Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments

Abstract

Increasing watershed disturbance regimes, such as from wildfire, are a growing concern for natural resource managers. However, the influence of watershed disturbances on event-scale rainfall–runoff patterns has proved challenging to disentangle from other hydrologic controls. To better isolate watershed disturbance effects, this study evaluates the influence of several time-varying hydrologic controls on event-scale rainfall–runoff patterns, including water year type, seasonality, and antecedent precipitation. To accomplish this, we developed the Rainfall–Runoff Event Detection and Identification (RREDI) toolkit, an automated time-series event separation and attribution algorithm that overcomes several limitations of existing techniques. The RREDI toolkit was used to generate a dataset of 5042 rainfall–runoff events from nine western US watersheds. By analyzing this large dataset, water year type and season were identified as significant controls on rainfall–runoff patterns, whereas antecedent moisture was pinpointed as a limited control. Specific effects of wildfire disturbance on runoff response were then demonstrated for two burned watersheds by first grouping rainfall–runoff events based on identified hydrologic controls, such as wet versus dry water year types. The role of water year type and season should be considered in future hydrologic analysis to better isolate the increasing and changing effects of wildfires on streamflow. The RREDI toolkit could be readily applied to investigate the influence of other hydrologic controls and watershed disturbances on rainfall–runoff patterns.