bluuebunny/crossref_metadata_2025 huggingface dataset:
Discussion and Supplement of Limestone Classification Schemes
Authors: Ma Xiangqian , Qiu Longwei , Wang Yelei , Yang Yongqiang
Published: March 2024

In the more than 120 years since Amadeus William Grabau (1870-1964) first proposed the classification of carbonate rocks in 1904, geologists have conducted in-depth research on this issue and made multiple modifications, forming the currently relatively comprehensive and systematic limestone classification scheme. Among them, Folk (1959, 1962) and Dunham (1962) classified limestones based on the perspective of structural maturity, which has become the most widely used classification scheme to this day. It is worth noting that Mei Mingxiang supplemented Wright VP’s (1992) classification scheme in 2001 by adding types of limestones formed by event sedimentation that cannot be explained by the perspective of structural maturity, such as seismolithic limestone, turbidite limestone, etc.However, there are also problems with previous classification schemes. First, limestone types similar in structure to clastic rocks formed by terrigenous carbonate particles under abnormal depositional processes are not included in the carbonate rock classification system; Second, travertine precipitated carbonate rocks are not included in the carbonate rock classification system. Based on studies of carbonate rudite in the Bengbu depression, conglomerate deposits of the Guanzhuang Formation in the Pingyi Basin, and travertine, as well as years of field work experience, the author has further modified the limestone classification system by adding a terrigenous limestone class formed under abnormal depositional conditions and a purely chemical limestone class.In view of the well-developed characteristics of terrestrial carbonate rocks in China, a terrigenous clastic limestone subclass was added under the abnormal deposition subclass, and a purely chemical limestone class with mud crystal components was added under the diagenetic class. The following changes have been made to the cause-structure classification table of limestones in this study: (1) The classification method of dividing limestones into depositional, biological and diagenetic classes remains unchanged, but the skeletal components are used as the basis for further subdivision. Compared with the previous scheme, more terrigenous clastic and mud crystal classes have been added to the skeletal components. (2) For limestones formed by terrigenous carbonate particles under abnormal depositional conditions, they are subdivided into rudite limestone, arenite limestone, mudstone limestone and their transitional types according to the particle size of the skeletal grains. (3) In the diagenetic class, the added purely chemical limestone refers specifically to travertine or travertine cemented collapse limestone (cave limestone), with mud crystal as its skeletal component.
Geological controls on groundwater chemistry in the Himalayan Indus River basin aquifers, India
Authors: Coomar Poulomee , Ahmed Suhail Lone , Jeelani Gh , Gupta Saibal , Mukherjee Abhijit
Published: March 2024

The upper Indus River basin aquifers spanning over the Himalayan, Karakoram, Hindu Kush ranges is a vast water scares region, which have escaped the notice of the groundwater scientists until recently. The work presented here aims to decipher the processes of groundwater-rock interactions in the shallow Trans-Himalayan Indus River basin aquifers of India. Located on the Indus-Tsangpo suture zone, the area provides a unique opportunity to study water-rock interaction processes in one of the coldest and highest inhabited regions of the world.Alkaline to circumneutral groundwater are collected from wells mostly located in the meta basics and associated volcanoclastic of the Dras Volcanics (DV) and the granitoids and their extrusive equivalents of the Ladakh Plutonic Complex (LPC). Waters mostly belong to Ca-HCO3 and Ca-Mg-HCO3 facies. Ca-Na-HCO3 occurs as minor facies. Among bivalent cations Ca and Mg shows high degree of correlation, with a low Ca/Mg ratio. Ca-Mg-HCO3 relations suggest bivalents come from Ca-Mg pyroxenes and calcite of the meta-basalts, and calcic plagioclase. Ca-Mg pyroxenes are sourced from the DV, while Ca-feldspars only from the LPC, given the ones in DV are albitised. Decreasing trends of calcite saturation with Ca/Mg ratio hints secondary calcite precipitation. Among monovalent ions, Na + K versus SO42- + Cl- relations suggests, waters owe their Na content to silicates or cation exchange reactions when the ratio >1, and to inputs from saline springs or compounds when the ratio falls below unity. Nearly 60% of samples have Na in excess of Cl- (Na*), but only a minority of them correlates well with dissolved silica. However, thermodynamic calculations suggest waters are mostly in equilibrium with kaolinite, along with some Ca -, Na – smectites and in disequilibrium with all sorts of feldspars suggesting both Na- and K-feldspar weathering from both meta-basics and felsic lithologies. The absence of Na*-Si correlativity indicates simultaneous Na addition through ion-exchange processes or dissolution of non-halite hydrothermal precipitates; borax, trona, burkeite, being the most common. Lack of co-relation between Cl- and SO42- suggest dissimilarity in their provenance.  High Ca/ SO42- ratio precludes inputs from gypsum or anhydrite, so SO42- can only stem from sulphide oxidation or dissolution of sulphates like thenardite or jarosite, which are known to occur in vicinity of local hot springs. 
STCCN: A spatiotemporal consistency constraint network for all-weather MODIS LST reconstruction by fusing reanalysis and thermal infrared data
Authors: Gong Yuting , Li Huifang , Li Jie
Published: March 2024

Land surface temperature (LST) is a critical parameter for understanding the physical properties of the boundary between the earth's surface and the atmosphere, and it has a significant impact on various research areas, including agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensing is often hindered by clouds and aerosols, resulting in gaps in LST data products, which hinders the practical application of these products. Therefore, reconstruction of cloud-covered thermal infrared LST is vital for the measurement of physical properties in land surface at regional and global scales. In this paper, a novel reconstruction method for Moderate Resolution Imaging Spectroradiometer (MODIS) LST data with a 1-km spatial resolution is proposed by a spatiotemporal consistency constraint network (STCCN) model fusing reanalysis and thermal infrared data. Firstly, a new spatio-temporal consistency loss function was developed to minimize the discrepancies between the reconstructed LST and the actual LST, by using a non-local reinforced convolutional neural network. Secondly, ERA5 surface net solar radiation (SSR) data was applied as one of the important factors for network inputs, it can characterize the influence of the Sun on surface warming and correct the LST reconstruction results. The experimental results show that (1) the STCCN model can precisely reconstruct cloud-covered LST, the coefficient of determination (R) is 0.8973 and the mean absolute error (MAE) is 0.8070 K; (2) with the introduction of ERA5 SSR data, the MAE of reconstructed LST decreases by 17.15% while the R is kept close, indicating that it is necessary and beneficial to consider the effects of radiation data on LST; (3) the analysis of spatial and temporal adaptability indicates that the proposed method exhibits strong resilience and flexibility in accommodating variations across different spatial and temporal scales, suggesting its potential for effective and reliable application in different scenarios; (4) referring to the SURFRAD station observations, the reconstructed R ranges from 0.8 to 0.9, and MAE ranges from 1 to 3 K, demonstrating the high effectiveness and validity of the proposed model for reconstructing regional cloud-covered LST.
Impacts and Implications of Airborne Microorganisms in a Warming Atmosphere
Authors: Schuster Stephan , Gusareva Elena , Ketan Kutmutia Shruti , Wittekindt Lennard , Lim Yee Hui , Uchida Akira , Sosa Elisa , Dällenbach Kaspar Rudolf , Haddad Imad El , Beer Martin Gysel , Egli Adrian , Mohr Claudia
Published: March 2024

Amid escalating concerns regarding climate change and air pollution, the intricate interplay between climate dynamics and air microbiomes remains inadequately understood. Our research team is dedicated to an in-depth exploration of bioaerosol dynamics through metagenomic analysis. This will establish linkages between resultant environmental microbiomes and a spectrum of physico-chemical factors. We will further evaluate potential implications of climate driven bioaerosol dynamics on human health. Consequently, our research initiative entails a comprehensive analysis of bioaerosol dynamics across distinct climate regimes, encompassing alpine, temperate, and tropical environments. Using high-volumetric air sampling technologies,  we have conducted environmental time series that offer high temporal and taxonomic resolution. In an interdisciplinary approach that integrates expertise from aerobiology, medicine, atmospheric physics, and climate modelling, we aim at assessing the impact of raising global temperatures on atmospheric bioaerosols and the global dispersal of airborne microorganisms. Our bioaerosol detection methodologies can be applied to both, historical and contemporary air samples, enabling to examine the bioaerosol dynamics preceding the current and most acute climate crisis. By integrating biological, chemical, and physical measurements collected from pristine alpine and metropolitan areas from temperate and tropical settings, we investigate the potential interconnections between climate-driven alterations in airborne microbial dynamics and their consequential effects on human and ecosystem health.
Diagnosing flavors of tropospheric Rossby wave breaking and their associated dynamical and sensible weather features 
Authors: LaChat Grant , Bowley Kevin A. , Gervais Melissa
Published: March 2024

Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. RWB events can lead to tropospheric impacts ranging from changes in intensity and position of the jet stream to extremes in precipitation resulting in significant societal impacts. Traditionally, RWB events are categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature (θ) and low θ air on a potential vorticity surface. Self-organizing maps (SOM), a machine learning method, was used to cluster RWB events into archetypal patterns, or “flavors”, for each RWB event type (i.e., AWB and CWB). This allowed for an examination of differences in RWB event flavors, and their associated tropospheric impacts, using the European Centre for Medium Range Weather Forecasts Reanalysis v5 (ERA5) dataset. AWB and CWB flavors capture variations in the θ minima/maxima of each streamer and the localized meridional θ gradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θ between flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Given these impacts and their importance for regional climates, it is important to also understand how RWB events, and their associated sensible weather features, are represented in climate models. Therefore, AWB and CWB events were identified from overturning isentropes on the dynamic tropopause (DT) in the Community Earth System Large Ensemble v2 (CESM-LENS2) climate model output during December, January, and February (DJF) 1980-2014 (i.e., historical period). RWB flavors are identified in the LENS2 for comparison to the ERA5 dataset for the same time period. Composites of tropospheric dynamic and thermodynamic fields were calculated for each RWB flavor in the LENS2 which allows for an evaluation of the impact of AWB and CWB structure on sensible weather extremes. First, the frequency of occurrence of each RWB flavor between datasets was found. Second, differences in the sensible weather features associated with each flavor were quantified. This process-orientated climate model evaluation of the LENS2 as compared to the ERA5 can provide insight into the source of model errors in the LENS2 climate model.
The Development of Forecasting System Flood Travel Time by Dam Release for Supplying Flood Information Using Deep Learning at Flood Alert Stations in the Seomjin River Basin
Authors: Kang Jieun , Lee Gayeong , Park Sooho , Jung Chunggil , Yu Juyeong
Published: March 2024

We propose a hybrid deep learning model that combines long short-term memory networks (LSTMs) to capture both spatial and temporal dependencies in the river system. The LSTM component processes spatial information derived from topographical data and river network characteristics, allowing the model to understand the physical layout of the river basin. Simultaneously, the LSTM component exploits temporal patterns in historical dam release and rainfall data, enabling the model to discern the dynamics of flood propagation. In comparison of previous study, previous results accepted only hydrological models such as HECRAS, FLDWAV, FLUMEN. But, this study accept combination of HECRAS and Deep Learning algorithm, LSTM. The goal of this study is to predict the river highest level and travel time by dam release 3 to 6 hours in advance throughout the Seomjin river basin. In order to achieve, this study conducted hydrological modeling (HECRAS) and developed a deep learning algorithm (LSTM). Afterward, the developed model combining HECRAS and LSTM was verified at six flood alert stations. Finally, the models will provide the river highest level and travel time information up to 6 hours in advance at six flood alert stations. To train and validate the model, we compile a comprehensive dataset of historical dam release events and corresponding flood travel times from a range of river basins. The dataset includes various hydrological and meteorological features to ensure the model's robustness in handling diverse scenarios. The deep learning model is then trained using a subset of the data and validated against unseen events to assess its generalization capabilities. Preliminary results indicate that the hybrid HECRAS-LSTM model outperforms traditional hydrological models in predicting flood travel times. The model exhibits improved accuracy, particularly in cases of complex river geometries and extreme weather events. Additionally, the model demonstrates its potential for real-time forecasting, as it can efficiently process and assimilate incoming data. In conclusion, our study showcases the effectiveness of using a hybrid HECRAS-LSTM model for forecasting flood travel time by dam release. By leveraging the power of deep learning, we pave the way for more precise and reliable flood predictions, contributing to the overall resilience and safety of communities located downstream of dam-controlled river systems.
Effects of land use change on ecosystem services in Africa's Great Green Wall Initiative (GGW) for dryland restoration
Authors: Wang Yizhuo , Dallimer Martin , Scott Cat
Published: March 2024

Dryland ecosystems worldwide face substantial degradation due to adverse climatic conditions and unsustainable land practices. This is particularly evident in Africa's Sahel region, where extensive land degradation and desertification pose significant challenges to local livelihoods and socio-economic stability. To address this issue, the Great Green Wall Initiative (GGW) aims to restore over 100 million hectares of degraded land to counter desertification and support rural communities.Here we assess how land use changes have altered ecosystem services within GGW implementation areas. We analyzed the spatiotemporal characteristics of land use change using the MODIS-Global Land Cover product from 2007 to 2019. Based on remote sensing data and established geospatial models, we evaluated five ecosystem services, namely carbon sequestration, soil conservation, sand fixation, water regulation, and food provision. We explored trends in ecosystem service changes, identified spatial clusters of high and low values, and evaluated synergies and trade-offs among these services by Pearson's coefficient and the bivariate Moran’s I method. The result showed that the level of various ecosystem services in GGW areas is heterogeneous, with a large spatial distribution. High values of ecosystem services are found in Burkina Faso, southern Nigeria and eastern Ethiopia. Synergies between ecosystem services are dominant, with the strongest synergies between carbon sequestration and soil conservation. Carbon sequestration and water regulation were clustered, but there were trade-offs with food provision. We quantified the contribution of land use to the changes in ecosystem services through the calculation of the Ecosystem Service Contribution Index (ESCI). The expansion of farmland and desertification have had significant negative impacts on ecosystem services and grassland conversion. This assessment provides critical insights into the efficacy of restoration efforts and aims to offer guidance for informed decision-making in sustainable management practices for dryland ecosystems.
The biophysical and biogeochemical impacts of global forest cover changes on land surface temperature from 2001 to 202
Authors: Li Yan , Chen Hongwen
Published: March 2024

The global forest cover is undergoing significant changes which affect climate through biophysical and biogeochemical processes. Using potential biophysical effects and carbon flux datasets, we quantify the biophysical and biogeochemical impacts of forest cover changes over the past two decades. The net loss of global forest cover from 2001 to 2020 resulted in global average warming of 0.0042°C, with biophysical and biogeochemical contributions of 0.0020°C and 0.0022°C, respectively. The biophysical impacts dominated most regions of the world (68%), and the biogeochemical impacts were mainly concentrated in Europe and the tropics. The tradeoff between biophysical and biogeochemical impacts was found for 58% of forest cover change areas, mainly in boreal regions, while synergy was distributed in the southeastern United States and tropical regions. The study highlights the urgent need to protect and manage forest cover to reverse the warming.
Hyperspectral imaging system for ice core studies
Authors: Kurbatov Andrei , Brook Edward , Buizert Christo , Carr Theodore , Fegyveresi John , Fudge Tyler , Hargreaves Geoffrey , Hoefen Todd , Kirkpatrick Liam , Labombard Curtis , Nunn Richard , Powers Lindsay , Rock Kevin , Zhizhin Mikhail
Published: March 2024

Hyperspectral imaging (HSI) technology has been increasingly used in Earth and planetary sciences. This imaging technique has been successfully tested on ice cores using VNIR (visible and near-infrared, 380-1000 nm) (Garzonio et al., 2018) and near-infrared (900 - 1700 nm) (McDowell et al, 2023)  line-scan cameras. Results show that  HSI data greatly expand ice core line-scan imaging capabilities, previously used with gray or RGB cameras (see summary in Dey et al., 2023). Combinations of selected HSI bands from the hyperspectral data cube improve feature detection in ice core stratigraphy, and map distribution of volcanic material, dust, air bubbles, fractures, and ice crystals in ice cores. Captured spectral information provides unique fingerprints for specific materials present in ice cores. This method helps to guide ice core sampling because it provides non-destructive, rapid visualization of microstructural properties, layering, bubble contents, increases in dust, or presence of  tephra material. Precise identification of these atmospheric components  is important for understanding past climate drivers reconstructed from ice cores. As part of the COLDEX project (Brook et al., this meeting) we adapted the SPECIM SisuSCS HSI system for ice core imaging. The ice core scanning system is housed inside the ca. -20ºC main NSF ICF freezer, and externally computer-controlled. The operator monitors scanning operations and communicates with personnel inside of the freezer via radio.  The system is equipped with a SPECIM FX10 camera that measures up to 224 bands in the VNIR range. We modified the ice core holder tray and installed a heated enclosure for the camera. The system uses SCHOTT DCR III Fiber Optic light sources with an OSL2BIR bulb from Thorlabs. IR filters are removed to extend the light spectral range beyond the 700 nm limit without heating the ice core surface during rapid (
Spatial Distribution Patterns and Susceptibility Assessment Via PSO-BP Neural Network of Landslides Triggered by the Jiuzhaigou Earthquake in Sichuan on August 8th, 2017
Authors: Li Qiang , Zhang Jingfa , Jiang Wenliang , Li Yongsheng
Published: March 2024

On August 8, 2017, a 7.0-magnitude earthquake occurred in Jiuzhaigou County, Ngawa Prefecture, Sichuan Province, China. This earthquake followed the Wenchuan earthquake in 2008 and the Lushan earthquake in 2013, both of which were strong earthquakes with magnitudes of 7.0 or above in Sichuan Province. The epicenter of this earthquake was in Zhangzha Town, Jiuzhaigou County. It is very important to interpret the spatial distribution of landslides quickly after an earthquake for disaster emergency rescue. The distribution of geological disasters in an earthquake area is obtained based on the interpretation of high-resolution remote sensing images after an earthquake. The main geological disasters are small landslides and collapses. In terms of the results of the spatial distribution of disasters, we analyzed the spatial distribution law and control factors of coseismic disasters (elevation, slope angle, slope aspect, peak ground acceleration, and proximity to faults). The study shows that the geological hazards are relatively developed along the gully, with an obvious fault effect, and concentrated within 2 km of the seismogenic fault. Based on the above foundation, a particle swarm optimization (PSO)–BP neural network model for predicting landslide susceptibility was established by optimizing the initial weight and threshold of a BP neural network using the PSO algorithm. The landslide susceptibility assessment considers eight factors contributing to landslide occurrence, including elevation, slope angle, slope aspect, proximity to stream network, lithology, density of geological boundaries, proximity to faults, and proximity to the road network. The validity and accuracy of the model was tested by calculating the area under the receiver operating characteristic curve, which was 0.918. The experimental results showed that the PSO–BP neural network model exhibited satisfactory accuracy in predicting landslide susceptibility.
Forward seismic modelling, a tool for the prediction of offshore sedimentary architectures. Application to the Roussillon Basin's Pliocene land-see prism (Gulf of Lion, France).
Authors: Widemann Teddy , Lasseur Eric , Lofi Johanna , Issautier Benoît , Grélaud Carine , Berné Serge , Pezard Philippe A. , Caballero Yvan
Published: March 2024

The prediction of offshore sedimentary architectures is a first plan approach to the geological study of continental margins. While such work is commonly led using seismic surveys tied to well-logs, we are interested in land-sea objects for which there is no direct tie between seismic signal and lithology. More precisely, we look at shoreline clinoforms and more continental deposits of which size is below used seismic resolution, and their integration at the shelf-scale. The Roussillon Basin’s Pliocene infill satisfies these criteria. It belongs to a progradational land-sea prism about 100km-long, displaying essentially clinothems and defining the Gulf of Lion modern shelf. It is described with high quality conventional seismic profiles offshore, while outcrops and drill-cores are available onshore. However, there is no data at the transition between the two domains.In order to predict the offshore sedimentary architecture, we establish classical equivalence hypotheses between seismic facies and expected associated sedimentary facies. This work is based on the seismic facies interpretation and on the lithologies known from outcrops and onshore drillings.Nonetheless, without directly tied-in seismic such hypotheses rely essentially on interpretation. This, together with seismic data vertical resolution (~15m in thickness) and the upscaling from direct observations onshore, introduce uncertainties.In order to produce more reliable sedimentary predictions, we test our hypotheses through forward seismic modelling using SeisRoX pro by NORSAR. We create small scale geological/impedance models based on onshore sedimentary observations coupled with well-logs petrophysical data (P-wave velocities). Then we simulate acoustic waves propagation through them and obtain theoretical seismic profiles that are subsequently compared to the seismic data.This method, including a geophysical control, allows for the testing of various geological hypothesis at the outcrop-scale, and for a more objective subsurface description.Among the results, we show that vertical velocity variations at a meter scale eventually get a specific seismic signature in terms of both geometry and amplitude on conventional seismic profiles. More generally, we illustrate different lithological models and their results, which allow for a high-resolution reconstruction of most parts of the Roussillon Basin’s Pliocene offshore prism.
Estimating CO2 Flux at 30 Meter Resolution Using Machine Learning
Authors: Granat Robert , Dara Andrey , Demidov Oleg , Toth Geza
Published: March 2024

We present an approach to using a machine learning based regression model to estimate CO2 fluxes at 30 meter spatial resolution. The method uses eddy covariance measurements of CO2 obtained from in situ stations (FLUXNET) as primary reference data.  Multispectral satellite observations collected by Landsat are combined with meteorological information to form feature vectors that are used as predictor variables. The XGBoost machine learning algorithm is used to train the regression models on a per-land cover basis. The resulting models can be used to estimate CO2 fluxes wherever Landsat satellite imagery is available.  Moreover, the approach provides a framework that is extensible to other satellite imagery types and will improve in accuracy as more primary reference data becomes available.  We present results of the method as applied to examples in the agricultural sector.
Response of irrigation water supply to demand in China and its effects on yields
Authors: Hou Chengcheng
Published: March 2024

Irrigation is the most significant human water withdrawal globally, playing a pivotal role in ensuring food security. However, the lack of detail irrigation datasets across spatial and temporal dimensions limits our comprehensive understanding of how historical irrigation water supply has responded to demand fluctuations and, consequently, its effect on agricultural yields. In this study, we employed a combination of remote sensing products, meteorological data, and various statistical datasets to estimate gridded monthly irrigation water demand and supply in China at a spatial resolution of 0.1° during the period 2000-2019. The results indicate that the national annual irrigation water demand is 122.23 km3, with rice accounting for the highest share (39.25%), followed by wheat (36%) and maize (24.75%). While the annual irrigation water supply is measured at 317.42 km3, with rice (62%) dominating, trailed by maize (21.13%), and wheat (16.87%) contributing the least. The mismatch in the distribution of irrigation water supply and demand among crops underscores variations in irrigation systems and the availability of water sources for irrigation. Notably, in the downstream of the Yellow River Basin and the Huaihe River Basin, the irrigation water supply falls short of demand when not accounting for irrigation efficiency, primarily attributed to a scarcity of water during the wheat growing season in spring (Mar. to May), indicating a potential water stress on wheat yield in this region. This study enhances our understanding of the intricate relationship between irrigation water supply and demand in China, offering valuable insights to support regional water resources management and allocation strategies.
Crustal structure in the Tengchong Volcanic Area based on the H-κ-c method
Authors: Zhang Yingying , An Yanru
Published: March 2024

The Tengchong volcano is the most famous active intraplate volcano in Southwest China, which has been dormant for hundreds. The significant seismicity, active geothermal activities and gas emissions are all indicate that the volcanoes still have potential for future eruptions. However, the origin of the Tengchong volcano is still on debate, which is strongly depend on the structure underneath. Here we employ a recently developed H-κ-c method to characterize the crustal structure with its thickness and the Vp/Vs ratio of the Tengchong volcanic area. A total of 4,040 receiver functions are obtained from 9 permanent seismic stations, providing an overall good coverage in both distance and azimuth of the analyzed data. After removing the back azimuthal effects of dipping Moho and/or crustal anisotropy, our results are more robust and highlight local variations. The crustal thickness increases from south to north, ranging from 33.5 to 41.9 km with an average of 37.6 km. The crustal thickness beneath stations RHT is thicker than the surrounding stations, suggesting a possible local crustal depression beneath this station. The Vp/Vs ratios vary from 1.75 to 1.79 and the average value is 1.76 with a standard deviation of 0.015. Stations with high Vp/Vs values indicate partial melting magma chambers in the crust beneath those stations.
Variability in strontium and lithium composition of ancient seawater from fluid inclusions in halite—implications for reconstructing drivers of seawater secular variability
Authors: Weldeghebriel Mebrahtu , Lowenstein Tim , Higgins John
Published: March 2024

Secular variations in the major ion chemistry and isotopic composition of seawater on multimillion-year time scales over the Phanerozoic are well documented, but the causes of these changes are debated. δ7Li and 87Sr/86Sr are widely utilized to interpret the driving mechanisms of secular changes in seawater chemistry, the tectonic history of the Earth and the link between paleo-ocean chemistry and the carbon cycle. These interpretations and models, however, are based on (1) few quantitative data on strontium concentration [Sr]SW in seawater calculated from the Sr/Ca ratios of marine carbonates and (2) the assumption that the Li concentration [Li]sw of seawater has been similar to modern [Li]sw. But those assumptions, if inaccurate, could undermine the validity of modeling results. The marine strontium and lithium cycles through time could be better reconstructed using coupled marine records of [Sr]SW, 87Sr/86Sr, [Li]sw and δ7Li. [Sr]SW and [Li]sw in ancient seawater would be particularly useful for examining which global processes, continental weathering or global volcanicity at seafloor hydrothermal systems and subduction zones, exerted the dominant control on the changes in seawater chemistry. Recent analytical advances using combined cryo-SEM-EDS and laser ablation ICP-MS now allow quantitative measurement of [Sr]FI and [Li]FI in fluid inclusions in halite. [Sr]SW and [Li]sw, reconstructed from chemical analyses of >1,000 fluid inclusions in more than 100 halite samples with marine 87Sr/86Sr values, varied seven-ten-fold and oscillated twice between high- and low-Sr and Li concentrations over the past 550 million years, in rhythm with Ca-rich and SO4-poor paleoseawater intervals, calcite-aragonite seas, supercontinent breakup, dispersal, and assembly cycles, greenhouse–icehouse climates, and modeled atmospheric pCO2. These data enable us to better constrain the Sr and Li cycle, and offer new insights into geochemical modeling of Phanerozoic seawater chemistry using multiple isotope systems and seawater concentrations. 
Long-term hydrological budget over urban areas: approaches and challenges
Authors: Lin Xu Yexia , Meili Naika , Fatichi Simone
Published: March 2024

Urbanisation substantially alters the permeability of the land surface and modifies the vegetation cover extent and type. These landcover changes profoundly affect the hydrological and energy budget. While the role of urbanization on the local flood response has been extensively studied, its effect on the long-term hydrological budget is much less known as computing the latent heat flux (evapotranspiration) in urban areas is still challenging. However, knowledge on the urban hydrological budget is important as many cities are planning water sensitive urban designs and water harvesting could be key to support an increasing amount of urban vegetation.This study quantifies how urbanization changed the long-term hydrological budget of the island city state Singapore for the period between 1982 to 2021. To do so, we use two state-of-the-art mechanistic models which include all the major hydrological components such as runoff, soil and interception water storage, transpiration, and evaporation. The first is Tethys-Chloris (T&C), which is a mechanistic ecohydrological model that can resolve the water, carbon and energy budgets at high spatio-temporal resolutions but considers the urban effects in a simplistic way by only modifying the impervious fraction of the land surface and its roughness. The second is its urban counterpart, Urban Tethys-Chloris (UT&C), which explicitly resolves shading and radiation reflection within an urban canyon accounting for different urban vegetation types and configurations and explicitly resolves the local urban climate and hydrology. UT&C is however too computationally costly to simulate an entire city at high spatial resolution. Hence, a machine learning approach, where a multimodal neural network comprising a Conv1D layer followed by LSTM layers for dynamic meteorological inputs and Dense layers for static inputs such as urban properties, is used to re-map UT&C outputs over the entire city based on a few selected covariates.By cross comparing the different approaches to compute the long-term water budget over urban areas, we highlight the involved uncertainties, and we can gain insights into the impact of urban development on modifying the water availability in Singapore. The island city-state relies on water harvesting as one source of water supply for households and knowledge of water availability is extremely important for long-term water management purposes.
A comparison of Lumped Convolution approach and Ensemble Hydrograph Separation in soil transit time distribution estimations: case study of San Francisco catchment
Authors: Peña Pablo , Windhorst David , Crespo Patricio , Timbe Edison , Samaniego Esteban , Breuer Lutz
Published: March 2024

Mean Transit Time (MTT) and Transit time distribution (TTD) functions are crucial for understanding the temporal dynamics of water flow through a catchment system, particularly in the context of rainfall-runoff processes that govern the solute storage and transport. Traditionally, these insights have been assessed using lumped TTD functions through models based on quasi-linearity and steady-state conditions. In contrast, the Ensemble Hydrograph Separation technique (EHS) presents a promising alternative for estimating TTD through multiple linear equations representing the relation between tracer fluctuations. This approach is advantageous, eliminating the need for continuous time series data of tracer measures and avoiding constraints related to the shape of transit distributions or system stationarity. However, EHS faces a sensitivity challenge in its regularization process, governed by a parameter denoted as "v," making the technique susceptible to either under-smoothing or over-smoothing the TTD function. Consequently, the judicious estimation of the regularization parameter within EHS becomes imperative.This study aims to investigate how both the traditional lumped TTD approach and the innovative EHS method contribute to our understanding of catchment hydrology. The present investigation was conducted using stable water isotope data of stream and soil water collected in a typical Andean tropical mountain cloud forest catchment. The sampling was conducted at six sites along two altitudinal transects (at elevations of 3000 m, 2000 m, and 1000 m), encompassing two distinct land covers (forest and pasture). At each site, soil water samples were collected at three different depths (0.10, 0.25, and 0.40 m below ground). The main objective is to assess the feasibility of substituting one method with the alternative by comparing their performance using different evaluation criteria such as the Nash-Sutcliffe coefficient (NSE), mean absolute error (MAE), and coefficient of determination (R2).Through Monte-Carlo simulations, we calibrated the “v” parameter and conducted a comprehensive comparison of both approaches. At 75% of the monitoring points, we observed NSE and R2 coefficients exceeding 0.65. These results align with previous studies, emphasizing the feasibility of assuming stationary conditions in humid tropical ecosystems. The study systematically examined the concordance between the Lumped TTD approach and Ensemble Hydrograph Separation (EHS) findings when utilizing similar TTDs. Furthermore, it provided a detailed analysis of the strengths and limitations of EHS implementation with actual real data. The insights gained from this research can be extrapolated to identify situations where each approach may be more suitable, offering valuable recommendations for their future application in various catchments.
Hall Nature Ahead of Dipolarization Fronts in the Earth's Magnetotail
Authors: Wang Lei , Huang Can
Published: March 2024

Dipolarization front (DF), a common magnetic structure at the leading edge of fast plasma jets in the Earth's magnetotail, plays an important role in magnetic energy release and may even modify the magnetotail energy budgets. To date, the physical process within the structure has been well studied. However, the effect of the structure on the background plasma sheet remains elusive. Using recent high-quality data from the Magnetospheric Multiscale (MMS) mission, we statistically investigate this issue. Ahead of the DF, we find a bipolar By structure, which is consistent with the local Hall current system. The main carriers of the current system are also studied. Our research advances the understanding of the interaction between the DF and ambient undisturbed plasma environment.
Spectral energy distribution of Nevado del Ruiz Volcano seismicity near eruptive events
Authors: Rave-Bonilla Yuly Paola , Rodgers Mel , Rodríguez-Cardozo Félix , Bruanmiller Jochen , Londoño John Makario
Published: March 2024

Volcano seismology is a key contributor for assessing the activity status of volcanoes and forecasting future eruptive behaviour. One measurement that may indicate a change in the volcanic behaviour is a temporal change in the spectral content, or frequency distribution, of seismic waveforms. Significant changes in frequency distribution have been observed at Soufrière Hills, Montserrat; Telica Volcano, Nicaragua; Redoubt Volcano, Alaska USA, and at other volcanoes prior to volcanic eruptions. Identification of such changes in spectral energy could indicate changes in volcanic activity, and hence be used in forecasting, as well as to investigate the physical processes behind eruptive processes. With the objective of identifying possible changes in the spectral energy distribution of the seismic sources of the Nevado del Ruiz Volcano (NRV) prior to eruptions, we analysed the spectral energy of volcano tectonic (VT), low frequency (LF) and hybrid seismic events during 2012, when the NRV had two Volcanic Explosivity Index (VEI) 2 eruptions on May 29th and June 30th. We analysed nearly 27,000 events and implemented a semi-automatic pre-processing pipeline in ObsPy for selecting the optimal stations and seismograms based on the signal-to-noise ratio and proximity to seismic clusters. Then, we cut the seismograms to isolate the seismic signals and bandpass-filtered the data before calculating metric such as dominant frequency, ratio of high to low spectral energy. In this work, we present preliminary results on the temporal changes in spectral energy of the seismic events at NRV and whether this could be linked, along with other geophysical measurements, with changes to eruptive behaviour.
Using a Citizen Science Approach to Assess Nanoplastics Pollution in Remote High-Altitude Glaciers
Authors: Jurkschat Leonie , Gill Alasdair J. , Milner Robin , Holzinger Rupert , Materić Dušan
Published: March 2024

Microplastics have been found in almost every environment on Earth. As it is known that microplastics gradually degrade into smaller particles, eventually reaching the nanoscale, one naturally expects to find nanoplastics in all of the places where microplastics are found, but in even greater numbers. Such nanoplastics have been shown to adsorb organic pollutants and to cross cell membranes in vitro. While not fully understood, they may have an adverse effect on human health, and therefore warrant further investigation.However, analysing nanoplastics is challenging. Firstly, the more commonly used measurement techniques have limitations at this scale. Secondly, while micro- and nanoplastic research has predominantly concentrated on marine and fluvial environments, atmospheric transport is believed to be significant, particularly for nanoplastics, and it is difficult to sample the atmosphere systematically.In this study, we combine high-sensitivity trace science methods with sampling the surface snows from high-altitude glaciers as a proxy for airborne micro- and nanoplastics. This was facilitated by a citizen science sampling strategy involving mountaineers from the HLR 22 Expedition (www.high-level-route.com). This enabled us to obtain samples from otherwise inaccessible high-altitude glaciers in the Alps, thereby gaining a better insight into nanoplastics' presence and distribution in remote Alpine areas.We analysed particles in the < 1 µm size fraction by thermal desorption-proton transfer reaction-mass spectrometry (TD-PTR-MS) using a method developed in previous studies. We fingerprinted the samples for common polymers (PE, PET, PP, PVC, PS and tire wear particles) and calculated a mass concentration for each polymer. Nanoplastics were detected at half of the sampled sites, with the majority by mass being PS and tire wear particles, showing just how pervasive nanoplastics are, even in places where humans rarely tread.Our results show the value of a citizen science approach to analysing nanoplastics in remote and pristine environments. Confounding factors in such a sampling strategy bring risks of lower reproducibility, human error and contamination. However, strategies can be implemented to reduce these risks, and the results obtained are a unique and valuable contribution to understanding nanoplastics pollution. We conclude that the trained citizen science sampling approach is feasible for expanding the analysis to remote regions worldwide.__PRESENT__PRESENT__PRESENT__PRESENT
Tracing the complementary and competitive water use patterns in a Theobroma cacao (cocoa) agroforestry system: A stable isotope approach
Authors: Farrick Kegan , Geris Josie , Ramjohn Priya , Durodola Oludare , McDonnell Jeffrey
Published: March 2024

In cocoa agroforesty systems, shade trees are used to create climate conditions that benefit cocoa growth and survival. However, the benefits may be offset by competition between shade trees and cocoa for water, especially in a changing climate.  Here we use stable isotope tracers to quantify  the patterns and depths of water uptake among shade timber trees, cocoa, and banana for a tropical agroforestry system in Trinidad. Rainfall was collected from August 2021 to September 2023. Three field campaigns were carried out at an upslope and downslope location representing different hydro-climatological conditions and at different times in the crop cycle. During each campaign and at each slope position, soil was collected from three pits at depths of 5, 15, 25, 50 and 75 cm below the surface, while up to 10 xylem cores were collected from the different plant species. Additional soil texture and soil moisture data were also collected. Cryogenic vacuum extraction was used to extract water from the soil and vegetation samples, while an Elementar Isoprime isotope ratio mass spectrometer was used to determine the δ2H and δ18O of the extracted water. These data were subsequently used for MixSIAR endmember mixing modelling. Our results suggest that cocoa and banana plants primarily use shallow soil water (0 – 10 cm below the surface), while shade and timber trees like Immortelle (Erythrina poeppigiana) and Cedar (Cedrela odorata) use water from deeper sources (20 – 50 cm below the surface). Spatially, plants located in upslope areas appear to use water from slightly deeper soil depths than downslope locations. Soils in the valley bottom were also wetter and had relatively higher clay content. This study indicates that shade trees do not compete with cocoa for water; however, bananas likely compete with cocoa making managing that co-cropping important.