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.
Zahra Abdollahi; Ataollah Kavian; kaka shahedi; neda Abdollahi; Mohammad Jafari
Abstract
Introduction: River discharge as one of the most important hydrology factors has a vital role in physical, ecological, social and economic processes. So, accurate and reliable prediction and estimation of river discharge have been widely considered by many researchers in different fields such as surface ...
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Introduction: River discharge as one of the most important hydrology factors has a vital role in physical, ecological, social and economic processes. So, accurate and reliable prediction and estimation of river discharge have been widely considered by many researchers in different fields such as surface water management, design of hydraulic structures, flood control and ecological studies in spetialand temporal scale. Therefore, in last decades different techniques for short-term and long-term estimation of hourly, daily, monthly and annual discharge have been developed for many years. However, short-term estimation models are less sophisticated and more accurate.Various global and local algorithms have been widely used to estimate hydrologic variables. The current study effort to use Lazy Learning approach to evaluate the adequacy of input data in order to follow the variation of discharge and also simulate next-day discharge in Talar River in KasilianBasinwhere is located in north of Iran with an area of 66.75 km2. Lazy learning is a local linear modelling approach in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries
Materials and Methods: The current study was conducted in Kasilian Basin, where is located in north of Iran with an area of 66.75 km2. The main river of this basin joins to Talar River near Valicbon village and then exit from the watershed. Hydrometric station located near Valicbon village is equipped with Parshall flume and Limnogragh which can record river discharge of about 20 cubic meters per second.In this study, daily data of discharge recorded in Valicbon station related to 2002 to 2012 was used to estimate the discharge of 19 September 2012. The mean annual discharge of considered river was also calculated by using available data about 0.441 cubic meters per second. To estimate the discharge of considered day, three methods of constant, linear and quadratic functionscontrollers based on the local linearization provided by the lazy learning algorithm were considered. Lazy learning is a memory-based linear technique for local modeling approach which is reported as a high-efficient algorithm for simulating variables with low input data.The series of input data was categorized into previous 6, 8, 10, 15 and 20 days, 1 and 2 months, 1, 2 and 3 seasons and also 1 and 2 years to evaluate which series is appropriately enough to predict next-day discharge inthe river. Then, mean absolute error and root-mean square error were calculated for all series and modelsin order to find the best estimator model and the most appropriate series of input data.
Results: Results showed that constant and linear model had the minimum root-mean square error of 0.001 and 0.057 respectivelywith previous 60 days’ data series. Whilethe quadratic model had its best estimation with previous 2 season data series with the minimum root-mean square error of 0.059. The result indicated that the more input data increase, the best quadratic model estimate until 60 days. But after 60 days, estimation error gradually increased. Consequently, not more data but adequate areneeded for accurate estimation. Also, RMSE in linear model had less fluctuation and therefore less sensitivity compared with other models. And quadratic model had less fluctuation and sensitivity to neighborhoods. Also, according to results, the more variation in each period increase, the better estimation is accrued by lazy learning algorithm. Hence, it was expected that next-day discharge prediction in low-water period needs longer data series than high-water period.
Conclusion: Regarding to thousands of prepared training models, constant model with previous 60 days’ data and minimum error of 0.0001 was selected as the most accurate estimatefor next-day river discharge in Talar River. Results showed that despite of some limitation and demerits, the local Lazy Learning algorithm has significant efficiency in time series simulating. Although the accuracy of simulation increase with more input data, but this algorithm can runby at least 5 training data. However we find lazy learning to be the best performing approach on average goodness indicators (such as mean absolute error and Root-mean square error). On the other hand, the lazy learning predictor can be quickly developed and easily kept up-to-date by adding new data to its database. Also, it does not face with overfitting problems which are common in global modeling approaches.According to some noteworthy features of lazy learning noticed in this regards, this approach will have good performance for time-series studies.
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.
M. Zarei; M. Habibnejad; K. Shahedi; M.R. Ghanbarpour
Abstract
Abstract
Major of hydrological systems are very complicated and it is not possible to understand them completely, therefore simplification is necessary to understand or control of a part of the system behavior such as water balance relationships. Hydrological models are simple structure of complicated ...
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Abstract
Major of hydrological systems are very complicated and it is not possible to understand them completely, therefore simplification is necessary to understand or control of a part of the system behavior such as water balance relationships. Hydrological models are simple structure of complicated systems in water cycle in the nature. The first goal of a hydrological model is function predict of complicated system and survey the impact of any kind of changes on system behavior. In this research IHACRES hydrological model was used to daily flow simulation and calculation of rainfall measure that be increase into streamflow, in the kasilian catchment (Area=342.86 km2) and kasilian sub catchment (Area=67.8 km2). The results was representative of delay naught between rainfall and flow in two catchments, also to values of two parameter coefficient of determination (D) and average relative parameter error (ARPE), the model streamflow in kasilian catchment more accuracy simulated than kasilian sub catchment. Altogether, in attention to values of error in flow volume (Bias), average of simulated streamflow by IHACRES model was more than observed streamflow in these catchments. Percentage of rainfall that bears hand to streamflow creation of kasilian catchment was calculated near third of catchment rainfall average and in the kasilian sub catchment for evaluation and calibration period 231 and 216 mm/yr, respectively.
Keywords: Hydrological Model, IHACRES, Calibration, Evaluation