Full access nearest neighbor-genetic algorithm for downscaling of climate change data from GCMs

Soojun Kim; Jaewon Kwak; Hung Soo Kim; Younghun Jung; Gilho Kim

doi:10.1175/JAMC-D-15-0100.1

Full access nearest neighbor-genetic algorithm for downscaling of climate change data from GCMs

Soojun Kim, Jaewon Kwak, Hung Soo Kim, Younghun Jung, Gilho Kim

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

The spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor-genetic algorithm (NN-GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN-GA. The NN-GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.

Original language	English
Pages (from-to)	773-789
Number of pages	17
Journal	Journal of Applied Meteorology and Climatology
Volume	55
Issue number	3
DOIs	https://doi.org/10.1175/JAMC-D-15-0100.1
State	Published - 2016

Keywords

Climate change
Hydrology
Hydrometeorology
Rainfall
Time series
Watersheds

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1175/JAMC-D-15-0100.1

Cite this

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title = "Full access nearest neighbor-genetic algorithm for downscaling of climate change data from GCMs",

abstract = "The spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor-genetic algorithm (NN-GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN-GA. The NN-GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.",

keywords = "Climate change, Hydrology, Hydrometeorology, Rainfall, Time series, Watersheds",

author = "Soojun Kim and Jaewon Kwak and Kim, {Hung Soo} and Younghun Jung and Gilho Kim",

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year = "2016",

doi = "10.1175/JAMC-D-15-0100.1",

language = "English",

volume = "55",

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AU - Kim, Soojun

AU - Kwak, Jaewon

AU - Kim, Hung Soo

AU - Jung, Younghun

AU - Kim, Gilho

PY - 2016

Y1 - 2016

N2 - The spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor-genetic algorithm (NN-GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN-GA. The NN-GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.

AB - The spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor-genetic algorithm (NN-GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN-GA. The NN-GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.

KW - Climate change

KW - Hydrology

KW - Hydrometeorology

KW - Rainfall

KW - Time series

KW - Watersheds

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U2 - 10.1175/JAMC-D-15-0100.1

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VL - 55

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JO - Journal of Applied Meteorology and Climatology

JF - Journal of Applied Meteorology and Climatology

IS - 3

ER -

Full access nearest neighbor-genetic algorithm for downscaling of climate change data from GCMs

Abstract

Keywords

UN SDGs

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