bluuebunny/crossref_metadata_2025 huggingface dataset:
Altitude as an indicator of biased sampling design in landslide prediction
Authors: Dornik Andrei , Drăguț Lucian , Oguchi Takashi , Hayakawa Yuichi , Micu Mihai
Published: March 2020

Variables related to terrain morphology are widely used and have proven particularly effective in landslides detection as well as susceptibility modelling. Altitude has often been found as one of the main predictors in landslide modelling, although it does not have clear conceptual or empirical justification as predisposing factor. As most other land-surface variables are derived from it, altitude might be just a surrogate for more meaningful predictors in a statistically-based landslide modeling. For instance, altitude might replace curvature simply because convexities tend to occur in upper parts of a landscape, while concavities are associated with lower altitudes. Our work intends to examine the hypothesis that altitude points out issues in sampling design when appears as a main predictor in landslide modeling. The tests were conducted in two study areas, one in the Buzău County, Romania and the other in the Shizuoka Prefecture, Japan, with landslide inventories available. Two sampling designs were tested in each study area: random sampling over the entire study area (random point allocation within each landslide scarp polygon and the same number of points randomly created outside landslide scarp area, as absence data), and stratified random sampling based on lithological strata. Following stratified random sampling based on lithological homogeneity, three study areas in Buzau and two in Japan resulted.  Variable importance analysis and prediction of landslide scarp were conducted with Random Forest (RF) on databases with presence/absence of landslide scarp and associated values of 14 terrain variables. The results of variable importance analysis showed that variable hierarchy changed significantly when using lithological stratified sampling. In the random sampling scenario, altitude showed as the second most important landslide predictor in both study areas. In four out of five cases, the lithologically stratified random sampling led to decrease of altitude importance as landslide predictor, in two cases altitude even being one of least important variables. The results of model performance metrics showed that in four out of five cases the lithologically stratified random sampling significantly improved the prediction. In both areas in Japan, all four metrics show improvement of lithological stratified sampling over random sampling, by 6 and 4 % for AUC, 3% for OOB, 3 and 5 % for OA, and 6 and 10 % for Kappa, respectively. We conclude that landslide modeling is sensitive to lithological homogeneity and the presence of altitude as an important predictor could indicate a bias in the sampling design.
Modulation instability of weakly nonlinear long internal wave packets
Authors: Talipova Tatiana , Pelinovsky Efim
Published: March 2020

We exam the problem of the modulation instability of long internal waves. Such weakly nonlinear weakly dispersive wave packets in one-modal approximation are described by the Gardner equation (Korteweg-de Vries equation with both, quadratic and cubic nonlinearity and necessity condition for modulation instability of such quasi-harmonic waves is the positive coefficient of cubic nonlinear term, which is realized for certain density stratification. Nevertheless the linear dispersive relation used within the Gardner equation is valid for very long waves and does not describe waves of moderate length. It is why some other nonlinear evolution equations are applied in the theory of long surface waves like the Benjamin-Bona-Mahony (BBM) and Whitham equations. We use the extended versions of these equations including cubic nonlinear term and express all  coefficients through modal functions and density stratification. Then, the modulational instability of weakly modulated wave packets is investigated after deriving the nonlinear Schrodinger equation. Improved dispersion relation influences on the increment and size of modulational instability. Obtained results are compared with those, which known within the Gardner model.
Geotechnical properties of marine sediments in the SW Iberia Margin – Implications to marine geohazards.
Authors: Piedade Aldina , Santos Nicole , Lemos Luís , Roque Cristina , Quinta-Ferreira Mário , Ramalho Inês , Omira Rachid
Published: March 2020

Submarine mass-failures are recognized worldwide as a potential source of marine geo-hazards. They can compromise the safety and integrity of seafloor and subsurface infrastructures through destroying offshore installations or triggering potential tsunamis. This applies to the SW Iberia Margin, where the occurrence of damaging and tsunamigenic underwater landslides were evidenced in various research works.

This work assesses the geotechnical properties of the marine sediments forming the slopes of the SW Iberia Margin and provides implications to the marine geohazard in the region. Taking advantage of the availability of the cores from previous projects and expeditions (i.e. CONDRIBER, and IODP Expedition-339), we perform conventional Triaxial laboratory tests. These tests allow determining the in-situ shear strength and stress deformation properties, pre-and post-rupture of the undisturbed sediments.

Furthermore, we present, through a landslide case-study in the SW Iberia Margin, a sensitivity analysis of the marine geohazards (sliding mass drag forces and tsunamigenesis) to the geotechnical properties of the marine sediments. We demonstrate that the geotechnical analysis is crucial for an accurate modelling of the submarine mass movements, their impact on offshore installations, and their induced tsunamis.

This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project MAGICLAND – Marine Geo-hazards Induced by Underwater Landslides in the SW Iberian Margin (PTDC/ CTA-GEO/30381/2017).
Cyclic micromechanical controls of transient crustal deformation
Authors: White Joseph Clancy
Published: March 2020

Episodic brittle-ductile behaviour reflects the complex interplay of micromechanical hardening and softening, often with some type of fluid pressure associated with introduction of new material that acts as the switch from coseismic to interseismic response. Brittle features observed in nature can in general be characterized as discrete surfaces or narrow zones across which fast particle displacements have occurred, with or without dilatant behaviour; this descriptively meets the criteria for generation of earthquakes. Likewise, non-brittle flow is a priori associated with slower particle velocities. This reduces the problem to one of how and why rocks cycle between slow and fast displacements. Particle displacement in the solid-state is limited to three processes: individual atoms, glide of packets of atoms and frictional displacement across an essentially free surface. Each of these processes, however large the feature being studied or rapid the displacements, necessitates the sequential overcoming of extant atomic bonding energies. Within the rock record, evidence of seismic events are embedded as new or reconstituted material introduced to the deforming host as a consequence of brittle deformation; for example, veins and pseudotachylyte. This new material acts as an important sink for strain energy whereby brittle responses are suppressed until such time as a new critical state is reached. In turn, the strain rate softening abetted by the new material provides a ductile overprint of their syn-fracture origin. Consequently, rheological transitions within Earth’s crust are spatially and temporally transient, evidence for which may be routinely lost. As part of this cyclic behaviour, localization of deformation can be viewed as the default state, with macroscopic deformation a result of organization into required dissipative structures.
Real-Time Detection of Water Stress in Corn Using Image Processing and Deep Learning
Authors: Soffer Mor , Lazarovitch Naftali , Hadar Ofer
Published: March 2020

Water limitation is one of the main environmental constraints that adversely aﬀects agricultural crop production around the world. Precise and rapid detection of plant water stress is critical for increasing agricultural productivity and water use efficiency. Numerous studies conducted over the years have attempted to ﬁnd eﬀective ways to correctly recognize situations of water stress in order to determine irrigation regimes.

Water stress detection is currently done by various methods that are not ideal; these methods are often very expensive, destructive and cumbersome. Water stress in plants is also expressed at diﬀerent visual levels. Image processing is alternative way to visually recognize water stress levels. Such analysis is non-destructive, inexpensive and allows to examine the spatial variability of stress level under ﬁeld conditions.

In our study, we propose a new method for detecting water stress in corn using image processing and deep learning. For the purpose of collecting the images, we performed a three-months experiment, in which we took images of ﬁve diﬀerent groups of corn. Each group had a diﬀerent irrigation treatment, which led to ﬁve diﬀerent levels of water stress. The images were collected using a web camera located approximately 2 m from the plants.

Stress classiﬁcation was done by inserting processed images into a Convolutional Neural Network (CNN). Training the network was done using transfer-learning techniques in order to exploit the performance of an already trained CNN, for a fast and efficient training over the dataset. Testing the quality of classiﬁcation was done using extra camera which took a diﬀerent set of images.

Results were tested upon two sub-experiments - classiﬁcation of three types of treatments and classiﬁcation of ﬁve types of treatments; the results were 98% accuracy in classiﬁcation into three types of treatments (well-watered, reduced-watered and draught stressed treatment), and 85% accuracy in classiﬁcation into ﬁve diﬀerent treatments. These initial results are deﬁnitely excellent and can certainly serve decision making for optimal irrigation.  
Sinkhole collapse and hydrogeological hazard assessment in covered karst terrains: Case study of a bridge project across the Loire River (Orleans, France)
Authors: Noury Gildas , Salquèbre Damien , Jacob Thomas , Baltassat Jean-Michel , Asfirane-Haddadj Fawzia , Buscarlet Etienne , Boularas Soraya
Published: March 2020

The floodplain of the Loire River around Orleans is a sinkhole-prone area due to the highly karstified calcareous Beauce Formation overlying by few meters of weathered materials and alluvial deposits. Such layout makes it necessary to carry out detailed geotechnical and hydrogeological investigations for any important building project.

This paper presents the first results of a study carried out by the French Geological Survey for a bridge project across the Loire River. The geological setting indicates that the study area is near the front door of a supposed-major cave system in which water, coming from the Loire River, not only provides drinking water for an important part of the region, but also supplies the main spring of the Loiret River, located a few kilometers further. The overview of past sinkhole collapses confirms that the study area is regularly concerned by ground collapses of several meters of diameter. Field investigations include microgravimetry (26 hectares), two electrical resistivity profiles (720 m and 470 m long), 149 cone penetration tests (around 15 m deep), a first sequence of 11 drillings (40 m deep) and videos, gamma-ray and sonar logs. A first sinkhole hazard assessment is now quite complete: very weak layers (possibly caves) of several decimeters to a few meters thick need specific mitigation measures to secure the construction project. Other investigations are still being analysed (a second sequence of drillings, dye tracing, injection of salt brine with resistivity profiling) and should help specify the area hydrogeological hazard.
Using InSAR to asses rock glacier movement in the Uinta Mountains, Utah
Authors: Brencher George , Handwerger Alexander , Munroe Jeffrey
Published: March 2020

Rock glaciers are perennially frozen bodies of ice and rock debris that move downslope primarily due to deformation of internal ice. These features play an important role in alpine hydrology and landscape evolution, and constitute a significant water resource in arid regions. In the Uinta Mountains, Utah, nearly 400 rock glaciers have been identified on the basis of morphology, but the presence of ice has been investigated in only two. Here, I use satellite-based interferometric synthetic-aperture radar (InSAR) from the Copernicus Sentinel-1 satellites to identify and monitor active rock glaciers over a 10,000 km2 area. I also compare the time-dependent motion of several individual rock glaciers over the summers of 2016-2019 to search for relationships with climatic drivers such as precipitation and temperature. Sentinel-1 data from the August-October of 2016-2019 are used to create 79 interferograms of the entire Uinta range and are processed with the NASA/JPL/Stanford InSAR Scientific Computing Environment (ISCE) software package. Temporal baselines of intrayear interferograms range from 6-72 days. We use average velocity maps to generate an active rock glacier inventory for the Uinta Mountains containing 196 active rock glaciers. Average rock glacier velocity is 3 cm/yr in the line-of-sight direction, but individual rock glaciers have velocities ranging from 0.3-15 cm/yr. Rock glacier speeds do have a seasonal component, and were fastest in August across all years. One rock glacier reached a speed of 40 cm/yr over a 12 day interval from August 5 to August 17 of 2017. Preliminary results suggest that active rock glaciers are found at altitudes 10 m higher on average than inactive and relic rock glaciers identified in the previous inventory. Rock glacier movement did not accelerate between 2016 and 2019, suggesting that rock glaciers in this part of the Rocky Mountains are not speeding up over time. Our results highlight the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the factors that control their kinematics.
Dissolution of amphibole asbestos in modified Gamble’s solution at pH 4.5: a combined ICP-OES, XPS and TEM investigation
Authors: pacella alessandro , nardi elisa , montereali maria rita , fantauzzi marzia , rossi antonella , viti cecilia , ballirano paolo
Published: March 2020

This study analizes the dissolution reactions, and the corresponding surface modifications, of two amphibole asbestos incubated for 1, 24, 48, 168 and 720 h in a modified Gamble’s solution at pH 4.5. The investigated samples are UICC crocidolite from Koegas Mine, Northern Cape (South Africa), and fibrous tremolite from Montgomery County, Maryland (USA). Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was used to monitor the ion release into solution, X-Ray Photoelectron Spectroscopy (XPS) was performed to unveil the chemistry of the leached surface, and High Resolution Transmission Electron Microscopy (HR-TEM) was exploited for monitoring the structural modifications of the fibres.

An incongruent cation mobilization was observed in both samples. Fe mobilization was detected only in UICC crocidolite, due to the occurrence of Fe-bearing accessory phases in the sample (siderite, iron carbonate, and minnesotaite, an iron-bearing phyllosilicate). Notably, tremolite lifetime is shown to be roughly ten times that of UICC crocidolite under the same experimental conditions. This result agrees with previous dissolution studies at pH 7.4 indicating a higher dissolution and surface alteration for UICC crocidolite with respect to tremolite.
Modelling an exceptional desert dust transport toward Portugal on February 2017
Authors: Costa Maria João , Couto Flavio , Cardoso Eduardo , Salgado Rui , Guerrero-Rascado Juan Luis
Published: March 2020

The terrain surrounding the Sahara desert is formed by some mountains ranges, as the Atlas mountain system in the northern edge of the desert and the Hoggar Mountains in Southern Algeria. Such orography, jointly with atmospheric circulation, plays an important role in the mobilization and transport of desert dust over medium and large distances. This study explores the interaction between complex terrain and atmospheric circulation in order to better understand an exceptional desert dust outbreak affecting Portugal in February 2017. The Meso-NH model is able to represent the atmospheric motions in different scales, and has been implemented with a rather complete parametrization package of physical processes in the atmosphere. The capability of the model to simulate dust emission is also explored. The on-line dust emission parametrization type is taken from the distribution of emitted dust of SURFEX with no need to use chemistry to activate dusts. A set of two simulations was performed for the period between 16 February at 0000 UTC to 24 February 1200 UTC, with the Meso-NH model configured in a single domain at 10 km horizontal resolution and 300x360 grid points. The experiments were defined as a) control experiment (CTRL), and b) dust experiment (DUST). From the large domain simulations, it was possible to assess the source of dust and its mobilization over Western Sahara desert, namely over the Northern part of Mauritania and Mali and Eastern part of Algeria. The formation of a cyclonic circulation at the surface favoured the dust uplifting. Such a surface low merged with a cut-off low that moved southward over the Iberian Peninsula and remained centred in the north of Morocco. Such pattern intensified the northward flow found at 700 hPa toward the Atlas Mountains range, inducing the dust transport above 3 km altitude. As expected, the simulations showed the ability to assess important details about the atmospheric circulation not resolved by low density of observations over the domain considered. Furthermore, the simulations were able to show the way that the atmospheric ingredients were brought together to produce the exceptional transport of desert dust toward Portugal. The orographic effects playing an important role in dust mobilization (convergence and cyclogenesis at the surface) and atmospheric circulation to the maintenance of the dust transport have been highlighted. Such event were responsible for the transport of high amount of dust toward the Iberian Peninsula.
A space-based perspective of trends in air quality in major cities in the UK and India
Authors: Vohra Karn , Marais Eloise , Kramer Louisa , Bloss William , Porter Peter , Van Damme Martin , Clarisse Lieven , Coheur Pierre-François
Published: March 2020

Air pollution is one of the leading global causes of premature mortality, necessitating routine monitoring of air quality in cities where most (55%) people now reside. Surface monitors are sparse and costly to operate, whereas satellites provide global coverage of a multitude of pollutants spanning more than 2 decades. Here we make use of the dynamic range of satellite products to understand long-term changes in air quality in target cities in the UK (London and Birmingham) and India (Kanpur and Delhi). These include nitrogen dioxide (NO2) from OMI for 2005-2018, formaldehyde (HCHO) from OMI for 2005-2016 to monitor non-methane volatile organic compounds (NMVOCs), ammonia (NH3) from IASI for 2008-2017 and aerosol optical depth (AOD) from MODIS for 2005-2018 to monitor PM2.5. Where surface observations are available (almost exclusively the UK), we first evaluate the ability of the satellite observations to reproduce variability in surface air pollution. We find temporal consistency for most pollutants (R >= 0.5), with the exception of MODIS AOD and surface PM2.5 (R = 0.3), but the decline in AOD (3.0% a-1) and surface PM2.5 (2.8% a-1), so far only evaluated for London, is similar. Inconsistencies result from seasonal variability in the planetary boundary layer, differences in sampling footprint between the satellite and surface monitors, and interferences in the surface measurements (as is the case for NO2). We find a decrease in all pollutants in Birmingham and London and an increase in all pollutants in Delhi and Kanpur, over the analysis period, but not all trends are significant. Birmingham and London NO2 both declined by 2.5% a-1, whereas Delhi NO2 increased by 2.0% a-1, so that by the end of 2018 Delhi and London have the same tropospheric column concentrations of NO2. Only Delhi exhibits a significant NMVOCs trend (increase) of 1.8% a-1. NH3 trends are not significant in any of the four cities, consistent with bottom-up inventories and lack of direct controls on emissions of this pollutant, mostly from agriculture. These data show no evidence of air quality improvements in Delhi, despite rollout of strict controls on industry and vehicles.
A Python package for data assimilation in the eWatercycle program – a hydrological framework
Authors: Wang Zhenwu , Hut Rolf , van de Giesen Nick
Published: March 2020

The eWaterCycle program provides a collaborative environment for hydrological modelers, developed by the Netherlands eScience Center together with the Delft University of Technology. It aims to build a community of scientists in hydrology who use different programming languages for their specific models. Python is the lingua franca of the eWaterCycle platform and requires no modification to a particular model, making the platform user-friendly and flexible. Therefore, it can readily be applied in other geoscientific models. Currently, the Python data assimilation package includes ensemble-type methods, particle filters, and their variants, which are all sequential data assimilation algorithms. The implementation of techniques related to localization and inflation methods is included in this package. Localization and inflation are effective ways to avoid the collapse of a filter, which happens commonly in high dimensional models. The package gives access to all tunable parameters by configuration files quickly. To evaluate the performance of data assimilation comprehensively, a series of metrics is provided. In addition, the package offers a set of visualization tools to explore the results of data assimilation and the improvement of models.
Advanced Exploratory Analysis of Air Pollution Multivariate Spatio-Temporal Data
Authors: Kanevski Mikhail , Amato Federico , Guignard Fabian
Published: March 2020

The research deals with an application of advanced exploratory tools to study hourly spatio-temporal air pollution data collected by NABEL monitoring network in Switzerland. Data analyzed consist of several pollutants, mainly NO2, O3, PM2.5, measured during last two years at 16 stations distributed over the country. The data are considered in two different ways: 1) as multivariate time series measured at the same station (different pollutants and environmental variables, like temperature), 2) as a spatially distributed time series of the same pollutant. In the first case, it is interesting to study both univariate and multivariate time series and their complexity. In the second case, similarity between time series distributed in space can signify the similar underlying phenomena and environmental conditions giving rise to the pollution. An important aspect of the data is that they are collected at the places of different land use classes – urban, suburban, rural etc., which helps in understanding and interpretation of the results.

Nowadays, unsupervised learning algorithms are widely applied in intelligent exploratory data analysis. Well known tasks of unsupervised learning include manifold learning, dimensionality reduction and clustering. In the present research, intrinsic and fractal dimensions, measures characterizing the similarity and redundancy in data and machine learning clustering algorithms were adapted and applied. The results obtained give a new and important information on the air pollution spatio-temporal patterns. The following results, between others, can be mentioned: 1) some measures of similarity (e.g., complexity-independent distance) are efficient in discriminating between time series; 2) intrinsic dimension, characterizing the ensemble of monitoring data, is pollutant dependent; 3) clustering of time series observed can be interpreted using the available information on land use.  
Application of linear dynamical mode decomposition to surface air temperature in 20th century
Authors: Gavrilov Andrey , Kravtsov Sergey , Mukhin Dmitry , Loskutov Evgeny , Feigin Alexander
Published: March 2020

According to recent study [1], the current state-of-the-art climate models lack the substantial part of internal multidecadal climate signal which is observed in the 20th century surface air temperature reanalysis data as a global stadium wave (GSW). In the presented work we further investigate this phenomenon using the recently developed method [2] of empirical spatio-temporal data decomposition into linear dynamical modes (LDMs). The important property of LDMs is their ability to take into account the time scales of the system evolution (they are extracted from observed dataset by the Bayesian optimization technique) better than some other linear techniques, e.g. traditional empirical orthogonal function decomposition. Like any linear decomposition, it provides the time series of principal components and corresponding spatial patterns.
We modify the initially developed LDM decomposition to make it possible to take into account a prescribed external forcing (like CO2 emissions, sun activity etc.) and then find part of variability which may be considered as an internal climate dynamics decomposed into set of modes with different time scales, and hence may be helpful in GSW interpretation. The results of applying the method to the 20th century surface air temperature with different ways of forcing inclusion will be presented and discussed.

1. Kravtsov, S., Grimm, C., & Gu, S. (2018). Global-scale multidecadal variability missing in state-of-the-art climate models. Npj Climate and Atmospheric Science, 1(1), 34. https://doi.org/10.1038/s41612-018-0044-6
2. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2018). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics. https://doi.org/10.1007/s00382-018-4255-7
Evaluation of CCHE2D hydrodynamic and sediment transport model to simulate erosional sources in row crop agriculture in Midwest US
Authors: Wells Robert , Jia Yafei , Momm Henrique , Castillo Carlos , Vieira Dalmo , Bingner Ronald , Bennett Sean , Locke Martin
Published: March 2020

Soil erosion due to rainfall and overland flow can be detrimental to agricultural management and long-term agricultural sustainability. Although numerous conservation measures and planning strategies have greatly reduced the amount of sediment moving within the landscape, there are still unresolved questions concerning initiation of particle motion, susceptibility to erosion, total soil loss, sediment transport and general measurement theory. Within agricultural fields, ephemeral erosion is particularly harmful because these sources can accelerate sediment transport, often yield more sediment than interrill sources and are more challenging to mitigate. In this study, terrain data were collected by aerial photogrammetry using an unmanned aerial system (UAS) following planting and approximately one month later, while climate variables during the period were collected using NexRad radar. Imagery was captured within seven agricultural fields (six in Iowa and one in Minnesota), ranging in size from 0.6 to 3.6 hectare (1.6 to 8.8 acre). Considering the small scale in topographic variation between two surveys, extreme efforts were applied to image processing and geospatial registration. Advanced models for camera calibration utilizing Micmac open-source photogrammetry software package were used to account for complex distortion patterns in the raw image data set. The undistorted images were then processed using Agisoft Photoscan for camera alignment, model georeferencing and dense point cloud generation (millions to billions of points per survey), from which digital elevation models (DEMs; 10 to 57 million cells) were produced. A physically-based finite element hydrodynamic and sediment transport model (CCHE2D, developed at the National Center for Computational Hydroscience and Engineering) was applied to simulate hydrological (runoff), sediment detachment (raindrop splash, sheet flow, and concentrated flow erosion) and sediment transport/deposition landscape evolution processes. Simulated geomorphological and sediment budget results over time were compared to field observations for model input parameter adjustment and consequently quantification of estimates. Integration of high-resolution spatial and temporal topographic measurements with physically-based numerical models support the development and validation of dynamic landscape evolution models needed for accurate prediction and quantification of gully initiation, evolution and impact on total soil loss and effective conservation management planning.
Changing of the Guard for the Total Solar Irradiance Record
Authors: Kopp Greg , Harber David , Heuerman Karl , Stone Brandon
Published: March 2020

The uninterrupted, 41-year-long, spaceborne total solar irradiance (TSI) record has recently undergone several changes in the instruments contributing to these measurements of the net incoming radiant energy providing nearly all the power driving the Earth’s climate system. Two long-term instruments, NASA’s SORCE/TIM and TCTE/TIM, have recently been powered off. This ends the 17-year record from the SORCE/TIM, which established the currently-accepted TSI value of 1361 W m‑2 after its launch in 2003. ESA’s SoHO/VIRGO continues to acquire measurements that extend its 24-year record, but data availability has been on hold as a new processing methodology is implemented. NASA’s recently-launched TSIS‑1/TIM is presently continuing the measurements of these stalwart legacy instruments. This new TSI instrument is demonstrating higher on-orbit accuracy than any prior such instrument has achieved, with daily measurement updates that are available to the community for climate- and solar-research purposes. I will discuss the many recent changes to the spaceborne TSI measurement record, the current measurement-accuracy improvements and stabilities achieved and their implications for Earth energy-balance studies, and the future plans to maintain measurement continuity.
Testing for Dynamical Dependence: Application to the Surface Mass Balance Over Antarctica
Authors: Dalaiden Quentin , Vannitsem Stephane , Goosse Hugues
Published: March 2020

Dynamical dependence between key observables and the surface mass balance (SMB) over Antarctica is analyzed in two historical runs performed with the MPI‐ESM‐P and the CESM1‐CAM5 climate models. The approach used is a novel method allowing for evaluating the rate of information transfer between observables that goes beyond the classical correlation analysis and allows for directional characterization of dependence. It reveals that a large proportion of significant correlations do not lead to dependence. In addition, three coherent results concerning the dependence of SMB emerge from the analysis of both models: (i) The SMB over the Antarctic Plateau is mostly influenced by the surface temperature and sea ice concentration and not by large‐scale circulation changes; (ii) the SMB of the Weddell Sea and the Dronning Maud Land coasts are not influenced significantly by the surface temperature; and (iii) the Weddell Sea coast is not significantly influenced by the sea ice concentration.
Are melting alpine glaciers a source of legacy priority contaminants to downstream environments? A high-frequency analysis of water chemistry in the Canadian Rockies.
Authors: Staniszewska Kasia , Cooke Colin , Reyes Alberto
Published: March 2020

Glaciers are retreating across the Canadian Cordillera. As this ice melts trace elements and other contaminants, accumulated from millennia of atmospheric deposition, are subject to release with uncertain consequences for downstream water quality. It is therefore imperative to constrain the rate and magnitude of contaminant input to river systems from glacierized watersheds. Meltwater chemistry was monitored and modelled at a high temporal frequency using a combination of grab-sampling and sondes for physical, chemical, and hydrological parameters at the outlet of proglacial Sunwapta Lake, Athabasca Glacier, Canada. Principal component analysis revealed that chemical parameters could be split into two groups with distinct seasonal trends. Group A encompasses solutes and endogenic bedrock weathering associated elements. Group B includes particulate, and exogenic dust-associated elements. Group A element concentrations were highest during low flow conditions and were correlated positively with conductivity. Group B element concentrations were highest during high flow conditions and had a moderate positive correlation with turbidity. Concentrations of potentially hazardous trace elements remained below Canadian Environmental Quality Guidelines throughout the hydrological season (THg < 2.7 ng/L; TPb < 1.7 µg/L; TAs < 0.34 µg/L; TCr < 1.9 µg/L). Trace element fluxes (kg/year) and yields (kg/year/watershed area) were modelled at a high temporal-resolution by pairing grab sampling results with corresponding strongly correlated high-frequency physical parameters: conductivity or turbidity. Annual fluxes and yields were comparable or lower than fluxes and yields from other glacial meltwater streams globally. Annual fluxes and yields were THg: 95 kg/yr  and 3.2 g/yr/km2; TPb: 34 kg/yr and 1.2 kg/yr/km2; TCr 39.5 kg/yr and 1.4 kg/yr/km2; TAs: 7.3 kg/yr and 0.25 kg/yr/km2. Numerous studies have suggested that glaciers are a significant source of high concentrations, fluxes, and yields of contaminants, including: pesticides; PAHs; PCBs; and toxic trace elements. In contrast, we found low concentrations, fluxes, and yields of trace elements in meltwater from the rapidly retreating Athabasca Glacier. Grab-sampling complemented by high-frequency monitoring of physical and chemical water parameters allowed a high-resolution view of water chemistry variation in meltwater from the Athabasca Glacier.
The allometric scaling of thermal emissions from temperate and tropical cities
Authors: Abdulrasheed Mukhtar
Published: March 2020

ABSTRACT

Cities around the world develop energy balances that are different to their surroundings. This study examines the application of allometric scaling to the thermal emission of cities in temperate and tropical regions. Overpasses of UK and Nigeria of the Moderate Resolution Imaging Spectroradiometer (MODIS), covering the period between 2000 and 2017 were sampled to examine the seasonal variability in night-time clear-sky upwelling long-wave energy for selected cities of the two countries. Total (area-integrated) emitted energy was calculated per city and interpreted by looking for ‘allometric’ (power law) scaling against the total population of the urban areas. Both sets of cities produce strong correlations (R2 ³ 0.8 and R2≥0.7) of log (total emission) against log (population). Total night-time emitted energy is found to scale sub-linearly (i.e. with power law index < 1) with population on both countries. However, the slope derived from UK allometry (0.85 ± 0.03) is quite different from that derived for cities in Nigeria (0.4 ± 0.05). When scaled against log (city area), both sets of cities produce linear scalings, demonstrating that the total area of built surface is a more general predictor of thermal emissions than total population, a surprising result given the differences in built form in the two sets of cities. These results are robust to the method chosen to delineate the city boundary. We further investigate the factors underlying these allometric relationships using Local Climate Zone (LCZ) classifications.    
Increasing risks of apple tree frost damage under climate change
Authors: Menke Inga , Pfleiderer Peter , Schleussner Carl-Friedrich
Published: March 2020

The impacts of global warming on agriculture and crop production are already visible today and are projected to intensify in the future. As horticultural and agricultural systems are complex organisms, their responses to changing climate can be non-linear and at times counter-intuitive. These systems undergo yearly cycles of growth with different plant characteristics in each of their phenological phases. They are thus especially sensitive to changes in seasonality besides changes in the annual mean and single extreme events.

Here we show that as a result of warmer winters, the risk of frost damages on apple trees in Germany is projected to be about 10% higher in a 2°C world compared to today. Warmer winters lead to less frost days but also to earlier apple blossom. This can result in overall increase in years where frost days occur after blossom.

Using large ensemble climate simulations, we analyze this compound event of frost days after blossom – frost days after warm winters. Although the projected shift in blossom day and the decrease in frost days is relatively homogeneous over Germany, the change in frost risk varies considerably between regions. Our results highlight the importance of treating frost risk as a compound event of frost days after warm winters instead of comparing the average shift in blossom days with the decrease in frost days.

Reference: Pfleiderer, P., Menke, I. & Schleussner, C.-F. Increasing risks of apple tree frost damage under climate change. Clim. Change (2019). doi:10.1007/s10584-019-02570-y
Drought monitoring Using Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI) and Normalized-Difference Snow Index (NDSI) with observational and ERA5 dataset, within the uremia lake basin, Iran
Authors: Habibi Maral , Schöner Wolfgang , Babaeian Iman
Published: March 2020

Abstract

In this study, droughts were assessed for the Uremia Lake Basin located in the North West of Iran which is facing the risk of drying over the last decades. Since long-term and spatially dense observational data are not available, in particular for the mountainous part of the Uremia lake basin, we successfully tested the performance of the ERA5 reanalysis data set for our purpose. By comparing time series plots of drought indices (SPI, SPEI), both indices were able to capture the temporal variation of droughts. SPIE identified more drought events but SPI, as it uses precipitation only as input, fails to show the increasing number of evaporation driven droughts in the Uremia Lake Basin, which were observed in particular for the most recent decade. SPEI was calculated using the monthly temperature and precipitation, the extremely dry conditions of the basin were observed in the mountainous area, it seems that based on SPEI index, the highest values of actual evapotranspiration happens near the lake and in high mountains. Moreover, in recent years, drought has become more extreme in higher elevated areas, then we focused on Snow cover which has a significant role in surface runoff and groundwater recharge in mountainous and semi-arid areas, like within the Uremia lake basin. In recent years climate change impact snow variations distribution, snow cover, and runoff in different scales. Therefore, spatial and temporal monitoring of the snow-covered surface and the impact of these changes is necessary. Consequently, the chances of snow cover (SCA) in the study area were studied using MODIS images by the NDSI index and snow cover data from the ERA5 dataset. Finally, we came to this conclusion that the temperature rise in recent decades led to a high amount of evaporation and consequently the snow surface area has decreased so that it could affect the region’s water reservoir in the future.

Key words: Drought monitoring,ERA5,MODIS,SPI,SPEI,NDSI
High spatial and temporal resolution land surface temperature for the Antarctic Dry Valleys
Authors: Lezama Valdes Maite , Katurji Marwan , Meyer Hanna
Published: March 2020

Anthropogenic Climate Change is expected to take a toll on the Antarctic environment and its biodiversity, which is concentrated on the continent’s few ice-free areas, such as the McMurdo Dry Valleys (MDV). To model the current terrestrial habitat distribution and predict possible climate induced changes, high spatial and temporal resolution abiotic variables, especially land surface temperature (LST) and soil moisture are needed, but are currently unavailable.

The aim of this project is to fill this gap and create a high resolution LST dataset of the Antarctic Dry Valleys. This variable is acquired in a high temporal resolution (sub-daily) by the MODIS sensor aboard Terra and Aqua satellites. However, as LST varies greatly in space, the spatial resolution provided by this data source (1000 m) is too low to give a meaningful impression of LST and to study biodiversity patterns. Therefore, we use data from Landsat and ASTER sensors as a reference to downscale MODIS LST to a spatial resolution of 30 m. 7 year’s worth of satellite data as well as terrain-derived auxiliary variables went into the development of the model, which predicts 30 m LST for the Antarctic Dry Valleys. 

To model complex relations between terrain, radiation, land cover and LST, machine learning models are used. Multiple algorithms (Random Forest, NN, SVM, Gradient Boosting) are compared to find the best approach for predicting high resolution LST based on MODIS data. Using the best performing model, a daily dataset is created that provides LST for the Antarctic Dry Valleys from 2002 on.
Exploring the role of observational uncertainty and resolution mismatch in the application of bias adjustment methods
Authors: Casanueva Ana , Herrera Sixto , Iturbide Maialen , Lange Stefan , Jury Martin , Dosio Alessandro , Maraun Douglas , Gutiérrez José M.
Published: March 2020

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many bias adjustment methods, which merely correct for deficiencies in the distribution, have been developed. Despite adjusting the desired features under historical simulations, their application in a climate change context is subject to additional uncertainties and modifications of the change signals, especially for climate indices which have not been tackled by the methods. In this sense, some of the commonly-used bias adjustment methods allow changes of the signals, which appear by construction in case of intensity-dependent biases; some others ensure the trends in some statistics of the original, raw models. Two relevant sources of uncertainty, often overlooked, which bring further uncertainties are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect).

In the present work, we assess the impact of these factors on the climate change signal of a set of climate indices of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature -empirical or parametric-, fitted parameters and preservation of the signals) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping -QDM-, scaled distribution mapping -SDM- and the method from the third phase of ISIMIP -ISIMIP3) preserve better the raw signals for the different indices and variables (not all preserved by construction). However they rely largely on the reference dataset used for calibration, thus present a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20km) and low (approximately 120km) spatial resolutions.
Temporal evolution of rain drops’ velocities in a turbulent wind field
Authors: Gires Auguste , Tchiguirinskaia Ioulia , Schertzer Daniel
Published: March 2020

It is commonly assumed that a rain drop falls vertically at a speed equal to its so called “terminal fall velocity” which has been determined both empirically and theoretically by equating the net gravity force with the drag force due to the fact the drop is moving in the atmosphere. This velocity depends on the size of the drop, usually characterized by its equivolumic diameter.

In this investigation we study the temporal evolution of the velocity of a rain drop falling through turbulent wind field. The equation governing a rain drop motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. The latter depends non-linearly on the instantaneous relative velocity between the drop and the local wind. The whole complexity of the resulting behaviour arises from this feature. In this work, the drag force is expressed in a standard way with the help of a drag coefficient, which is itself determined according to a Reynolds number. It should be mentioned that in this initial work, the strong assumption that the drops remain spherical in their fall is made. It is well known that its not true for drops greater than typically 1-2 mm which tend to become oblate, and potential effects on the results will be discussed.

An explicit numerical scheme is implemented to solve this equation for 3+1D turbulent wind field to study the temporal evolution of the velocities as well as the trajectories of rain drops over few hundreds of meters. The variations in both space and time of the wind field are simulated with the help of a Universal Multifractals which are a framework that has been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as wind.

Temporal multifractal analysis are then carried out on the simulated drop velocity, which enables to characterize the behaviour of drops according to their size, and notably a scale below which turbulent eddies have a limited impact on their motion. Finally the consequences of these findings on rainfall remote sensing with radars are briefly discussed.