dc.date.accessioned | 2020-06-12T18:04:14Z | |
dc.date.available | 2020-06-20T22:46:13Z | |
dc.date.created | 2019-08-08T10:14:11Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Li, Xiang-Quan Chen, Jie Xu, Chong-Yu Li, Lu Chen, Hua . Performance of post-processed methods in hydrological predictions evaluated by deterministic and probabilistic criteria. Water resources management. 2019, 33(9), 3289-3302 | |
dc.identifier.uri | http://hdl.handle.net/10852/76924 | |
dc.description.abstract | Meteorological Ensemble Streamflow Prediction (ESP), which uses Ensemble Weather forecasts (EWFs) to drive hydrological models, is a useful methodology for extending forecast periods and to provide valuable uncertainty information to improve the operation of future water resources. However, raw EWFs are usually biased and under-dispersive and so cannot be directly used in ESP, leading to the development of several post-processing methods. The performance of these methods needs to be evaluated/compared in building ESP based on deterministic and probabilistic criteria. In addition, likely influencing factors also need to be identified. This study evaluated the performance of four state-of-the-art methods: the Generator-based Post-Processing (GPP) method, Extended Logistic Regression (ExLR), Bayesian Model Averaging (BMA) and Affine Kernel Dressing (AKD), using a simple bias correction (BC) method as a benchmark. The evaluation was carried out over four watersheds with different basin areas in the humid region of central-south China based on the weather reforecasts from the Global Ensemble Forecasting System (GEFS). The results show that the performance of the post-processing methods varies with the forecast variable (precipitation, or air temperature or streamflow), but all of them outperform the BC and GEFS. For the four post-processing methods, the advantage of the generator-based methods (GPP and ExLR) lies in their probabilistic performance, which outperforms the distribution-based methods (BMA and AKD) by about 10% in precipitation forecasts and about 20% in streamflow forecasts, while the distribution-based methods (BMA and AKD) are better at their deterministic performance for precipitation forecasts, with a benefit of about 15%. Meanwhile, the post-processing methods generally perform better for precipitation and streamflow forecasts, but worse for air temperature forecasts for a bigger basin compared to the distribution-based methods. The results of this study emphasize the importance of considering the uncertainty of post-processing methods in ESP. | en_US |
dc.language | EN | |
dc.title | Performance of post-processed methods in hydrological predictions evaluated by deterministic and probabilistic criteria | en_US |
dc.type | Journal article | en_US |
dc.creator.author | Li, Xiang-Quan | |
dc.creator.author | Chen, Jie | |
dc.creator.author | Xu, Chong-Yu | |
dc.creator.author | Li, Lu | |
dc.creator.author | Chen, Hua | |
cristin.unitcode | 185,15,22,0 | |
cristin.unitname | Institutt for geofag | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1714767 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Water resources management&rft.volume=33&rft.spage=3289&rft.date=2019 | |
dc.identifier.jtitle | Water resources management | |
dc.identifier.volume | 33 | |
dc.identifier.issue | 9 | |
dc.identifier.startpage | 3289 | |
dc.identifier.endpage | 3302 | |
dc.identifier.doi | https://doi.org/10.1007/s11269-019-02302-y | |
dc.identifier.urn | URN:NBN:no-80037 | |
dc.type.document | Tidsskriftartikkel | en_US |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0920-4741 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/76924/1/Post-print85698.pdf | |
dc.type.version | AcceptedVersion | |