Storm surges caused by tropical cyclones can significantly impact on coastal areas in East Asia, including megacities e.g., in China. To inform effective adaptation and mitigation planning, a robust storm surge hazard assessment is essential. Unfortunately, the real frequency-intensity distribution of relevant storm-surge levels can only be estimated with large uncertainly based on limited historical observations.

This study demonstrates the successful development of a two-step, objective and automated identification and selection approach of storm-surge relevant TCs for large model data sets where no ground truth verification is possible. In our approach, we combine for the first time two established identification and tracking tools originally developed for extra-tropical cyclones and storms and apply these to identify tropical cyclones. In the first step, we adapted the widely used Murray & Simmonds (1991) University of Melbourne tracking scheme (MS-Track) to the specific conditions of TC tracking in the North-west Pacific. In the second step, we apply the windstorm tracking tool WiTRACK to TC-induced severe wind fields to provide and attach the potential storm-surge relevant information in addition to just the core track provided by the MS-Track.

By validating our results with ERA5 reanalysis data and IBTrACS, we show that our method is simple yet has a well comparable performance in detecting and assessing relevant TC events than more complex tracking approaches. Based on this performance this approach is well-designed and specifically intended to specific applications in CAT modelling approaches, e.g. for the creation of physically consistent event sets for storm surges.

How to cite: Zhang, X., Leckebusch, G. C., and Ng, K. S.: Objective Identification of Tropical Cyclones with Severe Storm Surge Potential for the North-west Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1525, https://doi.org/10.5194/egusphere-egu24-1525, 2024.

In the context of global climate change, windstorms pose significant environmental, ecological, and socioeconomic challenges. Mountainous and forested regions of Europe, including the Veneto region in northern Italy, have been devastated by unprecedented events such as the storms in July 2023 and Vaia in October 2018, raising the question whether such events may occur more frequently in the future. The probability of observing such extremes in present-day climate can be quantified using cumulative distribution functions of annual maxima wind speeds, obtained from extreme value analysis methods. Once these are derived, however, is it near to impossible to project future changes in these distributions as extreme wind speeds are caused by storms driven by diverse synoptic conditions, the characteristics and occurrence frequency of which may change differently in response to climate change.

This study introduces a method to derive cumulative distribution functions of annual maximum wind speeds explicitly considering the intensity and occurrence frequency of multiple types of storms. Independent windstorms are separated and their maximum hourly wind speed is isolated. Storms are then organized into types based on their local wind direction using a clustering technique. We then use a multi-type Simplified Metastatistical Extreme Value distribution (SMEV) to estimate the cumulative distribution function of annual maximum wind speed for the location of interest. The study focuses on mountainous areas, seeking a simpler relation between typical wind directions and synoptic conditions.

A thorough leave-one-out evaluation with benchmark models, including the traditional Generalized Extreme Value distribution (GEV) and a single-type SMEV, is conducted on 22 mountain stations in the Veneto region (northern Italy). We show that, overall, the proposed multi-type method provides estimates of extreme return levels that are comparable with the ones of single-type SMEV and GEV. Our results demonstrate that it is possible to derive cumulative distribution functions of annual maximum wind speeds explicitly considering storms emerging from different marginal processes. This paves the way to the use of projections of large-scale atmospheric dynamics from climate models to improve our prediction of future extreme wind speeds.

Keywords: Windstorm; Extreme events; Wind direction classification; Multiple types; Simplified Metastatistical Extreme Value (SMEV); Mountainous areas.

How to cite: Fathollahzadeh Attar, N., Marra, F., and Canale, A.: Assessing windstorm hazard emerging from multiple types of storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3613, https://doi.org/10.5194/egusphere-egu24-3613, 2024.

Tornadoes represent major meteorological hazards, in terms of damages to buildings, vehicles and structures and casualties. Because of their small space scale (order of 1km or less), duration (order of 1000s), strongly nonlinear and chaotic dynamics, tornadoes cannot be reproduced in operational weather prediction and climate models. It is important to develop approaches overcoming this limitation and capable of delivering reliable early warnings by civil protection services and estimating whether frequency and strength of tornadoes will change because of anthropogenic climate change. Recently, a probabilistic approach has been developed that resulted in analytical expressions of the probability of tornadoes occurrence based on meteorological parameters that can be extracted from weather prediction and climate models, such as W_MAX (updraft maximum parcel vertical velocity, linked to the Convective Available Potential Energy CAPE), WS₇₀₀ (the wind shear in the lower troposphere), LCL (the lifting condensation level), SRH₉₀₀ (low-level storm relative helicity). An example is the formula log₁₀(P)=-6.6+W_MAX/(3.1+5.2 · W_MAX/WS₇₀₀), which is meant to describe dependence of probability P of occurrence of a tornadoes  on the surrounding environmental conditions and to distinguish among conditions with low and high probability. In this study this and similar formulas are applied to hindcasting the probability of tornadoes using ERA5 data. The purpose is to assess the skill of the method for operational prediction and explore its validity for climate change studies.

The methodology supporting this formula is extensively described in Ingrosso, R., Lionello, P., Miglietta, M. M., and Salvadori, G.: Brief communication: Towards a universal formula for the probability of tornadoes, Nat. Hazards Earth Syst. Sci., 23, 2443–2448, https://doi.org/10.5194/nhess-23-2443-2023, 2023.

How to cite: Lionello, P. and Muhammadi, A.: Testing the skill of an analytical expression for the probability of occurrence of tornadoes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12741, https://doi.org/10.5194/egusphere-egu24-12741, 2024.

Climate change affects the severity and frequency of extreme meteorological events, including hailstorms. In this regard, it is imperative to understand the factors driving the intra- and interannual variability of hailstorms. In Switzerland, this remains insufficiently understood. To address this knowledge gap, our study conducts a long-term analysis to identify potential drivers and precursors of Swiss hailstorm variability. Due to the lack of long-term data on Swiss hailstorms, we developed statistical models reconstructing hail days from 1959 to 2022, utilizing radar-based hail observations and environmental data from ERA-5. Our hailday time series shows a statistically significant positive trend in yearly hail days in both southern and northern Switzerland. This trend is mainly attributed to heightened atmospheric instability and moisture content evident in recent decades' ERA-5 data. Noteworthy natural variability is observed in both regions. To delve into the large-scale mechanisms influencing Swiss hail activity, our study uses composites to explore potential drivers and precursors. Those include soil moisture conditions, sea surface temperature anomalies, large-scale variability patterns (Piper and Kunz 2017), central European weather types (e.g., Rohrer et al. 2018), cold fronts (Schemm et al. 2015, 2016), and atmospheric blocks (e.g. Barras et al. 2021). 

How to cite: Wilhelm, L., Martius, O., Schröer, K., and Schwierz, C.: Long-Term Trends and Drivers of Hailstorms in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19897, https://doi.org/10.5194/egusphere-egu24-19897, 2024.

Hail is by far the greatest contributor worldwide to insured losses from severe convective storms on an annual basis. Individual outbreaks can cause losses well above EUR 1 bn. In Italy, severe convective storm losses have been dominating the market in the last 5-7 years, with a record of EUR 1.4 bn in 2019 prior to year 2023. On 18-25 July 2023 an unprecedented outbreak brought large hail and strong winds to Lombardy, Veneto, Friuli-Venezia Giulia, Piedmont and Emilia-Romagna, with affected cities including Parma, Turin, Milan and Venice. There were many reports of large hailstones, causing significant damage to property and motor vehicle. The European hail record was breached too. Twice. On 19 July, a hailstone measuring 16 cm in diameter was recorded in Carmignano di Brenta, and broke the previous largest hail record in Europe, which was held by a 15 cm stone found in Romania in 2016. Just five days later, a new record was set, when a 19 cm hailstone was found in the town of Azzano Decimo. This is very close to the all-time largest hail recorded of 20.3 cm, found in 2010 in South Dakota, US. Total loss estimates, of which hail was by far the largest contributor, exceeds EUR 3 bn, of which 70-80% in the property sector (residential and commercial buildings), and the remaining 20-30% in the motor vehicle sector. These were the largest hail events in Italy in recorded history, and the costliest cat event in the third quarter of 2023 for the global insurance market.

Following in the footsteps of the severe convective storm outbreak that impacted France in June 2022, these storms came after a record-hot air mass that languished over Southern Europe much of the week prior. Persistent meteorological conditions conducive to rotating supercell thunderstorms were observed for several consecutive days. These compounded with local conditions favorable for the development of severe hail over the Po Valley. In this study we present a reconstruction of these events based on event reports from European Severe Weather Database. We analyze the synoptic configurations and pre-convective environments that characterized them, with focus on those properties and features that are peculiar to severe hail-forming thunderstorms. We look at different formulations of CAPE and vertical wind shear, as well as composite parameters such as the Significant Hail Parameter and the Supercell Composite Parameter. We make use of Gallagher Re’s Severe Convective Storm Index to contextualize these events historically, and to discuss climate change trends over Northern Italy. Finally, we discuss the implications that such events and their expected frequency under climate change have on the (re)insurance market.

How to cite: Panosetti, D. and Tomassetti, U.: The July 2023 Northern Italy hailstorms from a climatological and (re)insurance market perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5962, https://doi.org/10.5194/egusphere-egu24-5962, 2024.

To manage current flood hazard and help develop climate change adaptation strategies, the government-funded project “Mā te haumaru ō ngā puna wai: Increasing flood resilience across Aotearoa” aims to better understand flood hazard and risk across all Aotearoa New Zealand, now and in the future. A crucial part of this project is the generation of nationally consistent flood maps across the whole country for the current climate and future climate projections.

First, the workflow requires as input the identification of independent floodplains. Each floodplain will be associated to its catchment and be considered a computational unit. For each domain, a design storm is generated for a given scenario (Annual Exceedance Probability, climate projection, antecedent conditions) or an historical storm is used for validation purposes. The runoff and flow routing of streams and rivers on the steep part of the catchment are simulated with the NIWA TopNET model (McMillan et al. 2016). Used uncalibrated, this hydrological model was modified to include a physically realistic soil conductivity and provide a consistent response between gauge and ungauged catchments. The model is spun up to an average base flow with consistent soil and ground water antecedent conditions. The design storm is then run through the model to provide realistic flow boundary conditions to the hydrodynamic model in the populated lower catchment. Before the inundation modelling, the spatial maps are generated, using the GeoFabrics suite (Pearson et al. 2023), across the lower catchment, based on the latest LiDAR data available and complementary databases such as OpenStreetMap for infrastructure. This process produces a hydrologically conditioned DEM (Digital Elevation Model), including waterways opening and a basic riverbed estimation, associated to a roughness length map. Finally, the flood is simulated using the hydrodynamic model BG_Flood (Bosserelle et al. 2022). The model is a GPU-enabled inundation model using a modern shock-capturing St Venant solver. The model uses a quadtree type mesh that is well suited for GPU computation and allows iterative refinement of the mesh. A first coarse resolution run is used to define the expected flood extent. This flood extent and external data such as stop bank locations, is then used to produce a refinement map defining areas where higher resolution is needed. The model is then run a second time using the variably refined mesh.

Figure 1: Scheme of the cascade of model used to develop consistent flood maps in Aotearoa New Zealand.

This workflow has been validated on several historic flood events including a fluvial flood in Westport, ANZ (56h duration, 60-year flood), a fluvial and pluvial flood in Waikanae, ANZ (12h duration, 80-year flood) and the floods in the Hawkes Bay and Tairāwhiti regions (ANZ) following the Tropical Cyclone Gabrielle in February 2023 (over 100-year flood in some areas).

This workflow is based on open-sources tools; it is modular and automated for continual improvement, to enable data update and to facilitate the creation of new scenarios.

How to cite: Harang, A., Lane, E., Bosserelle, C., Pearson, R., Cattoën-Gilbert, C., Carey-Smith, T., Shiona, H., Dean, S., Srinivasan, R., Smart, G., and Wilkins, M.: Creation of an automatic workflow for a National Flood assessment in Aotearoa New Zealand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15295, https://doi.org/10.5194/egusphere-egu24-15295, 2024.

The flood events in Germany during the summer of 2021 have once again brought to the forefront the challenges in translating scientific knowledge into effective disaster risk management practices. This paper examines the critical gap between the scientific understanding of flood risks and the practical needs of those who manage these risks. We delve into the limitations of current scientific approaches, such as flood risk and hazard mapping, in fully addressing the complexities and nuances required for practical disaster risk management, especially in the face of uncertain climate change impacts. We examine the dynamics of how flood risk information, inclusive of uncertainties, is perceived and acted upon, highlighting the psychological factors influencing these processes. The paper discusses the challenges and opportunities in translating scientific risk assessments and forecasts into practical, actionable strategies for communities and stakeholders. By highlighting the disconnects and potential areas for improvement in the science-practice interface, this paper seeks to foster a more coherent and comprehensive approach to disaster risk management. Within the framework of the Safe Development Paradox, the importance of communicating uncertainties and evaluating their potential impacts on planning and emergency responses is discussed. This paper addresses uncertainties at multiple levels and for different stakeholders, highlighting the integration of uncertainty information as a vital step in preparing for surprises and ambiguities in the context of extreme meteorological and hydrological events induced by severe weather and climate change.

How to cite: Höllermann, B.: Navigating Uncertainty in Flood Risk Perception in the Context of Climate-Induced Extremes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18692, https://doi.org/10.5194/egusphere-egu24-18692, 2024.

When a flood disaster occurs, there is an opportunity for affected individuals and decision-makers to learn from the experience. However, this learning tends to be narrowly focused on the specific event, missing the chance to discuss and prepare for even more severe or different events. For instance, regions that have been spared from havoc might feel safe and underestimate the risk. We suggest spatial counterfactual floods to encourage society to engage in discussions about exceptional events and appropriate risk management strategies. We create a series of floods across Germany by spatially shifting the rainfall fields of the 10 most expensive floods, arguing that past storm tracks could have occurred several tens of kilometers away from their actual paths. The set of spatial counterfactual floods generated includes events that are more than twice as severe as the most devastating flood in Germany since 1950. Our approach obtains peak flows that exceed the current flood-of-record at more than 70% of the gauges (369 out of 516). Spatial counterfactuals are proposed as an easy-to-understand approach to overcome society's unwillingness to consider and prepare for exceptional floods, which are expected to occur more frequently in a warmer world.

How to cite: Merz, B., Nguyen, V. D., Björn, G., Han, L., Guan, X., Rakovec, O., Samaniego, L., Ahrens, B., and Vorogushyn, S.: Counterfactual floods: What if the storm track would have taken a different path?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19701, https://doi.org/10.5194/egusphere-egu24-19701, 2024.

Storm Daniel, the deadliest recorded Mediterranean tropical-like (medicane) storm, led to severe floods in large parts of the eastern-central Mediterranean, including Greece and northern Libya. Extreme rainfall, reaching more than 400 mm day^-1, triggered a flash flood in Wadi Derna (Libya)– an ephemeral river with a drainage area of 575 km² that crosses the city of Derna at its outlet to the Mediterranean Sea. Historical measures to mitigate flood risks included dam construction in the Wadi Dernah basin since the 1970s. However, during Storm Daniel, at least two of the dams were breached, resulting in a devastating flood that inundated much of the city of Derna, with over 4,000 casualties, 8,000 missing persons, and the displacement of tens of thousands. The devastating event was the focus of media coverage for a long time, but questions regarding the role of dams and their collapse remain open, and are relevant for other dammed regions as well: How extreme was the storm? How extreme the flood would have been if the dams had not been breached? What would the outcomes of the flood look like if dams were not built in the first place?

To analyze the characteristics of the storm over Wadi Derna, the catchment’s hydrological response, and the impact of the flood on the city of Derna, we integrate various datasets and models. Satellite-based precipitation estimations (IMERG) were used to quantify spatiotemporal storm properties and the catchment-scale rainfall, which were fed into the KINEROS2 hydrological model to quantify surface runoff upstream of the collapsed dams. The modeled flood hydrograph is then fed into a 2D hydraulic model (HEC-RAS) to test three end-member scenarios: (a) dam filling, overflow, and collapse, (b) dam overflow but no collapse, and (c) no dams exist in the wadi. This combination of methods reveals that the peak discharge during the flood was ~1,400 m³ s^-1, just below the expected maximum extreme flood for this region. In the dam-collapse scenario, the populated flooded area is 40% larger than the no-dam scenario. These results emphasize the anthropogenic influence of damming natural streams on flood impacts. Given the high variability of precipitation in arid and semi-arid areas and the projected increase in extreme precipitation intensity with climate change, the Wadi Derna flood should serve as a warning sign for other populated areas downstream of a man-made dam in similar environments.

How to cite: Dente, E., Armon, M., and Shmilovitz, Y.: The September 2023 flood in Derna, Libya: an extreme weather event or man-made disaster?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15755, https://doi.org/10.5194/egusphere-egu24-15755, 2024.

The intensification of extreme precipitation in a warming climate has been shown in observations and climate models to follow approximately theoretical Clausius-Clapeyron scaling. However, larger changes have been indicated in events of short-duration which frequently trigger flash floods or landslides, causing loss of life. Global analyses of continental-scale convection-permitting climate models (CPCMs) and new observational datasets will be presented that provide the state-of-the-art in understanding changes to extreme weather (rainfall, wind, hail, lightning) and their compounding effects with global warming. These analyses suggest that not only warming, but dynamical circulation changes, are important in the manifestation of change to some types of extreme weather, which must be addressed in the design of new CPCM ensembles. We use our projections to provide the first analyses of impacts on infrastructure systems using a new consequence forecasting framework and show the implications for adaptation. It will be argued that a shift in focus is needed towards examining extreme weather events in the context of their ‘ingredients’ through their evolution in time and space. Coupled with exploration of their causal pathways, sequencing, and compounding effects – ‘storylines’ –, this can be used to improve both early warning systems and projections of extreme weather events for climate adaptation.

How to cite: Fowler, H.: Rapidly intensifying extreme weather events in a warming world: how important are large-scale dynamics in generating extreme floods?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22472, https://doi.org/10.5194/egusphere-egu24-22472, 2024.

Reconstructing historical flood events can offer critical insight into past hydrological responses to extreme weather, informing contemporary flood risk management and infrastructure design. This study employs reverse engineering, based on historical data such as recorded rainfall, flood marks, visual records, and eyewitness accounts to reconstruct a flood event. Historical data was collected by the team during a workshop with the local community. The approach involves hydrological (HEC-HMS) and hydraulic (HEC-RAS) models to simulate the flood event. The July 1968 UK storm, remarkable for record rainfall reaching 175 millimetres within 18 hours, caused extensive devastation in south-west England. This study focuses on reconstructing the 1968 flash flood on the River Chew, notably the peak hydrograph in the village of Pensford. A Monte Carlo simulation approach is used in conjunction with the HEC-HMS and HEC-RAS models to produce a range of potential input hydrographs with uncertainty input parameters (primarily event rainfall and Manning’s roughness) that match the historical evidence.  In particular, the Monte Carlo approach is implemented using a series of Python scripts enabling multiple HEC-RAS simulations to be conducted and the results synthesised in the form of an uncertainty analysis of key parameters such as peak flow. 

How to cite: Sabeti, R., R. Kjeldsen, T., and Stamataki, I.: Reconstructing Historical Flood Events: A Monte Carlo-Based Uncertainty Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-108, https://doi.org/10.5194/egusphere-egu24-108, 2024.

Rainfall return levels are guiding hazard protection, insurance models, infrastructure design, construction, and planning of cities. When deriving information about the frequency and intensity of extremes by fitting extreme value models to pointwise observations, the regionalization of these models is challenging. Rain gauges are distributed unevenly, where some regions suffer from data scarcity in space and time. To address this, topographical and/or climatological covariates are often used for the spatial interpolation. On the other hand, high-resolution climate simulations are available to provide spatial information on rainfall extremes. However, these simulations are still governed by model biases, where the bias adjustment of extremes at ungauged locations is also inducing uncertainty.   

In this study, we propose a combination of observations and a high-resolution convection-permitting climate model simulation in the framework of smooth spatial Generalized Extreme Value (GEV) models in order to estimate spatial rainfall return levels. We choose a study area over southern Germany with complex terrain, which is densely monitored with 1132 rain gauges providing more than 30-year daily rainfall observations. There, a 30-year simulation of the Weather and Forecasting Research (WRF) model is available at 1.5 km resolution driven by ERA5 reanalysis data. We combine observations and covariates from the WRF simulation in the spatial GEV and refer to this approach as sGEV-WRF.

We want to answer three research questions to assess the added value of the proposed framework:

• Is it worth the effort? Does the sGEV-WRF improve the generation of rainfall return levels compared to the WRF alone?
• Does the WRF simulation as covariate add value? Can the sGEV-WRF outperform a topography-only spatial GEV?
• Does the dynamical downscaling at high resolution add value? Can sGEV-WRF outperform a spatial GEV based on observations and covariates from the coarser resolution ERA5?

By evaluating the percentage bias, mean absolute error, and root-mean-square error, we show that the combination of observations and WRF can improve the representation of 10-year and 100-year return levels of daily rainfall.

In addition, we aim to assess the performance of this framework under data-scarce conditions. Therefore, we devise an extensive cross-validation study. We select 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all 1132 rain gauges to re-build the spatial GEV models with 1000 random folds each. We show that the performance is robust under these conditions, highlighting the potential for the application in data-scarce regions. Furthermore, in a non-stationary setup with climate model future projections, it can serve as a reliable tool to assess climate change effects on heavy rainfall.     

How to cite: Poschlod, B. and Koh, J.: Combining observations and a high-resolution climate model for the generation of spatial rainfall return levels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5310, https://doi.org/10.5194/egusphere-egu24-5310, 2024.

The consensus of climatic research indicates that the likelihoods of extreme precipitation events are going to change significantly, but specific trends depend on the type of dominant weather system, and regional landscape and climate details. Despite the effort of increasingly accurate and converging global circulation models, the uncertainties in the ensemble of CMIP6 models, and the often course spatial resolution make translation of climate models to actionable information of flood forecasts complex and uncertain. We carried out an analysis of a large ensemble of Global Circulation Models (GCMs) of the CMIP6 ensemble that were downscaled statistically as part of the NASA NEX-GDDP-CMIP6 dataset. The analysis looked at segmented windowed return period analysis using the method of l moments to fit general extreme value distributions to global climate models. With analysis of 1, 3 and 7 day duration, median, 15 and 85 percent quantiles, between 5 and 100 year return period, and global spatial coverage, the results show variations in how precipitation events of various return periods and durational are predicted to change in GCMs, and what the associated uncertainty is for various regions of the world. Intermediate analysis outputs show artifacts in yearly extreme precipitation due to the applied statistical downscaling, but relative factors to be used in precipitation scaling under climate change resolves these. Average increases in precipitation extremes of percent are observed globally (+5.1%), with many local outliers for the SSP585 scenario in 2050 (e.g. regions such as the Himalayan region (+23.4 percent median), the Sahel region(+21.6%) or South-Western Spain (-3.9%)). The other SSP scenarios change the global average factors to +3.75% and +4.32% for SPP245 and SSP370 Respectively. Very low variability in the changes is observed for return periods, indicating that the intensity probability curves shift uniformly in the model output. Precipitation events duration does more significantly alter the analysis outputs, and various areas show differences here that correlate with flash and fluvial flood susceptibility. Finally, we open-source the analysis code and link the output as a built-in dataset in the fastflood.org rapid flood simulation platform. Here, automatically derived extreme precipitation events from era5 datasets can be rescaled under climate change conditions by applying the scaling factors derived in this work.

How to cite: van den Bout, B.: Global changes in extreme precipitation linked with rapid flood simulation tools, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7617, https://doi.org/10.5194/egusphere-egu24-7617, 2024.

Extreme hydro-meteorological events are increasingly observed in southern Europe and especially in the European Alps, where they threaten ecological and socio-economic systems. To detect such events and analyse the changes in their occurrence, a proper definition of an extreme event is needed. Statistically, we define extremes from the tails of the probability distributions. However, these events are not necessarily extreme in terms of impact, and impact-related thresholds may vary spatially and temporally, i.e., single absolute thresholds do not necessarily reflect the extremes at all locations, in all time periods and all seasons. Moreover, the availability of harmonized and consistent datasets is crucial for investigating extremes in a transnational context. In this study, we focus on the identification and characterisation of extreme hydro-meteorological events affecting a transboundary Alpine region between Austria and Italy from 2003 to 2021 based on different definitions of extreme events considering spatiotemporal aspects and multiple datasets. Daily accumulated precipitation is used as the main proxy parameter to describe the potential for severe consequences, as it as it is the most broadly available quantity across different datasets compared to e.g., sub-daily precipitation sums. Moreover, its role as a triggering factor for various hazards (e.g., landslides, debris flows, pluvial and fluvial floods) is widely recognised. We analyse three different statistical methods for the detection of extreme events: (i) the identification of the highest daily precipitation amounts on a regional scale, (ii) the detection of daily precipitation values of high intensity on a local scale and (iii) the identification of exceptional daily precipitation records not in absolute terms but with respect to average conditions associated to a specific period of the year. All detection algorithms are applied to four gridded precipitation datasets, including both observation and reanalysis products, with different technical specifications. Subsequently, identified events for each method-dataset combination are blended with existing records of gravitational mass movements and fluvial floods in the Austrian-Italian border region to analyse the suitability of each combination to detect actual occurred impacts. First results indicate that most detected precipitation extremes relate to actual observed impacts (e.g., 74% for regional scale identification with reanalysis data). However, different method-dataset combinations have different strengths and weaknesses, which reflect inherent characteristics of the dataset and/or of the statistical method employed. Furthermore, some combinations show lower performance in detecting impactful events, because the dataset and method applied conflict with each other (e.g., a coarse-resolution dataset not resolving local-scale features conflicts with a statistical method searching for locally high intensities). The findings could contribute to better inform civil protection authorities about risks related to extreme hydrometeorological events, possibly affected by climate change.

How to cite: Enigl, K., Crespi, A., Lehner, S., Haslinger, K., and Pittore, M.: Detection of past extreme precipitation events and connection to recorded impacts: a multi-data and multi-method assessment over the Central-Eastern Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10058, https://doi.org/10.5194/egusphere-egu24-10058, 2024.

Floods and their devastating effects on society and economy have increased dramatically in Germany, and Europe in recent years. At the end of 2023, rivers and streams across Germany burst their banks due to heavy rainfall, affecting property, transport and power supplies and necessitating rescue operations and evacuations to protect human lives. One measure to deal with flooding and safeguard lives and property is the implementation of early warning systems, such as the European Flood Awareness System (EFAS), which provides short-term hydrological forecasts in real time. However, preparedness is essential along the responders value chain and longer term forecasts are important to anticipate, take precautions, raise awareness and generally mitigate the effects of flooding. The objective of this study is to evaluate the performance of hydrological forecasting using the seasonal meteorological forecast at a spatio-temporal resolution of 1 arcmin and day over Germany including all transboundary catchments for the period from 1990 to 2020. The hydrological model used was LISFLOOD. In the first step, LISFLOOD was calibrated using the meteorological observations, the EMO 1arcmin dataset and the discharge data from the transnational hydrological portal for all federal states and neighboring countries. The characteristics of land use, land cover, soil, groundwater, and human activity referred to as surface fields for global environmental modelling, were provided by EFAS. The second step, downscaling of the seasonal (long-term) forecast meteorological forcing to 1arcmin, is performed using a Deep Residual Neural Network (DRNN), and a bilinear interpolation approach over the seasonal forecast information of atmospheric conditions up to seven months into the future provided by the European Center for Medium-Range Weather Forecasts (ECMWF), 25 ensemble members in total. In the third step, the discharge is simulated by feeding the LISFLOOD model with two meteorological forcing scenarios, the DRNN downscaled and the bilinear approach of the seasonal meteorological forecast, to finally compare the performance with the observed runoff using the modified Kling-Gupta efficiency criteria (KGE'). The calibrated and validated LISFLOOD parameters showed a good and acceptable performance in all catchments, KGE' between 0.6 and 0.9. The DRNN downscaling technique shows a promising result, providing a good agreement between downscaled and observed dataset. Finally, the hydrological performance, KGE', is expected to be improved by 0.05 to 1 in the hydrological stations with good and poor performance, respectively, by using the DRNN downscaled seasonal forecast.

How to cite: Espitia Espitia, E. F., Diaz Esteban, Y., Heidari, F., Lin, Q., and Xoplaki, E.: Evaluation of the performance of hydrological model LISFLOOD using the ECMWF seasonal meteorological forecast at 1arcmin-1day spatiotemporal resolution over German catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17041, https://doi.org/10.5194/egusphere-egu24-17041, 2024.

Modeling the joint behavior of flood characteristics under climate change is necessary for understanding the potential changes in associated flood risk and hazards. In this study, we assessed the changes in flood duration, peak, and volume between historical and future periods through copula-based flood frequency analysis, employing the Soil and Water Assessment Tool (SWAT) hydrological model for modeling flood risk in a tropical watershed (Govindpur) lying in eastern India. Observed streamflow at the watershed outlet is obtained for the baseline period (1990-2014) for flood analysis. A suitable copula model is selected for bivariate flood frequency analysis while assuming copula parameters vary between baseline and future periods under climate change. In this study, high-resolution (12-km) climate reanalysis dataset from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and future climate projections from general circulation models (BCC-CSM2-MR, MPI-ESM1-2-HR) after downscaling and bias correction, are used for simulating flood events using SWAT. The use of high-resolution climate data for hydrological modeling and flood frequency analysis is a novel aspect of the presented study. Uncertainty in the estimation of joint return periods of flood events under climate change due to climate model selection and assumption of stationarity is also quantified in this study for the near future (2041-2070) period under the shared socio-economic pathway (SSP585) scenario. Among the GCMs used, BCC-CSM2-MR performed relatively better in simulating baseline period streamflow in the study watershed. In this study, the Clayton copula is obtained as the most suitable based on its lowest Akaike information criterion (AIC) value, and joint return periods are then derived with the help of a conditional copula. It is found that flood events are projected to become more severe in the near future; the flood peak value increased by more than 90%, while the duration is projected to decrease. Flood volume may likely double in the future, as per our analysis, suggesting the need for mitigation and precautionary measures to reduce flood risk in the watershed. Based on the analysis, uncertainty in flood return period estimation under changed future climate is to be accounted for extreme event studies, and that can aid in managing and minimizing the flood-associated risks.

Keywords: Climate Change, Flood Frequency Analysis, Soil and Water Assessment Tool, Copula, General Circulation Model, Uncertainty Analysis

How to cite: Manekar, A. and Ramadas, M.: Modeling Uncertainty of Copula-based Joint Return Period of Flood Events under Climate Change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18684, https://doi.org/10.5194/egusphere-egu24-18684, 2024.

The physical mechanisms underlying extreme precipitation events linked to atmospheric moisture transport (IVT-P) are investigated in this study. It investigates changes in synoptic-scale weather patterns over the Indian subcontinent and identifies regions where IVT influences extreme precipitation. The study discovers a strong relationship between daily IVT and precipitation over the core monsoon region and the complex terrains of the Western Ghats and Himalayas. Event Coincidence Analysis reveals that extreme IVT can be used to forecast extreme precipitation in these regions. The dynamic component of moisture transport has a strong influence on daily and extreme precipitation over complex terrains. In contrast, the thermodynamic component has an influence on precipitation over regions with an abundance of water vapor and weak horizontal winds. The study also identifies synoptic features and moisture transport ahead of IVT-P events and finds intense low-pressure anomalies, the transition from ridge to trough patterns, and intense 700 hPa relative humidity in the specified regions. Overall, the study provides insights into the physical mechanisms underlying IVT-linked extreme precipitation events.

How to cite: Raghuvanshi, A. S. and Agarwal, A.: Deciphering the connections between extreme precipitation events, atmospheric moisture transport, and associated synoptic features over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1625, https://doi.org/10.5194/egusphere-egu24-1625, 2024.

Estimating the risk of river flooding under global warming is challenging, mainly due to the compound nature of the various drivers, which is not yet fully understood. Our study aims to quantitatively unravel the complex dynamics of multiple factors that interact and influence river flooding. Using interpretable machine learning techniques, we analyzed thousands of global catchments to identify the role of compounding drivers in river flooding. Our results indicate that these compounding drivers have played a significant role in increasing the magnitude of river floods over the past four decades. In particular, the influence of the interaction effects between these drivers becomes more pronounced with increasing flood magnitude, and the degree is modulated by specific physioclimatic conditions. Importantly, traditional flood analysis will underestimate the magnitude of extreme floods due to insufficient consideration of the varying compounding effects in flood generation. Overall, our results emphasize the need to more carefully incorporate compounding factors to improve extreme flood estimates.

How to cite: Jiang, S., Tarasova, L., Yu, G., and Zscheischler, J.: Compounding drivers amplify the severity of river floods , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5441, https://doi.org/10.5194/egusphere-egu24-5441, 2024.

Extratropical Rossby waves are a potential source of instability for driving convective disturbances in the tropics. In the South Pacific, island nations are subject to flooding associated with such convective disturbances, yet these have not been conclusively linked to any large-scale processes. Using an object-based approach, this study specifically explores in particular how Rossby waves propagating into the tropics can contribute to the formation of extratropical-tropical cloud bands, which can cause flooding events. These cloud bands are associated with substantial precipitation events and serve as easily detectable proxies to identify when such intrusions occur. Building upon this foundation we use ERA5 reanalysis along with a detection analysis for tropical-extratropical cloud bands and potential vorticity streamers and cutoffs to establish a climatology of such intrusions and cloud bands. This allows us to demonstrate the statistical association of extratropical intrusions with intensified deep convection, in particular over the tropical central South Pacific. We find that these intrusions contribute significantly to the occurrence of floods in the Polynesian islands. In summary, this study allows us to connect the interaction between the extratropics and the tropics with flood events in the South Pacific.

How to cite: Pilon, R., de Vries, A., and Domeisen, D.: Extratropical intrusions and their role in tropical flood events: A South Pacific perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9412, https://doi.org/10.5194/egusphere-egu24-9412, 2024.

Warmer temperatures are expected to cause more intense rainfall, primarily due to the rise in atmospheric moisture at the rate of 7%/K, as indicated by the Clausius-Clapeyron (CC) equation. To evaluate this effect, studies use a statistical approach known as precipitation-temperature scaling that involves fitting an exponential regression between observations of extreme rainfall events and local temperatures, resembling how saturation-vapor pressure scales with temperature. However, the estimated sensitivities (also called scaling rates), exhibit notable deviations from the CC scaling (7%/K). These rates remain mostly negative in the tropics as the rainfall extremes exhibit a general monotonic decrease with temperature and “hook-shape” structures in most parts of tropics and mid-latitudes.

Here we show that most of the variability in the observed scaling rates arises from the confounding radiative effect of clouds associated with rainfall events. Clouds substantially reduce the net radiative heating of the surface during the storms by up to 100 W/m² in the tropics, leading to the cooling of surface temperatures by up to 8K. This cloud-induced cooling results in a covariation between precipitation and local temperature, inducing a two-way causality in the observed scaling rates. To isolate this cooling effect, we used a thermodynamically constrained surface energy balance model and force it with radiative fluxes under both "clear" and "cloudy" sky conditions. We then quantified the changes in surface temperatures due to clouds and remove it from temperature observations during rainy days. After removing this effect, we found positive scaling across the global land areas, closely aligning with CC rates of 7%/K. We demonstrate that cloud radiative effects alone can explain the observed negative and hook-shaped relationships found in precipitation-temperature scaling.

Our findings imply that projected intensification of rainfall extremes with temperature by climate models is consistent with observations after the cloud-cooling effect is corrected for. Our results emphasize on making a clear distinction between causality and covariation by explicitly separating the temperatures before the rainfall event that are shaped by less clouds from temperature during the rainfall event which include clouds. This adds a crucial effect to the debate of interpreting observed precipitation - temperature scaling rates. Furthermore, our methodology of removing cloud effects on temperatures can be extended to estimate climate sensitivities from observations beyond precipitation extremes.

How to cite: Ghausi, S. A., Zehe, E., Ghosh, S., Tian, Y., and Kleidon, A.: Extreme precipitation – temperature scaling: disentangling causality and covariation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10848, https://doi.org/10.5194/egusphere-egu24-10848, 2024.

How to cite: Buonacera, G., Peres, D. J., Palazzolo, N., and Cancelliere, A.: Projecting Extreme Rainfall in Sicily: Integrating Simple Scaling and Hourly Projections into Depth-Duration-Frequency Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10737, https://doi.org/10.5194/egusphere-egu24-10737, 2024.

Extreme humid heat stress presents significant challenges to human health and productivity. Traditional heat action plans formulated to tackle dry heat stress are insufficient to address the complexities associated with humid heat stress. Furthermore, there is limited quantitative evidence on the evolving patterns of humid heat stress under changing climate. This study investigates the spatiotemporal trends of extreme heat stress across the Global South from 1964 to 2023, distinguishing between dry and humid heat, using high-resolution ERA5 reanalysis hourly data and the Heat Index (HI).

Notably, South Asia and the Middle East experience the highest frequency of extremely humid heat stress. Specific regions in peninsular South Asia have extremely humid heat stress hours from May to June due to persistent high humidity levels. In contrast, western regions of South Asia encounter extreme dry heat stress preceding the monsoon season, followed by a transition to humid heat stress immediately after the onset of the monsoon. The temporal analysis reveals a more rapid increase in the occurrence of extremely humid heat stress compared to that of dry heat stress from May to July over the past 60 years. This underscores the evolving nature of heat stress and the intensification of humid conditions compared to dry ones.

In conclusion, this study advocates for a shift from exclusively addressing dry stress to a comprehensive approach that accounts for the diverse impacts of humid heat stress, particularly on vulnerable populations. This understanding is critical for policymakers to formulate adaptive strategies tailored to the changing landscape of extreme heat stress. 

How to cite: Sojan, J. M. and Srinivasan, J.: Beyond Extreme Temperature: Spatiotemporal Analysis of Humid Heat Stress , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-395, https://doi.org/10.5194/egusphere-egu24-395, 2024.

Compound hot-dry events (CHDEs) are among the deadliest climate hazards and are occurring with increasing frequency under global warming. The Yangtze River Basin in China experienced a record-breaking CHDE in the summer of 2022, causing severe damage to human societies and ecosystems. Recent studies have emphasized the role of atmospheric circulation anomalies in driving this event. However, the contribution of land–atmosphere feedback to the development of this event remains unclear. Here, we investigated the impacts of soil moisture-temperature coupling on the development of this concurrent heatwave and drought. We showed that large amounts of surface net radiation were partitioned to sensible heat instead of latent heat as the soil moisture-temperature coupling pattern shifted from energy-limited to water-limited under low soil moisture conditions, forming positive land–atmosphere feedback and leading to unprecedented hot extremes in August. The spatial heterogeneity of hot extremes was also largely modulated by the land–atmosphere coupling strength. Furthermore, enhanced land–atmosphere feedback has played an important role in intensifying CHDEs in this traditional humid region. This study improves the understanding of the development of CHDEs from three aspects, including timing, intensity, and spatial distribution, and enables more effective early warning of CHDEs.

How to cite: Ni, Y., Qiu, B., Miao, X., Li, L., Chen, J., Tian, X., Zhao, S., and Guo, W.: Shift of soil moisture-temperature coupling exacerbated 2022 compound hot-dry event in eastern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1631, https://doi.org/10.5194/egusphere-egu24-1631, 2024.

Rising global average temperatures, as a consequence of climate change, have worsened the occurrences of extreme weather events, causing disruptions in rainfall patterns around the world. In Brazil, such effects are already observed with the increase of heat waves, floods, droughts, and wildfires. The correlation between disruptions in precipitation patterns and fires is complex, nevertheless, the intensity, frequency, and duration of drought events have significant impacts on fuel flammability and fire behavior. Drought monitoring is particularly relevant in Brazil, where the vast majority of forest fires have an anthropogenic ignition and prolonged dry periods favor such fires to spread out of control. The Standardized Precipitation Index (SPI) is one of the most important tools used to evaluate precipitation variability, offering simple yet robust statistical information on the distribution, duration, and frequency of rainfalls and, consequently, droughts. The SPI uses precipitation as input data to standardize the deviation of cumulated rainfall from the mean of historical precipitation, detecting water deficit (negative values) or water surplus (positive values) for a given location. In doing so, this index allows direct spatial comparability between arid and humid regions. This is an advantageous characteristic when drought analysis is applied to a country with different regional rainfall regimes, such as Brazil. The applicability of SPI as a source of drought prediction was investigated by observing its performance with historical climate simulations of the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and the fifth generation ECMWF atmospheric reanalysis of the global climate, ERA5. The direct comparison of the SPI data, employing the climatology extending from 1980-2014 in Brazil, derived both from the climate simulation model and the reanalysis data - which combines observations and models – has provided valuable insights. Preliminary results show an overall consistency in the calculated indexes from both sources, which are in line with seasonal regional rainfall patterns in Brazil. On average, the SPI indexes recognize water deficits for the North-east, north of the South-east and central regions of Brazil. During the months of winter, both indexes detect droughts in these regions, with ERA-5 SPI index registering severe droughts in central Brazil. These results suggest that the SPI index calculated using the reanalysis data seems to register droughts with greater severity and longer duration, identifying more precisely periods with little to no rainfall, whilst the SPI derived from the MIROC6 simulation data, although able to acceptably identify and delimitate droughts, records less severity for the same period. These findings are important to recognize the MIROC6-derived SPI index as a valuable tool in drought prediction. However, they also highlight the necessity of acknowledging the limitations of the model regarding the severity of droughts. The understanding and prediction of precipitation anomalies is fundamental to coping with the impacts of climate change on water resources, agriculture, and biodiversity, guiding mitigation and adaptation strategies in Brazil.

How to cite: Michel, G. P., Guida Barroso, A., Zanandrea, F., Aguiar Soares, M. V., Ferreira Subtil de Almeida, G., Cataldi, M., Esposte Coutinho, P., Sancho, L., and Galves, V. L.: Evaluating Standard Precipitation Index (SPI) using MIROC6 historicclimate simulations and ERA 5 reanalysis data as a tool to map theimpacts of climate change in rainfall regime in Brazil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10531, https://doi.org/10.5194/egusphere-egu24-10531, 2024.

Drought frequency and severity have increased over the water-stressed Arid regions. This research employs multiple CMIP6 global climate models (GCMs) for projecting droughts over the Arabian Peninsula (AP) until the end of the 21st century. We utilized the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) to generate projected future statistics of droughts along with uncertainties assessment from inter-model spread, scenarios, timescale, and methods therein.

For this purpose, after a meticulous analysis, we first identify the most suitable GCMs for better representation of AP's drought spatiotemporal pattern over the historical period (1985-2014). Our results indicate an increase in potential evapotranspiration (PET), which dominates simulated drought statistics relative to the precipitation. The projected evolution of the SPEI, which is derived from both precipitation and PET, indicates droughts  consistently increasing from low to high emission scenarios, In contrast, the SPI, owing to relatively-weaker amplification of the precipitation shows a moderately increasing wetness, except for a few northern regions where both indices evolve in agreement The fidelity of the simulated precipitation by many models over the historical period is also relatively poor compared to the PET, which may also be potentially adding to the uncertainties. In general, the principal sources of uncertainty in drought projections evolve from the choices of index, followed by scenarios, and inter-model variability, whereas methods and timescale mostly impact the magnitude of the trend in drought statistics.  

How to cite: Saharwardi, M. S., Dasari, H. P., Hassan, W. U., Gandham, H., Pathak, R., Ashok, K., and Hoteit, I.: Drought projections and associated uncertainties over the Arabian Peninsula from CMIP6 models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15012, https://doi.org/10.5194/egusphere-egu24-15012, 2024.