In lieu of the paired catchment approach: Hydrologic model change detection at the catchment scale

Abstract

The paired catchment approach has been the predominant method for detecting the effects of disturbance on catchment‐scale hydrology. Notwithstanding, the utility of this approach is limited by regression model sample size, variability between paired catchments, type II error, and the inability of locating a long‐term suitable control. An increasingly common practice is to use rainfall‐runoff models to discern the effect of disturbance on hydrology, but few hydrologic model studies (1) consider problems associated with model identification, (2) use formal statistical methods to evaluate the significance of hydrologic change relative to variations in rainfall and streamflow, and (3) apply change detection models to undisturbed catchments to test the approach. We present an alternative method to the paired catchment approach and improve on stand‐alone hydrologic modeling to discern the effects of forest harvesting at the catchment scale. Our method combines rainfall‐runoff modeling to account for natural fluctuations in daily streamflow, uncertainty analyses using the generalized likelihood uncertainty estimation method to identify and separate hydrologic model uncertainty from unexplained variation, and GLS regression change detection models to provide a formal experimental framework for detecting changes in daily streamflow relative to variations in daily hydrologic and climatic data. We include statistical analyses of climate variation and a two‐part evaluation to explore model performance and account for unexplained variation. Evaluations consisted of applying our method to a control catchment and to a period prior to harvesting in a treated catchment to demonstrate that our method was capable of capturing the absence of land use change in an undisturbed catchment and capturing the absence of land use change during a period of no disturbance in the harvested catchment. In addition, we explore the sensitivity of our method to model identification, number of simulations, and likelihood thresholds for model identification. We show that an increase in the number of model simulations does not necessarily result in increased change detection performance. Our method is a potentially useful alternative to the paired catchment approach where reference catchments are not possible and to stand‐alone hydrologic modeling for detecting the effects of land use change on hydrology.