Drought indicators are of critical importance in characterization and forecasting. The use of the Standardized Precipitation Index (SPI) has increasingly become the main tool for drought analysis; however, the index lacks hydrological information useful as a proxy for other types of droughts. This study aims at evaluating the SPI against the Standardized Precipitation Evapotranspiration Index (SPEI) in the South Central Region of Vietnam. The indices were calculated using monthly rainfall and temperature data measurements from 30 rainfall and 13 temperature stations, during the period from 1977 to 2014. The study focuses on the spatial-temporal variations of drought events and therefore an area of 1680 grid cells of 4x4 km was selected. Inverse distance weighting was used to interpolate grid rainfall and temperature prior to drought indices estimating at multiple time scales (3, 6, 9, and 12 months). Drought severity was classified from gridded SPIs and SPEIs using a Non-Contiguous Drought Area (NCDA) approach. The result indicated that drought characteristics using the SPEI and NCDA can capture better historical drought conditions than that using the SPI and NCDA. This suggests an important role of temperature factor in the degree of drought severity. The analysis of spatial-temporal drought on the SPEI showed that the occurrence of moderate droughts in the study area was 1-2 years, and the highest percentage of drought in the area was observed in the summer-autumn season at all SPEI time scales. The results of this study may extend our understanding of natural drought mechanisms.

A sufficient data length can play an important role in a proper estimation drought index, leading to a better appraisal for drought risk reduction. The South Central Region of Vietnam is one of drought prone areas but it has poor data conditions. A collection of meteorological data in the study area during a period of 38 years 1977-2014 found out a fact that there existed missing values in 10 out of 30 collected rainfall stations and 4 out of 13 collected temperature stations. Therefore, this study aims at evaluating the influence of three different infilling techniques (Inverse Distance Weighting, Multi-Linear Regression, and Artificial Neural Network) on 1-month Standardized Precipitation Evapotranspiration Index (SPEI1) drought indicator for the given region. The performance on rainfall and temperature infilling indicated that ANN technique achieved lower errors between observations and predictions than others. Infilled rainfall and temperature generated from different infilling techniques were then combined with the available data to calculate SPEI1. The results showed that infilling techniques seem to create the SPEI1 time series which have higher number of drought events, in comparison with that time series containing the observation values only. Otherwise, drought index seems to be insensitive to different infilling techniques.