IRAN-WATER RESOURCES RESEARCH
Year:2010 | Volume:6 | Issue:1 (16)|Papers Count:9

One of the major challenges in water resources management is estimation of missing or un-gauged data. Estimates of the unknown data in different points can be made by spatial interpolation of the observed data recorded in some special points of the study region. In case of spatial variations of precipitation data, there are many interpolation techniques changing from simple linear methods to complicated multivariate methods. Each method has its own necessities and constraints which result in different levels of accuracy and precision in the estimated values. Accordingly regarding the needed accuracy in different applications and the available time and data, different spatial analysis methods are utilized. In this paper different conventional and modern methods such as interpolation and Kriging are used for spatial analysis of the precipitation in the western part of Iran. Many different techniques have been applied to measure annual and monthly precipitation data at 38 stations for the period of 1967 to 2005 in the study area. The results are compared by estimation of the data for some stations in the region. The results showed that different methods of interpolation should be used with care considering some local characteristics that affect the climatic variables. In this case study, universal Kriging had the best performance among different interpolation techniques. Based on the best results of conventional and Kriging methods in this study, the locations of some new stations are proposed.

IntroductionMeta heuristics algorithms are usually used for the problems that are based on the behavior of real creatures. They basically try to combine basic heuristic methods in higher level frameworks aimed at efficiently and effectively exploring a search space. This class of algorithms includes Genetic Algorithms (GA), Iterated Local Search (ILS), Simulated Annealing (SA), Tabu Search (TS), Ant Colony Optimization (ACO), etc. ACO is a meta-heuristic approach proposed by Dorigo et al. (1991). The basic algorithm of ACO is Ant System (AS).

IntroductionFloods, droughts, water scarcity, water contaminants, and optimal use of water resources including reservoirs and aquifers are some of the many water problems present today. These will be even more noticeable in the future. The optimal operation of reservoirs is one of the best ways in water resources management to deal with such problems and other unwanted temporal and spatial distributions of water. Optimization techniques have become increasingly important in the management and operation of complex reservoir systems over the past three decades. Some researches have provided an extensive literature review and evaluation of various optimization methods and their corresponding models. Each optimization method has its advantages and disadvantages that make them suitable for some problems. Selection of each method depends on the characteristics of the system being considered, data availability, optimization objectives, and the problem constraints.

IntroductionIran is a country with arid and semi arid Climate. More than 94 percent of the water resources of the country is being used in the agriculture sector. This makes agriculture the main sector that uses water in Iran. With the increasing water scarcity in Iran, the virtual water content became an important subject in water resources management studies. Virtual water is defined as the volume of water required to produce a commodity or service (Allan, 1998). When these goods or commodities enter the global market, virtual water trade will occur. Zimmer and Renault (2003) estimated the global virtual water trade between nations as 1340×109 cubic meters in 2000. Of this total value, 60% was related to the vegetal products trade, 14% to the fish and seafood trade, 13% to the animal products (non-meat) trade, and 13% to the meat trade. The virtual water trade related to the crop and the livestock product trade was about 17% of the total water used for crop production (Chapgain & Hoekstra, 2003). The present study quantifies the Iranian virtual water trade related to the agricultural products according to the global studies on the virtual water flows (Hoekstra and Hung, 2002 and 2005, Zimmer and Renault, 2003, Oki et al., 2003, Chapagain and Hoekstra, 2003, and De Fraiture et al., 2004). This study focuses on the agricultural commodities due to their major part in global water use (Postel et al., 1996).

IntroductionFor years, it was believed that with the scientific progresses and the increase of human knowledge, uncertainties may diminish. But uncertainties are an unavoidable part of natural systems. Conventional methods based on statistics and probability theories are successful in facing uncertainties in many simple cases. However, when it comes to more complex systems these theories need many theoretical assumptions that, practically, make them awkward and cumbersome.

IntroductionSome characters of the time series probability distribution are described by the shape and the scale parameters. These parameters show the skewness and the kurtosis of the probability distribution. Suitable fitting of the probability distribution on the climatic elements, e.g. precipitation, can estimate the related scale and shape parameters.

IntroductionOrganic matter is a vital element in consolidating the soil against erosion. However, mechanism on its movement and controlling factors has not yet been properly documented. Most parts of the soil organic matter is dissolved and consequently transported by water (Brent et al., 2007). The relationship between the flow discharge and the loss of organic matter has just recently been taken into account (Towsend-Small et al., 2008). Therefore more attempts are required to fully understand and formulate the mechanism of the organic matter loss in different watersheds.

IntroductionDevelopment of drought plans is an efficient approach to reduce the consequent damages and improve responses. The effectiveness of these plans depends on the indicators and triggers that determine the timing and type of management actions (Steinmann and Cavalcanti, 2006). There are yet many questions about appropriate indicators, their combination, and threshold levels to define triggers.