Displays

Compound and cascading natural hazards usually cause more severe impacts than any of the single hazard events alone. Despite the significant impacts of compound hazards, many studies have only focused on single hazards. The aim of this paper is to investigate spatio-temporal patterns of compound and cascading hazards using historical data for dry hazards, namely heatwaves, droughts, and fires across Europe. We streamlined a simple methodology to explore the occurrence of such events on a daily basis. Droughts in soil moisture were analyzed using time series of a threshold-based index, obtained from the LISFLOOD hydrological model forced with observations. Heatwave and fire events were analyzed using the ERA5-based temperature and Fire Weather Index datasets. The data used in this study relates to the summer seasons from 1990 to 2018. Our results show that joint dry hazard occurrences were identified in west, central, and east Europe, and with a lower frequency in southern Europe and eastern Scandinavia. Drought plays a substantial role in the occurrence of the compound and cascading events of dry hazards, especially in southern Europe as it drives the duration of cascading events. Moreover, drought is the most frequent hazard-precursor in cascading events, followed by compound drought-fire events. Changing the definition of a cascading dry hazard by increasing the number of days without a hazard from 1 to 21 within the event (inter-event criterion), lowers as expected, the maximum number of cascading events from 94 to 42, and extends the maximum average duration of cascading events from 38 to 86 days. We had to use proxy observed data to determine the three selected dry hazards because long time series of reported dry hazards do not exist. A complete and specific database with reported hazards is a prerequisite to obtain a more comprehensive insight into compound and cascading dry hazards.

How to cite: Sutanto, S. J., Vitolo, C., Di Napoli, C., D'Andrea, M., and Van Lanen, H.: Droughts, heatwaves, and wildfires: exploring compound and cascading events of dry hazards at the pan-European scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-271, https://doi.org/10.5194/egusphere-egu2020-271, 2020.

Globally more than 600 million people reside in the low elevation (< 10 meters elevation) coastal zone. The densely populated low-lying deltas are vulnerable to flooding primarily in two ways: (1) Due to extreme coastal water level (ECWL) because of either storm surges or heavy rain-induced river floods generated by a severe storm episode. (2) Co-occurrence or successive occurrence of ECWL and river floods as a result of storm-producing synoptic weather conditions leading to compound floods that causes a severe impact than when each of these extremes occurs in an isolation at different times. Most of the earlier assessments that analyzed compound floods, often do not consider the delay between rainfall and streamflow events. River runoff, which also includes subsurface groundwater recharge component, cannot be adequately described by extreme precipitation alone. While most of the literature is limited to analyzing joint dependence between variables considering only central dependence, challenges to flood hazard assessment include difficulty in delineating the severity of riverine floods, especially due to long upper tails of the variables that influence interdependencies between underlying drivers. Despite uncertainties, utilizing the rich database of northwestern Europe, here we assess compound flood severity and its trend by examining spatial interdependencies between annual maxima coastal water level (as an indicator of ECWL) and d-day lagged peak discharge within ±7 days of the occurrence of the ECWL event. Our analysis reveals a spatially coherent dependence pattern with strong positive dependence for gauges located between 52° and 60°N latitude, whereas a weak positive dependence across gauges in > 60°N latitude. Based on a newly proposed index, Compound Hazard Ratio (CHR) that compares the severity of compound floods with at-site design floods, our proof-of-principal analysis suggests nearly half of the stream gauges show amplifications in fluvial flood hazard during 2013/2014’s catastrophic winter storm Xaver that affected most of northern Europe. Furthermore, a multi-decadal (1889 – 2014) temporal evolution of compound flood reveals the existence of a flood-rich period between 1960s and 1980s, especially for the mid-latitude gauges (located within 47° to 60°N), which might be closely linked to the North Atlantic Oscillation (NAO) teleconnection pattern prevailing in the region. On the other hand, gauges at high-latitude (> 60°N) show decreasing to no trend in compound floods. The approach presented here can serve as a basis for developing coastal urban flood risk management portfolios aiding improved resilience and reduce vulnerability in the affected areas.  

How to cite: Ganguli, P. and Merz, B.: Compounding Effects of Riverine and Coastal Floods and Its Implications for Coastal Urban Flood Resilience, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6439, https://doi.org/10.5194/egusphere-egu2020-6439, 2020.

Flood is one of the most frequent and severe natural disasters all over the world. Among different types of floods, the compound flood has been considered as an alarming threat over the years due to climate change. River flood synchronization acts as a compound event which is mainly occurred at the downstream of the large river confluence zones. When multiple rivers become flooded at the same time, the resultant flood magnitude and flood duration at their confluences zone raise drastically. Only a few previous research addressed synchronized flood risk at a local scale, where simply yearly peak synchronization has been considered to avoid the complexity of detecting multiple peaks. However, several rivers occasionally show significant multiple peaks in a single year and sometimes yearly peak stays much below the flood limit.  Therefore, the existing technique either over-estimate or under-estimate synchronized flood risk, apparently, it is not applicable at the global scale.

The quantitative analysis of synchronized flood at a global scale considering multiple peaks is still a big challenge. Here, we have developed a flood return period-based new methodology to quantify synchronous flood precisely as well as to detect the background of the synchronous flood, either multiple peak synchronization or yearly peak synchronization. To find out the suitable confluence points on the global scale, we set two conditions. Firstly, the drainage area of contributing rivers is large enough to become different hydrological features and secondly, both rivers contribute a significant amount of discharge for the generation of flood at the respective confluence points. The next-generation global river routing model, CaMa-Flood, has been employed to compute discharge for return period-based analysis of selected rivers and confluence points. Finally, we check the contributing rivers return period discharge when the respective confluence point is at flooded condition, and if both rivers exceed corresponding 2-year return period discharge, those events are considered as synchronized floods.

We have found 53 confluence points on the global scale where catastrophic flood hazards may occur due to flood synchronization. The historical floods in 49 confluence points show different degrees of synchronization. Moreover, the Confluence zone flood at high latitude mostly affected by yearly peak synchronization may be due to the snowmelt domination. In contrast, the historical synchronized floods in tropical and sub-tropical regions affected by different levels of multiple peak synchronization where rainfall timing might be playing a significant role. This method emerges the physical mechanism of the historical catastrophic fluvial flood that took place where the large rivers have merged.

How to cite: Islam, M. R. and Yamazaki, D.: The Quantitative Analysis of Synchronized River Flood on the Global Scale Considering Multiple Flood Peaks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4455, https://doi.org/10.5194/egusphere-egu2020-4455, 2020.

Multi-hazard events have the potential to cause damages to infrastructures and people that may differ greatly from the associated risks posed by singular hazards. Interrelations between natural hazards also operate on different spatial and temporal scales than single natural hazards. Therefore, the measure of spatial and temporal scales of natural hazard interrelations still remain challenging. The objective of this study is to refine and measure temporal and spatial scales of natural hazards and their interrelations by using a spatiotemporal clustering technique. To do so, spatiotemporal information about natural hazards are extracted from the ERA5 climate reanalysis. We focus here on the interrelation between two natural hazards (extreme precipitation and extreme wind gust) during the period 1969-2019 within a region including Great Britain and North-West France. The characteristics of our input data (i.e. important size, high noise level) and the absence of assumption about the shape of our hazard clusters guided the choice of a clustering algorithm toward the DBSCAN clustering algorithm. To create hazard clusters, we retain only extreme values (above the 99% quantile) of precipitation and wind gust. We analyse the characteristics (eg., size, duration, season, intensity) of single and compound events of rain and wind impacting our study area. We then measure the impact of the spatial and temporal scales defined in this study on the nature of the interrelation between extreme rainfall and extreme wind in the UK. We therefore demonstrate how this methodology can be applied to a different set of natural hazards.

How to cite: Tilloy, A., Malamud, B., Winter, H., and Joly-Laugel, A.: Measuring temporal and spatial scales of compound events in the United Kingdom, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1841, https://doi.org/10.5194/egusphere-egu2020-1841, 2020.

When two or more extreme weather events occur either simultaneously or in close succession, there may be more severe societal and economic impacts than when extreme hazards occur alone. Impacts may also cascade across different sectors of society or amplify impacts in another sector. Perturbed parameter ensemble simulations of projections to 2080 have been generated at the UK Met Office to cover the UK at high spatial (12 km or 2.2 km) and temporal resolution (daily or sub-daily) resolution as part of the “UK Climate Projections”. We use the regional 12 km model simulations at daily resolution to consider how the frequency, duration and spatial extent of multiple extreme hazard events in the UK changes over the 21^st century. We will show case studies of multiple extreme hazard pairings that pose a risk to UK sectors, for example, the risk of hot and dry weather to agricultural harvests. By working with stakeholders that have a good understanding of their vulnerabilities and exposure, we consider multiple extreme events in a risk projection framework. This work is funded under the Strategic Priority Fund for UK Climate Resilience.

How to cite: Garry, F. and Bernie, D.: Multiple hazards under future UK Climate Projections , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13408, https://doi.org/10.5194/egusphere-egu2020-13408, 2020.

Compound weather extremes including warm-wet and warm-dry events can lead to catastrophes such as wildfires, droughts and flooding. We use three large ensembles (3 × 50 members) of climate simulations to study the non-stationarity of compound events based on an ensemble pooling approach: the Canadian Regional Climate Model Large Ensemble (CanRCM4-LE), and the Canadian Large Ensembles Adjusted Datasets (CanLEAD1&2). The CanLEAD products include daily precipitation, maximum and minimum temperature from CanRCM4-LE that are bias-corrected using a novel statistical approach, which preserves the multivariate structure of the climate variables and corrects for univariate biases. Each ensemble member is validated against the NRCANmet observed data over Canada for 1951-2000 using a hierarchical Bayesian framework. Additionally, the performance of the models to mimic the dependence structure of the observation is tested using copulas. Extreme climate indices are estimated for a baseline period and changes in extremes are explored across four future warming scenarios corresponding to +1.5°C, +2.0°C, +3.0°C and +4.0°C warming above the pre-industrial period of 1850-1900. The ensemble pooling approach allows for the quantification of changes in the dependence structure and its subsequent effects on compound extremes in the future. 

Results show that the CanLEAD products can reduce warm and wet biases in CanRCM4-LE over the majority of Canadian regions in all seasons except for winter. The ensembles unanimously project significant warming and wetting trends over most of southern Canada excluding the Canadian Prairies in summer, which show a drying trend towards the end of the 21^st century. The overall trend shows an increase in hot extremes in central and southeastern Canada and a significant increase in wet extremes in western coastal regions. Results from compound extreme analysis show that there is significant under-estimation of extremes when the dependence between temperature and precipitation is ignored. For example, a 100-year hot and dry event under the assumption of independence becomes a ~60-year event when the dependence is characterized using copulas.

How to cite: Najafi, M. R., Singh, H., and Cannon, A.: Nonstationary Compound Weather Extremes in Canada based on Large Ensemble Climate Simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18987, https://doi.org/10.5194/egusphere-egu2020-18987, 2020.

The 2016 wheat harvest in France suffered from an unforeseen and unprecedented production loss. At 5.4 tonnes ha^-1, wheat yield was the lowest recorded since 1986 and 30% below the five-year average.  Crop yield forecasting can be considered as near-real-time impact modelling, but unfortunately, none of the forecasting systems in place anticipated the extent of the impact. The 2015/2016 growing season was characterized by compounding warm autumn temperatures and abnormally wet conditions in the following spring. High rainfall and high temperatures leading to fungal diseases, soil water lodging and anoxia, low radiation affecting grain filling, and leaching of nitrogen from the root-zone have all been suggested as important factors ultimately leading to the yield loss. The use of binomial logistic regressions accounting for autumn and spring temperatures and precipitation, suggests that the odds of an extreme yield loss in 2016 was times 35 higher than expected. The challenge now is to further identify the variety of biotic and abiotic processes interacting at different timescales. Collecting relevant insights on the field or from trial experiments, and confronting these with statistical and biophysical crop modelling will be key to achieve this. Improved impact relevant indicators will need to be integrated into operational crop yield forecasting systems in preparation for future compound events.

How to cite: Ben Ari, T.: Causes and implications of the unforeseen 2016 extreme yield loss in France’s breadbasket, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20116, https://doi.org/10.5194/egusphere-egu2020-20116, 2020.

The impacts of global warming on agriculture and crop production are already visible today and are projected to intensify in the future. As horticultural and agricultural systems are complex organisms, their responses to changing climate can be non-linear and at times counter-intuitive. These systems undergo yearly cycles of growth with different plant characteristics in each of their phenological phases. They are thus especially sensitive to changes in seasonality besides changes in the annual mean and single extreme events.

Here we show that as a result of warmer winters, the risk of frost damages on apple trees in Germany is projected to be about 10% higher in a 2°C world compared to today. Warmer winters lead to less frost days but also to earlier apple blossom. This can result in overall increase in years where frost days occur after blossom.

Using large ensemble climate simulations, we analyze this compound event of frost days after blossom – frost days after warm winters. Although the projected shift in blossom day and the decrease in frost days is relatively homogeneous over Germany, the change in frost risk varies considerably between regions. Our results highlight the importance of treating frost risk as a compound event of frost days after warm winters instead of comparing the average shift in blossom days with the decrease in frost days.

Reference: Pfleiderer, P., Menke, I. & Schleussner, C.-F. Increasing risks of apple tree frost damage under climate change. Clim. Change (2019). doi:10.1007/s10584-019-02570-y

How to cite: Menke, I., Pfleiderer, P., and Schleussner, C.-F.: Increasing risks of apple tree frost damage under climate change , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11527, https://doi.org/10.5194/egusphere-egu2020-11527, 2020.

Crop health and favourable yields depend strongly on precipitation and temperature patterns during the crop’s growing season. Compound events, such as co-occurring drought and heat can lead to extreme crop failure and cause larger damages than the impacts of the individual drought or heat alone.

Here we assess the relative role of hot and dry conditions (HDC) in crop yields and evaluate in what manner compound HDC enhance the probability of failure in rainfed cropping systems in the Iberian Peninsula. We use annual wheat yield data at the province level and cluster provinces with similar sensitivities of yields to climate conditions. Copula theory was applied to model the trivariate dependence between 3-monthly means of maximum temperature, 3-monthly means of precipitation and wheat yields. The climate variables and averaging periods have been chosen to maximize the dependence between the driver climate conditions during growing season and the annual yields. Copulas enable for the estimation of conditional probabilities of crop-loss under different hot and dry severity levels based on their trivariate joint distribution.

Our results demonstrate that the probability of wheat loss increases with the severity of the compound HDC and that losses are significantly larger during co-occurring drought and heat compared to individual water- or heat-stress. Moreover, the difference between heat impacts and compound heat and drought related impacts is larger than the difference between drought impacts and compound heat and drought related impacts, suggesting that water-stress is the major driver of wheat losses. These findings can help contribute to design management options to mitigate climate-related crop impacts and guide the decision-making process in agricultural practices.

Acknowledgements: A.F.S.Ribeiro would like to acknowledge the financial support through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under the projects UIDB/50019/2020 – IDL and PTDC/CTA-CLI/28902/201 (IMPECAF). A.F.S.Ribeiro is also thankful to FCT for the grant PD/BD/114481/2016 and to the COST Action CA17109 for a Short Term Scientific Mission (STSM) grant to develop the present work.

How to cite: Ribeiro, A., Russo, A., Gouveia, C., Páscoa, P., and Zscheischler, J.: Risk of crop-failure due to compound hot and dry extremes in the Iberian Peninsula, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-674, https://doi.org/10.5194/egusphere-egu2020-674, 2020.

Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. The identification of the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. Here we investigate whether key meteorological drivers of extreme yield loss can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment. 
We use yearly wheat yields as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth v2.3) under present-day conditions for the Northern Hemisphere. We define extreme yield loss as years with yield below the 5th percentile. We apply logistic Lasso regression to predict whether weather conditions during the growing season lead to crop failure. Lasso selects the most relevant variables from a large set of predictors that best explain the target variable via regularization. Our input variables include monthly averaged values of maximum temperature, vapour pressure deficit and precipitation as well as established extreme event indicators such as maximum and minimum temperature during the growing season, diurnal temperature range, total number of frost days, and maximum five-day precipitation sum.
We obtain good model performance in Central Europe and the American Corn Belt, while yield losses in Asian and African regions are less accurately predicted. Model performance and mean wheat yield strongly correlate, i.e. model performance is highest in regions with relatively large mean yield. Based on the selected predictors, we identify regions where crop loss is predominantly influenced by a single variable and regions where it is driven by the interplay of several variables, i.e. compound events. Especially in the Midwest and Eastern regions of the USA, several variables are required to correctly predict yield losses. This illustrates the importance of accounting for the interplay of various weather conditions over the course of the growing season to be able to determine crop yield losses more precisely.
We conclude that the Lasso regression is a useful tool to detect the compound drivers of extreme impacts, which can be applied for other impact variables such as fires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts. Furthermore, using the same model environment, the robustness of the identified relationships will be tested in a climate change context.

How to cite: Sauter, C., Deidda, C., Rahimi, L., Rivoire, P., Tschumi, E., Vogel, J., van der Wiel, K., and Zscheischler, J.: Identifying compound meteorological drivers of extreme wheat yield loss using Lasso regression, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18514, https://doi.org/10.5194/egusphere-egu2020-18514, 2020.

Droughts and heat waves have large impacts on the terrestrial carbon cycle. They lead to reductions in gross and net carbon uptake or anomalous increases in carbon emissions to the atmosphere because of responses such as stomatal closure, hydraulic failure and vegetation mortality. The impacts are particularly strong when drought and heat occur at the same time. Climate model simulations diverge in their occurrence frequency of compound hot and dry events, and it is unclear how these differences affect carbon dynamics. Furthermore, it is unknown whether an increase in frequency of droughts and heat waves leads to long-term changes in carbon dynamics, and how such an increase might affect vegetation composition.

To study the immediate and long-term effects of varying signatures of droughts and heat waves on carbon dynamics such as inter-annual variability of carbon fluxes and cumulative carbon uptake, we employ the state-of-the-art dynamic global vegetation model LPX-Bern (v1.4) under different drought-heat scenarios.

We have constructed five 100-yr long scenarios with different drought-heat signatures, representing a “control”, “close to mean seasonal cycle”, “drought only”, “heat only”, and “compound drought and heat” climate forcing to LPX-Bern. This is done by sampling daily climate variables from a 2000-year stationary simulation of a General Circulation Model (EC-Earth) for present-day climate conditions. Such a sampling ensures physically-consistent co-variability between climate variables in the climate forcing.

We investigate the carbon-cycle response and changes in vegetation structure to different drought-heat signatures on a global grid, representing different land cover types and climate zones. Our results provide a better understanding of the links between hot and dry conditions and carbon dynamics. This may help to reduce uncertainties in carbon cycle projections, which is important for constraining carbon cycle-climate feedbacks.

How to cite: Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., and Zscheischler, J.: Investigating the impact of different drought-heat signatures on carbon dynamics using a dynamic global vegetation model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9004, https://doi.org/10.5194/egusphere-egu2020-9004, 2020.

The Mediterranean Basin is known as a hot spot of climate change and therefore especially prone to increasing frequencies of warm spells and droughts. Investigating these events in isolation neglects their interactions, which illustrates the need to account for such compound events in a holistic manner. We analysed during which months the frequency of compound warm spells and droughts increased most over the 40-year period from 1979 – 2018. Warm spells and droughts were detected using daily maximum air temperature, precipitation and potential evaporation data from ERA 5. The two drought indices Standardised Precipitation Index (SPI) and Standardised Precipitation-Evapotranspiration Index (SPEI) were calculated.

Our results show the number of compound events increases substantially for almost the entire Mediterranean indicating that novel climatic conditions are occurring. The increases in compound events are predominantly driven by the rising number of warm spells, whereas SPI droughts remain almost constant. However, the rising temperatures lead to higher evapotranspiration, which alters the water balance in the Mediterranean. Therefore, the SPEI droughts shows significant increases in contrast to the SPI, indicating that even though the amount of precipitation does not decrease, the Mediterranean Basin is likely facing drier conditions due to increasing evapotranspiration. The highest changes in the number of compound warm spells and droughts occur in the time span from late winter to early summer. This finding is particularly relevant for Mediterranean ecosystems because this period encompasses the main growing season, and therefore ecosystem productivity and carbon sequestration might be reduced.

How to cite: Vogel, J. and Paton, E.: Increasing compound warm spells and droughts during the growing season in the Mediterranean Basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3707, https://doi.org/10.5194/egusphere-egu2020-3707, 2020.

Compound events are characterized as a combination of multiple drivers and/or hazards which contributes to societal, economical or environmental risk. In southern Norway, hydrometeorological compound events can trigger severe floods, for instance the joint occurrence of rainfall and snowmelt in south-eastern Norway in 1995 and 2013.  

Due to this high impact, the investigation of compound events is important, but is hampered by some limiting factors. The multivariate character and the associated very rare occurrence of these events require a large database in order to conduct statistically robust investigations, whereas the available meteorological observations are too scarce in space and time.
With this current study, we present a quantile-based framework to define and examine compound events within a single model initial condition large ensemble (SMILE). To overcome the limitation of data scarcity, we use 50 high-resolution climate simulations from the SMILE CRCM5-LE to investigate two hydrometeorological compound event types in southern Norway:

(1) Heavy rainfall on saturated soil during the summer months (June, July, August, September),

(2) Concurrent heavy rainfall and snowmelt (also often referred to as rain-on-snow).

Furthermore, the application of climate model data enables us to quantify the impact of climate change on the frequency and spatial distribution of both types of compound events. Thereby, we compare current climate conditions (1980-2009) with future conditions (2070-2099) under the high-emission scenario RCP 8.5. We find that the frequency of heavy rainfall on saturated soil increases by 38% until 2070-2099 on average. In contrast, the occurrence probability of rain-on-snow is projected to decrease by 48% over the whole study area, largely driven by decreases in snowfall. The spatial patterns of both events are found to shift. Additionally, we assess the range of the natural variability of the drivers and of the compound event probability within the 50 members of the CRCM5-LE. The univariate spread of the meteorological drivers is found to be relatively small, whereas the occurrence probability of both compound events shows a high inter-member variability. Hence, we conclude that the frequency of the joint occurrence of the contributing drivers is highly variable, which is why a SMILE is needed to assess this probability.

Our current work shows the limitations of regional climate models, stressing the need for even higher-resolution setups to resolve the complex topography of Norway. However, it also highlights the benefits of SMILE simulations for the analysis of compound events.

How to cite: Poschlod, B., Zscheischler, J., Sillmann, J., Wood, R. R., and Ludwig, R.: Climate change effects on hydrometeorological compound events over southern Norway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4590, https://doi.org/10.5194/egusphere-egu2020-4590, 2020.

Drought is a widespread climate phenomenon throughout the world, as well as one of the natural disasters that seriously impact agricultural. Losses caused by drought in China reach up to about 15 percent of the all losses caused by natural disasters every year. Therefore, to monitoring the drought real-time and effectively, to improving the level of drought monitoring and early warning capacity have important significance to defense drought effectively. Satellite remote sensing technique of drought developed rapidly and had been one of the significant methods that widely used throughout the world since 1980s. Studies have shown that remote sensing drought index, especially the Vegetation drought Index (VIs) is the most suitable one that can be used in semi-arid and semi-humid climate region. We choose semi-arid region of Longdong rain-fed agriculture area in the northwest of Gansu Province as the study area, which is the most frequency area in China that drought occurs. To estimate the drought characteristics from 1981 to 2010, monthly NDVI data, the VCI and AVI index data got from NDVI data, the Comprehensive meteorological drought Index (CI) data during this period, and soil moisture observation data in 20 cm were used. Results show that:

1. The frequency and severity of drought in Longdong region appeared a low-high-low trend from 1981 to 2010. 1980s showed a lowest value, 1990s showed a highest value and 2000s showed a falling trend in the frequency and severity.
2. AVI and VCI showed a good consistency of drought monitoring together with CI and soil moisture, but a higher volatility and lagged behind for 1 month.
3. A Winter Wheat Drought Index (WWDI) was proposed through the analyses of inter-annual NDVI data during the winter wheat growth period and it represents the drought degree in the whole growth period commendably. Thus provide an efficient index to the winter wheat disaster assessment.
4. The winter wheat drought degree in the study region from 1981 to 2010 was obtained using WWDI data. The most drought years got from WWDI data were 1995, 2000, 1992, 1996 and 1997, which displayed a very high consistency with the actual disaster situations.

How to cite: Guo, N., Wang, W., and Wang, L.: Drought monitoring over Semi-Humid rain-fed winter wheat region in northwest China using remote sensing data from 1981 to 2010, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6597, https://doi.org/10.5194/egusphere-egu2020-6597, 2020.

Climate change is one of the largest challenges currently faced by society, with an impact on many systems, such as hydrology. To locally assess this impact, Regional Climate Model (RCM) data are often used as an input for hydrological rainfall-runoff models. However, RCMs are still biased in comparison with the observations. Many methods have been developed to adjust this, but only during the last few years, methods to adjust biases in the variable correlation have become available. This is especially important for hydrological impact assessment, as the hydrological models often need multiple locally correct input variables. In contrast to univariate bias-adjusting methods, the multivariate methods have not yet been thoroughly compared. In this study, two univariate and three multivariate bias-adjusting methods are compared with respect to their performance under climate change conditions. To do this, the methods are calibrated in the late 20^th century (1970-1989) and validated in the early 21st century (1998-2017), in which the effect of climate change is already visible. The variables adjusted are precipitation, evaporation and temperature, of which the resulting evaporation and precipitation are used as an input for a rainfall-runoff model, to allow for the validation of the methods on discharge. The methods are also evaluated using indices based on the calibrated variables, the temporal structure, and the multivariate correlation. For precipitation, all methods decrease the bias in a comparable manner. However, for many other indices the results differ considerable between the bias-adjusting methods. The multivariate methods often perform worse than the univariate methods, a result that is especially pronounced for temperature and evaporation.

How to cite: Van de Velde, J., De Baets, B., Demuzere, M., and Verhoest, N.: Comparison of univariate and multivariate bias-adjusting methods for hydrological impact assessment under climate change conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17387, https://doi.org/10.5194/egusphere-egu2020-17387, 2020.

Key Words: Compound events; Multi-hazards; Industry application; Multivariate extreme value theory.

Abstract:

Resilience of pre-existing and new-build infrastructure to natural hazards is of key interest for many different industries (e.g. energy, water, transport). In most situations, studies analyzing the risk posed by single natural hazards have already been undertaken and relevant protection measures have been implemented. However, when considering the potential impacts of compound events or multi-hazards there can be less confidence in which combinations need to be considered and how to estimate the risks associated to these multi-hazard scenarios. Certain industries (e.g. nuclear) have already undertaken several projects on the occurrence and risks posed by multi-hazards (e.g. ASAMPSA-E, NARSIS), whereas other industries are still trying to understand their risks and which questions need to be posed.

The EDF Energy R&D UK Centre are part of an industry scheme funded by NERC called the Environmental Risks for Infrastructure Innovation Programme (ERIIP) which aims to connect academics to industrial organisations and undertake translational research. One of the key topics of further research identified by this group is the topic of compound events and multi-hazards. A recent review of knowledge on multi-hazards was undertaken by the British Geological Survey (BGS) which highlighted the state of knowledge across UK infrastructure owners.

This presentation will start by summarizing this review to pull out some key themes for future research in this area. Then, two different ongoing research projects will be outlined which look to address the key themes coming out of the review. One project is attempting to better understand the different multivariate statistical methods that are available for assessing the probability of multi-hazards. The application of the different models outlined in this work will be shown on an example of extreme precipitation and wind speed. The other project aims to better understand the overarching meteorological conditions that can lead to compound flooding at coastal sites around the UK. This focuses less on estimating joint probabilities, but more on producing clear visualisations for end-users.  

How to cite: Winter, H., Tilloy, A., Hendry, A., and joly-Laugel, A.: Building Industry Resilience to Compound Events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2327, https://doi.org/10.5194/egusphere-egu2020-2327, 2020.

Wind characteristics in 2019 on the Polish Baltic coast.

Katarzyna Starosta, Andrzej Wyszogrodzki

katarzyna.starosta@imgw.pl, andrzej.wyszogrodzki@imgw.pl

          Wind is one of the main complex elements that affects the climate and weather of our planet. The topic of our presentation is to show  characteristics of the wind for the  Polish coastal areas. In our presentation we show distribution  of wind speed and wind direction based on the COSMO (Consortium for Small-scale Modelling) model forecasts  at a mesh resolution of 2.8 km and their verification with 24 hour measurements for a five synoptic stations: Swinoujscie, Kolobrzeg, Ustka, Leba and Hel. The Polish Institute of Meteorology and Water Management – National Research Institute (IMWM-NRI) runs an operational model COSMO   using two nested domains at horizontal resolutions of 7 km and 2.8 km. The model produces 36 hour and 78 hour forecasts four times per day for 2.8 km and 7 km domain resolutions respectively. However, only the 00 UTC forecasts are  utilized in this study. We show wind analyzes at synoptic stations for different time scales (hours, days, months). We also analyze situations of extreme winds, such as for example passage of hurricane Alfrida in January 2019 on the Polish coast. The results show  characteristic distribution of wind speed and direction at the interface between sea and land . The wind is both destructive and conducive to human action. It causes local flooding, damage in ports, knocks down trees but also provides clean energy for wind farms or serves tourism activities as yachting or surfing.Poland plans recently to build an offshore wind farm in the Baltic Sea. Increasingly accurate wind forecasts are then one of the necessary elements for assessing the local climatology at the wind farm site and to further provide warnings and  decisive support to its operation. 

Presentation preference: POSTER

EGU/2020/session 35921

Institute of Meteorology and Water Management – National Research Institute

Podleśna 61

01-633 Warsaw

Poland

How to cite: Starosta, K. and Wyszogrodzki, A.: Wind characteristics in 2019 on the Polish Baltic coast, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3517, https://doi.org/10.5194/egusphere-egu2020-3517, 2020.

The Mediterranean coastal zone is a hotspot to the impact of extreme events due to the combination of high values at exposure (concentration of population, large urban areas, infrastructures), large vulnerability (natural protection provided by beaches decreasing due to coastal erosion) and presence of extreme hydro-meteorological events of marine (storms) and terrestrial (flash floods) origins. The Catalan coast (NE Spain) can be considered a paradigm of Med hotspots. On one hand, due to its climatic conditions, orography and land use, flash floods are one of the main causes of inundation risks in the coastal fringe, inducing numerous damages and even casualties (e.g. Llasat et al. 2013). On the other hand, coastal damage associated to the impact of marine storms have been increasing during the last decades along this coast (Jiménez et al. 2012). However, existing studies have not analysed their joint impact to assess the most hazardous conditions, when the coastal zone would be subjected to the combined action of both types of extreme events.

Within this context, this works analyses the combined presentation of extreme events of terrestrial (flash floods) and marine (storms) origin in the Catalan coast. First, extreme events causing significant damage (based on reported damages, insurance costs and casualties) along the coast were identified for the period 1981-2014. 69 events were identified and classified according their origin (marine and/or terrestrial). Each event was characterized in terms of their marine (wave height, period, direction, storm duration) and rainfall characteristics. Since the coastline length is about 600 km, these events verify at specific locations. To cover this spatial variability, storms were locally characterized by using data from existing rain gauges and radar stations along the territory as well as hindcasted wave conditions along the entire coastal fringe. To fully characterize these events, synoptic conditions were also recorded.

From this, first, we directly obtained the corresponding marginal probabilities of each event. Then, compound frequencies were assessed and compared to the marginal ones. Finally, we identified synoptic situations with higher probability of associated compound hazards and bound the range of corresponding wave and rain conditions. By jointly considering the location where they verified, we identify coastal areas (and corresponding geomorphologic conditions) with higher probabilities of suffering damages due to impact of compound extreme events.

This work was carried out within the framework of the M-CostAdapt (CTM2017-83655-C2-1-R) research project, funded by the Spanish Ministry of Economy and Competitiveness (MINECO/AEI/FEDER, UE).

Jiménez et al. 2012. Storm-induced damages along the Catalan coast (NW Mediterranean) during the period 1958–2008. Geomorphology 143, 24-33.

Llasat et al. 2013.  Towards a database on societal impact of Mediterranean floods within the framework of the HYMEX project. NHESS, 13, 1337-1350.

How to cite: Sanuy Vazquez, M., Llasat-Botija, M., Rigo, T., Jiménez, J. A., and Llasat, M. C.: On the joint impact of marine (storms) and terrestrial (flash floods) extreme events along the Catalan coast (NW Mediterranean), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19954, https://doi.org/10.5194/egusphere-egu2020-19954, 2020.

In recent years, the surface air temperature (SAT) anomalies in winter over North America show a “warm-West/cool-East” (WWCE) dipole pattern. The underlying mechanism of the North American WWCE dipole pattern has been an important research topic. This study examines the physical cause of the WWCE dipole generation.

It is found that the positive phase (PNA⁺) of the Pacific North American (PNA) pattern can lead to the generation of the WWCE SAT dipole. However, the impact of the PNA⁺ on the WWCE SAT dipole over North America depends on the type of the El Nino SST anomaly. When an Eastern-Pacific (EP) type El Nino occurs, the anticyclonic anomaly center of the PNA⁺ over the North American continent is displaced eastward near 100°W due to intensified midlatitude westerly winds over North Pacific so that its anticyclonic anomaly dominates the whole North America. In this case, the cyclonic anomaly of the PNA⁺ almost disappears over the North America. Thus, the WWCE SAT dipole over the North America is weakened. In contrast, when a central-Pacific (CP) type El Nino appears, the anticyclonic anomaly center of the associated PNA⁺ is located over the North America west coast due to reduced midlatitude westerly winds over North Pacific. As a result, the cyclonic anomaly of the PNA⁺ can appear over the east United States to result in an intensified WWCE SAT dipole over the North America

How to cite: Ge, Y. and Luo, D.: North American Warm-west/Cold-East air temperature pattern and its linkage to different El Nino types, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1219, https://doi.org/10.5194/egusphere-egu2020-1219, 2020.

Windstorms and flooding pose a significant socio-economic threat to the United Kingdom andcan cause significant financial loss. For example, the great October storm of 1987 damaged whole elements of the national electricity grid in the west of the UK. Storms can also be associated with heavy precipitation, for example, extensive inland flooding was caused by a series of slow-moving storms in the case of the winter floods of 2013/14 in the South East of England. The UK Met Office and Environment Agency estimated the financial loss attributable to the 1987 and 2013/14 events at €6.4bn and €1.5bn respectively. The question of correlations between windstorm and flood events remains open, for example the risk of a 1987-scale event "colluding" with the economically adverse meteorology of the 2013/14 season being poorly unquantified. If wind and flood risk is correlated then insurers are under-estimating both capital requirements and risk policy price, exposing them to very substantial liabilities.

Here, a collaborative project between academics and insurers has been undertaken to improve our understanding of the spatial-temporal distribution of risk from extreme, compounded windstorm and inland flood events in the UK. Statistical analysis of different data sets (~40 years of winter ERA5 reanalysis daily maximum winds, as well as observational precipitation and river flow gauge data) reveals wind and inland flooding are modestly correlated across the UK. In addition, we find substantially more compound events than expected by chance, some of which can be linked to named UK storms.

In terms of the large-scale atmospheric drivers, there appears to be no particular preferred path for the storms associated with compound wind and flood events. However, we find that compound events appear to be moderated by the amount of rainfall in the days preceding a windstorm, rather than the overall storminess of any given year. Further, we investigate the relationship in very extreme (200-year return period) windstorms and precipitation from the 1000-years of high-resolution HiGEM climate simulations.

How to cite: Halliday, O., Shaffrey, L., Tsaknias, D., Cloke, H., and Siddaway, A.: Understanding the relationship between extremes of wind and inland flooding in the UK, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20230, https://doi.org/10.5194/egusphere-egu2020-20230, 2020.

Climate change is set to result in an increase of extreme weather events such as extreme precipitation, heatwaves, floods, droughts etc. The study of the possibility of the increase of such events is of high importance, but equally important is to study the combination of these events, meaning the study of Compound Events. In our case we focus on the combination of extreme precipitation with extreme wind speed for the region of Greece.

Greece located in the region of the Eastern Mediterranean Sea is prone to Climate Change as the whole region of the Mediterranean Basin. So, it is crucial to understand how the country is affected by Compound Events of extreme precipitation and extreme wind speed. As a first step, we study the historic period 1980-2009 using the model output data. The data for the historic period analysis have been produced from Weather Research Forecast (WRF) 5km downscaled model output with temporal resolution of 6 hours, using as input ERAINTERIM data. The downscaling study that has produced the atmospheric model dataset is described in Politi, et al. (2018). The methodology for studying Compound Events in the area is presented together with the preliminary results. 

How to cite: Sfetsos, A., Markantonis, J., Karozis, S., Politi, N., and Vlachogiannis, D.: Preliminary study of Compound Events in Greece using high-resolution downscaled climate data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21541, https://doi.org/10.5194/egusphere-egu2020-21541, 2020.

GRIMASA project aims to develop a spatial (not only, but especially a gridded version) stochastic weather generator (WG) applicable at various spatial and temporal scales, for both present and future climates. The multi-purpose SPAGETTA generator (Dubrovsky et al, 2019, Theoretical and Applied Climatology) being developed within this project is based on a parametric approach suggested by Wilks (1998, 2009). It was presented already at EGU-2017 and EGU-2018 conferences. It is run mainly at daily time step and allows to produce multivariate weather series for up to 100 (approximately) grid-points. In developing and validating the generator, we employ also various compound weather indices defined by multiple weather variables, which allows to account for the inter-variable correlations in the validation process. In our first experiments, the WG was run at 100 km resolution (50 km EOBS data were used for calibrating the WG) for eight European regions, and its performance was compared with RCMs (CORDEX simulations for EUR-44 domain). In our EGU-2019 contribution, our WG was validated in terms of characteristics of spatial temperature-precipitation compound spells (including dry-hot spells). Most recently, after implementing wind speed and humidity into the generator, the WG was run at much finer resolution (using data from irregularly distributed weather stations in Czechia and Sardinia) and validated in terms of spatial spells of wildfire-prone weather (using Fire Weather Index) (results were presented at AGU-2019).

Present project activities aim mainly at (A) going into finer spatial and temporal scales, and (B) conditioning the surface weather generator on larger scale circulation simulated by circulation weather generator run at much coarser resolution. The development of the circulation generator (CIRCULATOR) has started in 2019. It is based on the first-order multivariate autoregressive model (similar to the one used in SPAGETTA), and the set of generator’s variables consists of larger scale characteristics of atmospheric circulation (derived from the NCEP/NCAR reanalysis), temperature and precipitation defined for a 2.5 degree grid. In our contribution, we will show results related to these two activities, focusing on (i) WG’s ability to reproduce spatial temperature-precipitation spells at various spatial scales (down to EUR-11 resolution) for eight European regions, (ii) validation of the circulation generator in terms of its ability to reproduce frequencies of circulation patterns and larger-scale temperature and precipitation characteristics for the 8 regions, and (iii) assessing an effect of using the circulation generator to drive the surface weather generator on its ability to reproduce the compound spells.

Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).

How to cite: Dubrovsky, M., Lhotka, O., and Miksovsky, J.: Simulation of Spatial Temperature-Precipitation Compound Events with Circulation-Conditioned Weather Generator, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17947, https://doi.org/10.5194/egusphere-egu2020-17947, 2020.