The Experts below are selected from a list of 11979 Experts worldwide ranked by ideXlab platform
Florian Pappenberger - One of the best experts on this subject based on the ideXlab platform.
-
continental and global scale Flood Forecasting systems
Wiley Interdisciplinary Reviews: Water, 2016Co-Authors: Florian Pappenberger, Albrecht Weerts, Rebecca Emerton, Elisabeth Stephens, T C Pagano, Andrew W Wood, Peter Salamon, J D BrownAbstract:Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and Forecasting of Floods at the global scale is crucial to preparing for severe events and providing early awareness where local Flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centers are increasingly using their meteorological output to drive hydrological models, creating hydrometeorological systems capable of Forecasting river flow and Flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data, and resources in recent years have made it possible to produce global scale Flood Forecasting systems. In this paper, the current state of operational large-scale Flood Forecasting is discussed, including probabilistic Forecasting of Floods using ensemble prediction systems. Six state-of-the-art operational large-scale Flood Forecasting systems are reviewed, describing similarities and differences in their approaches to Forecasting Floods at the global and continental scale. Operational systems currently have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products in real time across the globe, in support of national Forecasting capabilities. With improvements in seasonal weather Forecasting, future advances may include more seamless hydrological Forecasting at the global scale alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction. WIREs Water 2016, 3:391–418. doi: 10.1002/wat2.1137 For further resources related to this article, please visit the WIREs website.
-
probabilistic Flood Forecasting and decision making an innovative risk based approach
Natural Hazards, 2014Co-Authors: M. Dale, Florian Pappenberger, Ken Mylne, J. Wicks, Stefan Laeger, Steve TaylorAbstract:Flood Forecasting is becoming increasingly important across the world. The exposure of people and property to Flooding is increasing and society is demanding improved management of Flood risk. At the same time, technological and data advances are enabling improvements in Forecasting capabilities. One area where Flood Forecasting is seeing technical developments is in the use of probabilistic forecasts—these provide a range of possible forecast outcomes that indicate the probability or chance of a Flood occurring. While probabilistic forecasts have some distinct benefits, they pose an additional decision-making challenge to those that use them: with a range of forecasts to pick from, which one is right? (or rather, which one(s) can enable me to make the correct decision?). This paper describes an innovative and transferable approach for aiding decision-making with probabilistic forecasts. The proposed risk-based decision-support framework has been tested in a range of Flood risk environments: from coastal surge to fluvial catchments to urban storm water scales. The outputs have been designed to be practical and proportionate to the level of Flood risk at any location and to be easy to apply in an operational Flood Forecasting and warning context. The benefits of employing a benefit-cost inspired decision-support framework are that Flood Forecasting decision-making can be undertaken objectively, with confidence and an understanding of uncertainty, and can save unnecessary effort on Flood incident actions. The method described is flexible such that it can be used for a wide range of Flood environments with multiple Flood incident management actions. It uses a risk-based approach taking into account both the probability and the level of impact of a Flood event. A key feature of the framework is that it is based on a full assessment of the Flood-related risk, taking into account both the probability and the level of impact of a Flood event. A recommendation for action may be triggered by either a higher probability of a lower impact Flood or a low probability of a very severe Flood. Hence, it is highly innovative as it is the first application of such a risk-based method for Flood Forecasting and warning purposes. A final benefit is that it is considered to be transferrable to other countries.
-
Applying probabilistic Flood Forecasting in Flood incident management
2013Co-Authors: M. Dale, Ken Mylne, Florian Pappenberger, J. Wicks, Hannah L ClokeAbstract:In recent years, various probabilistic Flood Forecasting techniques have been developed and applied with some success in the UK and worldwide. Developments in quantitative precipitation Forecasting, probabilistic storm surge and Flood modelling all provide more information for Flood Forecasting. However, more information does not necessarily improve decision-making, particularly where the probabilistic forecasts are likely to contain conflicting predictions. In order for probabilistic forecasts to be used effectively, methods must assist in rapid decision-making in a real-time Flood environment. This report describes a practical approach for using probabilistic Flood forecasts to support decision-making in Flood incident management (FIM). Three decision support methods have been developed and tested on case studies. The report explains how these methods could be applied to a variety of Forecasting situations of different complexity and at different lead times ahead of an event. Also included is an outline of the datasets that would be required to use the decision-support methods in different Forecasting situations, and data requirements for real-time use. The report covers the likely operational benefits, opportunities and constraints of using probabilistic Flood Forecasting in FIM. This work provides a useful resource for suitably qualified professional to investigate how probabilistic Flood forecasts could be used to support decision making in Flood incident management.
-
Recent Advances and case studies in medium range Flood Forecasting: A follow on review
2011Co-Authors: Florian Pappenberger, Hannah L Cloke, J. Thielen, M.h. RamosAbstract:Ensemble Flood Forecasting systems are more and more widely used. Cloke and Pappenberger (2009) reviewed the scientific drivers of the shift towards such ensemble Flood Forecasting' and discussed several of the questions surrounding best practice in using EPS in Flood Forecasting systems.
-
ensemble Flood Forecasting a review
Journal of Hydrology, 2009Co-Authors: Hannah L Cloke, Florian PappenbergerAbstract:Operational medium range Flood Forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble Flood Forecasting’ and discuss several of the questions surrounding best practice in using EPS in Flood Forecasting systems. We also review the literature evidence of the ‘added value’ of Flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.
Kwok-wing Chau - One of the best experts on this subject based on the ideXlab platform.
-
a Flood Forecasting neural network model with genetic algorithm
International Journal of Environment and Pollution, 2006Co-Authors: Kwok-wing ChauAbstract:It will be useful to attain a quick and accurate Flood Forecasting, particularly in a Flood-prone region. The accomplishment of this objective can have far reaching significance by extending the lead time for issuing disaster warnings and furnishing ample time for citizens in vulnerable areas to take appropriate action, such as evacuation. In this paper, a novel hybrid model based on recent artificial intelligence technology, namely, a genetic algorithm (GA)-based artificial neural network (ANN), is employed for Flood Forecasting. As a case study, the model is applied to a prototype channel reach of the Yangtze River in China. Water levels at the downstream station, Han-Kou, are forecasted on the basis of water levels with lead times at the upstream station, Luo-Shan. An empirical linear regression model, a conventional ANN model and a GA model are used as the benchmarks for comparison of performances. The results reveal that the hybrid GA-based ANN algorithm, under cautious treatment to avoid over-fitting, is able to produce better accuracy in performance, although at the expense of additional modelling parameters and possibly slightly longer computation time.
-
a web based Flood Forecasting system for shuangpai region
Advances in Engineering Software, 2006Co-Authors: Kwok-wing Chau, Chun-tian ChengAbstract:Traditional Flood Forecasting and operation of reservoirs in China are based on manual calculations by hydrologists or through standalone computer programs. The main drawbacks of these methods are long Forecasting time due to time-consuming nature, individual knowledge, lack of communication, absence of experts, etc. A Web-based Flood Forecasting system (WFFS), which includes five main modules: real-time rainfall data conversion, model-driven hydrologic Forecasting, model calibration, precipitation Forecasting, and Flood analysis, is presented in this paper. The WFFS brings significant convenience to personnel engaged in Flood Forecasting and control and allows real-time contribution of a wide range of experts at other spatial locations in times of emergency. The conceptual framework and detailed components of the proposed WFFS, which employs a multi-tiered architecture, are illustrated. Multi-tiered architecture offers great flexibility, portability, reusability and reliability. The prototype WFFS has been developed in Java programming language and applied in Shuangpai region with a satisfactory result. sult.
-
comparison of several Flood Forecasting models in yangtze river
Journal of Hydrologic Engineering, 2005Co-Authors: Kwok-wing ChauAbstract:In a Flood-prone region, quick and accurate Flood Forecasting is imperative. It can extend the lead time for issuing disaster warnings and allow sufficient time for habitants in hazardous areas to take appropriate action, such as evacuation. In this paper, two hybrid models based on recent artificial intelligence technology, namely, the genetic algorithm-based artificial neural network (ANN-GA) and the adaptive-network-based fuzzy inference system (ANFIS), are employed for Flood Forecasting in a channel reach of the Yangtze River in China. An empirical linear regression model is used as the benchmark for comparison of their performances. Water levels at a downstream station, Han-Kou, are forecasted by using known water levels at the upstream station, Luo-Shan. When cautious treatment is made to avoid overfitting, both hybrid algorithms produce better accuracy in performance than the linear regression model. The ANFIS model is found to be optimal, but it entails a large number of parameters. The performanc...
-
Developing a Web-based Flood Forecasting system for reservoirs with J2EE
Hydrological Sciences Journal, 2004Co-Authors: Chun-tian Cheng, Kwok-wing ChauAbstract:Abstract Abstract A Flood Forecasting system is a crucial component in Flood mitigation. For certain important large-scale reservoirs, cooperation and communication among federal, state, and local stakeholders are required when heavy Flood events are encountered. The Web-based environment is emerging as a very important development and delivery platform for real-time Flood Forecasting systems. In this paper, the findings of a case study are presented of the development of a Web-based Flood Forecasting system for reservoirs using Java 2 platform Enterprise Edition (J2EE). J2EE of Sun Microsystems is chosen as the development solution for the Web-based Flood Forecasting system, Weblogic 6.0 of BEA as the container provider, and JBuilder 7.0 of Borland as the development tool. One of the key objectives in this project is to establish a collaborative platform for Flood Forecasting via Web technology in order to render hydrological models and data available to stakeholders and experts involved and thus offer a...
Hannah L Cloke - One of the best experts on this subject based on the ideXlab platform.
-
Applying probabilistic Flood Forecasting in Flood incident management
2013Co-Authors: M. Dale, Ken Mylne, Florian Pappenberger, J. Wicks, Hannah L ClokeAbstract:In recent years, various probabilistic Flood Forecasting techniques have been developed and applied with some success in the UK and worldwide. Developments in quantitative precipitation Forecasting, probabilistic storm surge and Flood modelling all provide more information for Flood Forecasting. However, more information does not necessarily improve decision-making, particularly where the probabilistic forecasts are likely to contain conflicting predictions. In order for probabilistic forecasts to be used effectively, methods must assist in rapid decision-making in a real-time Flood environment. This report describes a practical approach for using probabilistic Flood forecasts to support decision-making in Flood incident management (FIM). Three decision support methods have been developed and tested on case studies. The report explains how these methods could be applied to a variety of Forecasting situations of different complexity and at different lead times ahead of an event. Also included is an outline of the datasets that would be required to use the decision-support methods in different Forecasting situations, and data requirements for real-time use. The report covers the likely operational benefits, opportunities and constraints of using probabilistic Flood Forecasting in FIM. This work provides a useful resource for suitably qualified professional to investigate how probabilistic Flood forecasts could be used to support decision making in Flood incident management.
-
Recent Advances and case studies in medium range Flood Forecasting: A follow on review
2011Co-Authors: Florian Pappenberger, Hannah L Cloke, J. Thielen, M.h. RamosAbstract:Ensemble Flood Forecasting systems are more and more widely used. Cloke and Pappenberger (2009) reviewed the scientific drivers of the shift towards such ensemble Flood Forecasting' and discussed several of the questions surrounding best practice in using EPS in Flood Forecasting systems.
-
ensemble Flood Forecasting a review
Journal of Hydrology, 2009Co-Authors: Hannah L Cloke, Florian PappenbergerAbstract:Operational medium range Flood Forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble Flood Forecasting’ and discuss several of the questions surrounding best practice in using EPS in Flood Forecasting systems. We also review the literature evidence of the ‘added value’ of Flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.
-
Ensemble Flood Forecasting: a review.
Journal of Hydrology, 2009Co-Authors: Hannah L Cloke, Florian PappenbergerAbstract:Operational medium range Flood Forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble Flood Forecasting’ and discuss several of the questions surrounding best practice in using EPS in Flood Forecasting systems. We also review the literature evidence of the ‘added value’ of Flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.
-
Ensemble predictions and perceptions of risk, uncertainty, and error in Flood Forecasting
Environmental Hazards, 2007Co-Authors: David Demeritt, Hannah L Cloke, Florian Pappenberger, Jutta Thielen, Jens Bartholmes, Maria-helena RamosAbstract:Abstract Under the auspices of the World Meteorological Organization, there are a number of international initiatives to promote the development and use of so-called ensemble prediction systems (EPS) for Flood Forecasting. The campaign to apply these meteorological techniques to Flood Forecasting raises important questions about how the probabilistic information these systems provide can be used for what in operational terms is typically a binary decision of whether or not to issue a Flood warning. To explore these issues, we report on the results of a series of focus group discussions conducted with operational Flood forecasters from across Europe on behalf of the European Flood Alert System. Working in small groups to simulate operational conditions, forecasters engaged in a series of carefully designed Forecasting exercises using various different combinations of actual data from real events. Focus group data was supplemented by a follow-up questionnaire survey exploring how Flood forecasters understan...
Keith Beven - One of the best experts on this subject based on the ideXlab platform.
-
Application of data-based mechanistic modelling for Flood Forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)
Hydrology and Earth System Sciences, 2013Co-Authors: David Leedal, Albrecht Weerts, Paul Smith, Keith BevenAbstract:The Delft Flood Early Warning System provides a versatile framework for real-time Flood Forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new Flood Forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).
-
A Data Based Mechanistic real-time Flood Forecasting module for NFFS FEWS : DBM real-time Flood Forecasting
2012Co-Authors: David Leedal, Albrecht Weerts, Paul Smith, Keith BevenAbstract:The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for Flood Forecasting in several studies. The DELFT-FEWS open shell operational Flood Forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new Forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.
-
visualization approaches for communicating real time Flood Forecasting level and inundation information
Journal of Flood Risk Management, 2010Co-Authors: David Leedal, Keith Beven, Peter C. Young, Jeffrey C Neal, Paul D BatesAbstract:The January 2005 Flood event in the Eden catchment (UK) has focused considerable research effort towards strengthening and extending operational Flood Forecasting in the region. The Eden catchment has become a key study site within the remit of phase two of the Flood Risk Management Research Consortium. This paper presents a synthesis of results incorporating model uncertainty analysis, computationally efficient real-time data assimilation/Forecasting algorithms, two-dimensional (2D) inundation modelling, and data visualization for decision support. The emphasis here is on methods of presenting information from a new generation of probabilistic Flood Forecasting models. Using Environment Agency rain and river-level gauge data, a data-based mechanistic model is identified and incorporated into a modified Kalman Filter (KF) data assimilation algorithm designed for real-time Flood Forecasting applications. The KF process generates forecasts within a probabilistic framework. A simulation of the 6-h ahead forecast for river levels at Sheepmount (Carlisle) covering the January 2005 Flood event is presented together with methods of visualizing the associated uncertainty. These methods are then coupled to the 2D hydrodynamic LISFlood-FP model to produce real-time Flood inundation maps. The value of incorporating probabilistic information is emphasized.
-
THE UNCERTAINTY CASCADE IN Flood Forecasting
2006Co-Authors: Keith Beven, Florian Pappenberger, Renata J. Romanowicz, Peter C. Young, Micha WernerAbstract:A methodology for propagating and constraining the uncertainty inherent in real-time Flood Forecasting is presented and demonstrated on an application to the River Severn, UK. The Flood Forecasting system is based on a cascade of rainfall-runoff and Flood routing models, developed using stochastic transfer functions with state dependent parameterisations to allow for nonlinearity. The nonlinearities require a Monte Carlo sampling approach to propagation of uncertainty. Model updating and uncertainty constraint as new water level data become available is based on a Kalman filtering approach. The methodology is being implemented into the UK National Flood Forecasting System.
Chun-tian Cheng - One of the best experts on this subject based on the ideXlab platform.
-
a web based Flood Forecasting system for shuangpai region
Advances in Engineering Software, 2006Co-Authors: Kwok-wing Chau, Chun-tian ChengAbstract:Traditional Flood Forecasting and operation of reservoirs in China are based on manual calculations by hydrologists or through standalone computer programs. The main drawbacks of these methods are long Forecasting time due to time-consuming nature, individual knowledge, lack of communication, absence of experts, etc. A Web-based Flood Forecasting system (WFFS), which includes five main modules: real-time rainfall data conversion, model-driven hydrologic Forecasting, model calibration, precipitation Forecasting, and Flood analysis, is presented in this paper. The WFFS brings significant convenience to personnel engaged in Flood Forecasting and control and allows real-time contribution of a wide range of experts at other spatial locations in times of emergency. The conceptual framework and detailed components of the proposed WFFS, which employs a multi-tiered architecture, are illustrated. Multi-tiered architecture offers great flexibility, portability, reusability and reliability. The prototype WFFS has been developed in Java programming language and applied in Shuangpai region with a satisfactory result. sult.
-
Developing a Web-based Flood Forecasting system for reservoirs with J2EE
Hydrological Sciences Journal, 2004Co-Authors: Chun-tian Cheng, Kwok-wing ChauAbstract:Abstract Abstract A Flood Forecasting system is a crucial component in Flood mitigation. For certain important large-scale reservoirs, cooperation and communication among federal, state, and local stakeholders are required when heavy Flood events are encountered. The Web-based environment is emerging as a very important development and delivery platform for real-time Flood Forecasting systems. In this paper, the findings of a case study are presented of the development of a Web-based Flood Forecasting system for reservoirs using Java 2 platform Enterprise Edition (J2EE). J2EE of Sun Microsystems is chosen as the development solution for the Web-based Flood Forecasting system, Weblogic 6.0 of BEA as the container provider, and JBuilder 7.0 of Borland as the development tool. One of the key objectives in this project is to establish a collaborative platform for Flood Forecasting via Web technology in order to render hydrological models and data available to stakeholders and experts involved and thus offer a...