Haji Hosseini, Reza and Golian, Saeed and Yazdi, Jafar
(2018)
Evaluation of data-driven models to downscale rainfall
parameters from global climate models outputs: the case
study of Latyan watershed.
Journal of Water and Climate Change, 11 (1).
pp. 200-216.
ISSN 2040-2244
Abstract
Assessment of climate change in future periods is considered necessary, especially with regard to
probable changes to water resources. One of the methods for estimating climate change is the use of
the simulation outputs of general circulation models (GCMs). However, due to the low resolution of
these models, they are not applicable to regional and local studies and downscaling methods should
be applied. The purpose of the present study was to use GCM models’ outputs for downscaling
precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the
observation data from the Amameh station during the 1980–2005 period, 26 output variables from
two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three
data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest
neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS).
Comparison of the monthly results showed the superiority of KNN compared to the other two
methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed
satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the
study area.
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