bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of organic matter quality on organic matter degradability in river sediments
Authors: Zander Florian , Gebert Julia , Comans Rob N. J. , Groengroeft Alexander , Heimovaara Timo J. , Eschenbach Annette
Published: March 2020

The project BIOMUD, part of the scientific network MUDNET (www.tudelft.nl/mudnet), investigates the decomposition of sediment organic matter (SOM) in the Port of Hamburg. The microbial turnover of sediment organic matter under reducing conditions leads to the formation of methane, carbon dioxide and others gases causing a change in the sediment rheological parameters. BIOMUD is aiming to explain the effect of organic matter lability on the rheological properties impacting the navigable depth of the harbour.

Samples of freshly deposited material were taken in 2018 and 2019 at nine locations in a transect of 30 km through the Port of Hamburg. Analyses included abiotic parameters (among others grain size distribution, standard pore water properties, standard solid properties, stable isotopes, mineral composition) and biotic parameters (among others anaerobic and aerobic organic matter degradation, DNA, protein and lipid content, microbial population). At four locations, physical density fractions and chemical organic matter fractions were analysed.

The quality of organic matter was described by normalising carbon released from microbial degradation under both aerobic and anaerobic conditions to the share of total organic carbon (mg C/g TOC). Organic matter pools with different degradation rates were used to quantify the lability of organic matter. The share of faster degradable (more labile) pools correlated strongly with the size of the hydrophilic DOC fraction, confirming results of Straathof et al. (2014) who investigated dissolved organic carbon pools in compost. The hydrophilic DOC fraction was closely correlated to the polysaccharide concentration, explaining the input of easily degradable organic matter. Moreover, the amount of organic carbon present in the sediment’s light density fraction < 1.4 g/cm3 strongly correlated with the hydrophilic DOC fraction and, less strongly, with organic matter lability. High organic matter quality, i.e. the labile, easily degradable fraction, was further related to the chlorophyll concentration in the water column but also the ammonium concentration in the sediment’s pore water.

It was hypothesised that the observed toposequence of decreasing organic matter quality from upstream to downstream could be explained by a chronosequence of increasing degradation and therefore ageing of organic matter as the sediment passes through the harbour area. Further, it was hypothesized that the harbour received organic matter of higher degradability, originating from phytoplankton biomass, from the upstream part of the Elbe river, whereas the input from the tidal downstream area provided organic matter of lower quality (degradability).

This study was funded by Hamburg Port Authority.
Biologically driven variability in coastal carbon fluxes - A model study
Authors: Chavan Harshal , Hense Inga
Published: March 2020

Benthic-pelagic coupling is responsible for the sudden appearance and disappearance of many coastal plankton blooms. Whether this signature is also reflected in pCO2 and whether the processes involved are important for the carbon fluxes in the coastal ocean is unclear. To address these questions, we use an ecosystem model that accounts for benthic-pelagic coupling of three different functional phytoplankton groups. Coupled with the water column model GOTM, we investigate the air-sea CO2 fluxes in the Baltic Sea and compared them with observations. We show that the variability is very well captured by the model. The relative importance of the life cycle processes in regulating carbon fluxes is demonstrated.
Citizen science and crowdsourcing in the field of marine scientific research – the MaDCrow project
Authors: Diviacco Paolo , Nadali Antonio , Malfatti Francesca , Iurcev Massimiliano , Carbajales Rodrigo , Busato Alessandro , Pavan Alessandro , Grio Lorenzo , Nolich Massimiliano
Published: March 2020

The Oceans cover 70% of the surface of our planet and contain 99% of the living space on the planet. Surveying the blue planet  is a very demanding and expensive activity since requires large infrastructures and trained personnels. Research  institutions, on the contrary, have very limited funding to perform their studies  so that the seas remain, still, mostly unexplored. This urges for  a bold step towards a new paradigm for marine data acquisition. MaDCrow (Marine Data Crowdsourcing) is a marine technology research and development project co-funded by the European Regional Development Fund (ERDF), aiming  to create an innovative technological infrastructure for the acquisition, integration and dissemination of data on the marine ecosystem. This is coupled with the goal to increase  public awareness of environmental issues and in particular of climate changes as drawn within goal 13.1 of the UN Sustainable Development Goals. MaDCrow sensors acquire Temperature, Salinity, pH and Oxygen data in real time. These are placed in ad hoc housing  that can be installed  on citizen’s  vessels . Data acquired are transmitted onshore, stored, processed and integrated with other information sources in order to provide end-users with an App- or web-site-based  clear picture of the status of the marine environment to address relevant social questions (e.g.: where is a good place to swim?; is there an oil spill?; are the seawater conditions good for aquaculture and fishery?) The main idea behind the project is to bridge the gaps among three actors who are mutually interdependent, namely: (I) Researchers, (II) Policy makers and (III) and the Citizens.

From the point of view of the scientific community, data acquisition by volunteers is a mechanism that has many advantages. It keeps costs low while at the same time generates large quantities of information. We will discuss the pros and the cons of MaDCrow approach and the future development of this multi-stakeholder initiative.
Reconciling plate convergence and orogeny: The influence of India-Asia convergence rate on the formation of the Himalayas
Authors: Knight Ben S. , Capitanio Fabio A. , Weinberg Roberto F.
Published: March 2020

The collision of India and Eurasia since ~50 Ma has resulted in a broad range of deformation along the Himalaya-Tibetan orogeny, accommodating >2700 km of convergence. The region is characterised by the Tibetan Plateau, the Himalayan internal units and fold-and-thrust belt from North to South. These formed as a consequence of a convergence history characterised by a progressive decrease in velocity, from ~10 cm/yr 50 Ma, to ~8 cm/yr 42.5 Ma and to present-day values of ~4 cm/yr around 20 Ma. Here, we test the controls of such a convergence velocity history on the orogeny of a viscoplastic wedge during collision, above a subducting continental lithosphere. We compare numerical models simulating India-Asia plate convergence and collision, comparing the structures observed throughout the evolution with those observed in the Himalayan-Tibetan region. The models display distinct phases of growth and structural style evolution in the Himalayan-Tibetan region that are a result of the change in convergence velocity and long-term collision. After an initial stacking, the high convergence velocity forces deformation migration towards the upper plate, where a plateau forms, while late stage slowdown of collision favours the formation of the Himalayan fold-and-thrust belt. While the latter is in agreement with the structuring of the southermost domains and the South Tibetan Detachment (STD) fault, the former provide constraints to the initial uplift of the Tibetan Plateau.
Low-cost methane (CH4) sensors for use in in flux chambers
Authors: Bastviken David , Nygren Jonatan , Schenk Jonathan , Massana Roser Parrelada , Duc Nguyen Thanh
Published: March 2020

The lack of reliable low-cost greenhouse gas flux measurement approaches limit our ability quantify regulation and verify mitigation efforts at the local level.   Methane (CH4), one of the most important greenhouse gases, is particularly dependent on local measurements because levels are regulated by a complex combination of sources, sinks and environmental conditions. There are still major gaps in the global methane budget and the reasons for the irregular development over time remains unclear. Facilitation of local flux measurements in all parts of the world therefore seem important to constrain large-scale assessments. As the high cost of gas analysers is a limiting factor for flux measurements, we here present how low-cost CH4 sensors can be used outside their specified range to yield reasonably accurate chamber-based flux measurements. By using a two-step calibration approach, testing multiple alternatives on how to model interference from temperature and humidity, an R2 ≥ 0.99 was achieved over a CH4 concentration range of 2 – 700 ppm under variable temperature and relative humidity. We also demonstrate ways to reach such calibration results without complicated calibration experiments and instead using in the order of 20 in situ reference measurements at different environmental conditions. Finally we, constructed and described a make-it-yourself Arduino based logger with the tested sensors for CH4, temperature, humidity and carbon dioxide (CO2) intended for flux chamber use with a material cost of approximately 200 Euro. We hope that this can contribute to more widespread greenhouse gas flux measurements in many environments and countries.
Grazing and mowing impact on soil organic carbon and microbial activity in grassland soil
Authors: Gilmullina Aliia , Rumpel Cornelia , Blagodatskaya Evgenia , Dippold Michaela , Louault Frederique , Klumpp Katja , Chabbi Abad
Published: March 2020

Grassland management practices, such as grazing with varying animal density and mowing may impact the processes leading to soil organic carbon (SOC) accumulation. Although, they serve similar agricultural purposes, they differ in their effect on plant physiology and their influence on SOC remains uncertain. We hypothesised that both practices affect SOC storage differently due to an altered plant C input and changed growth and physiological response leading consequently to contrasting soil microbial activity.

Based on this, our experiment included the investigation of three grassland treatments: grazing at two intensities and mowing which are located at the experimental station of SOERE ACBB (Clermont-Ferrand, France). Additionally, we included bare soil and unmanaged abandoned site considering as negative and positive controls, accordingly. The aim of the study was to estimate how grazing and mowing affect SOC chemical characteristics and its link with microbial activity.

Our results show highest SOC contents under low grazing intensity, whereas SOC content under high grazing intensity was lower and did not differ from abandoned grassland. SOC content under mowing was lowest among all treatments but still higher compared to bare soil. Microbial biomass C (MBC) followed a similar pattern under high grazing intensity and positive control whereas it was similar under mowing and low grazing intensity and lowest under bare soil. Absolute enzyme activities showed a similar tendency as SOC content. However, enzyme activities per MBC resulted in highest values under low grazing intensity and similarly lower values under all other treatments.

These results demonstrate that microbial parameters responded to management in various ways most probably related to the differences in dung and litter inputs. We suggest that dung input under high grazing intensity increased MBC and consequently compensated for plant removal thus keeping SOC contents increasing. Consequently, grazing at both intensities allows to maintain SOC at similar levels as in absence of management. While on unmanaged land high SOC may be related to absence of harvest, on grazed land it may be related to stimulation of microbial activity due to animal activity. Mowing treatment on the other hand did not allow to increase SOC.

We conclude that the presence of animals in the system is essential to improve soil heath, biogeochemical cycling, and SOC storage.
Onset of Double-Diffusive Convection in the Ice Shelf/Ocean Boundary Layer
Authors: Middleton Leo , Vreugdenhil Catherine , Holland Paul , Taylor John
Published: March 2020

The interaction between Ice-Shelves and the Ocean is an important component of the response of ice sheets to future warming oceans. Observational data in the ocean boundary layer beneath ice shelves is limited and the turbulent flow in the boundary layer is not well characterised. Our work uses small scale (9m depth) direct numerical simulations (DNS) of the Ice-Shelf-Ocean Boundary Layer, inspired by field observations made beneath the George VI Ice Shelf. Here, warm water has been observed directly beneath the ice shelf, and yet the observed melt rates are modest. To study this scenario, we simulate a forced turbulent flow underlying an ice shelf where the ice base is represented by a dynamic melting boundary condition. As the ice melts, a pool of relatively cold, fresh water develops below the ice base. Thermal diffusion causes the underlying water to cool and can drive turbulent convection. At the same time, the salinity gradient in the halocline is stabilising, but develops over a longer time scale. As a result, two flow regimes exist: one with active turbulent convection driven by double-diffusion of heat and salt, and the other with stratified turbulence leading to mixing of the halocline. By varying control parameters, we identify the transition between the flow regimes in terms of the temperature contrast (thermal driving) and the level of turbulence in the far field. We consider the behaviour of the diapycnal buoyancy flux near the ice base, and it provides insight into the drivers of both the double-diffusive convection and its modification by ambient turbulence. Finally, we discuss how the double-diffusive process we have described applies to real-world ice-shelf ocean boundary layers, and how it may be quantified within observations.
An open rainfall-runoff measurement database
Authors: Devátý Jan , Beitlerová Hana , Lenz Jonas
Published: March 2020

Measurement of runoff events induced by natural rainfall or rainfall simulators of various construction and dimensions is a common method for obtaining data needed for run-off and soil erosion models calibration. As every simulator is different so are the methods for data collection, recording, processing and utilization. Mining the data from different sources for comparison or a common purpose can be quite exhausting as all the teams and workers use different software, workflows and structures for storing the data. The database presented is an attempt to provide a robust structure for storing experimental data together with its metadata, relationships between data sets and other information about the data collection and preprocessing. The desired state is where any record is back-trackable to the original source field record regardless if it was written by hand on paper or registred by digital logger.

The relational database is built in MySQL and provides a comprehensive structure for storing and retrieving the data and metadata. The access to the database is differentiated into multiple levels with different rights. A public web user interface allows low-level access to the data that can be viewed as tables and charts. Private web interface provides logged-in users the rights to add, delete and alter data. The web interface incorporates basic search, order and filter capabilities on the data. High level access by direct querying the DB is available for trusted users who are familiar with MySQL language and so are capable of creating their own complex queries. The direct access to the database is possible via any programing language with appropriate libraries. Querying the DB directly by code comes especially handy when preparing extensive datasheets for statistical evaluation or model calibration runs.

The database follows the “FAIR Guiding Principles for scientific data management and stewardship”.

So far the database was successfully tested on the data from the three institutions of the authors' affiliation . Further development and tuning of the DB to enable incorporation of wider range of data structures is desired and any suggestions are welcome. If you are dealing with measurements related to rainfall-runoff processes and are interested in making your data accessible, please bring a typical dataset or an overview of recorded parameters to this PICO.

 

The research has been supported by the research project QK1810341 of Czech National Agricultural Research Agency and the European Social Fund in the Free State of Saxony (Förderbaustein: Promotionen)
Global morphodynamic response of deltas to sea-level rise in the 21st century
Authors: Nienhuis Jaap , van de Wal Roderik
Published: March 2020

River deltas are low lying areas that will likely experience significant land loss because of relative sea-level rise. Most future projections of delta land loss, however, assume passive coastal inundation (using so-called “bath-tub” models) and as such they tend to be unvalidated and exclude morphodynamic processes such as sedimentation. To improve future projections of delta land area change, here we apply a morphodynamic model of delta response to RSLR to all 10,000 deltas globally. We use historic RSLR, sediment supply, and observed delta land area change from 1985-2015 to calibrate and validate this model for all these deltas. Applying our model using future RSLR scenarios, we find that by the end of this century deltas globally will have lost land under all RCP scenarios. Land loss is aggravated by river dams that have diminished sediment supply to many deltas. RSLR expected under RCP8.5 will force delta land loss at at rates exceeding 900 km2/yr by 2100. We predict cumulative land loss under RCP8.5 up to 2100 of ~35,000 km2, or about 4% of total global delta area.
Design of Microplastics Citizen Science Kit
Authors: Hughson-Gill Rachael
Published: March 2020

Microplastics are an ever-increasing problem. Every river that was tested in a recent study found the presence of microplastics, with 80% of all plastic in the ocean coming from upstream. Despite this, there is little understanding into the abundance of plastic, its characteristics and the full impact that is it having on marine, freshwater ecosystems and wider ecological systems.

 

Current fresh water monitoring does not consider the fluid dynamics of rivers, is difficult to use and is inaccessible to the wider public. My project will focus on creating a product that allows for the large-scale data collection of microplastic through citizen science. Allowing groups of people to analyse their local natural environment for the presence and abundance of microplastics within the water. This method of data collection could provide information on a scale that is not possible with traditional methods and would allow for the comparison between freshwater systems. This comparison is fundamental to begin to fill the knowledge gaps around the understanding of microplastics.

 

Inaccessibility of monitoring to the public is not just through tools but also through the current communication of data with research rarely breaking into the public domain. Citizen science offers not just an improvement in understanding but also offers an opportunity for engagement with the public body. Increasing awareness of the impact of habits round plastic through the sharing of monitoring data can generate the much-needed change on both an individual and policy level to address the problem from the source. This method of change through public opinion can be seen to have an effect on freshwater systems through microbeads ban, plastic bags, plastic straws and industrial pollution regulation.

 

Through the creation of this product a multidisciplinary approach that blends engineering and design practices is implemented. The wholistic approach to creation is something that is fundamental in the success of tools and therefore the success of the research that is implemented through them. A tool such as this whose function is within the public engagement of its use - increased awareness, as well as the outcome of its use - microplastics data, is required to have an engaging user experience as well as data integrity implemented through engineering design.

 

This project offers an opportunity to show the importance of the design process within research tools to aid the research process and the positive impact that can come from it.
Soil organic carbon along a geothermal gradient in North-West Canada
Authors: Peplau Tino , Gregorich Edward , Poeplau Christopher
Published: March 2020

Global warming will increase soil microbial activity and thus catalyse the mineralisation of soil organic carbon (SOC). Predicting the dynamics of soil organic carbon in response to warming is crucial but associated with large uncertainties, owing to experimental limitations. Most studies use in-vitro incubation experiments or relatively short-term in-situ soil warming experiments. Long-term observations on the consequences of soil warming on whole-profile SOC are still rare. Here, we used a long-term geothermal gradient in North-West Canada to study effects of warming on quantity and quality of SOC in an aspen forest ecosystem.

The Takhini hot springs are located within the region of discontinuous permafrost in the southern Yukon Territory, Canada. The springs warm the surrounding soil constantly and lead to a horizontal temperature gradient of approximately 10°C within a radius of 100 meters. As these natural springs heat the ground for centuries and the forest ecosystem surrounding the springs is relatively homogenous, the site provides ideal conditions for observing long-term effects of soil warming on ecosystem properties. Soils were sampled at four different warming intensities to a depth of 80 cm and analysed for their SOC content and further soil properties in different depths. 

For the bulk soil, we found a significant negative relationship between soil temperature and SOC stocks. This confirms that climate change will most likely induce SOC loss and thus a positive climate- carbon cycle feedback loop. The response of five different SOC fractions to warming will also be presented.
Regional climate simulations at kilometer-scale with RegCM4: Evaluation of precipitation and future projections
Authors: Stocchi Paolo , Pichelli Emanuela , Coppola Erika , Torres Alvarez Jose Abraham , Giorgi Filippo
Published: March 2020

The recent increase in climate modeling activities at convection permitting scales (grid spacing under 4 km) has strongly been motivated by the increased computer capacities in the last years with the aim to reduce the model errors associated with parameterized convection and a more detailed representation of present and future regional climate. Some Regional climate projects addressing on convection permitting modeling simulations and projections have been recently implemented to make more robust conclusions on the added value of convection permitting simulation to future climate projections. Here, we present convection resolving climate simulations performed in the framework the European Climate Prediction System (EUCP) project, using the non-hydrostatic version of the RegCM model. The RegCM simulations have a grid spacing of 3 km, over three different regions (Pan-Alpine, Central Europe, and South-East Europe). These simulations were driven by initial and boundary conditions built from intermediate 12 km simulations driven by the global climate model (GCM) HadGEM2-ES. We considered three time slices each one of them covering a 10-year period, the historical (1996-2005), the near future (2041-2050) and the far future (2090-2099) under the RCP8.5 scenario. The high resolutions (3 km) simulations, over the historical period, are evaluated through comparison with available observations data sets (including in-situ and satellite-based observation of precipitation) and coarse resolution (12 km) simulation is used as benchmark. The kilometer-scale RegCM4.7 scenario (RCP8.5) simulations, driven by HadGEM2-ES, near future (2041-2050) and the far future (2090-2099), are also analyzed and presented, focusing on the future change in terms of mean precipitation, precipitation intensity and frequency and heavy precipitation on daily and hourly timescales in different seasons.
Spatio-Temporal Modeling of Wind Speed Using EOF and Machine Learning
Authors: Guignard Fabian , Amato Federico , Robert Sylvain , Kanevski Mikhail
Published: March 2020

Spatio-temporal modelling of wind speed is an important issue in applied research, such as renewable energy and risk assessment. Due to its turbulent nature and its very high variability, wind speed interpolation is a challenging task. Being universal modeling tools, Machine Learning (ML) algorithms are well suited to detect and model non-linear environmental phenomena such as wind.

The present research proposes a novel and general methodology for spatio-temporal interpolation with an application to hourly wind speed in Switzerland. The methodology is organized as follows. First, the dataset is decomposed through Empirical Orthogonal Functions (EOFs) in temporal basis and spatially dependent coefficients. EOFs constitute an orthogonal basis of the spatio-temporal signal from which the original wind field can be reconstructed. Subsequently, in order to be able to reconstruct the signal at spatial locations where measurements are unknown, the spatial coefficients resulted from the decomposition are interpolated. To this aim, several ML algorithms were used and compared, including k-Nearest Neighbors, Random Forest, Support Vector Machine, General Regression Neural Networks and Extreme Learning Machine. Finally, wind field is reconstructed with the help of the interpolated coefficients.

A case study on real data is presented. Data consists of two years of wind speed measurements at hourly frequency collected by Meteoswiss at several hundreds of stations in Switzerland, which has a complex orography. After cleaning and handling of missing values, a careful exploratory data analysis was carried out, followed by the application of the proposed novel methodology. The model is validated on an independent test set of stations. The outcome of the case study is a time series of hourly maps of wind field at 250 meters spatial resolution, which is highly relevant for renewable energy potential assessment.

In conclusion, the study introduced a new way to interpolate irregular spatio-temporal datasets. Further developments of the methodology could deal with the investigation of alternative basis such as Fourier and wavelets.

 

Reference

N. Cressie, C. K. Wikle, Statistics for Spatio-Temporal Data, Wiley, 2011.

M. Kanevski, A. Pozdnoukhov, V. Timonin, Machine Learning for Spatial Environmental Data, CRC Press, 2009.
BEL1D: 1D imaging using geophysical data in the framework of Bayesian Evidential Learning
Authors: Michel Hadrien , Nguyen Frédéric , Hermans Thomas
Published: March 2020

BEL1D has been newly introduced to the community as a viable algorithm for the stochastic interpretation of geophysical data in the form of 1D geological models. It relies on a simplified version of the Bayesian problem in reduced space called Bayesian Evidential Learning. However, the method is closer to machine learning than classical McMC approaches since it can be separated into a learning process followed by a prediction part. The learning phase consists in constituting statistical relationships between models parameters and geophysical data from a training set of numerical models. The prediction phase then samples the previous relationships according to field data. Compared to other stochastic methods such as McMC, BEL1D as key advantages: 1) it converges easily as long as the prior is consistent with the unique input parameter being the size of the training set, 2) every model in the posterior is drawn independently, making it easy to trace back their origin, 3) the CPU times are similar to McMC, but the method can be fully parallelized and the learning process can be done before data acquisition, leading to quasi instantaneous prediction of the posterior. BEL1D already has led to successful applications on surface nuclear magnetic resonance data as well as dispersion curves from surface waves analysis. Nonetheless, the method is not limited to those two examples and can be implemented for any 1D geophysical method as long as a forward model is provided. Currently, the method is implemented for blocky imaging but will be extended to non-blocky models in the future. The open-source codes are readily available.
Flow recession behavior of dendritic subsurface flow patterns
Authors: Strüven Jannick , Hergarten Stefan
Published: March 2020

Discharge curves of springs are the fingerprint of aquifers. In particular, the recession of flow after strong recharge events has been widely used for aquifer characterization. While an exponential decay is often found at long time scales, the short-term behavior is less unique and widely used in the context of characterizing karst systems. Several empirical and a few physically-based models describing the short-term recession behavior were proposed.

This study investigates the flow recession behavior of aquifers with preferential flow paths with a structure according to the concept of minimum energy dissipation.
Assuming a power-law relationship between hydraulic conductivity and porosity, the subsurface flow patterns used in our model are organized towards an optimal spatial distribution of these two parameters in a way that the total energy dissipation of the flow is minimized. This leads to two-dimensional dendritic network structures similar to river networks. Starting from a steady-state initial condition with a constant recharge rate we model the decrease of discharge over time, under the assumption of a linear storage behavior.
As expected the long-term flow recession can be approximated by an exponential function. At short times, however, our model predicts a power-law behavior with exponents ranging from 0.7 to 0.9. For the most realistic scenario, a quadratic relationship between hydraulic conductivity and porosity, the power-law exponent approximates 0.8 which corresponds well to what other studies have found for suitable recession events of karst springs.
Variations revealed by INFREP Radio Network in correspondence of six earthquakes with MW greater than 5.0 occurred in the Balkan Peninsula and Adriatic Sea on 26 and 27 November, 2019
Authors: Biagi Pier Francesco , Nina Alexandra , Ermini Anita , Nico Giovanni
Published: March 2020

In this work we analyse variations in VLF/LF radio signal amplitudes recorded by the INFREP network in the period 16 November – 6 December, 2019 characterized by very intensive seismic activities in the Balkan peninsula, Crete, and Adriatic, Aegean and Black seas. Namely, 38 earthquakes with magnitude greater than 4.0 occurred in this area during the noticed period; the most intensive of them occurred on 26 and 27 November: three events in Albania (Mw= 6.4, 5.3, 5.1), one in Crete (Mw= 6), one in Bosnia and Herzegovina (Mw= 5.4) and two in Adriatic sea (Mw= 5.4, 5.3). We study both long- and short- term variations that are already recorded in earlier studies. The long-term variations relate to changes in the amplitude intensities in periods of several days and their existence is shown in many previous studies. The recent analyses also indicate short-term variations in signal amplitude noises started about several tents of minutes before the earthquake (Nina et al. 2020). In this work, we analyse different areas using INFREP network, which allow us to study local changes in the atmosphere. In order to examine possible precursors we considered longer time started and ended 10 days before and after the most intensive of the considered earthquakes, respectively.

This research is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, under the projects 176002 and III44002.

References

Nina, A., S. Pulinets, P. F. Biagi, G. Nico, S. T. Mitrović, M. Radovanović and L. Č. Popović. Science of the Total Environment 710 (2020) 136406
Trajectories of Major Martian Dust Storms
Authors: Wang Huiqun (Helen) , Richardson Mark , Toigo Anthony
Published: March 2020

      Major dust storms – including large regional and global dust storms – dramatically influence atmospheric optical depth, thermal structure, and circulation. They represent probably the most significant inter-annual variability in the Martian lower / middle atmosphere and are of particular concern for spacecraft missions. Great variability is exhibited not only in their timing and magnitudes, but also in histories of their evolution. Mars Daily Global Maps (MDGMs) over the past Mars decade have shown that all major dust storms develop through a combination of dust storm sequences evolving along one or more trajectories, where each dust storm sequence is composed of multiple dust storm members of various sizes and durations along a trajectory. In this presentation, we will illustrate the trajectories of major dust storms observed in MDGMs and examine their relationship with the general circulation.
Supporting risk management in the Caribbean by application of Deep Learning for object classification of aerial imagery with MATLAB
Authors: Bomberg Sebastian , Goel Neha
Published: March 2020

The presented work focuses on disaster risk management of cities which are prone to natural hazards. Based on aerial imagery captured by drones of regions in Caribbean islands, we show how to process and automatically identify roof material of individual structures using a deep learning model. Deep learning refers to a machine learning technique using deep artificial neural networks. Unlike other techniques, deep learning does not necessarily require feature engineering but may process raw data directly. The outcome of this assessment can be used for steering risk mitigations measures, creating risk hazard maps or advising municipal bodies or help organizations on investing their resources in rebuilding reinforcements. Data at hand consists of images in BigTIFF format and GeoJSON files including the building footprint, unique building ID and roof material labels. We demonstrate how to use MATLAB and its toolboxes for processing large image files that do not fit in computer memory. Based on this, we perform the training of a deep learning model to classify roof material present in the images. We achieve this by subjecting a pretrained ResNet-18 neural network to transfer learning. Training is further accelerated by means of GPU computing. The accuracy computed from a validation data set achieved by this baseline model is 74%. Further tuning of hyperparameters is expected to improve accuracy significantly.
Properties and challenges of mineral dust aerosol modelling in the latest Earth System Models
Authors: Checa-Garcia Ramiro , Balkanski Yves , Bergman Tommi , Carslaw Ken , Dalvi Mohit , Marticorena Beatrice , Michou Martine , Nabat Pierre , Nieradzik Lars , Noije Twan van , O’Donnell Declan , Olivie Dirk , O'Connor Fiona , Schulz Michael , Scott Catherine
Published: March 2020

Mineral dust aerosols participate in the climate system and biogeochemistry processes due to its interactions with key components of Earth Systems: radiation, clouds, soil and chemical components. A central element to improve our understanding of mineral dust is through its modeling with Earth Systems Models where all these interactions are included. However, current simulations of dust variability exhibit important uncertainties and biases, which are model-dependent, whose cause is our imperfect knowledge about how to best represent the dust life cycle. For these reasons a continuous evaluation of the performance and properties of the different models compared against measurements is a crucial step to improve our knowledge of the dust cycle and its role in the climate system and biogeochemical cycles. Here we present an exhaustive evaluation of mineral dust aerosols in CRESCEND-ESMs over global, regional and local scales. We compare models against three networks of instruments for total dust deposition flux, yearly surface concentrations, and optical depths. Global and regional dust optical depths are compared with MODIS and MISR derived products. Specific analyses are done over the Sahel region where improved and compressive dust observational datasets are available. The results indicate that all the models capture the general properties of the global dust cycle, although the role of larger particles remains challenging. Differences are partially due to surface winds as nudged simulations improve the inter-model comparison and the performance in optical depth compared to MODIS. At the regional scale, there is an optical depth reasonable agreement over main source areas, but a joint inter-comparison including fluxes and concentration indicates larger differences. At the local scale, the uncertainties increase and current models are not able to reproduce together several observables at the same time.
A new tool for identifying boundary conditions in coastal oceanic models : First tryouts with a tidal model
Authors: Koenig Guillaume , Aldebert Clément , Chevalier Cristèle , Devenon Jean-Luc
Published: March 2020

If lateral boundary conditions are crucial for physical modelling of the ocean dynamics, their estimate may lack of accuracy in coastal regions. Data-assimilation has been a long-used tool to improve accuracy, but most of the existing popular methods are difficult to implement. To solve this, we tried a new and an easy-to-implement method to estimate boundary conditions. This method uses data assimilation with a stochastic gradient descent  and successive approximations of the boundary conditions. We  tested it with twin experiments on a tidal model in the lagoon of Ouano, in New-Caledonia. The method was successful and provided robust estimation of the boundary conditions with various settings of subsampling and noise for the pseudo-data. Here we present those results and discuss about how the stochastic gradient descent and the approximations have to be tuned.
An integrative framework for data-driven investigation of environmental systems
Authors: Eggert Daniel , Dransch Doris
Published: March 2020

Environmental scientists aim at understanding not only single components but systems, one example is the flood system; scientists investigate the conditions, drivers and effects of flood events and the relations between them. Investigating environmental systems with a data-driven research approach requires linking a variety of data, analytical methods, and derived results.

Several obstacles exist in the recent scientific work environment that hinder scientists to easily create these links. They are distributed and heterogeneous data sets, separated analytical tools, discontinuous analytical workflows, as well as isolated views to data and data products. We address these obstacles with the exception of distributed and heterogeneous data since this is part of other ongoing initiatives.

Our goal is to develop a framework supporting the data-driven investigation of environmental systems. First we integrate separated analytical tools and methods by the means of a component-based software framework. Furthermore we allow for seamless and continuous  analytical workflows by applying the concept of digital workflows, which also demands the aforementioned integration of separated tools and methods. Finally we provide integrated views of data and data products by interactive visual interfaces with multiple linked views. The combination of these three concepts from computer science allows us to create a digital research environment that enable scientists to create the initially mentioned links in a flexible way. We developed a generic concept for our approach, implemented a corresponding framework and finally applied both to realize a “Flood Event Explorer” prototype supporting the comprehensive investigation of a flood system.

In order to implement a digital workflow our approach intends to precisely define the workflow’s requirements. We mostly do this by conducting informal interviews with the domain scientists. The defined requirements also include the needed analytical tools and methods, as well as the utilized data and data products. For technically integrating the needed tools and methods our created software framework provides a modularization approach based on a messaging system. This allows us to create custom modules or wrap existing implementations and tools. The messaging system (e.g. pulsar) then connects these individual modules. This enables us to combine multiple methods and tools into a seamless digital workflow. The described approach of course demands the proper definition of interfaces to modules and data sources. Finally our software framework provides multiple generic visual front-end components (e.g. tables, maps and charts) to create interactive linked views supporting the visual analysis of the workflow’s data.
Experimental study of resonant shallow flows past a lateral cavity: a benchmark test for high-resolution numerical models
Authors: Martínez Aranda Sergio , Navas-Montilla Adrián , Lozano Antonio , García-Navarro Pilar
Published: March 2020

The study of resonant shallow flows past a lateral cavity is of great relevance due to their interest in civil and environmental engineering [1]. Such flows exhibit the presence of a standing gravity wave, called seiche, which is coupled with the shedding of vortices at the opening of the cavity. A complete understanding of such phenomenon is necessary as it may determine the mass exchange between the main channel and the cavity [2]. A better insight into this phenomenon helps to improve the design and implementation of innovative river bank restoration techniques. An experimental study of the resonant flow in a laboratory flume with a single lateral cavity is herein presented. Five different flow configurations at a fixed Froude number (Fr=0.8) are considered. The main novelty of the present work is the use of a pioneering non-intrusive experimental technique [3] to measure the water surface at the channel-cavity region. This optical technique offers high resolution 2D data in time and space of the water surface evolution, allowing to determine the relevant features of the seiche oscillation, i.e. spatial distribution of oscillation nodes and anti-nodes, oscillation modes and amplitude of the oscillation. Such data are supplemented with Particle Image Velocimetry measurements to perform a more detailed study of the resonance phenomenon. High-resolution two-dimensional amplitude oscillation maps of the seiche phenomenon are presented for the experimental water depth. Experimental velocity fields inside the cavity are presented and confirm the inherent coupling between the unstable shear layer at the opening of the cavity and the gravity standing wave. The high quality of the experimental data reported in this work makes this data set a suitable benchmark for numerical simulation models in order to evaluate their performance in the resolution of turbulent resonant shallow flows.

[1] C. Juez, M. Thalmann, A. J. Schleiss & M. J. Franca, Morphological resilience to flow fluctuations of fine sediment deposits in bank lateral cavities, Advances in Water Resources, 115 (2018) 44-59.

[2] I. Kimura & T. Hosoda, Fundamental properties of flows in open channels with dead zone, Journal of Hydraulic Engineering 123 (1997) 98-107.

[3] S. Martínez-Aranda, J. Fernández-Pato, D. Caviedes-Voullième, I. García-Palacín & P. García-Navarro, Towards transient experimental water surfaces: a new benchmark dataset for 2D shallow water solvers, Advances in water resources, 121 (2018) 130-149.
New pathways for high-resolution weather radar products in the Hamburg metropolitan region
Authors: Burgemeister Finn , Finn Tobias Sebastian , Machnitzki Tobias , Clemens Marco , Ament Felix
Published: March 2020

The University of Hamburg operates a single-polarized X-band weather radar to investigate small scale precipitation in Hamburg’s center since 2013. This weather radar provides a temporal resolution of 30 s, a range resolution of 60 m, and a sampling resolution of 1° within a 20 km radius. The X-band observations refine the coarse measurements of the German nationwide C-band radars. On the one hand, the resolution enables new capabilities in research and detection of extreme events, e.g. flash floods or tornadoes in rain events. On the other hand, with the single polarization and small wavelength, attenuation, noise, and non-meteorological echoes become a challenging issue. How can we derive products from disturbed weather radar observations?

We demonstrate new methods to process X-band weather radar observations effectively using synthetic and real data. Firstly, we present our python package for local weather radars. This package combines all steps of processing our measurements and includes well-established algorithms of image processing and radar meteorology. Secondly, we study machine learning as a new and potential method for our weather radar products. The developed neural network uses raw reflectivity measurements as input and results in data, which is free of noise and non-meteorological echoes. We outline assets and drawbacks of both methods and show possible connections.

Further X-band weather radar systems are planned for 2020 to monitor precipitation for the Hamburg metropolitan region in a networked environment. The high-quality and -resolution weather radar products will be provided for urban hydrology research within the Cluster of Excellence CLICCS - Climate, Climatic Change, and Society.
Impact of satellite and in situ data assimilation on hydrological predictions
Authors: Musuuza Jude Lubega , Crochemore Louise , Gustafsson David , Pimentel Rafael , Pechlivanidis Ilias
Published: March 2020

The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimises model performance. We further assess the effect of assimilating different satellite products namely snow water equivalent, fractional snow cover, and actual and potential evapotranspiration; as well as in situ measurements of river discharge and local reservoir inflows. We finally investigate the combinations of those products that improve model predictions of the target variables and how the model performance varies through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without ET products improves the model performance.