bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of the North Atlantic for Heat Wave Characteristics in Europe
Authors: Bischof Sabine , Pilch Kedzierski Robin , Hänsch Martje , Wahl Sebastian , Matthes Katja
Published: March 2024

The recent severe European summer heat waves of 2015 and 2018 co-occurred with cold subpolar North Atlantic (NA) sea surface temperatures (SSTs). However, a significant connection between this oceanic state and European heat waves was not yet established.We investigate the effect of cold subpolar NA SSTs on European summer heat waves using two 100-year long AMIP-like model experiments: one that employs the observed global 2018 SST pattern as a boundary forcing and a counter experiment for which we removed the negative NA SST anomaly from the 2018 SST field, while preserving daily and small-scale SST variabilities. Comparing these experiments, we find that cold subpolar NA SSTs significantly increase heat wave duration and magnitude downstream over the European continent. Surface temperature and circulation anomalies are connected by the upper-tropospheric summer wave pattern of meridional winds over the North Atlantic European sector, which is enhanced with cold NA SSTs. Our results highlight the relevance of the subpolar NA region for European summer conditions, a region that is marked by large biases in current coupled climate model simulations.
Data-driven and data-agnostic stochastic parametrization of unresolved processes
Authors: Tucciarone Francesco , Mémin Etienne , Li Long
Published: March 2024

The simulation of planetary flows at all the scales that have a significant impact on the climate system is unachievable with nowadays computational resources. Large-scale simulations of the Ocean (as well as for Atmosphere) remains the primary tool of investigation while high resolution simulations can be obtained only for small geographical domains or short integration periods. The complex interdependence of mesoscale and sub-mesoscale dynamics, however, is lost in state-of-the-art simulations when performed at scales that are too large to capture these phenomena. Most of the modeling challenges arise from the representation of these effects in a parameterized manner. This work investigates the so called Location Uncertainty (LU) framework [1,2], that provides a solid theoretical background for the definition of a large-scale representation with an additional stochastic component representing the subgrid contribution, introducing new degrees of freedom to be exploited in the modeling of specific phenomena [2]. The model, that has been proven successful in several large-scale models for ocean dynamics [3,4,5,6], is implemented in the community ocean model NEMO (https://www.nemo-ocean.eu) in its hydrostatic primitive equation version, as outlined in [6] and already tested in [7]. An idealized double-gyre configuration is shown to be improved by the stochastic addition in both eddy permitting (~35km) and eddy resolving (~10km) regimes, under a variety of choices of the noise model including both data-driven and data-agnostic approaches. [1] E. Mémin Fluid flow dynamics under location uncertainty,(2014), Geophysical & Astrophysical Fluid Dynamics, 108, 2, 119–146.[2] G. Tissot, E. Mémin, Q. Jamet, (2023), Stochastic compressible Navier-Stokes equations under Location uncertainty, Stochastic Transport in Upper Ocean Dynamics, Springer. [3] W. Bauer, P. Chandramouli, L. Li, and E. Mémin. Stochastic representation of mesoscale eddy effects in coarse-resolution barotropic models. Ocean Modelling, 151:101646, 2020.[4] Rüdiger Brecht, Long Li, Werner Bauer and Etienne Mémin. Rotating Shallow Water Flow Under Location Uncertainty With a Structure-Preserving Discretization. Journal of Advances in Modeling Earth Systems, 13, 2021MS002492.[5] V. Resseguier, L. Li, G. Jouan, P. Dérian, E. Mémin, B. Chapron, (2021), New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics, Archives of Computational Methods in Engineering.[6] F.L. Tucciarone, E. Mémin, L. Li, (2022), Primitive Equations Under Location Uncertainty: Analytical Description and Model Development, Stochastic Transport in Upper Ocean Dynamics, Springer.[7] F.L. Tucciarone, E. Mémin, L. Li, (2023), Data driven stochastic primitive equations with dynamic modes decomposition, Stochastic Transport in Upper Ocean Dynamics, Springer.
Historical volcanic sulfur emissions and stratospheric sulfate aerosol optical properties for CMIP7
Authors: Aubry Thomas , Schmidt Anja , Kovilakam Mahesh , Toohey Matthew , Sigl Michael
Published: March 2024

Explosive volcanic eruptions injecting gases and aerosols into the stratosphere are a key natural driver of climate variability at annual to centennial timescales. They are thus one of the forcings considered by the Coupled Model Intercomparison Project (CMIP) Climate Forcings Task Team, in charge of identifying and implementing the next generation forcings for current and future generations of Earth System models. This presentation will provide an overview of ongoing work to produce volcanic forcing datasets for phase 7 of CMIP (CMIP7).The datasets we produce will cover the period from 1750 to 2022 at version 1 to meet to the need of modelling groups who might run extended historical simulations starting in 1750 instead of 1850. We are producing one volcanic stratospheric sulfur emission dataset catering for the needs of models which have a prognostic interactive stratospheric aerosol scheme, as well as a stratospheric sulfate aerosol optical property dataset required by models that cannot interactively simulate stratospheric sufate aerosols. For the satellite era (from 1979 onwards), sulfur emissions and sufate aerosol optical properties are based on NASA’s MSVOLSO2L4 and GloSSAC datasets, respectively. For the pre-satellite era (1750-1978), the emission dataset is based on ice-core datasets complemented by the geological record, whereas the aerosol optical property dataset is directly derived from emissions using the latest version of the Easy Volcanic Aerosol (EVA) model. This ensures methodological consistency between our emission and optical property datasets, further enhanced by the fact that EVA is calibrated using the same datasets we use for the satellite era. Our choice of methods aims to maximize consistency with methodologies used in individual model intercomparison projects (e.g. PMIP and VolMIP). A major focus of our task team is to produce well-documented datasets, which includes extensive meta-data and flags, detailed documentation, and provision of open-access scripts used to create the datasets, which should facilitate future development and operationalization by the community. We also discuss the most critical challenges for providing accurate volcanic forcing datasets, including the under-recording of small-to-moderate magnitude eruptions before the satellite era, and the Hunga Tonga-Hunga Ha'apai 2022 eruptions, which injected relatively small amounts of sulfur, but 150 Tg of water into the stratosphere.
Developing hybrid distributed models for hydrological simulation and climate change assessment in large alpine basins
Authors: Li Bu , Sun Ting , Tian Fuqiang , Ni Guangheng
Published: March 2024

Large alpine basins on the Tibetan Plateau (TP) provide abundant water resources crucial for hydropower generation, irrigation, and daily life. In recent decades, the TP has been significantly affected by climate change, making it crucial to understand the runoff response to climate change are essential for water resources management. While limited knowledge of specific alpine hydrological processes has constrained the accuracy of hydrological models and heightened uncertainties in climate change assessments. Recently, hybrid hydrological models have come to the forefront, synergizing the exceptional learning capacity of deep learning with a rigorous adherence to hydrological knowledge of process-based models. These models exhibit considerable promise in achieving precision in hydrological simulations and conducting climate change assessments. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information and describe alpine hydrological processes, which restricts their applicability in hydrological modeling and climate change assessment in large alpine basins. To address this issue, we develop a set of hybrid distributed hydrological models by employing a distributed process-based model as the backbone, and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. Results are compared to those obtained from hybrid lumped models, state-of-the-art distributed hydrological model, and DL models. A climate perturbation method is further used to evaluate the alpine basins' runoff response to climate change.Results indicate that proposed hybrid hydrological models can perform well in predicting runoff in large alpine basins. The optimal hybrid model with Nash-Sutcliffe efficiency coefficients (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid distributed model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. Besides, runoff exhibits an amplification effect in response to precipitation changes, with a 10% precipitation change resulting in a 15–20% runoff change in large alpine basins. An increase in temperature enhances evaporation capacity and changes the redistribution of rainfall and snowfall and the timing of snowmelt, leading to a decrease in the total runoff and a reduction in the intra-annual variability of runoff. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about runoff’s response to climate change in large alpine basins on the TP. 
Enhanced redox mapping at national scale of Denmark through integration of sediment color and groundwater chemistry in a machine learning framework
Authors: Koch Julian , Conde Joel , Hansen Birgitte , Kim Hyojin , Møller Ingelise , Thorling Lærke , Troldborg Lars , Voutchkova Denitza , Højberg Anker
Published: March 2024

Redox conditions play a crucial role in determining the fate of geogenic and anthropogenic contaminants in groundwater, impacting ecosystem services vital for both the aquatic environment and human water supply. For example, investigating the reduction of nitrate underscores the importance of data on redox conditions since denitrification takes places in anoxic environments. Specifically, knowledge of the depth to the uppermost reduced layer, i.e., first redox interface, can inform water and land management by identifying agricultural areas vulnerable or robust to nitrate leaching. Assessing redox processes is complicated by geological heterogeneities, resulting in complexities of local to regional groundwater flow paths. Geospatial machine learning techniques have previously successfully mapped redox conditions based on sediment color or water chemistry observations. This study introduces a novel approach that combines both data sources to enhance understanding of subsurface redox conditions in Denmark. In the first step, depth to the first redox interface is mapped using sediment color information from 26,800 boreholes. This depth is derived from sediment color changes, transitioning from oxic to reduced colors of quaternary sediments. The mapping utilizes a regression-based gradient boosting with decision tree algorithm trained against sediment color data and 20 covariates, encompassing information on hydrogeology, lithology, topography, and hydrology. In the second step, the depth of the first redox interface is compared against groundwater chemistry to classify continuous and discontinuous redox conditions. Continuous conditions exhibit the absence of oxic groundwater below the first redox interface, while discontinuous conditions show oxic groundwater below the interface. This classification is performed using a gradient boosting with decision tree algorithm utilizing the same 20 covariate maps and 21,800 classified groundwater samples. Both models undergo comprehensive cross-validation and feature importance analysis. The depth to the first redox interface is modeled with a mean error of 0.001 m and a root-mean-squared error of 8.3 m. The continuous/discontinuous classification attains an accuracy of 69.5 %. Both variables are mapped at a 25 m spatial resolution at the national scale of Denmark. Results indicate a mean depth to the first redox interface of 9.4 m and a standard deviation of 5.7 m, with spatial patterns largely driven by the groundwater table. 66.0% of Denmark is classified as discontinuous, indicating complex redox conditions, predominantly collocated with moraine clay. These maps contribute significantly to understanding subsurface redox processes, supporting national-scale land and water management.
Andosol genesis: Transition from silandic to aluandic properties and the related changes in organic carbon storage
Authors: Zieger Antonia , Kaiser Klaus , Kaupenjohann Martin
Published: March 2024

Andosols are commonly subdivided according to silandic and aluandic features. Both subgroups are considered to be end members of the Andosol genesis. Silandic Andosols are characterized by organic matter (OM) strongly bound to imogolite-type material (ITM), while Aluandic Andosols mainly consist of aluminium-OM complexes (Al-OM complexes). According to current theory, silandic and aluandic properties are direct results of primary weathering, assuming two separate lines of genesis.Our previous results, however, suggest that silandic horizons can transform into aluandic over time, resulting in additional carbon accumulation. This is likely caused by dissolved organic matter (DOM) entering the subsoil with the percolating soil solution, masking dissolved aluminium as soluble Al-OM complexes. This promotes the dissolution of ITM, releasing aluminium. The latter reacts with DOM, inducing the formation of insoluble, Al-OM complexes, which then precipitate.To test this hypothesis, we conducted a long-term percolation experiment with material of an Ecuadorian Andosol formed in a homogeneously tephra deposit. This soil exhibits aluandic properties in the topsoil and silandic properties in the subsoil. Ions, pH, and DOC in the feed and eluate solution were monitored over a period of 18 months. The convection-dispersion-equation as implemented in HYDRUS-1D was used to model the percolation experiment as a one-dimensional standard reactive solute transport.Our results revealed a strong carbon accumulation of 14 g·kg-1 in the silandic material after 18 months, with the vertical transport of Al-OM complexes only explaining up to 33 % of the carbon accumulation. The HYDRUS-1D model revealed that sorption of DOM dominates at the beginning of the experiment and explains up to 40 % of carbon accumulation (including vertically transported Al-OM complexes). For the silandic material, the results indicate that up to 91% of the carbon accumulation are due to ITM dissolution and subsequent formation of insoluble Al-OM complexes.In summary, our findings support the hypothesis, that ITM dissolution and the subsequent formation of Al-OM precipitates significantly contribute to the increase in carbon concentration in the silandic material, while the above aluandic material did not change.
Secondary Ice Processes during a Medicane Evolution
Authors: Sotiropoulou Georgia , Floka Foteini , Patlakas Platon
Published: March 2024

The Mediterranean basin is characterized by cyclonic activity that can often lead to adverse weather conditions. Lately, there is an increasing interest to specific types of cyclones, such as medicanes, due to their dynamic characteristics. However, these events can also lead to extreme precipitation, often resulting in flooding and causing severe damage, with potential human casualties. While there is continuous effort to understand the  dynamic evolution of these  systems, little is known about the underlying microphysical processes. Secondary Ice Production (SIP) processes are ice multiplication mechanisms that have been frequently linked to the onset of heavy precipitation and the generation of high concentrations of precipitation particles. In this study we investigate the impact of four SIP mechanisms (rime-splintering, collisional break-up, drop-shattering, sublimation break-up) on the evolution of medicane Qendresa using the Weather and Research Forecasting (WRF) model. Qendresa occurred in 2014 mainly in the vicinity of Italy and Malta, causing three fatalities and at least $250 million in damages in Italy.   
The impacts of several global change drivers on tropical root traits and dynamics
Authors: Lugli Laynara F. , Yaffar Daniella ,   TropiRoots Network - Tropical Root Trait Initiative
Published: March 2024

Tropical ecosystems are threatened by escalating anthropogenic activities that worsen global change, potentially disrupting the carbon (C) equilibrium in tropical forests and affecting global climate regulation. While considerable research has explored the impact of global change on aboveground tropical vegetation, our comprehension of belowground components, particularly roots that mediate plant-soil interactions, such as nutrient and water uptake, remains limited. We conducted an analysis of existing research on how tropical roots respond to key global change drivers, including warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO2, and fires. Drawing from tree species- and community-level outcomes from experimental studies, we compiled 266 root trait observations from 96 studies conducted across 24 tropical countries. From the existing knowledge, we noted in this review that tropical root systems tend to increase in biomass in response to warming and eCO2, but community-level experiments were rare for warming and non-existent for eCO2. Drought increased root:shoot ratio without changing root biomass, indicating a reduction in aboveground biomass. While N deposition may not greatly impact most tropical forests in the short term due to strong phosphorus limitation, mycorrhizal colonization and root phosphatase exudation were predominantly down- and up-regulated, respectively. Cyclones, fires, and flooding resulted in decreased root biomass, which, under elevated CO2 and warming, could lead to greater carbon losses from tropical soils. Cyclones and fires increased root production, potentially in response to plant community shifts and nutrient input, while flooding altered compounds related to plant regulatory metabolism due to low oxygen conditions. We also emphasize the importance of in situ studies, comparing adapted versus non-adapted species to these disturbances and the need for methodological consistency among experiments. Our findings underscore the necessity for further research to enhance our understanding of tropical root responses to global changes. The responses of root traits and dynamics to several global change drivers would affect the functioning of the whole forest and, consequently, carbon cycling and stocks above and belowground.
Deformation-induced Rare Earth Elements (REE) redistribution in apatite from the Fen Carbonatite Complex (Norway)
Authors: Menegon Luca , Valter Oleksandra , Dahlgren Sven
Published: March 2024

The Fen Carbonatite Complex in Norway contains the largest deposit of Rare Earths Elements (REE) in Europe, with estimated resources in the range of 30 – 50 Mt of total Rare Earth Oxides. If Fen will be targeted as an exploitable mineral resource, the geological processes that formed it must be understood, with specific emphasis on what controls the location and composition of the REE resources.The Fen complex formed at 580 Ma through different stages of carbonatite melt intrusions followed by hydrothermal alteration. Fluid- and melt-assisted deformation accompanied the intrusive and hydrothermal evolution of the Fen Complex extensively and resulted in the formation of shear zones and breccias. However, the mechanism and significance of carbonatite deformation are poorly understood, and so are the effects of post-crystallization deformation processes on the remobilization of trace elements in carbonatites.This study investigates deformation processes and REE remobilization in shear zones in dolomite-carbonatites from Fen. The shear zones display a compositional banding defined by alternating dolomite- and apatite-rich layers, where apatite grains are variably elongated with aspect ratio ranging from 2 to 11 and grain length from 50 to 500 µm. SEM images reveal the presence of carbonatitic melt pseudomorphs in the form of intergranular beads, cusps, films, and pools, which are particularly evident in the apatite layers, where individual grains are locally entirely rimmed by melt films. The apatite grains appear zoned in cathodoluminescence (CL) images, with dark cores and bright rims that are thicker parallel to the foliation. In the most elongated grains, the dark core forms less than 20% of the grain area, which is otherwise dominated by the bright rim. On the contrary, more equidimensional grains are dominated by the dark core. Hyperspectral analysis of CL images indicates that the elongated rims of apatite are enriched in REE (particularly in Nd) compared to their core. Electron backscatter diffraction (EBSD) analysis demonstrates that (1) the elongated apatite grains are internally strain free, and (2) grain elongation occurs parallel to apatite c-axis.Our data show that deformation of apatite occurred by melt-assisted dissolution-precipitation creep, which was responsible for grain elongation and remobilization of REE. Thus, post-crystallization deformation and melt-rock interaction played an important role in redistributing REE within the Fen Complex.
Controls on microplastic breakdown due to abrasion in gravel bed rivers
Authors: Ockelford Annie , Wu Xuxu , Parsons Daniel
Published: March 2024

Microplastic contamination of river sediments has been found to be pervasive at the global scale however, the physical controls governing the storage, remobilization and pathways of transfer in fluvial sediments remain largely unknown. The properties that make plastics useful - strength, flexibility, durability and resistance to degradation - also make their transport through the environment difficult to predict. Specifically, the risk profile associated with microplastic transfer is dynamic because their physical and chemical properties change over time as they persist in, or move through, the environment. For example, mechanical breakdown, due to abrasion, likely decreases the size of microplastic particles, increases their surface roughness and surface area to volume ratio, and influences the diversity and abundance of the microbial taxa that colonise them. However, the processes controlling the mechanical breakdown of plastic particles rivers by abrasion is poorly understood, particularly in gravel bed rivers where there are a range of grain sizes present with the bed sediment. Here we report a series of experiments designed to explicitly quantify the influence of sediment grain size on microplastic degradation and understand how this varies by microplastic type.Four sediment beds ((i) 0.8mm uniform sand; (ii)10mm uniform gravel; (iii) 20mm uniform gravel and (iv) bimodal sand gravel mix D50 14mm)) were seeded with either Nylon pellets (d= 1.2 g/cm3), Polycarbonate fragments (d=1.2 g/cm3) or Nylon fibres (d = 1.15g/cm3) at 0.005% concentration by mass. The sediment and plastic were placed into a cement mixer with 20L of water and tumbled for 100 hours. During each experiment, the cement mixer was periodically stopped and a sample removed to assess microplastic abrasion.Results indicate that fibres are abraded to the greatest degree in comparison to beads and fragments.  Results also indicate a clear relationship with sediment size where microplastic fragmentation rates increase with river sediment grain size. In all plastic types surface complexity increases with time which has implications for the ability of the plastics to potentially host microbial taxa.   
Deep Learning for Higher-Order Aftershock Forecasting in Near-Real-Time
Authors: Mizrahi Leila , Jozinović Dario
Published: March 2024

The use of machine learning (ML) methods for earthquake forecasting has recently emerged as a promising avenue, with several recent publications exploring the application of neural point processes. Such models, in contrast to those currently applied in practice, offer the flexibility to incorporate additional datasets alongside earthquake catalogs, indicating potential for enhanced earthquake forecasting capabilities in the future. However, with a forecasting performance that currently remains similar to that of the agreed-upon benchmark, the Epidemic-Type Aftershock Sequence (ETAS) model, the black-box nature of ML models poses a challenge in communicating forecasts to lay audiences. The ETAS model has stood the test of time and is relatively simple and comprehensively understood, with few empirically derived laws describing aftershock triggering behavior. A main drawback of ETAS is its reliance on large numbers of simulations of possible evolutions of ongoing earthquake sequences, which is typically associated with long computation times or resources required for parallelization.In this study, we propose a deep learning approach to emulate the output of the well-established ETAS model, bridging the gap between traditional methodologies and the potential advantages offered by machine learning. By focusing on modeling the temporal behavior of higher-order aftershocks, our approach aims to combine the interpretability of the ETAS model with the computational efficiency intrinsic to deep learning.Evaluated using commonly applied metrics of both the ML and earthquake forecasting communities, our approach and the traditional, simulation-based approach are shown to perform very similarly in describing synthetic datasets generated with the simulation-based approach. Our method has two major benefits over the traditional approach. It is faster by several orders of magnitude, and it is not susceptible to being influenced by the presence (or absence) of individual 'extreme' realizations of the process, and thus enables accurate earthquake forecasting in near-real-time.
The soil safety situation in Hungary, especially around the military sites.
Authors: Szűcs-Vásárhelyi Nóra , Pátzay György , Szécsy Orsolya , Koós Sándor , Uzinger Nikolett , Mészáros János , Dobor József , Árvai Mátyás , Szabó Anita , Garamhegyi Gábor , Szatmári Gábor , Kovács Zsófia Adrienn , Rékási Márk
Published: March 2024

Effective management of soil damage is essential for national defence. In such a situation, chemical analytical methods are used to assess the contamination, which, although accurate, are time-consuming, costly and do not provide sufficient information on the state of the pedosphere on their own. Rapid decision making is of paramount importance, especially in disaster situations, and a rapid procedure for in situ assessment of the damage site, complemented by interpretation of soil health data, would be needed. The combined use of near-surface remote sensing methods, non-destructive analytical techniques and ecotoxicology could provide a new, optimised approach to soil safety. For the measurements in my research, I use in situ applicable non-destructive instruments (so-called proximal soil sensing) in the designated military sample area. The detection of soil radiation was performed by means of airborne remote sensing using an unmanned aerial vehicle (uav) mounted radiation measuring device. The potentially toxic element content was measured using a hand-held X-ray fluorescence spectrometer (PXRF). Ecotoxicological tests, soil column and microcosm experiments were set up to investigate soil ecosystem sensitivity. My objectives are to (i) implement improvements to facilitate effective operations of disaster management agencies (ii) streamline procedures for individual risk reduction and (iii) place the importance of pedosphere ecosystem sensitivity testing in a disaster management context during the execution of operations.The research was funded by the National Research, Development and Innovation Fund of the National Defence Subprogramme of the Cooperative Doctoral Programme of the Ministry of Innovation and Technology.
Link between groundwater storage and landscape changes in mountainous areas: the Kahule Khola watershed (Nepal)
Authors: Teagai Kapiolani , Armitage John , Agélas Léo , Andermann Christoff , Hovius Niels
Published: March 2024

In many watersheds of various sizes, the role played by groundwater to sustain river flow is still misunderstood. This is the case in mountainous areas where geological features as fractures, altered or unaltered bedrocks and steep slopes notably play an important role for storing groundwater into the subsurface. The groundwater support to low flows was considered for a long time as a minor contribution, due to the steep slopes in those areas. But in Nepal, it is estimated that 2/3 of the volume of rivers comes from the exfiltration of groundwater through resurgences. Though several attempts were made with numerical modelling based on data monitoring and field surveys to quantify river-groundwater exchanged fluxes, some ambiguities remain. Especially regarding the impact of landscape change in a mountainous topography. The aim of this work is to characterize the subsurface infiltration, recharge, and storage mechanisms of a mountainous hydrogeological system in the Himalayas using field investigations and numerical modelling. In the Kahule Khola watershed (Nepal), a steep catchment of 33 km² whose altitudes range between 1000 and 3500 masl, various field experiments were made to identify groundwater pathways into the altered subsurface and to catch the river/groundwater interactions: seismic and electric surveys (ERT), infiltration tests, physical and isotopic measurements of springs/streams and the water tracking on the surface with loggers installed along gullies in the overall watershed. The region is submitted to intense rainfall as monsoon, intercalated by dry periods in which the river flow is still sustained. Moreover, by closing ancient fractures and opening new ones, earthquakes can deviate springs and change the surface water/groundwater pathways. This contributes to reshaping the landscape. However, the spatial and temporal contribution of groundwater to maintain a baseflow in the river is not quantified yet, in space and time. The ERT data from a time-lapse realized before and after monsoon show a deep alteration zone with a shallow humid layer of 10 m thick at least all year long under the slopes. Areas of low resistivity reveal infiltration zones and preferential flow paths. These areas are recharged in the wet season and drained in the dry season. At the surface, we estimate an average hydraulic conductivity at saturation of 3,5.10-5 m.s-1 in 150 cm depth which suggest an infiltration rate higher than the average rainfall rate (~3000 mm.year-1). In order to quantify the groundwater storage into the subsurface, a numerical groundwater model in 2D has been developed (Python) and is able to simulate and quantify the water storage dynamics of a spatial and temporal pre-defined domain. The data measured on the field will be used to define the initial conditions of future scenarios.
Multisource Data Fusion to Understand the Impact of Air Pollution and Climate Change on Asthma Prevalence over Dhaka City for Informed Policy Development
Authors: Siraj B M Zuhair , Khan Md. Firoz , Ahmed Md. Deen , Biswas Animesh , Khan Mohammad Adnan , Motalib Mohammad , Rahman Shahanaj , Rahman Md. Hafizur
Published: March 2024

Dhaka city has experienced heat waves and extreme weather events in recent years, leading to elevated levels of air pollution primarily attributed to vehicle emissions, industrial activities, and domestic burning. The World Health Organization has dubbed air pollution as the "silent killer," contributing to a global threat with 262.41 million asthma cases worldwide. Study has confirmed a strong correlation between asthma and both outdoor and indoor air pollutants. Despite the unknown impact of macro-environmental factors on asthma burden in Bangladesh, this study sheds light on the connection between indoor and outdoor air pollution, climate change, and respiratory health in Dhaka. It quantifies their influence on hospital admissions, highlighting the need for proactive measures to address these challenges. The research aims to investigate the influence of air pollution and climate change on asthma hospital admissions in Dhaka City over the past five years. This involves integrating ground measurements, satellite data (Sentinel 5p & CAMS), and community-level asthma patient data. The study unveils a significant relationship between climate change, air pollution, and asthma-related health issues in Bangladesh. Utilizing a Generalized Additive Modelling (GAM), the analysis of climate and air quality data from urban locations and various hospitals in Dhaka reveals a substantial association. The study explores the correlation between asthma-related hospital admissions and air quality parameters (NO2, CO, SO2, PM2.5, PM10, O3) as linear variables, with temperature, relative humidity, and wind speed as covariates. The findings from the Generalized Additive Modeling using ground data indicate that increased levels of PM2.5, SO2, PM10, O3, CO, and NO2 in Dhaka City are linked to progressively heightened risks of asthma hospital admissions. Immediate spikes in PM2.5, SO2, CO, and NO2 result in a higher relative risk of asthma prevalence, leading to an increased number of hospital admissions. In contrast, PM10 shows an immediate but minimal increase in risk, and O3 exhibits a time-delayed effect. The study integrates satellite data to provide a comprehensive view of pollution levels and utilizes community-level asthma patient data for informed policy development. Additionally, it identifies indoor pollution as a significant factor, given that urban families spend 80-90% of their time indoors, emphasizing its impact on asthma prevalence. The study also determines emission regions through reverse modeling.
A Multi-level Approach for Provenance Management And Exploration in Climate Workflows
Authors: Antonio Fabrizio , Rampazzo Mattia , Sacco Ludovica , Nassisi Paola , Fiore Sandro
Published: March 2024

Provenance and reproducibility are two key requirements for analytics workflows in Open Science contexts. Handling provenance at different levels of granularity and during the entire experiment lifecycle becomes key to properly and flexibly managing lineage information related to large-scale experiments as well as enabling reproducibility scenarios, which in turn foster re-usability, one of the FAIR guiding data principles.This contribution focuses on a multi-level approach applied to climate analytics experiments as a way to manage provenance information in a more structured and multifaceted way, and navigate and explore the provenance space across multiple dimensions, thus enabling the possibility to get coarse- or fine-grained information according to the actual requested level. Specifically, the yProv multi-level provenance service, a new core component within an Open Science-enabled research data lifecycle, is introduced by highlighting its design, main features and graph-based data model. Moreover, a climate models intercomparison data analysis use case is presented to showcase how to retrieve and visualize fine-grained provenance information, namely micro-provenance, compliant with the W3C PROV specifications.This work was partially funded by the EU InterTwin project (Grant Agreement 101058386), the EU Climateurope2 project (Grant Agreement 101056933)and partially under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 1031 of 17/06/2022 of Italian Ministry for University and Research funded by the European Union – NextGenerationEU (proj. nr. CN_00000013).
Utilizing Diffractions as a Tool to Study the Volcano-Tectonic Processes at the Christiana-Santorini-Kolumbo Volcanic Field
Authors: Ischebeck Lisa , Preine Jonas , Hübscher Christian
Published: March 2024

The interplay between volcanism and tectonics gives rise to a spectrum of geological phenomena, including eruptions, earthquakes, mass failures, and tsunamis, posing significant threats to both local and global environments. Seismic data interpretation serves as a crucial tool for reconstructing past volcanic-tectonic interactions, offering insights into potential precursors and triggers for future events. However, the inherent complexity of volcanic-tectonic regions often poses challenges for seismic imaging and interpretation, particularly regarding the delineation of faults, as well as the identification of volcanic structures, volcanic products, and mass transport events.In this study, we explore the frequently overlooked diffracted wavefield as a tool to aid seismic interpretation of volcano-tectonic structures. Wave diffraction occurs at geodynamically important features like faults, erosional surfaces or other small-scale scattering objects and encodes information on a sub-wavelength resolution. Our approach models and adaptively subtracts the reflected wavefield from the un-migrated seismic data before we focus the separated diffractions to generate diffraction-energy images. We will present two seismic profiles from the Christiana-Santorini-Kolumbo volcanic field, one of the most active volcano-tectonic fields in Europe. These profiles cross major rift basins, complex fault zones, volcanic edifices, and mass-transport deposits. Our derived diffraction images provide a unique window into the subsurface, highlighting important small-scale heterogeneities. We observe that diffractions cluster at geodynamically important subsurface structures, such as faults, volcanic cones, as well as distinct unconformities within the rift basins. Diffractions also cluster at seismic subunits previously interpreted as eruptive products such as ignimbrites and lava flows, as well as mass-wasting deposits. In contrast, we observe that little diffraction occurs in sedimentary strata interpreted to be the result of hemipelagic background sedimentation. Thus, this study strongly advocates for the integration of diffraction energy images into the standard practice of seismic data analysis and interpretation in the context of volcano-tectonic interactions.
Large Earthquakes Monitoring using High-Rate Global Navigation Satellite System Data through a Deep Learning approach
Authors: Quinteros-Cartaya Claudia , Quintero-Arenas Javier , Faber Johannes , Köhler Jonas , Srivastava Nishtha
Published: March 2024

The High-rate Global Navigation Satellite System (HR-GNSS) instruments are devices that can detect seismic wave arrivals and measure ground displacement generated by an earthquake with high precision. By integrating HR-GNSS data with other sensors and models, we can improve the accuracy of earthquake assessments and provide valuable information for early warning and disaster preparedness. Our focus lies in developing deep-learning models leveraging HR-GNSS waveform data. These models significantly empower our capacity to detect and estimate the magnitude of large earthquakes. Yet, the rapid analysis of HR-GNSS data using deep learning algorithms remains a current challenge. To overcome this challenge, it is crucial to have access to large and high-quality datasets. Since the presence of noise in GNSS recordings particularly impacts data quality, especially for earthquakes measuring below magnitude 7, our training of Deep Learning (DL) models primarily relies on the data available from the largest earthquakes. This comes with a trade-off as these events provide a limited dataset because they occur less frequently, making the data poorly representative for model training. To overcome this limitation, we have used both synthetic earthquake signals combined with synthetic and real noise for model training, validation, and testing. Our investigation explores how diverse factors, such as noise, earthquake magnitude, station density, distance from the epicenter, and duration of the signal, affect the performance of our models. We aim to generalize the detection methodology and magnitude estimation for real-time monitoring of large earthquakes across diverse tectonic regions. The DL models proposed in this work will be integrated as complementary algorithms to the open-source Python package SAIPy.
A multi-hazard framework integrating flood and landslide early warning systems: Application to the Tordera river basin (Catalonia, Spain)
Authors: Yeditha Pavan Kumar , Hürlimann Marcel , Berenguer Marc
Published: March 2024

Compounding and cascading effects of rainfall-induced floods and landslides pose significant challenges and existing research has predominantly focused on individual hazards and the multi-hazard aspect has been understudied. To bridge the gap, the current study develops a multi-hazard approach by building upon validated flood and landslide hazard assessment methods.The first steps in our approach involve determining the flood hazard in the river network and landslide warnings. With the landslide warnings denoting the initiation areas of the landslides, the warnings are combined with a morphological index to determine the potential sediment amount generated in subbasins and their connectivity to the drainage network. Finally, the multi-hazard aspect in the river network is obtained by integrating sediment transport and flood hazards. The results indicate the regions that have amplified hazards due to the combined effect of floods and sediment transport.An initial version of the proposed framework was tested in the Tordera River basin, with a focus on the Gloria storm that occurred in January 2020. This storm caused widespread floods and landslides across Catalonia (NE Spain), resulting in substantial damage. Choosing this area allowed us to see how well the framework works in understanding multi-hazard aspects. The results highlight the areas with the highest occurrence of landslides and also the regions in the drainage network where significant changes occurred due to floods and sediment transport. The identified areas match with regions where noticeable alterations were observed during the Gloria storm. The preliminary results showcase the capability of the framework to effectively capture the amplified hazard, providing valuable insights into the compounding and cascading effects of rainfall-induced floods and landslides.
Prediction of airborne allergenic pollen concentrations with machine learning  
Authors: Vovk Tetiana , Kryza Maciej , Tomczyk Szymon , Malkiewicz Małgorzata , Lipiński Piotr , Werner Małgorzata
Published: March 2024

Over the past 30 years, the prevalence of allergies has increased continuously. Allergic rhinitis and asthma are among the most frequent non-communicable diseases and cause serious public health concerns worldwide, with the highest prevalence rates among children and adolescents. Moreover, experts assume that climate change will worsen the impact of allergies within the next decades. Therefore, it is essential to develop high quality methods and tools that can forecast allergenic pollen in the air to prevent sensitized against contact to high concentrations of aeroallergens. 

In this study we aim to develop a tool for prediction of pollen concentrations based on machine learning (ML) methods with the use of measured pollen concentrations and modelled meteorological parameters. We focus on the birch pollen, which is the most allergenic tree taxon in Central Europe. We use daily pollen concentration from Wrocław aerobiological station (south-west Poland) for years 2006 – 2022. Pollen grains were gathered with the use of the Burkard trap and counted following the recommendations of the International Association for Aerobiology. Meteorological data for the analysed period were provided with the Weather Research and Forecasting (WRF) model. We test different machine learning algorithms including: Random Forest,  xgBoost, Support Vector Regression (SVR) and Multilayer Perceptron (MLP). The algorithms are used to detect the days with pollen concentrations exceeding the threshold levels of 20, 75 and 90 pollen m-3, which correspond to the first symptoms, symptoms in all subjects and severe symptoms, respectively. 

For each ML algorithm, the whole data set was split into training and testing subsets in a proportion where the training set was ¾ of the data and the rest was the independent test set for the final model verification. Each model was checked during cross-validation for optimal hyperparameters. We test different parameters, including temporal variables and lagged predictors (e.g. pollen concentrations, air temperature, relative humidity, wind speed, solar radiation, planetary boundary height, rainfall – all derived with the mesoscale meteorological WRF model) to choose the most significant for prediction of pollen concentrations.  We also compare the performance of the different algorithms in terms of such error metrics as F1 score, ROC-AUC and PR-AUC. The results of the analysis will be applied to forecast pollen concentrations based on the automatic pollen detector, newly installed at the station, and weather forecasts. 
The Narlı Fault: the characteristics of the surface rupture that initiated the 6 February 2023 Kahramanmaraş Earthquake Sequence (Türkiye)
Authors: Kıray Canik Havva Neslihan , Zabcı Cengiz , Akyüz Hüsnü Serdar , Kırkan Erdem , Sunal Gürsel , Yakupoğlu Nurettin , Sabuncu Asen , Akoğlu Ahmet Murat
Published: March 2024

The south-southeastern Türkiye was struck by a series of earthquakes in February 2023 causing the death of more than 50,000 people and billions of USD of economical loss. The largest of these events, the February 6th Mw 7.8 Pazarcık and Mw 7.5 Ekinözü earthquakes occurred only 9 hours apart of each other generating surface ruptures of about 450 km and the majority of the destruction. All these events took place in a complex tectonic system where the boundary structures of the Anatolian Block, the Arabian Plate and the Adana-Cilicia-Hatay Basin merge together. The Pazarcık earthquake was initiated along a subsidiary splay, the Narlı Fault (NF), and continued with the bilateral rupturing of faults over a length of 300 km.In this study, our aim is to document the surface deformation along the NF in order to have a better understanding of the initiation mechanism of the ‘multi-segment’ Pazarcık earthquake. In addition to our field observations, we operated a sUAS to map the rupture and measure offsets. We used a tape measure to determine the magnitude of slip across the well-established offset markers in the field, while the ultra-precise rupture mapping and a vast number of displacement measurements were made by using sUAS-based digital surface models (DSM) and orthophotos with a ground pixel resolution of ~3–5 cm.We mapped two sets of continuous surface ruptures, the 14 km-long southern and the 10 km-long northern parts, separated by a ‘gap’ of 8 km. The average sinistral offset along the ~N20-30°–striking southern rupture is about 2 m, whereas the maximum value reaches up to 3.7 ± 0.7 meters. On the other hand, the horizontal slip along the N25-35°–striking northern rupture barely exceeds 1.2 m. In this section, it is highly probable that a large portion of the total horizontal deformation is shared by widely distributed surface shears within the wedge between the East Anatolian Fault and the NF, which can be an analogue to a variant of a Prandtl Cell Model. In addition, we identified semi-circular/elliptical surface cracks at more than 10 locations to the south of the NF rupture. These are interpreted as seismically induced sink-holes, given the widely distributed Eocene limestones in the region. The preliminary analysis of the time series of interferograms between 2015–2022 suggests a long-term systematic subsidence, supporting our hypothesis.