Keywords
rainfall-runoff, ungauged catchments, regression, bayesian averaging
Start Date
1-7-2004 12:00 AM
Abstract
A main limitation of using conceptual models for predicting flow in ungauged catchments is theerrors in identified relationships between calibrated conceptual parameters and known (or estimated)catchment descriptors. It is hypothesised here that these errors may be reduced if the modeller does notexplicitly identify relationships, but applies all feasible models within a Bayesian averaging scheme. Thismaintains the information about parameter inter-dependencies obtained as part of local calibration, and alsoprovides a strong basis for integrating various sources of uncertainty into the predicted average flow andassociated confidence intervals. A case study of UK catchments provides encouraging results.
Tools and Approaches for Evaluating Uncertainty in Streamflow Predictions in Ungauged UK Catchments
A main limitation of using conceptual models for predicting flow in ungauged catchments is theerrors in identified relationships between calibrated conceptual parameters and known (or estimated)catchment descriptors. It is hypothesised here that these errors may be reduced if the modeller does notexplicitly identify relationships, but applies all feasible models within a Bayesian averaging scheme. Thismaintains the information about parameter inter-dependencies obtained as part of local calibration, and alsoprovides a strong basis for integrating various sources of uncertainty into the predicted average flow andassociated confidence intervals. A case study of UK catchments provides encouraging results.
