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Estimating the Role of Climate Internal Variability and Sources of Uncertainties in Hydrological Climate-Impact Projections

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  • Wenjun Cai

    (College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Jia Liu

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Xueping Zhu

    (College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Xuehua Zhao

    (College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Xiaoli Zhang

    (School of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

Hydrological climate-impact projections in the future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate change-impacted assessment, in a representative watershed of Northeastern China. Moreover, recent studies have indicated that the climate internal variability (CIV) plays an important role in various hydrological climate-impact projections. Six downscaled global climate models (GCMs) under two emission scenarios, and a calibrated Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need to be particularly partitioned and quantified. Moreover, it is worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and the sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominant contributors of runoff in the rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to the runoff in January to May, and October to December. The findings of this study advised that the uncertainty is complex in the hydrological simulation process; hence, it is meaningful and necessary to estimate the uncertainty in the climate simulation process. The uncertainty analysis results can effectively provide efforts for reducing uncertainty, and then give some positive suggestions to stakeholders for adaption countermeasures under climate change.

Suggested Citation

  • Wenjun Cai & Jia Liu & Xueping Zhu & Xuehua Zhao & Xiaoli Zhang, 2022. "Estimating the Role of Climate Internal Variability and Sources of Uncertainties in Hydrological Climate-Impact Projections," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12201-:d:925762
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    References listed on IDEAS

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    1. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    2. Bin Wang & De Li Liu & Cathy Waters & Qiang Yu, 2018. "Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia," Climatic Change, Springer, vol. 151(2), pages 259-273, November.
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