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**Title**Numerical Feasibility Study for Treated Wastewater Recharge as a Tool to Impede Saltwater Intrusion in the Coastal Aquifer of the Gaza – Palestine Supervisor Prof. Dr. rer. nat Manfred Koch By MSc. Hasan Sirhan**Saltwater intrusion can be defined as the invaded of**seawater inland into fresh groundwater aquifers as a results of: • Steeply overexploitation of the aquifer to meet the municipal water demand as well as extended agricultural activities. • Destruction of natural barriers had led to reduction or reversal of a groundwater gradient under unsteady-state conditions, where denser saline water displace fresh water.**High chloride concentration is used as an indicator that**seawater intrusion is occurring. Terms describing degree of salinity as used by USGS • The increase of salinity in water causes: • An increase in blood pressure for people, • Extreme damage to the soil and reduced crops yield, • Corrosion of water metal pipes.**Saline water in aquifers may be derived from any of the**following sources • Upconing of ancient saline water that entered aquifers during past geologic time into fresh water aquifer. • Intrusion of seawater into a coastal aquifer. • Return flows from irrigated lands and human saline waste. This study deal with the seawater intrusion as a source of salinity in the Gaza aquifer.**The simplest analyses of seawater intrusion adopt the**Ghyben-Herzberg relationship, which is based on the sharp interface method, assumes that: • The saltwater and freshwater are immiscible and no mixing between the two fluids. • Attributed to a hydrostatic equilibrium existing between the two fluids. hs= 40hf • Salt water occurred underground at a depth ‘‘hs’’ below sea level about 40 times the height of the fresh water above sea level ‘‘hf ’’. (Ghyben, 1989; Herzberg, 1901) Ghyben-Herzberg theory, Hydrostatic equilibrium between freshwater/seawater interface**Presence of salinity in coastal aquifers can be detected by:**• Geophysical Techniques: by using the profiling technique of frequency domain electromagnetics (FDEM). • Geochemical Analysis (Isotops) • Numerical Models Most popular models for seawater intrusion • Visual MODFLOW Pro 4.2 integrates SEAWAT • SUTRA • FEFLOW**Does proper management prevent salinization of aquifers?**• Not PREVENTING seawater intrusion • But CONTROLING seawater intrusion Once the groundwater is contaminated by saline water, it is very difficult to bring it back to its original quality, thus the clean-up of salinity-polluted aquifers will be a major challenge for the future.**Research Objectives: The overall objective of this research**is to develop a numerical model to study the problem of saltwater intrusion, using the artificial recharge option as an integrated approach and optimum scenario to impede the seawater intrusion in the Gaza coastal aquifer. Specific Objectives such as: • Setting up a conceptual numerical model using a finite difference model of Visual MODFLOW for the Gaza aquifer. • Applying the MODFLOW-2000 incorporating with MT3DMS in Visual MODFLOW for the contaminant solute-transport simulation in the aquifer system. • Simulate the future migration of the contaminant saline plumes under several management scenarios and strategies. • Applying a statistical model to predict groundwater levels using an Artificial Neural Network (ANN) approach as an alternative tool for traditional physical-based numerical models.**The Study Area**Gaza Strip**The Study Area**• Gaza Strip Geography Palestine is composed of two-separated areas, the Gaza strip and the West Bank. The Gaza Strip is a very small area located at the eastern coast of the Mediterranean sea in the southwest of Palestine. Its length 40 km while its width varies between 6 km in the north to 12 km in the south, with an avg. area of 365Km2.**Demography**• •The population density in the Gaza Strip is the highest in the world of almost 2,802 persons/Km2. • •The average annual growth rate is • 3.2%. • • More than 1.5 Million inhabitants are living now within the area of 365 km2,. By year 2020 the population will be around 2.3 Million**Hydrogeology**• The Coastal Aquifer extends from Gaza in the south to Mount Carmel in the north along some 120 km of Mediterranean coastline, and it is the only source of water supply. • • The Gaza coastal aquifer represents part of the whole coastal aquifer. • The width of the aquifer varies from 3-10 km in the north to about 20 km in the south. • • Under natural conditions, the groundwater flow in the Gaza Strip is towards the Mediterranean Sea.**Geology**• The Upper Sub-Aquifer The uppermost aquifer (classified as unconfined A-aquifer). • The Middle Sub-Aquifer This aquifer classified as confined/unconfined B1/B2-aquifer. The Lower Sub-Aquifer • The lower aquifer (classified as confined/unconfined • C-aquifer).**Under steady state condition the overall aquifer balance of**the Gaza Strip can be represented as:Balance = Sum (Inflows) – Sum (Outflows). Inflows • Effective recharge (rainfall) • Lateral inflow • Total return flow and • seawater intrusion Outflows • Domestic abstraction • Agricultural abstraction • Groundwater discharge**Recharge (Rain)**• Municipal & Agricultural • Abstraction • Return Flow: • Agriculture, • Pipe Leakage & • Wastewater Groundwater Discharge • Seawater Intrusion • Lateral Inflow**Groundwater quality**• • The coastal aquifer holds approximately 5000×106m3 of different groundwater quality. •Only 1400×106 m3 of this is freshwater, with Chloride (Cl-) content of less than 250 mg/l. • That means approximately 70% of the aquifer are brackish or saline with a chloride concentration exceeding 250 mg/l.Only 30% are fresh water found mainly in the Northern area.**This figure represents the chloride concentration at some**specified monitoring wells in the Gaza Strip. It is clear that most of the wells have a chloride concentration more than the WHO (250 mg/l), where the seawater intrusion had occurred.**The Numerical model**Visual MODFLOW model: • Visual MODFLOW package is a coupled three - dimensional groundwater flow and contaminant transport model based on the finite-difference method and give the most complete and powerful graphical interface. The linkage used MODFLOW-2000 (Harbaugh and McDonald, 1996) and MT3DMS (Zheng and Wang, 1999). SEAWAT 2000 package has now been included in Visual MODFLOW, allowing modeling of variable density flow such as seawater intrusion modeling.**Model Setup**The finite-difference grid method in Visual MODFLOW is formulated as such: • The model domain grid contains of 157 rows, 50 columns, and 7 layers. • The model of Gaza coastal aquifer has uniform cell sizes of 300 m by 300 m in the horizontal plane. The model domain with the grid origin and boundaries**Boundary Assigned**• Neumann boundary condition • • A Neumann influx-boundary condition was assigned at the top of the aquifer at the land surface representing groundwater recharge (infiltration). • Lateral no-flow boundaries • A zero flux imposed on parts of the northern boundary with Israel border, and southern boundary with Egypt. • b) Horizontal boundary conditions • • A Neumann-type of no-flux boundary conditions: It represents the base of the model boundary • 2) Dirichlet boundary condition • Assigned to the residual parts of the left and right boundaries • Constant flux boundary • constant flux representing the lateral inflow to the domain • Constant-head boundary • h = 0 m ASL along the coastline.**Neumann influx boundary**Dirichlet BC. Constant flux boundary Dirichlet BC. Constant head boundary Neumann no-flow boundary**Wells abstraction**More than 3850 active water wells have been used in the model as internal hydrologic stress and distributed between agricultural, municipal, and domestic wells in year 2000 Spatial distribution of the pumping wells across the Gaza Strip**Model Simulation**The groundwater flow of the aquifer system was simulation in two steps. • Firstly, steady- state water levels for the year 2000 were taken for the steady-state calibration of • Horizontal hydraulic conductivity. • Vertical hydraulic conductivity (10% of Kh) • In the second step transient conditions between years 2001-2007 were used to calibrate the storage coefficients, the specific yields and Porosity . Calibrated Parameters The calibrated are based on trial and error approach, • It is carried out to check that the model can reasonably well emulate the groundwater flow system to fit the observed hydraulic heads with an acceptable error. • The results show the calibrated parameters are well-calibrated.**Results of St-St. Calibration**The calculated versus observed heads and the summary of steady state calibration statistics are graphed and presented in the following Figures (A) Observed initial heads for year 2000, (B) Resulting heads for steady state simulation for year 2000.**The results indicate that the model represent the behavior**of the aquifer quite well under the existing conditions as such as: • R = 90.4 % • SEE = 0.084 m • RMS = 1.105 m • Normalised RMS = 6.124 % • < 10 % (preferable by many modeler. Calculated vs. observed heads and summary of steady state calibration statistics**Water Balance**The steady state mass balance was prepared and the total aquifer system inputs and outputs were calculated and summarized in the table below: Summary of year 2000 water balance from model calibration. Percentage volumetric water balance components**Validation also is applied between the period 2005-2007,**since this step is important. The purpose of model validation is to establish greater confidence in the model. Observed and calculated heads versus time for well A53.**Cont.**• Observed and calculated heads versus time for well E45.**Cont.**• Observed and calculated heads versus time for well L47**Model sensitivityanalysis**A sensitivity analysis is performed in order to; • Establish the effect of uncertainty resulting in inaccurate estimation or definition of boundary conditions, aquifer parameters and stresses on the calibrated model. The main type of prediction uncertainties is Parameter uncertainties, where it Can be quantified relatively well for both the hydraulic conductivity and recharge.**Summary**The tasks which have been completed by now: • Data collection and literatures • Define the hydrodynamic and the mechanisms of the seawater intrusion evolution in the Gaza aquifer. • Set-up of the conceptual groundwater modeling using Visual MODFLOW model • Steady-state and transient conditions calibration of the groundwater flow model. • Statistical models to predict groundwater level: • Artificial Neural Network (ANN) approach. Ongoing works • Applying the density-independent MODFLOW-2000, incorporating with MT3DMS in Visual MODFLOW to represent the contaminant solute-transport simulation as saline plume migration in the aquifer system of the Gaza. • Achieve the specific objectives that aforementioned .**Published papers**First paper Prediction of Dynamic Groundwater Levels Using an ANN Approach in the Gaza Coastal Aquifer, South Palestine First International Colloquium REZAS12: "Water resources in the arid and semi-arid regions-challenges and prospects. Case of the African continent" BeniMellal, Morocco, November 14-16, 2012, Presentation HasanSirhan* and Manfred Koch* • * Department of Geohydraulics and Engineering Hydrology, • Faculty of Civil and Environmental Engineering, Kassel University**Second paper**Numerical Modeling of the Effects of Artificial Recharge on Hydraulic Heads in Constant-Density Ground Water Flow to manage the Gaza Coastal Aquifer, South Palestine. Geomatic Science Meeting, Rabat, Morocco, April 8-9, 2013-Presentation HasanSirhan* and Manfred Koch* • * Department of Geohydraulics and Engineering Hydrology, • Faculty of Civil and Environmental Engineering, Kassel University**Content:**• Natural Neural Network • Definition of Artificial Neural Network • Why Artificial Neural Network • ANN Properties • Artificial Neural Networks Learning • Development of ANN model for prediction of groundwater levels. What is a Neural Network?**The Neural Network of the human brain can:**• Collect more than 10 billion interconnected “neurons”. • Transmit information and computes some function (biochemical reactions). • Takes input as treelike network dendrites. • Produces (output) and connected to each other by synapses (weights). • Can learn and makes appropriate decisions.**What is an Artificial Neural Network (ANN)?**• The first studies on Artificial Neural Networks (ANNs) were prompted based on computers mimic human learning and created in (1943). • Artificial neural networks are a simplified mathematical model of a natural neural network inspired by biological nervous of the brain. • A Computing system which can be model based on the simple quantifiable and highly interconnected input variables.**Why Artificial Neural Network?**• ANN’s are a relatively new approach for groundwater levels modeling and an attractive tool for traditional physical-based numerical models. • It is not necessary to characterize and quantify the physical properties in explicit way as in the numerical models. • The system can be model based on the simple quantifiable input variables.**An artificial neural network is a model of reasoning based**on the analogy with the human brain. Analogy between biological and artificial neural networks**ANNs Properties**• Inputs are flexible • Any real values • Highly correlated or independent • Fast evaluation and less time consumed compared to the traditional (numeric) models. • In training process, it is highly important to deal with consistent data set of patterns. • The neural network model act as a black box, therefore the function produced can be difficult for humans to interpret.**A typical ANN model includes**• N inputs, • One output, • A summation block (Adder) • An adder ‘Σ’ for collection of the weight • inputs and biass weight signals, which is • numerical estimate of the connection • strength. • An activation function.**Approach**• The activation values of the input nodes are weighted and accumulated at each node in the first layer. • The weighted input nodes are transformed by an activation function into the node’s activation value. • Take output from first layer neurons as input to the next layer, until eventually the output activation values are found. The neuron output O is given by the following relationship: Where • Wj is the input connection weight, • Pi is the input, • X0 is the biass (not an input) and • W0 is the biass weight.**Activation function**• The activation function determines the relationship between inputs and outputs of a node and a network. Sigmoid (logistic) function hyperbolic tangent (tanh) function linear function • Among them, logistic transfer function is the most popular choice. It has a nature nonlinearity and it can be used for both hidden and output nodes.**Artificial Neural Networks Learning**The back propagation (BP) neural network • The back propagation (BP) is considers the most common learning algorithm used for training MLP network. • The error back propagation algorithm can: • Computes current output through the network layer by layer (forward pass), • Works backward to correct error (backward pass). Approach: • Compute actual output target: O • Compare to desired output: d • Determine effect of each weight (w) on error () = d-o • Adjust weights and correct error**Application of Artificial Neural Network**• Application in hydrology An approximation of any continuous (non-linear) relationship can be carried out. • Application in groundwater Ground water levels predicting can be applied under variable weather conditions and under pumping conditions.**The Study Area**Gaza Strip**The Study Area**• Gaza Strip Geography Palestine is composed of two-separated areas, the Gaza strip and the West Bank. The Gaza Strip is a very small area located at the eastern coast of the Mediterranean sea in the southwest of Palestine. Its length 40 km while its width varies between 6 km in the north to 12 km in the south, with an avg. area of 365Km2.