bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Insights into Water Saturation Effects on CO2 Sequestration in Enhanced Coalbed Methane Recovery
Authors: Qin Chen Yang
Published: March 2024

The injection of carbon dioxide (CO2) into coal seams serves a dual objective, namely augmenting coalbed methane (CH4) production and providing a geological CO2 storage solution, thereby offering both economic and environmental advantages. Given the typically water-rich nature of underground porous media, the impact of water on CO2-enhanced coalbed methane (CO2-ECBM) becomes a crucial factor that cannot be ignored. In order to assess the influence of varying reservoir initial water saturation and the physical parameters of coal rock on the CO2-ECBM process, we have developed a comprehensive Thermal-Hydro-Mechanical-Chemical (THMC) model utilizing COMSOL software. The simulation results indicate a substantial enhancement in CH4 recovery with increased CO2 injection pressures, demonstrating a positive correlation between higher CO2 injection pressures, augmented CO2 storage rates, and enhanced CH4 production efficiency.
With the increase of displacement time and injection volume, the effective displacement radius increases, but the influence radius increment decreases gradually. The reason is that the competitive adsorption of CO2 in the matrix has a higher adsorption capacity than CH4, resulting in a decrease in reservoir permeability. In addition, the higher the water saturation, the lower the influence radius and the displacement effect. The reason is that the existence of reservoir water limits the migration speed of gas in the reservoir to a certain extent. In the mechanical field, the permeability of coal decreases slowly, and the output of methane gas in the production well is affected.
The mechanical properties of coal reservoir are closely related to the evolution of permeability. The mechanical parameters of coal in this paper are mainly Young 's modulus and Poisson 's ratio. The results show that low Young 's modulus and high Poisson 's ratio show higher deformation resistance, and the output effect of CH4 is better.
Advancements in Point-Cloud Processing for Geo-Environmental Modeling: A Case Study of The Dalles Dam Digital Twin Creation
Authors: Krapac Mary
Published: March 2024

The integration of point-cloud data in geo- and environmental sciences has become increasingly pivotal, with applications ranging from UAVs, spaceborne and airborne lidars to ground-based lidars and stereo-photogrammetric techniques. This session seeks contributions that delve into challenges related to classification, segmentation, and noise removal in the context of point-cloud data, crucial for facilitating change detection studies. Our study focuses on the Navigational Branch of the ERDC Coastal Hydraulics Laboratory tasked with developing a Digital Twin model for a Dam, exemplifying the complexities involved in creating CAD models of terrain and structures.To address the intricacies of point-cloud data processing, we employed both open-source and proprietary software solutions—Cloud Compare and Autodesk ReCAP— for noise reduction, ensuring the prepared data is seamlessly integrated into CAD modeling software, specifically Inventor. Surface modeling involved the strategic application of planes on cloud points to generate a foundation for sketching and subsequent solid surface extrusion.Classification of data points was initiated through the implementation of regions in the noise removal software, facilitating the depiction of various areas on the model. Further, color and material assignment in the CAD software enhanced the identification of distinct part areas. Microstation TopoDOT played a pivotal role in creating a detailed terrain model, complete with physical landmarks and water bodies specific to the Dalles dam site.The resulting models were exported in the desired file format, ensuring compatibility with sponsor requirements. This case study not only showcases the practical challenges encountered in working with point-cloud data but also highlights effective strategies for noise reduction, classification, and model exportation. The presented methodologies contribute to the broader spectrum of geo- and environmental sciences, emphasizing the significance of accurate point-cloud processing for comprehensive modeling endeavors.
Atmospheric dust record preserved in an ice core from Trambau Glacier, Nepal Himalaya
Authors: Esashi Nao , Tsushima Akane , Uemura Ryu , Matoba Sumito , Iizuka Yoshinori , Adachi Kouji , Kinase Takeshi , Kayastha Rijan B. , Fujita Koji
Published: March 2024

Mineral dust affects climate through direct radiative forcing by scattering and absorbing solar radiation in the atmosphere and by accelerating snow and ice melting through reduced albedo when deposited on snow surfaces. The concentration and composition of dust deposited on glaciers reflect the surface conditions of the source regions and atmospheric conditions during transportation. Dust records in ice cores provide insights into historical atmospheric and land surface environments. However, ice cores drilled in high-altitude Himalayan glaciers are limited. To investigate historical variations in dust concentration in the Himalayas, we conducted ice core drilling at an elevation of 5862m on the Trambau Glacier in the Rolwaling region of the Nepal Himalaya. The ice core, covering 146 years (1874-2019), was dated using seasonal variations in NO3- and Ca2+. The 81-m ice core was divided into 1637 samples (~5 cm interval), and dust concentration (particle size ranging from 0.6 to 10.0 µm) was measured using the Coulter Counter Multisizer TM3. The Trambau ice core exhibits a higher dust concentration than the other Himalayan ice cores, particularly with an abundance of small particles (
Cross- regional Crop Identification Using the Hypothesis Testing Distribution Method
Authors: Zeng Wenzhi , He Jie , Ren Zhipeng , Ao Chang , Ma Tao
Published: March 2024

To improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite images are employed for crop classification training and prediction in select farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The Hunts filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the Hypothesis Testing Distribution Method (HTDM). This method balances the distribution of input feature values in the test set even without known crop distribution, thereby enhancing the accuracy of the classification test set. This study utilizes 2019 data on crop planting types from Yushan and Longzhen farms in Heilongjiang Province for model training and data from 10 farms in the province from 2019 to 2022 for model testing. Results indicate that HTDM significantly improves prediction accuracy in cases of substantial image quality variance. After applying HTDM, the recognition accuracy of crop types for the Bawuba Farm in the years 2020 and 2021 reached 95.5% and 96.0%, an increase of 18.2% and 25% compared to before processing, respectively. In 2022, the recognition accuracy for crop types at all farms processed by HTDM was above 87%, showcasing the strong robustness of the HTDM. An analysis of input features using SHAP values revealed that the most impactful features for rice, corn, soybean, and wheat were LSWI in May (LSWI5), LSWI in May (LSWI5), RNDVI in August (RNDVI8), and IRECI in August (IRECI8) respectively.
A fast unsupervised deep learning algorithm using seismic records of a single station for roadside rockfall recognition
Authors: Li Yu-Ru , Chao Wei-An
Published: March 2024

The steep terrain in mountainous areas poses a significant threat to people's safety due to frequent geological hazards(e.g., rockfall and slope collapse), making effective management, monitoring, and timely issuance of alerts and warnings are crucial for highway authorities. Previous studies focus on studying the rainfall thresholds for possible rockfall occurrence. Recently, machine learning using seismic signals has been applied to detect rockfall events and monitor rockfall activity. However, supervised machine learning algorithms have relied on predefined labels, and the limited accumulation of data makes predicting model reliability challenging. The time-consuming model training can limit the practical application of the above models. In response to both aforementioned challenges, we first selected the roadside slope with relatively high activity of rockfalls and earthquakes as the study site and installed a seismic station on the crest of the slope. Then, we use an unsupervised machine learning framework to reveal patterns from unlabeled data and cluster seismic signals in continuous seismic records in the single three-component seismic station. Using continuous seismic data over one year, our approach combines a deep scattering network, features extraction, and features cluster to detect structures of signal segments. To illustrate the framework, a deep scattering network performs convolution and pooling on the three-component seismic signal data to extract multiscale information and construct scattering coefficients. For feature extraction, four different algorithms were employed: principal components analysis (PCA), independent components analysis (ICA), singular value decomposition (SVD), and Uniform Manifold Approximation and Projection (UMAP). Subsequently, we cluster the primary features using unsupervised learning algorithms such as K-means and Gaussian Mixture Model(GMM). We demonstrate the group categories belonging to rockfall events with in-situ data time-lapse images and videos. An approach proposed in this study could achieve rapid model training for building on-site rockfall warning systems using only single-station seismic records. Our high capability recognition model of rockfall events is ready to be implemented globally with high rockfall activity. Keywords: unsupervised machine learning, deep scattering network, rockfall, seismic records, on-site early warning 
Optimizing Flood Control: A Comprehensive 3D Computationsl Fluid Dynamic Study of the Comite Diversion Channel in the Comite and Amite River Basins
Authors: Denney Christopher , Savant Gaurav , Grant Abigail , McAlpin Tate , Jones Keaton
Published: March 2024

To address the escalating challenges posed by extreme weather events and the critical importance of water management, this presentation focuses on innovative methodologies in streamflow monitoring and prediction. Specifically, we present a comprehensive study undertaken to enhance flood mitigation in the Comite and Amite River Basins of central Louisiana.In response to the imperative need for effective flood control, a 12-mile diversion channel has been designed to redirect flow from the Comite River into the Mississippi River. Our research, commissioned by the United States Army Corps of Engineers, New Orleans District, aims to quantify the impact of design modifications on crucial flow parameters within the diversion structure. We employ advanced three-dimensional, multiphase computational fluid dynamics (CFD) modeling techniques, utilizing the open-source OpenFOAM library with the interFoam finite volume solver.The study evaluates the alignment of the diversion channel by analyzing flow diversion, velocity profiles, and streamlines within the channel and the associated hydraulic control structure. Special emphasis is placed on understanding the dynamics of the drop structure flow, interactions with upstream drainage features, and potential sediment accumulation risks. Our model, validated through perturbations in turbulence models, boundary roughness, and grid independence studies, provides valuable insights into the performance of the diversion structure under various flow conditions. In conclusion, our findings underscore the importance of informed engineering decisions for fostering climate resilience in riverine regions. By providing insights into the dynamics of the diversion channel and quantifying uncertainties associated with flow parameters, this study offers actionable solutions to enhance streamflow monitoring efficiency in the face of evolving hydrological challenges.
Investigating CMIP6 Models for Arctic Sea Ice Dynamics and Predictive Links with Monsoon Precipitation
Authors: Sardana Divya , Agarwal Ankit
Published: March 2024

The Arctic Sea plays a pivotal role in Earth's climate system by modulating ocean-atmospheric interactions, influencing heat exchange, with potential implications for regional phenomena such as Indian Summer Monsoon Rainfall (ISMR). We assess the performance of 26 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating Arctic Sea Ice based on historical data and climatology. The evaluation of selected models focus on their fidelity in accurately representing the intricate dynamics of Arctic Sea Ice by employing various statistical skill metric parameters. Additionally, we will investigate the potential connections between Arctic Sea Ice variability and ISMR using CMIP6 models. A multi-model ensemble mean of the top-performing models will be conducted to illuminate these associations. Further, the study will provide teleconnections and potential correlations through sea-level pressure (SLP) anomalies and velocity potential during ISMR. Overall, the research aims to evaluate the tropical-polar teleconnection and its predictive capacity for ISMR through CMIP6 models, thereby ensuring a better understanding of the critical climate dynamics.
The genesis, stability, and vertical structure of Jupiter polar vortices: from 2D to 3D perspectives
Authors: Chen Sihe , Li Cheng , Ge Huazhi , Ingersoll Andrew
Published: March 2024

Recent research has significantly advanced our understanding of the polar vortices of gas giants, yet a unified theory explaining their genesis remains elusive. Li et al. (2020) demonstrated that a finite radius of deformation and an anticyclonic ring (shielding) around each cyclone were required for a stable pattern. Conversely, Siegelman et al. (2022) found stable patterns without the formation of shielding, working with an infinite radius of deformation. There were also 3D modelling attempts. For example, Cai et al. (2021) used a dry 3D model to generate a closely packed lattice of cyclones.
This study employs the shallow water model based on the Athena++ SNAP architecture (Li & Chen, 2021) to analyze the conditions required for vortex lattice formation. We identified parameter sets needed to create a stable vortex lattice under ideal conditions in Jupiter's polar regions and shed light on why these parameters are efficacious. We then investigated this problem with the 3D cloud-resolving model, which has not been applied to the poles of Jupiter before, and analyzed the genesis of the vortices and their vertical structure. We connected the critical parameters, such as the forcing scale and radius of deformation, in 2D simulations, to the results obtained with 3D models.
Unveiling the Dynamics of Chlorophyll-a in Arid Oligotrophic Lakes: A Machine Learning Approach in Xinjiang, China
Authors: Tan Zhenyu , Duan Hongtao
Published: March 2024

This study focuses on estimating Chlorophyll-a (Chl-a) in nutrient-poor, oligotrophic lakes in Xinjiang, China, from 2020 to 2023. It identifies the limitations of traditional remote sensing methods in detecting low Chl-a levels due to the water's inherent properties. Using MODIS imagery and field data, the research evaluates different models, including empirical, semi-analytical, and machine learning approaches, for Chl-a estimation. Machine learning techniques, particularly the Mixed Density Network (MDN) and Automated Machine Learning (AutoML), proved to be more effective.
The research involved comprehensive data collection from 2002 to 2023, including seven detailed field samplings in northern Xinjiang's major lakes. MODIS Aqua data was extensively used, and an atmospheric correction was performed, with the POLYMER method showing high accuracy. The study compared various models for Chl-a estimation, finding that while empirical and semi-analytical models had limitations in accurately quantifying Chl-a, machine learning models, especially MDN and AutoML, showed superior performance.
MDN was chosen for its probabilistic nature and balanced error distribution. The model was applied to analyze nine major Xinjiang lakes over two decades. Despite significant pixel loss in daily MODIS images, 8-day Chl-a concentration data products were created using spatiotemporal interpolation. The study revealed spatial and temporal fluctuations in Chl-a concentrations across the lakes, with Jili Lake showing the highest concentration.
Overall, the study highlights the importance of advanced machine learning techniques in accurately estimating Chl-a in oligotrophic lakes in arid environments and contributes valuable insights for future environmental monitoring and management in Xinjiang.
Mathematics of Sea Ice and its Ecosystems
Authors: Golden Kenneth
Published: March 2024

The Arctic and Antarctic sea ice covers form key components of Earth's climate system. Their precipitous declines are impacting the polar marine environment and its ecosystems, with ripple effects felt far beyond the polar regions. As a material sea ice exhibits composite structure on many length scales. A principal challenge is how to use information on small scale structure to find the effective or homogenized properties on larger scales relevant to climate and ecological models. From tiny brine inclusions to rich ice pack dynamics on oceanic scales, and from microbes to polar bears, we'll consider recent advances in modeling sea ice and the ecosystems it hosts. In the spirit of MPE 2013, we’ll focus on the broad range of mathematics and physics being used. Percolation theory and statistical physics, fractal geometry, spectral analysis and random matrix theory, advection diffusion processes, topological data analysis, and uncertainty quantification for dynamical systems will arise naturally in considering various sea ice structures and organisms. This work is helping to advance how sea ice is represented in climate models, and to improve projections of the fate of Earth’s sea ice packs and the ecosystems they support.
Variability of advection and dispersion due to tides and wind forcing in a multiple-inlet coastal system
Authors: Fajardo-Urbina Jeancarlo Manuel , Duran-Matute Matias , Giesbergen Mart , Vankan Gert , Gräwe Ulf , Clercx J.H. Herman , Gerkema Theo
Published: March 2024

We present results on the Lagrangian transport of water parcels in a multiple-inlet coastal system: the Dutch Wadden Sea. We performed realistic hydrodynamic numerical simulations spanning 36 years, which are coupled to a Lagrangian module. The aim is to relate the characteristics of Lagrangian transport to the forcing. For this, the displacement of clouds of passive Lagrangian particles representing water columns is split into its advective and dispersive components, with the latter mainly due to chaotic tidal stirring. Advection is defined as the displacement vector of the center of mass of patches of passive particles, and the dispersion is quantified by the covariance matrix of their positions. Both quantities are determined for each tidal cycle spanning the duration of the simulations. Maps of advection reveal mostly large-scale features (basin-scale), whereas the spatial distribution of dispersion shows the predominance of localized structures covering mostly the regions around the inlets and strong bathymetric gradients. A strong correlation is found between the wind and the system-wide advection, showing a marked annual periodicity attributed to the seasonality of the wind forcing, which is stronger during winter and autumn. On the other hand, the magnitude of the tides (especially, due to the spring-neap cycle) governs the magnitude of the dispersion, i.e. mixing in the system. However, an enhancement in dispersion is observed during a few storms.  
Detection of iodine over the continental United States: Implications for ozone and mercury oxidation
Authors: Lee Christopher , Elgiar Tyler , David Liji , Wilmot Kai , Reza Margarita , Hirshorn Noah , McCubbin Ian , Lyman Seth , Hallar Gannet , Gratz Lynne , Volkamer Rainer
Published: March 2024

Most of our knowledge about the global distribution of atmospheric iodine is based on measurements of iodine monoxide (IO) radicals in the marine boundary layer and at high latitudes. Recent evidence of IO in the free troposphere is limited to few available aircraft measurements over oceans. We report the first ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of tropospheric IO radicals over the central continental United States. IO vertical columns measured at Storm Peak Laboratory, CO (40.455°N, 106.744°W, 3220 m.a.s.l.) are similar to those over oceans and vary significantly. A priori sensitivity studies indicate that IO mixing ratios increase with altitude. Back trajectory modeling indicates that IO is consistently observed in air masses quickly transported from over the Pacific Ocean. We compare the IO columns and their variability with predictions by a global model and use the observations to constrain a chemical box model to assess the atmospheric implications for ozone and mercury oxidation. Iodine-induced mercury oxidation is currently missing in atmospheric models, understudied, and helps to partially explain the elevated concentrations of oxidized mercury measured at SPL.
Disconnectivity matters: The outsized role of small ephemeral wetlands in landscape-scale nutrient retention
Authors: Cheng Frederick , Park Junehyeong , Kumar Mukesh , Basu Nandita
Published: March 2024

Wetlands protect downstream waters by filtering excess nitrogen (N) generated from agricultural and urban activities. Small ephemeral wetlands, also known as geographically isolated wetlands (GIWs), are hotspots of N retention but have received fewer legal protections due to their apparent isolation from jurisdictional waters and are typically left out of restoration efforts. Here, we hypothesize that the isolation of the GIWs make them more efficient N filters, especially when considering transient hydrologic dynamics. We use a reduced complexity model with thirty years of remotely sensed monthly wetland inundation levels in 3,700 GIWs across eight wetlandscapes in the United States to show how consideration of transient hydrologic conditions that capture disconnectivity dynamics can increase N retention estimates by up to 130%, with greater retention magnification for the smaller wetlands. This effect is more pronounced in semi-arid systems, where transient assumptions lead to 1.8 times more retention, compared to humid landscapes where transient assumptions only lead to 1.4 times more retention.  Our results highlight how GIWs have an outsized role in retaining nutrients, and this service is enhanced due to their hydrologic disconnectivity. Under the context of the new EU Nature Restoration Law and other global conservation efforts, these unique ecosystems must be protected and considered in restoration plans to maintain the integrity of downstream waters.
Deformation of the northeastern Tibetan Plateau and adjacent areas: Evidence from MT array data
Authors: Yin Haoxiang , Jin Sheng , Ye* Gaofeng
Published: March 2024

The uplift and growth of the Tibetan Plateau is an essential geologic issue. The closure of the Neo-Tethys Ocean and northward subduction of the Indian Plate formed the Tibetan Plateau and influenced the strain on its northeastern margin. We obtained the lithospheric electrical structure by inversion of MT array data collected at the Alxa and Ordos blocks neighboring the northeastern Tibetan Plateau. It shows the Ordos Block has noticeable electrical differences between the north and south parts. The northern lower crust to the upper mantle characterized large-scale low-resistivity anomaly, while the south is a stable craton block. The retreat of the Paleo-Pacific Plate caused the North China Craton to be in a tensional environment. With the northward subduction of the Indian lithosphere, the Tibetan Plateau continues to grow in a northeastern direction, resulting in an intensification of the subduction of the Alxa Block to the Ordos Block, and the north Ordos Block was pried up and in a weak state. The Asian asthenosphere became active under the influence of Indian lithospheric subduction. It jumped over the rigid Alxa and southern Ordos blocks to deform the northern part of the Ordos Block and form the large-scale partial melting. Since partial melt is more viscous than rigid blocks, it better equilibrates crustal deformation, resulting in flatter topography.
Overcoming challenges to data and software attribution throughout the research workflow: a publisher perspective
Authors: Vrouwenvelder Kristina , Raia Natalie , Stall Shelley
Published: March 2024

In an era when research is increasingly interdisciplinary and when the use of AI and ML methods across the Earth, space, and environmental science continues to grow, the importance of FAIR data is clear. The community has worked to elevate the importance of outputs beyond the research article; publishers, including the American Geophysical Union, now require authors to share data and code alongside their research articles in an effort to increase the transparency and reproducibility of science and enhance data and software reuse. Yet before we can fully realize the potential of FAIR data and software policies to advance the scientific enterprise, significant challenges remain. These include the need for education and efficient workflows to ease the burden on the researcher and increase their uptake of open, FAIR practices at the point of data and/or software publication as well as at article publication. Here, we will share a progress report from AGU on the effect of publisher policies on data and software sharing and discuss work by AGU and the broader community to break down barriers for the researcher and to ensure that data and software creators receive appropriate attribution for their work. 
Discovery of new cyanobacterial species from crusted soils of San Nicolas Island, California, from genera previously restricted to Brazil and Africa.
Authors: Johansen Jeffrey , Jusko Brian
Published: March 2024

We are currently revisiting a study by Flechtner, Johansen, and Belnap (2008) on the algae of biological soil crusts on San Nicolas Island.  A total of 200+ strains have been newly isolated, of which 78 cyanobacterial strains have been sequenced, representing 26 different species.  Of these 26 species, 23 appear to be phylogenetically new to science.  Several isolates show distinctive biogeography, belonging to genera recently described from Brazil. We found evidence of three species of the heterocytous genus Atlanticothrix, two species of Pycnacronema, and one species of Konicacronema.  Our species are morphologically consistent with species of these genera, but molecularly are clearly separated, particularly on the basis of ribosomal gene trees and analysis of the 16S-23S internal transcribed spacer region. There are presently no known vectors for transmission of taxa from the Atlantic Forest in Brazil to San Nicolas Island, or vice versa.  Wind patterns from Africa are known to bring dust from that continent to both South America and North America, but winds in the western hemisphere blow out into the Pacific Ocean in a westerly direction and do not cross the equator.  This suggests that at some time in the distant past, these microbes may have been seeded from Africa to North America, but have been in place long enough to become independent distinctive lineages worthy of recognition as different species from their southern hemisphere congeners. As preliminary evidence supporting an African origin for soil crust taxa, other workers have reported cyanobacterial genera in Africa (Pseudoacaryochloris, Aliterella, and Atlanticothrix) that also occur on San Nicolas Island. Future work will explore the cyanobacterial flora of all the Channel Islands as well as coastal southern California which could serve as a colonization source for cyanobacteria on the islands.
Probing relation between rainfall pattern and seismic detected water-and-sediment events
Authors: Huang Guan-Syun , Chao Wei-An
Published: March 2024

Southern Taiwan often experienced abundant monsoon seasons during seasonal transitions, and monsoons and typhoons controlled the rainfall patterns to be complex and varied, resulting the high intensity, prolonged duration, and high concentration. The aforementioned rainfall characteristics can increase the risk of water-and-sediment-related disasters.  To explore the correlation between rainfall patterns and water-and-sediment events, this study employs micro-seismic monitoring network, and the selected Putanpunuas River in southern Taiwan as a case study site. Frequent landslides in the middle and upper watershed supply the river with stable source of sediment materials. Consequently, during the periods with strong precipitation, our study site the shows high susceptibility of water-and-sediment events.  The seismic network comprises one station (BNAR) on the right bank and two stations (BNAL, BNAS) on the left bank downstream of the Putanpunuas River, and an additional station (BNAF) at the confluence of the Putanpunuas River and the Laonong River.  By conducting a series of spectrogram analysis, the average power spectral density (PSD) time series of each station can be computed. Then, we further quantified the seismic signal characteristic parameters for each water-and-sediment events.  This study initially employs various machine learning algorithms (Decision Tree, KNN, K-means, Auto-sklearn) to develop an optimized model for identifying water-and-sediment events, classifying different types of events, such as flooding (FD), debris flooding (DFD) and debris flow (DF), then providing a 4-year-length (2019~2023) catalog of water-and-sediment events.  Rainfall data including hourly precipitation and LiDAR estimated rainfall are collected from the rain gauge stations nearby study area. Using a certain definition (e.g., 4 mm/hr threshold for picking start time) of rain episodes, we calculated total number of episodes and established a rain episodes catalog.  The aforementioned datasets allow us to probe the relationship between rainfall patterns and water-and-sediment events, aiding in inferring the main rain episodes characteristics associated with water-and-sediment events . The  results of this study can be applied to predict potential water-and-sediment event types in Putanpunuas River using rainfall information as input. This can facilitate relevant early warning operations, reducing the societal impact of water-and-sediment disasters.Key words : Rainfall Patterns, Rain Episode, Micro-seismic monitoring network, Putanpunuas River, Water-and-Sediment Events, Machine Learning
Application of machine learning for modeling and characterizing electron and proton auroras on Mars
Authors: Dhuri Dattaraj , Atri Dimitra , Hseih Sonya
Published: March 2024

Auroras on Mars are known since their first discovery in 2005 by Mars Express and subsequently have been observed by Mars Atmosphere and Volatile Evolution (MAVEN) since 2014. Since 2021, Emirates UV spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) has been observing Martian auroras with an unprecedented frequency. These auroras are seen as FUV and EUV emissions of H, O, CO, and CO2 and are categorized based on their morphologies and the particles that are responsible for these emissions. Electron precipitation on the nightside causes discrete and diffuse auroras whereas solar wind protons penetrating the Mars atmosphere cause proton auroras on the dayside. EMUS also detected new discrete auroras extending thousands of km into the nightside with a sinuous morphology. The variety and abundance of Mars aurora occurrences make them an important tool for gaining new insights into solar wind interaction with Mars's magnetosphere. Mars aurora research therefore involves characterizing aurora occurences in terms of solar activity, seasonal variability, IMF orientation, crustal magnetic fields, and energies of precipitating particles. In this work, we present applications of machine learning for modeling proton auroras as well as automatically detecting discrete electron auroras, leveraging a plethora of MAVEN and EMUS observations. We also focus on explainability of these ML models, commonly perceived as “black-boxes”, and approaches to analyze and validate correlations learned by these models. We discuss in detail the characteristics of proton and electron auroras thus revealed by these models and present future directions for such applications on Mars and other planets.
MJO initiation and propagation simulated with a global kilometer-scale climate simulation: Implication for the cross-scale interaction
Authors: Takasuka Daisuke
Published: March 2024

The Madden–Julian oscillation (MJO) is the most predominant tropical intraseasonal variability and is a source of modulating global weather patterns. An accurate simulation of the MJO still remains a challenge for general circulation models (GCMs), and in fact, climate simulations with GCMs often struggle with the mean state-variability tradeoff. For this issue, a global storm-resolving climate simulation, which a recent increase in computing power makes possible, is expected to be a useful way because of its merit of direct coupling between moist processes and dynamics.The present study examines the MJO representation in an AMIP-type ~10-year simulation with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 3.5-km horizontal resolution, in comparison with that in one of conventional GCMs (MIROC6). This NICAM simulation successfully reproduces the realistic initiation frequency, propagation, and hierarchical structure of MJO convection, as well as realistic mean states (e.g., mean tropical precipitation), whereas MIROC6 overly underestimates the number of robust MJO events, and the activity of westward-propagating synoptic-scale waves embedded within MJO convective envelopes. As specific processes, the enhanced mixed Rossby-gravity wave-like systems seem to be a precursor and building blocks of the MJO simulated with NICAM at least over the Indian Ocean, consistent with several observational studies. In addition, NICAM-MJO propagation into the western Pacific is helped by high-frequency intermittent advective moistening that can be triggered by the upper-tropospheric PV intrusion from the extratropics. This feature is also found in some of observed MJO events.Our results suggest that good MJO simulations can attribute to representing the feedback from a complex of synoptic-scale waves onto MJO convective envelopes appropriately, and that the extratropics sometimes plays an active role in MJO dynamics. A success in simulating and scrutinizing these cross-scale interactions about the MJO is one of clear merits of kilometer-scale climate simulations without the assumption of a priori scale separation and quasi-equilibrium characteristics.
Mapping pure vegetation index values based on multisource remote sensing data over China for estimation of fractional vegetation cover
Authors: Zhao Tian , Song Wanjuan , Mu Xihan
Published: March 2024

Fractional Vegetation Cover (FVC) is an important vegetation structure factor for agriculture, forestry, and ecology. Due to its simplicity and reasonable precision, the vegetation index-based (VI-based) mixture model is commonly used to estimate vegetation cover from remotely sensed data. Improving the accuracy and computational efficiency of FVC estimations requires rapidly and precisely calculating the model's two most important parameters, namely the pure vegetation index of fully-vegetated and bare soil pixels. However, no pure normalized difference vegetation index (NDVI) values mapping has yet been produced. When there is a lack of pure pixels in many ecosystems, traditional empirical statistical approaches for obtaining pure vegetation index values are unreliable and challenging. In this study, the pure NDVI values mapping over China is achieved by combining the traditional empirical statistical method and the multi-angle remotely sensed inversion method (MultiVI), which can be adapted to various application scenarios for vegetation cover estimation when utilized with vegetation indices with different spatial and temporal resolutions. When the pure NDVI values extracted from a total of 19 GF-2 images in various parts of China were compared to those obtained in this study, the findings showed a good degree of accuracy. Furthermore, in semi-arid areas where fully-vegetated pixels are lacking and vegetation evergreen areas where bare soil pixels are lacking, this study can compensate for the fact that empirical statistical methods are unable to obtain accurate pure NDVI values and provide reasonable endmember NDVI values for vegetation cover estimation using the VI-based mixture model.
Improved Parameterizations of Ice-Ocean Boundary Layers
Authors: Zhao Ken , Chor Tomas , Skyllingstad Eric , Nash Jonathan
Published: March 2024

Glacial melt rates at ice-ocean interfaces are critical to understanding ice-ocean interactions in polar regions and are commonly parameterized as a turbulent shear boundary with a time-invariant drag coefficient. This assumes the exchange of heat and freshwater across the mm-scale diffusive thermal and salinity boundary layers varies proportionally with the strength of external momentum. However, this is only appropriate when melt/buoyancy-driven turbulence and the suppression of turbulence by stratification is weak.Guided by GPU-accelerated Direct Numerical Simulations (10 micron resolution) of the ice-ocean boundary layer for varying geometric and ocean forcing parameters, I will present an updated understanding of the basic principles of ice-ocean boundary layers as a complex interplay between diffusive freshwater/thermal and viscous shear layers nested within different types of turbulent boundary layers. I will present numerical simulation results that seek to merge the different turbulent ice-ocean boundary layer regimes: (1) meltwater-driven buoyancy, (2) meltwater-driven shear, and (3) externally-driven shear from both horizontal and vertical sources of momentum.This updated understanding allows us to develop more accurate predictions for the turbulently-constrained momentum, thermal, and freshwater boundary layer thicknesses, which is required to predict the ocean-driven melt rate of ice in polar regions.
Prediction of debris flow deposition based on particle segregation
Authors: Li Shuai , Chen Xiao-qing , Guo Xiao-jun , Chen Jian-gang , Chen Hua-yong , Wu Xu-yan
Published: March 2024

Predicting the deposition of debris flow is of great significance for hazard mapping, disaster reduction designing, construction of engineering, and settlements in vulnerable areas. There are many factors affecting debris flow deposition, such as landform, geological structure, debris flow characteristics, etc. However, these factors are finally realized by changing the migration and redistribution of particles in debris flow (particle sorting effect), and then through pore water pressure, shear force, friction resistance and momentum transfer. To this end, the laboratory flume experiments were conducted, focusing on the runout distance, deposit area, and maximum height, under different initial and boundary conditions such as water mass fraction and particle size. The experimental results reveal that the deposit morphology (e.g., runouts distance, deposit depth, and deposit width) of debris flow is closely related to the degree of particle size-segregation. Increasing water content first increase the degree of particle size-segregation which leads to longer longitudinal distance, however, too much water then reduced the degree of particle size-segregation, thus decreased the deposit distance. That is, the optimal water fraction corresponds to the well-distributed particle size-segregation, resulting in the longest deposit distance. In this condition, the friction among particles causes coarse particles to tend to move upward and forward, eventually accumulating at the front and surface, whereas fine particles tend to flow backward and downward, finally accumulating at the bottom and middle. Changing coarse particles size can only increases runout a little. However, well-distributed coarse particles can promote runout significant. The research can improve the understanding of debris flow accumulation and have important significance for quantitative risk assessment and risk zoning of debris flow.
'Witness mountains' on scarplands are expected outcomes of drainage capture dynamics
Authors: Peifer Daniel , Beer Alexander R. , Glotzbach Christoph , Neely Alexander B. , Bernard Thomas , Schaller Mirjam
Published: March 2024

Scarplands worldwide, underlain by tilted or undeformed sedimentary successions, display a distinct morphology with relief asymmetry between steep escarpments and gentle plateaus. This asymmetry drives escarpment retreat towards the plateau, gradually eroding the old plateau. Residual flat-topping landforms, commonly known as 'residual hills', 'mesas', 'buttes', or 'witness mountains,' prominently rise above the surrounding areas. Traditionally seen as remnants of an ancient plateau, these landforms are believed to endure escarpment retreat due to their capping by resistant rocks (e.g., sandstones, basalts, or duricrusts). This widely accepted conceptual explanation has yet to undergo testing through numerical modelling.Here, we propose a distinct mechanism for the rapid formation of 'witness mountains' — drainage capture dynamics during escarpment retreat. Using a 2-D numerical landscape evolution model incorporating stream-power-driven river incision and linear hillslope diffusion (representing mass wasting processes), we simulate escarpment evolution in tilted and undeformed multi-layered stratigraphy. As rivers draining down the escarpment capture low-sloping plateau rivers, a knickzone rapidly propagates upstream through the captured river system, followed by steepening hillslopes behind the escarpment front. This process results in flat-topped drainage divides bounded by marginal escarpments in both the front and back of the initial escarpment, forming 'witness mountains'. With equally steep slopes on both sides, these 'witness mountains' do not retreat towards the plateau. Instead, they gradually decay in elevation in situ until their distinctive morphology is no longer discernible. Similar results emerge in simulations assuming uniform lithology, suggesting drainage capture can sculpt 'witness mountains' beyond differential erosion resistance.Our simulations suggest that 'witness mountains' can be rapid outcomes of local drainage capture events affecting the scarpland foreland. This insight, supported by empirical observations in the Southwestern German Scarplands, challenges conventional interpretations of landscape evolution, offering valuable perspectives for understanding scarpland transience.
Measuring stream discharge using audible sound
Authors: Zreda Marek
Published: March 2024

Before we see a stream we can hear it. The discharge of that stream can be inferred from measurements of its sound. Sound pressure level is proportional to the energy of the flowing water and is related to discharge by a sound-discharge rating curve. Measurements with a hand-held sound level meter take seconds to acquire, allowing for high-resolution, long-term monitoring of stream discharge, campaign surveys, and ad hoc measurements. Sound measurements correlate well with the standard stream gauge data over the full range of discharges studied, from 0.02 m3/s to 33 m3/s. The following characteristics make the method an attractive alternative to the standard stream gauging: the instrumentation is simple and inexpensive; field deployment requires no built infrastructure; the instrument is suitable for rapid or emergency deployment; the measurements are non-invasive and non-contact, made at a distance from the stream, using a stationary or roving instrument; the acoustic response curve is linear; and the interfering sound sources are either negligibly small or easily removed.If there is enough time, attendees will be able to create their own sound-discharge rating curve using their cell phones and the Decibel-X app to measure sound intensity. The conference room's audio equipment will provide sound clips of an actual stream along with the independently measured discharges.
Recipe For Regular Machine Learning-based Earthquake Cataloging: A Systematic Examination in New Zealand, from Local to Regional Scale
Authors: Liao Wu-Yu , Lee En-Jui , Manea Elena , Aden Florent , Fry Bill , Kaiser Anna , Rau Ruey-Juin
Published: March 2024

Machine learning-based algorithms are emerging in mining earthquake occurrences from continuous recordings, replacing some routine processes by human experts, e.g., phase picking and phase association. In this study, we explore the combination of phase picker and phase associator with challenging application scenarios: the complex seismogenic structure, wide study area (15 degrees of both longitude and latitude and a depth of 600 km), hundreds of stations, and intensive seismicity during the 2016 Mw7.8 Kaikōura earthquake that correlates with at least seven faults. The deep learning-based phase pickers usually follow the prototype of PhaseNet, which maps the phase arrivals into truncated Gaussian functions with a customized model. Recent studies have shown poor generalizability of the advanced models on data out of the training distribution. In this study, we argue that appropriate data augmentation enables the RED-PAN model, trained on the Taiwanese data, to generalize well on New Zealand data even under intense seismicity. We applied RED-PAN on year-long continuous recordings over 439 stations of the GeoNet during 2016 and 2017. RED-PAN produces approximately three million P-S pairs over the New Zealand-wide network, enabling the exploration of the advanced phase associators' robustness on local and regional scales and under intense seismicity, e.g., back-projection, GaMMA, and PyOcto. Finally, we developed a six-stage automatic pipeline producing a high-quality earthquake catalog: phase picking, phase association, 3-D absolute location by NonLinLoc, magnitude estimation, weighted template matching, and 3-D relative location by GrowClust. 