bluuebunny/crossref_metadata_2025 huggingface dataset:
On the generalization of hydraulic-inspired graph neural networks for spatio-temporal flood simulations
Authors: Bentivoglio Roberto , Isufi Elvin , Jonkman Sebastian Nicolaas , Taormina Riccardo
Published: February 2023

The high computational cost of detailed numerical models for flood simulation hinders their use in real-time and limits uncertainty quantification. Deep-learning surrogates have thus emerged as an alternative to speed up simulations. However, most surrogate models currently work only for a single topography, meaning that they need to be retrained for different case studies, ultimately defeating their purpose. In this work, we propose a graph neural network (GNN) inspired by the shallow water equations used in flood modeling, that can generalize the spatio-temporal prediction of floods over unseen topographies. The proposed model works similarly to finite volume methods by propagating the flooding in space and time, given initial and boundary conditions. Following the Courant-Friedrichs-Lewy condition, we link the time step between consecutive predictions to the number of GNN layers employed in the model. We analyze the model's performance on a dataset of numerical simulations of river dike breach floods, with varying topographies and breach locations. The results suggest that the GNN-based surrogate can produce high-fidelity spatio-temporal predictions, for unseen topographies, unseen breach locations, and larger domain areas with respect to the training ones, while reducing computational times.
Global machine-learning model of naturally occurring fluoride in groundwater
Authors: Podgorski Joel , Berg Michael
Published: February 2023

Chronic consumption of elevated concentrations of fluoride in groundwater can cause detrimental health effects including dental mottling and skeletal fluorosis. However, the concentration of fluoride is not known in many aquifers. To help address this, we have used machine learning to create a global fluoride prediction map based on the WHO drinking-water guideline of 1.5 mg/L. Over 400,000 data points of fluoride in groundwater (10% greater than1.5 mg/L) from 77 countries were used along with 12 predictor variables out of an initial set of 62 spatially continuous variables relating to geology, soil, climate and topography. The model performs very well, (e.g. AUC of 0.90) and was used to produce a global prediction map. This helps gauge the scope of the problem and identify potential hotspots that should receive the focus of more groundwater testing, including parts of central Australia, western North America, eastern Brazil and many areas of Africa and Asia. This fluoride hazard model was also used to estimate the global at-risk human population at about 180 million people, most of whom live in Asia and Africa. Another model was created using additional physicochemical parameters measured in situ. Although this model (AUC of 0.95) could not be used to create a map, it helps to better understand the processes related to the dissolution and accumulation of fluoride. For example, both the spatially continuous and in-situ predictor variables confirm that arid conditions promote the dissolution of fluoride in groundwater.
Sequence stratigraphic synthesis of the Late Barremian-Aptian of the Wessex-Channel and Weald Basins: Insights from well-logs correlations and outcrop-based field geological studies, Isle of Wight, Southern England
Authors: Giannenas Panagiotis Athanasios , Robin Cécile , Guillocheau François , Gréselle Benjamin , Vennin Emmanuelle
Published: February 2023

In order to understand the effects of the OAE1a on the sedimentary system in a Source to Sink approach, we studied the Late Barremian-Aptian sediments of the Wessex-Channel and Weald Basins and present a synthesis of the sequence stratigraphic framework and sedimentary patterns of the siliciclastic system developed during this time interval. We present a regional and detailed sequence stratigraphic model for the Lower Greensand Group and provide new stratigraphic insights.This synthesis is based on field work (Isle of Wight) with focus on defining sedimentological and stratigraphic signatures using facies analysis and is coupled with published datasets and studies on bio- chemo-stratigraphy. The results are integrated with well-logs correlations for regional scale understanding.It presents updated composite lithostratigraphic logs for three areas of the island which are correlated and provide a better 3D understanding for this part of the basin. The analysis and interpretation of the well database (84 petroleum exploration wells) contributed to understanding the regional architecture including the extent, facies distributions and thickness variations of the sediments. The sequence stratigraphic model includes 3rd order (composite) sequences within the context of 2nd order super-sequences and a nomenclature in support of the model was proposed. Also, on the Isle of Wight, we present how the sedimentary system along with the relative sea level variations additionally recorded significant changes in terms of depositional environments. Finally, the applicability of the sequence stratigraphic model will be examined on a global scale for the proposal of a unified regional nomenclature in accordance with the global understanding of eustatic variations. Therefore, a long-distance correlation with already established schemes will be performed including France, Spain, the Southern Tethyan margin (Tunisia) and the Arabian plate.The project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No 860383 (https://www.s2s-future.eu/).
Development of algorithms based on the integration of meteorological data and remote sensing indices for the identification of low-productivity agricultural areas
Authors: Coluzzi Rosa , Di Paola Francesco , Imbrenda Vito , Lanfredi Maria , Pace Letizia , Ricciardelli Elisabetta , Samela Caterina , Tramutoli Valerio
Published: February 2023

Agricultural areas of Mediterranean regions host an extraordinary wealth of biodiversity and represent the source of income for a large population often living below the average economic conditions of the most advanced regions of Europe. In these areas, the semi-arid climates, the impact of climate change, the parcelization of land property, and the poor soils, contribute to create widespread conditions of low profitability of agricultural areas. This is likely to have an impact on the increasing occurrence of land abandonment phenomena and on growing hydrogeological risk linked to the lack of land maintenance.
The productivity estimation of these agricultural areas represents a crucial information to detect hotspots of degradation helping policy makers in taking specific actions to increase productivity and reduce migration fluxes.
In this work, realized in the framework of the ODESSA (On DEmand Services for Smart Agriculture) project (financed by the European Regional Development Fund Operational Programme 2014-2020 of Basilicata Region), the procedure adopted involves the use of climate and vegetation geospatial data, including both direct observational data (temperature, rainfall, etc.) and satellite-derived vegetation indexes. For the climatic component, we exploited a database of daily temperature and rainfall data (2000-2021) acquired by the agrometeorological network of ALSIA (Lucana Agency for Development and Innovation in Agriculture) and the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset providing rainfall data (1981-2020) at a spatial resolution of 0.050 to produce different diagnostic indices able to capture low-productivity areas. We tested this procedure in two districts of Basilicata (Southern Italy): the Vulture-Melfese and the Metapontino, representing the core areas of regional agricultural specialization for vineyards and intensive fruit and vegetable crops, respectively.
 
High-resolution quantification of aboveground carbon change over the tropics
Authors: Feng Yu , Ciais Philippe , Xu Yidi , Wigneron Jean-Pierre , Li Xiaojun , Fan Lei
Published: February 2023

Tropical ecosystems play an important role in regulating the global carbon balance. Existing studies have extensively analyzed the carbon dynamics of tropical forests, the largest terrestrial component of the global carbon budget, showing a likely neutral contribution of tropical forests to the global carbon cycle. However, high-resolution dynamics of aboveground carbon (AGC) change of the whole tropical terrestrial ecosystem and its processes remain rarely investigated. In this study, we first used low-frequency L-band passive microwave observations to derive wall-to-wall maps of annual AGC stocks over the tropics at 25-km spatial resolution. Using high-resolution satellite observations of land-cover change and biomass maps and random forest models, we separated the AGC stock into various ecosystems, including forest, shrub, and short-vegetation (grass and crop), and attributed the change to different degradation processes such as fires and deforestation at 100-m resolution. Our preliminary results show that total AGC stocks in tropical ecosystems increased by  +2.25 [+1.19,+3.29] PgC (the range represents the minimum and maximums of the multiple estimates) from 2010 to 2020. The coast of Brazilian Mata Atlantica, Central African Republic, and east Tanzania are the hotspots of net increase, while the Arc of Deforestation in the Amazon basin and the Congo Basin show substantial net losses. Gross losses from non-fire deforestation and fire totaled -1.62 [-1.38,-1.86] PgC yr-1. We also observed strong recovery in African burned regions, possibly due to post-fire regrowth and additional recovery resulting from declining fires in the region. Our results highlight the importance of explicit temporal and spatial mapping of tropical carbon dynamics at high resolution, which can help us better understand the role of tropical terrestrial ecosystems in the global carbon cycle.
Reproducible quality control of time series data with SaQC
Authors: Schäfer David , Palm Bert , Lünenschloß Peter , Schmidt Lennart , Bumberger Jan
Published: February 2023

Environmental sensor networks produce ever-growing volumes of time series data with great potential to broaden the understanding of complex spatiotemporal environmental processes. However, this growth also imposes its own set of new challenges. Especially the error-prone nature of sensor data acquisition is likely to introduce disturbances and anomalies into the actual environmental signal. Most applications of such data, whether it is used in data analysis, as input to numerical models or modern data science approaches, usually rely on data that complies with some definition of quality.To move towards high-standard data products, a thorough assessment of a dataset's quality, i.e., its quality control, is of crucial importance. A common approach when working with time series data is the annotation of single observations with a quality label to transport information like its reliability. Downstream users and applications are hence able to make informed decisions, whether a dataset in its whole or at least parts of it are appropriatefor the intended use.Unfortunately, quality control of time series data is a non-trivial, time-consuming, scientifically undervalued endeavor and is often neglected or executed with insufficient rigor. The presented software, the System for automated Quality Control (SaQC), provides all basic and many advanced building blocks to bridge the gap between data that is usually faulty but expected to be correct in an accessible, consistent, objective and reproducible way. Its user interfaces address different audiences ranging from the scientific practitioner with little access to the possibilities of modern software development to the trained programmer. SaQC delivers a growing set of generic algorithms to detect a multitude of anomalies and to process data using resampling, aggregation, and data modeling techniques. However, one defining component of SaQC is its innovative approach to storing runtime process information. In combination with a flexible quality annotation mechanism, SaQC allows to extend quality labels with fine-grained provenance information appropriate to fully reproduce the system's output.SaQC is proving its usefulness on a daily basis in a range of fully automated data flows for large environmental observatories. We highlight use cases from the TERENO Network, showcasing how reproducible automated quality control can be implemented into real-world, large-scale data processing workflows to provide environmental sensor data in near real-time to data users, stakeholders and decision-makers. 
Unravelling wildland fire morphology and structure using image velocimetry
Authors: Valencia Andres , Katurji Marwan , Lin Dongqi , Gross Shana , Zhang Jiawei , Pearce Grant , Finney Mark , Strand Tara
Published: February 2023

Wildland fires have been increasing in size, frequency and intensity during recent decades, affecting entire ecosystems and societies even in regions historically not considered fire prone. Some of those fires display dynamics of extreme fire behaviour, which chaotic and large-scale nature make them challenging to study. Thus, there is a need of new methodologies for wildland fire analysis, capable of capturing spatiotemporal characteristics suitable for this application. This work presents two applications of the image velocimetry technique applied to wildland fires, offering new details on the morphology and structure of large-scale medium-intensity prescribed shrubland fires, as well as an outlook on new applications in more complex scenarios. Fire flow displacement vectors and streamlines were calculated and mapped from a high-resolution overhead visible-spectrum (RGB) video acquired during a four-hectare prescribed gorse fire. This method allowed for identification of spatially interleaved flow convergence and divergence regions, providing insight on the high-level structure of the fire front and flaming zone. The method was further expanded to identify what we refer to as “fire sweeps”, via the application of a 2D convolution operation on the displacement vector based upon a kernel carefully designed to highlight the characteristics highly divergent fire flows.
Parametric study to characterize water-weakening effects in UDEC
Authors: Bu Fengchang , Jaboyedoff Michel , Derron Marc-Henri , Xue Lei
Published: February 2023

The Universal Distinct Element Code (UDEC) has been rising much prevalence and is a validated technique applied to simulations in varied branches of geotechnics. Nevertheless, the characterization of water-weakening effects on a rock model remains elusive, resulting in a poor constraint referring to water-induced simulations by UDEC. In this context, previous research has been attempting to conduct an intuitive link between modelling parameters and saturation degrees, Sr, to implement a water-weakening process in UDEC, leading to a detrimental potential devoid of the basic logic that modelling parameters determine macroscopic properties of a rock model in contrast to dominance by Sr owing to the discrepancy in a physical sense and spatial scale. To fill in this gap, a new methodology coupled with a parametric study is first proposed with procedures that macroscopic properties of actual rock with different Sr are input into parametric relations to acquire predicted modelling parameters, which will be sequentially calibrated and adjusted until simulations are in line with actual tests. Utilizing this methodology, water-weakening effects on macroscopic properties, mechanical behaviours, and failure configurations of numerical models in UDEC are commensurate with tested ones to the utmost with noticeable computational expediency harnessing the benefit of the parametric study, indicating the feasibility and simplicity of the methodology. Thus, in the implementation of a water-weakening process in UDEC, we suggest converting an embarking from an intuitive link between modelling parameters and Sr into a parametric analysis to determine modelling parameters according to macroscopic properties under different Sr.
Estimating river surface slopes from ICESat-2 to inform hydrodynamic models
Authors: Christoffersen Linda , Nielsen Karina , Bauer-Gottwein Peter , Sørensen Loiuse
Published: February 2023

ICESat-2 measures the ground surface elevation with 6 laser beams grouped in three pairs of two, and pairwise separated by 3.3 km across track. This measurement configuration provides an unprecedented opportunity to measure local the water surface slope. Local water surface slopes can inform hydrodynamic models and improve performance.The slopes are estimated by a linear regression of the water surface height as a function of the distance along the river. Water surface slopes are estimated for all intersections, independent of intersection angle, between ICESat-2 crossings and the river centerline where data is available and of reliable quality. The package is applied to the Amur river basin using 3.5 years of ICESat-2 data. More than 3700 slope estimates were produced with a median relative standard error of 2.1% across 1502 SWORD reaches out of 3360 reaches that intersect ICEsat-2 tracks.In this study, an automatic method for estimating river surface slopes is developed and implemented in an R package. The package uses ICESat-2 ATL13 data and the SWOT River Database (SWORD) as the only compulsory input data. The R package can be tuned to fit the application of interest based on the user settings. This enables slope estimates to be computed globally with minimal additional effort.This R package provides a tool that is easy to use and systematically gives local water surface slope estimates for a specified area of interest. Studies have shown how information of river slopes from twin in-situ gauge stations can improve discharge estimates from models. The global sparseness of in-situ stations limits the usability of models informed with slope estimates from gauge stations. Water surface slope estimates on local scale from satellite data increases the usability of these models.
Development of high-resolution particle concentration prediction model - an application of remote sensing and machine learning
Authors: Moni Mufaddal , Sahu Manoranjan
Published: February 2023

According to WHO, air pollution is associated with 7 million premature deaths annually. Lack of air pollution monitoring systems is one of the major challenges in developing regulations and policies for air pollution control as well as health risk assessments in majority of the developing nations. Ground based monitoring stations are sparsely distributed in major cities only. The major reason for lack of monitoring stations is the heavy cost associated with their establishment and operations, whereas low-cost sensors come with different variety of challenges related to the calibration, accuracy and reliability. This problem could be solved by combining remote sensing data with machine learning to estimate particulate matter concentrations. Modern satellite sensors hold the potential to provide high-quality aerosol optical depth (AOD) data at a resolution of 10m × 10m. Current approaches for estimating particle (PM2.5) concentrations rely on AOD with meteorological data such as relative humidity, ambient temperature, wind speed, etc. However, the prediction accuracy decreases dramatically if these results are extrapolated over a wider timeline and a broader region. This is owing to the fact that traditional methods take many assumptions based on the training data set. We implemented and validated the conventional method of predicting particle concentration to evaluate prediction accuracy using five distinct machine learning models. The light gradient boosting regression model generated the highest prediction accuracy, i.e., 92.46% for a training data set of one year (January 2019 – December 2019) for the city of Mumbai, with a resolution of 0.5° × 0.625° (latitude × longitude). This work presents a hybrid approach combining the physics-based relations and statistical methods, to predict surface level concentration. It uses the vertical distribution of aerosols along with the optical properties like single scattering albedo and angstrom exponent for determining particle characteristics and meteorological parameters for a greater prediction accuracy over a wider timeline and a broader region. Since AOD provides a measure of total particles above a location, we employed data from multi-angular satellite sensor (CALIOPS) to generate vertical distribution profiles and ultimately surface-level concentration. Also, the physics based empirical relations are considered while determining the input parameters for model training, which significantly increases the prediction accuracy of model. When the particle size distribution curve was combined with the surface level concentration from vertical distribution profile, a more accurate surface level PM2.5 concentration was obtained. Unlike previous approaches that make several assumptions based on the location of training data, this method, by removing those assumptions, is valid over a broader area and a wider timescale. 
Determination of anthropologically induced deformation along the Kenyan Rift system using Multitemporal InSAR analysis with Sentinel-1 data.
Authors: Kirui Pius Kipngetich , Riedel Björn , Gerke Markus
Published: February 2023

The Kenyan Rift system hosts various forms of land use, including residential, commercial, and agricultural. In addition, the geology of the Kenyan Rift, the geodynamic setting of the quaternary volcanoes along the rift axis, and the high temperatures associated with the hot asthenosphere along the Kenyan Rift system are favourable for the occurrence of geothermal fields, some of which have already been harnessed for the generation of electricity. The interplay of human activities along the Kenyan Rift system can cause deformation, which is also prone to deformation due to geophysical activities such as volcanism and magmatism. In our study, we utilized both conventional and optimized multitemporal InSAR analyses based on the SBAS method to quantify human-induced deformation along the Kenyan Rift. By directly estimating the tropospheric delay from Sentinel-1 SAR data, the optimized approach can reduce errors in InSAR derived displacement measurements. Nairobi, located on the eastern flank of the Kenyan Rift, has experienced significant deformation caused by urbanization and the overexploitation of groundwater. A maximum subsidence rate of approximately 55 mm/yr. was observed in one of the eight deformation units that are mainly located in residential areas. Njoro town and Nakuru town industrial zone have also been shown to be undergoing land subsidence of approximately 20 mm/yr. and 10 mm/yr., respectively, both of which are associated with the overexploitation of groundwater resources. In addition, land subsidence in the range of 20 mm/yr was observed at several flower farms in Naivasha, which can also be attributed to the overexploitation of groundwater. At Olkaria, we observed land subsidence in the seven geothermal fields in the range of 22-50 mm/yr., we also observed at Menengai Crater Land subsidence and uplift of approximately 8 mm/yr. and 6 mm/yr. respectively. There is a significant deformation in the Kenyan Rift as a result of human activities, and these results indicate that InSAR can be used to monitor deformation in regions that were previously unmonitored due to the associated costs of using other geodetic monitoring techniques. Similarly, correct estimation of tropospheric delay in InSAR not only leads to better time-series displacement estimation with a more apparent temporal trend but also reveals subtle deforming regions that are otherwise obscured by tropospheric delay in the conventional method.
Bottom-up estimation of traffic emissions in Munich based on macroscopic traffic simulation and counting data
Authors: Kühbacher Daniel , Aigner Patrick , Super Ingrid , Droste Arjan , Denier van der Gon Hugo , Ilic Mario , Chen Jia
Published: February 2023

Cities are home to more than half of the world’s population, a share that will continue to grow in the future and account for more than 70% of the global fossil fuel CO2 emissions. To avoid dangerous climate change, cities will be required to reduce their energy consumption and cut carbon emissions significantly. Emission inventories are the basis for any carbon mitigation efforts. They determine the current status, allocate emissions to various sectors and indicate their reduction potential. The ICOS-Cities project fosters this development and aims to set up integrated city observatories in three pilot cities (Paris, Zurich and Munich). Reliable prior data is essential for modeling efforts in this project and road transport is a key emission sector in urban areas.We present a newly developed, highly spatially and temporally resolved bottom-up traffic emission inventory for the area of Munich (311 km2), covering outer circle motorways as well as inner city roads. The inventory accounts for greenhouse gases (CO2, CH4) and co-emitted species/ air pollutants (CO, NO2, O3 and PM). It has a temporal resolution of one hour and is compiled for the years 2019 to 2022. The emissions are represented as line sources along the road network, which allows for emission sampling ranging from several tens of meters in densely interconnected inner-city environments to a kilometer-scale on highways.The inventory is based on the city’s official macroscopic traffic model (VISUM), which we validate using traffic counts from more than hundred permanent traffic monitoring stations in Munich since this data is not implemented in the traffic model. Additionally, we extrapolate the traffic model to unobserved days (e.g., weekends, holidays) by means of traffic counts, and distinguish between vehicle classes (private car, heavy duty vehicle, light duty vehicle, coach and motorbike) based on categorized traffic counts. HBEFA emission factors (Handbook for Road Transport Emission Factors) are applied to estimate the emissions.A comparison with the official emission numbers of the City of Munich and other spatially explicit inventories available in the same region, such as TNO GHGco database, is conducted. We will present the main discrepancies and provide insights for other cities aiming to develop similar inventories.
‘risk-calculator’: a tool for multi-hazard risk assessments
Authors: Evaz Zadeh Tara , Oostwegel Laurens Jozef Nicolaas , Lingner Lars , Shinde Simantini , Cotton Fabrice , Schorlemmer Danijel
Published: February 2023

Effects of a hazard can be expressed in terms of the losses and damages caused by it. These risk assessments help decision makers and disaster managers to better prepare for and cope with the disasters arising from hazards in all three phases (preparation, response and recovery) of disaster risk reduction and resilience. With the growth of disaster risk globally (GAR, 2022), and also the related research on different components of risk (hazard, exposure, vulnerability), there is a rising demand to compute risk for multi-hazard events, for which an integrated tool is very beneficial, but most of the tools used for multi-hazard risk assessment purposes are not available for public use (Cees J. van Westen and Stefan Greiving, 2017).The ‘risk-calculator’ is an open-source Python program that enables users to do multi-hazard scenario risk assessments. This program is  capable of assessing loss and damage for different sets of discrete or continuous vulnerability or fragility functions for different concurrent hazards, such as floods, earthquakes and tsunamis.The three main inputs are: (1) hazard, expressed as an intensity field (e.g ShakeMap for earthquakes or inundation field for tsunamis and floods), (2) the exposure model provided in a geospatial database or as CSV files to be imported, and (3) vulnerability/fragility functions describing the level of loss or damage at different intensity measure levels dependent on the asset taxonomy and the hazard type. The tool works on basis of the standard taxonomy as defined by the Global Earthquake Model. This taxonomy can also be expanded to include damage states of previous events for a loss computation of cascading events such an earthquake followed by a tsunami.Apart from using the program directly, users can connect to the API designed for this program to run loss assessments. The resulting losses and damages are provided in a geospatial database (SpatiaLite or GeoPackage), for sake of easier handling and plotting. This user-friendly program provides database views, connecting data in a meaningful way from different tables to allow for various ways of analyzing and visualizing the loss and damage assessments.
Rupture dynamics driven by strain localisation within fault gouges
Authors: Brantut Nicolas , Barras Fabian
Published: February 2023

During fast slip, fault strength may decrease due to weakening mechanisms linked to constitutive properties of the deformed material (e.g., flash heating, thermal pressurisation), but also due to structural effects driven by changes in strain distribution within the shear zone. Extensive theoretical work on thermally activated weakening mechanisms, such as thermal pressurisation of pore fluids, has shown that strain can spontaneously localise in very narrow zones during rapid shear, which promotes further macroscopic weakening of faults. Here, we develop a multiscale fault model which combines a detailed description of thermal pressurisation of fault gouges within large scale elastodynamic rupture simulations. We show that spontaneous strain localisation inside the fault gouge dramatically changes the dynamics of ruptures, and makes the faults more brittle, i.e., decreases the fracture energy and thus produces faster ruptures. We provide closed-form approximations for the resulting localised width and fracture energy as functions of rupture speed. Our work provides a link between structural observations and earthquake dynamics.
Timescales for Ice Shelf Collapse and MISI Initiation in the Filchner-Ronne Sector
Authors: Wolovick Michael , Humbert Angelika , Kleiner Thomas , Rückamp Martin , Timmermann Ralph
Published: February 2023

The Filchner-Ronne sector of Antarctica contains a number of deep-bedded ice streams and glaciers potentially vulnerable to the Marine Ice Sheet Instability (MISI).  Previous work has shown that, in a warming climate, the ocean circulation in the cavity underneath the Filchner-Ronne Ice Shelf (FRIS) could switch from its present cold state to an alternate warm mode, in which intrusions of Circumpolar Deep Water (CDW) cause high basal melt rates near the deep grounding lines of potentially vulnerable glaciers.  However, less work has been done on modeling the response of the ice sheet and ice shelf system to such a mode switch in the cavity circulation.  Here, we use the Ice-sheet and Sea-level System Model (ISSM) to simulate the response of the Filchner-Ronne sector of Antarctica over multi-centennial timescales to changes in basal melt rate caused by a mode switch in the cavity circulation.  We force ISSM with both melt rates directly calculated by the cavity-resolving Finite-Element Sea ice-Ocean Model (FESOM) and with parameterized melt rate forcing derived from CMIP6 global models.  We find that parameterized melt rates in high-emissions scenarios cause rapid grounding line retreat at almost all of the major glaciers and ice streams feeding the FRIS beginning in the 22nd century, followed by ice shelf collapse and rapid sea level rise in the 23rd.  Using FESOM simulated melt rates the destabilization of the FRIS sector proceeds more slowly. During the 22nd century retreat is concentrated in specific ice streams, reflecting the more heterogeneous distribution of melt rate in the ocean model as opposed to the parameterized forcing.  In the 23rd century retreat becomes more widespread, culminating in ice shelf collapse and rapid sea level rise in the 24th and 25th centuries.  
A long-term property earthquake insurance: illustration with the housing sector in California
Authors: pothon adrien
Published: February 2023

For several seismic-prone countries, current earthquake insurance solutions cover only a small part of the economic loss. Innovative insurance products like parametric insurance are emerging for which the compensation is calculated upon a trigger instead of a claim amount, covering more people but with drawbacks due to probable difference between the insurance compensation and the actual loss. In this paper, new insurance model is proposed, covering earthquake risk for residential houses. Its main characteristics are: (1) the compensation is to rebuild the insured house, instead of paying a financial amount; (2) the model leverages both on long-term financial investment and seismic retrofitting of the insured buildings to make the premium amount affordable; and (3) joint participation of the public authorities and the homebuilder companies in this insurance model are expected since the first ones are the key player in risk prevention plans and the second ones are the beneficiary of this new market (incentivizing repairs/reconstruction and retrofitting works). Results show that in most cases the price (i.e. premium amount and retrofitting costs) for this earthquake insurance model is lower than the premium amount considering the traditional earthquake insurance. For the optimal deductible amount, the decrease can even be three times lower than for classical model, by assuming a contribution from both the public authorities and the homebuilder companies. Such a decrease could raise the rate of California homeowners insured against earthquake risk from 15% up to 50%. 
Advancing the realistic simulations of N2O emissions in cold climate watersheds using Soil and Water Assessment Tool
Authors: Daggupati Prasad , Ghimere Uttam , Biswas Asim
Published: February 2023

Nitrous Oxide (N2O) emissions in Soil and Water Assessment Tool (SWAT) is heavily dependent on soil temperature and moisture. However, SWAT has been known to highly under-estimate soil temperature which limits movement of water and nutrients throughout the soil profile and does not replicate freeze-thaw cycles which is of paramount importance in the N2O emissions. Thus, we integrated modules developed by individual researchers pertaining to energy balanced snow melt, rain-on-snow, energy balanced soil temperature and N2O emission into a single SWAT model and termed in SWAT Cold Climate N2O (SWAT-CCN2O). SWAT-CCN2O was then tested for flows, sediments, soil temperature and N2O emission simulation in a representative watershed in Ontario, Canada, the Speed River basin. Compared with the unaltered SWAT model, SWAT-CCN2O was able to significantly capture the pre-spring snowmelt induced flows. A more realistic simulation of soil temperature (soil temperatures did not go below -4oC) and a satisfactory simulation of sediments and N2O emissions were observed in the basin, which highlights the potential to use SWAT-CCN2O for streamflow and N2O simulation in cold climatic catchments. This version of SWAT is made publicly available for further improvements and applications in similar watersheds.  
Exploring the fire danger and exposition of settlements at the wildland urban interface: a case study in Graz, Austria
Authors: Silva Andrade Mariana , M. Müller Mortimer , Dioszegi Gergo , Vacik Harald
Published: February 2023

Changing climate is likely to increase the intensity of forest fires in Austria. Consequently, the investigation of the fire danger situation at the wildland-urban interface is crucial for the planning of prevention measures against future damages. Hence, a comprehensive fire risk assessment study was performed for Austria, in which five components were considered (natural cause for ignition of forest fires, socioeconomic causes for ignition, vegetation structure, meteorological factors and exposition of important infrastructures). The approach for the fire danger assessment determined for the city of Graz, the second largest city in Austria, as high danger level for fire occurrences. Therefore, the present work introduces a case study for this city, focusing on the exposure of settlements and infrastructure to fire events. The relevant infrastructure features were selected, such as residential buildings, power lines, radio stations, railways, highways and traffic and communication buildings, as well as three types of forests with different management objectives (production forest, site protection forest and object protection forest). In order to analyze the interaction between settlements and vegetation in case of forest fires, buffers were used (i.e. buffer sizes for buildings are 10m, 50m and 150m and other settlements are 60m, 100m and 200m). Infrastructure and forest cover were also classified due to their importance: high, medium and low risk of exposition. For example, production forest was considered to bear a lower risk of exposition due to their economic importance compared to protection forests, that have to protect the city from natural hazards like rock falls or mudflows. On the other hand, radio stations carry a high risk of exposition because of their importance for facilitating the communication of the community. In combing the results of the fire danger assessment for the city of Graz with final exposition map the results indicate that areas around radio stations situated in the northwestern area of the city have a higher risk of exposition. As a result, firefighters must pay particular attention to this region for management and evacuation plans.
Using cGAN for cloud classification from RGB pictures
Authors: Rosenberger Markus , Dorninger Manfred , Weißmann Martin
Published: February 2023

Clouds of all kinds play a large role in many atmospheric processes including, e.g. radiation and moisture transport, and their type allows an insight into the dynamics going on in the atmosphere. Hence, the observation of clouds from Earth's surface has always been important to analyse the current weather and its evolution during the day. However, cloud observations by human observers are labour-intensive and hence also costy. In addition to this, cloud classifications done by human observers are always subjective to some extent. Finding an efficient method for automated observations would solve both problems. Although clouds have already been operationally observed using satellites for decades, observations from the surface shed a light on a different set of characteristics. Moreover, the WMO also defined their cloud classification standards according to visual cloud properties when observations are done at the Earth’s surface. Thus, in this work a utilization of machine learning methods to classify clouds from RGB pictures taken at the surface is proposed. Explicitly, a conditional Generative Adversarial Network (cGAN) is trained to discriminate between 30 different categories, 10 for each cloud level - low, medium and high; Besides showing robust results in different image classification problems, an additional advantage of using a GAN instead of a classical convolutional neural network is that its output can also artificially enhance the size of the training data set. This is especially useful if the number of available pictures is unevenly distributed among the different classes. Additional background observations like cloud cover and cloud base height can also be used to further improve the performance of the cGAN. Together with a cloud camera, a properly trained cGAN can observe and classify clouds with a high temporal resolution of the order of seconds, which can be used, e.g. for model verification or to efficiently monitor the current status of the weather as well as its short-time evolution. First results will also be presented.
Climate Digital Twin to support climate change adaptation efforts
Authors: Kontkanen Jenni , Acosta Mario , Bretonnière Pierre-Antoine , Castrillo Miguel , Davini Paolo , Doblas-Reyes Francisco , Früh Barbara , von Hardenberg Jost , Jung Thomas , Järvinen Heikki , Keller Jan , Klocke Daniel , Niemelä Sami , Stevens Bjorn , Thober Stephan , Manninen Pekka
Published: February 2023

Climate change is expected to have far reaching impacts on human and natural systems during the 21stcentury. To guide policy decisions on climate change adaptation and mitigation, there is a need for developing new types of climate information systems that can provide timely information on regional and local impacts of climate change. The European Commission’s Destination Earth (DestinE) programme aims towards this by developing high precision digital twins (DT) of the Earth. We present here the overview of one of the two first priority DTs, Climate Change Adaptation DT. The Climate DT will encompass a pre-exascale climate information system that can support climate change adaptation efforts.The Climate DT harnesses two different kilometer-scale Earth-system models (ESMs), ICON and IFS-FESOM/NEMO. The models will be adapted to two EuroHPC pre-exascale computing systems: LUMI that is currently in operation in Kajaani, Finland, and Mare Nostrum 5 that will be available during 2023 in Barcelona, Spain.The Climate DT introduces the idea of a generic state vector (GSV), which is evolved by the ESMs and streamed to applications. This enables the ESMs to work at an unprecedented scale (multi-decadal simulations on 5km or finer global meshes) and thus improves the fidelity of the information the models provide as well as its relevance for the users.  This approach also creates the basis for an information system that can scale across an unlimited number of applications and enable interactivity during the future phases of DestinE.Use cases from different impact sectors are implemented within Climate DT as applications that operate on the streamed GSV. The Climate DT will explore five use cases which will provide information on (1) wind energy supply and demand, (2) wildfire risk and emissions, (3) river flows, (4) hydrometeorological extreme events, and (5) heat stress in urban environments. Additional applications operating on the GSV include a quality assessment and uncertainty quantification framework, used for monitoring and evaluation of the GSV. This framework will utilize observational operators to evaluate the GSV against observational data, and in so doing enable the use of tools from numerical weather prediction for quality assessment and observation-based model tuning.In this presentation, we will give an overview of the Climate DT, describing the objectives for the first phase, technical design of the DT, and the progress made so far. The use cases and the new possibilities provided by the streaming of the GSV are discussed in more detail by Doblas-Reyes et al., and the development of the digital infrastructure for Climate DT by Narayanappa et al.
Human and natural influences impacting the Geodiversity and Geosystem services in mountain regions: An application of DPSIR framework
Authors: Khoso Rasool Bux , Guerini Michele , Giardino Marco , Viani Cristina , Acquaotta Fiorella
Published: February 2023

The DPSIR (Drivers, Pressures, State, Impact, Response) framework is a widely utilized tool for analyzing the relationship between human activities and their impacts on the environment (O’Neill et al., 2002). In this study, we applied the DPSIR framework to examine the loss of geodiversity and degradation of geosystem services in the Monte Rosa (North Western Italian Alps, Sesia Val Grande UNESCO Global Geopark).Using the DPSIR framework, we identified both the human and climatic drivers of change in geodiversity and geosystem services in the mountain areas, including glaciers, permafrost, temperature, precipitation, rivers and torrents. These drivers exert various pressures on the environment, including, the more frequent extreme events by rainfall, permafrost degradation, fluvial processes, glacial processes and glacial shrinkage. To accurately measure the drivers and pressures that are influencing mountain system, we compiled a list of data, sources, techniques and tools that are appropriate for this purpose. We assessed the current state of geodiversity and geosystem services in the area and evaluated the impacts of these pressures. Our results showed that the available services can be dilapidated overtime due to the active influence of these drivers of change.Our study demonstrates the utility of the DPSIR framework for understanding the complex interactions between human activities, climate change and the environment, and for identifying strategies for conserving and preserving geodiversity and geosystem services. The findings of this research can inform decision-making and policy development at the local, regional, and national levels, with the ultimate goal of mitigating the negative impacts of human and climatic activities on the environment.Keywords: DPSIR, geosystem services, geodiversityReferences: O'Neill, R. V., Krumhardt, K. M., & Brauman, K. A. (2002). The DPSIR framework. Environmental Science & Policy, 5(5), 347-359.
Old and modern challenges of Forensic Gemmology
Authors: Spoto Sebastiano Ettore
Published: February 2023

Forensic Gemmology is a branch of Forensic Science where the analysis of gemstones has legal implications, which cannot be set aside, improvised, or, even worse, done with approximation. Local and world markets and archaeological sites can currently encounter a wide range of gemmological objects that are incorrectly declared, treated, or classified. Materials in question are made by also using the latest technologies. Occasionally, cases are brought to court regarding the value of a precious gem in addition to its "authenticity," which often, to be resolved, require complex preparation. Therefore, keeping in mind the significance of gemstone identification, here are specific methodologies discussed to test the authenticity of the gemstones and to find out whether the gemstones are authentic or not. Modern challenges also concern determining whether gemstones were extracted under ethical conditions and determining whether gemstones are of synthetic or natural origins. Thus, forensic examination of gemstones becomes very difficult if proper procedures are not outlaid. The problems that need to be addressed at the international level remain relevant, for instance, creating a standard scheme for determining the ethical origin of colored gemstones, similar to the one existing in the diamond market.