S11

Abstract: Catastrophic extremes not only depend on the large-scale atmospheric circulation situations, but also on water vapor transport. In this study, we use an unsupervised neural network algorithm, self-organizing map (SOM) to identify and visualize the large-scale atmospheric circulation patterns (CPs) over the spatial domain of 10°S–70°N and 40°E–170°W, which represented by standardized anomalies of 850 hPa geopotential height. Theil-Sen estimator, Mann-Kendall (MK), and Pettitt test are chosen to investigate the change trends and abrupt points in the time series of extreme precipitation over the Central-Eastern China (CEC) during 1960–2015. Results show that extreme precipitation over the southeast CEC demonstrates a significant positive trend (at 90% significance level). Regarding the average abrupt points of extreme precipitation frequency and its amount are 1988.3 and 1988.7 respectively, the time series are divided into two periods, i.e., 1960 to 1989 and 1990 to 2015. Then, we objectively adopt 5×5 SOM nodes for each period to represent the atmospheric CPs. At first, the simultaneities for extreme precipitation of 228 rain gauges are examined by using event synchronization, and these gauges are separated into four clusters by using the modularity method. Based on the SOM results, by examining the synchronization degree between extreme precipitation occurrences across the four clusters, we found that the patterns characterized by obvious negative anomalies of 850 hPa geopotential height over the Eastern and Southern Asia continent are highly synchronized with extreme precipitation events around the CEC. Moreover, comparing for each cluster the most synchronized CPs to heavy events of the two periods, we found significant changes in the structure and occurrence of CPs driving heavy rainfall events, that might be a consequence of change of the global pole-equator and ocean-land contrast temperature gradients.

Keywords: extreme precipitation, large-scale circulation patterns, self-organizing map, event synchronization, integrated vapor transport

How to cite: Jiang, Q., Giannini, M., and Cioffi, F.: Potential links between large-scale circulation patterns and extreme precipitation events over the Central-Eastern China, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-37, https://doi.org/10.5194/iahs2022-37, 2022.

In response to climate change, torrential rains undergo changes in their magnitude and temporal properties. In many places, it is predicted that these extreme short-duration rainfalls will intensify, become more frequent, and shorten in duration. In climate change impact studies, the spatial aspect of torrential rain is often overlooked, despite many case studies demonstrating its importance. The main reason why changes in rainfall structure are ignored is the lack of information derived from convection-permitting models. They are capable of simulating the rainfall at the fine spatial and temporal scales required to accurately represent torrential rainfall under various warming scenarios, although they are very computationally demanding. Recent studies have shown a relationship between rainfall spatial structure and air temperature but the relationship is not universal and varies by location. In some places, increasing temperature is associated with an increase in rainfall heterogeneity and a decrease in storm area, while in other locations it is associated with rainfall intensification and an increase in storm area. For flood impact assessments, we propose a parameterization method to change the spatial structure of extreme short-duration rainfall. The method is applicable to either simulated data derived from convection-permitting models or observed data obtained from remote sensing devices (such as weather radar) and can be adapted to a variety of global warming scenarios. We will illustrate how the method can be applied to alter the spatial profile of a design storm and demonstrate its implications for assessing changes to flood statistics.

How to cite: Peleg, N., Leitão, J. P., Paschalis, A., Fatichi, S., Molnar, P., and Burlando, P.: Parameterizing the spatial structure of torrential rain for flood impact assessments, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-163, https://doi.org/10.5194/iahs2022-163, 2022.

The Julian Alps, located in the region of Friuli Venezia Giulia (FVG, Northeastern Italy), record the heaviest precipitation annual totals in the country. Due to the complex orography and several other prone factors, effects of both prolonged and extreme precipitation can be particularly damaging in this area, causing debris flow, flash floods, avalanches. A proper planning of protection against natural hazards then requires the understanding of possible modification in rainfall characteristics. Since the mountain watersheds of the Alpine area are characterized by a very short time of concentration and hydrological response, extreme events are of particular interest, and rainfall analyses at sub-daily scale could not be appropriate.

The region counts on a dense ground-station network which is managed by the regional Civil Protection Agency, constituted by 2 main rain-gauges networks, based on CAE and Micros-SIAP technology, respectively; this last is co-managed by the OSMER-ARPA (OSservatorio MEteorologico Regionale-Agenzia Regionale per la Protezione dell’Ambiente) FVG. The networks count a total of about 200 rain-gauges; for some stations, data at 5-minute resolution are available since the 1996 (CAE network), whereas Micros-SIAP works continuously and at high resolution since the early 2000s. Over the last two decades, the temporal resolution of stations has been progressively increased up to 1-minute step.

In this work, we propose a comprehensive analysis of the available dataset at high temporal resolution (i.e. 30 min, 5 min and 1 min) in order to verify whether trends in very short rainfall duration are underway. At this aim, we first analyzed the continuous time series of data recorded by a sample of rain-gauges by the two networks. A preliminary analysis aims at verifying the consistency of the dataset at the higher resolutions. Statistical trends are then assessed by comparing two methods, i.e., the classical Mann-Kendall and the quantile regression at different thresholds and durations. The quantile regression method, which is increasingly used in hydrology, allows to detect changes in the tails of the rainfall distributions and to screen the whole rainfall time series, differently than the traditional methods that require a subset of data (e.g., the rainfall annual maxima).

How to cite: Arnone, E., Treppiedi, D., and Noto, L. V.: High-resolution rain analysis in FVG, Northeastern Italy, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-177, https://doi.org/10.5194/iahs2022-177, 2022.

Water and disaster risk management require accurate information about hydrometeorological extremes. Estimation of rare events by extreme value analysis is hampered by short observational records. Probabilistic seasonal forecasts allow assessing the uncertainty in the estimation of extremes. From meteorological seasonal reforecasts and therewith driven hydrological simulations, we create hundred- to thousand-year-long surrogate timeseries across Europe. We identify independent samples based on the assessment of the forecast skill, and extract precipitation and streamflow extremes to explore the impact of sample size on return period estimations. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is reduced at 100-year samples, and stabilizes at very low uncertainty around 500 years. We discuss the benefits and limitations of this method, and how it can be applied to study climate change and multiple extremes.

How to cite: Berg, P., Klehmet, K., Bozhinova, D., Crochemore, L., Pechlivanidis, I., Photiadou, C., and Yang, W.: Robustness of precipitation and streamflow extremes in the surrogate world of seasonal forecasts, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-191, https://doi.org/10.5194/iahs2022-191, 2022.

Future rainfall extremes are expected to increase due to global warming based on both theoretical considerations and climate model outcomes. Common (yearly) extremes and rare (decennial or centennial) extremes may be affected differently. Here we show that the rarer the event, the more it is relatively expected to increase in a future climate. For the mitigation scenario SSP1-2.6 and the high emission scenario SSP5-8.5 daily land rainfall extremes will increase by 10.5 % and 28.2 %, respectively, for yearly events and by 13.5 % and 38.3, respectively, for 100-year events by the end of this century. These numbers are based on frequency and extreme value analyses applied to 25 different CMIP6 earth system models, weighted for independence and performance. The findings are consistent and statistically significant across all 25 earth system models, 102 different model runs, and four different possible climate futures. Our results show distinct regional differences, with some regions disproportionally affected relative to others. This has important implications for engineering design standards, which need to be raised more for systems designed for the rarest events.

How to cite: Gründemann, G., van de Giesen, N., Brunner, L., and van der Ent, R.: Rarest rainfall events have greatest relative increase under climate change, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-231, https://doi.org/10.5194/iahs2022-231, 2022.

Hydro-climatological applications often require global climate models (GCMs) outputs to assess the impacts of climate change. However, it is well known that the direct use of GCM simulations is limited as their spatial and temporal resolution are insufficient to provide output at the regional scale required in assessing changes in extreme rainfall. Although regional climate models (RCMs) forced with GCM data are widely used to resolve finer resolutions, their application is hindered by systematic biases contained in large-scale circulation patterns from driving GCM data. To deal with these considerable biases, recent studies have suggested the bias correction of the input boundary conditions of RCM.

This study focuses on the impact of bias corrections in the input boundary conditions of RCM on extreme rainfall events. Three bias correction methods are used: mean, mean and variance, and nested bias correction (NBC) that corrects lag-1 autocorrelations. RCM used here is the Weather Research and Forecasting model (WRF), and the European Center for Medium-Range Weather Forecast’s (ECMWF) ERA-Interim (ERA-I) reanalysis model is used as an “observational” reference for bias correction. The downscaling is performed over the Australasian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain.

Two quantitative measures are used to evaluate the impact of bias correction on the RCM output: root-mean-square errors (RMSE) and bias. Indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used to evaluate bias correction performance on extreme rainfall.

It is clear from the statistics used here that bias correction on the input boundary condition produces a noticeable improvement in daily precipitation percentile indices. The results also show that the sophisticated method representing rainfall variability and long-term persistence corrects details in simulating extreme rainfall.

How to cite: Kim, Y., Evans, J., and Sharma, A.: Impact of bias-corrected RCM lateral boundary conditions on precipitation extremes, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-312, https://doi.org/10.5194/iahs2022-312, 2022.

The majority of rainfall measurements in the world is at the daily scale, i.e. expressed over fixed 24-hours. It is then evident that the 24-hour annual maximum rainfall depths, which refer to a period starting at any instant, must not be less than the daily extremes, and are generally higher. The ratio between these extremes, called Hershfield factor (HF), has been studied to correct the errors between fixed time interval and sliding maxima, allowing to take advantage of the relevant amount of information included in historical records of daily extremes. For instance, before 1980, in the Italian Hydrological Yearbooks only a subset (< 50%) of the rain gauges was equipped with a recording device, from which annual maxima over 1, 3, 6, 12 and 24 consecutive hours can be derived.
In our study we investigated the possibility of using the daily data to enrich the availability of sliding maxima rainfall measurements included in the Improved Italian – Rainfall Extreme dataset (I²-RED), by selecting the Po river basin (North of Italy) as a case study. We retrieved from SCIA (http://www.scia.isprambiente.it/wwwrootscia/Home_new.html) and then we quality-controlled all the daily rainfall measurements available over this area from early 1900 until today. We computed the annual HF for all the stations and all the years where both the daily and the hourly extremes were available, to obtain data that can be analyzed in their interannual and spatial variability. 
Analyzing the HF spatial distribution within the Po basin we found values similar to the ones suggested in the literature. Hershfield himself originally estimated a mean value of about 1.13 for the United States, while further studies suggested values in the range of 1-1.2. The spatial distribution of the average HF values turns out to be related to the geographic position of the stations and entails the possibility to identify some distinct areas with a positive or negative anomaly. This map allowed us to reconstruct the missing 24-hour extremes in stations with only daily data, and improve the knowledge of the spatial variability of sub-daily rainfall extremes.

How to cite: Mazzoglio, P., Butera, I., and Claps, P.: Increasing rainfall data density in Northern Italy through daily extremes and the Hershfield factor, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-335, https://doi.org/10.5194/iahs2022-335, 2022.

Intense precipitation events often occur in Mediterranean regions. These phenomena depend on the presence of mountainous and hilly reliefs combined with masses of humidity caused by the proximity of the sea. Floods are the most significant natural hazards in the region that may cause widespread devastation. Therefore, a proper characterization of these extreme precipitation events is crucial.

Extreme Value Theory (EVT) is a branch of statistics that provides a suitable framework for the statistical modelling of extreme events. Owing to the spatial heterogeneity of the Mediterranean area, the distribution of extreme precipitation events is non-stationary in space. To take non-stationarity into account, the parameters of the distribution can be viewed as functions of covariates that convey information on the spatial heterogeneity. Such functions may be implemented as a generalized linear model (GLM) or with more flexible non-parametric non-linear models such as Artificial Neural Networks (ANN).

In this work, we aim at evaluating and comparing several statistical models that allow to interpolate spatially the distribution of intense precipitation events. The statistical models combine the distribution of extremes with a GLM and an ANN for the spatial interpolation of distribution parameters. Key issues are the proper selection of the complexity level of the ANN (i.e. the number of hidden units) and the proper selection of geographical covariates.

Three sites that form a north-south aridity gradient are included in our study : a region in the French Mediterranean, the Cap Bon area in North-East Tunisia and the Merguellil catchment in central Tunisia. The comparative analyses aim at assessing the genericity of state-of-the-art approaches to interpolate the distribution of extreme precipitation events.

• KEYWORDS: Intense precipitation events, non-stationarity in space, extreme value theory, spatial interpolation.

How to cite: Hammami, H., Carreau, J., Neppel, L., and Elasmi, S.: Spatial non-stationary extreme precipitation modelling in the Mediterranean region., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-597, https://doi.org/10.5194/iahs2022-597, 2022.

Droughts are one of the most severe natural hazards, causing extensive economical losses and often regional conflicts due to water scarcity. Central Asia is especially sensitive to droughts because the region heavily depends on water for hydropower and irrigation. The drought warning system is needed for water-related political discussions and decision-makers in the region to mitigate potential water governance and make action plans for agriculture depending on the water availability.

Central Asia has limited ground observations and additionally a lot of outdated measurement stations from the Soviet era, therefore the usage of remotely sensed data is beneficial in this region.

Here we use several drought indices to analyse historical droughts and possibly predict future droughts. Already developed indices such as Drought Severity Index (DSI) based on GRACE and GRACE-FO total water storage anomaly (TWSA) data and a widely used Standard Precipitation Index (SPI), based only on precipitation, were calculated. Additionally, other climate factors were investigated and their statistical relationship with DSI and SPI, such as groundwater and particularly, snow cover and snow water equivalent (SWE). We applied near real-time monthly TWSA data from GRACE/GRACE-FO and MSWEP (Multi-Source Weighted-Ensemble Precipitation). Daily snow cover data from MODIS was used and Copernicus products for groundwater and SWE.

In Central Asia, most of the water resources come from snow and glacier melt, coming from the Pamir, Tian Shan and Hindukush mountains, and the study was focused on assessing the snow cover and SWE in the winter months, particularly before the drought period. The goal was to understand and quantify the relationship between these climate factors and historical droughts based on DSI and SPI. Ultimately, the established relationship between climate factors and drought indices could provide an early warning for the upcoming arid and drought period. The obtained results show that drought conditions can be well identified using the remote sensing information in Central Asia.  The results also show that the region is experiencing more frequent drought conditions in the last 5 years.

How to cite: Latinovic, M. and Gafurov, A.: Estimating droughts in Central Asia based on several climate indices and climate factors, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-580, https://doi.org/10.5194/iahs2022-580, 2022.

In recent years, West Africa has experienced an increasing number of flood disasters, urging governments and decision makers to take adaptation measures, particularly in cities where the population is growing very fast.

The present study focuses on the hydrological hazard of the city of Niamey (capital of Niger) along the Niger River. The regime of the Niger river at Niamey is marked by two successive floods: a first flood (July-August) resulting from the contributions of local Sahelian catchment (Sahelian red flood) and a second flood (February-March) resulting from the contribution of upstream catchments (Guinean black flood). Past studies have shown how the 1st flood, which was almost non-existent in the 1950s and 1960s, has predominated since the 1980s causing the most damaging floods due to a combined effect of climate and land use/cover changes. However, there is no in-depth study on statistical flood modelling, which is an essential step in any flood management strategy. The aim of the study is to evaluate how Niger discharge regime has changed since the 1950s with a specific focus on floods with the aim of deciphering the impact of Guinean and local flows in a statistical model.

To do so, an original method is first proposed to isolate local flows from Guinean flows in the Niamey discharge data series. This separation allows us to distinguish three typologies of maximum annual floods according to whether they are generated by Guinean flows only, local flows only or a mix of the two. This observation leads us to propose different models of extreme discharge in Niamey based on the non-stationary extreme value theory able to consider both the typology of the floods and their temporal evolution.

How to cite: Sauzedde, E., Vischel, T., Panthou, G., Tarchiani, V., and Massazza, G.: Statistical model for compound flood hazard to separate local and upstream flow inputs on the Niger River at Niamey, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-341, https://doi.org/10.5194/iahs2022-341, 2022.

The understanding of the temporal properties of hydroclimatic extremes is critical to the mitigation of related risk as well as to society’s perception of risk. While the marginal properties of extremes have been extensively studied in the literature, their temporal behaviours have been rather overlooked, or approached via deterministic reasoning. We focus on the temporal variability and clustering mechanisms of extremes as seen in long-term hydroclimatic records, highlighting their links to the inherent stochastic properties of the parent hydrological process. To this aim, we apply a new simulation algorithm (Koutsoyiannis and Dimitriadis, 2021) capable of simultaneously reproducing the time dependence structure of a stochastic process, from short-term dependence to persistence (i.e. Hurst-Kolmogorov dynamics), its time directionality as well as its marginal distribution, irrespective of its type. The performance of the methdology in reproducing the observed extremal patterns is evaluated and the practical implications of the findings are discussed.

Reference: D. Koutsoyiannis, and P. Dimitriadis, Towards generic simulation for demanding stochastic processes, Sci, 3, 34, doi:10.3390/sci3030034, 2021.

How to cite: Iliopoulou, T., Dimitriadis, P., and Koutsoyiannis, D.: Investigating the clustering mechanisms of hydroclimatic extremes: from identification to modelling strategies, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-382, https://doi.org/10.5194/iahs2022-382, 2022.

One of the most impressive flash floods of the 20th century in France as well as in Catalunia occurred in the Eastern part of the Pyrenees on October 1940. 47 people were killed in France during this extraordinary event and more than 100 in Spain. It caused tremendous damages to buildings and, in particular, destroyed the center of the resort town of Vernet-les-Bains on the slopes of the mount Canigou. The maximum observed 24-hour accumulated rainfall reached locally 1000 mm and this remains until a record value for the French European territory. The flood has been abundantly documented by the technical State services as well as some scientists of the time and a large part of this documentation has been archived.

At the light of the recent advances in flash flood studies, this data set has been unearthed and the past analyses of the event have been deeply revisited. This revealed, in particular, that the peak discharge values, on which local risk assessment studies are based, had been largely over-estimated. This led to several mis-interpretations of the processes occurred during this flood such inundation dynamics or the driving factors of the tremendous observed scour and erosion volumes. Some particular features of this event could also be confirmed or revealed: (1) an impressive wave propagation of about four million cubic meters due to the breach of a natural dam due to a massive landslide in the upper part of the Tech river which occurred during the night, affected already destroyed areas and got therefore almost unnoticed, (2) a local major amplification of the inundation induced by the breach of a railway embankment, (3) the dynamics of scour and erosion. Beyond the conclusions drawn on this specific extreme flash flood, this study illustrates that our knowledge about extreme flood events is still limited by their poor and often biased documentation. New and adapted observation and documentation strategies, based for instance on the systematic collation and analysis of opportunistic data such as amateur videos, have to be implemented to enable a real progress in the science of extreme flash floods.

How to cite: Gaume, E., Payrastre, O., Coeur, D., and Kovacs, Y.: Reanalysis of the October 1940 record flash flood in the Pyrenees, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-384, https://doi.org/10.5194/iahs2022-384, 2022.

Dam breaks can origin important economic and human losses. Failure of such infrastructure may be due to flooding that exceeds the design capacity of spillways. Design floods are estimated through a statistical analysis of observed data, but observations are usually short. Furthermore, high return periods are used for estimating design floods and climate change is expected to increase the frequency and magnitude of floods in the future. Therefore, new methodologies to assess hydrological dam safety considering short observed data and climate change are required. 
A stochastic methodology to assess hydrological dam safety considering climate change is presented. The methodology is applied to the Eugui Dam on the River Arga in the north of Spain that has a gated spillway. 
The stochastic model STORAGE (De Luca and Petroselli, 2021) based on the Neymann-Scott Rectangular Pulse Model has been used to simulate long time series of precipitation with a time step of 15 minutes. Delta changes extracted from precipitation projections of 12 climate models, three periods and two emission scenarios (Garijo and Mediero, 2019) are used to consider climate change.
The precipitation time series generated stochastically are transformed into runoff time series by using the COSMO4SUB model (Grimaldi et al., 2021). It is a continuous model that uses a high-resolution digital terrain model, land cover and the precipitation supplied by the STORAGE model as input data, providing 15-min continuous runoff time series as output. The curve number and time of concentration variables have been calibrated by minimizing a set of objective functions.
Inflow hydrographs are extracted from runoff time series simulated by COSMO4SUB. The Volume Evaluation Method (MEV) (Girón, 1988) is applied to simulate the operation of spillway gates, obtaining maximum water levels in the reservoir and outflow hydrographs. The MEV method specifies when the gates should be opened and closed to reach the target water level. Hydrological dam safety is assessed with the frequency curve of maximum water levels in the reservoir for the climate models. Therefore, the methodology proposed allows practitioners and dam owners to assess the hydrological dam safety requirements detailed in the regulations, accounting for climate change.

How to cite: Soriano Martín, E., Mediero Orduña, L., Petroselli, A., Grimaldi, S., and De Luca, D. L.: Effects of climate change on hydrological safety of dams with gated spillways., IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-420, https://doi.org/10.5194/iahs2022-420, 2022.

Sub-Saharan Africa is affected by a high-level of temporal and spatial variability in climate, with large impacts on water resources, human lives and economies, notably through hydrological extremes, such as floods and droughts. Using a newly reconstructed 65-year daily streamflow dataset of over 600 stations distributed throughout sub-Saharan Africa, we first highlight that the frequency, intensity and duration of hydrological extremes are strongly impacted by decadal to multi-decadal variations. However, the key factors driving such decadal to multi-decadal variability remain poorly documented and understood. Thus, here, compiling information on local-scale drivers (precipitation, temperature, soil moisture) and large-scale drivers (sea-surface temperature modes of variability, such ENSO and AMO, in the different ocean basins), using relative importance analysis and multiple datasets (ERSSTv3, ERA5-land, REGEN), we investigate the respective contribution of large-scale versus regional-scale processes in driving decadal to multi-decadal variability in long-lived and short-lived flood and drought.

How to cite: Ekolu, J., Dieppois, B., Eden, J., Tramblay, Y., Villarini, G., Mahé, G., Paturel, J.-E., and van de Wiel, M.: Decadal variability in long- and short-lived hydrological extremes in Sub-Saharan Africa, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-422, https://doi.org/10.5194/iahs2022-422, 2022.

Recent record-breaking heat wave events around the world raised awareness and concerns that these extremes are more likely to happen due to global warming. Previous studies showed that coupling a 3D groundwater representation to the land surface of regional climate models impacts near-surface air temperatures (Keune et al., 2016; Barlage et al., 2021). However, the effect of groundwater dynamics on long-term simulations and climate projections remains to be explored. In order to advance our physical understanding, in climate time scale simulations, we applied the Terrestrial Systems Modelling Platform (TSMP, Shrestha et al., 2014), which closes the terrestrial water cycle and includes a 3D variably saturated groundwater flow representation. First, we evaluated the TSMP heat waves climatology forced by ERA-Interim (Furusho-Percot et al., 2019) in the context of a representative EURO-CORDEX Regional Climate Model ensemble, and an observational reference data (E-OBS). In our evaluation, TSMP shows consistently improved statistics with respect to observations in comparison to the RCM ensemble. Then, we used TSMP with 3D groundwater to downscale a CMIP5 Global Climate Model (MPI-ESM-LR, r1i1p1 experiment by the Max-Planck Institute) simulation over Europe and compared to RCM similar EURO-CORDEX downscaling experiments for the periods of 1976-2005 and 2006-2099 using the RCP2.6 and RCP8.5 scenarios. With the fully coupled TSMP simulations, we are able to address the following questions: How are future heat waves going to evolve in Europe under near-natural conditions? How does the evolution differ from the projections of climate models that do not consider groundwater dynamics explicitly? Future work will additionally consider groundwater use scenarios to identify the role of groundwater for drought and heat wave mitigation.

How to cite: Furusho-Percot, C., Poshyvailo-Strube, L., Hartick, C., Goergen, K., and Kollet, S.: Does Groundwater Dynamics Impact Heat Waves Simulations and Projections? Explicit Groundwater Representation in a State-of-the-art Regional Climate Model Ensemble over Europe, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-533, https://doi.org/10.5194/iahs2022-533, 2022.

Global warming is projected to result in changes in streamflow in West Africa with implications for recurrent droughts and floods in the region. This study assesses changes in the timing of low and high flows under climate change in the poorly gauged and transboundary Volta River basin (VRB) in West Africa. The mean annual minimum (MAM) flow of seven consecutive days is considered as low flow, while high flow is calculated as the annual maximum flow (MAF) corresponding to the highest peak flow in a calendar year. The method of circular statistics is used to estimate the timing of AMF and MAM based on the mean date of occurrence (D), and their seasonality based on the concentration of the dates of occurrence (r). River flow is simulated with the fully distributed mesoscale hydrologic model (mHM), which is thoroughly calibrated using a novel multivariate calibration based on streamflow and satellite data. The mHM model is forced with bias-corrected climate projection datasets consisting of 43 RCM and GCM model combinations from CORDEX-Africa under three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5). The changes in AMF and MAM are analysed over three future horizons (2021-2050, 2051-2080 and 2071-2100) relative to the historical baseline period (1991-2020).

The results show that the date of occurrence of AMF varies between the calendar days 246 and 252 across the three sub-basins (Black Volta, White Volta, Oti), and it is projected to drop by -2 days over the twenty-first century. A strong seasonality of high flows is observed as r exceeds 0.96 on average and hardly change in the future. The date of occurrence of MAM varies between the calendar days 132 and 139. In contrast to the AMF, there is a forward shift in the date of occurrence of MAM as it is projected to increase on average by +4 to +9 days across sub-basins, and up to + 14 days under RCP8.5, which might be explained by the forward shift of the rainy season. The r of MAM is 0.6 and slightly drops in the future, denoting a higher variation in the seasonality of low flows.

How to cite: Dembélé, M., Vrac, M., Ceperley, N., Zwart, S. J., Larsen, J., Dadson, S. J., Mariéthoz, G., and Schaefli, B.: Shifting of streamflow timing and seasonality under climate change in the Volta River basin, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-545, https://doi.org/10.5194/iahs2022-545, 2022.

As floods and droughts strongly influenced the navigability of the river and the commercial activity of the region, the Rhône at Beaucaire has always been monitored by the locals. Consequently, one of the French first permanent hydrological gauging stations was installed at Beaucaire at the beginning of the 19^th century. The Lower Rhône valley has remained a vulnerable area in terms of flood risk. A recent archival work (Pichard et al., 2017) allowed to build a continuous daily stage time series from 1816 to 2020. The goal of this work is to establish the corresponding series of discharge, considering the main sources of uncertainty, and to improve the statistical knowledge of the flood occurrence and variability in the Lower Rhône valley. The conversion from stage series to discharge series is not straightforward, especially for the ancient times for many reasons such as ungauged periods, rating shifts and morphogenic floods, metrological or climatic changes. First, the periods with stable stage/discharge relationships were determined by applying the segmentation procedure of Darienzo et al. (2021) to the available gaugings since 1845. This recursive segmentation procedure accounts for both gaugings and rating curve uncertainties, through a Bayesian framework. Then, the uncertain rating curves of each period have been estimated using the BaRatin SPD model (Mansanarez et al., 2019), allowing to include prior hydraulic knowledge and to transfer information across periods. After checking the homogeneity of this exceptionally long discharge series, a flood frequency analysis has been conducted in a Bayesian framework. Some assumptions have been tested to improve the estimation of the parameters of the GeV distribution, such as a regional estimation of the scale parameter, or the introduction of climatic covariates for a time-varying analysis. A key objective of this work was to account for uncertainties at all steps steps of the approach, from the stage measurements and the gaugings to the flood quantiles estimation.

How to cite: Lucas, M., Lang, M., Bard, A., Renard, B., Le Coz, J., Darienzo, M., and Pierrefeu, G.: Hydrological records of the Rhône River at Beaucaire from 1816 to 2020: accounting for uncertainty in the discharge estimation and flood frequency analysis, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-650, https://doi.org/10.5194/iahs2022-650, 2022.

Extreme rainfall and associated river floodings are important concerns in modern human societies. Despite a recent increase of extreme rainfall, there is no evidence of an increase of the intensity and frequency of flash floods in Southern Europe, and future projections of flash-floods are quite uncertain in part due to the coarse resolution of available climate model simulations.The recent development of convection-permitting climate models allow a better representation of precipitation extremes. This new generation of climate models have been little employed in combination with hydrological models up to now, and their added value for flash flood modeling remains to be identified.
In this work, a 2.5-km convection-permitting climate model (CNRM-AROME) simulation is used to force two hydrological models (CREST and GR5H). This new modeling chain is tested in a French mediterranean catchment, the Gardon at Anduze, that experienced severe flash flooding episodes over the last decades. Hydrological models are calibrated using the COMEPHORE 1 km observed precipitation dataset merging radar and rain gauge rainfall at the hourly time step. We compare the CNRM-AROME-based hydrological simulation to a benchmark run driven by a conventional CORDEX 12-km CNRM-ALADIN simulation. The analysis of the peak discharges simulated by both hydrological models driven by the different meteorological inputs allows to determine how higher resolution precipitation could improve the simulation of flash floods. 

How to cite: Poncet, N., Lucas-Picher, P., Tramblay, Y., and Thirel, G.: Does a convection-permitting climate model improve the simulation of flash floods ? A case study over a Mediterranean watershed, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-589, https://doi.org/10.5194/iahs2022-589, 2022.

The flood event in July 2021 caused more than 180 death tolls in Germany and several billion Euro damage. In particular, the Ahr valley was severely hit by the flood, which lead to unprecedented inundation, houses and bridge destruction. Flood response was based on the official flood hazard maps, which were designed in the course of the EU Flood Directive. We revisit flood hazard in the Ahr valley by considering historical flood events in the extreme value statistics. A state-of-the-art bootstrapping approach is applied to estimate flood quantiles considering uncertainties in estimating historical discharges. Inclusion of historical floods dramatically changes estimation of flood quantiles. Nevertheless, we observe significant deviations between empirical and theoretical flood distributions due to singular outstanding extremes and discuss the suitability of the GEV model. We further drive a 2D hydraulic model with the estimated quantiles to compute inundation areas, depths and flow velocities. Human stability indicator is additionally computed. We conclude that the consideration of historical events is paramount for assessing flood hazard. We recommend to revisit flood hazard maps and include historical floods either into extreme value statistics or as indication of potential extreme inundation.

How to cite: Vorogushyn, S., Apel, H., Kemter, M., and Thieken, A.: Statistical and hydraulic analysis of flood hazard in the Ahr valley, Germany considering historical floods, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-660, https://doi.org/10.5194/iahs2022-660, 2022.

Hydrological extremes can be particularly impactful in populated areas where they are modulated by human intervention such as reservoir regulation. However, little is known about how reservoir regulation affects hydrological extremes, particularly at a regional scale. Here, I present a large data set of natural and regulated catchment pairs in the United States and assess how reservoir regulation affects local and regional drought and flood characteristics. I show that (1) reservoir regulation affects drought and flood hazard at a local scale by reducing severity (i.e. intensity/magnitude and deficit/volume) but increasing duration; (2) regulation affects regional hazard by reducing spatial flood connectedness in winter and by increasing spatial drought connectedness in summer; (3) the local alleviation effect is only weakly affected by reservoir purpose for both droughts and floods. I conclude that both local and regional flood and drought characteristics are substantially modulated by reservoir regulation, an aspect that should neither be neglected in hazard nor climate impact assessments.

How to cite: Brunner, M. I.: How do reservoirs affect local and regional floods and droughts?, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-5, https://doi.org/10.5194/iahs2022-5, 2022.

The climate is a complex dynamical system involving interactions and feedbacks among different processes at multiple temporal and spatial scales. Although numerous studies have attempted to understand the climate system, nonetheless, the studies investigating the multiscale characteristics of the climate are scarce. Further, the present set of techniques are limited in their ability to unravel the multi-scale variability of the climate system. It is completely plausible that extreme events and abrupt transitions, which are of great interest to the climate community, are the result of interactions among processes operating at a multi-scale. For instance, storms, weather patterns, seasonal irregularities such as El Niño, floods and droughts, and decades-long climate variations can be better understood and even predicted by quantifying their multi-scale dynamics. This makes a strong argument to unravel the interaction and patterns of climatic processes at different scales. Our work will discuss the potential of multi-scale approaches, new methods developed using wavelet and complex network techniques and their applications in hydro climatology to better forecast extreme floods.

How to cite: Agarwal, A., Merz, B., and Kurths, J.: Multi-scale approaches in forecasting extreme floods and their global connections, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-9, https://doi.org/10.5194/iahs2022-9, 2022.

Recent studies have sought to characterize variations of the annual maximum flood discharge series over time and across space in Europe. To further support these studies, we conduct a pan-European assessment of process controls on key statistical properties of these series, including the mean annual flood (MAF), coefficient of variation (CV) and skewness (CS) of flood discharges. We analyse annual maximum flood discharge series from 2370 catchments in Europe without strong human modifications covering the period 1960-2010. We explore how the estimated moments MAF, CV and CS vary due to catchment size, climate and other controls across Europe.

The process controls on the flood moments are identified through correlation and multiple linear regression analyses and the interpretation is aided by a seasonality analysis. Precipitation-related covariates are found to be the main controls of the spatial patterns of MAF in most of Europe except for regions in which snowmelt contributes to MAF, where air temperature is more important. The Aridity Index is, by far, the most important control on the spatial pattern of CV in all of Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments.

Finally, to provide a performance baseline for more local studies, we assess the estimation accuracy of regional multiple linear regression models for estimating flood moments in ungauged basins.

How to cite: Lun, D., Viglione, A., Bertola, M., Komma, J., Parajka, J., Valent, P., and Blöschl, G.: Characteristics and process controls of statistical flood moments in Europe - a data-based analysis, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-68, https://doi.org/10.5194/iahs2022-68, 2022.

Earth system processes have complex physics and are dynamically interlinked, making modelling and predictions difficult. In particular, current challenges for hydroclimatic systems are in understanding nonstationarity and heterogeneity driven by climatic and human influences. Hence, studying the spatial and temporal occurrences and dependencies of hydroclimatic extremes is becoming increasingly important for water resources management and hydrological services. 

Research efforts that address these issues for extreme hydrological events are large and diverse in their approaches and methodologies. In this respect, multivariate statistical methods such as clustering are approaches commonly used to reveal mechanisms affecting floods and droughts in relation to their trends and magnitude as well as their variability in time and space. Clustering is a convenient tool to analyze large hydrometeorological datasets because of its unsupervised nature. However, there are no structured insights for hydrologists to reflect on the principles and findings of data clustering for hydroclimatic extremes. 

This contribution sheds light on the why’s and how’s of clustering methods for floods and droughts based on a systematic review of the literature. Our aim is to provide a synthesis of hydrological themes and methodological concepts found in papers that investigate floods and droughts through data clustering. These insights are valuable for guiding future applications of clustering methods while enabling wider discussions on the knowledge gaps for modelling extremes in hydroclimatic systems.

How to cite: Dogulu, N., Brunner, M. I., Fischer, S., and Knoben, W.: Clustering for hydroclimatic extremes: a retrospective synthesis of the literature, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-262, https://doi.org/10.5194/iahs2022-262, 2022.

Floods are a common natural hazard with costly and often fatal consequences. Under changing climatic conditions, extreme precipitation events have increased in severity. This is expected to affect flood magnitude. However, several previous studies were not able to reach comparable conclusions in regard to direction of flood trends and their drivers. We investigate some potential explanations for this inconsistency. These include (1) the importance of climate-dependent differences in flood generating processes and (2) the effect of alternative event sampling methods. We investigate these two aspects using a quasi-global dataset comprising catchment data from Australia, Brazil, Chile, Great Britain, and the United States. The compiled dataset represents a wide range of climates.

We first evaluate how streamflow reacts to precipitation under different initial soil moisture conditions. It is well known that extreme precipitation is more likely to cause flood events under high soil moisture conditions. Our results show that this interaction of precipitation, soil moisture, and streamflow changes with aridity. This indicates that the influence of soil moisture on flood trends might change with climate zone and should be considered in future flood projections.

Secondly, we compare two sampling strategies that are currently in use: sampling events by extreme precipitation and sampling events by extreme flow. Sampling events by extreme precipitation is likely to miss some significant flood events and include lower flow events due to the strong interaction between precipitation and soil moisture resulting in misclassification of flood trends. To summarise, we recommend that future studies of flooding (i) should stratify large samples according to flood process, e.g. by using climate as an indicator of process and (ii) should include extreme flow events which are caused by moderate precipitation combined with high initial soil moisture.

How to cite: Stein, L., Woods, R., Wasko, C., and Pianosi, F.: Using flood process information to support flood trend studies, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-324, https://doi.org/10.5194/iahs2022-324, 2022.