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.
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References
Ahmadi M, Moeini A, Ahmadi H et al (2019) Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran). Phys Chem Earth Parts A/B/C 111:65–77. https://doi.org/10.1016/j.pce.2019.05.002
Beven K (2002) Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system. Hydrol Processes 16:189–206. https://doi.org/10.1002/hyp.343
Brinkerhoff CB, Gleason CJ, Feng D, Lin P (2020) Constraining remote river discharge estimation using reach-scale geomorphology. Water Resour Res 56(11):e2020WR027949. https://doi.org/10.1029/2020WR027949
Chernykh DV, Samoilova GS (2011) Landscapes of Altai (Republic of Altai and Altai Krai). In: The 1:500 000 Map. FGUP Novosibirskaya Kartograficheskaya Fabrika, Novosibirsk (in Russian)
Conrad CP (2019) Seasonal precipitation influences streamflow vulnerability to the 2015 drought in the western United States. J Hydrometeor 20(7):1261–1274. https://doi.org/10.1175/JHM-D-18-0121.1
Corripio JG, López-Moreno JI (2017) Analysis and predictability of the hydrological response of mountain catchments to heavy rain on snow events: a case study in the Spanish Pyrenees. Hydrology 4(2):20. https://doi.org/10.3390/hydrology4020020
Feng Z, Niu W, Tang Z et al (2020) Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. J Hydrol 583:124627. https://doi.org/10.1016/j.jhydrol.2020.124627
Gvozdetskiy NA (1968) Physical-geographical zoning of the USSR. Characteristics of regional units. MSU Publishing house, Moscow (in Russian)
Gvozdetskiy NA, Mikhailov NI (1987) Physical geography of the USSR. The Asian part. Mysl’ Publishing House, Moscow (in Russian)
Iooss B, Lemaître P (2015) A review on global sensitivity analysis methods. In: Meloni C, Dellino G (eds) Uncertainty management in simulation-optimization of complex systems: algorithms and applications. Springer, Boston, pp 101–122. https://doi.org/10.1007/978-1-4899-7547-8_5
Kirsta YB (2011) Spatial generalization of climatic characteristics in mountain areas. World Sci Cult Educ 3:330–337. http://amnko.ru/index.php/english/journals/ Accessed 1 June 2020
Kirsta YB (2020) System-analytical modelling: 2.Assessment of runoff model sensitivity to environmental factor variations. EJMCA 8(3):67–77. https://doi.org/10.32523/2306-6172-2020-8-3-67-77
Kirsta YB, Puzanov AV (2020) System-analytical modelling: 1. Development of regional models for mountain river runoff. EJMCA 8(2):69–85. https://doi.org/10.32523/2306-6172-2020-8-2-69-85
Koch·M, Cherie N (2013) SWAT-modeling of the impact of future climate change on the hydrology and the water resources in the upper blue Nile river basin, Ethiopia. In: Proceedings of the 6th International Conference on Water Resources and Environment Research. Koblenz, Germany, pp 428–523
Kokorin AO (2011) Assessment report: climate change and its impact on ecosystems, population and economy of the Russian part of the Altai-Sayan Ecoregion. WWF-Russia, Moscow. https://wwf.ru/upload/iblock/7c5/assessment_climate_altai_eng_.pdf. Accessed 1 June 2020
Kundzewicz ZW, Su BD, Wang YJ et al (2019) Flood risk and its reduction in China. Adv Water Resour 130:37–45. https://doi.org/10.1016/j.advwatres.2019.05.020
Mirkin BM, Rozenberg GS (1978) Phytosociology: Principles and methods. Nauka, Moscow (in Russian)
Mohammad-Azari S, Bozorg-Haddad O, Loaiciga HA (2020) State-of-art of genetic programming applications in water-resources systems analysis. Environ Monit Assess 192:73. https://doi.org/10.1007/s10661-019-8040-9
Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulation. Trans ASABE 50(3):85–900. https://doi.org/10.13031/2013.23153
Mosavi A, Ozturk P, Chau K (2018) Flood prediction using machine learning models: Literature review. Water 10(11):1536. https://doi.org/10.3390/w10111536
Musselman KN, Lehner F, Ikeda K et al (2018) Projected increases and shifts in rain-on-snow flood risk over western North America. Nat Clim Chang 8(9):808–812. https://doi.org/10.1038/s41558-018-0236-4
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I – A discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nikolaev AN, Skachkov YB (2012) Snow cover and permafrost soil temperature influence on the radial growth of trees in Central Yakutia. J Siberian Federal Univ Biol 5(1):43–51 (in Russian). http://elib.sfu-kras.ru/handle/2311/3009. Accessed 1 June 2020
Parisouj P, Mohebzadeh H, Lee T (2020) Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States. Water Resour Manag 34:4113–4131. https://doi.org/10.1007/s11269-020-02659-5
Roessler O, Froidevaux P, Boerst U et al (2014) Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps – a matter of model limitations or unpredictable nature? Hydrol Earth Syst Sci 18:2265–2285. https://doi.org/10.5194/hess-18-2265-2014
Song X, Zhang J, Zhan C et al (2015) Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications. J Hydrol 523:739–757. https://doi.org/10.1016/j.jhydrol.2015.02.013
Tabari H (2019) Statistical analysis and stochastic modelling of hydrological extremes. Water 11:1861. https://doi.org/10.3390/w11091861
Tanasienko AA, Putilin AF, Artamonova VS (1999) Environmental aspects of erosion processes: An Analytical review. SPSTL SB RAS & ISSA SB RAS, Novosibirsk (Series “Ecology” No. 55) (in Russian). http://www.spsl.nsc.ru/win/lc3/tanasi.pdf. Accessed 1 June 2020
Tardy Y, Bustillo V, Boeglin JL (2004) Geochemistry applied to the watershed survey: hydrograph separation, erosion and soil dynamics: A case study: the basin of the Niger River, Africa. Appl Geochem 19:469–518. https://doi.org/10.1016/j.apgeochem.2003.07.003
Tsimbaley Y (2011) Landscape map of Altai Krai: Maps (with using the materials from Purdik LN, Bulatov VI, Kovanova AA, and with the participation of Chernykh DV, Smirnov SB, Vinokurova ОM). Library materials of IWEP SB RAS, Barnaul (in Russian)
Tullos D, Byron E, Galloway G et al (2016) Review of challenges of and practices for sustainable management of mountain flood hazards. Nat Hazards 83(3):1763–1797. https://doi.org/10.1007/s11069-016-2400-3
Wang F, Huang GH, Fan Y, Li YP (2020) Robust subsampling ANOVA methods for sensitivity analysis of water resource and environmental models. Water Resour Manag 34:3199–3217. https://doi.org/10.1007/s11269-020-02608-2
Wijayarathne DB, Coulibaly P (2020) Identification of hydrological models for operational flood forecasting in St. John’s, Newfoundland, Canada. J Hydrol Regional Stud 27:100646. https://doi.org/10.1016/j.ejrh.2019.100646
Wu WY, Emerton R, Duan QY et al (2020) Ensemble flood forecasting: Current status and future opportunities. Wiley Interdiscip Rev Water 7(3):e1432. https://doi.org/10.1002/wat2.1432
Zhang X, Peng Y, Xu W, Wang B (2019) An optimal operation model for hydropower stations considering inflow forecasts with different lead-times. Water Resour Manage 33(1):173–188. https://doi.org/10.1007/s11269-018-2095-1
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The work was carried out within the framework of the Research Program of the Institute for Water and Environmental Problems SB RAS (Project 0383-2019-0005).
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Y. B. Kirsta designed the study and conducted data analysis; O. V. Lovtskaya participated in the design of the study and GIS application. Both authors read and approved the final manuscript.
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Kirsta, Y.B., Lovtskaya, O.V. Good-quality Long-term Forecast of Spring-summer Flood Runoff for Mountain Rivers. Water Resour Manage 35, 811–825 (2021). https://doi.org/10.1007/s11269-020-02742-x
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DOI: https://doi.org/10.1007/s11269-020-02742-x