Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting

Zhangjun Liu, Shenglian Guo (), Honggang Zhang, Dedi Liu and Guang Yang
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Zhangjun Liu: Wuhan University
Shenglian Guo: Wuhan University
Honggang Zhang: Changjiang Water Resources Commission
Dedi Liu: Wuhan University
Guang Yang: Wuhan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 7, No 1, 2126 pages

Abstract: Abstract Accurate real-time flood forecasting is essential for flood control and warning system, reservoir operation and other relevant water resources management activities. The objective of this study is to investigate and compare the capability of three updating procedures, namely autoregressive (AR) model, recursive least-squares (RLS) model and hydrologic uncertainty processor (HUP) in the real-time flood forecasting. The Baiyunshan reservoir basin located in southern China was selected as a case study. These three procedures were employed to update outputs of the established Xinanjiang flood forecasting model. The Nash-Sutcliffe efficiency (NSE) and Relative Error (RE) are used as model evaluation criteria. It is found that all of these three updating procedures significantly improve the accuracy of Xinanjiang model when operating in real-time forecasting mode. Comparison results also indicated that the HUP performed better than the AR and RLS models, while RLS model was slightly superior to AR model. In addition, the HUP implemented in the probabilistic form can quantify the uncertainty of the actual discharge to be forecasted and provide a posterior distribution as well as interval estimation, which offer more useful information than two other deterministic updating procedures. Thus, the HUP updating procedure is more promising and recommended for real-time flood forecasting in practice.

Keywords: Flood forecasting; Real-time updating; Autoregressive model; Recursive least-squares model; Hydrologic uncertainty processor (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11269-016-1275-0

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