Search Results (6,727)

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter $\beta$, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions. Full article

This work aims to demonstrate the successful, long-term human use of an automatic scheduling-manual operation (AS-MO) precision irrigation tool by farmers on a medium-scale Jordanian farm. Innovation in low-cost, accessible, and water-efficient irrigation technologies is critical as water resources become scarce, especially on resource-constrained farms in the drought-prone Middle East and North Africa (MENA) region. Prior work has shown that a proposed AS-MO decision support tool could bridge the gap between fully manual irrigation—a common practice on many MENA farms—and existing precision agriculture solutions, which are often too expensive or complex for medium-scale farmers to adopt. Recent developments have also demonstrated that the scheduling theory behind the proposed AS-MO tool uses up to 44% less water compared to fully manual irrigation. However, a functional design of the AS-MO tool has not been realized nor has it been demonstrated on a farm with farmer users. This work documents the detailed design of an AS-MO tool’s human–machine interaction (HMI) and validates the human execution of the tool in context. Through an 11-week case study conducted on a Jordanian farm, we show that farmers used a functional prototype of the AS-MO tool as intended. The functional tool prototype was designed to deliver a long-term AS-MO user experience to study participants. The prototype monitored local weather conditions, generated water-efficient schedules using an existing scheduling theory, and notified users’ phones when they should manually open or close valves. The irrigation practices of participants using the AS-MO prototype were measured, and participants demonstrated successful use of the tool. Users correctly confirmed 93% of the scheduled events using the tool’s HMI. Despite manual operation, a majority of confirmed irrigation event durations fell within 15% of the automatically scheduled durations; relative to the length of scheduled irrigation event durations, the medians of confirmed and scheduled durations were 102% and 88%, respectively. These results demonstrate the success of the tool’s decision support ability. Feedback from study participants can support the AS-MO tool’s next design iteration and can inform the development of other decision support systems designed for resource-constrained, medium-scale farms. This work presents an important step towards developing a precision irrigation tool that, if adopted at scale, could increase the adoption of water-efficient irrigation practices on resource-constrained farms that are not served by existing technology, improving sustainable agriculture in MENA. Full article

Reliable observation of water resources is a major challenge for sustainable development, particularly in the river-centric deltaic countries like Bangladesh, where the data is generally scarce. Leveraging operational satellites, this study presents a real-time capable water level (WL), discharge (Q), and floodplain monitoring framework implemented for the Brahmaputra River in Bangladesh. The multi-satellite approach presented here combined satellite altimetry, synthetic aperture radar (SAR), and optical imagery. A set of WL time series is obtained first from Jason-2/3 and Sentinel-3 altimetry, while a combination of Sentinel-1 SAR and Sentinel-2 optical images is used to extract the floodplain extent. Seasonal Rating Curve (RC) models are then developed to estimate Q from the river WL (altimetry) and width (imagery). The altimetry WL measurement is further complemented by the width-based Q utilizing an inverse RC. Furthermore, the water level is combined with a floodplain map to extract floodplain topography and its evolution. The proposed framework provides consistent and reliable observations in the Brahmaputra River, with a bias, root mean-squared errors (RMSEs), and correlation coefficient of 0.03 m, 0.68 m, and 0.96 for WL, and −168.22 m³/s, 4161.46 m³/s, and 0.97 for Q, respectively, relative to a mean discharge of approximately 30,000 m³/s. The locations of high erosion–accretion across the river reach are also well-captured in the evolving floodplain maps. By integrating multiple satellite altimetry missions with SAR and optical imagery, the multi-satellite approach reduces the effective monitoring interval for both water level and discharge from approximately 10 days (single-mission altimetry) to about 4 days, enabling improved capture of extreme events such as floods. As the operational satellites used in this study are expected to provide long-term observations, the proposed framework supports sustainable monitoring of floodplain dynamics in Bangladesh and other similar data-poor environments, towards informed water management under ongoing climatic and anthropogenic changes. Full article

Groundwater is a critical resource for agriculture and livelihoods, particularly in semi-arid regions such as Gujarat and Rajasthan in India. However, unsustainable extraction has led to aquifer depletion and increased water insecurity. This study uses Ostrom’s design principles to evaluate how Village Groundwater Cooperatives (VGCs) are transitioning toward self-governance in managing groundwater commons. Through field research in Dharta (Rajasthan) and Meghraj (Gujarat), including 33 key informant interviews and nine focus group discussions, this study assesses institutional robustness, rule enforcement, and community participation. Findings reveal that VGCs have the potential to enhance groundwater security through collective water budgeting and recharge interventions, though institutional robustness is constrained by limited formal enforcement. In Hinta, pipelines connected four wells to distribute water equitably, while in Dharta and Meghraj, traditional water-sharing agreements (two-part and three-part systems) sustained cooperation. Groundwater monitoring by trained “Bhujal Jankaars” helped farmers plan crop cycles, supporting informed crop choices that better aligned with available water supply. Despite these successes, to strengthen VGCs for effective groundwater management, formal sanctioning mechanisms are needed to address rule violations. Additionally, women’s participation in groundwater management decisions and operationalising VGCs is low. Conflict resolution mechanisms are currently informal. This study suggests that because women primarily manage domestic water needs while men manage irrigation, integrating women into decision-making is essential to reconcile competing water demands and ensure the long-term viability of VGCs. The findings provide policy insights for scaling up community-led groundwater governance in semi-arid regions. Full article

Green roofs in Southern Europe are interest-growing nature-based solutions, capable of improving urban sustainability by positively impacting the water cycle, biodiversity, pollution, and, in some cases, energy consumption and carbon sequestration. Native plants adapted to Mediterranean climates exhibit drought-resistant traits, making them highly suitable for the challenging microclimate of green roofs. This microclimate features intense solar radiation, strong winds, and higher temperatures, in comparison to ground level, leading to increased atmospheric evaporative demand, driven by the interplay of radiation, wind, temperature, and humidity. Consequently, native plants from ecosystems resembling this microclimate are likely better suited for green roofs than local ground-level species. The current review synthesizes current knowledge on the use of native plants in Southern European green roofs, focusing on water management challenges given the region’s climate and scarce water resources. Out of roughly 12,500 native plant species in the Mediterranean basin, only about 124 have been examined in the past 20 years for green roof applications, with just 16% appearing in multiple scientific studies, highlighting a significant knowledge gap. The data indicate that ca. 85% of these species are perennials, valued for their low maintenance needs, a key consideration for green roof sustainability. Some of the studied species retain adequate aesthetic value when cultivated on green roofs with limited water availability. These species are mainly associated with four habitat types—rocky, coastal, dry, or well-drained environments—with a few linked to humid or adaptable conditions. This study aims to document the selection of drought-adapted native plant species best suited for green roof implementation in Southern Europe, contributing to enhancing sustainable urban design in the region, considering water management best practices and water use efficiency. Full article

The agri-food sector is a major contributor to global greenhouse gas emissions while facing increasing demand for food production driven by population growth. Transitioning towards sustainable and low-carbon agricultural systems is therefore critical. Green hydrogen, produced from renewable energy sources, holds significant promise as a clean energy carrier and chemical feedstock to decarbonize multiple stages of the agri-food supply chain. This systematic review is based on a structured analysis of peer-reviewed literature retrieved from Web of Science, Scopus, and Google Scholar, covering over 120 academic publications published between 2010 and 2025. This review provides a comprehensive overview of hydrogen’s current and prospective applications across agriculture and the food industry, highlighting opportunities to reduce fossil fuel dependence and greenhouse gas emissions. In agriculture, hydrogen-powered machinery, hydrogen-rich water treatments for crop enhancement, and the use of green hydrogen for sustainable fertilizer production are explored. Innovative waste-to-hydrogen strategies contribute to circular resource utilization within farming systems. In the food industry, hydrogen supports fat hydrogenation and modified atmosphere packaging to extend product shelf life and serves as a sustainable energy source for processing operations. The analysis indicates that near-term opportunities for green hydrogen deployment are concentrated in fertilizer production, food processing, and controlled-environment agriculture, while broader adoption in agricultural machinery remains constrained by cost, storage, and infrastructure limitations. Challenges such as scalability, economic viability, and infrastructure development are also discussed. Future research should prioritize field-scale demonstrations, technology-specific life-cycle and techno-economic assessments, and policy frameworks adapted to decentralized and rural agri-food contexts. The integration of hydrogen technologies offers a promising pathway to achieve carbon-neutral, resilient, and efficient agri-food systems that align with global sustainability goals and climate commitments. Full article

Soil erosion undermines the sustainable development of land—a vital resource for human survival. Research into the spatiotemporal dynamics of soil erosion is therefore crucial for formulating effective soil and water conservation strategies and advancing ecological protection efforts. In the domain of soil erosion research, the Universal Soil Loss Equation and Revised Universal Soil Loss Equation (USLE/RUSLE) model represent the dominant approach for quantifying soil erosion volumes. While this methodology yields reliable outcomes, it fails to incorporate an assessment of the relative significance of the factors embedded within the model. This study selected the Henan section of the Yellow River Basin as the research area, using monthly remote sensing data from 2010 to 2025 as the main data source. Taking into account factors such as rainfall, slope, elevation, vegetation coverage, and hydrological conservation measures, the RUSLE model was used to calculate and combine Geographic Information System (GIS) geographic detectors for quantitative analysis of soil erosion factors. The results showed the following: (1) The average soil erosion modulus in the study area from 2010 to 2025 was mainly micro and mild erosion. (2) Soil erosion exhibits a certain periodicity, with a year of significant soil erosion occurring every 3–4 years. The overall trend of soil erosion is a decrease. (3) Geographic detector analysis shows that slope has the greatest impact on soil erosion, with larger slopes leading to more severe soil erosion. The influence of each factor ranges from large to small as slope > water conservation measures > rainfall > vegetation coverage > elevation. (4) The interaction between factors can enhance the influence on soil erosion, and the interaction between vegetation cover factors and other factors significantly increases the influence; after interacting with various factors, the slope factor will significantly increase the influence of soil erosion. The research results can provide technical support and decision-making basis for ecological protection in the Yellow River Basin, such as through soil and water conservation, returning farmland to forests, and slope greening; The dominant factors and obvious interaction factors in the research area can provide a scientific basis for subsequent scholars to optimize the parameters of regional models. Full article

The flavor and color profiles of vegetables are crucial in determining their nutritional value, health benefits, taste, and visual appeal. The genomic characteristics of plants control these traits. Components such as sugars, organic acids, amino acids, phenolic compounds, and essential oils, as well as color pigments like anthocyanin, chlorophyll, carotenoid, and betalain, are synthesized in plants based on their genetic structure. Environmental factors like temperature, water, light, and soil can affect the production and intensity of these components. Long-term environmental changes, such as climate change, can significantly alter the dynamics of these components. This comprehensive review focuses on the genetic and environmental interactions underlying the flavor and color profiles of vegetables, with particular emphasis on the analysis of quantitative trait loci (QTL) associated with these traits. The article discusses the identification of genes that regulate taste and color in vegetables and how these genes have been localized in QTL mapping studies. It also discusses the influence of environmental factors on taste and color, as well as gene–environment interactions. Furthermore, it focuses on how this information can be used to improve plant breeding and sustainable agriculture and emphasizes that data from QTL analyses provide valuable insights into the integration of genetic and environmental approaches to improve vegetable quality and meet consumer preferences. In conclusion, the review aims to be a valuable resource for both researchers and professionals interested in the genetic and environmental aspects of taste and color in vegetables. Full article

Rigid polyurethane (PUR) foams are widely used across multiple industries due to their excellent thermal insulation and mechanical properties. However, their environmental impact, flammability, and limited thermal stability pose challenges for sustainable development. In this study, selected natural minerals—including talc, montmorillonite, halloysite, and diatomite—were incorporated into water-blown polyurethane foams to improve their performance while enhancing sustainability. The prepared foams were characterized in terms of apparent density, water uptake, compressive strength, dimensional stability, and thermal and fire resistance. The results indicate that the inclusion of mineral additives significantly improves the physical and mechanical properties of polyurethane foams, increasing durability, resistance to high temperatures, and fire safety. By using naturally occurring minerals, the study promotes the development of polyurethane foams with reduced environmental footprint, longer service life, and safer application potential in construction, automotive, and heating systems. These findings highlight the contribution of mineral-reinforced polyurethane foams to sustainable materials engineering and resource-efficient industrial applications. Full article

African vegetation responses to extreme drought represent a key challenge for global change research and sustainable water–land resource management. Satellite remote sensing provides long-term observations of vegetation dynamics, yet conventional analyses focus on vegetation structural, greenness, or productivity changes, lacking of understanding on physiological adaptation. This study applies a multi-model framework integrating high-temporal-resolution (4-day) and multi-spectral satellite data with machine learning to disentangle structural and physiological responses across Central and Western Africa. Three key indicators were used: evapotranspiration (ET), relative solar-induced chlorophyll fluorescence (SIF_rel), and the ratio of midday to midnight vegetation optical depth (VOD_ratio), which respectively, represent water flux, photosynthetic activity, and water regulation. A random forest model, combined with SHapley Additive exPlanations (SHAP) analysis, was used to separate vegetation anomaly signals and identify key climatic controls. The results reveal pronounced differences in vegetation responses between arid and humid climatic regions. In arid regions, near-infrared reflectance of vegetation (NIRv) and solar-induced chlorophyll fluorescence (SIF) exhibited clear negative anomalies and significant pre-drought declines, accompanied by marked changes in vegetation optical depth (VOD), indicating canopy structural damage and reduced photosynthetic activity. In contrast, trend analysis revealed that although SIF and NIRv in humid regions showed relatively strong responses during the pre-drought phase, they did not exhibit significant trends after the drought peak, and changes in VOD were comparatively small, suggesting that higher water availability partially buffered the prolonged impacts of drought on vegetation structure and function. Process analysis showed that three months before and after drought peaks, physiological indicators exhibited strong anomalies that closely tracked drought duration. SIF_rel, ET signals peaked earlier than water-content anomalies (VOD_ratio), suggesting a two-phase regulation strategy: early stomatal closure followed by delayed deep-root water uptake. Physiological anomalies accounted for over 88% of total vegetation anomalies during drought peaks, highlighting their dominant role in early-stage drought response. Precipitation and temperature emerged as primary drivers, explaining 76.8% of photosynthetic variation, 60.3% of ET variation, and 53.9% of water-content variation in the development. The recovery is influenced by the duration of drought and the regrowth of vegetation. By explicitly decoupling physiological and structural vegetation responses, this study provides refined, process-based insights into African ecosystem adaptation to water stress. These findings contribute to more accurate drought monitoring, water availability assessment, and climate adaptation strategies, directly supporting sustainable water and land management goals. Full article

The sustainable management of water resources and the development of climate-resilient infrastructure depend on the precise identification of water bodies in satellite imagery. This paper presents a novel deep learning architecture that integrates a convolutional block attention module (CBAM) into a modified EfficientNet–UNet backbone. This integration allows the model to prioritize informative features and spatial areas. The model robustness is ensured through a rigorous training regimen featuring five-fold cross-validation, dynamic test-time augmentation, and optimization with the Lovász loss function. The final model achieved the following values on the independent test set: precision = 90.67%, sensitivity = 86.96%, specificity = 96.18%, accuracy = 93.42%, Dice score = 88.78%, and IoU = 79.82%. These results demonstrate improvement over conventional segmentation pipelines, highlighting the effectiveness of attention mechanisms in extracting complex water-body patterns and boundaries. The key contributions of this paper include the following: (i) adaptation of CBAM within a UNet-style architecture tailored for remote sensing water-body extraction; (ii) a rigorous ablation study detailing the incremental impact of decoder complexity, attention integration, and loss function choice; and (iii) validation of a high-fidelity, computationally efficient model ready for deployment in large-scale water-resource and ecosystem-monitoring systems. Our findings show that attention-guided segmentation networks provide a robust pathway toward high-fidelity and sustainable water-body mapping. Full article

Understanding the behavior of light non-aqueous phase liquids (LNAPLs) in urban subsurface environments is essential to developing effective pollution control strategies, designing remediation systems, and managing waste and resources sustainably. Oil leakage from urban industrial facilities, underground pipelines, and fueling systems often leads to contamination that is challenging to characterize due to complex soil structures, limited access beneath densely built infrastructure, and dynamic groundwater conditions. In this study, we integrate a large-scale soil tank experiment with multiphase flow simulations to elucidate LNAPL distribution mechanisms under fluctuating groundwater conditions. A 2.4-m-by-2.4-m-by-0.6-m soil tank was used to visualize oil movement with high-resolution multispectral imaging, enabling a quantitative evaluation of saturation distribution over time. The results showed that a rapid rise in groundwater can trap 60–70% of the high-saturation LNAPL below the water table. In contrast, a subsequent slow rise leaves 10–20% residual saturation within pore spaces. These results suggest that vertical redistribution caused by groundwater oscillation significantly increases residual contamination, which cannot be evaluated using static groundwater assumptions. Comparisons with a commonly used NAPL simulator revealed that conventional models overestimate lateral spreading and underestimate trapped residual oil, thus highlighting the need for improved constitutive models and numerical schemes that can capture sharp saturation fronts. These results emphasize that an accurate assessment of LNAPL contamination in urban settings requires an explicit consideration of groundwater fluctuation and dynamic multiphase interactions. Insights from this study support rational monitoring network design, reduce uncertainty in remediation planning, and contribute to sustainable urban environmental management by improving risk evaluation and preventing the long-term spread of pollution. Full article

The growing demand for low-carbon, resource-efficient, and multifunctional construction materials has intensified interest in solutions that can support both circular economy strategies and sustainable urban development. Expanded perlite—a lightweight volcanic material with low embodied energy and multiple functional properties—is increasingly considered a potential component of circular and nature-based material systems. This paper critically examines whether expanded perlite can serve as a sustainable alternative in civil engineering applications, contributing to reduced material consumption, improved thermal performance, and lower environmental impact across the life cycle. The review provides an overview of current applications of expanded perlite in lightweight concretes, insulation systems, green roofs, water-retention substrates, and other technologies relevant to resilient and net-zero cities. It also identifies key research gaps related to long-term durability, large-scale implementation, and life-cycle assessment, while emphasizing the need for proper handling procedures due to health concerns associated with dust exposure. By situating expanded perlite within the context of circular design and low-carbon construction, the paper highlights its potential role in decarbonizing the built environment and advancing the transition toward climate-resilient and regenerative urban systems. Full article

Long-term continuous cropping in cotton fields of Southern Xinjiang has limited crop productivity. To investigate how subsoiling depth regulates ecosystem-level water partitioning and thereby enhances water productivity mechanisms, a two-year field experiment was conducted in a mulched drip irrigation cotton field in Southern Xinjiang. Using a non-subsoiled field in the current season (CT) as the control, three subsoiling depths were established: subsoiling at 30 cm (ST1), 40 cm (ST2), and 50 cm (ST3). Changes in evapotranspiration partitioning and water use efficiency were analyzed. The results showed that subsoiling enhanced the utilization of deep soil water. Compared with CT, the ST2 and ST3 treatments significantly reduced soil water storage in the 0–60 cm layer during the flower opening to boll-setting stages, while soil water consumption increased by 26.4 mm and 28.8 mm, respectively. We demonstrate that subsoiling depth exerts a predominant control on the partitioning of evapotranspiration. Increasing subsoiling depth systematically shifted water loss from non-productive soil evaporation to productive plant transpiration, with the ST2 and ST3 treatments decreasing seasonal soil evaporation by 24.1% and 25.1%, respectively, and increasing plant transpiration by 21.9% and 22.8%, and lowering the Es/ET (where Es is soil evaporation and ET is evapotranspiration) ratio by 22.1% and 27.1%. However, this maximal physiological water-saving did not yield the optimal agronomic return. We established a non-linear relationship in which the ST2 treatment uniquely achieved the maximum seed cotton yield, WUE (water use efficiency), and IWUE (irrigation water use efficiency) (increasing by up to 34.4%, 17.2%, and 23.4%, respectively). This optimal depth better balances water resource allocation and reproductive growth. We conclude that under sandy loam soil conditions in typical mulched drip-irrigated cotton areas of Southern Xinjiang, implementing an optimal subsoiling depth (40 cm) can engineer a more resilient soil–plant–water continuum, providing a feasible pathway toward enhancing water and crop production sustainability. Full article