Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios

OKKAN U., Inan G.

INTERNATIONAL JOURNAL OF CLIMATOLOGY, vol.35, no.11, pp.3274-3295, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 11
  • Publication Date: 2015
  • Doi Number: 10.1002/joc.4206
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3274-3295
  • Keywords: monthly inflows, climate change, predictor selection, statistical downscaling, general circulation models, NCEP, NCAR reanalysis data, multi-model ensemble, bias correction, SUPPORT VECTOR MACHINE, CLIMATE-CHANGE SCENARIOS, BIAS CORRECTION, CHANGE IMPACTS, MODEL OUTPUTS, PRECIPITATION, REGRESSION, RAINFALL, VARIABILITY, FORECASTS
  • Middle East Technical University Affiliated: Yes


In this study, statistical downscaling of general circulation model (GCM) simulations to monthly inflows of Kemer Dam in Turkey under A1B, A2, and B1 emission scenarios has been performed using machine learning methods, multi-model ensemble and bias correction approaches. Principal component analysis (PCA) has been used to reduce the dimension of potential predictors of National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. Then, the reasonable GCMs were selected by investigating the rank correlations between the selected predictors in NCEP/NCAR reanalysis data and those in GCMs for 20C3M scenario between periods 1979 and 1999. Upon the training of feedforward neural network (FFNN), least squares support vector machine (LSSVM) and relevance vector machine (RVM) downscaling models, the general performance of the downscaled predictions using NCEP/NCAR reanalysis data for Kemer watershed showed that the trained RVM model produced adequate results. The effectiveness of RVM model was illustrated by its integration with 20C3M scenario between periods 1979 and 1999 and A1B, A2, and B1 future climate scenarios between periods 2010 and 2039. Afterwards, the flow forecasts were obtained by building a multi-model ensemble through the selected GCMs followed by a bias correction approach. Finally, the significance of the probable changes in trends was identified through statistical tests based on the corrected forecasts. Results showed that decreasing flows trends in winter, spring and fall seasons have been foreseen over the study area for the period between 2010 and 2039.