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Accurate assessment of changes in water availability with changing climate is vital for effective mitigation and adaptation. In this research, we employ a parsimonious Budyko curve method to evaluate changes in water availability under low‐ (SSP126) and high‐emission (SSP585) scenarios for 331 river basins in the contiguous United States. We also assess the relative role of changes in precipitation (∆P) and potential evapotranspiration (∆PET) with changing climate on the increase in water availability vulnerability. Results highlight that around 43% (28%) of basins are projected to experience increased vulnerability to changing climate in high‐emission (low‐emission) scenarios. Sub‐humid basins are most often impacted, while arid and semi‐arid basins exhibit lower sensitivity to changes. Intriguingly, ∆PET emerges as the dominant control on vulnerability, surpassing ∆P, particularly under SSP585 scenario. The analysis prompts water managers to focus on long‐term mitigation planning and scientists to further constraint climate and water budget forecasts in affected basins. Plain Language Summary: Assessment of changes in water availability because of the changing climate is useful for mitigation and adaptation planning. In this study, water availability change in river basins in the contiguous United States is assessed. Which basins will become more vulnerable in terms of reduced water availability in the future climate and how the vulnerability changes within different aridity zones is studied. The study presents a novel methodology to assess and quantify whether the changes in potential evapotranspiration or precipitation are the main driver of future water vulnerability. We find that vulnerability increase due to an increase in potential evapotranspiration to be large. From the different aridity zones, sub‐humid basins are projected to most often experience an increase in vulnerability. Key Points: Changes in water availability under climate change scenarios are evaluatedSub‐humid basins are most often impactedVulnerability increase is large due to increase in Potential Evapotranspiration [ABSTRACT FROM AUTHOR]

Research background: Understanding how distortions in capital and labor markets affect corporate value and sustainable growth is crucial in today's economy. These distortions can disrupt resource allocation and economic sustainability. Additionally, the role of institutional quality in shaping these dynamics requires thorough exploration. Purpose of the article: We quantify the effect of capital and labor market distortions on corporate value and sustainable growth rate (SGR) and how this association is moderated by institutional quality. Methods: Stemming from the sample criteria, we calibrated a final sample of 1971 United States-listed manufacturing firms for 2012-2022. This research offers insights into market inefficiencies and institutional effects. Progressing towards objectives, we use advanced techniques like feasible generalized least squares and generalized methods of moments. These methods help us rigorously analyze complex relationships among study variables. Findings & value added: Three key findings emerge: first, capital and labor market distortions have a negative and significant influence on corporate value and sustainable growth. Our primary finding implies that increasing distortions significantly reduce sustainable growth's value and potential. Second, we find institutional quality has a positive significant effect on corporate value and sustainable growth. Third, institutional quality positively moderates the association between capital and labor market distortions, corporate value, and sustainable growth. Findings suggest that institutional quality, as a potential mechanism, improves the efficiency of resource allocation and optimizes the sustainable economic system to lessen the negative effect of factor market distortions on corporate value and SGR. Besides, we conduct robustness checks to validate our findings. Finally, we offer policymakers and stakeholders actionable insights. [ABSTRACT FROM AUTHOR]

This article compares the discursive construction of news values in English news reporting of China Daily and CNN about the COVID-19 vaccine, to reveal how the two media packaged the vaccine and improved the acceptance of the international community towards the vaccines made in the respective countries. Adopting corpus linguistic methods and Discursive news values analysis (DNVA) framework, this study examines news values through keywords, nomination strategies, and photographs. The results show that the two media outlets constructed the news value of Eliteness dominantly through a systematic combination of words and images, albeit in different ways. China Daily prominently consists of references to China's international communications in the production and application of the COVID-19 vaccine, at the same time packaging the COVID-19 vaccine as foreign aid to enhance diplomatic relations as well as protect and promote the order of the international community. In contrast, CNN chiefly demonstrates references to domestic medical experts and the well-known manufacturers of the COVID-19 vaccine, packaging the COVID-19 vaccine as the US' medical/commercial product. The respective ways in which they portray the COVID-19 vaccine have helped to bolster the acceptance of each country's vaccine by the international community. [ABSTRACT FROM AUTHOR]

Modeling dynamic geophysical phenomena is at the core of Earth and environmental studies. The geoscientific community relying mainly on physical representations may want to consider much deeper adoption of artificial intelligence (AI) instruments in the context of AI's global success and emergence of big Earth data. A new perspective of using hybrid physics‐AI approaches is a grand vision, but actualizing such approaches remains an open question in geoscience. This study develops a general approach to improving AI geoscientific awareness, wherein physical approaches such as temporal dynamic geoscientific models are included as special recurrent neural layers in a deep learning architecture. The illustrative case of runoff modeling across the conterminous United States demonstrates that the physics‐aware DL model has enhanced prediction accuracy, robust transferability, and good intelligence for inferring unobserved processes. This study represents a firm step toward realizing the vision of tackling Earth system challenges by physics‐AI integration. Plain Language Summary: Artificial intelligence (AI) learns and makes inferences from experience and resembles the way untaught humans learn. If scientists can manage to teach AI the physical rules of the world, the "educated" AI may be more intelligent in deductions. However, how to implant elements of physical representations into an AI system effectively and directly remains an open question. This study proposes a general framework and applicable solutions to this challenge in the context of Earth science. The novel framework has a specially structured design for an AI system to "memorize" physical rules behind system dynamics (i.e., how a geosystem evolves with time). Following this framework, we developed a hydrology‐aware deep learning model to simulate/predict runoff in 569 catchments across the conterminous United States. The results show that after "learning" a hydrologic model, the AI system has enhanced prediction accuracy and good intelligence to deal with unfamiliar regions and infer unobserved processes. The potential of AI for in‐depth information mining, in return, fills the knowledge gap existing in physical approaches. The symbiotic integration of physical approaches and deep learning represents a promising solution to improve AI system awareness of geoscience knowledge. Key Points: A novel approach to representing geosystem dynamics via a recurrent neural network within deep learning architectures is proposedThe physics‐aware AI system exhibits robust transferability and good intelligence for inferring unobserved processes in runoff modelingThe hydrology‐aware DL model can be an intelligent parameterization module for the encoded hydrologic model in cross‐region applications [ABSTRACT FROM AUTHOR]

All federal systems face the two fundamental dilemmas of federalism: too strong a center risks overawing the subnational units; and too weak a center risks free-riding that makes the system fall apart. Resolving the two dilemmas is problematic because mitigating one dilemma exacerbates the other. We develop a model of federal institutions that shows the circumstances under which both dilemmas can be solved so that federal institutions are self-enforcing. We apply our approach to modern Russia where we suggest that when the center is too strong, its ability to extract rents increases and the benefits for maintaining participation in the federal bargain disappears We also suggest strong parallels between Russia and those of the early United States under the Articles of Confederation. [ABSTRACT FROM AUTHOR]

The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download. [ABSTRACT FROM AUTHOR]