Geoethics provides a critical framework for understanding and guiding responsible human–Earth interactions, particularly within UNESCO Global Geoparks (UGGps), which function as living laboratories for geoconservation, geoeducation, and sustainable regional development. Despite growing recognition of geoethics within the geosciences, validated and standardized tools for assessing geoethical awareness—and for understanding how societal engagement with geoheritage varies across socioecological contexts—remain limited. This study addresses this gap by integrating the development, validation, and application of a Geoethical Awareness Scale (GAS) with a comprehensive mapping of residents’ geoethical perceptions and engagement profiles across nine Hellenic UGGps (Lesvos Island, Psiloritis, Chelmos–Vouraikos, Vikos–Aoos, Sitia, Grevena–Kozani, Kefalonia–Ithaca, Lavreotiki, and Meteora–Pyli).

Using an online questionnaire administered to 798 residents, we developed and psychometrically validated a 32-item GAS structured across 16 thematic axes. Exploratory and confirmatory factor analyses identified six robust dimensions of geoethical awareness: (1) geological heritage conservation and sustainable georesource use, (2) community engagement and collaborative governance, (3) sustainability through geoenvironmental education, (4) environmental challenges and risk adaptation, (5) sustainable geotourism, and (6) climate awareness and ecosystem resilience. These factors explained 60.12% of the total variance, with reliability indices ranging from acceptable to excellent. Structural equation modeling confirmed the internal validity and generalizability of the scale, establishing GAS as a reliable tool for assessing geoethical awareness in designated, protected, and managed socioecological systems.

Beyond scale validation, spatial and comparative analyses revealed generally high levels of geoethical awareness across Hellenic UGGps, alongside significant regional variability linked to local context, management visibility, and outreach practices. Sitia UGGp consistently exhibited the highest awareness levels, whereas Psiloritis and Lavreotiki UGGps showed lower scores in dimensions related to community engagement and sustainable geotourism, highlighting opportunities for targeted governance and educational interventions. Demographic and experiential factors—particularly age, education level, urban origin, prior visits to UGGps, and membership in environmental organizations—significantly influenced geoethical perceptions, underscoring the importance of experiential learning and direct engagement.

Cluster analysis further identified four distinct resident profiles: (1) highly engaged environmental stewards, (2) supportive but selective advocates, (3) moderately indifferent participants, and (4) disengaged or critical respondents. While nearly 70% of participants demonstrated strong or moderate alignment with geoethical principles and values, the remaining groups highlight the need for tailored education, participatory governance, and inclusive outreach strategies.

Overall, this integrated assessment demonstrates how validated measurement, spatial differentiation, and social profiling of geoethical awareness can inform adaptive governance and geoeducation strategies within UGGps. The findings support a transition from anthropocentric toward geocentric perspectives, positioning geoethical awareness as a key socioecological indicator for sustainability, resilience, and Earth-system stewardship in the Anthropocene.

How to cite: Koupatsiaris, A. A. and Drinia, H.: Measuring Geoethical Awareness and Engagement Profiles in UNESCO Global Geoparks: A Validated Scale and Evidence from Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1853, https://doi.org/10.5194/egusphere-egu26-1853, 2026.

Macaronesia (Azores, Madeira–Selvagens, Canary Islands, and Cape Verde) constitutes a natural laboratory for studying the interaction between intraplate volcanism, regional seismicity, and coastal hazards. This paper presents an integrated approach to assessing and communicating volcanic, seismic, and tsunami risks in the Canary Islands and their Macaronesian context, combining: (i) multiparametric monitoring data (IGN, INVOLCAN, CIVISA/IPMA), (ii) geophysical synthesis of the mantle structure beneath Macaronesia, and (iii) active learning experiences with university students. Case studies include the Tajogaite–Cumbre Vieja eruption (La Palma, 2021), with pre-eruptive seismic swarms, Strombolian emissions, and lava flows that affected infrastructure and necessitated evacuations; and the seismicity associated with volcanic systems and faults in the Canary Islands and Azores. The danger of tsunamis from volcanic landslides (prehistoric megatsunamis) and the UNESCO IOC NEAM early warning framework (with IPMA, INGV, CENALT, KOERI, NOA, PTWC, among others) are also discussed. Preliminary results show that integrating monitoring networks, propagation models, and educational activities based on real data improves risk understanding and community preparedness.

Goals

• To characterize the main geological hazards in the Canary Islands and Macaronesia (active volcanism, regional seismicity, and tsunami generation/propagation), integrating historical and instrumental data.
• Analyze the Tajogaite case (La Palma, 2021) as a recent example of risk management and civil response, highlighting lessons for monitoring and reconstruction.
• Exploring tsunami scenarios associated with volcanic flank collapses and early warning mechanisms in the NEAM region (capacities and limitations).
• Develop a program of academic activities with UNED students.

Methodology

• Data sources: IGN seismic catalogs (1585–2022), IPMA/CIVISA in the Azores, volcanic monitoring bulletins (IGN/INVOLCAN), and recent literature (Frontiers, MDPI).
• Analysis: review of eruptive chronologies and swarms (La Palma 2021), mapping of hypocenters and magnitudes, synthesis of mantle structure (tomography/seismicity), and evaluation of tsunami scenarios due to landslides.
• Alert framework: NEAMTWS (IOC ‑UNESCO), functions of NTWCs (IPMA, INGV, CENALT, KOERI, NOA) role of the PTWC/ITIC in interoperability.

Activities

• Seismic data practice (IGN/IPMA): download the catalog for the Canary Islands/Azores; filter by period, magnitude, and depth; visualization and heat map of hypocenters; discussion of active patterns (pre/post ‑eruptions).
• Analysis of the Tajogaite case (2021): timeline of previous seismicity, eruptive evolution, impacts on infrastructure and population; use of bulletins and technical articles (Frontiers/MDPI/IGN).
• Tsunami workshop: review of megatsunami deposits in the Canary Islands and basic wave attenuation modeling; coastal exposure maps; connection with Tsunami Ready (IOC).
• Macaronesian Geodynamics Seminar: Critical Reading of the Plume vs. Tectonics Debate; Implications for Risk; Relationship with Biodiversity and Human Occupation on Islands (Socio-environmental Context).

Results

• Technical skills: handling seismic catalogs and volcanic reports (IGN/INVOLCAN/IPMA/CIVISA), signal reading, and construction of hazard and exposure maps.
• Integrated risk understanding: connection between tsunami monitoring, geodynamics and warning in the NEAM system, with criteria for interpreting warnings and model limitations.
• Lessons from 2021 on La Palma: recognition of pre-eruptive indicators, evacuation logistics and reconstruction (slow cooling of lava flows, gases, geotechnical heterogeneity).
• Impact on resilience: improving community preparedness and a culture of prevention in island environments by connecting science, education, and citizens through replicable activities.

Bibliography

• IGN: Seismic Catalogue of the Canary Islands 1341–2022 (maps and relocations 1975–2000).
• IPMA/CIVISA: seismic networks and maps for Azores/Madeira.
• Geodynamic Macaronesia: Frontiers 2023 (mantle and plume review/alternatives).

How to cite: Delgado García, A.: Study of volcanic, seismic and tsunami risks in the Canary Islands and Macaronesia: integration of monitoring, models and university education for resilience., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2161, https://doi.org/10.5194/egusphere-egu26-2161, 2026.

In the field of science, there is a need for a more comprehensive assessment of the potential geopolitical impacts of the transition to renewable energy. The combination of risk-prone situations such as depleting mineral resources, increasing environmental problems, rising geopolitical risks, and regional and global conflict potentials with the energy transition’s intense demand for critical minerals has made uninterrupted access to critical minerals a highly sensitive issue for the countries’ national security. The importance of these minerals, which are also heavily used in sophisticated weapon systems and ammunition, is increasing day by day. Recent events such as trade disputes between countries, resource nationalism, the COVID-19 pandemic, the Russia-Ukraine War, US President Trump’s demand to “annex” Greenland and Canada for critical minerals deposits, the US-Ukraine Minerals Deal that ensures the control of US on the critical minerals deposits of Ukraine, and the US-China trade war depending on REE’s and critical minerals have made the risk of disruption to the global economy and security even more apparent. This situation has placed critical minerals in a sensitive position in the global political economy, necessitating a reassessment of the mutual economic and political relations between the major global economies of the 21st century and resource-rich developing countries. In this process, developed countries need to enter into a new economic structure with resource-rich countries in order to maintain their prosperity and national security. The ideological divisions of the Cold War era are giving way to new alliances based on economic and technological superiority. On the other hand, due to the vital importance of critical minerals, especially for leading economic and military powers such as the US, EU, China, Russia, Japan, and India, any disruptions these countries may experience in the access of critical minerals or mutual interventions between parties in resource-rich countries carry the risk of large-scale conflict worldwide.

Protectionist, control-oriented, import-substitutionist, and divisive policies are those that most countries have implemented or have been forced to contend with regarding “critical minerals.” This situation, which has led to a resurgence of resource nationalism worldwide, also signals the beginning of a new “mercantilist” era from a global perspective. These policies, reflect the fundamental characteristics of neo-mercantilism, have formed the main axis of many countries’ “critical minerals” strategies, especially since 2016. Moreover, the United States, one of the most important advocates of economic liberalism, is leading this new era globally. The US’s national interests are driving the country to pursue neomercantilist strategies regarding critical minerals. These strategies leave other countries with no choice but to either align with the policies they contain or respond to the US with similar counter-policies. In today’s climate of international insecurity, the implementation of neo-mercantilist policies on critical minerals is becoming a necessity rather than a choice for countries. Developments in the coming period will determine whether critical minerals will be a vital aid for the clean energy transition or a bottleneck for world politics and economics due to access risks. Geologists and policymakers will need to work together on this issue.

How to cite: Tuna, İ. K.: The United States' critical minerals security policies in the context of neomercantilism and their impact on global geological studies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7904, https://doi.org/10.5194/egusphere-egu26-7904, 2026.

Addressing contemporary environmental challenges, such as climate change, land degradation, and ecosystem transformation, requires not only scientific knowledge but also new ways of communicating complexity, uncertainty, and responsibility. Art–science collaborations are increasingly recognised as effective tools to engage diverse audiences emotionally and cognitively, fostering environmental awareness and sustainable mindsets. This contribution presents The Kaleidoscopic Lens of Art: Imaging the Environment, an interdisciplinary educational project that bridges Earth Observation science and artistic practice to promote environmental literacy and meaningful public engagement.

Developed within Italy’s PCTO (Pathways for Transversal Skills and Career Guidance) framework, the project involved third-year high school italian students working in close collaboration with researchers from the Institute of Methodologies for Environmental Analysis (IMAA) of the National Research Council (CNR) of Italy. Students analysed authentic satellite imagery and geospatial datasets related to environmental processes and human–environment interactions, including landscape change and urban–natural dynamics. Scientific data were then reinterpreted through multiple artistic languages transforming analytical evidence into visual narratives.

The educational pathway followed a blended methodology combining classroom instruction, field activities, laboratory sessions, and creative workshops. This iterative process guided students from scientific observation and data analysis to conceptual re-elaboration and artistic production. The resulting works—mixed-media paintings, architectural reinterpretations of landscapes, and digitally manipulated satellite imagery—functioned as hybrid artefacts, simultaneously conveying scientific content and eliciting emotional and ethical reflection on sustainability.

The collective exhibition COSMOS CREATIVO: Artistic Transformations of Earth from Space, presented during the European Researchers’ Night (2024–2025), demonstrated the potential of art–science collaboration to act as a powerful form of science communication. By translating complex environmental data into accessible and emotionally resonant forms, the exhibition fostered dialogue between students, scientists, and the wider public, highlighting the shared responsibility of scientific and artistic communities in communicating planetary boundaries and ecosystem fragility.

Aligned with the EU Key Competences for Lifelong Learning and SDG 4, the project offers a replicable model for integrating STEAM education, environmental awareness, and civic engagement. By positioning scientific data as both analytical tools and sources of aesthetic inspiration, The Kaleidoscopic Lens of Art illustrates how art–science collaborations can build bridges between disciplines, enhance public understanding of Earth system science, and support the cultural imagination needed to envision sustainable futures.

Keywords: interdisciplinary education, PCTO, STEM and art integration, environmental awareness, satellite imagery, geospatial data, creative learning, high school education 

How to cite: Coluzzi, R., Imbrenda, V., Fanti, L., Traino, W., de Carlo, M., Camardelli, V., Smilzo, A., Cordisco, M., Limone, G., Amato, L., Calamita, G., Ciancia, E., Gandolfi, I., Perrone, A., Salvatore, L., Pilogallo, A., Santopietro, L., and Giampaolo, V.: The Kaleidoscopic Lens of Art: Art–Science Collaborations for Environmental Literacy and Sustainable Futures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12137, https://doi.org/10.5194/egusphere-egu26-12137, 2026.

The capabilities and widespread availability of generative AI are potentially changing ways of working and studying. However, there are a lot of pitfalls and ethical questions to complicate use. Postgraduate taught (PGT) students typically study at the University of Glasgow for 12 months. They come from a wide range of institutions, where rigorous academic citation of information may not have been previously covered. Students have also been falling into the trap of AI hallucinations and losing academic integrity as they don’t realise generative AI can’t be relied upon. With this in mind a workshop was designed and run in Autumn 2025 to discuss finding reliable sources of information, how to manage/store information you find during research (including citation information), how to cite information correctly, and why this is important. The workshop included an explanation of Generative AI and student discussions on generative AI use and ethics. This work will discuss the workshop and reflect on what went well and what could be further improved. We need students to have a solid understanding of what generative AI can and can’t do, and the ethical background to decide if and when to use it, during their studies and in their future careers.

How to cite: Petrie, E.: Integrating Generative AI into good academic practice: a workshop for PGT students on sourcing, managing and citing information and Generative AI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13848, https://doi.org/10.5194/egusphere-egu26-13848, 2026.

• The Graveyard Orbit: The region located several hundred kilometres above the geostationary belt, where defunct satellites are "parked" in perpetuity to prevent interference with operational assets.
• The Spacecraft Cemetery: The South Pacific Ocean Uninhabited Area near Point Nemo, where controlled reentries are targeted to sink decommissioned hardware into the deep sea.

How to cite: Desai, R.: Ethics of Repair: From the Earth’s High Orbits to its High Seas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13997, https://doi.org/10.5194/egusphere-egu26-13997, 2026.

Early-career researchers play a central role in advancing geoscience, yet their research trajectories are shaped not only by scientific challenges, but also by structural conditions that influence access, recognition, and sustainability. While equal-opportunity frameworks aim to ensure fairness through consistent treatment, they may still produce uneven outcomes when differences in experience, workloads, contribution, and risk exposure are not fully recognised. These conditions are particularly consequential for early-career researchers navigating mobility and temporary contracts. The uneven distribution of invisible academic labour further shapes who remains visible and who is able to sustain a research career.

Framing these dynamics as shared research challenges allows early-career researchers to learn from one another’s experiences, reduce impostor syndrome, and make visible the human side of scientific work. Equity is a shared responsibility: institutions and organisations can improve transparency around structural conditions, while research communities and scientific societies can reduce inequities by shaping participation, recognition, and visibility within existing constraints. This includes flexible participation models, transparent evaluation practices, and greater recognition of non-visible contributions that support more equitable and inclusive research environments. By treating equality and fairness as shared problem-solving spaces rather than individual burdens, this perspective aims to support more inclusive and sustainable pathways for early-career researchers in geoscience.

How to cite: Tang, A. C. I.: Beyond Equality: Early-Career Perspectives on Equity in Geoscience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16189, https://doi.org/10.5194/egusphere-egu26-16189, 2026.

Underground mining supplies essential metals that are indispensable for the energy transition and digital technologies. In this context, mountain landscapes around the globe are profoundly transformed, not only at the surface, but also underground on a large scale. Hidden subsurface landscapes develop progressively below the earth surface. A better understanding of the interconnections between subterranean metal extraction, landscape change, energy use and metal consumption is essential for future visions of sustainable resource management. In the current study, the Harz Mountains in Northern Germany serves as a case study to analyze the development of historical mining landscapes in a spatio-temporal and interdisciplinary context including especially geological, geomorphological, hydrological and cultural aspects. The natural landforms has been transformed significantly by ore extraction forming a new hybrid mining landsape.

The project on mining landscapes is carried out at the UNESCO-World heritage site Samson Mine in St. Andreasberg, which was one of the deepest mines in the 19^th century and shows an almost 400-year mining history of silver. The research results are communicated to a wider public in the museum. In this regard the study is embedded in geographical environmental education (GEE), in which global learning and the Sustainable Development Goals (SDGs) form central components. Historical mining serves as a learning platform to reflect on current challenges of global metal extraction and energy use.

Historical perspectives reveal how mining landscapes have been shaped over centuries, how the rate of extraction increased with technical and social innovations or stagnated due to various crises, and they may show, most important, the cultural drivers of ore extraction. In this regard a geocultural concept for science communication has been developed for the Samson Mining Museum integrating digital forms of geovizualisations such as Structure-from-Motion (SfM), GIS-Applications and Augmented Reality (AR). They have the potential to make the underground visible and at the same time to show landscape changes over longer time periods. The fundamental starting element of the educational concept is the staff-guided mine tour through the original historical mine as an authentic and emotional experience. The didactic progression consists of the real-life experience in the mine, followed by locating, capturing, understanding, contextualizing, and reflecting mine-related topics in a local to global context through hybrid digital media in the museum to enhance geographical core competences, and finally transferring the acquired knowledge and interconnections to the real landscape – from Analog via Digital to Real-World explorations (ADR-Concept). The project is supported by fundings schemes on cultural heritage in Lower Saxony by the Ministry of Science and Culture of Lower Saxony (zukunft.niederdsachsen.de).

How to cite: Iturrizaga, L.: Geocultural Education and Digital Geovisualizations of Mountain Mining Landscapes: From Analog via Digital to Real-World explorations – a conceptional approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16962, https://doi.org/10.5194/egusphere-egu26-16962, 2026.

A key reason for the widespread use of peat-based growth media in horticulture is their reliable nutrient availability when supplemented with fertilisers. However, due to environmental concerns over continued peat-extraction and use, peat-alternatives (e.g., coir, wood fibre, composted bark, biochar) are increasingly being used commercially. These alternative media often blend multiple materials, making it crucial to understand elemental composition and nutrient interactions between components. This study evaluates whether benchtop Energy Dispersive X-ray Fluorescence (EDXRF) can provide a rapid method for determining the elemental composition of peat-alternative components.

Representative growing media components (peat, coir, wood fibre, composted bark, biochar, horticultural lime, perlite, slow-release fertilisers, and trace-element fertiliser) were blended in different ratios to generate industry-representative mixes. Individual components and prepared mixes were dried and milled to ≤80 μm. An industry-representative mix (QC-50: 50% peat, 30% wood fibre, 10% composted bark, 10% coir, with fertiliser and lime additions) and 100% peat were analysed by EDXRF (Rigaku NEX-CG) for P, K, Mg, Ca, S, Fe, Mn, Zn, Cu and Mo, and compared against ICP-OES reference measurements. The instrument’s fundamental parameters (FP) method using a plant-based organic materials library showed large discrepancies relative to ICP-OES (relative differences: 268–390 084%) for most elements in both QC-50 and peat, with the exception of Ca in QC-50 (11%). These results confirm that the FP approach combined with loose-powder preparation is unsuitable for accurate elemental analysis of organic growing media.

An empirical calibration was subsequently developed using 18 matrix-matched standards (CRMs, in-house growing media and individual component standards). Matrix matching is challenging because mixes are mostly organic by volume, yet variable inorganic amendments (e.g., lime, fertilisers, and sometimes perlite) can strongly influence XRF absorption/enhancement effects. Calibration performance was optimised iteratively using QC-50 as the validation sample, until relative differences were <15% for all elements. When applied to 100% peat, agreement with ICP-OES results improved substantially for some macro-elements (e.g. Mg 10%, Ca 1%, S 19%) but remained poor for most trace elements (28–96%), demonstrating limited transferability of this calibration method across different elements and matrices tested.

Overall, these results demonstrate that loose powder preparation does not provide sufficiently robust accuracy for EDXRF analysis of organic growing media even with meticulous empirical matrix-matched calibration. We are therefore developing a pressed pellet method using a low-cost wax binder to improve sample homogeneity (packing density) and calibration transferability. Twenty unknown mixes will be analysed using both loose powder and pressed-pellet calibrations, and agreement with reference data (ICP-OES) will confirm method validation, supporting the development of EDXRF as a novel approach for growing media analysis.

How to cite: De Silva, T., Tupaz, C., Croffie, M., Daly, K., Gaffney, M., Stock, M., and Corbett, E.: Developing Matrix-Matched Empirical Calibrations for EDXRF Analysis of Peat-Alternative Growth Media, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20122, https://doi.org/10.5194/egusphere-egu26-20122, 2026.

One of the most life-changing experiences for scientists is when real-world events challenge theoretical knowledge and standard models in the literature. When facing such circumstances, scientists, instead of feeling disappointment and discouragement, must seize the opportunity to expand their knowledge and adjust for flaws in their initial assumptions, as academic integrity is rooted in fundamental scientific values, such as honesty and fairness. Considering this, and after decades of post-graduate, PhD, and post-doctoral studies in the fields of Hydraulics, Hydrology, and Stochastics, we witnessed a series of unprecedentedly extreme events in academia involving the official regulations for tenured professorships in Greece. These regulations mandate the formation of an Academic Board for candidate evaluation by randomly drawing lots from a pool of professors whose scientific fields are relevant to the subject of the position. This is intended to avoid "pre-designed" boards (i.e., those formed by blocking certain experts —often highly qualified ones— from the draw and favouring others —often poorly qualified ones— who may have scientific and financial conflicts of interest regarding specific candidates), which can cause severe long-term degradation of the educational system. Unexpectedly, even after multiple repetitions and strong reassurance regarding the validity of the above procedure, the probability of the outcomes (specifically, the consistent drawing of a handful group of lots) reached the extreme order of millionths. In this presentation, we will discuss these experiences with extremes and whether the concepts of statistical significance and reliability indices in scientific literature and academic regulations should be revisited.

How to cite: Dimitriadis, P.: Extreme Academic Tales for Recorded Extreme Tails in Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21525, https://doi.org/10.5194/egusphere-egu26-21525, 2026.

The Amazon Basin (AB) is experiencing an intensification of hydroclimatic extremes, with droughts becoming more frequent, widespread, longer, and severe in the 21st-century. While precipitation deficits have historically been the primary driver of these events, the role of rising air temperatures and the consequent increase of atmospheric evaporative demand (AED) remains poorly quantified. Understanding the relative contributions of these factors is crucial for assessing AB resilience and potential tipping points under ongoing global warming. Here, we analyzed drought evolution across the AB over 45 years (1980–2024) using the Standardized Precipitation-Evapotranspiration Index (SPEI) derived from ERA5-Land reanalysis data. To isolate the contribution of atmospheric evaporative demand (CAED) to drought severity, we compared the standard SPEI with a modified SPEI version based on constant climatological AED.

Furthermore, we applied a rarity index to systematically rank drought events by intensity and spatial extent, enabling a standardized comparison of the exceptional 2023/24 event with historical benchmarks. Our analysis reveals that the 2023/24 drought (AD-23/24) was the most extreme event on record, affecting over 88% of the basin’s area and having a magnitude four times that of the average of the previous top-5 droughts. Notably, the recurrence of high-ranking drought years since 2020 underscores a persistence of extreme conditions in the 2020s. Crucially, the CAED analysis uncovers a distinct temporal regime shift occurring after 2005. While earlier droughts were primarily precipitation-driven, the post-2005 era is characterized by a predominantly evapotranspiration-driven regime, in which climate change-induced warming significantly amplifies drought intensity through increased AED. This intensification is further linked to sea surface temperature anomalies in the Tropical Indian, Tropical Pacific, and North Atlantic oceans. These findings demonstrate that the AB has entered a new hydroclimatic phase in which temperature-driven AED is overtaking precipitation deficits as the primary driver of exceptional drought events. This shift suggests that warming is likely exacerbating drought severity, posing unprecedented challenges for ecosystem stability and water security in the region.

How to cite: Albuquerque, R., M. dos Santos, D., F. V. V. Miranda, V., F. Peres, L., M. Trigo, R., M. B. Nunes, A., L. R. Liberato, M., M. Gouveia, C., and Libonati, R.: Temperature-driven shift intensifies 21st-century Amazon droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-557, https://doi.org/10.5194/egusphere-egu26-557, 2026.

This study provides the first high-resolution polarimetric radar observations of Overshooting Convective Storms (OCS) over the Western Ghats (WG), India using the newly installed SSPA-based X-band Radar at HACPL, Mahabaleshwar. Three post-monsoon OCS events (15, 23, and 24 October 2025) were analysed using PPI, RHI, CFAD products and ERA5 Atmospheric fields. All storms exhibited strong vertical growth, with echo-top heights of 17.6–19.8 km (15 Oct), 16.5 km (23 Oct), and 17.8 km (24 Oct), and peak reflectivity values of 59.6, 63.3, and 52.1 dBZ, respectively. Notably, significant reflectivity (>40 dBZ) persisted above 16 km, confirming deep overshooting intrusions. Polarimetric signatures showed clear mixed-phase and ice-growth processes, including KDP up to 3–4° km⁻¹, enhanced ZDR in the rainy regions, and reduced ρhv (0.92–0.96) within convective cores, indicating liquid water content, riming, and graupel/hail production. ERA5 diagnostics revealed favorable conditions for deep convection, with strong mid-tropospheric ascent (–0.6 to –0.8 Pa s⁻¹), high moisture, and pronounced convergence over the WG. These results demonstrate intense post-monsoon overshooting convection in complex terrain and highlight the capability of X-band Polarimetric radar to reveal the storm microphysics and vertical structure in an orographically challenging environment.

How to cite: Devisetty, H., Uriya Veerendra, M. K., Tyagi, B., Das, S. K., Chakravarty, K., Survase, C., and Burrala, P. K.: Radar Polarimetry to Characterize Overshooting Convection in the Western Ghats of India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-676, https://doi.org/10.5194/egusphere-egu26-676, 2026.

This study is motivated by the observation of a unique intraseasonal power in the upper tropospheric equatorial meridional winds over the Western Hemisphere during boreal winter. The presence of intraseasonal power at both westward and eastward wavenumbers is unusual and intriguing, as the dominant tropical modes of intraseasonal variability typically exhibit little amplitude in equatorial meridional winds and are instead characterized by strong zonal wind perturbations. Furthermore, the intraseasonal disturbances are confined to the upper troposphere.  The spatial structure and dynamical characteristics of these disturbances are consistent with those of mixed Rossby-gravity waves (MRGWs), indicating the presence of intraseasonal MRGWs in the atmosphere. A systematic relationship between the location and amplitude of the intraseasonal MRGWs and the upper-tropospheric westerlies suggests that background circulation is fundamental to their existence. This hypothesis is investigated through a set of diagnostic analyses guided by the dispersion relation of a linear shallow water model incorporating a homogeneous background flow. The results not only explain the emergence of intraseasonal MRGWs but also the overall distribution of meridional wind power throughout the troposphere. The strength and direction of the background flow govern the observed spatial and temporal characteristics of MRGWs via Doppler shifting of intrinsic MRGWs. Consequently, the spectral power distribution of equatorial meridional wind perturbations across pressure levels represents a composite of MRGWs that have been Doppler-shifted by a range of background flow regimes.

How to cite: Mehak, M. and Ettammal, S.: Can Background Circulation Facilitate Intraseasonal Mixed Rossby-Gravity Waves over theCentral-Eastern Pacific?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-691, https://doi.org/10.5194/egusphere-egu26-691, 2026.

Western Disturbances (WD) are key atmospheric phenomena over northern India, Pakistan, and the Western Himalayas, especially during winter months (December to February). In recent years, increasing variability in these systems has been observed across all seasons, notably pre-monsoonal months (March to May), although thorough investigation remains underexplored. The study evaluates the shifting behaviour and structure in WDs across two climatologically distinct periods – 1950 to 1976 and 1977 to 2022, corresponding to the well-documented 1976-1977 climate shift. In this study, vorticity-based WD track data, coupled with the ERA5 reanalysis dataset, have been utilised to analyse the shift. Behavioural changes are quantified through frequency trends, maximum vorticity distribution and mean track, while structural evolution is examined through composite vertical profiles of key atmospheric variables.  The study unravels notable increase in WD frequency during the pre-monsoon season in recent decades, accompanied by a westward shift in WD origins and longer track durations, thereby enhancing the potential for moisture transport. Furthermore, substantial strengthening of upper-level zonal winds, intensified mid-tropospheric convection, and atmospheric moisture availability have been observed through structural analysis. Such transformations indicate a transition of WD towards hybrid systems with enhanced convective features, thereby elevating the potential for extreme precipitation events during the pre-monsoon period. This improved understanding of the evolving WD dynamics is critical for hydrological planning, climate action, strategies and disaster preparedness in the highly vulnerable Himalayan and adjoining regions.

How to cite: Mitra, S., Sardana, D., and Agarwal, A.: Evolving Characteristics in Western Disturbances over the Hindu Kush Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-731, https://doi.org/10.5194/egusphere-egu26-731, 2026.

This work investigates a prominent dust event that occurred over Nainital, Uttarakhand, during 14-18 May 2025. Continuous micro pulse lidar (MPL) observations provided evidence of significant aerosol enhancement during the event. Distinct elevated aerosol layers were observed between 1 and 2.5 km above ground level, where backscatter coefficients increased to approximately 5×10⁻³ km⁻¹ sr⁻¹ and extinction values ranged between 0.8 and 1.2 km⁻¹. The persistence of these layers indicated long-range transport rather than local sources. Satellite-based aerosol optical depth (AOD) data from MODIS confirmed these enhancements, showing values doubling from 1.1-1.5 to above 2.2 during the peak dust intrusion. Meteorological observations documented elevated daytime temperatures between 20.1 ± 1.3 and 26.8 ± 1.6 °C and a marked reduction of relative humidity to below 50%, suppressing aerosol scavenging. Wind speeds intensified, with nocturnal maxima up to 5.6 ± 1.1 m/s and predominantly westerly to northwest directions (230°- 265°), favoring dust transport from western source regions. Synoptic-scale 850 mb wind analyses further corroborated persistent strong westerlies guiding mineral aerosols from the Thar Desert and Indo-Gangetic plains into the Himalayan foothills. The results highlight the importance of integrating lidar measurements with meteorological and reanalysis datasets to capture both vertical and horizontal characteristics of dust intrusions in mountainous regions. 

How to cite: Singh, S. K., Singh, N., Rawat, V., Chauhan, M., and Debnath, S.: Variation of atmospheric properties during a dust episode over central Himalayan region using Lidar observation and auxiliary data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1290, https://doi.org/10.5194/egusphere-egu26-1290, 2026.

With the intensification of global warming, deep convective system(DCS) precipitation over the Tibetan Plateau(TP) has become increasingly frequent, which plays a vital role in regulating the regional hydrological cycle. Previous studies have focused on instantaneous convective activity, little elaborating on the evolutionary processes and rainfall of DCSs throughout their whole lifecycle. Here, based on the continuous observations from the FY-4A geostationary satellite, this study investigates the characteristics and evolution of DCSs over TP from 2022 to 2023 through our previous full-lifecycle tracking algorithm from initiation to dissipation. Furthermore, the effects of key meteorological factors on DCSs evolution are revealed.

Results indicate that DCSs are mostly short-lived (3–6 h lifecycle), and more than 85% of convective precipitation occurs during summer from June to August. DCSs concentrate in the central-eastern TP, with an occurrence probability exceeding 12% in summer. Additionally, the area and rainfall rate of DCSs typically reach their peaks at the middle stage of the lifecycle. After the dissipation of the convective core, the persistence time of cirrus can reach 5%–28% of the core’s lifecycle. Controlled variable analysis reveals that convective available potential energy (CAPE) and precipitable water (PW) synergistically regulate the development of convective systems: under conditions of high CAPE (500-10³ J kg^-1) and high PW (>50 mm), the area of cores expands to the largest extend. However, the maximum lifecycle and peak precipitation of DCSs occur under conditions of moderate wind shear (5-10 m s^-1).

This study explores the full-lifecycle evolutionary patterns of DCS over the TP and the regulatory effects of meteorological conditions over TP, laying a theoretical foundation for future research on regional precipitation and climate change in the region.

How to cite: Guo, C., Yin, J., Pan, Z., Zang, L., and Mao, F.: Observed Lifecycle of Convective Precipitation over Tibetan Plateau Based on the FY-4A Geostationary Satellite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1839, https://doi.org/10.5194/egusphere-egu26-1839, 2026.

How to cite: Liu, Z.:  Global-scale compound Droughts and heatwaves: inland/coastal type grouping, diversity of temperature extremes, and dynamically-based Interpretable reconstruction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2053, https://doi.org/10.5194/egusphere-egu26-2053, 2026.

Carbon dioxide removal (CDR) is critical to net-zero pathways achieving the Paris Agreement 1.5°C target, yet its effectiveness in reducing humid heat stress risks remain uncertain. Here we examine the hysteresis and reversibility of humid heat stress in China under CDR scenarios. Humid heat responds asymmetrically during warming and CO₂ removal especially in eastern and southern China, producing a hysteretic and partially reversible trajectory. This results from unequal adjustments of temperature and relative humidity, which constrain heat-stress recovery even as global temperatures decline. Moist static energy analysis indicates suppressed vertical energy export over eastern China and enhanced transport of warm moisture from tropical oceans, sustaining humid heat during CO₂ removal. Consequently, severe humid heat stress persists, with over 6.4 billion people affected, more than 66% of which is due to hysteresis. These findings highlight enduring heat-related risks and the urgent need for adaptation alongside mitigation.

How to cite: Ma, Q.: Committed and Irreversible Humid Heat Stress Risk in China Despite Carbon Dioxide Removal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2384, https://doi.org/10.5194/egusphere-egu26-2384, 2026.

African Easterly Waves (AEWs) are a dominant synoptic-scale feature of the tropical atmosphere, widely recognized for their role as precursors of tropical cyclones and for modulating summertime rainfall over the Atlantic basin and adjacent regions. However, their potential influence on the transport of Saharan dust across the Atlantic and its impacts on air quality has received comparatively less attention. In this study, we investigate the role of AEWs in modulating Saharan dust transport and its relationship with high PM2.5 concentration episodes over the Yucatán Peninsula. Using reanalysis data, we document a pronounced seasonal cycle in dust transport, with maximum concentrations during boreal summer (June–August), coinciding with the peak activity of AEWs. Spectral analysis reveals a significant contribution at periods of 4–9 days, consistent with the characteristic timescales of AEWs. To quantify their impact on air quality, intense dust events associated with AEWs were identified based on anomalies exceeding one standard deviation and compared with episodes of poor air quality driven by particulate matter. Our results indicate that AEWs account for approximately 26–31% of PM2.5 pollution episodes linked to dust over the Yucatán Peninsula, with event durations ranging from 1 to 8 days. These findings highlight the important role of AEWs in shaping the synoptic-scale variability of aerosol transport and surface air quality in the Yucatán Peninsula and southern Mexico, underscoring their relevance beyond tropical cyclogenesis and precipitation, particularly during the boreal summer.

How to cite: Jaramillo-Moreno, A. and Vázquez-Jiménez, C. S.: African Easterly Waves as Drivers of Saharan Dust Transport and PM2.5 Extremes in the Intra-Americas Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4071, https://doi.org/10.5194/egusphere-egu26-4071, 2026.

The Madden–Julian Oscillation (MJO) is the dominant mode of intraseasonal variability (30–90 days) in the tropical atmosphere, characterized by eastward-propagating convective and circulation anomalies that strongly modulate tropical and regional climate. Through its large-scale dynamical perturbations, the MJO influences moisture transport, low-level circulation, and precipitation over regions far removed from its primary convective center. However, its role in regulating low-level moisture fluxes over northwestern South America has received comparatively limited attention. In this study, we investigate the influence of the MJO on moisture transport toward Colombia, with particular emphasis on its modulation of the Chocó Low-Level Jet. Using MERRA-2 reanalysis, we characterize intraseasonal variability in low-level moisture advection and wind fields associated with different MJO phases defined by the Real-time Multivariate MJO (RMM) index. The analysis examines changes in low-level moisture transport, wind intensity, and large-scale convergence associated with the zonal displacement of the MJO convective envelope. Results show that the strength of the Chocó Jet depends strongly on the longitudinal position of the MJO convective center. Certain MJO phases enhance moisture transport from the eastern Pacific toward Colombia, favoring orographic ascent along the Andes and organized convection over the Colombian Pacific region, while other phases are associated with weaker moisture fluxes and reduced convergence. These findings highlight the role of the MJO in regulating intraseasonal moisture transport and low-level circulation over northwestern South America.

How to cite: Toro-Arenas, J., Jaramillo-Moreno, A., Carvajal-Serna, L. F., and Mesa-Sanchez, Ó. J.: Intraseasonal Modulation of the Chocó Low-Level Jet by the Madden–Julian Oscillation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4128, https://doi.org/10.5194/egusphere-egu26-4128, 2026.

The Indian Summer Monsoon Rainfall (ISMR), occurring from the month of June to September, is characterized by intraseasonal variability in the form of active and break spells. Monsoon breaks are periods of sparse to no rainfall, marked by positive outgoing longwave radiation anomalies, above-normal pressures, and clear-sky conditions. These monsoon breaks can have socioeconomic impacts due to their effect on crop growth stages, irrigation planning, and water management. This study aims to investigate the synoptic-scale systems responsible for the onset and sustenance of monsoon breaks over the Western Ghats and other parts of India to improve future predictability of the same. Rainfall data from 1901 to 2025 is used to identify break spells and classify them into short, moderate, and long-duration events. Subsequently, decadal rainfall composites are constructed. These composites reveal patterns of rainfall suppression and enhancement over the Indian subcontinent, equatorial Indian Ocean and West Pacific. Although the spatial structure of rainfall anomalies remains consistent, slight decadal variabilities are observed. Composite analyses of outgoing longwave radiation, and upper and lower tropospheric winds are used to diagnose the synoptic features associated with monsoon breaks. Case studies of recent drought years, 2002 and 2015, highlight the role of upper tropospheric anticyclones, northward displacement of the monsoon trough, and dry air intrusion from West Asia in sustaining and prolonging the breaks, confirming previous studies. The influence of large scale climate oscillations such as ENSO, EQUINOO, and the Boreal Summer Intraseasonal Oscillation (BSISO) on monsoon break frequency and duration is investigated using statistical and machine learning tools, with the aim of informing the development of improved predictive frameworks for monsoon breaks.

How to cite: Aboobaker, J. and Singh, Dr. S.: Synoptic-Scale Mechanisms And Climate Oscillation Influences On The Monsoon Breaks Of The Indian Summer Monsoon System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4971, https://doi.org/10.5194/egusphere-egu26-4971, 2026.

This study develops a novel framework within the Weather Research and Forecast Model for modeling aerosol-cloud-lightning interactions. The framework explicitly represents aerosol-cloud interactions by prescribing aerosols with two configurations: an idealized setup, where both cloud condensation nuclei (CCN) and ice nucleating particles (IN) are assumed to have a single chemical composition and spatially uniform distributions; and a quasi-realistic configuration, with multi-species aerosols assigned spatially varying distributions, where hygroscopic components act as CCN, dust particles act as IN, and all aerosol species influence radiative transfer. Cloud microphysics is coupled with detailed charge separation and discharge processes to enable the lightning simulation. The framework is evaluated using two thunderstorms in Guangdong, China. For an isolated storm, the model successfully reproduces the observed tripolar charge structure (positive–negative–positive), demonstrating its capability in simulating cloud electrification. For a frontal storm, it captures well the observed precipitation and lightning, and shows that increasing CCN suppresses the rainfall while enhancing the lightning. Higher CCN concentrations produce more numerous but smaller cloud droplets, which suppresses the coalescence into rain droplets, allows a greater number of droplets to loft into the upper troposphere, and forms more but smaller cloud ice particles. This boosts graupel–ice collisions, intensifies non-inductive charging, strengthens the upper positive charge and the vertical electric-field gradient, ultimately increasing the lightning frequency. In contrast, no significant aerosol-induced invigoration of updrafts is observed. These results highlight the dominant role of aerosol microphysical effects over dynamical invigoration in modulating thunderstorm electrification and lightning activity.

How to cite: Wang, W., Chen, G., and Zhang, Y.: A framework for modeling aerosol-cloud-lightning interactions: Validation of charge structure and aerosol effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6493, https://doi.org/10.5194/egusphere-egu26-6493, 2026.

Variations in land surface characteristics directly alter surface biophysical properties like albedo, roughness length, and canopy resistance, leading to changes in surface radiative and turbulent fluxes. These changes influence sensible and latent heat fluxes, which can further regulate surface temperature, evapotranspiration, and near-surface moisture transport. Thus, variations in surface fluxes associated with changes in land-surface properties can regulate convective instability, moisture convergence, and can modulate short-range rainfall characteristics and their predictability. Such land–atmosphere feedbacks are particularly important over the Indian region, where strong seasonal contrasts and heterogeneous land surfaces play a critical role in shaping rainfall variability. Hence, this study investigates the sensitivity of short-range precipitation forecasts over India to decadal changes in land use and vegetation during the pre-monsoon and monsoon periods. USGS 24-category land use data based on the 1994 landscape is used as the control run. Seven different simulation experiments are conducted using WRF model with various land use and vegetation data from MODIS and ISRO; ie: Experiment1 (MODIS 2001, USGS LAI and VF default), Experiment 2 (MODIS 2001, LAI and VF default), Experiment 3 (MODIS 2019, LAI and VF default), Experiment 4 (MODIS 2001, with Urban Class of 2019, LAI, and VF default), Experiment 5 (MODIS 2001, with water bodies of 2019, LAI, and VF default), Experiment 6 (MODIS 2019, LAI and VF of 2019), Experiment 7 (ISRO 2018-2019, VF and LAI default). A comprehensive assessment based on quantitative error metrics and categorical forecast skill scores demonstrates statistically significant improvements in rainfall forecast performance following the assimilation of updated land-use and vegetation datasets.  These statistically robust improvements indicate that realistic representation of land-surface conditions contributes meaningfully to enhanced short-range precipitation predictability. The computed Extreme Dependency Index (EDI) values indicate an enhanced ability of the model to capture rare extreme rainfall events following the incorporation of updated land-use information. The incorporation of realistic land-use classifications derived from MODIS and ISRO datasets led to improved simulations of surface meteorological variables, including temperature, wind speed, relative humidity, surface pressure, and surface fluxes. Corresponding improvements were also observed in the vertical atmospheric profiles for wind, temperature, and specific humidity profiles. These enhancements indicate a more realistic depiction of land–atmosphere interactions and boundary-layer processes in the model.

How to cite: Putatunda, I., Vasudevan, R., and Singh, R.: Effect of decadal land use change on WRF model-simulated surface meteorological parameters over the Indian region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8881, https://doi.org/10.5194/egusphere-egu26-8881, 2026.

Rising global warming is accelerating climate extremes at regional scales worldwide. Capturing these extremes at the regional level requires high resolution climate modeling capable of representing complex topography and strong land atmosphere interactions. In this study, a high-resolution Non-Hydrostatic Regional Climate Model (NHRCM) is configured at a kilometer scale resolution (5 km) through dynamic downscaling of the MRI AGCM 3.2 outputs developed by the Meteorological Research Institute of Japan (MRI). Daily precipitation and temperature (maximum and minimum) data are generated at 5 km resolution for the historical period (1980–2000) and the future period (2081–2100) under the high emission climate scenario SSP585. The performance of the downscaled climate variables is evaluated against ERA5 Land data after resampling to the same resolution as the NHRCM. Statistical metrics and extreme climate indices are used to quantify model skill and biases at regional and sub regional scales over Pakistan. The results reveal a strong correlation in high elevation regions of Pakistan compared to the plains. After evaluating model performance, precipitation and temperature extreme indices are calculated for both historical and future periods. The findings indicate an increase in precipitation in the southern regions of Pakistan, accompanied by rising temperatures. These trends are also associated with an increase in short-duration intense rainfall events during summer and prolonged dry conditions in winter. Furthermore, the frequency of heatwaves is expected to rise by the end of the century across Pakistan, along with increasing temperatures in snow fed regions. Overall, this study highlights the added value of high-resolution nonhydrostatic regional climate modeling in understanding and assessing climate extremes over Pakistan, providing a robust foundation for future climate impact assessments and adaptation planning.

How to cite: Mamoor, M., Rahim, A., Yaseen, M., Naz, R., Kosar, T., Tariq, N., and Akif, A.: Kilometer Scale Climate Modeling of Extremes: Evaluation of the NonHydrostatic Regional Climate Model (NHRCM) over Pakistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9211, https://doi.org/10.5194/egusphere-egu26-9211, 2026.

In this study we analyze the occurrence of cold pools in the tropical Atlantic during the ORCESTRA field campaign (August to September 2024), using data collected from over 2000 soundings. We first tested whether the method to detect cold pools based on the mixed-layer height, developed for shallow convective cold pools in the winter trades during EUREC4A, is applicable to the deep convective regime of the ITCZ. The validation by a surface-based detection method and the investigated recovery behaviour of the mixed layer after a cold pool demonstrates its applicability to this environment. On this basis, we examine the distribution and properties of cold pools within the tropical Atlantic. A total of 26% of all ORCESTRA soundings detected cold pools, compared to only 7% during the EUREC4A campaign in the winter trades. The ITCZ region with the highest moisture content and presumably deepest convection features the largest number of cold pools. This presentation will further discuss how the cold pool strength and frequency correlate with wind, moisture and stability.

How to cite: Vogel, R., Mann, L., Robbins-Blanch, N., and Rochetin, N.: Characterizing cold pools in the ITCZ using soundings from the ORCESTRA campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14298, https://doi.org/10.5194/egusphere-egu26-14298, 2026.

Satellite data sets are the primary source of observations of cloud characteristics, but downward-looking passive sensors cannot see lower-level clouds obscured by higher clouds nor cloud bases. Observations of low clouds with downward-looking satellite IR are hampered by the small brightness temperature differences between the low cloud top and the underlying surface. In contrast, upward-looking thermal IR can readily distinguish warm clouds against the cold sky. By sampling thermal IR cloud characteristics across the diurnal cycle, upward looking thermal IR observations have the potential to yield improved understanding of transitions in cloudiness at sunrise and sunset and differences in the relative importance of different cloud processes with and without SW fluxes.

Our thermal IR all-sky camera was assembled from commercially available, off-the-shelf parts. The key components are a FLIR Boson thermal IR camera and a FLIR PTU-5 pan-tilt mount. The IR camera has a 50° field of view and a resolution of 640x500 pixels. To obtain imagery of the entire sky, the pan-tilt mount points the camera at 14 different directions, each varying in azimuth and elevation. The volume coverage pattern is executed once per minute, and the entire sky is sampled in less than 30 seconds. The images are then stitched together in software to yield a hemispherical array of IR brightnesses from horizon to horizon.

From the imagery we can infer cloud fraction, cloud coverage characteristics relating to the size and shapes of cloud elements, and estimate the altitude of cloud bases at all times of day. Sequences of images reveal the evolution of individual cloud elements and provide information on the phase space of cloud properties across the diurnal cycle and to related to air mass changes, such as the passage of fronts. Combined with other data from lidar and visible all sky cameras, the upward-looking thermal IR data on cloud outer surface temperature details at small spatial scale (10s of meters) and few minute time scale have high potential to yield new insights on cloud initiation and dissipation.

We will detail the performance of the thermal IR all-sky camera and analyze derived cloud characteristics in the context of data from visible wavelength all-sky imagery and additional atmospheric observations.

How to cite: Miller, M. and Yuter, S.: All-Sky Camera Upward-looking Thermal Infrared Cloud Characteristics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15029, https://doi.org/10.5194/egusphere-egu26-15029, 2026.

Reanalysis products, which blend weather model output with observations are commonly used as substitutes for observations to assess numerical weather prediction model forecast skill, the accuracy of climate model historical realizations, and AI training. However, the quality of reanalysis output is not uniform across all variables, times of day, seasons, or geographic settings. This study evaluates the strengths and weaknesses of ERA5 reanalysis (0.25° grid) over a multi-year period by comparing them to worldwide hourly surface observations from over 1200 stations, buoys, and radiosonde vertical profiles.

Our analysis focuses on several metrics across the diurnal cycle (7 AM and 3 PM local time) and during temperature outlier events (< 10th percentile and > 90th percentiles for the 30-year climatology). Results indicate that ERA5 provides reliable 2-meter air temperatures in most regions, but shows a frequent dry bias in dewpoint of greater than 3 ℃ more than 5% of the time for many stations in the Dry and Mediterranean climate zones. ERA5 often underestimates warm events (> 90th percentile), with the largest cold biases, less than -3 ℃ occurring more than 11% of the time in the Mediterranean climate zone. Temperature and dewpoint biases are amplified in complex terrain, and dewpoint biases tend to be larger near coastal locations. To investigate whether higher spatial resolution mitigates these issues, we also examine the performance of the ERA5-Land product  (0.1° grid). These findings emphasize the importance of evaluating the adequacy of purpose when using reanalysis for specific applications, since performance can vary significantly by variable, time of day, season, and climate zone.

How to cite: Lewis, W., Yuter, S., and Miller, M.: Is ERA5 Fit for Purpose? A Global Multi-Variable Evaluation of Reanalysis Strengths and Weaknesses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15265, https://doi.org/10.5194/egusphere-egu26-15265, 2026.

Persistent extreme weather anomalies lasting several weeks to months can lead to drought and compound dry–hot extremes, posing serious socio-economic risks in the Indian monsoon region. Although subseasonal-to-seasonal (S2S) prediction systems have advanced, the extent to which these models represent drought-relevant hydroclimatic variability over India has not been adequately quantified. Here, we focus on evaluating the hindcast quality of weekly accumulated precipitation and temperature from multiple S2S models with lead times up to six weeks during the JJAS season over India. These model hindcast outputs and IMD observations are regridded to a common 0.5° resolution and analyzed using deterministic forecast skill metrics at various lead times, statistical bias correction is then applied to isolate systematic model errors, followed by SPI-based drought diagnostics, and compound dry–hot extreme indices are derived and computed. The analysis reveals modest forecast skill at early lead times, followed by a systematic decline as lead time increases, with precipitation predictability deteriorating more rapidly than temperature predictability. Although the models generally capture the large-scale spatial distribution of drought-prone regions, they significantly underestimate the frequency and spatial extent of compound dry–hot conditions, exhibiting pronounced regional dependence across India. These results highlight key limitations and identify opportunities to enhance subseasonal drought early-warning systems. 

Keywords: Subseasonal-to-seasonal predictability, Indian Summer Monsoon, Drought, Climate extremes, Hindcast evaluation

How to cite: Sukumaran, S. and Agarwal, A.: S2S Forecast Skill Assessment for Summer Monsoon Drought Warning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16394, https://doi.org/10.5194/egusphere-egu26-16394, 2026.

Phthalate exposure has been rampant with the growing use of these compounds in personal care  and other plastic products. Phthalates have been associated with endocrine, neurological, and reproductive disorders resulting from their continuous release from plastic surfaces throughout their lifecycle. These endocrine disruptors are used in personal care products to increase their shelf life. This paper aims to identify phthalates and their concentration in three test materials, perfume, scented candles, and incense sticks, through a chamber experiment. This experiment aids in understanding phthalate emissions into air during the use of these materials in indoor environments. Sampling was performed using Tenax TA tubes, which were placed inside the glass chambers for 30 minutes while maintaining a positive flow rate using a mass flow controller and pump. The tubes capture gas-phase phthalates efficiently. The tubes were then further analyzed using TD-30 and GC/MS. Three phthalates, which were detected in the test materials, were dimethyl phthalate (DEP), dibutyl phthalate  (DBP), and butyl benzyl phthalate (BBP). The phthalate concentration was found to be high in incense sticks, with DEP being the most prominent phthalate, followed by DBP. Scented candles had the high concentration of DBP, followed by BBP. A similar pattern was observed in perfumes. The high concentrations of these compounds detected in the test materials underscore growing concerns about the widespread use of phthalates in  manufacturing of plastic products.

How to cite: Anshika, F. and Rappenglueck, B.: Investigation of phthalate emissions from incense stick, scented candle and perfume through a chamber experiment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18616, https://doi.org/10.5194/egusphere-egu26-18616, 2026.

Delhi is one of the most polluted megacities in the world. Since the ancient era, Delhi has been known for its rich heritage sites like the Red Fort, Humayun’s Tomb, and Qutub Minar, which are UNESCO World Heritage Sites. In this study, we investigate urban pollution-linked alterations at the heritage buildings (HBs) of Delhi, India, through a comparative characterization of deposited black crust (BC) and the underlying red sandstone (RS) collected from the exposed surfaces of the HBs. The BC on HBs can act as an integrative passive sampler of urban pollution, recording particulate matter, reactive gases (SO_x/NO_x), and associated heavy metal deposition. To achieve this objective, we applied a multi-analytical workflow combining X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), Fourier transform infrared spectroscopy (FTIR), carbonaceous analysis, and inductively coupled plasma mass spectrometry (ICP-MS) resolved phase assemblages, morphology, major-elemental composition profiles, and signatures of trace elements. The outcome suggests that the urban pollution sources, including vehicular emissions, road/construction/soil dust, industrial activity, and biomass burning, were identified as fingerprints of calcium (Ca) and sulfur (S) in the formed BC at the RS substrate. In this scenario, BC was enriched with Ca and S, which may cause sulfation phenomena to occur at the RS substrate as it contains low intrinsic Ca. Therefore, gypsum was identified as a dominant deteriorating agent, along with weddellite, bassanite, while carbon and heavy metals were also embedded in BC relative to the RS substrate. Additionally, in this work, a modeling approach is also utilized to assess the pollution dispersion impacts on the built HBs and linkage with BC deposition, which was employed using coupled WRF (Weather Research and Forecasting) and AERMOD (the American Meteorological Society/Environmental Protection Agency Regulatory Model) technique. Hence, considering these analytical and modelling approaches contributes to applying the site-specific conservation and preservation interventions in similar urban polluted environments.

How to cite: Kumar, G., Gurjar, B. R., Sharma, M., and Ojha, C. S. P.: Black crust as a passive sampler of urban pollution on the heritage buildings in Delhi, India: An analytical and modelling approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18929, https://doi.org/10.5194/egusphere-egu26-18929, 2026.

Tropical deep convection evolves as a cyclic process, but most observational and modeling studies diagnose convection through regional or domain-based contrasts, obscuring how key physical processes vary across different stages of the convective life cycle. The convective life cycle in the tropics is frequently conceptualized through recharge discharge processes. While valuable, this framework can be extended with more granular, phase specific diagnostics to better understand the distinct physical processes governing each stage of convection. Here, we build on existing phase-plane approaches to represent convection as a cyclic process, using column-integrated moist static energy (MSE) and its temporal tendency as the primary state variables. The phase plane is constructed with column integrated MSE along the horizontal axis and its temporal derivative along the vertical axis. While adhering to the established recharge–discharge paradigm, we extend this terminology by defining four distinct, cycle-consistent stages on the phase plane: Build-up, Cresting, Decay, and Recovery. Applying this framework to the Indian Summer Monsoon (ISM) core region, we map quasi-geostrophic (QG) omega-scaled precipitation components onto the MSE phase plane  to investigate the relative contributions of diabatic heating  and adiabatic forcing across the convective life cycle. These stage dependent signatures demonstrate the utility of the MSE phase plane for attributing and relative importance of dynamical and diabatic processes across the convective life cycle. Final results and extended analyses will be presented and discussed at the conference.

How to cite: Chakraborty, S., Nikumbh, A., Maithel, V., and Zore, T.: A Phase-Plane Representation of the Convective Life Cycle: Characterizing Diabatic and Adiabatic Drivers during the Indian Summer Monsoon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19311, https://doi.org/10.5194/egusphere-egu26-19311, 2026.

In Eastern Africa, subseasonal forecasts are critical for early warning systems as climate extremes severely impact food security and livelihoods. ECMWF Artificial Intelligence Forecasting System (AIFS ENS v1.0) , an ensemble-based probabilistic data-driven forecast model developed by ECMWF offers unprecedented opportunities for regional applications through AI-driven weather prediction, but GPU compute costs and data access challenges limit deployment. As participants in the ECMWF AI Weather Quest, we developed solutions enabling cost-effective, cloud-based AIFS ensemble forecasting tailored for regional climate centers.  

We implemented a workflow (https://github.com/icpac-igad/ea-aifs) leveraging Google Cloud Platform infrastructure. Initial conditions are accessed via ECMWF's IFS data stored at AWS (Amazon Web Service) open data program at S3 Cloud storage using GRIB index-kerchunk, and VirtualiZarr methods for efficient data streaming without local storage overhead. The workflow employs experimental FP16 (half-precision) inference on AIFS ensemble models along with the standard FP32, evaluating GPU memory requirements and enabling deployment on cost-effective T4/L4 GPUs rather than expensive A100 instances.  

Verification results from the SON (September-October-November) 2025 season as part of the AI Weather Quest demonstrates that Team Fahamu's submission using AIFS ensemble forecasts for temperature and mean sea-level pressure outperforms climatology benchmarks. Regional evaluation over East Africa reveals promising subseasonal skill for temperature at lead times of 2-4 weeks—critical timescales for agricultural planning and anticipatory drought/flood action—while evaluation of precipitation forecasts is ongoing. This method provides a scalable template for regional climate centers globally to operationalize state-of-the-art AI weather models cost-effectively, advancing the democratization of advanced forecasting capabilities. 

How to cite: Koech, E., Kalladath, N., Mwanthi, A., Ogelo, A., Kinyua, J., Koros, H., Lelaono, M., Misiani, H., Bekele, T., Seid, H., Gudoshava, M., and Amdihun, A.: Cost-Effective ECMWF AIFS Ensemble Inference for Subseasonal Forecasting in East Africa , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20505, https://doi.org/10.5194/egusphere-egu26-20505, 2026.

Persistent heatwaves across South Asia impose severe and growing impacts, yet the atmospheric processes that sustain extreme heat over multiple days remain incompletely understood. This study aims to determine whether heatwave persistence is driven by failures of nocturnal boundary-layer ventilation, rather than by daytime temperature extremes alone. We analyze pre-monsoon (March–May) heatwaves across South Asia (65°E–98°E, 5°N–35°N) from 1981 to 2024 using ERA5 hourly reanalysis, which includes near-surface air temperature, boundary-layer height, near-surface winds, and surface sensible heat flux. Heatwaves are identified using a percentile-based definition of daily maximum temperature, and nighttime conditions are diagnosed consistently using local solar time. Nocturnal ventilation is quantified through a physically interpretable ventilation potential combining nighttime boundary-layer height and near-surface wind speed, complemented by diagnostics of turbulent mixing and nocturnal cooling. We find that heatwave nights are consistently characterized by suppressed nocturnal ventilation, including shallow boundary layers, weak winds, and reduced turbulent exchange, and that reductions in nighttime ventilation are more strongly associated with heatwave duration and nighttime heat accumulation than daytime temperature anomalies. Composite analyses further indicate that ventilation and turbulent mixing weaken before heatwave onset and remain suppressed throughout the persistence phase, with particularly pronounced effects in humid regions such as Bangladesh. Our findings demonstrate that nocturnal boundary-layer ventilation failure is a central physical mechanism controlling heatwave persistence and suggest that incorporating nighttime atmospheric processes into heatwave monitoring and early-warning frameworks is essential for anticipating prolonged and high-impact heat extremes.

How to cite: Laskor, Md. A. H., Dipu, S. U. A., Bhuiyan, F., and Islam, A. K. M. S.: Nocturnal boundary-layer ventilation failure governs heatwave persistence in South Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21148, https://doi.org/10.5194/egusphere-egu26-21148, 2026.

Phenolic compounds are key precursors and intermediates in the formation and aging of secondary organic aerosols (SOA), particularly in biomass-burning plumes and urban atmospheres. However, their detection typically requires laboratory-based chromatographic or mass-spectrometric techniques, limiting rapid or on-site characterization. The current research present a fully 3D-printed electrode (3D-PE) platform produced via hybrid material extrusion additive manufacturing, providing a compact, low-cost, and field-deployable tool for electrochemical quantification of atmospheric phenolics. The device integrates PLA-based structural components with graphene and silver conductive layers deposited in a single manufacturing step. Cyclic voltammetry measurements demonstrate clear and distinct redox signatures for representative phenolic structures, with oxidation potentials of 0.48–0.68 V and well-resolved reduction peaks. These redox behaviors correspond to functional groups commonly found in lignin-derived and anthropogenically emitted aromatic species.

The 3D-PE operates with sample volumes as low as 50 µL, suitable for extracts from aerosol filters, cloud water, or fog samples. Its electroactive surface area (5.8–6.7 mm²) and high electron-transfer efficiency from the graphene electrode enable sensitive detection of trace phenolic compounds. The platform’s portability and rapid response offer new opportunities for quantifying oxidation intermediates during field campaigns, studying heterogeneous oxidation pathways, and investigating Secondary Organic Aerosol (SOA) formation dynamics.

This work demonstrates that additive manufacturing provides a promising route for developing next-generation, customizable atmospheric chemistry sensors. The 3D-printed electrochemical platform can complement established mass-spectrometric techniques by enabling low-cost, high-frequency measurements of reactive organic compounds that play central roles in SOA formation and atmospheric oxidative chemistry.

How to cite: Raj, A., Yadav, P., Sahai, A., and Sharma, R. S.: 3D-Printed Electrochemical Sensor for Rapid Detection of Phenolic Oxidation Products Relevant to Organic Aerosol Formation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-186, https://doi.org/10.5194/egusphere-egu26-186, 2026.

Abstract

The densely populated monsoon region of Pakistan, influenced by a diverse mix of natural and anthropogenic aerosols, provides a natural laboratory to investigate aerosol impacts on cloud properties. Using a decade-long (2015–2024) dataset from MODIS, MERRA-2, and ERA5, we examine the response of non-precipitating warm clouds to fine-mode and coarse-mode aerosols. We find positive correlations between aerosol optical depth (AOD) and cloud effective radius (CER), with stronger sensitivity to fine-mode AOD. The relationships of AOD with cloud optical thickness (COT) and liquid water path (LWP) are generally negative, but more pronounced for coarse-mode AOD. These aerosol-cloud relationships are strongly modulated by meteorological conditions: low relative humidity and low lower-tropospheric stability enhance the negative AOD–COT and AOD–LWP responses. Additionally, the sensitivity of aerosol-cloud relationships to meteorology is greater for fine-mode AOD than coarse-mode. These results highlight the importance of aerosol size and ambient meteorology in determining cloud microphysical responses, providing insight into aerosol cloud interactions in a region critical for South Asian climate.

How to cite: Anwar, K., Liu, Y., Labban, A., and Zeydan, Ö.: Aerosol-cloud interactions under fine-mode and coarse-mode aerosol conditions over the monsoon region of Pakistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-323, https://doi.org/10.5194/egusphere-egu26-323, 2026.

Accurate retrieval of the dry-air mole fraction of CO₂ (XCO₂) is essential for tracking emissions and supporting mitigation. However, aerosols significantly alter photon path lengths through scattering and absorption, making them the largest variable error source in XCO₂ retrieval. Current efforts to improve aerosol treatment in XCO₂ retrievals, such as the Atmospheric CO₂ Observations from Space (ACOS) algorithm for the Orbiting Carbon Observatory-2 (OCO-2), primarily focus on enhancing prior estimates of aerosol optical depth (AOD), vertical distribution, and optical properties. Yet, aerosol particle size distribution (PSD) parameters—a critical microphysical factor contributing to nonlinear variations in aerosol optical properties—are held fixed and excluded from the ACOS retrieval, thereby introducing additional biases into the XCO₂ results.

To address this challenge, we developed a Boosted Aerosol-Size-Integrated XCO₂ (BASIC) retrieval algorithm that concurrently retrieves XCO₂ and aerosol PSD parameters from OCO-2 observations. Validation at five Total Carbon Column Observing Network (TCCON) sites in East Asia shows that BASIC reduces the root-mean-square error (RMSE) by 30% and 13% compared to the standard and bias-corrected OCO-2 products, respectively. The improvement primarily stems from BASIC’s ability to generate forward-modeled spectra that more closely match observations than those from ACOS, particularly in the O₂ A-band, which is highly sensitive to aerosols. These results highlight the importance of incorporating variable aerosol PSD in retrievals and demonstrate that BASIC more accurately represents aerosol effects on radiative transfer. Our findings suggest that PSD-aware retrievals can significantly improve the accuracy of satellite-derived XCO₂ estimates under highly variable aerosol loading conditions, such as those in East Asia.

How to cite: Li, Z. and Li, S.: BASIC: A Boosted Aerosol-Size-Integrated XCO2 Retrieval Algorithm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1603, https://doi.org/10.5194/egusphere-egu26-1603, 2026.

To advance urban sustainability and achieve climate neutrality, cities are proposing various decarbonization strategies through nature-based solutions, including the implementation of green infrastructures (GI). Accurately quantifying urban biogenic CO₂ exchange is essential for developing robust greenhouse gas budgets and for distinguishing biogenic from anthropogenic contributions to atmospheric CO₂ in urban environments.

This study systematically reviews current approaches for estimating urban biogenic CO₂ fluxes from both measurement- and model-based perspectives, evaluating their advantages, limitations, and applicability in urban contexts. Building on this review, we present an optimized framework to quantify urban biospheric CO₂ fluxes using the Vegetation Photosynthesis and Respiration Model (VPRM) driven by a high-resolution land cover map and sentinel-2 satellite data. The framework is applied to the Metropolitan Area of Barcelona for April and December 2023 at a 10 m spatial resolution. Results show that evergreen needleleaf forests and croplands act as significant carbon sinks in April, with biogenic CO₂ uptake offsetting approximately 10% of anthropogenic CO₂ emissions in April and 4% in December. A cross-city comparison of urban vegetation cover, climatic conditions, and the biogenic offset effects indicates that increased vegetation cover does not necessarily translate into a proportionally stronger carbon sink. Nevertheless, this study proposes that a standardized framework for accounting for biogenic CO₂ fluxes and uptake should be established to provide critical support for GI-based mitigation strategies in urban planning.

How to cite: Luo, Q., Segura-Barrero, R., Badia, A., Lauvaux, T., Li, J., Chen, J., and Villalba, G.: Quantifying Urban Biogenic CO2 Fluxes in Greenhouse Gas Budgets: A Scalable Framework and Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2329, https://doi.org/10.5194/egusphere-egu26-2329, 2026.

This study presents a comprehensive climatological analysis of aerosol optical and microphysical properties over the Eastern Mediterranean, utilizing long-term AERONET measurements from Mersin/Erdemli (IMS-METU) site. The Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm was used to retrieve key parameters, including Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Volume Size Distribution, offering robust separation of surface and aerosol properties.

Results revealed a distinct seasonal cycle in aerosol loading, with AOD peaking in summer (July-August) due to fine-mode pollution and exhibiting a secondary peak in spring (April) driven by mineral dust transport from desert regions. The annual mean SSA displays a negative spectral slope, decreasing from ~0.93 at 440 nm to ~0.90 at 1020 nm, indicating a background atmosphere dominated by fine-mode urban-industrial aerosols. Although winter months exhibit the lowest total aerosol load due to wet scavenging, they display the strongest absorption characteristics. The imaginary refractive index significantly exceeds 0.015, and SSA values drop sharply during winter, attributed to Black Carbon emissions from domestic heating. The volume size distribution maintains a bimodal structure year-round; while the coarse mode dominates during spring dust events, the fine mode contribution remains substantial across all seasons. The aerosol population over the Eastern Mediterranean is characterized as a heterogeneous mixture where the anthropogenic fine-mode background is periodically modulated by natural mineral dust intrusions and local carbonaceous emissions.

How to cite: Çetiner, A. S. and Aslanoğlu, S. Y.: Long Term Analysis of Aerosol Properties Over the Eastern Mediterranean Using Grasp Retrieved AERONET Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3325, https://doi.org/10.5194/egusphere-egu26-3325, 2026.

 Meteorological normalization of surface ozone typically relies on air temperature to proxy both photochemical activity and boundary-layer dynamics. However, this approach implicitly assumes that the thermal state adequately represents radiative energy input—an assumption that remains untested in high-elevation environments where strong solar forcing and a thin atmosphere may decouple temperature from the surface energy balance. Here, we examine how surface energy forcing modulates ozone variability independently of air temperature using continuous station-level measurements in Lhasa (3650 m a.s.l.), Tibetan Plateau. By stratifying days based on net radiative input while explicitly constraining thermal conditions through a counterfactual matched-pair analysis, we isolate energy-driven processes without invoking reanalysis-based boundary-layer estimates. Results demonstrate that high-energy states consistently exhibit enhanced morning ozone growth (median +4.3 ppb h^-1) and elevated daytime concentrations relative to temperature-matched low-energy states. These enhancements are accompanied by coherent multi-tracer responses, including moisture drying and the dilution of primary pollutants, which provide observational constraints on energy-driven vertical coupling that are distinct from temperature-dependent photochemistry. Furthermore, a rate-based robustness analysis confirms that these signals persist across varying stratification thresholds. We conclude that surface energy forcing represents a previously under-constrained structural factor in conventional ozone attribution frameworks, particularly in complex terrain where thermal and radiative states frequently decouple. 

How to cite: Zhao, C., Wang, Y., Liu, Y., Li, W., Gongga, D., Quzhen, D., Ao, Y., Yue, J., Zhong, X., and Du, X.: Surface energy forcing modulates ozone variability independently of air temperature over the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4386, https://doi.org/10.5194/egusphere-egu26-4386, 2026.

As growing environmental pressures challenge urban resilience, sustainability, and human well-being, the lack of high-resolution geospatial information at the urban or intra-urban scale remains a critical limitation for effective and targeted decision-making. In the context of air quality and associated health impacts, this study addresses this gap by developing the Atmospheric SpatioTemporal Emissions Model (AtmoSTEM), a high-resolution spatiotemporal framework for representing atmospheric emissions, pollutant concentrations, and population exposure at 1 km² resolution. The application focuses on the Ioannina basin in Greece, where residential biomass burning (BB) constitutes a dominant emission source, especially during the cold season, frequently leading to pollution levels exceeding the World Health Organization (WHO) Air Quality Guidelines (AQG) and EU thresholds and underscoring the need for targeted interventions.

For this purpose, a high spatiotemporal emission inventory for Residential Heating is developed. The Copernicus Atmosphere Monitoring Service (CAMS) regional emission inventory, structured according to the GNFR classification and provided at a spatial resolution of 0.05° × 0.1°, serves as the baseline dataset. The downscaling process is based on publicly available, open-access, GNFR-dependent high-resolution spatial proxies, including the Coordination of Information on population density data from Global Human Settlement (GHSL), land-use classifications from the Copernicus Land Monitoring Service (CLC 2018), the OpenStreetMap (OSM) road network, and, where applicable in coastal and maritime domains, marine traffic density from the European Marine Observation and Data Network (EMODNet). Particular emphasis is placed on refining pollutant fields that are more relevant to BB activities, thereby improving the spatial representativeness of BB emissions within urban and peri-urban environments.

To capture the temporal variability of the emissions, CAMS is combined with CAMS temporal Regional Profiles (CAMS-TEMPO), enabling the generation of analytically resolved, hourly emission estimates. Pollutant concentrations are then estimated using a Random Forest machine learning model that integrates AtmoSTEM’s high-resolution emissions, with meteorological, satellite-derived, and spatial data, as well as in-situ air quality measurements provided by the University of Ioannina. The resulting high-resolution concentration fields are evaluated against independent in-situ measurements. Additionally, BB-related PM_2.5 fields are derived and analyzed, enabling improved source-specific characterization of residential heating contributions and providing a physically consistent basis for air-quality and exposure assessments.

How to cite: Kakouri, A., Filippis, G., Korras-Carassa, M.-B., Kuenen, J., Hatzianastassiou, N., Matsoukas, C., and Kontos, T.: AtmoSTEM: A high-resolution spatiotemporal emission model for urban air quality applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5251, https://doi.org/10.5194/egusphere-egu26-5251, 2026.

We present an automated procedure that combines lidar measurements, ADS-B flight tracks and ECMWF ERA5 meteorological data to detect and characterise nighttime aircraft contrails. Measurements have been carried out at the Observatory of Haute-Provence (OHP) in France. Lidar scattering-ratio profiles were processed with a sensitivity-driven spatio-temporal discrimination algorithm to identify contrail “spots” and aggregate them into contrail signatures. A parameter score identifies an optimal discrimination threshold set that balances sensitivity and false positives. In our case, these thresholds took these values: scattering ratio SR ≈ 2.1; temporal aggregation ≈ 7.2 min; vertical separation ≈ 0.3 km. Applied to five nighttime events, the method yields mean contrail altitudes of 8.7–10.3 km, geometrical thicknesses of 0.1–1.1 km, horizontal widths 2–3 km, and optical depths (COD) of ≈0.05–0.40. Persistent contrails are associated with ice-supersaturated layers and temperatures below −41 °C. Contrail optical depth resulted well correlated with both vertical thickness and horizontal extent. We have demonstrated that combining lidar with ADS-B and ERA5 substantially improves detection and discriminates contrails from natural cirrus at night, a regime where passive satellite retrievals are limited. This approach is automatic, transferable and reproducible, offering robust validation data for satellite algorithms and improved contrail parameterizations in climate models.

How to cite: Mandija, F., Keckhut, P., Alraddawi, D., Irbah, A., Sarkissian, A., Khaykin, S., Peyrin, F., and Baray, J.-L.: Automated nighttime contrail detection using spatio-temporal clustering of Raman lidar measurements , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5443, https://doi.org/10.5194/egusphere-egu26-5443, 2026.

Smoke aerosols constitute a critical component of atmospheric pollutants and radiative forcing agents. Northeast China is frequently afflicted by smoke episodes. Driven by the combined impacts of anthropogenic emissions, residential heating, agricultural biomass burning, and other factors, this region exhibits complex aerosol characteristics with pronounced seasonal variations. This study systematically evaluates the spatiotemporal evolution of smoke aerosols from 2015 to 2023 using GEOS-Chem global simulations (2°×2.5°), along with ground-based PM2.5 measurements, sun photometer AOD measurements in Harbin, MODIS, and VIIRS fire data. We classified the region into six distinct sub-regions based on smoke concentration characteristics: four urban zones (Dalian, Shenyang, Changchun, Harbin) and two rural zones (Eastern Coastal and Western Inland). Observational and simulation data demonstrates that the model captures regional seasonal variability and annual trends. Employing the HYSPLIT model and Concentration Weighted Trajectory (CWT) analysis, we identified potential external source regions and initially assessed the relative contribution of cross-regional transport (e.g., from Siberia or North China) to smoke episodes across different seasons. This comprehensive analysis provides a scientific basis for understanding the climatic effects of aerosols and formulating refined regional air quality management strategies in Northeast China.

How to cite: Gao, X. and Miatselskaya, N.: Long-term spatiotemporal evolution and source attribution of smoke aerosols in Northeast China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8607, https://doi.org/10.5194/egusphere-egu26-8607, 2026.

The Indo-Gangetic Plain (IGP) experiences strong seasonal and spatial heterogeneity in aerosol composition, driven by variations in emissions, meteorology, and regional transport. Capturing these variations requires measurement approaches that extend beyond conventional fixed-site monitoring. In this study, we deployed a mobile lab platform equipped with aerosol instrumentation, including a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), to investigate seasonal variability in organic aerosol (OA) sources during the post-monsoon and spring seasons across distinct urban environments in Lucknow, located in the central Indo-Gangetic Plain.

Field campaigns were conducted during the post-monsoon and spring seasons at Babasaheb Bhimrao Ambedkar University (BBAU), a site influenced by traffic near major highways, and at the Council of Scientific & Industrial Research–Central Institute of Medicinal and Aromatic Plants (CSIR-CIMAP), located adjacent to a forested area. Additional measurements were conducted during the spring season at the Uttar Pradesh Pollution Control Board (UPPCB) site, which represents a residential–commercial environment.

At each location, the mobile laboratory was operated for approximately 10–15 days, enabling continuous, near real-time characterization of fine particulate matter and associated co-pollutants. Measurements of non-refractory PM_2.5 (NR-PM_2.5) chemical composition (measured using HR-ToF-AMS) were supported by simultaneous observations of trace elements, black carbon, gaseous species, total PM_2.5 mass, and meteorological parameters. This integrated, multi-instrument framework allowed for a consistent comparison of aerosol chemical signatures across sites and seasons, while capturing short-term variability linked to local emissions, atmospheric processing, and regional transport.

Organic aerosol dominated the mass of NR-PM_2.5 across all sites and seasons, contributing more than 50% of the total NR-PM_2.5. Source apportionment using Positive Matrix Factorization (PMF) with the multilinear engine (ME-2) resolved hydrocarbon-like OA (HOA), biomass-burning OA (BBOA), oxidized biomass-burning OA (O-BBOA), and secondary oxygenated OA components (SVOOA and LVOOA). During the post-monsoon period, BBOA accounted for approximately 28–40% of total OA across the sites, indicating a strong combustion influence under shallow boundary-layer conditions. Traffic-related HOA contributed about 8–13% of OA, with enhanced fractions at the highway-influenced BBAU site, reflecting local vehicular emissions. In contrast, springtime conditions showed enhanced secondary OA contributions (70-60%), with trajectory-based analyses highlighting the role of long-range transport in shaping aerosol composition.

The use of a mobile laboratory enabled rapid deployment across diverse land-use environments while maintaining consistent instrumentation and methodology, allowing robust inter-site and inter-seasonal comparisons. This approach emphasises the significance of high-resolution mobile observations for elucidating the fine-scale spatial variability and seasonal evolution of organic aerosol sources in the complex urban regions of the IGP.

How to cite: Lakra, A., Tripathi, S. N., Sethi, D., Kumar, A., Bhowmik, H. S., and Shukla, A. K.: Seasonal variation of organic sources during the post-monsoon and spring seasons across multiple urban sites of the Indo-Gangetic Plain using a mobile lab platform, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8822, https://doi.org/10.5194/egusphere-egu26-8822, 2026.

The impact of increasing anthropogenic hydrogen (H₂) emissions on Earth’s radiative balance depends on the soil microbial H₂ sink—the largest and most uncertain term in the global H₂ budget. Soil moisture is a primary but poorly quantified control regulating the soil sink. Here, we assess the sensitivity of microbial H₂ oxidation to soil moisture in laboratory experiments with temperate and arid soils spanning distinct textures. H₂ oxidizer activity is observed down to –70 to –100 MPa water potentials across soils, which are among the driest conditions reported for microbial activity and are much drier than assumed in global simulations of H₂. Using genome-resolved meta-omics, we link H₂ oxidation dynamics in temperate soils to specific desiccation-adapted microbial taxa that contribute differentially to H₂ uptake along the moisture gradient. Through global simulations, we show that our observationally constrained drier moisture threshold increases the contribution of arid and semi-arid regions for soil H₂ uptake by 4-7 percentage points (pp), while decreasing the contribution of temperate and continental regions (−7 pp). Our results highlight the importance of H₂ uptake under extreme hydrological conditions, particularly the roles of desertification, dryland expansion, and H₂-oxidizer ecophysiology in modulating long-term changes in H₂ uptake.

How to cite: Reji, L., Bertagni, M., Paulot, F., Qin, Q., and Zhang, X.: Global Implications of a Low Soil Moisture Threshold for Microbial Hydrogen Uptake , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11855, https://doi.org/10.5194/egusphere-egu26-11855, 2026.

A closed to open-celled transition of mixed-phase clouds within a marine cold air outbreak (MCAO), driven by an occluded cyclone over the Nordic Seas, is interrogated with data acquired during the Cold Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) field campaign on 29 February 2024. The understanding of these transitions is a challenge for numerical prediction models due to their small scale but important for weather and climate prediction, as open celled conditions have stronger updrafts supporting locally high precipitation rates along with a lower cloud fraction (lower albedo) than closed celled conditions. Measurements indicate that a stratiform (closed cell convective) cloud deck with cloud top heights of ~1230 m and liquid water paths (LWP) of ~130 gm^-2, within a boundary layer with aerosol number/CCN surpassing 600 cm^-3, deepen to heights of ~1520 m with LWPs of ~270 gm^-2 over 250 km of fetch, before transitioning into an open-celled convective structure with cloud tops reaching up to 2220 m. Cooling free tropospheric temperatures with fetch, which reduce the inversion strength thereby enhancing growth by entrainment may encourage boundary layer growth. Open-cells are  more glaciated than closed-cells with mean LWPs falling from 270 gm^-2 to 80 gm^-2 across the transition, however isolated peaks of LWP within updrafts of open cells occasionally surpass 500 gm^-2. Minimal secondary ice production (SIP) is observed in closed cells with ice nucleating particle and ice number concentrations ~2 L^-1 with cloud temperatures between -20^oC and -15^oC. In open cellular convection (cloud temperatures between -22^oC and -15^oC), ice number concentrations reach ~10 L^-1 indicating SIP. High aerosol concentrations are hypothesized to support the maintenance of closed-celled convection, with 80% of drops smaller than 10 μm reducing the riming efficiency. Small droplets also limit the production of freezing drizzle, which is hypothesized to limit the potential of SIP due to freezing/fragmentation within closed cell convection. Only after aerosol concentrations are depleted through scavenging and/or entrainment are SIP processes able to become more effective and precipitation particles able to grow large and dense enough to reach the surface and form cold pools breaking up the cloud deck. Plumes of warm moist air lifting off the ocean surface, juxtaposed with cold pools and entrainment events penetrating to the surface are documented using the Multi-function Airborne Raman Lidar (MARLi) in the first observations of its kind.

How to cite: Ephraim, S., Zuidema, P., Bansemer, A., Cai, L., Cruikshank, O., Evengeliou, N., French, J., Geerts, B., Grasmick, C., Massling, A., McFarquhar, G., Noel, G., Petters, M., Rosky, E., Skov, H., Snider, J., Tatro, T., Wang, Z., Woods, S., and Zhang, L.: Closed to Open-Celled Mixed-Phase Cloud Transition Over the Nordic Seas Under High Aerosol Loading, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12630, https://doi.org/10.5194/egusphere-egu26-12630, 2026.

Urban vegetation plays a key role in modulating atmospheric carbon dioxide (CO₂) in megacities. However, studies that explicitly quantify the effect of urban vegetation on CO₂ remain scarce. This study investigates how vegetation cover affects CO₂ mixing ratios in the Metropolitan Area of São Paulo (MASP) during 2020–2022, using four monitoring sites with contrasting vegetation fractions: Pico do Jaraguá (PJ; 59.75%), IAG (36.38%), ICESP (22.42%), and UNICID (10.42%). Vegetation cover was derived from Sentinel-2 Level-2A imagery using NDVI-based pixel classification, while CO₂ observations were obtained from the METROCLIMA network and analyzed together with concurrent meteorological variables (temperature, humidity, and wind). The analysis comprised characterizing temporal variability and quantifying vegetation effects using regression models and probability distribution functions (PDFs). Clear seasonal and diurnal patterns were observed, with lower CO₂ concentrations during summer and afternoon hours (420-414 ppm), and higher values during winter and nighttime periods (447-425 ppm). The greener and less urban site, PJ, exhibited the lowest and most stable CO₂ levels, whereas the highly urban UNICID site showed the highest average mixing ratios. Elevated CO₂ values at IAG (428.30 ppm in summer and 435.49 ppm in winter), despite substantial vegetation cover, suggest the influence of local emissions and boundary-layer dynamics, while relatively low CO₂ values at ICESP (422.10 ppm in summer and 428.03 ppm in winter) likely reflect the elevated measurement height (~100 m a.g.l.), which favors regional-scale mixing and reduces sensitivity to local emission sources. NDVI revealed a bimodal phenological cycle (April–May and October–November), which was mirrored by CO₂ variability at PJ. Among 132 fitted PDFs, the Normal Inverse Gaussian distribution best captured CO₂ variability, with greener sites showing flatter and more symmetric distributions and urban sites showing increased skewness and peakedness. Regression results indicate a significant vegetation signal: a 0.1 increase in NDVI was associated with CO₂ reductions of ~3.92 ppm (PJ), 2.81 ppm (IAG), and 7.66 ppm (ICESP; likely conditional on site features), while no significant effect was detected at UNICID. Overall, urban vegetation influences both mean CO₂ levels and their distributional characteristics, supporting the role of green infrastructure and phenology in urban carbon management.

Keywords: carbon dioxide, vegetation cover, linear regression, Metropolitan Area of São Paulo.

How to cite: Piscoya, J., Franco, M. A., and Andrade, M. D. F.: Influence of Vegetation Cover on Atmospheric CO2 Mixing Ratios in the São Paulo Metropolitan Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13522, https://doi.org/10.5194/egusphere-egu26-13522, 2026.

Aerosol optical depth (τ) is routinely reported at wavelengths that are not directly measured by ground-based sun photometers, in particular at 550 nm for satellite validation and at longer wavelengths for short-wave infrared applications. These values are typically obtained by spectral interpolation or extrapolation, most often using linear or quadratic regressions in logarithmic space. However, the uncertainty structure of such regressions is frequently treated incorrectly, because measurement uncertainties in τ are absolute and approximately wavelength-independent in linear space, and therefore become wavelength-dependent in logarithmic space. As a result, the measurement covariance matrix must be explicitly accounted for in log–log regression, although this is rarely done in practice. This study provides both a formal and a practical framework for estimating τ at non-measured wavelengths together with its associated uncertainty. A rigorous formulation is presented for linear and quadratic regression in logarithmic space, including the propagation of random and systematic (bias-related) errors from the original spectral measurements to the interpolated or extrapolated wavelength.

Sensitivity analyses based on synthetic aerosol optical depth spectra generated with the GRASP forward model are used to compare six different approaches for deriving τ(550) and τ(2000), including linear and quadratic regressions over different spectral ranges as well as the GRASP-AOD method. When the covariance matrix is treated correctly, quadratic log–log regression is found to be the most robust method for estimating τ(550), and its results become essentially independent of the chosen spectral range. In contrast, when the covariance matrix is neglected, the same regression becomes highly sensitive to the selected wavelengths, and artificially improved performance is obtained when restricting the fit to the central AERONET channels. These findings are confirmed using real AERONET observations. When the full covariance treatment is applied, differences between estimates obtained using different spectral ranges remain below 0.002 at all sites analysed. When it is ignored, root-mean-square differences exceeding 0.01 are observed at sites dominated by fine-mode aerosols.

Finally, the uncertainty propagation framework is applied to real data and shows that the uncertainty of interpolated τ follows the expected Beer–Lambert law governing sun-photometer measurements, scaling with optical air mass. This provides an independent validation of the formal error model. Overall, this work establishes a consistent methodology for spectral interpolation and extrapolation of τ, ensuring both accurate values and physically meaningful uncertainties for satellite validation and related applications.

How to cite: Torres, B., Dubovik, O., Toledano, C., Fuertes, D., Momoi, M., Kazadzis, S., Marbach, T., Lind, E., Roman, R., Veloso Varela, M., and Barreto, A.: Inferring aerosol optical depth at unmeasured wavelengths from ground-based spectral photometer data: uncertainty-consistent regression, sensitivity tests, and application to real data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13698, https://doi.org/10.5194/egusphere-egu26-13698, 2026.

Past aerial hyperspectral mapping campaigns and pilot studies have demonstrated that highly concentrated plumes are a significant portion of California’s total methane emissions, including many unintentional leaks that can be fixed quickly when operators are notified. Satellite plume imagers such as Planet’s Tanager offer the capacity for repeated observations of known methane infrastructure, with enough spatial resolution and sensitivity to address a significant fraction of these leaks by identifying source facilities and contacting operators. Here we present system design and first results from the California Satellite Methane Project (CalSMP), a comprehensive multi-sector effort to notify individual operators of plumes within days of detection and ensure prompt mitigation when possible through a mix of direct regulation and voluntary dialogue with operators.

In May 2025, CARB began retrieving low-latency Tanager plume detections purchased from Carbon Mapper. Using a cloud-based system developed in-house, CARB employees oversee a semi-automated process to assign plumes to a source and facilitate information exchange with operators. The system generates a notification email with instructions and response forms tailored to the specific facility type (e.g. landfill, oil and gas, dairy biogas). These responses allow us to categorize emissions across sectors by emission type (e.g. unintentional, temporary, process) as well as identify sector-specific components or infrastructure (landfill gas collection system, gas well stuffing box) and details of any repairs.

CARB plans to expand its spatiotemporal coverage through additional satellites, with increased automation as we scale up. While the project’s initial focus is direct repair of unintentional leaks, operator responses also effectively survey underlying causes of point-source emissions and can inform future efforts to improve industry operational practices. CARB has dedicated community outreach funds to ensure methane observations are accessible, understandable, and useful to communities, and is committed to sharing technical details, project design, and lessons learned with other jurisdictions to maximize global mitigation efforts.

How to cite: Phillips, D., Yang, E., Schroeder, J., Zelinka, S., Frausto-Vicencio, I., Fibiger, D., and Herner, J.: From Detection to Mitigation: The California Satellite Methane Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14127, https://doi.org/10.5194/egusphere-egu26-14127, 2026.

We quantify climate effects of Tambora-scale eruptions under current and future warming using the SOCOL-MPIOM coupled atmosphere-chemistry-ocean model for 2020, SSP2-4.5, and SSP3-7.0 (2080) scenarios.

Global cooling amplifies counterintuitively with background warming: SSP3-7.0 shows 44% stronger cooling than present-day due to polar vortex intensification increasing from 5.5% to 44.5%. Regional responses reveal complex patterns: winter exhibits Arctic warming (+0.4-0.6°C) simultaneous with tropical cooling (up to -8°C) and continental extremes (up to +15°C). Summer brings widespread continental cooling (-2 to -4°C). Monsoon systems weaken by 20-35% while mid-latitude winter precipitation intensifies by 20-40%.

Results demonstrate that volcanic impacts under anthropogenic warming generate spatially heterogeneous extremes rather than uniform cooling, critical for agricultural and water resource risk assessment.

How to cite: Tkachenko, M. and Eugene, R.: Climate Response to Tambora-Scale Volcanic Eruptions Under Present and Future Climate Conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14645, https://doi.org/10.5194/egusphere-egu26-14645, 2026.

Cloud detection over snow- or ice-covered (S/IC) surfaces remains a critical challenge in satellite remote sensing. The cloud-like high surface albedo and ice-cloud-like brightness temperatures of these surfaces often lead to systematic misclassification in visible- and infrared-based algorithms, including the threshold-based cloud detection applied to Sentinel-5P atmospheric composition retrievals. Misclassified clear-sky scenes can introduce biases in the retrieved total columns of ozone, sulfur dioxide, and nitrogen dioxide, while misclassified cloudy scenes reduce the spatial coverage of satellite products.

To address this challenge, we develop a global cloud detection algorithm based on absorption images derived from Sentinel-5P oxygen A-band observations. The algorithm exploits the reduction of oxygen absorption in cloudy pixels, as cloud layers reflect solar radiation before it reaches the underlying surface, thereby shortening the radiative transfer path in the atmosphere and reducing absorption along the path. In addition, spatial texture information extracted from oxygen absorption images is incorporated to enhance sensitivity to optically thin and broken clouds, enabling more robust discrimination between clouds and bright underlying surfaces. This physical mechanism makes the algorithm insensitive to surface type, rendering it particularly suitable for global cloud detection, including over S/IC surfaces.

Validation against CALIPSO demonstrates a marked improvement in cloud detection performance across diverse surface and cloud conditions. The proposed algorithm achieves an overall accuracy of 91%, compared with 85% for the Suomi-NPP product and 48% for the operational Sentinel-5P product. Improvements are particularly pronounced over S/IC surfaces, where detection accuracy increases by 15% relative to Suomi-NPP. Additionally, detection accuracy for optically thin clouds improves by 20% globally, with the largest gains (up to 52%) observed over S/IC surfaces. These results demonstrate the value of oxygen absorption and spatial texture features for cloud detection, especially over S/IC surfaces, and support improved quality and consistency of satellite-based atmospheric observations over polar and other bright-surface regions.

How to cite: Liu, H. and Li, S.: Cloud Detection over Snow- or Ice-covered Surfaces Using Oxygen A-Band Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15531, https://doi.org/10.5194/egusphere-egu26-15531, 2026.

Simulations of aviation's air quality impacts near airports are critical for enhancing our understanding of how aviation impacts air quality regionally, and the potential effects of sustainable alternatives. In order to better understand such impacts, a research to investigate the effects of the resolution of the simulation grid and of the meteorology inputs on the air quality impact estimates due to aircraft emissions, specifically in the context of stretched gnomonic cubed-sphere grids for the simulation of a specific area was conducted. Such grids allow for the possibility of having a region with finer grid elements while coarsening the grid outside a specified area.

The research questions that the present research effort aims to address are: which parameter (grid resolution or meteorology resolution) impacts most the simulations, how grid and meteorology resolution impact air quality estimates, and whether stretched grids can be used for regional simulations.

To address the matter, we use the distributed-memory, high-performance version of the GEOS-Chem atmospheric chemistry-transport model to simulate the evolution of aviation attributable to Landing and Take-Off operations (LTO) emissions throughout the year of 2019. The LTO emissions were obtained from the OpenAVEM emissions inventory, whereas the remaining non-aviation emissions were taken from the default GCHP databases.

Three different grid resolutions were chosen to evaluate the impact of the horizontal grid resolution: C24, C36, and C48, with grid cell lengths ranging between 40 km, 30 km, and 20 km, respectively. All grids use the same stretch parameters, i.e., target latitude, target longitude, and stretch factor. These parameters were set so as to have a finer resolution around Europe. For the meteorology sensitivity, two resolutions were used, 2 ° ×2.25 ° and 0.5 ° ×0.625 ° from MERRA2 for the three grid resolutions aforementioned.

A comparison between the area weighted concentrations for NO₂, PM_2.5, and O₃ showed that the resolution of the meteorology plays a more important role than the horizontal grid resolutions, for the resolutions tested. For the human health impacts, the deaths attributable to each component have also been estimated and compared for each grid resolution.

How to cite: Kavabata, L., Quadros, F., Meijer, V., Snellen, M., and Dedoussi, I.: Horizontal grid and meteorology resolution impacts on aviation’s air quality impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15593, https://doi.org/10.5194/egusphere-egu26-15593, 2026.

Abstract. This work presents the incorporation of a tropospheric isoprene oxidation scheme into an Earth System Model to enhance the simulation of tropospheric ozone levels. Numerical experiments were performed using two distinct model setups: one accounting for isoprene oxidation and another in which this chemical pathway was not considered.

Keywords: Isoprene, tropospheric ozone, atmospheric chemistry, MIM1 mechanism

Understanding the processes of tropospheric ozone formation is of key importance both for the development of air quality control measures and for climate prediction, especially under conditions of changing anthropogenic and biogenic emissions. While on the global scale the production of tropospheric ozone is primarily governed by the oxidation of carbon monoxide and methane, in densely populated and industrial regions non-methane volatile organic compounds (NMVOCs) become the dominant contributors. Among these NMVOCs, isoprene plays a particularly important role, with the majority of its atmospheric emissions originating from vegetation.

The aim of this study is to further develop the INM RAS–RSHU chemical–climate model [1], which is a component of the Earth System Model (ESM), with an emphasis on a more accurate representation of tropospheric chemical processes. The primary focus is on the implementation of an improved chemical mechanism designed to enhance the accuracy of simulated concentrations of key atmospheric gaseous components. One of the main criteria in selecting the mechanism is achieving an optimal balance between the level of chemical detail and the computational efficiency of the model.

As part of the model development, a comparative analysis of several widely used chemical mechanisms was performed, including the Mainz Isoprene Mechanism (MIM1) [2], comprising 16 species and 44 reactions; MIM2, with 69 species and 178 reactions [3]; the Model for Ozone and Related Chemical Tracers (MOZART), including 151 species and 287 reactions [4]; and the Regional Atmospheric Chemistry Mechanism (RACM), which includes more than 100 species and 363 reactions [5]. Based on the results of this analysis, the MIM1 mechanism was considered the most appropriate for initial implementation in the ESM, as it was decided to begin with the most compact option while still providing sufficient accuracy in representing key tropospheric chemical processes.

To assess the impact of the MIM1 mechanism, two numerical experiments were conducted using identical model settings and boundary conditions. In the control simulation, a basic tropospheric chemistry scheme without isoprene was applied, whereas the MIM1 experiment implemented the full isoprene oxidation mechanism, including 44 chemical reactions.

 The study and the set of numerical experiments are aimed at optimizing the chemical component of the INM RAS–RSHU chemical–climate model in order to improve the accuracy of representing tropospheric processes while maintaining high computational efficiency. The obtained results provide a solid basis for further investigation of the interactions between chemical and dynamical processes in the atmosphere and will contribute to the development of approaches for forecasting atmospheric composition and its impact on regional and global climate change.

How to cite: Okulicheva, A., Tkachenko, M., and Smyshlyaev, S.: Numerical modeling of tropospheric chemistry in an Earth System Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17672, https://doi.org/10.5194/egusphere-egu26-17672, 2026.

The influx of anthropogenic metals into the atmosphere is expected to increase substantially due to the rapid growth of the space industry. More than 20 elements from re-entering spacecraft have been identified in sulphuric acid droplets in the Junge layer, with several estimated to surpass the background level from cosmic dust. While the atmospheric impact of these particles is uncertain, they have been widely hypothesised, including ozone destruction, increased polar stratospheric cloud formation, harmful surface deposition, a perturbed radiative balance and in turn, changes to global circulation.

The spacecraft ablation process and subsequent formation of space debris particles (SDPs) are not well defined. The dominant constituent of spacecraft is aluminium. If vaporised, aluminium is expected to undergo a series of reactions to form aluminium hydroxide (Al(OH)3). The initial form and size of the particles will strongly influence the coagulation, global transport, and atmospheric lifetime of the particles. Constraining these factors is vital to accurately assessing the impact SDPs have on the atmosphere.

This work provides an update on the work presented at EGU2025 (Egan et al., Modelling impacts of ablated space debris on atmospheric aerosols, EGU25-4460), using the Whole Atmosphere Community Climate Model with the Community Aerosol and Radiation Model for Atmospheres (WACCM-CARMA) to simulate the production and transport of SDPs. This work investigates the sensitivity of the initial particle radius to the transport, lifetime and surface deposition of particles.

How to cite: Hawkins, S., Egan, J., Plane, J., Marsh, D., and Feng, W.: Modelling the transport of ablated space debris particles in the atmosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19003, https://doi.org/10.5194/egusphere-egu26-19003, 2026.

Solid fuels such as fuelwood (FW), coal balls (CB), dung cake (DC), and agricultural residue (AR) are widely used for domestic heating and cooking in developing countries, particularly in rural and peri-urban regions. Combustion of these fuels is a major source of carbon-based gaseous emissions, notably carbon monoxide (CO) and carbon dioxide (CO₂), contributing to indoor air pollution, adverse health effects, and climate change. The emission characteristics of these gases are strongly influenced by fuel moisture content, elemental composition, and inorganic constituents. This study presents the development of emission factors (EFs) for CO and CO₂ from commonly used solid fuels and evaluates materials-based mitigation strategies using a laboratory-designed fixed-bed reactor system with a combustion chamber simulating real-world burning conditions.

Fuel samples were collected from representative domestic sources, air-dried, pulverized, and homogenized prior to analysis. Moisture content was determined gravimetrically by oven drying at 105 °C. Ultimate analysis of carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) was performed using a CHNS/O elemental analyzer, while anionic and cationic species were quantified using ion chromatography. Combustion experiments for emission factor development were conducted in a custom-designed fixed-bed reactor equipped with a controlled burning chamber to simulate domestic heating conditions. The reactor enabled stable combustion, controlled airflow, and downstream integration of mitigation materials for post-combustion treatment of exhaust gases. It also includes real-gas cylinders to generate the gas mixture representing smoke.

The results revealed notable variability in fuel composition and emission behavior. FW exhibited relatively efficient combustion with lower CO emissions, while DC, with higher moisture and lower carbon content, produced higher CO levels. CB showed high CO emissions despite its carbon content, whereas AR displayed intermediate emission characteristics. Elevated levels of alkali metals and anions, particularly in DC, were associated with reduced combustion efficiency and increased CO formation.

The hazards of these gases demands for removal. In this study, removal experiments were carried out by integrating advanced functional materials into the exhaust section of the fixed-bed reactor. Porous and nanostructured materials such as graphene-based materials, biochar, graphitic carbon nitride (g-C₃N₄), zeolites, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and silica-based materials were evaluated for CO oxidation and CO₂ capture. Material characterization using Brunauer–Emmett–Teller (BET) surface area analysis, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) confirmed high specific surface area, well-developed porous structures, and the presence of reactive surface functional groups, which directly enhance adsorption and catalytic conversion of carbonaceous gases.

Overall, the study demonstrates that fuel chemical composition and combustion conditions strongly influence CO and CO₂ emission factors from domestic heating activities. The integration of a designed fixed-bed reactor with a burning chamber and advanced materials-based mitigation strategies provides a robust experimental framework for reducing carbon-based emissions and improving air quality in regions dependent on traditional solid fuels.

How to cite: Sahu, D. and Pervez, S.: Emission of Carbon Monoxide (CO) and Carbon Dioxide (CO2) from Household Solid Fuels Burning Practices: Development of Real World Emission Factor and Removal Methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20738, https://doi.org/10.5194/egusphere-egu26-20738, 2026.

Field-based observations of Carbon dioxide (CO₂) exchange between soils and atmosphere are critical to accurately account for terrestrial carbon cycling in data-scarce West African savanna ecosystems. This study quantified soil CO₂ fluxes over two consecutive years (2023–2024) using a static chamber approach across four contrasting land-use systems namely forest, grassland, cropland, and rice fields. Measurements were conducted on weekly basis using replicated chambers to assess both spatial heterogeneity and interannual variability. Soil CO₂ fluxes were analysed in relation to key environmental drivers, including water-filled pore space (WFPS) and soil temperature, using mixed-effects statistical models to account for repeated chamber measurements. Across all land uses, CO₂ emissions increased markedly in 2024 compared to 2023. Median seasonal CO₂ fluxes ranged from 0.59 to 1.46 t C ha⁻¹ season⁻¹ in forest systems, 1.91 to 5.07 t C ha⁻¹ season⁻¹ in grasslands, 1.75 to 5.09 t C ha⁻¹ season⁻¹ in croplands, and 1.84 to 2.61 t C ha⁻¹ season⁻¹ in rice fields. Grasslands and croplands consistently exhibited the highest CO₂ emissions, with maximum values reaching 7.18 and 5.38 t C ha⁻¹ season⁻¹, respectively, highlighting the strong influence of land management and disturbance intensity. Forest soils showed comparatively lower CO₂ fluxes, reflecting reduced soil disturbance and more stable microclimatic conditions. Statistical analyses revealed that soil temperature was a dominant driver of CO₂ emissions across all ecosystems, while soil moisture exerted a secondary but significant control, particularly in managed systems. Higher WFPS and elevated soil temperatures during the wet season were associated with enhanced CO₂ release, indicating intensified microbial respiration and root activity. Interannual contrasts suggest that wetter and warmer conditions in 2024 amplified soil respiration across all land uses. Overall, our results demonstrate pronounced spatial and temporal variability in soil CO₂ fluxes in the Sudanian savanna and underscore the sensitivity of carbon emissions to land-use change and hydro-climatic variability. These findings provide critical baseline data for improving regional carbon budgets and for informing mitigation strategies in data-scarce tropical savanna regions.

How to cite: Oussou, F. E. and the WASCAL CONCERT Team: Assessing Spatial and temporal heterogeneity of Soil Carbon emissions across anthropized land use Gradient in the Sudanian savanna, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21511, https://doi.org/10.5194/egusphere-egu26-21511, 2026.

Anthropogenic fires linked to the burning of agricultural biomass residues represent a recurring environmental disturbance in the province of Tucumán, northwest Argentina. These events are predominantly associated with land clearing and post-harvest burning in sugarcane fields concentrated in the region’s lowland plains, where agro-industrial activity is most intense. Such practices contribute significantly to greenhouse gas (GHG) emissions, vegetation degradation, and a range of socio-economic impacts. This study integrates satellite-derived fire indices with environmental economic tools to quantify the spatial and temporal effects of fire over the past five years (2021–2025) and assess their implications for climate change mitigation and policy.

Multispectral data from Sentinel‑2 and atmospheric composition products from Sentinel‑5P were processed via Google Earth Engine to calculate vegetation and fire severity indices including NDVI, dNBR, and BAI. Additionally, tropospheric CO and CO₂ concentrations were used to evaluate atmospheric impacts. The spatial distribution of fire activity—primarily in the eastern and southern lowlands—was cross-referenced with the Global Fire Emissions Database (GFED) and IPCC Tier 1 emission factors to estimate fire-related GHG emissions. Preliminary analyses indicate an average of 45,000 ha affected annually, mainly in sugarcane-dominated landscapes, resulting in estimated emissions of approximately 382,500 t CO₂-equivalent per year.

To evaluate broader socio-environmental impacts, economic losses were estimated across multiple dimensions: reduced land productivity, costs of ecosystem restoration, loss of ecosystem services (e.g. carbon sequestration, water retention), and public health expenses related to degraded air quality. Additional indirect impacts include traffic accidents due to smoke-induced low visibility and recurring property damage reported in local media. These preliminary estimates suggest combined annual damages of approximately USD 46.5 million, underscoring the considerable burden imposed by current fire management practices.

It is important to note that this work presents ongoing research, and all results are preliminary. The estimates provided will be further refined through continued integration of field data, emission modeling, and economic valuation methods.

This integrative approach demonstrates the value of combining Earth observation technologies with environmental economics to support climate-oriented decision-making. By quantifying the environmental and economic impacts of anthropogenic fires, this study provides critical evidence for the development of cross-sectoral policies aimed at regulating biomass burning, improving land management practices, and strengthening resilience to climate risks. The case of Tucumán underscores the urgent need for sustainable alternatives to current residue management practices and for aligning agricultural production with mitigation goals.

How to cite: Reynoso Posse, F., Zbrun Luoni, J. P., and Aguilera Sammaritano, M.: Greenhouse gas emissions and socio-environmental costs of anthropogenic fires in Tucumán (Argentina): A remote sensing and environmental economics approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21709, https://doi.org/10.5194/egusphere-egu26-21709, 2026.

The new satellite missions including active sensors (e.g. EarthCare), passive multi-angular polarimeters (e.g. PACE/SPEXone, PACE/HARP-2) and single-viewing instruments (e.g. OLCI), together with synergies among existing sensors, are foreseen to characterize aerosols and clouds with high accuracy. However, robust validation activities are essential to ensure the quality of the new satellite products.

In this study, we focus on the evaluation of the aerosol optical properties synergistically retrieved from three sensors, i.e., TROPOMI, OLCI-A, and OLCI-B, within the framework of the AIRSENSE ESA project (https://www.grasp-earth.com/portfolio/airsense/). The derived optical properties include the aerosol optical depth (AOD), Ångström exponent (AE), coarse- and fine-mode AOD, and single-scattering albedo (SSA). Validation is performed against ground-based sun-photometer observations from five ACTRIS/AERONET stations across Europe (https://aeronet.gsfc.nasa.gov/). The results show good agreement for AOD with root-mean-square errors (RMSE) ranging from 0.006 to 0.09. In contrast, AE and SSA show lower agreement, with RMSE values of 0.27 and 0.02, respectively, at the Limassol station, even when quality flags are applied.

Moreover, we evaluate the aerosol properties retrieved using PACE/SPEXone observations. PACE (Plankton, Aerosol, Cloud, and ocean Ecosystem) mission was launched in February 2024 and employs advanced passive polarimetric observations to enhance the aerosol characterization. In addition to aerosol optical properties (e.g., AOD, AE) the PACE/SPEXone products generated within the framework of AIRSENSE, include the aerosol layer height (ALH), a parameter that is critical for quantifying aerosol-cloud interactions. Since EarthCARE/ATLID provides vertically resolved aerosol profiles, it offers an independent reference for the assessment of ALH. Here, we present first comparison results of the PACE/SPEXone ALH product over the ocean, produced with two algorithms, RemoTAP and FastMAPOL, compared to EarthCARE/ATLID weighted backscatter heights. Overall, RemoTAP ALH products are systematically lower than those derived from EarthCARE/ATLID, whereas FastMAPOL retrieves a larger number of ALH estimates but exhibits lower overall agreement with the EarthCARE/ATLID reference. As a next step, we intend to expand the area of interest and increase the number of collocations.

Acknowledgements:

This research is financially supported by the PANGEA4CalVal project (Grant Agreement 101079201) funded by the European Union  and the AIRSENSE (Aerosol and aerosol cloud Interaction from Remote SENSing Enhancement) project, funded by the European Space Agency under Contract No. 4000142902/23/I-NS.

How to cite: Voudouri, K. A., Tsekeri, A., Karipis, A., Litvinov, P., Lopatin, A., Dubovik, O., Hasekamp, O., and Amiridis, V.: Evaluation of new polarimetric products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21713, https://doi.org/10.5194/egusphere-egu26-21713, 2026.

This work evaluates EarthCARE cloud products against ground-based Cloudnet retrievals at multiple sites in Europe. We focus on the comparison between the EarthCARE synergetic target classification product (AC-TC), with the Cloudnet target classification product, both derived from the synergy of lidar/radar measurements. As the two classifications have different aerosol/cloud types, a new common classification with the following classes is defined and used for direct comparison: Unknown, Clear, Liquid (Droplets T>0°C), Supercooled Liquid (Droplets T<0°C), Drizzle or rain, Drizzle & droplets, Ice, Ice & droplets, Melting ice possibly coexisting with droplets, Insects, Aerosol. Each AC-TC or Cloudnet target is assigned with a new class. Spatiotemporal collocation criteria are considered, along with visual inspection of the collocated scenes, to limit the dataset in homogenous scenes where the satellite and suborbital platform has detected similar clouds. Additionally, retrieved ice and liquid water cloud contents from Cloudnet and EarthCARE are compared to evaluate cloud microphysical properties. Τhe geographical diversity of the Cloudnet network, provides the advantage of investigating different atmospheric conditions in terms of clouds and aerosols, with abundant ice cloud occurrences in the northern sites and frequent liquid water clouds at the southern sites. This analysis aims to assess the consistency of cloud categorization and microphysical retrievals between the satellite and suborbital measurements, and to investigate the strengths and limitations of both approaches.

How to cite: Tsikoudi, I., Marinou, E., Pfeitzenmaier, L., Mashon, S., O'Connor, E., Karkani, D., Karipis, A., Voudouri, K. A., Kollias, P., Treserras, B. P., and Battaglia, A.: Cloud typing and microphysics: An EarthCARE-Cloudnet Comparison, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21851, https://doi.org/10.5194/egusphere-egu26-21851, 2026.

Remote sensing of aerosol variability over the Central Himalayas remains challenging because of complex terrain, strong elevation gradients in surface reflectance, and frequent cloud and snow contamination, which can bias passive aerosol optical depth (AOD) retrievals. In this study, we examine an apparent MODIS-derived AOD enhancement in 2011 over the Central Himalayas that deviates from the expected seasonal pattern of reduced aerosol loading during the monsoon and post-monsoon periods. The central objective is to determine whether this apparent anomaly represents a physically meaningful aerosol enhancement or is influenced by retrieval limitations in high-relief environments. We evaluate the MODIS anomaly using collocated CALIPSO observations, including vertically resolved aerosol extinction profiles and aerosol-layer optical depths. CALIPSO measurements show no evidence of persistently elevated aerosol layers corresponding to the MODIS enhancement, and aerosol extinction remains vertically shallow, indicating that the observed AOD anomaly is not associated with strong free-tropospheric aerosol intrusion. These results suggest that the apparent MODIS “spike” likely reflects a column-integrated enhancement dominated by near-surface aerosol and/or terrain–cloud–snow-related retrieval effects rather than a sustained elevated aerosol event. This study highlights the importance of integrating active lidar profiling with passive satellite retrievals to improve the interpretation of aerosol anomalies over mountainous regions and strengthens the basis for aerosol–cloud interaction assessments in the Himalayas.

How to cite: Bello, A., Bhardwaj, A., and Sam, L.: CALIPSO validation of an apparent MODIS AOD spike over the Central Himalayas (2011), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23052, https://doi.org/10.5194/egusphere-egu26-23052, 2026.

Tropospheric Emissions: Monitoring of Pollution (TEMPO) is observing air quality and
atmospheric composition over North America from a geostationary orbit since its operations
started in August 2023. TEMPO observes the continent every 40 to 60 minutes at a spatial
resolution on the order of ~ 2 x 4.5 km 2 . Together with the Geostationary Environment
Monitoring Spectrometer (GEMS, launch 2020) monitoring Asia and the Sentinel-4/UVN
(launch 2025) monitoring Europe, TEMPO is part of the current global constellation of
geostationary sensors devoted to the observation of air quality. Like GEMS and Sentinel-4/UVN,
TEMPO uses backscattered ultraviolet and visible solar radiation to retrieve atmospheric
amounts of key trace gases and aerosols associated with air quality and atmospheric chemistry.
Among the species retrieved from TEMPO observations of nitrogen dioxide and formaldehyde
are important to understand emissions and atmospheric chemistry, including the formation and
destruction of tropospheric ozone.

After multiple version updates over the first two years of the mission, the TEMPO Level 2 NO 2
and HCHO products have undergone significant enhancements to improve the performance and
accuracy of the slant column retrievals, air mass factor calculations and post-processing
corrections including destriping for NO 2 and background for HCHO. We illustrate the
performance of both retrievals (version 3 & 4), evaluating their fitting uncertainty and showing

comparisons with independent correlative measurements and other satellite products showcasing
small noise levels and remarkable accuracy with well quantified biases. We continue by
illustrating the capacity of TEMPO products focusing on different case studies showing
TEMPO’s high temporal and spatial resolution. We finalize discussing aspects of the retrieval
subject to improvement and our plans to address them.

How to cite: Gonzalez Abad, G., Nowlan, C. R., Chance, K., Liu, X., Chong, H., Fasnacht, Z., Flittner, D. E., Ghahremanloo, M., Henderson, B., Hou, W., Houck, J., Judd, L., Knowland, K. E., Shah, V., Wales, P., Qin, W., Valin, L., and Wang, H.: First two years of TEMPO nitrogen dioxide and formaldehyde observations:algorithm status and highlights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23274, https://doi.org/10.5194/egusphere-egu26-23274, 2026.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission is part of a global constellation of geostationary satellites, along with GEMS and Sentinel-4, dedicated to monitoring air quality across the Northern Hemisphere. TEMPO is the first geostationary satellite instrument to monitor air pollutants over North America on an hourly basis at nearly neighborhood-scale resolution, covering an area from Mexico City to the Canadian oil sands and from the Atlantic to the Pacific Ocean.TEMPO measures backscattered ultraviolet and visible radiation to observe several trace gases important to air quality, including ozone, nitrogen dioxide, and formaldehyde, with observations every 40–60 minutes and at a high spatial resolution of approximately 2 × 4.75 km². TEMPO was successfully launched in April 2023 and began nominal operations in October 2023. Since then, it has been continuously monitoring atmospheric pollutants across its observation domain.
This presentation summarizes the Version 4 (V04) updates and improvements to the TEMPO total-ozone (O3TOT) and ozone-profile (O3PROF) retrieval algorithms. This presentation also presents the evaluation of the upcoming V04 TEMPO O3TOT product through comparisons of total ozone columns (TOCs) with measurements from other satellite instruments (e.g., OMPS and TROPOMI) and ground-based instruments, including Pandora, Brewer, and Dobson spectrometers. The V04 TEMPO O3PROF algorithm, which is UV-only, is validated through comparisons of ozone profiles, tropospheric ozone, and 0–2 km ozone columns with those from the Tropospheric Ozone Lidar Network (TOLNet) and aircraft observations, as well as through validation with MLS, EPIC, TROPOMI, and OMI observations.

How to cite: Park, J., Liu, X., Bak, J., Chong, H., Chance, K., Hou, W., Houck, J., Abad, G. G., Nowlan, C. R., Wang, H., Yang, K., Flynn, L. E., Haffner, D. P., Flittner, D. E., Knowland, K. E., Johnson, M., Demetillo, M. A. G., Spurr, R., Li, C., and Zhao, X. and the TEMPO Validation Team and NASA's GMAO Team: The Status of the TEMPO Total-Ozone and Ozone-Profile Algorithm: V04 Updates and Comprehensive Evaluations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23276, https://doi.org/10.5194/egusphere-egu26-23276, 2026.

Low-cost air quality sensors (LCS) offer opportunities for expanding air monitoring networks in regions where reference-grade instrumentation is limited. Within the framework of the Improving Air Quality in West Africa (IAQWA) project, we deployed Real-time Affordable Multi-Pollutant sensors (RAMPs) in Abidjan (Côte d'Ivoire) and Accra (Ghana), to characterize fine particulate matter (PM_2.5) in contrasting urban environments. Prior to field deployment, each RAMP underwent a co-location period with reference monitors, and city-specific multilinear calibration models were developed incorporating both RAMP-reported PM_2.5 and relative humidity (RH). These calibration models were applied to correct the sensor data and improve measurement reliability under varying atmospheric conditions.

From February 2020 to June 2021, five (5) measurement sites in Abidjan and four (4) sites in Accra were monitored using a 15-second temporal resolution. These sites were selected to represent the dominant pollution sources in West Africa, particularly domestic fires and road traffic. The calibrated dataset enabled comparative analysis of diurnal, daily, and seasonal PM_2.5 variability. Both cities exhibited pronounced morning PM_2.5 peaks associated with traffic, while evening increases were more visible in residential areas, indicating contributions from domestic combustion. Seasonal contrasts were marked, with highest concentrations occurring during the long dry season (Harmattan), when long-range Saharan dust transport significantly enhanced particulate loading. During an intense dust episode in January 2021, calibrated RAMP data underestimated PM_2.5 relative to reference measurements, highlighting a known limitation of optical LCS under high mineral dust conditions.

Annual mean PM₂.₅ concentrations ranged from 17 to 26 µg m^-3 across sites, exceeding both the 2005 and 2021 WHO air quality guidelines. Variability within each city, especially between traffic-influenced and urban background locations, was greater than variability between the two cities. These findings demonstrate both the value of rigorously calibrated low-cost sensors for improving air quality knowledge in data-scarce urban regions, and the need for sensor performance considerations in environments influenced by episodic dust intrusions.

How to cite: Bahino, J., Giordano, M., Beekmann, M., Ramanchandran, S., and Yoboué, V.: Field-Calibrated Low-Cost Sensor Networks for PM2.5 Monitoring in West African Urban Environments: Insights from Abidjan and Accra, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-130, https://doi.org/10.5194/egusphere-egu26-130, 2026.

Harmful Algal Blooms (HABs) represent a growing threat to marine ecosystems, aquaculture, and public health in the Central Eastern Arabian Sea (CEAS). This study utilized 18S rRNA metabarcoding to characterize the absolute abundance and community composition of potentially toxigenic diatoms and dinoflagellates in the coastal waters of Goa. The analysis reveals a distinct and alarming prevalence of multiple genera associated with diverse toxin syndromes.

The dataset was dominated by a massive proliferation of the dinoflagellate Karenia (linked to Neurotoxic Shellfish Poisoning and ichthyotoxicity), which reached extreme abundances exceeding 51,000 reads per sample at the most impacted sites. Co-occurring with this bloom, spatially distinct hotspots of Paralytic Shellfish Toxin (PST) producers were identified, specifically Alexandrium and Gymnodinium spp., with Alexandrium counts peaking at over 5,200 reads. Notably, the potent PST producer Alexandrium tamiyavanichii was positively identified, alongside detections of Gymnodinium catenatum.

The diatom community also exhibited significant toxicity potential; the Amnesic Shellfish Poisoning (ASP) genus Pseudo-nitzschia displayed high relative abundance (up to ~3,700 reads), including the presence of P. pungens. Furthermore, vectors for Diarrhetic Shellfish Poisoning (DSP), including Dinophysis spp. and Phalacroma rotundatum, and Yessotoxin producers (Lingulodinium polyedra, Gonyaulax spinifera) were ubiquitously present at lower background levels.

These findings highlight a complex, multi-risk scenario where ASP, PSP, NSP, and DSP vectors coexist within the same coastal system. The distinct spatial separation observed between peak Karenia, Alexandrium, and Pseudo-nitzschia events suggests that heterogeneous environmental drivers are influencing specific HAB assemblages. This data underscores the critical need for broad-spectrum toxin monitoring beyond single-species surveillance in the region.

How to cite: Zedi, S. and Khandeparker, R.: Hidden Hazards in the Central Eastern Arabian Sea: Metabarcoding Reveals Co-occurrence of ASP, PSP, and NSP Vectors in the Coastal Waters of Goa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-414, https://doi.org/10.5194/egusphere-egu26-414, 2026.

Secondary organic aerosol (SOA) has been shown to significantly impact climate, air quality, and human health. Hydroxyl dicarboxylic acids (OHDCA) are generally of secondary origin and ubiquitous in the atmosphere, with high concentrations in South China. This study explored the formation of representative OHDCA species based on time-resolved measurements and explainable machine learning. Malic acid, the most commonly studied OHDCA, had higher concentrations in the noncontinental air (63.7 ± 33.3 ng m^–3) than in the continental air (7.5 ± 1.4 ng m^–3). Machine learning quantitatively revealed the high relative importance of aromatics and monoterpenes SOA, as well as aqueous processes, in the noncontinental air, due to either shared precursors or similar formation pathways. Isoprene SOA, particle surface area, and ozone corrected for titration loss (O_x) also elevated the concentrations of malic acid in the continental air. Aqueous photochemical formation of malic acid was confirmed given the synergy between LWC, temperature, and O_x. Moreover, the OHDCA-like SOA might have facilitated a relatively rare particle growth from early afternoon to midnight in the case with the highest malic acid concentrations. This study enhances our understanding of the formation of OHDCA and its climate impacts.

How to cite: Li, H. and Lyu, X.: Investigating the formation mechanisms of hydroxyl dicarboxylic acids based on machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2796, https://doi.org/10.5194/egusphere-egu26-2796, 2026.

Air quality over western India is impacted by both regional emissions and transport from the Indo-Gangetic Plain (IGP). Effective mitigation requires identifying the dominant emission sectors that govern regional air quality during different seasons. In this regard, the present study examines the impact of emissions from major anthropogenic sectors (power, residential, transport and industries) on ambient fine particulate matter (PM_2.5)concentrations over the western Indian region. For this, high-resolution (12km x 12km) regional model (WRF-Chem v3.9.1) simulations have been conducted using the EDGAR v5.0 inventory for anthropogenic emissions. Simulations are conducted for the year 2019, for winter and post-monsoon seasons when PM_2.5 concentrations are typically elevated in this region, and anthropogenic emissions effects are the highest. During winter, the residential sector is seen to dominate, contributing 20% to PM_2.5 concentration over western India, followed by power (9%) and industry (∼8%). The trans-regional pollution from the IGP and central India is also dominated by residential emissions (25%), followed by the power sector (∼8%). In contrast to winter, the dominant source during post-monsoon is the power sector (∼14%), followed by the industry (∼12%) and the residential (∼9%) sectors. Trans-regional impact also shows a similar pattern and dominance of power (∼15%) and industrial (∼10%) sectors. This seasonal shift in the dominant sector is driven by the seasonal variation in emissions. The results also reveal large spatial heterogeneity in sectoral influence, highlighting that dominant emission sources vary at the local scale within western India in both seasons. The study offers model-based insights for more effective planning of regional air pollution mitigation in western India.

How to cite: Shekhar, S., Dhaka, S., Vaishya, A., Ojha, N., Pozzer, A., and Sharma, A.: Sector-segregated anthropogenic impacts on PM2.5 over western India based on regional air quality modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5792, https://doi.org/10.5194/egusphere-egu26-5792, 2026.

 Background and Objective

Exposures to fine and ultrafine particles (i.e., PM_2.5 and PM₁) are widely accepted as a major environmental risk factor and is known to cause adverse human health outcomes. Most epidemiological research as well as regulatory frameworks rely on using PM_2.5 as the ‘reference’ exposure metric to assess health risks. However, this approach does not adequately quantify size-specific PM effects that are critical for dose-based health assessments. Size-segregated particulate matter assessment of exposures and effects are limited in resource-limited settings. The objective of this study is to estimate lung deposition doses for size-fractionated PM measured using low-cost sensors.

Methods

We utilized the data obtained from an ongoing study conducted in South Indian villages. Here, the household energy use is dominated by biomass combustion and the adoption of cleaner cooking fuels like liquefied petroleum gas (LPG) is relatively low. Ambient PM measurements were carried out continuously over a period of 1 year in 80 rural households in southern India, using real-time, optical, low-cost PM sensors. In order to capture the household-level exposure characteristics, indoor PM measurements were also carried out in a subset of households. Minute averaged, PM mass concentrations in three discrete size fractions: PM₁, PM₂.₅ and PM₁₀ were provided by the low-cost sensors.  The temporal variability in PM concentrations was derived using the time-series data obtained from the sensors. Daily and monthly mean concentrations captured the short-term exposure peaks as well as day to day variability.

Results  

A mathematical model using a non-linear least squares method was developed to transform the measured PM concentrations into a continuous size distribution. Respiratory deposition doses were estimated by feeding the size distribution to a computational model of the lung designed to simulate the spatial and temporal distribution of particles within the human respiratory system, incorporating various deposition models. The estimates of deposition doses ranged from ~0.2µg/min to ~1µg/min in the total lung. The coarse particles contributed to about 20% of the total lung dose, whereas the remaining 80% of the respiratory dose was predominantly of fine and ultrafine particles.

Conclusions

This study demonstrates that physiologically relevant, size-fractioned lung deposition doses can be estimated using limited size-bin data obtained from low-cost sensors. Since low-cost air quality monitoring networks are critical in regions that lack regulatory-grade instrumentation, the proposed analytical framework provides a benchmark for translating low-cost sensor-based air pollution measurements into relevant health-based dose metrics. The proposed analytical framework can be readily modified to incorporate satellite-derived PM inputs alongside low-cost sensor data, enabling improved spatial scaling of size-resolved, dose-relevant exposure estimates.

How to cite: Sai Ganesan, K. D., Puttaswamy, N., Sendhil, S., Natesan, D., Ramasami, R., Desai, M., Pillarisetti, A., Vakacherla, S., Krishnan, R., Sambandam, S., Ramaswamy, P., and Balakrishnan, K.: Estimating Size-Resolved Lung Deposition Doses of Particulate Matter (PM) Using Low-Cost Sensor Data in Rural India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6264, https://doi.org/10.5194/egusphere-egu26-6264, 2026.

The growing urgency of climate action at the city level has led to an exponential rise in documents that describe a city’s policy, action plan, or progress towards climate action. The increased number of documents has made it increasingly difficult for governments to track commitments and compare approaches across jurisdictions. These documents are essential for informed decision-making, but extracting useful information from unstructured PDF reports remains a largely manual, resource-intensive, and inconsistent process. Recent advances in AI and large language model (LLM) based document understanding offer strong potential, but their application in urban climate governance workflows is still limited. Integrating AI-driven document analysis into this workflow offers opportunity for building scalable, standardized, and transparent climate policy assessment.

This study presents an AI-assisted natural language processing (NLP) pipeline that automatically extracts, segments, and classifies climate actions from diverse policy documents. The workflow integrates layout-aware text extraction with an action-segmentation mechanism to identify action statements across heterogeneous formats. A fine-tuned, two-stage ClimateBERT classifier then categorizes actions: Stage 1 differentiates mitigation and adaptation measures (F1 = 93%), while Stage 2 assigns domain-specific sub-categories, achieving 92% F1 for mitigation and 91% for adaptation. An equity-detection module further identifies references to vulnerable groups, inclusivity, and justice-oriented themes.

The pipeline significantly reduces manual review effort and enhances consistency in understanding climate action. By enabling standardized comparisons, the approach directly supports mayors, policymakers, and urban practitioners in evaluating progress and designing more effective and equitable interventions.

As AI capabilities advance, such automated tools will strengthen climate governance by improving the accessibility, reliability, and strategic value of climate policy data.

How to cite: Sahu, S. and Shanker, S. and the MU NLP team: Automated Analysis of City Level Climate Action Plans using Natural Language Processing Technique, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6386, https://doi.org/10.5194/egusphere-egu26-6386, 2026.

Nitrogen oxides (N₂O, NO, and NO₂) serve as critical linkages connecting climate systems, ecosystems, and atmospheric chemistry, with soils acting as a primary natural source. Adopting a multi-scale framework spanning global, regional, and field scales, we systematically examine the spatiotemporal heterogeneity of nitrogen oxide emissions from cropland soils. Spatially, emissions exhibit a latitudinal gradient, decreasing from low to high latitudes, with hotspots concentrated in agriculturally intensive regions. Temporally, emissions display multi-scale rhythmic patterns aligned with crop growth stages, seasonal cycles, and diurnal variations, tightly coupled to soil carbon-nitrogen transformation processes. From the perspective of carbon-nitrogen coupling mechanisms, we reveal how land management practices—including nitrogen fertilization, conservation tillage, and precision irrigation—regulate emissions by modulating soil organic carbon content, carbon-nitrogen ratios, and pore structure. Concurrently, climate change drivers such as rising temperatures, elevated CO₂ concentrations, and extreme precipitation alter microbial-mediated carbon-nitrogen transformation efficiency, collectively shaping the core mechanisms governing nitrogen oxide emissions. A meta-analysis further investigates light effects on soil nitrogen oxide emissions, demonstrating significant impacts: light exposure increased N₂O and NO fluxes by 57.28% and 116.19%, respectively. Notably, heightened UV-B radiation reduced N₂O emissions by 6.85%, whereas shading increased them by 77.23%, with crop-specific responses observed. Mechanistically, light regulates emissions by modifying soil physicochemical properties and restructuring nitrogen-cycling microbial communities. Current emission mitigation faces challenges, including underdeveloped monitoring systems, limited prediction accuracy due to multifactor coupling complexities, and poor regional adaptability of existing technologies. Integrating multi-source data (field observations, remote sensing inversion, laboratory experiments) with advanced modeling approaches—such as climate-soil-crop coupling models and machine learning algorithms—offers viable pathways to enhance emission prediction precision and optimize mitigation strategies. Looking ahead, priorities include establishing multi-scale automated monitoring networks, developing carbon-nitrogen coupling-driven predictive models, promoting regionally tailored carbon sequestration and nitrogen emission reduction technologies, and combining policy incentives with public engagement to reduce uncertainties in global carbon-nitrogen cycle projections. These efforts aim to strengthen scientific support for sustainable agricultural development.

How to cite: Li, D. and Shen, W.: Nitrogen oxide emissions from cropland soil: spatiotemporal heterogeneity, carbon-nitrogen coupling mechanisms, and mitigation strategies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9022, https://doi.org/10.5194/egusphere-egu26-9022, 2026.

ABSTRACT

The Taihu Lake region has experienced rapid land use intensification, characterized by conversions from natural wetlands (NW) to conventional rice-wheat rotation fields (RW) and further to greenhouse vegetable fields (GH), driven by economic interests. While such transformations are widespread, their combined effects on greenhouse gas (GHG) emissions and underlying soil microbial mechanisms remain poorly understood. This integrated study addresses these gaps through multi-faceted analyses of GHG fluxes, soil microbial communities, and nitrogen (N)-cycling functional genes across NW, RW, and GH sites. Two-year in-situ field experiments revealed significant GHG emission shifts: land use intensification reduced methane (CH₄) emissions (NW: 970.66 ± 100.09 kg C ha⁻¹; RW: 896.71 ± 300.44 kg C ha⁻¹; GH: 71.23 ± 63.62 kg C ha⁻¹) but markedly increased nitrous oxide (N₂O) emissions (NW: 3.35 ± 0.44 kg N ha⁻¹; RW: 14.38 ± 4.09 kg N ha⁻¹; GH: 81.62 ± 4.89 kg N ha⁻¹). Global warming potential followed the order RW > NW > GH, indicating intensified comprehensive greenhouse effects during NW→RW conversion and mitigation during RW→GH conversion. Microbial community analyses showed land use intensification directly altered bacterial and fungal compositions, with stronger impacts on bacteria. Bacterial communities correlated closely with soil NO₃⁻-N, pH, and electrical conductivity, exhibiting decreased deterministic processes (opposite to fungi). Arable lands (RW/GH) displayed more complex microbial networks, and seasonal variations (notably summer) influenced microbial diversity and function, though less strongly than land use effects. Integrating quantitative PCR and metagenomics uncovered microbial mechanisms driving N₂O emissions: intensification reshaped N-cycling microbial communities, depleting nitrogen fixation, dissimilatory nitrate reduction to ammonium, and anammox marker genes in GH soils. Denitrifying communities segregated similarly to total N-cycling assemblages, with increased network complexity but divergent stability. Critically, intensification amplified N₂O emission potential by elevating Pseudomonadota harboring nirK/norB genes (and associated communities) while reducing nosZ (encoding N₂O reductase) abundance—directly linking microbial functional imbalance to emission increases. Collectively, this study demonstrates that land use intensification in the Taihu Lake region drives GHG emission trade-offs (reduced CH₄ but amplified N₂O) and restructures soil microbial communities and N-cycling functions. These findings highlight the need to prioritize microbial functional balance (e.g., restoring nosZ-carrying taxa) in mitigation strategies, providing critical insights for sustainable land management in wetland-agricultural transition zones.

Acknowledgment

This study was funded by the National Key Research and Development Program of China (2023YFF0805403, 2025YFD1700403) and National Natural Science Foundation of China (42377311).

How to cite: Shen, W., Xiong, R., Qin, D., and Gao, N.: Integrated impacts of land use intensification on greenhouse gas emissions and soil microbial communities in the Taihu Lake Region: patterns, mechanisms, and implications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9373, https://doi.org/10.5194/egusphere-egu26-9373, 2026.

Dense fog events across India severely disrupt aviation, surface transportation, and daily activities during winter months, with northern districts experiencing extended periods of visibility issues. Building upon the WRF-based ensemble fog forecasting over the Indo-Gangetic Plain and BOFFIN-Melbourne's Bayesian Decision Network framework, this study proposes a continuous risk monitoring and decision support system at district-level (admin-2).

The operational system will conduct daily continuous risk assessment, leveraging satellite observations from MODIS/VIIRS/INSAT-3D and the ECMWF IFS ensemble forecasts (51 members, 0.25° resolution) including probabilistic meteorological predictions of temperature, dewpoint, wind speed, boundary layer height, relative humidity profiles, and cloud cover, to characterize antecedent fog conditions and to establish baseline occurrence patterns.

 A Bayesian Network will integrate these layers to provide real-time short-term forecasts using pre-defined conditional probability tables which encode relationships between stable boundary layer conditions, radiative cooling, and regional fog formation mechanisms. The operational output of the algorithms will be in the form of traffic light decision matrix for each district: Green (Minimal/Low risk - Monitor), Yellow (Moderate risk - Be Aware), Orange (High risk - Be Prepared), Red (Extreme risk - Take Action).

This paper will present the validation results from pilot districts and the development framework for scaling to nationwide continuous risk assessment, demonstrating the system's potential for proactive decision-making in transportation management, aviation operations, and public safety advisories.

References

• Parde, Avinash N., et al. "Operational probabilistic fog prediction based on ensemble forecast system: A decision support system for fog." Atmosphere 13.10 (2022): 1608.
• Boneh, Tal, et al. "Fog forecasting for Melbourne Airport using a Bayesian decision network." Weather and Forecasting 30.5 (2015): 1218-1233.

How to cite: Guttikunda, S. K., Kalladath, N., Tucci, R. R., Ouma, J., Amdihun, A., and Dammalapati, S. K.: Fog Risk Monitoring and Assessment for India Using Bayesian Networks and ECMWF IFS Ensemble Prediction System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9696, https://doi.org/10.5194/egusphere-egu26-9696, 2026.

Understanding vegetation responses to climate variability is essential for assessing long-term ecosystem dynamics. Leaf Area Index (LAI) is a widely used variable to characterise vegetation state and productivity. However, attributing observed global LAI trends to specific climatic drivers remains challenging due to non-linear interactions, strong spatial heterogeneity, and scale-dependent processes.

This study is conducted within the framework of the PROFECIA project, which aims to improve the monitoring and interpretation of vegetation responses to climate change by combining remote sensing observations and artificial intelligence techniques. We analyse global LAI trends over the period 1982–2022 using the GEOV2-AVHRR long-term satellite record and examine their relationship with trends in key climatic variables obtained from the ERA5 reanalysis, including temperature, precipitation, radiation, and several indicators of water availability and drought conditions. All trends are computed consistently over the 1982-2022 temporal record to ensure a homogeneous assessment of long-term vegetation–climate relationships at the global scale.

The vegetation–climate relationships are modelled using a suite of machine learning approaches, including tree-based methods and neural networks, designed to capture non-linear responses across diverse climatic and ecological conditions. Particular emphasis is placed on the role of the training strategy: different spatio-temporal sampling schemes are evaluated to assess their impact on model performance, robustness, and generalisation capability when analysing long-term trends at the global scale.

To move beyond purely predictive modelling, the study systematically applies explainable artificial intelligence (XAI) techniques to interpret the trained models. Methods such as SHAP-based attribution and partial dependence analyses are used to quantify the relative contribution of individual climatic drivers to observed LAI trends and to examine how these contributions vary across regions and time periods.

Overall, this work highlights the importance of combining robust machine learning training strategies with interpretability tools to improve the attribution of long-term vegetation trends to climatic drivers, providing new insights into global vegetation–climate interactions over the last four decades.

How to cite: García-Diaz, D., Aguilar, F., Schauman, S., and Verger, A.: Machine learning analysis of global LAI trends and their relationship with climate variability (1982–2022), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10657, https://doi.org/10.5194/egusphere-egu26-10657, 2026.

Particulate matter (PM) pollution represents a significant public health concern, in Lima, Peru. This issue is further compound by the lack of accurate forecasting tools due to limited monitoring networks. This study addresses this gap by developing and validating a hybrid model combining a Multilayer Perceptron (MLP) neural network and a type of rule-based Cellular Automata (CA) simulation. This model simulates and forecasts the spatiotemporal dispersion of PM10 and PM2.5. Using a decade of historical PM data (2015-2024) from seven monitoring stations and NASA's meteorological data, an optimized MLP was trained to learn the complex, non-linear transition rules from 47 engineered features. The model demonstrated remarkable performance in historical validation (R² > 0.90), outperforming standard baseline models. When fed with weather forecast data, the model can operate as an Early Warning System (EWS), providing a reliable prediction horizon to anticipate the exceedance of Air Quality Standards. The resulting hotspot maps accurately identify high-risk areas, confirming the potential of this hybrid model as a robust, proactive, and quantitative tool for air quality management and public health protection in complex urban environments.

How to cite: Maita, B., Condezo, P., Llanto, J., Huaman, S., and Sanabria, J.: A Hybrid Neural Network and Cellular Automata Model for spatiotemporal Forecasting of PM10 and PM2.5 in Lima, Peru, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10823, https://doi.org/10.5194/egusphere-egu26-10823, 2026.

In India, ensuring clean air for all is vital and should not be limited to urbanites. However, the current air quality monitoring networks and clean air strategies are limited to cities. Notably, the air quality status across regions is yet to be measured, and comprehensive regional management plans are non-existent. To address these significant research gaps, the Ambient air quality Monitoring over Rural areas using Indigenous Technology (AMRIT) project was envisioned and implemented by the Centre of Excellence Advanced Technologies for Monitoring Air-quality iNdicators (CoE – ATMAN), Indian Institute of Technology Kanpur. For the first time, over 1,400 low-cost PM_2.5 sensors were installed across the states of Uttar Pradesh and Bihar at the block level. The sensor locations encompass a diverse range of land-use and land-cover categories, demographics, and communities. By leveraging a dense network of low-cost sensors, we developed a data-driven machine learning framework to delineate airsheds for regional air quality management for the first time. We utilized a recurrent neural network–based long short-term memory (LSTM) and hierarchical clustering algorithms with PM_2.5 and meteorological data to delineate airsheds. The LSTM embeddings learn latent representations from PM_2.5–ventilation coefficient (VC) time series, capturing spatiotemporal patterns and inter-variable relationships. These embeddings were then hierarchically clustered to delineate airsheds. Applying this framework, our results for Bihar show five distinct airsheds; three prevail in the north of the Ganges River, and two prevail in the south of the Ganges. Notably, Airshed 1, located in the northwest region, is highly polluted. However, during the post-monsoon and winter across the airsheds, PM_2.5 levels were two to three times higher than the national standard (60 µg/m³), and on ~90% of days, people breathe unhealthy air. Similarly, we identified multiple distinct airsheds in Uttar Pradesh, as well as common airsheds that prevail across Bihar and Uttar Pradesh states. This emphasizes not only shared regional influences but also the need for an integrated approach to reduce PM_2.5 pollution. Therefore, the identified airsheds would be instrumental in targeting the reduction of fine particulate pollution across the Bihar and Uttar Pradesh states. Furthermore, the scalable, data-driven airshed delineation framework using low cost sensors could be implemented across India and potentially globally. Thus, this will facilitate airshed-based air quality management plans and integrated policy interventions to ensure “clean air for all.”

Keuwords: Air Quality; Low-Cost Sensors; Airshed;  Indo-Gangetic Plain; Machine Learning

How to cite: P Chandrasekaran, A., Tripathi, S. N., Godhani, N., Pandey, M., Rai, P., Agrawal, N., Kumar, A., and Ballav, S.: Regional Air Quality Management: A Scalable, Data-Driven Airshed Framework using Low-Cost Sensors across the Indo-Gangetic Plain, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11502, https://doi.org/10.5194/egusphere-egu26-11502, 2026.

Indoor environments account for most human exposure to air pollution, yet indoor air quality (IAQ) monitoring and control remain fragmented across devices, platforms, and proprietary building automation systems. Commercial IAQ monitors and smart thermostats are widely available, but they typically operate in closed ecosystems with limited interoperability. In parallel, open-source communities have demonstrated the potential of low-cost sensing networks, yet these solutions rarely connect to building-level control systems capable of simultaneously reducing pollutant exposure and energy use. To address this gap, we present an open, interoperable framework that integrates open-source and commercial technologies for IAQ monitoring, data management, and automated building control[1]. The framework, developed within the EU-funded healthRiskADAPT project, is built on an open, production-ready IoT infrastructure for indoor environments. At the edge, low-cost sensor nodes collect and transmit environmental data. A web-based interface allows users to register locations, nodes, and sensors, and provides near-real-time visualization, historical analytics, and an interactive map of the sensor network. Beyond monitoring, the framework enables direct integration with commercial control devices such as smart thermostats, smart plugs, and filtration systems. This interoperability supports data-driven control strategies, including increasing ventilation during indoor pollution events, activating filtration during periods of poor outdoor air quality, and dynamically adjusting HVAC operation to balance comfort, energy use, and exposure reduction. By combining continuous mass-balance modeling[2] with real-time sensor data, the system will deliver actionable indoor-outdoor (I/O) ratios and exposure indicators. These outputs could drive automated responses but also support informed user behavior, such as choosing higher-efficiency filters during high-pollution episodes, using kitchen exhaust during cooking, or understanding the trade-offs between energy costs and health risks. In this way, the platform functions not only as a control system but also as an educational and decision-support tool for occupants and building managers. This presentation demonstrates how open-source hardware, open APIs, and modular integration pathways can create a flexible, transparent, and scalable ecosystem for IAQ management. The framework supports diverse use cases, homes, schools, workplaces, and research settings, while offering a roadmap toward energy-efficient, healthier indoor environments driven by interoperable technologies rather than isolated products.

[1] https://particularmatter.org

[2] Dallo, Federico, Thomas Parkinson, Carlos Duarte, Stefano Schiavon, Chai Yoon Um, Mark P. Modera, Paul Raftery, Carlo Barbante, and Brett C. Singer. "Using smart thermostats to reduce indoor exposure to wildfire fine particulate matter (PM2. 5)." Indoor Environments 2, no. 2 (2025): 100088.

How to cite: Dallo, F., Tenti, L., Palo, A., Parkinson, T., and Duarte, C.: Open-Tool Frameworks for Cross-Platform Indoor Monitoring and Optimized Air Cleaning Strategie, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14158, https://doi.org/10.5194/egusphere-egu26-14158, 2026.

Fine particulate matter (PM_2.5) is among the foremost environmental determinants of human health, contributing to cardiovascular disease, respiratory illness, and premature mortality. In rapidly urbanizing regions of the Global South, accurate spatial characterization of PM_2.5 exposure requires spatially continuous concentration surfaces that also provide reliable uncertainty estimates, yet ground-based monitoring networks remain severely sparse. Kolkata, India’s third-largest metropolitan area (population 14.9 million), exemplifies this challenge: only seven regulatory monitoring stations cover the entire city, leaving large areas unobserved.

This study evaluates how different PM_2.5 surface generation strategies—satellite-based machine learning (ML) and spatial interpolation—differ not only in predictive accuracy but also in their ability to provide decision-relevant uncertainty under sparse monitoring conditions. Using six years of daily observations (2019–2024), we compare two complementary approaches. The first employs satellite-based ML, integrating Sentinel-5P trace gases, MODIS aerosol optical depth, ERA5 meteorological reanalysis, and static urban features (VIIRS nightlights, population density) to predict PM_2.5. The second evaluates spatial interpolation methods—ordinary kriging, inverse distance weighting (IDW), and simple averaging—using station observations alone.

For satellite-based ML (Random Forest), the station-level model achieved R² = 0.79 under leave-one-station-out (LOSO) validation, while grid-based model trained on kriging-interpolated targets reached R² = 0.70 under temporal out-of-sample validation (train: 2019–2022, test: 2023–2024). Feature importance analysis consistently identified dewpoint temperature, air temperature, and surface albedo as dominant predictors, indicating that atmospheric conditions exert stronger control on PM_2.5 variability than emission proxies or land-use variables.

For spatial interpolation evaluated under daily LOSO, all methods achieved comparable point prediction accuracy (R² ≈ 0.85). However, uncertainty calibration diverged sharply. Ordinary kriging achieved 88% empirical coverage for nominal 95% prediction intervals (90% when including observation noise)—approaching theoretical calibration—whereas IDW and simple averaging exhibited severe under-coverage (45–52%), substantially underestimating true prediction error.

These findings yield three key insights: (1) satellite-derived predictors enable spatially complete PM_2.5 estimation beyond monitoring locations, though with moderate accuracy; (2) when temporally aligned station data are available, interpolation achieves higher point accuracy than satellite-based ML; and (3) regardless of estimation strategy, only geostatistical approaches provide uncertainty estimates suitable for health-protective decision-making. We conclude that hybrid frameworks combining satellite-based spatial prediction with kriging-derived uncertainty characterization offer a principled pathway for generating spatially complete and risk-aware PM_2.5 maps in data-sparse urban environments.

How to cite: Raj, A., Dasgupta, T., Sinha, M., and Mitra, A.: Satellite-Based PM2.5 Estimation in Data-Sparse Urban Environments: Comparing Machine Learning and Geostatistical Approaches in Kolkata, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18107, https://doi.org/10.5194/egusphere-egu26-18107, 2026.

Machine Learning models are rapidly becoming popular for complementing, enhancing, and in some cases, replacing traditional numerical models. This study presents a data-driven framework for predicting 24-hour tropical cyclone intensification over the North Indian Ocean using supervised machine learning and ERA5 reanalysis data. Cyclones that formed over Bay of Bengal and the Arabian Sea during the period 1990–2024 are considered here.  We have integrated environmental parameters from ERA5 with intensity records from the IBTrACS archive, excluding early developmental stages and retaining only dynamically mature systems. Intensification is formulated as a binary classification problem based on the sign of the 24-hour change in maximum sustained wind speed. While this captures general strengthening behaviour, it does not distinguish between moderate and rapid intensification, nor does it estimate the magnitude of intensity change. Five machine learning models—Logistic Regression, Random Forest, Extra Trees, Support Vector Machine, and Multilayer Perceptron—are trained and evaluated. Results indicate that the Random Forest classifier has achieved the highest accuracy. Feature-importance analysis reveals strong physical consistency, highlighting the dominant roles of upper-level circulation, sea surface temperature, vertical wind shear, and atmospheric moisture in regulating short-term intensification. Cyclone Montha (2025) is used as a test case to illustrate the model's real-world applicability and is validated outside of historical data. The model-predicted intensification probability is estimated as 0.943, which indicates good performance. Although a single case study does not constitute statistical validation, this illustrates the applicability of data-driven models in tropical cyclone intensity estimation. The results encourage further investigations into the use of such data-driven models in tropical cyclone intensity prediction, which aids disaster management efforts.

How to cite: Madhu, D., Binu, N. M., and Ramesh, M. V.: Machine Learning-Based Prediction of Tropical Cyclone Intensification Over the North Indian Ocean Using ERA5 Reanalysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20844, https://doi.org/10.5194/egusphere-egu26-20844, 2026.

Traditional solid fuels are extensively used for domestic heating and cooking in developing countries, especially in rural and semi-urban regions. Combustion of these fuels is a major source of nitrogen oxides (NO_x), sulfur dioxide (SO₂), and volatile organic compounds (TVOCs), which significantly contribute to air pollution, respiratory disorders, secondary aerosol formation, and atmospheric photochemical reactions. The generation and release of these pollutants are strongly influenced by fuel moisture content, elemental composition, and inorganic constituents. This study presents a comprehensive investigation of the chemical characteristics of commonly used solid fuels and evaluates the potential of advanced functional materials for mitigating NO_x, SO₂, and TVOC emissions from domestic combustion sources.

Representative fuel samples, including fuel wood (FW), coal balls (CB), dung cake (DC), and crop residues (CR), were obtained from the Raipur–Durg–Bhilai region of Chhattisgarh, India, selected based on their prevalence and area-specific usage patterns. The samples were air-dried, pulverized, and homogenized prior to analysis. Moisture content was determined gravimetrically by oven drying at 105 °C. Ultimate analysis of carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O) was performed using a CHNS/O elemental analyzer. Ionic species, including nitrate, sulfate, chloride, and major alkali and alkaline earth metals, were quantified using ion chromatography to assess their role in pollutant formation and combustion behavior. These chemical parameters were used to infer emission potential for NO_x, SO₂, and TVOCs.

NO emissions were generally higher for AR and DC, while FW showed the lowest NO EF. SO₂ emissions followed a similar trend, with DC producing the highest levels and FW the lowest. TVOC emissions were elevated for fuels with higher moisture and inorganic content, such as AR and DC, whereas FW exhibited the lowest TVOC emission potential. CB displayed intermediate to high emissions, with particularly high TVOC formation due to its variable composition. Emission factors developed in simulated experimental chambers were validated against real-world measurements, indicating that domestic household emissions closely correspond to chamber-based estimates.

To address post-combustion emission control, advanced materials including graphene-based materials, biochar, graphitic carbon nitride (g-C₃N₄), metal oxides (MnO₂/TiO₂), zeolites, metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and silica-based adsorbents were considered for NO_x, SO₂, and TVOC mitigation. Materials were characterized using BET surface area analysis, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), confirming high surface activity and strong gas affinity. Their physicochemical properties, high specific surface area, tunable pore size distribution, and surface functional groups, enable efficient adsorption and catalytic transformation of pollutants. Graphene-based materials and biochar adsorb acidic gases through π–π interactions and surface oxygen functional groups, while g-C₃N₄ facilitates photocatalytic oxidation of NO_x under visible light. Metal oxides such as MnO₂/TiO₂ catalyze the oxidation of SO₂ to sulfate and TVOCs to less harmful products via surface redox cycles. Zeolites and MOFs provide selective adsorption of NO_x and TVOCs through microporous confinement and acid–base interactions.

How to cite: Pervez, S., Sahu, D., Pervez, Y. F., Karbhal, I., and Deb, M. K.: Hazardous gaseous pollutants (NOx, SO2, TVOCs) emission from solid fuels combustion and their mitigation using novel adsorbent materials, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21338, https://doi.org/10.5194/egusphere-egu26-21338, 2026.

Correction of high-frequency spectral losses is a major technical challenge of the eddy covariance (EC) technique. If not properly accounted for during post-processing, these losses can result in a systematic underestimation of the measured gas fluxes exchanged between the ecosystem and the atmosphere. To address this issue, several methods have been developed, with experimental approaches relying on the definition of a transfer function and its associated cut-off frequency to describe the EC system as a first order low-pass filter.

One still debated yet fundamental choice is whether to use power spectra or co-spectra to derive the system cut-off frequency. In this study, we present a systematic, multi-site, data-driven comparison of the these two methods. To do so, we used one year of CO₂ and H₂O data from all of 38 ICOS Class 1 and Class 2 stations (Integrated Carbon Observation System, www.icos-cp.eu), all equipped with a standard setup comprising the LI-7200 enclosed path analyser and the HS-50 sonic anemometer.

We showed that the corrections were limited for both approaches, especially for CO₂, ranging from 1 to 1.2, generally higher for H₂O, ranging from 1 to 2, and overall consistent across sites. This highlighted the good spectral performance of the enclosed path analyser as well as the effectiveness of the setup standardisation. Nonetheless, the results showed that differences in correction factors between the methods existed. They were analysed for all sites, separately for stable and unstable conditions. They increased with atmospheric stability and attenuation level, and decreased with measurement height above the canopy. In particularly, they were systematically the highest in stable conditions. However, when assessing the impact of the two corrections on cumulative u*-filtered fluxes, we found that rejections of most stable conditions through this standard post-processing filtering led to differences under 3% for CO₂ in 89% of sites and under 6% for H₂O in 79% of sites.

With this specific experimental setup, we suggest prioritising the co-spectral for two main reasons. First, sensor separation is a dominant part of the high-frequency attenuation and is treated experimentally in the co-spectral method, whereas the spectral approach relies on a fully theoretical formulation. Second, the spectral method requires a robust denoising procedure, which is not needed in the co-spectral approach. Finally, while recognising its crucial importance at a network level, we highlight the complexity of having a fully automatic pipeline for spectral corrections.

How to cite: Faurès, A., Papale, D., Nicolini, G., Sabbatini, S., and Heinesch, B.: A multi-site comparison of spectral and co-spectral approaches for correction of turbulent gas fluxes with ICOS set-up, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21667, https://doi.org/10.5194/egusphere-egu26-21667, 2026.

The Cumbrian Wildlife Trust (CWT) is undertaking the most ambitious attempt to revive Britain’s lost temperate rainforest in Skiddaw, northern Lake District, over the next 100 years. This involves the restoration of native Atlantic tree and treeless communities, including peatland in the area. There are ongoing ecological surveys to map the current vegetation and assess peatland status to establish baseline for detailed framework to restore degraded bogs. In collaboration with the CWT, this study employs different lines of palaeoecological evidence to investigate Skiddaw bog’s ecological history, the degree of its degradation over time, and the role of climatic and anthropogenic factors in shaping the landscape. This long-term perspective complements ongoing ecological appraisals by establishing a comprehensive baseline to predict changes in the bog and develop robust restoration and conservation frameworks against future warming climates.

How to cite: Adeleye, M., Essell, H., Handley, J., and Arizpe, A.: Long-term peatland ecological assessment in England’s largest national park (Lake District) for restoration under changing climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-257, https://doi.org/10.5194/egusphere-egu26-257, 2026.

In recent years, the triple-oxygen isotopic composition (Δ′¹⁷O) of CO₂ has emerged as a powerful tracer of atmospheric carbon cycling. The Δ′¹⁷O signature of tropospheric CO₂ is controlled by key processes, including global biosphere-atmosphere CO₂ exchange, tropospheric residence times, and stratosphere-troposphere mixing, each of which modifies CO₂ composition through dynamic isotopic fractionation. High-precision measurement of Δ′¹⁷O is essential for constraining models that predict future changes in atmospheric CO₂, yet current datasets remain limited owing to extreme low abundance of ¹⁷O and the considerable analytical challenges involved for accurate and precise isotopic measurement. A further obstacle is the absence of a well-constrained global background Δ′¹⁷O value for atmospheric CO₂ that restricts proper evaluation of deviations arising from diverse source contributions. As a result, model predictions of tropospheric Δ′¹⁷O(CO₂) often diverge substantially from observational constraints.

To address this gap, we have initiated high-precision measurements of δ¹³C, δ¹⁸O, and Δ′¹⁷O in atmospheric CO₂ using TILDAS (Tunable Infrared Direct Laser Absorption Spectroscopy) at the Stable Light Isotope Laboratory in University of Cape Town, South Africa. Bi-monthly air samples have been collected at the Global Atmospheric Watch (GAW) Cape Point station, South Africa, since December 2024. Given the station’s location, sampling is preferentially conducted under south-easterly wind conditions to minimize local anthropogenic influence. CO₂ is extracted, purified, and analysed with a precision of ±10 ppm (1σ). We will present the Δ′¹⁷O record and evaluate its correspondence with existing predictive models. We will also discuss perturbation of local Δ′¹⁷O values by regional fluxes, such as anthropogenic inputs or seasonal biospheric exchange. This initiative aims to provide the first annual Δ′¹⁷O (CO₂) baseline from the Southern Hemisphere and improve the accuracy of predictive models of the carbon cycle.

How to cite: Ghoshmaulik, S., Labuschagne, C., and Hare, V.: Insights into global carbon cycling using bi-monthly measurements of triple oxygen isotopes in CO₂ from Cape Point, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-644, https://doi.org/10.5194/egusphere-egu26-644, 2026.

The rapid technological developments of recent years have enabled new methods for acquiring aerial photographs and high-spectral-resolution imagery. In this context, unmanned aerial vehicles (UAVs) offer significant potential for high-resolution Land Use/Land Cover (LULC) mapping, allowing clear distinction between natural and human-made features. UAV-based approaches provide high accuracy, faster data acquisition, and cost-effective solutions for detailed LULC analyses. However, there is a fertile ground in evaluating different methodological approaches and testing different algorithms for obtaining robust and transferable results. To this end, the present study aims at comparing two advanced classification techniques for mapping agricultural areas using multispectral UAV data over a typical agricultural site. The area selected for the study consists of crops and agricultural land located near the town of Amygdales, in the regional unit of Grevena. The two techniques are SVM (Support Vector Machines) and MLC (Maximum Likelihood Classification). In overall, results showed that the SVM proved to be more accurate with an overall accuracy of 79.45% compared to 78.91% for MLC, while both methods achieved a Kappa coefficient of 0.72. The statistical significance of the findings was further confirmed from the Mc-Nemar statistical significance results which were also computed. The results evidenced the capability of both methods obtaining LULC maps at very high spatial resolution. All in all, the methodological approach presented herein provides potentially a low-cost solution in mapping agricultural areas at very high spatial resolution which may be also fully transferable and reproducible to other locations too, which offer potentially important pathways to be used in precision agriculture applications. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions for field management.

Keywords: Precision Agriculture, Mapping, UAVs, Classification, Machine Learning, Support Vector Machine, Maximum Likelihood

Acknowledgement

The participation of George P. Petropoulos study is financial supported by supported by the ACCELERATE MSCA SE program of the European Union’s Horizon research and innovation program under grant agreement No. 101182930.

How to cite: Charisoulis, P., Petropoulos, G. P., Detsikas, S. E., Volianaki, E., and Litke, A.: Evaluating different methodological approaches for very high spatial resolution mapping of agricultural areas exploiting UAV data: a case study from Greek agricultural site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1693, https://doi.org/10.5194/egusphere-egu26-1693, 2026.

 Resource Use Efficiency (RUE) serves as a critical indicator of forest ecosystem functionality, reflecting the efficiency of forests in utilizing light, water, and carbon for biomass production. This study investigates the spatiotemporal dynamics of RUE across 14 major Indian forest types from 2014 to 2023 by integrating Light Use Efficiency (LUE), Water Use Efficiency (WUE), and Carbon Use Efficiency (CUE) derived from MODIS satellite products. Using an eco-hydrogeological framework coupled with Random Forest modeling, the study evaluates the influence of climatic, topographic, and hydrological variables on forest productivity. Results reveal considerable spatial heterogeneity and temporal variation in RUE, with the highest efficiencies observed in wet evergreen and semi-evergreen forests and the lowest in dry deciduous and thorn forests. WUE demonstrated substantial variability across forest types and years, particularly impacted by the 2016 drought. CUE was strongly influenced by elevation (R2 = 0.82), and slope emerged as a limiting factor in drier ecosystems. The study highlights that subtropical pine and montane forests exhibit resilience and adaptive efficiency, while arid-zone forests remain vulnerable to climatic stressors. These findings provide actionable insights for sitespecific sustainable forest management and climate resilience planning in India’s diverse forest landscapes. 

How to cite: Raj, A. and Kumar, R.:  Resource Use Efficiency (RUE) Dynamics of Indian Forests Through an Eco-Hydrogeological Approach Using Machine Learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2734, https://doi.org/10.5194/egusphere-egu26-2734, 2026.

Organic soils, especially peat soils, store large amounts of carbon and are therefore considered a significant source of greenhouse gas (GHG) emissions, disproportionately contributing to land-use emission estimates in countries with extensive organic soil areas. When synthesising emission factors (EFs), data are typically stratified by broad climate zones such as temperate and boreal. However, given the spatial distribution of available forest-soil GHG data, such stratification is suboptimal. Temperate forest soils remain less studied than boreal soils, although recent research has expanded the number of temperate estimates from 8 used in the IPCC default EF compilation to at least 91, with the most recent flux data originating from the Baltic states (56 sites). When deriving EFs for specific applications, it is preferable to pool data from regions with similar climatic conditions rather than restricting analyses to unnecessarily small datasets defined by national borders or aggregating data across overly broad and climatically diverse zones.

We reassessed forest-soil GHG emissions in the Baltic states by supplementing regional data with additional sites sharing the same Dfb climate zone under the Köppen–Geiger climate classification. This approach expanded the dataset from 56 to 98 sites, improving EF accuracy and enhancing comparability between neighbouring GHG emission estimates, regardless of whether countries rely solely on domestic data. While such analyses typically focus on drained organic soils, we also included undrained soils to support the establishment of emission baselines for assessing forest management impacts.

Average annual organic-soil emissions in the Dfb climate zone  (mean ± SE, per hectare of forest) were estimated at 0.22 ± 0.18 t CO₂-C, 1.17 ± 1.58 kg CH₄, and 2.82 ± 0.59 kg N₂O for drained soils, and −0.60 ± 0.37 t CO₂-C, 92.88 ± 78.05 kg CH₄, and 2.81 ± 1.07 kg N₂O for undrained soils. Expressed as CO₂ equivalents using AR5 GWPs, total emissions were 1.59 ± 0.46 t CO₂ eq. for drained and 1.15 ± 6.70 t CO₂ eq. for undrained soils. Owing to high natural variability between site-level fluxes, the effect of drainage on GHG emissions remains uncertain. Although the mean difference between drained and undrained soils (0.44 t CO₂ eq.) may indicate a long-term drainage effect, this estimate is highly doubtful. The dataset indicated that simple averaging across all sites is not well suited to deriving EFs, as CO₂ emissions from drained organic soils showed dependence on nutrient status and linkage to dominant tree species and stand age. Drained soils in young stands tended to act as emission sources, whereas older stands increasingly functioned as carbon sinks, with a transition at approximately 25 years of stand age. However, additional observations are required to accurately quantify this dynamic across the forest growth cycle. To illustrate the implications for national upscaling, we derived a Latvia-specific drained organic-soil CO₂ EF that accounts for the distribution of dominant tree species and stand types characterising soil nutrient availability, yielding a weighted EF of 0.14 t CO₂-C ha⁻¹ yr⁻¹.

This work was supported by PeatTransform with co-funding from the European Union and the State Budget of Latvia (6.1.1.2/1/25/A/001).

How to cite: Butlers, A., Bārdule, A., Lazdiņš, A., and Kamil-Sardar, M.: Synthesis of greenhouse gas emission factors for forest organic soils in the Dfb zone of the Köppen–Geiger climate classification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3062, https://doi.org/10.5194/egusphere-egu26-3062, 2026.

Monitoring the fractional cover of vegetation and bare soil is essential for sustainable land management, soil erosion control, and precision agriculture. However, accurately estimating these fractions from conventional satellite imagery is challenging due to mixed ground cover and limitations such as cloud contamination and coarse spatiotemporal resolution. High-resolution UAV imagery provides an effective solution by capturing fine-scale heterogeneity, enabling the application of spectral mixture modeling techniques to decompose each pixel into proportions of vegetation, bare soil, and other components. Understanding how GSD influences the performance of such mixture models is critical for optimizing UAV-based monitoring strategies and ensuring reliable, quantitative estimates of soil and vegetation fractions for informed land management decisions.

The objective of this study is to evaluate the sensitivity of fractional vegetation estimates to different ground sampling distances (GSDs) derived from unmanned aerial vehicle (UAV) imagery. Multispectral data were acquired using a UAV equipped with RGB, near-infrared, red, and red-edge sensors, flown at altitudes of 40 m, 80 m, and 120 m above ground level. The study area is a heterogeneous vineyard located in Drama, Macedonia, northern Greece. Image acquisition took place on 30 July 2025, under stable atmospheric and illumination conditions.

An object-based image analysis (OBIA) approach was applied to the UAV imagery, and the data were classified into three main land cover classes: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. Fractional vegetation cover estimates derived at each flight altitude were compared in order to assess the influence of spatial resolution on classification performance and vegetation fraction retrieval. Validation of the classification results was performed using an independent dataset generated through direct photo interpretation, allowing for an objective assessment of accuracy across the different GSDs.

This contribution aims to evaluate the effects of different ground sampling distances (GSDs) on the estimation of fractional vegetation cover (FVC) using multispectral UAV imagery over commercial vineyards in Northern Greece. The study highlights the influence of spatial resolution on canopy representation, particularly in young or sparsely developed vineyards, and supports the development of robust UAV-based tools for precision viticulture

Keywords: UAV, Vineyard, Fractional Vegetation Cover , ACCELERATE

Acknowledgement: This study is supported by ACCELERATE research project which has received funding from the European Union’s Horizon  research and innovation program under grant agreement No.101182930.

How to cite: Tselos, G.-N., Detsikas, S. E., Petropoulos, G. P., Grigoriadis, K., Polychronos, V., Mamagiannou, E.-M., Balomenou, P., Ramnalis, D., and Masouridis, P.: Assessing the effect of different ground sampling distances for drone-based mapping of fractional cover: a case study from a vineyard field in Northern Greece , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3144, https://doi.org/10.5194/egusphere-egu26-3144, 2026.

Bamboo expansion constitutes a significant process altering forest ecosystem structure and function. However, quantitative research remains scarce on how its long-term evolution influences the critical relationship between ecosystem productivity and biodiversity. This study aims to evaluate the long-term ecological effects of bamboo expansion in Wuyishan National Park (a biodiversity hotspot in China).

By analysing Landsat time-series imagery (1986–2020) and existing land cover dynamic classification maps, seven land cover types—including bamboo forests and broad-leaved forests—were identified. Analysis of surface classification results revealed an overall upward trend in bamboo forest area, with expansion primarily occurring at the expense of broad-leaved forests. Notably, 48% of the newly added bamboo forest area resulted from the conversion of broad-leaved forests.

To assess ecosystem responses to bamboo expansion, spectral diversity was quantified using Rao's Q index (functional diversity) and Shannon index (species diversity), calculated from NDVI and NDMVI. Ecosystem productivity was characterised via habitat indices (DHIs) derived from NDVI time series. Results indicate that regional ecosystem productivity has steadily increased, whereas spectral diversity has markedly declined, with both Rao's Q and Shannon indices showing significant downward trends. Specifically, bamboo forest patches exhibited higher Cumulative DHI (8.8 ± 0.65) than broadleaf forests, yet lower Rao's Q indices (0.010 ± 0.004), whereas broadleaf forests recorded (8.7 ± 0.69) and (0.011 ± 0.003), respectively. Moreover, farmland and tea plantations exhibited abnormally high Rao's Q values, likely attributable to fragmentation and edge effects (small patches embedded within forest backgrounds) rather than genuine species richness.

The study employed Theil–Sen trend estimation and Mann–Kendall significance testing to investigate correlations between bamboo forest area changes and biodiversity. Results revealed a significant negative correlation between bamboo forest expansion and spectral diversity indices (R²≈−0.36), suggesting bamboo encroachment may diminish biodiversity.

The observed trend of increasing productivity coupled with declining spectral diversity warrants further analysis to elucidate underlying drivers. Future research should integrate additional vegetation indices and morphological parameters for diversity calculations. Furthermore, long-term assessments of animal habitat suitability and ecosystem stability require combined ground-truthing and modelling approaches.

How to cite: Shi, W., Zhang, Q., and Qiu, F.: Bamboo Expansion Drives Divergence in Productivity and Spectral Diversity in Wuyishan National Park over Nearly Four Decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3212, https://doi.org/10.5194/egusphere-egu26-3212, 2026.

Java Island is experiencing severe degradation of natural mangrove forests due to anthropogenic pressure, particularly in Probolinggo Regency. Although restoration programs have been widely implemented, the success rate remains very limited due to unsuitable planting technique and suboptimal species selection. This study provides the first baseline ecological assessment of vegetation status and estimation of carbon stock to support more effective restoration planning. Using a quantitative random sampling method, data on species identification, vegetation height, and diameter at breast height (DBH) were collected from 33 plots across eight sub-districts. Avicennia marina, Rhizophora mucronata, and Avicennia alba, are the dominant species with relative abundance varied by location. Saplings represented the most abundant growth stage, while trees exhibited the lowest abundance, indicating high past historical degradation. The westernmost sub-district exhibited the lowest Shannon–Wiener diversity index (H' = 0.9), suggesting higher anthropogenic pressures than others. Species richness, evenness, and dominance remain substantially varies across sub-districts. The total estimated carbon stock was 292 Mg C ha⁻¹, comparatively low for Indonesian mangroves ecosystems. The natural mangrove forests in Probolinggo Regency are in early-mid successional stage, reflecting strong past degradation. These findings highlight the urgency of restoration program to improve the total carbon stock across all sub-districts, particularly western areas, with careful consideration of site-species suitability. 

This research was conducted at the Warm-Temperate and Subtropical Forest Research Center, National Institute of Forest Science (Project No. FE-2022-04-2025).

How to cite: Qurani, C. G., Rizal, M., and Sugiana, I. P.: Baseline ecological insights of vegetation assessment and carbon stock estimation in natural mangrove forests of Probolinggo Regency, Indonesia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6317, https://doi.org/10.5194/egusphere-egu26-6317, 2026.

Stable carbon and nitrogen isotopes are widely applied to infer plant water-use efficiency and nutrient dynamics; however, their physiological interpretation remains uncertain when isotopic signals are not supported by functional measurements. Here, we combine elemental and isotopic analysis (EA-IRMS; δ¹³C, δ¹⁵N, %C, %N and C/N ratio) with fast chlorophyll a fluorescence assessed in vivo through the JIP-test (Handy-PEA) to resolve the physiological meaning of isotopic variability across two genotypes and three geographical contexts of typical Italian red chicory.
In December 2024, leaves and roots of two Cichorium intybus cultivars (“Rosso precoce di Chioggia” and “Rosso precoce di Treviso”) were sampled across three sites, resulting in four genotype–site combinations. Plants were collected at their areas of origin, as defined by Protected Geographical Indication (PGI, Chioggia and Treviso), and outside the PGI area in Massenzatica (Ferrara, Italy), where both cultivars are cultivated in a sandy coastal soil. Isotopic and elemental analyses revealed a pronounced site-dependent differentiation. Leaf and root δ¹³C values varied among sites, indicating substantial long-term differences in C discrimination, related to the balance between transpiration and CO₂ assimilation, which seemed more favourable in Chioggia. δ¹⁵N further discriminated sites and cultivars, highlighting marked differences in N dynamics and internal allocation, although without clear attribution to specific sources.
To assess whether isotopic shifts reflected adaptive regulation or functional impairment, chlorophyll fluorescence transients (OJIP) were analysed using JIP-test parameters. All samples exhibited the typical polyphasic OJIP pattern, yet clear differences emerged between cultivars and sites. Across the entire induction curve, the Treviso PGI transient were consistently higher at the J and I steps than the other samples. This pattern was associated with increased light absorption and energy dissipation per photosystem II reaction centre (ABS/RC, DI₀/RC), reduced electron transport efficiency (ET₀/RC), and lower probabilities of electron transfer to photosystem I end acceptors (ψ(RE₀), δ(RE₀)), indicating a tendency to over-reduce the electron transport chain, probably driven by downstream limitations in electron utilisation.
Overall, our results demonstrate that a more negative δ¹³C, while implicating a better water use efficiency, does not necessarily reflect an overall better plant adaptation, which could be affected by  some biochemical photosynthetic constraints. Integrating EA-IRMS with JIP-test fluorescence analysis provides a robust framework to discriminate between stomatal regulation and biochemical limitation, improving the mechanistic interpretation of isotopic markers for geographical fingerprinting and genotypic differentiation in climate-sensitive agroecosystems.

This research was allowed by phD fellowship granted by EUROPEAN SOCIAL FUND P L U S - The ESF+ 2021-2027
Programme of the Regione Emilia Romagna

How to cite: Martina, A., Ferroni, L., and Marrocchino, E.: From isotopic fingerprints to functional diagnosis in Italian red chicory: linking δ¹³C and δ¹⁵N to photosynthetic performance across geography and genotype, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8969, https://doi.org/10.5194/egusphere-egu26-8969, 2026.

Sustaining rice productivity in intensive rice-rice systems requires comprehensive soil management, with diagnosis of key soil physical, chemical, and biological indicators that need attention. In a 16-year long-term experiment (established in 2005-06 and ongoing) of the irrigated double rice system of Eastern India, we investigated the effect of key soil drivers on rice productivity.

The experiment assessed the effect of control (no N fertilizer application), imbalanced fertilization (N/NP/PK), balanced and recommended NPK and 150% NPK, NPK with lime, micronutrient additions (Zn with/without S or B), and integrated nutrient management with FYM (with/without lime), Composite surface soil samples (0-15cm) were collected after harvest of the 32^nd rice season for evaluation of soil physical, chemical, and biological properties. Rice grain yield after the 32^nd season was recorded at 14% grain moisture.  

To identify key soil drivers, an interpretable machine learning framework was used, specifically a conditional random forest-based yield model, permutation-based variable importance, and accumulated local effect (ALE) plots. The model described the yield variability very well (mean RMSE 305 kg ha^-1, R² 0.88, MAE 254 kg ha^-1). Variable importance screening highlighted total K, protease, and urease activities, as well as permanganate-oxidizable carbon (POC), as dominant predictors. ALE-based effect sizes suggested these properties accounted for ~400 (total K), ~250 (protease), ~200 (urease), and ~140 (POC) kg yield variability.

Overall, the results indicate that potassium dynamics are a primary constraint in intensive rice-rice systems, with risks associated with continuous K mining, and emphasize the importance of routine monitoring of biological activity indicators for long-term sustainability.

Keywords: Conditional random forest; Soil quality index (SQI); Long-term fertilizer application; K-dynamics; Soil enzymes; Cattle manure

How to cite: Garnaik, S., Samant, P. K., Mandal, M., Wanjari, R. H., Sinha, N. K., Mohanty, M., and Lenka, N. K.: How Soil Quality Affects Long-Term Rice Productivity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11249, https://doi.org/10.5194/egusphere-egu26-11249, 2026.

Support Vector Machine (SVM) classifiers are widely used for satellite-based crop mapping, yet hyperparameter tuning is often treated as a black-box process, with limited insight into how individual parameters influence classification performance. This limitation becomes critical when deploying SVM models across heterogeneous agricultural landscapes, where robustness and transferability are required. This study systematically investigates the sensitivity of SVM hyperparameters for crop type discrimination using Sentinel-2 NDVI time series over the Al Haouz plain in central Morocco, a heterogeneous irrigated agricultural region comprising winter cereals and perennial orchards. An exhaustive grid search was conducted across multiple orders of magnitude for the regularization parameter C (0.01–1000) and the RBF kernel coefficient γ (0.001–10). Model performance was evaluated using F1-score, Recall, and Overall Accuracy for six crop classes with contrasting phenological patterns.

Results reveal a pronounced asymmetry in hyperparameter influence. The regularization parameter C exhibits a high degree of robustness: once a moderate threshold is reached (C ≥ 1), classification performance stabilizes and remains insensitive to further increases. In contrast, γ shows a narrow optimal range (0.1–1.0), beyond which performance rapidly deteriorates. High γ values induce overfitting, particularly among crops with similar seasonal dynamics, as evidenced by persistent confusion between citrus and olive classes. The optimal configuration (C = 1, γ = 1) achieved an F1-score of 0.80 and an Overall Accuracy of 81%. More importantly, sensitivity analysis demonstrates that γ plays a dominant role in model calibration. These findings provide practical guidance for deploying robust SVM classifiers in data-limited agricultural contexts, where extensive hyperparameter tuning is often impractical.

How to cite: Ben zhair, F., Elyoussfi, H., Alami Machichi, M., Azamz, R., El Kasri, J., Boufous, B., and Belaqziz, S.: Hyperparameter Sensitivity Analysis of Support Vector Machine for Crop Type Classification Using Sentinel-2 NDVI Time Series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12087, https://doi.org/10.5194/egusphere-egu26-12087, 2026.

Estuarine systems are crucial in deciphering coastal ocean dynamics and biogeochemistry, including the vital role they play as ecological sequesters of greenhouse gases. We present a modelling framework that combines the Estuary Box Model (EBM) with the Biogeochemical Flux Model (BFM) to simulate the interplay between physical dynamics and biogeochemical processes. The EBM is a robust, yet simplified model that represents estuarine hydrodynamics, addressing salinity, temperature, and freshwater discharge variations. The BFM simulates nutrient cycling, microbial interactions, phytoplankton dynamics, organic matter mineralization and particulate sedimentation across chemical functional families and living functional groups. To realistically simulate estuarine scenarios, the passive tracer transport equation was adapted to include explicit biogeochemical reaction terms within a time-varying estuarine simplified control volume, furthermore, accounting for riverine nutrient inputs, vertical mixing, tidal exchange and various biological feedback. Additional alterations were made to accommodate burial and sequestration parameters better representing estuarine zones.
The coupled framework was applied to the Po di Goro estuary in northern Italy, and the simulations were conducted for the period 2010 to 2023. The results were validated by comparing the Chlorophyll concentration outputs against satellite and in-situ buoy observations. The outcomes show a strong correlation between phytoplankton biomass and residence time during periods of algal blooms, whereas a rapid shift to zooplankton propelled top-down grazing control during prolonged periods of stable conditions. The model effectively replicates the organic matter sedimentation dynamics typical of deltaic environments, offering insights into the scale and factors controlling the burial and sequestration of organic matter in these ecosystems. The coupled EBM-BFM system is a highly computationally efficient and scalable framework for understanding the estuarine ecosystem drivers, with important potential applications in biogeochemical variability, nutrient retention, and climate-driven changes in coastal zones.

How to cite: Santhoshkumar, S., Verri, G., Vigiak, O., Niroumand, M., Riminucci, F., Silvestri, S., and Mentaschi, L.: A computationally efficient framework for modelling estuarine biogeochemistry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12306, https://doi.org/10.5194/egusphere-egu26-12306, 2026.

The European Environment Agency data on nitrates levels gives us a ratio of 8:1 for nitrates in groundwater versus river water, when we analyse the data across 27 countries. The “missing” nitrate, at an average of about 89%, matches the levels of “missing” nitrate due to capture of nitrate on the river bottom, by microbes known as diatoms, which take up 65-95% of the water nitrate load.

Diatoms convert nitrate to ammonium in daily cycles, that are linked to sunlight and Oxygen abundance, encountered in typical river and lake conditions.

We can identify that the major pathway of nitrate into rivers and lakes is through groundwater feeds, which average 25% of surface waterway volumes worldwide -because their nitrate levels dwarf those from any other source. We can also identify that the main mechanism of nitrates removal in river bottoms is diatom capture, where diatoms take up the bulk loads of nitrate arriving in the groundwaters beneath.

Diatoms' virtual monopoly on nitrates conversion may allow us to control N2O and global warming levels, by intercepting the conveyor belt system of nitrates to diatoms in waterways. We can capture and repurpose the nutrient for use as farm fertilizer and harvest diatom ammonium as a carrier for Hydrogen fuel. Diatoms are already farmed commercially for fish food, showing they are amenable to farming, and they are already a source of soil conditioner for farms. Ammonium is harvested in wastewater plants for Hydrogen fuel purposes already, and diatoms offer a low carbon method of ammonium production.

The junction between the UN, EEA and microbial data also allows us to calculate the world processing levels of nitrate in terms of both natural and human produced components. We obtain a range around 300,000 kilotons per annum as being processed by surface waters worldwide from all sources. About 120,000 kilotons of the load comes from human produced sources.

Ammonium nutrient from diatom nitrate conversion is quickly absorbed by aquatic plants and riverside trees, but there is a risk of high levels on hot days in lowered Oxygen conditions. Trees draw up around 1000 litres a day of groundwaters in river basins, so that ammonium and nitrates consumption by trees is additionally a main mechanism of Nitrogen reduction around the riverbed.

How to cite: Hall, C., Smith, D., Munro, A., and Sgroi, A.: Microbial breakthroughs in 2022 now allow us to link United Nations water volumes with EEA nitrates data, to reveal world nitrates processing loads in kilotons, including how much nitrate is from natural sources, and how much is from human activity., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13318, https://doi.org/10.5194/egusphere-egu26-13318, 2026.

Twenty years of MODIS satellite data (2002-2022), TROPOMI glyoxal observations (2018-2022), and ground-based isoprene measurements were used to examine vegetation greenness (NDVI) and atmospheric glyoxal over Houston, Texas. Biogenically produced glyoxal grew by 51% between 2018 and 2022, despite a 2% per decade decrease in summer vegetation greenness and continued urbanization. Ambient mixing ratios of isoprene, the main biogenic glyoxal precursor, paradoxically dropped by 14% within the same time frame. Temperature (+0.68°C/year), ozone (+28%), and photochemical oxidants all significantly increased over this time, according to analysis of concurrent environmental data. The results indicate that higher temperature-driven isoprene emissions (+35%) and accelerated photochemical oxidation (+10%) overcame the declining vegetation signal, resulting in net increases in atmospheric glyoxal. This suggests that Houston's remaining flora is experiencing temperature-driven changes in biogenic volatile organic compound (VOC) emissions per unit area, even while its greenness has reduced.

Keywords: MODIS NDVI; TROPOMI glyoxal; Isoprene emissions; Photochemical oxidation

How to cite: Bibi, S. and Rappenglück, B.: Vegetation Dynamics and Atmospheric Glyoxal in Houston, Texas (2018-2022), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13501, https://doi.org/10.5194/egusphere-egu26-13501, 2026.

Stubble burning after harvest is known to degrade soil organic carbon (SOC). However, research on its long-term impacts on SOC is scarce and inconclusive. To address this gap, we introduce a data-driven modeling approach for SOC quantification by integrating remote sensing data with machine learning models to quantify changes in SOC during 2004-2021 across burnt rice areas in Punjab, India. This involved synthesizing literature to obtain SOC values pre- and post-burning, as well as intersecting MODIS burnt areas with rice crop maps to identify stubble burning areas in Punjab from 2004 to 2021. Post synthesis and identification, MODIS satellite band values were extracted for the synthesized experimental plots on pre- and post-burning dates. Further, remote sensing indices, which are sensitive to SOC changes such as NDVI, NBR, RECI, and BSI, were calculated for the pre- and post-burning dates. Using these indices and band values as predictors and literature-derived observed SOC values as response variables, multiple machine learning models were trained, whereby an R² value of 0.3 was obtained. While further efforts are required to improve model accuracy, our study revealed a significant decline in SOC from 2004 to 2018, ranging from 0.1  to 12.5 %,  whereas from 2019 to 2021, SOC increased by 0.7 to 7 % in various districts in Punjab. More specifically, these districts-Sangrur, Ludhiana, and Kapurthala have had the most significant decline from 2014 to 2018, whereas Rupnagar, Patiala, and Fatehgarh Sahib exhibit the highest increase in SOC from 2019 to 2021. The decline in SOC could be attributed to accelerated mineralization driven by combustion and the loss of SOC in the form of CO₂ emissions. Whereas the increase in SOC could be attributed to a reduction in stubble burning and incomplete combustion of residue, leading to the return of unburnt organic matter to the soil. These findings highlight the efficacy of integrating remote sensing frameworks with data-driven machine learning models in monitoring SOC and other aspects of soil health.

How to cite: Hoojon, J., Narayanan, M., and Ilampooranan, I.: Data-driven modelling to quantify soil organic carbon in burnt croplands: An integration of remote sensing and machine learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13704, https://doi.org/10.5194/egusphere-egu26-13704, 2026.

Drylands are mostly covered by biocrusts and are sensitive to climate change, which will likely affect nitrogen (N) transformation. However, it remains unclear the response of N transformation-related variables (N transformation rates, microbial biomass, and enzyme activity) to warming in biocrust-dominated dryland ecosystems. Here, we examined three soil cover types (bare soil, cyanobacteria- and moss-dominated soil) over a full year as we conducted a warming treatment (open top chambers) in the Tengger Desert. In order to quantify the response of N transformation-related variables to warming, we defined the warming effects (WEs) as the increment of N transformation-related variable per-unit variation of temperature. Our results showed that the presence of biocrusts can significantly increase the WEs of soil N mineralization rates (Rmin), nitrification rates (Rnit), the content of microbial biomass carbon (MBC) and nitrogen (MBN), and the activities of soil nitrate reductase (S-NR) and urease (S-UE). Microbial biomass under biocrusts was more sensitive to warming followed by enzyme activity. Meanwhile, the WEs in spring and fall were higher than those in winter and summer. The cumulative rainfall was the driving factor affecting the seasonal change of WEs. Therefore, the defining and studying warming effects expand our understanding of seasonal dynamics of N transformation, microbial biomass and enzyme activity, and emphasize the important roles of biocrusts as modulators of N cycling under climate change in dryland ecosystems.

How to cite: Hu, R. and Zhang, Z.: Biocrusts mediate seasonal warming effects of soil N transformation in drylands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15468, https://doi.org/10.5194/egusphere-egu26-15468, 2026.

Kathmandu has undergone significant urbanization over the past decade, resulting in consistently warmer peri-urban regions compared to nearby rural landscapes due to the urban heat island effect. This study examines how baseline warming interacts with monsoon droughts to affect grassland ecosystems.

Using the Kathmandu Valley as a case study, we analyzed monsoon-season (June–October) data from 2000 to 2022, comparing land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS, volumetric soil moisture from ERA5-Land, and reference evapotranspiration (ET₀) from Terra-Climate between peri-urban and rural grasslands. Grasslands were considered instead of agricultural regions to avoid the effects from irrigation. The premises of Tribhuvan University were chosen as the peri-urban location for their closeness to the core-city region and Changunarayan (Bhaktapur) was chosen as a rural location. The Standardized Precipitation Index (SPI-3) was derived from historical precipitation records (1980–2020) and drought years were identified by negative monsoon-mean SPI values.

The results reveal a persistent peri-urban heat penalty throughout the study period. On average, peri-urban grasslands were 0.94°C warmer than their rural counterparts. This contrast increased to 1.15°C during non-drought years but narrowed to 0.5°C during drought years, as rural grasslands experienced sharper warming related to soil moisture depletion and reduced evaporative cooling. Despite the partial thermal convergence, the peri-urban zone experienced greater ecological stress during droughts, with NDVI declining by approximately 4% relative to rural areas as soil in peri-urban region are 1.12% drier during droughts compared to rural grasslands. An average potential evapotranspiration difference of 23.6 mm exists between the region, and during droughts, the evapotranspiration is 2.66% higher in peri-urban region.

These findings demonstrate that monsoon drought reduces spatial thermal contrasts but does not eliminate peri-urban vulnerability. Persistent background heating in peri-urban landscapes results in elevated vegetation stress even when meteorological drought conditions are similar. These results highlight the importance of peri-urban land management and thermal mitigation strategies in reducing ecological stress under increasing climate variability in rapidly growing cities.

How to cite: Dahal, P. and Lamichhane, S.: Peri-urban heat amplification of monsoon drought impacts on grasslands in the Kathmandu Valley, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15799, https://doi.org/10.5194/egusphere-egu26-15799, 2026.

The intensive irrigation-linked groundwater abstraction in North China Plain (NCP) is dramatically affecting the hydrological processes and regional climate. Impacts from these anthropogenic groundwater withdrawals are evident in the fluctuation of each component in the terrestrial water cycle, the lack of groundwater sustainability, and regional climate extremes. Ensuring future groundwater security within this context will largely depend on how accurately the human activities in the Human-Earth system model were represented. However, to date, most hydrological models and land surface models either ignore the representation of human intervention or realistically model sophisticated human activity processes. In this study, we incorporated two groundwater-fed irrigation schemes in the Noah-MP model and further used realistic irrigation water use results constraining irrigation water withdrawals. We evaluate the influence of the groundwater pumping representation on the simulation of evapotranspiration and groundwater water table depth using Fluxnet-MTE ET data and observational groundwater well data, respectively. The Noah-MP simulation with groundwater-fed irrigation produced ET that matched the magnitude of observations-based Fluxnet-MTE ET values. Observational well-depth anomaly fluctuations can be reproduced in irrigated areas within the groundwater-fed simulation. In addition, the improvement of groundwater pumping also helps to improve terrestrial water storage estimates in higher resolution. We estimated that, over a seasonal cycle, groundwater-fed irrigation in the model can account for 80% of the declining terrestrial water storage trend from 2003 to 2016. Our approach and results reinforce the importance of parameterizing human activities in the Human-Earth system model and better address the water security challenges under climate change and human interventions.

How to cite: dai, D.: Modelling the groundwater pumping for agriculture in the Noah-MP model to support sustainable water management over the North China Plain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16034, https://doi.org/10.5194/egusphere-egu26-16034, 2026.

Soil-derived N₂O represents a critical climate feedback in semi-arid agriculture. With a global warming potential (GWP) 298 times greater than CO₂, 6.2 TgN₂O-N is emitted annually from agricultural soils. The atmospheric acceleration with a growth rate >1.0 nmol mol^-1 y^-1 remains unexplained by fertilizer use alone, suggesting climate change as a critical driver of enhanced emissions, particularly through extreme precipitation and droughts. Rajasthan’s 4.6 million hectares of climate-resilient millet cultivation experience intense droughts and severe monsoon variability. Both these factors lead to drought-rewetting cycles and impact N₂O emissions, which remain unquantified at regional scales.

This study integrates satellite-derived measurements coupled with multi-model ensemble projections to model N₂O emission hotspots in the millet croplands at the district level in this state, which is a major producer of millet in India. Published millet area datasets for spatial distribution and water-filled pore space (WFPS) thresholds (80 - 95%, for optimal denitrification) with soil moisture proxies (NDVI, LST) are integrated to quantify N₂O flux. Standard precipitation index from CMIP6 models (SSP2-4.5, SSP5-8.5) is applied to quantify temporal shifts in wet and dry frequencies. 

The results indicated that the denitrification-dominated pathways have dominated during rewetting phases, with N₂O peaks lagging behind soil moisture recovery by 48–72 hours, consistent with the Birch effect. Meta-analytical synthesis suggests rewetting pulses release 5 - 10 times higher N₂O flux than constant moisture conditions. CMIP6 scenarios project 20 - 35% intensification in drought frequency by 2050, driving 15 - 25% increases in cumulative annual N₂O emissions under high-emission scenarios. The regional assessment enables evidence-based fertilizer timing and supports India’s Nationally Determined Contributions (NDCs) by quantifying emissions, thereby paving the way for more effective mitigation strategies.

How to cite: Aarav, P., Burman, P., Phartiyal, G., and Sharma, S.: Quantifying N₂O Pulses from Millet Croplands: The Role of Drought-Rewetting Cycles Observed via Remote Sensing and CMIP6, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16677, https://doi.org/10.5194/egusphere-egu26-16677, 2026.

Apple flowering in the Himalayan region depends on winter chill and spring heat, which are changing under a warming climate. These changes have increased uncertainty in flowering intensity and timing across different elevations. In this study, high-resolution UAV imagery and a YOLOv8-based segmentation model were utilized to map tree-level flowering intensity across three apple orchards situated along an elevation gradient in the northwestern Himalayas. The YOLO model was found to reliably detect flower clusters and showed strong agreement with manual counts, with an R² value of 0.85. This allowed consistent comparison of flowering intensity across sites. The winter chill was estimated using the Dynamic Model, expressed as chill portions derived from ERA5 Land hourly temperature data. Spring heat accumulation was quantified using growing degree days. Flowering varied clearly with elevation. Mid-hill orchards bloomed earlier and showed lower visible flowering during UAV surveys. Higher-elevation orchards bloomed later and exhibited higher flowering intensity. The winter chill was sufficient at all sites. Flowering responses were mainly controlled by the combined effects of chill and spring heat. The results demonstrate that integrating UAV-based deep learning with climate indices provides a practical framework to assess climate-driven changes in apple phenology in mountain environments. This approach can support climate risk assessment and adaptive orchard management in the face of continued warming.

How to cite: Shukla, Y., Gupta, V., and Himanshu, S. K.: Apple Flowering Response to Climate Variability along the Himalayan Elevation Gradient, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17076, https://doi.org/10.5194/egusphere-egu26-17076, 2026.

Since 2011 in Italy the surface area cultivated with pear tree (Pyrus communis L.) decreased from 35,400 ha to approximately 23,700 ha in 2023, driven by escalating production costs, stagnant market prices, and recurrent phytopathological challenges. Additionally, this decline has been hypothesized to be causally linked to progressively unfavorable climatic conditions, characterized by rising temperatures, frequent summer heatwaves exceeding 35°C, and reduced precipitation. The objective of this study is to identify the most effective soil–plant relationships across Emilia-Romagna, which is the leading Italian region for pear production. We aim at providing a scientific basis for adaptive management strategies linked to regional pedoclimatic variability, so as to support the future of the Italian periculture.
Three experimental orchards were located in the provinces of Ferrara, Modena and Ravenna, which are key districts in Italy for periculture were set up in 2023. Agro-meteorological conditions were continuously monitored through an online automated system. Soil samples were thoroughly characterized with respect to their texture, calcium carbonate content, pH, loss on ignition LOI, multi-element profiling by X-ray fluorescence (XRF). To link soil properties with the plant performance, fast chlorophyll a fluorescence was measured in leaves in June 2025 on BA-29 grafted trees and the PI_tot (total performance index) was calculated, which represents a synthetic indicator of photosystem II efficiency.

Soils at the Modena site were predominantly sandy, deriving from Apennine sediments, whereas soils from Ferrara and Ravenna sites exhibited a higher clay content resulting in greater, water-holding capacity. XRF analyses indicate that elemental concentrations at all sites were within expected background levels. LOI and calcimetric analyses were higher in Ravenna soils compared to those from Ferrara and Modena, indicating a greater organic matter and carbonate content. At the Modena site, trees exhibited significantly lower PItot values than those observed in  Ferrara and Ravenna sites, This pattern is attributable to the higher sand fraction, which promotes rapid water drainage and reduced nutrient retention in contrast to clay-rich soils where enhanced water and nutrient availability can support improved photosynthetic performance performance. A relationship could also be envisaged found between soil organic carbon content and PItot. Because meteorological conditions were comparable and the germoplasm was uniform across the three sites, the observed differences in photosynthetic performance can be primarily ascribed to soil properties. These location-specific soil–plant correlations can inform precision agriculture practices, rootstock-scion selection, and adaptation strategies to enhance the resilience of pear orchards under changing climate conditions.

Research funded by the European Union – NextGenerationEU, Ministero dell’Università e della Ricerca - Piano Nazionale di Ripresa e Resilienza, D.M. 630/2024.

How to cite: Bigoni, M., Marrocchino, E., Viola, I., Zaccarini, M., Farinelli, A., and Ferroni, L.: Linking soil texture and organic carbon to leaf chlorophyll fluorescence in Pyrus communis orchards: a multi-site study in Emilia-Romagna, Italy , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18092, https://doi.org/10.5194/egusphere-egu26-18092, 2026.

Understanding the interactions (synergies and trade-offs) among ecosystem services (ESs) and their driving factors is crucial for sustainable ecosystem management under intensifying climate change and anthropogenic disturbances. In recent years, machine learning approaches have demonstrated strong potential in capturing nonlinear relationships and exploring the driving mechanisms of ES interactions. However, most existing studies provide unified explanations at the global scale and often overlook the spatial heterogeneity and spatial dependence inherent in geographic locations, thereby limiting the ability to reveal the differentiated effects of the same driving factors on ES synergies and trade-offs across regions. This gap becomes particularly critical in large river basins, where pronounced environmental gradients, spatial connectivity, and heterogeneous human activities jointly drive strong spatial differentiation in ecosystem processes and services.

In this study, we develop a geospatially explainable machine learning framework to more explicitly characterize the spatial variability of ES interactions and their formation mechanisms in the Yangtze River Basin, China. Specifically, six key ESs, including food supply (FS), water yield (WY), water purification (WP), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ), were quantitatively assessed for the period from 2000 to 2023. Spearman correlation analysis and geographically weighted regression (GWR) were then employed to identify the ES relationships and their spatial distribution patterns. Furthermore, the GeoShapley method was introduced to incorporate geographic location into the model interpretation process, thereby enhancing the transparency and interpretability of machine learning decisions. From a spatial interaction perspective, this approach enables the analysis and visualization of the differentiated driving effects of climate conditions, topography, land use, and human activities on ES synergies and trade-offs across different spatial locations.

This study shows that the geospatially explainable framework enhances insights into the formation mechanisms of ES interactions and provides scientific support for implementing zoned ecosystem management and targeted regulation strategies under ongoing global environmental change.

How to cite: Jiang, C., Yao, Y., and Ma, L.: Ecosystem service interactions and their driving factors based on a geospatially explainable framework: A case study in the Yangtze River Basin, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18238, https://doi.org/10.5194/egusphere-egu26-18238, 2026.

Accurate estimation of gross primary productivity (GPP) in croplands is essential for quantifying terrestrial carbon uptake and understanding carbon–water coupling under increasing agricultural water stress. Conventional Light Use Efficiency (LUE) models typically rely on evaporative fraction (EF), derived from total evapotranspiration (ET), which does not distinguish between productive transpiration and non-productive evaporation. In contrast, transpiration-based framework explicitly represent the physiological coupling between carbon assimilation and water loss regulated by stomatal conductance. In this study, transpiration is estimated using a Leaf Area Index (LAI)-based approach driven by remotely sensed MODIS data and environmental variables within the underlying Water Use Efficiency (uWUE) framework.

We evaluate the transpiration-based uWUE model against an EF-based LUE model for GPP estimation using eddy covariance observations from 51 globally distributed cropland sites. The dataset includes 6 sites from India (Flux Tower and INCOMPASS networks), 3 sites from Japan (AsiaFlux), 9 sites from Europe, and 33 sites from the United States (FLUXNET), spanning a wide range of hydro-climatic and management conditions. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), and bias.

The transpiration-based uWUE model showed overall better agreement with observed GPP than the EF-based LUE model across the global set of crop sites. Improvements were evident in both the strength of the relationship with observations and the reduction of estimation errors. At the site level, uWUE more frequently achieved higher R² together with lower RMSE, demonstrating consistent performance across multiple evaluation metrics at a larger number of sites. Superior performance was observed at 28 sites, driven by the model’s ability to capture coupled carbon–water dynamics under varying crop types, canopy structures, and climatic conditions. In contrast, the EF-based LUE model showed advantages at a limited number of sites characterized by distinct water stress regimes or vegetation properties.

Overall, the results highlight the critical role of transpiration dynamics in GPP estimation, with higher GPP values associated with dense canopies and favorable environmental conditions. By explicitly isolating transpiration from total evapotranspiration, the uWUE framework provides a more physically meaningful representation of carbon–water interactions than ET-based approaches. These findings demonstrate that incorporating transpiration-based constraints improves GPP estimation in croplands and has important implications for large-scale agricultural carbon cycle assessments under future climate scenarios characterized by increased water stress and drought frequency.

How to cite: Harde, S. and Rajasekaran, E.: Improved Estimation of Gross Primary Productivity in Global Croplands Using a Transpiration-Based uWUE Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18930, https://doi.org/10.5194/egusphere-egu26-18930, 2026.

Climate change and intensifying droughts represent a critical challenge to agricultural productivity and soil sustainability. This study evaluated the effect of two superabsorbent amendments with contrasting chemical compositions (polyacrylate-based polymer and a biodegradable polysaccharide nanocomposite) on carbon dynamics in common beans grown under drought conditions. The experimental design included analyses of morphological parameters, elemental composition, ¹³C allocation, and soil density fractionation. The results showed that drought drastically reduced biomass and nitrogen in leaves and roots, increased C:N ratios, and decreased root-derived carbon (RDC) incorporation, especially in stable soil fractions. The application of superabsorbents reversed these effects, increasing ¹³C translocation to roots and RDC in soil. NSN stood out for its ability to increase total RDC compared to the drought control parallelling the irrigated control in the heavy fraction associated with minerals, a key indicator of stable carbon sequestration. In contrast, Com mainly promoted flow to labile fractions, with less impact on stabilisation. These findings demonstrate that superabsorbents might be an effective tool for sustaining crop productivity and strengthening carbon sequestration in agroecosystems under conditions of increasing aridity.

How to cite: Guarda Reyes, M., Calabi Floody, M., Biron, P., Salvidar, M., Mora, M. D. L. L., and Rumpel, C.: Enhancing carbon sequestration in water stressed plant–soil systems through soil amendment with a Superabsorbent Nanocomposite derived from natural materials, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19408, https://doi.org/10.5194/egusphere-egu26-19408, 2026.

Continuous, spatially explicit estimates of environmental attributes are increasingly provided as gridded data. The accuracy of gridded data, including classifications derived from remotely-sensed data, is typically evaluated using measures based on confusion matrices with site-specific class allocations; however, these measures are defined for categorical variables and are therefore not applicable to ratio-scale attribute estimates representing quantities, such as canopy height or population abundance.

We present an approach that extends commonly used agreement measures, i.e. the Jaccard index, Precision, Recall, and F-score, to non-negative, continuous ratio-scale attributes. The extended measures (cJaccard, cPrecision, cRecall, and cF-score) are viable equivalents to their binary counterparts, invariant to data imbalance and suitable for evaluating the agreement of various types of data representing ratio-scale attribute estimates. The cJaccard measure has proven useful for a range of applications in the geospatial domain, illustrating the broader potential of these measures for evaluating large-scale environmental gridded data products and beyond.

The aim of this contribution is to showcase and discuss the practical application of these continuous agreement measures to real-world gridded datasets representing spatial-environmental variables. Through applied examples, we demonstrate how cPrecision and cRecall enable a directional interpretation of disagreement, disentangling commission and omission errors in the total proportion of misallocated magnitudes. We further illustrate how cJaccard provides a bounded, scale-independent measure of agreement that complements typically used error-based measures (such as Mean Absolute Error or Root Mean Square Error) in the data comparison process.

How to cite: Krasnodębska, K., Goch, W., Uhl, J. H., Verstegen, J. A., and Pesaresi, M.: Agreement measures for continuous, ratio-scale data: cJaccard, cPrecision, cRecall and cF-score, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20818, https://doi.org/10.5194/egusphere-egu26-20818, 2026.

The increasing demand for reliable food authentication highlights the need for scalable and innovative tools to link geochemical fingerprints in food products with their geographic provenance. Food authentication is not only essential for preventing fraud but also offers a unique opportunity to relate agricultural products to their underlying geochemical signatures. Here we present a unified framework that combines stable isotopes (e.g. ⁸⁷Sr/⁸⁶Sr) and trace-element fingerprints measured in food products with explainable and cost-aware machine learning to support provenance verification.

We first develop a cost-aware binary classification model for French sparkling wines, demonstrating how high-precision ⁸⁷Sr/⁸⁶Sr ratios can be partially substituted by low-cost elemental proxies (e.g. Rb) while maintaining strong discriminative power. To address scalability constraints, we extend this approach to a multiclass setting using cost-sensitive logistic regression to classify wines from multiple Portuguese and Chilean regions, explicitly handling class imbalance and feature redundancy. Finally, we introduce TeaPrint, an unsupervised multimodal clustering framework that jointly integrates isotopic, elemental and volatile organic compound data to uncover coherent regional geochemical patterns in international tea samples without requiring prior labels.

Across these case studies, we show that food products carry integrated geochemical signatures that can be exploited for robust provenance authentication across heterogeneous datasets. By bridging forensic geochemistry and explainable machine learning, our approach offers a cost-efficient and scalable pathway towards robust provenance authentication and transparent food supply chains.

How to cite: Lu, Y., Doerr, C., and Sebilo, M.: From Geochemical Fingerprints to Food Authentication: Integrating Explainable and Cost-Aware Machine Learning for Provenance Analysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21380, https://doi.org/10.5194/egusphere-egu26-21380, 2026.

Cropland abandonment is a key land-use change process within the human-environment system, shaped by diverse environmental and socio-economic determinants. However, many studies overlook the complex interrelationships among these determinants, which may result in the reconfiguration of agricultural landscapes. Here, we developed an analytic framework based on social-ecological system theory to map cropland abandonment archetypes in Sichuan Province, Southwest China, using a combination of biophysical conditions, proximity characteristics, socio-economic determinants, and the extent, cumulative proportion, and spatial configuration of abandoned croplands. We implemented self-organizing feature maps using a nested clustering approach, which resulted in 25 sub-archetypes and 6 meta-archetypes. We used random forest regressions to quantify the relative importance of explanatory determinants influencing archetype geographies. Our results revealed diverse cropland abandonment archetypes, with meta-archetype area shares ranging from 4.4 % to 48.4 %. The most widespread archetype was characterized by favorable terrain, low cropland per capita, and low cumulative proportions of abandonment. Determinants of meta-archetypes varied in their importance but consistently highlighted the role of environmental determinants (i.e., topography, temperature), as well as productivity-related and socio-economic determinants (i.e., employee wages, pension insurance, high-value crops) as the most important determinants. Our findings argue against one-size-fits-all solutions and are highly relevant to nuance existing regional land-use policies addressing cropland abandonment. They further allow targeting key determinants of cropland abandonment and considering regional and local socio-ecological contexts in decision-making processes.

How to cite: Hong, C.: Revealing nested archetypes of cropland abandonment based on social-ecological system theory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21572, https://doi.org/10.5194/egusphere-egu26-21572, 2026.

Improving olive yield in Moroccan agro-ecosystems requires a better understanding of the interactions between water availability, soil properties, and management practices. The complexity and non-linear nature of these interactions limit the effectiveness of conventional analytical approaches. This study applies machine learning methods to predict olive yield and to assess how the importance of yield determinants varies under contrasting water regimes. A multi-site dataset from Moroccan olive groves, including more than 2,000 observations, was analyzed. Machine learning models showed high predictive accuracy across water regimes. Under rainfed conditions, CatBoost achieved the best performance (R² = 0.845), indicating that yield variability is mainly driven by soil properties and spatial context. Under irrigated conditions, XGBoost provided the highest accuracy (R² = 0.855), highlighting the increasing role of management practices such as planting density and nitrogen fertilization. Under intensive irrigation, fruit-related variables, particularly 100-fruit weight, became the dominant predictors, while the influence of edaphic constraints decreased.

Overall, the results demonstrate that irrigation does not simply increase olive yield but fundamentally alters the hierarchy of factors controlling production. These findings emphasize the need for data-driven, site-specific management strategies to enhance the sustainability and efficiency of olive production in Morocco.

How to cite: Azamz, R., Elyoussfi, H., Benzhair, F., El Mousadik, R., and Belaqziz, S.: Deciphering Olive Yield Determinants under Contrasting Water Regimes: A Multi-Site Machine Learning Approach in Morocco Agro-Ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21818, https://doi.org/10.5194/egusphere-egu26-21818, 2026.

Future food security will increasingly depend on the development of crop ideotypes that produce higher yields per unit of water used. Stomata are central to developing water-efficient crop ideotypes, as they serve as the primary gateway for carbon and water exchange. Process-based crop models are essential tools for testing crop phenotypes with favorable stomatal traits, as they can explain how changes in stomatal traits propagate to whole canopy carbon gain and water use. Yet, current models still struggle to connect leaf-level physiology to season-long canopy performance (e.g., yield) under realistic climate variability.

Current process-based models have one of these limitations: (i) lack of explicit biochemical photosynthesis module for C3 or C4 crops, preventing mechanistic analysis of crop phenotypes; and (ii) models that explicitly represent biochemistry ignore leaf energy balance dynamics and assume leaf temperature (T_leaf) equal to air temperature (T_air), ignoring the feedback between stomatal conductance, transpiratory cooling. As a result, they require extensive empirical calibration and are not recommended for exploring novel stomatal phenotypes, such as lower stomatal density and pore size. In particular, current efforts to manipulate stomatal traits in crops cannot be reliably evaluated using these simplifications, as they do not account for how changes in stomata affect CO₂ diffusion and canopy energy balance.

This study presents a novel cross-scale framework, vLeaf@DSSAT, where we couple a process-based leaf model with the CERES-Maize growth model and introduce a two-leaf (sunlit–shaded) canopy representation. The explicit consideration of energy balance makes this framework distinct from similar attempts in the past. CERES-Maize provides daily crop state variables such as leaf area index (LAI), phenology, soil water status, and nitrogen status. Using these, vLeaf then computes hourly net assimilation and transpiration rates for both sunlit and shaded leaf areas. It computes photosynthesis, stomatal conductance, boundary-layer conductance, and leaf energy balance simultaneously in an iterative loop. Root water uptake from CERES-Maize constrains canopy transpiration; vLeaf then reruns under these constraints and updates T_leaf and gas exchange rates. The resulting canopy-scale assimilation from vLeaf drives the biomass accumulation in CERES-Maize on the next day, closing the loop between leaf biophysics and crop growth.

Simulations for climates based on a US Midwest reference site show that neglecting leaf energy balance results in sizeable errors in both carbon gain and water use. For cooler climates, forcing T_leaf = T_air underestimates seasonal carbon gain by ≈ 9% and transpiration by ≈ 30%. For warmer climates, the bias in carbon gain is small, but transpiration is overestimated by 5–10%. These errors can create uncertainty in ranking crop phenotypes with favorable stomatal traits. vLeaf@DSSAT provides a practical approach to testing stomatal manipulation, irrigation strategies, and climate-resilient ideotypes under realistic climate conditions, while also connecting leaf biophysics to field-scale yield and water use.

How to cite: Srivastava, A.: vLeaf@DSSAT: integrating leaf energy balance and biochemistry into CERES-Maize to reassess water-efficient ideotypes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-231, https://doi.org/10.5194/egusphere-egu26-231, 2026.

Urbanization, together with increasing global population pressure and climate variability, has introduced heat-related challenges across urban areas, severely impacting humans and the Earth. The rapid population growth has placed India at the top of the global population ranking. Demographic surges concentrate stress on existing urban systems, making Indian metropolises-both inland hubs and rapidly transforming coastal centers-critical laboratories for studying UHI dynamics.

The understanding of patterns and possible causes of the UHI effect due to urbanization-induced anthropogenic activities is a vital area in urban climate research. This study presents an overall multi-decadal day/night spatiotemporal seasonal analysis and trends in LST and UHI for six Indian cities: Ahmedabad, Mumbai, Panjim, Mangalore, Kochi, and Thiruvananthapuram, spanning the last three decades.

MODIS LST and AOD data are used to explain the possible reasons for the change in LST and UHI, focusing on the seasonal thermal behavior of cities under prevailing atmospheric, meteorological, and anthropogenic conditions. The Landsat series datasets are used to develop LULC maps and delineate high-resolution UHI zones, to explain shared trajectories and city-specific patterns that expose complex vulnerabilities within urban ecosystems in India. The findings, which integrate multi-decadal 30-year satellite-derived LST, LULC, and AOD data, demonstrate that greater increases in nighttime LST are associated with a decrease in the diurnal temperature range across all cities. Mumbai consistently showed lower mean LST values compared to Ahmedabad, which exhibited substantially higher values and extreme seasonal amplitudes ranging from 17.23 °C to 50.05 °C. Goa and Mangalore depicted a 1-4 °C increase in seasonal mean LST between 1993 and 2023. Corresponding to a rise in built-up area and a decline in vegetation, Kochi too exhibited a rise in LST. Thiruvananthapuram showed a strong warming, with a mean LST increase of about 3°C. AOD patterns also demonstrated similar spatial and temporal gradients across cities, helping to reinforce land-use change, urban expansion, and inland-coastal climatic contrasts as the significant causes of LST trends.

Collectively, these findings reveal how land-use transition, and climatic variability, significantly alters the thermal environment of Indian cities; making such studies important for climate-responsive planning and better urban management to enhance resilience and thermal comfort.

How to cite: Rai, R. and Murali R, M.: Utilising geospatial data to understand urban heat island and its effect on urban thermal comfort in selected Indian cities using Remote Sensing and GIS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-488, https://doi.org/10.5194/egusphere-egu26-488, 2026.

Seagrass ecosystems in Indonesia provide a diverse array of services and societal benefits that helps to mitigate the impacts of climate change, yet they face complex threats and ongoing declines. Assessing their diverse values through pluralistic lenses can provide important baseline information to guide future conservation and restoration policies. Utilising the Seagrass Ecosystem Contributions to People (SCP) framework, a systematic review was conducted in representative seagrass ecosystem regions—west (Bintan), central (Selayar), and east (Ternate)—to examine the current state of research on their diverse values. Specifically, we identified the presence, perceived worldviews, characteristics, specific values, and their overlaps of SCP categories.

Publications were retrieved from Scopus, Web of Science, and the first ten pages of Google Scholar between February and April 2025. Then, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guideline, a thorough literature analysis was conducted using six established inclusion criteria. Present SCPs, perceived worldviews, specific values, and their overlaps were assessed in accordance with core meanings derived from McKenzie and Colleagues, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) report on the diverse values of nature, and Himes and Colleagues’ publication, respectively.

Overall, 54 publications were included in this review, spanning the years 1989 to 2025 and identifying 25 SCPs across three regions, with a decreasing number of studies for each SCP from west to east. There were imbalances in the SCP perception, with bioindicator and scientific research being perceived in all regions and having twice the number of publications compared to other SCPs. These two prevalent SCPs generally studied the ecological status of seagrasses, their associated biota, and experimental results on their monitoring methods (e.g. remote sensing).  

Biocentric and anthropocentric worldviews were researched more and dominated interchangeably between SCPs, focusing on seagrass conservation values and societal perceptions of its services. Meanwhile, the pluricentric worldview had singular studies limited to several Material and Nonmaterial SCPs. Excluding the east region, all three specific values (intrinsic, instrumental, and relational) were present, with instrumental as the most frequent overlapping value. These value overlaps demonstrated fuzzy boundaries between material and nonmaterial groups, but not with the regulating SCPs, which were all studied with a singular specific value.

Regional differences in seagrass ecosystem management, benefit utilizations, and research focus might reflect the variability of the perceived SCPs, worldviews, and value overlaps. Although these perceptions are still biased towards tangible benefits for human ends. However, most of the reviewed publications were short-term studies and distinct from each other, demonstrating the need for local experts’ knowledge elicitation to further weave the information. Our study further developed the SCP framework by incorporating the concept of nature’s diverse values, which is highly relevant and applicable to the seagrass socio-ecological setting and management initiatives in Indonesia.

How to cite: Widanto, D. S., Sasmito, S. D., and Waltham, N.: A systematic review of the diverse values of Seagrass Contributions to People (SCP) in Indonesia: A pilot study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-984, https://doi.org/10.5194/egusphere-egu26-984, 2026.

The EU Nature Restoration Regulation is the first continent-wide, comprehensive law of its kind and is a key component of the EU Biodiversity Strategy. This sets binding targets to restore degraded ecosystems, particularly those with the most potential to capture and store carbon and to prevent, and reduce, the impact of natural disasters. The EU Nature Restoration Regulation contains seven specific targets, including:

‘marine ecosystems – restoring marine habitats such as seagrass beds or sediment bottoms that deliver significant benefits, including for climate change mitigation…….’

The 800 hectares of seagrass meadows in South-West England are rightly regarded as important biodiversity ‘hot spots’, providing habitats for many species of juvenile fish, cuttlefish and sea horses. Many of the meadows in the region have been impacted by a ‘Seagrass Wasting Disease’ in the 1930s and the more recent damage caused by the anchoring of small boats (usually pleasure craft). This damage is being rectified by re-planting of seagrass, but many of those engaged in this work do not appreciate the full story behind the present distribution and development of the meadows.

The oldest seagrasses are known from the Maastrichtian of The Netherlands and are found in the Maastricht Chalk Formation (70 million years’ old). Between that time and the present day there are very few direct records of seagrass fossils, and this is because:

• Seagrass meadows from the tidal/inter-tidal boundary are not in an environment that is commonly preserved in the geological record; and
• In modern seagrass meadows, the plants are rarely – if ever – preserved and are rarely found in cores drilled into the meadows below ~30 cm.

In marine cores taken in Plymouth Sound and elsewhere in Southern England there are only low levels of carbon (spores, pollen, dinoflagellates, seeds, and organic debris) and not the high levels that would be required to suggest that sea grasses sequester high levels of ‘blue carbon’ for extended periods of time. The accumulation of low carbon levels can be explained by the enhanced sedimentation created by the seagrass, the so-called allochthonous carbon. The one element of carbon sequestration that is often ignored is that of foraminifera, ostracods and bryozoans, all of which are extremely abundant in meadow sediments. In many cases, the biodiversity of the foraminifera (>100 species) dwarfs the usual biodiversity counts of larger organisms. Such fixed calcium carbonate does have a long-term storage potential.

The other issue that can be ignored by those studying seagrasses is that the marine environment has undergone significant change throughout the 1 million years of the Pleistocene/Holocene and many of the seagrass meadows have only just re-established themselves following the Last Glacial Maximum, when sea levels were 120‒130 m below present-day levels. How seagrass migrated back into the present-day coastal areas is not yet fully understood, including the separation of the inter-tidal and sub-tidal taxa.

How to cite: Hart, M. and Fisher, J.: South-West England seagrasses: ecology, evolution and contribution to biodiversity and carbon sequestration , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4468, https://doi.org/10.5194/egusphere-egu26-4468, 2026.

To support resilient community planning and informed hazard mitigation decisions, having an effective flood risk evaluation is very important especially in coastal and flood prone areas. This presentation is focused on the development of an interactive web-based decision-making platform designed to analyze future flood risk and elevation ordinance impacts across five parishes in Louisiana, USA. The website allows users to explore long-term flood risk projections and ordinance related costs over multiple future decades from 2030 to 2100. The platform integrates various geospatial datasets including multi-return-period flood depth projections, decadal population forecasts, and building inventories. Flood depth raster datasets are converted from raster to point data using python and then assigned to building data obtained from Coastal Protection and Restoration Authority (CPRA) using spatial join. Then the obtained datasets are used to calculate Average Annual Loss (AAL) for different elevation ordinances. This framework incorporates a range of flood elevation ordinances, including ASCE 24-14, ASCE 24-24, and freeboard-based standards (BFE +1’, +2’, and +3’), with ordinance costs and risk outcomes by decade. ArcGIS Pro is used for spatial analysis and 3D geospatial visualization, while interactive webpages and different elevation ordinance scenario comparisons are implemented with react vita app. To improve accessibility for non-technical users, the website integrates AI-driven features that assist users in navigating the tool, interpreting results, and comparing ordinance scenarios. The platform supports hotspot analysis, side-by-side visualization of present and future flood risks, and iterative refinement through user feedback sessions. Overall, this tool provides planners, homeowners, and policymakers with a forward-looking environment to assess flood mitigation strategies, ordinance performance, and population-driven risk changes over time by combining advanced spatial analytics with interactive and user-centered design.

How to cite: Kunku, L. P., Friedland, C. J., and Mostafiz, R. B.: Assessing Long-Term Flood Risk and Elevation Ordinance Comparisons Using a Web-Based Geospatial Decision Tool, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4510, https://doi.org/10.5194/egusphere-egu26-4510, 2026.

The attenuation of atrazine in the saline water of the hypersaline Pétrola lake from a natural reserve (SE Spain) was studied to get more insight into the processes governing the fate of the contaminant in highly saline environments. In microcosms, the water column was spiked with 15.4 mg/L of atrazine for 24 days. Before atrazine amendment, the initial distribution of bacterial community was mostly composed of Proteobacteria (25.5 %), Cyanobacteria (25.2 %), Actinobacteriota (18.5 %), Verrucomicrobiota (11.5 %) and Bacteroidota (10.1 %). Within the mayor phyla, the most abundant families were identified as Cyanobiaceae (25 %), Rhodobacteraceae (10 %), Alcaligenaceae (9.9 %), Microbacteriaceae (7.3 %) and a poorly described PeM15 (6.2 %).

The reduction of atrazine concentration in the water column reaches 86.9 %, which means a reduction of dissolved atrazine mass of 98.1 %. Parallel to the decrease in atrazine, the amount of its intermediate degradation metabolite, desethylatrazine, increased. Desethylatrazine was the major short-term metabolite within the first 8-12 days, indicating the potential activity by atrazine-degrading bacteria. No deisopropylatrazine was detected in the saline water above detection limit. Microbiology results showed that atrazine can be removed from the saline lake environment. After atrazine amendment, the taxonomic bacterial composition at phylum level shifted to Proteobacteria (60.8 %), Patescibacteria (9.7 %), Bacteroidota (7.3 %), Campylobacterota (6.4 %) and Actinobacteriota (2.1 %). Atrazine supplementation suggested a selective pressure on bacterial structure morphology through the emergence of different dominant groups (i.e., Campylobacterota) or even the eradication of those phyla of bacteria capable of photosynthesis (i.e., Cyanobacteria). At family level, Rhodobacteraceae (16.7 %), Burkholderiaceae (11.7 %), Thiomicrospiraceae (7.9 %), Methylophagaceae (6.7 %), Sulfurimonadaceae (4.4 %) and Solimonadaceae (3.3 %) were the most abundant in the water column at the end of the experiment.

Related atrazine-degrading families established at the end of the experiment were Rhodobacteraceae, Solimonadaceae, Pseudomonadaceae, Rhizobiaceae, Chromatiaceae, Bacillaceae, Xanthomonadaceae, Caulobacteraceae, Moraxellaceae, Streptomycetaceae, Microbacteriaceae and Nannocystaceae. Related candidates such as Pseudomonas paralactis, Microbacterium sp., or Arthrobacter sp., among others, were isolated in water samples from previous studies. The bacterial candidates for atrazine degradation identified in the water column indicate that the herbicide acted as a selective pressure factor, altering the composition of the bacterial pattern. The water column would constitute a reactive environment which may govern the fate of pesticides in saline surface water bodies.

How to cite: Espín Montoro, Y., Martínez Couque, G., Fernández Pérez, J. A., Álvarez Ortí, M., and Gómez Alday, J. J.: Selective pressure of atrazine on bacterial pattern in a hypersaline lake environment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5322, https://doi.org/10.5194/egusphere-egu26-5322, 2026.

Microorganisms in estuarine and coastal ecosystems are subject to changes in salinity and nutrient loads as well as threats from emerging contaminants, sea level rise, extreme weather patterns, and human encroachment. These ecosystems are of economic and ecological importance due to their biological diversity as well as their role in carbon sequestration, flood protection, and erosion prevention. However, the microbial community structure in the sediment of estuarine systems remains under researched despite the abundant ecosystem services they provide. In this study, we surveyed 25 sites within Econfina River State Park located in the eastern portion of the Northern Gulf of Mexico along a salinity gradient from freshwater upstream to coastal seagrass beds downstream. DNA extracted from the top 10 cm of sediment were 16S rRNA amplicon sequenced to determine microbial community structure. Results contribute to the understanding of microbial ecology of coastal ecosystems in the Northern Gulf of Mexico.

How to cite: Parker, S. G., Tryzbiak, B., Patel, A., Pereira, G., Chambers, L., and Beazley, M.: Spatial Variation in Sediment Bacterial Communities Along a Salinity Gradient in a Northern Gulf of Mexico Coastal Ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6116, https://doi.org/10.5194/egusphere-egu26-6116, 2026.

The Lomagundi-Jatuli event (2.3-2.0 Ga) is one of the grandest carbon isotopic (δ¹³C_carbonate) excursion events in the Earth’s history, marked by anomalous δ¹³C_carbonate reaching up to + 30 ‰. Several hypotheses have been proposed to explain this excursion; however, they remain inadequate due to associated drawbacks. The conventional explanation is organic carbon burial due to enhanced productivity. But, the lack of organic rich stratas synchronous with the excursion demands the reconsideration of alternative biogeochemical processes to explain this isotopic anomaly. Moreover, the excursion is observed only in the evaporitic and nearshore carbonates, with no evidence from open ocean; demanding facies based biogeochemical explanation. Here, we explore the possibility of CO₂ outgassing and calcite precipitation as potential drivers responsible for this excursion as these two processes remain the least explored among the proposed hypotheses. Through sedimentological evidences from previous studies and Rayleigh fractionation calculations, we argue that dominant loss of DIC through CO₂ outgassing in the evaporitic facies and calcite precipitation in the nearshore facies along with a well-mixed DIC reservoir in the open ocean led to observed Lomagundi Excursion.

How to cite: Janaarthanan, P. A. and Kumar, S.: Can CO2 outgassing explain Lomagundi Excursion?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7342, https://doi.org/10.5194/egusphere-egu26-7342, 2026.

Fire is a dominant disturbance in tropical and subtropical dry forests and a major contributor to variability in carbon emissions, atmospheric composition, and land–atmosphere interactions. Despite their global extent and rapid transformation, the processes controlling fire size and extreme fire events in dry forest systems remain less understood than in savannas or humid tropical forests.

We investigated the controls on fire size using the South American Gran Chaco as a representative large-scale tropical dry forest system spanning strong climatic, ecological, and land-use gradients. We analyzed two decades (2001–2022) of satellite-derived fire patches from the FRY v2.0 burned-area database, combined with ERA5-Land meteorology and Fire Weather Index diagnostics, land-cover composition, landscape fragmentation metrics, topography, and anthropogenic pressure proxies. Our analysis focuses explicitly on fire size rather than fire occurrence, using statistical approaches and machine learning tools such as Random Forest models with SHAP-based interpretation to disentangle the relative and interacting roles of atmospheric forcing, landscape structure, and human-driven land transformation.

Our results show that fire size distributions are highly skewed across the region, with a small fraction of large and extreme events accounting for a disproportionately large share of total burned area. Wind and atmospheric dryness exert a strong influence on the final shape and size. At the same time, precipitation plays opposing roles by constraining fire spread through fuel moisture and enhancing fuel accumulation in fuel-limited environments. Landscape structure mediates the translation of meteorological extremes into large burned areas, with land-cover composition, fuel continuity, and fragmentation consistently ranking among the most influential predictors of burned area. Topography systematically emerges as the dominant predictor across subregions and seasons, acting not as a direct driver of fire spread but as an integrative proxy capturing hydrological gradients, vegetation structure, and human accessibility. Direct anthropogenic proxies show weaker importance at the event scale but exert strong indirect control through long-term land-use change and fuel reorganization, which in turn modulate fuel continuity and landscape configuration.

These results highlight tropical dry forests as a distinct fire domain where fire size emerges from coupled climate–biosphere–human interactions. By combining Earth observation fire products with explainable machine-learning approaches, this study advances understanding of fire–Earth system interactions and supports improved fire-risk assessment in rapidly transforming dry forest regions.

How to cite: San Martín, R., Ottlé, C., Sorenssön, A., Mouillot, F., and Vaittinada Ayar, P.: Atmospheric and landscape controls on fire size in tropical dry forests: insights from the South American Gran Chaco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7609, https://doi.org/10.5194/egusphere-egu26-7609, 2026.

Evidence suggests cetaceans utilise magnetoreception to navigate using magnetic fields. However, disturbances in the geomagnetic field from solar storms and anthropogenic activity can lead to beachings. Previous studies indicate fluctuations around 50 nT are large enough to influence strandings. Ísafjarðardjúp is home to a large humpback whale population in the summer, but marine activity, including fish farms, vessel traffic and coastal structures such as the Bolafjall radar station, makes the fjord prone to magnetic interference, potentially intefering with magnetoreception.  

How to cite: Rahman, A.: Magnetic Disturbances to Cetaceans in Isafjörðurdjup, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8273, https://doi.org/10.5194/egusphere-egu26-8273, 2026.

Land-use change in tropical regions has profoundly altered forest structure, disturbance regimes, and recovery pathways over recent decades, with important implications for fire activity and land–atmosphere interactions. Medium-resolution global land cover products are widely used to analyze these dynamics in climate and Earth system studies, yet their capacity to distinguish native forests from planted tree crops across disturbance and recovery phases remains limited. This raises critical questions about how forest loss, recovery, and resilience are inferred from satellite-based observations in human-modified tropical landscapes.

Here, we examine how forest-to-tree-crop transitions are represented in the ESA CCI Land Cover medium-resolution land cover (MRLC) product at 300 m spatial resolution over the period 1992–2022, and how these transitions relate to fire occurrence across Southeast Asia. We combine MRLC land cover maps and land cover transition layers with a high-resolution global dataset of planted tree crops providing spatial extent and year of establishment (Descals et al., 2024), together with fire information from FireCCI v5.1 at 250 m resolution (2001–2022) and fire polygon data from FRY v2.0. This integrated framework allows us to place land-cover changes associated with plantation establishment and maturation in the context of disturbance–recovery processes.

Our analysis focuses on land cover trajectories from native evergreen broadleaf forest to mosaic classes during plantation establishment, followed by reclassification to broadleaf tree cover as plantations mature. We examine the timing and duration of these transitions and their association with fire occurrence during land-use change. Preliminary results show systematic patterns in which oil palm expansion is linked to transient forest loss and elevated fire activity during early plantation stages, followed by reduced fire occurrence as plantations develop before being mapped again as tree cover.

These results demonstrate that confusion between native forests and planted tree crops in medium-resolution land cover products can lead to misleading interpretations of post-disturbance recovery and forest resilience. In particular, apparent forest recovery detected by satellite products may in some cases reflect a land-use replacement rather than true ecosystem recovery with important implications for the interpretation of disturbance–recovery dynamics, as well as for climate modeling and projections in human-modified tropical landscapes. This highlights the need for complementary high-resolution land cover information, such as that developed within the ESA CCI High Resolution Land Cover (HRLC) project, to better disentangle recovery from land-use change.

How to cite: San Martín, R., Ottlé, C., Peylin, P., Lamarche, C., and Mouillot, F.: From native forests to planted tree crops: disentangling tree cover transitions driven by deforestation, fire, and plantation development using satellite observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12372, https://doi.org/10.5194/egusphere-egu26-12372, 2026.

Methane emissions from natural wetlands are the largest and most uncertain component in the global methane budget. Conventionally they are estimated using two main methods. Top-Down methods work by inverting atmospheric concentrations of methane into fluxes, using a chemistry transport model. This is often done by optimizing the scaling factors of the inventory flux maps, but this approach decouples the fluxes from their physics and has limited predictive capabilities. Conversely, Bottom-up methods use biogeochemical models to directly estimate the fluxes, but they are severely affected by the scarcity of in-situ flux observations to calibrate them. To bridge this gap, we present the development and validation of a new fully coupled Methane Data Assimilation System (CH4DAS). This system integrates these two techniques, and provides constraints to the bottom-up model (ie., optimizing its main parameters) from both satellite concentrations and site-level fluxes. Such integration ensures that flux estimates remain consistent with physical drivers, simultaneously addressing data scarcity and enabling predictive capability.

CH4DAS is developed within the Community Inversion Framework (Berchet et al., 2021) and couples SatWetCH4 (Bernard et al., 2025), a simple bottom-up wetland methane model with the LMDz-SACS chemistry-transport model. This system can simultaneously assimilate both satellite concentrations (GOSAT) and site-level in-situ fluxes (FLUXNET-CH4) within a variational assimilation scheme to constrain the model parameters. To address the scale challenges while simultaneously assimilating observations of different streams, we run two instances of SatWetCH4. The first, driven by global forcing is coupled with LMDz-SACS and constrained by Satellite observations. While the second instance driven by site-level forcing is constrained by in-situ fluxes. This way, the shared internal temperature sensitivity parameter Q₁₀⁰ is jointly constrained by two data streams, while site-level and regional base rate parameter K account for data-specific variability.

We mathematically validate the system using an Identical Twin Observing System Simulation Experiment (OSSE), demonstrating its capacity to constrain the control variables. Further, we apply the system to real-world data to demonstrate that the system can successfully reduce the mismatches in the prior to match the spatiotemporal gradients observed by GOSAT, enabling insights on regional CH4 budgets.

How to cite: Vetriselvan, R., Rayner, P., Berchet, A., Peylin, P., Salmon, E., Saunois, M., Bernard, J., and Singh, A.: Beyond Static Fluxes: Constraining parameters of a wetland methane model in a new fully coupled CH4DAS using Satellite Concentrations and In-Situ Fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12474, https://doi.org/10.5194/egusphere-egu26-12474, 2026.

Terrestrial ecosystems currently absorb a substantial proportion of anthropogenic carbon dioxide (CO₂) emissions, yet the persistence of this land carbon sink under accelerating climate change remains largely uncertain. While process-based vegetation models project continued enhancement of carbon uptake through CO₂ fertilization, there are growing observational evidence that suggests that climate extremes, particularly 3^oC global warming level, may substantially constrain or reverse these gains through droughts and heatwaves. Recent global carbon budget assessments also show a rapid weakening of the land carbon sink, with dynamic global vegetation model ensembles and atmospheric inversions indicating a large decline in net land uptake between 2022 and 2023. Here we present an observation-constrained, species-specific quantification of global terrestrial carbon fluxes across major terrestrial biomes. We integrate a combination of satellite-derived vegetation indices, a geo-referenced global database of climate-induced tree mortality, and dynamic global vegetation models from the TRENDY intercomparison models to map spatial and temporal variability in carbon uptake, storage, and loss under historical and future climate conditions. Model simulations are forced with regionally downscaled climate projections and explicitly constrained using observed mortality signals to quantify the effects of CO₂ fertilization. Our results reveal a widespread divergence between modelled and observation-constrained carbon fluxes, with some biomes exhibiting a reduced carbon sink. Species adapted to moderately moist climate conditions show strong sink-to-source transitions, while drought-tolerant species exhibit greater resilience but limited long-term sequestration capacity. These findings demonstrate that climate extremes impose substantial limits on terrestrial carbon sequestration. By linking species-level ecological responses with carbon flux dynamics, our study provides a more realistic assessment of the future role of terrestrial ecosystems in regulating the global carbon cycle under ongoing climate change, as well as show species and regions for optimal sequestration.

Key words: Carbon Flux, Carbon Dioxide Removal, Biomes, Dynamic Vegetation Models, Drought, Heatwave

How to cite: Lawal, S. and Stewarts, W.: Mapping Global Carbon Flux in Species across Terrestrial Biomes Under Climate Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14745, https://doi.org/10.5194/egusphere-egu26-14745, 2026.

Canada’s terrestrial ecosystems play a critical role in the global carbon cycle and are being affected by unprecedented climate change and wildfire disturbance. However, we have an incomplete understanding of Canada’s historical carbon cycle. Existing assessments, conducted at varying spatial scales, use a wide range of data sources and methodologies, which lead to significant differences in the estimated strength of Canada’s land carbon sink over recent decades. Moreover, many approaches (e.g., inversions and data-driven estimates) have a limited ability to disentangle the relative contributions of different processes to the carbon sink over the recent past (1700 - 2022). We addressed this gap using a land surface model recently tailored to Canada and the most comprehensive information depicting wildfire disturbance and timber harvest available to make, to our knowledge, the first physically coherent wall-to-wall estimates of all major carbon pools and fluxes for Canada. We show that Canada’s terrestrial ecosystems have been a carbon sink since the mid-20th-century, due to the influence of wildfire and timber harvest before 1940. Since the early 2000s, wildfire disturbance has been driving Canadian forests towards becoming a carbon source. Based on our findings from a purely process-oriented perspective, projected increases in wildfire activity will further impact the strength and direction of Canada's terrestrial carbon sink.

How to cite: Curasi, S., Melton, J., Humphreys, E., Arora, V., Beaver, J., Cannon, A., Chen, J., Hermosilla, T., Lee, S.-C., and Wulder, M.: Canada’s forests shifting from a recovery-driven carbon sink to a disturbance-driven carbon source, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14857, https://doi.org/10.5194/egusphere-egu26-14857, 2026.

Sea level rise (SLR) is a key driver in altering spatial boundaries, species distribution, and functioning of coastal wetlands in the 21st century. Wetland adaptation to SLR depends on vertical accretion and landward migration; however, the magnitude and rate of these processes remain uncertain and are constrained by factors such as sediment supply and the availability of inland accommodation space. To enhance adaptive capacity, spatially explicit assessments are critical for identifying areas suitable for wetland migration. Nevertheless, much of the existing literature emphasises global-scale analyses with coarse spatial resolution and limited use of local SLR projections, which reduces their applicability for regional and local decision-making.

We conducted a scenario-based assessment of the potential extent of landward migration for mangroves and saltmarshes in Victoria, Australia, using high-resolution (10 m) regional spatial data under two socio-economic pathways (SSP2 and SSP5) for 2070 and 2090. The results indicate that, for both ecosystems, potential area gains from landward migration exceed losses from seaward inundation. For saltmarshes, ecosystem losses are driven more by mangrove encroachment (53%) than by inundation (47%). Land tenure emerges as a key factor shaping wetland migration capacity. Future accommodation space for mangroves is largely under public ownership (56%), primarily within protected areas and nature reserves (56.2%), providing a relatively secure buffer for inland migration. In contrast, most accommodation space for saltmarshes is privately owned (56.4%) and predominantly associated with primary production land use (45.4%). Without specific management actions, saltmarshes are likely to experience losses from both expanding mangroves and ongoing land-use pressures. Overall, these findings provide valuable insights to support stakeholders in scenario-based planning and the management of coastal and urban areas to enable wetland adaptation to rising sea levels.

How to cite: Perera, N., Wartman, M., Nuyt, S., Macreadie, P., and Duarte de Paula Costa, M.: Landward Migration of Coastal Wetlands under Land-Use Constraints in Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15259, https://doi.org/10.5194/egusphere-egu26-15259, 2026.

         To address the intensifying urban heat island (UHI) effect driving by rapid urbanization, current research reveals a significant scale discontinuity between macro-level strategies, such as regional cooling network design, and micro-level studies that focus on localized cooling mechanisms of individual green patches. Macro-scale approaches often overlook small cooling islands embedded within dense urban fabrics, while micro-scale investigations lack systematic understanding of inter-patch connectivity. This study proposes a multi-scale cooling island ventilation network to synergistically mitigate UHI impacts across spatial hierarchies. Using the core area of Changsha City as a case study, the research introduces an innovative three-tier scale classification framework incorporating building density. By integrating relative land surface temperature and morphological spatial pattern analysis, the study identifies core cold island sources. Further, a cold island ventilation resistance surface is constructed using the CRITIC objective weighting method, enabling the identification of key nodes and corridors for establishing a comprehensive multi-scale ventilation network. Findings reveal that, amidst urban expansion and increasing building/road densities, landscape fragmentation has led to a “shrinking-in-size, growing-in-number” trend for both primary and secondary cold island sources. From 2009 to 2016, the total area of primary-scale cold sources declined sharply from 45 km² to 19.8 km², while their number rose from 130 to 151. The average patch size fell from 0.35 km² to 0.07 km², and the minimum temperature increased from 28.7 °C to 35.3 °C-signaling a depletion risk. Similarly, secondary cold sources shrank from 215.38 km² to 144.83 km², as their number increased from 123 to 169, with average patch size dropping from 1.75 km² to 0.86 km²-weakening their thermal buffering capacity. Despite this, ventilation corridors peaked in 2020, totaling 371 in number and 528.5 km in length, continuing to act as "relay stations" transmitting peripheral cooling effects to the urban core. Notably, tertiary cold sources rebounded after 2016 due to strengthened ecological conservation efforts, expanding by 237.5 km² by 2020. Their temperatures stabilized between 35–38 °C—significantly cooler than the urban core—demonstrating sustained cooling potential. Policy recommendations are proposed across three spatial scales: 1) primary scale, remove obstructions at cold source points to broaden cooling supply channels; 2) secondary scale: prioritize the protection of key corridors and junctions to preserve inter-patch connectivity and maintain dynamic cold air flow; 3) tertiary scale: safeguard and enhance core ecological areas to ensure stable and continuous cooling output. By identifying cold island sources and constructing a multi-scale ventilation network, this study offers a science-based framework for optimizing thermal environments in high-density urban areas.

Graphical Abstract

How to cite: Zhong, X., Wei, B., Liu, L., and Huang, Y.: Constructing a Multi-Scale Urban Cooling Island Ventilation Network to Mitigate the Urban Heat Island Effect: A Case Study of Changsha, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15541, https://doi.org/10.5194/egusphere-egu26-15541, 2026.

Climate change accelerates the global hydrological cycle, which has escalating impacts on human health and the socioeconomic development. However, many existing Earth system models neglect the more complex processes of topography-driven vegetation-surface-groundwater interactions, thereby failing to accurately capture climate-hydrological responses. To address this gap, we integrate the three-dimensional surface-subsurface hydrological model ParFlow with the dynamic global vegetation model LPJ-GUESS to investigate how lateral groundwater flow and vegetation dynamics jointly regulate hydrological fluxes. The fully coupled ParFlow-LPJ-GUESS (PF-LPJG) model (with and without lateral flow) and stand-alone LPJ-GUESS model were used to run hydrological simulations over a 38-year period at a resolution of 10 km across the Danube River Basin. The results demonstrate that the PF-LPJG model substantially improves streamflow and surface soil moisture simulations without requiring parameter calibration compared to stand-alone LPJ-GUESS, mitigates the underestimation of summer low flows during dry years, increases the accuracy of peak flow timing in wet years, and achieves a Kling-Gupta Efficiency (KGE) > 0.5 and Spearman’s ρ > 0.80 at over 80 % of gauging stations. Seasonal soil moisture anomalies are better captured (R = 0.51) compared to satellite-based products. Additionally, the modelled WTD agrees well with in-situ monitoring-well data, as indicated by a low RSR value (~1.31). Notably, the coupled model improves the representation of bare-soil evaporation and reduces transpiration-to-evaporation (T/E) ratio fluctuations, aligning more closely with the GLEAM v4.2 product. Sensitivity analysis reveals that shallow-rooted vegetation exhibits strong decreases in LAI and AET when lateral flow is removed, while slightly increasing LAI and AET in deep-rooted regions. The coupled model PF-LPJG entails a mechanistic framework for capturing bidirectional interactions among surface-subsurface water, vegetation dynamics and ecosystem biogeochemical processes, which can be applied to other catchments or climatic conditions to deeply analyze climate-induced modification on vegetation-water-carbon interactions. Future work will focus on how the lateral flow affects vegetation greening under different climatic conditions.

How to cite: Jia, Z., Fu, Y. H., Chen, S., and Tang, J.: Advancing Ecohydrological Modelling with Coupled ParFlow-LPJ-GUESS: Role of Lateral Flow in Vegetation and Hydrology Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16516, https://doi.org/10.5194/egusphere-egu26-16516, 2026.

Mediterranean forest ecosystems are currently facing severe challenges due to the combined pressures of biotic and abiotic stressors. In particular, Holm oak (Quercus ilex L.) forests are experiencing a widespread decline driven by the effects of climate change-induced drought and the aggression of the soil pathogen Phytophthora cinnamomi. This decline threatens not only forest biodiversity but also the stability of essential ecosystem services. In response to this problem, the LIFE RECLOAK project aims to restore and improve the conservation status of these threatened habitats. The project adopts an approach involving the genetic selection of drought-tolerant and pathogen-resistant genotypes, their micropropagation, and subsequent planting in pilot sites across Italy, Spain, and Malta. Within this framework, Work Package 7 (WP7) is dedicated to the "Evaluation of ecosystem functioning and climate mitigation effects," validating the success of the reforestation efforts. Therefore, the primary objective of WP7 is to quantify the restoration of ecosystem processes and the enhancement of climate change mitigation potential provided by the selected resistant genotypes compared to traditional forest stock.

The activities of WP7 integrate field monitoring and modelling tasks. Firstly, the project will assess vegetation growth and biodiversity. In the pilot sites, key morphological parameters of Q. ilex (such as Basal Diameter (BD), Plant Height (PH), and Leaf Area Index (LAI)) will be measured annually to track biomass accumulation. Concurrently, biodiversity indexes (BI) will be calculated to monitor the recovery of understory vegetation. To evaluate the restoration of below-ground processes, soil quality will be assessed through measurements of soil respiration, Soil Organic Carbon (SOC), erosion rates, and Water Holding Capacity (WHC). Recognizing the slow growth rate of holm oaks, these monitoring activities are planned to continue for ten years after the project's conclusion, ensuring a long-term perspective on ecosystem recovery. Data collected from vegetation and soil monitoring will be used for the assessment of carbon sequestration, calculating the CO₂ absorbed by both the woody biomass and the soil compartment. WP7 will also develop a monitoring tool using the Biome-BGC Musso ecohydrological model. By integrating site-specific pedoclimatic data with the physiological traits of the resistant genotypes, this model will be calibrated to simulate carbon and water pathways (e.g., Water Use Efficiency) over the plantation's lifespan. This approach quantifies the added value of the resistant genotypes, demonstrating their superior resilience under changing environmental and management conditions.

Finally, under the coordination of the Democritus University of Thrace, WP7 will integrate these findings to analyze broader Ecosystem Services (ES), including provisioning, regulating, and supporting functions. Ultimately, by validating biological indicators and establishing a robust carbon monitoring protocol, WP7 will demonstrate the effectiveness of using improved genotypes, offering a replicable model for restoring Mediterranean areas affected by forest dieback.

How to cite: gori, A., beltrami, S., alderotti, F., ferrini, F., dibari, C., ferrise, R., paffetti, D., and brunetti, C.: Restoring Mediterranean holm oak forests: ecosystem functioning and climate mitigation in the LIFE RECLOAK project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16743, https://doi.org/10.5194/egusphere-egu26-16743, 2026.

Water bodies play a crucial role in controlling urban heat by acting as a sink and enhancing evaporative cooling. However, rapid urbanisation in India has led to the progressive encroachment and shrinkage of water bodies, which threatens the urban thermal environment. In this study, we investigate the impact of urban water body encroachment on surrounding temperature using multidecadal Landsat-derived land surface temperature (LST) data at 30 m spatial resolution and water body datasets. The LST of Water bodies and surrounding urban areas within their vicinity are estimated to assess spatiotemporal changes in LST. Our results reveal a substantial increase in LST in urban regions surrounding water bodies in recent decades, indicating a decline in their local cooling effectiveness. Furthermore, encroached water bodies exhibit a pronounced rise in surface water temperature than non-encroached water bodies. The warming of both water surfaces and adjacent urban areas highlights the compound thermal impacts of water body encroachment. The findings indicate that the loss of urban water bodies due to encroachments contributes to the warming of urban areas. The study underscores the importance of protecting and restoring urban water bodies as effective nature-based solutions for mitigating rising urban temperatures and enhancing climate resilience in rapidly growing urban cities.

How to cite: Naik, M. N. and Mishra, V.: Assessing the Impacts of Water Bodies Encroachment on Urban Land Surface Temperature, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16835, https://doi.org/10.5194/egusphere-egu26-16835, 2026.

Magnetotactic bacteria (MTB) are an ancient microbial lineage that navigate geomagnetic field lines via intracellular magnetosomes, and their unique multi-disciplinary properties have long drawn research attention. This study focuses on MTB’s behavioral and metabolic adaptations under high magnetic fields—including extreme environments six orders of magnitude stronger than the geomagnetic field—and explores their application potential.Our recent progress is outlined as follows: 1) Using Magnetospirillum gryphiswaldense MSR-1 as the model strain, we analyzed how high magnetic fields reprogram MTB metabolism, modulate biomineralization dynamics, and impact MmaK scaffold assembly as well as magnetofossil genesis.2) We clarified the regulatory role of MTB-derived Mms6 protein in biomineralization, and synthesized magnetosome-mimetic nanocrystals in vitro that match natural magnetosomes in cuboctahedral morphology, soft ferromagnetic behavior, and high saturation magnetization. 3) We built a magnetic nanorobot-based navigation system to realize precise spatial control and trajectory planning of MTB, paving new ways for MTB-mediated nanodrug delivery and magnetic navigation.

References

[1] WAN, Hengjia, et al. Assembly dynamics of magnetotactic bacterial actin-like protein MamK under shielded geomagnetic fields: In vitro evidence of inhibited filament formation. International Journal of Biological Macromolecules, 2025, 320: 145863.

[2] Tao, Tongxiang, et al. "Boosting SARS-CoV-2 enrichment with ultrasmall immunomagnetic beads featuring superior magnetic moment." Analytical Chemistry 95.30 (2023): 11542-11549.

[3] Ma, Kun, et al. "Magnetosome-inspired synthesis of soft ferrimagnetic nanoparticles for magnetic tumor targeting." Proceedings of the National Academy of Sciences 119.45 (2022): e2211228119.

[4] TAO, Tongxiang, et al. A Precise BSA Protein Template Developed the C, N, S Co-Doped Fe3O4 Nanolayers as Anodes for Efficient Lithium-Ion Batteries. ACS Applied Energy Materials, 2022, 5.8: 10254-10263..

How to cite: Wang, J. and Ma, K.: Research progress on magnetotactic bacteria under high magnetic field, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17838, https://doi.org/10.5194/egusphere-egu26-17838, 2026.

Tropical forests provide vital ecological, economic, cultural and climate-regulating services to local and global communities. However, these ecosystems are threatened by deforestation, often driven by complex and region-specific factors. Numerous studies have been conducted to predict the spatial distribution of deforestation risk, yet little research has explored the possible advantages of predicting deforestation intensity patterns. To support more effective forest management and conservation planning, this study examines the use of deep learning for predicting the spatial patterns of deforestation intensity.

This research develops and evaluates a regression-based ResUNet architecture for predicting deforestation intensity patterns. 
The deforestation datasets are, in most cases, highly skewed and zero-dominated, which poses the first challenge since this can significantly affect the predictive performance of the regression model. Several loss functions have been evaluated to mitigate this effect. The results illustrate how the Tweedie loss performs best. Furthermore, with a Root Mean Squared Error (RMSE) of 0.00494 on all values and 0.0169 on non-zero values, the Tweedie ResUnet model consistently outperforms the baseline XGBoost regression model. 

To test the model's cross-regional generalizability, four tropical regions were selected, each located on a different continent and characterised by varying deforestation drivers and dynamics. The Tweedie-ResUNet architecture was trained and tested on each study area. The differences in performance could be explained by regional characteristics such as data quality, topography, and seasonal cloud cover. However, the results still demonstrate a strong potential for the model's applicability to other tropical regions. 

The overall findings of this study suggest that deep learning models can be utilised to offer valuable insight into spatial patterns of deforestation intensity. 

How to cite: Sillem, K. and Cue la rosa, L.: Beyond Risk: Predicting Tropical Deforestation Intensity Patterns with Regression-Based Fully Convolutional Neural Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18862, https://doi.org/10.5194/egusphere-egu26-18862, 2026.

Climate change is increasing the frequency and intensity of drought and heat events in Mediterranean regions, urging the need for resilient fruit crops that link stress tolerance to relevant fruit quality traits. My PhD project focuses on the strawberry tree (Arbutus unedo L.), an underutilised Mediterranean evergreen fruit species of considerable ecological relevance in Southern Europe, with a recognised capacity to cope with diverse abiotic and biotic constraints. The project objective is to characterise six Italian A. unedo populations in terms of physiological performance under water-limited conditions and fruit quality characteristics. The primary step is to select provenances with contrasting pedoclimatic characteristics and to use calculated climatic indices (e.g., Emberger’s Q2) across an aridity–temperature gradient. These provenances are then screened for constitutive traits under well-watered conditions combining measurements of xylem vulnerability to embolism formation (P50), water-use strategy (e.g., minimum epidermal conductance, g_min; specific leaf area, SLA), and cellular drought tolerance derived from pressure–volume analysis (turgor loss point, TLP; osmotic potential at full turgor, π₀; and modulus of elasticity, ε).  After that, inducible trait modifications are tested under water stress and recovery conditions, monitoring gas exchange, plant water relations, chlorophyll fluorescence parameters, hydraulic resistance, and growth. In parallel,  a core component of the work investigates fruit physiological performance across different ripening stages (identified by skin colour) and during post-harvest storage. Fruits, harvested at the yellow–orange stage, are analysed for respiratory activity using a custom fruit chamber coupled to a LI-COR 6800 system. Post-harvest dynamics are monitored under two storage treatments (ambient temperature vs. 4 °C) and related to quality attributes, including colour development, soluble solids (°Brix), firmness, weight loss, pectin content, sugar profile, and polyphenol-related traits. Within this post-harvest study, the project also considers the use of edible coatings as a practical tool to modulate storage techniques and help preserve the quality attributes of this perishable fruit. Therefore, integrating provenance screening with fruit respiration and post-harvest physiology provides a practical basis for selecting A. unedo genotypes with improved drought resilience and high fruit yield and quality, thereby fostering A. unedo cultivation in Mediterranean areas under a changing climate.

How to cite: Pascucci, G., Alderotti, F., Lo Piccolo, E., Beltrami, S., Detti, C., Baptista, A., Palchetti, V., Bini, L., Ferrini, F., Antunes, M. D., Giordani, E., Brunetti, C., and Gori, A.: Drought resilience and fruit performance in strawberry tree (Arbutus unedo L.) populations: ecophysiological screening and postharvest behaviour , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21475, https://doi.org/10.5194/egusphere-egu26-21475, 2026.

Biological soil crusts (biocrusts) are increasingly recognized as important components of ecosystems. Biocrusts are instrumental in maintaining functions such as soil stability and reduced abundance of invasive annual grasses. Impacts of fire are likely to be less severe where invasive plants are reduced and biocrusts are abundant. These organisms can thrive under drought conditions and help intact ecosystems be more resilient to drought. However, expertise on these organisms remains limited.  Practitioners are often interested in the topic but might feel they would benefit from additional resources on how to identify these organisms. We have developed a single-page, front and back guide that enables anyone, regardless of previous experience, to recognize biocrusts in the field and categorize them into ecologically meaningful groups, with known functional roles. The guide has been tested both in the field and by examining biocrust samples in hand. Data associated with testing the guide was used to improve it. The resulting guide describes higher-level groups of algae, mosses, and lichens. Light algal crusts are described as their own group. Dark algal crusts are presented with gelatinous lichens due to similar ecologies and increased accuracy of identification when these groups are combined. The determination of algal crusts was accepted as correct, regardless of the light or dark designation, due to the quality of our samples. Mosses are split into short and tall. In addition to the gelatinous lichens / dark algal crust group, lichens were classified into five additional categories: crustose, cup, foliose, fruticose, and scale. This tool has been created from the ground up, driven largely by the accuracy with which non-experts can correctly classify presented groups. In addition to reporting on the accuracy of each group, we explain how sample type (resin, dried and in petri dishes, or enlarged photographs) played a role in the accuracy of identification. We describe the ecology and functional roles of the presented groups, giving further justification for their classification. We anticipate that adoption of the guide is likely to have far-reaching implications, such as an increase in the number of studies on biocrusts at the level of functional roles. 

How to cite: Condon, L., Barker, C., and Coates, P.: Alleviating Biocrust Blindness: An Easy Guide to Morphogroups of Biocrusts , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22143, https://doi.org/10.5194/egusphere-egu26-22143, 2026.

Mesozoic Oceanic Anoxic Events (OAEs) are critical geological episodes linked to global carbon cycle perturbations, climate warming, and ecosystem restructuring. However, the regional expression of OAEs in the eastern Tethys remains insufficiently constrained. This study focuses on the Early Jurassic Toarcian OAE (T-OAE)—integrating petrological, mineralogical, and geochemical analyses of two key sections to reconstruct Early Jurassic sedimentary evolution, paleoclimate-paleoenvironment dynamics, and their responses to the T-OAE. Pronounced negative carbon isotope excursions (CIEs) are recorded in both marine strata, correlatable with global T-OAE records. Intensified continental chemical weathering  and enhanced terrigenous detrital input are common responses of the eastern Tethys to T-OAE, driven by global warming. Redox proxies reveal oxic-suboxic conditions in open marine settings of the eastern Tethys during OAEs, regulated by regional factors (water depth, basin restriction, freshwater input), contrasting with the anoxic-euxinic environments in the western Tethys. Bioproductivity showed spatial heterogeneity: organic matter accumulation was controlled by redox conditions and productivity, with high accumulation in restricted lagoons versus low-moderate in open shelves.

This study reveals the regional response patterns of the eastern Tethys to Mesozoic OAEs, highlighting the spatial heterogeneity of redox and productivity dynamics, and provides new insights into the Mesozoic climate-ocean-biosphere system.

How to cite: Yi, J. and Fu, X.: Sedimentary Environment Evolution and Response to Mesozoic Toarcian Oceanic Anoxic Event (T-OAE) in the Eastern Tethys, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-279, https://doi.org/10.5194/egusphere-egu26-279, 2026.

Microbially Induced Sedimentary Structures (MISS) are syndepositional primary structures that occur both in some of the earliest forms of life and in modern environments. Throughout geological time, microorganisms developed metabolic strategies that enabled their establishment and proliferation in a wide range of settings, including arid environments such as deserts. However, MISS are widely recognized in tidal flats and other shallow-marine environments, whereas examples preserved in continental deposits remain comparatively scarce. A comprehensive review of Precambrian MISS occurrences indicates a notable expansion of documented records during the Mesoproterozoic, coincident with the assembly of the Columbia supercontinent and a concurrent rise in atmospheric oxygenation. These global transitions may have promoted the ecological diversification of microbial communities and facilitated their dispersal into progressively drier continental interiors. Under favorable conditions, microorganisms proliferate and form microbial mats that interact with external factors such as sedimentation, currents, erosional processes, and other physical drivers. Their presence in arid terrestrial deposits is thus of considerable importance, as it underscores how microbial communities evolved and developed adaptive capabilities that enabled them to colonize and persist within intermittently wet landscapes subjected to elevated environmental stress. This study documents the occurrence of MISS within continental desert depositional systems of the Mangabeira Formation, São Francisco Craton, Brazil (1.6 Ga), preserved in wet sandsheet deposits. These occurrences broaden the sparse record of MISS in Proterozoic desert environments and offer new constraints on the capacity of early microbial communities to endure highly stressful and intermittently wet conditions. To investigate the conditions that enabled microbial establishment in this ancient desert, this study applies a multi-method approach integrating sedimentological, stratigraphic, petrographic, and microtextural datasets. The vertical succession reveals six distinct drying-upward cycles, each associated with fluctuations in groundwater level that periodically generated stable, moisture-rich surfaces suitable for microbial mat development. Within these intervals, MISS occur in millimetric heterolithic laminites displaying wavy–crinkly lamination, wrinkle marks, roll-up structures, deformational features, authigenic minerals with convolute morphologies, trapped grains, and organic carbon remnants. Complementary Raman analyses reveal characteristic carbonaceous peaks (at ~1370, 1590, and 1610 cm⁻¹), confirming the presence of organic carbon and kerogen. Collectively, the integrated dataset indicates that microbial colonization in the Mangabeira Formation was episodically favored by groundwater-controlled moisture stability, which enhanced substrate cohesion and enabled the formation of distinctive biosedimentary fabrics. These findings, contextualized within the broader Mesoproterozoic expansion of MISS, highlight the capacity of early microbial communities to establish themselves in hydrologically stressed desert landscapes and refine the sedimentological and geochemical criteria necessary for recognizing MISS in deep-time continental systems.

How to cite: Feitosa, A., Bállico, M., Souza, E., Scherer, C., Callefo, F., Balbinot, V., Tatsch, G., Yokoyama, E., Leite, A., Reis, A., Silva, S., and Santos, A.: Microbially Induced Sedimentary Structures in a Mesoproterozoic Erg System: A Case Study from the Mangabeira Formation, Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-633, https://doi.org/10.5194/egusphere-egu26-633, 2026.

The impacts of climate change are directly visible in the intensification and increasing frequency of extreme climate events, such as floods and droughts. Since droughts result from complex, multivariate, and non-linear land-atmosphere interactions, understanding these relationships is crucial for developing impactful future measures to reduce or mitigate drought impacts. Many studies have performed correlation analysis among these variables, but (1) correlation does not fully resolve causality and complexity of drought occurrence, due to which (2) the nonlinear behavior of drought propagation remains poorly understood. This study applies conditional independence tests, such as the Peter and Clark Momentary Conditional Independence algorithm (PCMCI+), to identify and analyze the causal drivers of drought at different lag periods using multivariate time series data. We investigated the influence of seven important variables for drought incidences on drought-induced vegetation responses in the drought-prone Bundelkhand region of Central India as well as its seven districts (Banda, Chitrakoot, Hamirpur, Jalaun, Jhansi, Lalitpur, Mahoba) separately. We used ERA-5 land monthly data at 0.1˚ spatial resolution for climatic variables, including precipitation, temperature, evaporation, relative humidity, soil moisture, and biophysical variables as Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI) which measures vegetation health via greenness used as a proxy for drought-induced vegetative stress, were taken from Peking University’s Global Inventory Modelling and Mapping Studies version 1.2 (PKU GIMMS) at 0.0833˚ spatial resolution. The analysis spans 32 years temporally from 1990 to 2021 and is carried out at a monthly scale by temporally aggregating the data through monthly averages.

For each variable, PCMCI+ measures partial correlation  as a function of the maximum time delay and the significance threshold applied.  Results here are presented for a maximum time lag of 3 months and a significance value of 0.05. At the investigated spatiotemporal scales, precipitation is the primary driver of soil moisture in Bundelkhand given a 3-month lag. Temperature primarily affects LAI with a 1-month lag, while accumulated warmth supports vegetation on longer timescales (3-month lag). Among atmospheric factors, relative humidity emerges as the strongest control on vegetation greenness and canopy development, influencing both NDVI, and LAI. The results also reveal important land-atmosphere feedback. The negative feedback between soil moisture, NDVI, and LAI indicates self-limiting plant growth under water stress 2-3-month lag. Vegetation contributes to surface cooling as expected, reflected in the inverse relationship between LAI and temperature. Furthermore, vegetation regulates evaporation, with NDVI affecting evaporation at a 2-month lag and LAI at a 3-month lag. Spatially, district-level patterns generally mirror the regional findings, except for Lalitpur, where fewer and different causal links were identified. Overall, the study shows that humidity-driven vegetation dynamics and multi-lag feedback between the land surface and atmosphere are central to drought evolution, highlighting the importance of explicitly representing these coupled processes in ecohydrological assessments. Future work should translate these identified causal pathways into next-generation drought monitoring and forecasting systems that incorporate lag-aware vegetation-climate interactions to improve drought early-warning capabilities and anticipatory mitigation planning.

How to cite: Sahu, H., Garg, P. K., Vijay, S., and Dasgupta, A.: Uncovering Causal Pathways of Agricultural Droughts using Climate and Vegetation Signals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1007, https://doi.org/10.5194/egusphere-egu26-1007, 2026.

The Central Ganga Plain (CGP), a key sector of the Indo-Gangetic foreland basin, contains thick, continuous Quaternary alluvial sequences. Its rapidly subsiding basins preserve a high-resolution terrestrial archive, ideal for reconstructing Indian Summer Monsoon (ISM). This study examines sedimentary profiles from distinct river systems in the Central Ganga Plain (CGP) using a multi-proxy framework. A Late Quaternary trench from the Gomti river (26°52′ N, 80°56′ E, Lucknow) and a Holocene section from the Betwa river (25°28′ N, 79°5′ E, Hamirpur). Sediment sample from Lucknow profile were analysed for CHNS, AMS ¹⁴C dating, mineral magnetism, and bulk geochemistry (major, trace, and REE), while those from Hamirpur were analysed using OSL and AMS ¹⁴C dating, alongside CHNS. The established chronology or Lucknow trench, record from ~24 to 3 kyr BP. The CHNS data shows a significant shift at ~20 kyr, marked by high TOC (3.97%) and C/S ratio (~ 300) indicating enhanced organic productivity and freshwater conditions. Concurrent mineral magnetic signatures (χlf, SIRM and ꭓARM) suggest strong detrital input linked to weaker monsoon. This evolving climatic condition is further investigated through bulk geochemistry, (major, trace and REE), which provide critical insights into sediment provenance, weathering regimes, and paleo-hydrological conditions. The chronology for the Hamirpur trench covers from ~800-12000 years BP and the CHNS data provide distinct environmental phases, marked by a sharp peak in TC (3.31%), TOC (1.56%) and C/N ratio (~439), indicating a enhanced terrestrial organic matter preservation in a low-energy, waterlogged setting around ~3000 kyr BP. This integrated high-resolution multiproxy record from the two distinct river systems provides new insights into monsoon variability and sedimentary responses in the Central Ganga Plain during the late Quaternary.

How to cite: Jayakumar, J., ms, A., phartiyal, B., sharma, A., Kumar, P., D. Chauhan, G., and kannan, P.: Multi-Proxy Reconstruction of Late Quaternary Monsoon Variability and Fluvial Response in the Central Ganga Plain, India: Insights from magnetic, CHNS and geochemistry records., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1046, https://doi.org/10.5194/egusphere-egu26-1046, 2026.

How to cite: Gkatzogias, A., Bitas, D., Georgiou, K., Amditis, A., and Michalis, P.: From Divers to Communities: An IoT-Based Crowdsourcing Sensing Approach to Protect Underwater Heritage Sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1934, https://doi.org/10.5194/egusphere-egu26-1934, 2026.

Underwater cultural heritage (UCH) is threatened by climatic risks, natural hazards, pollution and human induced activities, which increases the need for integrated monitoring approaches that combine advanced technologies with reliable in situ observations. This study presents the experience gained during underwater operations carried out in THETIDA project. This involved the deployment of coordinated teams of specialized and recreational divers across four Mediterranean pilot sites for data collection and documentation in support of integrated monitoring and protection of UCH sites. Diving teams were systematically deployed to collect various datasets (e.g. high-resolution photographic and video data), perform archaeological measurements, mapping using established underwater archaeology techniques and provide ground truth and spatial referencing data using a series of underwater technologies (e.g. wearable sensors, hyperspectral cameras, autonomous under water vehicles, among others).

At the 18th-century Nissia shipwreck (Cyprus), diving operations were carried out in parallel with a site excavation, hyperspectral imaging of wooden structures, material and biofouling sampling and the deployment of wearable, seabed and boat operated environmental sensing systems. Comparable methodologies were applied at deeper sites, including the WWII Equa shipwreck and the Roman Albenga II shipwreck of Gallinara Island (Italy), as well as the WWII B-24 Liberator aircraft (Portugal). Across these sites, divers performed detailed photogrammetric surveys and 3D reconstructions, in operations under constrained visibility and challenging conditions, putting into practice the validation of the performance and durability of prototype underwater sensing devices. Diver observations obtained at sites were also considered essential for the identification of site-specific risks, such as sediment mobility, biological colonization and physical disturbances. In addition to scientific data acquisition, the underwater operations supported participatory monitoring through citizen-science activities (operation of boat sensing devices), aiming to contribute to long-term site and data continuity.

The obtained results demonstrate that diving underwater operations are considered to be a key complementary component for integrated UCH monitoring, merging knowledge from specialist expertise with sensor-based systems in an effort to enhance informed conservation and protection strategies. Data gathered is also essential for the development of hazard and risk models that allow the prediction and aid the management of these UCH. The experience gained indicates that diving data collection is essential for integrating archaeological documentation, environmental sensing, and survey data under real field conditions. Underwater diver-led operations can serve as both primary data collectors and ground-truth contributors effectively bridging together human expertise with advanced monitoring technologies for the protection of underwater cultural heritage.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under THETIDA project (Grant Agreement No. 101095253) (Technologies and methods for improved resilience and sustainable preservation of underwater and coastal cultural heritage to cope with climate change, natural hazards and environmental pollution).

How to cite: Michalis, P., Demesticha, S., Giatsiatsou, P., Demetriou, A., Ruberti, F., Gabotto, G., Martins, F., Mazzoli, C., and Amditis, A.: Underwater Operations for Data Collection in Integrated Cultural Heritage Monitoring and Protection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1982, https://doi.org/10.5194/egusphere-egu26-1982, 2026.

Our cultural memory is permanently endangered and, often, damaged irretrievably, given that cultural disasters, are quite repetitive, whether in cases of man-made or of natural disasters, armed conflicts and climate change combined with earth’s tectonic activity constituting, potentially, the primary causes.

The southwestern Peloponnese in Greece, precisely the region of Pylia, due to its proximity to the Hellenic trench, is considered to be tectonically as one of the most active areas in Greece, representing a major subduction zone. At the same time, it constitutes also a broad area with a very long history and a wealth of archaeological sites and relics. Focusing on the western coastline of the said region, directly exposed to variations of sea level height, as well as to conditions of rapid erosion due to the aggressive components of seawater, it should be considered as an area of ​​urgent priority to be monitored and protected.

Specifically, Voidokilia bay at the western coast of Messenia Prefecture, to the north of Navarino bay, a highly fragile ecosystem, actually under a NATURA Network protection status, constitutes the related case-study. Nevertheless, Voidokilia bay, is also one of the most attractive landscapes worldwide, thus facing rapid tourism challenges, ending up in increased disaster risks, both for the cultural as well as for the environmental assets. Accordingly, an interdisciplinary methodology within the scientific field of digital humanities has been applied for digitizing archaeological sites, excavated or not, underwater or not, as well as for highlighting their interdependence with the wider Messenia region’s archaeological sites network, further combined with trade routes connecting southwestern Peloponnese and the Aegean islands, via southeastern Peloponnese and Attica, thus fulfilling the notion of applied archaeology.

In this context, applying geoinformatics proved to be the most effective methodology for the related holistic cultural heritage management, in the perspective of an effective strategic planning on the part of the State apparatus, whether for private or public works, taking also into consideration charters, European directives and good practices, already applied worldwide, such as people’s community inclusion, in the direction of a public archaeology model.

The cornerstone of the specific research procedure has been extensive documentation, integration of different data types, such as archaeological, bibliographic, (palaeo)environmental, geospatial, remotely sensed imagery, for building up the sites’ multidimensional profile and revealing spatial relations and settlements’ interdependence, further highlighting the related buffer zones, in the perspective of delineating wider areas of archaeological profile, for anticipating the long-standing threats to archaeological assets such as rapidly increasing tourism, mismanaged development, poor excavation and looting, lack of conservation, climate change posing further significant threats to cultural heritage assets.

Conclusively, further constituting a potential contribution to the archaeological cadastre, already established by the Hellenic Republic, as well as proposing mild tourism development for keeping the balance between urban regeneration and environmental protection, in accordance with the Sustainable Development Goal 11-Sustainable cities and communities, one of the 17 SDGs established by the United Nations General Assembly in 2015, with the official mission to “Make cities inclusive, safe, resilient and sustainable”.

How to cite: Chroni, A. and Karathanassi, V.: The western Peloponnese coastline cultural landscape: cultural heritage management policy tools for making cities inclusive, safe, resilient and sustainable, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2191, https://doi.org/10.5194/egusphere-egu26-2191, 2026.

Instrumental long-term climate records are scarce worldwide, especially in tropical regions such as the Brazilian Amazon. The lack of systematic data prior to the 20th century limits understanding of long-term climate variability and extreme events. This study is situated within the field of Historical climatology. It aims to contribute to knowledge of climate extreme events that occurred during periods of limited climatological data through analysis, focusing on the flooding events of 1859/60 and 1892 in the Amazon River basin. The research aligns 19th-century historical documents, such as newspapers, periodicals, official correspondence, and travel logs, with climate information from dendrochronological studies to reconstruct the magnitude, duration, and impacts of these flood events. Methodologically, it is constructed through an interdisciplinary approach combining environmental history, historical climatology, and hydrology, using written records as climate proxies that provide crucial information on river levels, rainfall seasonality, and flood persistence. Analysis of tree-rings from the Amazonian trees known as “Cedro-Vermelho” (Cedrela Odorata) indicates that the floods of 1859/60 and 1892 were among, if not the, most severe flooding extremes of the 19th century on the Amazon River basin. Historical descriptions of damage to livestock, farming, agriculture, urban infrastructure, and the living conditions of the population, which mainly consisted of “ribeirinhos”, a traditional culture and way of life near the rivers, back this up. The results demonstrate that rescuing and systematizing historical climate information from a region with a traditional lack of instrumental records helps fill a gap in tropical data. Regions such as those analyzed in this study have often been overlooked in historical climate research. The potential of historical records combined with dendrochronological analysis has proven extremely promising. It allows not only cross-validation of information but also the recovery of climate data through a non-conventional method of analyzing climate before the 20th century, helping to build a more comprehensive understanding of past climate in the vast Amazonian territory.

How to cite: Sousa, K., Granato, D., Neto, A., and Nunes, E.: Historical floods of the 19th century in the amazon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3138, https://doi.org/10.5194/egusphere-egu26-3138, 2026.

The preservation of underwater and coastal cultural heritage is challenged by climate change, including sea-level rise, coastal erosion, extreme events and long-term environmental degradation. These threats require not only scientific monitoring and risk assessment, but also active engagement of local communities and stakeholders to foster awareness and citizen centered resilience.

This contribution presents the Augmented Reality (AR) crowdsourcing mobile application, to engage citizens, divers and local communities in exploring heritage sites, understanding climate-related risks and contributing to resilience strategies with collection of ground-truth data. The AR mobile application focuses on providing complex scientific knowledge into intuitive, place-based experiences accessible to non-expert audiences through interactive 3D reconstructions, contextualized information supported by visualizations of environmental and site-specific data over time. The AR mobile app supports enhanced learning and strengthens connections between citizens, heritage sites and scientific evidence, by allowing users to visualize digital content within their physical surroundings.

The application has been deployed across seven underwater and coastal pilot sites: the Equa Shipwreck (La Spezia, Italy), the Albenga A Shipwreck at Gallinara Island (Italy), the Hiorthhamn Arctic mining station (Svalbard, Norway), Lake IJssel (The Netherlands), the B-24 Liberator aircraft wreck (Algarve, Portugal), the Castle of Mykonos (Greece) and the Nissia Shipwreck (Cyprus). Each pilot features a tailored AR campaign reflecting its specific heritage value, ranging from Roman cargo vessels and WWII wrecks to Arctic industrial remains and coastal fortifications. Site-specific content visualises relevant climate hazards such as erosion, sea-level rise, storm impacts and material decay, while enabling users to explore excavation layers, alternative site states and historical reconstructions. The AR experiences are built using optimised 3D scans, reconstruction models, archival imagery, curated scientific content with interactive Points of Interest. Dynamic visualisations illustrate processes such as habitat formation, sediment movement, and structural transformation, supporting a deeper understanding of how environmental change affects heritage over time. All content has been developed in close collaboration with domain experts to ensure scientific accuracy and educational value.

A citizen-engagement study has been conducted to assess usability, user motivation, and the application’s effectiveness in raising awareness of climate risks to cultural heritage. Full validation across all pilot sites is taking place, ensuring that results reflect the cultural, geographic and environmental diversity of the seven pilot sites.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under THETIDA project (Grant Agreement No. 101095253) (Technologies and methods for improved resilience and sustainable preservation of underwater and coastal cultural heritage to cope with climate change, natural hazards and environmental pollution).

How to cite: Koukoudis, K., Katika, T., Touramanis, A., Amditis, A., and Michalis, P.: Connecting Citizens, Science, and Vulnerable Heritage: An AR-Based Approach to Climate Resilience , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3862, https://doi.org/10.5194/egusphere-egu26-3862, 2026.

Tide-gauge observations are among the most reliable sources for assessing sea-level variability and its seasonal and temporal changes in coastal regions. This study analyzes sea-level records obtained from tide gauges along the Angolan coast for the period 2015–2020, with the objective of characterizing the seasonal and interannual variability of tides. The methodology included quality control and pre-processing of hourly and monthly sea-level data, removal of non-tidal signals, harmonic tidal analysis, and the assessment of seasonal variability and its statistical significance. Despite limitations related to data gaps, limited temporal resolution, and the lack of complementary oceanographic data, the results reveal pronounced seasonal and interannual variability in sea level. This variability reflects the combined influence of tidal dynamics, regional ocean circulation, wind forcing, and climate-related processes. The analysis highlights the importance of continuous and homogeneous tide-gauge records along the Angolan coast for improving the detection and interpretation of sea-level variability. The findings contribute to coastal monitoring efforts and provide relevant information for coastal management, risk assessment, and the development of adaptation strategies in the context of sea-level change.

Keywords:  Sea level variability, Tide-gauge observations, Seasonal and interannual variability, Angolan coast.

How to cite: Guilherme, F., Neves, M., and Lamas, L.: Seasonal and Interannual Variability of Tide-Gauge Records along the Angolan Coast for the period 2015 – 2020 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4120, https://doi.org/10.5194/egusphere-egu26-4120, 2026.

To understand the climate conditions in Japan before the commencement of modern official meteorological observations, it is necessary to indirectly estimate them using proxy data that serve as climate indicators.

In Japan, there is a nearly continuous annual dataset of Lake Suwa's freezing records spanning over 580 years. Furthermore, diaries from various parts of Japan contain daily weather records. By utilizing these records, daily climate data with the minimum temporal resolution can be obtained. By leveraging these proxies, it is possible to reconstruct the climate of the cold season, which has been previously less understood, across various temporal and spatial scales.

The objective of this study is to reconstruct the changes in cold-season climate in Japan over the past several hundred years with high temporal resolution.

The proxy data currently used include: lake and terrestrial sediments (Lake Suwa, approximately 1000 years), records of cherry blossom flowering and full bloom dates primarily collected in Kyoto (approximately 1000 years), tree rings (approximately 300 years), daily weather records from diaries (approximately 200 years), freezing records of Lake Suwa and Lake Jusan (approximately 580 and 150 years, respectively), early-meteorological observation data (approximately 50 years), and Japan Meteorological Agency observation data (approximately 150 years).

Firstly, the most extensive dataset, the cherry blossom flowering data, is used as a reference. Next, proxy variables are standardized after removing trends caused by human activities. Subsequently, regression analysis is performed for each period where variations either coincide or do not coincide. Furthermore, for each proxy variable, spatial correlations were calculated using 20th-century meteorological observation data to identify the regions represented by that proxy variable.

(This research was funded by JSPS Grant-in-Aid for Scientific Research (24H00118).

How to cite: Hasegawa, N., Katata, G., Hirano, J., Yonenobu, H., Yasue, K., Kumon, F., Hatano, N., Takahashi, H., Zaiki, M., and Mikami, T.: Reconstruction of Japan's Cold-Season Climate in the Past Few Hundred Years Using High-Resolution Multi-Proxy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4208, https://doi.org/10.5194/egusphere-egu26-4208, 2026.

Tropical convectionanomaly could serve as a crucial driver of global atmospheric teleconnections and weather extremes around the world. However, quantifying the dominances of convection anomalies with regional discrepancies, relevant for the variations of global atmospheric circulations, remains challenging. By using a network analysis of observation-based rainfall and ERA5 reanalysis datasets, our study reveals that El Niño-like convection is the most primary rainfall pattern driving the global atmospheric circulation variations. High local concurrences of above-normal rainfall events over equatorial central-eastern Pacific amplify their impacts, even though the most intense rainfall anomalies are observed near the Maritime Continent. Furthermore, we find that the impacts of El Niño- like convection will be tripled by the end of this century, as projected consistently by 23 climate models. Such “rich nodes get richer” phenomenon is probably attributable to the dipolar rainfall changes over theequatorial western-central Pacific. This study highlights the dominant role of El Niño- like convection on the global climate variations, especially under the future changing climate.

How to cite: Lin, S., Cai, F., Gerten, D., Yang, S., Jiang, X., Su, Z., and Kurths, J.: Intensified dominance of El Niño-like convection relevant for global atmospheric circulation variations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4441, https://doi.org/10.5194/egusphere-egu26-4441, 2026.

The contribution of EM4C to the TRIQUETRA project addressed a central challenge in contemporary cultural heritage protection: transforming complex scientific risk assessment knowledge into practical, operational tools that support informed decision-making by professionals and heritage authorities. From the outset, EM4C adopted an application-oriented approach, extending beyond academic research to the development of structured methodologies and digital decision-support tools aligned with real-world conservation needs.

EM4C’s involvement spanned the full project lifecycle, from methodological design and knowledge structuring (WP3) to validation and evaluation (WP6), assessment of exploitation potential and future development pathways (WP7), and contribution to reporting and documentation activities (WP1). This integrated engagement ensured continuity between research, implementation, evaluation, and long-term usability of project outcomes.

Within WP3, and particularly Task 3.6, EM4C acted as task leader for the development of tools, methods, and technologies aimed at mitigating risks to cultural heritage sites. The work recognized that heritage vulnerability results from the interaction of multiple factors, including construction materials, environmental conditions, historical interventions, patterns of use, and diverse natural hazards exacerbated by climate change. Rather than addressing these factors in isolation, EM4C developed a structured framework reflecting real-world site behavior, where risks emerge through combined and cumulative effects over time.

A major challenge identified was the extreme complexity of potential risk scenarios. Initial theoretical analysis showed that more than 1.8 million combinations could arise when accounting for all variables, rendering manual assessment impractical. EM4C addressed this through a rational reduction process, grouping construction materials into four realistic tri-material combinations commonly found in heritage sites. This filtering reduced the scenario space to 15,120 valid and prioritized cases, maintaining representativeness while ensuring usability.

These scenarios were implemented digitally through two decision-support tools: the M REPORT ENGINE for monument-scale assessments and the LS REPORT ENGINE for landscape-scale risk management. Both tools generate structured technical outputs based on user-selected parameters such as materials, hazards, and risk intensity. Crucially, the proposed conservation and protection measures are grounded in an extensive manual synthesis of scientific literature, technical guidelines, and recognized good practices, ensuring technical accuracy, consistent terminology, and non-commercial neutrality.

The developed tools were evaluated within WP6 through presentations and hands-on assessments involving conservators, engineers, and cultural heritage authorities, including representatives of the Hellenic Ministry of Culture. Feedback collected through questionnaires and qualitative observations confirmed the tools’ clarity, relevance, and capacity to support structured decision-making, while also identifying directions for future refinement.

Within WP7, EM4C assessed the exploitation potential of the model and tools, demonstrating their adaptability to diverse institutional contexts and their suitability as flexible decision-support systems. The work highlighted their potential evolution into more specialized, data-integrated applications.

Overall, EM4C’s contribution effectively bridged theory and practice, delivering scientifically robust yet operationally meaningful tools that enhance the long-term impact and applicability of the TRIQUETRA approach to cultural heritage risk management.

How to cite: Spyrakos, V.: ABSTRACT - Overall Contribution of EM4C to the TRIQUETRA Project , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5292, https://doi.org/10.5194/egusphere-egu26-5292, 2026.

The Makgadikgadi Basin (MKB), in the central Kalahari Basin of northeastern Botswana, currently consists of a wide complex playa lake system, a relic of the Paleolake Makgadikgadi. Reconstructing the Quaternary depositional, environmental, and climatic history of the lacustrine-playa system has great significance for revealing the basin's evolution. However, its sedimentary record remains largely unexplored due to methodological challenges. In this study, four sediment cores, up to 1.6 m deep, were collected along a generally E-W transect from the western to central parts of the Ntwetwe pan, MKB.  Our multi-proxy record, including sedimentology, chronology, ostracod-based biostratigraphy, and clumped (∆₄₇) isotope geochemistry from these cores, reveals three complex hydroclimatic sequences that refine the environmental and climatic evolution of the MKB for the past 29 cal ka BP. The computed Bayesian age depth model and preliminary clumped isotope analysis on ostracod valves suggest the late Pleistocene (~29-19.5 cal ka BP) hypersaline-saline phase occurred under relatively low temperature conditions (∆₄₇-T = ~18.5-21°C), aligning with global glacial cooling and supporting interpretations of severe aridity in the Kalahari during the Last Glacial Maximum. The shift to a freshwater ostracod assemblage by ~5.2 cal ka BP partly corresponds to the termination of the African Humid Period (AHP), with a mean temperature (∆₄₇-T) of ~16.8°C. However, our record reveals significant complexity during the Late Holocene. The dominance of brackish water assemblage from ~4-1.6 cal ka BP suggests a prolonged transitional phase toward aridity, consistent with the broad trend of ITCZ retreat. Most notably, the late Holocene (~1.6-1 cal ka BP) assemblage, indicating a mix of brackish and freshwater taxa alongside extreme and warmer temperatures (∆₄₇-T = ~28.5°C). This implies a period of complex hydrological variability, potentially driven by increased summer rainfall variability or episodic flood inflow. Consequently, the Late Pleistocene and Middle Holocene data align with regional patterns, while the Late Holocene sequence particularly highlights the current extreme climate in the region, suggesting ostracod growth under extreme ephemeral playa lake conditions.

How to cite: Kahsay, T., Asrat, A., Sinha, N., Marchegiano, M., and Franchi, F.: Late Pleistocene to Holocene Multi-proxy Paleoenvironmental and Paleoclimatic Reconstruction of the Makgadikgadi Basin, Central Kalahari, Botswana, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7064, https://doi.org/10.5194/egusphere-egu26-7064, 2026.

The Million Year Ice Core (MYIC) Project is an Australian Antarctic Program initiative to recover a continuous ice core spanning the mid-Pleistocene Transition (MPT; 700–1,250kyr). MYIC pilot drilling and borehole reaming for casing installation started in the 2024/25 austral summer at Dome C North (DCN, 75.04220S, 123.63120E, ice depth of 3064 m). DCN is 9 km NE of Concordia Station and 45 km NE of the European Beyond EPICA Oldest Ice site at Little Dome C (LDC). In the 2025/26 season, casing was installed and deep drilling commenced using a new AAD deep drill system. Completion of drilling to bedrock is scheduled for the 2028/29 season.

One-dimensional ice modelling, constrained by ice penetrating radar and isochrones traced back to the original EPICA Dome C ice core site, indicate an age above the basal ice at DCN potentially reaching 2 million years (Ma) and a resolution at 1.5 Ma of 10,000 years per metre or better (Chung et al., 2023).

Laboratory capabilities for MYIC are directed at measurements required to test hypotheses on the cause of the MPT. Ice core continuous flow analysis (CFA) for conductivity, particles and soluble ions are underway, with fraction-collected aliquots taken for measurement of cosmogenic ¹⁰Be. Gas and water isotope measurements on the returned ice are scheduled to start this year. The new gas laboratory developed for the project combines a small-sample sublimation extraction system coupled to a Quantum Cascade Laser spectrometer and dual inlet mass spectrometry for combined measurement of CO₂, δ¹³C-CO₂, CH₄, and N₂O, as well as the main air isotopes. There are opportunities for measurements of other parameters through national and international collaboration.

How to cite: Pedro, J. B. and the Million Year Ice Core Project Team: The Million Year Ice Core Project at Dome C North, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8488, https://doi.org/10.5194/egusphere-egu26-8488, 2026.

The Norian-Rhaetian boundary (NRB) marks a critical interval of Late Triassic global environmental instability, ecological crisis, and climatic transition, which preceded the sustained biodiversity decline culminating in the end-Triassic mass extinction. Despite its significance, the drivers of carbon cycle and biotic disturbances across the NRB remain unresolved. In most cases, these major mass extinctions in geological history are interpreted as chain reactions triggered by volcanic activity. Interestingly, the NRB and Rhaetian intervals lack compelling evidence for synchronous, precisely dated, large-scale volcanism with demonstrable global effects. In this context, the Central Atlantic Magmatic Province (CAMP) erupted later at ca. 201 Ma, while other impact-related triggers and/or proposed large igneous provinces (LIP), such as the Angayucham LIP in Alaska (214 ± 7 Ma), remained weakly constrained in magmatic timing, magnitude, and environmental significance. In the absence of significant volcanism, the mechanisms underlying carbon cycle perturbations and ecological crises become even more enigmatic.

Here, we present a high-resolution carbonate carbon isotope (δ¹³C_carb) profile spanning the Late Triassic to Early Jurassic from South China. Through independent U-Pb dating and cyclostratigraphic analysis, a high-precision astronomical timescale was established. Carbon isotope variations are strongly controlled by orbital cycles, and the record reveals two large-magnitude negative carbon isotope excursions (CIEs) at ca. 205 Ma and 201 Ma, corresponding to the NRB and Triassic-Jurassic Boundary (TJB), respectively. Our study posits that astronomically driven climate change persistently influenced the NRB and subsequent Rhaetian intervals, triggering a series of chain reactions involving climate, vegetation, carbon burial, greenhouse gas emissions, and other factors. Ultimately, it acted as an amplifier in the NRB event, leading to carbon cycle perturbations and ecological crises during this period, thus potentially preconditioning the Earth system for the subsequent end-Triassic mass extinction. This study further highlights the significance of low-latitude coastal areas as dynamic amplifiers of carbon cycle instability and underscores the vulnerability of modern carbon reservoirs under ongoing climate change.

How to cite: Lu, T. and Fu, X.: Astronomically Driven Climate Change as an Amplifier of Carbon Cycle Instability and Ecological Crisis at the Norian-Rhaetian Boundary, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8903, https://doi.org/10.5194/egusphere-egu26-8903, 2026.

Cultural heritage is exposed to a wide range of risks arising from natural processes, extreme events and human activities, making heritage resilience a challenging and complex issue. Existing risk assessment and management approaches often lack cohesion, difficult to access or insufficiently aligned with the everyday needs of heritage managers and local communities, resulting in gaps in understanding, as well as preparedness and response capacity.

This contribution focuses on addressing these challenges by merging scientific knowledge, field-based experience, and community generated awareness through an integrated digital environment. Within the European project THETIDA, a web-based visualization and decision-support platform has been developed with the main objective of supporting a holistic understanding of cultural heritage resilience. The platform integrates hazard information, environmental monitoring data, socio-economic indicators and spatial representations within a single, accessible interface, enabling users to explore and understand how multiple risks interact and affect heritage assets and their surrounding environments.

The platform delivers three main categories of services: (i) Remote Sensing–Based Services, including inundation and flood prediction, coastal erosion monitoring, material degradation mapping, land-use change detection, and geo-hazard assessment; (ii) In-Situ Sensing Services, supporting on-site monitoring and material characterization; and (iii) a Decision Support System providing seismic hazard analysis, multi-risk assessment, and socio-economic impact evaluation. Interactive geospatial functionalities allow users to explore datasets through structured spatial representations, such as hexagonal grid systems and visualize multiple data layers simultaneously. The system operates through standard web browsers without the need for specialized GIS software, ensuring accessibility for diverse user groups, including heritage professionals, decision-makers and local communities. Multiple data formats, such as GeoJSON, TIFF, PDF, 3D models and imagery, are processed and visualized in near real time within the platform.

Τhe results demonstrate that the integration of digital tools is not only considered as a technological advancement but also as a key enabler for collaboration, participation and sustainable heritage management. Interactive and cooperative digital environments can significantly enhance the resilience of cultural heritage sites to climate and disaster-related risks, supporting informed, inclusive and actionable management strategies.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation program under THETIDA project (Grant Agreement No. 101095253) (Technologies and methods for improved resilience and sustainable preservation of underwater and coastal cultural heritage to cope with climate change, natural hazards and environmental pollution).

How to cite: Georgiou, K., Routsis, K., Michalis, P., and Amditis, A.: Digital Integration of Environmental, Socio-Economic and Hazard Data for Heritage Resilience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9183, https://doi.org/10.5194/egusphere-egu26-9183, 2026.

The aim of this study is to explore links between large-scale atmospheric circulation and meteorological and hydrological drought events in the Danube basin.

Based on previous studies on the relationship between large-scale atmospheric circulation and the occurrence of extreme events in the Danube basin, especially in the middle and lower Danube basin, two climate indices were considered. The first index characterizes the Greenland-Balkan Oscillation (GBOI) and the second is the well-known index associated with the North Atlantic Oscillation (NAOI). For meteorological and hydrological drought, the Palmer Drought Severity Index (PDSI), with applications especially in the agricultural field, and, respectively, the Palmer Hydrological Drought Index (PHDI), especially useful for estimating drought affecting water resources on longer timescales, were taken into account.

To find the type of connection (linear/non-linear) between large-scale climate indices (GBOI, NAOI) and those at the regional scale (PDSI, PHDI), elements from information theory, such as mutual information, were applied. To get time-frequency details, bivariate and multivariate wavelet transforms were used.

The analyses were performed separately for each season. The most statistically significant results were obtained both for the link between GBOI and PDSI, in the winter season, and for that between GBOI and the two analysed Palmer indices, in the spring season.

Regarding the influence of NAOI, it is much less than that of GBOI, but it can be considered relatively significant in winter on PDSI and in spring on PHDI.

From the wavelet coherence analyses it was observed that the significant coherences between the large-scale atmospheric indices and the analysed Palmer drought indices are located in frequency bands, corresponding to ~11–year, 22-year and 33-year period bands, that can be associated with the Schwabe, Hale and even Bruckner solar activity cycles.

In exploring regional-scale droughts, for the future studies, it appeared evident the importance of taking into account of the simultaneous or delayed influence of solar activity on terrestrial climate variables.

How to cite: Mares, C., Mares, I., Dobrica, V., and Demetrescu, C.: On the  links between large-scale atmospheric circulation and extreme events in the Danube basin, identified by Palmer drought indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10629, https://doi.org/10.5194/egusphere-egu26-10629, 2026.

Urban areas are increasingly exposed to flood hazards due to climate change, densification and growing urbanization. Remote sensing datasets can be used to monitor the floods and warn the population. Much more, simulations of hazards, using high resolution geospatial datasets combined with meteorological data can be derived. In this case, solutions prior to the events can be implemented, so increasing the resilience of cities to natural hazards.

This study presents a high-resolution 3D urban model of Brașov, Romania, developed from an airborne photogrammetric datasets acquired in 2025, and its application in urban flood risk assessment. The 3D building models were obtained using footprints from the national topographic database and the height derived from the computed normalized Digital Surface Model (nDSM). For the flood modelling the hydrological network and land cover data were used.

To assess flood risk, time series precipitation dataset was analyzed and used in the modelling framework. The combined analysis under different scenarios, enabled the identification of flood areas and the estimation of the number of exposed buildings. The results highlight the importance of high-resolution 3D urban data for understanding flood dynamics in complex urban settings and support decision-making processes related to urban planning, risk mitigation, and climate resilience. The output also represents a starting point for a Digital Twin for Brașov.

How to cite: Pârvu, I., Cuibac, I., Pârvu, A., Pârvulescu, N., Corneanu, I., Cheval, S., Crăciunescu, V., Dumitrescu, A., Amihaesei, V., Gabrian, Ș., Dinicila, Ș., and Tudose, N.: Urban Flood Risk Assessment Using High-Resolution 3D Building Models and Multi-Temporal Meteorological Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11292, https://doi.org/10.5194/egusphere-egu26-11292, 2026.

The Eocene experienced pronounced temporal changes in temperature and atmospheric pCO₂, with multiple warming phases from the early to late middle Eocene. High-resolution, sub-annual palaeoclimate reconstructions are essential to evaluate the impact of elevated pCO₂ on seasonal climate dynamics, providing critical insights for mitigating future climate crises. Middle Eocene Climatic Optimum (MECO), the Lutetian–Bartonian boundary warming event (~41 Ma) is particularly relevant, as current pCO₂ levels are rising rapidly and could reach similar concentrations within a century.

Bivalvia shells, growing incrementally, record seasonal to even sub-daily climatic and environmental fluctuations throughout their life. Shells of the oyster Cubitostrea cubitus, a shallow-to-marginal marine cementing bivalve from the Sables du Guépelle Formation (~41 Ma) of the Paris Basin (~41° N palaeolatitude), contain very low Mn and Fe concentrations (<250 µg/g), indicating their pristinity. These shells are used as a palaeoclimate archive in a multiproxy approach that combines LA-ICP-MS trace element analyses and clumped isotope thermometry (Δ₄₇), integrated with simulations from the Community Earth System Model (CESM). Sub-annual periodic variations in trace elements to Ca ratios along the oyster hinge indicate an oyster lifespan of ~16 months when aligned with monthly temperature variability from CESM simulations. Clumped isotope thermometry (Δ₄₇-T) records a seasonal sea surface temperature (SST) amplitude of ~8 °C, where the summer temperature reaching 28.3 ± 4.4 °C (68% CI) and winter temperatures of  19.6± 3.5 °C. Summer δ¹⁸O_w (-1.1± 0.9  ‰), consistent with Bartonian seawater compositions (-0.5 to -1.0 ‰), indicate a strong seasonal marine influence in early Bartonian Paris Basin. In contrast, significantly lower winter δ¹⁸O_w values (-2.9± 0.7 ‰) reflect enhanced freshwater input, which is further supported by relatively lower Sr/Ca profile, a salinity indicator consistent with increased winter rainfall predicted by CESM simulations.

In summary, our preliminary results indicate that during the MECO, the Paris Basin experienced seasonal sea-surface temperature variability comparable to that of modern shallow waters along the French North Sea coast, but with higher temperatures of approximately 10 °C throughout the year. In contrast to the modern climate (in the region of : 0–5° E, 46–50° N), where annual precipitation is relatively evenly distributed, rainfall during the MECO appears to have been strongly seasonal.

How to cite: Mitra, A., Goderis, S., Baatsen, M., Zhao, X., Chaudhuri, S., Ledésert, B. A., Claeys, P., and Müller, I. A.: Oyster shells record seasonal climate variability in the middle Eocene Paris Basin under higher-than-modern temperatures and seasonal rainfall patterns, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12459, https://doi.org/10.5194/egusphere-egu26-12459, 2026.

Limestone cultural heritage has increasingly been threatened by the complex interplay of climatic stressors, air pollution, and biological colonization. In this STECCI study, a bio-geochemical dose-response framework was introduced to quantify and interpret the decay of stećci-medieval tombstones constructed from locally sourced limestone, across fifteen culturally significant sites in Southeastern Europe. While existing dose-response functions (DRFs) have traditionally been applied to climatic, chemical and physical weathering, biological link has often been in the Blind Spot, despite mounting evidence that lichens, mosses, and microbial taxa contribute actively to stone decay.

Two widely used DRF models Lipfert (1989) and Kucera et al. (2007) were applied to multi-decadal environmental data (1992-2023), accounting for variations in precipitation, temperature, and pollutant load (SO₂, NOₓ, PM₁₀). Bioassement surveys were conducted to record biological colonization using a modified Braun-Blanquet scale and photographic quadrat sampling. At the same toime, spatial overlays of DRF results and biological data were produced to identify zones of specific vulnerability, where climatic exposure and biodeteriogen presence were observed to overlap. As expected, the Lipfert model responded more strongly to high-precipitation karstic settings, while the Kucera model captured the cumulative effect of pollutants and humidity in urban sites. However, both models were shown to underestimate decay in areas with extensive lichen or moss coverage, highlighting the need for biotic factors to be integrated into predictive modeling. To address this, a multi-stressor approach was developed, coupling DRF-predicted surface recession with biological indicators and  introdicing b coficient within the both mathematical models, as Lithobiontic organisms, such as Lobothallia cheresina, Xanthoria elegans, and Grimmia pulvinata, were found to contribute to micro-fracturing, mineral leaching, and, most importantly, moisture retention, often acting synergistically with atmospheric deposition. Based on these insights, a STECCI Preservation Measures Assessment tool was proposed to classify heritage sites according to modeled decay, biocolonization intensity, and conservation urgency.

This integrative methodology was conducted to sharp the diagnostic capacity of DRFs and enabled the generation of science-based insights, integrating risk assessment models for heritage exposed to climatic, natural, and anthropogenic hazards. In light of projected climate shifts and persistent anthropogenic emissions, it is recommended that heritage conservation efforts adopt bio-geo diagnostics to transition from reactive toward preventive conservation strategies. The approach presented here is transferable to other limestone heritage materials and contributes to the growing discourse on climate-resilient cultural heritage preservation.

Acknowledgement: The STECCI project has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101094822 (STECCI), managed by the European Research Executive Agency (REA).

How to cite: Radulović, S., Anačkov, G., Radak, B., Ilić, M., Radulović, B., Novković, M., Djug, S., Smailagić Vesnić, L., Ibragić, S., and Dresković, N.: Breaking Disciplinary Boundaries: Bringing the Biological Role out of the Blind Spot in DRF-Based Assessments of Limestone Weathering under a Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14335, https://doi.org/10.5194/egusphere-egu26-14335, 2026.

This study develops a novel multi-criteria framework for the identification of intra-urban heat islands by integrating indicators related to three-dimensional urban morphology (e.g., height-to-width ratio and sky view factor), land cover characteristics, satellite-derived land surface temperature, and thermal comfort conditions. The proposed framework practically enables the delineation of urban areas with distinct thermal and morphological profiles, thereby providing a robust basis for targeted, site-specific intervention strategies.

Subsequently, a range of bioclimatic heat mitigation measures is assessed for selected hotspots, including nature-based solutions such as increased tree planting and green roofs, as well as the application of high-albedo (cool) materials. The effectiveness of these measures is evaluated using advanced urban climate simulation models (ENVI-met and UT&C), allowing for a comparative assessment of their performance under varying spatial configurations and microclimatic conditions.

Overall, the study provides evidence-based guidance for urban heat mitigation and supports climate-resilient urban planning in Mediterranean cities, with Athens serving as a representative case study.

How to cite: Oikonomou, M., Agathangelidis, I., and Cartalis, C.: Development of a Multi-Criteria Framework for Identifying Intra-Urban Heat Islands in Support of Urban Heat Mitigation in Athens, Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14400, https://doi.org/10.5194/egusphere-egu26-14400, 2026.

The Paraná basin is composed of stratigraphic units that record distinct paleoenvironmental settings, organized into six supersequences. The Gondwana I Supersequence  (Permian) records a transgressive-regressive cycle associated with tectonic and climatic changes, in which periglacial successions (Itararé Group), coastal and marine (Guatá Group), and continental deposits (Passa Dois Group) are preserved. These sedimentary units crop out along the eastern margin of the Paraná Basin in a complex structural configuration that reveals significant tectonic displacements attributed to normal faults, resulting in the lateral juxtaposition of stratigraphically distinct units. Due to this arrangement, volcanogenic deposits play a fundamental role as stratigraphic markers, as they allow the establishment of precise geochronological correlations. This study presents geochronological data obtained from a volcanogenic deposit at Morro dos Conventos outcrop, and compares it with a compilation of the ages of volcaniclastic sediments interbedded with sedimentary deposits from the Rio Bonito Formation, aiming to constrain the evolution of depositional systems that enabled the preservation of such volcanogenic deposits within this interval. The detailed stratigraphic section of the outcrop was conducted and samples were collected for geochronological analysis. U-Pb zircon ages were determined by LA-MC-ICP-MS from a volcanogenic layer. The results reveal a unimodal zircon population with a concordia age of 286 ± 1.4 Ma (N = 9; MSWD = 1.2), allowing correlation of the deposit with the Artinskian Stage. Sedimentological and stratigraphic analysis of the section indicates a paleoenvironment of storm wave-dominated shelf, with interbedded subsystems recording high-frequency cycles associated with changes in sea level or sedimentation rates within the second-order Permian transgressive sequence. Sedimentological and geochronological data suggest that the studied succession correlates with the upper portion of the Rio Bonito Formation, in a context of progressive drowning by the Palermo Sea. At the top of the section, a progradation of subsystems is observed, characterized by the arrangement of subaerial sequences under humid backshore conditions. A similar configuration has been documented in other areas of the basin during the Cisuralian, where pelitic successions associated with coal deposits preserve centimeter-thick intercalated volcanic ash layers. The preservation of these features is attributed to paleoenvironmental conditions of subsystems developed along the margins of subaqueous bodies, dominated by low-energy settings with limited reworking, favoring the deposition of fine-grained sediments. In the studied outcrop, the preservation of the volcanogenic deposit is interpreted as a result of deposition within fine-grained sediments characterized by redoximorphic structures, indicative of fluctuating conditions between dry and wet periods typical of subaerial environments influenced by aqueous systems. A similar preservation context is observed in volcanogenic deposits recorded both in CPRM wells (Brazilian Geological Survey) and in nearby outcrops of this stratigraphic interval. The coexistence of low-energy depositional systems and episodes of high-magnitude explosive volcanism along the western margin of Gondwana enabled the preservation of ash-fall deposits in the Paraná Basin stratigraphic record, commonly associated with the Choiyoi Magmatism during the proposed Rio Bonito Formation sedimentation interval.

How to cite: Ribeiro Franqueira, A. V., Bettarel Bállico, M., Moreira Florisbal, L., Oliveira Manna, M., and Marlon dos Santos Scherer, C.: Controls on Ash-Fall Deposit Preservation in Low-Energy Depositional Systems of the Rio Bonito Formation, Paraná Basin, Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14428, https://doi.org/10.5194/egusphere-egu26-14428, 2026.

Quantitative estimates of Holocene climate conditions have been developed since the 1960s using space‑for‑time calibration approaches. Although hundreds of reconstructions are now available globally, questions persist regarding their accuracy. We evaluate quantitative reconstructions derived from three sources: site‑specific studies, regional reconstruction compilations, and regional products generated from global databases. The focus is on Holocene pollen‑based reconstructions, which remain the most widely used indicators of terrestrial paleoclimate and on North American Arctic and treeline regions. Reconstructions developed at individual sites often display substantial high‑frequency variability, including anomalous values and abrupt shifts, reflecting in part calibration-related artifacts. Regional averages (“stacks”) reduce some of this variability, yet comparisons based on different reconstruction sources reveal divergences. Conversely, studies analyzing paired cores from a single lake or from closely spaced sites frequently demonstrate strong replication and relatively low reconstruction error. Multi‑proxy analyses of Arctic cores likewise reveal both areas of agreement and persistent discrepancies. Addressing these inconsistencies remains a challenge for Holocene climate reconstruction.

How to cite: Gajewski, K.: Analysis of quantitative pollen-based reconstructions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15510, https://doi.org/10.5194/egusphere-egu26-15510, 2026.

The Pliocene (5.33-2.58 Ma) was comparatively warmer (+ 1.8-3.6 ⁰C) than today and was characterized by elevated CO₂ concentrations (400 ppmv). Thus, studying sedimentary sequences dated to this interval can serve as excellent analogues for comparing present conditions and provide tools for better modeling future trends. Yet, while most studies rely on marine archives, continental data dating back to this interval are scarce, particularly from boundary regions such as the Levantine Corridor. Sediments from the Erk-el-Ahmar Fm. (lacustrine, 3.9 Ma, Jordan Valley, Israel) and Bnot Lot member of the Sedom Fm. (lagoonal/lacustrine, 3.2-4.0 Ma, Dead Sea, Israel) highlight as one of the few well-exposed continental archives in the region that date back to that time.

In the present contribution, we explore these two sedimentary archives and integrate in a multi-proxy fashion the physical, chemical, and biological properties of both outcrop and core sections (with the latter only retrieved from the Erk-el-Ahmar sequence). This study aims to reconstruct the paleoenvironmental setting and changing hydroclimatic conditions in the Levantine Corridor during these time intervals. By amalgamating the datasets, we show that while the region is characterized by increased warmth and augmentation in precipitation patterns, occasional cooling phases coupled with drought punctuate the Pliocene climatic history in the Levantine region.

By synthesizing these diverse datasets into a consistent narrative, the project illuminates how precipitation, evaporation, and ecosystem processes interact under high-CO₂ and high-temperature conditions. The outcomes provide the first robust benchmark of Pliocene hydroclimate evolution in the Levantine Corridor, offering critical insight into thresholds of lake resilience, feedback mechanisms, and the persistence of aquatic systems under sustained global warmth.

How to cite: Waldmann, N., Yunfa, M., Olaopa, O., Danish, M., Niu, G., Greenlee, J., Parth, S., Mazzini, I., Castañeda, I., and Taha, N.: Sharp turnovers in Pliocene hydroclimate variability in the Levantine Corridor, East Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17979, https://doi.org/10.5194/egusphere-egu26-17979, 2026.

Cloudbursts, defined as sudden, intense rainfall episodes, are increasingly frequent extreme weather events in the Indo-Himalayan region, causing widespread devastation to human life and property; yet understanding their causal mechanisms and improving predictability remains constrained by incomplete knowledge of atmospheric and land-based precursors. Particularly, the role of soil moisture as a vital land-surface component has been underexplored in the context of cloudburst formation. This study hypothesizes that increased soil moisture from agricultural irrigation amplifies atmospheric moisture fluxes via land-atmosphere coupling and contributes to enhanced cloudburst risk. The objective here is to attribute moisture source locations, identify critical pre-event land-atmospheric indicators, and assess soil–atmosphere coupling through the analysis of IMD-specified cloudburst events from 1991 to 2020 using the Indian Land Data Assimilation System (ILDAS) dataset. We employ NOAA's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory model and create Integrated Vapor Transport (IVT) maps, composited with winds, surface pressure, and sea level pressure, to trace moisture source locations. Pre-event anomaly detection and change-point analysis are performed using the Pruned Exact Linear Time (PELT) algorithm on soil moisture, precipitation, evaporation, and runoff variables across nine spatially proximate grid cells per event. Additionally, extreme percentile threshold exceedances and non-parametric persistence metrics quantify the early-warning potential. Decadal NDVI trends contextualize Land Use/Land Cover (LULC) influences. Results reveal moisture source hotspots in regions undergoing land-use transitions, with steep pressure gradients establishing strong circulation patterns that contribute moisture to multiple cloudburst events. Significant temporal anomalies occur across all four variables, with threshold exceedances and change-point detections ranging from 2 to 10 occurrences per event and anomaly persistence spanning 2 to 8 days for soil moisture. Early warning lead times of 15 to 120 days are identified for soil moisture, precipitation, evaporation, and runoff anomalies preceding the cloudburst events. These findings suggest that further quantifying the causal links among these variables can better help understand soil–atmosphere coupling and substantially improve early warning systems for detecting extreme rainfall events.

How to cite: Kaushal, A., Saharia, M., and Rajagopalan, B.: Land-Atmosphere Drivers of Cloudburst Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19097, https://doi.org/10.5194/egusphere-egu26-19097, 2026.

One approach to reduce the uncertaintites in the black carbon (BC) emissions estimated using the  bottom-up inventories is by integrating the atmospheric models with observational data. In this study, we estimate aerosol emissions  over Europe (15◦W–35◦E, 33–73 ◦N) by assimilating surface observations of BC from EBAS network using Local Ensemble Transform Kalman Filter (LETKF) in the LOTOS-EUROS chemical transport model. Sensitivity experiments indicate that an ensemble size of 24 and a localization distance of 300 km provide optimal performance. Furthermore, we assess the influence of CAMS BC boundary conditions on the emission estimates and find that these boundary conditions tend to overestimate BC concentrations near the domain boundaries.

Our results show that the bottom-up approach generally overestimates BC emissions across Europe. Quantitatively, the posterior emissions are found to be 21% and 30% lower than the prior emissions for the years 2011 and 2021, respectively. A reduction in both emissions and associated uncertainties is observed over central Europe, where the observations are dense. Seasonal analysis reveals that emission decreases are most pronounced over the central domain during autumn and winter. Finally, the validation of optimized BC concentrations with independent observations showed a decrease in bias and RMSE, however the correlation remains poor compared to the background concentrations.

How to cite: George, B., Thomasson, A., Roldin, P., Segers, A., and Schutgens, N.: Optimizing Aerosol Emissions over Europe using Surface Black Carbon Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19525, https://doi.org/10.5194/egusphere-egu26-19525, 2026.

Underwater cultural heritage sites are under increasing pressure from emerging environmental risks: warmer waters and changing seawater chemistry are accelerating corrosion processes in ways that remain difficult to quantify in terms of their impacts on protected archaeological metals. This work proposes an experimental approach that makes these changes measurable and comparable across sites using metallic coupons carefully selected to match materials revealed from a series of wrecks. Coupons were deployed at different depths at four different locations, and retrieved at fixed time intervals. The development of corrosion layers, concretions, and biofouling in the natural environment was investigated. Observations were integrated with results from a second experimental approach. The same set of coupons was exposed to controlled environmental conditions using a custom Micro-Environment Simulator (MES). MES was set to reproduce marine conditions at 4 bar gauge pressure (40 m depth), 20 °C water temperature, and pH 7.7, simulating ocean acidification by the end of this century according to the CMIP6 projections for the Mediterranean under the SSP5-8.5 Warming 4 °C scenario. Results have shown a significant shift in electrochemical equilibria under declining pH, significantly influencing the stability of the corrosion products, and determining a shift in the behaviour of the corrosion layers from protective barrier to pathway for continued metal loss. By linking corrosion behaviour to specific environmental settings, the approach provides indicators of when and where deterioration is likely to accelerate under future scenarios. These outputs support preventive strategies for underwater metallic heritage by identifying high-risk wreck contexts, and guiding actions before irreversible loss occurs.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under THETIDA project (Grant Agreement No. 101095253) (Technologies and methods for improved resilience and sustainable preservation of underwater and coastal cultural heritage to cope with climate change, natural hazards and environmental pollution).

How to cite: Cesareo, L. P., Germinario, L., Salvemini, F., MacLeod, I. D., Marchettoni, E., Ambrosi, C., Donnarumma, L., Sorci, A., and Mazzoli, C.: How climate change rewrites metal decay: forecasting ancient shipwreck corrosion under acidified seawater, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20603, https://doi.org/10.5194/egusphere-egu26-20603, 2026.

Assam, one of India’s most flood-prone states, has a vulnerability to climate change that is shaped by a complex socio-political context and increasing biophysical pressures. A range of policies and on-the-ground initiatives have been introduced to support climate change adaptation (CCA). However, understanding what works, where, and how remains critical for advancing effective and equitable adaptation. This study addresses this gap by examining Assam, a historically flood-prone state that experiences significant loss and damage each year.

Despite widespread exposure to floods, loss and damage across Assam is uneven. In several districts, high vulnerability rather than flood intensity drives severe economic and non-economic loss and damage. In some cases, districts with relatively low flood hazards experience high loss and damage due to social, economic, and institutional vulnerabilities. Differences in the type and quality of adaptation implementation further shape these outcomes.

To examine the role of adaptation in reducing and managing loss and damage, qualitative fieldwork was conducted in three districts representing different drivers of flood impacts. Majuli, a river island that experiences floods almost every year, records relatively low loss and damage. This is largely due to lower vulnerability and the presence of effective adaptation measures. Long-term structural interventions and community-led practices have enabled adaptation to move beyond coping towards more transformative pathways.

In contrast, Barpeta, a district exposed to high flood hazard, experiences high loss and damage due to high socio-economic vulnerability. Deep inequalities, uneven community distribution, limited adaptive capacity, and local political dynamics constrain effective adaptation. As a result, adaptation efforts in Barpeta have largely progressed only from coping to intermediate, incremental adaptation, despite decades of recurrent flooding.

The third case, Udalguri, is located farther from the Brahmaputra and is primarily affected by flooding from smaller tributaries. Although flood intensity is relatively low, the district experiences high loss and damage, particularly loss of human life. Flooding is a relatively recent phenomenon in this area, and communities are poorly prepared. High vulnerability, driven by inadequate adaptation strategies, persistent social inequality, and pronounced caste–class differentiation, has kept adaptation responses at the coping stage, with little progression towards incremental change.

Insights from expert interviews, key informant interviews, focus group discussions, and community interactions reveal that adaptation planning in Assam is largely guided by flood hazard levels rather than vulnerability. This hazard-focused approach results in unequal protection and leaves highly vulnerable communities exposed to severe loss and damage, not only economic but also high non-economic, which is not even documented.

Overall, the findings demonstrate that flood impacts cannot be understood through water levels alone. Vulnerability fundamentally shapes how floods are experienced and how damaging they become, particularly in relation to non-economic loss and damage. By foregrounding lived experiences and overlooked forms of loss, this study argues for a shift in adaptation planning beyond physical flood control. Policies must recognize vulnerability, systematically document non-economic losses, and support locally grounded, socially just adaptation pathways that protect people, not only infrastructure. This is especially critical in regions like Assam, where social vulnerability continues to turn even moderate floods into human tragedies.

How to cite: Vyas, S., Barua, A., and Mallikarjuna, C.: When Adaptation Follows Hazard, Not Vulnerability: Flood Loss and Damage in Assam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21729, https://doi.org/10.5194/egusphere-egu26-21729, 2026.

Temperature essential for all the life form, but when the same temperature crosses its threshold limit it can become a threat for the same. In the recent decades the world has experienced a shift in temperature range both minima and maxima, as both are shifting towards the higher tail. And this is detrimental for health and air quality. Also very few studies talk about how rising temperature can impact the air quality and vice versa. Therefore, in the present work we have studied the long term heatwave pattern over Delhi, India using the ground based and satellite data observations. Delhi is known for its hot summers, landlocked geography and dense population.  During May 2022 , city experienced long heat wave event where daily maximum temperature observed higher than 40 ^OC   for consecutively  around 10 days  which not only  causes heat stress but also the pollution stress over the city as the concentration of Particulate matter (PM₁₀ and PM_2.5) observed significantly higher than the  non-heatwave period of the month. Air Quality Index (AQI) was moved from moderate to very poor during the heatwave period compared to non-heatwave where AQI showed satisfactory to poor condition. Further we observed that the Temp and Demand data increases monotonically during this period from 30 °C with demand ≈4500–5200 MW to about 38 °C with demand ≈6800–7070 MW, indicating a strong positive linear response. The regression analysis showed with 1°C increase in air temperature can increase the city demand by 97MW with r=0.61. We have further calculated night vs day slope, indicate that when night stays hot (>35°C) people might be keep cooling system running more intensely or for longer hours. And each degree increase in nighttime temperature put much larger load on demand compared to same warming during the day.

How to cite: Masiwal, R., Ganguly, D., and Kunchala, R.: Heatwave and Air pollution, a synergetic effect or not: A case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21797, https://doi.org/10.5194/egusphere-egu26-21797, 2026.

Water resources worldwide are increasingly threatened by growing anthropogenic pressures and inherent hydrogeological constraints, raising concerns about their suitability for domestic use. This study aims to assess the physicochemical quality of certain springs in the Agadir Ida-Ou-Tanane region and evaluate compliance with international thresholds established by the World Health Organization (WHO) for drinking water. A total of twenty-six water samples were collected across the studied region and analyzed for key parameters including Electrical Conductivity (EC), Total Dissolved Solids (TDS) and Total Hardness (TH). The EC values ranged from 275 µS/cm to 4210 µS/cm with an average of 1446.15 µS/cm. For Total dissolved solids, values ranged from 135 ppm to 7140 ppm, with Total hardness presented a maximum value of 3217.02 mg/L and minimum value of 188.9 mg/L. Water Quality Index (WQI) was calculated to provide an integrated evaluation of the overall water quality.Spatial distribution of water quality was further examined through Inverse Distance Weighting (IDW) interpolation. WQI based classification  revealed that 73.1% of the springs were in acceptable quality categories, with 34.6% classified as excellent and 38.5% as good. Despite this generally favorable status, TDS values approach or exceed international thresholds in several locations, indicating the need for region-wide monitoring and treatment strategies. Considering the heavy dependence of rural communities on spring water, these findings underscore the importance of investing in adequate treatment infrastructure and implementing robust protection measures for sustainable water resource management.

How to cite: Raïs, A., Tairi, A., El Mouden, A., Ijlil, S., Ait Moh, H., Hssaisoune, M., and Bouchaou, L.: Geo-statistical and hydrochemical assessment of spring water quality and water sustainability based on WHO standards in the Agadir Ida-Ou-Tanane region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-873, https://doi.org/10.5194/egusphere-egu26-873, 2026.

Sub-daily extreme rainfall (SDER) events frequently lead to natural disasters, including flash floods, urban floods, landslides, and soil erosion. It is essential to make a reliable prediction of its frequency at the local/regional spatial scale for the future period, in order to devise improved disaster mitigation and adaptation strategies. It has been observed that current CMIP6 GCMs have limitations in simulating short-duration (sub-daily) heavy rainfall events, and large biases are often observed in the control run simulations compared to historical observations at various locations worldwide. Hence, there is a lack of confidence in considering the crucial future projections obtained from those GCMs as reliable. In this study, we present a novel statistical methodology for predicting the seasonal frequency of SDER for 99 river sub-basins (RSBs) in India, encompassing tropical, temperate, arid, and polar climates across various topographies. The methodology identifies the scaling relationship between the SDER frequency and the associated potential atmospheric variables/drivers for each RSB. Results indicated that the seasonal frequency of SDER scales with (i) near-surface air temperature (SAT), and (ii) moisture content in the air, which is measured by near-surface dew-point temperature (DPT). The scaling relationship exhibits an increasing (scaling) phase followed by a decreasing (reverse scaling) phase as the (dew point) temperature increases. The range of SAT and DPT in the scaling relationship varies with RSB and climate. The SAT and DPT values at peak frequency are high for mountainous areas and lower for non-mountainous areas. The effectiveness of those scaling relationships in predicting SDER frequency at the seasonal scale was assessed/validated for the recent past (1981-2020). The method performed fairly well for RSBs with non-mountainous topography and moderately well for RSBs with mountainous topography across climate zones, except for years with an abnormally high or low SDER frequency. A finer spatial-resolution scaling relationship is deemed necessary for mountainous topographies where SDER exhibits a rather local nature. In addition, the time trends in simulated and observed frequencies closely matched. The proposed methodology is applied to predict the future seasonal frequency of SDER in the RSBs for different SSP climate scenarios till the end of the twenty-first century. The performance of various GCMs in projecting the seasonal frequency of SDER is also evaluated.

How to cite: Varshney, A. and Srinivas, V. V.: A Statistical Methodology for Regional Scale Future Projection of the Seasonal Frequency of Sub-daily Extreme Rainfall Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1058, https://doi.org/10.5194/egusphere-egu26-1058, 2026.

In various parts of arid and semi-arid region of India such as Rajasthan people are mainly depended on groundwater for fulfil their daily demands like drinking and watering their crops. However, in the Khetri mining region of Jhunjhunu district, extensive mining and smelting of copper and associated sulfide minerals have led to heavy metal contamination and a deterioration in groundwater quality. Therefore, this study evaluates the overall groundwater quality and the related human health risks in the region severely affected by copper mining and metallurgical activities. We have collected total 59 groundwater samples from both pre- and post-monsoon periods and were examined for physicochemical parameters, cations, anions, and heavy metals (Pb, Cd, Cr, Cu, Fe). Multivariate analysis, including PCA and correlation, revealed that geogenic processes, such as carbonate and silicate weathering, dominate natural groundwater chemistry. Whereas anthropogenic inputs from mining, ore processing, agriculture, and industrial waste significantly elevate toxic metal concentrations. The elevated level of Pb, Cd and Cr were detected across many locations, often exceeding permissible limits. Non-carcinogenic risks (HI) for Cr, Pb and Cd surpassed the safe thresholds in many locations, and carcinogenic risks (CR) for Cr, Cd, and Pb exceeds the permitted limit of 1 × 10⁻⁴ at multiple sites, indicating significant long-term health threats. The integrated EWQI–Monte Carlo framework thus combines the objectivity of entropy-based weighting with the statistical power of probabilistic simulation, enabling a more realistic and comprehensive evaluation of both groundwater quality and the related human health risks. In addition of this, the risk assessment for human health (HRA) revealed that children are at more danger than adults due to their greater exposure per body weight. These findings clearly indicate an urgent need for groundwater quality management through the adoption of remediation actions and the exploration of alternative sources to protect community health from contaminated groundwater.

How to cite: Kumar, M., Pathania, T., and Gaurav, K.: Integrated EWQI–Monte Carlo framework for assessing groundwater quality and health risk in the Khetri mining region of Jhunjhunu district, Rajasthan, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1227, https://doi.org/10.5194/egusphere-egu26-1227, 2026.

Riverbank filtration (RBF) is a managed aquifer recharge (MAR) technique applied worldwide, operating at the river–groundwater interface and offering the potential to enhance both groundwater quantity and quality, thereby improving drinking water supply security. However, its sustainable implementation requires a robust understanding of hydraulic interactions between surface water and groundwater as well as hydrochemical processes, supported by targeted local and regional monitoring strategies. Moreover, recharge efficiency and water quality benefits may vary in response to seasonal and event-based fluctuations in river flow, upstream contaminant inputs, and site-specific aquifer heterogeneity. In our study, we investigated river water–groundwater mixing, along with bank filtrate residence times, to improve the understanding of recharge dynamics at the Kępa Bogumiłowicka RBF site, a key regional water supply system located near Tarnów, southern Poland. Environmental tracers, including stable water isotopes, chloride concentration, water temperature and specific electrical conductance, were combined with high-resolution hydrological, meteorological and groundwater abstraction records. The results demonstrate that RBF is the dominant aquifer recharge mechanism, contributing more than 90% of the year-round yield from seven production wells located near the riverbank. Based on this case study, we propose a practical and transferable framework for efficient RBF monitoring and management. The approach integrates multi-tracer observations with ensemble end-member mixing analysis (EEMMA), combining discrete sampling with continuous physicochemical and hydrometeorological monitoring over at least one hydrological year. This cost-effective workflow enables robust recharge-source assessment, supports the evaluation of both quantitative and qualitative groundwater status, and facilitates proactive responses to upstream pollution events and rapid hydrological changes. As such, it provides a valuable template for the long-term, sustainable and resilient management of MAR-based drinking water resources in shallow alluvial aquifers.

How to cite: Janik, K., Rein, A., and Sitek, S.: sMARt riverbank filtration monitoring: how environmental tracers and high-resolution data support resilient drinking water supply, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3097, https://doi.org/10.5194/egusphere-egu26-3097, 2026.

Accurate or exact estimations of hydraulic conductivity (K) and infiltration rate are crucial for understanding soil-water interactions, optimising irrigation practices, evaluating groundwater recharge potential, and designing drainage systems. Conventionally laboratory permeability tests and in-situ infiltrometer tests, provide direct estimates of soil hydraulic behaviour. However, these methods have limitations of their point-specific nature and are unable to capture subsurface heterogeneity across larger spatial scales. In contrast, the electrical resistivity tomography (ERT) technique offers a non-invasive geophysical approach that is capable of detecting subsurface variations in soil electrical resistivity properties. The electrical resistivity estimates can further be interpreted to analyse soil types, soil layer structures, moisture and mineral contents, and pore connectivity. These are ultimately related to soil hydraulic properties, such as hydraulic conductivity and soil-water interaction behaviour, such as vertical infiltration rates. One of the accurate methods of estimating K is a pumping test, which is expensive and time-consuming. Other methods include laboratory permeameter tests, which require the collection of soil samples from the field, which often are disturbed ones and thus may produce K values with considerable uncertainties. The primary goal of this study is to establish the relationship between hydraulic conductivity (K) and electrical resistivity (ER) to replace the tests mentioned above. The second objective of this study is to establish an ER-infiltration rate relationship to convert point-based infiltration measurements into area-wide infiltration maps using resistivity data, minimizing the number of infiltrometer tests needed, saving time, manpower, and resources. Field investigations executed here involve ERT surveys using different electrode configuration arrays, such as the Wenner, Schlumberger, and dipole-dipole, across selected test sites that represent various soil textures and moisture conditions. The resistivity profiles are inverted to generate 2D subsurface sections, enabling identification of moisture zones and shallow saturation patterns. Parallelly, laboratory permeability tests are carried out on undisturbed soil samples to determine hydraulic conductivity, while infiltrometer tests are performed to obtain field-scale infiltration characteristics and steady-state infiltration rates. The combined dataset provided a comparative evaluation of resistivity variations in relation to measured soil-hydraulic parameters. Once these relationships are established, ERT can move beyond the simple imaging and serve as a fast and cost-effective way to estimate how water moves through the soil over a wider area. This will significantly reduce the need for frequent point-based tests and help capture natural variations in soil conditions that are often required in hydrological studies. Site evaluations can thus become faster and efficient, while areas with higher infiltration potential can be identified with greater confidence, and the overall planning of irrigation, drainage, and groundwater recharge strategies becomes more informed and robust.

Keywords: Electrical Resistivity Tomography (ERT); Hydro-geophysical characterization; Hydraulic Conductivity; Infiltration Rate; Groundwater recharge; Soil Heterogeneity.

How to cite: Jaiswal, M., Ganguly, S., and Prashanth, T.: Integration of electrical resistivity tomography, permeability and infiltrometer tests for modelling hydraulic conductivity and infiltration rates in the field, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3766, https://doi.org/10.5194/egusphere-egu26-3766, 2026.

Lake Cuitzeo is the second biggest lake in Mexico. It is placed in a semi-graben
structure, linked to volcanic rocks and fault systems. On the lake shoreline,
hydrothermal bodies emerge. These present arsenic and boron concentrations and
are used in thermal spas. Nevertheless, it is necessary to study the original and
behavior of these hydrothermal bodies, which provides information for the
sustainable management in order to benefit the local users.
The objective of this work is to determine the spatial distribution of the
hydrothermal manifestations, as well as their hydrogeochemical characteristics and
the temperature they reach at depth. The methodology consisted of sampling thermal wells and springs, along with laboratory determination of major ions and
trace elements. Subsequently, hydrogeochemical diagrams, isoline maps, and
geochemical indicators were used to understand their behavior. The results show
that the thermal sites have higher temperatures at depth and are associated with
the presence of faults.
Finally, the information compiled in this study may be useful for defining a safe and
feasible use of the geothermal resource for the communities inhabiting the study
area, whether for energy generation or for direct-use applications.

How to cite: Sierra Garcia, B. A., Olea, S., Israde Alcántara, I., Villanueva Estrada, R. E., Morales Casique, E., Zamora Martínez, O., Tadeo León, J., Gómez Vasconcelos, M. G., Avellán Denis, R., and Ramírez Serrato, N.: “Environmental implications of natural sources of arsenic and boron in hydrothermal bodies in the second biggest lake of México.”, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4336, https://doi.org/10.5194/egusphere-egu26-4336, 2026.

The global demand for groundwater is expected to increase due to changing hydrological and population patterns. Hence, spatio-temporal characterization of groundwater is crucial for its sustainable management options. This study introduces a fully distributed groundwater simulation model, based on the MODFLOW framework, to understand the fluctuations in the groundwater table and basin-scale hydrological dynamics of the Damodar River basin (DRB) in India. Monthly simulations were conducted over a 5-year period (2015–2020) to quantify changes in groundwater head and interactions between the river aquifer and the watershed. Real field abstraction data were used to represent pumping components of water use in the MODFLOW model. Parameter Estimation Test (PEST) based calibration and validation were performed to estimate the unknown hydraulic conductivity and recharge. The model outcomes reasonably align with heads at the observation wells, thereby improving our understanding of the water table in large river basins. The findings highlight the influence of monsoon precipitation and the overall changes observed in DRB. Furthermore, the study combined the tributary stream network and PEST-calibrated recharge, which further enhanced the physical representation and accuracy of the model despite the additional data requirements. Decline in groundwater level was observed, which potentially highlights unsustainable water management practices in the river basin. The results underscore the significance of numerical groundwater models, which are crucial for informed decision-making in robust groundwater planning interventions.

Keywords: Groundwater, MODFLOW, PEST, recharge, river basin

How to cite: Kumari, A. and Pathania, T.: Assessing spatio-temporal variations of groundwater level in the Damodar River basin, India using MODFLOW, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5224, https://doi.org/10.5194/egusphere-egu26-5224, 2026.

Uranium contamination in shallow aquifers is emerging as a concern for groundwater-quality issues across several parts of India. The present study evaluates the spatial distribution, concentration levels in shallow groundwater systems of selected semi-urban regions of India. Secondary data used in this assessment were obtained from the Central Ground Water Board (CGWB), covering semi-urban areas across all Indian states. The results reveal pronounced spatial heterogeneity in uranium concentrations, with numerous locations exceeding the permissible limits prescribed by the World Health Organization (WHO) and the Bureau of Indian Standards (BIS) for drinking water. Analysis of uranium concentration data for the period 2024–2025 indicates that shallow aquifers in parts of Karnataka, Punjab, and Rajasthan exhibit average uranium concentrations of approximately 133 ppb, 48 ppb, and 79 ppb, respectively, while maximum concentrations of 488 ppb, 202 ppb, and 119 ppb respectively, were recorded at select locations. A substantial proportion of groundwater samples were found to exceed WHO guideline values, highlighting widespread contamination concerns. The findings of this study offer critical insights for water-resource managers and policymakers in developing strategies to protect drinking-water security in uranium-affected regions of India.

How to cite: Kumar, D., Khare, S., and Kurre, S.: Spatial Variability of Uranium in Shallow Aquifers of Semi-Urban Indian Landscapes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6358, https://doi.org/10.5194/egusphere-egu26-6358, 2026.

A 2024 climate change study in Taiwan indicated an increase in rainfall of approximately 10-35%, causing flooding in some urban areas where stormwater drainage systems exceeded their original design protection standards. Furthermore, urban stormwater drainage systems improvements in Taiwan often face complex and intertwined spatial issues related to road traffic and underground utility lines, making rapid engineering improvements difficult. Therefore, to address the threats already posed by climate change, the use of big data monitoring of urban areas and surrounding regions, along with rapid AI-powered algorithms for drainage systems, is imperative. The New Taipei City Government, in order to manage urban water information, has developed a series of adaptation strategies for its drainage system. These strategies address environmental factors such as drainage sections affected by tides and storm surges, rainfall characteristics in nearby mountainous areas, and sections with gates and pumping stations that cannot drain by gravity. The aim is to lower urban drainage levels to prevent flooding and shorten flooding duration. This includes practical operational recommendations and early flood warnings. The method is based on historical practical experience and AI-generated water level forecasts to conduct drainage system decision analysis and management value setting. It combines real-time rainfall data from the Internet of Things, road flooding sensors, road CCTV, stormwater sewer water levels, and pumping station water levels. The data used includes actual data from the past 3 hours, forecasted rainfall for the next 6 hours, tidal changes, and real-time water level information at various monitoring locations to formulate adjustment strategies. Synchronous information is released within the drainage system to systematically set stormwater sewer water levels, treating stormwater sewers as flood retention spaces for monitoring and water level control. Based on operational experience gained from the past 3 years of implementation, this method will be used in the future to address the threats posed by increased rainfall due to climate change and to formulate urban flood control strategies to reduce disaster losses.

How to cite: Yang, S., Song, D., Huang, M., Pan, J., Wang, X., Chen, C., and Yeh, K.: Research on the Adaptation Strategies of Urban Stormwater Drainage to Increased Rainfall Due to Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7784, https://doi.org/10.5194/egusphere-egu26-7784, 2026.

The Doongmabulla Springs Complex (DSC) is a cluster of groundwater-dependent wetlands located in central Queensland, Australia. The DSC has over 160 individual springs ranging from over 9 ha, with pools of water, down to vents less than 10 cm across, supporting individual grasses. The springs are home to a variety of plant species (many endemic) that are adapted to the unique physico-chemical parameters of the groundwater discharge on which they rely. Wetlands and scalds of the DSC support a listed Threatened Ecological Community of species dependent on this natural discharge of groundwater from underlying artesian aquifers of the Galilee Basin. The springs are protected under Australia’s State and Commonwealth Environmental legislation. 

Aquifer depressurisation due to mine dewatering occurring to the east of the springs has the potential to threaten spring biodiversity in future by reducing groundwater discharge and consequent wetland persistence. Assessing the likelihood and possible magnitude of these threats requires a multi-disciplinary modelling approach to address complex groundwater – surface water – ecosystem interactions: 1) define groundwater pressure change probabilities; 2) simulate likely wetland response; and 3) evaluate potential ecological impacts. Once such a modelling chain is in place, impact mitigation may be assessed, considering the effect of interventions at each stage of the chain.

To support the numerical modelling, extensive hydrological, ecological and physio-chemical data collection and analysis was undertaken to understand spring wetland area and species microhabitats and distributions over multiple scales and time frames, including seasonal and inter-annual variability. Generation of realistic and defensible conceptualisations for the springs is critical and is described in a companion paper (Cresswell, et al., these proceedings).

Regional numerical groundwater modelling (MODFLOW) generated potential groundwater pressure change responses relevant to the DSC source aquifers. Potential groundwater depressurisation over time at the individual spring locations was utilised in a wetland water balance model (GoldSim) to describe wetland persistence, size and seasonality. Water balance outputs were translated into spatial representations of predicted spring hydrology (TUFLOW), generating area and shape configurations that could be compared to historical wetland persistence data and then utilised to predict potential effects on suitable habitat for key flora species under a range of scenarios including mining-related groundwater drawdown and climate change using maximum entropy species distribution modelling (MaxEnt). This latter process relates known species’ occurrences to measurable variables that describe the environment (such as soil moisture, soil salinity and pH) to predict the presence or absence of a species at unsampled locations and under future groundwater drawdown scenarios. 

The results of the application of this novel chain of models have informed a revised impact assessment for the potential impacts of mine dewatering on the unique vegetation communities at the Doongmabulla Springs Complex and enables development of targeted mitigation approaches for individual wetlands and species.

How to cite: Gibson, A., Cresswell, R., Muller, J., Capon, S., Grieger, R., Lloyd, P., Stanton, D., and Yeates, M.: Using a novel chain of models to mimic source aquifer depressurisation  impacts across the Doongmabulla Springs Complex , Queensland, Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8457, https://doi.org/10.5194/egusphere-egu26-8457, 2026.

Understanding seasonal water uptake depth (WUD) in trees is critical for assessing trees’ physiological responses to seasonal variability in microclimatic conditions and soil water availability. While broadleaf and conifer species in the Northern Hemisphere have been widely studied, studies of seasonal changes in WUD of large and long-lived evergreen conifers in the Southern Hemisphere are rare.

We investigated the effect of season and tree size on the water uptake pattern of Agathis australis (kauri), a large and long-lived endemic conifer, over an 18-month period. Our study site, a remnant kauri-dominated forest, is located in West Auckland, northern New Zealand. We collected stem cores from seven kauri trees (n = 3 < 50 cm diameter, n = 4 > 100 cm diameter) and soil samples underneath each of the seven trees (organic layer (OL), 0-10 cm, 10-20 cm, 20-30 cm, 30-50 cm, 50-70 cm, 70+ cm) across six seasons (austral spring 23,  austral summer 23-24, austral autumn 24, austral winter 24, austral spring 24, and austral summer 24-25). Water from soil and stem cores was extracted using cryogenic vacuum extraction. We measured δ²H and δ¹⁸O in all samples and used a Bayesian mixing model (MixSIAR) to determine WUD.

Our preliminary results show that across season and tree size, kauri obtained a larger proportion of water from the shallow layer (OL to 30 cm depth; ~ 60%) compared to ~ 40% sourced from layers below 30 cm. There was greater reliance on water from the shallow layer (up to 75%) during austral summer 23-24 and 24-25. We did not observe strong differences in WUD between small and large trees across our study seasons. These insights advance ecohydrological research on Southern Hemisphere evergreen conifers and highlight the importance of understanding species-specific response to microclimatic conditions and changing water availability.

How to cite: Boseren, M., Schwendenmann, L., and Boswijk, G.: Seasonal water uptake pattern in Agathis australis (kauri), a large and long-lived Southern Hemisphere conifer, using water stable isotopes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8525, https://doi.org/10.5194/egusphere-egu26-8525, 2026.

In arid and semi-arid regions, the agriculture thrives on the use of irrigation systems such as drip and sprinkler irrigation which ensures the higher irrigation application efficiencies. However, the planning and design of drip irrigation systems continue to rely on generic understanding and manual hydraulic calculations which often leads to sub-optimal performance. The present study addresses this gap by using a numerical model for analysing the drip irrigation for Okra Cultivation in a semi-arid district of Udaipur, Rajasthan, India. Therefore, the objectives of this study are analysing the hydraulic performance of the given network of drip irrigation using a numerical model and evaluate its adequacy and operational efficiency.

The hydraulic adequacy is determined using EPANET 2.2 modelling tool employing pressure driven demand (PDD) approach. The temporal variability in the behaviour of system was captured by running the extended period simulation model. The model incorporates operational control rules to define the variable demands for the different phases of the growth of the plant and scheduling the pump and valve operations thereby enabling the digital twin of the drip irrigation system. The source of water taken as well is explicitly represented in the model while the filtration unit is represented as a non-return valve with high loss coefficient. In addition to the watering, the fertigation of the crops is also simulated in the model according to the fertigation schedule.

The hydraulic performance of the irrigation system is evaluated using standard performance indicators  such as the Coefficient of Uniformity, Coefficient of Variation, and Distribution Uniformity. Furthermore, the reliability of system performance is assessed using network reliability parameter.  Thus, the study will assist farmers and stakeholders in achieving optimal operation of drip irrigation systems by addressing and minimizing the multiple technical and operational challenges associated with this irrigation method.

How to cite: Gupta, K.: Numerical Modelling of Drip Irrigation to Improve Water Use Efficiency in Semi-Arid Agroecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8945, https://doi.org/10.5194/egusphere-egu26-8945, 2026.

Urban and peri-urban regions in semi-arid India are increasingly exposed to contrasting climatic extremes, namely water scarcity and flooding, driven by complex interactions between anthropogenic pressures and biophysical processes. In this context, the present study investigates long-term changes in the annual water yield (AWY) ecosystem service across sub-watersheds of the Godavari River Basin encompassing the semi-arid Warangal district, Telangana, India. The InVEST AWY model is applied for two representative years, 1995 and 2025. The results indicate that AWY ranges from 460-890 mm yr⁻¹ in 1995, with relatively higher values in the north-eastern sub-watersheds, but declines across most sub-watersheds by 2025 to 220-690 mm yr⁻¹. The annual precipitation found to be 1400 to 1230 mm yr⁻¹ over the study period, while potential evapotranspiration increase substantially from 2253 to 2955 mm yr⁻¹, enhancing atmospheric evaporative demand and reducing water availability. Sensitivity analysis (expressed in terms of elasticity, E), shows that AWY is highly sensitive to precipitation variability (E = 1.84) and moderately negatively sensitive to urban-related biophysical parameters (root restricting depth: E = -0.42, crop coefficient (K_c): E = -0.39).  In contrast, sensitivity to potential evapotranspiration is lower (E = -0.36), highlighting the combined influence of climatic forcing and urban expansion. Spatially, urban land use in 1995 is concentrated in the central region, with cropland and forest dominating the western and eastern parts, respectively, yielding a mean AWY of 718.51 mm yr⁻¹. By 2025, relatively higher AWY zones shift toward the north-eastern region, reflecting reduced evapotranspiration associated with urban expansion; however, the overall mean AWY declines to 476.36 mm yr⁻¹, indicating that land-use changes influenced spatial patterns while climatic factors governed the temporal decline. The decline in AWY between 1995 and 2025 corresponds with reduced ecosystem service values (ESV) for water-yield related regulation services, particularly water regulation (ESV₁₉₉₅ = 16.37 to ESV₂₀₂₅ = 12.91 million US$) and water supply (ESV₁₉₉₅ = 84.73 to ESV₂₀₂₅ = 73.69 million US$). Overall, the findings demonstrate the joint role of climate variability and urbanization in shaping sub-watershed water yield and associated ecosystem services, providing insights for climate-responsive urban and landscape management.

How to cite: Dasari, S., Pal, M., and Keesara, V. R.: Ecohydrological Modelling of Annual Water Yield and Water-Related Ecosystem Services in the Semi-Arid Region of Warangal, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9012, https://doi.org/10.5194/egusphere-egu26-9012, 2026.

Scarcity of groundwater level (GWL) data poses a significant challenge to effective groundwater resource modeling, particularly in urban and peri-urban regions where anthropogenic influences further complicate hydrological processes. In this study, a machine learning–based framework is developed to predict groundwater levels for Bhubaneswar city, India, using Long Short-Term Memory (LSTM) neural networks. Given the data-driven nature of machine learning models and the limited availability of long-term observations, a regionalized modeling approach is adopted by coalescing GWL measurements from 31 closely located monitoring wells. To enable the model to capture well-specific variability, each well is characterized using static indicators derived from hydrological and socio-environmental datasets. Multiple combinations of predictor variables are evaluated to identify those most effective in representing groundwater level dynamics. The optimal model, trained on aggregated regional data, demonstrates strong predictive performance during testing, with a correlation coefficient (R) of 0.89 and a Nash–Sutcliffe Efficiency (NSE) of 0.79. The proposed regionalized LSTM framework shows promise for reliable groundwater level prediction at individual wells in data-scarce urban settings, offering a practical tool for groundwater assessment and management.

How to cite: Anshuman, A. and Panigrahi, M.: Groundwater Level Prediction in Urban Areas under Data Scarcity Using a Regionalized LSTM Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10663, https://doi.org/10.5194/egusphere-egu26-10663, 2026.

Extreme precipitation events (EPEs) are expected to increase in frequency and intensity under global warming and can trigger various impacts such as floods and landslides, which can undermine the socio-economic stability by raising the risk of loss of human lives, infrastructure failure and agricultural losses. Consequently, the study of hydroclimatic extremes has grown substantially in the recent decades, supported by high-resolution data and multivariate event-based analytical frameworks that improve understanding and resilience to climate-related risks. The rainfall induced impacts often show a compound nature because the underlying processes are scale-dependent and can overlap and intensify each another. Therefore it is important to consider extremeness across spatio-temporal scales when assessing EPEs. However, the the complex topography and diverse climatic conditions in the Indian Peninsular region pose a key challenge in assessing and characterizing the EPEs. In this context, a comprehensive ranked catalog of EPEs is developed from the 73 year long data set, based on their extremity across spatio-temporal scales. To increase the robustness of the underlying statistical analysis and to make an optimal use of the data, a combination of the peak-over-threshold (POT) method and the cross-scale weather extremity index (xWEI) is introduced to quantify the spatiotemporal extremity. In addition, the study exemplifies the applicability of POT method and compares the resulting extremeness with the conventional annual maxima approach. The catalog identifies EPEs that are jointly extreme across spatial and temporal scales and distinguishes short-lived localized storms from persistent, widespread events, thereby enabling a systematic characterization of EPE typologies. By linking each EPEs xWEI value to the season and meteorological divisions, the catalog offers a consistent basis for comparing historical events, and advances process-based understanding of regional hazard regimes. In summary, the resulting catalog can be a valuable tool in improving the robustness of quantitative risk assessments and enhancing the reliability of climate change attribution analyses.

How to cite: Ganapathiraju, S. A., Voit, P., Marwan, N., and Rathinasamy, M.: Ranked Multiscale Catalog of Precipitation Extremes using Cross Scale Extremity for the Indian Peninsular Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10790, https://doi.org/10.5194/egusphere-egu26-10790, 2026.

Groundwater contamination arising from mining activities represents a persistent and complex environmental challenge, particularly in coal-bearing regions where sulfide-rich mine overburden is extensively exposed to atmospheric conditions. Upon interaction with oxygen and moisture, pyrite oxidation generates acidic by-products and mobilizes dissolved constituents, such as ferrous, ferric iron, sulfate, and hydrogen ions. Understanding and predicting the spatiotemporal evolution of contaminant plumes in such systems remains challenging due to the coupled nature of variably saturated flow, multicomponent geochemical reactions, and microbially mediated processes. This study develops a comprehensive numerical modeling framework for simulating contaminant transport and remediation processes associated with oxidation reactions in unsaturated mine overburden systems. Variably saturated flow is represented through discretization of the governing flow equation in the vertical domain using hydraulic head-based parameters, and the resulting tridiagonal system of linear equations is efficiently solved using the Thomas algorithm. The model couples variably saturated groundwater flow, represented by Richards’ equation, with multicomponent reactive transport equations describing the generation and migration of key oxidation products (Fe²⁺, Fe³⁺, SO₄²⁻, and H⁺). In addition, sulfate reduction mediated by sulfate-reducing bacteria (SRB) is incorporated to capture biologically driven attenuation mechanisms relevant to natural and engineered remediation scenarios. Simulations are performed for a total of 22 years (8030 days). A time step of 0.1 day and a grid size of 0.2 m are identified as the optimal choices for the simulations. The simulation results indicate that the concentrations of oxidation-derived species decrease significantly from 200 to 40 mol/m³ in clay, 300 to 95 mol/m³ in loam, and 1 to 0.2 mol/m³ in sand. Sensitivity analysis shows that peak sulphate sensitivity in clay with a sensitivity index (SI) of 0.65 and in loam with an SI of 0.5 under high saturation condition (water content, w_c = 0.9), while ferrous ions exhibit maximum sensitivity in loam under low saturation condition (w_c = 0.2) with an SI of 750. The findings support the development of predictive frameworks that can inform sustainable groundwater management, optimize remediation strategies, and address key challenges in the practical application of contaminant transport models.

How to cite: Roy, G. and Sivakumar, B.: Hydrogeochemical Forensics of Pyrite Oxidation in Unsaturated Mine Overburden: A Numerical Simulation Framework for Groundwater Contaminant Migration., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12747, https://doi.org/10.5194/egusphere-egu26-12747, 2026.

Quarry lakes, primarily located in alluvial plains, form as a result of the deepening of quarry excavations beyond the water table of the shallow aquifer.

They allow for full exploitation of the deposit without excessively damaging the landscape, limiting land consumption. Quarry lakes play also an important ecological and landscape role, because they provide (i) habitats for aquatic plants, aquatic animal species and birds, and (ii) recreational opportunities. Additionally, they contribute to management of water resources and mitigation of flood risks.

In the Piedmont region (NW Italy), quarry lakes are numerous and of considerable size due to the high market demand for concrete and aggregates. These quarry lakes are mostly located along the Po River, the main river of the region, and its main tributaries.

This study focused on six active quarry lakes and, primarily, a hydrogeological reconstruction of the surrounding areas was carried out. Lake water samples were collected in the summer and autumn of 2025 and analysed for hydrochemical composition. Field parameters, including pH, electrical conductivity, and water temperature, were also recorded.

The hydrochemical results, compared with data from the regional network of groundwater monitoring wells, reveal a strong correlation between lake waters, the surface aquifer, and watercourses. The chemical characterization of these quarry lakes supports the study of their  photochemical activity, and the assessment of their potential use as nature-based basins for quaternary treatment of water, thus allowing to minimize the overexploitation of groundwater resources in a context of more frequent drought events due to climate change.

How to cite: Pigozzi, G., Bianco Prevot, A., Biasio, L., Carena, L., Cocca, D., De Luca, D. A., Egidio, E., Lasagna, M., and Mittaridonna, A.: Hydrogeological and Hydrochemical Characterization of Quarry Lakes in the Piedmont Alluvial Plain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13428, https://doi.org/10.5194/egusphere-egu26-13428, 2026.

The nitrate input in the groundwater and surface water is the main source of contamination in many areas of the world. In Mexico, agricultural activities depending of groundwater and surface water. The groundwater flow system (GFS) is a single system with recharge and discharge zone were the interactions between surface and groundwater are present. In Mexico, the Cuitzeo GFS is a is one of the most agriculturally developed areas in central Mexico and includes the second and third largest lakes, lakes Cuitzeo and Patzcuaro.
The main object of this work is to analyze the nitrate behavior in groundwater and lake waters to understand the spatial changes over two years.
The methodology includes sampling of major ions of 39 sites, including wells, dugwells, springs, and lakes in the dry season for the years 2024 and 2025. Additionally, hydrogeochemical diagrams and spatial analysis were developed. The nitrate concentrations in this country are regulated by Mexican rules.
The results show that 11 sites exceed the permitted limit of concentrations according to these rules. Nitrates predominate in the zone of major population close to Morelia city and close to Lake Cuitzeo. Whereas ammonium is present close to the lake Patzcuaro. These distributions are in groundwater and surface waters, reflecting the same processes in both water bodies. This area presents a rapid expansion and intensification of berry and avocado cultivation, which have displaced local crops and driven unsustainable patterns of agricultural water use.
This study provided valuable information about the source and quantification of nitrate species contaminations, which can help to generate new management strategies.

How to cite: Llanos Solis, A. G., Olea Olea, S., Morales Casique, E., Zamora Martínez, O., Tadeo León, J., Goméz Vasconcelos, M. G., Avellán, D. R., and Ramírez Serrato, N.: Nitrate behavior in a groundwater flow system that discharges into the largest lakes of Mexico, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15246, https://doi.org/10.5194/egusphere-egu26-15246, 2026.

Groundwater is a critical water resource in Morocco, particularly in semi-arid regions where agricultural demand and local uses place increasing pressure on aquifers. In this setting, a clear and spatially explicit assessment of groundwater quality is essential to support monitoring strategies and contribute to more sustainable water management.

This study presents an approach for characterizing groundwater quality in a semi-arid area of Morocco based on physico-chemical analyses of groundwater samples collected from wells and springs. Water quality is identified through the computation of a groundwater quality index derived from multiple measured parameters, providing a synthetic and comparable metric across sampling points. The results are then integrated within a Geographic Information System (GIS) framework to explore spatial patterns and support interpretation at the territorial scale. Spatial interpolation is used to map the distribution of both the individual parameters and the quality index, highlighting local contrasts and potential hotspots within the study area.

Overall, the findings indicate generally satisfactory groundwater quality, while also revealing localized variations that justify targeted follow-up and site-specific attention. The proposed workflow is transferable and can be adapted to other semi-arid settings in Morocco to support diagnosis, prioritization of actions, and long-term, sustainable groundwater resource management.

How to cite: Mouncif, A., Nait-Taleb, O., Karroum, M., Krimissa, S., Namous, M., and Elaloui, A.: Groundwater quality assessment in semi-arid Morocco: spatial analysis and monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15319, https://doi.org/10.5194/egusphere-egu26-15319, 2026.

The subsurface architecture of paleochannel and floodplain deposits, as well as their hydrogeological significance, remains insufficiently characterized in the Ganges upper delta region. The present study evaluates the hydrogeological implications for groundwater resource assessment and aquifer vulnerability in Chakla, North 24 Parganas, West Bengal. Descriptions and discrimination of different subsurface regimes are provided based on electrical resistivity. Vertical Electrical Sounding (VES) surveys are conducted at 51 sites, distributed across paleochannel (n_p = 31) and floodplain (n_f = 20) geomorphic settings. Six-layer resistivity models are developed for each site through inversion analysis. Regime-specific hydrogeological properties are quantified through non-parametric statistical testing (Wilcoxon rank-sum and Kruskal-Wallis) on the modeled VES data. Furthermore, longitudinal conductance and transverse resistance, as obtained from the Dar-Zarrouk parameter analysis, are explored. VES inferences are found to be in well accordance with the borehole lithology from six cores. Paleochannel aquifers and floodplain aquitards exhibit significantly different resistivity distributions due to different grain sizes and saturation. Paleochannel sites reveal higher median resistivity (49.5 Ω·m) and coarser grain sizes, indicating high-capacity aquifers with enhanced investigation depth. On the other hand, floodplain sites are characterized by lower resistivity (19.2 Ω·m), finer grain sizes, and low-permeability confining layers. The findings of the present study support targeted groundwater exploration in paleochannel zones and aquifer protection in floodplain areas, providing key insights for water supply assessment and contaminant vulnerability.

Keywords: Vertical Electrical Sounding (VES), Paleochannel aquifer, Electrical resistivity, Dar-Zarrouk parameters, Hypothesis testing, Non-parametric statistics

How to cite: Dutta, A. D., Yadav, A. K., Mukherjee, A., and Sengupta, P.: Characterization of Paleochannel and Floodplain Aquifers Using Vertical Electrical Sounding: A Case Study from the Western Part of Bengal Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16051, https://doi.org/10.5194/egusphere-egu26-16051, 2026.

The hyporheic zone serves as a critical hotspot for nitrogen attenuation, driven by flow in streambed sediments, biogeochemical reactions, and enhanced microbial activity. It has, however, yet to be determined how the interaction of heterogeneity in sedimentary physical (e.g., permeability) and chemical (e.g., organic matter content) properties influences nitrogen cycling in complex hyporheic environments. Here we developed numerical models coupling porous flow, reactive transport, and microbial dynamics for realistic heterogeneous streambed scenarios. Simulations reveal that small-scale spatial variations in sediments physical and chemical properties exert negligible effects on nitrogen removal, whereas the spatial heterogeneity in functional microbial biomass dominates nitrogen removal dynamics. This is caused by biofilm-induced bioclogging that drastically reduces hyporheic exchange, thereby weakening the role of sedimentary heterogeneity. This study represents the first quantitative assessment of how sedimentary and microbial spatial heterogeneities jointly regulate nitrogen removal in hyporheic systems, offering critical insights for predictive modeling of bedform interfaces.

How to cite: Xian, Y., Xiao, Z., Wen, Z., and Krause, S.: Microbial Heterogeneity Outweighs Sediment Variability in Regulating Hyporheic Nitrogen Removal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16155, https://doi.org/10.5194/egusphere-egu26-16155, 2026.

Rapid groundwater depletion, driven by intensive pumping and growing climate variability, poses a critical threat to water security across India. Concurrently, climate change is intensifying the frequency and magnitude of flood events, generating episodic but potentially significant opportunities for natural aquifer replenishment. However, the contribution of floods to groundwater in India remains poorly quantified. In this study, we systematically quantify flood‐driven groundwater recharge across the major river basins of India. Using the integrated, physically based ParFlow-CLM hydrological model, we evaluate three fundamental attributes of flood recharge: (i) the contribution of flood runoff to total groundwater recharge, (ii) the temporal lag between flood peaks and aquifer response, and (iii) the persistence of flood‐induced recharge signals following an event. These metrics are evaluated across diverse hydrogeological settings to identify where floodwaters are most effectively captured and retained within aquifers. Our results show strong spatial contrasts in flood recharge efficiency. The highly permeable alluvial aquifers of the Indus, Ganga and Brahmaputra basins exhibit the highest flood-to-recharge contribution and the longest persistence, indicating a strong capacity to capture and retain floodwater. In contrast, less permeable and fractured hard-rock aquifers in large parts of central and southern India show weaker and shorter-lived recharge responses to floods. By explicitly linking flood dynamics to subsurface hydrologic response, this study provides a framework for identifying priority regions for flood‐based groundwater management. The results demonstrate how increasing flood extremes under climate change can be strategically harnessed to enhance the resilience of India’s groundwater resources.

How to cite: Roy, R. and Mishra, V.: Flood-Driven Groundwater Recharge for India , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16236, https://doi.org/10.5194/egusphere-egu26-16236, 2026.

Groundwater drought poses a growing threat to water security in India, as groundwater supplies support agriculture, ecosystems, and domestic use. Although meteorological and hydrological droughts and their propagation have been studied, the propagation of drought into groundwater systems in India has not been examined. In this study, we employed Standardized Precipitation Index (SPI), the CGWB-based Standardized Groundwater Index (SGI), and the GRACE-based Groundwater Storage Anomaly (GWSA) to investigate meteorological drought and groundwater drought across the Indian region. We estimated drought propagation duration, recovery duration, mean drought duration, and maximum drought duration. The results show that regions in the north, northwest, northeast, and a few regions in southern India have the longest propagation time from meteorological to groundwater drought, while other zones, such as central India, have relatively shorter propagation times. We also find that regions in northeast and northwest India recover faster from groundwater droughts than other regions. Our results also show that the Dryness Index (DI), Seasonality Index (SI), and Land Surface Controls (NDVI, soil moisture (SM), and Evapotranspiration (ET)) play a significant role in the propagation time of meteorological to groundwater droughts across different zones. Overall, understanding the propagation and recovery plays a vital role in aiding effective management and planning of groundwater resources in India.

How to cite: Aayush, A. and Mishra, V.: Propagation of Meteorological Drought to Groundwater Drought in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16340, https://doi.org/10.5194/egusphere-egu26-16340, 2026.

Hydraulic redistribution (HR), the passive movement of water through plant root systems from wet to dry soil layers, plays a critical role in maintaining plant water status and nutrient uptake in water-limited environments. While HR is well-documented under drought, its dynamics become significantly more complex in saline conditions where total soil water potential is driven by both matric and osmotic components.

In this study, we employed a split-root experimental design using young avocado trees to isolate and quantify HR. The root system of each tree was divided between two pots: a "wet pot" maintained at field capacity and a "drying pot" where irrigation was withheld. We utilized high-precision weighing lysimeters to monitor nocturnal weight changes in the drying pot, alongside soil moisture sensors and isotopic water labelling to track water movement.

Our preliminary results confirm the occurrence of HR in young avocado trees under non-saline control conditions. The phenomenon was clearly identified in two out of three trees monitored during the initial experimental phase, as evidenced by nocturnal increases in soil water content and corresponding weight changes in the drying pots. These findings provide a foundational baseline for the next phase of the research, which aims to evaluate how increasing levels of salt stress (NaCl) in the wet pot influence the osmotic gradients and root hydraulic conductivity that drive HR. By comparing control and saline treatments, we seek to determine whether salinity-induced changes in total water potential suppress or shift the patterns of hydraulic redistribution.

How to cite: Hochman, D. and Schwartz, N.: Understanding the drivers of hydraulic redistribution under salt stress, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17910, https://doi.org/10.5194/egusphere-egu26-17910, 2026.

Soil Aquifer Treatment (SAT) relies on biogeochemical processes occurring within the vadose zone to improve the quality of secondary treated wastewater during infiltration. Dissolved organic matter (DOM) is a key driver of these processes; however, its depth-dependent transformation within the soil profile remains insufficiently resolved at the molecular level under field conditions.

In this study, we investigated the vertical evolution of fluorescent dissolved organic matter (fDOM) within the soil profile of a full-scale SAT infiltration basin. Soil samples were collected from successive depths along the vadose zone, representing progressive stages of soil–water interaction during infiltration. DOM composition was characterized using excitation–emission matrix (EEM) fluorescence spectroscopy with inner-filter correction and Raman normalization. Fluorescence data were analysed using Coble peak integration and Parallel Factor Analysis (PARAFAC) to resolve independent fluorescent components and assess their depth-dependent behaviour.

The results reveal pronounced vertical stratification of DOM composition within the soil profile. Shallow soil layers are dominated by protein-like fluorescence associated with labile, wastewater-derived organic matter. With increasing depth, these protein-like signals show strong attenuation, while humic-like fluorescence becomes increasingly dominant. Coble peak analysis indicates preferential removal of tryptophan- and tyrosine-like peaks (B and T), whereas humic-like peaks (A, C, and M) persist at depth. PARAFAC modelling further identifies distinct fluorescent components exhibiting contrasting depth trends, with protein-like components rapidly decreasing in intensity and humic-like components remaining relatively stable or proportionally enriched.

These findings demonstrate that SAT acts as a selective biogeochemical filter within the soil profile, where biodegradation and sorption processes preferentially remove reactive DOM fractions in the upper vadose zone while more refractory humic material persists at depth. The combined use of EEM–PARAFAC provides mechanistic insight into DOM transformation pathways during soil aquifer treatment and highlights the importance of depth-resolved fluorescence analysis for improving process-based understanding of SAT performance.

How to cite: Adler‬‏, O.: Vertical transformation of fluorescent dissolved organic matter within the soil profile of a soil aquifer treatment basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19818, https://doi.org/10.5194/egusphere-egu26-19818, 2026.

The Brahmaputra River Basin is among the most flood-prone regions globally, experiencing recurrent large floods with severe socio-economic and ecological impacts. Despite extensive flood management interventions, forecasting skill remains limited due to the basin’s complex hydrology, strong monsoon variability, and pronounced land–atmosphere interactions. This study investigates the drivers and dynamics of large floods in the Brahmaputra Basin, with a particular emphasis on coupled land–atmosphere processes. We conduct a composite analysis of major flood events using reanalysis datasets, satellite observations, and hydrological records. Our results show that large floods are consistently associated with anomalously high atmospheric moisture content, extreme and spatially extensive precipitation, and elevated antecedent soil moisture that amplifies runoff generation. The concurrence of saturated catchments with persistent multiday monsoon rainfall leads to rapid escalation of flood magnitude and prolonged flood duration. In addition, enhanced moisture transport into the basin emerges as a critical contributor to the development of large flood events. By integrating these insights into coupled land–atmosphere modeling frameworks, we demonstrate that improved representation of soil moisture dynamics, rainfall persistence, and moisture transport pathways can substantially enhance flood predictability. This work advances the understanding of flood-generating mechanisms in monsoon-dominated river basins and provides actionable insights for improving early warning systems and adaptive flood risk management in the Brahmaputra Basin.

How to cite: Vangala, G. and Mishra, V.: Controls and Predictability of Large Floods in the Brahmaputra River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20263, https://doi.org/10.5194/egusphere-egu26-20263, 2026.

Reliable baseflow estimation plays a crucial role in water resource management across India's monsoon-dominated landscapes, where groundwater contributions sustain river flows through extended dry periods. This study addresses persistent data limitations in Central and Southern India by first compiling daily streamflow records from 4,862 basins sourced from the India Water Resources Information System (IWRIS), followed by rigorous pre-processing and quality control steps that yielded suitable data for analysis across hundreds of representative basins. A comprehensive evaluation of 12 baseflow separation methods was then conducted using Kling-Gupta Efficiency (KGE) against hydrologically verified baseflow benchmarks, revealing digital filter techniques, particularly the Eckhardt filter (median KGE of 0.88) as superior to conventional graphical methods across diverse hydrological regimes. These findings affirm digital filters' reliability for capturing baseflow variability in monsoon recharge areas and arid inland zones, laying a strong foundation for advanced hydrological modeling in data-constrained environments.

Keywords: Baseflow Separation, Digital Methods

How to cite: Samyuktha, P., Dubey, S., and Gautam, S.: Comprehensive Evaluation of Baseflow Separation Methods for Peninsular India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21596, https://doi.org/10.5194/egusphere-egu26-21596, 2026.

Abstract

Reliable hydrological modelling tools that can operate with the type and quality of data commonly available at the basin scale are essential for effective water resources planning. In recent years, the HIDROTURK model has been developed to support national hydrological assessments in Türkiye, especially in modelling tasks undertaken as part of river basin management planning processes. This study presents one of the first comprehensive evaluations of the newly updated HIDROTURK Phase II model and compares its performance with the AQUATOOL + EVALHID hydrological modelling system. The North Marmara River Basin was selected as the test region due to its complex hydrological structure and diverse sub-basin characteristics.

Hydrological simulations were carried out using long-term meteorological inputs derived from precipitation and evapotranspiration records for the period 1989–2014, enabling the examination of the models under a wide range of climatic conditions. Streamflow outputs were compared with observations at 14 calibration points, and model performance was assessed using the Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) indicators.

The results indicate that the meteorological inputs generated through HIDROTURK’s internal processing tools show a high level of agreement with data prepared using more traditional methods, and that both models produced comparable flow patterns under similar conditions. Overall, the findings demonstrate that HIDROTURK Phase II exhibits stable behavior even at this early stage of development and provides a practical and reliable alternative for hydrological simulations.

Keywords: Hydrological Modelling; Basin-Scale Simulation; Model Comparison; Model Performance Evaluation; Streamflow Calibration

ACKNOWLEDGEMENT: The authors would like to express their gratitude to the projects “Technical Assistance on Preparation of River Basin Management Plans for Six Basins (EuropeAid/140294/IH/SER/TR)” and “Development and Sustainability of the HIDROTURK Model Project” for their support. The authors also thank the Directorate General for Water Management, the State Hydraulic Works, and the General Directorate of Meteorology for providing essential data.

How to cite: Kacikoc, M., Mesta, B., Fistikoglu, O., Ozkaya, H., and Ozdemir-Calli, K.: Comparative Evaluation of the Newly Developed HIDROTURK-Phase II Hydrological Model in the North Marmara River Basin, Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1436, https://doi.org/10.5194/egusphere-egu26-1436, 2026.

In the context of the global hydrological cycle, runoff generated from snowmelt plays a key role in water availability, particularly in cold and mountainous regions. In many parts of Canada and the western United States, mountain snowpacks act as natural reservoirs by storing precipitation during cold seasons and releasing it during spring and summer. Accurate estimation of snow water equivalent (SWE) is therefore essential for hydropower reservoir operation, snow-related hazard assessment, and hydrological modeling. However, the coarse spatial resolution of widely available SWE products in northern latitudes, combined with complex mountain topography, introduces substantial uncertainty in their direct application to hydrological models. High-resolution SWE mapping remains a major challenge in these environments. In this study, we propose a multifactor SWE downscaling framework based on a Long Short-Term Memory (LSTM) deep learning approach, applied to the Nechako River watershed in British Columbia, Canada. The framework uses ERA5-Land SWE at 10 km resolution as the target variable, with predictor variables including precipitation, minimum and maximum temperature, solar radiation, and 2-m dewpoint temperature, together with static physiographic information such as elevation and land cover. Daily data from 1981 to 2024 are considered. The model is trained and evaluated at the 10 km resolution before being applied to generate SWE at 5 km resolution, corresponding to the spatial resolution of the CEQUEAU hydrological model. The downscaled SWE fields are designed to retain the large-scale snow patterns provided by ERA5-Land, while adding more spatial detail based on local elevation and land cover. Current work focuses on incorporating these downscaled SWE estimates into the CEQUEAU hydrological model and comparing the resulting runoff simulations with those obtained using CEQUEAU’s internal SWE representation. Rather than aiming to demonstrate clear improvements at this stage, the goal is to better understand how different SWE inputs influence the simulated hydrological response. Preliminary results suggest that LSTM-based downscaling offers a flexible and promising way to generate intermediate-resolution SWE fields in mountainous regions. This approach shows potential as a practical link between coarse-resolution reanalysis products and distributed hydrological models used for water resources and hydropower studies.

How to cite: moghadam, S. H., Arsenault, R., St-Hilaire, A., and Talbot, F.: Evaluating a Long Short-Term Memory (LSTM) approach for Snow Water Equivalent (SWE) downscaling and hydrological modeling in mountainous terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2612, https://doi.org/10.5194/egusphere-egu26-2612, 2026.

The complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations 
present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims 
to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River 
Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance 
Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for 
estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation meth
ods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model 
(WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are 
more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian 
summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating 
the inadequacy of using a constant lapse rate for hydrological modelling in high- elevation regions like the TP. The geostatistical 
technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation 
performance based on cross- validation results. However, although KED provides superior results based on cross- validation per
formance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination 
of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting 
the importance of not solely relying on cross- validation results but also considering the practical implications of interpolation 
methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource 
management in a data- scarce, high- elevation region like the TP.

How to cite: guede, K. G., Yu, Z., and Hofmeister, F.: Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data- Scarce Region in the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4141, https://doi.org/10.5194/egusphere-egu26-4141, 2026.

A numerical investigation is conducted to study the steady-state concentration field
when a solute is released from multiple continuous line sources in an ice-covered
channel with an absorbing bed under turbulent flow conditions. The governing
equations are solved using the Crank-Nicolson scheme by adopting a two-power law
velocity and a quartic eddy diffusivity profile, which is influenced by the roughness of
the bed layer and the ice cover. Validation against earlier numerical results for a
specific case reveals strong consistency in the concentration profiles. The findings
highlight how the roughness of the boundaries affects the solute concentration. It
further demonstrates the effect of the bed absorption parameter in the early mixing
stages when solute is released near the bed. For zero bed absorption, the solute
concentration asymptotically attains a uniform far-field value of unity, while any non-
zero bed absorption leads to complete depletion of solute downstream.

How to cite: Paul, S. and Ghoshal, K.: Solute dispersion from continuous point sources in ice-covered turbulent flows with bed absorption, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5160, https://doi.org/10.5194/egusphere-egu26-5160, 2026.

Water balance modeling plays a pivotal role in sustainable water management, as it underpins the understanding of hydrological processes that govern resource distribution, ecosystem stability, and long-term environmental planning. Accurate and efficient computational tools are essential to capture the spatial and temporal dynamics of water balance, particularly in complex geological and urban environments. WaterpyBal is an innovative modeling framework specifically designed to construct spatial-temporal water balance models. It effectively integrates multiple stages of hydrological assessment-including data interpolation, evapotranspiration estimation, and infiltration computation-while accounting for soil heterogeneity and components of the urban water cycle. The tool demonstrates robust performance when applied to both synthetic and experimental datasets, providing reliable and scalable results.

In the context of the exascale era, where data-intensive environmental models demand unprecedented computational power, High-Performance Computing (HPC) frameworks are essential to ensure scalability and efficiency. To this end, WaterpyBal has been enhanced through its integration with PyCOMPSs, the Python binding of the COMPSs programming model. PyCOMPSs enables the transparent parallelization of Python applications by identifying task-level parallelism through annotated methods and dynamically constructing a task-dependency graph during runtime. This graph-driven execution model allows efficient scheduling and data management across distributed computing infrastructures such as clusters and cloud platforms.

The integration of WaterpyBal with PyCOMPSs significantly improves its computational performance, enabling the simulation of large-scale, high-resolution water balance models within feasible timeframes. This work demonstrates the potential of combining advanced hydrological modeling with state-of-the-art parallel computing frameworks to address emerging challenges in environmental modeling and resource management at scale.

How to cite: Castillo Reyes, O., Li, J., Hassanzadeh, A., and Vázquez-Suñé, E.: High-performance task-based water balance modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5899, https://doi.org/10.5194/egusphere-egu26-5899, 2026.

The intensification of hydrological cycle driven by global warming leads to an increase in extreme precipitation events, prolonged droughts and higher rates of evaporation. This global change has altered the global hydrological cycle and effect on the long term water availability in many watersheds worldwide. The impact of climate change is usually assessed by using ratio of stream flows and climate variables, which are formally defined as the climate elasticity of water availability This study examines how the hydrological cycle is intensifying over the Kabul Watershed and how this affects water availability using the water balance (P–E) approch.  Here, we used ERA5 land data of annual total precipitation (P) and total surface evaporation (E) from 1976 to 2024 to understand how the land and atmosphere interacted.  The climate elasticity of (P-E) to annual water availability is determined for 1976-2010 and validated for 2011-2024. The results reveal 0.8 °C rise in temperature, 12% decline in annual precipitation, and  7% increase in evaporation in the past 25 years. This caused 15% reduction in the P–E balance, which directly reduced the annual water availability. The climate elasticity factor of 0.55 has been determined to water availability in Kabul for the period of 1976-2010. By using this elastic factor, the average water availability of 19.54 MAF is predicted for the period of 2011-2024 whereas the observed water availability is 20.43 MAF. This finding reflects the sensitivity of a watershed to P-E alteration for water availability and underscore the urgent need of climate resilient water management strategies to mitigate the future impacts of climate change in the Kabul watershed.

How to cite: Kosar, T., Rahim, A., Yaseen, M., Naz, R., Mamoor, M., and Akif, A.: Climate Driven Hydrological Intensification and Its Implications for Water Availability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6269, https://doi.org/10.5194/egusphere-egu26-6269, 2026.

The Critical Zone, extending from the land surface through the vadose zone to groundwater, can store and transfer substantial carbon as CO₂ and dissolved inorganic carbon (DIC). Yet CO₂ behavior below the first meters of soil remains poorly constrained, particularly where water-table fluctuations, gas-water exchange, and water-rock reactions interact. In these settings, deep vadose CO₂ may exhibit atmospheric and soil-respiration signatures with contributions linked to groundwater degassing and carbonate-system reactions, potentially creating transient subsurface CO₂ reservoirs that couple the aquifer and the atmosphere.

We present a repeated sampling design to characterize carbon cycling across the Critical Zone in semi-arid southeastern Spain. We sampled the air columns of 11 boreholes belonging to six groundwater bodies during four campaigns (spring 2022, autumn-winter 2022, spring-summer 2024, and spring-summer 2025). In borehole air, we measured CO₂, H₂O vapor, and its carbon isotopes composition (δ¹³C-CO₂); air was stored in gas-tight bags and analyzed by cavity ring-down spectroscopy (Picarro G2508 and G2201-i). In parallel, groundwater was sampled at each site. In situ, we measured pH, temperature, oxidation–reduction potential (ORP), HCO₃-, and electrical conductivity. In the laboratory we analyzed pH, alkalinity, major ions, total organic carbon and total nitrogen, carbon isotopes of dissolved inorganic carbon (δ¹³C-DIC), and water isotopes (δ²H, δ¹⁸O). Water-table position at the time of sampling was used to interpret gas-water contact.

Critical Zone CO₂ concentrations in borehole air ranged from 614 to 128700 ppm (pCO₂=0.000587-0.102287 atm). Groundwater CO₂ was estimated with the PHREEQC software, yielding values between 2240 and 9550 ppm (pCO₂=0.002240-0.009550 atm), allowing comparison between the air column and the saturated zone, and evaluation of disequilibrium and exchange potential as the water-table varies. Carbon isotopes signatures constrain sources and transformations: δ¹³C-CO₂ ranged from -11.14 to -23.62‰, δ¹³C-DIC from -6.27 to -20.11‰, and host-rock δ¹³C from 2.37 to -7.12‰. All values (δ¹³C‰) are reported relative to VPDB (Vienna Pee Dee Belemnite). Joint interpretation across gas, DIC, and rock enabled discrimination among biogenic CO₂ production, atmospheric mixing, carbonate dissolution/precipitation (based on the saturation indices of the main carbonate mineral phases), and CO₂ transfer from the aquifer to the deep vadose zone. The multi-campaign design provided a basis for quantifying seasonal and interannual shifts in these boreholes and for identifying hydrogeochemical conditions (e.g., pH-alkalinity evolution and redox state) that promote storage/mineralization versus release of CO₂.

Our experimental design characterizes subsurface CO₂ storage and transport at the Critical Zone scale. It identifies when the deep vadose environments act as reservoirs, conduits, or sources linking groundwater and the atmosphere. This information is rarely available but critical for improving carbon budgets and models for the Critical Zone.

This work was supported by the Spanish Ministry of Science and Innovation (projects PID2024-158786NB-C21 and PID2024-158786NB-C22, NATURAL), the University of Granada (project PPJIB2024-53), and the Regional Ministry of University, Research and Innovation, the Spanish Government and the and European Union – NextGenerationEU (projects BIOD22_001 and PCBIO).

How to cite: Echeverría-Martín, E., Fernández-Cortés, Á., Sánchez-Cañete, E. P., Serrano-Ortiz, P., Oyonarte, C., Riba Palou, A., Kowalski, A. S., and Domingo, F.: CO2 Dynamics and Carbon Sources in the Critical Zone: An Isotopic Study in Aquifers of Southeastern Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7262, https://doi.org/10.5194/egusphere-egu26-7262, 2026.

Hydroclimatic extremes have extensive social, economic and ecological impacts, thereby making it highly imperative to develop disaster assessment and mitigation strategies. The frequency of extreme events like heatwaves and droughts is  intricately linked with the rising temperature trends and the changing precipitation patterns worldwide. Moreover, lagged responses amongst such extremes can occur across temporal scales due to the existing large-scale climate linkages. However, the association between present-day occurrences of concurrent high temperature and low precipitation days (HTLPs) with the frequency of heatwaves and droughts of a subsequent period is not fully explored. In this global analysis, we estimate the frequency of heatwaves and droughts based on 1-year temporally lagged HTLPs. Our results reveal a significant rising trend in the average number of heatwaves with an increase in the number of HTLPs of the previous year, while no significant trend is observed for droughts. However, a high number of HTLPs (over 100 events) is associated with a slight reduction in the number of heatwaves (5.9 to 5.6) but a pronounced increase in the number of droughts (1.8 to 2.4). During a 10-year validation period, 81% of heatwaves and 85% of droughts globally remain consistent with the HTLP–conditioned behavior inferred from the 34-year training period of the model. Our findings thus demonstrate the applicability and effectiveness of HTLPs in predicting heatwaves and droughts. This study can be used to develop stochastic models to predict heatwaves and droughts with HTLP as a predictor, and hazard-specific probabilistic assessments that can support and improve resource allocation at regional and global scales.

How to cite: Sinha, D. and Rajulapati, C.: The role of High Temperature-Low Precipitation conditions in shaping heatwaves and droughts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8393, https://doi.org/10.5194/egusphere-egu26-8393, 2026.

The intensification of the global hydrological cycle is a well-established consequence of anthropogenic climate change. However, how this intensification manifests across the diurnal cycle remains poorly understood, representing a critical blind spot in climate risk assessments. While daily-aggregated metrics consistently suggest a "wetter and more extreme" climate, they mask fundamentally different responses of daytime and nighttime precipitation to warming.Here we analyse high-resolution observational records from 2,399 stations across China spanning 1972–2024 and identify a distinct nighttime intensification regime that is increasingly dominant under warming. In regions experiencing active wetting, extreme precipitation (R95p) intensifies more rapidly at night than during the day, both in magnitude and spatial extent.This diurnal asymmetry reflects contrasting physical controls. Nighttime wetting is driven almost exclusively by increases in precipitation intensity (p < 0.001, Wilcoxon signed-rank test) and exhibits a tight thermodynamic scaling with background warming. By contrast, daytime precipitation changes arise from a heterogeneous combination of intensity and frequency adjustments, indicating a greater role for dynamical modulation.

These findings reveal a previously underappreciated amplification of nocturnal hydrometeorological hazards, including flash floods and landslides, that is systematically underestimated by daily-mean indicators. As global warming continues, the emerging dominance of nighttime precipitation extremes underscores the urgent need to incorporate diurnally resolved processes into climate risk assessment, infrastructure design and early-warning systems.

How to cite: Meng, J. and Xia, H.: Asymmetric intensification of nighttime versus daytime precipitation extremes under warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9421, https://doi.org/10.5194/egusphere-egu26-9421, 2026.

Surface-groundwater interactions (SGI) plays a crucial role in maintaining stream thermal regime and ecological balance, keeping a check on this is logistically and financially challenging. This study utilises multi-temporal Landsat-8 Thermal Infrared Sensor (TIRS) data to compute Stream Surface Temperature (SST), its anomaly (SSTA), Robust Thermal Deviation Index (R-TDI) and further classifies groundwater influence on the Kharun River, a semi-arid urban catchment in India (approximately 4109 km²).

Due to changes in weather and season, surface water is subject to heating and cooling, but the water system beneath the land surface will be at a constant temperature. The stream reach, influenced by groundwater, will show a relatively stable thermal signature across all seasons. Stream Surface Temperature (SST) derived through radiometric calibration and emissivity-adjusted retrieval across pre-monsoon, monsoon, and post-monsoon periods. To isolate localized hydrological processes from regional climatic forcing, we computed Stream Surface Temperature Anomalies (SSTA) by subtracting reach-wise median SST from pixel-scale values. To account for the non-normal nature of SST, a Robust Thermal Deviation Index (R-TDI) framework was utilised which minimizes atmospheric noise and mixed-pixel interference, allowing for the isolation of persistent thermal signals.

Using statistically defined TDI thresholds, a classification approach was finalised putting stream stretches into high, moderate, and low groundwater influence zones. Results identify spatially consistent cold-water anomalies indicative of groundwater discharge primarily during pre-monsoon and warmer-water anomalies during post-monsoon seasons when thermal contrasts are most pronounced.  These zones coincide with structurally controlled segments and urbanized stretches, suggesting a complex interplay between hydrogeology and anthropogenic modifications. By leveraging open-access satellite data, this research provides a scalable tool for evidence-based river restoration and climate-resilient water management in rapidly urbanizing regions.

Key Words: Thermal remote sensing; Landsat-8 TIRS; Stream surface temperature; Thermal anomaly; Surface–groundwater interaction; Data-scarce catchments

How to cite: Chandel, R. and K. Singh, C.: Thermal Remote Sensing for Qualitative Analysis of Surface Water and Groundwater Interaction: A Case Study of the Kharun River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9498, https://doi.org/10.5194/egusphere-egu26-9498, 2026.

Understanding catchment behaviour is essential for effective water resources planning and sustainable watershed management. Traditional model evaluation approaches based solely on time-series performance metrics often fail to capture the full spectrum of hydrological functioning. This study employs a hydrological signature–based evaluation framework to assess the capability of the Soil and Water Assessment Tool (SWAT) model in reproducing the dominant hydrological processes in the Bharathapuzha River Basin, a monsoon-dominated river system in southern India. The SWAT model was implemented using spatial datasets of topography, land use, and soil characteristics, together with long-term hydro-meteorological inputs, and calibrated and validated against observed daily streamflow. Beyond conventional performance indices, key hydrological signatures including flow duration curves, runoff ratio, baseflow index, seasonal flow patterns, and characteristics of low- and high-flow events were extracted from both observed and simulated datasets. Comparison of observed and simulated signatures provided a process-oriented evaluation of model behaviour, offering key insights into how well runoff generation, seasonal variability, and hydrological extremes are represented. These perspectives are not readily evident from traditional model performance metrics alone. This study demonstrates the value of hydrological signatures as diagnostic tools for enhancing model realism and improving confidence in hydrological simulations for climate impact assessment and water resources management in monsoon-driven catchments.

How to cite: Krishnan Sreelatha, G., Anupama Rajith, A., Sreekumar, A., Girish, G., and Reghunath, G.: Signature-Based Evaluation of Hydrological Processes Using the SWAT Model in the Bharathapuzha River Basin, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9647, https://doi.org/10.5194/egusphere-egu26-9647, 2026.

Global water security can be achieved by the systematic assessment of available water resources in both large and small river basins. This study investigated the stable isotopic composition of river water in the Yamuna basin, India to fingerprint the major contributing sources of water and their spatial variability along the main river channel. The Yamuna river originates at an altitude of about 6300 m asl in Yamunotri glacier near Bandarpunch, Uttarakhand Himalayas and flows through several states of India like Haryana, Punjab, Madhya Pradesh, Rajasthan and Uttar Pradesh. In this study, the Yamuna river water samples have been collected along main channel from Yamunotri to its confluence with Ganga river during pre-monsoon and post-monsoon seasons of the year 2024. The measured stable isotope ratios of oxygen (δ¹⁸O) and hydrogen (δ²H) in river water are in the range of -2.7 – -11.2 ‰ and -23.4 – -75.2 ‰ respectively for the sampling period. This study reports for the first time that there is a significant spatial variability in the source water of Yamuna river as fingerprinted by the stable isotopic composition. The Yamuna river at upper reaches receives water from sources that are depleted in heavier isotopic content mainly from glacial melt. The higher amount of water diversion to canal networks at different stages as well as water mixing from industrial and urbanized regions have led to relative water degradation of Yamuna river in middle reaches. The downstream isotopic composition reflects possible interaction with groundwater, higher water influx from Peninsular tributaries, and evaporation effect. Seasonality in source water contribution to Yamuna river discharge along the entire stretch has also been traced using stable isotopic composition of water.

How to cite: Tripti, M., Khobragade, S. D., and Rao, S. M.: Stable isotopic fingerprinting of hydrological variability along the Yamuna river, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10986, https://doi.org/10.5194/egusphere-egu26-10986, 2026.

Over the past decade, droughts have drawn increasing attention due to their substantial agricultural and economic consequences, particularly in the U.S. Great Plains area (e.g., the 2012 Central US event and the 2017 Northern Plains event). Although certain large-scale atmospheric and oceanic patterns are necessary for drought development, land- atmosphere interactions can play an important role in the intensification of drought conditions, especially for flash drought. This study aims to predict drought conditions over the U.S. Great Plains at 1-3-week lead times using a convolutional neural network (CNN) model. To forecast drought categories derived from the US Drought Monitor (USDM), the models are trained using multi-source atmospheric and land-surface variables, including 500 hPa geopotential height, precipitation, wind speed, surface radiation, humidity, and temperature from ERA5, soil moisture from Global Land Evaporation Amsterdam Model (GLEAM) and North American Land Data Assimilation System (NLDAS), and Normalized Difference Vegetation Index (NDVI) from satellite products. Model performance is evaluated to unravel the atmospheric and land-surface processes that drive droughts at different lead times and identify their relative contributions to drought development and intensification.

How to cite: Rippeteau, L. and Chen, L.: Drought prediction and understanding the drivers of drought development using a machine learning approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12345, https://doi.org/10.5194/egusphere-egu26-12345, 2026.

Extreme hydrological events have become increasingly frequent in Ukraine in recent decades due to climate change and structural weaknesses in water resources management. According to the Water Strategy of Ukraine up to 2050, inadequate governance practices remain a major source of anthropogenic pressure on water bodies, while climate change creates additional risks through prolonged droughts interrupted by intense rainfall events, leading to flooding. These challenges are particularly critical for southern Ukraine, where limited water resources require extensive hydrotechnical regulation and adaptive management.

Flash floods represent one of the most dangerous manifestations of hydrological extremes. Characterised by rapid water-level rise and high flow velocities, they pose severe risks to settlements, infrastructure, and agriculture due to their sudden onset.

The north-western part of the Black Sea region has experienced several severe flash flood events over the past decade. One of the most significant cases occurred in September 2013 in the Kogilnyk River basin., when anomalously high precipitation totals of 41 - 270 mm were recorded from 10 and 14 September. These extreme rainfall conditions were associated with a stationary cold atmospheric front linked to the Asia Minor depression, resulting in prolonged convective rainfall with thunderstorms, squalls and wind gusts of up to 22 m/s in the southern districts of the Odesa region.

The total volume of storm rainfall during this event is estimated at approximately 250 million cubic meters, which exceeded the mean annual runoff of the Kogilnyk River by a factor of 5.5. Precipitation affected an area of about 1,400 km², corresponding to 35% of the total river basin area. As a result, flash flooding impacted multiple settlements, located in the south-western part of Odesa Oblast as well as extensive agricultural lands in there.

Another notable episode occurred in early August 2019, when unstable atmospheric conditions and active cyclones caused intense rainfall across southern and eastern Ukraine. On 3 - 4 August, precipitation amounts reached 130–220% of the monthly norm in several locations. In the Odesa region, rainfall totals of up to 126 mm - equivalent to nearly three months of precipitation—met the criteria for hazardous meteorological phenomena and triggered debris flows and localized flash flooding, particularly in the village of Moloha (Bilhorod-Dnistrovskyi district).

More recently, in September 2025, an urban flash flood in Odesa highlighted the increasing vulnerability of urban areas to extreme rainfall. Prolonged heavy rains caused widespread flooding, significant damage, and human losses, prompting large-scale rescue operations..

The analysed events indicate a clear increase in flash flood intensity and impacts in the north-western Black Sea region. Under continued climate change, enhanced hydrological monitoring, early warning systems, climate-adaptive urban planning, and integrated water resources management are urgently required in southern Ukraine.

ACKNOWLEDGEMENTS

This contribution builds on the conceptual framework of the applied research project “Sustainable Development of Water Resources Management and Modelling in the North-Western Black Sea Region under Conditions of Increasing Climate Extremes and Anthropogenic Pressure”, approved for funding by the Ministry of Education and Science of Ukraine (Order No. 23, 9 January 2026, see https://surl.li/omqxph).

How to cite: Ovcharuk, V. and Khomenko, I.: Flash Flood Events in the Northwestern Black Sea Region under Climate Change , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14233, https://doi.org/10.5194/egusphere-egu26-14233, 2026.

Although drought indices can be evaluated employing linear and non-linear algorithms, most contributions in the literature have not adequately quantified geospatial-temporal volatility, leading to Type II errors. This study addresses these gaps by comparing ten drought indices across the Colorado and Louisiana regions of the United States over 75 years, examining non-linear and spatio-temporal patterns to ensure a robust assessment of drought. High-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) gridded monthly total precipitation data for 75 years (1950-2024) were used to evaluate the drought indices. The spatial clustering of precipitation patterns was quantified using the second-order semi-parametric eigen-decomposition geospatial autocorrelation to geolocate hot and cold spots of precipitation. We employed the Autoregressive Integrated Moving Average (ARIMA) model, coupled with the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, and compared five ARIMA-GARCH variants across nine error distributions to address non-asymptotic conditional volatility and temporal persistence in precipitation. Drought indices were examined across five temporal scales and contrasted with simulated parameters derived from the Community Earth System Model (CESM). The temporal lag relationship between meteorological and agricultural droughts was evaluated using the non-parametric Time-Varying Distance Cross-Correlation Function (TV-DCCF). The findings revealed that the ARIMA-eGARCH(1,1) model with a Student’s t distribution precisely detected the non-asymptotic conditional volatility in the precipitation time series. The Standardized Precipitation Index (SPI), China Z Index (CZI), and Z-Score Index (ZSI) were the most applicable indices for drought monitoring in both regions. TV-DCCF revealed that meteorological droughts significantly influenced agricultural droughts, with a lag of up to four months. CESM-derived drought indices were mainly within the ERA5-Land uncertainty range, except for CZI and aSPI, attributable to CESM’s lower spatial resolution and limited sensitivity to localized extreme events.

Keywords: Standardized Precipitation Index (SPI); Global Moran’s Index; Autoregressive Moving Integrated Average (ARIMA); Generalized Autoregressive Conditional Heteroscedastic Model (GARCH); ERA5-Land; Community Earth System Model (CESM).

How to cite: Choudhari, N., Elshorbany, Y., Jacob, B., and Collins, J.: Prognosticative De-Volatility Modeling for Empirically Quantifying CESM and ECMWF Space-Time Heterogeneity of Drought Indices Across Colorado and Louisiana Regions of the USA, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14649, https://doi.org/10.5194/egusphere-egu26-14649, 2026.

The application of isotopic tracers provides a powerful means to unravel complex hydrological systems, including groundwater (GW)-surface water (SW) connectivity. This study investigates the interacting hydrological and geochemical processes within a temperate volcanic lake basin in west-central Mexico, with the objective of assessing hydrogeological connectivity between groundwater and the lacustrine system. Spatially distributed sampling was conducted for major ions, nitrate, strontium, and stable water isotopes (δ¹⁸O and δ²H) across multiple water sources, including precipitation, rivers, lakes, wells, and springs.

Results indicate that direct infiltration of precipitation constitutes the dominant groundwater recharge mechanism in high-elevation, forested zones, where waters exhibit a Ca–Mg–HCO₃⁻ hydrochemical facies. Mixing with deeper groundwater components is also evident, as reflected by elevated temperatures and isotopic compositions indicative of enhanced water-rock interaction. Surface waters, particularly lakes, display pronounced evaporative enrichment, while elevated nitrate concentrations in shallow groundwater point to anthropogenic inputs associated with irrigation return flows and urban activities.

Although sampling was conducted during the dry season and therefore may not capture the full range of annual hydrological variability, the identification of local and regional recharge zones provides a robust framework for future investigations of precipitation-driven recharge and GW-SW interactions. Additionally, strontium concentrations proved effective for tracing subsurface flow paths and fluid exchange along fault-controlled structures, offering valuable insights into hydrogeological processes in tectonically active volcanic settings. The integrated use of hydrochemical and isotopic tracers highlights their critical role in supporting sustainable water-resource management and protecting groundwater quality in complex temperate, semi-arid lake systems increasingly impacted by anthropogenic pressures.

How to cite: Ramírez González, L., Olea Olea, S., Sánchez Murillo, R., Villanueva Estrada, R. E., González Mejía, M. A., González Hita, L., Morales Casique, E., Zamora Martínez, O., Gómez Vasconcelos, M. G., Denis Ramón, A., and Ramírez Serrato, N.: Tracing Groundwater-Surface Water Mixing Using Isotopes in a Semi-Arid Volcanic Lake Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15165, https://doi.org/10.5194/egusphere-egu26-15165, 2026.

Flood hazard assessments are commonly based on rainfall magnitude and frequency; however, flood responses to similar rainfall intensities may change over time due to evolving land use, river morphology, and hydraulic controls. This study investigates temporal changes in flood inundation patterns between 2014 and 2024 over a highly flood prone urban coastal catchment in India under comparable rainfall forcing, with the objective of improving understanding of non-stationary flood behavior. Rainfall events were identified and grouped based on intensity and duration using long-term precipitation records. For each selected event, flood extents were mapped using satellite-based inundation detection implemented on cloud computing platforms, and, where appropriate, complemented by physically based hydraulic modeling. This combined rainfall–flood framework enables consistent inter-annual comparison of flood patterns under equivalent meteorological conditions. The methodological approach focuses on isolating the influence of landscape and hydraulic evolution on flood response by analyzing spatial characteristics of inundation independent of rainfall variability. By integrating remote sensing and hydraulic modeling within a long-term analysis, the study provides a transferable framework for assessing how flood behavior evolves in response to environmental and anthropogenic changes. This work is relevant to flood risk management and climate adaptation, particularly in rapidly changing river basins where traditional stationary assumptions may no longer be valid. The approach supports improved interpretation of historical floods and more robust planning under future uncertainty.

How to cite: Ghosh, M. and Karmakar, S.: Assessing changes in flood inundation patterns using rainfall-controlled event analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15655, https://doi.org/10.5194/egusphere-egu26-15655, 2026.

Understanding hydrological responses to high-intensity rainfall is critical for water resource management in humid tropical regions, where climate change is increasing the frequency and magnitude of extreme storm events. However, runoff generation mechanisms and flow pathway activation in small tropical catchments remain poorly understood. This study investigates hydrological connectivity, flow pathways, and streamflow source contributions in the Acono watershed, Trinidad and Tobago.

A multi-tracer approach combining stable isotopes (δ²H, δ¹⁸O), radioisotopes (³H, ²²²Rn), and major ion geochemistry (SO₄²⁻, Na⁺, Mg²⁺, Ca²⁺, Cl⁻) was applied to characterize water sources and residence times under contrasting hydrological conditions. Periodic sampling was conducted over a 22-month period, complemented by event-based sampling during a minimum of five high-intensity rainfall events. Samples were collected from rainfall, streams, springs, shallow soil water (10–80 cm), and deep groundwater, alongside continuous monitoring of rainfall, soil moisture, and water levels across the catchment. End-member mixing analysis was used to quantify source contributions to streamflow.

Preliminary results indicate that streamflow is predominantly sourced from pre-event (“old”) water under low flow and moderate wet-season conditions, with old water and spring inputs frequently accounting for 60–99% of flow. Direct rainfall contributions are generally limited (average ~7%) and rarely exceed ~30–37%, suggesting strong subsurface buffering and rapid mobilization of stored water rather than dominant overland flow. In contrast, the onset of wetter conditions in early 2025 triggered pronounced, non-linear shifts in source contributions, including sharp increases in deep groundwater and spring contributions (up to ~89% and ~80%, respectively), alongside elevated event water fractions. These patterns suggest threshold-controlled activation of deeper storage and fast-responding subsurface pathways during periods of sustained or intense rainfall.

Data collect is ongoing and additional analyses are expected to improve our understanding of the translation from rainfall to streamflow. This research provides a novel approach to understanding hydrological processes in small island developing states (SIDS).

How to cite: Ramjohn, P. and Farrick, K.: High Intensity Rainfall Event Contributions to Stormflow and Stream Residence Time in the Acono Watershed, Trinidad., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15744, https://doi.org/10.5194/egusphere-egu26-15744, 2026.

Climate change is driving a marked intensification of hydrological extremes, including both droughts and floods. When these opposing conditions occur in close succession, known as hydrological whiplash, they generate compounded impacts on ecosystems, infrastructure, and human livelihoods. We analyze hydrological whiplash across India using observed streamflow data and simulations from the validated H08-CaMa-Flood model. The results indicate that nearly 90% of streamflow stations experienced at least one whiplash event, with drought-to-flood transitions being both more common and more abrupt than flood-to-drought shifts. These events are concentrated primarily during the monsoon season, but their occurrence has increased in the non-monsoon months in recent decades, particularly in high-elevation regions. Moreover, we find that whiplash events are becoming more frequent and more intense, while the interval separating dry and wet extremes is shrinking, signaling an escalation of hydrological volatility across the country. Together, these patterns underscore the need for strengthened monitoring, early warning capabilities, and adaptive water management strategies to reduce the growing risks associated with rapid hydrological transitions under a warming climate.

How to cite: Sharma, P. and Mishra, V.: Hydrological Whiplashes Over India: Patterns, Drivers, and Recurrence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16235, https://doi.org/10.5194/egusphere-egu26-16235, 2026.

Terrestrial water availability in India is increasingly affected by both climate variability and human activities such as groundwater extraction, reservoir operations, and the expansion of irrigation. However, the observed trends in these factors and their future changes at the river basin level are still not well quantified, making it difficult to plan effective water management strategies. In this study, we assess the individual and combined impacts of climate change and human interventions on the water budgets of major Indian river basins using an ensemble framework that includes the Community Water Model (CWatM) hydrological models. We specifically analyze the changes in sectoral water demands, including agricultural, domestic, and industrial, analyzing their historical progression and projected changes from 1951 to 2100. Based on IMD datasets and CMIP6 scenarios, we identify key regions likely to face water stress in the future and estimate uncertainties in water availability. These findings support the development of sustainable water management plans in response to evolving sectoral trends and climate-related challenges across the Indian subcontinent.

How to cite: Kushwaha, A. P. and Mishra, V.: Dynamics of Sectoral Water Demand and Future Water Stress Hotspots in Indian River Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16329, https://doi.org/10.5194/egusphere-egu26-16329, 2026.

Agriculture poses severe impacts on water quality and health due to diffuse pollution via pesticide use. In this study, the impact of pesticide use on water quality in the Polatlı district—a region of intensive agriculture within the Ankara River Watershed—was assessed using the Grey Water Footprint (GWF) methodology. The study analyzed 34 active ingredients utilized in the 2023 production cycle of wheat, barley, onion, and sugar beet. District-level data on cultivated areas (ha) for each crop were obtained from the Turkish Statistical Institute (TURKSTAT) for 2023. For the GWF calculations, the natural background concentration was assumed to be zero, while the maximum allowable concentrations for each pesticide were retrieved from local regulations. A watershed-scale hydrological model, namely Soil and Water Assessment Tool (SWAT) were constructed for the study area and calibrated against observed streamflow data to ensure reliable simulation of pesticide transport. Pesticide applications were integrated into the model based on actual usage data. The pollutant loads transported from the Polatlı district to the Ankara River were calculated and subsequently utilized in the grey water footprint equation.

Our findings reveal that pesticide impacts vary significantly with respect to crop and active ingredient levels. For example, SWAT model simulation results for deltamethrin reveal a high environmental transport efficiency despite its low application rate (250 ml/ha) compared to other pesticides. This pesticide has an extremely high affinity for soil particles as clear from the organic carbon-water partition co-efficient value (K_oc = 1,000,000 L/kg); it binds strongly to soil rather than dissolve in water. The transport of deltamethrin is entirely driven by soil erosion, leading to its accumulation in riverine sediments. Due to its extreme K_oc value, the pesticide remains associated with suspended solids and bed sediments, posing a significant long-term threat to benthic organisms and aquatic biodiversity. This sediment-related pollution indicates that the GWF of the basin is not only a function of dissolved pollutants, but it can be heavily influenced by sediment quality. No leaching to groundwater or dissolved transport was observed, confirming its strong soil-binding behavior. This substantial variability in GWFs underscores the necessity for region-specific water quality standards to more accurately assess and manage the environmental impact of pesticide use. Our analysis addresses the complexities of mixed cropping systems typical of semi-arid regions, where water scarcity and intensive pesticide use converge to create critical water quality challenges. This study provides a framework for similar assessments in other agricultural regions, aiding in the development of more informed pesticide management strategies to enhance water resource sustainability. Our results highlight specific pesticides requiring priority attention: replacing or limiting high-GWF pesticides is essential for progress toward sustainable water management in the Ankara River basin.

How to cite: Dogan, F. N. and Capar, G.: Assessment of Pesticide-Related Water Pollution in the Ankara River Watershed: A Combined SWAT and Grey Water Footprint Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16687, https://doi.org/10.5194/egusphere-egu26-16687, 2026.

Chemical weathering of continental rocks plays a key role in regulating river water chemistry and global biogeochemical cycles. This study investigates the spatial and seasonal variability in major and trace element geochemistry of rivers in the Ganga basin to constrain dominant weathering processes and their downstream evolution. A total of 200 water samples and sediment samples were collected across different hydrological regimes and physiographic settings. The results indicate that dissolved loads are primarily controlled by the weathering of silicate and carbonate lithologies. In the Himalayan headwaters, river chemistry is dominated by carbonate and Ca²⁺–Mg²⁺-rich silicate weathering, with Ca²⁺ + Mg²⁺ and HCO₃⁻ contributing approximately 85% of total cations and anions. In contrast, downstream reaches exhibit a systematic decrease in Ca²⁺ + Mg²⁺ contributions and an increase in Na⁺ + K⁺ proportions (up to ~50%), suggesting enhanced influence of silicate weathering and/or alkaline soil inputs. Trace elements such as  Pb, Hg, Th, Sr, Rb, Mo, U, Ba, and V reveal spatially variable source contributions across different catchments. Sodium-normalized trace metal ratios and Ca/Na* relationships indicate additional contributions from carbonate or Ca²⁺–Mg²⁺-rich silicates, particularly during high-discharge periods. Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) of the Ganga River reflect chemical weathering and sediment sources in the Himalayan region. The river drains diverse lithologies of the Himalaya, causing spatial and seasonal isotopic variations. Higher ⁸⁷Sr/⁸⁶Sr values indicate silicate weathering of radiogenic continental crust, especially during monsoon periods. Lower ratios reflect inputs from carbonate rocks and recycled sediments. Sr isotopes highlight the role of Himalayan weathering in controlling riverine Sr flux.These observations highlight the combined influence of lithology, hydrology, and seasonal discharge on riverine geochemistry and provide new constraints on chemical weathering processes and trace element fluxes from the Ganga basin to the ocean.

How to cite: Parida, R. K. and Rai, S. K.: Chemical Weathering Dynamics and Riverine Geochemistry of the Ganga River Basin: Spatial and Seasonal Controls on Elemental Fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16801, https://doi.org/10.5194/egusphere-egu26-16801, 2026.

Hydrodynamic processes strongly influence coastal and estuarine landscapes, especially in low-lying deltaic regions like the Meghna Estuary. Islands in the lower Meghna and at the Padma-Meghna confluence face increased risk of submergence due to sea-level rise and intensified precipitation under future climate scenarios. This study analyzes the long-term hydrodynamic changes in the Meghna Estuary using Delft3D simulations for 2000, 2024, and 2050, focusing on islands such as Nijhum Dwip, Moulovi Char, Domar Char, Char Kukri Mukri, Rajrajeshwar, Hatiya, and Manipura. The model covers the area from Baruriya Transit to the sea, integrating tidal and riverine dynamics. Future discharge for 2050 was generated from MIROC6 climate projections under SSP2-4.5 and SSP5-8.5 scenarios, bias-corrected and simulated via HEC-HMS. HEC-HMS was calibrated using 2022 data and validated with 2023 discharge records from Bhairab Bazar, while Delft3D was calibrated and validated using observed water level data from Daulatkhan over the same period. Results show rising tidal amplitudes and water levels, with high tides near Char Kukri Mukri increasing by 30 to 35 cm by 2050. Tidal inundation is expected to expand during monsoons, increasing flood risk in low-lying areas. Islands like Char Kukri Mukri and Hatiya are losing relative elevation, heightening their vulnerability to flooding and storm surges. Hydrodynamic projections indicate an average increase in water depth of 0.5 to 0.8 m around Rajrajeshwar, Hatiya, and Manipura by 2050, suggesting enhanced tidal energy and flow velocities that are likely to accelerate shoreline erosion and land loss, particularly along their southern and eastern margins. These findings highlight the increasing vulnerability of the Meghna Estuary’s islands to climate change–driven hydrodynamic shifts, emphasizing the urgent need for targeted adaptive management, improved flood risk mitigation, and resilience-building measures to protect the region’s communities and ecosystems from future inundation and erosion risks.

How to cite: Ahmed Dipu, S. U., Gemintang, E., Haque Laskor, Md. A., Bhuiyan, F., Galib Fardin, M. A., Rahman, Md. M., Rabbany, Y., and Islam Fahim, S.: Hydrodynamic Changes of Estuarine Islands in the Meghna River under Future Climate Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17766, https://doi.org/10.5194/egusphere-egu26-17766, 2026.

Wetlands play a vital role in the hydrologic cycle since they impact stream flow, add to water storage capacity, provide habitat for many species, and provide resiliency in ecosystems. Ondiri Swamp, which is a peatland located in Kenya with approximately an area of 33 hectares and is the headwaters of Nairobi River has very little hydrological understanding, especially regarding subsurface and groundwater contributions since there have not been any continuous in-situ data measurements. This study aims to quantify water storage and investigate potential groundwater presence using an embedded, multi-sensor dataset.
To overcome the limitation of only using traditional optical index methods for surface water detection under dense vegetation, water occurrence data (Global Surface Water), Sentinel-1 SAR and Sentinel-2 multitemporal optical images, DEM images (Copernicus DEM), and NDVI derived vegetation index data will be combined. Measurements of swamp depth and peat thickness will be collected from short-term field campaigns for calibration of volume estimates and provide preliminary data for a preliminary water balance. The precipitation data (CHIRPS) and ET data (FAO WaPOR) will be combined with inflow and outflow estimates to create a preliminary water balance. Surface storage will be estimated, and potential groundwater contributions will be inferred without long-term observatory data sources. The methods used for the quantitative and qualitative assessment of wetland water resources will generate probabilistic wetland water maps using a multi-temporal remote sensing-based classification of existing datasets, as well as using terrestrial calibrations from field data. 
The study will be able to quantify total wetland water storage, determine the degree to which groundwater may influence wetlands, and identify the seasonal dynamics of wetland hydrology. Through a combination of remote sensing, existing datasets, and terrestrial calibrations from field studies, the study provides a strong, scalable framework for conducting wetland hydrology research, managing wetland ecosystems and planning wetland water resources in areas where very few, if any, hydrological observations are available.

How to cite: Ouedraogo, A.: Estimating Surface and Subsurface Water in Ondiri Swamp, Kenya, Using Multi-Sensor Embedded Data and Preliminary Water Balance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19536, https://doi.org/10.5194/egusphere-egu26-19536, 2026.

Snow accumulation and melt dynamics govern water availability and flood timing across high-latitude catchments, yet observational gaps constrain understanding of scale-dependent hydrologic processes. Traditional snow monitoring relies on sparse gauge networks or coarse satellite products, preventing observation of sub-catchment patterns critical for hydrologic connectivity. Recent advances in Sentinel-1 SAR (10m, all-weather) and Sentinel-2 optical (10m, 20m, 60m) constellations offer transformative observational capabilities, yet systematically exploiting their complementary information for continuous fine-resolution snow monitoring across cloud-prone regions remains an operational challenge.

We present an innovative all-weather snow monitoring system that fuses Sentinel-1 SAR backscatter (sensitive to snow wetness and surface properties) with Sentinel-2 optical imagery (discriminating snow from clouds and bare ground) to deliver 10m resolution fractional snow cover estimates across boreal Finland. This fusion approach explicitly addresses the fundamental limitation of optical-only monitoring: persistent cloud contamination prevents observations during critical winter periods in high-latitude regions. Our methodology incorporates quality-aware atmospheric corrections (cloud masks, aerosol optical thickness, water vapor) to extract reliable snow information despite challenging atmospheric conditions.

A data-driven multi-resolution framework bridges the critical scale gap between fine-resolution satellite observations (10m) and operational hydrologic models requiring catchment-aggregated snow states. The system learns scale-dependent aggregation and disaggregation functions directly from observations, preserving fine-scale spatial patterns essential for understanding snow redistribution by wind, sublimation, and terrain-driven processes. This approach captures heterogeneity at forest-canopy scales while remaining compatible with distributed hydrologic model architectures.

Operational validation demonstrates that the system achieves physically realistic snow patterns with spatially coherent uncertainty estimates that appropriately elevate at snow-land boundaries where hydrologic transitions occur. These calibrated uncertainty bounds are critical for risk-informed water management and probabilistic flood forecasting, enabling downstream hydrologic models to appropriately weight observational constraints.

Key scientific innovations: (1) Demonstrated feasibility of all-weather snow monitoring by effectively combining complementary SAR and optical signatures, overcoming the cloud-cover limitation that constrains optical-only approaches during 60-80\% of winter days in boreal regions. (2) Developed a principled multi-scale learning framework that explicitly captures scale-dependent aggregation and disaggregation properties, bridging satellite observations and hydrologic model requirements. (3) Resolved sub-catchment snow heterogeneity previously masked in operational products (MODIS: 500m, VIIRS: 375m), enabling new insights into snow redistribution and hydrologic connectivity across fragmented landscapes. (4) Quantified spatial structure in prediction uncertainty, enabling probabilistic hydrologic forecasting that appropriately reflects observational constraints.

This next-generation observational capability addresses critical scientific and operational data gaps: calibrating distributed snow models at relevant scales, improving melt timing predictions through continuous all-weather depletion monitoring, validating snow-pack simulations in data-sparse headwater regions, and quantifying snow-climate feedbacks across heterogeneous landscapes. The framework's transferability to pan-Arctic and mountain regions demonstrates how integrating complementary space-based observations through data-driven fusion unlocks fine-scale process understanding previously limited by observational constraints, advancing our capacity for water security assessment and climate adaptation planning in snow-dependent regions.

How to cite: Demil, G., Humayun, M. F., Westerlund, T., Heikkonen, J., and Oussalah, M.: Identifying Scale-Dependent Snow patterns from learned fusion of Multi-modal, Multi-resolution satellite observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21040, https://doi.org/10.5194/egusphere-egu26-21040, 2026.

The Syr Darya River Basin is a highly glacierized transboundary system where future water availability is strongly influenced by climate change, reservoir operations, and population growth. This study investigates the role of reservoirs in regulating water supply under future climate scenarios using the Water Evaluation and Planning (WEAP) model. Climate projections from the ISIMIP framework under Shared Socioeconomic Pathways SSP1-2.6, SSP3-7.0, and SSP5-8.5 are used to drive hydrological inputs, including streamflow, precipitation, and temperature, while population growth projections represent evolving water demands.

 Results indicate a strong increase in summer unmet demand by 2050, intensifying further by 2090, with peak deficits occurring in July–August. Reservoir refilling remains seasonal across all scenarios but becomes more variable and less reliable by 2090, with deeper drawdowns and reduced buffering capacity under higher-emission pathways.

How to cite: Abdul Jalil, U., D'Haeyer, B., Prakash, S., and Huang, J.: The Role of Reservoirs in a Glacierized Basin Under Climate Change: An Analysis Using the WEAP Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21606, https://doi.org/10.5194/egusphere-egu26-21606, 2026.

Developing hydrological models, which are process-aware and reliably transferable across diverse environments remains a challenge. We benchmark Shyft – an open-source, fully FAIR (findable, accessible, interoperable and reusable) flexible modeling framework, across 109 catchments in mainland Norway to evaluate how model structure, forcing uncertainty and calibration objective jointly shape streamflow simulation performance. We adopt large sample hydrology perspective to probe five models “stacks”, providing alternative process choices, such as evapotranspiration (Penman-Monteith vs Priestley-Taylor), snowmelt (temperature-index vs semiphysical) and runoff response (Kirchner vs HBV tank and soil) with multiple goal functions drawn from KlingGupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE), with and without catchment specific precipitation correction. We use a suite of evaluation metrics targeting bias, hydrograph dynamics, low flows and interannual variability. We move beyond crude mean-flow benchmarks toward simple climatological benchmarks, providing an objective context for model skill evaluation, given the seasonal nature of Norwegian catchments.

The evaluation revealed that configurations containing temperature-index snow simulation and Kirchner runoff offer the greatest robustness and generality across all hydrological regimes. In terms of objective functions, KGEbased targets outperform NSE-based targets, with metric combining KGE and box-cox transformed KGE (KGE_bcKGE) identified as a promising generalist objective, which performs well across diverse metrics, including low-flow targeted (KGE(1/Q)) and interannual NSE. Furthermore, precipitation correction was found to be essential for improving performance in Mountain and Inland regimes, suggesting snow undercatch as a primary source of precipitation uncertainty. Among simple benchmarks, daily mean was found to be best predictor setting model expectations for future model intercomparisons in the region. Our results demonstrate the need for balance of structural adequacy, forcing uncertainty and equifinality.

This project is supported by Norwegian Research Council NFR project 336621.

How to cite: Silantyeva, O., Huang, S., and Xu, C.-Y.: Benchmarking flexible modelling framework Shyft across mainland Norway, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22432, https://doi.org/10.5194/egusphere-egu26-22432, 2026.

The climate-related risks in South Africa’s Raymond Mhlaba Municipality and similar rural regions include erratic rainfall, recurring droughts, heatwaves, and shifting seasons. These directly threaten agricultural productivity, leading to frequent crop losses and food insecurity. Vulnerability is heightened by reliance on rain-fed small-scale farming, minimal irrigation infrastructure to buffer against climatic shocks, and the use of old farming methods.

The government uses radio, television, newspapers, and flyers to communicate climate change, and universities are trying to produce more extension officers to assist farmers, but the challenge remains unaddressed.  The root causes of this vulnerability are multi-layered: weak data dissemination systems, socio-economic marginalization, land tenure insecurity, infrastructure deficits, and regulatory and governance gaps. Consequently, farmers make key agricultural decisions such as when and what to plant without critical zone science knowledge, leading to frequent crop losses, wasted inputs, and heightened poverty.

As such, having a Climate-Adapt-Farm-Wise-AI (CAFW-AI) which can inform the farmer about the climate change and provide customised suggestions to a farmer to a) use conservation agriculture, drought-tolerant crop varieties, and precision irrigation to enhance productivity and climate resilience b) integrate adaptation and mitigation strategies across the entire food value chain to ensure sustainable food production and reduce greenhouse gas emissions c) employ Sustainable Agricultural Practices (SAPs) such as agroforestry and millet resilience to improve soil health and enhance food security in climate-vulnerable regions, based on their geographical area.

These techniques are crucial for fostering innovation and resilience in agricultural economies, especially in the face of climate change. By integrating these innovations, farmers can enhance productivity, reduce environmental impact, and ensure food security.

The proposed solution to the problem 

The initiative introduces an AI-enabled, open-source mobile platform that delivers localized, real-time agricultural advisories to rural small-scale farmers in climate-vulnerable regions such as the Eastern Cape. Its strategies are threefold:

• Localized Climate-Smart Decision Support:

By integrating real-time weather data from IoT sensors (local weather stations), information, and Indigenous Knowledge Systems (IKS), the AI model generates tailored recommendations on crop selection, planting times, and resource use. This ensures that decisions are data-driven, context-specific, and actionable for farmers with limited resources.

• Accessible Communication Channels: The platform disseminates advisories via SMS/USSD in local languages (e.g., isiXhosa), bridging the digital divide for communities with limited or no smartphone access.
• Feedback-Driven Learning: Farmers contribute local observations (e.g., rainfall, soil moisture, pest outbreaks) into the system. AI processes these inputs alongside satellite and meteorological data, enabling continuous model refinement and ensuring the system evolves with changing conditions.

What sets this initiative apart is the role of real-time weather data from IoT sensors (local weather stations), AI in combining heterogeneous data sources (real-time weather, soil characteristics, and farmer inputs) to generate hyper-local insights that would not be possible through traditional extension methods. Previously, climate advisories were generalized, delayed, and fragmented; now, AI enables predictive analytics and personalized recommendations at scale, even in remote areas.

How to cite: Vambe, W. T.: Climate-Adapt-Farm-Wise-AI (CAFW-AI): Utilizing IoT, AI, and Machine Learning to Enhance Decision-Making and Protect Crops More Effectively Against Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23177, https://doi.org/10.5194/egusphere-egu26-23177, 2026.

A major threat to Falkland Islands (FI) biodiversity and livelihoods is a drying climate. As warming continues, FI water security is a growing concern, but we lack baseline data to inform mitigation and adaptation. This study applies an innovative remote sensing approach to monitor long-term surface water variability across the Falkland Islands, aiming to support climate-resilient water management. Using the Google Earth Engine (GEE) platform, historical dynamics from 1999–2021 were derived from the Global Surface Water (GSW) Explorer dataset, while recent trends (2021–2025) were assessed using Harmonized Sentinel-2 MSI Level-2A imagery. Together, these enable the first continuous, multi-decadal assessment of pond, wetland, and lake dynamics across East Falkland, West Falkland, and Lafonia. Preliminary results show a relative decline in surface water extent across East Falkland, West Falkland, and Lafonia from 1999 to 2021. More recent Sentinel-2 observations reveal regionally distinct trends from 2021 to 2025: East Falkland remains relatively stable, West Falkland shows a modest increase, and Lafonia exhibits a pronounced rise with strong seasonal variability. These results align with limited ground observations from the water level monitoring site, where satellite-derived surface water area strongly correlates with recorded maximum water levels, confirming the hydrological consistency of the satellite data. Despite limited ground validation, this proof-of-concept highlights the capability of cloud-based remote sensing tools to monitor hydrological variability at regional scale. This approach illustrates how open-access Earth observation data and hydroinformatics tools can aid early detection of climate-driven water changes and strengthen water management in data-scarce areas. It also establishes a basis for future studies linking satellite data with peatland hydrology and ecosystem resilience.

Keywords: Climate Change Impacts, Surface Water Variability, Remote Sensing, Sentinel-2, Google Earth Engine

How to cite: Ko, N. T., Baylis, A., Davies, G. M., Barlow, D., and Evans, C.: Surface Water Dynamics under Changing Climate: Integrating Multi-Sensor Satellite Observations (1999–2025) across the Falkland Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-245, https://doi.org/10.5194/egusphere-egu26-245, 2026.

Long-Short Term Memory (LSTM) shows exceptional performance for rainfall-runoff modelling, but lacks physical realism. Efforts to integrate mass conserving into the model architecture didn’t translate to significant improvement in predictive accuracy. We build on the Mass-Conserving LSTM (MC-LSTM) by proposing a novel architecture that incorporates two complementary cell states to represent distinct fast and slow hydrologic memory components while maintaining strict mass balance. We introduce a new partition gate for segregating the mass input for long- and short-term memory, and made required architectural changes to incorporate additional cell state. We benchmarked our model against LSTM and MC-LSTM on CAMELS-IND (158 basins) and CAMELS-US (531 basins) using NSE, KGE, Pearson-r, FHV, FLV, and peak timing/magnitude. For the Indian dataset, MC-LSTM-DS surpasses both LSTM and MC-LSTM across all metrics except Pearson-r and FLV, where it exceeds the performance of LSTM but falls short of MC-LSTM. In the low flow regime (FLV), our model decreases the overestimation of LSTM significantly, while MC-LSTM shows severe underestimation. Analysis of the spatial distribution revealed it to be aligned with hydroclimate, where all the models performed better in humid/tropical climates, while performance lacked in arid regions. Investigation of the cell states revealed that the added cell state represents the long-term processes effectively, while the original cell state captures short-term processes. The change in their relative contributions according to the climate characteristic is observed, thus confirming our hypothesis and also providing an interpretable decomposition of the simulated flows. On the CAMELS-US dataset, MC-LSTM-DS demonstrates equal performance as MC-LSTM and LSTM on NSE, and outperforms all the models in KGE, FHV, and Pearson-r. In FLV, it outperforms all the mass-conserving models by a huge margin and is just short of LSTM. This study proposes a novel mass-conserving model that provides an interpretable prediction. We claim MC-LSTM-DS to be the current state-of-the-art for large sample rainfall runoff modelling, as it showed superior performance across two diverse regions. To the best of our knowledge, this study is the first to investigate the effects of strict mass conservation on the diverse Indian region.

How to cite: Toraskar, S., Naik, M. N., Singh, A., and Gaurav, K.: Mass Conserving LSTM with Dual States for Improved Streamflow Prediction through Quickflow and Slow Storage Separation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1031, https://doi.org/10.5194/egusphere-egu26-1031, 2026.

Arid and semi-arid regions are facing more frequent and severe droughts, with annual rainfall often below 200 mm. Large-scale, intensive irrigation further strains these limited water resources. Under these conditions, growers need practical tools to estimate yield and monitor tree health at high spatial detail so they can better manage irrigation and inputs. This work develops and tests an automated, data-driven pipeline for estimating citrus yield at the individual-tree level using UAV imagery and Deep Learning. The pipeline comprises three main components. First, individual trees and orchard rows are segmented using a lightweight Tiny U‑Net model. Second, a CNN-based model predicts tree-level yield from vegetation indices and field measurements. Third, these predictions are validated through detailed fruit sampling.

The study was conducted in a commercial citrus orchard in a semi-arid region under climate and water stress. High‑resolution UAV imagery was processed into orthomosaics and vegetation index maps, and the Tiny U‑Net was optimized for fast, near real‑time semantic segmentation, enabling precise tree crown delineation and accurate tree and row counts. For yield prediction, the CNN model exploited spatial features from vegetation indices combined with in‑situ data. The validation relied on direct comparison between UAV‑based yield estimates and yields obtained from field sampling and laboratory weighing. Both mean and median yields per tree were computed to capture tree‑level variability. The final dataset, consisting of 34 trees and approximately 340 fruit samples, provided a robust basis for assessing model performance. The Tiny U‑Net segmentation model reached high accuracy, with precision and recall of 94.74% and 94.88%, and an inference time of 12.55 ms per image tile. This shows the model is suitable for real‑time or on‑board use and can reliably map orchard structure at large scale. Tree and row counts derived from the segmentation achieved an R² greater than 0.99, confirming the robustness of the approach. For yield estimation, the CNN model outperformed other machine learning methods, achieving an R² of 0.88 at tree level. Field validation confirmed the practical usefulness of the pipeline, UAV‑predicted yields closely matched ground‑truth values, with both indicating an average yield of roughly 50 kg per tree. Most trees fell between 40 and 70 kg, and the model’s output histogram mean 50.9 kg, and median 51.4 kg aligned well with these field observations.

This robust agreement between model outputs and independent field validation data underscores the system's reliability and operational readiness for accurate, tree-level yield mapping. By integrating precise tree segmentation, high-resolution vegetation indices, and rigorously collected ground truth measurements, this study demonstrates that automated yield maps can be produced with sufficient accuracy to support operational decisions in orchards. This offers a cost-effective and scalable tool for precision agriculture, enabling optimized resource allocation, improved harvest planning, and adaptive management under increasing climate stress.

How to cite: Bakas, K., Saddik, A., Dliou, A., Hssaisoune, M., El Hachemy, S., Ait Ichou, H., Hmache, F., El Hafyani, M., Labbaci, A., and Bouchaou, L.: Real-Time UAV-Deep Learning System for Citrus Orchard Structure and Yield Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2054, https://doi.org/10.5194/egusphere-egu26-2054, 2026.

The Souss-Massa region is known as the most important agricultural area in Morocco, and one of the most affected regions by climate change and over-exploitation. This situation has required the intervention of new tools to improve water resource management. In this context, the Unmanned Aerial Vehicles (UAVs) images data were used for weeds detection in a Citrus orchard farm. Two sites were considered, the first one planted with 12-years-old and 1.5 years-old clementine trees. After a panoply of image processing from the data collection, following by the georeferencing, the creation of the digital elevation model, the digital surface model, and the elaboration of the orthomosaic image, the machine learning algorithms (MLA) such as Maximum Likelihood Classification, Minimum Distance Classification, Support Vector Machine, were applied for weeds detection and mapping. For both sites, all MLA showed a Cohen’s kappa coefficient higher than 0.6 and an overall accuracy higher than 60%. This study demonstrates how this emerging technology offers farmers opportunities to enhance production while optimizing water usage.

How to cite: El Hafyani, M., Chaaou, A., Sadik, A., Labbaci, A., Hssaisoune, M., Tairi, A., Abdelfadel, F., Taia, S., Ait-Ichou, H., Elhaid, I., and Bouchaou, L.: A combined approach of UAV data and machine learning algorithms in weeds detection , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2155, https://doi.org/10.5194/egusphere-egu26-2155, 2026.

Coastal wetlands, characterized by their geomorphological sensitivity and tidal dependence, exhibit pronounced vulnerability under global warming. While the persistent threat of sea-level rise to coastal wetlands has been extensively documented at the macroscale, there remains a lack of systematic quantitative frameworks for mapping these trends to the microscale dynamics of wetland evolution. To address this gap, this paper proposes WetFramework, a novel approach for joint modeling of spatial structure and temporal variation in wetlands. (1) In the encoder, Transformer and Mamba modules are integrated to enhance multiscale feature representation through the synergy of global attention and implicit sequence modeling, with a Token-Driven Attention Mechanism (TDAM) designed to facilitate deep interactions between features. (2) In the decoder, a Wavelet-Enhanced Reconstruction Module (WERM) is introduced to improve spatial structure modeling via wavelet transforms, thereby optimizing boundary delineation and fine detail representation for precise mapping of coastal wetland extents. (3) To capture periodic inundation characteristics, a Fourier-Based Inundation Estimation Module (FBIEM) is further proposed, incorporating tidal-height observations to enable unsupervised modeling of pixel-level hydrological responses and quantitative expression of inundation rhythms. Extensive experiments conducted in four representative coastal regions—Yancheng and Dongying (China), Mont-Saint-Michel Bay (France), and San Francisco Bay (USA)—demonstrate that the proposed framework outperforms state-of-the-art models across multiple evaluation metrics and exhibits robust cross-regional generalization and dynamic modeling capabilities. This study provides an effective paradigm for intelligent remote sensing-based wetland identification and long-term hydrological modeling, and offers key hydrological information to support inundation-dynamics monitoring and management decision-making.

How to cite: Liang, J.: WetFramework: A Deep Learning Framework for Coastal Wetland Boundary Extraction and Inundation Frequency Estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2567, https://doi.org/10.5194/egusphere-egu26-2567, 2026.

In 2001, the tamarisk leaf beetle (Diorhabda spp.) was released as a biological control agent for invasive tamarisk (Tamarix spp.), which dominates many floodplains in the western United States (US) and substantially alters riparian ecosystem structure and function. Since its release, the beetle has expanded across thousands of river kilometers, repeatedly defoliating tamarisk far beyond original release sites. Although biological control offers an alternative to mechanical or chemical removal, its ecological benefits and tradeoffs remain uncertain. Here, we synthesize current understanding of one of the most extensive biological control programs implemented in North America, evaluating impacts on riparian evapotranspiration (ET) and riverine hydrology. We assess ongoing challenges and opportunities associated with tamarisk biocontrol and consider how western US riparian forests may evolve under reduced tamarisk dominance.

Early management efforts were driven by the assumption that tamarisk consumed exceptionally large volumes of water, motivating legislative and large-scale removal programs. Subsequent studies, however, demonstrated that tamarisk water use is highly variable and comparable to native riparian vegetation such as cottonwood (Populus spp.) and willow (Salix spp.), as well as mixed shrub communities. Reported tamarisk ET since 2000 ranges widely (109–1456 mm yr⁻¹), with mean values near 850 mm yr⁻¹, depending on stand age, density, health, groundwater depth, soil properties, and salinity.

Defoliation by Diorhabda spp. was expected to enhance streamflow by reducing riparian ET, yet observed hydrologic responses have been inconsistent. In past research ET declines exceed 40% relative to healthy tamarisk at some locations, whereas at other sites, reductions are modest or absent, particularly where baseline ET is low. In this current study, we reassess post-defoliation dynamics by analyzing ET across 27 riparian sites from 2014–2023 using Landsat-derived Nagler ET(EVI2) estimates and gridded climate data. Approximately half of the sites exhibited sustained ET reductions averaging a loss of 18% (−142 mm yr⁻¹), while the remainder showed negligible change or increases in ET of 9% (+54 mm yr⁻¹), likely reflecting tamarisk regrowth or replacement by other vegetation. Across all sites, net water savings were modest, averaging a loss of 7% (−48 mm yr⁻¹), consistent with earlier estimates.

These findings reinforce that hydrologic benefits from tamarisk biocontrol are site-specific, often transient, and frequently offset by vegetation recovery or compositional shifts. Consequently, biological control alone is unlikely to yield substantial or reliable increases in water availability for agricultural or municipal use. Predicting future structure and function of western US riparian forests under tamarisk biocontrol requires explicit consideration of ecosystem complexity, spatial heterogeneity, and interacting drivers that will shape whether alternative states favor native vegetation recovery or secondary invasions.

How to cite: Nagler, P., Palmquist, E., Snyder, K., Jimenez-Hernandez, E., and Hultine, K.: Revisiting a riparian invasive shrub and its biocontrol in the western United States: Measured Changes in Water Use, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3158, https://doi.org/10.5194/egusphere-egu26-3158, 2026.

Farm roadway networks are an important infrastructure in grassland farms providing access between the farmyard and grazing fields. However, during livestock movement, excreta is deposited on the roadways, especially on bends, T-junctions and at corners where their movement is impeded. Nutrient-enriched soiled runoff generated on these roadways can contribute significantly to water quality degradation if connected to waters (including man-made open drainage ditches). Quantifying the risk associated with farm roadway runoff delivery to waters includes mapping the roadway and drainage networks and identifying sections which contain high pollutant loads and have the potential of generating, mobilising and delivering surface runoff to the drainage channels. In this study, a deep learning (DL) approach was employed to automatically identify internal farm roadway networks and open drainage channels in 5 grassland farms. Aerial imagery and LiDAR-derived digital terrain models were used to train the DL models for identifying farm roadways and open drainage ditches, respectively. The flow direction and flow accumulation were determined using digital elevation models to map farm roadway sections that have the potential to generate and deliver runoff to the drainage network.

Across the 5 farms, a total of 16.7 km of roadway and 13.5 km of drainage channels were identified by the DL models, achieving precisions of 79 % and 64 %, and accuracies of 90 % and 96 %, respectively. Flow accumulation maps were established for each farm to assess delivery pathways and the potential of roadway runoff connectivity to waters. Flow pathways through roadway junctions and at corners were considered critical outranking those on straight roadway sections. Breaking the runoff pathway at these locations will help prevent delivery to waters. The findings of this study indicate that mapping of open drainage channels and internal farm roadways in grassland farms can be automated by using deep learning models. Integrating the automated mapping and hydrological modelling enables more precise identification of critical roadway sections, supporting targeted mitigation to reduce soiled runoff from entering waters and thus enhance water quality protection in grassland farming systems.

How to cite: Sifundza, L. S., Murnane, J. G., Daly, K., Adams, R., Tuohy, P., and Fenton, O.: Integrating deep learning and hydrological modelling to assess farm roadway runoff risk to inform targeted mitigation in grassland systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6937, https://doi.org/10.5194/egusphere-egu26-6937, 2026.

Abstract: 

In this work, we develop a hydrological model designed to simulate the water balance and runoff processes at the catchment-scale. Instead of using a rectangular grid discretization, the model represents the catchment using a non-homogeneous two-dimensional triangular mesh framework (similar to a triangular mesh in the Finite Element method). This discretization fits a more flexible representation of complex topography and land boundaries. The model is implemented in the Fortran programming language. It depends on the Digital Elevation Model (DEM) to extract the flow pathways starting from upstream and reaching downstream. That guarantees a physically consistent and explicit flow-routing structure across the triangular mesh.

Evapotranspiration is calculated using the Penman–Monteith equation, as the parameters are considered to suit coastal climate conditions. The model utilizes temperature, solar radiation, wind speed, and vapor pressure as atmospheric inputs. The SCS Curve Number method is used to estimate the surface runoff, considering slope, land cover, and soil properties. Meteorological data measurements, including precipitation, temperature, humidity, as well as inflow and outflow discharges, are integrated into the simulations.

Due to its efficient numerical structure, the model supports simulations with numerous spatial elements and long time series while maintaining the computational cost at its lowest limits. This makes it well-suited for large-scale watershed applications and provides a strong basis for future high-performance computing developments.

Keywords: hydrological modeling, watershed triangulation, flow routing, numerical simulation

How to cite: Dali, N.: Hydrological Modelling Framework for Large-Scale Catchments using triangular nonhomogeneous spatial discretization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8171, https://doi.org/10.5194/egusphere-egu26-8171, 2026.