bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of detecting algorithm for potential refreezing rain area using the road icing accidents report 
Authors: Kwon Soohyun , Lee Jeong-Eun , Lee Seungwoo , Choi Hee-Jeong
Published: March 2024

Freezing rain is a meteorological phenomenon in which precipitation melts in the upper atmosphere, transforming into super-cooled droplets near the ground due to lower temperatures. In Northern Europe and North America, strong winter storms often accompany freezing rain, leading to road or facility damage. In Korea, several traffic accident have also occurred due to road icing caused by freezing rain, demanding the development of monitoring technologies for enhanced safety measures. In order to provide the information about the road hazard warning and ensuring safety, we analyzed the atmospheric condition and dual-polarimetric characteristics for road icing and developed the algorithm to detect the potential refreezing rain area by using dual polarization radar and 3-D wet-bulb temperature. We selected road icing accidents including precipitation and inversion layer events from 2019 to 2021, and analyzed the changes in surface temperatures and wet-bulb temperatures at surface and the hydrometeor classified using dual-polarization variables at upper layer. The hydrometeor at the accident sites were classified with rain or super-cooled droplet, and wet-bulb temperatures ranged between -2 to 1.5 degrees. This information was used to determine the potential refreezing rain area. The inversion layer was also analyzed by the calculation of 3-dimensional wet-bulb temperatures through multi-quadratic interpolation using various observations (AWS, sounding, Buoy, etc.) and the Korea Local Analysis and Prediction System (KLAPS). The dual-polarization variables were employed to classify the hydrometeor type and investigate the possibility of ice particle melting within the inversion layer. The area for potential refreezing rain was designated as dangerous/cautious zones based on ground temperature conditions when snow particles melted within the inversion layer.The performance of the algorithm for potential refreezing rain areas was evaluated during cold seasons when incidents of refreezing rain, often referred to as black-ice events occurred. We analyzed the hourly and monthly frequencies of detecting dangerous/cautious zones during traffic accidents caused by refreezing rain.※ This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.
MEDUSA - Modelling Equity and DistribUtional impacts for Socioeconomic Analysis
Authors: Alonso-Epelde Eva , Rodés Clàudia , Moyano-Reina María
Published: March 2024

Addressing the major challenges of the 21st century, such as climate change, will require complex and ambitious policies that promote social justice. To do so, it is necessary to design efficient policies that do not exacerbate existing inequalities, such as gender or income inequality. In this sense, it is essential to carry out impact analyses of policies from a holistic perspective that evaluates the economy, energy, land, and water systems in an integrated manner before implementing them. While Integrated Assessment Models (IAMs) have been a fundamental tool in the past, micro-simulation models for distributional analysis have the advantage of providing more heterogeneous results that help to more robustly identify the socio-economic impacts of the policies to be implemented. These analyses make it possible to identify the people who will be most affected by policies and to implement compensatory measures to make the policy fairer. Thus, the combination of both models (IAMs and microsimulation models) can provide valuable results for decision making.  MEDUSA is an R package that allows the development of distributional analyses in isolation or in connection with other models such as GCAM. Its extensive database allows for highly disaggregated results, taking into account numerous socio-economic and demographic characteristics of households, such as income level, place of residence, type of family or the degree of feminisation of the household. At the moment, the prototype works for Spain, but the idea is to extend it to all EU countries in the short term. However, the package could be extended to all countries that are able to provide the raw data of the model.
Interactions Between a Marine Heatwave and Tropical Cyclone Amphan in the Bay of Bengal in 202
Authors: Rathore Saurabh , Goyal Rishav , Jangir Babita , Ummenhofer Caroline , Feng Ming , Mishra Mayank
Published: March 2024

Interactions are diagnosed between a marine heatwave (MHW) event and tropical super cyclone Amphan in the Bay of Bengal. In May 2020, an MHW developed in the Bay of Bengal driven by coupled ocean-atmosphere processes which included shoaling of the mixed layer depth due to reduced wind speed, increased net surface shortwave radiation flux into the ocean, increased upper ocean stratification, and increased sub-surface warming. Ocean temperature, rather than salinity, dominated the stratification that contributed to the MHW development and the subsurface ocean warming that also increased tropical cyclone heat potential. The presence of this strong MHW with sea surface temperature anomalies >2.5°C in the western Bay of Bengal coincided with the cyclone track and facilitated the rapid intensification of tropical cyclone Amphan to a super cyclone in just 24 h. This rapid intensification of a short-lived tropical cyclone, with a lifespan of 5 days over the ocean, is unprecedented in the Bay of Bengal during the pre-monsoon period (March-May). As the cyclone approached landfall in northern India, the wind-induced mixing deepened the mixed layer, cooled the ocean's surface, and reduced sub-surface warming in the bay, resulting in the demise of the MHW. This study provides new perspectives on the interactions between MHWs and tropical cyclones that could aid in improving the current understanding of compound extreme events that have severe socio-economic consequences in affected countries.
The 2D evolution of the M-I responses to solar wind/foreshock transients based on the coordinated observation between THEMIS and ground-based ASI
Authors: Wang Boyi , Xu Xinyu
Published: March 2024

Dynamic pressure variations that are upstream from the magnetopause can interact with the coupled magnetospheric and ionospheric system, causing significant auroral responses, which indicates magnetospheric compressions, wave propagations and disturbances of current system. Recent studies have shown that not only large-scale solar wind structures but also locally-generated foreshock transients can be associated with strong dynamic pressure variations and further induce such auroral responses. However, how these auroral responses evolve in 2D perspective and how the corresponding current system and electron precipitation evolve in 2D perspective are still unclear. Thus, in our study, we used the coordinated observations between THEMIS probes and the ground-based all-sky images at South Pole to statistically investigate the 2D evolution of discrete and diffuse auroral responses to both solar wind structures and foreshock activities. The discrete auroral evolution shows that the dynamic pressure variations upstream from the magnetopause can induce upward field-aligned currents near the magnetopause in the magnetosphere. These currents can extend earthward and most of them extend duskward, causing the bifurcation of auroral oval. The diffuse auroral patterns reveal that in the ionosphere, the area with compression-related electron precipitations propagates poleward. The azimuthal motion of compression-related electron precipitations can be either dawnward or duskward, depending on the azimuthal propagation of the magnetospheric compressions. We further found that the location, size and associated dynamic pressure change of the upstream solar-wind or foreshock transients can modify the shape and 2D evolution of the corresponding auroral patterns.
Remote sensing based analysis of urban tree temperatures during extreme heat and drought
Authors: Zandler Harald , Samimi Cyrus
Published: March 2024

Trees have a pivotal role in mitigating heat island effects and several studies analyzed temperature differences of various species. However, the potential of trees to decrease temperatures during heat waves, drought and extreme events is inadequately studied, particularly at the city scale. Therefore, we present a remote sensing based approach that evaluates surface temperatures of trees in the urban environment of Forchheim (Germany) during the heatwave 2022. To provide an example of extreme conditions, we conducted measurements on July 20, 2022, the day with the current absolute daily heat record of the region since temperature measurements started in 1949. The three-month period before the survey flight (May-July) was the second warmest and third driest May-July period ever measured, leading to an ideal setting to assess the role of trees in urban regions during projected climate extremes. Analyzing such situations is highly relevant for city planning as existing research showed that sap flow is only reduced after several weeks of drought. We performed a low-altitude flight campaign (350 meters above ground) during the daily maximum temperature period (2-4 pm) with a thermal camera (Optris PI 450) for surface temperatures and a multispectral camera (Micasense RedEdge M) for vegetation parameters and land cover. We compared derived surface temperatures at field mapped locations of more than 3000 trees covering more than 30 species (n ≥ 20) to assess species patterns and the influence of urban parameters such as imperviousness. We show differences between species and interrelationship with vegetation parameters (e.g. NDVI) to provide insights into mitigation effects and patterns of urban trees during extreme events.
Simplifying properties of glacier runoff resulting from Ice-albedo feedback
Authors: Banerjee Argha
Published: March 2024

The instantaneous rate of glacier melt depends strongly on the surface albedo. For example, snowfall increases surface albedo and reduces melt rate, while dark impurities deposited on the surface enhances melt. We discuss two interesting consequences of this feedback, which lead to simplifications in models describing the runoff of glacierised catchments.  We investigated the interannual variability of modelled streamflow on two Himalayan catchments. On an excess-precipitation year, glaciers receives more snow, which reduces melt. In contrast, on a precipitation-deficient year, glaciers have less snowcover and they produce more meltwater. This behaviour makes the annual runoff contribution from the glaciers in any given catchment to be either weakly sensitive or insensitive to the interannual variability in precipitation. Further, this characteristic is shown to be independent of the climate setting of the glacier, or the models used. A general linear-response expression for the streamflow response to climatic perturbations is proposed, where the glacierised parts respond to temperature variability and the non-glacierised parts respond to precipitation variability. This simple expression reproduces several well known characteristics of the variability of the runoff of glacierised catchments, e.g., glacier-compensation effect, buffering effect, peak-water effect etc.  The melt enhancing effects of dark supraglacial impurities lead to the formation of tiny cylindrical cryoconite holes that are commonly seen on glacier surfaces across the globe. Their contribution to glacier mass balance and runoff generation is debated. We build an idealised model of the evolution of these holes on sunny days, and show that the holes of a given diameter reach a steady depth, which scales linearly with the diameter. The predicted depth-diameter scaling is consistent with available global-scale observations. This scaling imply that the total melt contribution of the holes to glacier runoff is likely to be negligible, and that the formation of these holes provides an effective mechanism for restricting excess melt when supraglacial impurities are present.  
Development of continuous cover forests under different levels of global warming – a methodological case study in Southern Germany
Authors: Djahangard Marc , Costa Maximiliano , Bugmann Harald , Yousefpour Rasoul
Published: March 2024

Informing forest decision makers about the impacts of climate change on forests is challenging because the representative concentration pathway scenarios (RCPs) impose deep uncertainty and complexity that is difficult to integrate in management planning. A user-oriented translation of the RCPs would facilitate the integration of climate change impacts in forest decisions and improve the understanding of how different climate policy actions would affect forests.We applied a translation of the RCPs by analyzing how three global warming scenarios related to climate policy actions – the Paris targets (1.5°C and 2°C warming) and a higher warming level without climate policy (3°C) – would impact forest dynamics. We developed indices of forest processes (e.g., species succession, biomass, harvest) that capture changes induced by the global warming scenarios relative to a reference period (1981 – 2010). The methodology was adapted from the JRC PESETA IV project, where climate indices had been developed and impacts on forest vulnerability was explored.We applied this method with a large-scale forest model (LandClim) on a complex and highly diverse 5000 ha forest landscape over an elevation gradient from lowland deciduous to high montane conifer forests in the area of Freiburg, Southern Germany. Simulations started from the state of the forest in the year 2010, and both no-management and a business-as-usual management (BAU) was simulated. For the initial state of the forest, we applied a state-of-the-art initialization procedure that makes use of a detailed inventory network (over 2000 inventory points in the study area) to depict the current forest conditions (e.g., species distribution, stem numbers, tree ages, stem diameters at breast height) at high resolution. BAU was applied in the form of close-to-nature management based on the guidelines by the State Forestry Department. It includes >10 forest types with both younger and older stands.Simulation results indicate reductions of biomass and species richness at lower elevations, including both lowland and submontane zones, connected to an upslope shift of species. As these changes intensify with increasing global warming, the largest impacts are observed under the 3°C warming scenario, leading to the loss of biodiversity associated with dominant species capitalizing on the changing ecological conditions.In summary, by applying this method for a diversity in continuous cover forests over a large elevation gradient, our study outlines important forest dynamics representative for temperate forests in Central Europe under three global warming scenarios. Moreover, the evaluation of close-to-nature management can give important insights for forest decision making.
Improving Groundwater Level Prediction in the Zhuoshui River Basin, Taiwan, through Advanced Deep Learning Methods
Authors: Tsai Wen-Ping , Wang I-Hsaun , Ho Shao-Wei
Published: March 2024

Despite its abundant rainfall, Taiwan faces persistent water scarcity issues due to its steep terrain, rapid runoff, and uneven precipitation distribution. The limited effectiveness of reservoirs, owing to the country's small land area, has led to an increased reliance on groundwater, surpassing thirty percent of the total water usage and exceeding contributions from reservoirs. Understanding groundwater level dynamics, especially in critical regions like the central area around the Zhuoshui River, is imperative. This comprehensive study endeavors to collect data from multiple groundwater and hydrological monitoring stations, encompassing parameters such as temperature and precipitation. The collected data will undergo meticulous purification, ensuring the acquisition of high-quality information for subsequent in-depth analysis. Utilizing Long Short-Term Memory (LSTM), a deep learning module, this research aims to predict groundwater level changes. The study will identify key parameters influencing these changes by exploring the correlation of various factors with groundwater levels. The anticipated outcomes are poised to deliver valuable insights for the enhancement of planning and management strategies concerning groundwater recharge and extraction. This, in turn, will contribute to the more effective and sustainable utilization of Taiwan's groundwater resources, addressing water scarcity challenges and fostering environmental resilience in the future.
Advancing polar airborne geophysics data management at the UK Polar Data Centre 
Authors: Fremand Alice , Bodart Julien , Jordan Tom , Fretwell Peter , Tate Alexander
Published: March 2024

In the last 50 years, the British Antarctic Survey (BAS, https://www.bas.ac.uk/) has been a key player in acquiring airborne magnetic, gravity and radio-echo sounding data in Antarctica. These data have been central to many studies of the past, present and future evolution of the Antarctic Ice Sheet but until recently they were not accessible to the community.In the last three years, the UK Polar Data Centre (https://www.bas.ac.uk/data/uk-pdc/) has made considerable efforts to standardise these datasets to comply with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles and develop the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/). Workflows from collection to publication have been updated, data formats standardised, and Jupyter Notebooks created to improve reuse and comply with the needs of the scientific community [1].Following this experience and to promote open access, the UK Polar Data Centre led the management of 60 years of international Antarctic ice thickness data through the Bedmap3 project (https://www.bas.ac.uk/project/bedmap/), an international project supported by the Scientific Committee of Antarctic Science. This time, it’s 80+ million points of ice thickness, ice surface and bed elevation from 270+ surveys collected from 50+ international partners that have been standardised and assimilated in the Bedmap data portal (https://bedmap.scar.org/) [2].Today, airborne data are acquired using new types of platforms including uncrewed aerial systems (UAV) adding new challenges and opportunities to set up new standards and data management practices.As part of this presentation, we will present the different workflows and data management practices that we are developing to make Antarctic science open and FAIR.[1] Frémand, A. C., Bodart, J. A., Jordan, T. A., Ferraccioli, F., Robinson, C., Corr, H. F. J., Peat, H. J., Bingham, R. G., and Vaughan, D. G.: British Antarctic Survey's aerogeophysical data: releasing 25 years of airborne gravity, magnetic, and radar datasets over Antarctica, Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022 , 2022.[2] Frémand, A. C., Fretwell, P., Bodart, J., Pritchard, H. D., Aitken, A., Bamber, J. L., ... & Zirizzotti, A.: Antarctic Bedmap data: Findable, Accessible, Interoperable, and Reusable (FAIR) sharing of 60 years of ice bed, surface, and thickness data, Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, 2023.
Synthesizing regional irrigation data using machine learning – towards global upscaling via metamodeling 
Authors: Kragh Søren , Schneider Raphael , Stisen Simon , Fensholt Rasmus , Koch Julian
Published: March 2024

Knowledge on irrigation is key to sustainable water resource management, but spatio-temporal irrigation data are rarely available. Recent advances are based upon satellite remote sensing data to quantify irrigation at high spatial resolution, and this study utilizes published irrigation datasets at regional scale to develop a metamodel approach to synthesize the available irrigation knowledge. We investigate the potentials and limitations of a Random Forest-based metamodeling approach that predicts irrigation at monthly timescale using only globally available and easily accessible features related to hydroclimatic and vegetation variables. The training dataset consists of three irrigation water use datasets derived from the soil moisture-based inversion framework and covers a variety of climatic conditions and irrigation practices in Spain, Italy, and Australia. Further, the study includes irrigation predictions from three test sites representing major global hot spots for unsustainable irrigation management: the North China Plain, Indus, and Ganges Basins. Our study aims to test the model transferability in space and time based on a series of split-sample experiments. We quantify and outline model transferability based on the area of applicability analysis, showing that although the feature space was mostly well represented, the magnitude of the target variable was equally important for assessing model transferability. A comprehensive feature importance analysis reveals that ranking of the most important input features depends on geographical extent of the training dataset. We find that model transferability was more robust across space than time within the small study areas, mainly because of the small geographical extents of the training datasets. The developed metamodel demonstrates satisfying performance with less than 10% bias and 3 mm/month mean error for a successful model transferability outside the training study areas and predicted spatial patterns of irrigation closely linked to climate and vegetation features. Given the increase in published regional irrigation datasets, we see great potential for further developing metamodel approaches for synthesizing existing knowledge and work towards global upscaling opportunities.
Gas buffering of magma chamber contraction during persistent explosive activity at Mt. Etna volcano
Authors: Carbone Daniele , Cannavò Flavio , Montagna Chiara , Greco Filippo
Published: March 2024

Decrease of the local gravity field and ground deflation were observed at Mt. Etna through continuous measurements, during a 2-month period when more than 20 short-lasting explosive eruptions took place. Results from the joint inversion of the gravity and ground deformation data are cross-checked against the output of a numerical code providing independent geochemical insight on how the density of the magmatic liquid/gas mixture in the source reservoir varies as a function of the pressure. This cross-analysis provides a framework to explain why (i) the bulk volume reduction sensed by the ground deformation data is much lower than the volume of the erupted products and (ii) the observed gravity changes point to a strong mass decrease, that is incompatible with a pure mechanism of magma withdrawal. Contraction of the source reservoir was mostly buffered by pressure-driven exsolution and expansion of H2O and CO2, which compensated the withdrawal of magma and led to the inferred mass decrease.
Measures to Reflect the Climate Change Uncertainties in Water Resources Plans in South Korea
Authors: Lee Moonhwan , Seo Seung Beom , Yang Iljoo , Ahn Jongho
Published: March 2024

The climate change impact assessment of the water resources in Korea has been carried out in many research projects and researchers for a long time, but there are no cases in which climate change impacts are reflected in water resources plan due to a variety of reasons. According to Article 27 of the Framework Act on Water Management in Korea, it is required to formulate a Master Plan for National Water Management including measures to respond to the vulnerabilities of water management to climate change, but there is a limit to reflecting climate change effects. In addition, the Board of Audit and Inspection in Korea pointed it out and demanded improvement measures. Various barriers that make it difficult to reflect climate change in water resources plan are climate change uncertainties, insufficient evidence, and limited cost-benefit analysis and so on. For these reasons, this study aims to derive technical and institutional limitations for establishing a water resources plan in consideration of climate change, and to establish a system that can formulate plans to reflect the climate change uncertainties. This study proposes short- and long-term improved measures to establish a water use plan in consideration of climate change such as 1) production and standardization of climate change scenario with multi-model ensemble for water sector applications, 2) preparation of a framework for analyzing water supply and demand considering climate change, 3) publication of national report for climate change impact and risk assessment on water resources, and 4) development of standard guidelines for water resources planning considering climate change. The details of these parts will be shown at the presentation.[Acknowledgements]This study was supported by grants through the project (2022003570007) of developing environmental technologies responding to a new climate regime funded by the Ministry of Environment and the project (WO2023-11) funded by Korea Environement Institute.
Assessment of the predictability of cold-wet-windy Pan Atlantic compound extremes
Authors: Krouma Meriem , Messori Gabriele
Published: March 2024

Occurrence of cold spells in different North American regions has been related to concurrent wet and windy extremes in Western Europe. This link is driven by an anomalous state of the North Atlantic storm track. Two dynamical pathways have been defined as potential origins of the Pan-Atlantic compound extremes. The first pathway is linked to a Rossby wave train propagating from the Pacific toward the Atlantic, associated with a pronounced Alaskan ridge. The second pathway is characterized by the presence of a high west of Greenland, that favors simultaneously a southward displacement of a trough over eastern USA and an upper-level trough over South western Europe. The aim of this study is to assess the predictability of these two pathways in the ERA5 reanalysis using dynamical systems indicators. These indicators are the local dimension and the persistence of the large-scale atmospheric flow, and can be used as proxies for the predictability of each pathway. We complement this analysis using the ECMWF ensemble reforecasts at different lead times, and computing skill scores for the two pathways.
Toward a Polarity Focal Mechanism Estimation via Deep Learning for small to moderate Italian earthquakes
Authors: Tavani Flavia , Artale Harris Pietro , Scognamiglio Laura , Meier Men-Andrin
Published: March 2024

One of the main tasks in seismology is the source characterization after an earthquake, in particular the estimate of the orientation of the fault on which an earthquake occurs and the direction of the slip. Currently, most seismological observatories compute moment tensor solutions for earthquakes above a certain magnitude threshold, but, for small to moderate earthquakes (i.e. aftershock sequences), or for large but close in time events, focal mechanism by first arrival polarities are often the only source information available (Sarao et al., 2021).Focal mechanisms are important to better define the activated faults, to help in understanding the seismotectonic process, to improve the predicted ground shakings for early warning, the tsunami alert and the seismic hazard assessment. For these purposes, it becomes essential to produce and disseminate an estimate of the earthquake source parameters even for small events. Recently, machine learning techniques have gained significant attention and usage in various fields, including seismology where these algorithms have emerged as powerful tools in providing new insight into the earthquakes data analysis such as the prediction of the seismic wave's first arrivals polarities which can be used to compute focal mechanisms.We present here a workflow developed to obtain earthquake focal mechanisms starting from the first p-wave polarities estimated through the method proposed by Ross et al (2018).Our procedure consists of two stages: in the first stage, we use a combination of the available INGV web services (Bono et al., 2021) and the ObsPy functions to download the earthquake hypocentral location. We recover the waveforms recorded by the stations in the 0 -120 km distance range, and we create an input file with the appropriate information required for the prediction of the polarities for each waveform. We then use the convolutional neural network (CNN) proposed in Ross et al (2018) to obtain the polarities for each waveform, which can be UP, DOWN, or UNKNOWN. The second stage of the developed procedure aims to use the polarities that have been predicted to determine the focal mechanisms of the selected earthquakes. To do this, we use a modern Python implementation of HASH code (originally proposed in Fortran by Hardebeck et al. 2002, 2003) called SKASH (Skoumal et al. submitted). Finally, we present an application of this procedure to the September 2023, Marradi (Central Italy), seismic sequence that has been characterized by a magnitude Mw 4.9 mainshock followed by over 70 aftershocks in the magnitude range 2 - 3.4. Here, we focused on the estimation of the focal mechanism for events down to M 2.0. The application of the presented workflow permits to gain useful information about the kinematics of the earthquakes in the sequence, obtaining thus a more precise characterization of the activated structures.
Separation and recovery of anions from aqueous solution through iron oxide nanoparticle impregnated biochar derived from waste wood
Authors: Das Sneha , Mondal Sandip
Published: March 2024

Through this study, a novel method for producing magnetic biochar from waste wood sourced from Acacia auriculiformis is achieved. Slow pyrolysis method of biochar was used for the conversion of waste wood which in turn proved to be more efficient than other procedures. Nanoparticles which were successfully deposited onto the surface of biochar were derived from iron powder transformed into iron-oxide. High specific surface area of 266.564 m2g-1 was achieved through Brunauer–Emmett–Teller (BET) analysis. Scanning Electron Microscopy (SEM) images demonstrate the formation of triangular pyramid-shaped nanoparticles in the adsorbent's inner and outer wall pores. Fe3O4 was coated on the surface of the adsorbent in a crystalline, carbonaceous form, as indicated by XRD peaks. A number of hydroxyl and aliphatic stretching bonds of carbon serve as functional groups in the impregnation and anionic targeted pollutants adsorption process, according to FTIR research. The establishment of the best-fit model for several anionic pollutants followed the method of multi-layer heterogeneous adsorption. Removal efficiencies of 95%, 85%, and 80% of arsenic, chromium, and fluoride are attained, respectively. Kinetic models were used to determine the adsorption process. Surface mechanism involved electrostatic attraction followed by pseudo first, second order, Bangham equation and Weber Morris intra-particle diffusion and complexation helping in adsorption of the anionic ions. Whereas, chromium and fluoride followed Temkin and Dubinin-Radushkevich adsorption isotherms. The maximum capacity of the manufactured biochar for arsenic, chromium and fluoride is estimated to be 294.1176, 204.22 and 102.36 mgg-1 respectively. Regeneration studies showed that upto 80-90% of ions can be recovered from the magnetic biochar.
Flux tracking of groundwater via integrated modelling for abstraction management
Authors: Liu Leyang , Bianchi Marco , Jackson Christopher , Mijic Ana
Published: March 2024

In systems where surface water and groundwater interact, management of the water resource often involves conflicting objectives between water supply and baseflow maintenance. Balancing such objectives requires understanding of the role of groundwater in integrated water systems to inform the design of an efficient strategy to minimise abstraction impacts. This study first develops a reduced-complexity, processed-based groundwater model within the water systems integration modelling framework (WSIMOD). This model is applied to the Lea catchment, UK, as a case study and evaluated against monitored groundwater level and river flow data. A flux tracking approach is developed to reveal the origins of both river baseflow at a critical assessment point and abstracted groundwater across the systems. The insights obtained are used to design two strategies for groundwater abstraction reduction. Results show that the model has good performance in simulating the groundwater and river flow dynamics. Three aquifer bodies that contribute the most to the river baseflow in the dry season at the assessment point are identified; contributions being 17%, 15%, and 5%. The spatial distribution of abstracted groundwater originating from these aquifer bodies is illustrated. Compared to the default equal-ratio reduction, the strategy prioritising abstraction reduction in these three aquifer bodies increases a similar amount of baseflow (13%) by reducing much less abstraction (23%). The other strategy, which further decreases abstraction in the adjacent aquifer bodies, increases more baseflow (16%) with a similar abstraction reduction (30%). Both strategies can more efficiently improve the baseflow. The flux tracking approach can be further implemented to trace water from other origins, including runoff, stormwater, and wastewater, to enable coordinated management for better systems-level performance.
On the Contribution of Atmospheric Nitrogen Deposition to Nitrogen Burden in an Eutrophic Lake in Eastern China
Authors: Li Weikun , Wang Xia , Zhang Zhongyi , Liu Xiaodong , Geng Lei
Published: March 2024

Atmospheric deposition of natural and anthropogenic sourced reactive nitrogen (Nr, mainly including NH3, NH4+, NOx, NO3- and etc.) has substantial influence on terrestrial and aquatic ecosystems, driving global nutrient imbalances and increasing risks to human health. Although it has been demonstrated that atmospheric Nr deposition has a substantial impact on nitrogen pools in remote and/or sensitive lakes, there is a scarcity of systematic evaluations regarding atmospheric Nr deposition's impact on the nitrogen burden in eutrophic lakes with riverine input as the primary source. Utilizing a regional chemical transport model, combined with observations of riverine nitrogen input, we investigate the contribution of atmospheric Nr deposition to a eutrophic Lake Chaohu in eastern China. The results indicate that riverine total nitrogen (TN) input to the lake was 11553.3 t N yr-1 and atmospheric TN deposition was 2326.0 t N yr-1 in the year of 2022. For Nr species which are directly available for the biosphere supporting algae and plant growth, riverine NH4+ input was 1856.1 t N yr-1 and atmospheric NHx (NH3 and NH4+) deposition was 824.5 t N yr-1. The latter accounts for ~ 1/3 of total NHx input to the lake. For NOy (HNO3 and NO3-) species, atmospheric deposition was estimated to also contributes a similar amount to the NHx species. The results suggest that even in regions with dense human activities with primary riverine N input, atmospheric deposition of Nr could also contribute significantly to the bio-available nitrogen in lake systems, and addressing eutrophication in Lake Chaohu and other eutrophic lakes will also need to consider the reduction of NH3 and NOx (i.e., NO + NO2, the precursor of NOy) emissions, in addition to the mitigation of riverine N input.
The Partitioning Processes of Sea Ice Associated Ice Nucleation Particles Impacting the Arctic Clouds 
Authors: Jensen Lasse Z. , Kjærgaard Eva R. , D’Agostino Martina , Søgaard Dorte , Rosati Bernadette , Bilde Merete , Lund-Hansen Lars C. , Finster Kai , Šantl-Temkiv Tina
Published: March 2024

The Arctic is a region that is particularly vulnerable to climate change, as it is warming at a much faster rate than the rest of the globe. This warming causes a decline in multiyear sea ice cover, which results in an increase in ice-free waters with a much lower albedo compared to ice, therefore leading to a positive feedback and enhanced warming. Another factor that plays a role in regulating the temperature in the Arctic is the type and extent of cloud cover. Aerosols, that can serve as cloud condensation nuclei or ice nucleating particles (INPs), are key for cloud formation and aggregation state of water; liquid or ice. Some microorganisms produce INPs, but it is not well understood which microorganisms are most relevant, which environments they inhabit, and how active they are. In this study, we investigated the partitioning of INPs between the Arctic marine and atmospheric environment by combining in situ measurements with laboratory experiments, deploying cold-stage INP measurements and amplicon sequencing of the bacterial and eukaryotic communities. First, we determined if sea ice acts as a reservoir for INPs and, whether the INPs are partitioned into the sea ice during its formation or whether they are produced by microorganisms within the sea ice. We used a modified ice-finger to grow sea ice using sea water collected in West Greenland. We found that INPs are not enriched in the ice, but they stochastically incorporate into the ice fraction during sea ice formation. Next, we studied the temporal and spatial dynamics of INPs in Arctic sea ice cores that were collected before and during the spring sea ice phytoplankton bloom. We observed a higher concentration of INPs active at -10 °C (INP-10) present towards the bottom of the sea ice core with concentrations 10-100-fold higher than those in the under-ice water. Finally, we determined the potential of sea-ice as a source of atmospheric INP. In separate experiments, we filled bulk water and water from melted sea ice from Nuuk and Station Nord into a temperature-controlled sea spray simulation chamber and quantified the microorganisms and INPs present in the bulk water and air before and after the water bubbling. We observed that the highly active INPs are efficiently aerosolized by bubble-bursting together with specific bacterial and eukaryotic taxa. In light of these findings, this study provides new insights into the role of Arctic sea ice as a reservoir for INPs and the microorganisms that potentially produce them. Additionally, it explores the mechanisms by which INPs are released into the Arctic atmosphere. Last, these results can be used to improve cloud and climate model predictions in the Arctic region.
Constraining latent heat of hydration using combined thermal- and garnet diffusion modelling
Authors: Schorn Simon , Moulas Evangelos , Stüwe Kurt
Published: March 2024

Hydration and retrogression are common processes in many reworked polymetamorphic terranes. The incorporation of water into a new, hydrated assemblage obliterates precursor high-grade parageneses and releases significant latent heat proportional to the degree of hydration. The hydration of dry gneisses (containing 1–2 wt.% water) at greenschist-facies conditions, may result in an approximate doubling of the bulk water content (4–5 wt.% water). This equates to a gain of 20–40 g of water per kg of retrogressed rock. Considering an average latent heat release of 4 kJ per g of water accommodated in hydrous minerals (Connolly & Thompson, 1989), hydration leads to the release of 80–160 kJ per kg of retrogressed rock. Consequently, this effect causes a significant, albeit short-lived, perturbation of the local thermal conditions. Cooling of the affected rock pile is delayed while the additional energy enhances thermally-activated processes such as diffusive loss of radiogenic Argon. This, in turn, may contribute to a significant rejuvenation of apparent 40Ar/39Ar ages in white mica of up to ~10 % (Schorn et al., 2023). In this study, we present results of multicomponent diffusion modelling of garnets hosted in pervasively hydrated micaschist from the polymetamorphic eclogite-type locality (Koralpe–Saualpe, Austria). Using simple thermokinematic models, we explore exhumation paths that encompass variable degrees and conditions of hydration, along with latent heat to, constrain a set of plausible, yet non-unique, temperature–time histories for the investigated rocks. These cooling paths serve as input for garnet diffusion modelling (Fig. 1), used to determine an appropriate set of thermal parameters for the investigated samples. Our findings contribute to discussions on the broader implications of pervasive fluid–rock interaction in a collisional setting, emphasizing the significance of the often-overlooked effects of retrogression in reworked metamorphic terranes.

Figure 1 - Multicomponent diffusion modelling of a polyphasic garnet. Yellow dots: EMPA data; red line: initial composition; blue line: calculated composition.
REFERENCES
Connolly, J. A., & Thompson, A. B. (1989). Fluid and enthalpy production during regional metamorphism. Contributions to Mineralogy and Petrology, 102(3), 347-366.
Schorn, S., Moulas, E., & Stüwe, K. (2023). Hot when wet: the consequences of exothermic hydration on geochronology (No. EGU23-5769). Copernicus Meetings.
Using mobile measurements of methane to detect natural gas leaks in two medium sized UK cities
Authors: Moore Thomas , Budisulistiorini Sri Hapsari , Shaw Marvin , Carslaw David , Lee James
Published: March 2024

Reducing fugitive emissions from natural gas networks is an important area to consider for future management of a country's methane emissions. The main problem with reducing these emissions comes from locating where leaks are commonly occurring, in order to determine which areas should be prioritised for replacing ageing or leaky pipes. This problem is especially relevant in the United Kingdom which has an extensive but ageing natural gas network. Two cities in the UK of varying sizes were selected in order to conduct vehicle-based measurement campaigns in summer and winter with a three-fold method to detect natural gas leaks; locating methane enhancements using 1Hz methane measurements; determining which areas had a recurring methane enhancement; confirming if these enhancements are natural gas emissions by use of ratios of other components contained within the natural gas mixture. This information can then be used to quantify the detected natural gas leaks and compare these to existing inventories.
Mapping the nationwide crop phenology stages in Saudi Arabia using machine learning and Sentinel-2 NDVI time series
Authors: Li Ting , Lopez Valencia Oliver Miguel , Johansen Kasper , McCabe Matthew Francis
Published: March 2024

Vegetation phenology, encompassing critical events like leaf emergence and maturity, serves as an important indicator of adaptive plant responses to environmental factors. In the context of Saudi Arabia, existing crop phenology retrieval methods encounter several challenges related to local farm management operations. These can include unstable crop calendars with planting and harvesting at any time throughout the year, uncertainty in sub-field management with independent control of areas within a center-pivot field, and diverse crop rotations between fodder and non-fodder crops. To address these challenges, we present an innovative framework utilizing machine learning and Sentinel-2 NDVI time series data for mapping phenology stages of key crops at a national scale. The framework is composed of three modules that are implemented step-wise, including: (1) a within-field dynamic clustering module (termed WithinFDy) that monitors fields for potential subdivision based on pixel-level NDVI temporal dynamics; (2) a phenology estimation module (termed PhenoEst) that segments NDVI time series into growing seasons and extracts essential phenology stages (e.g., planting and harvesting dates) for each season; and (3) a crop type discrimination module (termed CropDis) that utilizes extracted phenology information as input features to discriminate between different crop types. Evaluated on 1,000 randomly selected fields in northern Saudi Arabia, our framework achieved overall accuracies of 93.38%, 96.40%, and 94.39% for WithinFDy, PhenoEst, and CropDis modules, respectively. When applied nationwide in 2020, the framework revealed valuable insights. In terms of field management, 21.8% of the fields were divided into two distinct subfields, featuring different planting and harvesting dates - and sometimes crop type, while 73.2% showed consistent practices across the entire field. For seasonal dynamics, 53.4%, 36.3%, and 8.7% of fields supported crops for one, two, and three seasons annually, respectively. Main planting and harvesting activities occurred during winter seasons (November to February), with another peak observed in June. Approximately 30% of fields were under production for 5 to 6 months, and 15.7% were under production year-round. The dominant crop types in 2020 were fodder crops (e.g. alfalfa and Rhodes grass), followed by winter crops like winter wheat. Our methodology represents a substantial advancement over previous approaches, expanding applicability beyond crops with regular growth patterns. The results not only enrich agricultural datasets in Saudi Arabia but also hold promise for enhancing food and water security studies globally.
The similarity between ruptures in scaled laboratory seismotectonic models and slow earthquakes 
Authors: Corbi Fabio , Mastella Giacomo , Tinti Elisa , Gualandi Adriano , Sandri Laura , Rosenau Matthias , Pardo Silvio , Funiciello Francesca
Published: March 2024

Modeling the seismic cycle requires multiple assumptions and parameters. Providing a quantitative assessment of the model behavior is pivotal for determining the degree of similarity between different scales and modeling strategies and for exploring dependencies with respect to selected parameters. Here we compare stick-slip ruptures nucleating spontaneously in scaled seismotectonic models (i.e., laboratory experiments capturing the first-order physics of the seismic cycle of subduction megathrusts) with slow earthquakes in nature. We rely on two non-dimensional parameters, namely the Ruina number (Ru) and system dimension (D) to quantify model behavior. Ru is proportional to the ratio of the asperity size to the critical nucleation size. Within the rate- and state friction framework, for velocity weakening asperities Ru controls the behavior of the system, which can be either periodic or not, and it can exhibit both slow and fast ruptures. D measures how complicated the system evolution is. D reveals how many variables are required to describe the seismic cycle because it tells us the minimum dimension needed to embed the observed dynamics. By coupling the Simulated Annealing algorithm and quasi-dynamic numerical simulations, we retrieve rate and state friction parameters characterizing single asperity models with different lateral extent of the velocity weakening patch. Similarly to slow earthquakes, we found optimal rate and state parameters indicative of low (< 4) Ru. We also found a direct proportionality between Ru and the lateral extent of the asperity. Next, we implement tools from non-linear time-series analysis and Extreme Value Theory to compute D from models of different sizes, materials, deformation rates and frictional configurations (single or twin asperities along strike). Our analysis supports the existence of a low dimensional attractor (D