bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-range hydroacoustic observations of the Monowai Volcanic Centre as a proxy for seasonal variations in sound propagation
Authors: Smets Pieter , Weemstra Kees , Evers Läslo
Published: March 2020

Hydroacoustic activity of the submarine Monowai Volcanic Centre (MVC) is repeatedly observed at two distant triplet hydrophone stations, south of Juan Fernandez Islands (H03S, 9,159km) and north of Ascension Island (H10N, 15,823km). T-phase converted energy recorded at the broadband seismic station Rarotonga on Cook Island (RAR, 1,845km) is used as a reference for the cross-correlation analysis. A detailed processing scheme for the calculation of the daily cross-correlation functions (CCF) of the hydroacoustic and seismic data is provided. Preprocessing is essential to account for the non-identical measurements and sensitivities as well as the different sample rates. Further postprocessing by systematic data selection has to be applied before stacking CCFs in order to account for the non-continuous activity of the MVC source. Daily volcanic activity is determined for the period from 2006 until 2018 using the signal-to-noise ratio of the CCFs assuming sound propagation in the SOFAR channel. Monthly stacked CCFs with clear volcanic activity are used to study seasonal variations in sound propagation between the MVC and the hydrophone stations. In winter, however, a faster than expected signal is observed at H10N which is hypothesized to (partial) propagation through the formed sea ice along the path near Antarctica.
EO Satellite Based system for monitoring Bracken Fern in Scotland
Authors: Marzialetti Pablo , Fusilli Lorenzo , Laneve Giovanni , Cadau Enrico
Published: March 2020

Bracken fern (Pteridium) is ranked among the most invasive species of the world (Holland & Aplin 2013). The fern’s intrusion has caused great reduction in the quantity and quality of land accessible for grazing (Birnie & Miller 1986). In some cases, farmers permanently abandon agricultural land severely invaded by bracken fern (Schneider & Geoghegan 2006).

Literature has shown that the fern also obstructs secondary forest reestablishment, and does not deliver adequate quality biomass, which improves soil nutrients regeneration (Schneider & Geoghegan 2006; Oldham et al. 2013). In some cases, bracken fern patches have excluded conifer seedlings despite several post-harvest planting efforts (Ferguson and Adams 1994), and even when seedlings do survive, bracken fern can retard seedling growth (Dimock 1964). Bracken fern spread is also a strong obstacle for re-introducing the autochthone fauna.

Empirical evidence from literature has demonstrated that spatial data on bracken fern’s spread, its life cycle and fern status cannot be accurately mapped using field surveys in the remote and inaccessible mountainous environments in many parts of the world (Mehner et al. 2004; Ngubane 2014; Odindi et al. 2014). Several studies have used available remote sensing platforms for detection and mapping bracken fern spatial distribution at various scales (e.g. Miller et al. 1990; Holland & Aplin 2013; Ngubane 2014; Singh et al. 2014).

This work concerns the feasibility of developing an EO satellite-based system capable of mapping the presence of bracken fern vegetation and monitoring its distribution in a predefined area of western highlands in Scotland.

The study considers also the impact of clouds, often present in the area of interest, and assesses the suitability of different available satellite sensors and their products (in terms of spatial, spectral and temporal resolution) as a means for achieving the objective.

The challenges encountered include problems of similarity in the spectral signatures of bracken and other vegetation species, leading to low classification accuracy. This aspect has been minimized by using an approach which considers the specific phenology of the behaviour of the vegetation of interest. Preliminary results shown summer months (June, July) as the best period during the year to monitor this area of interest, due to the minimum presence of clouds and shadow areas. Regarding the use of SAR imagery, also foreshortening  and layover effects caused in this mountainous area limit the possibility to monitor the evolution of these plants.
Drought-induced decline in oak Mediterranean forests: insights from wood anatomical traits
Authors: Colangelo Michele , Rita Angelo , Borghetti Marco , Camarero Jesus Julio , Gentilesca Tiziana , Pericolo Osvaldo , Ripullone Francesco
Published: March 2020

Increased forest vulnerability to drought and heat spells is being reflected as more widespread and severe dieback events. In this regard, the Mediterranean Basin is revealing a high susceptibility to these phenomena across several tree taxa with a high ecological and socio-economic importance, particularly pines and oaks. For instance, oaks are particularly vulnerable to spring-summer droughts with important losses in term of growth and productivity accompanied by rising mortality rates and declining growth rates, despite some of these species are theoretically considered well-adapted to tolerate drought stress. Dendroecological studies using retrospective analysis of wood anatomical traits and tree-rings have demonstrated their potential to supply useful information on the long-term patterns of forest dieback in several oak species.  

In this study, we explored the xylem anatomical plasticity through time by performing a long-term (1980-2017) reconstruction of wood anatomical traits, aiming at investigate the drought stress effects on dieback of oak species.To this aim, we carried out some field experiments in Italy on four oak species differing in drought tolerance, i.e. Quercus robur, Quercus cerris, Quercus frainetto and Quercus pubescens, considered to have low to high tolerance, respectively, but showing recent decline phenomena. We cored asymptomatic (ND) and symptomatic (D) coexisting trees showing low and high defoliation levels, respectively, and for all sampled species we measured the following anatomical traits in the xylem: vessel area, Dh, vessel density.

Climate-traits relationships over the last century explained the recent growth divergence observed between D and ND trees because D trees were more sensitive[U1]  to drought stress and summer warm temperatures leading to high evapotranspiration rates. Here, we discuss: i) the relationships between radial growth, changes in wood anatomy and hydraulic functioning of trees to highlight the triggers of oak dieback; ii) the associations between climate, growth and anatomy data to explain likely the differences in acclimation/plasticity to short/long-term changes in environmental conditions.
Soil organic carbon dynamics as affected by climate warming in a semiarid alpine region
Authors: Zhu Meng , Liu Wei , Feng Qi , Jia Bing , Zhang Chengqi
Published: March 2020

An evaluation to soil organic carbon (SOC) stock dynamics in alpine regions is crucial for the adaptive management of regional carbon budget under the elevation-dependent warming in mountainous regions. Here, we evaluated the dynamics of SOC stock to 60 cm depth in the Qilian Mountains (1700~5100 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning technique (i.e. random forest, RF). Our results revealed that the combination of systematic measurements with the RF model allowed spatially explicit estimates to be made. The average SOC density (SOC amount per unit area, SOCD) in the middle Qilian Mountains will decrease under future climate change. However, the size and direction of carbon change are elevation- or vegetation-dependent. Specifically, in comparison with the baseline year (1970~2000), the mean annual precipitation will increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature will increase by 1.9, 2.4 and 2.9°C, respectively, under the RCP2.6 (representative concentration pathway), RCP4.5 and RCP8.5 scenarios in 2050s. Accordingly, the mean SOCD decreased by 0.59, 0.93 and 1.05 kg C m-2, the SOC stock decreased by 6.23, 9.75 and 11.07 Tg C, respectively under the RCP2.6, RCP4.5 and RCP8.5 scenarios. In addition, the mid-elevation zones (3100-3900 m), especially the subalpine shrub-meadow zone, will be characterized by the strongest carbon loss due to the high standing organic carbon stock under climate warming. By contrast, the high elevation zones (> 3900 m), especially the alpine desert zone, which will experience increase in accumulative temperature, prolongation in growing season, and consequently enhancement in plant productivity due to future climate warming, will be characterized by significant carbon accumulation in the future. Thus, the mid-elevation zones, especially the subalpine shrub-meadow zone should be given priority in terms of reducing CO2 emissions under future warming in alpine regions.
Seismically induced ground motions and source mechanism passively retrieved from remote infrasound detections
Authors: Shani-Kadmiel Shahar , Averbuch Gil , Smets Pieter , Assink Jelle , Evers Läslo
Published: March 2020

The amplitude of ground motions caused by earthquakes and subsurface explosions generally decreases with distance from the epicenter. However, in the near-source region, other factors, e.g., near surface geology, topography, and the source radiation pattern, may significantly vary the amplitude of ground motions. Although source location and magnitude (or yield), can be rapidly determined using distant seismic stations, without a dense seismological network in the epicentral region, the ability to resolve such variations is limited.

Besides seismic waves, earthquakes and subsurface explosions generate infrasound, i.e., inaudible acoustic waves in the atmosphere. The mechanical ground motions from such sources, including the effects from the above mentioned factors, are encapsulated by the acoustic pressure perturbations over the source region. Due to the low frequency nature of infrasound and facilitated by waveguides in the atmosphere, such perturbations propagate over long ranges with limited attenuation and are detected at ground-based stations. In this work we demonstrate a method for resolving ground motions and the source mechanism from remotely detected infrasound. This is illustrated for the 2010 Mw 7.0 Port-au-Prince, Haiti earthquake, and the 6th and largest nuclear test conducted by the Democratic People's Republic of Korea in 2017.

Such observations are made possible by: (1) An advanced array processing technique that enables the detection of coherent wavefronts, even when amplitudes are below the noise level, and (2) A backprojection technique that maps infrasound detections in time to their origin on the Earth's surface.

Infrasound measurements are conducted globally for the verification of the Comprehensive Nuclear-Test-Ban Treaty and together with regional infrasound networks allow for an unprecedented global coverage. This makes infrasound as an earthquake disaster mitigation technique feasible for the first time and contributes to the Treaty's verification capacity.
Measuring bedload transport rates in a laboratory flume: fluctuations and uncertainties
Authors: Pascal Ivan , Ancey Christophe
Published: March 2020

Measuring sediment fluxes in rivers and laboratory flumes has long been a challenge. Different definitions of sediment transport rates have been proposed over the past decades. Most measurement techniques involve collecting a volume of sediment in a sampler or counting the number of particles crossing a reference section within a given time interval. In laboratory experiments, scientists routinely use high-speed cameras and particle tracking techniques for monitoring bedload transport, but measuring the relevant transport parameters (i.e. the number of moving particles, their velocities and size, etc.) remains a demanding task. Moreover, no clear consensus has emerged on how to define mean bedload transport rates. To address this controversy, we ran an experiment in which we measured the particle flux in different places along a flume using high-speed cameras. Furthermore, we also determined the number of particles moving in a fixed control volume, their trajectories, and their velocities. Even under steady-state conditions, particle transport rates exhibited significant non-Gaussian fluctuations, which caused the time-averaged transport rate to fluctuate widely. In such a situation, determining the mean transport rate becomes a non-trivial operation. To solve this issue, we developed a procedure for estimating the uncertainties associated with the time-averaged transport rates. The theoretical underpinnings are provided by a Markovian model of bedload transport. We demonstrated its versatility by applying it to other laboratory and field cases with different monitoring systems.
Whole Earth Full-Waveform Inversion With Wavefield Adapted Meshes
Authors: Thrastarson Solvi , van Herwaarden Dirk-Philip , Krischer Lion , Boehm Christian , van Driel Martin , Afanasiev Michael , Fichtner Andreas
Published: March 2020

With the steadily increasing availability and density of seismic data, full-waveform inversion (FWI) can reveal the Earth's subsurface with unprecedented resolution. FWI, however, carries a significant computational burden. Even with the ever-increasing power of high-performance computing resources, these massive compute requirements inhibit substantial progress, and require algorithmic and technological innovations for global and continental scale inversions.
In this contribution, we present an approach to FWI where we achieve significant computational savings through wavefield adapted meshing [1] combined with a stochastic optimization scheme [2]. This twofold strategy allows us (a) to solve the wave equation at lower costs, and (b) to reduce the number of required simulations. In laterally smooth media, we can construct meshes which are adapted to the expected complexity of the wavefield. By optimally designing a unique mesh for each source, we can reduce the computational cost of the forward and adjoint simulations by an order of magnitude. The stochastic optimization scheme is based on a dynamic mini-batch L-BFGS approach, which adaptively subsamples the event catalogue and requires significantly fewer wavefield simulations to converge to a model than conventional FWI. An additional benefit of the dynamic mini-batches is that they seamlessly allow for the inclusion of more sources in an inversion without a considerable additional computational cost.
We demonstrate a prototype FWI for this approach towards a global scale inversion with real data.

[1] Thrastarson, S., van Driel, M., Krischer, L., Afanasiev, M., Boehm, C., van Herwaarden, DP., Fichtner, A., 2019. Accelerating numerical wave propagation by wavefield adapted meshes, Part II: Full-waveform inversion. Submitted to Geophysical Journal International
[2] van Herwaarden, DP., Boehm, C., Afanasiev, M., Krischer, L., van Driel, M., Thrastarson, S., Trampert, J., Fichtner, A. 2019. Accelerated full-waveform inversion using dynamic mini-batches. Submitted to Geophysical Journal International
Quantitative reconstruction of past climate mean states in the Atacama Desert using hydrogen and triple oxygen isotopes of gypsum hydration water
Authors: Voigt Claudia , Herwartz Daniel , Staubwasser Michael
Published: March 2020

Gypsum crystals capture the isotopic composition (δ2H, δ17O, and δ18O) of ambient water in their structurally bonded water and may serve as a useful tool to reconstruct paleoclimate. Relative humidity, water temperature, wind speed, along with the isotopic composition of atmospheric vapor and inflowing water control, to a variable degree, the relative proportion of equilibrium and kinetic isotope fractionation during evaporation, and, thus, ultimately determine the d-excess and 17O-excess of gypsum-bonded water. Here, we demonstrate that the respective best fit of these variables through measured gypsum-bonded water isotope data using the classic Craig-Gordon evaporation model provides apparent absolute values for the fundamental climate mean state variables humidity and temperature and an empirical wind speed parameter of the geologic past.

In this proof-of-concept study, we sampled gypsum crystals within individual stratigraphic units of Pliocene lacustrine deposits from the Atacama Desert, extracted their structurally bonded water, and analyzed the hydrogen and triple oxygen isotope composition. The spread of measured isotope data within each sampled stratigraphic unit suggests variable degrees of evaporation between individual gypsum samples along a common evaporation trajectory. We used the Craig-Gordon evaporation model together with a Monte Carlo simulation to determine the limits of climate mean state variables that fit the measured isotopic data.

Our results demonstrate that primary isotope signatures of marine and continental source waters are preserved in structurally bonded gypsum waters. The data coherently suggest a slightly warmer (18-35°C), less windy and much more humid (50-75%) climate for the Pliocene Atacama, which is consistent with marine records and global circulation climate models that agree on “permanent El Niño” conditions for the Pliocene in the equatorial East Pacific.

Under the assumption that mixing of different brines or multiple sources is insignificant - as would be evident from scattering of isotopic data below the evaporation trajectory in 17O-excess over δ18O – the combined hydrogen and triple oxygen isotope analyses of gypsum-bonded water provides a powerful tool to quantify past mean states of humidity and temperature, and to estimate paleo-wind conditions.
Anomaly Detection by STL Decomposition and Extended Isolation Forest on Environmental Univariate Time Series
Authors: Sezen İsmail , Unal Alper , Deniz Ali
Published: March 2020

Atmospheric pollution is one of the primary problems and high concentration levels are critical for human health and environment. This requires to study causes of unusual high concentration levels which do not conform to the expected behavior of the pollutant but it is not always easy to decide which levels are unusual, especially, when data is big and has complex structure. A visual inspection is subjective in most cases and a proper anomaly detection method should be used. Anomaly detection has been widely used in diverse research areas, but most of them have been developed for certain application domains. It also might not be always a good idea to identify anomalies by using data from near measurement sites because of spatio-temporal complexity of the pollutant. That’s why, it’s required to use a method which estimates anomalies from univariate time series data.

This work suggests a framework based on STL decomposition and extended isolation forest (EIF), which is a machine learning algorithm, to identify anomalies for univariate time series which has trend, multi-seasonality and seasonal variation. Main advantage of EIF method is that it defines anomalies by a score value.

In this study, a multi-seasonal STL decomposition has been applied on a univariate PM10 time series to remove trend and seasonal parts but STL is not resourceful to remove seasonal variation from the data. The remainder part still has 24 hours and yearly variation. To remove the variation, hourly and annual inter-quartile ranges (IQR) are calculated and data is standardized by dividing each value to corresponding IQR value. This process ensures removing seasonality in variation and the resulting data is processed by EIF to decide which values are anomaly by an objective criterion.
Soil organic carbon and soil total nitrogen stocks, soil quality and vegetation composition during natural revegetation processes in a Mediterranean mid-mountain area
Authors: Nadal Romero Estela , Sánchez Navarrete Pedro , Khorchani Makki , Medrano-Moreno Luis Miguel , Lasanta Teodoro
Published: March 2020

Mediterranean mid-mountain areas have been subject to significant human pressure through deforestation, cultivation of steep slopes, fires and overgrazing. However, during the 20th century, the mountainous areas of the northern rim of the Mediterranean region were affected by abandonment of cultivated fields and natural revegetation processes. Natural revegetation occurred in most of the lands where human activity (farming on steep slopes, grazing) declined in intensity or was abandoned, resulting in the expansion of shrubs, bushes and forests. What are the consequences of such processes on soil quality, soil organic carbon (SOC) and soil total nitrogen (TN) stocks and vegetation composition? What are the differences between the different land uses and land covers (LULCs)? The general aim of this study is to study the effects of natural revegetation processes after land abandonment on soil quality, SOC and soil TN stocks and vegetation composition in the Leza Valley (Iberian System, Spain). We hypothesized that natural revegetation processes improves soil quality and higher SOC and TN stocks. For this purpose, we analyzed 60 soil samples, from 5 LULCs and four depths (0-10, 10-20, 20-30, 30-40 cm): pasture, shrubs characterized by the presence of Cistus laurifolius, bushed characterized by the presence of Juniperus communis, Young forest (Quercus faginea), and old forest or dehesa. In addition, plant species inventories were carried out in each LULC.

The results related to physico-chemical soil properties indicated: (i) significant differences in soil quality between the first stages of natural revegetation (pasture and shrubs) and young forest (limited to the first 20 cm between shrub and young forest); (ii)  significant differences in SOC stocks between the first stage of natural revegetation (pasture) and young and old forests; (iii) significant differences in soil TN stocks between pasture and shrubs and young and old forests; and (iv) significant differences between the shrub families. Final results obtained through a Principal Component Analysis with all the variables differentiate forests from shrubs, bushes and pastures confirming our first hypothesis. We can conclude that natural revegetation is an effective strategy to improve soil quality and increase SOC and soil TN stocks.
Creation of detailed soil properties maps of the Czech Republic based on national legacy data and digital soil mapping
Authors: Minařík Robert , Žížala Daniel , Juřicová Anna
Published: March 2020

Legacy soil data arising from traditional soil surveys are an important resource for digital soil mapping. In the Czech Republic, a large-scale (1:10 000) mapping of agricultural land was completed in 1970 after a decade of field investigation mapping. It represents a worldwide unique database of soil samples by its national extent and detail. This study aimed to create a detailed map of soil properties (organic carbon, ph, texture, soil unit) by using state-of-the-art digital soil mapping (DSM) methods. For this purpose we chose four geomorphologically different areas (2440 km2 in total). A selected ensemble machine learning techniques based on bagging, boosting and stacking with random hyperparameters tuning were used to model each soil property. In addition to soil sample data, a DEM and its derivatives were used as common covariate layers. The models were evaluated using both internal repeated cross-validation and external validation. The best model was used for prediction of soil properties. The accuracy of prediction models is comparable with other studies. The resulting maps were also compared with the available original soil maps of the Czech Republic. The new maps reveal more spatial detail and natural variability of soil properties resulting from the use of DEM. This combination of high detailed legacy data with DSM results in the production of more spatially detailed and accurate maps, which may be particularly beneficial in supporting the decision-making of stakeholders.

The research has been supported by the project no. QK1820389 " Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping" funding by Ministry of Agriculture of the Czech Republic.
Learning main drivers of crop dynamics and production in Europe
Authors: Mateo Sanchis Anna , Piles Maria , Amorós López Julia , Muñoz Marí Jordi , Camps Valls Gustau
Published: March 2020

An expanding world population combined with challenges brought by climate change pose totally new scenarios for managing agricultural fields and crop production. In the last decades, a variety of ground-based, modeled, and Earth observation (EO) data have been used to characterize crop dynamics and, ultimately, estimate yield. Typically, optical vegetation indices and, in particular, metrics like their maximum peak or integral during the growing season are exploited to estimated crop yield. Also, most studies are focused on large areas with homogeneous agricultural landscapes in which cultivation/production is centred in a unique main crop (e.g. the U.S. Corn Belt or the Indian Wheat Belt). 

In this study, we study the transportability of machine learning models for crop yield estimation across different regions and the relative relevance of agro-ecological drivers (input features). We use a previous methodology presented in [1] that synergistically combined optical and microwave vegetation data for crop yield prediction. We apply this methodology, which was trained in the homogeneous area of the US Corn Belt, to the highly heterogeneous agricultural landscapes across Europe. The fragmented and diverse European agro-ecosystems poses a greater challenge for the combination of multi-sensor data, and we need to establish first which is the set of variables providing the best skill for yield estimation of the main crops grown in Europe (corn, barley and wheat) under this new scenario. Subsequently, we study whether these variables are also able to capture potential disruptions on crop dynamics deriving from extreme events and their influence in final crop production. 

[1] Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation. Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay and Gustau Camps-Valls. Remote Sensing of Environment 234:111460, 2019.
IRON: a permanent dense nationwide radon network to approach the challenge of monitoring seismic regions
Authors: Soldati Gaia ,   IRON working team
Published: March 2020

The deployment of multi-station and multi-parameter networks is considered fundamental in view of the investigation of Earth’s internal processes from which volcanic and seismic activity originate. The different changes often observed before the occurrence of strong earthquakes or eruptions (anomalies in sub-soil gas emission, hydrothermal discharge, chemical composition of groundwaters, Earth’s electromagnetic field) highlight the key role of fluids in the generation of these natural phenomena. Since they transfer from the underground to the surface messages about how the natural systems work, geochemistry can actively interact in a multidisciplinary context for investigating natural processes. While observational seismology has witnessed tremendous advances in the last twenty years, thanks to the development of very dense networks of stations measuring ground displacement, deformation and acceleration, the system of geochemical observations did not follow the same growth. The creation, ten years ago, of the Italian Radon mOnitoring Network (IRON) was motivated by the need for a permanent and dense network of stations aimed to make radon time series analysis a complement to traditional seismological tools. In fact, its radioactive nature makes radon a powerful tracer for fluid movements in the crust. The further step was the integration of IRON into a nationwide multi-parameter monitoring network, consisting so far of 10 homogenous sites including velocimeters, accelerometers, GPS sensors, and instruments measuring the Earth’s electromagnetic field. The potential of IRON as a tool to study the relationship between radon variability and the preparation process of earthquakes is discussed by means of two practical applications: to the 2016 Amatrice-Visso-Norcia seismic sequence and to the shorter sequence following the Ml 4.4 earthquake of 7 November 2019 in the Frusinate region.
Fractal reconstruction of the subgrid scales in turbulence models in applications to cloud microphysics
Authors: Akinlabi Emmanuel , Waclawczyk Marta , Malinowski Szymon
Published: March 2020

Modelling of small-scale turbulence in the atmosphere play a significant role in improving our understanding of cloud processes, thereby contributing to the development of better parameterization of climate models. One of the important problems is related to the transport of cloud particles, their activation and growth, which are influenced by small-scale turbulence motions. The idea presented in this work is to use fractal interpolation to reconstruct structures which are typically not resolved in the Large Eddy Simulations (LES) of clouds. Known filtered values of velocities on LES are basis of the reconstruction. The reconstructed small scales depend on the stretching parameter d, which is related to the fractal dimension of the signal. In many previous studies, the stretching parameter values were assumed to be constant in space and time. We modify this approach by treating the stretching parameter as a random variable with a prescribed probability density function (pdf). This function can be determined from a priori analysis of numerical or experimental data and within a certain range of wavenumbers it has a universal form, independent of the Reynolds number. We show, such modification leads to improvement in terms of reconstruction of two-point statistics of turbulent velocities. Preliminary results of simulations with Lagrangian particles (superdroplets) in the reconstructed field show the fractal model properly mimics the turbulent mixing processes at subgrid scales.
On the Rapid Spectral Evolution of Steep Wave Groups with Directional Spreading at Intermediate Depths
Authors: Barratt Dylan , Bingham Harry B. , Taylor Paul H. , van den Bremer Ton S. , Adcock Thomas A. A.
Published: March 2020

We have performed numerical simulations of steep three-dimensional wave groups, formed by dispersive focusing, using the fully-nonlinear potential flow solver OceanWave3D. We find that third-order resonant interactions result in directional energy transfers to higher-wavenumber components, forming steep wave groups with augmented kinematics and a prolonged lifespan. If the wave group is initially narrow banded, quasi-degenerate interactions resembling the instability band of a regular wave train arise, characterised by unidirectional energy transfers and energy transfers along the resonance angle, ±35.26°, of the Phillips ‘figure-of-eight’ loop. Spectral broadening due to the quasi-degenerate interactions eventually facilitates non-degenerate interactions, which dominate the spectral evolution of the wave group after focus. The non-degenerate interactions manifest primarily as a high-wavenumber sidelobe, which forms at an angle of ±55° to the spectral peak. We consider finite-depth effects in the range of deep to intermediate waters (5.592 ≥ kpd ≥ 1.363), based on the characteristic wavenumber (kp) and the domain depth (d), and find that all forms of spectral evolution are suppressed by depth. However, the quasi-degenerate interactions exhibit a greater sensitivity to depth, suggesting suppression of the modulation instability by the return current, consistent with previous studies. We also observe sensitivity to depth for kpd values commonly considered "deep", indicating that the length scales of the wave group and return current may be better indicators of dimensionless depth than the length scale of any individual wave component. The non-degenerate interactions appear to be depth resilient with persistent evidence of a ±55° spectral sidelobe at a depth of kpd =1.363. Although the quasi-degenerate interactions are significantly suppressed by depth, the interactions do not entirely disappear for kpd =1.363 and show signs of biasing towards oblique, rather than unidirectional, wave components at intermediate depths. The contraction of the wavenumber spectrum in the ky-direction has also proved to be resilient to depth, suggesting that lateral expansion of the wave group and the "wall of water" effect of Gibbs & Taylor (2005) may persist at intermediate depths.
Investigating the effects of land use change on ecosystem services: the Basilicata region (Italy) case study
Authors: Pilogallo Angela , Saganeiti Lucia , Scorza Francesco , Murgante Beniamino
Published: March 2020

By the end of this century effects of land-use change on ecosystem services are expected to be more significant than other world-wide transformation processes such as climate change, altering atmospheric concentrations of greenhouse gases or distribution of invasive alien species.

In recent years, scientific literature has been embellished with numerous land-use models that aim to explore the behaviour of land use systems under changing environmental conditions and different territorial transformations explaining the different dynamics that contribute to it, and to formulate scenario analyses to be followed up by development strategies.

In addition, it should be noted that a dimension of the nexus between planning and sustainability that is important but still too little explored, is the assessment of territorial changes and development dynamics through the alterations analysis induced on processes, functions and complex systems.

While land-use models can help investigate the effects of a combination of drivers at different scales, ecosystem services approach can help in better understand the trade-offs between different development scenarios making explicit the relations that every variation induces within the relationship between man and territory  and among different environmental components.

In this framework is set the present work that aims to integrate scenario analysis of the Basilicata region (Italy) development with assessments of alterations induced on the capacity to deliver ecosystem services. Although this region is very poorly populated and characterised by low settlement density, it is not immune to the global phenomenon of land take associated with high territorial fragmentation.

The building stock increase due to real development dynamics and relative demographic increase typical of the post-war period, was followed by a further built up environment growth - in contrast with the demographic trend - and a significant land take due to massive construction of renewable energy production plants.

Changing model have been applied to identify and classify the driving forces for land use changes and predict future development scenarios.

In order to contribute to the development of decision support systems, scenarios resulting from the implementation of different policies are analyzed with the ecosystem services approach.
Wetter is better: rewetting of minerotrophic peatlands increases plant production and moves them towards carbon sinks
Authors: Schwieger Sarah , Kreyling Jürgen , Couwenberg John , Smiljanić Marko , Weigel Robert , Wilmking Martin , Blume-Werry Gesche
Published: March 2020

In their natural state peatlands are effective carbon sinks as more biomass is produced than decomposed under the prevalent anoxic conditions. Draining peatlands results in release of the stored carbon. Rewetting may or may not restore the original carbon sink. Patterns of plant production and decomposition in rewetted peatlands and how they compare to the drained state remain largely unexplored.

We measured annual above- and belowground biomass production and decomposition in three different drained and rewetted peatland types: alder forest, percolation fen and coastal fen. We also used standard material (green and rooibos tea) to compare decomposition between the sites, regardless of the decomposability of the local plant material.

Rewetted sites had higher root and shoot production in the percolation fen, and higher root production in the coastal fen but similar root and leaf production in the alder forest (excluding woody biomass). Decomposition rates were similar in drained and rewetted sites, only in the percolation fen and alder forest aboveground litter decomposed faster in the drained sites. The rewetted percolation fen and the two coastal sites have the highest projected potential for organic matter accumulation due to high production and low decomposition rates. Roots accounted for 23–66% of total biomass production, and the importance of belowground biomass, rather than aboveground biomass, for organic matter accumulation increased with time. This highlights the significance of roots as main peat forming element in these graminoid-dominated fen peatlands and their crucial role in carbon cycling. Notably, increased production compensated for loss by decomposition even during the exceptionally dry year 2018.

Rewetted sites generally had a more productive plant community compared to drained sites, only tree stem biomass increment was higher in the drained alder forest site. High biomass production supported the peatlands’ function as carbon sink even during a dry year and roots were more important than shoots in establishing this sink, especially in the graminoid dominated sites. Rewetted peatlands may cope better with the extreme weather conditions that will occur more frequently in the future, emphasizing the case for rewetting those systems
Predicting and mapping of soil salinity using machine learning algorithms in central arid regions of Iran
Authors: Taghizadeh-Mehrjardi Ruhollah , Toomanian Norair , Shamshirband Shahab , Mosavi Amir , Behrens Thorsten , Schmidt Karsten , Scholten Thomas
Published: March 2020

In this study, we predicted and mapped soil salinity using machine learning (ML) and digital soil mapping (DSM) approaches. Support vector regression (SVR) and the hybrid of SVR with wavelet transformation (W-SVR) where applied to correlate soil salinity of the upper 200 cm of soil to a wide range of environmental covariates derived from a digital elevation model (DEM), remote sensing (RS) and climatic data. Results indicate that W-SVR performed better in predicting soil salinity at all depth intervals with scattered index ranging from 1.45 to 1.68 compared to the standalone SVR. This is particularly true at the lowest soil depth when W-SVR indicated ~1.5 times higher accuracy compared to the SVR. At this soil depth topographic features are the main covariates in the models. For topsoil salinity, land use represented by RS features controls the spatial distribution of the salinity widely. Independent from soil depth, climatic features are the most important predictors for soil salinity in all ML models. The predicted salinity maps show the highest salinity for soils in the eastern parts of central Iran. Furthermore, the importance of topographic features for all ML algorithms coincides with most landform characteristics in central Iran and confirms a close relation of soil salinity not only to land use practices like irrigation but also to soil-landscape relationships in this dry region.

Keywords: Soil salinity, machine learning, spatial variation, central Iran, support vector regression, wavelet transformation

Acknowledgments

Ruhollah Taghizadeh-Mehrjardi has been supported by the Alexander von Humboldt Foundation under the grant number: Ref 3.4 - 1164573 - IRN - GFHERMES-P.
Convective and stratiform precipitation: A PCA-based clustering algorithm for their identification
Authors: Francipane Antonio , Sottile Gianluca , Adelfio Giada , Noto Leonardo V.
Published: March 2020

The increasing occurrence of flood events in some areas of the Southern Mediterranean area (e.g., Sicily), over the last few years, has contributed to raising the importance of characterizing such events and identifying their causes. Since most of these events can be related to high-intensity rainfalls, which, in turn, are usually due to convective rainfall, it is very important to understand which factors could be recognized as drivers of such extreme events. Nevertheless, the way to distinguish between convective and stratiform rainfall is still an open issue and not easy to solve.
With this regard, starting from precipitation time series recorded at different rain gauge stations of Sicily, which is the greatest Mediterranean island, we propose an algorithm capable to classify precipitation distinguishing between their convective and stratiform components.
In order to do that, a dataset from the regional agency SIAS (Servizio Informativo Agrometeorologico Siciliano - Agro-meteorological Information Service of Sicily) has been used because of its high temporal resolution, quality, and availability of up-to-date data. Specifically, data from rain gauge stations spread over the entire island have been collected for the period 2003 - 2018 and with a temporal resolution of 10 minutes.
In order to classify the precipitation in convective and stratiform components, the functional PCA-based clustering approach (denoted by FPCAC) has been applied, which can be considered as a variant of a k-means algorithm based on the principal component rotation of data. In order to evaluate the validity of the proposed algorithm, finally, the results have been compared to some ERA5 reanalysis products.
PhenoCar: Assessment of phenology of thousands of trees along an environmental gradient using car mounted cameras and deep-learning based image segmentation
Authors: Basler David , Richardson Andrew D.
Published: March 2020

The length of the period of vegetation activity is a significant driver of the global carbon cycle. Thus, the observation of plant phenology and seasonal vegetation dynamics has become an essential tool to quantify the impact of climate change on ecosystems. However, the accurate prediction of potential shifts of plant phenology in a warmer future requires a detailed spatio-temporal quantification of phenological patterns observed today. While phenological data derived from satellite-based remote sensing platforms often lack the spatial and/or temporal resolution to resolve the responses on the species level or to even reveal intraspecific patterns across the landscape, accurate visual observations by a human observer for thousands of trees are often not feasible due to time constraints. Therefore, we here present a novel near-surface remote sensing method that allowed the accurate tracking of tree phenology along an elevation- and urbanization gradient using a car-mounted camera. Using deep-learning-based image segmentation, we were able to track distinct patterns in the timing of leaf phenology of tens of thousands of trees along a nearly 100 km transect in New England throughout two growing seasons. The efficient collection of such high-resolution, multi-species, spatiotemporal data provides an excellent opportunity to quantify variation in tree phenology down to the level of individual organisms, across landscape and regional scales and for the fine-tuning of phenological models.
Pleistocene sea-level record in low latitude settings: the Cape Verde Islands.
Authors: Bardají Teresa , Goy José Luis , Zazo Caridad , Hillaire-Marcel Claude , Dabrio Cristino J. , Gozález Ángel , Ghaleb Bassam , Cabero Ana , Soler Vicente , Lario Javier
Published: March 2020

The volcanic Cape Verde archipelago constitutes one of the few sites in low latitude eastern Atlantic Ocean, where a long record of Pleistocene sea-level indicators develops, particularly beach deposits and marine terraces. The extreme aridity of the easternmost islands (Sal, Boa Vista and Maio) allows the exposure of long sedimentary sequences, the altitudinal and spatial distribution of which must be related both to sea level behaviour in low latitude settings and also to the volcanic nature of the archipelago.

The particular case of Maio Island reveals the occurrence of a flight of at least 18 marine terraces, between +85 and 0m. The chronology has been approached by a paleomagnetic sequence (Early - Middle Pleistocene transition), U-series measurements (Last Interglacial deposits) and 14C (Holocene units).

The results have revealed a differential behaviour in the vertical motion of the island along the Pleistocene, with unequal uplift rates during Early and Middle Pleistocene. An anomalously low-lying MIS5 unit in this island fits well with the predictions done by GIA models of Crevelling et al., (2017) although the proper evolution of volcanic islands cannot be discarded.

A comparison with Sal (Zazo et al., 2007, 2010) and Boa Vista islands is done, especially in what the MIS5 sea level record is concerned. MIS 5e deposits are scarce along the coasts of Maio and Boa Vista, and always at very low heights above mean sea level (0-0,5 m). On the island of Sal the deposits corresponding to the MIS 5e are located at a maximum height of +2.5m asml, in its most southern sector, being also very frequent to find them at 0m (Zazo et al., 2010).

The geomorphological distribution of the Pleistocene sedimentary sequences along these three islands reveals a complex history of uplift and subsidence that must be conciliated with the far-field sea level behavior, especially for the MIS5 units.

Creveling et al., 2017. QSR 163.

Zazo et al., 2007. QSR 26.

Zazo et al., 2010. GPCh 72.

Acknowledgements: This work has been supported by FEDER-MINECO Spanish project CGL15-69919-R.
Building a perfomance protocol of a low cost seismograph
Authors: Avlonitis Markos , Krokidis Spiridon , Vlachos Ioannis , Karakostas Vasileios , Kostoglou Anastasios
Published: March 2020

The low cost seismograph under study consists of low cost open source hardware and software microprocessor boards (Arduino Uno R3  and Raspberry Pi 3 B+ ), customized low noise design signal amplifiers, low power dissipation sophisticated power supply, two (2) kinds of earth ground shaking sensors a) Ceramic Accelerometer with cutoff frequency fc=0.15Hz and b) moving coil geophone with cutoff frequency fc=4.5Hz. The signals from the two sensors are amplified independently, while an active second order low-pass anti-alias filter and an 8th order active low-pass anti-alias filter have been used. Finally, a low-cost microprocessor board is responsible for digitizing the analog data from the amplified signal of the sensors with a frequency sampling rate of 345Hz. The aim of the present work is to design and test a systemic protocol in order to evaluate the performance of the proposed low cost seismograph for monitoring local to regional seismicity and micro seismicity. The proposed low-cost system was installed in an area of high seismic activity (Lefkada Island – Village Evgiros) and the recordings are transmitted to the database continuously from the day of its installation up today. Thus have create an amount of data for more than 280 days and all of those data have been stored to our database. Collocated with a high resolution 24 bits digitizer equipped with a broad band seismometer give us the opportunity to compare the recordings. To this end, a testing list of 15 local events has been created with different epicenters and magnitudes. For each event the recording signals have been analyzed in terms of a) power spectrum analysis, b) estimation of first arrival times of both P and S waves, c) signal amplitudes and d) earthquake duration. The choice of those specific measures was done in order to evaluate the performance of the low-cost seismograph in terms of certain seismic parameters such as magnitude, epicenter and source properties. Initial results in terms of the proposed protocol are also presented showing an adequate performance of the propose low cost seismograph.

 

Keywords:
low-cost instruments, Ionian islands, performance protocol

Acknowledgements
«Telemachus – Innovative Seismic Risk Management Operational System of the Ionian Islands» which is part of the Operational Program «Ionian Islands 2014-2020» and is co-financed by the European Regional Development Fund (ERDF) (National Strategic Reference Framework - NSRF 2014-20).