Identification of hydrologic drought triggers from hydroclimatic predictor variables

Drought triggers are patterns in hydroclimatic variables that herald upcoming droughts and form the basis for mitigation plans. This study develops a new method for identification of triggers for hydrologic droughts by examining the association between the various hydroclimatic variables and streamflows. Since numerous variables influence streamflows to varying degrees, principal component analysis (PCA) is utilized for dimensionality reduction in predictor hydroclimatic variables. The joint dependence between the first two principal components, that explain over 98% of the variability in the predictor set, and streamflows is computed by a scale-free measure of association using asymmetric Archimedean copulas over two study watersheds in Indiana, USA, with unregulated streamflows. The M6 copula model is found to be suitable for the data and is utilized to find expected values and ranges of predictor hydroclimatic variables for different streamflow quantiles. This information is utilized to develop drought triggers for 1 month lead time over the study areas. For the two study watersheds, soil moisture, precipitation, and runoff are found to provide the fidelity to resolve amongst different drought classes. Combining the strengths of PCA for dimensionality reduction and copulas for building joint dependence allows the development of hydrologic drought triggers in an efficient manner.