Climate change is associated with increasing frequencies and intensities of extreme weather events. These can, directly and indirectly, shape human (im)mobility. While most research on migration in the context of climate change focuses on climate as a migration driver in origin areas, there is a gap in knowledge on the role of migration for climate resilience in the destination areas. This paper studies differences in resilience (resistance and recovery) to climatic shocks between migrant and non-migrant households in Ethiopia, a country that is highly exposed and vulnerable to climate change. We use longitudinal data from the Living Standards Measurement Study (LSMS) conducted by the World Bank to construct a comprehensive Well-Being Index, which is used to analyze the impacts of climatic shocks and identify households that are more or less able to resist and recover from shocks. We use fixed effect panel regression approaches to model the impacts of climatic shocks on well-being over time for migrant and non-migrant households. Further explorative mediation analyses yield insights into mechanisms explaining differences between households. We find that migrant households have an overall lower climate resistance as they experience double as high well-being impacts when exposed to climatic shocks compared to non-migrant households. Climatic shocks significantly reduce the food security of all affected households and, in addition, negatively impact access to basic infrastructures and health for migrant households. Mediation analyses suggest that these differential climatic impacts are mainly driven by characteristics of migrant-origin regions, including poverty. Migrant households originating from less prosperous regions still face disadvantages even if they now reside in more prosperous regions. This contrasts the experience of non-migrant households whose resilience benefits from increased prosperity in their region of residence. While migrant households show a lower resistance to climate shocks, they recover faster from climatic shocks, which can be associated with diversified livelihoods and remittances that take time to unfold. This research is highly relevant to policy as it improves the understanding of underlying factors shaping differential vulnerability to climate change impacts and supports targeted interventions to increase the resilience of affected households.

How to cite: Link, A.-C. and Hoffmann, R.: The Tail End of Migration: Assessing the Climate Resilience of Migrant Households in Ethiopia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2160, https://doi.org/10.5194/egusphere-egu24-2160, 2024.

Climate change is causing temperatures around the globe to rise, leading to an increase in the frequency and intensity of hot days and heatwaves. In urban areas, this trend is further exacerbated by urban characteristics, such as the high building density and degree of sealing, the high concentration of anthropogenic heat sources or the reduced outgoing radiation. Extreme heat puts a strain on health, especially for elders and people with pre-existing illnesses. For effective and targeted prevention of heat-related morbidity and mortality, information on the spatial variance of people’s exposure and sensitivity, but also their adaptability towards heat can be of great importance.

A common practice for determining the distribution of vulnerable population groups within a city or an area is to construct a spatial Heat Vulnerability Index (HVI) based on findings and data from natural and social sciences, including e.g. socio-economic data, health data, remote sensing data, and climate data. However, there is no standardized workflow but a variety of approaches for the construction of an HVI, which may lead to significant differences in the calculated index ranks. In order to assess the impact of changes in the method design on the resulting index, we test different input data sets, weighting methods and spatial scales for the construction of a spatial HVI for the city of Graz (Austria). The input parameters for the HVI include temperature data, derived from satellite data and weather stations, as well as spatial socio-economic data that describe the population’s sensitivity towards heat and the capability to adapt to high temperatures. By conducting an uncertainty analysis and a global variance-based sensitivity analysis, the partial contribution of changing input variables, chosen weighting methods and different spatial scales to the output’s variance is determined. In addition, a local sensitivity analysis compares the application of land surface temperature derived from thermal satellite imagery to the use of station temperature data for the construction of an HVI.

How to cite: Löffler, K., Damm, A., Gallaun, H., Köberl, J., Kortschak, D., Miletich, P., Oberhuber, L., and Strohmaier, M.: Using data and findings from natural and social sciences to assess urban heat vulnerability: a comparison of different methodologies., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3460, https://doi.org/10.5194/egusphere-egu24-3460, 2024.

Drought, flood, hail and low temperature frost (LTF) are the main agrometeorological disasters in China. However, a comprehensive and quantitative study on the long-term trend of farmland and economic damage across the country is still lacking and needs to be carried out urgently. Based on historical statistical data from yearbooks and bulletins, the overall characteristics of the impacts of provincial meteorological disasters on population, economy and farmland during 1989-2022 were analyzed by using Mann-Kendall trend test at yearly and provincial scales in China. The results showed that the proportion of direct economic losses caused by meteorological disasters to GDP showed a decreasing trend. The SGD13.1 index, based on the number of deaths and the value of disaster losses, shows that there are abrupt years on the time scale under the Mann-Kendall trend test. In the past 30 years, crop loss in China has increased first and then decreased under natural disasters, and drought is the most serious type of disaster that causes farmland loss. The Person correlation analysis combining disaster intensity index and multiple factors shows that agricultural economic output has a significant negative correlation with disaster intensity, SDG13.1 and total precipitation, and a positive correlation with average annual temperature. There was a significant positive correlation between SDG13.1 and disaster intensity index. The results of this study systematically reveal the damage characteristics of meteorological disasters to socio-economic system in China, which are critical and necessary for disaster risk reduction and adaptive strategy development.

How to cite: airiken, M. and Li, S.: Spatiotemporal variations in damages to socio-economic system from meteorological disasters in mainland China during 1989–2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4576, https://doi.org/10.5194/egusphere-egu24-4576, 2024.

Achieving climate-smart nutrition security in sub-Saharan Africa is an urgent challenge due to increasing climate risks to agricultural production, population growth and food price volatility This necessitates an integrated evidence base that takes into account not only future food system modelling but wider academic expertise and stakeholder knowledge and the plausible and desirable transformations that these information streams can provide. Accordingly, we use the integrated Future Estimator for Emissions and Diets (iFEED) to explore scenarios of food system transformation towards nutrition security. iFEED integrates climate, crop and land use modelling to explore scenarios of relevance to the policy landscape, as informed by stakeholders, assessing the adequacy of energy and nutrient supplies to meet dietary requirements at a population level. Our results show that calories are not always sufficient at the population level in extremely hot and dry years by mid-century in Zambia, even when maximising food production on available land. The majority of micronutrients also remain below population requirements. An alternative scenario where crops for population level nutrition security are prioritised shows that there are larger calorie shortfalls in extremely hot and dry years, although more micronutrient requirements are met than in the production-focused scenario. Both scenarios show benefits, and we point to ways forward that address the challenges to achieving climate-resilient nutrition security in the region. We also introduce our latest thinking on a new inclusive assessment framework that aims to expand iFEED to incorporate bottom-up disruptive seeds work and top-down modelling across spatial scales to deliver socially-equitable nutrition security in Kenya.

How to cite: Jennings, S., Challinor, A., Macdiarmid, J., Pope, E., Crocker, T., Anderson, W., King, R., Whitfield, S., Sarku, R., Chomba, C., Nawiko, M., Rutting, L., and Veeger, M.: An inclusive assessment framework for exploring climate-resilient nutrition security in sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8637, https://doi.org/10.5194/egusphere-egu24-8637, 2024.

Coastal communities are expected to be highly exposed to rising sea levels and more frequent and intense tropical storms in the coming decades, with forced migration (or displacement) highly likely in many of these places. The exposure to these hazards is driven not just by climate change, but also by growing populations and rapid urbanisation of coastal cities. However, the extent of forced migration will be highly variable, and will be dependent on pre-existing physical and social vulnerabilities present in each location. Therefore, in order to reliably forecast future forced migration due to sea-level rise and tropical storms, it is necessary to construct spatially explicit displacement curves that link hazard levels to the migratory response of communities. This study has calibrated displacement curves through regression analysis for the Philippines based on historical internal migratory movements due to coastal flooding and tropical storms. The data for calibration was obtained from the Internal Displacement Monitoring Centre and governmental disaster reports, and the calibration was performed at the level 3 administrative boundaries. With the displacement curves, critical thresholds of flood and wind damage, at which point forced migration occurs, are identified. Subsequently, these displacement curves are combined with projections of future sea-levels and tropical storms in order to forecast the forced migration of communities under climate change. The displacement curves can be used by researchers, planners and policymakers to understand the varied migratory response of communities to sea-level rise and its associated hazards. This will allow for effective adaptation plans to be devised in advance in order to manage such forced migration in a manner that allows communities, including vulnerable ones, to relocate and avoid the adverse impacts of a changing climate.

How to cite: Lallemant, D., Manimaran, S., Housset, T., and Ponserre, S.: Calibrating Displacement Curves to Forecast Forced Migration due to Sea-Level Rise and Tropical Storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14994, https://doi.org/10.5194/egusphere-egu24-14994, 2024.

Assessing the spatio-temporality of risks associated with climate change have become dominant in disaster risk research. However, integrated assessments of spatio-temporal aspects combing hazard, exposure and social vulnerability is still under-researched, especially in the fields extreme heat events and heightened ozone concentrations. Studies frequently tend to concentrate either solely on the hazard dimension, such as heatwaves and ozone exceedances, neglecting their interactions (Feron et al. 2023), or solely on isolated spatio-temporal assessments of social vulnerability and exposure (Santos et al. 2022). Using the recent risk conception of the latest IPCC report, we analyze risk as the cumulative interaction of hazard, exposure and vulnerability for historical trends and near future scenarios.

A novel data set allows for an integrated assessment of historic spatio-temporal trends as well as near-future trends using different SSP-RCP combinations (SSP2-4.5 & SSP3-8.5) at census tract level. To assess the combined impact of temperature and ozone extremes, we utilize bias-corrected model fields from high resolution runs of the coupled chemistry-climate model WRF-Chem. Population data was projected until 2050 by combining historical growth rates for selected indicators with national change rates from the Shared Socio-economic Pathways (SSP) database by IIASA (Riahi et al. 2017). Regional variations in national SSP change rates are weighted with regionalized projections for population and age groups, and historic data on income and education from the Eurostat Database.

Methodologically, we use the Adjusted Mazziotta-Pareto Index (AMPI) normalization method to overcome the limitations of comparing z-scored values over time as reported by Santos et al. (2022). This has the advantaged that all values across all periods of time are considered in normalization (Mazziota & Pareto 2022). Bases on the integration into a composite indicator, we, first, performed a multivariate analysis of how sub-indicators for hazard, exposure and social vulnerability relate to each other for Austria. Second, we applied global and local Moran’s I statistics to analyze if the spatial patterns have changed in terms of spatial heterogeneity or spatial clustering over time.

The paper concludes by highlighting the needs of integrated risk assessments and discusses the potentials and limitations of our assessment approach. Finally, possible benefits of the interdisciplinary and small-scale use of SSP-RCP combinations for a more comprehensive formulation of informed policy guidelines.

Feron, S., Cordero, R. R., Damiani, A., Oyola, P., Ansari, T., Pedemonte, J. C., ... & Gallo, V. (2023). Compound climate-pollution extremes in Santiago de Chile. Scientific Reports, 13(1), 6726.

Mazziotta, M., & Pareto, A. (2022). Normalization methods for spatio‐temporal analysis of environmental performance: Revisiting the Min–Max method. Environmetrics, 33(5), e2730.

Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., ... & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change, 42, 153-168.

Santos, P. P., Zêzere, J. L., Pereira, S., Rocha, J., & Tavares, A. O. (2022). A novel approach to measuring spatiotemporal changes in social vulnerability at the local level in Portugal. International Journal of Disaster Risk Science, 13(6), 842-861.

How to cite: Friesenecker, M., Thaler, T., Mayer, M., Rieder, H., Formayr, H., Schmidt, C., and Fabian, L.: An integrated assessment of future risks of climate change for Austria: spatio-temporal trends of ozone, heat, and social vulnerability , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20205, https://doi.org/10.5194/egusphere-egu24-20205, 2024.

The characterization of drought hazards remains a complex endeavor, primarily due to the absence of a universally accepted definition for a "drought event." Different deficits across various parts of the water cycle contribute to a spectrum of drought consequences, rendering the definition contingent upon the impacts incurred. Moreover, quantifying drought vulnerability poses challenges given the intricate interplay among socioeconomic, political, and environmental factors that influence the relationship between a drought event and its impacts on exposed production systems, people and nature. 
Our work addresses these challenges by introducing a novel data-driven methodology employing an array of drought indices and several datasets on observed drought impacts. Applying decision tree-based AI techniques, this method identifies combinations of hydrometeorological conditions known to generate societal consequences, and as such is able to estimate probabilistic drought disaster risk.

The presented impact-based approach is generalizable and impacts evaluated include energy production losses, internal displacement, crop and livestock damage, malnutrition, ecosystem health degradation, and strains on drinking water utilities. Illustrated through a case study in the Horn of Africa, this contribution exemplifies the quantification of expected annual drought impact, whereby impact is measured as the number of drought-induced internally displaced persons (IDPs). Drawing on the latest IDMC Displacement Tracking Matrix data, we assessed drought displacement risks under current and projected climate scenarios for Somalia and Ethiopia. Both countries grapple with complex human mobility dynamics, driven by a multitude of push and pull factors. Our findings reveal average annual IDPs up to 2% in some regions in Ethiopia, rising to 3% with unmitigated climate change. In Somalia, the majority of regions are anticipated to experience on average >10,000 drought-induced IDPs annually, under all future projections. Our model demonstrates proficiency in distinguishing prolonged and flash droughts as drivers for displacement. Furthermore, it facilitates the identification of hotspot areas, thereby supporting drought disaster risk reduction decisions and proactive policies.

How to cite: Wens, M., de Moel, H., van Loon, A., Isabellon, M., Ottonelli, D., Ponserre, S., and Rossi, L.: A data-driven approach to predict water security and societal impacts: the risk of drought-induced internal displacement in the Horn of Africa., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19158, https://doi.org/10.5194/egusphere-egu24-19158, 2024.

The 2022 Pakistan floods have been unprecedented in their extent. They affected around 33 million people, caused about 15 billion USD in damages, and took the lives of more than 1,800 persons, dominantly in the southern parts of the country.

Effective disaster response requires fast assessments of likely impacts from hazardous weather to inform decision-makers and guide relief efforts for early action. Displacement modeling is a key technique towards these goals. However, displacement modeling which accounts for socio-economic components and uncertainties is methodologically challenging, and quantitative evidence largely remains limited and fragmented. Much work is needed to resolve these.

This study aims at providing a case study for disaster displacement modeling by using the open-source impact assessment platform CLIMADA to investigate the extent by which flood-related hazards can be used to quantify displacement numbers in a data-limited region. Here, we estimate displacement from the 2022 Pakistan floods in Sindh province as a case study. We combine data on flood depth, exposed population, and provide impact functions that relate vulnerability of people likely to be displaced. We further use published numbers of affected people as target data for our model. The centerpiece of our analysis is the choice of impact functions. We test different forms of impact functions as well as assumptions about critical flood depths to proxy the number of displaced people, first using ex-ante assumptions, and then a numerically optimized version.

With ex-ante assumptions, our model predicts a range of 1.94 to 5.65 million of displaced people in Sindh province, as compared to a total number of 6.76 million as reported by government sources. When we apply numerically optimized impact functions, the results closely resemble those obtained using the ex-ante assumptions, indicating that the current methods underestimate the extent of displacement. Additionally, we have evaluated the relationship between local vulnerability and the level of urbanization, and our findings reveal a negative correlation.

We use this model to explain different displacement estimates for the 2022 floods across Pakistan and thereby contribute a case study to the growing field of displacement models, and towards the development of more refined ones. It highlights opportunities as well as limitations, and is a quantitative contribution to an existing discussion on how much disaster-related displacement can be modelled, and in how far assumptions can be generalized. These insights also support a better understanding of displacement and migration from future climate risks.

How to cite: Lohrey, S., Kam, P. M., Biess, B., Cache, T., Di Vincenzo, S., Horton, R. M., and Thalheimer, L.: Modeling human displacement in the 2022 Pakistan floods: Current gaps and opportunities., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16735, https://doi.org/10.5194/egusphere-egu24-16735, 2024.

The water footprint of maize production is an indicator that provides information not only about direct water use for crop yields but also about indirect water use and virtual water trade. The general aim of the present research is to enlarge the knowledge about climate variability's impact on agriculture concerning improving sustainable water use for crop production. The accent of the proposed work will be on the assessment and analysis of green (rainfed production) and blue (irrigation water) water used for growing maize crops in the Danube Plain (Bulgaria).

The investigation is based on the following data: climatic data (air temperature, precipitation, wind speed, relative humidity); statistical data from agriculture, local authorities, and farmers (data about crop parameters and yields, and irrigation), and geographical data (climatic maps, maps about land use, soil maps, maps of main agricultural plants dissemination). The calculation and assessment of the water footprint of growing maize is done by the application of Cropwat software. The water needed for irrigation under various crop management options is determined. The main investigated period is 1961-2022 but special attention is given to water footprints of maize production during the extreme dry and extreme wet years. The results of the present work allow us to identify the hotspots regarding water use and water scarcity. The knowledge about the water footprint and climate-agriculture relationship could be used in water resources management and for effectively coping with the environmental and economic problems related to water scarcity and drought.

Acknowledgments: This study has been carried out in the framework of the project “The Nexus Approach in Agriculture. The water-food nexus in the context of climate change”, supported by the Ministry of Education and Science (MES) of Bulgaria (Agreement № КП-06-КОСТ-2/17.05.2022 and is based upon work from COST Action NEXUSNET, CA20138, supported by COST (European Cooperation in Science and Technology).

How to cite: Nikolova, N. and Matev, S.: Water Footprints of Growing Maize Crops in the Danube Plain (Bulgaria), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13023, https://doi.org/10.5194/egusphere-egu24-13023, 2024.

Extreme weather and climate events, such as extreme temperatures, droughts, and floods, cause significant yield losses and threaten global food security. Their frequency and intensity have increased in recent decades, a trend expected to continue. China is the world's largest grain producer and also a country where extreme events occur frequently. Nevertheless, the influence of extreme weather and climate events on crop yields in China is not yet well understood. This study quantified the impact of heat waves, frost, droughts, and floods on the yields of wheat, maize, rice, and soybean in China from 1970 to 2019, using the superposed epoch analysis (SEA) method, agricultural statistics collected from the National Bureau of Statistics of China, and crop calendar reanalysis dataset. Furthermore, the performance of 13 global gridded crop models (GGCMs) in simulating these impacts is evaluated. The results show that heat waves, frost events, droughts, and floods significantly decrease crop yields by 2.1%, 1.0%, 2.2%, and 1.7% for wheat, maize, rice, and soybean, respectively, accounting for 23.6%, 10.5%, 21.4%, and 18.9% of the interannual variability. Yields of different crop types in China are sensitive to specific extreme weather events. The GGCMs effectively capture the impact of droughts, with nine out of thirteen models detecting a significant effect, yet they struggle to accurately simulate the effects of heat waves, frost events, and floods, with only five, two, and two models detecting these impacts, respectively.

How to cite: Yin, D. and Li, F.: Influence of Extreme Weather and Climate Events on Crop Yields in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4863, https://doi.org/10.5194/egusphere-egu24-4863, 2024.

The unequivocal global warming of the climate system and the clear influence of human activities underscore the urgency of addressing the present challenge of Earth's warming. The exploration of past climate patterns presents significant opportunities in this regard.

Past climate information in high-mountain-areas, such as the Catalan or Aranese Pyrenees, is often still scarce. This is attributed to various reasons. On one hand, instrumental data series for these regions during the 20th century are not abundant and/or frequently start only from the 1960s. On the other hand, concerning climate information derived from historical documents for the past centuries in some of these regions, although its potential has been demonstrated in previous studies, it remains largely unexplored. Given all of this, it is not difficult to realize that these high-mountain-regions may exhibit a particular vulnerability in the face of current conditions of global warming. At the same time, its reactivity allows for the swift documentation of changes, as observed in the rapid regression of permanent Pyrenean glaciers over the past 50 years.

It is important to note that, given the strategic position of many of these locations as passages and border areas, especially from the mid-17th century onward, with the consolidation of European nation-states, there comes the implementation of the concept of political borders, various events throughout history (such as fires, wars, etc.) have led to the total or partial destruction of numerous documents. Frequently, the history of certain events is only preserved through oral accounts passed down from generation to generation.

Life in the Pyrenees has often been challenging, sustained by those individuals who have remained faithful, resisted, and persevered. The people of the Pyrenees have relied on the forest, pastures, and rather lean lands for their livelihood, and transportation has consistently posed difficulties. Additionally, sporadic phenomena of various kinds, whether historical, economic, or natural (avalanches, floods, earthquakes...), the latter strongly impacting the natural hazards in mountainous areas, have triggered changes in the villages or, in the worst cases, their abandonment and/or disappearance. The impact on these communities has often resulted from a combination of phenomena that is challenging to disentangle.

Here, we present an initial exploration of abandoned villages in the Catalan and Aranese Pyrenees during the Little Ice Age and the 20th century. The developed methodology includes the classification of depopulated areas based on various attributes: moment of disappearance, cause, altitude, and location. We have examined the climatic trends that could have affected the regions of the depopulated areas at different times. Causes include natural phenomena such as avalanches and landslides, as well as other factors like epidemics or plagues. The combination of these physical and biological factors can produce strong economic crisis at different scales. In extreme cases, this deterioration leds to the abandonment of specific villages. It is worth noting the centrifugal effect of large industrial and service agglomerations located in proximity, which have significantly contributed to the depopulation of Pyrenean settlements, whether seasonally (especially in the 19th century) or permanently (particularly in the 20th century).

How to cite: Cisneros, M., Barriendos, J., Barriendos, M., Esteban i Amat, A., Simó, C., Aventín-Boya, C., and Sigró, J.: Abandoned villages in the Catalan and Aranese Pyrenees during the Little Ice Age and the 20th Century: exploration of climate forcings through historical documents, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18736, https://doi.org/10.5194/egusphere-egu24-18736, 2024.

Recent studies that address the impacts of extreme weather on crop yields, are predominantly focused on expansive geographical scales and generally ignore the role of management practices in modulating the dynamics of weather-crop sensitivities. In our study, a unique dataset containing data from the Dutch Minerals Policy Monitoring Program and the Farm Accountancy Data Network (FADN) is used to explore the relationship between extreme weather and crop yields at farm level in the Netherlands. The dataset consists of unbalanced panel data from the years 2006 to 2021 including an average of about 1,500 farms. The Standardized Precipitation Evapotranspiration Index (SPEI) is used to reflect weather anomalies, both extreme wet and dry conditions. The climatological variables necessary to compute the SPEI are estimated at field-level using data gathered by the Royal Netherlands Meteorological Institute from 277 precipitation stations and 18 climate stations. In total, ten types of crops are covered and the role of soil type, irrigation and nutrient application in modulating the relationship between extreme weather and crops is elucidated. Distinction is made between drought and excessive precipitation during the planting-, growing- and harvesting period. The results show substantial impacts from drought during the growing- and harvesting period and excessive precipitation during the planting- and growing period. Severe droughts show statistically significant (p≤0.05) reductions in yield for nine crops, and lead to yield reductions ranging from 10 to 25 percent when only occurring during the growing period. Meanwhile, eight crops show statistically significant (p≤0.05) reductions in yield due to severe precipitation excess, with reductions ranging from 5 to 20 percent from excessive precipitation during the planting period. Soils such as sand or loess amplify the negative impact of drought on crop yield, while softening the impact of excessive precipitation. Furthermore, irrigation and nutrient application (both nitrogen and phosphate) are shown to moderately decrease the impact of extreme weather on crop yield, with substantial differences depending on crop type and the period in which the extreme weather event occurred. The findings of this study provide valuable insights to guide local adaptation priorities which are critical given the projected increase in the intensity and frequency of extreme weather under climate change.

How to cite: van der Veer, S., Hamed, R., Karabiyik, H., and Roskam, J.: Investigating the Effects of Extreme Weather and their Interactions with Farm Management on Crop Yields in the Netherlands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17120, https://doi.org/10.5194/egusphere-egu24-17120, 2024.

The global surge in displacement, with nearly 110 million people uprooted due to violence, underscores the pressing need to comprehend the challenges faced by refugees. Population growth, environmental crises, and political instability contribute to this crisis, projecting an escalating trend in the decades ahead. While hosting countries strive to address concerns related to labour markets, state provisions, and cultural integration, understanding the well-being of refugees upon entry needs to be more adequately explored. This study focuses on refugee stability and integration, employing Austria as a case study. Utilising comprehensive administrative data spanning November 2022 to November 2023, we examine residence movements as a proxy for stability. Our findings reveal a stark contrast in the stability of refugees compared to other migrant groups. Analysing movement profiles, we establish that refugees exhibit significantly higher rates of residential mobility than their counterparts, especially among male refugees. This imbalance persists even when comparing refugees to migrants from top refugee-sending countries without official refugee status. This study contributes valuable insights into the intricate dynamics of refugee stability, shedding light on the enduring challenges faced by this population. By examining movement patterns as a key indicator, we provide a nuanced understanding of the residential experiences of refugees, that can inform targeted policies and interventions for enhanced refugee well-being and integration.

How to cite: Ali, O., Dervic, E., Stütz, R., Nedelkoska, L., and Prieto-Curiel, R.: Quantifying the Stability of Refugee Populations: A Case Study in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4065, https://doi.org/10.5194/egusphere-egu24-4065, 2024.

Nowadays, the rise in the global temperatures are a source of concern, particularly in the Mediterranean region, where Spain is already witnessing notable consequences for its aging population. Predictions for the end of the XXI century reveal a persistent increase in air temperatures along with an increment of extreme episodes. Abnormal heat, once considered an 'environmental accident', is now a serious public threat. This contribution endeavors to quantify the added effects of heat wave exposure on mortality by demographic and socioeconomic strata during the period of 45 years in Spain at the provincial level. Moreover, we aim to explore the temporal evolution in these effects and variations in its spatial patterns, especially focusing on the inequality aspects that shape the health outcomes in an increasingly aging population.

Here we leverage daily individual mortality data and other contextual data on population from the National Institute of Statistics of Spain and air temperature estimates from the ERA5 global reanalysis. We also use the historical settlement data as a proxy for population distribution from 1975 onward. To estimate the main and added effects of heat waves we fit a quasi-Poisson time-series regression model using a distributed lag non-linear model with 10 days of lag, controlling for trends and day of the week.

We analyze approximately 15.8 million of deaths registered in Spain between 1975 and 2019. During the selected time window, we expect to see a shift in the temperature-mortality association from a V-shape in the first decades of the observation to a U-shape by the end of the period all across the provinces, thus revealing a progressive flattening of the exposure-response curve. We also expect to observe an overall reduction in the mortality burden associated with the temperatures. In particular, we anticipate more significant and rapid decline in the cold-related risks and attributable fractions in comparison with the heat-related ones, with some latitudinal variations across the country.

On the other hand, we witness a steady increase in the incidence of the heat wave episodes with time all over the country. We expect to see a positive added effect of heat wave on mortality, however this effect is assumed to be smaller than the primary effect. In addition, we anticipate observing variations in the effect depending on the heat wave order, duration, intensity, geographic location and demographic strata. The largest added effects are expected for the longest and strongest heat waves in the oldest-old population in the less accustomed to extreme heat areas.

How to cite: Ordanovich, D., Casanueva, A., Tobías, A., and Ramiro, D.: The lethal grip of heat: mapping the heat wave-mortality nexus in Spain (1975-2019), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3565, https://doi.org/10.5194/egusphere-egu24-3565, 2024.