The Colorado Basin River Forecast Center (CBRFC) provides forecasts of streamflow for purposes such as flood warning and water supply. Much of the water in these basins comes from spring snowmelt, and the forecasters at CBRFC currently employ a suite of models that include a temperature-index snowmelt model. While the temperature-index snowmelt model works well for weather and land cover conditions that do not deviate from those historically observed, the changing climate and alterations in land use necessitate the use of models that do not depend on calibrations based on past data. This dissertation reports work done to overcome these limitations through using a snowmelt model based on physically invariant principles that depends less on calibration and can directly accommodate weather and land use changes. The first part of the work developed an ability to update the conditions represented in the model based on observations, a process referred to as data assimilation, and evaluated resulting improvements to the snowmelt driven streamflow forecasts. The second part of the research was the development of web services that enable automated and efficient access to and processing of input data to the hydrological models as well as parallel processing methods that speed up model executions. These tasks enable the more detailed models and data assimilation methods to be more efficiently used for streamflow forecasts.