Hydrologic Modeling of an Extreme Flood in the Guadalupe River in Texas¹

Abstract

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.

Comparison of SWAT and GSSHA for High Time Resolution Prediction of Stream Flow and Sediment Concentration in a Small Agricultural Watershed

“…Various studies have successfully applied GSSHA model to a wide range of watershed scales for predicting hydrological response particularly during extreme storm events [10][11][12]22,23]. The fully distributed GSSHA model is based on a structured grid and uses physically-based partial diffusive wave equations for two-dimensional overland flow (Explicit and Alterative Direction Explicit (ADE)) and one-dimensional channel flow (up-gradient explicit).…”

“…The fully distributed GSSHA model is intuitively more realistic compared to a lumped HEC-HMS model in terms of land use change. In addition, GSSHA has been successfully applied to small to Hydrology 2017, 4, 27 2 of 15 medium size watersheds with event-based and continuous configuration for predicting both stream flow and sediment discharge [10][11][12].…”

Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

“…GSSHA model has been used to study different flooding events, including the 2002, 2004, and 2007 storm events in San Antonio, Texas [23,41], the June 2002 storm event in the upper Guadalupe River watershed [4], and the November 2004 flooding event in the middle Guadalupe River watershed and Bull Creek watershed, Austin, Texas [18,19].…”

“…Numerous studies have conducted inter-comparisons between MPE precipitation estimates and rain gauge observations (e.g., [4,[15][16][17][18][19][20][21]). These studies showed that the calibrated radar products such as NEXRAD Stage ІІІ and MPE yielded more accurate streamflows than the rain gauge network alone.…”

Physically, Fully-Distributed Hydrologic Simulations Driven by GPM Satellite Rainfall over an Urbanizing Arid Catchment in Saudi Arabia

“…Different satellite products we used as input to GSSHA in simulation of several floods over a 3000 km 2 catchment in Texas [30] with satellite products of higher spatiotemporal resolutions producing the most reasonable runoff estimates. Another study over the Guadalupe River in Texas demonstrated that GSSHA was more successful in simulating events with multiple rainfall hiatuses than the HEC model [33]. An experiment using rainfall forecasts over a semi-arid urban catchment in Colorado demonstrated that GSSHA was able to produce reasonable forecasts of inundation and peak discharge for lag times of up to 70 min [34].…”