Probabilistic inflow forecasts can quantify the uncertainty involved in the forecasting process and provide useful risk information for reservoir management.This study proposed a probabilistic inflow forecasting schem...Probabilistic inflow forecasts can quantify the uncertainty involved in the forecasting process and provide useful risk information for reservoir management.This study proposed a probabilistic inflow forecasting scheme for the Three Gorges Reservoir(TGR)at 1-3 d lead times.The post-processing method Ensemble Model Output Statistics(EMOS)is used to derive probabilistic inflow forecasts from ensemble inflow forecasts.Considering the inherent skew feature of the inflow series,lognormal and gamma distributions are used as EMOS predictive distributions in addition to conventional normal distribution.Results show that TGR's ensemble inflow forecasts at 1-3 d lead times perform well with high model efficiency and small mean absolute error.Underestimation of forecasting uncertainty is observed for the raw ensemble inflow forecasts with biased probability integral transform(PIT)histograms.The three EMOS probabilistic forecasts outperform the raw ensemble forecasts in terms of both deterministic and probabilistic performance at 1-3 d lead times.The EMOS results are more reliable with much flatter PIT histograms,coverage rates approximate to the nominal coverage 89.47%and satisfactory sharpness.Results also show that EMOS with gamma distribution is superior to normal and lognormal distributions.This research can provide reliable probabilistic inflow forecasts without much variation of TGR5s operational inflow forecasting procedure.展开更多
Poyang Lake, the largest freshwater lake in China, and its surrounding sub-basins have suffered frequent floods and droughts in recent decades. To better understand and quantitatively assess hydrological impacts of cl...Poyang Lake, the largest freshwater lake in China, and its surrounding sub-basins have suffered frequent floods and droughts in recent decades. To better understand and quantitatively assess hydrological impacts of climate change in the region, this study adopted the Statistical Downscaling Model （SDSM） to downseale the outputs of a Global Climate Model （GCM） under three scenarios （RCP2.6, RCP4.5 and RCP8.5） as recommended by the fifth phase of the Coupled Model Inter-comparison Project （CMIP5） during future periods （2010-2099） in the Poyang Lake Basin. A semi-distributed two-parameter monthly water balance model was also used to simulate and predict projected changes of runoff in the Ganjiang sub-basin. Results indicate that： 1） SDSM can simulate monthly mean precipitation reasonably well, while a bias correction procedure should be applied to downscaled extreme precipitation indices （EPI） before being employed to simulate future precipitation; 2） for annual mean precipitation, a mixed pattern of positive or negative changes are detected in the entire basin, with a slightly higher or lower trend in the 2020s and 2050s, with a consistent increase in the 2080s; 3） all six EPI show a general increase under RCP4.5 and RCP8.5 scenarios, while a mixed pattern of positive and negative changes is detected for most indices under the RCP2.6 scenario; and 4） the future runoff in the Ganjiang sub-basin shows an overall decreasing trend for all periods but the 2080s under the RCP8.5 scenario when runoff is more sensitive to changes in precipitation than evaporation.展开更多
基金This study is supported by the National Key Research and Development Plan of China(No.2016YFC0402206)the National Natural Science Foundation of China(Grant Nos.51879192,91647106).Thanks are also given to CWRC for providing necessary data and the three anonymous reviewers’valuable suggestions to improve our manuscript.
文摘Probabilistic inflow forecasts can quantify the uncertainty involved in the forecasting process and provide useful risk information for reservoir management.This study proposed a probabilistic inflow forecasting scheme for the Three Gorges Reservoir(TGR)at 1-3 d lead times.The post-processing method Ensemble Model Output Statistics(EMOS)is used to derive probabilistic inflow forecasts from ensemble inflow forecasts.Considering the inherent skew feature of the inflow series,lognormal and gamma distributions are used as EMOS predictive distributions in addition to conventional normal distribution.Results show that TGR's ensemble inflow forecasts at 1-3 d lead times perform well with high model efficiency and small mean absolute error.Underestimation of forecasting uncertainty is observed for the raw ensemble inflow forecasts with biased probability integral transform(PIT)histograms.The three EMOS probabilistic forecasts outperform the raw ensemble forecasts in terms of both deterministic and probabilistic performance at 1-3 d lead times.The EMOS results are more reliable with much flatter PIT histograms,coverage rates approximate to the nominal coverage 89.47%and satisfactory sharpness.Results also show that EMOS with gamma distribution is superior to normal and lognormal distributions.This research can provide reliable probabilistic inflow forecasts without much variation of TGR5s operational inflow forecasting procedure.
基金Acknowledgements This study was supported by the National Nature Science Foundation of China （Grant Nos. 51539009 and 51190094）, and the National Key Research and Development Plan of China （2016YFC0402206）. The authors thank the editor and anonymous reviewers for their comments and suggestions, and Prof. Chong-Yu Xu and Dr. David E. Rheinheimer whose cornments and English language editing helped to clarify and improve the quality of this paper.
文摘Poyang Lake, the largest freshwater lake in China, and its surrounding sub-basins have suffered frequent floods and droughts in recent decades. To better understand and quantitatively assess hydrological impacts of climate change in the region, this study adopted the Statistical Downscaling Model （SDSM） to downseale the outputs of a Global Climate Model （GCM） under three scenarios （RCP2.6, RCP4.5 and RCP8.5） as recommended by the fifth phase of the Coupled Model Inter-comparison Project （CMIP5） during future periods （2010-2099） in the Poyang Lake Basin. A semi-distributed two-parameter monthly water balance model was also used to simulate and predict projected changes of runoff in the Ganjiang sub-basin. Results indicate that： 1） SDSM can simulate monthly mean precipitation reasonably well, while a bias correction procedure should be applied to downscaled extreme precipitation indices （EPI） before being employed to simulate future precipitation; 2） for annual mean precipitation, a mixed pattern of positive or negative changes are detected in the entire basin, with a slightly higher or lower trend in the 2020s and 2050s, with a consistent increase in the 2080s; 3） all six EPI show a general increase under RCP4.5 and RCP8.5 scenarios, while a mixed pattern of positive and negative changes is detected for most indices under the RCP2.6 scenario; and 4） the future runoff in the Ganjiang sub-basin shows an overall decreasing trend for all periods but the 2080s under the RCP8.5 scenario when runoff is more sensitive to changes in precipitation than evaporation.
