bluuebunny/crossref_metadata_2025 huggingface dataset:
The Development of a Water Quality Forecasting System for Recreational Coastal Bathing Waters in Ireland
Authors: Hawtree Daniel , O'Sullivan John , O'Hare Gregory , Görgü Levent , Muldoon Conor , Meijer Wim G. , Masterson Bartholomew , Gitto Aurora , Taylor Malcolm , Mitchell Elaine
Published: March 2020

The European Bathing Water Directive (BWD; 76/160/EEC 2006) requires the implementation of early warning systems for bathing waters which are subject to short-term pollution events. To this end, the EU SWIM project is developing coastal water quality prediction models and alert systems at nine beach sites in the Republic of Ireland and Northern Ireland, which represent a range of baseline water quality and site conditions.

At each site, statistical / machine-learning predictive models are being developed based on their site-specific relationships between fecal indicator bacteria and multiple environmental variables. A unique aspect of the approach being developed is the use of a historical back-cast climate data (Met Éireann's MÉRA dataset) as the foundation of model development, and the use of a related climate forecast dataset (Met Éireann's Harmonie dataset) for forecasts. By integrating these datasets into a predictive system, environmental variables can be utilized at spatial and temporal resolutions exceeding what is typically available from alternative data sources (e.g. weather station gauges). This approach enables the production of a continuous stream of short-term water quality forecasts, which can then be validated against data collected by routine compliance sampling, as well as targeted supplementary water quality sampling.

This presentation provides an overview of the end-to-end prediction system, a summary of the underlying models, and a discussion of the challenges and opportunities presented by this forecasting framework.
Controls over phosphorus mineralization and immobilization rates in different tropical soils
Authors: Fuchslueger Lucia , Zezula David , Püspök Johann , Van Langenhove Leandro , Margalef Olga , Canarini Alberto , Ranits Christian , Quesada Carlos Alberto , Salinas Norma , Cosio Eric , Penuelas Josep , Wanek Wolfgang , Richter Andreas , Janssens Ivan
Published: March 2020

Highly weathered soils depleted in minerals and phosphorus (P) support large tracts of the tropical rainforests in the Central Amazon, which significantly contribute to the global carbon (C) sink. In these soils (oxisols and ferrasols), P is either occluded in Al/Fe-oxides, bound to the soil mineral matrix or in soil organic matter, and therefore not directly available for uptake as inorganic phosphate (Pi). To liberate Pi for plant or microbial uptake two processes are key: (i) changes of sorption-desorption equilibria of Pi with the soil matrix and (ii) the release of Pi from organic compounds (Po) catalyzed by enzymes, such as phosphatases. Plant roots and soil microbes have developed strategies to stimulate the release of P by accelerating P dissolution and desorption and by releasing extracellular phosphatases into the soil environment, which requires however C and energy investment. Because of P limitation in this ecosystem, the relative contributions of abiotic and biotic controls over P mineralization is of pivotal importance. Yet conclusive results are still scarce.

We therefore aimed to disentangle abiotic and biotic controls over P mineralization in tropical soils. To achieve this, we collected forest soils from the Amazon Basin covering a range of soil texture and P concentrations, determined soil mineralogy and measured gross P desorption and mineralization rates using a 33P isotope pool dilution assay. Moreover, we determined acid phosphatase activity rates and microbial biomass C and P. We found significant differences between the studied sites in gross P influx and efflux rates into the Pi pool. Gross influx rates (i.e. the sum of Pi desorption and organic P mineralization) exceeded efflux (i.e. sorption or biotic Pi uptake rates) only in sandy and silty soils, while in clayey soils efflux rates dominated P fluxes indicating a very high Pi sorption capacity. However, gross influx and efflux rates were not related to total or dissolved P. Microbial biomass and acid phosphatase activity normalized to microbial biomass C were highest in sites with overall low total P microbial biomass P accounting for up to 40 % of total P in low P soils. We therefore conclude that in low P soils organic P turnover plays a major role in soil P cycling, and despite of the high P sorption capacity of clay rich soils, microbes can be strong competitors for plant available P.
ESM-projected global change in indices of extreme precipitation using the TR3S method of bias-correction
Authors: Gomez-Garcia Martin , Matsumura Akiko , Ogawada Daikichi
Published: March 2020

The post-processing of the Earth System Models (ESMs) outputs has become a routine step that is taken in climate change impact assessments with the aim of (i) reproducing the probability distribution of the corresponding observed data and (ii) correcting the biases in the probability distributions of projected future climate. To responsibly support the decision‐making processes, the climate‐modeling community has been discussing about the conceptual requirements that bias‐correction methods should fulfill to avoid altering the relevant information that is provided by ESMs, like the climate trends or the inter-variable physical dependence structure. Bearing in mind these discussions, a recently proposed method of bias-correction, based on TRend-preserving Synthetic Samples of Stable Distributions (TR3S), decomposes the atmospheric variables into three temporal elements that represent the climate mean state, the interannual variability, and the daily variability. This decomposition is aimed at correcting the biases at one time scale without affecting the projected climate trend or the distributional properties at other time scales. The novelty of this approach is, nevertheless, marked by the adjustment of interannual and daily variability that is made by replacing the ESM‐simulated variability with synthetic samples drawn from Stable Distributions (SDs) that were previously fitted to the observed variability. The replacement prevents the transfer of the sampling variability of the calibration period while giving the corrected data the distributional properties of the observed climate. The employment of SDs was motivated by the fact that the ESM-projected changes in the scale, the symmetry, and the frequency of extremes can be measured and applied to the SDs of the observed data. In this work, we correct the biases in the global precipitation datasets generated by several ESMs using the TR3S method and present the projected changes of a few indices of extremes using online interactive maps. Furthermore, the TR3S method allowed us to document the spatial distribution of the biases in the distributional properties (i.e., scale, symmetry, and frequency of extremes) of daily and interannual variability of each ESM. We hope that the bias-corrected information can be useful to end-users in impact assessments and the analytical framework of model biases can be used by modelers to identify ways in which the ESM parameterizations could be improved.
A high resolution reanalysis for the Mediterranean Sea
Authors: Escudier Romain , Clementi Emanuela , Drudi Massimiliano , Pistoia Jenny , Grandi Alessandro , Cipollone Andrea , Lecci Rita , Omar Mohamed , Aydogdu Ali , Masina Simona , Coppini Giovanni , Pinardi Nadia
Published: March 2020

In order to be able to predict the future ocean climate and weather, we need to understand what happened in the past and the mechanisms responsible for the ocean variability. This is particularly true in a complex area such as the Mediterranean Sea with diverse dynamics such as deep convection and thermohaline circulation or coastal hydrodynamics. To this end, effective tools are reanalyses or reconstructions of the past ocean state. 

Here we present a new physical reanalysis of the Mediterranean Sea at high resolution, developed in the CMEMS Med-MFC framework. The hydrodynamic model is based on the Nucleus for European Modelling of the Ocean (NEMO) combined with a variational data assimilation scheme (OceanVAR). A series of system developments have been carried out to upgrade the current Med-MFC reanalysis to the new one with high resolution, including new NEMO version and configuration, the new version of atmospheric forcing (ERA-5) datasets and revised OceanVAR scheme.

The model has a horizontal resolution of 1/24° and 141 vertical z* levels and provides daily and monthly 3D values of temperature, salinity, sea level and currents. Hourly ERA-5 atmospheric fields force the model and daily boundary conditions in the Atlantic are taken from the global CMCC C-GLORS reanalysis. 39 rivers model the freshwater input to the basin plus the Dardanelles. The reanalysis covers 30-years, initialized from World Ocean Atlas climatology in January 1985, getting to a nominal state after a two years spin-up and ending in 2018. In-situ data from CTD, ARGO floats, XBT are assimilated into the model in combination with satellite altimetry data.

This reanalysis has been validated and assessed through comparison to in-situ and satellite observations as well as literature climatologies. The results show good agreement with observations and a better representation of the main dynamics of the region compared to the previous, lower resolution (1/16°) reanalysis. The new reanalysis will allow the study of physical processes at multi-scales, from the large scale to the transient small mesoscale structures.
Analysis of Rice Yield in CHINA by Climate Change using Deep Neural Network
Authors: Cho Su-Bin , Lee Yang-Won
Published: March 2020

Climate change is an important factor in crop growth, and it is significant to understand the relationship between climate change and rice yield. This study used annual rice yield from the USDA(United States Department of Agriculture) for each of China’s 16 administrative regions from 1979 to 2009, as well as average climate data from July to August, which were meteorological observations collected from the CRU(Climate Research Unit). The relationship between selected rice yield and climate change was nonlinear and modelled using a deep neural network to train even rows and verify odd rows of data. This study is expected to contribute to better food self-sufficiency and forecast future grain yields in China.
Detecting Groundwater Level Changes with Radar Interferometry
Authors: Heuff Floris , Hanssen Ramon
Published: March 2020

The Dutch are known for their dewatered peat pastures known as polders. These pastures are used for heavy agricultural and have to be continuously drained to compensate for the subsiding top layer due to oxidation. Additionally, the top part of the peat soil responds to changes in temperature and precipitation. Driven by moisture changes, the peat soils shrink as water is evaporated during dry, warm periods, while they swell in periods with lots of precipitation. During these dry periods, the groundwater level drops as well, mirroring the behavior of the surface. As the groundwater level drops, more organic material is exposed to air and more greenhouse gases are emitted. Monitoring the movement of the surface of the pasture could provide indirect measurements of the groundwater level and used to reveal areas that are more or less affected by a rainfall deficit. Efforts to reduce emissions can then be focused on more vulnerable areas. However, this dynamical behavior is hard to monitor with conventional geodetic means, as it is near impossible to install the required benchmarks on the soft surface of the pastures, which are needed for repeated surveying.  

Radar Interferometry presents an opportunity to observe this dynamic behavior without the need of installing equipment. The Sentinel-1a/b satellites pass the Dutch peat soils four times per week, providing the data necessary to observe the shrinking and swelling of the soils. We applied the technique to two study areas in the Netherlands, one between Delft and Rotterdam, where most of the pastures are situated on peat or peaty soils, and one above Zwolle in the center of Netherlands, near Staphorst, a peat-rich area. We processed all radar acquisitions between 2017 and 2019, which were averaged to 200 by 200 meter square windows to suppress noise. This is than further processed to obtain deformation time series. Based on these time series, areas more vulnerable to droughts were identified. Notably, 2018 – a very dry year, with a very large rainfall deficit – caused significantly more shrinkage than observed in 2017. We estimate that some areas shrunk up to 50 percent more. The associated drop in groundwater level exposed fresh peat to air for the first time, potentially increasing the emission of greenhouse gases significantly.

Climate change exposes peat soils to new and more extreme weather conditions. Radar Interferometry can monitor the impact of these conditions on the soils and can be used to reduce greenhouse emissions more effectively.
Nutrient regeneration and benthic fluxes in the Coastal Baltic and North Sea
Authors: Dähnke Kirstin , Neumann Andreas , Sanders Tina
Published: March 2020

Sediments in the coastal ocean can play an important role in nutrient regeneration and in recharging the water column with dissolved inorganic nutrients. This function, however, depends on various variables, such as physical characteristics, but also on biological traits like fauna composition and activity. To unravel and quantify these effects, we investigated nutrient fluxes and nitrate stable isotope composition in water samples along a North Sea – Skagerrak – Baltic Sea gradient during the Maria S. Merian cruise MSM 50 in January 2016.

Especially in the North Sea and the Skagerrak region, d15N values of nitrate were unexpectedly high, suggesting that underlying sediments with relatively enriched isotope signatures were a source of nitrate. This nitrification signal, however, resembled an autumn situation rather than the expected winter values. Parallel sediment incubations confirm that the benthic rates of oxygen consumption and nutrient turnover were indeed very similar to respective rates in autumn and that the sediment was a source of recycled nitrate. From the North Sea towards the Baltic Sea, we found, in accordance with previous studies, a depletion in nitrate stable isotope values. This is indicative of different nitrate sources in the respective basins: in the North Sea region, N of anthropogenic origin leads to high N values in surface sediments and in newly generated nitrate. Due to a higher share of nitrogen fixation, the nitrogen stable isotope signal of surface sediments in the Baltic Sea was depleted, which in turn was mirrored in lower nitrate isotope values in the water column above the sediment.

Overall, the data highlight the importance of nitrate regeneration. Parallel flux measurements reveal that faunal activity shifts the nutrient balance from sequestration to regeneration. Seasonal differences enable us to unravel seasonal effects of fauna and microbiota on nutrient budgets.
Spatio-temporal variability of global soil salinization delineated by advanced machine learning algorithms
Authors: Hassani Amirhossein , Azapagic Adisa , Shokri Nima
Published: March 2020

Soil salinity is among the major threats affecting the soil fertility, stability and vegetation. It can also accelerate the desertification processes, especially in arid and semi-arid regions. An accurate estimation of the global extent and distribution of the salt-affected soils and their temporal variations is pivotal to our understanding of the salinity-induced land degradation processes and to design effective remediation strategies. In this study, using legacy soil profiles data and a broad set of climatic, topographic, and remotely sensed soil surface and vegetative data, we trained ensembles of classification and regression trees to map the spatio-temporal variation of the soil salinity and sodicity (exchangeable sodium percentage) at the global scale from 1980 to 2018 at a 1 km resolution. The User’s Accuracies for soil salinity and sodicity classification were 88.05% and 84.65%, respectively. The 2018 map shows that globally  ̴ 944 Mha of the lands are saline (with saturated paste electrical conductivity > 4 ds m-1), while  ̴ 155 Mha can be classified as sodic soils (with sodium exchange percentage > 15%). Our findings and provided dataset show quantitatively how soil salinization is influenced by a broad array of climatic, anthropogenic and hydrologic parameters. Such information is crucial for effective water and land-use management, which is important for maintaining food security in face of future climatic uncertainties. Moreover, our results combined with the quantitative methodology developed in this study will provide us with an opportunity to delineate the role of anthropogenic activities on soil salinization. This information is useful not only for developing predictive models of primary and secondary soil salinization but also for natural resources management and policy makers.
Current and future CML-rainfall estimation in Germany: Improved data processing, real-time rainfall maps and fusion with weather radar data
Authors: Chwala Christian , Smiatek Gerhard , Graf Maximilian , Polz Julius , Winterrath Tanja , Kunstmann Harald
Published: March 2020

Many cell phone base stations are connected by a network of commercial microwave links (CMLs). At the typically used frequencies between 15 GHz and 40 GHz, precipitation along the path of a CML leads to significant attenuation of the signal. The path-averaged rain rate along a CML can therefore be derived from measurements of the attenuation.

In cooperation with Ericsson, we record attenuation data of 4000 CMLs across Germany with our own open source data acquisition software. The data is acquired every minute and is available to us in real time. The dataset is continuously growing and now spans more than two and a half years. 

Here we present and discuss results from our current processing chain for hourly country-wide CML-derived rainfall fields. We show the effect of improved rain event detection in the raw attenuation time series and the necessity to correct for wet antenna attenuation (Graf et al., 2019). Validation is done via the gauge-adjusted radar product RADOLAN-RW of the German meteorological service. For summer months the pearson correlation between CML and radar data reaches up to 0.85, but is substantially worse during the winter months. The presented processing chain is fast enough to be applied in real-time, which will be illustrated in a live-demo. Furthermore, since Germany has both, a large network of CMLs and a modern weather radar network, we also work on the combination of these data sources. We will present first results of an approach where CMLs are used as an additional source for weather radar rain rate adjustment similarly to the existing gauge-adjustment done in RADOLAN.

Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: Optimized processing and validation for one year of data, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-423, 2019
Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy
Authors: Schillaci Calogero , Tomasoni Edoardo , Acutis Marco , Perego Alessia
Published: March 2020

To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R2=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.
Benchmarking machine learning algorithms for Non-Intrusive Water Monitoring
Authors: Cominola Andrea , Becker Marie-Philine , Taormina Riccardo
Published: March 2020

As several cities all over the world face the exacerbating challenges posed by climate change, population growth, and urbanization, it becomes clear how increased water security and more resilient urban water systems can be achieved by optimizing the use of water resources and minimize losses and inefficient usage. In the literature, there is growing evidence about the potential of demand management programs to complement supply-side interventions and foster more efficient water use behaviors. A new boost to demand management is offered by the ongoing digitalization of the water utility sector, which facilitates accurate measuring and estimation of urban water demands down to the scale of individual end-uses of residential water consumers (e.g., showering, watering). This high-resolution data can play a pivotal role in supporting demand-side management programs, fostering more efficient and sustainable water uses, and prompting the detection of anomalous behaviors (e.g., leakages, faulty meters). The problem of deriving individual end-use consumption traces from the composite signal recorded by single-point meters installed at the inlet of each household has been studied for nearly 30 years in the electricity field (Non-Intrusive Load Monitoring). Conversely, the similar disaggregation problem in the water sector - here called Non-Intrusive Water Monitoring (NIWM) - is still a very open research challenge. Most of the state-of-the-art end-use disaggregation algorithms still need an intrusive calibration or time- consuming expert-based manual processing. Moreover, the limited availability of large-scale open datasets with end- use ground truth data has so far greatly limited the development and benchmarking of NIWM methods.

In this work, we comparatively test the suitability of different machine learning algorithms to perform NIWM. First, we formulate the NIWM problem both as a regression problem, where water consumption traces are processed as continuous time-series, and a classification problem, where individual water use events are associated to one or more end use labels. Second, a number of algorithms based on the last trends in Artificial Intelligence and Machine Learning are tested both on synthetic and real-world data, including state-of-the-art tree-based and Deep Learning methods. Synthetic water end-use time series generated with the STREaM stochastic simulation model are considered for algorithm testing, along with labelled real-world data from the Residential End Uses of Water, Version 2, database by the Water Research Foundation. Finally, the performance of the different NIWM algorithms is comparatively assessed with metrics that include (i) NIWM accuracy, (ii) computational cost, and (iii) amount of needed training data.
Regionalization of Vapor Pressure Deficit (VPD) in Spain
Authors: Tomas-Burguera Miquel , Beguería Portugués Santiago , Serrano-Notivoli Roberto , Cabos William
Published: March 2020

Vapor Pressure Deficit (VPD) is an atmospheric variable that represents the extra water vapor that air may contain prior to condensation. It is a relevant variable for climatology due to its consideration as a measure of ‘air dryness’, but also for hydrology and ecohydrology. In spite of its relevance, studies focusing on VPD are scarce, especially when comparing with other variables such as temperature or precipitation. To obtain VPD values, temperature and air humidity data are required at the same time and location, which is difficult to obtain even in dense observational networks. While temperature is positively related with VPD, relative humidity shows a negative relation with VPD.

Within the framework of the CLICES Project, a spatial regionalization of VPD will be performed for mainland Spain. This project is focused on the climatic reconstruction of the last century and, for the most recent decades, data from a Regional Climate Model (RCM) simulation will be used as a complement of the observational data. Specifically, the climate of Spain for the period 1980-2017 at 3-hourly time step was simulated using REMO. Among a high amount of available methods, a bias correction procedure based on a quantile-quantile mapping in spatial coherent regions will be tested for the RCM correction. In order to implement this methodology, the VPD spatial regionalization is required and it will be addressed using a clustering methodology. Furthermore, regionalization of VPD will sharply improve our knowledge of this variable in Spain, a region showing high spatial contrasts affecting temperature, precipitation and wind speed. It is expected that the combination of the dissimilarities between temperature and precipitation will emerge in the regionalization of VPD values.
Heterogeneity of unsaturated flow measured in the dry summer of 2018 in Germany recorded by the combination of ERT and soil data
Authors: Susanne Stadler , Olga Fishkis , Ursula Noell
Published: March 2020

In 2018 the weather in Germany was extreme: The highest temperatures since 1881 (= start of regular weather recording) were observed during the months of April – August (temperature anomaly of +3.6 K) and the second lowest precipitation amounts (anomaly of -150 mm). In that year, we measured the soil conditions (soil water tension, water content, electrical resistivity, temperature, seepage water at suction plates) in a maize field in Northern Germany continuously down to a depth of about 1.5 m using a combined geophysical and soil scientific small-scale instrumentation array.

This unique dataset revealed the heterogeneity of the subsurface water content, changes in soil water conductivity, heterogeneity of the water retention function, indications for preferential flow after the onset of precipitation (and locally increased nitrate concentrations) in seepage water. The electrical resistivity (ERT) data clearly detected the infiltration of local rainfall events by the change of near surface resistivity. The resistivity changes differ spatially reflecting dm-scale variations most probably caused by the dense maize plants. Soil water contents measured by TDR detected the summer rainfall events in some locations, in others, very small-scale preferential flow paths were found overlooked by ERT. The detected changes in pore water conductivity need to be taken into account when recalculating water contents from ERT data. Our data allow for a description of different scale effects on the derivation of flux processes and total flux estimations under extreme weather conditions but also show that cross-scale methods are needed for an adequate assessment of unsaturated flow.
Investigation of Mars Seismic Attenuation Using InSight SEIS Data.
Authors: Kawamura Taichi , Margerin Ludovic , Drilleau Mélanie , Ménina Sabrina , Lognonné Philippe , Schmerr Nicholas C. , Stäehler Simon , Driel Martin van , Banerdt Bruce
Published: March 2020

 NASA InSight (the Interior Exploration using Geodesy and Heat Transport) has placed the first broadband seismometer (SEIS) on the Martian surface and now continuously monitoring Martian seismic activity. Since the first detection of a marsquake in March 2019, SEIS detected more than 200 marsquakes and Mars has been revealed to be a seismically active planet. The dataset can now be used to perform the seismic investigation of the Mars interior and interpret this in a comparative manner by referring to the examples from the Earth and the Moon.

In this study, we investigate the seismic attenuation on Mars and compare this with the Earth and the Moon. Attenuation can be described as a combination of inelastic absorption and elastic diffusion of energy. Such properties will give important constraints on the composition of the Mars interior and also its thermal state. Another interesting aspect will be to discuss the water content with respect to the attenuation. Given the large variety of water content for the Earth, the Moon and Mars, the attenuation feature will be likely to differ significantly between these planets and satellite. Here we use the seismic dataset obtained by InSight SEIS and construct a 1D structure of seismic attenuation on Mars. Then we refer to the values obtained for the Earth and the Moon to discuss the possible implication on their differences and similarities.

　The presentation aims to summarize the results from different approaches taken by the authors. The approach includes; 1) spectral analyses of seismic signals and spectral decay fitting, 2) seismic coda analyses with coda rise time and decay, 3) numerical coda simulation with diffusion theory on seismic energy. With these approaches we will be constraining seismic quality factor Q and diffusivity D for different depth range. Different approaches have sensitivities to different depth and prarameters and we aim to provide our view on the martian attenuation and diffusion to date by summarizing the obtained results.
Salinity reduces coastal wetland potential for climate change mitigation
Authors: Molini Annalisa , Perri Saverio , Porporato Amilcare
Published: March 2020

Coastal wetlands represent a vital component of the global carbon cycle due to the inherent capability of sequestering carbon in both biomass and sediments. Their future ability to act as carbon sinks largely depends on how plant communities will adapt to sea-level rise, and its most direct consequences: submergence and salinization. Although tidal ecosystems can actively contrast SLR through vertical and lateral soil accretion, the impacts of salinity on soil-plant-water interactions, species succession, and ecosystem productivity remain unaccounted for in carbon budget models.

Salinity is known to limit plants capability to uptake water, and as such, it is expected to have an impact on the ecosystem productivity, the soil water balance, and water table dynamics. Here, we model the response of coastal ecosystems to salt stress, and we show that salinity and plant salt tolerance exert a dominant control on how coastal plant communities interact with water table dynamics. We demonstrate that salinization and shifts in groundwater regimes may trigger abrupt successions from the highly productive salt-sensitive species dominating freshwater coastal wetlands, to the less productive salt-tolerant species now confined within the intertidal fringe. Our theoretical results explain recent eco-hydrological patterns observed in the Florida Everglades and indicate that shifts from salt-sensitive to salt-tolerant communities could cause a drastic reduction of coastal wetland productivity.
Efficient global ionospheric modeling based on multi-source and massive observation data
Authors: Jin Xulei , Song Shuli , Li Wei , Cheng Na
Published: March 2020

Abstract Ionosphere is an important error source of satellite navigation and a key component of space weather. With the rapid development of multiple Global Navigation Satellite System (GNSS) and other ionospheric research technologies, and the high precision and near real-time requirements for ionospheric products, it is necessary to carry out a research on multi-source data fusion, massive data processing and near-real-time solution of global ionosphere model (GIM); therefore, we modified the traditional ionospheric modeling technology and generate the GIM products (GIM/SHA). In view of the defect of ground-based GNSS data missing in the ocean regions, the method of adding virtual observation stations to the data missing regions in a large range was adopted, which not only enhanced the accuracy of the GIM in the ocean regions, but also avoided the weight determination among different data sources. In terms of near-real-time modeling, the multi-threaded parallel modeling strategy was adopted. Four GNSS (GPS, GLONASS, BEIDOU, Galileo) observation data, eight virtual observation stations and a server with a CPU frequency of 2.1 GHz and 16 threads were utilized. It took less than 30 minutes to construct the GIM by using parallel modeling strategy, which was 10.3 times faster than serial modeling strategy. The accuracy of the GIM/SHA was verified by using the ionospheric products of International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) in the period of day of year (DOY) 200-365, 2019. Compared with the ionospheric products of CODE, ESA/ESOC, JPL, UPC, EMR, CAS and WHU, the vertical total electron content (VTEC) root mean squares (RMSs) were 1.09 TEC units (TECu), 1.51TECu, 2.32TECu, 1.88TECu, 2.24TECu, 1.25TECu and 1.38TECu, respectively. The result shows that the GIM/SHA have comparable accuracy with IGS ionospheric products. Satellite altimetry data was exploited to verify the accuracy of GIM/SHA in ocean regions, and it can be concluded that the accuracy of the GIM in ocean regions can be significantly reinforced by adding virtual observation stations in ocean regions. Multi-system and multi-frequency differential code bias (DCB) products (DCB/SHA) were simultaneously generated. Compared with IGS DCB products, the satellite DCB RMSs of DCB/SHA were 0.16ns for GPS, 0.08ns for GLONASS, 0.17ns for BEIDOU and 0.14ns for Galileo; the GNSS receiver DCB RMSs of DCB/SHA were 0.69ns for GPS, 1.06ns for GLONASS, 0.75 for BEIDOU and 1.03ns for Galileo. It can be proved that the accuracy of DCB/SHA are comparable to IGS DCB products.

Keywords Multi-GNSS; GIM; Virtual observation station; Near real-time; VTEC; DCB
Numerical experiments with idealised pore space geometries and shear-thinning fluids.
Authors: Lanzendörfer Martin
Published: March 2020

In an endeavour to describe quantitatively the water flow and solute transport in soils and other heterogeneous porous media, various different approaches have been introduced in the past decades, including double porosity, double permeability and other multiple-continua approaches. Recently, a promising methodology to identify experimentally the pore structure of porous media has been proposed, where a discrete distribution of effective pore radii is established based on saturated flow experiments with non-Newtonian (shear-thinning) fluids, as described by Abou Najm and Atallah (2016) and in other works. In this particular concept, the porous media is idealised as a bundle of capillaries with only a reasonably small number of distinct values of their radii. This allows to identify the pore radii and the contributions of the corresponding pore groups to the total flow by performing and evaluating a reasonable number of flow experiments.

In an attempt to understand better the relation of the effective discrete pore radii distribution concept (with a given number of distinct pore radii allowed) to the structure of the porous media, we perform numerical experiments with other idealised geometries of the pore space. The saturated flow experiments with shear-thinning fluids are simulated by finite element method and then, based on the resulting flow, the discrete pore radii distributions are established and compared with the original geometry. For simplicity, we stick to one-dimensional models analogous to Poiseuille or Hagen-Poiseuille flow. The idea is to examine pore size distributions that are continuous rather than discrete, while keeping the advantage of a perfectly controlled and comprehensible idealised geometry. This in-silico approach may later serve as a supporting tool for studying various aspects of the addressed experimental methodology, e.g., in taking into account realistic non-Newtonian rheology, proposing an optimal set of experiments, or contemplating links with solute transport models.
Imaging shallow structures in Dublin city using seismic interferometry of seismic waves generated by train traffic
Authors: Rezaeifar Meysam , Maggio Giuseppe , Xu Yihe , Bean Chris , Lavoué François , Boué Pierre , Pinzon-Rincon Laura , Brenguier Florent
Published: March 2020

Although train-induced vibrations are mainly regarded as a source of unwanted noise for classical seismological applications, these vibrations act as powerful sources for seismic imaging using seismic interferometry. Most of the seismic interferometry studies to date have concentrated on using the ambient seismic field generated by natural processes but the appropriate use of train-induced vibrations could result in higher resolution images.

In this study, we present results of seismic interferometry applied on 3 days of railroad traffic data recorded by an array of 3-component seismographs along a railway in Dublin, Ireland. Train-generated waves show a significantly higher frequency range than those recovered from typical ambient noise interferometry. Analysing the recorded signal, we have been able to distinguish between different train types (e.g. cargo vs. passenger trains) and train lengths (3-4, 5-6, 7-9, and/or 10-11 wagons).

For seismic interferometry, a Common Mid-Point – Cross-Correlation (CMP-CC) stack approach has been used to directly image the structures beneath the array. This approach produces a reflection image with interfaces consistent with nearby borehole data at ~450-500 m and ~1350-1400 m depth.

In addition to this reflection image, our results document a strong relation between the ambient source location (trains in this case) and the retrieved seismic reflection image. Since we have train location GPS data, we extracted 2-s time windows for when the train is 1500 m, 1000 m, and 500 m away from the first sensor and we applied the CMP-CC procedure to produce reflection images. As expected, the reflection images are sensitive to the location of the ambient noise source.

Numerical forward modelling of seismic wavefields for various source-receiver configurations also documents a strong correlation between the source location and the retrieved reflection image.

This research emanates from PACIFIC - Passive seismic techniques for environmentally friendly and cost-effective mineral exploration - which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No~776622. We also acknowledge support from the European Research Council under grant No.~817803, FAULTSCAN.
AMOC response to changing resolution in the Finite-volumE Sea ice–Ocean Model
Authors: Sidorenko Dmitry , Danilov Sergey , Koldunov Nikolay , Scholz Patrick
Published: March 2020

The Atlantic meridional overturning circulation (AMOC) is the most common diagnostics of numerical simulations. Generally it is computed as a streamfunction of zonally averaged flow along the constant depth. More rarely it is computed as zonally averaged along constant isopycnals. The latter computation, however, allows one to better distinguish between water masses and physical processes contributing to the meridional transport. We analyze the AMOC in global simulations based on the Finite-volumE Sea ice–Ocean Model (FESOM 2.0) using eddy permitting to eddy resolving configurations in the North Atlantic. We (1) split the AMOC computed in density space into the constitutes induced by surface buoyancy fluxes and cross isopycnal transformations, (2) identify the water masses which contribute to the formation of the North Atlantic Deep Water and (3) study the AMOC response to the permitting or resolving eddies in the North Atlantic ocean.
Changes in soil properties on agricultural land with the impact of water and tillage erosion in the last 60 years
Authors: Juřicová Anna , Chuman Tomáš , Žížala Daniel
Published: March 2020

The decline in soil organic carbon (SOC) is generally perceived as a major threat to the sustainability of the soil due to its key role in many productive and non - productive soil functions. The aim of this research is to assess the intensity of changes and the spatial variability of SOC and soil depth in the last 60 years. Estimation of spatial variability of soil properties was performed by using digital soil mapping. A study area is located in the chernozems area in south Moravia (Czechia). This region is traditionally intensively cultivated with the strong impact of water and tillage erosion. The study is based on the analysis of historical data that comes from the Large-scale mapping of Agricultural Soils in Czechoslovakia soil database. Our dataset contained data from 120 soil profiles. A new field investigation shows significant SOC losses on steep slopes and slope shoulders with a decrease of depth of the humic horizon. As a result, there is a gradual transformation of soil units from the former Calcic Chernosems into the Haplic Calcisols. These findings are the result of ongoing environmental changes with the strong impact of historical agricultural policy and inappropriate interference in the landscape.
Determining the Thermal Inertia of the UTPS-TB simulant for different grain sizes and densities
Authors: Pál Bernadett , Miyamoto Hideaki , Niihara Takafumi , Sakatani Naoya , Takano Amiko , Kereszturi Ákos
Published: March 2020

The Martian moons, Phobos and Deimos are exciting new targets for future in-situ, and possibly future human explorations. The mission of JAXA, Martian Moons eXplorer (MMX), is scheduled to launch in 2024 to perform observations of both moons, landing on one of them. How the landing modules should be designed depends greatly on the surface conditions, thus studying the surface of Phobos (the likely candidate for landing) is highly important. The exact composition of the regolith covering the surface of the moon is still under debate; but even with the chemical compositions and size distributions of the grains established, numerous mechanical properties remain problematic to estimate. The thermal inertia of the regolith determines the amount of heat the soil can store, as well as how quickly the heat is reradiated. It is also possible to estimate the particle diameter and porosity of the regolith, if thermal inertia is measured, however, the Hayabusa2 and OSIRIS-REx missions showed that the actual grain sizes can vary greatly. In our study we work with the Tagish Lake-based simulant developed at the University of Tokyo (UTPS-TB). Using a thermostatic chamber and a vacuum chamber at the ISAS/JAXA laboratory in Sagamihara, we measure and calculate the thermal inertia of UTPS-TB samples with different grain sizes and densities. This work was supported by the by Campus Mundi short scientific research programme, and the ÚNKP-19-3 New National Excellence Program of the Ministry for Innovation and Technology. 
Mintell4EU – Mineral Intelligence for Europe – a GeoERA project to improve and sustain the European raw materials knowledge base.
Authors: Whitehead David , Flindt Jørgensen Lisbeth , Pedersen Mikael , Brown Teresa , Kumelj Špela , Aslaksen Aasly Kari , Clain Ulrich
Published: March 2020

There is a need for comprehensive, up-to-date, reliable and harmonised cross-border information on raw materials to improve resource efficiency across Europe. The Mintell4EU project builds on the achievements of previous projects such as Minerals4EU, ProSUM and Minventory to deliver data on the spatial distribution, production, trade, resource potential and levels of exploration activity to support decision making in government and industry.

The project has four principle components. The first component involves updating production, trade and exploration statistical data within the electronic European minerals yearbook. The second component includes extending the spatial coverage and improvement of spatial data quality within the Minerals4EU database. The third component will demonstrate how the application of the United Nations Framework Classification (UNFC) will provide a tool that can be used to more accurately assess European mineral inventories. The final component involves consolidating the electronic European minerals yearbook into the Minerals4EU database used for external systems such as the European Geological Data Infrastructure (EGDI) and the Joint Research Center’s Raw Materials Information System (RMIS). Another important goal of the project is to create a sustainable platform for raw materials.

The project works in collaboration with other GeoERA projects within the theme of raw materials such as FRAME and the GeoERA Information Platform Project (GIP-P). This collaboration is critical in ensuring data harmonisation across projects, regions and focus areas. Improvements in the quality and availability of data that are available through the web portal on the project home page https://geoera.eu/projects/mintell4eu7/ have already been achieved. Work will continue on improving the availability and relevance of raw material data throughout the remainder of the project. This will lead to improved foresight of the raw material supply situation and potential of Europe within the framework of the United Nations Sustainable Development Goals (SDGs).

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731166
Characteristics and process controls of statistical flood moments in Europe - a data based analysis
Authors: Lun David , Viglione Alberto , Komma Jürgen , Bertola Miriam , Parajka Juraj , Blöschl Günter
Published: March 2020

Characteristics and process controls of statistical moments of annual maximum peak discharges, including the mean annual flood (MAF), the coefficient of variation (CV) and the coefficient of skewness (CS), are analyzed for flood series in Europe. The data set consists of observations from 2370 catchments with an average record length of 48 years. The controls are identified by investigating dependencies between the flood moments and catchment area, flood seasonality, climate and catchment characteristics in five regions. The covariates providing the most explanatory power for within-region variability are identified in a regression framework. Preliminary results indicate: MAF and CV are strongly correlated with hydroclimatic catchments characteristics, and to a lesser degree with topography and land use. In the Atlantic region, precipitation is the most important control on the spatial patterns of MAF and CV; in the Mediterranean it is precipitation and aridity; and in Northeastern Europe it is air temperature.