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

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.

[1]  Perdinan,et al.  Selection of climate information for regional climate change assessments using regionalization techniques: an example for the Upper Great Lakes Region, USA , 2015 .

[2]  J. Chormański,et al.  Evaluation and verification of the WetSpa model based on selected rural catchments in Poland , 2010 .

[3]  S. Klein,et al.  GFDL's CM2 Global Coupled Climate Models. Part I: Formulation and Simulation Characteristics , 2006 .

[4]  N. Stern.  The Economics of Climate Change: Implications of Climate Change for Development , 2007 .

[5]  H. Safavi,et al.  A modified regionalization weighting approach for climate change impact assessment at watershed scale , 2015, Theoretical and Applied Climatology.

[6]  W. Cline.  Global Warming and Agriculture: Impact Estimates by Country , 2007 .

[7]  N. Reynard,et al.  The effects of climate change due to global warming on river flows in Great Britain , 1996 .

[8]  B. Bobée,et al.  The use of the Pearson type 3 and log Pearson type 3 distributions revisited , 1977 .

[9]  T. Wigley,et al.  Implications for climate and sea level of revised IPCC emissions scenarios , 1992, Nature.

[10]  J. R. Wallis,et al.  Regional Frequency Analysis: An Approach Based on L-Moments , 1997 .

[11]  Keith Beven,et al.  Flood frequency estimation by continuous simulation under climate change (with uncertainty) , 2000 .

[12]  Beni P. Sangal,et al.  Practical Method of Estimating Peak Flow , 1983 .

[13]  H. Fill,et al.  Estimating Instantaneous Peak Flow from Mean Daily Flow Data , 2003 .

[14]  M. Semenov.  Development of high-resolution UKCIP02-based climate change scenarios in the UK , 2007 .

[15]  Julien Boé,et al.  Statistical and dynamical downscaling of the Seine basin climate for hydro‐meteorological studies , 2007 .

[16]  J. Blanchet,et al.  The Canadian Climate Centre Second-Generation General Circulation Model and Its Equilibrium Climate , 1992 .

[17]  M. Déqué,et al.  The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling , 1994 .

[18]  John F. B. Mitchell,et al.  The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments , 2000 .

[19]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[20]  Johann Stötter,et al.  Assessment of climate change impacts on flood hazard potential in the Alpine Lech watershed , 2012 .

[21]  M. Semenov,et al.  Use of multi-model ensembles from global climate models for assessment of climate change impacts. , 2010 .

[22]  Deliang Chen,et al.  Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation , 2001 .

[23]  Okke Batelaan,et al.  A distributed model for water and energy transfer between soil, plants and atmosphere (WetSpa) , 1996 .

[24]  Erwin Schmid,et al.  High resolution climate data for Austria in the period 2008–2040 from a statistical climate change model , 2013 .

[25]  P. Coulibaly,et al.  Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models , 2005 .

[26]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[27]  Shyamal Ghosh,et al.  Impact of climate change on flood characteristics in Brahmaputra basin using a macro-scale distributed hydrological model , 2012, Journal of Earth System Science.

[28]  Muhammad Zaffar Hashmi,et al.  Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed , 2011 .

[29]  C. Kucharik,et al.  Impacts of recent climate change on Wisconsin corn and soybean yield trends , 2008 .

[30]  M. Hulme,et al.  Relative impacts of human-induced climate change and natural climate variability , 1999, Nature.

[31]  D. Goldblum.  Sensitivity of Corn and Soybean Yield in Illinois to Air Temperature and Precipitation: The Potential Impact of Future Climate Change , 2009 .

[32]  Khaled H. Hamed,et al.  Flood Frequency Analysis , 1999 .

[33]  S. Durrans.  Parameter estimation for the Pearson type 3 distribution using order statistics , 1992 .

[34]  Christian W. Dawson,et al.  SDSM - a decision support tool for the assessment of regional climate change impacts , 2002, Environ. Model. Softw..