South-Central Section - 51st Annual Meeting - 2017

Paper No. 4-2
Presentation Time: 8:25 AM


YANG, Zong-Liang1, ZHAO, Long1, WEI, Jiangfeng1, LIN, Peirong1, LI, Lingcheng1, CALDWELL, Todd G.2, DAS, Narendra3, HOPPER, Larry4 and ZEITLER, Jon4, (1)Department of Geological Sciences, University of Texas at Austin, 1 University Station C1100, Austin, TX 78712, (2)Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, University Station, Box X, Austin, TX 78713, (3)NASA, Jet Propulsion Laboratory, Pasadena, CA 91109, (4)Climate and Aviation Focal Points, Austin/San Antonio National Weather Service WFO, 2090 Airport Road, New Braunfels, TX 78130,

Texas is subject to recurrent floods and droughts. In order to improve our predictive capability for these extreme events, we have taken advantage of existing hydrometeorological models, in situ and satellite measurements, data assimilation, and back-trajectory analyses. In this presentation, we will summarize four lines of concurrent collaborative research activities.

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.