Nicoleta Cristea's student page
Developing a Framework for Testing Distributed Hydrologic Models at the Hillslope Scale
There are numerous hydrologic models that solve Richards equation for the variably saturated subsurface domain. However, the scarcity of measured hydrologic states and variables and the scale discrepancies between observations and simulations pose a challenge in testing and evaluating such models. We develop a flexible framework for testing distributed hydrologic models at the hillslope scale. First we generate "hypothetical realities" representing the hydrologic response of a synthetic watershed modeled after the 10.5 ha Tarrawarra catchment in Australia, and then we test other distributed hydrologic models against this error-free data set. Stream temperature prediction with neural networks
Artificial neural networks (ANNs) are powerful regression techniques that have been widely applied to model environmental systems. We are investigating the most useful types of ANNs and input variables to predict hourly values of stream temperature. | Generating hourly potential evapotranspiration timeseries with minimal climatological input
Potential evapotranspiration (PET) is required to run hydrologic models. In some areas, the weather data is insufficient to calculate PET with the full Penmann-Montieth method. We are investigating several radiation based methods to generate hourly PET timeseries with minimal climatological input. |
Last modified: 3/30/2008 8:32 PM