IRAN-WATER RESOURCES RESEARCH
Year:2011 | Volume:7 | Issue:1 (19)|Papers Count:11

Groundwater aquifers are important and vital resources in coastal zones. Serious environmental problems often occur in coastal areas that are densely populated or have been industrialized. The common problem is the saltwater intrusion into the freshwater aquifer system, due to inappropriate exploitation and over-pumping of coastal aquifers. This causes more limitations for the meaningful operation policies.A proper and reliable picture of the groundwater system in the coastal aquifer and the saltwater intrusion phenomenon are required to keep away from the possible threats and to develop sufficient management strategies. This study, presented an efficient combined numerical simulation optimization model for optimal management of a coastal aquifer, while controlling the saltwater intrusion process through controlled pumping. The proposed approach employs improved Elitist Continuous Ant Colony Optimization (ECACO) algorithm as an optimization tool in the model. In the numerical simulation approach, objective functions with extensive range of constraints for coastal aquifer management is considered. In this model, through some simple scheme, such as continuity equations in the porous media cells and existing hydraulic systems in the study area, more complex phenomenon representing real systems can be investigated.This model is verified using application examples available in the literature. The success and efficiency of the proposed ECACO algorithm in optimal management of actual coastal aquifers is shown.

Fuzzy logic (FL) as a soft computing method based on human inference is widely used for water resources issues such as reservoir operation. However, common to any FL modeling approach, the definition of membership functions (MF) and the inference rules (IR) are difficult and time consuming practices that construct the objective of this paper. Self-organizing feature maps (SOFM) method is applied to create MF. Also Fuzzy associative maps (FAM) and Bootstrap fuzzy relevance test (BFRT) are applied to generate IRs.Zayandeh-rud dam in Esfahan Province, Iran, was taken as the case study. The results showed that the suggested classification by SOFM to define MF is quite acceptable. But, the resulted rules from FAM and BFRT need more evaluation and screening before application. Nevertheless, the applied methods play a significant role in reducing the required time to set up the final fuzzy model.

River stage or flow rates are required for the design and evaluation of hydraulic structures and water resources management systems. Accordingly flood routing techniques are used to predict the river stage and flow rates while flood wave propagates along the river. The aim of this paper is to compare the Muskingum–Cunge method with constant and variable parameters with Dynamic flow routing. The results showed that the errors in application of Muskingum-Cunge method in rivers with low slope are high and therefore some concerns should be taken when using Muskingum-Cunge in low slopes rivers. Sensitivity analysis showed that the most dominant parameters on the peak outflow are orderly the peak inflow rate, the roughness coefficient, and the bed slope.Also, the most important parameters on peak timing of outflow are orderly the channel length and the roughness coefficient.

Improving the performance of water conveyance networks is one of the key issues in saving limited water resources. The first step for this improvement is performance evaluation and then presenting the solutions. One of the practical and efficient approches for performance improvement is to extract the homogenous area out of the irrigation network based on the physical and technical features. The main idea behind this research is to present a quantitative benchmark for exploring homogenous areas with similar physical attributes and present the abilites of this method for a real case study. K-Means clustering algorithm is applied to spatial clustering of irrigation networks based on physical attributes. Data was arranged based on the “objects” and the “features” in the matrix language. Ghazvin irrigation network data was used to form the input matrix. This matrix consisted of 162 rows and 5 columns. Using Davies and Bouldin (DB) index as the cluster validity index, it has been shown that the optimum number of clusters is 10. Each cluster represented a homogenous area in the irrigation network district. Clustering reduces the dimension of assessments from a large extended irrigation district to a limited number of homogeneous regions and provide a context for better and easier decision making, performance evaluation, and allocation of facilities and budget to different regions.

The Urmia Lake is one of the largest and most important natural ecosystems in Iran. Temperature increases, high fluctuation in rainfall, and frequent droughts have caused high fluctuations in the lake water level. In this research, the monthly change of the Urmia Lake water level under the climate change condition was simulated by the Adaptive Neuro-fuzzy inference system (ANFIS) from 2000-2100 by the HadCM3 model under SRES emission scenario (A2 and B2). Monthly inputs to the model are the rain on the lake, the temperature mean values, and the total inflow discharge. The effect of climate change on future water level based on the projection of the results from HadCM3 model under A2 scenario has shown the increase in average annual temperature and decrease in average annual lake level by 2.80°C and 4.60m, respectively. The B2 scenario model predicted increase in the average annual temperature and decrease in the average annual lake level by 2.35°C and 3.93 m, respectively. Comparin the A2 and B2 scenarios, it was concluded that the A2 scenario predicted a more critical state for the Urmia Lake.

Over the course of recent decades, agricultural and industrial activities have led to a considerable pollution in rivers. To control the river pollution level, the waste water discharge should be regulated. Pollutions have been dispersed in longitudinal, lateral, and vertical direction. The longitudinal dispersion of pollutants in river is important. In this study, a comprehensive equation was proposed for longitudinal dispersion coefficient using dimensional analysis.Dimensional and statistical analysis indicated that longitudinal dispersion coefficient depends on roughness coefficient and the ratio of width to depth of river. Sensitivity analysis indicated that roughness coefficient had the highest effect on longitudinal dispersion coefficient. The proposed methodology is a new approach to estimate dispersion coefficient in streams and can be implemented into mathematical models of pollutant transfer.

Selecting the suitable landfill site can prevent inappropriate ecological and socio economic effects. Industrial and economic development of the Esfahan metropolitan area and the fast increase in population and life standards has changed the amount and the components of the generated waste load. The present landfill has already reached its capacity and also the city is expanding toward this center.The fundamental studies for selecting suitable landfill sites should therefore be considered. Landfill site selection requires the analysis of spatial data, regulations and accepted criteria. In this research the multi criteria evaluation method by emphasis on GIS techniques was used for identifying suitable landfill sites for Esfahan city. Then the Analytic Network Process (ANP) was applied for prioritizing the suggested landfill sites. ANP is the modified version of AHP method which is based on the super matrix approach. This method is capable of applying for complex problems with non-hierarchical structure and also gives the possibility of considering interdependence relationships of decision criteria within and between levels. In this paper, in addition to evaluating the effective criteria in site selection and allocating the proposed landfill sites, the variation and relative importance of elements of these sites that cannot be considered in other decision making methods like AHP have been considered within the context of ANP method. The results indicated the significant importance of water resources protection in landfill site selection.

The main purpose of this Study is to recognize and evaluate the environmental impacts of aquifer depletion in Jiroft plain in Kerman Province, Iran, during several past years and Prediction of the future status. In this regard, all relevant data, including meteorological, hydrological, hydrogeological, geological, and geographical data have been gathered and evaluated. Isocheimal maps and groundwater table maps of this plain were drawn using ArcGIS software. Accordingly the over-extraction, water table decline rate, excess discharge, as well as chemical changes of aquifers has been determined.Assuming that the past and present trends would continue in the future, the status of aquifers fot the next 10 years has been determined. Results indicated that the overdraft has led to several environmental impacts including groundwater quality variation, increase in energy consumption for groundwater extraction, enhancement of susceptibility to drought, ground subsidence, the occurrence of cracks and joints in buildings, etc. If no drastic actions are taken, the plain will not only be faced with much more severe environmental impacts, but will also encounter economic and social problems.

A proper architectural design of the Artificial Neural Network (ANN) models can provide a robust tool in water resources modeling and forecasting. The performance of different neural networks in a groundwater level forecasting was examined by researchers in order to identify an optimal ANN architecture that can provide accurate predictions up to 24 months ahead. In this study the Saadat-shahr Plain in Fars Province in central Iran was chosen as the study area. All networks were trained for an 8-year period of data and calibrated for a 24-month period. Experimental results showed that the most accurate forecast (for up to 24 months ahead) is achieved with an FNN trained with the LM algorithm.