Search Results (3,767)

Climate change constitutes a growing challenge for high-Andean communities worldwide, whose livelihoods depend directly on agriculture, livestock farming, and the stability of local ecosystems. In this context, the study seeks to understand the construction of social imaginaries among agricultural producers regarding the dynamics of climate variability, with the aim of analyzing both the vulnerabilities and adaptive capacities that emerge in their everyday practices. Based on a qualitative approach, supported by 32 interviews with key informants from 16 communities, 04 focus groups, and documentary analysis, field data were collected and processed using Atlas.ti software. The testimonies of community members from Cojata, Puno–Peru, revealed social imaginaries and collective responses linked to this phenomenon. The findings show feelings of concern and uncertainty, diverse interpretations of climate change dynamics, reconfiguration of cultural meanings, and the deployment of hybrid adaptation strategies that combine ancestral knowledge with contemporary resources. Overall, these findings show that social imaginaries play a central role in how communities face the climate crisis, revealing both the persistence of structural inequalities and the need to strengthen intercultural territorial policies that recognize local knowledge, promote communal cooperation, and foster a horizon of resilience and climate justice. Full article

Global water management faces a critical challenge: whilst scholarly consensus recognises that multiple, interacting drivers fundamentally shape water availability and management capacity, operational governance frameworks fail to systematically incorporate this understanding. This disconnect is particularly acute in public good contexts where incomplete knowledge, diverse stakeholder values, and statutory planning mandates create distinct challenges. Using Australia’s Murray–Darling Basin as a pilot case, this research develops and demonstrates a rapid, policy-relevant heuristic for identifying, prioritising, and incorporating drivers of change in complex socio-ecological water systems. Through structured participatory deliberation with 70 experts spanning research, policy, industry, and community sectors across three sequential workshops and 15 semi-structured interviews, we systematically identified key drivers across environmental, governance, economic, social, and legacy dimensions. A risk and sensitivity assessment framework enabled prioritisation based on impact, vulnerability, and urgency. Climate change, drought, water quality events, and cumulative impacts emerged as the highest-priority future drivers, with climate change acting as a threat multiplier, whilst governance drivers show declining relative significance. Using these methodological innovations, we synthesise the I-PLAN heuristic: five interdependent dimensions (Integrative Knowledge, Prioritisation for Management, Linkages between Drivers, Adaptive Agendas, and Normative Collaboration) that provide water planners with a transferable, operational tool for driver identification and bridging to planning and management in data-sparse contexts. Full article

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks. Full article

Coastal lagoons are highly vulnerable to human and climatic pressures, yet long-term ecological changes remain poorly quantified. We analyzed four decades (1980–2020) of data from fisheries from six lagoons in the Amvrakikos Gulf, Greece, using ecological indicators to assess trophic structure, exploitation status, and ecosystem responses. Cluster analysis of species level fishery production revealed a distinct temporal regime shift in the late 1990s–early 2000s, reflecting a major reorganization of species contributions to total yield. Mean total yield (Y), showed a consistent declining trend across lagoons, ranging from 2.7 ± 2.0 to 7.2 ± 5.0 t km⁻². Primary Production Required (PPR) declined (0.8–1.5 × 10¹⁰g C km⁻² yr⁻¹), while Mean Temperature of the Catch (MTC) increased in five lagoons (19.0–21.4 °C) and remained stable in one (20.0 ± 0.9 °C). Pelagic to demersal (P/D) ratios generally decreased (0.09–1.26), and Q-90 values were variable (0.8–2.2), highlighting site specific ecological dynamics. Short term yield predictions for 2021–2025 ranged from 0.78 to 6.75 t km⁻², remaining comparable to recent historical levels, while the estimated carrying capacities varied from 1.79 to 9.11 t km⁻², reflecting contrasting exploitation states among lagoons. These results demonstrate that multi-indicator, fishery-based analyses provide a robust framework for quantifying ecological change and guiding adaptive management in lagoon ecosystems. Full article

This study presents a validated framework to quantify regional sea-level risk on U.S. coasts by (i) extracting trends and seasonality from satellite altimetry (ADT, GMSL), (ii) learning regional dynamical regimes via PCA-embedded KMeans on gridded ADT time series, and (iii) coupling these regimes with socioeconomic exposure (population, income, ocean-sector employment/GDP) and wetland submersion scoring. Relative to linear and ARIMA/SARIMA baselines, a sinusoid+trend fit and an LSTM forecaster reduce out-of-sample error (MAE/RMSE) across the North Atlantic, North Pacific, and Gulf of Mexico. The clustering separates high-variability coastal segments, and an interpretable submersion score integrates elevation quantiles and land cover to produce ranked adaptation priorities. Overall, the framework converts heterogeneous physical signals into decision-ready coastal risk tiers to support targeted defenses, zoning, and conservation planning. Full article

As the frequency and severity of extreme weather events may increase due to climate change, understanding their impacts on water systems, resources, and infrastructure becomes very important. This study contributes to the growing body of knowledge on how extreme storms and floods disrupt interrelated elements comprising water systems by examining the case of Storm Daniel, which struck the Thessaly region of Greece in September 2023. Using a multi-source approach, including field data, institutional reports, scientific assessments, and publications, the study systematically identifies and categorizes the impacts of the storm and the ensuing flood across surface waters, drinking water supply, and wastewater infrastructure and other water-related systems through various mechanisms. The findings provide an overview of how such extreme storms may affect such systems and reveal widespread, interconnected disruptions that highlight systemic vulnerabilities in both natural and engineered systems, synthesizing these impact pathways. The study presents evidence of poor resilience against extreme events and climate change hazards in water-related infrastructure. Full article

Japan has largely achieved the “first half” of SDG 6—universal access to safe drinking water and sanitation—through decades of intensive investment in water supply and sewerage systems, implementation of the Total Pollutant Load Control System, and stringent regulation of industrial effluents. National indicators show that coverage of safely managed drinking water and sanitation services is nearly 99%, and domestic statistics report high compliance rates for BOD/COD-based environmental standards in rivers, lakes, and coastal waters. Conversely, the “second half” of SDG 6 reveals persistent gaps: ambient water quality (6.3.2) remains at 57% (2023 data), while water stress (6.4.2) is at approximately 21.6%. Furthermore, SDG 6.6.1 shows that 3% of water basins are experiencing rapid changes in surface water area (2020 data), with ecosystems increasingly threatened by hypoxia in enclosed bays and climate-induced vulnerabilities. Drawing on global comparisons, this review synthesizes Japan’s progress toward SDG 6, elucidates the structural drivers for remaining gaps, and proposes policy pathways for a nature-positive water future. Using national statistics (1970–2023) and the DPSIR framework, our analysis confirms that improvements in BOD/COD compliance plateaued around 2002, reinforcing concerns that point-source measures alone are insufficient to address diffuse pollution, groundwater nitrate contamination, and emerging contaminants like PFAS. We propose six strategic directions: (1) climate-resilient water systems leveraging groundwater; (2) smart infrastructure renewal; (3) advanced treatment for emerging contaminants; (4) basin-scale IWRM enhancing transboundary cooperation; (5) data transparency and citizen engagement; and (6) scaled nature-based solutions (NbS) integrated with green–gray infrastructure. The paper concludes by outlining priorities to close the gaps in SDG 6.3 and 6.6, advancing Japan toward a sustainable, nature-positive water cycle. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as a defensible biophysical metric. We employed both a dual-stream deep learning architecture and a Random Forest model to predict 2023 NDVI anomalies across Bangladesh croplands using a 22-year time series (2001–2023) of MODIS vegetation indices, ERA5 climate variables, and static environmental covariates. A spatially aware block cross-validation strategy ensured rigorous, independent performance evaluation. Results demonstrated that the Random Forest model (R² = 0.70, RMSE = 197.03) substantially outperformed the deep learning architecture (R² = 0.02, RMSE = 357.57), explaining 70% of cropland stress variance and enabling early detection of vulnerable areas three months before harvest. Feature importance analysis identified recent climate variables, March precipitation, February NDVI, and vapor pressure deficit as primary vulnerability drivers. Spatial analysis revealed distinct vulnerability patterns, with Natore and Magura districts exhibiting elevated stress consistent with 2023 drought conditions, threatening the productivity of the region’s critical irrigation-dependent rice cultivation. These findings demonstrate that simpler, interpretable models can sometimes outperform complex architectures while providing useful information for early warning systems and precision targeting of climate adaptation interventions. Full article

The continuous degradation of mangrove ecosystems, considered among the most vulnerable worldwide, reveals multiple threats driven by human activities and climate change. In the Peruvian context, particularly in the Tumbes Mangrove ecosystem, these pressures are intensified by the absence of integrated spatial and educational infrastructures capable of supporting conservation efforts while engaging local communities. In response, this research proposes a Sustainable Interpretation Center for Conservation and Environmental Education in Ecologically Sensitive Areas of the Tumbes Mangrove, Peru. The methodology includes climate data analysis, identification of local flora and fauna, and site topography characterization, supported by digital tools such as Google Earth, AutoCAD 2025, Revit 2025, and 3D Sun Path. The results are reflected in an architectural proposal that incorporates sustainable materials compatible with sensitive ecosystems, including eco-friendly structural solutions based on algarrobo timber, together with resilient strategies addressing climatic variability, such as lightweight structures, elevated platforms, and passive environmental solutions that minimize impact on the mangrove. Furthermore, the proposal integrates a photovoltaic energy system consisting of 12 solar panels with a unit capacity of 450 W, providing a total installed capacity of 5.4 kWp, complemented by a 48 V LiFePO₄ battery storage system designed to ensure energy autonomy during periods of low solar availability. In conclusion, the proposal adheres to principles of sustainability and energy efficiency and aligns with the Sustainable Development Goals (SDGs) 7, 8, 12, 14, and 15, reinforcing the use of clean energy, responsible tourism, sustainable resource management, and the conservation of marine and terrestrial ecosystems. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

The erratic nature, increasing prevalence, and intensity of extreme meteorological phenomena are forcing researchers and farmers to urgently develop adaptation practices to enhance the resilience of agroecosystems to climate change. It is strategically crucial to identify farming systems that have successfully endured recent climatic disturbances and understand the agroecological attributes that enabled them to resist and/or recover from droughts and hurricanes. This paper describes a number of methodologies utilized by Latin American researchers to assess agroecosystem resilience by estimating the vulnerability and the response capacity of selected farming systems to cope with climatic threats. The methodologies utilize a set of socio-ecological indicators that can be easily evaluated in the field, allowing farmers to determine whether their farms can withstand a drought or a major storm and, based on this information, select agroecological practices able to enhance the resiliency of their farms in preparation for future events. The principles and practices of resilience identified on successful, climate-resistant farms can be shared with thousands of producers, facilitating the broader adoption and scaling up of agroecological adaptation strategies. Full article

Climate change and the resulting projected rise in sea level put densely populated urban communities at risk of river flooding, storm surges, and subsurface flooding. Miami finds itself in an increasingly vulnerable position, as compound inundation seems to be a constant and unavoidable occurrence due to its low elevation and limestone geomorphology. Several recent studies on compound overflows have been conducted in Miami-Dade County. However, in-depth research has yet to be conducted on its economic epicenter. Owing to the lack of resilience to tidal surges and extreme precipitation events, Miami’s infrastructure and the well-being of its population may be at risk of flooding. This study applied HEC-RAS 2D to develop one- and two-dimensional water flow models to understand and estimate Miami’s vulnerability to extreme flood events, such as 50- and 100-year return storms. It used Hurricane Irma as a validation and calibration event for extreme event reproduction. The study also explores novel machine learning metamodels to produce a robust sensitivity analysis for the hydrologic model. This research is expected to provide insights into vulnerability thresholds and inform flood mitigation strategies, particularly in today’s unprecedented and intensified weather events. The study revealed that Miami’s inner bay coastline, particularly the downtown coastline, is severely impacted by extreme hydrometeorological events. Under extreme event circumstances, the 35.4 km² area of Miami is at risk of flooding, with 38% of the areas classified as having medium to extreme risk by FEMA, indicating severe infrastructural and community vulnerability. Full article

Climate change poses a threat to global rice production by increasing the frequency and intensity of extreme weather events. The widespread cultivation of genetically uniform modern varieties has narrowed the genetic base of rice, increasing its vulnerability to these increased pressures. Rice landraces are traditional rice varieties that have been cultivated by farming communities for centuries and are considered crucial resources of genetic diversity. These landraces are adapted to a wide range of agro-ecological environments and exhibit valuable traits that provide tolerance to various biotic stresses, including drought, salinity, nutrient-deficient soils, and the increasing severity of climate-related temperature extremes. In addition, many landraces possess diverse alleles associated with resistance to biotic stresses, including pests and diseases. In addition, rice landraces exhibit great grain quality characters including high levels of essential amino acids, antioxidants, flavonoids, vitamins, and micronutrients. Hence, their preservation is vital for maintaining agricultural biodiversity and enhancing nutritional security, especially in vulnerable and resource-limited regions. However, rice landraces are increasingly threatened by genetic erosion due to widespread adoption of modern high-yielding varieties, habitat loss, and changing farming practices. This review discusses the roles of rice landraces in developing resilient and climate-smart rice cultivars. Moreover, the Pantiange Heigu landrace, cultivated at one of the highest altitudes globally in Yunnan Province, China, has been used as a case study for integrated conservation by demonstrating the successful combination of in situ and ex situ strategies, community engagement, policy support, and value-added development to sustainably preserve genetic diversity under challenging environmental and socio-economic challenges. Finally, this study explores the importance of employing advanced genomic technologies with supportive policies and economic encouragements to enhance conservation and sustainable development of rice landraces as a strategic imperative for global food security. By preserving and enhancing the utilization of rice landraces, the agricultural community can strengthen the genetic base of rice, improve crop resilience, and contribute substantially to global food security and sustainable agricultural development in the face of environmental and socio-economic challenges. Full article

Assessing the vulnerability of species to climate change is currently one of the hot issues in ecology and conservation biology. Although species sensitivity and adaptability play a crucial role in determining species vulnerability to climate change, most studies have only focused on habitat exposure, hindering a comprehensive understanding of species vulnerability to climate change and the implementation of effective conservation actions and policies. Here, we performed a comprehensive evaluation of the species sensitivity, habitat exposure and vulnerability of 122 Chinese Theaceae species and the spatial distribution patterns of their sensitivity, habitat exposure and vulnerability, as well as the effectiveness of China’s protected area network in protecting these species under future climate change. Our analyses suggest that species vulnerability was mainly determined by species sensitivity rather than habitat exposure. In addition, these species generally exhibit a high sensitivity and vulnerability to temperature-related variables, such as the annual mean temperature and temperature annual range, while exhibiting a high exposure to precipitation variables, such as total annual precipitation and precipitation seasonality. Furthermore, our analyses show that the high-vulnerability areas are mainly distributed in western and eastern China. However, no more than 17% of the high-vulnerability areas would be covered by China’s protected area network and no more than 15% of the median- and low-vulnerability areas would be covered by China’s protected area network. These findings can contribute to a new understanding of the vulnerability of the 122 Chinese Theaceae species to future climate change and guide effective conservation prioritizing in a rapidly changing climate. Full article