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.
Satellite snow cover area (SCA) mapping using optical sensors has been known to suffer severe obstruction due to vegetation canopy and cloud cover. Several algorithms have been developed to reduce cloud cover contamination and enhance the SCA mapping. In this study we introduce the use of a daily SCA product from the Multisensor Snow and Ice Mapping System (IMS) at a nominal resolution of 4 km, assess its accuracy and error levels against in situ observations, and compare the IMS SCA product with the SCA products from moderate resolution imaging spectroradiometer (MODIS), a combination of daily Terra and Aqua satellites. The results show that the snow accuracies are higher during winter for both IMS and MODIS, and that there is not much difference between MODIS at 500 m and upscaled at 4 km. The IMS SCA mapping accuracies are significantly higher than MODIS accuracies for all sky conditions, while they are similar to or slightly lower than MODIS accuracies in clear sky conditions. The overestimate error of snow cover using IMS is higher (lower) than that of MODIS during ablation (accumulation) periods. Both MODIS and IMS show a similar pattern of underestimation errors of snow cover with the IMS being slightly higher than the MODIS. It is concluded that the IMS SCA product has potential as a good alternative for the MODIS daily SCA products or replacing those cloud pixels in the MODIS daily or multiday products.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.