With global climate change affecting the frequency and severity of extreme meteorological and hydrological events, it is particularly necessary to develop models and methodologies for a better understanding and forecasting of present day weather induced hazards. Future changes in the event characteristics as well as changes in vulnerability and exposure are among the further factors for determining risks for infrastructure and society, and for the development of suitable adaptation measures. This session considers extreme events that lead to disastrous hazards induced by severe weather and climate change. These can, e.g., be tropical or extratropical rain- and wind-storms, hail, tornadoes or lightning events, but also floods, long-lasting periods of drought, periods of extremely high or of extremely low temperatures, etc. Papers are sought which contribute to the understanding of their occurrence (conditions and meteorological development), to assessment of their risk and their future changes, to the ability of models to reproduce them and methods to forecast them or produce early warnings, to proactive planning focusing to damage prevention and damage reduction. Papers are also encouraged that look at complex extreme events produced by combinations or sequences of factors that are not extreme by themselves. The session serves as a forum for the interdisciplinary exchange of research approaches and results, involving meteorology, hydrology, hazard management and applications like insurance issues.
vPICO presentations: Tue, 27 Apr
Heavy precipitation events (HPEs) in the densely populated eastern Mediterranean trigger natural hazards, such as flash floods and urban flooding. However, they also supply critical amounts of fresh water to this desert-bounded region. The impact of global warming on such events is thus vital to the inhabitants of the region. HPEs are poorly represented in global climate models, leading to large uncertainty in their sensitivity to climate change. Is total rainfall in HPEs decreasing, as projected for the mean annual rainfall? Are short duration rain rates decreasing, or rather increasing as expected from the higher atmospheric moisture content? Where are the changes more pronounced, near the sea or farther inland towards the desert? To answer these questions, we have identified 41 historical HPEs from a long weather radar record (1990-2014) and simulated them in the same resolution (1 km2) using the convection-permitting weather research and forecasting (WRF) model. Results were validated versus the radar data, and served as a control group to simulations of the same events under ‘pseudo global warming’ (PGW) conditions. The PGW methodology we use imposes results from the ensemble mean of 29 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for the end of the century on the initial and boundary conditions of each event simulated. The results indicate that HPEs in the future may become more temporally focused: they are 6% shorter and exhibit maximum local short-duration rain rates which are ~20% higher on average, with larger values over the sea and the wetter part of the region, and smaller over the desert. However, they are also much drier; total precipitation during the future-simulated HPEs decreases substantially (~-20%) throughout the eastern Mediterranean. The meteorological factors leading to this decrease include shallower cyclones and the projected differential land-sea warming, which causes reduced relative humidity over land. These changing rainfall patterns are expected to amplify water scarcity – a known nexus of conflict and strife in the region – highlighting the urgent need for deeper knowledge, and the implementation of adaptation and mitigation strategies.
How to cite: Armon, M., Marra, F., Garfinkel, C., Rostkier-Edelstein, D., Adam, O., Dayan, U., Enzel, Y., and Morin, E.: Global warming decreases rainfall but increases short-duration rain-rates during heavy precipitation events in the eastern Mediterranean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1573, https://doi.org/10.5194/egusphere-egu21-1573, 2021.
Extreme precipitation is considered to be one of the natural disasters with greatest impact on human society, leading to floods and debris flows. To better understand the spatio-temporal effects on extreme precipitation, and to predict the intensity of extreme precipitation ahead in different return periods, this study focus on quantifying both climate and spatial effects on the intensity of extreme precipitation in coastal areas of southeast China, considering different weather system. A hierarchical Bayesian model with generalized extreme value distribution (GEV) is applied to maximum daily precipitation through 94 stations in study area from 1964 to 2013 in JAS. Tropical cyclone (TC) and non-TC influenced extreme precipitation are analyzed separately. Climate and spatial effects are introduced through regression models associating parameter values in GEV with different covariates, such as climate indices and distance to coastline. It was observed that SST anomaly in North Pacific, SLP anomaly above North India Ocean are found to be the main climate indices that influence extreme precipitation in coastal areas of southeast China. Using SST, we can predict the intensity of extreme precipitation in different return period at 6-month lag. Extreme precipitation was found to decrease as distance to coastline increase. In addition, different performances of extreme precipitation along with distance to coastline were found among various subregions and weather systems.
How to cite: Qian, W. and Sun, X.: Spatio-temporal effects on extreme precipitation in the coastal areas of southeast China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2043, https://doi.org/10.5194/egusphere-egu21-2043, 2021.
Climate change is a phenomenon that is claimed to be responsible for a significant alteration of the precipitation regime in different regions worldwide and for the induced potential changes on related hydrological hazards. In particular, some consensus has raised about the fact that climate changes can induce a shift to shorter but more intense rainfall events, causing an intensification of urban and flash flooding hazards. Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted. For this reason, it is of key importance to ascertain the quality of regional climate models (RCMs), especially with reference to their ability to reproduce the main climatological regimes with respect to an historical period. To this end, several studies have focused on the analysis of annual or monthly data, while few studies do exist that analyze the sub-daily data that are made available by the regional climate projection initiatives. In this study, with reference to specific locations in eastern Sicily (Italy), we first evaluate historical simulations of precipitation data from selected RCMs belonging to the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) with high temporal resolution (three-hourly), in order to understand how they compare to fine-resolution observations. In particular, we investigate the ability to reproduce rainfall event characteristics, as well as annual maxima precipitation at different durations. With reference to rainfall event characteristics, we specifically focus on duration, intensity, and inter-arrival time between events. Annual maxima are analyzed at sub-daily durations. We then analyze the future simulations according to different Representative concentration scenarios. The proposed analysis highlights the differences between the different RCMs, supporting the selection of the most suitable climate model for assessing the impacts in the considered locations, and to understand what trends for intense precipitation are to be expected in the future.
How to cite: Nanni, P., Peres, D. J., Musumeci, R. E., and Cancelliere, A.: Analysis of EURO-CORDEX sub-daily rainfall simulations and derived event characteristics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2119, https://doi.org/10.5194/egusphere-egu21-2119, 2021.
Extreme precipitation and winds can have a severe impact on society, particularly when they occur at the same place and time. Studies have investigated the frequency of co-occurring extreme precipitation and wind using observational data. However, due to the rarity of very extreme events, these results are limited, since a large number of samples is needed to get robust estimates. Additionally, it is very difficult for estimates based on observations alone to help us understand the risk of future unprecedented events. Using the UNSEEN method (UNprecedented Simulated Extremes using ENsembles) this risk can be estimated from large ensembles of climate simulations. The Met Office's Global Seasonal forecast system version 5 (GloSea5) model ensembles are evaluated against ERA5 reanalysis data to find out how well they represent extreme precipitation, extreme wind and extreme co-occurring events over Europe. This model has not been evaluated in such a way before and this is needed before the model can be used to estimate the likelihood of unprecedented events using the UNSEEN method. We find that although the intensity of precipitation and wind extremes differ between the model and observations (by up to 12 mm and 9 m/s), the frequency of co-occurring events is well represented. The extremal dependency measure, χ, which measures co-occurrence, compares well spatially over Europe between GloSea5 and ERA5. However, significant differences in χ are found over areas of high topography, over the North Atlantic, Western Europe and the Norwegian Sea. Generally, GloSea5 underestimates χ over the ocean, and performs better over land. Mean sea level pressure anomaly composites for co-occurring extreme events show that at a number of selected locations, the co-occurring extremes are produced by very similar synoptic situations in the model and reanalysis. This gives increased confidence in the model. The model ensembles can then be used to assess the present day likelihood of unprecedented 3 hourly compound precipitation and wind extremes for winter over Europe, and to find out how the NAO index influences the frequency of co-occurring events over Europe.
How to cite: Owen, L., Catto, J., Stephenson, D., and Dunstone, N.: Model evaluation of compound precipitation and wind extremes over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2168, https://doi.org/10.5194/egusphere-egu21-2168, 2021.
Supercell thunderstorms are often associated with severe weather conditions, such as tornadoes, hail, strong wind gusts, heavy rainfall, and flash-floods, producing damage to populations and assets. The goal of the study is to analyze and improve our understanding of pre-convective environments conducive for supercell development in the different regions of Spain. We use 2014-2020 data from the Spanish Supercell Database (Martin et al., 2020), ERA-5 reanalysis, and a dynamical downscaling with WRF-ARW model to a 9 km spatial resolution to be able to generate sounding-derived parameters at the moment of formation of each supercell. Results indicate that supercells are more common in high values of CAPE and Shear 0-6 Km, but in the south-western of Spain predominates supercells of HSLC (High Shear-Low CAPE) in the cold season.
How to cite: Calvo-Sancho, C. and Martín, Y.: Supercell Pre-convective Environments in Spain: a dynamic downscaling of ERA-5 Reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2967, https://doi.org/10.5194/egusphere-egu21-2967, 2021.
The Mississippi River Basin (MRB) is a flash flood hotspot in the United States, receiving the most frequent floods and highest rainfall accumulations across the country. In a future warmer climate, this region exhibits some of the greatest increases in rainfall associated with storms that produce flash floods. In order to better understand these future changes, convection-permitting simulations of a current and future climate are utilized to study changes to storm dynamics and precipitation in these convectively-driven flash flood-producing storms.
First, nearly 500 flash flood-producing storms in the MRB are examined under a pseudo-global warming framework to examine the role of vertical velocity in modulating future rainfall changes. Three different categories of storms are designated based on their vertical velocity magnitude in the current climate–weak, moderate, and strong. While all storm categories display an increase in future rainfall accumulation, the amount of increase varies by the storm’s vertical velocity magnitude, which also changes in the future.
Second, idealized WRF simulations are run based on a composite sounding of the flash flood-producing storms in the MRB that occurred during the warm season. Future temperature, moisture, and horizontal wind perturbations are added to the initial sounding using the CESM Large Ensemble Data Set under the RCP 8.5 emissions scenario. In these idealized simulations, the contribution of different precipitation modes to future changes in rainfall are examined. The relationship between changes in future precipitation mode and storm dynamics provides a better understanding of how storm processes influence future changes in rainfall in a flash flood prone region in the United States.
How to cite: Dougherty, E., Rasmussen, K., Newman, A., and Gutmann, E.: Changes in Vertical Velocity and Precipitation Mode in Flash Flood-Producing Storms in the Mississippi River Basin in a Future Climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3451, https://doi.org/10.5194/egusphere-egu21-3451, 2021.
Daily precipitation extremes are projected to intensify with global warming. Here the focus is on how extreme precipitation scales with the changing global mean surface air temperature (GSAT) and how much their inherent seasonality will change, using historical and SSP5-8.5 scenario simulations from 18 CMIP6 models for different sub-domains over Europe. With strong future global warming, the annual maximum precipitation (RX1DAY) is found to occur later in the year, although this shift is model-dependent and hardly significant in the multi-model distribution. Using generalized extreme value theory also provides evidence for the intensification of wet extremes in the future. In addition, we use monthly model outputs to decompose changes in RX1DAY occurring at the peak of the extreme season into several contributions, which gives insights into the underlying physical mechanisms that control the response of precipitation extremes and their inter-model spread.
How to cite: John, A., Douville, H., and Yiou, P.: Changing intensity and seasonality of wet extremes over Europe , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7554, https://doi.org/10.5194/egusphere-egu21-7554, 2021.
Between 7-10 January 2020, severe snowfall and precipitation event swept over south, center and eastern Spain, with a total amount of precipitation of more than 200 mm on the south, snowfall accumulations of 50 cm or more on widespread areas of center Spain and 25 cm on Zaragoza and Ebro valley.
The low, called Filomena, was an unusual event with excessive social impact. In this study we will present the synoptic framework, characterized by the presence of three different air masses: cold air mass on low levels, more humid Mediterranean air mass on low-mid levels, at around 2-3 kilometres from surface; and a wet and warm subtropical air mass from the south. The interaction of these three air masses lead to the exceptional precipitation and snow accumulations. For this end, ERA-5 reanalysis and satellite images will be used. For mesoscale analysis, WRF-ARW will be used with both GFS and ERA-5 reanalysis. This extreme event, although it was generally predictable, had key points of low predictability in some parts with high social impact, including very populated areas.
How to cite: Rodríguez-Sánchez, A., Granda-Maestre, R., Calvo-Sancho, C., and Oliver-García, Á.: An approach to Storm Filomena severe snowfall and precipitation in Spain: preliminary results, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10406, https://doi.org/10.5194/egusphere-egu21-10406, 2021.
Precipitation extremes are widely thought to intensify with the global warming due to exponential growth, following the Clausius-Clapeyron (C-C) equation of atmosphere water holding capacity with rising temperatures. However, a number of recent studies based on station and reanalysis data for the contemporary period showed that scaling rates between extreme precipitation and temperature are strongly dependent on temperature range, region and moisture availability. Here, we examine the scaling between daily precipitation extremes and surface air temperature over Russian territory for the last four decades using meteorological stations data and ERA-Interim reanalysis. The precipitation-temperature relation is examined for total precipitation amount and, separately, for convective and large-scale precipitation types. In winter, a general increase of extreme precipitation of all types according to C-C relation is revealed. For the Russian Far East region, the stratiform precipitation extremes scale with surface air temperature following even super C-C rates, about two times as fast as C-C. However, in summer we find a peak-like structure of the precipitation-temperature scaling, especially for the convective precipitation in the southern regions of the country. Being consistent with the C-C relationship, extreme precipitation peaks at the temperature range between 15 °C and 20 °C. For the higher temperatures, the negative scaling prevails. Furthermore, it was shown that relative humidity in general decreases with growing temperature in summer. Notably, there appears to be a temperature threshold in the 15-20 °C range, beyond that relative humidity begins to decline more rapidly. This indicates that moisture availability can be the major factor for the peak-shaped relationship between extreme precipitation and temperature revealed by our analysis.
How to cite: Aleshina, M., Semenov, V., and Chernokulsky, A.: A relation between extreme precipitation and surface air temperature over Russia in the last four decades, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9166, https://doi.org/10.5194/egusphere-egu21-9166, 2021.
In the field of hydrogeological risk, rainfalls represent the most important triggering factor for superficial terrain failures such as shallow landslides, soil slips and debris flow. The availability of local rain gauges measurements is fundamental for defining the cause-effect relationship for predicting failure scenarios. Unfortunately, these hydrogeological phenomena are typical triggered over mountains regions where the density of the ground-based meteorological network is poor, and the local effects caused by mountains topography can change dramatically the spatial and temporal distribution of rainfall. Therefore, trying to reconstruct a representative rainfall field across mountain areas is a challenge but is a mandatory task for the interpretation of triggering causes. We present a reanalysis of an ensemble of extreme rainfall events happened across central Alps and Pre-Alps, in the northern part of Lombardy Region, Italy. We have investigated around some critical aspects such as their intensity and persistency also proposing a modelling of their meteorological evolution, using the Linear Upslope-Rainfall Model (LUM). We have considered this model because it is designed for describing the mechanism of orographic precipitation intensification that was identified as the main cause of that extreme events. To test and calibrate the LUM model we have considered local rain gauges data because they represent the effective rainfall poured on the ground. These punctual data are generally considered for landslide assessment, in particular for rainfall induced phenomena such as shallow landslides and debris flows. Considering our test cases, the results obtained have shown that the LUM has been able to reproduce accurately the rainfall field. In this regard, LUM model can help to address further information around those ungauged area where rainfall estimation could be critical for evaluating the hazard. We are conscious that our and other studies around this topic would be propaedeutic in the next future for the adoption of an integrated framework among the real-time meteorological modelling and the hydrogeological induced risk assessment and prevision.
How to cite: Abbate, A., Longoni, L., and Papini, M.: Reconstruction of a realistic rainfall field for extreme events happened in mountain area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9176, https://doi.org/10.5194/egusphere-egu21-9176, 2021.
It is difficult to predict the occurrence and rain volume of torrential rainfalls, such as guerrilla rain, rain band with typhoon and linear precipitation zone. As heavy rain area is spatially localized and the parent thunderstorm tends to develop within a short time, it makes difficult to accurately predict the occurrence location/time and rain volume. Recently, the machine learning technique is remarkably developed with the improved processing speed of computers and with a huge amount of the data. In addition to this, the application of the machine learning methods to the meteorological fields is intensively tried in the world. Since 2017, we started installing the automatic weather observation system (AWS) named as P-POTEKA in Metro Manila, the Philippines, which is one of the cities suffering from the torrential rainfall and related flood. So far, we installed 35-P-POTEKAs in Metro Manila and continue the weather observations (rain volume, temperature, air pressure, humidity, wind speed, wind direction and solar radiation) with the time resolution of 1 min. In this study, we used both P-POTEKA rain volume data and machine learning model (ConvLSTM: Convolutional Long-Short Term Memory) in order to predict the near future (< 1hour) rain volume and distribution. At the presentation, we will show the results derived from the machine learning prediction of the rain volume and distribution more in detail.
How to cite: Noda, A.: Machine Learning Prediction of Precipitation in Metro Manila, Philippines, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11927, https://doi.org/10.5194/egusphere-egu21-11927, 2021.
Record-breaking amount of Mei-yu rainfall around the Yangtze River has been observed in the 2020 Mei-yu season. This shows the necessity and urgency of accurate prediction of extreme Mei-yu precipitation over China for the current and future climate. Such information could further improve the decision and policy making in the region. Many studies in the past have shown that large-scale modes, e.g. western north Pacific subtropical high and the south Asia high, play a role in controlling extreme Mei-yu precipitation over China. Although the spatial resolution of typical climate models might be too coarse to simulate extreme precipitation accurately, they are likely to simulate large-scale modes reasonably well. One might be possible to construct a causally guided statistical model based on those known large-scale modes to predict extreme Mei-yu precipitation.
In this presentation, we show preliminary results of the relationship between known large-scale atmospheric and oceanic modes and extreme Mei-yu precipitation in the two regions of China, i.e. Yangtze River Valley and Southern China, using the causal network discovery approach. The relationships between large-scale modes and extreme Mei-yu precipitation on different time scale are explored. Implication of relationships in constructing statistical predictive model is also discussed.
How to cite: Ng, K., Leckebusch, G., and Hodges, K.: An evaluation of the effectiveness of known large-scale modes for predicting extreme Mei-yu precipitation over China using causality driven approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10568, https://doi.org/10.5194/egusphere-egu21-10568, 2021.
Convective hazards such as large hail, severe wind gusts, tornadoes, and heavy rainfall cause high economic damages, fatalities, and injuries across Europe. There are insufficient observations to determine whether trends in such local phenomena exist, however recent studies suggest that the conditions supporting such hazards have become more frequent across large parts of Europe in recent decades.
We model the occurrence of these hazards using Generalized Additive Models (GAM) to investigate the existence of such long-term trends, and to enable objective probabilistic forecasts of these hazards. The models are trained with storm reports from the European Severe Weather Database (ESWD), lightning observations from the EUCLID network, and predictor parameters derived from the ERA5 reanalysis. Our work is based on the framework AR-CHaMo (Additive Regression Convective Hazard Models), previously developed at ESSL.
Preliminary results include a spatial depiction of the environmental conditions giving rise to convective hazards at a higher resolution than was possible before. The skill of hail models developed using AR-CHaMo has been shown to be superior to composite parameters used by weather forecasters for the occurrence of large hail, such as the Supercell Composite Parameter (SCP) and the Significant Hail Parameter (SHP). Likewise, for tornadoes, more skillful models can be constructed using the AR-CHaMo framework than predictors such as the Significant Tornado Parameter (STP).
The developed models have use both in climate studies and in medium-range severe weather forecasting. We will report on initial efforts to detect long term (1979-2019) trends of convective hazards and present how these models can be used to support severe weather forecasting using medium-range ensemble forecasts.
How to cite: Battaglioli, F., Groenemeijer, P., Pucik, T., Ulbrich, U., Rust, H., Kühne, T., and Taszarek, M.: Modelling the occurrence of convective hazards using ERA5 reanalysis data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15165, https://doi.org/10.5194/egusphere-egu21-15165, 2021.
Thunderstorms can have a wide range of impacts on modern societies and their assets. Severe thunderstorms associated with thunder squall, hail, tornado, and lightning cause extensive damage and losses to lives, especially in the densely populated sub-tropical countries like Bangladesh. In this study the future changes in thunderstorm conducive environments, in terms convective available potential energy (CAPE), have been assessed under the RCP 8.5 scenario for the selected major cities of Bangladesh. Results show an increase in CAPE for all the selected cities and in the range of 44%–106%. Later, a statistical thunderstorm frequency prediction model has been developed based on CAPE and convective precipitation and the probable scenario of thunderstorm frequency in the 21st century under future climate has been projected. The simulations were carried out for three different time slices (Early, Mid and Late 21st century) with CMCC-CM (Centro Euro-Mediterraneo per Cambiamenti Climatici Climate Model) model data. The future projection of thunderstorm shows an increase in thunderstorm frequency for all the season in a warmer future climate. But pre-monsoon and monsoon are found to be the most thunderstorm frequent season. Given the substantial damage from severe thunderstorms in the current climate, such increases imply an increasing risk of thunderstorm-related damage in this disaster-prone region of the world.
How to cite: Esha, S. A. and Jahan, N.: Change in thunderstorm activity in a projected warmer future climate over Bangladesh, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8518, https://doi.org/10.5194/egusphere-egu21-8518, 2021.
Sub-daily precipitation extremes over Europe induce hazards such as mass movements and floods. These hazards are impacting the society in terms of financial losses, which is of great interest for insurance companies. The occurrence probability of heavy rainfall events is often assessed by calculating rainfall return periods. Though, these estimations are governed by uncertainties due to the natural variability of the climate system.
Here, we quantify the range of sub-daily extreme precipitation due to natural variability within the single model initial-condition large ensemble featuring 50 members of the Canadian regional climate model, version 5 (CRCM5) under the high-emission scenario Representative Concentration Pathway 8.5. Therefore, we calculate 10-year return levels of sub-daily precipitation for hourly to 24-hourly aggregations in a European domain for each of the 50 ensemble members. The analysis is carried out for four time periods covering 1980 to 2099: the reference period (1980 – 2009) and three future periods (2010 – 2039, 2040 – 2069, 2070 – 2099).
We find that the rainfall intensities of the 10-year return levels increase by 5 – 9 % on areal average for every future 30-year period. There, short-duration rainfall intensities increase to a greater extent than longer-duration rainfall intensities. Natural variability as uncertainty source is quantified as the range between the median of the 50 members and the 5th and 95th quantile, respectively. This spread is between -16 % – 20 % for hourly duration and -13 % – 17 % for 24-hourly duration.
These findings highlight the large impact of natural variability on the estimation of extreme precipitation return levels. This database also allows us to identify regions in Europe, where future median extreme precipitation exceeds the 95th quantile of the reference period. These regions of significant changes are in northern Europe, central Europe and the eastern part of the Mediterranean.
How to cite: Poschlod, B. and Ludwig, R.: Climate change effects on sub-daily extreme precipitation over Europe and the role of natural variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9431, https://doi.org/10.5194/egusphere-egu21-9431, 2021.
As the global temperature increases, the likelihood of extreme temperature and precipitation events occurring is expected to change across many parts of the world. In particular, a warmer world can alter the spatiotemporal characteristics (e.g., intensity, magnitude, distribution, frequency) and patterns of such events. The changing character of extreme events in the future can have substantial impacts (e.g., flooding, drought) that affect our society, built and natural environments, and food, water, and energy systems. We therefore must better understand and quantify how the distribution of temperature and precipitation are changing. In this study, the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations are used to characterize shifts in the distribution of temperature and precipitation as they vary across space and time using both historical simulations and projections. This research demonstrates how different parts of these distributions exhibit nonlinear changes (e.g., the hottest and wettest events) in the future. This study also characterizes inter-model differences to better assess uncertainty across historical simulations and projections as well as how human activities influence extreme events.
How to cite: Huning, L.: How are Global Extreme Temperature and Precipitation Events Changing?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10530, https://doi.org/10.5194/egusphere-egu21-10530, 2021.
Hydrometeorological droughts are complex hazards that cover wide areas with typically slow-onset and can affect different social, economic, environmental sectors at different spatial and temporal scales. However, it is challenging to investigate changes in hydrometeorological drought and their propagation from precipitation deficit, to soil moisture, discharge and groundwater deficits and to ascertain to what extent climatic change may affect drought characteristics (e.g. magnitude, frequency and duration). This research explores changes in hydrometeorological drought characteristics and their propagation from meteorological to hydrological drought states using climate model simulations from CMIP6 to force a conceptual hydrological model. Using a sample of 30 catchments in Ireland, we examine changes in hydrometeorological drought using standardised indices of precipitation (SPI), soil moisture deficits (SPEI), runoff (SRI) and baseflow (SBI). We find that downscaled CMIP6 projections are poor at capturing droughts at shorter timescales, however performance increases depending on bias correction technique and drought accumulation period. Largest uncertainties in drought projections emanate from climate models, outweighing the role of hydrological model parameter uncertainty, bias correction and emissions scenarios. Projected changes in drought characteristics strongly covary for SPI and SPEI, however covariation in changes is weaker for SRI and SBI. The propagation of meteorological to hydrological drought increases over time, with proportional increases for moderate, severe and extreme droughts. Across the catchment sample the average lag time between meteorological and hydrological drought occurrence in the baseline period is 3-5 months, with lag times likely to increase with climate change. Therefore, results suggest that while the propagation of meteorological droughts to hydrological events (SRI/SBI), increases, the time taken for precipitation anomalies to become apparent in hydrological variables increases. Such changes in drought propagation need to be considered in adaptation planning.
How to cite: Meresa, H., Murphy, C., and Fealy, R.: Climate change impact on the hydrometeorological drought propagation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8285, https://doi.org/10.5194/egusphere-egu21-8285, 2021.
According to widely accepted definition of drought, meteorological and hydrological droughts originally develop from rainfalls and runoffs deficits, respectively. Runoffs deficit is mainly derived from rainfalls deficit. Therefore, hydrological drought is essentially propagated from meteorological drought, which is critical for agricultural water management. Investigation of the propagation from meteorological to hydrological drought is important for drought early warning, preparedness and mitigation. Nevertheless, the characteristics and dynamic of drought propagation in spatiotemporal scale remain unresolved. To this end, the characteristics and dynamic of drought propagation in different seasons and their linkages with key forcing factors are evaluated. In this study, the meteorological drought and hydrological drought are characterized by Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), respectively. The propagation time is identified by the corresponding timescale of the maximum correlation coefficient between SPI and SRI. Then, a 20-year sliding window is adopted to explore the propagation dynamic in various seasons. Furthermore, the multiple linear regression model (MLR) is established to quantitatively explore the influence of meteorological factors, underlying surface features and teleconnection factors on the propagation time variations. The Wei River Basin (WRB), which is a typical Loess Plateau watershed in China, is selected as a case study. Results indicate that: (1) the propagation time from meteorological to hydrological drought is shorter in summer (2 months) and autumn (3 months), whilst that is longer in spring (8 months) and winter (13 months). Moreover, the propagation rates exhibit decreasing trend in warm seasons, which however show increasing trend in cold seasons; (2) a significant slowing propagation in autumn is mainly caused by the decreasing soil moisture and precipitation, while the non-significant tendency in summer is generally induced by the offset between insignificant increasing precipitation and significant decreasing soil moisture; (3) the replenishment from streamflow to groundwater in advance prompts the faster propagation from meteorological to hydrological drought in spring and winter; (4) teleconnection factors have strong influences on the propagation in autumn, in which Arctic Oscillation (AO), El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) mainly affect participation, arid index and soil moisture, thereby impacting drought propagation.
How to cite: Ma, L., Huang, Q., Huang, S., Liu, D., Leng, G., Wang, L., and Li, P.: The propagation dynamics and causes of hydrological drought in response to meteorological drought at seasonal timescale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3818, https://doi.org/10.5194/egusphere-egu21-3818, 2021.
The effects of climate change on precipitation patterns can be observed on global scale, however, global climate change affects different regions more or less severely. Because of the high variability of precipitation in particular, future changes related to precipitation can be very different, even opposite on continental/regional scale. Even within Europe, the detected trends in precipitation patterns and extremes differ across the continent. According to climate model simulations for the future, Northern Europe is projected to become wetter, while the southern parts of the continent will tend to become drier by the end of the 21st century. The frequency and intensity of extreme precipitation will also increase in the whole continent. The possible shifts in precipitation patterns from wetter to drier conditions with fewer but increased extreme precipitation events can cause severe natural hazards, such as extended drought periods, water scarcity, floods and flash floods, therefore appropriate risk management is essential. For this purpose the analysis of possible hazards associated to specific precipitation-related weather phenomena is necessary and serves as key input.
Since plain regions play an important role in agricultural economy and are more exposed to floods because of their geographic features and the gravitational movement of surface water, our primary goal was to examine temporal and spatial changes in extreme precipitation events and dry spells in three European lowlands, located in the southern part of the continent. We selected the following regions: the Po-Valley located in Italy with humid subtropical climate; the Romanian Plain in Romania, and the Pannonian Plain covering different parts of Hungary, Serbia, Slovakia, Croatia, Romania and Ukraine with humid continental climatic conditions.
Precipitation time series were used from the E-OBS v.22 dataset on a 0.1° regular grid. The dataset is based on station measurements from Europe and are available from 1950 onward with daily temporal resolution. For the analysis of main precipitation patterns, dry spells and extreme events, we use 17 climate indices (most of them are defined by the Expert Team on Climate Change Detection and Indices, ECCDI). The analysis focuses on annual and seasonal changes in the three regions. The selected indices are capable to represent the differences and similarities between and within the plains. Our preliminary results show that the occurrence and intensity of extreme precipitation events increased in all regions, while the trends of duration and frequency of dry spells show both intra- and inter regional variability across the plains.
How to cite: Berényi, A., Pongrácz, R., and Bartholy, J.: Trend in precipitation and drought extremes in southern lowland regions of Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12005, https://doi.org/10.5194/egusphere-egu21-12005, 2021.
Industrial facilities, like any building or installation, are designed to withstand defined levels of natural hazards during their lifetime. These levels are requested by the regulations, and are commonly estimated by use of the Statistical Extreme Value theory ahead of the building in order to support the design of the planned asset. However, for long lasting installations, climate change may change the frequency of the level defined at the time of the building, so that the protection is not as high as initially expected. This work presented here aims at describing and testing a way to estimate Return Levels for precipitation in different locations in Europe at the 2050 time horizon. The methodology is based on the definition of a variable whose extremes can be considered as stationary, so that future Return Levels are obtained from those of this variable and the climate model changes in mean, standard deviation and rainy day frequency at the desired future horizon (Acero et al. 2017). The methodology is first tested in a cross-validation setting over the historical period using 15 rainfall observation time series in Europe provided by the ECA&D dataset and CMIP5 climate model simulations. Then, estimates of the 50-year Return Levels in 2050 are computed. The methodology is then applied to the gridded E-OBS dataset with the objective of producing risk maps at the European scale. The first step is then to compare the estimations previously obtained for the station time series to those obtained for the nearest E-OBS grid points, in order to assess the ability of gridded data to faithfully represent the behavior of the extremes. Depending on the results, advices can be given about the most suited way to map future rainfall extremes in Europe in relation to the adaptation of industrial facilities.
Acero F.J., Parey S., Hoang T.T.H., Dacunha-Castelle D., Garcia J.A. and Gallego M.C.: Non-stationary future Return Levels for extreme rainfall over Extremadura (SW Iberian Peninsula). Hydrological Sciences Journal, 2017, DOI: 10.1080/02626667.2017.1328559
How to cite: Parey, S. and Michelangeli, P.-A.: Estimation of future precipitation Return-Levels in Europe for the adaptation of industrial facilities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2101, https://doi.org/10.5194/egusphere-egu21-2101, 2021.
Maghreb countries, like the rest of the Mediterranean region, are vulnerable to flood events which often cause disastrous damages and a large number of fatalities. In Europe, this problematic has been addressed by the implementation of the Copernicus European Flood Awareness System (EFAS) that, together with the national and regional flooding schemes, provide a robust tool for flood forecasting. Nevertheless, Maghreb countries do not have such national or regional flooding schemes and, although EFAS covers their northern territories, its forecast capability for these regions is limited as its hydrological model (LISFLOOD) remains uncalibrated due to data unavailability. As data become available, daily river discharge data of 21 Tunisian basins from 1980 to 2018 was used to implement and compare different flood modelling strategies including LISFLOOD and simpler models such as GR4J and IHACRES, which were calibrated for each basin separately. The LISFLOOD model was first implemented with its default parametrization to the 21 basins considered using both, the ERA5 dataset, and observed precipitation data from rain-gauges. Although the use of observations slightly increases the model performance, the performances achieved are substantially lower than with simpler calibrated hydrological models (i.e. GR4J and IHACRES); whereas these simpler models generally present KGE values over 0.4, just four out of the 21 catchments have positive KGE values when discharge is simulated with LISFLOOD.
The model sensitivity to six of its main parameters (Xinanjiang, preferential flow, upper groundwater time constant, lower groundwater time constant, percolation and Manning’s coefficient) was assessed through the application of the Latin hypercube sampling (LHS) scheme. The LHS was used to generate 1000 near-random samples of LISFLOOD parameters sets, to investigate the model sensitivity to these parameters within the 21 basins. This process was repeated constraining the parameter range progressively in order to achieve an optimal parameter set for each catchment, as well as an additional parametrization that could be used in all catchments while resulting into satisfactory performances. Additionally, a Sobol sensitivity analysis was conducted to further investigate the sensitivity of the parameters mentioned above. This analysis revealed that, for extreme discharge values, for extreme discharge values, the most sensitive parameters are the Upper and Lower groundwater time constants and the exponent in Xinanjiang equation for the soil infiltration capacity. Different calibration and validation experiments were carried out with different objective functions, in order to identify the best parameters sets suitable for flood modelling at regional scale.
How to cite: Cantoni i Gomez, E., Tramblay, Y., Dakhlaoui, H., Thiemig, V., and Salamon, P.: Flood modelling in Tunisia: On the suitability of a large-scale hydrological model for flood forecasting at basin scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5446, https://doi.org/10.5194/egusphere-egu21-5446, 2021.
Large parts of East and South Asia were affected by heavy precipitation and flooding during early summer 2020. This study provides both a statistical and dynamical characterisation of these events. By aggregating daily and monthly precipitation over river basins across Asia, it is shown that the Yangtze River Basin (YRB) is one of the areas that was particularly affected. June and July 2020 rainfalls were higher than in the previous 20 years, and the YRB experienced anomalously high rainfall across most of its sub-basins. An automated method detecting the daily position of the East Asian Summer Monsoon Front (EASMF) is applied to show that the anomalously high YRB precipitation was associated with an anomalously slow northward progression of the EASMF and prolonged Mei Yu conditions over the YRB lasting more than one month. Lagrangian trajectory analysis is employed to study the convergence of air masses in the EASMF during two 5-day heavy-precipitation episodes, 12-16 June and 4-8 July 2020. Despite heavy precipitation and the convergence of monsoonal and subtropical air masses seen in both episodes, clear differences are identified between these episodes in the location/strength of the Subtropical Westerly Jet and the location of the Western North Pacific Subtropical High. This study contextualises heavy precipitation in Asia in summer 2020 and showcases a number of analysis tools developed by the authors for the study of such events.
How to cite: Muetzelfeldt, M., Volonté, A., Schiemann, R., Turner, A., and Klingaman, N.: Dynamical drivers of the 2020 Mei Yu floods over China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8386, https://doi.org/10.5194/egusphere-egu21-8386, 2021.
Anthropogenic activities have accelerated the global warming phenomena, causing a rapid change in the weather patterns, especially the extremes. Intensification of magnitude and frequency of extreme events have increased the stress on water infrastructures. Hence design methods have to be updated to build climate-resilient infrastructures. Intensity-Duration-Frequency (IDF) curves play a vital role in flood risk assessment and impact the region's socio-economic structure. In this study, a non-stationary modelling approach is proposed to develop IDF curves under changing climate using Global Climate Models (GCMs). Non-Stationary Generalized Extreme Value Distribution (NS-GEVD) location parameter is modelled as a linear function of GCM outputs. Data used for analysis is the annual maximum daily precipitation generated at a Hyderabad city station, India using 27 GCMs of Coupled Model Intercomparison Project Phase-5 (CMIP-5). The analysis is carried out for the baseline period of 1971 to 2005 and the future time-period of 2006 to 2100. Corrected Akaike Information Criterion test statistic is used to identify the best NS-GEVD model. The results indicate that NS-GEVD model could capture the non-stationary behaviour and predicted an average increase of 7% in extreme rainfall intensity for the future. Besides, it is observed that six GCM covariates outperform other GCMs. The outcomes of this study will benefit the city municipality, practitioners and decision-makers in identifying future risk for water infrastructures.
How to cite: Sojan, J. M. and Srivastav, R.: Intensity-Duration-Frequency (IDF) Curves Under Changing Climate – A Non-Stationary Modelling Approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8577, https://doi.org/10.5194/egusphere-egu21-8577, 2021.
Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs). To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing 12.000 simulated years. LAERTES-EU is adapted and applied for the use in an HM to calculate discharges for large river catchments in Central Europe, where the Rhine catchment serves as the pilot area for calibration and validation. Quantile mapping with a fixed density function is used to correct the bias in model precipitation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values of precipitation and discharges. While for the Rhine catchment a clear added value is identified, the results are more mixed for other catchments (e.g., the Upper Danube).
How to cite: Ehmele, F., Kautz, L.-A., Feldmann, H., He, Y., Kadlec, M., Kelemen, F. D., Lentink, H. S., Ludwig, P., Manful, D., and Pinto, J. G.: Adaptation and Application of the large LAERTES-EU RCM Ensemble for Hydrological Modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9598, https://doi.org/10.5194/egusphere-egu21-9598, 2021.
Rain-on-snow (ROS) floods are responsible for the overwhelming majority of floods affecting multiple major river basins simultaneously in Europe during the last century. These widespread floods have serious negative economical, social and ecological effects, and knowledge about their rate of occurrence is critical for future projections in the face of climate change.
Recent studies have shown that ROS events (with flood-inducing potential) in Europe increase and decrease based on the elevation range considered since 1950 and there appears to be a clustering pattern of flood-poor and flood-rich periods since 1900. Our goal is to analyze if these changes in frequency can be realistically described by a stationary process (or a combination thereof) or if there must be hidden time-dependent driving factors to explain the observed clustering. To test this theory we analyze a simulation for the time period 1901-2010 based on ERA-20C dynamically downscaled using a coupled RCM. We apply a method from scan statistics and confirm the existence of significant periods poor and rich in ROS events with regards to the reference condition of independent and identically distributed random events and present their position in time. The same procedure is applied to the ROS event constituents (rainfall and snowmelt), where we identify such periods in the rainfall, but not in the snowmelt time series. We construct a stochastic ROS model by modelling precipitation and snowmelt via stationary gamma distributions fitted to our data but are unable to reproduce the observed clustering behaviour using the combined signal.
This study confirms that the observed ROS floods in Central Europe are unlikely to be the result of stationary processes which hints at climate drivers for the compound rain-on-snow process in Europe.
How to cite: Kirschner, M. J., Krug, A., David, L., and Ahrens, B.: Evaluating the clustering of Central European rain-on-snow events with flood-inducing potential, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9778, https://doi.org/10.5194/egusphere-egu21-9778, 2021.
Storm surges caused by extreme meteorological conditions are a major natural risk in coastal areas, especially in the context of global climate change. The increase of future sea-levels caused by continuing global warming, may endanger human lives and infrastructure through inundation, erosion and salinization.
Hence, the reliable estimation of the occurrence probability of these extreme events is crucial to quantify risk and to design adequate coastal defense structures. The probability of event occurrence is typically estimated by modelling observed sea-level records using one of a few statistical approaches.
The traditional Extreme Value Theory is based on the use of the Generalized Extreme Value distribution (GEV), fitted either by considering block (typically yearly) maxima, or values exceeding a high threshold (POT). This approach does not make full use of all observational information, and thereby does not minimize estimation uncertainty.
The recently proposed Metastatistical Extreme Value Distribution (MEVD), instead, makes use of most of the available observations and has been shown to outperform the classical GEV distribution in several applications, including hourly and daily rainfall, flood peak discharge and extreme hurricane intensity.
Here, we comparatively apply the MEVD and the GEV distribution to long time series of sea-level observations distributed along European coastlines (Venice (IT), Hornbaek (DK), Marseille (FR), Newlyn (UK)). A cross-validation approach, dividing available data in separate calibration and test sub-samples, is used to compare their performances in high-quantile estimation.
The MEVD approach is based on the definition of an “ordinary values” distribution (here a Generalized Pareto distribution), whose parameters are estimated using the Probability Weighted Moments method on non-overlapping sub-samples of fixed size (5 years). To address the problems posed by observational samples of different sizes, we explore the effect on uncertainty of different calibration sample sizes, from 5 to 30 years. In this application, we find that the GEVD-POT and MEVD approaches perform similarly, once the above parameter choices are optimized. In particular, when considering short samples (5 years) and events with a high return time, the estimation errors show no significant differences in their median value across methods and sites, all approaches producing a similar underestimation of the actual quantile. When larger calibration sample sizes are considered (10-30 yrs), the median error of MEVD estimates tends to be closer to zero than those obtained from the traditional methods.
Future projections of sea-level rise until 2100 are also analyzed, with reference to intermediate and extreme representative concentration pathways (RCP 4.5 and RCP 8.5). The probability of future storm surges along European coastlines are then estimated assuming a changing mean sea-level and an unchanged storm regime. The projections indicate future changes in mean sea-level lead to increases in the height of storm surges for a fixed return period that are spatially heterogeneous across the coastal locations explored.
How to cite: Caruso, M. F. and Marani, M.: Extreme Storm Surge estimates and projection through the Metastatistical Extreme Value Distribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10706, https://doi.org/10.5194/egusphere-egu21-10706, 2021.
In recent years, flash floods occurred repeatedly in temperate regions of central Western Europe (e.g., Orlacher Bach (GER), Hupselsebeek (NL), White Ernz (LUX)). This type of extreme flood events is unusual for these regions, as opposed to Mediterranean catchments that are more prone to flash floods. In the second half of the 20th century, and more specifically in the 1990’s, westerly atmospheric fluxes were the dominating triggering factor of large scale (winter) floods in central Western Europe.
With a view to gain a better understanding of the mechanisms controlling the recent flash flood events at higher latitudes, we explore various avenues related to the non-stationarity of environmental systems. We hypothesize that an increase in the occurrence of flash flood prone atmospheric conditions has recently led to higher precipitation totals and a subsequent increase in flash flood events in central Western Europe.
Therefore, we first analysed relevant atmospheric parameters from the ERA 5 reanalysis dataset. Second, we linked the atmospheric parameters to the concept of general circulation patterns as per Hess and Brezowsky (1977). Third, we analysed precipitation data from a set of stations located in the Moselle river basin (35.000 km2). These three pillars build the base for identifying flash flood prone atmospheric conditions over space and time that are then compared to actual occurrences of extreme discharge events in streams within the Moselle river basin.
To validate our hypothesis, spatial and temporal patterns in the occurrence of extreme precipitation and discharge events need to match atmospheric patterns. Preliminary results suggest that daily precipitation data and meridional circulation patterns do not show a clear trend towards an increased occurrence of precipitation events or higher precipitation amounts. Due to the limitations inherent to the available long-term dataset of daily data, the hypothesis can only be partly evaluated, and more detailed analyses are added. For that reason, discharge data with a 15-minute resolution, along with precipitation radar data of 5-minute time steps will be employed at a limited spatial extent in future analyses. In case of rejection of our working hypothesis this may pinpoint to other flash flood triggering mechanisms, such as changes in land use, soil moisture conditions or cultivation methods.
How to cite: Meyer, J., Douinot, A., Neuper, M., Mathias, L., Tamez-Meléndez, C., Zehe, E., and Pfister, L.: Identifying and linking flash flood prone atmospheric conditions to flooding occurrences in central Western Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12522, https://doi.org/10.5194/egusphere-egu21-12522, 2021.
In Europe, flash floods are one of the most significant natural hazards, causing serious risk to life and destruction of buildings and infrastructure. The intense rain causing those floods has a few different names, however, with very similar meaning. The term chosen in this study, ‘cloudburst’, was introduced by Woolley (1946) as “…a torrential downpour of rain which by its spottiness and relatively high intensity suggests the bursting and discharge of the whole cloud at once”. While these events play an important role in the ongoing flood risk management discussion, they are under-represented among flood models.
The main aim of this study is to demonstrate an approach by showing how methods and techniques can be integrated together to construct a catastrophe model for flash flooding of Jönköping municipality in Sweden. The model is developed in the framework of the ‘Oasis Loss Modelling Framework’ platform, jointly with end-users from the public sector and the insurance industry. Calibration and validation of the model were conducted by comparisons against three historical cloudburst events and corresponding insurance-claim data.
The analysis has shown that it is possible to get acceptable results from a cloudburst catastrophe model using only rainfall data, and not surface-water level as driving variable. The approach presented opens up for such loss modelling in places where complex hydraulic modelling cannot be done because of lacking data or skill of responsible staff. The Swedish case study indicates that the framework presented can be considered as an important decision making tool, by establishing an area for collaboration between academia; insurance businesses; and local authorities, to reduce long-term disaster risk in Sweden.
Woolley, Ralf R., "Cloudburst Floods in Utah 1850-1938" (1946). Elusive Documents. Paper 55.
How to cite: Karagiorgos, K., Halldin, S., Haas, J., Knos, D., Blumenthal, B., and Nyberg, L.: Cloudburst catastrophe modelling: Case study – Jönköping municipality, Sweden , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12684, https://doi.org/10.5194/egusphere-egu21-12684, 2021.
Monitoring of equatorial wave activity and understanding their nature is of high priority for scientists, weather forecasters and policy makers because these waves and their interactions can serve as precursors for weather-driven natural hazards, such as extreme rain and flood events. We studied such precursors of the January 2019 heavy rain and deadly flood in the central Maritime Continent region of southwest Sulawesi, Indonesia. It is shown that a convectively coupled Kelvin wave (CCKW) and a convectively coupled equatorial Rossby wave (CCERW) embedded within the larger-scale envelope of the Madden-Julian Oscillation (MJO), contributed to the onset of a mesoscale convective system. The latest developed over the Java Sea and propagated onshore, resulting in extreme rain and devastating flood.
For the analysis of the January 2019 flood, we explored large datasets and detected interesting features to find multivariate relationships through visualization. We used SpectralWeather – a new tool supporting tropical weather training, research and forecasting, easily accessible at https://www.spectralweather.com. Extending Cameron Beccario's earth.nullschool.net project, SpectralWeather focuses on spectral decomposition of meteorological and oceanic fields into equatorial waves – CCKW, MJO, CCERW and Mixed Rossby-Gravity waves. SpectralWeather uses ECMWF ERA5 reanalysis at several levels, NASA GPM rainfall datasets, OMI OLR index, NEMO SST, AVISO sea surface height, and OSCAR currents.
This new visualization tool can help to quantify and understand factors triggering natural hazards in the global tropics. We will discuss its interface and available features, based on the example of the January 2019 Sulawesi flood and other flood and extreme rain events in the Maritime Continent.
How to cite: Latos, B., Lefort, T., Flatau, M. K., Flatau, P. J., Baranowski, D. B., Szkółka, W., and Peyrillé, P.: Application of SpectralWeather to prediction of flood and extreme rain events in the Maritime Continent, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14574, https://doi.org/10.5194/egusphere-egu21-14574, 2021.
Hydro-meteorological disasters, particularly the extreme rainfall events (EREs) and associated flash floods, are very frequent in the major metro cities in India during recent years and in many occasions they cause massive destruction to life and property which in long run make adverse socio-economic impacts over the country. Hence, it makes formost importance and has great societal relevance to modellers working such area to develop an advance prediction system for such disasters in India.A strategic framework combining modelling and data analytics is integral part of developing advanced warning system for preparedness during such disasters. In this study, the role of landuse/landcover like built-up, vegetation, barrenland and waterbodies over the Bangalore city in flash flood occurrence is examined using multispectral spatio-temporal satellite data.The recent LULC map evidences a drastic changes in urban landscape that resulted in loss of natural drainage and waterbeds causing frequent floods. Digital Elevation Map (DEM) is analysed to know the low-lying and high elevation topography compared with Mean Sea Level(MSL)to quantify the impact of flooding during Extreme Rainfall Events(ERE) on the different part of the Bangalore city. Using Triangular Irregular Network (TIN), flood simulation is carried out for highland and lowlandarea to study immediate affected areas during EREs Storm Water Modelling is carried out for different regions in the city to obtain flood pattern, time and volume during selected EREs. The framework developed and simulation results are very useful in generation of management and mitigation strategy by various user agencies.
How to cite: Purwar, S., Mohapatra, G., and Vasudevan, R.: Startegic framework for integrated flood Disaster modelling over Bangalore city of India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15002, https://doi.org/10.5194/egusphere-egu21-15002, 2021.
Global warming is expected to enhance El Niño Southern Oscillation (ENSO) with potential impacts on rainfall and flood risk in numerous countries of the Asia-Pacific region. Modeling studies have suggested that positive and negative ENSO phases may intensify by as much as 25% under extreme climate projections. However, the influence of ENSO variability on flood risk in Asia-Pacific countries is still largely unexplored. Here, we aim to shed light into the link between ENSO, flood risk, and insured losses in New Zealand by combining rainfall observations and state-of-the-art flood risk models. We draw on 60 years of daily precipitation measurements to quantify the statistical correlations between the rainfall principal components and the ENSO historical time series. This allows us to generate 50,000 years of stochastic daily rainfall maps correlated with a long-term, synthetic ENSO time series. The stochastic precipitation maps are then used to drive streamflow and flood simulations at 20 m spatial resolution. Our results indicate that positive and negative ENSO phases increase the flood risk in different regions of New Zealand, and that extreme ENSO events tend to cause more severe flood events. We finally investigate the potential differences in economic losses during positive and negative ENSO phases by combining modeled flood footprints with exposure and vulnerability data. These results may guide the implementation of effective adaptation and mitigation strategies against the increasing risk of flood events in warming climate.
How to cite: Comola, F., Scudeler, C., Satyam, S., and Nicotina, L.: Impacts of El Niño Southern Oscillation on flood risk and insured losses in New Zealand, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15107, https://doi.org/10.5194/egusphere-egu21-15107, 2021.