Urban and suburban forests provide major cultural ecosystem services, yet planning still often relies on destination-based indicators (e.g., nearest forest, simple distance buffers). These measures miss how real access is shaped by corridor continuity, available transport modes, and last-mile connections to entrances. As a result, they can misrepresent both visitation pressure and equity patterns across a metropolitan region.
We analyse forest recreation in the Vienna Metropolitan Area using representative Public Participation GIS (PPGIS) data (n = 3,121). We link anonymised home locations to reported forest destinations and entrances, derive origin–destination (OD) flows, and assess accessibility using both Euclidean distance and mode-specific network travel times for walking, cycling, public transport, and car. To move beyond a destination-only assessment, we apply density-based OD flow clustering (DBSCAN/HDBSCAN) to detect corridor-like patterns and compare clusters by travel time, mode share, and visitation frequency.
We identify six visitor groups (with sub-clusters in the two largest), differing in mobility profiles and spatial structure. We find a clear distance–decay relationship: each additional kilometre to the forest is associated with ~11% fewer annual visits. Importantly, distance alone does not explain use. Corridor structure matters. Multimodal “belts” around the city support access within feasible travel times, while other areas remain underused despite being geographically close, suggesting gaps in connectors and continuity rather than limited forest supply.
This corridor-based perspective complements destination-centric metrics and supports more actionable planning and mitigating environmental impacts. Strengthening gateways and last-mile links, protecting high-performing multimodal corridors, and targeting specific accessibility gaps can improve equity while limiting car dependence.
How to cite: Stefan, F.: Beyond distance: mapping multimodal forest recreation corridors in the Vienna metropolitan area using PPGIS and origin–destination flow clustering, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11718, https://doi.org/10.5194/egusphere-egu26-11718, 2026.
The European Commission’s Joint Research Centre (JRC) produces open and free gridded data on human settlements and population at the European and global level. These datasets provide robust sources for decision making, planning, disaster risk management and scientific research. In this talk, we will provide an overview of recent developments and advances with this regard. Specifically, we will highlight ongoing work, novel datasets and underlying methods, including global, gridded future projections (GHS-WUP-POP; 1-km population estimates from 1980 to 2100), historical gridded population data for Europe since the 1960s using spatially-explicit backcasting models and innovative, chain-linking based dasymetric population downscaling, including age-sex disaggregations, as well as global historical gridded population data from 1900 onwards produced by integrating historical, long-term land-use models with data from the Global Human Settlement Layer.
For robust and transparent gridded population data production, uncertainty awareness and -quantification is key. Hence, at the JRC, we explore novel ways to conduct accuracy assessments of gridded population data. For example, we benchmark our datasets against increasingly available authoritative gridded population and other official data reported by national census agencies, and develop new metrics tailored to estimate the accuracy of gridded population data and similar datasets in meaningful and intuitive ways. In our talk, we will highlight recent methodological advances on gridded population data quality assessments and showcase exemplary results of benchmarking and cross-comparing different gridded population datasets. Moreover, we will reflect on pitfalls and caveats that may occur when gridded population data accuracy assessments involve unsuitable data processing or sampling design and highlight the importance of reflected considerations of the fitness-for-use of these datasets.
How to cite: Uhl, J., Schiavina, M., Pigaiani, C., Batista e Silva, F., Alessandrini, A., Freire, S., Krasnodębska, K., Carioli, A., Pesaresi, M., Kemper, T., and Dijkstra, L.: Advances in producing and evaluating gridded population data at the European Commission’s Joint Research Centre, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20475, https://doi.org/10.5194/egusphere-egu26-20475, 2026.
With the rapid development of artificial intelligence (AI), energy consumption and carbon emissions from high-demand computing power have gradually attracted widespread attention in the environmental field. Existing research largely focuses on data centers, which are the infrastructure that directly generates AI-related carbon emissions. However, the users who truly drive computing power demand have long been neglected. A major reason is the difficulty in tracking and accurately locating users, so that AI-related carbon emissions from users’ perspective have lacked systematic identification and discussion so far. It is worth noting that with the wide application of AI, the primary source of carbon emissions has shifted from model training to large-scale and multi-domain usage. This means that understanding the spatial distribution pattern of AI users is crucial to explore demand-side emission reduction in AI, especially during periods of bottlenecks in production-side emission reduction, such as the slow green transformation of the electricity energy mix. Demand-side management can, to some extent, contribute to mitigating AI-related carbon emissions. In this study, we aim to display the spatial distribution of AI users within the city and assess whether variations in usage across different wards may lead to potential spatial inequalities in AI-related carbon emissions. Taking London as a case study, we utilize regional AI penetration rates and AI user profiles to spatially decompose urban AI users at a more granular scale, quantifying the corresponding AI-related carbon emissions and comparing the proportion of AI-related carbon emissions in residents' carbon footprints and potential inequalities. We expect to find spatial clustering of AI-related carbon emissions and a positive correlation with the distribution of educational resources and wealth. Our study may provide an empirical basis for understanding the new environmental inequalities brought about by AI development and offers key references for future green digital governance on the demand side.
How to cite: Yin, Y., Chu, Y., and Chen, Y.: Mapping AI Users and Potential Inequality Pattern: A Spatial Downscaling Study in London, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-895, https://doi.org/10.5194/egusphere-egu26-895, 2026.
Whistler-Blackcomb is a premier ski resort in Canada, attracting approximately 2 million visitors annually and is about a two-hour drive from Vancouver, British Columbia. Whistler-Blackcomb has approximately 3,300 hectares of skiable terrain, a peak elevation of 2,240 meters, and a vertical drop of approximately 1,565 meters. Located at the ski resort are two weather stations: one at 659 meters (the resort Village) and a second at 1,835 meters (Roundhouse Lodge). These weather stations have been collecting daily data on air temperature, snowfall, rainfall, and ground snow depth since the 1970s. The Village weather station data record spans from 1977 to 2025. At this weather station, minimum temperatures, averaged for the winter season, are rising much faster than maximum temperatures (0.44 vs 0.10 °C per decade). Snowfall and rainfall show no noteworthy trends at the Village from 1977 to 2008. Measurements of these two variables were not made from 2009 to 2025. Ground snow depth appears to have declined significantly since 2009. The Roundhouse Lodge weather station data record spans from 1974 to 2025. At this location, average winter minimum temperatures are also rising faster than maximum temperatures (0.22 vs 0.11 °C per decade). No meaningful change in snowfall was observed at Roundhouse Lodge. However, winter rainfall has increased considerably since the early 2000s. Ground snow depth during the winter season shows no trend at the Roundhouse location. Finally, a stochastic weather generator, combined with an eight-member AR6 climate model ensemble (with an equilibrium climate sensitivity of 3.2 °C) and the emission scenarios SSP2-4.5 and SSP5-8.5, is employed to predict how daily minimum and maximum temperatures averaged over the winter season will change from 2030 to 2090.
How to cite: Pidwirny, M.: Changes in Temperature, Snowfall, Rainfall, and Ground Snow Depth Observed in Winter Daily Weather Station Data Collected at 659 and 1835 Meters from the 1970s to 2025 at Whistler-Blackcomb Ski Resort., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8452, https://doi.org/10.5194/egusphere-egu26-8452, 2026.
Climate change significantly impacts health, environments, and socioeconomics, but these effects are not evenly distributed globally. Variations in the spatial distribution, age and sex structure, and rate of growth of human populations drive different vulnerabilities to climate change. Maps of future population scenarios are therefore essential for understanding, planning, and responding to these impacts now and long into the future.
While efforts have been made to generate gridded future population maps, key gaps remain: a) consistency with historical datasets containing population (e.g., HYDE) for climate simulations; b) updates aligned with the latest SSP estimates; c) use of up-to-date data and methods; d) high-resolution outputs (100m) to support detailed climate impact studies; e) disaggregation by age/sex to assess specific vulnerabilities; and f) inclusion and communication of uncertainty. To address these, we launched the FuturePop project.
Here we present the latest updates to FuturePop. FuturePop V0.2 (Bondarenko et al., 2025) produced 1km global maps for population count between 2025 to 2100 from the latest Shared Socio-economic Pathway (SSP) population estimates (SSP Database V3.2), with these maps now extended to 2300. In turn this FuturePop data has been harmonized with past (HYDE & GHS-Pop) and present (WorldPop) population data to contribute to CMIP7 forcing data (Paprotny et al., 2025), with extensions made until 2300.
We present our initial maps for FuturePop V1.0. FuturePop V1.0 adds enhancements by explicitly incorporating SSP urbanisation rates and using SSP informed building volume estimates for spatial disaggregation. The latest work to create sub-national SSP population estimates and progress to create age/sex disaggregated maps will also be introduced.
Lastly, we present initial maps from “FuturePop Japan.” These are informed by Japanese adaptations of the SSPs (Chen et al., 2020), which provide greater national nuance than the global SSPs. Japan is a particularly interesting case, as its population is ageing and declining. It also had a high building vacancy rate of 22% in 2015, projected to reach 66–78% by 2100 (Yoshikawa et al., 2025). Although Japan is extreme, understanding how to spatially disaggregate shrinking populations is critical, as nearly 60% of countries are projected to decline by 2100 under SSP Database V3.2. We focus on the Japan SSP1 scenario, which includes planned urban compaction through the government-led “compact plus network” initiative.
- Bondarenko, M., Tejedor Garavito, N., Priyatikanto, R., Zhang, W., Fang, W., Nosatiuk, B., & Tatem, A. (2025). Global 1-km population projections for 2025–2100 under SSP3.2 (v0.2). University of Southampton. https://doi.org/10.5258/SOTON/WP00849
- Paprotny, D., Hawker, L., Bondarenko, M., Hilton, J., Garavito, N. T., Noi, E., & Tatem, A. (2025). input4MIPs: CMIP7 PIK-CMIP-1-0-0. Oak Ridge National Laboratory. https://doi.org/10.25981/ESGF.input4MIPs.CMIP7/2583900
- Chen, H., Matsuhashi, K., Takahashi, K., Fujimori, S., Honjo, K., & Gomi, K. (2020). Adapting shared socioeconomic pathways for Japan. Sustainability Science, 15(3), 985–1000. https://doi.org/10.1007/s11625-019-00780-y
- Yoshikawa, S., Imamura, K., Takahashi, K., & Matsuhashi, K. (2025). Development of scenarios for climate impacts in Japan. In N. Mimura & S. Takewaka (Eds.), Climate Change Impacts and Adaptation in Japan (Springer). https://doi.org/10.1007/978-981-96-2436-2_36-2436-2_3
How to cite: Hawker, L., Bondarenko, M., Hilton, J., Noi, E., Tejedor Garavito, N., Priyatikanto, R., McKeen, T., Tanaka, T., and Tatem, A.: FuturePop - Constructing global gridded population maps at multiple scales for SSP scenarios , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15386, https://doi.org/10.5194/egusphere-egu26-15386, 2026.
Nature-based recreational activities are highly sensitive to climate change. Particularly, hiking tourism is exposed to weather and climate variability that can affect the accessibility and attractiveness of trekking routes and alter tourism seasonality and flows. Thus, there is an urgent need for climate adaptation actions for effectively responding to climatic and environmental pressures and ensuring continuity of outdoor tourism experiences.
So far, responses within the tourism sector have been largely managed by individual operators, through unsustainable coping measures aimed at managing climate variability and related shifts in supply and demand patterns. Integrated approaches that could promote more effective, long-term climate adaptation, while enhance landscape heritage resources and prioritize the needs of the local community remain weak and isolated. This challenge is even more pressing in rural communities where nature-based tourism is envisioned as a sustainable driver for economic revitalization and socio cultural innovation against depopulation and aging. However, they frequently lack adequate resources, institutional support, and policy frameworks to implement effective adaptation strategies, while short-term management decisions and low public awareness further exacerbate vulnerabilities.
This contribution presents a participatory adaptive planning approach for nature-based tourism in rural contexts. The case study involves six small municipalities in the Fiastra river valley (Marche region, Italy), where a cultural trekking route – the Anello della Val di Fiastra – has been developed to promote responsible territorial enhancement by combining slow tourism, unique natural landscapes and the local cultural heritage. A scenario-based planning workshop was organized to engage stakeholders in discussing plausible future climate conditions for thevalley. Participants were projected to the year 2068, characterized by rising temperatures, increased frequency of heatwaves and tropical nights, and more intense rainfall events. They were asked to identify landscape assets most at risk and to co-design adaptive solutions to preserve territorial attractiveness and ensure the walkability of the route throughout the year. Environmental hiking guides, tourism operators, heritage managers, and representatives of local cultural associations collectively mapped vulnerable and exposed places along the route and discussed potential responses, spatializing them where possible. Proposals ranged from long-term strategies to operational measures and tactical interventions, including nature-based and engineering solutions, financial instruments, tourism supply management, training and awareness-raising initiatives, and governance actions.
The workshop was conducted within the newly established landscape observatory of the Fiastra Valley, a local entity studying landscape dynamics and risk conditions towards bettermanagement policies. Findings provide insights into the actors and planning instruments required for effective adaptive decision-making in nature-based tourism. Moreover, this studyhighlights the value of community-based and interdisciplinary research in fostering mutual learning and co-creation of knowledge, by redefining spaces and modes of relationship between local authorities and actors for risk management and climate adaptation.
The research is part of the project “QUI Val di Fiastra”, funded by the Italian National Recovery and Resilience Plan, Intervention 2.1 – Attractiveness of historic villages.
How to cite: Baldassarre, B., De Luca, C., Giacomelli, M., and Barchetta, L.: Participatory adaptive planning for nature-based tourism in a changing climate: the case of “Anello della Val di Fiastra” hiking path, Marche region, Italy , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20623, https://doi.org/10.5194/egusphere-egu26-20623, 2026.
The construction of a network for assigning users to essential socio-economic services at the urban level provides a powerful framework to represent the web of functional connections that are exposed to natural hazards. Such a representation is particularly relevant for natural risk assessments, as it enables the analysis not only of direct damages to assets and services, but also of indirect and cascading impacts arising from service disruptions and user reallocation processes (e.g. during flood events). Building this type of network requires an understanding of decision-making factors, both individual and non-individual, which depend on multiple parameters, from economic to social. Despite this complexity, it is possible to reduce the modelling of these mechanisms to the analysis of a limited set of behavioural variables, such as the distance between the service and the user’s residence.
Based on millions of human movements, we highlight how to generate realistic flows of home–essential service users on an urban scale according to a distance-based universal law of service attractiveness. To do this, we incorporate the city road network into the distribution of populated buildings using demographic data, assigning an attractiveness value to the path to the service among all possible choices.
By showing how the universal law of service attractiveness depends on the size of the city, our study demonstrates that the larger the city under analysis, the more rapidly the attractiveness distribution of the service declines, and vice versa. Moreover, we highlight how service attractiveness is influenced by the type of essential service selected, distinguishing those for which people are most willing to travel long distances in order to benefit from them.
Our model, in addition to enabling the generation of a socio-economic network of assigned users to essential services—useful for various areas of research such as epidemiology and urban risk—bridges the gap between distance- and opportunity-based models of human mobility, characterising users’ decision-making mechanisms across multiple spatial scales and for different types of essential services through a distance-based universal law of service attractiveness.
How to cite: Arosio, M., Fidelibus, N., and Starnini, M.: From human mobility to urban service networks: a distance-based model for systemic risk assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19312, https://doi.org/10.5194/egusphere-egu26-19312, 2026.
The impact of climate change on human populations is already being felt around the world. Both its effects, and the human populations affected, are unevenly distributed, driving differential exposure and vulnerabilities. To better understand, plan for, and respond to climate change impacts, mapped estimates of population projected under future SSP (Shared Socioeconomic Pathway) scenarios have been developed.
With a growing number of SSP-consistent gridded population datasets being developed - over thirty to date - the comparability of these datasets needs to be understood. If these datasets are used in hazard exposure analyses or vulnerability assessments, the choice of gridded population dataset potentially has a considerable impact on the population estimated to be at risk. Research on the impact of dataset choice in such analyses has been very limited. In this work, we start to address these knowledge gaps. Firstly, we introduce results of a comparative review of existing gridded future population estimates. We explore how differences in: (i) SSP database versions, (ii) downscaling methods, and (iii) classification of built settlement and urban areas, translate into variability at the grid cell level. The results of our comparative analysis show that fundamental differences exist between the various SSP-consistent future gridded population datasets.
Secondly, we focus on the challenges that differences in gridded population dataset bring for downstream data users, with an example of assessing future population exposure to flood hazards in parts of China and Italy. Using flood extents, derived from a high-resolution hydrodynamic flood model, for four time points (2020, 2050, 2070 and 2100), we calculate an estimate of exposed population based on each gridded population dataset. Preliminary results show that flooding exposure estimates vary considerably depending on which gridded population dataset is used. Our results underscore the critical role that accurate future small area population estimates have in robust exposure and vulnerability analyses.
How to cite: Chamberlain, H., Savage, J., and Hawker, L.: The Role of Gridded Population Data in Shaping Future Exposure Estimates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20185, https://doi.org/10.5194/egusphere-egu26-20185, 2026.
The majority of the world’s population live in cities, making urban food environments an important driver of global diets and their associated health and environmental impacts. However, only a few dietary and food-system assessments have been conducted at the city level, often with important shortcomings which limit consistent policy planning. Existing studies cover only a few cities and mostly large ones, leaving many smaller cities without estimates. Further, most simply scaled national estimates of food intake – either from food balances or surveys – to city populations. We combined dietary data for urban residences by age and sex, gridded age and sex structures from WorldPop, and urban settlement polygons from the Global Urban Polygons and Points Dataset (GUPPD), to estimate the dietary intake in 5,500 cities with populations over 100 thousand inhabitants. Our estimates indicate that diets in most cities contained greater amounts of foods compared to a country’s average intake in 2020. As a result, cities in most regions were responsible for a larger share of food-related environmental resource use and pollution compared to their share of population. This was mostly driven by increased intake of animal source foods in cities included in our impact assessment. Cities were also responsible for a large share of diet-related health burden and an outsized share of health-related costs, in line with the generally higher cost levels observed in cities. Dietary changes to healthier and more sustainable diets could substantially reduce the environmental, health, and cost impacts associated with city diets, but are dependent on consistent policy approaches and support.
How to cite: Caleffi, S., Springmann, M., Rawden, J., and Auclair, O.: The environmental and health impacts of diets and dietary change in 5,500 cities worldwide, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20901, https://doi.org/10.5194/egusphere-egu26-20901, 2026.
Protecting and restoring natural spaces is critical in the face of climate risks and environmental change, whilst at the same time, access to natural space plays an important role in population health and well-being. Understanding visitation patterns to natural spaces aids planning, maintenance, and land use, and allows us to evaluate the impact of interventions designed to benefit both nature and society. While surveys can provide snapshots of information about visits to natural spaces, robustly measuring visitor patterns on broad scales remains a challenge. Moreover, we lack the tools required to provide visitation estimates under the range of scenarios involved in land use and natural space planning. In this research, we develop scalable tools to predict visitor counts along paths in the UK located in natural spaces using Machine Learning methods, expanding on previous work by the Office for National Statistics. We employ a range of linear, tree-based, and time series models trained on automated footplate counter data and test our models across a range of spatial and temporal scenarios. Our models demonstrate promising ability to replicate historical visitation patterns at many sites, suggesting data-driven methods could offer valuable insights into the sustainable management of natural spaces. We also highlight areas for future improvement, such as improving the spatial generalisability of the models, which could inform future visitation monitoring strategies. Finally, we use Explainable AI approaches to investigate the characteristics of natural space visitation, providing information for planning and interventions which we explore in this study using a storytelling approach.
How to cite: Galloway, E., Chai, Y., Langford, P., and Challenor, P.: Machine learning tools for estimating visitation to natural spaces in the UK, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20620, https://doi.org/10.5194/egusphere-egu26-20620, 2026.
The global climate crisis poses a growing and multifaceted threat to human health. Assessing and mitigating these climate-related health risks requires spatially explicit understanding of where populations are exposed and vulnerable to climate hazards. Advances in geospatial technologies and the increasing availability of satellite and remote sensing data have enabled the development of high-resolution global gridded population datasets, which have become critical infrastructure for climate-health research. These datasets support the analysis of population exposure and vulnerability across regions and scales, and are increasingly important for scenario-based assessments aligned with future climate and socioeconomic pathways.
This study systematically reviews academic literature published since 2015 to assess how gridded population data are being used in climate change and health research. Specifically, we examine who is using gridded population datasets, in which geographical regions, and for what types of climate-related health analyses. We assess the types of gridded population products used, including their spatial resolution and levels of demographic disaggregation, and how population data are integrated with climate and health information. Where reported, we also evaluate how study results are interpreted and applied to inform policy or decision-making.
The review of 222 academic peer-reviewed studies demonstrates that i) gridded population data have become foundational infrastructure for climate–health research, with a marked increase in publications since 2015; ii) applications span multiple health domains; iii) there is a substantial geographical imbalance; iv) gridded population data enable assessments of population exposure and vulnerability; v) use of age- and sex-disaggregated data is limited.
Overall, the review highlights gridded population data as a crucial bridge between climate science and public health action, emphasising the need for continued dataset development, interdisciplinary collaboration, and integration with future climate and socio-economic scenarios.
How to cite: Woods, D., Esepey, J., and Bonnie, A.: A systematic review of the use of gridded population datasets in the assessment of climate-health risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19353, https://doi.org/10.5194/egusphere-egu26-19353, 2026.
The present study aims to identify potential areas for the development of sustainable ecotourism in the Naryn Region of the Kyrgyz Republic using geographic information systems (GIS) and weighted overlay methods based on Earth remote sensing data. Ecotourism is one of the most dynamically developing and economically promising sectors oriented toward sustainable territorial development. The Naryn Region possesses significant potential for ecotourism development due to its mountainous terrain, unique natural landscapes, rich biodiversity, and cultural heritage. The weighted overlay method is an effective and visually intuitive tool for comparing multiple thematic layers, whose values are determined based on natural, environmental, and socio-economic factors. The study utilizes open-access geospatial data, including satellite imagery and digital elevation models. Data processing and analysis are carried out using ArcGIS software and specialized remote sensing applications. Seven thematic layers are employed in the analysis: elevation above sea level, land use and land cover, proximity to water bodies, transportation accessibility, population density, proximity to protected areas, and natural and cultural heritage sites. Based on the physical-geographical and socio-cultural characteristics of the Naryn Region, weighting coefficients are assigned to each thematic layer, followed by an integrated suitability analysis. As a result, an ecotourism suitability map is generated and classified into five categories from very high to very low suitability. The results demonstrate the potential of specific areas within the Naryn Region for sustainable ecotourism development while simultaneously accounting for environmental protection constraints.
How to cite: Darylkan kyzy, K., Lehnert, L., and Atyshov, K.: Geospatial Assessment of Sustainable Ecotourism Potential in Naryn, Kyrgyz Republic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3779, https://doi.org/10.5194/egusphere-egu26-3779, 2026.
While ongoing climate change is projected to expand environmentally suitable cropland toward high-latitude regions, the practical utilization of this potential is increasingly shaped by socio-economic constraints. Previous studies have suggested that agricultural workforce availability, as a proxy for socio-economic transitions and interactions, acts as a bottleneck for cropland supply potential. In this study, we assess spatially explicit practical cropland supply potential by incorporating agricultural workforce constraints using gridded population datasets from WorldPop. A weighting map of agricultural workforce distribution was constructed based on national-level minimum distance thresholds between population pixels and cropland pixels, and was used to allocate agricultural labor spatially. Future cropland potential was then derived by applying land-to-labor ratios that represent technological advancement. Within this workflow, urbanization levels were reviewed by comparing WorldPop Global 1 and Global 2 datasets and population projection datasets, all classified based on DEGURBA definitions (EUROSTAT), with national urbanization statistics from the World Bank. In addition, agricultural workforce shares between rural and urban pixels were evaluated through comparison with ILO statistics. Our results indicate that agricultural workforce availability constrains the northward expansion of cultivable land. A southward retreat of workforce-available cropland potential is also projected in some regions, such as Central Asia, despite increasing environmental suitability. Beyond regional projections, this study further demonstrates an application channel through which high-resolution population datasets can be used to constrain and quantify human influences on the Earth system.
Acknowledgment: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (RS-2021-NR055516, RS-2025-02312954).
How to cite: Forsell, N., Lee, H., and Kim, H.: Application of a Gridded Population Dataset to the Projection of Cropland Potential under Workforce Constraints, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16302, https://doi.org/10.5194/egusphere-egu26-16302, 2026.
The valorization of intangible cultural heritage represents a cornerstone for developing resilient
and sustainable tourism models in rural areas. Within the framework of the transborder project
CulinaryTrail.eu, this study focuses on the Gagauzia region (Republic of Moldova), a unique
cultural enclave in the Danube basin. Our research aimed to identify, document, and inventory
specific culinary assets that define the identity of the Gagauz community and assess their potential
to catalyze Community-Based Sustainable Tourism (CBST).
The methodology integrated ethnographic field research, semi-structured interviews with local
practitioners, and participatory mapping. The resulting inventory comprises 20 distinct units of
culinary heritage, classified into traditional dishes (e.g., kaurma, gözleme), specific processing
techniques (such as the use of traditional ovens and clay vessels), local beverages, and communitydriven
gastronomic events.
The analysis reveals that these culinary assets are not merely food products, but "living artifacts"
that encode migration history, adaptation to the steppe environment, and social cohesion. We
argue that the systematic integration of this inventory into the Culinary Trail network can:
Redirect tourist flows from oversaturated centers toward the Danubian hinterland—a
territory that remains peripheral yet profoundly authentic;
Ensure economic circularity by establishing direct links between small-scale local producers
and the regional hospitality sector;
Safeguard local biocultural identity by revitalizing authentic recipes and indigenous
ingredients.
The findings presented in this work demonstrate how a data-driven culinary inventory serves as a
vital tool for policymakers and local stakeholders in designing a tourism product that is both
economically viable and culturally respectful.
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How to cite: Căpățînă, L. and Odnostalco, I.: Mapping the Culinary Heritage of the Bugeac Steppe: A Strategic Inventory for Community-Based Sustainable Tourism in Gagauzia(Republic of Moldova), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21259, https://doi.org/10.5194/egusphere-egu26-21259, 2026.
Gridded population datasets play a pivotal role in a wide range of contemporary research and development, such as the distribution of aid, public health campaigns, as well as disaster risk management. However, the selection of the appropriate existing population dataset remains a non-trivial task, resulting in many practitioners choosing based on convenience or familiarity, rather than explicit use-case suitability.
In our contribution we present a user-requirement driven review of major gridded population datasets, in particular reviewing the wide array of the WorldPop suite, including their bespoke datasets, LandScan (HD), Kontur, Facebook HRSL, GPW, and GHS-Pop. We first consolidate key requirements of users in applied human-environment research and policy, based both on a literature review as well as key-informant-interviews of practitioners in the Humanitarian sector. Our synthesis reveals barriers to informed dataset choice, including scattered and inadequate documentation, limited uncertainty quantification and communication, and a lack of explicit suitability statements.
We then systematically evaluate, based on Riedler et al., 2025 (under review), how current existing datasets perform with respect to spatial granularity, temporal consistency, sensitivity to input data, the influence of settlement type on accuracy, and transparency of the product.
Based on the combination of both findings, we derive a set of generalised guiding questions for practitioners, as well as a decision tool for use-case specific dataset choice. We, furthermore, illustrate the effects of different dataset choices on down-stream applications and their potential impact on decision-making, as well as discussing alternative methods to establish population estimates and their suitability for studies and policies.
By shifting the perspective from dataset-centric descriptions to user-centred logic our review provides a foundation for operational decision-support and better understanding of gridded population products for domain agnostic users.
How to cite: Klaussner, E. S.: Towards Informed Use of Gridded Population Data: A User-Driven Selection Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1294, https://doi.org/10.5194/egusphere-egu26-1294, 2026.
European coastal regions represent a substantial share of the European tourism economy, but they are also among the places where climate change is most likely to be felt by visitors and businesses alike. Rising temperatures, changing precipitation regimes, and altered wind and cloudiness patterns can directly affect thermal comfort and perceived “beach quality,” with implications for visitation, seasonality, and local economies. This study quantifies how climate shapes coastal-beach tourism demand across Europe and translates these relationships into forward-looking, risk-based scenario insights.
Climate suitability is characterized using the Holiday Climate Index for beach tourism (HCI:Beach; Scott et al., 2016), a bioclimatic indicator integrating temperature, precipitation, humidity, wind, and cloudiness to reflect tourists’ stated preferences and destination comfort. This indicator is employed in a monthly panel tourism-demand model estimated on historical regional observations of tourism activity, alongside sector-specific controls and fixed effects. The resulting estimates indicate a statistically significant link between HCI:Beach and tourism demand, and a clear north-south pattern in demand changes in observed , with northern regions benefiting and southern regions experiencing significant reductions, particularly in higher warming scenarios.
To evaluate future impacts, monthly HCI:Beach projections through 2100 are generated using an ensemble of ten regional climate models, and corresponding changes in tourism demand are simulated. Uncertainty is represented from two sources: (i) climate model spread, by sampling across the ensemble projections of the underlying climate variables, and (ii) statistical uncertainty, by repeatedly drawing from the estimated parameter distribution of the demand model. These components are combined in a Monte Carlo framework, producing distributions of future demand outcomes.
Results are reported under two emissions pathways (RCP4.5 and RCP8.5) and, to support policy-relevant interpretation, are also summarized for four global warming levels (1.5°C, 2°C, 3°C, and 4°C). Across European coasts, projections reveal strong spatial and seasonal heterogeneity: climate change can improve suitability in some destinations and months (often in shoulder seasons) while degrading peak-season conditions elsewhere, implying shifts in the timing and geography of demand.
The risk assessment translates probabilistic projections into decision-ready metrics, such as the probability of peak-season demand losses exceeding specified thresholds, the likelihood of shoulder-season demand gains, and the emergence of “high-risk months” in which unfavorable beach conditions become consistently more common. Robust signals are further identified (high agreement across climate models and stable econometric effects) versus deep-uncertainty cases where adaptive strategies should remain flexible.
Finally, building on these findings, adaptation options tailored to regional and seasonal risk profiles are discussed, including spreading demand though season extension and product diversification, or managing heat and comfort through services and information. By integrating a preference-based climate index, econometric demand estimation, multi-model climate projections, and probabilistic risk metrics, this study provides a transparent framework to anticipate where, when, and how European coastal tourism may change.
Keywords: climate change impacts, coastal tourism demand, panel data analysis, HCI:Beach (Holiday Climate Index)
How to cite: Matei, N. A.: Tides of Change: How Climate Will Reshape Coastal Tourism in Europe. Destination Shifts, Economic Impacts, and Adaptation Options, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21281, https://doi.org/10.5194/egusphere-egu26-21281, 2026.
