bluuebunny/crossref_metadata_2025 huggingface dataset:
Intertidal blue carbon ecosystems and their socio-economic value at Lindisfarne, northern England 
Authors: Dunn Rebecca , Hudson Paul , Garrett Ed
Published: March 2024

Coastal Blue carbon ecosystems offer a range of ecosystem services including, for example, carbon sequestration, feeding grounds for birds and flood defence. Therefore, the conservation and management of these ecosystems can act as a nature-based solution that contributes to multiple ecological and climate goals. Blue carbon ecosystems achieve this due to their multifaceted nature and how the communities surrounding these ecosystems interact with them.  An example of blue carbon ecosystems can be found in Lindisfarne national nature reserve (NNR) located on the Northumberland coastline in the Northeast of England. The Lindisfarne NNR contains saltmarshes, seagrass meadows and sandflats in close proximity to each other. Moreover, Lindisfarne not only has value as a source of blue carbon but has further human value due to it being a destination for pilgrimages, bird watchers and holiday makers, as well containing a residential population with a small-scale fishery. Therefore, they represent locations of both natural and cultural capital. The close proximity of multiple blue carbon ecosystems and various human interactions provides an opportunity for a significant comparative analysis of the above blue carbon ecosystems under similar socio-environmental conditions. We do so in terms of both their carbon sequestration potential as well as their socio-economic importance. Through our comparative analysis we present two strands of work. The first is our findings regarding the estimated total sediment and vegetative carbon stock within the Lindisfarne NNR, including two seagrass meadows with contrasting sediment profiles. Additionally, we quantify the various pools of carbon storage: labile, refractory and organic; and the carbon accumulation rates of each studied ecosystem, using 210Pb and 137Cs dating analysed within a Bayesian framework. We also consider factors associated with carbon sequestration, such as vegetation coverage, surface elevation and sediment profile, in our analysis. In doing so this project will be one of the first to calculate carbon accumulation rates and carbon pools in UK seagrass meadows. The second strand of work is generated through a survey of the perceptions and values that people have towards coastal environments via discrete choice experiment survey.  Taken together these results will provide insight into the design of coastal  management plans which focus on using the above blue carbon ecosystems as a tool for climate change mitigation through their long-term carbon sequestration as further contextualised through the priorities and values identified via the social survey.
Impact of climate and agricultural management practices on carbon fluxes using a CLM5 land surface model
Authors: Reddy K Narender , Baidya Roy Somnath
Published: March 2024

Carbon exchange from agroecosystems contributes to the fluctuations of the carbon cycle. The present research employs the Community Land Model version 5 (CLM5) to examine the effects of climate and agricultural management methods, such as fertilization and irrigation, on carbon fluxes in the primary agroecosystems of India. In this study, CLM5 is calibrated and validated against the crop phenology dataset of spring wheat and rice. The crop phenology data is an unprecedented dataset that we have compiled by gathering information from many agricultural institutes around India. The crop dataset covers the period from 1970 to 2020. We have comprehensively tested and validated the CLM5 crop module in the Indian region. Subsequently, regional-scale simulations were conducted. The findings indicated that there are large variations in fluxes among different climatic regions of India, primarily due to disparities in growing circumstances. Throughout the study period, all fluxes exhibited statistically significant upward trends (p
Harmonization of the assumptions of atmospheric aerosol properties in  climate models and remote sensing approaches
Authors: Dubovik Oleg , Litvinov Pavel , Lapyonok Tatyana , Momoi Masahiro , Lopatin Anton , Fougnie Bertrand , Remi Samuel , Checa-Garcia Ramiro , Flemming Johannes
Published: March 2024

The assimilation and reanalysis efforts have essentially advanced in last decades benefiting from the progress in both modeling and observations. However, there is an essential gap between aerosol modelling approaches used in remote sensing algorithms and in the global climate models. This complicates the utilization of the remote sensing data in the reanalysis for constraining  global climate models and  improving their forecast capabilities. This gap also has a restraining effect on the evolution of remote sensing. For example, the predicted or climatological aerosol information from global climate models (e.g., aerosol type, vertical profiles, etc.) can be a valuable source of a priori information to constrain the retrievals, while some inconsistences in aerosol representations complicate the use of this information  in the remote sensing algorithms.The presentation discusses the harmonization and aligning aerosol modeling assumptions used in CAMS  and MERRA-2 model and aerosol retrieval from multi-angular polarimetric data realized by the GRASP/Components approach.  The GRASP/Components approach uses quite similar aerosol modeling concept to that of aerosol transport models. Indeed, it considers aerosol as a mixture of  aerosol components with known index of refraction and derives particle size distribution and fractions of different aerosol components. The aerosol components used in CAMS  and MERRA-2 and  were compared  with GRASP and analyzed by outlining possible advantages and shortcoming in both concepts. As a result, several modifications were recommended for both models and GRASP in order to improve consistency of two approaches. It is expected that once the suggested modifications are realized the aerosol parameters derived from remote sensing are more adapted for  the assimilation of the observation and the use of model forecast data (e.g., aerosol vertical distribution, changes in aerosol properties due to humidity variations, etc.) are straight forward for constraining the remote sensing retrieval.
Effects of PETM in SouthWestern Anatolia: preliminarily results
Authors: Danacı Füsun , Karakuş Erhan , Tokatlı Dilek , Türkmen Bozkurt Banu
Published: March 2024

Paleocene - early Eocene units within the Isparta Bend where located in the west of the Taurus mountain belt consist of reddish-gray micritic limestones, silty mudstones, mudstones and sandstones. The sediments in question contain a late Tantian (NP9 according to Martini 1971 Nannoplankton zoning) aged nannofossil assemblage. In the samples taken from these sediments, many nannofossil species belonging to the Discoaster and Fasciculithus genera, which have oligotrophic hot environmental conditions, were identified. In the quantitative analyses made on the nanofossil contents of the samples taken from the study area, the abundance of species belonging to the Discoaster and Fasciculithus genera shows remarkable increases at upper levels. This situation can be interpreted as a change in oligotrophic-hot environmental conditions. This possibly be an indication of an increase in sea surface water temperature within the early PETM, possibly in the period just before the Paleocene-Eocene transition, which corresponds to the NP9-NP10 boundary in nannoplankton biozonation. The samples taken from the corresponding sediments on these samples contain nannofossil assemblages (Clausicoccus norrisii Bown & Newsam, Sphenolithus radians Deflandre, Ellipsolithus macellus (Bramlette & Sullivan)) giving an early Ypresian age (NP11). According to the results of the quantitative analysis, the diversity of nannofossil species and A significant decrease in the number of species has been observed, and Fasciculithus species are also very rare. This may be signs that the environmental conditions that existed in the late Thanetian changed greatly in the early Ypresian. Planktonic foraminifera, oxygen isotope and carbon isotope analyses in the samples continuing.
Agrohydrological modelling approach for assessing the impact of climate change on water resources and land management in the Messinian region, Greece.
Authors: bouizrou ismail , Castelli Giulio , Cabrera Gonzalo , Villani Lorenzo , Bresci Elena
Published: March 2024

The Mediterranean region is highly susceptible to the consequences of warming, leading to an increasing of extreme events such as droughts, severe heat waves, and precipitation events. The Messinia watershed (MW) is predominantly characterized by olive cultivation, encompassing approximately 70% of the landscape. These olive orchards constitute a vital component of the Mediterranean ecosystem, playing a crucial role in regional agriculture. The MW is a perfect illustration of a Mediterranean watershed significantly impacted by climate change, as well as soil degradation and a lack of effective land management practices.In this context, agro-hydrological modelling emerges as a potent tool to address soil degradation and enhance water resource retention within the olive orchards at the watershed scale. To achieve this objective, the SWAT+ agrohydrological model was chosen for a comprehensive assessment of the potential impacts of climate change on water resources and ecosystems in the Messinia region. The adopted modelling approach involved both hard and soft calibration techniques, simulating four sub-watersheds of Messinia by incorporating remote sensing data, including evaporation and soil moisture, for multi-criteria model calibration.The calibrated model was subsequently employed to assess the potential impacts of climate change on water resources and ecosystems in the Messinia region, utilizing various RCM climate scenarios. Our findings are valuable for addressing soil degradation, as well as for guiding land and water management practices in the Messinian watershed.  This research was carried out within the SALAM-MED project, funded by the Partnership for Research and Innovation in the Mediterranean Area Programme (PRIMA).The content of this abstract reflects the views only of the author, and the Commission cannot be held responsible for any use that may be made of the information contained therein. 
Precambrian mixed clastic-carbonate systems of Saudi Arabia: Integrated sedimentological, micropaleontological and geochemical characterization
Authors: Malik Muhammad Hammad , Koeshidayatullah Ardiansyah , Herlambang Adhipa , Amao Abduljamiu , Bello Abdulwahab M. , Al-Ramadan Khalid
Published: March 2024

The Precambrian sedimentary successions in the northwestern part of the Arabian plate are found in various isolated basins, associated with the rifting phases of Precambrian Najd fault system. While some preliminary work focusing on the low-resolution geochemistry and radiometric age dating has been performed in some basins, it is considered insufficient in explaining the depositional system of these sediments. This absence of comprehensive sedimentological control has resulted in previously formulated conclusions which fail to clarify the extent and depositional environment of these sediments leading to debate and confusion, hampering research progress in the region. In this context, we focused on the Dhaiqa Basin of NW Saudi Arabia due to its exceptional and continuous exposures and perform integrated high resolution micropaleontological, geochemical and isotope analysis to unravel the origin and evolution of these deposits. Our sedimentological analysis revealed that contrary to common belief, the Ediacaran sequence in this basin contains a mixed clastic-carbonate system rather than a continuous carbonate succession lying conformably on sandstone layers. The deposited sediments include basal conglomerates, covered by shallow water mixed clastic-carbonates and microbialites facies of limestones. Overlying these carbonates are thinly laminated sandstones which also suggest a shallow-water origin, predominately lacustrine, overlain again by the mixed clastic carbonate deposits. The mineralogical data extracted using X-Ray diffraction and X-Ray florescence techniques aided with Axioscan and QEMScan analysis, also reveals a proximity to the source of sediments. Recovered Ediacaran microfossils from cherty carbonates, sandy microbialites as well as from siliceous mudstones also signify a shallow water environment. Stable isotope results are also found to consistent with these observations with δ13C values hardly reaching -2‰ VPDB, not consistent with Ediacaran isotopic values of global seas extracted from neighboring country of Oman where values go as negative as -12‰. This stratal pattern explicates the Dhaiqa basin to be a potentially fault-controlled basin, originating at the flak of a normal fault developed during some stage of the left-lateral transcurrent Najd Fault system with no evidence of connection with the global seas so far. These acquired results will not only contribute to the correlation of these sediments across the Arabian plate but will also facilitate comparisons with similar sediments worldwide.
Observations and modelling of the solar wind composition variations
Authors: Rouillard Alexis , Lomazzi Paul , Thomas Simon , Réville Victor , Gannouni Bahaeddine , Poirier Nicolas , Dakeyo Jean-Baptiste , Hou Chuanpeng
Published: March 2024

The physical mechanisms that regulate the abundance of heavy ions in the solar wind are not well understood. Variations in composition are measured in the charge state of heavy ions as well as the abundance of alpha particles and of elements with low first ionisation potential (FIP). The ionisation state and the abundance of heavy ions remain unchanged beyond the solar corona in the solar wind. Since the slow and fast solar winds have very different compositions, the slow wind being enriched in low-FIP elements, it has been argued that they must form through different processes in the solar corona. We analyse solar wind data taken in situ at different points in the inner heliosphere by Parker Solar Probe, Solar Orbiter and ACE to study the relation between ion abundances and solar wind properties (focusing on plasma moments, cross-helicity and non-thermal particles). This leads us to classify the different solar wind types according their composition and Alfvénicity during the different phases of the solar cycle. We then compare this classification with recent results of a new multi-species model of the solar corona and solar wind to study the various mechanisms potentially controlling solar wind composition, including diffusion processes and wave-particle interactions, to regulate heavy ion abundances. This work was funded by the ERC SLOW SOURCE project.
ShellSetHPC – Parallel dynamic neotectonic modelling
Authors: May Jon Bryan , Bird Peter , Carafa Michele Matteo Cosimo
Published: March 2024

Geodynamic forward models are typically computationally expensive, they generally draw on a large set of input and testing data and will normally have quite large model domains in order to ensure that the results are practical and applicable. To reduce simulation time many programs will use some form of parallel computing in their calculations, e.g., programs may use the message passing interface (MPI) communication protocol or OpenMP API to partition the domain into reasonable chunks divided between multiple processes, this can improve performance by having multiple processes solve smaller parts of a large problem in parallel.Geodynamic inverse modelling involves using known phenomena to constrain a model, or set of models, to improve knowledge of unknowable or unmeasurable parameters involved within a dynamic system. For example, one could use measured GPS velocities and seafloor spreading rates to infer changes to mantle convection or combine GPS velocities with near-surface temperatures to monitor the growth of magma chambers.Since geodynamic inversion involves running multiple constrained forward models it naturally suffers from the performance issues linked to the forward models themselves, briefly: a poorly performing forward model will mean a poorly performing inverse model. More than that, the inverse model must perform multiple (the more the better) forward models with updated parameters. These requirements, a good enough forward model, and an efficient method of performing multiple models in parallel while optimizing the desired parameters, leads to an obvious conclusion: to perform forward models in parallel while searching for an optimal model.To this end we present a geodynamic inversion model, which we call ShellSetHPC, which uses a combination of existing, well known, and robust software to model the neotectonics of planetary lithosphere. This is further combined with an efficient, de-centralised random search algorithm able to generate testable models within a user defined N-dimensional space. This algorithm is also able to launch multiple models in parallel thanks to an MPI framework. The forward model makes use of Intel’s thread safe math kernel library (MKL) to solve the linear system in parallel. This hybrid approach lends itself to use on high performance computing (HPC) machines which would allow a more complete utilisation of these features.In this work we will present scaling results on an HPC cluster and compare these with results obtained from more typical search algorithms. All tests are performed within a realistic geological setting, the results of which will be used to gather insight into the performance of the driving model generators and their settings.
Investigating the surface energy budget of the continental boundary layer in the Arctic
Authors: Ravetta François , Raut Jean-Christophe , Maillard Julia , Fochesatto Javier , Dieudonné Elsa , Law Kathy S , Brett Natalie , Ciarletti Valérie , Delanoë Julien , Kivi Rigel , Pirrazini Roberta
Published: March 2024

Recent field campaigns in Alaska and Finland combining in situ observations and remote sensing are analysed to investigate the components of the surface energy budget of stable atmospheric boundary layers during Winter and Spring. Modulation of stability by a local flow in Central Alaska during the ALPACA (Alaskan Layered Pollution and Chemical Analysis) 2019 and 2022 campaigns is discussed. The representation of surface inversions over boreal forests by the surface layer scheme of WRF (Weather Research and Forecasting) mesoscale model is also evaluated. The impact of mixed phase clouds on the surface energy budget is discussed relying on observations combining radar, lidar and radiometric measurements in Northern Finland during the IMPECCABLE (IMPact on the Energy budget of mixed-phase Clouds and Canopy in the Arctic Boundary LayEr) 2022-2023 campaign. The variability of surface emission is addressed using airborne drone measurements.
A Multi-dimensional Framework for Assessing Disaster Recovery Pathways: Lessons and Experiences from Germany and Nigeria 
Authors: Okunola Olasunkanmi , Werners Saskia
Published: March 2024

The recent increase in the frequency of extreme events worldwide highlights the urgent need for comprehensive and coordinated efforts to achieve sustainable recovery and resilience. However, the recovery process following such events has often been prolonged and uneven, and it is frequently overlooked in global disaster management policies. This study aims to address this gap by providing a comprehensive assessment of disaster recovery pathways after extreme flood events, by developing a multidimensional framework.The research questions guiding this study are: What are the key elements of disaster recovery? What factors act as barriers or facilitators to recovery pathways after disasters? How do these factors contribute to building back better? To answer these questions, the study combines conceptual and empirical insights, including a literature review and an assessment of recovery pathways from recent extreme flood events in Germany and Nigeria.A multi-methods approach was utilized, encompassing in-depth interviews with representatives from government, NGOs, the private sector, community members, and disaster recovery experts. A total of thirty-eight in-depth interviews were conducted with stakeholders from both countries. Additionally, grey literature and policy documents were analyzed. Qualitative content analysis was employed to analyze the data derived from the in-depth interviews and policy documents.The findings highlight the significant role of relief organizations in driving recovery efforts, with a particular focus on humanitarian aspects. Moreover, sustainable changes have been observed, such as the implementation of communal heating systems and the use of sustainable materials in building reconstruction. Religious organizations have played a crucial role in providing social and psychological support during the recovery process.Despite these positive developments, the study underscores a lack of concerted efforts to truly "build back better" during the recovery process, primarily due to political, financial, and institutional constraints. This observation holds across different cases, including the Ahr and Erftstadt in Germany and Lagos in Nigeria. These themes have been explored within the broader scope of the Sendai Framework for Disaster Risk Reduction 2015 - 2030.
Evaluation of atmospheric circulation of CMIP6 models for extreme temperature events using Latent Dirichlet Allocation
Authors: Malhomme Nemo , Podvin Bérengère , Faranda Davide , Mathelin Lionel
Published: March 2024

Climate models aim at representing as closely as possible the statistical properties of the climate components, including extreme events on which fine-tuning data may be less available. This is a fundamental requirement to correctly project changes in their dynamics due to anthropogenic forcing.In order to evaluate how closely models match observations, we need algorithms capable of selecting, processing and evaluating relevant dynamical features of the climate components. This has to be reiterated efficiently for large datasets such as those issued from the Coupled Model Intercomparison Project 6 (CMIP6). In this work, we use Latent Dirichlet Allocation (LDA), a statistical soft clustering method initially designed for natural language processing, to extract synoptic patterns from sea-level pressure data and evaluate how close the dynamics of CMIP6 climate models are to ERA5 reanalysis, both in the general case and in the case of extreme temperature events.LDA allows for learning a basis of decomposition of maps into objects called "motifs". From the ERA5 sea-level pressure data, the method robustly extracts a basis of motifs that are interpretable objects at synoptic scale, i.e. cyclones or anticyclones. Pressure data can be projected onto this basis, yielding motif weights that contain local information about the large-scale atmospheric circulation. LDA decomposition is efficient and sparse: most of the information of a given map is contained in few motifs. It is therefore possible to decompose any map in a limited number of easy-to-interpret synoptic objects. This allows for a variety of new angles for statistical analysis.The weights statistics can be used to characterize the general and extreme dynamics in reanalysis and model data. By comparing the statistics obtained from reanalysis data with those obtained from a selection of CMIP6 models, we can quantify errors on each localized circulation pattern and identify model-specific and model-agnostic errors. We found that, on average, large-scale circulation is well predicted by the models, but model errors are increased for extreme events such as heatwaves and cold spells. Additionally, Mediterranean motifs were found to be associated with significant model errors for all the considered models in all cases.
Lightning-ignited fires in Portugal: A Comparison of satellite-derived and in-situ Databases
Authors: Menezes Lucas S. , Russo Ana , Ramos Alexandre M. , Trigo Ricardo M. , Gouveia Célia M. , Benali Akli , Pereira José M. C. , Deus Ricardo , Libonati Renata , Morales Rodriguez Carlos A.
Published: March 2024

Fire databases typically contain information regarding the location, timing, and duration of fire occurrences, as well as the cause (natural or human-induced). These databases allow for the analysis and understanding of the circumstances surrounding the ignition and propagation of wildfires, being highly relevant when addressing fire suppression and management plans, or for improvement of prevention policies. In Portugal, the Mediterranean country with the highest burned area over the last five decades, a large number of fires in the official database have unknown causes, indicating that further investigation is required to understand to what extent lightning ignitions are a relevant fire cause over the country.  In this context, we aim to investigate the spatial-temporal variability of lightning-induced fires in Portugal using the official fire database from the Portuguese Institute for Nature Conservation and Forests (ICNF) from 2003 to 2020. Then, we evaluate the potential of remote sensing data retrieved daily from the Moderate Resolution Imaging Spectroradiometer (MODIS) to act as a complementary and alternative information source when identifying fire causes. Our results revealed that lightning-ignited fires are less relevant to the fire regime than claimed by the current official report about fire causes in the country, accounting for only 1% of the fire occurrences instead of 2% and 4.6% of the total burned area, instead of 5.6% in the 18-year period under analysis. Despite accounting for a low percentage of the total amount of fires, lightning ignitions presented a higher relevance among larger fires (above 100 hectares) and contributed significantly to the total area burned during the unusual 2003 and 2017 fire seasons. Besides often occurring in remote areas, which delays the initial suppression and contributes to the larger fire size, as its importance in these extreme years suggests, lightning can trigger large fire events when in conjunction with extreme events such as droughts and heatwaves. Our results reveal that the use of remote sensing data resulted in reasonable precision when classifying the ignition cause of fires. However, due to its inherent limitations, the remote sensing data failed to account for the smaller fires, which represent the majority of the occurrences in the Portuguese database.
Stone decay in the underwater environment: examples from Mediterranean archaeological sites
Authors: Germinario Luigi , Moro Isabella , Crocetta Fabio , Tomasin Patrizia , Moschin Emanuela , Cibecchini Franca , Demesticha Stella , Gallocchio Enrico , Gatt Judith , Mazzoli Claudio
Published: March 2024

This contribution presents one of the research directions of the project WATERISKULT (https://wateriskult.geoscienze.unipd.it), involving the decay of underwater archaeological sites, in particular of submerged structures and artifacts made of stone. The pilot sites of this project are located in the western, central, and eastern Mediterranean Sea, and include the Roman complex of Baia in Italy, the Hellenistic harbor of Amathus in Cyprus, and the Roman port structures of Anse des Laurons in France. Diving and sampling campaigns were organized therein in the first half of 2023, and were followed by laboratory analyses that explored the state of conservation of different archaeological stone materials (limestones, marbles, tuffs, sandstones, etc.). Microscopic techniques were applied for investigating the stone composition, biofouling, and chemical alteration, observing the surface and stratigraphic features of the sampled materials. Moreover, 3D morphometric techniques allowed for the quantification of the physical damage of the archaeological surfaces. The analytical results were combined with site-specific topographic information collected during the dives and environmental data provided by seawater monitoring agencies. In that way, the interaction between ancient materials and the underwater environment was explored, discussing the relationship between deterioration and a range of different stone and seawater properties.
Are users drawing the same conclusions from different climate data portals?
Authors: Lavoie Juliette , Caron Louis-Philippe , Logan Travis , Barrow Elaine
Published: March 2024

Climate data portals serve as important tools for climate services providers to effectively communicate with decision-makers. With a rapid increase in the number of such portals and the datasets underlying them, critical questions arise: do users encounter similar narratives across these platforms, and does their choice of portal influence decisions on adaptation measures? This presentation conducts a comparative analysis of two prominent Canadian portals, namely Climate Data Canada and Portraits Climatiques. Both portals feature bias-adjusted CMIP6 simulations that differ in various aspects, including bias-adjustment methodology, climate of reference, ensemble composition, and emissions scenarios. The impact of these choices is explored by assessing three core variables (daily maximum temperature, daily minimum temperature, and daily precipitation) and examining five case studies from the agriculture, transport, and health sectors. Our findings reveal significant disparities between the portals in terms of climate indicator values at the end of the century, while projected changes compared to the present climate are often more similar. Moreover, we observe a strong influence of the reference dataset choice on threshold-based indicators. Despite these discrepancies, users commonly make similar final decisions when employing both platforms, as adaptation measures are not markedly sensitive to the distinctions and as other non-climate-related factors must be considered in the decision-making process. This study sheds light on differences and similarities between climate data portals, emphasizing the need for climate services organizations to transparently communicate the implications of their choices to users, in order to guide the formulation of effective adaptation strategies.
Climate drivers of meteorological droughts in north-western Europe (1836-2022)
Authors: Neimry Emile , Goosse Hugues , Jonard Mathieu
Published: March 2024

Droughts have garnered global attention due to their adverse effects on crops, ecosystems, and society. Despite their frequent occurrence in north-western Europe, the causes of these droughts remain poorly understood. This study investigates the historical climate drivers of meteorological droughts in the region. The identification of drought events since 1836 is conducted using the Standardized Precipitation Evapotranspiration Index at a 3-month scale, based on reanalysis datasets (ERA5 and 20CRv3). Subsequently, by employing clustering methods, we categorize the diverse atmospheric conditions leading to droughts into discernible patterns. Our next objective is to assess the long-term variability and trends within these patterns. This research provides a long-term regional analysis of meteorological drought drivers, contributing to a deeper understanding of regional climate changes over the past two centuries.
Multi dimensional Assessment of Air Pollution Evolution in the Sundarban Deltaic Region in context of Climate Change and Socio-Economic Dynamics.
Authors: Ganguly Varsha , Mallik Chinmay
Published: March 2024

Sundarbans is a biodiversity hotspot sprawling across eastern India and Bangladesh, often subject to various biospheric research. Despite being one of the keys to coastal resilience building and climate change mitigation,  it remains one of the most vulnerable areas in terms of environmental degradation and economic instability. This multi-dimensional study aims to look into the temporal evolution of air pollutants like nitrogen oxides (NOx), particulate matter (PM), Sulfur dioxide (SO2), carbon monoxide (CO) and carbon dioxide (CO2) along with the urbanization of the highly sensitive biodiversity hotspots of Sundarban Deltaic Region by using long-term satellite and reanalysis datasets. The time series analysis of the air pollutants, including PM, NOx, CO, and CO2 showed an increasing trend of the pollutant concentrations, mostly owing to anthropogenic sources and climate change. The enhancement of air pollutants along with climate parameters like temperature indices within densely vegetated regions such as the Sundarbans, a biodiversity hotspot, raises considerable concern especially when the region’s socio-economic statistics have also deteriorated over the years.This study also aims to assess the effectiveness of NBS in carbon sequestration through mangrove plantations implemented by diverse stakeholders over time. Further, the socio-economic dynamics of communities depending on mangrove resources were studied by utilizing various district-level surveys, plantation statistics, field surveys and stakeholder consultations. It was found that the communities have been most dependent on the mangrove species for firewood even after the advent of LPG which does undermine the Government’s efforts for clean fuels in homes. Though the households continue to graze cattle and use firewood, the awareness amongst the vulnerable populations regarding the importance of mangroves has improved. The planting of mangrove trees has not only contributed to ecological benefits but also brings in economical benefits for the communities involved. The initiative carried out in areas identified as vulnerable under the Panchayat's Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) 100-day work scheme, aims to provide employment opportunities to rural households by guaranteeing at least 100 days of wage employment per year, and in the context of mangrove plantation, it aligns both environmental conservation, economic development goals and women participation. Women's participation is actively observed in the plantation and maintenance of mangroves, which gives them economic benefits at the same time aligning with the goals of habitat preservation and climate mitigation. This symbiotic relationship proves to be the key to several potential environmental initiatives that positively impact the livelihoods of local populations.
Exploring Regional Ocean Climate Variability: Insights from Integrated Clustering and Principal Component Analysis.
Authors: Radin Cristina , Nieves Veronica
Published: March 2024

Ocean regional climate variability is a part of the Earth's complex system that can influence the occurrence and intensity of extreme weather events. Variability in ocean temperature can either amplify or mitigate the impact of these events. For example, the El Niño phenomena affect weather conditions in various parts of the world, leading to droughts, floods, and altered precipitation patterns. Furthermore, regional climate variability is also linked to changes in sea level. Understanding regional variability is crucial for predicting how sea level changes will vary in different parts of the world, which has profound implications for coastal communities and infrastructure. To contribute to this understanding, we have developed a novel method that combines K-means clustering and Principal Component Analysis to extract ocean climate modes at a regional scale worldwide. This integrated approach automatically identifies regions of variability, allowing for the emulation of coastal and regional sea level variations across multiple timescales. It also has the potential to offer valuable insights into the significance of temperature across multiple depth layers extending up to 700 meters. The produced set of regional sea-level emulators are a complementary source of information in coastal areas, especially in situations where satellite altimetry encounters challenges and/or tide-gauge sensor records are incomplete, thereby supporting well-informed decision-making.
Forecasting PM2.5 concentrations using machine learning approaches: added value of low-cost monitoring and regional modeling
Authors: Hernández K. Santiago , Nieves Duvan , Pérez-Carrasquilla Jhayron S. , Montoya Paola , Zuluaga Manuel D. , Ramírez Mauricio
Published: March 2024

Machine Learning (ML) techniques have acquired great importance for forecasting air pollution events, due to their relatively low computational cost and skillful results within horizons of up to 3 days. In this study, we forecast PM2.5 concentrations measured by low-cost sensors in the Aburrá Valley, a densely populated and complex terrain region in the Colombian Andes. ML models such as Artificial Neural Networks, Random Forest, Gradient Boosting, and Support Vector Regression, were trained for each forecast horizon (up to 72 hours) using data from satellites and global atmospheric models, which are available in other cities with little in-situ information. The information includes 2-meter temperature, boundary layer height, latent heat flux, winds at different levels and precipitation from the Global Forecasting System (GFS); total aerosol optical thickness (AOD), dust AOD, black carbon AOD and sea salt AOD data from the CAMS Global Atmospheric Composition Forecast; and an index calculated from predicted back-trajectories and the fire radiative power derived from MODIS satellite-monitored hotspots, which allows accounting for long-range transport of biomass burning aerosols. As an added value, we investigated the effect of including data from real-time PM2.5 concentrations from low cost sensors, as well as operational forecast information from the Early Warning System of Medellín and the Aburrá Valley (SIATA) with the WRF regional model. The predictions were evaluated across multiple performance metrics and during an air quality special period in which air pollution increases in the region. Our results show that ML-based forecasts perform better than those obtained directly from CAMS. By including real-time measured information, forecast performance significantly improves during the first 24 hours after initialization. In addition, meteorological data obtained from the WRF model are useful for extending the usefulness of the forecasts to longer horizons (2 to 3 days). Since this approach is based on satellite data and global atmospheric models, it can be easily replicated in other cities with scarce in-situ information. Finally, this work highlights the usefulness of these tools for air quality management and serves as a reference framework for the implementation of forecasting tools in other cities with scarce air quality data.
Lunar Impact Flashes and their Resultant Craters 
Authors: Sheward Daniel , Avdellidou Chrysa , Cook Anthony , Delbo Marco
Published: March 2024

Impact craters have been identified on almost every type of celestial body, and are among the most destructive processes. The lunar surface is covered in craters ranging from 2500km in diameter, down to sub-millimetre scale, and >600 lunar impact flash (LIF) events have been observed by ground based telescopes, detecting the generated light. Despite this large volume of data, previously only three freshly formed craters had been both located within LROC imagery, and have the forming LIF documented.Using PyNAPLE (Sheward et al., 2022) - software which locates fresh craters from the selenographic latitude, longitude, and epoch of a LIF - a search was performed upon the 22 most energetic LIFs within literature. For completeness, this included the three LIF events with already identified craters. There were sufficient LROC images to locate six new freshly formed craters, in addition to the three already identified. For these nine events, the likely parent meteoroid stream for each event is identified to constrain the velocity, impact geometry, and impactor properties. From this, the pre-impact kinetic energy could be obtained from an estimation for the luminous efficiency, and the luminous energy released by the LIF.Furthermore, using the crater scaling laws from Melosh (1989), both the predicted crater size from the kinetic energy, and the predicted energy from the observed crater size, could be calculated for each event. From this, it was found that the predicted crater diameter was consistently larger than the observed crater. While there are several factors that could contribute to this, the single most likely factor is the poorly constrained luminous efficiency. Under this assumption, a more accurate value for the luminous efficiency can be calculated using the observed craters. Using a rearrangement of the crater scaling laws, with the kinetic energy equation, and luminous efficiency, η = Elum/Ekwhere Elum is the energy released by the LIF, and Ekis the kinetic energy. After outlier removal and meteoroid stream identification, this produces an average value of η=0.0171324. While this is slightly larger than the typically used values of between 10−2and 10−4, the difference is not drastic.References Melosh, H. J. (1989). Impact cratering : a geologic process.Sheward, D. et al (2022). MNRAS, 514(3):4320–4328
Improving the detection accuracy of vegetation destruction events using bands sensitive to vegetation foliage, canopy and water content
Authors: Zhao Chuanwu , Pan Yaozhong , Ren Shoujia , Ma Gelilan , Gao Yuan , Wu Hanyi , Zhu Yu
Published: March 2024

Frequent climate changes and intense human activities increase the risk of vegetation destruction. Spectral index-based change detection can identify vegetation destruction from multi-temporal images, providing valuable insights for vegetation management and post-disaster recovery efforts. However, we still face the challenge of the spectral diversity of vegetation destruction and the complexity of the background environment. Existing spectral indices (VIs) often struggle to accurately detect vegetation destruction in complex scenarios. These VIs focus on specific aspects of vegetation, such as leaf, canopy or water content, limiting their effectiveness in capturing vegetation dynamics. In addition, they are susceptible to background environment changes. To overcome these challenges, this study proposes a new metric called Slope Vegetation Index (SVI) using bands that are sensitive vegetation leaf, canopy, and water content (i.e., green, near-infrared (NIR), and short-wave infrared (SWIR) bands). The performance of SVI was verified by the dual time-phase difference method, and five widely used VIs were selected for detailed comparison. In addition, the performance of SVI was evaluated using PROSAIL simulation data, various vegetation change scenarios, and real vegetation destruction cases. Moreover, we assessed the applicability of SVI to other multispectral sensors. The results showed that compared with existing VIs, SVI exhibited the highest sensitivity to vegetation changes under different chlorophyll and water content conditions. In various vegetation change scenarios and vegetation destruction cases, SVI consistently had the best performance, with Producer’s Accuracy (PA), User’s Accuracy (UA), and F1 scores all exceeding 0.90. In complex scenarios, SVI could better highlight vegetation changes while suppressing background environment changes. Additionally, SVI performed well on other Landsat-8/9 images, with an F1 score exceeding 0.89. This study confirms that SVI is valid for vegetation destruction detection and has potential for large-scale and high-frequency vegetation monitoring.
Deep Learning-Based Monitoring of Artisanal Mining to Tackle Environmental Degradation: A SERVIR West Africa Case Study in Ghana
Authors: Gelaye Kidia , Asare Emmanuel , Amponsah Mary , Bartel Paul , Traore Pierre CS , Abramowitz Jacob , Cherrington Emil , Mensah Foster
Published: March 2024

Deep Learning-Based Monitoring of Artisanal Mining to Tackle Environmental Degradation: A SERVIR West Africa Case Study in Ghana
Kidia Gelaye1, Emmanuel Asare2, Mary Amponsah2,  Paul Bartel1,  Pierre CS Traore3, Jacob Abramowitz4,5, Emil Cherrington4,5,  Foster Mensah2 
1 International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Accra, Ghana,
2 University of Ghana, Center for Remote Sensing and Geographic Information Services (CERSGIS), Accra, Ghana,
3 International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Dakar, Senegal,
4 Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL, USA
5 NASA SERVIR Science Coordination Office, Marshall Space Flight Center, Huntsville, AL, USA

Artisanal mining, a prevalent form of mineral extraction and income generation activity in Africa, is widely practiced in Ghana. Small-scale gold mining is a common livelihood in rural areas, often conducted in forest reserves, cocoa farms, lands along riverbanks, and sometimes within rivers. It involves traditional techniques such as digging pits, washing soil, and extracting gold through sieving or amalgamation using mercury (Hg). However, due to the decentralized and small-scale nature of this practice, monitoring, regulating activities, and making timely decisions become challenging. Consequently, in recent years, gold mining has expanded rapidly into agricultural lands, protected forest reserves, and some parts of the savannah regions leading to severe land degradation, biodiversity loss, pollution of water bodies, and soil acidification, adversely affecting both biophysical and socio-economic environments. To counteract these issues, real-time remote sensing is employed to monitor large and remote active gold mining sites in Ghana. SERVIR West Africa is implementing Earth observation services using Fully Convolutional Neural Networks (FCNN) approach to support decision-making. The data preparation involves digitizing active mining areas, inactive mines, and polluted rivers using Planet/NICFI imagery. The FCNN shows high accuracy in classifying land use features. For instance, distinguishing between mining and farm or forest areas with accuracy matric (F1) score of 0.99, while differentiating active from inactive mines achieved an F1 score of 0.94, and classifies polluted versus unpolluted rivers with moderate F1 score (0.91). The high accuracy of FCNN classifications is attributed to the distinct visual difference between vegetation-covered land features and bare lands (mining sites). 
 
Keywords: Decision making, Deep-Learning, FCNN, Ghana, Planet/NICFI, Small-scale gold mining 
A Long-term Spatial Runoff and Flood Prediction Method in Higher Accuracy
Authors: Wang Jiaqing , Zhao Jianshi , Wang Quanjun
Published: March 2024

When predicting future long-term runoff using hydrological models, the large uncertainty associated with general circulation models (GCMs) pose significant limitations. Additionally, current accurate long-term runoff predictions are restricted to specific locations with gauge stations, hindering basin-wide water resource planning and management. To address these challenges, this study proposes a hybrid Hydrological model, Empirical Orthogonal Function analysis, Gaussian Process Regression (HEG) model, which demonstrates higher accuracy in daily runoff prediction across the entire basin compared to the traditional multi-model ensemble mean method, with KGE improved by 0.09~0.11, and NSE improved by 0.08~0.32). Moreover, to enhance the estimation of future extreme flood risks which are of great concern of the public but are often predicted with high uncertainty, the model incorporates uncertainty interval information into prediction and is called HEGU model. Evaluations conducted in the topographically and climatically diverse Brahmaputra River Basin confirm the effectiveness of the HEGU model. The relative error of peak discharge (REPD) is reduced to an average of ~46% of that obtained through the ensemble mean method, while the correlation coefficient (CC) for flood volume estimation during the monsoon period increases from -0.054 to 0.645. Furthermore, the HEGU model demonstrates the potential to improve overall runoff prediction accuracy across the basin when the data quality of extremely few grids in the high-fidelity dataset is enhanced. The enhancement can be achieved through the incorporation of additional runoff gauge stations, remote sensing data, and other data augmentation techniques. These findings underscore the practical significance of the HEGU model, indicating its high effectiveness and applicability in real-world future hydrological projection and water resource management scenarios.