Journal of Irrigation and Water Engineering
Year:2013 | Volume:4 | Issue:13|Papers Count:10

Infiltration study as a component of hydrological cycle is necessary for water projects management and planning. Because infiltration rate measurement is very time-consuming and costly in field, therefore various models are used for the infiltration rate estimation; however each model, in certain circumstances may show the best fitness for experimental data. Choice of suitable model is important in water resources management, thus in this research, first, infiltration rate measured in four land uses including forest, garden, agriculture and seacoast using double ring infilterometer, and then infiltration models of American soil conservation service (SCS), Philip, Kostiakov, Green-Ampt and Horton used and thus evaluated by coefficient of determination (R2), Nash-Sutcliffe sufficiency score (NSSS), root mean squared error (RMSE) and mean error (ME). The results showed that Kostiakov model had the best fitness to experimental data with maximum of R2 and NSSS and minimum of RMSE in all land uses. Also, ME values showed that Kostiakov model in garden land use, overestimated the infiltration rate, but in other land uses, model had underestimates. Besides, SCS model in all land uses estimated the infiltration rate less than real values and thus showed stable function than the other models. Totally, Kostiakov model, for all land uses, was placed at the first rank and SCS, Philip, Green-Ampt and Horton were placed in next ranks, respectively.

Snowfall has an important role in the amount and distribution of river flows in mountainous basins. Therefore, it is crucial to assess the application of river flow forecasting models that employ an index of snow in their structure. In this paper, a range of forecasting models that have been in use since decades ago are presented and compared with the more recently developed ones. These include some conventional models based on simple statistical methods and models that use more sophisticated methods such as Artificial Neural Networks (ANN) and Geographic Information Systems (GIS) with remotely sensed snow covered area (SCA) data obtained through NOAA satellites. Moreover, it is shown that the use of test periods is important in reaching realistic conclusions about the models. In addition, a testing method is presented that through an iterative process, evaluates the whole period of available data. Data from the Dez Basin in Iran is used to carry out the calculations. Furthermore, the role of spring rainfalls and their simultaneous implication in the forecasting models is investigated.

One method of decreasing loss resulted from flooding is to determine flood productive regions and applying appropriate methods to control flooding. In this study, by using HEC-HMS model accompanied by GIS technology the contribution rate of different sub-basins located upstream of Chamchit hydrometry station in Dalaki basin based on 2, 50 and 100 years return period is determined. In this research upper sub-basins of Chamchit hydrometry station ranked due to flooding using Single Successive Subwatershed Elimination method in HEC-HMS environment. Results of this study showed that sub-basin contribution rate in whole output not only affected by area and peak discharge of sub-basin but also factors like location of sub-basin, distance from output, CN, and Flood Routing role in main river have significant impact on the portion of sub-basin in production of the whole basin flood (sub-basin flooding). Except above mentioned, it was found that there is no significant change in sub-basin flooding priority in different return periods.

Water supply networks as a hydraulic transmission and distribution system has been always the focus of researchers. Water transmission in networks is due to energy difference (pressure head). However, the added pressure in networks causes undesirable leakage, which is directly related to the pressure. Leakage always ends in lack of water resources and exerts pumping, treatment and water transfer costs on producers and this amount of water exits the consumption cycle. In this study one of water supply networks of South-Khorasan Rural Water and Wastewater Company is selected. The purpose of research is pressure and leakage control, so a conventional demand-based approach (DDSM) is chosen, since in this method the demand is fixed in the nodes. This model was hydraulically analyzed using WaterGEMS. So PRV-valves are installed in critical points and are time-adjusted in order to provide standard pressure in all network nodes. Then the effect of intelligent pressure-management on network is evaluated. Then its influence on leakage control was investigated. Results showed that intelligent pressure-control by valves is a suitable method for optimal management of water consumption and causes significant reduction in network leakage, so that with 14.23 percent decrease in average pressure, the amount of 26585 cubic meters water saved (15.75 percent) and 27.44 percent of leakage decreased.

Land subsidence due to excessive extraction of groundwater has been reported in different parts of the world and several plains and Iran. Mashhad Plain during the past 40 years has been faced with 60 m drawdown of water table. This drawdown caused land subsidence in some areas of the plain. Land subsidence effects appear as rupture and curvature of well casings and they cause destruction of subsurface structures in wells. Based on data available from three GPS stations in Mashhad plain, and with respect to geological parameters of the study area, effective factors on rupture of well casings which account for abundant damages every year, have been investigated in this paper for Toos area, northwest of Mashhad City. A deep grain size profile of underground layers has been provided for problematic area in the study area. Also, soil grain size profile in two levels, above and below the groundwater surface, has been provided using ArcGIS software for investigation of current situation and anticipation of continuity conditions of land subsidence and therefore its resulted destruction in the future.

Estimating reference evapotranspiration (ETo) is one of the most important variables at water supply and distribution, irrigation management, irrigation systems design, agriculture and hydrological operations. The need for accurate estimates of ETo, complexity of ETo, unknowing mathematical of phenomenon, lack of reliable meteorological data, the cost of using lysimeters and their absent in most areas magnifies the need for developing new data mining methods. In this paper, the Least Square Support Vector Machine (LSSVM) model with three kernels function of RBF, Linear and Polynomial based on Gamma test used for estimating ETo and their results compared with other methods including Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models. In this analysis, annual daily meteorological data of Kerman synoptic station and ETo lysimeter data used. In this research, Gamma test was used for selecting the best combinations of input parameters for various models used instead of using trial and error classic methods. The combination including maximum and dew point temperatures, relative humidity, wind speed, and solar radiation selected as the best combination for estimating ETo and modeling was based on this combination. The LSSVM model with RBF kernel performed better than the SVM model with polynomial and linear kernels. Additionally, the distribution of prediction error was calculated that the ANFIS and LSSVM-RBF created less error in train and test steps, respectively. At the end study, Monte-Carlo uncertainty analysis was performed on results of different models that were used in this study. According to the results, predictions of LSSVM models showed less uncertainty than the ANFIS and ANN models were used.

Shortage of water resources and rainfall is an undeniable reality in Kerman province. Total water use within a single country is not a proper measure for the actual harvest from the world's water resources. Water ecological footprint is an index to determine the actual amount of water consumption. Therefore, analyzing water ecological footprint and indices of strategic products virtual water are useful for optimal planning and programming of water resources. Thus, in the present study, ecological footprint of water in strategic agricultural products of Kerman province (pistachio and date) was calculated to investigate the ability of the region to meet the water requirements for domestic production. In this study, Hoekstra & Hung (2002) and Hoekstra et.al (2009) methods were used for calculations. Water ecological footprint index of the province in agriculture sector is estimated to be 5.56 billion cubic meters, which is 2097.2 cubic meters for each person yearly. Kerman province exports virtual water and has low dependence on external water resources. Water deficit index or intensity of water use in agriculture sector of the province is estimated to be 123.3 percent of all renewable water resources of the province. According to the definition of UN commission on sustainable development, once the volume of water used in each country is more than 40 percent of total annual renewable water resources, then the country in considered low water. Therefore, Kerman province is low water and reducing the pressure on domestic water resources of the province may require using more virtual water trade strategy in importing products with a high water requirement.

Karoonbasin is one of the most important watersheds in southwestern of Iran. Great amount of precipitation in this region fall in the form of snow. Studying about accumulated snow and snow water equivalent in this basin can help to knowledge about hydrologic condition and better management on water sources. This research was carried out in the Col Chari region as an indicator region for snow measuring in north Karon basin. In this research snow height were measured directly with ruler. Also snow volume (with specific cylinder), weight (with digital scale) and snow water equivalent (according to snow bulk density) were determined. In order to snow measuring, seven sites were selected and three scaled indexes (accurate to one cm and four m in length) were established in each site. This research was performed for six years. In each year, inventory and data collection were started after the first snow and snow height were measured weekly. Also three samples of snow profile were supplied. The results indicated that snow stability had been differed from 104 to 193 days during six years. Maximum height of snow was observed in February. Also, we concluded that, maximum height of accumulated snow and mean of annually snow height were varied from 103 to 197 cm and from 39 to 100 cm respectively. Maximum amount of snow water equivalent and its annually mean were determined between 37 and 110 cm and between 14 and 44 cm respectively also.

Rainfall intensity changes during storm time advancement has more influence on shape of hydrograph and the time of peak storm flood discharge. One of the most important inputs in storm flood simulation models is rainfall pattern that applied in rainfall- runoff simulation section and affect the hydraulic characteristics of streams in water conduits, both open and closed. There are several methods for determination of rainfall temporal patterns. In this study Yen & chow, Chicago and Synthetic Block methods are used. Because of high imperviousness and small area of Babolsar Urban catchment, 2 hour design storm with 10 year return periods was accepted. 329 continuous storm among the 1966 to 1998 was selected. Storms was separated in 5 categories, less than 6 hr, 6-12 hr, 12-18 hr, 18-24 hr and longer than 24 hr. IDF curves are calculated and drawn tp/td. value was calculated by Yen & Chow method, and used instead of advancement coefficient (r) in Chicago method. Centre of storm duration in Synthetic Block, determined by tp/td value. Results of this research emphasis that by Yen & Chow method can be used as a trustable method for determination of rainfall advancement coefficient in one station or an area.

Spatial distribution of snow reserves, in order to more accurately estimate the runoff from their understanding of the importance of water is snow. Spatial distribution of snow depth required to achieve the observational data and the compressed scale, be done. However, due to practical limitations, collect information, particularly on the scale, it is difficult and sometimes impossible. Methods can not measure the depth of snow in places, and also to estimate the extent of their use in this regard is essential. The study a range an area of 16 acres in the watershed Taft called the Skhvyd and using 216 data deep snow, using geostatistical kriging, Cokriging, Radial Basis Function (RBF) and inverse distance method (IDW) was. Variogram plotted snow depth was measured using 216 points. Variogram obtained showed that the most appropriate variogram model is exponential, so that the variogram has a Nugget of 0.05 and a Sill of 0.533, has been, which means that there is a strong spatial structure of the data. Variogram analysis and verification statistics (RMSE) between the methods of extraction methods showed that kriging is better in snow depth zonation. The results showed that the universal kriging, kriging methods with minimum error (11.49) and the best estimate of snow depth in the study area.