bluuebunny/crossref_metadata_2025 huggingface dataset:
Modelling the spatial variation in susceptibility to gully initiation and stabilization in the Southern Main Ethiopian Rift Valley
Authors: Belayneh Liuelsegad , Dewitte Olivier , Gulie Guchie , Poesen Jean , Stal Cornelis , Kervyn Matthieu
Published: March 2022

Gullying is a common soil erosion process in Southern Ethiopia, damaging agricultural landscapes and contributing sediments to water bodies. River catchments extending across the rift escarpment within lakes Abaya and Chamo Basin in the Southern Main Ethiopian Rift are areas experiencing such problems. Impact of gullying depends on the state of activity of gullies, i.e. inactive gullies might have had a role in the landscape formation, but they can now be considered stable and not contributing to sediment delivery to the downstream, unlike the active gullies. In order to reduce the impact of active gullies, understanding the conditions under which gullies initiate, expand and stabilize is vital. In this paper, the location where new gullies initiate and where they stabilize is modelled spatially. To determine factors controlling for the initiation of new gullies, the potential gully initiation points were extracted along more than 4520 active gullies using slope and drainage area thresholds. The susceptibility of gully initiation is then modelled using logistic regression and frequency ratio methods, with a set of 14 predisposing factors. The conditions for gully stabilization are assessed by modeling the location of the head of more than 1080 inactive gullies. Highly susceptible areas for gully initiation are mainly modelled in rejuvenated landscapes downslope of knickpoints, where steep slopes have been recently formed by knickpoint propagation.most susceptible areas for gully initiation are observed in concave slopes with high topographic wetness index, whereas heads of inactive gully stabilized when slopes become convex with a lower topographic wetness index. The area under receiver characteristics curve (AUC) of the validation data ranges from 0.75 to 0.85 for all susceptibility models; prediction rate of gully initiation and stabilization vary from 70 to 93%. Our results indicate that the applied models are reliable and have very good prediction performance of gully initiation and stabilization and that such approach contrasting the gully initiation point and the gully head location enable to better understand the gullying process. The resulting susceptibility maps are a step towards contributing to the decision-making process on the optimized locations of soil and water conservation measures, and thus contributing to landscape sustainability.
Experimental petrology and spectroscopy: building analogue samples in laboratory for planetary exploration
Authors: Pisello Alessandro , Tolomei Pietro , Brucato John Robert , Poggiali Giovanni , Petrelli Maurizio , Porreca Massimiliano , Perugini Diego
Published: March 2022

Interpretation of spectral data acquired remotely and/or in situ from other planets requires an exhaustive database taking into account well-characterized spectra. Silicate glasses are one of the main constituents of volcanic rocks and a deep knowledge of their spectral response is fundamental to characterize volcanic terrains that we can observe on other planets.

We will show a study concerning spectroscopy properties of silicate glasses, which were synthesized at the Petro-Volcanology Laboratory of the University of Perugia. This study has the main objective to simulate and characterize putative compositions of lavas present on the Northern Volcanic Province of Mercury.

Glasses were synthesized mixing pure oxides and melting them at high temperatures. Once produced, glasses were partly crushed to powders in order to obtain different grain size classes and distributions. Furthermore, they were partly embedded as bulk fragments in epoxy and irradiated by laser ablation at different powers to simulate space weathering effects on Mercury.

The spectroscopic characterization of the samples was performed at the INAF-Astrophysical Observatory of Arcetri, Firenze, where mid-IR biconical diffuse reflectance FTIR spectra in the range 7-15 µm range were acquired on samples characterized by different granulometry. Spectroscopic measurements were performed first on 7 different homogeneous granulometric classes (ranging from 25 to 500 µm), then on six heterogeneous granulometric classes presenting gaussian distributions with varying values of average granulometry and standard deviation. Finally, spectra were acquired on slabs of glasses, which were previously irradiated by laser ablation simulating both weathered surface and re-deposited fine material after meteoritic impacts.

The results showed that spectroscopic features depend on the grain sizes, and in particular they are strongly influenced by presence of fine materials in the heterogeneous samples. Such information was used to retrieve detailed granulometrical data of the bulk samples which were covered by ablated and redeposited particles.

The study shows that experimental petrology is indeed a powerful tool to obtain planetary analogues of any terrestrial and planet product. The spectral characterization and space weathering simulation in the laboratory represent useful techniques to develop instrumental and analytical knowledge for space and planetary exploration. This study was performed on a specific Mercurian product, but in general this kind of approach can be preparatory to design future exploration missions of any planetary/asteroidal site in particular.
Dominance of Pacific Sourced Deep Water in the Atlantic sector of the Southern Ocean during the last glacials
Authors: Hallmaier Moritz , Rückert Eva M. , Link Jasmin M. , Lütkes Laura , Frank Norbert
Published: March 2022

The deep Southern Ocean (SO) circulation is of major significance for the understanding of the ocean´s impact on Earth’s climate as uptake and release of CO­­­2 depend strongly on the redistribution of well and poorly ventilated water masses.

Neodymium isotopes preserved in deep sea sediment have proven useful to study the deep ocean circulation and water mass provenance thanks to basin scale isotope gradients between the Pacific and the North Atlantic. Here we present novel neodymium isotope data (εNd) of three sediment cores in 2.8, 3.3 and 3.6 km depth in the Atlantic sector of the SO to assess the presence of old and poorly ventilated Pacific sourced Deep Water (PDW) during the past 150 ka.

The sediment cores indicate dramatic temporal changes of εNd spanning a range of 7.7 ε-units from -1.0 to -8.3. While the εNd variability of the two deeper cores is driven by changes in ocean circulation, the shallowest drilling site is likely influenced by a local source of radiogenic Nd, such as weathering of volcanic material.

During peak glacial periods with maximum ice extent and a shoaled AMOC we observe radiogenic εNd values of ~-2.5 to -3.5. This confirms a predominance of glacial PDW at depths of >3 km with proportions close to 100% and thus increasing the water volume portion with enhanced respired carbon. We further advocate for the persistent presence of PDW even during interglacials although with a much smaller proportion.

Hence, our results enforce the leading role of the SO in storing and reinjecting respired CO2 into the deep Atlantic Ocean and the Atmosphere during glacial-interglacial terminations.
Sulphur dioxide (SO2) emissions by means of miniDOAS measurements during the 2021 eruption of Cumbre Vieja volcano, La Palma, Canary Islands
Authors: Albertos Violeta T. , Recio Guillermo , Alonso Mar , Amonte Cecilia , Rodríguez Fátima , Rodríguez Claudia , Pitti Lia , Leal Victoria , Cervigón Germán , González Judith , Przeor Monika , Santana-León José Manuel , Barrancos José , Hernández Pedro A. , Padilla Germán D. , Melián Gladys V. , Padrón Eleazar , Asensio-Ramos María , Pérez Nemesio M.
Published: March 2022

Cumbre Vieja is the most active volcano of the Canary Islands since it has been the scenario of  8 of 17 historical eruptions in this archipelago. A recent magmatic reactivation started at Cumbre Vieja volcano on October 2017, and 9 additional seismic swarms occurred until the recent eruption which started on September 19, 2021, and ended on December 13, 2022 after 85 days of eruption. Since the first day of the eruption, extending to current days, INVOLCAN performed the monitoring of SO2 realesed by this eruption using a miniDOAS on terrestrial (car), sea (ship) and air (helicopter) mobile position. More than 360 measurements of SO2 emission rates were carried out daily. The standard deviation of the estimated values obtained daily was ~ 20%. During the first days of the eruption, estimated SO2 emission rates reached more than 30,000 tons/day, and maintaining weekly average values above 10,000 tons/day until the end of the eruption. After a final paroxysmal phase with an eruptive column of 8,500 m altitude, decreased significantly to averages values of 250 tons/day. Estimated SO2 emission rates from the 2021 Cumbre Vieja eruption became a powerful tool to contribute to the understanding of eruptive dynamics.
Quality Assessment of SARAH-3: The new regional satellite-based Surface Solar Radiation data set from the CM SAF
Authors: Trentmann Jörg , Pfeifroth Uwe , Drücke Jaqueline , Cremer Roswitha
Published: March 2022

The incoming surface solar radiation has been defined as an essential climate variable by GCOS. Long term monitoring of this part of the earth’s energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models.

The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving climate data records (CDRs) from geostationary and polar-orbiting satellite instruments. Within the CM SAF these CDRs are accompanied by operational data at a short time latency to be used for climate monitoring. All data from the CM SAF are freely available via www.cmsaf.eu.

Here we present the new edition of the SARAH climate data record of surface solar radiation from the CM SAF. The regional SARAH-3 climate data record (Surface Solar Radiation Dataset – Heliosat) is based on observations from the series of Meteosat satellites. SARAH-3 provides high-resolution data (temporal and spatial) of the surface solar radiation (global and direct) and the sunshine duration from 1983 to 2020 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). For the first time, this edition of the SARAH data record also provides user-oriented data of spectral radiation, namely the photosynthetic active radiation (PAR) and the daylight (DAL); UV radiation parameters are also available upon request.

In this contribution we introduce the results from the comparison of the satellite-derived surface radiation with available surface measurements; the evaluation addresses the accuracy and the temporal stability of the satellite data using data from regional and global networks, e.g., BSRN, GEBA, ECA&D, CLIMAT, as well as, in the case of PAR and DAL, from individual stations. We present the improvements of the edition 3 of the SARAH data record compared to previous editions, in particular over snow-covered surfaces. The high accuracy and stability of these data records allow the assessment of the spatial and temporal variability and trends.
A global-scale vulnerability assessment of human displacement for floods and tropical cyclones
Authors: Mester Benedikt , Frieler Katja , Schewe Jacob
Published: March 2022

Floods and tropical cyclones displaced more than 275 million people between 2008 and 2020, with the two hazards together being responsible for 86% of all displacements. It is important to understand the socio-economic drivers of displacement vulnerability to quantify future changes in risk, for instance, due to climate change, economic development, or social inequities. Here, we investigate globally and event-by-event the displacement vulnerability due to flooding and tropical cyclones (TCs), using remote sensing-derived hazard data. We create a database of displacement events associated with spatially explicit flood or TC hazard, by matching displacement data from the Internal Displacement Monitoring Center (IDMC) spatially and temporally with satellite imagery from the recently published Global Flood Database and a collection of tropical cyclone data. The resulting hazard footprints are overlaid with gridded population data to derive the number of affected people for each event, which is compared with estimated displacement to determine the event-specific vulnerability. Between and within continental regions, displacement vulnerability varies by several orders of magnitude. We generally find a negative trend between displacement vulnerability and increasing (socio-)economic prosperity indicators, such as GDP per capita or the Human Development Index (HDI). Indicator binning reveals further insights, for instance, a higher proportion of urbanization or female population tends to indicate a lower susceptibility towards TC impacts. We analyze the uncertainty associated with different population datasets and methods to compute the number of affected people. Our analysis provides new insights into patterns and potential drivers of displacement vulnerability across space and between socio-economic groups. To our knowledge, the usage of the extensive set of observational satellite imagery is an unprecedented approach for global flood vulnerability analysis, posing remote sensing as a suitable alternative for global models for future studies. 
Postprocessing of gridded precipitation forecasts using conditional generative adversarial networks and quantile regression
Authors: Hemri Stephan , Bhend Jonas , Spirig Christoph , Nerini Daniele , Moret Lionel , Furrer Reinhard , Liniger Mark A.
Published: March 2022

Probabilistic predictions of precipitation call for rather sophisticated postprocessing approaches due to its low predictability, high spatio-temporal variability and highly positive skewness. Moreover, the large number of zeros makes the generation of physically realistic postprocessed forecast scenarios using standard approaches like ensemble copula coupling (ECC) rather difficult. In addition to classical statistical approaches, recently, machine learning based methods gained increasing popularity in the field of postprocessing of probabilistic weather forecasts.

In this study, we compare conditional generative adversarial network (cGAN) based postprocessing of daily precipitation with a quantile regression based approach. In principle, an appropriately trained cGAN model should be able to generate postprocessed forecast scenarios that improve forecast skill and cannot be distinguished from observed data in terms of spatial structure. While we use ECC to generate physically realistic forecast scenarios from quantile regression, cGAN does not need any additional ECC steps. For training and verification, we use COSMO-E ensemble forecasts with a grid resolution of about 2 km over Switzerland and the corresponding CombiPrecip observations, which are a gridded blend of radar and gauge observations. Preliminary results suggest that it is possible to generate realistic looking forecast scenarios using cGAN, but up to now, we have not been able to increase forecast skill. On the other hand, quantile regression seems to increase forecast skill at the expense of relying on an additional ECC step to generate forecast scenarios.
Retrieving freeze/thaw-cycles using Machine Learning approach in Nunavik (Québec, Canada)
Authors: Chen Yueli , Wang Lingxiao , Bernier Monique , Ludwig Ralf
Published: March 2022

In the terrestrial cryosphere, freeze/thaw (FT) state transition plays an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes.

Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Many algorithms have been developed and evaluated over time in this scope. With the advancement of data science and artificial intelligence methods, the potential of better understanding the cryosphere is emerging.

This work is dedicated to exploring an effective approach to retrieve FT state based on microwave remote sensing data using machine learning methods, which is expected to fill in some hidden blind spots in the deterministic algorithms. Time series of remote sensing data will be created as training data. In the initial stage, the work aims to test the feasibility and establish the basic neural network based on fewer training factors. In the advanced stage, we will improve the model in terms of structure, such as adding more complex dense layers and testing optimizers, and in terms of discipline, such as introducing more influencing factors for training. Related parameters, for example, land cover types, will be included in the analysis to improve the method and understanding of FT-related processes.
Phase Field Modelling of Interactions Between Hydraulic Fractures and Natural Fractures
Authors: Li Xiaoxuan , Hofmann Hannes , Yoshioka Keita , Luo Yongjiang , Liang Yunpei
Published: March 2022

Hydraulic fracturing is widely applied in unconventional reservoirs to generate fracture networks for productivity enhancement. Interactions between hydraulic fractures and natural fractures have a great impact on fracture propagation. In this study, we use a two-dimensional phase field model to investigate interactions between hydraulic fractures and different frictional or cemented fractures under different in-situ stress, injection rate, natural fracture orientation and strength. We find that with the increasing stress anisotropy, hydraulic fracture is more likely to cross natural fracture and leads to a lower fracture complexity. A moderate injection rate is conducive for complex fractures. The approaching angle between the hydraulic fracture and natural fracture impact fracture topology. Complex fractures are formed when the angles are not so steep. With the increasing strength contrast between natural fractures and the rock matrix, the material heterogeneity increases for hydraulic fractures to generate complex fractures. Compared with frictional NFs, opening stronger cemented NFs requires more pressure than hydraulic fracture propagating outside the interface. The numerical investigations in this study can provide theoretical support and design guidance for fracturing operations in complex geological conditions.
Climate ambiguity and optimal allocation of renewable energy capacities in the 2050 EU
Authors: Sirotko-Sibirskaya Natalia , Benth Fred Espen , Zeyringer Marianne
Published: March 2022

The Paris Agreement aims at creating the climate-neutral world by the middle of the 21st century. One possible and currently popular solution to achieve this goal is to integrate renewable energies into the existing power grid. However, this is a highly complex task, since renewable energies are intermittent due to their weather dependency and predicting weather long-term both at a sufficient time and spatial resolution is non-trivial. It is often the case that both weather predictions based on historical data and future climate predictions are of very limited precision. To accommodate both for aleatoric and epistemic uncertainties we address the problem of optimal allocation of renewable energy capacities in the context of distributionally robust optimization, where the best possible capacity allocation is found given the worst possible weather conditions. We determine the optimal mix for the future 2050 climate-neutral Europe in the financial portfolio framework, where we view mean production of renewable power plants as expected returns on the assets. In the suggested framework portfolio risk is diversifed based on probabilistic characteristics of the underlying assets. Taking into account probabilistic nature of weather variables provides additional safety guarantees which are of primary importance when modelling renewable-based power systems. We limit our analysis to solar and wind energy capacities and implement suggested approach using both historical data-based forecasts and climate predictions models for the EU. 
Autonomous Assessment of Source Area Distributions for Sections in Lagrangian Particle Release Experiments
Authors: Trahms Carola , Handmann Patricia , Rath Willi , Renz Matthias , Visbeck Martin
Published: March 2022

Lagrangian experiments for particle tracing in atmosphere or ocean models and their analysis are a cornerstone of earth-system studies. They cover diverse study objectives such as the identification of pathways or source regions. Data for Lagrangian studies are generated by releasing virtual particles in one or in multiple locations of interest and simulating their advective-diffusive behavior backwards or forwards in time. Identifying main pathways connecting two regions of interest is often done by counting the trajectories that reach both regions. Here, the exact source and target region must be defined manually by a researcher. Manually defining the importance and exact location of these regions introduces a highly subjective perspective into the analysis. Additionally, to investigate all major target regions, all of them must be defined manually and the data must be analyzed accordingly. This human element slows down and complicates large scale analyses with many different sections and possible source areas.

We propose to significantly reduce the manual aspect by automatizing this process. To this end, we combine methods from different areas of machine learning and pattern mining into a sequence of steps. First, unsupervised methods, i.e., clustering, identify possible source areas on a randomized subset of the data. In a successive second step, supervised learning, i.e., classification, labels the positions along the trajectories according to their most probable source area using the previously automatically identified clusters as labels. The results of this approach can then be compared quantitatively to the results of analyses with manual definition of source areas and border-hitting-based labeling of the trajectories. Preliminary findings suggest that this approach could indeed help greatly to objectify and fasten the analysis process for Lagrangian Particle Release Experiments.
Coincident signals on multiple counters in a neutron monitor: Sparse vs. dense atmospheric secondary particles from cosmic ray showers 
Authors: Chaiwongkhot Kullapha , Ruffolo David , Chaiwongkhot Poompong , Sáiz Alejandro , Banglieng Chanoknan
Published: March 2022

Neutron monitors were designed to measure atmospheric secondary neutrons from cosmic ray showers in order to track the cosmic ray flux vs. time.  Furthermore, at the Princess Sirindhorn Neutron Monitor (PSNM), an 18-counter NM64 detector at 2560-m altitude at Doi Inthanon, Thailand, the leader fraction (inverse multiplicity) inferred from time delay distribution between successive neutron events in the same counter has been used to track spectral variations.  More recent measurements of time delays between neutron events in different counters, as a function of counter separation, have confirmed that 1) the product neutrons from the interaction of a single atmospheric secondary neutron can spread among neighboring counters, with a cross-counter leader fraction that depends on whether the first counter is an end or middle counter, and 2) coincident counts between distant counters can be produced by multiple atmospheric secondary neutrons from the same primary cosmic ray, with a leader fraction that depends on whether the second counter is an end or middle counter.  Here we report on measurements of neutron signals in amplifier outputs at PSNM using a 4-channel oscilloscope, in order to further investigate these phenomena.  For a pair of neighboring counters located at the edge of the counter array, we find roughly equal event rates in either neighbor following a neutron trigger in one of them, implying that the difference in leader fraction relates to the base count rate of the first counter and is therefore lower if that is an end counter. In addition, an FPGA-based readout system was developed for more efficient collection of neutron events on two distant counters (Tubes 2 and 18) that were coincident within a 250-microsecond time window, while also monitoring Tube 10 in between.  The time distributions and neutron multiplicities indicate that a small fraction of the cosmic ray events that triggered both Tubes 2 and 18 also led to neutron events on Tube 10 with an enhanced rate of high multiplicity, indicating a few air shower events that densely “carpeted” the neutron monitor, while the majority of such coincidences apparently involved a sparse distribution of isolated secondary particles near Tube 2 and near Tube 18 and not near the intermediate Tube 10, which is consistent with a cross-counter leader fraction dependence on the count rate of the second counter.  This research was supported by the postdoctoral research sponsorship of Mahidol University, Thailand, by grant RTA6280002 from Thailand Science Research and Innovation, and by grant NSRF from the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, NXPO, Thailand [grant number B05F640051]. 
Magnetospheric Responses to Solar Wind Pc5 Density Fluctuations: Results from 2D Hybrid Vlasov Simulation
Authors: Zhou Hongyang , Turc Lucile , Tarvus Vertti , Pfau-Kempf Yann , Battarbee Markus , Dubart Maxime , Ganse Urs , Alho Markku , Grandin Maxime , George Harriet , Suni Jonas , Bussov Maarja , Papadakis Konstantinos , Manglayev Talgat , Horaites Kosta , Zaitsev Ivan , Cozzani Giulia , Palmroth Minna
Published: March 2022

Ultra-low frequency (ULF) waves in the Pc5 range, with periods between 150 – 600 s, play a key role in the dynamics of Earth’s magnetosphere, in particular through their interaction with radiation belt electrons. One important source of magnetospheric Pc5 waves are fluctuations of the upstream solar wind parameters in the same frequency range. Pressure variations in the solar wind are thought to result in a forced breathing of the magnetosphere, as the magnetosphere would expand and compress in response to the changing upstream conditions, which drives ULF waves inside the magnetosphere. The details of the interaction of these solar wind variations with the Earth’s bow shock and magnetosheath, their impact on the magnetosheath plasma properties and how the fluctuations would change before reaching the magnetopause, remain however unclear. In this study, we investigate the influence of externally-driven variations across near-Earth space using global 2D simulations performed with the hybrid-Vlasov model Vlasiator. The new time-varying boundary setup in Vlasiator allows us to set Pc5 periodic density pulses coming from the upstream. The density pulses cause the breathing motion of the bow shock, create clear stripes of variations inside the magnetosheath, and modulate the electromagnetic ion cyclotron (EMIC) and mirror modes. We characterize the spatial-temporal variations of waves on the simulation plane within the magnetosheath and discuss the potential impact on the near-Earth environment.
Are winds and moisture necessary to cause Indian summer monsoon extremes?
Authors: Singhai Priyanshi , Chakraborty Arindam , Rajendran Kavirajan , Surendran Sajani
Published: March 2022

The extremes of Indian summer monsoon rainfall (ISMR) are largely driven by the modulation of vertically integrated moisture flux over the Arabian sea (70oE) and the Bay of Bengal (90oE). The droughts and floods are resulted due to strong divergence and convergence of the moisture-laden winds over India associated with various external forcings. Therefore, identifying the association of the zonal moisture flux with ISMR in the observation and Climate Forecast System version 2 (CFSv2) model is essential to improve the prediction of the ISMR extremes. We find that, unlike observation, ISMR extremes in CFSv2 are all ENSO-related and mainly driven by the moisture flux over the Bay of Bengal and remain unresponsive to eastward boundary flux at 70oE. Further decomposition of the fluxes into dynamical (winds) and thermodynamical (moisture) components shows that both moisture and winds terms over the Arabian sea are necessary for determining extremes. However, in CFSv2, only the winds component of the eastern boundary (90oE) flux plays a significant role in driving the ISMR extremes. It is due to the presence of strong heating over the Western Pacific which results in strong eastward moisture flux over the Bay of Bengal through a Gill-type response.    
Temporal compound events: Are they represented in catastrophe models?
Authors: Hodsman Stephanie
Published: March 2022

Temporal compound events are defined in recent literature as successive events which impact the same geographical region. These kinds of events have the ability to cause catastrophic impacts. If we treat them as single events in a catastrophe model, the overall event magnitude, impact, and subsequent losses would be underestimated. The United Kingdom is vulnerable to temporally-compounding events due to low-pressure systems from the north Atlantic Ocean: the storms Desmond, Eva, Frank that occurred in December 2015 and Ciara, Dennis, Jorge that occurred in February 2020 are some recent, notable temporally compounding events that caused large economic losses.

 

For insurers and reinsurers to appropriately manage their exposure, it is imperative the tools they use truthfully reflect the risk of an insured asset being inundated several times due to temporal compound events. It has been recognised in previous research that catastrophe models are limited in their ability to handle connected, multi-hazard events. In addition, the risk of loss from temporal compound events should be demonstrated accordingly as the loss from a second event may not be as severe as the initial impact. Therefore, the definition of an event within a catastrophe model’s event set is extremely important. This provided the motivation to review temporal compound event representation in JBA Risk Management’s stochastic event set.

 

We manipulated various versions of stochastic event sets for known historical temporal compound events, and we explored how these different event sets alter the losses from catastrophe models. This research allowed us to interpret the impact various modelling strategies would have on (re)insurance companies should similar events occur in the future and provided further questions on how is best to model natural catastrophes.

 
A reconstruction of June–July temperature since AD 1383 for Western Sichuan Plateau, China,using tree-ring width
Authors: Li Jinjian
Published: March 2022

Global warming has a significant impact on the ecosystem of the high-altitude areas of the Tibetan Plateau, such as land degradation and the rapid retreat of glaciers. It is important to understand the effects of global warming occurring over hundreds or thousands of years. However, the long-term reconstruction of temperature is still lacking for the region of Western Sichuan Plateau (WSP). In this study, we reconstructed summer (June–July) temperatures back to 1383 for the WSP region based on a significant positive correlation between the standard chronology and summer temperature. The reconstruction passed statistical tests and explained 55.4% of the climatic variance in the period for which data are available (1962 to 2005). There have been five periods with warm summers and five with cold summers during the past 623 years. Warm periods occurred in 1383–1445, 1525–1590, 1700–1724, 1825–1870, and 1930–1960, while relatively cold periods occurred in 1446–1524, 1591–1675, 1725–1824, 1900–1929, and 1961–1990. A comparison with observed data and regional temperature reconstruction series shows that the reconstruction has a high degree of consistency and is indicative and reliability. The spatial correlation was analyzed with Climatic Research Unit gridded data to detect the spatial representativeness of the reconstruction, confirming that the reconstruction has a strong regional temperature signal for the WSP. Furthermore, ensemble empirical mode decomposition indicates quasi-oscillations of 2.8–3.3 years, 6.4–8.3 years, 11.1–14.2 years, 25–38.4 years, 89 years, and 138 years in the reconstructed temperature time series during the past 623 years. El Niño Southern Oscillation cycles, solar activity, and Atlantic multi-decadal oscillation can potentially be the primary forcing factors. Meanwhile, the coordinated action of temperature-induced drought stress with a late growing season related to delayed snowmelt can potentially be attributed to the divergence problem (observed around 2005).
Characterising trend changes of groundwater quality in Wallonia (Belgium)
Authors: Verstraeten Elise , Alonso Alice , Vanclooster Marnik , Collier Louise
Published: March 2022

Over the last decades, various regulations and good management practices have been implemented to reverse the increasing concentration of nitrates and pesticides in our groundwaters. The effects of these efforts on groundwater quality are complex to assess because of the non-linearity and the spatial heterogeneity of the hydrological fluxes, the numerous potential pollution sources, and the temporal delays due to the infiltration time in the vadose zone.

We use the information encoded in the monitoring data of 52 groundwater wells and galleries in Wallonia (Belgium) to assess the effectiveness of the regulations and management practices. We analyze the trends of the time series and subsequently seek to explain their spatial and temporal variability by looking into the characteristics likely to support or undermine the regulations and good practices such as water depth or urban development.

Our results should contribute to a better understanding and prediction of the groundwater quality trends at the catchment scale in Wallonia, providing guidance to catchment management. 
The legacy of hard coal mining in Germany: hydrochemistry and stable isotopes of mine waters
Authors: Weisser Oliver , Strauss Harald
Published: March 2022

           Underground hard coal mining occurred in Germany over several centuries in the Ruhrgebiet in Northrhine-Westphalia and ceased in 2018. Nineteen mine waters draining historic and (sub)recent mines were studied in respect to their hydrochemistry and selected stable isotopes (H, O, C, S) in order to identify prevailing processes that determine their chemical composition.

All mine waters show near neutral pH values. Electrical conductivity displays a wide range with values between 370 and 1690 μS/cm (average value of 700 μS/cm). Major ions are in decreasing importance: dissolved inorganic carbon, calcium, sulfate, magnesium, sodium and chloride.

Sulfate concentrations range from 27 to 363 mg/L, showing a positive correlation with electrical conductivity. Sulfate sulfur and oxygen isotopes display some variability between -4.9 and +20.4 ‰ (average value of +4.5 ‰) and between +0.7 and +12.7 ‰ (average value of +4.5 ‰), respectively. Most isotope values suggest that sulfate derived from pyrite oxidation and/or the oxidation of organo-sulfur compounds in the coal. In contrast, the very positive sulfur and oxygen isotopes could indicate dissolution of sulfate minerals.

Apart from sulfate, dissolved inorganic carbon (DIC) is a major ion with concentrations ranging from 60 to 600 mg/L (average value of 249 mg/L). The carbon isotopic composition of DIC is somewhat variable with values between -19.3 and -4.5 ‰. Samples center around an average value of -11,40 ‰, suggesting the dissolution of carbonate minerals through carbonic acid.

In summary hydrochemical and stable isotope data from these mine waters suggest water-rock interaction as the main compositional driver, specifically carbonate dissolution, pyrite oxidation and sulfate dissolution.
Clustering networks: reducing the complexity of urban hydrology models with graph partitioning for fast and flexible simulations
Authors: Dobson Barnaby , Muhandes Samer , Borup Morten , Mijic Ana
Published: March 2022

Graph partitioning algorithms separate nodes of a graph into clusters, resulting in a smaller graph that maintains the connectivity of the original. In this study we use graph partitioning to produce reduced complexity sewer networks that can be simulated by a novel urban hydrology model. We compare a variety of algorithms, including spatial clustering, spectral clustering, heuristic methods and we propose two novel methods. We show that the reduced network that is produced can provide accurate simulations in a fraction of the time (100-1000x speed up) of typical urban hydrology models. We address some likely use cases for this approach. The first is enabling a user to pre-specify the desired size of the resultant network, and thus the fidelity and speed of simulation. The second is enabling a user to preserve desired locations that must remain in their own cluster, for example, locations with complex hydraulic structures or where monitoring data exists. The third is a case where detailed sewer network data is not available and instead the network must be simulated hundreds of times in a random sampling of network parameters, something that is only possible with the speed gains that our method allows. We envisage that this reduced complexity approach to urban hydrology will transform how we operate and manage sewer systems, enabling a far wider range of model applications than are currently possible, including optimisation and scenario analysis.
Assessment and Validation of Daily Enlil and EUHFORIA Simulations During 2019–2021
Authors: Barnes David
Published: March 2022

The Heliospheric Expert Service Centre (H-ESC), part of ESA’s space weather service network, serves to provide existing heliophysics models for the provision of alerts and forecasts of space weather conditions at Earth and throughout the heliosphere. This is achieved by means of remote sensing and in-situ measurements of space weather transients, including Coronal Mass Ejections and high-speed solar wind streams, combined with advanced MHD modelling to predict their arrival times at points of interest in the heliosphere. Two such models include the European Heliospheric Forecasting Information Asset (EUHFORIA) and the ENLIL model operated by the UK Met Office, both of which are currently federated through the H-ESC portal. As a means to test both models, we perform daily runs of the EUHFORIA model using the same inputs (GONG magnetograms and CME cone-files) as the Met Office ENLIL simulations since the beginning of 2019, which are compared to real solar wind observations near Earth. We employ well-established model validation methodology by deriving contingency tables and the associated skill scores for both models as a means to assess their ability to make accurate space weather forecasts during this period of parallel operation.
Flood damage model bias caused by aggregation
Authors: Bryant Seth , Kreibich Heidi , Merz Bruno
Published: March 2022

Reducing flood risk through improved disaster planning and risk management requires accurate and reliable estimates of flood damages.  Damage models commonly provide such information through calculating the impacts or costs of flooding to exposed assets, such as buildings within a community. At large scales, computational constraints or data coarseness leads to the common practice of aggregating asset data using a single statistic (e.g., the mean) prior to applying non-linear damage models. While this simplification has been shown to bias model results in other fields, like ecology, the influence of object aggregation on flood damage models has so far not been investigated. This study quantifies such errors in 12 published damage function sets and three levels of aggregation using simulated water depths. Preliminary findings show bias as high as 20% (of the damage estimate), with most damage functions having a positive bias for shallower depths (< 1 m) and a negative bias for larger depths (> 1 m). In other words, compared to an analogous model with object-specific asset data, aggregated models overestimate damages at shallow depths and underestimate damages at large depths. These findings identify a potentially significant source of error in large-scale flood damage assessments introduced, not by data quality or model transfer, but by modelling approach. With this information, risk modellers can make more informed decisions about when, where, and to what extent aggregation is appropriate. 
A global 3D map of marine plastic litter: a data assimilated modelling framework  
Authors: Kaandorp Mikael , Lobelle Delphine , Kehl Christian , van Sebille Erik
Published: March 2022

Estimates of plastic quantities entering our oceans are not yet consistent with observed concentrations in the marine environment. This has led to the often-quoted statement that 99% of the marine plastics are missing. Here, we present a framework where the global transport of marine plastics is modelled over long time scales, in which the effects of different sources and sinks is investigated. Data assimilation techniques are used to inform unknown parameters regarding these sources and sinks, enabling us to quantify their role on the global plastic mass budget.

State-of-the-art numerical models are included in the framework to capture for the first time the combined effect of marine plastic beaching, resuspension, biofouling, turbulent mixing, and fragmentation. The relative importance of different marine plastic sources is investigated, such as mismanaged coastal plastic waste, riverine outflow, and fishing activity. Unknown parameters are found by means of calibration to a large set of observational data of plastic concentrations in the ocean surface water, water column, ocean floor, and on coastlines.

We show that with this framework, the global marine plastic mass budget can be closed. An overview is given of which environmental reservoirs are likely to contain most of the plastic mass, which sources are contributing to most of the pollution, and what the residence times of litter in the marine environment is. With the model calibration approach, we additionally get a better insight in the physics governing the transport of marine litter. 
AtmoDist as a new pathway towards quantifying and understanding atmospheric predictability
Authors: Hoffmann Sebastian , Deng Yi , Lessig Christian
Published: March 2022

The predictability of the atmosphere is a classical problem that has received much attention from both a theoretical and practical point of view. In this work, we propose to use a purely data-driven method based on a neural network to revisit the problem. The analysis is built upon the recently introduced AtmoDist network that has been trained on high-resolution reanalysis data to provide a probabilistic estimate of the temporal difference between given atmospheric fields, represented by vorticity and divergence. We define the skill of the network for this task as a new measure of atmospheric predictability, hypothesizing that the prediction of the temporal differences by the network will be more susceptible to errors when the atmospheric state is intrinsically less predictable. Preliminary results show that for short timescales (3-48 hours) one sees enhanced predictability in warm season compared to cool season over northern midlatitudes, and lower predictability over ocean compared to land. These findings support the hypothesis that across short timescales, AtmoDist relies on the recurrences of mesoscale convection with coherent spatiotemporal structures to connect spatial evolutions to temporal differences. For example, the prevalence of mesoscale convective systems (MCSs) over the central US in boreal warm season can explain the increase of mesoscale predictability there and oceanic zones marked by greater predictability corresponds well to regions of elevated convective activity such as the Pacific ITCZ. Given the dependence of atmospheric predictability on geographic location, season, and most importantly, timescales, we further apply the method to synoptic scales (2-10 days), where excitation and propagation of large-scale disturbances such as Rossby wave packets are expected to provide the connection between temporal and spatial differences. The design of the AtmoDist network is thereby adapted to the prediction range, for example, the size of the local patches that serve as input to AtmoDist is chosen based on the spatiotemporal atmospheric scales that provide the expected time and space connections.

By providing to the community a powerful, purely data-driven technique for quantifying, evaluating, and interpreting predictability, our work lays the foundation for efficiently detecting the existence of sub-seasonal to seasonal (S2S) predictability and, by further analyzing the mechanism of AtmoDist, understanding the physical origins, which bears major scientific and socioeconomic significances.
Predicting regional soil moisture dynamics using machine learning techniques and a dense observational network 
Authors: Shokri Nima , Bakhshian sahar , Zarepakzad Negar , Nevermann Hannes , Hohenegger Cathy , Or Dani
Published: March 2022

Soil moisture has a direct impact on ecosystem functioning, vegetation and crop production, environmental health and affects the stability of rural communities. Soil moisture plays a crucial role in all aspects of land-atmosphere interactions including extreme events such as heatwaves, droughts and floods. The highly localized and complex nature of soil moisture present a major challenge to its accurate estimation. Notwithstanding recent advances in satellite-based monitoring, the temporal and spatial resolution and shallow observation impede their application to mechanistic modeling and to highly resolved applications. Motivated by the importance of soil moisture on many hydrologic processes, the objective of the present study is to develop a predictive tool capable of describing the relationship between soil moisture and a wide range of climatic and soil related parameters. Within this context, we report a dense in-situ measurement networks that offer valuable ground truthing supplemented by physics informed machine learning (ML) techniques. We conducted a detailed observational campaign covering 100,000 m2 in Falkenberg in Germany by deploying a dense network of sensors to measure soil moisture (at 29 locations), ambient temperature and relative humidity, wind speed, near-surface radiation fluxes and soil temperature. We also determined soil characteristics and important properties (e.g., particle size distribution). We used static and dynamic climatic and soil-related predictors (covariates) for training the ML models to capture the complex relationship between the soil moisture and predictor covariates. Following Hassani et al. [2020], we employ different ML algorithms for model training to evaluate their performance in forecasting soil moisture dynamics in space and time using rigorous cross-validation. This work will shed new lights on the interaction and relationship between soil moisture dynamics and a variety of climatic and soil parameters.

 

Reference

Hassani, A., Azapagic, A., Shokri, N. (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale, Proc. Nat. Acad. Sci., 117(52), 33017-33027, doi.org/10.1073/pnas.2013771117