bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural inheritance and the evolution of an incipient rift: interaction between the Eger Graben and the Elbe Zone, Central Europe
Authors: Ulicny David , Cajz Vladimír , Mach Karel , Špičáková Lenka , Machek Matěj , Čech Stanislav , Grygar Radomír , Mrlina Jan , Havlíček Filip
Published: March 2024

The present-day surface morphology and fabric of the lithosphere of west-central Europe in the foreland of the Alpine orogen have been significantly affected by formation of a system of rifts and associated volcanic domains during the Oligocene and Neogene, known as the European Cenozoic Rift System (ECRIS). In order to better understand the geodynamic causes of formation of ECRIS and its volcanism, it is important to improve the knowledge of chronology of tectonic events in the entire ECRIS, and to test the validity of existing palaeostress interpretations. The Oligo-Miocene Eger Rift, so far the least-studied part of ECRIS, has the potential to bring new clues to some persisting controversies.The axis of the Eger Rift roughly follows the trend of a major Variscan lithosphere-scale boundary, the Teplá-Barrandian/ Saxothuringian suture (TSS) formed during the collisional phases about 380-320 Ma. Following the Variscan collision, the lithosphere of the Bohemian Massif was affected by formation of a Late Paleozoic extensional basin system which in the western part of the Bohemian Massif largely follows the NE strike of the TSS. Another major structure in the basement underlying the Eger Rift is the WNW-striking Elbe Zone, with main periods of activity during the Paleozoic and Mesozoic through early Cenozoic.We present a synthesis of presently available structural and stratigraphic data and a resulting first-order interpretation of tectonic evolution of central and eastern Eger Rift. The main data sources were borehole, outcrop, seismic reflection data, targeted field mapping, digital elevation models, and gravity data from both public and industry sources. Several stratigraphic levels (within the Neogene, Cretaceous, and top of Late Palaeozoic) were used as structural datums.Analysis of fault populations in central and eastern Eger Rift shows that overall, the Late Paleozoic fracturation of the upper crust of the Bohemian Massif was key for localization of the main fault systems of the Eger Rift. This includes dextral shearing within the Elbe Zone that affected the basement structural grain responsible for segmentation of the Eger Rift during the Cenozoic. Changes between oblique and orthogonal extension modes are interpreted from the geometries and temporal relationships of key structures - both in time, likely due to a changing regional paleostress field, and in space, due to different orientations of basement structures between the rift segments.This research has been supported by the Czech Science Foundation (GAČR) project 22-13980S.
Enhancing Rainfall Estimates in East Africa by Merging TAHMO Precipitation Gauge Data with Remote Sensing Rainfall Products
Authors: Hoogelander Vincent , van de Giesen Nick , Hut Rolf , Dong Jianzhi , Le Coz Camille , Sserwada George
Published: March 2024

Sub-Saharan Africa heavily relies on remotely-sensed rainfall measurements due to a lack of in-situ rainfall data. While a high number of satellite-based rainfall products do exist, they are typically developed and tested in regions with a high density of ground data. The Trans-African Hydro-Meteorological Observatory (TAHMO) aims to tackle the ground data gap by installing and operating a dense network of weather stations in Sub-Saharan Africa. As part of the TEMBO Africa project, TAHMO data were used to make a new regional rainfall product in East Africa based on the SM2Rain algorithm.  Subsequently, this regional product was merged with a reanalysis product (ERA5) and two MW/IR-based rainfall products (IMERG-L and CHIRPS) based on the Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER). Within this framework, merging weights are based on error variances of the rainfall products determined from quadruple collocation on a pixel-to-pixel basis. The merged product and the individual products are evaluated using data of the individual TAHMO stations. Our findings indicate that the merged product outperforms the individual products in most selected evaluation metrics.  ERA5 has the highest contribution in the merged product, followed by SM2Rain. Both IMERG and CHIRPS have limited contribution in the merged product due to a high error variance. The ultimate goal of this study was to develop a workflow to enhance the accuracy of rainfall measurements in Sub-Saharan Africa by leveraging information from TAHMO data and different existing products, contributing to the improvement of remotely-sensed rainfall measurements in Sub-Saharan Africa.We welcome suggestions on possible improvements and operational implementation, as well as ideas on how to use this merged product to understand the sources of error in satellite-based rainfall measurements in Sub-Saharan Africa. TEMBO Africa: The work leading to these results has received funding from the European Horizon Europe Programme (2021-2027) under grant agreement n° 101086209. The opinions expressed in the document are of the authors only and no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information
Attempts to include geomagnetic anomalies into the existing Romanian Operational Earthquake Forecast
Authors: Moldovan Iren Adelina , Toader Victorin Emilian , Mihai Andrei , Marmureanu Alexandru , Ionescu Constantin
Published: March 2024

Our study analyzes the possibility to include geophysical parameters in the existing OEF (Operational Earthquake Forecasting) application based on the geochemical detected anomalies correlated with short-term changes in seismicity rates and occurrence of medium sized intermediate depth earthquakes.The study aims to decide which of the geomagnetic anomalous signals can be considered to be a reliable precursor of Vrancea, Romania moderate sized earthquakes that occurred in the last decade. The anomalies were observed using different processing methods: polarization, diurnal variation, differential analysis between two stations or simple visualization at only one station and the standard deviation from the mean value.The existing OEF application for the Vrancea area based on geochemical parameters is using the standard deviation, time gradient, cross correlation, and linear regression customized for the geological specificity of the area under investigation. For anomaly detection is used the short-time-average through long-time-average trigger (STA/LTA) method on time-integral data. The daily–seasonal variation of parameters is correlated with atmospheric conditions and temperature in the borehole to avoid false decisions. The probability and epistemic uncertainty of the gas emissions act as input into a logical decision tree.During the study period, in Vrancea seismogenic zone there have been recorded 25 earthquakes with moment magnitude Mw>4.5, at intermediate depth. The Geomagnetic data are obtained from Muntele Rosu (MLR) Seismological Observatory and Plostina (PLOR) of NIEP, situated inside Vrancea seismogenic zone as primary station, and from Surlari (SUA) National Geomagnetic Observatory, part of the International Real-time Magnetic Observatory (Intermagnet), as remote station, unaffected by medium size earthquake preparedness processes. We have assumed that the zone of effective manifestation of the precursor deformations is a circle with the radius taken from the equation of Dobrovolsky, 1979.  Geomagnetic indices taken from GFZ (https://www.gfz-potsdam.de/kp-index) were used to separate the global magnetic variation from possible local seismo-electromagnetic anomalies, that might appear in a seismic area like Vrancea zone and to ensure that observed geomagnetic fluctuations are not caused by solar-terrestrial effect.Acknowledgments: This paper was carried out within AFROS Project PN-III-P4-ID-PCE-2020-1361, 119 PCE/2021, supported by UEFISCDI, Nucleu Program SOL4RISC, supported by MCI, project no PN23360201, and PNRR- DTEClimate Project nr. 760008/31.12.2023, Component Project Reactive, supported by Romania - National Recovery and Resilience Plan
Laboratory study of dust impact ion free expansion
Authors: Nouzak Libor , Fontanese John , Edwards Kathryn R. , Horanyi Mihaly , Sternovsky Zoltan
Published: March 2024

This study presents the investigation of the angular and velocity distribution of ions expanding freely from a dust impact generated plasma plume. The characteristic angular and velocity distributions are relevant for the design of dust detector and analyzer instrument, or for the interpretation of electric field antenna signals generated by dust impacts on the spacecraft body.  Iron dust particles of micron and sub-micron size are accelerated to velocities 2–40 km/s using the electrostatic dust accelerator operated at the University of Colorado. A unique experimental setup with a delay line detector (DLD) is used to measure the properties of the expanding ion cloud. The DLD provides the position and the time of impact for individual ions originating from the tungsten impact plate. The angular distribution of ions with respect to target normal is calculated from these positions. The velocity distribution is determined from arrival times of the ions on the detector. The experimental results show that the impact-generated ions expand in the form of a plume with angular distribution following a cosine law and half angle 25°. The velocity distribution consists from different parts which correspond to expansion of a distinct plasma components. The slowest component of the distribution has the most probably speed around 5 km/s and the fastest component around 30 km/s. The contribution of the components changes according to velocity of dust.
Anisotropic structure of the central European lower crust from ambient noise inversion
Authors: Kvapil Jiří , Plomerová Jaroslava , Working Groups and AdriaArray Seismology Group AlpArray, AlpArray-EASI, PACASE
Published: March 2024

Previous research of the Bohemian Massif (BM) crust with the use of ambient noise tomography (ANT) indicates a transversely isotropic structure of the lower crust (Kvapil et al. 2021). In this study, we have developed a new approach for evaluations of localised seismic anisotropy by travel time integration method.The method calculates synthetic Rayleigh (vertical ZZ correlation) and Love (transverse TT correlation) velocities and derives the vSH/vSV from the initial 3D isotropic vSV model. The higher ratio of measured vSH/vSV to synthetic vSH/vSV indicates the existence of velocity anisotropy in the lower crust of the BM in the reference ANT model (Kvapil et al., 2021). The new method evaluates azimuthal variations of the synthetic parameters due to heterogeneities reflecting local geology effects and corrects the observed velocity ratios. Then the 1D stochastic joint (ZZ, TT) inversion is applied to retrieve the depth dependence of the velocity ratio. We use cross-correlation of ambient noise and earthquake data from seismic stations included in the AlpArray, PACASE, and Adria Array passive seismic experiments and data from the PASSEQ experiment, which complement the sparse coverage in the northern part of the BM.Seismic anisotropy records the stress/strain conditions of each originally independent tectonic microplates during the formation of the BM crust. We demonstrate that synthetic modelling over the reference isotropic velocity model is an efficient tool for extracting radial and azimuthal shear velocity anisotropy in the lower crust directly from Rayleigh and Love wave dispersion curves. Regions with consistent parameters of seismic anisotropy correlate well with the major tectonic units of the BM. We show that variations in azimuthal and radial anisotropy of the lower crust on a regional scale can provide constraints for the reconstruction of geodynamic processes during the formation of the BM. We present complementary studies of the anisotropic structure of the mantle lithosphere in contributions by Zlebcikova et al. (GD7.1, EGU 2024), which shows an anisotropic model of the upper mantle derived from a 3D coupled anisotropic-isotropic teleseismic tomography (code anitomo), and in contributions by Vecsey et al. (GD7.1, EGU 2024), suggesting a new method for evaluation of anisotropy from shear waves.
Benchmarking seismic phase associators: Insights from synthetic scenarios
Authors: Puente Huerta Jorge Antonio , Münchmeyer Jannes , McBrearty Ian , Sippl Christian
Published: March 2024

In seismology, accurately associating seismic phases to their respective events is crucial for constructing reliable seismicity catalogs. This study presents a comprehensive benchmark analysis of five seismic phase associators, including machine learning based solutions, employing synthetic datasets tailored to replicate the seismicity characteristics of real seismic data in a crustal and a subduction zone scenario.The synthetic datasets were generated using the NonLinLoc raytracer, using real station distributions and velocity models and simulating a large range of seismic events across different depths. In order to generate sets of picks with quality and diversity similar to a real-world dataset, some modifications such as adjustments to arrival times simulating picking errors, selective station exclusion, incorporation of false picks, were included. Such a controlled environment allowed for the assessment of associator performance under a range of different conditions.As part of project MILESTONE, we compared the performance of five state-of-the-art seismic phase associators (PhaseLink, GaMMA, REAL, GENIE, and PyOcto) across multiple scenarios, including low-noise environments, high-noise background activity, out-of-network events, and complex aftershock sequences. Each associator's accuracy in identifying and associating true events amidst noise picks and its ability to handle overlapping sets of arrival times from different events were rigorously evaluated.Additionally, we conducted a systematic comparison of the advantages and disadvantages of each associator, attempting a fair and unbiased evaluation. This included assessing their processing times, a critical factor in operational seismology. Our findings reveal significant differences in the precision and robustness of these associators.This benchmark study not only underscores the importance of robust phase association in seismological research but also paves the way for future enhancements in seismic data processing techniques. The insights gained from this analysis are expected to significantly contribute to the ongoing efforts in seismic monitoring and hazard assessment, particularly in the realm of machine learning applications.
A WEF NEXUS tool for integrating soil moisture and meteorological IoT data.
Authors: Straface Salvatore , Brunetti Guglielmo Federico Antonio , Maiolo Mario , Brunetti Giuseppe , Scozzari Andrea
Published: March 2024

According to the European Parliamentary Research Service, agriculture is a major user of ground and surface water in the Mediterranean region. Agriculture accounts for more than 40% of water use in the EU and most freshwater abstraction is for agricultural use. Water applied as irrigation enables crop production in arid regions and replenishes soil moisture in humid regions when rainfall during the growing season is insufficient. It helps to increase crop productivity, but it also poses a threat to the conservation of water resources. The issue of water scarcity therefore requires careful consideration of the trade-off between increased agricultural productivity and the degradation of water resources. Ensuring food security in the face of climate change requires improved water management capacity.Nowadays, the interest in estimating the average soil moisture content (SM) and its variability is a cross-cutting issue in many areas of scientific research in the natural sciences. The water contained and transiting in the vadose zone is involved in and plays a central role in many natural processes related to plant physiology and agriculture, soil microbial activity, groundwater pollution and, more generally, eco-hydrological and bio-geochemical processes.SM depends either on soil characteristics, i.e. hydraulic conductivity, porosity, soil texture, etc., or on meteorological forcing, i.e. precipitation, temperature, evapotranspiration, etc. Knowing the soil characteristics, a numerical model for unsaturated flow (Hydrus-1D) can be calibrated using time-lapse measurements of meteorological forcing and SMs obtained by IoT enabled sensors. After the calibration, the numerical model can generate a very large number of SMs for many meteorological forcings. With these data, a WEF Nexus tool, based on a machine learning approach, integrates the SM and meteorological IoT data to estimate crop water demand.This research aims to develop and test a building block for possible future water demand estimation tools. As a future perspective, further development and integration may lead to new tools with user-friendly interfaces.
High-resolution meteorological CO2 enhancements of German metropolitan areas using WRF
Authors: Pilz Lukas , Lüken-Winkels Christopher , Gałkowski Michał , Ho David , Chen Fei , Vardag Sanam N.
Published: March 2024

Verifying greenhouse gas (GHG) mitigation efforts of governments using atmospheric observations is a task which is rapidly gaining scientific interest and attention. The United Nations Framework Convention on Climate Change (UNFCCC) requires the compilation of National Inventory Reports and recommends augmenting them with observational data. The joint project ITMS (Integriertes Treibhausgas-Monitoringsystem für Deutschland) is Germany's national contribution to the World Meteorological Organization’s Integrated Global Greenhouse Gas Information System (IG3IS). It will establish the scientific basis and methodology for integrating GHG observations into the national emissions inventories. Our focus within the ITMS joint project is to optimize observation strategies for monitoring fossil CO2 emissions in German urban and metropolitan areas using synthetic studies. Focusing on cities is especially relevant as cities are substantial contributors to total anthropogenic CO2 emissions. 

Our study uses the Weather Research and Forecasting model (WRF v4.3.3) with ECMWF-ERA5 as meteorological input and boundary conditions. As a first step, we have optimized model transport such that our results are representative of real-world conditions as much as possible. Within comprehensive sensitivity studies, we have analyzed the optimal model settings for German urban areas. Our sensitivity studies focus on the Rhine-Neckar region and compare 16 different physics configurations of WRF for 4 months of 2020, representative of the four seasons. Modeled meteorological variables were compared against 19 meteorological observation stations operated by the German Weather Service and 2 radiosonde stations. We found the setup using Mellor-Yamada-Janjic boundary layer, Noah MP land surface, Monin-Obukhov surface layer and BEP urban parametrization scheme has the overall best performance for our use case. 

Using the optimal setup for urban areas in WRF, we have generated a year-long, 1km resolved dataset of German metropolitan areas. This dataset contains meteorological and sector specific CO2 enhancement data for the year 2018. These metropolitan areas include the Rhine-Neckar, Berlin, Rhine-Ruhr, Nuremberg and Munich metropolitan areas. We showcase the usefulness of the dataset by comparison to actual observations in cities.
Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal Timescales?
Authors: Kiefer Selina M. , Lerch Sebastian , Ludwig Patrick , Pinto Joaquim G.
Published: March 2024

For many practical applications, e.g. agricultural planning, skillful weather predictions on the subseasonal timescale (2-4 weeks in advance) are key for making sensible decisions. Since traditional numerical weather prediction (NWP) models are often not capable of delivering such forecasts, we use an alternative forecasting approach combining both, physical knowledge and statistical models. Selected meteorological variables from ERA-5 reanalysis data are used as predictors for wintertime Central European mean 2-meter temperature and the occurrence of cold wave days at lead times of 14, 21 and 28 days. The forecasts are created by Quantile Regression Forests in case of continuous temperature values and Random Forest Classifiers in case of binary occurrence of cold wave days. Both model types are evaluated for the winters 2000/2001 to 2019/2020 using the Continuous Ranked Probability Skill Score for the continuous forecasts and the Brier Skill Score for the binary forecasts. As a benchmark model, a climatological ensemble obtained from E-OBS observational data is considered. We find that the used machine learning models are able to produce skillful weather forecasts on all tested lead times. As expected, the skill depends on the exact winter to be forecasted and generally decreases for longer lead times but is still achieved for individual winters and in the 20-winter mean at 28 days lead time. Since machine learning models are often subject to a lack of interpretability and thus considered to be less trustworthy, we apply Shapley Additive Explanations to gain insight into the most relevant predictors of the models’ predictions. The results suggest that both Random-Forest based models are capable of learning physically known relationships in the data. This is, besides the capability of producing skillful forecasts on the subseasonal timescale, a selling point of the combination of physical knowledge and statistical models. Finally, we compare the skill of our statistical models to subseasonal state-of-the-art NWP forecasts.
Stochastic modelling of thermokarst lake distributions
Authors: Reinken Constanze , Brovkin Victor , de Vrese Philipp , Nitze Ingmar , Bergstedt Helena
Published: March 2024

Thermokarst lakes are among the most common and dynamic landscape features in ice-rich permafrost regions. They form due to melting of ground ice and subsequent ground subsidence. Their presence and dynamic behavior do not only influence the carbon exchange with the atmosphere by accelerating permafrost thaw and facilitating the production of methane, but also have an impact on soil hydrology as well as biophysical fluxes between atmosphere and land surface, such as energy and water transfer. These feedbacks have implications for both the regional and global climate and are therefore highly relevant when investigating the climate response to changes in permafrost systems under different future carbon emission and warming scenarios. Despite their significant role in the climate system, thermokarst lakes are only rudimentarily or not at all represented in Earth system models. Because the involved hydrological processes are complex and depend on small-scale sub-surface heterogeneities that are difficult to measure, we treat them as probabilistic and use a stochastic approach to create a model of thermokarst lake dynamics (formation, expansion and drainage). More specifically, we utilize common stochastic approaches, such as the Poisson process and Brownian motion, as tools to simulate changes in lake density, size distributions and fractions of water and drained area. Recent advancements in remote sensing offer an opportunity to use high-resolution satellite data products for model calibration and the parameterization of inherent and/or climate-induced thermokarst lake dynamics. We expect our approach and the results of our simulations to contribute to a more accurate representation of permafrost dynamics in Earth system models.
Monitoring seismic velocity changes at Campi Flegrei (Naples) using seismic noise interferometry - Do we see precursors of the future volcanic activity?
Authors: van Laaten Marcel , Müller Jozef , Wegler Ulrich
Published: March 2024

Campi Flegrei is a volcanic field located in the immediate vicinity of the densely populated area of Naples, Italy. Since 2005, the area has been experiencing a new bradyseismic crisis, i.e., a slow uplift of the subsurface caused by rising fluids in the subsurface. The uplift is accompanied by earthquake activity that has been steadily increasing for years, culminating in the strongest earthquake (ML 4.2) in the last 40 years on September 27, 2023. Such uplift and earthquakes can cause changes in seismic velocity and are often succeeded by a volcanic eruption. In this study, we utilize seismic noise to calculate velocity changes at different levels/frequencies over a 7-year period using passive image interferometry. The observed long-term velocity decrease of 1.39 % near the surface over the period from 2016 to 2023 can be explained by a volume increase of the hydrothermal system at the depth of 3 km. In 2023, the Campi Flegrei underwent several phases of velocity change. After a period of minor velocity changes, there was a gradual 0.7 % increase in velocity starting in May. Following the onset of the earthquake swarm in August, the velocity slowly decreased once again.
Consistency of resilience indicators in terrestrial vegetation models
Authors: Bathiany Sebastian , Blaschke Lana , Morr Andreas , Boers Niklas
Published: March 2024

Terrestrial ecosystems are affected by climate change, deforestation and other human influences. There is concern that the resilience of these ecosystems, i.e. their ability to recover from perturbations, is thereby decreased and that their sensitivity to environmental change is increased. In the extreme case, this sensitivity could diverge at a “tipping point”, and propel systems into alternative states. A prominent example is the potential dieback of the Amazon rainforest and the transition to a savanna-like state.The notion of resilience is a highly complex and multi-faceted concept. Ecological resilience theory and the mathematical properties of dynamical systems suggest that a number of different resilience quantifiers are related to each other, or even equivalent, which would allow improved “resilience monitoring” from space. For instance, indicators based on the phenomenon of “critical slowing down” (CSD) like variance and autocorrelation, and related indicators have been used to detect changes over time. In contrast to empirical recovery rates, these indicators do not require one to directly observe the recovery from rare extreme disturbances. Also, they do not rely on the observation or attribution of the responsible environmental drivers.Based on the assumption that fluctuations in remotely sensed proxies of vegetation properties (like biomass or vegetation greenness) behave like iconic one-dimensional stochastic models (most importantly, the Ornstein-Uhlenbeck process), CSD-based indicators should be related to empirical recovery rates after perturbations, to the more general Kramers-Moyal coefficients rooted in statistical mechanics, and to the sensitivity of a dynamical equilibrium state to environmental change. It has been shown that in observations, the theoretically expected relationships between some of these measures roughly hold. At the same time, process-based models, as well as observations, can deviate from such simple stochastic models, e.g. when multiple plant types affect the resilience of an ecosystem but not its sensitivity to environmental change. In our contribution, we show and discuss examples for such deviations in a global vegetation model LPJ. In addition, we compare resilience indicators across a number of state-of-the-art models from CMIP6 and compare the results to an assessment of observations, in order to separate limitations that are related to the practical measurement process (e.g. uncertainties related to retrieval algorithms) from limitations that are associated with unjustified theoretical assumptions. Our results are meant to guide resilience monitoring toward meaningful indicators and to focus on regions and observable properties that can warn of future loss of ecosystem services.
An Austrian case study on empowering ReduceData solar power forecasting using a ML-driven semi-synthetic data generator
Authors: Papazek Petrina , Gfäller Pascal , Schicker Irene
Published: March 2024

Heterogenous, location dependent solar power/PV installations entail individually different production. This is a challenge for power grid operators as to feed-in PV-production, besides its vast output variability, the grid operators need very high-resolution (temporal and spatial) power forecasts, ideally tailored to each of these sites. Technological advances along with the expansion of solar energy will often modify the initial setup of a production site, thereby significantly altering the production data over their record time. Inevitably inconsistent presentations of historic data or short record periods (e.g.: in case of newly build sites) pose challenges in the renewable sector. This induces a common issue in AI driven post-processing:  machine learning and AI powered forecasts heavily rely on sufficient, consistent historic data, more so if simulating expected production peaks in high temporal resolution is part of the requirements. To address the need of such reduced historic data, we aim at generating semi-synthetic data within the ReduceData project by providing a sufficiently represented and continuous data set across multiple data sources. Building on random forest models, we exploit spatial and temporal strongly associated non-reduced auxiliary data, such as satellite data products (e.g.: CAMS) and reanalysis fields (e.g.: ERA5).  Due to their limited nature, PV production records and high-resolution numerical models (e.g.: AROME) will be targeted by our semi-synthetic data generator. The presented case study focuses on nowcasting- to short-range forecasts in 15-minute update frequency tailored to selected solar power production sites in East-Austria. We study to what extent deep learning methods benefit from a consistent semi-synthetic data set built on different raw data sources, highlighting the added value of combining various sources via deep learning. Inputs for the AI-driven post-processing are, for instance, the climatology of satellite data and reanalysis, pvlib’s estimations, AROME surface parameters, and in-house nowcasting models (e.g.: IrradPhyD-Net). Different settings of the semi-synthetic data generator are evaluated by cross-validation. In most studied cases, we achieve a high skill compared to available classical and standard methods (e.g.: persistence, climatology). 
Mapping the perceived potential of energy harvesters to increase the resilience of European water and energy infrastructure 
Authors: Stepanovic Ivana , Frigerio Steven , Guðlaugsson Bjarnhéðinn , Finger David
Published: March 2024

Energy harvesters (EH) are devices designed to capture and convert mechanical energy from ambient sources, which can be converted into electrical energy employing piezoelectric materials. Energy harvesters can capture and convert energy from vortex-induced vibrations in water flows such as water piping, open channels, and natural streams. Harvested energy can be used or stored to power small electronic components such as wireless sensors. These renewable and environmentally friendly energy sources present a tremendous opportunity for clean, reliable off-grid energy production. In the EU–funded project H-HOPE (https://h-hope.eu/), energy harvesters are being designed and deployed for various environments to improve and enhance water and energy resilience. In Reykjavik, Iceland, EH can be implemented in geothermal pipes, providing energy for a sensor network in volcanically active areas where traditional powered sources may be unavailable. In Izmir, Turkey, EH can be implemented in the water supply systems, offering reliable electricity for monitoring drinking water quality. In Padova, Italy, EH can be installed in sewage systems, providing electricity for continuous water quality monitoring. In natural streams like fjords (West Fjords, Iceland) and lagoons (Venice, Italy), EH might be upscaled to power remote communities. However, the perceived potential for EH by local energy stakeholders is unknown. To address this, we conducted semi-structured interviews and expert surveys with relevant stakeholder groups to assess the perceived opportunities and challenges of implementing EH in the mentioned case studies. Preliminary results are visualized in causal diagrams, identifying positive and negative feedback loops of stakeholder perceptions. This analysis identifies both enablers and barriers to EH implementation. These findings will be used to develop a strategy for energy and water service providers to enhance the resilience of existing water and energy infrastructure across Europe and assess the potential uptake and validation of such technology by stakeholders.
Assessing the Impact of Wave Age on GNSS-R L1 products and L2 Wind Speed Inversion
Authors: Chen Mingyi , Liou Yufu , Yang Kanghung , Chien Hwa
Published: March 2024

    This study delves into the impact of wave age on GNSS-R L1 observations, with a specific emphasis on Delay-Doppler Map (DDM) and Normalized Bistatic Radar Cross Section (NBRCS) signal strength, as well as its consequent effects on L2 wind speed inversion. Conventionally, DDMs have been simplified in wind speed inversion algorithms to solely represent sea surface roughness influenced by wind speed. However, this research underscores the significant, yet often overlooked, role of sea surface roughness induced by wave characteristics in the reflection of microwave signals. Wave age, denoting the delay of waves under wind influence and the proportion of swell to wind waves, is identified as a crucial factor affecting sea surface roughness and, thereby, the scattering properties of ocean surface signals.    Recognizing the intricate physical correlation between wave age and sea surface wind speed, along with the multi-variable dependency inherent in GNSS-R observation theory, the study employed machine learning techniques to assess the extent of wave age's influence throughout the observation to wind speed inversion process. For this purpose, the Resnet18 deep convolutional neural network was chosen for its adept handling of the complex features present in DDM data, which can be considered as images. This choice was anchored in Resnet18’s robust feature extraction abilities and its proven track record in tasks requiring high-accuracy image classification.    This study utilized CyGNSS L1 data along with corresponding ECMWF wind speed and sea surface parameter data for specific time and location. To conduct a comparative analysis, two methodologies were used: a traditional geophysical model function (GMF) developed by our self and machine learning. Preliminary testing indicated a marked enhancement in the accuracy of wind speed predictions when incorporating wave age into both GMF and machine learning approaches. The root mean square error notably decreased from approximately 1.8-2 meters per second to about 1.1 meters per second. The study also found a link between wave age and NBRCS intensity distribution, noting that larger inverse wave ages correlate with more signal scattering and weaker signal strength, underlining the vital impact of wave age on NBRCS intensity distribution.
Subsurface transport of microplastics in riverine sediment: Impacts of different rain events and particle density
Authors: Singh Jaswant , Dehbandi Reza , Chauhan Neeraj , Schneidewind Uwe , Haverson Lee , Yadav Brijesh K , Krause Stefan
Published: March 2024

Microplastics (MPs) have emerged as a growing concern, posing potential risks to both marine and terrestrial environments. While surface soils are recognised as a primary sink for these particles, the vertical mobility of MPs in the subsurface remains uncertain due to a lack of comprehensive scientific data. Here, we conducted column experiments to study the transport behaviour of MPs through and retention in subsurface sediment. Two types of pre-stained MPs (median size 50.4 µm) with densities greater than (polystyrene) and smaller than (polyethylene) water were added to the top of large (110 cm) wet-packed fine gravel columns - the most common gravel found in the subsurface zone of the riverine environment. The concentration of deposited MPs was 50,000 particles per kilogram of sediment, derived from an extensive literature survey of polluted sites. Various scenarios, including continuous rain, wet-dry cycles, and dry conditions (characterised by a single rain event followed by a subsequent drying period), were implemented to simulate diverse rain events. 20 mL of water samples were systematically collected at specified intervals from different ports of the column at depths of 30, 50 and 70 cm. Additionally, continuous effluent collection took place at the bottom port (90 cm), which was connected to a pump that maintained a controlled flux at around 4.6 mL/min. At the end of the experiment, gravel samples were methodically collected from discrete sediment layers within the columns (0–5 cm (top of the source layer), 5–10 cm (source layer), 10–30 cm, 30–50 cm, 50–70 cm, 70–90 cm) to quantify the MP mass retained in the column. Results showed that the smallest PS-MPs with a continuous flow system exhibit the highest potential for transport due to higher density and less hydrophobicity compared to PE. With increasing rain events, MPs in the source sediment layer decreased, while MPs concentrations in deeper column layers increased significantly. Furthermore, an intriguing observation indicates that as these MPs undergo more wet-dry cycles, their penetration depth substantially increases. The results indicate that sediment may not only act as a sink for MPs but also as a possible entry point to subsurface receptors such as subterranean fauna and aquifers. This research underscores the intricate dynamics of MPs in sediment and raises awareness regarding the potential environmental consequences. Keywords: Microplastics, Transport, Raining events, Density, Hydrophobicity 
Why the 2022 Po River drought is the worst ever observed
Authors: Montanari Alberto , Nguyen Hung , Rubinetti Sara , Ceola Serena , Galelli Stefano , Rubino Angelo , Zanchettin Davide
Published: March 2024

The causes of recent hydrological droughts and their future evolution under a changing climate are still poorly understood. By analysing a a 216-year river flow time series of the Po River at the closure section, we show that the 2022 hydrological drought is the worst event (30% lower than the second worst, with a six-century return period) ever observed. We prove that the 2022 drought is part of an increasing trend in severe drought occurrence. The decline in summer river flows (−4.14 cubic meters per second per year), which is more relevant than the precipitation decline, is attributed to a combination of changes in the precipitation regime, resulting in a decline of snow fraction (−0.6% per year) and snowmelt (−0.18 millimeters per day per year), and to increasing evaporation rate (+0.013 cubic kilometers per year) and irrigated areas (100% increment from 1900). Our study presents a compelling case where the hydrological impact of climate change is exacerbated by local changes in hydrologic seasonality and water use.
Projected impacts of climate change in forest fires and wind damage in Fennoscandia
Authors: Kinnunen Outi , Backman Leif , Aalto Juha , Markkanen Tiina
Published: March 2024

Climate change alters boreal forest dynamics. The risk of boreal forest disturbances are expected to increase by the end of the century compared to the current state. However, projecting the future impacts of climate change on forest disturbances inherently contains uncertainties related to the global climate models.Here, we study the impact of climate change on forest fires and wind damage using ecosystem model (JSBACH) simulations from 1951 to 2100. The simulations are driven by output from three global climate driver models that have been bias-corrected and downscaled (CORDEX EUR-44 domain). The global models from CMIP5 were run under two forcing scenarios, RCP 4.5 and RCP 8.5. To tackle the uncertainty of climate change projections, we use six climate projections.In our simulations the fire season in Fennosscandia is projected to extend in both spring and autumn. The fire season is estimated to lengthen by 20-52 days, starting 10-23 days earlier and ending 10-30 days later, by the end of the century. In general, it is expected that the number of fires and burnt area are projected to increase from the reference period (1981-2010) to the end of the century (2071-2100) due to rising temperatures, despite increases in precipitation. However, the amount and direction of change varies significantly between climate projections and locations.Our preliminary results implicate that the risk for wind damage may change and affect to the number of fires. Wind damage affects the size of litter pools that change the amount of fuel available for fires. Finally, our aim is to study the interaction between forest fires and wind damage in boreal forests.
Deformation Time-series Analysis and Disaster Potentiality Inversion by Short Baseline Interferometry Measurement
Authors: Xiaoqing Wang , Junli Wu , Peng Zhang , Zhanyi Sun , Yongshang Wang , Qinglan Zhang , Shenghao Liang
Published: March 2024

Synthetic aperture radar interferometry (InSAR) measurement technology is a new remote sensing technology that can effectively monitor slight land deformation. Compared with traditional monitoring technology, InSAR technology has the advantages of wide coverage, all-weather and low cost, providing a technical means of high-resolution, high-precision and low-cost for hidden geological hazard identification and deformation monitoring along pipelines. For purpose of this paper, considering the complex terrains of research area, SBAS-InSAR technology was adopted for deformation information extraction. This technology performs better in identifying fast deformation by controlling temporal and perpendicular baseline thresholds, which is able to prevent temporal de-coherence caused by fast deformation. In this paper, we performed deformation time-series comprehensive processing and analysis on gas pipeline based on Sentinel-1 image data through short baseline data processing, obtained deformation results in terms of time series by calculating linear deformation rate and nonlinear deformation phase based on residual phase separation nonlinear deformation phase and atmospheric effect phase, and then conduct parameter calculation, linear deformation rate calibration, accumulative deformation quantity calculation, etc. Finally, we extracted pipeline deformation quantities from 2020 to 2022. The result showed that, the land deformation rate of the ascending track data during this period ranges from -43 mm/year to 25 mm/year, and that of the descending track data from -66 mm/year to 33 mm/year. The results show that the area along the gas pipeline is in stable condition on the whole, deformation mainly occurred along a section in the northwest of Haidian District, and a large quantity of deformation occurred since January of 2020 until December of 2021, with the maximum deformation quantity of -70mm, This result provided a reliable reference for safety monitoring and repair & maintenance of the gas pipeline.Further more, The possible causes of the deformations mainly include surface subsidence, groundwater mining and such factors. Precautionary measures need to be enhanced for the area at potential risk of surface subsidence, to get rid of the threat of sudden geological disasters and resulting losses particularly in extreme weather conditions. At the same time, terrains and geographical realities should be considered to further determine whether there exists any hidden geological hazard like unstable slope, surface subsidence or landslide and decide the potential risk the pipeline is faced in combination with actual conditions.
Establishment and verification of HFC-134a-gridded emission inventory in China
Authors: Wu Jing , Wang Tong , Ding Shan , An Minde , Liu Zehua , Peng Lin
Published: March 2024

Hydrofluorocarbon-134a (HFC-134a) has been experiencing an annual increase in global emissions and atmospheric concentration in recent years, as a major substitute for ozone-depleting substances (ODSs). It has attracted considerable global attention owing to its double environmental effects, including high global warming potential and degradation to form trifluoroacetic acid (TFA). There are discrepancies in the results of existing top-down and bottom-up studies, and existing studies have only estimated the total HFC-134a emissions at national or regional scales. The lack of methods for building and verifying high-spatial-resolution emissions inventories makes it difficult to analyze the spatial distribution of emissions and regional contributions, as well as to identify emission hotspot grids. This study utilized emission factors and an atmospheric dispersion model to establish a methodology for calculating and validating a gridded emission inventory of HFC-134a, and evaluated its dual environmental impacts. This study focused on China and calculated a gridded emission inventory of HFC-134a for the period from 1995 to 2020. The results showed that the banks and emissions of HFC-134a increased from 0.9 kt and 0.1 kt yr-1 in 1995 to 301 kt (273-332 kt) and 48 kt yr-1 (39-56 kt yr-1) in 2020, respectively. Guangdong, Jiangsu, and Shandong provinces in eastern China had the largest cumulative emissions, with a total cumulative emission amount of 98 kt, accounting for 28% of the national emissions, and were also the provinces where the hotspot grids were mainly distributed. The high spatial resolution emission inventories can provide important input data for atmospheric models to simulate transport and transformation processes and assess environmental impacts, thus improving the accuracy of modelling and prediction. In addition, prediction results showed that if HFC-134a was phased out solely in accordance with the emission reduction requirements of the Kigali Amendment, there would still be HFC-134a banks and emissions in China by the year 2060. If emissions were reduced according to the carbon-neutral emission reduction path proposed in this study to meet a high consumption demand for HFC-134a, it was feasible to achieve nearly zero emissions in 2060. Nevertheless, if HFO-1234yf and R-513A were selected as substitutes for HFC-134a, it could result in the production of more TFA through atmospheric degradation, which could have an adverse impact on the aquatic ecosystem and plants. Therefore, it is necessary to actively explore more environmentally friendly alternatives in the future.