Volume 23 - 2021

Field investigations were conducted to study the seasonal variation of hydrodynamics and sediment transport in Indus River Estuary (IRE), Pakistan. The data of water levels, currents, salinity, and suspended sediment concentration (SSC) were collected hourly covering both wet and dry seasons. Tidal amplitudes were higher near the mouth than those at the middle and upper estuary. The ebb phase lasted longer than that of the flood during the wet season. The asymmetric tidal pattern with higher ebb velocity was observed during the wet season. A slight difference in current velocity was found during the dry season. The flood currents were higher at middle estuary than those in wet season. During the wet season, salinity variation within a tidal cycle slightly increased from the upper estuary to the mouth. Salinity was substantially higher during the dry season than the wet season at all three stations, with the absence of the flood-ebb variation, showing a strong saltwater intrusion. The SSC data revealed that the sediments were mainly brought into the estuary by freshwater discharge during the wet season. Sediment re-suspension process persists during the dry season, due to the tidal currents. A stronger saltwater intrusion occurred in the dry season due to weak river discharge. An estuarine turbidity maximum zone was formed near station-2 due to the combined effects of tides, river discharge and saltwater intrusion. Overall, field observations have shown a significant spatial and temporal variation in flood/ebb and wet/dry seasons for hydrodynamics and sediment transport in IRE.

In the urban area, surface runoff can be utilized effectively to improve groundwater table through rainwater harvesting. The main aims of this study were to:1) investigate the potential of the surface runoff to inject into Urmia aquifer and increase groundwater recharge of this aquifer using SWMM - MOFLOW Model, 2) to investigate the quality of the urban runoff for aquifer recharge, and 3) to investigate the feasibility and effeteness of the artificial recharge via injection wells in a semi-arid area. Urmia city with an area of 930 Km2 is located at the West of the Urmia Lake in the North-West of Iran. The study aquifer has a negative groundwater budget, while some of the sub basin in the study watershed is prone to flood in the falling season. In this study, based on the location of surcharged channels, the quantity of rechargeable surface runoff to inject into the aquifer was estimated via SWMM model. Calibrated MODFLOW model was applied to predict the potential effects of the injectable water runoff on the groundwater surface. Estimated runoff by SWMM model was used as the input of the MODFLOW model. The quantity of the heavy metals (Fe2+, Zn2+, Mn2+, Pb and Cu2+) TDS and pH were measured to control runoff quality. According to the results, 1.12 million cubic meters (MCM) per year of runoff can be injected to the aquifer via 9 designed injection wells. This amount is equivalent to the annual loss of the aquifer (about 20 centimeters per year) that can ensure the stability of the aquifer in the injection area.

Climate data composes of time series and space series with unknown. These unknown series contains complex co-variation relations of climate data. The extraction of these relations is essential for further revealing the complex representations between time series and space series in climate data. As an important application, through extracting these co-variation relations, we can further predict the change of climate to provide early warning for natural disasters, e.g., Greenhouse effect. Hence, it is a challenge to explore the relations between climate data. To address this, this work propose a deep neural network. Based on Brenier theorem, the loss function is derived. Since Brenier theorem rigorously proves that the data distribution in background space is consistent with the data distribution in the feature space with greatest probability, ensuring that the relations extracted from the latent space are as close to that of in background space as possible. Then, the parameters of time series consisting of eight variables are encoded by the first hidden-layer in the proposed model. The remaining two hidden-layers encode the latitude and longitude in spatial series, respectively. Experimental results show that the proposed method outperforms the state-of-the-art methods with respect to climate relations extracted. Hence, the proposed method is considered a good alternative in capturing relations between climate variables, as well as, between carbon dioxide (CO2) and surface temperature

Weibull Cumulative Distribution Function (C.D.F.) has been employed to assess and compare wind potentials of two wind stations Europlatform and Stavenisse of The Netherland. Weibull distribution has been used for accurate estimation of wind energy potential for a long time. The Weibull distribution with two parameters is suitable for modeling wind data if wind distribution is unimodal. Whereas wind distribution is generally unimodal, random weather changes can make the distribution bimodal. It is always desirable to find a method that accurately represents actual statistical data. Some well-known statistical methods are Method of Moment (MoM), Linear Least Square Method (LLSM), Maximum Likelihood Method (M.L.M.), Modified Maximum Likelihood Method (MMLM), Energy Pattern Factor Method (EPFM), and Empirical Method (E.M.), etc. All these methods employ Probability Density Function (PDF) of Weibull distribution, except LLSM, which uses Cumulative Distribution Function (C.D.F.). In this communication, we are presenting a newly proposed method of evaluating Weibull parameters. Unlike most methods, this new method employs a cumulative distribution function. A MATLAB® GUI-based simulation is developed to estimate Weibull parameters using the C.D.F. approach. It is found that the Mean Square Error (M.S.E.) is the lowest when using the new method. The new method, therefore, estimates wind power density with reasonable accuracy. Wind Power (W.P.) is estimated by considering four different Wind Turbine (W.T.) models for two sites, and maximum W.P. is found using Evance R9000.