نشریه پژوهش های حفاظت آب و خاک
سال شانزدهم شماره 1 (بهار 1388)

In order to study the effects of climate and geomorphology on the development of soils and their physico-chemical peroperties, two transects on the loess parent material were selected in two regions of Golestan Province. The regions were: Huttan (Maraveh Tappeh area) and Gorgan with aridic-thermic and xeric-thermic soil moisture and temperature regimes, respectively. Four pedons in Hutan and five in Gorgan were studied. Results revealed that in Hutan area, Aridisols formed on summit and toeslope positions while on the shoulder and footslopes, Entisols were dominant. In contrast, in Gorgan region, Inceptisols were the major members of the summit, shoulder and footslope and Mollisols were dominant on the toeslopes. There were significant differences in soil physico-chemical properties such as texture, CaCO3 content and its leaching depth, organic matter, saturation percentage, acidity, electrical conductivity, cation exchange capacity and soil solution cations and anions in the two studied regions. There was higher clay in Gorgan area. In Huttan however more silt were observed. Carbonate was leached from the surface horizons and accumulated in the depth of soils of Gorgan area. Considering the organic matter and CEC, soils of Gorgan had more favorable conditions. Depth of leaching of calcium carbonate, its dissolution-precipitation processes, and the accumulation of organic matter were among the most important soil forming processes occurred in Gorgan area.

Gorganrood river is one of the three main rivers in Golestan province have a basic role in Golestan water supply. To study Gorganrood water quality and estimating maximum allowable pollutant, water quality data was used from 8 stations. Purification of this river has been studied by two statistical and experimental methods. Each method showed that the Gorganrood river at present condition has no problem for purification. Average measured BOD in this river on Tamar-Ghareghoozy station was 9.7 mg/Lit and maximum observed BOD on up stream of Voshmgir dam was, some times larger than allowable amount but on the down stream there wasnt any problem. Average BOD in Gorganrood varies from 1.7 mg/Lit on Gonbad entrance station to 2.48 mg/Lit on Tamar-Ghareghoozy and Agh ghala entrance stations. This river has no main problem to fall pollutants.

Infiltration rate is one of the most important soil physical parameters and is a basic input data in irrigation and drainage projects. Although, a number of theoretical or experimental based equations are presented to describe this phenomenon but the evaluation of some new sciences such as artificial neural networks, for prediction of the phenomenon can be investigated. Generally, the infiltration rate is a function of different soil factors, such as: organic materials; porosity; pH; EC; Na and Ca+Mg. Aburaihan campus belongs to the University of Tehran. It has two teaching and research centres, one of them is located in the Waramin lowlands of eastern Tehran (Ghezlagh farm). The farm has about 120 hectares of fertile lands. The present research plan aims to model the infiltration of the soils involving the Artificial Neural Networks (ANN) and the Statistics models. The performances of different types of neural networks, relevant functions and processing elements were examined using mean square error (MSE) as the criterion. The Multilayer perceptorns (MLPs, feed-forward network) with one hidden layer (three layers in total) including five neurons as neural network type and momentum as learning rule were the final option, which were chosen to built up the ANN model. Data from a previous study were used for this purpose. Also, a regression model involving the SPSS software has been used to predict the basic infiltration rate (Ibas). Results obtained from the artificial neural network and the regression models were compared in terms of correlation coefficient between measured and estimated values. The calculated correlation coefficients between the predicted and measured data were found to be 0.94 and 0.98 for the neural network and regression methods, respectively. Results indicated that the variable, Ibas, was predicted more efficiently by the regression model than the ANN model. However, from the encouraging results, it can be concluded that the use of a neural network model can be efficient for prediction of the basic infiltration rate.

Nowadays, occurrence of mass movement in the northern forests of Iran, especially in the Golestan province is turning to be a usual phenomenon. Because of decay of vegetation cover, prediction of landslide occurrence will prevent its damages. Landslide hazard zonation is a suitable way in this case. Selection of the best model is a way to decrease costs and time. Consequently, land slide hazard zonation with best models will cause getting better results. In this study, capability of two experimental models including Mora-Varson and Haeri-Sameai methods, a statistic model together with relative effect model have been tested for land slide hazard zonation in the Seyed Kalateh watershed at Ramian, Golestan province, using Sum of Quality for evaluation of efficiency of landslide hazard zonation methods. Results showed that relative effect method could better separate the hazard zones compared to other used methods. Comparing the experimental methods, Haery method is more effective than the Mora method.

Spur dike is an important element for protection of river banks. Spur dike causes variations in flow field, sediment transport and bed topography. The mechanism of flow and sediment transport in a channel bend is very complex, especially when a spur dike is constructed in a bend. In this paper the time variations of dimension of the scour hole and the bed’s topography were considered around a T shaped spur dike located in a 90 degree bend. For this purpose, the experiments were carried out in a 90 degree channel bed with uniform sediments having average diameter of 1.28 mm under clear water condition. The time variations of parameters such as length of the spur dike and its wing, the location of the spur dike in the outer part of the bend and Froude number on dimensions of scour hole around a T shaped spur dike and the bed’s topography were studied. The results showed that by increasing the length of the spur dike, decreasing the wing length of the spur dike, increasing the Froude number and changing the location of spur dike towards the down stream of bend, the dimension of scour hole increased.

Nowadays Artificial Neural Networks (ANNs) are being applied in several problems of water engineering where there is no clear relationship between effective parameters on the estimation of phenomenon. This research was used to measure aerodynamic data inside and outside greenhouse for estimating reference evapotranspiration in greenhouse by using ANNs. ANN was used with perceptron multilayer structure and Back Propagation with one hidden layer for estimating evapotranspiration by using meteorological parameters. Results showed, with regard to Root Mean Square Error (RMSE), ANNs wasable to estimate reference evapotranspiration with low error. Inside greenhouse, ANN showed a best estimation maximum temperature (Tmax), minimum temperature (Tmin), extraterrestrial radiation (Ra), actual vapor pressure (ea) and sunshine (n) in entrance layer and found as the best model for estimating inside greenhouse reference evapotranspiration with RMSE equal to 1.1 mm day-1. Outside greenhouse, ANN was found as best model which can use maximum temperature (Tmax), minimum temperature (Tmin), and sunshine (n) in entrance layer estimating inside greenhouse reference evapotranspiration with RMSE equal to 1.01 mm day-1.

Hershfield method is commonly used for computing probable maximum precipitation (PMP) in most parts of the world for which the frequency factor (Km) is considered to be 15 for computing 24-h PMP (PMP24). This value may not be suitable for different stations under different climatic conditions. In 1986 the world meteorological organization standardized the Hershfield method and introduced the record length and the maximum value observed for the correction of the Km. In 2001 in Malazia Desa et al. introduced a new approach to the Hershfield method. Only the maximum value was considered in this approach, and caused a severe decrease in Km which was more rational. In this paper maximum daily rainfall values for 46 raingage stations located in north-east of Iran with record length of 13 to 48 years was adopted to estimate the appropriate Km values. For the first approach, Km was found to be varied in the range of 16.7 and 19.5 and the PMP24 was in the range of 138.6 and 410.3 mm. This high range is in part influenced by short record length of the stations. The corresponding values of Km for the second approach was 1.8 and 6.2 and for PMP24 was 62.7 and 207.7 mm. Numerous data in the literature supported that the ratio of PMP24 to maximum of p24, as a criteria independent of climatic conditions, was consistent for the second approach and was in harmony with multi-station method that uses all of the data at a time. Based on the literature, it was shown that ratio of statistical PMP24 to synoptic PMP24 is closer to 1 for the second approach. Finally, the map of PMP24 isochrones was prepared for the second approach which was assumed to be a sound estimation of PMP24 for the north-east of Iran. This map may be in use for runoff control of rivers which are drained either to other countries or to the interior deserts.

A local scouring phenomenon is one of the important problems in hydraulic design of groynes. Due to constriction and downward flow, the scouring can occur around the groynes. Nowadays, the artificial neural networks have a lot of applications in various water engineering problems where there is not any specific relation between effective parameters. In this study, the Artificial Neural Networks (ANNs) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for estimating the maximum depth of scour around L-head groynes. The results were compared with experimental relations. One hidden layer with five neurons was used for ANNs. The activation function for hidden layer was tangent hyperbolic while for output layer was sigmoid function. The first order Sugeno fuzzy model with hybrid learning algorithm was used in ANFIS. The correlation coefficient of test data for ANNs, ANFIS and experimental relation were 0.97, 0.99 and 0.93 respectively. The comparison of results with experimental relation showed the ability of artificial intelligent system (especially ANFIS) for learning and estimatign maximum depth of scour around L-head groynes.

Changes in land use can significantly influence the hydrological cycle and hence affect water resources. Understanding the impacts of land use changes on hydrology response at the watershed scale can facilitate development of sustainable water resources strategies. This paper investigates the hydrological effects of land use change in the Zanjanrood basin of Iran, during last 40 years. The study reveals how land use changes affected the hydrological regimes at the watershed scale. The water balance was simulated with the use of the Soil and Water Assessment Tool (SWAT2005). This is a hydrological model that requires the meteorological data, land use, soil and DEM of the inspected areas, in the form of a digital map. After calibrating and modeling accuracy determination, the effect of land use changes on hydrological responses was evaluated using, land use of 1967, 1994, 2007 years set as model input. The results indicated that land use change had significant impact on water yield and river discharge. The land use changes over the period 1967 to 2007 led to a 33% increase in the amount of surface runoff and a 22% decrease in the ground water storage. Furthermore, the area of subbasins that influenced by high annual group runoff (14-28 mm) increased about 16%