Applications of Smart Technologies in Climate Risk and Adaptation

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 4885

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Guest Editor
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung, Taiwan
Interests: nonlinear wave dynamics; coastal oceanography; computational fluid dynamics; artificial intelligence
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Special Issue Information

Dear Colleagues,

The efforts to mitigate the impacts of climate change have been progressing slowly and adaptation is now one of the major strategies which is being considered by both developed and developing countries. Developing products and services that leverage these technologies to mitigate and adapt to climate change can increase our resilience against the impacts of a changing climate. Given the urgency of addressing climate change, it is imperative to explore innovative solutions that can enhance resilience and sustainability. This Special Issue seeks to advance the understanding and application of emerging technologies for climate change mitigation and adaptation. We are especially interested in the topics listed below:

Novel strategies, technologies, and policies;

Climate change mitigation and adaptation;

Protection and restoration of biodiversity and ecosystems;

Sustainable use and protection of water and marine resources;

Convergence technologies for sustainable climate change challenges.

Prof. Dr. Wen Cheng Liu
Dr. Chih-Chieh Young
Guest Editors

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Keywords

  • climate change mitigation
  • climate change adaptation
  • pollution prevention and control
  • carbonation technology
  • greenhouse gas (GHG)
  • biodiversity
  • ecosystem
  • sustainable development
  • water resources
  • marine resources

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Published Papers (3 papers)

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Research

22 pages, 9741 KiB  
Article
Assessing Green Strategies for Urban Cooling in the Development of Nusantara Capital City, Indonesia
by Radyan Putra Pradana, Vinayak Bhanage, Faiz Rohman Fajary, Wahidullah Hussainzada, Mochamad Riam Badriana, Han Soo Lee, Tetsu Kubota, Hideyo Nimiya and I Dewa Gede Arya Putra
Climate 2025, 13(2), 30; https://doi.org/10.3390/cli13020030 - 31 Jan 2025
Viewed by 1641
Abstract
The relocation of Indonesia’s capital to Nusantara in East Kalimantan has raised concerns about microclimatic impacts resulting from proposed land use and land cover (LULC) changes. This study explored strategies to mitigate these impacts by using dynamical downscaling with the Weather Research and [...] Read more.
The relocation of Indonesia’s capital to Nusantara in East Kalimantan has raised concerns about microclimatic impacts resulting from proposed land use and land cover (LULC) changes. This study explored strategies to mitigate these impacts by using dynamical downscaling with the Weather Research and Forecasting model integrated with the urban canopy model (WRF-UCM). Numerical experiments at a 1 km spatial resolution were used to evaluate the impacts of green and mitigation strategies on the proposed master plan. In this process, five scenarios were analyzed, incorporating varying proportions of blue–green spaces and modifications to building walls and roof albedos. Among them, scenario 5, with 65% blue–green spaces, exhibited the highest cooling potential, reducing average urban surface temperatures by approximately 2 °C. In contrast, scenario 4, which allocated equal shares of built-up areas and mixed forests (50% each), achieved a more modest reduction of approximately 1 °C. The adoption of nature-based solutions and sustainable urban planning in Nusantara underscores the feasibility of climate-resilient urban development. This framework could inspire other cities worldwide, showcasing how urban growth can align with environmental sustainability. Full article
(This article belongs to the Special Issue Applications of Smart Technologies in Climate Risk and Adaptation)
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16 pages, 2534 KiB  
Article
Mapping Methane—The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning
by Hanqing Bi and Suresh Neethirajan
Climate 2024, 12(12), 223; https://doi.org/10.3390/cli12120223 - 15 Dec 2024
Cited by 1 | Viewed by 1311
Abstract
Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, [...] Read more.
Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, using data collected between January 2020 and December 2022. By integrating advanced analytics, we identified key drivers of methane concentrations, including herd genetics, feeding practices, and management strategies. Statistical tools such as Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) addressed multicollinearity, stabilizing predictive models. Machine learning approaches—Random Forest and Neural Networks—revealed a strong negative correlation between methane concentrations and the Estimated Breeding Value (EBV) for protein percentage, demonstrating the potential of genetic selection for emissions mitigation. Our approach refined concentration estimates by integrating satellite data with localized atmospheric modeling, enhancing accuracy and spatial resolution. These findings highlight the transformative potential of combining satellite observations, machine learning, and farm-level characteristics to advance sustainable dairy farming. This research underscores the importance of targeted breeding programs and management strategies to optimize environmental and economic outcomes. Future work should expand datasets and apply inversion modeling for finer-scale emission quantification, advancing scalable solutions that balance productivity with ecological sustainability. Full article
(This article belongs to the Special Issue Applications of Smart Technologies in Climate Risk and Adaptation)
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19 pages, 7060 KiB  
Article
A Comparison between Radar Variables and Hail Pads for a Twenty-Year Period
by Tomeu Rigo and Carme Farnell
Climate 2024, 12(10), 158; https://doi.org/10.3390/cli12100158 - 4 Oct 2024
Cited by 1 | Viewed by 1423
Abstract
The time and spatial variability of hail events limit the capability of diagnosing the occurrence and stones’ size in thunderstorms using weather radars. The bibliography presents multiple variables and methods with different pros and cons. The studied area, the Lleida Plain, is annually [...] Read more.
The time and spatial variability of hail events limit the capability of diagnosing the occurrence and stones’ size in thunderstorms using weather radars. The bibliography presents multiple variables and methods with different pros and cons. The studied area, the Lleida Plain, is annually hit by different hailstorms, which have a high impact on the agricultural sector. A rectangular distributed hail pad network in this plain has worked operationally since 2000 to provide information regarding different aspects of hail impact. Since 2002, the Servei Meteorològic de Catalunya (SMC) has operated a single-pol C-band weather radar network that volumetrically covers the region of interest. During these years, the SMC staff has been working on improving the capability of detecting hail, adapting some parameters and searching for thresholds that help to identify the occurrence and size of the stones in thunderstorms. The current research analyzes a twenty-year period (2004–2023) to provide a good picture of the hailstorms occurring in the region of interest. The main research result is that VIL (Vertically Integrated Liquid) density is a better indicator for hailstone size than VIL, which presents more uncertainty in discriminating different hail categories. Full article
(This article belongs to the Special Issue Applications of Smart Technologies in Climate Risk and Adaptation)
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