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    AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts

    Source: Journal of Hydrologic Engineering:;2020:;Volume ( 025 ):;issue: 008
    Author:
    Xini Zha
    ,
    Lihua Xiong
    ,
    Shenglian Guo
    ,
    Jong-Suk Kim
    ,
    Dedi Liu
    DOI: 10.1061/(ASCE)HE.1943-5584.0001955
    Publisher: ASCE
    Abstract: The common strategy for real-time streamflow forecasting involves a precalibrated rainfall-runoff model for streamflow simulation together with a statistical postprocessing model of simulation errors for updating simulated streamflow. Recognizing both autocorrelation and heteroscedasticity inherent in the simulation errors of rainfall-runoff models, the autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) model is introduced as a statistical postprocessing model of simulation errors in this study, in which the AR model is used to forecast the mean process of simulation errors, and the GARCH model to forecast the variance process of simulation errors. For investigating how well incorporating exogenous variables that contribute to heteroscedasticity improves update accuracy by comparing the GARCH model with and without exogenous variable, in this study, two rainfall-runoff models (one lumped and one distributed) have been chosen to simulate streamflow in three basins. Case studies show that (1) the AR-GARCH model with an exogenous variable showed advantages over AR-GARCH without exogenous variables through both increased forecast accuracy and reduced uncertainty during the validation period, and (2) more than 90% of error heteroscedasticity is due to the internal variable and less than 10% is due to the exogenous variable in this study. Only one exogenous variable was considered in this study, so further research is necessary to identify more exogenous variables which may have a greater contribution to simulate error heteroscedasticity. In conclusion, the developed AR-GARCH model with exogenous variables has the flexibility to deal with the characteristics of error series including autocorrelation and heteroscedasticity. Although the application of this study focused on streamflow forecasting, the developed methodology may be generalized and implemented in other applications to time-series data with complex errors.
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      AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4269049
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    contributor authorXini Zha
    contributor authorLihua Xiong
    contributor authorShenglian Guo
    contributor authorJong-Suk Kim
    contributor authorDedi Liu
    date accessioned2022-01-30T21:54:54Z
    date available2022-01-30T21:54:54Z
    date issued8/1/2020 12:00:00 AM
    identifier other%28ASCE%29HE.1943-5584.0001955.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4269049
    description abstractThe common strategy for real-time streamflow forecasting involves a precalibrated rainfall-runoff model for streamflow simulation together with a statistical postprocessing model of simulation errors for updating simulated streamflow. Recognizing both autocorrelation and heteroscedasticity inherent in the simulation errors of rainfall-runoff models, the autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) model is introduced as a statistical postprocessing model of simulation errors in this study, in which the AR model is used to forecast the mean process of simulation errors, and the GARCH model to forecast the variance process of simulation errors. For investigating how well incorporating exogenous variables that contribute to heteroscedasticity improves update accuracy by comparing the GARCH model with and without exogenous variable, in this study, two rainfall-runoff models (one lumped and one distributed) have been chosen to simulate streamflow in three basins. Case studies show that (1) the AR-GARCH model with an exogenous variable showed advantages over AR-GARCH without exogenous variables through both increased forecast accuracy and reduced uncertainty during the validation period, and (2) more than 90% of error heteroscedasticity is due to the internal variable and less than 10% is due to the exogenous variable in this study. Only one exogenous variable was considered in this study, so further research is necessary to identify more exogenous variables which may have a greater contribution to simulate error heteroscedasticity. In conclusion, the developed AR-GARCH model with exogenous variables has the flexibility to deal with the characteristics of error series including autocorrelation and heteroscedasticity. Although the application of this study focused on streamflow forecasting, the developed methodology may be generalized and implemented in other applications to time-series data with complex errors.
    publisherASCE
    titleAR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts
    typeJournal Paper
    journal volume25
    journal issue8
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0001955
    page16
    treeJournal of Hydrologic Engineering:;2020:;Volume ( 025 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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