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Floods have an impact on human survival, economic development and environmental sustainability through loss of life and significant economic loss as a consequence of the failure of structures (e.g. dams and culverts).  Estimates of design floods are required for the design of hydraulic structures and to quantify the risk of failure of the structures. Information on flooding is also essential for the development of safe human settlements, particularly in low lying areas.

Historical records of floods are used to both directly estimate design floods from the observed gauged data and to develop methods to estimate the design floods at ungauged sites. The longer the period of available gauged flow data, the more reliable the design floods and methods. Most of the methods currently used for design flood estimation in South Africa were developed in the late 1960s and early 1970s and are in need of updating with more than 40 years of additional data currently available and with new approaches used internationally.  In addition, climate change is expected to influence the magnitude and frequency of flooding, and hence this increased variability in flows need to be accounted for when determining flood risk.

Given the above, the South African National Committee on Large Dams identified the urgent need to update the data and methods used for design flood estimation in South Africa and, in conjunction with the Water Research Commission, initiated a National Flood Studies Programme (NFSP) to update these.  This paper will briefly summarise the performance of empirical, event-based deterministic and regional approaches currently used in South Africa and will provide an overview of new developments to date. This will include updates to design rainfall and probable maximum precipitation estimation, the identification and recommendation of the best probability distribution to use in flood frequency analyses, the development of regional quantile regression and index flood approaches, the determination of locally derived Curve Numbers for the SCS model using both observed and simulated flows, the investigation of an ensemble approach for the SCS-SA model, and the development of a continuous simulation approach to flood estimation on a national scale.

How to cite: Smithers, J.: The Initiation of a National Flood Studies Programme for South Africa: Overview and Progress to Date, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-229, https://doi.org/10.5194/iahs2022-229, 2022.

The Nature-based Solution for reducing flood peaks known as Natural Flood Management is an increasingly popular policy option for flood mitigation in the UK and other parts of the world.   It involves a variety of different types of management intervention, including both in-stream and off-line storage elements.   These are often implemented on the basis of convenience in the choice of both sites and materials without any consideration of the importance of unimpeded flow during normal events, and the need for cumulatively large volumes of storage during extremely high flows that could flood downstream communities.   Yet past work (e.g. Metcalfe et al. HESS 2018) has suggested that there may be a rather subtle effect of retention times on peak flows.   If storage elements fill too quickly the effect on downstream hydrograph peaks during events that flood properties will be small.   Equally, if storage elements fill and then drain too slowly, their effectiveness during multi-peaked extreme events will be much reduced. Given the cost of adding sufficient storm-effective storage to a catchment is high, it is important that the interventions are constructed to work for those design events that affect flood risk in the downstream community.   This paper describes a simple tool for evaluating the retention times of in-stream structures of different types that can be used to make natural flood management more effective.

How to cite: Beven, K., Follett, E., Hankin, B., Mindham, D., Page, T., and Chappell, N.: The importance of retention times in Natural Flood Management interventions, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-453, https://doi.org/10.5194/iahs2022-453, 2022.

Floods are generated by complex interactions between different processes that are still poorly understood especially at the large scale. Here we investigate 177,267 rainfall-runoff events across 373 German catchments to identify which spatial and temporal properties of precipitation events and wetness state of catchments lead to the emergence of floods by applying two machine learning approaches based on decision trees and random forests. We found that in wet mountainous catchments with rather shallow soils, rainfall characteristics play an ultimate role in modulating flood occurrence. Instead, in the drier catchments the occurrence of flood events is driven primarily by the pre-event wetness conditions and spatial interplay between soil moisture and rainfall within the catchment. Snow cover additionally enhances flood occurrence in all study catchments. The identified ingredients and regional flavors of flood events in Germany provide new insights on the spatial dynamics of hydro-meteorological processes leading to floods and might become an essential tool to foster regionally-coordinated flood management strategies and early warning systems.

How to cite: Merz, R., Tarasova, L., and Basso, S.: The German flood cooking book: ingredients and regional flavors of floods, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-203, https://doi.org/10.5194/iahs2022-203, 2022.

Reliable appraisal of the occurrence of extreme flood events, their magnitude and probability are crucial for a multitude of societal and economic activities. Extreme floods are however elusive by nature and difficult to estimate through current methods, which heavily rely on limited flood records unable to characterize processes that might be more variable than suggested by available observations.

In this work, we first capitalized on advances in the mechanistic-stochastic descriptions of precipitation, soil moisture and runoff dynamics in river basins to derive a new tool: the Physically-based Extreme Value (PHEV) distribution of river flows. PHEV provides a theoretical underpinning to the study of manifold flood-related issues, such as the emergence of heavy tails in streamflow and flood distributions, flood rich and poor periods, and the watershed features leading to the occurrence of extreme flood events.

We then utilized PHEV to aid investigation of the latter phenomenon in a large set of catchments in the USA and Germany. We positively verified its capability to identify rivers which exhibit a sharp increase of the magnitude of the rarer floods, and the flood value for which this marked increment of magnitude occurs, which we label flood divide. We then leveraged the mechanistic nature of PHEV and a dimensional analysis tool to identify key hydroclimatic and geomorphologic determinants of the occurrence of a flood divide. The hydrograph recession exponent, which embodies the branching pattern of the stream network, and the river flow regime characterized by its streamflow variability play a pivotal role in this regard, as confirmed by observations from the large dataset analyzed in this study.

Finally, we applied binary logistic regression using hydrograph recession exponent and streamflow variability as explanatory variables to predict in what basins the occurrence of flood divides and extreme floods shall be expected. Analyses for independent realizations of subsets of data indicate high prediction accuracy. This remarkable result emphasizes the feasibility of inferring river basins which are prone to generating extreme floods by means of two simple hydrologically-meaningful and readily available indices, thus raising awareness of the intrinsic peril of floods in these cases.

How to cite: Basso, S., Merz, R., and Miniussi, A.: Risky rivers: physioclimatic controls of basins' penchant for extreme floods, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-70, https://doi.org/10.5194/iahs2022-70, 2022.

In July 2021 Germany has experienced devastating flooding with unprecedented impacts of around 180 fatalities and 30 billion € economic losses. To understand the main causes of this flood disaster, we analyze this event along the flood process chain, from the triggering atmospheric processes through catchment and river system processes to its impacts on people and the built environment. We discuss to which extent current approaches for flood mapping and risk assessment are able to provide sound information for preparing citizens, disaster management and administration against such events. We argue that risk assessments should be based on broader approaches that attempt to include a larger breadth of possible events, failure scenarios and impacts. Approaches, such as worst-case scenarios, downward counterfactuals, or historical storylines, and more comprehensive and refined impact modeling can substantially contribute to a better understanding of the potential for disasters.

How to cite: Merz, B., Apel, H., Kreibich, H., and Vorogushyn, S.: Disastrous flooding in July 2021 in Germany – Event analysis and consequences for risk assessment approaches, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-183, https://doi.org/10.5194/iahs2022-183, 2022.

The July 2021 floods costed up to 200 lives and 30 billion euros of damages in Germany alone. The aftermath of this exceptional event was a wake-up call for questioning the ability of the hydrometeorological tools in providing timely and reliable flood forecasts, knowing that this kind of events will become more frequent due to global warming. For these events, we simulated the hourly streamflows of seven catchments in the west of Germany, by combining nine, partly polarimetric, radar-based rainfall products with two hydrological models: GR4H (Ficchì et al., 2019, 10.1016/j.jhydrol.2019.05.084), a bucket-style, lumped model, and ParFlow-CLM (Kollet and Maxwell, 2006, 10.1016/j.advwatres.2005.08.006), a distributed model that couples 3D sub-surface and overland flows. GR4H parameters were calibrated using more than 10 years of rainfall-runoff data, with emphasis on reproducing high flows, whereas ParFlow-CLM parameters were estimated based on landscape and soil properties. The key findings are as follows: (1) In comparison with estimates from rain gauges, all radar-based precipitation products underestimate the total precipitation depths for the 14^th of July, with significant differences among the products. This is most likely due to strong coalescence processes below the height monitored by the radars. (2) The hydrographs simulated by GR4H and ParFlow-CLM are similar for 3 of the 7 catchments, whereas the differences in the remaining ones could be explained by the effect of human water management, not represented in ParFlow-CLM. (3) The ability to detect the exceedance of the 100-year flood highly depends on the precipitation product used, highlighting the need for reliable precipitation estimates to reproduce such exceptional events.

How to cite: Saadi, M., Furusho, C., Belleflamme, A., Chen, J.-Y., Trömel, S., and Kollet, S.: Testing state-of-the-art radar-based precipitation estimates to simulate the exceptional European floods of July 2021, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-164, https://doi.org/10.5194/iahs2022-164, 2022.

Accounting for the variability of processes and climate conditions between catchments and within catchments remains a challenge in hydrological modelling. To address this issue, various approaches were developed over the past decades. Among them, multi-model approaches provide a way to quantify and reduce the uncertainty linked to the choice of model structure, and semi-distributed approaches propose a good compromise to account for spatial variability of the processes by dividing the catchment in sub-catchments while maintaining a limited level of complexity. However, these two approaches were barely applied together. The aim of this work is to answer the following question: can we improve the efficiency of hydrological models by implementing a multi-model approach within a semi-distributed framework? In this work, the benchmark considered is a lumped model with a fixed structure.

To this end, a large set of 147 catchments in France was assembled, with precipitation, evapotranspiration and flow data at an hourly time step over the 1998-2018 period. The semi-distribution set-up was kept simple by considering a single intermediate catchment between a downstream station and one or more upstream catchments. The multi-model approach was implemented with two versions of the GR model (namely GR4H and GR5H). Within a semi-distributed framework, the two models were either used individually, i.e. applied on all sub-catchments (called GR4H-SD and GR5H-SD respectively), or combined using a simple and a weighted mean.

The first step of this work was to check whether past conclusions published in the scientific literature, obtained with lumped multi-models, were the same in a semi-distributed framework. In other words, does the multi-model approach generate better performance than individual models in a semi-distributed context?

Another possible combination of the semi-distributed and the multi-model approaches would be to make different choices of model structures or combinations on each sub-catchment. Intuitively, it makes sense to propagate the flow simulated by the best model from upstream to downstream. The second analysis therefore focuses on the following question: is the best upstream model always the most useful downstream?

The results and the operational implications of this work will be analyzed in the case of the Rhône basin.

How to cite: Thébault, C., Perrin, C., Andréassian, V., Thirel, G., and Legrand, S.: Combining multiple hydrological model structures in a semi-distributed modelling environment., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-490, https://doi.org/10.5194/iahs2022-490, 2022.

Semi-distributed hydrological models have the potential to improve the efficiency of flood forecasting chains. Such models take into account the spatial distribution of both meteorological forcings and soil moisture states to predict streamflow along the river network and allow the assimilation of streamflow observations on multiple internal flow gauges. How to update the model states on ungauged upstream sub-catchments remains however a challenge. Indeed, the actual relative contribution of each upstream sub-catchment to the observed streamflow at the outlet cannot be observed but simply estimated by the hydrological model from the simulated upstream streamflow and routing-lag. In this work, we test the following hypotheses:

• the simultaneous assimilation of streamflow observations at internal gauges should improve streamflow predictions at the main downstream outlet
• accounting for time lags between flow gauges is needed to efficiently update model states in cases where no streamflow observations are available at internal gauges

The analysis is performed with a semi-distributed version of the hourly GR5H model (de Lavenne et al., 2019; Peredo-Ramirez et al., 2021) on a large dataset of French gauged catchments, each one having at least one internal gauged station. Several experiments were set up in gauged and pseudo-ungauged contexts to test both hypotheses. Two updating schemes were used: a particle filter (Piazzi et al., 2021) and a direct insertion method used in the operational flood forecasting model GRP (Furusho et al., 2016).

References

de Lavenne, A., Andréassian, V., Thirel, G., Ramos, M.-H., & Perrin, C. (2019). A regularization approach to improve the sequential calibration of a semidistributed hydrological model. Water Resources Research, 55, 8821–8839, 2018WR024266.

Furusho, C., Perrin, C., Viatgé, J., Lamblin, R., and Andréassian, V. (2016). Collaborative work between operational forecasters and scientists for better flood forecasts, La Houille Blanche, 102:4, 5-10.

Peredo-Ramirez, D., Ramos, M.-H., Andréassian, V., and Oudin, L. (2021). Investigating hydrological model versatility to simulate extreme flood events. Hydrological Sciences Journal, in review.

Piazzi, G., Thirel, G., Perrin, C., and Delaigue, O. (2021). Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin scale. Water Resources Research, 57(4), e2020WR028390.

How to cite: Royer-Gaspard, P., Bourgin, F., de Lavenne, A., Perrin, C., and Thirel, G.: Seeking best streamflow assimilation scheme in a semi-distributed hydrological model for flood forecasting, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-339, https://doi.org/10.5194/iahs2022-339, 2022.

Assessing the impact of climate change scenarios on flood regimes is a crucial issue when evaluating the future resilience of flood protection systems. Mainly due to the high computational effort and to the scarcity of hourly climate projections, expected changes in future floods are often simulated by hydrological models on a daily basis, even for basins with short response times, where hourly simulations would be needed.

In this work, the expected occurrence and magnitude of future flood events is modelled through the coupling of bias-corrected local climate scenarios at hourly time scale and continuous rainfall-runoff modelling. The case study for testing the procedure refers to the Panaro river (one of the OpenAir Laboratories in the OPERANDUM H2020 project).

The analysis entailed first the collection, validation and spatialization of historical meteorological ground data over the catchment, to be used both to calibrate the hydrological model and in the bias-correction procedure.

Secondly, the precipitation and temperature timeseries available at hourly time-scale for a set of climate modelling chains based on the same RCM nested in different GCMs, under RCP 8.5, are identified in the EURO-CORDEX ensemble and processed. The comparison with observed spatial fields obtained from weather stations and from gridded E-OBS products allows assessing the biases affecting “raw” data. The Scaled Distribution Mapping (SDM) bias correction procedure is then applied to “adjust” the raw model output towards hourly observations in a post processing step. The strength of such a procedure relies on preserving raw climate model projected changes in the bias-corrected series and on avoiding assumptions about stationarity.

A semi-distributed, continuously simulating HBV-type rainfall-runoff model is parameterised, especially focusing on the reproduction of past flood events, and then run to reproduce the streamflow in the Panaro river, providing in input i) historical meteorological forcing based on ground stations, ii) raw and bias-corrected climate scenarios over the control period, iii) bias-corrected climate scenarios for the future decades. Finally, the flood events are extracted from the continuous streamflow simulations and the changes in the flood signals expected over the future decades are analysed, in terms of both peaks and volumes.

How to cite: Neri, M., Reder, A., Rianna, G., and Toth, E.: Simulating future occurrence and magnitude of flood events through bias-correction of hourly climate scenarios and semi-distributed rainfall-runoff modelling: application to the Panaro river (Northern Italy), IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-369, https://doi.org/10.5194/iahs2022-369, 2022.

Flood risk management requires the knowledge of flood quantiles associated with high return periods. To date and due to the short temporal length of the maximum available flow records, these present very high uncertainties. This scarcity makes unfeasible the use of classical statistical methods, becoming necessary to advance in a methodological approach of flood frequency analysis based on the understanding of the main hydrological processes and taking advantage of the better information on extreme rainfall at regional scale. In this article, a storm modeling approach with a weather generator (WG) and distributed hydrological modeling including sediments (TETIS model) are proposed to support the frequency analysis, being implemented in a case study: the Segura river basin (Murcia, Spain) with 14,000 km². Specifically, the methodology consists of the following steps:

• Perform a regional study of annual maximum daily precipitation.
• Calibration of a WG on a daily scale and generation of a long daily precipitation series (5,000 years)
• Extreme storm selection (698) and temporal disaggregation into sub-daily scale (hourly)
• Implementation of TETIS at hourly resolution and hydrological simulation of selected storms
• Final peak flow frequency analysis and validation of the methodology

A validation of the methodology has been carried out with historical flood data considering six catastrophic flood events since 1825. According to the results obtained, a significant reduction in the uncertainty associated with both rainfall and flow quantiles has been achieved. For the last major flood in September 2019, the results have shown that, in the Abanilla tributary (one of the main flow contributors to the flood zone), the recorded peak flow is highly likely to correspond with an event with a medium probability of flooding (around a 100-year return period), although it corresponds with a rainfall event with a return period between 10 and 1000-year, depending on the location of the rain gauge within the Abanilla subcatchment.

Finally, it is possible to advance in an analysis of risk management measures to evaluate their effectiveness and viability, comparing present conditions and future scenarios and considering different strategies (structural and non-structural measures or nature-based solutions).

How to cite: Beneyto, C., Aranda, J. Á., Salazar-Galán, S., García-Bartual, R., Albentosa-Hernández, E., and Francés, F.: Expanding information for flood frequency analysis using a weather generator and distributed hydrological modelling in a Spanish Mediterranean catchment, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-275, https://doi.org/10.5194/iahs2022-275, 2022.

A catchment is in a continuous state of recession although this can be difficult to observe due to added moisture from precipitation or snowmelt. For the updating procedure developed in this study, we need to use the runoff and the subsurface storage estimated for a state of pure recession. For an observed runoff value, the runoff caused by pure recession at the next timestep need to be estimated. This is obtained by the recession characteristic Lambda=log(Q(t)/Q(t+1)), which is estimated through an iteration procedure. Then, the subsurface storage for a state of pure recession can be estimated by assuming that the catchment behaves as a linear reservoir, with parameters specific for the moment of update. The rate constant of the linear reservoir is not all a constant but a function of the recession characteristic. The difference between observed runoff and runoff due to pure recession (distributed in time due to an UH estimated using the recession characteristic and the distance distribution describing the distances from points in the hillslopes to the river network) is the moisture needed to be added to the subsurface state of pure recession, giving us an estimate of subsurface state for the observed runoff. If the subsurface state in the model is different, then we need to add or subtract water to the model so that so the modelled subsurface state and the estimated subsurface state due to the observed runoff are equal. The model with the updated subsurface state has improved runoff predictions, and increased precision can be observed for several timesteps ahead. These are very preliminary, but promising results, and will, if successful, be of considerable value for the forecasting services.

How to cite: Skaugen, T.: Improving runoff predictions by updating the subsurface state in the DDD model from observed runoff, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-405, https://doi.org/10.5194/iahs2022-405, 2022.

The main objective of this study is to investigate how innovative satellite Earth observation techniques that allow for the estimation of soil moisture and the mapping of flood extents can help in reducing errors and uncertainties in hydro-meteorological modelling especially in ungauged areas where potentially no or limited runoff records are available. A conceptual hydrological model is loosely coupled with a shallow water model allowing for the simulation of soil moisture and flood extent. Using as forcing of this model rainfall and air temperature time series provided in the globally and freely available ERA5 database it is then possible to carry out long-term simulations of soil moisture, discharge and flood extent. Next, time series of soil moisture and flood extent observations derived from freely available satellite image databases are jointly assimilated into the hydrological model in order to retrieve optimal parameter sets. For this assimilation experiment, we take benefit of recently introduced Particle Filters with tempering that circumvent some of the usual particle filter limitations such as degeneracy and sample impoverishment. As a proof of concept, we set up an identical twin experiment based on synthetically generated observations and we evaluate the performance of the calibrated model.

How to cite: Hostache, R., Matgen, P., van Leuuwen, P.-J., Nichols, N., Chini, M., Pelich, R., and Delenne, C.: A joint assimilation of satellite soil moisture and flood extent maps to improve a flood hazard modelling., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-359, https://doi.org/10.5194/iahs2022-359, 2022.

Urban floods are amongst the most dangerous and frequent natural hazards. Despite the great uncertainty and errors of the computation and input information, the estimation of flow discharges and flood levels are paramount for a successful flood management. In small watersheds with high slopes and small concentration time, reliable early warning systems should be based on real-time monitoring of the forcing variables and hydrology and hydraulic modelling to evaluate the flooded areas and the inundation levels in the vulnerable area.

In this work, hydrologic analysis and hydrodynamic modelling as a support for an early warning system are presented. Together with the modelling component, the monitoring setup including meteorological and hydrometric stations is showed. The case study focuses on the Vinhas Creek basin, where steep slopes from the mountainous headwaters converge to a final flat reach located in the highly urbanized area of the city of Cascais, close to the Tagus estuary mouth. This area is rather prone to torrential floods that inundate the lower part of the city.

The hydrologic and hydraulic modelling were conducted with the HEC models. After the definition and characterization of the watershed, the first modelling step was made by applying the HEC-HMS software coupled with geomorphological analysis and landcover to estimate the curve number and all related variables to generate flood hydrographs. Calibration was achieved using data from the historical flood of February of 1983.

As for the hydraulic modelling of the flood wave it was performed using HEC-RAS 2D software. A Digital Terrain Model was constructed taking advantage of 2x2m² topographic information. The model was validated against data from the flood of February 2021 for the validation of the results.

Finally, the integration of the validated HEC models for hydrology and hydrodynamics in the Water Information Forecast Framework is briefly presented. Based on meteorological data, this system aims at providing real time flood levels in the vulnerable areas.

How to cite: Fernandes, J., Rodrigues, R., Jesus, G., Rogeiro, J., and Oliveira, A.: Hydrologic and Hydraulic modelling as a support for an early warning system for flash floods, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-715, https://doi.org/10.5194/iahs2022-715, 2022.

Southern France is frequently hit with severe flash floods. These events have caused numerous casualties as well as considerable economic losses. In France, crisis inducing floods are managed at a local scale by municipalities. Current forecasting models are rarely used by these crisis managers. This is due to the fact that these models often fail to convey relevant information in an appropriate form. This work aims to determine the adequate output variable in a crisis management context with a special focus on the needs and vision of local crisis managers.

The study area is the 545 sq. km. sized Gardon in Anduze in the Cévennes range, France, which is often subject to important flash floods. A feed-forward multilayer perceptron, a type of neural networks where one of the inputs is the measured output, is used to forecast water discharge. Neural networks are good candidates to model nonlinear phenomenon. Both the properties of parsimony - they provide relevant results while requiring a limited number of parameters – and of universal approximation are very helpful in hydrology. Various rainfall and discharge measurements are considered as inputs. The output variable for this model is water flow. Data ranges from 2002 to 2019 at a 30 minutes’ time step. Events are extracted from this database to compose a training set, a test set and an early stopping set. Complexity and variable selection is made using cross-validation, allowing to select the most robust model. To make the model easier to understand by end users, discharges are replaced by water levels. Both these results are compared, especially regarding their ability to reach crisis management plans levels without delay. Secondly, ensemble models based on different initializations of the parameters of the neural model during the training step are developed. They give an uncertainty margin, often desired by end users.

The results are enriched through the collection of background information (end users’ opinions and habits) in order to enhance the assessment of performance. Further analysis is carried to determine the pros and cons of each approach (discharge or levels to crisis management plans levels and uncertainties representation and communication).

How to cite: Sadkou, S., Artigue, G., Ayral, P.-A., Pistre, S., Sauvagnargues, S., and Johannet, A.: Towards a flash flood forecasting model for local crisis managers, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-379, https://doi.org/10.5194/iahs2022-379, 2022.

The estimation of storage and fluxes in surface hydrology is an essential scientific question related to major socio-economic issues, especially when forecasting extreme floods and droughts with the undergoing climate change. Advanced spatially distributed modeling tools are critically needed to perform reliable and skillful local forecasts. Nevertheless, hydrological modeling remains a challenging task because of limited observations of physical processes and modeling uncertainties. In particular, given the spatial sparsity of constraining discharge data, hydrological modeling is faced with the challenge of producing predictions at ungauged locations based on the regionalization of the model parameters. Despite the overparameterization problem in spatially distributed modeling, Jay-Allemand et al. (2020) presented promising results for estimating the spatial variability of the distributed parameters within a catchment using only downstream discharge observations. However, providing better spatial constrains on the estimated parameters patterns, inside or outside calibration catchments in a regionalization perspective, remains a challenge.

This contribution presents a regionalization approach based on: (i) the SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) hydrological modeling and assimilation platform (Haruna et al., 2021) underlying the French national flash flood forecasting system Vigicrues Flash (Javelle et al., 2019); (ii) the variational assimilation algorithm from Jay-Allemand et al. (2020), adapted to high dimensional inverse problems; (iii) spatial constraints added to the optimization problem, based on masks derived from physiographic maps (e.g., soil occupation and nature, bedrock type, terrain slope); (iv) multi-objective optimization which targets independent watersheds. This method gives a regional estimation of the distributed parameters over the modeled area. Performances of the model and the parameters robustness are evaluated on a large sample of French catchments and flash floods in spatio-temporal extrapolation based on cross-validation experiments. Effects of the spatial constraints (regularization and multi-objective optimization) are discussed in the light of adjoint sensitivity maps. Further work aims to improve the global search of prior parameter sets and to better balance the adjoint sensitivity with respect to the spatial constraints resolution and catchment characteristics. This will ensure a better consistency of simulated fluxes variabilities and enhance the applicability of the regionalization method at higher spatial scales.

How to cite: Jay-Allemand, M., Demargne, J., Garambois, P.-A., Javelle, P., Gejadze, I., Colleoni, F., Organde, D., Arnaud, P., and Fouchier, C.: Spatially distributed calibration of a hydrological model with variational optimization constrained by physiographic maps for flash flood forecasting in France, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-166, https://doi.org/10.5194/iahs2022-166, 2022.

Flash floods can be considered among the worst natural hazards that usually impact small river basins, often ungauged, characterized by few hours or less of response time to short and high-intensity rainfall events. The events are characterized by sudden, small-scale heavy rains and unpredictable peak discharges. In this context, the definition of design hyetographs and hydrographs, at the basis of flood risk assessment, is a paramount task, because of the great uncertainty with concern with ungauged river basins and the short time series. The definition of synthetic hyetographs based on past observed storm events, scaled and transposed, in order to improve the flash flood risk assessment is presented here. Particularly, the spatial distribution of past observed storm events is analyzed and the Areal Reduction Factors (ARFs), to scale the observed hyetographs, for different river basins extensions, are considered. The hyetographs of past observed storm events, scaled with the ARF according the sizes of the investigated river basins,  are then applied on different locations with a  Stochastic Storm Transposition and used as input of hydrological models. The entire methodology is applied to a set of river basins in Northern Tuscany (central Italy).  The Stochastic Storm Transposition involves resampling and transposing storm events to generate synthetic events from a collection of realistic events. The synthetic hyetographs obtained by the past events are tested on river basins with a similar rainfall statistical distribution of the place in which the events occur, i.e. over catchments that can statistically have similar events, avoiding unreal comparison with the design hydrographs. The derivation of synthetic hyetographs is made by scaling recent short duration events occurred in the area to identify if the design hydrographs commonly used in the region underestimate the flash flood risk. It is shown that peak discharges of short-duration events are usually greater than the design floods, of the small river basins with an area generally less than 30 km².

How to cite: Lompi, M., Caporali, E., Mediero, L., and Mazzanti, B.: On extreme rainfall scaling and stochastic storm transposition to enhance the design hydrographs definition and flash flood risk assessment, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-666, https://doi.org/10.5194/iahs2022-666, 2022.

Flash Floods are damaging natural hazards which often occur in the European Alps. Precipitation patterns and intensity may change in a future climate affecting their occurrence and magnitude. For impact studies, flash floods can be difficult to simulate due the complex orography and limited extent & duration of the heavy rainfall events which trigger them. The new generation convection-permitting regional climate models (CP-RCMs) improve the representation of the intensity and frequency of heavy precipitation. Therefore, this study combines such simulations with high-resolution distributed hydrological modelling to assess changes in flash flood frequency over the Alpine domain. We use output from a state-of-the-art CP-RCM to drive a high-resolution distributed hydrological wflow_sbm model (0.0088333 degree, ~1km2) covering most of the Alpine mountain range on an hourly resolution. First, the hydrological model was validated by comparing ERA5 driven simulation with streamflow observations from 130 stations (across Rhone, Rhine, Po, Adige and Danube basins). Second, an hourly wflow_sbm simulation driven by the CP-RCM downscaled ERAInterim simulation was compared to databases of past flood events to evaluate if the model can accurately simulate flash floods and to determine a suitable threshold definition for flash flooding and to determine a suitable threshold definition for flash flooding. Finally, simulations of the future climate RCP 8.5 for the end-of-century (2096-2105) and current climate (1998-2007) are compared for which the CP-RCM is driven by a Global Climate Model. The simulations are compared to assess if there are changes in flash flood frequency and magnitude using a threshold approach. Results show a similar flash flood frequency for autumn in the future, but a decrease in summer. However, the Future Climate simulations indicate an increase in the maximal flash flood severity in both summer and autumn leading to more severe flash flood impacts.

How to cite: Zander, M., Viguurs, P., Sperna Weiland, F., and Weerts, A.: Future changes in flash flood frequency and magnitude over the European Alps, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-186, https://doi.org/10.5194/iahs2022-186, 2022.

Flood forecasting is particularly complicated in karstic Mediterranean area. Forecasters are faced to complex relationships between surface and ground water. During low-flow periods, when water table is low, karst can store a significant quantity of water in its vadose zone. Conversely, during periods of high water, its storage capacity is limited, leading to an increase in runoff due to a refusal to infiltrate and also to rapid underground transfers that can amplify floods downstream of karst basins (Fleury et al., 2010 ; 2013).

Agly river is located in the department of Pyrénées-Orientales in the South of France, its catchment is over 1000 km². It flows partly over the karst terrains of the Corbières. The hydrogeological functioning of this system has been studied by the BRGM (Dorfliger, 2008). This river is known for its flash floods which can cause significant damages to the population. This river is monitored by the SPC (Service de Prévision des Crues, the French flood forecasting office), which undergoes forecasting difficulties. In this context, BRGM carried out a study in order to improve flood process understanding and to make a simple and robust forecasting tool on the area.

These studies made it possible to identify an observation well in the karst that is representative of the saturation level of the karst. A semi-distributed numerical model including five sub-catchments was used to simulate the water level in the karst as well as the flow at the outlet of each of these sub-catchments.

At the end of this modelling work, the model was used in forecasting mode using numerous climatic scenarios. A double abacus type tool was built. The first allows the characterisation of the storage capacity of the karst from the initial piezometric level measured, the second provides the response in terms of flow following a rainfall episode (total rainfall and intensity). The tool has been used for two years in real time by warning managers during heavy rainfall events. Results in terms of vigilance (green, orange, red) were satisfactory.

How to cite: Fleury, P. and Maréchal, J.-C.: Floods forecasting on karstic basin – Agly river, south of France, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-512, https://doi.org/10.5194/iahs2022-512, 2022.

The floods that have affected many African countries south of the Sahara have not exempted Ivory Coast, especially since the 1970s. The impact of these events has led to major climatic disruptions, particularly the occurrence of extreme rainfall. In the Bandama catchment area, in September 2018 alone, the heavy rains that fell led to a flooding of the Bandama River. The towns of Zenoula and Bouaflé were flooded, plantations destroyed. According to the Agence de Presse Ivoirienne (AIP) on 22 September 2018, a total of 2017 people including 1130 children are affected in Bouaflé. In this context, it is important to better understand how the irregularity and distribution of extreme rainfall occurs in order to adopt preventive measures. However, the lack of studies on climate trends does not allow us to anticipate environmental problems. Indeed, the simplest way to understand this variability, although the necessary concepts and notions must be available, is to consider that there is an elementary and scale-invariant process that reproduces this variability from scale to scale. The possibility of characterising and modelling the variability of precipitation from scale to scale rather than at a given scale has led to a number of studies on multifractal precipitation (Lovejoy et al., 1987; Hubert et al., 2002). The objective of this study is to highlight the occurrence of extreme rainfall in the Bandama catchment using multifractal analysis. For this work, daily data from 2005 to 2010 from 10 stations (Yamoussoukro, Bouaflé, Dimbokro, Ferkessedougou, Niakara, Séguela, Bouaké, Tiassalé, Grand-Lahou, Zuenoula) were considered. Three intensity thresholds (with respective values of 20; 35; 50 mm/d) were studied. For these thresholds, the respective fractal dimension is 0.321; 0.249; 0.232.  The probability of obtaining a rainy day with thresholds set at 50 mm, 35 mm, 20 mm is respectively 0.069; 0.074; 0.093 over one month. The probability of obtaining rainy days for the three thresholds is spatialized over the catchment.

Keywords: Occurrence of extreme rainfall, multifractal, Bandama basin, Ivory Coast

How to cite: Meledje, N. E. H., Koffi Bi, K. F., Kouassi, K. M., N'go, Y. A., and Kouassi, K. L.: Risk of extreme rainfall in tne Bandama basin (West Africa): Contribution of multifractal analysis, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-4, https://doi.org/10.5194/iahs2022-4, 2022.

A coincidence in the timing of floods and their drivers can be used as a proxy for the causality of flood generation. By investigating flood generation mechanisms, we can better understand how runoff is generated and its predominant flow paths. However, so far, no study has explored the drivers of flood seasonality on a large scale in Brazil, particularly the roles of intense rainfall and soil moisture. Here, we investigate the relationship between the seasonality of floods, maximum annual rainfall, and maximum annual soil moisture data of 886 basins in Brazil for 1980-2015 to shed light on flood generation mechanisms. We analyze circular correlation of the variables’ timing and compare their mean dates of occurrence. Floods generally occur around February in central Brazil, April in Amazonia’s southern tributaries, June in Amazonia’s northern tributaries, and between austral autumn and spring in southern Brazil. On average across Brazil, floods tend to occur at the same time of year as soil moisture peaks and lag behind rainfall peaks by three weeks. In Amazonia, central and northern Brazil, flood timing is more highly correlated with the timing of soil moisture peaks than with that of rainfall peaks. In these regions, rainfall usually peaks early or mid-wet season even though floods and soil moisture peaks usually occur at the end of the wet season. We suggest that such delays between rainfall peaks and floods are associated with high subsurface water storage capacities. On the other hand, in southern and southeastern Brazil, flood timing is highly correlated with the timing of both soil moisture and rainfall peaks. Intense rainfall quickly saturates the soil and generates floods, indicating a predominance of low subsurface water storage capacities. These findings give a large-scale indication of how floods and runoff are generated in Brazil, supporting flood forecasting and climate-change impact studies.

How to cite: B P Chagas, V., L B Chaffe, P., and Blöschl, G.: Flood Seasonality Mechanisms in Brazil, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-550, https://doi.org/10.5194/iahs2022-550, 2022.

Despite the assumption of a homogeneous sample in many flood frequency analyses, flood events can be generated by highly different meteorological and catchment conditions. In large river basins, the diversity of flood generation is increased even more due to spatial and temporal variability of meteorological loads and hydrological processes within the basin and the resulting event-specific flood superposition of main river and tributary floods. Any consideration of additional attributes increases the complexity of statistical characterisation of flood events and scenarios. However, consideration of such differences is imperative if the effectiveness of flood control measures is to be assessed in a spatial context. This is mostly done by flood scenarios, which are usually derived using individual historical floods along with deterministic model-based simulations. We, instead, performed hydrograph-based flood-type classification and volume-based runoff analyses to estimate the contributions of sub-basins to floods in large basins. Using this information, we generated long synthetic samples of peak-volume-pairs to apply a multivariate statistical flood-frequency model that yields a type-specific conditional probability of a flood peak given the peaks in tributary stations. The results show that only some combinations of flood types may result in extreme peaks downstream of confluences. They also highlight the need to distinguish runoff-generation mechanisms for the larger floods from ones that drive smaller, more frequent events. Finally, design floods for different scenarios of flood-type combinations and assigned probabilities are derived, an approach that can be used to assess possible climate impacts to flood frequency. Case studies in several large river basins in Central Europe demonstrate the importance of distinguishing between flood types and flood scenarios, especially for extreme flood events.

How to cite: Fischer, S. and Schumann, A.: Multivariate statistical assessment of flood scenarios in large river basins under consideration of tributary impacts and flood types, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-57, https://doi.org/10.5194/iahs2022-57, 2022.

Changing climate and changes in watersheds raise questions about the safety of flood control structures. Evaluating the present safety of past designs where storage was involved requires the entire hydrograph or, at least, the flood peak-volume-shape estimates related to the present or future conditions. Therefore, studying the relationships between these quantities is exciting both from the scientific hydrological and engineering points of view. The statistical analysis of flood peaks and volumes in engineering hydrology practice was often dealt with in a multivariate frequency framework. However, examining the interplay of climatic and catchment processes in influencing the probabilities of flood wave peaks, volumes and shapes is a challenging problem for safety evaluation of flood control structures. The proposed framework for Slovakia is based on the inclusion of climatic and hydrologic controls on the flood peak-volume-wave shape relationships and their statistical models. Rather than examining the safety based on a single control flood wave, a scheme was proposed allowing us to arrive at a set of control flood waves with associated probabilistic parameters. These permit to study of several aspects of reservoir safety (hydrologic, hydraulic and geotechnical). The concept is based on flood events assigned to the prevailing flood process types in the region with discernible flood-peak-volume-shape relationships. A set of process representative flood waves is created by base-flow separation. The joint probability distribution of peaks and volumes is copula-based, which are selected by a process-based approach. Besides that, process characteristic wave shapes are scaled to a set of dimensionless shapes. These allow selecting a dimensionless control flood wave shape according to an empirical shape quantile. Finally, a set of control flood waves is constructed by combining volumes conditioned on peaks and associated with shapes of given quantiles. That provides a good variety of control waves for assessing the safety of water structures under extreme situations in a probabilistic and process-based framework in typical risks of failure scenarios.

Acknowledgements: This work was supported by the Slovak Research and Development Agency under Contract No. APVV-20-0374 and the VEGA Grant Agency No. 1/0782/21.

How to cite: Valent, P., Liová, A., Hlavčová, K., Kohnová, S., Bacigál, T., Výleta, R., and Szolgay, J.: A process-based multivariate framework for reservoir safety evaluation for Slovakia, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-355, https://doi.org/10.5194/iahs2022-355, 2022.

Global warming impacts are known as increasing in rainfall magnitude and frequency that lead to more intense and frequent river flooding. In recent years, there is an upward trend of floods in West African regions. As result, many countries like Togo and Benin have had catastrophic floods that affected thousands of people with loss of lives, damages, and properties. Mono catchment is indeed subject to flooding due to anthropogenic and natural climate changes impacts. This study aims to predict streamflow for the period between 1990 and 2020 using hydrological modeling at gauge stations; to analyze flood frequency and magnitudes over the Mono catchment for the historical period through an investigation of hydroclimate indices. Moreover, projected changes in floods hydroclimate indices in the catchment under representative concentration pathway (RCP) from a multi-model ensemble model are investigated. The results show that hydrological modeling reproduces seasonal streamflow with acceptable performance with Kling-Gupta Efficiency (KGE) and percent over calibration and validation periods. Finally, an upward flood frequency and magnitude are observed both for historical and future periods. The outcomes of this study suggest an urgent need to improve the functionality of early warning systems and increase societal resilience to warming climates over the catchment through sustainable policy strategies and governance measures.

Keywords: Flood Frequency and magnitude, Flood forecasting, Mono catchment, Togo-Benin

How to cite: Koubodana, H. D., Tall, M., Nonki, R. M., Djaman, K., Patel, N., and Atchonouglo, K.: Analysis of Historical Flood Frequencies and Magnitudes and Future Scenarios Forecasting in the Mono River Basin, West Africa, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-553, https://doi.org/10.5194/iahs2022-553, 2022.

Understanding the variability in extreme hydrological events is the first step toward the reduction of its impacts on livelihoods. This work aims to identify possible changes in the hydrological regime of the West African basins which could explain the recent flooding observed across the region. For that purpose, 56 discharge stations were selected with the data ranging between 1922 and 2017. Multiple trend patterns on moving time slots of 20 years for stations having at least 40 years of data were performed using the modified Mann Kendall test and the strength of the trend computed using the Sen’s slope. Statistically significant trends in annual maximal discharge (AMD) have been found for many stations and distinct periods with different change directions. While a decreasing tendency was found for the AMD between the 1950 and 1970s, an increasing frequency was depicted from the 1980s to recently (2017) for most of the stations. The magnitude of change per year in AMD as depicted from Sen’s slope is substantially variable ranging from -14 to 12% relative to the mean discharge of the considered period. The highest increases in AMD were found for the stations located toward the Gulf of Guinea. The temporal variation in AMD is in line with the interdecadal rainfall variability in West Africa. 

How to cite: Hounkpè, J. and Badou, D. F.: Assessment of the long term change in the extreme hydrological events across the West African basins, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-460, https://doi.org/10.5194/iahs2022-460, 2022.

Poland is characterized by hydrometeorological variability, where conditions such as ice melt, snowmelt, extreme precipitation, and other factors might cause river floods. Climate change has had a direct influence on it, due to the shifts in parameters such as precipitation and temperature over the world. Therefore, evaluating the historical flood trends allow to establish a link between hydrology and water resource management.

The purpose of this study was to present trends in selected river flood indicators (magnitude, frequency, and timing) for a data set of 146 stream flow gauges, using the annual maximum daily flow and peak-over-threshold approaches. The gauges are also free from major human influences on flow regimes such as large dams. Two periods (1956-2019 and 1981-2019) were analyzed. The first one focused on a large temporal coverage (58 flow gauges), while the second one maximized spatial coverage (146 flow gauges). Trends were calculated using the non-parametric Mann-Kendall test (MK) and Sen slope for changes in magnitude and timing, while flood frequency trend was detected by chi-square test on parametric Poisson regression.

The flood trend detection in river flood indicators allowed us to identify areas with predominant decreases or increases. The northeast region of Poland showed a downward trend in flood magnitude with changes between -5 to -15% per decade using both methods, whereas the south part showed a predominantly upward trend.  The results are in general agreement with previous studies based on annual maximum flow. No evident trends were detected in flood frequency, where detected changes generally did not exceed ± 0.2 events per decade. An annual flood timing average on February-April was found in the northern and central parts of Poland, while May-June was the average in the southern-most basins. The flood timing showed a strong pattern of earlier flood occurrence in the southern half of the country, and an increasing trend (later flood occurrence) in the north-eastern and north-western parts, with changes of 4 and 8 days. The results showed the first analyses of a rather under-studied topic in Poland and leave an opportunity to study the attribution of changes in floods.

How to cite: Venegas Cordero, N., Kundzewicz, Z., Jamro, S., and Piniewski, M.: Detection of trends in observed river floods in Poland, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-402, https://doi.org/10.5194/iahs2022-402, 2022.

Despite massive flood protection efforts, undertaken in China for millennia, disastrous floods continue to plague the country. We discuss flood hazard and flood risk in China, starting from observed changes and variability and then dealing with flood risk management and projections. We also refer to recognized unsolved problems in hydrology.

Observed changes and variability

First, we review results of change detection in observed records of variables related to water abundance, such as intense precipitation, high river flow, and flood damage in China. There is a strong inter-annual and inter-decadal variability, part of which may be due to climate variability, i.e. oscillation in the ocean-atmosphere system (ENSO - El Niño-Southern Oscillation and PDO – Pacific Decadal Oscillation). Particular large flood events (e.g. in 1998 and 2016) coincide with high values of ENSO indices.

Flood risk management

In its efforts to reduce flood risk, China has undertaken both structural and non-structural measures. An emergency manager's nightmare is when several serious emergencies (e.g., covid pandemic and flooding) happen at once, as in the unprecedented covid year 2020, when also record-breaking precipitation occurred in China. Yet, emergency management made it possible to constrain the flood loss.

Flood risk projections

In the warming climate, flood risk is likely to increase over much of China. Projections of flood loss changes for global warming scenarios from 1.5° to 4.0°C above the preindustrial temperature are presented. Projections are based on runoff simulations by a hydrological model driven by multiple downscaled general circulation models, the national GDP projected at shared socio­economic pathways, and the “intensity–loss rate” function. Flood losses in China are projected to soar in the future, particularly in lowland regions subject to rapid economic growth. There is a considerable difference between flood losses corresponding to two global climate policy target thresholds, 1.5° and 2.0°C, amounting to over US$60 billion.

Available flood projections for China are not consistent. Since the spread is large, projections have to be interpreted with caution, because of the impact on decisions related to climate change adaptation and flood risk reduction.

How to cite: Kundzewicz, Z., Jiang, T., Su, B., Huang, J., and Wang, Y.: Flood risk in China: observed changes and variability, management, and projections, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-245, https://doi.org/10.5194/iahs2022-245, 2022.

In the context of an ever-increasing vulnerability to floods in Africa, it is important to encourage innovative research on the understanding of these extreme events, notably to improve warning systems and increase the resilience of socio-economic systems to global changes. A recently developed database with discharge data covering most regions of the African continent makes possible a continental-scale assessment of the main flood drivers. Two complementary analyses were performed; the first based on directional statistics to compare the timing of floods with annual maximum rainfall and soil moisture and the second one relying on a process-based classification of flood drivers. The results indicate that the annual maximum floods are more strongly related to the timing of the annual peak of soil moisture than of annual maximum precipitation. Furthermore, temporal changes in flood magnitudes are better explained by the variability of annual maximum soil moisture than by the variability in the annual maximum precipitation. About the dominant flood drivers, excess rainfall over saturated soils and long rainfall events are the main flood generating mechanisms, accounting for more than 75% of floods across the African continent. The relative contribution of the different flood’s drivers across a range of different catchments being strongly related to aridity conditions. The main implication of these results is that precipitation extremes should not be analyzed alone to explain the occurrence and temporal evolution of floods.

How to cite: Tramblay, Y., Villarini, G., El Mehdi Saidi, M., Gründemann, G., El Khalki, E. M., Massari, C., Stein, L., and Hughes, D.: On the importance of soil moisture dynamics for flood generation in Africa, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-26, https://doi.org/10.5194/iahs2022-26, 2022.

Exceptional floods are caused by extreme meteorological conditions matching critical space-time scales of flood generation processes in a catchment. Although these events are very rare, they are possible and it would be helpful to know what might happen in these cases for risk assessment. Fortunate, for most of the flood events some factors are not “optimal” to cause an exceptional flood. This study investigates which reasonable changes on the flood producing storms may lead to exceptional floods. For that, observed storms are stochastically modified and analysed regarding its maximisation potential for floods. First the spatial patterns are modified, then the intensities are modified and finally both conditions are modified together. The pattern modification is based on space-time simulation conditioned on observations from a conventional rainfall station network. The rainfall intensities are modified with moisture maximisation but keeping the observed pattern constant according to radar rainfall observations. Many space-time rainfall realisations are produced and used as input for a rainfall-runoff model. Simulated annealing is used as conditional rainfall simulation approach and the HBV model to simulate the floods. This case study uses data from the Mulde river catchment in Germany and applies the methodology to a set of selected large flood events. It is expected, that the results reveal how extreme the floods could have become and whether increased intensities or pattern modification contributes more to the maximisation potential.

How to cite: Haberlandt, U. and Thiele, L.-B.: Investigation of the maximisation potential of large floods by spatio-temporal simulation of rainfall, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-230, https://doi.org/10.5194/iahs2022-230, 2022.

This study is motivated by showing how future floods predictions in arid areas respond to uncertainty in climatic parameters—a question, if explored, that bridges a gap in water resources management plans. To address this question, we projected the changes, over Qatar, in four climatic parameters (surface air temperature, precipitation, wind speed, and potential evapotranspiration) from eight Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) during the middle of the 21^st century (2031–2050). Using topographic and groundwater data, a physically-based water balance model was built to simulate future floods under all these scenarios. Findings show high uncertainty in climatic parameters. Relative to the historical period, values varied under RCP4.5 (RCP8.5) from +1.8 to +3.4 (+3.8 to +5.6)^°C for average temperature, -48% to +15% (-60% to +6%) for annual precipitation, -0.23 to +0.1 (-0.27 to +0.04) m/hour for wind speed, and from -5.7 to +12.8 (+4.3 to +17) mm for annual potential evapotranspiration. Uncertainty in climatic parameters caused significant uncertainty in future floods estimations. During the middle of the 21^st century, floods simulations varied from -67% to +64% with an average value of -20% under RCP4.5, and from -81% to +8% with an average value of -36% under RCP8.5. The greatest uncertainty resulted from the driving models, whereas the choice of emission scenario had a secondary impact. Since floods studies are critical to save lives and assets, the study’s findings emphasize the importance of both considering the uncertainty associated with climatic parameters and the regional climatic information chosen.

How to cite: Ajjur, S. B. and Al-Ghamdi, S. G.: Quantifying the uncertainty associated with future floods simulations from regional climate models, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-703, https://doi.org/10.5194/iahs2022-703, 2022.

Urban catchments are very complex systems due to urban development. In these contexts, the intense urban expansion induces local changes in the hydrological cycle and increases flood risk by extending impervious surfaces and altering the natural watercourses. Therefore, modelling them is a challenging task specially in data-sparse regions. Indeed, a compromise has to be reached to take into account the heterogeneity while avoiding over parameterisation. The Oued Fez catchment located in the northern part of Morocco is a typical example of urban catchments: its land cover is highly heterogeneous and has channelised water courses. In this work, the ATHYS modelling platform is used to simulate 59 selected flood events monitored on the urban and peri-urban parts of the Oued Fez catchment over the 2008-2018 periods. Two production functions, SCS (Soil Conservation Service) and a linear reservoir are combined with the lag and route transfer function. The drainage network is derived based on a high-resolution Digital Elevation Model (Source Dem from ALOS PALSAR RTC, https://asf,alaska,edu/datasets/sar-data-sets/alos-palsar/, 12,5m resolution). The road network is used as a proxy of the stormwater network to force the drainage directions along the motorways and three types of land use classes are used to parameterize the two production functions. Tests are carried out at hourly and sub-hourly time steps using both the natural and modified drainage networks. The main results show that using the road network and minimal land use classes improves model performance. The field data also shows that despite lower rainfall values, the highest peak flow values are recorded in 2017/2018.  This is due to stream channelisation and increased stormwater network coverage.

Keywords: Hydrological modelling; Flood events; Urbanization; Distributed hydrological models; ATHYS, Data-scarce regions ; Oued Fez catchment.

How to cite: Bouizrou, I., Chahinian, N., Perrin, J.-L., Müller, R., and Rais, N.: Assessing the effects of urbanization on flood events using hydrological modelling: A case study of the Oued Fez urban catchment (Morocco), IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-6, https://doi.org/10.5194/iahs2022-6, 2022.

How to cite: Nortes Martinez, D., Grelot, F., Choley, C., and Finaud-Guyot, P.: Flood impact assessment in urban context. How important is it to couple hydraulic and economic models at a fine scale?, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-71, https://doi.org/10.5194/iahs2022-71, 2022.

How to cite: Grelot, F., Bailly, J.-S., and Dorchies, D.: Flood scenarios sampling effect on annualised flood damage estimation, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-40, https://doi.org/10.5194/iahs2022-40, 2022.

As an alternative to straightforward numerical integration of annual damage costs as function of either the return period or the exceedance probability, the expected annual damage, EAD, can be estimated analytically by using a log-linear model for the damage curve. With this model, it is found that it is important to assess the return period where damage begins as precisely as possible, and that the relative error in damage data gives rise to an error in EAD of the same order of magnitude. Moreover, the model allows for extrapolation above the largest return period for known damage costs. The precision of the EAD estimation is analysed in detail, and it is found that usual numerical integration over either the return period or the exceedance probability can give contradictory results, when only few data is available for the damage function. Using a piecewise log-linear damage function is show to be less sensitive to limited data availability. Finally, the necessary corrections when damage occurs below the 1-year return period are determined.

How to cite: Rosbjerg, D.: Estimation of expected annual damage, EAD, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-11, https://doi.org/10.5194/iahs2022-11, 2022.

Hydrogeomorphic scaling laws, providing consistent flood flow depth estimations as a function of contributing drainage areas, are widely used by DTM-based parsimonious floodplain delineation methods. Recent research demonstrated the suitability of hydrogeomorphic floodplain delineation models from basin to global scale across diverse climatic and morphological settings. However, distributed analysis of scaling laws performances in semi-arid to humid and low-gradient to steep basins are missing. In this work, we considered eleven basins in the west-central United States to assess flow depths – contributing areas scaling law parameters with varying basin slope and average annual rainfall. The outcomes of this scaling law parametrizationanalysis were used to test improved performances of the GFPLAIN hydrogeomorphic floodplain delineation model. Outcome of this work show improved performances and effectiveness of the GFPLAIN modelwith varying morphometric and climatic factors using as main input largely and freely available global climate and topographic datasets.

How to cite: Annis, A., Karpack, M., Morrison, R. R., and Nardi, F.: Impact of river basin morphology and climate on geomorphic scaling laws for floodplain mapping, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-540, https://doi.org/10.5194/iahs2022-540, 2022.

The disastrous floods of July 2021 in Germany has shown, that abstract forecasts of river discharge or water levels at selected gauges do not provide sufficient information for timely and location specific warning of the population and targeted disaster management actions. The forecasts as well as the available flood hazard maps did neither allow for detailed information, which areas will actually be affected, nor could any information about the severity of the flooding derived. In order to provide better informed flood forecasts, RIM2D was developed. It solves the inertial formulation of the shallow water equations on a regular grid, and is highly parallelized on Graphical Processors (GPUs). Moreover, the modelling concept is parsimonious and allows for fast model setup. We will show that hydraulic simulations driven by the available gauge forecasts would have been feasible with sufficient lead time to provide spatially explicit forecasts of inundation depths and flow velocities. Moreover, we also show that impact forecast derived from the simulations would in terms of hazard maps indicating human instability in water and building failure hazard can be additionally derived for the operational forecasts. We argue that using these hydraulic and impact forecasts would have had an substantial impact on the flood alertness of the population and responsible authorities, enabling a better early warning and disaster management, which eventually saves lifes in such extreme flash flood conditions.

How to cite: Apel, H., Vorogushyn, S., and Merz, B.: RIM2D – fast 2D hydraulic modelling for operational flood and flood impact forecast, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-634, https://doi.org/10.5194/iahs2022-634, 2022.