The 2018 compound of hot and dry conditions in Central Europe are unprecedented in magnitude, duration and spatial extent since measurements started in 1881. During spring and summer, these compounding of extreme conditions caused a series of severe impacts on several sectors including agriculture, forestry, transport, energy and water supply. At the beginning of the same year, windstorm Friederike concurrent with heavy snowfall caused severe damages in Ireland, Great Britain, northern France, Belgium, the Netherlands, Germany, Czech Republic and Poland. Friederike reached wind gusts of the order of 100 – 150 km/h, up to 173 km/h at Sněžka in Czech Republic and 203 km/h at Brocken in Germany.

Along the trajectory from large to the local scale, the drivers and dynamics of these events are analyzed and the impacts of the compound events are provided. Exemplary for 2018, the impacts of the compound events comprise traffic disruption, power outages, property damage by e.g., falling trees, and fatalities after the windstorm. Unprecedented winter wheat yield reductions were observed as well after the hot and dry spring and summer growing season. The impact of the drought and heat wave compound further facilitated the outbreak of bark beetle in 2018 and the following years, as a cumulative hazard and increased the probability of a dry surface water anomaly to an unexpected 68 %.

Taking advantage of the transdisciplinary research and gathered expertise in the frame of the coordinated German ClimXtreme project network (www.climxtreme.net), we analyze and characterize these 2018 events that link with severe impacts in Germany and neighboring countries in Central Europe. We focus on two key storylines with respect to the selected case studies of compound wind & rain and drought & heat. We provide a detailed overview of the data, methods and approaches used, the scales and aspects involved as well as the events’ drivers/dynamics and their multi-sectorial impacts. We finally demonstrate the importance of considering the various facets of the compound nature of extremes and respond to timely research questions that the ClimXtreme research network addresses, such as: attribution of changing compound events to climate change, understanding the variability of clustered storms, understanding the role of decadal variations on compound heat metrics, understanding and predicting the effects of climate change on landslides, analysis of past and future changes in the frequencies of compound events.

How to cite: Ellsäßer, F. and Xoplaki, E. and the The climXtreme research network on climate change and extreme events: Compound events in Germany: drivers and case studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11534, https://doi.org/10.5194/egusphere-egu22-11534, 2022.

In river flowing areas, the co-occurrence of high temperature and low streamflow may cause compound hydrologic hot-dry events (CHHDEs). When thermal and hydrological extremes interact, the impact can be worse than when they occur individually. Evidence shows that CHHDEs have severe socio-economic effects, such as increasing pollutant concentration, endangering aquatic species, and reducing power generation. Despite the importance, large-scale risk quantification of CHHDEs remains rarely studied due to the lack of enough simulated data at the global scale.

Therefore, the objectives of this study are threefold: (1) developing the first global hydrologic hot-dry event dataset from 1901 to 2014 (containing four attributes: duration, intensity, severity, and magnitude) based on a state-of-the-art physically-based Tightly Coupled framework for Hydrology of Open water Interactions in River–lake network (TCHOIR) model, which dynamically simulates thermal and hydrological regimes; (2) developing a robust statistical framework to conduct attribution analysis to identify drivers of compound risk (distinguishing high temperature-driven, low streamflow-driven, and dependence-driven); (3) quantifying the impact of river order and hydrologic belt on compound risk to pinpoint CHHDEs hotspots.

CHHDEs have multi-sectoral impacts, including water availability, food security, and energy production. The compound risk analysis provides crucial insights to maintain regional resilience and guide adaptation strategies.

How to cite: An, R., Liu, P., and He, X.: Global Assessment of Compound Risk of High Temperature and Low Streamflow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4916, https://doi.org/10.5194/egusphere-egu22-4916, 2022.

The recent 2021 spring-to-summer season was characterized by co-occurrent hot, dry and extremely wet extremes around the globe, raising questions regarding changing likelihoods of such extreme years in a changing climate. To address this question, we assess the likelihood of spatially compounding hot, dry and wet extremes under historic and present climate as well as under different future warming levels. The occurrence-probability of spatially compounding events and area affected in future climates under scenarios at 1.5°C, 2°C and higher levels of global warming is determined using Earth System model simulations from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). As climate change impacts are particularly severe when spatially compounding events occur in multiple regions with high exposure of people or crops, this study focuses on densely inhabited regions and important agricultural areas. 

How to cite: Biess, B., Gudmundsson, L., and Seneviratne, S. I.: Changes in likelihood and intensity of spatially co-occurring hot, dry and wet extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7281, https://doi.org/10.5194/egusphere-egu22-7281, 2022.

While it is known that severe winter wind storms are related with strong impacts, this study investigates the enhanced impact of compound precipitation and wind extremes. Therefore, we analyse the co-occurrence of extreme wind and precipitation using ERA5 reanalysis data for the European winter season. Co-occurring events are defined by simultaneous threshold exceedance of daily wind speed and precipitation in same or neighbouring areas.

For the quantification of impacts, we are using daily insurance records of damages for residential buildings over Germany provided by the German Insurance Association (GDV). Using the definition of co-occurring extremes, those damage records can be grouped into compound and non-compound events. Analysing insurance loss data between 1997-2016 allows comparisons of the distribution of both groups. There are much more events in the non-compound group. On the other hand, the distribution of the compound group is shifted towards higher damages with an increased median of a factor of ten.

How to cite: Grieger, J. and Ulbrich, U.: Enhanced impacts of compound precipitation and wind extremes on residential buildings, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4371, https://doi.org/10.5194/egusphere-egu22-4371, 2022.

Extreme precipitation events are associated with cyclones, atmospheric fronts or convective storms which produce high winds as well. This fact increases the probability of compound wind-precipitation events. Such events can cause even more damage than single precipitation and wind events because, for example, soil moisture makes trees less stable. The joint effect is even more significant in case of solid precipitation due to snow accumulations on trees. However, as the orographic precipitation enhancement increases mainly cold-season precipitation totals in highlands, the altitude makes the difference in the seasonal distribution of precipitation in Czechia. Thus, the local lowlands and highlands also partly differ in terms of the frequency of compound wind-precipitation events. We present this fact on data series of maximum daily wind gusts, daily precipitation totals and inter-diurnal increases in show depth from the period 1961 – 2020 at selected Czech weather stations, located in various altitudes. Extreme events are defined by the method of percentiles; frequencies of compound events are evaluated in comparison to the stochastic frequencies.

How to cite: Müller, M., Kašpar, M., and Křížová, M.: Differences between lowlands and highlands in terms of compound wind-precipitation events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9715, https://doi.org/10.5194/egusphere-egu22-9715, 2022.

Over recent years, research on compound weather and climate event has emerged as a new research frontier at the interface of climate science, climate impact research, engineering and statistics. Compound weather and climate events refer to the combination of multiple drivers and/or hazards that contribute to environmental or societal risk. Compound event analysis combines traditional research on climate extremes with impact-focused bottom-up assessments, thereby providing new insights on present-day and future climate risk. In this talk, I will illustrate my own trajectory into compound event analysis and highlight current and future challenges in this novel and exciting field of research. 

How to cite: Zscheischler, J.: The emergence of compound event analysis as a new research frontier, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7784, https://doi.org/10.5194/egusphere-egu22-7784, 2022.

Recent studies on compound flooding have considered the interaction of storm surge and fluvial or pluvial flood drivers, whereas the contribution of waves to compound flooding has so far been neglected. In this study, we assess compound flooding from waves, tides and river discharge at Breede Estuary, South Africa, using a hydrodynamic model. We estimate the contribution of extreme waves to compound flooding by analysing the driver interactions and by quantifying changes in flood characteristics. We further consider the effect of waves on flood timing and compare results of compound flood scenarios to scenarios in which single drivers are omitted. We find that flood characteristics are more sensitive to river discharge than to waves, particularly when the latter only coincide with high spring tides. When interacting with river discharge, however, the contribution of waves is high, causing larger flood extents and higher water depths. With more extreme waves, flooding can begin up to 12 hours earlier. Our findings provide insights on the magnitude and timing of compound flooding in an open South African estuary and demonstrate the need to account for the effects of waves during compound flooding in future flood impact assessments of similar coastal settings with similar wave climates.

How to cite: Kupfer, S., Santamaria-Aguilar, S., van Niekerk, L., Lück-Vogel, M., and Vafeidis, A. T.: Compound flooding due to interaction of waves and river discharge at Breede Estuary, South Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1222, https://doi.org/10.5194/egusphere-egu22-1222, 2022.

Compound events are extreme events whose impact is enhanced by the synergy, in time and/or space, of multiple variables. An example of this typology of events is provided by compound flooding. In this case, the resulting flooded area is increased by several factors, combining together; for example, the contemporaneous occurrence of high sea level and heavy precipitation (multivariate event), the presence of high soil saturation prior to rainfall events (preconditioned event), a precipitation event affecting several basins (spatially compounding event), or a succession of precipitation events (temporally compounding event). In this respect, we have adopted a compound analysis to study a series of floods that affected the town of Como (Northern Italy). Indeed, the town experiences recurrent damages due to the flooding of the nearby lake. In particular, we collected and analyzed 53 flood events, covering the period 1981-2020, in order to gain a better and more in-depth understanding of the phenomenon. This may eventually have important implications for the prediction and risk reduction of compound flooding.

How to cite: Banfi, F. and De Michele, C.: Investigating compound flooding in Como, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7426, https://doi.org/10.5194/egusphere-egu22-7426, 2022.

Many large population centres are located along estuaries where freshwater flows merge with tidally-driven sea water. In these intertidal zones the river water levels are directly affected by the upstream flow and the downstream coastal conditions. Naturally, such coastal zones can be vulnerable to flood events both from a single driver or several drivers acting in a combination. The compound coastal floods levels may generate extreme impacts even if hazards from individual drivers in isolation would be unlikely. Moreover, the complexity of compound flooding is exacerbated by the presence of interactions (e.g. tide and surge) or dependencies between drivers (e.g. river discharge and surge). To fully understand the multi-driver flood dynamics, the multiple drivers and their impacts need to be assessed in an integrated manner.

In this study the statistical and hydrodynamic models are linked to determine probabilities of multiple-driver flood events and associated risks. Cork City on the south coast of Ireland, frequently subject to complex coastal-fluvial flooding is used as a study case.  The research shows that in Cork Harbour and estuary, the tide-surge interactions have a damping effect on the total water level while dependencies between the surge residual and river flow amplify the risk of flooding. The study also shows that for the most accurate assessment of flood hazard, these phenomena need to be accounted for in the joint probability analysis. From a range of uni- and multivariate scenarios, the multivariate joint exceedance probability AND scenario that includes dependence between multiple drivers represents the most realistic representation of flood probabilities. The outputs from the statistical analysis were used to force the hydrodynamic model of Cork City floodplains. The MNS_Flood model was found to be a robust tool for mapping coastal flood hazards in tidally active river channels. Ultimately, the model results were used to build a machine-learning-based flood forecasting tool. A range of machine learning algorithms were tested to explore relationships between the flood drivers and the resulting spatially variable inundation patterns.

The information derived from the integrated statistical, hydrodynamic and machine learning tools can provide a significant support for short-term early-warning applications as well as for the long-term flood management.

How to cite: Olbert, A. I., Nash, S., Comer, J., and Hartnett, M.: Linking statistical, hydrodynamic and machine learning models for assessment of compound floods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4388, https://doi.org/10.5194/egusphere-egu22-4388, 2022.

Due to climate change, extreme floods are projected to increase in the 21st century in Europe. As a result, flood risk and flood related losses might increase. It is therefore essential to simulate potential floods not only relying on the historical but also include future projecting data. Such simulations can give necessary information for development of flood protection measures and spatial planning. This paper analyzes the risk of compound flooding in the Dane River under different river discharge and Klaipeda Strait water level probabilities. Additionally, we examined how water level rise of 1 meter in the Klaipeda Strait could impacts Dane River floods in Klaipeda City. Flood extent was estimated with Hydrologic Engineering Center's River Analysis System (HEC-RAS) and visualized with ArcGIS Pro. Research results show that the rise of the water level in the Klaipeda Strait has a greater impact on the Central part of Klaipeda City, while the maximum discharge rates of the river—on the Northern upstream part of the analyzed river section. Sea level rise of 1 m could lead to the increase of area affected by Dane floods up to three times. Floods can cause significant damage to the infrastructure of Klaipeda Port City, urbanized territories in the City Center and residential areas in the Northern part of the City. Our results confirm that, in the long run, sea level rise will significantly impact the urban areas of the Klaipeda City situated to Baltic Sea coast.

How to cite: Čepienė, E., Dailidytė, L., Stonevičius, E., and Dailidienė, I.: Sea Level Rise Impact on Compound Coastal-river Flood Risk in Klaipeda city (Baltic coast, Lithuania) , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6089, https://doi.org/10.5194/egusphere-egu22-6089, 2022.

From a risk management perspective, the relevance of compound events lies in the fact that they can significantly increase the intensity and/or the spatial and temporal extension of the impact (and damage) due to the synergic and/or cumulative action of different hazards. This compounding effect may overwhelm the capability of emergency-response services since these have to tackle an “unusual” high-damaging situation, they have to respond to a large number of emergency situations throughout the region at the same time, and/or they have to maintain the level of response during a relatively long period. Due to this, from this perspective, it would be important to incorporate the emergency/recovery services responsiveness to identify these events, as well as to evaluate their probability of occurrence. In this work we investigate this by parameterising this response as a time window between individual extreme events (rainfall and waves) to define the presence of a compound event. This time window depends on the intrinsic capacity of response of the available services, but also on the magnitude of contributing events as well as their spatial scale. In this work we analyse the variation of the probability of occurrence of compound heavy rainfall and wave storms events along the Catalan coast (NW Mediterranean) as a function of the responsiveness.

This work was supported by the Spanish Agency of Research in the framework of the C3RiskMed (PID2020-113638RB-C21/ AEI /  10.13039/501100011033)

How to cite: Jiménez, J. A., Costa, J., Ortego, M., and Llasat, M. C.: Integrating responsiveness in the identification and characterization of compound heavy rainfall and wave storms events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7724, https://doi.org/10.5194/egusphere-egu22-7724, 2022.

The successive occurrence of extreme precipitation events on sub-seasonal (weekly to monthly) timescales can lead to large precipitation accumulations and severe impacts for humans and ecosystems. We take here a global perspective to explore the spatio-temporal distribution of sub-seasonal temporal clustering of extreme precipitation (TCEP) and the physical mechanisms that are responsible for it. We first discuss the seasonal distribution of TCEP and its statistical significance, assessed with Ripley’s K function. Though TCEP is mainly confined to the tropical oceans, it is also significant regionally in the Northern Hemisphere extra-tropics, especially along the eastern margins of ocean basins. We then examine thanks to Generalized Linear Models how large-scale modes of variability and regional dynamics affect the occurrence of temporal clustering across the world. In the tropics, ENSO, the Indian Ocean Dipole and the MJO all modulate TCEP frequency, while the effect of the North Atlantic Oscillation and Pacific North American pattern dominate in the Northern Hemisphere. We conclude with an impacts-focused discussion of how TCEP affects river discharge across Europe. TCEP leads to a higher and more prolonged discharge response, especially in pluvial-dominated catchments, and thus to higher flooding risk.

How to cite: Tuel, A., Schaefli, B., Zscheischler, J., and Romppainen-Martius, O.: Sub-seasonal temporal clustering of extreme precipitation: Spatio-temporal distribution, physical drivers and impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1055, https://doi.org/10.5194/egusphere-egu22-1055, 2022.

Wintertime extremes such as cold spells and heavy precipitation events can have severe societal impacts, disrupting critical infrastructures, traffc and affecting human well-being. Here, we relate the occurrence of local and concurrent cold and wet wintertime extremes in North America and Western Europe to a recurrent, quasi-hemispheric wave-4 Rossby wave pattern in the Jetstream. We identify this pattern as a fundamental mode of Northern Hemisphere (NH) winter circulation exhibiting phase-locking behavior as the associated atmospheric circulation and surface anomalies re-occur over the same locations when the pattern's wave amplitude is high. The wave pattern is strongest over the pan-Atlantic region, and is associated with an increased probability of extreme cold or wet events by up to 300 % in certain areas of North America and Western Europe. We identify a significant increase in frequency over the past four decades (1979- 2021), which we hypothesise may derive from increased convective activity in the tropical Pacific, from where the pattern originates, while a weakened meridional temperature gradient linked to Arctic warming appears to have no direct effect on its occurrence. The identified pattern and its remote forcing might provide pathways for early prediction of local and concurrent cold or wet wintertime extremes in North America and Western Europe.

How to cite: Kornhuber, K. and Messori, G.: Compounding Wet and Cold-Extremes driven by an increasing Pan-Atlantic wave-4-pattern , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6351, https://doi.org/10.5194/egusphere-egu22-6351, 2022.

The analysis of climate change impacts involves the utilisation of climate model output. Quite often, quantities of interest are compound events rather than “raw variables” such as temperature. Questions such as "what is the probability that temperature will exceed a high threshold for five consecutive days and how will this change in the future?" are quite common. Statistical (probabilistic) modelling of climate model output can be used to answer such questions by stochastically simulating the raw variables and then quantifying the compound events as a “by-product”. This is particularly useful since any compound event can be investigated using the same approach – since the raw variables are the ones being modelled.

Such approaches however do not always scale well with big data sets and are often too complicated to even interpret appropriately. Here we present a way of analysing such data, using the (well-established) idea of a ‘moving window’ in conjunction with penalised smoothing splines and Generalised Additive Models (GAMs). The probabilistic nature of the resulting predictions provides a way of extrapolating beyond the range of the original data to robustly quantify the likelihood of rare events and their future changes. The approach is implemented in the Bayesian framework which results in full quantification of the associated uncertainty in using this method, e.g. increased uncertainty for extreme events way outside the range of the original data.

The method is both scalable and paralleliseable and we present it in quantifying changes in regional climate model output. Due to the simplicity of the components that make up the approach, it can be argued that it is highly interpretable as well as robust to the choice of variables – we demonstrate this using temperature as well as humidity and precipitation, variables which are known to have very different statistical behaviour. We also demonstrate how the approach can be extended to capture the behaviour of more that one variable and use it to quantify the changes in compound hazard events such as the frequency of “warm-dry” days.

How to cite: Economou, T. and Garry, F.: Probabilistic modelling and simulation of big spatio-temporal climate data for quantifying future changes of compound events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3392, https://doi.org/10.5194/egusphere-egu22-3392, 2022.

There is ample evidence that the occurrence of deep convection changes as a result of global warming and that, across Europe, increases in convective instability as measured by CAPE are an important driver in many regions. This study is a first step in disentangling the role that climate change induced changes in flow pattern occurrence plays on the evolution of the frequency of thunderstorms. Here we evaluate the association between large-scale flow patterns with the (temporal and spatial) distribution of lightning in Europe as detected by the Met Office Arrival Time Difference Network (ATDnet). The seasonal cycle shows that the largest number of lightning days occurs in the summer from May to August, the period we, therefore, focus on. The large-scale flow pattern is expressed using the daily mean 500 hPa geopotential extracted from ERA5 reanalysis data. A hierarchical clustering algorithm (Ward's method) is applied to the daily mean geopotential heights in the selected four-month period between 2007 and 2019. The algorithm produces 9 patterns (Fig. 1), with cluster 1 being the most frequent, occurring around 20% of the time and pattern 3 being the least frequent, occurring around 4% of the time. The distributions of lightning associated with the clusters show that lightning often occurs in synoptically quiescent conditions or even underneath a ridge. Furthermore, lightning occurrence over western Europe seems to be more dependent on the synoptic situation, where it is strongly associated with clusters that have a southerly flow at 500 hPa, compared to lightning over the Alpine range or south-eastern Europe.

Fig. 1: Large-scale flow patterns are shown in nine clusters, geopotential heights of ERA5 at 500 hPa are plotted in the foreground with 50hPa intervals, and the mean number of lightning strikes per day is shown as filled contours.

How to cite: Ghasemifard, H., Groenemeijer, P., Battaglioli, F., and Pucik, T.: Dependence of lightning occurrence in Europe on large-scale flow patterns, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5346, https://doi.org/10.5194/egusphere-egu22-5346, 2022.

The Mediterranean region is an area of increasing interest due to its unique climate. Nowadays, climate change has already evident consequences, such as the rise of extreme weather events, which significantly affect peoples’ life in the highly populated urban areas of the Mediterranean. Thus, in this study, ten metropolitan cities from the wider Mediterranean region with different climatic characteristics have been selected to study the frequency and the multidecadal trends of extreme events, as well as their possible connection with the large scale and synoptic scale atmospheric variability.

Four combined extreme indices have been evaluated on annual and seasonal basis for the period 1950-2018 using the high-resolution E-OBS gridded daily mean temperature and precipitation datasets (0.1° x 0.1°; v.19e) from the European Climate Assessment & Dataset (ECA&D, Klein Tank et al. 2002, www.ecad.eu). These combined extreme indices refer to the joint modes of temperature and precipitation extremes, concerning the co-occurrence of Cold/Dry days (CD), Cold/Wet days (CW), Warm/Dry days (WD), Warm/Wet days (WW), which can reflect extreme conditions better than temperature or precipitation statistics considered separately (Beniston, 2009; 2011). The links of the extreme events with the atmospheric variability are investigated based on large-scale teleconnection indices and spatiotemporal distribution of cyclonic activity. Toward this, the comprehensive climatology of Mediterranean cyclones assembled was used by applying a cyclone tracking algorithm (Murray and Simmonds, 1991; Flocas et al., 2011) with respect to the ECMWF ERA5 Interim mean sea level pressure fields since 1950.

The findings of the analysis showed distinct temporal and spatial variations of the combined extremes occurrences in the cities across the Mediterranean, which can be attributed to the effects of its complex topography, as well as to the non-uniform influence of the atmospheric variability. Specifically, the CD and WD indices have higher annual occurrences than the CW and WW, which indicates that the wider Mediterranean region experiences more dry days, either cold or warm, than wet days. The urban areas most affected by cold/dry events are located on the western Africa, while almost all urban areas around the Mediterranean coast are impacted by higher number of warm/dry events, with increasing trends.

References: Beniston M., 2009, Geophys. Res. Lett., 36, L07707; Beniston M., and Coauthors, 2011, Int. J. Climatol., 31, 1257-1263; Murray and Simmonds, 1991 Aust Met Mag 39 155 166; Flocas et al., 2010, J Climate, 23(19), 5243-5257

How to cite: Polychroni, I., Hatzaki, M., Nastos, P. T., Kouroutzoglou, J., and Flocas, H. A.: Climate extremes in Mediterranean metropolitan cities and atmospheric variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10342, https://doi.org/10.5194/egusphere-egu22-10342, 2022.

Global climate change and more frequent and severe compound events poses a threat to agricultural productivity in China with important impacts on human development, and social stability. China has 18% about 25% of the world's grain production--accounting rice up to 34% of it.  Much of the existing research has focused on the important average effects of climate warming on rice yields showing. However, there is evidence about nonlinear interactions when compound events being present (ie. frost and heavy rainfall). As some of the major natural disasters in China at present, the overall spatial extent of drought and floods in China are expected to change significantly in the future, with more extreme events resulting. This paper analyzes total factor productivity growth in China's rice production to compute technological progress as an adaptative factor for total factor productivity growth response to compound extreme events. Labor inputs, education, fertilizer application and energy use are considered as control factors, jointly with socio-economic factors the the adoption of agricultural technology by growers. The Levinsohn-Petrin consistent semi-parametric estimation method was used to empirically analyze input-output panel data on rice yields in 30 Chinese provinces from 1990 to 2019 and to simulate the level of rice yield at the end of the 21st century under different RCPs scenarios. The model has stronger prediction ability for the central-eastern and southern production areas of China and reveals that rice yields may show opportunities of increase under average conditions for some climate scenarios, but it shows a bigger risk and vulnerability to compound extreme events.

How to cite: Quiroga, S., Suárez, C., Wang, H., and Hernanz, V.: Interactions between compound extreme events and technological change over rice yield in China as an opportunity to adapt. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8014, https://doi.org/10.5194/egusphere-egu22-8014, 2022.

The Mediterranean region has been identified as a hotspot of climate change characterized by a large tree mortality. Especially holm (Quercus ilex L.) and cork oak trees (Quercus suber L.) in high-value and nature-based agroforestry systems (in Spain known as dehesa) have multiple positive effects, e.g., on the microclimate, carbon storage, erosion prevention, increase of soil water content and soil nutrient concentration. Many studies dealing with the oak decline (also called seca) reported the infestation by root pathogens, in particular the soil-born pathogen Phytophthora cinnamomi, as the main driver. However, rapidly, the focus shifted to the interaction of the pathogen and single abiotic conditions like drought.

We assume that compound events (co-occurring warm spells and soil drought) have a larger correlation with vegetation indices than single climatic drivers. We analyse time series of two vegetation indices, namely the Normalized Difference Vegetation Index (NDVI) and the kernel Normalized Difference Vegetation Index (kNDVI) as an indicator for greenness and vitality. In particular, we focus on the trend of both indices over about two decades (2003-2021) in eight different plots in our study area, on a dehesa in Huelva province, Andalusia. Subsequently, we correlate them with the decomposed signal of compound events.

Based on precipitation and temperature data, we calculated two drought indices, namely the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). We then used these indices together with temperature to calculate so-called compound events, a co-occurrence of extreme values in multiple environmental drivers. To assess the status of the vegetation, we calculated the NDVI and its newly proposed kernel variant kNDVI from MODIS (MYD13Q1) and Landsat (4-5, 7,8) data in eight different plots in our study area. The kNDVI is a non-linear generalization of the NDVI and showed good behaviour in the Mediterranean and correlates stronger with the gross primary productivity (GPP) than the original NDVI. To extract physically meaningful information, we decomposed the time series signals with the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method by Torres et al. (2011) into seasonality, trend, and a remainder part. CEEMDAN is suitable for non-linear and non-stationary time series. To analyse the relationships between vegetation indices and possible climatic drivers, we subsequently calculate lagged cross-correlations (i.e., correlation between different time series) between the Intrinsic Mode Functions (IMFs) of the signal expressing the trend and different seasonalities.

We extracted different positive and significant (p < 0.01) NDVI trend signals from the MODIS time series. The seasonal component corresponded to the expected annual cycle. Based on these first results, we will correlate the NDVI and kNDVI trend signals with the calculated compound events to observe their role in the oak tree mortality.

How to cite: Reddig, F., Bareth, G., and Bogner, C.: Effect of compound events on oak tree vitality in a climate change hotspot: analysis of time series in a traditional Spanish dehesa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9852, https://doi.org/10.5194/egusphere-egu22-9852, 2022.