A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model (DEM)-based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc-second (10m) Height Above Nearest Drainage (HAND) raster data for the conterminous U.S. (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc-second CONUS HAND raster dataset was computed in 1.5 days on the CyberGIS ROGER supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model (NWM) forecast data to enable near real-time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process was profiled to gain insights into the computational characteristics in high-performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies

Application of cellular automata approach for fast flood simulation

CCWI 2011: Computing and Control for the Water Industry, 5-7 September 2011, University of Exeter, UKThe increasing pluvial flooding in many urban areas of the world has caused tremendous damage to societies and has drawn the attention of researchers to the development of a fast flood inundation model. Most available models are based on solving a set of partial differential equations that require a huge computational effort. Researchers are increasingly interested in an alternative grid-based approach called Cellular Automata (CA), due to its computational efficiency (both with respect to time and computational cost) and inherent parallel nature. This paper deals with the computational experiment with a new CA method for modelling 2D pluvial flood propagation. A Digital Elevation Model (DEM) comprising square grids forms the discrete space for the CA setup. Local rules are applied in the von Neumann Neighbourhood for the spatio-temporal evolution of the flow field. The proposed model is applied to a hypothetical terrain to assess its performance. The results from the CA model are compared with those of a physically based 2D urban inundation model (UIM). The CA model results are comparable with the results from UIM model. The advantages of low computational cost of CA and its ability to mimic realistic fluid movement are combined in a novel and fast flood simulation model

Developing an effective 2-D urban flood inundation model for city emergency management based on cellular automata

Flash floods have occurred frequently in the urban areas of southern China. An effective process-oriented urban flood inundation model is urgently needed for urban storm-water and emergency management. This study develops an efficient and flexible cellular automaton (CA) model to simulate storm-water runoff and the flood inundation process during extreme storm events. The process of infiltration, inlets discharge and flow dynamics can be simulated with little preprocessing on commonly available basic urban geographic data. In this model, a set of gravitational diverging rules are implemented to govern the water flow in a rectangular template of three cells by three cells of a raster layer. The model is calibrated by one storm event and validated by another in a small urban catchment in Guangzhou of southern China. The depth of accumulated water at the catchment outlet is interpreted from street-monitoring closed-circuit television (CCTV) videos and verified by on-site survey. A good level of agreement between the simulated process and the reality is reached for both storm events. The model reproduces the changing extent and depth of flooded areas at the catchment outlet with an accuracy of 4 cm in water depth. Comparisons with a physically based 2-D model (FloodMap) show that the model is capable of effectively simulating flow dynamics. The high computational efficiency of the CA model can meet the needs of city emergency management

Enable High-resolution, Real-time Ensemble Simulation and Data Assimilation of Flood Inundation using Distributed GPU Parallelization

Numerical modeling of the intensity and evolution of flood events are affected by multiple sources of uncertainty such as precipitation and land surface conditions. To quantify and curb these uncertainties, an ensemble-based simulation and data assimilation model for pluvial flood inundation is constructed. The shallow water equation is decoupled in the x and y directions, and the inertial form of the Saint-Venant equation is chosen to realize fast computation. The probability distribution of the input and output factors is described using Monte Carlo samples. Subsequently, a particle filter is incorporated to enable the assimilation of hydrological observations and improve prediction accuracy. To achieve high-resolution, real-time ensemble simulation, heterogeneous computing technologies based on CUDA (compute unified device architecture) and a distributed storage multi-GPU (graphics processing unit) system are used. Multiple optimization skills are employed to ensure the parallel efficiency and scalability of the simulation program. Taking an urban area of Fuzhou, China as an example, a model with a 3-m spatial resolution and 4.0 million units is constructed, and 8 Tesla P100 GPUs are used for the parallel calculation of 96 model instances. Under these settings, the ensemble simulation of a 1-hour hydraulic process takes 2.0 minutes, which achieves a 2680 estimated speedup compared with a single-thread run on CPU. The calculation results indicate that the particle filter method effectively constrains simulation uncertainty while providing the confidence intervals of key hydrological elements such as streamflow, submerged area, and submerged water depth. The presented approaches show promising capabilities in handling the uncertainties in flood modeling as well as enhancing prediction efficiency

Continental-scale high-resolution river geometry and real-time inundation mapping

Flooding is the most threatening natural disaster worldwide considering the fatalities and property damage it causes. Recent flood disasters have raised concerns for accurate and responsive inundation forecast due to the rapid spread and astonishing destructive power of these events. Although recent development in large scale hydrologic simulation has enabled the real-time streamflow simulation operating on millions of river reaches, a framework for converting the forecast discharge into corresponding water surface elevation and inundation maps at a continental-scale is absent to better support local flood response. To accurately map flood inundation extent, a comprehensive description of the geometry of the channel is indispensable. As such, this dissertation presents an innovative approach for estimating river geometry and conducting inundation mapping at a continental-scale with a high spatial resolution. This approach is based on the concept of Height Above Nearest Drainage (HAND). Advanced hydrologic terrain analysis workflows have been designed to derive channel hydraulic properties, stage-discharge rating curves, and inundation extents using HAND. After the mechanism being presented, the implementation of this approach across the contiguous United States has been demonstrated using the 10-meter National Elevation Dataset. The integrity of the outputs has been validated through the comparison with best available references at multiple test sites. Considering the increasingly availability of high-resolution topographic data derived from lidar technology, the dissertation further presents how advanced geomorphic feature extraction tools are integrated into the proposed approach to overcome the challenges associated with the enrichment of terrain details. At last, this dissertation presents how banklines, an essential piece of river geometry characteristic as the boundary differentiates channel zone from floodplain, is detected with enhanced geomorphic feature extraction tools for improving large-scale hydrologic simulation and inundation mapping accuracy.Civil, Architectural, and Environmental Engineerin

Lidar In Coastal Storm Surge Modeling: Modeling Linear Raised Features

A method for extracting linear raised features from laser scanned altimetry (LiDAR) datasets is presented. The objective is to automate the method so that elements in a coastal storm surge simulation finite element mesh might have their edges aligned along vertical terrain features. Terrain features of interest are those that are high and long enough to form a hydrodynamic impediment while being narrow enough that the features might be straddled and not modeled if element edges are not purposely aligned. These features are commonly raised roadbeds but may occur due to other manmade alterations to the terrain or natural terrain. The implementation uses the TauDEM watershed delineation software included in the MapWindow open source Geographic Information System to initially extract watershed boundaries. The watershed boundaries are then examined computationally to determine which sections warrant inclusion in the storm surge mesh. Introductory work towards applying image analysis techniques as an alternate means of vertical feature extraction is presented as well. Vertical feature lines extracted from a LiDAR dataset for Manatee County, Florida are included in a limited storm surge finite element mesh for the county and Tampa Bay. Storm surge simulations using the ADCIRC-2DDI model with two meshes, one which includes linear raised features as element edges and one which does not, verify the usefulness of the method

Accuracy and computational efficiency of 2D urban surface flood modelling based on cellular automata

There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy.This is the final version of the article. Available from Elsevier via the DOI in this record.There is an emerging abundance freely available of high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and Wastewater systems in the UK contain over 700,000 km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required calling for the most computational efficient models possible. Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES, a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy

Multi-Block Computation for Flood Inundation Studies

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Formulation of fast 2D urban pluvial flood model using cellular automata approach

Copyright © 2013 IWA Publishing. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics Vol 15 (3), pp. 676–686 (2013), DOI: 10.2166/hydro.2012.245 and is available at www.iwapublishing.comWith the increase in frequency and severity of flash flood events in major cities around the world, the infrastructure and people living in those urban areas are exposed continuously to high risk levels of pluvial flooding. The situation is likely to be exacerbated by the potential impact of future climate change. A fast flood model could be very useful for flood risk analysis. One-dimensional (1D) models provide limited information about the flow dynamics whereas two-dimensional (2D) models require substantial computational time and cost, a factor that limits their use. This paper presents an alternative approach using cellular automata (CA) for 2D modelling. The model uses regular grid cells as a discrete space for the CA setup and applies generic rules to local neighbourhood cells to simulate the spatio-temporal evolution of pluvial flooding. The proposed CA model is applied to a hypothetical terrain and a real urban area. The synchronous state updating rule and inherent nature of the proposed model contributes to a great reduction in computational time. The results are compared with a hydraulic model and good agreement is found between the two models.Engineering and Physical Sciences Research Council (EPSRC