Abstract:
Stochastic model is necessary for planning and implementation of water works in ephemeral rivers of arid and semi-arid regions. Accurate estimation of stream flow in arid and semi-arid regions is an important, critical and crucial task in many cases, however daily discharge data is usually limited in such regions. It even occurs for short term rainfall series, whereas monthly and yearly rainfall series usually do not show such manner In this study a model is considered for daily discharge data of Firoozabad basin of Kerman province, Iran which includes identification of some factors including occurrence of flow time, flow increment, flow increment magnitude and calculation of flow reduced decrement. The Markov chain, increments of the rising limb and an exponential recession of the hydrograph were used. The observed daily gauging flow data were used for parameter estimation as well. The model is then applied to daily flow series records which showed that not only the model can show the long-term characteristics of the hydrograph but also it can predict its short-term characteristics.
Machine summary:
It even occurs for short term rainfall series, whereas monthly and yearly rainfall series usually do not show such manner In this study a model is considered for daily discharge data of Firoozabad basin of Kerman province, Iran which includes identification of some factors including occurrence of flow time, flow increment, flow increment magnitude and calculation of flow reduced decrement.
Kisi and Cigizoglu( 2007) compared of different artificial neural network (ANN) techniques in short- and long-term continuous and intermittent daily streamflow forecasting and concluded that radial basis function-based neural networks (RBF) to be superior to the other ANN techniques and a time series model in terms of the selected performance criteria.
In this study it is aimed to apply a stochastic model for simulating daily stream flows of ephemeral streams in the southeastern Iran due to the scarcity of data available and several observations with zero values in the time series.
2. Daily stream flow model In many hydrological studies, the use of series of short term flow data are necessary not only for planning of water supply projects but also for ecology, ecohydrology and water quality monitoring during low flows which is mostly apparent in arid regions (Kavvas and Delleur, 1984).
Daily streamflow series (view the image of this page) (view the image of this page) Mean flow values of the estimated and observed recession as shown in Table 3 along with the percentage of relative errors (RE) of each month indicates that the total number of recession curves are 212 and 228 while the recession coefficients for the first and second steps are 0.