bluuebunny/crossref_metadata_2025 huggingface dataset:
Tectonic Influence on Bed-Character Variability under Thwaites Glacier, West Antarctica
Authors: Borthwick Louise , Muto Atsuhiro , Anandakrishnan Sridhar
Published: March 2021

Subglacial topography and bed character are important controls on glacier and ice-sheet flow. Previous studies using reflection-seismic data from the upper half of Thwaites Glacier, West Antarctica, have shown variations in the bed character in the along-flow direction with continuous soft bed in the flatter “lowland” areas and a mix of soft and hard bed over more elevated, rugged “highland” areas. Here we use long-offset reflection/refraction seismic and aerogravity data over a ~40-km section 230-km inland of the current grounding line to model the upper-crustal structures and relate them to the previously identified bed-character variability. We identified a sedimentary basin ~11-km long and up to ~400-m deep beneath the lowland area with continuous soft bed. The downstream end of this sedimentary basin aligns with the transition from the lowland to highland area which indicates its existence could be related to the formation of the subglacial topography. The sedimentary basin is a graben or half-graben potentially formed due to rifting associated with the development of the West Antarctic Rift System, suggesting tectonic influence on the bed character variability and, in turn, on the glacier flow. We will further analyze the seismic reflection data and also add aeromagnetic data to model the crustal structures more accurately and clarify the potential tectonic control on bed-character variability.
First geodetic mass balance estimate of the bulk of the South Shetland Islands ice caps
Authors: Shahateet Kaian , Seehaus Thorsten , Navarro Francisco , Braun Matthias
Published: March 2021

The Antarctic Peninsula ice sheet is an important contributor to sea-level rise and the glaciers in its peripheral islands have a large potential to increase their contribution under a warming climate. This region has undergone a complex history of climate change during recent decades, which justifies a close monitoring of their glaciers. The South Shetland Islands (SSI) is one of the northernmost archipelagos in this region, but it is lacking a geodetic mass balance (GMB) calculation for the entire archipelago. We have estimated the GMB of the SSI over a 3-4 years period within 2013-2017 (depending on the data availability for each island). Our estimation is based on remotely-sensed multispectral and interferometric SAR data covering 96% of the glacierized areas of the islands considered in our study, and 73% of the total glacierized area of the SSI archipelago (Elephant, Clarence and Smith Islands were excluded due to overly large slopes for SAR or limited input data). Our Results show a close-to-balance overall status during the analyzed period, with specific mass balances ranging from -0.680±0.071 to 0.209±0.025 m w.e. a-1 on Low and Livingston islands, respectively. The average specific mass balance for the whole area is -0.064±0.015 m w.e. a-1, representing an ice mass loss of 0.144±0.035 Gt a-1. This result is consistent with the cooling trend observed in the region between 1998 and 2017, and with the mass balance estimates by the glaciological method performed in various glaciers in the AP region (and the SSI in particular).
Rainfall-Runoff Simulation and Interpretation in Great Britain using LSTMs
Authors: Lees Thomas , Buechel Marcus , Anderson Bailey , Slater Louise , Reece Steven , Coxon Gemma , Dadson Simon
Published: March 2021

Techniques from the field of machine learning have shown considerable promise in rainfall-runoff modelling. This research offers three novel contributions to the advancement of this field: a study of the performance of LSTM based models in a GB hydrological context; a diagnosis of hydrological processes that data-driven models simulate well but conceptual models struggle with; and finally an exploration of methods for interpreting the internal cell states of the LSTMs. 

In this study we train two deep learning models, a Long Short Term Memory (LSTM) Network and an Entity Aware LSTM (EALSTM), to simulate discharge for 518 catchments across Great Britain using a newly published dataset, CAMELS-GB. We demonstrate that the LSTM models are capable of simulating discharge for a large sample of catchments across Great Britain, achieving a mean catchment Nash-Sutcliffe Efficiency (NSE) of 0.88 for the LSTM and 0.86 for the EALSTM, where no stations have an NSE < 0. We compare these models against a series of conceptual models which have been externally calibrated and used as a benchmark (Lane et al., 2019). 

Alongside robust performance for simulating discharge, we note the potential for data-driven methods to identify hydrological processes that are present in the underlying data, but the FUSE conceptual models are unable to capture. Therefore, we calculate the relative improvement of the LSTMs compared to the conceptual models, ∆NSE. We find that the largest improvement of the LSTM models compared to our benchmark is in the summer months and in the South East of Great Britain. 

We also demonstrate that the internal “memory” of the LSTM correlates with soil moisture, despite the LSTM not receiving soil moisture as an input. This process of “concept-formation” offers three interesting findings. It provides a novel method for deriving soil moisture estimates. It suggests the LSTM is learning physically realistic representations of hydrological processes. Finally, this process of concept formation offers the potential to explore how the LSTM is able to produce accurate simulations of discharge, and the transformations that are learned from inputs (temperature, precipitation) to outputs (discharge).

References:
Lane, R. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P., Greene, S., Macleod, C. J., and Reaney, S. M.: Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain, Hydrology and Earth System Sciences, 23, 4011–4032, 2019.
Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.
Authors: Boukachaba Niama , Reale Oreste , L. McGrath-Spangler Erica , Ganeshan Manisha , McCarty Will , Gelaro Ron
Published: March 2021

Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.
Sapwood-Heartwood isotopic composition in four forest species: implications for isotopes studies
Authors: Fabiani Ginevra , Penna Daniele , Klaus Julian
Published: March 2021

Obtaining reliable estimates of isotopic composition of xylem water transported in tree stems is crucial for ecohydrological studies. In most tree species xylem consists of two physiologically different parts, sapwood and heartwood. The former functions as the flowpath for sap flow, whereas the latter does not conduct water and provides mechanical support to the stem. However, some studies highlighted that water stored in heartwood might sustain transpiration by providing water during dry periods. Therefore, assessing how the isotopic composition in sapwood and heartwood compartments changes over time is critical to explain tree hydraulic.

Typically, studies rely on wood cores from the tree trunk to isotopically characterize xylem water in order to assess water sources for tree use (e.g., soil water from different depths, groundwater, stream water or a mixture of those), and only few studies specified which functional portion of the wood was sampled. There is currently a lack of knowledge on the possible isotopic difference between heartwood and sapwood potentially leading to uncertainties on the origin of the extracted water.

In the present study, we investigate four forest species characterised by different xylem anatomy, wood density, and timing of physiological activity to evaluate the degree of differentiation in isotopic composition between sapwood and heartwood.

We carried out biweekly sampling campaigns over one growing season (March-October 2020) in a central European forest (Luxembourg) to assess sapwood and heartwood isotopic composition and water content of European beech (Fagus sylvatica), sessile oak (Quercus petraea), douglas fir (Pseudotsuga menziesii), and spruce (Picea abies).

Preliminary results showed a temporal variation in isotopic composition both in sapwood and heartwood for all investigated species. In conifers, we found a stronger difference in isotopic composition and water content between sapwood and heartwood compared to broadleaved species suggesting a larger degree of compartmentalization. Heartwood displays consistently heavier oxygen delta values compared to sapwood in all species whereas sapwood shows heavier hydrogen delta values compared to heartwood only in conifers. These preliminary results suggest that the occurrence of potential mixing between older water stored in the heartwood and newly uptake water flowing in the sapwood should be taken into account in tree water uptake studies.
AirSeaFluxCode: Open-source software for calculating turbulent air-sea fluxes from meteorological parameters
Authors: Biri Stavroula , Kent Elizabeth , Berry David , Cornes Richard , Yelland Margaret
Published: March 2021

The exchanges, or fluxes, of heat, moisture and momentum between the atmosphere and the Earth's surface play a crucial role in the Earth's climate system, but the quantification of these fluxes remains challenging due to the non-linearity of surface dynamics and turbulent processes. Bulk formulae are frequently used to estimate surface turbulent fluxes from observed mean (or bulk) meteorological quantities. Uncertainties are inherent in the parameterisations as they rely on observations that are themselves uncertain and may not sample the full range of important conditions or include all of the variables that may affect the fluxes. As a consequence, different parameterisations of the bulk formulae may give different estimates of the fluxes.

AirSeaFluxCode is an open-source software package implemented in Python 3.6 for the computation of surface turbulent fluxes of heat (latent and sensible) and momentum. It includes ten different parameterisations, each based on published algorithms. The parameterisations implemented in the new package can be used to caclulate the fluxes using a small number of input parameters that are typically observed or available as model output: wind speed; air temperature; sea surface temperature; atmospheric pressure; and humidity. Some parameterisations require the input of a "skin" sea surface temperature so code to relate skin temperature to temperature at depth is included. In addition the code can be used to do height corrections of mean parameters.

This open-source software package is intended to be accessible, easy to use in its default implementation, and to provide a more informed choice of suitable parameterisations for particular applications.
The influence of water depth and forcing factors on the performances of Machine Learning approaches for the simulation of lake surface water temperature
Authors: Yousefi Azadeh , Toffolon Marco
Published: March 2021

Some attempts to predict water temperature in lakes by means of machine learning (ML) approaches have been pursued in recent years, relying on the performances that ML showed in many different contexts. The existing literature is focused on specific applications, and does not provide a general framework. Therefore, we systematically tested the role of different forcing factors on the accuracy of the simulation of lake surface water temperature (LSWT), comparing ML results with those obtained for a synthetic case study by means of a physically-based one-dimensional model, GLM. Among the available supervised ML tools, we considered artificial neural network (ANN) with back propagation, one of the most common and successful methods.

In our modelling exercise, we found that the two most important factors influencing the ability of ML to predict LSWT in temperate climates are air temperature (AT) and the day of the year (DOY). All the other meteorological inputs provide only minor improvements if considered additionally to AT and DOY, while they cannot be used as single predictors. The analysis showed that an important role is played by lake depth because a larger volume per unit of surface area implies a larger heat capacity of the lake, which smooths the temporal evolution of LSWT.  Such a filtering behaviour of deep lakes is not reproduced by standard ML methods, and requires an ad hoc pre-processing of AT input, which needs to be averaged with a proper time window. Moreover, while shallow lakes tend to be relatively well-mixed also in summer, deeper lakes can develop a strong stratification that tends to isolate the surface layer, modifying the thermally reactive volume and thus affecting the temporal evolution of LSWT. These considerations suggest that the physical dynamics of lakes, and especially of deep lakes, needs to be carefully considered also when adopting “black-box” approaches such as ML.

 
A novel approach for the geospatial modelling and resource assessment of tailings storage facilities
Authors: Blannin Rosie , Frenzel Max , Gutzmer Jens
Published: March 2021

Tailings are the fine-grained residues of ore processing operations, typically stored in dedicated tailings storage facilities (TSFs). Despite being viewed as ‘waste’ materials, tailings can contain significant amounts of valuable metals which were not recovered by original processing techniques or were previously not of economic interest. Re-processing of tailings deposits for the recovery of remaining metals has the additional benefits of mitigation of environmental hazards posed by the TSFs, such as Acid Mine Drainage (AMD). The estimation of mineral resources requires the construction of accurate and reproducible geospatial models. However, the sedimentary-style deposition and subsequent weathering of tailings results in a complex internal structure which is challenging to model, with a laterally and vertically heterogeneous distribution of the minerals comprising the residues. The present study investigates a novel approach for the geospatial modelling of a TSF case study. The surface of the tailings deposit was densely sampled in order to assess the intrinsic horizontal variability. Drill core samples were taken from a depth of 1-3 m, on a 30 m grid and nested grids of 15 m and 7.5 m, with additional random and twin holes. The entire depth of the TSF was sampled in 2 m intervals with a total of 10 drill holes to assess vertical variability. All drill core samples were analysed with x-ray fluorescence spectrometry and inductively coupled plasma mass spectrometry. The compositional data was log-ratio-transformed and variography and subsequent ordinary kriging and co-kriging were performed on the surface samples. The variogram models obtained for the surface samples were then applied for kriging of the deeper layers. Historical photographs of the surface of the TSF were used to improve estimates with co-kriging for the corresponding layers. The entire data set will be used to determine the most efficient sampling approach for the resource estimation of TSFs.
The Global Centroid Moment Tensor Catalog: Heterogeneities and improvements
Authors: González Álvaro
Published: March 2021

Statistical seismology relies on earthquake catalogs as homogeneous and complete as possible. However, heterogeneities in earthquake data compilation and reporting are common and frequently are not adverted.

The Global Centroid Moment Tensor Catalog (www.globalcmt.org) is considered as the most homogeneous global database for large and moderate earthquakes occurred since 1976, and it has been used for developing and testing global and regional forecast models.

Changes in the method used for calculating the moment tensors (along with improvements in global seismological monitoring) define four eras in the catalog (1976, 1977-1985, 1986-2003 and 2004-present). Improvements are particularly stark since 2004, when intermediate-period surface waves started to be used for calculating the centroid solutions.

Fixed centroid depths, used when the solution for a free depth did not converge, have followed diverse criteria, depending on the era. Depth had to be fixed mainly for shallow earthquakes, so this issue is more common, e.g. in the shallow parts of subduction zones than in the deep ones. Until 2003, 53% of the centroids had depths calculated as a free parameter, compared to 78% since 2004.

Rake values have not been calculated homogenously either. Until 2003, the vertical-dip-slip components of the moment tensor were assumed as null when they could not be constrained by the inversion (for 3.3% of the earthquakes). This caused an excess of pure focal mechanisms: rakes of -90° (normal), 0° or ±180° (strike-slip) or +90° (thrust). Even disregarding such events, rake histograms until 2003 and since 2004 are not equivalent to each other.

The magnitude of completeness (Mc) of the catalog is analyzed here separately for each era. It clearly improved along time (average Mc values being ~6.4 in 1976, ~5.7 in 1977-1985, ~5.4 in 1986-2003, and ~5.0 since 2004). Maps of Mc for different eras show significant spatial variations.
Mining Flood Insurance Big Data to Reveal the Determinants of Humans' Flood Resilience
Authors: Veigel Nadja , Kreibich Heidi , Cominola Andrea
Published: March 2021

Human behavior has shown to have a significant impact on future flood risk. The state-of-the-art research regarding human behavior before, during and after flood events is predominantly based on site- and event-specific survey data or psychological theories. In recent years, the availability of large-scale databases provides an empirical basis for dynamical approaches to model the impacts of heterogeneous individual and societal behavioral patterns of flood risk. The US Federal Emergency Management Agency has recently released household-scale data on national flood insurance policies in-force since 2009, covering the whole US. Providing access to flood insurance is an effective strategy to increase resilience by enabling inhabitants in flood prone areas and their property to quickly recover from flood events. In this work, we analyze flood insurance purchase, considered as a proxy of flood awareness and preparedness, by data mining techniques, spatially correlating and modeling insurance ratios and socioeconomic data in official floodplains. Recent or regular exposure to flood events has shown to be another important factor influencing flood risk perception, in addition to socio-economic variables. Therefore, the effect of flood experience on flood insurance uptake is analyzed. This study ultimately contributes a data-driven approach to identify the main determinants and dynamics of flood insurance purchase throughout different states and social backgrounds. Understanding the factors driving people’s choices regarding flood insurance purchase is the first step to improve the National Flood Insurance Program's strategies and address societal inequalities in disaster risk management.
Microplastic detection in terrestrial systems using a classification on optical values and surface characteristics 
Authors: Zeyer Tabea , Fiener Peter
Published: March 2021

There is a growing concern that the steady increase in plastic production is leading to a substantial contamination of our environment with microplastic particles. While aquatic ecosystems are more and more studied, there is still a substantial lack in knowledge regrading terrestrial (mainly soil) system. This knowledge gap is partly related to the challenges to detect and analyses microplastic particles in soils. Firstly, it is difficult to extract microplastic from a matrix of organic and inorganic particles of similar size. Secondly, the well-established spectroscopic methods to detect microplastic in water samples are sensitive to organic material and are moreover very time consuming. Eliminating very stable organic particles (e.g. lignin) from soil samples without affecting the microplastic to be measured is hardly possible. Hence, a robust analytical approach is needed to tackle the microplastic detection in soils. In this study, we combine a density separation scheme, a 3D Laser Scanning Confocal Microscope (Keyence VK-X1000, Japan) and a machine learning algorithm to classify and analyses microplastic particles in soil samples. For the analysis a silty loam (16% sand, 59% silt, 25% clay, 1.3% organic carbon) and a loamy sand (72% sand, 18% silt, 10% clay, 0.9% organic carbon) were spiked with different concentrations of high density Polyethylene (HDPE), low density Polyethylene (LDPE), Polystyrene (PS) and Polybutylene adipate terephthalate/Ploy lactic acid (PBAT/PLA) microplastic (HDPE 50 - 100 and 250 - 300 µm, LDPE <50 and 200 - 800 µm, PS <100 µm, PBAT/PLA < 2 mm). The classification with a machine learning algorithm is an essential data processing step to distinguishes between plastic, mineral as well as organic particles left after density separation. In case microplastic adopts the soil color, a combination of optical information and surface characteristics are used for a successful classification. Overall, the 3D Laser Scanning Confocal Microscopy in combination with a machine learning algorithm is a promising tool to detect, quantify and analyses microplastic in soils.
Portable XRF analyzer: a powerful tool for multiscale assessment of soil contamination
Authors: Mileti Florindo Antonio , Agrillo Antonietta , Manna Piero , Giuliano Langella , Terribile Fabio , Vingiani Simona
Published: March 2021

All around the world, a wide range of rural and industrial areas contaminated by PTE (potentially toxic elements) is affected by general lack of ex-ante information on type, quantity and location of potentially hazardous substances, hence the increasing request of proper investigation tools enabling preliminary screening of soil environment. Besides, spatial understanding of soil contamination is a prerequisite for the achievement of both proper site characterisation and reclamation.

Continuous aquisition in field of physical soil properties (such as apparent electrical conductivity by means of EMI equipments or natural gamma-ray dose rate by gamma-ray spectrometer) is of great importance to follow soil short range spatial variability. However, when the key parameter to be monitored is the soil PTE concentration (to assess, for example, exceeding of the established screening values - CSCs), a different tool is required.  

In a farmland of South of Italy, confiscated by the Italian Judiciary due to past illegal burial of industrial wastes, a portable handheld XRF analyzer (pXRF) was used at field scale to measure PTE (As, Cd, Cr, Ni, Pb) content on soil samples collected on a regular sampling grid of 20x20 m, at three depths (0-20, 30-60, 70-90 cm). On the basis of the contaminant content, distribution maps were outlined and “spatial pollution hot spots” revealed.

In correspondence of the most contaminated areas, 8 soil trenches and 5 profiles were dug. In one of the most representative soil trenches, a large (depth/height = 200 cm and width = 500 cm) wall was in situ analysed with high detail by using the pXRF at pedon scale (measurement distance of 10 cm vertically and between 20-35 cm horizontally). The use of the software Surfer 12 enabled the spatialization and mapping of the in depth contamination. Results showed a moderate but diffuse and homogeneous Cr contamination in the topsoil (400 mg/kg), a higher but point-source (2-3%) contamination in the subsoil and uncontaminated soil (40 mg/kg) below 2 m of depth. Most contaminated soil/wastes were then morphologically described and collected, bulk samples for chemical analyses and undisturbed samples for micromorphological thin sections.

pXRF analyzer was also used at microscopical scale on soil thin sections, using a small spot collimator (analysis area of 0.07 cm2), to preliminary detect and select contaminated micro – pedofeatures, to be further sub-microscopically (SEM-EDS) analysed.
Unexpectedly strong shape anisotropy of out-of-phase magnetic susceptibility due to eddy currents of internally isotropic copper cylinders
Authors: Ježek Josef , Hrouda František
Published: March 2021

In order to better understand the anisotropy of out-of-phase magnetic susceptibility (opAMS) due to eddy currents of rocks and minerals, we investigated the ipAMS and opAMS of copper cylinders, because copper is diamagnetic, internally isotropic magnetically, and its opMS is no doubt due to eddy currents. Results of laboratory measurements were compared to theoretical models.  The degree of AMS (P=k1/k3, k1>k2>k3) of internally isotropic substance is controlled by the internal susceptibility and demagnetizing factors solely controlled by the substance shape. If the internal susceptibility is very low and this is the case of magnetostatic susceptibility of most diamagnetic substances, there is virtually no shape anisotropy of such material. The magnetostatic susceptibility of copper is about -8 x 10-6 [SI units], which gives rise to undetectably low degree of magnetostatic AMS. The ipMS is also negative, but several orders lower depending on grain size, grain shape, and operating frequency. The opMS is on the other hand positive, relatively high and also dependent on operating frequency. Calculated from signed (negative) k1, k2, k3 values, the degree of ipAMS, P<1, and decreases with increasing elongation of copper cylinders. It also depends on grain size and operating frequency. The degree of opAMS, P>1 and increases with increasing cylinder elongation. It also depends on grain size and operating frequency. Parallel to the axes of elongated cylinders are the ipk1 and opk3 directions, while in flattened cylinders are parallel to the cylinder axes the ipk3 and opk1 directions. The above results are useful in the structural interpretation of the opAMS of rocks and ores whose opAMS is due eddy currents (in electrically conductive materials).
The effect of human influence on wet and dry European summers
Authors: Christidis Nikolaos , Stott Peter
Published: March 2021

As the climate becomes warmer under the influence of anthropogenic forcings, increases in the concentration of the atmospheric water vapour may lead to an intensification of wet and dry extremes. Understanding regional hydroclimatic changes can provide actionable information to help communities adapt to impacts specific to their location. This study employs an ensemble of 9 CMIP6 models and compares experiments with and without the effect of human influence using detection and attribution methodologies. The analysis employs two popular drought indices: the rainfall-based standardised precipitation index (SPI), and its extension, the standardized precipitation evapotranspiration index (SPEI), which also accounts for changes in potential evapotranspiration. Both indices are defined relative to the pre-industrial climate, which enables a comparison between past, present and future climatic conditions. Potential evapotranspiration is computed with the simple, temperature-based, Thornthwaite formula. The latter has been criticised for omitting the influences of radiation, humidity and wind, but has been shown to yield very similar trends, spatial averages and correlations with more sophisticated models. It is therefore deemed to be adequate in studies assessing the broader overall effect of climate change, which are more concerned with wet and dry trends and changes in characteristics of extremes rather than the precise estimation of drought index values. The rainfall-based index suggests a shift towards wetter conditions in the north and dryer in the south of the continent, as well as an overall increase in variability. Nevertheless, when the temperature effect is included, the wet trends in the north are largely masked leading to increasingly drier summers across most of the continent. A formal statistical methodology indicates that the fingerprint of forced climate change has emerged above variability and is thus detectable in the observational trends of both indices. A broadening of the SPI distribution also suggests higher rainfall variability in a warmer climate. The study demonstrates a striking drying trend in the Mediterranean region, suggesting that what were extremely dry conditions there in the pre-industrial climate may become normal by the end of the century.
Quantifying Local-scale Forest Degradation Intensity from Charcoal Production Using a Fusion of GEDI and Landsat Data
Authors: Liang Mengyu , Duncanson Laura , Sedano Fernando
Published: March 2021

Deforestation and degradation are two major threats to the global forest that jeopardize their functions to store carbon and mitigate climate change. Forest degradation undermines the health and functions of the forest to perform ecosystem services and is a stepping stone to deforestation. However, forest degradation has not been sufficiently monitored and quantified due to the varying intensity of disturbance and usually inconsistent spectral signals reflected in optical remote sensing. Drivers of forest degradation can be natural and/or human-related, and charcoal production is a key driver of forest degradation in sub-Saharan Africa due to the high demands for charcoal for energy consumption and the increasing rate of population growth and urbanization. In this study, we focus on charcoal production-driven forest degradation that occurred at the Mabalane district in Southern Mozambique from 2008 to 2018. We intend to demonstrate the potential of combining Global Ecosystem Dynamics Investigation (GEDI) data and Landsat time stacks for inspecting the changes in forest structure and aboveground biomass (AGB). To do so, we categorize the degraded forest by the year of disturbance based on a disturbance map produced for the study area for 2008-2018 by Sedano et al. (2019) and analyze the first year of publicly-released GEDI data to characterize forest structure and AGB at different disturbance classes. We also compare the GEDI L4A biomass with three other global and continental AGB products to understand the pre-disturbance biomass storage and the degradation patterns. Lastly, we build an empirical model between GEDI biomass and Landsat spectral bands and vegetation indices to quantify the biomass removal and regrowth from 10-year charcoal production. Uncertainties from the GEDI-Landsat models are estimated using Monte Carlo Simulations to propagate errors. The study improves the current understanding of forest degradation and carbon dynamics associated with it in tropical dry forests of sub-Saharan Africa. It also demonstrates the potential of combining spaceborne lidar missions and Landsat archives to facilitate accurate mapping of forest structural and AGB change in the degraded forest at a local scale. 
Increased Temperatures Overwhelm Precipitation Changes Leading to Streamflow Declines in the Colorado River Basin
Authors: Whitney Kristen , Vivoni Enrique , Bohn Theodore , Wang Zhaocheng , Xiao Mu , Mascaro Giuseppe
Published: March 2021

The Colorado River Basin (CRB) has experienced widespread and prolonged drought in the 21st century with recent precipitation (P) up to 25% below historical means and air temperature (T) up to 0.8 oC warmer. The extent that continued warming will lead to streamflow (Q) decline is unclear given the high interannual variability of P. Here we explore physically plausible ways that climate change could impact Q using the Variable Infiltration Capacity (VIC) model. We integrated advances in VIC using Landsat- and MODIS-based products to produce more realistic land surface conditions and used this setup to simulate long-range Q projections. Meteorological datasets were sourced from gridded daily observations (1950-2013) and downscaled historical (1950-2005) and future projections (2006-2099) derived from multiple CMIP5 models under a low and a high emission scenario to explore forcing uncertainties and cases where P increase could offset warming. We compared the impacts of anticipated climate change on hydrologic responses in subbasins key for water management to gauge their importance for basin-wide water budgets and how these relationships could evolve in time, as this has been a largely unexplored aspect in the CRB. Results showed that spatial gradients in seasonal P changes led to contrasting seasonal responses in runoff (R) across the CRB. Whereas most of the Upper Basin had a shift to greater R during the winter, summer R declined over most of the CRB due to heightened evapotranspiration in the northwest (Green, Upper Colorado, Glen Canyon, and Grand Canyon subbasins) and large P decline in the southeast (San Juan, Little Colorado, and Gila subbasins). The strength of seasonal runoff signals across different climate models and their impacts to annual Q were dependent on subbasin area and emission scenario. Annual Q at the CRB outlet declined in most cases, however, reflecting the pervasive drying effect of warming.
Evaluation of the physics suite in NOAA’s GFSv16 using field-campaign observations and diagnosis of physics tendencies
Authors: Bao Jian-Wen , Michelson Sara , Grell Evelyn
Published: March 2021

Shallow cumulus clouds play an important role in the weather in the Atlantic Tropical Convergence Zone.  Their interaction with the atmospheric environment and oceanic mixing processes has a significant impact on the convective organization and tropical dynamics.  It is still a scientific challenge for numerical weather prediction models to accurately simulate them due to deficiencies in the model’s representation of physical processes. 

In this study, we investigate how the physics parameterization schemes in NOAA’s most recent operational global forecast system (GFSv16) perform in the simulation of shallow cumulus clouds in the western Atlantic in terms of their interaction with the large-scale atmospheric dynamics.  Previous studies have indicated that the impact of physics parameterization schemes on model’s tendencies during the first few hours can provide critical information on their suitability for short- and medium-range forecasts.  Therefore, we first evaluate the GFSv16 forecasts against the observations obtained from the European field campaign called the ATOMIC/EUREC4A that occurred between 12 January and 23 February 2020.  We then diagnose the sensitivity of the GFSv16 physics tendencies to changes to the physics parameterization schemes over the first 6 hours of the forecast, which is the timescale before dynamical feedback becomes significant. Using the information from the observational evaluation and physics tendency diagnosis, we further explore possible improvement in the physical process representation that can positively affect the physics tendencies and lead to overall forecast improvement beyond 6 hours.
Complexity-based approach for El Nino magnitude forecasting before the spring predictability barrier
Authors: Meng Jun
Published: March 2021

The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy(SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn(complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error=0.25◦C for the average of the individual datasets forecasts). For the recent two 2018 and 2019 El Niño events, our method forecasted weak El Niños with magnitudes of 1.11±0.23◦C and 0.69±0.25◦C, both within one root-mean-square error comparing to the observed magnitudes, i.e. 0.9◦C and 0.6◦C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.
Groundwater flow in Naturally Occurring Asbestos (NOA) rich settings: new findings on the relation among concentration, types and mobility of mineral fibres, and geological characteristics of aquifer formations.
Authors: Avataneo Chiara , Belluso Elena , Capella Silvana , Lasagna Manuela , De Luca Domenico Antonio
Published: March 2021

Weathering and erosion of asbestos-bearing rocks, such as meta-ophiolites (e.g. serpentinite rocks and metabasites), is the principal natural cause of asbestos water dispersion in Naturally Occurring Asbestos (NOA) rich settings. Water pollution by asbestos may occur as a consequence of superficial and groundwater flow through natural rock formations with NOA, depending on several characteristics of either the rocks (e.g. mineralogical composition, fracture grade) and hence the water (e.g. pH, speed).

Given the importance of groundwater resources for both drinking water and agricultural and industrial activities, groundwater asbestos pollution represents an environmental problem and could even constitute a risk for human health. In fact, waterborne asbestos can come into contact with human beings as airborne fibres after water vaporization, or by ingestion, especially if they are present in drinking water. While a lot is known about diseases caused by airborne asbestos respiration, not enough has been yet understood about potential noxiousness of its ingestion. For this reason, the necessity to set a Maximum Contaminant Level (MCL) for asbestos in potentially usable water is still debated.

As the North-Western and Central Alps are rich in NOA and also in naturally occurring asbestiform minerals non-asbestos classified, it’s essential to understand if, how and which type of mineral fibres can eventually be released into water and to correlate them to the geolithological and hydrogeological characteristics of the area.

The results of a surface water and groundwater sampling and analysis campaign, settled in the North-Western Alps, will be presented. The main aim is to investigate the principal aspects related to asbestos and asbestiform fibres presence in water, in particular their natural occurrence in groundwater, linked to hydrological and geolithological characteristics of the reservoir. Furthermore, laboratory test to study the flow of polluted water through a packed column will be designed and observations on the methodology to evaluate waterborne mineral fibres behaviour into porous media will be presented.

These data are fundamental to monitor asbestos (and asbestiform) fibres transportation due to water flowing into NOA and to better understand the relationship among geology, hydrogeology and mineral fibres presence in water.
Insights into the environmental impacts of large igneous province volcanism from volcanology, atmospheric modelling and sedimentary archives
Authors: Mather Tamsin , Schmidt Anja , Percival Lawrence
Published: March 2021

Episodes of large igneous province (LIP) volcanism punctuate Earth history. LIPs are anomalous geologically rapid large‐volume accumulations of igneous rock on the Earth’s surface and in the shallow crust. Periods of LIP emplacement are often temporally associated with times of profound environmental and climatic change throughout Earth history, particularly during the last 300 million years. The fluxes of gas and particles emitted during LIP volcanism are key candidates for triggering these Earth system responses. Understanding these events, their feedbacks and impacts on the Earth system requires collaboration between the fields of volcanology, atmospheric science, ocean chemistry, sedimentology and palaeobiology amongst other fields. This presentation will explore how evidence of the environmental impacts of LIP volcanism and the processes leading to these effects is best combined often from disparate sources including: (1) temporal associations between the dates or proxies of LIPs and evidence of environmental change captured in the geological record; (2) historical records or monitoring studies of the effects of large‐scale recent volcanic activity such as the Laki eruption in 1783–1784 CE and its deposits; and (3) scaling up from observations and measurements of the environmental impacts of present‐day volcanism such as the 2014–2015 Holuhraun eruption and the 2018 Lower East Rift Zone eruption at Kīlauea, Hawai‘i. Recent progress in each of these areas sets the scene for future advances in our understanding of these profoundly important events in Earth’s history.
The Near-Sun Streamer Belt Solar Wind: Turbulence and Solar Wind Acceleration
Authors: Chen Christopher , Chandran Benjamin , Woodham Lloyd , Jones Shaela , Perez Jean , Bourouaine Sofiane , Bowen Trevor , Klein Kris , Moncuquet Michel , Kasper Justin , Bale Stuart
Published: March 2021

The fourth orbit of Parker Solar Probe (PSP) reached heliocentric distances down to 27.9 Rs, allowing solar wind turbulence and acceleration mechanisms to be studied in situ closer to the Sun than previously possible. The turbulence properties were found to be significantly different in the inbound and outbound portions of PSP's fourth solar encounter, likely due to the proximity to the heliospheric current sheet (HCS) in the outbound period. Near the HCS, in the streamer belt wind, the turbulence was found to have lower amplitudes, higher magnetic compressibility, a steeper magnetic field spectrum (with spectral index close to -5/3 rather than -3/2), a lower Alfvenicity, and a "1/f" break at much lower frequencies. These are also features of slow wind at 1 au, suggesting the near-Sun streamer belt wind to be the prototypical slow solar wind. The transition in properties occurs at a predicted angular distance of ~4 degrees from the HCS, suggesting ~8 degrees as the full-width of the streamer belt wind at these distances. While the majority of the Alfvenic turbulence energy fluxes measured by PSP are consistent with those required for reflection-driven turbulence models of solar wind acceleration, the fluxes in the streamer belt are significantly lower than the model predictions, suggesting that additional mechanisms are necessary to explain the acceleration of the streamer belt solar wind.
Paradigm Shift in distribution preferences for Flood Frequency Analysis and the ‘LMoFit’ R-Package
Authors: Zaghloul Mohanad Ashraf , Papalexiou Simon Michael , Elshorbagy Amin
Published: March 2021

Safe and economical design of dams, highways, bridges, and other infrastructures require accurate estimates of the magnitude and frequency of peak floods obtained by flood frequency analysis (FFA). The Generalized Extreme Value (GEV) distribution is the traditional preference for FFA along with other distributions having location, scale, and shape parameters. In this poster, two alternative power-type distributions comprising one location and two shape parameters are explored, these are Burr type III (BrIII) and Burr type XII (BrXII) distributions. The performances of BrIII and BrXII are compared against that of GEV in describing annual maximum streamflow records at 1088 sites across Canada. A generic L-moment algorithm is developed to fit these distributions regardless of the unavailability of some of their analytical L-moment expressions. This algorithm is devised in the R package “LMoFit” on CRAN. The latter comparison shows that: (1) the three distributions perform equally-well in describing the observed peaks; (2) the BrIII and the BrXII distributions predict larger streamflow peaks increasing the heaviness of their right tails compared to that of the GEV distribution; (3) the predictions of the GEV distribution reach the upper limits of the distribution in 39% of the sites, while the corresponding predictions of BrIII and BrXII are not limited and exceed the reached limits of GEV; (4) the GEV distribution might be underestimating the risk of extreme events, especially for large return periods. Accordingly, there are potential limitations in using the GEV distribution for FFA and the findings suggest BrIII and BrXII distributions as consistent alternatives for future FFA practices. The “LMoFit” R package is devised to facilitate the future application of the suggested distributions.