58.7’) and August 1982 (‘82.8’) and the proposed model was applied to select the optimal escape routes on a typical floodplain area of the LYR for these two floods. Model predictions indicated that: (i) the optimal escape routes for these two floods were the same for all three starting locations, and the optimized routes provided 3 h more time for evacuees to escape; and (ii) the time of evacuation would need to be earlier for the ‘58.7’ flood because of its larger amount of water volume and higher peak discharge

Pluvial flooding: high-resolution stochastic hazard mapping in urban areas by using fast-processing DEM-based algorithms

Climate change and rapid expansion of urban areas are expected to increase pluvial flood hazard and risk in the near future, and particularly so in large developed areas and cities. Therefore, large-scale and high-resolution pluvial flood hazard mapping is required to identify hotspots where mitigation measures may be applied to reduce flood risk. Depressions or low points in urban areas where runoff volumes can be stored are prone to pluvial flooding. The standard approach based on estimating synthetic design hyetographs assumes, in a given depression, that the T-year design storm generates the T-year pluvial flood. In addition, urban areas usually include several depressions even linked or nested that would require distinct design hyetographs instead of using a unique synthetic design storm. In this paper, a stochastic methodology is proposed to address the limitations of this standard approach, developing large-scale ~ 2 m-resolution pluvial flood hazard maps in urban areas with multiple depressions. The authors present an application of the proposed approach to the city of Pamplona in Spain (68.26 km2)

Evaluation of GPU Acceleration for WRF–SFIRE

WRF–SFIRE is an open source, atmospheric–wildfire model that couples the WRF model with the level set fire spread model to simulate wildfires in real time. This model has many applications and more scientific questions can be asked and answered if the model can be run faster. Nvidia has put a lot of effort into easing the barrier of entry for accelerating applications with their tools to be run on GPUs. Various physical simulations have been successfully ported to utilize GPUs and have benefited from the speed increase. In this research, we take a look at WRF-SFIRE and try to use the Nvida tools to accelerate portions of code. We were successful in offloading work to the GPU. However, the WRF-SFIRE codebase contains too many data dependencies, deeply nested function calls and I/O to effectively utilize the GPU’s resources. We look at specific examples and try to run them on a Titan V GPU. In the end, the compute intensive portions of WRF-SFIRE need to be rewritten to avoid data dependencies in order to leverage GPUs to improve the execution time

Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations

High computational cost is often the most limiting factor when running high-resolution hydrodynamic models to simulate spatial-temporal flood inundation behavior. To address this issue, a recent study introduced the hybrid Low-fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behavior of flood inundation extent by upskilling simulations from a low-resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high-resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high-resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent-based LSG model rather than deriving it from water depth predictions.Niels Fraehr, Quan J. Wang, Wenyan Wu, and Rory Natha