Climate

25 pages, 5531 KiB

Open AccessArticle

Transitions of Carbon Dioxide Emissions in China: K-Means Clustering and Discrete Endogenous Markov Chain Approach

by Shangyu Chen, Xiaoyu Kang and Sung Y. Park

Climate 2025, 13(8), 165; https://doi.org/10.3390/cli13080165 - 3 Aug 2025

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While [...] Read more.

This study employs k-means clustering to group 30 Chinese provinces into four CO₂ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

► Show Figures

Figure 1

20 pages, 7986 KiB

Open AccessArticle

Investigating the Gender-Climate Nexus: Strengthening Women’s Roles in Adaptation and Mitigation in the Sidi Bouzid Region

by Houda Mazhoud, Arij Boucif, Abir Ouhibi, Lobna Hajji-Hedfi and Fraj Chemak

Climate 2025, 13(8), 164; https://doi.org/10.3390/cli13080164 - 1 Aug 2025

Abstract

Tunisia faces significant challenges related to climate change, which deeply affect its natural and agricultural resources. This reality threatens not only food security but also the economic stability of rural communities and mainly rural women. This research aims to assess the impact of [...] Read more.

Tunisia faces significant challenges related to climate change, which deeply affect its natural and agricultural resources. This reality threatens not only food security but also the economic stability of rural communities and mainly rural women. This research aims to assess the impact of climate change on rural women in the agricultural development group in Sidi Bouzid, focusing on the strategies adopted and the support provided by various stakeholders to mitigate this impact. To achieve this, we developed a rigorous methodology that includes structured questionnaires, focus group discussions, and topological analysis through Multiple Correspondence Analysis (MCA). The results revealed that rural women were categorized into three groups based on their vulnerability to climate change: severely vulnerable, vulnerable, and adaptive. The findings highlighted the significant impact of climate change on water resources, which has increased family tensions and reduced agricultural incomes, making daily life more challenging for rural women. Furthermore, a deeper analysis of interactions with external stakeholders emphasized the important role of civil society, public organizations, and research institutions in strengthening the climate resilience of rural women. Given these findings, strategic recommendations aim to enhance stakeholder coordination, expand partnerships, and improve access to essential technologies and resources for women in agricultural development groups. Full article

► Show Figures

Figure 1

20 pages, 621 KiB

Open AccessArticle

Support Needs of Agrarian Women to Build Household Livelihood Resilience: A Case Study of the Mekong River Delta, Vietnam

by Tran T. N. Tran, Tanh T. N. Nguyen, Elizabeth C. Ashton and Sharon M. Aka

Climate 2025, 13(8), 163; https://doi.org/10.3390/cli13080163 - 1 Aug 2025

Abstract

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building [...] Read more.

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building efforts. Grounded in participatory feminist research, this study employed a multi-method qualitative approach, including semi-structured interviews and oral history narratives, with 60 women in two climate-vulnerable provinces. Data were analyzed through thematic coding, CATWOE (Customers, Actors, Transformation, Worldview, Owners, Environmental Constraints) analysis, and descriptive statistics. The findings identify nine major climate-related events disrupting livelihoods and reveal a limited understanding of HLR as a long-term, transformative concept. Adaptation strategies remain short-term and focused on immediate survival. Barriers to HLR include financial constraints, limited access to agricultural resources and technology, and entrenched gender norms restricting women’s leadership and decision-making. While local governments, women’s associations, and community networks provide some support, gaps in accessibility and adequacy persist. Participants expressed the need for financial assistance, vocational training, agricultural technologies, and stronger peer networks. Strengthening HLR among agrarian women requires gender-sensitive policies, investment in local support systems, and community-led initiatives. Empowering agrarian women as agents of change is critical for fostering resilient rural livelihoods and achieving inclusive, sustainable development. Full article

► Show Figures

Graphical abstract

11 pages, 985 KiB

Open AccessArticle

Strengthening Western North Pacific High in a Warmer Environment

by Sanghyeon Yun and Namyoung Kang

Climate 2025, 13(8), 162; https://doi.org/10.3390/cli13080162 - 1 Aug 2025

Abstract

The geographical response of western North Pacific subtropical high (SH) to environmental conditions such as the El Niño-Southern Oscillation (ENSO) and global warming has been one of the main concerns with respect to extreme events induced by tropical convections. By considering observed outgoing [...] Read more.

The geographical response of western North Pacific subtropical high (SH) to environmental conditions such as the El Niño-Southern Oscillation (ENSO) and global warming has been one of the main concerns with respect to extreme events induced by tropical convections. By considering observed outgoing longwave radiation (OLR) as the strength of subtropical high, this study attempts to further understand the geographical response of SH strength to ENSO and global warming. Here, “SH strength” is defined as the inhibition of regional convections under SH environment. A meridional seesaw pattern among SH strength anomalies is found at 130°–175° E. In addition, the La Niña environment with weaker convections at lower latitudes is characterized by farther westward expansion of SH but with a weaker strength. Conversely, the El Niño environment with stronger convections at lower latitudes leads to shrunken SH but with a greater strength. The influence of the seesaw mechanism appears to be modulated by global warming. The western North Pacific subtropical high strengthens overall under warming in both the La Niña and El Niño environments. This suggests that the weakening effect by drier tropics is largely offset by anomalous highs induced by a warming atmosphere. It is most remarkable that the highest SH strengths appear in a warmer El Niño environment. The finding implies that every new El Niño environment may experience the driest atmosphere ever in the subtropics under global warming. The value of this study lies in the fact that OLR effectively illustrates how the ENSO variation and global warming bring the zonally undulating strength of boreal-summer SH. Full article

► Show Figures

Figure 1

23 pages, 2122 KiB

Open AccessArticle

Climate Change of Near-Surface Temperature in South Africa Based on Weather Station Data, ERA5 Reanalysis, and CMIP6 Models

by Ilya Serykh, Svetlana Krasheninnikova, Tatiana Gorbunova, Roman Gorbunov, Joseph Akpan, Oluyomi Ajayi, Maliga Reddy, Paul Musonge, Felix Mora-Camino and Oludolapo Akanni Olanrewaju

Climate 2025, 13(8), 161; https://doi.org/10.3390/cli13080161 - 1 Aug 2025

Abstract

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in [...] Read more.

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives, 2nd Edition)

► Show Figures

Figure 1

35 pages, 1524 KiB

Open AccessArticle

Unveiling the Interplay of Climate Vulnerability and Social Capital: Insights from West Bengal, India

by Sayari Misra, Md Saidul Islam and Suchismita Roy

Climate 2025, 13(8), 160; https://doi.org/10.3390/cli13080160 - 26 Jul 2025

Abstract

This study explores the interplay of climate vulnerability and social capital in two rural communities: Brajaballavpur, a high-climate-prone village in the Indian Sundarbans characterized by high ecological fragility, recurrent cyclones, and saline water intrusion affecting water access, livelihoods, and infrastructure; and Jemua, a [...] Read more.

This study explores the interplay of climate vulnerability and social capital in two rural communities: Brajaballavpur, a high-climate-prone village in the Indian Sundarbans characterized by high ecological fragility, recurrent cyclones, and saline water intrusion affecting water access, livelihoods, and infrastructure; and Jemua, a low-climate-prone village in the land-locked district of Paschim Bardhaman, West Bengal, India, with no extreme climate events. A total of 85 participants (44 in Brajaballavpur, 41 in Jemua) were selected through purposive sampling. Using a comparative qualitative research design grounded in ethnographic fieldwork, data were collected through household interviews, Participatory Rural Appraisals (PRAs), Focus Group Discussions (FGDs), and Key Informant Interviews (KIIs), and analyzed manually using inductive thematic analysis. Findings reveal that bonding and bridging social capital were more prominent in Brajaballavpur, where dense horizontal ties supported collective action during extreme weather events. Conversely, linking social capital was more visible in Jemua, where participants more frequently accessed formal institutions such as the Gram Panchayat, local NGOs, and government functionaries that facilitated grievance redressal and information access, but these networks were concentrated among more politically connected individuals. The study concludes that climate vulnerability shapes the type, strength, and strategic use of social capital in village communities. While bonding and bridging ties are crucial in high-risk contexts, linking capital plays a critical role in enabling long-term social structures in lower-risk settings. The study contributes to both academic literature and policy design by offering a relational and place-based understanding of climate vulnerability and social capital. Full article

(This article belongs to the Special Issue Sustainable Development Pathways and Climate Actions)

► Show Figures

Figure 1

16 pages, 421 KiB

Open AccessReview

Applications of Machine Learning Methods in Sustainable Forest Management

by Rogério Pinto Espíndola, Mayara Moledo Picanço, Lucio Pereira de Andrade and Nelson Francisco Favilla Ebecken

Climate 2025, 13(8), 159; https://doi.org/10.3390/cli13080159 - 25 Jul 2025

Abstract

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates [...] Read more.

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates everything from precise forest health assessments and real-time deforestation detection to wildfire prevention and habitat mapping. Other significant advancements include species identification via computer vision and predictive modeling to optimize reforestation and carbon sequestration. Projects like SILVANUS serve as practical examples of this approach’s success in combating wildfires and restoring ecosystems. However, for these technologies to reach their full potential, obstacles like data quality, ethical issues, and a lack of collaboration between different fields must be overcome. The solution lies in integrating the power of machine learning with ecological expertise and local community engagement. This partnership is the path forward to preserve biodiversity, combat climate change, and ensure a sustainable future for our forests. Full article

(This article belongs to the Topic Disaster Risk Management and Resilience)

► Show Figures

Figure 1

24 pages, 319 KiB

Open AccessArticle

Indigenous Contestations of Carbon Markets, Carbon Colonialism, and Power Dynamics in International Climate Negotiations

by Zeynep Durmaz and Heike Schroeder

Climate 2025, 13(8), 158; https://doi.org/10.3390/cli13080158 - 24 Jul 2025

Abstract

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the [...] Read more.

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the height of their negotiation during COP25 and COP26 by drawing attention to their role in perpetuating “carbon colonialism,” thereby revealing deeper power dynamics in global climate governance. Utilising a political ecology framework, this study explores these power dynamics at play during the climate negotiations, focusing on the instrumental, structural, and discursive forms of power that enable or limit Indigenous participation. Through a qualitative case study approach, the research reveals that while Indigenous Peoples have successfully used discursive strategies to challenge market-based solutions, their influence remains limited due to entrenched structural and instrumental power imbalances within the UNFCCC process. This study highlights the need for equitable policies that integrate human rights safeguards and prioritise Indigenous-led, non-market-based approaches to ecological restoration. Full article

18 pages, 411 KiB

Open AccessArticle

Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia

by Andreea Bratu, Aayush Sharma, Carmen H. Logie, Gina Martin, Kalysha Closson, Maya K. Gislason, Robert S. Hogg, Tim Takaro and Kiffer G. Card

Climate 2025, 13(8), 157; https://doi.org/10.3390/cli13080157 - 24 Jul 2025

Abstract

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand [...] Read more.

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand whether climate risk perceptions differ across workers between industries. We conducted an online survey of British Columbians (16+) in 2021 using social media advertisements. Participants rated how likely they believed their industry (Natural Resources, Science, Art and Recreation, Education/Law/Government, Health, Management/Business, Manufacturing, Sales, Trades) would be affected by climate change (on a scale from “Very Unlikely” to “Very Likely”). Ordinal logistic regression examined the association between occupational category and perceived industry vulnerability, adjusting for socio-demographic factors. Among 877 participants, 66.1% of Natural Resources workers perceived it was very/somewhat likely that climate change would impact their industry; only those in Science (78.3%) and Art and Recreation (71.4%) occupations had higher percentages. In the adjusted model, compared to Natural Resources workers, respondents in other occupations, including those in Art and Recreation, Education/Law/Government, Management/Business, Manufacturing, Sales, and Trades, perceived significantly lower risk of climate change-related industry impacts. Industry-specific interventions are needed to increase awareness of and readiness for climate adaptation. Policymakers and industry leaders should prioritize sectoral differences when designing interventions to support climate resilience in the workforce. Full article

► Show Figures

Figure A1

19 pages, 2340 KiB

Open AccessArticle

Analysis of Olive Tree Flowering Behavior Based on Thermal Requirements: A Case Study from the Northern Mediterranean Region

by Maja Podgornik, Jakob Fantinič, Tjaša Pogačar and Vesna Zupanc

Climate 2025, 13(8), 156; https://doi.org/10.3390/cli13080156 - 23 Jul 2025

Abstract

In recent years, early olive fruit drop has been observed in the northern Mediterranean regions, causing significant economic losses, although the exact cause remains unknown. Recent studies have identified several possible causes; however, our understanding of how olive trees respond to these environmental [...] Read more.

In recent years, early olive fruit drop has been observed in the northern Mediterranean regions, causing significant economic losses, although the exact cause remains unknown. Recent studies have identified several possible causes; however, our understanding of how olive trees respond to these environmental stresses remains limited. This study includes an analysis of selected meteorological and flowering data for Olea europaea L. “Istrska belica” to evaluate the use of a chilling and forcing model for a better understanding of flowering time dynamics under a changing climate. The flowering process is influenced by high diurnal temperature ranges (DTRs) during the pre-flowering period, resulting in earlier flowering. Despite annual fluctuations due to various climatic factors, an increase in DTRs has been observed in recent decades, although the mechanisms by which olive trees respond to high DTRs remain unclear. The chilling requirements are still well met in the region (1500 ± 250 chilling units), although their total has declined over the years. According to the Chilling Hours Model, chilling units—referred to as chilling hours—represent the number of hours with temperatures between 0 and 7.2 °C, accumulated throughout the winter season. Growing degree hours (GDHs) are strongly correlated with the onset of flowering. These results suggest that global warming is already affecting the synchrony between olive tree phenology and environmental conditions in the northern Mediterranean and may be one of the reason for the green drop. Full article

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

► Show Figures

Figure 1

30 pages, 459 KiB

Open AccessReview

Recent Advances in Long-Term Wind-Speed and -Power Forecasting: A Review

by Jacqueline Muthoni Mbugua and Yusuke Hiraga

Climate 2025, 13(8), 155; https://doi.org/10.3390/cli13080155 - 23 Jul 2025

Abstract

This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of [...] Read more.

This review examines advancements and methodologies in long-term wind-speed and -power forecasting. It emphasizes the importance of these techniques in integrating wind energy into power systems. Covering a range of forecasting timeframes from monthly to multiyear projections, this paper highlights the diversity of applications and approaches. These applications and approaches are essential for managing the inherent variability and unpredictability of wind energy. Various forecasting methods, including statistical models, machine-learning techniques, and hybrid models, are discussed in detail. The review demonstrates how these methods improve forecast accuracy and reliability across different temporal and geographical scales. It also identifies significant challenges such as model complexity, data limitations, and the need to accommodate regional variations. Future improvements in wind forecasting include enhancing model integration, employing higher resolution data, and fostering collaborative research to further refine forecasting methodologies. This comprehensive analysis aims to advance knowledge on wind forecasting, facilitate the efficient integration of wind power into global energy systems, and contribute to sustainable energy development goals. Full article

(This article belongs to the Special Issue Wind‑Speed Variability from Tropopause to Surface)

► Show Figures

Figure 1

21 pages, 991 KiB

Open AccessArticle

Strengthening Agricultural Drought Resilience of Commercial Livestock Farmers in South Africa: An Assessment of Factors Influencing Decisions

by Yonas T. Bahta, Frikkie Maré and Ezael Moshugi

Climate 2025, 13(8), 154; https://doi.org/10.3390/cli13080154 - 22 Jul 2025

Abstract

In order to fulfil SDG 13—taking urgent action to combat climate change and its impact—SDG 2—ending hunger and poverty—and the African Union CAADP Strategy and Action Plan: 2026–2035, which’s goal is ending hunger and intensifying sustainable food production, agro-industrialisation, and trade, the resilience [...] Read more.

In order to fulfil SDG 13—taking urgent action to combat climate change and its impact—SDG 2—ending hunger and poverty—and the African Union CAADP Strategy and Action Plan: 2026–2035, which’s goal is ending hunger and intensifying sustainable food production, agro-industrialisation, and trade, the resilience of commercial livestock farmers to agricultural droughts needs to be enhanced. Agricultural drought has affected the economies of many sub-Saharan African countries, including South Africa, and still poses a challenge to commercial livestock farming. This study identifies and determines the factors affecting commercial livestock farmers’ level of resilience to agricultural drought. Primary data from 123 commercial livestock farmers was used in a principal component analysis to estimate the agricultural drought resilience index as an outcome variable, and the probit model was used to determine the factors influencing the resilience of commercial livestock farmers in the Northern Cape Province of South Africa. This study provides a valuable contribution towards resilience-building strategies that are critical for sustaining commercial livestock farming in arid regions by developing a formula for calculating the Agricultural Drought Resilience Index for commercial livestock farmers, significantly contributing to the pool of knowledge. The results showed that 67% of commercial livestock farming households were not resilient to agricultural drought, while 33% were resilient. Reliance on sustainable natural water resources, participation in social networks, education, relative support, increasing livestock numbers, and income stability influence the resilience of commercial livestock farmers. It underscores the importance of multidimensional policy interventions to enhance farmer drought resilience through education and livelihood diversification. Full article

► Show Figures

Figure 1

21 pages, 3532 KiB

Open AccessReview

Climate Hazards Management of Historic Urban Centers: The Case of Kaštela Bay in Croatia

by Jure Margeta

Climate 2025, 13(7), 153; https://doi.org/10.3390/cli13070153 - 19 Jul 2025

Abstract

The preservation and protection of historic urban centers in climate-sensitive coastal areas contributes to the promotion of culture as a driver and enabler of achieving temporal and spatial sustainability, as it is recognized that urban heritage is an integral part of the urban [...] Read more.

The preservation and protection of historic urban centers in climate-sensitive coastal areas contributes to the promotion of culture as a driver and enabler of achieving temporal and spatial sustainability, as it is recognized that urban heritage is an integral part of the urban landscape, culture, and economy. The aim of this study was to enhance the resilience and protection of cultural heritage and historic urban centers (HUCs) in the coastal area of Kaštela, Croatia, by providing recommendations and action guidelines in response to climate change impacts, including rising temperatures, sea levels, storms, droughts, and flooding. Preserving HUCs is essential to maintain their cultural values, original structures, and appearance. Many ancient coastal Roman HUCs lie partially or entirely below mean sea level, while low-lying medieval castles, urban areas, and modern developments are increasingly at risk. Based on vulnerability assessments, targeted mitigation and adaptation measures were proposed to address HUC vulnerability sources. The Historical Urban Landscape Approach tool was used to transition and manage HUCs, linking past, present, and future hazard contexts to enable rational, comprehensive, and sustainable solutions. The effective protection of HUCs requires a deeper understanding of the evolution of urban development, climate dynamics, and the natural environments, including both tangible and intangible urban heritage elements. The “hazard-specific” vulnerability assessment framework, which incorporates hazard-relevant indicators of sensitivity and adaptive capacity, was a practical tool for risk reduction. This method relies on analyzing the historical performance and physical characteristics of the system, without necessitating additional simulations of transformation processes. Full article

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

► Show Figures

Figure 1

28 pages, 2140 KiB

Open AccessArticle

Application of the GEV Distribution in Flood Frequency Analysis in Romania: An In-Depth Analysis

by Cristian Gabriel Anghel and Dan Ianculescu

Climate 2025, 13(7), 152; https://doi.org/10.3390/cli13070152 - 18 Jul 2025

Abstract

This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may [...] Read more.

This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article

(This article belongs to the Special Issue Hydroclimatic Extremes: Modeling, Forecasting, and Assessment)

► Show Figures

Figure 1

21 pages, 6605 KiB

Open AccessArticle

Analysis of Spatial and Temporal Dynamics of Climate Aridization in Rostov Oblast in 1951–2054 Using ERA5 and CMIP6 Data and the De Martonne Index

by Denis Krivoguz

Climate 2025, 13(7), 151; https://doi.org/10.3390/cli13070151 - 17 Jul 2025

Abstract

Rostov Oblast is one of the key grain-producing regions in Russia, accounting for 6% of the total grain production. However, it faces an increasing risk of climate aridization, which requires an accurate scientific assessment to ensure the food security of the country. The [...] Read more.

Rostov Oblast is one of the key grain-producing regions in Russia, accounting for 6% of the total grain production. However, it faces an increasing risk of climate aridization, which requires an accurate scientific assessment to ensure the food security of the country. The present study analyzes the spatial and temporal dynamics of climate aridification in the Rostov region for the period 1951–2054. This analysis is based on ERA5 reanalysis data and CMIP6 forecast models (MPI-ESM1-2-HR, CanESM5, BCC-CSM2-MR). The analysis indicates that the annual mean temperature in the region has increased by 2–3 °C since the 1950s, reaching 12 °C in 2023. At the same time, precipitation shows significant interannual variability with no detectable long-term trend. Spatial analysis reveals a stable meridional temperature gradient and zonality of precipitation distribution. The southeastern parts of the region are characterized by the highest degree of aridification. Projection models indicate further warming (+1.5–3 °C by 2054) and increasing contrasts between western (wetter) and eastern (drier) areas. Projections derived from the CMIP6 models indicate an intensification of aridification, accompanied by a decrease in the De Martonne index of 15–25% by the year 2054. The area of territories with arid climates is expected to increase from 30% to 40%. The most vulnerable regions will be in the southeast part of Rostov Oblast, where the De Martonne index values are predicted to decrease to less than 10. The potential increase in temperature and evapotranspiration, coupled with spatial differentiation, could pose significant risks to the sustainability of the agro-industrial complex, particularly in the southeastern part of the region. Full article

► Show Figures

Figure 1

17 pages, 4652 KiB

Open AccessArticle

Challenge and Bias Correction for Surface Wind Speed Prediction: A Case Study in Shanxi Province, China

by Zengyuan Guo, Zhuozhuo Lyu and Yunyun Liu

Climate 2025, 13(7), 150; https://doi.org/10.3390/cli13070150 - 17 Jul 2025

Abstract

Accurate prediction of wind speed is critical for wind power generation and bias correction serves as an effective tool to enhance the precision of climate model forecasts. This study evaluates the effectiveness of three bias correction methods—Quantile Regression at the 50th percentile (QR50), [...] Read more.

Accurate prediction of wind speed is critical for wind power generation and bias correction serves as an effective tool to enhance the precision of climate model forecasts. This study evaluates the effectiveness of three bias correction methods—Quantile Regression at the 50th percentile (QR50), Linear Regression (LR), and Optimal Threat Score (OTS)—for improving wind speed predictions at a height of 70 m from the NCEP CFSv2 model in Shanxi Province, China. Using observational data from nine wind towers (2021–2024) and corresponding model hindcasts, we analyze systematic biases across lead times of 1–45 days. Results reveal persistent model errors: overestimation of low wind speeds (<6 m/s) and underestimation of high wind speeds (>6 m/s), with the Root Mean Square Error (RMSE) exceeding 1.5 m/s across all lead times. Among the correction methods, QR50 demonstrates the most robust performance, reducing the mean RMSE by 11% in October 2023 and 10% in February 2024. Correction efficacy improves significantly at longer lead times (>10 days) and under high RMSE conditions. These findings underscore the value of regression-based approaches in complex terrain while emphasizing the need for dynamic adjustments during extreme wind events. Full article

(This article belongs to the Special Issue Wind‑Speed Variability from Tropopause to Surface)

► Show Figures

Figure 1

17 pages, 2951 KiB

Open AccessArticle

Long-Term Rainfall–Runoff Relationships During Fallow Seasons in a Humid Region

by Rui Peng, Gary Feng, Ying Ouyang, Guihong Bi and John Brooks

Climate 2025, 13(7), 149; https://doi.org/10.3390/cli13070149 - 16 Jul 2025

Abstract

The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various [...] Read more.

The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various rainfall events during fallow seasons in Mississippi by applying the DRAINMOD model. The analysis revealed that the average rainfall during the fallow season was 760 mm over the past 100 years, accounting for 65% of the annual total. In dry, normal, and wet fallow seasons, the average rainfall was 528, 751, and 1010 mm, respectively, corresponding to runoff of 227, 388, and 602 mm. Runoff frequency increased with wetter weather conditions, rising from 16 events in dry seasons to 23 in normal seasons and 30 in wet seasons. Over the past century, runoff dynamics were predominantly regulated by high-intensity rainfall events during the fallow season. Very heavy rainfall events (mean frequency = 11 events) generated 215 mm of runoff and accounted for 53% of the total runoff, while extreme rainfall events (mean frequency = 2 events) contributed 135 mm of runoff, making up 34% of the total runoff. Water table depth played a critical role in shaping spring runoff dynamics. As the water table decreased from 46 mm in March to 80 mm in May, the soil pore space increased from 5 mm in March to 14 mm in May. This increased soil infiltration and water storage capacity, leading to a steady decline in runoff. The study found that the mean daily runoff frequency dropped from 13.5% in March to 7.6% in May, while monthly runoff decreased from 74 to 38 mm. Increased extreme rainfall (R95p) in April contributed over 45% of the total runoff and resulted in the highest daily mean runoff of 20 mm, compared to 18 mm in March and 16 mm in May. The results from this century-long historical weather data could be used to enhance field-scale water resource management, predict potential runoff risks, and optimize planting windows in the humid east-central Mississippi. Full article

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

► Show Figures

Figure 1

21 pages, 2800 KiB

Open AccessArticle

Integrating Socioeconomic and Community-Based Strategies for Drought Resilience in West Pokot, Kenya

by Jean-Claude Baraka Munyaka, Seyid Abdellahi Ebnou Abdem, Olivier Gallay, Jérôme Chenal, Joseph Timu Lolemtum, Milton Bwibo Adier and Rida Azmi

Climate 2025, 13(7), 148; https://doi.org/10.3390/cli13070148 - 14 Jul 2025

Abstract

This paper examines how demographic characteristics, institutional structures, and livelihood strategies shape household resilience to climate variability and drought in West Pokot County, one of Kenya’s most climate-vulnerable arid and semi-arid lands (ASALs). Using a mixed-methods approach, it combines household survey data with [...] Read more.

This paper examines how demographic characteristics, institutional structures, and livelihood strategies shape household resilience to climate variability and drought in West Pokot County, one of Kenya’s most climate-vulnerable arid and semi-arid lands (ASALs). Using a mixed-methods approach, it combines household survey data with three statistical techniques: Multinomial Logistic Regression (MLR) assesses the influence of gender, age, and education on livestock ownership and livelihood choices; Multiple Correspondence Analysis (MCA) reveals patterns in institutional access and adaptive practices; and Stepwise Linear Regression (SLR) quantifies the relationship between resilience strategies and agricultural productivity. Findings show that demographic factors, particularly gender and education, along with access to veterinary services, drought-tolerant inputs, and community-based organizations, significantly shape resilience. However, trade-offs exist: strategies improving livestock productivity may reduce crop yields due to resource and labor competition. This study recommends targeted interventions, including gender-responsive extension services, integration of indigenous and scientific knowledge, improved infrastructure, and participatory governance. These measures are vital for strengthening resilience not only in West Pokot but also in other drought-prone ASAL regions across sub-Saharan Africa. Full article

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

► Show Figures

Figure 1

22 pages, 2775 KiB

Open AccessArticle

Surface Broadband Radiation Data from a Bipolar Perspective: Assessing Climate Change Through Machine Learning

by Alice Cavaliere, Claudia Frangipani, Daniele Baracchi, Maurizio Busetto, Angelo Lupi, Mauro Mazzola, Simone Pulimeno, Vito Vitale and Dasara Shullani

Climate 2025, 13(7), 147; https://doi.org/10.3390/cli13070147 - 13 Jul 2025

Abstract

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface [...] Read more.

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature (

T_{a}

), relative humidity, and the ratio of water vapor pressure to

T_{a}

. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

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

► Show Figures

Figure 1

Journal Description

Climate

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI