INTRODUCTION
* Land use map prepared from IRS images for July 2007* Climatologic data from seven rainfall stations, five temperature stations located inside and around the basin and also two stream gauges from the Water Resource Company for 1992 till 2004MATERIALS AND METHODSNumerous parameters are recognized for comprehensive simulation by complex hydrological models (Eckhardt and Arnold, 2001) where, interaction of parameters requires attention by experts. Abbaspour et al. (2007), states two very different parameters sets produce similar signals in the observed values in the calibration process. A comprehensive, complex hydrologic model is also characterized by a multitude of parameters (Eckhardt and Arnold, 2001). The real magnitude of many parameters is not exactly known due to spatial variability, inaccurate measurements and so on. Therefore, for recognize the correct value of each parameter calibration of model to be used to estimate them as correct as possible. Godio (2009), focused on snow pack parameters on density and thickness of snowpack to compare the data were calibrated and compared with the results coming from direct measurements of the density and thickness.[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]In this research four major input data including Digital Elevation Model (DEM), land use map, soil map, climatologic data and stream gage data are collected and used as given below:Therefore, the main objective of this study is validating the applicability of the SUFI-2 in Taleghan River Basin in Northwest of Tehran with particular interest on setting up a runoff component in SWAT model to improve hydrologic modeling in the Taleghan River Basin.Description of SWAT: The Soil and Water Assessment Tool (SWAT) is a semi-distributed conceptual model that operates continuously on a daily time step (Arnold et al., 1998). It is a comprehensive tool that enables the impact of land management practices on water, sediment and agricultural chemical yields to be predicted over long periods of time for large complex watersheds that have varying soils, land use and management practices (Neitsch et al., 2005). SWAT was developed to simulate the major processes of the hydrologic cycle and their interactions as simply and realistically as possible and to use input data that is readily available for large scale catchments so that it can be used in routine planning and decision making (Ogden et al., 2001). One of the main advantages of SWAT is that it is computationally efficient for even the largest of catchments, which makes it of practical use to land and water resources managers. The model was designed for the prediction of long-term yields rather than single flood events (Arnold et al., 1998).Study area: The study area is the upper part of Taleghan dam watershed and located in north western of Tehran, capital of Iran, with an approximate area of 800.5 [km.sup.2] and lies within 50 [degrees] 38'-51 [degrees] 12' E longitude and 36 [degrees] 04'-36 [degrees] 21' N latitude. Figure 1 shows the location of the study area named as Taleghan watershed. The summary of hydro morphological characteristics is illustrated in Table 1. The outlet stream gauge is located at Galinak which has an area of 800.5 [km.sup.2] with 28 sub basins (Fig. 2).* Radar Digital Elevation Model with 85 meter resolution from National Geographic Center of IranIn the study catchment topographical elevation varies between 1775 and 4362 amsl with weighted average elevation of 2753 amsl. The hypsometric information of the study area shows that the maximum elevation class of 35.48 % of the catchment area belongs to the 2500-3000 m while the 4000- 4500 class has the minimum as 0.06% of total area. The Frequency Distribution of the Slope Classes shows more than 52 percent of area located at slope class >40 %.In last decades, hydrological models are more broadly applied by hydrologists and water resource managers as tools to analyses water resource management systems. Hydrological models usually involve a large number of parameters that are used for consideration of surface and subsurface runoff, groundwater, deep percolation, evapotranspiration, soil properties, land use, precipitation (Sorooshian and Gupta, 1995) and water quality components (Yu and Salvador, 2005). The development of these kinds of models requires adequate observed data in time series and field experience which are often unavailable in developing countries (Ndomba et al., 2005). Lack information on water resources is very important especially in qualitative studies (Yisa and Jimoh, 2010).* Classified soil map and field work with 1/50000 scale obtained from Faculty of Natural Resources of Tehran UniversityThe main restricting factor in models performance is lack of strategies that explicitly account for model error calculation during calibration (Yapo et al., 1996). Users' experience in modeling and in recognizing parameters are two main significant skills to reach success in manual calibration of models (Eckhardt and Arnold, 2001). Many hydraulic and hydrologic modeling have been performed in the world where according to Neitsch et al. (2005) Civita et al. (2009) most of the researchers applied manual calibration to obtain optimum parameter values. Few models were calibrated and evaluated by sensitivity and auto calibration procedures. The hydrological model that is used in this study is the Soil and Water Assessment Tool released in 2009 and named SWAT2009. Development of the SWAT model has taken place since early 1990s. Widely distributed versions of the model include SWRRB, SWAT94.2, SWAT96.2, SWAT98.1 and SWAT99.2.[FIGURE 3 OMITTED]The SWAT model was developed by United States Department of Agriculture-Agricultural Research Service (USDA-ARS) to predict the impact of land management practices on water, sediment and agricultural chemical yields in large engaged basins (Arnold et al., 1995). Sequential Uncertainty fitting (SUFI-2) is a program that is linked with ArcSWAT and was used for calibration and validation analysis by Abbaspour et al. (2007). SUFI-2 is one of five different programs (SUFI2, ParaSol, GLUE, MCMC and PSO) that are linked with SWAT in the package called SWAT Calibration Uncertainty Programs (SWAT-CUP). Its main function is to calibrate SWAT and perform validation, sensitivity and uncertainty analysis for a watershed model created by SWAT. Beside, the SWAT model is able to estimate pollutant losses. The Soil and Water Assessment Tool model was used to identify critical source areas of phosphorus and sediment in the Wister Lake basin in southeastern Oklahoma, USA (Busteed et al., 2009). This model is compatible with GIS and RS in natural resources projects (Eyad et al., 2008).
Description of SUFI-2: Various SWAT parameters for estimation discharge were estimated using the SUFI-2 program (Abbaspour et al., 2007). Uncertainty is defined as discrepancy between observed and simulated variables in SUFI-2 where it is counted by variation between them. SUFI-2 combines calibration and uncertainty analysis to find parameter uncertainties while calculating smallest possible prediction uncertainty band. Hence, these parameters uncertainty reflect all sources of uncertainty, i.e. conceptual model, forcing inputs (e.g., temperature) and the parameters themselves. In SUFI-2, uncertainty of input parameters is depicted as a uniform distribution, while model output uncertainty is quantified at the 95 % prediction of uncertainty (95PPU). The cumulative distribution of an output variable is obtained through Latin hypercube sampling. SUFI-2 starts by assuming a large parameter uncertainty within a physically meaningful range, so that the measured data initially fall within 95PPU, then narrows this uncertainty in steps while monitoring P_factor and R_factor. The P_factor is the percentage of data bracketed by 95 % prediction uncertainty (95PPU) and R_factor is the ratio of average thickness of 95PPU band to the standard deviation of the corresponding measured variable. A p-factor of 1 and R-factor of zero is a simulation that exactly corresponded to measured data. In the each iteration, previous parameter ranges are updated by calculating the sensitivity matrix and the equivalent of a Hessian matrix (Magnus and Neudecker, 1988), followed by the calculation matrix. Parameters are then updated in such a way that new ranges are always smaller than previous ranges and are centered on the best simulation (Abbaspor et al., 2007). These two measured factors can be used as statistical analysis instead of the usual equations such as coefficient of determination (R2), Nash-Sutcliffe (Nash and Sutcliffe, 1970) which only compares two signals. Other statistical analyses in this study are coefficient of determination R2 multiplied by the coefficient of the regression line (BR2) and Mean Square Error (MSE). In this study all six mentioned variables were examined for testing calibration and validation of the simulated runoff in Taleghan basin.
