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Contrasting Uncertainties in Estimating Floods and Low Flow Extremes

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Abstract

Evaluation of possible sources of uncertainty and their influence on water resource planning and extreme hydrological characteristics are very important for extreme risk reduction and management. The main objective is to identify and holistically address the uncertainty propagation from the input data to the frequency of hydrological extremes. This novel uncertainty estimation framework has four stages that comprise hydrological models, hydrological parameter sets, and frequency distribution types. The influence of uncertainty on the simulated flow is not uniform across all the selected eight catchments due to different flow regimes and runoff generation mechanisms. The result shows that uncertainty in peak flow frequency simulation mainly comes from the input data quality. Whereas, in the low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty in the estimation of QT90 (extreme peak flow quantile at 90-year return period) quantile shows the interaction of input data and extreme frequency models has significant influence. In contrast, the hydrological models and hydrological parameters have a substantial impact on the QT10 (extreme low flow quantile at 10-year return period) estimation. This implies that the four factors and their interactions may cause significant risk in water resource management and flood and drought risk management. Therefore, neglecting these factors in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to significant impact.

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Acknowledgments

The authors thank two reviewers for helpful comments in improving the manuscript. This study is supported by the CAS Pioneer Talents Program,the National Natural Science Foundation of China (Grant No. 41971032), and the CAS-CSIRO drought propagation collaboration project. We extend our thanks to Jinkai Luan for kindly providing the hydro-meteorological data of China.

Funding

This study is supported by the CAS Pioneer Talents Program and the National Natural Science Foundation of China (Grant No. 41971032).

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  1. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Hadush Meresa & Yongqiang Zhang

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  1. Hadush Meresa
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H. M. designed and performed the experiments, computational framework, derived the models, and analyzed the data. Also worked out almost all the technical details and performed the numerical calculations for the suggested experiment. H. M., Y. Z. and J. T. contributed to the design and execution of the research, the analysis of the results, and the manuscript’s writing. Finally reviewed, edited, and approved by Y. Z..

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Correspondence to Yongqiang Zhang.

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Meresa, H., Zhang, Y. Contrasting Uncertainties in Estimating Floods and Low Flow Extremes. Water Resour Manage 35, 1775–1795 (2021). https://doi.org/10.1007/s11269-021-02809-3

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