Speaker: Dr. Yuning Shi
Topic: "Towards Improved High-Resolution Land Surface Hydrologic Reanalysis Using a Physically-Based Land Surface Hydrologic Model and Data Assimilation"
Room: 529 Walker Building
Time: Fri Apr 03, 2015 03:45pm to 04:45pm
Abstract: Data assimilation technique (e.g., the ensemble Kalman filter) provides an efficient method of model-data synthesis and model calibration for computationally intensive hydrologic models. A coupled physically based land surface hydrologic model, Flux-PIHM, has been developed by incorporating a land surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. A Flux-PIHM data assimilation system has been developed by incorporating EnKF for model parameter and state estimation. Both synthetic and real data assimilation experiments have been performed at the Shale Hills watershed (0.08 km2). Synthetic experiment results show that the data assimilation system is able to simultaneously provide accurate estimates of multiple parameters. In the real data experiment, the EnKF estimated parameters and manually calibrated parameters yield similar model performances, but the EnKF method significantly decreases the time and labor required for calibration. Essential observations for model calibration are available with national and even global spatial coverage (e.g., MODIS surface temperature, SMAP soil moisture, and the USGS gauging stations). Therefore the Flux-PIHM EnKF data assimilation system could be readily expanded to other watersheds to provide regional scale land surface and hydrologic reanalysis with high spatial temporal resolution.