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Sharif, Hatim O., Almoutaz A. Hassan, Sazzad Bin‐Shafique, Hongjie Xie, and Jon Zeitler, 2010. Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas. Journal of the American Water Resources Association (JAWRA) 1‐11. DOI: 10.1111/j.1752‐1688.2010.00459.x Abstract:  Many of the storms creating the greatest rainfall depths in Texas, measured over durations ranging from one minute to 48 hours, have occurred in the Texas Hill Country area. The upstream portion of the Guadalupe River Basin, located in the Texas Hill Country, is susceptible to flooding and rapid runoff due to thin soils, exposed bedrock, and sparse vegetation, in addition to the Balcones Escarpment uplift contributing to precipitation enhancement. In November 2004, a moist air mass from the Gulf of Mexico combined with moist air from the Pacific Ocean resulted in the wettest November in Texas since 1895. Although the peak discharges were not the highest on record, the U.S. Geological Survey (USGS) stream gauge on the Guadalupe River at Gonzales, Texas reported a daily mean discharge of 2,304 m3/s on November 23, 2004 (average discharge is 53 m3/s). In this paper, we examine the meteorological conditions that led to this event and apply a two‐dimensional, physically based, distributed‐parameter hydrologic model to simulate the response of a portion of the basin during this event. The study results clearly demonstrate the ability of physically based, distributed‐parameter simulations, driven by operational radar rainfall products, to adequately model the cumulative effect of two rainfall events and route inflows from three upstream watersheds without the need for significant calibration.

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This study evaluates the May and October 2015 flood prediction skill of a physically based model resembling the U.S. National Water Model (NWM) over the Texas Hill Country. It also investigates hydrometeorological factors that contributed to a record flood along the Blanco River at Wimberley (WMBT2) in May 2015. Using two radar-based quantitative precipitation estimation (QPE) products—Stage IV and Multi-Radar Multi-Sensor (MRMS)—it is shown that the event precipitation accuracy dominates the prediction skill, where the finer-resolution MRMS QPE mainly benefits basins with small drainage areas. Overall, the model exhibits good performance at gauges with fast flood response from causative rainfall and gauges that are not forecast points in the National Weather Service’s Advanced Hydrometeorological Prediction System, showing great promise for forecasts, warnings, and emergency response. However, the model suffers from poor prediction skill over regions without rapid flood response and regions with human-altered flows, suggesting the need to revisit the channel routing algorithm and incorporate modules to represent human alterations. Two contrasting flood events at WMBT2 with similar meteorological characteristics are examined in greater detail, revealing that the location of intense rainfall combined with land physiographic features are key to the flood response differences. Model sensitivity tests further show the record flood peak could be better obtained by tuning the deep-layer soil wetness and the flow velocity field in the river network, which offers hydrometeorological insights into the causes and the complex nature of such a flood and why the model struggles to predict the record flood peak.