مجله علوم و مهندسی آبخیزداری ایران
پیاپی 14 (بهار 1390)

Ground water resources of Iran are depleted because of an increase in water demand. Since, in Iran, precipitation is inadequate and occasionally results in violent runoff, artificial ground water recharge can be an appropriate method to optimize use of runoff in recharging the groundwater. In the operation of ground water recharge, Choosing criteria and appropriate method for suitable site selection is important. In this paper, factors such as: slope, depth of groundwater, land use and electrical conductivity of water, geology and potential of spate are considered to determine the areas most suitable for groundwater recharge in Shemil Ashkara plain in the southern part of Iran. Thematic layers for the mentioned parameters were prepared, classified, weighted and integrated in a GIS environment by means of Simple Additive Weighted and Analytic Hierarchical Process methods. The results of each method were multiplied by constraint map and their outputs were classified in four categories: constrained, unsuitable, moderately suitable and suitable. Comparison of the maps produced by these two different methods show that Analytic Hierarchical Process beside Eigenvector technique creates more reliable results.

The SCS-CN method as a traditional method is widely used for the estimation of direct runoff for a given rainfall event from small agricultural watersheds. In this method, the ratio of initial abstraction (Ia) to maximum potential retention (S) that is defined as initial abstraction ratio (λ=Ia/S) is equal to 0.2. This constant (λ=0.2) is the most ambiguous assumption and requires considerable refinement. The objectives of this study were assessment of capability of the SCS-CN method in estimating runoff, and determine the initial abstraction ratio, by analyzing measured rainfall-runoff events. Rainfall data was taken from a recording rain gauge and runoff data obtained from measurement of runoff volume in 36 dry-farming lands in the Hashtrood, northwestern Iran in a two-year period from March 2004 to March 2007. The analysis of 41 rainfall-runoff events data indicated that runoff generation is linearly (R2=0.68, P<0.001) related to rainfall height. Out of 41 rainfall events only 13 events have a rainfall height value bigger than the initial abstraction value and so based on the SCS-CN method they had a potential to generate runoff. Average observed runoff values in 41 events were 2.988 times higher than the estimated runoff values in the study area. The correlation between the observed and estimated runoff was low (R2= 0.21). The efficiency coefficient (E) of the SCS-CN model in 36 study lands was low, with an average of -8.032. The results revealed that the SCS-CN method has a low accuracy in estimating runoff in the study area due to considering a high initial abstraction ratio value (Ia/S= 0.2). Data analysis showed that an initial abstraction ratio of 0.02 can modify the runoff estimation of the SCS-CN method in the study area. With using of this constant (λ=0.02), the correlation between the observed and estimated runoff values increased to 0.53 and also the model efficiency coefficient (E) improved to -0.194.

Snowmelt runoff prediction is one of the important challenges in watershed management. The present research was carried out for snowmelt runoff prediction using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the Taleghan watershed located in Alborz province. For this research, 38 MODIS instrument images have been obtained from Iranian space agency for years of 2003, 2004, 2005 and 2006. Snow cover area (SCA) was extracted from all images. Then, snow water equivalent volume was computed using SCA and snow depth and density for mentioned years. Daily rainfall, temperature, snow water equivalent variables were used as inputs and daily discharge as output to multilayer feedforward perceptrons using backpropagation algorithm and compared with artificial neural fuzzy interference system (ANFIS). The results reveal that for the Galink hydrometry station, it was found that ANN with RMSE=0.133 m3/s and R2=0.71 in the validation stage are superior in snowmelt runoff forecasting than the ANFIS with RMSE=0.84 m3/s and R2=0.52, respectively. For this station, the ANN models without daily snow water equivalent as input are superior than the ANN models with daily snow water equivalent as input. An increase in the number of inputs from one previous time period to three consecative previous time period proved to be an excellent alternative to perform high quality daily snowmelt runoff prediction. In the other part of study, these comparisons were performed for three other gauging stations, it was found that the ANN in the validation stage is superior in snomelt runoff forecasting than the ANFIS. The ANN models with daily snow water equivalent as input are superior than the ANN models without daily snow water equivalent as input and also an increase in the number of inputs from one previous time period to three consecative previous time period proved to be an excellent alternative to perform high quality daily snowmelt runoff prediction for two stations.

In this study streamflow drought was analyzed in Karkheh, Karoon and Dez basins in southwestern Iran. The main aims of this paper are to determine, 1) spatial aspects of streamflow drought, 2) homogenous regions, 3) the effective factors that affect streamflow drought and 4) regional regression models. To attain such aims daily time series of discharges were considered, tested and adjusted in 54 hydrometric stations. Based on truncation level method (at 70% level) the annual maximum series (AMS) drought deficit volume and duration series were extracted. Maps of spatial variation of these indices were showed. Frequency analysis was carried out for mentioned time series. Probability distribution functions including Gamma (Pearson’s, type III), Weibull, Logarithmic-normal, Johnson, Double exponential, Generalized Pareto (GP) were used and best models were determined by goodness of fit test. More than 35 important characteristics including physiographic, climatic, geologic, and vegetation cover were considered as influential factors in the regional drought analysis. According to the results of factor analysis, six most effective factors were identified as area, rainfall from December to February, the percent of area with Normalized Difference Vegetation Index (NDVI) of less than 0.1, the percent of convex area, drainage density and the minimum of watershed elevation as a total, these six fact explain more than 90.9% variance. Based on conclusions of factor analysis, homogenous regions were determined by cluster analysis and discriminate function analysis. Suitable multivariate regression models were performed and evaluated for streamflow drought deficit volume with 2, 50, and 100-year return periods. The significance level of regression models was 0.01. The results showed that the watershed area is the most effective factor with high correlation with deficit volume. Drought duration was not a suitable drought index for regional analysis.

Estimating soil moisture content is very important in soil science and hydrologic studies. Time domain reflectometry (TDR) has been suggested for measuring soil moisture. Soil moisture as a porous environment can be predicted by TDR (wavelength) with probe installed in the environment. Since the reflected wave back under the influence of environment, soil moisture content can be measured. Therefore, the method can be used in the natural conditions of soil moisture without time consuming and with high accuracy and it is important application. Our objectives were to evaluate the ability of published models to fit TDR calibration data for the soils of different texture (clay, clay loam, loam, sandy clay, silty clay). An artificial neural network (ANN) was used to predict the Ka–θv relationship considering soil physical parameters. The parameters that give the most significant reduction in the root mean square error (RMSE) are bulk density and clay content. The results showed that ANN predictions are better than other models such as Birchak et al. (2), De Loor (3), Malicki et al. (4), Topp et al. (4), Whalley (5) with comparable coefficient of determination and RMSE. Topp et al. model is showed poor result among models under study. Thus, by using ANN, highly accurate data can be obtained without need for elaborating soil specific calibration experiments.

In this research, three different uncertainty and sensitivity analysis methods, i.e. “First Order Variance Estimation Method”, “Harr’s point Estimate Method” and “Monte Carlo Simulation with Latin Hypercubic Sampling” are applied to four different empirical equations for flow through rockfill. These equations are: 1- McCorquodale et al. equation, 2- Stephenson equation, 3- Adel equation and 4- Wilkins equation. To conduct this study, in laboratory, a rockfill with an arithmetic mean size of about 37 mm was constructed and 15 random samples were drawn from the material. Then, physical characteristics of the samples, related to different empirical equations, were measured or estimated. The results of applying different uncertainty analysis methods showed that “Monte Carlo Simulation with Latin Hypercubic Sampling” is more conservative compared to the other methods. It was also found that for computation of hydraulic gradient, McCorquodale et al. equation showed the highest level of uncertainty, while Stephenson equation had the lowest uncertainty level. Also, sensitivity analysis of Stephenson equation showed that harmonic mean size and porosity were two significant parameters, respectively.

Estimation of groundwater recharge has an essential role in water resources management. Base flow component of stream flow is one of the main recharge sources. Base flow is sometimes used as an approximation of recharge when underflow, evapotranspiration from riparian vegetation, and other sources of ground water transfer to/ or from the watershed are minimal. In this research, Honifaghan station on Honifaghan River with 415 km2 watershed area in 110 km southwestern Shiraz was selected. Recharge estimates were made using streamflow records collected during 1353-1384.The base flows were predicted by PART Program and groundwater recharges were predicted by RORA program. The result shows that PART and RORA programs show the same results with high correlation to each other. Also it is found that baseflow component shows about 87 percent of streamflow in average. The study shows the importance of managing surface and groundwater together.

Some Iranian weather stations do not have sufficient instruments to measure climatic parameters such as pan evaporation, solar radiation and sunshine hours and/ or lack long-term and continuous data. A Pedo-transfer function (PTF) is an indirect method to predict parameters that are difficult to measure. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) were used to develop the PTFs. In this study, application of PTFs for estimation of climatic parameters was conducted for synoptic weather station of Tabriz. The objective of this study was to predict monthly parameters as solar radiation, evaporation, and sunshine hours from readily available parameters of weather. The results indicated that PTFs estimated solar radiation, evaporation, and sunshine hours well. The performance of both MLR and ANN were almost similar, only for solar radiation, ANN predictions were better than regression equation. The estimation of pan evaporation and sunshine hours were better than solar radiation in both PTF functions (MLR and ANN).