T. J. Hegdahl, K. Engeland, I. Steinsland, A. Singleton
{"title":"Pre- and postprocessing flood forecasts using Bayesian model averaging","authors":"T. J. Hegdahl, K. Engeland, I. Steinsland, A. Singleton","doi":"10.2166/nh.2023.024","DOIUrl":null,"url":null,"abstract":"\n In this study, pre- and postprocessing of hydrological ensemble forecasts are evaluated with a special focus on floods for 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature and precipitation with a lead time of up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. A Bayesian model averaging processing approach was applied to preprocess temperature and precipitation forecasts and to postprocessing streamflow forecasts. Ensemble streamflow forecasts were generated for eight schemes based on combinations of raw, preprocessed, and postprocessed forecasts. Two datasets were used to evaluate the forecasts: (i) all streamflow forecasts and (ii) forecasts for flood events with streamflow above mean annual flood. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead time of 2–3 days, whereas preprocessing temperature and precipitation improved the forecasts for 50–90% of the catchments beyond 3 days lead time. We found large differences in the ability to issue warnings between spring and autumn floods. Spring floods had predictability for up to 9 days for many events and catchments, whereas the ability to predict autumn floods beyond 3 days was marginal.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hydrology Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/nh.2023.024","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 2
Abstract
In this study, pre- and postprocessing of hydrological ensemble forecasts are evaluated with a special focus on floods for 119 Norwegian catchments. Two years of ECMWF ensemble forecasts of temperature and precipitation with a lead time of up to 9 days were used to force the operational hydrological HBV model to establish streamflow forecasts. A Bayesian model averaging processing approach was applied to preprocess temperature and precipitation forecasts and to postprocessing streamflow forecasts. Ensemble streamflow forecasts were generated for eight schemes based on combinations of raw, preprocessed, and postprocessed forecasts. Two datasets were used to evaluate the forecasts: (i) all streamflow forecasts and (ii) forecasts for flood events with streamflow above mean annual flood. Evaluations based on all streamflow data showed that postprocessing improved the forecasts only up to a lead time of 2–3 days, whereas preprocessing temperature and precipitation improved the forecasts for 50–90% of the catchments beyond 3 days lead time. We found large differences in the ability to issue warnings between spring and autumn floods. Spring floods had predictability for up to 9 days for many events and catchments, whereas the ability to predict autumn floods beyond 3 days was marginal.
期刊介绍:
Hydrology Research provides international coverage on all aspects of hydrology in its widest sense, and welcomes the submission of papers from across the subject. While emphasis is placed on studies of the hydrological cycle, the Journal also covers the physics and chemistry of water. Hydrology Research is intended to be a link between basic hydrological research and the practical application of scientific results within the broad field of water management.