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Florian Pappenberger - One of the best experts on this subject based on the ideXlab platform.
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A vision for improving global Flood Forecasting
Environmental Research Letters, 2019Co-Authors: David A. Lavers, Shaun Harrigan, Erik Andersson, David S. Richardson, Christel Prudhomme, Florian PappenbergerAbstract:Global hydrological Forecasts are now produced operationally on a daily basis. However, the lack of global river discharge observations precludes routine Flood Forecast evaluation, an essential step in providing more skilful and reliable Forecasts. A vision is expounded for greater and more timely exchange of global river discharge observations, which would result in improved Flood awareness and socioeconomic benefits in some of the World's most vulnerable countries.
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Willingness-to-pay for a probabilistic Flood Forecast : a risk-based decision-making game
2016Co-Authors: L. Arnal, Fredrik Wetterhall, M.h. Ramos, E. Coughlan, H.l. Cloke, E. Stephens, S-j Andel, Florian PappenbergerAbstract:Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the Forecaster and the user of the Forecasts. Many authors have demonstrated the added (economic) value of probabilistic Forecasts over deterministic Forecasts for a diversity of activities in the water sector (e.g. Flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of Flood protection mitigation, called “How much are you prepared to pay for a Forecast?”, will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic Forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a Forecast, the results of the game showed that the value (or the usefulness) of a Forecast depends on several factors, including the way users perceive the quality of their Forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having Forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological Forecasting community in the future.
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Willingness-to-pay for a probabilistic Flood Forecast: a risk-based decision-making game
Hydrology and Earth System Sciences, 2016Co-Authors: L. Arnal, Schalk Jan Van Andel, Fredrik Wetterhall, M.h. Ramos, H.l. Cloke, E. Stephens, E. Coughlan De Perez, Florian PappenbergerAbstract:Probabilistic hydro-meteorological Forecasts have over the last decades been used more frequently to communicate Forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the Forecaster and the user of the Forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic Forecasts across the water sector (e.g. Flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of Flood protection mitigation, called “How much are you prepared to pay for a Forecast?”. The game was played at several workshops in 2015, which were attended by operational Forecasters and academics working in the field of hydrometeorology. The aim of this game was to better understand the role of probabilistic Forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a Forecast, the results of the game show that the value (or the usefulness) of a Forecast depends on several factors, including the way users perceive the quality of their Forecasts and link it to the perception of their own performances as decision-makers.
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a pan african medium range ensemble Flood Forecast system
Hydrology and Earth System Sciences, 2015Co-Authors: Vera Thiemig, Florian Pappenberger, B Bisselink, Jutta ThielenAbstract:The African Flood Forecasting System (AFFS) is a probabilistic Flood Forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFlood, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important Floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various Flood archives. Results showed that AFFS detected around 70 % of the reported Flood events correctly. In particular, the system showed good performance in predicting riverine Flood events of long duration (> 1 week) and large affected areas (> 10 000 km 2 ) well in advance, whereas AFFS showed limitations for small-scale and short duration Flood events. The case study for the Flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in Forecasting timing and severity of the Floods, gave an example of the clear and concise output products, and showed that the system is capable of producing Flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African Flood Forecasting system, although issues related to the practical implication will still need to be investigated.
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Visualizing probabilistic Flood Forecast information: Expert preferences and perceptions of best practice in uncertainty communication
Hydrological Processes, 2013Co-Authors: Florian Pappenberger, David Demeritt, Schalk Jan Van Andel, Elisabeth Stephens, Jutta Thielen, Peter Salamon, Fredrik Wetterhall, Lorenzo AlfieriAbstract:The aim of this article is to improve the communication of the probabilistic Flood Forecasts generated by hydrological ensemble prediction systems (HEPS) by understanding perceptions of different methods of visualizing probabilistic Forecast information. This study focuses on interexpert communication and accounts for differences in visualization requirements based on the information content necessary for individual users. The perceptions of the expert group addressed in this study are important because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to nonexperts. In this article, we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about the best practice for HEPS visualization and communication. We suggest that specific training on probabilistic Forecasting would foster use of probabilistic Forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic Forecasts and what they consider the essential information that should accompany plots and diagrams. In this article, we propose a list of minimum properties that, if consistently displayed with probabilistic Forecasts, would make the products more easily understandable. © 2012 John Wiley & Sons, Ltd.
Fredrik Wetterhall - One of the best experts on this subject based on the ideXlab platform.
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Willingness-to-pay for a probabilistic Flood Forecast : a risk-based decision-making game
2016Co-Authors: L. Arnal, Fredrik Wetterhall, M.h. Ramos, E. Coughlan, H.l. Cloke, E. Stephens, S-j Andel, Florian PappenbergerAbstract:Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the Forecaster and the user of the Forecasts. Many authors have demonstrated the added (economic) value of probabilistic Forecasts over deterministic Forecasts for a diversity of activities in the water sector (e.g. Flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of Flood protection mitigation, called “How much are you prepared to pay for a Forecast?”, will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic Forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a Forecast, the results of the game showed that the value (or the usefulness) of a Forecast depends on several factors, including the way users perceive the quality of their Forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having Forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological Forecasting community in the future.
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Willingness-to-pay for a probabilistic Flood Forecast: a risk-based decision-making game
Hydrology and Earth System Sciences, 2016Co-Authors: L. Arnal, Schalk Jan Van Andel, Fredrik Wetterhall, M.h. Ramos, H.l. Cloke, E. Stephens, E. Coughlan De Perez, Florian PappenbergerAbstract:Probabilistic hydro-meteorological Forecasts have over the last decades been used more frequently to communicate Forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the Forecaster and the user of the Forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic Forecasts across the water sector (e.g. Flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of Flood protection mitigation, called “How much are you prepared to pay for a Forecast?”. The game was played at several workshops in 2015, which were attended by operational Forecasters and academics working in the field of hydrometeorology. The aim of this game was to better understand the role of probabilistic Forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a Forecast, the results of the game show that the value (or the usefulness) of a Forecast depends on several factors, including the way users perceive the quality of their Forecasts and link it to the perception of their own performances as decision-makers.
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Visualizing probabilistic Flood Forecast information: Expert preferences and perceptions of best practice in uncertainty communication
Hydrological Processes, 2013Co-Authors: Florian Pappenberger, David Demeritt, Schalk Jan Van Andel, Elisabeth Stephens, Jutta Thielen, Peter Salamon, Fredrik Wetterhall, Lorenzo AlfieriAbstract:The aim of this article is to improve the communication of the probabilistic Flood Forecasts generated by hydrological ensemble prediction systems (HEPS) by understanding perceptions of different methods of visualizing probabilistic Forecast information. This study focuses on interexpert communication and accounts for differences in visualization requirements based on the information content necessary for individual users. The perceptions of the expert group addressed in this study are important because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to nonexperts. In this article, we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about the best practice for HEPS visualization and communication. We suggest that specific training on probabilistic Forecasting would foster use of probabilistic Forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic Forecasts and what they consider the essential information that should accompany plots and diagrams. In this article, we propose a list of minimum properties that, if consistently displayed with probabilistic Forecasts, would make the products more easily understandable. © 2012 John Wiley & Sons, Ltd.
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coupling ensemble weather predictions based on tigge database with grid xinanjiang model for Flood Forecast
Advances in Geosciences, 2011Co-Authors: L N Zhao, Y He, Zhijia Li, Fredrik Wetterhall, Hannah Cloke, Florian Pappenberger, D ManfulAbstract:Abstract. The incorporation of numerical weather predictions (NWP) into a Flood Forecasting system can increase Forecast lead times from a few hours to a few days. A single NWP Forecast from a single Forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for Flood Forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for Flood Forecast. This paper presents a case study using the coupled Flood Forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of Flood Forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of Flood events several days ahead.
Roman Krzysztofowicz - One of the best experts on this subject based on the ideXlab platform.
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probabilistic Flood Forecast exact and approximate predictive distributions
Journal of Hydrology, 2014Co-Authors: Roman KrzysztofowiczAbstract:Summary For quantification of predictive uncertainty at the Forecast time t 0 , the future hydrograph is viewed as a discrete-time continuous-state stochastic process { H n : n = 1 , … , N } , where H n is the river stage at time instance t n > t 0 . The probabilistic Flood Forecast (PFF) should specify a sequence of exceedance functions { F ‾ n : n = 1 , … , N } such that F ‾ n ( h ) = P ( Z n > h ) , where P stands for probability, and Z n is the maximum river stage within time interval ( t 0 , t n ] , practically Z n = max { H 1 , … , H n } . This article presents a method for deriving the exact PFF from a probabilistic stage transition Forecast (PSTF) produced by the Bayesian Forecasting system (BFS). It then recalls (i) the bounds on F ‾ n , which can be derived cheaply from a probabilistic river stage Forecast (PRSF) produced by a simpler version of the BFS, and (ii) an approximation to F ‾ n , which can be constructed from the bounds via a recursive linear interpolator (RLI) without information about the stochastic dependence in the process { H 1 , … , H n } , as this information is not provided by the PRSF. The RLI is substantiated by comparing the approximate PFF against the exact PFF. Being reasonably accurate and very simple, the RLI may be attractive for real-time Flood Forecasting in systems of lesser complexity. All methods are illustrated with a case study for a 1430 km 2 headwater basin wherein the PFF is produced for a 72-h interval discretized into 6-h steps.
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probabilistic Flood Forecast bounds and approximations
Journal of Hydrology, 2002Co-Authors: Roman KrzysztofowiczAbstract:Abstract The probabilistic river stage Forecast (PRSF) specifies a sequence of exceedance functions { Ψ n :n=1,…,N} such that Ψ n (h n )=P(H n >h n ), where Hn is the river stage at time instance tn, and P stands for probability. The probabilistic Flood Forecast (PFF) should specify a sequence of exceedance functions { F n :n=1,…,N} such that F n (h)=P(Z n >h), where Zn is the maximum river stage within time interval (t0,tn], practically Zn=max{H1,…,Hn}. In the absence of information about the stochastic dependence structure of the process {H1,…,HN}, the PFF cannot be derived from the PRSF. This article presents simple methods for calculating bounds on F n and approximations to F n using solely the marginal exceedance functions Ψ 1 ,…, Ψ n . The methods are illustrated with tutorial examples and a case study for a 1430 km2 headwater basin wherein the PRSF is for a 72-h interval discretized into 6-h steps.
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recent advances associated with Flood Forecast and warning systems
Reviews of Geophysics, 1995Co-Authors: Roman KrzysztofowiczAbstract:Floods remain one of the most frequent and devastating natural hazards worldwide. In the United States alone, there are 20,000 Flood-prone communities; 3,000 of them receive site-specific Flood Forecasts from the National Weather Service (NWS), and 1,000 have local warning systems; the remaining communities receive county-wide warnings. Between 1965 and 1985, Floods accounted for 63% of the federally declared disasters (337 out of 531), took 1,767 lives, and caused $5 billion worth of damage annually, on the average. The Great Flood of 1993, documented by the National Oceanic and Atmospheric Administration [NOAA, 1994], provided a vivid demonstration of Cromwell's rule: one should never assign the exceedance probability of zero to any prior observation. The duration (from March to November), the extent (over nine states) and the magnitude (exceeding previous Floods of record at 95 Forecast points) made this the most catastrophic Flooding in modern U.S. history: 54,000 persons were evacuated, 50,000 homes were damaged, and economic losses of $15–20 billion have been estimated. The event also tested the limits of the Nation's Forecast and warning services as Flood stages were exceeded at about 500 Forecast points.
Akira Mano - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty assessment for short-term Flood Forecasts in Central Vietnam
River Basin Management VI, 2011Co-Authors: Hoai Nam, Keiko Udo, Akira ManoAbstract:Accurate Flood Forecasts with greater lead-times are very important in development of Flood mitigation measures, especially in short response catchments. The Flood Forecasts based on numerical weather prediction (NWP) and runoff models have demonstrated its breakthrough to extend the Forecast lead-time over traditional Flood Forecast methods, for instance, those are based on rainfall information from rain-gages. However, given the imperfectness either in the specification of initial states or in the formulation of NWP models, rainfall prediction for example, the driving factor for Flood Forecast, has been recognised as a major source of uncertainty in the generation of river flow. This paper presents the uncertainty assessment for a short-term Flood Forecast model that is coupled by the short-range global NWP model, 0.5 degree spatial resolution, with the distributed rainfall runoff model, for a large sized basin (Thu Bon River, 3,150km) located in Central Vietnam. To reduce uncertainty of runoff Forecasts by means of increasing the rainfall prediction skill, first the model output statistic technique has been employed to downscale the large scale prediction Forecasts directly derived from the NWP model output to the basin scale by using the artificial neural network with the back-propagation method. Skill scores of the downscaled precipitation are investigated with increasing lead-time and compared to those obtained using the large scale precipitation Forecasts. Uncertainties of runoff prediction are assessed by quantifying the relative error of Forecasts and estimates of confidence interval for the mean error. Results show that larger uncertainties along with the Forecast lead-times are observed; however, the model is able to predict reliable river flows with lead-time of the order of 6-18 hours. This demonstrates great benefits in Flood Forecasting practices for many developing countries where ground weather observation is scarce and access to high resolution NWP models is limited.
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DEVELOPMENT OF SHORT-TERM Flood Forecast MODEL - A CASE STUDY FOR CENTRAL VIETNAM
2010Co-Authors: Hoai Nam, Keiko Udo, Akira ManoAbstract:This paper presents the development of a short-term Flood Forecast model by coupling the relatively high resolution (0.5 O ) global numerical weather prediction model (NWP) with the distributed rainfall runoff model. The case study was conducted for a medium sized basin (the Ve River) located in Central Vietnam. Model output statistic (MOS) was applied to improve quantitative precipitation Forecast (QPF) derived from the NWP model. Separate regression equations for single storm events and continuous storm events were formulated based on training data of the wet season, 2008. Results of 24-hour lead time Flood Forecast using MOS-derived QPF were comparable to those obtained using raingages. Model validation demonstrated that the short-term Flood Forecast model is encouraging for the further extension of Flood Forecast lead time at global-scale applicability.
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Uncertainty on a Short-Term Flood Forecast with Rainfall-Runoff Model
Advances in Water Resources and Hydraulic Engineering, 2009Co-Authors: Hadi Kardhana, Akira ManoAbstract:Precipitation Forecast has been become a useful asset for Flood prediction using rainfall-runoff model. An uncertainty that appears on the Forecast affects accuracy of Flood prediction, added to that is which possessed in the rainfall-runoff model. The case is in Shichikashuku Dam basin in Japan. The precipitation Forecast is a product of short range Forecast of Japan operational numerical weather prediction based on Mesoscale Model (MSM) and Regional Scale Model (RSM). The rainfall-runoff model based on distributed tank model. This research estimates total uncertainty by quantifying mean error and standard deviation on the precipitation and discharge Forecast. The result has shown that the precipitation Forecast is more uncertain than discharge’s. Uncertainty is significantly increased after twelve hour and draws a common characteristic between both models.
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Flood Forecast based on numerical weather prediction and distributed runoff model
WIT Transactions on Ecology and the Environment, 2007Co-Authors: Hadi Kardhana, H Tatesawa, Akira ManoAbstract:The accuracy of quantitative precipitation Forecast from numerical weather prediction (NWP) grows as higher resolutions are achieved by the computation capacity of supercomputers. Various distributed dataset, globally and locally, with better spatial and temporal resolution has been rapidly developed. The objective of this paper is to have a Flood Forecast model that utilises these great benefits and gives reliable accuracy. Another asset of a Flood Forecast model is a conceptual-distributed runoff model; it has been chosen because of its simplicity. Calibration and validation of the model has been met by good agreement for events in 2002 in the case of a 237 km operational scale basin. These results are based on input Grid Point Value (GPV) precipitation of Japan radar observation. Forecasted Precipitation was based on a GPV Mesoscale Model of Japan NWP. It had an 18 hour lead time and updated four times a day. Flood Forecasting based on input from Forecasted precipitation shows that the accuracy decreases as lead-time increased. It is clear that Flood Forecasting depends on precipitation Forecast accuracy. Observed precipitation and discharge are used for model updating to determine initial data for Flood Forecasting. The simplicity of the runoff model gives advantage on water content estimation in soil storage. It is necessary because the runoff model might have basic errors and it needs to have better initial data. Updating calculated discharge with observed discharge approximates the estimation. By estimating more correctly, the model shows to be more reliable.
Schalk Jan Van Andel - One of the best experts on this subject based on the ideXlab platform.
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Willingness-to-pay for a probabilistic Flood Forecast: a risk-based decision-making game
Hydrology and Earth System Sciences, 2016Co-Authors: L. Arnal, Schalk Jan Van Andel, Fredrik Wetterhall, M.h. Ramos, H.l. Cloke, E. Stephens, E. Coughlan De Perez, Florian PappenbergerAbstract:Probabilistic hydro-meteorological Forecasts have over the last decades been used more frequently to communicate Forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the Forecaster and the user of the Forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic Forecasts across the water sector (e.g. Flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of Flood protection mitigation, called “How much are you prepared to pay for a Forecast?”. The game was played at several workshops in 2015, which were attended by operational Forecasters and academics working in the field of hydrometeorology. The aim of this game was to better understand the role of probabilistic Forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a Forecast, the results of the game show that the value (or the usefulness) of a Forecast depends on several factors, including the way users perceive the quality of their Forecasts and link it to the perception of their own performances as decision-makers.
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Visualizing probabilistic Flood Forecast information: Expert preferences and perceptions of best practice in uncertainty communication
Hydrological Processes, 2013Co-Authors: Florian Pappenberger, David Demeritt, Schalk Jan Van Andel, Elisabeth Stephens, Jutta Thielen, Peter Salamon, Fredrik Wetterhall, Lorenzo AlfieriAbstract:The aim of this article is to improve the communication of the probabilistic Flood Forecasts generated by hydrological ensemble prediction systems (HEPS) by understanding perceptions of different methods of visualizing probabilistic Forecast information. This study focuses on interexpert communication and accounts for differences in visualization requirements based on the information content necessary for individual users. The perceptions of the expert group addressed in this study are important because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to nonexperts. In this article, we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about the best practice for HEPS visualization and communication. We suggest that specific training on probabilistic Forecasting would foster use of probabilistic Forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic Forecasts and what they consider the essential information that should accompany plots and diagrams. In this article, we propose a list of minimum properties that, if consistently displayed with probabilistic Forecasts, would make the products more easily understandable. © 2012 John Wiley & Sons, Ltd.