Assessment of multiple precipitation interpolation methods and uncertainty analysis of hydrological models in Chaohe River basin, China

Authors

  • Binbin Guo 1. Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China; 2. College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China; 3. Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
  • Jing Zhang Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
  • Tingbao Xu Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia
  • Yongyu Song Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
  • Mingliang Liu Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
  • Zhong Dai College of Geography and Tourism, Hengyang Normal University, Hengyang 421002, China

DOI:

https://doi.org/10.17159/wsa/2022.v48.i3.3884

Keywords:

precipitation interpolation, ANUSPLIN, hydrological model, uncertainty, multi-objective method

Abstract

Precipitation interpolation is widely used to generate continuous rainfall surfaces for hydrological simulations. However, increasing the precision of values at the unknown points generated by different spatial interpolation methods is challenging. This study used the Chaohe River Basin, which is an important source of Beijing’s drinking water, as a research area to comprehensively evaluate several precipitation interpolation methods (Thiessen polygon, inverse distance weighting, ordinary kriging and ANUSPLIN) for inputs in hydrological simulations. This research showed that the precipitation time-series surface generated using the ANUSPLIN interpolation method had higher accuracy and reliability. Using this precipitation input to drive the hydrological models, we explored the parameter uncertainties of four typical hydrological models (GR4J, IHACRES, Sacramento and MIKE SHE) based on the multi-objective generalized likelihood uncertainty estimation (GLUE) method. The GLUE method was used to study the parameter sensitivity and uncertainty of the model. Results showed that the ANUSPLIN precipitation interpolation surface combined with the Sacramento model performed best. The multi-objective GLUE method had obvious advantages in parameter uncertainty analysis in hydrological models. Simultaneously exploring the convex line and point density distributions of the behavioural parameters with multi-objective functions determined their distribution and sensitivity more effectively.

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Published

2022-07-27

Issue

Vol. 48 No. 3 July (2022): Water SA

Section

Research paper

License

Copyright (c) 2022 Binbin Guo, Jing Zhang, Tingbao Xu, Yongyu Song, Mingliang Liu, Zhong Dai

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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The content of this journal is licensed under a Creative Commons Attribution Licence.  Users are permitted to read, download, copy, distribute, print, search or link to the full texts of the articles in this journal under the terms of this Licence, without asking prior permission from the publisher or the author, provided the source is attributed. Copyright is retained by the authors.

How to Cite

Binbin Guo (2022) “Assessment of multiple precipitation interpolation methods and uncertainty analysis of hydrological models in Chaohe River basin, China”, Water SA, 48(3 July). doi:10.17159/wsa/2022.v48.i3.3884.