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Applications of Smart Technologies in Climate Risk and Adaptation
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Applications of Smart Technologies in Climate Risk and Adaptation

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3979

Special Issue Editors

Prof. Dr. Wen Cheng Liu

E-Mail Website
Guest Editor
Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36063, Taiwan
Interests: hydrology and hydraulics; environmental fluid mechanics; disaster prevention and ecological engineering; contaminant transport modeling; environmental monitoring; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Chih-Chieh Young

E-Mail Website
Guest Editor
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung, Taiwan
Interests: nonlinear wave dynamics; coastal oceanography; computational fluid dynamics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

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 1127
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Study area map representing (<b>a</b>) Indonesia and the domains for the Weather Research and Forecast (WRF) model and the location of the Nusantara capital city and (<b>b</b>) the location of the government center core area (KIPP), the main area of Nusantara (KIKN), and the entire area, including the Nusantara capital city future development plan (IKN) and the actual land use and land cover (LULC) classes across the Nusantara region.</p> Full article ">Figure 2
<p>Variations in the LULC data for the KIKN area used for the WRF numerical simulations: (<b>a</b>) scenario 1 (before development); (<b>b</b>) scenario 2, representing the baseline (50% greenery and 50% urban); (<b>c</b>) scenario 3 (50% grasslands and 50% urban); (<b>d</b>) scenario 4 (50% mixed forest and 50% urban); (<b>e</b>) scenario 5 (65% greenery and 35% urban); (<b>f</b>) scenario 6 (35% greenery and 65% urban).</p> Full article ">Figure 3
<p>Spatial distributions of LULC (left column) and category-wise surface air temperature probability distributions (right column) at 01:00 and 16:00 local time for the Nusantara area domain for different scenarios: scenario 1 (<b>a</b>,<b>b</b>), scenario 2 (<b>c</b>,<b>d</b>), scenario 3 (<b>e</b>,<b>f</b>), scenario 4 (<b>g</b>,<b>h</b>), scenario 5 (<b>i</b>,<b>j</b>), and scenario 6 (<b>k</b>,<b>l</b>). The dashed lines indicate the north–south (NS) and east–west (EW) cross-sections used in <a href="#climate-13-00030-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 4
<p>Differences in surface air temperature (Temp) and wind speed (WS) at 01:00 and 16:00 local time at the east–west (EW) and north–south (NS) cross-sections before (scenario 1) and after (scenarios 2–6) Nusantara city development; Temp and WS differences (<b>a</b>) between scenario 1 and scenario 2, (<b>b</b>) between scenario 1 and scenario 3, (<b>c</b>) between scenario 1 and scenario 4, (<b>d</b>) between scenario 1 and scenario 5, and (<b>e</b>) between scenario 1 and scenario 6. <a href="#climate-13-00030-f003" class="html-fig">Figure 3</a> shows the locations of the EW and NS cross-sections. The mitigation measures were incorporated into scenarios 2–6 by adjusting the albedo values to 0.8 for roofs and 0.7 for walls. (For references to color blocks in this figure legend, please see <a href="#climate-13-00030-f003" class="html-fig">Figure 3</a>).</p> Full article ">Figure 5
<p>Comparisons of the simulated surface air temperature (Temp) and wind speed (WS) from scenario 2 at 01:00 and 16:00 local time along (<b>A</b>) EW1, (<b>B</b>) EW2, (<b>C</b>) NS1, and (<b>D</b>) NS2 cross-sections and (<b>E</b>) the LULC pattern of scenario 2 with the locations of the cross-sections.</p> Full article ">Figure 6
<p>Comparison of the average hourly surface air temperature over computational domain 3 for various scenarios (<b>a</b>) with water bodies, (<b>b</b>) without water bodies, and (<b>c</b>) the difference in air temperature between (<b>b</b>) and (<b>a</b>).</p> Full article ">Figure A1
<p>(<b>a</b>) Observed monthly variations in rainfall and surface air temperature in the study area from Jan 2016–Dec 2020, and (<b>b</b>) the highest average surface air temperature occurred on 21 October 2020 during the five-year period, as indicated by the red circle.</p> Full article ">Figure A2
<p>Paired comparison of simulated surface air temperature with wind speed before and after Nusantara city development: scenario 2 (<b>a</b>,<b>b</b>), scenario 3 (<b>c</b>,<b>d</b>), scenario 4 (<b>e</b>,<b>f</b>), scenario 5 (<b>g</b>,<b>h</b>), and scenario 6 (<b>i</b>,<b>j</b>). (For references to color blocks in this figure legend, please see <a href="#climate-13-00030-f003" class="html-fig">Figure 3</a>).</p> Full article ">Figure A2 Cont.
<p>Paired comparison of simulated surface air temperature with wind speed before and after Nusantara city development: scenario 2 (<b>a</b>,<b>b</b>), scenario 3 (<b>c</b>,<b>d</b>), scenario 4 (<b>e</b>,<b>f</b>), scenario 5 (<b>g</b>,<b>h</b>), and scenario 6 (<b>i</b>,<b>j</b>). (For references to color blocks in this figure legend, please see <a href="#climate-13-00030-f003" class="html-fig">Figure 3</a>).</p> Full article ">Figure A3
<p>Spatial wind patterns at 01:00 and 16:00 local time on 21 October 2020. (For references to color blocks in this figure legend, please see <a href="#climate-13-00030-f003" class="html-fig">Figure 3</a>).</p> Full article ">
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
Viewed by 1082
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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Figure 1

Figure 1
<p>Schematic representation of the data analysis process, including data collection, feature analysis, and machine learning modeling to assess the relationship between dairy farm characteristics and methane emissions.</p> Full article ">Figure 2
<p>Visual representation of the correlations between various dairy farm factors. The color intensity indicates the strength of correlation, with darker colors representing stronger correlations.</p> Full article ">Figure 3
<p>Correlation of dairy farm factors with methane concentration. Bar chart showing the correlation coefficients between individual dairy farm characteristics and methane concentration (ppb). Positive values indicate positive correlations, while negative values indicate inverse relationships.</p> Full article ">Figure 4
<p>Performance comparison of machine learning models for methane prediction. Comparison of R-squared values and mean squared errors (MSEs) for Random Forest and Neural Network models applied to datasets processed with Variance Inflation Factor (VIF) and Principal Component Analysis (PCA).</p> Full article ">Figure 5
<p>Feature importance in predicting methane emissions from dairy farms. Ranking of dairy farm characteristics based on their importance in predicting methane emissions, as determined by the Random Forest model. Higher values indicate greater importance in the prediction model.</p> Full article ">
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 1319
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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Figure 1

Figure 1
<p><b>Top left</b>: Map of Western Europe. The area included in the rectangle is the region of study. <b>Bottom right</b>: Zoom in on the region of interest. The dots indicate the location of the radars and the circles indicate the 50 (dots) and 100 (straight) km range for each radar. The red shaded area marks the region covered by the hail pad network. “LMI”, “CDV”, “PBE”, and “PDA” indicate the locations of the radars of La Miranda, Creu del Vent, Puig Bernat, and Puig d’Arques, respectively.</p> Full article ">Figure 2
<p>Example of hit hail pad corresponding to the event of 29 August 2023. The image has been filtered to highlight the impacts over the plaque. The striped rectangle in the middle of the pad corresponds to the calibration area (see [<a href="#B19-climate-12-00158" class="html-bibr">19</a>] for more information).</p> Full article ">Figure 3
<p>(<b>A</b>) CAPPI at 3 km height at 17.12 UTC on 28 July 2028. The black arrow line shows the cross section segment shown in panel (<b>B</b>). (<b>B</b>) Cross section of the thunderstorm over the region of interest at the same time as panel (<b>A</b>). (<b>C</b>) Maximum VIL field for the whole day of 28 July 2028. The dots indicate the maximum hail size registered by the different hail pads. (<b>D</b>) Same as panel (<b>C</b>), but for the maximum VIL density field.</p> Full article ">Figure 4
<p><b>Top</b>: each dot corresponds to an analyzed hail pad during the event of 5 July 2012. <b>Below</b>: normalized coordinates of the same points centered in (0, 0).</p> Full article ">Figure 5
<p><b>Top</b>: Hail size distribution (in logarithm) for the whole dataset of hail pad registers. <b>Bottom</b>: Linear fitting of the distribution.</p> Full article ">Figure 6
<p>From top to bottom: distribution of graupel (grey), hail (blue), and severe hail (red) for the week of the year (<b>A</b>), the month (<b>B</b>), the year (<b>C</b>), and the maximum daily surface temperature (<b>D</b>).</p> Full article ">Figure 7
<p>As in <a href="#climate-12-00158-f006" class="html-fig">Figure 6</a>, but for the percentage distribution (only for cases with plaques with impacts). (<b>A</b>–<b>C</b>) panels correspond to weekly of the year, monthly, and yearly distributions, respectively.</p> Full article ">Figure 8
<p>Box plots of the VIL for the four hail categories detected in the hail pads (from left to right: no hail, graupel, hail and severe hail).</p> Full article ">Figure 9
<p>Same as <a href="#climate-12-00158-f008" class="html-fig">Figure 8</a>, but for VIL density.</p> Full article ">Figure 10
<p>VIL (<b>top</b>) and VIL density (<b>bottom</b>) distributions for all the four categories (black: no hail; grey: graupel; blue: hail; and red: severe hail).</p> Full article ">Figure 11
<p>Total sample of hail records (dots) for 2000–2023 (dark grey for null, grey for graupel, cyan for hail, and orange for severe hail). The dashed lines correspond to the 10th percentile of occurrence for each category (black for null, green for graupel, blue for hail, and red for severe hail), showing the usual behavior of the hailfall in the region concerning the center of the event.</p> Full article ">Figure 12
<p>Graupel (<b>A</b>), hail (<b>B</b>), and severe hail (<b>C</b>) spatial distributions estimated using maximum daily VIL fields for 2013–2023. Dotted, dashed, and straight lines indicate the 10th, 50th, and 90th ground observations percentiles.</p> Full article ">Figure 13
<p>Same as <a href="#climate-12-00158-f012" class="html-fig">Figure 12</a>, but for VIL density (Graupel, hail, and severe hail in panels (<b>A</b>–<b>C</b>), respectively).</p> Full article ">
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