Search Results (9,623)

Food loss and waste (FLW) represent a significant source of resource inefficiency in water-scarce economies. This study quantifies the water footprint (WF) of FLW in Saudi Arabia using product-level blue, green, and grey WF coefficients from the Water Footprint Network database. Our analysis covers 3.997 million tons of FLW across 19 commodities grouped into cereals, fruits, vegetables, and meat. Results indicate that FLW is associated with a total blue and green WF of 7.3 billion m³, of which 2.1 billion m³ is blue water directly associated with managed water resources. The blue WF is equivalent to approximately 20% of agricultural water withdrawals and 62% of domestic water demand. Despite constituting only 13% of total FLW by mass, meat products account for 53% of the total water footprint, driven by their exceptionally high water intensity (7474 m³/ton). The consumption stage dominates water losses, contributing 56% of the total blue and green WF. Based on alternative water supply cost benchmarks, the blue WF embedded in FLW corresponds to an indicative production-cost equivalent ranging from 1.03 to 6.5 billion SAR. A 25% reduction in FLW could save over 500 million m³ of blue water annually. These findings demonstrate that FLW reduction represents an important supporting strategy for water resource management and provides a quantitative basis for prioritizing intervention across food groups and supply-chain stages. Full article

To address runoff forecasting inaccuracies caused by data gaps in reservoir operations, this paper proposes a collaborative modeling framework integrating deep learning, signal decomposition, uncertainty quantification, and transfer learning. Validated on the Wei River (source basin) and Yongding River (target basin) with similar hydrological characteristics, the framework first constructs a Pyraformer-BiLSTM-LSS point forecasting model to enhance characterization of non-stationary runoff sequences. Then, the BLSO-VMD optimization decomposition technique filters and reconstructs forecasting noise, improving model robustness. Subsequently, a probabilistic interval forecasting model is developed via multi-task learning to reliably quantify uncertainty. To tackle data scarcity in the target domain, a “decomposition–reconstruction–transfer” learning mechanism transfers model knowledge from the source domain to the target domain. Results show that the framework achieves excellent performance in the source domain and successfully transfers to the data-scarce target domain, significantly enhancing the accuracy and stability of both point and interval forecasts. By establishing a collaborative modeling framework combining transfer learning and multi-task learning, along with an adaptive signal decomposition method based on BLSO and a multi-scale deep learning model, this study effectively addresses the challenges of accuracy and reliability in runoff forecasting for data-scarce basins. It provides a transferable and scalable technical pathway for runoff simulation and reservoir operation in hydrologically underserved regions, supporting sustainable water resource management and ecological protection. Full article

Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979–2020. Rainfall trends were investigated using non-parametric Mann–Kendall test (MK test) and Sen’s slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen’s β slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +19.2%. During 1979–2020 post-monsoon rainfall showed the highest increase (62–70%) while, post 2001, monsoon rainfall declined substantially (1.7–3.3 mm/year) across all districts, with Balasore showing the largest decrease (−3.3 mm/year). The earlier period (1979–2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change. Full article

Marine ecological assets (MEAs) comprise habitats, living resources, and ecosystem services and are globally fundamental to biodiversity conservation, climate regulation, and sustainable development. However, the establishment of systematic frameworks for MEA definition, classification, and valuation faces considerable conceptual and methodological challenges, particularly in rapidly industrializing nations with urgent marine conservation priorities. We reviewed the theoretical evolution, methodological development, and practical implementation of MEA accounting in China and propose an integrated framework that bridges conceptual gaps and supports evidence-based policy for sustainable marine governance. Our comprehensive analysis covers domestic and international literature, policy documents, technical standards, and case studies. We developed a definition that incorporates ownership attributes and dynamic management elements, and constructed a three-in-one classification system that integrates resource characteristics, ecological functions, and governance needs from existing international frameworks adapted to the governance context in China. We identified seven multidimensional MEA attributes and systematically evaluated mainstream valuation methods (market-based, alternative market, and hypothetical market approaches) across different marine ecosystem types (e.g., estuaries, coral reefs, mangrove forests). The review provides a coherent theoretical foundation for advancing MEA accounting in China and offers practical pathways for integrating accounting outcomes into policy mechanisms such as ecological compensation, blue carbon trading, and marine spatial planning. Our framework contributes to the operationalization of the philosophy that “lucid waters and lush mountains are invaluable assets” in marine governance and may provide preliminary insights for other nations developing MEA capital accounts to achieve sustainable development goals, although cross-national comparative validation is necessary to assess applicability beyond the Chinese context. Full article

Diospyros comorensis Hiern is a medicinal plant traditionally utilized in the management of cardiovascular disorders. Despite its common use, the pharmacological properties and phytochemical composition remain unexplored. This study aimed to evaluate the vasorelaxant, diuretic, and antioxidant activities, as well as toxicity and phytochemical profiling, of a methanol–water extract of D. comorensis leaves (MDCR) and a decoction of D. comorensis leaf (DDCR) extract. The main phytochemicals were quantified using High-Performance Liquid Chromatography (HPLC). Antioxidant capacity was assessed using DPPH and FRAP assays. The vasorelaxant effect was evaluated in vitro on phenylephrine-precontracted aortic rings. Diuretic activity was determined by measuring Wistar rats’ urine output and electrolyte levels (Na⁺, Cl⁻, and K⁺). Toxicity was assessed using Swiss mice. The extracts showed a total phenolic content (TPC) of 29,693.02 ± 3493.75 mg GAE/100 g DW (Folin–Ciocalteu method), which was markedly higher than the total phenolics quantified by HPLC (3743.12 ± 457.32 mg/100 g DW, representing 76.38% of the total bioactive fraction). Among the quantified constituents, ellagic acid (56.36%) was the main compound. Both extracts exhibited marked antioxidant capacity along with significant vasorelaxant effects on phenylephrine-precontracted rat aorta rings, with EC50 values of 3.83 ± 0.81 µg/mL for MDCR and 4.87 ± 0.79 µg/mL for DDCR. Acute toxicity was not observed with either extract. The identified compounds may be involved in the observed antioxidant and pharmacological effects. These results show experimental evidence useful to support the traditional use of D. comorensis leaves in managing high blood pressure and highlight the antihypertensive potential of this Comorian endemic species. Further studies are necessary to characterize the biological mechanisms involved and relative bioactive substances. Reporting the pharmacological activities of D. comorensis may contribute to the sustainable use of natural resources in the Comoros Islands and Madagascar. Full article

Dissolved oxygen (DO) is a critical indicator for assessing marine ecological health and hypoxia risk. Most existing DO prediction studies rely on time-series forecasting models, which require continuous temporal observations and are often unreliable in practical marine monitoring scenarios due to sparse sampling, missing records, and heterogeneous measurement conditions. To address this limitation, this paper investigates the problem of non-temporal DO prediction, aiming to learn a direct nonlinear mapping between environmental drivers and DO concentration. To explicitly model nonlinear pairwise interaction effects between environmental variables, we propose a Factor-Interaction Neural Network (FINN), which decomposes DO estimation into main effects and structured pairwise interaction effects. This interaction-driven design enhances both representation capacity and interpretability compared with conventional multilayer perceptrons. Furthermore, we develop a physics-informed extension, termed PI-FINN, by incorporating oceanographic-consistent regularization priors that reflect key DO formation mechanisms, including temperature-related solubility behavior, depth-wise smoothness associated with stratification, and chlorophyll-driven biological oxygen production tendencies. To evaluate the physical plausibility of model predictions beyond standard accuracy metrics, we introduce a physics-consistency assessment protocol based on Physics Consistency Violation Rate (PCVR) and its robust variant, and further analyze their convergence stability under different driver-weight configurations. Extensive experiments on a real-world marine dataset demonstrate that FINN achieves competitive predictive accuracy compared with strong machine learning baselines (e.g., SVR, Random Forest, and XGBoost), while the proposed physics-informed design mainly improves the physical consistency, robustness, and interpretability of DO estimation under heterogeneous environmental regimes, although it does not necessarily guarantee superior RMSE or MAE performance compared with purely data-driven models. Specifically, FINN achieves an RMSE of 0.3130, an $R^2$ of 0.9831, and a PCVR of 0.4826 on a dataset composed of key environmental variables, including depth, temperature, salinity, and chlorophyll-a, collected under sparse and irregular sampling conditions. Ablation studies confirm the effectiveness of both factor-interaction modeling and physics-guided regularization components. Overall, the proposed framework further provides a reliable tool for sustainable environmental monitoring by enabling physically consistent dissolved oxygen prediction under sparse observational conditions. Such capability is critical for supporting sustainable water resource management, hypoxia risk assessment, and long-term ecological protection. Full article

Although South Africa has an extensive water infrastructure, it continues to face significant water scarcity due to its semi-arid climate, increasing urbanisation, ageing infrastructure, and pollution. These challenges, coupled with climate change and increasing water demand, have led to inefficiencies across the water value chain, particularly in rural areas. This review paper evaluates the current adoption of predictive analytics in South Africa’s water management system through a systematic literature review. It identifies the current applications, implementation gaps, and key system components that are suitable candidates to enhance efficiency, resource planning, and long-term sustainability in the sector. The findings show that while predictive models are being applied in urban systems for demand forecasting and proactive maintenance, only 15% of the reviewed studies address their actual adoption in rural or under-resourced contexts. This underscores the need for more inclusive development strategies to ensure equitable water service delivery. Although strides have been made in research and innovation, a major barrier is the slow transition from research to operational deployment, which hinders the full realisation of these technologies’ benefits that are essential for water supply sustainability and availability. Full article

Scientific knowledge-based fishery management is essential to ensure the sustainability of marine resources, particularly in regions where fisheries are data-limited. This study assessed the stock status of six shallow-water demersal fish species (Bagre marinus, Cathorops mapale, Diapterus rhombeus, Eucinostomus argenteus, Haemulopsis corvinaeformis, and Lutjanus synagris) in the Colombian Caribbean Sea using three complementary length-based models: length-based indicators (LBIs), length-based spawning potential ratio (LBSPR), and the Length-Based Bayesian Biomass estimator (LBB). The integrated results demonstrated that five species (C. mapale, D. rhombeus, E. argenteus, H. corvinaeformis, and L. synagris) are currently overexploited (F/M > 1 and B/B_MSY < 1), while B. marinus is experiencing overfishing (F/M > 1 and B/B_MSY > 1), with a high risk of surpassing its maximum sustainable yield. These outcomes confirm that demersal fish populations in the Colombian Caribbean are being exploited beyond sustainable biological limits. With the aim of promoting stock recovery and long-term sustainability, this study recommends the implementation of recently evaluated management measures focused on (i) the implementation and enforcement of Bycatch Reduction Devices (BRDs); (ii) the regulation and monitoring of trawl net mesh sizes to improve selectivity patterns; (iii) the establishment of spatial and temporal closures in critical spawning areas for demersal fish species; and (iv) the strengthening of fishery monitoring and data collection systems. The findings provide critical baseline information and a methodological framework to support evidence-based fishery management and conservation strategies in tropical multispecies fisheries under data-limited conditions. Full article

Significant cyanobacterial proliferations dominated by Lyngbya have been increasingly reported since the 2000s, posing environmental, economic, and human-health risks. This review synthesizes their distribution, predictors, toxicity, and management strategies of all identified Lyngbya species, including species historically classified as Lyngbya despite later taxonomic changes. Research has focused mainly on Lyngbya majuscula and Lyngbya wollei. For L. majuscula, bloom initiation is driven by proximate abiotic factors such as nutrients, light, and temperature; while broader conditions, including bottom currents, sediment nutrients, rainfall, and land use, set the stage for proliferation. Toxin production appears related to nutrient levels and temperature, although mechanisms remain poorly understood. Management of L. wollei commonly relies on copper-based chelated algaecides, despite their risks to non-target organisms, highlighting the need for more sustainable tools used in managing other cyanobacteria. Existing predictive models for Lyngbya proliferation show limited accuracy, partly due to insufficient in situ data. This review argues that novel monitoring approaches could provide the data needed to strengthen predictive models, also offering insights into a new modeling approach, supporting more proactive and effective Lyngbya bloom management. It is particularly valuable for research in water resource management and environmental science, as it synthesizes current knowledge essential for advancing management strategies. Full article

River water quality prediction is a crucial aspect of water environment management and ecological conservation, holding significant importance for ensuring the sustainable utilization of water resources. As a key indicator for assessing river self-purification capacity and aquatic ecosystem health, the accurate prediction of dissolved oxygen (DO) is particularly vital for water quality early warning. To address the challenges that single deep learning models face in collaboratively modeling long- and short-term dependencies, and that most hybrid methods fail to adequately consider the characteristic differences in various components within a time series, this paper proposes an STL-TCN-LSTM model for predicting DO concentration in river water. The proposed model first employs seasonal-trend decomposition using Loess (STL) to decompose the original time series into three components: trend, seasonality, and residual, aiming to separate features at different time scales. Then, three parallel Temporal Convolutional Networks (TCNs) are utilized to extract temporal features from each component and reconstruct the sequence. Finally, the reconstructed results are fed into a Long Short-Term Memory (LSTM) network to further model their dynamic temporal dependencies, thereby enhancing prediction accuracy. The performance of the proposed model is validated on three river water quality datasets from different river basins with varying sampling frequencies. The experimental results on the three river datasets show that the STL-TCN-LSTM model consistently outperforms all baseline models, including LSTM, TCN, BiLSTM, GRU, CNN-LSTM, and XGBoost. Specifically, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by an average of 14.47%, 14.51%, and 14.27%, respectively, while the coefficient of determination (R²) improves by an average of 0.79%. The Wilcoxon signed-rank test confirms that all performance improvements are statistically significant (p < 0.05). These results demonstrate that the proposed model achieves higher prediction accuracy and exhibits stronger generalization capability in DO forecasting, thereby offering a reliable tool for water quality early warning and aquatic environmental management. Full article

The desalination of brackish and seawater has emerged as a critical strategy to address growing water scarcity in regions experiencing water stress, particularly within the context of sustainable water resource management. Among available technologies, forward osmosis (FO) has gained increasing attention due to its potential for lower energy consumption and reduced environmental impact compared to conventional desalination processes. In this study, commercial HFFO₂ (Aquaporin Inside) membrane from FO was used. A complete factorial design with three factors was used: feed solution concentration (1.5 and 3 g/L NaCl), draw solution concentration (15, 25, and 35 g/L NaCl), and feed solution flow rate (600 and 1000 mL/min) on the percentage of recovery and water flux. Tests showed that as the feed concentration decreases from 3 to 1.5 g/L of NaCl, water recovery improves by 23.6%. The results revealed that increasing the concentration of the draw solution from 15 to 25 g/L of NaCl increased water recovery by 22.2%. However, for a concentration variation of 25 to 35 g/L, this increase is insignificant at 0.92%. The results showed that, with a concentration of 1.5 g/L of NaCl, a feed flow rate of 1000 mL/min, and a concentration of 25 g/L of NaCl as the draw solution, a higher water recovery rate (95.4839%) was achieved. Similarly, average water flux values of 2.18, 2.43, and 2.68 $L{m}^{-2}{h}^{-1}$ were observed when using draw solutions of 15, 25, and 35 g/L of NaCl, respectively. In addition, increasing the FS flow rate slightly reduces water recovery (from 76.04% to 74.06%). Consequently, the forward osmosis process has proven to be effective, practical, viable, and environmentally friendly for water desalination, as well as being applicable to the treatment of wastewater with high electrical conductivity. Full article

Rain gauge networks are the core infrastructure for hydrological and water resource monitoring, flood control and disaster mitigation early warning, and water resource planning and regulation. The rationality of their layout directly determines the accuracy, representativeness, and economy of regional precipitation data acquisition. Considering that information entropy can accurately characterize the spatial distribution law and information complexity of rainfall, and spatiotemporal deep learning models have strong capabilities in fitting spatiotemporal features, this paper couples mutual information entropy with a spatiotemporal deep learning model and proposes a novel optimal layout method for rain gauge networks. Daily observed rainfall data from 50 ground-based rain gauges in the upper reaches of the Tuojiang River during 2015–2024, as well as the PERSIANN-CCS remote sensing precipitation product for the same period, were used in the study. A CNN-LSTM spatiotemporal deep learning model integrating spatial features and temporal dependence was constructed, coupled with the mutual information entropy index, and the GA-PSO hybrid optimization algorithm was applied for solution. The superiority of the proposed method was verified by comparison with the calculation results of the traditional mutual information entropy-based greedy optimization algorithm. The results show that the hybrid optimization algorithm driven by the spatiotemporal deep learning model coupled with mutual information entropy is significantly superior to the comparison algorithm in terms of the rationality of the station network structure, the ability to characterize spatial rainfall distribution, the control of average relative error, and the improvement of total information entropy. After optimization, the number of rain gauges in the upper reaches of the Tuojiang River can be reduced from 50 to 25. While greatly reducing the number of stations, the optimized network can still relatively accurately reflect the spatiotemporal characteristics of rainfall in the basin, which can provide a theoretical basis and technical support for the optimal layout of basin rain gauge networks and water resource management. Full article

Rice cultivation in climate-sensitive regions necessitates adaptive irrigation and nutrient management strategies to enhance resource utilization efficiency and mitigate operational uncertainty. This study investigated the operational feasibility of an Internet of Things (IoT)-based monitoring and recommendation system for real-time soil moisture and nutrient-related operational monitoring in large-scale rice farming environments in Thailand. An integrated IoT-assisted monitoring and recommendation framework comprising sensing, communication, analytics, and recommendation components was developed and evaluated under practical field-deployment conditions. The system incorporated soil moisture monitoring and nutrient-related operational sensing, cloud-based data processing, machine learning-assisted prediction, and mobile notification services to support irrigation and fertilizer management. A comparative evaluation between conventional and IoT-assisted management conditions revealed lower irrigation water use (947.38 vs. 7638.38 m³/ha), reduced fertilizer utilization (41.40 vs. 347.56 kg/ha), and lower production costs (4230.88 vs. 30,664.69 THB/ha) under IoT-assisted conditions. Average profit also increased from 2357.68 to 23,920.00 THB/ha. User evaluation indicated high overall satisfaction (mean = 4.28/5.00). The findings suggest that integrating IoT-based sensing, machine learning-assisted prediction, and optimization-driven recommendation workflows within a unified field-deployment framework may improve adaptive irrigation management, resource-allocation efficiency, and operational decision support under climate-sensitive rice cultivation environments. Full article

As a result of long-term groundwater overexploitation, the thickness of the vadose zone in the NCP has significantly increased, leading to changes in moisture transport patterns and groundwater recharge processes. This research gathers data on soil water potential and moisture content by conducting in situ profile monitoring of a 30.4 m thick vadose zone. A 44.5 m geological borehole was drilled for the purpose of measuring the hydraulic parameters of undisturbed soil samples, collecting ³⁶Cl isotope tracer samples, and constructing a coupling model of the unsaturated–saturated zone with a depth of 47 m. The research objectives were to examine the moisture transport law and infiltration recharge mechanisms in deep vadose zones. Comprehensive analysis shows that the average infiltration velocity is 0.661–0.743 m/a and the average recharge intensity is 103.1–115.9 mm/a. The depth and silty clay play an important role in affecting the infiltration process. The characteristics of infiltration can be divided into three segments: rapid, slow, and stagnant. The residual pore gases in the clay strata have a certain inhibitory effect on moisture transport. The time required for precipitation infiltration is 75.14 years for a 44.5 m thick vadose zone; thereafter, new water replaces old water to continue recharging the aquifer. In recent years, the government has taken multiple actions to alleviate this continuous downward trend in groundwater levels, including river ecological flow replenishment and groundwater extraction reduction. Additionally, increased precipitation since 2021 has objectively halted the previous thickening trend of the vadose zone. It is recommended to further strengthen groundwater resource management and enhance groundwater-level monitoring and warning to prevent further declines. This research holds significant implications for the evaluation and sustainable management of groundwater resources in large-scale plains in semi-humid areas. Full article

Mountain vegetation in dryland regions is highly sensitive to climatic variability, particularly changes in water availability and atmospheric demand. This study assessed the relationships between vegetation coverage and climatic factors in the Chinese Altay Mountains from 2000 to 2024 using MODIS NDVI data, meteorological observations, drought indices, and extreme climate indicators. Pixel-based correlation analysis and directional interaction classification were used to evaluate the spatial consistency and divergence between vegetation dynamics and climate variability. The results showed that water availability was the dominant factor controlling vegetation cover. Annual precipitation, SPEI, and precipitation-related extreme indices were generally positively associated with vegetation coverage, whereas warmth-related indices such as GSL, WSDI, and TX90 were mostly negatively associated with vegetation coverage. Temperature showed a spatially variable effect, with warming tending to suppress vegetation in water-limited low- and middle-elevation areas but potentially benefiting vegetation in cold-limited high-elevation zones. SPEI showed a more consistent relationship with vegetation coverage than TVDI, indicating that cumulative climatic water balance better captured regional vegetation drought responses than surface dryness alone. These findings highlight the importance of vegetation–atmosphere water deficit in regulating mountain vegetation dynamics and provide a scientific basis for ecological conservation and water resource management in the Altay Mountains. Full article