See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/334553144 Discharge Interval method for uncertain flood forecasts using a flood model chain: City of Kulmbach Article in Journal of Hydroinformatics · July 2019 DOI: 10.2166/hydro.2019.131 CITATION READS 1 82 7 authors, including: Md Nazmul Azim Beg J. Leandro Tulane University Technische Universität München 16 PUBLICATIONS 31 CITATIONS 108 PUBLICATIONS 903 CITATIONS SEE PROFILE SEE PROFILE Punit Bhola Iris Konnerth Technische Universität München Technische Universität München 27 PUBLICATIONS 54 CITATIONS 8 PUBLICATIONS 21 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: PRESSURES AND VELOCITIES MEASUREMENTS IN PROTOTYPE. THE CASE OF FOZ TUA DAM PLUNGE POOL./Instrumentação da Barragem do Tua View project Experimental and numerical investigation of pluvial flood flows and pollutant transport at and between system interface points. View project All content following this page was uploaded by Md Nazmul Azim Beg on 18 September 2019. The user has requested enhancement of the downloaded file. ELECTRONIC OFFPRINT Use of this pdf is subject to the terms described below This paper was originally published by IWA Publishing. The author’s right to reuse and post their work published by IWA Publishing is defined by IWA Publishing’s copyright policy. 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Full details can be found here: http://iwaponline.com/content/rights-permissions Please direct any queries regarding use or permissions to [email protected] 925 © IWA Publishing 2019 Journal of Hydroinformatics | 21.5 | 2019 Discharge Interval method for uncertain flood forecasts using a flood model chain: city of Kulmbach Md Nazmul Azim Beg, Jorge Leandro, Punit Bhola, Iris Konnerth, Winfried Willems, Rita F. Carvalho and Markus Disse ABSTRACT Real-time flood forecasting can help authorities in providing reliable warnings to the public. Ensemble Md Nazmul Azim Beg (corresponding author) Rita F. Carvalho prediction systems (EPS) have been progressively used for operational flood forecasting by European Marine and Environmental Sciences Centre, Department of Civil Engineering, hydrometeorological agencies in recent years. This process, however, is non-deterministic such that University of Coimbra, – uncertainty sources need to be considered before issuing forecasts. In this study, a new Rua Luís Reis Santos Pólo II, 3030-788 Coimbra, Portugal methodology for flood forecasting named Discharge Interval method is proposed. This method uses E-mail: [email protected] at least one historical event hindcast data, run in several ensembles and selects a pair of best Jorge Leandro Punit Bhola ensemble discharge results for every certain discharge level. Later, the method uses the same Iris Konnerth parameter settings of the chosen ensemble discharge pair to forecast any certain flood discharge Markus Disse Chair of Hydrology and River Basin Management, level. The methodology was implemented within the FloodEvac tool. The tool can handle calibration/ Technical University of Munich, Arcisstrasse 21, 80333 München, validation of the hydrological model (LARSIM) and produces real-time flood forecasts with the Germany fl associated uncertainty of the ood discharges. The proposed methodology is computationally Winfried Willems fi efficient and suitable for real-time forecasts with uncertainty. The results using the Discharge Interval IAWG, Of ce for Applied Hydrology, Water Resources Management and method were found comparable to the 90th percentile forecasted discharge range obtained with the Geoinformatics, Alte Landstr. 12-14, 85521 Ottobrunn, Ensemble method. Germany Key words | calibration, forecasting, hydrological modelling, uncertainty, validation INTRODUCTION The economic loss within the European Union due to flood atmospheric physics and also because of the limited resol- issue exceeded 60 billion euros from 1998 to 2009 with ution of simulated atmospheric dynamics (Lorenz ; 1,126 fatalities (EEA ; Kauffeldt et al. ) making Buizza et al. ; Kauffeldt et al. ). Uncertainty may flood resilience one of the prominent issues for many also arise from the inherent issues with the structures of a cities. The loss increased in the past decade as a result of cli- hydrological model (Renard et al. ). Combining these mate change, increasing city population and increase in per reasons, hydrological forecast models contain uncertainty capita wealth (EEA , ). Improved disaster risk man- to a great extent (Beven & Binley ; Boyle et al. ; agement through early warning information is one of the Refsgaard et al. ; Wani et al. ). Assessing uncertainty critical procedures to reduce flood losses. Significant suc- in the model results is an integrated part for hydrologic mod- cess in flood forecasts lies in the accuracy to predict the elling and considered necessary in research and practice, state of future atmospheric conditions. Yet, numerical especially when models are used for water management weather forecasts are still inaccurate due to the limitation issues (Beven ; Refsgaard et al. , ; Vanden- in mathematically representing the non-linear and complex berghe et al. ; Todini ; Barbetta et al. ). doi: 10.2166/hydro.2019.131 926 M. N. A. Beg et al. | Discharge Interval method for uncertain flood forecasts: city of Kulmbach Journal of Hydroinformatics | 21.5 | 2019 Different methods have been developed for uncertainty With the advancement of radar technology, it is possible quantification in flood forecasting. Some methods focus on to obtain spatial rainfall data (Codo & Rico-Ramirez ). the model input uncertainty such as meteorological input Recent improvements of radar rainfall data accuracy and forcing and model initial states as, e.g., van Andel et al. resolution have shown possibilities (Pedersen et al. ) () and Li et al. (), respectively, and others focus to replace point rain gauge data by spatially variable rainfall on the hydrological model parameters such as Benke et al. forecasts in the near future. However, rain gauge data are () as well as the hydrological model itself (Coccia & still considered better regarding measurement accuracy Todini ; Deletic et al. ). (Muthusamy et al. ). One of the significant challenges Among different uncertainty estimation approaches, of using rain gauge data is to convert the point measure- Bayesian inference methods are very popular as they can uti- ments to spatially distributed data as most of the popular lise multiple parameter values within model structure hydrological models are lumped catchment area models limitations. One example is the Generalized Likelihood and for which the point measurements must be upscaled Uncertainty Estimation (GLUE) methodology, described for the whole catchment area. The uncertainty contribution by Beven & Binley (). A large number of Monte Carlo of the spatial distribution error becomes important when simulations are required in GLUE, yet accepting parameter point rainfall data are needed to be interpolated to an set criteria is subjective and based on a user-defined area. Geospatial simulations such as Kriging is also a threshold (Dotto et al. ). The results can be sensitive to better option for spatial rainfall interpolations as this the choice of the threshold value (Freni et al. ). The method considers spatial dependence structures of the Bayesian Model Averaging (BMA) method is considered a data (Mair & Fares ; Ly et al. ). Kriging with external better approach as it optimises the model posterior density drifts showed effective and reliable ways to improve the by estimating different weights (Raftery et al. ; Vrugt quality of spatial rainfall distribution (Berndt et al. ; & Robinson ). BMA applies predictive probability den- Cecinati et al. ). sity function considering weights of discrete bias corrected In the FloodEvac project, a real-time flood forecasting forecasts. This method reflects the relative contributions to tool was developed which can forecast flood discharges the predictive skill of the model by discouraging ensembles and flood extents with the inclusion of uncertainties (Disse with lower weights, which can be useful in reducing compu- et al. ). The tool can be utilised using Ensemble-based tation costs of running large numbers of ensembles (Raftery prediction to consider both model input and model par- et al. ). Hydrological Ensemble Prediction System ameter uncertainties with flood discharge forecast. The (widely known as HEPS or only EPS: Ensemble Prediction current study explains the hydrological forecast efficiencies System) is one of the most practised methodologies to pre- and proposes a new alternate methodology to reduce fore- dict river flow, which is mainly based on the BMA cast computational time by optimising the ensembles of method. In this process,