Water quality is the most important parameter of the portable water. Therefore, water quality simulation is of utmost importance, along with carrying out the hydraulic analysis of a water distribution network. In the current study it has been attempted to carry out the water quality simulation of the proposed distribution network for the University of Kashmir using EPANET 2.0 software. The study also aims at obtaining the optimal performance of the designed network in terms of water quality parameters. Furthermore, the leakage modelling for the network has been carried out using the EPANET extension- WaterNetGen. It was found that the important water quality parameters like Residual chlorine at nodes and Water Age were within the standard ranges throughout the simulation period. Also, the percentage of the leakage discharge at the nodes was very insignificant.

Cyanobacteria rigorously affect water quality that human use for various activities ranging from drinking, to fishing and recreation, as indicated in the investigation protocol of World Health Organization (WHO). The current state of the lake necessitates constant monitoring and remote sensing is an adequate tool for the constant monitoring of the whole water body. In the present study, multi-temporal sentinel-2 images were used to assess surface water quality based on the concentration of chlorophyll a (Chl-a) of cyanobacteria and dissolved oxygen of water. Chl-a was used as an indicator for cyanobacteria bloom and dissolved oxygen was used as an indicator of water quality. Dissolved oxygen was generated using Sentinel 2 dataset. For the present study two wetlands Wadhwana and Timbi Vadodara City, Gujarat, India were assessed from 2018 to 2022. Analysis showed that, dissolved oxygen is an important environmental factor that influenced cyanobacteria abundance. It was seen that the increased concentration of chlorophyll a, was associated with reduction in dissolved oxygen and hence deteriorated the water quality.

Station observation is a good data source to monitor the potential evapotranspiration (PET) of a specific site particularly for the purpose of crop irrigation activities; however it represents only the site geographic characteristics and provides real time or historical records. Hence, there was an urgent need to find a promising tool and a simple empirical to predict/project the PET in locations where station observation is not available. The Hargreaves–Samani method (HS) method is recommended after the Penman-Monteith equation. Also, it can operate on daily and monthly time scales. To handle this issue, the regional climate model (RegCM4) was used to compute the PET using the HS. ERA-Interim reanalysis of 1.5 degrees (EIN15) was used to downscale the RegCM4 with 25 km grid spacing over Egypt for the period 1979-2017. A comparison was conducted between the original HS formula and its calibrated version with respect to the high-resolution ERA5-land derived reanalysis product (hereafter ERA5). Results showed that calibrating the HS coefficients indicates a considerable improvement in estimating the PET (relative to the original equation) when it is compared with ERA5. Such improvement is confirmed by a significant low mean bias. Therefore, the RegCM4 can be used to estimate the PET using a calibrated version of the HS equation either for making a daily forecast (e.g., for 5 days in advance) or projecting the future PET under different global warming scenarios.

The study presents a spatial modeling approach of soil erodibility (K factor) by water (rainfall and irrigation) on conventional tillage (CoT) and no tillage (NOT) treatments of a Helianthus annuus crop using field measurements, soil analyses, GIS and Kriging geostatistical models. A split-plot design with 4 treatments×3 replications of experimental field plots (with a south-east facing 7.5 % slope) was used. At each plot, topsoil samples (0–5 cm) were collected in a Grid pattern to determine texture (clay, silt, sand, very fine sand and gravel contents), organic matter content (OM), soil’s structure and soil’s water permeability classes. A Global Positioning System (GPS) receiver was used to identify the sampling locations and the 40 top soil samples were air-dried, and passed through a 2 mm mesh to determine soil texture by the Bouyoucos method. Organic matter was analyzed by chemical oxidation with 1 mol L^-1 K₂Cr₂O₇ and titration of the remaining reagent with 0.5 mol L^-1 FeSO₄. The soil structure and the soil’s water permeability classes were determined according to USDA classification system. Soil erodibility by water modeling of K factor (t ha h ha−1 MJ−1 mm−1) was based and estimated using the nomograph equation of Wischmeier model by incorporating it in a developed GIS spatial model using Kriging geostatistical models. Results of Anova statistical analysis (P=0.05) between K data groups revealed that the 2 tillage systems and the 4 management treatments were significantly different. Moreover, it was found that no tillage system and treatment-No Tillage with Vegetation Cover were the best tillage and agricultural practices for hillslope farmfields, and can be regarded as potential ecological good agricultural practices, in order to reduse soil erodibility by water, erosion and runoff risk and preserve the soil environment and its valuable nutrients.

Floods may get lower or less frequent in certain regions due to climate change, while increasing rapidly or more frequent in other places. Changes in the intensity and frequency of heavy precipitation events may have an impact on the extent and occurrence of river flooding as warmer temperature cause more water to evaporate from the land and oceans (see the Heavy Precipitation Indicator). Flood patterns can also be impacted by variation in streamflow, the timing of snowmelt (see the Streamflow Indicator), and the quantity of snowfall that accumulates over the winter (see the Snowpack indicator). Due to the heavy monsoon rainfall cause the flood in Khyber Pakhtunkhwa, Sindh, and parts of Baluchistan, from the year 2010. Over 20 million people and over 20 % of Pakistan land area were impacted. 1985 people had loss the lives by the disaster. According to the world Bank, the country already faced significant nonagricultural water shortage threats before the floods. Water demand is expected to rise by about 60% in a high growth (4.9 percent per year) and high warming (3 degree Celsius by 2047) scenario due to increase the development of household and industrial sector. Up to 15% of this growth in demand will be caused by climate change. Additionally, climatic shocks like the recent floods will continue to make it more difficult for populations to secure clean water. Thirty-three percent of Pakistan was affected by the floods in 2022, affecting around 33million people. According to information provided by National Disaster Management Authority, 81 district in Sindh, Punjab, Baluchistan and KPK have been officially designated as “calamity affected” with around 1400 deaths on 14 June. According to this study, NDMA decided to take the step for the management of this flood extreme events and used the best water conservation strategies for save the life and reduce the disaster.

Membranes are used in desalination or water treatment to separate pollutants from water based on characteristics such as size or charge. Nanofiltration (NF), ultrafiltration (UF), microfiltration (MF), and reverse osmosis (RO) are typical membrane techniques. However, traditional membranes have a number of disadvantages, including fouling both on surfaces and in internal structure, uncontrollable pore size, and membrane features. Smart membranes, also known as stimuli-responsive membranes, have recently attracted attention due to their selectivity, tunable permeability, and tunable and/or reversible attributes. This new generation of smart membranes is created by integrating various stimuli-responsive materials into membrane substrates. These multi-functional smart membranes can self-adjust their physical and chemical features in response to environmental signals such as temperature, pH, light, and other stimuli. Thermo-responsive membranes, pH-responsive membranes, ion-responsive membranes, molecule-responsive membranes, UV-light-responsive membranes, glucose-responsive membranes, magnetic-responsive membranes, and redox-responsive membranes are the current kinds of smart membranes. Because of their smart structures, they have the potential to improve performance by providing high selectivity without reducing permeability, high mechanical stability, and high resistance against fouling, and can meet requirements such as molecular weight cut-off (MWCO), removal efficiencies, and wastewater quality. Smart membranes can show tuneable features based on the condition of the stimulus or stimuli present internally or externally, resulting in improved and desirable controllability over the process of pollutant removal from water. Because of their physicochemical stability, repeatability, and long life, stimuli-responsive smart materials (mainly adsorbents and filtration membranes) have the potential to be key materials for membrane production, particularly in the field of water treatment. Smart membranes have a bright future, and it is important to investigate and encourage their use and advancement. This review provides a comprehensive overview of smart membranes.

Human activities and specific geological and oceanographic conditions can affect the exposure of small river coastal parts to erosion and flooding. This study presents the development of a vulnerability assessment methodology combining both hydraulic and oceanographic values to evaluate the fragility of the island’s coast, subject to floods and climate change impact. The study area covers the coastal part from Petra to Molyvos in West–North Lesvos Island, Greece. Petra stream 1.75 km in length drains the catchment area of 7.97 km², with a mean flow of 3.52 m³/s, a mean width of 6.15 m, a mean slope is 1.03% and a mean depth is 1.63 m, and flows into the Aegean Sea. The flooded sections of the river’s coastal part are analyzed by the HEC-RAS model, while the coastal vulnerability index (CVI) was calculated by the Invest model. The HEC-RAS model estimated the critical cross-sections (CSs) 2 and 3 at the stream mouth are the first to show overflow equal to 8 m³/s reducing the upstream flow which could reach 11m³/s with appropriate riverbed shaping. CS 26 (depth 0.54m and width 4.43m, upstream of the mouth at 650 m) reduces the upstream drainage of the stream and overflow with a flow rate of 1.5 m³/s. While the INVEST computes the CVI by combining the ranks of up to seven ecological and physical variables at each shoreline point. Ranks vary from very low exposure (rank=1) to very high exposure (rank=5), based on a mixture of user- and model-defined criteria. A change in the geomorphology of the estuary was observed during the summer period, due to the river sediment dredging and small delta reclamation processes. The CVI results are expected to show that 10% of the coastal zone is in high hazard.

The study of water quality is crucial given the amount of industrial, agricultural, and other human activities in the sampling sites. The aim of the study was to assess the physicochemical characteristics and heavy metals contamination of water samples in Nigeria. This study used conventional analytical techniques to analyze the physicochemical parameters in water samples from 33 sampling sites (dug wells, boreholes, rivers, and rainwater) in three different states (Ekiti, Osun, and Ondo) of Nigeria. These parameters included pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Temperature, Relative Humidity (RH), and four elemental parameters (Ca, Na, Fe, and Cu). The Enrichment Factor (EF), Contamination Factor (CF), and Metal Index (MI) were used to characterize the data. Temperature (28.17^oC), TDS (130.2 mg/L), EC (260.0 μS/cm), pH (6.88), Na (14.47 ppm), Ca (25.74 ppm), Fe (0.49 ppm), and Cu (0.08 ppm) were the average values from the results. Na and Ca had relationships with one another. The levels of heavy metals were below those recommended by the Standard Organization of Nigeria (SON) and the World Health Organization (WHO). The metal levels in the water samples were over 1.5, which is the threshold value indicated by the EF classifications. Particularly, EFs were moderate to significantly enriched. All element CFs were below the 1-level pollution threshold. The water samples are pure based on the MI's rating of water quality. Human and natural activities may represent a risk to the local public health, hence it is highly advised that all stakeholders adopt rapid and long-lasting collective action to limit pollution levels as part of the water quality governance system.

Water is a crucial component of ecosystems and a critical resource that cannot be replaced for social progress or human life. It supports temperature variation, the cycling of carbon, and other ecological processes. In this study, Agulu Lake, an inland water body located in Anambra, south-east Nigeria, was mapped, classified, and delineated with remote sensing and GIS tools with the aim of analyzing the spatial-temporal changes that occur in the lake’s surface water from 2000 to 2022. To achieve this goal, remotely sensed data from the United States Geological Survey (USGS) portal, including Landsat 7 TM (2000), ETM+ (2011), and Landsat 8 OLI (2022), were used to create various classification maps, including land and water surface classes. The Google Earth image was also used to show the general overview of Agulu Lake and its environs. The ArcGIS 10.5 software and maximum likelihood classifier were used to classify the images. The findings demonstrate that over the study period, the land surface class has grown whereas the water surface class (Agulu Lake) has shrunk. The research demonstrates that GIS and remote sensing are useful methods for tracking, mapping, and defining lakes and other open water bodies. The study is recommended as a starting point for monitoring developments that influence the lake’s water quality.

Climate change has brought tremendous impact for people around the world. Indonesia is identified as one of country that are vulnerable to disaster, especially of which induced by climate change such as floods, droughts, sea level rise, changing rainfall patterns and temperature raise. The Indonesia Meteorology, Climatology and Geophysical Agency (BMKG) predicted that rising rain precipitation in Indonesia in 2023 which would likely increase the threat of hydrometeorological disasters such as floods and landslides. Although it is believed that the climate change produces a higher risk of hydro meteorological disasters, however, a study found that Indonesian has the highest climate change deniers among 23 other countries that participate in the study which shows that Indonesians people are still unaware of the impact of climate change to their environment. Environmental awareness is one of the important factors that need to be integrated into the disaster risk reduction planning. Therefore, identifying the environmental awareness of community live in the hydrological disaster-prone area would be beneficial for designing a disaster mitigation plan of the area and ensure the attainment of the SDG-11 sustainable cities and communities. Therefore, aimed of this research is to describe the environmental awareness of people live in vulnerable area which is prone to hydrological disaster. This research employs a survey method. This study involved 357 participants live in hydrological disaster-prone area in Indonesia. The environmental awareness scale was given to the participants. The result showed that the majority of the participants have a low level of environmental awareness.