Novel concept for water scarcity quantification considering nonconventional and virtual water resources in arid countries: Application in Gulf Cooperation Council countries.

The lack of perennial streams or surface water in most arid countries necessitates input modification and water scarcity/security equation calculation as per the water resource systems and physiographic conditions in these countries. The contributions of nonconventional and virtual water resources to water security have been disregarded or undervalued in previous research on global water scarcity. This study addresses this knowledge gap by developing a new framework for estimating water scarcity/security. The proposed framework considers the contributions of unconventional and virtual water resources and the roles of economics, technology, water availability, service accessibility, water safety and quality, water management, and resilience to threats on water and food security, and considers institutional changes required to adjust to water scarcity. To manage water demand, the new framework incorporates metrics for all categories of water resources. Although the framework was specifically designed for arid regions, particularly the Gulf Cooperation Council (GCC) countries, it is applicable to non-arid nations too. The framework was implemented in GCC countries, which are suitable examples of arid countries with notable virtual commerce. The ratio of abstraction from freshwater resources to renewability from conventional water sources was calculated to determine the extent of water stress in each country. The values obtained from measurement varied from 0.4 (the optimal threshold level for Bahrain) to 22 (severe water stress/low water security in Kuwait). Considering the nonconventional and abstracted nonrenewable groundwater volumes from the total water demand in the GCC, the minimum water stress value measured was 0.13 in Kuwait, suggesting considerable reliance on nonconventional water resources along with little domestic food production to achieve water security. The novel water scarcity/stress index framework was found to be appropriate for arid and hyper-arid regions, such as the GCC, where virtual water trade has a major positive impact on water security.

Groundwater recharge estimation using in-situ and GRACE observations in the eastern region of the United Arab Emirates.

The intensive agricultural expansion and rapid urban development in Abu Dhabi Emirate, United Arab Emirates (UAE) have resulted in a major decline in local and regional groundwater levels. By using the latest release (RL06) of Gravity Recovery and Climate Experiment (GRACE) satellite measurements and Global Land Data Assimilation System (GLDAS) products, the groundwater storage change was computed and compared with the time series of in-situ monitoring wells over the period of 2010-2016. The RL06 GRACE products from Jet Propulsion Laboratory (JPL), University of Texas Center for Space Research (CSR), German Research Center for Geosciences (GFZ), and JPL mass concentrations (MASCON) were assessed and have shown satisfactory agreements with the monitoring wells. The JPL MASCON reflected the in-situ groundwater storage change better than the other GRACE products (R = 0.5, lag =1 month, RMSE = 13 mm). The groundwater recharge is estimated for the study area and compared with the in-situ recharge method that considers multi recharge components from the rainfall, irrigation return flow and internal fluxes. The results show that the agreements between in-situ and GRACE-derived recharge estimates are highly agreeable (e.g., R2 = 0.91, RMSE = 1.5 Mm3 to 7.8 Mm3, and Nash-Sutcliff Efficiency = 0.7). Using the Mann-Kendall trend test and Sen's slope, the analyses of policies, number of wells, and farm areal expansion with groundwater time series derived from GRACE helped to validate GRACE and emphasize the importance of regulations for sustainable development of groundwater resources. The impacts of subsidy cuts after 2010 can be captured from the GRACE data in the eastern region of Abu Dhabi Emirate. The linear trend of groundwater storage anomaly obtained from GRACE over the period from 2003 to 2010 is -6.36 ± 0.6 mm/year while it showed a decline trend of -1.2 ± 0.6 mm/year after the subsidy cut. The proposed approach has a potential application for estimating groundwater recharge in other arid regions where in-situ monitoring wells are limited or absent.

Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia.

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Suspended sediment load prediction using long short-term memory neural network.

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988-1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

Complex Extreme Sea Levels Prediction Analysis: Karachi Coast Case Study.

In this study, the analysis of the extreme sea level was carried out by using 10 years (2007-2016) of hourly tide gauge data of Karachi port station along the Pakistan coast. Observations revealed that the magnitudes of the tides usually exceeded the storm surges at this station. The main observation for this duration and the subsequent analysis showed that in June 2007 a tropical Cyclone "Yemyin" hit the Pakistan coast. The joint probability method (JPM) and the annual maximum method (AMM) were used for statistical analysis to find out the return periods of different extreme sea levels. According to the achieved results, the AMM and JPM methods erre compatible with each other for the Karachi coast and remained well within the range of 95% confidence. For the JPM method, the highest astronomical tide (HAT) of the Karachi coast was considered as the threshold and the sea levels above it were considered extreme sea levels. The 10 annual observed sea level maxima, in the recent past, showed an increasing trend for extreme sea levels. In the study period, the increment rates of 3.6 mm/year and 2.1 mm/year were observed for mean sea level and extreme sea level, respectively, along the Karachi coast. Tidal analysis, for the Karachi tide gauge data, showed less dependency of the extreme sea levels on the non-tidal residuals. By applying the Merrifield criteria of mean annual maximum water level ratio, it was found that the Karachi coast was tidally dominated and the non-tidal residual contribution was just 10%. The examination of the highest water level event (13 June 2014) during the study period, further favored the tidal dominance as compared to the non-tidal component along the Karachi coast.

Optimised neural network model for river-nitrogen prediction utilizing a new training approach.

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.

Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting.

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.

Characterisation of the impact of dissolved organic matter on iron, manganese, and arsenic mobilisation during bank filtration.

Bank filtration (BF) has been used for many years as an economical technique for providing high-quality drinking water. However, under anaerobic conditions, the aquifer release of undesirable metal(loid)s, such as iron manganese, and arsenic, reduces the bank filtrate quality and thus restricts the application of this technique. This study investigates the impact of the organic-matter composition of source water on the mobilisation of Fe, Mn, and As during the anaerobic BF process. A laboratory-scale column study was conducted at a controlled-temperature (30 ± 2 °C) using different feed water sources. The organic matter characteristics of the feed water were elucidated using excitation-emission spectroscopy techniques integrated with parallel factor framework clustering analysis (PFFCA) model. The experiment was performed at redox conditions between 66 mv and -185 mv. Moreover, batch studies were implemented to study the effect of natural organic matter type (humic, fulvic and tyrosine) and concentration on the mobilisation of the selected metal(loids). The laboratory experiments demonstrated that the mobilisation of Fe, Mn and As during the BF are varied with the organic water concentration and composition of the source water. The fluorescence results revealed that terrestrial and condensed structure humic compounds are more capable to release Fe into the filtrate water. In contrast, Mn exhibited an equal tendency of mobilisation towards all the humic compounds regardless of its origin and structure. However, at a humic concentration higher than 5 mg-C/L, Mn showed more affinity towards lower molecular weight humic compounds. Arsenic was found to be the least impacted by the alteration in the source water organic matter composition; its mobilisation was highly correlated with iron releasing process. On the other hand, the biodegradable organic matter at high concentration (>10 mg-C/L) was found to be highly effective to turn the infiltration area into Fe-reducing environment and thereby elevating Fe and As concentrations in the pumped water. In conclusion, this study revealed that the DOM composition and concentration of the raw water could play an important role in the mobilisation of metal(loids) during the BF processes.

Impacts of seawater rise on seawater intrusion in the Nile Delta Aquifer, Egypt.

Several investigations have recently considered the possible impacts of climate change and seawater level rise on seawater intrusion in coastal aquifers. All have revealed the severity of the problem and the significance of the landward movement of the dispersion zone under the condition of seawater level rise. Most of the studies did not consider the possible effects of the seawater rise on the inland movement of the shoreline and the associate changes in the boundary conditions at the seaside and the domain geometry. Such effects become more evident in flat, low land, coastal alluvial plans where large areas might be submerged with seawater under a relatively small increase in the seawater level. None of the studies combined the effect of increased groundwater pumping, due to the possible decline in precipitation and shortage in surface water resources, with the expected landward shift of the shore line. In this article, the possible effects of seawater level rise in the Mediterranean Sea on the seawater intrusion problem in the Nile Delta Aquifer are investigated using FEFLOW. The simulations are conducted in horizontal view while considering the effect of the shoreline landward shift using digital elevation models. In addition to the basic run (current conditions), six different scenarios are considered. Scenarios one, two, and three assume a 0.5 m seawater rise while the total pumping is reduced by 50%, maintained as per the current conditions and doubled, respectively. Scenarios four, five, and six assume a 1.0 m seawater rise and the total pumping is changed as in the first three scenarios. The shoreline is moved to account for the seawater rise and hence the study domain and the seaside boundary are modified accordingly. It is concluded that, large areas in the coastal zone of the Nile Delta will be submerged by seawater and the coast line will shift landward by several kilometers in the eastern and western sides of the Delta. Scenario six represents the worst case under which the volume of freshwater will be reduced to about 513 km(3) (billion m(3) ).