Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 2
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.
The paper investigates the potential of Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Temporal Fusion Transformers (TFTs), to enhance the prediction of coastal dynamics along the Western Black Sea coast. We aim to bridge the gap between in-situ observations from five meteo-oceanographic stations and modelled geospatial marine data from the Copernicus Marine Service. TFTs are employed to refine predictions of shallow water dynamics by considering atmospheric influences, focusing on wave-wind correlations. Atmospheric pressure and temperature are treated as latitude-dependent constants, with specific investigations into extreme events like freezing and solar radiation-induced turbulence.
The analysis utilizes a dataset of meteorological information collected by the Maritime Hydrographic Directorate (MHD) since 2015. The study relies on data gathered from seven automated weather stations at lighthouses along the Romanian coastline. The stations, part of the Romanian Navy - Marine Meteorological Surveillance Network, continuously gather meteorological parameters at specific ground-level heights, including wind speed and direction. The Copernicus Marine Service (CMEMS) wave reanalysis dataset for the Black Sea provides a comprehensive record of wave conditions with a spatial resolution of approximately 2.5 km and hourly temporal resolution.
Explainable AI (XAI) is exploited to ensure transparent model interpretations and identify key influential input variables, including static, encoder, and decoder variables. Data attribution strategies address missing data concerns, while ensemble modelling enhances overall prediction robustness. The models demonstrate a significant improvement in prediction accuracy compared to traditional methods. This research provides a deeper understanding of atmosphere-marine interactions and demonstrates the efficacy of AI/ML in bridging observational and modelled data gaps for maritime safety and coastal management along the Western Black Sea coast.
Acknowledgements: The research of the M.E.M., P.P., M.G.I., and L.D. was conducted as part of the "Forecasting and observing the open-to-coastal ocean for Copernicus users" FOCCUS Project (https://foccus-project.eu/), funded by the European Union (Grant Agreement No. 101133911). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them.
The presented results of the M.E.M., P.P., M.G.I., and L.D. have been carried out with financial support from the Sectorial Research-Development Plan of the Romanian Ministry of National Defence, PSCD 2021–2024 Project (097/2021, 092/2022, 097/2023, 097/2024): „Development of an integrated monitoring system to increase the quality of hydro-oceanographic data in the area of responsibility of the Romanian Naval Forces".
Thanks are extended to the relevant departments of INOE-2000 for their help through the "Core Program with the National Research Development and Innovation Plan 2022-2027" with the support of MCID, project no. PN 23 05/2023, contract 11N/2023.
How to cite: Mihailov, M. E., Ichim, M. G., Chirosca, A. V., Chirosca, G., Dutu, L., and Popov, P.: Temporal Fusion Transformers for Improved Coastal Dynamics Forecasting in the Western Black Sea , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9680, https://doi.org/10.5194/egusphere-egu25-9680, 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.
The maritime transport industry faces a significant challenge: reducing its greenhouse gas (GHG) emissions by 50% compared to 2008 levels. A crucial factor in calculating and optimizing these emissions is accurately predicting ship speed through water. While various models exist, few effectively combine both physical principles and machine learning approaches, leading to limitations in prediction accuracy.
The paper proposes a hybrid model with two main components: ''Solve'' Component: A physics-based approach that uses a Physics-Informed Neural Network (PINN) to determine the theoretical speed a ship would achieve in calm water conditions, based on fundamental physical principles and equations. ''Predict'' Component: A data-driven approach that takes the theoretical calm water speed and adjusts it based on real-world conditions using machine learning algorithms, producing actual speed predictions.
The Solve Phase centers around a differential equation relating three key parameters: propulsion power (P), draft (T), and speed through calm water (Vw), the equation takes the form:
The model uses a PINN to solve a differential equation that links propulsion power (P), draft (T), and calm water speed (Vw) to generate initial speed estimates. The PINN uses a loss function that incorporates both initial conditions and differential equation residuals. A major challenge arises because Vw is theoretical and cannot be directly measured. This issue is addressed using historical data by identifying periods when sea conditions were calm to use as training data.
The model creates a bridge between its solve and predict phases. In the first approach, focused on training data generation, the system utilizes the trained PINN to generate collocation points. From these points, it creates training triplets consisting of propulsion power (Pi), draft (Ti), and calm water speed (Vwi). This approach uses a straightforward mean squared error loss function to train the neural network. The second approach takes a different path by using propulsion power (P) and draft (T) as direct inputs to the neural network. What makes this approach unique is that it incorporates the PINN directly into the loss function. This integration allows physical principles from the differential equation to directly influence the predictions, creating a stronger connection between the physical model and the machine learning component.
The predict phase begins by taking the calm water speed predictions generated from the solve phase and enhances them by incorporating various real-world factors that affect ship movement. These factors include maritime conditions, meteorological data, and current conditions, providing a comprehensive view of the actual sailing environment. To process this combined data, we use machine learning algorithms such as Xgboost. The final output of this phase is the real speed through water (Vwr), which represents a more realistic prediction that accounts for all environmental factors affecting the ship's speed.
The model offers a groundbreaking approach to maritime speed prediction by generalizing across vessel types and integrating physical principles with machine learning. By incorporating operational and meteorological data, it provides more accurate speed predictions that optimize fuel consumption and support the maritime industry's greenhouse gas emission reduction goals, bridging environmental protection with operational efficiency.
How to cite: Atanane, A., Elmimouni, Z., and Benabbou, L.: A Hybrid Machine Learning Model For Ship Speed Through Water: Solve And Predict, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14039, https://doi.org/10.5194/egusphere-egu25-14039, 2025.
The pressing demand for urbanised land due to the growing global population has led to increased land consumption, posing significant challenges to sustainable urban development. The European Union's No Net Land Take (NNLT) 2050 initiative aims to mitigate this issue by curbing urban expansion through urban densification or circular construction. Therefore, it is imperative to study the existing demand trajectory and their effects on the current development situation.
Wallonia, the southern region of Belgium, characterised by urban and peri-urban development, predominantly experience urban expansion. As a solution to that, government implemented various plans which focuses on revitalizing urban cores, addressing vacant buildings, and promoting the regeneration of central areas to prevent further urban sprawl. These ongoing urban pressures, makes it an ideal study area for conducting research on strategic planning.
In this study, we develop a Multinomial Logistic based Cellular Automata (MNL-CA) model calibrated using geophysical, accessibility, socioeconomic and spatial zoning data. Hereto, the model simulate futuristic urban growth until 2050 under two distinct scenarios:
- Business-As-Usual where urban growth continues following the historical demand trends within existing policies.
- Growth-As-Usual represents a scenario of latest observed built up demand trend along a constant rate .
The BAU scenario demonstrates a marked decline in urban expansion rates, stabilizing at 0 hectares per day by 2040. This trajectory reflects a shift toward densification and more spatially cohesive urban development. Meanwhile, the GAU scenario forecasts a sustained expansion rate of 2.51 hectares per day, resulting in a projected 49.20% increase in urban land by 2050. Together, these scenarios provide complementary insights: BAU serves as a valuable reference point for understanding controlled growth dynamics, while GAU offers a perspective for exploring the implications of constant expansion, thereby enhancing the robustness of future urban planning strategies.
While BAU offers a pathway aligned with policy goals, incorporating elements from GAU scenarios allows policymakers to "stress-test" urban strategies. This dual approach can enhance resilience and flexibility in urban planning, enabling better accommodation of future growth challenges while adhering to sustainability principles.
How to cite: Chakraborty, A., Mustafa, A., and Teller, J.: Monitoring the Impact of Urban Growth Scenarios on No Net Land Take of Wallonia, Belgium Using a Cellular Automata Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13665, https://doi.org/10.5194/egusphere-egu25-13665, 2025.
Abstract
Yasooj, with its rich cultural, historical, and musical heritage, stunning natural landscapes, and a young, educated workforce, is one of Iran's cities with significant potential to become a leading example of sustainable development. However, challenges such as weak urban management, lack of economic development, inadequate infrastructure, and natural constraints hinder its progress. In this context, assessing the current situation and providing solutions to enhance urban management and formulate sustainable development strategies are of great importance.
The objective of this study is to identify the capabilities and challenges of Yasooj's urban management and to develop a strategic vision for sustainable development, based on the SWOT model and the SPACE matrix. This research is applied-developmental in nature and employs a descriptive-analytical research method. The statistical population consists of 40 urban experts, and the required data were collected through field observations, reviews of comprehensive and detailed plans, and questionnaires.
Data analysis was conducted using the SWOT model, which identified the strengths, weaknesses, opportunities, and threats of Yasooj City. The findings indicate that the final score of the IFE matrix (internal factors of strengths and weaknesses) is 2.10, and the score of the EFE matrix (external factors of opportunities and threats) is 2.37, both of which are significantly below the average score of 2.5. The internal-external (IE) matrix analysis revealed that Yasooj is in a defensive (WT) position, requiring a review of management structures and the formulation of new operational plans.
Additionally, Yasooj's potential in tourism, agriculture, industry, and services has been identified as a key opportunity for sustainable development. To achieve sustainable development in Yasooj, urban management must revisit its structures and plans, focusing on enhancing inter-institutional cooperation and fostering greater citizen participation in decision-making processes.
Utilizing the city's natural, cultural, and social capacities, strengthening academic tourism through Yasooj University, hosting cultural festivals; and supporting agro-tourism are among the proposed solutions. Furthermore, Yasooj should aim to establish itself as a successful model of sustainable urban development by improving infrastructure, enhancing good urban governance, and fostering partnerships with the private sector and civil society.
Step-by-step and participatory planning, along with a thorough review of past strategies and the definition of clear visions, will contribute to achieving this goal. Establishing closer links between academic institutions and urban management will also play a key role in the successful implementation of development strategies.
Keywords: Sustainable Development, Yasooj City, SWOT Model, Urban Management, Tourism.
How to cite: salamatnia, A., balist, J., and Nahavandchi, M.: Assessment and Development of a Sustainable Development Strategy for Yasooj City Using the SWOT Model and SPACE Matrix, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3305, https://doi.org/10.5194/egusphere-egu25-3305, 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.
Central Italy experienced a catastrophic seismic sequence that suddenly started on 24 August 2016 at 1:36:32 UTC with an Mw = 6.0 earthquake. Buildings damaged by the shaking of this event caused about 300 fatalities, and several towns (e.g., Amatrice, Accumuli, Arquata del Tronto) were destroyed entirely. A seismic sequence started from this event, and the largest event occurred more than two months later on 30 October 2016 at 6:40:17 UTC with magnitude Mw = 6.5. On 18 January 2017, a resurgent of the seismic sequence occurred with four events of magnitude equal to or greater than 5.0 in a Southern sector of the interested region (close to Capitignano/Montereale/Campotosto Lake). Then, the sequence followed a typical multi-year decay. The impact was huge, and from an energetic point of view, the event of 30 October 2016 was one of the largest recorded in the last 40 years in Italy.
Considering this particular case study, we developed a multidisciplinary and multiparametric Jupyter Notebook which can be run, e.g. in a Virtual Research Environment (VRE). The Open Source Code and friendly environment of Jupyter Notebook permit future users to adopt the same VRE to study other earthquakes.
The Jupyter Notebooks retrieves data mainly from the European Plate Observing System (EPOS) platform (Bailo et al., 2023, https://doi.org/10.1038/s41597-023-02697-9), integrating with other sources such as climatological archives and Swarm magnetic satellites of European Space Agency (ESA). EPOS is a European research infrastructure devoted to understanding plate tectonics through multidisciplinary and multiparametric studies. EPOS has already implemented a portal (https://www.epos-eu.org/dataportal, last accessed 10 January 2024) where users can retrieve data grouped into 10 disciplines (Thematic Core Services – TCS).
The Italian seismic sequence interests the extensional plate typical of the Central Apennine Mount Chain, and multiparametric data can help to understand the physical and chemical processes that could occur before and during the earthquake. The VRE relies on the results published by (Marchetti et al., 2019) but using updated algorithms such as the one used to study the Arabian Plate earthquake doublets (Ghamry et al., 2024, https://doi.org/10.3390/atmos15111318). We will also include other atmospheric investigations of specific parameters (e.g., Piscini et al., 2017, https://doi.org/10.1007/s00024-017-1597-8). Such previous studies propose evidence for anomalies in the organised chain of lithosphere, atmosphere, and ionosphere that were identified before the Italian seismic sequence 2016-2017.
These preliminary studies contribute to investigating the relations between geo-layers in our Earth’s system and the influence of seismic activity on them. Furthermore, this VRE adds a tool to the EPOS platform with potentially several applications, such as investigations of other significant earthquakes or other natural hazards, such as volcano eruptions.
How to cite: Marchetti, D., Bailo, D., Michalek, J., Paciello, R., and Falcone, G.: A Jupyter Notebook devoted to a multiparametric investigation of the Amatrice-Norcia Italian seismic sequence 2016-2017, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13827, https://doi.org/10.5194/egusphere-egu25-13827, 2025.
Stratospheric aerosol injection (SAI) is a proposed climate intervention that involves injecting aerosols (or aerosol precursors) into the stratosphere to reduce global warming and associated devastating impacts. In this study, I estimate the socioeconomic effects of future SAI using model results from the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS-SAI) and the Assessing Responses and Impacts of Solar Climate Intervention on the Earth System (ARISE-SAI) as inputs to the Climate Framework for Uncertainty, Negotiation, and Distribution Integrated Assessment model (FUND). GLENS-SAI and ARISE-SAI are an ensemble of SAI simulations between 2020 and 2100 (GLENS) and 2035-2064 (ARISE-SAI-1.5) using the Community Earth System Model, wherein SAI is simulated to offset the warming produced by a high-emission scenario (RCP 8.5) and a middle of the road (SSP2-4.5). FUND's components include agriculture, forestry, heating, cooling, water resources, tropical and extratropical storms, biodiversity, cardiovascular and respiratory mortality, vector-borne diseases, diarrhea, migration, morbidity, and rising sea levels. These aggregate impacts culminate in net damages, calculated as a percentage of gross domestic product (GDP). In both emission scenarios, global damages take a more linear trend in time, with up to 1% of global GDP loss under SSP2 - 4.5, as opposed to 6% under RCP8.5 (Figure 1). Under GLENS and ARISE SAI, damages follow a beneficial pathway, resulting in up to 0.6% and 1% savings of global GDP, respectively (Figure 1). Significant aspects of net damages include cooling and heating demand, agriculture, and water resources. Whereas cooling costs rise under both warming scenarios, savings accrue from avoided heating costs. However, SAI elicits the opposite effect. Additionally, the Dynamic Integrated Climate-Economy model, a neoclassical IAM, was tailored similarly to give further insight into damages. A nonlinear regression approach was then applied to climate and economic data to validate the results from the integrated assessment models. Finally, a cost-benefit analysis was performed on the GLENS and ARISE scenarios using operational and deployment cost estimates from Wagner and Smith (2018). SAI benefits (savings) are more than sufficient to cover the costs of operation and deployment. Even in the extreme case (GLENS-SAI), cost peaks at around 0.03% of global GDP (Figure 2). This analysis will be pivotal in advising policymakers on the economic outcomes and feasibility of SAI.
Figure 1 ( Damages as a percentage of global GDP. Left: SSP2-4.5 and ARISE-SAI. Right: RCP8.5 and GLENS-SAI)
Figure 2 (SAI costs as a percentage of Global GDP. Blue: ARISE-SAI, Yellow: GLENS-SAI)
References
Smith, W., & Wagner, G. (2018). Stratospheric aerosol injection tactics and costs in the first 15 years of deployment. Environmental Research Letters, 13(12), 124001.
How to cite: Ansah, P.: Leveraging Integrated Assessment Models to Assess Socioeconomic Impacts of Potential Stratospheric Aerosol Injection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19957, https://doi.org/10.5194/egusphere-egu25-19957, 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.
Different types of water bodies, such as streams, creeks, irrigation ponds, and paddies, form networks in low-elevation mountainous areas, referred to as Interconnected Small Water Bodies in Slopeland (ISWBS) in the context of Taiwan. Little is known about the ecosystem functions and conservation potential of ISWBS, and an assessment framework is proposed using an integration of remote sensing and field survey data. We analyze whether network characteristics, node characteristics, and landscape factors impact ecological functions and estimate the services related to sediment reduction, agricultural production, and biodiversity that ISWBS provides.
In this preliminary study, we focused on irrigation and natural ponds as important nodes within ISWBS. Monitoring stations were established to record the micro-climate factors of the ponds, and surveys of benthic macro-invertebrates were conducted in 2024. Using a framework of functional feeding groups, the ponds are categorized based on the relative abundance of collector-gatherers, which significantly affect the results of ordination. Community analysis shows little and non-significant relationships between community composition and environmental factors, namely variations in water depth, landscape indices, and irrigation use. However, some factors, such as water depth variation during low depth periods, total edge length, and canal connection, show potential to contribute to future analyses.
Regarding the remote sensing analysis, we find that the distance between nodes has decreased over the past 40 years. Nevertheless, no biodiversity records are available to determine the impact of landscape change. The effects of changing network characteristics on community composition and functional groups are unclear due to insufficient sampling of biodiversity data. Further biodiversity sampling and the study of network characteristics are critical to determine how ISWBS functions in ecosystem processes, especially for sediment detention and nutrient cycling at a landscape scale.
How to cite: Hsieh, C.-K., Chen, S.-C., and Liang, M.-C.: Assessment Framework of Ecosystem Services and Functions of Interconnected Small WaterBodies in Slopeland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15571, https://doi.org/10.5194/egusphere-egu25-15571, 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.
The laurel forest (laurisilva) represents a unique and biodiverse ecosystem currently confined to subtropical regions with specific climatic conditions. In the Canary Islands, these laurisilva forests, constrained to areas with high humidity and stable temperatures as the northern slopes of Tenerife, La Gomera, and La Palma, are of particular ecological importance hosting numerous endemic species. However, climate change poses a significant threat to these fragile habitats, with potential shifts in their distribution at both regional and global scales with new regions emerging as potential refuges for laurisilva forests. The main scope of this study is to explore the current distribution of laurisilva forests in the Canary Islands and projects to the future under different climate change scenarios for mid-century and end-century, its potential range in other archipelagos of Macaronesia and selected regions worldwide with similar climatic conditions.
Using Maxent as the primary modeling tool, we first trained the model by means of high-resolution bioclimatic indicators specifically designed for the Canary Islands, the so-called BICI-ULL dataset. This dataset was generated taking into account the intricate topography and diverse microclimatic patterns of the archipelago, providing a robust framework to delineate the current distribution of laurisilva. Once the model was trained, we used the global bioindicators from WorldClim and Chelsa to project the potential future distribution of laurisilva.
Thus, this methodology based on BICI-ULL allowed us to develop a detailed understanding of laurisilva distribution in the Canary Islands, while WorldClim and Chelsa facilitated the extrapolation of projections to broader geographic scales offering a framework for identifying potential refugia and new habitats for conservation planning of the laurisilva forests. These findings underline the importance of combining regional expertise with global datasets to inform conservation strategies for biodiverse but threatened ecosystems like laurisilva.
How to cite: Sosa-Guillén, P., Simon Tondreau, P., Barragán, R., González, A., Pérez, J. C., Expósito, F. J., and Díaz, J. P.: Modeling the Future of Laurisilva Forests: Integrating Regional and Global Bioclimatic Datasets for Projections Beyond the Canary Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18096, https://doi.org/10.5194/egusphere-egu25-18096, 2025.
