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Good-quality Long-term Forecast of Spring-summer Flood Runoff for Mountain Rivers

Author

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  • Yuri B. Kirsta

    (Institute for Water and Environmental Problems SB RAS)

  • Ol’ga V. Lovtskaya

    (Institute for Water and Environmental Problems SB RAS)

Abstract

The universal simulation model was developed with the use of system-analytical modeling to ensure a long-term forecast of mountain river runoff during spring-summer floods. Prediction quality of this SAM-model is characterized by Nash-Sutcliffe efficiency of 0.68–0.88 and is very high for long-term flood forecasts, including ones for inundations and mountain reservoirs filling in spring. The model was tested on the example of 34 medium and small rivers (1630 values of runoff observations for 1951–2016) located in the Altai-Sayan mountain country (2,000,000 km2). Its input factors include monthly precipitation, monthly mean air temperature, GIS data on landscape structure and orography of river basins. Meteorological factors are calculated as percentage of their “in situ” long-term mean values averaged for the whole study area. This helps to explain and quantify the influence of autumn-winter-spring soaking, freezing and thawing of mountain landscape soils on spring-summer flood. We apply a simple novel method to evaluate model sensitivity to variations in environmental factors expressed in terms of their contribution to variance of the observed flood runoff. It turns out that sensitivity of the latter decreases in the following sequence of factors: autumn precipitation, landscape structure of river basins, winter precipitation, winter air temperature, landscape altitude. The developed SAM-model provides a three-month lead-time estimate of runoff in a high water period with the threefold less variance as compared to forecasts based on the observed long-term mean values.

Suggested Citation

  • Yuri B. Kirsta & Ol’ga V. Lovtskaya, 2021. "Good-quality Long-term Forecast of Spring-summer Flood Runoff for Mountain Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 811-825, February.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-020-02742-x
    DOI: 10.1007/s11269-020-02742-x
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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. Desirée Tullos & Elizabeth Byron & Gerald Galloway & Jayantha Obeysekera & Om Prakash & Yung-Hsin Sun, 2016. "Review of challenges of and practices for sustainable management of mountain flood hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1763-1797, September.
    3. Xiaoli Zhang & Yong Peng & Wei Xu & Bende Wang, 2019. "An Optimal Operation Model for Hydropower Stations Considering Inflow Forecasts with Different Lead-Times," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 173-188, January.
    4. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
    5. Keith N. Musselman & Flavio Lehner & Kyoko Ikeda & Martyn P. Clark & Andreas F. Prein & Changhai Liu & Mike Barlage & Roy Rasmussen, 2018. "Projected increases and shifts in rain-on-snow flood risk over western North America," Nature Climate Change, Nature, vol. 8(9), pages 808-812, September.
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    Cited by:

    1. Hui Hu & Jianfeng Zhang & Tao Li, 2021. "A Novel Hybrid Decompose-Ensemble Strategy with a VMD-BPNN Approach for Daily Streamflow Estimating," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5119-5138, December.

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