The evolution of life on Earth has been bracketed by two momentous events. The first was enabling: the rise in atmospheric oxygen from photosynthetic cyanobacteria during the early Proterozoic. The second was disrupting: the alteration of all subsystems comprising the Earth System by Homo sapiens beginning in the Late Pleistocene and intensifying through the Holocene. Using this lens, the Anthropocene that has morphed this century from a term to a concept to a keyword to a zeitgeist is a profound, albeit in many ways inadvertent, outcome of the transformed pure-to-applied geology profession. It instructively highlights the natural to unnatural transition of Earth history with the human-modified upper part of the lithosphere as the archaeosphere which straddles the Geological and Archaeological Timescales. In Earth System terms, it informs a new ethos to challenge the estrangement from nature by most non-indigenous peoples and the blinkered approach to climate change deliberations by most policymakers.

In contrast to the Anthropocene Event approach by a diverse group who considered all of humanity’s Earth-surface-altering impacts, the stratigraphically focused Anthropocene Working Group (AWG) proposed a post-Holocene epoch/series with a 1952 GSSP centered on the mid-20^th-century Great Acceleration and peak of atomic bomb testing fallout. Starting its advocacy in 2015 in the Bulletin of the Atomic Scientists, which was created at the urging of Albert Einstein and Manhattan Project researchers to reflect on the catastrophic weaponry used at Hiroshima and Nagasaki, was a questionable anomaly in Geological Timescale practice. Rejected in 2024 by the Subcommission on Quaternary Stratigraphy, International Commission on Stratigraphy and International Union of Geological Sciences, the AWG’s proposal also ignored several relevant breakthroughs in the human psyche. These included the Rockefeller Foundation–Lancet Commission on Planetary Health in 2015 and the UN’s Transforming our World agenda from 2015-2030.

Today, the sciences and humanities would be wise to integrate the prescient realizations of Alexander von Humboldt (1769-1859) that nature would exist in the absence of humanity but that humanity cannot exist without nature and of James Lovelock (1919-2022) that there is no prescription for living with Gaia, only consequences. Arguably, today’s biggest dividend of Anthropocene thinking is that it provides a holistic foundation for urgent Earth System governance.

How to cite: Koster, E., Gibbard, P., and Gibling, M.: The Anthropocene as Earth’s natural to unnatural history transition, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1432, https://doi.org/10.5194/egusphere-egu26-1432, 2026.

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.

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.

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.

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.

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.

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.

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.

The Integrated Forecasting System, extended for atmospheric composition modelling (IFS-COMPO), is a global forecasting system for atmospheric trace gases and aerosols. It provides the global component for the Copernicus Atmosphere Monitoring Service (CAMS). In the operational suite of the IFS-COMPO aerosol concentrations are constrained by assimilating aerosol optical depth (AOD) from different satellites. Here, we test the system by adding assimilating of ground-based lidar and ceilometer observations from the European E-Profile network. The performance is investigated by comparison to non-assimilated E-Profile stations, AERONET AOD observations, and aerosol ground concentrations from AirBase. E-Profile assimilation strongly reduces biases and root mean square errors (RMSE) of model-equivalent profiles of the attenuated backscatter coefficient. When constraining aerosols with AOD observations only, surface concentrations of particles smaller than 2.5 μm (PM2.5) are often overestimated in summer, and concentrations of particles smaller than 10 μm (PM10) are frequently underestimated. Additional assimilation of E-Profile observations can lower the RMSE of PM2.5 by up to 50% and of PM10 by up to 10 %. However, as the IFS-COMPO analysis system uses the total aerosol mass mixing ratio as control variable, the positive PM2.5 bias and the negative PM10 bias cannot simultaneously be improved. In most cases the PM2.5 bias is reduced, while the PM10 bias is degraded. The reason is that fine particles make the dominant contribution to the optical cross sections per mass. Different configurations of the assimilation-system have been tested, showing that the best overall performance is achieved by describing optical properties of dust with a spheroid model, suppressing vertical correlations in the background error covariances, and using an aggressive cloud mask.

How to cite: Kahnert, M., Ades, M., Backles, M., Flemming, J., Guidard, V., Haefele, A., Hogan, R., Rémy, S., and Sauvageat, E.:  Assimilation of lidar and ceilometer observations from the E-profile network of European ground-based stations into ECMWF’s Integrated Forecasting System (IFS-COMPO), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12255, https://doi.org/10.5194/egusphere-egu26-12255, 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.

Methane is a powerful greenhouse gas, currently responsible for 0.5 degrees of warming. Because methane acts on a short timescale, reducing its emissions can limit temperature in the coming decades. However, rising global methane emissions highlight the need for enhanced efforts. In addition to methane emissions reduction, a new field of Atmospheric Oxidation Enhancement (AOE) is emerging that may reduce climate risks by artificially accelerating the natural breakdown of methane in the atmosphere.

Recent studies of atmospheric oxidation enhancement (AOE) involving chlorine atoms have relied on simplified assumptions, such as constant surface fluxes of reactive chlorine, or a catalytic iron-salt-aerosol efficiency that is independent of environmental parameters. While such studies highlight the scale of potential effects, these parameterizations obscure key issues of catalytic efficiency and deployment feasibility. Disagreement between global model parameterizations for iron-salt-aerosol exacerbates the problem of resolving these issues and can lead to contradictory conclusions.  Here we present independent lines of reasoning—based on elementary reaction kinetics, modelling, laboratory studies, and field analyses—that demonstrate the complexity that needs to be considered to assess the viability of AOE involving chlorine atoms. We show that effective deployment depends critically on location and conditions—sunlight, aerosol acidity, sea spray, background species (NOx, O3, acids, oxidants) and dispersion. In this context, plumes emerge as a distinct and attractive reactor class: local, temporary (days), and capable of entraining and processing large air volumes. Finally, we present recent advances in monitoring of enhanced atmospheric methane oxidation. This creates a route towards field studies providing data to validate models, and towards MRV approaches to validate hypothetical future atmospheric methane removal.

How to cite: van Herpen, M. M. J. W. and Johnson, M. S.: Atmospheric Oxidation Enhancement with chlorine atoms: Efficiency, Plume Dynamics, and New Mechanistic Insights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18912, https://doi.org/10.5194/egusphere-egu26-18912, 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.

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.

The terrestrial biosphere is responsible for most CO₂ exchanges between land surfaces and the atmosphere, with ecosystems functioning as either carbon sinks or sources. These exchanges are primarily controlled by photosynthesis and ecosystem respiration, which depend on vegetation traits, environmental drivers, and soil water availability. Under drought conditions, plants tend to reduce stomatal conductance to conserve water, decreasing photosynthetic efficiency and limiting atmospheric CO₂ uptake.

In Argentina, observational studies of ecosystem CO₂ fluxes remain scarce, partly due to the high costs of instrumentation. Models such as the Vegetation Photosynthesis and Respiration Model (VPRM) provide an alternative approach to estimate ecosystem–atmosphere carbon exchange using meteorological forcing and satellite-derived vegetation indices. Recent developments include a modified VPRM formulation that explicitly accounts for water availability effects on respiration (Gourdji et al., 2022), which may improve model skill during drought. Additionally, high-resolution satellite observations have been demonstrated to more accurately represent heterogeneous agricultural landscapes, such as the crop mosaics characteristic of central Argentina.

In this work, we assess the ability of a modified VPRM driven by high-resolution satellite data to reproduce net ecosystem exchange (NEE) under contrasting hydroclimatic conditions. We use eddy-covariance observations from three sites (two grasslands and one under crop rotation cycles) in the Argentine Pampas. For each site, information on vegetation conditions in the vicinity of the flux tower was extracted from MODIS Terra and Sentinel-2 images. Time series of the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI) were derived. NEE was simulated using both the original and the modified VPRM forced by each satellite data source, evaluating all model–satellite combinations. Drought conditions were characterized using the Standardized Precipitation–Evapotranspiration Index (SPEI) computed from CRU TS v4 gridded data at the nearest grid cells. Based on SPEI thresholds, the observational period was classified into “normal”, “mild drought”, and “moderate-to-severe drought”. Also, model performance statistics were computed for each regime.

Across sites, the configuration combining the modified VPRM with Sentinel-2 inputs (VPRMnew_S2) achieved improved skill (R² = 0.49, 0.24, 0.65) compared with the original VPRM driven by MODIS imagery (R² = 0.43, 0.23, 0.52). For the grassland sites, VPRMnew_S2 consistently outperformed the other configurations across all moisture regimes (higher R², lower RMSE, and near-zero bias). At the cropland site, VPRMnew_S2 showed similar skill to the original model in terms of R² and RMSE, but substantially reduced bias under water-limited conditions. These findings suggest that high-resolution satellite indices, coupled with drought-sensitive parameterizations, better capture NEE responses to water stress in the Argentine Pampas. Improved modelling of drought impacts on CO₂ exchange is essential to reduce uncertainty in regional carbon budgets and to assess ecosystem vulnerability under increasing drought frequency.

Keywords: Net Ecosystem Exchange, Eddy Covariance, MODIS, Sentinel-2, VPRM (Vegetation Photosynthesis and Respiration Model)

Acknowledgements

This research was financed by the UBACyT 2020–2025 program (N° 20020190100128BA) and by PIP-CONICET (N° 11220200100794CO) grants. Rodrigo Merino is supported by a scholarship granted by CONICET.

How to cite: Gassmann, M., Merino, R., Tonti, N., Covi, M., and Pérez, C.: Improved net ecosystem exchange (NEE) modelling under drought conditions in the Argentine Pampas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4100, https://doi.org/10.5194/egusphere-egu26-4100, 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.

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.

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.

Droughts across India exhibit variability arising from the complex interaction between atmospheric forcing and terrestrial hydrological processes. Although precipitation deficits are generally considered the primary trigger for drought, changes in terrestrial water storage dictate how drought evolves and recovers. It is therefore essential to understand how surplus and deficits in water balance governs not only the drought periods across different hydroclimatic zones of India but also the subsequent influence on vegetation health. In this study, we analyze how water balance components regulate vegetation by assessing the elasticity of vegetation to climatic and catchment storage variables. A dominant driver approach is used to evaluate whether vegetation response is mainly controlled by meteorological or terrestrial variability. Furthermore, we analyzed the influence of key drought attributes, including severity, duration, development and recovery speeds, on vegetation elasticity with respect to climate and catchment variables. The results show a shift from precipitation-dominated vegetation control during mild drought conditions to storage-driven regulation under extreme droughts. These findings highlight the role of subsurface water storage in buffering vegetation against severe drought stress across India. Overall, this analysis offers valuable insights into the processes controlling vegetation resilience and susceptibility, allowing for a more refined understanding of vegetation-catchment-climate interactions across diverse drought conditions.

How to cite: Bilal, S. B. and Gupta, V.: Role of Water Balance Components in Regulating Vegetation Response to Drought , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18248, https://doi.org/10.5194/egusphere-egu26-18248, 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.

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.

A new low-cost device based on Internet-of-Things (IoT) communication has been developed within the RemoTrees project to monitor climate-change effects in remote forest ecosystems. One of the key component of this device, referred to as the RemoTrees - beta, is a multispectral chipset composed of four sensors (three AS7265X and one AS7341), providing 26 spectral channels covering the range 410–940 nm. The chipset is equipped with a 1-inch diffuser designed to collect hemispherical solar radiation over incidence angles θ ∈ [−90°, +90°], with an angular response close to the cosine law. Here we present laboratory characterisation and calibration results obtained for 15 replicate RemoTrees - beta units. The spectral performance was highly consistent across devices, with central-wavelength variations below ~2 nm. Full width at half maximum (FWHM) values ranged from 19.17 to 47.93 nm, with standard deviations between 0.32 and 1.74 nm and a maximum relative expanded uncertainty of 0.90%. Because the devices will operate under highly variable illumination conditions (time of day, season, latitude, altitude, cloudiness, and canopy cover), optimisation of integration time (IT) and gain (G) is essential to avoid low digital-number (DN) values and insufficient use of the sensor dynamic range. As commonly applied in field spectrometry, automated IT/G optimisation and scan averaging are recommended to maximise signal-to-noise ratio (SNR) and minimise measurement uncertainty. When IT settings alone are insufficient to reach a satisfactory fraction of the dynamic range (≈65 000 DN; target ≥50%), summing of consecutive readings can be used to effectively increase the integration time while limiting saturation risks under rapidly changing sub-canopy light conditions. Radiometric sensitivity was evaluated by varying G and IT. Under optimised settings, SNR values up to ~5000 were achieved. For AS7265X sensors, gains G > 16 combined with IT optimisation increased SNR by up to ~4×, while for AS7341 gains G > 2 with IT optimisation yielded improvements up to ~5×. Detector nonlinearity contributes an expanded uncertainty of up to ±2.98% (k = 2) if uncorrected, which decreases to ≤±1.24% when nonlinearity correction is applied. The calibration coefficients derived from the tested devices showed moderate inter-device variability, with a maximum variation of approximately 10% for each spectral band. The RemoTrees - beta light sensor demonstrates stable spectral performance, high achievable SNR, and manageable inter-device variability, supporting its suitability for large-scale deployment in forest monitoring networks. Proper optimisation of integration time, gain, and signal averaging is essential to fully exploit the sensor dynamic range and minimise uncertainties under highly variable illumination conditions. Ongoing field deployment will further validate these strategies and refine operational protocols for long-term climate monitoring applications.

How to cite: Mihai, L., Toma, C., Mihalcea, R., Sakowska, K., Vescovo, L., Belelli Marchesini, L., Coppola, V., and Valentini, R.: Performance and optimisation strategy of a multispectral sensor as part of a newly developed low-cost IoT device for forest monitoring (RemoTrees - beta), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21910, https://doi.org/10.5194/egusphere-egu26-21910, 2026.

Bog peatlands in northwestern Germany release high amounts of greenhouse gas (GHG) emissions. Most of these bogs were drained, resulting in intensively used grasslands primarily for dairy production. While dairy production is economically highly important in the region drained bogs with intensive grassland use show high CO₂ emissions. To reduce GHG-emissions from intensively managed grasslands on bogs used for dairy production, the GreenMoor project investigates the effects of different water management approaches, such as such as subsurface irrigation and ditch blocking, as well as different usage practices including different fertilization intensities and pasture or cutting regimes. With a unique and expansive setup, we investigate the full GHG-balance of these different variants using manual chambers. We aim to present preliminary results from the first project phase including preliminary GHG-balances and an outlook on the potential success of different management approaches to reduce GHG-balances from drained intensively used bogs.

How to cite: Taube, R., Köhn, D., Gerken, H., Krause, A., and Jurasinski, G.: Impact of Water Management and Land Use Practices on Greenhouse Gas Emissions from an Intensively Farmed Bog Grassland in Northwest Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23185, https://doi.org/10.5194/egusphere-egu26-23185, 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.

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.

Plant responses to dry environments are shaped by diverse adaptive strategies linked to plant hydraulic traits, as reflected by the coexistence of deciduous and evergreen tree species in tropical seasonally dry forests. Empirical evidence suggests that leaf shedding is associated with declining leaf water potential, while leaf flushing depends on xylem rehydration. However, these physiological mechanisms are rarely incorporated into Dynamic Global Vegetation Models (DGVMs), which typically represent drought deciduous phenology using highly-simplified, threshold-based schemes with fixed rates of leaf phenological change. Here, we develop a stress–gradient based phenology scheme in which leaf shedding and leaf flushing are driven by the temporal gradients of leaf water potential and xylem water potential, respectively. This gradient–driven phenology mechanism was implemented in the LPJ-GUESS DGVM and validated with in-situ observations of phenological responses along hydraulic gradients. The new model has successfully reproduced phenological dynamics for the 7 selected locations and substantially improves model performance in simulating plant transpiration. We provide evidence that plant hydraulics are key controls for the phenological dynamics of tropical dry forests. The proposed stress-gradient phenological mechanism, linking phenology to plant hydraulic status, is an efficient approach to represent landscape phenology and improve simulations of water and carbon cycling over the tropical drylands. It may also help improve our understanding of forest response to drought stress, which remains largely unknown under warming climates.

How to cite: Zhu, Y., Pugh, T. A. M., and Wu, M.: Modelling Tropical Dry Forest Phenology from a Plant Hydraulic Perspective, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12529, https://doi.org/10.5194/egusphere-egu26-12529, 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.

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.

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.

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.

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.

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.

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.

How to cite: Pan, G. and Fu, X.: Orbital eccentricity pacing of hydroclimate variability in Qinghai-Tibet Plateau during the middle–late Eocene, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7110, https://doi.org/10.5194/egusphere-egu26-7110, 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.

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.

A realistic representation of sea surface temperature (SST) variability in climate models is essential for seasonal-to-interannual forecasting and for understanding large-scale climate oscillations. This study evaluates the impact of three initialization strategies in the NorCPM coupled climate model on the structure and temporal evolution of the leading modes of global SST variability over the 1980–2010 period. The analyzed strategies include a free-running simulation (FREE), ocean data assimilation using an ensemble Kalman filter (ODA), and atmospheric nudging of wind and temperature anomalies (NUDA_UVT). Model results are evaluated against the HadISST observational dataset.

Empirical Orthogonal Function (EOF) analysis is applied to monthly SST anomalies computed over the full globe and using all calendar months, without regional restriction or seasonal stratification. This framework enables a consistent comparison of the dominant large-scale SST variability modes across all datasets. The results indicate that ocean data assimilation (ODA) best reproduces the leading ENSO-related mode, achieving a spatial correlation of 0.98 and the lowest root mean square error in its principal component (RMSE = 1.70). For the second mode, associated with lower-frequency variability, atmospheric nudging (NUDA_UVT) shows improved spatial agreement (correlation = 0.90) compared to ODA. The free-running simulation captures the main spatial structures but displays systematically larger temporal errors.

These findings demonstrate that ocean data assimilation is the most effective strategy for representing ENSO-like variability in NorCPM, while atmospheric nudging provides added value for lower-frequency modes. As a perspective, this work will be extended to investigate the impact of initialization strategies on atmospheric fields, as well as to explore SST variability at specific seasonal and regional scales.

How to cite: Moutachaouiq, K., Bari, D., Omrani, N.-E., and Nafiri, S.: Impact of Different Initialization Strategies on the Representation of Dominant SST Variability Modes in the NorCPM Coupled Climate Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17596, https://doi.org/10.5194/egusphere-egu26-17596, 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

Most studies on estuarine compound flooding patterns and processes either utilize paired station data of streamflow and sea level (or simulated data), readily available at national, continental and global scales, or are confined to one estuary system and specific events, where detailed water level observations exist. These approaches do not provide a full understanding of the processes underlying the compound events, as reflected in observed spatial patterns and temporal variation.

We call for revised observational strategies for integrating estuary-scale process and the manifestation of compound water level extremes in the estuary with larger-scale patterns and trends. Proving methodological insights from a review of existing compound flooding literature and our multi-scale analysis in Europe, we establish recommendations for such a setup.

As a baseline, our analysis uses regional-scale patterns and long-term trends in compound flooding potential in three contrasting countries in Europe (Norway, Finland and Spain, differing in terms of tidal amplitude, seasonality and relative sea level trends), quantified with state-of-the-art methods. We then document the availability of direct estuarine water level data for validating the inferred flooding potential. Finally, three different field setups in northern Norway, central Finland and northern Spain are used to test how compounding streamflow and sea level conditions inferred from national stations are visible in different parts of the estuary. Unlike previous studies, we also examine the manifestation of low water conditions to maximize the usefulness of the acquired data for extreme-event studies. Moreover, we test how robust the state-of-the-art infrastructure is in extreme conditions, including meso-tidal variation, river ice, snowmelt-induced flooding and drying up of estuaries.

This multi-scale study allows us to present recommendations for robust observational strategies that allow inference of processes governing compound water level extremes at multiple scales, and explaining the observed patterns and trends. Such strategies have potential in improving our understanding of current and future compound hazards. Accommodating low-water conditions and hydrometeorological extreme conditions facilitates continental and global comparative studies.

How to cite: Nylén, T. and Tolvanen, H.: Revisiting the observational strategy: Toward more robust inference of compound water level extreme processes in estuaries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20425, https://doi.org/10.5194/egusphere-egu26-20425, 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.

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.

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.

The hydraulic jump is considered to have one of the largest energy losses in the field of Hydraulics. These losses are caused during transition from super-critical to sub-critical flow conditions in the case of open-surface flows. In this study, we focus on a laboratory-scale hydraulic jump combining both experimental measurements and model simulations using theoretical arguments. The main objective is to identify, quantify, and interpret the uncertainty in both cases through key parameters, such as the (sub/super) critical depths and channel geometry, for various flow conditions, with emphasis on energy dissipation, turbulence, mixing, regime transitions, and flow stability characteristics .

How to cite: Cuny, S., Dimitriadis, P., Koutsoyiannis, D., Sargentis, G.-F., and Iliopoulou, T.: Uncertainty Evaluation of Hydraulic Jumps in Open-Surface Flows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8394, https://doi.org/10.5194/egusphere-egu26-8394, 2026.

The frequency of disasters induced by heavy precipitation (HP) in the upper reaches of the Yellow River Basin (URYR) has increased notably. This study had further elucidated the structure and interactions of synoptic systems across different pressure levels and quantitatively characterized the anomalous driving factors. Four weather types had been identified: Xinjiang Trough (Type1, constituting 35% of HP), Mongolian Trough (Type2, 14%), Westward–Extension Western Pacific Subtropical High (WPSH) (Type3, 43%), and Cut–Off Cyclone (Type4, 8%). Influenced by the troughs, the moisture anomalies are transported by the southwesterly jet originating from Bay of Bengal low-pressure systems. In Type3, the WPSH and South Asian High demonstrate the greatest zonal expansion and central intensity (reaching 12610 gpm); this type distinguished by maximal moisture and energy, exhibits the most pronounced extreme properties. The most notable characteristic of Type4 is its stability and persistence presented the most favorable dynamic conditions, despite occurring with the lowest frequency. Due to the anomalous evolution of atmospheric circulation, the anomalies in potential vorticity, column-integrated precipitable water, and convective available potential energy increase; negative anomalies in vertical velocity and moisture flux divergence decline dramatically within 12 to 6 hours preceding HP, signaling anomalous moisture convergence coupled with ascending motion. Low-level moisture is impeded and diverted by the TP topography, generating northerly flow along its eastern flank and forming a distinct “moisture corridor”. Orographic uplift introduces pronounced vertical component to the moisture flux vectors and intensifies local circulations, thereby promoting the initiation and organization of mesoscale systems. The vertical moisture advection serves as dominant mechanism driving HP, while zonal or meridional moist enthalpy predominantly contributes to the physical processes driving the ascending motion under different patterns. These findings may offer a scientific basis for the prediction of HP events in the region. 

How to cite: Tan, C., Ma, Y., Chen, X., Li, W., and Zhang, Q.: Analysis of Heavy Precipitation and its Typical Weather Patterns over the Upper Reaches of the Yellow River, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8717, https://doi.org/10.5194/egusphere-egu26-8717, 2026.

Monitoring of inland water bodies is considered crucial for effective water resource management. In this study, a combination of satellite imagery and altimetry products was utilized to monitor changes in water level and surface extent during the different operational filling phases of the Grand Ethiopian Renaissance (GER) Dam. The primary objective was to utilize diverse remote sensing products to provide an accurate estimation of water volume changes over time. Sentinel-1 data were processed using an unsupervised edge Otsu algorithm to map reservoir extents. These output maps were validated against Planet and Sentinel-2 water masks, and a high level of agreement was observed, with overall accuracy values ranging from 0.97 to 0.99. Furthermore, various Surface Water and Ocean Topography (SWOT) satellite products were evaluated for the estimation of reservoir extents. It was found that the SWOT Lake Single Product performed poorly, with an Intersection over Union (IOU) value of approximately 0.33 being recorded. In contrast, moderate agreement with validation sets was demonstrated by the SWOT water mask raster and pixel cloud products, with overall accuracy values ranging from 0.78 to 0.89 being observed. Volume variation across different dam operational phases was estimated through the application of satellite-based observations and a DEM contouring method. Although a high correlation (R² value of 0.98) was exhibited by both methods, significant differences in absolute values were identified (RMSE value of 2736.35 km³). These discrepancies are attributed to a potential scaling error and the inherent water slope present within the GER Dam reservoir.

How to cite: Tazwar, M., Rietbroek, R., Maathuis, B. H. P., and Shakya, A.: Tracking The GER Dam Impoundment Stages Using SWOT and Other Radar Altimetry Products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8732, https://doi.org/10.5194/egusphere-egu26-8732, 2026.

Baseflow is a crucial component of streamflow, essentially driven by changes in groundwater storage, and is vital for sustaining flows during dry periods. Traditional techniques for baseflow quantification using graphical analysis or digital filters require long-term river discharge observations, which are often limited in their spatial extent. However, with the launch of the Surface Water and Ocean Topography (SWOT) mission, global estimates of river discharge are now available over a period of two years, offering a high-resolution dataset with at least one observation every 21 days. Despite its relatively coarse temporal resolution, prior studies have demonstrated SWOT’s ability to accurately estimate average baseflow even at one observation per cycle, based on synthetic SWOT discharge estimates. The high spatial resolution provided by ‘SWOT discharge’ can be utilized to estimate baseflow at a reach-scale and gain new insights into groundwater-surface water interactions in water-stressed river basins.

In this study, we will utilize SWOT’s discharge products over Indian river basins to characterize baseflow dynamics at reach-scale resolution and examine the effects of climate variability and land-use changes on baseflow. By accurately estimating the baseflow recession parameter (k), this study will be able to identify the gaining-to-losing transition in a basin. Furthermore, the research will explore SWOT’s ability to detect temporal shifts in the baseflow recession parameter (k) during the pre-monsoon period and evaluate the effects of anthropogenic extractions on the groundwater table. Finally, these estimates will be integrated into a mass-balance model, baseflow will be converted into upstream groundwater storage (GWS) changes and validated against independent GWS anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) satellites. This study will demonstrate the capability of SWOT to bridge the gap between reach-scale hydraulics and basin-scale storage, providing a vital tool for sustainable water resource management in water-stressed regions.

How to cite: Deshpande, R. S., Vidushi, V., and Syed, T. H.: Characterizing Baseflow in Indian River Basins Using SWOT Discharge Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10090, https://doi.org/10.5194/egusphere-egu26-10090, 2026.

In a context where energy efficiency is a major concern, studying linear and local head-losses in hydraulic networks is essential. These losses are mainly caused by internal fluid friction and network singularities (such as bends, section changes, valves, etc.), have a direct impact on water transport efficiency and management. In this study, we focus on a laboratory-scale hydraulic network combining both experimental measurements and model simulations using theoretical arguments and the EPANET software. The main objective is to identify, quantify, and interpret the uncertainty in both linear and typical local head-losses through key parameters, such as the friction factor and the local-loss coefficient, for various flow conditions.

How to cite: Bourbon, M., Sargentis, G.-F., Iliopoulou, T., Koutsoyiannis, D., and Dimitriadis, P.: Uncertainty Evaluation of Hydraulic Losses in Closed Pipes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10560, https://doi.org/10.5194/egusphere-egu26-10560, 2026.

Climate variability represents one of the most severe threats to lacustrine ecosystems worldwide, leading to water loss in nearly half of the world’s lakes and reservoirs. On the one hand, this variability is associated with drought conditions, manifested by a decline in precipitation. On the other hand, it is linked to rising temperatures, which enhance evaporation rates at lake surfaces.

In Senegal, a Sahelian country, the prolonged drought period of the 1970s led to the desiccation of several water bodies, including Lake Tanma. In this context, the present study contributes to a better understanding of the drying process of Lake Tanma under climate variability conditions, using remote sensing techniques. Lake Tanma is located in Thiès region, approximately 70 km from Dakar.

To achieve this objective, Landsat Earth observation products were used at the beginning and end of each decade between 1984 and 2024. The time series consists of multispectral images acquired in October, corresponding to the end of the rainy season in Senegal. This choice ensures the capture of the lake’s maximum water extent, thereby minimizing seasonal fluctuations. All data were acquired and processed using the Google Earth Engine platform. The Modified Normalized Difference Water Index (MNDWI) was computed for the entire time series to accurately delineate and characterize water-covered surfaces.

The results reveal a highly variable evolution of the inundated surface area of Lake Tanma, with a variation coefficient of 57.8%. The largest flooded area was observed in 1984, covering 969.33 ha, while the smallest extent was recorded in 2024, with only 76.18 ha. Analysis of intra-decadal variations shows a slight decrease (7%) in the flooded surface, between 1984 and 1989. In contrast, subsequent decades exhibit a marked and progressive regression of the lake’s water surface, reaching 21% during the 2000–2009 decade, 61% during 2010–2019, and up to 89% over the 2020–2024 period.

These decrease trends highlight the influence of hydro-climatic parameters, particularly precipitation and evaporation, which constitute the primary drivers of lake recharge and desiccation. Consequently, further investigation, of hydro-climatic factors, namely rainfall and temperature, is required, to better understand the drying process of Lake Tanma and to assess the impacts of hydro-climatic variability on its long-term dynamics.

How to cite: Ndeye, M. D. B., Diene, S. M., Ndao, S., Sarr, S., Guèye, A., and Tamba, S.: Contribution of Remote Sensing to the Analysis of the Drying Process of Lake Tanma under Strong Climate Variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10679, https://doi.org/10.5194/egusphere-egu26-10679, 2026.

Tunisian agriculture remains a crucial component of the country’s economic development and faces considerable constraints related to increasing water demand and reducing water resources’ availability. Improving the assessment of irrigation water use is a prerequisite for sustainable water management. The present study aims to evaluate the quality of water consumption estimates in the Public Irrigated Area of Lakhmess using open-source data. High-resolution (10 m) Sentinel 2 images, combined with ERA5-land meterological data, were used to assess monthly and seasonal actual evapotranspiration (ET) and water use through the implementation of the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine (GEE) environment. The calculated water uses were combined with the seasonal supplied water to the PIA Lakhmess, collected at plot level.

This study was conducted over eight agricultural campaigns from 2015-2016 to 2022-2023. The method is validated for three sectors Sidi Jaber, Gantra and Gabel, comparing the seasonal water use estimates to water meter observations. Correlation analysis between estimated water use from open-access data and  in-situ measurement yielded correlation coefficients of 0.76, 0.75 and 0.73, with corresponding RMSE values of 0.461, 0.425, and 0,391 mm/day, respectively. In addition, SEBAL-derived evapotranspiration estimates were evaluated through comparison with reference evapotranspiration computed using the FAO-56 Penman-Monteith, resulting in an R²  of 0,68 and an RMSE of 0.315 mm/day. Overall, the methods were deemed satisfactory, as they facilitated the monitoring of excessive water usage by identifying areas where water losses occurred.

Key words: Evapotranspiration, Irrigation, water use, Remote sensing, GEE, SEBAL.

How to cite: Belhaj Kilani, A., Alonso, A., Bousselmi, A., Khlifi, S., and Vanclooster, M.: Estimation of Crops Water Consumption by Remote Sensing: SEBAL Model Calculations Versus Ground Observation In The Irrigated Area of Lakhmess (Siliana, Northern Tunisia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12323, https://doi.org/10.5194/egusphere-egu26-12323, 2026.

Effectively characterizing uncertainty and error in flood prediction is essential for informed decision-making. This study combines advanced deep neural network architectures, i.e., Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS), and Long Short-Term Memory (LSTM), with multiple uncertainty quantification frameworks to evaluate flood forecasts across several watersheds in the southeastern United States. Bayesian inference, Monte Carlo–based methods, and quantile regression are applied to estimate predictive uncertainty. The comparative analysis examines how different uncertainty approaches perform across a range of flood magnitudes, highlighting their respective advantages and limitations at multiple scales. Results indicate that N-HiTS generally yields narrower and more reliable uncertainty bounds than LSTM. The findings further demonstrate that prior specification in MCMC sampling strongly influences uncertainty estimates and requires careful calibration. While Monte Carlo dropout, which is an approximate Bayesian technique, primarily captures uncertainty near flood peaks, MCMC offers a more complete characterization across the full hydrograph. In addition, this study investigates multi-site training to evaluate model adaptability under diverse hydrological regimes. Collectively, these results advance the integration of deep neural networks and uncertainty quantification to enhance flood modeling capabilities and risk management.

How to cite: Saberian, M., Samadi, V., Wagener, T., and Popescu, I.: Uncertainty-Aware Flood Prediction Using Deep Neural Networks Across Multiple Watersheds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15829, https://doi.org/10.5194/egusphere-egu26-15829, 2026.

Bofedales are high-altitude wetlands whose functioning is closely linked to surface water availability, playing a key role in hydrological regulation and ecosystem resilience in the Andes. Despite their importance, spatially explicit information on their surface water dynamics remains limited, particularly in data-scarce mountain regions. This study investigates the ecohydrological dynamics of bofedales in the Alto Pampas sub-basin (Huancavelica, Peru) over the 2015–2024 period using a multitemporal remote sensing approach combined with climatic information. Seasonal patterns of bofedal extent and surface water presence were mapped from Landsat 8 imagery using vegetation and moisture indices (NDVI and NDII), together with topographic variables. Bofedales were identified through a Random Forest classification framework and subsequently categorized as permanent or seasonal based on the temporal persistence of hydric signals. Changes in surface water extent and bofedal productivity were quantified, and temporal trends were assessed using the Mann–Kendall test. In addition, generalized additive models were applied to examine potentially nonlinear relationships between climatic drivers and key ecohydrological indicators. The results reveal contrasting surface water trajectories among bofedales, reflecting heterogeneous sensitivity to climate variability within the sub-basin. These findings demonstrate the value of satellite-based monitoring for assessing surface water dynamics in high-Andean wetlands and provide relevant insights for water resources management and climate change adaptation in data-poor mountainous regions.

How to cite: Sansoni-Koga, S., Laurente-Torres, J., Ccaico-Atoccsa, M., Flores-Quispe, S., Meza-Fajardo, G., and Cárdenas-Gaudry, M.: Ecohydrological and multitemporal analysis of Andean wetlands under climate variabilitiy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16435, https://doi.org/10.5194/egusphere-egu26-16435, 2026.

Abstract. Predicting flood risk is a complex phenomenon. Several factors influence flood behavior generation and intensity such as intricate interactions between hydrological dynamics, meteorological variability, the overarching influence of climate change and land-use changes. This study explores flood risk within the watershed of Tassaout River located in the central region of Morocco. Three advanced machine learning algorithms were chosen to evaluate flood risk. These algorithms are Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest (RF) and Support Vector Machine (SVM). The models are trained based on 11 different factors derived from remote sensing data. From ALOS digital elevation model, 8 factors are developed: Elevation, Slope, Aspect, Plan Curvature, Profile Curvature, Stream Power Index (SPI), Topographical Wetness Index (TWI), and Surface Roughness. In addition, from Landsat 9 imagery, three flood susceptibility factors are extracted: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST). The predictive performance of each model was assessed using standard classification metrics: accuracy, recall, and F1-score. Results indicate that the RF model performed the best with an accuracy of 100%, SVM algorithm achieved good performance, attaining 68% in accuracy and more than 80% in f1-score. However, the ANN model underperformed compared to the other algorithms, with an accuracy of only 59% in accuracy and 70% in f1-score highlighting its limitations in capturing the decision boundaries within the current data configuration. Furthermore, the Shapley Additive exPlanations model (SHAP) was used to enhance the transparency and interpretability of the modelling results.

How to cite: Legsabi, H., Tiai, S., Boussabou, S. M., Najaoui, N., El Mansouri, B., and Erraioui, L.: Modeling Flood Risk in Kalaa Sraghna Region in Morocco Using Explainable Artificial Intelligence Techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19676, https://doi.org/10.5194/egusphere-egu26-19676, 2026.

On September 10, 2023, the city of Derna in northeastern Libya experienced one of the deadliest flood disasters in Mediterranean and African history. Storm Daniel delivered unprecedented rainfall, with Al-Bayda recording 414 mm and Derna receiving over 100 mm within 24 hours, approximately 270 times the region's typical September average of 1.5 mm. This study employs Synthetic Aperture Radar (SAR) from Sentinel and high-resolution Planet imagery to provide a comprehensive analysis of the flood's spatial extent, infrastructure damage, and the interplay between natural and anthropogenic factors that amplified this disaster.

Our flood extent mapping reveals catastrophic impacts on urban infrastructure. The river channel expanded dramatically from 50 meters to approximately 500 meters in width, while the maximum inundated area extended 1.2 km² from the collapsed dams to the Mediterranean Sea over a distance of 2.5 km. The analysis identifies critical damage to infrastructure including the collapse of two upstream dams, destruction of five road flyovers, and significant damage to ports, bridges, and residential areas.

The disaster's severity was substantially amplified by anthropogenic factors. Historical urban development had rerouted the river through artificial canals, with roads and settlements subsequently constructed on the natural riverbed. The two dams, built in the 1970s and unmaintained since 2002, catastrophically failed, releasing an estimated 30 million cubic meters of water. Mann-Kendall trend analysis of 122-year climatic records reveals a statistically significant warming trend (p ≈ 0, Sen's slope = 0.00798) alongside decreasing overall precipitation (p = 0.027, Sen's slope = -0.0389), suggesting a paradoxical pattern where less frequent but more intense rainfall events are becoming more likely.

The socio-economic impacts were devastating, with nearly 4,000 confirmed fatalities in Derna alone, over 10,000 missing, and economic losses estimated at $80 million. Our findings underscore the critical vulnerability created when urban expansion encroaches upon natural floodplains without adequate infrastructure resilience.

This study demonstrates the power of multi-source satellite remote sensing for rapid disaster assessment and highlights the urgent need for integrated flood risk management that considers both climatic extremes and anthropogenic modifications to natural water systems. The lessons from Derna have profound implications for urban planning, dam safety protocols, and climate adaptation strategies in vulnerable Mediterranean regions facing increasingly extreme weather events.

How to cite: Agarwal, V. and Kumar, M.: Man-Made or Natural: Deciphering the Complex Factors Behind the 2023 Derna FloodDisaster, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22246, https://doi.org/10.5194/egusphere-egu26-22246, 2026.

How to cite: Barella-Ortiz, A., Quintana-Seguí, P., Cid-Giménez, J., Clavera-Gispert, R., Altés-Gaspar, V., Villar, J. M., Munier, S., Laluet, P., Olivera-Guerra, L. E., and Merlin, O.: Comparison of Irrigation Scenarios in the Ebro Basin Using the SASER Modelling Chain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22992, https://doi.org/10.5194/egusphere-egu26-22992, 2026.

• Rainfall-induced shallow landslides pose a persistent hazard in the Eastern Himalaya, particularly along the strategically important Balipara–Charduar–Tawang (BCT) corridor in western Arunachal Pradesh, India. This study develops a region-specific rainfall threshold framework by integrating long-term rainfall trend analysis with empirical landslide-triggering thresholds to enhance early warning capabilities in this data-scarce, high-relief terrain. Daily gridded rainfall data from the India Meteorological Department (2000–2020) and an inventory of 236 landslide events recorded between 2008 and 2015 were analyzed. Trend analysis reveals a statistically significant decline in annual rainfall (–81.05 mm yr⁻¹), accompanied by pronounced inter-annual variability and persistent monsoonal dominance. Empirical analysis indicates that short-term antecedent rainfall plays a critical role in slope failure initiation, with 3-day and 5-day cumulative rainfall showing the strongest correlation with landslide occurrence (R² = 0.508 and 0.480, respectively). Corresponding 80th percentile thresholds of ≥89.24 mm (3-day) and ≥118.80 mm (5-day) are proposed as practical triggering criteria. In addition, an intensity–duration (I–D) threshold derived from 95 rainfall-induced landslides follows a negative power-law relationship (I = 17.26·D⁻⁰·¹⁰), capturing the influence of short-duration, high-intensity rainfall events. The combined use of antecedent rainfall and I–D thresholds effectively represents both progressive soil saturation and rapid-onset rainfall triggers. This integrated threshold framework provides a robust and scalable basis for landslide early warning system development along the BCT corridor and offers broader applicability to similar monsoon-dominated Himalayan regions.

How to cite: kar, S., Chaturvedi, P., and Negi, H. S.: Integrating Intensity–Duration and Antecedent Rainfall Thresholds for Shallow Landslide Prediction in the Eastern Himalaya, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1658, https://doi.org/10.5194/egusphere-egu26-1658, 2026.

Extreme hydro-meteorological events associated with the Coastal El Niño phenomenon represent a critical threat to the socioeconomic stability of the northern coast of Peru. In particular, the Piura River basin, characterized by its complex topography and short concentration times, requires precise monitoring and modeling to address these episodes. Currently, the National Meteorology and Hydrology Service of Peru (SENAMHI) employs a semi-distributed system (ARNO/VIC coupled with RAPID) for the operational assessment of flood risk. However, the increasing intensity and frequency of recent events highlights the need for tools that explicitly represent physical processes at higher resolution. This research proposes the implementation of the fully distributed WRF-Hydro model, focusing the methodology on the reconstruction and analysis of the main extreme flood events within the period covered by the PISCOp_h product, a gridded hourly precipitation observational dataset developed by SENAMHI for 2015–2020. The methodological strategy is based on generating a hybrid meteorological forcing to feed the hydrological model. For this purpose, an atmospheric simulation is carried out with WRF, forced by initial and boundary conditions from the GFS, obtaining high-resolution distributed atmospheric fields. Given the uncertainty of the modeled precipitation, the rainfall field generated by WRF is replaced by the hourly gridded observations from PISCOp_h, ensuring controlled and realistic forcing. With this configuration, model calibration and validation are performed. Calibration prioritizes the highest-magnitude events, highlighting the 2017 Coastal El Niño episode for the adjustment of physical parameters, while validation considers a set of floods recorded between 2015 and 2020, evaluating the robustness of the system. It is expected to demonstrate that this combination of atmospheric dynamics and observational accuracy constitutes a physically consistent and operationally viable tool for predicting intense floods, strengthening flood risk management in Peru.

How to cite: Tufino, J. C., Huerta, A., Lavado-Casimiro, W., De la Cruz, G., Saavedra, D., and Ibañez, A.: Implementation and Evaluation of the WRF-Hydro Model for Hydrometeorological Forecasting in the Piura River Basin, Peru, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3205, https://doi.org/10.5194/egusphere-egu26-3205, 2026.

Accurate runoff estimation is fundamental to improving streamflow forecasting, particularly in large river basins with sparse or uneven rain-gauge coverage. This study investigates the identification of representative rain gauges from a densely but randomly distributed network to support reliable runoff simulation in data-limited regions. The Middle Tapi Basin (MTB), comprising 26 operational rain gauges and extensive ungauged areas, is used as a case study. Four approaches—Hall’s method, K-means clustering, hierarchical clustering (HC), and self-organizing maps (SOM)—are applied to identify key rain gauges that effectively capture the spatial variability of basin-scale rainfall. Hall’s method selected 15 representative stations, whereas the clustering-based approaches identified nine stations each. The performance of the resulting rain-gauge networks is evaluated by simulating basin runoff using a lumped hydrological model. Results indicate that the rain-gauge network derived from Hall’s method consistently produces superior runoff simulations compared to the clustering-based networks, demonstrating improved representation of rainfall inputs at the basin scale. Based on these findings, the use of 15 key rain gauges identified through Hall’s method is recommended for runoff prediction in the Middle Tapi Basin. The proposed framework is transferable and can be applied to other large basins with heterogeneous rainfall patterns and limited monitoring infrastructure, offering a practical approach for optimizing rain-gauge networks to enhance hydrological modelling and flood forecasting.

How to cite: Yadav, S. and Panchal, A.: Designing efficient rain-gauge networks for improved flood forecasting in a large river basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3693, https://doi.org/10.5194/egusphere-egu26-3693, 2026.

Rapid dam construction, rising water demand, climate change, and increasing pollution are exposing critical weaknesses in the governance of freshwater systems worldwide, particularly in shared river basins. Although environmental flows (e-flows) are widely recognised as essential for sustaining riverine ecosystems and long-term water security, their integration in transboundary water governance has remained largely symbolic, weakly enforced, and poorly adapted to climatic uncertainty. Even durable agreements in several regions prioritise volumetric allocation and procedural cooperation, offering limited mechanisms to safeguard e-flow regimes, as illustrated by treaties such as the Indus Water Treaty and the Ganga Water Sharing Treaty. This study argues that the persistent failure to operationalise transboundary e-flows in national and transboundary river basin governance frameworks reflects a deeper and systematic governance implementation gap that has not been adequately addressed in existing literature. Much of the literature examines legal provisions, economic instruments, and monitoring systems as separate domains rather than as interdependent components of operational governance. As a result, many transboundary river agreements pair legal allocation rules with flow monitoring but fail to link these to enforceable e-flow obligations or adaptive responses. To investigate this gap, the study undertook a structured comparative analysis of ten major international treaties and river basin agreements across Asia, Africa, Europe, and North America, covering both bilateral and multilateral transboundary river systems. Existing treaties were assessed to identify why most fail to deliver implementable e-flow solutions, while arrangements where elements of effective implementation exist were examined to extract transferable best practices for future transboundary water agreements. Based on the findings, the study proposes a three-tier governance framework to operationalise transboundary e-flows under climate uncertainty. The framework integrates climate-adaptive legal obligations, economic and financial mechanisms, and monitoring, reporting, and verification systems supported by remote sensing and GIS. By reframing e-flows as an implementable component of cooperative water security, this study makes both a conceptual and practical contribution to transboundary water governance, with implications for ecological resilience, conflict reduction, and long-term regional stability.

Keywords : Water demand, Climate change, River basin treaties, Ecological resilience

How to cite: Malhotra, K. B. and Nema, A. K.: Implementing Environmental Flows in Transboundary Rivers under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4740, https://doi.org/10.5194/egusphere-egu26-4740, 2026.

Effective management of eutrophication in inland lakes requires spatially continuous information on key water-quality variables at management-relevant scales. However, metre-scale mapping of total phosphorus (reported as “Phosphorus, Total”, PPUT; µg/L) remains difficult to achieve using conventional in-situ sampling, and nearshore gradients and tributary plumes are often poorly resolved by medium-resolution satellite sensors. In this study, we exploit multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid, physics-informed AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance is combined with short-term meteorological descriptors (3–7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure) and in-situ Secchi depth (SSD) to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes. Inclusion of SSD yields a substantial performance gain, with mean R² increasing from ~0.67 (SSD-free) to ~0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on phosphorus retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, we implement a teacher–student knowledge-distillation scheme in which an SSD-aware teacher transfers its representation to a student using only satellite and meteorological inputs. The optimal student, based on a compact subset of 40 predictors, achieves R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L on unseen monitoring stations, and is applied to 2020–2025 PlanetScope scenes to generate metre-scale PPUT maps. A 26 July 2024 case demonstrates that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but spatially coherent hotspots >20 µg/L coincide with tributary mouths and narrow channels, highlighting priority areas for management intervention. Although demonstrated here for phosphorus, the PlanetScope–KD framework is model-agnostic with respect to the target variable and can be retrained for other water-quality parameters with optical or hydro-meteorological controls, such as chlorophyll-a, dissolved oxygen, and surface water temperature. This opens a pathway toward unified, high-resolution, multi-parameter lake water-quality prediction to support adaptive monitoring and lake-basin management.

How to cite: Deng, Y., Pan, D., Yang, S., and Gharabaghi, B.: Knowledge Distillation of PlanetScope Imagery for Metre-Scale Lake Water-Quality Mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8119, https://doi.org/10.5194/egusphere-egu26-8119, 2026.

How to cite: Cheung, P. K.: Is blue-green infrastructure effective in reducing urban flood depth and area?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8693, https://doi.org/10.5194/egusphere-egu26-8693, 2026.

Impact of Organoclay Content on Hydraulic Performance of Filter Strips to Treat Urban Runoff

Yaren Ozturk ¹, Derya Ayral Cınar ² 

¹ Marmara University, Istanbul, Turkiye

² Gebze Technical University, Kocaeli, Turkiye  

Abstract. 

Due to urbanization and climate change, it has become common for urban runoff to carry pollutants to surface water bodies, wastewater treatment plants, infrastructure systems and groundwater. Pollutants transported include heavy metals, solids, nutrients, pathogens and various organic substances such as pesticides and polycyclic aromatic hydrocarbons (PAH). It is proposed to manage this pollutant load at source before it reaches receiving environments.  Nature-based solutions such as filter ditches, infiltration ponds or rain gardens are considered more efficient to manage urban runoff. Among these methods, filter ditches have the highest potential to treat pollutants. It is thought that the use of organoclays, synthesized by the integration of surfactants into the clay mineral structure, as filter material may increase a common contaminant in urban runoff -PAH- removal compared to conventional clay minerals. In addition to treatment efficiency, another important parameter in designing filter ditches is the hydraulic permeability of the filter material. It is desirable that the infiltration rate of the surface flow is slow enough to allow time for pollutant removal and fast enough to prevent ponding on the filter. This study investigated how organoclays, which are proposed to enhance PAH removal from urban runoff, affect the hydraulic permeability of the filter material. Organoclay synthesized by Ca-montmorillonite and HDTMA is used at different percentages in the filter material mixture and hydraulic permeability was determined. Hydraulic conductivity of sand was 4.5x10^-4 cm/s and it dropped to 2.4x10^-5 cm/s and 2.3x10^-5 cm/s when 10% and 20% clay was used, respectively. On the contrary, organoclay at 10% and 20% did not decrease the hydraulic conductivity significantly (to 1.5x10^-4 cm/s and 1.4x10^-4 cm/s, respectively). As hydraulic conductivity is suggested to be 0.3-1.4 x 10^-4 cm/s for surface runoff treatment systems, it appeared that using 20% organoclay is promising to treat emerging pollutants such as PAHs without comprimising the hydraulic performance of the filter system.

Keywords: Nature based solutions, urban runoff, climate change, filter strips, organoclay

How to cite: Ozturk, Y. and Ayral Cınar, D.: Impact of Organoclay Content on Hydraulic Performance of Filter Strips to Treat Urban Runoff, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8965, https://doi.org/10.5194/egusphere-egu26-8965, 2026.

To address climate change and population growth, Central Asia must urgently adopt a holistic water resource management strategy, moving beyond traditional sectoral approaches to embrace a water-energy-food-ecosystems (WEFE) nexus approach.  The WEAP model, a key research tool, integrates hydrological data and socioeconomic factors to create scenarios considering glacier melt, irrigation expansion, and energy generation. WEAP, unlike hydrological models, highlights unmet demand, demonstrating the sectoral impacts of water scarcity for decision-makers. The Nexus approach uses the WEAP model to optimize the Vakhsh hydropower cascade (Nurek and Rogun plants), balancing energy security, environmental flows, and predictable agricultural water supply. The WEAP model assesses innovative irrigation technologies in the Zarafshon basin to enhance food security and cross-border cooperation between Tajikistan and Uzbekistan. The scenario analysis shows that modernizing irrigation systems reduces the burden on the ecosystem and ensures stable harvests even in dry years. Integrating climate forecasts into WEAP allows for water availability scenarios, enabling adaptation measures like optimized cropping and expanded runoff management. WEAP modeling in the Vakhsh and Zarafshon basins highlights the importance of cross-sectoral considerations for water resource management in Tajikistan, providing a basis for sustainable water system decisions.

How to cite: Niyazov, J.: A Nexus-Based Approach to Water Resources Assessment: Practical Application of the WEAP Model in Tajikistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9585, https://doi.org/10.5194/egusphere-egu26-9585, 2026.

The primary goal of hydrological modeling is to generate reliable forecasts of future changes in water resources. In the arid conditions of Central Asia, where irrigated agriculture demands significant water resources during summer, early forecasting is crucial for planning water allocation between upstream and downstream regions.

The WEAP model offers a flexible and user-friendly framework for addressing various water resource management challenges. It supports decision-makers and experts in constructing and selecting optimal solutions for water management. Accurate hydrological forecasts of water availability during the growing season are essential for effective water resource planning. National hydrometeorological services in Central Asia are adopting and adapting modern, effective methods for hydrological forecasting. The primary goal of developing a methodology for forecasting river water content in the Kyrgyz Republic, using the WEAP model, is to create a calculation algorithm, simulate a water management model, and implement this methodology into the practices of the Kyrgyz Republic's National Hydrometeorological Services. This approach will be applied to forecast water availability in the Naryn River during the growing season, monitor changes in the Toktogul Reservoir's water volumes, support hydroelectric power production, and facilitate agricultural irrigation. Advanced forecasts of low water availability during the growing season are vital for implementing preventive measures to ensure efficient water use by water and energy management organizations.

The WEAP model allows for the use of various scenarios, such as climatic ones, with a focus on the national level, while introducing various innovative technologies for irrigation and energy conservation in the upcoming years. This is significant for long-term planning in water management activities and the energy strategy of both the country and the region.

How to cite: Kalashnikova, O.: Predictive WEAP modeling for NEXUS management in the Naryn River basin (Kyrgyzstan), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9627, https://doi.org/10.5194/egusphere-egu26-9627, 2026.

Inter-state agricultural trade plays a critical role in redistributing water resources across India, particularly for water-intensive crops such as rice. This study examines the structure of inter-state rice virtual water trade in India using a directed, weighted complex network approach. Physical inter-state rice trade data covering all Indian states were obtained from the Directorate General of Commercial Intelligence and Statistics (DGCIS) and transformed into virtual water flows using crop-specific virtual water content coefficient for rice (m³/ ton), assumed to be uniform across states. This transformation enables an assessment of trade relationships in terms of embodied water transfers rather than physical commodity volumes. States are represented as nodes and directed edges denote rice virtual water flows from exporting to importing states, weighted by total virtual water volumes (m³). Network properties were analysed using strength-based measures to quantify import and export intensities, betweenness centrality to identify states functioning as key intermediaries in trade pathways, and PageRank to assess systemic importance within the national virtual water trade system. These metrics jointly allow differentiation between dominant exporting states, import-dependent states, and structurally central states influencing the overall redistribution of water through trade. The analysis reveals a highly centralized rice virtual water trade network, characterised by a small group of states accounting for a disproportionate share of total virtual water exports. States such as Punjab, Haryana, Andhra Pradesh, Chhattisgarh, Uttar Pradesh, Odisha, and Madhya Pradesh emerge as major exporters, while several other states rely predominantly on inter-state imports to meet rice demand. The concentration of virtual water exports among a limited number of producing regions indicates strong structural dependencies within the national trade network. Several major exporting states like Punjab are also subject to increasing pressure on water resources, the observed trade patterns raise concerns regarding the sustainability of current production-trade configurations. By integrating crop-specific virtual water accounting with complex network analysis, this study provides a quantitative framework for identifying key contributors, dependencies, and structural vulnerabilities in India’s inter-state agricultural water redistribution system. The methodology is transferable to other crops, years, and regional contexts and offers a basis for informing discussions on sustainable agricultural trade and water resource management.

How to cite: Badoni, A. and Reddy, M. J.: Mapping Inter-State Rice Virtual Water Trade in India Using Complex Network Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11912, https://doi.org/10.5194/egusphere-egu26-11912, 2026.

Water scarcity across the Mediterranean is increasingly forcing economies that are deeply integrated into global markets to balance export performance with long-term water sustainability. Research on “virtual water” and trade-related water footprints has grown rapidly, yet it remains fragmented: studies rely on diverse frameworks (physical accounting, MRIO, life-cycle assessment, and hybrids), use non-uniform scarcity metrics, and often treat climate projections and adaptation only implicitly. This review asks: which methods are currently used to estimate the water footprint of trade under climate constraints, what limits their comparability, and what methodological protocol is needed for a robust, policy-relevant application to Morocco? By framing trade-related water footprints as part of coupled human–water systems, the review highlights how economic structures, trade choices, and climate-driven water scarcity interact and generate feedbacks relevant for water governance and policy design.

We follow a PRISMA-type workflow based on systematic searches in Scopus and Web of Science, with explicit inclusion/exclusion criteria and standardized data extraction. Studies are coded along five dimensions: (i) data type (monetary vs. physical); (ii) modelling approach (IO/MRIO, LCA, hybrid); (iii) treatment of scarcity (stress factors, availability indicators, scarcity-adjusted footprints); (iv) integration of climate change (scenarios, downscaling, hydrological modelling); and (v) potential to inform policy (efficiency improvements, reallocation options, abstraction caps, and economic or trade-related instruments). Institutional sources are used in a complementary way to document indicator frameworks and datasets, without replacing the peer-reviewed evidence base.

The review delivers (1) an operational typology of methods used to quantify trade-related water footprints under climate stress; (2) a diagnosis of key comparability barriers (spatial resolution, upstream embodied water through inputs, and the limited use of dynamic approaches); and (3) a practical empirical agenda linking hydrological projections, water-extended input–output frameworks, and decision-relevant scarcity metrics. Outputs will be shared through a reusable coding grid and an analytical framework diagram to support country studies and comparative work across the Mediterranean.

How to cite: Tasra, F. and Mafamane, D.: How Much Water Is Embedded in Trade? A Systematic Review and Research Roadmap for Morocco under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14286, https://doi.org/10.5194/egusphere-egu26-14286, 2026.

Urban water pollution remains a major challenge for sanitation management in Brazil and other tropical regions. In areas served by separate sewer systems, illicit domestic sewage connections to stormwater drainage networks represent a significant source of contamination of urban runoff and receiving water bodies. Conventional inspection techniques for identifying such contributions are often operationally complex, spatially limited, and therefore rarely applied. Distributed temperature sensing techniques have been successfully used in temperate regions to detect sewage inputs based on thermal contrasts; however, their applicability under tropical conditions remains poorly explored.

This study investigates the thermal signature of domestic sewage in a tropical urban environment and evaluates the detectability of illicit sewage discharges in stormwater systems using a simplified thermal mixing model. Sewage temperature was monitored using thermocouples connected to a data logger with 1-minute temporal resolution in a sewer interceptor located at the São Carlos School of Engineering, University of São Paulo, Brazil, in an area characterized by student housing and food service facilities. Two monitoring campaigns were conducted. Mean sewage temperatures of 27.45 ± 0.45 °C (November 2024–April 2025) and 24.21 ± 0.54 °C (September–November 2025) were observed. A moderate Pearson correlation between sewage temperature and local air temperature (r = 0.58, p < 0.05, n = 140) indicates that atmospheric conditions partially influence sewage thermal variability.

Based on the monitored sewage temperatures (T₂) and stormwater temperature data (T₁) from the literature, a preliminary theoretical model was developed using an instantaneous energy balance approach. The model relates the detectable temperature variation (ΔT) to the sewage fraction (f), defined as the ratio between sewage discharge (Q₂) and stormwater flow (Q₁). Results indicate an exponential relationship between f and ΔT for different thermal contrasts (T₂ − T₁). The minimum detectable sewage discharge was found to be highly sensitive to ΔT, associated with the thermal resolution of the sensing system, while showing direct proportionality to stormwater flow and inverse proportionality to the thermal contrast between sewage and runoff. Future work will focus on model validation under field conditions and its extension to non-stationary flow regimes.

How to cite: de lima neto, E., Bertotto, L. E., and Wendland, E. C.: Applicability of distributed thermal sensing for identifying illicit sewage connections in urban drainage networks under tropical climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15359, https://doi.org/10.5194/egusphere-egu26-15359, 2026.

Deep learning models, particularly LSTMs, have transformed large-sample hydrology by achieving high streamflow predictive performance, yet they remain largely black-box approaches with limited physical interpretability and no explicit representation of multiphysical hydrological processes. Differentiable, learnable process-based models (or δ-models) overcome these limitations by embedding neural networks within differentiable physics frameworks. While existing benchmarks like HBV-δ have proven this concept across 671 US basins, they rely on conceptual foundations (e.g., empirical beta-functions) that approximate, rather than resolve, underlying soil physics. This study introduces MILC-δ (Modular Differentiable Physic-Informed Learning), designed to bridge this gap. The MILC model utilizes continuous soil water retention curves and physically derived drainage laws, which can aid in more accurate hydrological flux simulation. Thus, we developed a MILC-δ - a hydrologic model embedded with neural networks and trained in a differentiable programming framework. Consequently, MILC-δ is anticipated to match or exceed HBV-δ by leveraging neural networks to map static catchment attributes directly to physically measurable properties (e.g., pore size distribution, hydraulic conductivity) rather than abstract calibration parameters. Initial testing of the developed model shows that the model performs at par in some basins and better than HBV-δ in other basins. This approach gives LSTM-level accuracy and generalizability as well as the clear physical story stakeholders actually need to explain the decline in baseflow, threats to the groundwater recharge, etc.

How to cite: Sharma, V., Barbhuiya, S., and Gupta, V.: Differentiable, Learnable MILC: Balancing Predictive Skill and Physical Interpretability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16119, https://doi.org/10.5194/egusphere-egu26-16119, 2026.

Lakes and associated hydrological processes are sensitive indicators of environmental change and climate variability. Variations in lake water level and storage reflect the combined effects of atmospheric forcing (precipitation, evaporation, and temperature regime) and anthropogenic interventions, including irrigation, drainage, and hydraulic infrastructure development. Continuous monitoring of lake level dynamics is therefore essential for water resources management, evaluation of regional climate impacts, and assessment of environmental risks in arid and semi-arid regions.

Central Asia has experienced pronounced hydrological transformations over recent decades as a result of climate warming, altered precipitation patterns, and intensified human water use. These changes are manifested in contrasting lake responses, ranging from the dramatic desiccation of the Aral Sea to the expansion of endorheic water bodies receiving anthropogenic inflows. Lake Sarikamish, one of the largest lowland lakes in the region, is located along the Uzbekistan–Turkmenistan border near the escarpment of the Ustyurt Plateau and represents a key example of such coupled natural–human system dynamics.

This study investigates water level variability of Lake Sarikamish over the period 2001–2024 using satellite altimetry observations from the Global Reservoirs and Lakes Monitor (G-REALM) database. The dataset, provided at a 10-day temporal resolution in NetCDF format, was processed to construct a continuous long-term time series. Short data gaps were filled using linear interpolation, a method previously shown to yield robust performance for altimetric lake level records. Descriptive statistics and trend analyses were applied to quantify intra-annual variability, interannual fluctuations, and long-term tendencies.

The minimum lake level during the observation period was recorded in February 2002 (4.23 m), while the maximum level occurred in April 2018 (8.84 m). The time series exhibits substantial interannual variability, with a standard deviation of 0.91 m. Four distinct phases of lake level evolution were identified: (i) a rapid increase during 2001–2007 at a rate of +0.56 m yr⁻¹, (ii) a short-term decline in 2008–2009 (−0.60 m yr⁻¹), (iii) a prolonged period of moderate increase during 2010–2020 (+0.15 m yr⁻¹), and (iv) a renewed decrease during 2021–2024 (−0.36 m yr⁻¹). Despite the recent downward trend, the overall period is characterized by a net positive trend of +0.16 m yr⁻¹.

The observed post-2020 decline suggests an increasing influence of regional climate change, particularly rising air temperatures and reduced effective precipitation. Continued water level lowering may have negative consequences for local ecosystems, biodiversity, and environmental stability. The results highlight the value of satellite altimetry for long-term lake monitoring and emphasize the need for integrated assessments of climatic and anthropogenic drivers of lake hydrological change in Central Asia.

How to cite: Umirzakov, G., Kalabaev, S., Gafurov, A., and Turgunov, D.: Remote Sensing–Based Monitoring of Lake Sarikamish Water Level Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17089, https://doi.org/10.5194/egusphere-egu26-17089, 2026.

The present study investigates the hydrological performance of two Nature Based Solutions (NBS) realised within the urban requalification project of the former military area “Caserma Gavoglio” (now public park), in one of the most heavily urbanized districts of the city of Genoa (Italy). The rapid expansion of urbanization has led to an increase in impervious surfaces and a consequent increase in runoff generation, flood volume and flood peak. Since the required expansion of the stormwater drainage capacity is neither economically nor environmentally sustainable, innovative stormwater management strategies are required. In this context, NBSs represent effective solutions to mitigate runoff generation and peak flows and restore natural infiltration processes.

A resin gravel permeable pavement (PP) was used for the paving of about 40% of the park surfaces while a bioswale was realised alongside the sport field to manage stormwater excess.  

The PP was preliminarily tested in the laboratory by monitoring the outflow from a standardized test bed under various rainfall input and slope conditions. The results of the tests were interpreted mathematically using the analogy of the step response function of first- and second-order dynamic systems. This allows to transfer the laboratory results for comparison with field conditions, even if these were not precisely reproduced in the laboratory tests.

Both NBSs were monitored in the field with the objective to measure the outflow rate, representing the inflow to the urban drainage system, and to compare it with the corresponding rainfall input.

Two hydrometric measurement stations and one rain gauge station were installed. Since the stormwater drainage system was already in place, water stage probes were housed inside existing manholes equipped with suitable “V-shaped” weirs. Due to non-standard operational conditions, the measurement stations were preliminarily tested in the laboratory to verify their accuracy prior to field installation.

From the monitored rainfall events, direct comparisons between the measured precipitation and the outflow hydrographs were performed. These analyses enabled the quantification of the retention and detention effects due to the NBSs and their improvement relative to typical impervious paving solutions. The following performance indicators were derived for each significant precipitation event that exceeded the retention capacity of the NBS: (i) the outflow coefficient, defined as the ratio between total outflow and rainfall volumes, (ii) the peak reduction coefficient, i.e. the ratio between peak discharge and peak rainfall intensity and (iii) the system response delay, i.e. the time lag between the centre of mass of the flow hydrograph and that of the rainfall.

Acknowledgements

This work was conducted in the framework of the Urban Nature LABs (UNALAB) project, under the “HORIZON 2020” programme, Smart and sustainable Cities-SCC-02-2016-2017, as a collaboration between the University of Genova (DICCA) and the Municipality of Genova (project partner).

How to cite: Ferro, M., Chinchella, E., Cauteruccio, A., and Lanza, L. G.:  Laboratory testing and in-situ monitoring of the hydrological response of a resin gravel permeable pavement and a bioswale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18311, https://doi.org/10.5194/egusphere-egu26-18311, 2026.

Climate change, rising water demand, and ecosystem stress are intensifying the reliance on groundwater while limiting the capacity of many basins to effectively monitor and manage subsurface water resources. In data-scarce and conflict-affected regions where monitoring networks are sparse, decision-makers increasingly require reliable, high-resolution information to support drought preparedness, climate adaptation, and sustainable groundwater governance. The present study proposes an evidence-based machine-learning framework for the purpose of enhancing the monitoring of groundwater storage anomaly (GWSA) through the process of downscaling GRACE and GRACE-FO observations from ~3° to 0.1°. The reconstruction of monthly GRACE/GRACE-FO gaps was performed using a Seasonal-Trend Decomposition based on Loess (STL), and a Random Forest model was trained with hydroclimatic and land-surface predictors, including soil moisture, snow water equivalent, evapotranspiration, precipitation, land-surface temperature, and the normalized difference vegetation index (NDVI). The performance of the model was evaluated by comparing the model's results with the existing in-situ groundwater-level observations in the Kabul River Basin. The results indicate that satellite-inferred groundwater losses in Afghanistan are persistent, with a rate of −0.71 cm yr^-1, ranging from basin-scale depletion of −0.77 cm yr^-1 in the Helmand River Basin to −0.40 cm yr^-1 in the Northern River Basin. Recent conditions indicate intensified depletion during 2018–2022, with year-sum GWSA declines reaching ~145 cm in the Harirod–Murghab River Basin, while the Northern River Basin shows comparatively lower losses (~80 cm). The 0.1° downscaled product improves agreement with observations (root mean square error (RMSE) reductions up to 77.8%) and reveals spatially heterogeneous hotspots that are not detectable at coarse GRACE resolution. Generally, the proposed framework translates coarse satellite gravimetry into actionable, basin-relevant information for climate-resilient groundwater management, while underscoring the necessity for uncertainty-aware, multi-source monitoring under increasing hydroclimatic extremes. The approach enables the early detection of emerging depletion hotspots, thereby supporting proactive planning for future water security. This includes targeted demand management, drought response, and adaptation investments in groundwater-dependent regions.

How to cite: Azizi, A. H., Akhtar, F., Borgemeister, C., and Tischbein, B.: Assessing Groundwater Storage Changes in Data-Scarce Basins of Afghanistan: A Machine-Learning Based Downscaling of GRACE(-FO) Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18956, https://doi.org/10.5194/egusphere-egu26-18956, 2026.

Geoid height values obtained in 1984, 1996 and 2008 were used to study the thermal evolution (considering density variations caused by temperature alterations) of a sedimentary basin located in the central eastern part of the Atlantic Ocean region. Historical and registered earthquakes have been detected in this basin.

The results obtained show a heterogeneous basin with increases/decreases in density (temperature) values occurring in the time interval between measurements and points with identical values in different years, separating regions of warming from regions of cooling. It is also observed that the maximum values obtained increase from 1984 to 1996 and 2008, occurring at different latitudes.

The minimum values obtained in 1984 are clearly higher than values obtained in 1996 and 2008 at same latitudes. The minimum values obtained in 2008 are higher than those of 1996 in latitudes between 35.8 and 36.2 N and also for latitudes equal to or greater than 36.6 N. At intermediate latitudes, the values obtained in 2008 are lower than those obtained in 1996.

Climate data presented on IPMA website show high values of precipitation data occurring in 1996 in months with lowest temperature values in Mainland Portugal, suggesting that the low values of temperature found may be related with infiltration of cold water and to an increase of water pressure in depth.

In the present work, special attention is given to the western boundary of the basin, where it is possible to observe high temperature values associated with lateral cooling of seamounts linked to cooling in the sedimentary basin, and a consequent increase in temperature in the inner part of the seamount. The location of 3 earthquakes recorded in May, July, and August 2005 showed  that they occurred near points without changes, separating a warming area (on the West side) from a cooling area (on the East side). The earthquakes are located in the warming area.

The analyzed data show that the region under study experienced warming in the past and is now in a heterogeneous cooling phase. Areas in a warming phase can be identified with the 2008 geoid height values, after been cooled in 1996.

Climate data was used to identify temporal relationships between geoid height values and precipitation and temperature values in mainland Portugal.

Earthquakes with magnitude greater than 4.0 were identified in the region in 2005. They are located close to the crossing points of geoid height values between 1996 and 2008, which separate areas under heating from areas under cooling, giving rise to different horizontal thermal and pressure gradients in the western and eastern side of the point with no changes in density (temperature) and possible contribution to the occurrence of earthquakes.

How to cite: Duque, M. R.:   Using geoid height changes to study the thermal evolution of a sedimentary basin and possible relation with earthquake occurrence in the region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1467, https://doi.org/10.5194/egusphere-egu26-1467, 2026.

A remote sensing index is often used to identify meteorological and agricultural droughts. Google Earth Engine analyzes CHIRPS data from 2015 to 2024 and Landsat-8/Sentinel-2 data from 2020 to 2024. The Vegetation Condition Index (VCI), Temperature Condition Index (TCI), composite Vegetation Health Index (VHI), and Standardized Precipitation Index (SPI) were calculated for four seasons using NDVI, EVI, LST, and CHIRPS precipitation data to explain specific spatiotemporal trends. Meteorological and agricultural droughts include precipitation deficits and vegetation stress. From the study, pre-monsoon analysis reveals significant intra-seasonal correlations between VCI and VHI (0.84) and TCI and VHI (0.75), indicating that moisture reserves and thermal stress influence vegetation health during arid periods. The VCI-VHI correlation (0.91) predominates during the monsoon season, indicating plant growth amidst substantial precipitation. As the season nears peak aridity, the correlations between post-monsoon and winter TCI-VHI increase (0.81 and 0.83), signifying thermal stress. A weak correlation (≤ 0.50) between SPI and vegetation indices across the seasons indicates that current precipitation does not succeed in reliably predicting vegetation stress, since vegetation depends on accumulated soil moisture rather than instantaneous rainfall. Vegetation indices exhibit substantial temporal persistence: Pre-monsoon VCI conditions are strong predictors of winter VCI (0.98), VHI forecasts winter VHI (0.92), and TCI predicts winter TCI (0.87), thereby enabling nine-month drought forecasting. The findings demonstrate that vegetation indices serve as drought indicators for seasonal water resource planning and agricultural vulnerability assessment in monsoon-affected nations.

How to cite: Thounaojam, L. and Oinam, B.: Spatio-temporal dynamics of meteorological and agricultural droughts: A multi-seasonal analysis of Vegetation Health and Climate Indices Using Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2205, https://doi.org/10.5194/egusphere-egu26-2205, 2026.

The objectives of the experiment were the following:

• Evaluation of continuous ground motion from satellite data (European Ground Motion Service for the period 2019-2023 and IRIDE for 2024);
• Analysis of different types of landslides (active landslides, dormant landslides, landslide-prone areas, subsidence);
• Identification of elements for the Emergency Limit Condition (CLE) analysis near areas affected by specific ground motions derived from satellite data in the period 2019-2024
• Identification of buildings in the municipality of Perugia near areas affected by specific ground motions derived from satellite data in the period 2019-2024
• Submission of the results to all regional offices that authorize, evaluate, design, or schedule interventions on the territory and to the regional civil protection agency.

QGIS version 3.42 software was used for the experiment.

The following databases were imported into QGIS:

• European Ground Motion Service satellite data.
• IRIDE satellite data – Cross Monitoring of Ground Motion and "Hot Spots" of Cover Change.
• PAI geomorphological landslide hazard maps.
• Local Seismic Hazard Map of the Umbria Region.
• Umbria Region Emergency Limit Condition Analysis (CLE) maps.
• Geological Map of the Umbria Region.
• Building database of the Umbria Region's land registry system for the Municipality of Perugia.
• Administrative Boundaries of the Umbria Region and base maps such as the Regional Technical Map and Google Satellite.

Spatial analyses were performed using the GIS on the collected data to homogenize and select specific information useful for subsequent processing.

Multiple analyses werw performed for 2 specific case studies.

All objectives were achieved: assessment of continuous ground motion from satellite data (two different databases: IRIDE for 2024 and EGMS 2019-2023); subsequent analysis using different types of landslides (active landslides, dormant landslides, landslide-prone areas, subsidence); subsequent assessment using the CLE (Emergency Limit Condition) elements; subsequent assessment using buildings in the Municipality of Perugia.

How to cite: Motti, A. and Natali, N.: Experimentation with the use of EGMS and IRIDE satellite data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3065, https://doi.org/10.5194/egusphere-egu26-3065, 2026.

Earthquake clusters can be broadly classified into two types: swarm-like sequences and mainshock–aftershock sequences. The spatial organization of the two types provides important insights into underlying tectonic processes and fluid migration in earthquake source regions. In this study, we apply the nearest-neighbor distance approach on the Southern California focal-mechanism earthquake catalog (the CNN_SoCal catalog) and introduce two new statistical indicators-skewness and kurtosis to distinguish between these two classes of earthquake clusters. We find that the square root of kurtosis and skewness provide effective and interpretable indicators for clusters classification. In the kurtosis–skewness diagram, swarm-like sequences and mainshock–aftershock sequences tend to occupy distinct regions, enabling a practical distinction between the two sequence types without relying on subjective inspection of individual clusters. Overall, the proposed approach offers an efficient way to differentiate swarm-like and mainshock–aftershock seismicity in large catalogs. The method is computationally light, easy to implement, and suitable for rapid screening of earthquake sequence types in high-resolution regional datasets.

How to cite: Fan, Y. and Hu, F.: A New Statistical Method to distinguish Different Earthquake Cluster Types, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4745, https://doi.org/10.5194/egusphere-egu26-4745, 2026.

The Himalayan river Basins frequently experience devastating floods. These river basins require accurate predictions and timely warnings to support effective flood risk management. While accurate prediction is crucial for saving lives, disaster managers often face a difficult trade-off between computational cost and warning lead time. High-fidelity physics-based models are precise but are computationally expensive for rapid decision-making, whereas low-fidelity geo-spatial models often lack accuracy in data-scarce regions. Our proposal is a framework to improve the flood inundation prediction in the Himalayan basin by combining the reliability of hydrodynamic modeling with the speed of machine learning.

In this study, we developed a 2D HEC-RAS model using a Rain-on-Grid approach to simulate the historical floods. We utilize the developed hydrodynamic model to generate a dataset of flood inundations that captures the basin's flow dynamics. These datasets will serve as the foundation for training advanced machine learning algorithms, including a Random Forest Regressor (RF) and a Convolutional Neural Network (CNN), to identify and predict flood patterns. Our model will integrate critical landscape features, including elevation, slope, land-use characteristics, the Normalized Difference Vegetation Index (NDVI), and satellite-derived rainfall data, to approximate the complex physical processes embedded in the hydrodynamic model. This allows the machine learning approach to achieve comparable predictive accuracy while reducing computational time. Through comprehensive validation against established benchmarks and real-world flood events, our research aims to deliver a scalable, computationally efficient, and highly accurate flood prediction tool. This framework has the potential to transform disaster preparedness and response capabilities in the Himalayan region by enabling timely, data-driven policy planning and proactive risk mitigation strategies.

How to cite: Duwal, S., Pradhan, P. M., Liu, D., and Bhattarai, Y.: Coupling Hydrodynamic Modeling with Machine Learning for Flood Risk Assessment in the Himalayan River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7810, https://doi.org/10.5194/egusphere-egu26-7810, 2026.

Remote sensing enables spatially continuous and timely monitoring of hydro-climatological extremes by capturing key land–atmosphere variables across large regions, including for data-scarce areas. The rising frequency of heat extremes across India in recent decades underscores the need for effective monitoring, especially in data-scarce regions. This study evaluates the potential of monitoring heat extremes over the Indian sub-continent using satellite based observations and data driven approaches. For this, MODIS land surface temperature (LST) along with NDVI, land use/land cover and elevation information is used with traditional machine learning models namely Random Forest (RF) and XGBoost. Subsequently, the performance of the two ML models in estimating maximum temperatures across the Indian subcontinent was evaluated and validated using in situ temperature observations from the Indian Meteorological Department. Heat extremes were identified using both absolute temperature percentile thresholds and Standardized Temperature Index based heat stress categories. The performance of ML models was evaluated using station‑wise categorical verification metrics such as hit rate, false alarm ratio, and critical success index. Results show that the ML models exhibit higher accuracy in predicting mean temperatures compared to extremes, and XGBoost outperforms the RF model with lower RMSE and higher R². The results further reveals that ML model prediction skill exhibits considerable geographic variability across the sub-continent, with reduced performance over mountainous areas. This study demonstrates that integrating satellite-based data with machine learning provides an effective approach for monitoring heat extremes across the Indian subcontinent, particularly in data-scarce environments.

How to cite: Remesh Ancy, A. and Mitra, S.: Monitoring Heat Extremes over India Using Earth Observations and Data Driven Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16372, https://doi.org/10.5194/egusphere-egu26-16372, 2026.

Land subsidence poses growing risks to urban infrastructure, water resources, and long-term resilience, requiring assessment frameworks that link present-day observations with planning-relevant forecasts. This study develops an integrated approach for land subsidence susceptibility mapping and trend forecasting over multi-year horizons. The analysis uses SBAS-InSAR deformation time series derived from Sentinel-1 observations from 2017 to 2025 to characterize subsidence patterns across East Baton Rouge Parish, Louisiana. Subsidence susceptibility is modeled using an ensemble machine-learning framework that combines Extra Trees and Random Forest regressors and incorporates geological, topographic, hydrological, land use, infrastructure, and climatic conditioning factors. The susceptibility results highlight the dominant influence of land use, elevation, proximity to faults and rivers, and terrain-hydrology interactions on subsidence patterns. To extend assessment beyond observation periods, a physics-informed long short-term memory (LSTM) ensemble is introduced for forecasting. The model integrates data-driven learning with physically motivated constraints to ensure stable and realistic deformation trajectories. The forecasts preserve observed spatial patterns while exhibiting physically consistent temporal evolution and quantified uncertainty. The results demonstrate that combining InSAR observations with physics-informed deep learning enables robust, planning-scale subsidence assessment and forecasting. The proposed framework is transferable to other urban settings where long-term subsidence poses increasing societal risk.

How to cite: Kangah, D. and Abdalla, A.: Near-Decadal Land Subsidence Susceptibility and Trends Using Physics-Informed LSTM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16451, https://doi.org/10.5194/egusphere-egu26-16451, 2026.

Earthquake catalogue declustering is a critical preprocessing step in time-dependent seismicity analyses (Gardner and Knopoff, 1974; Reasenberg, 1985), yet its systematic influence on conditional earthquake probability estimates remains insufficiently quantified, particularly in tectonically complex continental collision zones such as the Himalayas (Bungum et al., 2017). Renewal-based recurrence models typically assume that declustered catalogues isolate tectonically driven mainshock recurrence by removing dependent events. However, recent advances in declustering theory demonstrate that methodological choices, ranging from fixed spatio-temporal windows to adaptive and stochastic approaches, can substantially modify inter-event time statistics and inferred recurrence memory (Zaliapin et al., 2008; Zaliapin & Ben-Zion, 2020; Teng & Baker, 2019). Despite these developments, the implications of declustering-induced variability for time-dependent conditional probabilities remain underexplored in active orogenic belts.

In this study, we explicitly quantify how alternative declustering strategies influence time-dependent recurrence behavior and conditional rupture probabilities across selected Himalayan seismic source zones. Inter-event time series were constructed for moderate-to-large earthquakes (M ≥ 4.0) using both raw (non-declustered) and declustered catalogues derived from regional earthquake compilations. Declustering was performed using commonly applied fixed-window and adaptive approaches to capture epistemic variability associated with catalogue preprocessing. The resulting inter-event times were analyzed within renewal process models, including Brownian Passage Time (BPT), Lognormal, Weibull, and Gamma distributions, to estimate conditional probabilities as functions of elapsed time since the most recent major event.

Results show that declustered catalogues consistently yield smoother initial probability gradients and delayed probability peaks relative to raw catalogues, reflecting reduced short-term temporal clustering in inter-event time distributions. These shifts correspond to systematic changes in inferred renewal memory parameters, with declustering suppressing short-term contagion effects while largely preserving long-term mean recurrence intervals. In the Himalayas, collision-driven aftershock swarms and spatially heterogeneous fault interactions amplify these effects, introducing substantial epistemic uncertainty in early-time conditional probabilities, which can locally exceed factors of two to three depending on the declustering strategy employed. In contrast, long-term probability remains comparatively robust across declustering scenarios, consistent with steady-state tectonic strain accumulation.

These findings identify catalogue declustering as a dominant and often underappreciated source of uncertainty in time-dependent seismic probability modelling, reinforcing recent calls for ensemble-based and transparent pre-processing strategies in probabilistic seismic hazard workflows. This study advances a methodological framework for interpreting renewal-based conditional probabilities in clustered tectonic regimes. The Himalayas emerge as a natural laboratory where combined raw and declustered analyses can yield more resilient probabilistic interpretations.

How to cite: Pratap, B. and Sharma, M. L.:  Sensitivity of Time-Dependent Earthquake Conditional Probabilities to Catalogue Declustering in the Himalayas , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17842, https://doi.org/10.5194/egusphere-egu26-17842, 2026.

Landslides have become one of the most destructive geological hazards in the Himalayan region, exhibiting a significant increase in both occurrence and intensity in recent decades. This increasing trend poses serious threats to human life, infrastructure, and essential public assets, underscoring the need for comprehensive risk evaluation in these highly vulnerable mountainous terrains. The present study offers an extensive assessment of landslide hazard, vulnerability, and associated risk in the Darma Valley of the Kumaun Himalaya, India. Landslide susceptibility was modelled using a Multilayer Perceptron (MLP) neural network, and the model’s predictive performance was validated through ROC–AUC analysis. Vulnerability was quantified by integrating land-use/land-cover categories with their respective economic valuations. Furthermore, rainfall and seismic intensity maps were combined with the susceptibility outputs to derive a detailed landslide hazard map. The results indicate that roads are the most vulnerable elements, followed by settlements and dam infrastructures, largely due to their substantial reconstruction costs and higher exposure levels. The final risk map, produced by integrating hazard and vulnerability layers, reveals that approximately 9% of the study area falls within high to very high risk zones, 22% within moderate risk, 26% within low risk, and 43% within very low risk zones. These findings offer essential guidance for promoting sustainable development and supporting land-use planning that accounts for environmental risks. They also contribute to more informed and effective decision-making aimed at strengthening the resilience of the fragile and sensitive Himalayan landscape.

How to cite: Shawez, M., Kumar, S., Gupta, V., Kumar, P., and Rawat, G.: Landslide Hazard, Vulnerability, and Risk Analysis (HVRA) Using Machine Learning and AI: A Case Study of the Darma Valley, Kumaun Himalaya, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18022, https://doi.org/10.5194/egusphere-egu26-18022, 2026.

Slow-moving, deep-seated landslides represent a significantly underestimated geologic hazard, incurring huge economic loss and persistent long-term risk to communities annually. Further, they have the potential to evolve into catastrophic events, which necessitates continuous monitoring to better understand their dynamics, minimize potential losses, and implement appropriate mitigation measures. The present study aims at understanding the dynamics of the slow-moving slopes housing villages such as Bhatwari, Raithal, and Barsu in the Bhagirathi Valley, Uttarakhand Himalaya, by means of PS-InSAR techniques. A total of 129 ascending-pass and 114 descending-pass scenes of Sentinel-1, from January-2021 up to March-2025, have been utilized to estimate slope velocities along the radar line-of-sight (LOS) for each pass, using open-source tools such as ISCE and StaMPS.  Further, these LOS velocities were decomposed to obtain vertical (up-down) and horizontal (east-west) velocities. The results reveal that Raithal (elevation ~2150 m), on middle of the slope, is subsiding at ~3 mm/year with an eastward movement of ~5 mm/year. Bhatwari (1650 m), on the lower slope, shows eastward creep at ~4 mm/year and upliftment at ~2 mm/year, suggesting rotational landslide activity. Barsu (2262 m), situated at a slope ~3 km upstream, exhibits eastward movement at ~6 mm/year and subsidence at ~3 mm/year. Field investigations corroborate these findings, revealing features such as scarps, cracks, tilted structures, disrupted roads, and longitudinal and transverse ponds. The persistent creeping suggests the potential for sudden slope failure during heavy rainfall or earthquakes, which may dam the Bhagirathi River, and the impoundment may further trigger cascading downstream hazards. Therefore, there is a need for a comprehensive investigation integrating the PS results with the slope stability analysis that assesses the role of geology, rainfall, and earthquakes. This integration shall assist in estimating the risk posed by the failure and further help in mitigation planning.

How to cite: Gupta, A. K., Luirei, K., Gupta, V., and Shawez, M.: PS-InSAR based Slope Deformation Monitoring in the Bhagirathi Valley, Uttarakhand Himalaya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18221, https://doi.org/10.5194/egusphere-egu26-18221, 2026.

Chișinău, the capital of the Republic of Moldova, faces significant geohazard challenges due to its unique geological setting on loess-covered plateaus dissected by river valleys and ravines. Urban expansion and infrastructure development have intensified landslide susceptibility in this region, threatening residential areas, transportation networks, and critical infrastructure. This study presents a comprehensive analysis of urban landslides in Chișinău using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique applied to Sentinel-1 satellite data spanning the last five years (2019-2025).

The PS-InSAR methodology provides millimeter-level precision in detecting and monitoring ground deformation over time, making it particularly suitable for identifying slow-moving landslides and ground subsidence in urban environments. We processed ascending and descending Sentinel-1 SAR imagery to generate time-series deformation maps and identify persistent scatterers across the Chișinău metropolitan area. The analysis revealed multiple zones of significant ground displacement, with deformation rates ranging from -15 to +25 mm/year, concentrated primarily in areas with steep terrain, proximity to water courses, and urban development on historically unstable slopes.

The susceptibility map derived from our analysis indicates high-risk zones in the northern and western sectors of Chișinău, particularly around suburb localities Vatra, Ghidighici, and Durlești, where loesslike deposits on valley slopes are subjected to both natural erosion processes and anthropogenic pressures. The southeastern areas near locality Bubuieci also show elevated landslide susceptibility, correlating with urban expansion into previously undeveloped terrain. Integration of PS-InSAR results with geological maps, digital elevation models, and land-use data enabled the development of a comprehensive landslide susceptibility assessment framework.

Key findings reveal that ground deformation patterns in Chișinău exhibit strong seasonal variations, with accelerated movement during spring months corresponding to snowmelt and precipitation events. Urban infrastructure, including roads, buildings, and utilities, located within identified high-risk zones, shows structural damage consistent with slow-moving landslide activity. The study identifies critical infrastructure corridors, including major transportation routes (E583, E581) traversing the study area, that require enhanced monitoring and mitigation measures.

Acknowledgements: This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS – UEFISCDI, project number 40PCBROMD within PNCDI IV.

How to cite: Sandric, I., Nicoara, I., Spian, C., Tambur, A., Ilinca, V., Jeleapov, V., Irimia, R., Daia-Creinicean, T., and Alexandru, N.: Urban Landslide Monitoring Using PS-InSAR Sentinel-1 Data in Chișinău, Republic of Moldova (2019-2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19054, https://doi.org/10.5194/egusphere-egu26-19054, 2026.

Rapid glacier retreat in the Nepal Himalaya has accelerated the formation and expansion of glacial lakes, increasing the likelihood of glacial lake outburst floods (GLOFs) with potentially severe downstream consequences. Existing GLOF studies in Nepal are largely site-specific and lack national-scale consistency, limiting their utility for systematic hazard planning. Here, we present a comprehensive, multi-scenario GLOF hazard assessment for Nepal based on three decades of satellite observations (1990–2023) and large-scale hydrodynamic modelling. Using multi-temporal remote sensing, we mapped 1,232 glacial lakes, including 265 newly formed lakes, and estimated lake volumes and peak discharges using established empirical relationships. Downstream flood propagation was simulated using the LISFLOOD-FP hydrodynamic model, enabling consistent, high-resolution inundation mapping across the country. To examine plausible future conditions under continued glacier retreat, we implemented scenario-based lake-volume increases of 10–50%, representing optimistic, intermediate, and pessimistic states. Results indicate a ~26.9% increase in total glacial lake area since 1990, with the most pronounced expansion in the Koshi and Karnali provinces. Modelled inundation extents and flood depths, particularly exceeding 3.5 m, increase substantially under higher-volume scenarios. Koshi and Karnali consistently emerge as the most exposed regions, with heightened impacts on settlements, hydropower infrastructure, and transport networks. The resulting national-scale GLOF hazard atlas provides a coherent framework for visualising present and future flood hazards and offers a practical basis for climate adaptation planning and disaster risk reduction in high-mountain regions.

How to cite: Saha, S., Singh, H., and Mohanty, M. P.: Nationwide Multi-Scenario GLOF Hazard Mapping in Nepal Using Remote Sensing and Hydrodynamic Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1600, https://doi.org/10.5194/egusphere-egu26-1600, 2026.

Disasters triggered by natural hazards increasingly unfold as compound and cascading events, placing extraordinary demands on the institutions responsible for coordination and decision-making. Emergency Operations Centers (EOCs) sit at the nexus of these multi-hazard crises, linking infrastructures, agencies, and communities, yet their organizational design is rarely examined through a systemic resilience lens. This presentation contributes empirical insights into how EOCs enable—or constrain—resilience under escalating uncertainty.

Drawing on qualitative analysis of U.S. Federal and state EOC doctrine and training materials, this study conceptualizes EOCs as socio-technical systems operating along a continuum between mechanistic (hierarchical and rule-based) and organic (networked and adaptive) organizational structures. Findings reveal that while formal guidance emphasizes mechanistic control to ensure accountability and resource tracking, effective EOC performance during complex and cascading disasters depends heavily on organic processes such as lateral information sharing, informal coordination, and emergent problem-solving. These adaptive mechanisms—critical for responding to interacting hazards and rapidly shifting conditions—remain largely undocumented and are instead learned through experience and social networks.

The analysis further identifies predisaster networking among EOC participants as key enabling conditions for systemic resilience. Pre-established relationships enhance information flow, reduce coordination friction, and support adaptive decision-making when conventional procedures are strained by compound hazards. From a resilience perspective, EOCs function not merely as coordination hubs but as institutional platforms where resistance, recovery, adaptation, and potential transformation are negotiated in real time.

This presentation advances the disaster- and climate-resilience discourse by reframing EOC design as a resilience-building intervention. It offers actionable strategies for strengthening systemic resilience, including integrating organic coordination mechanisms into doctrine, redesigning training and exercises to emphasize adaptive capacity, and evaluating EOC performance beyond compliance metrics. By explicitly addressing institutional dynamics within multi-hazard contexts, this work bridges theory and practice in climate-resilient development.

How to cite: Chang, R.: Designing Institutional Resilience for Compound Disasters: EOC Structures, Networks, and Adaptive Operations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6236, https://doi.org/10.5194/egusphere-egu26-6236, 2026.

Urban fire risk in heritage cities threatens lives, livelihoods, and irreplaceable historical monuments. Nepal's heritage cities, rich in cultural landmarks, face acute vulnerability due to dense settlement patterns driven by uncontrolled urbanization. Fragmented data availability prevents stakeholders from implementing effective fire risk mitigation measures at the community level, which intensifies the existing vulnerabilities. In this study, we address this challenge by developing comprehensive data through collaborative public-private partnerships involving multiple stakeholder experts. We propose scalable interventions designed to reduce fire risk while strengthening community resilience in ways that align with heritage preservation objectives. This integrated approach ensures that safety measures protect both people and the cultural assets that define these historic urban centers.

Our study area is Bhaktapur Municipality, a UNESCO World Heritage site rich with traditional wooden architecture. Our approach combines municipal planning data, private building inventories, community knowledge, and emergency response databases for fire hazards. We integrate Analytical Hierarchy Process (AHP) with GIS technology across three domains: hazard factors, vulnerability indicators, and response capacity. We establish public-private partnerships to gain access to previously prepared fire incident datasets while we protect commercial interests. We establish multi-stakeholder data protocols and develop community-centered collection mechanisms that respect local knowledge systems. We leverage real field knowledge from community-level surveys to assess the present scenario and propose upgrades to current practices. We perform dynamic vulnerability assessments that support both emergency planning and heritage conservation. Through weighted overlay analysis, we determine optimized fire hydrant placement for narrow streets that existing firefighting services cannot access. This spatial analysis ensures that infrastructure improvements respect the historic urban fabric while they enhance emergency response capabilities.

We expect collaborative data partnerships to enhance decision-making through three key contributions: (i) bridge critical information gaps that have long hindered effective fire risk management, (ii) support sustainable development, cultural preservation, and community resilience as interconnected goals and (iii) offer scalable lessons for complex urban management challenges in resource-constrained environments. This integrated framework demonstrates how heritage cities can balance safety imperatives with conservation priorities through evidence-based interventions.

How to cite: Ghimire, S., Dangol, S., Khatri, S., Duwal, S., and Bhattarai, Y.: A Framework for Fire Risk Assessment in Heritage Cities through Multi-Stakeholder Data Integration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7909, https://doi.org/10.5194/egusphere-egu26-7909, 2026.

Early warning systems are fundamental to cyclone risk reduction, yet evacuation outcomes depend on whether warnings trigger timely household action and whether households can physically reach shelters. This study quantifies evacuation thresholds, time-to-action, and mobility constraints using georeferenced survey data from 1,126 households across two cyclone-prone coastal unions in Bangladesh. Using household-level GPS data, we measured distance to the nearest cyclone shelter for each household and analysed evacuation behaviour across spatial distance thresholds.

Early warning message (EWM) coverage was high, with 93.5% of households reporting receipt of warnings, yet only 80.8% evacuated, indicating a persistent warning–action gap. Logistic regression shows that households receiving EWMs had more than twice the odds of evacuation (OR = 2.13, 95% CI: 1.27–3.55, p < 0.01), although evacuation likelihood varied significantly by distance to shelters, and road conditions. Distance to shelters and road conditions were also significantly associated with evacuation outcomes (p < 0.001).

Time-to-action analysis indicates delayed mobilisation after warnings: only 39.8% of households began preparation within 1 hour, and 9.3% delayed action beyond 6 hours. Distance and road conditions compounded these delays: evacuation times rose sharply beyond 1 km and were significantly longer where roads were reported poor or waterlogged during the cyclone, suggesting that delayed mobilisation increases exposure to peak travel constraints.

Spatial constraints also explain non-evacuation among warned households. Among households (18%) that received warnings but did not evacuate, the dominant barriers were distance from shelters (50.0%), shelter overcrowding and lack of privacy or maternal facilities (48.9%), and lack of transportation (45.7%), alongside caregiving and health-related constraints. Only 2.4% cited lack of knowledge about shelter locations, indicating that non-evacuation reflects spatial and mobility exclusion rather than information failure.

These findings demonstrate that cyclone evacuation is a threshold-based and constrained mobility process, where warnings increase evacuation odds but do not guarantee timely action for households facing greater distance, degraded road conditions, and care burdens. Strengthening anticipatory action therefore requires addressing spatial inequalities in last-mile accessibility, reducing response delays, and improving shelter suitability for households with health and caregiving needs in high-risk coastal settings.

How to cite: Islam, M. R., Rahman, M. H., Ahmed, F. A., and Salehin, Dr. M.: Warning is not enough: time delays and spatial inequalities in household-scale cyclone evacuation in coastal Bangladesh, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10195, https://doi.org/10.5194/egusphere-egu26-10195, 2026.

Resilience and sustainability are widely recognized as desirable properties of infrastructure systems.  Although related, they can become conflicting objectives, especially when resources available to enhance them are limited, making trade-offs between short-term resilience and long-term sustainability inevitable. Despite growing needs of increasing both resilience and sustainability, systematic analyses of such trade-offs remain limited.  In this work, we address this gap by developing a stylized, minimalistic stochastic model of system functionality under a sequence of disruptions.  The results reveal the nature of the trade-offs between short-term resilience and long-term sustainability and show that, depending on the effectiveness of investments in each, sub-optimal allocations may arise and should be avoided.  The analysis establishes clear relationships demonstrating how physical system features and investment strategies interplay to influence the nature of such resilience-sustainability trade-offs.

How to cite: Muneepeerakul, R. and Lin, N.: Trade-off between short-term resilience and long-term sustainability in infrastructure systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13004, https://doi.org/10.5194/egusphere-egu26-13004, 2026.

Background: The Danube Delta, a UNESCO Biosphere Reserve and one of Europe's most important wetland ecosystems, faces increasing environmental pressures from climate change, including altered hydrological regimes, flooding patterns, and ecosystem degradation. Effective climate adaptation and nature-based solutions in such regions require not only hazard modeling but also robust tools for assessing how local communities perceive their environment and the governance structures meant to protect it. Understanding these perceptions helps designing risk communication strategies and fostering behavioral preparedness.

Methods: This study presents the development and psychometric validation of two scales measuring (1) perceptions of natural resources and (2) perceptions of local development and quality of life among Danube Delta inhabitants. A cross-sectional survey was conducted with 503 residents (76.3% female; M age = 24.8 years). Exploratory and confirmatory factor analyses were employed to establish the factorial structure and validity of both instruments.

Results: Descriptive findings revealed that residents perceive estate-level government engagement in ecosystem conservation as notably low (M = 46/100), significantly lower than local government engagement—a finding with direct implications for implementing top-down nature-based adaptation strategies. The Natural Resources Perception Scale yielded a 6-item, two-factor structure with excellent fit indices (CFI = .97, TLI = .96, RMSEA = .08): Factor 1 captures environmental quality (air, water, soil), while Factor 2 captures biodiversity (fish, birds, animals). The Local Development and Quality of Life Scale retained 12 items across two factors (CFI = .95, TLI = .94, RMSEA = .07): Factor 1 addresses tourism and infrastructure development, while Factor 2 encompasses governance engagement, ecosystem conservation mechanisms, and inhabitants' quality of life. Both scales demonstrated good internal consistency (α = .83 and α = .92, respectively).

Conclusion: These instruments offer researchers and practitioners standardized tools for assessing community perceptions in climate-vulnerable regions. Such assessments can inform the design of locally-relevant risk communication and identify gaps in perceived governance effectiveness. Future applications may include longitudinal tracking of perception changes following climate events or conservation interventions.

How to cite: Avram, E., Iacob, C. I., Ionescu, D., and Armas, I.: Development and validation of scales measuring natural resources and local development perceptions in the Danube Delta, a climate-vulnerable ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14542, https://doi.org/10.5194/egusphere-egu26-14542, 2026.

How to cite: Mayacela-Salazar, B. and Torres-Ramirez, R.: Linking paleochannel evidence and physical vulnerability to urban flooding: a spatial analysis in Ibarra, Ecuador , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15733, https://doi.org/10.5194/egusphere-egu26-15733, 2026.

This presentation outlines a multi-faceted framework for addressing climate change adaptation. The methodology is built upon key pillars, including the resilience and integrity of cultural heritage assets, responsible resource use, and effective mitigation of hazard impacts. The success of any adaptation initiative depends on a holistic evaluation that considers not only technical feasibility and cost, but also its broader societal, cultural, community, and economic impacts, including the project's carbon footprint and adherence to principles of the circular economy.
The management of cultural heritage threated by increasing climate change hazards needs a multi-criteria evaluation framework. A structured approach to decision-making ensures that all intervention strategies prioritize conservation, resilience, and long-term sustainability. Criteria and their respective measurement spaces include technical feasibility, cost (which may be incured looking towards the benefit of increasing climate resilience for culturally significant buildings and unbuilt spaces), adherence to regulatory compliance, as well as impact on society and culture. Authenticity, values, and integrity of the heritage building or unbuilt space must be kept. Thus framework emphasizes key climate-specific metrics: hazard impact mitigation, proactive adaptability (such as preventive retrofit), efficient resource use (including materials, time and workforce as they may be depicted in devices for costs calculation), minimizing carbon footprint, and aligning with the circular economy. Actually building retrofit by itself as a reuse strategy illustrates the principles of circular economy itself at its best in the built environment. Such a retrofit project must demonstrate community acceptance (for example by respecting the mental map of landmarks to be kept in case of reconstruction, following Kevin Lynch principles as well as a psychogeograhic parcours), offer educational value, and ensure positive economic impact. 
This strategic management model follows a four-level hierarchy, from the definition of the overarching mission and objectives (Level 1), over problem definition, diagnosis, and stakeholders analysis (Level 2) to defining the challenge and identifying opportunities (Level 3). The highest level of decision-making (Level 4) - implementation - involves setting evaluation criteria, criterion weighting, and decision rules to inform the final choice and design of the intervention. The outcome is an action plan comprising operational and communication (a higher level of participation) means, demonstrated in a model project aiming at long-term resilience and effective climate risk management.

An analysis of various climate change-related events (floods: Elbe/East Germany 2013, Passau 2013, Florence 1966, Bosnia Herzegovina 2025, and winter storms: Lothar 1999, and Atlantic storm in 2013) is included, detailing, along with articles and online exhibitions, for each event:

• Structures: Locations and specific areas affected, such as the Elbe and Bosna rivers, the Black Forest, inner-city forests in Karlsruhe, and the Pena Palace park in Sintra.
• Damage: The impacts range from common effects like flooding of streets, transport disruption, and damage to lower levels of buildings (incl. economic impacts and activity disruption) to specific damage like forest destruction and agricultural land saturation from freaic water.
• Intervention:
  • Short-term/Emergency: Early warning, sandbags, closing roads, removing fallen trees.
  • Long-term: Awareness campaigns, a flood museum (Passau), landscape solutions (river renaturation, changing vegetation to more storm-resilient species in affected forests)

How to cite: Bostenaru Dan, M. and the Climate Adaptation Working Group at ICOMOS Iscarsah: A Structured Framework for Climate-Adaptive Cultural Heritage Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15909, https://doi.org/10.5194/egusphere-egu26-15909, 2026.