bluuebunny/crossref_metadata_2025 huggingface dataset:
A comprehensive Applied Space Weather Research graduate program for professionals to meet academia and commercial needs for space weather expertise
Authors: Poh Gang Kai , Uritsky Vadim , Feuillet Léa , Robinson Robert , Kraemer Steve
Published: February 2023

Space weather is the study of the changes in Earth’s space environment with solar activities that impact technical systems, such as communication, navigation, aviation, satellite command and control, and electric power.  With the increasing human presence in space and blossoming of the New Space industry, understanding the physics of the heliosphere and the ability to accurately predict space weather events became essential to government agencies and private industry. The need for “in-house” professional experts on space weather becomes important for agencies/businesses to understand the potential implications of upcoming space weather events and make informed decisions to protect equipment, infrastructure and ensure health and safety. The Catholic University of America Physics department (CUA-Physics) has developed one of the U.S. first M.S. program in Applied Space Weather Research (ASWR), taught by faculty in the Dept. of Physics, researchers from our NASA/PHaSER cooperative agreement, and NASA civil-servant scientists. The CUA Master’s Degree program in Applied Space Weather Research is designed to provide graduate students the knowledge that will be needed in government and industry to confront the increasing impacts of space weather as human presence in space expands. In addition to core courses in thermodynamics, electromagnetism, and statistical mechanics, the program includes three new courses that cover the fundamental physical processes of space weather from the surface of the Sun to Earth’s atmosphere, in addition to courses in plasma physics and numerical methods for space weather. The technical and health impacts of space weather are integrated into these courses and also taught as part of a space weather seminar series. The program is open to students from any of the sciences, mathematics, and engineering disciplines, and is designed to teach students from diverse backgrounds the fundamentals of space weather. The program makes use of models and data streams available through CUA’s on-going partnership with NASA Goddard Space Flight Center. A key goal of the program is to provide our students with ample opportunity to learn both the scientific and practical aspects of space weather that will position them for jobs in academia, government and industry through active mentorship with scientists and researchers at Catholic University, NASA, and university partners. With emphasis on both the applied and research aspects of space weather, the program provides the unique interdisciplinary education needed to address the challenges of understanding, forecasting, and mitigating the threats from space weather. 
Extended-range predictability of stratospheric extreme events using explainable neural networks
Authors: Wu Zheng , Beucler Tom , Domeisen Daniela
Published: February 2023

Extreme stratospheric events such as extremely weak vortex events and strong vortex events can influence weather in the troposphere from weeks to months and thus are important sources of predictability of tropospheric weather on subseasonal to seasonal (S2S) timescales. However, the predictability of weak vortex events is limited to 1-2 weeks in state-of-the-art forecasting systems, while strong vortex events are more predictable than weak vortex events. Longer predictability timescales of the stratospheric extreme events would benefit long-range surface weather prediction. Recent studies showed promising results in the use of machine learning for improving weather prediction. The goal of this study is to explore the potential of a machine learning approach in extending the predictability of stratospheric extreme events in S2S timescales. We use neural networks (NNs) to predict the monthly stratospheric polar vortex strength with lead times up to five months using the first five principal components (PCs) of the sea surface temperature (SST), mean sea level pressure (MSLP), Barents–Kara sea-ice concentration (BK-SIC), poleward heat flux at 100 hPa, and zonal wind at 50, 30, and 2 hPa as precursors. These physical variables are chosen as they are indicated as potential precursors for the stratospheric extremes in previous studies. The results show that the accuracy and Brier Skill Score decrease with longer lead times and the performance is similar between weak and strong vortex events. We then employ two different NN attribution methods to uncover feature importance (heat map) in the inputs for the NNs, which indicates the relevance of each input for NNs to make the prediction. The heat maps suggest that precursors from the lower stratosphere are important for the prediction of the stratospheric polar vortex strength with a lead time of one month while the precursors at the surface and the upper stratosphere become more important with lead times longer than one month. This result is overall consistent with the previous studies that subseasonal precursors to the stratospheric extreme events may come from the lower troposphere. Our study sheds light on the potential of explainable NNs in searching for opportunities for skillful prediction of stratospheric extreme events and, by extension, surface weather on S2S timescales.
Simulating hydrology and tracer dynamics in a subglacial environment underneath the Greenland ice sheet
Authors: Pramanik Ankit , Arndt Sandra , Werder Mauro , Pattyn Frank
Published: February 2023

The Greenland ice-sheet surface melt has increased substantially in intensity and spatial extent over the recent decades. The rapid migration of melt towards upstream areas of Greenland ice sheet is expected to incur major changes in hydrological behaviour of the ice-sheet and outlet glaciers along with changes in export fluxes of carbon, methane, and other nutrient fluxes, which, in turn, will further affect the downstream ecosystem of rivers, fjords and oceans. Subglacial environments are emerging as ecological hotspots, urging detailed understanding of interaction between subglacial-hydrology and biogeochemistry. However, due to their inaccessibility, the hydrology and biogeochemistry of subglacial environment thus far lacks a detailed understanding. Numerical models are, in combination with observational data, ideal tools to advance our understanding.Here, we developed a novel process-based model to investigate the interplay between subglacial-hydrology and (passive and active) tracer dynamics underneath the rapidly changing Greenland ice sheet on seasonal, inter-annual and climate warming relevant timescales. We set up the subglacial-hydrology model GlaDS (Glacier Drainage System model) to simulate seasonal and interannual evolution of distributed and channelized subglacial water flow for Leverett glacier (Southwest Greenland) to explore the geometry, connectivity, and flow dynamics in the seasonally evolving drainage system.We then use the GlaDS results to inform a reaction-transport model (RTM) of Leverett’s subglacial system following the GlaDS set-up. The RTM is run to conduct a series of idealized tracer experiments with the aim of disentangling the transport and reaction controls on subglacial tracer distribution and outflow. Tracers are injected to the system through moulins with the surface meltwater and are either passively transported (passive) or also consumed/produced (active) during their transit through the system. Model results are validated with long-term measurements in this area. Results show that the tracer transport is primarily controlled by subglacial drainage system efficiency, which is regulated by discharge magnitude, topography and moulin locations. The spatial and temporal variation in tracer concentration is further dependent on hydrological interaction between different subglacial components (cavities and channels), location and type of branching of channels, and bed properties.In the future, we will extend the model to wider area of Greenland ice sheet and couple it to multi-component biogeochemical reaction networks with the. aim to understand the evolution of biogeochemical process along with the evolution of hydrology in warming climate.
Sensitivity study of the effects of black carbon on Arctic sea ice using CICE sea-ice model
Authors: Wang Yu , Su Jie
Published: February 2023

Black carbon (BC) is one of the most important absorbing particles in the atmosphere. BC can reduce the albedo of snow/ice and enhance the absorption of solar radiation at ultraviolet (UV) and visible wavelengths when it deposited on snow/ice surface. The deposition of BC can lead to an acceleration of the melting of snow/ice. To quantify the changing process of BC in snow/ice and its contribution to the melting of snow/ice, a series of sensitivity numerical experiments including the impacts of BC species (hydrophobic and hydrophilic), deposition rate, and scavenging efficiency of BC was completed using the Icepack one-dimensional column model of CICE. Further, we evaluate the effects of BC deposition on Arctic albedo and ice thickness, forced by ERA5 reanalysis data and BC deposition rate from CMIP6, including two simulation results of the historical experiments with GISS-E2 model and EC-Earth3 model. The results indicate that the hydrophobic BC can cause a reduction of snow/ice albedo by 0.43% in the melting season, which is 35% larger than hydrophilic BC with the same deposition rate. When only the hydrophilic BC was considered, the impact on scavenging efficiency halved to BC content in snow/ice is similar to double the deposition rate in the melting season. Additionally, the 2D model results indicate that the existence of BC in snow could enhance the absorption of solar radiation in the snow layer and reduce the transmittance of radiation to the ice layer, leading to a thicker ice thickness before the melting season. The thermodynamic impact of BC is more significant in the marginal ice zone than that in the central Arctic, especially from Barents Sea to Laptev Sea. In this paper, we quantify the effects of BC on the melting of Arctic snow and sea ice and discuss the problems of the parameterizations of BC’s effect. This may contribute to the improvement of the sea ice model.Key words: Black carbon; CICE model; Sensitivity experiment; Scavenging efficiency; Albedo
GLAC3: Joint glaciological model and visco-elastic earth model history matching of the last glacial cycle: Greenland and Antarctica components
Authors: Tarasov Lev , Lecavalier Benoit , Balco Greg , Hillenbrand Claus-Dieter , Milne Glenn , Roberts Dave , Woodroffe Sarah
Published: February 2023

We present the Antarctic and Greenland components of an extensivehistory matching for last glacial cycle evolution and regional earthrheology from glaciological modelling with fully coupled regionalvisco-elastic glacio-isostatic adjustment.  Of further distinction isthe accounting for model structural uncertainty. The product is a highvariance set of joint chronologies and earth model parameter vectorsthat are not inconsistent with available constraints givenobservational and model uncertainties.Ensemble parameters are from Markov Chain Monte Carlo sampling withBayesian artificial neural network emulators.  The glaciological modelis the Glacial Systems Model with hybrid shallow shelf and shallow icephysics and a coupled energy balance climate model. It includes a muchlarger set of ensemble parameters (34 and 38 respectively forGreenland and Antarctica) than other paleo ice sheet models tofacilitate more complete assessment of past ice sheet evolutionuncertainty. The history matching is against a large curated set ofrelative sealevel, vertical velocity, cosmogenic age, and marineconstraints as well as the present-day physical and thermalconfiguration of the ice sheet.The careful assessment of uncertainties, breadth of modelledprocesses, and sampling approach has resulted in NROY (not ruled outyet) chronologies and rheological inferences that contradict previousmore limited model-based reconstructions.  For instance, in contrastto most previous inferences for the Antarctic contribution to the lastglacial maximum (LGM) low-stand (with inferred values of about 10 m iceequivalent sea-level (mESL), our NROY set includes chronologies withLGM contributions of up to 23 mESL.  This result represents apotentially significant contribution towards addressing the challengeof LGM missing ice.
Machine learning and hydrological sciences: A systematic overview of review papers
Authors: Dogulu Nilay , Olusola Adeyemi Oludapo , Papacharalampous Georgia
Published: February 2023

Water sciences have greatly contributed to the proliferation of machine learning in the twenty-first century, especially through engineering hydrology. This process has consequently necessitated transfer of core theory and knowledge of machine learning to the domain of hydrological sciences. In this regard, it is noteworthy that published academic literature played a substantial role in supporting development and learning of hydrologists. Specifically, research articles (and book sections) that review machine learning concepts and algorithms along with their applications in hydrology bolster progress of science by presenting encapsulated information (e.g, in the form of literature synthesis). Despite the rapid increase in the number and scope of such research articles, a systematic understanding of how this line of research publications has evolved with respect to their scientific context, objectives, and methods is still lacking. Hereby, we present an analysis of review papers in hydrology and machine learning based on a  systematic search strategy. The overview includes analysis of bibliographic information, review types (objective, focus theme, etc.), review methodologies (narrative, systematic, etc.) as well as thematic context (hydrology subjects and machine learning topics). We believe that our analysis can provide important insights into topics and discussions in hydrology and machine learning that need further exploration by hydrologists. Furthermore, the public online library on Zotero (https://www.zotero.org/groups/4828386/machine_learning_hydrology_review_papers/library) might encourage more participation towards sustainable literature search and active reading on this subject at the intersection of two fundamental disciplines, machine learning and hydrology.
Modulation of Tropical Instability Waves and chlorophyll concentration by equatorial waves during the 2021 Atlantic Niño
Authors: Tuchen Franz Philip , Perez Renellys C. , Foltz Gregory R. , Brandt Peter
Published: February 2023

During the boreal summer of 2021, the central and eastern equatorial Atlantic experienced persistent sea surface temperature (SST) anomalies of more than +1°C for several months. These episodic extreme events are referred to as Atlantic Niños, with 2021 marking the strongest event since 1984. Atlantic Niños are known to have far-reaching impacts on, for instance, rainfall over the surrounding continents, and on ocean dynamics through changes in thermohaline gradients and circulation. A pronounced event like the 2021 Atlantic Niño in combination with the steadily expanding coverage by satellite and in-situ observations provides the rare opportunity to study an Atlantic Niño event in unprecedented detail. Here we focus on the influence of the 2021 Atlantic Niño on tropical instability wave (TIW) activity and surface chlorophyll concentration.The 2021 Atlantic Niño was initiated by a strong downwelling Kelvin wave excited by westerly wind bursts in the western and central equatorial Atlantic. The eastward propagating Kelvin wave induced strong eastward flow anomalies on the equator causing a reduction of the meridional shear of the near-surface zonal flow in the central equatorial Atlantic. At the same time, the Kelvin wave-induced deepening of the thermocline weakened the seasonal development of the equatorial Atlantic cold tongue. The reduction of both the meridional SST gradient and the meridional shear of zonal velocity largely suppressed barotropic and baroclinic instability, which is required for the generation of TIWs. Consistent with these changes, we find that 2021 was one of the least active years in terms of TIW-related temperature, salinity, sea level, and current variability. The overall reduction in TIW activity is characterized by weak TIW activity before and enhanced TIW activity after the climatological TIW peak resulting in a delay of the TIW season. This delayed onset of TIW activity is expected to have considerable consequences for the local heat and freshwater budgets. Low TIW activity and positive SST anomalies also impacted the concentration and distribution of surface chlorophyll as observed by daily gap-free satellite observations. After the initial Kelvin wave, surface chlorophyll concentration dropped to extraordinarily low values and was anti-correlated with the evolution of SST anomalies. The absence of TIWs is also apparent in weaker than normal surface chlorophyll concentration variability on intraseasonal time scales, highlighting the interplay of TIWs and chlorophyll. Our results demonstrate how the 2021 Atlantic Niño impacted oceanic variability, but further analysis is needed to better understand the consequences of such events for regional heat and freshwater budgets as well as for nutrients, productivity, and marine ecosystems.
Understanding the patterns of microbial diversity across the seismogenic zones of Koyna (India) and South African gold mine through scientific drilling
Authors: Sar Pinaki , Sahoo Rajendra , Onstott Tullis , Kieft Thomas , Wiersberg Thomas , Ogasawara Hiroshi , Kazy Sufia , Roy Sukanta
Published: February 2023

This study reports deep biosphere related outcomes of the scientific drilling into the seismogenic zones of Koyna (India) and South African gold mine and presents a comparative account of microbial ecology across the continental settings (by including geomicrobiological data from Fennoscandian and SAFOD deep drilling / biosphere sites as well). ICDP DSeis project (SA) and Ministry of Earth Sciences (India) - ICDP Koyna scientific drilling allowed us to drill and recover Archean granitic rock cores from deep Earth crust (~3-4 km) and investigate the patterns of deep biosphere microbial ecosystems. Deep crystalline rock hosted microbial communities are elucidated by sequencing the 16S rRNA genes from extracted total DNA of the rock samples. Two rock core samples obtained from borehole B (~3330 meter below surface; representing quartzite from the Babrasco Formation) drilled at the Moab Khotsong Gold mine and several rock samples obtained from various depths of Koyna exploratory and pilot boreholes are used. Microbial taxa affiliated to Burkholderiace dominated (>30% relative abundance) the SA deep rocks. Predominance of multiple hydrogen metabolizing genera such as Comamonas, Ralstonia, Burkholderia-Caballeronia-Paraburkholderia, Enhydrobacter, extremotolerant Corynebacterium and C1- compound metabolizing Methylobacterium could be noted. In contrast, the deep biosphere of Koyna (down to 2910 meter below surface) is found to be constituted by multiple taxa with relatively lower abundance. Abundance of extremotolerant Actinobacteria (Micrococcaceae and Corynebacteriace), Pseudomonadaceae, Burkholderiaceae and Opitutaceae is noted. WPGMA based clustering suggests that microbial communities of Deccan trap subsurface are significantly different from its South African counterparts; and the latter are somewhat similar to microbial communities previously reported from Fennoscandian deep biosphere. Presence of several unique taxa is identified in each of these habitats. Members of Thermoactinomycetaceae, Planococcaceae, Babeliaceae, Thermomonosporaceae and several Clostridial families are found as unique taxa in SA deep biosphere, while Thermoanerobaculaceae, Cyclobacteriaceae, Acidothermaceae, Thioalkalispiraceae, etc. represented those of the deep biosphere underneath the Deccan. The study highlights the pattern of deep granitic crust hosted microbial communities and reinforces the need to develop and adopt more universal sampling and experimental approaches in deep life research.
Impacts of oceanic warming patterns versus CO2 radiative forcing on the Hadley Circulation
Authors: Sun Yong , Ramstein Gilles , Fedorov Alexey V. , Ding Lin , Liu Bo
Published: February 2023

The Hadley circulation (hereafter HC) is one of the most prominent meridional overturning circulations in the climate system. In addition to maintaining energy balance and momentum exchange in tropics and extratropics, it can also shape the Intertropical Convergence Zone (ITCZ) and subtropical dry arid zones by regulating the hydrological cycle in tropical and extratropical regions. Weakening and expanding HC and narrowing of the ITCZ are projected with human greenhouse gas emissions. However, no consensus has been achieved regarding the relative importance of direct CO2 radiative effect and indirect effects via SST changes in shaping the future HC changes. This limits our deep understanding of the climate impacts imposed by changes in the HC. Here we analyze a broad range of CMIP5 experiments and show that future changes in SST patterns play the leading role in the determining the future changes in HC and ITCZ. In addition, a series of individual basin perturbation experiments were conducted at 1.5°C, 2°C, and 3°C temperature thresholds to identify key basins that determine HC strength, edges, and ITCZ locations. Our work highlights the overwhelming role of future tropical Indian Ocean warming on the HC and ITCZ changes.
Impacts of biogeochemical processes on hydrological extremes under future SSP585 scenario over East Asia
Authors: Lee Jaehyeong , Kim Yeonjoo
Published: February 2023

This study assesses future extreme hydrological conditions in East Asia based on four Global Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 with biogeochemistry (CMIP6-BGC), which provides layers of vegetation and soil to estimate carbon and nitrogen cycle. We estimate the frequency and severity of extreme dry and wet conditions based on runoff using the threshold level method under Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) 585 scenarios. The differences in extremes between “standard”, which does not consider the detailed biogeochemical processes, and the “BGC” simulations are estimated to quantify the impacts of biogeochemical processes on the hydrological extremes. This study shows that the “standard” case is predicted to be more severe and pronged in intensity and duration of extremes in the future than that of the “BGC” case. For example, the duration of extreme dry and wet conditions in the “BGC” case shows less duration, about 21% and 12%, respectively, in the future than in the “standard” case in three GCMs ensemble. We demonstrate that the effects of the biogeochemical process should be considered to project future extremes because these extremes could be overestimated in “standard” simulations. AcknowledgementsThis work was supported by the Basic Science Research Program (2020R1A2C2007670) and the Framework of International Cooperation Program (2021K2A9A2A06038429) through the National Research Foundation of Korea (NRF).
Future Land Use Change projection under SSP-RCP scenarios over Ethiopia
Authors: Brhane Ermias Sisay , Dairaku Koji
Published: February 2023

Land use land cover (LULC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LULC products at a global and regional scale for public use has been difficult, especially in developing countries/regions (Doelman et al., 2018). Land Use Land Cover (LULC) change simulation models are a powerful tool for analyzing the causes and effects of LULC dynamics under different scenarios. Scenario-based simulations of future land-use change can provide important information for evaluating the impacts of land strategies under different conditions. In this study, we project the future land use data at a 1-km resolution that comprises six land use types, adopting the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) over Ethiopia. To generate this high-resolution land-use product, we use the FLUS model to simulate future land-use dynamics. The process of developing a future land dataset for Ethiopia can be divided into two parts. The first part is the estimation of the future area demands of different land use types under different SSP-RCP scenarios extracted from the LUH2 (Land-Use Harmonization 2) datasets which is available for free at http://luh.umd.edu/index.shtml. This dataset comprises a global projection of multiple land types for successive years from 2015 to 2100 under different SSP-RCP scenarios with a 0.25° resolution (approximately 25 km at the equator). The second part is conducting a 1-km spatial land simulation using the future land use simulation (FLUS) model under the macro constraints of the demands. In this sense, we select a series of relevant spatial driving factors, such as socioeconomic (GDP, population), distance factors (urban center, roads, and rivers), and natural factors (climate, topography, and soil quality). On this basis, a new set of land use projections, with a temporal resolution of 10 years and a spatial resolution of 1km, in eight SSP-RCP scenarios, comprising six land use types in Ethiopia is produced. This dataset shows good performance compared to remotely sensed ESA CCI-LC data. The results show that our land use simulation yields a satisfactory accuracy (Kappa = 0.8, OA = 0.9, and FoM = 0.1). Because of the advantages of the fine resolution, current scenarios, and multiple land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models.
The impact of training dataset on a vision-based smart road sensor to measure the level of flood on the streets
Authors: Mohammadi Jahromi Mahnoush , Honarparvar Sepehr , Saeedi Sara , Liang Steve
Published: February 2023

With recent advancements in Artificial Intelligence (AI) and decreases in computing costs, early warning systems have become affordable and popular in many different applications such as smart cities, and disaster management. AI and the Internet of Things (IoT) enable the automatic detection of different incidences, such as flash flooding. The IoT solutions at the scene of an emergency, enable capturing various data and sharing in real-time with first responders. The accuracy of flood level estimation is critical for proper emergency response. This is while the accuracy depends on multiple factors, such as camera properties, the brightness of the environment, training data size, etc. In this research, we focus on the impact of training dataset size, the number of classes, and the labelling method of training data on the accuracy of the proposed Machine Learning (ML) method. A vision-based smart road sensor was developed to detect flood occurrences in urban areas, which can be used as a sub-module of an early warning system. We used the portion of cars’ tires that is visible to the camera as an indicator to detect and classify the depth of standing water into four classes: “no-water”, “low-level”, “high-level”, and “water-splash”. A Convolutional Neural Network (CNN) called You Only Look Once (YOLO) was trained for this matter. The results showed that the accuracy of the trained CNN depends on multiple factors. In this study, we evaluated and studied the impact of the training data size, the number of classes, and the labelling method of training data on the accuracy of the early warning system.
Automatic labeling of the Training Dataset for Individual and Group Activities Detection
Authors: Honarparvar Sepehr , Mohammadi Jahromi Mahnoush , Saeedi Sara , Liang Steve
Published: February 2023

Real-time activity detection is one of the interesting fields of research due to its wide range of applications in human-computer interaction, health monitoring, security, workers' performance monitoring, safety, etc. Computer vision and deep learning is one of the most popular solutions to detect human activities. However, these solutions often suffer from inadequate training datasets in terms of quantity and quality. Additionally, the procedure of data labeling is time-consuming and expensive. This paper proposes an automated method for labeling data for automatic individual and group activity detection. The proposed algorithm takes advantage of combining simple object classes (such as hands, and persons) and spatial relationship rules to label the training dataset for detecting activities like covering coughs with an arm or hand, hugging, and hand-shaking. These activities were used for Covid risky activities monitoring at workplaces. To implement this algorithm, we used You Only Look Once (YOLO) and Mask Reginal Convolutional Neural Network (Mask-RCNN) object detection and we applied the transfer learning from the Common Object in Context (COCO) CNN model. The results showed 96% precision of data labeling for covering coughs with a hand, 93% for covering coughs with an arm, 89% for hugging, and 87% for handshaking activities. This is while, the average precision of activity detection using automatically annotated data, only decreased by 3% for Mask RCNN and 5% for YOLO. Also, the separation of the individual and group activity classes led to more detection precision. In conclusion, the proposed automatic data labeling algorithm preserved the quality of data annotation while outperforming the training-data annotation speed. 
Developing a physics-guided neural network to predict soil moisture with remote sensing evapotranspiration and weather forecasting
Authors: Ma Zonghan , Wu Bingfang , Chang Sheng , Yan Nana , Zhu Weiwei
Published: February 2023

The short-term prediction of soil moisture variation is a decisive indicator of irrigation scheduling and crop management in agriculture. Traditional soil water dynamic models require complex descriptions of water movement and multiple parameters to calibrate for specific fields, which limit the model’s capability of generalization. Machine learning methods based on large sample datasets can automatically learn the most accurate way of predicting soil moisture with numerous related input variables. However, it could be time consuming in training and model optimization to improve performance. Combining the advantages of both methods, we designed a new soil moisture prediction neural network guided by the water transport driving mechanism. The water balance principle is used to limit the training process with remote sensing-based field-scale evapotranspiration, meteorological rainfall and primary soil water changes calculated from a simplified soil water model. By adding the physics layer to neural network, the demand for large datasets and the requirements of training and optimization are reduced. The prediction of soil moisture is at a half-monthly scale, and we tested the model during the winter wheat growing period. The results show that it requires less training capability to achieve high accuracy. Physics-guided neural networks could act as a better framework for parameter prediction in further researches.
Assessment of Microplastic Abundance and Discharge from Greywater of Ships
Authors: Jang Yu Lee , Jeong Jongwook , Eo Soeun , Hong Sang Hee , Shim Won Joon
Published: February 2023

Greywater drained from showers, washbasins, laundry, and dishwashers can be discharged to the sea without further treatment unless in no discharge zone. In 2021, International Maritime Organization indicated greywater generated from ships as one of the possible sources of sea-based microplastics. However, there are only a few studies on microplastics in greywater, most of which have used the scientific literature for statistical estimation of the amount of microplastics in cruise ships. It is necessary to evaluate the amount of microplastics in real ships’ greywater for accurate calculation of emission. This study aims to measure microplastics in a ship’s greywater by its usage and to estimate the preliminary global emission of microplastics from a ship’s greywater discharge into the ocean.Greywater was collected in three different holding tanks at 5-day intervals from a research vessel Onnuri of Korea Institute of Ocean Science and Technology (1,370 tons) before and during the cruise for 18 days (April 21–May 9, 2022) with 33 persons on board. A total of 83 m3 of greywater was generated during the cruise. The average microplastic abundance was 135,563±87,141 n/m3 (range: 60,500–322,500 n/m3) in greywater. There were no significant differences in the abundance of microplastics in greywater usage (mainly galley, laundry and shower, and cabin washbasin). The level of microplastics in greywater in this study was several orders of magnitude higher than those in effluents from terrestrial wastewater treatment plants in the literature.The greywater generation rate during navigation was 0.15 m3/person/day, which was comparable to previous studies with various types of ships. The greywater sub-flows accounted for 51%, 17%, and 32% of tank A (drained from the galley and 5 cabins with showers), tank B (18 cabins, 2 laboratory rooms, and 4 washbasins), and tank C (laundry, shower, and washbasin), respectively. The number of microplastic from total greywater was highest in tank C (44%), followed by tank A (29%) and tank B (18%). However, there was no difference in the number and mass of microplastics. The annual microplastic load in greywater from R/V Onnuri was estimated to be 1.2ⅹ108 n/year or 91 g/year. Based on the greywater microplastic emission factor abundance in this study, it is estimated that about 28ⅹ1012 particles or 13–29 tons of microplastics could enter the global oceans from greywater of global fleets (>100 gross tonnages) in a year.
The transformation of the Altyn Tagh fault in its intersections with the Qilian Shan and Eastern Kunlun Shan explained by triple junction analysis
Authors: Yi Kexin , Guo Zhaojie
Published: February 2023

The Altyn Tagh fault (ATF) is a large lithospheric left-lateral strike-slip fault, marking the northwestern boundary of the Tibetan Plateau. Understanding the tectonic history of the ATF provides insights into the growth pattern of the Tibetan plateau, as well as the deformation mechanism of complex fault systems.However, despite numerous research efforts, the deformation of the ATF is still a subject of discussion, especially its interaction with the other two major faults in the northeast Tibetan Plateau: the strike-slip Eastern Kunlun fault and the Qilian Shan fold-thrust belt. The triple junction analysis has proven successful in explaining the spatial-temporal variations of fault kinematics. Therefore, here we use the principles of triple junctions to discuss the transformation of the ATF in its intersections with the Qilian Shan and Eastern Kunlun Shan, with the assistance of geological evidence from fieldwork and satellite images. We propose that the initiation of the left-lateral motion of the Eastern Kunlun fault led to an FFF triple junction in the former western end of the ATF. Meanwhile, the deformation on the southern Qilian Shan forms an TFF triple junction with the splays of the ATF. The unstable triple junctions will trigger the growth of the ATF and complicate the deformation the Qilian Shan and the Eastern Kunlun Shan. Our research firstly applies triple junction principles to both ends of the ATF, and presents a new model of the evolution of the ATF and its surrounding orogens, shedding lights on the history of Tibetan Plateau.
Multi-Objective Operation- Decision-Making-Risk Propagation Analysis for Cascade Reservoirs Affected by Future Streamflow Process Variations
Authors: Yang Zhe , Wang Yufeng
Published: February 2023

Significant climate variations have decreased the stability of water resource systems, leading to multiple uncertainties in streamflow response, reservoir operation optimization, decision-making, and adaptive adjustments for water resource scheduling. Understanding the impact of climate change on reginal streamflow is necessary and crucial to identifying reservoir operation strategies and decision-making responses. In this study, we created an integrated systematic “uncertain streamflow responses”– “reservoir operation”– “optimization”– “decision-making risk analysis” chain. Three bias-corrected and downscaled general circulation models (GCMs) were used to analyze the inter-model uncertainties under three representative concentration pathways (RCPs). The streamflow responses and uncertainty in the future were determined using a distributed hydrological model and the fuzzy extension principle under predefined scenarios and uncertainty levels. Then, a stochastic simulation model and modified stochastic multi-criteria decision-making model were applied to identify the effects of climate change projections and streamflow responses on reservoir multi-objective operation and decision-making. Moreover, risk quantification indices were used to determine the uncertainty propagation and potential risks accumulated in the chain. We applied this framework to cascade reservoirs in the Qing River Basin. The results indicate that the mean annual streamflow projected using selected GCMs will increase, enhancing the hydropower response and weakening the ecological benefit response. The Pareto non-dominated solutions optimized based on the streamflow projections obtained using the GCMs (under the same RCP) and hydrological model are more distinct than those based on different RCPs and the same GCM. Moreover, a high emission scenario may increase the uncertainty of the streamflow projections and reservoir operation responses, which is consistent with the finding that the decision-making process becomes more variable and sensitive with increasing streamflow uncertainty. Finally, we identified the preferred solutions for reservoir operation under different uncertainties, the respective expected values, and the 95% confidence interval bands to enhance the adaptability of future reservoir operation.
Climate whiplash in California: too much to bear, too little to handle?
Authors: Facincani Dourado Gustavo , Rheinheimer David E. , Abatzoglou John T. , Viers Joshua H.
Published: February 2023

Inter- and intra-annual water availability is naturally highly variable in Mediterranean regions, with swings between extremes costing nations potentially billions of dollars in damages and threatening lives. In California, future projections foresee an increase in the bimodal distribution of hydrological extremes, leading to greater hydroclimatic whiplash. Here, we quantify the relative impact of hydroclimatic whiplash on hydropower systems, flood control and water deliveries in the Central Sierra Nevada, California. We aim to explore at what point these services become less resilient to drought, and if wet whiplash years can re-establish an ‘average’ system state. To represent a wide range of wet, dry and dry-to-wet transitions, we sampled water years from upper (floods) and lower (droughts) quintiles, with replacement, across 30 years of future streamflow projections (2030-2060) from 10 global circulation models. Synthetic hydrological sequences of 2 to 5 dry years, followed by 1 to 2 wet years form a total sample of 200 whiplash sequences for the Stanislaus, Tuolumne, Merced and Upper San Joaquin River basins. This stress test indicates that the intensification of whiplash cycles would seriously challenge existing hydropower production, water storage and flood control operating rules. Compared to baseline averages, all basins had negative impacts on hydropower generation, with losses varying from 6% in the Merced to almost around 67% in the Upper San Joaquin, depending on the whiplash sequence. Agricultural and/or urban demands are most impacted in the Tuolumne and the Upper San Joaquin, in particular for all sequences. Historically, this basin has had more than 70% of outflows delivered to irrigation districts, therefore whiplash sequences tend to disrupt these services more easily. Meanwhile, carryover storage is negatively affected in all basins, but more noticeably in the Merced and Stanislaus basins, with losses of 7-60% and 15-31%, respectively, due to their small overall storage capacity. The small reservoirs in the upper watersheds and inflexible operating flood control rules constitute a challenge to accommodate whiplash impacts in the region. These results show heterogenous sensitivities of flood control releases, environmental flows and agricultural/urban deliveries with projected climate whiplash conditions, with varying degrees of annual, time and volumetric reliability. These services compete for scarce water supply within the low-elevation terminal dams in each basin. This analysis identifies perspectives on the challenges and risks of regional climate whiplash effects and adaptation strategies to include extremes and their impacts on water allocation to human and environmental purposes.
Using GAN for Imputation of Missing Recorded Data to Improve Groundwater Level Prediction Based on Deep Learning Methods
Authors: Chen Hsin Yu , Lo Wei-Cheng , Huang Chih-Tsung
Published: February 2023

　　The development of civilization and the preservation of environmental ecosystems are strongly dependent on water resources. Typically, the insufficient supply of surface water resources for domestic, industrial, and agriculture needs is often supplemented by the ground water resources. However, the groundwater is a natural resource that must be accumulated over many years and cannot be recovered after a short period of recharge. Therefore, the long-term management of groundwater resources is an important issue for the sustainable development. The accurate prediction of groundwater levels is the first step to evaluate the total water resources and its allocation.
　　However, in the process of data collection, data may be missing due to various factors. Thus, retracting the missing data is a main problem which any research field must deal with. It has been well known that to maintain the data integrity, one of the effective approaches is to choose missing value imputation (MVI) for tackling the problem. In addition, it has been demonstrated that the method of the machine learning may be a better tool. Therefore, the main purpose of this study is to utilize a generative adversarial network (GAN) that consists of a generative model and a discriminative model for imputation. Our result shows that GAN can improve the accuracy of water resource evaluations.
　　In the current study, two interdisciplinary deep learning methods, Univariate and Seq2val, are used for groundwater level estimation. In addition to addressing the significance of the parameter conditions, the advantages and disadvantages of these two models in hydrological simulations are also discussed and compared. Finally, Seq2seq is employed to examine the limit of the models in long-term water level simulations. Our result suggests that the interdisciplinary deep learning approach may be beneficial for providing a better evaluation of water resources.
Keywords: GAN，CNN，LSTM，Imputation，Groundwater prediction
Detecting seismic velocity change by noise-based direct surface wave
Authors: Liu Zhiqiang
Published: February 2023

Recently, advances in the ambient noise analysis provide new ways to detect the velocity changes in the volcanic region by measuring the time delay of the daily cross-correlation functions (CCFs). Despite abundant studies on coda waves, studies examining the direct surface waves are relatively rare because of the influence of passive noise sources. However, direct surface waves have stronger energy and carry depth information, which can be obtained by the dispersion inversion. The direct surface waves' propagation direction along the great circle path is also beneficial for conducting tomography by finding a stable passive noise source, which is key to extracting depth-dependent velocity changes. Here, we used direct surface wave to detect velocity change caused by 2018 KIlauea volcano eruption and 2019 Ridgecrest earthquake. The results show that the noise-based direct surface wave is a powerful tool to study the change of earth medium caused by geological hazards, and can accurately obtain the information of the lateral position and depth position of the change. 
Rare earth elements and Yttrium (REY) abundances and distribution characteristics depending on lithofacies in the South Pacific sediment
Authors: Kim Yuri , Hong Sung Kyung , Kim Yoon-Mi , Lee Changyoon , Hong Seok-Hwi
Published: February 2023

Since rare earth elements and Yttrium (REY) were considered critical resources in modern technological and economic industries, deep-sea sediments in the world oceans have started to gain attention as an essential source of REY. In particular, recent studies have discovered that deep-sea sediments in the western North Pacific Ocean near Minamitorishima island have the highest REY concentrations (over 5,000 ppm of REY). In this study, our goals are to identify the existence potential of REY-rich sediment in the South Pacific Ocean and investigate REY abundance and distribution characteristics depending on lithofacies. We acquired the sediment samples from seven sites (U1365, U1366, U1367, U1368, U1369, U1370, U1371) recovered from Integrated Ocean Drilling Project (IODP) Expedition 329. The sediment samples were analyzed for bulk chemical composition, mineral composition, and sedimentary facies. The results indicate that REY concentrations ranged from 53 to 4,177 ppm. U1365 and U1366 sediments showed extremely high REY abundances over 2,000 ppm. On the other hand, U1368 and U1371 sediments showed the lowest contents of REY, less than 200 ppm. Based on the geochemical results, the sediments were divided into six lithofacies: Bioapatite-rich clay, Fe or Mn-rich clay, zeolitic clay, pelagic clay, siliceous ooze, and calcareous ooze. Bioapatite-rich clay with high P2O5 content contained the highest REY peak layers (1,141 ~ 4,177 ppm). We observed abundant fish teeth debris in the sediments composed of biogenic calcium phosphate. Fe or Mn-rich clay contained an average of 1,073 ppm of REY, indicating the second-highest abundance among the six lithofacies. XRD analysis and wet sieving results suggested that Fe or Mn originated mainly from goethite derived from hydrogenous and hydrothermal origins. Zeolitic clay and pelagic clay contained an average of 729 ppm and 344 ppm of REY, respectively. In addition, siliceous ooze and calcareous ooze showed an average of 152 ppm and 130 ppm of REY, respectively. These results imply that clay deposits are expected to have high REY contents than biogenic ooze. In addition, it implies that the main host phases of REY from deep-sea clay in the South Pacific are bioapatite and Fe or Mn (oxyhydr)oxides.