| Abstract: | Remote sensing technology has matured significantly over the past decade. Operational satellites provide reliable, periodic coverage for all areas of the Earth. Data from these satellites are in a digital format that provides enhanced flexibility in hydrological modelling. Considerable advances in acquiring hydrological data from airborne and in situ sensors have also been achieved. Additionally, data from non-traditional remote sources such as weather radar from which spatial and temporal rainfall rates may be estimated are widely available. These new data acquisition capabilities have been paralleled by equal advancements in digital array processing and geographic information systems, which allow the effective extraction of both temporal and spatial information. This paper examines the use of object-oriented programming techniques to create dynamic hydrological models, and explores their potential to receive real and near real-time data from remote sensors as input to improve hydrological forecasting. In particular, the COE SSARR model is used to illustrate how an established hydrological model may be adapted to create a dynamic object model. |
