Hydrochemical characteristics of irrigation water and their spatiotemporal variations can provide critical information for ensuring healthy crop growth and determining the best water management practices. The Lower Rio Grande Valley (LRGV) is heavily dependent upon ditch irrigation to deliver water from the Rio Grande River to support its staple crop production. To date, no studies were conducted to quantify the water quality and its variations along the distribution system. This research measured water quality parameters at seven sites in the LRGV irrigation water in 2021. Chemical indices including salinity hazard (SH), sodium adsorption ratio (SAR), sodium percentage (Na%), residual sodium carbonate (RSC), magnesium hazard (MH), Kelly's Ratio (KR), and permeability index (PI) were calculated. Classification diagrams were prepared. Results revealed the locations that had doubtful water for irrigation use and more problematic water quality index levels. June and August had the highest index levels which may have been attributed to the large rainfall events in May and July. The SH, Na%, KR, and MH indices exceeded recommended levels. ANOVA analyses showed significant temporal variations in SAR, RSC, MH, KR, and PI. These findings indicate the importance of incorporating water quality spatiotemporal variation information in routine irrigation planning and management.

Although previous studies have analyzed eco-hydrological changes, external pressure sources on the eco-hydrological regime have rarely been clarified, and knowledge about the individual impacts of large reservoir regulation is still insufficient. In this study, we reconstructed the natural flow in the middle reaches of the Yangtze River (MRYR) not regulated by the Three Gorges Reservoir (TGR) based on a long–short-term memory model. We then evaluated the dynamics of eco-hydrological regimes using environmental flow components and eco-hydrological risk indicators and quantified the impacts of the TGR. The results showed that the hydrological stability has increased after TGR construction, with significant decreases in the frequency and duration of extremely low-flows, large floods, and small floods. In addition, there is a high eco-hydrological risk in the MRYR, with a higher ecodeficit and showing a continuous upward trend. The impact of the TGR on environmental flow components decreases along the river, averaging 42.1%, with the strongest impact on small floods, accounting for 56.2%. Overall, reservoir regulation has counteracted the increased eco-risk caused by climate change. Considering only the TGR, the ecosurplus in the spring and winter seasons increased, while the ecodeficit increased in the autumn season, with corresponding contribution rates of 81.4, 82.7, and 53.1%, respectively.

In this research, the water level (surface profile) in compound channels with convergent and divergent floodplains using soft computing models including the Multi-Layer Artificial Neural Network (MLPNN), Group Method of Data Handling (GMDH), Neuro-Fuzzy Group Method of Data Handling (NF-GMDH) and Support Vector Machine (SVM) was modeled and predicted. For this purpose, laboratory data published in this field were used. Parameters including convergence angle (with a positive sign) and divergence angle (with a negative sign), relative depth, and relative distance were used as input variables. The results showed that all the used models have appropriate performance, the best performance was related to the support vector machine model with statistical indicators of R² = 0.998 and RMSE = 0.008 in the test stage. The use of the adaptive fuzzy approach in the development of the GMDH model led to a remarkable increase in this model and reached the values of statistical indicators R² = 0.985 and RMSE = 0.203 in the test stage. It was found that the best performance of the activator and kernel functions in the development of the artificial neural network model and the support vector machine is related to the sigmoid and radial tangent functions (kernel).

Natural biofilms, which are widely distributed in various aquatic environments, can not only serve as bioindicators of various anthropogenic contaminants but also participate in the purification and degradation of various pollutants. However, the inherent purification capacity of natural biofilms and their physiochemical and biological properties are still poorly understood. In this study, outdoor sampling and indoor experiments were used to explore the purification abilities of natural biofilms. The physiochemical and biological properties of natural biofilms were further investigated to reveal their purification mechanism. The results demonstrated that natural biofilms had an excellent purification effect on heavily polluted water. Indoor experiments showed that the purification capacity of natural biofilms was dominated by microbial biodegradation rather than physical biosorption, and after 14.0 days of incubation, the removal rates of COD, TP, NH₄⁺-N, and NO₃^--N could reach 93.6, 80.83, 85.93, and 81.03%, respectively. The SEM, FTIR spectra, and component analyses revealed that natural biofilms were mainly composed of polysaccharides and proteins. The dominant phyla in the bacterial community structure were Campilobacterota, Proteobacteria, Bacteroidota, Firmicutes, and Desulfobacterota, and the major phyla in the fungal community structure were Chytridiomycota and Ascomycota. These microorganisms might be the main degraders of riverine pollutants.

This systematic review aims to understand how previous studies have determined the environmental performance of urban water systems (UWSs) and to identify the main environmental impacts caused by these systems and their respective sources. A systematic review strategy based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was used. The publications were selected by searching the Scopus and Web of Science databases last updated on 8 February 2022 and according to the eligibility criteria that were set for the present study. A total of 25 publications were analyzed in their entirety. Six publications presented methodological contributions by proposing frameworks and models for the analysis of environmental and sustainability parameters of urban water and sewage systems. Nineteen publications conducted case studies where life cycle assessment (LCA) and mass balance methods were employed. The categories of environmental impacts most frequently analyzed in the publications included climate change, eutrophication, acidification, and human toxicity. Moreover, the sources of these negative impacts were identified as high electricity consumption, chemical use, and nutrient discharge into the environment. The present review analyzes the contributions to each stage of the systems that are associated with the characteristics of the systems and the level of detail of the used data.

Runoff is affected by natural and nonnatural factors in the process of formation, and the runoff series is generally nonstationary time series. How to improve the accuracy of runoff prediction has always been a difficult problem for hydrologists. The key to solve this problem is to reduce the complexity of runoff series and improve the accuracy of runoff prediction model. Based on the aforementioned ideas, this article uses the complementary set empirical mode decomposition to decompose the runoff series into multiple intrinsic components that retain time–frequency information, thus reducing the complexity of the runoff series. The particle swarm optimization (PSO) adaptive neuro-fuzzy system is used to predict each intrinsic component to improve the accuracy of runoff prediction. After that, the trained intrinsic components of the model are reconstructed into the original runoff series. The example shows that the absolute relative error of the runoff forecasting model constructed in this article is 0.039, and the determination coefficient is 0.973. This model can be applied to the annual runoff series forecasting. Comparing the prediction results of this model with empirical mode decomposition algorithm-ANFIS model and ANFIS model, complementary set empirical mode decomposition algorithm-PSO-ANFIS model shows obvious advantages.

The world is facing the greatest and most complex twin challenges of water insecurity: scarcity and excess, with their adverse consequences on health, well-being, and developmental outcomes. Against this backdrop, we analyzed the challenges households face due to ‘too much and too little water’. The research employed a qualitative methodology in which data were collected through 40 key informant interviews, informal conversations, and observations during 2020–2021 including a relevant literature review. We note that both ‘too much and too little water’ pose risks to water insecurity. Also, water security cannot be ensured by only dealing with water inadequacy without building a resilient water system and robust institutions. We found that water scarcity has affected other components of water security such as equity, quality, and affordability. Excess water has impacted water infrastructures degrading the water quality, and risking human health and well-being. The responses to the water challenges were hindered by several constraints such as the limited capacity of the water institutions, frequent leadership changes, political influence, and emerging challenges in the federal context. We suggest timely planning and adopting site-specific innovations to address water scarcity and excess challenges, which include strengthening water infrastructures, water supply services, water institutions, and governance.

Water scarcity has resulted in lower yield (7–8 t ha⁻¹) and a decline in citrus orchards in central India. In this scenario, optimal deficit irrigation (DI) is a potential option to sustain citrus production. To optimize the DI schedule in relation to yield, water productivity, fruit quality, and production economics, water was applied at 30% of full irrigation (FI, 100% crop evapotranspiration), 50% of FI, and 70% of FI and compared with FI in drip-irrigated citrus at Nagpur, India during 2008–2010. Fruit yield under 50% FI (11.48 t ha⁻¹) was statistically at par with FI (13.14 t ha⁻¹). However, a 50% reduction in water supply (1,343 m³ ha⁻¹) resulted in a 75% improvement in water productivity in DI at 50% FI than FI (water productivity, 4.89 kg m⁻³). The fruit quality in DI at 50% FI (acidity, 0.83%; total soluble solids, 10.3 °Brix) was superior to FI (acidity, 0.85%; total soluble solids, 9.80 °Brix). The highest net profit (INR 94,300 ha⁻¹) and net economic water productivity (70.19 INR m⁻³) were generated under DI at 50% FI. The study inferred that irrigation at 50% FI could be a water-saving and profitable option for citrus production in central India.

Tanks are one of the most important traditional sources of water for irrigation and other livelihood purposes in southern India. The present study was conducted to develop the strategies for enhancing the land and water productivity in tank command areas in the rain-fed regions of Telangana. Three strategies were identified, these strategies include change in the cropping pattern, water saving technologies and conjunctive use of tank with open well/bore well water. Due to change in cropping pattern from paddy to irrigated, dry crops like maize, chillies and cotton increased the cultural command area of the tanks by 79% and net reruns by 43%. The conjunctive use of tank water and well water is the better strategy for optimal resource use and higher returns in tank command areas. Results also showed that the alternate wetting and drying strategy in tank command areas increased the command area by 35%. These strategies can be implemented to increase the water use efficiency, cultural command area and net returns in the tank command areas of Southern India.

In this study, to evaluate the trend of precipitation change, the Mann–Kendall method has been used. The studied area is Lorestan province located in the western part of Iran. To achieve this goal, time series of annual and monthly rainfall data were collected for different statistical periods. Moreover, in order to analyze the drought, the standard precipitation index and the non-parametric Mann–Kendall test were used. To predict the meteorological drought in this province, the monthly time series of the precipitation parameter was incorporated. The results showed that most parts of Lorestan province are facing an extreme drought and such conditions will happen again in the future. Furthermore, the amount of precipitation was predicted until 2032, and the trend of predicted precipitation data in the entire Lorestan province showed that there is a significant trend in most months. The results of the research on an annual scale showed that all stations have a significant negative trend at the level of 5%, which indicates the existence of a negative trend, or in other words, a decrease in irrigation in the studied stations. Therefore, according to the obtained results, it is necessary to plan the water consumption in Lorestan province toward sustainable management.