EVALUATION OF REGIONAL CLIMATE MODELS WITH REMOTELY SENSED EVAPOTRANSPIRATION DATA

Water is one of the most precious resources on Earth. Managing water resources is a complex discipline that requires accurate data, which in turn means that the management of water resources is limited by the availability and quality of these datasets. Evapotranspiration (ET) is one of these key datasets, but is also one that is lacking in large-scale spatial distribution with traditional methods such as weighing lysimeters or Bowen ratio. This is a quantity that needs to be evaluated in regional and global climate models since it is a substantial component of the land surface-atmosphere interaction. In order to overcome the limitations imposed by point wise calculation of ET, a new dataset based on a surface energy balance model Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) constrained by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery have been developed. A Fully Automated Python implementation of METRIC model, as well as a script which generates 15-day Reference ET Fraction (ETrF) composites were needed and developed to cover the Contiguous United States (CONUS) due to the high computational time for manual processing of METRIC. In this study, the new ET dataset will be used to evaluate how well the Weather Research and Forecasting Model, coupled with Community Land Model\u27s (WRF-CLM) as well as Noah-MP and Bucket Land Surface Model, evaluate ET. CLM, Noah-MP and Bucket are the models used to understand the processes between land and atmosphere and also climate change, and contain crucial but poorly known parameterizations for ET. Advisers: Robert Oglesby and Ayse Kili