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Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones' features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an R 2 of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. [ABSTRACT FROM AUTHOR]

The document "Digitalization for Resilient and Sustainable Energy Transitions" discusses the impact of the global energy crisis on the energy sector, particularly due to the COVID-19 pandemic. It highlights challenges such as disruptions in green energy technology supply chains, energy price hikes, and energy security issues in regions like Europe. The document emphasizes the role of digitalization in enabling decentralized and renewable-based energy systems as a key theme for a resilient, sustainable, and equitable energy transition. Additionally, it explores the use of artificial intelligence (AI) for energy security and the importance of decentralized energy systems in advancing renewable energy solutions. [Extracted from the article]

Analyzing agricultural and hydrological drought at different timescales is essential for designing adaptation strategies. This study aimed to assess agricultural and hydrological drought in the Abbay Basin of Ethiopia by using multiple indices, namely the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and drought severity index (DSI). Climate extremes were assessed over the Abbay Basin between 1981 and 2022. The results indicate that the years 1982 and 2014 were the most drought-prone, while the year 1988 was the wettest year in the Abbay Basin. The results revealed the presence of extremely dry and severely dry conditions, potentially impacting agricultural output in the region. Agricultural drought was identified during the main crop seasons (June to September). The VCI results indicated the presence of extremely wet and severely wet conditions. In 2012, 65% of the area was affected by extreme drought conditions, while nearly half of the Basin experienced extreme drought in 2013 and 2022. The DSI results indicated the occurrence of agricultural drought, although the spatial coverage of extreme dry conditions was lower than that of the other indices. In 2003, 78.49% of the Basin experienced moderate drought conditions, whereas severe drought affected 20% of the region. In 2010, about 90% of the Basin experienced moderate drought. This study provides valuable insights for agricultural communities, enabling them to mitigate the impact of drought on crop yields by utilizing different adaptation strategies. An adequate knowledge of agricultural and hydrological drought is essential for policymakers to assess the potential effects of drought on socioeconomic activities and to recognize the significance of implementing climate change adaptation measures. [ABSTRACT FROM AUTHOR]

Climate change scenarios have been used to evaluate future climate change impacts and develop adaptation measures to mitigate potential damage. This study investigated strategies to reduce nonpoint source loads in an agriculturally dominated watershed and adapt to climate change despite uncertainty. We also investigated strategies for adapting to future meteorological conditions characterized by uncertainty. We utilized the latest future climate change scenarios—shared socioeconomic pathways—and explored measures to reduce nonpoint source loads by implementing nonpoint pollution abatement facilities in a watershed model. The simulation results indicate that the future frequency of rainfall events may decrease based on observations and the types and features of rainfall events in the scenarios. However, the variability of runoff loads in the context of future climate scenarios may increase because of factors influencing surface runoff, including the amount and intensity of rainfall. Nonpoint source loads are expected to exhibit high uncertainty in the future. Finally, the optimal solution can be determined through a simulated evaluation of the cost–benefit of installing the abatement facilities, considering the abatement efficiency and maintenance period. Overall, implementing effective management practices is crucial for reducing nonpoint source loads resulting from agricultural activities while adapting to increasingly variable meteorological conditions. [ABSTRACT FROM AUTHOR]

The skill, assumptions, and uncertainty of machine learning techniques (MLTs) for downscaling global climate model's precipitation to the local level in Bolivia were assessed. For that, an ensemble of 20 global climate models (GCMs) from CMIP6, with random forest (RF) and support vector machine (SVM) techniques, was used on four zones (highlands, Andean slopes, Amazon lowlands, and Chaco lowlands). The downscaled series' skill was evaluated in terms of relative errors. The uncertainty was analyzed through variance decomposition. In most cases, MLTs' skill was adequate, with relative errors less than 50%. Moreover, RF tended to outperform SVM. Robust (weak) stationary (perfect prognosis) assumptions were found in the highlands and Andean slopes. The weakness was attributed to topographical complexity. The downscaling methods were shown to be the dominant source of uncertainties. This analysis allowed the derivation of robust future projections, showing higher annual rainfall, shorter dry spell duration, and more frequent but less intense high rainfall events in the highlands. Apart from the dry spell's duration, a similar pattern was found for the Andean slopes. A decrease in annual rainfall was projected in the Amazon lowlands and an increase in the Chaco lowlands. [ABSTRACT FROM AUTHOR]

Peanut production plays a crucial role in global food security, particularly in developing countries, where it provides essential nutrition and income. This paper examines the optimization of peanut production through genetic advancements, climate adaptation strategies, and sustainable practices. The primary objective is to increase yields by addressing challenges related to climate change, pests, and resource constraints. Globally, peanut production is hindered by rising temperatures, irregular rainfall, and declining soil quality, impacting both yield and quality. Developing countries, especially in Africa and Asia, face additional challenges, such as limited access to advanced agricultural technologies, inadequate infrastructure, and insufficient support for smallholder farmers. The vital issues include genetic vulnerabilities to pests, climate stress, and inefficient water use. Recent genetic research has provided insights into breeding more resilient, drought-resistant varieties, offering hope for improving yields, despite environmental challenges. The adoption of climate adaptation strategies, precision farming, and integrated pest management is essential for boosting productivity. These, along with optimized irrigation and nutrient management, have significantly impacted peanut production in resource-limited settings. Additionally, drought-resistant varieties have proven crucial, enabling farmers to increase resilience and yields in areas facing climate stress. In conclusion, optimizing peanut production requires continued investment in genetic advancements, infrastructure, and sustainable practices. Future efforts should focus on improving climate adaptation and sustainable farming techniques for long-term success. [ABSTRACT FROM AUTHOR]

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity. [ABSTRACT FROM AUTHOR]

Groundwater depletion in Bangladesh's Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation. [ABSTRACT FROM AUTHOR]

One of the main challenges in modern animal husbandry in North Kazakhstan is ensuring an uninterrupted supply of sufficient fodder crops. This research, conducted from 2019 to 2023, aimed to develop strategies for cultivating environmentally sustainable fodder crops capable of providing a stable fodder crop base under the changing climatic conditions of the North Kazakhstan region. The studies included analysis of air temperature and precipitation data as well as monitoring of fodder grass mixtures within a green fodder conveyor system. Different sowing dates for fodder crops and mixtures were selected for the development of the conveyor system. The range of experimental variants included fodder crops and their mixtures from various botanical families. The experiment involved both perennial (alfalfa and festulolium) and annual (corn, pea, sunflower, Sudan grass, oats, and rapeseed) crops. The highest green mass yields were achieved by the following variants: fodder crops of corn + pea—74.40 c/ha; mixtures of annual legume–grass crops in the pea + oats variant of the first sowing date—43.64 c/ha; Sudan grass + pea—45.72 c/ha; mixtures of perennial grasses in the second utilization term of alfalfa + festulolium—64.9 c/ha; and rapeseed sown at the first sowing date—46.61 c/ha. In terms of crude and digestible protein content, the best among the annual grass variants was the mixture of Sudan grass and pea (crude protein—33.59 g/kg, digestible protein—24.5 g/kg), and the best among the perennials was the variant of the first utilization term (crude protein—50.42 g/kg, digestible protein—38.2 g/kg). Regarding metabolizable energy content, the annual crop variant of corn + pea had a yield of 1.92 MJ/kg, and in the perennial variant, the mixture of alfalfa and festulolium in the first utilization term had a yield of 2.68 MJ/kg. Such an approach to creating green fodder conveyors can be crucial for developing effective strategies for adapting agriculture to climate change, including the selection of promising fodder crops and optimization of their placement. The results obtained can contribute to enhancing the productivity and sustainability of agricultural production in the North Kazakhstan region. [ABSTRACT FROM AUTHOR]

Climate change is severely affecting all regions of the world, and urban water management has become a major urban challenge. Although nature-based solutions (NBSs) have been widely implemented in developed countries in the Global North to address stormwater-related challenges in urban areas, implementation of such approaches in Viet Nam and other Asian countries remains limited. In addition, comprehensive and critical reviews of NBS adoption and development processes in Viet Nam are scarce. This study aims to clarify several aspects through a literature-based review: to understand the development of urban water management in Europe and Asia (China and Southeast Asian countries) along with the drivers for NBS implementation in Viet Nam, to explain the barriers to NBS adoption in Viet Nam, to present feasible solutions for promoting NBS adoption, and to explore future perspectives for NBS development in the context of Viet Nam. Although significant barriers exist, opportunities for NBS implementation are evident. The findings of this study can be used to promote NBS in other municipalities in developing countries. [ABSTRACT FROM AUTHOR]

Dominant species are crucial in regulating the structure and productivity of plant communities. Adaptation strategies to climate change vary among the dominant species of different life types. However, the responses of the ecological niches of dominant species to warming and precipitation in semi-arid grasslands and their impacts on community structure and function are unknown. This study involved conducting a long-term experimental simulation of warming and increased precipitation on grasslands in Inner Mongolia and studying population dynamics, ecological niches, and their responses to the structure and function of the community species of two dominant plants, L. chinensis (perennial rhizome grass) and S. krylovii (perennial clumped grass). The results show that the niche width of L. chinensis increased and S. krylovii decreased under warming and increased precipitation conditions. The overlap of L. chinensis and S. krylovii decreased under the same conditions. The niche widths of L. chinensis and S. krylovii were 1.22 for the control (C), 1.19 and 1.04 under warming (W) conditions, 1.27 and 0.97 under warming plus precipitation (WP) conditions, and 1.27 and 1.24 under the conditions of precipitation addition (P). The niche overlap of L. chinensis and S. krylovii were 0.72 in C, 0.69 in W, 0.68 in WP, and 0.82 in P. The biomass share and importance value of L. chinensis increased, and those of S. krylovii decreased in response to warming and precipitation. The effects of warming on species diversity and community stability are primarily influenced by the effects on the niche breadth of S. krylovii. Combined with our previous study, L. chinensis will offer more resources in communities in warmer and wetter steppe climates in the future. However, this is not conducive to community diversity. [ABSTRACT FROM AUTHOR]

The document "Soil Health and Crop Management in Conservation Agriculture" discusses the importance of conservation agriculture in addressing global food production challenges while maintaining soil health. Conservation practices like minimum tillage and crop diversification have shown to enhance soil aggregation, biological activity, water retention, and nutrient cycling. Various studies highlighted in the document demonstrate the positive impact of practices such as biochar application, cover crops, and advanced tillage methods on soil fertility, crop yield, and environmental sustainability. Overall, the research emphasizes the potential of conservation agriculture to create resilient and productive farming systems for a sustainable food future. [Extracted from the article]

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is a widely recognized phenomenon consisting of heat accumulation by dense urban construction and human activities, resulting in higher temperatures across urban areas compared to their surroundings. This article aims to quantify the UHI effect on several areas throughout the city of Lisbon, Portugal, with the main goal of validating, evaluating, and reinforcing urban climate adaptation and resilience strategies proposed in the recent scientific literature. A set of nine quality-controlled weather stations from the "Lisboa Aberta" network that are compliant with the World Meteorological Organization (WMO) standards and installation requirements were used to characterize Lisbon's UHI, in comparison to a reference weather station from the Instituto Português do Mar e da Atmosfera (IPMA), located at Lisbon Airport. By applying a principal component analysis (PCA) in an innovative way to 10 urban indexes, it is shown that the thermal inertia in Lisbon's urban areas is positively correlated with the UHI intensity and urban density, regardless of the daily heating/cooling cycle. Furthermore, the results show that land use also has an impact on the UHI effect, with continuous, vertical building areas showing the greatest deviations in comparison to the reference, averaging +1.8 °C. Contrastingly, horizontal building areas reveal an average deviation of +1.3 °C, with sparse, discontinuously built areas representing an average UHI effect of +0.2 °C. Finally, through a climatope analysis, it is determined that, across Lisbon, high-density urban areas and ventilation corridors are responsible for inducing average UHI effects of +1.7 °C and +0.2 °C, respectively. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems' responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management. [ABSTRACT FROM AUTHOR]

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple Linear Regression, K-Nearest Neighbors, and XGBoost Regressor, were employed to forecast multi-scale SPI values (for 6-, 9-, 12-, and 24-month) considering various lag times. Results indicated that the ML model with the most robust performance varied depending on station and SPI duration. Furthermore, ANN demonstrated robust performance for SPI estimations at Shiraz station, whereas no single model consistently outperformed the others for Tridolino station. These findings were further validated through the confidence percentage analysis performed on all ML models in this study. Across all scenarios, longer SPI durations generally yielded better model performance. Additionally, for Shiraz station, optimal lag times varied by SPI duration: 6 months for the 6- and 9-month SPI, 4 months for the 12-month SPI, and 2 months for the 24-month SPI. For Tridolino station, on the other hand, no definitive optimal lag time was identified. These findings contribute to our understanding of predicting drought indicators and supporting effective water resource management and climate change adaptation efforts. [ABSTRACT FROM AUTHOR]

Progressive climate change (CC) forces people—particularly in the Global South—to adapt to its consequences, some of which include droughts, flooding, and new diseases. This study investigates the determinants of behaviors for adapting to these threats in a population from the region of Cusco (Peru). Data were gathered via a cross-sectional interview-based survey in 2016, using random-route sampling. For up to 542 cases, we regressed a scale combining performed behaviors and intentions on psychological constructs, for the entire and sub-samples (n > 179, allowing to detect an R2 of 10% with a power of 80% at p = 0.05). Behavior-specific evaluations—particularly perceived feasibility (β = 0.355), descriptive norms (β = 0.267), and cost-benefit evaluations (β = 0.235)—can explain most of the variance (44% with a total R2 = 61%). Furthermore, trust in specific sources (β = 0.106), general trust (β = 0.098), and negative attitudes toward nature (β = 0.077) are positively related to adaptation, particularly regarding public behaviors (supporting community projects and policies). However, evaluations directly related to CC, such as risk perception (β = 0.010) or how much a behavior helps prevent damage (adaptation efficacy, β = −0.042)), do not explain adaptation, except for an effect of adaptation efficacy on changing daily behaviors. Experiences with and expectations of CC consequences are mostly unrelated to adaptation. However, worries about such events are correlated with adaptation (r between 0.097 and 0.360). We conclude that, to promote adaptation behaviors in this region, the focus should be on the characteristics of the behavior performance (e.g., its costs or feasibility), not on the expected risks of extreme events because of CC. [ABSTRACT FROM AUTHOR]

Climate change is a global phenomenon impacting ecosystems, economies, and livelihoods. This research carried out in Bol in the Lake Province of Chad, a region heavily affected by climate change, aims to analyze the gender-differentiated impacts of this phenomenon. It was carried out using the rapid analysis and participatory planning (RAPP) method and structural analysis for social systems (SAS2). Meteorological and socioeconomic data were collected through interviews, household surveys, and focus groups. The results indicate variability in rainfall, with a slight downward trend and an increase in temperature. The women identified an increase in the cost of living, human and material losses, warmer housing, and health problems as socioeconomic socioeconomic consequences of climate change. Their coping strategies include community self-help, humanitarian aid, and welfare activities. Obstacles to full participation in the search for solutions include access to education, low decision-making power, and political representation. This research enriches our understanding of the interactions between gender, climate change, adaptation, and inclusive policy importance. [ABSTRACT FROM AUTHOR]

According to current forecasts, global heating is likely to exceed 2.8 °C by the end of this century. This makes substantial adaptation measures necessary to secure a broad basis for livelihood provision and the conservation of biodiversity. While the implementation of top-down and technocratic adaptation efforts predominates, related adaptation shortcomings of a socio-economic and ecological nature are becoming more and more apparent. Community-based adaptation (CBA), with its participatory, inclusive and needs-based bottom-up approach, offers a promising and powerful alternative. This article uses a semi-systematic literature review approach to screen the current literature and identify core issues of CBA. Linking communality, locality, multidimensionality, power imbalances, transformative potential, localisation, the triad of adaptation metrics and nature-based adaptation to corresponding potential actions for practical implementations provides a more holistic conceptualisation and broadens the horizons for further learning, research and improved applications. [ABSTRACT FROM AUTHOR]

The aim of this communication article is to present a successful irrigation advisory scheme on the island of Crete (Greece) provided by the Hellenic Agricultural Organization (ELGO DIMITRA), which is well adapted to the different needs of farmers and water management agencies. The motivation to create this advisory scheme stems from the need to save water resources while ensuring optimal production in a region like Crete where droughts seem to occur more and more frequently in recent years. This scheme/approach has three different levels of implementation (components) depending on the spatial level and end-users' needs. The first level concerns the weekly irrigation bulletins in the main agricultural areas of the island with the aim of informing farmers and local water managers about crop irrigation needs. The second level concerns an innovative digital web-based platform for the precise determination of the irrigation needs of Crete's crops at a parcel level as well as optimal adaptation strategies in the context of climate change. In this platform, important features such as real-time meteorological information, spatial data on the cultivation type of parcels, validated algorithms for calculating crop irrigation needs, an accurate soil texture map derived from satellite images, and appropriate agronomic practices to conserve water based on cultivation and the geomorphology of a farm are considered. The third level of the proposed management approach includes an open-source Internet of Things (IoT) intelligent irrigation system for optimal individual parcel irrigation scheduling. This IoT system includes soil moisture and atmospheric sensors installed on the field, as well as the corresponding laboratory soil hydraulic characterization service. This third-level advisory approach provides farmers with specialized information on the automated irrigation system and optimization of irrigation water use. All the above irrigation advisory approaches have been implemented and evaluated by end-users with a very high degree of satisfaction in terms of effectiveness and usability. [ABSTRACT FROM AUTHOR]

This study investigates the cooling potential of vegetation in rural landscapes of the Czech Republic to mitigate heat-related issues. Using remote sensing, the Cooling Capacity Index (CCI) is assessed to measure green spaces' ability to lower air temperatures using evapotranspiration and shading. Landsat 8/9 and meteorological data are utilised, with CCI calculated based on vegetation cover, albedo, and evapotranspiration. Our results demonstrate significant variations in cooling capacity across different land use types. Forests exhibited the highest cooling potential, while urban areas, characterised by heat-absorbing materials, displayed the least. We analysed temporal and spatial variations in cooling capacity using various visualisation tools and validated the results against the InVEST software (v3.14.0). This study highlights the effectiveness of remote sensing in quantifying ecosystem functions, particularly the cooling services provided by vegetation. Our findings emphasise the crucial role of vegetation in mitigating urban heat islands and addressing climate change. This research provides valuable insights for developing climate change adaptation strategies in rural landscapes. [ABSTRACT FROM AUTHOR]

Soil-based emissions from land conversions are often overlooked in climate planning. The objectives of this study were to use quantitative data on soil-based greenhouse gas (GHG) emissions for the state of Georgia (GA) (USA) to examine context-specific (temporal, biophysical, economic, and social) climate planning and legal options to deal with these emissions. Currently, 30% of the land in GA has experienced anthropogenic land degradation (LD) primarily due to agriculture (64%). All seven soil orders were subject to various degrees of anthropogenic LD. Increases in overall LD between 2001 and 2021 indicate a lack of land degradation neutrality (LDN) in GA. Besides agricultural LD, there was also LD caused by increased development through urbanization, with 15,197.1 km2 developed, causing midpoint losses of 1.2 × 1011 kg of total soil carbon (TSC) with a corresponding midpoint social cost from carbon dioxide (CO2) emissions (SC-CO2) of USD $20.4B (where B = billion = 109, $ = U.S. dollars (USD)). Most developments occurred in the Metro Atlanta and Coastal Economic Development Regions, which indicates reverse climate change adaptation (RCCA). Soil consumption from developments is an important issue because it limits future soil or forest carbon (C) sequestration potential in these areas. Soil-based emissions should be included in GA's carbon footprint. Understanding the geospatial and temporal context of land conversion decisions, as well as the social and economic costs, could be used to create incentives for land management that limit soil-based GHG emissions in a local context with implications for relevant United Nations (UN) initiatives. [ABSTRACT FROM AUTHOR]

In Nepal, climate change is projected to cause a rapid increase in air temperature, erratic rainfalls, and other changes that could negatively impact agricultural productivity. Given the crucial role of agriculture in household income and consumption, Nepal is particularly vulnerable to these impacts. Organic farming has the potential to enhance environmental protection and contribute positively to climate change mitigation and adaptation. This study aims to identify suitable crops for individual wards within the Dolakha district under changing climatic conditions. The EcoCrop model was applied to crops pre-selected by local small farmers to assess their suitability under both current and projected climate conditions in 2050. According to the model, the most successful crops under both current and future climate conditions were beans and colocasia, garlic, local radish, and finger millet. The modeling results were then compared to the preferences of local farmers as revealed through a questionnaire survey. Most crops selected by the model were also selected as suitable by local farmers, with beans being the exception. These findings have the potential to assist local stakeholders, including farmers, planners, and local authorities, in promoting successful organic farming by selecting suitable crops, thereby aiding the region in better adapting to expected climate change. [ABSTRACT FROM AUTHOR]

Olive cultivation, an icon of Mediterranean agriculture, economy, and cultural heritage, faces significant challenges due to climate change and soil degradation. Climate projections indicate that altered precipitation patterns, rising temperatures, and increased frequency of extreme weather events will adversely affect olive tree growth, fruit quality, and yield. This review provides a novel perspective on addressing these challenges through both long-term and short-term adaptation strategies, emphasizing innovative products, advanced technologies, and practical solutions that must work synergistically and be tailored to regional conditions. Long-term practices refer to proactive strategies for enduring climate resilience, including cover cropping, mulching, soil amendments, and breeding programs which enhance soil health, improve water retention, and increase the trees' resilience. Short-term strategies focus on immediate impacts, offering immediate stress relief and enhanced plant physiological responses, including optimized irrigation systems, pruning management, particle coating films, biostimulants, and plant growth regulators. The review underscores the importance of aligning agricultural practices with sustainability goals and evolving environmental policies and the education of farmers and policymakers. By integrating adaptive practices and technological advancements, the olive sector can better address climate challenges, contribute to global food security, and advance environmental sustainability. [ABSTRACT FROM AUTHOR]

The increase in global temperatures over the past few decades due to greenhouse gas emissions has raised concerns and necessitated further research in climate change mitigation and adaptation. Methane is a prominent greenhouse gas that significantly contributes to climate change, with a substantial amount generated through fermentation processes occurring in the rumen of ruminant animals. The potential of plant secondary metabolites, especially those derived from tannin-rich plants, warrants investigation to modify rumen fermentation and mitigate methane emissions in livestock diets. The objective of this study was to assess the impact of extracts obtained from green and black tea waste on rumen fermentation dynamics and gas (methane) production, utilizing in vitro methods. For this purpose, rumen fluid was collected from two fistulated sheep and subjected to three treatments: (1) a basal diet (control), (2) a basal diet + green tea waste extract (5% of dry matter), (3) a basal diet + black tea waste extract (5% of dry matter). The study assessed the effects of incorporating extracts from green and black tea waste on various parameters, including digestibility, protozoa population, ammonia nitrogen levels, volatile fatty acids, and methane gas production following a 24-h incubation period. Statistical analysis of the data was conducted using SAS software within a completely randomized design framework. The findings indicated that the addition of green and black tea waste extracts significantly decreased methane gas production (p < 0.05), protozoa count (p < 0.05), and ammonia nitrogen concentrations in rumen fluid (p < 0.05) when compared to the control group. The addition of green and black tea waste extracts has significantly altered the concentration of VFAs in rumen fluid (p < 0.05). Specifically, the addition of green tea waste extract has led to a highly significant reduction in acetic acid, (p < 0.01) and the addition of both extracts has resulted in a significant increase in propionic acid (p < 0.05). Consequently, the results suggest that the inclusion of green and black tea waste extracts in livestock diets may effectively mitigate methane emissions in the rumen, thereby reducing feed costs and reducing environmental pollution. [ABSTRACT FROM AUTHOR]

This study investigated the relatively unexplored topic of the collective memory of environmental disasters from a psychosocial perspective. To achieve this, we conducted an exploratory case study on the Vaia storm, which hit the Italian Alps in October 2018, causing significant social and ecological damage. We carried out thirteen in-depth semi-structured interviews with members of the enunciatory community of Vaia as follows: groups of people who either experienced the disaster firsthand, studied it, or had a particular interest in it. Through a thematic analysis of the interview transcripts, the four following key themes emerged that illustrate the forms taken by the collective memory of the disaster: (1) a sensory dimension; (2) an emotional dimension; (3) a narrative dimension; and (4) a material dimension. Our findings suggest a non-anthropocentric, hybrid understanding of collective memory, aligning with emerging concepts in the psychosocial literature on the emotional impacts of environmental disasters. We also advance potential future research questions at the intersection of memory studies, resilience, and sustainability and stress the practical implications of collective memory in fostering sustainable practices and enhancing climate resilience. [ABSTRACT FROM AUTHOR]

This study quantitatively evaluates the effectiveness of three main options for the adaptation of crop farming to climate change (i.e., shift of planting dates, increase/addition of irrigation, and resilient hybrids/cultivars) in Greece, a country in southern Europe. The potential effect of each option on the yields of several crops in all Greek regions is estimated for 2021–2040 and 2041–2060 and compared with those under the historical local climate of 1986–2005, by using agronomic and statistical regression models, and data from different climatic simulations and climate change scenarios. Our results reveal that all the adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021–2040 when for many regions and crops more than half of the losses can be compensated for. Notably, in some cases during this period, the measures examined resulted in crop yields that are higher than those under the historical climate. However, the effectiveness of the measures diminished significantly in 2041–2060 under very adverse climate change conditions, highlighting the dynamic nature of adaptation. Assessing the effectiveness of combined adaptation options and evaluating additional criteria (e.g., feasibility) represent essential areas for future research. [ABSTRACT FROM AUTHOR]

Climate change poses a significant risk to the long-term viability of smallholder agriculture in developing countries. Climate change has a direct effect on agricultural output, ultimately impacting the food security of smallholder farmers. However, the link between climate change and food security in developing countries is underexplored. This article investigates the effect of climate change adaptation strategies on household food security. A survey was conducted among 400 smallholder farmers in the uMkhanyakude district of KwaZulu-Natal using a stratified random sampling procedure. Survey data were analysed using descriptive statistics. The findings indicate that factors such as access to credit, access to government funding, and participation in agricultural groups play an important role in supporting adaptation to climate change. Adaptation to climate change is associated with reduced levels of food insecurity. These results underscore the important role of climate change adaptation in enhancing household food security. The study recommends that programmes that target smallholder farmers should focus on enhancing the adaptive capacity of smallholder farmers. [ABSTRACT FROM AUTHOR]

Exploring the response of spatial and temporal characteristics of ecological quality change to aridity on the Qinghai–Tibet Plateau (QTP) can provide valuable information for regional ecological protection, water resource management, and climate change adaptation. In this study, we constructed the Remote Sensing Ecological Index (RSEI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on the Google Earth Engine (GEE) platform with regional characteristics and completely analyzed the spatial and temporal variations of aridity and ecological quality on the QTP in the years 2000, 2005, 2010, 2015, and 2020. Additionally, we explored the responses of ecological quality to aridity indices at six different time scales. The Mann–Kendall test, correlation analysis, and significance test were used to study the spatial and temporal distribution characteristics of meteorological aridity at different time scales on the QTP and their impacts on the quality of the ecological environment. The results show that the ecological environmental quality of the QTP has a clear spatial distribution pattern. The ecological environment quality is significantly better in the south-east, while the Qaidam Basin and the west have lower ecological environment quality indices, but the overall trend of environmental quality is getting better. The Aridity Index of the QTP shows a differentiated spatial and temporal distribution pattern, with higher Aridity Indexes in the north-eastern and south-western parts of the plateau and lower Aridity Indexes in the central part of the plateau at shorter time scales. Monthly, seasonal, and annual-scale SPEI values showed an increasing trend. There is a correlation between aridity conditions and ecological quality on the QTP. The areas with significant positive correlation between the RSEI and SPEI in the study area were mainly concentrated in the south-eastern, south-western, and northern parts of the QTP, where the ecological quality of the environment is more seriously affected by meteorological aridity. [ABSTRACT FROM AUTHOR]

The present paper reviews the hot topics at the 18th Conference on Sustainable Development of Energy, Water, and Environment Systems (SDEWES) held from 24 to 29 September 2023 in Dubrovnik, Croatia. The selected papers, including aspects of biomass energy application, energy saving in building, low-carbon development and climate change adaptation, hybrid application of sustainable energy, energy storage system, and other measures in sustainable development, are published in this Special Issue of Energies. The 12 selected papers in Energies and corresponding literature that relates to the above topics and has been published in the most recent year are reviewed. In particular, diversifying energy applications are trending towards comprehensive integration with more efficient clean energy capacities for global energy procurement, which can also offer increased resilience and sustainability, reduce reliance on fossil fuels, mitigate environmental impacts, and enhance energy security through a mix of renewable sources and innovative technologies. Meanwhile, energy storage has become increasingly important, which can manage the intermittent of renewable energy, stabilize the grid, and improve energy security and resilience against disruptions. In addition to conventional solar energy storage, biomass energy storage, building energy storage, water storage, etc., can also reduce the overall energy costs, deferring investments in additional transmission infrastructure. [ABSTRACT FROM AUTHOR]

A significant number of people, either seasonally or permanently, migrate from the Thar Desert in Pakistan each year due to droughts caused by climate change. This study aims to investigate the determinants and consequences of these migration decisions, examine the effectiveness of migration as a climate adaptation strategy, and identify challenges in adapting to these changes. Data were gathered from 400 migrated households in the Mithi sub-district. A mixed-method approach was used, combining qualitative and quantitative methods. The findings revealed that threats to the standard of living, including lack of food and clean drinking water, unemployment, and limited educational and medical opportunities, were the primary reasons for permanent and temporary migration from ancestral locations. Migration significantly impacted the origin and destination regions, with positive or negative effects. Specifically, migrants identified various consequences for both the origin and destination communities, including population decline (63%), changes in age structure, increased demand for housing, economic fluctuations (73%), alterations in healthcare services, and increased psychological stress (77%). The study also revealed that individuals who migrated from the Thar Desert experienced improved conditions compared to their previous location, such as diversification of income sources, increased job stability, access to clean water and food, reduced health risks, and overall improvements in their living conditions. However, the destination communities faced significant challenges due to widespread resource depletion and environmental deterioration. Migrants encountered barriers to developing resilient livelihoods in destination areas, including lack of proper knowledge and information, institutional and government issues, environmental and technological challenges, and social and cultural issues. The study highlights the urgent need for comprehensive policies and sustainable solutions to address the root causes of migration and support the resilience of vulnerable populations. [ABSTRACT FROM AUTHOR]

Investigations into the impacts of climate change on water conservation capacity in the upper Mekong River Basin (UMRB) are important for the region's sustainability. However, quantitative studies on isolating the individual contribution of climate change to water conservation capacity are lacking. In this study, various data-driven SWAT models were developed to quantitatively analyze the unique impact of climate change on water conservation capacity in the UMRB. The results reveal the following: (1) From 1981 to 2020, the annual water conservation capacity ranged from 191.6 to 392.9 mm, showing significant seasonal differences with the values in the rainy season (218.6–420.3 mm) significantly higher than that in the dry season (−57.0–53.2 mm). (2) The contribution of climate change to water conservation capacity is generally negative, with the highest contribution (−65.2%) in the dry season, followed by the annual (−8.7%) and the rainy season (−8.1%). (3) Precipitation, followed by evaporation and surface runoff, emerged as the critical factor affecting water conservation capacity changes in the UMRB. This study can provide insights for water resources management and climate change adaptations in the UMRB and other similar regions in the world. [ABSTRACT FROM AUTHOR]

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The sustainability of global agricultural produce supply chains is crucial for ensuring global food security, fostering environmental protection, and advancing socio-economic development. This study integrates bibliometric analysis, knowledge mapping, and the ABCDE framework to conduct a comprehensive qualitative and quantitative analysis of 742 relevant articles from the Web of Science core database spanning January 2009 to July 2023. Initially, bibliometric analysis and knowledge mapping reveal the annual progression of research on the sustainability of global agricultural produce supply chains, the collaborative networks among research institutions and authors, and the geographic distribution of research activities worldwide, successfully pinpointing the current research focal points. Subsequently, the ABCDE framework, constructed from the quantitative findings, helps us identify and comprehend the antecedents, barriers and challenges, impacts, and driving forces affecting the sustainability of these supply chains. The study identifies globalization and technological advancement as the primary forces shaping the sustainability of agricultural produce supply chains, despite them also posing challenges such as resource constraints and environmental pressures. Moreover, the application of innovative technologies, the optimization of organizational models, and active stakeholder engagement are key to propelling supply chains toward more sustainable development, exerting a profound impact on society, the environment, and the economy. In conclusion, this study suggests future research directions. The integrated methodology presented offers new perspectives and deep insights into the complexities of sustainable global agricultural produce supply chains, demonstrating its potential to foster knowledge innovation and practical applications, providing valuable insights for academic research and policy formulation in this domain. [ABSTRACT FROM AUTHOR]

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs' contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities. [ABSTRACT FROM AUTHOR]

The future of humanity depends on successfully adapting key cropping systems for food security, such as corn (Zea mays L.), to global climatic changes, including changing air temperatures. We monitored the effects of climate change on harvested yields using long-term research plots that were established in 2001 near Fort Collins, Colorado, and long-term average yields in the region (county). We found that the average temperature for the growing period of the irrigated corn (May to September) has increased at a rate of 0.023 °C yr−1, going from 16.5 °C in 1900 to 19.2 °C in 2019 (p < 0.001), but precipitation did not change (p = 0.897). Average minimum (p < 0.001) temperatures were positive predictors of yields. This response to temperature depended on N fertilizer rates, with the greatest response at intermediate fertilizer rates. Maximum (p < 0.05) temperatures and growing degree days (GDD; p < 0.01) were also positive predictors of yields. We propose that the yield increases with higher temperatures observed here are likely only applicable to irrigated corn and that irrigation is a good climate change mitigation and adaptation practice. However, since pan evaporation significantly increased from 1949 to 2019 (p < 0.001), the region's dryland corn yields are expected to decrease in the future from heat and water stress associated with increasing temperatures and no increases in precipitation. This study shows that increases in GDD and the minimum temperatures that are contributing to a changing climate in the area are important parameters that are contributing to higher yields in irrigated systems in this region. [ABSTRACT FROM AUTHOR]

The climate crisis is widely regarded as the most significant challenge facing humanity in the 21st century In light of these concerns, Pope Francis announced the encyclical Laudato Si' in 2015, which conveyed both concern and hope in the fight to mitigate and adapt to climate change. This is further reinforced by the publication in 2023 of the encyclical Laudate Deum, which once again emphasises the relationship between religion and ecology. In this regard, an educational intervention was conducted to ascertain the extent of knowledge and acceptance of these texts and their premises among first-year high-school students. The action comprised an initial classroom analysis of the encyclicals Laudato Si' and Laudate Deum, during which various passages were read and commented on. This was followed by a second phase, in the form of a focus group, during which the students, in groups of five, were invited to share their perspectives on the relationship between faith and environmental stewardship. The preliminary study was conducted with a sample of 90 students in the second year of Baccalaureate from a secondary school in Barcelona, Spain. [ABSTRACT FROM AUTHOR]

Global climate change is intensifying and extreme weather events are occurring frequently, with far-reaching impacts on agricultural production. The Songnen Plain, as an important maize production region in China, faces challenges posed by climate change. This study aims to explore the effects of climate extremes on maize yield and provide a scientific basis for the adaptation of agriculture to climate change in this region. The study focuses on the spatial and temporal evolution characteristics of climate extremes during the maize reproductive period from 1988 to 2020 in the Songnen Plain and their impacts on maize yield. Daily temperature and precipitation data from 11 meteorological stations were selected and combined with maize yield information to assess the spatial and temporal trends of extreme climate indices using statistical methods such as the moving average and Mann–Kendall (M-K) mutation test. Pearson correlation analysis and a random forest algorithm were also used to quantify the degree of influence of extreme climate on maize yield. The results showed that (1) the extreme heat and humidity indices (TN90p, TX90p, CWD, R95p, R10, and SDII) tended to increase, while the cold indices (TN10p, TX10p) and the drought indices (CDD) showed a decreasing trend, suggesting that the climate of the Songnen Plain region tends to be warmer and more humid. (2) The cold indices in the extreme temperature indices showed a spatial pattern of being higher in the north and lower in the south and lower in the west and higher in the east, while the warm indices were the opposite, and the extreme precipitation indices showed a spatial pattern of being higher in the east and lower in the west. (3). Both maize yield and trend yield showed a significant upward trend. Maize meteorological yield showed a fluctuating downward trend within the range of −1.64~0.79 t/hm2. During the 33 years, there were three climatic abundance years, two climatic failure years, and the rest of the years were normal years. (4) The cold index TN10p and warm indices TN90p and CWD were significantly correlated with maize yield, in which TN90p had the highest degree of positive correlation with yield, and in the comprehensive analysis, the importance of extreme climatic events on maize yield was in the order of TN90p, TN10p, and CWD. This study demonstrates the impact of extreme climate indices on maize yield in the Songnen Plain, providing a scientific basis for local agricultural management and decision-making, helping to formulate response strategies to mitigate the negative effects of extreme climate, ensure food security, and promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Within the Earth's terrestrial environment, evapotranspiration significantly contributes to the hydrological cycle, accounting for around 80% of the precipitation on landmasses to be reintroduced into the atmosphere. This mechanism profoundly affects the distribution and availability of surface water resources throughout the ecosystem. Gaining insight into the factors influencing local evapotranspiration fluctuations in response to varying climatic and vegetative scenarios is crucial for effective water management strategies and rehabilitating ecosystem resilience. To this end, our study focuses on the Jing River Basin in the Loess Plateau, utilizing multi-source remote sensing data and climatic information to investigate the spatiotemporal dynamics of evapotranspiration from 1984 to 2018 through the application of the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model. Our research results indicate a general ascending tendency in evapotranspiration across the investigated region, demonstrating a notably discernible escalation at a pace of approximately 3.11 mm/year (p < 0.01), with an annual vegetation ET volume reaching 533.88 mm. Across different vegetation types in the Jing River Basin between 1984 and 2018, the mean yearly ET was observed to be highest in forests (572.88 mm), followed by croplands (564.74 mm), shrublands (536.43 mm), and grasslands (503.42 mm). The leaf area index (LAI) demonstrated the strongest partial correlation with ET (r = 0.35) and contributed the most significantly to the variation in ET within the Jing River Basin (0.41 mm/year). Additionally, LAI indirectly influences ET through its impact on vapor pressure deficit (VPD), precipitation (Pre), and temperature (Temp). Radiation is found to govern most ET changes across the region, while radiation and precipitation notably affected ET by modulating air temperature. In summary, these radiant energy changes directly affect the evaporation rate and total evapotranspiration of surface water. It provides important support for understanding how evapotranspiration in the Jing River Basin is adjusting to climate change and increased vegetation cover. These findings serve as a theoretical foundation for devising sustainable vegetation restoration strategies to optimize water resource utilization within the region. [ABSTRACT FROM AUTHOR]

Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change. [ABSTRACT FROM AUTHOR]

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics. [ABSTRACT FROM AUTHOR]

Climate change increases the vulnerability of relict forests. To address this problem, regional Forest Services require silvicultural and conservation actions to designate specific forest management alternatives. In this context, the main objective of this study was to develop a methodology to map complex Abies pinsapo forest typologies using multispectral and low-density airborne LiDAR data and machine learning. Stand density, species composition and cover were used to identify seven forest typologies. Random forest resulted as the more accurate model (OA = 0.62; Kappa = 0.43) to classify those types based on multispectral and LiDAR data, although showing a moderate model performance. Classification performance showed great differences between forest types with better results for the uneven-aged stands compared to the even-aged and two-aged stands. The developed typology was applied to supply local forest managers with more accurate forest maps that can be used to improve forest management plans. The typology proposed is easy to apply in forest management practices since it only uses as input the diameter at breast height, tree density and specific composition. The study demonstrated the potential of low-density LiDAR data combined with spectral information from high-resolution orthophotos to predict the structural characteristics of complex forest typologies. [ABSTRACT FROM AUTHOR]

This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM). The approach demonstrated improvements in modeling coastal river dynamics, particularly during extreme events like Hurricane Matthew. The coupled model successfully captured tidal influences, storm surge effects, and complex river–river interactions that the standalone NWM missed. The approach revealed more accurate representations of peak discharge timing and magnitude as well as water storage and release in coastal floodplains. However, we also identified challenges in reconciling variable representations between hydrological and hydraulic models. This work not only enhances the understanding of coastal–riverine interactions but also provides valuable insights for the development of next-generation hydrological models. The improved modeling capabilities have implications for flood forecasting, coastal management, and climate change adaptation in vulnerable coastal areas. [ABSTRACT FROM AUTHOR]

Climate change, a pressing global concern, poses significant challenges to agricultural systems worldwide. Among the myriad impacts of climate change, the cultivation of kiwifruit trees (Actinidia spp.) faces multifaceted challenges. In this review, we delve into the intricate effects of climate change on kiwifruit production, which span phenological shifts, distributional changes, physiological responses, and ecological interactions. Understanding these complexities is crucial for devising effective adaptation and mitigation strategies to safeguard kiwifruit production amidst climate variability. This review scrutinizes the influence of rising global temperatures, altered precipitation patterns, and a heightened frequency of extreme weather events on the regions where kiwifruits are cultivated. Additionally, it delves into the ramifications of changing climatic conditions on kiwifruit tree physiology, phenology, and susceptibility to pests and diseases. The economic and social repercussions of climate change on kiwifruit production, including yield losses, livelihood impacts, and market dynamics, are thoroughly examined. In response to these challenges, this review proposes tailored adaptation and mitigation strategies for kiwifruit cultivation. This includes breeding climate-resilient kiwifruit cultivars of the Actinidia species that could withstand drought and high temperatures. Additional measures would involve implementing sustainable farming practices like irrigation, mulching, rain shelters, and shade management, as well as conserving soil and water resources. Through an examination of the literature, this review showcases the existing innovative approaches for climate change adaptation in kiwifruit farming. It concludes with recommendations for future research directions aimed at promoting the sustainability and resilience of fruit production, particularly in the context of kiwifruit cultivation, amid a changing climate. [ABSTRACT FROM AUTHOR]

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil's water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC. [ABSTRACT FROM AUTHOR]

Eco-spatial indices are commonly used tools to improve the quality of the environment in cities. Initially modelled on the Berlin BAF, indices have evolved to address current challenges, particularly climate change adaptation. The Ratio of Biologically Vital Areas (RBVA), introduced in Poland in the mid-1990s, is an early planning tool for implementing Nature-based Solutions (NbSs) at the site level. This research aimed to assess the effectiveness of the RBVA in Poland compared to its counterparts in Oslo and Malmö. The study employed a serious simulation game developed under the Norwegian-funded CoAdapt project, testing six development scenarios, varied in terms of applied NbSs, for a typical multi-family housing estate. The adaptive potential of the tested scenarios was assessed based on the values of five environmental parameters calculated in the game; that is, air temperature, oxygen production, CO2 sequestration, rainwater harvesting, and biodiversity. The findings revealed that the RBVA, in its current form, has limited effectiveness in supporting climate adaptation. Its two-dimensional nature makes it less effective than the more comprehensive Green Factors used in Oslo and Malmö. The research presented in the article proves that better-constructed indices result in the efficiency of applied NbSs and consequently better adaptation to climate change. [ABSTRACT FROM AUTHOR]

Extreme climatic conditions cause a decrease in ecosystem services, the disruption of the ecological balance, and damage to human populations, especially in areas with socially vulnerable groups. Nature-based solutions applying blue-green infrastructure (BGI) against these negative impacts of climate change have an important role in planning sustainable cities. This study aims to identify priority areas and develop scenarios and strategies for spatial planning to understand the tradeoffs in approaches and to maximize the benefits of ecosystem services provided by BGI in cities with arid and semi-arid climates, using Phoenix, Arizona, a swiftly urbanizing city in the Sonoran Desert, as the study area. Using GIS-based multi-criteria decision-making techniques and the Green Infrastructure Spatial Planning model integrated with the city's existing water structures, this study is conducted at the US census scale. The hotspots for BGI are mapped from the combined GIS-based multi-criteria evaluation and expert stakeholder-driven weighting. In the hotspots where priority areas for BGI in Phoenix are identified, the city center area with a high density of impervious surfaces is identified as the highest priority area. It is revealed that social vulnerability and environmental risks (flooding, heat) have a positive correlation in Phoenix, and stormwater management and the urban heat island are the criteria that should be considered first in BGI planning. [ABSTRACT FROM AUTHOR]

The aim of this study is to synthesise the scientific evidence on the implications of urban and rural environments on older adults' adaptation strategies to heatwaves. Methods: The methodology follows PRISMA guidelines, which involves a systematic search of the scientific literature in selected electronic databases, including Web of Science and Scopus, to assist in the selection, identification and retention of studies. Results: The search identified 4991 potentially eligible articles, of which 17 met the criteria, mainly from developed countries, emphasising a multidisciplinary approach and moderate to low quality. Urban and rural environments influence older adults' adaptation strategies to heatwaves through the adequacy of the physical environment of the dwelling (design, materials, equipment) and the neighbourhood (green spaces, residential density, land use), the social environment (support network and type of assistance) and risk management (protective measures and heat warning systems). At the individual level, adaptation to heatwaves is determined by age, gender, health status, socio-economic status and social isolation. Discussion and conclusions: Differences in perceptions of heat adaptation were found according to location, typology and environmental characteristics, especially in developing countries. The findings contribute to promoting the adoption of protective measures, the design of heat warning systems, and the planning of friendly and resilient cities and rural communities to support the adaptation of an ageing society to the climate warming. [ABSTRACT FROM AUTHOR]

Regenerative design is being increasingly explored in urban environments to counteract and adapt to the changing climate and degradation of ecosystems. A critical success factor for the implementation of regenerative design is the evaluation of urban and building systems in relation to ecological performance and benefits. In biological ecosystems, the availability of high-quality energy, called exergy, and structural order can be used as indicators of the efficiency of on-going ecological processes. Structural order refers to the organization and systematic arrangements of biotic and abiotic elements within an ecosystem based on the available space and interactions with the goal to form a functional system. Ecological processes use the available exergy and generate ecosystem services (ESs) upon which human survival and that of other living organisms depend. In this article, structural order and ESs generation are proposed as indicators for exergy efficiency and accumulation in building systems, respectively, which can evaluate to what extent they are ecologically functional and regenerative. Based on this insight, design strategies are derived from the functioning of ecosystems that describe how buildings could become habitats that host living, non-living, and hybrid systems with optimized thermodynamic efficiency and that can generate ESs. This research suggests that when buildings improve structural order (an ecological concept) and implement ESs generating processes similar to biological ecosystems, they can facilitate regenerative processes more effectively that consume and generate resources and, with this, destroy but also accumulate exergy. [ABSTRACT FROM AUTHOR]