Assessment of the climate change impacts on flood frequency (case study: Bazoft Basin, Iran)

• Published in: Stochastic environmental research and risk assessment 2017-07, Vol.31 (5), p.1171-1182
• Main Authors: Almasi, Parisa, Soltani, Saeid
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Atmosphere; Atmospheric models; Chemistry and Earth Sciences; Circulation; Climate; Climate change; Computational Intelligence; Computer Science; Computer simulation; Distribution functions; Earth and Environmental Science; Earth Sciences; Empirical equations; Energy transfer; Environment; Environmental assessment; Environmental impact; Flood frequency; Floods; Frequency analysis; General circulation models; Hydrologic models; Hydrology; Math. Appl. in Environmental Science; Mathematical models; Original Paper; Physics; Probabilistic methods; Probability distribution; Probability distribution functions; Probability Theory and Stochastic Processes; Runoff; Scale (ratio); Series (mathematics); Soils; Stations; Statistics for Engineering; Uncertainty; Waste Water Technology; Water Management; Water Pollution Control; Weighting
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-016-1263-1

The present study attempts to investigate potential impacts of climate change on floods frequency in Bazoft Basin which is located in central part of Iran. A combination of four general circulation models is used through a weighting approach to assess uncertainty in the climate projections. LARS-WG model is applied to downscale large scale atmospheric data to local stations. The resulting data is in turn used as input of the hydrological model Water and Energy Transfer between Soil, plants and atmosphere (WetSpa) to simulate runoff for present (1971–2000), near future (2020–2049) and far future (2071–2100) conditions. Results demonstrate good performance of both WetSpa and LARS-WG models. In addition in this paper instantaneous peak flow (IPF) is estimated using some empirical equations including Fuller, Sangal and Fill–Steiner methods. Comparison of estimated and observed IPF shows that Fill–Steiner is better than other methods. Then different probability distribution functions are fit to IPF series. Results of flood frequency analysis indicate that Pearson III is the best distribution fitted to IPF data. It is also indicated that flood magnitude will decrease in future for all return periods.