bluuebunny/crossref_metadata_2025 huggingface dataset:
Jupiter’s Frequency-Dependent Love Number estimation through joint analysis of JUICE-3GM and Europa Clipper radio science measurements and one century of astrometry data
Authors: Magnanini Andrea , Fayolle Marie , Lainey Valery , Zannoni Marco , Dirkx Dominic , Tortora Paolo , Mazarico Erwan , Iess Luciano
Published: February 2023

The future JUICE and Europa Clipper missions will probe the Jovian system performing several flybys of the moons Europa, Ganymede, and Callisto. The precise radio tracking data will provide an accurate estimation of the gravity and ephemerides of the Galilean moons. This is especially true for Ganymede, after JUICE insertion in a low circular orbit around the moon for at least four months. The evolution of the orbits of the Galilean moons will allow for an estimation of the dissipation in Jupiter at the orbital frequency of each Galilean moon, represented/parameterized through the imaginary part of its degree-2 Love number.The imaginary part of the Jovian Love number  is a key parameter to evaluate the long-term orbital evolution of the Galilean moons and the Laplace resonance stability. Its secular effect on the orbit of the moons produces an acceleration both in the radial and tangential direction, and the longer the time span of observation, the better it can be estimated. However, the dissipations at the different satellite orbital frequencies are highly correlated due to the Laplace resonance, complicating their estimation. JUICE and Europa Clipper missions cover less than 5 years, but the accuracy in the determination of the moon’s orbit is good if not exquisite. This broad data set can be complemented by high quality astrometry measurements collected by ground observatories starting from 1891, past spacecraft missions (Voyager, Galileo) optical images and radar data. This approach has the potential to greatly improve the estimation accuracy for the dissipation parameters in Jupiter.Future work will include the addition of radio-tracking measurements from the spacecraft Galileo and Juno, which together with JUICE and Europa Clipper will offer a 30 year time span of radio-metric data.Unfortunately JUICE and Clipper, unlike Galileo and Juno, will fly never by Io, the moon which dominates the evolution of the Laplace resonance and the dissipation in the Jovian system. However Io can be observed from ground telescopes, and the available astrometric observations of the moon may allow a significant reduction of Io’s state solution uncertainties and correlations.In this study, we analyze the attainable uncertainties for the parameters characterizing the dissipation in Jupiter’s system and the ephemerides of the Galilean moons combining simulated range & range-rate radio tracking data from JUICE and Europa Clipper with astrometry data, showing the synergies and the possible improvements in the uncertainties and correlations of the joint analysis, together with a discussion of the problems associated to the fusion of data sets of very different type.
Climate-driven variations in functional strategies of temperate forest ecosystems
Authors: Liu Daijun , Esquivel-Muelbert Adriane , Fridman Jonas , Benito Paloma Ruiz , Miguel Ángel Zavala Girones , Astigarraga Julen , Susanne Suvanto , Talarczyk Andrzej , Cienciala Emil , Kunstler Georges , Schelhaas Mart-Jan , Sadler Jon , Matthews Tom , Woodall Chris , Acil Nezha , Zhang Chao , Pugh Thomas
Published: February 2023

Tree functional strategies play a crucial role regulating the fitness and ability of forests to cope with water stress. However, current understanding on community-level functional strategies of forest ecosystems and how they vary with geographic patterns is still limited. We combined eight functional traits (e.g. leaf nitrogen content, xylem conductivity, leaf area to sapwood area ratio, leaf mass area, xylem water potential at 50% loss of conductivity, slope for the curve between P50-P88, leaf turgor loss point and wood density) with forest inventory data across the USA and Europe (12,332 0.25° gridcells) to identify functional strategies with respect to water stress and to analyse their relationships with climate factors and across functional groups. Principal components analysis suggests that functional strategies at species-level could be captured at community-level. Acquisitive-conservative strategies loaded along the first dimension, while the water storage and isohydricity strategies loaded along the second dimension. Spatial patterns of community-level strategies showed more explanatory power with temperature than aridity. Multiple community-level strategies at a grid cell were observed at water-limited sites, which were broadly captured by broad functional groups based on leaf type and phenology. Our findings promote the understanding of forest adaptation to drought and provide a basis for improving the ability of ecosystem models to predict the patterns of tree mortality and forest biomass accumulation.
Magnetotail configurations during ballooning-interchange instability signatures
Authors: Panov Evgeny V. , Kubyshkina Marina V. , Sergeev Victor A. , Nakamura Rumi , Baumjohann Wolfgang
Published: February 2023

In situ observations revealed both linear and non-linear signatures of the kinetic (electron) ballooning-interchange instability (BICI) operating in the Earth’s magnetotail, which appear to drive auroral beads and pseudo-breakups in the ionosphere. Observation of the magnetotail configurations with inversed gradient of the vertical magnetic field, which is believed to drive the BICI is challenged by sparse spacecraft coverage. We use the adapted modeling based on in situ spacecraft observations, and low-altitude particle observations to extract some information on such magnetotail configurations. We present two in situ events with both linear and non-linear BICI development signatures and compare the magnetotail configurations during the two events using the two methods.
The UK Hydrology Skills and Satisfaction Survey
Authors: Skinner Christopher , Ockelford Annie , King Andy , Goodship Esther , Harfoot Helen
Published: February 2023

The UK’s 25-year Flood Hydrology Roadmap was published in 2022. The Roadmap was developed by the UK hydrology community to identify the areas of greatest need and to deliver actions across four themes: Ways of Working, Data, Methods, and Scientific Understanding. Ways of Working Action W5 aims to build the ‘skills, esteem and value’ of flood hydrology but this is currently not possible as there is no baseline available. To provide a baseline, a UK-wide survey of hydrologists and the users of hydrology was conducted.The survey was designed after consultation with hydrologists working in academia, consultancies, and other authoritative bodies in the UK. The objective of the survey was to baseline the current number, skills, satisfaction, backgrounds, and diversity of hydrologists practicing in the UK. A further consideration was to understand how prepared hydrology is as a discipline for anticipated changes in methods and skill requirements. The survey covered both low and high flow hydrology, not just flood hydrology.In this presentation, we will summarise the key results of the survey and highlight the implications for hydrology training, education, and teaching. Finally, we will share our recommendations, from the perspective of operational users of hydrology, for the future skills needs in hydrology.
Understanding monsoonal rainfall patterns with a complex network approach
Authors: Bishnoi Guruprem , Donner Reik , Dhanya Chandrika Thulaseedharan , Khosa Rakesh
Published: February 2023

The Indian summer monsoon (ISM), which accounts for the majority of India’s yearly rainfall, has a significant influence on the nation’s economy. Understanding monsoonal dynamics is a challenge because of the related small-scale processes and their spatiotemporal complexity. Nevertheless, in the past decades, complex networks have become a key mathematical tool in the analysis of complex systems like the monsoon. However, multi-scale interactions and the coupling between rainfall and atmospheric circulation have remained underrepresented in the corresponding functional network studies. In this study, we exploit coupled rainfall networks to investigate simultaneous interactions of rainfall with other atmospheric variables. Firstly, rainfall networks are investigated by considering various network measures. Secondly, a coupled network is developed based on several atmospheric variables and their point-wise correlation with rainfall fields. Furthermore, the contrasts between the rainfall network and its coupled equivalent are emphasized. By comparison, the resulting coupled network includes both horizontal and vertical interconnections of the spatially enclosed time sequences, representing both the inherent structure of a single meteorological variable and the interaction structure with rainfall fields. It is expected to help with understanding the dynamics of monsoonal rainfall. This study, therefore, demonstrates the application of a complex network approach to studying highly dynamic phenomena such as the ISM. Our results are anticipated to provide the scientific community with new insights into how the interplay of the atmospheric systems leads to the heavy rainfall episodes that take place during the ISM.  
Comparison of PM2.5 concentrations prediction model performance using Artificial Intelligence
Authors: Wang Kyung-Hui , Lee Chae-Yeon , Lee Ju-Yong , Jung Min-Woo , Kim Dong-Geon , Han Seung-Hee , Choi Dae-Ryun , Yun Hui-young
Published: February 2023

Since PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) directly threatens public health, in order to take appropriate measures(prevention) in advance, the Korea Ministry of Environment(MOE) has been implementing PM10 forecast nationwide since February 2014. PM2.5 forecasts have been implemented nationwide since January 2015. The currently implemented PM forecast by the MOE subdivides the country into 19 regions, and forecasts the level of PM in 4 stages of “Good”, “Moderate”, “Unhealthy”, and “Very unhealthy”.Currently PM air quality forecasting system operated by the MOE is based on a numerical forecast model along with a weather and emission model. Numerical forecasting model has fundamental limitations such as the uncertainty of input data such as emissions and meteorological data, and the numerical model itself. Recently, many studies on predicting PM using artificial intelligence such as DNN, RNN, LSTM, and CNN have been conducted to overcome the limitations of numerical models.In this study, in order to improve the prediction performance of the numerical model, past observational data (air quality and meteorological data) and numerical forecasting model data (chemical transport model) are used as input data. The machine learning model consists of DNN and Seq2Seq, and predicts 3 days (D+0, D+1, D+2) using 6-hour and 1-hour average input data, respectively. The PM2.5 concentrations predicted by the machine learning model and the numerical model were compared with the PM2.5 measurements.The machine learning models were trained for input data from 2015 to 2020, and their PM forecasting performance was tested for 2021. Compared to the numerical model, the machine learning model tended to increase ACC and be similar or lower to FAR and POD.Time series trend was showed machine learning PM forecasting trend is more similar to PM measurements compared with numerical model. Especially, machine learning forecasting model can appropriately predict PM low and high concentrations that numerical model is used to overestimate.Machine learning forecasting model with DNN and Seq2Seq can found improvement of PM forecasting performance compared with numerical forecasting model. However, the machine learning model has limitations that the model can not consider external inflow effects.In order to overcome the drawback, the models should be updated and added some other machine learning module such as CNN with spatial features of PM concentrations. 
Acknowledgements
This study was supported in part by the ‘Experts Training Graduate Program for Particulate Matter Management’ from the Ministry of Environment, Korea and by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (ME) of the Republic of Korea (NIER-2022-04-02-068).
 
Impact of the Wadden Sea-North Sea interactions on the sea water carbonate system
Authors: Ourradi Yasmina , Ossebaar Sharyn , Reichart Gert-Jan , Humphreys Matthew
Published: February 2023

Despite the small area that shelf seas occupy, these regions play a significant role in the global carbon cycle. Through the so-called continental shelf pump mechanism they support more than 20% of the global marine uptake of atmospheric CO2. However, these regions are highly dynamic and the complex interactions within them make it difficult to identify and estimate the contribution of the different processes involved and changes therein. The North Sea, a shelf sea situated on the north-western European shelf is an iconic example of such a shelf sea, making it ideal to study these processes, connections and their variability. The North Sea is connected with the tidal basin of the Wadden Sea via channels that allow not only exchange of water but also dissolved and suspended materials. Through these connections, the Wadden Sea supplies large amount of dissolved inorganic carbon (DIC) but also total alkalinity (TA) generated from processes such as organic matter reduction and as a conduit from riverine inputs. In this study, pH, DIC and TA were monitored on discrete samples allowing the highest possible accuracy and precision at the NIOZ monitoring platform at the edge of the Marsdiep channel, through which most of the water exchange occurs between Wadden Sea and the North Sea. The flow direction reverses with the tides, so both North Sea and Wadden Sea waters are dominant at different points through a tidal cycle. The platform is also equipped with salinity, temperature and pH sensors, which measure every 10 minutes. Here, we use these data to assess the impact of the Wadden Sea on the North Sea, quantifying the TA and DIC supply to the North Sea and the role of the Wadden Sea in regional CO2 uptake. Through cross calibrating these parameters we aim to unravel the relative impacts of the different processes involved in mixing in a tidal inlet. The permanent observatory moreover allows us to investigate the drivers of variability in the TA and DIC exchange from diurnal to seasonal and even multi-annual timescales. 
Pyrogenic carbon redistribution in the landscape: example of a small, cultivated temperate watershed
Authors: Lebrun Thauront Johanne , Walter Christian , Ascough Philippa , Barre Pierre , Abiven Samuel
Published: February 2023

Naturally occuring pyrogenic carbon (PyC) is produced during wildfires under oxygen limiting conditions. After a fire event, PyC is fragmented, dissolved and transported at the soil surface1,2 and/or downward into the soil3,4⁠. PyC represents on average 15 % of organic carbon in soils and sediments5⁠.Its residence time in soil ranges from 50 to 1000 years6⁠,which makes it the most persistent form of organic carbon in soils. However, at the mouth of the world’s largest rivers, PyC is on average 16,000 years old. This difference is probably due to isolated measurements of turnover time in surface soil horizons which does not take into consideration transport and accumulation processes happening at the landscape scale. We make the following hypothesis : (i) PyC accumulates at depth in soil and in lowland and hill-foot positions, and (ii) PyC in accumulation zones is significantly older than PyC from other sites/depths.We studied the dynamics of PyC in a well characterized, 120 ha watershed in Brittany, France (ORE AgrHys). We collected soil cores at different topographic positions along three transects and quantified PyC using standard (chemo-thermal oxidation, hydrogen pyrolysis) and novel (Rock-Eval thermal analysis) analytical methods. We also measured the 14C ages of the PyC fraction. We show that relative to total SOC, PyC is preferentially redistributed to depth and that the subsoil (30 to 60 cm) represents about a third of the total soil PyC stock. We do not observe accumulation of PyC at the hill-foot except where superficial erosion products are retained before reaching the stream. We discuss the potential sources and redistribution mechanisms of PyC in the area over the last 10000 years.1. Bellè, S.-L. et al. Biogeosciences Discuss. 1–35 (2021)2. Rumpel, C., Ba, A., Darboux, F., Chaplot, V. & Planchon, O. Geoderma 154, 131–137 (2009).3. Soucémarianadin, L. et al. Soil Biol. Biochem. 133, 12–15 (2019).4. Schiedung, M., Bellè, S. L., Sigmund, G., Kalbitz, K. & Abiven, S. Biogeosciences 17, 6457–6474 (2020).5. Reisser, M., Purves, R. S., Schmidt, M. W. I. & Abiven, S. Front. Earth Sci. 4, 1–14 (2016).6. Singh, N., Abiven, S., Torn, M. S. & Schmidt, M. W. I. Biogeosciences 9, 2847–2857 (2012).
Organic matter decay response to root functional types and simulated sea-level rise in a temperate salt marsh
Authors: Arnaud Marie , Geairon Philippe , Bakhos Melissa , Rumpel Cornelia , Dignac Marie-France , Norby Richard J. , Kostyrka Pierre , Deborde Jonathan , Gernigon Julien , Lemesle Jean-Christophe , Bottinelli Nicolas , Polsenaere Pierre
Published: February 2023

Salt marshes are carbon rich ecosystems, but they are threatened by sea-level rise. Root litter dynamics have been little studied, despite contributing three times more to carbon burial than leaves litters and being important for salt marsh resilience to sea level rise. Root litter is defined as one single pool in salt marshes, yet this approach might fail to accurately assess root litter decay, as shown in terrestrial ecosystems where a root functional framework proved to be more accurate. We investigated if root functional types (fine absorptive roots, fine transportive roots and rhizomes) and inundation duration affected OM decay rate in a temperate salt marsh. We used a marsh organ and litterbags.We found that the decay rate strongly varied between root functional types, with fine absorptive roots decaying 30% slower than fine transportive roots and 14% slower than rhizomes.The decay rate was poorly explained by C:N ratio, N and C contents, while better correlated with root lignin composition. The duration of inundation had little effect on the belowground OM decay and did not interact with the decay of root functional types. Therefore, a shift toward experimental methods separating roots by function seems necessary to accurately assess the contribution of root litter to carbon burial in salt marshes. Overall, our results suggest that the current global carbon decay of salt marshes may have been overestimated and that fine absorptive roots could be a larger source for carbon burial than previously thought in salt marshes.
Modelling sub-canopy landscape-scale shortwave radiation in Eucalyptus forests using a modified Beer-Lambert law combined with airborne LiDAR
Authors: Lyell Christopher , Nyman Petter , Duff Thomas , Newnham Glenn , Inbar Assaf , Lane Patrick , Brown Tegan , Sheridan Gary
Published: February 2023

In forest systems, direct shortwave radiation (SWR) plays a vital role in fundamental energy and water processes that require high-resolution modelling at the landscape scale. We propose an alternative approach to modelling high resolution, landscape scale, direct SWR transmittance through forest canopies. This approach utilises airborne LiDAR (AB LiDAR) to calibrate a modified Beer-Lambert Law. Over a three-year period, we established the most comprehensive spatial and temporal sub-canopy dataset of 1-minute pyranometer measurements over 31 diverse sites with varying forest densities and age classes in south-eastern Australia. Measuring below canopy SWR at sub-daily and seasonal variations in zenith angle, as well as peak daily and accumulative radiation loads. The modified Beer-Lambert Law (Rbc = Race-kL), utilises path length through the canopy (L) and AB LiDAR as a representation of the sun's beam to measure transmittance (Rbc/Rac) of above canopy (Rac) to below canopy (Rbc) radiation; To calculate a site-specific extinction coefficient (k). This approach links the theoretical framework of the Beer-Lambert law with the canopy penetrating properties of AB LiDAR, allowing for large-scale spatial extrapolation of SWR transmittance in forest canopies. This differs from previous studies, which either: apply the Beer-Lambert law or the LiDAR penetrating properties separately, use AB LiDAR to represent the vegetation structure from which a Leaf Area Index (LAI) is calculated and transmittance modelled using specific leaf projection functions, or use computationally intense approaches such as ray tracing. These approaches have limitations as they either require site-specific calibration at the point scale, don’t account for seasonal variations in beam penetration angle, are difficult to parameterise across the landscape, or are too computationally intense to feasibly run at the landscape scale. The proposed model combined with LiDAR calibration addresses these limitations as the path length changes with zenith angle, and the calibration of the extinction using LiDAR allows for landscape-level parameterisation in a computationally friendly workflow. With the expanding availability of AB and spaceborne LiDAR, the linking of the penetrating properties of LiDAR with the theoretical concept of the Beer-Lambert law will allow below canopy direct SWR to be modelled with improved accuracy at large scales over daily and seasonal timespans. This improves our ability to model radiation loading below forest canopies across diverse landscapes and terrains, improving the modelling of hydrological, micro-climate, energy and water processes.
Remote sensing salt marsh biomass: a dialogue between technical approach and spatial patterns of ecogeomorphological complexity
Authors: Blount Tegan , D'Alpaos Andrea , Silvestri Sonia , Marani Marco
Published: February 2023

Salt marshes provide a multitude of ecosystem services while simultaneously being susceptible to habitat loss and degradation in response to climatic and anthropogenic disturbances. Thus, the spatially explicit characterisation, monitoring and sustainable management of these environments is crucial. Particularly as salt marshes are considered a blue carbon ecosystem due to their enhanced ability to produce and sequester organic carbon, acting as long-term reservoirs with a role in climate change mitigation. Since tidal wetlands are bio-geomorphologically intricate, biotic and abiotic coevolution is a key factor in the landscape development. Given the complexity of the processes and the interactions which underlie the system, research in this field requires a multidisciplinary approach. Remote sensing is a facet of this approach, which can enable the cost-effective analysis of salt marsh systems across a range of temporal and spatial scales.Herein we analyse the application of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) and optical sensors as tools to derive salt marsh biomass spatial distribution and structure in the Venice Lagoon (Italy). All validation and empirical relations are based on in situ data. Our results allow us to (1) derive digital terrain and vegetation models (DTM and DVM) and canopy structure using an efficient and open-access procedure; (2) examine the effect which scan angle, post-processing and variation in ecogeomorphological characteristics have on the accuracy of remote sensing results and; (3) further elucidate good practice guidelines for UAV based remote sensing of salt marsh topography and biomass. The results indicate that a linear feedback exists between the LiDAR scan angle and the DTM elevation error, notable for angles above 10 degrees. Furthermore, there is a dialogue between the accuracy of the remote sensing derived data and the spatial patterns driven by salt marsh ecogeomorphological complexity. Thus, characteristics such as vegetation density, elevation transitions between geomorphological structures and differences along the marsh gradient result in spatially variable levels of uncertainty. Overall, our analyses support salt marsh sustainable management as well as enhance the understanding of salt marsh ecogeomorphological complexity.
Long-range transport effect on the ice nucleation properties of Saharan dust
Authors: Piedehierro Ana A. , Welti André , Mustonen Linnea , Meinander Outi , Viisanen Yrjö , Laaksonen Ari
Published: February 2023

Mineral dust is the main responsible ice nucleating particle (INP) type for mixed-phase clouds and has an influence over regions far from the dust source. Parameterizations of the dust ice nucleation (IN) ability used in models are normally obtained from experimental characterization of dust collected from the surface at origin (Ullrich et al., 2017). However, IN properties of long-range transported dust may differ from the dust at origin due to different mineralogy and physical or chemical transformations along the transport route. Here we present preliminary results of ice nucleation properties of Saharan dust transported to Spain and Finland in the temperature range relevant for mixed-phase cloud formation.In this work, the IN ability in immersion mode is evaluated using an Mk-1 droplet-freezing cold stage (Sikora Scientific Instrumentation). The drop-freezing assays are performed by depositing a set of dust-containing droplets (1 μL) onto a hydrophobic glass that rests atop the cold stage, whose temperature can be varied at a specific cooling rate. A high-resolution camera captures images tracking the freezing events, while the temperature is decreased. Dilutions of the original suspensions are prepared to explore the colder temperature range. Heat treatments are applied to the samples to investigate the contribution of the sample’s biological-origin components to its ice nucleation ability. The BET surface area is measured for selected samples.This work was supported by the Academy of Finland Flagship ACCC (grant no. 337552) and MEDICEN project (grant no. 345125).Ullrich, R. et al. (2017). A New Ice Nucleation Active Site Parameterization for Desert Dust and Soot J. Atmos. Sci., 74, 699-717.
The propagation and 3D VHF polarization properties of recoil leaders
Authors: Hare Brian , Scholten Olaf , Buitink Stijn , Dwyer Joseph , Liu Ningyu , Sterpka Chris , ter Veen Sander
Published: February 2023

Lightning dart and recoil leaders are difficult to understand, as they have a different (often smoother) propagation mode than stepped leaders, and re-ionize a previously ionized channel. In order to understand them better, we have imaged recoil leaders with the LOFAR radio telescope (30-80 MHz), and will present 3D polarization, speed, and intensity data from multiple recoil leaders. We will show that many recoil leaders with high VHF intensity have VHF polarization that is very parallel to the recoil leader channel, with an opening angle as small as 15 degrees. Recoil leaders with lower VHF intensity have larger polarization opening angles, but it is not clear if this is physical or instrumental. In addition, VHF emission from recoil leaders comes from a sub-meter thin channel. Finally, we will show that the propagation speed and VHF intensity are strongly correlated; almost following a power-law or exponential relationship. These results probe the streamer behavior of recoil leaders, and thus provide significant clues to how recoil and dart leaders propagate. The fact that recoil leaders are very VHF thin is consistent with small polarization opening angles, and demonstrates that recoil leaders have significant streamer activity in their core and their corona sheath is VHF silent. The power-law/exponential relationship between speed and VHF intensity, however, is very difficult to explain.
Estimation of global extreme temperature trends by merging Annual Maxima and Peaks Over Threshold
Authors: Nerantzaki Sofia , Papalexiou Simon Michael , Rajulapati ‪Chandra Rupa , Clark Martyn
Published: February 2023

Annual Maxima (AM) and Peaks over Threshold (POT) are the two most common approaches to define extreme time series in hydroclimatic variables. Both methods present limitations. AM frequently fails to include significant extremes that occur during the same year. Conversely, POT may only include clustered values from a few years thus excluding many years from the analysis, especially when the threshold is set high. Additionally, a big challenge in POT is identifying the threshold which can markedly affect the results.Here, we merge notions from both AM and POT, preserving the strengths of each approach, to extract extreme temperature series and estimate the trends in their frequency and magnitude. We select the values larger than or equal to the minimum of the AM series as high temperatures (HT) (lower than or equal to the maximum of the Annual Minima as the low temperatures – LT). Thus, each year of the HT, LT series has at least one extreme value (H1, L1). We apply the method to 4797 quality-controlled raw station observations from a global dataset of maximum and minimum temperatures over 1970-2019 when warming accelerates. To examine changes in H1-L1 frequency and magnitude, we estimate the ratio of observed to expected H1 (L1) annual occurrences, and the difference between the observed and expected mean H1 (L1) annual temperature values, respectively. We estimate the regression slopes of these ratios at the station level, regionally in 2°×2° grids, and globally. We then compare these trends with the ones obtained from AM and POT series. The proposed method adapts the threshold for each sample, and finds a compromise among all tested methods, thus being a flexible approach that can be applied to any non-intermittent variable. AcknowledgmentThis research was supported by a GWF Ph.D. Excellence Scholarship from the Global Institute for Water Security (GIWS), University of Saskatchewan
Time lag detection between raw eddy-covariance data by prewhitening and cross-correlation analysis
Authors: Vitale Domenico , Papale Dario
Published: February 2023

Variables sampled by eddy-covariance (EC) systems are not temporally aligned because, to avoid possible wind flow distortions, sensors are not perfectly co-located. If not properly treated, such a temporal mis-alignment constitutes a source of systematic error in the derived EC fluxes. In most of EC data processing pipelines, the time lag is detected by assessing the cross-covariance function between the vertical wind speed and the atmospheric concentration of the scalar of interest. In particular, the optimal time lag is detected in correspondence of the lag that maximizes (in absolute terms) the cross-covariance function between raw, high-frequency, time series. Such a procedure is effective when the cross-covariance function exhibits a distinct and pronounced peak, a condition occurring under second-order stationary conditions and when the signal-to-noise ratio is moderate/high. In other circumstances, the cross-covariance function can be characterized by multiple local minima or maxima of similar magnitude, making the detection of the optimal time lag problematic. This often occurs for trace gases or during dormant/senescence periods when  fluxes are of small magnitude.This work introduces a new procedure being computationally efficient and completely data-driven where time lag is detected by assessing the statistical significance of the cross-correlation estimates between raw EC data subject to a preliminary (linear) transformation known as prewhitening. Prewhitening avoids the risk of nonsense (or spurious) correlations, making it more realistic and informative the assessment of the cross-correlation function, and then the detection of the optimal time lag.The procedure consists of the following steps: i) removal of the serial correlation from at least one of the two series involved in the cross-correlation function using an autoregressive integrated moving average  (ARIMA) model; ii) filtering of the other time series using an ARIMA model with the same parameters estimated in the previous step; iii) evaluation of the cross-correlation function between the transformed variables (i.e. between the model residuals). The effectiveness of the procedure is evaluated for the detection of time lag affecting CO2, H2O, N2O and CH4 variables. Results indicate that applying the proposed approach and using sonic temperature instead of vertical wind speed greatly facilitates the detection of the optimal time lag, even in the case of low magnitude fluxes.
Changes in the global upper ocean with new NEMOv4 features
Authors: Cocetta Francesco , Iovino Doroteaciro , Moulin Aimie , Masina Simona
Published: February 2023

New and updated physics and parameterizations implemented in the NEMO ocean model from version 4 onwards are tested in a global eddying ocean/sea ice configuration, specifically the GLOB16 system. Such configuration is at the base of the operational short-term Global Ocean Forecast System (GOFS) adopted at the Euro-Mediterranean Center on Climate Change (CMCC) and uses a nonuniform tripolar grid with 1/16° horizontal resolution (corresponding to 6.9 km at the Equator) and 98 vertical levels. We performed a set of short-term simulations forced by the ECMWF operational atmospheric fields at 1/10° spatial resolution.Among all the recent functionalities of the NEMO model, this work focuses on the new features that could impact the ocean energy budget. The new formulation of tides, the parameterization of the mixing induced by breaking internal waves and the formulation of the surface wave-induced mixing are selected. Test simulations are compared against a control run employing a set of metrics computed on the global domain and regional ocean sectors. Additionally, model results are evaluated against available satellite estimates to provide a first validation of the variability of upper ocean energy budget.In the simulation in which the surface wave-induced mixing is included, external input forcings are needed to provide an accurate representation of the surface wave processes. Here, integrated wave parameters from WAVEWATCH III model feed the NEMO ocean model, in the forced mode.Our analysis shows that all new ocean implementations impact global and regional patterns of sea surface salinity and sea surface height; conversely, only enhanced surface mixing affects the sea surface temperature and the mixed layer depth. However, all experiments showed the tendency to reduce the surface and basin-averaged ocean energy with updated mixing processes.
Spatio-temporal inversion of atmospheric microplastics emmisions using block-coordinate descent method
Authors: Tichý Ondřej , Šmídl Václav , Evangeliou Nikolaos
Published: February 2023

We propose an inversion methodology that allows to estimate large-scale spatio-temporal emission profiles from deposition measurements of airborne microplastics from the Western USA. Traditionally, each spatio-temporal element is solved separately using linear inverse model, however, it is hard or even impossible to deduce which part of measurements is reconstructed by which spatio-temporal element. In this contribution, we treat the unknown spatio-temporal source term as a single unknown variable of a large scale optimization problem. To achieve a tractable algorithm, we propose to use the block coordinate descent (BCD) approach with each spatial element being a block of coordinates. The implied inversion method is an iterative procedure with selected linear inverse method in the inner loop. We have tested the standard linear inversion with Tikhonov regularization as well as self-tuning LS-APC (Least Squares with Adaptive Prior Covariance) Bayesian inversion as inner loop algorithms. The method converges within a small number of iterations. The results are compared with previous approaches for spatio-temporal emission estimation and the potential of the novel method is demonstrated.
Windthrow detection with moderate to high resolution optical imageries across the European forests
Authors: Galizia Luiz , Nasrallah Ali , Elkhoury Charbel , Coutu Sylvain , Castet Christelle , Voituron Quentin
Published: February 2023

Windthrow is one of the major disturbances for forests and can affect large areas, causing extensive economic and ecological impacts across the different types of forests. Remote sensing is an effective tool with potential to cost-efficiently map large wind-affected regions, with a specific revisit time and spatial resolution, depending on the sensors used. Windthrow detection relies mostly on spatiotemporal changes of forest reflectance. However, one of the main drawbacks in using optical images is the cloud cover, and thus the availability of cloud-free images, especially during the cool season, when most of the windthrow events occur. For this reason, relying solely on the twin satellite Sentinel-2 may not be enough as data with 5-day revisit time are only available since 2016. On the other hand, different approaches have been developed to deploy radar images for scene changes detection, nonetheless, for forests, both L- and C-bands shall be integrated to capture the changes in the different layers of the forest, rendering the process very complicated. In this study, we developed two different approaches for windthrow detection based on the difference between the surface reflectance composite of the image’s (Sentinel-2 and Landsat-8/9) bands, before and after a windthrow event. First, a global machine learning model was developed using multiple windthrow events across Europe in order to classify windthrow events at continental scale. Then, a local machine learning model was developed using samples of damaged areas and non damaged areas of the same forest type in order to classify windthrow events within the same satellite image. Overall, our preliminary results showed that the global model presented relatively lower accuracy and F1 score. This finding is most probably due to the different types of forests, which present different spectral signatures and hamper the correct classification of the affected areas. Conversely, the local model presented higher accuracy and F1 score due to the homogeneity in the selected forest type. Our preliminary results thus indicate that windthrow detection at large scales is still challenging and local models may be a reliable alternative for assessing the wind-affected forests.
Multi-parameter flood risk assessment towards efficient flood management in highly dense urban river basins in the Region of Attica, Greece
Authors: Tsouni Alexia , Sigourou Stavroula , Dimitriadis Panayiotis , Pagana Vasiliki , Iliopoulou Theano , Sargentis G.-Fivos , Ioannidis Romanos , Chardavellas Efthymios , Dimitrakopoulou Dimitra , Mamasis Nikos , Koutsoyiannis Demetris , Kontoes Charalampos (Haris)
Published: February 2023

Flood risk assessment in vulnerable areas is crucial for efficient flood risk management, including the analysis and design of civil protection measures and the implementation of studies with proper interventions towards mitigating flood risk. This is even more crucial in highly dense urban river basins such as the ones in the region of Attica, which is hosting Athens, the capital of Greece, as well as critical infrastructures and important social economic activities. In the framework of the Programming Agreement with the Prefecture of Attica, the Operational Unit BEYOND Centre of EO Research and Satellite Remote Sensing of the Institute of Astronomy, Astrophysics, Space Applications & Remote Sensing (IAASARS) of the National Observatory of Athens (NOA), in cooperation with the Research Group ITIA of the Department of Water Resources and Environmental Engineering of the School of Civil Engineering of the National Technical University of Athens (NTUA), study five flood-stricken river basins in the region of Attica, which affect 23 Municipalities. The research teams collect all available data, conduct detailed field visits, run hydrological and hydraulic models, and assess flood hazard, flood vulnerability and eventually flood risk in every area of interest. Furthermore, high-risk critical points are identified, and mitigation measures are proposed, both structural and non-structural, in order to achieve effective crisis management for the protection of the population, the properties and the infrastructures. In addition, the BEYOND Centre has developed a web GIS platform where all the collected and produced data, the flood hazard, vulnerability and risk maps, as well as the identified critical points, the refuge areas and escape routes are stored and made available. All the relevant stakeholders and the competent authorities, who are directly or indirectly involved in civil protection, participate in dedicated workshops designed for their needs, and moreover, the studies’ general outcomes are disseminated to the wider public for raising awareness purposes. The response of the end users is very positive, and their feedback very constructive. The methodology and the outputs of the project are in line with the requirements for the implementation of the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, as well as the GEO’s Societal Benefit Areas.
Real-time Rainfall Estimation Using Binarized Rain Streak Images in Surveillance Cameras
Authors: Byun Jongyun , Lee Jinwook , Kim Hyeon-Joon , Jun Changhyun
Published: February 2023

Real-time monitoring and analysis of rainfall are important in reducing potential damage and losses in water-related disasters. Nowadays, IoT sensor data is being widely used in weather observation because of cost-effectiveness with high spatiotemporal resolutions. This study proposes a novel approach to estimate rainfall intensity from binarized rain streak images in surveillance cameras. Here, several background subtract algorithms are considered to extract rain streak images from raw video data recorded by surveillance cameras installed in six different points in Seoul, Korea. Various ranges of binarization threshold values are also used to calculate the number of white pixel values from rain streak images. As results, it indicates that rainfall intensity is properly estimated from binarized rain streak images with a relation equation between the number of white values and observation rainfall intensity data, which shows high dependence on the amount of illumination and recording environment characteristics (e.g. rainfall type, camera parameter, etc.).Keywords: Rainfall Estimation, Rain Streak, CCTV, Computer Vision, KoreaAcknowledgementThis work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01910 and in part supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).
Machine learning models to predict groundwater level in a Semi-arid river catchment, Central India
Authors: Bhadani Vipul , Singh Abhilash , Kumar Vaibhav , Gaurav Kumar
Published: February 2023

We applied different machine learning algorithms to predict the groundwater water fluctuation in a semi-arid river basin. Precipitation, temperature, evaporation, relative humidity, soil type, and groundwater lag were modeled as input features. Feature importance analysis of the input features indicate that the groundwater lag is the most relevant whereas the soil type is the least relevant input features. We applied the backward elimination approach to eliminate the less relevant features in mapping groundwater. Using the relevant input features we trained different machine-learning models (random forest, decision tree, neural network, linear regression, ridge regression, support vector regression, k-nearest neighbors, recurrent neural network). All these algorithms predicted the groundwater level with a high correlation of coefficient (R) ranging from 0.83 to 0.91 and a Root Mean Square Error (RMSE) ranging from 1.61 to 2.17 m.  We found that the random forest algorithm outperforms the other algorithms with a RMSE of 1.61 and R = 0.91. 
Numerical simulation and evaluation of global ultrafine particle concentrations
Authors: Kohl Matthias , Chowdhury Sourangsu , Sharma Disha , Cheng Yafang , Tripathi Sachchida Nand , Sebastian Mathew , Pandithurai Govindan , Wang Hongli , Lelieveld Jos , Pozzer Andrea
Published: February 2023

Ultrafine particles (UFP), i.e. particles with an aerodynamic diameter below 100 nm, have a significant impact on public health and the hydrological cycle. Recent studies showed that their ability to penetrate more deeply into the lungs and potentially into the blood stream may cause an increased incidence of cardiovascular and cerebrovascular diseases. Additionally, UFPs significantly contribute to cloud condensation nuclei concentrations. However, knowledge on global distributions of UFPs is scarce.We present a global simulation of UFP concentrations using the ECHAM/MESSy Atmospheric Chemistry Model (EMAC), including tropospheric and middle-atmospheric processes, and the modal aerosol microphysics submodel GMXe. Due to the high sensitivity of UFP concentrations to the size distribution of emitted particles, we derived emission median diameter for primary emissions from various sectors and species based on existing literature. We show the importance of primary emissions and nucleation on UFP concentrations as well as their composition, seasonality and vertical distributions.Model results were evaluated over Europe, the United States, India and China, using particle size distribution and particle number concentration measurements from available datasets and the literature. We obtain reasonable agreement between the model results and observations. However, the highest values of observed, street-level UFP concentrations are systematically underestimated, whereas in rural environments close to urban areas they are generally overestimated by the model. As the relatively coarse global model does not resolve concentration gradients in urban centres and local UFP hotspots, high-resolution data of anthropogenic emissions is used to account for such differences in each model grid box. This downscaling further improves the agreement with observations, decreasing the root mean squared logarithmic error and removing discrepancies associated with air quality and population density gradients within the model grid boxes.
Mobility of total carbon, nitrogen and polyphenols from grape pomace in the soil column
Authors: Korz Sven , More Camilla , Sadzik Sullivan , Buchmann Christian , Richling Elke , Munoz Katherine
Published: February 2023

Grape pomace (GP) can be legally applied as an organic fertilizer in the vineyards in Germany. Some risks are associated with this common practice, since grape pomace is observed to have a high carbon to nitrogen ratio and contains bioactive secondary metabolites. Despite these concerns, up to date little is known about the mobility of substances in the vineyard soil. In this study, our goal was to investigate the mobility of the macronutrient content of GP, derived from four Rhineland palatinate grape varieties, in three different soils in a column model. We used a three-step lab-scale approach that included the analysis of total carbon (C), nitrogen (N) and polyphenolic content (TPC) to analyse the mobility in:1) the GP, representing the maximum total amount2) the rainwater, representing the aqueous extractable fraction of the total amount3) the soil column, as the soil-mobile fraction, as well as the leachateOur results showed that up to 4 % of the total polyphenolic content of the pomace is leached into the soil. The recovery in the soil strongly depends on the combination of soil type and grape variety investigated. Generally, sandy and acidic soils showed an even distribution of phenolics with a high recovery rate (up to 92 %) of the water extractable amount. Most polyphenols could be recovered from the upper soil layer (0-10 cm). Despite the low pH of GP, there was no effect on soil pH. The same holds true for the C/N ratio. These results give a first impression of the mobility of macronutrients in the soil using a column model, supporting the need for incubation experiments that aim for the effect of the application on biogeochemical processes.