Efficient data integration is one of the most challenging problems in data management, interoperation and analysis. The Earth science data which are heterogeneous are collected at various geographical locations for scientific studies and operational uses. The intrinsic problem of archiving, distributing and searching such huge scientific datasets is compounded by the heterogeneity of data and queries, thus limiting scientific analysis, and generation/validation of hydrologic forecast models. The data models of hydrologic research communities such as National Weather Service (NWS), National Oceanic and Atmospheric Administration (NOAA), and US Geological Survey (USGS) are diverse and complex. A complete derivation of any useful hydrological models from data integrated from all these sources is often a time consuming process.

One of the current trends of data harvesting in scientific community is towards a distributed digital library initiative. However, these approaches may not be adequate for data sources / entities who do not want to "upload" the data into a "data pool." In view of this, we present here an effective architecture to address the issues of data integration in such a diverse environment for hydrological studies. The heterogeneities in these datasets are addressed based on the autonomy of data source in terms of design, communication, association and execution using a hierarchical integration model. A metadata model is also developed for defining data as well as the data sources, thus providing a uniform view of the data for different kind of users. An implementation of the model using web based system that integrates widely varied hydrology datasets from various data sources is also being developed.