bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Water Resource Availability and Utilization in the Mahi River Basin: A Comprehensive Analysis using Water Accounting Plus (WA+)
Authors: Patle Pooja , Sharma Ashutosh
Published: March 2024

Water resource evaluation and management rely heavily on detailed and well-classified data on water supply, demand, consumption, and withdrawals. The Water Accounting Plus (WA+) framework provides an effective platform to anticipate water flows by integrating remote sensing data to analyze water flows within a basin while accounting for land use. For the Mahi River, we used the WA+ framework to get insights about water inflows, outflows, consumption, withdrawals, and storage changes. The WaterPix (soil moisture water balance) model was employed to simulate hydrological processes at the pixel level. Here, we also employed blue and green water estimates to classify between irrigated and rainfed regions. The present state of the basin's water resources was also computed using performance indicators. As per our study, the Mahi River basin experiences water scarcity and heavily depends on groundwater (GW) for agriculture. From 2012-2020, there has been an average annual outflow of 20.65 BCM, with average annual flows of exploitable and available water being 34.07 BCM/year and 30.38 BCM/year, respectively. Furthermore, the average vertical GW recharge and outflow were 17.47 BCM/year and 21 BCM/year, respectively. Though, the average surface water (SW) withdrawal was lower and concentrated in a few regions. The outcomes from the GW sheets demonstrated a considerable dependence on GW, with 95% of the used flow coming from GW. Notably, over the study period, there was a 25% reduction in water storage, emphasizing the challenges of excessively using GW for irrigation and the decrease in water storage within the Mahi River basin. The conclusions of our study give local and national authorities important new information that they can use to spot regions with poor water management practices and create water management strategies and programs that are suitable for the basin's requirements.   
Urban hydrogeologic uncertainty characterisation to evaluate risk of groundwater flooding
Authors: Ntigkakis Charalampos , Birkinshaw Stephen , Stirling Ross , Thomas Brian
Published: March 2024

Groundwater flooding within the urban infrastructure can play a major role in determining the resilience of urban environments. Urban groundwater models can be used to simulate the complex interactions between surface water and groundwater within the urban system and can be developed to jointly account for groundwater-surface water processes and subsurface characterization. They can be used to simulate potential groundwater flooding and help understand the role of groundwater in urban resilience to climate change. However, urban groundwater is a component of the wider urban water system that has traditionally been overlooked, and the complex interactions between surface water and groundwater may obscured by urban infrastructure and its influence on groundwater flow. Furthermore, the subsurface characterisation is an integral part of any groundwater model, however it’s influence on model performance is not yet fully understood. Therefore, the inherent complexities of the urban environment, combined with the scarcity of appropriate groundwater and subsurface data, can lead to increased model uncertainty. It is argued that robust urban groundwater modelling depends on a strong conceptual understanding of the groundwater system, and constraining the uncertainty in the subsurface characterisation.This project aims to assess model sensitivity to the geological interpretation in simulating groundwater dynamics that represent regions of groundwater flooding. It accounts for uncertainty in the subsurface information to develop an ensemble of different geological interpretations and evaluate the influence of the subsurface characterisation on groundwater flow model performance, within the Ouseburn watershed in the greater Newcastle upon Tyne area.
High-Cr and High Al chromite from the Nidar Ophiolite Suite, Ladakh, India: implications for its petrogenesis and tectonic evolution
Authors: Kumar Alok , Barman Kalpajyoti , Verma Annapurna , Barman Prasenjit , Malviya Vivek Prakash , Koutsovitis Petros
Published: March 2024

The Nidar Ophiolite Suite (NOS) is well exposed in the Indus Suture Zone that separates the Indian plate in the south from Asia to the north, where the ophiolite sequence is best exposed in the Nidar Valley and the outcrop is elongated NW-SE along the regional tectonic trend. The Nidar ophiolite contains both mantle and crustal sections. The mantle section includes ultramafic rocks, namely lherzolite, harzburgite, dunite and serpentinized peridotite with chromite. In the present study, our focus is on chromium-spinel (chromite), which is a common mineral in ophiolitic rocks, and the study of this mineral from the mantle sections of NOS ophiolites can shed light on their petrogenetic origin and tectonic setting.NOS contains disseminated chromite grains in mantle harzburgites and podiform chromitites associated with dunites and serpentinized peridotites. Due to alteration, most chromite grains display compositional zoning, but the fresh cores preserve primary igneous compositions. Podiform chromitites in the NOS dunites and serpentinized peridotites are compositionally similar to typical ophiolitic chromitites elsewhere. NOS chromite samples exhibit two clusters based on Mg and Cr numbers, indicating two different chromite formation stages.They are in the initial chromite precipitating stage and later form chromite pods due to the accumulation of chromite crystal precipitation. Chromite from can be texturally and chemically classified into two main types: primary high-Al (spinel Cr# < 0.67) and high-Cr (spinel Cr# > 0.75) chromite. High Cr/Al ratios of the investigated spinel cores (Cr# 0.7– 0.81) point to a higher degree of partial melting of the depleted mantle source. Low (Cr# 0.27-0.4) indicates a lower degree of partial melting. MORB-like tholeiitic melt generated during proto-forearc spreading at the onset of subduction leads to the generation of high-Al chromite. In contrast, the latter was formed from boninitic melts resulting from the high degree melting of the sub-arc depleted mantle in slab-derived fluids at a mature-arc stage.However, chromite grains in the peridotites show mixed MORB and arc signatures. Thus, the mineralogy and geochemistry of the NOS peridotites suggest that the chromite in the NOS formed in a forearc tectonic setting during a reaction between boninitic melts and MORB-type harzburgite in a supra-subduction zone (SSZ) mantle wedge.
Indoor 222-Rn Modeling in Data-Scarce Regions: An Interactive Dashboard Approach for Bogotá, Colombia
Authors: Dominguez Duran Martín , Sandoval Garzón María Angélica , Huguet Carme
Published: March 2024

Radon (222Rn) is a naturally occurring gas that represents a health threat due to its causal relationship with lung cancer. Despite its potential health impacts, several regions have not conducted studies, mainly due to data scarcity and/or economic constraints. This study aims to bridge the baseline information gap by building an interactive dashboard that uses inferential statistical methods to estimate indoor radon concentration’s (IRC) spatial distribution for a target area. We demonstrate the functionality of the dashboard by modelling IRC in the city of Bogotá, Colombia, using 30 in situ measurements. IRC measured were the highest reported in the country, with a geometric mean of 91 ±14 Bq/m3 and a maximum concentration of 407 Bq/m3. In 57 % of the residences RC exceeded the WHO's recommendation of 100 Bq/m3. A prediction map for houses registered in Bogotá’s cadastre was built in the dashboard by using a log-linear regression model fitted with the in-situ measurements, together with meteorological, geologic, and building specific variables. The model showed a cross-validation Root Mean Squared Error of 56.5 Bq/m3. Furthermore, the model showed that the age of the house presented a statistically significant positive association with RC. According to the model, IRC measured in houses built before 1980 present a statistically significant increase of 72 % compared to those built after 1980 (p-value = 0.045). The prediction map exhibited higher IRC in older buildings most likely related to cracks in the structure that could enhance gas migration in older houses. This study highlights the importance of expanding 222Rn studies in countries with a lack of baseline values and provides a cost-effective alternative that could help deal with the scarcity of IRC data and get a better understanding of place-specific variables that affect IRC spatial distribution.
Leaf conductance, isohydric strategy, and Ѱ50 shape drought responses of European tree species in a dynamic vegetation model
Authors: Meyer Benjamin F. , Darela-Filho João , Gu Qiaolin , Gregor Konstantin , Krause Andreas , Papastefanou Phillip , Buras Allan , Hesse Benjamin , Asuk Sijeh Agbor , Liu Daijun , Grams Thorsten E. E. , Zang Christian S. , Rammig Anja
Published: March 2024

Increasingly frequent and intense drought events can jeopardize the current and future productivity and health of forests. Consequently, the ability of dynamic vegetation models (DVMs) to simulate drought impacts is paramount to improving their representation of the carbon cycle. To capture the physiological damage inflicted by drought, many state-of-the-art DVMs have implemented representations of plant hydraulic architecture in recent years. Although the understanding of the underlying processes governing hydrodynamic behavior in plants has steadily increased, the parameterization of hydraulic traits for different plant functional types (PFTs) remains a source of uncertainty in model output – in part due to limited data availability.  Here, we use LPJ-GUESS-HYD, an extension of LPJ-GUESS with new parameters and processes to simulate plant hydraulic architecture, isohydrodynamic water-potential regulation, and hydraulic failure mortality. Using latin hypercube sampling we create 6000 sets of hydraulic parameter combinations based on values found in the literature. Based on these parameter sets, we conduct a comprehensive variance-based sensitivity analysis for a set of 12 common European tree species across 37 sites from the FLUXNET 2020 warm winter dataset, encompassing a wide range of European ecosystems. Subsequently, we determine which parameters and parameter interactions contribute the most to variations in model outputs.  Our results indicate that of the seven parameters used in the hydraulic architecture model of LPJ-GUESS-HYD, only a few have a significant effect on the model outcomes. More specifically, Ѱ50, the water potential at which 50 percent of conductance is lost, and maximum specific leaf conductance had the largest impact on simulated processes. Parameters related with the isohydric strategy of plants, had a lesser but still substantial role in shaping the model output.  These results suggest that certain hydraulic parameters – and combinations thereof –  play a disproportionate role in modulating simulated forest fluxes and states in LPJ-GUESS-HYD. Specific parameterization choices can drastically alter model performance, including whether PFTs can survive in a given climate or not. Aside from encouraging careful consideration of the available trait data when parameterizing new PFTs, our results may guide future experiments in choosing which hydraulic traits to focus on.
The impact of atmospheric forcing on wintertime sea-ice lead patterns in the Southern Ocean
Authors: Dubey Umesh , Willmes Sascha , Frost Alexander , Heinemann Gunther
Published: March 2024

Sea-ice leads are narrow, linear fractures in sea ice, and are an important basis for understanding the mechanism of the atmosphere-sea ice-ocean system in the Southern Ocean. We use monthly sea-ice lead frequencies based on satellite thermal imagery with 1 km2 grid resolution to investigate potential causes for the observed spatial and temporal variabilities of sea-ice leads during wintertime (April-September), 2003-2023, using ERA5 winds and sea level pressure, as well as climate indices El Niño–Southern Oscillation (ENSO) and Southern Annular Mode (SAM). The presented investigation provides evidence for correlations between mean monthly lead frequency and monthly wind divergence, as well as monthly sea level pressure across the majority of the circum-Antarctic regions (significantly in the Weddell Sea, Ross Sea and Amundsen & Bellingshausen Sea). Furthermore, our investigation evaluates the influence of wintertime ENSO and SAM on sea-ice lead patterns in the Southern Ocean. Results reveal a positive correlation between sea-ice leads and SAM, in the Weddell Sea and specific regions of the Ross Sea. Moreover, a positive correlation is found between sea-ice leads and ENSO, particularly in the Ross Sea, Western Pacific Ocean, and certain portions of the Indian Ocean. While the driving mechanisms for these observations are not yet understood in detail, the presented results can contribute to opening new hypotheses on atmospheric forcing and sea-ice interactions. The contribution of atmospheric forcing to regional lead dynamics is complex, and a more profound understanding requires detailed investigations in combination with considerations of ocean processes. This study provides a starting point for further research into the detailed relationships between sea-ice leads and atmosphere, ocean, combined effect of ENSO-SAM, respectively in the Southern Ocean.
Mechanism and Forecast Potential of North Pacific Marine Heatwaves inferred from Adjoint Sensitivities
Authors: Wang Xiaoxue , Köhl Armin , Stammer Detlef
Published: March 2024

The increasing frequency and intensity of heatwave events have led to a significant rise in heat-related threads on land and in the ocean during recent years. A classic example of a marine heat wave (MHW) is the 2014 – 2016 warm event that spread across the northeastern Pacific (NEP) Ocean—an event that researchers coined “the blob”. Here we use an adjoint sensitivity approach to shed new light on potential causes for reoccurring NEP marine heatwaves events in the region of the NEP. The study is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint, for which the mean sea surface temperature (SST) of different target regions (region 1: 145°~ 160°W, 48°~ 56°N; region 2: 130°~ 145°W, 40°~ 48°N) and different target years (e.g. year 2014) was set as objective function. The adjoint sensitivities show that during the year of emergence, air-sea turbulent surface heat flux is the dominant atmospheric driver. The horizontal temperature advection, i.e., the impact of the basin-wide ocean circulation, is found to be less important, but might act as a preconditioning of MHW through climate oscillations (e.g. NPGO). Because atmospheric forcing anomalies occurring within the 18 months prior to the MHW event play a particularly critical role in driving the overall response locally through air-sea interactions, the leading 18 month atmospheric conditions in the central North Pacific can be considered as predictive signals for later marine heatwave events. Based on our preliminary findings, it can be concluded that 2024 may not be a heatwave year for NEP region. 
Rainfall partitioning by vegetation in China
Authors: Zhang Yafeng , Yuan Chuan , Chen Ning , Levia Delphis
Published: March 2024

Rainfall partitioning into stemflow, throughfall, and interception loss by vegetation alters hydrological and biogeochemical fluxes between vegetation and soil, and further affects water and nutrient balances at local, catchment, and regional scales. Here, we compiled a comprehensive dataset of rainfall partitioning by vegetation (forests, shrublands, croplands, and grasslands) in China. Based on this dataset, we delineate the general characteristics of rainfall partitioning in China from field observations. We summarize the best-fit functions reported for rainfall partitioning fluxes as a function of rainfall amount, as well as the rainfall thresholds for throughfall and stemflow initiation. We explore the patterns of the proportions of stemflow, throughfall, and interception loss to the gross rainfall across vegetation types in China. We determine whether and to what extent the chemical composition of rainwater is altered during rainfall partitioning processes. We use a machine learning method (boosted regression trees) to model the relative effects of cross-site biotic and abiotic predictors on each of the rainfall partitioning fluxes (%) and on the magnitude of chemical alteration in throughfall and stemflow. Our study avails a global readership to the findings of a large cache of Chinese studies that have been inaccessible hitherto, would aid in an accurate estimation of water and nutrients budget in vegetated ecosystems worldwide, and are helpful for making viable strategies to enhance forestry water resources management.
Comparison between model and observational cloud fraction adjustment using explainable machine learning
Authors: Jia Yichen , Andersen Hendrik , Cermak Jan
Published: March 2024

This ongoing study uses machine learning to quantify and compare observation- and global climate model-based sensitivities of cloud fraction (CF) for marine boundary layer clouds (MBLCs) to atmospheric aerosols. In addition, differences in the meteorological influence on these sensitivities between the model and observation are examined.Aerosol-cloud interactions in MBLCs remain one of the most substantial sources of uncertainties in climate simulations. Recent studies have reported that climate forcing from an increase in low-level liquid cloud fraction due to aerosol perturbations may be dominant. However, the impact of ambient meteorological conditions on the aerosol influence on CF continues to pose challenges as their covariability and interactions obscure the quantification of the aerosol–CF relationship.We established a data-driven framework based on cloud droplet number concentration (Nd, as a proxy for aerosol) and CF retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological parameters from the European Centre for Medium-Range Weather Forecasts Reanalysis v.5 (ERA5). The eXtreme Gradient Boosting (XGBoost) machine learning is applied to the daily collocated MODIS-ERA5 data (2011-2019) from 60°N to 60°S to predict CF with Nd and meteorological predictors. The Nd–CF sensitivity and its dependence on meteorological factors are quantified by SHapley Additive exPlanation (SHAP) values and SHAP interaction values. We found that both CF sensitivities and their interactions with meteorology derived from the SHAP approach exhibit distinct and coherent regional characteristics.The ongoing work is intended to implement an identical XGBoost-SHAP setup on outputs from the ICOsahedral Non-hydrostatic-Hamburg Aerosol Module (ICON–HAM) global atmospheric-aerosol model, and to compare the magnitudes and geographical patterns of the sensitivities and interactive effects derived from observations with those from ICON-HAM. Discrepancies may point to the physics parameterization schemes in ICON-HAM which may need further evaluation of their representativity with respect to relevant processes. This novel explainable machine learning framework can potentially provide insights into parameterization tuning and enhance our knowledge of the complex aerosol-cloud-climate system.
Operational soil moisture-based threshold for the assessment of landslide occurrence over Umbria region, central Italy
Authors: Ciabatta Luca , Galeazzi Sara , Brocca Luca , Ponziani Francesco
Published: March 2024

Landslides are one of the most dangerous natural hazards, causing every year fatalities, considerable damage and relevant economic losses. Early warning systems (EWS) for rainfall-induced landslides represent an useful tool for mitigating the impact of such hazard. The Umbria Regional Civil Protection Service developed a system able to take into account also the soil moisture conditions over the regional territory based on set of soil moisture-based thresholds. By identifying the soil saturation conditions before and after the rainfall event (obtained through a hydrological model), it has been seen that most of the activations occurred when the soil reached saturation. In this way, an alert can be issued when the amount of rainfall needed by the soil to reach saturation is observed. The amount of rainfall needed to reach saturation has been calculated through the definition of soil hydraulic parameters and the saturation degree at the start of the rainfall event. The obtained threshold is based on soil characteristics and it is independent by the input data (no need for recalibration or threshold adjustment). The proposed methodology is able to identify correctly most of the proposed events with a very limited amount of false alarms considering all the rainfall events occurred during the 1989-2022 period. Moreover, the use of high-resolution rainfall and soil moisture satellite-derived products has been tested for a limited time window to test whether these new sources of information can be used with benefit, even for operational purposes.
Complex flow of mud over cold and frozen surfaces in low pressure: Insights from physical experiments
Authors: Krýza Ondřej , Brož Petr , Fox-Powell Mark , Sylvest Matthew , Patel Manish
Published: March 2024

Extensive fields of sub-kilometre- to kilometer-scale edifices have been discovered on Mars and the process of subsurface sediment mobilization has been proposed as their formation mechanism. However, as igneous volcanism might form similarly looking features, it is currently unknown how they formed. Previously it was shown that when low viscosity muds would be exposed over cold sandy surfaces under the reduced martian atmospheric pressure, such muds would behave in similar fashion as Pahoehoe lavas on Earth. This shows how difficult it can be to distinguish mud volcanoes from igneous volcanoes based on morphology alone.However, the composition of the propagating mud as well as of the substrate might be crucial to the overall dynamics and the finite pattern of developed flow features on Mars. On the Red planet, a wide range of substrates is expected to be present globally; covering a transition from dry and warm unconsolidated regolith to permafrost with a higher content of ice. Therefore, to get a better understanding of the behavior of muds exposed to reduced atmospheric pressure and the resulting shapes of putative martian mud volcanoes, we performed a set of experiments, in which we studied the effect of warm, pre-cooled or continuously frozen substrates on general flow properties. We also considered different granular materials, transitional compositions or their spatial sequencing, using mainly silica sand, flour or pure water ice. All tested scenarios showed a significantly contrasting style in mud spreading over the various surfaces. The streaming style and finite morphology of the flows differed from fast, flat spreadings, with the levitation component of transport, to slow and narrow flows with a characteristic ropy pattern. The most important observed feature was an alternation of melting and recrystallization of the ice substrate, caused by interplay between the latent heat release and consumption in between the mud and substrate. Importance of ice in the substrate was also shown through rapidly extended boiling potential and prolonged flow ability of mud, probably due to combination of phase transitions in mud-permafrost and mechanical properties of the substrate itself. These findings are interesting for an evaluation of mud behavior in various environments occurring on Mars or other bodies within the Solar system where the sedimentary volcanism or cryovolcanism might be expected.
Exploring Unsupervised Clustering of Seismic Noise Sources in Urban DAS Data: A Methodology Guide
Authors: Kiel Antonia , Hadziioannou Céline , Hammer Conny
Published: March 2024

Seismic measurements record the superposition of many seismic sources, with anthropogenic ones dominating frequencies above 1 Hz. While the anthropogenic seismic vibrations in urban areas are too small to influence daily human life, measurements in high precision physics experiments, such as those carried out at the Deutsche Elektronen-Synchrotron (DESY) particle accelerators in Hamburg can be negatively influenced by these vibrations. To gain insight into the seismic wavefield at DESY, distributed acoustic sensing measurements were started in the WAVE initiative (www.wave-hamburg.eu). The goal of this study is to utilize unsupervised machine learning tools to detect and identify different anthropogenic seismic noise sources. Two different approaches were tested: the seismic measurements are clustered using a temporal average of one second on time-frequency representations and a deep embedded clustering technique. For the first method, the clustering methods fuzzy-c-means, Gaussian mixture model (GMM), hierarchical clustering and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were used. The clustering performance of all methods was compared using car signals on a short DAS fiber section as our ground truth data. Furthermore, the usage of spectrograms and continuous wavelet transforms was compared on the ground truth data set, with the continuous wavelet transform giving better results.In a next step, the best-performing clustering methods GMM and HDBSCAN of the temporal average and deep embedded clustering were applied to the entire 12 km fiber to cluster seismic noise sources. Based on the results, the respective advantages and disadvantages of the different approaches were determined. The study was concluded with a "recipe'' on how to approach unseen DAS data based on scientific objectives and physical properties of interest, paving the way for an optimized DAS data analysis. 
The impact of Atlantic Multidecadal Variability on Baltic Sea temperatures limited to winter
Authors: Börgel Florian , Gröger Matthias , Meier H. E. Markus , Dutheil Cyril , Radtke Hagen , Borchert Leonard
Published: March 2024

We analyze multidecadal temperature fluctuations of the Atlantic Ocean and their influence on Northern Europe, focusing on the Baltic Sea, without a priori assuming a linear relationship of this teleconnection. Instead, we use the method of low-frequency component analysis to identify modes of multidecadal variability in the Baltic Sea temperature signal and relate this signal to the Atlantic climate variability. Disentangling the seasonal impact reveals that a large fraction of the variability in Baltic Sea winter temperatures is related to multidecadal temperature fluctuations in the North Atlantic, known as Atlantic Multidecadal Variability (AMV). The strong winter response can be linked to the interaction between the North Atlantic Oscillation and the AMV and is maintained by oceanic inertia. In contrast, the AMV does not influence the Baltic Sea’s summer and spring water temperatures.
Country-to-country exchanges of PM2.5 related mortality over the Mediterranean
Authors: Akritidis Dimitris , Pozzer Andrea
Published: March 2024

Fine particulate matter (PM2.5) is detrimental to human health. Long term exposure to ambient PM2.5 is associated with excess mortality from respiratory, cardiovascular, and other non-communicable diseases. The mixture of anthropogenic and natural aerosols, as well as the prevailing atmospheric conditions, make the broader Mediterranean region one of the most polluted areas around the world. The national anthropogenic emissions and demographics, as well as the atmospheric pollution transport pathways shape the import and export of PM2.5 and associated mortality in a country level. Here, we perform an assessment of the anthropogenic PM2.5 related excess mortality exchanges between countries of the broader Mediterranean region using the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) and the GBD (Global Burden of Disease) 2019 methodology for the mortality calculations. The EMAC simulations are carried out in a T106 horizontal resolution (equivalent to 1.1 x 1.1 degree at the equator) for the year 2015, nudged towards the ERA5 dynamics, and following a zero-out approach (turn-off) for the CEDS (Community Emissions Data System, 2020-v1) anthropogenic emissions of each country. The results indicate that the hot spot countries of anthropogenic PM2.5 related mortality import and export are mainly driven by the countries’ population and emissions, respectively, and their relative location.       DA acknowledges support for enhancing the operation of the National Network for Climate Change (CLIMPACT), National Development Program, General Secretariat of Research and Innovation (2023ΝΑ11900001 - Ν. 5201588). AP acknowledges the European Commission Horizon Europe project FOCI (grant agreement No 101056783).
Study on year-end water level optimization of  multi-year regulation reservoirs under uncertain inflow conditions
Authors: Li Jieyu , Zheng Jiayun , Li Hang , Wang Yuanjian
Published: March 2024

The year-end water level of multi-year regulation reservoirs is key in harmonizing current-year and future power generation benefits. Taking the multi-year regulating Longyangxia Reservoir and Liujiaxia Reservoir in the upper reaches of the Yellow River as a case study, a multi-objective stochastic optimization model for cascade reservoirs considering the uncertain inflow was established. Then, the best scheme from the Pareto solution set of the current-year power output and the year-end water level was obtained based on the TOPSIS decision-making method. Finally, the effects of runoff frequency and initial water level on year-end water level and power output were investigated, and the reliability of year-end expected water level on multi-year power output was verified. The results show a competitive relationship between the year-end expected water level of Longyangxia Reservoir and the annual expected power output of Longyangxia Reservoir and Liujiaxia Reservoir under the uncertainty inflow scenarios. The lower the frequency of runoff and the higher the year-start water level to Longyangxia Reservoir, the higher the year-end water level of Longyangxia and the higher the power output of Longyangxia Reservoir and Liujiaxia Reservoirs. The optimal year-end water level of Longyangxia Reservoir calculated by the stochastic optimization model should be controlled between 2580~2590 meters, which greatly reduces the range of year-end water level under the current dispatching mode. When the optimal year-end expected water level obtained by stochastic optimization is used to control Longyangxia Reservoir, the reliability of guaranteeing the power generation benefit is above 98%.Keywords: multi-year regulating reservoir; year-end water level; uncertainty; stochastic optimal operation
Probabilistic Seismic Hazard Assessment of Sweden
Authors: Lund Björn , Joshi Niranjan , Roberts Roland
Published: March 2024

Sweden is a low-seismicity, stable continental region where seismic hazard assessment is non-trivial. Diffuse seismicity, low seismicity rate, few large magnitude earthquakes and little strong motion data makes it difficult to estimate recurrence parameters and determine appropriate attenuation relationships. Here we present a probabilistic seismic hazard assessment of Sweden based on a recent earthquake catalogue which includes earthquakes with magnitudes ranging from -1.4 to 5.9. The large number of events enables recurrence parameters to be calculated also for smaller source areas, in contrast to previous studies, and with less uncertainty. We use recent ground motion models developed specifically for stable continental regions, including Fennoscandia, and calculate hazard using the OpenQuake engine. The results are presented in the form of mean peak ground acceleration (PGA) maps at 475 and 2500 year return periods, hazard curves for four seismically active areas in Sweden and deaggregation for the area of highest hazard. We find the highest hazard in the northernmost part of the country, in the post-glacial fault province. This is in contrast to previous studies, which have not considered the high seismic activity on the post-glacial faults. We find relatively high hazard along the northeast coast and in southwestern Sweden, whereas the southeast and the mountain region to the northwest have low hazard. For a 475 year return period we estimate the highest PGAs to be 0.04 0.05g, in the far north, and for a 2500 year return period it is 0.1-0.15g in the same area. Significant uncertainties remain to be addressed with regards to the intraplate seismicity in Sweden and surroundings, such as the homogenization of magnitude scales, the occurrence of large events in areas with little prior seismicity and the uncertainties surrounding the potential for very large earthquakes on the post-glacial faults in northern Fennoscandia.
A Bayesian framework to model speleothem oxygen isotope data with age uncertainties
Authors: Axelsson Josefine , Nilsson Andreas , Suttie Neil , Sjolte Jesper
Published: March 2024

Age-depth models are widely used to build chronologies from proxy records and are most often implemented for speleothems due to age uncertainties and lower resolutions. In this study, we use a variation of the accumulation rate method to perform a pseudo-proxy reconstruction of large-scale variability in monsoon precipitation using synthetic oxygen isotope records from speleothem sites and the isotope-enabled ECHAM/MPI-OM climate model. We present a probabilistic approach to synchronize speleothems by informative priors of oxygen isotope data and individual independent age constraints. This is achieved by co-estimating the regional δ18O variations through time, where δ18O variability is modeled using Gaussian processes and a Bayesian model is further used for the individual speleothem chronologies. The method is tested using synthetic speleothem data generated from the last millennium-long climate model simulation and corrupted through realistic noise from speleothems in the Indian Ocean region from the SISALv2 database. Through the creation of a millennium-long reconstruction, we aim to study the atmospheric dynamics from the reconstruction over the Asian region to help us further constrain the drivers, responses to, and changes in the variability of the monsoon. By synchronizing the time series of oxygen-isotope data through the incorporation of accurate and realistic depth-dependent age uncertainties, this modeling approach may lead to advancements in handling speleothem data and climate model simulations for regional to global evaluations of variability and past climate reconstructions.
Preliminary investigation of earth dam using multiple geophysical imaging techniques
Authors: Roshdy Eslam , Majdański Mariusz , Marciniak Artur , Oryński Szymon , Popielski Paweł , Kowalczyk Sebastian , Mieszkowski Radosław , Cader Justyna , Trześniowski Zygmunt , Ostrzołek Ireneusz
Published: March 2024

Every dam site must have a complete stability assessment and seismic integrity evaluation under high ground vibration because aging, foundation deterioration, seepage phenomena, internal erosion, cavities, and cracks have resulted in dam damage. Often ERT technique and in-situ measurement in boreholes are used to monitor the state of the dam. In this project, we propose to use variety of seismic techniques both active and passive seismic combined with 3D ERT and spectral GPR measurements on the same profile.
In this study, compression and shear wave velocities were integrated to delineate elastic properties of the dam's materials and evaluate the seismic stability subjected to local soil stiffness and site response at the Rybnik dam and Orzepowice embankment, Poland. To asses those values we used seismic travel time tomography for general recognition of spatial differences, MASW technique to assess water infiltration and reflection seismic imaging to recognize the geological structures under the dam.
Here we present initial results and gathered data. We used standard standalone 3C seismic stations combined with Distributed Accousting Sensing (DAS). As sources both sledgehammer and industrial S wave source were used. This setup allows comparison of multicomponent data for both source types.
We plan to record seismic data on exactly the same lines next year to obtain time laps observation and recognize the different in the medium.  
This research was funded by National Science Centre, Poland (NCN) project number 2022/45/B/ST10/00658.
The impact of floods on plastic pollution
Authors: van Emmerik Tim
Published: March 2024

Reducing plastic pollution requires a thorough understanding of its sources, sinks, abundance, and impact. The transport and retention dynamics of plastics are however complex, and assumed to be driven by natural factors, anthropogenic factors, and plastic item characteristics. Current literature shows diverging correlations between river discharge, wind speed, rainfall, and plastic transport. However, floods have been consistently demonstrated to impact plastic transport and dispersal. Here, we present a synthesis of the impact of floods on plastic pollution in the environment. For each specific flood type (fluvial, pluvial, coastal and flash floods), we identified the driving transport mechanisms from the available literature. We introduce the plastic-flood nexus concept, which is the negative feedback loop between floods (mobilizing plastics), and plastic pollution (increasing flood risk through blockages). Moreover, we assessed the impact of flood-driven plastic transport, and argue that increasing flood resilience also reduces the impact of floods on plastic pollution. In this paper we provide a perspective on the importance of floods on global plastic pollution. We argue that increasing flood resilience, and breaking the plastic-flood nexus, are crucial steps towards reducing environmental plastic pollution.
Geospatial modelling of soil organic carbon density in 3D across the northern circumpolar permafrost region
Authors: Röseler Friedrich , Treat Claire , Heuvelink Gerard
Published: March 2024

The northern circumpolar permafrost region contains up to half of the global soil carbon pool and twice as much carbon as currently is in the atmosphere. At the same time, the Arctic is rapidly warming due to climate change, causing the permafrost to thaw. There is a risk that substantial amounts of soil organic carbon (SOC) may be released into the atmosphere as greenhouse gases during this process. This makes permafrost carbon a potentially strong climate feedback that could further amplify global warming.Currently, only a few studies attempted to quantify this permafrost carbon on a global scale. Despite the advances in estimating how much SOC is stored in the northern circumpolar permafrost region, there are still large uncertainties. Modelling permafrost carbon is particularly challenging due to the scarce availability of reference datasets on SOC content and great subsurface variability in the Arctic environment caused by cryoturbation. The high lateral (i.e. horizontal) and vertical (i.e. along the soil profile) variability results in several obstacles when mapping SOC in permafrost regions.While previous studies on modelling permafrost carbon focused on quantifying its spatial heterogeneity, they lacked in capturing the complex (vertical) distribution of SOC as a function of depth. Furthermore, they often rely on discrete models to estimate the spatial variation. In this work, we focus on providing more accurate high-resolution, continuous global maps of permafrost SOC density using a 3D digital soil mapping approach. Digital soil mapping has shown to be a valuable tool in mapping SOC, as it can better capture the continuous variation of soil properties. Here, we used a random forest machine learning model to predict SOC based on a number of spatial variables representing soil forming factors (such as topographic attributes, climate, carbon age and land cover). The reference dataset that we used to train the model consists of soil profile observations from the permafrost region of the Northern Hemisphere, excluding alpine permafrost. We harmonised this dataset from existing databases and recent studies that provide information on carbon content from soil core measurements. Information on the bulk density was needed to calculate the SOC density and estimated for missing observations using pedotransfer functions. Results indicate that 3D modelling of permafrost carbon produces substantially different results than conventional 2D approaches. Furthermore, accounting for the vertical variation in SOC improves the prediction accuracy.
Exploring the effectiveness of Artificial Intelligence-powered insights in drought study in Nagaland, India
Authors: Kikon Ayilobeni
Published: March 2024

Drought, as a natural calamity, has serious economic and environmental implications, especially as the impacts of climate change continue to escalate globally. In many regions, monitoring and comprehending changes in drought patterns have become imperative. As climate change increasingly influences hydrological cycles, there is a need to grasp and interpret drought behaviour in diverse geographical areas. This study is particularly focused on a landlocked state in the north-eastern region of India, which is characterised by a predominantly monsoon climate with high humidity and an annual rainfall of 1800–2500 mm. The study focuses on the state of Nagaland, India, and is aimed at evaluating the efficacy of artificial intelligence (AI) models such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Genetic-Algorithm Adaptive Neuro-Fuzzy Inference System in predicting drought. For analysing the drought conditions, the Effective Drought Index (EDI) is used. By utilising rainfall data from 1987–2021, the EDI drought index has been computed, recognising the pivotal role of rainfall in comprehending prevailing drought conditions. The drought conditions are categorised from extremely dry to near normal, excluding the wet conditions in the study region. The investigation into the effectiveness of AI in predicting and detecting drought yielded insightful results, highlighting the informative and promising capabilities of AI models. The results of the study facilitate a comparative analysis of the three models, MLP, LSTM, and GA-ANFIS, using the evaluation metrics. The study findings indicate that LSTM exhibits superior prediction accuracy in the study region in terms of its ability to predict drought conditions in the given geographical area. This outcome is crucial for understanding and addressing the impacts of drought. This study contributes to the broader understanding of drought prediction and emphasises how AI models can improve their ability to predict drought conditions, which will ultimately contribute to enhanced water resource management and climate adaptability.
Changes in the Variability and Teleconnections of the Northeastern Tropical Atlantic Upwelling Region around 2
Authors: Lübbecke Joke , Rodríguez-Fonseca Belén , Martin-Rey Marta , Losada Teresa , Mohino Elsa , Polo Irene
Published: March 2024

Sea Surface Temperatures (SST) in the Northeastern Tropical Atlantic upwelling region off Senegal and Mauritania feature pronounced variability on interannual time scales with impacts on the marine ecosystem. While part of this variability results from wind stress and wind stress curl-driven changes in local upwelling, the roles of air-sea heat fluxes, horizontal advection and potentially remotely forced thermocline variations have also been discussed. Here the relative roles of these forcing mechanisms and how they change over the time period from 1958 to 2020 are investigated utilizing reanalysis products as well as output from a general ocean circulation model (NEMO) forced by the atmospheric JRA55-do forcing. In the configuration analyzed (VIKING20X), oceanic resolution is increased to 1/20º over the Northern Atlantic via a two-way nesting approach, allowing for a better representation of the near-coastal upwelling region.Interestingly, while interannual SST variability in the eastern equatorial Atlantic and the Angola Benguela region has decreased since 2000 and is projected to further decrease in the future, there is an increase of SST variability in the Northeastern Tropical Atlantic. To understand this increase, we address the roles of changes in local wind forcing and the connection to the equatorial region via the propagation of equatorial and coastal trapped waves. Along with the altered SST variability, teleconnection patterns related the Northeastern Tropical Atlantic, in particular with the El Niño – Southern Oscillation, also changed.    
Greenhouse gas emissions under nanomaterial co-application with reduced fertiliser input
Authors: Chadwick Jessica , Lynch Iseult , Ullah Sami
Published: March 2024

With the advent of synthetic nitrogen fertiliser, the proportion of reactive nitrogen (Nr) in terrestrial ecosystems has doubled. While this has enabled high crop productivity, it has also triggered mass environmental effects, with almost half of the applied fertiliser lost into air (in the form of N2O or NH3) as well as nutrient runoff and leaching of nitrates into water bodies. Nanomaterials present an opportunity to improve nutrient use efficiency of crops and minimise agricultural pollution via reducing losses. This study screened engineered nanomaterials, including zeolites and metal oxides, to assess their impact on greenhouse gas (CH4, N2O and CO2) emissions when co-applied with NPK (nitrogen, phosphorus and potassium) fertiliser to grow lettuce (Lactuca sativa). The findings show that there are highly differential emissions from soils with nanomaterial co-application with reduced fertiliser application rates. One of the zeolites used, ZSM-5-15, when co-applied with a reduced dose (50% of RB209 nutrient management guide recommendation) of NPK fertiliser, increased N2O emissions relative to reduced NPK fertiliser alone and negative controls. Another zeolite, BEA-19, had limited effect on either NH3 or N2O volatilization, but did reduce the concentration of ammonium in the leachate. Nitrate leaching gradually rose over the course of the 8-week experiment for full NPK fertiliser dose application, reduced NPK dose and negative control. This pattern was altered by BEA-19, triggering elevated nitrate leaching earlier in the experiment, peaking in week 1 compared to week 4 for other treatments. While nanomaterial treatment was able to increase lettuce biomass accumulation compared to full NPK and negative fertiliser treatments, understanding the impact of nanomaterials on N cycling has proven more complex. The mechanism for N loss from soils triggered by ZSM-5-15 application is unknown, potentially through impact on denitrifying enzymes. My work posits that the earlier release of nitrate from BEA-19 application is due to selective binding of the NPK to the nanomaterial surface. More data on nanomaterial endpoints is pending and may help elucidate the nature of this binding and nutrient release mechanisms.