This study delves into the environmental impact of inland aquaculture on estuarine ecosystems by examining the water quality of four estuarine streams within the key inland aquaculture zone of South India. In this region, extensive and intensive aquaculture practices are common, posing potential challenges to estuarine health. The research explores the predictive capabilities of the Gaussian elimination method (GEM) and machine learning techniques, specifically multi-linear regression (MLR) and support vector regressor (SVR), in forecasting the water quality index of these streams. Through comprehensive evaluation using performance metrics such as coefficient of determination (R2) and average mean absolute percentage error (MAPE), MLR and SVR demonstrate higher prediction efficiency. Notably, employing key water parameters as inputs in machine learning models is also more effective. Biochemical oxygen demand (BOD) emerges as a critical water parameter, identified by both MLR and SVR, exhibiting high specificity in predicting water quality. This suggests that MLR and SVR, incorporating key water parameters, should be prioritized for future water quality monitoring in intensive aquaculture zones, facilitating timely warnings and interventions to safeguard water quality.

Rivers are vital and complex natural systems that provide a wide range of ecosystem services. This study presents a methodology for assessing the riverine provisioning and supporting ecosystem services, whose applicability has been demonstrated over the Budhabalanga River Basin of India. The Soil and Water Assessment Tool (SWAT) is used to generate streamflow time series at various ungauged sites, and then the streamflow is characterized for the evaluation of provisioning services. Further, the diversity and abundance of macroinvertebrates, along with the Lotic-invertebrate Index for Flow Evaluation (LIFE), is used to study the riverine supporting ecosystem services. The streams show intermittent behavior and strong seasonality for low flows, which limits the water availability, particularly during pre-monsoon season. The Baseflow Index (BFI) is greater than 0.6, indicating that groundwater contributes more than 60% of the total streamflow. Interestingly, despite the high BFI, the streams did not conform to the prevailing opinion that a greater baseflow contribution results in a later commencement of the low-flow period in the hydrological year. Furthermore, the study depicts significant variations in the diversity and abundance of the macroinvertebrates across the various sampling sites. However, the LIFE score across the sites remained consistent within a narrow range, i.e., 8 to 9, suggesting a steady supply of supporting ecosystem services. The results of the study can help the policymakers towards an informed decision making and the simplistic methodology proposed in this study can be replicated in other river basins for identifying vulnerable watersheds and prioritizing management actions.

The Egyptian Ministry of Water Resources and Irrigation launched in 2020 the national project to rehabilitate the canals network to rationalize the use of water resources to face the scarcity problems. The aim of study is to evaluate the impact of canal rehabilitation on the performance of irrigation water delivered laterally to Mesqa\'s and longitudinally to the end of canal. Qaraqoul Canal et al.-Mallah Area, Alexandria, Egypt, was modeled using Hydrologic Engineering Center\'s-River Analysis System (HEC-RAS) to simulate water levels in the canal before and after rehabilitation using four discharge scenarios: 1.82, 3.7, 2.2, 7.87 m3/s. The calibration before rehabilitation shows that HEC-RAS simulated water levels corresponding to a discharge of 2.2 m3/s were in a good agreement with the actual field measured water levels. HEC-RAS results demonstrated that rehabilitation hydraulically improved the efficiency and performance of water conveyed by the canal. On the other hand, second scenario can be considered as suitable to keep water to reach the canal downstream with minimum suitable discharge, providing the need of two emergency pumps at last two branch canals called Mesqa\'s. An ideal cross-section is also simulated using HEC-RAS which produced an efficient alternative with 40% less cost than the constructed alternative.

Understanding the impacts of microplastics (MPs) on aqueous environments requires understanding their transport dynamics and how their presence affects other natural processes and cycles. In this context, one aspect to consider is how MPs interact with freshwater snow (FWS), a mixture of algae and natural particles. FWS is one of the primary drivers of the flux of organic matter from the water surface to the bottom sediment, where zooplankton, diurnal migration, fish faecal pellets settling and turbulent mixing can also play prominent roles. Understanding how MPs and FWS heteroaggregation affects their respective settling velocities is important to assess not only MPs fate and transport but also their ecological impacts by altering FWS deposition and thereby nutrient cycling. In this present study, we obtained a mechanistic understanding of the processes controlling MPs settling dynamics and heteroaggregation with FWS and the subsequent impacts on the settling rates of both MPs and ballasted FWS. Here we used a plexiglass column equipped with a stereoscopic camera system to track the settling velocities of (1) MPs of various compositions, densities and morphologies, (2) FWS flocs and (3) MP-FWS agglomerates. For each experimental set, thousands of particles were tracked over a series of image sequences. We found that agglomerates with high-density MPs settled at least twofold faster than FWS alone, implying a much smaller residence time in the water column, except for cases with MP fibres or low-density plastics. These findings will help to refine MP fate models and, while contingent on MPs number, may impact biogeochemical cycles by changing the flux of nutrients contained in FWS to the sediment.

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In order to explore the evolution law and driving mechanism of aerobic denitrification bacteria in Baiyangdian Lake under different hydrological scenarios, based on water quality survey and high-throughput sequencing technology, this study conducted a water quality factor analysis and aerobic denitrification bacteria α-diversity analysis, species composition, and network analysis. The results showed that the water body of Baiyangdian Lake was weakly alkaline, with the highest T and the lowest DO in the rainy season and the lowest T and the highest DO in the freezing season. There were significant differences between NH4+-N, NO2--N, NO3--N, TN, permanganate index, Fe, and Mn in Baiyangdian water under different hydrological scenarios (P < 0.01), and there was no significant difference in TP under different hydrological scenarios (P > 0.05). The largest category in water bodies under different hydrological scenarios was Proteobacteria, and the genera with a higher relative abundance were Magnetospirillum, Aeromonas, Pseudomonas, Azospirillum, and Bradyrhizobium. In addition, within the aerobic denitrifying bacteria community, there were significant differences in α-diversity (P < 0.001), with the highest abundance of microbial communities occurring during the freezing period, and the highest diversity and evenness of microbial communities during the dry and freezing periods. According to the RDA and Mantel analyses, the water quality driving factors of flora were different under different hydrological scenarios. The water quality driving factors of flora in the dry season were pH, NO3--N, NO2--N, and permanganate index; the driving factors of flora in the rainy season were pH, T, DO, NO2--N, and TP; the driving factors of flora in the normal season were NO2--N, Fe, and permanganate index; and the driving factors of flora in the freezing season were NO3--N and NONO2--N. Network analysis showed that there were temporal differences in species related to water quality driving factors. The genera related to water quality driving factors during the dry season were Magnetospirillum, Aeromonas, and Azoarcus, whereas the genera related to the rainy season were Magnetospirillum, Pseudomonas, and Aeromonas. The genera related to the normal season were Magnetospirillum, Pseudomonas, and Limnohabitans, and the genera related to the freezing period were Magnetospirillum, Azoarcus, and Pseudomonas. The relationship between key water quality factors (mainly T, DO, NO3--N, and permanganate index) and aerobic denitrification flora in different hydrological scenarios was gradually changing with time. In conclusion, the study on the evolution characteristics of aerobic denitrification bacteria in Baiyangdian Lake under different hydrological scenarios and the driving mechanism of environmental factors could provide a basis for understanding the evolution mechanism of aerobic denitrification bacteria in the natural environment.

Effectively managing water resources in karst systems requires a thorough understanding of their general conduit network along with their seasonal dynamics. Their investigation has involved well construction or several advanced natural tracer data, most of which are not always available. Hence, this work showcases a pragmatic approach that makes use of basic hydrochemical variables of springs with coarse temporal resolution in characterising a karst system. In this study\'s example, physicochemical variables like major ion concentrations/ratios, Electrical Conductivity (EC), pH and water temperature (Tw) were measured on 20-day basis for a hydrological year at the Louros Catchment, Greece. We further performed the frequency distribution and variation analysis of EC and Tw, principal component analysis (PCA), scatter plots of carbonate ions vs sulphate and hydrochemographs to determine relevant hydrochemical processes and hydrogeological features. PCA and the scatter plots showed that the simple-type upper karst level is entirely dominated by carbonate dissolution, whereas the complex-type middle and lower levels also involve gypsum and dolomite dissolution. Presence of mixing between karst units was also detected. EC and Tw analyses revealed the degree of karstification of different units and relative depths of flow systems. Hydrochemographs reflected the seasonality of limestone and gypsum dissolution\'s contributions linked to the dominant flow type (conduit vs diffuse). This study thus was able to demonstrate the usefulness of such holistic hydrochemical analyses to better understand karst systems. Given their cost-effectiveness, they can be easily applied to any understudied karst system worldwide.

The southwestern region of China is the largest exposed karst area in the world and serves as an important ecological security barrier for the upstream of Yangtze River and Pearl River. Different from the critical zone of non-karst areas, the epikarst, formed by an interwoven network of denudation pores, is the core area of karst critical zone. Water is the most active component that participates in internal material cycle and energy flow within the critical zone. We reviewed relevant research conducted in the southwestern region from three aspects: the characte-rization of critical zone structure, the hydrological processes of soil-epikarst system, and their model simulations. We further proposed potential research hotpots. The main approach involved multi-scale and multi-method integrated observations, as well as interdisciplinary collaboration. Precisely characterizing the eco-hydrological processes of the vegetation-soil-epikarst coupling system was a new trend in the future research. This review would provide scientific reference for further studies on hydrological processes in critical zones and regional hydrological water resource management in karst areas.
中国西南地区是世界上喀斯特出露面积最大的区域,也是长江和珠江上游重要的生态安全屏障区。区别于非喀斯特关键带,由溶蚀性孔隙网络结构交织而成的表层岩溶带是喀斯特关键带的核心区,水则是参与和联系关键带内部物质循环、能量流动过程最活跃的因子。本文从关键带结构刻画、土壤-表层岩溶带系统水文过程以及模型模拟3方面回顾和总结了西南喀斯特地区开展的相关研究以及存在的问题,并对潜在的研究热点进行了展望。多尺度、多方法综合观测以及多学科交叉是开展系列研究的主要途径,精准刻画植被-土壤-表层岩溶带耦合系统的生态水文过程是未来研究的主要趋势和重点。本文旨在为深化喀斯特关键带水文过程研究和区域水文水资源管理提供科学参考。.

Plastic pollution has emerged as a global environmental concern, impacting both terrestrial and marine ecosystems. However, understanding of plastic sources and transport mechanism at the catchment scale remains limited. This study introduces a multi-source plastic yield and transport model, which integrates catchment economic activities, climate data, and hydrological processes. Model parameters were calibrated using a combination of field observations, existing literature, and statistical random sampling techniques. The model demonstrated robust performance in simulating both plastic yield and transport from 2010 to 2020 in the upper and middle Mulan River Catchment, located in southeast China. The annual average yield coefficients were found to closely align with existing estimations, and the riverine outflow exhibited a high correlation coefficient of 0.97, with biases ranging from -63.0 % to -21.4 % across all monitoring stations. The analysis reveals that, on average, 12.5 ± 2.5 % of the total plastic yield is transported to rivers annually, with solid waste identified as the primary source, accounting for 37.8 ± 20.7 % of the total load to rivers, followed by agricultural film (26.4 ± 9.8 %), impermeable surfaces (21.5 ± 10.3 %), urban and rural sewage (10.4 ± 5.0 % and 3.0 ± 1.5 %, respectively), and industrial wastewater (0.9 ± 0.7 %). The annual average outflow was estimated to between 9.3 and 43.0 ton/year (median: 23.1) at a 95 % confidence level. This study not only provides insights into the primary sources and transport pathways of plastic pollution at the catchment scale, but also offers a valuable tool for informing effective plastic pollution mitigation strategies.

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.