Applied Water Science最新文献

This study investigates the spatial variability of surface water balance within the Semen Omo Zone in Ethiopia, leveraging data from the Global Land Data Assimilation System (GLDAS) and Empirical Bayesian Kriging (EBK) techniques. The primary parameters analyzed include total precipitation rate (TPR), evapotranspiration (ET), storm surface runoff (SRO), and baseflow groundwater runoff (BF). The study focuses on two scenarios: Scenario I, which considers only surface water components (TPR-ET-SRO), and Scenario II, which incorporates partial groundwater (TPR-ET-SRO-BF). In Scenario I, significant variations in water balance were identified across different watersheds. Watersheds such as WS16, WS15, and WS14 exhibited surplus water, while WS3 showed a notable deficit, indicating insufficient precipitation compared to evapotranspiration and runoff. Scenario II provided a more comprehensive analysis, revealing that watersheds WS17, WS14, and WS6 experienced substantial water deficits when both surface and groundwater components were considered. Conversely, watersheds like WS21 and WS19 were identified as water-efficient areas. The geological context significantly influenced the water balance outcomes. Regions underlain by old crystalline granite schist diorite and marine sediments demonstrated higher water budgets in Scenario I. Scenario II indicated the crucial role these formations play in groundwater recharge and storage. The findings underscore the necessity of integrated water management practices that consider both surface and groundwater resources alongside geological variability. This comprehensive analysis offers valuable insights for policymakers and water resource managers in developing targeted strategies for sustainable water management, ensuring long-term water resource sustainability in the Semen Omo Zone and potentially other similar regions.

This study aimed to optimize water productivity and wheat yield in the rainfed wheat systems of the Honam plain, a critical region in the upper Karkheh River basin of Iran. In the first two years of research (2013–2014 and 2014–2015), the prevailing status of the region was investigated with regards to wheat yield and rainfall productivity under rainfed conditions. Thereafter, different management scenarios were defined and investigated to improve wheat yield, rainfall productivity, and water productivity. In the second year of research (2014–2015), the best management scenarios selected from the first two years were tested in some selected rainfed wheat farms in the Honam plain. The results showed that wheat biomass and grain yields from these best scenarios under rainfed and single irrigation (SI) conditions could be accurately predicted using the AquaCrop model. At the model validation stage, the RMSE was 0.16 for grain yield and 0.32 ton ha⁻¹ for biomass and the NRMSE was 5 and 4%, respectively. Whether for grain yield or crop biomass, the coefficient of determination was about 0.86. The proposed scenarios for AquaCrop modelling were then trialed for rainfed wheat and showed better agronomic advantages than the traditional crop management practices. By applying a single irrigation in spring, the mean total water productivity (rainfall + irrigation) for wheat increased to 0.70 kg m⁻³, being 74% higher than that under rainfed conditions. The best management plan in the Honam plain was the combination of superior crop management with single irrigation in spring (60 mm) during the mid-flowering period, which increased the grain yield by 176% and rainfall productivity by 134%. The results from this management scenario were satisfactorily simulated by the AquaCrop model.

Ethiopia confronts considerable challenges pertaining to the availability of clean drinking water, impacting numerous communities throughout the nation. This review critically evaluates the present condition of water quality and sanitation in Ethiopia, underscoring significant barriers and proposing feasible strategies to guarantee access to potable water and sufficient sanitation facilities. The investigation explores the determinants contributing to the insufficiency of water supply and sanitation infrastructure, pinpointing fundamental issues such as inadequate infrastructure development, restricted water distribution networks, ineffective waste management practices, and the overuse of insecticides and synthetic fertilizers. Untreated sewage, industrial effluents, and agricultural runoff further intensify contamination risks. Utilizing a comprehensive analysis of 36 scientific journals, studies, and articles acquired from repositories such as PubMed, Google Scholar, ResearchGate, and various indexed scholarly journals, the review elucidates disparities in water quality across various regions. While certain locales exhibit moderate water quality, others contend with severe contamination, presenting significant public health hazards. The results accentuate the imperative of enacting measures to improve water quality and ensure equitable access to clean drinking water for all populations. Proposed strategies advocate for substantial investments in water and sanitation infrastructure that are congruent with sustainable development objectives. Policy initiatives should prioritize the enhancement of water reservoirs, the expansion of distribution systems, and the promotion of environmentally sustainable agricultural practices. Moreover, capacity-building initiatives for healthcare institutions, researchers, policymakers, and stakeholders are essential for effectively addressing these challenges. Fortifying these efforts will contribute to alleviating water pollution, enhancing sanitation services, and protecting public health for forthcoming generations. Furthermore, the findings provide valuable lessons for other developing countries facing similar water quality challenges, and contribute to international efforts to achieve Sustainable Development Goal 6 (clean water and sanitation for all).

Phthalates can enter bottled water during production, packaging, and storage due to inadequate contact between the polymer and the chemical used. The research utilized several databases such as Web of Science, Scopus, and PubMed. Following an extensive search for duplicate and unnecessary information, a total of 10 research selected from a total of 2359 initial publications. The mentioned databases included articles dated from the first of February 2000, to June 10, 2025. The results show that Elham Khanniri, Mohammed F. Zaater, and Iman Al-Saleh had the highest mean concentrations of DEP (0.97 µg/l), DEHP (3.56 µg/l), DBP (6.53 µg/l), and BBP (1.19 µg/l). Based on the result, phthalate concentrations in bottled drinking water across the EMRO region are significantly influenced by storage temperature and duration. High temperatures (25 °C and 40 °C) markedly accelerate the migration of phthalates (like DEP and DEHP) from the plastic, while low-temperature storage (4 °C) effectively prevents this increase.

Zoos are significant water consumers, exacerbating water scarcity challenges and impacting animal welfare. Despite the urgent need for effective water management in zoos, research on water saving remains limited. This study analyzes water consumption and potential of water saving strategies for animal welfare, using to Barcelona Zoo as a case study. Barcelona zoo’s historical water consumption averages 423,747 m³ annually, equivalent to the daily water usage of approximately 10,554 people. Fluctuations in consumption are linked to renovations, rather than seasonal variations, due to the zoo’s effective water ponds renewal, cleaning procedures, and filtration systems. Water is mainly sourced from potable water (68% of total input), with seawater utilized in certain animal habitats, including for Spheniscus humboldti (Humboldt penguins) and Zalophus californianus (California sea lions). The animal ponds with the highest water consumption are Choeropsis liberiensis (Pygmy hippopotamus), Ursus arctos arctos (Eurasian brown bear), and Hippopotamus amphibius (Hippopotamus). While water consumption remains stable year-round, opportunities for water reuse, particularly in cleaning and filtration processes, are identified as critical for improving water efficiency. This study emphasizes the need for targeted water management strategies in zoos, emphasizing the importance of recycling wastewater, optimizing filtration systems, and exploring water conservation initiatives. The findings from Barcelona Zoo offer transferable sustainable water management practices for zoological and wildlife facilities, reducing demand and enhancing environmental sustainability.

Background

During dry seasons, accurate predictions of the reference evapotranspiration (ET₀) are crucial for effective water management and irrigation. Machine learning (ML) models rely on existing data to make predictions; however, they struggle to perform in new locations where data are insufficient. Methods: This study improved ET₀ prediction in diverse locations with limited data by proposing regional scenarios that utilize datasets from a wider region for training. Historical weather data from four California weather stations were used to evaluate classical ML models: linear regression (LR), ridge regression (RR), multilayer perceptron (MLP), and support vector regression (SVR), along with ensemble methods, such as: random forest (RF), extra trees (ETs), extreme gradient boosting (XGB), and gradient boosting regression (GBR). The performance was assessed using the mean absolute error (MAE) and root mean square error (RMSE) in both the local and regional scenarios. Results: ET and GBR showed significant improvements in regional scenarios. After validation at two new stations, ET consistently outperformed GBR as a robust global model for ET₀ prediction in new California locations with minimal data. The performance remained near the minimum error, with MAE values of 0.1001 (RMSE: 0.1582) in Ferndale, 0.1494 (RMSE: 0.2279) in Linden, and 0.0974 (RMSE: 0.1495) in Smith River. Conclusion: A regional approach enhanced ML-based ET₀ predictions, particularly in data-scarce areas. These findings support the adoption of smart farming and sustainable water resource management.

Irrigation is a fundamental scheme for poverty reduction, food security, and proved to better farmer economy through additional income during the dry season. Fuzzy logic algorithm was used in this study because it offers a more nuanced, scalable, and realistic approach to surface irrigation suitability mapping than traditional binary methods making it particularly suitable for complex watershed environments like Keleta. The main aim of this study was to identify suitable potential zones for surface irrigation in the Keleta watershed using a fuzzy logic algorithm in GIS. Fuzzy logic algorithm was used in this study because it offers a more nuanced, scalable, and realistic approach to surface irrigation suitability mapping than traditional binary methods making it particularly suitable for complex watershed environments like Keleta. Factors considered are rainfall deficit, Soil Capability Index, land use land cover, slope, Hydrogeology, Groundwater yield, and proximity to (urban, roads, rivers, and wells). Potential evapotranspiration was simulated based on the modified Penman–Monteith method. Relative weight for each factor was determined by the geometric mean method of Fuzzy AHP. To overlay, GIS-based gamma 0.9 fuzzy weighted overlay operators were used. The results indicate that, based on surface water sources, 18.56% of the area is highly suitable, 33.78% moderately suitable, 30.16% marginally suitable, and 16.86% not suitable for surface irrigation. This means that approximately 52% of the watershed is suitable for surface irrigation without requiring significant land modification. Regarding groundwater potential, 8.34% of the area is highly suitable, while 12%, 32.87%, and 48.01% fall into moderately, marginally, and not suitable categories, respectively. Additionally, a restricted area covering 5.28 km² (0.65%) was identified due to environmental or physical limitations. Overall, the findings confirm the feasibility of expanding surface irrigation in the Keleta watershed. The study offers a valuable tool for policymakers and planners to prioritize irrigation development and provides a foundation for researchers and development agencies to collaborate in enhancing land suitability and promoting sustainable and economically viable irrigation practices in the region.

Water scarcity and climate change pose significant challenges for Sudan, leading to considerable migration. A total of 1 million hectares of arable land are irrigated, while 6.7 million hectares employ semi-mechanized rainfed agricultural practices. In contrast, a significant 9 million hectares depend solely on conventional rainfed techniques. GWFP deals with precipitation stored in the soil as moisture and consumed in biomass production, as agricultural products are usually irrigated with rainwater and thus more dependent on green water sources. Calculating the green water footprint is important for developing sustainable agricultural practices and effectively managing water resources. The accurate estimation of the GWFP value is very important in economics as an approach to foster the virtual green water trade and improve human well-being. This research aims to assess the efficacy of machine learning models in predicting the green water footprint (GWFP) of cotton within the framework of climate change. By examining a range of input variables, including climatic conditions, agricultural data, and remote sensing indices, the study explores their impacts on cotton cultivation over the time frame from 2001 to 2020. A total of seven models were implemented, comprising random forest (RF), Extreme Gradient Boosting (XGBoost), and support vector regressor (SVR), along with hybrid combinations such as RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, across five scenarios (Sc) incorporating diverse variable combinations utilized throughout the investigation. The maximum and minimum RMSE values varied between 31.35 m³ t⁻¹ and 166.37 m³ t⁻¹, based on the RF-XGB-SVR hybrid model and the RF model, respectively, under Sc5 (Peeff, and Tmax). The highest R² values were achieved with hybrid ML models, whether double or triple, across all scenarios, reaching values of 1.0 or 0.99. The lowest R² value, recorded at 0.0676, was noted under SVR and Sc3, followed closely by XGB and Sc3 with a value of 0.0767. The box plot for GWFP of cotton indicated that the XGB-SVR and Sc3 exhibited the lowest interquartile range (IQR) at 0.047, succeeded by the RF-XGB-SVR model with Sc3 at a value of 0.052; however, the XGB-SVR hybrid model displayed the highest IQR in Sc5 at 0.098. The research concludes that hybrid models outperformed single models in forecasting cotton GWFP. Furthermore, remote sensing indices showed a negligible positive impact on GWFP prediction, with Sc3 yielding the lowest statistical results across all models. The study recommends the employment of hybrid models to reduce the error term in predicting cotton GWFP.