Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios
Year: 2014
Type: article
Abstract: ABSTRACT In this study, statistical downscaling of general circulation model (GCM) simulations to monthly inflows of Kemer Dam in Turkey underA1B,A2, andB1emission 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 reasonableGCMswere selected by investigating the rank correlations between the selected predictors inNCEP/NCARreanalysis data and those inGCMsfor20C3Mscenario 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 usingNCEP/NCARreanalysis data for Kemer watershed showed that the trainedRVMmodel produced adequate results. The effectiveness ofRVMmodel was illustrated by its integration with20C3Mscenario between periods 1979 and 1999 andA1B,A2, andB1future climate scenarios between periods 2010 and 2039. Afterwards, the flow forecasts were obtained by building a multi‐model ensemble through the selectedGCMsfollowed 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.
DOI: 10.1002/joc.4206
Authors: Umut Okkan, Gul Inan
Institutions: Balıkesir University, Middle East Technical University
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1521873257