Climate

20 pages, 2611 KiB

Open AccessArticle

Global Meta-Analysis of Innovation Attributes Influencing Climate-Smart Agriculture Adoption for Sustainable Development

by Chin-Ling Lee, Ginger Orton and Peng Lu

Climate 2024, 12(11), 192; https://doi.org/10.3390/cli12110192 - 20 Nov 2024

Viewed by 384

Abstract

Climate-smart agricultural technologies offer transformative potential for achieving Sustainable Development Goals, especially in mitigating extreme weather impacts and enhancing food security. Despite this potential, adoption rates remain limited due to various factors, with perceived complexity playing a significant role. This study conducted a [...] Read more.

Climate-smart agricultural technologies offer transformative potential for achieving Sustainable Development Goals, especially in mitigating extreme weather impacts and enhancing food security. Despite this potential, adoption rates remain limited due to various factors, with perceived complexity playing a significant role. This study conducted a systematic review and meta-analysis to assess the influence of perceived innovation complexity on adopting climate-smart technologies. Using frameworks of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, we systematically reviewed 28 studies and conducted a meta-analysis of 15 studies across diverse geographic contexts. Our findings from the systematic review indicate inconsistent results on the impact of complexity on adoption due to the different items and scales used to measure the concepts of complexity across contexts, suggesting that there is a need for the development of a standardized scale to measure complexity. Results from the meta-analysis generated a summary effect size (r = 0.51, 95% CI = [0.05, 0.72], z = 6.78, p ≤ 0.0001), revealing a significant relationship between perceived complexity and adoption intent. The effect size of 0.51 indicates that higher complexity levels significantly decrease the likelihood of adoption intent for climate-smart technologies. Differences in CSA research trends across geographic regions highlight the need for tailored approaches to technology adoption that take into account the specific capabilities and constraints of each region. These findings provide valuable insights for policymakers, Extension professionals, and technology developers to design interventions to promote ease of use and enhance technology diffusion in sustainable farming practices and food security. These findings contribute to ongoing efforts to foster sustainable agricultural innovations, offering guidance to accelerate the global transition to more resilient farming systems. Full article

(This article belongs to the Special Issue Climate Change and Food Insecurity: What Future and New Actions?)

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16 pages, 5224 KiB

Open AccessArticle

Factors Influencing Rural Women’s Adoption of Climate Change Adaptation Strategies: Evidence from the Chivi District of Zimbabwe

by Johanes Belle, Tendai Mapingure and Solomon Temidayo Owolabi

Climate 2024, 12(11), 191; https://doi.org/10.3390/cli12110191 - 20 Nov 2024

Viewed by 432

Abstract

The socio-cultural leadership system in rural communities of developing countries is generally gender-biased, thus rendering female-headed households (FHHs) vulnerable to climate change risk. This study explored the factors influencing FHHs’ adoption of a climate change adaptation strategy (CCAS) in Chivi District, Zimbabwe. We [...] Read more.

The socio-cultural leadership system in rural communities of developing countries is generally gender-biased, thus rendering female-headed households (FHHs) vulnerable to climate change risk. This study explored the factors influencing FHHs’ adoption of a climate change adaptation strategy (CCAS) in Chivi District, Zimbabwe. We used a multistage sampling technique and logistic regression to evaluate 107 women household heads’ livelihood and their decision to adopt the CCAS in Ward 25 of the Chivi District. The results show that the age of the female head significantly influenced the CCAS decision (R² = −0.073), along with marital status (R² = 0.110), agricultural training (R² = 0.133), club membership (R² = 0.084), and farm size (R² = 0.014). Access to formal agricultural training plays a prominent role. At the same time, the institutional framework showed variations and laxity on the part of the local government, as access to extension services varies significantly. In addition, education level was reported to have an insignificant (p = 0.098) influence on CCAS adoption. Overall, multiple institutional and socio-economic factors are essential in influencing CCAS decisions. Hence, central and local governments are encouraged to improve outreach strategies on deploying supporting tools, extension agents, and vital stakeholders for strategic information dissemination to sensitize rural dwellers and community leaders on women’s and FHHs’ crucial role in food security and their resilience to climate change risk. Moreover, the educational syllabus can be enhanced at all rural education levels to reshape the norms of future generations against the customary impact of old age on farming approaches and to encourage women’s participation in decision making and interventions, particularly those sensitive to their societal contributions. Full article

(This article belongs to the Collection Adaptation and Mitigation Practices and Frameworks)

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22 pages, 5572 KiB

Open AccessArticle

Application of Machine Learning and Hydrological Models for Drought Evaluation in Ungauged Basins Using Satellite-Derived Precipitation Data

by Anjan Parajuli, Ranjan Parajuli, Mandip Banjara, Amrit Bhusal, Dewasis Dahal and Ajay Kalra

Climate 2024, 12(11), 190; https://doi.org/10.3390/cli12110190 - 17 Nov 2024

Viewed by 625

Abstract

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), [...] Read more.

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. SPI values were calculated from the precipitation data obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a hydrological model for calculating discharge and drought in the overall basin. It uses random forest (RF) and support vector regression (SVR) as machine learning models for SSI prediction for time scales of 1- and 3-month periods, which are widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the machine learning model’s results. The results from the prediction model, hydrological model, and the station data show better correlation. The coefficients of determination obtained for 1-month SSI are 0.842 and 0.696, and those for the 3-month SSI are 0.919 and 0.862 in the RF and SVR models, respectively. These results also revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one, with the better prediction result being from the SVR model. The hydrological model-evaluated SSI has 0.885 and 0.826 coefficients of determination for the 1- and 3-month time durations, respectively. The results and discussion in this research will aid planners and decision-makers in managing hydrological droughts in basins. Full article

(This article belongs to the Special Issue Coping with Flooding and Drought)

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13 pages, 4985 KiB

Open AccessArticle

Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study

by Sergei Soldatenko and Yaromir Angudovich

Climate 2024, 12(11), 189; https://doi.org/10.3390/cli12110189 - 15 Nov 2024

Viewed by 353

Abstract

This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two [...] Read more.

This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two versions of the classical Lorenz model with a small time-scale separation factor, is treated as an atmosphere–ocean research simulator. In numerical experiments, the number of hidden layers and the number of nodes in each hidden layer varied from 1 to 5 and from 16 to 256, respectively. The basic configuration of the surrogate model, determined experimentally, has three hidden layers, each comprising between 16 and 128 nodes. The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy. As the forecast horizon increases, the accuracy of forecasts deteriorates, which was quite expected, primarily due to the chaotic behaviour of the fast subsystem. All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy. The obtained results indicate that the quality of short-term forecasts with a lead time of up to 0.75 model time units (MTU) improves most significantly. The predictability limit of the fast subsystem (“atmosphere”) is somewhat greater than the Lyapunov time. Full article

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

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21 pages, 16910 KiB

Open AccessArticle

Extreme Precipitation Events During the Wet Season of the South America Monsoon: A Historical Analysis over Three Major Brazilian Watersheds

by Aline Araújo de Freitas, Vanessa Silveira Barreto Carvalho and Michelle Simões Reboita

Climate 2024, 12(11), 188; https://doi.org/10.3390/cli12110188 - 15 Nov 2024

Viewed by 461

Abstract

Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas [...] Read more.

Most of South America, particularly the region between the southern Amazon and southeastern Brazil, as well as a large part of the La Plata Basin, has its climate regulated by the South American Monsoon System. Extreme weather and climate events in these areas have significant socioeconomic impacts. The Madeira, São Francisco, and Paraná river basins, three major watersheds in Brazil, are especially vulnerable to wet and drought periods due to their importance as freshwater ecosystems and sources of water for consumption, energy generation, and agriculture. The scarcity of surface meteorological stations in these basins makes meteorological studies challenging, often using reanalysis and satellite data. This study aims to identify extreme weather (wet) and climate (wet and drought) events during the extended wet season (October to March) from 1980 to 2022 and evaluate the performance of two gridded datasets (CPC and ERA5) to determine which best captures the observed patterns in the Madeira, São Francisco, and Paraná river basins. Wet weather events were identified using the 95th percentile, and wet and drought periods were identified using the Standardized Precipitation Index (SPI) on a 6-month scale. In general, CPC data showed slightly superior performance compared to ERA5 in reproducing statistical measures. For extreme day precipitation, both datasets captured the time series pattern, but CPC better reproduced extreme values and trends. The results also indicate a decrease in wet periods and an increase in drought events. Both datasets performed well, showing they can be used in the absence of station data. Full article

(This article belongs to the Special Issue Impacts of Extreme Weather on Hydrological Process, Water Quality and Ecosystem in Agricultural and Forested Watersheds under the Changing Climate)

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15 pages, 3276 KiB

Open AccessArticle

Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach

by Luiz Augusto Ferreira Monteiro, Francisco Ivam Castro do Nascimento, José Francisco de Oliveira-Júnior, Dorisvalder Dias Nunes, David Mendes, Givanildo de Gois, Fabio de Oliveira Sanches, Cassio Arthur Wollmann, Michel Watanabe and João Paulo Assis Gobo

Climate 2024, 12(11), 187; https://doi.org/10.3390/cli12110187 - 15 Nov 2024

Viewed by 452

Abstract

Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an [...] Read more.

Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an initial approach, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with the observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7 mm to 11.2 mm, and the coefficient of determination (R²) was low in some series. Extensive analyses showed a low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, this study lays a foundation for future advancements in climate modeling and water resource management in the BLA. Full article

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

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17 pages, 2287 KiB

Open AccessArticle

Economic Impact of Droughts in Southern Brazil, a Duration Analysis

by Jorge Luis Tonetto, Josep Miquel Pique, Adelar Fochezatto and Carina Rapetti

Climate 2024, 12(11), 186; https://doi.org/10.3390/cli12110186 - 14 Nov 2024

Viewed by 696

Abstract

Hydrometeorological hazards are currently a cause for great concern worldwide. Droughts are among the most recurrent events, causing significant losses. This article presents a study on the duration of droughts in the southernmost state of Brazil, which has a large agricultural sector and [...] Read more.

Hydrometeorological hazards are currently a cause for great concern worldwide. Droughts are among the most recurrent events, causing significant losses. This article presents a study on the duration of droughts in the southernmost state of Brazil, which has a large agricultural sector and experiences frequent drought events. The approach focuses on the economic recovery time of municipalities affected by the drought in 2020, 2022 and 2023, using the total value of invoices issued within each municipality between companies and from companies to consumers. The Kaplan–Meier estimator and Cox regression models are applied, incorporating covariates such as the size of the municipality, geographic location, and primary economic activity sector. The results show that the longest recovery period is concentrated in small cities, particularly in those where agriculture or livestock is the primary economic activity. The greatest resilience is observed in cities within the metropolitan region, where economic activity is more concentrated in services and industry and where populations are generally larger. The study identifies that after each drought event, at least 75% of municipalities achieve economic recovery within 3 months. These findings support better planning for both drought prevention and impact reduction and they are relevant for the development of economic and social policies. Full article

(This article belongs to the Special Issue Global Warming and Extreme Drought)

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18 pages, 3729 KiB

Open AccessArticle

Wildlife Tourism and Climate Change: Perspectives on Maasai Mara National Reserve

by Catherine Muyama Kifworo and Kaitano Dube

Climate 2024, 12(11), 185; https://doi.org/10.3390/cli12110185 - 11 Nov 2024

Viewed by 769

Abstract

The impact of climate change on nature-based tourism is gaining significance. This study evaluated the impacts of climate change and tourism stakeholders’ perspectives on the subject in the Maasai Mara National Reserve and World Heritage Site. Surveys and interviews were used to collect [...] Read more.

The impact of climate change on nature-based tourism is gaining significance. This study evaluated the impacts of climate change and tourism stakeholders’ perspectives on the subject in the Maasai Mara National Reserve and World Heritage Site. Surveys and interviews were used to collect data. The main climate-related threats to tourism were heavy rain, floods, and extreme droughts. These events adversely impacted infrastructure, such as roads, bridges, and accommodation facilities, and outdoor tourism activities, such as game viewing, cultural tours, birdwatching, and hot air ballooning. They also exacerbated human–wildlife conflicts. The key challenges identified in dealing with impacts were poor planning, non-prioritizing climate change as a threat, a lack of expertise, inadequate research, and a lack of internal early warning systems. The key recommendations included prioritization of climate change planning, development of internal early warning systems, and building resilience toward climate-related disasters. This study contributes to practice by making recommendations for management and other stakeholders. It also extends the discussions of climate change and tourism to wildlife tourism destinations in Africa. Full article

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16 pages, 12600 KiB

Open AccessArticle

Species Distribution Modeling of Ixodes ricinus (Linnaeus, 1758) Under Current and Future Climates, with a Special Focus on Latvia and Ukraine

by Volodymyr Tytar, Iryna Kozynenko, Mihails Pupins, Arturs Škute, Andris Čeirāns, Jean-Yves Georges and Oksana Nekrasova

Climate 2024, 12(11), 184; https://doi.org/10.3390/cli12110184 - 11 Nov 2024

Viewed by 566

Abstract

This study assesses the impact of climate change on the distribution of Ixodes ricinus, which transmits Lyme disease, a growing public health concern. Utilizing ensemble models from the R package ‘flexsdm’ and climate data from WorldClim, ENVIREM, and CliMond, we project habitat [...] Read more.

This study assesses the impact of climate change on the distribution of Ixodes ricinus, which transmits Lyme disease, a growing public health concern. Utilizing ensemble models from the R package ‘flexsdm’ and climate data from WorldClim, ENVIREM, and CliMond, we project habitat suitability changes for the focus species. The models, validated against Lyme disease incidence rates, predict a 1.5-fold increase in suitable habitats in Latvia, contrasted with a 4.5-fold decrease in suitable habitats within Ukraine over the coming decades. SHAP values are analyzed to determine the most influential climatic features affecting tick distribution, providing insights for future vector control and disease prevention strategies. The optimal bioclimatic environment for I. ricinus seems to be an intricate balance of moderate temperatures, high humidity, and sufficient rainfall (bio7, 14, 18, 29). Also, radiation during the wettest quarter (bio24) significantly influences tick distribution in northern countries. This implies an increased presence of ticks in Scandinavian countries, Baltic states, etc. These findings largely coincide with our projections regarding bioclimatic suitability for ticks in Latvia and Ukraine. These shifts reflect broader patterns of vector redistribution driven by global warming, highlighting the urgent need to adapt public health planning to the evolving landscape of vector-borne diseases under climate change. Full article

(This article belongs to the Special Issue Ecological Modeling for Adaptation to Climate Change)

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21 pages, 6197 KiB

Open AccessArticle

Impact of Climate Change on the Bioclimatological Conditions Evolution of Peninsular and Balearic Spain During the 1953–2022 Period

by Christian Lorente, David Corell, María José Estrela, Juan Javier Miró and David Orgambides-García

Climate 2024, 12(11), 183; https://doi.org/10.3390/cli12110183 - 8 Nov 2024

Viewed by 495

Abstract

Climate change is altering the temperature and precipitation patterns in the Iberian Peninsula and on the Balearic Islands, with potential impacts on the distribution of plant communities. This study analyses the evolution of bioclimatic units in this region during the 1953–2022 period. Data [...] Read more.

Climate change is altering the temperature and precipitation patterns in the Iberian Peninsula and on the Balearic Islands, with potential impacts on the distribution of plant communities. This study analyses the evolution of bioclimatic units in this region during the 1953–2022 period. Data from 3668 weather stations distributed throughout the study area were analysed. Two 35-year periods (1953–1987 and 1988–2022) were compared to assess changes in macrobioclimates and bioclimates. The results showed expansion of the Mediterranean macrobioclimate, whose total area increased by 6.93%, mainly at the expense of the Temperate macrobioclimate. For bioclimates, a trend towards more xeric and continental conditions was observed in the Mediterranean region, while temperate areas moved towards homogenisation of climate conditions. Likewise, two new bioclimates were detected, which indicate the emergence of new climate conditions. These results suggest a reorganisation of bioclimatic conditions, with particular implications for biodiversity in mountainous and transitional areas, where endemic species face higher risks of habitat loss. This study provides useful information for developing targeted conservation strategies, establishing a baseline for monitoring future changes and developing early warning systems for vulnerable ecosystems, thus supporting the design of climate-adapted conservation measures in the region studied. Full article

(This article belongs to the Special Issue Climate Variability in the Mediterranean Region)

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19 pages, 2687 KiB

Open AccessArticle

An Assessment of the Carbon Budget of the Passively Restored Willow Forests Along the Miho River, Central South Korea

by Bong-Soon Lim, Seung-Jin Joo, Ji-Eun Seok and Chang-Seok Lee

Climate 2024, 12(11), 182; https://doi.org/10.3390/cli12110182 - 8 Nov 2024

Viewed by 666

Abstract

Climate change is rapidly progressing as the carbon budget balance is broken due to excessive energy and land use. This study was conducted to find and quantify new carbon sinks to implement the carbon neutrality policy prepared by the international community to solve [...] Read more.

Climate change is rapidly progressing as the carbon budget balance is broken due to excessive energy and land use. This study was conducted to find and quantify new carbon sinks to implement the carbon neutrality policy prepared by the international community to solve these problems. To reach this goal, an allometric equation of the willow community, which dominates riparian vegetation, was developed and applied to calculate the net primary productivity of the willow community. Furthermore, after the amount of carbon emitted via soil respiration was quantified, the net ecosystem production was calculated by subtracting the amount of soil respiration from the net primary productivity. In comparisons of the results obtained via this process with those obtained from forest vegetation, the willow community, representative of riparian vegetation, showed a much higher carbon sequestration rate than forest vegetation. Considering these results comprehensively, the willow community could be a new and significant carbon absorption source. In this context, proper river restoration should be realized to contribute to carbon neutrality and secure various ecosystem service functions. Full article

(This article belongs to the Special Issue Climate Change—Achieving the UN Sustainable Development Goals in Urban Contexts)

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28 pages, 10629 KiB

Open AccessArticle

The Impact of Marine Heatwaves on Isotherm Displacement and Tuna Distribution in Vanuatu

by Hannah Weinberg, Jessica Bhardwaj, Andrew B. Watkins and Yuriy Kuleshov

Climate 2024, 12(11), 181; https://doi.org/10.3390/cli12110181 - 8 Nov 2024

Viewed by 623

Abstract

Marine heatwaves (MHWs) have intensified in frequency, duration, and severity over recent decades. These events, defined by unusually warm sea surface temperatures (SSTs), can cause significant ecological impacts. This is particularly so for Pacific Island countries, such as Vanuatu, where communities rely on [...] Read more.

Marine heatwaves (MHWs) have intensified in frequency, duration, and severity over recent decades. These events, defined by unusually warm sea surface temperatures (SSTs), can cause significant ecological impacts. This is particularly so for Pacific Island countries, such as Vanuatu, where communities rely on marine resources for their food and livelihoods. A common ecological response to MHWs is the movement of oceanic species to cooler waters. Predicting such shifts through monitoring SST isotherms can help identify thermal boundaries that marine species favor. This study explores the connection between MHWs, SST isotherm movement, and tuna abundance in Vanuatu. The displacement of the 28 °C isotherm was analyzed across three major MHW events (2008–2009, 2016, and 2021–2022). It was found that MHWs with longer duration and greater intensity caused more significant isotherm displacement. Additionally, the El Niño–Southern Oscillation had an important influence on MHW formation and isotherm movement. The effects of these displacements on tuna distribution varied between events. The ability to monitor MHWs and SST isotherm movement could be an effective instrument for the prediction of areas of suppressed or abundant tuna activity and can be used to aid in the proactive management of food security and fishery sectors. Full article

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19 pages, 1075 KiB

Open AccessArticle

The Impact of Climate Change on Migration Patterns in Coastal Communities

by Umar Daraz, Štefan Bojnec and Younas Khan

Climate 2024, 12(11), 180; https://doi.org/10.3390/cli12110180 - 7 Nov 2024

Viewed by 921

Abstract

Climate change is a major global challenge affecting migration patterns, particularly in coastal communities vulnerable to sea-level rise, flooding, and extreme weather. Pakistan, with its extensive coastline and diverse environmental conditions, faces significant climate-induced migration issues, especially in Karachi, Thatta, Gwadar, Badin, and [...] Read more.

Climate change is a major global challenge affecting migration patterns, particularly in coastal communities vulnerable to sea-level rise, flooding, and extreme weather. Pakistan, with its extensive coastline and diverse environmental conditions, faces significant climate-induced migration issues, especially in Karachi, Thatta, Gwadar, Badin, and Muzaffargarh. This study aims to investigate the impact of climate change on migration patterns in these five selected regions of Pakistan. By analyzing climate variables and socio-economic factors, the research seeks to provide a localized understanding of how climate change drives population movements. A cross-sectional survey design was employed to gather data from 350 participants across these regions. Stratified random sampling ensured representation from each area, and data were collected using a structured questionnaire administered online. Statistical analyses included multiple linear regression, logistic regression, and structural equation modeling (SEM). This study found a strong positive relationship between climate change variables (sea level rise, temperature increases, and flooding) and migration patterns. Both direct impacts of climate change and indirect socio-economic factors influenced the likelihood of migration. The SEM analysis revealed that climate awareness partially mediates the relationship between climate change and migration. In conclusion, climate change significantly drives migration in Pakistan’s coastal communities, with both direct environmental impacts and socio-economic conditions playing crucial roles. Enhanced climate awareness and comprehensive adaptation strategies are essential. Policies should focus on climate resilience through infrastructure improvements, early warning systems, and socio-economic support programs. Strengthening education and economic opportunities is vital to build community resilience and effectively manage climate-induced migration. Full article

(This article belongs to the Special Issue Coastal Hazards under Climate Change)

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25 pages, 3719 KiB

Open AccessArticle

Impact of Climate Change on Biodiversity and Implications for Nature-Based Solutions

by Cor A. Schipper, Titus W. Hielkema and Alexander Ziemba

Climate 2024, 12(11), 179; https://doi.org/10.3390/cli12110179 - 7 Nov 2024

Viewed by 1872

Abstract

The Intergovernmental Panel on Climate Change (IPCC) provides regular scientific assessments on climate change, its implications, and potential future risks based on estimated energy matrixes and policy pathways. The aim of this publication is to assess the risks climate change poses to biodiversity [...] Read more.

The Intergovernmental Panel on Climate Change (IPCC) provides regular scientific assessments on climate change, its implications, and potential future risks based on estimated energy matrixes and policy pathways. The aim of this publication is to assess the risks climate change poses to biodiversity using projected IPCC climate scenarios for the period 2081–2100, combined with key species-sensitivity indicators and variables as a response to climate change projections. In doing so, we address how climate-change-driven pressures may affect biodiversity. Additionally, a novel causal relationship between extreme ambient temperature exposure levels and the corresponding effects on individual species, noted in this paper as the Upper Thermal-Tolerance Limit and Species Sensitivity Distribution (UTTL-SSD), provides a compelling explanation of how global warming affects biodiversity. Our study indicates that North American and Oceanian sites with humid continental and subtropical climates, respectively, are poised to realize temperature shifts that have been identified as potential key tipping-point triggers. Heat stress may significantly affect approximately 60–90% of mammals, 50% of birds, and 50% of amphibians in North American and Oceanian sites for durations ranging from 5 to 84 days per year from 2080. In the humid temperate oceanic climate of European sites, the climate conditions remain relatively stable; however, moderate cumulative effects on biodiversity have been identified, and additional biodiversity-assemblage threat profiles exist to represent these. Both the integration of IPCC-IUCN profiles and the UTTL-SSD response relationship for the species communities considered have resulted in the identification of the projected threats that climate pressures may impose under the considered IPCC scenarios, which would result in biodiversity degradation. The UTTL-SSD responses developed can be used to highlight potential breakdowns among trophic levels in food web structures, highlighting an additional critical element when addressing biodiversity and ecosystem concerns. Full article

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

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28 pages, 45519 KiB

Open AccessArticle

A Novel Input Schematization Method for Coastal Flooding Early Warning Systems Incorporating Climate Change Impacts

by Andreas G. Papadimitriou, Anastasios S. Metallinos, Michalis K. Chondros and Vasiliki K. Tsoukala

Climate 2024, 12(11), 178; https://doi.org/10.3390/cli12110178 - 5 Nov 2024

Viewed by 651

Abstract

Coastal flooding poses a significant threat to coastal communities, adversely affecting both safety and economic stability. This threat is exacerbated by factors such as sea level rise, rapid urbanization, and inadequate coastal infrastructure, as noted in recent climate change reports. Early warning systems [...] Read more.

Coastal flooding poses a significant threat to coastal communities, adversely affecting both safety and economic stability. This threat is exacerbated by factors such as sea level rise, rapid urbanization, and inadequate coastal infrastructure, as noted in recent climate change reports. Early warning systems (EWSs) have proven to be effective tools in coastal planning and management, offering a high cost-to-benefit ratio. Recent advancements have integrated operational numerical models with machine learning techniques to develop near-real-time EWSs, leveraging data obtained from reputable databases that provide reliable hourly sea-state and sea level data. Despite these advancements, a stepwise methodology for selecting representative events, akin to wave input reduction methods used in morphological modeling, remains undeveloped. Moreover, existing methodologies often overlook the significance of compound extreme events and their potential increased occurrence under climate change projections. This research addresses these gaps by introducing a novel input schematization method that combines efficient hydrodynamic modeling with clustering algorithms. The proposed methodοlogy, implemented in the coastal area of Pyrgos, Greece, aims to select an optimal number of representative sea-state and water level combinations to develop accurate EWSs for coastal flooding risk prediction. A key innovation of this methodology is the incorporation of weights in the clustering algorithm to ensure adequate representation of extreme compound events, also taking into account projections for future climate scenarios. This approach aims to enhance the accuracy and reliability of coastal flooding EWSs, ultimately improving the resilience of coastal communities against imminent flooding threats. Full article

(This article belongs to the Special Issue Coastal Hazards under Climate Change)

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18 pages, 3137 KiB

Open AccessReview

Sustainable Strategies to Current Conditions and Climate Change at U.S. Military Bases and Other Nations in the Arctic Region: A 20-Year Comparative Review

by Vinayak Kaushal and Amey Kashyap

Climate 2024, 12(11), 177; https://doi.org/10.3390/cli12110177 - 4 Nov 2024

Viewed by 867

Abstract

Amidst the backdrop of growing great power competition, heightened United States presence via military bases has manifested in the Arctic. However, the then design and implementation have hampered the resilience of these bases in a region warming at nearly four times the rate [...] Read more.

Amidst the backdrop of growing great power competition, heightened United States presence via military bases has manifested in the Arctic. However, the then design and implementation have hampered the resilience of these bases in a region warming at nearly four times the rate of the rest of the globe. Two-thirds of the United States’ 79 military bases in the Arctic remain underprepared against permafrost thaw and rising sea levels despite rampant calls for sustainable strategies. Damages emanating from climate-related failures will continue to cost the U.S. billions of dollars and render crucial infrastructure unusable. The objective of this study is to present a comprehensive literature review of the extent of Arctic warming and its significance for U.S. bases, the negative implications of military infrastructure deterioration, and methods to adapt both existing and forthcoming bases to a rapidly warming atmosphere. Eighty published papers that directly or indirectly referenced U.S. military bases or climate-oriented engineering in the aforementioned contexts were identified and analyzed over a 20-year period from 2004 to 2024. The literature review concludes that warming concerns were often not taken into much account by civil engineers during initial base construction, an oversight that now jeopardizes runways, docks, and highways. Other nations that have a sizeable footprint in the Arctic Circle, such as Canada and Russia, have demonstrated progress by utilizing pile-driven substructures, thawing permafrost before construction, and ventilated crawlspaces. Alternative solutions, such as cooling permafrost via thermosiphons or refrigeration systems, employing spatially oriented foundations composed of specific materials, and preventative measures such as floodwalls and revetments, have also shown considerable promise in simulations and practice. A table illustrating a holistic literature summary of sustainable strategies to current conditions and climate change at U.S. Military Bases in the Arctic region is also developed. Modeling successful engineering concepts and incorporating existing innovations into military infrastructure should be at the forefront of the United States’ sustainable policy. 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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15 pages, 436 KiB

Open AccessArticle

Exploring the Effects of Greenhouse Gases and Particulate Emissions on Quality of Life: A Country-Level Empirical Study

by Dongli Zhang, Wullianallur Raghupathi and Viju Raghupathi

Climate 2024, 12(11), 176; https://doi.org/10.3390/cli12110176 - 2 Nov 2024

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Abstract

This study explores the relationship between greenhouse gases (GHGs) and particulate emissions and quality of life. The aim is to understand how emissions affect quality of life globally—across countries, regions, and the global population. Statistical methods were used to examine the impact of [...] Read more.

This study explores the relationship between greenhouse gases (GHGs) and particulate emissions and quality of life. The aim is to understand how emissions affect quality of life globally—across countries, regions, and the global population. Statistical methods were used to examine the impact of various emissions’ indicators on different aspects of quality of life. The study highlights the urgent need for climate change action and encourages policymakers to take strategic steps. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that higher CO₂ and methane emissions and air pollution negatively impact quality of life. CO₂ emissions are positively associated with electricity while air pollution is positively associated with GDP and negatively with unemployment. Air pollution has an adverse effect on all three aspects of the children’s welfare dimension of quality of life. These results provide timely and convincing insights for policy- and decision-making aimed at mitigating the impact of emissions on quality of life. 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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15 pages, 4552 KiB

Open AccessArticle

Associations of Climatic Variables with Health Problems in Dairy Sheep Farms in Greece

by Eleni I. Katsarou, Daphne T. Lianou, Charalambia K. Michael, Natalia G. C. Vasileiou, Elias Papadopoulos, Efthymia Petinaki and George C. Fthenakis

Climate 2024, 12(11), 175; https://doi.org/10.3390/cli12110175 - 1 Nov 2024

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Abstract

This study aimed to study the potential effects of climatic conditions prevalent at the locations of sheep farms in the country. The specific objectives were to explore associations between climatic variables and the incidence of four clinical problems in sheep farms and, moreover, [...] Read more.

This study aimed to study the potential effects of climatic conditions prevalent at the locations of sheep farms in the country. The specific objectives were to explore associations between climatic variables and the incidence of four clinical problems in sheep farms and, moreover, to compare these to the health management practices applied in the farms. Our hypothesis was that climatic factors may be associated with the prevalence of diseases in sheep farms; this will provide information regarding potential weather effects, to take into account in the efforts for control of the diseases. Data were obtained during a large cross-sectional investigation performed across Greece involving 325 sheep flocks. Climatic variables prevailing at the location of each farm were derived from ‘The POWER Project’. The annual incidence rate for abortion was 2.0% (95% confidence intervals: 1.9–2.1%), for clinical mastitis 3.9% (3.8–4.0%), for lamb pneumonia 1.4% (1.3–1.4%) and for lamb diarrhoea 7.9% (7.8–8.1%). In multivariable analyses, climatic variables emerged as significant predictors for abortion—high annual precipitation at the farm location (p = 0.024)—and for lamb diarrhoea—high average annual temperature range at the farm location (p < 0.0001)—but not for clinical mastitis or lamb pneumonia. The potential effects of climatic variables were found to be more important in lambs than in adult animals. Future studies may focus on how variations in temperature and precipitation can be translated into on-farm metrics to understand the impacts on sheep health and welfare. Full article

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20 pages, 2205 KiB

Open AccessArticle

Educational Strategies for Teaching Climate and Bioclimate in Response to Global Change

by Ana Cano-Ortiz, Carmelo Maria Musarella and Eusebio Cano

Climate 2024, 12(11), 174; https://doi.org/10.3390/cli12110174 - 31 Oct 2024

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Abstract

This work establishes the relationship between climate, bioclimate, and forest ecosystems and highlights the need to teach these topics in educational institutions. It was found that such knowledge is not currently taught in universities, leading to scarce or non-existent teacher training in these [...] Read more.

This work establishes the relationship between climate, bioclimate, and forest ecosystems and highlights the need to teach these topics in educational institutions. It was found that such knowledge is not currently taught in universities, leading to scarce or non-existent teacher training in these areas. However, the teaching of bioclimatic aspects over a three-year period as a basis for land use planning, has shown highly positive results. The objective is to propose the teaching of bioclimatology to future managers and teachers in order to obtain a balanced environmental development. The analysis of bioclimatic diagrams makes it possible to stipulate the duration of the water reserve in the soil. This is essential for agricultural and forestry management. The edaphic factor and the bioclimatic ombrotclimatic (Io) and thermoclimatic (It/Itc) indexes condition the types of forests and crops that can exist in a territory, with the particularity that the ombrotype is conditioned by the edaphic factor, which allows a decrease in the ombrothermal index, expressed by the ombroedaphoboxerophilic index (Ioex). The humid ombrotypes condition the presence of Abies pinsapo, Quercus pyrenaica, Q. broteroi, and Q. suber, and the dry ones Q. rotundifolia and Olea sylvestris. Full article

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

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21 pages, 10021 KiB

Open AccessArticle

Glacial Lake Outburst Flood Susceptibility Mapping in Sikkim: A Comparison of AHP and Fuzzy AHP Models

by Arindam Das, Suraj Kumar Singh, Shruti Kanga, Bhartendu Sajan, Gowhar Meraj and Pankaj Kumar

Climate 2024, 12(11), 173; https://doi.org/10.3390/cli12110173 - 30 Oct 2024

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Abstract

The Sikkim region of the Eastern Himalayas is highly susceptible to Glacial Lake Outburst Floods (GLOFs), a risk that has increased significantly due to rapid glacial retreat driven by climate change in recent years. This study presents a comprehensive evaluation of GLOF susceptibility [...] Read more.

The Sikkim region of the Eastern Himalayas is highly susceptible to Glacial Lake Outburst Floods (GLOFs), a risk that has increased significantly due to rapid glacial retreat driven by climate change in recent years. This study presents a comprehensive evaluation of GLOF susceptibility in Sikkim, employing Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) models. Key factors influencing GLOF vulnerability, including lake volume, seismic activity, precipitation, slope, and proximity to rivers, were quantified to develop AHP and FAHP based susceptibility maps. These maps were validated using Receiver Operating Characteristic (ROC) curves, with the AHP method achieving an Area Under the Curve (AUC) of 0.92 and the FAHP method scoring 0.88, indicating high predictive accuracy for both models. A comparison of the two approaches revealed distinct characteristics, with FAHP providing more granular insights into moderate-risk zones, while AHP offered stronger predictive capability for high-risk areas. Our results indicated that the expansion of glacial lakes, particularly over the past three decades, has heightened the potential for GLOFs, highlighting the urgent need for continuous monitoring and adaptive risk mitigation strategies in the region. This study, in addition to enhancing our understanding of GLOF risks in Sikkim, also provides a robust framework for assessing and managing these risks in other glacial regions worldwide. Full article

(This article belongs to the Special Issue Coping with Flooding and Drought)

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23 pages, 24978 KiB

Open AccessArticle

Drought Characteristics and Drought-Induced Effects on Vegetation in Sri Lanka

by Deepakrishna Somasundaram, Jianfeng Zhu, Yuan Zhang, Yueping Nie, Zongke Zhang and Lijun Yu

Climate 2024, 12(11), 172; https://doi.org/10.3390/cli12110172 - 29 Oct 2024

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Abstract

Understanding the spatiotemporal characteristics of drought and its impacts on vegetation is a timely prerequisite to ensuring agricultural, environmental, and socioeconomic sustainability in Sri Lanka. We investigated the drought characteristics (duration, severity, frequency, and intensity) from 1990 to 2020 by using the Standardized [...] Read more.

Understanding the spatiotemporal characteristics of drought and its impacts on vegetation is a timely prerequisite to ensuring agricultural, environmental, and socioeconomic sustainability in Sri Lanka. We investigated the drought characteristics (duration, severity, frequency, and intensity) from 1990 to 2020 by using the Standardized Precipitation Evapotranspiration Index (SPEI) at various timescales and the cumulative and lagged effects on vegetation between 2000 and 2020 across the climatic zones of Sri Lanka (Dry, Wet, and Intermediate). SPEI indexes at 1-, 3-, 6-, 12-, and 24-month scales were used to analyze the drought characteristics. Frequent droughts (~13%) were common in all zones, with a concentration in the Dry zone during the last decade. Drought occurrences mostly ranged from moderate to severe in all zones, with extreme events more common in the Dry zone. This research used SPEI and the Standardized Normalized Difference Vegetation Index (SNDVI) at 0 to 24-month scales to analyze the cumulative and lagged effects of drought on vegetation. Cumulated drought effects and vegetation had maximum correlation coefficient values concentrated in the −0.41–0.98 range in Sri Lanka. Cumulated drought effects affected 40% of Dry and 16% of Intermediate zone vegetation within 1–4 months. The maximum correlation between the lagged drought effect and vegetation SNDVI showed coefficient values from −0.31–0.94 across all zones, and the high correlation areas were primarily distributed in Dry and Intermediate zones. Over 60% of the Dry and Intermediate zones had a lagged drought impact within 0 to 1 month, while 52% of the Wet zone experienced it over 11 months. The resulting dominant shorter timescale responses indicate a higher sensitivity of vegetation to drought in Sri Lanka. The findings of this study provide important insights into possible spatiotemporal changes of droughts and their possible impact on vegetation across climate zones. Full article

(This article belongs to the Special Issue Coping with Flooding and Drought)

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14 pages, 4414 KiB

Open AccessArticle

Carbon Stocks in Two Aquatic Marshes on the Caribbean and Pacific Coast of Panama

by Andrés Fraiz-Toma, Paola Gastezzi-Arias, Brillit Della Sera, Antonio Clemente, Mileika González, Alex Espinosa, Benjamín Braghtley, Edgar Arauz and Karen Domínguez

Climate 2024, 12(11), 171; https://doi.org/10.3390/cli12110171 - 25 Oct 2024

Viewed by 755

Abstract

Wetlands are critical ecosystems globally, boasting significant ecological and economic value. They play a crucial role in the hydrological cycle by storing water and carbon, thereby helping to mitigate climate variability. But in Panama, little is known about the carbon stored in freshwater [...] Read more.

Wetlands are critical ecosystems globally, boasting significant ecological and economic value. They play a crucial role in the hydrological cycle by storing water and carbon, thereby helping to mitigate climate variability. But in Panama, little is known about the carbon stored in freshwater wetlands. This research presents the estimation of the carbon stocks of two freshwater wetlands in Panama, located on both sides of the Caribbean (Portobelo) and Pacific (Tonosi) coasts. The methodology consisted of transects of 125 m and 40 m wide, with six circular plots every 25 m; in each transect, the diameter of the tree trunk was measured at breast height (1.3 m) and the species was recorded, and in the same plots, soil samples were collected in triplicate by depth intervals. The average total ecosystem carbon storage (TECS) for the aquatic wetlands of Tonosí was 106.26 ± 18.3 Mg C ha⁻¹, and for Portobelo, it was 355.09 ± 70.02 Mg C ha⁻¹. These recorded values can contribute to the conservation of wetlands, supporting Panama’s nationally determined NDC contributions. However, despite the acceptance that wetlands are important nature-based solutions, national data on soil carbon stocks in freshwater wetlands are still scarce and their protection should be increased. Full article

(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)

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19 pages, 1819 KiB

Open AccessArticle

The Role of Fairness for Accepting Stricter Carbon Taxes in Sweden

by Daniel Lindvall, Patrik Sörqvist, Sverker Carlsson Jagers, Mikael Karlsson, Stefan Sjöberg and Stephan Barthel

Climate 2024, 12(11), 170; https://doi.org/10.3390/cli12110170 - 24 Oct 2024

Viewed by 926

Abstract

Carbon taxes are considered to be an efficient method to reduce greenhouse gas emissions; however, such taxes are generally unpopular, partly because they are seen as unfair. To explore if public acceptance of a stricter carbon tax in Sweden can be enhanced, this [...] Read more.

Carbon taxes are considered to be an efficient method to reduce greenhouse gas emissions; however, such taxes are generally unpopular, partly because they are seen as unfair. To explore if public acceptance of a stricter carbon tax in Sweden can be enhanced, this study investigates the effectiveness of three different policy designs, addressing collective and personal distributional consequences and promoting procedural aspects (democratic influence). A large-scale (n = 5200) survey is applied, combining a traditional multi-category answer format with a binary choice format. The results show that support for higher carbon taxation can be enhanced if tax revenues are redistributed to affected groups. Policies with collective justice framings can change the attitudes of individuals who express antagonistic attitudes to increased carbon taxation and influence groups comparably more affected by carbon taxes, such as rural residents, low-income groups, and people who are driving long distances. Policy designs addressing collective distributional consequences are, however, less effective on individuals expressing right-leaning ideological views and low environmental concern. Policies addressing personal distributional outcomes, or perceptions of procedural injustice, had no significant effect on policy acceptance. Full article

(This article belongs to the Section Policy, Governance, and Social Equity)

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22 pages, 2141 KiB

Open AccessArticle

Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia

by Fekadie Bazie Enyew, Dejene Sahlu, Gashaw Bimrew Tarekegn, Sarkawt Hama and Sisay E. Debele

Climate 2024, 12(11), 169; https://doi.org/10.3390/cli12110169 - 22 Oct 2024

Viewed by 1033

Abstract

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias [...] Read more.

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures. Full article

(This article belongs to the Section Climate and Environment)

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9 pages, 620 KiB

Open AccessArticle

Threat Severity and Threat Susceptibility Are Significantly Correlated with Climate Distress in Australian Mothers

by Jennifer L. Barkin, James Dimmock, Lacee Heenan, James Clancy, Heather Carr and Madelyn K. Pardon

Climate 2024, 12(11), 168; https://doi.org/10.3390/cli12110168 - 22 Oct 2024

Viewed by 774

Abstract

Climate change presents a critical global crisis, characterized by rising temperatures, extreme weather events, and shifting climate patterns. Vulnerable populations bear a disproportionate share of these impacts, with women at heightened risk due to unequal access to resources, decision-making power, and social roles. [...] Read more.

Climate change presents a critical global crisis, characterized by rising temperatures, extreme weather events, and shifting climate patterns. Vulnerable populations bear a disproportionate share of these impacts, with women at heightened risk due to unequal access to resources, decision-making power, and social roles. Postpartum women specifically face further unique challenges as they strive to protect their children, amplifying the psychological toll of climate change. The current study explores climate distress in a sample of 101 postpartum women in Australia (M_age = 31.14 years), whose youngest child was (on average) 5 months of age, examining factors associated with their psychological responses to climate threats. Correlational analyses reveal that perceptions of threat severity (r = 0.621, p ≤ 0.01) and susceptibility (r = 0.695, p ≤ 0.01) are strongly linked to climate distress. These findings highlight the need to further investigate the distinct psychological pathways climate-related anxiety operates through in postpartum women. The study underscores the importance of targeted interventions to support this vulnerable population as they face increasing climate-related stressors. 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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Climate, Volume 12, Issue 11 (November 2024) – 25 articles

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