bluuebunny/crossref_metadata_2025 huggingface dataset:
Flood susceptibility mapping using neural network based models in Morocco: Case of Souss Watershed
Authors: Aghenda Mohamed , Labbaci Adnane , Hssaisoune Mohamed , Bouchaou Lhoussaine
Published: March 2024

The global climate situation becomes more and more critical due to the impacts of climate change especially when dealing with flood hazard causing major human and economic losses every year. In Morocco, the Souss-watershed is one of the most vulnerable regions in term of flooding and land degradation. The climate conditions, population growth affect more the land use conditions. The present work introduces a novel approach to assess flood risk in Souss watershed using 4 neural network based models in Google Colab: Artificial neural networks (ANN), Recurrent Neural networks (RNN), One dimensional (1DCNN) and Two dimensional Convolutional neural networks (2DCNN). The models input were constructed using 4 features chosen from 17 of the most triggering flood factors that describe the characteristics of the watershed, including topography, vegetation and soil ones. The Pearsons correlation factor was applied to evaluate the correlation between the features, the variance inflation factor analysis (VIF) was applied to diagnose the collinearity and the Shapley Additive Explanations (SHAP) was applied to evaluate the importance of a factor in the prediction model. For the evaluation and validation process, the calculation of the Mean Absolute Error (MAE) and loss was used to evaluate the accuracy of predictions along with the calculation of the ROC (Receiver Operating Characteristic) and the AUC (Area Under the ROC Curve) to compare between the four models, the results demonstrated that the RNN has the highest performance with an accuracy of 96% and a validation loss of 0.0984 and a validation MAE of 0.2553, followed by the ANN with a slightly lower accuracy of 95% , 2DCNN and 1DCNN demonstrated lower accuracies of 87% and 81%. These findings have demonstrated that in the flood susceptibility mapping context, the application of complex neural networks such as 1DCNN and 2DCNN calls for more tuning and optimizing to overcome over-fitting issues, and that using simple neural networks such as RNN and ANN can be more effective in achieving more accurate predictions.
A comparison of linear and non-linear theories for modelling solid Earth dynamics
Authors: Yu Ziheng , Maitra Matthew , Al-Attar David
Published: March 2024

To date, most computational work in solid Earth geophysics has been based on linearised continuum mechanics. This is justified so long as deformation from the reference state remains sufficiently small. The dependence on linearisation also reflects computational limitations of the past: most tractable problems relied on geometric symmetries along with linearity to reduce the calculation to the solution of decoupled systems of ordinary differential equations.Increases in computational power have allowed for increasingly routine applications of fully numerical techniques such as finite-difference, finite-element, and finite-volume methods. This has allowed geophysical problems to be solved in increasingly realistic Earth models. Although for the most part, the equations being solved are the same as linearised ones used previously, keeping nonlinear terms significantly increases the complexity of solution schemes. Within the context of fully numerical methods, non-linear problems are solved using iterative schemes that involve repeated solution of the corresponding linearised equations. This implies that solving non-linear equations should only be appreciably more expensive if non-linear effects are physically important.Within this presentation, we compare the use of linearised and non-linear equations of motion, focusing on quasi-static elastic and viscoelastic loading problems of relevance to studies of glacial isostatic adjustment. This is achieved using the open-source finite-element package FeniCSx which facilitates rapid development and testing. Starting from simple representative examples, we quantify the errors associated with linearisation along with the added cost of solving non-linear problems.
The 2021-22 volcanic unrest at Vulcano Island (Italy) inside of 50 years of activity observed with geophysical data.
Authors: Privitera Laura , Bonforte Alessandro , Gambino Salvatore , Guglielmino Francesco
Published: March 2024

Vulcano Island is characterized by transitions from phreatomagmatic to minor magmatic activity. The last eruption in 1888–90 saw powerful explosive pulses and this eruption defines what we call today ‘vulcanian’ also for other volcanoes worldwide. Since then, volcanic activity at Vulcano has been limited to the form of fumarolic emanations of variable intensity and temperature, mainly concentrated at “La Fossa” crater and on its northern slope.In September 2021, La Fossa entered a new phase of unrest with a sudden and significant increase of degassing activity from the crater and with outstanding variations to fumarole gas composition, temperature, plume emissions, diffuse soil CO2 degassing, local seismicity and ground deformation.We discuss a set of geophysical monitoring parameters which started evidencing changes since middle 2018, and showed an important expansion of La Fossa Cone in 2021 as the result of an increased activity inside the shallow hydrothermal system just below La Fossa.We discuss the 2021 episode within 50 years of ground deformation and seismicity data collected by INGV, trying to define the elements of comparison, similarities and differences with the hydrothermal unrests already observed during the late 1970s to early 1990s.
A Hybrid Machine Learning Climate Simulation Using High Resolution Convection Modelling
Authors: Briant James , Giles Dan , Morcrette Cyril , Guillas Serge
Published: March 2024

Underrepresentation of cloud formation is a known failing in current climate simulations. The coarse grid resolution required by the computational constraint of integrating over long time scales does not permit the inclusion of underlying cloud generating physical processes. This work employs a multi-output Gaussian Process (MOGP) trained on high resolution Unified Model (UM) simulation data to predict the variability of temperature and specific humidity fields within the climate model. A proof-of-concept study has been carried out where a trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model (AGCM) named SPEEDY. The temperature and specific humidity profiles of the SPEEDY model outputs are perturbed at each timestep according to the predicted high resolution informed variability. 10-year forecasts are generated for both default SPEEDY and ML-hybrid SPEEDY models and output fields are compared ensuring hybrid model predictions remain representative of Earth's atmosphere. Some changes in the precipitation, outgoing longwave and shortwave radiation patterns are observed indicating modelling improvements in the complex region surrounding India and the Indian sea.
Reversal of Precipitation Trend in Hothouse Climates
Authors: Liu Jiachen , Yang Jun , Ding Feng , Chen Gang , Hu Yongyun
Published: March 2024

Hydrologic cycle has wide impacts on the ecosystem, atmospheric circulation, ocean salinity and circulation, and carbon and nitrogen cycles. Under anthropogenic global warming, previous studies showed that the intensification of the hydrologic cycle is a robust feature. Whether this trend persists in hothouse climates, however, is unknown. Here we show that mean precipitation first increases with surface temperature, but it decreases with surface warming when the surface is hotter than ~320-330 K. This non-monotonic phenomenon is robust to the warming trigger, convection scheme, ocean dynamics, atmospheric mass, planetary rotation, gravity, and stellar spectrum. The weakening is because of the existence of an upper limitation of outgoing longwave emission and the continuously increasing shortwave absorption by H2O, and is consistent with the strong increase of atmospheric stratification and dramatic reduction of convective mass flux. Our results have wide implications for the climates and evolutions of Earth, Venus, and potentially habitable exoplanets.
Improving the Completion of Weather Radar Missing Data with Deep Learning
Authors: Gong Aofan , Chen Haonan , Ni Guangheng
Published: March 2024

Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.
Deciphering pollution loads in the Middle-Lower Yangtze River by coupling water quality models with machine learning
Authors: Huang Sheng , Xia Jun , Wang Yueling , Wang Gangsheng , She Dunxian , Lei Jiarui
Published: March 2024

Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river’s pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollution sources. Specifically, anthropogenic activities’ contribution to the pollution have been underestimated in previous research. Here, we coupled a hydrodynamic-based water quality (HWQ) model with a machine learning (ML) model, namely attention-based Gated Recurrent Unit, to decipher the daily pollution loads (i.e., chemical oxygen demand, COD; total phosphorus, TP) and their sources in the Middle-Lower Yangtze River from 2014 to 2018. The coupled HWQ-ML model outperformed the standalone ML model with KGE values ranging 0.77–0.91 for COD and 0.47–0.64 for TP, while also reducing parameter uncertainty. When examining the relative contributions at the Middle Yangtze River Hankou cross-section, we observed that the main stream and tributaries, lateral anthropogenic activities, and parameter uncertainty contributed 15%, 66%, and 19% to COD, and 58%, 35%, and 7% to TP, respectively. For the Lower Yangtze River Datong cross-section, the contributions were 6%, 69%, and 25% for COD and 41%, 42%, and 17% for TP. The primary drivers of the anthropogenic pollution sources, in decreasing order of importance, were temperature (reflecting seasonality), date, and precipitation. This study emphasizes the synergy between physical modeling and machine learning, offering new insights into pollution load dynamics in the Yangtze River.
Kinetics controlling the removal of pharmaceuticals in aquifers: estimates from laboratory and field scales
Authors: Nikolenko Olha , Villa Alejandra , Ginebreda Antoni , Jurado Anna , Pujades Estanislao
Published: March 2024

Presence of pharmaceuticals in freshwater resources is a burning issue in Europe as it is important to understand their impact on human health and living organisms. For this purpose, the mechanisms of their transport, transformation and accumulation in aquatic systems have to be clarified. Up to now there are a number of studies focusing on the estimation of the attenuation of different groups of pharmaceurticals under different physico-chemical conditions at the laboratory scale. Nevertheless, such studies often lack the geochemical modelling part, which allows determining accurately the kinetic properties of pharmaceuticals. In addition, it is of paramount importance to transfer and verify the magnitudes of kinetic parameters obtained from the lab experiments to/at the in-situ field scale.
In this study, reactive modelling (PHREEQC) was used to assess the impact of redox conditions on the fate of pharmaceuticals namely, lamotrigine, carbamazepine, irbesartan, metoprolol and atenolol in the shallow sub-oxic part (< 0.5 mg/L) of the redox stratified aquifer of the Besòs River Delta (Barcelona, Spain). A previous multivariate data analysis and mixing modelling study has identified that the presence of irbesartan, metoprolol and atenolol in the studied groundwater is governed by the river inflow. At the same time, lamotrigine and carbamazepine revealed the tendency towards the accumulation in the aquifer but the modalities of the distributions of their concentrations showed that the concentration of carbamazepine represented better all factors that control the hydrochemistry of the studied aquifer system.
To clarify and verify the obtained evidence about the dynamics of the above mentioned pharmaceuticals, batch experiments, which mimicked temperature and sub-oxic conditions of the aquifer, were carried out to measure their degradation. The obtained results were used to run the PHREEQC model to define their kinetic parameters (i.e. maximum rate constants, half-saturation concentrations and inhibition constants). The computed values from the lab studies were used as an input for simulating the behavior of lamotrigine, carbamazepine, irbesartan, metoprolol and atenolol at the field scale.
 The results of the study helped to obtain a better insight into the attenuation properties of lamotrigine, carbamazepine, irbesartan, metoprolol and atenolol under the sub-oxic conditions of the aquifer. This information assists in the assessment of the potential of those pharmaceuticals to be used as quantitative or semi-quantitative tracers.
 
Future Forest: A Decision Support System for Smart and Sustainable Forest Management.
Authors: Catalli Flaminia , Faßnacht Fabian , Kerber Jonas , Költzow Jonathan , Mohr Johannes , Rammer Werner , Reitz Thorsten , Schiller Christopher
Published: March 2024

Future Forest is an “AI Lighthouse” project funded by the German Ministry of the Environment that has two main objectives: develop a decision support system for forest management and build the foundations for a forest transformation data space.The Future Forest decision support system is based on a chain of AI/numerical models. The information used to analyse the best alternatives in an area of interest comes from state-of-the-art process-based forest simulations of specific forest management scenarios, AI-based upscaling techniques, and remotely sensed data on current forest composition and health. This data will cover Germany’s forests wall-to-wall with an unprecedented resolution of 100m for the management scenarios and climate data, and up to 10m for other variables.Creating such a system is impossible without having an accessible pool of data. Since much of the needed information is not freely available, data is collected and organized as an IDSA-compliant data space. Such a data space serves as a platform where various data holders and users converge, exchanging information and analytical applications within a structured data governance framework. This arrangement empowers platform users to retain comprehensive control over their data and enables them to share information with third parties in a controlled and secure environment. Future Forest is one year away from completion, and we can now present the first results on our way towards a forest management 2.0 system. This system is designed to offer a spectrum of alternatives for effectively managing local forest stands in response to climate change. Considering the forest owner's management objectives, such as timber production or biodiversity, the system proposes alternatives using various ecosystem indicators, encompassing wood production, carbon storage, and biodiversity considerations. The final ranking of the alternatives is based on a multi-criteria decision analysis algorithm, which incorporates also a comprehensive robustness and sensitivity analysis.In this contribution, we outline the tools utilized to make informed decisions, from the neuronal networks for forest classification to the forest dynamic simulations, and the decision support system. We discuss the constraints encountered and highlight the innovations incorporated in each of these tools. We will discuss the attempt made to offer an explainable or even interpretable model, as far as this was possible. 
Environmental risk modelling of pharmaceuticals in the water environment: towards eco-directed prescribing in Scotland
Authors: Glendell Miriam , Niemi Lydia , Gagkas Zisis , Taggart Mark , Gibb Stuart , Arakawa Naoko , Anderson Claire , Pfleger Sharon
Published: March 2024

Pharmaceutical pollution is a globally recognised public health and environmental issue that can negatively affect aquatic organisms, impact on drinking water quality and contribute to the spread of antimicrobial resistance. In this research, we developed the first UK multi-criteria decision support tool (DST) for medical practitioners to encourage eco-directed prescribing that considers environmental risk factors alongside medical considerations. The probabilistic DST, based on Bayesian networks, implemented a novel decision-making framework as a blueprint to predict environmental risk and inform eco-directed prescribing for an initial list of priority pharmaceuticals. The risk criteria agreed with stakeholders from both healthcare and environmental sectors included pharmaceutical physico-chemical properties; prescription and excretion rates; sewage treatment removal rates and dilution in the freshwater environment.The priority compounds were selected through surveys, facilitated discussion and voting by stakeholders across the environment, medicines regulation, prescribing, public health and pharmaceutical industry sectors. Based on clinical and environmental factors, four pharmaceuticals were selected: carbamazepine, clarithromycin, fluoxetine, and propranolol. Expert consultation and literature review identified data on the environmental exposure and hazard of selected pharmaceuticals. Data was collated into a database, following a classification system based on prescribing data (population standardised, by mass), ecotoxicological data, environmental monitoring data, and drug physicochemical properties. Scotland-wide risk simulation models were developed, with mapping to visualise risk levels in freshwater catchments. The models show a gradient of risk in Scotland’s freshwaters, with greatest risk in the most highly populated areas.The project has helped to increase awareness on environmental impact of pharmaceuticals, and has progressed cross-sector activity to develop support tools to introduce environmental data into prescribing decision-making in Scotland.
Including glacier storage change and reservoir management into the Community Water Model to assess vulnerabilities and enhance resilience in the Climate-Land-Energy-Water nexus
Authors: Fennell Jessica , Burek Peter , Smilovic Mikhail , Virk Zeeshan , Torabi Haghighi Ali , Eisner Stephanie , Beldring Stein , Kwok Wong Wai , Kværner Jens , Berg Peter , Bossard Thomas , Klöve Björn
Published: March 2024

Water, energy, and food security are threatened by changes in climate. Shifts in rainfall patterns and increases in temperature affect availability of catchment water resources, particularly when hydrological regimes are rainfall-limited, snow-dominant or influenced by glaciers. The sectors dependent on those water resources are therefore more at risk. To evaluate resource availability, sector interdependencies and overall vulnerability of a catchment, a nexus approach can be used. More holistic solutions can then be developed, increasing the catchment’s resilience to changes in future. However, difficulties lie in capturing the dynamics of climate, land, energy, and water systems together. In Norway for instance, this often includes snow, glaciers, and the management of reservoirs for hydropower production, and few nexus methods include these features. To address this, we selected the Community Water Model (CWatM) and made several new developments. CWatM is a widely available, easily adjustable hydrological model on a 1km x 1km daily resolution. It has the facility to include multiple crop types, and domestic, agriculture and industry water demands, therefore highly suitable for nexus assessment. The new developments to CWatM mean that seasonal changes in both reservoir and glacier water storage can now be assessed, so how these have affected, and may affect resilience to changes in climate in future could be evaluated. To test the model developments, we applied the CWatM model to the Otta catchment in Innlandet, Norway. Three large glacial bodies, and four hydropower reservoirs provide water storage to an otherwise rain-limited catchment (~300mm/year). Water resources are required for consistent hydropower production throughout the year, agriculture, and forestry, as well as white water rafting-dependent tourism. These competing demands, alongside the melting of glaciers due to climate change, have the potential to put a large amount of strain on the limited water resources. Results showed that CWatM with the new developments successfully represented the dynamics of stream discharge, glaciers, and reservoir water storage in the Otta catchment. Future work will focus on assessing the vulnerability and resilience of the Otta catchment to climatic extremes given historic and potential future changes in storage with climate change. Wider application of CWatM and the new developments could improve nexus evaluation of other catchments in Norway and worldwide and highlight opportunities for greater resilience to changes in climate.
Exploring uncertainty of trends in the lower-tropospheric North Pacific Jet
Authors: Keel Tom , Brierley Chris , Frame Thomas , Edwards Tamsin
Published: March 2024

The underlying dynamics responsible for the climatological position of jet streams are complex. In a warming world, there is mounting evidence from modelling and observational studies that amplified upper‐level tropical warming will have a poleward impact on the latitude of the tropospheric jet streams, which will continue across this century. However, existing research has also created confusion over these exact movements/trends, and as such they remain without consensus at any scale nor in any region. Here, we argue that this is in part due to the wide variety of statistics that have been used to define ‘jet latitude’ – one such method of quantifying the jet position, from which to calculate climatological shifts.In this talk, trends associated with the latitude of the lower tropospheric jet streams are examined over the North Pacific using seven unique jet latitude statistics, four modern climate reanalysis products and CMIP6 historical simulations and future projections. Using these, we assess the relative importance of various associated uncertainties arising from choice of data, scenario, or statistic. The results show that the winter North Pacific Jet is moving polewards within both the reanalysis and climate models. The climatological trend of the North Pacific jet is found to vary by season in the reanalysis, and is most robust to choice of statistic and reanalysis dataset in winter. Finally, a poleward end-of-century shift of the jet position is shown that is robust to choice of statistic and model for autumn.
EECO, LLTE and MECO events in the Eocene succession of the Isle of Wight and adjacent areas.
Authors: Hart Malcolm , Smart Christopher
Published: March 2024

The Eocene sediments of the London and Hampshire basins, which include the Isle of Wight, have long been a challenge for those interested in foraminifera and biostratigraphy. Despite many attempts at creating a viable zonation, this has proven impossible with Bowen (1954) stating that: “it is evident that no zonal scheme can be advanced for the [London Clay] formation base upon foraminifera”.          Here we present a zonation for the London Clay Formation based on smaller benthic foraminifera (SBF) and, in addition, indicate that the Early Eocene Climatic Optimum (EECO) event may have generated a glacio-eustatic signal. In the same way, using SBF and larger benthic foraminifera (LBF), the Late Lutetian Thermal Event (LLTE) and the Middle Eocene Climatic optimum (MECO) can also be identified, confirming the suggestion of Dawber et al. (2011). The LLTE and MECO events record the most northerly occurrence of numulitids in the UK, with the LLTE event particularly characterised by the northward limit of alveolinids and other LBF. Offshore Selsey Bill, which is east of the Isle of Wight, there is a distinctive carbonate-rich rock that is almost entirely formed of alveolinids and other bioclastic debris. Known since the 19th century it was, at one time, quarried as a building stone for local use. In the marine area around the Channel Islands, and on the Cotentin Peninsula, there are further occurrences of alveolinids, probably generated by a distinct glacio-eustatic event at this level in the succession. These Early and mid-Eocene glacio-eustatic events clearly pre-date the first, significant, glacio-eustatic event (Oi1) at the Eocene/Oligocene boundary (Westerhold et al., 2020).Bowen, R.N.C. 1954. Foraminifera from the London Clay. Proceedings of the Geologists’ Association, 65(2), 125–174. https://doi.org/10.1016/S0016-7878(54)80004-6Dawber, C.F., Tripati, A.K., Gale, A.S., MacNiocaill, C., Hesselbo, S.P., 2011. Glacioeustacy during the Middle Eocene? Insights from the stratigraphy of the Hampshire Basin, UK. Palaeogeography, Palaeoclimatology, Palaeoecology, 300, 84–100.Westerhold, T., and 23 others, 2020. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science, 369(6509), doi: 10.1126/science.aba6853.
HVSR analysis to investigate a possible correlation to a gas shallow reservoir in a mud volcanic field: the case of Nirano (MO).
Authors: Brindisi Albachiara , Albarello Dario , Carfagna Nicolò , Paolucci Enrico
Published: March 2024

Multiple studies highlight the evidence of a trough within the low-frequency range in HVSRs measurements performed over a gas field and attribute it to the presence of a hydrocarbon reservoir (Lambert et al., 2007; Saenger et al., 2007; Panzera et al., 2016). To explain the natural emission of low-frequency signals Saenger et al. (2007) and Lambert et al. (2007) consider hydrocarbon-reservoir related microtremor, assuming that the reservoir itself acts as a (secondary) source of low-frequency seismic waves by a resonant amplification effect. Furthermore, Panzera et al. (2016) observe that the minimum is identified by an “inverse eye-shaped” feature in the Fourier spectra, related to an amplitude increase in the vertical component of motion due to a velocity inversion. This study focuses on the investigation of the spectral anomaly described above at Nirano mud volcano field, conducted through the analysis of the results obtained by seismic arrays and three directional velocimetric stations (HVSR) deployed in the site. After a cluster analysis carried out on HVSRs have been identified 3 groups of measurements, one of which include HVSRs located in the caldera-like basin area, marked by a minimum in the seismic spectrum at 0.53 Hz. The joint inversion procedure based on Genetic Algorithms of the HVSR curves and the Rayleigh waves dispersion curve shows that the minimum is well reproduced even without a velocity inversion. This proves that it is not uniquely correlated to the mechanisms proposed above and that, therefore, it may be linked to a stratigraphic effect that unites all the measurements concentrated in the group under examination or to the surface wave model used.ReferencesLambert M., Schmalholz S. M., Saenger E. H. and Podladchikov Y. Y.; 2007: Low-frequency anomalies in spectral ratios of single-station microtremor measurements: Observations across an oil and gas field in Austria. In SEG Technical Program Expanded Abstracts 2007 (pp. 1352-1356). Society of Exploration Geophysicists.Panzera F., Sicali S., Lombardo G., Imposa S., Gresta S. and D’Amico S.; 2016: A microtremor survey to define the subsoil structure in a mud volcanoes area: the case study of Salinelle (Mt. Etna, Italy). Environmental Earth Sciences, 75, 1-13.Saenger E.H., Torres A., Rentsch S., Lambert M., Schmalholz S.M. and Mendez-Hernandez E.; 2007: A hydrocarbon microtremor survey over a gas field: Identification of seismic attributes. 77th SEG meeting, San Antonio, Texas, USA, Expanded Abstracts, 1277–1281.
Glow-terminating terrestrial gamma-ray flashes observed during the ALOFT Campaign
Authors: Cummer Steven , Pu Yunjiao , Mezentsev Andrew , Pazos Marni , Cohen Morris , Ostgaard Nikolai , Stanley Mark , Lang Timothy , Marisaldi Martino , Grove J. Eric , Quick Mason , Christian Hugh , Schultz Christopher , Blakeslee Richard , Adams Ian , Bitzer Phillip , Fullekrug Martin , Qureshi Bilal , Husa Bendik , Heymsfield Gerald ,   additional members of ALOFT team
Published: March 2024

The ALOFT campaign targeted aircraft measurements of terrestrial gamma-ray flashes (TGFs) through NASA ER-2 overflights of strong thunderstorms.  We report here the analysis of glow-terminating TGFs (GT-TGFs) that occur at the end of some gamma-ray glows.  GT-TGFs were generated by most of the observed storms during the campaign and were prolifically generated by two specific storms that were particularly active in gamma ray production.  One unique feature of GT-TGFs is that they always occur within several tens of microseconds of a narrow bipolar event (NBE).  The characteristics of GT-TGFs and the associated NBE radio emissions will be described in detail.
Evaluation of Climate and Land Use Change impacts on ecosystem services that support Food Security in Siem Reap Province, Cambodia
Authors: Ahmed Fahad , Loc Ho Huu
Published: March 2024

Nutrient delivery and water yield are key ecosystem functions that impact food security. Climate and Land use Land cover (LULC) changes are the main driving factors that affect these water related ecosystem services. By recognizing the value of ecosystem services, the efforts to manage ecosystem services have increased. One such tool to help manage ecosystem services is the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, a new but powerful ecosystem service model. However, the InVEST model still requires testing in various geographic regions. This study assessed the performance of the InVEST water yield and nutrient delivery models in Siem Reap Province, Cambodia. The climate scenarios were projected using CMIP6 for two pathways namely SSP2-4.5 and SSP5-8.5. Past, Present, and future scenarios were developed for two InVEST models including Annual water yield (AWY) and Nutrient delivery ratio (NDR) to evaluate the impacts of Climate change and LULC. In the past and present, water yield dropped by 52-69% from 2018 to 2022, with nitrogen and phosphorus exports rising by 627 and 186 tons, respectively. In future scenarios, from SSP2-4.5 to SSP5-8.5, water yield in Near Future (NF) decreased by 6-8%, while in Mid Future (MF), it increased by 10-12%, and in Far Future (FF), it decreased by 1-2%. Future nutrient delivery showed minor changes, nitrogen exports dropped by 0.42 tons for NF and increased slightly by 3 tons for MF, also increasing by 2.4 tons for FF. Phosphorus exports decreased by 0.07 tons for NF and increased slightly by 0.8 tons for MF, with a 0.7-ton increase for FF in Siem Reap province. Climate change primarily impacts water yield, with LULC governing nutrient delivery. Expanding croplands and urban areas heighten pollutants and threaten food security, while diminishing forests and vegetation reduce water yield, intensifying challenges in securing a stable food supply in Siem reap province of Cambodia.
Drivers of interannual salinity variability in the Arctic Ocean
Authors: Hochet Antoine , Lique Camille , Sévellec Florian , Llovel William
Published: March 2024

Accurate projections and attributions of Arctic ocean changes in climate models require a good understanding of the mechanisms underlying interannual salinity variability in the region. Although some mechanisms have been extensively studied in idealized settings, in particular for the dynamics of the Beaufort Gyre (BG), their applicability to the more complex system remains unclear. This study introduces a new diagnostic based on the salinity variance budget to robustly assess the mechanisms of salinity variations. The diagnostic is then applied to the Estimating the Circulation and Climate of the Ocean state estimate. The results indicate that the advection of salinity anomalies in the direction of the mean salinity gradient, produced by velocity anomalies is the primary source of interannual salinity variability. These velocities are primarily caused by fluctuating winds via Ekman transports.Fluctuating surface freshwater fluxes from the atmosphere and sea ice are the second most important source of variability and cannot be neglected. The two sinks of interannual salinity variance are associated with the erosion of large-scale  mean circulation gradients by eddies and to a lesser extent to the diffusive terms. Over continental shelves, particularly over the East Siberian Shelf (ESS), ocean surface freshwater fluxes and diffusion play a more important role than in the deep basins.We also report a strong intensification of all sources and sinks of interannual salinity variability in the BG and an opposite weakening in the ESS in the second decade of the analysis (2004-2014) with respect to the first (1993-2003). 
Quantifying the dynamic of cereals and broad leaves plants in semi-arid ranging grasslands using PlanetScope® satellite imaging
Authors: Mor-Mussery Amir , Zaady Eli , Blank Lior
Published: March 2024

The vegetation composition in grasslands is temporarily changed during its lifespan, due to the different physiological development of broadleaves and cereals species. Determination of the temporal vegetation composition and its dynamic, which is additionally termed: 'phenological state', is needed for maximal pasture utilization and sustainability. The widely used low-spatial and temporal resolution satellites are primarily suited to flat landscapes and low frequent imaging. Therefore, they are less suitable for the patchy landscape of arid regions and their plant development, characterized by rapid physiological processes. However, determining these processes using high-resolution satellite imagery needs to be studied better. Therefore, the following objectives were defined: (i) Determining the dynamic of cereals and broad-leaves vegetation in an arid grassland using timelines of High-resolution satellite imagery and (ii) Determining the influence of different grazing intensities on grassland vegetation phenology. The study site is in Migda experimental farm, northern Negev, Israel. Fifty-three grassland plots, sized 10X10m, were located. Seventeen plots were grazed, six of them heavily (90 min. per plot), five moderately (60 min.), and six lightly (30 min.). The grazing was carried out by a small ruminant herd, composed of 120- 130 animals, mainly sheep, at the cereals fallow stage (Between April end and May beginning) during 2019 and 2023. As a reference, thirty-six fenced and ungrazed plots were located; half of them are covered by cereals (Conserved), and half are covered by broad- leaves, due to spraying selective anti-cereals herbicide in January of each year (Sprayed). Imagery data from PlanetScope® was acquired (Spatial resolution: 3m pixel -1, Temporal: Once in three days, Spectral: RGB and NIR). Normalizing the different treatments NDVI, which was found to be the most suitable vegetation index by the Conserved values, indicates a phenology of a three-phase scheme. The first and the third phases (absent in part of the treatments) resemble a parabolic increase of Fresh vegetation reflectance (FVR) from the treatment, and the second resembles a parabolic increase in FVR from the Conserved. In most treatments, the duration and maximal NDVI of the first phase were positively correlated to the rainfall amounts, while a less dominant trend was found in the second phase. A rehabilitation trend over the years was calculated for the first and third phases of the lightly and moderately grazed treatments, correlated to increased broadleaf biomass, and a degradation trend was calculated for the heavily grazed. The finding indicates a parabolic change of vegetation in arid grassland. This novel methodology may be suited to other ecosystems.
Quantifying Methane Emissions Using Satellite Data: Integrated Methane Inversion (IMI) Model Application for Denmark
Authors: Vara-Vela Angel , Karoff Christoffer , Rojas Benavente Noelia , Nascimento Janaina
Published: March 2024

After decades of steady growth, even reaching a growth rate of approximately zero from 2000 to 2006, the atmospheric methane (CH4) has returned to values observed in the second half of the twentieth century, and in recent years it has increased at a faster rate (Palmer et al., 2021). In this context, major initiatives involving the use of satellite-based inversion approaches have been implemented to respond to a growing demand from the climate community. One of this initiatives is the Integrated Methane Inversion (IMI, Varon et al., 2022). IMI is a cloud-based facility developed to infer regional CH4 emissions at 0.25° × 0.3125° resolution, with dynamic boundary conditions from a global archive of smoothed TROPOspheric Monitoring Instrument (TROPOMI) data. Three monthly IMI simulations were conducted over Denmark to estimate CH4 emissions before (June 2018), during (June 2020), and after (June 2021) the COVID-19-related lockdowns. The calculated a posteriori emissions for these periods were 0.579 Tg yr-1, 0.396 Tg yr-1, and 0.553 Tg yr-1, respectively. The approximately 31% emission reduction in June 2020 was almost swiftly reversed in June 2021, with a reduction of emissions in June 2021 by less than 5% compared to the same period in 2018. As many months other than June do not frequently meet the IMI preview configuration (a model feature to rate the quality of a proposed inversion without actually performing the inversion), multi-period simulations are being conducted to characterize CH4 emissions across the country. The new CH4 emissions data set will serve as a benchmark to evaluate the model performance of the Aarhus University Methane Inversion Algorithm (AUMIA, Vara-Vela et al., 2023). Currently under development, AUMIA is a satellite-based tool designed to quantify CH4 emissions over Europe, with a specific focus on anthropogenic activities.ReferencesPalmer, P. L., Feng, L., Lunt, M. F., Parker, R. J., Bosch, H., Lan, X., Lorente, A., and Borsdorff, T.: The added value of satellite observations of methane for understanding the contemporary methane budget, Philos. T. R. Soc. A., 379, 2210, https://doi.org/10.1098/rsta.2021.0106, 2021.Vara-Vela, A. L., Karoff, C., Benavente, R. N., and Nascimento, J. P.: Implementation of a satellite- based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data, Geosci. Model Dev., 16, 6413-6431, 2023.Varon, D. J., Jacob, D. J., Sulprizio, M., Estrada, L. A., Downs, W. B., Shen, L., Hancock, S. E., Nesser, H., Qu, Z., Penn, E., Chen, Z., Lu, X., Lorente, A., Tewari, A., and Randles, C. A.: Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high- resolution methane emissions from TROPOMI satellite observations, Geosci. Model Dev., 15, 5787-5805, 2022.
Deciphering Antarctic Ice Sheet Mass Loss: A Modeling Approach to Distinguish Climate Change from Natural Variability
Authors: Beckmann Johanna , Seroussi Hélène , Bird Lawrence , Caillet Justine , Jourdain Nicolas , McCormack Felcity , Mackintosh Andrew
Published: March 2024

The Antarctic Ice Sheet (AIS) is currently undergoing accelerated mass loss, significantly contributing to rising sea levels (SLR). Despite numerous observations, uncertainties persist in understanding the drivers and dynamic responses of AIS mass loss. Climate variability strongly influences AIS dynamics, but limited observational data hinders precise attribution to climate change or natural variability. This study addresses this gap by employing advanced modeling techniques to assess the extent to which observed and future AIS mass loss can be attributed to climate change versus variability. Utilizing a unique "initialization method" with the ISSM model, we approximate the AIS state circa 1850, a period minimally affected by anthropogenic forces. From this baseline, we project AIS development using UKESM1 forcing, comparing scenarios with and without anthropogenic influence. This investigation aims to enhance our understanding of the impact of climate change on the AIS and its implications for future SLR.
A Unified Model of Crustal Stress Heterogeneity from Borehole Breakouts and Earthquake Focal Mechanisms 
Authors: Hardebeck Jeanne , Luttrell Karen
Published: March 2024

Observations of crustal stress orientation from the regional inversion of earthquake focal mechanisms often conflict with those from borehole breakouts. In particular, stress orientations from focal mechanism inversion tend to show little heterogeneity on length scales of kms to 10s of km, while borehole stress measurements often exhibit substantial short-length-scale heterogeneity.  Some of the difference may be because the two methods sample different locations within the crust, possibly indicating local stress heterogeneity, either laterally or with depth. We attempt to reconcile these two types of stress measurements, and investigate the implications for crustal stress heterogeneity. We compiled SHmax estimates from previous studies for 57 near-vertical boreholes with measured breakout azimuths across the Los Angeles region. We identified subsets of earthquake focal mechanisms from established earthquake catalogs centered around each borehole with various criteria for maximum depth and maximum lateral distance from the borehole. Each subset was independently inverted for 3-D stress orientation, and the SHmax direction compared with the corresponding borehole breakout-derived estimate. We find good agreement when both methods sample the basement stress (breakouts are close to the sediment-basement interface), or when both methods sample the mid- basin stress (sufficient earthquakes are present within a sedimentary basin). Along sedimentary basin margins, in contrast, we find acceptable agreement only when focal mechanisms are limited to shallow and close earthquakes, implying short-length-scale heterogeneity of
Water quality and physicochemical conditions drive chlorophyll-a concentrations in two connected subtropical off-stream reservoirs
Authors: Kuo Yi-Ming (AiR)
Published: March 2024

The assessment of reservoir water quality is vital for preserving ecosystems and ensuring the sustainable use of water resources. Chlorophyll-a (Chl-a) acts as a vital bioindicator, reflecting the dynamics of phytoplankton populations and trophic status of aquatic ecosystems. In this study, we use Generalized Additive Mixed Model (GAMM) models to analyze variations in Chl-a concentration in two connected subtropical off-stream reservoirs (Ren-Yi and Lan-Tan). Water temperature and rainfall are the only two important variables appearing in the optimal GAMM models for both reservoirs. However, Ren-Yi's optimal model additionally includes NH3, total phosphorus (TP), and water level, suggesting these factors may play a larger role in its nutrient levels and fluctuations. This is supported by the significantly higher chemical oxygen demand (COD), TP, and total nitrogen (TN) levels in Ba-Zhang River, which recharges Ren-Yi Reservoir. Lan-Tan's optimal GAMM model incorporates 'sampling depth' variables due to significant differences between shallow and deep sites. Interestingly, in larger datasets (300 points), 'season' emerges as a crucial variable, highlighting intensified seasonal variations in denser data. Therefore, incorporating 'season' as a nominal variable is essential for accurate modeling. Variance structures of Chl-a vary within Lan-Tan by season and within Ren-Yi by sampling site (RY100) and water temperature (RY300). The optimal GAMM captures this inherent variability by incorporating sampling sites or seasons as random effects. The relationship between dissolved oxygen (DO), COD, and Chl-a concentrations is complex and influenced by multiple factors, including nutrient dynamics, algal activity, water circulation patterns, and local conditions. GAMMs are well-suited to capturing the potentially nonlinear and time-varying nature of these relationships, leading to more accurate modeling.
Poleward intensification of midlatitude extreme winds under warmer climate
Authors: Gentile Emanuele Silvio , Zhao Ming , Hodges Kevin
Published: March 2024

In this work, we investigate the global impact of midlatitude cyclones on the geographical distribution and intensity of near-surface extreme wind speeds in a warmer climate. We use  state-of-the-art high-resolution general circulation models developed by the Geophysical Fluid Dynamics Laboratory. Results indicate a clear poleward shift of extreme wind speeds, driven by the associated shift in midlatitude storm tracks, and attributed to global warming and associated changes in general circulations. The total number of midlatitude cyclones decreases by roughly 4%, but the proportion of cyclone-associated extreme wind speed events increases by 10% in a warmer climate. Notably, the research has identified Northwestern Europe, the British Isles, and the West Coast of North America as hot spots with the greatest socio-economic impacts from increased cyclone-associated extreme winds. In addition, we also use the GFDL ultra-high resolution global storm resolving model to study cyclone-associated extreme winds.
The Life Methanic: Microbial Activity in the Deep Ocean Methanosphere of the Southern California Borderland
Authors: Treude Tina , Kloniki- Ference Emily , Liu Jiarui , Homola Kira , Li Yuhe , Utter Daniel R. , Wipfler Rebecca L. , Mayr Magdalena J. , Magyar John S. , Orphan Victoria J. , Goffredi Shana , Levin Lisa A.
Published: March 2024

The deep ocean methanosphere is defined by the microbial communities that cycle methane, the animals that directly consume or form symbioses with methane-consuming microbes, and the transitional animal communities that gain energy indirectly from methane and/or take advantage of the methane-derived authigenic carbonate. Our research seeks to redefine our understanding of the fate and footprint of methane on Pacific continental margins. By applying molecular, isotopic, geochemical, and radiotracer tools to the seep microbes and fauna we hope to better understand the contribution of methane to deep-sea diversity and ecosystem function. During the 2023 expedition AT50-12 with the RV Atlantis and the submersible Alvin we explored a set of methane seeps located in the Southern California Borderland. Samples were taken from seep carbonates, sediments, and the water column surrounding methane vents to study microbial methanotrophic activity and its relevance for methane removal, habitat engineering, and primary productivity. This poster will provide a first glance into new datasets on methanotrophy generated during the expedition and into the heterogeneity of deep ocean methane seeps off the coast of southern California.