bluuebunny/crossref_metadata_2025 huggingface dataset:
Extract an accurate river network
Authors: Chen Qiuyang , Mudd Simon , Attal Mikael , Hancock Steven
Published: February 2023

Due to the limited resolution of freely available digital elevation models (DEM), DEM-derived river products cannot provide accurate flow lines in inland and lowland areas. Stream burning is used to improve the accuracy of extracted flowlines in addition to other flow routing algorithms (from LSDTopotools). Using the latest DEM products (Copernicus 30m DEM and FAB 30m DEM) and other remote sensing data (Sentinel-1, Sentinel-2), the framework is tested on different geomorphological features (meanders, branches) and weather conditions (with and without clouds). The results show a significant improvement in the accuracy of the flow lines compared with existing global hydrography products.
An oscillating Atlantic Meridional Overturning Circulation during the last glacial period
Authors: Romé Yvan , Ivanovic Ruza , Gregoire Lauren
Published: February 2023

Abrupt climate changes over the last glacial period (~ 115 to 12 thousand years ago) are often associated with reorganisation of the Atlantic Meridional Overturning Circulation (AMOC). It has been suggested that the AMOC can exist in more than one stable mode, but the mechanisms leading to switches between different regimes are still not understood. It is also unclear how disruptions of the ocean circulation are connected to millennial-scale climate variability, such as Dansgaard-Oeschger events or abrupt transitions during the late last deglaciation. 

Most attempts at theorising glacial millennial-scale variability have involved looking at heat and salt transfers between the subtropical and subpolar gyres. This is often referred to as the ‘salt oscillator’ mechanism, which in turn controlled the intensity of the North Atlantic current. We propose that the salt oscillator is in fact part of a larger motion combining harmonic and stochastic dynamics spanning through all components of the climate system when triggered by an initial excitation. Only under certain combinations of boundary conditions and forcings can multiple stable states coexist, sometimes leading to the activation of a pseudo-oscillating regime for thousands of years. 

Based on a new set of last glacial maximum (~21 thousand years ago) simulations that oscillate when forced with snapshots of the early last deglaciation meltwater history, we propose a new way of visualising the stability of the AMOC and its shifts between different stable modes. We provide a detailed analysis of the heat and salinity tendencies in a comprehensive description of the different oscillating modes. Finally, we discuss how the freshwater forcing framework fits into the broader theory of glacial abrupt climate changes.
Towards the effective autoencoder architecture to detect weather anomalies
Authors: Fister Dusan , Pérez-Aracil Jorge , Peláez-Rodríguez César , Drouard Marie , G. Zaninelli Pablo , Barriopedro Cepero David , García-Herrera Ricardo , Salcedo-Sanz Sancho
Published: February 2023

To organise weather data as images, pixels represent coordinates and magnitude of pixels represents the state of the observed variable in a given time. Observed variables, such as air temperature, mean sea level pressure, wind components and others, may be collected into higher dimensional images or even into a motion structure. Codification of formers as a spatial and the latter as a spatio-temporal allows them to be processed using the deep learning methods, for instance autoencoders and autoencoder-like architectures. The objective of the original autoencoder is to reproduce the input image as much as possible, thus effectively equalising the input and output during the training. Then, an advantage of autoencoder can be utilised to calculate the deviations between (1) true states (effectively the inputs), which are derived by nature, and the (2) expected states, which are derived by means of statistical learning. Calculated deviations can then be interpreted to identify the extreme events, such as heatwaves, hot days or any other rare events (so-called anomalies). Additionally, by modelling deviations by statistical distributions, geographical areas with higher probabilities of anomalies can be deduced at the tails of the distribution. The capability of reproduction of the (original input) images is hence crucial in order to avoid addressing arbitrary noise as anomaly. We would like to run experiments to realise the effective architecture that give reasonable solutions, verify the benefits of implementing the variational autoencoder, realise the effect of selecting various statistical loss functions, and find out the effective architecture of the decoder part of the autoencoder.
Geothermal heat extraction from abandoned mines
Authors: van Hunen Jeroen , Mouli-Castillo Julien , Sweeney Alexandra , Chapman Sophie , Adams Charlotte , Townsend David
Published: February 2023

Water from abandoned, flooded mines can form an excellent low-enthalpy, renewable long-term heat source, provided it is managed properly. Its sustainability, however, is only as good as its proper management. The poor understanding of the condition of the mine, post-closure makes the investment in these projects risky compared to other alternatives. Our modelling allows us to explore uncertainties and reduce a variety of project risks.By combining numerical and analytical methods with digitised legacy mine data, we developed a tool to estimate the variations in the abstraction water temperature over the lifetime of a project. We couple the heat transfer approximation method originally proposed by Rodriguez and Diaz (2009) to that of flow in a pipe network as described by Todini and Pilati (1987). We refine the original heat transfer approximation by accounting for a flow regime specific heat transfer coefficient between the rock mass and the water, as prescribed by Loredo et al. (2017). We also develop a novel weighting function to account for the interference between adjacent mine galleries.This method is applied to investigate the scenario in which multiple users will extract heat from the same mine water block. We investigate the interference resulting from heat extraction at multiple locations, using a mine system from the North East of England as a study case. The results of this study provide constraints on the maximum mine water extraction rates and proximity of the different users. The poorly constrained connectivity (through mine shafts, connecting roadways or porous flow) between mine workings from different coal seams is shown to be one of the most significant uncertainties in assessing the feasibility of a mine system as a sustainable heat source.References:Loredo C, Banks D, Roqueñí N. Evaluation of analytical models for heat transfer in mine tunnels. Geothermics 2017; 69; 153-164.
Rodriguez R and Díaz M. Analysis of the utilization of mine galleries as geothermal heat exchangers by means a semi-empirical prediction method. Renewable Energy 2009; 34(7), 1716-1725.
Todini E and Pilati S. A gradient method for the analysis of pipe networks. Computer app. In water supply 1987; 1-20, v1.
Site specific emulators for tsunami run-up simulations
Authors: Storrøsten Erlend , Gibbons Steven , Løvholt Finn
Published: February 2023

Local Probabilistic Tsunami Hazard Analysis (PTHA) aims to quantify the probability distribution of inundation intensity parameters, such as maximum flow-depth, at a given location over a specified period of time. In a Monte Carlo framework such an analysis is dependent on the simulation of a large number of scenarios. A particularly expensive step, from a computational point of view, is the solving of the nonlinear shallow water equations associated with the tsunami run-up. This problem is even more pronounced in the context of Tsunami Early Warning and Probabilistic Tsunami Forecasting (PTF). A site specific (local) tsunami run-up emulator, trained on precalculated simulation results, enables rapid estimation of inundation maps allowing an assessment of a large number of scenarios with limited computational resources. While high dimensional input and output, dependence on topography and nonlinear dynamics has made the problem intractable for traditional statistical methods, the problem has recently been approached using new techniques developed within the field of Machine Learning. In this work we consider the problem of predicting onshore maximal flow-depth based on timeseries associated with simulated offshore gauge measurements. The site of study is the town of Catania in eastern Sicily. The dataset comprises more than 32,000 tsunami simulations for different earthquake sources in the Mediterranean Sea. Promising results have been obtained using only a small fraction of the total number of simulations as training data. The ML-based inundation predictions for locations close to the water's edge, which are flooded in many of the scenarios, show excellent correspondence with the numerical simulation results. Predicting inundation at locations further inland, which are flooded in only a small number of the simulations, is more challenging.
Development of a phase-resolving nearshore wave model for run-up assessment
Authors: Mihami Fatima-Zahra , Roeber Volker
Published: February 2023

Quantitative assessments of wave-driven run-up and inundation scenarios have been of high interest to coastal residents, engineers, emergency managers, and scientists. Though it is possible to obtain general estimates of wave run-up based on empirical formulae, such approximations are often of limited applicability, especially in areas with complex shorelines and bathymetries. Due to the substantial increase in computing power, numerical models have emerged as reliable and cost-effective tools for nearshore wave assessment. Accurate computation of wave run-up requires the numerical models to account for phase-dependent processes that can pose challenges to the quality and computational complexity of the numerical solution. We present the strategic development of a new Boussinesq-type model with the objective of building a reliable tool for operational run-up forecasting systems.  The effort has led to a computer code where multiple fundamental features are optimized for both computational efficiency and accuracy to achieve fast and reliable solutions of nearshore waves. This requires the model to answer several challenges including wave breaking, numerical diffusion, moving boundaries, and computational complexity. To solve for these contradicting requirements, the model relies on a lean numerical structure that supports mass and momentum conservation across discontinuities and long-distance propagation of irregular waves. An eddy viscosity closure approach based on temporally and spatially varying turbulent kinetic energy alleviates the chronic limitation of depth-integrated dispersive solutions to wave breaking. In an attempt to achieve real-time run-up computations over large domains, the new model efficiently uses commodity graphics cards hardware, and targeted grid refinement through locally nested domains.Finally, the new model has been verified and validated with standard benchmark tests for problems involving commonly encountered nearshore wave processes that are crucial for accurate run-up computations. 
Evaluation of Native Earth System Model Output with ESMValTool
Authors: Schlund Manuel , Hassler Birgit , Lauer Axel , Andela Bouwe , Bock Lisa , Jöckel Patrick , Kazeroni Rémi , Loosveldt Tomas Saskia , Medeiros Brian , Predoi Valeriu , Sénési Stéphane , Servonnat Jérôme , Stacke Tobias , Vegas-Regidor Javier , Zimmermann Klaus , Eyring Veronika
Published: February 2023

Projections from Earth system models (ESMs) are essential to allow for targeted mitigation and adaptation strategies for climate change. ESMs are state-of-the-art numerical climate models used to simulate the vastly complex Earth system including physical, chemical, and biological processes in the atmosphere, ocean, and on land. Progress in climate science and an increase in available computing resources over the last decades has led to a massive increase in the complexity of ESMs and the amount of data and insight they provide. For this reason, innovative tools for a frequent and comprehensive model evaluation are required more than ever. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), an open-source community diagnostic and performance metrics tool.Originally designed to assess output from ESMs participating in the Coupled Model Intercomparison Project (CMIP), ESMValTool expects input data to be formatted according to the CMOR (Climate Model Output Rewriter) standard. While this CMORization of model output is a quasi-standard for large model intercomparison projects like CMIP, this complicates the application of ESMValTool to non-CMOR-compliant data, like native climate model output (i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute). Recently, ESMValCore, the framework underpinning ESMValTool, has been extended to enable reading and processing native climate model output. This is implemented via a CMOR-like reformatting of the input data during runtime. For models using unstructured grids, data can optionally be regridded to a regular latitude-longitude grid to facilitate comparisons with other data sets. The new features are described in more detail in Schlund et al., 2022 (https://doi.org/10.5194/gmd-2022-205) and in the software documentation available at https://docs.esmvaltool.org/en/latest/input.html#datasets-in-native-format.This extension opens up the large collection of diagnostics provided by ESMValTool for the five currently supported ESMs CESM2, EC-Earth3, EMAC, ICON, and IPSL-CM6. Applications include assessing the models’ performance against observations, reanalyses, or other simulations; the evaluation of new model setups against predecessor versions; the CMORization of native model data for contributions to model intercomparison projects; and monitoring of running climate model simulations. Support for other climate models can be easily added. ESMValTool is an open-source community-developed tool and contributions from other groups are very welcome.
Data infrastructure for nitrogen compound emissions monitoring
Authors: Bertocci Daniel , Celikkol Burcu , Zhuang Shaojie , Fabius Jasper
Published: February 2023

Emissions of nitrogen compounds, including nitrogen dioxide (NO2) and ammonia (NH3), have significant impacts on air quality and the environment. To effectively monitor the spatial and temporal variability of these emissions and the efficacy of emission mitigation measures, OnePlanet Research Center is developing a low-cost sensor system to monitor outdoor NO2 and NH3concentrations. This sensor system is designed to be deployable in fine-grained networks to accurately capture the dispersion from an emitting source. The deployment of multitudes of such sensor systems will result in large volumes of data. For this purpose, we developed a data infrastructure using the OGC SensorThings API and TimescaleDB, a time-series database extending PostgreSQL. This infrastructure allows for the efficient storage, management, and analysis of large volumes of spatiotemporal data from various sources, such as air quality monitoring networks, meteorological data, and agricultural practices. We demonstrate the potential of this infrastructure by using it in citizen science project COMPAIR, combining data from various sensors to gain insights on the air quality impact of urban circulation policies. The resulting data platform will facilitate the development of decision support tools and the implementation of targeted emission reduction strategies.
On the relationship between Atmospheric Blocking and Arctic Amplification
Authors: Cadau Marco , Fosser Giorgia , Bordoni Simona , Sannino Gianmaria , Gaetani Marco
Published: February 2023

Atmospheric blocking is known to be one of the most important drivers of large-scale atmospheric variability at mid-high latitudes. Blocking events consist of a disruption and/or deceleration of the mean westerly circumpolar flow, and are generally associated with large-scale high-pressure patterns, which may be connected with the occurrence of climate extremes, such as heat waves and cold spells. Atmospheric dynamics in the Arctic region may be very important in shaping the spatial and temporal patterns of blocking at mid-high latitudes, and consequently the occurrence of associated climate extremes. In particular, Arctic Amplification (AA), namely the recent amplified warming in the Arctic region compared to lower latitudes, has recently been argued to have an impact on blocking patterns and behaviour at mid-high latitudes.The objective of this study is to investigate the most relevant mechanisms playing a role in the relationship between blocking and Arctic Amplification, by analysing the variability and frequency of the associated spatial patterns at various timescales using variables from the ERA5 reanalysis dataset for the time interval 1959-2022. Blocking events are detected based on geopotential height gradients between mid- and high-latitude regions, while Arctic Amplification is quantified as the difference of 1000hPa temperature between high and mid latitudes.The climatological number of events per year and their average lifetime, along with the long-term trends and their relationship with the AA are analysed. Furthermore, possible mechanisms linking blocking variability and the AA are explored through the analysis of the jetstream dynamics and teleconnection patterns in the Northern Hemisphere.
pyESD: An open-source Python framework for empirical-statistical downscaling of climate information
Authors: Mutz Sebastian G. , Boateng Daniel
Published: February 2023

The nature and severity of climate change impacts varies significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has lead to an increase in the coupling of Empirical Statistical Downscaling (ESD) models to General Circulation Model (GCM) simulations of future climate. Here, we present a new open-source Python package (pyESD; github.com/Dan-Boat/PyESD) that implements several Perfect Prognosis ESD (PP-ESD) methods and the whole downscaling cycle. The latter includes routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualisation, and more. The package includes a collection of well-established Machine Learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, hyperparameter optimization, etc., in relatively few lines of codes. The package is highly modular and flexible, and allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed or even glacial retreat. We demonstrate the effectiveness of the new PP-ESD framework by generating station-based downscaling products of precipitation and temperature for complex mountainous terrain in Southwest Germany.
Landslide probability in the German low mountain regions under climate change conditions
Authors: Nissen Katrin , Wilde Martina , Ulbrich Uwe , Damm Bodo
Published: February 2023

The influence of meteorological (pre-) condition on landslide probability in the German low mountain regions is assessed and effects arising from climate change are investigated. The landslide events analysed for this study are taken from the landslide database for Germany (Damm and Klose, 2015) and from an event inventory from the German railway company (Deutsche Bahn). We follow two different approaches in order to determine the influence of atmospheric conditions on hillslope failure.The first approach is based on weather types. Each day is assigned one of 28 Lamb-style weather types. The meteorological variables used to classify the weather types are sea level pressure and anomalies of the atmospheric water content. We were able to identify 4 patterns associated with a statistically significant increase for landslide frequency. The climate change signal of the frequency for the occurrence for these weather types is investigated in a multi-model ensemble of regional climate simulations (EURO CORDEX). The majority of the models shows a decrease in the frequency of those relevant patterns under RCP8.5 scenario conditions. In most models this decrease is, however, not statistically significant. The second approach is based on logistic regression. The logistic regression model was fitted using meteorological observations close to the landslide sites. Conditions at the day of the event as well as the pre-conditions from the days leading up to the event were considered. In order to select the best statistical model we tested a large number of physically plausible combinations of meteorological predictors. Each model was checked using cross-validation. The decision on the final model was based on the value of the logarithmic skill score and on expert judgement. As relevant predictors we identified daily precipitation, frost, and a soil moisture proxy determined from multi-day accumulated precipitation and potential evapotranspiration. The climate change signal is determined by applying the statistical model to the output of a multi-model ensemble of climate scenario simulations. Damm, B. and Klose, M. (2015): The landslide database for Germany: Closing the gap at national level, Geomorphology, 249, 82-93, https://doi.org/https://doi.org/10.1016/j.geomorph.2015.03.021. 
Recent progress in land surface models related to the hydrological cycle
Authors: Cuntz Matthias
Published: February 2023

What is the difference between a hydrologic model and a land surface model (LSM)? While hydrologic models concentrate on water fluxes and stores, LSMs describe the coupled energy, water and carbon cycles. There are also little conceptual LSMs so that they can best be compared to so-called process-based hydrologic models. Quite a few of the LSMs were developed as part of Earth System Models. Their primary output variables are hence the exchange fluxes with the atmosphere and they are often operated on continental to global scale, which implies very coarse spatial resolutions compared to hydrologic models.Here I will describe how state-of-the-art LSMs describe the water fluxes and how the fluxes are evaluated. I will outline current developments in the LSM community, focusing on the developments related to the hydrologic cycle. I will discuss current trends amongst developers of LSMs and problems that originate from these trends. I will also point to the challenges that come from ever increasing model resolutions. I will discuss in this context the scaling issue of, for example, soil parameters and how specific choices lead to problems in other parts of the LSM.
 Classification of flying insects in NEXRAD polarimetric weather radar using machine learning and aphid trap data
Authors: Kwakye Samuel , Kalesse-Los Heike , Maahn Maximilian , Seifert Patric , van Klink Roel , Wirth Christian , Quaas Johannes
Published: February 2023

Over the past decades, studies have observed strong declines in biomass and individual numbers of especially flying
insects. Despite strong evidence on the extent of these declines, there are many locations where no surveys of insect biomass
or abundance are available. Radar is known to provide quantitative estimates of flying insect biomass and abundance, and can
therefore be used to fill knowledge gaps in space and time. However, weather radar is designed to observe meteorological targets
rather than insects, thus the derivation of insect echoes needs special techniques. For the automation of insect retrieval methods
from weather radars artificial intelligence approaches are well suited. In this study, radar echoes from Level-II (Base) Data and
the Hydrometeor classification of the Level-III products from Next Generation Weather Radar (NEXRAD) are distinguished
and classified from a machine learning approach at Morris, Illinois, and Ames, Iowa. Weekly aphid counts from suction traps
are used as validation data. The variability and distribution of the insect echoes are assessed. Low variability of differential
reflectivity is found for precipitation scatterers and centered around zero at Ames but peaks were negative at Morris. The
differential reflectivity distributions caused by insects are broad with higher medians at Ames. The reflectivity distribution of the
scatterers was distinctive. Decision tree, random forest, and support vector machine models were generated to distinguish seven
combinations of scatterers. Generated models are cross-tested on the alternate station. Decision trees had the best accuracy to
distinguish all the scatterers at Morris and Ames. Random forest and decision tree classifiers are seen to perform reasonably
better in distinguishing the radar signals. This study presents the initial steps of distinguishing insect echoes, and evaluation of
insect concentration.
First results on interplanetary electron events obtained by joint observations of remote-sensing and in-situ instruments on Solar Orbiter
Authors: Warmuth Alexander , Schuller Frederic , Gómez-Herrero Raúl , Rodríguez-Pacheco Javier , Carcaboso Fernando , Krucker Sam , Pacheco Daniel , Wimmer-Schweingruber Robert , Kollhoff Alexander , Dresing Nina , Fedeli Annamaria , Paipa David , Maksimovic Milan , Vilmer Nicole , Barczynski Krzysztof , Podladchikova Olena , Mason Glenn , Rouillard Alexis ,   Joint STIX-EPD-RPW-EUI Working Group
Published: February 2023

Impulsive electron events observed in interplanetary space are believed to be generated by acceleration in solar flares. This notion has been supported by correlations between the characteristics of energetic electrons detected in-situ near 1 au and those in solar flares derived from hard X-ray observations. However, the details of this relation are still unclear, presumably because of the complex combination of acceleration, injection, and transport effects that are involved.We present the first statistical results on impulsive electron events obtained by joint observations of remote-sensing and in-situ instruments on Solar Orbiter. We use the suite Energetic Particle Detector (EPD) to measure the properties of the electrons (time profile, anisotropy, maximum energy, inferring the injection time at the source, etc.), as well as to determine the particularities of the composition in the suprathermal energy range. Also, X-ray observations from the Spectrometer/Telescope for Imaging X-rays (STIX) constrain the energetic electrons in the solar flare in terms of timing, spectrum, and location. Type III radio bursts detected by the Radio and Plasma Waves (RPW) instrument are used to link the nonthermal X-ray peaks to the interplanetary electron beams. Finally, the Extreme Ultraviolet Imager (EUI) provides context on the flare evolution. We use a large event sample obtained during the first 2.5 years of the Solar Orbiter mission, which covers a wide range of radial distances ranging from as close as 0.33 au to 1.02 au.
Glass Transition Temperature Prediction via Machine Learning
Authors: Armeli Gianluca , Peters Jan-Hendrik , Koop Thomas
Published: February 2023

Knowledge of the glass transition temperature of molecular compounds in atmospheric aerosol particles is important for estimating their viscosity, which directly influences chemical reaction kinetics and phase state. While there is a great diversity of organic compounds present in aerosol particles, experimental glass transition temperatures are known of only a minor fraction of them. Therefore, we have developed a machine learning model in Python designed to predict the glass transition temperature of organic molecular compounds based on molecule-derived input variables. The extremely randomized trees (extra trees) procedure was chosen for this objective. Two approaches using different sets of input variables were followed. The first one uses the number of predefined functional groups present in the compound, while the second one generates descriptors from a SMILES (Simplified Molecular Input Line Entry System) string. For improved results both approaches can be combined with the melting temperature of the compound as an additional input variable, if known. The results show that the SMILES-based predictions had a slightly lower mean absolute error (MAE), but both approaches had a similar MAE of about 12-13 K. Furthermore, we also show that its performance exceeds that of previous parametrizations developed of this purpose and performs better than existing machine learning models. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science.
Monitoring of drought in the Netherlands in an online portal
Authors: van Huijgevoort Marjolein , Brakkee Esther , van den Eertwegh Gé , Vonk Erwin , van Deijl Dion , Bartholomeus Ruud
Published: February 2023

In 2018-2020 water managers in the Netherlands were confronted with extreme drought. This event had a large impact on nature, agriculture, shipping and drinking water supply. To better anticipate dry conditions and improve water management during a drought, up-to-date and accurate information about the meteorological and hydrological situation is crucial. During the 2018 drought it became clear that current information about groundwater levels was scattered across many different organisations. In addition, each organisation had different methods to compare current groundwater levels with historical data to indicate the severity of the drought event. There was a clear need for an uniform indication of drought severity.We developed an online information portal with up-to-date measurements for precipitation and groundwater levels. To quantify the drought severity, the Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiraton Index (SPEI) and Standardized Groundwater Index (SGI) are determined. The availability of long-term records (30> years) of groundwater observations is limited for most regions in the Netherlands. Therefore, the SGI is based on simulations with a time series model for all locations for the same period (27 years). Time series models are developed for 5818 wells with observations. Several criteria have been applied to evaluate the time series model, for example, a minimum value of the explained variance, resulting in 1931 wells for which SGI values are calculated. We have also compared SGI values directly derived from observations with the SGI values from simulated groundwater levels for locations with longer time periods. This comparison indicated that due to errors or missing values in observations, the SGI values from simulations are more reliable to gain a global overview of the drought situation.By combining the information on meteorological and hydrological drought in one decision-support system (www.droogteportaal.nl), water managers and stakeholders can now get an up-to-date overview of the current situation. Due to the uniform determination of drought severity, regions within the Netherlands can be compared. This can help to implement targeted water management decisions for adaptation measures for mitigating drought impacts. Part of the information of the portal is also included in the national drought monitor of Rijkswaterstaat (Dutch Ministry of Infrastructure and Water Management). At the moment, the portal gives forecasted information for 7 days, but the data provides an excellent opportunity to include forecasts on longer timescales ((sub-)seasonal) to improve water management.
The role of antecedent conditions in the generation of large floods using long continuous simulations
Authors: Staudinger Maria , Kauzlaric Martina , Viviroli Daniel
Published: February 2023

Continuous simulation has proven to be a promising base for flood frequency analysis since it avoids some of the shortcomings of other methods, such as assumptions about antecedent conditions or omission of relevant processes. In the EXAR project (hazard information for extreme flood events on the rivers Aare and Rhine), we have elaborated long continuous simulations of streamflow using a hydrometeorological modeling chain. This chain consists of a stochastic weather generator that provides precipitation and temperature series to a hydrological model, whose outputs are finally processed with a hydrological routing system, including and emulating the effect of regulated lakes, bank overflow and floodplain retention. As a result, distinctively long (several 100’000 years) continuous simulations are available at hourly resolution.These simulations do not only cover streamflow but also other model internal fluxes and such as snow pack and soil moisture storage. With that, they allow to infer which hydro-meteorological story lines lead to extreme floods, i.e. floods with return periods of 100 years and higher. The story lines cover the important aspects of antecedent conditions, triggering precipitation, and their spatial and temporal interaction from the sub-catchment scale up to the large basin scale. The resulting story lines already cover a very broad range of possibilities due to the recombination of observations by the stochastic weather generator and the continuous simulation. They may help to further develop targeted story lines beyond what we already observed in changing climatic conditions.
Possible causes for regional zero emissions commitment signals
Authors: Hohn David , Martin Torge , Mengis Nadine , Monteiro Estela
Published: February 2023

In scenarios with abrupt cessation of CO2 emissions (ZECMIP) the committed warming is expected to be 0±0.3K [1]. It is imperative to understand and narrow this uncertainty range because it is similar in size to the remaining allowable warming until 1.5K. We have shown that temperature changes up to 3K are present at regional scales [2]. Furthermore significant differences between models are observed which are important to understand the uncertainties in the zero emissions committed warming.Analysing the ZECMIP simulations of nine ESMs, we identify common climate patterns and notable differences in an idealised zero CO2 emissions scenario. We distinguish between patterns within and outside of natural model climate variability, and will present first results for likely causes in commonalities linked to ocean circulations and predominant climate modes. References:[1] A. H. MacDougall et al., Is There Warming in the Pipeline? A Multi-Model Analysis of the Zero Emissions Commitment from CO2, Biogeosciences 17, 2987 (2020).[2] A. H. MacDougall, J. Mallett, D. Hohn, and N. Mengis, Substantial Regional Climate Change Expected Following Cessation of CO2 Emissions, Environmental Research Letters 17, 114046 (2022).
Shear resistance and near-field stresses on rough faults
Authors: Tal Yuval , Wise Lior
Published: February 2023

Natural faults are rough at all scales and can be described with fractal geometry. This deviation from planarity results in geometric asperities and a heterogeneous stress field. Analytical and numerical studies have shown that roughness introduces additional shear resistance on the fault and promotes failure in the medium surrounding the fault because of the elevated stresses. These studies generally assume a small slip on the fault, i.e., much smaller than the minimum roughness wavelength, λmin. It is important to examine the effects of roughness on shear resistance and near-fault stresses at large sliding, as well as the assumptions incorporated in the derivation of the analytical solutions.In this study, we examine the effects of fault geometry on the shear resistance and near-fault stresses at large sliding numerically, using a method that is based on the mortar finite element formulation, in which non-matching meshes are allowed across the fault, and the contacts are continuously updated. This enables modeling slip larger than λmin and the overriding of asperity contacts. We begin with simulations of an elastic medium and show that, indeed, the roughness results in large and heterogeneous stresses on and off the faults, which increase with the roughness level. However, except for small slip values, the increase of shear resistance with slip is much smaller than the linear increase predicted by the analytical models, which assume small and uniform slip. For self-similar geometry, with Hurst exponent of H = 1, the average shear resistance increases with slip at a decreasing rate. For self-affine geometry, with H < 1, it initially increases with slip, then decreases at a slip larger than λmin /2. Although overriding of asperities is allowed in the simulations, as slip increases, significant stress concentrations are developed on the fault, which may not be realistic for natural rock surfaces. To account for that, we implement wear laws into the method and model the evolution of stresses during a quasistatic slip and cycles of dynamic earthquakes. The wear process redistributes and bounds the stresses on the fault and allows a more realistic characterization of stress distribution near the fault.
Deriving the triggering potential in the Lower Rhine Embayment using background seismicity near Weisweiler, Germany
Authors: Roth Marco Pascal , Harrington Rebecca M , Knapmeyer-Endrun Brigitte , Finger Claudia , Dietl Marco
Published: February 2023

The Earth’s crust is permeated with faults and fractures due to its long tectonic history. Faults and fractures not only act as zones of weakness but can increase permeability and act as conduits to circulate fluids in the underground, making them ideally suited for geothermal energy production. However, human-induced changes to in-situ pressure fields have a documented history of leading to fault (re-)activation in the form of earthquakes (seismic slip) or aseismic slip that does not generate measurable ground motion.This study aims to quantify the current background earthquake activity, the spatiotemporal relationship to the seismotectonic setting, and the remote earthquake-earthquake triggering propensity in the Lower Rhine Embayment (LRE) in western North-Rhine Westphalia. The study area is targeted for extensive exploration activities focused on geothermal energy production. While regional mean slip rates do not exceed 0.1 mm/yr, paleo-seismic studies suggest that the normal faulting system has hosted a series of ~14 earthquakes of Mw > 5.0 since the 14th century, including the 1992 Mw 5.3 Roermond earthquake. Therefore, estimating background seismicity rates independent of anthropogenic stress perturbation is one important element in developing strategies to minimize the probability of felt earthquakes caused by industrial activity. We evaluate waveform data from a temporary deployment of 48 seismic stations operating between July 2021 and May 2022 in a radius of roughly 10 km around a future exploration well drilling site in Weisweiler. We implement a machine learning-based earthquake detection algorithm, SeisBench, to detect earthquakes and denoise the continuous seismic waveforms (DeepDenoiser), estimate P- and S-phase arrivals (PhaseNet and Generalised Phase Detection; GPD), and associate earthquake phases using a Bayesian Gaussian mixture model (GaMMA). We locate 81 local earthquakes to complement the 14 recorded in the Earthquake Observatory Bensberg (BNS) catalog for a total of 95 seismic events recorded between July 2021 and December 2021 with magnitudes ranging from 0 < ML < 1.3. In addition, we use the continuous BNS catalog to evaluate the dynamic triggering potential in the LRE starting in 1990. We select 20 teleseismic mainshocks with M > 6 (1990 – 2015) and M > 7 (2016 – present), as well as the 1992 Roermond Mw 5.3 due to the high amplitude shaking it caused within the study area. Preliminary remote dynamic triggering results suggest that the passing surface waves of the July 2021 M 8.2 Chignik, Alaska earthquake may have triggered a seismic sequence of about 16 locatable earthquakes. The migrating aftershock sequence of the Roermond earthquake also suggests the mainshock caused limited dynamic triggering within the study area, which lies outside of the classical aftershock zone of ~2-3 fault lengths.
Integrating green and grey measures for rockfall protection: technical and economic aspects
Authors: Bruzzese Stefano , Blanc Simone , Brun Filippo , Taboni Battista , Umili Gessica , Ferrero Anna Maria
Published: February 2023

Forests can effectively mitigate rockfall risk in mountainous contexts, acting as “nature-based solutions” (NBS). However, the use of artificial structures, for example, rockfall barriers, is usually necessary and complementary to ensure continuous and effective protection of specific elements at risk. In this work, we evaluate the forest protection service and the complementary effectiveness between NBS and rockfall barriers. The case study analysed, known as Alpe di Bazena, is located in the Italian Alps (municipality of Breno); it is a coniferous forest of about 8 hectares that, together with existing flexible barriers, protect from recurrent rockfall phenomena the underlying provincial road SP 345. At present, the forest partly fulfils its protective role, having been damaged by storm Vaia in 2018. Results from recent studies focused on the identification of rockfall source areas and block volume distribution were used for characterizing rockfall phenomena. For the economic evaluation of the forest protection service, the ASFORESEE model was used and a scenario analysis was carried out with four different levels of protection desired by stakeholders: 25, 50, 75 and 100%. The results show the effectiveness of the forest protection for all scenarios, with an estimated annual unit value of approximately 7,000 € ha-1 y -1 for both the first three scenarios, where the role played by the forest is sufficient, and in the last scenario, where an undersized rockfall barrier is required to complement the forest's action. This study proves quantitatively that the integration of green and grey measures could represent an optimization strategy in terms of costs and environmental benefits when dealing with rockfall phenomena.
Dynamics of pulsatory magma discharge at Fagradalsfjall volcano during Jul-Aug 2021: insights from observations, tremor locations and numerical models.
Authors: Melnik Oleg , Soubestre Jean , Shapiro Nikolai , Caudron Corentin
Published: February 2023

Fagradalsfjall eruption showed a remarkable pulsatory magma discharge activity in Jul-Aug 2021, with a characteristic timescale of ~36 hours (with cycles varying from 17 to 76 hours) and a duration of lava outflow from the crater of 10 to 70 hours. Active lava discharge coincides with the presence of both shallow and deep volcanic tremors that stops abruptly as soon as the active phase of the cycle finishes. The initial phase of each eruption cycle is characterized by some shifts of the tremor source between a depth of ~ 5 km and a shallow level, active degassing, and appearance of fresh lava at the top of the crater. Deep tremor source might be continuously active.We propose that the pulsatory activity is caused by the dynamics of magma flow in a feeding dike. The model assumes purely elastic wall-rocks rheology and Newtonian temperature-dependent magma viscosity. Elastic displacement of host rocks is calculated by means of the analytical solution for an elliptic cavity subject to fluid overpressure. We assume that surrounding rocks temperature is linearly increasing with depth and the heat transfer from the magma following Newton’s law. The influx of the magma at the base of the dike is controlled by the dike overpressure. For reasonable values of governing parameters, the system shows pulsatory activity in accordance with the observed timescales. During low discharge rate magma viscosity in the upper part of the dike increases dramatically, magma flow stops, and the dike starts to inflate at depth storing large amounts of magma. As the pressure increases the flow of the fresh hot magma destroys the plug and discharge episode occurs. The dike deflates and the flow rate decreases leading to consequent cooling of the magma and blockage of the dike.Parametric study reveals the influence of controlling parameters (magma influx rate, elastic modulus of rocks, heat exchange coefficient end others) on the period of discharge and the presence of pulsatory activity.
The influence of temperature variability on the Greenland ice sheet
Authors: Lauritzen Mikkel , Aðalgeirsdóttir Guðfinna , Rathmann Nicholas , Grinsted Aslak , Noël Brice , Hvidberg Christine
Published: February 2023

The projected contribution of the Greenland ice sheet to sea-level rise in response to future warming relies upon the state of the present-day ice sheet, and one of the main contributors to uncertainties in projections is due to uncertainties in the initial state of the simulated ice sheet. A previous study showed that including the inter-annual climate variability in an idealized ice sheet model leads to an increased mass loss rate, but the effect on the Greenland ice sheet is not known. Here we present a study using the PISM model to quantify the influence of inter-annual variability in climate forcing on the Greenland ice sheet. We construct an ensemble of climate-forcing fields that account for inter-annual variability in temperature using reanalysis data products from RACMO and NOAA-CIRES, and we investigate the steady state and the sensitivity of the simulated Greenland ice sheet under these different scenarios.We find that the steady state volume decreases by 0.24-0.38% when forced with a variable temperature forcing compared to a constant temperature forcing, corresponding to 21.7±5.0 mm of sea level rise, and the response to abrupt warming is 0.03-0.21 mm SLE a-1 higher depending on climate scenario. The northern basins are particularly sensitive with a change in volume of 1.2-0.9%. Our results emphasize the importance of including climate variability in projections of future mass loss.