IRAN-WATER RESOURCES RESEARCH
Year:2010 | Volume:5 | Issue:3 (15)|Papers Count:11

IntroductionHydrogeochemical modeling of an aquifer cannot only characterize the spatial and temporal variations, but can also be useful in understanding the interaction between the groundwater and the aquifer matrix. Different techniques and methods of studying the chemical composition of the groundwater provided an understanding of the main factors that contribute to the groundwater salinity (Hammer et al., 2005, Wischusena et al., 2004). Composite diagrams have been used as a tool to interpret the groundwater chemistry data in various parts of the world. Back (1966), Howard and Mullings (1996), Marie & Vengush (2001), Gosselin et al. (2001), and Cloutier (2004) used composite diagrams to identify salinity sources. The saturation indices (SI) for representing minerals of the rock can serve as a useful tool in understanding the interactions between the groundwater and the host rock. Calculation of these saturation indices has been performed by a number of researchers (Nordstorm et al., 1989, Jeong, 2001).

IntroductionMoisture is one of the most important variables in studies and management of soil and water resources. Soil moisture is also a limiting factor for vegetation growth in arid and semi-arid areas. Therefore the vegetation index calculated using satellite images may in some degree reflect the change of soil moisture. Traditional methods and field measurements of soil moisture are costly and time-consuming and cannot properly indicate the spatial variability on a watershed scale. Satellite images provide better spatial resolution data and are especially useful for isolated locations where meteorological observations are sparse. Because of the close relationship between vegetation growth and water availability, the NDVI (Normalized Difference Vegetation Index) is frequently used to study the soil moisture (Tucker & Choudhury, 1987, Townshend, 1994, Wang, 2005).

IntroductionThe remarkable quantity/quality of growth in agricultural productions and urban development has caused the use of underground water resources to go beyond safe capacity measures. Financial problems for new projects together with scarce resources of fresh water call for practical and proper water resources management practices.

IntroductionStreamflow forecasts are of great importance in water resources management. However, due to the high uncertainty in the rainfall-runoff simulation procedure, forecasts are usually problematic. Uncertainty analysis is traditionally applied for streamflow forecasts using statistical approaches. But data-driven models like the Artificial Neural Network (ANN) and the Adaptive Network Fuzzy Inference System (ANFIS) have been rarely considered. The objective of this paper is to determine the uncertainty associated with ANN and ANFIS models for 1 to 3 months lead time forecasts and compare these models in this regard. To explore the methodology, Sofy-chai river at Tazkand station in north-western Iran is selected.

IntroductionOpen channel transitions are short hydraulic structures widely used for changing the flow cross section. Rahman and Chaudhry (1997), Ming et al. (2004) and Krüger and Rutschmann (2006) were amongst the recent researchers who studied the flow behavior in transitions. For the numerical analysis of flow in transitions, the shallow water equations including the initial and boundary conditions may be solved by finite difference methods. Thus, the computational domain may be discretized into a regular mesh of nodes. While increasing the number of nodes may reduce the truncation error, extra nodes boost up the round off error. Moreover, the computational time for solving the partial differential equations escalates considerably. Taking advantage of a grid speed technique at the required times, the nodes may be clustered in places where the flow characteristics vary intensively and scattered where they are uniform. Hindman and Spencer (1983) and Rai and Anderson (1982) modified their computational network using this technique.

IntroductionKarst as a compound geological landscape represents special hydrological characteristics underlain by limestone, dolomite, gypsum, salt, and other soluble rocks created in the form of a cavity and cave. Development of karst formation is different from one region to another due to the climate as well as the process of forming karstic water resources [1]. Humid area dominating over European countries has provided conditions for the formation of karstic phenomena in these countries. whilst in Iran, located in an arid and semi-arid area, the formation of this phenomenon is limited.

IntroductionIn many cases there is no flood data on a specific river basin and therefore simulation is necessary. In the SCS (CN) method initial losses of 0.2 were studied by Woodward et al (2003). The initial value of 0.05 was proper for shallow storms with low CN. Another Iranian investigator reported that storm type II usually gives better results in the SCS (CN) method for Iran. In the current research these two topics were studied.

IntroductionThe analysis of the flowing water over a dam spillway and in the stilling basin using physical models is time consuming and costly. Fast development of computational fluid dynamics (CFD) in recent years promotes the wider use of numerical models for practical applications. The numerical simulation of the flow-field in the stilling basin using FlOW-3D is presented in this paper.