bluuebunny/crossref_metadata_2025 huggingface dataset:
The dynamics of floodplain urbanization and hydraulic engineering development
Authors: Lin Peirong , Gao Shang , Zeng Zhenzhong , Wang Jida , Zheng Kaihao , Yin Ziyun , Yuan Zimin , Huang Zhou , Zhou Xudong , Lei Xiangyong
Published: February 2023

Floodplain urbanization is accelerating at an alarming rate in recent decades, which has posed grand challenges to flood risk management. Hydraulic engineering infrastructure is often co-developed alongside this acceleration to mitigate flood risks, but to what extent this co-development matches up with the floodplain urbanization rate needs to be better quantified. In this study, we leverage a range of multi-source geospatial datasets including high-resolution floodplain maps, urban impervious area maps, global dam/reservoir datasets, and flood fatality data from the Emergency Events Database (EM-DAT) to assess the dynamics of floodplain urbanization (1985–2015) and its interplay with hydraulic engineering development at the global scale. We will report our assessments at both the basin level and the country level to promote the understanding of the hydroclimatic and socioeconomic contexts of floodplain urbanization. Ultimately, results from this study are expected to inform the prioritized regions for flood risk mitigation.
The project “Characterization of Ionospheric Turbulence Level by Swarm Constellation”: Results and Prospects
Authors: De Michelis Paola
Published: February 2023

The project “Characterization of Ionospheric TurbulENce Level by Swarm Constellation (INTENS)”, which was completed in 2021, was a collaboration between two Italian research institutes (INGV and INAF) and the National Observatory of Athens (NOA). It was funded by the European Space and was devoted to the investigation of turbulence and complexity in the ionospheric F2 region using data coming from the Swarm constellation. The obtained results were very intriguing because they provided the first global-scale characterization of the scaling properties of electron density and magnetic field fluctuations at the Swarm altitude as a function of geomagnetic activity and orientations of interplanetary magnetic fields. They also provided the opportunity to quantify changes in the complexity of the magnetosphere-ionosphere coupling system and to comprehend how it reacts to the onset and evolution of intense magnetic storms using entropy measures.Despite the fact that the INTENS project is no longer active, research continues. The aim of this presentation is to summarize some recent results and to show how turbulence can be one of the most significant processes for the generation of plasma density irregularities which strongly affect the Global Navigation Satellite System. A thorough understanding of turbulent ionospheric fluctuations can be crucial in the future creation of GPS Loss of Lock hazard maps, greatly advancing the effort to minimize the effects of space weather. Any future satellite mission that can accurately measure the magnetic field and electron density at high frequency, as the NanoMagSat mission should be able to, will be necessary to extend the reliability of the results to ever smaller spatio-temporal scales.  
Bayesian logistic regression and optimized XGBoost models for landslide susceptibility assessment
Authors: Mirus Benjamin , Woodard Jacob
Published: February 2023

Bayesian logistic regression with vague priors and optimized XGBoost models are two contrasting and commonly used approaches for modeling landslide susceptibility. Logistic regression calculates the log odds of a binary outcome (i.e., landslide or no landslide) given some predictor data (e.g., slope, elevation, and geology) that describes the terrain of each mapping unit used to divide the terrain for susceptibility evaluation. The Bayesian implementation incorporates uncertainty into the model by using probability distributions of the model parameters. Weakly informative priors ensure that the likelihood function (i.e., observational data) dominates posterior distributions, which can be estimated using the statistical software Stan. Like logistic regression, the gradient boosting decision tree machine learning algorithm XGBoost requires the predictor data of each mapping unit to output a probability of an event. Decision trees are a non-parametric learning tool that uses a set of if-then-else decision rules to predict the expected model outcome. Gradient boosting is a method of sequentially adding more decision trees to improve the model output until the lowest model residual levels are reached while penalizing for the level of complexity added to the model. We optimize the model parameters using a Bayesian cross-validation procedure on a portion of the training data. To obtain distributions of the level of susceptibility from XGBoost, a 10-fold cross-validation procedure with ten iterations is implemented. Evaluation of both Bayesian logistic regression and XGBoost algorithms is performed using the area under the curve of the receiver operator characteristics and the Brier score, but any other common metric for evaluation is possible. Model development and evaluation is carried out through the computational environment R. These methods have been applied with success to many diverse regions of the United States and would benefit from testing with the benchmark datasets proposed by the conveners.
Comparison of vegetation indices using measurement techniques on a scale from plant leaves to plots
Authors: Zsigmond Tibor , Zagyva Imre , Horel Ágota
Published: February 2023

In agricultural systems, rapid information from data collection and processing is an important factor for stakeholders and researchers to correctly account for the spatial and temporal variability of crop and soil factors. The aim of the present study was to investigate soil-plant-water systems and interactions using manual and remote sensing techniques in a small agricultural catchment. Four land use types of forest, grassland, vineyard, and cropland (sunflower) were investigated in different slope positions. At the same time, three different tillage practices were applied in the vineyard between the rows: grassed (NT), cover cropped (CC), and tilled (T) inter rows. We evaluated NDVI measurements from three different sources (PlantPen - PP, Meter Group - MG, Sentinel-2 - S2) representing different scales (leaves, 0.33m2, and 100m2). We also compared ground and satellite measurements of varying vegetation indices.Spectral reflectance sensors were used on the slopes of grassland, cropland, and three vineyard sites. The Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI) sensors were used to measure leaf reflectance. A hemispherical sensor set was used for each measurement. Hand-held instruments were used to measure the topsoil soil water content (SWC) and temperature, leaf NDVI and chlorophyll concentrations, and Leaf Area Index (LAI) every two weeks. Satellite data, such as NDVI, green (GCI) and red edge (RECI) chlorophyll indices, and soil-adjusted vegetation index (SAVI), were obtained from the Sentinel-2 database on days when both ground and satellite overpass occurred within 24 hours.Land use types and slope position have a strong influence on vegetation growth. The highest overall NDVI and leaf chlorophyll values were observed in vineyard and forest samples, and the lowest in grassland. SWC and temperature were the lowest in the forest and vineyards. SWCs were significantly different for T and CC samples (p<0.05) based on slope positions, while soil temperatures were not significantly different between upper and lower slope positions (p>0.05). For the other three land use types, there were no significant differences in values between slope positions. Chlorophyll data showed a very strong correlation between Sentinel-2 retrieved data and hand-held measurements, with r=0.84 for grassland (GCI), r=0.83 for NT (GCI), and r=0.87 for T (RECI). Strong correlations were found between the different sources of NDVI for the grassland samples (e.g. r=0.97, p<0.05 for S2 and MG). Weaker correlations were observed between different inter-row managed vineyard samples (e.g. for tilled inter-row r=0.70 between S2 and PP and r=0.35 between S2 and MG). Since inter-row management strongly influences the overall values of S2, adjustments are needed.
Global trends of vegetation leaf moisture content and extreme weather since the 1980s
Authors: Schmidt Luisa , Li Wantong , Forkel Matthias
Published: February 2023

Climate change leads to a change of precipitation frequency and quantity as well as to increased temperature inducing extreme weather events like floods but also more intensive and longer drought periods.The response of the vegetation to these trends is of high interest because vegetation regulates interception, transpiration and is a water storage which is important for plant productivity, agriculture, carbon cycling and the danger of wild fire occurrence. Reduced precipitation in combination with increased temperature lead to water stressed vegetation which might not only behave different in regards of evapotranspiration but are also prone to wildfires. However, currently we don’t know how the water status changes in the long-term. A long-term time series of the vegetation leaf moisture content can help to understand the consequences of changing environmental conditions on the vegetation layer as part of the water as well as the carbon cycle.Measurements of vegetation leaf moisture are usually only available for single test-sites (missing spatial coverage), often measured for a short time span and might hold missing data. Estimations of vegetation leaf moisture are able to provide consistent time series but are mostly done on regional scale which are also missing spatial transferability. However, long-term data with a consistent time series and large spatial coverage are necessary to address a reliable time series analyses in the context of climate change.Our trend analysis will focus on the live-fuel moisture content (LFMC) which is based on the vegetation optical depth (VOD) and Leaf Area Index (LAI). LFMC is defined as the water mass of living vegetation to the dry mass of the vegetation, usually expressed in percentage. LFMC is an important variable in the field of wild fire analyses as it is one of the key predictors for risk and development of a fire. LFMC can be estimated on ecosystem level due to its independence of plant type. Here we use VODCA VOD and GLOBMAP LAI data to create a longer time series of LFMC for the period 1988-2017 on global scale to analyse temporal changes in LFMC. Initial results indicate a heterogeneous pattern of LFMC trends which depend on land cover type, e.g., with a decreasing trend for shrublands but an increasing trend for needle-leaved forests. We compare the trends in LFMC with trends in heat and drought events as well as fire weather indices. Inter-annual changes in LFMC correspond to multi-year drought events.
High-relief exhumation history in the Japanese Alps within the past 1 Ma inferred from trapped charge thermochronometry
Authors: Bartz Melanie , King Georgina E. , Herman Frédéric , Anderson Leif S. , Sueoka Shigeru , Tsukamoto Sumiko , Tagami Takahiro
Published: February 2023

The interactions between Earth surface processes, climate and tectonics determine the landscape in mountain regions. The Japanese Alps uplifted throughout the Quaternary and now reach elevations of up to 3,000 m. However, quantifying relief changes in response to tectonic activity, magmatism and Late Quaternary glaciation is challenging due to the young age of the Japanese Alps and the difficulty of measuring surface processes at the timescale of glacial-interglacial cycles. Here, we use ultra-low temperature thermochronometers based on the luminescence of feldspar minerals and the electron spin resonance (ESR) of quartz minerals, in combination with inverse modelling to derive rock cooling rates and exhumation rates histories at 104-106 years timescales. We focus on the Tateyama region in the Hida range of the Japanese Alps, which was glaciated during the late Quaternary period. In total, 19 new samples were analyzed by luminescence and ESR thermochronometry. While most luminescence signals have already reached their upper dating limit, ESR signals (Al and Ti centres) yielded ESR ages of between 0.5-0.9 Ma. In general, thermal stability is lower for the Al centre compared to that of the Ti centre, but both centres constrain similar exhumation rates. Inversions reveal rock cooling rates on the order of 30-80 °C/Ma, which can be inverted to erosion rates of <1 mm/a within the past 1 Ma. In the next step, we will relate these rates to the climate and tectonic history of the Tateyama region.
Clustering Geodata Cubes (CGC) and Its Application to Phenological Datasets
Authors: Nattino Francesco , Ku Ou , Grootes Meiert W. , Izquierdo-Verdiguier Emma , Girgin Serkan , Zurita-Milla Raúl
Published: February 2023

Unsupervised classification techniques are becoming essential to extract information from the wealth of data that Earth observation satellites and other sensors currently provide. These datasets are inherently complex to analyze due to the extent across multiple dimensions - spatial, temporal, and often spectral or band dimension – their size, and the high resolution of current sensors. Traditional one-dimensional cluster analysis approaches, which are designed to find groups of similar elements in datasets such as rasters or time series, may come short of identifying patterns in these higher-dimensional datasets, often referred to as data cubes. In this context, we present our Clustering Geodata Cubes (CGC) software, an open-source Python package that implements a set of co- and tri-clustering algorithms to simultaneously group elements across two and three dimensions, respectively. The package includes different implementations to most efficiently tackle datasets that fit into the memory of a single machine as well as very large datasets that require cluster computing. A refining strategy to facilitate data pattern identification is also provided. We apply CGC to investigate gridded datasets representing the predicted day of the year when spring onset events (first leaf, first bloom) occur according to a well-established phenological model. Specifically, we consider spring indices computed at high spatial resolution (1 km) and continental scale (conterminous United States) for the last 40+ years and extract the main spatiotemporal patterns present in the data via CGC co-clustering functionality.  
On the role of soil water storage capacity and soil nutrients on tree growth of selected tree species in Central Europe.
Authors: Gadermaier Josef , Wächter Elisabeth , Grabner Michael , Vospernik Sonja , Katzensteiner Klaus
Published: February 2023

While numerous correlational studies on the impact of climatic variation on tree ring formation consider plant functional traits, masting cycles and stochastic disturbances, the role of soil properties is frequently neglected due to insufficient data availability. Using a homogenous dataset of increment cores collected in mature stands at 1562 different forest sites with detailed plot specific climate, stand and soil information in the province of Styria in Austria we are focusing on the role of soil water storage (plant available water capacity – AWC) and soil nutrient status on tree ring formation. The study area covers a wide altitudinal and climate gradient with mean annual temperatures ranging from 2.1°C to 10.2°C and mean annual precipitation ranging from 695 mm to 2024 mm. Generalized Additive Mixed Models (GAMM) for annual tree ring width over 38 years (1980 to 2018) were fitted for six tree species (Abies alba, Fagus Sylvatica, Larix decidua, Picea abies, Pinus sylvestris, Quercus robur/petrea). Individual tree characteristics, stand attributes, general site characteristics (terrain information calculated from high resolution ALS), downscaled climate information in high temporal resolution, and soil information were used as independent variables. Soil data was derived from a morphological description of 80 cm soil pits dug on each corresponding forest site. Via pedo-transfer-functions (using functions available from the literature as well as derived from laboratory analyses of approximately 25% of the pits), soil characteristics such as AWC and soil nutrient status were calculated.In a two-step procedure, we first developed a general tree growth model, including solely tree and stand attributes (e.g. age, competition) and general site specific information available in high spatial resolution (e.g. slope, aspect, irradiance, mean annual temperature, precipitation). Subsequently, we added soil attributes to the model and checked for their effect on model parameters. AWC and soil nutrient status do have significant influence on tree ring formation when added to the tree ring model. However, this effect varies amongst tree species. The results are consistent with tree species specific traits as available from literature: E.g. deep rooting species like Quercus benefit more from high water storage capacity than shallow rooting Picea abies; the effect of soil nutrient status is most pronounced for Fagus sylvatica, which has high nutritional requirements and more negligible for Pinus sylvestris, with low nutritional requirements. As soil formation itself depends upon geological substrate, landform and climate, the improvement of model quality when adding additional soil information is moderate.
Frontal processes of Sermeq Kujalleq in West Greenland observed with repeated UAV surveys
Authors: Kneib-Walter Andrea , Jouvet Guillaume , Wehrlé Adrien , Nap Ana , Walter Fabian , Lüthi Martin P.
Published: February 2023

Outlet glaciers and ice streams transport ice from the ice sheets to the ocean, where the glaciers lose mass by iceberg calving. Sermeq Kujalleq (SKK, Jakobshavn Isbræ) is one of the largest and most dynamic ice streams of the Greenland Ice Sheet with ice flow velocities up to 40 m/day. With extensive fieldwork and detailed repeated UAV surveys we aim at understanding the complex processes occuring at the ice stream margins and at the calving front of SKK. Such processes are often neclected in numerical models inducing uncertainties in projections of the ice sheet evolution.Within the framework of the COEBELI project we conducted drone photogrammetry surveys in July 2022 at SKK along other field measurements including in-situ GPS, GPRI, seismometers, and time-lapse imagery. Despite challenging weather conditions and constraints due to flying restrictions, we acquired 17 repeated flight surveys over the calving front of SKK during two weeks. As a result, we produced a large imagery data set, which was processed to infer high-resolution ortho-images and digital elevation models (DEM). Comparing the different products enables us to estimate changes in surface topography and ice dynamics. During the observation period several large calving events occurred allowing us to investigate the interaction between frontal processes and ice flow dynamics. With the very detailed data we can study crevasse opening, acceleration at the front, weaknesses in the ice and their origin, and the reaction of the glacier to large calving events.
Sedimentary evidence of a late Holocene tsunami at Loch Flugarth (Shetland Islands, UK)
Authors: Engel Max , Hess Katharina , Patel Tasnim , Kempf Philipp , Koutsodendris Andreas , Vakhrameeva Polina , Klemt Eckehard , Dawson Sue , Schön Isa , Heyvaert Vanessa M.A.
Published: February 2023

Tsunami deposits are required to assess the long-term hazard of tsunamis, in particular in regions with a short and fragmented historical record. In the North Sea region, evidence of tsunamis is scarce. However, the Shetland Islands are an exception, as they provide abundant deposits of the Storegga tsunami c. 8150 cal. a BP, and more fragmented evidence of younger events ~5500 and ~1500 cal. a BP. Sediments of the youngest tsunami – the so-called Dury Voe event – are considered uncertain and have only been found at two sites so far, as thin landward fining and landward thinning sand sheets which are vertically confined by peat.

Here, we present sedimentary evidence for a tsunami from the small coastal lake of Loch Flugarth, northern Mainland. Three gravity cores of up to 91 cm length were taken behind the barrier separating the lake from a shallow marine embayment. The cores show organic-rich background deposition with many sub-cm-scale sand layers, reflecting recurring storm overwash and a sediment source limited to the active beach and uppermost subtidal. A basal 13 cm-thick sand layer, dated to 426–787 cal. a CE based on 14C-AMS, 137Cs and Bayesian age-depth modelling, was found in all three cores. High-resolution grain-size analysis identified four normally graded sublayers with inversely graded traction carpets in the lower part of two sublayers. An organic-rich “mud” drape and “mud” cap, respectively, cover the upper two sublayers, which also contain small rip-up clasts. Grain-size distributions shows a difference between the basal sand layer and the thin storm layers, which are coarser and better sorted. Principal component analysis of XRF core scanning data also distinguishes both sand units, mostly driven by Zr, Fe and Ti in the basal sand. This, in combination with increased magnetic susceptibility, may be related to higher heavy mineral content reflecting an additional marine sediment source for the tsunami deposit. Based on reinterpretation of chronological data from the two published sites and the chronostratigraphy of the present study, the Dury Voe tsunami seems to be younger than previously estimated, i.e. closer to 1400–1300 cal. a BP. The source of the tsunami remains unclear. As there is no evidence for this event outside the Shetlands, the size of the tsunami is expected to be smaller than the Storegga tsunami, which affected most of the North Sea basin.
Parameter sensitivity analysis of vegetation and carbon dynamics using land surface model (CLM5-FATES) at European forest sites.
Authors: Naz Bibi S. , Poppe Christian , Hendricks Franssen Harrie-Jan
Published: February 2023

Changing environmental conditions impact ecosystem dynamics which have important implications for the land–atmosphere carbon and water exchanges. Land surface models coupled with dynamic vegetation models can be used to understand the impact of changes in terrestrial ecosystems on carbon and water cycles and their interactions with climate. However, process-based vegetation models are highly parameterized and have a large number of uncertain parameters, which lead to uncertainties in the model outputs. Here, we use a dynamic vegetation model, the Functionally Assembled Terrestrial Simulator (FATES) coupled to the Community Land Model (CLM v5) to analyze parameter sensitivities and its effects on forest growth, carbon storage and fluxes. We first calibrate allometry parameters to accurately describe plant functional types, representative of most abundant tree species across Europe (such as Norway spruce and European Beach), at three different European sites. Further, an ensemble of model simulations with perturbed parameters were performed and compared against observations to explore uncertainties in simulated vegetation structure and distributions (forest density, tree basal areas and above ground biomass) and their effects on ecosystem functioning (carbon, water and energy fluxes). Comparison with observation shows that the CLM5-FATES model is able to capture the interannual variability well for water and carbon fluxes (such as ET and GPP), but shows larger uncertainties for simulated forest structure (growth, establishment, and mortality). Future work will focus on parameter optimization to further improve model performance in simulating vegetation growth and composition for different vegetation distributions and climate conditions.
State-of-the-Art Review of Machine Learning Models in Civil Engineering: Based on DAMIE Classification Tree
Authors: Kim Jaehyun , Jung Donghwi
Published: February 2023

For recent years, Machine Learning (ML) models have been proven to be useful in solving problems of a wide variety of fields such as medical, economic, manufacturing, transportation, energy, education, etc. With increased interest in ML models and advances in sensor technologies, ML models are being widely applied even in civil engineering domain. ML model enables analysis of large amounts of data, automation, improved decision making and provides more accurate prediction. While several state-of-the-art reviews have been conducted in each sub-domain (e.g., geotechnical engineering, structural engineering) of civil engineering or its specific application problems (e.g., structural damage detection, water quality evaluation), little effort has been devoted to comprehensive review on ML models applied in civil engineering and compare them across sub-domains. A systematic, but domain-specific literature review framework should be employed to effectively classify and compare the models. To that end, this study proposes a novel review approach based on the hierarchical classification tree “D-A-M-I-E (Domain-Application problem-ML models-Input data-Example case)”. “D-A-M-I-E” classification tree classifies the ML studies in civil engineering based on the (1) domain of the civil engineering, (2) application problem, (3) applied ML models and (4) data used in the problem. Moreover, data used for the ML models in each application examples are examined based on the specific characteristic of the domain and the application problem. For comprehensive review, five different domains (structural engineering, geotechnical engineering, water engineering, transportation engineering and energy engineering) are considered and the ML application problem is divided into five different problems (prediction, classification, detection, generation, optimization). Based on the “D-A-M-I-E” classification tree, about 300 ML studies in civil engineering are reviewed. For each domain, analysis and comparison on following questions has been conducted: (1) which problems are mainly solved based on ML models, (2) which ML models are mainly applied in each domain and problem, (3) how advanced the ML models are and (4) what kind of data are used and what processing of data is performed for application of ML models. This paper assessed the expansion and applicability of the proposed methodology to other areas (e.g., Earth system modeling, climate science). Furthermore, based on the identification of research gaps of ML models in each domain, this paper provides future direction of ML in civil engineering based on the approaches of dealing data (e.g., collection, handling, storage, and transmission) and hopes to help application of ML models in other fields.
Using detrital thermochronology on moraine deposits to infer glacial erosion patterns and rock thermal history : insights from the Arve and Maurienne valleys (Western European Alps)
Authors: Guillaume Benjamin , Cogné Nathan , Gallagher Kerry , Valla Pierre G. , Crouzet Christian
Published: February 2023

This study tests the application of combined detrital apatite fission track (AFT) and U-Pb dating to infer both glacial erosion spatial patterns and long-term rock cooling histories in Alpine mountainous settings. We have dated 716 detrital apatite grains from glacial sediments collected in the Maurienne and Arve valleys (Western European Alps, France) from moraine deposits corresponding to different stages of glacial retreat since the Last Glacial Maximum (LGM, ca. 24-21 ka).The Maurienne valley crosses the internal and external Alps, which exhibit contrasting in-situ AFT and U-Pb ages. Here, we present the measured distribution of both detrital AFT and U-Pb ages at 6 locations along the valley, with catchment elevations ranging from 390 to 1740 m. We show that during glacial retreat, erosion is mainly concentrated in the downstream part of the glacier, near the sampled moraine deposits. This inference suggests that during glacial retreat, glacial erosion is more effective below the ELA (Equilibrium Line Altitude) and specifically close to the glacier front, in areas where ice flow velocity is high and subglacial water is abundant, as predicted by ice-dynamics reconstructions in the European Alps over the last 20 ka.In the Arve valley, previous studies showed that in situ AFT ages are systematically younger than 7 Ma for the Mont-Blanc massif. We compare the thermal history obtained from these literature bedrock-derived data to that derived from the new detrital AFT data collected in the Little Ice Age (LIA) moraine, just at the front of the Bossons glacier (~1300 m elevation). We also compare our results with 5 other samples down the valley at catchment elevations between 460 and 1050 m to evaluate potential changes in the detrital AFT signal as well as the consistency in the retrieved long-term cooling history.Based on these first results, we plan to extend our study to other areas (e.g., Patagonia) to investigate both (1) spatial patterns of glacial erosion for older glacial periods (pre-LGM), and (2) long-term rock cooling histories from moraine deposits where modern bedrock is inaccessible (e.g. under modern glaciers or ice fields).
Towards a better understanding of large-scale rock slope dynamics with seismic interferometry
Authors: Chmiel Małgorzata , Walter Fabian , Husmann Lena , Gassner Johannes , Kienholz Christian
Published: February 2023

The Kandersteg region, Switzerland, has a history of catastrophic rock slope failures that repeatedly occurred throughout the Holocene, with volumes reaching hundreds of millions of cubic meters. In recent years, the rock slope near "Spitze Stei" has exhibited elevated displacement rates exceeding 10 cm per day, suggesting a growing instability of up to 20 million m3.Due to the destructive potential of the Spitze Stei rockslide, extensive monitoring has been put in place since 2018, including borehole temperature logging, water pressure measurements and surface displacement monitoring. Borehole temperature measurements and direct observations highlight the presence of degrading permafrost, possibly on planes of enhanced gliding and shear deformation. However, point-like borehole measurements and sensing technology focusing on the slope surface cannot fully describe processes influencing slope dynamics, such as freeze-thaw cycles, varying water pressure, and progressive damage within the slope. These processes have lateral and depth-dependent sensitivity, causing changes in the rock's elastic properties, thus impacting seismic velocities. Here, we aim to provide a better understanding of these primary processes driving the dynamics of Spitze Stei. To this end, we analyze variations in relative seismic velocities (dv/v) measured through continuous seismic data and seismic interferometry. With this technique we transform seismic noise into coherent signals through cross-correlations of data from five 3-component seismometers.The initial results of the time series of relative seismic velocity variations (dv/v) constrain the lateral and depth-dependent extent of subsurface changes. The results indicate that a substantial decrease in relative seismic velocity occurs at the times of rather heavy rain (rainfall >10 mm d-1). This suggests that dv/v reflects material changes caused by pore pressure increase and reduction in material strength. The shallowest dv/v measurements agree with surface displacements displaying cyclic slipping of material.We discuss how our observations help identify the primary processes controlling the dynamics of the Spitze Stei rockslide, give quantitative insight into rock damage, and allow separating effects from irreversible damage growth and reversible thermoelastic and hydrologic variations. This knowledge is needed to better understand the development of large rock failures and potentially improve warning systems.
Microscale physicochemical- and biological configurations regulate microbial biogeochemical processes and functionality
Authors: Wang Gang , Zhu Kun , R. Johnson David , Ruan Chujin , Ramoneda Josep , Gogia Guram , Ran Hongyu , Zhang Peixuan
Published: February 2023

The microscale spatiotemporal heterogeneities of physicochemical- and biological properties and their variational dynamics are key factors regulating microbial activity, soil quality and functionalities. Yet little is known about the underlying mechanisms and impacts on the functioning biogeochemical processes of the earth-surface ecosystems typically the soil-water-microbe-climate nexus. Employing microscale experiments, we illustrate how small-scale water and nutrient configurations as well as those of biological (populations) and chemical (such as O2 and pH) gradients regulate microbial interactions and functionality, and impacts on soil carbon (C) and nitrogen (N) cycling. We firstly use pairs of fluorescently labelled bacterial strains and a hyphae-forming fungal strain that expand together across a nutrient-amended surface, and show that flagellar motility drives bacterial dispersal along the hyphal network, which counteracts the purifying effects of ecological drift at the expansion frontier and thereby increases the spatial intermixing and extent of range expansion of the bacterial strains. We further demonstrate that fungal hyphae are important regulators of bacterial diversity and promote plasmid-mediated functional novelty during range expansion in an interaction-independent manner. In addition, we employed a soil column experiment and illustrated that sufficient labile carbon from plant residues such as straw induced fast O2 consumption with microoxic development in the straw-soil interfaces. In the meantime, the porous structure of straw materials could enhance O2 diffusive inputs in the core area, and subsequently formed a a concentric ring-like microoxic area around the straw patch. Such enriched oxic-microoxic transient zones would induce nitrification coupled denitrification, which led to the high N2O emissions. Additionally, the microbial degradation of straw resulted in a pulse decline of soil pH, which possibly inhibited the N2O reductases, yielding enhanced N2O emissions. These results contribute to a better understanding of the driving factors for microbial interactions and possible impacts of soil key element (such as C and N) cycling.
Revisiting the impact of anomalous precipitation on the mass budget of the Amundsen Sea Embayment ice streams
Authors: Davison Benjamin , Hogg Anna , Rigby Richard , Veldhuijsen Sanne , van Wessem Melchior , van den Broeke Michiel , Holland Paul , Selley Heather , Dutrieux Pierre
Published: February 2023

Mass loss from the West Antarctic Ice Sheet is dominated by glaciers draining into the Amundsen Sea Embayment (ASE). The majority of that mass loss is driven by decadal variations in submarine melt rates. However, periods of extremely high or low precipitation can compound or mitigate ocean-driven mass losses, yet the impact of anomalous precipitation on the mass balance of the ASE is poorly known. We present a 25-year (1996-2021) record of ASE input-output mass balance and evaluate how two periods of anomalous precipitation affected its sea level contribution. Since 1996, the ASE has lost 3331±424 Gt ice, contributing 9.2±1.2 mm to global sea level. Overall, surface mass balance changes contributed just 7.7 % to total mass loss; however, two anomalous precipitation events had a larger, albeit short-lived, impact on rates of mass change. During 2009-2013, persistently low snowfall, due to anomalously zonal circulation, led to an additional 51±4 Gt yr-1 mass loss in those years (contributing positively to the total loss of 195±4 Gt yr-1). Contrastingly, extreme precipitation in the winters of 2019 and 2020 decreased mass loss by 60±16 Gt yr-1 during those years (contributing negatively to the total loss of 107±15 Gt yr-1). These results demonstrate that extreme snowfall variability can have a substantial impact on the short-term sea level contribution from West Antarctica and show that mass changes do not necessarily scale with grounding line discharge anomalies.
Loads and soil deformations from agricultural vehicles and their influence on soil water retention properties
Authors: Germer Kai , Weise Maike , Lorenz Marco
Published: February 2023

Agricultural sites are often influenced by soil deformation and compaction through vehicular traffic. Both can lead to changes in pore-size distribution and consequently in changes at the water retention properties. Compaction usually has the disadvantage that crop yields are reduced and, conversely, avoiding soil compaction has the potential to increase yields and is also more sustainable for the soil. By compaction mainly the macro pores are affected and the lost in pore volume comes from a transformation of macro pores and coarse pores to middle and fine pores.In this study presented here agricultural sites in Lower Saxony (Germany) were soil sampled before and after vehicle wheelings with different machines. The samples were lab investigated to become soil moisture to pore pressure pairs and based on these pairs samples retention curves were yield by fittings with the traditional constrained van Genuchten model using the HYPROP-Fit software. The goal of the study is mainly to compare the van Genuchten parameters θs (saturated water content) and α (van Genuchten shape parameter) between unwheeled and wheeled situations and this in relation to the used vehicles, depth and position in the field.The results show that soil compaction mainly occurs as a result of vehicular traffic, which is reflected in predominantly decreasing θs values. In most cases, the van Genuchten parameter α is significantly reduced after traffic loading, indicating that the mean and dominant pore size range is shifting towards finer pores. Based on these results, it may be possible in the future to identify vehicles and types of traffic where the slightest possible negative soil changes are to be expected.
Efficient indoor radon reduction by over pressure ventilation
Authors: Altendorf Diana , Grünewald Hannes , Dehnert Jörg , Duzynski Michal , Trabitzsch Ralf , Weiß Holger
Published: February 2023

Since new radon risk maps for Germany were published in 2021, in Saxony the highest spatial accumulation of precautionary areas can be found. Reasons are the geological subsoil like the Ore Mountains, as well as a historical mining industry and intensive uranium mining from 1946 to 1990.Close to a heap, various ventilation experiments for indoor radon reduction were performed in a two-room flat (ground floor) in Bad Schlema (Germany). As an innovative approach to eliminate indoor radon and prevent new radon from entering, the focus is on creating an over pressure within the flat. This developed ventilation mode, which aims to ensure that more fresh air enters the room than leaves it, is called differential pressure mode with a forced over pressure.Therefore, a decentralised ventilation system with heat-recovery from inVENTer (Germany) was installed. Throughout numerous different ventilation experiments, radon activity concentrations [Rn] were continuously measured in all rooms (including basement and balcony) using Radon Scout Plus devices from SARAD (Germany). Thereby, room-specific radon behaviour with and without ventilation was found.Despite a strong seasonal trend with significantly higher indoor radon levels in Sep.-Nov. and Dec.-Feb. than in June-Aug., an overall reduction of indoor radon of up to 80 % was achieved. Important to mention is that different ventilation modes in combination with different fan performance levels resulted in different indoor radon reductions.Here, in particular, the experiments with forced over pressure (up to +5 Pa) led to significant results in summer and winter, even in rooms with higher [Rn]. For example, measured [Rn] of 7.000 Bq/m3 within the kitchen could be reduced to 300 Bq/m3 and maintained for the entire duration of the respective ventilation experiment.In this work, the performed experiments as well as the room-specific ventilation effect will be presented. Furthermore, this work analyses the dependencies between the reduction of indoor radon activity concentration and the corresponding environmental parameters.
Development of gridded monthly reference evapotranspiration dataset for Germany for long term trend analysis
Authors: Sourya Daneti Arun , Ramadas Meenu
Published: February 2023

Estimation of reference evapotranspiration (ETo) is necessary for hydrological modeling, water stress adaptation and agricultural water management. While numerous studies have addressed changes in temperature and precipitation patterns at different spatiotemporal scales for assessing hydroclimatic variability, similar analyses on regional evapotranspiration trends are limited. This can be attributed to lack of observed records of ETo at regional scales, and highlights the need for developing better models for estimating this variable. To address this research gap, we developed monthly 0.5° gridded ETo dataset for entire Germany using machine learning techniques and then investigated the temporal trends in ETo over the region. We utilized flux tower data (sensible heat flux, net radiation, soil heat flux, and latent heat flux) from multiple locations in the region to compute observed ETo using the surface energy balance method. Then, fed-forward back-propagation method (BPNN) is used for predicting monthly ETo with easily available input predictors such maximum temperature, minimum temperature, precipitation, soil moisture, short wave radiation, and wind speed. The BPNN is trained with various input combinations in order to estimate ETo with minimal input predictors, and their performance is assessed using metrics: coefficient of determination, mean absolute error, and root mean square error. The results showed that with all the input parameters, the coefficient of determination  for training and testing are 0.89 and 0.93 respectively, while the best parsimonious model (precipitation and downward shortwave radiation as predictors) gives 0.88 and 0.93 respectively. Gridded ETo estimated using the best parsimonious model is then used for assessing spatially varying trends in the variable at monthly and annual time scales over Germany using Mann-Kendall test and Sen’s slope. The long term analysis helps us to identify critical regions in the study area that needs attention for water resources management, drought mitigation and improved adaptation to changing climate.Keywords: Reference Evapotranspiration, Flux Tower, Surface Energy Balance, Feed Forward Back Propagation (BPNN), Trend Analysis, Mann-Kendall Test
Evaluation of dynamically downscaled CMIP6 models using CCAM over Australia. New appraoch to added value of downscaling using information on climate extremes
Authors: Syktus Jozef , Chapman Sarah , Trancoso Ralph
Published: February 2023

High-resolution climate change projections are increasingly required to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using CCAM (Conformal Cubic Atmospheric model) for SSPs 126, 245 and 370. We compared the host CMIP6 models and downscaled simulations to the AGCD observational dataset, and evaluated performance using Kling-Gupta efficiency, and the Perkins skill score. The new added value index was derived by asssing the daily, monthly (annual cycle and amplitude) and seasonal climate for 1981-2100 period by comparing combined skill of CMIP6 host models and CCAM downscaled simulations. In addition to assesing the Perkins score for entire PDF,  the 5 and 95 percentlile for mean, minimum and maximum teperatures and fraction of dry days and 95 percentile of precipitation were considered. The combined skill score index/added value of downscaling was normalised and relative skill score of individual models can be compared for major IPCC regions and local gavernment planninga areas in Australia. Downscaling CMIP6 models improved performance for seasonal  and annual cycles for temperature (10% and 6%) and precipitation (43% and 13%). CCAM downscaling also improved the fraction of dry days, reducing the bias for too many low rain days by nearly half. The largest improvements were found in extremes, with improvements to extreme minimum temperatures in all seasons (varying from 142 to 201%), and improvements of 52% to extreme precipitation in Austral winter (JJA) and 47% in summer (DJF). The ensemble average overall skill score improved by 19% with downscaling. Temperature and precipitation biases were also reduced in mountainous and coastal areas. CCAM downscaling therefore adds value to the CMIP6 models,  so this dataset will be a valuable resource for understanding future climate changes in Australia. The new CMIP6 dataset is the largest in term of number of simulations and resolution for Australia and will contributed to CORDEX and Australian Climate Service database. Work is continuing on deriving climate extreme indices such as drought, heatwaves, fire weather, tropical cycliones and convective extrems, incliuding speciifc focus on climate hazards. . 
Near real-time subsurface structure imaging using airborne electromagnetic data based on deep learning
Authors: Wu Sihong , Huang Qinghua , Zhao Li
Published: February 2023

The airborne electromagnetic (AEM) method is a modern technique in geophysical surveys with the merits of terrain adaptability and acquisition efficiency, and can image the electrical structure of the Earth’s subsurface down to several hundred meters. AEM survey has been applied extensively in mineral exploration, groundwater monitoring, environment investigation and geological mapping. However, the expansion of survey area and spatial sampling bring about huge volumes of AEM data, which present a serious computational challenge for rapid AEM inversion. Inspired by Google’s neural machine translation system, we develop a fast inversion system guided by deep learning to translate AEM data directly into subsurface resistivity structures. Synthetic tests demonstrate that our proposed inversion system has strong noise robustness and can provide reliable inversion results much more efficiently than the conventional Gauss-Newton algorithm. In the field data application, our system obtains robust inversion results of more than 740,000 AEM soundings acquired by the U.S. Geological Survey from the Leach Lake Basin in California in seconds on a common PC. The inverted images coincide exactly with previous studies of the local geological environment and clearly outline the geometries of the lake, faults and surrounding mountains. Our inversion system can support near real-time underground subsurface imaging for AEM surveys and inject new vigor into resource exploration and tectonic studies.