Research Article, J Hydrogeol Hydrol Eng Vol: 2 Issue: 1
Estimation and Propagation of Parameter Uncertainty in Lumped Hydrological Models: A Case Study of HSPF Model Applied to Luxapallila Creek Watershed in Southeast USA
| Jairo N. Diaz-Ramirez1*, Billy E. Johnson2, William H. McAnally3, James L. Martin4, Vladimir J. Alarcon5 and Rene A. Camacho6 | |
| 1Director, Mississippi River Research Center, Alcorn State University, 1000 ASU Drive # 209, Alcorn State, MS, USA | |
| 2Research Civil Engineer, Engineer Research and Development Center (ERDC), 3909 Halls Ferry Road, Vicksburg, MS, USA | |
| 3Reasearch Professor, Department of Civil and Environmental Engineering, Mississippi State University, P.O. Box 9546, Mississippi State, MS, USA | |
| 4Professor, Department of Civil and Environmental Engineering, Mississippi State University, P.O. Box 9546, Mississippi State, MS, USA | |
| 5Assistant Research Professor, Geosystems Research Institute, Mississippi State University, Box 9627, Mississippi State, MS, USA | |
| 6Graduate Research Assistant, Department of Civil and Environmental Engineering, Mississippi State University, USA | |
| Corresponding author : Jairo N. Diaz-Ramirez, Director Mississippi River Research Center, Alcorn State University, 1000 ASU Drive # 209, Alcorn State, MS, USA Tel: (601) 877-3368 E-mail: jdiaz@alcorn.edu |
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| Received: October 25, 2012 Accepted: March 12, 2013 Published: March 18, 2013 | |
| Citation: Diaz-Ramirez JN, Johnson BE, McAnally WH, Martin JL, Alarcon VJ, et al. (2013) Estimation and Propagation of Parameter Uncertainty in Lumped Hydrological Models: A Case Study of HSPF Model Applied to Luxapallila Creek Watershed in Southeast USA. J Hydrogeol Hydrol Eng 2:1. doi:10.4172/2325-9647.1000105 |
Abstract
Estimation and Propagation of Parameter Uncertainty in Lumped Hydrological Models: A Case Study of HSPF Model Applied to Luxapallila Creek Watershed in Southeast USA
Explicit quantification of the uncertainty associated to the predictions of a hydrologic model is a necessary activity to objectively evaluate and report the limitations of the model caused by different sources of error. The current state of the practice of hydrologic modeling indicates that parametric uncertainty is considered as one of the most important sources of uncertainty. Some of the most relevant problems remaining in the practice include the identification of the principal parameters affecting model predictions and quantification of parameter ranges. This study evaluated stochastically one of the most popular deterministic watershed water quality models for decision making in USA.
