Beyond Precipitation: Reassessing Drought Severity Using Multiple Variables

Univariate assessments of drought such as the Standardized Precipitation Index (SPI) may be insufficient for detecting all types and severities of drought. Bivariate assessments of drought, such as combining SPI and the Standardized Soil Moisture Index (SSI) to create the Multivariate Standardized Drought Index, predict drought onset and longevity better than SSI and SPI compared to SSI alone. While drought risk is normally evaluated with precipitation alone, we investigate drought risk with precipitation and temperature combined. Using Weibull’s method and statistical copulas, we compare univariate and bivariate return periods in Northern Georgia and Central Iowa. Results show that using only a single variable to define drought gives the possibility of overestimating or underestimating drought risk. As shown in this study, using precipitation data joined with temperature data provides a return period that is more meaningful and more accurately describes drought conditions in an area. Methods to account for multiple variables are particularly important given the uncertain impacts of climate change; in which small changes in precipitation extremes may be exacerbated by large changes in temperature extremes. Understanding the interaction between precipitation and temperature will allow decision makers to plan ahead and act accordingly during times of drought