جستجوی مقالات مرتبط با کلیدواژه « حوضه آبریز کرخه » در نشریات گروه « علوم پایه »

The HBV (Hydrologiska Byråns Vattenbalansavedlning) is a conceptual model widely used for hydrological forecasting and water resource studies. In this study, sensitivity analysis of parameters of the HBV model is investigated for Karkhe basin and its sub-basins for four different periods 1, 5, 10 and 25 years with four methods including FAST (Fourier Amplitude Sensitivity Test), RSA (Regional Sensitivity Analysis), Sobol and regression. After determining the most sensitive parameters, the model is calibrated using Nondominated Sorting Genetic Algorithm (NSGA) method. In all statistical periods, one year has been used for warm-up to eliminate the effects of initial conditions. In this study, the MOUSE Toolbox is used to analyze the sensitivity of the HBV model parameters. This software is based on Java programming language. To analyze the sensitivity of the HBV model parameters based on the Monte Carlo sampling method and the Halton sequence method for each of the samples (time periods) in each sub-basin separately, 1000 samples are taken for the set of input parameters with a specified range for each parameter taken. Objective functions for evaluating performance of model are NSE, RMSE, RSR and BIAS. The results of sensitivity analysis of the parameters show that Sobol and RSA are more reliable methods because of variability in time intervals and different sub-basins. Fast and regression methods in the Karkheh basin and its sub-basins for different time periods show similar results that considering the change in hydroclimate conditions in this basin, isn't practical and the results of these methods can not be used for investigating sensitivity of parameters and their identification in the studied basin. The most sensitive parameters of HBV model for Karkheh basin and its sub-basins in soil routine is maximum soil moisture content (Fcap) and in the response routine is the storage of soil surface moisture content (hl1). These parameters have shown the most sensitive factor in minimum fluxes. The snow routine parameters, especially the threshold temperature for ice freezing (ttlim), are sensitive in the sub-basins of Ghare Sou and Kashkan in short periods (1 and 5 years). For a specific sub-basin, the sensitivity of the parameters in different time periods is not completely stable and a little variability has been observed in different periods. But the most sensitive parameters (hl1 and fcap) have maintained their sustainability almost in all periods. Parameters of response and soil routines are more sensitive to the parameters of snow and routing routines. The results of the interaction between the parameters using the Sobol method in different sub-basins indicate that the strongest interactions are between the soil routine parameters, especially Fcap, with the response routine parameters and also the response routine parameters with each other. The time variability of parameters indicates that the soil routine and response parameters in the minimum discharge show the most sensitivity. Other parameters are more sensitive in the dry season of the basin (summer and autumn). The HBV model has the ability to simulate runoff in the Karkhe basin and its sub-basins with high precision. This study shows that selection of shorter period of calibration gives better simulation results. For one year's period the best NSE, RSR and RMSE are in Gamasyab sub-basin respectively 0.95, 0.21 and 1.4 and the best BIAS is in Kashkan sub-basin and Karkhe basin with 0.13.

The outputs of General Circulation Models (GCMs) which are available for the users based on predefined scenarios have a low spatial resolution. Hence، downscaling techniques should be used for regional studies. Since climate is an effective factor in natural phenomena، a time series of future weather data is required for meteorological، agricultural and hydrological prediction or pre-warning applications. It is also important to select and utilize better and more accurate techniques for theses purposes. Generally، downscaling methods are classified into two categories: dynamical and statistical. The statistical downscaling is commonly considered due to its simplicity and wide applicability. As an example، LARS-WG is a parametric or semi-parametric model which has been used widely، but it underestimates monthly variances. Undoubtedly، it is more useful to use an approach having a non-parametric structure so that it does not rely on a statistical structure by default. These models use a set of observations in the simulation process not a certain value namely the «parameter». The aim of this study was to use a nonparametric approach for downscaling the GCM outputs. This approach is composed of one weather generator (WG) and a technique called strategic re-sampling which creates series that match the GCM output. The weather generator itself is based on a Kernel Density Estimator (KDE) and it is a multivariate weather generator. In the KDE method، all of the observations with a definite kernel function، commonly standardized and normalized، are used. Firstly، one of the normal kernels (with probability 1/n) is selected randomly and its mean is considered as the basic vector. Bandwidth (h) is the only parameter that should be estimated. The strategic resampling method includes a stochastic function based on the definition of the «shape parameter»، which determines the tendency of the new series. At the first step، a strategic re-sampling is run and then strategic series are prepared as input to the WG and the simulating climate prospect. The study area is Karkheh Basin in Khuzestan Province، Southwest of Iran. Downscaling was done for two periods، 30 years (2011 to 2040) and 60 years (2011- 2060) for monthly rainfall and air temperature variables based on the A1B scenario of the CGCM3T63 model. The results can be divided into three groups: an estimate of the strategic re-sampling parameter، evaluation of the weather generator applicability and finally، the climate change simulation. Results showed that in case of temperature، by selecting a unit value for the shape parameter، the generated series coincide with the observed or historical series. Substituting the values less or more than one resulted in warmer and colder simulated series، respectively. Similarly، for rainfall series the optimum value was 0. 9. Accoring to the results، the ability of the WG in simulation of moments of different orders (mean، variance and skewness coefficient) was acceptable and the coefficients were cross validated. The applied GCM showed warmer and drier series for both study periods. The findings of the study revealed that future climate would be simulated accurately and non-significantly different from GCM outputs. In general، the suggested non-parametric approach can be recommend due to the following features: non existence of a default pattern in its structure، the least number of parameters for running، coincidence of the high accuracy in downscaling with the GCM outputs and simplicity. More case studies are recommended for further scrutiny.