An Ensemble Flow Forecast Method Based on Autoregressive Model and Hydrological Uncertainty Processer

Xin Yang,Yurong Wang,Jianzhong Zhou,Wei Fang

doi:10.3390/w12113138

Abstract

In the process of hydrological forecasting, there are uncertainties in data input, model parameters, and model structure, which cause a deterministic forecasting to fail to provide useful risk information to decision-makers. Therefore, the study of ensemble forecasting and the analysis of hydrological uncertainty are of great significance to guide the actual operation of reservoirs in the flood season. This study proposed a Bayesian ensemble forecast method, comprising of a Gaussian mixture model (GMM), a hydrological uncertainty processer (HUP), and an Autoregressive (AR) model. First, the GMM is selected as the marginal distribution function to estimate the uncertainty of observed and modelled data. Next, the AR model is used to correct the forecast rainfall data. Then, a modified HUP is used to deal with the uncertainty of hydrological model structure and rainfall input data. In the end, the ensemble flow forecast results are composed of the expected values of the posterior distribution obtained by HUP under different rainfall conditions. Taking the Three Gorges Reservoir (TGR) as a case study, the ensemble flow prediction in the forecast period is calculated by using the above method. Results show that the method proposed in this paper can improve the accuracy of runoff forecasts and reduce the uncertainty of the hydrological forecast.

Highlights

Flooding is the most common natural hazard and third most damaging globally after storms and earthquakes [1]
The precipitation data of the Global Ensemble Forecast System (GEFS) was postprocessed by an Autoregressive (AR) model, and a precipitation-dependent hydrological uncertainty processor based on Gaussian mixture model (GMM) (PD-hydrological uncertainty processer (HUP)-GMM) was proposed to generate the ensemble flow forecast
The Three Gorges Reservoir (TGR) was selected as a case study

Summary

Introduction

Flooding is the most common natural hazard and third most damaging globally after storms and earthquakes [1]. Flood forecasting can provide key information for disaster warning and flood control, which plays an important role in reducing the damage caused by flooding [2,3]. The common flood forecasting methods generally use a deterministic hydrological model to predict the future flood process [4,5,6,7]. Ensemble weather forecasting (EWF) plays an important role in ensemble flow forecasting [11]. EWF contains more information, which can provide more comprehensive meteorological data for a hydrological forecast in a future period [12]. Research shows that combining EWF with a hydrological forecast model can improve the reliability and accuracy of forecast results [13]. Due to the diversity of atmospheric conditions and topography, the simplification of physical and thermodynamic processes, the uncertainties of parameterization of

Objectives

Methods

Results

Conclusion