Chapter 8
Advanced Topic: Rain-Gauge Rainfall Data Augmentation and Radar Rainfall Data Analysis
Publication: Radar Rainfall Data Estimation and Use (MOP 139)
Abstract
Hydrologic modeling needs continuous rainfall data without gaps. However, rainfall data gaps of different length are often unavoidable due to random and systematic errors. This chapter focuses on spatial and temporal analysis of rainfall and estimation of missing rainfall data using radar-based rainfall estimates. It discusses functional forms for estimating missing rainfall data with linking radar and rain gauge data and geographically weighted optimization methods. The chapter also describes geospatial grid-based transformation of radar data from one spatial coordinate system to another. Deterministic weighting and stochastic interpolation methods have been used for the spatial construction of rainfall fields or for estimating missing rainfall values in space. However, recent studies have reported limitations of spatial interpolation methods. Correlation-weighting techniques and artificial neural network methods are conceptually superior to deterministic approaches compared with the traditional inverse distance weighting method and its variants.
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Information & Authors
Information
Published In
Radar Rainfall Data Estimation and Use (MOP 139)
Pages: 95 - 110
Editors: Chandra S. Pathak, Ph.D., P.E., D.WRE, and Ramesh S. V. Teegavarapu, P.E.
ISBN (Print): 978-0-7844-1511-5
ISBN (Online): 978-0-7844-8176-9
Copyright
© 2018 American Society of Civil Engineers.
History
Published in print: Dec 2, 2018
Published online: Dec 4, 2018
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