Search Results (47)

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

Drought is a key limitation for rice productivity. While oxidative stress markers like hydrogen peroxide (H₂O₂) are important for drought adaptation, the predictive value of combining root anatomical and physiological traits is underexplored. We assessed 20 rice cultivars under drought and control conditions using a random forest, a multi-layer perceptron, and a SHAP-optimized stacking ensemble. The stacking ensemble achieved the highest classification accuracy (81.8%) and identified hydrogen peroxide, relative water content, and endodermis inner circumference as key predictors. SHAP analysis revealed important interactions between root anatomical and physiological traits, providing new biological insights into drought tolerance. Our integrative approach, supported by robust cross-validation, improves predictive power and transparency for breeding drought-resilient rice cultivars. Full article

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

The cocoa production in Ghana, largely reliant on rainfall and undertaken by smallholder farmers, is increasingly endangered by climate change-induced water scarcity. Although supplemental irrigation has been posited as an adaptive measure, its hydrological impacts remain understudied. This current study seeks to bridge this knowledge gap by employing the Soil and Water Assessment Tool (SWAT) to evaluate the hydrological and water resource implications of supplemental irrigation within the Upper Offin sub-basin of Ghana. High-resolution spatial data and field survey inputs were used to model dry period baseline and irrigation scenarios for cocoa farms with gentle slopes (2%). The results reveal that supplemental irrigation from the shallow aquifer can sustainably support irrigation for up to 5% of the cocoa area (4760 ha) without adversely affecting groundwater flow. Extending irrigation to 30% of the cocoa area (28,540 ha) is feasible with minimal reduction in catchment water yield. This study’s novelty lies in integrating high-resolution data with localized management practices to provide actionable insights for balancing cocoa productivity and water sustainability. The findings offer practical recommendations for policymakers, emphasizing that through solar-powered irrigation the shallow groundwater is a pathway to enhance climate resilience of cocoa productivity. Full article

Rice sustains a large global population, making its sustainable production vital for food security. Alternate wetting-and-drying (AWD) irrigation offers a promising approach to reducing water use in rice paddies but can impact grain yields. Plant growth-promoting rhizobacteria (PGPR) can enhance rice productivity under AWD cultivation conditions. This review explores integrating PGPR into AWD systems, focusing on their mechanisms for promoting growth and water stress resilience. It examines diverse microbial communities, particularly bacteria, and their contributions to nutrient acquisition, root development, and other beneficial processes in rice under fluctuating moisture, as well as the influence of AWD on rice’s structural and physiological development. The challenges and opportunities of AWD are also addressed, along with the importance of bacterial selection and interactions with the native soil microbiome. This synthesizes current research to provide an overview of PGPR’s potential to improve sustainable and productive rice cultivation under AWD. Future studies can leverage powerful tools such as e-DNA and NGS for a deeper understanding of these complex interactions. Full article

Climate change disrupts global food systems by affecting water, energy, ecosystems, and agricultural productivity. Building climate resilience demands integrated approaches that recognize interdependencies among water, energy, food, and environmental (WEF-E) systems. This review synthesizes current research on how the WEF-E nexus can guide climate adaptation strategies. It highlights interdisciplinary solutions—such as solar-powered irrigation, agrivoltaics, agroforestry, conservation agriculture, and nature-based water management—that enhance resource efficiency, stabilize yields, and reduce environmental degradation. Effective implementation requires governance innovation, stakeholder participation, and coherent cross-sector policies. The paper also outlines research priorities, including the development of resilience metrics, modeling tools, and inclusive decision-making mechanisms. Emphasizing both adaptation and mitigation, the WEF-E nexus offers a transformative lens for sustainable, equitable, and climate-resilient food systems. As climate pressures intensify, advancing this integrated framework presents both an urgent necessity and a strategic opportunity to align food security with environmental stewardship. Full article

Water scarcity necessitates desalination technologies, yet their high energy demands and brine disposal challenges hinder sustainability. This research study evaluates the energy footprint and carbon emissions of thermal- and membrane-based desalination technologies, alongside Minimal/Zero Liquid Discharge (MLD/ZLD) frameworks, with a focus on renewable energy source (RES) integration. Data revealed stark contrasts: thermal-based technologies like osmotic evaporation (OE) and brine crystallizers (BCr) exhibit energy intensities of 80–100 kWh/m³ and 52–70 kWh/m³, respectively, with coal-powered carbon footprints reaching 72–100 kg CO₂/m³. Membrane-based technologies, such as reverse osmosis (RO) (2–6 kWh/m³) and forward osmosis (FO) (0.8–13 kWh/m³), demonstrate lower emissions (1.8–11.7 kg CO₂/m³ under coal). Transitioning to RES reduces emissions by 90–95%, exemplified by renewable energy-powered RO (0.1–0.3 kg CO₂/m³). However, scalability barriers persist, including high capital costs, RES intermittency, and technological immaturity in emerging systems like osmotically assisted RO (OARO) and membrane distillation (MD). This research highlights RES-driven MLD/ZLD systems as pivotal for aligning desalination with global climate targets, urging innovations in energy storage, material robustness, and circular economy models to secure water resource resilience. Full article

This article examines the social aspects of seawater desalination, especially as a strategy for community adaptation and resilience to water scarcity in arid and coastal regions, focusing on Baja California, México. This study aims to understand how social, cultural, political-institutional and territorial factors influence the acceptance and implementation of desalination technology. Through an inductive analysis based on a grounded theory methodology, this research employed a literature review and stakeholder semi-structured interviews to identify the potential impacts and key factors affecting desalination projects. Two research questions guided the investigation: (1) What sociopolitical factors influence the implementation of desalination projects in coastal communities? (2) How do these factors shape community responses to desalination initiatives? Through a case study of San Quintin, Baja California, it was found that stakeholder perspectives varied significantly regarding water management strategies. This analysis revealed that successful desalination implementation depends on four key factors: local governance structures, power relations among stakeholders, community engagement processes, and territorial characteristics. These findings suggest that effective desalination projects require integrating technical solutions with robust social processes that include meaningful community participation and the consideration of local contexts. This study contributes to the water adaptation and resilience literature and provides practical insights for policymakers and project developers working on desalination initiatives in similar coastal regions. Full article

Efficient reservoir management is essential for ensuring water security and flood control, as well as hydroelectric power generation. Accurate volume measurements are key to optimizing these functions, but traditional methods—such as in situ measurements and physical surveys—are often time-consuming, costly, and unfeasible in many regions due to financial or geographical limitations. This study introduces a novel globally accessible remote sensing tool designed to overcome these challenges by providing a more effective approach to reservoir volume estimation. The tool leverages high-resolution satellite imagery from Sentinel-2 and integrates it with official storage capacity data and the GLOBAthy dataset to calculate surface area and reservoir volume across varying water levels over user-defined timeframes. Users can select reservoirs, date ranges, and cloud cover thresholds via an intuitive interface, which then generates time-series data of reservoir volumes. The tool employs machine learning algorithms to improve the precision of water surface delineation and volume calculations, accounting for complex environmental factors like cloud cover and built structures such as bridges. This remote sensing tool was tested on reservoirs of varying sizes and topographies in Portugal and California, USA, demonstrating a high accuracy with a Mean Absolute Percentage Error (MAPE) of 5.35% and a correlation coefficient (R²) of 0.90 when compared to official records. By offering a cost-effective, scalable, totally remote, and timely solution, the tool enables improved reservoir monitoring, particularly in remote or otherwise inaccessible areas. Ultimately, this research contributes to global water resources management, enhancing the sustainability and resilience of reservoir operations around the world. Full article

The protection and reclamation of surface waters, particularly lakes, necessitates the implementation of novel approaches that effectively integrate natural biological processes with sophisticated technological solutions. This paper examines the potential of bioremediation techniques utilising artificial aeration systems, with renewable energy sources serving as a viable power source. The objective of the analysis is to enhance the sustainability of the remediation of aquatic ecosystems. A multi-criteria analysis (MCA) was employed to evaluate the performance and environmental impact of the selected methods. Based on the results of the MCA, the SHPP (small hydro power plant) technology achieved the highest score for both lakes, 0.85 and 0.78, respectively, making it the optimal technology. In comparison, wind energy scored around 0.5 and photovoltaic (PV) around 0.6, showing a poorer fit with local conditions. By integrating reclamation with renewable energy applications, this research presents a strategy for developing more resilient and environmentally sound water management strategies. Full article

This study proposes developing a resilient machine learning algorithm based on neural networks to classify naval images used in surveillance, search, and detection operations in vast coastal and marine environments. Coastal areas critical for water resource management often face challenges such as illegal fishing, trafficking, piracy, and other illicit activities that require robust monitoring systems powered by computer vision. However, real-world datasets in such environments can be compromised by label noise due to random inaccuracies or deliberate adversarial attacks, leading to decreased accuracy in machine learning models. Our innovative approach employs Rockafellian Risk Minimization (RRM) to mitigate the impact of label noise contamination, crucial to maintaining data integrity in water-related security and governance operations. Unlike existing methodologies that rely on extensively cleaned datasets, our two-step process adjusts neural network weights and manipulates nominal probabilities of data points to isolate potential data corruption effectively. This technique reduces dependence on meticulous data cleaning, thereby increasing data processing efficiency in water resources and coastal management. To validate the effectiveness and reliability of the proposed model, we apply RRM in various parameter settings to datasets specific to naval environments and evaluate its classification accuracy against traditional methods. By leveraging the proposed model, we aim to reinforce the robustness of ship detection models, ultimately contributing to developing more reliable automated maritime surveillance systems. Such systems are essential for strengthening governance, security, and water management and curbing illegal activities at sea. Full article

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems. Full article

The Canary Archipelago, in general, and the island of Gran Canaria, in particular, operate with an independent energy system (SIE), which depends largely on local power generation. Today, its energy supply comes mainly from two sources: (a) Renewable energy, accounting for 19.90%, and (b) Fossil fuel combustion in thermal power plants, contributing the remaining 80.10%. The existing energy infrastructure faces challenges due to aging technology, requiring either modernization or replacement to prevent a potential energy crisis and ensure a sustainable production cycle. A transformative step to improve the system is the completion and commissioning in 2030 of the Chira-Soria pumped hydroelectric energy storage (PHES) plant. This installation will allow water to be transported to high altitudes by pumping, to be deposited until the right time and to be turbined to generate electricity in optimal conditions. To fully understand the impact of this integration, detailed analyses of annual energy production patterns, equipment performance, and real-time demand data (collected at five-minute intervals) will be conducted. These assessments will provide insights into how the Chira-Soria PHES can be seamlessly integrated into Gran Canaria’s energy network. Furthermore, they will help identify both the strengths and limitations of this storage solution, paving the way for a more resilient and efficient energy future for the island. Full article