Title:
A New Hydrologic Routing Model with Applications for Georgia Rivers
A New Hydrologic Routing Model with Applications for Georgia Rivers
| dc.contributor.author | Kim, Dong Ha | en_US |
| dc.contributor.author | Georgakakos, Aristidis P. | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology | en_US |
| dc.contributor.editor | Carroll, G. Denise | en_US |
| dc.date.accessioned | 2013-02-22T20:26:26Z | |
| dc.date.available | 2013-02-22T20:26:26Z | |
| dc.date.issued | 2011-04 | |
| dc.description | Proceedings of the 2011 Georgia Water Resources Conference, April 11, 12, and 13, 2011, Athens, Georgia. | en_US |
| dc.description.abstract | In this paper, a new hydrologic river routing model is developed to identify storage-outflow relationships for different reaches of a river. The model is then incorporated into a Bayesian forecasting framework (BFF) to generate ensemble forecasts of river flows that incorporate hydrologic and model uncertainties. The routing model assumes that a river reach can be viewed as a cascade of conceptual reservoirs, each of which receives water from the upstream and releases water to the downstream according to a release rule. Additionally, these release rules are assumed to follow monotonically increasing storage-outflow relationships. Without any assumption on the mathematical structures of the rules, a Linear Quadratic Regulator (LQR) is used to identify the storage-outflow relationships. The routing model was tested on the Equatorial Lakes in East Africa because this system is a series of cascading reservoirs and because actual observations of the storageoutflow relationships are readily available. Given the initial storage of each lake, the model was able to find storage-outflow relationships that closely approximate the observed data, as depicted in Figure 1.. The storage-outflow relationships were then used to generate ensemble forecasts of river flows. Under this forecasting scheme, a historical analog method is used to select an ensemble of system inflows. Each inflow trace is then simulated with the previously estimated storageoutflow relationships to generate ensembles of river flows. In order to improve forecast performance, a Bayesian forecasting framework (BFF) was developed and used to generate updated river flow ensembles. The distributions of the river flow forecasts at the outlet of the lake system before and after the application of the BFF are shown in Figure 2. It can be seen that the variances of the BFF distributions (blue-colored box plots) are smaller than those of the pre-BFF distributions (gray-colored box plots), while the actual river flows that materialized (red dots) still fall within the forecasted ranges. The BFF derived forecasts therefore provide more concise forecasts without significant loss of reliability. The new routing model will be tested under various flow and terrain conditions for various rivers in Georgia. Comparisons with existing methods, such as Muskingum, Muskingum-Cunge, method of characteristics, and explicit/implicit routing schemes will be carried out to test model accuracy and efficiency. | en_US |
| dc.description.sponsorship | Sponsored by: Georgia Environmental Protection Division U.S. Geological Survey, Georgia Water Science Center U.S. Department of Agriculture, Natural Resources Conservation Service Georgia Institute of Technology, Georgia Water Resources Institute The University of Georgia, Water Resources Faculty | en_US |
| dc.description.statementofresponsibility | This book was published by Warnell School of Forestry and Natural Resources, The University of Georgia, Athens, Georgia 30602-2152. The views and statements advanced in this publication are solely those of the authors and do not represent official views or policies of The University of Georgia, the U.S. Geological Survey, the Georgia Water Research Institute as authorized by the Water Research Institutes Authorization Act of 1990 (P.L. 101-307) or the other conference sponsors. | en_US |
| dc.identifier.isbn | 0-9794100-24 | |
| dc.identifier.uri | http://hdl.handle.net/1853/46232 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Warnell School of Forestry and Natural Resources, The University of Georgia | en_US |
| dc.relation.ispartofseries | GWRI2011. Environmental Protection | en_US |
| dc.subject | Water resources management | en_US |
| dc.subject | Hydrologic river routing model | en_US |
| dc.subject | Bayesian forecasting framework | en_US |
| dc.subject | Linear quadratic regulator | en_US |
| dc.subject | Storage-outflow relationships | en_US |
| dc.title | A New Hydrologic Routing Model with Applications for Georgia Rivers | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Georgakakos, Aristidis P. | |
| local.contributor.corporatename | Georgia Water Resources Institute | |
| local.contributor.corporatename | School of Civil and Environmental Engineering | |
| local.contributor.corporatename | College of Engineering | |
| local.relation.ispartofseries | Georgia Water Resources Conference | |
| relation.isAuthorOfPublication | ec69ec36-b20e-4231-be3e-cfe0dd51ea86 | |
| relation.isOrgUnitOfPublication | 8873b408-9aff-48cc-ae3c-a3d1daf89a98 | |
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| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| relation.isSeriesOfPublication | e0bfffc9-c85a-4095-b626-c25ee130a2f3 |
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