bluuebunny/crossref_metadata_2025 huggingface dataset:
Flood risk mapping using multi-criteria analysis (TOPSIS) model through geospatial techniques- A case study of the Navsari city, Gujarat, India
Authors: Pathan Azazkhan Ibrahimkhan , Agnihotri Dr. Prasit Girish , Said Dr. Saif , Patel Dr. Dhruvesh , Prieto Dr. Cristina , Mohsini Usman , Patidar Nilesh , Gandhi Dr.Pankaj , Jariwala Khushboo , Đurin Bojan , Azimi Mohammad Yasin , Rasuli Juma , Dummu Kalyan , Raaj Saran , Shaikh Arbaaz A. , Salihi Muqadar
Published: March 2022

Flood is one of the most devastating natural disasters that cause enormous socioeconomic and environmental destruction. The severity of flood losses has evoked emphasis on more comprehensive and vigorous flood modeling techniques for alleviating flood damages. Flood vulnerability in Navsari is intensifying due to urbanization, industrialization, and population growth. Although there has been a significant increase in research on flood assessment at a local scale in Navsari, there remains a lack of tools developed which utilize the risk map of the city. In response to this prerequisite, in this study we have employed a GIS-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis model to develop a flood risk map for Navsari city in Gujarat, India, to determine the vulnerable areas that are more susceptible to flooding. To estimate the extent of flood hazard, vulnerability, and risk intensities in terms of area covered, the city was divided into ten zones (i.e. NC1 to NC10) and classified into five classes: very high, high, moderate, low, and very low. A total of seven hazard forming spatial layers (i.e. slope, elevation, soil, rainfall, flow accumulation, distance to a river, and drainage density) and seven vulnerability forming spatial layers (i.e. female population, population density, land use, household, distance to hospital, road network density, and literacy rate) were appraised for evaluating the risk of flooding. The generated flood risk map has been compared with the extent of flood calculated based on field data collected from thirty-six random places. The outcome of the model unveiled the capability of the TOPSIS model since it capitulate low RMSE value varied between 0.95 to 0.43 and high R square value ranged from 0.78 to 0.95. The zones indicated under ‘high’ and ‘very high’ categories (i.e. NC8, NC6, NC4, NC1, NC7, and NC10) demand abrupt flood control action to alleviate the severity of flood risk and subsequent damages. The approach implemented in the study can be applied to any flood-sensitive region around the globe to accurately evaluate the risk of flood. Lastly, flood risk mapping using TOPSIS based geospatial techniques divulge the novel and efficacious approach, especially for data-sparse regions.
Predictability of onset, duration, and intensity of hot temperature events in the ECMWF subseasonal forecast system
Authors: Pyrina Maria , Domeisen Daniela
Published: March 2022

Extended range forecasts can directly contribute to positive health and economic outcomes, making sub-seasonal forecasting highly relevant for society. However, the summer temperature prediction skill over Europe (for both average and extreme temperatures) quickly decreases beyond timescales of two weeks. The origins of prediction errors of sub-seasonal forecast systems in the onset, intensity, and duration of hot temperature events are not yet fully understood. We investigate the predictability and drivers of the prediction skill of hot events in the sub-seasonal forecast system of the ECMWF (European Centre for Medium-Range Weather Forecasts). The analysis is conducted over six European regions and for different lead times (7-21 days) during the period 1998-2017. The onset and intensity of hot temperature events is better predicted by the ECMWF model at shorter lead times, but there are lower errors in duration at longer lead times. Compared to ERA-Interim reanalysis data, the ECMWF model overestimates the duration and underestimates the intensity of hot extremes for all European regions and lead times considered. Overall, the errors in hot event duration and intensity increase in the higher temperature percentiles, with large inter-event variability in the errors estimated for the 50-75 percentile range. 
A flashback to changes across the Cretaceous-Paleogene boundary (K/Pg) in Turkey:  a faunal and environmental portray
Authors: Karabeyoglu A. Uygar , Spangenberg Jorge , Adatte Thierry
Published: March 2022

The interplay between continental flood basalt volcanisms and major mass extinctions has been known for long. The importance of the K/Pg boundary extinction is due to the coincidence of two cataclysmic events (the Deccan volcanism and the Chicxulub impact, respectively) in a very short time. The effects of Deccan activity on the ecosystem have to be scrutinized in order to understand the causality between volcanic eruptions and associated environmental stress. To do so, here we document this relation in terms of high-resolution biostratigraphy, quantitative species analysis, and isotope and geochemical measurements on two complete sections in Mudurnu-Göynük and Haymana basins, Central, Anatolia, Turkey.

Quantitative analysis on planktonic foraminifera of the Haymana Basin reveals a systematic reduction in the species richness through the late Maastrichtian, and then sudden annihilation of large, ornamented ecological specialists (e.g. Globotruncanids, Racemiguembelinids, Planoglobulinids) at the boundary. This extinction level (i.e., the K/Pg boundary) corresponds to a 2-3 mm thick reddish oxidized layer in which we found enrichments in Ir, Te, Hg, Ba, Ni, Cr, Co. Additionally, measurements on Maastrichtian and Danian samples show a significant correlation between Hg and Te. Moreover, acmes of Thoracosphaera and Guembelitria cretacea demonstrate ecosystem collapse after the K/Pg event. The same levels correspond to a surge in fecal pellets.

Overall, our results show that the deteriorating effects of Deccan volcanism had already started before the Chicxulub impact which predisposed faunas to eventual extinction at the K/Pg boundary.
The fast-changing coast of Tuktoyaktuk Peninsula (Beaufort Sea, Canada): geomorphological controls on changes between 1985 and 2020
Authors: Costa Bernardo , Vieira Gonçalo , Whalen Dustin
Published: March 2022

The average rate of coastal change in the Arctic Ocean is -0.5 m/yr, despite significant local and regional variations, with large areas well above -3 m/yr. Recent data suggest an acceleration of coastal retreat in specific areas due to an increasingly shorter sea ice season, higher storminess, warmer ocean waters and sea-level rise. Moreover, climate warming is inducing the subaerial degradation of permafrost and increasing land to sea sediment transportation. This work consists of the characterization and analysis of the main controlling factors influencing recent coastline change in the Tuktoyaktuk Peninsula, Northwest Territories, Canada. The specific objectives are I. mapping Tuktoyaktuk Peninsula’s coastline at different time-steps using remote sensing imagery, II. quantifying the recent coastal change rates, III., characterizing the coastal morphology, IV. identifying the main controlling factors of the coastal change rates. A very high-resolution Pleiades survey from 2020, aerial photos from 1985 and the ArcticDEM were used. Results have shown an average coastline change rate of -1.06 m/yr between 1985 and 2020. While this number is higher than the Arctic average rate, it neglects to show the significance of extreme cases occurring in specific areas. Tundra cliffs are the main coastal setting, occupying c. 56% of the Tuktoyaktuk Peninsula coast and foreshore beaches represent 51%. The results display an influence of coastal geomorphology on change rates. The coastal retreat was higher in backshore tundra flats (-1.74 m/yr), whereas more aggradation cases exist in barrier beaches and sandspits (-0.81 m/yr). The presence of ice-wedge polygons contributes to increasing cliff retreat. Foreshore assessment may be crucial, as beaches present a hindering impact on coastal retreat (-0.76 m/yr), whereas foreshore tundra flats promote it (-1.74 m/yr). There are 48 areas with retreat rates higher than -4 m/yr, most being submersion cases.
Global gravity field modelling by the finite element method involving mapped infinite elements.
Authors: Macák Marek , Minarechová Zuzana , Čunderlík Róbert , Mikula Karol , Tomek Lukáš
Published: March 2022

We present a numerical approach for solving the oblique derivative boundary value problem (BVP) based on the finite element method (FEM) with mapped infinite elements. To that goal, we formulate the BVP consisting of the Laplace equation in 3D semi-infinite domain outside the Earth which is bounded by the approximation of the Earth's surface where the oblique derivative boundary condition is given. At infinity, regularity of the disturbing potential is prescribed. As the numerical method, we have implemented the FEM with mapped infinite elements, where the computational domain is divided into
two centrical parts, one meshed with finite elements and one with infinite ones. In numerical experiments, we firstly test a convergence of the proposed numerical scheme and then we deal with global gravity field modelling using EGM2008 data. To perform such numerical experiments, we create a special discretization of the Earth's surface to fulfil the conditions that arise from correct geometrical properties of finite elements. Then a reconstruction of EGM2008 aims to indicate efficiency of the presented numerical approach.
The short-term prediction of LOD  introducing atmospheric angular momentum by  1D-Convolutional Neural Networks (1D-CNN )
Authors: Guessoum Sonia , Belda Santiago , Ferrándiz José Manuel , Modiri Sadegh , Heinkelmann Robert , Schuh Harald
Published: March 2022

Abstract
Knowledge of the Earth orientation parameters (EOP) is essential for numerous practical and scientific applications including, positioning and navigating in space and on Earth.
The LOD (length of day), which represents the variation in the Earth's rotation rate, is the most difficult to forecast since it is primarily affected by the torques associated with changes in atmospheric circulation. Therefore, accurate LOD predictions are an ongoing challenge and are the focus of this work.
Consequently, there is a compelling need to identify next-generation time series prediction algorithms to be integrated into an operational processing chain. Of specific interest is the emergence of deep learning methods.  These methods tend to behave as highly adaptive and versatile fitting algorithms and can thus replace conventional fitting functions for enabling more accurate predictions.
In this study, the 1D-Convolutional Neural Networks (1D-CNN) is introduced to model and to predict the LOD using the IERS EOP 14 C04 and the axial Z component of the atmospheric angular momentum (AAM) taken from the German Research Centre for Geosciences (GFZ), since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: First, we detrend the LOD and Z-component series by using the LS method, then, we obtain the residuals series of each one to be used in the 1D-CNN prediction algorithm. Finally,  we analyzed the results before and after introducing the AAM function. These results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict LOD for up to 7 days.
Keywords

1D-Convolutional Neural Networks (1D-CNN);  Length of the day;  atmospheric angular momentum(AAM) function; prediction
Analysis of the dependency of atmospheric formaldehyde - as a proxy for bVOC emissions - on vegetation status over a Central European city and potential implications for surface ozone exceedances
Authors: Trimmel Heidelinde , Mayer Monika , Schreier Stefan , Schmidt Christian , Checa-Garcia Ramiro , Eitzinger Josef , Fitzky Anne Charlott , Karl Thomas , Huszár Peter , Karlický Jan , Hamer Paul , Koehler Philipp , Frankenberg Christian
Published: March 2022

In the city centre of Vienna, Austria ozone (maximum 8 hour mean) mda8 exceedances of the threshold value of 120 μg/m³ can occur from as early as March until September, which coincides with the main local vegetation season. Biogenic volatile organic compounds (bVOCs), which are mainly emitted by forests, but also other vegetation as agricultural field crops and are precursor substances to atmospheric formaldehyde (HCHO). Thereby they contribute to the production of ozone in and around the city. On the other hand, vegetated areas reduce the ozone concentration by uptake via stomatal and cuticular pathways and soil uptake.

In this study the dependency of HCHO mixing ratios, obtained from path averaged MAX-DOAS UV retrievals over the Vienna city centre, on meteorological parameters (air temperature, global radiation, boundary layer height) and vegetation drought stress indicators are analysed, focusing on the difference between drought and non-drought conditions. Following indicators are used: standardized precipitation index (SPI), relative soil saturation from the Agricultural Risk Information System (ARIS), vapour pressure deficit and satellite-based photosynthetically active radiation anomaly (fAPAR) as well as solar-induced chlorophyll fluorescence (SIF).

A clear dependency of the HCHO on vegetation-related parameters and the area of origin of HCHO and its precursor substances is found. However, the strength of the relationship between the parameters changes depending on the vegetation status. The results of the observational HCHO analyses spanning 2017-2021 are compared with bVOCs estimates of the Model of Emissions of Gases and Aerosols from Nature (MEGAN). The observed ozone concentrations are compared with the ozone mixing ratios and dry deposition rates calculated by the chemical transport model developed at Meteorological Synthesizing Centre-West within the European Monitoring and Evaluation Program (EMEP MSC-W model), which includes the Deposition of Ozone for Stomatal Exchange (DO3SE) model, to better understand timing and magnitudes of sources and sinks. Possible consequences for exceedances of the mda8 ozone target value in the study region are discussed.
A 1921 Western Australian tropical cyclone underscores the utility of historical records for hazard analysis in areas of marginal cyclone influence.
Authors: Switzer Adam D. , Christensen Joseph , Aldridge Joanna , Taylor David , Churchill Jim , Watson Holly , Fraser Matthew W. , Shaw Jenny
Published: March 2022

Shark Bay Marine Park is a UNESCO World Heritage Property in a region of marginal tropical cyclone influence and its sustainability requires a deep consideration of cyclone hazards. Here, we analyse historical records of a large storm surge from a Tropical Cyclone in 1921 that generated remarkable overland flow leaving fish and sharks stranded over 9 km inland. We weight information from the historical archives in a new framework and model event scenarios to reconstruct its magnitude. The plausible event scenarios imply that the cyclone was a marginal Category 4 or 5 storm with a return interval equivalent or slightly greater than the regional planning level. The outcome underscores the importance of examining the pre-instrumental events in areas of marginal cyclone influence as they are commonly of key economic importance.  Our work also implies that TC risk affects marine conservation in the Shark Bay World Heritage Property and requires attention.
Making sense of Isogeometric Analysis for geothermal applications: Parametric geomodelling (NURBS) for fast model construction, simulation and adaptation
Authors: Escallon Daniel , Degen Denise , Moulaeifard s.Mohammad , Wellmann Florian
Published: March 2022

Geological modelling is an essential aspect of a wide variety of geophysical and geological investigations related to geo-energy exploration and monitoring. A commonly-applied procedure is to use 3D geological models (often referred to as static models) to characterise the spatial distribution of material properties, which are then used in subsequent process simulations. The physical processes are described with partial differential equations that can be solved using different numerical methods by creating a discretisation in the space of the geometric object (i.e., a mesh). However, mesh generation can be a time-consuming step that generally only allows an approximation of the true geometric model. Several methods have been proposed to resolve these issues. We investigate here the use of the isogeometric analysis (IGA) technique, which exploits the finite element method (FEM) to numerically solve differential equations without the need of creating a mesh. Instead, it uses computer-aided design (CAD) tools, specifically Non-Uniform Rational B-splines (NURBS), to accurately represent any form of conic sections geometry. 

This presentation shows the link between NURBS representing geological interfaces and subsequent geothermal process simulations. The link is implemented in a user-friendly Python package (https://github.com/danielsk78/pygeoiga) with a simple but clear interface. It differs from other implementations by dealing with multipatch structures and focusing on geological modelling with multiple subdomains. A series of numerical examples are presented to show the use of the technique for solving the two-dimensional heat conduction problem. Results are contrasted to the results of a traditional FEM approach. The comparison shows that IGA requires fewer degrees of freedom (DoF) for convergence of the solution. Further, IGA provides a way to ease the workflow from the geological modelling to the results of process simulations, enabling tighter integration between modelling and simulation. Lastly, we describe shortly how the IGA concept can be implemented on top of existing standard FEM libraries.
Possible precursory indicators for the devastating fire in North Evia island during August 2021, using remotely sensed and Earth-observation data
Authors: Gemitzi Alexandra , Koutsias Nikos
Published: March 2022

The present work aims at unveiling possible precursory signals of the devastating fire in North Evia island, during August 2021, that destroyed approximately 400 km2 of forest and cultivated land. Therefore, the time series of two environmental parameters known to be related to wild fire occurrence, i.e. soil moisture and Normalized Difference Vegetation Index (NDVI) were extracted and analyzed. Soil moisture in the top soil layer from 0 to 7cm was extracted from the ERA5-Land Monthly Averaged - ECMWF Climate Reanalysis data set at a spatial resolution of 9 km. The time series of remotely sensed NDVI was accessed through the Landsat 8 mission, at a spatial resolution of 30m, with a 32-day time step. Both time series covered the period from January 2015 to October 2021. Results indicated two specific patterns in the examined time series. Soil moisture time series in the affected areas demonstrated a shard declining trend since 2018, reaching its lowest value just prior the fire events in North Evia. The NDVI time series did not show any distinctive trend during the examined period in the affected sites, however comparing it to surrounding unaffected areas with the same extent, occupied from the same land cover types, an alarming finding was revealed; the NDVI time series in the affected sites demonstrated statistically significant lower variability compared to unaffected ones. This difference corresponds to a more homogeneous vegetation and possible absence of fire breaks in the burned areas compared to the ones that were not affected. Findings of the present work may help in highlighting areas with specific characteristics related to soil moisture and NDVI, that indicate a high risk of fire occurrence.
Explainable deep neural networks for exploring spatial variability of soil properties in Germany
Authors: Taghizadeh-Mehrjardi Ruhollah , Scholten Thomas
Published: March 2022

Digital soil mapping approaches predict soil properties based on the relationship between soil observations and related environmental covariates using machine learning models. In this research, we applied deep neural networks to predict the spatial distribution of soil properties in Germany using 1976 soil observations and 170 environmental covariates which are derived from several sources (e.g., remote sensing data). However, a major problem with using deep neural networks is that the exact contribution of environmental covariates in the overall result is unknown. To address this issue and improve the interpretability of deep neural networks, several model-agnostic interpretation tools (i.e., post hoc analyses and techniques) are used to understand previously trained "black-box models" or their predictions. For example, a permutation feature importance technique ranked remote sensing images as the most important predictors to explain the spatial variability of soil organic carbon in the study area. This is the first study to use deep neural networks with explainable algorithms to explore and visualize the spatial distribution of soil properties in Germany.

Keywords: explainable machine learning; deep neural networks; soil properties; Germany
New AI based weighting strategy for 2m temperature and 10m wind speed forecasting over Moroccan airports  using the analog ensemble method.
Authors: Alaoui Badreddine , Bari Driss , Ghabbar Yamna
Published: March 2022

In front of determinism limitations, ensemble forecasting provides competitive advantage assessing uncertainty and helping weather information users in decision-making. Analog ensemble method (AnEn) is one of the most intuitive and computationally cheap ensemble methods that leverages a single deterministic model integration to produce probabilistic information. This method builds an ensemble forecast from a set of past observations of the target variable, neatly selected from a historical training dataset. For a given location, the most similar past forecasts to the current prediction are identified and the associated  past observations are nominated  as members of the analog ensemble forecast. However, The  AnEn forecasting quality is tightly affected by the process of skillful analogs selection in the training data which depends on predictor’s weighting among other factors. This work presents a new weighting strategy based on machine learning techniques (XGBoost, Random Forest and Linear regression) and assesses the impact of its application on the AnEn performance  for 10m wind speed  and 2m temperature forecasting over 13 Moroccan airports in the short term forecasting framework (24 hours). To achieve this, hourly forecasts from the operational mesoscale AROME model and the verifying observations covering 5 year period (2016-2020) are used.  The predictors include 2m temperature, 2m relative humidity, 10m wind speed and direction, mean sea level pressure and surface pressure,  meridonal and zonal components of 10m wind. The basic configuration of Delle Monache et al. (2013) -DM13- where all the predictor’s weights are equal to one is used here as a benchmark. The best weights are computed independently from one airport to another. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, and hence preserve physical meaning and correlations of the used weather variables, the AnEn performances are improved by up to 50 % for bias and by 30% for RMSE for most airports. This improvement varies as function of lead-times and seasons compared to AROME and DM13’s configuration. Results show also that AnEn performance is geographically dependent where a slight worsening is found for some airports.

 

Keywords : Analog Ensemble,  Machine Learning, Predictors Weighting Strategies, 2m Temperature, 10m Wind Speed, XGBoost, Linear Regression, Random Forest, Ensemble Forecasting.
A storyline view of the projected role of remote drivers on summer air stagnation in Europe and the United States
Authors: Garrido-Pérez José M. , Ordóñez Carlos , Barriopedro David , García-Herrera Ricardo , Schnell Jordan L. , Horton Daniel Ethan
Published: March 2022

Air pollutants accumulate in the near-surface atmosphere when atmospheric scavenging, horizontal dispersion, and vertical escape are reduced. This is often termed "air stagnation". Recent studies have investigated the influence that climate change could exert on the frequency of stagnation in different regions of the globe throughout the 21st century. Although they provide a probabilistic view based on multi-model means, there are still large discrepancies among climate model projections. Storylines of atmospheric circulation change, or physically self-consistent narratives of plausible future events, have recently been proposed as a non-probabilistic means to represent uncertainties in climate change projections. This work applies the storyline approach to 21st century projections of summer air stagnation over Europe and the United States. For that purpose, we use a CMIP6 ensemble to generate stagnation storylines based on the forced response of three remote drivers of the Northern Hemisphere mid-latitude atmospheric circulation: North Atlantic warming, North Pacific warming, and tropical versus Arctic warming.

Under a high radiative forcing scenario (SSP5-8.5), strong tropical warming relative to Arctic warming is associated with a strengthening and poleward shift of the upper westerlies, which in turn would lead to decreases in stagnation over the northern regions of North America and Europe, as well as increases in some southern regions, as compared to the multi-model mean. On the other hand, North Pacific warming tends to increase the frequency of stagnation over some regions of the U.S. by enhancing the frequency of stagnant winds, while reduced North Atlantic warming does the same over Europe by promoting the frequency of dry days.

Given the response of stagnation to these remote drivers, their evolution in future projections will substantially determine the magnitude of the stagnation increases. Our results show differences of up to 2%/K (~2 stagnant days in summer per degree of global warming) among the storylines for some regions. We will discuss the combination of remote driver responses leading to the highest uncertainties in future air stagnation separately for Europe and the U.S.
A Split-Explicit Runge-Kutta methods for 3D hydrodynamic equations for coastal applications
Authors: Ishimwe Ange , Lambrechts Jonathan , Legat Vincent , Deleersnijder Eric
Published: March 2022

Numerical models of marine hydrodynamics have to deal with processes exhibiting a wide range of timescales. These processes include fast external gravity waves and slower internal fully three-dimensional motions. In order to be both time-efficient and numerically stable, the temporal scheme has to be chosen carefully to cope with the characteristic time scale of each phenomenon. An usual approach is to split the fast and slow dynamics into separate modes. The fast waves are modeled with a two-dimensional system through depth averaging while the other motions, where characteristic times are much longer, are solved in a three-dimensional. However, if the splitting is inexact, for instance in projecting the fields in a new 3D mesh, this procedure can lead to improper results in regards to the physical properties such as mass conservation and tracer consistency. In this work, a new split-explicit Runge-Kutta scheme is adapted and developed for the Discontinuous-Galerkin Finite Element method in order to obtain a new second-order time stepping, yielding more accurate results. This method combines a three-stage low-storage Runge-Kutta for the slow processes and a low-storage one of two-stage for the fast ones. The 3D iterations are not affecting the surface elevation, hence an Arbitrary Lagrangian Eulerian implementation is straightforward. Water volume and tracers are conserved. A set of test cases for baroclinic flows as well as a laboratory application demonstrate the performance of the scheme. They suggest that the new scheme has little numerical diffusion.
Why do some Recurving Tropical Cyclones Impact Europe as Post-Tropical Cyclones?
Authors: Sainsbury Elliott , Schiemann Reinhard , Hodges Kevin , Baker Alexander , Shaffrey Len , Bhatia Kieran
Published: March 2022

Post-tropical cyclones (PTCs) are often associated with high winds and extreme precipitation over Europe. For example, ex-hurricanes Debbie (1961) and Ophelia (2017) were both responsible for national wind speed records in Ireland, and further east across Europe, ex-hurricane Debby (1982) caused significant wind damage over Finland. In previous work, we show that despite comprising only 1% of European impacting cyclones during hurricane season, almost 10% of those cyclones with storm force (>25ms-1) are PTCs, indicating that PTCs are disproportionately responsible for European windstorm risk.

By tracking and identifying observed TCs in two reanalyses, we explore the physical drivers for recurving TCs impacting Europe. Our methods of cyclone tracking and TC identification allow for a detailed analysis of the post-tropical stage of the TCs in the observational record, allowing us to separate the recurving TCs based on whether they impact Europe.

Using a composite analysis, we show that recurving TCs which impact Europe are significantly stronger at their lifetime maximum intensity, and for several days during and after extratropical transition. They are also 65% more likely to reintensify in the midlatitudes after completing extratropical transition. The Europe impacting recurving TCs interact more favourably with an upstream upper-level trough, which steers the TCs on a more poleward trajectory across a midlatitude jet streak. It is during the jet streak interaction that extratropical reintensification often occurs.

We show that TC lifetime maximum intensity and whether extratropical reintensification occurs both modulate the likelihood that a recurving TC will impact Europe as a PTC. Our results highlight the challenges of projecting PTC impacts over Europe in a future climate. Some climate model projections indicate a poleward shift in the jet, possibly indicating less opportunity for recurving TCs to interact with the jet and reintensify. However, sea surface temperatures are projected to warm, and lifetime maximum intensity may therefore increase. If the change in TC intensity outweighs any poleward shift in the jet, then a larger proportion of recurving TCs could reach Europe in the future.
Impact of microphysical uncertainty on the evolution of a severe hailstorm
Authors: Kuntze Patrick , Miltenberger Annette , Hoose Corinna , Kunz Michael , Frey Lena
Published: March 2022

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so do potentially uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties on the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.

Here, we focus the investigation on the effects of parametric uncertainty in a perturbed parameter ensemble, using the ICON model (with 2-moment cloud scheme, at 1 km grid spacing). A latin hypercube sampling is used to generate systematic variations of selected microphysical parameters from an eight-dimensional parameter space. Considered processes include riming, diffusional growth of ice and snow, CCN and INP activation, as well as the mass-diameter and mass-velocity relations. Isolated sensitivity experiments show distinct influences of all parameters on hail related variables, where the strongest impacts are found in simulations with reduced CCN and INP activation. We will present a detailed analysis of the simultaneous influence of parameter perturbations on the cloud microphysical evolution of the storm.
Modelling spatial and temporal patterns of fire due to human activity 
Authors: Perkins Oliver , Millington James , Matej Sarah , Erb Karlheinz
Published: March 2022

Despite recent climate change producing more favourable conditions for landscape fire in many regions, studies of remote sensing data have suggested that global burned area is declining. The reasons for this are poorly understood but land use change, landscape fragmentation and CO2 fertilisation have all been suggested as contributing factors. Understanding human-fire interactions has been hampered by fragmentation of work across multiple disciplines – including geography, anthropology, land economics and ecology – and much case-study work in specific local locations. Consequently, coherent understanding of how contemporary anthropogenic land use and associated fire management strategies influence spatial and temporal patterns of fire globally has not yet been established.

To address this challenge, we have developed WHAM! – the Wildfire Human Agency Model - parameterised using the global empirical Database of Anthropogenic Fire Impacts (DAFI, [1]). This new model is driven by explicit representations of human behaviour, drawing on agent functional types to capture categorical differences in anthropogenic approaches to fire management globally. We present initial results and evaluate WHAM! using land management data based on the Human Appropriation of Net Primary Production (HANPP) and find good agreement between model outputs and these independent data. Further, to enable a like-for-like comparison with moderate resolution remote sensing products, we present a model emulator to screen model outputs of small agricultural fires (0.5-21 ha). 

We discuss how WHAM! shows land use intensification in South America, itself driven by increases in global demand for meat, has led to a substantial decline in anthropogenic fire use. This provides a partial process-based explanation of declines in global burned area observed from remote sensing. We discuss implications for understanding global spatio-temporal patterns of wildfire and share how fellow modellers can access model data and code. 

[1] Perkins and Millington (2021) DAFI: a global database of Anthropogenic Fire. Figshare. https://doi.org/10.6084/m9.figshare.c.5290792.v1 
Morphodynamics of Brahmani River, Odisha, and its implication on Riverbank failure
Authors: Anand Aditya Kumar , Pradhan Sarada Prasad
Published: March 2022

In the middle and upper reaches of the Brahmani River, Odisha is prone to riverbank erosion and the avulsion process. The river devours a colossal landmass due to major floods and rainfall in the monsoon season. In the present study, morphodynamics of the Brahmani river has been studied for the past two decades (2000-2019). The study has been carried out using LANDSAT data to determine the changes in the channel belt of the river. Channel area, bar area, sinuosity, and braid-channel ratio have been determined to understand the avulsion and bank line shifting. The Brahmani River has been classified into ten classes based on their geomorphological and geological characteristics and further subdivided into 87 reaches. It has been observed that the middle and upper reaches (2000-2015) of the river depict variation in channel area and bar area. The variation in the channel area and bar area is less during 2015-2019 due to the construction of embankments and groyne on river banks. The higher sinuosity values are observed in the lower reaches of the Brahmani River. The major flood of 2011 had significantly affected the sinuosity pattern of the river. Similar trends have been observed in the braid channel ratio, which has been observed in the channel area. These parameters have been correlated with India Meteorological Department's gridded rainfall data (0.25°*0.25°) and discharge data collected from the Department of Water Resources, Odisha. Avulsion threshold index (ATI) was determined to classify the 87 reaches into stable, moderately stable, Critical, and most critical zones. The soil samples have been collected from the critical zones of the Brahmani River to determine their shear strength properties. A detailed geotechnical investigation of riverbank soil samples has been done in the laboratory. The shear strength properties of soil samples have been determined in consolidated undrained (CU) and unconsolidated undrained (UU) conditions. The numerical simulation of bank slopes has been done using PLAXIS 2D software under different conditions. The results obtained from numerical simulation determined the potential zones of failures.
Deployment of a local seismic network around the northern Guadiana Menor River (Betic Cordillera, Spain)
Authors: Garrido Jesús , Peláez José A. , Henares Jesús , Marín Carlos
Published: March 2022

The Guadiana Menor River, included in the Guadalquivir foreland basin, at the northern border of the Betic Cordillera, has suffered in the last decade some low to moderate magnitude seismic series. For instance, the so called 2012-2013 Sabiote-Torreperogil seismic sequence, 1 to 5 km deep, being the biggest recorded event a mbLg 3.9 earthquake, and the so called 2016-2018 Jódar-Peal de Becerro seismic series, less than 2 km and 9 to 13 km deep, 20 km southeast of the previous one, being a mbLg 4.1 event the greatest recorded magnitude. In both cases, seismic series show focal mechanism solutions mostly with strike-slip and some dip-slip and NW-SE compression with no clear tectonic features at surface, due to the sedimentary infill. The Spanish Instituto Geográfico Nacional (IGN) national seismic network recorded in the last years, mainly in the region of the 2016-2018 seismic sequence, some low magnitude earthquakes, showing that the fault that hosted these events continues to be active.

This was the main reason to develop a little local seismic network in the region, designed with the aim to study this persistent seismicity in terms of locations and focal mechanism solutions when there could be available data. It is equipped with six triaxial broadband sensors specifically deployed for this purpose, and also sharing data coming from a near IGN seismic station in the region. The seven seismic stations present as uniform azimuthal distribution as possible around the seismicity, being the nearest station less than 5 km from the seismicity area, and the farthest about 30 km away. Anyway, the seismic network spatial distribution has been conditioned for the fact that they are not permanent housing stations, requiring electric power. Most of the seismic stations are recording data from September 2021.

Real-time records are shared with the Spanish IGN seismic network in order to improve regional locations. Until now, several low magnitude earthquakes, below the perceptibility level of the national seismic network, have been located. In addition, focal mechanisms have been computed for some low magnitude events (~ mbLg 2.0), congruent with strike-slip solutions.
Effects on soil and vegetation of prescribed burn in the southeast of the iberian peninsula
Authors: Fajardo Álvaro , Moya Daniel , Peña Esther , Plaza-Álvarez Pedro , González Javier , Díaz Asunción , Botella Raúl , Borja-Lucas Manuel-Esteban , Gómez Elena , De las Heras Jorge
Published: March 2022

With current global warming, the exacerbation of climate change and the progression of neglect in rural areas, forest fires are increasing in extent and severity. To alleviate these changes in the fire regime and seek the reduction of large severe fires, the use of fire as a preventive management tool is being implemented through the application of prescribed burns in Mediterranean forests, used in forestry actions to reduce the understory and break both vertical and horizontal continuity of fuels. In this study we want to see the efficacy of the treatment by determining the effects of the prescribed burns on the soil, both physical-chemical and biological parameters, as well as on the vegetation, in a semi-arid forest ecosystem. The prescribed burns were performed in the municipality of Ayna, Albacete (SE Spain). To evaluate and monitor the ecological damage to the soil in the short term (1 year), CO2 flow measurement cameras were used to measure soil respiration, mini-disk infiltrometers, with a monthly monitoring period throughout the year. The results do not show significant alterations in all the parameters studied due to these prescribed burns over a long period, stabilizing together with the unburned plots. However, in the short period of time (3 months later), some variables are affected. This study aims to observe, and make known, the effects that these actions have on the soil, being these of great relevance to carry out a design, management, and application of these tools to forest management in the Mediterranean area.
Demand or Supply? An empirical exploration of theeffects of climate change on the macroeconomy
Authors: Marotta Fulvia
Published: March 2022

Using an original panel data set for 24 OECD countries over the sample 1990-2019 and a standard empirical macroeconomic framework for business cycle analysis, the paper tests the combined macroeconomic effects of climate change, environmental related policies and technology. Overall, we find evidence of significant macroeconomic effects over the business cycle: physical risks act as negative demand shocks while transition risks as downward supply movements. The disruptive effects on the economy are exacerbated for countries that did not adopt a carbon tax or with a high exposure to natural disasters. In general, we find evidence that green technological development that is not supported by the right policy mix may result in market failures that have different sizes for different countries with heterogeneous consequences on the phases and duration of their respective  cycles. A  coordinated  approach  on  climate  policies  would  therefore  be  essential for instance in a monetary union with common monetary and financial objectives.
Modelling flood events in Venice Lagoon with a cumulant CO lattice Boltzmann shallow water model
Authors: Padrone Jessica , Di Francesco Silvia , Venturi Sara
Published: March 2022

In this work a multi-relaxation time (MRT) Lattice Boltzmann model based on the use of non-conventional collision operator is used to simulate the flood event in Venice Lagoon.

Numerical methods (finite difference, finite volume and finite element methods) that solve the macroscopic equations of fluid mechanics (Navier Stokes equations), are usually employed for these aims. Most of these methods put in evidence that the application of bed slope and friction forces can lead to inaccurate solutions due to numerical errors.

In addition, the extension of these schemes to complex geometries is not straightforward and some of these approaches are very computational expensive if applied to real flows. Since the problems are posed at a large scale, it should be the aim to develop a simple and accurate representation of the source term to simulate realistic shallow water flows.

The LBM approach is a versatile method and it has been extensively applied in different fields.

Non-conventional Lattice Boltzmann models based on central moments and cumulants collision operators allows to simulate large-scale hydraulic problems such as flooding events and the use of a GIS environment allows to set the information related to topography, initial conditions (water depth and velocity values distribution), boundary conditions (position and type of solid and inlet/outlet boundaries), external force (value and distribution of roughness coefficients, obstacles position) and to make this data available for the execution of the numerical model.

In order to validate the correctness of the proposed mathematical model for Venice Lagoon, the real flood event that took place on November 12, 2019 is simulated: several field data are available for this test case; the results, in terms of water level and velocity field are compared with recorded data, verifying the accordance. Moreover, technical solutions for hydraulic risk evaluation and mitigation, taking account of the expected sea level rise, due to climate change, are proposed.
How to interpret Cosmogenic Radionuclides Exposure (CRE) ages on river-incised gorges undergoing rock falls: application to Southern French Alps and Provence
Authors: Petit Carole , Cardinal Thibaut , Rolland Yann , Audin Laurence , Braucher Régis
Published: March 2022

River channels and hillslopes are shaped by the joint action of localized, vertical fluvial incision along the channel and of diffuse surface creep, landslides or rock falls on the adjacent slopes, the latter being often gathered under the generic term of hillslope processes. The interplay between river incision and hillslope processes is responsible for various landscape forms, from smooth, low-relief areas to sharp and deeply incised domains. In areas where river incision is the dominant erosive process, determining the gradual exposure of river gorge walls  by cosmogenic radionuclide dating permits to estimate the long-term (several ka) local incision rate. However, strongly cohesive rocks like massive limestones or sandstones may be prone, from time to time, to abrupt and localized degradation by rock falls. On a gorge wall, besides resetting the exposure age signal on the area where a block has been detached, rock fall events also produce debris that transiently protect the bedrock from river incision and the bottom of the gorge wall from cosmic radiations. In some extreme cases, rock fall events can even lead to the formation of epigenetic gorges. Although gorge walls appear as good markers of river incision, the random occurrence of rock falls may therefore add complexity to the interpretation of exposure ages, to the point where the actual river incision imprint is barely discernable. In this presentation, we simulate the 1D evolution of topography and Cosmogenic Radionuclides Exposure (CRE) ages on a gorge wall progressively formed by river incision and randomly reshaped by rock falls, in order to evaluate the imprint of these events on the CRE signal. We then discuss the implications of these models on the sampling strategy and on the interpretation of previously dated river gorges in the Southern French Alps and Provence.