bluuebunny/crossref_metadata_2025 huggingface dataset:
More data and increased automation leads to better quality for GLIMS and RGI glacier data sets
Authors: Raup Bruce , Maussion Fabien , Paul Frank , Berthier Etienne , Bolch Tobias , Kargel Jeffrey , Racoviteanu Adina
Published: March 2022

GLIMS, Global Land Ice Measurements from Space, is an initiative that involves ~250 analysts from 34 countries and has the purpose of mapping all glaciers in the world (excluding the Greenland and Antarctic ice sheets) on a periodic basis.  The GLIMS Glacier Database, which became an official product of the NASA NSIDC DAAC (Distributed Active Archive Center) in 2019, contains time series of glacier outlines from different data sources.  Various parts or facies of glaciers are mapped, including the full glacier extent, debris-covered parts, internal rock outcrops, and glacial lakes.  The Randolph Glacier Inventory (RGI) is a snapshot map of glaciers, with one outline per glacier, as close as possible to a target date.

In the last year, GLIMS and the RGI working group have been working closely together to ingest new data into GLIMS and to improve GLIMS and RGI software tools. The goal is to improve data completeness and quality and to make the creation of the RGI smoother and more transparent (Maussion et al., EGU22-4484).  New data include approximately 60,000 outlines from 14 regions in all parts of the Earth, with times ranging from the Little Ice Age to 2018. Software improvements include more quality-control checks and constraints, such as separating multi-polygons into individual ones. 

The presentation will provide an overview on the latest data additions and software developments in GLIMS and the synergy with RGI production.
An open-source framework for nearly exact solutions to complex geoscience interactions (AEM)
Authors: Steward David R.
Published: March 2022

An open-source framework is presented to support geoscientific investigations of flow, conduction, and wave propagation. The Analytic Element Method (AEM) provides nearly exact solutions to complicated boundary and interface problems, typically with 6-8 significant digits. Examples are presented for seepage of water through soil and aquifers including fractured flow, groundwater/surface water interactions through stream beds, and ecological interactions of plant water uptake. Related applications include waves near coastal features and propagation of tsunamis through bathymetric shoals. This presentation overviews the concise AEM representation from Steward (2020), "Analytic Element Method: Complex Interactions of Boundaries and Interfaces", where solutions discretize the domain into features, develop mathematical representations of interactions, and develop coupled systems of equations to solve boundary conditions.  The companion site at Oxford University Press contains a wide range of open-source solutions to these problems and related applications across the geosciences.
Exploring novel Compound Specific Stable Isotopes (CSSIs) tracers with conventional fingerprinting properties for sediment source apportionment in an arable lowland catchment in Central Germany
Authors: Abbas Ghulam , Jomaa Seifeddine , Fink Patrick , Brosinsky Arlena , Nowak Karolina Malgorzata , Kümmel Steffen , Rode Michael
Published: March 2022

Soil erosion and associated sediment transport can cause severe water quality and ecosystems health deterioration. The fingerprinting approach has widely been applied for sediment source apportionment using a variety of sediment tracers. This study evaluates the applicability of the Compound Specific Stable Isotope (CSSI) fingerprinting technique of fatty acids to identify crop-specific soil loss and the importance of upland erosion compared to river bank erosion. We tested this new technique with fallout radionuclides, geochemical and spectral tracers in a small agricultural loess soil catchment (Geesgraben, 75 km²) within the lowland Bode river catchment in Central Germany. The CSSI tracer was combined with a linear multivariate mixing model to discriminate soil loss from areas with specific crop types (e.g., C3 vegetation/wheat and C4 vegetation/maize) and identify the share of river bank sediment source on total sediment loss. We compared the CSSI technique with fallout radionuclides, geochemical and spectral fingerprinting properties for tracing subsurface sediment sources. We found that the CSSI fingerprinting technique of fatty acids allowed to decipher surface sediments from wheat and maize fields. The CSSI δ¹³C-fatty acids were also used to disentangle arable and river bank sediment sources. The crop-specific soil loss from wheat and maize was 40% and 11%, respectively. Relative sediment contribution from river banks was up to 49%. The outcomes using the CSSI tracer were consistent and similar to those using fallout radionuclides, geochemical and spectral fingerprinting properties for arable land and river bank sediment sources, which indicated a mean sediment source contribution of 46% from river bank and 54% from surface sources, respectively. Our results showed that the stable isotope composition of fatty acids could discriminate C3 and C4 vegetation sources, and such information is of prime importance for decision making. Furthermore, the relatively high proportion of sediment losses from river banks has clear implications for management measures to reduce sediment losses in these agricultural loess areas. 

Keywords: sediment fingerprinting, CSSI, fatty acids, C3 and C4 vegetation sources, sediment sources.
A New Generation of Turnkey Broadband Solutions to Support Ocean Bottom Research
Authors: Perlin Michael
Published: March 2022

The densification of offshore observatories is the next important challenge for scientists as described in the New Advances of Geophysics’ (NAG) meeting in November 2018 in Edinburgh. 

Nanometrics is combining our latest land based technology with our proven OBS technologies to enable the next steps in offshore observation.  Specifically, we are building both 360 second and 120 second corner observatory class seismometers with the same performance specifications as land based instruments, but in a form factor allowing deployments to 6000m.   

 

These seismometers come in a form factor unique to the OBS community allowing exceptional advances in SWaP (size, weight and power), critical to reducing the expensive logistics of OBS work, and are suitable for autonomous stations and cabled stations.  Power usage and volume are reduced 60-70% versus previous generation options.

These new instruments expand Nanometrics' range of products enabling new ocean bottom science, reducing integration risk and time to deploy, while improving outcome certainty.  

Authors: Michael Perlin, Geoffrey Bainbridge, Bruce Townsend, Valarie Hamilton, Tim Parker
Linking citizen scientists with technology to reduce climate data gaps
Authors: Shein Karsten
Published: March 2022

Among the greatest constraints to accurately monitoring and understanding climate and climate change in many locations is limited in situ observing capacity and resolution in these places. Climate behaviours along with dependent environmental and societal processes are frequently highly localized, while observing systems in the region may be separated by hundreds of kilometers and may not adequately represent conditions between them. Similarly, generating climate equity in urban regions can be hindered by an inability to resolve urban heat islands at neighborhood scales. In both cases, higher density observations are necessary for accurate condition monitoring, research, and for the calibration and validation of remote sensing products and predictive models. Coincidentally, urban neighborhoods are heavily populated and thousands of individuals visit remote locations each day for recreational purposes. Many of these individuals are concerned about climate change and are keen to contribute to climate solutions. However, there are several challenges to creating a voluntary citizen science climate observing program that addresses these opportunities. The first is that such a program has the potential for limited uptake if participants are required to volunteer their time or incur a significant cost to participate. The second is that researchers and decision-makers may be reluctant to use the collected data owing to concern over observer bias. This paper describes the on-going development and implementation by 2DegreesC.org of a technology-driven citizen science approach in which participants are equipped with low-cost automated sensors that systematically sample and communicate scientifically valid climate observations while they focus on other activities (e.g., recreation, gardening, fitness). Observations are acquired by a cloud-based system that quality controls, anonymizes, and makes them openly available. Simultaneously, individuals of all backgrounds who share a love of the outdoors become engaged in the scientific process via data-driven communication, research, and educational interactions. Because costs and training are minimized as barriers to participation, data collection is opportunistic, and the technology can be used almost anywhere, this approach is dynamically scalable with the potential for millions of participants to collect billions of new, accurate observations that integrate with and enhance existing observational network capacity.
Magnetic reconnection and particle acceleration in the solar wind: theory, observations and opinions
Authors: Khabarova Olga
Published: March 2022

The solar wind has historically been considered as a far simpler medium than the solar or magnetospheric plasma. From the beginning of the space era through to XXI century, it has been supposed that all solar wind structures freely expand and can be modeled in 2D. Correspondingly, current sheets have been pictured as thin planar objects carrying the electric current and separating magnetic fields of different directions. Before 2010th, Petscheck magnetic reconnection was considered as the only possible mechanism transforming the magnetic energy into the thermal energy at current sheets in the solar wind. Acceleration of particles to suprathermal energies was thought to be impossible there because of too slow inflow speeds. Interpretations of observations totally followed the theoretical dominant paradigm mainly because of the insufficiency of observational material. Things began to change when more and more theoretical and observational studies in magnetospheric and solar physics appeared pointing to the complex character of magnetic reconnection. In particular, ideas about stochastic or turbulent reconnection at current sheets in realistic space plasmas become dominating. In turn, observations of the fine structure of current sheets in the solar wind as well as evidence for local acceleration of energetic particles found with help of modern missions, including Parker Solar Probe, allow re-considering views on solar wind current sheets and better understanding physics of the processes associated with magnetic reconnection.   
Demonstration of the Use of Bayesian Priors in GHG retrievals from Laser Heterodyne Radiometer Measurements
Authors: Miller J. Houston , Flores Monica , Bomse David
Published: March 2022

George Washington University and Mesa Photonics are developing and deploying a Laser Heterodyne Radiometer (LHR) that simultaneously measures CO2, CH4, H2O, and O2. Because oxygen concentrations are nearly invariant throughout the troposphere and lower stratosphere its line shape is dependent only on pressure and temperature, and analysis of its line shape can be used to improve GHG retrieval precision and provide dry-air corrections. To constrain these fits, pressure and temperature profiles for our LHR data retrieval algorithm can be obtained from the weather data measured by radiosondes as part of NOAA’s Integrated Global Radiosonde Archive (IGRA). In a recent paper, we reported on the statistical analysis of this data and highlighted how it can not only be used to constrain both the temperature and pressure profiles, but also the vertical profiles of water mixing ratios.  Not only do mean values of radiosonde temperature, pressure, and humidity provide useful priors in column retrievals, but the narrow distributions above near-surface altitudes create realistic constraints to retrieval results.

For other greenhouse gases (specifically CO2 and CH4), prior data to constrain these vertical profiles is much sparser and a different approach is required.  The Bayesian paradigm applies prior knowledge and observations to a model being tested. It is the foundation upon which inverse modeling in the atmospheric sciences is built and involves weighting the error the find the optimal value of a state vector given the observations.  In this presentation we demonstrate how continuous LHR data from a stationary sensor can be used to refine an initial prior based on available (and widely distributed spatially and temporally)  global GHG vertical profiles to constrain data from site-specific installations.  Further, we will demonstrate the robustness of this technique to follow temporal excursions such as surface emission events.  Initially, this algorithm is applied to synthetic data with a goal of application to the data stream from a sensor scheduled to go on line at the Smithsonian Environmental Research Center (Edgewater, Maryland, USA) in the 2nd quarter of 2022.
Suitability Analysis for Wind Farm Establishment and Wind Energy Potential Investigation: The Case of Upper Blue Nile River Basin, Ethiopia
Authors: Tegegne Eyale Bayable , Berhanu Yewubdar , Wang Binbin
Published: March 2022

The Upper Blue Nile Basin (UBNB) in Ethiopia has a huge hydropower development potential and it covers a considerable amount of the present hydropower consumptions in the country. However, as hydropower is basically rainfall-dependent, its future sustainable utilization under climate change is highly uncertain. As a result, it is critical to identify the amount of wind energy that can be harnessed in the area since wind is a good substitute for hydropower. Nonetheless, wind energy suitability studies and potential estimations are rarely researched in the UBNB. The objective of this study is, therefore, to investigate wind farm suitability based on multi-criteria decision method using Geographic Information System (GIS) and to determine the energy potentials of those suitable areas in UBNB. Wind speed, slope, land use/land cover, distance from grids, roads, urban areas, and protected areas were considered to identify suitable wind farm sites. The relative weights of these factors were calculated and overlaid by the principle of pairwise comparison in the context of the Analytic Hierarchy Process. From the total area it was found that 1498.69 km2 was highly suitable. The suggested highly suitable areas for wind farm sites fall in the northeastern part of the study area. For wind power potential investigation, wind speed data of ten sites with 15 min intervals of four years (2017-2020) were accessed from National Meteorology Agency (NMA). And it was statistically analyzed using statistical methods and software like MS-Excel and MATLAB programs. The best Weibull parameters estimator was identified based on the statistical test results for each station. From the wind power density analysis using 15-minute interval wind speed, the highest wind power density was recorded in Wogeltena followed by Gatira. Finally, the power density was higher during dry and short rainy season and can be said that wind is a good complementary to hydropower. In conclusion, most of the wind speed data in this study were not enough for wind energy potential estimations at large scales. This may be because the meteorological stations in the study area may not be located at ideal places for wind energy potential estimations. Thus, different wind speed measuring tools (i.e. taller wind mast) are suggested for additional wind energy potential investigations. Relatively, the northeastern parts of the study area are the most promising sites discovered in this study. Hence, these areas might be regarded as feasible for various wind energy applications (i.e. grid-connected and stand-alone).
Modified AquaCrop-OpenSource tool for data-scarce regions
Authors: Bruckmaier Felix , Adla Soham , Disse Markus , Tripathi Shivam
Published: March 2022

The Food and Agriculture Organization (FAO) AquaCrop model has demonstrated its ability to accurately simulate the growth of various crops. However, the quality of simulation results depends on the calibration of the model, which in turn requires field observations of model inputs and parameters. This limits the utility of AquaCrop in data-scarce regions, such as the Global South. A user-friendly method to analyze parameter sensitivities and model output uncertainties could facilitate the assessment of the model output reliability. The AquaCrop version provided by FAO, however, is run through a standalone graphical user interface (GUI) and therefore does not allow for systematic calibration. Besides, the user cannot customize parameter-specific features like irrigation scheduling.

This work presents a tool that enhances the MATLAB-based open-source application of AquaCrop, AquaCrop-OS (AOS), with the following functionalities: A Bayesian modeling feature is designed to calibrate the AOS model considering input data uncertainty, while the MATLAB toolbox Sensitivity Analysis For Everybody (SAFE) is integrated to automate sensitivity and uncertainty analysis. Irrigation schedules may now also be created dynamically and depending on different simulated parameters like the rooting depth. The user can distinguish between different environmental stresses, either by cause or affected variable. Every functionality is supplemented with intuitive graphics. The tool will be released under an open-source license on GitHub. A standalone executable version with a GUI will cater for non-MATLAB users.

The AOS model is calibrated on field data from an experimental agricultural plot in the Ganga River basin in Kanpur, India, for two wheat cropping seasons between 2018 and 2019. The proposed tool is used to quantify the uncertainty in the model input data and parameters and their effects on model outputs.
Climate evolution during the past 250 million years simulated by the Community Earth System Model
Authors: Li Xiang , Guo Jiaqi , Lan Jiawenjing , Lin Qifan , Yuan Shuai , Yang Jun , Liu Yonggang , Hu Yongyun
Published: March 2022

Global climates have undergone tremendous fluctuations during the past 250 million years, primarily driven by variations in tectonic dynamics, atmospheric greenhouse gases, and solar irradiance. Paleoclimate modeling has offered a feasible approach to investigating secular climate change for such a long span of time deep in the past. Nevertheless, global mean surface temperatures (GMSTs) simulated by previous studies scarcely depict the trend of past climate change. In this study, using the Community Earth System Model version 1.2.2 (CESM1.2.2), we present an ensemble of snapshot simulations during the past 250 million years based on the reconstructed GMSTs. An energy balance analysis is carried out to explore and quantitatively describe the causes of temperature change for the past 250 million years. We find that different levels of global mean warming for the past 250 million years compared with the pre-industrial period predominantly results from relative increase in greenhouse gas emissivity (12.2 °C), with the changing paleogeography (5.6 °C) and solar constant (3.0 °C) playing secondary roles. It is highlighted that the individual effect of heat transport convergence varies inconspicuously in spite of considerable changes of paleogeography and mean climate states during this time. The simulations are potentially valuable resources for extensive studies including climate dynamics analysis in geological timescales and paleoclimate-proxy intercomparison.
Using proxy data to characterize the spatio-temporal structure of climate variablity
Authors: Hébert Raphaël , Kunz Torben , Laepple Thomas
Published: March 2022

The spatial scale of climate fluctuations, or effective spatial degrees of freedom (ESDOF), depends on the timescale and the forcing: while local scale variability between far away locations may be independent on short timescales, they may become coherent over sufficiently long timescales, or if they are driven by a common forcing. While ESDOF have been estimated from instrumental data over the historical period and climate model simulations, it remains difficult to perform such analysis on paleoclimate data given the time uncertainty and proxy-specific bias. We take advantage of a database of absolutely dated annual proxies comprising tree ring, corals and varved sediments in order to provide the first estimate of ESDOF for longer than multi-decadal timescales based on proxy-data.
Machine learning-based multilayer soil moisture datasets: SoMo.ml
Authors: Oh Sungmin , Orth Rene , Park Seon Ki
Published: March 2022

Soil moisture information is valuable for a wide range of applications in various fields such as hydrology, agriculture, and climate. Although spatially continuous soil moisture data can be obtained from satellite observations or model simulations, each type of data has its own uncertainty and bias. In this study, we use machine learning as a hydrologic model and generate a gridded soil moisture dataset—SoMo.ml—that can complement existing soil moisture datasets (O and Orth, 2021). We train a Long Short-Term Memory neural network model using in-situ measurements to extrapolate daily soil moisture dynamics in space and in time. The first version of the data, SoMo.ml v1, provides multilayer soil moisture (0-10cm, 10-30cm, and 30-50 cm) at 0.25° and daily resolutions for the period 2000-2019; it has been actively used for drought analysis, data comparison, and other relevant research. The dataset is freely available from https://www.bgc-jena.mpg.de/geodb/. Given the growing needs for this unique soil moisture dataset, SoMo.ml v2 is currently under development, which aims to provide soil moisture data over Europe with a higher spatial resolution (0.1°). In this presentation, we will introduce the SoMo.ml datasets and show examples of data applications in other studies.

 

Reference: O and Orth, Global soil moisture data derived through machine learning trained with in-situ measurements. Sci Data, (2021). https://doi.org/10.1038/s41597-021-00964-1
Ocean surface wave and turbulence characteristics from direct measurements with a velocity sensor deployed in a buoy
Authors: Ocampo-Torres Francisco J. , Osuna Pedro , Esquivel-Trava Bernardo , Rascle Nicolas , García-Nava Héctor
Published: March 2022

There is great interest in acquiring directional ocean surface wave direct measurements in order to better determine sea state conditions in open waters as well as in harbors and nearshore sites. Typical applications range widely over coastal and oceanic engineering, naval architecture and safety at sea, for design and construction of vessels and infrastructure, as well as for maintenance and marine operations. In this work we explore the influence of the buoy motion and we are able to detect some turbulence characteristics of the near surface flow. Full motion of the buoy structure is recorded by an Inertial Motion Unit within the velocimeter case, and after applying motion corrections directional wave and some turbulence characteristics are analyzed. The buoy responde is readily defined and the final results are compared with corresponding measurements from a bottom fixed acoustic Doppler current profiler. Details of the groupinness behaviour of the wave field in a nearshore site are given, showing some enhancement of turbulence intensity during the passage of relatively high wave groups. Some attempts to quantify the kinetic energy dissipation rate are explained. Final results show similar turbulence intensity values from the buoys measurements when compared with those from the fixed ADCP.
Extreme rainfall quantile estimation based on SSP scenarios: Focusing on the Hangang river basin
Authors: Kim Sunghun , Kim Heechul , Kim Gyobeom , Heo Jun-Haeng
Published: March 2022

This study attempts to estimate the extreme rainfall quantile using the climate model data of the Shared Socioeconomic Pathways (SSP) scenarios presented in the sixth Assessment Report (AR6), published by the Intergovernmental Panel on Climate Change (IPCC). Generally, the applied research related to climate change is conducted using numerical simulation data from various climate models. Generally, an ensemble scenario based on various regional climate models is used as a way to reduce the uncertainty from one climate model. In this study, the ensemble rainfall data (based on HadGEM3-RA, WRF, CCLM, GRIMs, and RegCM4) were obtained from the climate information portal (CIP, http://www.climate.go.kr/). The observed rainfall data was extracted and the regional quantile delta mapping (RQDM) method was applied for bias correction. Regional frequency analysis (RFA) was performed to estimate the rainfall quantile. In addition, the generalized extreme value (GEV) distribution was applied as an appropriate probability distribution and the L-moments method was used for parameter estimation. As a result, the rainfall quantiles were estimated, and the effects of climate change were analyzed quantitatively in the study area.
Can the ultraviolet bursts be generated in the low solar chromosphere? 
Authors: Ni Lei
Published: March 2022

Ultraviolet (UV) bursts and Ellerman bombs (EBs) are small scale magnetic reconnection events in the highly stratified low solar atmosphere. The plasma density, reconnection mechanisms and radiative cooling/transfer process are very different at different atmospheric layers. EBs are believed to form in the up photosphere or low chromosphere. It is still not clear whether UV bursts have to be generated at a higher atmospheric layer than the EBs or UV bursts and EBs can actually both appear in the low chromosphere.   We numerically studied the low beta magnetic reconnection process around the solar temperature minimum region (TMR) by including more realistic physical diffusions and radiative cooling models. We aim to find out if UV bursts can be formed in the low chromosphere and investigate the dominant mechanism that transfer the magnetic energy to heat in an UV burst.The single-fluid MHD code NIRVANA was used to perform simulations. The time dependent ionization degrees of Hydrogen and Helium are included in the code, which lead to the more realistic magnetic diffusion caused by electron-neutral collision and ambipolar diffusion. The more realistic radiative cooling model from Carlsson& Leenaarts 2012 is included in the simulations. The high resolution simulation results indicate that the plasmas in the reconnection region can be heated above 20,000 K as long as the reconnection magnetic fields reach above 500 G, which further proves that UV bursts can be generated in the dense low chromosphere. When the reconnection magnetic fields are stronger than 900 G, the width of the synthesized Si IV 1394 A line profile with multiple peaks can reach above 100 km s-1, which is consistent with the usually observed broad-line-width UV bursts. The dominate mechanism that converts magnetic energy to heat in an UV burst in the low chromosphere is Joule heating that is contributed by magnetic diffusion caused by electron-ion collision in the reconnection region. The average power density that is converted to the thermal energy in the reconnection region is about 100 erg cm-3 s-1, which is comparable to the average power density of the released heat in an UV burst.

 
Energetic Neutral Atoms at Mars: Predicted Distributions Based on MAVEN Measurements
Authors: Ramstad Robin , Brain David , Dong Yaxue , Halekas Jasper , McFadden James , Espley Jared , Jakosky Bruce
Published: March 2022

Measurements of Energetic Neutral Atoms (ENAs) provide information about both the plasma and neutral components along the line-of-sight for any ENA instrument, though the individual influences of the plasma and neutral environments are convoluted due to the nature of the charge-exchange ENA generation process. We combine ion flux and magnetic field measurements from the Mars Atmosphere and Volatile EvolutioN (MAVEN) orbiter at Mars with models for the Martian exospheric components to predict the average observable 10 eV – 10 keV oxygen and hydrogen ENA distributions from virtual orbits in the near-Mars space environment. The predicted distributions are consistent with past ENA measurements, informing and constraining future ENA investigations of the neutral and plasma near-Mars space environments.
Hysteresis of terrestrial carbon cycle to CO2 ramp-up and -down forcing
Authors: Park Sowon , Kug Jong-Seong
Published: March 2022

To prevent excessive global warming, we have faced a situation to reduce the net carbon dioxide (CO2) emission. However, how the Earth’s terrestrial biosphere behaves under negative emission is highly uncertain. Here we show that there is a strong hysteresis in terrestrial carbon cycle in response to CO2 ramp-up and -down forcing. Due to this strong hysteresis lag, terrestrial biosphere stores more carbon at the end of simulation than its initial state, lessening the burden on the net negative emission. This hysteresis is latitudinally-dependent, showing a longer timescale of reversibility in high-latitudes and particularly carbon in boreal forests can be stored for a long time. However, the hysteresis of the carbon cycle in the pan-Arctic region strongly depends on the presence of permafrost processes. That is, an unexpected irreversible carbon emission might occur in permafrost even after achieving net-zero emission, which implies the importance of the permafrost processes, highly uncertain in our current knowledge.
Understanding the responses of river floods to warming climate across the United States
Authors: Shen Mingxi , Chui Ting Fong May
Published: March 2022

How river floods respond to warming climate has drawn increasing attention in recent years. One concern is whether floods would respond similarly to global warming as extreme precipitation, since extreme precipitation is usually the main trigger of flooding. Another argument is flood investigation should not solely focus on precipitation, while antecedent soil moisture condition and snowmelt are also important issues in flood generation. To address these concerns, we first implemented scaling analysis to illustrate the sensitivity of extreme precipitation and flood to warming climate across the Continental United States (CONUS). We found both similar and opposite scaling patterns in extreme precipitation and streamflow to temperature. Generally, the similar scaling was found in regions where extreme precipitation and flood timing is correspondent, e.g., the west coast and southern plains. For regions with dissimilar timing distribution of extreme precipitation and floods, the scaling patterns are much different. To further explain the contrasting scaling patterns, we used interpretable machine learning to investigate the extent to which different variables/processes contribute to the floods across the CONUS. We formulated four common flood generating mechanisms, including (1) precipitation, (2) precipitation excess, considering precipitation infiltration upon unsaturated soil, (3) snowmelt, (4) effective precipitation, considering both snowmelt and infiltration. Tree-based regression models (XGBoost, random forest) combined with interpretable machine learning (SHAP, ALE) were then used to quantify the relative contributions of these mechanisms to floods. The results show that the relative contributions of these mechanism show clear regional patterns. The scaling patterns of extreme precipitation and floods across the CONUS are in line with the contribution patterns of the four flood generating mechanisms. It demonstrates that the sensitivity of floods to warming climate is determined by local flood generating mechanisms. Therefore, more attention should be given to flood generating mechanisms when exploring flood responses to global warming.
GPS data reveal the evolution of liquid water  retention within the Greenland Ice Sheet at the seasonal and inter-annual time scale
Authors: Ran Jiangjun , Ditmar Pavel
Published: March 2022

The Greenland ice sheet (GrIS) is currently losing mass, as a result of complex mechanisms of ice-climate interaction that need to be understood for reliable projections of future sea level rise. Previous authors have intensively investigated the total GrIS mass balance, Surface Mass Balance (SMB), and ice discharge at different temporal scales. However, knowledge of the remaining mass variations due to supra-, en- and sub-glacier meltwater retention, is limited. In this study, we make a first attempt to quantify the temporal pattern of meltwater retention at different locations within the GrIS by analyzing the bedrock’s elastic response caused by meltwater mass variations. To that end, we use vertical displacements observed by Global Positioning System (GPS) stations. We estimate, for the first time, the evolution of meltwater storage variations at both the seasonal and inter-annual time scale. We find, among others, that the annual cycle of vertical displacements, after subtracting the signals related to SMB and other known mass transport processes, demonstrates at many GPS stations a consistent subsidence from May to July. This is an indication of a mass gain, which starts in May and peaks in July. We infer that this mass gain signal is due to the meltwater accumulation within the ice sheet. An in-depth investigation of this process by Geodetic data is critical for better understanding the hydrological cycle and the further evolution of the GrIS.
Integration of the Terrain Characteristics and Data of Gaging Station for Mimicking the Plausible Surface of Slope Failures.
Authors: Wong Hock-Kiet , Wang Chih-Ling , Ma Ching-Yuan , Tai Yih-Chin
Published: March 2022

An Idealized curve surface (ICS) with two constant curvatures was proposed in Tai et al. (2020) for mimicking the plausible landslide failure surface in numerical simulation.  For ease of illustrating the ICS, Ko et al. (2021) suggested the concept of a reference ellipse for constructing the associated ICS, i.e. the ellipse-ICS method. Hence, with respect to a landslide-prone area, the most appropriate ICS can be figured out by translating, rotating and side-tilting the reference ellipse.

In the present study, the criteria for the searching the most appropriate ICS consist of the terrain characteristics (cracks, scarps, erosion gullies) and the data of the gauging station (inclinometer and groundwater indicators), where the terrain characteristics indicate the plausible boundary of the failure area, the records of inclinometer help to identify the (local) depth of sliding surface. Since the inclinometer and groundwater indicators provide the local data only, the proposed ellipse-ICS method is employed as an efficient tool to construct the plausible ICS and to investigate the impacts of the groundwater distribution on the slope stability.

The ellipse-ICS method is therefore applied to two potential large-scale landslide areas in Taiwan, i.e., the T003 at Yanping Township in eastern Taiwan and the T002 at Fuxing District in northern Taiwan. The ICSs are identified with respect to the failure depths measured by inclinometer, where the safety factors are estimated. Together with the numerical approach given in Tai et al. (2019), the subsequent flow paths of post-failure can be estimated and may serve as useful information for hazard assessment.

 

Keywords:

ellipse-ICS, inclinometer, groundwater level, safety factors, flow paths

 

References

Tai, Y. C., Heß, J., & Wang, Y. (2019). Modeling Two‐Phase Debris Flows with Grain‐Fluid Separation over Rugged Topography: Application to the 2009 Hsiaolin Event, Taiwan. Journal of Geophysical Research: Earth Surface, 124(2), 305-333.

Tai, Y. C., Ko, C. J., Li, K. D., Wu, Y. C., Kuo, C. Y., Chen, R. F., & Lin, C. W. (2020). An idealized landslide failure surface and its impacts on the traveling paths. Frontiers in Earth Science, 8, 313.

Ko, C. J., Wang, C. L., Wong, H. K., Lai, W. C., Kuo, C. Y. & Tai, Y. C. (2021). Landslide Scarp Assessments by Means of an Ellipse-Referenced Idealized Curved Surface. Frontiers in Earth Science, 9,862.
Dynamics and attribution of exceptional Mediterranean heatwave in August 2021
Authors: Fuckar Neven-Stjepan , Allen Myles , Obersteiner Michael
Published: March 2022

Extreme weather and climate events - such as intense heatwaves, prolonged droughts and extensive wildfires - are aspects of the evolution of the climate system that are becoming more frequent and stronger in many parts of the world. Extremes can have substantial environmental and socio-economic impacts depending on vulnerability of exposed population, as well as present infrastructure and assets. In August 2021 extreme heat affected the broader Mediterranean region: on 11 August, a weather station in Syracuse (the birthplace of Archimedes on Sicily), Italy, reached 48.8 °C, the European near-surface temperature record, while Kairouan, Tunisia, reached a record 50.3 °C on the same day. 47.4 °C in Montoro set the national record for Spain on 14 August, while on the same day Madrid had its hottest day on the record with 42.7 °C. This was caused by an extensive heat dome, a large area of high pressure in the upper atmosphere leading to strong downward motion that compresses and heats up air in addition to the contribution from radiative heating. Furthermore, this heating was accompanied by devastating wildfires in several Mediterranean countries. Our study utilises a set of observations and reanalysis products combined with large ensembles of CMIP5/6 simulations to examine the role of anthropogenic drivers in this extreme event. We also use large ensembles of specifically designed historical/factual and natural/counterfactual simulations of EC-Earth3.3 coupled climate model at the standard resolution (T255L91 ORCA1L75) to assesses to what extent anthropogenic forcing modified the probability and magnitude of this event involving conditional perspective of the atmospheric circulation. The preliminary results points to a substantial role of the global climate change in modifying likelihood of this extreme event.
Causality Analysis in the Water-Energy-Food Nexus in the Canadian Prairies 
Authors: Saed Behdad , Elshorbagy Amin , Razavi Saman
Published: March 2022

As global water, energy, and food (WEF) demands are continuously increasing because of population growth, climate change, and the modernization of the human lifestyle, sustainable resource management is of prime importance. Societies have been struggling with the planning and management of WEF resources under changing population, climate, and ecosystem. Integrated resource management is essential to achieve optimal and sustainable WEF management as sector-centric (e.g. water-centric) management can lead to poor outcome. To that end, WEF nexus as a multi-centric approach has been introduced to emphasize interlinkages among WEF sectors. Such interlinkages need to be identified, quantified, and analyzed to facilitate sustainable WEF resources management.

This study aims to conduct a quantitative data analysis within the WEF nexus context to identify the interrelationships among WEF sectors and to understand how each sector interacts with other sectors in the Canadian Prairie provinces (Alberta, Saskatchewan, and Manitoba) individually, and as a whole over the period 1990-2020. Historical data used in this study are at annual temporal and provincial spatial resolution. A correlation-and-causality analysis has been conducted for different pairs of WEF sectors to measure the degree of relationships and to explore the cause-and-effects between each pair of sectors. The Multispatial Convergent Cross Mapping method, as a causal inference tool, has been used for identifying and assessing the causal relations. Determining the causal relationships among WEF sectors helps researchers identify critical components, of a large and complex system, for further investigation and modelling. It can also guide policy-makers for better allocation of resources.

Results showed that water has a stronger influence on food and energy than the other way around in the upstream province of Alberta. It was also found that food had more influence on energy than the other way around in the three prairie provinces. This study is a step forward toward a better understanding of the WEF nexus by using causal inference methods for tracking the strength of interactions to identify dominant sectors at both the provincial and regional scales. This can help build more parsimonious and efficient WEF nexus models for further simulation and scenario analysis.
Regional applicability of earthquake forecasts using geoelectric statistical moments: Application to Kakioka, Japan
Authors: Chen Hong-Jia , Hattori Katsumi , Chen Chien-Chih
Published: March 2022

Electromagnetic anomalies have become promising for short-term earthquake forecasting. One forecasting algorithm based on statistical moments of geoelectric data was developed and applied in Taiwan. The objective of our research was to investigate such a reliable, rigorously testable algorithm to issue earthquake forecasts. We tested the applicability of the forecasting algorithm and applied it to geoelectric data and an earthquake catalog in Kakioka, Japan with a long-term period of 26 years. We calculated the variance, skewness, and kurtosis of the geoelectric data each day, determined their anomalies, and then compared them with earthquake occurrences through the forecasting algorithm. We observed that the anomalies of variance, skewness, and kurtosis significantly precede earthquakes, suggesting that the geoelectric data distributions deviate from normal distributions before earthquakes. Furthermore, the forecasting algorithm can select robust optimal models and produce explicit forecasting probability for two-thirds of all experimental cases. Therefore, we concluded that the forecasting algorithm based on statistical moments of geoelectric data is universal and may contribute to short-term earthquake forecasting.
Evolution and variation of the heliospheric current sheet during 1995-2009
Authors: Liou Kan , Wu Chin-Chun
Published: March 2022

The heliospheric current sheet (HCS) is the largest known solar wind structure that exists persistently and continuously within the heliosphere. While it is discovered for more than half a century ago, owing to very limited and scattered in-situ solar wind observations in the heliosphere, the shape and evolution of the HCS are still little known and constitute the key subject in the study of global-scale solar wind. Currently the morphology of the HCS is derived largely from simple kinematic approaches that map the neutral sheet observed at 2.5 solar radii outward into the heliosphere. The dynamical effect of solar wind interactions on the evolution and global structure of the HCS is still poorly understood. Here we present results from a study of the HCS from 1995 to 2009 using time-dependent, three-dimensional, global magnetohydrodynamic (MHD) model simulations. We focus on the radial and solar cycle variations of the HCS tilt angle from 18 solar radii to 7 AU. We also compare our result with those obtained from ballistic outward projections of the neutral sheet observed at 2.5 solar radii. We present our analysis result and discuss possible implications.
Evaluating Petermann Gletscher ice-shelf basal melt and ice-stream dynamics from high-resolution TanDEM-X elevation data.
Authors: Ciracì Enrico , Rignot Eric , Milillo Pietro , Dini Luigi
Published: March 2022

Petermann Gletscher drains 4% of the Greenland Ice Sheet that contains an estimated volume of ice equivalent to a 0.5 m global sea-level rise. It terminates in the longest floating ice shelves in the Northern Hemisphere. A significant portion of the glacier’s drainage basin is grounded below sea level on a downsloping bed, hence prone to rapid retreat if the glacier was pushed out of equilibrium by climate warming. Previous studies documented near-zero mass balance and a steady grounding line position during the last three decades. However, more recent observations revealed the transition to a new phase characterized by rapid grounding line retreat and accelerated ice flow after 2016. Increased basal melt due to warming ocean temperatures has been identified as the physical mechanism driving the retreat process. Nonetheless, a comprehensive evaluation of the magnitude and spatial variability of basal melt has not been performed yet. Its contribution to the ice mass loss remains, for this reason, poorly constrained.

In this study, we achieve this goal by employing high-resolution digital elevation models acquired by the German Aerospace Centre (DLR) TanDEM-X mission between 2011 and 2021. We derive basal melt estimates from ice elevation changes computed in a Lagrangian framework. The extended temporal coverage provided by TanDEM-X data allows mapping changes in basal malt over different temporal scales with unprecedented resolution and highlights increased melt rates during the second part of the observation period. The melt rate spatial distribution is consistent with the recent inland migration of the grounding line with peak values above 60 meters per year measured along with the western, central, and eastern sectors of the grounding zone.
News from the GGOS DOI Working Group
Authors: Elger Kirsten ,   GGOS DOI Working Group
Published: March 2022

The “GGOS Working Group on Digital Object Identifiers (DOIs) for Geodetic Data Sets” is entering its third year of regular meetings and discussions to develop best practices, recommendations and advocate for improved global coordination for using DOI to geodetic data and products. The group was established by the International Association of Geodesy’s (IAG) Global Geodetic Observing System (GGOS) and includes international representatives of IAG Services and geodetic data centres and associated members.

Data publications with digital object identifiers (DOI) are best practice for FAIR sharing data. They are fully citable in scholarly literature and many journals require the data underlying a publication to be available. Initial metrics for data citation allows data providers to demonstrate the value of the data collected by institutes and individual scientists. This possibility to get credit for providing data products and running data services has been identified in the group as key requirement for the motivation to implement DOIs to geodetic data.

Our group activities include the collection of data products and discussions on already existing and planned DOI activities for IAG services and geodetic data centres, including for recent projects, like FAIR GNSS. Whenever possible, we recommend that DOIs shall be included in standard data formats (e.g. Rinex) and cited when using the data. This presentation will give an update of the group activities.