Key Themes:
Processes:
Physical processes involved in hydro-climatic extremes and compound hazards (e.g., droughts-heatwaves-fires), their precondition factors, enabling mechanisms, feedbacks, emergent properties, and synergistic effects. Interaction and impact of such events in the physical system, ecosystems, and human population.
Methods & techniques:
Integration of remote sensing, data mining, and machine learning approaches to enhance the detection, monitoring, and prediction of hydro-climatic extremes and compound events. Combination of physically-based hydrological and climatological models with AI-driven simulations, as well as applications across multiple spatial and temporal scales, from local case studies to regional and global assessments.
Orals: Mon, 4 May, 16:15–18:00 | Room N2
Land surface hydrology is a collection of complex processes. The spatial variability both the land surface properties (soil and vegetation) as well as the meteorological inputs (precipitation and radiation) play an important role in hydrology. Satellite remote sensing has a broad spatial and repeat temporal view of the land surface and is able to provide observations for use in hydrology such as soil moisture, surface temperature and vegetation density. The variability of the water cycle causes extremes such as droughts and floods and these have an impact on society. In addition, landslides, permafrost thaw and wildfires are the three other hydrological extremes that impact society. In the past two decades with the advent of improved satellite sensors, modeling and in-situ observations, quantification of the water cycle and its extremes has become possible. These satellite sensors include - microwave observations for soil moisture and precipitation; visible/near infrared for vegetation and evapotranspiration, gravity for groundwater/total water and thermal observations for surface temperature.
How to cite: Lakshmi, V.: Observing hydrological extremes from Space, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1293, https://doi.org/10.5194/egusphere-egu26-1293, 2026.
The impact of dams on the hydrological conditions and ecological functions of wetlands has not been extensively researched. Rivers and wetlands are crucial environmental components connected to both natural and human ecosystems, making it essential to study eco-hydrological planning and its implications for human well-being. This study examines the impact of the Bijnor barrage on the hydrological prosperity and eco-hydrological alterations in Hastinapur Wildlife Sanctuary (HWS) from 1983 to 2023. The research utilizes the Indicator of Hydrological Alteration (IHA) to assess eco-hydrological thresholds, failure rates, impact magnitudes, and eco-hydrological deficits and surpluses in the river section and adjacent wetlands. The findings reveal that the percentage of very high hydrological prosperity increased to 43.703% in 2023 from 31.431% in 1983, and this is due to the disappearance of major portions of very low and low zones of hydrological prosperity. However, the total area of wetlands decreased by 62.55% and 38.12% during the pre- and post-monsoon periods, respectively. This decline corresponds with a rising failure rate of ecological optima, leading to increased eco-hydrological deficits and indicating heightened ecological distress, which could adversely affect natural and human well-being. Hydrological prosperity maps demonstrate a significant reduction in water-rich areas, with zones of "very high" and "high" prosperity in 1983 being replaced by "moderate" to "very low" zones by 2023. This trend aligns with global observations of declining wetland hydrology due to anthropogenic influences. These changes underscore the critical need for hydrological prosperity-driven ecosystem-based adaptation strategies to enhance wetland resilience and reverse negative trends. Future research should focus on quantifying the impacts of these strategies and developing tailored solutions to sustain hydrological prosperity in HWS.
How to cite: Kundu, S., Rana, N. K., and Sharma, V. N.: Geospatial Assessment of Dam-Induced Hydrological Prosperity and Eco-Hydrological Health in Hastinapur Wildlife Sanctuary, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1185, https://doi.org/10.5194/egusphere-egu26-1185, 2026.
The catastrophic Los Angeles Fires of January 2025 underscore the urgent need to understand the complex interplay between hydroclimatic variability and wildfire behaviour. This study investigates how sequential wet and dry periods, hydroclimatic rebound events, create compounding environmental conditions that culminate in extreme fire events. Our results show that a cascade of moisture anomalies, from the atmosphere to vegetation health, precedes these fires by around 6–27months. This is followed by a drying cascade 6 months before ignition that results in anomalously high and dry fuel loads conducive to fires. These patterns are confirmed when analysing recent (2012–2025) extreme fire events in Mediterranean and Desert Californian biomes. We find hydroclimatic rebound as a key mechanism driving extreme wildfire risk, where moisture accumulation fuels vegetation growth that later dries into highly flammable fuel. In contrast, extreme fires in the fuel-rich Forested Mountain regions are less influenced by the moistening cascade and more impacted by prolonged drought conditions, which typically persist up to 11months prior to fire occurrence. These insights improve fuel-informed operational fire forecasts for the January 2025 Los Angeles fires, particularly when year-specific fuel conditions are included. This underscores the value of incorporating long-memory variables to better anticipate extreme events in fuel-limited regions.
How to cite: McNorton, J., Keune, J., Di Giuseppe, F., Turco, M., and Moreno, A.: Hydroclimatic rebound drives extreme fire in California's non-forested ecosystems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6633, https://doi.org/10.5194/egusphere-egu26-6633, 2026.
Agricultural drought threatens food security and livelihoods in the Middle Ganga Plain (MGP),India. This study identifies agricultural drought hotspots using a multi-parameter approach, integrating a Drought Vulnerability Index (DVI) from 16 parameters and a Drought Preparedness Index (DPI) from 22 indicators. These indices were combined within a novel Vulnerability–Preparedness Framework to systematically delineate high-risk areas. The Artificial Neural Network (ANN) model has been employed to identify the hotspot zones The results show that 17.46% of the region is a drought 'Hotspot', with a critical 6.57% classified as an 'Intense Hotspot' concentrated in the districts of Gazipur, Jaunpur, Mirzapur, and Varanasi in the south western part of the study region. Analysis of the Standardized Precipitation Index (SPI) for these districts confirmed a history of recurring meteorological dry spells. Correlation analysis linked hotspot formation to high population density, a large agricultural labor force, and significant groundwater extraction. The model’s robustness was validated, demonstrating high accuracy with an Area Under the Curve (AUC) of 0.889 and strong agreement between predicted and observed data on the Taylor diagram. This study advances SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) by mapping agricultural drought risks to guide sustainable water use and build climate resilience. These findings provide crucial spatial intelligence for policymakers to develop targeted interventions and site-specific water management strategies to enhance agricultural resilience in the MGP.
How to cite: Kundu, B., Rana, N. K., and Sharma, V. N.: Agricultural Drought Hotspot Assessment in the Middle Ganga Plain,India, Using Multi-Parameter Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1186, https://doi.org/10.5194/egusphere-egu26-1186, 2026.
Under accelerating global climate change, the increasing frequency and intensity of extreme precipitation events (EPEs) pose severe threats to socioeconomic and ecological security, highlighting the critical importance of satellite precipitation products (SPPs) for EPE monitoring. However, comprehensive multi-scale, multi-characteristic evaluations of different SPP types during EPEs remain limited. This study systematically evaluated five SPPs from three categories—satellite-derived products (IMERG-Early, IMERG-Late, IMERG-Final), reanalysis products (ERA5-Land), and merged products (MSWEP-NRT)—during an EPE in Guangdong Province, China (August 16–21, 2024), across three temporal scales (3-hour, 12-hour, 24-hour) and four precipitation characteristics (amount, frequency, intensity, duration). All SPPs exhibit significant scale dependence and systematic biases in reproducing EPEs. The IMERG near-real-time products (Early/Late) provide the best overall multi-scale performance, demonstrating superior spatial fidelity and preservation of dynamic features like intensity gradients and duration. In contrast, ERA5-Land and MSWEP-NRT suffer from excessive smoothing, while the bias-corrected IMERG-Final overly suppresses heavy rainfall intensity. A key limitation across all products is a severe underestimation of precipitation peaks. This study provides critical guidance for SPP selection in EPE monitoring and identifies that future algorithmic improvements must focus on enhancing the identification and quantitative retrieval of convective precipitation to improve reliability.
How to cite: Zhou, Z., Huang, W., Wu, H., Shen, Z., and Yu, L.: Capturing Precipitation Characteristics Across Multiple Temporal Scales: Evaluation of Satellite Precipitation Products During an Extreme Precipitation Event in Guangdong, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11949, https://doi.org/10.5194/egusphere-egu26-11949, 2026.
High-frequency climatic extremes in rapidly urbanizing areas are becoming prominent and often reflected through enhanced thermal stress, changed moisture conditions, and heavy diurnal asymmetries, but the quantification of their spatio-temporal changes is still underestimated. This study focuses on that aspect in the National Capital Region of India with long-term satellite-derived land surface temperature (MODIS 2003-2021) with high-resolution in-situ measurements of air temperature, humidity, and wind (AWS-IMD). A spatio-temporal analytics framework, based on physical diagnostics, time-series mining, and interpretable pattern learning, is used to describe surface and atmospheric urban heat islands (UHI), urban dry islands (UDI), and the question of emergent thermal hotspots at urban-peri-urban-rural gradients.
Results indicate an increase in surface thermal extremes, where daytime SUHI warming rates are approximately 0.19°C/ yr in urban cores and as high as 0.23 °C /yr in inner-urban regions. Increase in the night-time surface temperature was more prominent, especially in inner-city areas (~0.15 °C /yr), a phenomenon suggesting the rise of nocturnal heat stress. The atmospheric UHI peaks were as high as 2.0-2.3 °C, particularly during winter mornings and pre-monsoon nights. The space-time cube hotspots analysis reveals that the persistent hotspots experienced between 2003 and 2011 have evolved to become more intense and expanse beyond 2011 with evident outward movements to the peri-urban areas. At the same time, dry seasons in urban dry islands were highly coupled between thermal and moisture extremes with −13 to −15 g /m³ (urban dry islands). In general, the results show that there is a systematic increase and spatial expansion of coupled heat and dry island extremes, which implies that urban areas with rapid urbanization are changing to more volatile and persistence urban thermal stress regime.
How to cite: Pramanik, S.: Urban Expansion Reshapes Surface and Atmospheric Heat Islands and Moisture Regimes in NCR-Delhi, India: Evidence from In-Situ and Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17125, https://doi.org/10.5194/egusphere-egu26-17125, 2026.
Flash droughts (FD) are characterised by rapid depletion of soil moisture conditions. A heatwave (HW) is a period of abnormally hot weather (typically defined as lasting for three or more consecutive days). While HWs intensify through ongoing atmospheric heating, FDs result from a sudden drop in soil moisture brought on by increased evaporative demand and precipitation deficiencies. When combined, FD–HW compound occurrences can cause ecosystem disruption, hydrological stress, and significant agricultural losses. In Europe, flash droughts (FD) and heatwaves (HW) are becoming more dangerous due to changes in land-atmosphere coupling and increased warming. However, because conventional free-running climate model simulations are not the best solution to replicate the observed dynamic circumstances that drive actual events, their evolution under future warming requires a different approach.
Here, we employ a storyline-based method that imposes counterfactual warming levels (Pre-Industrial (PI), Present-Day (PD), +2 K, and +3 K worlds) while reconstructing the synoptic conditions of recent European extremes (2018-2024) using spectrally nudged simulations of AWI-CM-1-1-MR, which are constrained toward ERA5 circulation. This approach avoids the sampling constraints of historical analogues, maintains the physical structure of the observed FD–HW sequences, and produces dynamically consistent representations of warm worlds. The mesoscale Hydrologic Model (mHM), which measures soil moisture anomalies, spatial drought extent, and compound FD–HW features throughout Europe, is driven by these climate forcings.
Our findings demonstrate intensification in the FD and HW separately as well as when they occur simultaneously. FD events are expected to approximately double in the same time frame, while heatwaves are expected to occur 5 times more frequently and have an average magnitude more than 12 times greater in a 4K world compared to pre-industrial levels. When they happen together in a difference of less than or equal to three pentads, such events are expected to become more than 7 times more common. This work offers a solid foundation for climate-risk assessment and drought preparedness throughout Europe.
How to cite: Yadav, D., Sanchez Benitez, A., Goessling, H., Athanase, M., Kettaren, R., Kumar, R., and Rakovec, O.: Storyline attribution of flash drought-heatwave compound extreme to global warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1037, https://doi.org/10.5194/egusphere-egu26-1037, 2026.
Compound climate hazards, where extreme events co-occur, pose increasing risks to our socio-ecological systems, yet their spatial dynamics remain poorly understood. We introduce a novel metric to quantify simultaneous drought and heatwave exposure, applying it to Niger’s Dosso region over a 24-year period (2000–2023) using remote sensing and GIS-based techniques. Our analysis reveals distinct spatiotemporal patterns: Southern and northern municipalities emerge as heatwave hotspots, while drought frequency shifts from southern dominance during peak rainy seasons to central and northern prevalence throughout the rainy season, with most droughts classified as mild. The metric identifies critical years of profound compound hazard occurrence—2000, 2002, 2009, 2011, 2015, and 2021— in northern and central-eastern municipalities. By integrating multi-hazard dynamics, this innovative approach enhances understanding of localised compound climate hazard exposure and lays the groundwork to inform targeted adaptation strategies in climate-vulnerable regions.
How to cite: Gonzalez Grandon, T., Rombach, S., Cheo, E., and Bell, R.: Understanding Compound Climate Hazards and Exposure form a Spatial Perspective: A Case Study in the Dosso Region, Niger, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22368, https://doi.org/10.5194/egusphere-egu26-22368, 2026.
Risk arising from waterlogging in low-relief floodplain areas is manifested primarily not only by extreme rainfall but also by large linear infrastructures such as elevated railway lines or road embankments, which disrupt natural drainage pathways. Conventional flood mapping approaches often fail to capture these anthropogenic controls. This study presents an integrated framework combining machine learning techniques and Sentinel-1 synthetic aperture radar (SAR) data to map flood extent and identify infrastructure-induced waterlogging along a railway corridor in Keonjhar district, Odisha, eastern India. Time series Sentinel-1 SAR data were analysed to extract inundation and surface moisture signatures using few flood indices. The infrastructure-induced topographic modification has been quantified using two Digital Elevation Models (DEM) representing two different time periods: the first one is the pre-infrastructure SRTM DEM, and the second one is the recent high-resolution DEM generated from drone-based orthophotos. Flow accumulation and watershed boundaries have been independently derived from both DEMs to evaluate changes in drainage pathways caused by the railway embankment. After watershed delineation from two DEMs, runoff coefficients were estimated, allowing a comparative assessment of pre- and post-infrastructure hydrological response. These terrain- and watershed-based variables, together with station-based rainfall data and SAR backscatter features, were used as input parameters in a Random Forest model to classify flooded, waterlogged, and non-inundated areas, with particular emphasis on zones adjacent to the railway alignment and cross-drainage structures. The results reveal that the persistent inundation patterns is largely as a consequence of natural flow obstruction by the railway embankment and inadequate cross-drainage connectivity. By highlighting these problems, the proposed methodology helps to identify infrastructure-driven flood augmentation and supports informed planning for designing any drainage-railway crossings, strategies related to flood mitigation, and climate-resilient transport infrastructure in vulnerable regions.
How to cite: Mondal, D.: Integrating machine learning and SAR-derived flood indices to assess the railway-induced waterlogging extent , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13317, https://doi.org/10.5194/egusphere-egu26-13317, 2026.
Heavy rainfall is one of the most impactful hydrometeorological extremes, frequently causing floods, landslides, and severe socioeconomic damage worldwide. Continuous, high-temporal-resolution monitoring of heavy rainfall is essential for disaster risk reduction and early warning. Recent advances in satellite remote sensing and artificial intelligence (AI) have opened new possibilities for global-scale observation and analysis of extreme precipitation by integrating multi-platform satellite data within a unified framework. In this study, we develop a harmonized global geostationary satellite dataset by integrating observations from multiple operational platforms, including the GEO-KOMPSAT-2A (GK2A), Meteosat Second Generation (MSG), and the Geostationary Operational Environmental Satellite (GOES). To address differences in temporal sampling and radiometric characteristics among these satellites, we apply a deep learning–based video frame interpolation (VFI) technique. This approach enables temporally consistent interpolation across overlapping satellite domains and facilitates the construction of seamless global cloud maps with high temporal continuity. Heavy rainfall characteristics are analyzed by linking the harmonized geostationary cloud-top observations with satellite-derived precipitation estimates produced using AI-based retrieval algorithms. These AI-driven precipitation products are designed to capture nonlinear relationships between cloud properties and surface rainfall, providing enhanced sensitivity to intense precipitation events. To assess their robustness and physical consistency, the AI-based precipitation estimates are systematically compared with conventional satellite precipitation products derived from traditional physically based or empirically calibrated retrieval methods. This comparison allows us to evaluate the added value of AI-based precipitation retrievals in representing heavy rainfall intensity and occurrence at the global scale. The analysis focuses on identifying global and regional characteristics of heavy rainfall in relation to cloud-top temperature, emphasizing climatic contrasts across tropical, subtropical, and midlatitude regimes, as well as land–ocean differences. This study demonstrates that the synergy between harmonized multi-geostationary satellite observations and AI-based precipitation retrievals provides a powerful framework for global heavy rainfall analysis. The physically interpretable relationships identified between cloud-top signals and heavy rainfall establish a solid observational basis for future AI-driven or hybrid early warning systems. By combining continuous geostationary monitoring with advanced AI methodologies, this work contributes to improved global assessment of heavy rainfall risk and supports the development of more reliable hydrometeorological early warning capabilities.
How to cite: Choi, Y., Ryu, H. G., Choi, S., Park, J., Rao, M., and Myung, K.: Global Characteristics of Heavy Rainfall from Harmonized Geostationary Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18835, https://doi.org/10.5194/egusphere-egu26-18835, 2026.
Drought is a natural hazard that has serious impacts on the environment and human society including agricultural, industrial, and domestic sectors, especially in the era of climate change. For Taiwan, drought poses a challenge particularly to the water-intensive semiconductor manufacturing industry. Comprehensive assessment is therefore necessary to identify key regions and sectors with high risk. This study utilized a combination of Analytic Network Process (ANP) and Artificial Neural Network (ANN) in an ensemble learning method to evaluate and map drought risk in Taiwan. ANP constructs a network and assigns weights to indicators while the ANN model uses these indicators to predict drought risk classes. Twenty indicators were selected representing socio-economic and environmental factors which are categorized into hazard, exposure, and vulnerability components for risk assessment. The environmental condition during the 2021 spring drought was selected to represent the drought hazard in Taiwan. The trained ANN model showed effective prediction of drought risk as indicated by performance metrics of accuracy, precision, recall, F1 score, and Kappa Index with values 0.940, 0.946, 0.938, 0.942, and 0.923, respectively. The final drought risk map was validated through fieldwork and independent statistical data. Overall accuracy values ranging 0.717-0.851 by comparing drought risk classes with indicators related to damaged crops, converted damage areas, and estimated product losses. The prediction and validation results highlight the reliability of the framework for rapid and accurate risk assessment. The framework can be applied to different natural and socioeconomic backgrounds for effective drought management to inform future long-term adaptation strategies.
How to cite: Liou, Y.-A., Vo, T.-H., Tran, D.-P., Bui, H.-A., and Nguyen, K.-A.: A Comprehensive Assessment Framework for Drought Risk in Taiwan Using a Combined ANP-ANN Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2805, https://doi.org/10.5194/egusphere-egu26-2805, 2026.
Understanding spatial patterns of flood susceptibility is essential for targeted mitigation and resilient land-use planning, especially in ecologically sensitive zones. We present a comparative flood-susceptibility modelling framework that integrates a multi-criteria AHP (analytic hierarchy process) weighted criteria-based overlay and a data-driven neural-network (NN) classifier. The classification models are trained on a binary flood inventory map (0=No flood, 1=Flood) in Kerala, a coastal state in western India, and part of the ecologically sensitive zone of the Western Ghats. The flood inventory was developed using the microwave remote sensing data (Sentinel-1 SAR of 2018 and 2020) through Google Earth Engine (GEE) and validated through ground-based event (Actual Flood Occurrence). The study compiles an extensive set of 18 conditioning factors spanning climate and hydrology (annual precipitation, drainage density, flow accumulation, stream power), topography and morphometry (elevation, slope, profile curvature, TPI, TRI), soil wetness and permeability (soil type, soil moisture, TWI, erodibility), vegetation dynamics (NDVI, SAVI), and anthropogenic influence (built-up index, population density, built-up/impervious indices, distance to road, distance to river). Feature preprocessing included resampling, scaling, and inversion (where needed), and stratified random sampling 10 million labeled pixels (train: test = 8:2). AHP pairwise comparisons produced λmax ≈ 5.2, CI ≈ 0.05 and CR ≈ 0.05, indicating acceptable consistency. Model outputs comprised hydrological, morphometric, permeability, LULC, anthropogenic susceptibility maps and composite flood-susceptibility zonation maps from both AHP and NN workflows. Validation was performed using ROC-AUC and confusion-matrix analyses to assess predictive skill and class-level accuracy. Comparative analysis reveals that the NN approach improves predictive discrimination and spatial detail compared to the expert-driven AHP map, while AHP offers more interpretable insights of the factor weights. A Python-based application has been developed to automate flood-susceptibility mapping using dynamic precipitation and vegetation data, supporting long-term prediction and the development of mitigation measures. We discuss implications for operational flood risk mapping, targeted adaptation measures, and how combining knowledge-driven and data-driven methods can provide robust, actionable susceptibility maps for decision-makers.
How to cite: Naskar, S., Tripathi, L., Das, P., and Mukherjee, S.: Python-based Automated Tool for Flood Susceptibility Modelling in Kerala, a part of Ecologically Sensitive Western Ghats, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-778, https://doi.org/10.5194/egusphere-egu26-778, 2026.
Fire plays a central role in shaping ecosystem dynamics, biodiversity conservation, and the provision of ecosystem services; however, its role varies markedly among ecosystems. This is particularly critical in Brazil, a country that hosts globally important biomes and underpins vital functions such as climate regulation and the water–energy–food nexus. Recent observational studies indicate that Brazil is already undergoing shifts in the occurrence of extreme heat and drought events, and climate model simulations suggest that these trends will intensify in the future. However, the implications of these shifts for future fire risk patterns remain insufficiently explored, especially within an integrated risk framework that assesses how climate-driven hazard interacts with the heterogeneous resilience of ecosystems across the country.
Here, we ask how likely Brazilian ecosystems are to experience extreme fire danger conditions under future climates, and map how this hazard relates to both historical and projected patterns of landscape resilience. To this end, we perform a nationwide assessment of future fire danger using the Canadian Fire Weather Index (FWI) derived from daily CMIP6-based climate projections retrieved from the CLIMBra dataset, which was developed specifically for Brazil's climate conditions using an observational-based dataset. Employing a novel heatwave-based framework, we identify extreme fire danger events and characterize future changes in their intensity, duration, frequency, and spatial extent. Beyond this climate-based assessment, we contrast these changes from an ecosystem resilience perspective by integrating future fire danger projections with projections of landscape resilience. A Random Forest model, trained on the relationship between land cover and a map of landscape resilience classes, is applied to multiple future land-cover scenarios to estimate concurrent changes in both climate-driven fire danger and landscape resilience. This integrated approach allows us to pinpoint areas where high future fire danger overlaps with low landscape resilience.
Our results project up to approximately 30 additional compound hot-dry days per year by the end of the century across the country. These changes are expected to create a more challenging scenario for fire management, with a widespread increase in extreme fire danger across Brazil. For instance, the spatial extent and number of extreme fire danger days are projected to rise by approximately 69% and 42% on average, respectively, under intermediate-emission scenarios in the first half of the century. This integrated mapping enables us to reveal where projections of intensifying fire weather converge with those of future low landscape resilience, thereby highlighting priority regions and protected areas for targeted action. We believe that our framework will enable the integrated assessment of future fire danger and ecosystem vulnerability. These findings can guide national landscape and territorial policies by helping to prioritize actions in regions facing significant novel fire threats (transformative risk) or intensifying fire regimes (adaptive risk). They underscore the need for proactive fire management and conservation/restoration strategies that explicitly account for both climatic intensification and landscape resilience. Despite inherent uncertainties in climate and land-cover projections, our study provides a critical foundation for supporting more effective environmental planning and decision-making under a changing climate.
How to cite: Simões Ballarin, A., Simões Ballarin, C., S. M. Uchôa, J. G., Brandão, A., M. Mendiondo, E., A. A. Anache, J., Zaerpour, M., Hatami, S., R. Vargas Godoy, M., Wendland, E., S. Oliveira, P. T., and de Oliveira Roque, F.: Mapping Future Fire Danger Against Brazil's Landscape Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13539, https://doi.org/10.5194/egusphere-egu26-13539, 2026.
Severe droughts that occur alongside high temperatures and depleted soil moisture lead to compound dry–hot extremes (CDHE), having profound consequences for food security, water availability, human health and economic stability. This study uses the Blended Dry and Hot Events Index (BDHI) to identify CDHEs and to evaluate their characteristics over historical and future periods across the different climatic regions of the Indian subcontinent. The BDHI is constructed using combinations of multiple standardized indices, derived from precipitation, soil moisture and air temperature data. A novel framework is employed to identify compound events and to examine their evolution and propagation concurrently across spatial and temporal scales. The framework, identified events of varying degrees over the Indian subcontinent, including the mega-events of 2002 and 2009, and noted considerable increases in CDHEs during the recent decades. Climate change analysis using CMIP6 model projections reveal that CDHE events are projected to increased considerably under a 3oC warming world. The study improves understanding of how CDHE stresses may differentially affect regions across the Indian subcontinent, thereby supporting climate adaptation planning and risk management in climate-vulnerable areas.
How to cite: Ashokan, A. and Mitra, S.: Compound Dry and Hot extremes over the Indian subcontinent, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9442, https://doi.org/10.5194/egusphere-egu26-9442, 2026.
Compound climate extreme events has inflicted enormous damage since it amplifies their impacts on societies and ecosystems. However, it remains challenging to quantify its interaction and influences due to the vulnerability of drylands. We quantified the spatial and temporal pattern change, climate drivers of fire during 2001-2020 and investigated the interaction between the dry-hot conditions and fire events. The results show that fires mostly occurred in spring and autumn among three typical hotspots located in Southern of the East Europe and Central Asia, northeastern of East Asia, and Indian Peninsula. Fires in croplands accounted for 70.5% of all fire events in Eurasian drylands, with a limited size of 2.01±0.22 km2 in average. The most extensive fires were observed in grasslands, forests, shrublands, woody savannas, while the average fire burned area decreased by 0.30 km2/yr in the Eurasian dryland during 2001-2020, while dry-hot compounded fires burned area increase in 0.78 km2/yr. Dry-hot condition in early stage will increase the frequency and intensity of fire, mainly through affecting the fuel flammability and abundance. Our findings highlight the importance to understand the interrelated co-occurring climate extremes, and further efforts for monitoring and take action to reduce its threat.
How to cite: Yu, H.: Escalate Dry-hot compounded fires threaten Eurasian drylands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15235, https://doi.org/10.5194/egusphere-egu26-15235, 2026.
Unlike many natural hazards whose impacts are largely localized (e.g., volcanic eruptions), droughts can generate far-reaching spillover effects that extend well beyond their region of occurrence, producing socio-environmental consequences at continental and even global scales. Moreover, severe seasonal droughts may occur even in regions typically characterized by high levels of humidity, challenging conventional perceptions of hydroclimatic vulnerability. In particular, droughts affecting the Amazon Basin – the world’s largest watershed, characterized by high water availability and exceptional biodiversity – pose significant risks to the global climate system. Given the basin’s central role in regulating the global hydrological cycle, drought events may propagate beyond local riverine livelihoods, disrupting large-scale hydroclimatic processes and ecosystem functioning.
This study assesses whether drought records in the Amazon exhibit stationary behavior by combining the Standardized Precipitation Index (SPI), a widely used multi-timescale indicator of meteorological, with record-based stationarity tests designed to detect non-stationarity specifically in distribution tails. Monthly precipitation series from 272 rain gauge stations, each with at least 30 years of data, were transformed into SPI at a 6-month timescale. The analysis focuses on October SPI values, which integrate precipitation anomalies accumulated over the preceding dry season, allowing a consistent seasonal basis for comparison across the basin.
Stationarity is tested under the i.i.d. record hypothesis (record probability ) using non-parametric statistics proposed by Cebrián; Castillo-Mateo; Asín (2022), from the RecordTest package including the record-count -test and a weighted variant with linear weights, the likelihood-ratio test (LR), and the Foster–Stuart test, all applied to lower records representing drought extremes. Statistical significance is assessed using Monte Carlo resampling with 10,000 simulations.
The application of record-based stationarity tests indicates that drought records are predominantly stationary across the Amazon Basin. Out of the 272 analyzed stations, approximately 82% show no statistically significant departures from the i.i.d. record hypothesis in any of the applied tests. Strong and consistent evidence of non-stationarity is rare, with fewer than 3% of the stations showing simultaneous rejection across all tests. Spatially, the stations identified as non-stationary are broadly dispersed across the domain, indicating the absence of coherent regional clustering or directional gradients. These results support the hypothesis that, for the SPI-6 October series representing dry-season accumulation, the statistical behavior of drought extremes remains largely stationary at the basin scale, despite recent severe drought events reported in the literature. Overall, the proposed framework is distribution-free, tail-oriented, and computationally scalable, offering a robust methodological basis for monitoring changes in drought extremes and supporting early-warning systems and long-term water resources management in a changing Amazonian climate.
How to cite: Vale, I. and Fernandes, W.: Assessing Stationarity of Drought Records in the Amazon Basin using SPI and Record Theory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21663, https://doi.org/10.5194/egusphere-egu26-21663, 2026.
Droughts, heatwaves and wildfires represent an increasing risk for both human society and the environment. Despite Southern Europe being considered one of the most vulnerable regions, ongoing recent climate change has also negatively impacted the intensity, duration, and impacts of these extreme events in Central European countries. Therefore, here we present a newly compiled database of droughts, heatwaves, and wildfires in the Central European region spanning the 2000–2025 period. The database, primarily based on newspaper and online media reports, provides information about the occurrence and duration of more than 600 extreme events, the affected areas, their impacts, or societal responses. Based on newly available data, a severity index was calculated, and the severity of individual events was assessed according to several key characteristics. Moreover, several cross-border events negatively affecting Central European countries were detected, and their joint impacts were described. Lastly, the database was utilised to identify compound events of drought-wildfire and drought-heatwave. Despite the differences among individual countries (in terms of climate conditions, landscape, population, or GDP), similar impacts and societal responses to extreme events can be observed. Analysis of these compound events revealed several joint patterns (e.g., increased mortality rates, household water supply issues, rising food prices) as well as weaknesses on the international level (e.g., a lack of available firefighting equipment during intensive wildfire periods). The obtained results support the urgent need to develop a monitoring and forecasting tool for the occurrence of drought, heatwave, and wildfire events in the Central European region and implement it in national forecasting services to mitigate the negative impacts of these extreme events.
This research is supported by the OP JAK funding under Grant No. CZ.02.01.01/00/22_008/0004635 “Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (AdAgriF)”.
How to cite: Dolak, L., Rehor, J., Plackova, B., and Reznickova, L.: Central European droughts, heatwaves, and wildfires in the 21st century: compound events through the lens of media, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12735, https://doi.org/10.5194/egusphere-egu26-12735, 2026.
Environment degradation driven by changing climatic pattern and land deterioration poses significant challenges to semi-arid region by impacting water cycle dynamics, edaphic system and landscape resilience. The Chambal basin, which is environmentally fragile and climatically unstable, studies integrating climatic variability, soil erosion and land surface assessment are limited. While addressing the gap, this study aims to assess how climatic variability influences soil erosion dynamics and land surface stability in the Chambal basin. The Modified Mann-Kendall trends were used to assess climate variability, RUSLE-based modelling was used to estimate soil erosion, and the Bare Soil Index was used to map bare soil exposure for 2001, 2012, and 2024. The findings revealed that the Modified MK Z-values for rainfall ranging from −0.83 to 3.94, illustrated heterogeneous rainfall variability indicating both declining and increasing rainfall pockets, erratic rainfall zones. While minimum temperature shows substantial variability (Z = 2.70–4.08), particularly in the southwest and northeast, maximum temperature indicates a considerably increasing but spatially consistent trend with low variability (Z = 0.33–0.75). The estimates of soil erosion vary from 0 to 11.93 t ha⁻¹ yr⁻¹ with over 98% of the basin has very low erosion (<5 t ha⁻¹ yr⁻¹), but only a few steep, riparian, and dissected areas have slight to moderate erosion. The percentage of bare soil exposure decreased dramatically from 11.56% in 2001 to 9.53% in 2012 and then to 4.89% in 2024, showing better land cover conditions. The results indicate that despite the Chambal basin's increasing climatic stress, the terrain is still mostly stable with localized erosion vulnerability. These insights are important for planning for erosion reduction, managing watersheds responsively to climate change, and enhancing the basin's environmental resilience.
How to cite: Kumar, A.: Assessing Climate Variability and Landscape Vulnerability in the Chambal Basin, Central India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5359, https://doi.org/10.5194/egusphere-egu26-5359, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot 3
EGU26-16372 | ECS | Posters virtual | VPS12
Monitoring Heat Extremes over India Using Earth Observations and Data Driven ApproachesMon, 04 May, 14:15–14:18 (CEST) vPoster spot 3
Remote sensing enables spatially continuous and timely monitoring of hydro-climatological extremes by capturing key land–atmosphere variables across large regions, including for data-scarce areas. The rising frequency of heat extremes across India in recent decades underscores the need for effective monitoring, especially in data-scarce regions. This study evaluates the potential of monitoring heat extremes over the Indian sub-continent using satellite based observations and data driven approaches. For this, MODIS land surface temperature (LST) along with NDVI, land use/land cover and elevation information is used with traditional machine learning models namely Random Forest (RF) and XGBoost. Subsequently, the performance of the two ML models in estimating maximum temperatures across the Indian subcontinent was evaluated and validated using in situ temperature observations from the Indian Meteorological Department. Heat extremes were identified using both absolute temperature percentile thresholds and Standardized Temperature Index based heat stress categories. The performance of ML models was evaluated using station‑wise categorical verification metrics such as hit rate, false alarm ratio, and critical success index. Results show that the ML models exhibit higher accuracy in predicting mean temperatures compared to extremes, and XGBoost outperforms the RF model with lower RMSE and higher R². The results further reveals that ML model prediction skill exhibits considerable geographic variability across the sub-continent, with reduced performance over mountainous areas. This study demonstrates that integrating satellite-based data with machine learning provides an effective approach for monitoring heat extremes across the Indian subcontinent, particularly in data-scarce environments.
How to cite: Remesh Ancy, A. and Mitra, S.: Monitoring Heat Extremes over India Using Earth Observations and Data Driven Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16372, https://doi.org/10.5194/egusphere-egu26-16372, 2026.
EGU26-2205 | ECS | Posters virtual | VPS12
Spatio-temporal dynamics of meteorological and agricultural droughts: A multi-seasonal analysis of Vegetation Health and Climate Indices Using Google Earth EngineMon, 04 May, 14:18–14:21 (CEST) vPoster spot 3
A remote sensing index is often used to identify meteorological and agricultural droughts. Google Earth Engine analyzes CHIRPS data from 2015 to 2024 and Landsat-8/Sentinel-2 data from 2020 to 2024. The Vegetation Condition Index (VCI), Temperature Condition Index (TCI), composite Vegetation Health Index (VHI), and Standardized Precipitation Index (SPI) were calculated for four seasons using NDVI, EVI, LST, and CHIRPS precipitation data to explain specific spatiotemporal trends. Meteorological and agricultural droughts include precipitation deficits and vegetation stress. From the study, pre-monsoon analysis reveals significant intra-seasonal correlations between VCI and VHI (0.84) and TCI and VHI (0.75), indicating that moisture reserves and thermal stress influence vegetation health during arid periods. The VCI-VHI correlation (0.91) predominates during the monsoon season, indicating plant growth amidst substantial precipitation. As the season nears peak aridity, the correlations between post-monsoon and winter TCI-VHI increase (0.81 and 0.83), signifying thermal stress. A weak correlation (≤ 0.50) between SPI and vegetation indices across the seasons indicates that current precipitation does not succeed in reliably predicting vegetation stress, since vegetation depends on accumulated soil moisture rather than instantaneous rainfall. Vegetation indices exhibit substantial temporal persistence: Pre-monsoon VCI conditions are strong predictors of winter VCI (0.98), VHI forecasts winter VHI (0.92), and TCI predicts winter TCI (0.87), thereby enabling nine-month drought forecasting. The findings demonstrate that vegetation indices serve as drought indicators for seasonal water resource planning and agricultural vulnerability assessment in monsoon-affected nations.
How to cite: Thounaojam, L. and Oinam, B.: Spatio-temporal dynamics of meteorological and agricultural droughts: A multi-seasonal analysis of Vegetation Health and Climate Indices Using Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2205, https://doi.org/10.5194/egusphere-egu26-2205, 2026.
EGU26-7810 | Posters virtual | VPS12
Coupling Hydrodynamic Modeling with Machine Learning for Flood Risk Assessment in the Himalayan River BasinMon, 04 May, 14:21–14:24 (CEST) vPoster spot 3
The Himalayan river Basins frequently experience devastating floods. These river basins require accurate predictions and timely warnings to support effective flood risk management. While accurate prediction is crucial for saving lives, disaster managers often face a difficult trade-off between computational cost and warning lead time. High-fidelity physics-based models are precise but are computationally expensive for rapid decision-making, whereas low-fidelity geo-spatial models often lack accuracy in data-scarce regions. Our proposal is a framework to improve the flood inundation prediction in the Himalayan basin by combining the reliability of hydrodynamic modeling with the speed of machine learning.
In this study, we developed a 2D HEC-RAS model using a Rain-on-Grid approach to simulate the historical floods. We utilize the developed hydrodynamic model to generate a dataset of flood inundations that captures the basin's flow dynamics. These datasets will serve as the foundation for training advanced machine learning algorithms, including a Random Forest Regressor (RF) and a Convolutional Neural Network (CNN), to identify and predict flood patterns. Our model will integrate critical landscape features, including elevation, slope, land-use characteristics, the Normalized Difference Vegetation Index (NDVI), and satellite-derived rainfall data, to approximate the complex physical processes embedded in the hydrodynamic model. This allows the machine learning approach to achieve comparable predictive accuracy while reducing computational time. Through comprehensive validation against established benchmarks and real-world flood events, our research aims to deliver a scalable, computationally efficient, and highly accurate flood prediction tool. This framework has the potential to transform disaster preparedness and response capabilities in the Himalayan region by enabling timely, data-driven policy planning and proactive risk mitigation strategies.
How to cite: Duwal, S., Pradhan, P. M., Liu, D., and Bhattarai, Y.: Coupling Hydrodynamic Modeling with Machine Learning for Flood Risk Assessment in the Himalayan River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7810, https://doi.org/10.5194/egusphere-egu26-7810, 2026.
EGU26-2262 | ECS | Posters virtual | VPS12
Machine learning based prediction of long-term drought persistence over the Arabian PeninsulaMon, 04 May, 14:33–14:36 (CEST) vPoster spot 3
The Arabian Peninsula is among the most water-stressed regions globally, where limited precipitation, high evapotranspiration and rapid socio-economic development exacerbate vulnerability to drought. Emerging evidence indicates a significant intensification of drought conditions in recent decades, driven by climate variability and long-term warming trends posing serious challenges to water security, ecosystem stability and socio-economic resilience. Therefore, understanding historical drought dynamics, together with reliable drought prediction, is essential for strengthening drought monitoring and mitigation strategies in arid environments and for reducing drought-related risks. However, accurate drought prediction at fine resolution scale remains challenging due to the sparse distribution of meteorological stations. This study investigates the performance of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-, 6- and 12-month timescales using precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evapotranspiration derived from the TerraClimate dataset, respectively, for pixel-level drought assessment over the period 1992-2024. The historical dynamics were studied using Mann-Kendall trend, Sen’s slope and hotspot analysis. Random Forest (RF) was employed to assess its applicability for drought prediction in arid environments using satellite data, owing to its widespread adoption in global drought-prediction studies. The analysis demonstrates that the RF model exhibits high predictive performance under the studied conditions, with robust performance for SPEI-6 (R² = 0.92, RMSE = 0.12, NSE = 0.92) and satisfactory results for SPEI-12 (R² = 0.77, RMSE = 0.22, NSE = 0.77). These findings confirm enhanced predictability of seasonal to long-term drought variability across the Arabian Peninsula using a satellite-driven RF framework. The results showed the dominance of antecedent SPEI variables (>90%) indicating that cumulative moisture deficits and rising atmospheric evaporative demand primarily govern seasonal to long-term drought evolution over the Arabian Peninsula. In contrast, the consistently low contribution of SPI based indices (<3%) underscores the limited standalone role of precipitation variability in sustaining drought conditions in this arid region. Consistent with these predictive results, spatial trend analysis reveals pronounced heterogeneity in drought evolution across the Arabian Peninsula, with SPI exhibiting mixed and weak precipitation-driven signals, whereas SPEI shows widespread and statistically significant drying, particularly at 6- and 12-month timescales. This divergence further confirms that increasing evaporative demand and regional warming are the primary drivers of long-term drought intensification, reinforcing the dominant role of evapotranspiration processes identified by the machine-learning models. Therefore, the integration of satellite-derived pixel-level datasets with the RF model provides an effective framework for drought prediction across the Arabian Peninsula, offering valuable insights for water resource managers and policymakers to support the development of robust early warning systems and targeted mitigation strategies.
How to cite: Mushtaq, F. and Alhems, L. M.: Machine learning based prediction of long-term drought persistence over the Arabian Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2262, https://doi.org/10.5194/egusphere-egu26-2262, 2026.
