UCLA

The role of satellite-based remote sensing in improving hydrologic and water resources studies in data-scarce regions is investigated. Specifically, the dissertation focuses on the development of a 1) validation framework for remotely sensed precipitation and evapotranspiration without the use of ground-based observations, 2) methodological framework for calibration of large scale hydrologic models with multiple fluxes, and 3) a seasonal hydropower planning framework for data-scarce regions. In the first part of the dissertation, a root mean square error (RMSE)-based error metric capable of translating individual biasies in precipitation and evapotranspiration onto the Budyko space is developed. It is shown that the framework succeeds in arriving at the same conclusions as a traditional validation method. In the second part, the value of incorporating multiple hydrologic variables such as evapotranspiration, soil moisture and streamflow into model calibration is investigated. It is shown that parameters which are insensitive to individual model responses can influence the trade-off relationship between them. Finally, the potential of using remotely sensed precipitation and evapotranspiration datasets in generating reliable seasonal reservoir inflow forecasts for hydropower planning is investigated. Results highlight the importance of accounting for input and parameter uncertainty in hydropower planning.