Dust storms pose significant environmental challenges in arid and semi-arid regions, causing serious health, environmental, and socio-economic impacts. Traditional dust modeling approaches, like numerical methods, often struggle to balance accuracy, computational efficiency, and data availability. This study employed a Physics-Informed Neural Network (PINN) model for event-based dust storm modeling, integrating the physical principles of dust dynamics with data-driven methods. We demonstrated the applicability of the above framework in the Lake Urmia Basin, where the lake desiccation and external dust sources have triggered local dust storms. To this end, we first analyzed ground-recorded PM₁₀ and weather data to identify dusty days between 2004 and 2019. Next, we trained an initial neural network (NN) model with remote sensing data that describe meteorological and boundary layer characteristics at the locations of pollution monitoring stations. This approach allowed us to generate gridded PM₁₀ data, overcoming the limitations posed by insufficient and non-continuous data for directly training PINN. Finally, the PINN model was trained and validated on 21 selected dust events from three stations chosen for their spatial distribution and sufficient availability of PM₁₀ data throughout the events. Analysis revealed that the initial NN model achieved R² of 62% and mean absolute error (MAE) of 65  on the test data. The PINN model demonstrated substantial improvement with mean R² of 93% and mean MAE of 9  on the gridded PM₁₀, and MAE of 39  when validated against ground observations. Furthermore, the model yielded lower prediction accuracy in urban compared to rural stations, which is attributed to the bias imposed by the influence of terrestrial and industrial pollutions. This study demonstrates the effectiveness of PINNs in tackling dust transport modeling challenges in data-sparse regions, providing a novel way to combine physical principles with data-driven techniques for large-scale environmental applications.

How to cite: giahchin, K. and danesh-yazdi, M.: Event-Based Physics-Informed Neural Networks for Dust Storm Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15382, https://doi.org/10.5194/egusphere-egu25-15382, 2025.

Marine heat waves (MHWs) pose significant threats to coastal ecosystems, with particularly severe impacts in shallow waters where their magnitude is often amplified. The Chesapeake Bay, the largest estuary in the United States, is highly vulnerable to these events, which have increased in frequency and duration in recent decades. MHWs in the Chesapeake Bay have critical implications for its ecological balance, including effects on fish populations, habitat degradation, and water quality. Despite their growing prevalence, the underlying causes of these events and the factors regulating their variability remain poorly understood. Our study employs machine learning approaches to elucidate the drivers of marine heat waves in the Chesapeake Bay and to quantify their contributions to these extreme temperature events. By incorporating a comprehensive set of potential predictors, including local air temperature, wind forcing, river discharge, and Atlantic Ocean temperature, the model reveals the key mechanisms driving the onset, intensity, and persistence of MHWs in the Chesapeake Bay. Advanced feature selection techniques isolate the most relevant variables, while model outputs are validated against observed data to ensure accuracy and robustness. Our results suggest that local air temperature and ocean temperature anomalies from the Atlantic Ocean are dominant in triggering MHWs. These findings shed light on the complex interactions between atmospheric, hydrological, and oceanographic processes in shaping extreme thermal events in estuarine systems.

How to cite: Li, C., Xiong, N., and Zhao, J.: Understanding Marine Heat Waves in the Chesapeake Bay: Drivers, Variability, and Predictive Insights Using Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7577, https://doi.org/10.5194/egusphere-egu25-7577, 2025.

Shipping remains a crucial element of global trade and commerce, facilitating over 90% of international trade by volume. The maritime industry’s advanced logistics chains are vital for the timely delivery of goods, supporting both economic growth and employment. However, it is also a significant source of pollution, accounting for approximately 3% of global greenhouse gas (GHG) emissions, and contributing 13% of nitrogen oxides (NOx) and 12% of sulfur oxides (SOx). Additionally, shipping emits harmful pollutants, including particulate matter (PM), black carbon (BC), and methane (CH4). These emissions not only impact the global climate but also pose severe health risks to communities near shorelines, contributing to asthma, respiratory and cardiovascular diseases, lung cancer, and premature death.

The International Maritime Organization (IMO) is actively engaged in mitigating these environmental impacts as part of its support for the UN Sustainable Development Goal 13, which addresses climate change in alignment with the 2015 Paris Agreement. The IMO has implemented several regulations to curb GHG emissions from shipping, beginning with mandatory energy efficiency measures introduced on July 15, 2011. Subsequent regulations include the Initial IMO GHG Strategy (2018) and the updated Strategy on Reduction of GHG Emissions from Ships (2023). The 2023 strategy sets ambitious targets to achieve near-zero GHG emissions from international shipping by around 2050, with interim goals of reducing emissions by at least 20% by 2030 and 70-80% by 2040. It also aims to cut the carbon intensity of international shipping by at least 40% by 2030, measured as CO2 emissions per unit of transport work. As of January 1, 2023, ships are required to calculate their Energy Efficiency Existing Ship Index (EEXI) and establish an annual operational Carbon Intensity Indicator (CII), with ratings from A to E indicating energy efficiency (International Maritime Organization).

In response to evolving regulations aimed at reducing GHG emissions, we propose a machine learning framework to improve emission predictions, with a particular focus on the Saint Lawrence River. Currently, emissions in the Canadian shipping sector are calculated a posteriori, with Environment and Climate Change Canada (ECCC) providing a national marine emissions inventory and a comprehensive visualization tool. This tool enables users to analyze shipping activities and emissions across Canada by filtering data through various parameters.

Our proposed work is designed to predict GHG emissions for vessels navigating the Saint Lawrence River, with plans for broader application across Canada. By employing a bottom-up methodology, we create a detailed emissions inventory based on individual vessel activities, leveraging Automatic Identification System (AIS) data to capture the spatiotemporal dynamics of shipping (Spire). To enhance accuracy, we incorporate vessel-specific information from CLARKSONS, including engine type, fuel type, and power, along with meteorological data such as current speed to account for external factors affecting emissions. Machine learning models, particularly deep learning techniques, are employed in the prediction phase, enabling the model to continually improve with new data. This scalable approach not only enhances environmental monitoring but also supports national efforts to reduce GHG emissions from marine transportation across Canada.

How to cite: El aissi, A. and Benabbou, L.: Predicting GHG Emissions in Shipping: A Case Study Of Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14016, https://doi.org/10.5194/egusphere-egu25-14016, 2025.

Extreme warming events in Texas have far-reaching environmental, economic, and societal consequences, including impacts on agriculture, energy demand, public health, and infrastructure. These events underscore the urgent need for reliable prediction systems that can anticipate their occurrence and inform mitigation and adaptation strategies. In this study, we develop machine-learning-based models to predict extreme temperature events across Texas by identifying and modeling the key drivers of these phenomena. The predictive framework incorporates the influences of large-scale climate modes and processes from both the Pacific and North Atlantic Oceans, including the El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Warm Pool (WP), and Atlantic Multidecadal Oscillation (AMO). By integrating these climate indices with regional atmospheric and surface data, the model captures the complex interactions between large-scale climate variability and regional temperature extremes. The contributions of each climate mode are quantified and analyzed to determine their relative importance in driving warming events across different temporal and spatial scales. To ensure the robustness of the predictions, the model outputs are further validated against physical mechanisms linking large-scale climate modes to atmospheric circulation patterns. This validation process provides a mechanistic understanding of the statistical relationships uncovered by the machine-learning models, ensuring that the predictions align with established climate dynamics. The findings from this study enhance our understanding of regional climate dynamics in Texas and demonstrate the potential of machine-learning approaches for improving the predictability of extreme temperature events.

How to cite: Chen, A. and Zhao, J.: Machine Learning-Based Prediction of Extreme Temperature Events in Texas: Understanding the Role of Large-Scale Climate Modes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7566, https://doi.org/10.5194/egusphere-egu25-7566, 2025.

The dynamic nature of Earth's lithosphere necessitates comprehensive tools for integrating geological data with plate tectonic frameworks across vast spatiotemporal scales. To address this challenge, EarthBank, in collaboration with the Earthbyte Group and Lithodat, has developed LithoPlates - a cloud-based deep-time reconstruction tool designed to support the visualisation and analysis of geological features within their paleogeographic contexts. LithoPlates leverages Earthbyte’s GPlates Web Service, enabling users to access pyGPlates functionalities and advanced plate tectonic models, offering researchers an intuitive platform for spatiotemporal analyses.

LithoPlates incorporates ten plate tectonic models, including the latest model published in 2024, which extends reconstructions back to 1.8 billion years. These models are seamlessly integrated into EarthBank’s public geochemistry data platform, enabling researchers to explore the tectonic settings and geological histories of their area of interest. By applying age-specific filters, users can visualise data within any chosen reconstruction timeslice within 1Ma steps, facilitating precise spatio-temporal analyses of geological processes such as formation, deformation, and material transport across Earth’s surface.

The platform’s dual capability to analyse data in both present-day and palinspastic geography significantly enhances its utility for geoscientific research. LithoPlates supports the reconstruction of geochronological and thermochronological data, providing a robust framework for investigating the evolution of Earth’s lithosphere. Its integration with EarthBank’s relational database further enables on-the-fly analysis of both data and metadata, offering real-time insights into complex geological systems. Robust export functionalities are also present including an open REST API, enabling users to seamlessly integrate their data and share results for further analysis.

Future advancements for LithoPlates include the integration of additional plate tectonic models, enhanced visualisation tools, and advanced filtering capabilities to refine comparative analyses across multiple reconstruction scenarios. These updates will improve uncertainty quantification, allow for more sophisticated model-data fusion, and facilitate the analysis of geophysical and geochemical datasets within a unified paleogeographic framework. 

LithoPlates represents a transformative tool for advancing Earth system reconstructions by addressing key challenges in the integration of geological, geophysical, and environmental data. Its interdisciplinary approach aligns with the broader scientific goal of developing digital twins of our planet, contributing to fields as diverse as resource exploration, paleoclimatology, and environmental risk assessment.

This tool exemplifies the potential of combining advanced modeling techniques with expanding geochemical and geophysical datasets, offering a scalable solution for analyzing the spatiotemporal evolution of Earth’s lithosphere. By providing access to comprehensive plate tectonic models and enabling precise spatiotemporal analyses, LithoPlates paves the way for groundbreaking research in understanding Earth’s dynamic geological history and its implications for modern and future challenges.

How to cite: Kohlmann, F., Noble, W., Qin, X., Higton, J., Beucher, R., Theile, M., McInnes, B., and Mueller, D.: Deep-Time Digital Twins: Integrating LithoPlates with the EarthBank Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9058, https://doi.org/10.5194/egusphere-egu25-9058, 2025.

Future climate projection data are increasingly employed to evaluate the potential impacts of global warming across a wide range of domains, including meteorological variables (e.g., temperature and precipitation), hydrological processes, ecosystems, human health, and societal activities. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides an extensive dataset produced through international collaboration, incorporating multiple General Circulation Models (GCMs), diverse future scenarios, and numerous initial conditions. Despite the comprehensive nature of these datasets, most impact assessments rely on a limited subset of realizations, with no standardized methodology guiding their selection. This lack of consensus introduces potential biases into the outcomes of impact studies. This study quantitatively assesses the influence of realization selection on future climate impact assessments. Monthly precipitation and temperature data from CMIP6 were analyzed for both historical experimental periods and multiple Shared Socioeconomic Pathways (SSP) scenarios. Comparisons were conducted between outcomes obtained using all available realizations for each GCM and those derived from a single realization per GCM. Additionally, combinations of GCMs and realizations commonly used in prior studies were evaluated for their representativeness. The findings reveal that global average monthly precipitation is consistently higher when all realizations are utilized compared to scenarios based on a single realization. The inclusion of all realizations captures a broader range of variability, whereas subsets exhibit narrower variability and more localized trends. These results emphasize the significant impact of realization selection on future climate prediction outcomes. Moreover, an analysis of existing studies indicates that while selected datasets often reflect average trends, their overall representativeness requires further scrutiny. This research highlights the necessity of adopting uncertainty-aware methodologies in climate change studies. The findings offer valuable insights for improving the robustness and reliability of future climate impact assessments, paving the way for more informed decision-making in addressing climate change challenges.

How to cite: Nagata, K.: Quantitative analysis of the impact of realization selection on future climate change impact assessments using CMIP6 data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9648, https://doi.org/10.5194/egusphere-egu25-9648, 2025.

Environmental challenges have had a negative impact on African forest resources, which has subsequently adversely affected some ecosystem services that are required for the survival of people. We conducted a comparative study in the wet and dry woodlands in Zambia to establish the formation of tree growth rings and determine the relationship between the growth ring width and rainfall. Through the four successful Africa Dendrochronological Fieldschools that were conducted from 2021 to 2024, we collected samples from the wet miombo woodlands on the copperbelt province and the dry miombo and Baikiaea woodlands on the southern province of Zambia. From 2021 to 2023, we recorded 49 tree species from the wet miombo woodlands and found that the Fabaceae family plants had the highest species richness with 28.5%. We determined a series intercorrelation of 0.45 and average mean sensitivity of 0.465 from a master chronology of 14 tree species. The dendroclimatic study found a significant positive relationship (r-value =0.589, p-value = 0.0005) between ring width of a mixed species chronology of Brchaystegia longifolia and Julbernadia paniculata, and precipitation totals for Zambia’s wet season (October–April). In 2024, studies were conducted in the dry miombo and Baikiaea woodlands. Through this study, 16 distinct species were identified in the Baikiaea woodlands with Baikiaea plurijuga being the abundant species. We determined series intercorrelation of 0.31 and an average mean sensitivity of 0.50 from a mixed tree species from the Baikiaea woodlands. A precipitation correlation with Brachytegia longifolia from the miombo woodlands found that previous December and Current March precipitation have positive influence on tree growth. In both, dry and wet woodlands, we found that trees produce annual growth rings that are responsive to seasonal climate, and are useful for dendrochronology

How to cite: Ngoma, J. and the Justine Ngoma: A comparative study of the dendroclimatic potential of selected tree species of the tropical dry and wet woodlands of Zambia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16432, https://doi.org/10.5194/egusphere-egu25-16432, 2025.

Migration is one of human’s most drastic adaptation strategies against unfavorable conditions. With flows from and to origins and destinations, migration data are necessarily network data. Embedded within network data is interdependency among data points (flows) that renders some traditional statistical analyses, including causal inference techniques, inappropriate. To address this issue, we have developed a novel analysis, combining causal inference techniques with quadratic assignment procedure (QAP) to infer causal relationships from network data and applied it to the datasets that include migration flows and their potential drivers – these include socioeconomic, political, and environmental factors (e.g., flood and drought). We implemented this analysis for the African region data. The preliminary results are reported; the limitations and future work are discussed. We anticipate that this novel method will be applicable to a wide variety of network data in other fields.

How to cite: Muneepeerakul, R.: Causal linkages of human migration flow networks: A regional analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6756, https://doi.org/10.5194/egusphere-egu25-6756, 2025.

The Port of Manzanillo, Colima, serves as a pivotal infrastructure for Mexico’s international trade network. In response to increasing operational demands and advance modernisation efforts, an ambitious expansion into the Laguna de Cuyutlán has been proposed. This initiative includes the construction of specialised container terminals and supporting infrastructures. However, the area’s vulnerability to geological and hydrometeorological hazards—such as earthquakes, volcanic activity, tsunamis, landslides, and tropical storms—raises critical concerns regarding the durability and sustainability of these developments.

This study introduces a hybrid risk management approach that combines the principles of the PMBOK’s plan risk management methodology with the analytical precision of fuzzy set theory. Comprehensive historical data on natural hazards were systematically gathered from risk atlases, scientific research, and official reports. The model applies fuzzy membership functions to evaluate the likelihood and impact of risks. Additionally, tools like fuzzy Delphi, fuzzy DEMATEL, and fuzzy ANP facilitate the structured analysis and prioritisation of potential threats.

The primary aim is to create a robust system for addressing the uncertainties associated with complex risk environments. By integrating advanced analytical methods with established risk management practices, the model provides a foundation for designing effective mitigation strategies. These measures are essential for maintaining operational reliability, enhancing infrastructure resilience, and minimising socio-economic impacts. This research highlights the value of interdisciplinary methodologies that link scientific advancements with practical solutions, tackling the intricate challenges posed by climatic and geological extremes in dynamic contexts.

How to cite: Cardenas, E., Delgadillo-Partida, J., Mendoza-Rosas, A. T., and Zarate-Ramirez, F.: Comprehensive Risk Assessment at the Port of Manzanillo: A Model Based on PMBOK and Fuzzy Logic., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13886, https://doi.org/10.5194/egusphere-egu25-13886, 2025.

Abstract

Climate Change Impacts in the Transboundary Prespa-Ohrid watershed.

(The strategic impact identified in the SWOT analysis)

Brisilda Stafa¹; Emanuela Kiri¹

¹ Institute of Geosciences, UPT, Tirana, Albania. 

This study investigates the impacts of climate change on open surface water in the transboundary Prespa-Ohrid Lake area using a SWOT analysis based on lake level and meteorological data. Rising temperatures, altered precipitation patterns, and increased evaporation rates have been identified as critical factors influencing water levels, particularly in Prespa Lake. The SWOT framework helps in systematically evaluating the internal strengths and weaknesses of the region’s hydrological systems and external opportunities and threats posed by climate change.

Strengths include the availability of long-term lake level and meteorological data, which provide a robust foundation for assessing climate impacts and water management strategies. Weaknesses highlight the vulnerability of shallow lakes like Prespa to evaporation and reduced inflows, compounded by inconsistent monitoring across national borders. Opportunities lie in the potential for enhanced regional cooperation, ecosystem-based adaptation, and the development of sustainable water management policies that account for climate variability. However, the region faces significant Threats, including further reductions in water availability, degradation of water quality, loss of biodiversity, and socio-economic impacts on agriculture and tourism.

The study emphasizes the need for transboundary cooperation and adaptive strategies to mitigate these risks, with a focus on integrating climate and water data to guide future decision-making in the region.

Keywords: climate change, transboundary watershed, SWOT analysis, socio–economic impact. 

How to cite: Stafa, B. and Kiri, E.: Climate Change Impacts in the Transboundary Prespa-Ohrid watershed. (The strategic impact identified in the SWOT analysis), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18927, https://doi.org/10.5194/egusphere-egu25-18927, 2025.

Heat stress, next to drought, is one of the major constraints to maize growth, development and sustainable yield in tropical and sub-tropical regions. Hence, there is a dire need to explore strategies that alleviate adverse effects of heat stress. In this regard, silicon (Si) is an important plant nutrient which may support crop in alleviating heat stress-induced damages by modulating plant defense mechanisms. Si seed inoculation can be an ecofriendly mitigation strategy to ameliorate adverse effects of heat stress in maize. Yet, to date, limited knowledge is available on how Si modulates plant defense mechanisms to induce heat tolerance in maize. Therefore, a consecutive two years field trials were conducted in arid climatic conditions to evaluate the effects of six Si seed inoculation levels (0.00 to 6.00 mM) on the phenological, physiological, growth, antioxidant mechanisms, and yield components of (heat tolerant and heat sensitive) maize hybrids under normal temperature regime and heat stress conditions at the sixth leaf and 50% tasseling growth stages over a period of 8 consecutive days. Previously, the maize hybrids were selected on the basis of traits performance through screening in the glasshouse where hybrids were tested at different heat stress levels at sixth leaf stage-V6. Results showed that when the heat stress was imposed at sixth leaf stage then seed inoculation with 4.5 mM Si produced significant better cob length (15.0 cm, 16.7 cm), grains per cob (480, 500), thousand grains weight (211.6 g, 224.3 g), grain yield (6.58 t ha^-1, 7.11 t ha^-1) and biological yield (13.1 t ha^-1, 14.5 t ha^-1),  respectively for 2023 and 2024 growing seasons (years) as compared to other Si levels. Whereas, the same Si inoculation also produced the maximum cob length, grains per cob, thousand grains weight, grain yield (6.24 t ha^-1, 6.74 t ha^-1) and biological yield (13.7 t ha^-1, 15.2 t ha^-1), respectively for both growing seasons as compared with other Si inoculation when heat stress imposed at 50% tasseling stage. These results owing to increased physiological mechanism, growth, antioxidant activities, and osmolytes accumulation under heat stress conditions. Moreover, the interactive effects of heat stress and hybrids revealed that the maize hybrid DK-6103 (prior defined as heat tolerant) produced more grain yield (6.02 t ha^-1, 6.50 t ha^-1) and biological yield (11.4 t ha^-1, 12.6 t ha^-1), respectively during both years when the heat stress was imposed at six leaf stage. While, hybrid SW-1080 produced on an average 13.5% and 14.8% less grain and biological yields, respectively as attained by DK-6103. Therefore, the Si seed inoculation (4.5 mM) may be good strategy to alleviate the adverse effects of the heat stress in maize hybrids. Future studies are also needed to explore the role of Si in alleviating the adverse impacts of combined drought and heat stress under contrasting environmental conditions.

Keywords: Sixth leaf and tasseling phenological stages, physiological mechanism, antioxidants, grain yield, arid and semi-arid climatic regions

How to cite: Habib-Ur-Rahman, M., Hussain, I., Ikram, R. M., Hussain, M. B., Hoffmann, M., and Roetter, R. P.: Silicon seed inoculation improves growth, physiological mechanisms, grain and biological yields in maize hybrids under heat stress at vegetative and tasseling stages, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15934, https://doi.org/10.5194/egusphere-egu25-15934, 2025.