Stochastic simulation of water resources time series in general and hydrologic time series in particular has been widely used for several decades for various problems related to planning and management of water resources systems. Typical examples are determining the capacity of a reservoir, evaluating the reliability of a reservoir of a given capacity, evaluation of the adequacy of a water resources management strategy under various potential hydrologic scenarios, and evaluating the performance of an irrigation system under uncertain irrigation water deliveries (Salas et al, 1980; Loucks et al, 1981).

Stochastic simulation of hydrologic time series such as streamflow is typically based on mathematical models. For this purpose a number of stochastic models have been suggested in literature (Salas, 1993; Hipel and McLeod, 1994). Using one type of model or another for a particular case at hand depends on several factors such as, physical and statistical characteristics of the process under consideration, data availability, the complexity of the system, and the overall purpose of the simulation study. Given the historical record, one would like the model to reproduce the historical statistics. This is why a standard step in streamflow simulation studies is to determine the historical statistics. Once a model has been selected, the next step is to estimate the model parameters, then to test whether the model represents reasonably well the process under consideration, and finally to carry out the needed simulation study.

The advent of digital computers several decades ago led to the development of computer software for mathematical and statistical computations of varied degree of sophistication. For instance, well known packages are IMSL, STATGRAPHICS, ITSM, MINITAB, SAS/ETS, SPSS, and MATLAB. These packages can be very useful for standard time series analysis of hydrological processes. However, despite of the availability of such general purpose programs, specialized software for simulation of hydrological time series such as streamflow, have been attractive because of several reasons. One is the particular nature of hydrological processes in which periodic properties are important in the mean, variance, covariance, and skewness. Another one is that some hydrologic time series include complex characteristics such as long term dependence and memory. Still another one is that many of the stochastic models useful in hydrology and water resources have been developed specifically oriented to fit the needs of water resources, for instance temporal and spatial disaggregation models. Examples of specific oriented software for hydrologic time series simulation are HEC-4 (U.S Army Corps of Engineers, 1971), LAST (Lane and Frevert, 1990), and SPIGOT (Grygier and Stedinger, 1990).

The LAST package was developed during 1977-1979 by the U. S. Bureau of Reclamation (USBR). Originally, the package was designed to run on a mainframe computer (Lane, 1979) but later it was modified for use on personal computers (Lane and Frevert, 1990). While various additions and modifications have been made to LAST over the past 20 years, the package has not kept pace with either advances in time series modeling or advances in computer technology. This is especially true of the computer graphics. These facts prompted USBR to promote the initial development of the SAMS package. The first version of SAMS (SAMS-96.1) was released in 1996. Since then, corrections and modifications were made based on feedback received from the users. In addition, new functions and capabilities have been implemented leading to SAMS 2000, which was released in October, 2000.

The most current version is SAMS 2007, which includes new modeling approaches and data analysis features. SAMS 2007 has the following capabilities:

1. Analyze the stochastic features of annual and seasonal data.
2. It includes several types of transformation options to transform the original data into normal.
3. It includes a number of single site, multisite, and disaggregation stochastic models that have been widely used in hydrologic literature.
4. It includes two major modeling schemes for data generation of complex river network systems.
5. The number of samples that can be generated is unlimited.
6. The number of years that can be generated is unlimited.

The main purpose of SAMS is to generate synthetic hydrologic data. It is not built for hydrologic forecasting although data generation for some of the models can be conditioned on most recent historical observations.