Esmaeel Dodangeh; .Kaka Shahedi; karim solymani
Abstract
Introduction: The proper management of water resources in a watershed requires precise understanding and modelling of the hydrological processes. HSPF model uses an infiltration excess mechanism to simulate streamflow and requires the hourly precipitation data as input. Despite the high accuracy of the ...
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Introduction: The proper management of water resources in a watershed requires precise understanding and modelling of the hydrological processes. HSPF model uses an infiltration excess mechanism to simulate streamflow and requires the hourly precipitation data as input. Despite the high accuracy of the HSPF model, the lack of rainfall data at short time scales (hour and less than hour) restricts implementation of the model especially for long time simulations. Some studies have applied simple division for daily rainfall disaggregation into the hourly values to provide data required by the HSPF model. In simple division, each rainfall event is divided into 24 pulse stochastically and the peak flows may not be simulated correctly due to the lower rainfall intensities.
Materials and Methods: In this study, Random Parameter Bartlet-Lewis Rectangular Pluse (BLRPM) model was used for daily rainfall disaggregation into the hourly values to provide data needed by the HSPF model. The model parameters were calibrated using the 1, 24 and 48 hour rainfall data time series of the rain gauge stations inside (Jovestan and Zidasht) and outside (Kalk Chal) the watershed for the period of 2006-2009. To cluster the wet days, the BLRPM model was run several times and a generated sequence which had the best match with the original one in terms of daily totals was selected. Then, the synthetic sequence of hourly rainfall depths was modified based on a proportional adjusting procedure to add up exactly to the given daily depths. The calibrated model was then implemented to disaggregate the daily rainfall data of the watershed for the period of 1995-2005. The resultant hourly rainfall data were then fed into the HSPF hydrologic model to simulate the daily runoff. Parameterization of the BLRPM and HSPF models was also done while keeping the Kalk Chal station out of the calibration.
Results and Discussion: Sum of weighted squared error was calculated to be 1.03 when the data recorded in Kalk Chal station was also considered for parameter estimation of the BLRPM model. Maximum weighted square error was equal to 0.7 for lag-1 auto covariance of daily rainfall data. Keeping the Kalk Chal station out of the BLRPM model parameterization resulted in improved performance of the model. Sum of the weighted error decreased to 0.36 by removing the Kalk Chal station data. The results indicated that the weighted square error values decreased for all of the BLRPM model parameters when Kalk Chal station was not considered for calibration. The lag-1 auto covariance of daily rainfall data had the greatest reduction in weighted square error from 0.7 to 0.07 with and without including the Kalk Chal data set, respectively. The BLRPM model parameters also varied when data of the Kalk Chal station were removed from the calibration process. The k parameter value increased and the values of λ, and v decreased due to removal of the Kalk chal station data. The highest variation was observed for v decreased from 2.74 to 0.33 by removing the Kalk Chal station. The calibrated BLRPM model, with and without taking into account the Kalk Chal station data set, was employed to disaggregate daily rainfall data into the hourly values. The HSPF model was calibrated using the daily observed streamflow data recorded in Galinak station to simulate daily streamflow in reach 27. The daily streamflow simulations in reach 27 were conducted by implementing the hourly generated rainfall data sets. The results showed that inclusion of the hourly rainfall data recorded in Kalk chal station for parameterization of the BLRPM model caused the reproduction of high-intensity rainfall data in disaggregation process and consequently led to the overestimation of peak flows by HSPF model. Exclusion of the Kalk Chal station for BLRPM model parameterization improved the daily streamflow simulation with Nash-Sutcliff efficiency = 0.76, coefficient of determination = 0.79 and RMSE = 7.11 m3.s-1. These results demonstrated the sensitivity of HSPF model to the weather station selection and rainfall intensities.
Conclusions: The Kalk Chal station located outside of the studied region, with high intensity-short duration rainfall pattern caused heterogeneity of the input hourly rainfall data for parameter estimation of BLRPM model. Parameter estimation of the BLRPM model with inclusion of the hourly rainfall data of Kalk Chal station resulted in generation of greater intensities in disaggregation process. Despite the same values of daily rainfall data in streamflow simulations, the high rainfall intensities caused by the data set of Kalk Chal station led to the overestimation of peak flows. The results indicated the high sensitivity of HSPF model to the rainfall intensities.
M.R. Tabatabaei; K. Shahedi; karim solymani
Abstract
The estimation of suspended sediment load is very important for water resources quantity and quality studies. The suspended sediment load is generally calculated by direct measurement of suspended sediment concentration (SSC) of a river or by using sediment rating curve (SRC) method. Direct measurement ...
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The estimation of suspended sediment load is very important for water resources quantity and quality studies. The suspended sediment load is generally calculated by direct measurement of suspended sediment concentration (SSC) of a river or by using sediment rating curve (SRC) method. Direct measurement of the SSC is the most reliable but it is very expensive and time consuming. Also, the efficiency of the SRC method is low because it can substantially underpredict the high and overpredict the low loads. In this research, in order to consider the possibility of estimating the fluvial SSC, using reflectance of satellite images, the correlation between red and infrared bands of MODIS sensor and SSC of Karoun river at Molasani station for a period of 9 years (2003-2011) was considered. In this relation, two models (statistical simple linear regression and feed forward back propagation ANN) were used. The evaluation of models results showed that the efficiency of ANN model with having R2 =0.89 and RMSE=122mg/l was better than the regression relation with R2 =0.49 and RMSE=204mg/l. The research results showed that MODIS sensor images and ANN can be used together to estimate fluvial daily SSC in large rivers.
A. Azmoodeh; A. Kavian; K, Solymani; Gh. Vahabzadeh
Abstract
Abstract
Land use change, especially natural ecosystems transformation, has high effects on soil physico-chemical as well as biological characteristics and also, has effect on runoff and soil erosion. Analysis of runoff and soil erosion dynamics can be useful in improvement of management proposals and ...
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Abstract
Land use change, especially natural ecosystems transformation, has high effects on soil physico-chemical as well as biological characteristics and also, has effect on runoff and soil erosion. Analysis of runoff and soil erosion dynamics can be useful in improvement of management proposals and quantifying runoff and soil erosion changes. So, this research was carried out to investigate runoff and soil erosion in forest soils and to compare its results with dry farming and garden soils, which resulted from forest land use change. Also, effective quantitative soil parameters on runoff and soil erosion processes have recognized. For doing this, runoff and soil erosion were measured using simulated rainfall with 2 mm/min intensity and 15 min duration and soil samples were gathered in 15 replicates. Results showed that runoff was highest in native forest while the lowest was from garden land. Sediment yield was increased by 1.76 and 1.36 in conversion from forest to farming and garden land, respectively. Based on the results obtained from the correlation matrix, silt fraction, bulk density and antecedent soil moisture showed positive correlation with runoff and organic matter showed negative correlation with runoff. Also organic matter, clay and sand fraction reveal negative correlation with soil erosion, while antecedent soil moisture and silt fraction reveal positive correlation with erosion.
Keywords: Rainfall Simulator, Land Use Change, Runoff, Soil Erosion
