Articles | Volume 20, issue 6
https://doi.org/10.5194/hess-20-2437-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2437-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Feng Ma
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
Linying Wang
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Ziyan Zheng
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Zhuguo Ma
RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Aizhong Ye
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
Shaoming Peng
Yellow River Engineering Consulting Co. Ltd., Zhengzhou, 450003, China
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- Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting J. Musuuza et al. 10.1029/2022WR033655
- A nonparametric standardized runoff index for characterizing hydrological drought on the Loess Plateau, China J. Wu et al. 10.1016/j.gloplacha.2017.12.006
- Potential of Pan-European Seasonal Hydrometeorological Drought Forecasts Obtained from a Multihazard Early Warning System S. Sutanto et al. 10.1175/BAMS-D-18-0196.1
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- An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity L. Arnal et al. 10.1175/JHM-D-16-0259.1
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- Quantitative Analysis of the Uncertainty of Drought Process Simulation Based on Atmospheric–Hydrological Coupling in Different Climate Zones H. Xu et al. 10.3390/w15183286
- Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo P. Abbaszadeh et al. 10.1016/j.advwatres.2017.11.011
- Seasonal Drought Prediction: Advances, Challenges, and Future Prospects Z. Hao et al. 10.1002/2016RG000549
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- Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China F. Ma et al. 10.1007/s00382-017-3577-1
- Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil A. Costa et al. 10.1007/s40333-021-0097-y
- What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. 10.1029/2019WR026987
- Moving from drought hazard to impact forecasts S. Sutanto et al. 10.1038/s41467-019-12840-z
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- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
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Saved (preprint)
Latest update: 02 Nov 2024
Short summary
An experimental seasonal hydrological forecasting system is established over the Yellow River basin to provide adaptive support in a changing environment. The system consists of downscaled NMME climate prediction, hydrological models calibrated against naturalized streamflow along the mainstream, and a post-processor to account for the human interventions implicitly. As the first paper of a two-part series, this paper investigates the hydrological predictability by using reverse ESP simulations.
An experimental seasonal hydrological forecasting system is established over the Yellow River...