bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinematics Characterization of Slow-Moving Landslide using InSAR Time Series Analysis in the South-Central Andes of NW Argentina
Authors: M. Aref Mohammad , Bookhagen Bodo , R. Strecker Manfred
Published: March 2022

Slow-moving landslides pose significant natural hazards to humans and infrastructure.  Analysis of Interferometric Synthetic Aperture Radar (InSAR) time series provide the opportunity to monitor unstable hillslopes in difficult to access terrains at large spatial scales.

The geological conditions and land cover of the eastern Central Andes in northwestern Argentina ranges from densely vegetated areas in the low elevation foreland at around 1000 metres to arid, vegetation free conditions at high elevations at about 6000 metres. The land cover has a significant impact on the spatial and temporal InSAR signal decorrelation and deformation estimation. In our study, we extract InSAR time series from Sentinel-1 ascending and descending data acquired between 2014 and 2021 using both linear small baseline technique and non-linear phase inversion techniques to have a better understanding of deformation rate estimation techniques for landslide detection in complex areas. We identified several landslides including three main translational bodies with areas exceeding 1 km2 and downslope deformation rates in excess of 5-10 cm/yr. 

Our study is influenced by ionospheric total electron content variation for the C band Sentinel-1 ascending phase observations. We applied the split range-spectrum technique to minimize the ionospheric contribution on the phase measurements. The tropospheric signal was estimated using both statistical approaches based on topography and weather models to reduce the effects of atmospheric water vapor during South American Monsoon activity. We explore the impact of topographic relief on tropospheric phase delay. We compared our deformation-rate estimates with a double-differencing time series with local and regional spatial filters to mitigate tropospheric noise and unwrapping problems in the time series. We take advantage of connected component analysis and hierarchical clustering approaches on the mean velocity from the double-difference time series and vertical component derived from the 3D decomposition of InSAR time series to map landslides with similar characteristics. Our results highlight the importance of the several processing parameters during InSAR time-series analysis and their sensitivity toward slow-moving landslide detection.
Improved soil moisture-atmospheric boundary layer interactions by assimilation of Cosmic-Ray Neutron counts
Authors: Patil Amol , Fersch Benjamin , Hendricks Franssen Harrie-Jan , Kunstmann Harald
Published: March 2022

The Cosmic-Ray Neutron Sensing (CRNS) technology determines soil moisture for a few tens of hectares in a non-invasive way. These measurements, however, can be used to extend soil moisture characterization at regional scales using data assimilation. In the present study, we deployed the Ensemble Adjustment Kalman Filter (EAKF) to assimilate the CRNS neutron counts in order to update the spatial soil moisture, soil infiltration, and evapotranspiration parameters of the Noah-MP land surface model witch is also part of the WRF-Hydro modelling system. The study was conducted in the southern part of Germany, which includes the Rott and Ammer catchments within the TERENO Pre-Alpine observatory. The assimilation was carried out for both, a Noah-MP standalone scenario with observed rainfall as input and a coupled WRF-Hydro scenario with simulated rainfall to fully evaluate the added value of the assimilation. The assimilation performance was analysed at local and regional scale using independent soil moisture observations across the modelling domain. During the assimilation period, the Noah-MP standalone findings demonstrate a significant improvement in field scale soil moisture characterisation. The RMSE of simulated soil moisture was decreased by up to 66 % at field scale and up to 23 % at catchment scale. Additionally, the spatial patterns in the field scale soil moisture have showed improvement with reduction in spatial Bias by 0.025 cm3/cm3. The initial results from coupled WRF-Hydro scenario demonstrate that the soil moisture and parameter estimation experiment had a significant impact on estimated soil moisture and, humidity and evapotranspiration at regional scale. These findings support the use of the CRNS technique to improve the land surface and coupled hydro-atmospheric modelling.
Variability of the AMOC and water mass properties at the GO-SHIP OVIDE section over 2002-2018
Authors: Lherminier Pascale , Mercier Herlé , Carracedo Lidia , Pérez Fiz F. , Velo Anton , Desbruyères Damien , Lopez-Mozos Marta , Fontela Marcos
Published: March 2022

The OVIDE section, composed of a hundred top-to-bottom stations from Portugal to Greenland, has been visited biennially since 2002. Collected data show a strong variability of both the Atlantic Meridional Overturning Circulation (AMOC) and of the water mass properties. The OVIDE-AMOC timeseries built upon the Argo array and altimetry has been updated and validated with the in-situ cruise estimates. It shows a strong seasonal variability and, on longer time scales, significant transition in 2014, from moderate (19 Sv) to strong (23 Sv) amplitude, along with the development of a fresh and cold anomaly in the upper 800m over the eastern subpolar Atlantic, discussed in the literature and observed at the OVIDE section. Through a composite analysis of both transport and property data, we compare the 2002-2012 OVIDE average with the 2014-2018 average and analyze the evolutions of the transports of the different water masses with special attention to LSW, which has been largely renewed since 2014 through deep convection in the western subpolar gyre. 
Water Drop Penetration Time Revisited
Authors: Berli Markus , Shillito Rose , Inouye Shelby , Nikolich George , Etyemezian Vic
Published: March 2022

Water drop penetration time (WDPT), i.e. the time it takes for a water drop to be absorbed by the soil, is widely used as a measure of soil water repellency. Despite its popularity, little is known about the processes that govern WDPT and how WDPT is related to other soil hydraulic properties such as sorptivity. To shed some light on the physics of the WDPT, we measured apparent contact angles of sessile water drops on water repellent sand using a goniometer and compared apparent with effective contact angles of the same sand. Results showed that WDPT can be related to sorptivity by means of apparent and effective contact angles.
Decision-support modelling for an uncertain future: developing forecasts of sea level rise impacts on groundwater
Authors: Chambers Lee , Hemmings Brioch , Moore Catherine , Cox Simon , Levy Richard
Published: March 2022

The low-lying coastal urban area of South Dunedin, New Zealand, is particularly susceptible to the impacts of sea-level rise, which is projected to rise by as much as 1.2 m by 2100 under high emissions scenarios.  Currently, more than 2,500 homes are < 50 cm above mean sea level and groundwater levels are typically < 1 m below the surface.  As sea levels rise, groundwater levels are also predicted to rise, increasing the probability of inland groundwater inundation (groundwater flooding) throughout South Dunedin.  It is therefore imperative to develop an improved understanding of the physical controls, and the uncertainty associated with these controls, on the occurrence and severity of the groundwater inundation hazard caused by rising sea levels.  We deploy a simple and fast-running model within a highly-parametrised Uncertainty Quantification (UQ) workflow to investigate the adequacy of steady-state-only versus transient calibration when assessing the risks of groundwater inundation.  The decision to proceed beyond a steady-state-only calibration is time-consuming and costly (often vastly so) and requires careful attention and further research in practical application.  The reduction in uncertainty of decision-relevant forecasts accrued through implementing a transient calibration procedure (or lack thereof), given existing and yet to be acquired data, is the metric by which the modelling is judged.  Firstly, the workflow involves history matching and uncertainty analysis implemented through PESTPP-IES to explore and reduce the uncertainty of decision-relevant forecasts (spatial groundwater elevation and drain fluxes).  Secondly, a paired complex-simple model analysis is used to: explore 1) the potential uncertainty reductions in decision-relevant forecasts achieved through transient calibration and 2) the potential introduction of unquantifiable bias of decision-relevant forecasts introduced by the competing calibration procedures.
Understanding the deforestation process may mitigate environmental risks in recent agricultural frontier expansion in Ecuador
Authors: Urgilez-Clavijo Andrea , Tarquis Alfonso Ana María
Published: March 2022

The agricultural expansion frontier in Ecuador is mainly attributed to the deforestation process. Exacerbated rates of forest loss have been motivated more significant impacts in the territory, even negatively affecting the new uses of land, agriculture, and livestock. Climatic conditions have been changing in larger deforested areas, increasing landslides, floods, water level rise, and drought. This work aims to go deeper in understanding the deforestation process patterns by analyzing the structure of the expansion at the patch level.

We used the concept of patch skeletons and the Local connected fractal analysis (LCFA) through its temporal dynamics to identify complex hotspots inside the new agricultural areas. The K-means algorithm was used to perform LCFA segmentation and colouring to identify the complex intensity of the deforestation structure. This may indicate active expansion areas associated with high risked areas to perform agriculture and livestock systems because of high ecosystem dynamics recovery.

Hot spotting derived from the fractal analysis and k-means clustering not only serves for reforestation but will also lead to decision-makers for monitoring other associated environmental impacts. Most of the deforested areas in Ecuador after 5 to 7 years in agriculture were abandoned because of the nutrient loss and agricultural failure activities because of feeble farming systems infrastructure. LCFA and colouring communicate in a straightforward spatially explicit visualization strategy the hot spot method to geographically allocate the complex points of the deforested structure of the patches.

Acknowledgements

The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.

References

Andrea Urgilez-Clavijo, J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, 2021, 72(2), 635-655. https://doi.org/10.1111/ejss.13032

Andrea Urgilez-Clavijo, D. Rivas-Tabares, J.J. Martín-Sotoca and Ana M. Tarquis. Local fractal connections to characterize the spatial processes of deforestation in Ecuadorian Amazon. Entropy, 23(6), 748, 2021, https://doi.org/10.3390/e23060748
Insidious Retreat of the Holderness Coastline: Capturing Spatial and Temporal Patterns of Failure using Terrestrial Laser Scanning (TLS) 
Authors: Teasdale Serena L , Hackney Christopher R , Milan David J , Bennett Georgina L , Parsons Daniel R
Published: March 2022

The Holderness coastline of Eastern England is the fastest eroding coastline in Europe. The coast is characterised by ‘soft sediment’ tills, which make it distinctly susceptible to cliff retreat, in turn, these pose a socio-economic threat to local communities. The controls and future projections of the rates and patterns of retreat rely upon robust monitoring and process-based understanding of the geomorphological processes. Herein, we report on a 12-month monitoring study (June 2019 to May 2020) along a 220 m stretch of the Holderness coastline (Withernsea), whereby the spatial and temporal patterns of failure were captured using terrestrial LiDAR. Failure footprint, volumetric change and total eroded volume of the cliffs were estimated and compared against local hydrodynamic and meteorological records. The results reveal that >36% of individual failure events occurred solely in the upper portions (upper 75% vertical height) of the cliff, with a further >38% over the central section of the cliff face, with <26% occurring solely at the cliff toe (lower 25% cliff height). These findings disprove the widely accepted assumption that failure is primarily driven by wave attack, and we instead propose that instability in soft cliffs occurs as a result of moisture-driven ‘structural weakening’ with the influence of wave action primarily acting to remove failed material.
The impact of heat waves in forest fires over the Amazon rainforest
Authors: Narcizo Luiza , Santos Filippe LM , Peres Leonardo F. , Trigo Ricardo , Libonati Renata
Published: March 2022

Wildfires have become an imminent threat to ecosystems, consequently leading to economic loss and generating negative impacts on population health. Considering IPCC’s projection of a significant increase in the frequency of these events, it is important to understand which conditions lead to a fire intensification, as recently happened in California, Australia, and Brazilian Pantanal. Some of the greatest wildfires registered in North America and in Europe occurred in concomitance to intense heat waves and drought events. The lack of a comprehensive understanding of the physical mechanisms associated with extreme wildfire events in the Amazon rainforest, underlines the current inability to properly prevent them. Therefore, this study aimed to identify the role of extreme temperature events, such as heat waves (HW), in forest fires behaviour in the Brazilian Amazon during extreme drought years. The relationship between wildfires and HWs was hereby analysed during both dry and wet years in the Amazon Forest, in order to understand the association between different time and spatial scale events in forest fires magnitude. Accordingly, CPC/NOAA reanalysis data of daily maximum temperature between 1979 and 2019 were used as input to determine HW events in a multi-method global heatwave and warm-spell data record and analysis toolbox1. A standard HW definition was applied, where an event corresponds to at least three consecutive days in which the maximum temperature exceeds the 90th percentile for that day. Wildfire magnitude analyses were calculated through active fire (AF) and fire radiative power (FRP) data from MODIS C6 sensor, obtained at FIRMS/NASA for the comprehended period between 2003 and 2019. Spatial intensity of HW was classified and then confronted with precipitation anomaly in both normal and dry years. Also, statistical comparison of fire magnitude (i.e., AF and FRP) in HW and non heat wave (NHW) days was analysed to measure extreme temperature events impacts in wildfire. Results showed a significant increasing trend in HW occurrences in recent decades, with peaks in known drier years. An increase of AF counting and fire intensity was noticed during HW events. This latter effect appears even when the HW occurs during extremely dry seasons, such as happened at the Amazon Forest in 2005, 2010 and 2015. Extreme values of AF and FRP were a quarter higher in 2005, doubled in 2010 and tripled in 2015 at HW days when compared to NHW days.

 

References 

[1] Raei, E., Nikoo, M., AghaKouchak, A. et al. GHWR, a multi-method global heatwave and warm-spell record and toolbox. Sci Data 5, 180206 (2018).

Acknowledgements

This study was supported by FAPERJ project number E26/202.714/2019. L. N. was supported by CNPq PIBIC  number 160099/2021-8.
Choose your own weather adventure: deep weather generation for “what-if” climate scenarios
Authors: Watson Campbell , Guevara Jorge , Szwarcman Daniela , Oliveira Dario , Tizzei Leonardo , Garcia Maria , Avegliano Priscilla , Zadrozny Bianca
Published: March 2022

Climate change is making extreme weather more extreme. Given the inherent uncertainty of long-term climate projections, there is growing need for rapid, plausible “what-if” climate scenarios to help users understand climate exposure and examine resilience and mitigation strategies. Since the 1980s, such “what-if” scenarios have been created using stochastic weather generators. However, it is very challenging for traditional weather generation algorithms to create realistic extreme climate scenarios because the weather data being modeled is highly imbalanced, contains spatiotemporal dependencies and has extreme weather events exacerbated by a changing climate.

There are few works comparing and evaluating stochastic multisite (i.e., gridded) weather generators, and no existing work that compares promising deep learning approaches for weather generation with classical stochastic weather generators. We will present the culmination of a multi-year effort to perform a systematic evaluation of stochastic weather generators and deep generative models for multisite precipitation synthesis. Among other things, we show that variational auto-encoders (VAE) offer an encouraging pathway for efficient and controllable climate scenario synthesis – especially for extreme events. Our proposed VAE schema selects events with different characteristics in the normalized latent space (from rare to common) and generates high-quality scenarios using the trained decoder. Improvements are provided via latent space clustering and bringing histogram-awareness to the VAE loss.

This research will serve as a guide for improving the design of deep learning architectures and algorithms for application in Earth science, including feature representation and uncertainty quantification of Earth system data and the characterization of so-called “grey swan” events.
Comparative analyses on geophysical survey responses for various numerical models constructed based on field data
Authors: Yu Huieun , Kim Bitnarae , Cho Ahyun , Joung In Seok , Jeong Juyeon , Jang Hanna , Jang Soojin , Nam Myung Jin
Published: March 2022

For the detection of contaminated zones based on geophysical surveys, we make numerical experiments since it is not easy to make or maintain contaminated test-beds for field surveys. In the numerical experiments, we numerically simulate and analyze the responses of geophysical surveys including electrical resistivity tomography (ERT), induced polarization (IP) and ground penetrating radar (GPR), for numerical models, each of which was constructed based on the results of field geophysical survey obtained from contaminated site. ERT, which can image electrical resistivity of subsurface, is one of the most common geophysical tools, while IP survey can provide additional electrical information of the subsurface. Besides GPR can suggest the structure of geology.

For each model, whose geological structure was composed as similar to that of the corresponding field-survey site, we first numerically simulated corresponding field surveys performed in the site and compare with field data to verify the properness of the model. For the verified models we performed numerical simulation of geophysical surveys about various scenario of contaminations (e.g., oil pollution, leachate, heavy metal, etc.), and analyzed resulting responses in order to make strategies for the detection of contaminated zones bases on geophysical surveys. Further, we considered time-lapse geophysical surveys for changing contamination scenarios with respect to time. In the case of models including clay media, the responses of IP were remarkable useful in locating the clay media when compared ERT-only survey.

This work was supported by the Energy Efficiency & Resources of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20194010201920) and Korea Ministry of Environment as "The SEM projects; 2018002440005"
Space weather predictions of CMEs and SEPs through the inner heliosphere
Authors: Palmerio Erika
Published: March 2022

Coronal mass ejections (CMEs) and solar energetic particles (SEPs) are manifestations of the dynamic and explosive nature of solar activity and major drivers of space weather events. They often occur in concert, especially in the case of large and fast CMEs that are associated with strong flares and that are able to accelerate SEPs at the shocks ahead of them. Modelling efforts that aim to forecast and mitigate the effects of these solar phenomena range from empirical to analytical to numerical. Although the primary focus of space weather forecasts is naturally on Earth, the increased amount of heliospheric and planetary missions launched in the past ~15 years has provided new opportunities for CME and SEP measurements at other locations in the inner solar system. This has resulted in the possibility to test space weather forecasting models at multiple locations well separated in both heliocentric distance and longitude within the same event, which in turn represents a novel way to benchmark and validate the present capabilities.

In this presentation, we will first briefly review the current status of CME and SEP space weather forecasting, with particular attention given to the main challenges to overcome for advancing predictions. We will then present a few examples of CME and SEP events that were detected in situ at multiple locations in the inner heliosphere and show forecasting—or actually hindcasting—results for each of them. Finally, we will conclude by addressing possible future improvements that take advantage of model validation via multi-spacecraft measurements.
Simultaneous estimation of snow depth and freezing-ground uplift by GPS Interferometric Reflectometry over a permafrost area
Authors: Hu Yufeng , Wang Jiatong
Published: March 2022

Ground surface movements and snow cover during freeze/thaw cycles of permafrost are important variables for studying climate change. GPS-IR has emerged as an effective technique to estimate the relative elevation changes of ground surface such as the thaw subsidence of frozen ground and snow depth variations. In permafrost areas, the freezing process of the ground is always accompanied by the snow accumulations, making it hard for GPS-IR to separate these two distinct signals from the estimated elevation changes. In this study, using the Signal to Noise Ratio (SNR) collected by a permafrost GPS site SG27 (Northern Alaska) in 2018, we proposed a physical model-based method to simultaneously estimate the daily snow depths and freezing-ground uplifts with GPS-IR. First, we applied GPS-IR to the SNR data to obtain the daily elevation changes of the ground surface from September 1 in 2018 to August 31 in 2019. The elevation change measurements indicate the onset of snow season on October 18 in 2018 and the end of snow-cover on June 15 in 2019. Second, we used the thermal index Accumulated Degree Days of Freezing (ADDF) calculated from the temperature records to determine the onset of the permafrost freezing season as of September 17 in 2018. Third, we fitted the Stefan function to the estimated elevation changes (i.e. freezing-ground uplifts) from September 17 to October 18 in 2018. The Stefan model agrees with the freezing uplifts with an R2 of 0.65. Forth, we extended the fitted model to the time when the ground was completely frozen (November 1) to estimate daily freezing-ground uplifts up to 1.75 cm under the snowpack. Last, we extracted the snow depths from the estimated elevation changes by subtracting the corresponding freezing-ground uplifts. Our study is the first attempt to simultaneously estimate the daily freezing-ground uplifts and snow depths over the permafrost area with GPS-IR, providing the measurements to understand the coupling effects of the permafrost and snow cover.
Can reforestation help stabilize the climate in net-zero pathways? 
Authors: MacIsaac Alexander , Zickfeld Kirsten
Published: March 2022

Reforestation is a nature-based climate solution (NbCS) that can serve to sequester and store large quantities of atmospheric carbon dioxide. It requires no new technological advancements for deployment, is relatively cost-effective, and it would lead to important co-benefits for ecosystems and ecosystem services. For these reasons reforestation is a key measure in deep-mitigation and net-zero pathways. However, reforestation at scale alters land-surface biophysical properties (albedo, evapotranspiration and latent heat release, and sensible heat flux) that can induce either a warming or cooling effect on  surface temperature. The magnitude and sign of this temperature response depend on the background climate state and latitude over which reforestation is implemented. Therefore, depending on the scale and region of reforestation, these biophysical effects could lead to additional warming in emission pathways that use reforestation to compensate for residual CO2 emissions. 

Our research investigates the effectiveness of reforestation at stabilizing global mean temperature when used to compensate for residual CO2 emissions. Using a climate model of intermediate complexity (the UVic-ESCM v2.10) we conduct a set of idealized simulations where fossil fuel emissions decline towards zero by 2050 but remain at 1 and 5, Gt CO2/yr between 2050 to 2100 to represent emissions that are difficult to eliminate. Meanwhile reforestation is implemented globally and in different latitudinal zones (tropics, mid-latitudes, and high-latitudes) at an areal coverage appropriate to sequester the ongoing emissions so that cumulative CO2 emissions between 2050 and 2100 are net-zero. From these simulations we quantify the effectiveness of reforestation at stabilizing global mean temperature under consideration of biogeochemical and biophysical effects and feedbacks. 

While we expect our results to show that the carbon sequestration from reforestation could be effective at stabilizing global mean temperature, the biophysical effects could also induce important variations in global mean temperature. As such, our research is intended to provide an Earth system analysis of reforestation that can inform forestation carbon markets and net-zero policy frameworks.  
Magnetohydrodynamic simulation of multiple coronal mass ejection (CME) events: Effects of input parameters and CME-CME interactions
Authors: Wu Chin-Chun , Liou Kan , Wood Brian
Published: March 2022

Geomagnetic storms are one of the most important terrestrial space weather events and often commence in association with the arrival of coronal mass ejections (CMEs). When a CME is explosively released into the heliosphere, a shock wave can be formed in front of the dense, supersonic CME material. Thus, the first indication of the arrival of a CME at the Earth is a sudden increase in the global magnetic intensity due to magnetospheric compression by the CME-driven shock. Predictions of the arrival of the shock are a key element in space weather forecasting. Several different variety of methods, including numerical simulations, have been applied to predict the shock arrival time but with mediocre results, with an average uncertainty of ~10 hr. In this study we will use magnetohydrodynamic (MHD) simulations (Wu et al., 2020) to examine a number of input parameters such as the CME initial speed and release time in MHD simulation of CMEs and demonstrate their effect on the shock arrival time. We also explore effects of CME-CME interactions on the propagation of the CME/shock events. The multiple CME events that occurred during 6-29 July 2012 are simulated to highlight the importance of these factors on the prediction of shock arrival time using MHD simulations.
Comparing Academia's Perception of Needed SDG Research to SDG Progress Reports and Known SDG Synergies and Tradeoffs
Authors: Chaney Hannah , Abou Najm Majdi , Jose Lopez Serrano Maria
Published: March 2022

The Sustainable Development Goals (SDG) are a set of 17 goals that was released by the United Nations (UN) in 2015. Each goal has a target figure that countries and, ideally, the world should aim to reach in order to create sustainability within that sector for current and future generations. Seven years after the SDGs were released, thousands of studies and academic articles have promoted the SDGs, as well as regular updates that have been released by the UN on goal progress specific to each country. In addition, multiple studies have highlighted synergies and tradeoffs between SDGs that have the potential to significantly influence goal completion (Biggeri et. Al, 2019; Moyer & Bohl, 2019; Jose-Serrano, 2022; Zhao et. al, 2021). With this information in mind, this study aims to conduct a large-scale network analysis of research articles concerning SDG progress to answer the following questions: Which SDGs receive the most attention from researchers? What are the perceptions in academia regarding the synergies/ trade-offs between the SDGs? The network analysis will be conducted using the search engine SCOPUS resulting in hundreds of retrieved papers for each category within the SDGs. Results from this study will be compared to current SDG progress and known synergies and tradeoffs within the SDGs in order to determine how the perception of the SDGs compare with research conclusions and known SDG goal progress. This information will serve as an indication of which goals, synergies, or tradeoffs researchers and industries are aware of and readily researching and which of these categories needs more attention within academic circles. The ultimate goal for this research is that the results can be used as a tool to advocate for what SDG research is most needed in order for SDG goals to reach completion by 2030.
Predicted mapping of bed sediments in the estuarine turbidity maxima of Yangtze River based on multibeam data
Authors: Xu Wei , Cheng Heqin
Published: March 2022

Predictive mapping of seabed sediments based on multibeam bathymetric (BM), and backscatter (BS) data is effective for mapping the spatial distribution of the substrate. The sediment samples were collected by the box dredge, and then measured and analyzed by the LS13320 produced by BECKMAN. Raw BM and BS were collected using a 200/400 kHz SeaBat 7125 multi-beam echo sounder system (MBES) (Teledyne Reson, Slangerup, Denmark) in the area of turbidity maximum zone (TMZ) of the Yangtze River Estuary (YRE). The raw BS was processed by the HIPS and SIPS 11.0 software. The raw BM was processed by the PDS 2000 software. We extract bedform features by the GRASS GIS version 7.6.1 (GRASS Development Team, Beaverton, OR, USA) from BM data. The bedform features were classified as plane, pit, ridge, channel, peak, and pass by Wood's Criteria, based on the adjusting the value of slope tolerance. The bedform features were classified as ﬂat, pit, ridge, valley, peak, shoulder, spur, slope, hollow, and foot-slope by Geomorphons method. At last, a robust modeling technique, the random forest decision tree (RFDT), was used to predict the seabed sediments in the study area.
How to utilize deep learning to understand climate dynamics? : An ENSO example.
Authors: Shin Na-Yeon , Ham Yoo-Geun , Kim Jeong-Hwan , Cho Minsu , Kug Jong-Seong
Published: March 2022

Many deep learning technologies have been applied to the Earth sciences, including weather forecast, climate prediction, parameterization, resolution improvements, etc. Nonetheless, the difficulty in interpreting deep learning results still prevents their applications to studies on climate dynamics. Here, we applied a convolutional neural network to understand El Niño–Southern Oscillation (ENSO) dynamics from long-term climate model simulations. The deep learning algorithm successfully predicted ENSO events with a high correlation skill of 0.82 for a 9-month lead. For interpreting deep learning results beyond the prediction skill, we first developed a “contribution map,” which estimates how much each grid point and variable contribute to a final output variable. Furthermore, we introduced a “sensitivity,” which estimates how much the output variable is sensitively changed to the small perturbation of the input variables by showing the differences in the output variables. The contribution map clearly shows the most important precursors for El Niño and La Niña developments. In addition, the sensitivity clearly reveals nonlinear relations between the precursors and the ENSO index, which helps us understand the respective role of each precursor. Our results suggest that the contribution map and sensitivity would be beneficial for understanding other climate phenomena.
Acceleration of Organic Matter Decomposition by Tillage
Authors: Ghezzehei Teamrat , Alvarez-Sagrero Jennifer , Perez-Rojas Yulissa
Published: March 2022

Organic matter is a hallmark of healthy soils and soil functions. It is critical in developing a stable structure and is a significant reserve of resources for soil life. Soil carbon flux is also an essential regulator of atmospheric GHG concentrations and climate. Therefore, the stability and persistence of soil organic matter are considered a critical soil resiliency metric. Although the acceleration of soil carbon loss via disturbances such as tillage is widely recognized, we lack a predictive modeling framework that relates the tillage intensity to mineralization rates. Here, we show a framework that combines a model of soil structure evolution and water-retention-curve-based microbial moisture sensitivity function.  
Tree water-use recovery after drought-intermittent rainfall events
Authors: Shekhar Ankit , Paul-Limoges Eugénie , Zweifel Roman , Buchmann Nina , Gharun Mana
Published: March 2022

Short drought-intermittent rainfall (SDIR) events, i.e., short rainfall events after long dry spells, typically re-wet top few centimeters of soil layer and often has little impact on the water balance of the ecosystem. Nevertheless, these SDIR events could provide the much-needed short-term water supply during dry spells to the terrestrial ecosystem, especially trees. An efficient use of these SDIR events could provide the trees with a buffer to withstand longer drought conditions, which will become more frequent and intense with the upcoming changes in climate.

This study aims to quantify the tree water use (TWU) recovery in terms of sapflow recovery following SDIR events across multiple global forest sites (35 sites) spanning over 250 site-years. SDIR events are identified probabilistically as rainfall periods (1-2 days with daily rainfall < 75th percentile) occurring after an extreme dry spell (> 90th percentile of dry spell duration) during the growing season. For each tree, TWU recovery (RTWU) is estimated based on percentage increase in sapflow rate after the SDIR event (Sa), compared to before the SDIR event (Sb), and standardized by seasonal maximum sapflow (Smax). The inter-species RTWU and intra-species RTWU relationship with tree allometry (height and diameter) is used to explain the recovery rates. The main hypothesis tested here is that the intra-species differences in RTWU are positively related to tree size due to better root development in larger trees. The understanding of RTWU provides a new axis to understand and predict tree recovery after drought events.
A critical review of conceptual and empirical approaches to characterize infiltration
Authors: Abou Najm Majdi , Basset Christelle , Angulo-Jaramillo Rafael , Bagarello Vincenzo , Di Prima Simone , Iovino Massimo , Lassabatere Laurent , Stewart Ryan
Published: March 2022

Over the past two centuries, studying the infiltration process has received significant efforts resulting in numerous infiltration models being developed. These models depended on specific soil properties, and were influenced by initial and boundary conditions. They were also classified into two major categories: empirical and conceptual models, although the boundaries between those two categories can be debated for several models. The empirical models solved the infiltration problem by curve-fitting measured data into algebraic equations. In contrast, the conceptual approaches built on earlier concepts, mainly derived from the fundamental flow models, and then formulated analytical solutions applied to the infiltration problem. In this review, we create an inclusive survey covering the diverse spectrum of published infiltration modeling to understand the philosophy and evolution of those empirical and conceptual models across the years. After providing a full historical retrospective of infiltration models, we explored the model parameters and their evolution with time. We also reviewed the different methods applied to estimate the basic and common infiltration parameters, as well as the challenges that arise by such methods.
The impact of greenhouse gas and ozone forcing on the Southern Hemisphere climate system
Authors: Daher Houraa , Kirtman Ben
Published: March 2022

Anthropogenic climate change in the Southern Hemisphere is driven by two forces, the greenhouse gas emissions and the stratospheric ozone levels. In the past, the combination of increasing greenhouse gas emissions and ozone depletion over Antarctica worked together leading to an increase in sea surface temperatures and a poleward shift of the storm tracks. With the ozone expected to recover by mid-century, however, the greenhouse gas and ozone forces will oppose each other and the changes observed previously will begin to weaken or reverse. The role the greenhouse gases and the ozone recovery play in the Southern Hemisphere climate system are examined using Community Climate System Model, version 4 (CCSM4) coupled ocean eddy-parameterized and eddy-resolving simulations. The greenhouse gas emissions and ozone levels are specified independently to represent the two extremes, peak greenhouse gas emissions and a recovered ozone. In the eddy-parameterized simulations, the ozone recovery signal is found to be stronger on average. In the case of the eddy-resolving simulations, however, the increase in greenhouse gases is stronger especially in eddy-rich regions like western boundary current regions and the Antarctic Circumpolar Current. The volume transport is also calculated for the Southern Hemisphere western boundary currents (Agulhas, Brazil, and East Australian Currents) and the two external forces are found to not play an important role in the mean transports, but the model resolution does. The eddy-parameterizing simulations yield a more accurate transport than the eddy-resolving simulations. The eddy-resolving simulations however, are able to resolve a more accurate eddy field in these highly active regions. The relationship between the sea surface temperatures in the western boundary currents and regional precipitation over nearby South Africa, South America, and Australia is then analyzed in greater detail.
Multi-case analysis of ice particle properties of stratiform clouds using in-situ aircraft observations in Hebei, China
Authors: Liu Siyao , Zhao Chuanfeng
Published: March 2022

 

This study investigates the size distribution, the mean diameter and the concentration of ice particles within stratiform clouds by using in-situ observations from 29 flights in Hebei, China. Furthermore, it examines the empirical fitting of ice particle size distributions at different temperatures using Gamma and exponential functions. Without considering the first three bins of ice particles, the mean diameter of ice particles (size range 100 – 1550 µm) is found to increase with temperature from -15 ℃ to -9 ℃ but decrease with temperature from -9 ℃ and 0 ℃. By considering the first three bins of ice particles using the empirical Gamma fitting relationship found in this study, the mean diameter of ice particles (size range 25 – 1550 µm) shows similar variation trend with temperature, while the turning point changes from -9℃ to -10℃. The ice particle number concentration increases from 13.37 L-1 to 50.23 L-1 with an average of 31.27 L-1 when temperature decreases from 0 ℃ to -9 ℃. Differently, the ice concentration decreases from 50.23 L-1 to about 22.4 L-1 when temperature decreases from -9 ℃to -12 ℃. The largest mean diameter of ice particles at temperatures around -9 ℃ and -10 ℃ is most likely associated with the maximum difference of ice and water supersaturation at that temperature, making the ice particles grow the fastest. These findings provide valuable information for future physical parameterization development of ice crystals within stratiform clouds.
A review of the range of fibrous amphibole crystal morphologies between asbestiform fibers and cleavage fragments as revealed by TEM/SEM studies and how their interpretation impacts analytical testing results used in asbestos exposure studies.
Authors: Bailey R. Mark
Published: March 2022

A review of a range of TEM and SEM studies of fibrous amphiboles has been undertaken. Of particular relevance, are TEM studies where fibers were viewed down the fiber axis which identified the following common features to a greater or lesser degree: 1) crystal structure defects & stacking faults, 2) the presence of fiber cross-sectional edges which are rounded, irregular, faceted, or hybrid, and 3) intergrowths with other crystalline phases. Also of relevance are SEM studies where external fiber morphology can be studied in detail. The features identified in these studies have been used by various parties to either include or exclude identified fibers as asbestos. This review contrasts and compares the fiber images presented in the TEM/SEM studies and attempts to summarize the issues and arguments being made by various parties, including mineralogists, regulators, litigators in asbestos injury lawsuits, and others, in the debate as to whether fibrous amphiboles are, or are not, asbestos and to be included during analytical testing.
High-resolution topography project on the rock walls of the Mont-Blanc massif to reconstruct volume change
Authors: Uhlmann Daniel , Jaboyedoff Michel , Derron Marc-Henri , Ravanel Ludovic , Vicari Joelle , Wolff Charlotte , Fei Li , Choanji Tiggi , Gutierrez Carlota
Published: March 2022

Before modern remote sensing techniques, quantifying rock wall retreat due to rockfall events in the high alpine environment was limited to low-frequency post-event measurements for high-magnitude events. LiDAR and SFM now provide precise and accurate 3D models for computing 3D volume changes over time. Otherwise, mid- and low-sized events can remain unobserved due to the remoteness of the rockwalls and the lack of remnant evidence due to the rapid sequestration of ice in surrounding valley and cirque glaciers. To extend rockfall event measurement an initial measurement (t0) is necessary. The Mont-Blanc Massif (MBM, European Alps) High Resolution Topography Project is currently completing high-precision 3D models in the MBM using ground-based and aerial LiDAR, and drone-based structure-from-motion (SFM). In 2021, we began acquisition with initial measurements of 11 major sectors of the massif, representing about 80 km2 of rock and ice slopes, between 1700m - 4810m in elevation. By choosing a study area with robust existent photographic and film archives, such as the MBM, it is possible to extend 3D models back in time for comparison with current datasets. Despite existent high-quality image archives, SFM processing is more challenging and error-prone than from contemporary images due to a lack of metadata, such as camera and lens type, precise dates of images, and the general degradation of the original material.  Despite these limitations, the use of historical-image-based SFM in combination with modern LiDAR data can allow the reconstruction of significant slopes of the MBM over several decades in order to i) obtain estimates of erosion rates, ii) to document rockfall events, and iii) to quantify the extent change and volume loss of hanging glaciers and ice aprons. We thus explore geomorphic processes in the high mountain environment in context of warming climate, as well as the limits of input data (image sets) in terms of practical output resolution.
Dust detection by antenna instruments with applications to the STEREO spacecraft
Authors: Sternovsky Zoltan , Garzelli Alessandro , Horanyi Mihaly , Malaspina David , Pokorny Petr , Juhasz Antal
Published: March 2022

Plasma Wave antenna instruments are employed on a range of space missions and can also be used to characterize the population of cosmic dust particles. Such measurements are complementary to those made by a dedicated dust instrument and suitable for the detection of larger (> 1 micron) particles. These booms or deployed wires with receiving elements are sensitive to the plasma cloud generated by the hypervelocity impact of a dust particle on the spacecraft, or the antenna itself. The dust impact is registered as a transient voltage signal (waveform) that is due to the charging of the spacecraft/antenna, and the induced charging from the part of the plasma cloud that is expanding from the impact location. Recent advancements provide the capability of obtaining the mass of the impacting particle from the measured waveforms. The new models are based on first principles and account for the parameters of the impact plasma (in terms of effective temperatures and the geometry of the expansion), the parameters of the ambient space environment, and the geometry of the spacecraft. The latter two allow for determining the approximate impact location on the spacecraft and thus constrain the incoming direction of the dust particle. Once the expansion of the transient impact plasma is over, the spacecraft and the antennas discharge through the ambient environment and relax back to their equilibrium potentials. The analysis of the measured waveforms thus also provides information on the density of the ambient plasma and its temperature. The numerical model is applied for the reanalysis of the measurements made by the STEREO spacecraft.