scholarly journals Framework for Offline Flood Inundation Forecasts for Two-Dimensional Hydrodynamic Models

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 346 ◽  
Author(s):  
Punit Bhola ◽  
Jorge Leandro ◽  
Markus Disse
Keyword(s):  
Real Time ◽  
Warning System ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Database Queries ◽  
Inundation Maps ◽  
Online Maps ◽  
The City ◽  
Selection Of

The paper presents a new methodology for hydrodynamic-based flood forecast that focuses on scenario generation and database queries to select appropriate flood inundation maps in real-time. In operational flood forecasting, only discharges are forecasted at specific gauges using hydrological models. Hydrodynamic models, which are required to produce inundation maps, are computationally expensive, hence not feasible for real-time inundation forecasting. In this study, we have used a substantial number of pre-calculated inundation maps that are stored in a database and a methodology to extract the most likely maps in real-time. The method uses real-time discharge forecast at upstream gauge as an input and compares it with the pre-recorded scenarios. The results show satisfactory agreements between offline inundation maps that are retrieved from a pre-recorded database and online maps, which are hindcasted using historical events. Furthermore, this allows an efficient early warning system, thanks to the fast run-time of the proposed offline selection of inundation maps. The framework is validated in the city of Kulmbach in Germany.

scholarly journals Dynamic Flood Inundation Forecast for the City of Kulmbach Using Offline Two-Dimensional Hydrodynamic Models

10.29007/c4gq ◽  
2018 ◽  
Author(s):  
Punit Bhola ◽  
Jorge Leandro ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
Markus Disse
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Water Levels ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Database Queries ◽  
Inundation Maps ◽  
Inundation Map ◽  
The City

The paper presents a new methodology for hydrodynamic-based flood forecast focusing on sce- nario generation and database queries to select the appropriate flood inundation map in real-time. In operational flood forecasting, discharges are forecast at specific gauges using hydrological models. The water levels are obtained from a rating curve designed for each respective gauge. Particularly for higher discharges when the flow over-spills the side banks, these curves are highly uncertain. Hy- drodynamic models are then required to produce realistic inundation maps and water levels. Hydro- dynamic models are computationally expensive and therefore not feasible for real-time forecasting. Alternatively, pre-calculated inundation maps can be stored in a database which contains a substantial number of scenarios, and used for extracting the most likely map in real-time. This study investigates the application of offline inundation forecast in the city Kulmbach in Germany.

scholarly journals A 2D Real-Time Flood Forecast Framework Based on a Hybrid Historical and Synthetic Runoff Database

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 114
Author(s):  
Giampaolo Crotti ◽  
Jorge Leandro ◽  
Punit Kumar Bhola
Keyword(s):  
Real Time ◽  
River Discharge ◽  
Hybrid Approach ◽  
Flood Forecast ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Inundation Maps ◽  
Natural Streams ◽  
Historical Database ◽  
Non Gaussian

Operational real-time flood forecast is often done on the prediction of discharges at specific gauges using hydrological models. Hydrodynamic models, which can produce inundation maps, are computationally demanding and often cannot be used directly for that purpose. The FloodEvac framework has been developed in order to enable 2D flood inundations map to be forecasted at real-time. The framework is based on a database of pre-recorded synthetic events. In this paper, the framework is improved by generating a database based on rescaled historical river discharge events. This historical database includes a wider variety of runoff curves, including non-Gaussian and multi-peak shapes that better reflect the characteristics and the behavior of the natural streams. Hence, a hybrid approach is proposed by joining the historical and the existing synthetic database. The increased number of scenarios in the hybrid database allows reliable predictions, thus improving the robustness and applicability of real-time flood forecasts.

scholarly journals Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1283 ◽  
Author(s):  
Li-Chiu Chang ◽  
Mohd Amin ◽  
Shun-Nien Yang ◽  
Fi-John Chang
Keyword(s):  
Real Time ◽  
Warning System ◽  
Google Earth ◽  
Flood Inundation ◽  
Self Organizing Map ◽  
Storm Events ◽  
Inundation Maps ◽  
Forecast Models ◽  
Som Network ◽  
Precautionary Measures

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

Building the Flood Early Warning System in Guyana at the National scale, with real-time forecast of inundated areas for selected flood prone communities.

2020 ◽  
Author(s):  
Alessandro Masoero ◽  
Imra Hodzic ◽  
Colis Allen ◽  
Andrea Libertino ◽  
Andrea Giusti ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Flood Forecasting ◽  
Local Scale ◽  
Civil Defense ◽  
Data Availability ◽  
Inundation Maps ◽  
Extreme Flood

<p>Within the framework of the project “Strengthening Disaster Management Capacity of Women in Guyana and Dominica”, the National Flood Early Warning System (NFEWS) for Guyana is currently under development. The technical component of the system aims at implementing an operational flood forecasting modelling chain linking meteorological, hydrological and inundation models to provide timely early warnings and predicted flood scenarios, allowing the decision maker to take prevention actions and reduce the impacts of the forecasted event.</p><p>The objective is to implement, together with the Hydromet Service of Guyana, a technical tool able to provide daily forecasts of extreme flood events 1 to seven 7 days in advance, up to the local scale of inundation maps for selected locations.</p><p>The forecasting chain implemented is composed of five (5) main components: i) the weather forecasts, using the limited area WRF model executed twice a day at Hydromet; ii) observational inputs preparation, in particular rain maps through conditional merging between local ground stations and satellite information; iii) rainfall downscaling in several equiprobable scenarios using RAINFARM stochastic model; iv) the distributed hydrological model CONTINUUM, able to estimate river discharge and soil moisture conditions from the meteorological inputs (observation and forecasts), and v)the hydraulic model HYDRA-2D, that using a simplification of the shallow water equations allows fast and reliable inundation mapping.</p><p>At four (4) selected locations, corresponding to relevant flood-prone communities in Guyana, an innovative coupling between the hydrological and the inundation models allows to trigger an operational execution of several hydraulic simulations, resulting in real-time probabilistic forecast of inundation maps. The outflow volumes, derived from CONTINUUM hydrological routing, for different rainfall scenarios are used as inflow inputs for HYDRA-2D. Scalability between hydrological (1.5km) and hydraulic (12m) scales has been achieved through detailed field data collection, that was also used, together with local knowledge, to calibrate the inundation model.</p><p>Through the complete flood forecasting chain set up for Guyana, probability of exceeding significant water depths can be provided in advance to involved stakeholders, triggering early actions and thus enhancing flood resilience at the local scale.</p><p>The hydrological component of the forecasting chain has been implemented at the national level for the whole country, at a feasible spatial and temporal resolution based on a balance between input data availability and expected response time for civil defense activities.</p><p>Being developed using an open source model, as for all the other elements of the forecasting system, the hydraulic modelling component can be, in future, extended and replicated in other areas of interests.</p>

scholarly journals Risk-Based and Hydrodynamic Pluvial Flood Forecasts in Real Time

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1895
Author(s):  
Julian Hofmann ◽  
Holger Schüttrumpf
Keyword(s):  
Real Time ◽  
Flood Risk ◽  
High Efficiency ◽  
Computation Time ◽  
Warning Systems ◽  
Hydrodynamic Models ◽  
Model Concept ◽  
Urban Flood ◽  
Real Time Simulations ◽  
The City

The effective forecast and warning of pluvial flooding in real time is one of the key elements and remaining challenges of an integrated urban flood risk management. This paper presents a new methodology for integrating risk-based solutions and 2D hydrodynamic models into the early warning process. Whereas existing hydrodynamic forecasting methods are based on rigid systems with extremely high computational demands, the proposed framework builds on a multi-model concept allowing the use of standard computer systems. As a key component, a pluvial flood alarm operator (PFA-Operator) is developed for selecting and controlling affected urban subcatchment models. By distributed computing of hydrologic independent models, the framework overcomes the issue of high computational times of hydrodynamic simulations. The PFA-Operator issues warnings and flood forecasts based on a two-step process: (1) impact-based rainfall thresholds for flood hotspots and (2) hydrodynamic real-time simulations of affected urban subcatchments models. Based on the open-source development software Qt, the system can be equipped with interchangeable modules and hydrodynamic software while building on the preliminary results of flood risk analysis. The framework was tested using a historic pluvial flood event in the city of Aachen, Germany. Results indicate the high efficiency and adaptability of the proposed system for operational warning systems in terms of both accuracy and computation time.

scholarly journals Real-Time Flood Mapping on Client-Side Web Systems Using HAND Model

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 65
Author(s):  
Anson Hu ◽  
Ibrahim Demir
Keyword(s):  
Flood Mapping ◽  
Flood Inundation ◽  
Inundation Maps ◽  
Hand Model ◽  
Digital Elevation ◽  
Insurance Rate ◽  
Elevation Model ◽  
Client Side ◽  
High Degree ◽  
The City

The height above nearest drainage (HAND) model is frequently used to calculate properties of the soil and predict flood inundation extents. HAND is extremely useful due to its lack of reliance on prior data, as only the digital elevation model (DEM) is needed. It is close to optimal, running in linear or linearithmic time in the number of cells depending on the values of the heights. It can predict watersheds and flood extent to a high degree of accuracy. We applied a client-side HAND model on the web to determine extent of flood inundation in several flood prone areas in Iowa, including the city of Cedar Rapids and Ames. We demonstrated that the HAND model was able to achieve inundation maps comparable to advanced hydrodynamic models (i.e., Federal Emergency Management Agency approved flood insurance rate maps) in Iowa, and would be helpful in the absence of detailed hydrological data. The HAND model is applicable in situations where a combination of accuracy and short runtime are needed, for example, in interactive flood mapping and supporting mitigation decisions, where users can add features to the landscape and see the predicted inundation.

Sign in / Sign up

Export Citation Format

Share Document