College of Forestry
time series
In Lieu of the Paired Catchment Approach: Hydrologic Model Change Detection at the Catchment Scale
Nov-23-2010
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.
Corrected Prediction Intervals for Change Detection in Paired Watershed Studies
Jan-20-2012
Autoregressive process pHydrological data may be temporally autocorrelated requiring autoregressive process parameters to be estimated. Current statistical methods for hydrological change detection in paired watershed studies rely on prediction intervals, but the current form of prediction intervals does not include all appropriate sources of variation. Corrected prediction intervals for the analysis of paired watershed study data that include variation associated with covariance and linear model parameter estimation are presented. We provide an example of their application to data from the Hinkle Creek Paired Watershed Study located in the western Cascade foothills of Southern Oregon, USA. Research implications of using the correct prediction limits and incorporating the estimation uncertainty of aarameters are discussed.
