The applicability of real-time flood forecasting correction techniques coupled with the Muskingum method

Ruixiang Yang,Xuelei Zhang,Haiying Yu,Baodeng Hou,Baoqi Li,Chuan Liang,Weihua Xiao

doi:10.2166/nh.2019.128

Abstract

Abstract Improving flood forecasting performance is critical for flood management. Real-time flood forecasting correction techniques (e.g., proportional correction (PC) and Kalman filter (KF)) coupled with the Muskingum method improve the forecasting performance but have limitations (e.g., short lead times and inadequate performance, respectively). Here, particle filter (PF) and combination forecasting (CF) are coupled with the Muskingum method and then applied to 10 flood events along the Shaxi River, China. Two indexes (overall consistency and permissible range) are selected to compare the performances of PC, KF, PF and CF for 3 h lead time. The changes in overall consistency for different lead times (1–6 h) are used to evaluate the applicability of PC, KF, PF and CF. The main conclusions are as follows: (1) for 3 h lead time, the two indexes indicate that the PF performance is optimal, followed in order by KF and PC; CF performance is close to PF and better than KF. (2) The performance of PC decreases faster than that of KF and PF with increases in the lead time. PC and PF are applicable for short (1–2 h) and long lead times (3–6 h), respectively. CF is applicable for 1–6 h lead times; however, it has no advantage over PC and PF for short and long lead times, respectively, which may be due to insufficient training and increase in cumulative errors.

Highlights

Flood forecasting is a non-engineering measure for flood control development that resulted from the attempt to address the occurrence of flood disasters (Ryder )
For 3 h lead time, Traditional forecasting (TF) results are corrected by the real-time correction techniques of proportional correction (PC), Kalman filter (KF) and particle filter (PF)
The weights reflect that PC performs significantly better for [1,2] h lead time, while PF performs significantly better for [3,4,5,6] h lead time

Summary

Introduction

TF errors may be generated and accumulated gradually for a variety of reasons, mainly including model structure errors, model parameter errors and the errors caused by water projects With the development of automatic hydrological monitoring and information transmission technology, real-time flood forecasting is commonly achieved by proportional correction (PC) and Kalman filter (KF) to correct TF using real-time hydrological data (Calvo & Savi ; Liu et al ) These two correction techniques have numerous advantages that improve the forecasting performance to some extent; for example, the PC approach boasts a low computational cost, and KF has an unbiased minimum variance (Ocio et al ).

Methods

Results

Discussion

Conclusion