Open AccessBias correcting instantaneous peak flows generated using a continuous, semi‐distributed hydrologic model
Author(s) -
Spellman P.,
Webster V.,
Watkins D.
Publication year - 2018
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12342
Subject(s) - streamflow , watershed , flood myth , environmental science , soil and water assessment tool , forcing (mathematics) , calibration , series (stratigraphy) , flow (mathematics) , climate change , hydrology (agriculture) , hydrological modelling , statistics , computer science , meteorology , climatology , mathematics , geology , geography , drainage basin , geotechnical engineering , paleontology , oceanography , geometry , cartography , archaeology , machine learning
Flood risk analysis in the United States follows the guidelines recommended in Bulletin 17B, which inherently ties the probability of exceeding a selected peak flow to the historical record. However, insufficient streamflow data, land use change, or climate change can render these recommended techniques impractical, forcing alternative measures. One such alternative is hydrological models which use climate and landscape data to simulate streamflow. Daily time steps or finer, however, are required to extract the necessary peak flow series. Accurately simulating flows at a daily time step can be challenging, and bias in prediction of hydrological extremes can be large. In this analysis, we compare three types of statistical bias‐correction measures for peak flow series data extracted from simulations using the Soil and Water Assessment Tool (SWAT), a widely used continuous, semi‐distributed hydrologic model. Results show that a correction method that only adjusts the mean of the peak flow series was the most robust. This efficacy is likely a result of persistent bias in watershed simulation due to uncertainty in calibration parameters and simplified watershed processes, suggesting that correcting for the mean of the peak flow series may be sufficient when applying bias corrections for flood risk analysis using hydrological models.