bluuebunny/crossref_metadata_2025 huggingface dataset:
Venus Atmospheric Structure Investigation (VASI) on the DAVINCI Probe
Authors: Lorenz Ralph ,   VASI Team
Published: February 2023

The only near-surface temperature/pressure profile of the atmosphere of our twin planet, Venus, was obtained in 1985 by the VEGA-2 lander. The handful of other probe missions have very limited vertical resolution, or sensor failures in the lowest few km.  Unlike altitudes above 40km, which have been relatively well-surveyed by radio occultation profiles from orbiter missions, the fine temperature structure of lowest part of the Venus atmosphere must be interrogated by direct measurement. This structure is important in several respects. First, the structure and composition reflects the interactions between surface and atmosphere of an ‘exoplanet in our back yard’ which may be much more typical than are those of Earth. Secondly, there are indications that particularly interesting phenomena may occur on Venus, not seen in the atmospheres of Earth, Mars or Titan (but analogous to aspects of ocean stratification on Earth): the VEGA-2 profile is impossible to reconcile with a profile that is both convectively stable and compositionally uniform. A favored hypothesis is that the lowest few kilometers are compositionally denser (lower N2). The supercritical thermodynamics of carbon dioxide add to the rich possibilities in this region.The exchange of angular momentum between the retrograde, slowly-rotating Venus and its dense atmosphere is reflected in the wind profile, which can now be interpreted by global circulation models. Again, while cloud-top (60-70km) winds are now well-known from Akatsuki and preceding missions, very little data exist on winds in the hidden lowest 40km.  Doppler tracking, turbulence measurements, and trajectory reconstruction from descent imaging will shed unprecedented light on the lower atmospheric dynamics.DAVINCI was selected for flight in 2021 and is presently under development for launch in 2029. This presentation will review how the VASI’s measurements of pressure, temperature and wind, far superior in resolution and/or quantity to those of previous missions, may improve our understanding of Venus and complement DAVINCI’s composition measurements and imaging.
Training risk managers in the climate change and energy transition narrative to avoid maladaptation to the emerging 21st century paradigm.
Authors: Mille Jonathan , Charlton Danielle , De Ruiter Marleen , Haklay Muki , Edwards Stephen
Published: February 2023

As the effects of climate change intensify and energy supply issues become more prominent (ie: tackling the rise of CO2 emissions, conflict in Ukraine), the potential impacts of climate and energy variability on anthropogenic systems and question the ability of organisations to maintain their vital services and supply chains in the future.However, there are many uncertainties surrounding climate change and the energy transition. As risk management is directly dependent on environmental conditions and energy supplies, it is necessary for risk managers to understand how these intertwined phenomena may alter current risk management strategies.Although climate change is discussed and highlighted amongst the Disaster Risk Reduction community, the issue around the functioning of the energy system is not yet widely discussed and integrated into risk reduction strategies. This research focuses on assessing the perception of risk managers on environmental and energy risks in order to help them integrate climate change and the energy transition into risk management strategies. Our objective is to paint a picture of the global energy system and to integrate its future developments and limitations in order to prepare risk managers for the systemic changes of the 21st century.
COAWST-XBeach nesting strategy validation for the Celia storm at the Barceloneta beach
Authors: Sánchez-Artús Xavier , Gracia Vicente , Espino Manuel , Liste María , Mestres Marc , Sánchez-Arcilla Agustín
Published: February 2023

Celia storm was an intense event that hit the NW Mediterranean Spanish coast during March 2022. During those dates a study was conducted where 2 instrumented platforms were installed, together with the gathering of high-resolution topobathymetric information at the Sant Sebastià and Sant Miquel beaches (Barcelona). The information taken from this campaign helped to calibrate and validate separately the hydrodynamics of the models XBeach and COAWST. A nesting strategy was done between these models in order to improve the validation of XBeach hydrodynamics. The approach is designed for a pass from a pre-operational to an operational framework in the future.The aim of the study is to integrate both models within a reasonable computational time and good accuracy which will help to forecast possible flooding and erosion hazards and consequently create an Early Warning System in the area. The full strategy comprises atmospheric and hydrodynamic forcings from CMEMS into COAWST that feed the hydrodynamic conditions of XBeach. The simulations were done using the MPI modules of the models to reduce the computational expense and the post-processing using Python/Matlab.Results from the validation of the models will be presented as well as the inundation and the erosion derived from the storm impact at the area. Also, some methodologies that could be applied for validate both outputs will be discussed.Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101037097 (REST-COAST project).Acknowledgments: The first author has the support of the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.
High-resolution sulfur isotopes from ice cores: improved estimates of the volcanic forcing of climate
Authors: Burke Andrea , Innes Helen , Crick Laura , Anchukaitis Kevin , Hutchison William , McConnell Joseph , Rae James , Sigl Michael , Wilson Robert
Published: February 2023

The record of the volcanic forcing of climate over the past 2500 years is reconstructed primarily from sulfate concentrations in ice cores. Of particular interest are stratospheric eruptions, as these afford sulfate aerosols the longest residence time and largest dispersion in the atmosphere, and thus the greatest impact on radiative forcing.  Sulfur isotopes can be used to distinguish between stratospheric and tropospheric volcanic sulfate in ice cores since stratospheric sulfur aerosols are exposed to UV radiation which imparts a mass independent fractionation (Savarino et al., 2003). Thus, sulfur isotopes in ice cores provide a means to identify stratospheric eruptions and calculate the proportion of sulfate deposited from a volcanic event that came the stratosphere, allowing us to refine the historic record of explosive volcanism and its forcing of climate.  Here we present high-resolution (sub-annual) sulfur isotope data from both Greenland and Antarctica across a suite of unidentified eruptions from the anomalously cold decades of the 530s CE, 1450s CE and 1600s CE, as well as the newly identified eruption of Okmok in 43 BC (McConnell et al., 2020), to investigate the stratospheric sulfur loading and climate forcing potential of these eruptions.  Savarino, J., Romero, A., Cole Dai, J., Bekki, S., & Thiemens, M. H. (2003). UV induced mass‐independent sulfur isotope fractionation in stratospheric volcanic sulfate. Geophysical Research Letters, 30(21). http://doi.org/10.1029/2003GL018134McConnell, J. R., Sigl, M., Plunkett, G., Burke, A., Kim, W. M., Raible, C. C., et al. (2020). Extreme climate after massive eruption of Alaska’s Okmok volcano in 43 BCE and effects on the late Roman Republic and Ptolemaic Kingdom. Proc Natl Acad Sci USA, 117(27), 15443.
Modelling spatial variation of thermal comfort indices in urban settings performed within an open source GIS
Authors: Lindberg Fredrik , Bernard Jeremy , Wallenberg Nils , Bäcklin Oskar , Lönn Jessika , Thorsson Sofia , Kalori Karzo
Published: February 2023

The Urban Multi-scale Environmental Predictor (UMEP) is a city based climate service tool that facilitate user-friendly open source capabilities to combine models and tools essential for climate simulations. The tool is designed for a broad range of users, both within academia as well as practitioners and non-expert users. UMEP is available as a plugin in QGIS, a free and open source geographic information system (GIS) available on all common platforms. One main purpose with UMEP is to include pre-processing of geo- and weather data, process calculations as well as post-processing and visualisation in the same tool.Recent developments in UMEP enables creation of all essential input variables required to generate high-resolution raster grid of common human thermal comfort indices such as Physiological Equivalent Temperate (PET), Universal Thermal Comfort Index (UTCI), Comfort Formula (COMFA) etc. This work presents initial results and methodology used to compute these indices within UMEP. Examples of workflow throughout the process, all the way to the final result, will be presented and discussed.
Deep Learning-based earthquake catalog and tomography reveal the rupture process of the 2022 Mw 6.9 Chihshang earthquake sequence
Authors: Sun Wei-Fang , Pan Sheng-Yan , Huang Chun-Ming , Guan Zhuo-Kang , Yen I-Chin , Kuo-Chen Hao
Published: February 2023

The Longitudinal Valley in eastern Taiwan, the arc-collision boundary between the Eurasian and Philippine Sea plates, is one the most seismic active areas in the world. On September 18, 2022, the Mw 6.9 Chihshang earthquake struck the south half of the valley and caused severe damage. Since November 2021, we have installed a five-station permanent broadband seismic array with station spacings of 10-20 km around the Chihshang area, and right after the Mw 6.5 foreshock occurred, we further installed a 46-station temporary dense array of nodal seismometers with station spacings of 2-5 km for 35 days. We use SeisBlue, a deep-learning platform/package, to extract the whole earthquake sequence including the Mw 6.5 foreshock, the Mw 6.9 main shock, and over 5,000 aftershocks from the broadband array, and to obtain over 40,000 aftershocks from the dense nodal array. With the high quality and quantity of P- and S-wave arrival times, we apply the finite difference travel time tomography, developed by Roecker et al. (2006). The improved resolution at the shallow part of the crust (at depth < 10 km) provides new constraints to get detailed (with grid spacing 1 km) and reliable Vp, Vs, and Vp/Vs velocity models at the local scale for the first time. Combined with the high-resolution velocity models and the much more complete seismicity, our results clearly depict not only the Central Range fault and the Longitudinal fault but also several local, shallow tectonic structures that have not been observed along the southern Longitudinal Valley.
Identifying radon priority areas by mapping geogenic radon potential of soils in Baden-Wuerttemberg (Germany) using machine learning algorithms
Authors: Kölbl Alexandra , Blaschek Michael
Published: February 2023

Radon (222Rn) is a radioactive gas of the uranium-radium decay chain and occurs naturally in soil air. Primarily by diffusion, radon migrates to the surface and can accumulate in buildings, where it is harmful to human health because of its radioactivity. To tackle this particular health hazard, radon mitigation was recently introduced into national law. Among other things, the law demanded the designation of radon priority areas by the end of 2020. These areas are defined as administrative areas with a large proportion of land affected by high radon concentrations inside buildings. In the state of Baden-Wuerttemberg these areas were selected at municipality level based on a 10x10 km national map of geogenic radon potential (GRP) provided by the German Federal Office for Radiation Protection (BfS) accompanied by a state-specific map of uranium concentrations. With the prospect of future designations, this work aims at replacing the national GRP map used in 2020 by a more sophisticated state-specific version. In doing so, we improve spatial resolution, focus on covariates that only represent the state-relevant features of geology and soil and move away from a kriging-based mapping approach towards machine learning.This ongoing study is currently based on 580 radon measurements in soil gas at 1 m depth from different surveys spread irregularly over Baden-Wuerttemberg in southeast Germany. This point dataset is combined with a set of covariates from factors that influence radon concentrations such as soil parameters, geology, relief, climate and a map of uranium concentrations created from over 4000 heavy metal measurements. Modelling is done using a random forest (RF) approach as implemented in the R packages ranger and mlr3. Preliminary results indicate that the new GRP map with a spatial resolution of 250 m is highly useful in classifying communities as vulnerable areas, which were previously not called due to uncertain underlying data. In addition, model output confirms up to 80 % of already identified radon priority areas.Machine learning algorithms such as RF with its precise learning progressions can be used to create GRP maps at regional scale at high resolution. Besides further improving the RF model, next steps will focus on explainable machine learning, i.e. to produce features that support policy makers in finding acceptance by the public in sight of a sensitive topic. This includes variable importance plots, uncertainty measures and maps representing areas of applicability. The latter will also be used to help guiding the ongoing radon measurement programme of Baden-Wuerttemberg, which currently comprises approximately 100 new locations per year.
Reconciling differences in CO2 emissions and removals from LULUCF by separating natural and land-use CO2 fluxes at the country level
Authors: Schwingshackl Clemens , Obermeier Wolfgang A. , Bultan Selma , Grassi Giacomo , Canadell Josep G. , Friedlingstein Pierre , Gasser Thomas , Houghton Richard A. , Kurz Werner A. , Sitch Stephen , Pongratz Julia
Published: February 2023

Anthropogenic and natural CO2 fluxes on land constitute substantial CO2 emissions and removals but are usually not well distinguished in national greenhouse gas inventories (NGHGIs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC). Instead, countries frequently include natural and indirect human-induced CO2 fluxes on managed land in their estimates of CO2 fluxes from land use, land-use change, and forestry (LULUCF), mostly due to methodological constraints. Comparisons of anthropogenic LULUCF flux estimates from global models and from NGHGI reports thus reveal a substantial gap. Globally, this gap could be successfully reconciled by considering the different definitions used by global models and by NGHGI reports. Recent improvements in LULUCF flux modelling enable such a reconciliation now also at the country-level.We separate natural and land-use-related CO2 fluxes from NGHGI reports in eight countries using global models to assess and improve the attribution of land CO2 fluxes to direct anthropogenic activities. In most investigated countries, the gap between model-based and report-based CO2 flux estimates is reduced (by up to 70%) if natural and indirect human-induced CO2 fluxes on managed land are considered. This confirms that the methodological discrepancies between NGHGI reports and global model estimates of LULUCF emissions are primarily due to differing estimation and reporting definitions, which need to be considered when accounting for country contributions to global climate mitigation targets. Further examinations show that remaining differences are linked to country-specific discrepancies between model-based and report-based estimates, such as incomplete reporting by countries, uncertainties in historical land-use dynamics, and model limitations. Moreover, most countries report the areas considered as managed without explicit information on their location, which prevents a precise spatial identification necessary for a detailed comparison of natural fluxes in managed forests with model-based estimates.Reconciling estimates of LULUCF fluxes in individual countries by separating natural and land-use-related CO2 fluxes at national scales provides an important step toward a transparent assessment of LULUCF fluxes from NGHGI reports and supports a fair burden sharing of climate mitigation across countries.
Deep learning and chemical constraints allow accurate segmentation of µCT data from metamorphic rocks
Authors: Rizzo Roberto Emanuele , Freitas Damien , Gilgannon James , Seth Sohan , Butler Ian B. , Wheeler John , Marone Federica , Schlepuetz Christian , McGill Gina , Plümper Olivier , Fusseis Florian
Published: February 2023

X-ray tomographic imaging has become a very valuable tool for the analysis of (rock) materials, both for visualising complex 3D microstructures and for imaging internal features such as damage, mineral reaction, and fluid/rock interactions quantitatively. The validity of the results derived from X-ray tomography, however, hinge on the  accuracy of the image segmentation. There are many methods for image segmentation (from simple manual thresholding to machine learning and deep learning approaches), which can produce a high range of variability in the segmentation results. Accuracy of segmentation results is seldom checked and thus calling the reproducibility of the results into question. In this contribution we show how metamorphic reactions themselves can be used to constrain accuracy and highlight the benefits of deep learning methods to extend this over many large datasets efficiently.Here, we demonstrate a methodology that uses deep learning to achieve reliable segmentation of time-series volumetric images of gypsum dehydration reaction, on which standard segmentation approaches fail due to insufficient contrast. We implement 2D U-net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how labelled data obtained via machine learning (i.e., Random Forest Classification) can be used as input data and enhance the neural network performances. The developed deep learning algorithm proves to be incredibly robust, as it is able to consistently segment volume phases within the whole suite of experiments. In addition, the trained neural network exhibits short run times (<7 minutes for ~250 MB of image volumes) on a local workstation equipped with a GPU card.  To confirm the precision achieved by our workflow, we consider the theoretical and measured molar evolution of gypsum (CaSO4.2H2O) to bassanite (CaSO4.½H2O) during the dehydration. Within all time-series experiments, errors between the predicted theoretical and the segmented volumes fall within the 5% confidence intervals of the theoretical curves. Thus, the segmented CT images are very well suited for extracting quantitative information, such as mineral growth rate and pore size variations during the reaction. To our knowledge, this is the first time an internal standard is used to unequivocally measure the accuracy of a segmentation model.  Being able to accurately and unambiguously measure the volumetric evolution during a reaction enables high-level modelling and verification of the physical (hydraulic and mechanical) properties of rock materials involved in tectono-metamorphic processes.
Submarine groundwater discharge and methane seepage driven by Fennoscandian Ice Sheet dynamics offshore northern Norway
Authors: ten Hietbrink Sophie , Kim Ji-Hoon , Sen Arunima , Lepland Aivo , Szymczycha Beata , Saghravani Seyed Reza , Knies Jochen , Hong Wei-Li
Published: February 2023

Freshwater in sediment pore fluids and methane seepage from the seafloor have often been observed concurrently in Arctic regions where submarine groundwater discharge (SGD) occurs, with advective flows potentially reintroducing ancient carbon into the modern ocean. It is hypothesized that hydraulic loading by ice sheets enhances submarine groundwater discharge, and subsequently methane transport. In the presence of microbial communities, large amounts of methane can be consumed by the anaerobic oxidation of methane (AOM), inducing precipitation of authigenic carbonates that inherit unique carbon isotopic signatures from methane. At an SGD site offshore Lofoten Islands, northern Norway (ca. 800 meters water depth), in the vicinity of the maximum extent of the Fennoscandian Ice Sheet, we observed a downcore decreasing chlorinity profile and a linear relation between δ18O and δ2H of the porewater, known as the local meteoric water line. This demonstrates a meteoric water contribution to the porewater. We also found methane-derived authigenic carbonates (MDACs) with depleted δ13C values (< -30 ‰ VPDB), suggesting that microbial (or thermogenic) methane was incorporated during MDAC precipitation. Moreover, δ18O values (> 2.5 ‰ VPDB) of MDACs indicate precipitation in the presence of 18O-enriched water, possibly a result of past hydrate dissociation. To assess the carbon cycle and timing of the methane seepage at the Lofoten SGD site, we investigated the radiocarbon contents of Total Organic and Inorganic Carbon in sediments (TOC, TIC), as well as Dissolved Inorganic Carbon (DIC) in porewater from multiple sediment horizons. The radiocarbon contents of DIC have the lowest values among the three carbon pools, in the order of 10-30 percent modern carbon. Their radiocarbon ages (~ 17,000 years BP) are in the order of the Last Glacial Maximum. Consequently, the DIC must have been closed off from the atmosphere due to long groundwater retention times. Alternatively, a methane source low in radiocarbon could have contributed to the DIC pool through AOM. The TIC pool showed radiocarbon content half of that of the TOC in the same sediment horizons, which can be explained by carbonate precipitation from the radiocarbon-depleted DIC pool. To further constrain in situ carbon cycling and advection velocities, a reaction-transport model using mass balance calculations of 12C, 13C and 14C has been applied. Radiocarbon content profiles of the DIC indeed imply the advection of old groundwater into the marine sediment porous media.
Emerging of surface ozone pollution beyond summer season over the North China Plain
Authors: Li Ke , Zhang Danyuting , Hou Jie
Published: February 2023

Surface ozone pollution is of great concern in current air quality management in China. The North China Plain (NCP), which is home to 300 million people, has the highest ozone level and increasing trend.  While earlier studies of ozone pollution focused on summer season, here we show that ozone pollution out of summer season over the North China Plain is also very emerging.  Firstly, ozone has been very low during winter haze (particulate) pollution episodes. However, the abrupt decrease of NOx emissions following the COVID-19 lockdown in January 2020 reveals a switch to fast ozone production during winter haze episodes with MDA8 ozone of 60 to 70 ppb. This remarkable switch to an ozone-producing regime in January–February following the lockdown illustrates a more general tendency since 2013 of increasing winter–spring ozone in the North China Plain and increasing association of high ozone with winter haze events, as pollution control efforts have targeted NOx emissions while VOC emissions have limited regulations. Secondly, we find that ozone episodes comparable with those in summer season can also occur in early-autumn over NCP, resulting in the emerging of two-peak ozone pollution (June and September) for some years. The statistical analysis show that this interannual variation is driven by the anomalies in the large-scale sea surface temperature (SST) patterns. These results highlight the urgency of ozone pollution controls out of summer season in China.
Towards a generic clustering approach for building seismic catalogues from dense sensor networks
Authors: Rimpot Joachim , Hibert Clément , Malet Jean-Philippe , Forestier Germain , Weber Jonathan
Published: February 2023

In the context of climate change, the occurrence of geohazards such as landslides or rockfalls might increase. Therefore, it is important to have the ability to characterise their (spatial and temporal) occurrences in order to implement protection measures for the potential impacted populations and infrastructures. Nowadays, several methods including Machine Learning algorithms are used to study landslides-triggered micro-seismicity and the associated seismic sources (eg. rockfalls and  slopequakes). Those innovative algorithms allow the automation of the processing chains used to build micro-seismicity catalogues, leading to the understanding of the landslide deformation pattern and internal structure. Unfortunately, each landslide context has its own seismic signature which requires the use of the most complete and handmade training samples to train a Machine Learning algorithm. This is highly time consuming because it involves an expert that needs to manually check every seismic signal recorded by the seismic network, which can be thousands per day.The aim of this study is to develop semi-supervised and unsupervised clustering methods to characterise the micro-seismicity of landslides in near real time. Here, we present the preliminary results obtained for creating a landslide micro-seismicity catalogue from the analysis of a dense network of 50 seismic stations deployed temporarily at the Super-Sauze landslide (French Alps). First, we present the performance of supervised Random Forest and XGBoost trained models on the event signals. Then, an approach aimed at processing streams of raw seismic data based on 18s-length windows is explored. Finally, we discuss the clustering results and the transferability possibilities of the approach to other landslides and even environments (glaciers, volcanoes).
Assessing the size and uncertainty of remaining carbon budgets
Authors: Lamboll Robin , Nicholls Zebedee , Smith Chris , Kikstra Jarmo , Byers Edward , Rogelj Joeri
Published: February 2023

The remaining carbon budget (RCB), the net amount of carbon dioxide humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5C are small, and minor changes in their calculation can therefore result in large relative shifts. Here we evaluate recent RCB assessments by the IPCC and explain differences between them. We present calculation refinements together with robustness checks that increase confidence in RCB estimates. We conclude that the RCB for a 50% chance of keeping warming to 1.5C is around 250 GtCO2 as of January 2023, around 6 years of current CO2 emissions. This estimate changes to 480 and 60 GtCO2 for a 33% and 66% chance, respectively. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and potential warming after net zero is reached. 
A Python tool to monitor noise characteristics in large seismic data bases
Authors: Lehr Johanna , Stammler Klaus
Published: February 2023

Continuous monitoring of data quality is a major issue in seismology because the achievement of robust scientific results depends on the reliability of the underlying data resources. We present a Python package which provides means to perform a systematic analysis of noise data in the time and frequency domain. The tool is designed to process large amounts of channels and years of data. In a first step, average amplitude levels and power spectral densities are computed for large parts - preferably the whole available time range - of the data of a station. Depending on the size of the data set, this processing takes minutes to hours. Therefore, the results are stored in rapidly accessible HDF5-files. Subsequently, they are visualized using color-coded matrix displays (spectrograms) and interactive 3D-figures. The resulting figures give insight to characteristic noise patterns at the station and possible noise sources, like various forms of anthropogenic noise or wind generated noise. Furthermore, changes in noise levels or noise patterns are easily detectable. Such changes either indicate changes in the environmental conditions at the recording site or changes in the recording hardware improperly reflected by the station metadata, often signaling a problem with the metadata. Furthermore, the processed data can easily be restricted to selected times, e.g. to investigate the influence of day/night cycles or to obtain wind-speed dependent spectrograms. In this manner, a comprehensive picture of relevant characteristics at a station site may be acquired.The package is build from established Python libraries like obspy, scipy and h5py. Matplotlib and plotly are used for data visualization. The core functionalities are accessible via command line interface while the underlying API allows for more customized workflows.
Sensitivity and uncertainty analysis for predicted soil test phosphorus using the APLE model
Authors: Bolster Carl , Vadas Peter
Published: February 2023

The long-term application of inorganic fertilizer and animal manure to agricultural fields has resulted in soil test P (STP) concentrations that greatly exceed the agronomic critical level in many areas of the world. This build-up of soil P, often referred to as legacy P, can increase the risk of P being mobilized during runoff and leaching events, even years or decades after P inputs have decreased or ceased. If this mobilized P reaches P-limited surface waters, eutrophication can result, leading to serious water quality problems that can adversely impact the environment, human health, and recreational activities. Due to the cost and effort involved in soil, manure, and runoff sampling and testing, as well as implementation of best management practices, nutrient management policies and strategies are often guided by computer model predictions. However, computer model predictions are inherently uncertain, thus it is important to account for this uncertainty when interpreting modeling data and using modeling results to guide decision makingIn this study we conducted a sensitivity and uncertainty analysis using the Annual P Loss Estimator (APLE) model focusing on model predictions of STP. We calculated and evaluated the sensitivity coefficients of predicted STP and changes in STP using 1- and 10-yr simulations with and without P application. We also compared two methods for estimating prediction uncertainties: first-order variance approximation (FOVA) and Monte Carlo simulation (MCS). Finally, we compared uncertainties in APLE-predicted STP to uncertainties in measured STP collected from multiple sites in Maryland under different manuring and cropping treatments. Results from our sensitivity analysis showed that predicted STP and changes in STP for 1-yr simulations without P inputs were most sensitive to initial STP whereas model STP predictions were most sensitive to manure and fertilizer application rates when sensitivity analyses included P inputs. For the 10-yr simulations without P application inputs, the range in sensitivity coefficients for crop uptake and precipitation were much greater than for the 1-yr simulations. Prediction uncertainties from FOVA were comparable to those from MCS for model input uncertainties up to 50%. Using FOVA to calculate APLE STP prediction uncertainties using the Maryland data set, the mean measured STP for nearly all site years fell within the 95% confidence intervals of the STP prediction uncertainties. Our results provide users of APLE insight into what model inputs require the most careful measurement when using the model to predict changes in STP under conditions of P drawdown (i.e., no P application) or P buildup. Our results also demonstrate the importance of including model prediction uncertainties when estimating long-term drawdown of STP in agricultural fields.
MultiPAC as a tool to monitor the sustainability of riverbed restoration measures
Authors: Haun Stefan , Negreiros Beatriz , Schwindt Sebastian , Aybar Galdos Alcides , Noack Markus , Wieprecht Silke
Published: February 2023

Many water bodies are, as a result of anthropogenic influences, such as river straightening, river bank fixation, or damming in accordance with the EU Water Framework Directive (2000/60/EC) not in a good ecological state anymore. With the aim to return to a good ecological status for surface waters, restoration measures are implemented in many rivers. However, the success and sustainability of such measures are often site-dependent and require hence an objective assessment.In this study, the Multi-Parameter Approach to assess Clogging (MultiPAC) was used to assess the suitability and sustainability of different riverbed restoration strategies. MultiPAC is based on several measured physico-chemical parameters, which enable a detailed investigation of in-situ conditions of gravel-bed rivers. The approach includes measurements of the sediment composition for identifying surface and subsurface grain size distributions and fine sediment fractions. In addition, measurements of the porosity are obtained by using Structure-from-Motion and the Water Replacement Method of freeze-core samples. Finally, measurements of the interstitial oxygen concentration and so-called slurping rates, which are converted into hydraulic conductivity, were performed with a double-packer system called VertiCo.The residual river stretch between Jettenbach and Töging at the Inn River in Germany provided a means to evaluate riverbed restoration measures, implemented in February and March 2020. Investigations were performed for several gravel bars, where sediment was replenished and a mechanical break-up of the bed armour layer was conducted. The MultiPAC investigations were performed before measure implementation, shortly afterwards (March 2020) and in November 2020 to investigate the impact of a flood event with a 10-year return period, which occurred in August 2020, and thus may have influenced the sustainability of the restoration measures.From the measurements, it can be seen that sediment replenishment and the mechanical break-up of the armour layer significantly improved the ecological functioning of the riverbed. However, it became evident that the increase in the quality of the riverbed was only temporary. Hence, these measures will need to be repeated regularly with the aim of maintaining ecologically-valuable riverbed habitat conditions. The results of this study also showed that MultiPAC provides detailed insights into the riverbed sediments, their composition, and the permeability of the riverbed.
DL-AISLE: A Deep Learning framework using Active Learning on Satellite imagery for Landslide identification
Authors: Sharma Nirdesh , Saharia Manabendra
Published: February 2023

A landslide database is of utmost importance for hazard management as well as early warning systems. Historically landslides were manually identified by ground surveys or remote sensing data, but with development in satellite technology open-source satellite imagery has emerged as a preferred data source for landslide identification due to its cost effectiveness. On the other hand, an increase in computing power made computer vision methods especially deep learning popular for satellite image segmentation. Deep learning models require a large amount of data to reach operational performances, however there is very little labelled landslide data present. Labelling satellite imagery is costly and time consuming. Active learning remedies this by optimally selecting the data to label thereby maximizing the performance of the model given the limited data. In this study we present an active learning-based framework to train a segmentation model to identify landslides. The pre- and post-landslide images from sentinel 2 are merged with terrain features to create input data bands. The model is tested on a test database using metrics like IOU. The methodology has been developed with an application in India but can be applied globally. 
Seasonal dynamics of 2H2O passage and 15N accumulation in a dual-label pulse-chase experiment in a mature, irrigated Scots pine forest
Authors: Marshall John , Lehmann Marco , Hagedorn Frank , Saurer Matthias , Treydte Kerstin , Gessler Arthur
Published: February 2023

Tree roots are responsible for the bulk of water and nitrogen (N) uptake by forests, but the detailed mechanism of resource uptake and their role in the spatial exploitation of water and nitrogen resources is incompletely understood. Theory and empirical evidence suggest that there are two processes delivering N to root surfaces for uptake: 1) convection of dissolved N carried in soil water as it is drawn toward the roots and 2) diffusion of dissolved N in still water toward the low concentrations at the root surface.  Quantifying these processes has, however, been difficult, particularly in forest trees. Recently paper by Henriksson et al. Our objective here was to make a similar comparison in an irrigation experiment.We applied highly labelled 2H2O and 15N on a small area at the beginning of the growing season a similar and tracked 2H and 15N in tissues of adjacent trees and shrubs, aiming to assess not only correlations between the water and N tracers at the end of the season, but also the dynamics of label passage and accumulation through repeated measurements. Synchronicity of the nitrogen accumulation with the water label passage would strengthen evidence for the role of water uptake in nitrogen uptake.We found that the 2H-label in water, as measured in extracted water, passed through the system as a pulse, disappearing by late in the season. In contrast, the 15N label, as measured from leaf tissue, accumulated toward an asymptotic maximum. Among tree individuals, the rate of 15N increase was correlated with the plant-water 2H2O labelling at any point in time, supporting the notion that the 15N uptake was predominantly driven by water uptake. No difference was detected between the irrigated and the non-irrigated plots, perhaps because high rainfall overrode any irrigation effects.Next steps will be to also compare the cumulative δ2H of the wood in the new growth ring to the δ2H of the xylem water that passed through individual trees. If the correlation is strong, we will map the patterns of water and N uptake around the labelled plots to provide a detailed spatial description of the correlated water and N uptake processes. When combined with the current study, these results will show the spatial and temporal extent to which root water uptake facilitates nitrogen delivery to the roots.
Nord Stream methane leaks case: ICON-ART model simulation versus observations at various ICOS stations
Authors: Sunkisala Anusha , Mamtimin Buhalqem , Rösch Thomas , Roth Franziska , Kaiser-Weiss Andrea
Published: February 2023

Methane is emitted during the production and transportation of fossil fuels. Methane emissions result also from intensive livestock farming and agricultural practices as well as by the decay of organic waste. The leakage throughout the extraction, processing and transportation of natural gas releases methane straight into the atmosphere. Due to the damage to the Nord Stream gas pipelines on 26 September 2022 leaks have appeared close to the Danish island of Bornholm in the Baltic Sea, which releases large amounts of methane in the pipeline into the atmosphere within just a few days. In our study, we simulated the transport of methane plume in Nord Stream case by using DWD’s regional Icosahedral Nonhydrostatic (ICON) model with its transport scheme ART (Aerosols and Reactive Trace gases) extension.The model is run for Limited Area Mode (LAM) with a horizontal spatial resolution of 6.5 km and 60 model levels. As source strength of methane emissions were used the estimates which were calculated by the German Federal Environmental Agency (Umwelt Bundesamt). An assumption, that a constant 700 kg/s of gas had been leaking since September 26 was used for the hourly model run to simulate the methane plume between September 26 and October 1 2022.The model results had been compared to the potential methane signals of Nord Stream leaks detected at Integrated Carbon Observation System (ICOS) stations. According to our simulations, we found a good fit with respect to ICOS observations for the stations Hyltemossa, Birkenes and Norunda. Further analysis has been conducted to look at vertical profiles at different heights and also into correlation coefficients between the model and observations.In this Nord Stream case, our simulation demonstrates modelling capabilities of the ICON-ART model and its associated quantitative assessment of methane emissions.This work has been funded by the German Federal Ministry for Digital and Transport programme for Development and Implementation of Copernicus services for public needs within the HoTC project.
Modelling of atmospheric transport of SEP-induced cosmogenic 10Be using CCM SOCOL-AER2-BE
Authors: Golubenko Kseniia , Rozanov Eugene , Kovaltsov Gennady , Baroni Mélanie , Usoskin Ilya
Published: February 2023

Be is a cosmogenic isotope continuously produced in the Earth’s atmosphere by galactic cosmic rays (GCRs) and sporadically by solar energetic particles (SEPs). The long-living isotope, as measured in polar ice cores, typically with an annual resolution, serves as a proxy for long-term cosmic-ray variability, whose signal can, however, be distorted by atmospheric transport and deposition that need to be properly modelled. Atmospheric transport of 10Be depends on production, atmospheric circulation, and local orography. For an accurate physical description of the isotope's transport and deposition, we use the chemical climate model (CCM) SOCOL-AER2-BE. In combination with the production model CRAC, our model was verified using real measurements of beryllium in ice cores for Antarctic and Greenland locations. The model results agree with the measurements at the absolute level, implying that the production, decay, and lateral deposition are correctly reproduced. However, the exact time variability is not always well reproduced, particularly for the Greenland shore sites implying significant regional effects. Potentially, extreme SPEs that are orders of magnitude stronger than those observed during the recent decades can be recorded in cosmogenic isotope data, and a proper model is needed to study them. Here we present a model of the production and transport of 10Be for a major solar energetic particle event (GLE 69) and analyze the geographical pattern of the beryllium concentration.
Destination Earth Data Lake
Authors: Duatis Juarez Jordi , Schick Michael , Puechmaille Danaele , Stoicescu Miruna , Saulyak Borys
Published: February 2023

Destination Earth is an operational service under the lead of the European Commission being implemented jointly by ESA, ECMWF and EUMETSAT.The presentation will provide insights into the EUMETSAT Data Lake Service component of the Destination Earth undertaking.The objective of the European Commission’s Destination Earth (DestinE) initiative is to deploy several highly accurate digital replicas of the Earth (Digital Twins) in order to monitor and simulate natural as well as human activities and their interactions, to develop and test “what-if” scenarios that would enable more sustainable developments and support European environmental policies. DestinE addresses the challenge to manage and make accessible the sheer amount of data generated by the Digital Twins and observation data located at external sites such as the ones depicted in the figure below. This data will be made available fast enough and in a format ready to support analysis scenarios proposed by the DestinE service users. Figure:  DestinE Data Sources (green) and Stakeholders (orange) The “DestinE Data Lake” (DEDL) is one of the three Destination Earth components interacting with:the Digital Twin Engine (DTE), which runs the simulation models, under ECMWF responsibility
the DestinE Core Service Platform (DESP), which represents the user entry point to the DestinE services and data, under ESA responsibility
The DestinE Data Lake (DEDL) fulfils the storage and access requirements for any data that is offered to DestinE users. It provides users with a seamless access to the datasets, regardless of data type and location. Furthermore, the DEDL supports big data processing services, such as near-data processing to maximize throughput and service scalability. The data lake is built inter alia upon existing data lakes such as Copernicus DIAS, ESA, EUMETSAT, ECMWF as well as complementary data from diverse sources like federated data spaces, in-situ or socio-economic data. The DT Data Warehouse is a sub-component of the DEDL which stores relevant subsets of the output from each  digital twin (DT) execution being powered by ECMWFs Hyper-Cube service.
Human interventions impacts: the role of reservoir operations on drought propagation
Authors: Senbeta Tesfaye B. , Karamuz Emilia , Kochanek Krzystof , Napiorkowski Jaroslaw J. , Bogdanowicz Ewa
Published: February 2023

The reservoir is a hydroengineering structure to regulate discharge in rivers and store water. It can be used for flood control, water supply, irrigation, power generation, etc. It is also used for physical water management to cope with droughts at the catchment scale. The reservoir operations can have a mitigating and/or enhancing impact on droughts and their propagation from meteorological to agricultural and hydrological drought.The aim of the study is to assess the role of reservoir operation on drought propagation using the Sulejow and Wiory reservoirs as case studies in the catchments of the Pilica and Kamienna rivers (central Poland), respectively. Two approaches, namely hydrological modelling and the observation-based approaches, were used for the study. In the hydrological modelling method, the naturalised hydrological variables in the post-dam period simulated using the Soil and Water Assessment Tool (SWAT) were compared with the observed values in the same period, while in the observation-based approach, the upstream and downstream hydrological variables such as soil moisture (remote sensing data) and observed river discharge were used. In addition, the SWAT with reservoir was considered by applying the target reservoir release method for simulating the downstream hydrological variables and comparing it with the method without reservoir. The threshold method, based on the parameter transfer method, was applied in the analysis of drought conditions to account for the non-stationarity of the hydro-climatic variables.Preliminary results suggest that the two approaches are consistent in showing the impact of reservoir operations on the propagation and characteristics of droughts. In addition, the comparative analysis between the reservoirs shows differences based on their purpose. The results of the study can be used to understand the propagation of drought in human-altered watersheds and to appropriately manage water resources for drought mitigation.AcknowledgementsThis work was supported by the HUMDROUGHT (https://humdrought.igf.edu.pl) project carried out at the Institute of Geophysics of the Polish Academy of Sciences and funded by the National Science Centre (contract 2018/30/Q/ST10/00654).