Evaluation of Uncertainty in Stream Flow Prediction Using Monte Carlo Simulation for Watershed-Scale Hydrological Modeling
Publication: Journal of Hydrologic Engineering
Volume 29, Issue 1
Abstract
Uncertainty analysis is crucial for the quality control and application of hydrological models in water resources management. For improving the reliability of model outcomes, the identification of uncertainties originating from the knowledge gap and stochastic behavior of the model is required. To demonstrate a detailed investigation of uncertainty in hydrological model prediction, we developed a Hydrological Simulation Program-FORTRAN (HSPF) model for simulating streamflow at the South Chickamauga Creek watershed, Tennessee, and quantified uncertainty in the model outputs resulting from the input parameters using Monte Carlo simulation. Additionally, we compared the relative efficiency of Latin hypercube (LH) and Monte Carlo (MC) methods used for parameter distribution for the Monte Carlo simulation. Both methods produced similar results, where annual average runoff had the lowest, peak flow had the medium, and low flow had the highest level of uncertainty. Notably, our investigation revealed that extreme flow conditions exhibited higher uncertainty in prediction. Findings from this study will help in better interpretation of model results to devise more informed watershed management decisions.
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Data Availability Statement
The BASINS/HSPF application, which was used to develop the watershed model, can be obtained from the USEPA at https://www.epa.gov/ceam/basins-download-and-installation, and discharge data can be obtained from USGS station 03567500 at https://waterdata.usgs.gov/nwis. Watershed hydrologic characteristics (HUC 06020001), NLDAS meteorologic data, STATSGO soils data, and 2016 NLCD landcover data can be retrieved from the USEPA’s BASINS 4.5 (https://www.epa.gov/ceam/basins-download-and-installation) interface.
Acknowledgments
The authors would like to acknowledge the funding support from the Tennessee Higher Education Commission: Center of Excellence in Applied Computational Science and Engineering (CEACSE) Grant Competition (Grant No. R041302256) at the University of Tennessee at Chattanooga.
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Journal of Hydrologic Engineering
Volume 29 • Issue 1 • February 2024
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© 2023 American Society of Civil Engineers.
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Received: Jan 31, 2023
Accepted: Sep 1, 2023
Published online: Nov 3, 2023
Published in print: Feb 1, 2024
Discussion open until: Apr 3, 2024
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