bluuebunny/crossref_metadata_2025 huggingface dataset:
Enrichment Processes in Throughfall and Stemflow in a Mixed Temperate Forest
Authors: Grundmann Maria , Floriancic Marius G. , Molnar Peter
Published: March 2022

Forest canopies redistribute precipitation by processes of interception and stemflow which also change the chemical signature of incoming precipitation. Understanding what controls these transformations and how they evolve across seasons is key to assess forest water cycling and nutrient transport. At the Waldlabor Zurich ecohydrological observatory (Switzerland) we measure the amount and chemical signature of precipitation since April 2020. Our measurement setup focusses on spruce (Picea abies) and beech (Fagus silvatica) trees, as they are the two most common species across Switzerland. In addition to the ion and isotope concentrations in precipitation, throughfall, stemflow as well as in the soil at different depths (10, 20, 40 and 80 cm), we also assess the canopy density in weekly resolution, groundwater depth and streamflow amount at the outlet of our forested catchment, as well as their chemical and isotopic composition

We assessed the seasonal variability of throughfall and stemflow and their relation to canopy density measurements for beech, spruce and young spruce trees. Canopy density had little to no effect on interception and stemflow fractions. We found almost half of the total annual precipitation is intercepted in the canopies of spruce and beech trees, this is because most precipitation events were quite small, resulting in almost no throughfall at all. However, in general, the fraction of interception decreased with increasing event size, on the other hand events below 4 mm did not produce significant amounts of throughfall and stemflow. Water chemistry is showing that major enrichment processes took place in the canopy, subsequently the ion signature was different in throughfall and stemflow compared to open field precipitation. Ion concentrations of sodium, chloride, nitrate, ammonium and potassium were 2 - 10 times higher in throughfall and up to 14 times higher in stemflow compared to concentrations measured in open field precipitation. We hypothesize this is the result of accumulation of wind deposits, especially of anthropogenic contaminants on the tree stem, branches and leaves, as we found concentrations where generally higher during events succeeding long periods without precipitation. In accordance with the much rougher surface of spruce needles and stems compared to beech leaves and stems, we found much higher concentrations in throughfall and stemflow below spruce trees and elevated ion concentrations in the soil waters up to 40 cm depth. Overall, our results highlight the importance of forests, not only in redistributing precipitation, but also in changing the chemical signal of precipitation and thus the forest water and nutrient cycle.
Quantifying long-term sediment dynamics of a proglacial river in an alpine catchment 
Authors: Piermattei Livia , Heckmann Tobias , Altmann Moritz , Betz-Nutz Sarah , Fleischer Fabian , Haas Florian , Pfeifer Norbert , Ressl Camillo , Rom Jakob , Becht Michael
Published: March 2022

Alpine rivers have experienced considerable changes in channel morphology over the last century. The main problem of current studies is the lack of information over a longer period. In order to reliably assess the magnitude of the channel change processes and/or their frequencies due to recent climate change, the investigation period needs to be extended to the last century, ideally back to the end of the Little Ice Age. In addition, a high temporal resolution is required to account for the history of changes in channel morphology and for better detection and interpretation of related processes.

The increasing availability of digitized historical aerial images, together with advances in digital photogrammetry, provides the basis for reconstructing and assessing the long-term evolution of the surface, both in terms of mapping of historic planimetric position and generation of historical digital elevation models (DEMs). We use photogrammetric analysis of recent and historical images, together with LiDAR and drone-based photogrammetric DEMs, to quantify channel changes and the net sediment balance of a main alpine river in a glaciated catchment (Kaunertal, Austria) over nineteen periods from 1953 to 2019. Based on DEMs of difference, we estimate the spatio-temporal patterns of erosion and deposition. We show that geomorphic changes are mainly driven by deglaciation, i.e. glacier retreat, and sediment delivery from recently deglaciated steep lateral moraines, and from extreme runoff events. Overall, this work contributes to better understanding the main factors influencing river changes and the links between channel changes and climatic factors.
Analysis of low-frequency drone-borne GPR for soil surface electrical conductivity mapping
Authors: Wu Kaijun , Lambot Sébastien
Published: March 2022

In the VHF frequencies, the sensitivity of the reflection coefficient at the air-soil interface with respect to the soil electromagnetic properties, i.e., the dielectric permittivity and electrical conductivity, varies with frequency. The lower the frequency is, the lower the sensitivity to permittivity is and the larger the sensitivity to conductivity is. In this study, we investigated low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil surface electrical conductivity characterization. In order to have a good sensitivity to electrical conductivity, we operated in the 15-45 MHz frequency range. We conducted both numerical and field experiments, under the assumptions that the soil magnetic permeability is equal to the magnetic permeability of free space, and that the soil permittivity and conductivity are frequency-independent. Through the numerical experiments, we analyzed the sensitivity of the soil permittivity and electrical conductivity by plotting the objective function in the inverse problem. In addition, we analyzed the effects of modelling errors on the retrieval of the permittivity and conductivity. The results show that the soil electrical conductivity is sensitive enough to be characterized by the low-frequency drone-borne GPR. The depth of sensitivity was found to be around 0.5-1 m in the 15-45 MHz frequency range. Yet, the effects of permittivity cannot be neglected totally, especially for relatively wet soils. For validating our approach, we conducted field measurements with the drone-borne GPR and we compared results with electromagnetic induction (EMI) measurements considering two different offsets, i.e., 0.5 and 1 m, respectively. The lightweight GPR system consists of a handheld vector network analyzer (VNA), a 5-meter half-wave dipole antenna, a micro-computer stick, a GPS receiver, and a power bank. The good agreement in terms of absolute values and field structures between the GPR and EMI maps demonstrated the feasibility of the proposed low-frequency drone-borne GPR method, which appears thereby to be promising for precision agriculture applications.
Complex, slow-moving landslide dynamics: implications from a long-term monitoring setup on the Hofermühle landslide in Lower Austria
Authors: Stumvoll Margherita J. , Philipp Marr , Robert Kanta , Jiménez Alejandra , Thomas Glade
Published: March 2022

Slow-moving landslides play an important role in both theoretical slope evolution and practical landslide hazard and risk research. Their process rates impede the quantitative analysis of related dynamics over short time periods, given that the actual changes are often lying within the error margins of the respective methodological approaches. In this study, current results are presented for a long-term monitoring setup of a slow-moving earth slide – earth flow system in the Flysch and Klippen Zone of Lower Austria. The aim is to further assess surface and subsurface characteristics, their interrelations, and implications on spatio-temporal landslide dynamics.

The research strategy comprises the utilization and analysis of both surface and subsurface monitoring data. The methodology includes the application of Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) based Structure from Motion (SfM). Geotechnical methods, such as penetration tests, percussion drilling and inclinometer measurements are used to gain information about subsurface characterization. A meteorological station and piezometer measurements provide information on hydro-meteorological conditions. Surface monitoring data is available since 2015, subsurface monitoring started in 2018.

Results suggest that a) very high-resolution surface data is necessary to capture real surface changes and that TLS is more suited for processes such as these than UAV based SfM, b) the interpretation of morphological features based on multi-temporal mapping can increase the DoD based level of surface change detection, c) only prolonged observation periods can reveal interrelations on surface and sub-surface dynamics and d) that in-depth knowledge on the study area is important to interpret results and that the impact of natural, but especially artificial disturbances of the hillslope system more or less temporarily close to recent process activities remains difficult to evaluate.

Current monitoring results reveal the complexity and non-linearity of slow-moving, complex landslide behaviour. Both high spatial and temporal resolution of on-going monitoring data enables an assessment of low rates and changes. However, the slower the process, the longer the observation needs to be. Otherwise the actual process dynamics might be misinterpreted, e.g. the data might be superimposed by technical restrictions. 
Present and future land surface and wet bulb temperatures in the Arabian Peninsula
Authors: Safieddine Sarah , Whitburn Simon , Clarisse Lieven , Clerbaux Cathy
Published: March 2022

The Arabian Peninsula exhibits extreme hot summers and has one of the world's largest population growth. We use satellite observations and reanalysis as well as climate model projections to analyze morning and evening land surface temperatures (LST), to refer to processes at the surface, and wet bulb temperatures (WBT) to measure human heat stress. We focus on three regions: The Persian Gulf and Gulf of Oman, the inland capital of Saudi Arabia, Riyadh and the irrigated agricultural region in Al-Jouf, Saudi Arabia. This study shows that the time of the day is important when studying LST and WBT, with current and future WBT higher in the early summer evenings. It also shows that the effect of humidity brought from waterbodies or through irrigation can significantly increase heat stress.

Over the coasts of the Peninsula, humidity decreases LST but increases heat stress via WBT values higher than 25°C in the evening. Riyadh, located in the heart of the Peninsula has lower WBT of 15°C to 17.5°C and LST reaching 42.5°C. Irrigation in the Al-Jouf province decreases LST by up to 10° with respect to its surroundings, while it increases WBT by up to 2.5°. Climate projections over the Arabian Peninsula suggest that global efforts will determine the survivability in this region. Even under the sustainability scenario, the projected increase in LST and WBT reaches +10° and +5°C respectively in the Persian Gulf and Riyadh by 2100 posing significant risk on human survivability in the Peninsula.
Influence of intraday irregularities in the operation of large hydroelectric power plants on the hydrodynamic and hydrochemical regimes of the upper pools of hydroelectric complexes
Authors: Lyubimova Tatyana , Lepikhin Anatoliy , Parshakova Yanina , Lyakhin Yury , Bogomolov Andrey
Published: March 2022

A characteristic feature in the operation of hydroelectric power plants is their technological ability to promptly and quickly change the generation of electricity. By this quality, they fundamentally differ from thermal and nuclear power plants, which require a stable operating mode. The population of large cities consumes electricity very non-uniformly throughout the day: consumption is minimal at night and maximum in the morning and afternoon. To ensure this non-uniformity, it is very convenient to use hydroelectric power plants. The non-uniformity of power generation on them is determined by the non-uniformity of the discharge of water from the upper pools to the lower ones. Sharp changes in the discharge of water through the dams of hydroelectric power plants affect the hydrodynamic regimes of not only the lower, but also the upper pools. For the city of Perm, the hydrodynamics of the upper pool of the Kamskaya HPP is of particular importance, since the main drinking water intake of the city of Perm - the Chusovskoy wastewater treatment plant (ChOS) is located there.

The high sensitivity of the quality of the water withdrawn at the ChOS to the hydrodynamic regime in the upper pool of the Kamskaya HPP is due to the fact that in the winter period the hardness of the withdrawn water is largely determined by the peculiarities of the confluence of the Chusovaya and Sylva rivers [1]. In order to objectively assess the impact of the operation of the hydroelectric power plant on the quality of the water taken under these conditions, a complex of coupled field studies and computational experiments in 3D formulation was carried out. In the course of field studies, daily measurements of the hydrodynamic characteristics of the flow and fluctuations of temperature and specific electrical conductivity of water were carried out using a flow rate meter MIDAS ECM manufactured by Valeoport. Computational experiments were carried out using the commercial package ANSYS FLUENT. The studies carried out not only proved the importance of the influence of the intraday irregularity of the HPP operation on the hydrodynamic and hydrochemical regimes of the upper pool of the hydroelectric complex, but also made it possible to work out the technology for its minimization.

The study was supported by the Russian Science Foundation (grant 17-77-20093) and by the Russian Foundation for Basic Research (RFBR) and Perm Krai (grants 19-41-590013 and 20-45-596028).

1. Lyubimova T., Lepikhin A., Konovalov V., Parshakova Ya., Tiunov A. Formation of the density currents in the zone of confluence of two rivers. Journal of Hydrology. 2014. Vol. 508. pp. 328-342.
Enrichment of trace metals from acid sulphate soils in sediments of the Kvarken Archipelago, eastern Gulf of Bothnia, Baltic Sea
Authors: Virtasalo Joonas , Österholm Peter , Kotilainen Aarno , Åström Mats
Published: March 2022

Rivers draining the acid sulphate soils of western Finland are known to deliver large amounts of trace metals (e.g. Al, Cd, Co, Cu, La, Mn, Ni and Zn) with detrimental environmental consequences to the recipient estuaries in the eastern Gulf of Bothnia, northern Baltic Sea. However, the distribution of these metals in the coastal sea area, and the relevant metal transport mechanisms have been less studied.

This study investigates the spatial and temporal distribution of metals in sediments at 9 sites in the Kvarken Archipelago, which is the recipient of Laihianjoki and Sulvanjoki rivers that are among the most acid sulphate soil impacted rivers in Europe. Metal contents increase in the studied cores during the 1960s and 1970s due to the intensive artificial drainage of the acid sulphate soil landscape. The metal deposition has remained at high levels since the 1980s and the metal enrichment in seafloor sediments is currently visible at least 25 km seaward from the river mouths. Comparison to sediment quality guidelines shows that the metal contents are very likely to cause detrimental effects on marine biota more than 12 km out from the river mouths. The dynamic sedimentary environment of the shallow archipelago makes these sediments potential future sources of metals to the ecosystem. Finally, the strong association of metals and nutrients in the same sediment grain size class of 2–6 µm suggests that the transformation of dissolved organic matter and metals to metal-organic aggregates at the river mouths is the key mechanism of seaward trace metal transport, in addition to co-precipitation with Mn-oxyhydroxides identified in previous studies. These findings are important for the estimation of environmental risks and the management of biologically-sensitive coastal sea ecosystems.

This study resulted from the SmartSea project, funded by the Strategic Research Council at the Academy of Finland (grant number 292 985). M.E.Å. additionally acknowledges the Swedish Research Council Formas (grant number 2018-00760). The study has utilized research infrastructure facilities provided by FINMARI (Finnish Marine Research Infrastructure network).

Original publication: Virtasalo, J. J., Österholm, P., Kotilainen, A. T., and Åström, M. E.: Enrichment of trace metals from acid sulfate soils in sediments of the Kvarken Archipelago, eastern Gulf of Bothnia, Baltic Sea. Biogeosciences, 17, 6097–6113, https://doi.org/10.5194/bg-17-6097-2020, 2020.
Characterising the multi-decadal evolution of highland ecosystems, Sibinacocha, Peru, using GoogleEarth Engine
Authors: Castro Joshua , Montoya Nilton , Quincey Duncan , Potter Emily
Published: March 2022

The Sibinacocha catchment is located in the southern region of Peru, inside the Vilcanota Urubamba Basin (VUB) system, and provides a range of important ecosystem services that local people depend on in their daily lives. Mapping highland ecosystems such as these is challenging because of cloud cover, and thus large-scale mapping activities are frequently applied. For this reason, there is a lack of studies focused on annual-scale land cover changes that may reveal sudden changes, or expose the interaction of changes between ecosystems. In this study, we identify five different land covers comprising the Sibinacocha catchment, namely glaciers, water bodies, wetlands, pastures, and low-vegetation areas. The evolution of the land cover of these ecosystems is mapped using a Random Forest classification model, which is a supervised machine-learning algorithm developed in Google Earth Engine. We apply it to a 36 year-long stack of Landsat images (Landsat 5, 7, and 8) from 1984 to 2020, using five classification criteria such as different normalized indices and a slope discrimination criteria obtained from SRTM information. Overall results were validated using the Kappa coefficient (K; 0.97) and overall accuracy analysis (97%) both based on collected field data, highlighting a good performance of the Random Forest model at classifying highland ecosystems. The results of the land cover evolution from 1984 and 2020, show significant area changes mainly on glaciers (-35%), wetlands (-17%), and water bodies (+14%) with noticeable trends, and low changes in pastures (+2%) and low-vegetation areas (+8%). For the time period of analysis, we identify an increase less than +0.8ºC in the annual temperature and 20 mm in annual precipitation. Using simple linear regression and correlation analysis, the changes we observe can be explained by the ecosystem responding to a warming climate. As glaciers recede, they are replaced by water bodies and low-vegetation ecosystems, low-vegetation ecosystems have generally become wetter, and wetlands and pastures transition backward and forward depending on their management. With these results, it is possible to understand the ecosystem’s natural evolution, enhanced by external factors, and to observe that it is ultimately conditioned by accelerated glacier retreat in the catchment headwaters.
Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales
Authors: Teufel Bernardo , Carmo Fernanda , Sushama Laxmi , Sun Lijun , Khaliq Naveed , Belair Stephane , Shamseldin Asaad Yahia , Nagesh Kumar Dasika , Vaze Jai
Published: March 2022

The high computational cost of super-resolution (< 250 m) climate simulations is a major barrier for generating climate change information at such high spatial and temporal resolutions required by many sectors for planning local and asset-specific climate change adaptation strategies. This study couples machine learning and physical modelling paradigms to develop a computationally efficient simulator-emulator framework for generating super-resolution climate information. To this end, a regional climate model (RCM) is applied over the city of Montreal, for the summers of 2015 to 2020, at 2.5 km (i.e., low resolution – LR) and 250 m (i.e., high resolution – HR), which is used to train and validate the proposed super-resolution deep learning (DL) model. In the field of video super-resolution, convolutional neural networks combined with motion compensation have been used to merge information from multiple LR frames to generate high-quality HR images. In this study, a recurrent DL approach based on passing the generated HR estimate through time helps the DL model to recreate fine details and produce temporally consistent fields, resembling the data assimilation process commonly used in numerical weather prediction. Time-invariant HR surface fields and storm motion (approximated by RCM-simulated wind) are also considered in the DL model, which helps further improve output realism. Results suggest that the DL model is able to generate HR precipitation estimates with significantly lower errors than other methods used, especially for intense short-duration precipitation events, which often occur during the warm season and are required to evaluate climate resiliency of urban storm drainage systems. The generic and flexible nature of the developed framework makes it even more promising as it can be applied to other climate variables, periods and regions.
Constraints on the lunar magnetic sources location using orbital magnetic field data
Authors: Oliveira Joana S. , Vervelidou Foteini , Wieczorek Mark A. , D. Michelena Marina
Published: March 2022

Orbital magnetic field observations of the Moon show several magnetic anomalies distributed heterogeneously across its surface. These observations and results from paleomagnetic studies on lunar rocks corroborates that the lunar crust is locally magnetized. The origin of these magnetic field anomalies is still debated, as most of them are not related to known geological structures or processes. Some of the current suggestions to explain the origin of the anomalies sources include contamination from impactors that could deliver iron-rich material to the lunar surface, and heating associated with localized magmatic activity that could thermochemically alter rocks to produce strong magnetic carriers. Both hypotheses need however an inducing field to magnetize the lunar crust, and strong evidence from previous studies argues in favor of this being a global magnetic field generated by a core dynamo.

In this work, we aim to elucidate the origin of the magnetic anomalies by constraining the location and shape of the underlying magnetization. We do so by inferring the magnetization geometry from orbital magnetic field measurements using an inversion scheme that assumes unidirectional magnetization while making no a priori assumptions about its shape. This method has been used up to now to infer the direction of the underlying magnetisation but it has not yet been used to infer the geometry of the sources. We test the performance of the method by conducting a variety of synthetic tests using magnetized bodies of different geometries such as basins, dykes, and lava tubes, each corresponding to a different possible origin scenario for the observed magnetic anomalies.  Results from our synthetic tests show that the method is able to recover the location and shape of the magnetized volume. We explore how different input parameters, such as shape, depth, thickness, and field direction influence the method’s performance in retrieving the characteristics of the magnetized volume. Such an analysis can be performed on many lunar magnetic anomalies, including those which are not related to swirls or impact craters, i.e., the mechanisms that have been most studied up to now. This will help elucidate the geological history of the Moon and key features of the lunar dynamo evolution.
The effects of parameter choices in basal sliding laws on a modelled ice flow response to perturbations in forcing
Authors: Barnes Jowan , Gudmundsson Hilmar
Published: March 2022

Ice flow models include processes which cannot be determined from observational data, but must be represented mathematically in order to simulate the physical system. One such process is the interaction between the ice and the bedrock, basal sliding, which enters models in the form of a sliding law describing the relationship between basal drag and velocity. Basal sliding is a major factor in ice flow, and therefore how it is represented is one of the most important modelling decisions.

 

Several sliding laws have been proposed and used in ice flow models, representing different types of bed. In general, these comprise some combination of a Weertman-style power law and Coulomb friction. The equations for sliding laws contain parameters which are usually given constant, uniform values. The responses to perturbations in the ice flow system differ depending on the sliding law used, and on the parameter choices made.

 

In this study, we use the ice flow model Úa to run experiments for a range of different sliding laws, and different values for parameters within these laws. In each case, we test the response of the model to perturbations in the ice shelf melt rate. We investigate the differences between our model outputs, and assess the relationships between sliding law parameter choices and the resulting changes in ice flow.

 

Our model domain covers the Amundsen Sea Embayment, which we break down into separate catchment areas during our analysis in order to capture localised variation in our results.
Spatio-temporal evolution of rift volcanism driven by progressive crustal unloading
Authors: Ferrante Gaetano , Rivalta Eleonora , Maccaferri Francesco
Published: March 2022

Continental rifting can be accompanied by a large amount of volcanism, which is often observed to shift from the inside of the rift basin to its flanks and conversely, but the controls on this variability are still unclear. Maccaferri et al. (2014) proposed that elastic stresses under rifts are dominated by gravitational unloading due to the excavation of the graben. According to this model, off-rift volcanism follows the creation of a stress barrier below the rift that drives dikes diagonally away from the rift axis, or stops their ascent altogether so that they get stuck as lower crustal sills. The Maccaferri et al. (2014) model is however based on simplyfied assumptions that need to be relaxed to further test its validity. In particular, the model neglects the effect of the accumulating crustal intrusions on ascending dikes. Here we build on this model to explain the spatio-temporal evolution of rift volcanism in terms of the reorientation of principal stresses in the crust due to the progressive unloading of a rift basin with time. To do so, we extend the dike propagation boundary element code used by Maccaferri et al. (2014) to account for the stresses generated by previously ascended dikes. We find that volcanism in rift zones starts inside the rift depression for small values of basin depth. The deepening of the rift is accompanied by the development of a stress barrier under the basin which deflects ascending dikes, causing a shift of surface volcanism from the inside to the flanks. The intensification of the barrier due to further deepening of the basin promotes the formation of lower crustal sill-like structures that pile up under the rift, shallowing the depth at which magma is injected. This eventually leads to dikes being injected from above the stress barrier, moving surface volcanism back to the axial part of the rift. We compare the results of our model to observations of evolving volcanism and crustal structure for rifts of different graben width and depth.
Many-hazard Risk Assessment with the CLIMADA Data API
Authors: Stalhandske Zélie , Schmid Emanuel , Steinmann Carmen B. , Kropf Chahan , Bresch David  N.
Published: March 2022

As the climate and the risks of extreme weather to society change, access to tools for researchers and decision makers to assess the possible evolution of impacts should be facilitated. The open-source modelling platform CLIMADA (CLIMate ADAptation) allows to investigate the present and future statistical risk of natural hazards to human and economic systems, from the local to the global scale. One of the latest additions to the platform is an Application Programming Interface (API) providing access to exposure and hazard data to perform risk assessments on a consistent 4km grid. Hazard sets for tropical cyclones, droughts, heat-waves, wildfires, river floods, and crop-yield are, or will imminently be available at a worldwide scale on the API. In addition, region-specific hazards such as European winter storms are available. As for the exposures at risk, both population count and assets can be considered based on the data produced trough the CLIMADA LitPop module.

Owing to the availability of globally consistent hazard and exposures datasets through the CLIMADA API, it is now possible to compute and combine the impacts from several hazards. In this first study making use of the API, we calculate global probabilistic economic impacts for tropical cyclones, river floods and reduced crop yields for historical data, as well as for future time steps based on the RCP2.6 and RCP8.5 climate scenarios. From these hazard sets, we compute probabilistic annual impact sets for each hazard. In the case that impacts are provided on an event-base and not on a yearly basis, the probabilistic annual impact sets are created by randomly sampling the number of events per year following a Poisson distribution. From the impact sets per hazard, we finally quantify the total combined cost in a same year and grid cell in order to investigate temporal and spatial correlations of the different hazards.
BASEveg: A modelling framework integrating vegetation dynamics and river hydro-morphodynamic processes
Authors: Caponi Francesco , Vetsch David F. , Siviglia Annunziato , Vanzo Davide
Published: March 2022

Riparian vegetation and river hydro-morphodynamic processes are strongly interconnected by feedback mechanisms that act at various spatial and temporal scales. Such feedbacks affect water and sediment fluxes along river channels and across floodplains, in turn shaping the river planform style and vegetation structure. In the face of profound changes in climate and increasing anthropogenic pressure, the quantification of these processes is paramount to understand future river dynamics and better design restoration projects and management solutions.

Despite recent advances in river eco-morphodynamic modelling, numerical models including feedbacks between plants, flow, and sediment transport in rivers are still limited. Here we introduce BASEveg, a modelling framework that allows combining the freeware tool BASEMENT, simulating river hydro-morphodynamic processes, and an open-source python module for vegetation growth simulations. The model structure and implementation follow the basic assumption that morphodynamic processes and vegetation growth occur at very different timescales. We consider that over long periods of time the riverbed is essentially stable because of the low flow discharges and modifies only when discharges peak, generating erosion and deposition processes. When the discharge is low enough to expose bare surfaces, vegetation can grow undisturbed until the next high discharge peak. During this time, plants can be uprooted by the flow or buried under sediments.

Here we present a model test case based on the Alpine Rhine river, Switzerland, to illustrate the main functionalities and potentials of BASEveg. The vegetation growth module simulates the plant growth rate depending on the water table level fluctuations during low flow, vegetative periods. This results in a vegetation distribution that well compares with observations and previous modelling results. We show also how the vegetation pattern and riverbed topography co-evolve depending on species-specific traits, which can be simulated within the model. Although the model includes experimental features that still require proper data for calibration and validation, it represents an important step towards the integration of river hydro-morphodynamic processes and vegetation dynamics in common 2D numerical models. The model can be used to understand long-term river morphological trajectories depending on the hydrological regime and climate forcing, helping the design of river restoration projects and management practices. 
Analysis of the spatio-temporal evolution of the seismicity induced by hydraulic fracturing operations in Preston New Road, UK
Authors: Minetto Riccardo , Helmstetter Agnès , Edwards Ben
Published: March 2022

In August 2019 an hydraulic fracturing operation was carried out at the PNR-2 well in Preston New Road, UK. Hydraulic fracturing caused abundant seismic activity that culminated with a ML 2.9 event. This event prompted the operator (Cuadrilla Resources Ltd.) to halt any further stimulation of the well. The seismic activity was recorded by a downhole array of 12 sensors located in a monitoring well (PNR-1z). The operator released a seismic catalog created in real time during the fracturing operation. The catalog consists of 55555 events detected and located with a coalescence microseismic mapping method. The catalog also reports moment magnitudes, but no precise information on the method and on the parameters used to estimate them is available. In our study, we attempt to improve the number of detections and the location accuracy of the events by applying template matching and a double-difference relocation method, respectively. We also recalculate moment magnitudes using spectral fitting to look for any inconsistencies in the real-time catalog. Finally, we use the new information to better understand the spatio-temporal evolution of the seismicity and the dynamics that led to the ML 2.9 event.
Peculiarities of geomagnetic field measurements at Lampedusa island in relation with geomagnetic activity, seasons and tidal modes: possible link with the sea level rise and current flows in the Mediterranean
Authors: Regi Mauro , Guarnieri Antonio , Lepidi Stefania , Di Mauro Domenico
Published: March 2022

In the frame of the geomagnetic depth sounding (GDS) using Single Station Induction Arrows (SSIA), data from an island and nearby coastal sites can provide useful information regarding the surrounding crust structure. In this regard, ultra low frequency (ULF, 1 mHz–5 Hz) waves, originated by solar wind-magnetosphere interactions, represent a natural probing source of the ground from sea-crust surface down to the mantle due to their skin depth in the salt water (1-D infinite half space) of the order of ~104 m for a frequency of ~1 mHz and a conductivity of ~3 S/m. The INGV geomagnetic observatory installed at Lampedusa island (geographic coordinates 35°31′N; 12°32′E, altitude 33 m a.s.l.), and operating since 2007, offers a great opportunity for probing the Earth’s crust in the frame of 1-D models. In this work, geomagnetic field variations collected from 2017 to 2021 are examined by using spectral approaches, providing evidence of the influence of sea salt water with the SSIA approach in relation to different geomagnetic activity levels, seasons and tidal modes. We also study a possible relationship with the sea level variations driven by astronomical tides. A possible link between SSIA variations with water flow modifications, probably linked to the induced electric field of sea salt water flows embedded into the main Earth’s magnetic field, is shown.
Decomposition of long-lived greenhouse gases by atmospheric streamers
Authors: Francisco Hani , Ebert Ute , Fullekrug Martin , Plane John
Published: March 2022

Sulfur hexafluoride (SF6) and carbon tetrafluoride (CF4) are inert gases in the atmosphere that can absorb infrared radiation and affect the climate. They have lifetimes up to 1278 years for SF6 and 50000 years for CF4. The International Panel on Climate Change lists the two as part of the most influential long-lived, well-mixed greenhouse gases, with SF6 having the highest identified global warming potential. Both gases have anthropogenic major sources. SF6 is used as an insulating gas in the electrical power industry, and CF4 is a by-product of aluminum manufacturing. In this study, we question whether atmospheric electricity significantly influences the atmospheric concentrations of these molecules. We aim to investigate SF6 and CF4 decomposition within streamers at different altitudes in the atmosphere, and then estimate the global occurrence rate of such streamers and their impact. To accomplish this, we simulate positive streamers in synthetic air that contains a small concentration of the two gases. From our simulations, we identify relations between streamer properties and the amounts of SF6 and CF4 destroyed, which can be used to estimate the rates of chemical processes in observed streamer events.
Hydrological drivers of bedload transport in an Alpine watershed
Authors: Antoniazza Gilles , Nicollier Tobias , Boss Stefan , Mettra François , Badoux Alexandre , Schaefli Bettina , Rickenmann Dieter , Lane Stuart
Published: March 2022

Understanding and predicting bedload transport is an important element of watershed management. Yet, predictions of bedload remain uncertain by up to several order(s) of magnitude. In this paper, we use a five-year continuous time-series of streamflow and bedload transport monitoring in a 13.4 km2 snow-dominated Alpine watershed in the Western Swiss Alps to investigate the hydrological drivers of bedload transport. Following a calibration of the bedload sensors, and a quantification of the hydraulic forcing of streamflow upon bedload, a hydrological analysis is performed to identify daily flow hydrographs influenced by different hydrological drivers: rainfall, snow-melt, and mixed rain and snow-melt events. We then quantify their respective contribution to bedload transport. Results emphasize the importance of mixed rainfall and snow-melt events, for both annual bedload volumes (77% in average) and peaks in bedload transport rate. Results further show that a non-negligible amount of bedload transport may occur during late summer and autumn storms, once the snow-melt contribution and baseflow have significantly decreased (9% of the annual volume in average). Although rainfall-driven changes in flow hydrographs are responsible for a large majority of the annual bedload volumes (86% in average), the identified melt-only events also represent a substantial contribution (14 % in average). Through a better understanding of the bedload magnitude-frequency under different hydrological conditions, the results of this study may help to improve current predictions of bedload transport, and we further discuss how bedload could evolve under a changing climate through its effects on Alpine watershed hydrology.
Additional climate impacts of overshoot scenarios
Authors: Pfleiderer Peter , Lejeune Quentin , Schleussner Carl-Friedrich , Sillmann Jana
Published: March 2022

We are approaching the 1.5°C temperature goal of the Paris agreement at a worrisome pace. Achieving this global temperature goal is physically still possible but would require drastic greenhouse gas emission reductions as well as the deployment of some level of carbon dioxide removal. Most scenarios that limit global warming to 1.5°C by the end of the century experiment an overshoot – a temporary exceedance of this level of global warming followed by a decrease in global mean temperature once global greenhouse gas emissions become net-negative in the second half of the century.

However, besides a number of well documented tipping points our understanding of the reversibility of climate impacts remains limited. It is indeed not well understood for which climate and sectoral impacts one can expect reversibility or not, and over which time scale it would occur.

Here we attempt to present an overview of changes that an overshoot would bring to the climate system. We analyze standard climate indicators as well as extreme event indicators in overshoot scenarios including the SSP119 and the SSP534 scenarios for a range of CMIP6 models. Comparing climate projections at a fixed warming level before and after global warming has peaked reveals significant differences in local climatic conditions, with precipitation pattern changes being particularly affected. This preliminary investigation will help to identify regions of interest for which the mechanisms that hinder reversibility could be analyzed in more depth in future research.
Multi-station Multivariate Multi-step Convection Nowcasting with Deep Neural Networks
Authors: Chkeir Sandy , Anesiadou Aikaterini , Biondi Riccardo
Published: March 2022

Extreme weather nowcasting has always been a challenging task in meteorology. Many research studies have been conducted to accurately forecast extreme weather events, related to rain rates and/or wind speed thresholds, in spatio-temporal scales. Over decades, this field gained attention in the artificial intelligence community which is aiming towards creating more accurate models using the latest algorithms and methods.  

In this work, within the H2020 SESAR ALARM project, we aim to nowcast rain and wind speed as target features using different input configurations of the available sources such as weather stations, lightning detectors, radar, GNSS receivers, radiosonde and radio occultations data. This nowcasting task has been firstly conducted at 14 local stations around Milano Malpensa Airport as a short-term temporal multi-step forecasting. At a second step, all stations will be combined, meaning that the forecasting becomes a spatio-temporal problem. Concretely, we want to investigate the predicted rain and wind speed values using the different inputs for two case scenarios: for each station, and joining all stations together. 

The chaotic nature of the atmosphere, e.g. non-stationarity of the driving series of each weather feature, makes the predictions unreliable and inaccurate and thus dealing with these data is a very delicate task. For this reason, we have devoted some work to cleaning, feature engineering and preparing the raw data before feeding them into the model architectures. We have managed to preprocess large amounts of data for local stations around the airport, and studied the feasibility of nowcasting rain and wind speed targets using different data sources altogether. The temporal multivariate driving series have high dimensionality and we’ve  made multi-step predictions for the defined target functions.

We study and test different machine learning architectures starting from simple multi-layer perceptrons to convolutional models, and Recurrent Neural Networks (RNN) for temporal and spatio-temporal nowcasting. The Long Short-Term Memory (LSTM) encoder decoder architecture outperforms other models achieving more accurate predictions for each station separately.  Furthermore, to predict the targets in a spatio-temporal scale, we will deploy a 2-layer spatio-temporal stacked LSTM model consisting of independent LSTM models per location in the first LSTM layer, and another LSTM layer to finally predict targets for multi-steps ahead. And the results obtained with different algorithm architectures applied to a dense network of sensors are to be reported.
Changing snow conditions and shrub expansion alter above- and belowground seasonal dynamics in alpine grasslands
Authors: Broadbent Arthur , Bahn Michael , Pritchard William , Newbold Lindsay , Goodall Tim , Guinta Andrew , Snell Helen , Cordero Irene , Michas Antonios , Grant Helen , Soto David , Kaufmann Ruediger , Schloter Michael , Griffiths Robert , Bardgett Richard
Published: March 2022

Climate change is disproportionately impacting mountain ecosystems, leading to large reductions in winter snow cover, earlier spring snowmelt and widespread shrub expansion into alpine grasslands. Yet, the combined effects of shrub expansion and changing snow conditions on abiotic and biotic soil properties remains poorly understood. We used complementary field experiments to show that reduced snow cover and earlier snowmelt have effects on soil microbial communities and functioning that persist into summer. However, ericaceous shrub expansion modulates a number of these impacts and has stronger belowground effects than changing snow conditions. Ericaceous shrub expansion did not alter snow depth or snowmelt timing, but did increase the abundance of ericoid mycorrhizal fungi and oligotrophic bacteria, which was linked to decreased soil respiration and nitrogen availability. Moreover, by combining molecular sequencing, enzyme assays, greenhouse gas flux measurements, soil biogeochemical analyses, and 15N labelling, we show that reduced winter snow cover and shrub expansion alter the seasonal dynamics of plant growth (i.e., net ecosystem exchange and plant N-uptake), with important consequences for the seasonal dynamics of soil microbial communities, their functioning, and alpine biogeochemical cycles. In conclusion, our findings suggest that changing winter snow conditions have cross-seasonal impacts on biotic and abiotic soil properties, but shifts in vegetation can modulate belowground effects of future alpine climate change.
Comparison of simulated and radar-determined accumulation and melt at a high glacier accumulation site in the Alps
Authors: Lambrecht Astrid , Heilig Achim , Mayer Christoph
Published: March 2022

The quantification of snow accumulation and the temporal evolution of the snow pack is essential when investigating the mass balance conditions of mountain glaciers. In particular, accumulation regions become smaller due to the gradual increase of the equilibrium line, thus reducing mass input into the glacier system. This will have severe consequences on ice flux and thus the mass balance conditions across many mountain regions worldwide. The mass redistribution within the accumulation regions is considerably influenced by migration of melt water in the snow and firn pack and the induced mass and density changes. Here, we study snow and firn processes at a high mountain accumulation plateau on 3470 m asl at Vernagtferner, Austria. Vernagtferner is a major glacier in the drainage basin of Rofenache, with an area of about 6.9 km², covering altitudes between 2900 m and 3550 m. A snow monitoring station, including an upward-looking ground penetrating radar (upGPR) was installed at the highest accumulation basin in 2018. This station allows the continuous determination of the snow pack stratigraphy and of the snow water equivalent (SWE) (Heilig et al. (2009, 2010), Schmid et al. 2014, Heilig et al., 2015). We compare numerical simulations of the 1-dimensional snow cover model SNOWPACK (Bartelt and Lehning, 2002), driven by automatic weather station data, with continuous observations of the installed upGPR system and bi-annual in-situ data. The analysed upGPR data enable continuous evaluation of the SNOWPACK simulations over several melt and accumulation seasons. The upGPR data show that even at high elevations frequent melt-freeze crusts develop during the accumulation period. Even though the crusts are several centimetres, melt water rapidly percolates trough these layers, once the snow pack reaches isothermal conditions in late spring. The simulation results demonstrate, that SNOWPACK is able to reproduce this fast advance of the melt front accurately, while the up-GPR measurements provide an independent proof of the model performance. These measurements also show that firn layers (previous summer surfaces) block water infiltration into depth only for a very short period, indicating that SWE measurements of glacier accumulation only provide realistic values, if carried out before or just at the onset of spring melt. This feasibility study provides important indication on how to extend such studies to larger glacier systems, also in less monitored regions, where in-situ data might be sparse.