Climate

21 pages, 1951 KiB

Open AccessArticle

A Farmer-Centric Cost–Benefit Analysis of Climate-Smart Agriculture in the Gandaki River Basin of Nepal

by Shobha Poudel, Rabin Thapa and Bhogendra Mishra

Climate 2024, 12(9), 145; https://doi.org/10.3390/cli12090145 - 16 Sep 2024

Climate-smart agriculture (CSA) is a climate-resilient practice that stands out globally as an important practice through which we can deal with emerging challenges through adaptation and mitigation to increase crop productivity and resilience. Despite its significance, a comprehensive cost–benefit analysis of the adoption [...] Read more.

Climate-smart agriculture (CSA) is a climate-resilient practice that stands out globally as an important practice through which we can deal with emerging challenges through adaptation and mitigation to increase crop productivity and resilience. Despite its significance, a comprehensive cost–benefit analysis of the adoption of these practices has not yet been carried out. This study aims to bridge the knowledge gap between the cost and effectiveness of CSA practices adopted by small-scale farmers in growing rice, wheat and maize, the most staple crops in the Gandaki River Basin of Nepal. In this study, net present value (NPV), internal rate of return (IRR), benefit–cost (BC) ratio, net benefit investment (NK) ratio and payback period, along with the value of externalities (social and environmental), were employed to assess the profitability of CSA practices. The findings indicate that almost all the CSA practices analyzed were profitable, with the exception of solar water management in maize with very low IRR (6%) and a longer payback period. The outcome of this study offers valuable insights for farmers in choosing profitable CSA technology and for policy makers in promoting better CSA technology, upscaling CSA practices, and formulating new agricultural policies and programs in the context of the changing climate. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)

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22 pages, 985 KiB

Open AccessArticle

Synergistic Impacts of Climate Change and Wildfires on Agricultural Sustainability—A Greek Case Study

by Stavros Kalogiannidis, Dimitrios Kalfas, Maria Paschalidou and Fotios Chatzitheodoridis

Climate 2024, 12(9), 144; https://doi.org/10.3390/cli12090144 - 14 Sep 2024

Abstract

Climate change and wildfire effects have continued to receive great attention in recent times due to the impact they render on the environment and most especially to the field of agriculture. The purpose of this study was to assess the synergistic impacts of [...] Read more.

Climate change and wildfire effects have continued to receive great attention in recent times due to the impact they render on the environment and most especially to the field of agriculture. The purpose of this study was to assess the synergistic impacts of climate change and wildfires on agricultural sustainability. This study adopted a cross-sectional survey design based on the quantitative research approach. Data were collected from 340 environmental experts using an online questionnaire. The results showed that extreme weather events such as heavy rains or extreme droughts negatively influence agricultural sustainability in Europe. The results showed that disruptions in ecosystems caused by climate change have a significant positive impact on agricultural sustainability in Europe. Furthermore, forest regeneration after wildfires showed statistically significant positive influence on agricultural sustainability in Europe. The economic impact of fire on crops, cattle, and farms can be estimated. This information can be used to develop and plan agricultural regions near fire-prone areas; choose the best, most cost-effective, and longest-lasting cultivar; and limit fire risk. It is also clear that increased wildfire smoke negatively affects agricultural sustainability. Full article

(This article belongs to the Special Issue Climate Adaptation Ways for Smallholder Farmers)

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20 pages, 3056 KiB

Open AccessArticle

The Signature of Climate in Annual Burned Area in Portugal

by Carlos C. DaCamara

Climate 2024, 12(9), 143; https://doi.org/10.3390/cli12090143 - 12 Sep 2024

Abstract

Portugal is by far the country most affected by wildfires in Mediterranean Europe. The increase in frequency and severity of extreme years in the last two decades calls for a better understanding of the role played by climate variability and climate change. Using [...] Read more.

Portugal is by far the country most affected by wildfires in Mediterranean Europe. The increase in frequency and severity of extreme years in the last two decades calls for a better understanding of the role played by climate variability and climate change. Using data covering a period of 44 years (1980–2023), it is shown that the distribution of annual burned area in Portugal follows a Rayleigh distribution whose logarithm of the scale parameter depends linearly on Cumulative Daily Severity Rate (

C D S R

). Defined for each year as the sum of the mean Daily Severity Rate over Portugal from 1 January to 31 December,

C D S R

is a measure of the dryness of dead fuels as induced by atmospheric conditions. Changes along the years of the modeled average explain 56% of the interannual variability of the annual burned area. When comparing the model for 30-year subperiods 1980–2009 and 1994–2023, large decreases are observed in return periods of annual burned area amounts, from 35% for amounts greater than 120 thousand hectares up to 49% for amounts greater than 200 thousand hectares. The proposed model is a useful tool for fire management under present and future climate conditions. Full article

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28 pages, 10631 KiB

Open AccessArticle

Optimizing Local Climate Zones through Clustering for Surface Urban Heat Island Analysis in Building Height-Scarce Cities: A Cape Town Case Study

by Tshilidzi Manyanya, Nthaduleni Samuel Nethengwe, Bruno Verbist and Ben Somers

Climate 2024, 12(9), 142; https://doi.org/10.3390/cli12090142 - 10 Sep 2024

Abstract

Studying air Urban Heat Islands (AUHI) in African cities is limited by building height data scarcity and sparse air temperature (T_air) networks, leading to classification confusion and gaps in T_air data. Satellite imagery used in surface UHI (SUHI) applications overcomes [...] Read more.

Studying air Urban Heat Islands (AUHI) in African cities is limited by building height data scarcity and sparse air temperature (T_air) networks, leading to classification confusion and gaps in T_air data. Satellite imagery used in surface UHI (SUHI) applications overcomes the gaps which befall AUHI, thus making it the primary focus of UHI studies in areas with limited T_air stations. Consequently, we used Landsat 30 m imagery to analyse SUHI patterns using Land Surface Temperature (LST) data. Local climate zones (LCZ) as a UHI study tool have been documented to not result in distinct thermal environments at the surface level per LCZ class. The goal in this study was thus to explore relationships between LCZs and LST patterns, aiming to create a building height (BH)-independent LCZ framework capable of creating distinct thermal environments to study SUHI in African cities where LiDAR data are scarce. Random forests (RF) classified LCZ in R, and the Single Channel Algorithm (SCA) extracted LST via the Google Earth Engine. Statistical analyses, including ANOVA and Tukey’s HSD, assessed thermal distinctiveness, using a 95% confidence interval and 1 °C threshold for practical significance. Semi-Automated Agglomerative Clustering (SAAC) and Automated Divisive Clustering (ADC) grouped LCZs into thermally distinct clusters based on physical characteristics and LST data internal patterns. Built LCZs (1–9) had higher mean LSTs; LCZ 8 reached 37.6 °C in Spring, with a smaller interquartile range (IQR) (34–36 °C) and standard deviation (SD) (1.85 °C), compared to natural classes (A–G) with LCZ 11 (A–B) at 14.9 °C/LST, 17–25 °C/IQR, and 4.2 °C SD. Compact LCZs (2, 3) and open LCZs (5, 6), as well as similar LCZs in composition and density, did not show distinct thermal environments even with building height included. The SAAC and ADC clustered the 14 LCZs into six thermally distinct clusters, with the smallest LST difference being 1.19 °C, above the 1 °C threshold. This clustering approach provides an optimal LCZ framework for SUHI studies, transferable to different urban areas without relying on BH, making it more suitable than the full LCZ typology, particularly for the African context. This clustered framework ensures a thermal distinction between clusters large enough to have practical significance, which is more useful in urban planning than statistical significance. Full article

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25 pages, 3148 KiB

Open AccessReview

Systematic Mapping of Climate Change Impacts on Human Security in Bangladesh

by Ferdous Sultana, Jan Petzold, Sonali John, Verena Muehlberger and Jürgen Scheffran

Climate 2024, 12(9), 141; https://doi.org/10.3390/cli12090141 - 9 Sep 2024

Abstract

There is an increasing consensus that climate change undermines human security by exacerbating existing challenges, acting as a “threat multiplier”. Bangladesh is a climate hot spot due to its geographical location, dense population and vulnerable socio-economic infrastructure. Although there is an increasing number [...] Read more.

There is an increasing consensus that climate change undermines human security by exacerbating existing challenges, acting as a “threat multiplier”. Bangladesh is a climate hot spot due to its geographical location, dense population and vulnerable socio-economic infrastructure. Although there is an increasing number of studies on the impacts of climate change in Bangladesh, aggregated research that combines this evidence and provides a comprehensive overview is lacking. The aim of this research is to thoroughly investigate the characteristics of the academic literature on the complex pathways through which climate variability affects different components of human security in Bangladesh, allowing for determination of the trends and research gaps, as well as whether they lead to conflict or cooperation. We used a systematic mapping methodology to search and screen 1839 publications in Web of Science, including 320 publications for the final synthesis. We found a predominant research focus on rural areas, especially in the southwestern region, with declining crop yield, economic loss, migration, water shortage, food scarcity and health hazards being the highlighted impacts of climate change for Bangladesh. The impacts on food, economic, environmental, health and water security have been well studied, but we found significant research gaps in some human security components, especially energy security. Women and the economically disadvantaged are disproportionately affected, and the causal pathways between conflict or cooperation and the ever-changing climate lack research efforts, implying a dire need to focus on under-researched areas before they become more complex and difficult to address. Policies and interventions should prioritise climate-resilient agricultural practices and infrastructure in high-risk areas, building local capacities and integrating climate risk assessments into urban planning, considering the high influx of environmental migrants. This systematic map provides a foundation for future longitudinal studies, establishes a baseline for this era for future comparisons and serves as a reliable database for relevant stakeholders and policy makers. Full article

(This article belongs to the Special Issue Climate Change Impacts at Various Geographical Scales (2nd Edition))

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17 pages, 4369 KiB

Open AccessArticle

A Multi-Hazard Approach to Climate Migration: Testing the Intersection of Climate Hazards, Population Change, and Location Desirability from 2000 to 2020

by Zachary M. Hirsch, Jeremy R. Porter, Jasmina M. Buresch, Danielle N. Medgyesi, Evelyn G. Shu and Matthew E. Hauer

Climate 2024, 12(9), 140; https://doi.org/10.3390/cli12090140 - 7 Sep 2024

Abstract

Climate change intensifies the frequency and severity of extreme weather events, profoundly altering demographic landscapes globally and within the United States. This study investigates their impact on migration patterns, using propensity score matching and LASSO techniques within a larger regression modeling framework. Here, [...] Read more.

Climate change intensifies the frequency and severity of extreme weather events, profoundly altering demographic landscapes globally and within the United States. This study investigates their impact on migration patterns, using propensity score matching and LASSO techniques within a larger regression modeling framework. Here, we analyze historical population trends in relation to climate risk and exposure metrics for various hazards. Our findings reveal nuanced patterns of climate-induced population change, including “risky growth” areas where economic opportunities mitigate climate risks, sustaining growth in the face of observed exposure; “tipping point” areas where the amenities are slowly giving way to the disamenity of escalating hazards; and “Climate abandonment” areas experiencing exacerbated out-migration from climate risks, compounded by other out-migration market factors. Even within a single county, these patterns vary significantly, underscoring the importance of localized analyses. Projecting population impacts due to climate risk to 2055, flood risks are projected to impact the largest percentage of areas (82.6%), followed by heatwaves (47.4%), drought (46.6%), wildfires (32.7%), wildfire smoke (21.7%), and tropical cyclone winds (11.1%). The results underscore the importance of understanding hyperlocal patterns of risk and change in order to better forecast future patterns. Full article

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16 pages, 1010 KiB

Open AccessArticle

Set When the Sun Rises, Rise When the Sun Sets: Climate Impact on Health, Safety, and Wellbeing of Smallholder Farmers in Vietnam

by Miranda Dally, Thuy Thi Thu Tran, Thanh Le Nhat Nguyen, Quynh Nguyen, Lee S. Newman, Mike Van Dyke, Marcela Tamayo-Ortiz, James Crooks, Lyndsay Krisher and Megan Cherewick

Climate 2024, 12(9), 139; https://doi.org/10.3390/cli12090139 - 7 Sep 2024

Abstract

Vietnam is a country most at risk for experiencing climate change effects, especially increasing temperatures. Agricultural production is one of the biggest contributors to Vietnam’s economy. Recent research has explored how climate change will impact agriculture in Vietnam. However, the impact of climate [...] Read more.

Vietnam is a country most at risk for experiencing climate change effects, especially increasing temperatures. Agricultural production is one of the biggest contributors to Vietnam’s economy. Recent research has explored how climate change will impact agriculture in Vietnam. However, the impact of climate change to the health, safety, and wellbeing of Vietnamese farmers is often overlooked. In this study, we conducted five focus groups with 46 farmers representing three provinces of Vietnam. We used a convergent mixed-methods design and a Total Worker Health^® framework to assess how farmers in Vietnam experience climate-change-related hazards and describe how famers associate these hazards with impacts to their health, safety, and wellbeing. Multi-dimensional scaling suggests farmers conceptualize hazards separately from health, safety, and wellbeing outcomes, but a thematic analysis of our data indicated that farmers perceive both direct and indirect impacts of climate change to their health, safety, and wellbeing. Direct impacts of climate change described included physical health effects such as heat stress. Indirect impacts included mental health stressors due to productivity demands. Gaps in available health and safety trainings for farmers were also identified. This project demonstrates the need to co-develop safety and health trainings with farmers. System-level approaches both at the societal and community levels are needed. The local governments, cooperatives, Women’s Unions, and Farmers’ Unions are trusted sources of information that could implement and disseminate these trainings. Full article

(This article belongs to the Special Issue Confronting the Climate Change and Health Nexus: Interactions, Impacts, and Adaptation Strategies)

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33 pages, 1890 KiB

Open AccessArticle

Assessing the Vulnerability of Farming Households on the Caribbean Island of Hispaniola to Climate Change

by Jacky Duvil, Thierry Feuillet, Evens Emmanuel and Bénédique Paul

Climate 2024, 12(9), 138; https://doi.org/10.3390/cli12090138 - 6 Sep 2024

Abstract

This article assesses the individual vulnerability of 550 farming households, 430 in Haiti and 120 in the Dominican Republic, on the Caribbean island of Hispaniola to the impacts of climate change. This assessment is based on an integrated approach, using socio-economic and biophysical [...] Read more.

This article assesses the individual vulnerability of 550 farming households, 430 in Haiti and 120 in the Dominican Republic, on the Caribbean island of Hispaniola to the impacts of climate change. This assessment is based on an integrated approach, using socio-economic and biophysical variables. The variables collected for each farm household were grouped into three categories: adaptive capacity, sensitivity, and exposure. Multiple correspondence analysis (MCA) was used to develop a vulnerability index for each farm household, enabling them to be classified according to their level of vulnerability to the impacts of climate change. A logistic regression model was then used to identify the main factors influencing their vulnerability. The results revealed that on the island of Hispaniola, 33.91%, 32.09%, and 34% of farming households were classified as very vulnerable, vulnerable, and less vulnerable. In Haiti, these proportions were 36.74%, 36.51%, and 26.75%, while in the Dominican Republic, they were 20%, 20%, and 60%. Agricultural households with highly accessible credit (OR = 0.16, p < 0.001) and university education (OR = 0.05, p < 0.001) were relatively less vulnerable to climate change impacts compared to their counterparts. Full article

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26 pages, 1751 KiB

Open AccessArticle

Are Big Cities Ready to Mitigate Climate Change? Evidence from Sydney, Australia

by Ozgur Gocer, Anusha Roy, Shamila Haddad, Chirag Deb and Thomas Astell-Burt

Climate 2024, 12(9), 137; https://doi.org/10.3390/cli12090137 - 4 Sep 2024

Abstract

Governments across the world are facing challenges in urgently responding to the adverse impacts of climate change. Australian cities have been proactively working on various climate action plans. Despite this, the Climate Action Tracker rates Australia’s climate net zero targets, policies, and climate [...] Read more.

Governments across the world are facing challenges in urgently responding to the adverse impacts of climate change. Australian cities have been proactively working on various climate action plans. Despite this, the Climate Action Tracker rates Australia’s climate net zero targets, policies, and climate finance as “Insufficient”, highlighting the urgent need for substantial improvements to align Australia’s climate policies and commitments towards the Paris Agreement. This study explores the readiness of Australian cities towards climate change mitigation, with a focus on Sydney. It identifies prioritized cooling measures and proactive local governments in Great Metropolitan Sydney, through an analysis of official documents and policy statements. Interviews were conducted with local governments to gain insights into implementation processes, perceived effectiveness, challenges, and opportunities related to heat mitigation initiatives. The results reveal efforts to amend local environmental and development control plans to mitigate the urban heat island effect and create cooler, more comfortable built environments. However, challenges exist, including limited authority of local governments in urban planning, as national and state governments set stringent codes and regulations for heat mitigation. Financial constraints pose challenges, particularly in maintaining and monitoring strategic plans during their implementation stage, leading to the potential removal of sustainability measures from designs. Full article

(This article belongs to the Section Climate Adaptation and Mitigation)

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20 pages, 633 KiB

Open AccessReview

The Mediterranean Diet in the Era of Climate Change: A Reference Diet for Human and Planetary Health

by Chrysi C. Koliaki, Nicholas L. Katsilambros and Charilaos Dimosthenopoulos

Climate 2024, 12(9), 136; https://doi.org/10.3390/cli12090136 - 4 Sep 2024

Abstract

Nowadays, climate change constitutes an enormous global threat for human health and environmental sustainability. The expanding world population and the increased global need for food production have an important negative impact upon the environment. Diet can link human health with environmental sustainability. Food [...] Read more.

Nowadays, climate change constitutes an enormous global threat for human health and environmental sustainability. The expanding world population and the increased global need for food production have an important negative impact upon the environment. Diet can link human health with environmental sustainability. Food production systems are closely related to anthropogenic greenhouse gas emissions and the aggravation of climate change, and current Western-type, animal-based dietary patterns may lead to adverse environmental footprints. In this present narrative review, we address the interconnection of the Mediterranean diet (MD) with climate change and sustainability. The MD is a highly recommended dietary intervention for the prevention and management of various endocrine and cardiometabolic diseases. Beyond its evidence-based, health-promoting effects, it also has a beneficial environmental impact, reducing greenhouse gas emissions and enhancing biodiversity, food security, and sustainability. Based on the evidence reviewed herein, the MD should be incorporated within the framework of a “One Health” model, which involves the improvement not only of human health but also of planetary health and food system sustainability. Our review aims to provide a stimulus for health professionals to strongly recommend the implementation of the MD under the current pressure of climate change, despite all barriers, targeting both human health preservation and planetary well-being. Full article

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24 pages, 10548 KiB

Open AccessArticle

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

by Nadia Politi, Diamando Vlachogiannis and Athanasios Sfetsos

Climate 2024, 12(9), 135; https://doi.org/10.3390/cli12090135 - 3 Sep 2024

Abstract

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in [...] Read more.

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change. Full article

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20 pages, 2417 KiB

Open AccessArticle

Indigenous Subsistence Practices of the Sakha Horse Herders under Changing Climate in the Arctic

by Lena Popova

Climate 2024, 12(9), 134; https://doi.org/10.3390/cli12090134 - 3 Sep 2024

Abstract

This article provides, firstly, an overview of Arctic traditional horse herding as one of the Indigenous subsistence practices of the Republic of Sakha (Yakutia). It discusses the origins, characteristics, and spiritual and material importance of Sakha horses and horse herding practices to inform [...] Read more.

This article provides, firstly, an overview of Arctic traditional horse herding as one of the Indigenous subsistence practices of the Republic of Sakha (Yakutia). It discusses the origins, characteristics, and spiritual and material importance of Sakha horses and horse herding practices to inform the overall understanding of this traditional subsistence activity, which remains largely unexplored. Secondly, by conducting in-depth semi-structured interviews with Indigenous Sakha horse herders, this study explores the ways in which Indigenous subsistence practices are evolving and reacting to the climate and environmental changes. Results show that climate change is altering the local ecosystem and introducing new challenges to communities in Central Yakutia. Local herders describe climate change as a complex interplay of diverse transformations rather than a singular phenomenon. While historical adaptation strategies relied on the flexibility of traditional practices, today, this flexibility is often hindered by non-climatic factors. This article further discusses adaptability of Indigenous practices to climate change and offers recommendations for their development, particularly traditional horse herding. Future research related to climate change and Arctic Indigenous communities should encompass deeper and broader aspects, covering historical, cultural, social, and economic contexts and the worldviews of Indigenous peoples, distinct from Western perspectives. Full article

(This article belongs to the Special Issue Climate, Climate Change and the Arctic: Environment, Infrastructure, Health and Well-Being)

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16 pages, 4170 KiB

Open AccessArticle

Development of a Spatial Synoptic Classification Scheme for East Africa with a Focus on Kenya

by Daniella C. Alaso, Jason C. Senkbeil and Scott C. Sheridan

Climate 2024, 12(9), 133; https://doi.org/10.3390/cli12090133 - 2 Sep 2024

Abstract

Despite the wide range of applications of the Spatial Synoptic Classification (SSC), its expansion and utility in the tropics remains limited. This research utilized the fifth generation of European ReAnalysis (ERA5) data to develop an SSC scheme tailored for East Africa with a [...] Read more.

Despite the wide range of applications of the Spatial Synoptic Classification (SSC), its expansion and utility in the tropics remains limited. This research utilized the fifth generation of European ReAnalysis (ERA5) data to develop an SSC scheme tailored for East Africa with a focus on Kenya. The SSC method classifies weather into seven types: Dry Polar (DP), Dry Moderate (DM), Dry Tropical (DT), Moist Polar (MP), Moist Moderate (MM), Moist Tropical (MT), and Transitional (TR). Frequency and trend analysis between 1959 and 2022 show that the MT and DM weather types are the dominant types in Kenya. The DM type is dominant in the December–February (DJF) dry season while the MT type is common from April to September. We find statistically significant decreasing trends in the DM, MP, and MM weather types and increasing trends in the DT and MT weather types. The results suggest that, generally, the number of days with cool and moderate conditions in Kenya is decreasing, while the number of days with warmer conditions is increasing. This research indicates the potential for the SSC to be utilized in different applications in East Africa including investigating heat vulnerability, as increasing temperatures could be a significant risk factor to human health. Full article

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18 pages, 3196 KiB

Open AccessArticle

Risk Assessment Protocol for Existing Bridge Infrastructure Considering Climate Change

by Shereen Altamimi, Lamya Amleh and Liping Fang

Climate 2024, 12(9), 132; https://doi.org/10.3390/cli12090132 - 2 Sep 2024

Abstract

The escalating impact of climate change on global weather patterns threatens the functionality and resilience of infrastructure systems. This paper presents a rigorous risk assessment protocol tailored to existing bridge infrastructure, integrating climate change projections, structural integrity, and socioeconomic factors. The protocol’s application [...] Read more.

The escalating impact of climate change on global weather patterns threatens the functionality and resilience of infrastructure systems. This paper presents a rigorous risk assessment protocol tailored to existing bridge infrastructure, integrating climate change projections, structural integrity, and socioeconomic factors. The protocol’s application involves five sequential steps: selecting a bridge, disassembling the structure into components, calculating utilization factors for design and projected temperatures, evaluating severity factors encompassing structural and socioeconomic aspects, and ultimately determining an overall risk rating. To demonstrate the protocol’s effectiveness, a case study was conducted on the Westminster Drive Underpass in London, Ontario. This study shows how the protocol systematically evaluates the vulnerability of each bridge component to projected temperatures under the Representative Concentration Pathway 6.0 model. The protocol provides a holistic risk assessment by incorporating both the structural response and socioeconomic implications of failure. The results rank the bridge’s risk level and highlight the urgency of intervention. The protocol emerges as a robust tool for decision-makers, practitioners, and engineers, offering a comprehensive approach to strengthen bridge infrastructure against the challenges of climate change. Full article

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17 pages, 2889 KiB

Open AccessArticle

Artificial Neural Networks for Drought Forecasting in the Central Region of the State of Zacatecas, Mexico

by Pedro Jose Esquivel-Saenz, Ruperto Ortiz-Gómez, Manuel Zavala and Roberto S. Flowers-Cano

Climate 2024, 12(9), 131; https://doi.org/10.3390/cli12090131 - 27 Aug 2024

Abstract

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state [...] Read more.

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R² (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better. Full article

(This article belongs to the Section Weather, Events and Impacts)

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16 pages, 3803 KiB

Open AccessArticle

Quantitative Testing of a SOLO-Based Automated Quality Control Algorithm for Airborne Tail Doppler Radar Data

by Robert Pasken and Richard Woodford

Climate 2024, 12(9), 130; https://doi.org/10.3390/cli12090130 - 26 Aug 2024

Abstract

An automated quality control pre-processing algorithm for removing non-weather radar echoes from airborne Doppler radar data has been developed. The proposed algorithm can significantly reduce the time and experience level required for interactive radar data editing prior to dual-Doppler wind synthesis or data [...] Read more.

An automated quality control pre-processing algorithm for removing non-weather radar echoes from airborne Doppler radar data has been developed. The proposed algorithm can significantly reduce the time and experience level required for interactive radar data editing prior to dual-Doppler wind synthesis or data assimilation. As important as reducing the time required and skill level necessary to process an airborne Doppler dataset can be, the quality of the automated analysis is paramount. Retrieved wind data, recovered perturbation pressure data (with associated momentum check values) and correlation coefficients were computed. To quantitatively test the quality of the automated quality control algorithm, spatial Pearson correlation coefficients and momentum check values were computed. Four different (published) Electra Doppler Radar (ELDORA) datasets of convective echoes were used to stress the algorithm. Four distinct threshold levels for data removal in the automated quality control algorithm were applied to each of four ELDORA datasets. The algorithm threshold levels were labeled as follows: extremely low, low, medium, and high. Extremely low algorithm cases were deemed necessary during the data analyses and were added to the low, medium and high cases. A description of each case and the differences in the perturbation pressure momentum check values and correlation coefficients between the interactively edited fields were computed. These comparisons along with a subjective visual inspection show that the automated quality control algorithm can produce an analysis comparable—and in some cases superior—to an interactive analysis when used properly. A key benefit of this algorithm is that the skill level of a relatively inexperienced airborne radar meteorologist may be effectively increased by using the SOLO QC algorithm. Full article

(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)

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13 pages, 1322 KiB

Open AccessArticle

Assessing the Value of a Human Life in Heat-Related Mortality: Lessons from COVID-19 in Belgium

by Koen De Ridder

Climate 2024, 12(9), 129; https://doi.org/10.3390/cli12090129 - 26 Aug 2024

Abstract

This study evaluates the cost of heat-related mortality using economic impacts and mortality data from the COVID-19 pandemic in Belgium as a proxy. By examining the economic loss measured by gross domestic product (GDP) decline and excess mortality during the first COVID-19 wave [...] Read more.

This study evaluates the cost of heat-related mortality using economic impacts and mortality data from the COVID-19 pandemic in Belgium as a proxy. By examining the economic loss measured by gross domestic product (GDP) decline and excess mortality during the first COVID-19 wave (March–June 2020), a new estimate for avoided heat-related mortality is derived. The results show that the cost per avoided death is EUR 377,000 ± EUR 222,000, significantly lower than numerical values of the commonly used Value of a Statistical Life (VSL). However, when this cost is divided by the expected remaining (eight) life years at the age of death, the resulting monetary value for a saved life year, in a EUR 47,000 ± EUR 28,000 range, aligns well with commonly used values for the Value of a Life Year (VOLY). Thus, the present study contributes to the ongoing debate on the most appropriate methods for valuing human life in the context of heat-related mortality. By comparing our results with both VSL and VOLY, we underscore the limitations of VSL in the context of heat-related mortality and advocate for VOLY as a more accurate and contextually relevant metric. These findings may offer useful insights for policymakers in evaluating and prioritizing investments in heat-related mortality-prevention strategies. Full article

(This article belongs to the Special Issue Confronting the Climate Change and Health Nexus: Interactions, Impacts, and Adaptation Strategies)

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21 pages, 4854 KiB

Open AccessArticle

Seasonal Ecophysiological Dynamics of Erythroxylum pauferrense in an Open Ombrophilous Forest of the Brazilian Atlantic Forest

by João Everthon da Silva Ribeiro, Ester dos Santos Coêlho, Francisco Romário Andrade Figueiredo, Walter Esfrain Pereira, Thiago Jardelino Dias, Marlenildo Ferreira Melo, Lindomar Maria da Silveira, Aurélio Paes Barros Júnior and Manoel Bandeira de Albuquerque

Climate 2024, 12(9), 128; https://doi.org/10.3390/cli12090128 - 25 Aug 2024

Abstract

Seasonal forests are characterized by seasonal dynamics that influence the growth and ecophysiology of forest species. Erythroxylum pauferrense is an understory species endemic to the Northeastern region of Brazil, with a distribution limited to Paraíba, Brazil. In this study, how the physiological characteristics [...] Read more.

Seasonal forests are characterized by seasonal dynamics that influence the growth and ecophysiology of forest species. Erythroxylum pauferrense is an understory species endemic to the Northeastern region of Brazil, with a distribution limited to Paraíba, Brazil. In this study, how the physiological characteristics of E. pauferrense vary in response to seasonal changes in an open ombrophilous forest of the Brazilian Atlantic Forest was investigated. Precipitation, air and soil temperature, and leaf area index were monitored and correlated with gas exchange, chlorophyll fluorescence, chlorophyll indices, and leaf morphofunctional attributes. The results show that E. pauferrense exhibits ecophysiological plasticity, adjusting its photosynthesis rates, stomatal conductance, and water use efficiency according to seasonal changes. During the rainy season, photosynthesis and stomatal conductance were higher than in the dry season, indicating more excellent photosynthetic activity due to increased water availability. Water use efficiency varied, with more efficient use in the dry season, which is crucial for survival in conditions of low water availability. Thus, this study contributes to understanding the ecology of endemic understory species in seasonal tropical forests, such as Erythroxylum pauferrense. Full article

(This article belongs to the Special Issue Forest Ecosystems under Climate Change)

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14 pages, 4013 KiB

Open AccessArticle

Harnessing Machine Learning to Decode the Mediterranean’s Climate Canvas and Forecast Sea Level Changes

by Cristina Radin, Veronica Nieves, Marina Vicens-Miquel and Jose Luis Alvarez-Morales

Climate 2024, 12(8), 127; https://doi.org/10.3390/cli12080127 - 22 Aug 2024

Abstract

Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs, gaps in historical data, and bridging the gap between short-term [...] Read more.

Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs, gaps in historical data, and bridging the gap between short-term and long-term forecasting intervals. Our study addresses these challenges by combining advanced machine learning techniques to provide region-specific sea level predictions in the Mediterranean Sea. By integrating high-resolution sea surface temperature data spanning 40 years, we employed a tailored k-means clustering technique to identify regions of high variance. Using these clusters, we developed RNN-GRU models that integrate historical tide gauge data and sea surface height data, offering regional sea level predictions on timescales ranging from one month to three years. Our approach achieved the highest predictive accuracy, with correlation values ranging from 0.65 to 0.84 in regions with comprehensive datasets, demonstrating the model’s robustness. In areas with fewer tide gauge stations or shorter time series, our models still performed moderately well, with correlations between 0.51 and 0.70. However, prediction accuracy decreases in regions with complex geomorphology. Yet, all regional models effectively captured sea level variability and trends. This highlights the model’s versatility and capacity to adapt to different regional characteristics, making it invaluable for regional planning and adaptation strategies. Our methodology offers a powerful tool for identifying regions with similar variability and providing sub-regional scale predictions up to three years in advance, ensuring more reliable and actionable sea level forecasts for Mediterranean coastal communities. Full article

(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)

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