bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristics of compound flooding along the Indian coastline: Seasonal and interannual variability
Authors: Dutta Diljit , Srinivas Venkata Vemavarapu , Bala Govindasamy
Published: March 2024

The Indian coastline, flanked by the Bay of Bengal and the Arabian Sea, is prone to the impact of intense low-pressure systems, specifically tropical cyclones and monsoon depressions and lows, which are accompanied by extreme rainfall and storm surges. The vulnerability of the Indian coastline to compound flooding, characterized by concurrent occurrence of extreme rainfall with extreme storm surge (SS-RF) or extreme rainfall with extreme sea level (SL-RF), poses a significant challenge in the face of changing climatic conditions. Analysing the past changes in the characteristics of compound flood events is essential to understanding the changing flood risks associated with concurrent extremes along the Indian coastline. This study utilises hourly sea level data from 8 tide gauge stations operated by Survey of India and daily rainfall data at those stations prepared from 0.25° gridded rainfall product of the India Meteorological Department (IMD). The skew surge time series corresponding to the stations are prepared by harmonic analysis of sea level data, and daily maxima of the time series which represent storm surge are analyzed. The concurrent extremes are identified as events where extremes of rainfall, sea level, and skew surge exceeded their respective 95th percentile thresholds concurrently. Our findings reveal distinct seasonal patterns, with higher occurrences of extreme sea level-rainfall (SL-RF) and extreme storm surge-rainfall (SS-RF) events during the summer monsoon (June to September) and post-monsoon (October to December) seasons along the east coast. Conversely, along the west coast, there are negligible SL-RF events throughout the year and the SS-RF events are clustered in the summer monsoon season only. The variability in frequency and intensity of concurrent extremes is higher in the post-monsoon than in the summer monsoon season along the east coast. The interannual variability of compound extremes on the east coast is primarily influenced by the El Niño Southern Oscillation (ENSO). During El Niño conditions, a decreasing trend in the frequency and intensity of concurrent extremes is observed, while La Niña conditions contribute to an increasing trend. ENSO impact also extends to the frequency and intensity of tropical cyclones during the post-monsoon season, also contributing to the interannual variability of concurrent extremes. The findings underscore the complex dynamics of the compound flood risk along the Indian coastline and provide valuable insights for assessing and managing flood risk under changing climate.Figure 1: The number of compound extremes witnessed at typical locations along the east-coast of India during (a) the summer monsoon (JJAS) and (b) post-monsoon (OND) seasons. The El Nino and La Nina composite of the frequency of compound extremes are plotted for JJAS in (c), (d) and for OND in (e), (f).
Solar wind deceleration in the foreshock as the source of the foreshock compressive structures
Authors: Xirogiannopoulou Niki , Goncarov Oleksandr , Safrankova Jana , Nemecek Zdenek
Published: March 2024

The foreshock is a turbulent region located upstream of the quasi parallel bow shock. It is dominated by waves and reflected back-streaming particles that interact with each other and result in the creation of different foreshock phenomena. Xirogiannopoulou et al. (2023) found that the deceleration of the solar wind in the foreshock region plays a major role in the creation of subsolar structures with enhanced density or/and magnetic field magnitude, like plasmoids, SLAMS and mixed structures. Moreover, they have found that their formation is increasing with increased velocity of the pristine solar wind. Previous studies, established that foreshock structures are connected with MSH jets (Raptis et al., 2022).  Simultaneously, Koller et al. (2023) researched the connection between the MSH jets and solar wind structures and concluded that the high-speed streams (HSS) create a more favorable environment for the jet creation. Following these results, we use measurements of the Magnetospheric Multiscale Spacecraft (MMS) and OMNI solar wind database between the years 2015 and 2018 and present a statistical analysis considering the presence of solar wind phenomena and their effect in the appearance rate of the compressive foreshock structures. We attempt to explore the origin of these structures and analyze their connection with the notable decrease (10–15 %) of the solar wind speed inside the foreshock region.
Multiscale mapping and monitoring of green biodiversity in urban areas 
Authors: Bottaro Chiara , Sona Giovanna
Published: March 2024

Urban areas are being recognized as ecosystems, where biodiversity is essential for sustaining their functionality. Healthy ecosystems offer numerous services that contribute to the well-being of human populations. The EU Biodiversity Strategy and the Nature Restoration law call for no-net loss of urban green spaces and a minimum 10% tree canopy cover in European cities. This commitment is driving a surge in city tree planting projects, with expectations for further increase in the coming years.In this context, we present an investigation focused on the multiscale mapping and monitoring of urban green biodiversity, with a primary emphasis on trees. Urban trees, besides providing various ecosystem services, play a crucial role in mitigating the urban heat load during summers, thereby alleviating adverse effects on human health, and reducing energy consumption. Unfortunately, the challenging conditions within urban environments, including increased temperatures and water scarcity due to impervious surfaces, can impact the phenology and physiology of trees, often compromising their health and functionality.In order to better explore these aspects, we introduce an application based on Google Earth Engine with the aim to extract geospatial data related to tree cover and temperatures across various spatial scales in urban environments. Utilizing machine learning algorithms, the application downscales thermal infrared satellite imagery and classifies vegetation features. Users have the flexibility to investigate the relationship between temperatures and vegetation by selecting specific time windows and areas of interest, along with access to significant spectral indices and correlation coefficients. In a pilot case study on the city of Milan, we use the application to perform a detailed analysis at the tree species level, involving the assessment of individual tree canopy temperature response in different areas of the city.This application aims to provide researchers, urban planners, green managers, and other professionals with a valuable tool to comprehend the spatial dynamics of vegetation in urban environments, assess the impacts of stressors on their fitness, and in the long run to evaluate the effectiveness of mitigation efforts, such as urban reforestations and tree planting.
Welcome to Fourier-Land: Deep-Learning based downscaling of near-surface winds and drifting snow using WRF simulations over synthetic topographies
Authors: Saigger Manuel , Mölg Thomas
Published: March 2024

Redistribution of snow by the wind has been shown to greatly influence local snow accumulation in alpine terrain. Due to the small-scale nature of this process, previous studies either concentrated on short case studies over small areas or relied on highly simplified wind fields. To bridge the gap towards an assessment of the importance of snow drift over alpine glaciers on seasonal scales we present a new approach using simulations with the Weather Research and Forecasting (WRF) model and deep learning as a computationally efficient downscaling tool for near-surface winds and snow redistribution over complex topography.We created a training data set of high-resolution (dx=50 m) WRF simulations coupled to a novel drifting-snow module that is representative for winter-time alpine environments. The idealized setup allows us to control the degrees of freedom that the final model has to learn. We developed a new technique to create synthetic topographies with similar spectral information as real terrain employing inverse Fourier transforms of scaled fields of random noise. Initial conditions for the WRF simulations are taken to represent the distribution of atmospheric and snow conditions over a winter season. This training data set we feed into a U-Net shape architecture using convolutional neural networks.Here we present first results using a training data set with a reduced number of degrees of freedom as a prove of concept. Future developments will involve adding more complexity to the initial conditions as well as applying it to real-world settings. For this we will couple the model to a glacier mass balance model and run it with real-world atmospheric fields in order to asses the overall importance of drifting snow for alpine glaciers.
Physical processes influencing the Asian climate due to the black carbon emissions over China and India
Authors: Luo Feifei
Published: March 2024

Current and expected future aerosol emission changes are particularly strong in East and South Asia, where high population densities imply high potential climate risk. Hence, there is an urgent need for improved knowledge about the near-term influences of changes in aerosol emissions. Here we have developed a set of Systematic Regional Aerosol Perturbations (SyRAP) using the reduced complexity climate model FORTE 2.0 to explore the effects of aerosol-driven climate change. Results show that the increased Black Carbon(BC) concentrations over China and India lead to decreased local surface Temperature (Ts) and precipitation, with seasonal differences in the spatial distribution. Chinese (Indian) BC emissions also impact on Indian (Chinese) climate in specific seasons. The changes of shortwave radiation (SW) dominate the surface cooling and the lower tropospheric warming due to the absorption of BC. The reductions of column-intergrated diabatic cooling lead to the decreased local precipitation, while the changes in atmospheric circulation play an opposite role (weakened EAWM, enhanced EASM and ISM). The horizontal/vertical distributions of air temperature anomalies can induce the changes in cloud cover and atmospheric circulation, which further impact on the radiation flux and precipitation. Additionally, the increased surface albedo in winter is helpful to decrease Ts and precipitation.
A deep learning method to automatically measure tide and surge in coastal areas
Authors: Sabato Gaetano , Scardino Giovanni , Kushabaha Alok , Casagrande Giulia , Chirivì Marco , Fontolan Giorgio , Fracaros Saverio , Luparelli Antonio , Spadotto Sebastian , Scicchitano Giovanni
Published: March 2024

Advances in machine learning and deep learning approaches have drawn substantial interest across diverse research domains, including environmental studies. These innovative techniques have transformed the approaches to measuring marine parameters by facilitating automated and remote data collection. This study focuses on the implementation of a deep learning model to automatically assess tide and surge, aiming for precise outcomes through the analysis of surveillance camera imagery.Utilizing the Inception v3 structure, the deep learning model was applied to predict tide and storm surge from surveillance cameras strategically positioned in two distinct coastal regions, namely Santa Lucia in southeastern Sicily and Lignano Sabbiadoro in Friuli Venezia Giulia, Italy. The deep learning model is based on classification methods to assign a value of water level to a given frame. This approach is particularly advantageous in scenarios where traditional tide sensors face inaccessibility or are distant from measurement points, especially during extreme events demanding accurate surge measurements. The dataset used for the training and validation of the deep learning model covers the entire tide values that could be observed in the study areas. Predictions of the deep learning model were compared with tide gauge values in order to assess the system accuracy.The conducted experiments demonstrate the efficiency of the model in remotely and effectively measuring tide and surge, achieving an accuracy exceeding 90% while maintaining a loss value below 1 for the deep learning model. These findings underscore its potential to fill the data collection gap in challenging coastal environments, offering valuable insights for coastal management and hazard assessment. This study makes an important contribution to the rapidly growing field of remote sensing and machine learning applications in environmental monitoring, facilitating greater comprehension and decision-making in coastal areas.
Rigorous Identification of Variations and Changepoints in the Observed Rates of Radiocarbon Samples Over Time 
Authors: Heaton Timothy J , Al-assam Sara , Bard Edouard
Published: March 2024

A commonly-used approach to estimate changes in the frequency of past events or the size of populations looks at variations in the rate of archaeological and environmental samples (e.g., charcoal from fires, human/animal bones, or other evidence of occupation) found at a site over time. Time periods with large numbers of samples suggest increased activity, while those with few samples indicate a reduced level of activity. Variations and abrupt changes in the rate of observed samples might suggest the influence of important external environmental factors. This paradigm is known as “dates-as-data”.The reliability of such a “dates-as-data” approach is highly dependent upon our ability to estimate the calendar ages of the discoveries. Most archaeological/environmental dates are obtained using radiocarbon (14C). All 14C determinations need to be calibrated in order that they can be understood on the calendar scale. This introduces considerable uncertainties in the resultant calendar ages and complicates the identification of changepoints in the calendar year rates at which samples occur.In this talk, we provide a statistically rigorous approach to overcome these challenges. We model the occurrence of events (each assumed to leave a 14C sample in the archaeological/environmental record) as an inhomogeneous Poisson process, estimating the varying rate of samples using reversible-jump Markov Chain Monte Carlo. Given a set of radiocarbon samples, we aim to reconstruct how their occurrence rate varies over calendar time and identify if there are statistically significant changepoints in the rate at which the samples arise (i.e., specific times at which the rate of events abruptly changes).We will demonstrate our approach on data exploring the expansion of humans, and the parallel disappearance of megafauna, in the Yukon and Alaska in the late Pleistocene and early Holocene: investigating both the timings of such migrations in comparison with the climatic changes known to have occurred during this period, and the potential interactions between humans and the various species in the region.
Increased evapotranspiration in the Yellow River basin brings additional precipitation locally and downwindwards
Authors: Zhang Haoyu , Wang Shuai
Published: March 2024

The ecological restoration benefits in the Yellow River Basin (YRB) are significant, characterized by increased vegetation and reduced sediment. However, afforestation has resulted in elevated water consumption, posing a threat to the sustainability of ecological functions and socio-economic water use. Previous studies treating evapotranspiration (ET) as absolute water consumption and neglecting the precipitation increase from water recycling, have introduced considerable uncertainty and limited our understanding and prediction of the process. By combining GLEAM ET data and UTrack data, we depicted the contribution of ET in the YRB to local and surrounding basin precipitation .Our study reveals a substantial increase in ET in the YRB from 1980 to 2020. ET in this basin contributes to precipitation in both local and downstream areas through moisture recycling. On average, ET contributes 107 mm/yr of precipitation locally (21%), with the primary contribution from the Upper and Middle region. Additionally, ET contributes 63, 23, 20, and 20 mm/yr of precipitation to the Haihe River Basin, Yangtze Basin, Huaihe River Basin, and Songliao River Basin, respectively. Alongside the increase in ET, its contribution to precipitation is also rising, diminishing outward from the YRB. The increased ET brings about approximately 11 mm/yr of additional precipitation to YRB, offsetting about a quarter of the ET increase. We also provide a schematic diagram illustrating the water cycle in the YRB, elucidating the proportions of each component. This work contributes to a clearer understanding of the basin's hydrological processes, offering scientific support for water resource management and sustainable development in the changing conditions of the YRB.
Investigating Extreme Wave-Induced Runup in Villanova, Spain: A Comparative Analysis of Extreme Value Models
Authors: Anton Iulia , Paranunzio Roberta , Bendoni Michele , Nalakurthi Sudha-Rani , Gharbia Salem , Baldini Luca
Published: March 2024

Coastal cities are increasingly vulnerable to the impacts of extreme wave-induced runup (ssh-runup), which can cause significant damage to infrastructure, ecosystems, and human life. A comprehensive understanding of the characteristics and future trends of extreme ssh-runup is crucial for effective coastal risk management and adaptation strategies. This study employs extreme value analysis (EVA) to investigate wave-induced runup (ssh-runup) in Villanova, Spain, a coastal community participating in the SCORE project's Coastal City Living Labs initiative.Historical (1956-2005), evaluation run (1980-2018), and future (2015-2094) ssh-runup data are analyzed under two representative concentration pathways (RCP 4.5 and 8.5). Four statistical models are applied for EVA: Block Maxima Generalized Extreme Value (GEV) with L-moments using Gumbel and Peak Over Threshold (POT) Generalized Pareto Distribution (GPD) with a 98% threshold and a constant threshold (0.82). Model performance is evaluated using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), as well as different plots (e.g., QQ plot). Results indicate that the GPD model performs consistently better than the other methods in all datasets. The GPD model exhibits a slight improvement over GEV and other models in the historical and evaluation runs, while it outperforms GEV and other models significantly in future projections. This suggests that the GPD model is better suited for capturing the increasing trend in extreme ssh-runup under climate change scenarios.The findings of this study provide valuable insights into the characteristics and future trends of wave-induced runup in Villanova, aiding in coastal risk assessment and adaptation planning. Applying different EVA techniques highlights the importance of selecting the most appropriate model for the specific data and context. These findings contribute to the understanding of coastal hazards and inform the development of effective adaptation strategies to mitigate the risks associated with extreme wave-induced runup.
Melting the mantle of Mercury and the compositional diversity of the crust
Authors: Wu Peiyan , Xu Yongjiang , Lin Yanhao , Saracino Fabrizio , Namur Olivier , Cartier Camille , Charlier Bernard
Published: March 2024

The compositional diversity of the crust of Mercury revealed by NASA’s MESSENGER spacecraft is interpreted to result from partial melting of a heterogeneous sulfur-rich Mercurian mantle. Major magmatic activity and the building of its secondary volcanic crust are restricted to the first billion year of the planet evolution. In order to understand the causes for the production of diverse lavas and the early death of major volcanism, we have performed a suite of high-pressure and high-temperature partial melting experiments under reduced conditions at temperature and pressure conditions relevant the mantle (1450-1750°C; 5, 3.5, and 1.5 GPa) of potential primordial S-free and S-saturated mantles and obtained crystallization sequences, solidus and liquidus of the residual mantle of Mercury with the Mg/Si ratio of 1.02 (Mer8) and 1.35 (Mer15) contains under above conditions. Our experimental data reveal that the majority of chemical composition of the highest Mg/Si region (HMR) on the Mercury’s surface can result from ~25±15 wt.% melting of a deep primitive mantle. Additionally, the possibility that garnet was abundant in the deep mantle could explain that Mercury rocks but the High-Mg province overlap the terrestrial “Al-undepleted” array, consistent with low-pressure melting of a garnet-free mantle.
Calculating exposure to extreme sea level risk will require high resolution ice sheet models
Authors: Williams C. Rosie , Thodoroff Pierre , Arthern Robert J. , Byrne James , Hosking J. Scott , Kaiser Markus , Lawrence Neil D. , Kazlauskaite Ieva
Published: March 2024

The West Antarctic Ice Sheet (WAIS) is losing ice and its annual contribution to sea level is increasing. The future behaviour of WAIS will impact societies worldwide, yet deep uncertainty remains in the expected rate of ice loss. High impact low likelihood scenarios of sea level rise are needed by risk-averse stakeholders but are particularly difficult to constrain. Here we combine traditional model simulations of the Amundsen Sea sector of WAIS with Gaussian process emulation to show that ice-sheet models capable of resolving kilometre-scale basal topography will be needed to assess the probability of extreme scenarios of sea level rise. This resolution exceeds many state-of-the-art continent-scale simulations. Our model simulations show that lower resolutions tend to overestimate future sea level contribution and inflate the tails of the distribution. We therefore caution against relying purely upon low resolution simulations when assessing the potential for societally important high impact sea level rise.
First Determination in the Extended Corona of the 2D Thermal Evolution of a Current Sheet after a Solar Eruption
Authors: Bemporad Alessandro , Shi Guanglu , Li Shuting , Ying Beili , Feng Li , Lin Jun ,   Metis Team
Published: March 2024

For the first time the evolution of the coronal reconfiguration after a Coronal Mass Ejection (CME) was observed by the multi-channel Metis Coronagraph on-board the ESA - Solar Orbiter mission. The images acquired in the Visible Light (VL) between 3.0 and 5.4 Rsun show the formation after a CME of a bright elongated radial feature interpreted as a post-CME Current Sheet (CS). This interpretation is supported by the appearance of the same feature as an intensity decrease in the UV Lyman-alpha images. The unique combination of VL and UV images allowed for the first time to map in 2D the time evolution of multiple plasma physical parameters inside and outside the CS region. In particular, the CS electron temperature reached peak values higher than 1 MK, more than 2 times larger than the surrounding corona in the covered altitude range. An elongated vertical diffusion region (DR), characterized as a region of much higher thermal pressure and lower magnetic pressure, is observed to slowly propagate outward during 13 hours of observations. Inside this region the Alfvènic Mach number is on the order of MA ≃ 0.02 - 0.11, the plasma β is close to unity, and the level of turbulence is higher than the surrounding corona, but decreases slowly with time. All these results provide one of the most complete pictures of these features, and support the idea of a magnetic reconnection coupled with turbulence and occurring in multiple smaller scale CSs resulting in the much broader macroscopic feature observed with the Metis coronagraph. This allows magnetic reconnection to provide a significant heating of the local plasma, despite the weakness of involved coronal magnetic fields in the considered altitude range.
Modelling of impacts of combined sewer overflows pollution on urban and rural canal networks
Authors: Masi Matteo , Masseroni Daniele , Castelli Fabio
Published: March 2024

The rapid and chaotic expansion of many urban areas, associated with increasing frequency of extreme events, may significantly affect the rainfall-runoff processes and pollutant fate and transport in surface waters. The consequences of such trends affect the combined sewerage systems (CSSs) where wastewater and rainwater mix, leading to a reduction of the efficiency of the existing drainage infrastructures and to the release of pollutants to the water bodies. In this work we developed a coupled hydrologic, hydraulic, and water quality model to simultaneously assess the effects of hydrologic events and CSS discharge on receiving waterbodies (RWBs) on both water quantity and water quality. The modelling framework consists of three modules: (i) the MOBIDIC-U software which is a distributed and raster-based hydrological model to simulate runoff and propagation in canal network in urban and rural areas, (ii) a reactive-transport module able to simulate the advective and dispersive transport and bio-chemical reactions of pollutants in the network, and (iii) an additional software module to assess the mitigation of pollution through the implementation of nature-based solutions (e.g., constructed wetlands). The main quality parameters in the model are: carbon, ammonia nitrogen (NH3 and NH4+), nitrate NO3-, total suspended solids (TSS) and dissolved oxygen. The application of the model to rural areas is particularly critical due to poor availability of data, in particular those related to the morphological characteristics of the network. To overcome this deficiency, we developed an algorithm that automatically extracts the topological/topographical data of the network (e.g., cross sections, elevations) to be provided as input to the model from the digital terrain model. We showcase the application of the model to a case study located in a suburban area of Milan (Italy) to evaluate the effects of sewerage overflows and runoff from polluted urban and agricultural surfaces on the water quality status of the RWBs. The results demonstrate that is possible to define mitigation strategies through the implementation of constructed wetlands with the purpose of obtaining a quality suitable for agricultural and environmental reuses.
Chemical and isotopic composition of steam from fumaroles in Hengill, SW Iceland
Authors: Óskarsson Finnbogi , Jolie Egbert
Published: March 2024

The greater Hengill volcanic complex in SW Iceland is located on a triple junction between the rift zone of the Reykjanes peninsula, the Western rift zone of Iceland and the South Iceland seismic zone. It is divided into three volcanic systems; Hengill, Hrómundartindur and Grændalur, each of which has associated fissure swarms. The Hengill volcanic complex is host to several geothermal areas that are spread over most of its extent, with geothermal surface manifestations generally connected to the tectonic features.Three of the geothermal systems within the youngest volcanic system (that of Mt. Hengill) have been exploited by Reykjavík Energy which runs two geothermal power plants: Hellisheiði and Nesjavellir. The power plants produce a total of 423 MWe which is sold on the national grid and 500 MWth in the form of heated groundwater for direct use in the capital area.We report samples of steam, gas and condensate collected from 12 fumaroles in 11 geothermal subfields defined within the Mt. Hengill fissure swarm (Nesjavellir, Nesjalaugagil, Köldulaugagil, Hagavíkurlaugar, Fremstidalur, Miðdalur, Innstidalur, Sleggjubeinsdalur, Skarðsmýrarfjall, Skíðaskáli and Hverahlíð) in October 2019. The samples were analysed for concentrations of all major gases (CO2, H2S, H2, CH4, N2, O2), stable noble gases (He, Ne, Ar, Kr, Xe) as well as the 3He/4He isotopic ratio and the stable isotope ratios of carbon (δ13CCO2), hydrogen (δD) and oxygen (δ18O). This study is relevant for the chemical characterization of the key upflow zones in the volcano-tectonic system as well as the identification of promising target areas for the installation of advanced gas monitoring systems. Monitoring data can contribute to an improved understanding of processes in the subsurface related to volcanic and seismic activity, but also to geothermal reservoir operations.The steam has generally rather low gas concentrations, with a total gas content of less than 500 mmol/kg steam for all samples except the few that had lost steam to condensation before sampling. All samples have CO2 as the most abundant gas followed H2S and H2. Gas geothermometry suggests temperatures ranging from about 250°C to more than 320°C. The stable water isotope values of the most powerful fumaroles are in good agreement with those obtained for well fluids, but the isotope values for steam from weaker fumaroles show signs of subsurface condensation. Carbon isotopes suggest that CO2 has a magmatic origin, as is most common for Icelandic high-temperature geothermal areas. Helium isotopes show clear mantle signatures with 3He/4He as high as 16 Ra but mixing with atmosphere is also observed.
Glider, ship and satellite measurements of marine bioluminescence in the Mediterranean Sea: the BIOLUMOPS project
Authors: Jourdin Frédéric , Martini Séverine
Published: March 2024

In the ocean, bioluminescent organisms are ubiquitous (e.g. Martini and Haddock 2017) anddeeply related to the ocean dynamics at multiple-scale (e.g. Piontkovski et al 2023).Bioluminescent organisms range from bacteria (bioluminescent bacteria have ecologicalimportance in the biological carbon pump, e.g. Tanet et al 2020) to fishes, not forgetting inparticular many zooplankton species and most dinoflagellates (these bioluminescent organismsemit light through mechanical stimulation allowing in situ sensing of these biological tracers).BIOLUMOPS (“BIOLUminescence Marine, Observations spatio-temporelles in situ par PlaneurSous-marin”) is a project running for 3 years from January 2024 to December 2026. The studyfocuses on the Gulf of Lion, in the Mediterranean Sea, which is known to be an area where winterdeep convection occurs recurrently, in correlation with bioluminescence signals (Martini et al 2014).The project aims at observing the bioluminescence and their related physical and biogeochemicalvariables at multiscale: from the fine scale vertical sawtooth paths sampling of an ocean glider tothe large scale surface ocean colour sampling of satellite remote sensors, not mentioning thediscreet sampling of the ship measurements. The four main tasks of this project aremultidisciplinary: 1. integrating two (reference and innovative) bioluminescence sensors on a sameglider; 2. deploying ship and glider in the Gulf of Lion over three surveys; 3. data processing usingclassification of organisms, in relation with biochemical and hydrodynamic variables; 4. validatingocean colour satellite image of dinoflagellates in the highly dynamic waters of the Gulf of Lion.ReferencesMartini, S., Nerini, D., Tamburini, C. (2014), Relation between deep bioluminescence and oceanographicvariables: A statistical analysis using time–frequency decompositions, Progress in Oceanography, 127, 117-128, https://doi.org/10.1016/j.pocean.2014.07.003Martini, S., Haddock, S. (2017), Quantification of bioluminescence from the surface to the deep seademonstrates its predominance as an ecological trait, Sci Rep 7, 45750, https://doi.org/10.1038/srep45750Piontkovski, S. A., Melnik, A. V., Serikova, I. M., Minsky, I. A., Zhuk, V. F. (2023), Bioluminescent eddies ofthe World Ocean, Luminescence, 38(4), 505, https://doi.org/10.1002/bio.4475Tanet, L., Martini, S., Casalot, L., and Tamburini, C. (2020), Reviews and syntheses: Bacterialbioluminescence – ecology and impact in the biological carbon pump, Biogeosciences, 17, 3757–3778,https://doi.org/10.5194/bg-17-3757-2020.
Xarray-regrid: regridding with ease
Authors: Schilperoort Bart , Donnelly Claire , Liu Yang , Koren Gerbrand
Published: March 2024

In geosciences different sources of data are often on different grids. These can be at different resolutions, but also have the grid centers at different locations. To be able to use these different sources of data in models or analyses, they have to be re-projected to a common grid. Popular tools for this are the command-line tool ‘Climate Data Operators’ (CDO) and the Earth System Modeling Framework (ESMF).

These tools work well but have some downsides: CDO is a command-line tool and as such the regridded data has to be written to disk. ESMPy, the Python package for ESMF, is only available on Linux and Mac OSX, and does not support out-of-core computing. Both tools rely on binary dependencies, which can make them more difficult to install. Additionally, many geoscientists already use xarray for analyzing and processing (netCDF) data.

For this use case we developed xarray-regrid, a lightweight xarray plugin which can regrid (rectilinear) data using the linear, nearest-neighbor, cubic, and conservative methods. The code is open source and modularly designed to facilitate the addition of alternative methods. Xarray-regrid is fully implemented in Python and therefore can be used on any platform. Using Dask, the computation is fully parallelized and can be performed out-of-core. This allows for fast processing of large datasets without running out of memory.

Xarray-regrid is available on the Python Package Index (pip install xarray-regrid), and its source code is available on GitHub at https://github.com/EXCITED-CO2/xarray-regrid 
Investigating the human-water dynamics leading to increased drought and flood risk in Kitui, Kenya.
Authors: Barendrecht Marlies H , Weesie Ruben , Matanó Alessia , Mazzoleni Maurizio , Van Loon Anne F.
Published: March 2024

During the past decades, the county of Kitui in Kenya, has experienced severe droughts. Both rain seasons have failed for several years in a row. While the region is known for its aridity and the droughts it experiences, the region also experiences regular flooding. Both drought and flood events have had devastating impacts, leading to widespread water and food insecurity. In this study, we developed a system-dynamics model to investigate the interplay between drought and flood risk and how this is influenced by human-water interactions. We model the system’s hydrology, as well as drought and flood impacts and human actions and adaptation. We aim to estimate model parameters using hydrological and impact data and fit the model to the case study areas. The fitted model is used to investigate changes in drought and flood risk over the years and how these vary across three different case study areas. We investigate how both climatic drivers and human actions and responses to the changing environment influence drought and flood risk. This analysis provides insights into the main drivers of drought and flood risk and the model allows for an exploration of the policies and measures that could be implemented to reduce risk in the future.
Inferred source models for Alpine Fault Earthquake Scenarios and influence on seismic hazard.
Authors: Holden Caroline , Townend John , Chamberlain Calum , Warren-Smith Emily , Juarez-Garfias Carmen , Pita-Sllim Olivia , Van Wijk Kasper , Denolle Marine
Published: March 2024

As part of the Southern Alps Long Skinny Array (SALSA) project, ~35+ seismometers have been deployed with 10–12 km spacing along a 450 km-long   section of the Alpine Fault. SALSA is focused on determining the ground motions likely to be produced by a future Alpine Fault earthquake. This project is addressing three principal objectives: (1) Determine the Alpine Fault’s subsurface geometry, present-day slip rates, and spatial variations in how tectonic stresses are currently accumulating on the fault, (2) Estimate the ground shaking that would be recorded at seismometers throughout central and southern New Zealand by localised slip at different points on the Alpine Fault, focusing on the synthesis of long-period Green's functions  representing accurate path effects between sources distributed along the fault and population centres throughout the South Island, and (3) Calculate the ground shaking hazard from geologically informed earthquake rupture scenarios. In this presentation we will address the influence of inferred Alpine Fault source models derived from empirical data as well as current knowledge of the fault geological and geophysical parameters on regional seismic hazard.
Stability analysis of the GNSS reference stations for displacement monitoring
Authors: Stepniak Katarzyna , Wielgosz Pawel , Kurpinski Grzegorz , Seta Mateusz , Paziewski Jacek , Sieradzki Rafal
Published: March 2024

Satellite measurements are of great importance in monitoring the deformations of the Earth's crust, which have resulted from the impact of natural forces and human activities. The example of human activities that cause surface deformations is mining activity. Geodetic monitoring of deformations and displacements caused by mining activities is of key importance for ensuring the safety of people's activities on the surface. To perform geodetic surveys, the establishment and maintenance of a local control network with reference stations of the highest accuracy is essential. The stability of the reference network should be constantly monitored and controlled to assessed the quality of the actual solutions of the reference stations and the reliability of the entire network.The goal of this study is to perform the stability analysis of the GNSS permanent stations located at a mining area where significant mining activity is observed. Daily coordinate solutions obtained from over 2 years of GNSS data processing performed in Bernese GNSS Software v.5.4 are collected and analyzed. The results show that the station coordinates are characterized by high stability over time. The standard deviation is below 2 mm for the horizontal components and below 3.5 mm for the height for all stations. The results also reveal a continuous movement of one station located near a mine shaft. In addition, a jump in the station coordinates was registered which was caused by a mining shock on the 7th of April, 2022. As a results, the station moved 50 mm northwards, 48 mm in the eastwards and 88 mm downwards.
Understanding the mechanisms of fluid flow and fracturing in poorly consolidated porous media
Authors: Konstantinou Charalampos , Papanastasiou Panos
Published: March 2024

The fundamental processes of fluid flow and fracturing within porous media play a crucial role in various applications within the fields of energy geomechanics and groundwater hydrology. Design parameters include the composition and properties of the fluid, the hydraulic and mechanical properties of the porous media, and the injection methods. These parameters are selected based on the specific objectives of each application, such as inducing fractures or uniformly replacing pore fluids through infiltration, the depth of the operation, and whether the application is conducted in soil or rock.Many of these operations take place in weakly-cemented and poorly-consolidated sands, which constitute the host rock for a significant portion of active aquifers and oil and gas reservoirs. These materials exhibit higher porosity and permeability compared to stronger rocks, resulting in a different response under fluid injection conditions. Fluid experiments are conducted on artificially cemented porous media to reveal the underlying mechanisms. The synthetic rock specimens used in the experiments are generated via microbially induced carbonate precipitation (MICP), a method that leads to the formation of calcium carbonate around silica particles. The resulting bio-treated specimens have prescribed properties (i.e., permeability, porosity, strength). The experimental behavior is then analyzed, encompassing factors such as infiltration/fracturing response regimes and fracturing patterns. Critical concepts from advanced geomechanics and groundwater hydrology are utilized to interpret the findings, including the brittleness index (BI), the cavity expansion theory, and the sand erosion problem.
Conformal Prediction Intervals For Water Demand Forecasting
Authors: Wewer Christiaan , Taormina Riccardo
Published: March 2024

In a world with accelerating climate change, rapid population increase and urbanization, urban water systems are under a growing stress. Precise short- and medium-term water demand forecasting are needed to optimize water supply operations. While machine learning methods are commonly used for this task, most studies rely on point predictions which lack a robust characterization of prediction errors. This undermines decision making under uncertainty and related applications. In this work, we employ real data to demonstrate the advantages of probabilistic water demand forecasting up to a week ahead. In particular, we explore the benefits of conformal predictions, a set of novel techniques providing distribution-free prediction intervals. Conformal predictions are model agnostic and may guarantee the validity of the prediction intervals under some assumptions. We apply the conformal prediction framework on several ML models, including tree-based methods, deep neural network models and classical time series analysis. We compare these conformalized approaches against traditional probabilistic methods such as quantile regression and Monte-Carlo dropout.
Foreshock fluctuation and its behaviors
Authors: Pi Gilbert , Nemecek Zdenek , Safrankova Jana
Published: March 2024

The foreshock region is a turbulent area that forms before a quasi-parallel shock. It is caused by the interaction between reflected particles from the bow shock and oncoming waves in the solar wind. Typically, the foreshock is located on the dawn side. However, when the interplanetary magnetic field (IMF) points in a radial or anti-radial direction, the foreshock region will move to the nose of the bow shock, covering almost the entire dayside magnetospheric system. This change triggers various phenomena in the magnetospheric system, such as magnetopause expansion and the generation of foreshock transients like sHFA. Our previous studies have shown that foreshocks behave differently depending on different upstream cone angles. The fluctuation is enhanced when approaching the bow shock with an IMF near the Parker spiral structure, but for radial IMF, it remains almost constant. In this study, we investigate the frequency, polarization, and amplitude of fluctuations for different cone angles to uncover the underlying mechanisms.
On the improvement of catchment scale simulations of nitrate transport through tile drains
Authors: Hojberg Anker Lajer , Schneider Raphael , Christiansen David Terpager , Stisen Simon
Published: March 2024

Nitrate transport from cultivated areas poses a significant risk to water quality of inland and marine water bodies. During subsurface transport nitrate may undergo reduction under anaerobic conditions. However, in temperate climates tile drainage are widely used in agriculture, which provides short-circuits between the root zone and surface water systems, with limited or no nitrate reduction. To identify areas most prone to loss of nitrate to the aquatic environment, it is thus vital to assess and quantify not only the transport out of the root zone, but also the fraction of nitrate being transport by tile drains vs. groundwater transport. The spatio-temporal pattern of tile drainage can be estimated by use of physically-based distributed hydrological models, but their setup and evaluation are generally challenged by limited data on drains with respect to both the tile drain network and in particular with respect to the efficiency of the drains, i.e. the amount of recharging water that is transported via drains. To support water management, the models must cover relevant scales (100 – 1000 km2) posing an additional upscaling modelling challenge.As part of nitrogen usage regulation in Denmark, a national nitrogen model has been developed, which is currently under revision. An important task is to improve the description of drain transport. This is achieved through detailed hydrological modelling of fields with drain flow observations from which drain fractions, i.e. the fraction of precipitation being drained, are calculated for each model grid. A machine learning algorithm (gradient boosted decision tree) is then used to regionalise the drain fraction to the national scale. Results are used in model calibration to improve the spatial and temporal description of drain flow. While the drainage estimates are needed at a fine scale, preferably at grid scale, data to evaluate model accuracy in terms of nitrate transport is not available at this scale. At catchment scale, the seasonal dynamics of observed nitrogen transport in streams provide valuable information on the amount and temporal variation of the contributions from drains. Analyses of the observed time series are used to further constrain nitrate drainage transport at catchments scale. Uncertainty in model results is assed using a stochastic approach calling for numerous model runs. To limit computational time, model simulations are carried using different spatial resolutions (100 and 500 m grids), where the coarser model is run for an entire 30-year period while the finer resolution is only run for a decade. Results from the overlapping simulation period is used for tuning a downscaling of the drain flow in 500 to a 100 m resolution, thereby providing model results of nitrate transport via drainage at a 100 m resolution for the entire 30 years.