Search Results (101)

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article

Salinity intrusion is a growing threat to freshwater resources, particularly in low-lying coastal and estuarine regions, necessitating the development of effective early warning systems (EWS) to support timely mitigation. Although various water quality monitoring technologies exist, many face challenges related to long-term sustainability, ongoing maintenance, and accessibility for local users. This study introduces a novel hybrid real-time salinity intrusion early warning system that uniquely integrates fixed and portable monitoring technologies with strong community participation—an approach not yet widely applied in comparable urban-adjacent delta regions. Unlike traditional systems, this model emphasizes local ownership, flexible data collection, and system scalability in resource-constrained environments. This study presents a real-time salinity intrusion early warning system for Bang Kachao, Thailand, combining eight fixed monitoring stations and 20 portable salinity measurement devices. The system was developed in response to community needs, with local input guiding both station placement and the design of mobile measurement tools. By integrating fixed stations for continuous, high-resolution data collection with portable devices for flexible, on-demand monitoring, the system achieves comprehensive spatial coverage and adaptability. A core innovation lies in its emphasis on community participation, enabling villagers to actively engage in monitoring and decision-making. The use of IoT-based sensors, Remote Telemetry Units (RTUs), and cloud-based data platforms further enhances system reliability, efficiency, and accessibility. Automated alerts are issued when salinity thresholds are exceeded, supporting timely interventions. Field deployment and testing over a seven-month period confirmed the system’s effectiveness, with fixed stations achieving 90.5% accuracy and portable devices 88.7% accuracy in detecting salinity intrusions. These results underscore the feasibility and value of a hybrid, community-driven monitoring approach for protecting freshwater resources and building local resilience in vulnerable regions. Full article

The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines Sentinel-2 imagery with Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) models. Our approach achieves a 5.4× increase in data coverage over traditional methods, demonstrating the effectiveness of machine learning in environmental monitoring. Predictive accuracy was evaluated across Support Vector Machine (SVM), ARIMA, and Amazon Forecast models. Results indicate that SVM, optimised through RBF kernel and grid search, outperforms other models for Chlorophyll-a (RMSE: 1.8), while ARIMA exhibits superior performance for Secchi Depth (RMSE: 0.2) and Trophic State Index (RMSE: 0.8). The study also introduces Aqua Sight, a web-based visualisation tool built on Google Earth Engine, enabling stakeholders to access real-time water quality forecasts. These findings highlight the potential of integrating satellite-derived data with machine learning to enhance early warning systems and support environmental decision making in coastal ecosystems. Full article

The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments. Full article

Pig slurry fertigation can modify soil biochemical properties by promoting glomalin production and shifting microbial communities; however, its impacts under varying water regimes remain insufficiently quantified. We assessed irrigated and rainfed systems by integrating the soil quality index (SQI) with total and easily extractable glomalin (T-GRSP, EE-GRSP), determining microbial diversity via eDNA amplicon sequencing, and evaluating enzyme activities across three soil depths (0–10, 10–20, and 20–30 cm). Robust regression revealed that T-GRSP and EE-GRSP accounted for 75% of the SQI variability in irrigated soils and 46% in rainfed soils (p < 0.001), with the strongest correlations in the 0–10 cm layer. Irrigation increased T-GRSP concentrations by 66% (1.78 vs. 1.07 mg g⁻¹) and raised its contribution to total soil carbon from 2.0% to 3.2%. The EE-GRSP levels were slightly lower in the irrigated soils (0.73 vs. 0.76 mg g⁻¹) yet remained a sensitive early-warning indicator of moisture stress in rainfed plots. Microbial profiling showed a 19% increase in Shannon bacterial diversity (3.44 vs. 2.89), even more bacterial communities under irrigation, intermediate fungal diversity, higher fungal abundance, and no detectable arbuscular mycorrhizal fungi in either system. Combining GRSP fractions with microbial and enzymatic markers provides a responsive framework for assessing soil health and guiding organic amendment strategies in fertigation-based agriculture under fluctuating water availability. Full article

Artificial intelligence (AI) is transforming water environment management, creating new opportunities for improved monitoring, prediction, and intelligent regulation of water quality. This review highlights the transformative impact of AI, particularly through hybrid modeling frameworks that integrate AI with technologies like the Internet of Things (IoT), Remote Sensing (RS), and Unmanned Monitoring Platforms (UMP). These advances have significantly enhanced real-time monitoring accuracy, expanded the scope of data acquisition, and enabled comprehensive analysis through multisource data fusion. Coupling AI models with process-based models (PBM) has notably enhanced predictive capabilities for simulating water quality dynamics. Additionally, AI facilitates dynamic early-warning systems, precise pollutant source tracking, and data-driven decision-making. However, significant challenges remain, including data quality and accessibility, model interpretability, monitoring of hard-to-measure pollutants, and the lack of system integration and standardization. To address these bottlenecks, future research should focus on: (1) constructing high-quality, standardized open-access datasets; (2) developing explainable AI (XAI) models; (3) strengthening integration with digital twins and next-generation sensors; (4) improving the monitoring of trace and emerging pollutants; and (5) coupling AI with PBM by optimizing input data, internal mechanisms, and correcting model outputs through validation against observations. Overcoming these challenges will position AI as a central pillar in advancing smart water quality governance, safeguarding water security, and achieving sustainable development goals. Full article

Effective water resource management requires an understanding of the interactions between water and environmental parameters, especially in regions with limited data availability. This study used generalized additive models (GAMs) to investigate the relationship between climatic and hydrological factors, namely river flow, rainfall, air temperature, and physicochemical water quality parameters in the Kiso River, Japan. Seasonal and non-seasonal GAMs models were developed for each water quality parameter, resulting in 7 non-seasonal models and 28 seasonal models based on Japan’s meteorological seasons (winter, spring, summer, fall). The findings demonstrated how seasonal models captured seasonal variability, significantly outperforming the non-seasonal models. For example, turbidity in winter (R² = 0.5030) showed significant improvement compared with non-seasonal models (R² = 0.1470), and organic pollution in fall (R² = 0.4099) increased compared with non-seasonal models (R² = 0.2509). Beyond assessing the influence of environmental drivers on water quality, these findings are crucial in regions with limited data, emphasizing the role of model–based seasonal analysis in identifying high-risk contamination periods, and supporting targeted and effective water management and early warning systems. Full article

Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), as a critical indicator for measuring water self-purification capacity and ecological health status, has been widely applied in water quality monitoring and early warning systems. Therefore, accurate prediction of dissolved oxygen concentration is of significant importance for the ecological and environmental protection of river basins. This study introduces a hybrid prediction model combining Variational Mode Decomposition (VMD), Improved Fruit Fly Optimization Algorithm (IFOA), and Attention-based Gated Recurrent Unit (Attention-GRU). The model first decomposes preprocessed dissolved oxygen data through VMD to extract multiple intrinsic mode functions, reducing non-stationarity and high-frequency noise interference. It then utilizes the Improved Fruit Fly Optimization Algorithm to adaptively optimize key parameters of the Attention-GRU network, enhancing the model’s fitting capability. Experiments demonstrate that the VMD-IFOA-Attention-GRU model achieves 0.286, 0.302, and 0.915 for Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²), respectively, significantly outperforming other comparative models. The results indicate that this method can provide a reference for intelligent water quality prediction in typical regions such as the Yangtze River Basin. Full article

Cyanobacteria harmful blooms (Cyano-HABs) have become a globally critical environmental issue, threatening freshwater ecosystems by degrading water quality and posing risks to human and aquatic life. Chlorophyll-a (Chl-a), a key biomarker of bloom intensity, offers crucial insights into algal bloom dynamics. However, predicting Chl-a concentrations remains challenging due to the complex interactions between various environmental factors. This study utilizes machine learning (ML) models to predict Chl-a concentrations, focusing on Lake Taihu in China, a large eutrophic lake that serves as an example of numerous freshwater lakes suffering from Cyano-HABs. The research leverages nine critical water quality parameters—water temperature, pH, dissolved oxygen, turbidity, electrical conductivity permanganate index, ammonia nitrogen, total phosphorus, and total nitrogen—to develop an ensemble ML model using XGBoost, known for its ability to handle nonlinear relationships and integrate multiple variables. The XGBoost model achieved superior predictive accuracy with an R² value of 0.78 and RMSE of 8.97 mg/m³ on the test set, outperforming traditional models like linear regression, decision trees, multi-layer perceptrons, support vector regression, and random forests. Feature importance analysis identified electrical conductivity, turbidity, and water temperature as the most significant predictors of Chl-a levels. This study further enhances model interpretability through Pearson correlation analysis, which quantifies the relationships between Chl-a concentrations and other water quality factors. Additionally, we employed principal component analysis (PCA), mutual information, Spearman rank correlation coefficients, and SHAP models to analyze feature importance and model interpretability in ML. The model’s robustness was tested across multiple monitoring sites in Lake Taihu, demonstrating its potential for broader application in other eutrophic lakes facing similar environmental challenges. By providing a reliable tool for forecasting Chl-a concentrations, this research contributes to the development of early warning systems that can help mitigate the impacts of Cyano-HABs, aiding in more effective water resource management. Full article

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

Quantifying the dynamic changes and relationships between ecosystem services (ESs) and land use change is critical for sustainable ecosystem management and land use optimization. However, comprehensive discussions on the spatiotemporal variations of ESs and their relationships with land use intensity (LUI) are lacking, particularly in the context of significant climate warming. Systematic analyses of the forestry management unit scale are limited, leading to considerable uncertainty in sustainable ecosystem management, especially in alpine ecosystems of the Northern Hemisphere, where ESs have significantly degraded. The study focuses on the Daxing’anling forest area, Inner Mongolia (DFIAM), a representative sensitive alpine ecosystem and crucial ecological security barrier in Northern China. Utilizing the InVEST model, we analyzed the spatiotemporal variations in land use and four essential ESs, water yield (WY), carbon storage (CS), soil conservation (SC), and habitat quality (HQ), from 2013 to 2018. We also assessed the dynamic relationships between LUI and these ESs using a four-quadrant model. Our findings indicate the following: (1) Land use types in DFIAM remained relatively stable between 2013 and 2018, with forest being the dominant type (approximately 93%). During this period, areas of forest, cropland, impervious surfaces, and bare land increased, while areas of grassland, water, and wetland decreased. Although the overall change of LUI was gentle, a spatial pattern of “high in the southeast and low in the northwest” emerged, with low LUI areas showing slight expansion. (2) WY, SC, and HQ decreased, while CS increased from 2013 to 2018. The spatial distributions of these ESs showed higher values in the center and lower values at the edges, with forests demonstrating a strong capacity to provide multiple ESs. (3) The relationship between LUI and the four ESs from 2013 to 2018 was predominantly negative, primarily situated in Quadrant II, indicating that increased LUI inhibited ES supply capacity. Within Quadrant II, the distribution range of LUI, WY, and HQ decreased, while CS remained stable and SC increased. Furthermore, Quadrant III (positive correlation) accounted for a large proportion (19.23%~42.31%), highlighting the important role of non-anthropogenic factors in ES changes. Overall, most ESs in the DFAIM showed a decline while LUI remained relatively stable, with predominantly negative correlations between LUI and ESs. The increased LUI driven by human activities, and other non-human factors, may have contributed significantly to ES degradation. To improve ESs, we proposed implementing differentiated land use planning and management, systematic ecological protection and restoration strategies, a multi-level ecological early-warning monitoring and evaluation network, ecological corridors and buffer zones, and a collaborative management system with multiple participation. These results provide scientific guidance for the sustainable management of alpine ecosystems, enhancement of ESs, and formulation of land resource protection policies. Full article

Harmful algae blooms (HABs) pose critical threats to aquatic ecosystems and human economies, driven by their rapid proliferation, oxygen depletion capacity, toxin release, and biodiversity impacts. These blooms, increasingly exacerbated by climate change, compromise water quality in both marine and freshwater ecosystems, significantly affecting marine life and coastal economies based on fishing and tourism while also posing serious risks to inland water bodies. This article examines the role of hyperspectral imaging (HSI) in monitoring HABs. HSI, with its superior spectral resolution, enables the precise classification and mapping of diverse algae species, emerging as a pivotal tool in environmental surveillance. An array of HSI techniques, algorithms, and deployment platforms are evaluated, analyzing their efficacy across varied geographical contexts. Notably, hyperspectral sensor-based studies achieved up to 90% classification accuracy, with regression-based chlorophyll-a (Chl-a) estimations frequently reaching coefficients of determination ($R^2$) above 0.80. These quantitative findings underscore the potential of HSI for robust HAB diagnostics and early warning systems. Furthermore, we explore the current limitations and future potential of HSI in HAB management, highlighting its strategic importance in addressing the growing environmental and economic challenges posed by HABs. This paper seeks to provide a comprehensive insight into HSI’s capabilities, fostering its integration in global strategies against HAB proliferation. Full article

The aquatic environment in aquaculture serves as the foundation for the survival and growth of aquatic animals, while a high-quality water environment is a necessary condition for promoting efficient and healthy aquaculture development. To effectively guide early warnings and the regulation of water quality in aquaculture, this study proposes a predictive model based on a dual-channel and dual-attention mechanism, namely, the DAM-ResNet-LSTM model. This model encompasses two parallel feature extraction channels: a residual network (ResNet) and long short-term memory (LSTM), with dual-attention mechanisms integrated into each channel to enhance the model’s feature representation capabilities. Then, the proposed model is trained, validated, and tested using water quality and meteorological parameter data collected by an offshore farm environmental monitoring system. The results demonstrate that the proposed dual-channel structure and dual-attention mechanism can significantly improve the predictive performance of the model. The prediction accuracy for pH, dissolved oxygen (DO), and salinity (SAL) (with Nash coefficients of 0.9361, 0.9396, and 0.9342, respectively) is higher than that for chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), nitrite (NO₂⁻), and active phosphate (AP) (with Nash coefficients of 0.8578, 0.8542, 0.8372, and 0.8294, respectively). Compared to the single-channel model DA-ResNet (ResNet integrated with the proposed dual-attention mechanism), the Nash coefficients for predicting pH, DO, SAL, COD, NH₃-N, NO₂⁻, and AP increase by 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, and 14.99%, respectively. Compared to the single-channel DA-LSTM model (LSTM integrated with the proposed dual-attention mechanism), the corresponding increases in Nash coefficients are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, and 10.2%, respectively. Compared to the ResNet-LSTM (ResNet and LSTM in parallel) model without the attention mechanism, the improvements in Nash coefficients are 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, and 4.13%, respectively. The predictive performance of the model fulfills the practical requirements for accurate forecasting of water quality in nearshore aquaculture. Full article

Lake water pollution under anthropogenic influences exhibits characteristics of high uncertainty, rapid evolution, and complex control challenges, presenting substantial threats to ecological systems and human health. Quantitative risk prediction provides crucial support for water quality deterioration prevention and management. This study employed the Copula Bayesian Network model to conduct a comprehensive risk assessment of water quality in Qilu Lake, China (2010–2020), incorporating inter-indicator correlations and multiple uncertainty sources. Analysis revealed generally “worse” water quality conditions (5.10 ± 1.35) according to established index classifications, with predicted probabilities of reaching “deteriorated” status ranging from 11.80% to 47.90%. Significant spatial and temporal variations in water quality and pollution risk were observed, primarily attributed to intensive agricultural non-point source loading and water resource deficiency. The study established early warning thresholds through key indicator concentration predictions, particularly for the southern region where “deteriorated” risk levels corresponded to specific ranges: TN (3.42–8.43 mg/L), TP (0.07–1.29 mg/L), and COD_Cr (27.75–67.19 mg/L). This methodology effectively characterizes lake water quality evolution while enabling risk prediction through key indicator monitoring. The findings provide substantial support for water pollution control strategies, risk management protocols, and regulatory decision-making for lake ecosystem administrators. Full article

As global climate change intensifies and population growth continues, water scarcity has emerged as a critical constraint to sustainable agricultural development. Conservation management, an effective water-saving technique, plays a crucial role in enhancing soil water content (SWC) and promoting sustainable agriculture. This study utilizes CiteSpace to perform a bibliometric analysis of research literature on the effects of conservation management on SWC, encompassing publications indexed in the Web of Science database from 1992 to 2024. By systematically examining 599 papers, we analyzed key research institutions, authors’ collaborative contributions, keyword co-occurrences, and shifts in research hotspots related to conservation management and its impact on SWC. The results reveal that significant topics in this field include “conservation agriculture”, “water use efficiency”, and “conservation tillage”. China (225, 38%) and the United States (129, 22%) lead in publication volume, whereas European countries and institutions show a higher degree of collaboration. The research focus has transitioned from examining the impacts and mechanisms of conservation tillage on crop yield and soil physical and chemical properties to long-term monitoring, water use efficiency, and mitigation. Furthermore, keyword co-occurrence and temporal analysis highlight a growing emphasis on soil quality and greenhouse gas emissions. In the future, it remains imperative to enhance the implementation of automated monitoring systems, secure long-term continuous monitoring data, promote conservation agriculture technology, and bolster the early warning network for extreme climate events. These measures are crucial for preserving soil nutrient levels and ensuring the sustainable development of agriculture. Full article