Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 4
This study explores studies on lakes and paleolakes originating from natural effects. The main objective is to perform a bibliometric analysis of research on naturally occurring lake environments worldwide, covering the period from 2014 to 2024. Data extracted from 1687 documents in the Scopus database were analyzed using VOSviewer software. The results reveal a strict trend towards a focus on geosciences and the environment, underlined by research. This study particularly highlights the relationships between authors, co-authors, keywords, and publishers of specialized journals in this research field, thus providing essential information to guide future research and to value the role of these geological environments, which are rare in the world, based on essentially multidisciplinary geoscience approaches.
How to cite: Abbach, J., El Moussaoui, S., El Talibi, H., and Bouiss, C. E.: bibliometric analysis of natural lakes and paleolakes origin of natural events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4994, https://doi.org/10.5194/egusphere-egu25-4994, 2025.
Land degradation is a critical challenge to sustainable development, impacting ecosystems, economies, and communities globally. As part of the FAIR-EASE Earth Critical Zone (ECZ) pilot, this study develops a tailored Land Degradation Assessment tool based on the Trends.Earth approach. The tool aims to enhance data accessibility, integration, and usability across environmental domains, supporting decision-making and policy frameworks aligned with the United Nations Sustainable Development Goals (SDGs).
Building upon the robust Trends.Earth implementation, we can integrate customized workflows and datasets to reflect regional variability in degradation indicators, including vegetation productivity, soil health, and land cover changes. Our approach prioritizes FAIR (Findable, Accessible, Interoperable, and Reusable) principles to ensure broad usability and collaboration across scientific and policy communities.
Preliminary results demonstrate the tool's capacity to enhace the detail of the analysis and to identify degradation hotspots. Furthermore, the integration of open-source geospatial tools and standards supports a scalable framework applicable to diverse environmental contexts.
The tool is designed to be embedded within the LandSupport platform, a geospatial decision support system, further enhancing its accessibility and integration into decision-making processes for land management.
This work contributes to advancing interdomain digital services and illustrates the potential of FAIR principles in addressing complex environmental challenges. We invite feedback from the community to refine, expand and customise the tool's application, fostering collaboration for sustainable land management.
How to cite: Mauriello, I. E., Langella, G., Terribile, F., and Miralto, M.: Customizing Trends.Earth for land degradation assessment in the earth critical zone: a FAIR-EASE approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19225, https://doi.org/10.5194/egusphere-egu25-19225, 2025.
The Uniform Data Access Layer (UDAL), a central component within the FAIR-EASE project, is designed to revolutionize how researchers access, integrate, and utilize diverse scientific datasets. FAIR-EASE prioritizes FAIR (Findable, Accessible, Interoperable, Reusable) principles to ensure that data becomes a powerful enabler of scientific discovery and informed decision-making.
The UDAL concept brings a modular and re-usable approach to choosing and using data in data processing workflows. It materializes as a software package that users can use in their pipelines. UDAL serves as a middleware layer, offering a standardized, user-centric framework for data access. By bridging the gap between complex infrastructures and researchers, UDAL simplifies data retrieval, integration, and usage. This solution decouples data usage from technical complexities, ensuring that researchers can focus on analysis without needing detailed knowledge of access protocols or data formats. Its adaptability to a wide range of technologies and protocols enables interoperability across disciplines and geographic regions. UDAL's innovative approach has been validated with data providers such as Argo and Blue-Cloud and various technology stacks and formats like NetCDF, Beacon, SPARQL endpoint, HTTP REST API, demonstrating its capacity to unify diverse datasets into a single, intuitive system.
A key feature of UDAL is its "named query" mechanism, which standardizes and reuses specific data requests. This enhances reproducibility, shields users from the intricacies of data filtering and retrieval, and promotes efficiency. Additionally, UDAL’s technology-agnostic approach accommodates centralized and distributed data architectures, supporting innovation in data management and usage strategies.
By addressing critical challenges in data management—such as technical barriers and the diversity of data sources—UDAL aligns with the broader goals of FAIR-EASE. It empowers both researchers and data providers, fostering cross-domain collaboration and innovation. Beyond its technical contributions, UDAL embodies a vision of “data as a commodity,” promoting the sustainability and accessibility necessary for open science. While it does not directly address equitable benefit distribution, its transparent usage measurement capabilities lay a foundation for future policy and governance frameworks.
In conclusion, UDAL represents a transformative advance in data-driven research, harmonizing access across disciplines and platforms while accelerating discovery and fostering innovation. As a cornerstone of FAIR-EASE, UDAL is set to establish new standards for simplicity, usability, and sustainability in scientific data management.
How to cite: Mendes, J. and Portier, M.: Uniform Data Access Layer: Advancing Data FAIRness in FAIR-EASE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15569, https://doi.org/10.5194/egusphere-egu25-15569, 2025.
The Er-Rich region is a focal area for understanding the geological evolution of the central-eastern High Atlas, which it covers almost entirely along a north-south transverse line. It is a hinge region between the two major tectonic structures of the High Atlas (the North and South Atlas faults), which reveal a framework of Meso-Cenozoic carbonate, detrital and magmatic rocks.
Previous studies have highlighted the complexity of mapping in this area. To date, no detailed geological map has been produced for this study area, with the exception of the old provisional 1:200,000 map of the Midelt-Rich High Atlas. Remotely-sensed mapping initiatives have also been carried out in the region, except that they do not provide a final interpretation as a geological map, supported by geological maps covering neighboring regions. A detailed geological map of the Er-Rich region, based on the results of remote sensing and field data, is therefore needed in the area. For this purpose, remote sensing geological mapping techniques have been applied to two types of satellite data: 1) Landsat 8 OLI (Optical Land Imager) multispectral optical data, and the Spot 5 panchromatic band acquired by the HRG-2 (High Geometric Resolution) instrument; 2) Sentinel-1 SAR data with dual polarisation (HV-HH).
All the data underwent several pre-processing or correction stages using appropriate software, in particular radiometric and atmospheric correction for Landsat 8 OLI (Optical Land Imager) images using ENVI software. The corrected product of the three Landsat 8 OLI scenes covering the region were then spatially enhanced using the Spot 5 panchromatic band to produce a multispectral image with a high spatial resolution of 5 m using ENVI software. The Sentinel-1 radar data were pre-processed using SNAP toolbox software by applying a series of corrections.
The results obtained by applying the Optimum Index Factor (OIF) method and Principal Component Analysis (PCA), allowing us to select the most significant colored compositions. Moreover, this combination enabled us to delineate with great precision the large outcrops of carbonate rocks (limestones, marl), siliciclastic rocks (conglomerates, sandstones and silts) and magmatic rocks (igneous intrusions).
The lineaments were extracted manually by visual interpretation of Sentinel-1 radar images, after applying directional filtering folowing four general orientations (N0, N45, N90, N135), enabling us to generate a synthetic structural map of the region.
The results obtained were compared with data from geological maps of adjacent areas and approved by field observations, leading to the production of a high-precision geological map, compiled with pre-existing geological literature.
How to cite: Hdoufane, M., Zafaty, O., and Ettaki, M.: Integrated remote sensing data and field investigations for geological mapping and structural analysis in the Er-Rich area (High Atlas, Morocco), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13782, https://doi.org/10.5194/egusphere-egu25-13782, 2025.
The ESA Directorate of Earth Observation Programmes has been actively leveraging satellite-based environmental information to address fragility contexts, focusing on areas such as environmental crimes, crimes against humanity, cross-border crimes, and onset of crises. Over the past decade, ESA has explored digital intelligence crime analysis by employing advanced data mining and machine learning tools to uncover hidden patterns and relationships in historical crime datasets, enabling better detection, prediction, and prevention of criminal activities.
Despite these advancements, the integration of Earth Observation (EO) capabilities into investigative practices remains limited. This is due to several challenges, including low awareness of EO's potential, a lack of illustrative use cases showcasing its benefits, inconsistencies in satellite data collection compared to investigative needs, high costs of very high-resolution imagery, and restricted access to national intelligence sources. To overcome these barriers, ESA has been investigating strategies to systematically incorporate EO-derived information into investigative frameworks also as legal evidence, aiming to enhance situational awareness and support stakeholders in developing procedures to exploit EO and OSINT for addressing international crimes and assessing fragility contexts, in cooperation with international organizations including Interpol, UNODC and ICC.
Recent developments in EO technology and methodologies have created significant opportunities for more impactful applications. ESA has focused on tailoring EO-based services and OSINT to meet the case-sensitive requirements of security and development end-users, enabling better integration of EO-derived insights into intelligence models. These efforts include developing advanced EO information products that go beyond routine offerings, testing and evaluating these products in collaboration with end-users, and demonstrating their value in operational settings.
The GDA Fragility, Conflict, and Security initiative has been a cornerstone of ESA’s work, involving partnerships with International Financial Institutions (IFIs) to co-design tools that provide precise and timely information. These tools have supported initiatives aimed at reducing inequalities, promoting economic development, and enhancing environmental safety in fragile and conflict or post conflict-affected areas. By combining geospatial data with diverse data sources, ESA has delivered customized analyses and reports to improve emerging threats analysis and decision-making processes.
Several ESA initiatives have demonstrated the benefits of EO services for assessing fragility risk exposure, characterizing dynamic needs in fragile contexts, planning post-conflict reconstruction, and managing natural resources. ESA constantly engages with stakeholders, including the OECD, security organizations, and humanitarian actors, and its community of industries and research centres to promote the adoption of EO in international development, humanitarian aid, and peacebuilding. Through these efforts, ESA continues to advance the role of EO in supporting justice, accountability, and sustainable recovery in fragile settings.
How to cite: Corvino, M. and the Michela Corvino: Leveraging EO for Security and Resilience, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17156, https://doi.org/10.5194/egusphere-egu25-17156, 2025.
INTRODUCTION. InSAR or Interferometric Synthetic Aperture Radar is a technique for mapping ground deformation using radar images of the Earth's surface collected from orbiting satellites. DInSAR or Differential SAR Interferometry is an active remote sensing technique based on the principle that, due to the very high stability of the satellite orbits, it is possible to exploit the informative contribution carried by the phase difference between two SAR images looking at the same scene from comparable geometries.
AIM. In this setting, the main objective of this study is to evaluate the region near the closed rock salt mine in the south of Albania. Our input for this exercise will be two images of the land near the former rock salt mine in Dhrovjan near the Blue Eye (Saranda, Albania).
RESULTS. By combining the phases of 2 images we produce an interferogram where the phase is correlated to the terrain topography and deformation so if the phase shifts related to the topography are removed from the interferogram, the difference between the resulting products will show surface deformation patterns or cure between the two acquisition dates and this methodology is called differential interferometry Processing, Phase Unwrapping, and at the end creating the displacement map. We use in our study the difference in time with the algorithm which consists of working step by step with these operators: Read the two split products, Applying Orbit files, Back-Geocoding, Enhanced Spectral Diversity, Interferogram, TOPSAR Deburst, and Write. The resulting difference of phases is called an interferogram containing all the information on relative geometry. Removing the topographic and orbital contributions may reveal ground movements along the line of sight between the radar and the target.
The next algorithm we worked with these operators: Read the debursted interferogram, TopoPhaseRemoval, Multilook, Goldstain Filtering, and Write. At the same time from Goldstain Filtering, we add the Snaphu Export operator.
Correct phase unwrapping procedures must be performed to retrieve the absolute phase value by adding multiples of 2π phase values to each pixel to extract accurate information from the signal. In this study, we will use SNAPHU, which is a two-dimensional phase unwrapping algorithm consists of working step by step with these operators: read (the wrapped image) and read (2) the unwrapped image, Snaphu Import, PhaseToDisplacement, and Write. We can display it in Google Earth after saving it as .kmz and also make a profile of the displacements.
DISCUSSION AND CONCLUSIONS
One of the SAR Interferometry applications is deformation mapping and change detection. This work demonstrates the capability of interferometric processing for the observation and analysis of instant relative surface deformations in the radar LOS direction. When two observations are made from the same location in space but at different times, the interferometric phase is proportional to any change in the range of a surface feature directly. All three stages of the work are important and require accurate interpretation knowledge, especially when working with the Snaphu program.
KEY-WORDS
InSAR, DInSAR, Interferogram
How to cite: Belba, P.: The use of InSAR and DInSAR for detecting land subsidence in Albania, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11808, https://doi.org/10.5194/egusphere-egu25-11808, 2025.
In today's changing climate, there is an urgent need to understand the adverse impacts of climate change on natural environments, infrastructures, and industries.Particularly permafrost regions in the Arctic are highly vulnerable to global warming, impacting both the environment and socioeconomic aspects. Thus, systematic monitoring of such environments, is of paramount significance. Advances in Geoinformation technologies, and in particular in Earth Observation (EO), cloud computing, GIS, web cartography create new opportunities and challenges for Arctic research examining the socioeconomic impact of climate change.The rapid advancements in EOin particular have led to an exponential increase in the volume of geospatial data that come from spaceborne EO sensors. This surge, combined with the fast developments in GIS and web cartography present significant challenges for effective management, access, and utilization by researchers, policymakers, and the public. Consequently, there is a growing need for advanced methodologies to organize, process, and deliver geospatial information that comes from EO satellites in an accessible and user-friendly manner.
Recognizing thepromising potential of geoinformation technologies, the European Union (EU) has funded several research projects that leverage advanced technologies such as geospatial databases and WebGIS platforms to streamline EO data handling and dissemination. One such project is EO-PERSIST (http://www.eo-persist.eu), which aims to create a collaborative research and innovation environment focusing on leveraging existing services, datasets, and emerging technologies to achieve a consistently updated ecosystem of EO-based datasets for permafrost applications. To formulate the socioeconomic indicators, the project exploits state of the art cloud processing resources, innovative Remote Sensing (RS) algorithms, Geographic Information Systems (GIS)-based models formulating, exchanging also multidisciplinary knowledge.EO-PERSIST innovative approach is anticipated to contribute to more informed decision-making and broader data accessibility for researchers, policymakers, and other stakeholders.
The present contribution aim is two-fold: at first, to provide an overview of EO-PERSIST Marie Curie Staff Exchanges EU-funded research project; second, to present some of the key project outputs delivered so far relevant to the selected Use Cases of the project and the geospatial database developed for assessing the socioeconomic impacts of climate change in the permafrost Arctic regions.
This study is supported by EO-PERSIST project which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101086386.
KEYWORDS:earth observation, cloud platform, Arctic, socioeconomic impact
How to cite: Tselos, G.-N., Detsikas, S. E., Kroszka, B., Grzybowski, P., and Petropoulos, G. P.: Enhancing the use of Geoinformation technologies to assess the socioeconomic impacts of climate change in the Arctic: Insights from the EO-PERSIST Project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6240, https://doi.org/10.5194/egusphere-egu25-6240, 2025.
The U.S. territory of Guam is threatened annually by high-intensity storms and typhoons due to its location in the western Pacific Ocean. The island’s infrastructure – buildings, roads, and utilities – bear the brunt of typhoon damage, which in turn affects public health, the economy, and natural resources. Traditionally, these impacts have been observed via satellite, radar, and official weather stations. Damages are assessed in the aftermath of the typhoon with a manual, on-the-ground approach led by the National Weather Service (NWS). This is often exhaustive and time-consuming for the assessment team. Observations from the ground can inadvertently create data gaps on damage assessments due to inaccessible areas caused by vegetative and construction debris, and flooded roads and pathways. This may not capture many impacts eligible for local or federal assistance. To address these data gaps and augment damage assessments, the University of Guam (UOG) Drone Corps program aims to assist local and federal government agencies (e.g., utility companies, public health, emergency services, and natural resource management) by collecting high-resolution aerial imagery to help prioritize and allocate limited resources. This presentation highlights the results of this novel collaboration of UOG, NWS, Guam Homeland Security (GHS), and the Office of the Governor of Guam in the creation of the damage assessment of Typhoon Mawar, which ravaged Guam on 24-25 May 2023. Following the typhoon, UOG worked with NWS to identify and capture imagery of vulnerable sites that were heavily impacted. This presentation will also share how UOG Drone Corps’ data was disseminated among other agencies as supplemental data for natural disaster recovery efforts. The presentation will conclude with a summary of the UOG Drone Corps program model as a resource for developing resiliency strategies for vulnerable island communities using advanced and emerging technologies.
How to cite: Fausto, K. and Sayama, J.: Deploying UAV technology to assess typhoon impacts in vulnerable communities in Guam , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8063, https://doi.org/10.5194/egusphere-egu25-8063, 2025.
Coral reefs in the Mariana Islands serve important roles for the islands’ ecology and economy, contributing to the region’s fisheries, tourism, coastal protection, education, and cultural histories. Despite their immense value, the resilience of these marine ecosystems is threatened by an array of climate-change induced stressors, including ocean acidification and coral bleaching. In response, a team from the University of Guam (UOG) launched a large-scale coral reef mapping campaign to monitor priority reef sites throughout the Mariana Islands using drone technology. The UOG team, consisting of researchers and remote pilots funded by the USGS, Pacific Islands Climate Adaptation Science Center, NASA Guam EPSCoR, and NASA Guam Space Grant, has been conducting drone-based missions to capture high-resolution imagery of priority coral reef sites across Guam and Saipan. Their efforts aim to gather aerial data of coral reefs in Micronesia, providing resource managers with essential information regarding response and recovery. Initially, the campaign used of NASA’s fluid lensing technology developed by Chirayath (2019) for coral reef mapping. This technology combines unmanned aerial systems (UAS), off-the-shelf technology, and machine learning algorithms to create detailed coral reef maps by filtering out distortions caused by light and ocean waves, resulting in clear, high-resolution imagery. In 2024, this process was augmented to employ a new methodology that strategically uses RGB sensors and low tides. This system allows the remote pilots to capture the areas and produce orthomosaic maps at much more efficient rates while maintaining high-resolution quality. By providing these datasets within a shorter turn-around time, local natural resource managers are able to get a timely snapshot of the coral reef sites – providing crucial data of the ecosystem’s health that can help inform conservation decisions. This presentation will outline the collaborative efforts between UOG and regional partners, demonstrating how drones and fluid lensing technology are innovating coral reef monitoring efforts. It will explore how the collected data can help local resource managers make informed decisions regarding coral reef management, showcase coral reefs to the general public, ultimately transforming how local communities can contribute to coral reef resilience.
How to cite: Sayama, J. and Fausto, K.: Innovating Coral Reef Mapping with Drones & NASA Fluid Lensing Technology in the Mariana Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5359, https://doi.org/10.5194/egusphere-egu25-5359, 2025.
The Geostationary Ocean Color Imager-II (GOCI-II), which was launched on February 19, 2020, offers an increased observation times within a day and finer spatial resolution than those of its predecessor, the Geostationary Ocean Color Imager (GOCI), which was launched in 2010. To ensure the reliability of GOCI-II data for practical applications, the accuracy of remote sensing products must be validated. In this study, we employed in situ data from Lake Taihu for validation. We assessed the accuracy of GOCI-II products, including the remote sensing reflectance inverted via two atmospheric correction algorithms (ultraviolet (UV) and near-infrared (NIR) atmospheric correction algorithms), as well as the chlorophyll a (Chl-a) concentration, total suspended matter (TSM) concentration, and phytoplankton absorption coefficient (aph). Our results revealed that the UV atmospheric correction algorithm provided a relatively higher accuracy in Lake Taihu, with average absolute percentage deviations (APDs) of the remote sensing reflectance across different bands of 25.17% (412 nm), 29.69% (443 nm), 22.27% (490 nm), 19.38% (555 nm), 36.83% (660 nm), and 33.0% (680 nm). Compared to the products generated using the NIR atmospheric correction algorithm, the derived Chl-a concentration, TSM concentration, and aph products from the UV algorithm showed improved accuracy, with APD values reduced by 16.92%, 3.32%, and 10.91%, respectively. When using UV correction, the 412 nm band performed better than the 380 nm band, likely due to the lower signal-to-noise ratio of the 380 nm band and smaller extrapolation errors when assuming a zero signal for the 412 nm band. Considering that the NIR algorithm is suitable for open ocean waters while the UV algorithm demonstrates higher accuracy in highly turbid environments, a combined UV-NIR atmospheric correction algorithm may be more suitable for addressing different types of water environments. Additionally, more suitable retrieval algorithms are needed to improve the accuracy of Chl-a concentration and aph in eutrophic waters.
How to cite: Zhao, M., Li, H., Li, H., Zhang, X., Ding, X., and Gong, F.: Assessment of GOCI-II satellite remote sensing products in Lake Taihu, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2237, https://doi.org/10.5194/egusphere-egu25-2237, 2025.
The abandonment of agricultural land in India, especially paddy fields, has emerged as a significant challenge for food security and ecosystem sustainability in the country. Although rice production is vital for national food security, research on paddy land abandonment in India remains limited. Some Indian states have reported an alarming decline in paddy cultivation area over the past two decades. The study employs the Udupi district of Karnataka, India, a high-rainfall coastal region where paddy has traditionally been the dominant crop and where paddy land abandonment has been observed, as the study area. This study addresses crucial research gaps by framing these objectives for the study: (1) developing a deep learning framework that utilizes both intensity and phase information from polarimetric Synthetic Aperture Radar (SAR) data for abandoned paddy land detection, (2) leveraging recurrent neural networks (RNNs) to capture temporal patterns in abandonment, and (3) demonstrating an automated, all-weather monitoring approach that overcomes the limitations of traditional optical remote sensing in tropical regions.
Conventional monitoring approaches struggle with persistent cloud cover in tropical regions which limits effective assessment of abandonment patterns. SAR data provides unique capabilities for continuous monitoring under all weather conditions, making it particularly well-suited for tropical regions. However, previous studies have primarily underutilized SAR's potential by concentrating solely on backscattering intensity from ground range detected (GRD) products, overlooking the valuable phase information that could offer deeper insights into land use changes. In this study, we employ Sentinel-1 Single Look Complex (SLC) data, which offers both intensity and phase information. Considering the temporal nature of paddy land abandonment, we developed a deep learning framework utilizing RNNs viz. LSTM, BiLSTM and BiGRU to effectively capture time-series patterns in the data. This framework analyzes backscattering coefficients (VV and VH polarizations) and polarimetric parameters (entropy, anisotropy and alpha angle) derived from SLC data collected during the Kharif seasons from 2017 to 2024. We carried out extensive ground truth data collection of active and abandoned paddy lands to train and validate our models. The backscattering coefficients were processed through orbit correction, radiometric calibration, TOPSAR deburst, multi-looking, speckle filtering and terrain correction. For deriving the polarimetric parameters, after basic preprocessing steps, the covariance matrix was generated followed by the polarimetric decomposition of the phase-preserved data. Results indicate that our RNN models show promising performance in detecting temporal patterns of paddy land abandonment. The method exhibits a robust ability to produce reliable abandoned land maps in regions prone to cloudy and rainy conditions. Future research should explore polarimetric features across various vegetation types in abandoned lands, expand the methodology to other agricultural systems, and examine the impact of socio-economic and topographical factors on abandonment patterns to support evidence-based land management policies.
How to cite: Kasture, S., Hegde A, A., and Umesh, P.: Deep Learning based Paddy Land Abandonment Detection Using Multitemporal Polarimetric SAR Patterns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18606, https://doi.org/10.5194/egusphere-egu25-18606, 2025.
Rock masses are characterized by the complex hierarchical structures involving various scale levels. The deformation of rock masses is primarily controlled in weak structural layers between rocks, whereas the rock block can be regarded as a non-deformable block and can move as a whole. In consequence, a new dynamic phenomenon, namely the pendulum-type wave, has emerged, which is a kind of nonlinear displacement wave caused by the overall movement of relatively intact large-scale rock blocks. Aiming at the complex hierarchical structures of rock masses and low-frequency characteristics of pendulum-type waves, the blocky rock masses composed of granite blocks and rubber interlayers are simplified into the block-spring model and wave motion model. Based on Bloch theorem and d’Alembert’s principle, the dispersion relation and equations of motion of 1D blocky rock masses are determined. Research shows that with the increase of the rock size and geomechanical invariant, the initial frequency of the first attenuation zone gradually decreases, and only the low-frequency waves lower than that frequency can propagate in blocky rock masses, which reveals the mechanism of low-frequency characteristics of pendulum-type waves theoretically. The equivalent substitution for the two models and their errors are given, and the results show that the equivalent substitution of the two models is not universal and unconditional. Finally, the influence of hierarchical structures on the dispersion relation and dynamic response is further studied. The larger the stiffness ratio, or the higher the order of hierarchical structures, the smaller is the effect of ignoring the high-order hierarchical structures.
How to cite: Jiang, K. and Qi, C.: Research on dispersion relation and dynamic properties of pendulum‑type waves in 1D blocky rock masses with complex hierarchical structures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5033, https://doi.org/10.5194/egusphere-egu25-5033, 2025.
Poincaré established a framework for understanding the dependence of a dynamical system's properties on its topology. Topological properties offer detailed insights into the fundamental mechanisms — stretching, squeezing, tearing, folding, and twisting — that govern the shaping of a dynamical system's flow in state space. These mechanisms serve as a conduit between the system's dynamics and its topology [Ghil & Sciamarella, NPG, 2023]. A topological analysis based on the templex approach [Charó et al., Chaos, 2022] involves finding a topological representation of the underlying structure of the flow by the construction of a cell complex that approximates its branched manifold and a directed graph on this complex. A pivotal feature of the cell complex that facilitates the characterization of the flow dynamics is the joining locus, upon which all the fundamental mechanisms that sculpt the flow leave a pronounced signature.
The local dimension d(x) and the inverse persistence θ(x) of the state x of a dynamical system [Lucarini et al., 2016; Faranda et al., Sci. Rep., 2017] provide information on the rarity and predictability of specific states, respectively. We demonstrate herein that these two measures, d and θ also provide information about the localization of the joining locus.
The present work proposes a new topological method for fingerprinting a system’s nonlinear behavior using the concept of persistent generatexes. This novel approach integrates the strengths of two topological data analysis methods: the templex and persistent homologies. Rather than employing a single cell complex and a digraph to characterize the flow of the system, our approach emphasizes the localization of the joining locus through the calculation of local dimension and the inverse persistence, leading to the construction of a family of nested digraphs. The dynamical paths, namely the nonequivalent ways of travelling through the flow, are found to be the most persistent cycles; here the concept of persistence is used in the sense of the persistent homology approach [Edelsbrunner & Harer, Contemporary mathematics, 2008]. The dynamical paths give us the ‘topological fingerprinting’ of a system’s dynamics.
How to cite: Charó, G. D., Faranda, D., Ghil, M., and Sciamarella, D.: Topological fingerprinting of dynamical systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12947, https://doi.org/10.5194/egusphere-egu25-12947, 2025.
The impacts of global change, such as extreme heat and water scarcity, are increasingly threatening urban populations. Evapotranspiration (ET) plays a vital role in mitigating urban heat islands and reducing the effects of heat waves. It also serves as a proxy for vegetation water use, making it a critical tool for designing resilient green cities. Despite its importance, high-resolution mapping of urban ET that captures both spatial and temporal dynamics remains limited. This study focuses on the Paris metropolitan area, analyzing ET variability across multiple spatial scales (from 10 m to 10 km) using Sentinel-2 data from the Copernicus system. The Normalized Difference Vegetation Index (NDVI) is calculated with observation scale of 10 m, and then used as a proxy for ET. Universal Multifractal analysis, which have been widely used to characterize and model geophysical fields extremely variable across wide range of space-time scales, are implemented on this new data set. This framework is parsimonious since it basically relies on three parameters only: the mean intermittency codimension C1, the multifractality index a and the non-conservation parameter H. Specifically, the multifractality index α (1.3–1.5) and the mean intermittency codimension C1 (~0.02) were derived to quantify the spatial and temporal heterogeneity of ET. The analysis, spanning 2019–2023, revealed noticeable temporal and spatial variability in ET. The study focuses on a square region of approximately 60 km × 60 km within the area around Paris. This region was further divided into multiple portions of size ranging from 2 to 10 km to assess potential variability over the studied areas. By incorporating both yearly and monthly data, the analysis captured seasonal trends as well as interannual variability, with higher variability observed during the summer months, driven by increased vegetation activity and water demand. Spatially, yearly data was analyzed and ET variability was most pronounced in densely populated areas, such as central Paris, where anthropogenic influences dominate. In contrast, forested areas and urban parks demonstrated significantly more stable ET patterns, underscoring the moderating effect of vegetation cover. These findings highlight the critical role of urban greening in mitigating extreme variability and stress on urban ecosystems.
How to cite: Zhu, S., Gires, A., Schertzer, D., Tchiguirinskaia, I., and Maksimovic, C.: Temporal and Spatial Dynamics of Urban Evapotranspiration in Paris: A Multiscale Perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16203, https://doi.org/10.5194/egusphere-egu25-16203, 2025.
The presence of rain in wind farms involves several modeling challenges, as the momentum exchanges between turbulent wakes and the particle phase present subtle phenomena. For instance, rain droplets are typically large enough to exhibit inertia relative to the air carrier phase. Under these conditions, it has been found that the gravitational settling of particles in turbulent flows may be either enhanced or hindered compared to stagnant conditions. While this has significant implications for rainfall transport, ash pollutants, and pollen dispersion, very few studies have been conducted in field conditions. Moreover, the scaling laws and non-dimensional parameters governing this phenomenon have not yet been properly identified, and determining which configurations result in the enhancement or hindrance of settling velocity remains an open question.
We propose a hybrid experimental/numerical approach. Field data from a meteorological mast located at a wind farm in Pays d’Othe, 110 km South-East of Paris, France, were used to characterize the background turbulent flow through a set of sonic anemometers. Additionally, disdrometers were employed to characterize the settling velocity of raindrops, discriminating by particle size. Numerical simulations complement this data analysis. Specifically, 3D space and time vector fields that realistically reproduce the observed spatial and temporal variability of wind fields are generated using multifractal tools. Then, 3D trajectories of non-spherical particles are simulated and their settling velocity derived.
Our findings indicate that the presence of turbulence significantly hinders the settling velocity of raindrops in turbulent environments. Our study covers several distinct rainfall events, allowing us to analyze the influence of turbulent flow properties on this phenomenon.
How to cite: Obligado, M. and Gires, A.: Rainfall Dynamics in Wind Energy Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19070, https://doi.org/10.5194/egusphere-egu25-19070, 2025.
Over thirty years ago, Y. Kagan speculated that seismicity could fruitfully be considered as “the turbulence of solids”. Indeed, fluid turbulence and seismicity have many common features: they are both highly nonlinear with huge numbers of degrees of freedom. Beyond that, Kagan recognized that they are both riddled with scaling laws in space and in time as well as displaying power law extreme variability and – we could add – multifractal statistics.
Kagan was referring to seismicity as usually conceived, as a sudden rupture process occurring over very short time periods. We argue that even at one million year time scales, that the movement of tectonic plates is “quake-like” and is quantitatively close to seismicity, in spite of being caused by relatively smooth mantle convection.
To demonstrate this, we develop a multifractal model grounded in convection theory and the analysis of the GPlates data-base of 1000 point trajectories over the last 200 Myrs. We analyzed the statistics of the dynamically important vector velocity differences where Dr is the great circle distance between two points and Dt is the corresponding time lag. The longitudinal and transverse velocity components were analysed separately. The longitudinal scaling of the mean longitudinal difference follows the scaling law <Δv(Δr)> ≈ ΔrH with empirical H close to the mantle convection theory value H = 1. This high value implies that mean fluctuations vary smoothly with distance. Yet at the same time, the intermittency exponent C1 is extremely high (C1 ≈ 0.55) implying that from time to time there are enormous “jumps” in velocity: “Plate quakes”. For comparison, laminar (nonturbulent) flow has H = 1 but is not intermittent (C1 = 0), whereas fully developed isotropic fluid turbulence has the (less smooth) value H = 1/3 (Kolmolgorov) but with non-negligible intermittency C1 ≈ 0.07 and seismicity has very large C1 ≈ 1.3. Our study thus quantitatively shows how smooth fluid-like behaviour for the longitudinal velocity component can co-exist with highly intermittent quake-like behaviour.
Whereas the longitudinal component is well modelled by (highly intermittent) convection, the transverse velocity is well modelled by Brownian motion. In the temporal domain both components (including their strong correlations) display such diffusion behaviour (i.e. with classical exponent H = ½), but are highly intermittent (C1time = C1space/2 ≈ 0.27). Finally, the extreme velocity differences (that appear as occasional spikes in the velocities) have power law probability tails; the “Guttenberg-Richter” exponents in the seismology literature.
The advection - diffusion model is based on an underlying multifractal space-time cascade process. Using mantle convection theory, we show how the driving multifractal flux (ψ) is related to vertical heat fluxes, expansion coefficients, densities, viscosities and specific heats. Taking typical values predict driving fluxes very close to the observed mean <ψ> ≈ 1/(400 Myrs). Trace moment analysis shows that the outer space-time scales of the cascade process are ≈17000 km in space and ≈ 50Myrs in time. Whereas the former corresponds to half the Earth’s circumference, the latter is the typical time required for a plate to randomly “walk” the same distance.
How to cite: Lovejoy, S., Spiridonov, A., and Balakauskas, L.: The turbulence of solids: a multifractal plate tectonic model with Guttenberg-Richter plate “quakes” , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20653, https://doi.org/10.5194/egusphere-egu25-20653, 2025.
The Canary Eddy Corridor is a dynamic region of mesoscale eddy activity, playing a critical role in the transport of physical properties (heat and salt) and biogeochemical properties (nutrients, larvae, plankton) in the eastern North Atlantic. This study investigates the Lagrangian evolution of the trapping capacity of mesoscale eddies according to their lifecycle phases and vertical structure (surface vs. subsurface eddies).
We combine OceanParcels (an open-source Python toolbox) and an eddy identification and tracking algorithm with the GLORYS12V1 reanalysis product and altimetry data from AVISO to simulate particle release and track trajectories within eddies. Applying the eddy tracking algorithm at surface and subsurface levels in GLORYS12V1 reveals that subsurface eddies with a surface signal exhibit subsurface rotational velocities at the eddy core that occasionally exceed those of surface eddy cores. This highlights the potential misrepresentation of eddy transport capacity when relying solely on altimetry data, without accounting for the vertical structure, which can be better resolved through a combination of model outputs and observational data, such as non-standard Argo float configurations. Furthermore, a detailed analysis of the eddy lifecycle phases shows that mature eddies exhibit substantially greater trapping depths compared to their growth and decay stages. These findings align with earlier modeling analyses of dipoles originating south of Madagascar, which also highlight enhanced trapping depths in mature eddies.
The results provide a comprehensive view of the trapping capacity of mesoscale eddies throughout their lifecycle and vertical structure, emphasizing their critical role in biophysical coupling, ecological connectivity, and the transport of biogeochemical properties, as well as microplastics and other pollutants.
Acknowledgments: The first author is grateful for the internship grants ERASMUS +, AMI-MESRI, and TIGER.
How to cite: Vacca, D., Aguiar-González, B., and Morris, T.: Lagrangian Evolution of the Trapping Capacity of Mesoscale Eddies in the Canary Eddy Corridor: A Numerical Modeling Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17965, https://doi.org/10.5194/egusphere-egu25-17965, 2025.
