bluuebunny/crossref_metadata_2025 huggingface dataset:
How do river channels aggrade? An investigation into the importance of upstream drivers (water discharge and sediment supply) on sediment aggradation using analogue modelling
Authors: Watkins Stephen E. , Sharma Nikhil , Valero Luis , Tremblin Maxime , Zaki Abdallah S. , Arlaud Frédéric , Simpson Guy , Castelltort Sébastien
Published: March 2020

Stratigraphic architecture of fluvial deposits is often interpreted as a record of changes in accommodation created by absolute sea-level change, subsidence, or a combination of both (downstream drivers). An increase or decrease in accommodation causes the fluvial system to respond by either aggrading or degrading to a new equilibrium slope. However, in recent years the role of upstream drivers, such as water discharge and sediment supply (volume and grain-size distribution), in controlling equilibrium slopes has gained more importance, however we still lack significant understanding of these upstream processes. It is important to be able to differentiate between stratigraphy influenced by upstream and downstream drivers in the field because fluvial deposits represent an important archive of environmental changes.  Traditionally, downstream drivers are often invoked to explain past accommodation changes, but in actuality there are rarely robust constraints on the cause of these space changes. At present there is still no well-documented examples of upstream versus downstream driven stratigraphic architecture. One way to address this issue is by undertaking analogue modelling (i.e. flume experiments) as this permits the isolation of individual parameters, such as water discharge, and allows us to investigate their role on the fluvial system in a controlled environment.

 

In the first part of the project that we present here, we investigate how sediment aggradation within a channel develops through time by using a quasi-2D flume.  We have designed and manufactured a narrow (0.05 m), long (2.4 m) flume with an initial gradient of zero.  We aim to (i) investigate how aggradation occurs through time using a series of different water discharges, sediment supplies and sediment concentrations and observe the resulting equilibrium slopes; (ii) perturb the system once equilibrium is reached to observe the readjustment of the system to new conditions; (iii) carry out a series of experiments varying downstream drivers (i.e. sea-level) which theoretically produce the same amount of aggradation as the upstream parameters we have used do, we will then be able to compare any similarities or differences in stratigraphy.  Ultimately we will use these results to scale up to a fully three-dimensional analogue model (i.e. a wide flume, approximately 1 m) that produces channels and floodplains.  We can then investigate how the upstream and downstream changes seen in the narrow flume are translated into the wider flume.
Heatwaves and Predictability - the Role of Rossby Waves and Atmospheric Waveguides
Authors: White Rachel , Prodhomme Chloé , Fragkoulidis Georgios , Materia Stefano , Ardilouze Constantin
Published: March 2020

Heat waves can have a devastating impact on human society and ecosystems, and thus improved understanding and predictability of such events would provide huge benefits. It has been shown previously that many extreme temperature events are associated with quasi-stationary, or recurrent, Rossby waves (hereafter QSWs). We show that these QSWs are often associated with atmospheric waveguides, providing some dynamical understanding of why such weather patterns persist. In the context of this framework, we study the subseasonal-to-seasonal (S2S) predictability of heatwaves, QSWs, and atmospheric waveguides. Operational seasonal forecasts can reproduce the observed climatological statistics of QSWs, and the observed connection between QSWs and extreme temperatures over Europe, although with some biases. To better understand the underlying dynamics of the seasonal forecast models, we explore whether such models are capable of reproducing the observed connection between QSWs and atmospheric waveguides,  linked to persistent, and thus high impact, extreme heat events. We examine the S2S predictability of atmospheric waveguides and high amplitude QSW events, to better understand the potential S2S predictability of heatwaves.
UAS magnetics as a non-invasive exploration technology
Authors: Madriz Yuleika , Zimmermann Robert , Shaik Fareedh Junaidh , Lorenz Sandra , Gloaguen Richard
Published: March 2020

The growing demand for innovative and sustainable exploration technologies is boosting opportunities for non-invasive geophysical surveys using unmanned aerial systems (UASs). During the last few years lightweight magnetometers have been increasingly developed for their use on UASs. Aeromagnetic surveys can provide a rapid and cost-effective technology to improve the detection of shallow targets and to delineate magnetite-pyrrhotite-rich mineralizations. With low altitude flights and tight flight lines, magnetometers lifted by rotary wing UAS systems can deliver high resolution maps in small-to-medium scale areas (<100 sq.km). We propose an adaptive workflow for aeromagnetic survey acquisitions by using multi-copters that in combination with a programmed processing tool can efficiently achieve valid observations and reliable maps. Results suggest that minimizing and compensating for the magnetometers attitude changes during flight as well as the removal of temporal variations plays an important role to avoid small anomalies to go undetected. For this study we present a comprehensive data set where UAS aeromagnetic surveys aids to overcome the scale gap between ground and airborne magnetics in potentially hazardous environments where UAS have operational advantage over traditional techniques.
Dispersion Model Evaluation for the Sulfur Dioxide Plume from the 2019 Raikoke Eruption using Satellite Measurements.
Authors: de Leeuw Johannes , Schmidt Anja , Witham Claire , Theys Nicolas , Pope Richard , Haywood Jim , Osborne Martin , Kristiansen Nina
Published: March 2020

Volcanic eruptions pose a serious threat to the aviation industry causing widespread disruption. To identify any potential impacts, nine Volcanic Ash Advisory Centres (VAACs) provide global monitoring of all eruptions, informing stakeholders how each volcanic eruption might interfere with aviation. Numerical dispersion models represent a vital infrastructure when assessing and forecasting the atmospheric conditions from a volcanic plume.

In this study we investigate the 2019 Raikoke eruption, which emitted approximately 1.5 Tg of sulfur dioxide (SO2) representing the largest volcanic emission of SO2 into the stratosphere since the Nabro eruption in 2011. Using the UK Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME), we simulate the evolution of the volcanic gas and aerosol particle plumes (SO2 and sulfate, SO4) across the Northern Hemisphere between 21st June and 17th July. We evaluate the skills and limitations of NAME in terms of modelling volcanic SO2 plumes, by comparing our simulations to high-resolution measurements from the Tropospheric Monitoring Instrument (TROPOMI) on-board the European Space Agency (ESA)’s Sentinel 5 – Precursor (S5P) satellite.

Our comparisons show that NAME accurately simulates the observed location and shape of the SO2 plume in the first few weeks after the eruption. NAME also reproduces the magnitude of the observed SO2 vertical column densities, when emitting 1.5 Tg of SO2, during the first 48 hours after the eruption. On longer timescales, we find that the model-simulated SO2 plume in NAME is more diffuse than in the TROPOMI measurements, resulting in an underestimation of the peak SO2 vertical column densities in the model. This suggests that the diffusion parameters used in NAME are too large in the upper troposphere and lower stratosphere.

Finally, NAME underestimates the total mass of SO2 when compared to estimates from TROPOMI, however emitting 2 Tg of SO2 in the model improves the comparison, resulting in very good agreement with the satellite measurements.
Continental and global hydro-climatic forecasting services to address user needs for the water-related sectors
Authors: Bosshard Thomas , Arheimer Berit , Crochemore Louise , Gyllensvärd Frida , Pechlivanidis Ilias , Photiadou Christiana
Published: March 2020

Addressing the user needs at the local and large scales remains an ongoing scientific and operational effort to the various hydro-climatic service providers. The evolution of hydro-climatic services has received high attention, particularly given the recent scientific and computational advancements that have led to skillful meteorological forecasts at time horizons from sub-seasonal (up to 6 weeks ahead) to seasonal (up to a year ahead). Sub-seasonal to seasonal (S2S) forecasts have great potential for user groups that are affected by climatic variations and that could manage such variations to their advantage through better predictions. Therefore the Swedish Meteorological and Hydrological Institute co-developed with users from the water-related sectors a demonstrator interface to communicate the ensemble of pan-European and global hydro-climatic indicators at the catchment scale.

 

Here we present these operational hydro-climatic services for the long time horizons, and focus on the setup, the implementation and the challenges. The provided hydro-climatic forecasts are based on the bias-adjusted meteorological forecasts from ECMWF (i.e. daily precipitation and daily mean, maximum and minimum temperature) and the pan-European E-HYPE and global WW-HYPE hydrological models (http://hypeweb.smhi.se/). The forecasts are updated frequently when the newly initialised forecasts become available. Hydro-climatic information for variables such as river flow, water discharge, actual and potential evapotranspiration, soil water content, precipitation and temperature is presented as maps and graphs, for both climatology and forecast period. The service provides also the option to download the forecast information (catchment scale) including also the metadata and forecast skill information. The map shows the anomaly for each catchment and lead time using as reference either the catchment’s normal conditions (based on terciles) or extremes (10th and 90th percentiles) for the period of interest. To overcome misinterpretation of the forecasted information, we set as default the option to the user to mask the catchments in which forecasts have no skill (based on re-forecast analysis); meaning that climatology is more predictive than ECMWF forecasts. The graphs display the median and different percentiles of the ensemble of forecasts, and the high and low thresholds of the normal and extreme conditions for the period of interest.

 

Keywords

Seasonal hydro-meteorological forecasting, Copernicus C3S, global climate services
Retrieval of Stable Water Vapour Isotopologues from the TROPOMI Instrument
Authors: Trent Tim , Boesch Hartmut , Somkuti Peter , Schneider Matthias , Khosrawi Farahnaz , Diekmann Christopher , Sodemann Harald
Published: March 2020

Atmospheric moisture is a crucial factor for the redistribution of heat in the atmosphere, with a strong coupling between atmospheric circulation and moisture pathways which are responsible for most climate feedback mechanisms. Conventional satellite and in situ measurements provide information on water vapour content and vertical distribution; however, observations of water isotopologues make a unique contribution to a better understanding of this coupling.

In recent years, observations of water vapour isotopologue from satellites have become available from nadir thermal infrared measurements (TES, AIRS, IASI) which are sensitive to the free troposphere and from shortwave-infrared (SWIR) sensors (GOSAT, SCIAMACHY) that provide column-averaged concentrations including sensitivity to the boundary layer. The TROPOMI instrument on-board Sentinel 5P (S5p) measures SWIR radiance spectra that allow retrieval of water isotopologue columns but with much improved spatial and temporal coverage compared to other SWIR sensors promising a step-change for scientific and operational applications.

Here we present the development of the retrieval algorithm for water isotopologues from TROPOMI as part of the ESA S5p Innovation programme.  We also discuss the validation of these type of satellite products with fiducial in situ measurements and challenges when comparing with other satellite measurements. Finally, we outline the roadmap for assessing the impact of TROPOMI data against state-of-the-art isotope enabled models.
A comparative analysis of property level flood mitigation behaviour in the regions of England
Authors: Barendrecht Marlies H , McCarthy Simon , Viglione Alberto
Published: March 2020

Even though there has been a move towards a more integrated approach to flood risk management, with a stronger focus on property level measures to reduce flood risk, the uptake of property level measures remains low. Experience has been found to influence the uptake of measures, but even property owners with experience do not always take measures to prepare for future flooding. In this paper we investigate the variations in the relationship between experience and preparedness (i.e. the uptake of property level measures) for the different regions of England. We use survey data collected among the population at risk in the years 1996 to 2004 and perform a hierarchical beta regression to determine the differences between the seven regions. We find that the South West and Southern regions have a higher increase in preparedness with increasing experience compared to other regions. In the Thames, Midlands and North West the preparedness increases less with increasing experience. Based on an analysis of additional data sources (e.g. surveys and maps of structural protection and population) we provide a possible explanation as to why the behaviour of property owners in these regions could be different.
Fertilization effects on the fungal biomass in grasslands
Authors: Barreiro Ana , Fox Aaron , Lüscher Andreas , Widmer Franco , Dimitrova Mårtersson Linda-Maria
Published: March 2020

Fertilisation is a common practise in grass production systems performed to increase primary production, a supporting ecosystem service essential for other services. However, different fungal groups, like saprothropic fungi (SF) and the obligate symbionts arbuscular mycorrhizal fungi (AMF), have potential differential response to the fertilizer concentration and composition. Three controlled field experiments were utilised in our study, two medium-term (6 years) in the south of Sweden (SE) and one long-term experiment (46 year) in Switzerland (CH), all sampled in 2018. The Swedish sites included the same two factor treatment, i.e. four different plant mixtures and two (SE-Lanna) or three (SE-Alnarp) nitrogen fertilization levels (0, 60, 120 kg ha-1 yr-1); while the Swiss experiment  included different proportions of N, P and K fertilization under different cutting regimes (CH-Bremgarten). The PLFA and NLFA (phospholipid- and neutral lipid fatty acid) analysis was used to estimate the fungal biomass (SF+AMF). The application of N was associated with a decrease in the AMF biomass, with significant effects with the application of 60 and 120 kg N ha-1 in SE-Alnarp, and 75 and 150 kg N ha-1 in CH-Bremgarten. On the other hand, the SF biomass was only negatively affected by the N fertilization in SE-Lanna (60 kg N ha-1) under the plant mixture that showed the biggest SF biomass in the unfertilized plot; and by the highest application of N in CH-Bremgarten. Our findings indicate that nitrogen fertilization influences microbial community structure and reduces the abundance of AMF, with these being more sensitive than SF to fertilizer application.
Quantifying the added value of downscaling in extreme precipitation attribution
Authors: Eden Jonathan , Dieppois Bastien
Published: March 2020

While there is a discernible global warming fingerprint in the increase observed daily temperature extremes, there is far greater uncertainty of the role played by anthropogenic climate change with regard to extreme precipitation. A logical progression of thought is that an increase in extreme precipitation results from the 7% increase in atmospheric moisture per 1°C global temperature increase predicted by the Clausius-Clapeyron (CC) relation.  While this is supported by observations on the global scale, rates of extreme precipitation at smaller spatial and temporal scales are influenced to a far greater extent by atmospheric circulation and vertical stability in addition to local moisture availability. Many of these processes and other features of extreme precipitation events are not sufficiently represented in general circulation model (GCM) simulations. Meanwhile, limited observational networks mean that many short-term convective events are not accurately represented in the observational data.  

Errors and biases are common to all global and regional climate models, and many users of climate information require some form of statistical correction to improve the usefulness of model output. As so-called bias correction has become commonplace in climate impact research, its development has been hastened by a sustained debate regarding model correction in general leading to techniques that merge statistical correction and downscaling, represent random variability using stochasticity and are explicitly applicable to extremes. To date, attribution of extreme precipitation has not fully utilised the tools available from recent advances in bias correction, stochastic postprocessing and statistical downscaling. In the same way that GCMs are the most important tool in making climate change projections, understanding the degree to which the nature of a particular weather event has changed due to global warming requires long-term simulations of global climate from the pre-industrial era to the present day.  The lack of a correction and/or downscaling step in almost all precipitation event attribution methodologies is therefore surprising. 

Here, we present a multi-scale attribution analysis of a sample of extreme precipitation events across Europe using a blend of observation- and model-based data. Attribution information generated using the raw output of global and regional climate model ensembles will be compared to that generated using the same set of models following a statistical postprocessing and downscaling step. Our conclusions will make recommendations for the value and wider application of downscaling methodologies in attribution science.
Estimating the SEP Flux for the Upcoming Solar Cycle 25 Using LSTM Network
Authors: Nedal Mohamed , Kozarev Kamen
Published: March 2020

Estimating space weather parameters for the solar cycle 25, which has already started, is essential to anticipate the behavior of the near-Earth space environment. Artificial Neural Networks have in recent years become very widely used in several scientific fields owing to the advancement in computational power and the availability of big data. In this work, we take advantage of utilizing Recurrent Neural Network models in time-series analysis. We have developed and trained a Long-Short Term Memory (LSTM) model, in order to make long-term predictions of the hourly-averaged energetic proton fluxes at 1AU. We have used as input a combination of solar and interplanetary magnetic field indices (from the OMNI database) from the past four solar cycles and generated predictions of the solar energetic proton fluxes at three energies. So far, we found that the root-mean-square errors for the predictions over a three-month period were 0.0240, 0.0173, and 0.0309, respectively. We also found that the model underestimates the prediction at the highest energy band. We will extend the model architecture in order to estimate the future SEP fluxes over the whole solar cycle.
Elastic properties of rocks: Why shouldn’t they be constant?
Authors: Sandrone Federica , Pimienta Lucas , Gastaldo Laurent , Violay Marie
Published: March 2020

         Over past decades, different approaches have been suggested for assessing the elastic constants of materials. In mechanics, the elastic properties are evaluated according to Hooke’s law from the static stress-strain curves, in the strain range before the material failure. In rock mechanics, this approach is used as well for characterizing elastic constants of rocks. Moreover, thanks to development of seismology and applied geophysics, seismic wave velocities were found to allow evaluating rock elastic properties. This approach has been largely developed by the rock physics/petrophysics community as a simple and non-destructive  mean of characterization of rock elastic constants.
         Ideally, being expected to probe the same material constants, the two approaches should yield the same results. However, in practice, the results seldom compare for a number of potential reasons, such as strains rate and amplitude.
This work aims to investigate, discuss and – possibly – reconcile these two approaches. Different igneous and sedimentary rocks are tested in the laboratory to investigate the influence of different potential factors. Three measuring methods are used: i) static stress-strains, ii) ultrasonic waves velocities, iii) stress-strains oscillations of varying amplitude. The experimental results are then discussed on the basis of existing theories.  
Comparison of observed lower stratospheric ozone changes with free-running chemistry climate models
Authors: Ball William , Chiodo Gabriel , Abalos Marta , Alsing Justin
Published: March 2020

The ozone layer was damaged last century due to the emissions of long-lived ozone depleting substances (ODSs). Following the Montreal Protocol that banned ODSs, a reduction in total column ozone (TCO) ceased in the late 1990s. Today, ozone above 32 km displays a clear recovery. Nevertheless, a clear detection of TCO recovery in observations remains elusive, and there is mounting evidence of decreasing ozone in the lower stratosphere (below 24 km) in the tropics out to the mid-latitudes (30-60°). Chemistry climate models (CCMs) predict that lower stratospheric ozone will decrease in the tropics by 2100, but not at mid-latitudes.
 
Here, we compare the CCMVal-2 models, which informed the WMO 2014 ozone assessment and show similar tendencies to more recent CCMI data, with observations over 1998-2016. We find that over this period, modelled ozone declines in the tropics are similar to those seen in observations and are likely driven by increased tropical upwelling. Conversely, CCMs generally show ozone increases in the mid-latitude lower stratosphere where observations show a negative tendency. We provide evidence from JRA-55 and ERA-Interim reanalyses indicating that mid-latitude trends are due to enhanced mixing between the tropics and extratropics, in agreement with other studies. 

Additional analysis of temperature and water vapour further supports our findings. Overall, our results suggest that expected changes in large scale circulation from increasing greenhouse gases may now already be underway. While model projections suggest extra-tropical ozone should recover by 2100, our study raises questions about their ability to simulate lower stratospheric changes in this region.
Reviewing our options: How can we address climate change impacts in hydrogeological studies?
Authors: Birk Steffen , Collenteur Raoul
Published: March 2020

Arguably, the groundwater community has responded more slowly to the challenges posed by climate change than other fields of (hydrological) science. However, in recent years a strong increase in studies addressing climate change impacts on groundwater is observed, and recommendations on the methodology of such studies have been developed and discussed (e.g. Holman et al., Hydrogeology Journal, 2012). Following the common practice in other fields of climate change research, it was suggested that assessments of climate change impacts on groundwater should be based on multiple emission scenarios and a range of global and regional climate models. This scenario-based, top-down approach involves the propagation of multi-model ensembles through a model chain starting from emission scenarios to global and regional climate models to impact models such as hydrological and groundwater models. However, as the uncertainty increases at each step of the model chain, the uncertainty in the assessment of local climate change impacts and the resulting recommendations for adaptation options likely are very high and thus of little use in practice. A vulnerability-based, bottom-up approach starting from the identification and analysis of the factors that are relevant for coping with climate change in a given system, therefore, was proposed as a complementary approach (e.g. Wilby and Dessai, Weather, 2010). “Storylines” (Shephard et al., Climatic Change, 2018) that aim at representing uncertainty in physical aspects of climate change in an event-based rather than probabilistic way appear to be consistent with the latter concept. In this poster we relate these concepts of climate change research to methodological frameworks established in hydrogeological research (e.g. multi-model approaches). We present an overview of potential tools, such as trading-space-for-time, historical data analysis, sensitivity analysis, climate projections and controlled experiments, that can be used to study climate change impacts, and we discuss their role and applicability within more general methodological frameworks.
The role of increasing vertical resolution on the detection and attribution of North Atlantic storms
Authors: Walker Erin , Mitchell Daniel , Seviour William , Valdes Paul , Collins Mat
Published: March 2020

Accurately determining extratropical cyclone paths is key in determining regional impacts associated with precipitation and wind. It is known that the stratosphere plays an important role in atmospheric dynamics and can extend its influence down to the surface. Despite this, many attribution studies have not included a stratosphere in their experiments. We believe that not considering the stratosphere could affect the results of these experiments, so the role it has on North Atlantic storm tracks is analysed using an idealised, atmospheric only model named Isca. With the aim of identifying clear implications of including the stratosphere in storm track analysis in the North Atlantic basin, a large ensemble formed of 4 separate experiments is set up for the winter of 2013/2014. The four experiments are as follows; 1) no vertical layers in the stratosphere, 2) vertical levels extended to the upper stratosphere, 3) doubling of vertical levels throughout the atmosphere, and finally, 4) an increase of vertical levels at the tropopause. We expect that including the stratosphere, in addition to increasing vertical resolution, will help improve model representation of storm tracks and their intensities during the 2013/2014 winter. The results of this study hope to highlight how the inclusion of the stratosphere and increased vertical resolution can lead to the improvement in modelling storm track statistics, which in turn will help to make more reliable attribution statements in the future.
Mars’ thermal evolution from machine-learning-based 1D surrogate modelling
Authors: Agarwal Siddhant , Tosi Nicola , Breuer Doris , Padovan Sebastiano , Kessel Pan
Published: March 2020

The parameters and initial conditions governing mantle convection in terrestrial planets like Mars are poorly known meaning that one often needs to randomly vary several parameters to test which ones satisfy observational constraints. However, running forward models in 2D or 3D is computationally intensive to the point that it might prohibit a thorough scan of the entire parameter space. We propose using Machine Learning to find a low-dimensional mapping from input parameters to outputs. We use about 10,000 thermal evolution simulations with Mars-like parameters run on a 2D quarter cylindrical grid to train a fully-connected Neural Network (NN). We use the code GAIA (Hüttig et al., 2013) to solve the conservation equations of mantle convection for a fluid with Newtonian rheology and infinite Prandtl number under the Extended Boussinesq Approximation. The viscosity is calculated according to the Arrhenius law of diffusion creep (Hirth & Kohlstedt, 2003). The model also considers the effects of partial melting on the energy balance, including mantle depletion of heat producing-elements (Padovan et., 2017), as well as major phase transitions in the olivine system. 

To generate the dataset, we randomly vary 5 different parameters with respect to each other: thermal Rayleigh number, internal heating Rayleigh number, activation energy, activation volume and a depletion factor for heat-producing elements in the mantle. In order to train in time, we take the simplest possible approach, i.e., we treat time as another variable in our input vector. 80% of the dataset is used to train our NN, 10% is used to test different architectures and to avoid over-fitting, and the remaining 10% is used as test set to evaluate the error of the predictions. For given values of the five parameters, our NN can predict the resulting horizontally-averaged temperature profile at any time in the evolution, spanning 4.5 Ga with an average error under 0.3% on the test set. Tests indicate that with as few as 5% of the training samples (= simulations x time steps), one can achieve a test-error below 0.5%, suggesting that for this setup, one can potentially learn the mapping from fewer simulations. 

Finally, we ran a fourth batch of GAIA simulations and compared them to the output of our NN. In almost all cases, the instantaneous predictions of the 1D temperature profiles from the NN match those of the computationally expensive simulations extremely well, with an error below 0.5%.
RETREAT - a REal-time TREmor Analysis Tool
Authors: Smith Patrick , Bean Chris
Published: March 2020

The EUROVOLC project aims to promote an integrated and harmonised European volcanological community, and one of its main themes focuses on understanding sub-surface processes. Early identification of magma moving towards the surface is very important for the mitigation of risks from volcanic hazards, and joint research activities within the project aim to develop and improve volcano pre-eruptive detection schemes. Volcanic tremor is a sustained seismic signal associated with eruptions and is often linked to movement of magmatic fluids in the subsurface. However, it can occur pre-, syn- and post-eruption, and signals with similar spectral content can also be generated by several other processes (e.g. flooding, rockfalls). Hence one of the best ways of distinguishing between the processes underlying tremor generation is through tracking the evolution of its spatial location. Due to its continuous nature tremor cannot be located using classical seismological methods and so its source must be determined using alternatives such as seismic array analysis.

This work presents RETREAT, a REal-time TREmor Analysis Tool developed under EUROVOLC, that uses seismic array data and array processing techniques to detect, quantify and locate volcanic tremor signals. It is an open-source python-based tool that utilizes existing routines from the open-source obspy framework to carry out analysis of seismic array data in real-time. The tool performs f-k (frequency-wavenumber) analysis using beamforming to calculate the back azimuth and slowness in overlapping time windows, which can be used to detect and track the location of volcanic tremor sources.

A graphical and web-based interface has been developed which allows adjustment of highly configurable input parameters. These include options for setting the data source, pre-processing, timing and update options as well as the parameters for the seismic array analysis which must be carefully selected and tuned for the specified array. Once configured the tool fetches waveform data in real time and updates its output accordingly, returning plots of the array processing results (slowness and back azimuth values) as well as plots of the seismic waveform, envelope and spectrogram. The tool has been tested on real-time data using the obspy FDSN (International Federation of Digital Seismograph Networks) client to fetch data from the IRIS datacenter, using example array data from the small aperture SPITS seismic array in Spitsbergen, Svalbard. A 'replay’ mode using existing archive (non real-time) data has also been implemented and tested on array data from the 2014 eruption at Holuhraun and Bardarbunga volcano in Iceland, collected as part of the FUTUREVOLC project. The RETREAT tool is now ready for testing and implementation in a volcano monitoring setting at observatories. It will also be made freely available to download as a EUROVOLC community tool.
Predict urban growth in a low-density context: Basilicata region study case
Authors: Saganeiti Lucia , Mustafà Ahmed , Teller Jacques , Murgante Beniamino
Published: March 2020

This paper presents a spatiotemporal analysis to simulate and predict urban growth. It is important to study the growth of cities to understand their implications for environmental sustainability and infrastructure needs. The aim of this work is to predict future scenarios in low-density settlements in order to control and regulate the processes of urban transformation.

We applied an integrated approach based on the multinomial logistic regression (MLR) and the cellular automata (CA) for urban sprinkling modelling. Our case study is the Basilicata region, in southern Italy, which is affected by urban sprinkling literally defined as: “a small amount of urban territory distributed in scattered particles".

Built-up density maps were created on the basis of three regional building datasets (1989, 1998 and 2013) with four density classes: no built up, low density, medium density and high density. These sources were used for calibrating and validating the model as well as for future simulation of urban sprinkling. Two components were considered for the calculation of the transition potential from one density class to another. For the first built up development causative factors were calibrated using the MLR for the expansion and densification processes. The causative factors consider elevation and slope as physical factors, Euclidian distance to railway station, different type of street, large size city and medium size city as proximity factors, population density and employment rate as socio economic factors and zoning for land use policies in the study area.

The second causative factor is the CA neighbourhood effects that have been calibrated using a multi objective genetic algorithm (MOGA) as in (Mustafa et al., 2018). The transition potential was calibrated for the 1989-1998 time period and the calibration outcomes were used to simulate the 2013 map. The calibrated map was then used for the simulation of the 2013 map which was compared with the actual map of 2013 (validation). We then used our calibrated model to simulate urban growth in the year 2030.

The robustness of MLR has been evaluated with the Receiver Operating Characteristic (ROC) index. The Fuzzines index has been used for evaluating the validation process accuracy.

The results of the 2030 prediction show the greatest variations in class 1 (low density) that represent the sprinkling of urban cells in the territory.

Keywords: Low-density, Cellular Automata, Multinomial Logistic Regression, Urban Sprinkling, Basilicata Region.
An evaluation of the mixed precision version of NEMO 4.0.1
Authors: Tintó Oriol , Paronuzzi Ticco Stella Valentina , Acosta Mario C. , Castrillo Miguel , Serradell Kim , Doblas-Reyes Francisco J.
Published: March 2020

One of the requirements to keep improving the science produced using NEMO is to enhance its computational performance. The interest in improving its capability to efficiently use the computational infrastructure its two-fold: on one side there are experiments that would only be possible if a certain threshold of throughput is achieved, on the other side any development that achieves an increase in efficiency would help saving resources while reducing the environmental impact of our experiments. One of the opportunities that raised interest in the last few years is the optimization of the numerical precision. Historical reasons brought many computational models to over-engineer the numerical precision: solving this miss-adjustment can payback in terms of efficiency and throughput. In this direction, a research was carried out in order to safely reduce the numerical precision in NEMO which led to a mixed-precision version of the model. The implementation has been developed following the approach proposed by Tintó et al. 2019, in which the variables that require double precision are identified automatically and the remaining ones are switched to use single-precision. The implementation will be released in 2020 and this work presents its evaluation in terms of both performance and scientific results.
Energy spectrum from single TGFs detected by ASIM
Authors: Lindanger Anders , Marisaldi Martino , Østgaard Nikolai , Mezentsev Andrey , Neubert Torstein , Reglero Victor , Kochkin Pavlo , Lehtinen Nikolai , Sarria David , Carlson Brant E. , Maiorana Carolina , Skeie Chris Alexander , Bjørge-Engeland Ingrid , Ullaland Kjetil , Genov Georgi , Christiansen Freddie , Köhn Christoph
Published: March 2020

Terrestrial Gamma-ray Flashes (TGFs) are sub milliseconds bursts of high energy photons associated with lightning flashes in thunderstorms. The Atmosphere-Space Interactions Monitor (ASIM), launched in April 2018, is the first space mission specifically designed to detect TGFs. We will mainly focus on data from the High Energy Detector (HED) which is sensitive to photons with energies from 300 keV to > 30 MeV, and include data from the Low Energy Detector (LED) sensitive in 50 keV to 370 keV energy range. Both HED and LED are part of the Modular X- and Gamma-ray Sensor (MXGS) of ASIM.

The energy spectrum of TGFs, together with Monte Carlo simulations, can provide information on the production altitude and beaming geometry of TGFs. Constraints have already been set on the production altitude and beaming geometry using other spacecraft and radio measurements. Some of these studies are based on cumulative spectra of a large number of TGFs (e.g. [1]), which smooth out individual variability. The spectral analysis of individual TGFs has been carried out up to now for Fermi TGFs only, showing spectral diversity [2]. Crucial key factors for individual TGF spectral analysis are a large number of counts, an energy range extended to several tens of MeV, a good energy calibration as well as knowledge and control of any instrumental effects affecting the measurements.

We strive to put stricter constraints on the production altitude and beaming geometry, by comparing Monte Carlo simulations to energy spectra from single ASIM TGFs. We will present the dataset and method, including the correction for instrumental effects, and preliminary results on individual TGFs.

Thanks to ASIM’s large effective area and low orbital altitude, single TGFs detected by ASIM have much more count statistics than observations from other spacecrafts capable of detecting TGFs. ASIM has detected over 550 TGFs up to date (January 2020), and ~115 have more than 100 counts. This allows for a large sample for individual spectral analysis.

References:

Dwyer, J. R., and D. M. Smith (2005), A comparison between Monte Carlo simulations of runaway breakdown and terrestrial gamma-ray flash observations, Geophys. Res. Lett., 32, L22804, doi:10.1029/2005GL023848.

Mailyan et al. (2016), The spectroscopy of individual terrestrial gamma-ray flashes: Constraining the source properties, J. Geophys. Res. Space Physics, 121, 11,346–11,363, doi:10.1002/2016JA022702.
Internal and external sources of CO2 release in subarctic lakes
Authors: Verheijen Hendricus , Klaus Marcus , Seekell David , Karlsson Jan
Published: March 2020

Internal and external sources of CO2 release in subarctic lakes

Most lakes are supersaturated with CO2, and are thus net sources of CO2 to the atmosphere. Lakes can only sustain annual net CO2 emissions through either 1) mineralization of terrestrial carbon flushed into the system or 2) transfer of inorganic carbon from the terrestrial landscape, but the relative contribution of these sources are unknown. We studied 14 lakes in the Southern Swedish Scandes during the open-water season. We used loggers to infer CO2-emissions and net internal production of CO2 during the open water season, and DIC mass balance to determine winter CO2-buildup and emission at spring ice-off. All lakes emitted CO2 to the atmosphere on an annual basis, even though some took up CO2 during the open water season. Emissions during spring ice off dominated (mean 7,5 g C m-2 yr-1) the yearly CO2-balance of the lakes. The CO2-emissions from the lakes increased with increasing DOC concentrations, similar to findings from many other regions. However, only a small part of the CO2-emissions originated from the internal production of CO2 (mean 31%), implying dominance of external CO2 sources in these lakes. The external source contribution also increased over the DOC gradient, from 30% in clear lakes to 91% in high DOC-lakes. Our results highlight the importance of seasonal and landscape factors in maintaining net CO2 release from lake waters.
Spatio-temporal decomposition of geophysical signals in North America
Authors: Brady Aoibheann , Rougier Jonathan , Vishwakarma Bramha Dutt , Ziegler Yann , Westaway Richard , Bamber Jonathan
Published: March 2020

Sea level rise is one of the most significant consequences of projected future changes in climate. One factor which influences sea level rise is vertical land motion (VLM) due to glacial isostatic adjustment (GIA), which changes the elevation of the ocean floor. Typically, GIA forward models are used for this purpose, but these are known to vary with the assumptions made about ice loading history and Earth structure. In this study, we implement a Bayesian hierarchical modelling framework to explore a data-driven VLM solution for North America, with the aim of separating out the overall signal into its GIA and hydrology (mass change) components. A Bayesian spatio-temporal model is implemented in INLA using satellite (GRACE) and in-situ (GPS) data as observations. Under the assumption that GIA varies in space but is constant in time, and that hydrology is both spatially- and temporally-variable, it is possible to separate the contributions of each component with an associated uncertainty level. Early results will be presented. Extensions to the BHM framework to investigate sea level rise at the global scale, such as the inclusion of additional processes and incorporation of increased volumes of data, will be discussed.
Examining the respective roles of greenhouse-gas and aerosol forcing for modes of multi-decadal variability
Authors: Dittus Andrea , Hawkins Ed , Wilcox Laura , Hodson Dan , Robson Jon , Sutton Rowan
Published: March 2020

The respective roles of aerosol and greenhouse-gas forcing in modulating the phasing and amplitude of large-scale modes of multi-decadal variability remain poorly understood, despite the attention that has been devoted to trying to separate the influence of forcing from internal variability in modes such as the Atlantic Multidecadal Variability and the Pacific Decadal Oscillation, for instance. However, understanding what drives multidecadal variability in these basins is imperative for improving near-term climate projections.

Here, we show how aerosol and greenhouse-gas forcing interact with internal climate variability to generate indices of multi-decadal variability in the Atlantic, using a large ensemble of historical simulations with HadGEM3-GC3.1 for the period 1850-2014, where anthropogenic aerosol emissions are scaled to sample a wide range in historical aerosol forcing. These results are complemented by early results from new stabilised warming simulations with the same climate model and analysis of future projections from models partaking in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6).
The Potential Water Quality Impacts of Shale Gas Exploitation
Authors: Hsu Hsiao-Yuan Tammy , Worrall Fred , Aplin Andy
Published: March 2020

     The potential development of shale gas has brought with it several concerns about environmental impacts, these include: induced seismicity, air pollution, and groundwater contamination. During hydraulic fracturing for shale gas, large volumes of oxic and acidic water are injected into the gas-bearing formations. The injected fluids contain a range of additives and will mix and react with the in-situ groundwater and shale rock with the potential to drive water-rock interactions; release metal contaminants; alter the permeability of the bedrock; with each of these affecting the transport and recovery of water, hydrocarbons, and contamination. The purpose of this study is to understand the geochemical processes and inorganic metals release during hydraulic fracturing to assess the potential contribution of fluid-rock interaction for the composition of produced waters and alteration of shale mechanical properties.
     The study has: 
i) Statistically analysed the chemical composition of hydraulic fracturing in USGS dataset to create prior distributions for the prediction of the salinity and chemical composition of flowback fluids in the UK. 
ii) Statistically analysed the composition and controls on geothermal waters in the UK. Deep geothermal waters are an analogue for the in-situ groundwater composition with which injected fracking fluids will react and mix.
iii) Both sources of information have assisted in the design of the high pressure, high temperature experiments that will simulate the fracking fluid processes
iv) Undertaken sequential extraction of target shales to understand the data from existing batch experiments undertaker

     Future work will include isotope proxy and mineralogical texture studies to predict flowback fluid composition and the post-frack condition of the shale.