Search Results (131)

The ESTIMET (Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration algorithm) model provides continuous, spatially distributed daily ET, essential for model calibration in data-scarce environments where conventional hydrological monitoring is unavailable. The challenge of applying SWAT in arid regions without ground observations, this study proposes a remote-sensing-based calibration approach using ESTIMET to overcome data scarcity. Daily satellite-derived evapotranspiration (ET) data to assess the performance of the Soil and Water Assessment Tool (SWAT) was used to evaluate the performance of the SWAT in a desertified watershed in Brazil, aiming to assess ESTIMET’s effectiveness in supporting SWAT calibration, quantify sediment yield, and examine the influence of land-use changes on environmental quality over 21-years period. The results highlight a distinct hydrological response in SWAT initially underestimated ET, contrasting with patterns typically observed in other semi-arid applications and demonstrating that desertified environments require distinct calibration strategies. Performance indicators showed strong agreement between observed and simulated ET (R² = 0.94; NSE = 0.76), supporting satellite-based ET as a valuable source for improving SWAT performance in watersheds where empirical hydrometeorological data are sparse or unevenly distributed. Sediment yield was generally low to moderate, with degradation concentrated in bare-soil areas associated with deforestation. Full article

This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments. Full article

Hydrological forecasting has evolved rapidly in response to intensifying climate variability, increasing data availability, and advances in computational modeling. This review synthesizes developments from 2006 to 2025, examining four major forecasting domains: statistical approaches, physically based models, data-driven machine learning and deep learning techniques, and hybrid or emerging physics–AI frameworks. Recent literature shows a decisive shift toward integrated, data-rich systems that leverage remote sensing, IoT networks, and artificial intelligence to overcome limitations in traditional forecasting. While hybrid and physics-informed AI models achieve notable improvements in accuracy, lead time, and scalability, persistent challenges remain, especially regarding data scarcity, model interpretability, cross-basin generalization, climate non-stationarity, and operational computational demands. This review highlights these limitations and outlines future directions needed to strengthen hydrological forecasting as a tool for climate adaptation, early warning systems, and long-term water resource planning. By consolidating methodological advances and emerging gaps, the study provides insights into how hydrological forecasting can transition toward more resilient, transparent, and decision-oriented frameworks. Full article

In Mediterranean climate regions, water scarcity, seasonal droughts and hydrological extremes are exacerbated by climate change. In these areas, small dams are increasingly used as decentralized water infrastructure for water supply, especially in agricultural areas. However, several challenges must overcome when planning and managing small reservoirs. This review combines evidence from case studies to analyze the benefits and challenges of small dams. The findings show that small reservoirs may offer a wide array of ecological, agricultural, hydrological, and socio-economic benefits when strategically planned and properly maintained, providing water and contributing to groundwater recharge, vegetative restoration, and biodiversity conservation, while simultaneously controlling flash floods in a cost-effective and participatory manner. On the other hand, evaporation losses and sedimentation may affect water quality and reduce storage capacity. In addition, small dams may negatively affect river ecosystems. Persistent disturbance of downstream flow and sediment regime contributes to altered river morphology and habitat, with effects on biota, and may reduce river system resilience. These impacts are context-dependent, influenced by dam density, geomorphic setting, and climate. Finally, this study highlighted the importance of governance and maintenance practices. Polycentric and participative systems may promote more adaptable responses to water stress, whereas fragmented institutions exacerbate trade-offs between water supply and ecological integrity. Full article

Dryland, which mainly retains rain-fed agriculture, is the main type of farmland in China and widely distributed in the northern regions. Rainfall scarcity limits the development of maize at the seedling stage, which adversely affects the increase in maize yields in this region. A planting method that allows variable sowing depths based on the uneven distribution of soil moisture was proposed in this study. This site-specific planting method which fully utilizes available soil water is able to overcome the above problem. The framework of variable depth seeding suitable for this region was constructed: Within the depth range of 5.5 to 8.5 cm in the soil, maize seeds should be sown to a position with a relative soil moisture of 70%. For some drylands without such moisture conditions, seeds can be placed at the position with the highest relative soil moisture in this depth range. Taking the conventional planting method as the control group, the performance of the variable depth planting method in improving maize seedling growth was evaluated. The results showed that the proposed planting method not only increased the emergence rate and the seedling uniformity by 9.31% and 25.29%, respectively, but also raised the mean leaf number and the mean plant height in the same growth period, having a remarkable effect in improving the maize seedling traits. This planting method is easy to be embedded into precision control systems of the maize planter, and will promote the application of soil moisture-based planting technology and thus increase the yield per hectare of maize. Full article

Floating photovoltaic systems represent a promising solution for renewable energy generation, offering an alternative to agricultural land consumption. However, these installations have the potential to exert an effect on the aquatic ecosystem, emphasizing the necessity of effective monitoring strategies also related to system management issues. In this paper, the use of an unmanned surface vehicle, which can also operate as an autonomous surface vehicle, is proposed to overcome many difficulties of maintenance and monitoring in aquatic environments. A review of the extant literature reveals the scarcity of a cohesive monitoring framework for these plants, highlighting the urgent need for standardized guidelines for plant management and water quality monitoring. The implementation of automated plants directly addresses this gap by providing a tool for efficient and sustainable monitoring tasks, enabling, at the same time, aquatic ecosystem protection and energy production optimization. To address these challenges, a low-cost prototype of an autonomous surface vehicle is proposed. Preliminary test results on trajectory control and obstacle recognition are reported. Full article

Concentrated Solar Power (CSP) technology offers significant potential for stable and dispatchable renewable electricity generation through integration with thermal energy storage. However, adoption remains limited due to high capital costs, technical complexity, and market competition from photovoltaic (PV) systems. This review systematically synthesises recent literature on CSP and applies a hybrid SWOT–Analytic Hierarchy Process (AHP) methodology to quantitatively evaluate key internal and external factors influencing CSP deployment. The analysis identifies major strengths such as high-capacity factors and grid stability enabled by thermal storage, as well as weaknesses including high initial investment and site requirements. Opportunities stem from technological innovation, supportive policy frameworks, and potential for local job creation, while threats include rapid cost reductions in PV systems, water scarcity, and market and regulatory uncertainties. The integrated SWOT–AHP approach provides a robust decision-making framework and strategic insights for stakeholders seeking to promote CSP technology in diverse market contexts. The findings underscore the importance of tailored policy support and targeted investment to overcome barriers and realise CSP’s full potential within the renewable energy landscape. Full article

The lack of freshwater and the low efficiency of the traditional solar stills have led to the search to find a technology that can enhance desalination by use of vacuum-enhanced solar still technology. This review intends to investigate the impact of integrating a vacuum into solar stills, which include vacuum membrane distillation (VMD), nanoparticle-enhanced solar stills, multi-effect/tubular solar stills, geothermal integration and parabolic concentrator solar stills. The most important findings show that the productivity improves greatly: vacuum-assisted solar stills give up to 133.6% more product using Cu₂O nanoparticles, and multi-effect tubular stills under vacuum (40−60 kPa) show a doubling in freshwater productivity (7.15 kg/m²) in comparison to atmospheric operation. Geothermal cooling and vacuum pump systems show a 305% increase in productivity, and submerged VMD reached 5.9 to 11.1 kg m⁻² h⁻¹ with solar heating. Passive vacuum designs further reduce the energy used down to a specific cost, using as little as USD 0.0113/kg. Nevertheless, membrane fouling, initial cost, and the complexity of the system can still be termed as the challenges. This review highlights the significance of vacuum-enhanced solar stills to address the critical issue of freshwater scarcity in arid regions. The integration of vacuum membrane distillation, nanoparticle and heat recovery into vacuum-enhanced solar stills enabled us to improve the economic feasibility. We conclude that vacuum technologies significantly boost the efficiency and economic feasibility of solar desalination as a potential approach to sustainable desalination. Specifically, these inventions will contribute to providing a renewable and cost-effective solution for freshwater production. Further investigations are required to overcome the existing challenges, such as system complexity and membrane fouling, to effusively comprehend the efficacy of vacuum-enhanced solar stills to ensure sustainable water management. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

This study addresses the critical challenge of optimizing water resource allocation in fragmented citrus cultivation zones, particularly in Anfusi Town, a key citrus production area in China’s middle-lower Yangtze River region. To overcome the limitations of traditional deterministic models and spatially heterogeneous water supply–demand dynamics, an innovative framework integrating interval two-stage stochastic programming (ITSP) with long short-term memory (LSTM) neural networks is proposed. The LSTM component forecasts irrigation demand and supply under climate variability, while ITSP optimizes dynamic allocation strategies by quantifying uncertainties through interval analysis and balancing economic returns with hydrological risks. Key results demonstrate an 8.67% increase in system-wide benefits compared to baseline practices in the current year scenario. For the planning year (2025), the model identifies optimal water distribution thresholds: an upper limit of 3.85 × 10⁶ m³ for high-availability zone A and lower limits of 1.62 × 10⁶ m³ for moderate-to-low-availability zones B and C. These allocations minimize water scarcity penalties while maximizing net benefits, prioritizing local over external water sources to reduce costs. The study innovates by integrating stochastic-economic analysis with spatial prioritization of high-marginal-benefit zones and uncertainty robustness via interval analysis and two-stage decision making. By bridging a research gap in citrus irrigation optimization, this approach advances sustainable water management in complex agricultural systems, offering a scalable solution for regions facing fragmented landscapes and climate-driven water scarcity. Full article

Ecological restoration in arid coal-mining regions faces extreme challenges due to soil infertility, salinization, and water scarcity. This study addresses these limitations in the Santanghu Shitoumei No. 1 open-pit mine (Xinjiang), where gypsum gray-brown desert soil, minimal rainfall (199 mm/yr), high evaporation (1716 mm/yr), and persistent gale-force winds exacerbate revegetation efforts. To overcome the high cost, short lifespan, and poor practicality of commercial water-retaining agents, we developed a novel humic acid (HA) and sodium carboxymethyl cellulose (CMC) composite water-absorbing resin (HA-CMC). Optimal synthesis parameters—identified as acrylic acid (AA)–carboxymethyl cellulose (CMC)–humic acid (HA)–Acrylamide (AM)–N,N’-methylene diacrylamide (MBA)–Ammonium persulphate (APS) = 100%:15%:4.5%:25%:0.6%:0.8%—yielded effective crosslinking, confirmed via FTIR and SEM. Performance benchmarking against existing agents demonstrated superior attributes. Field application in the mine’s demonstration area significantly enhanced surface vegetation and soil fertility, confirming the resin’s potential for large-scale soil remediation and ecological restoration in arid mining environments. Full article

Green hydrogen, produced via renewable-powered electrolysis, offers a promising path toward deep decarbonisation in energy systems. This study investigates the major technological, infrastructural, and economic challenges facing green hydrogen production in Jordan—a resource-constrained yet renewable-rich country. Key barriers were identified through a structured survey of 52 national stakeholders, including water scarcity, low electrolysis efficiency, limited grid compatibility, and underdeveloped transport infrastructure. Respondents emphasised that overcoming these challenges requires investment in smart grid technologies, seawater desalination, advanced electrolysers, and policy instruments such as subsidies and public–private partnerships. These findings are consistent with global assessments, which recognise similar structural and financial obstacles in scaling up green hydrogen across emerging economies. Despite the constraints, over 50% of surveyed stakeholders expressed optimism about Jordan’s potential to develop a competitive green hydrogen sector, especially for industrial and power generation uses. This paper provides empirical, context-specific insights into the conditions required to scale green hydrogen in developing economies. It proposes an integrated roadmap focusing on infrastructure modernisation, targeted financial mechanisms, and enabling policy frameworks. Full article

Organic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure–property relationships, and enabling the cross-scale optimization of OFMs. Methodologically, ML workflows—spanning data construction, feature engineering, and model optimization—accelerate candidate screening, inverse design, and mechanistic interpretation, as demonstrated in gas separations and nascent liquid-phase applications. Key findings reveal that ML identifies critical structural descriptors and environmental parameters, guiding the development of high-performance membranes that surpass traditional selectivity–permeability limits. Challenges persist in liquid separations due to dynamic operational complexities and data scarcity, while emerging frameworks offer untapped potential. The integration of interpretable ML, in situ characterization, and industrial scalability strategies is essential to transition OFMs from laboratory innovations to sustainable, adaptive separation systems. This review underscores ML’s transformative capacity to bridge computational insights with experimental validation, fostering next-generation membranes for carbon neutrality, water security, and energy-efficient industrial processes. Full article

While at the global level, water stress does not seem to present a serious threat to the sustainability of freshwater withdrawal and use, the situation appears much grimmer if a closer look is given to the status of the freshwater resources at basin and sub-basin levels. Unfortunately, such information is often not available to water managers and decision-makers, due both to the scarcity of sufficient data and also to the lack of methods capable of transforming the existing data into usable information. Hence, disaggregating water stress at basin and sub-basin levels is fundamental to provide a finer view of both its causes and effects, allowing the targeting of interventions at areas with high water stress and sectors with high water use. The spatial disaggregation of SDG indicator 6.4.2 by major river basin already implemented at a global scale showed the existence of a water stress belt running across the globe approximately between 10 and 45 degrees north, with a few other areas above and below it. The value of SDG indicator 6.4.2 at the country level is influenced by its size: the larger the country, the more the national average masks local variability. When the disaggregation is performed at sub-basin level, there is the possibility that the same amount of water is counted twice or even more (double counting), as it flows from one sub-basin to the neighbouring ones. Current water accounting methods do not allow this issue to be overcome. This causes an underestimation of water stress and an overestimation of the water resources available for human use in a given area. This paper presents a new methodology to assess SDG indicator 6.4.2 (water stress) seasonally and at the sub-basin level, addressing double counting by factoring in water demands between upstream and downstream sub-basins. This approach supports more informed water management. A corresponding plugin for the WEAP tool was developed, tested in the Senegal River basin countries, and is available online with a user manual in English, French, and Spanish. Full article

Often thought of as a mesic paradise, forest ecosystems are a mosaic of microhabitats with temporal oscillations that cause significant environmental stresses, providing habitats for extremophilic and extremotolerant fungi. Adapted to survive and thrive under conditions lethal to most mesophiles (e.g., extreme temperatures, pH, water potential, radiation, salinity, nutrient scarcity, and pollutants), these species are increasingly recognized as vital yet underappreciated elements of forest biodiversity and function. This review examines the current understanding of the roles of extremophilic fungi in forests, scrutinizing their presence in these ecosystems with a critical eye. Particularly under severe environmental conditions, extremophilic fungi play a crucial role in forest ecosystems, as they significantly enhance decomposition and nutrient cycling, and foster mutualistic interactions with plants that increase stress resilience. This helps to maintain ecosystem stability. We examine the definition of “extreme” within forest settings, survey the known diversity and distribution of these fungi across various forest stress niches (cold climates, fire-affected areas, acidic soils, canopy surfaces, polluted sites), and delve into their possible ecological functions, including decomposition of recalcitrant matter, nutrient cycling under stress, interactions with plants (pathogenesis, endophytism, perhaps mycorrhizae), bioremediation, and contributions to soil formation. However, the review stresses significant methodological difficulties, information gaps, and field-based natural biases. We recommend overcoming cultural constraints, enhancing the functional annotation of “omics” data, and planning investigations that clarify the specific activities and interactions of these cryptic creatures within the forest matrix to further advance the field. Here, we demonstrate that moving beyond simple identification to a deeper understanding of function will enable us to more fully appreciate the value of extremophilic fungi in forest ecosystems, particularly in relation to environmental disturbances and climate change. Full article