A variety of weather-driven natural hazards, such as droughts and heatwaves, often occur throughout the world [1]. The resultant impacts of the occurrence of these extremes have been exacerbated by an increase in their frequency and intensity over the past few decades in Europe [2-6]. Due to the projected increase of these extreme events [2-3,6-7], combined with larger exposure, exceptional risk to humans and ecosystems is expected [8]. Therefore, progressive intensification of these hazards represents one of the most challenging impacts of climate change on society [8,9].

Natural hazards often result from interacting physical processes across a wide range of spatial and temporal scales [P1- P4,6,9-14]. These types of interacting events are commonly referred as compound or cascading. Similarly to individual extremes, positive trends in the frequency and severity of compound hot and dry events (hereafter CHD) have also been reported [6,9,12], being responsible for wide-ranging impacts, namely morbidity and mortality [15-16], wildfires [17], air pollution [16,18], and agricultural losses [19].

Several studies have addressed individual and CHD events in order to assess the costs and benefits of disaster risk reduction measures, both structural and nature-based. However, most of the approaches do not account for the interplay between multiple hazards when addressing the impacts on natural and socioeconomic sectors, although that might lead to the underestimation of risk [12]. Following the considerations formulated under the Sendai Framework for Disaster Risk Reduction, which highlights the need for multi-hazard early warning systems, an urgent call has been issued to assess compound disasters and the associated risks rather than focusing on single hazards. Therefore, several researchers started to focus their attention to the application of multi-hazards and compound approaches to tackle the caveats resulting from centering on single hazards. Consequently, clear relationships were identified in Southern Europe between hot and dry extremes [e.g. P1], which resulted in sectoral impacts [e.g. P2, P4,11,18].

DHEFEUS overarching goal is to improve the understanding on CHD and further associate them to the occurrence of fires and pollution events in Mediterranean Europe (EU), by focusing on their intricate chain of events through a compound-oriented framework.

Therefore, DHEFEUS will focus on 1) the driving mechanisms of individual and extreme events; 2) the assessment of the interaction between CHD events and furthermore, between a) CHD and fires and b) CHD and pollution events from a multidisciplinary perspective, including past, present and future climate conditions. This will be accomplished based on innovative methods (e.g., compound event-oriented approaches [P1-P4,1,9-12-14,20,23-24,32], copula analyses [19] and climate-change sensitive methods [30,32], supported by satellite-based datasets [23-24,27] and high-resolution regional modelling (Regional Climate Models’ outputs and regional simulations from WRF-Chem) of surfaceatmosphere feedbacks [30,32].

By studying on weather, fire activity and air pollution together, DEFHEUS will take into consideration the Paris Agreement and the UN Sustainable Development Goals to improve lives in the present and under future climate warming conditions. This integrative approach enables to account for synergies between different hazards, which constitutes an emerging and crucial line of research within the scope of climate change. The proposed approach characterizes the multiple causes (low precipitation, extreme temperatures) of single or compound outcomes (fires, high pollution), instead of tracing the impact of a single hazard to a certain outcome [10,12,18,20].

DHEFEUS intends to answer several emerging and still unanswered target questions (TQ): TQ1: How do CHD events contribute do exacerbate the impacts on air quality in different sectors of Europe? TQ2: What is the role of climate forcing factors (e.g., anomalous Sea Surface Temperature in the Atlantic, or predominant phases of the main modes of atmospheric circulation like the North Atlantic Oscillation or Eastern Atlantic Oscillation) to the occurrence of CHD events? TQ3: How much the synergetic effect of CHD events and wildfires affect the terrestrial carbon budget? TQ4: What are the main impacts of the CHF events, under future climate change conditions, in terms of extreme pollution events in different sectors of the Europe? DHEFEUS proposes an integrated approach which will be carried out in 7 tasks (T#, see DHEFEUS_cronograma.pdf). T1 will be devoted to data acquisition and validation. T2 will address single and compound heatwaves and droughts, identifying synergies, driving mechanisms and dominant atmospheric modes controlling single and combined events. T3 will analyze fires and their association to single and compound events as identified in T2. T4 will examine air pollution events, especially those associated with wildfires and from long range transport. T2-T4 will allow resolving TQ1 and TQ2. T5 and T6 rely on T1-T4, focusing on more than two hazards (e.g., droughts, heatwaves and wildfires), providing an overview of the likely impacts of fire emissions on terrestrial carbon cycle and of climate feedbacks in presence of single and combined hazards, answering to TQ3. T7 is devoted to climate change and proposes to analyze how extreme events will evolve in the future, answering to TQ4.

Soon available

To answer to the proposed TQ following a multi-hazard and multidisciplinary perspective, we propose to implement the following dedicated methodologies:

A)Drought and heatwaves will be identified using multiindicator approaches [P1-P3,10]. The indicators will use long meteorological datasets (1979 to present), integrating recent gridded high-resolution data together with high spatialresolution satellite information (e.g., MTG and Sentinel) with shorter duration, although allowing a good characterization of droughts and heatwaves. In particular, multi-scalar drought indices like the Standardized Precipitation and Evapotranspiration Index (SPEI) [6], will be used to quantify the severity of the hazard. Exposure analysis and return periods will be identified over EU to assist stakeholders’ decision-making process and the implementation of mitigation strategies.

B) Fires over Europe (since 2001) will be analyzed and characterized regarding its duration, intensity, and total burned area based on national statistics available on EFFIS and from high temporal resolution remotely sensed products (c.f. T1, DS8 dataset). The meteorological and surface forcing mechanisms responsible for triggering large fires will be assessed [5]. Case studies of extreme events (e.g., the 2017 and 2020 events in Portugal, 2021 in Greece and Turkey and 2022 in Ukraine) will be identified and characterized using ERA-5 reanalysis datasets, WRF-Chem outputs and remote sensing data (c.f. T1, DS1, DS7, DS8 datasets).

C) The identification of synergies, driving mechanisms and dominant atmospheric modes controlling single and CHD events will be attempted, considering a wide spatial Europe-Atlantic region, using multivariate statistical analysis [P1,P3- P4,5,9] and compound approaches [P2,10-14]. Synergies will be identified, together with their main drivers [P3,9], allowing the characterization of the dependence structure between droughts and heatwaves. The probabilities of one hazard becoming extreme given the occurrence of another hazard will be estimated, which constitute a valuable and attractive tool in risk assessment [P2].

D) The links between fire activity and pollutants’ concentrations, namely contribution of biomass smoke to records of air pollution and the importance of long-range transport, will be assessed over the EU, namely for case studies using in-situ data from air quality monitoring stations and from Copernicus Atmosphere Monitoring System (CAMS). Carbon emissions will be derived from the Fire radiative power (FRP) [25] obtained from SEVIRI, MODIS/VIIRS and SENTINEL and Ozone will be obtained from CAMS, allowing to assess the impact of the recent extreme fires on ozone and terrestrial carbon budget and therefore evaluate the impact of single or CHD events on nowadays carbon budget. This will rely on DS5, DS7, DS8 datasets (cf. T1).

E) CHD, forest fires and pollution events over Europe will be addressed based on twofold approach: 1) focusing on the involved mechanisms, based on trend and extreme analysis, together with coupling metrics [e.g., P3, 9] and 2) focusing on the characterization of the joint behavior of multiple hazards and the consequent risks based on statistical (Copulas) and dynamical (WRF-Chem) approaches [P2,12,19,32]. A full characterization of the linear and nonlinear dependences between variables allows for the characterization of the dependence structure between variables and the estimation of conditional probabilities of one event given the occurrence of another event, constituting a valuable and attractive tool in risk analysis [P2]. This twofold approach will permit to assess the role played by preceding and simultaneous dry and/or hot conditions in the exacerbation of forest fires and resultant air pollution [23], and the identification of the key moments (months, seasons) and timescales of dry and/or hot conditions involved in the reinforcement and triggering both fires and air pollution events [23]. The use of both dynaical and statistical approaches relates to the fact that both have advantages and caveats. WRF-Chem can predict meteorological conditions along with pollution level [32], although having limitations on the parametrizations (e.g., terrain). Conversely, statistical approaches require much less computational power allowing for similar results, but large training data sets are usually required to improve outputs’ accuracy and minimize uncertainty [39]. This two-fold approach will be complementary and secure the proposed outcomes.

F) The evaluation of the impacts on the vegetation productivity of the synergetic effects of carbon and ozone extremes associated with fires and CHD extremes will be performed using satellite derived information of vegetation gross primary productivity. The disturbances on vegetation productivity will be analyzed using vegetation parameters available from the LSA-SAF (LAI, FAPAR) and the NPP MODIS datasets (DS4 and DS5). Statistical approaches, such as correlation and logistic regression, will be used to evaluate the effect of ozone extremes (DS9) associated with severe fires (DS8) on the exacerbation of vegetation productivity losses (DS5), namely in water stress conditions (DS1). G) Climate change assessment will be performed based on the ability of GCMs and RCMs to represent extremes, relying preferentially on the CMIP6 and WRF-Chem runs. At the regional scale, climate change assessment will be performed based on the full EURO-CORDEX regional climate simulations from CMIP6 [30] to feed the WRF-Chem model [32]. These will be validated and ranked, aiming to build a multi-model ensemble based on the RCMs individual qualities to represent the actual main drivers of CHD extremes in the IP, and their impacts on fires and mortality/morbidity outcomes. The statistical comparison of the multi-model ensembles, future and present climates, will permit the quantification of future changes on the drivers and properties of the hazards, and to characterize the uncertainty of the projections. Result will be used to support the construction of the warning system, having in mind the amplitude of the uncertainty of future hazards.

• [1] Ward, P.J., Blauhut, V., Bloemendaal, N., Daniell, J.E., de Ruiter. M.C., Duncan, M.J., Emberson, R., Jenkins S.F., Kirschbaum, D., Kunz, M., Mohr, S., Muis, S., Riddell, G.A., Schäfer, A., Stanley, T., Veldkamp, T.I.E., Winsemius, H.C. (2020). Review article: Natural hazard risk assessments at the global scale, Nat. Hazards Earth Syst. Sci., 20, 1069–1096, doi:10.5194/nhess-20-1069-2020.

• [2] Spinoni, J., Naumann, G., Vogt, J., Barbosa, P. (2016). Meteorological Droughts in Europe: Events and Impacts – Past Trends and Future Projections. Publications Office of the European Union, Luxembourg, EUR 27748 EN, doi:10.2788/450449.

• [3] Russo, S., Sillmann, J., Fischer, E.M. (2015). Top ten European heatwaves since 1950 and their occurrence in the coming decades, Environmental Research Letters, 10, 12, 124003, doi:10.1088/1748-9326/10/12/124003

• [4] Sánchez-Benítez, A., García-Herrera, R., Barriopedro, D., Sousa, P.M., Trigo, R.M. (2018). June 2017: The earliest European summer mega-heatwave of reanalysis period. Geophysical Research Letters, 45, 1955–1962. doi: 10.1002/2018GL077253

• [5] Turco, M., Jerez, S., Augusto, S., Tarín-Carrasco, P., Ratola, N., Jiménez-Guerrero, P., Trigo, R.M. (2019). Climate drivers of the 2017 devastating fires in Portugal. Sci Rep 9, 13886, doi:10.1038/s41598-019-50281-2

• [6] Vicente-Serrano, S.M., Lopez-Moreno, J.-I., Beguería, S., et al (2014). Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ Res Lett 9:44001. doi: 10.1088/1748-9326/9/4/044001.

• [7] Libonati, R., Geirinhas, J.L., Silva, P.S., Russo, A., Rodrigues, J.A., Belém, L.B.C., Nogueira, J., Roque, Rodrigues, J.A., Belém, L.B.C., Nogueira, J., Roque, F.O., DaCamara, C.C., Nunes, A.M.B, (2022). Environmental Research Letters, Volume 17, Number 1, 015005, doi:10.1088/1748-9326/ac462e

• [8] Seneviratne, S.I. et al. (2012). Changes in climate extremes and their impacts on the natural physical environment, in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) , C. B. Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M.D. Mastrandrea, K.J. Mach, G. K. Plattner, S. K. Allen, M. Tignor, P. M. Midgley, Eds. (Cambridge Univ. Press, 2012), chap. 3, pp. 109–230.

• [9] Miralles, D.G., Gentine, P., Seneviratne, S.I., Teuling, A.J. (2019). Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges, Ann. N. Y. Acad. Sci., 1436 (1), 19-35, doi:10.1111/nyas.13912

• [10] Hertig E., Russo A., Trigo R.M. (2020) Heat and Ozone pollution waves in Central and South Europe - Characteristics, Weather Types, and association with Mortality. Atmosphere, 11(12), 1271, doi:10.3390/atmos11121271

• [11] Sutanto, S.J., Vitolo, C., Di Napoli, C., D'Andrea, M., Van Lanen, H.A.J. (2020). Heatwaves, droughts, and fires:Exploring compound and cascading dry hazards at the pan-European scale. Environ Int. 2020 Jan; 134:105276. doi: 10.1016/j.envint.2019.105276.

• [12] Zscheischler, J., Seneviratne, S.I. (2017). Dependence of drivers affects risks associated with compound events, Science Advances, 3, 6, e1700263, doi: 10.1126/sciadv.1700263

• [13] Zscheischler, J. et al. (2018). Future climate risk from compound events. Nat. Clim. Change, 8: 469-477, doi:10.1038/s41558-018-0156-3

• [14] AghaKouchak, A., Chiang, F., Huning, L.S., Love, C.A., Mallakpour, I., Mazdiyasni, O., Mo􀀄akhari, H., Papalexiou, S.M., Ragno, E., Sadegh, M. (2020). Climate Extremes and Compound Hazards in a Warming World, Annual Review of Earth and Planetary Sciences 2020 48:1, doi:10.1146/annurev-earth-071719-055228

• [15] Gasparrini, A. et al. (2017). Projections of temperature-related excess mortality under climate change scenarios. The Lancet Planetary Health 1(9) E360-E367, doi:10.1016/S2542-5196(17)30156-0

• [16] Shaposhnikov, D. et al. (2014). Mortality related to air pollution with the Moscow heat wave and wildfire of 2010. Epidemiology 25(3) 359-364, doi:10.1097/EDE.0000000000000090

• [17] Brando, P.M. et al. (2014). Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl. Acad. Sci. 111 6347-52

• [18] De Sario. M., Katsouyanni, K., Michelozzi, P. (2013). Climate change, extreme weather events, air pollution and respiratory health in Europe. European Respiratory Journal, 42 (3), 826-843, https://doi.org/10.1183/09031936.00074712

• [19] Ribeiro, A.F.S., Russo, A., Gouveia, C. M., Páscoa, P., and Zscheischler, J. (2020). Risk of crop failure due to compound dry and hot extremes estimated with nested copulas. Biogeosciences, 17, 4815–4830

• [20] Vitolo, C., Di Napoli, C., Di Giuseppe, F., Cloke, H.L., Pappenberger, F. (2019). Mapping combined wildfire and heat-stress hazards to improve evidence-based decision making, Environ. Int., 127 (2019), 21-34, doi:10.1016/j.envint.2019.03.008

• [21] UNDRR: Global Assessment Report 2015, Making Development Sustainable: The Future of Disaster Risk Management, UN Office for Disaster Risk Reduction, Geneva (2015).

• [22] Turco, M., von Hardenberg, J., AghaKouchak, A., Llasat, M.C., Provenzale, A., Trigo R.M. (2017). On the key role of droughts in the dynamics of summer fires in Mediterranean Europe. Scientific Reports, 7:81. DOI:10.1038/s41598-017-00116-9, https://doi.org/10.1038/s41598-017-00116-9

• [23] Liu, J.C., Wilson, A., Mickley, L.J., Dominici, F., Ebisu, K., Wang, Y., et al. (2017). Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 28 77–85.

• [24] Liu, J.C., Mickley, L.J., Sulprizio, M.P., Yue, X., Peng, R.D., Dominici, F., Bell, M.L. (2016). Future respiratory hospital admissions from wildfire smoke under climate change in the Western US. Environmental Research Letters, 11. doi:10.1088/1748-9326/11/12/124018

• [25] Price, O.F., Williamson, G.J., Henderson, S.B., Johnston, F., Bowman, D.M.J.S. (2012). The Relationship between Particulate Pollution Levels in Australian Cities,Meteorology, and Landscape Fire Activity Detected from MODIS Hotspots. PLoS ONE 7(10): e47327.doi:10.1371/journal.pone.0047327

• [26] Deb, P., Moradkhani, H., Abbaszadeh, P., Kiem, A.S., Engström, J., Keellings, D., Sharma, A. (2020). Causes of the widespread 2019–2020 Australian bushfire season. Earth's Future, 8, e2020EF001671. https://doi.org/10.1029/2020EF001671

• [27] Cavaleiro, R.. Russo, A., Sousa, P.M., Durão, R. (2021). Association between Prevailing Circulation Patterns and Coarse Particles in Portugal. Atmosphere 2021, 12(1), 85

• [28] Rammig, A., Wiedermann, M., Donges, J.F., Babst, F.,von Bloh, W., Frank, D., Thonicke, K., Mahecha, M.D. (2015). Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity Biogeosciences 12 373-85

• [29] Wu, X., Hao, Z., Hao, F., Li, C., Zhang, X. (2019). Spatial and temporal variations of compound droughts and hot extremes in China, Atmosphere 2019, 10(2), 95; https://doi.org/10.3390/atmos10020095

• [30] Soares, P.M.M., Cardoso, R.M., Lima, D.C.A., Miranda, P.M.A. (2017). Future precipitation in Portugal: high-resolution projections using WRF model and EURO-CORDEX multi-model ensembles. Climate Dynamics, 49:2503–2530, doi:10.1007/s00382-016-3455-2.

• [31] Ruffault, J., Curt, T., Moron, V., Trigo, R.M., Mouillot, F., Koutsias, N., Pimont, F., Martin-StPaul, N., Barbero, R., Dupuy, J.-L., Russo, A., Belhadj-Khedher, C. (2020). Increased Likelihood of Heat-Induced Larfe Wildfires in the Mediterranean Basin. Scientific Reports, 10, 13790, doi: 10.1038/s41598-020-70069-z

• [32] Zhang, J., Gao, Y., Luo, K., Leung, L. R., Zhang, Y., Wang, K., and Fan, J. (2018). Impacts of compound extreme weather events on ozone in the present and future, Atmos. Chem. Phys., 18, 9861–9877, doi:10.5194/acp-18-9861-2018

• [33] Lei, Y., Yue, X., Liao, H., Zhang, L., Yang, Y., Zhou, H., Tian, C., Gong, C., Ma, Y., Gao, L., and Cao, Y. (2021). Indirect contributions of global fires to surface ozone through ozone–vegetation feedback, Atmos. Chem. Phys., 21, 11531–11543, doi:10.5194/acp-21-11531-2021.

• [34] Hao, Z., Hao, F., Xia, Y., Singh, V.P., Zhang, X. (2019). A monitoring and prediction system for compound dry and hot events, Environ. Res. Lett.14 114034, doi:10.1088/1748-9326/ab4df5

• [35] Vieira, I., Russo, A., Trigo, R.M. (2020). Identifying Local-Scale Weather Forcing Conditions Favorable to Generating Iberia’s Largest Fires, Forests 11(5), 547, doi:10.3390/f11050547

• [36] Huijnen, V., Wooster, M., Kaiser, J. et al. (2016). Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci Rep 6, 26886. doi:10.1038/srep26886

• [37] Geirinhas, J.L., Trigo, R.M., Libonati, R., Castro, L.C.O., Sousa, P.M., Coelho, C.A.S., Peres, L.F., Magalhães, M.D.A.F.M. (2019). Characterizing the atmospheric conditions during the 2010 heatwave in Rio de Janeiro marked by excessive mortality rates. Sci. Total Environ., 650, 796–808, doi:10.1016/j.scitotenv.2018.09.060

• [38] Russo, A., Trigo, R.M., Martins, H., Mendes, M.T. (2014). NO2, PM10 and O3 urban concentrations and its association with circulation weather types in Portugal. Atmos. Environ., 89, 768-785.doi:10.1016/j.atmosenv.2014.02.010

• [39] Russo, A., Lind, P. G., Raischel, F., Trigo, R., Mendes, M. (2014). Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmospheric Pollution Research 6 (2015) 540?549 doi:10.5094/APR.2015.060

• [40] Bento, V.A., Ribeiro, A.F.S., Russo, A., Gouveia, C., Cardoso, R.M.,Soares P.M.M. (2021). The impact of climate change in wheat and barley yields in the Iberian Peninsula. Scientific Reports, 11, 15484, doi:10.1038/s41598-021-95014-6

• [41] Lin, M., Horowitz, L.W., Xie, Y., Paulot, F., Malyshev, S., Shevliakova, E., Finco, A., Gerosa, G., Kubistin, D. and Pilegaard, K. (2020). Vegetation feedbacks during drought exacerbate ozone air pollution extremes in Europe. Nature Climate Change, 10(5), 444-451.