TOWARDS AN ADVANCED ANALYSIS, SIMULATION, AND FORECASTING CAPABILITY FOR THE WATER CYCLE IN TEXAS
a) Water cycle analysis
We use observation-based meteorology data and a back-trajectory moisture-tracking method to investigate the evaporative moisture sources supplying Texas rainfall. It is found that the Gulf of Mexico is the dominant moisture source and the eastern north Pacific also has larger contributions during cold seasons.
b) Hydrological simulation over the Edwards Plateau
We develop a methodology to improve groundwater recharge over the Edwards Plateau using a high-resolution Noah-MP land surface model. Simulated soil moisture and streamflow are evaluated against in-situ observations from the Texas Soil Observation Network (TxSON) and the US Geological Survey, respectively.
c) Land data assimilation
The above simulations of streamflow and groundwater recharge could be further improved by assimilating satellite observations of soil moisture. Towards this goal, the 3-km and 9-km retrievals from the Soil Moisture Active Passive (SMAP) satellite are first scaled to the Noah-MP modeled climatology through CDF-matching, and then assimilated into the high-resolution Noah-MP through an Ensemble Kalman Filter (EnKF). Preliminary results suggest that while SMAP retrieved soil moisture matches well with in-situ observations, data assimilation can slightly improve open-loop estimates.
d) Flood prediction
Currently, the operational implementation of the US National Water Model (NWM) has provided unprecedented streamflow forecasting capability for the nation. However, the model performance needs to be carefully assessed. In this study, using WRF-Hydro-RAPID, a prototype NWM framework implemented at UT-Austin, we present the evaluation of the model performance in predicting four flood events in central Texas in 2015. The influence of Multisensor Precipitation Estimate (MPE), initial wetness conditions, and input data on the model performance is discussed in detail to inform operational forecasting.