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

Date

2015-09-01

Author

OKKAN, UMUT
İnan, Gül

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

211
views

0
downloads

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.

Subject Keywords

Atmospheric Science

URI

https://hdl.handle.net/11511/51561

Journal

INTERNATIONAL JOURNAL OF CLIMATOLOGY

DOI

https://doi.org/10.1002/joc.4206

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Optimally merging precipitation to minimize land surface modeling errors Yılmaz, Mustafa Tuğrul; Shrestha, Roshan; Anantharaj, Valentine G. (American Meteorological Society, 2010-03-01) This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minim...
Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis Yılmaz, Mustafa Tuğrul (American Meteorological Society, 2014-06-01) Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has sp...
Comparison of homogeneity tests for temperature using a simulation study Yozgatlıgil, Ceylan (Wiley, 2016-01-01) Homogeneity testing is one of the most important analyses in climate-related studies as it underpins the reliability of any inferences. The effects not directly related with climate are identified and removed from the meteorological variables, and then the obtained homogeneous variables are used to present an enhanced picture of the current situation and produce realistic forecasts based upon the variables. In this study, we investigate the performances of well-known homogeneity tests and introduce some tes...
Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting Yılmaz, Koray Kamil; Hsu, KL; Sorooshian, S; Gupta, HV; Wagener, T (American Meteorological Society, 2005-08-01) This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agre...
Nonstationarity impacts on frequency analysis of yearly and seasonal extreme temperature in Turkey Aziz, Rizwan; Yücel, İsmail; Yozgatlıgil, Ceylan (Elsevier BV, 2020-07-01) This study investigates the temporal variability in yearly and seasonal extreme temperatures across Turkey using stationary and nonstationary frequency analysis. The analyses are conducted using Generalized Extreme Value (GEV), Gumbel and Normal distributions for minimum and maximum temperatures during historical (1971-2016) and projection period (2051-2100). The future nonstationarity impacts are quantified using a 12-member ensemble of The Coordinated Regional Downscaling Experiment (CORDEX) regional clim...

Citation Formats

U. OKKAN and G. İnan, “Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios,” INTERNATIONAL JOURNAL OF CLIMATOLOGY, pp. 3274–3295, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51561.