Abstract

Key Points A significant potential source of error exists in mosaicked radar-rainfall maps. Different radar calibration offsets lead to misestimation of rainfall amounts. Systematic error in rainfall significantly affects hydrologic predictions. This study addresses a significant potential source of error that exists in radar-rainfall maps that are combined using data from multiple WSR-88D radars of the Next Generation Radar (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to misestimation of rainfall over basins and subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar-rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar-rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the borderline that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically based, data-intensive, calibration-free, hillslope-link hydrologic model called CUENCAS and demonstrated CUENCAS's ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the misestimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions. ©2013. American Geophysical Union. All Rights Reserved.

Department(s)

Civil, Architectural and Environmental Engineering

Publication Status

Free Access

Keywords and Phrases

precipitation; radar-rainfall

International Standard Serial Number (ISSN)

1944-7973; 0043-1397

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Wiley; American Geophysical Union, All rights reserved.

Publication Date

01 May 2013

Download

Full Text Link

Share

 
COinS