Abstract—in streamflow simulation, the first-order gamma autoregressive (GAR(1)) model [2] has been found to be very effective for annual data. This paper presents some attempts to apply the GAR(1) model to the simulation of monthly streamflows. To this aim, we propose two models, namely the GAR(1)-Monthly and GAR(1)-Fragments models that will be compared with the popular Thomas-Fiering model. Based on actual data of monthly streamflows at three stations and generated series of monthly data for 1000 years, it was found that both GAR(1)-Monthly and GAR(1)-Fragments models can reproduce very well all statistical descriptors, namely mean value, standard deviation and skewness coefficient, of the historical monthly series. Moreover, the GAR(1)-...
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and m...
One major acknowledged challenge in daily precipitation is the inability to model extreme events in ...
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and m...
Many stochastic processes in practice having the sequences of random variables are generally skewed ...
Computer simulation is used to study many stochastic processes. Most of these stochastic processes i...
Stochastic models are required to generate synthetic values of flows statistically similar to observ...
The partial auto-correlation coefficients of most of the series of monthly stream flows recorded in ...
In this paper, we develop a simple analysis method to infer some properties of the watershed process...
Daily peak stream discharge data, collected over time, are typically characterized by a few large pe...
This study aims to develop an improved time series model to overcome difficulties in modeling monthl...
Using entropy theory, a new method for single-site monthly streamflow simulation is developed, which...
Conventional streamflow models operate under the assumption of constant variance or season-dependent...
This study was designed to find the best stochastic model (using of time series analysis) for annual...
Abstract – Multiplicative seasonal autoregressive integrated moving average models are appropriate f...
Online access for this thesis was created in part with support from the Institute of Museum and Libr...
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and m...
One major acknowledged challenge in daily precipitation is the inability to model extreme events in ...
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and m...
Many stochastic processes in practice having the sequences of random variables are generally skewed ...
Computer simulation is used to study many stochastic processes. Most of these stochastic processes i...
Stochastic models are required to generate synthetic values of flows statistically similar to observ...
The partial auto-correlation coefficients of most of the series of monthly stream flows recorded in ...
In this paper, we develop a simple analysis method to infer some properties of the watershed process...
Daily peak stream discharge data, collected over time, are typically characterized by a few large pe...
This study aims to develop an improved time series model to overcome difficulties in modeling monthl...
Using entropy theory, a new method for single-site monthly streamflow simulation is developed, which...
Conventional streamflow models operate under the assumption of constant variance or season-dependent...
This study was designed to find the best stochastic model (using of time series analysis) for annual...
Abstract – Multiplicative seasonal autoregressive integrated moving average models are appropriate f...
Online access for this thesis was created in part with support from the Institute of Museum and Libr...
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and m...
One major acknowledged challenge in daily precipitation is the inability to model extreme events in ...
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and m...