Assessment of climate change impacts on water quality parameters of Lake Burullus, Egypt

A real-time simulation model of water quality with the impact of internal pollution for water source reservoir

The water source reservoirs are the important urban water source in northern China. Although external pollution has been greatly improved, the internal pollutants in reservoirs continue to accumulate with the complex deposition and release processes, resulting in potential risks to water supply safety. To address the aforementioned issue, this paper proposed a simulation model of water quality named ECOlab EU1-WSR to simulate the spatio-temporal changes of water quality under the influence of internal pollution for the water source reservoirs. Based on the analysis of the water quality characteristics and the distribution of benthic vegetation in the reservoir, a three-dimensional hydrodynamic model was established based on MIKE3, the corresponding parameters and the related state variables were set, the ECOlab EU1-WSR model was established by secondly developing the original ECOlab EU1 template, and the real-time dynamic outputs of pollutant content in sediment were added to link the water quality index with sediment nutrition index for better revealing the impact of the internal pollution on the water quality. The performance of the model was evaluated by the case application on the water quality simulation of Daye reservoir and the optimization of the connection project between Daye reservoir and Xueye reservoir in Shandong Province China. The results showed that the model can accurately and simultaneously simulate the pollution in water and sediment by the comparative verification of hydrodynamics, water temperature, and water quality. Moreover, the model can effectively reflect the influence of the accumulation, deposition, and release of internal pollution on water quality by analyzing the correlation between the content of various pollution in water body and those in sediment, such as the total nitrogen and total phosphorus in the water body at the bottom of the water intake, were negatively correlated with the total nitrogen and total phosphorus in the sediments with correlation coefficients of 0.538 and 0.917, respectively. In addition, the optimal water inlet position and water flow rate of the connection project can be optimized and determined by using the model to effectively control water quality. The established model will be a useful tool for the design and management of a reservoir, the interconnection projects, and other water bodies by adaptively recoded.

Hydro-meteorological factors and inflowing nutrients drive water quality in an impounded lake of China's South-to-North Water Diversion Project

Freshwater lakes play a vital role in global hydrological and biogeochemical cycles, serving various functions and maintaining ecological balance. However, freshwater resources are more vulnerable to deterioration due to multiple stressors. Gaoyou Lake is one of the impounded lakes of the Eastern route of South-to-North Water Diversion Project in China, and as an important source of drinking water, the lake has been routinely monitored. Long-term monitoring of water quality in Gaoyou Lake showed that concentrations of nutrients and chlorophyll a as well as trophic state in the water column increased while water transparency decreased, indicating that the water quality has declined during the last 12 years. Specifically, there was a notable and statistically significant increase in chlorophyll a concentrations, averaging an annual rate of 9.9%. Despite a slight decline in trophic level index until 2014, subsequent years saw an upward trend, ranging from 50.7 to 56.4 and indicating a light eutrophic state. Spatially, the western area displayed higher nutrient and chlorophyll a concentrations. Changes in hydro-meteorological variables and nutrients from inflowing rivers were the main factors correlated with water quality in Gaoyou Lake. Thus, pollution source apportionment and management within Huaihe River basin should be considered to reduce the external loadings of nutrients in order to improve and sustain long-term water quality.

Integrated Management and Environmental Impact Assessment of Sustainable Groundwater-Dependent Development in Toshka District, Egypt

Egypt has recently inaugurated a mega development project aiming to alleviate the overpopulation along the Nile River and to meet the looming food gap. Toshka is a promising area where groundwater-dependent activities are being expanded adjacent to Lake Nasser. Thus, it is of utmost importance to provide a sustainable development approach and to assess the resulting environmental implications. Accordingly, a coupled groundwater flow and transport model was invoked. The generated model was successfully calibrated for the observed water levels and salinity. The proposed exploitation regime of 102 wells each pumping 1000 m3/day was simulated for a 100-year test period. The maximum resulting drawdown was about 25 m, compatible with the advocated sustainable restriction limit. Climate change (CC) impacts of reducing the lake’s storage and increasing the crops’ water requirements were investigated. The lake’s water level fluctuations were a key factor in the aquifer hydraulics and flow direction. The drawdown breakthrough considering the CC catastrophic scenario (RCP8.5) has increased by about 20%. The developed solute transport model was utilized to simulate the salinity spatial distribution and the lateral movement of leaking pollutants from the underway activities. Cultivation activities were found feasible up to 80 km away from the lake border where salinity does not exceed 2000 ppm. Yet, a protection strip of not less than 4.8, 6.0, and 7.2 km according to the lake operating condition is inevitable to ensure that pollutants do not intrude into the lake. These findings will assist the decision-makers in scheming environmental impact assessment criteria for sustainable development.

Projection of temperature extremes of Egypt using CMIP6 GCMs under multiple shared socioeconomic pathways

Global warming has amplified the frequency of temperature extremes, especially in hot dry countries, which could have serious consequences for the natural and built environments. Egypt is one of the hot desert climate regions that are more susceptible to climate change and associated hazards. This study attempted to project the changes in temperature extremes for three Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, and SSP5-8.5 and two future periods (early future: 2020-2059 and late future: 2060-2099) by using daily maximum (T_max) and minimum temperature (T_min) of general circulation model (GCMs) of Coupled Model Inter-comparison Project phase 6 (CMIP6). The findings showed that most temperature extreme indices would increase especially by the end of the century. In the late future, the change in the mean T_min (4.3 °C) was projected to be higher than the mean T_max (3.7 °C). Annual maximum T_max, temperature above 95th percentile of T_max, and the number of hot days above 40 °C and 45 °C were projected to increase in the range 3.0‒5.4 °C, 1.5‒4.8 °C, 20‒95 days, and 10‒52 days, respectively. In contrast, the annual minimum of T_min, temperature below the 5th percentile, and the annual percentage of cold nights were projected to change in the range of 2.95‒5.0 °C, 1.4‒3.6 °C, and - 0.1‒0.1%, respectively. In all the cases, the lowest changes would be for SSP1-2.6 in the early period and the greatest changes for SSP5-8.5 in the late period. The study indicates that the country is likely to experience a rise in hot extremes and a decline in cold extremes. Therefore, Egypt should take long-term adaptation plans to build social resiliency to rising hot extremes.

Eutrophication risk assessment of a large reservoir in the Brazilian semiarid region under climate change scenarios

The present study assesses the risk of eutrophication of a large semiarid reservoir under SSP2-4.5 and SSP5-8.5 scenarios for three future periods and different conditions of influent total phosphorus (TP) concentration and reservoir withdrawal. An integrated approach coupling climate, hydrological and water quality models was proposed for forecasting the climate change impacts on the trophic condition of the reservoir. The projected TP concentrations were organized as probability-based cumulative distribution functions to quantify the risk of eutrophication. The results indicated changes of eutrophication status in the three future periods, with the end of the 21st century experiencing the highest impacts on water quality. On the other hand, major reductions both in the inlet TP concentration and the reservoir withdrawal are necessary to significantly improve the trophic status and minimize the risk of eutrophication. The results also showed that the dry period is more susceptible to eutrophication than the rainy period, suggesting that tropical semiarid reservoirs are more vulnerable to eutrophication under climate change than reservoirs in other regions of the world. The proposed approach and model results are important to better understand the impact of climate change on reservoir water quality and improve water resources management in tropical semiarid regions.

Impacts of Land Use on Surface Water Quality Using Self-Organizing Map in Middle Region of the Yellow River Basin, China

The Yellow River is one of the most important water sources in China, and its surrounding land use affected by human activities is an important factor in water quality pollution. To understand the impact of land use types on water quality in the Sanmenxia section of the Yellow River, the water quality index (WQI) was used to evaluate the water quality. A self-organizing map (SOM) was used for clustering analysis of water quality indicators, and the relationship between surface water quality and land use types was further analyzed by redundancy analysis (RDA). The results showed that WQI values ranged from 82.60 to 507.27, and the highest value was the sampling site S3, whose water quality grade was "Likely not suitable for drinking", mainly polluted by agricultural non-point sources ammonia nitrogen pollution. SOM clustered the sampling sites into 4 groups according to the water quality indicators, the main influencing factors for different groups were analyzed and explored in more depth in relation to land use types, suggesting that surface water quality was significantly connected with the proportion of land use types at the watershed scale in the interpretation of water quality change. The negative impact of cropland on surface water quality was greater than that of other land use types, and vegetation showed a greater positive impact on surface water quality than other land uses. The results provide evidence for water environment conservation based on land use in the watershed.

Human-induced sediment degradation of Burullus lagoon, Nile Delta, Egypt: Heavy metals pollution status and potential ecological risk

Heavy metals contamination level and their ecological risk of the Burullus lagoon were estimated using four cores and twelve surficial sediment samples. The distributions maps of Fe, Mn, Zn, Hg, Cu, and Pb indicate an increase toward drains areas, whereas Cd rises toward the Boughaz El-Burullus. The geoaccumulation index (I_geo) and contamination factor (CF) for heavy metal displayed the following order: Cd > Zn > Fe > Cu > Pb > Mn > Hg. The degree of contamination (C_d) indicates a considerable degree of contamination for 81.25% of the studied stations, and the pollution load index (PLI) suggested deterioration in 100% of sediments. Regarding the potential ecological risk (RI), the metals were arranged as: Cd > Hg > Cu > Pb > Zn, with considerable risk at the eastern part. According to sediment quality guidelines (SQGs), Zn concentrations suggest frequently adverse impacts on biota while Cu and Cd indicating an occasional adverse impact. Periodic monitoring of heavy metals in aquatic organisms is recommended to assess their toxic risk.

Loss of Coastal Wetlands in Lake Burullus, Egypt: A GIS and Remote-Sensing Study

Lake Burullus is the second largest lake at the northern edge of the Nile Delta, Egypt, and has been recognized as an internationally significant wetland that provides a habitat for migrating birds, fish, herpetofauna, and mammals. However, the lake is experiencing severe human impacts including drainage and conversion to agricultural lands and fish farms. The primary goal of this study was to use multispectral, moderate-spatial-resolution (30 m2) Landsat satellite imagery to assess marsh loss in Lake Burullus, Egypt, in the last 35 years (1985–2020). Iterative Self-Organizing Data Analyses (ISODATA) unsupervised techniques were applied to the Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager–Thermal Infrared Sensor (OLI–TIRS) satellite images for classification of the Lake Burullus area into four main land-use classes: water, marsh, unvegetated land surfaces (roads, paths, sand sheets and dunes), and agricultural lands and fish farms. The overall classification accuracy was estimated to be 96% and the Kappa index was 0.95. Our results indicated that there is a substantial loss (44.8% loss) in the marsh aerial coverage between 1985 and 2020. The drainage and conversion of wetlands into agricultural lands and/or fish farms is concentrated primarily in the western and southern part of the lake where the surface area of the agricultural lands and/or fish farms doubled (103.2% increase) between 2000 and 2020. We recommend that land-use-policy makers and environmental government agencies raise public awareness among the local communities of Lake Burullus of the economic and environmental consequences of the alarming loss of marshland, which will likely have adverse effects on water quality and cause a reduction in the invaluable wetland-ecosystem services.

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031-2065 and 2066-2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB.

A Three-Dimensional Coupled Hydrodynamic-Ecological Modeling to Assess the Planktonic Biomass in a Subalpine Lake

In this study, a coupled three-dimensional hydrodynamic-ecological model was developed to comprehensively understand the interaction between the hydrodynamics and ecological status of a lake. The coupled model was utilized to explore the hydrodynamics, water quality, and ecological status in an ecologically rich subalpine lake (i.e., Tsuei-Feng Lake (TFL), located in north-central Taiwan). The measured data of water depth, water temperature, water quality, and planktonic biomass were gathered to validate the coupled model. The simulated results with a three-dimensional hydrodynamic and water quality-ecological model reasonably reproduced the variations in observed water depth, water temperature, water quality, and phytoplankton and zooplankton biomass. Sensitivity analysis was implemented to determine the most influential parameter affecting the planktonic biomass. The results of sensitivity analysis indicated that the predation rate on phytoplankton (PRP) significantly affects the phytoplankton biomass, while the basal metabolism rate of zooplankton (BMZ) importantly affects the zooplankton biomass. Furthermore, inflow discharge was the most important environmental factor dominating the phytoplankton and zooplankton biomass of TFL. This implies that the runoff in the catchment area caused by rainfall and the heavy rainfall induced by climate change may affect the planktonic biomass of the lake.

Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH₃), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.