Stochastic Analysis , Modeling , and Simulation ( SAMS ) Version 2007-User ' s Manual by

iv PREFACE Several computer packages have been developed since the 1970's for analyzing the stochastic characteristics of time series in general and hydrologic and water resources time series in particular. For instance, the LAST package was developed in 1977-1979 by the US Bureau of Reclamation (USBR) in Denver, Colorado. Originally the package was designed to run on a mainframe computer, but later it was modified for use on personal computers. While various additions and modifications have been made to LAST over the past twenty years, the package has not kept pace with either advances in time series modeling or advances in computer technology. These facts prompted USBR to promote the initial development of SAMS, a computer software package that deals with the Stochastic Analysis, Modeling, and Simulation of hydrologic time series, for example annual and seasonal streamflow series. It is written in C, Fortran, and C++, and runs under modern windows operating systems such as WINDOWS XP. This manual describes the current version of SAMS denoted as SAMS 2007.

[1]  Robert H. Shumway,et al.  Time Series Analysis and Its Applications (Springer Texts in Statistics) , 2005 .

[2]  J. Stedinger,et al.  Water resource systems planning and analysis , 1981 .

[3]  R. Shanmugam.  Introduction to Time Series and Forecasting , 1997 .

[4]  J. Salas,et al.  Modeling the Dynamics of Long-Term Variability of Hydroclimatic Processes , 2003 .

[5]  Stephen G Walker,et al.  A Note on the Innovation Distribution of a Gamma Distributed Autoregressive Process , 2000 .

[6]  Jose D. Salas,et al.  Prediction of Extreme Events in Hydrologic Processes that Exhibit Abrupt Shifting Patterns , 2005 .

[7]  Jose D. Salas,et al.  Gamma‐Autoregressive Models for Stream‐Flow Simulation , 1990 .

[8]  W. L. Lane,et al.  Applied Modeling of Hydrologic Time Series , 1997 .

[9]  竹安 数博,et al.  Time series analysis and its applications , 2007 .

[10]  David Mautner Himmelblau,et al.  Applied Nonlinear Programming , 1972 .

[11]  J. M. Mejía,et al.  Disaggregation models in hydrology revisited , 1976 .

[12]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[13]  R. Fisher.  The Advanced Theory of Statistics , 1943, Nature.

[14]  J. Filliben.  The Probability Plot Correlation Coefficient Test for Normality , 1975 .

[15]  J. Stedinger,et al.  Multisite ARMA(1,1) and Disaggregation Models for Annual Streamflow Generation , 1985 .

[16]  Clifford M. Hurvich,et al.  A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION , 1993 .

[17]  R. Valencia,et al.  Disaggregation processes in stochastic hydrology , 1973 .

[18]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[19]  Jose D. Salas,et al.  Estimation of ARMA Models with seasonal parameters , 1982 .

[20]  A. J. Lawrance,et al.  A new autoregressive time series model in exponential variables (NEAR(1)) , 1981, Advances in Applied Probability.

[21]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[22]  W. L. Lane,et al.  Use of Stochastic Hydrology in Reservoir Operation , 1986 .

[23]  Multivariate Shifting Mean Plus Persistence Model for Simulat- ing the Great Lakes Net Basin Supplies , 2006 .