Texas Water Digital Library

Near Real-Time Runoff Estimation Using Spatially Distributed Radar Rainfall Data

DSpace/Manakin Repository

Show simple item record

dc.creator Hadley, Jennifer Lynn
dc.date.accessioned 2007-11-19T22:37:08Z
dc.date.accessioned 2011-05-19T14:14:17Z
dc.date.available 2007-11-19T22:37:08Z
dc.date.available 2011-05-19T14:14:17Z
dc.date.issued 2003-12
dc.identifier.uri http://hdl.handle.net/1969.1/6124
dc.description.abstract he purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, rain gauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the rain gauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as rain gauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 iv produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data. en
dc.language.iso en_US en
dc.publisher Texas Water Resources Institute en
dc.relation.ispartofseries TR-240;
dc.title Near Real-Time Runoff Estimation Using Spatially Distributed Radar Rainfall Data en
dc.type Technical Report en

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account