Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty

The effect of land-use or land-cover change on stream runoff dynamics is not fully understood. In many parts of the world, forest management is the major land-cover change agent. While the paired catchment approach has been the primary methodology used to quantify such effects, it is only possible for small headwater catchments where there is uniformity in precipitation inputs and catchment characteristics between the treatment and control catchments. This paper presents a model-based change-detection approach that includes model and parameter uncertainty as an alternative to the traditional paired-catchment method for larger catchments. We use the HBV model and data from the HJ Andrews Experimental Forest in Oregon, USA, to develop and test the approach on two small (< 1 km(2)) headwater catchments (a 100% clear-cut and a control) and then apply the technique to the larger 62 km(2) Lookout catchment. Three different approaches are used to detect changes in stream peak flows using: (a) calibration for a period before (or after) change and simulation of runoff that would have been observed without land-cover changes (reconstruction of runoff series); (b) comparison of calibrated parameter values for periods before and after a land-cover change; and (c) comparison of runoff predicted with parameter sets calibrated for periods before and after a land-cover change. Our proof-of-concept change detection modelling showed that peak flows increased in the clear-cut headwater catchment, relative to the headwater control catchment, and several parameter values in the model changed after the clear-cutting. Some minor changes were also detected in the control, illustrating the problem of false detections. For the larger Lookout catchment, moderately increased peak flows were detected. Monte Carlo techniques used to quantify parameter uncertainty and compute confidence intervals in model results and parameter ranges showed rather wide distributions of model simulations. While this makes change detection more difficult, it also demonstrated the need to explicitly consider parameter uncertainty in the modelling approach to obtain reliable results.