An approach for identifying drought typologies: Drivers, characteristics, and impacts of streamflow drought across the Delaware River Basin

Connecting drought drivers to drought conditions and drought conditions to consequences are two key components of drought risk assessment. We propose a two-step data-driven approach that contributes to advancing this capability. First, we use machine learning feature selection techniques to transform hydrologic model outputs into calculated indices and determine which features are most predictive of streamflow drought duration and severity. Second, we use the selected features to cluster drought events from 1985–2015 and examine drivers of drought in each cluster, spatial and temporal patterns, frequency over time, event characteristics, and connections to water management metrics. Our approach is implemented in the Delaware River Basin (DRB), a highly managed basin in the northeastern United States with a history of consequential droughts. Feature selection identified 19 features that can predict drought duration and severity better than models fit to all features. K-means clustering identified three distinct drought types: one capturing summer and fall droughts, largely in the lower basin; one capturing winter droughts, concentrated in the upper basin; and one capturing long-duration droughts across regions and seasons. Although annual drought frequency has decreased slightly over the period of record, those declines have not been split equally between clusters. Analysis of water management metrics revealed that soil moisture indices at 3-6 month timescales are the most able to differentiate the more consequential long-duration droughts from other drought types. This work contributes both specific insights for stakeholders in the DRB and a generalizable approach for similar analyses and explorations in other basins.