The major socioeconomic issue affecting the nation's economy is flooding. The use of hydrologic models to predict floods has been the subject of numerous well-founded research, but little is still known about how spatial variability of rainfall affects the flood modeling. The purpose of this study is to comprehend, using the spatial variability index, how rainfall spatial variability affects flood characteristics and its importance in the selection of the hydrological models. The spatial variability index classifies the rainfall events into spatially homogenous (Class A) or spatially varying (Class B) by analyzing the spatial variability of rainfall and catchment properties. Further in this study, both lumped and distributed models have been set-up to understand whether the segregation of events prior to the flood modeling improves the model efficiency or not. Both the models were calibrated separately for the Class A and Class B events. The results shown that Class A events performed better in the lumped model with percentage error in peak flow (PEPF) of 18.45% and 20.32% in calibration and validation respectively. Whereas, the Class B events performance was better in the distributed model with PEPF of 11.5% and 15.85% in calibration and validation. These results were compared against the model calibrated using the traditional method, i.e. without segregating the events. The results show that performance of the lumped model is deteriorated. Similarly, distributed model performance was better when the class B events are separated instead of the combined events. Therefore, segregation of the rainfall events prior to the flood modeling helps in improving the model selection and its performance, which also reduces the assumptions in the model selection.

How to cite: Indhu, D. and Vema, V. K.: Does the spatial variability of the rainfall events affect the efficacy of the hydrological models?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-143, https://doi.org/10.5194/egusphere-egu23-143, 2023.

Flash floods resulting from torrential precipitation events cause devastating destruction and loss of human lives, as numerous events in Central Europe have demonstrated in recent years. To assess the damage potential of such events, using hydrodynamic 2D-models is the most accurate method to simulate all hydraulic and hydrological processes at the earth's surface.

Beside infiltration, roughness is the key parameter for hydraulic surface runoff simulations. Particularly roughness for near surface runoff is affected by high uncertainties, as numerous available values are only valid for higher water depths. The choice of the roughness coefficient influences the flow velocity and therefore the accumulation of surface runoff. This potentially leads to an inaccurate planning of flood protection measures.

Based on artificial rainfall experiments on natural hillslopes, available in literature, we estimate the roughness coefficient (Manning’s n). The experiments have been conducted on a wide range of different sites, whose properties differ in vegetation type (pasture, crops, bare soil), vegetation density (0-100%) and slope (10-30%). A framework evaluating rainfall intensity and surface runoff with the aim to separate the impact of infiltration rate and roughness on the shape of the hydrograph is developed. This avoids complex measurements of flow velocity and water depth during the field experiments.

To verify the validity of the framework, three water depth-dependent formulations of roughness and a constant Manning coefficient are used to simulate the measured hydrograph with an idealized hydraulic 2D-model. This finally results in a robust parameterisation of near surface roughness for a water depth below 1 cm. A strong dependence of the roughness coefficient on the degree of vegetation cover and a correlation between rain intensity and roughness was found. In addition, the temporal change of the infiltration rate during the rainfall experiment could precisely be calculated through the determination of roughness. Therefore, the developed framework also allows a better calibration of infiltration models based on artificial rainfall experiments. In conclusion, this study reduces uncertainties in 2D-hydraulic flash flood modeling by providing empirical near surface roughness coefficients.

How to cite: Feldmann, D., Laux, P., Heckl, A., Schindler, M., and Kunstmann, H.: Determining near surface roughness based on artificial precipitation experiments on natural hillslopes for the application in hydrodynamic flash flood modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2261, https://doi.org/10.5194/egusphere-egu23-2261, 2023.

Precipitation scenario analysis is a crucial step in flood risk assessment, in which storm events with different probabilities are defined and used as input for the hydrological/hydrodynamic calculations. Rainfall generators may serve as a basis for the precipitation analysis. With the increase in the use of high resolution spatially-explicit hydrological/hydrodynamic models in flood risk calculations, demand for synthetic gridded precipitation input is increasing. In this work, we present a dynamic spatiotemporal rainfall generator. The model is capable of generating catchment-scale rainfields containing moving storms, which enable physically-plausible and spatiotemporally coherent precipitation events. This is achieved by the tools event-based approach, where dynamic storms are identified as clusters of related data that occur at different locations in space and time, and are then used as basis for event regeneration. The implemented methodology, mainly inspired by Dierden et al. (2019), provides an improvement in the spatial coherence of precipitation extremes, which can in turn be beneficial in flood risk calculations.

The model has been validated under different databases such as the radar-based RADALON dataset or spatially-interpolated historical raingauge timeseries of different catchments in Germany, which is also presented in this work. The validation indicates the models ability to adequately preserve observed storm statistics in the generated timeseries. The generator is developed as an extension to the state-of-the-science flood risk modelling tool ProMaIDes (Promaides 2023). The model also puts great focus on user accessibility with offering features such as an easy installation process, support for most operating systems, a user interface and an online user manual.

Diederen, D., Liu, Y., 2020. Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes. Stoch Environ Res Risk Assess 34, 1369–1383. https://doi.org/10.1007/s00477-019-01724-9

ProMaIDes (2023): Protection Measures against Inundation Decision support. https://promaides.h2.de

How to cite: Khosh Bin Ghomash, S., Bachmann, D., Caviedes-Voullième, D., and Hinz, C.: Introducing a dynamic spatiotemporal rainfall generator for flood risk analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2599, https://doi.org/10.5194/egusphere-egu23-2599, 2023.

The length and quality of observed discharge data is frequently insufficient to derive robust extreme discharge values, which are crucial for water management planning for the present and future climates. Using long timeseries of synthetic weather data generated by stochastic weather generators (SWGs) as input to rainfall-runoff models to derive extreme discharge peaks could be of value for basins with missing or short observation timeseries. While multi-site generators preserve spatial correlation between stations, their complexity also limits them in other ways, such as implementing only simple parametric distributions that don’t adequately represent tails of distributions and extreme events. Single-site generators offer more complex parametric distributions, but don’t preserve correlation between stations, making them unsuitable for distributed hydrological modeling. In the context of hydrological modeling with emphasis on extremes, the question arises if the lumped output of a multi-site generator outperforms a single-site generator in combination with a lumped hydrological model, or if the advantages of a heavy-tailed distribution can outweigh the averaging across a heterogenous catchment. This work examines the transfer of synthetic weather data into runoff extremes in the alpine watershed of the Austrian Ybbs River by driving the rainfall runoff model COSERO with meteorological timeseries from the single- and multi-site SWGs WeaGETS and MulGETS. A GEV distribution was fit to each timeseries of annual runoff maxima to derive events with return periods of 30, 100 and 300 years. The single-site generators and their superior parametric distribution functions did not outweigh the averaging effect, and hydrological simulations were greatly biased, significantly underestimating flood peaks. However, also the results of the flood frequency analysis using multi-site synthetic data underestimated the results using observed data for all return periods by at least 30 %. The findings show that the application of single- and multi-site SWGs to estimate runoff extremes may not be applicable and must be critically reviewed.

How to cite: Ehrendorfer, C. and Herrnegger, M.: Applying single- and multi-site weather generators for the estimation of extreme floods in the Austrian Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13267, https://doi.org/10.5194/egusphere-egu23-13267, 2023.

In the context of a Flood Risk Management Plan (FRMP), flood frequency analysis is of paramount importance to obtain high return period flood quantiles. However, these estimations still present high uncertainty as a result of the short temporal length of the maximum available flow records; the data recording errors (principally on large floods); the high variance and asymmetry of the maximum flows; the hypothesis considered to evaluate the initial soil moisture conditions and the spatio-temporal variability of storms; the extended use of the iso-frequency hypothesis, among others. One of the prominent approaches to deal with these issues is the use of process-based flood frequency analysis. This way the main hydrological processes are considered at the same time that better information on extreme rainfall and historical floods can be incorporated. In this article, a weather generator (GWEX) and a distributed hydrological model (TETIS) are integrated in a cascade modelling approach, expanding the information to support the frequency analysis in a case study: the Segura River basin (Murcia, Spain) with 14,000 km2. Specifically, the methodology consists of the following steps: a) a regional study of annual maximum daily rainfall; b) Calibration of the WG on a daily scale and generation of a long daily precipitation series (5,000 years); c) Extreme storm selection (698) and temporal disaggregation into sub-daily scale (hourly); d) calibration and validation of hydrological model and simulation of selected synthetic storms with the hourly temporal resolution and a spatial resolution of 200 m; e) flood frequency estimation considering synthetic annual maximum instantaneous floods.

Finally, the methodology was validated with six historical catastrophic flood events since 1825. According to the results, the last major flood in September 2019 in the “Rambla de Abanilla” (one of the main tributaries to the flood prone area via flash flood processes) would correspond with an event with a low probability of flooding (around a 400-year return period). But the rainfall event generating it has an assigned return period ranging from 10 to 1000 years, depending on the geographical point or rain gauge considered in the Abanilla sub-catchment. This result shows that the classical approach of iso-frequency is not feasible to use in this kind of catchments. Using this methodology, it was possible to estimate the effect of the introduction of flood risk mitigation measures via scenario modelling. In this same tributary, the proposed reforestation in the FRMP will reduce the high frequency quantiles in a 10% and the structural measures a 65% the 100-year quantile. Finally, based on the results obtained, it is possible to move towards an analysis of the efficiency of the measures and to support scientifically sound decision making.

How to cite: Beneyto, C., Salazar-Galan, S., Aranda, J. Á., García-Bartual, R., Albentosa-Hernández, E., and Francés, F.: A process-based flood frequency analysis using a weather generator and distributed hydrological modelling in a Spanish Mediterranean catchment: the Segura River basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14250, https://doi.org/10.5194/egusphere-egu23-14250, 2023.

The engineering design of hydraulic infrastructures in either urban or rural environments requires flow hydrograph scenarios to be tested in numerical models. Whilst peak flow discharge is calculated according to a return period analysis of historical data,  the definition of the corresponding hydrograph duration and shape is not unique. The first one is typically considered either equal to the concentration time of the catchment or dependent on rainfall event intensity and duration. The shape of the hydrograph is then defined as a rectangle (e.g., constant flow discharge) or other simple shapes (e.g., triangle) according to empirical rules. In this work, we propose the definition of an average hydrograph shape based on the stochastic analysis of the Compound Poisson Process, which is usually considered as a proxy model for hydrological data in several applications. Once the peak flow discharge value is derived by means of the Peak Over Threshold analysis and a baseflow value is set, we calculate the duration of both the raising and the falling limbs of the hydrograph based on the concept of mean first passage time across thresholds for non-Markovian processes. As this technique considers the ensemble of the infinite possible stochastic trajectories reaching the threshold, it then returns a more comprehensive description of the possible mean shape of the hydrograph. Such a shape can also be approximated by using relationships already available in the literature (e.g., the Yevdjevich function). In definitive, the proposed approach provides more reliable results when the hydraulic processes being modelled (e.g., flow erosion) require that not only the peak but also the shape and the hydrograph duration are important for verification and design purposes.

How to cite: Calvani, G. and Perona, P.: Stochastic description of mean flow hydrograph shape for flood modeling and river engineering design purposes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14306, https://doi.org/10.5194/egusphere-egu23-14306, 2023.

Cork city is considered to be in a vulnerable region to flooding and is expected to face significant economic and social damage due to the effects of climate change in the upcoming future. An increasing trend in the precipitation and temperature coupled with a rise in severity and frequency of storm events leads to a higher frequency of expected flooding for Cork city in the upcoming decades. More floods and severe heatwaves during the summer months will hugely impact Cork’s large agricultural sector and property in the urban, suburban and rural areas surrounding the city. In 2009, Cork city experienced a severe flood event that devastated property resulting in €90 million worth of damages only to the city centre.

This study focuses on the identification of the most vulnerable regions in Cork city based on the historically observed flood-related data. Furthermore, the changes in flood vulnerability and risk for the city in the future corresponding to different climate change scenarios have been explored. Three regional climate models (RCMs) and two Representative Concentration Pathways (RCPs) has been considered to obtain future projected meteorological variables, which were used to simulate future projected streamflow using Soil Water Assessment Tool (SWAT) model for each RCM and RCP. The daily simulated streamflow in the future was used to first extract the annual maximum flow, and then to obtain flood quantiles corresponding to different return periods using generalized extreme value distribution. The flood quantiles were then fed into the HEC-RAS model to generate flood inundation maps for Cork city. Comparison of flood inundation maps for a chosen return period for the historical and future period corresponding to different climate change scenarios revealed that the flood depth and flood extend is expected to increase in the future for the majority of the climate change scenarios.

A detailed risk assessment based on those developed flood inundation maps were then performed will then be performed for Cork to identify the most vulnerable areas. Subsequently, the social and economic impact of flooding has been quantified in this study. It has been noted that due to climate change, the expected damage from future flood events will ellipse the damage seen in the 2009 flood at Cork city.

How to cite: Basu, B. and O Sullivan Greene, M.: The impacts of climate change on the flood risk for Cork city: A case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14957, https://doi.org/10.5194/egusphere-egu23-14957, 2023.

Floods represent one of the most frequent natural hazards in the world and have a high impact on society. In central Europe and countries like Germany, flooding severity has increased in the last 10 years with devastating effects in well-being and high economic loses. Currently, the European Flood Awareness System (EFAS) is preparing a major release of the system at a spatial scale of 1 arc min. EFAS uses a specific number of hydrological stations for calibration, nevertheless, the availability of information at a higher resolution allows for a further update of the current system, over specific areas and catchments. The objective of this study is to evaluate the performance of the fully distributed rainfall-runoff model by calibrating and testing LISFLOOD for flood forecasting in Germany at 1 arc min with daily resolution. For this study we selected all major rivers and catchments that flow through Germany, choosing a period of analysis from 1990 to 2020. The meteorological forcing entails precipitation, temperature, solar radiation, and vapor pressure, from the high-resolution multi-variable gridded meteorological data set for Europe, EMO-1arcmin (Thiemig et al., 2022; http://data.europa.eu/89h/0bd84be4-cec8-4180-97a6-8b3adaac4d26). The discharge data were collected from the transnational flood portal for all states in Germany and the neighboring countries. The maps, the static maps that characterize the land use, land cover and human activity on the surface, soil and groundwater characteristics of the study area are kindly provided by EFAS. The calibration was conducted using the non-dominated sorting genetic algorithm-II (NSGA-II) in a top-down approach where the Kling–Gupta efficiency criteria (KGE) acted as the objective function for evaluating the model performance. The results show that the calibrated LISFLOOD model could be use in flood scenarios with reduced performance along the coastal areas.

Reference

Thiemig, Vera; Ramos Gomes, Gonçalo Nuno; Skøien, Jon Olav; Ziese, Markus; Rauthe-Schöch, Armin; Rustemeier, Elke; Rehfeldt, Kira; Walawender, Jakub; Kolbe, Christine; Pichon, Damien; Schweim, Christoph; Salamon, Peter (2022). EMO-1arcmin: A high-resolution multi-variable gridded meteorological data set for Europe (1990-2021). [Data set]. European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26

How to cite: Espitia Sarmiento, E. F., Heidari, F., Lin, Q., and Xoplaki, E.: Evaluation of the performance of the 1-arc min hydrological model LISFLOOD in German catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15296, https://doi.org/10.5194/egusphere-egu23-15296, 2023.

Flood risk analysis is of the utmost importance for policy makers and water managers as an input for the design and management of water bodies. In order to assess the frequency and severity of potential extreme floods, both data analysis and modelling, or even a combined approach can be employed. However, the spatial and temporal context of flood events is often complex, in particular when the extreme water levels can be caused by a combination of extreme upstream discharges, extreme downstream water levels and/or extreme wind events, and given the additional impact of climate change. This complexity hampers a straightforward analysis and extrapolation of rare flood events. We therefore present a semi-probabilistic flood risk analysis, that combines an ensemble approach, using different hydrological models and various climate scenarios, with a methodology that describes the extreme domain of the different flood drivers by a nested Copula. The latter Copula is based on the individual univariate extreme value distributions of each of the drivers. Synthetic design conditions for different return periods can be generated by a stratification of the obtained probability domain for extreme events. An application for the catchment of the River Scheldt in Belgium will be used to illustrate the presented approach for flood risk analysis, including an ensemble of 3 hydrological models, multiple climate scenarios for different time horizons and different projections of sea level rise.

How to cite: Pereira, F., Nossent, J., Leyssen, G., Van Uytven, E., Blanckaert, J., Adams, R., and Franken, T.: Semi-probabilistic flood risk analysis including climate change uncertainties, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15596, https://doi.org/10.5194/egusphere-egu23-15596, 2023.

Extreme events as the ones which occurred in July 2021 mostly in Belgium, Germany and the south of the Netherlands are causing important human and material losses. This event broke many records in a meteorological point of view as well as in the behaviour of the rivers, hence it urges several procedures of water and flood management to be revisited or better understood. However, hydrological modelling of such phenomena is challenging as it should cope with historical rain and discharge flow intensities never recorded before in the region but also deal with human construction and behaviour such as dams. Furthermore, most of the measurement devices in the Vesdre river were swept away or damaged during this flood engendering a paucity of reliable data for this catchment.

This presentation will focus on the hydrological simulation and a model comparison in the Vesdre and Amblève catchments in Belgium during the episode of July 2021. Both neighbour catchments were among the most damaged and displays anthropogenic characteristics. The former lacks accurate data along the watercourse but chronicles of the water depths and the manoeuvres of the sluice gates in the dams are exploitable to reconstruct the inputs and outputs. While the latter contains a rich database of flow measurements during that period and spatially well distributed to capture all main tributaries in the Amblève.

The Amblève river was favoured to perform a calibration of the models and analysis of the evolution of the flow properties along its stream. With this aim in mind, the modular structure of the hydrology package in the WOLF software, developed by the HECE team at the University of Liège, was employed to carry out distributed event hydrology simulations. Semi-distributed optimisations were performed, considering the functioning of the dams. The impact of an increasing complexity of the models and a comparison with celebrated models such as GR4H and VHM was investigated, as well as the effect of the source of rain data and their quality on the results.

This study emphasised that, for all models, most of the flow in the surface drained by the dams in the Vesdre was mainly caused by runoff/overland flows. This effect was observed in some subbasins in the Amblève displaying the same types of land uses, but also in more urban areas. Nonetheless, head catchments in the Ambève were mostly composed of groundwater flow. Moreover, it shows the importance of accurately distributed rain and confirms the local character of the phenomenon and its effect on the hydrological properties evolution

How to cite: Dessers, C., Archambeau, P., Dewals, B., Erpicum, S., and Pirotton, M.: Hydrological modelling of July 2021 floods in Vesdre and Amblève catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15619, https://doi.org/10.5194/egusphere-egu23-15619, 2023.