Downscaling of monthly precipitation using CMIP5 climate models operated under RCPs

Abstract

Downscaling of general circulation model (GCM) outputs extracted from CMIP5 datasets to monthly precipitation for the Gediz Basin, Turkey, under Representative Concentration Pathways (RCPs) was performed by statistical downscaling models, multi-GCM ensemble and bias correction. The output databases from 12 GCMs were used for the projections. To determine explanatory predictor variables, the correlation analysis was applied between precipitation observed at 39 meteorological stations located over the Basin and potential predictors of ERA-Interim reanalysis data. After setting both artificial neural networks and least-squares support vector machine-based statistical downscaling models calibrated with determined predictor variables, downscaling models producing the most suitable results were chosen for each meteorological station. The selected downscaling model structure for each station was then operated with historical and future scenarios RCP4.5, RCP6.0 and RCP8.5. Afterwards, the monthly precipitation forecasts were obtained from a multi-GCM ensemble based on Bayesian model averaging and bias correction applications. The statistical significance of the foreseen changes for the future period 2015-2050 was investigated using Student's t test. The projected decrease trend in precipitation is significant for the RCP8.5 scenario, whereas it is less significant for the RCP4.5 and RCP6.0 scenarios.

Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study

“…, Ahmed et al . , Okkan and Kirdemir , utilized several artificial intelligence models (e.g., support vector regression, ANN, genetic programing) for downscaling GCM outputs. Hashmi et al .…”

“…While this is the first study to use ELM for statistical downscaling of GCM outputs to monthly precipitation over Minab Basin, it is worthwhile to relate the performance of the applied methods in the current research with those presented in other studies that closely relate to this study. Chen et al [18], Goyal and Ojha [19], George et al [47], Ahmed et al [48], Okkan and Kirdemir [49], utilized several artificial intelligence models (e.g., support vector regression, ANN, genetic programing) for downscaling GCM outputs. Hashmi et al [5] utilized Gene Expression Programming for simulating watershed precipitation and they found R 2 of 0.5 in the test period.…”

Predicción de precipitación mensual mediante Redes Neuronales Artificiales para la cuenca del río Cali, Colombia

“…At present, projections of different hydrometeorological variables have been created worldwide using General Circulation Models (GCMs), a tool that allows the future behavior of variables such as precipitation and temperature to be examined in order to gain a better understanding of Climate Change (CC) and its after-effects in the near and distant future [1,2]. However, despite the practical uses of low-resolution projections, researchers have been working In statistics, when compared with other regression techniques, automatic learning techniques like Artificial Neural Networks (ANNs) are more efficient and present more correlated values, due to their capacity for learning from the data and their use of computer algorithms [10].…”

“…However, despite the practical uses of low-resolution projections, researchers have been working In statistics, when compared with other regression techniques, automatic learning techniques like Artificial Neural Networks (ANNs) are more efficient and present more correlated values, due to their capacity for learning from the data and their use of computer algorithms [10]. Research has been carried out globally that involves ANNs in the downscaling of Climate Change scenarios, and these studies show this method to be adequate and with low computational cost [2,11,12].…”

Farklı yanlılık düzeltme yöntemlerinin istatistiksel ölçeğe indirgenmiş yağış projeksiyonlarına uygulanması

“…Örnek olarak ele alınan Gediz Havzasının su kaynakları yönetimi için çoklu kriterli karar verme süreci modellemesi Yilmaz ve Harmancioglu[29,30] tarafından gerçekleştirilen çalışmalarda daha önce ele alınmıştır. Gerek Okkan ve Kirdemir[3,4] tarafından yürütülen çalışmalar, gerekse bu çalışma ile elde edilen yansız yağış projeksiyonları kullanılarak havzadaki akışların ve çok kriterli karar verme sürecinin ne ölçüde etkileneceği gelecek çalışmalarımızda ayrıca sorgulanacaktır.…”