Search Results (1,717)

This research assesses four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) concerning precipitation quantiles across Korea, utilizing the CMIP6 multi-model ensemble comprising 23 General Circulation Models alongside observational data to project future changes. Precipitation quantiles, derived from regional frequency analysis conducted at 615 sites, are calculated as annual averages for the period from 2015 to 2024. Each SSP scenario is evaluated for its spatial distribution through the application of observational data and chi-square tests, with the results indicating that the SSP3-7.0 ensemble most accurately reflects the current quantile estimates derived from observational data. Furthermore, interannual precipitation quantiles are projected to extend to the year 2100 to discern long-term trends within each reproducible period. It is anticipated that precipitation associated with the 100-year reproducible period will increase by over 20% in most regions across the nation by the century’s end, with this increase becoming more pronounced in accordance with the severity of the pathway. These findings underscore significant increases in extreme rainfall events under high-emission scenarios and highlight the critical need for the integration of enhanced flood mitigation, water resource management, and climate adaptation strategies within Korea’s planning framework. Full article

Healthcare institutions are under increasing pressure to deliver high-quality, patient-centered care while reducing their environmental footprint. Integrating quality and environmental management systems (ISO 9001 and ISO 14001) into a unified integrated management system (IMS) offers a potential pathway to meet these dual imperatives. This study investigates the effects of IMS implementation in three European hospitals through a comparative qualitative analysis of institutional reports, audit documentation, and performance indicators. The methodology combines a literature-informed conceptual framework with a multi-case analysis guided by four domains: environmental impact, care quality, process efficiency, and stakeholder engagement. The data were collected from institutional documentation over a six-year period (three years before and after IMS implementation), covering key indicators such as energy and water consumption, medical waste recycling, audit compliance, and patient satisfaction. The findings show that IMS adoption was associated with a 20–28% improvement in resource efficiency, increased recycling rates, and consistent gains in compliance and satisfaction metrics. These results were supported by strategic leadership, cross-functional training, and digital monitoring tools. The study concludes that IMS enhances institutional performance and sustainability while aligning healthcare operations with broader governance and policy goals. Further research is recommended to explore the long-term impacts and generalize the findings across healthcare systems. Full article

Climate and land use changes significantly affect runoff and hydrological drought, presenting challenges for water resource management. This study focuses on the Naoli River Basin, utilizing the SWAT model integrated with PLUS land use projections under the CMIP6 SSP245 and SSP585 scenarios to assess trends in runoff and drought characteristics from 2025 to 2100. The Standardized Runoff Index (SRI) and run theory are applied to analyze drought frequency and duration. Key findings include the following: (1) Under the SSP585 scenario (2061–2100), land use changes—specifically, a reduction in cropland and an increase in forest cover—resulted in a 12.59% decrease in runoff compared to the baseline period (1970–2014), with notable differences when considering climate-only scenarios. (2) The SSP585 scenario exhibits a significant rise in drought frequency and duration, particularly during summer, whereas SSP245 shows milder trends. (3) Based on the Taylor plot evaluation, the ensemble average MMM-Best (r = 0.80, RMSE = 26.15) has been identified as the optimal prediction model for the 2025–2100 period. Deviation analysis revealed that NorESM2-MM and IPSL-CM6A-LR demonstrated the greatest stability, while EC-Earth3 exhibited the largest deviation and highest uncertainty. (4) Land use changes under the SSP245 scenario help mitigate drought by enhancing water retention, although their effectiveness diminishes under SSP585 due to the dominant influence of climate factors, including increased temperature and precipitation variability. And (5) SRI-3 mutation analysis indicated that the mutation point occurred in July 2074 under the SSP245 scenario and in April 2060 under the SSP585 scenario (p < 0.05). The trend for SSP245 revealed significant fluctuations, with the number of crossover points rising to 40 following land use changes; conversely, the SSP585 trend remained stable with only seven crossover points, as high-emission scenarios predominantly influenced early mutations. These findings illuminate the interactive effects of land use and climate change, providing a scientific foundation for optimizing water resource management and developing effective drought mitigation strategies. Full article

Agricultural waste poses significant environmental, economic, and social challenges globally, with estimates indicating that 10–50% of agricultural products are discarded annually as waste. This review explores strategies for managing agricultural waste to mitigate its adverse impacts and promote sustainable development. Agricultural residues, such as those from sugarcane, rice, and wheat, contribute to pollution when improperly disposed of through burning or burying, contaminating soil, water, and air. However, these residues also represent untapped resources for bioenergy production, composting, mulching, and the creation of value-added products like biochar, bioplastics, single-cell protein and biobased building blocks. The paper highlights various solutions, including integrating agricultural waste into livestock feed formulations to reduce competition for human food crops, producing biofuels like ethanol and biodiesel from lignocellulosic materials, and adopting circular economy practices to upcycle waste into high-value products. Technologies such as anaerobic digestion for biogas production and gasification for synthesis gas offer renewable energy alternatives and ample feedstocks for gas fermentation while addressing waste management issues. Composting and vermicomposting enhance soil fertility, while mulching improves moisture retention and reduces erosion. Moreover, the review emphasizes the importance of policy frameworks, public-private partnerships, and farmer education in promoting effective waste management practices. By implementing these strategies, agricultural waste can be transformed into a resource, contributing to food security, environmental conservation, and economic growth. Full article

Algal blooms pose significant risks to public health and aquatic ecosystems, highlighting the need for real-time water quality monitoring. Traditional manual methods are often limited by delays in data collection, which can hinder timely response and effective management. This study proposes a solution by integrating automated monitoring systems (AMSs) with advanced machine learning (ML) techniques to predict chlorophyll-a ($Chl-a$) concentrations. Utilizing low-cost and readily available input variables, we developed energy-efficient ML algorithms optimized for deployment on buoys with a battery and hardware resources. The AMS employs preprocessing methods like the SMOTE and Random Forest (RF) for feature selection and ranking. Deep feature extraction is performed through a ResNet-18 model, while temporal dependencies are captured using a Long Short-Term Memory (LSTM) network. A Softmax output layer then predicts $Chl-a$ concentrations. An alert system is incorporated to warn when $Chl-a$ levels exceed 10 μg/L, signaling potential bloom conditions. The results show that this approach offers a rapid, cost-effective, and scalable solution for real-time water quality monitoring, enhancing manual sampling efforts and improving management of water bodies at risk. Full article

Water plays a critical role in ensuring sustainable food security, particularly in the face of increasing freshwater scarcity and climate variability. This study examines virtual water use and virtual water trade in Ghana’s vegetable production sector over a 30-year period (1994–2023), focusing on four key crops: tomato, pepper, onion, and eggplant. Using secondary data on production volumes, trade flows, and virtual water content, the research quantifies imported and exported virtual water volumes and assesses net virtual water trends. The results reveal a substantial increase in virtual water use for most crops, with the exception of pepper, which experienced a marked decline. Onion and tomato are identified as the dominant contributors to both imports and exports of virtual water, while pepper and eggplant play relatively minor roles. The study finds that Ghana is a net importer of virtual water in vegetable trade, emphasizing the need for integrated water resource management to balance agricultural growth with water sustainability. A gravity model analysis was applied to identify the primary determinants of virtual water trade, revealing that GDP per capita, population size, distance, land availability, virtual water use, and border-sharing significantly influence trade patterns. The findings suggest that enhancing domestic production capacity and promoting efficient water use practices can reduce Ghana’s reliance on imports and improve resilience against water-related risks. This research provides valuable insights for policymakers, researchers, and practitioners aiming to develop sustainable water and food systems in Ghana and similar contexts. Full article

Groundwater is a critical resource in semi-arid regions like Morocco’s Guire Basin, yet pollution and overexploitation threaten its sustainability. This study evaluates the groundwater quality of the Guire aquifer (Eastern High Atlas) using an integrated approach combining hydrochemical, isotopic (δ¹⁸O, δ²H, δ¹³C), multivariate statistical, and Geographic Information System (GIS) analyses alongside the Water Quality Index (WQI). Sixteen wells were monitored for physicochemical parameters (pH: 7–7.9; EC: 480–3004 μS/cm; BOD5: 1.03–30.5 mg/L; COD: 10.2–45.75 mg/L) and major ions, revealing widespread exceedances of Moroccan standards for Cl⁻, HCO₃⁻, Mg²⁺, Ca²⁺, and NH₄⁺. WQI classified 81% of samples as “Poor” to “Unsuitable for drinking” (WQI: 51–537), driven by elevated Cl⁻, Na⁺, and SO₄²⁻ from Triassic evaporite dissolution and NO₃⁻ (up to 45 mg/L) from agricultural runoff. Stable isotopes (δ¹⁸O: −7.73‰ to −5.08‰; δ²H: −66.14‰ to −44.20‰) indicate Atlantic-influenced recharge at 900–2200 m altitudes, with a δ¹⁸O-δ²H slope of 5.93 reflecting evaporation during infiltration. Strontium (Sr²⁺/Ca²⁺: 0.0024–0.0236) and bromide (Br/Cl: 8.47 × 10⁻⁵–9.88 × 10⁻⁴) ratios further confirm evaporitic dominance over anthropogenic contamination. This work provides actionable insights for policymakers, advocating for targeted restrictions on fertilizers, enhanced monitoring near evaporite zones, and artificial recharge initiatives. By linking geogenic/anthropogenic contamination to governance strategies, this study advances sustainable groundwater management in semi-arid regions. Full article

The water issue is undoubtedly one of the most fundamental challenges and controversial issues of the current century. These days, the best options for managing water resources can be chosen by considering several indexes, such as political, social, and environmental criteria. The overall goal of this research is to propose an integrated model of fuzzy Shannon entropy and Fuzzy Additive Ratio Assessment (ARAS) that uses risk indexes to manage water resources in drought conditions. To achieve the goal of this research, first, risk factors are identified and selected based on the literature review. In previous studies, risk indicators were employed for water resource management, separately. However, this paper extracted an extensive list of risk indicators from prior studies and employed all these indicators for water resource management. Furthermore, four scenarios for water resource management in Chaharmahal and Bakhtiari province are introduced according to the geographical characteristics, climate, economic and agricultural conditions in this province. Then, a questionnaire is designed and distributed among experts in the field of water resource management. After collecting data, the proposed method is implemented on the data. The fuzzy Shannon entropy method is used to determine the weights of risk indicators, while the fuzzy ARAS method is applied for ranking water resource management scenarios. The results of applying fuzzy Shannon entropy reveal that the three indicators of volume reliability, vulnerability, and sustainability of the water supply system, with weight values of 0.124, 0.119, and 0.118, respectively, are the most effective risk indexes. The results of implementing fuzzy ARAS show that changing the cultivation pattern with a score of 0.936 is placed in the first priority, reducing the demand of the agricultural sector with a score of 0.922 is placed in the second priority, and the type of irrigation system with a score of 0.896 is placed in the third priority, and the reduction of industrial and drinking water consumption with a score of 0.882 is placed in the fourth priority. Finally, the results of implementing the proposed model of fuzzy Shannon entropy and fuzzy ARAS reveal an increase in volume reliability in the field of cropping pattern change in the studied province. Full article

In the context of ongoing climate change, extreme weather events are becoming increasingly frequent and unpredictable, posing significant challenges for traditional probability-based methods. This study presents an innovative uncertainty-based Box–Cox regression framework to assess the impacts of climate change factors, specifically temperature and precipitation, on the volatility of extreme weather events in the Chengdu-Chongqing region. To address data imprecision, we establish a new estimation theorem for the Extended Least Squares Estimator (ELSE), proving its existence, uniqueness, unbiasedness, and variance consistency under uncertainty theory. The Mann–Kendall trend test is applied to detect event frequency trends, and a coupling coordination degree model is employed to evaluate the dynamic relationship between climate resources and economic development. The results show that (1) temperature has a more significant impact on the volatility of extreme weather events than precipitation; (2) the thermal resource–economy coupling degree has remained above 0.45 since 2015, indicating a strengthening relationship but suboptimal coordination; and (3) since 2014, the water resource–economy coupling degree has consistently exceeded 0.5, reaching optimal levels and highlighting the growing importance of water resources in regional development. Based on these findings, we recommend enhancing extreme weather monitoring systems, improving infrastructure resilience, optimizing climate-related resource management, and fostering regional cooperation. This study provides a rigorous theoretical and empirical basis for integrating uncertainty modeling into climate–economy analysis. Future work should further explore alternative modeling strategies and validate conclusions using extended datasets. Full article

This study combines artificial intelligence (AI) with mathematical modeling to improve the forecasting of the water cycle mechanism. The proposed model simulates the development of global temperature, precipitation, and water availability, integrating key climate parameters that control these dynamics. Using a system of fractional-order differential equations in the fractal–fractional sense of derivatives, the model captures interactions between solar radiation, the greenhouse effect, evaporation, and runoff. The deep learning framework is trained on extensive climate datasets, allowing it to refine predictions and identify complex patterns within the water cycle. By applying AI techniques alongside mathematical modeling, this procedure provides valuable insights into climate change and water resource administration. The model’s predictions can contribute to assessing future climate states, optimizing environmental policies, and designing sustainable water management strategies. Furthermore, the hybrid methodology improves decision-making by offering data-driven solutions for climate adaptation. The findings illustrate the effectiveness of AI-driven models in addressing global climate challenges with improved precision. Full article

This study investigates the impact of climate variability on wheat production in Algeria’s semi-arid interior plains from 2014 to 2024, aiming to curb the challenges of rainfed wheat cultivation, optimize irrigation, and improve water productivity. The Soil–Water–Atmosphere–Plant (SWAP) model-driven approach refined irrigation scheduling to mitigate climate-induced losses and improve resource efficiency. Using historical climate data, soil properties, and wheat growth observations from the experimental farm of the Technical Institute for Field Crops, the SWAP model was calibrated and validated using one-factor-at-a-time sensitivity analysis, achieving a coefficient of determination (R²) of 0.93 and a Normalized Root Mean Squared Error (NRMSE) of 17.75. Two drought-based irrigation indices, Soil Moisture Drought Index (SMDI) and Crop Water Stress Index (CWSI), guided adaptive irrigation strategies, showing a significant reduction in crop failure during drought periods. Results revealed a strong link between rainfall variability and wheat yield. Adopting a 9-day irrigation interval could increase water productivity to 18.91 kg ha⁻¹ mm⁻¹, enhancing yield stability under varying climatic conditions. The SMDI approach maintained soil moisture during extreme drought, while CWSI optimized water use in normal and wet years. This study integrates SMDI and CWSI into a validated irrigation framework, offering data-driven strategies to enhance wheat production resilience. Findings support sustainable water management and provide practical insights for policymakers and farmers to refine irrigation planning and climate adaptation, contributing to long-term agricultural sustainability. Full article

This study aimed to reveal the characteristics of returned water in paddy fields at different scales and the rules of its reuse in China’s Ganfu Plain Irrigation District through multiscale (field, lateral canal, main canal, small watershed) observations, thereby optimizing water resource management and improving water use efficiency. Subsequent investigations during the 2021–2022 double-cropping rice seasons revealed that the tillering stage emerged as a critical drainage period, with 49.5% and 52.2% of total drainage occurring during this phase in early and late rice, respectively. Multiscale drainage heterogeneity displayed distinct patterns, with early rice following a “decrease-increase” trend while late rice exhibited “decrease-peak-decline” dynamics. Smaller scales (field and lateral canal) produced 37.1% higher drainage than larger scales (main canal and small watershed) during the reviving stage. In contrast, post-jointing-booting stages showed 103.6% higher drainage at larger scales. Return flow utilization peaked at the field-lateral canal scales, while dynamic regulation of Fangxi Lake’s storage capacity achieved 60% reuse efficiency at the watershed scale. We propose an integrated optimization strategy combining tillering-stage irrigation/drainage control, multiscale hydraulic interception (control gates and pond weirs), and dynamic watershed storage scheduling. This framework provides theoretical and practical insights for enhancing water use efficiency and mitigating non-point source pollution in plain irrigation districts. Full article

Water management is a long-standing source of dispute between the riparian states of Karnataka and Tamil Nadu. Recently, these disputes have intensified due to impacts from climate change and Bangalore’s rapid growth to megacity status. Despite well-defined national water governance instruments, competition between state actors and limited access to reliable hydrometric data have led to a fragmented regulatory regime, allowing unchecked exploitation of surface and groundwater resources. Meanwhile, subsidised energy for groundwater pumping incentivises the unsustainable irrigation of high-value, water-intensive crops, resulting in overextraction and harm to aquatic ecosystems. Here, we employ a water–energy–food–environment (WEFE) nexus approach to examine the socio-political, economic, and environmental factors driving unsustainable irrigation practices in the Cauvery River Basin (CRB) of Southern India. Our methodology integrates spatially explicit analysis using digitised irrigation census data, theoretical energy modelling, and crop water demand simulations to assess groundwater use patterns and energy consumption for irrigation and their links with governance and economic growth. We analyse spatio-temporal irrigation patterns across the whole basin (about 85,000 km²) and reveal the correlation between energy access and groundwater extraction. Our study highlights four key findings. First, groundwater pumping during the Rabi (short-rain) season consumes 24 times more energy than during the Kharif (long-rain) season, despite irrigating 40% less land. Second, the increasing depth of borewells, driven by falling water table levels, is a major factor in rising energy consumption. Third, energy input is highest in regions dominated by paddy cultivation. Fourth, water pumping in the Cauvery region accounts for about 16% of India’s agricultural energy use, despite covering only 4% of the country’s net irrigated area. Our study reinforces the existing literature advocating for holistic, catchment-wide planning, aligned with all UN Sustainable Development Goals. Full article

Sustainable transboundary water governance in Central Asia faces significant challenges, including political tensions, ecological issues, such as the drying Aral Sea, and seasonal hydropower disruptions impacting downstream countries. This study aims to address these problems by examining the complexities of water resource governance in the region, emphasizing the interplay between national interests and regional cooperation. We analyze how social, economic, environmental, and political factors influence water diplomacy among Central Asian states. Key challenges include water scarcity, climate change impacts and the growing tensions over transboundary river basins, particularly in the Aral Sea basin (i.e., the development of the Kushtepa Canal in Afghanistan). The intricate linkages between water, energy, and agriculture further complicate decision-making processes among riparian nations. While recent diplomatic efforts signal a shift towards enhanced regional cooperation, existing agreements remain fragmented, and a sustainable, long-term governance framework is still lacking. Our findings highlight the importance of an integrated, basin-wide approach to transboundary water management. We argue that a cohesive regional water strategy—grounded in international legal frameworks and supported by collaborative governance mechanisms—can mitigate conflicts and promote water security in Central Asia. The significance of this study lies in its potential to inform policy decisions and promote sustainable practices in transboundary water governance, ultimately contributing to the broader goals of sustainable development and regional cooperation. Full article

The Devonian–Mississippian Bakken Formation in western North Dakota (USA) is one of the largest hydraulic fracturing oil fields in the world. Streamflow analysis showed that the average seven-day low flows in the region surprisingly increased during the recent oil boom. The increase, ranging from 88% to 3648%, was largely due to the fact that the region had received 20% more precipitation than normal during that time period. To study the impact of hydraulic fracturing at Bakken on regional streamflow under normal precipitation and other scenarios, we integrated a socioeconomic agent-based model that simulates the hydraulic fracturing water uses with a hydrological model that simulates the streamflow in the Little Muddy River in the region. Our results showed that compared to the existing (baseline) scenario, the average seven-day low flows in the Little Muddy River decreased from 18% to 88%, while the annual average flows did not change much under drier to normal precipitation scenarios. Our research also finds that climate factors and water management policies were more influential than hydraulic fracturing and population growth. The emergency water management policies implemented at the peak of shale oil development had mitigated the hydraulic fracturing impact on regional streamflow at low-flow conditions and improved water resource sustainability in the region. Full article