مجله علوم و مهندسی آبخیزداری ایران
پیاپی 33 (تابستان 1395)

A large amount of spatial information and criteria is required to determine zones with groundwater recharge potential. The objective of this research is an application of integrated AHP-GIS approach for determination of groundwater recharge potential. Therefore, thematic layers in the field of slope, distance from drainage, soil order, climate, land cover, land use, lithology and distance from fault have been used. The thematic layers have been prepared in ArcGIS9.3 environment. The AHP method has been used to weigh the criteria and indices being investigated. The factors have been carefully analyzed and integrated so that the groundwater recharge potential map of the study area is prepared. Maps of selected thematic layers have been integrated according to Raster Calculator function, in ArcGIS environment. By integrating rated maps with resulting weights in AHP procedure, the study area has been divided into inadequate, intermediate and adequate classifications. The results show that all the selected recharge sites are significantly appropriate from the scientific and applied point of view; thus using GIS and AHP weighting method present a desirable performance in determining zones with groundwater recharge potential. Also, the lithology theme has been determined as the most efficient factor.

The correct estimation of river discharge is an important issue in forecasting of drought and floods, designing of water structures, dam reservoir operation and sediment control. For this reason, water resources managers used intelligent techniques such as Artificial Neural Networks and data mining methods such as Decision Tree to reliably estimate the discharge in a river. In this study, the Elman Neural Networks (ENN) and M5 model trees were used to forecast daily discharge of Aharchay River. The daily discharge data of Aharchay River measured at the Orange hydrometric station was used for modeling. The results showed that for the forecasting discharge of one day ahead, the ENN method presents more accurate results in compression with M5 model. For forecasting discharge of one day ahead, the best scenario of ENN model with a relatively complicated structure of 9-3-1 that indicating 9 nodes in input layer, 3 nodes in hidden layer and 1 node in output layer, the calculated error measures were R2=0.90, RMSE=0.028 (m3/s) and MAE=0.001 (m3/s). The corresponding values for M5 model with only two input parameters including the discharge of current and last day, were R2=0.83, RMSE=0.734 (m3/s) and MAE=0.317 (m3/s). Comparing the performance of ENN and M5 models indicated that, however the ENN approach may present more accurate results than the M5 model tree, but the M5 model provides more understandable, applicable and simple linear relation in forecasting daily discharge. In addition, the number of required input parameter for M5 model is less than ENN model. Thus, the M5 model tree can be used as an alternative method in forecasting daily discharge.

Assessment of Changes in runoff and soil erosion in adjacent soils under different land use is essential. This study aimed to investigate the interaction effects of slope gradient and rainfall intensity on runoff and sediment production on rangeland and agricultural fields in the Senobar watershed using rainfall simulator. Therefore, based on the topographic map of the region, three-classes of slope 0-10, 10-30 and more than 30% were selected. Also, three rainfall intensity values 0.9, 1.1 and 1.4 mm min-1 to correspond return periods of 10, 25 and 100- years, respectively were chosen. Then, based on workable unit map, 18 points were identified in different areas in the rangeland and agriculture randomly. The results of the analysis of the main effects of the variables on runoff and sediment yield data showed significant differences for slope gradient and different intensities in Senobar watershed. The interaction effect between slope gradient and intensity of any of the parameters measured in the two land uses does not show significant difference in five percent confidence level. The results also show that the volume of runoff and sediment yield in rangeland was higher than in agriculture.

This study was conducted in order to amend a saline-sodic soil with Azolla compost and polyacrilamide (PAM) in a laboratory experiment. A factorial design of completely randomized test with three replications was used to compare the effects of the amendments. The soil sample was collected from saline-sodic marls of hilly lands around Manjil dam. Both azolla compost and PAM were used in four rates of 0, 5, 10 and 15 ton per hectare, and 0, 25, 50 and 75 kg per hectare, respectively. Soil samples were packed in a 30×35 cm drainable detachment tray, and subjected to a rainfall intensity of 95 mm h-1for 40 minutes. The runoff rate, sediment concentration and splash erosion rate were measured during the experiments, and sodium adsorption ratio and exchangeable sodium percentage of the soil were determined after the experiment. Results showed that the best treatment for reducing sodium adsorption ratio and exchangeable sodium percentage of the the minimum application of one of the amendments (25 kg ha-1 PAM, or 5 ton ha-1 Azolla compost). For decreasing sediment concentration and rainfall erosion rate, application of 5 ton ha-1 Azolla compost was the most appropriate treatment than the other combined treatments. Runoff rate was affected only by Azolla compost, and the application of 5 ton ha-1 Azolla compost resulted in the minimum runoff. In addition to these positive effects, Azola compost as an organic material may have effective functions in improvement of vegetation cover if it used in the field.

The concentration of human population in urban areas with urbanization and changing the face of the earth's natural has caused an increase in impervious surfaces and change in hydrological cycle. To evaluate the effect of urbanization on the runoff volume entering in urban drainage of Zanjan City, the trend of urbanization were processed by using Thematic Mapper images in IDRISI Selva and ArcGIS 9.3 software. Land use maps of the 1956, 2000 and 2012 were prepared. Analysis of hydrological and hydraulic behavior of urban development impact on the volume of runoff was carried out by using Stormwater Management Model (SWMM). Based on model calibration by observation of rainfall-runoff events, the results of calibration and verification have confirmed the accuracy of simulations. The trend of urbanization shows that urban area has been expanded in 2012 compared to 2000 and 1956, 22.59 and 923.88 percent and in 2000 compared to 1956, 543.06 percent, respectively. The results of SWMM model shows that urbanization and convert the land to impervious surfaces have caused an increase in the runoff volume, so that, runoff volume average has increased in 2012 compared to 2000 and 1956 64.45 and 698.58 percent and in 2000 compared to 1956, 376.91 percent, respectively.

Groundwater drought is one of the drought types that caused by lack of sufficient groundwater recharge. Groundwater Resources Index (GRI) is a method to express the state of this type of drought using ground water level data. Various methods and models have been presented in order to forecast and model, but selecting a reliable model is a difficult task. So, it would be better to use a combination of acceptable models instead of using just one model. In this study, the GRI values over 1984-2011 period were calculated in south of Qazvin province and its relationship with meteorological parameters such as precipitation, discharge, evapotranspiration, temperature (Mean, Max, Min) and large scale climate signals (MEI, SOI, AMM, AMO, PDO) was modeled by artificial neural network based on the Gamma test and in three structures. The results show that SOI and temperature have higher significant correlation with GRI values and also using the meteorological parameters as input parameters lead to improving the artificial neural network performance. Moreover, the ARIMA (1, 1, 3) (2, 0, 1) was selected for forecasting of GRI based on evaluation measures such as AIC and SBC. Finally, ANN-ARIMA modeling revealed better performance compared with the ANN and ARIMA(R2=0.94, RMSE= 0.05).

One of the basic strategies for floods control, is identify and spatial prioritization of flood prone areas within a watersheds. However, several methods have been introduced to identify flood generation areas, but in recent years a new method called "Successive Single Sub-watershed Elimination (SSSE)", is used to identify sub basins that contributed to flood generation in a catchment. In this method, the flood potential areas are determined by hydrograph analysis in watershed outlet; while the maximum flood may not occur in watershed outlet. Thus, it is necessary to investigate the whole tributaries and sub- watersheds in order to determine the location of maximum flood formation. In this article employ the above approach to determine flood prone areas in Shamsabad basin using stream-flow routing technique. The Shamsabad basin with an area of 1680 km ² and an elevation range of 1,200 to 2,500 meters was divided into 28 sub-basins. Sub-basin physical characteristics were measured in geographical information system (GIS). Design storm return period for 2, 5, 20, 50 and 100 years in 2, 5 and 12 hour duration were calculated using meteorological data. Then hydrologic HMS model was run. The results showed that the highest flood rate has occurred in the central basin at junction-5. The only half of the upper parts of watershed contributed in this junction flood. It was concluded that identification and prioritization of the risk of flood prone areas should be based on the location of maximum flood within the catchment not rely on outlet hydrograph analysis. Prioritization of flood prone areas showed that, the B sub-watershed has the most contribution in J-5 flood formation and after that are C, A, G1 and H1 sub-watersheds.

One of the most important tasks of remote sensing technology is to producing land use maps. In this study, in order to produce land use map of abolabbas basin, landsat satellite image of TM scanner acquired on 01 June 2009 were employed. the image classified by using three-layer perceptron neural network, support vector machine with the radial basis kernel function and Maximum Likelihood algorithm. So, The performance of different classification algorithms in producing land use maps were investigated using overall accuracy and kappa coefficient. Results showed that Nonparametric algorithms such as artificial neural network (with 95.8% overall accuracy and 0.95 kappa coefficient) and support vector machine with the radial basis kernel function (with 95.8% overall accuracy and 0.94 Kappa coefficient) with the same performance were better than the third method which is Parametric maximum likelihood algorithm (with 93.7% overall accuracy and 0.91 Kappa coefficient). Overall, this study showed that three classification algorithms, neural network, support vector machine and maximum likelihood are capable to generate land use maps with high accuracy.