Although the European Forest Fire Information System (EFFIS), provided by the Copernicus Emergency Management Service, offers three different methods for determining forest fire danger, the Canadian method is usually used and accepted in Croatia. The Canadian Fire Weather Index (FWI) estimates the forest fire danger level based on meteorological parameters (air temperature, humidity, wind speed and precipitation amount) related to 12 UTC for the given day at the meteorological station or to a grid point of a numerical weather prediction model.

Thanks to the EFFIS statistics portal, it is possible to see the extent to which Croatia has been at risk from forest fires in recent years based on the areas burned and the number of fires. The Copernicus Climate Change Service (C3S) provides a much more detailed overview of the burned areas. The combination of data from the Climate Change Service and the Emergency Management Service can provide a better overview of forest fires in Croatia. The forest fire danger levels are analyzed spatially between different regions such as the continental, mountainous and Adriatic parts of Croatia. In order to find an appropriate duration of the fire season, the forest fires within and outside the fire season are listed. The aim of the spatio-temporal analysis is to show the most endangered areas and the seasonal trend of forest fires in Croatia.

How to cite: Škurić Kuraži, D. and Herceg Bulić, I.: Spatio-temporal analysis of forest fires in Croatia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15531, https://doi.org/10.5194/egusphere-egu25-15531, 2025.

This communication presents a unified modeling framework for human-caused wildfire ignitions across representative European regions (pilot sites, PS), aiming to enhance understanding of ignition drivers and support wildfire risk management. Our approach models ignition probability at a fine spatial resolution (100 m), identifies key influencing factors, and enables cross-regional comparisons.

We calibrated Random Forest models using historical fire records and geospatial datasets, including land cover, accessibility, population density, and dead fine-fuel moisture content (DFMC). Models were developed individually for each PS and compared to a comprehensive model integrating all PS. Spatial autocorrelation effects on model performance were also evaluated.

Model performance was robust, with AUC values ranging from 0.70 to 0.89. DFMC anomaly emerged as the most influential variable across all PS. Among human-related factors, proximity to the Wildland-Urban Interface was most significant, followed by distance to roads, population density, and wildland coverage. The full model achieved an AUC of 0.81, highlighting mean DFMC and anomaly as dominant ignition drivers modulated by accessibility and population density. Local model performance, however, dropped by 0.10 AUC in regions such as Southern Sweden and Attica, Greece.

These findings underscore the importance of integrating fine-scale spatial and environmental data for wildfire ignition modeling. The developed models provide valuable insights into wildfire ignition hazards and support the implementation of targeted mitigation policies in fire-prone European landscapes.

How to cite: Gelabert Vadillo, P. J., Jiménez-Ruano, A., Ochoa, C., Alcasena, F., Sjöström, J., Marrs, C., Ribeiro, L. M., Palaiologou, P., Bentué-Martínez, C., Chuvieco, E., Vega-García, C., and Rodrigues, M.: Modeling Human-Caused Wildfire Ignition Probability Across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11004, https://doi.org/10.5194/egusphere-egu25-11004, 2025.

Wildfires are an intensifying global challenge, driven by climate change, which increases their frequency, severity, and spatial extent. Accurate wildfire risk assessment and forecasting are essential for effective mitigation, resource allocation, and long-term planning. The Canadian Fire Weather Index (FWI) is a widely used fire danger rating system that integrates four primary daily meteorological variables—24-hour accumulated precipitation, wind speed, relative humidity, and temperature—into six components representing fuel moisture, ignition probability, and fire spread potential. Its temporal "memory" feature, which tracks moisture changes over time, makes it particularly valuable for capturing wildfire dynamics.

However, the FWI reliance on specific daily input data at noon poses challenges for its application in regions or scenarios lacking such precise temporal measurements. To address this limitation, FWI proxies computed using daily mean data offer a practical alternative. Yet, these proxies often lack the fidelity required to fully replicate the FWI values.

This study focuses on enhancing the emulation of the original FWI using daily mean data and other proxy variables by leveraging advanced deep learning techniques. We explore a spectrum of architectures, ranging from conventional machine learning models to state-of-the-art approaches like convolutional neural networks (CNNs) and Convolutional Long Short-Term Memory (ConvLSTM) networks. These models are tailored to capture the spatial and temporal complexities of wildfire behavior while maintaining robustness in the face of variable data availability.

Our research centers on the Iberian Peninsula, a Mediterranean region highly vulnerable to extreme wildfire events. By utilizing high-resolution, geo-referenced datasets, we validate the ability of these models to emulate the original FWI with high accuracy. To enhance model interpretability, we integrate eXplainable Artificial Intelligence (XAI) techniques, providing actionable insights into the decision-making processes and addressing concerns about the "black box" nature of deep learning.

This work demonstrates how daily data, combined with cutting-edge deep learning methods, can effectively emulate the FWI, offering a scalable and reliable solution for wildfire risk prediction in regions where traditional inputs are unavailable. The proposed models bridge the gap between limited data availability and the growing need for precise fire danger indices, enabling improved assessment and planning for wildfire-prone regions.

By advancing the science of wildfire modeling through daily data-driven approaches, this study contributes to a deeper understanding of spatial and temporal wildfire dynamics. It highlights the potential of integrating geoscience, climatology, and artificial intelligence to develop practical tools for wildfire risk mitigation, resilience, and decision-making in a rapidly changing climate.

Acknowledgments: This research work is part of R+D+i project CORDyS (PID2020-116595RB-I00) with funding from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033. O.M. has received the research grant PRE2021-100292 funded by MCIN/AEI /10.13039/501100011033.

How to cite: Mirones, Ó., Baño-Media, J., and Bedia, J.: Daily Data-Driven Emulation of the Fire Weather Index: Deep Learning Solutions for Wildfire Risk Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16116, https://doi.org/10.5194/egusphere-egu25-16116, 2025.

The radon-deficit technique has proven to be a valuable tool for environmental site characterization, particularly in detecting subsurface organic contamination. This work highlights its successful application in two contaminated sites, validated by consulting firms and supported by independent data collection campaigns. In the first case study, the technique effectively identified previously undetected DNAPL (Dense Non-Aqueous Phase Liquid) accumulations and optimized the placement of monitoring wells. Similarly, in the second case, radon-deficit data delineated areas potentially impacted by LNAPL (Light Non-Aqueous Phase Liquid) contamination, refining the sampling approach and complementing existing geochemical methods.

Building on these findings, a study is underway to integrate long-term radon data with machine learning (ML) techniques. By analysing environmental variables such as soil moisture, temperature, and atmospheric conditions, this approach aims to reduce the uncertainties inherent in radon-deficit data interpretation. Preliminary results indicate that ML models, such as Random Forest and Artificial Neural Networks, can enhance the predictive accuracy and reliability of the technique, paving the way for standardized protocols in site assessments. This integration represents a significant step toward more robust and scalable applications of radon-deficit methods in environmental monitoring.

How to cite: Barrio-Parra, F., Lorenzo Fernández, D., Cecconi, A., Serrano-García, H., Izquierdo-Díaz, M., and De Miguel García, E.: Application of Radon-Deficit Technique for Site Characterization and Machine Learning Integration: Case Studies and Emerging Insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5524, https://doi.org/10.5194/egusphere-egu25-5524, 2025.

Urgent Computing (UC) refers to the use of High-Performance Computing (HPC) and High-Performance Data Analytics (HPDA) and Artificial Intelligence (AI) modules during or immediately following emergencies. It typically integrates complex end-to-end workflows with scalable computing resources, where multiple model realizations are necessary to account for input and model uncertainties, all under strict time-to-solution constraints. Enabling urgent HPC in unpredictable events such as earthquakes can significantly enhance resilience and response efforts. The temporal horizon for UC usually spans from minutes to a few hours, providing decision-makers with rapid estimates of the potential outcomes of emergency scenarios. In particular, high-resolution synthetic ground motions for earthquakes can complement the tools used by seismological services for impact analysis. Here, the Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) is proposed as an innovative UC seismic workflow designed to rapidly generate synthetic estimates of the consequences (such as synthetic time histories, shakemaps, PGA/PGV, among other proxies) of moderate to large earthquakes (M > 6). Over the last six years, UCIS4EQ has been developed from scratch and received contributions within the framework of three European projects (DT-GEO, eFlows4HPC, and ChEESE CoE). In this work, we demonstrate the technological maturity of UCIS4EQ and its operational readiness in collaboration with the Mexican Seismological Service (SSN). Furthermore, this work addresses the challenges we face to reach operational maturity addressing the specific requirements of a seismological service for an urgent computing framework providing reliable outcomes for decision making with global coverage.

How to cite: Monterrubio-Velasco, M., Boehm, C., Iglesias, A., Diez, G., Bhihe, C., Esquivel, L., Zamora, N., Tuinstra, K., and de la Puente, J.: Earthquake Shaking Simulation Workflow for Urgent Computing Services: Challenges and Advances, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17470, https://doi.org/10.5194/egusphere-egu25-17470, 2025.

Subsidence is a geological phenomenon that continuously affects Mexico City. Over time, the impact of this phenomenon has been extensively studied using various methodologies, primarily at a regional scale. In recent years, efforts have shifted toward mapping subsidence at a local scale using technologies such as photogrammetry and LiDAR. These studies aim to establish a reference database to validate or complement regional-scale initiatives.

Field-based studies on subsidence often involve identifying problematic areas and analyzing topographical changes and structural damage over time. However, it is crucial to quantify and understand the limitations and capabilities of these techniques to establish a reference framework and ensure the reliability of the obtained data. Currently, precision methodologies are within everyone's reach thanks to technologies like photogrammetry and LiDAR from smartphones.

To achieve this, two controlled experiments (one conducted in the field and one in a laboratory setting) were carried out, in which 3D reconstructions of a box with known dimensions were made. Ten photogrammetry and ten LiDAR surveys were performed to compare the measurements obtained from the digital model with those taken from the physical object.

In the laboratory experiments, the average percentage error using photogrammetry was 1.03% (0.20 cm). Specifically, the error for a 16-cm-tall box was 1.44% (0.27 cm), while for a 20-cm-tall box, it was 0.61% (0.12 cm). For LiDAR, the average percentage error was 1.51% (0.27 cm), with errors of 1.50% (0.26 cm) for the 16-cm box and 1.52% (0.27 cm) for the 20-cm box. In field experiments, photogrammetry yielded an average percentage error of 0.88% (0.3 cm), whereas LiDAR showed an average error percentage of 2.17% (0.62 cm).

These findings confirm LiDAR and photogrammetry's potential for high-precision subsidence monitoring, providing a robust and accessible validation method. Utilizing mobile devices such as the iPhone 13 Pro Max extends the reach of these methodologies, enabling more accessible and practical research in urban contexts where subsidence poses significant challenges to infrastructure and quality of life.

How to cite: Vidal, G., Ramírez, N. L., Jácome, M. P., López, N., Reyes, T. A., and Yépez, F. D.: Accuracy Analysis of Photogrammetry and LiDAR Point Clouds Using an iPhone 13 Pro Max, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7731, https://doi.org/10.5194/egusphere-egu25-7731, 2025.

Mining conflicts sustainable environment and causes disturbances for the livelihoods of people. Given the adverse impact on environment, indigenous community including Sami people and domesticated reindeer, it is of critical importance to peruse mining expansion and reclamation in Lapland, Finland. For the first time, this study employs a spatial-temporal deep learning architecture called ConvoLSTM, which enables accurate predictions of mining activities by capturing spectral, spatial, and temporal dependencies. Our custom model integrates a 2-Dimensional Convolutional Neural Network (2D-CNN) with a Long Short-Term Memory (LSTM) component. Using 10-meter Sentinel-2 imagery, we generated time-series land use/land cover (LULC) maps from 2015 to 2024 to track changes in mining extent. The performance of the spatial-temporal model was carefully evaluated against a Random Forest (RF) and a standalone 2D-CNN model, where it achieved superior accuracy. In the post-analysis phase, the Change Vector Analysis (CVA) technique was applied to quantify the magnitude and direction of change in mining activities over the past decade. The unique contribution of this study lies in implementing a custom spatial-temporal deep learning model to map decade-long mining activities and detect changes using publicly available satellite data. The resulting time-series maps demonstrate significant conversion of forest land and bare soil into mining areas, highlighting the rapid expansion of mining activities in Lapland which indicates a growing environmental concern in the arctic-boreal forest region. These findings offer critical insights and a valuable resource for policymakers, researchers, and reindeer herders, facilitating informed decision-making for sustainable environmental management and natural resource conservation in Finland.

Keywords: Mining Mapping, Environmental Impact, Remote Sensing, Deep Learning, CVA. 

How to cite: Hasan, I. and Liu, D.: Quantifying Surface Mining Expansion and Reclamation Using Deep Learning-based ConvoLSTM Model and Satellite Images: A Case Study in Lapland Region of Finland., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4913, https://doi.org/10.5194/egusphere-egu25-4913, 2025.

Gravitational mass movements are recurrent events in Brazil, usually triggered by intense rainfall. When such rainfall events occur in urban areas, particularly on slopes, they often result in disasters, causing loss of human lives, social impacts, and economic damage. Thus, mapping and monitoring landslide susceptible areas are extremely important, as well as the implementation of a system capable of predicting their occurrence in advance. In this context, this study aims to assess the efficiency of the TRIGRS numerical model as a component of a prediction system for landslides on urban slopes. As a first step, the influence of the drainage network, which is altered due to urbanization on slopes, will be analyzed in relation to the safety factor, moisture profile, and pore pressure. The drainage network was calculated using a digital terrain model derived from LIDAR data. The TRIGRS model was applied to a small watershed located in the municipality of Campos do Jordão, São Paulo, Brazil. During the 72 hours analyzed period, two heavy rainfall events stroke the area and landslides were registered. The registered landslides show the model efficiency on the identification of the most susceptible areas, because they happened in areas identified by TRIGRS as extremely susceptible to landslides. The combined geotechnical and geophysical methodology for soil characterization and the use of more realistic drainage network feeding the TRIGRS has shown to be useful urban planning and early warning systems. This study is part of Brazilian Council for Scientific and Technological Development (CNPq) Project coordinated by GEGEP/UFPE, with the participation of Cemaden, and in collaboration under development with the National Research Council of Italy (CNR). It aims to implement a methodological procedure.

How to cite: Ernesto de Moraes, M. A., M. Mendes, R., Bortolozo, C. A., Metodiev, D., das Dores S. Medeiros, M., R. M. Andrade, M., S. G. Mendes, T., and Q. Coutinho, R.: Effects of Drainage Network on the Identification of Landslide-Susceptible Areas Using the TRIGRS Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21040, https://doi.org/10.5194/egusphere-egu25-21040, 2025.

This study addresses slope stability challenges at the All-India Radio Telecommunication Tower site in Kodagu, Coorg, Karnataka, India. The hillock supporting the tower exhibited signs of instability following the monsoon of 2022, prompting the need for effective reclamation strategies to prevent future landslides. A detailed spatial analysis was conducted using open-source Digital Elevation Models (DEM) and the Scoop 3D spatial Limit Equilibrium Method (LEM) tool to identify critical regions susceptible to failure. To ensure robust and sustainable slope stabilization, geocell retaining walls were selected as an innovative solution. This technique promotes biotechnical stabilization by integrating structural reinforcement with natural vegetation, aligning with sustainability principles. The three-dimensional geometry of the proposed solution was modelled, and Finite Element Method (FEM) simulations were performed using PLAXIS 3D to evaluate the design’s performance under static and pseudo-static conditions, both with and without reinforcement. The analysis revealed that the geocell-based retaining system significantly enhances the slope's stability, achieving a Factor of Safety improvement of more than 10%. This solution not only addresses immediate stability concerns but also aligns with the United Nations Sustainable Development Goals (SDG) 9 and 11, emphasizing resilient infrastructure and sustainable urban development. The study concludes by recommending the implementation of this hybrid geocell retaining system to effectively mitigate future landslides and protect the telecommunication tower site.

How to cite: Menon, V. and Kolathayar, S.: Innovative Geocell-Based Slope Stabilization for Sustainable Protection: A Case Study of a Radio Tower Site in Kodagu, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3939, https://doi.org/10.5194/egusphere-egu25-3939, 2025.

Climate change has intensified droughts in many parts of the world, severely impacting different sectors. In particular, the agricultural sector is highly sensitive to precipitation deficits and the resulting soil moisture deficit, leading to a drastic reduction in crop productivity. There is an urgent need to ensure access to food for a growing population in future, making it essential to address agricultural drought induced crop yield losses. Multimodal satellite and reanalysis climate data archives, coupled with advancements in machine learning, offer a promising avenue to address this issue, but studies are often limited to the calculation of drought indices. In order to produce actionable insights and allow for time to prepare for drought-related food production deficits, specific information on crop losses is needed. Therefore, this study demonstrates the potential of the machine learning algorithm Random Forest (RF) for annual crop yield forecasting using multimodal datasets, for two agriculturally important drought-prone regions in India and Germany. Using 11 climate variables from ERA5 data and PKU GIMMS NDVI (version 1.2) from 1990 to 2021, an RF model was trained to predict crop yields for two common crops across the study sites. The model was evaluated at different spatial scales and the spatial transferability of the model was also tested, using Root Mean Square Error (RMSE; absolute error metric) and Mean Absolute Percentage Error (MAPE; relative error metric). Feature importance was also assessed across scales and across different study sites, using the mean decrease in impurity as a post-hoc explainability tool. Results show that different features are important for accurate crop yield predictions in different regions, for different crops, and across different space-time scales. Spatial transferability requires retraining the model with local data, due to the strong influence of local climatic and agricultural conditions as well as data availability. Findings pave the way for long lead time predictions of drought impacts on agricultural productivity purely open source data, contributing directly to improving global food security equitably, as the methods are equally applicable in data-rich and data-poor contexts. 

How to cite: Sahu, H., Garg, P. K., Vijay, S., and Dasgupta, A.: Estimating drought impacts on crop yield using AI and EO, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2768, https://doi.org/10.5194/egusphere-egu25-2768, 2025.

The Amazon Basin (AB), the largest hydrographic basin in the world, spans across seven countries in South America. It constitutes a highly intricate system, rich in natural resources, and is marked by substantial biological heterogeneity. The AB plays a pivotal role in the regulation of environmental processes, serving a key component of the global hydrological cycle and climate systems. Understanding the increasing frequency, intensity and spatial extent of extreme drought events in this region is vital for safeguarding the regional ecosystem. This study aims to classify extreme drought events in the AB using the Standardized Precipitation-Evapotranspiration Index (SPEI), derived from ERA5 reanalysis data, covering the period from 1980 to 2024. To assess both agricultural and hydrological droughts, this research incorporates the accumulation periods of 6 and 12 months (SPEI-6 and SPEI-12). The ranking methodology accounts for various SPEI time scales, the extent of the affected area, and the average SPEI intensity within that area. The results highlight that the 2023/24 drought episode was the most intense ever recorded in the AB, with over 90% (80%) of the region affected for the month of January for SPEI-6 (SPEI-12), surpassing known past mega-events, such as the 2005, 2010 and 2015/16 episodes. These extreme conditions were observed across all timespans. Specifically, for January 2024 under the SPEI-6 and for September 2024 under the SPEI-12, more than half of the AB was categorized as experiencing exceptional drought, as established by the 1st percentile of the SPEI distribution. Furthermore, the results underscore the persistence of consecutive periods of drought, especially since the beginning of 2020. With the climate projections indicating continued warming in the region, increased evapotranspiration and lower rates of rainfall are expected, potentially leading to even drier periods. This marks the significance of studies focused on understanding the development and impacts of droughts, as they play a critical role in the mitigation of future environmental risks.

How to cite: Albuquerque, R., Monteiro dos Santos, D., Miranda, V., Gouveia, C., Liberato, M., Trigo, R., Peres, L., and Libonati, R.: Ranking of extreme drought events in the Amazon Basin between 1980 and 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14311, https://doi.org/10.5194/egusphere-egu25-14311, 2025.

In territories with complex orography such as the Macaronesian archipelagos of Madeira, Azores, Canary Islands, and Cape Verde, the spatial resolution of the Coupled Model Intercomparison Project Phase 6 (CMIP6) is not sufficient to account for all the atmospheric phenomena that occur in these archipelagos with such a complex microclimatic structure. This research presents a climatic dataset at a spatial resolution of 3x3 km² in all the Macaronesian archipelagos derived from high-resolution regional climate simulations performed with Weather Research and Forecasting (WRF) model, applying the pseudo-global warming (PGW) method. The dataset is focused on the following parameters: temperature, precipitation, solar radiation, wind, and cloud coverage. Meteorological stations (ECAD and METAR) and reanalysis ERA5 data were used for the validation of the model results in the recent past period (1982–2019). We worked with two periods for future projections (2030–2059 and 2070–2099) under two representative scenarios (SSP2.6 and SSP8.5). These indicators include annual and seasonal statistics and variability for each parameter. The dataset aims to support regional climate adaptation strategies, contributing to the broader scientific understanding of climate in insular environments.

How to cite: Barrancos, J., Carrillo, J., Tondreau, P. S., Expósito, F. J., Pérez, J. C., González, A., and Díaz, J. P.: Pseudo–Global Warming climate projections at convection-permitting resolution in the Macaronesia region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7204, https://doi.org/10.5194/egusphere-egu25-7204, 2025.

The Nearest Neighbour declustering technique is utilized to differentiate dependent events, such as aftershocks and foreshocks from independent events, such as isolated and mainshocks events . The estimated background field could either be stationary or non-stationary over time and may exhibit patterns that depend on both space and time. Any residual deviations from a time-stationary and spatiotemporally-independent Poisson point field could offer insights into regional loading processes and merit further investigation (Zaliapin and Ben-Zion, 2020). We apply the adopted technique on the Southern California region, an area that includes four significant events with magnitudes greater than 7, over the years 1981- 2021 and the catalog's completeness ranges between magnitudes 2 to 3 (Zaliapin and Benzion, 2020). For generating the complete background set, both outdegree and closeness centrality yielded nearly identical mainshock node counts for background detection in our study region, highlighting the robustness of these centrality measures.   In a tree network, hierarchy identification might not be straightforward, but utilizing centrality can aid in placing elements accurately. Higher centrality values indicate a simpler structure compared to lower centrality values. Although the traditional highest magnitude method produces results almost similar to those of the centrality measure from network analysis, the network-based approach offers new possibilities for future research in the study of earthquake sequences and their evolution. In a spatially inhomogeneous, temporally homogeneous Poisson process (SITHP), there is a strictly positive probability that two events may occur arbitrarily close to each other  and NN method works better for declustering with this condition (Luen and Stark, 2012). In this study, three temporal statistical tests have been conducted: the Conditional Chi square(CC) test, the Brown-Zhao(BZ) test, and the Kolmogorov smirnov (KS) test on the complete background set. It was found that the KS test, which assumes the time series follows a uniform distribution and does not require any adjusting parameters, is more reliable than the other two tests(requires more tuning constants). For almost all magnitude cut-offs, the temporal tests fail the null hypothesis; however, for a magnitude of 3.4, the temporal test is satisfied, but the space time test ( Luen and Stark test) fails the null hypothesis. For the nearest neighbour (NN) method, the null hypothesis is rejected for all magnitude ranges in our study region. Consequently, it can be concluded that NN declustering is not effective for this dataset or the number of data points is low. Notably, the Luen and Stark space time test yielded a value of 0 for most magnitudes, except for magnitudes 4 and 4.2. This suggests two potential scenarios: either the earthquakes are inadequately declustered, leading to some background events being overlooked or there is another possibility that this model is not fit for the Poisson process and suggesting a need for an alternate conditional model.

References:

Luen, B., & Stark, P. B. (2012). Poisson tests of declustered catalogues. Geophysical journal international, 189(1), 691-700. https://doi.org/10.1111/j.1365-246X.2012.05400.x.

Zaliapin, I., & Ben‐Zion, Y. (2020). Earthquake declustering using the nearest‐neighbour approach in space‐time‐magnitude domain. Journal of Geophysical Research: Solid Earth, 125(4), e2018JB017120. https://doi.org/10.1029/2018JB017120.

How to cite: seal, A. and Jana, N.:  Statistical Analysis on Background Seismicity of Southern California Region: Application of Nearest Neighbour Declustering and Network  Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-693, https://doi.org/10.5194/egusphere-egu25-693, 2025.

This study investigates the collective narratives of informal settlement communities in Southeast Asia through the lens of participatory mapping, thereby elucidating geonarratives that encapsulate their lived experiences of extreme urban heat. As urban environments increasingly confront the challenges of rising temperatures—particularly evident in cities such as Bangkok in Thailand, and Quezon City in the Philippines—the integration of community perspectives into risk assessments becomes paramount. The heightened vulnerability of informal settlements to these climatic stressors necessitates a thorough examination of the insights provided by residents. Through participatory mapping exercises and focus group discussions, this research actively engages community members in articulating their lived experiences and adaptive strategies in response to extreme heat. The findings reveal that while these communities develop coping mechanisms to mitigate the impacts of heat, such strategies may inadvertently intensify their vulnerabilities and impose additional burdens. The geonarratives that emerge from these collective stories illustrate the interplay between vulnerabilities and adaptive capacities, illuminating the complexities of resilience. By fostering an inclusive participatory framework, this research enables community members to identify the local conditions and challenges that shape their resilience and coping strategies. By prioritising the voices of marginalised populations, this study underscores the necessity of integrating community insights into urban planning and climate adaptation strategies, thereby enhancing resilience in the face of escalating climate risks.

How to cite: Macagba, S. F. A. and Delina, L.: Geonarratives of Resilience and Coping: Understanding Lived Experiences of Urban Extreme Heat in Southeast Asia’s Informal Settlement Communities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2880, https://doi.org/10.5194/egusphere-egu25-2880, 2025.