Trivariate Copula Function Model for Mapping Vegetation Drought Vulnerability

This study evaluates the negative effects of various meteorological drought stresses on vegetation drought. We propose a probabilistic model based on the copula theory that constructs a multivariate joint probability distribution describing the interrelationship between climate and vegetation information. The vulnerability of vegetation can be quantified by deriving a conditional probability distribution of vegetation under a meteorological drought scenario through joint probability distribution modeling. Meteorological drought conditions are divided into two aspects: atmospheric moisture supply and moisture demand. We utilize the Standardized Precipitation Index (SPI), a drought index based on precipitation amount on range of timescales, and the Evaporative Demand Drought Index (EDDI), a drought index based on potential evapotranspiration for a period of interest. In addition, a normalized VTCI (nVTCI) modified from the Vegetation Temperature Condition Index (VTCI) is proposed to estimate vegetation drought. We estimate SPI, EDDI, and nVTCI at a high spatial resolution (0.05° by 0.05°) using remote sensing data, and construct a trivariate joint probability model between nVTCI and two meteorological drought indices. The vulnerability of vegetation for various meteorological drought scenarios is quantified and spatially presented to identify vulnerable regions. We expect the proposed method and simulated outcomes will improve describing vegetation response to various drought scenarios in different regions in a more realistic way. Acknowledgement This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1A2C1003114).