Construction of fine resolution bioclimatic variables for Central Highlands and Southern Central Coast of Vietnam

Pham Bach Viet; Hoang Minh Duc; Tran Van Bang; Luu Hong Truong; Nguyen Dang Quang; Diep Dinh Phong

doi:10.15625/2615-9023/16062

Author affiliations

Authors

Pham Bach Viet Vietnam National Space Center, VAST, Vietnam
Hoang Minh Duc Southern Institute of Ecology, VAST, Vietnam
Tran Van Bang Vietnam National Space Center, VAST, Vietnam
Luu Hong Truong Southern Institute of Ecology, VAST, Vietnam
Nguyen Dang Quang Center for Hydro-Meteorological Technology Application, Vietnam
Diep Dinh Phong Southern Institute of Ecology, VAST, Vietnam

DOI:

https://doi.org/10.15625/2615-9023/16062

Keywords:

Bioclimatic variables, climate data, downscaling, multiple linear regression, precipitation, temperature

Abstract

In this study, 19 surface bioclimatic variables of high spatial resolution 0.00226^o (~ 250 m) are generated in a Geographic Information System by the combination of (1) the raster dataset of monthly temperature and precipitation obtained from the global WorldClim database at 0.00833^o spatial resolution for the period of 1960–2000; and (2) the climate data (temperature and precipitation) of the Central Highlands and Southern Central Coast collected from the 31 temperature and 97 precipitation recording sites for the period of 1991–2015. The statistical downscaling method is applied, using multiple linear regression analysis, in which elevation, geographic coordinates, and distance from the coast are treated as independent variables, to estimate the distribution of temperature; and the B-Spline interpolation method combined with multiple linear regression analysis is employed on precipitation over the study area. The outcomes of the two main analyses are computed to create 19 high spatial resolution bioclimatic variables. While using only local climate data on analyzing the regression models results in high fluctuation of estimated temperature, the combination of the two datasets is more informative. The spatial distribution of our interpolated precipitation is similar to the WorldClim data but has a smaller difference in the mean annual precipitation. The results, which shows higher spatial resolution and are closer to the observed data than those from the WorldClim, could be better applied for predicting species distribution in the region.

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