IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING
Year:2017 | Volume:10 | Issue:35 |Papers Count:9

For the last four decades, different artificial recharge methods have been implemented in Iran and in the nearly two decades attention was paid to floodwater spreading method. Generally, the implementation of civil projects with specific goals associated with positive and negative consequences. Floodwater spreading projects are not the exception to this general rule and may have undesirable effects. This study seeks to identify factors that have positive or negative effects on the floodwater spreading projects so eventually lead to success or failure of them. Also, the impact of factors on the performance of artificial groundwater feeding will be discussed. The present study indeed is a retrospective study that has attempted to identify the important factors with the assistant of experts and researchers. The experts participated in the poll based on their experiences. Due to our experiences, more than 50 researchers participated in the survey. The results showed that according to circumstances prevailing in the country, 17 factors or criteria have very important role in the success of projects. By using inductive method, the seven criteria were identified as the main criteria.

Rainfall erosivity is defined as the power of raindrops to erosion, which can be expected to change in correspondence to changes in rain characteristics, particularly intensity and duration. This study was conducted to assess the effects of intensity and duration on rainfall erosivity in a semi-arid region in North West of Zanjan, Iran. Soil loss was measured at forty eight plots (60 cm Í 80 cm) under the simulated rainfalls with eight rainfall intensities and six durations at five replications in a sloped land (8%). Based on the results, out of 240 rainfall events, only 180 events caused soil loss at the plots. Rainfall intensity of 15 mm h-1 was threshold intensity of the rainfalls from view point of soil erosion at the plots. Total rainfall energy of rainfall (E) obtained based on the sum of energies of all raindrops had a better correlation with the amount of soil loss at the plots (r= 0. 92). Soil loss strongly correlated with an index obtained from the multiplication of maximum 7. 5 minute intensity (I7. 5) and rainfall energy (E), EI7. 5. About 96 percentage of soil loss at the plots could be explained by EI7. 5. The results indicated that, EI7. 5 as compared to the EI30 can properly predict soil loss at the small plots similar to what usually happens in the surface erosion.

Monthly streamflow forecasting plays an important role in long-lead water resources planning and management. In the current paper, model fusion technique has been used in order to increase the accuracy of monthly streamflow forecast of Karkheh River at the entrance of Karkheh reservoir in winter. For this purpose, five models including: Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Linear Regression (LR) with optimized structure have been applied as individual forecasting models (IFMs). In order to combine the IFM models, two model fusion strategies including constant and variable weighting based on ordered weighted averaging (OWA) have been performed, where the Orlike method has been applied to determine the weights of IFMs. The results show that variables weighting strategy is more performable than constant weighting strategy in order to promote the accuracy of the forecast results. In addition, the comparison of the two strategies including model fusion with artificial neural network and selecting the best IFM reveals that variable weighting strategy can significantly promote the accuracy of the forecast results than the latest strategies; such that this strategy increases the accuracy of the results 51. 8, 38. 1, and 44. 5 percent as compared to ANN model fusion, and 7. 6, 132, and 52. 9 percent as compared to the best IFM for January, February, and March, respectively.

Groundwater level has dropped due to an additional utilization than recharge of these resources in the last decades. Management and prevention of these problems through exploration commensurate with aquifer potential is one of important chosen Strategic. Considering the number of parameters identifying zones or areas appropriate for assessment of groundwater potential zone, particularly aimed at feeding aquifers, the use of modern methods of production and new methods of synthesis and the decision making is inevitable. The purpose of this study is use of decision support systems and geographic information systems in order to achieve the appropriate groundwater potential zone in Dehgolan plain. In this study, the extraction of potential map is based on input effective parameters data such as: slope, geology, surface permeability, elevation, density drainage and density fault of the study area. The Satty’ s analytical network processing (ANP) is used as a MCDM technique to normalize the weights of various thematic layers and their classes in Super Decision software. Thematic layers were integrated using weighted overlay method in a GIS environment to generate groundwater potential and recharge zone maps. Finally, the potential map of groundwater was produced in five grades from high potential to without potential. Accordingly, many potential area with 9. 6 % of total area, are located in a thick layer of Quaternary deposits and close to major waterways. The produced map was verified against existing well data (yield, transmissivity, water level). It was found that the model was useful for assessment of groundwater potential zone.

This study aimed to present Climatic Research Unit (CRU) data and assess the accuracy of this data with the latest GPCC updates against the synoptic stations data during 2014-1985. For this purpose, monthly precipitation from GPCC Full Data Version 7, monthly precipitation and temperature from CRU TS3. 23 with 0. 5 × 0. 5 degree spatial resolution, monthly total precipitation, and average of minimum, maximum and mean daily temperature of 88 synoptic stations with proper distribution in Iran were gathered and analyzed. To assess the databases that mentioned R square, RMSE, IA, Bias, slope of the regression and EF indicators was used. Statistical comparisons revealed that, although accuracy of CRU precipitation dada in Iran, especially in the western half of country, is well, but in some regions, especially coastal areas, is less reliable. Because the R square in these areas is less than 0. 5, while the accuracy of GPCC data in all areas of Iran even coastal stations is very high. So, the amount of its precipitation in different regions of Iran is very close to the amount of precipitation recorded in the synoptic stations. Temperature components (average, minimum and maximum) assessment shows that although CRU amounts in the Caspian coastal area have more deviation than other areas, but, more than 95% of the observed data variance across the country explained by CRU and most of the CRU data distributed around the regression line. This term represents very high accuracy and reliability of CRU gridded temperature data in Iran. Based on the results of this study suggested that in the regions with no data, GPCC precipitation data and CRU temperature data to be used.

In recent decades, the rapid growth of industrial activities has caused imbalance in climate of the earth which is so called climate change. This phenomenon directly affects meteorological parameters such as temperature and precipitation. The objective of this research is investigation of the impacts of climate change on precipitation and maximum and minimum temperatures in Karkheh River Basin during the period of 2010-2039. The representative climate model of the region using AOGCM and observed data period of 1971-2000 was selected. Comparison of performance indicators of few AOGCM models for rainfall and temperature simulation showed that generally HadCM3 model is suitable for the region using synoptic and climatological weather stations of the region. Statistical and regression downscaling was carried out for the selected AOGCM. Statistical and regression downscaling was performed using statistically dynamic model of SDSM. The final results for near future, 2010-2039, shows 2% reduction in rainfall for both synoptic stations of Kermanshah and Khoramabad in the north of the basin and 4% reduction in Hamidieh climatological weather station in the south of the basin. The increase in maximum temperature for above stations are estimated as 119 and 3% and increase in minimum temperature are 24, 4 and 1%, respectively. Using HadCM3 and SDSM for near future, period of 2010-2039, simulation shows that as one moves from north to the south of the basin (colder climate to warmer climate) the effects of climate change on maximum and minimum temperature are less pronounced while the trend for rainfall, although small, is opposite and is 2% for the north and 4% for the south.

Splash rain erosion, as the first event in soil erosion, causes the movement of soil particles and lumps, and it is considered as an important process in erosion. Given the complexity of this process in nature, one way of identifying and modeling the process is to use a rain simulator and to study it in the laboratory. For this purpose, in the present study the simulation of the amount of material transported in the various intensities of rainfall and for different amounts of poly-acryl-amide was carried out. After measuring the amount of material transported in different durations and amounts of poly-acryl-amide, using artificial neural networks, ANFIS and SVM, the modeling of transported materials was done. The results showed that among the three methods used, the best values of evaluation criteria are related to SVM and ANFIS, respectively. Among the three studied durations, also the best results are related to the experiment with duration of 30 minutes. The results showed that, based on available data, by increasing the number of membership functions, extra-fitting happens in the ANFIS method. To reduce the complexity of the model and the likelihood of extra-fitting, some of the rules was eliminated. The results showed that the performance of the model was improved by eliminating some rules.

Nowadays, groundwater quality change and salinization of water resources is a major hazard to developing of agriculture, particularly in dry lands. In this study, the application of Fuzzy Set theory to evaluate quality of groundwater was studied. In recent years, fuzzy-logic-based methods have adjusted to spot uncertainty in environmental problems. In this study, the 49 groundwater samples of Sarab plain in 2014 that analyzed in Hydrogeology laboratory of Tabriz University was used. Values of irrigation indices including the Sodium Absorption Ratio, Permeability Index, the Kelley Ratio, Magnesium Adsorption Ratio, Residual Sodium Bicarbonate, Dissolved Sodium Percentage and Electrical Conductivity as an indicator of total dissolved solids in water are used in this research. Based on Fuzzy Water Quality Model, the groundwater quality is classified in three categories; desirable, acceptable and unacceptable. Results showed 41 Samples come in desirable category with certainty level of 65 to 83 percent and 26 samples classified in the acceptable category whose certainty level ranged from 37 to 65. 4 percent and the remaining three samples were in rejected category with the maximum certainty level of 23. 4 percent, respectively.

The purpose of this research was watershed management on sediment control in dryland region. To done this research collected inform, maps, reports about rain, sediment and runoff. In second steep done field works and recorded data, collected inform of checkdams dimensions. The measurement rainfall during research. Measurer put in dam stores to measure of sediment. Measuring specific erosion and specific sediment using PSIAC model. The data indicated that the 11 case rain occur more than 10mm in study area which 6 cases has been runoff, 3 cases rain controlled in dams reservoir and 2 cases has been, not runoff. During the research 545525 m3 flood came to dams reservoir which 0. 037 of them was soil particles and other material. Data collected indicate that the 19824 m3 sediment control through dams that each hectare has crated 10 m3 sediment in study area during 2 years.