Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows

A key-predictand and key-station approach was employed in downscaling general circulation model outputs to monthly evaporation, minimum temperature (Tmin) and maximum temperature (Tmax) at five observation stations concurrently. Tmax was highly correlated (magnitudes above 0.80 at p ≤ 0.05) with evaporation and Tmin at each individual station, hence Tmax was identified as the key predictand. One station was selected as the key station, as Tmax at that station showed high correlations with evaporation, Tmin and Tmax at all stations. Linear regression relationships were developed between the key predictand at the key station and evaporation, Tmin and Tmax at all stations using observations. A downscaling model was developed at the key station for Tmax. Then, outputs of this downscaling model at the key station were introduced to the linear regression relationships to produce projections of monthly evaporation, Tmin and Tmax at all stations. This key-predictand and key-station approach was proved to be effective as the statistics of the predictands simulated by this approach were in close agreement with those of observations. This simple multi-station multivariate downscaling approach enabled the preservation of the cross-correlation structures of each individual predictand among the stations and also the cross-correlation structures between different predictands at individual stations.

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General Circulation Model Development

10.1016/s0074-6142(00)x8046-1 ◽

2000 ◽

Keyword(s):

General Circulation Model ◽

General Circulation ◽

Model Development ◽

Circulation Model

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Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

Xplore Journal of Statistics ◽

10.29244/xplore.v8i1.275 ◽

2019 ◽

Vol 8 (1) ◽

Author(s):

Khairunnisa Khairunnisa ◽

Rizka Pitri ◽

Victor P Butar-Butar ◽

Agus M Soleh

Keyword(s):

Statistical Downscaling ◽

General Circulation ◽

Principal Component Regression ◽

Circulation Model ◽

Principal Component ◽

Meteorological Station ◽

Grid Size ◽

Partial Least Square ◽

Least Square ◽

Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

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