Articles | Volume 383
https://doi.org/10.5194/piahs-383-13-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-383-13-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Precipitation and water stage variability under rapid developments of urbanization in Taihu Basin
Juan Wu
Bureau of Hydrology Information Center of Taihu Basin Authority,
Shanghai 200434, China
Hejuan Lin
Bureau of Hydrology Information Center of Taihu Basin Authority,
Shanghai 200434, China
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Song Jin
Bureau of Hydrology Information Center of Taihu Basin Authority,
Shanghai 200434, China
Jian Wu
Urban and Rural Development Bureau of Suzhou New
District, Suzhou 215163, China
Haiping Ji
Bureau of Hydrology Information Center of Taihu Basin Authority,
Shanghai 200434, China
Min Liu
Bureau of Hydrology Information Center of Taihu Basin Authority,
Shanghai 200434, China
Yueyun Gan
Bureau of Hydrology Information Center of Taihu Basin Authority,
Shanghai 200434, China
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A spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method is proposed. The calibration model is built by post-processing ECMWF raw forecasts, while the bridging models are built using atmospheric intraseasonal signals as predictors. The calibration model and bridging models are merged through a Bayesian modelling averaging (BMA) method. The results indicate that the newly developed method can generate skilful and reliable sub-seasonal precipitation forecasts.
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The relationship between atmospheric intraseasonal signals and precipitation is highly uncertain and depends on the region and lead time. In this study, we develop a spatiotemporal projection, based on a Bayesian hierarchical model (STP-BHM), to address the above challenge. The results suggest that the STP-BHM model is skillful and reliable for probabilistic subseasonal precipitation forecasts over China during the boreal summer monsoon season.
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Manuscript not accepted for further review
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The paper attempt to quantitatively link vertical wind velocity (an atmospheric dynamical variable) to droughts, which try to solve hydrological communities' concerns using meteorological theories and approaches. Performance over simulation and prediction of droughts over China is promising. Achievements herein can help further explore antecedent drought-inducing signals of atmospheric/oceanic anomalies, which may provide a fundamental approach for drought prediction with long lead times.
Zhenchen Liu, Guihua Lu, Hai He, Zhiyong Wu, and Jian He
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Process prediction of seasonal drought is the goal of our study. We developed a drought prediction model based on atmospheric–oceanic anomalies. It is essentially the synchronous statistical relationship between atmospheric–oceanic anomalies and precipitation anomalies, forced by seasonal climate forecast models. It can predict seasonal drought development very well, despite its weakness in drought severity.
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Short summary
This is our latest research on hydrological regime change, possible reasons for Taihu water stage increase and flood risks in Taihu Basin. Quantitative evaluation of hydrological response to climate change and human activities affecting Taihu water stage. The findings are useful for improved understanding of changing hydrological processes in the Taihu Basin under rapid development of urbanization.
This is our latest research on hydrological regime change, possible reasons for Taihu water...