bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensitivity of surface wave and gravity data to velocity and density structure in the mantle – insights from transdimensional inversion
Authors: Szwillus Wolfgang
Published: February 2023

Determining the thermochemical structure of the mantle is crucial for understanding its evolution and dynamics. Temperature variations have long been known as important driving forces of mantle convection; however compositional differences can also influence dynamics. Additionally, compositional differences can act as indicators left behind by processes operating in the past. Both aspects have played a role in the ongoing discussions on the Large Low Shear Wave Velocity Provinces (LLSVP), the proposed Bridgmanite Enriched Ancient Mantle Structures (BEAMS) and the fate of subducted oceanic crust.A prerequisite for determining compositional differences in terms of major oxides with geophysical techniques is a joint determination of several geophysical properties. A single geophysical property (density, velocity) could almost always be explained by temperature or composition variations alone – except in pathological edge cases. The geophysical signature of composition lies in the pointwise relation between properties. This pointwise relation can be distorted by spectral filtering or inversion smoothing and damping.In this contribution, I parametrize the mantle as a collection of discrete spatial anomalies in terms of seismic velocity and density. Surface wave phase speed and satellite gravity data are used to constrain the anomalies. A transdimensional Monte Carlo Markov Chain method is used to generate ensembles of solutions that try to balance model complexity and data fit. An important aspect of this setup is that the two data sets used are complementary: While satellite gravity data are available (nearly) globally with homogeneous quality, coverage of phase speed data depends on the spatial distribution of seismic stations and large earthquakes. Conversely, the gravity field lacks true depth sensitivity, which surface wave data can provide by combining several frequencies.I will present synthetic investigations that aim at determining how accuracy and coverage affect the simultaneous recoverability of seismic velocity and density.
Insights into the terminal Ediacaran marine carbonate record from shale-hosted carbonate carbon isotopes
Authors: Yilales Mariana , Bowyer Fred , Wood Rachel , Poulton Simon
Published: February 2023

The marine carbon isotope record (δ13C) used for chemostratigraphy and reconstruction of carbon cycle dynamics is constructed using carbonate rocks, but there is evidence that carbonate cements hosted within fine-grained clastics (shales and mudstones) in some settings may also express δ13C trends that covary with the record from carbonates. We present new carbon and oxygen isotopic data from shale-hosted carbonate cements (δ13Ccarb-sh and δ18Ocarb-sh, n = 107, <16 wt% CaCO3) of the terminal Ediacaran Nama Group, Namibia (≥550.5 to <539.6 Million years ago; Ma). These data are compared with the published carbon and oxygen isotopic record from coeval carbonates (δ13Ccarb and δ18Ocarb, n = 1611) and total organic carbon (TOC) concentrations. We show that δ13Ccarb-sh compositions in samples of intermediate to high CaCO3/TOC can approximate contemporaneous δ13Ccarb in open marine mixed carbonate-clastic settings. By contrast, δ13Ccarb-sh values in samples with low CaCO3/TOC that were deposited in clastic settings distant from the locus of carbonate deposition are more negative than contemporaneous δ13Ccarb. These data suggest that δ13Ccarb-sh may approach seawater composition in samples of low TOC when deposited in high dissolved inorganic carbon (DIC) settings, where carbonate can rapidly precipitate from seawater during early diagenesis. However, the use of δ13Ccarb-sh to infill gaps in the existing δ13Ccarb record remains uncertain, even when these criteria are fulfilled. Intervals of δ13C-δ18O co-variability in the Nama Group succession appear to correlate with units where seawater mixing with meteoric fluids was more likely during early diagenesis, such as clastic-dominated settings, which also show significant decreasing δ18O through time with gradual sub-basin infill.We further consider uncertainties in lithostratigraphic correlation of the upper Urusis Formation of the Nama Group that enable three new possible correlations to be proposed for δ13Ccarb-sh data within the terminal Ediacaran to lower Cambrian (<542.65 Ma to >535 Ma) regional and global δ13Ccarb records.
Further development of a numerical modeling concept of the coupled THM behavior within a generic nuclear waste repository in crystalline rock
Authors: Guevara Morel Carlos , Maßmann Jobst , Thiedau Jan
Published: February 2023

Crystalline rock is one of the potential rocks under consideration in Germany for hosting a heat-generating nuclear waste repository. The focus of this contribution is a repository system, which relies on the Containment Providing Rock Zone (CRZ) in a crystalline rock as the principal containment barrier. In compliance with the German safety requirements, the integrity of the CRZ is required, i.e. to keep its containment capabilities for a period of 1 million years. This implies that the formation of new pathways must be excluded, temperature development must not significantly impair the barrier effect and anticipated stresses and fluid pressures should not exceed the dilatancy strength and the fluid pressure capacity, respectively. To meet this requirement a safety-oriented assessment of mechanical, hydraulic, thermal and chemical processes, as well as their couplings, occurring in the host rock due to the storage of heat-generating nuclear waste, excavation and/or gas production among others, is needed and numerical modelling is an essential and powerful tool for it.Fractures and other types of discontinuities, which usually characterize crystalline rock, are expected to influence the hydraulic behavior of the system. Thus, an adequate representation of the fracture network is required in order to capture its relevant properties, which will ultimately define the hydraulic boundary conditions surrounding the CRZs. Typically it is only possible to characterize fracture networks statistically. This requires a systematic investigation to quantify the influence of multiple realizations on the repository system.The decay-heat produced by the heat-generating nuclear waste leads to an increase of the local temperature through which the host rock as well as the fluid will expand. Consequently, a change in the effective stresses is also expected. With the aim of obtaining a better representation of the temperature-induced fluid pressure increase a bi-directional coupling of the hydraulic-mechanical (HM) processes is being tested.Previous work done by [1] based on a one-directional coupling between the HM processes for the estimation of the fluid pressure and one realization of a statistically generated generic geological model. This contribution aims to further develop the work from [1] by using a bi-directional coupling between the HM processes, implemented in the open-source finite element code OpenGeoSys version 6, to calculate the thermal induced fluid pressure increase and quantify the impact on the integrity criteria. Moreover, preliminary results for an approach used to evaluate the influence of multiple statistically equivalent fracture networks on the repository will be presented.References[1] Thiedau, J., et al.: CHRISTA-II - Analysen zur Integrität von geologischen Barrieren von Endlagersystemen im Kristallin. Ergebnisbericht, Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, 202
Measuring the Carbon Footprint from Wildfires and Crop Burning Using Satellite Data
Authors: Jain Raunaq , Odhiambo Mitchell , Kaushal Nikita , Mishra Abhilash , Gorana Yash
Published: February 2023

Climate change and altered land use patterns have increased the risk and frequency of wildfires over the last decade. Today's estimates of the carbon impact of wildfires deploy a bottom-up approach to estimate the amount of carbon released into the atmosphere. Various parameters and variability in this approach make it difficult to make quick and accurate estimations of the carbon emissions from wildfires. We propose using satellite data from NASA’s OCO-2 and MODIS satellites to directly estimate the amount of CO2 released into the atmosphere from wildfires. A similar approach can be used to measure carbon emissions from crop-burning activities in the Gangetic plains- another significant source of carbon emissions. Direct measurements of carbon emissions can help policymakers and researchers make data-based decisions to prevent forests from becoming carbon sources instead of carbon sinks. 
In our presentation, we will present a new data platform to estimate carbon emissions from localized wildfires and crop-burning instances using publicly available satellite data. The platform has pre-built functions and pipelines, allowing researchers to perform data analysis without the need to write cumbersome code. The underlying data lake combines NASA’s Orbiting Carbon Observatory (OCO-2 and OCO-3) data with other data sources (e.g., MODIS-based fire data) that facilitate more accurate and complete modeling of the dynamics of biomass burning and the impact they have on their immediate geography and the planet’s climate system at large.
Quantification of uncertainty using artificial neural networks for mapping of soil properties in Germany
Authors: Taghizadeh-Mehrjardi Ruhollah , B.M. Heuvelink Gerard , Scholten Thomas
Published: February 2023

Machine learning models have been widely used in digital soil mapping to predict the spatial distribution of soil properties across the landscape. However, due to the unpredictable behavior of soil properties, insufficient training sample size, input data error, uncertainty in model parameters and structure, uncertainty inherently exists in machine learning predictions. Therefore, knowledge of prediction uncertainty is vital for decision-makers and end-users. This study quantifies the overall uncertainty of digital soil maps of Germany for soil acidity, organic carbon, cation exchange capacity, and clay using quantile regression forest (QRF) and artificial neural network (ANN) models. Here, we propose the use of a novel ANN model that directly estimates the lower and upper bounds of a prediction interval by using an architecture with two output neurons. A multi-objective evolutionary algorithm (i.e., non-dominated sorting genetic algorithm II) was employed to parameterize the ANN weights. The results of the modeling indicated that ANN performed better than the QRF for predicting soil properties. Additionally, the ANNs produced narrower prediction intervals in comparison to the QRF. Most importantly, ANN yielded prediction interval coverage probabilities that were more closely aligned to their associated confidence levels in comparison to the QRF. In general, the ANNs were not only effective in predicting soil properties, but they were also effective in constructing reasonable prediction intervals for soil characteristics; and therefore, it is recommended to be used for predicting soil properties and quantifying their uncertainty in digital soil mapping.Keywords: uncertainty estimation, prediction interval, multi-objective optimization, artificial neural networks, machine learning, Germany
Chemometric evaluation of the field burn severity indexes
Authors: González-Pérez José A. , Jiménez-Morillo Nicasio T. , Vega José A. , Fernández Filgueira Cristina , Fontúrbel Teresa , Almendros Gonzalo
Published: February 2023

For temperate regions in forests and scrublands, a 5-level categorization was established to operationally define soil burn severity (SBS). This classification is important for estimating post-fire erosion risks and scheduling post-fire rehab activities [1]. This work describes the relationships between field SBS and molecular-level changes in the soil organic matter (SOM). Direct analytical pyrolysis [2] was used to study unburned (SBS-0) and burned soils (SBS 1 to 5) at two scenarios: 1) wildfire, and 2) burning laboratory experiment. The pyrolysis compounds were identified and plotted in modified 3D van Krevelen diagrams [3]. Moderate chemical changes occurred between stages SBS-0 to SBS-3, such as, dehydration, demethylation, depletion of polymethylene structures, carbohydrate rearrangements, lignin demethoxylation and triterpene preservation. Clear SOM transformations occurred at SBS-4 onwards with concentration of alkyl structures (triterpenes). Progressive changes in terms of SBS were not linear for all compounds. The subtraction surfaces suggest two major stages: SBS (0–1) generation of pyrolytic anhydrosugars; and SBS (3–4) generation of condensed compounds. Molecular-level changes in SOM after wildfire and laboratory experiment were similar. Nonetheless, the former showed incorporation of alkyl compounds and methoxyphenols, probably inputs of charred biomass from the vegetation.Keywords: Soil Burn Severity; Statistical Analysis; Analytical PyrolysisReferences:[1] Vega, JA. et al. 2013. Plant Soil 369, 73–91[2] Jiménez-Morillo, NT. et al. 2016. Catena 145, 266–273[3] Almendros, G. et al. 2018. J. Soils Sediments 18, 1303-1313Acknowledgment:Funding projects: MARKFIRE (EU-FEDER Andalusia; P20_01073), EROFIRE (FCT Portugal; PCIF-RPG-0079-2018). Contract: N.T.J.M. (FCT CEECIND/00711/2021 and RYC2021-031253-I). Technical assistance D. Monis and A. Carmona.
Direct Observation of Grain Boundary Sliding in Forsterite Bicrystals
Authors: Singh Shobhit Pratap , Thom Christopher , Hansen Lars , Marquardt Katharina , Wheeler John , Mariani Elisabetta , Mecklenburgh Julian
Published: February 2023

Olivine is the most abundant mineral in Earth’s mantle, and its rheology is likely to control upper-mantle convection. While the rheology of olivine is widely studied, little is known about the rheology of olivine grain boundaries and their effect on deformation in the mantle. Forsterite bicrystals, synthesized by direct bonding of highly polished single-crystal plates, were tested in this study to investigate sliding along the single grain boundary at high temperature (1300°C). Prior to deformation, the bicrystals were polished and scratch markers were scribed perpendicular to the grain boundary to track grain-boundary sliding. Bicrystals were deformed in shear loading between two alumina pistons in a uniaxial creep apparatus at 1 atm with applied axial stress ranging from 1 to 30 MPa. The specimen deformation was measured in real time using a high-resolution (~1 μm) linear variable differential transducer. Each test was carried out until attainment of a quasi-steady state deformation rate to determine the creep parameters. Post-deformation microstructural analysis was conducted using a scanning electron microscope (SEM) and electron backscattered diffraction. Our study established that the creep-rate law for bicrystals is different than single and polycrystalline forsterite. Bicrystals are weaker and shows up to 1 order of magnitude higher deformation rates. SEM microstructures reveal the sliding of scratch markers, which is direct evidence of grain-boundary sliding in forsterite. However, the strain geometry is complex, and further experiments are necessary to determine the overall strain distribution in the sample. Here we present the rationale of our research, and we compare our results on grain-boundary sliding in forsterite with the earlier literature.
A TS-InSAR clustering approach to detect spatio-temporal changes inground deformation
Authors: Rygus Michelle , Hussain Ekbal , Novellino Alessandro , Bateson Luke , Meisina Claudia
Published: February 2023

SAR images can be used to measure changes in the surface of the Earth over time using Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) techniques. TS-InSAR enables the detection and measurement of very small changes in surface deformation, often on the order of millimetres or less. This makes it a powerful tool for monitoring a wide range of natural and man-made phenomena, such as tectonic activity, subsidence, ground water extraction, and the behaviour of engineered structures like buildings and bridges. While TS-InSAR provides deformation measurements, further analysis must be taken to understand the underlying cause of the deformation. In this study, a novel framework has been developed to extract the vast amount of information embedded within the large number of ground deformation Measurement Points (MPs) derived from the Small BAseline Subset (SBAS; Berardino et al., 2002) TS-InSAR technique. The proposed automatic data-mining approach begins with clusterization the TS-InSAR MPs by applying a nonlinear dimensionality-reduction technique, Uniform Manifold Approximation and Projection (UMAP; McInnes et al., 2018), prior to performing clustering with Hierarchical Density based Spatial Clustering of Applications with Noise (HDBSCAN; Campello et al. 2013) in order to group together MPs exhibiting similar deformation behaviour on a large scale. Next, every extracted cluster time series is further investigated by applying a piecewise linear function as a method to detect and quantify accelerations and decelerations of deforming areas.A test of the method has been conducted over the Bandung Basin (Indonesia) using Sentinel-1 data from October 2015 to December 2020. Application of the method provides an objective way to identify changes in displacement rates over time and provides a wealth of information on the dynamics of surface displacement over a large area. The displacement rates, their spatial variation, and the timing and location of accelerations and decelerations can be used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to causal and triggering factors.ReferencesBerardino, P., Fornaro, G., Lanari, R., Sansosti E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40 (11) (2002), pp. 2375-2383.Campello, R.J., Moulavi, D., Sander, J. Density-based Clustering Based on Hierarchical Density Estimates. In Advances in Knowledge Discovery and Data Mining, Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining; Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu,G., Eds.; Springer: Berlin, Germany, 2013; pp. 160–172 McInnes, L. and Healy, J. UMAP: uniform manifold approximation and projection for dimensionreduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
Benchmark of the high-resolution Nemo-SI3-XIOS configuration SEDNA on an ARM-based HPC system, Fugaku.
Authors: Odaka Tina , Gouaillardet Gilles , Talandier Claude , Lique Camille , Dérouillat Julien , Meurdesoif Yann
Published: February 2023

We have developed the ocean-sea ice Arctic regional configuration SEDNA (Sea ice - EDdy resolving ocean paN-Arctic configuration) at ultra-high resolution (800 m in the horizontal and 150 vertical levels) based on the NEMO-SI3 numerical platform, in order to investigate how the dynamics of mesoscale turbulent eddies in the Arctic Ocean interplay with sea ice. The configuration was initially developed on the AMD-based HPC system Joliot-Curie ROME based at CEA in France, which has achieved a Linpack performance of 12 PFlop/s ranked number 33 of June 2020 TOP500 list (https://top500.org/system/179700/).Thanks to a European PRACE allocation of nearly 40 million CPU hours, we were able to run a 8 year-long simulation. Although promising to understand part of the small-scale dynamics at play, this length of simulation will likely be a limiting factor in the investigation of the eddy dynamics which is known to equilibrate over several decades.To overcome this limitation, here we investigate the feasibility of running SEDNA over several decades but at a realistic time cost. To that aim, a benchmark has been performed on the ARM based HPC system Fugaku based at RIKEN in Japan, which has archived a Linpack performance of 442 PFlop/s ranked number 1 of November 2020 TOP500 list (https://www.top500.org/system/179807/). Such a benchmark has required adaptation of the compiling and placements of NEMO and XIOS MPI processes in order to fit on the Fugaku architecture compared to standard X86_64 based HPC systems like Joliot-Curie ROME. In this presentation we will share the tips and lessons learned from our benchmarks and will report the benchmark results.  Our insights on model MPI placements for efficient post processing of huge models will be discussed as well.
Structural evolution of long-lasting fault zones developed in dolostones in the south-western Matese area (southern Apennines, Italy).
Authors: Diamanti Renato , Camanni Giovanni , Vitale Stefano
Published: February 2023

In the southern Apennines, Mesozoic carbonates are characterized by the superimposition of different brittle deformation structures formed during different phases of rifting, orogenic shortening and post-orogenic extension. In the western Matese area, fault zones developed in the Triassic dolostones commonly exhibit the complex juxtaposition of different fault rocks, wide damage zones and pulverized dolomite rocks. Significantly, pulverization causes a drastic change in the petrophysical behavior of deformed rock masses through dynamic shattering. For these reasons, a better description of the processes that steer the “dolomite flour” spatial distribution through fault zones represents a fundamental aspect of the mechanical stratigraphy assessment related to subsurface infrastructure building and of the fault zone hydraulic behavior.In this work, we present a multi-scalar and multi-methodological approach to provide a possible structural evolutionary scenario for the Matese Triassic dolostones. We analyzed fault zones outcropping along the eastern side of the Telese plain in the Ailano and Alife area. The fault zones consist of mature damage zones where pulverized rocks occur in patches of variable size (10s 100s of meters) immediately adjacent to the fault core. The fault zones show complex arrays of slip surfaces, including a major N-S normal fault crosscut and reactivated by NE-SW and NW-SE strike-slip faults. Extensive powder bodies are heterogeneously distributed within the damage zone and adjacent to the fault cores of both systems of faults. Fault zone architecture is associated with different brittle structural facies: i) cataclastic to ultra-cataclastic facies; ii) wide volumes of pulverized “dolomite flour”; iii) foliated cataclasite; iv) cataclastic shear bends; v) hydrothermalized dolomite; vi) unstrained lithons.Fault analysis suggests that the N-S normal fault systems accommodated extension during the Mesozoic passive margin extensional phases. The NE-SW and NW-SE strike-slip fault systems developed in a successive phase of orogenic shortening. The shortening-related strike-slip faults propagation overprint the inherited Mesozoic fault zone through extensive pulverization processes of dolostones with the formation of a wide volume of “dolomite flour”.
A model inter-comparison and sensitivity analysis of three urban canopy models
Authors: Gal Csilla
Published: February 2023

This study summarizes the inter-comparison of three urban canopy models that are able to derive local-scale urban air temperature from rural atmospheric data and urban land use and built form characteristics at an hourly resolution. The focus of this presentation are the findings of the sensitivity analysis and the lessons learned in the process. The evaluated models are the Urban Weather Generator (UWG), the Vertical City Weather Generator (VCWG), the Surface Urban Energy and Water Balance Scheme (SUEWS). The evaluation is done against a two-week-long air temperature and relative humidity measurement conducted in the neighborhood of Újlipótváros in Budapest, Hungary.  The study found a good agreement between modeled and observed air temperature values with a root mean square error (RMSE) remaining between 1–2ºC when calculated for the entire period. However, when separated per day- and nighttime, as well as per cyclonic and anticyclonic periods, the RMSE of the models increased up to 2–3ºC—particularly when calculated for nighttime and/or for anticyclonic periods. The sensitivity analyses shed light on additional shortcomings in the models. It revealed UWG’s low sensitivity to trees and vegetation. In this regard, especially the presence of trees in the urban canopy were not captured by the model. The analysis also found discrepancies regarding VCWG’s model physics, as the model responded to the increase in shortwave radiative forcing with decreasing air temperatures. The study will conclude with a set of recommendations for model developers and users.
Probabilistic Precipitation Nowcasting with Physically-Constrained GANs
Authors: Choma Matej , Murín Matej , Bartel Jakub , Troller Milly , Najman Michal
Published: February 2023

It is generally accepted that weather forecasts contain errors due to the chaotic nature of the atmosphere. Regression models, such as neural networks, are traditionally trained to minimize the pixel-wise difference between their predictions and ground truth. The major shortcoming of these models is that they express uncertainty about prediction with blurring, especially for longer prediction lead times. One way to tackle this issue is to use a generative adversarial network, which learns what real precipitation should look like during training. Coupled with a loss, such as Mean Squared or Mean Absolute Error, these networks can produce highly accurate and realistic nowcasts. As there is an inherent randomness in those networks, they allow to be sampled from, just like ensemble models, and various probabilistic metrics can be calculated from the samples. In this work, we have designed a physically-constrained generative adversarial network for radar reflectivity prediction. We compare this network to one without physical restraints and show that it predicts events with higher accuracy and shows much less variance among its samples. Furthermore, we explore fine-tuning the network to the prediction of severe weather events, as an accurate prediction of these benefits both automated warning systems and forecasters.
Proton energy spectra of energetic storm particle events and their relation with magnetic turbulence and intermittency nearby interplanetary shocks
Authors: Lepreti Fabio , Chiappetta Federica , Laurenza Monica , Benella Simone , Consolini Giuseppe
Published: February 2023

Shock waves propagating in the interplanetary space are efficient sources of energetic particles. In situ spacecraft observations, especially particle fluxes which can be used to obtain energy spectra, provide very useful data for the investigation of the acceleration mechanisms occurring at shocks. In this work we analyse the kinetic energy spectra of several proton flux enhancements associated with energetic storm particle (ESP) events observed by various spacecraft. ESP events occurring both in association with and in absence of Solar Energetic Particles (SEPs) are considered. Moreover, ESP events associated both with quasi-perpendicular and quasi parallel shocks are investigated.  Different functional forms (i.e. Weibull function, double power law, and Ellison-Ramaty) are used to fit the observed spectra and the obtained results are discussed in relation to the shock properties and to the magnetic turbulence and intermittency in the upstream and downstream regions. More specifically, the properties of magnetic turbulence and intermittency are analysed by calculating power spectral densities and structure functions of the fluctuations of the magnetic field components and the implications for particle acceleration are examined.This research has been carried out in the framework of the CAESAR project, supported by the Italian Space Agency and the National Institute of Astrophysics through the ASI-INAF n. 2020-35-HH.0 agreement for the development of the ASPIS prototype of scientific data centre for Space Weather.
Maize and wheat yield forecasting in the Pannonian Basin using extreme gradient boosting and its performance in years of severe drought
Authors: Bueechi Emanuel , Fischer Milan , Crocetti Laura , Trnka Miroslav , Zappa Luca , Grlj Ales , Dorigo Wouter
Published: February 2023

The increasing frequency and intensity of severe droughts over recent decades have significantly impacted crop production in the Pannonian Basin in southeastern Europe. Related crop yield losses can be substantial and require logistic compensation on an international level. To plan such compensations, seasonal crop yield forecasts have proven to be a valuable tool to support decision-makers in taking timely action. However, the impact of severe droughts on crop yields is often underestimated by such forecasts. To address this issue, we developed a maize and wheat yield forecasting system based on extreme-gradient-boosting machine learning for 42 regions in the Pannonian Basin. The used predictors describe vegetation state, weather, and soil moisture conditions derived from Earth observation, reanalysis, in-situ data, and seasonal weather forecasts. The wide range of predictors was selected to represent the state of the crops and the conditions they are facing and are expected to face. We expected it to be crucial, especially during severe drought years, to provide the model with sufficient information about the drought and its impacts. Afterwards, the model was validated, with a focus on drought years. 

Our results show that crop yield anomaly estimates in the two months preceding harvest have better performance than earlier in the year (relative root mean square errors below 17%) in all years. The models have their clear strength in forecasting interannual variabilities but struggle to forecast differences between regions within individual years. This is related to spatial autocorrelations and a lower spatial than temporal variability of crop yields. In years of severe droughts, there is a clear improvement in the forecasts with a 2-month lead time over longer forecasts too. The crop yield losses remain underestimated, but the wheat model performs in drought years better than for average years with errors below 12%. The errors of the maize forecasts in drought years are larger than for non-drought years: 30% two months ahead and 20% one month ahead. The feature importance analysis shows that in general wheat yield anomalies are controlled by temperature and maize by water availability during the last two months before harvest. In severe drought years, soil moisture is the most important predictor for the maize model and the seasonal temperature forecast becomes key for wheat forecasts two months before harvest. Going forward, a finer spatial resolution of the predictors will be tested to better distinguish the yields between the different regions. In addition, longer time-series of crop yield data, including more data during severe drought years, will help to test the findings of this study. 
Buoy-based detection of low-energy cosmic-ray neutrons to monitor the influence of atmospheric effects
Authors: Schrön Martin , Rasche Daniel , Weimar Jannis , Köhli Markus , Boehrer Bertram , Dietrich Peter , Zacharias Steffen
Published: February 2023

Neutron monitors on the Earth’s surface are usually used to observe the dynamics of highly energetic cosmic-ray particles, assuming that local environmental conditions do not influence the measurement. In another young research field, low-energy cosmic-ray neutrons are used to monitor local dynamics of environmental water content. Water in soil, air, snow and vegetation determines the amount of ground albedo neutrons in the sensitive energy range from 1 eV to 100 keV. Plenty of small neutron detectors are operated on natural or agricultural sites all around the world. A major issue is the modulation of the neutron flux by the dynamics of incoming high-energy cosmogenic particles. Conventionally, independent data from neutron monitors are consulted to serve as a reference for the correction of the local detectors. However, the performance of this comparative correction approach is unreliable, because it does not account for geographical displacement, different energy windows of the detectors, or potential influence of atmospheric conditions on the referenced neutron monitor.To test the traditional correction approaches for incoming cosmic radiation, air pressure, and air humidity, an experimental setup should avoid any influence of changes due to soil moisture. Therefore, a set of neutron detectors have been deployed in a buoy at the center of a lake for six months. The measurement period also included a Forbush Decrease in September, 2014. We found that the neutron signals correlated with air pressure, air humidity, and secondary cosmic radiation. The thermal neutron response to air humidity has been revealed to be different from the epithermal neutron response, while air pressure and incoming radiation similarly   influenced the thermal and epithermal signals. The results have been used to evaluate different existing strategies for air humidity correction of low-energy neutron data. Additionally, the potential effect of lake temperature on the thermal neutron count rate has been investigated. We have also analyzed the performance of the buoy  signal together with different neutron monitors in their capability to correct for the changes of incoming radiation and for the Forbush Decrease during the measurement period.Overall, the study demonstrates how low-energy neutron detectors on a buoy  could be used to assess the influence of atmospheric and cosmogenic factors on the signal without the influence of soils. Despite the low count rate over water, the general principle could also serve as an alternative to remote neutron monitors as a more local reference signal at more comparable energies.
Modelling Of Hydro-Geomorphological Processes Related To Sediment Transport: Case Study of the Baganza River (Italy)
Authors: Khan Usman Ali , Vacondio Renato , Dazzi Susanna , Ferrari Alessia , Valentino Roberto
Published: February 2023

The accuracy and reliability of sediment transport models is crucial for understanding and predicting geomorphological changes in river systems, which can have important implications for conserving riverine ecosystem. This information can be used to make better informed decisions about the management of the river, as well as to predict and prepare for potential hazards such as flash flooding. In the past, various approaches have been used to model these processes for suspended and bedload sediment transport. However, many of these models have limitations including spatial and temporal scales, data requirements, model complexity, numerical stability and computational cost, particularly when it comes to simulating the transport of bedload sediments.In this study, we tried to address these limitations by testing a 2D weakly coupled numerical model for bedload transport in a real application. The model was implemented by adopting schemes presented in previous works (Vacondio et al. 2014, doi.org/10.1016/j.envsoft.2014.02.003; Juez et al. 2014, doi.org/10.1016/j.advwatres.2014.05.014). The advantage of using a weakly coupled model is that it is flexible, computationally efficient and can be used to simulate bedload transport in large-scale systems while producing consistent and reliable results over time. It is based on the finite-volume method and uses the Shallow water and Exner equations for the liquid and solid phases, respectively. High computational efficiency is guaranteed by parallelization on Graphics Processing Units.We selected the case study of the Baganza River (Italy), characterized by a catchment area of 228 km2 and a total length of 55 km. We focused on the 28 km-long stretch between Calestano and Parma, with an average slope in the order of 0.8-1.5% and grain sizes in the order of 2-30 mm. For the topography, we used high-resolution digital elevation model (DEM) from 2008, while grain size distribution data were obtained through a hybrid technique combining field sediment sampling and photogrammetry. The adopted approach for the characterization of fluvial sediments at different points along the river is desirable in order to accommodate the full range of particle sizes inside the riverbed. The inflow boundary condition is the 2008-2014 series of floods on the Baganza River, including a destructive flood that occurred in October 2014.The riverbed topography resulting from the numerical simulation was compared with the one extracted from the DEM provided by a LiDAR survey carried out after the October 2014 event. The overall fair agreement between measured and simulated results suggests the usefulness of 2D weakly coupled numerical model for simulating hydro-geomorphological processes in the Baganza River. Moreover, the hybrid technique adopted for the grain characterization provides realistic representation of the sediments and increases the accuracy and reliability of the model predictions.
Nitrogen and Phosphorus co-limitation impact on temperate forests productivity 
Authors: Nakhavali Andre (Mahdi) , Mercado Lina M. , Hartley Iain P. , Sitch Stephen
Published: February 2023

Global process-based models with dynamic vegetation and functions show an increasing ongoing land carbon (C) sink, primarily due to CO2 fertilization effects (Sitch et al., 2008; Schimel et al., 2015). However, the phosphorus (P) and Nitrogen (N) related processes may constrain the plant’s photosynthetic and growth capacity and the C sink magnitude in temperate forests consequently (Aerts and Chapin, 1999; Vitousek et al., 1997; Vitousek, 2004). The co-limitation of N and P changes the plant C fixation capacity and the uptake of the atmospheric CO2 consequently. However, modelling studies are incapable of reproducing the productivity decrease due to these limitations (Fleischer et al., 2019). In this study, we introduced the P dynamics, and its interaction with N and C cycles in the Joint UK Land Environment Simulator (JULES-CNP), and parametrized and calibrated it using a well-site Free-Air Carbon Enrichment (FACE) facility in Mill Haft woodland, England. We further investigate the role of N and P availability and limiting impact on C cycles under different CO2 conditions. Furthermore, we modified the SOC representation in JULES (RothC model) to simulate observed Soil C soil stabilization capacity that is mainly related to soil texture. Our study shows an NPP reduction due to N and P limitation under both ambient and elevated CO2 conditions at the study site. Furthermore, using an improved SOC model in JULES, our results show an increase in soil C turnover with rising CO2 which leads to lower equilibrium soil C stocks than expected from the rise in soil C input alone.

 

Reference:

 

Aerts, R. and Chapin, F. S.: The Mineral Nutrition of Wild Plants Revisited: A Re-evaluation of Processes and Patterns, Adv. Ecol. Res., 30, 1–67, https://doi.org/10.1016/S0065-2504(08)60016-1, 1999.

Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F., Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M., Haverd, V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn, B. E., Mercado, L. M., Norby, R. J., Pak, B., von Randow, C., Quesada, C. A., Schaap, K. J., Valverde-Barrantes, O. J., Wang, Y.-P., Yang, X., Zaehle, S., Zhu, Q., and Lapola, D. M.: Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition, Nat. Geosci., 12, 736–741, https://doi.org/10.1038/s41561-019-0404-9, 2019.

Vitousek, P. M.: Nutrient Cycling and Limitation: Hawai’i as a Model System, Princeton University Press, 2004.

Vitousek, P. M., Mooney, H. A., Lubchenco, J. M., and Melillo, J. M.: Human Domination of Earth Ecosystems, Science (80-. )., 278, 21, 1997.

 
Microscale inversion modelling of Black Hill Norite, South Australia
Authors: mcenroe suzanne , Lee Madeline , Pastore Zeudia , Church Nathan , Schmidt Phillip
Published: February 2023

In this work, we conduct inverse modelling of all opaque minerals in a thin section from the Black Hill Norite (BHN), South Australia. The BHN is a mafic intrusion and one of three plutons within the Black Hill Complex. The BHN is important as it has yielded an Early Ordovician paleomagnetic pole position for Australia. Paleomagnetic measurements from the BHN exhibit an intensity and direction different from the local magnetic inclination and declination, indicating a strong and stable NRM.The total magnetic moment of the thin section was measured in a cryogenic magnetometer at Geological Survey of Norway and yielded D = 171°, I = 3°, and m = 4.5 x 10-7 Am2. An electron backscatter image was made of the entire thin section using a Phenom XL scanning electron microscope to map and identify the opaque minerals. The opaque minerals were ilmenite and magnetite. The thin section was then scanned using NTNU’s Scanning Magnetic Microscope in a near free field environment, therefore mapping the sample’s remanent anomalies. All opaque mineral grains in the thin section were modelled using a multistep parametric inversion approach. Each grain was modelled in two different 3-D geometries, as a single frustum volume and as a series of smaller tabular array volumes. This bulk and discretized modelling allowed for a multi-scale analysis of each grain. The calculated total moment of the frustum and tabular array modelling was D = 356°, I = 6.2°, m = 2.2x10-7 Am2, and D = 359°, I = 8.3°, m = 3.3x10-7 Am2 respectively.Three modelled remanent magnetization populations were identified in this sample: discrete paramagnetic ilmenite, ilmenite with magnetite reduction-exsolution lamellae, and discrete magnetite with single-phase oxy-exsolved ilmenite. Although discrete ilmenite is paramagnetic, magnetic modelling of this thin section indicates that ilmenite grains without lamellae can be attributed inaccurate modelled magnetizations due to nearby magnetite grains.
Using stakeholder-developed forest management maps to model fire reduction treatment effects on forest fire
Authors: Asif Bushra Sanira , Fiorucci Paolo , Perello Nicolò
Published: February 2023

Over the past century, severe wildfire events have been recorded globally due to climate change. Changes in climatic conditions may change forest landscape by modifying rates of vegetation formation, shifts in temperature and tree species shift. There was a near-complete loss of native tree species in some affected areas and loss of these species were most strongly linked to burn frequency. Many questions remain regarding how these changes will occur across landscapes and how disturbances such as wildfires may interact with changes to climate and vegetation. Forest management is used to proactively modify forest structure and composition to improve fire resilience. Yet, research is needed to assess how to best utilize the resources to reduce damages due to forest fires. Human communities also exist within these landscapes, and decision regarding how to manage forests must carefully consider how management will affect such communities.
The scientific literature recognizes the importance of incorporating stakeholders' knowledge and the active role of local communities to enhance and strengthen adaptive capacities to fire risk management. However, the research in this area seems to be still at the initial stage, and this gap needs to be addressed through actions that value the knowledge and voices of stakeholders and local communities. This research aims to contribute to this gap by sharing the process with the application of participatory mapping GIS involving forest stakeholders in a forest fire risk area in Liguria, Italy. To investigate local community preferences for forest management, public participation geographic information systems (PPGIS) mapping exercise is a good tool for local residents to express their views on fire reduction treatments (for example, commercial harvest, non-commercial harvest and prescribed fire). Emergent themes from the mapping exercise can be used to inform alternative management scenarios to explore the usefulness of using PPGIS to generate modelling inputs. Scenarios will be ranged from restoration-only treatments to short-rotation commercial harvest. The use of PPGIS is useful for outlining the range of forest management preferences within the local community, for identifying areas of agreement among residents who have otherwise polarized views, and for generating modelling inputs that reflect views that may not be obtained through existing official channels for public participation. Involving forest stakeholders in the decision-making process may increase public acceptance of the forest fire treatments needed to modify wildfire trajectories under future climate conditions and to improve forest resilience.
Weather-dependent climate change
Authors: Goessling Helge
Published: February 2023

Climate is defined by the statistics of the weather. We are thus used to the notion that weather depends on climate in the sense that a weather state is a quasi random realization drawn from the climatological distribution of weather states. Consequently, weather obviously also depends on climate change. Here it is argued that it can be very instructive to consider this dependence the other way around and to investigate how climate change depends on the weather. More specifically, the question is how different parts of the climatological distribution change, depending on certain relevant characteristics of the weather. For example, one may ask how the climate at some time of the year and at some location changes between a pre-industrial and a globally +4K warmer climate, considering only days where the local winds at some height blow from a specific direction. Alternatively, one may investigate how the climate change pattern differs between certain large-scale atmospheric circulation regimes, such as NAO+ and NAO- situations. While such conditional climate change analyses can be based for example on reanalysis or CMIP-type climate model data, a more extreme variant are storyline simulations where the evolution of the large-scale circulation is imposed in a climate model using different climate backgrounds, allowing to assess climate change conditional on a specific evolution of the large-scale circulation. Storyline simulations inevitably ignore possible changes in the likelihood of circulation patters. In contrast, analyses based on sufficiently large samples of reanalysis or CMIP-type data also allow for quantifying changes in likelihoods and constitute a proper decomposition of the complete (unconditional) climate change signal. Here the concept of weather-dependent climate change is explained and its potential to help unravel the complexities of climate change is demonstrated.