Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal scales of analysis

Abstract

Summary Methods for predicting streamflow in areas with limited or nonexistent measures of hydrologic response commonly rely on regionalization techniques, where knowledge pertaining to gauged watersheds is transferred to ungauged watersheds. Hydrologic response indices have frequently been employed in contemporary regionalization research related to predictions in ungauged basins. In this study, we developed regionalization models using multiple linear regression and regression tree analysis to derive relationships between hydrologic response and watershed physical characteristics for 163 watersheds in the Great Lakes basin. These models provide an empirical means for simulating runoff in ungauged basins at a monthly time step without implementation of a rainfall–runoff model. For the dependent variable in these regression models, we used monthly runoff ratio as the indicator of hydrologic response and defined it at two temporal scales: (1) treating all monthly runoff ratios as individual observations, and (2) using the mean of these monthly runoff ratios for each watershed as a representative observation. Application of the models to 62 validation watersheds throughout the Great Lakes basin indicated that model simulations were far more sensitive to the temporal characterization of hydrologic response than to the type of regression technique employed, and that models conditioned on individual monthly runoff ratios (rather than long term mean values) performed better. This finding is important in light of the increased usage of hydrologic response indices in recent regionalization studies. Models using individual observations for the dependent variable generally simulated monthly runoff with reasonable skill in the validation watersheds (median Nash–Sutcliffe efficiency = 0.53, median R 2 = 0.66, median magnitude of the deviation of runoff volume = 13%). These results suggest the viability of empirical approaches to simulate runoff in ungauged basins. This finding is significant given the many regions of the world with sparse gauging networks and limited resources for gathering the field data required to calibrate rainfall–runoff models.