bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER
Authors: Bathiany Sebastian , Rechid Diana , Pfeifer Susanne , El Zohbi Juliane , Goergen Klaus , Wagner Niklas , Ney Patrizia , Belleflamme Alexandre
Published: March 2021

Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the subsequent dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned with such interannual variability and extremes. Yet, it often remains unclear what long-term adaptation options are most suitable in the context of climate change, mainly because climate projections have uncertainties and are usually not tailored to meet requirements, measures and scales of the individual practicioners. In the ADAPTER project, we explore regional and local change on the weather- and climate-related time scales and together with stakeholders (administration, plant breeders, educators, agricultural advisors), we co-design tailored climate change indices and usable products.

In this contribution, we provide a snapshot view of our stakeholders' requirements regarding information about climate change over the next decades. We then focus on the analysis of three groups of indices based on 85 regional climate model simulations from Coordinated Downscaling Experiments over Europe - EURO-CORDEX: (i) changes in daily temperature variability, (ii) occurrence of agricultural droughts in summer, (iii) compound events of combined dryness and elevated temperatures during the same events. We show that these user-oriented, newly constructed indices can capture relevant changes during important phenological development states of typical crops. Finally, we discuss first implications of our findings for different adaptation strategies in Mid-Europe, such as alternating crop rotations, irrigation strategies or plant breeding. The analysis products presented are interactively and publicly available through a product platform (www.adapter-projekt.de) for agricultural stakeholders.
Hydraulic roughness estimation in vegetated floodplains
Authors: Herrera Gomez Lisdey Veronica , Ravazzani Giovanni , Ferri Michele , Mancini Marco
Published: March 2021

The continuous interaction between riparian vegetation and water has important effects on the hydraulics of a river, mainly onto the flood events propagation. Vegetation is a fundamental part of the river ecosystem, but its stage and growth need to be monitored and controlled, especially when the river passes through a densely urbanized area. In fact, vegetation obstructs the streamflow by reducing the hydraulic cross-section area and increasing the roughness of the floodplains and the relative flood risk.

In this study, experiments have been performed at the Fantoli Hydraulic Laboratory at Politecnico di Milano, to validate the methodologies that estimate the hydraulic roughness of vegetated river floodplains, starting from the vegetation properties such as size, density and elastic modulus of a case study. A model based on the mechanical properties of vegetation was used to identify the most suitable material to reproduce the dynamic behaviour of real vegetation on a laboratory scale. The tests were carried out for different spatial configurations of trees, densities and submerged conditions.

The analysis, in addition to relying on experimental work, involves the installation of six piezoresistive pressure sensors located both in the floodplains and in the main channel, to monitor head losses in a representative reach of the river under study. The field measurements allow validation of the approach used in laboratory tests.
Needle properties and a new higher altitude negative leader structure; observations by the LOFAR radio telescope
Authors: Hare Brian , Scholten Olaf , Dwyer Joseph , Ningyu Liu , Strepka Chris ,   LOFAR CR KSP
Published: March 2021

Recently, Hare et al. 2020 found that individual leaders steps could be imaged in the VHF band, and for leaders below 5 km altitude, the radio emission from each step is mostly consistent with a point-source. We will report on new observations of negative leaders above 7 km altitude that behave significantly differently than lower altitude leaders. These higher- altitude leaders are a few 100 meters wide and have step lengths a few 100 meters long, as opposed to lower altitude leaders that are at most 10 meters wide with 10 meter stepping lengths. Furthermore, unlike lower altitude leaders, the radio emission from individual steps of higher altitude shows extensive structure. Each step shows a burst of radio radiation, followed by the growth of multiple filamentary structures. The nature of these filaments is presently unclear, but they could be long streamers or leader branches. We have observed one leader that clearly starts at low altitude and propagates to higher altitude. This leader shows that the transition from the low altitude mode of propagation to the higher altitude mode does not occur smoothly as one may expect, but occurs abruptly at around 6 km altitude within only one kilometer, somewhat similarly to a phase change.

Previous work has measured 100 m long stepping lengths of higher altitude leaders, and it is often assumed that this is a simple pressure scaling effect. However, our data shows that the stepping process at lower altitudes and higher altitudes appears very differently in VHF, and that the transition between the two modes occurs rapidly. This implies higher and lower altitude leaders actually have different propagation modes, and are not merely pressure-scaled versions of each other.

We will also present new detailed VHF measurements of needle activity. We will show that needle twinkles have a wide range of propagation speeds, from 105 to 107 m/s, and that needle twinkles sometimes show stepping behavior, which strongly implies that needle twinkles can propagate similar to stepped leaders or dart leaders depending on the conductivity of the needle. We will also show that recoil leaders can quench needle activity, which leads to a cycle of increasing needle activity followed by quenching by a recoil leader, as originally predicted by Hare et al. 2019.
Multi-scale input data assessment for harmonized index-based aquifer vulnerability evaluations across Europe
Authors: Broda Stefan ,   HOVER WP7 Team
Published: March 2021

Work package 7 of the GeoERA HOVER project deals with groundwater vulnerability assessment to pollution of the shallow upper aquifer. We present vulnerability assessments across Europe applying the DRASTIC method in 11 pilot areas and the COP method for karst systems in 5 pilot areas. The presented assessments are carried out at multiple scales (between 1:1K and 1:250K), pilot areas sizes (catchment to national scale; 15 to 338 000 km²) and hydro-climatic contexts (ranging from extremely high and steady recharge to very low and seasonal recharge). The core item of this presentation is a detailed investigation and statistical assessment on respective data availability, data density and methodologies applied to retrieve input parameters for the assessment (e.g., groundwater recharge) and how this affects the final vulnerability assessments. We also focus on the definition (numerical ranges) of the individual vulnerability classes, which are valid across all pilots.

In an attempt to generate information summarizing affected aquifer volumes, a method based on a lumped index and 2D conceptual cross-sections is proposed. It was originally designed for sea-water intrusion assessments, and has been adapted and applied in some pilot areas to estimate aquifer volumes of each individual vulnerability class.
Dusk side clockwise vortex generation and aurora intensity decrease after Solar Wind Dynamic Pressure Decrease
Authors: Zhao Jinyan , Shi Quanqi , Tian Anmin , Guo Ruilong , Shen Xiao-Chen
Published: March 2021

A solar wind dynamic pressure increase/decrease leads to the compression/expansion of the Earth’s magnetosphere. In response, field-aligned currents, which are carried by precipitating or escaping plasma particles, are generated in the magnetosphere and in lead to variations in the auroral intensity. In this study, we investigate magnetospheric and ionospheric responses (including magnetospheric plasma vortex, ionospheric currents and aurorae) to a sudden decrease in solar wind dynamic pressure (SW Pdyn), which is critical for further understanding of the solar wind-magnetosphere-ionosphere coupling. We focused on a SW Pdyn decrease event that monitored by OMNI. A counter-clockwise plasma vortex was generated in the dusk side magnetosphere uncovered by using MHD simulation method and a clockwise equivalent ionospheric currents (EIC) vortex was generated in the dusk side ionosphere within about ten minutes after the pressure pulse arrival. Simultaneously, the observation results of Spherical Elementary Currents (SECs) showed that the EIC vortex region is dominated by downward field-aligned currents and the ground-based All-Sky Imager (ASI) observations in the vicinity of this EIC vortex showed that the aurorae diminished. These observations are consistent with the scenario proposed by Shi et al. (2014) that flow vortices in the magnetosphere generated by SW Pdyn sudden decrease carry downward field-aligned currents into the dusk side ionosphere, generating ionospheric current vortex and thereby modulating auroral activity on the dusk side.
HYPSO: a new multiresolution global raster digital elevation model for small-scale terrain mapping
Authors: Samsonov Timofey
Published: March 2021

During the last decade a significant progress in methods and techniques of elevation data acquisition has been achieved. With lidar-derived digital elevation models it is now possible to investigate landforms with precision and detail which was never possible before. The resolution of global and continental elevation models is approaching first meters, which enables detailed geomorphometric analysis and mapping in wide spatial extents. At the same time, Earth scientists are interested not only in learning the properties of small landforms, but also in investigating the large regional terrain features, as well as hierarchical properties of terrain structure. For this, small details must be omitted from digital elevation model, and the analyzed dataset is expected to have coarser resolution. Currently available coarse-resolution global digital elevation models such as GMTED2010, GEBCO_2019 and ETOPO1 are characterized by spatial resolution ranging from 7.5” to 1’, which is approximately equal to cell size of 250-2000 m on the equator. Such resolution fits well into the small-scale mapping and analysis context. However, these models have excessive level of detail in relation to their resolution, which is a consequence of the method of their derivation — mainly aggregation and resampling of more detailed data. As a result, terrain maps created using these models, are characterized by lack of generalization, which prevents realistic portray of large terrain forms. To solve the problem, the new high-quality mutiresolution digital elevation model HYPSO has been developed. HYPSO is derived based on GEBCO_2019 model (15” resolution) using the structural generalization, during which the less detailed terrain surface is reconstructed from characteristic stream and watershed lines. HYPSO includes eight levels of detail (LoDs) with resolutions 30”, 1’, 2’, 4’, 8’, 16’, 32’ and 64’ which are suitable for mapping any region on the Earth including the seabed at scales 1:1 000 000 and smaller. The sequence of LoDs is characterized by sequential decrease in detail, which enables production of multiscale maps. Additionally, HYPSO is spatially conflated with river/lake centerlines in popular Natural Earth cartographic database and can be used as a background terrain layer in production of general geographic (base) maps. While the primary purpose of HYPSO is hypsometric mapping, it is also suitable as a data source for performing the geomorphometric analysis aimed at investigating the properties of large terrain landforms, which is demonstrated on several examples.

The study was supported by the Russian Science Foundation grant No. 19-77-10071
Spatiotemporal patterns of global land use change: Understanding processes and drivers
Authors: Winkler Karina , Fuchs Richard , Rounsevell Mark , Herold Martin
Published: March 2021

Land use change is a major contributor to greenhouse gas emissions and biodiversity loss and, hence, a key topic for current sustainability debates and climate change mitigation. To understand its impacts, accurate data of global land use change and an assessment of its extent, dynamics, causes and interrelations are crucial. However, although numerous observational data is publicly available (e.g. from remote sensing), the processes and drivers of land use change are not yet fully understood. In particular, current global-scale land change assessments still lack either temporal consistency, spatial explicitness or thematic detail.
Here, we analyse the patterns of global land use change and its underlying drivers based on our novel high-resolution (~1x1 km) dataset of global land use/cover (LULC) change from 1960-2019, HILDA+ (Historic Land Dynamics Assessment+). The data harmonises multiple Earth Observation products and FAO land use statistics. It covers all transitions between six major LULC categories (urban areas, cropland, pasture/rangeland, forest, unmanaged grass-/shrubland and no/sparse vegetation).
On this basis, we show (1) a classification of global LULC transitions into major processes of land use change, (2) a quantification of their spatiotemporal patterns and (3) an identification of their major socioeconomic and environmental drivers across the globe. By using temporal cross-correlation, we study the influence of selected drivers on processes such as agricultural land abandonment, deforestation, forest degradation or urbanisation.
With this, we are able to map the patterns and drivers of global land use change at unprecedented resolution and compare them for different world regions. Giving new data-driven and quantitative insights into a largely untouched field, we identify tele-coupled globalisation patterns and climate change as important influencing factors for land use dynamics. Learning from the recent past, understanding how socio-economic and environmental factors affect the way humans use the land surface is essential for estimating future impacts of land use change and implementing measures of climate mitigation and sustainable land use policies.
The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models
Authors: Sanchez-Leon Eduardo Emilio , Brandhorst Natascha , Waldowski Bastian , Pui Hung Ching , Neuweiler Insa , Hendricks Franssen Harrie-Jan , Cirpka Olaf A.
Published: March 2021

The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km2, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.
A new glacier inventory for Svalbard from Sentinel-2 and Landsat 8 for improved calculation of climate change impacts
Authors: Paul Frank , Goerlich Franz , Rastner Philipp
Published: March 2021

Svalbard is famous for its numerous surge-type glaciers as well as for the harsh weather conditions of a highly maritime Arctic island, making regular observations of its glaciers challenging. However, the rapid changes of glacier geometry require a frequent update of their extent to perform accurate glacier-specific calculations such as their mass balance or contribution to sea level. The last inventory for Svalbard has been compiled by Nuth et al. (2013) from about 40 satellite scenes acquired by three different sensors (ASTER, Landsat, SPOT) on 30 unique days over a period of 10 years. Accordingly, any change assessment or other time dependent calculations are difficult to perform and a temporarily more consistent dataset is urgently required.

In this study we present the results of a new glacier inventory for Svalbard that has been derived from two Sentinel-2 swaths acquired for the main island within 3 days of 2017 and on 1 day in 2016 from Landsat 8 for Nordaustlandet. The images had overall very good snow conditions but in some regions late seasonal snow was hiding glaciers. Glacier mapping under local clouds in the very north and south could be performed by using further scenes from 2017 processed with GEE. We applied a simple red/SWIR band ratio to map clean ice and corrected wrong classifications (sea ice, lakes) or missing parts (debris cover) manually. New drainage divides and topographic parameters were derived from the ArcticDEM.

The new inventory counts 3136 glaciers >0.01 km2 covering an area of 32,948 km2. Of these, glaciers < 1 km2 cover 1.3% of the area but nearly 44% of the number whereas glac-iers >10 km2 cover 91% of the area and 10% by number. Compared to the previous inventory we have 1468 glaciers more and 2.5% area less. However, when excluding the 2025 glaciers <1 km2, we only identified 1111 glaciers, i.e. 557 less than in the previous inventory. The differences are mostly due to newly considered entities, different drainage divides, glacier retreat and advance/surging. By excluding surge-type glaciers, a more meaningful determination of climate-related area changes can be performed. The presentation will discuss the differences of the new inventory to the RGI dataset, the specific glacier mapping challenges and our approach to solve them.
Making Cyclone Tracking accessible to end users for Climate Research and Applications
Authors: Pagé Christian , Plieger Maarten , Som de Cerff Wim , Spinuso Alessandro , Filgueira Rosa , Atkinson Malcolm , Themeli Chrysoula , Klampanos Iraklis , Karkaletsis Vangelis
Published: March 2021

Climate impact and adaptation measures are becoming urgent to be put in place and anticipated. During the past years, climate change effects have been producing adverse conditions in many parts of the world, with significant societal and financial impacts. Advanced analysis tools are needed to process ensembles of simulations of the future climate, in order to generate useful and tailored products for end users.

An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know how storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like the IS-ENES climate4impact (C4I) can enable the use of those tools by a larger number of researchers and end users.

Integrating this type of complex tool is not an easy task. It requires significant development effort, especially if one of the objectives is also to adhere to FAIR principles. The DARE Platform enables research developers to faster develop the implementations of scientific workflows more rapidly. This work presents how such a complex analysis tool has been implemented to be easily integrated with the C4I platform. The DARE Platform also provides easy access to e-infrastructure services like EUDAT B2DROP, to store intermediate or final results and powerful provenance-powered tools to help researchers manage their work and data.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements N°824084 and N°777413.
Deep geothermal-induced seismicity: controlling factors and hazard mitigation measures
Authors: De Santis Francesca , Klein Emmanuelle , Thoraval Alain
Published: March 2021

As many industrial activities impacting the underground, deep geothermal projects can be associated with the occurrence of induced seismic events. This seismicity is sometimes a direct consequence of stimulation operations needed to enhance the permeability of geothermal reservoirs, but, in other cases, it can also occur in different phases of geothermal projects, as during wells shut-in, after injection operations, or during the production phase, which generally implies lower flow rates and injection pressures. The intensity of this seismicity, in terms of magnitudes of seismic events, can be extremely variable, from microseismic events (M < 2), not felt at the surface, to large earthquakes (M > 5) that pose a serious risk to neighboring populations and may lead to the abandon of geothermal projects. In this context, it is of paramount importance to: i) better characterize and understand the interactions between natural and anthropogenic factors which may lead to geothermal-induced seismicity and ii) evaluate currently applied approaches to handle and minimize associated risks.

The objective of this work is to establish a state of the art about deep geothermal-induced seismicity, by describing factors that have a bearing on the generation of seismic events, as well as by discussing existing means to handle their occurrence. Based on a worldwide review of geothermal projects, we created a large database describing each selected case study in terms of geological properties and tectonic setting, operational parameters and type of geothermal systems, as well as spatio-temporal characteristics of the observed induced seismicity. Collected data are analyzed in order to better understand possible cause-effect relationships between induced seismicity and geothermal operations with the aim of identifying the most important preexisting and anthropogenic factors, as well as their interactions, which may have a key role on the occurrence of seismic activity.       
Smaller land-sea contrast on probability of precipitation (POP) of warm clouds over globe
Authors: Liu Jihu , Wang Minghuai , Rosenfeld Daniel , Zhu Yannian
Published: March 2021

Proper observation of global warm rain and understanding of its formation processes can significantly advance our understanding on aerosol-cloud-precipitation interactions. Previous study shows that due to smaller cloud effective radii (Re), rain from liquid clouds over land is sharply reduced compared to oceans (Mülmenstädt, 2015). However, in our study, we use A-Train satellite observations to show that there should be smaller land-sea difference on probability of precipitation (POP) of warm clouds between land and oceans. The discrepancy is probably because the algorithm bias in CloudSat precipitation flag products over land, which may mistakenly treat drizzle as no rain. We also find that if Re is smaller than 14 mm, no matter how thick the warm cloud is it can hardly produce significant precipitation (here defined as radar reflectivity factor lager than 0dBZ), which can generate dynamic feedback on the development of clouds.
A review on share of river bank erosion to the total sediment load with increasing catchment size
Authors: Abbas Ghulam , Jomaa Seifeddine , Rode Michael
Published: March 2021

Information on the share of river bank erosion to the total sediment load at catchment scale by using the fingerprinting approach is important to address our knowledge of erosion processes to better target soil erosion control measures. In particular, river bank erosion is affected by many factors such as spatial and temporal variables and is difficult to quantify the relationship of the share of bank erosion to catchment size and upland erosion rate without extensive fieldwork and data analysis. Potential tracers including geochemical, fallout radionuclides, bulk and compound-specific stable isotopes, and magnetic properties have been used, often in combination with sediment source apportionment. In this worldwide review, the global dataset for percent share of river bank and surface erosion using fingerprinting approach was collected to establish the significance of catchment size and other physical controls on river bank erosion. Google Scholar and Web of Science were used to review research articles that included river bank/subsurface as one of the sediment sources in the study areas. This database showed that the UK (n = 84), USA (n = 14) and Brazil (n = 10) had the highest number of catchments, followed by Iran (n = 4), Southern Zambia (n = 1), Australia (n = 1), Spain (n = 1), Mongolia (n = 1) and Burkina Faso (n = 1) ranging in size from 0.31 to 15000 km2, predominately agriculture. Based on published studies, there is a clear shift of sediment sources from surface erosion to river bank erosion with increasing catchment size. The results show the wide range of relative contributions of surface and river bank sources to the catchment sediment yield around the globe. There are a number of catchments with river bank contribution exceeding 25% and surface contribution exceeding 90% of total sediment loss. This diversity highlights the many factors that influence river bank erosion. In addition to the wide range, sediment source contribution in the range 1-25% from river bank is generally representative around the World. We recommend that long term monitoring of sediment load and surface and river bank sources at nested sites within a catchment are indispensable. Furthermore, limited information on the share of sources often makes it difficult to target mitigation measures reducing sediment loads at the catchment scale.

Keywords: Sediment load, catchment size, fingerprinting approach, river bank share
MSWI fly ash particle size chemical and mineralogical characterization, before and after leaching tests, aimed to reuse. 
Authors: Destefanis Enrico , Caviglia Caterina , Agostino Angelo , Bernasconi Davide , Pastero Linda , Bonadiman Costanza , Pavese Alessandro
Published: March 2021

Municipal solid waste incinerator (MSWI) fly ash can represent a sustainable source of construction materials, but it needs to be treated in order to remove dangerous substances as chlorides, sulfates, and heavy metals. The concentration of salts and heavy metals in fly ash usually exceeds the law threshold and so they are considered a hazardous waste, unsuitable for reuse in concrete and civil engineering applications.In this work, a complete characterization of fly ash coming from a northern Italy thermovalorization plant was investigated, both on the solid and leachates composition, focused on the particle size, by X-Ray fluorescence and X-Ray diffraction on the solid matrices and ICP-MS analysis on the leachates.Using mechanical sieving on several subsamples of fly ash, six different particle size were separated and analyzed, and compared to the bulk fly ash composition.The most abundant elements are represented by Ca, Cl, S, and Si; trace elements and heavy metals are mainly represented by Zn, Fe, Al, Pb. The XRF and ICP-MS analysis show a general increasing trend, as the particle size decrease, of Na, K, Cl, S, as well as Cr, Cd, Cu, Pb, Sb, Zn, Ba, both on solid and leachates composition; on the contrary Ca and Si decrease.After leaching Cl and K decrease consistently, while it can be observed an increase of all the other elements, due to the weight loss attributable mainly to the leaching of Na-K chlorides, that is confirmed also by the X-Ray diffraction analysis.
Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline
Authors: Beloiu Mirela , Poursanidis Dimitris , Hoffmann Samuel , Chrysoulakis Nektarios , Beierkuhnlein Carl
Published: March 2021

Recent advances in deep learning techniques for object detection and the availability of high-resolution images facilitate the analysis of both temporal and spatial vegetation patterns in remote areas. High-resolution satellite imagery has been used successfully to detect trees in small areas with homogeneous rather than heterogeneous forests, in which single tree species have a strong contrast compared to their neighbors and landscape. However, no research to date has detected trees at the treeline in the remote and complex heterogeneous landscape of Greece using deep learning methods. We integrated high-resolution aerial images, climate data, and topographical characteristics to study the treeline dynamic over 70 years in the Samaria National Park on the Mediterranean island of Crete, Greece. We combined mapping techniques with deep learning approaches to detect and analyze spatio-temporal dynamics in treeline position and tree density. We use visual image interpretation to detect single trees on high-resolution aerial imagery from 1945, 2008, and 2015. Using the RGB aerial images from 2008 and 2015 we test a Convolution Neural Networks (CNN)-object detection approach (SSD) and a CNN-based segmentation technique (U-Net). Based on the mapping and deep learning approach, we have not detected a shift in treeline elevation over the last 70 years, despite warming, although tree density has increased. However, we show that CNN approach accurately detects and maps tree position and density at the treeline. We also reveal that the treeline elevation on Crete varies with topography. Treeline elevation decreases from the southern to the northern study sites. We explain these differences between study sites by the long-term interaction between topographical characteristics and meteorological factors. The study highlights the feasibility of using deep learning and high-resolution imagery as a promising technique for monitoring forests in remote areas.
COVID-19 Lockdown and its Effects on Aerosol Optical and Radiative Properties over Indo-Gangetic Plain
Authors: Yadav Sarla , Srivastava Atul Kumar , Ahlawat Ajit , Mishra Sumit Kumar
Published: March 2021

Aerosol optical and radiative properties, measured with theground based sun-sky radiometer (under AERONET) were explored at Kanpur (26.5°N, 80.2°E) in the central and Gandhi College (Ballia) (25.8°N, 84.1°E) in the eastern Indo Gangetic Plain (IGP) innorthern India. The measurements were carried out during the period from January to July 2019 (non-lockdown) and 2020 (lockdown). Significant changes were observed in aerosol properties during the lockdown period due to COVID-19 pandemic.This year marked an overall reduction of 15.79% and 3.39% in aerosol optical depth (AOD) than year 2019 at Kanpur and Gandhi College, respectively. Lockdown phase1 (23rd March to 14th April, 2020) showed reduction of 52.52 % and 42.22% in AOD compared to pre-lockdown condition at Kanpur and Gandhi College, respectively. In addition to lockdown Phase 1 observations, an increasing trend in AOD values was found at both locations for subsequent lockdown phases. Higher values of angstrom exponent measured at Eastern region (1.13 ± 0.17) than central region (0.97 ± 0.26) indicating the dominance of fine particles at Gandhi College. During lockdown Phase-1, the values of atmospheric forcing and heating rate were decreased about 23.48% and 15.07% at Kanpur and Gandhi College,respectively compared to year 2019 values.There was overall 1.31% reduction in SSA at Kanpur while 4.34% reduction was observed at Gandhi college in year 2020 than 2019. Current year marked reduction in SSA by 1.56% at Gandhi College than Kanpur representing the absorbing particles due to biomass burning event during the lockdown period. The effects of lockdown were prominently seen for Kanpur region in terms of variations in aerosol properties. The resultant atmospheric forcing and heating rate shows 11.86% reduction at Kanpur while 35.27% increased at Gandhi College during the lockdown period in 2020 compared to 2019.As the lockdown progressed, increasing trend was observed in atmospheric forcing and heating rate.
Crowd, Crops and Machines: How crowdsourced annotations can help towards crops classification
Authors: Guillot Alexis , You Shaodi , van't Woud Hans , Perenboom Matthijs , Kruijver Amanda , Foing Bernard
Published: March 2021

The use of artificial intelligence and specifically deep learning (DL) approaches in the domain of remote sensing is increasing. Such methods provide excellent results and show great potential for future applications. Earth observation sensors are able to deliver data with higher spatial, spectral and temporal resolutions. In this project, we use Sentinel-2 multispectral data and couple this input with a crowd annotated very high resolution (VHR) map which is generated in the video-game Cerberus, developed by the company BlackShore. In Cerberus, players are able to map features, like buildings, forest and specific types of crop fields, that are subsequently used as input for the Machine Learning (ML) pipeline. The ML pipeline is applied to classify crop fields in a larger region.

The main objective of this research is to study the accuracy of a model in detecting and describing the type of crop and whether the addition of a temporal dimension increases the accuracy. We will be experimenting with different methods that take their root in DL. The study region shown to Cerberus-players is Oromia in Ethiopia, south of the capital Addis Ababa. Using Sentinel-2 data, we aim to extend the generated maps to cover Ethiopia.

First, we will implement two DL methods; Random Forest (RF), and a 3D Convolutional Neural Network (CNN) that do not make use of the temporal dimension in order to have a baseline of the expected accuracy from a single multi-spectral image. Next, we will investigate four models that make use of time series: 1) a hybrid convolutional neural network-random forest (CNN-RF); 2) a 3D CNN that takes as input the output of a stack of 3D CNNs; 3) a model based on Recurrent Neural Networks (RNNs) performing pixel-based classification; and 4) an innovative method that combines the strength of RNNs, CNNs and Generative Adversarial Networks. 

We are now implementing the methods and shall report on results at EGU April 2021. For future research, it could be a very interesting case to study the possibility of generalizing the combined approach of crowd annotated training data with extended classification over larger regions and generalizing to other areas.
A hybrid method to calculate teleseismic body waves in a regional 3D model using GEMINI and SPECFEM.
Authors: Möller Thomas , Friederich Wolfgang
Published: March 2021

Modeling waveforms of teleseismic body waves requires the solution of the seismic wave equation in the entire Earth. Since fully-numerical 3D simulations on a global scale with periods of a few seconds are far too computationally expensive, we resort to a hybrid approach in which fully-numerical 3D simulations are performed only within the target region and wave propagation through the rest of the Earth is modeled using methods that are much faster but apply only to spherically symmetric Earth models.

We present a hybrid method that uses GEMINI to compute wave fields for a spherically symmetric Earth model up to the boundaries of a regional box. The wavefield is injected at the boundaries, where wave propagation is continued using SPECFEM-Cartesian. Inside the box, local heterogeneities in the velocity distribution are allowed, which can cause scattered and reflected waves. To prevent these waves from reflecting off the edges of the box absorbing boundary conditions are specifically applied to these parts of the wavefields. They are identified as the difference between the wavefield calculated with SPECFEM at the edges and the incident wavefield.

The hybrid method is applied to a target region in and around the Alps as a test case. The region covers an area of 1800 by 1350 km centered at 46.2°N and 10.87°E and includes crust and mantle to a depth of 600 km. We compare seismograms with a period of up to ten seconds calculated with the hybrid method to those calculated using GEMINI only for identical 1D earth models. The comparison of the seismograms shows only very small differences and thus validates the hybrid method. In addition, we demonstrate the potential of the method by calculating seismograms where the 1D velocity model inside the box is replaced by a velocity model generated using P-wave traveltime tomography.
Decoupling the effect of the forest pest damage from the effects of meteorology using space-borne remote sensing and modelling
Authors: Marjanovic Hrvoje , Kern Aniko
Published: March 2021

The EU’s climate change mitigation plans of 55% reduction in greenhouse gas emission by 2030 and reaching climate-neutrality by 2050 rely significantly on maintaining and increasing the carbon sink in European forests. In addition to direct consequences of climate change and ageing forests, this sink is becoming threatened by the new invasive forest pests which can decrease forest productivity. The Oak lace bug (Corythucha arcuata, Say 1832), native to North America, is a new invasive species rapidly spreading since 2012 from the east to the west of Europe. The oak lace bug (OLB) after establishment in an area shows no signs of retreating and negatively affects the tree photosynthetic capacity by feeding on leaf sap. The consequences of such new and persistent pest, which are not imminently life-threatening to trees but are long-lasting, have yet to be determined.

In our study, we used remotely sensed MODIS NDVI (MOD09Q1), gridded meteorological data (FORESEE), soil water content (ERA5 Land), available national forest management and land cover data to develop methods for detecting the presence and the assessment of the impact of the OLB. The study was focused on the modelling tools to decouple the effects caused by the environmental variables from the pest damage on the measured NDVI. To this different NDVI models were created based on the Least Absolute Shrinkage and Selection Operator (LASSO) technique and the most influential periods, to support accurate forest pest detection. We investigated forests containing oak trees in the transboundary area of Hungary and Croatia. The results show that the LASSO technique is a promising tool in NDVI modelling using meteorological and environmental data. The performance of the models based on the Most Influential Periods (MIP) of the different variables showed just slightly worse results, although their application is more intuitive. In the case of the OLB, the damage assessment results with the LASSO and MIP methods showed that the pest-caused NDVI decrease in pure oak stands during the late August to early September period can be as much as -14.5% and -15.6%, respectively.

 

Asknowledgments:

The research has been supported by the Croatian Science Foundation project MODFLUX (HRZZ IP-2019-04-6325), by the Hungarian Scientific Research Fund (OTKA FK-128709) and by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
Two sides of the same inverse problem, in identifying the pore size distribution based on experiments with non-Newtonian fluids
Authors: Lanzendörfer Martin
Published: March 2021

Following the capillary bundle concept, i.e. idealizing the flow in a saturated porous media in a given direction as the Hagen-Poiseuille flow through a number of tubular capillaries, one can very easily solve what we would call the forward problem: Given the number and geometry of the capillaries (in particular, given the pore size distribution), the rheology of the fluid and the hydraulic gradient, to determine the resulting flux. With a Newtonian fluid, the flux would follow the linear Darcy law and the porous media would then be represented by one constant only (the permeability), while materials with very different pore size distributions can have identical permeability. With a non-Newtonian fluid, however, the flux resulting from the forward problem (while still easy to solve) depends in a more complicated nonlinear way upon the pore sizes. This has allowed researchers to try to solve the much more complicated inverse problem: Given the fluxes corresponding to a set of non-Newtonian rheologies and/or hydraulic gradients, to identify the geometry of the capillaries (say, the effective pore size distribution).

The potential applications are many. However, the inverse problem is, as they usually are, much more complicated. We will try to comment on some of the challenges that hinder our way forward. Some sets of experimental data may not reveal any information about the pore sizes. Some data may lead to numerically ill-posed problems. Different effective pore size distributions correspond to the same data set. Some resulting pore sizes may be misleading. We do not know how the measurement error affects the inverse problem results. How to plan an optimal set of experiments? Not speaking about the important question, how are the observed effective pore sizes related to other notions of pore size distribution.

All of the above issues can be addressed (at least initially) with artificial data, obtained e.g. by solving the forward problem numerically or by computing the flow through other idealized pore geometries. Apart from illustrating the above issues, we focus on two distinct aspects of the inverse problem, that should be regarded separately. First: given the forward problem with N distinct pore sizes, how do different algorithms and/or different sets of experiments perform in identifying them? Second: given the forward problem with a smooth continuous pore size distribution (or, with the number of pore sizes greater than N), how should an optimal representation by N effective pore sizes be defined, regardless of the method necessary to find them?
Validation of the ALES Coastal Altimetry Dataset against the Norwegian Tide Gauges
Authors: Mangini Fabio , Bonaduce Antonio , Chafik Léon , Bertino Laurent
Published: March 2021

Satellite altimetry measurements, complemented by in-situ records, have made a fundamental contribution to the understanding of global sea level variability for almost 30 years. Due to land contamination, it performs best over the open ocean. However, over the years, there has been a significant effort to improve the altimetry products in coastal regions. Indeed, altimetry observations could be fruitfully used in the coastal zone to complement the existing tide gauge network which, despite its relevance, does not represent the entire coast. Given the important role of coastal altimetry in oceanography, we have recently decided to check the quality of a new coastal altimetry dataset, ALES, along the coast of Norway. The Norwegian coast is well covered by tide gauges and, therefore, particularly suitable to validate a coastal altimetry dataset. Preliminary results show a good agreement between in-situ and remote sensing sea-level signals in terms of linear trend, seasonal cycle and inter-annual variability. For example, the linear correlation coefficient between the inter-annual sea level variability from altimetry and tide gauges exceeds 0.8. Likewise, the root mean square difference between the two is less than 2 cm at most tide gauge locations. A comparison with Breili et al. (2017) shows that ALES performs better than the standard satellite altimetry products at estimating sea level trends along the coast of Norway. Notably, in the Lofoten region, the difference between the sea level trends computed using ALES and the tide gauges range between 0.0 to 0.7 mm/year, compared to circa 1 to 3 mm/year found by Breili et al. (2017). These preliminary results go in the direction of obtaining an accurate characterization of coastal sea-level at the high latitudes based on coastal altimetry records, which can represent a valuable source of information to reconstruct coastal sea-level signals in areas where in-situ data are missing or inaccurate.