Month: February 2024

Assessment of future changes in drought characteristics through stochastic downscaling and CMIP6 over South Korea

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Abstract

Assessments of future droughts are essential tools due to the potential for serious damage to the environment, economy, and society, particularly under climate change. This study proposes a framework for assessing drought characteristics at different scales, periods, and emission scenarios modeled by phase 6 of the Coupled Model Intercomparison Project. The four drought characteristics were determined by applying the Run theory to a standardized precipitation index time series, and the severe drought areas were detected by the Jenks Natural Breaks and Kriging methods. The study produced four main findings. (1) A stochastic weather generator, AWE-GEN, captures the variability of precipitation with inter- and intra-annual stochastic properties, and presents naturally occurring variability as an ensemble. (2) According to the ensemble average of drought characteristics, future droughts are projected to become less frequent with similar durations and intensity due to future rise in precipitation. However, the ensemble (stochastic or natural) and spatial variabilities are expected to increase, making drought management difficult (e.g., future decrease of 18% in \({DE}_{max}\) for END585). (3) Different temporal scales can affect the detection and characterization of drought events. Smaller temporal scales identify mild drought events of short duration and low severity, while larger scales merge and extend drought events, resulting in more prolonged and severe droughts. (4) Severe drought areas can expand compared with a control period for drought duration and severity, but may decrease for drought interval and frequency especially for the END period (e.g., 24% and 17% increase for \({DD}_{max}\) and \({\left|DS\right|}_{max}\) , and 85% and 78% decrease for \({DI}_{mean}\) and \({DE}_{max}\) for SPI3 and END585). The framework proposed in this study is expected to provide important information for the building of strategies required to adapt to and mitigate the potential impacts of drought in the future.

Marine ecosystem-based management: challenges remain, yet solutions exist, and progress is occurring

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Abstract

Marine ecosystem-based management (EBM) is recognized as the best practice for managing multiple ocean-use sectors, explicitly addressing tradeoffs among them. However, implementation is perceived as challenging and often slow. A poll of over 150 international EBM experts revealed progress, challenges, and solutions in EBM implementation worldwide. Subsequent follow-up discussions with over 40 of these experts identified remaining impediments to further implementation of EBM: governance; stakeholder engagement; support; uncertainty about and understanding of EBM; technology and data; communication and marketing. EBM is often portrayed as too complex or too challenging to be fully implemented, but we report that identifiable and achievable solutions exist (e.g., political will, persistence, capacity building, changing incentives, and strategic marketing of EBM), for most of these challenges and some solutions can solve many impediments simultaneously. Furthermore, we are advancing in key components of EBM by practitioners who may not necessarily realize they are doing so under different paradigms. These findings indicate substantial progress on EBM, more than previously reported.

“Frequently Asked Questions” About Genetic Engineering in Farm Animals: A Frame Analysis

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Abstract

Calls for public engagement on emerging agricultural technologies, including genetic engineering of farm animals, have resulted in the development of information that people can interact and engage with online, including “Frequently Asked Questions” (FAQs) developed by organizations seeking to inform or influence the debate. We conducted a frame analysis of FAQs webpages about genetic engineering of farm animals developed by different organizations to describe how questions and answers are presented. We categorized FAQs as having a regulatory frame (emphasizing or challenging the adequacy of regulations), an efficiency frame (emphasizing precision and benefits), a risks and uncertainty frame (emphasizing unknown outcomes), an animal welfare frame (emphasizing benefits for animals) or an animal dignity frame (considering the inherent value of animals). Animals were often featured as the object of regulations in FAQs, and questions about animals were linked to animal welfare regulations. The public were represented using a variety of terms (public, consumer) and pronouns (I, we). Some FAQs described differences between technology terms (gene editing, genetic modification) and categorized technologies as either well-established or novel. This framing of the technology may not respond to actual public concerns on the topic. Organizations seeking to use FAQs as a public engagement tool might consider including multiple viewpoints and actual questions people have about genetic engineering.

Extraction of Surface Water Bodies using Optical Remote Sensing Images: A Review

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Abstract

Surface Water Mapping (SWM) is essential for studying hydrological and ecological phenomena. SWM holds significant importance in water resource management, environmental conservation, and disaster preparation. Recently, rapid urbanization, overutilization, and environmental degradation have seriously impacted surface water bodies. Rapid advancement in remote sensing data and technologies has promoted the SWM to a new era. Timely and precise SWM is crucial for water resource preservation and planning. This paper critically reviews the extraction of surface water bodies from optical sensors using Spectral Indices (SI), Machine Learning (ML), Deep Learning (DL), and Spectral unmixing with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages, especially over the last decade. The extensive review of SWM reveals that DL outperforms ML and SI. DL outperforms other methods because it incorporates crucial elements in network design, like skip connections, dilation convolution, attention mechanisms, and residual blocks. The spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating the DL technique with spectral unmixing, fusing multisource data (SAR and optical) and integrating it with ancillary data (DEM) is the future direction for improved SWM.

Improving flood forecasts capability of Taihang Piedmont basin by optimizing WRF parameter combination and coupling with HEC-HMS

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Abstract

Based on numerical weather prediction model Weather Research and Forecasting (WRF) and Hydrologic Modeling System (HEC-HMS), a coupling model is constructed in Taihang Piedmont basin. The WRF model parameter scheme combinations composed of microphysics, planetary boundary layers, and cumulus parameterizations suitable for the study area are optimized. In both time and space, we tested the effects of the WRF model by a multi-index evaluation system composed of relative error, root meantime square error, probability of detection, false alarm ratio, and critical success index and established this system in two stages. A multi-attribute decision-making model based on Technique for Order Preference by Similarity to an Ideal Solution and grey correlation degree is proposed to optimize each parameter scheme. Among 18 parameter scheme combinations, Mellor-Yamada-Janjic, Grell-Devinji, Purdue-Lin, Betts-Miller-Janjić, and Single-Moment6 are ideal choices according to the simulation performance in both time and space. Using the unidirectional coupling method, the rolling rainfall forecast results of the WRF model in the 24 h and 48 h forecast periods are input to HEC-HMS hydrological model to simulate three typical floods. The coupling simulation results are better than the traditional forecast method, and it prolongs the flood forecast period of the Taihang Piedmont basin.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China

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Abstract

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China’s near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China’s eastern region, with population exposure risks mostly ranging from 0.8 to 5.

The Functional and Semantic Category of Appeal as a Linguistic Tool in Political Propaganda Texts (in the Example of the English Language)

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Abstract

The relevance of the research is defined by the need to create a set of linguistic means, which would contribute to effective communication with the general public, and the need to study different functional-semantic categories, including appeals, for the competent formation of public opinion in the political context. The research aims to comprehend the functioning of linguistic means used as appeals in the example of political propaganda texts in the English media field. The methodology is based on the theoretical study of the works of modern linguists, linguistic, structural, and communicative analysis of appeal linguistic units and contexts. The research considered the linguistic means that form the functional-semantic category of the appeal, examples of political contexts from the British and American media were presented, the functions of appeal were presented using specific examples, an idea of the communicative side of political propaganda texts and audience participation in this process was formed, emphasis was placed on different types of propaganda (white, black, gray), the following groups of appeals were characterized: imperatives (volitional and involuntary). The materials presented in this research can be used to form an idea of the functional-semantic category of appeal, the choice of linguistic means for the purpose of information promotion in the media or social networks, the study of communicative strategies in linguistics and their successful implementation, consideration of political propaganda texts, increasing efficiency when influencing the audience, further implementation of means of appeal in machine learning.

The Functional and Semantic Category of Appeal as a Linguistic Tool in Political Propaganda Texts (in the Example of the English Language)

Latest Results

Abstract

The relevance of the research is defined by the need to create a set of linguistic means, which would contribute to effective communication with the general public, and the need to study different functional-semantic categories, including appeals, for the competent formation of public opinion in the political context. The research aims to comprehend the functioning of linguistic means used as appeals in the example of political propaganda texts in the English media field. The methodology is based on the theoretical study of the works of modern linguists, linguistic, structural, and communicative analysis of appeal linguistic units and contexts. The research considered the linguistic means that form the functional-semantic category of the appeal, examples of political contexts from the British and American media were presented, the functions of appeal were presented using specific examples, an idea of the communicative side of political propaganda texts and audience participation in this process was formed, emphasis was placed on different types of propaganda (white, black, gray), the following groups of appeals were characterized: imperatives (volitional and involuntary). The materials presented in this research can be used to form an idea of the functional-semantic category of appeal, the choice of linguistic means for the purpose of information promotion in the media or social networks, the study of communicative strategies in linguistics and their successful implementation, consideration of political propaganda texts, increasing efficiency when influencing the audience, further implementation of means of appeal in machine learning.

Development of a greenhouse gas – air pollution interactions and synergies model for Korea (GAINS-Korea)

Latest Results

Abstract

This study aimed to create Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS)-Korea, an integrated model for evaluating climate and air quality policies in Korea, modeled after the international GAINS model. GAINS-Korea incorporates specific Korean data and enhances granularity for enabling local government-level analysis. The model includes source-receptor matrices used to simulate pollutant dispersion in Korea, generated through CAMx air quality modeling. GAINS-Korea's performance was evaluated by examining different scenarios for South Korea. The business as usual scenario projected emissions from 2010 to 2030, while the air quality scenario included policies to reduce air pollutants in line with air quality and greenhouse gas control plans. The maximum feasible reduction scenario incorporated more aggressive reduction technologies along with air quality measures. The developed model enabled the assessment of emission reduction effects by both greenhouse gas and air pollutant emission reduction policies across 17 local governments in Korea, including changes in PM2.5 (particulate matter less than 2.5 μm) concentration and associated benefits, such as reduced premature deaths. The model also provides a range of visualization tools for comparative analysis among different scenarios, making it a valuable resource for policy planning and evaluation, and supporting decision-making processes.

Affective, defective, and infective narratives on social media about nuclear energy and atomic conflict during the 2022 Italian electoral campaign

Latest Results

Abstract

In the digital age, poor public communication catalyzes the spread of disinformation within public opinion. Anyone can produce political content that can reach a global audience, and social media has become a vital tool for political leaders to convey messages to the electorate. The 2022 Italian election campaign has seen the term “nuclear” debated with two different declinations: on the one hand, regarding nuclear energy for civilian use, and on the other hand, regarding the fear of an escalation of the conflict in Ukraine and the use of atomic weapons. This research aims to analyze the social media debate by exploring multiplatform dynamics to qualitatively identify and analyze the connections between social media platforms that we have termed Bridges, a concept drawn from Transmedia Theory to describe the narrative relationship between platforms. The methodological approach will follow an explanatory sequential design that will rely on digital methods to identify connections between platforms (bridges) and then apply an exploratory qualitative approach to enrich the data and capture the nuances of the debate. As expected, we found polarized positions and fragmentation on both issues of civilian nuclear energy and the atomic conflict narrative. Primary evidence shows bridges spreading affective, defective, and infective content across platforms in a multifaceted social media ecosystem. Affective refers to rhetoric that appeals to people’s feelings. Defective means the discussion that brings attention to hyper-partisan news channels, fake news, and misinformation. Infective means bridges with below-the-radar platforms, niche channels, or pseudo-information channels. They use bridges with mainstream platforms to gain the potential to go viral. The paper highlights the importance of cross-platform and interdisciplinary approaches to addressing disinformation in a media ecosystem where social media plays an increasing role in a country’s democratic dynamics.