Improving the accuracy of coupling of water and carbon cycles in land surface models has been emphasised in recent studies as a major priority for research. Reliable quantification of carbon and water balances is required in order to effectively estimate gross primary production (GPP) and evapotranspiration (ET) across space and time. The P model (for ‘production’) is grounded in plant functional ecology and links the carbon and water cy- cles via a theory of stomatal optimization and photosynthetic acclimation. It has the mathematical form of a LUE model while being traceable to first principles, including the standard model of photosynthesis, for the prediction of GPP. The model has only three free parameters, of which two are estimated from independent observations on leaf stable carbon isotopes and leaf-level electron transport capacity. The model requires only elevation, CO2 concentration, solar radiation, vapour pressure deficit (VPD) and temperature as input. We will present a demonstration application of the P model using a novel approach that extends the algo- rithm to create a prototype of a universal transpiration (T) product using Sentinel 3 data. Both GPP and T outputs will be evaluated against FLUXNET observations. Stomatal conductance will be calculated based on the model’s predictions of GPP and the ratio of internal to external CO2 partial pressure, allowing transpiration to be calculated from VPD. The P model has many advantages, including its strong theoretical and empirical basis, extremely parameter-sparse nature, and the fact that it does not require the prior specification of plant functional types or land cover types. The research presented here will extend its application from primary production monitoring to include carbon-water cycle coupling and water resources assessment.

The relationship between erosion and biodiversity is reciprocal. Soil organisms can both reduce soil loss, by improving porosity, and increase it, by diminishing soil stability as a result of their mixing activities. Simultaneously, soil runoff has ecological impacts on belowground communities. Despite these obvious interactions, soil erosion models do not consider biodiversity in their estimates and soil ecology has poorly investigated the effects of erosion. In order to start filling in these knowledge gaps, a novel biological factor was developed and introduced, for the first time, into a well-known soil erosion model (the revised universal soil loss equation, RUSLE). Furthermore, insights to advance soil erosion ecology were proposed. Thanks to available data on both soil erosion (Panagos et al., 2015) and earthworm diversity (Rutgers et al., 2016), an “earthworm factor” was generated and applied to produce the first maps of a modified soil erodibility. The incorporation of “earthworm factor” reduces soil erodibility and as a consequence soil erosion. On the other hand, also the erosive events affect the soil-living communities. Potential consequences of soil erosion on soil life were also identified: migration, invasion, violence, defence, rebuilding and functional effects were described. The results showed how new estimates of soil loss can be produced by adding biological factors to soil erosion models. The earthworm factor represents the first step towards the inclusion of a wider biological factor, which takes into account the whole soil-living community, into erosion modelling. Fostering the development of soil erosion ecology for better understanding the reciprocal effects of erosion on soil life, will contribute towards this achievement. The increasing availability of large-scale data on soil biodiversity distribution opens up the possibility of incorporating a biological component into bio-physical models such as the soil erosion one. The model integration between the soil ecologists and soil erosion modellers is key point for identifying the interactions between soil organisms and soil loss

Different spectroscopy methods such as visible near-infrared (Vis-NIR) spectroscopy have proven to provide useful information on soil physical and chemical properties. The majority of previous studies have focused on multivariate regression methods such as partial least squares regression (PLSR) to predict soil characteristic features from soil spectra. The objective of these efforts was to provide precision data for agricultural and land management practices where the knowledge about the soil differences in each part of the field can be used for variable-rate irrigation, seeding, liming, fertilising and pesticide application. Since the currently available machines can only apply few (less than 10) variable classes of seed, fertiliser, etc., using soil classification seems to be a more appropriate option compared to regression. As a part of Danish National Soil Survey in 1980s, 2460 soil samples were collected from 789 soil profiles in 4 depths (0-30, 30-60, 60-100 and 100-200cm) throughout Denmark (some profiles did not have samples from all 4 depths) and tested for several soil characteristics including complete soil texture, organic carbon content and calcium carbonate in the lab. Based on these soil characteristics, all soil samples were classified into 8 soil types or 12 soil classes in the Danish Soil Classification System (JB system). Later the Vis-NIR spectra of samples were measured using a FOSS DS2500 spectrometer in the range of 350-2500nm. Partial least squares and support vector machines discriminant analyses (PLS-DA and SVM-DA, respectively) were used to calibrate and cross-validate classification models where soil Vis-NIR spectra were used to classify each sample from each depths into its corresponding JB soil type and soil class. The results show excellent classification accuracy and specificity (>80% and >90%, respectively) for samples from the same depths and when samples from all depths were combined. We found that the high false negative rate (low sensitivity) was mainly due to the models classifying samples in the neighbouring classes of the actual class (e.g. classifying a sample in JBC 2 as belonging to JBC 1 or JBC 3). This clearly indicates that calibrating the classification model on the uncertain hydrometer data (with 1.4-2% reproducibility error) was the main reason for classification of samples in the neighbourhood of the actual class. In conclusion, given the highly uncertain reference methods for soil classification, using Vis-NIR spectroscopy with PLS-DA provides a very rapid, inexpensive, and highly reliable method for soil texture classification on a national scale.

Submarine groundwater discharge (SGD) is highly variable in spatial and temporal terms due to interplay of several terrestrial and marine processes. In contrast to discrete in-situ measurements, remotely sensed thermal infrared radiation has proven to reveal horizontal SGD variability in a spatially continuous context. Yet, it lacks temporal information that is crucial to understand highly dynamic systems as represented by many coastal environments. Here we report the results of temporal continuous Unmanned Aerial Vehicle (UAV)-based measurements of thermal radiances in a highly saline environment - the Dead Sea. Instead of flying over the study area we exploit the UAV ability to observe predefined locations by hovering above a predefined SGD spot to study spatio-temporal characteristics of SGD. Through hovering and the so obtained high temporal and spatial resolution of thermal radiances, we can show influences of crossflows and interaction of nearby SGDs on the final thermal radiance pattern, horizontal pattern shifts, pattern size variation of up to 600% and a periodic discharge behaviour at intervals of 20-78 seconds. These numbers could not be revealed with classical spatially discrete methods and thus mean an asset for groundwater discharge and process understanding. Besides the advantages and thus opportunities for groundwater investigation, we will likewise point out limitations during the conduction (flight stability, flight duration, thermal sensor drift) and processing (image registration) but also future improvements that may influence UAV-based groundwater investigations.

Given the dynamics of the atmosphere, the ozone hole is accompanied by episodes of exchange between the polar vortex and the mid-latitudes and the tropics (Bencherif et al., 2007). During these events, the polar vortex is deformed and ozone-poor polar air-masses move towards the tropics. We call these episodes of isentropic exchanges “Ozone Secondary Effects” (OSE), and several events have been observed during the last decade. For example, an event observed on October 14, 2012 reached the south of Brazil (Peres et al., 2017), causing 13.7% reduction in the total columns of ozone. Recently, Bresciani et al. (2018) reported on a major OSE event (23% reduction in TCO) and its influence in southern Brazil and Uruguay that occurred in October 2016. Such OSE episodes may last up to 3 weeks, causing a potential increasing in the UV radiation levels leading to public health issues as well as impacts on the fauna and flora, with notable risks for the biodiversity and the agriculture. In the present study, we investigate the use of Long Short-Term Memory recurrent neural networks to forecast OSE events, more specifically the Causal LSTM model proposed by Wang et al. (2018), which has been successfully applied to the analysis of video images. In our study, however, the input data is composed by historical data for the total column of ozone (TCO), obtained by satellites. We aim at using the resulting forecasts to detect, monitor and classify OSE events by isentropic levels, latitudinal bands and geographical areas. We believe that these results can be integrated into a weather forecast workflow to improve the accuracy of existing models. Further experiments include the conjoint analysis of other atmospheric markers such as the potential vorticity (PV). Together with auxiliary parameters such as cloud fraction, aerosol optical depth, nebulosity and altitude, these forecasts could be used to produce realistic UV-Index estimations.

Scientists generally agree that, by 2100, the annual extraction of an average of 3.3 Gt of carbon equivalent from the atmosphere will be necessary to limit the increase in global average temperature to 2°C relative to pre-industrial levels. A greenhouse gas (GHG) removal technology (GGRT) is one that can remove a GHG from the atmosphere. Enhanced silicate rock weathering for long-term CO2 sequestration has considerable potential as a GGRT, but depends on the availability of suitable rocks coupled with proximity to locations where it can be applied. Extracting, processing, transporting and applying ground rock to land consume energy and thus generate GHG emissions, which must be balanced against the sequestration potential. In this study, we i) assess the environmental impacts of the practice, using existing basalt rock quarrying in Sao Paulo (SP) state by considering the emissions associated with the rock extraction, comminution, transport and application through a Life Cycle Assessment (LCA) approach; ii) estimate the contribution of the different processes involved with soil carbonation and enhanced weathering and iii) assess the potential net CO2 removal of Sao Paulo agricultural land through soil carbonation and weathering of basalt rock. Our results show that enhanced weathering and carbonation respectively emit around 75 and 135 kg CO2eq per tonne of CO2eq removed (considering an average quarry to field distance of 65 km.) We underline transportation as the principal process negatively affecting the practice and establish a limiting road travel distance from quarry to field of 540 ± 65 km for carbonation and 990 ± 116 km for enhanced weathering. Above these distances, the GHG emissions exceed the potential sequestration. The application of crushed basalt at 1 t/ha to all of Sao Paulo State’s 12 million hectares of agricultural land could capture around 1.3 to 2.4 Mt CO2eq through carbonation and enhanced weathering, respectively. If 50 t/ha is applied, the sequestration potential could reach up to 40 to 76 % of the total Sao Paulo State emissions in 2016 through carbonation and enhanced weathering respectively, assuming no loss in the effectiveness of basalt at high application rates. This study lowers previous net sequestration estimates from Kantola et al. (2017) by around 50% and, as various techniques are increasingly promoted as efficient solutions to remove or sequester CO2, it emphasizes the need to consider the practice in its entirety to show the limitations and potential caveats. The holistic approach of LCA helps identify processes with the most impact and their significance. However, its accuracy and reliability is inevitably limited by the amount, quality and representativeness of the input data. Therefore, detailed studies on each process stage should be conducted to increase accuracy, focusing on local practices especially transport.

Modern seismic networks provide a huge amount of data received in real-time, being impossible the manual identification of relevant events useful to monitor the activity of the volcano. Thus, many volcano observatories are interested in tools to perform an online, automatic analysis of the seismic activity. Machine Learning area provides various of Volcano-Seismic Recognition (VSR) systems designed to classify seismic events in real-time. However, only a few approaches can also detect them in a continuous data streams. Most of those VSR systems are based on the 2-step supervised paradigm: 1. A training database (X-DB) of a given volcano ’X’ is prepared with hundreds of events manually detected and classified according to their physical origin. 2. Statistical models are built analysing this DB, and are later used to automatically identify events in new data recorded at the volcano X. This supervised procedure is the major drawback to achieve a fast deployment of a VSR system for another volcano Y, as the preparation of its own Y-DB takes considerable time, and requires qualified operators and previous recordings, which is difficult for volcanoes without recent activity or which haven’t been monitored. In order to overcome these limitations, the EU-funded project ’VULCAN.ears’ focused on real-time, Volcano-Independent VSR (VI.VSR) approaches. It proposes alternative solutions based on state-of-the-art technologies as universal DBs and models, waveform standardisation and parallel architectures. Recent results obtained by mixing DBs from Popocatépetl, Colima, Deception and Arenal active volcanoes will be presented. We apply VULCAN.ears technologies to evaluate VSR systems on joint DBs built with data of several volcanoes. We also use volcano-independent models to automatically classify events of another volcano, analysing how the recognition accuracy varies as the training DB becomes more complex. All tests are carried out by an easy to use, user-friendly graphical application (geoStudio). All these achievements produce new insights useful to redesign the next-generation, portable and robust VSR systems.

Because of the global warming, urban planning strategies must be investigated to reduce the building energy consumption and increase the thermal comfort in cities. In the framework of Energy Strategy 2050 of Switzerland,it is important to highlight the impact of future climate change on urban planning and proposes strategies to help urban planners and policymakers face this new challenge particularly in a future where heat waves are going to become common at mid-latitudes. However, to do so in the best possible way, the models currently used have to be robust enough in complex regions (with lakes, mountains and urban areas) to evaluate future planning scenarios.Simulations are performed over Switzerland at high resolution using a mesoscale numerical weather prediction system. The results from the simulations are compared with multiple meteorological stations located in the domain and is also used to evaluate the urban heat island. We demonstrate that the models performs well in plateaued regions but some important deviations are noted in particularly complex region with complex topography. The importance of Lake Geneva in the mesoscale dynamics in the region is also highlighted

It has been long known that the preferred crack alignments and fractures in rock cause seismic anisotropy and corresponding S-wave birefringence. Here the S-wave birefringence caused by the preferred alignment of cracks due to subcritical stress in the granitic basement of north central Oklahoma is examined. The data used for this study was collected during the IRIS Community Wavefields Experiment (CWE) in June and July of 2016, where more than 300 3-component (3-C) short-period seismographs were deployed in dense arrays with station spacings of 100m or less. These arrays were also in an area where many small earthquakes were occurring on a monthly basis, so that many were recorded by these arrays. The seismic data were cut from the original 30-day field records to produce short 3-C SEG-Y records of local earthquakes and explosions at a variety of back azimuths from the CWE array. Each 3-C record was rotated to the back azimuth of the source, such that the components become vertical, radial, and transverse with respect to the source. Vertical components are dominated by P arrivals, while radial components are dominated by SV arrivals and transverse components are dominated by SH arrivals. Seismic sections of the radial components of the array are then plotted in one color and overlain by the corresponding transverse components from the same array in another color. S-wave birefringence can then be directly observed as the time difference between arrivals, particularly first arrivals. S-wave birefringence is proof of seismic anisotropy and some characteristics of that anisotropy can be observed in record sections. For instance, birefringence is high along some azimuths and absent along other azimuths. Where birefringence is absent arrivals coincide, this could be either due to arrivals crossing or arrivals meeting but do not crossing. The latter case is important because it is the direction of an axis of symmetry for the anisotropic medium where the arrivals are propagating. This is also the case is observed in the record sections. The axis of symmetry observed in the record sections has an azimuth of 335 +/- 5 degrees. The fact that an axis of symmetry with no birefringence exists in the horizontal plane indicates that the anisotropic medium exhibits hexagonal symmetry, also known as horizontal transverse isotropy (HTI). It indicates that cracks and fractures are open perpendicular to the axis of symmetry and closed in other directions. Because of the large number of earthquakes occurring in this area a number of studies have been conducted on the in-situ stress orientations. In the sedimentary section overlaying the basement close to the CWE seismic array, borehole image logs indicate that the azimuth of drilling induced tensile fractures is 59 +/- 12 degrees. This is interpreted as the orientation of the maximum horizontal stress in the borehole. The axis of seismic anisotropy, which indicates the direction of minimum horizontal stress.

In the call of 2015, the European Geosciences Union funded the workshop “Water as hazard and water as heritage”, to take place in Rome in the consecutive year. The frame for the workshop was given by the “Spazi aperti” (=open spaces) event at the Accademia di Romania a Roma, where the convener (Maria Bostenaru Dan) was a fellow that time on a topic related to water and architecture. Spazi aperti XIV, the edition of 2016, was curated by Roxana Mihaly, former fellow of Accademia di Romania a Roma. The integration reached over the day of oral presentations, as posters of the workshop were displayed in frame of the exhibition in a creative way. Accademia di Romania a Roma opens in frame of Spazi aperti all its spaces, not only the conference room, and the posters were displayed in an underground corridor ending in spaces with related artistic works (for example the convener participated with an installation). The artists invited come from other foreign accademies in Rome, Rome being a city to be visited by scientists from humanities and artists since a number of centuries (“all ways lead to Rome”). This ensured international participation from Europe and beyond. The geosciences were also well represented, which enabled the dialogue between science and the arts. After the talks guided tours were organised to related exhibitions on water in related museums which emphasized the archaeology dimension. Apart of this event there were a number of scientific and musical events accompanying the exhibition, strenghening the dialogue. The event was part of the “month of international culture” in Rome, highly mediatised, and different of the exhibitions of just own fellows in other foreign academies. In 2018 the book was published and is subject of a poster in another session (EGU2019-2000) which all attendees are invited to see. More about the event can be followed on the webpage https://sites.google.com/site/egutopicalevent/

“Water as hazard and water as heritage” was an international workshop funded by the European Geosciences Union and run in frame of the “Spazi aperti” international event of the Romanian Academy in Rome, along with other cultural events. The “Spazi aperti” (open spaces) event is an international exhibition inviting the scholars in arts and humanities at other foreign academies in Rome, an international city. The convener of the workshop, the first author, was Vasile Pârvan fellow at that time in Rome, on a topic about water, and volunteered to the “Spazi aperti” event, whose curator was Roxana Mihaly. Fellows from different academies contributed to the book. Participation was multidisciplinary, from geosciences to archeology, art history, architecture and urban and landscape planning. Participation was also international, from Romania, Italy, the USA, the UK and Germany. In 2018 the report was published, with support of the Marie Curie Alumni Association, which also supports participation to present the book at the EGU GA in 2019. Marie Curie Alumni contributed to the book. Working on the report has been supported by two short term scientific missions in frame of the COST action TD1406 (“Intelligent management of heritage buildings”), both under the mentorship of Alex Dill, co-editor of the book, a conference stay in Karlsruhe funded by the Marie Curie Alumni Association and a mobility project of the Romanian science authority (PN-III-P1-1.1-MC2017-1334), all leading to Karslruhe, Germany. The book has also been presented at the Romanian Architecture Biennale 2018, an event in frame of the European Year of Cultural Heritage of the European Commission. The book has also been announced on platforms such as the Hungarian Academy of Sciences and CORDIS of the European Union (https://cordis.europa.eu/news/rcn/129019_de.html). A contact has been established to present also on the EGU blog. The book contains an overview of the event (both the Spazi aperti exhibition and the workshop, including its webpage) and the abstracts of the presentations, along with images and the CVs of the presenters. The presentations include: symbols of water in the work of Carlo Scarpa, water as hazard in case of earthquakes, postwar riverfront reconstruction, water in the arts, waterways across Europe, water and archaeology (architecture and archaeology, Roman baths, virtual museums), water sketching, waterscapes, precipitation estimation, water and natural risks in smart cities, pastoralism and water, new forms of water in landscape. The countries covered are in Europe and beyond (near East), including different countries in Europe than those of the authors (such as Danemark and the Balkans for example). The contributions display the dual character of water, between hazard (geoscience) and heritage (arts), and thus link to previous contributions of the first author to the EGU, co(convening) since 2002 the session on natural hazards’ impact on urban areas and infrastructure.

In Portugal, the official curriculum of Natural Sciences (NS) in middle and high schools has highlighted the importance of a greater school-environment relationship, and calls for the need to complement activities in the classroom with the development of practical activities in the field. The development of activities of this type, taking into account the curriculum and the potentialities of the region where the schools are located, is considered an innovative and motivating strategy to be implemented in middle and high schools, in Science Education in general, and Geosciences in particular. Recognizing the importance of these activities in teaching and learning Geology, a fieldwork activity planned and applied to students of the 7th grade, in the discipline of NS, was carried out at the end of the 2017/2018 academic year, at Pombal Middle and High School. The field activity took place in the Serra of Sicó (central Portugal), in the Leiria district because this area has a great geological interest, namely diverse karst formations and geological structures. The organization of the fieldwork followed the guidelines of the Orion model and the field script that was presented made it possible to understand scientific concepts, to favour the participation of students in their learning, to promote the scientific education of school-age citizens and to stimulate awareness of the value and preservation of the geological heritage. The results of the activity evaluation indicate that the students considered their experience in this fieldwork activity very helpful, and that it helped in the acquisition of knowledge and development of skills.

The identification of temporal rainfall patterns is important both in hydrological studies and in water resources management. Computational methods employed for such identification are briefly reviewed with emphasis on design hyetographs. In the sequel, the computational processes of this paper are described and are summarized here as follows: Raw Pluviograph data were acquired from the Greek National Bank of Hydrological and Meteorological Information for all available stations. A Poisson process hypothesis was used for the division of the raw time series to independent rainstorm events, assuming that their inter-arrival time is distributed exponentially per station and month. Unitless Cumulative Hyetographs (UCHs) were compiled and the null hypothesis of random structures in them was tested. The applicability of Principal Components Analysis and large Self-Organizing Maps (SOM) were examined as ways to represent these data. Subsequently, a stepwise procedure that utilized SOM as a clustering technique was applied. In each step a different map was used in order to create a low dimensional view of the data and consequently a limited number of rainfall temporal distribution patterns. Finally, these temporal distribution patterns were presented in a probabilistic way. In conclusion: a) A monthly temporal pattern for the extraction of independent rainstorm events was found for Greece, b) the hypothesis that the UCHs contains random data was rejected, c) as a result of SOM analysis, a limited number of temporal rainfall patterns emerged, in terms of seasonality and different characteristics and d) the classification of the rainstorm events was made in an unsupervised manner.

Already vulnerable monsoon regions like those in populous Africa are expected to be hard-hit by the effects of anthropogenic global warming. Therefore, such areas could benefit from low global warming levels. Using the Community Earth System Model low-warming simulations, we study the avoided impacts over the southern Africa monsoon region (SAMR) from limiting warming to 1.5℃ instead of 2℃. There is a tendency of dryer and significant warmer conditions being projected over SAMR at the 1.5℃warming world and is amplified at the 2℃warming world with warming exceeding the mean global warming rates. Specifically, a change of 0.63℃  0.04℃ in surface temperature is projected from 1.5℃ to 2℃ warmer world. Consequently, extreme climate events in the SAMR are also estimated to respond to this warming albeit precipitation based extremes showing higher uncertainty levels than temperature extremes. In particular, there is a projected increase in severity of heatwaves and the number of cumulative dry days accompanied by an increasing rainfall intensity events from 1.5 to 2℃ warming in SAMR. This may have severe implications for run-off characteristics in the region especially when half of the land area here is being projected to have a 20% increase in extreme precipitation. We also estimate an increase in cumulative dry days in the SAMR where a decrease in rainfall is also expected with warming. This may exacerbate drought occurrence in the region under a 2℃ warmer climate than under a 1.5℃ one. Restricting warming to 0.5℃ lower than 2℃ is projected to result in 5 to 10 mm/day reductions in the intensity of rainfall extremes across the SAMR. This restriction in warming is also projected to lead to 29% (27-31%) to 42% (39-48%) reduction in severity and frequency of high-temperature extreme events. Once-in-10/20-year heat extremes may also be reduced by as much as 28% (25-34%) to 37% (34-40%) from limiting warming to half a degree lower than 2℃ in the region. Moreover, the probability of occurrence of the record-breaking extreme drought experienced in summer of 1991/1992 in southern Africa is projected to be reduced by about 20%  4% from limiting warning to 1.5℃ instead of 2℃. This probability is heavily linked to changes in temperature associated with that drought. Therefore, restricting warming to low levels may indeed translate to substantial benefits in the Southern Africa Monsoon region.

The Norwegian meteorological institute (MET Norway) routinely collects and archives in-situ observations measured by conventional weather stations following the WMO standard. However, it is apparent that non-conventional observations, those shared by private companies and citizens, cannot be ignored. Over the last couple of years, the number of such observations has constantly risen. From the point of view of a national meteorological service,this data comes with a number of issues, such as insufficient metadata and a total lack of control on both the measurement practices applied and the instrumentation used. On the other hand, this large volume of non-conventional data (up to hundreds of observations per km square per minute) allows for the near-surface atmospheric state to be observed at an unprecedented level of detail thus opening new possibilities for disaster risk reduction and research in atmospheric sciences.Redundancy is the key factor that helps transform otherwise unreliable data into usable data for national meteorological services. MET Norway has recently improved the temperature forecasts on Yr.no by introducing amateur station data into the processing chain. Yr.no has millions of users per week so this improvement is beneficial fora large community. This has required a tailored system based on two aspects: (1) distributed storage and (2) data quality control.We present our plans for a distributed database for mass storage and analysis of in-situ data. This storage backend will lay the foundation for products based on Big data, IoT and machine learning. To match a constant increase in data load, it becomes necessary to scale out and embrace the nature of distributed systems - a constant compromise between performance (availability) and information consistency. We favor availability at the expense of eventual consistency (milliseconds). For transactions that require higher consistency, distributed database management systems like Cassandra (C*) allow clients to specify the level of consistency. C* also supports ordered columns and a time-window compaction strategy, making C* performant for time series data. In terms of redundancy, a C*cluster employs leaderless replication and therefore has no single point of failure. This makes C* popular - it is the main technology behind Netflix’s time series storage solution of customer viewing history.C* may sound perfect for time series data, however there is a need for other access patterns, and unlike a relational database this comes at a cost, denormalization - SQL-like operations such as joins are not supported. To overcome these constraints, we denormalize the data into gridded, time series, and point cloud representations. For the point cloud representation we are currently testing a model that distributes data across the cluster via geohash, and allows for data selection within a geofence.

In our previous work [Ramouz et al. 2018], during the gravity field determination via Least Squares Collocation (LSC) in Iran, it was detected that localizing covariance modeling shows better performance than using one uniform covariance for all the under investigation regions. Now the question is which criteria should be used for dividing the region into subareas for localization the covariance estimation? Tscherning et al. 1994 stated that data distribution could significantly affect the covariance estimation and consequently the LSC gravity modeling. As Iran has a rough topography and at the same time suffers from lack of a good coverage and homogenous terrestrial gravity network, covariance analysis in this area is not a trivial task. Four local case studies with different roughness and data distributions but with the same window size were selected. In each case study and based on Remove – Restore technique, the global and topographic parts of the gravity signal were removed from the observations. To do so, global gravity model EIGEN-6C4 up to d/o 360 and RTM method with the topographic information supplied by SRTM 1 arc-second height model, were used respectively. After that, residual gravity anomalies went through analytical covariance estimation by make use of Tscherning – Rapp 1974 covariance model. Indeed, covariance estimation in LSC method consists of two steps: calculation of an empirical covariance function from the residual gravity anomalies, and fitting an analytical covariance model to it. In this study, we focus on the considerations about data and its distribution which must be taken into account during empirical and analytical covariance determination. In case of not well-distributed input data, excavating analytical covariance model parameters is a challenging task. In some cases, this sensitivity causes difficulty even in choosing initial values for inverse adjustment of these parameters, which improper initial values lead to wrong parameters selections. Also, the distribution of data in each case study was manipulated to analyze its influence on the covariance estimation. To make an assessment, in each case study, the residual gravity anomalies were split into two datasets; first as observations input for LSC, and the second, as control points to evaluate the accuracy of the LSC gravity modeling and the covariance estimation. Then the interdependency and effect of Tscherning – Rapp covariance model parameters on the covariance estimation were investigated in each case study. Evaluation of the results in the case studies shows that the accuracy of the gravity modeling, directly dependent on the distribution of the data and the roughness of the topography, among other parameters. Finally, enhancing the covariance estimation based on presented approach, lead to about 10% improvement of the accuracy in terms of STD through LSC gravity modeling.

I present a game for teaching plate tectonics in the secondary school. The subject of the game is the interactions of plates and phenomena connected to it. The game is composed by cards that represent oceanic or continental lithospheres and a dice that indicated the type of boundary: convergent, divergent or transform. Students, generally in groups, collect “Events Cards”, for example “Earthquake” or “Volcano” or “Oceanic Trench” if they are able to identify the correct event associated to the boundary. The winner is who collects all “Events Cards”. The game is a cognitive model for involving students in a stimulating activity and for applying knowledge acquired in the process of problem-solving. Working in groups is an opportunity of peer to peer learning, where the content transmission occurs ‘horizontally’ with the teacher who acts as facilitator. The sharing of common rules within the class groups has also positive effects on group cohesion, also in contexts outside the school, implying teaching and educational values on social scale.

Authors present their investigation of spatial and temporal changes of methane emission from the surface of the Mozhaisk reservoir, situated 120 km to the west from Moscow, Russia. Seasonal changes in the content and specific flow of methane were revealed for different morphological areas of the reservoir based on the data of field observations in 2015-2018. In the low-flow Mozhaisk reservoir, the methane content in the surface and bottom layers of the deep-water areas at the end of the summer stratification period may differ by three orders of magnitude. According to the results of flux measuring by «floating chambers» in the entral area of the reservoir from the beginning of June to the end of the period of direct stratification (August-September) methane flux increased from less than 1 to 16 mg 4-C/(m**2 hour). The simultaneous measurements with “floating chambers” of 2 types (simple – for taking samples of the integral flux, and with a shield, rejecting bubbles – for samples of diffusion flux) revealed typical values of these flux components and their change during the sampling period. In the beginning of stratification diffusive flux predominates with mean values 0,2 mg4-/(m2 hour) and forms 100% of the total flux while methane concentration near bottom is low.The increase of the total methane flux is associated with an increase of the bubble component: its ratio grows from 75% to 98.7%. Integral methane flux rapid increase is observed when the upper edge of bottom anoxic zone reaches the lower edge of epilimnion. Supported by RGS 17-05-41095.