Comparing Three Hydrological Models for Flash Flood Simulations in 13 Humid and Semi-humid Mountainous Catchments

2021 ◽  
Vol 35 (5) ◽  
pp. 1547-1571
Author(s):  
Xiaoyan Zhai ◽  
Liang Guo ◽  
Ronghua Liu ◽  
Yongyong Zhang ◽  
Yongqiang Zhang
Keyword(s):  
Flash Flood ◽  
Hydrological Models ◽  
Mountainous Catchments ◽  
Flood Simulations

Impact of the models structure uncertainty in flood simulations: Simbach case study

2020 ◽  
Author(s):  
Qing Lin ◽  
Jorge Leandro ◽  
Markus Disse ◽  
Daniel Sturm
Keyword(s):  
Flash Flood ◽  
Drainage System ◽  
Model Structure ◽  
Flood Simulation ◽  
Hydraulic Models ◽  
Uncertainty Factor ◽  
Model Component ◽  
Model Structure Uncertainty ◽  
Flood Simulations ◽  
Model Structures

<p>The quantification of model structure uncertainty on hydraulic models is very important for flash flood simulations. The choice of an appropriate model structure complexity and assessment of the impacts due to infrastructure failure can have a huge impact on the simulation results. To assess the risk of flash floods, coupled hydraulic models, including 1D-sewer drainage and 2D-surface run-off models are required for urban areas because they include the bidirectional water exchange, which occurs between sewer and overland flow in a city [1]. By including various model components, we create different model structures. For example, modelling the inflow to the city with the 2D surface-runoff or with the delineated 1D model; including the sewer system or use a surrogate as an alternative; modifying the connectivity of manholes and pumps; or representing the drainage system failures during flood events. As the coupling pattern becomes complex, quantifying the model structure uncertainty is essential for the model structure evaluation. If one model component leads to higher model uncertainty, it is reasonable to conclude that the new component has a large impact in our model and therefore needs to be accounted for; if one component has a less impact in the overall uncertainty, then the model structure can be simplified, by removing that model component.</p> <p>In this study, we set up seven different model structures [2] for the German city of Simbach. By comparison with two inflow calculation types (1D-delineated inflow or 2D-catchment), the existence of drainage system and infrastructure failures, the Model Uncertainty Factor (MUF) is calculated to quantify the model structure uncertainties and further trade-off values with Parameter Uncertainty Factor (PUF) [3]. Finally, we can obtain a more efficient hydraulic model with the essential model structure for urban flash flood simulation.</p> <p> </p> <ol>1. Leandro, J., Chen, A. S., Djordjevic, S., and Dragan, S. (2009). "A comparison of 1D/1D and 1D/2D coupled hydraulic models for urban flood simulation." Journal of Hydraulic Engineering-ASCE, 6(1):495-504.</ol> <ol>2. Leandro, J., Schumann, A., and Pfister, A. (2016). A step towards considering the spatial heterogeneity of urban, key features in urban hydrology flood modelling. J. Hydrol., Elsevier, 535 (4), 356-365.</ol> <ol>3. Van Zelm, R., Huijbregts, M.A.J. (2013). Quantifying the trade-off between parameter and model structure uncertainty in life cycle impact assessment, Environ. Sci. Technol., 47(16), pp. 9274-9280.</ol> <p> </p>

scholarly journals Flash flood simulations for an Egyptian city - mitigation measures and impact of infiltration

2020 ◽  
Vol 17 (5) ◽  
pp. 396-406 ◽  
Author(s):  
Franziska Tügel ◽  
Ilhan Özgen-Xian ◽  
Ester Marafini ◽  
Ahmed Hadidi ◽  
Reinhard Hinkelmann
Keyword(s):  
Flash Flood ◽  
Mitigation Measures ◽  
Flood Simulations

scholarly journals Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland)

2019 ◽  
Vol 19 (1) ◽  
pp. 19-40 ◽  
Author(s):  
Manuel Antonetti ◽  
Christoph Horat ◽  
Ioannis V. Sideris ◽  
Massimiliano Zappa
Keyword(s):  
Predictive Power ◽  
Hydrological Model ◽  
Expert Knowledge ◽  
Flash Flood ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Short Period ◽  
Set Up

Abstract. Flash floods evolve rapidly during and after heavy precipitation events and represent a potential risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a flash-flood forecasting chain is presented based on (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip); (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E); (iii) operationally available soil moisture estimations from the PREVAH hydrological model; and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types, which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is event-based and parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-sized flash-flood-prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of runoff types, which allowed sensitivity of the forecast performance to the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including an original version of the runoff generation module of PREVAH calibrated for one event. Results showed a low sensitivity of the predictive power to the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of runoff types with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill to a prediction system including a conventional hydrological model. In the small nested subcatchments, although the process-based forecasting chains outperformed the original runoff generation module, no forecasting chain showed satisfying skill in the sense that it could be useful for decision makers. Despite the short period available for evaluation, preliminary outcomes of this study show that operational flash-flood predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

FUZZY LOGIC BASED FLASH FLOOD FORECAST

2020 ◽  
Author(s):  
Petr Janál ◽  
◽  
Tomáš Kozel ◽  
Keyword(s):  
Fuzzy Logic ◽  
Czech Republic ◽  
Flash Flood ◽  
Fuzzy Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
The Czech Republic ◽  
Flood Model ◽  
Flash Flood Forecasting

The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurrence was assembled on the bases of the fuzzy logic. The model covers whole area of the Czech Republic and the nearest surroundings. The domain is divided into 3245 small catchments of the average size of 30 km2. Real flood episodes were used for the calibration and future flood events can be used for recalibration (principle of adaptability). The model consists of two fuzzy inference systems (FIS). The catchment predisposition for the flash flood occurrence is evaluated by the first FIS. The geomorphological characteristics and long-term meteorological statistics serve as the inputs. The second FIS evaluates real-time data. The inputs are: The predisposition for flash flood occurrence (gained from the first FIS), the rainfall intensity, the rainfall duration and the antecedent precipitation index. The meteorological radar measurement and the precipitation nowcasting serve as the precipitation data source. Various precipitation nowcasting methods are considered. The risk of the flash flood occurrence is evaluated for each small catchment every 5 or 10 minutes (the time step depends on the precipitation nowcasting method). The Fuzzy Flash Flood model is implemented in the Czech Hydrometeorological Institute (CHMI) – Brno Regional Office. The results are available for all forecasters at CHMI via web application for testing. The huge uncertainty inherent in the flash flood forecasting causes that fuzzy model outputs based on different nowcasting methods could vary significantly. The storms development is very dynamic and hydrological forecast could change a lot of every 5 minutes. That is why the fuzzy model estimates are intended to be used by experts only. The Fuzzy Flash Flood model is an alternative tool for the flash flood forecasting. It can provide the first hints of danger of flash flood occurrence within the whole territory of the Czech Republic. Its main advantage is very fast calculation and possibility of variant approach using various precipitation nowcasting inputs. However, the system produces large number of false alarms, therefore the long-term testing in operation is necessary and the warning releasing rules must be set.

scholarly journals Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash flood event in Genoa, Italy, in the framework of the DRIHM project

2015 ◽  
Vol 15 (3) ◽  
pp. 537-555 ◽  
Author(s):  
A. Hally ◽  
O. Caumont ◽  
L. Garrote ◽  
E. Richard ◽  
A. Weerts ◽  
...  
Keyword(s):  
Flash Flood ◽  
Convective Precipitation ◽  
Limited Area ◽  
Hydrological Models ◽  
Atmospheric Models ◽  
Meteorological Model ◽  
Ensemble Simulations ◽  
Model Configuration ◽  
The Eu ◽  
Model Approach

Abstract. The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its ability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developed within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH and AROME) and a downscaling algorithm (RainFARM) in a seamless fashion. In addition to this multi-model configuration, some of the models were run in probabilistic mode, thus giving a comprehensive account of modelling errors and a very large amount of likely hydrometeorological scenarios (> 1500). The multi-model approach proved to be necessary because, whilst various aspects of the event were successfully simulated by different models, none of the models reproduced all of these aspects correctly. It was shown that the resulting set of simulations helped identify key atmospheric processes responsible for the large rainfall accumulations over the Bisagno basin. The DRIHM e-Science environment facilitated an evaluation of the sensitivity to atmospheric and hydrological modelling errors. This showed that both had a significant impact on predicted discharges, the former being larger than the latter. Finally, the usefulness of the set of hydrometeorological simulations was assessed from a flash flood early-warning perspective.

scholarly journals The use of distributed hydrological models for the Gard 2002 flash flood event: Analysis of associated hydrological processes

2010 ◽  
Vol 394 (1-2) ◽  
pp. 162-181 ◽  
Author(s):  
Isabelle Braud ◽  
Hélène Roux ◽  
Sandrine Anquetin ◽  
Marie-Madeleine Maubourguet ◽  
Claire Manus ◽  
...  
Keyword(s):  
Flash Flood ◽  
Hydrological Processes ◽  
Flood Event ◽  
Event Analysis ◽  
Hydrological Models ◽  
Distributed Hydrological Models

scholarly journals Flash Flood Simulations Based on Shallow Water Equations to Investigate Protection Measures for El Gouna, Egypt

10.29007/kh16 ◽  
2018 ◽  
Author(s):  
Ester Marafini ◽  
Franziska Tügel ◽  
Ilhan Özgen ◽  
Reinhard Hinkelmann ◽  
Michele La Rocca
Keyword(s):  
Shallow Water ◽  
Flash Flood ◽  
Flash Floods ◽  
Flood Event ◽  
Water Model ◽  
Protection Measures ◽  
Environmental Disasters ◽  
Whole Process ◽  
Flood Simulations ◽  
The City

Severe and sudden events like flash floods are considered to be one of the most hazardous environmental disasters. Therefore, predicting the whole process of flooding is fundamental to prevent urban damages. In this context, the simulation of flash floods is an important tool to analyse the flow processes in order to find solutions to the problem. In this work, a case study of the flash flood event of 9th March 2014 in the city of El Gouna in Egypt was carried out using the Hydroinformatics Modeling System (hms), a two-dimensional (2D) shallow water model developed at the Chair of Water Resources Management and Modeling of Hydrosystems, Technische Universität Berlin. The flooding processes are simulated in great detail on unstructured grids. The aim of this work is to investigate the flow field around the settlement of the study area, when structures such as storage basins and dams are adopted as protection measures for the city. Different scenarios are analyzed to find out the most suitable one, which is able to minimize the risk during the flash flood event.

scholarly journals A physically-based distributed karst hydrological model (QMG model-V1.0) for flood simulations

2021 ◽  
Author(s):  
Ji Li ◽  
Daoxian Yuan ◽  
Fuxi Zhang ◽  
Yongjun Jiang ◽  
Jiao Liu ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Complex Model ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Average Value ◽  
Distributed Models ◽  
Physically Based ◽  
Flood Simulations ◽  
Distributed Hydrological Models ◽  
Hydrogeological Data

Abstract. Karst trough valleys are prone to flooding, primarily because of the unique hydrogeological features of karst landform, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially so for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting the Karst flooding is highly dependent on distributed structrues modeling, complicated boundary parameters setting, and tremendous hydrogeological data processing that is both time and computational power consuming. Proposed here is a distributed physically-based karst hydrological model, known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and the parameters are less than those of the current distributed models. This allows modeling in karst areas with only a small amount of necessary hydrogeological data. 18 flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations, for which the average value of Nash–Sutcliffe coefficient was 0.92. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient, and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding by the proposed QMG model promotes a better mechanistic depicting of runoff generation and confluence in karst trough valleys.

scholarly journals Hydrometeorological multi-model ensemble simulations of the 4 November 2011 flash-flood event in Genoa, Italy, in the framework of the DRIHM project

2014 ◽  
Vol 2 (11) ◽  
pp. 6653-6701
Author(s):  
A. Hally ◽  
O. Caumont ◽  
L. Garrote ◽  
E. Richard ◽  
A. Weerts ◽  
...  
Keyword(s):  
Flash Flood ◽  
Convective Precipitation ◽  
Limited Area ◽  
Hydrological Models ◽  
Atmospheric Models ◽  
Meteorological Model ◽  
Ensemble Simulations ◽  
Model Configuration ◽  
The Eu ◽  
Model Approach

Abstract. The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its capability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developped within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS, and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH, and AROME) and a downscaling algorithm (RainFARM) in a seamless fashion. In addition to this multi-model configuration, some of the models were run in probabilistic mode, thus allowing a comprehensive account of modelling errors and a very large amount of likely hydrometeorological scenarios (>1500). The multi-model approach proved to be necessary because, whilst various aspects of the event were successfully simulated by different models, none of the models reproduced all of these aspects correctly. It was shown that the resulting set of simulations helped identify key atmospheric processes responsible for the large rainfall accumulations over the Bisagno basin. The DRIHM e-Science environment facilitated an evaluation of the sensitivity to atmospheric and hydrological modelling errors. This showed that both had a significant impact on predicted discharges, the former being larger than the latter. Finally, the usefulness of the set of hydrometeorological simulations was assessed from a flash-flood early-warning perspective.

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