Impact of Possible Climate Change on Extreme Annual Runoff from River Basins Located in Different Regions of the Globe


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Abstract

For 11 large river basins (the Rhine, Tagus, Ganges, Lena, Upper Yellow, Upper Yangtze, Niger, Mackenzie, Upper Mississippi, Upper Amazon and Darling) located on different continents under a wide variety of natural conditions, series of annual river runoff were calculated by means of the land surface model SWAP for the period of 1962–2099. For the historical (base) period (1962–2005), meteorological forcing data were taken from the global WATCH data set. For the projection period (2006–2099), the results of simulations from five Atmosphere and Ocean General Circulation Models (AOGCMs: HadGEM2-ES, IPSL-CM5A -LR, MIROC-ESM-CHEM, GFDL-ESM2M, and NorESM1-M) obtained for four climate change scenarios of the RCP-family were applied. The obtained series of annual runoff for each river basin were used to calculate climatic values and standard deviations of annual runoff for four climatic periods (1962–2005, 2006–2035, 2036–2065 and 2066–2099), which were then averaged over all AOGCMs and RCP-scenarios and used to construct distribution functions of annual runoff (for each river basin and climatic period) approximated by the lognormal distribution function of random variables. The constructed annual runoff distribution functions were applied for estimating the probabilities of occurrence of extremely high and extremely low values of annual runoff for each river and climatic period.

About the authors

E. M. Gusev

Water Problems Institute, Russian Academy of Sciences

Author for correspondence.
Email: sowaso@yandex.ru
Russian Federation, Moscow, 119333

O. N. Nasonova

Water Problems Institute, Russian Academy of Sciences

Email: sowaso@yandex.ru
Russian Federation, Moscow, 119333

E. E. Kovalev

Water Problems Institute, Russian Academy of Sciences

Email: sowaso@yandex.ru
Russian Federation, Moscow, 119333

G. V. Ayzel

Water Problems Institute, Russian Academy of Sciences

Email: sowaso@yandex.ru
Russian Federation, Moscow, 119333

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