Search Results (93)

Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

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

This study presents an integrated spatiotemporal assessment of drought conditions in the Thung Kula Ronghai region of Northeastern Thailand from 2001 to 2023. Multiple satellite-derived drought indices, including SPI, SPEI, RDI, and AI, together with NDVI anomalies, were used to detect seasonal and long-term drought dynamics affecting rainfed Hom Mali rice production. The results show that dry season droughts now affect up to 17 percent of the region’s agricultural land in some years, while severe drought zones persist across more than 2.5 million hectares over the 20-year period. In the most recent 5 years, approximately 50 percent of cultivated areas experienced moderate to severe drought conditions. The RDI showed the strongest correlation with NDVI anomalies (r = 0.22), indicating its relative value for assessing vegetation response to moisture deficits. The combined index approach delineated high-risk sub-regions, particularly in central Thung Kula Ronghai and lower Surin, where drought frequency and severity have intensified. These findings underscore the region’s increasing exposure to dry-season water stress and highlight the need for site-specific irrigation development and adaptive cropping strategies. The methodological framework demonstrated here provides a practical basis for improving drought monitoring and early warning systems to support the resilience of Thailand’s high-value rice production under changing climate conditions. Full article

Increasing wildfire activities across the Great Plains has raised concerns about the effectiveness and safety of prescribed fire as a land management tool. This study analyzes wildfire records from 1992 to 2020 to assess spatiotemporal patterns in wildfire risk and evaluate the role of prescribed fires through the combined analysis of wildfire and prescribed fire data. Results show a threefold increase in both wildfire frequency and area burned, with fire size increasing from east to west and frequency rising from north to south. Wildfire seasons are gradually occurring earlier due to climate change. Negative correlation between prescribed fires in spring and wildfires in summer indicated the effectiveness of prescribed fire in mitigating wildfire risk. Drought severity accounted for 51% of the interannual variability in area burned, while grass curing accounted for 60% of monthly variability of wildfires in grasslands. The ratio of wildfire area burned to total area burned (dominated by prescribed fires) declined from over 20% in early March to below 1% by early April. The results will lay a foundation for the development of a localized fire risk assessment tool that integrates various long-term, mid-term, and short-term risk factors, and support more effective fire management in this region. Full article

Climate change refers to a significant and measurable alteration in the climate’s state, evident through shifts in the average and variability of key climate factors. Although the onset of climate change spans several decades, recent studies reveal a concerning intensification that is increasingly driven by anthropogenic activities, with the construction sector emerging as a significant contributor. The present paper investigates climate-conscious innovations within Romania’s construction industry, with a specific focus on the implementation of adaptive strategies. Through a narrative review methodology, this study synthesizes diverse sources, including scientific literature, technical reports, urban policy documents and relevant websites, to map the integration of sustainable construction practices in response to climate pressures. The findings highlight a range of local approaches, including passive design, green infrastructure, and reversible architecture, reflecting Romania’s gradual alignment with broader European environmental objectives. Despite Romania’s relatively low green contribution on a global scale, the country faces significant climate risks, including heatwaves, intense rainfall, and droughts. This evolving climate context necessitates a comprehensive adaptation of architectural practices, construction processes, material selection, and design strategies to mitigate environmental impact and enhance resilience. However, the narrative review approach has inherent limitations, including the potential for selection bias and limited replicability, which constrain the generalizability of the findings. Future research should employ quantitative and empirical methods to validate the effectiveness of climate-adaptive measures in structural engineering. Key areas include the integration of climate-resilient materials, structural performance under climate-induced stressors, and lifecycle carbon assessments of building components. Additionally, further investigation is needed into the development of predictive simulation models that assess the long-term structural impacts of evolving climate scenarios specific to Romania’s geographic and climatic conditions. Full article

The growing frequency and severity of climate-induced disasters—such as floods, heatwaves, droughts, and wildfires—pose significant threats to sustainable development worldwide. Integrating Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) has emerged as a strategy imperative for enhancing societal resilience and protecting developmental gains. This review synthesizes the current knowledge and practice at the intersection of CCA and DRR, drawing on international frameworks, national policies, and local implementation strategies. We assess the role of the Sendai Framework for Disaster Risk Reduction (2015–2030), the Paris Agreement, and the 2030 Agenda for Sustainable Development in promoting policy coherence and multi-level governance. Particular attention is given to the effectiveness of Nature-Based Solutions (NBS), Ecosystem-Based Adaptation (EbA), and community-based approaches that address both climate vulnerabilities and disaster risks while delivering co-benefits for ecosystems and livelihoods. Case studies from regions highly exposed to climate-related hazards, including the Global South and Europe, illustrate how integrated approaches are operationalized and what barriers persist, including institutional silos, limited financing, and data gaps. For example, Bangladesh has achieved over a 70% reduction in flood-related mortality, while Kenya’s drought-resilient agriculture has increased food security by 35% in affected regions. The review highlights best practices in risk-informed planning, participatory decision-making, and knowledge co-production, emphasizing the need for inclusive governance and cross-sector collaboration. By critically examining the synergies and trade-offs between adaptation and risk reduction, this paper offers a pathway to more resilient, equitable, and sustainable development. It concludes with recommendations for enhancing integration at the policy and practice levels, supporting both immediate risk management and long-term transformation in a changing climate. Full article

The water issue is undoubtedly one of the most fundamental challenges and controversial issues of the current century. These days, the best options for managing water resources can be chosen by considering several indexes, such as political, social, and environmental criteria. The overall goal of this research is to propose an integrated model of fuzzy Shannon entropy and Fuzzy Additive Ratio Assessment (ARAS) that uses risk indexes to manage water resources in drought conditions. To achieve the goal of this research, first, risk factors are identified and selected based on the literature review. In previous studies, risk indicators were employed for water resource management, separately. However, this paper extracted an extensive list of risk indicators from prior studies and employed all these indicators for water resource management. Furthermore, four scenarios for water resource management in Chaharmahal and Bakhtiari province are introduced according to the geographical characteristics, climate, economic and agricultural conditions in this province. Then, a questionnaire is designed and distributed among experts in the field of water resource management. After collecting data, the proposed method is implemented on the data. The fuzzy Shannon entropy method is used to determine the weights of risk indicators, while the fuzzy ARAS method is applied for ranking water resource management scenarios. The results of applying fuzzy Shannon entropy reveal that the three indicators of volume reliability, vulnerability, and sustainability of the water supply system, with weight values of 0.124, 0.119, and 0.118, respectively, are the most effective risk indexes. The results of implementing fuzzy ARAS show that changing the cultivation pattern with a score of 0.936 is placed in the first priority, reducing the demand of the agricultural sector with a score of 0.922 is placed in the second priority, and the type of irrigation system with a score of 0.896 is placed in the third priority, and the reduction of industrial and drinking water consumption with a score of 0.882 is placed in the fourth priority. Finally, the results of implementing the proposed model of fuzzy Shannon entropy and fuzzy ARAS reveal an increase in volume reliability in the field of cropping pattern change in the studied province. Full article

Sustaining instream flows is becoming increasingly critical due to the combined pressure of climate change and intensive reservoir operations in multi-dam watersheds. This study evaluates instream flow sustainability in the Seomjin River basin by integrating the SWAT and K-WEAP models with CMIP6-based climate scenarios. Two contrasting dam operation strategies—firm and deficit supply—were assessed over multiple temporal scales, including hydrological seasons and agricultural activity. Sustainability was quantified using the Sustainability Index (SI), which integrates reliability, resilience, and vulnerability. The probabilistic assessment revealed that the relative performance of the two strategies varied depending on the season and flow conditions. The firm supply generally exhibited higher sustainability under drought and low-demand periods, effectively reducing the probability of unsustainable outcomes. In contrast, the deficit supply often achieved higher sustainability under wet conditions or peak agricultural demand, although it was occasionally linked to extremely low SI values. These findings underscore the importance of season-specific, risk-informed dam operation planning over reliance on a single strategy and emphasize the need for flexible management frameworks capable of responding to diverse hydrological futures. Full article

Vulnerability impacts have increased in an unprecedented way with the effects of global warming, climate change, erosion, sea level rise, tsunami, flood, and drought—natural events that jointly cause geomorphological changes, especially in coastal zones. There are no analytical mathematical formulations under a set of assumptions due to the complexity of the interactive associations of these natural events, and the only way that seems open in the literature is through empirical formulations that depend on expert experiences. Among such empirical formulations are the Coastal Vulnerability Index (CVI), the Environmental Vulnerability Index (EVI), the Socioeconomic Vulnerability Index (SVI), and the Integrated Coastal Vulnerability Index (ICVI), which is composed of the previous indices. Although there is basic experience and experimental information for the establishment of these indices, unfortunately, logical aspects are missing. This paper proposes a Coastal Fuzzy Vulnerability Index (CFVI) based on fuzzy logic, aiming to improve the limitations of the traditional Coastal Vulnerability Index (CVI). Traditional CVI relies on binary logic and calculates vulnerability through discrete classification (such as “low”, “medium”, and “high”) and arithmetic or geometric means. It has problems such as mutation risk division, ignoring data continuity, and unreasonable parameter weights. To this end, the author introduced fuzzy logic, quantified the nonlinear effects of various parameters (such as landforms, coastal slope, sea level changes, etc.) through fuzzy sets and membership degrees, and calculated CFVI using a weighted average method. The study showed that CFVI allows continuous transition risk assessment by fuzzifying the parameter data range, avoiding the “mutation” defect of traditional methods. Taking data from the Gulf of Mexico in the United States as an example, the calculation result range of CFVI (0.38–3.04) is significantly smaller than that of traditional CVI (0.42–51), which is closer to the rationality of actual vulnerability changes. The paper also criticized the defects of traditional CVI, being that it relies on subjective experience and lacks a logical basis, and pointed out that CFVI can be expanded to integrate more variables or combined with other indices (such as the Environmental Vulnerability Index (EVI)) to provide a more scientific basis for coastal management decisions. This study optimized the coastal vulnerability assessment method through fuzzy logic, improved the ability to handle nonlinear relationships between parameters, and provided a new tool for complex and dynamic coastal risk management. Further research possibilities are also mentioned throughout the text and in the Conclusion section. Full article

This study presents a spatial methodology for integrating climate change (CC) risks and ecosystem service (ES) assessments into strategic spatial planning, applied to the Metropolitan Plan of the Province of Rimini (Emilia-Romagna, Italy). The proposed approach combines IPCC-aligned climate vulnerability analysis with ecosystem service mapping based on the methodology developed by CREN. Climate risks, including urban heat islands, droughts, and urban floods, were assessed using satellite-derived indices such as Land Surface Temperature (LST), Vegetation Health Index (VHI), and hydraulic modeling. For ESs, nine key services were evaluated and mapped by integrating land use, forest cover, and habitat data with biophysical modulation factors (e.g., slope, carbon stock, infiltration capacity). The results highlight priority areas where climate adaptation and ecological functions converge, enabling targeted interventions. This integrated workflow offers a replicable and scalable planning tool to support evidence-based decision-making at the metropolitan level. Its adoption is recommended by other local and regional authorities to strengthen the climate and ecological responsiveness of spatial planning instruments. Full article

As a critical intervention for enhancing inland navigation efficiency, waterway regulation projects profoundly modify riverine hydrodynamic conditions while optimizing navigability. This study employs the MIKE21 hydrodynamic model to establish a two-dimensional numerical framework for assessing hydrological alterations induced by channel regulation in the Hui River, China. Through comparative simulations of pre- and post-project scenarios across dry, normal, and wet hydrological years, the research quantifies impacts on water levels, flow velocity distribution, and geomorphic stability. Results reveal that channel dredging and realignment reduced upstream water levels by up to 0.26 m during drought conditions, while concentrating flow velocities in the main channel by 0.5 m/s. However, localized hydrodynamic restructuring triggered bank erosion risks at cut-off bends and sedimentation in anchorage basins. The integrated analysis demonstrates that although regulation measures enhance flood conveyance and navigation capacity, they disrupt sediment transport equilibrium, destabilize riparian ecosystems, and compromise hydrological monitoring consistency. To mitigate these trade-offs, the study proposes design optimizations—including ecological revetments and adaptive dredging strategies—coupled with enhanced hydrodynamic monitoring and riparian habitat restoration. These findings provide a scientific foundation for balancing navigation improvements with the sustainable management of fluvial systems. Full article

Effective and sustainable water reserve management faces increasing challenges due to climate-induced variability, data fragmentation, and the limitations of traditional, static modeling systems. This study introduces a conceptual framework designed to address these challenges by integrating digital twins, IoT-driven real-time monitoring, game engine simulations, and AI-driven decision support systems (AI-DSS). The methodology involves constructing a digital twin ecosystem using IoT sensors, GIS layers, remote-sensing imagery, and game engines. This ecosystem simulates water dynamics and assesses policy interventions in real time. AI components, including machine-learning models and retrieval-augmented generation (RAG) chatbots, are embedded to synthesize real-time data into actionable insights. The framework enables the continuous assessment of hydrological dynamics, predictive risk analysis, and immersive, scenario-based decision-making to support long-term water sustainability. Simulated scenarios demonstrate accurate flood forecasting under variable rainfall intensities, early drought detection based on soil moisture and flow data, and real-time water-quality alerts. Digital elevation models from UAV photogrammetry enhance terrain realism, and AI models support dynamic predictions. Results show how the framework supports proactive mitigation planning, climate adaptation, and stakeholder communication in pursuit of resilient and sustainable water governance. By enabling early intervention, efficient resource allocation, and participatory decision-making, the proposed system fosters long-term, sustainable water security and environmental resilience. This conceptual framework suggests a pathway toward more transparent, data-informed, and resilient decision-making processes in water reserves management, particularly in regions facing climatic uncertainty and infrastructure limitations, aligning with global sustainability goals and adaptive water governance strategies. Full article

Bayesian methods have revolutionised hydrological modelling by providing a framework for managing uncertainty, improving model calibration, and enabling more accurate predictions. This paper reviews the evolution of Bayesian methods in hydrology, from their initial applications in flood-frequency analysis to their current use in streamflow forecasting, flood risk assessment, and climate-change adaptation. It discusses the development of key Bayesian techniques, such as Markov Chain Monte Carlo (MCMC) methods, hierarchical models, and approximate Bayesian computation (ABC), and their integration with remote sensing and big data analytics. The paper also presents simulated examples demonstrating the application of Bayesian methods to flood, drought, and rainfall data, showcasing the potential of these methods to inform water-resource management, flood risk mitigation, and drought prediction. The future of Bayesian hydrology lies in expanding the use of machine learning, improving computational efficiency, and integrating large-scale datasets from remote sensing. This review serves as a resource for hydrologists seeking to understand the evolution and future potential of Bayesian methods in addressing complex hydrological challenges. Full article

The 21st‑century Maritime Silk Road initiative highlights the importance of oceans as hubs for resources, ecology, and trade, yet a comprehensive understanding of marine natural disaster risks within this region remains limited. This study focused on 30 countries along the Maritime Silk Road and developed a multi-hazard natural disaster risk assessment framework tailored for large-scale regional evaluation. It goes beyond single-factor or single-disaster assessments to enhance disaster resilience and support effective disaster response strategies. The framework integrates 65 indicators across four dimensions: disaster-causing factors, disaster-conceiving environments, disaster-bearing bodies, and disaster reduction capacities. It employs five single-indicator evaluation models alongside a combination assessment method based on maximum deviations to evaluate national-scale natural disaster risks. Results reveal spatial consistency in risk evaluations and capture the exposure and sensitivity of 30 countries to different hazards. South Asia exhibits higher seismic risks, while Saudi Arabia consistently receives the lowest risk. Tropical countries like Vietnam and the Philippines face significant storm risks. Drought hazard risk is higher in the Middle East and East Africa, while it is lower in Brunei, Indonesia, and Malaysia. Flood risks are notably higher in Bangladesh, while Iran and Tanzania consistently receive lower risk ratings. Overall, South Asia exhibits higher multi-hazard risks, with medium-to-low risks along the Mediterranean and Southeast Asia. These findings provide technical support for disaster risk reduction by identifying high-risk areas, prioritising resource allocation, and strengthening disaster reduction strategies. Full article