Selection of the key earth observation sensors and platforms focusing on applications for Polar Regions in the scope of Copernicus system 2020-2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.Peer ReviewedPostprint (published version

The 2020 oil price war has increased integration between G7 stock markets and crude oil WTI

This paper aims to examine whether the oil price war between Saudi Arabia and Russia has increased integration between the Crude Oil WTI Spot oil index and the G7 stock markets, namely France (CAC 40), Germany (DAX 30), USA (DOW JONES), UK (FTSE 100), Italy (FTSE MID), Japan (Nikkei 225), Canada (S&P TSX), from January 2018 to January 2021. The results show that in the period before the oil price war, the G7 stock markets and the WTI index had 29 integrations (out of 56 possible). The WTI index is integrated with the UK stock markets (FTSE 100), and Japan (NIKKEI 225), and is integrated into the Japanese market. In the period of the oil price war, the G7’s stock markets and the Crude Oil WTI Spot index had 43 integrations (out of 56 possible), namely the WTI, Dow Jones, and Nikkei 225 indexes, with all their peers (7 out of 7 possible). When comparing the period before and during the 2020 oil crash, we found that integrations increased significantly from 29 to 43 (out of 56 possible); we also found that the Crude Oil WTI Spot index is no longer a safe haven for portfolio diversification in G7 stock markets. These findings validate our research issue, i.e., the oil price war between Saudi Arabia and Russia had increased integrations, and this evidence could question portfolio diversification.info:eu-repo/semantics/publishedVersio

Empirical Evidence of Co-Movement between the Canadian CDS, Stock Market And TSX 60 Volatility Index: A Wavelet Approach

Purpose- The prime objective of this study was to find the co-movement between the Canadian credit default swaps market, the Stock market and volatility index (TSX 60 Index) Design/ Methodology- To achieve this purpose, daily data containing 2870 observations starting from the 1st of January 2009 to the 30th of December 2019 were analyzed. This study employed the wavelet approach to present results in short-term, medium-term, long-term, and very long time. Findings- The findings of this study showed a negative correlation between the CDS market, stock market, and the TSX 60 index in the short-term as well as in the long-term term, while in medium-term and very long-term period correlation is strongly positive. The wavelet co-movement results in the short-term and long-term were negative, while this relationship in the medium-term and very long-term period was strongly positive. Practical Implications- This research provides simultaneous valuable information for investment decisions in the short, medium, and long term time horizons, as well as for the policymakers in the Canadian credit default swaps market, stock market, and the volatility index (TSX 60 Index)

Fossil fuel divestment strategies: Financial and carbon related consequences

Hunt, Chelsie & Weber, Olaf (accepted), Fossil fuel divestment strategies: Financial and carbon related consequences, Organization & Environment. Copyright © [2018] (SAGE Publications). Reprinted by permission of SAGE Publications.Fossil fuel divestment is discussed controversially with regard to its financial consequences and its effect on decarbonizing the economy. Theory and empirical studies suggest arguments for both, financial underperformance and outperformance of divestment. Therefore, our first research objective is to understand the financial effect of divestment. The second objective is to analyze the influence of divestment strategies on the carbon intensity of portfolios. Empirically, our analysis is based on the Canadian stock index TSX 260 for the time between 2011 and 2015. The results of the study suggest higher risk-adjusted returns and lower carbon intensity of the divestment strategies compared to the benchmark. We conclude that divestment is not only an ethical investment approach, but that it is able to address financial risks caused by climate change and, at the same time, is able to reduce the carbon exposure of investment portfolios.SSHR

Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis

Wuhan (China) is facing severe consolidation subsidence of soft soil and karst collapse hazards. To quantitatively explore the extent and causes of land subsidence in Wuhan, we performed multitemporal interferometry (MTI) analysis using synthetic aperture radar (SAR) data from the TerraSAR-X satellite from 2013 to 2017 and the Sentinel-1A satellite from 2015 to 2017. MTI results reveal four major subsidence zones in Wuhan, namely, Hankou (exceeding −6 cm/yr), Xudong-Qingshan (−3 cm/yr), Baishazhou-Jiangdi (−3 cm/yr), and Jianshe-Yangluo (−2 cm/yr). Accuracy assessment using 106 levelling benchmarks and cross-validation between the two InSAR-based results indicate an overall root-mean-square error (RMSE) of 2.5 and 3.1 mm/yr, respectively. Geophysical and geological analyses suggest that among the four major subsiding zones, Hankou, Xudong-Qingshan, and Jianshe-Yangluo are located in non-karstic soft soil areas, where shallow groundwater (< 30 m) declines driven by engineering dewatering and industrial water depletion contribute directly to soft soil compaction. Subsidence in the Baishazhou-Jiangdi zone develops in the karst terrain with abundant underground caves and fissures, which are major natural factors for gradual subsidence and karst collapse. Spatial variation analysis of the geological conditions indicates that the stage of karst development plays the most important role in influencing kart subsidence, followed by municipal construction, proximity to major rivers, and overlying soil structure. Moreover, land subsidence in this zone is affected more via coupling effects from multiple factors. Risk zoning analysis integrating subsidence horizontal gradient, InSAR deformation rates, and municipal construction density show that the high-risk areas in Wuhan are mainly distributed in the Tianxingzhou and Baishazhou-Jiangdi zone, and generally spread along the metro lines. © 202

Investigating dynamic interdependencies between traditional and digital assets during the COVID-19 outbreak: Implications for G7 and Chinese financial investors

This paper discusses the relationship between the volatilities of traditional and digital assets before and during the COVID-19 pandemic. Using daily data relevant to the period ranging from January 4, 2016, to April 15, 2020, the results of the DCC-MVGARCH model indicate that the stock markets responded to the Coronavirus outbreak as the crypto market with worrying volatility. Before this outbreak, Bitcoin and gold are considered as a hedge for US, English, French, German, and Italian financial investors. The conditional correlation between stock indices and other assets was skyrocketing during this pandemic, except for the couple SSE-Ripple

Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture

We provide sea ice classification maps of a subweekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar (SAR) and optical data in other MOSAiC studies

Uncertainty Assessment of Ice Discharge Using GPR-Derived Ice Thickness from Gourdon Glacier, Antarctic Peninsula

Ice cliffs within a glacier represent a challenge for the continuity equations used in many glacier models by interrupting the validity of input parameters. In the case of Gourdon Glacier on James Ross Island, Antarctica, a ∼300–500 m high, almost vertical cliff, separates the outlet glacier from its main accumulation area on the plateau of the island. In 2017 and 2018 we conducted ice thickness measurements during two airborne ground penetrating radar campaigns in order to evaluate differences to older measurements from the 1990s. The observed differences are mostly smaller than the estimated error bars. In comparison to the in situ data, the published “consensus ice thickness estimate” strongly overestimates the ice thickness at the outlet. We analyse three different interpolation and ice thickness reconstruction methods. One approach additionally includes the mass input from the plateau. Differences between the interpolation methods have a minor impact on the ice discharge estimation if the used flux gates are in areas with a good coverage of in situ measurements. A much stronger influence was observed by uncertainties in the glacier velocities derived from remote sensing, especially in the direction of the velocity vector in proximity to the ice cliff. We conclude that the amount of in situ measurements should be increased for specific glacier types in order to detect biases in modeled ice thickness and ice discharge estimations

Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations