How Essential is Hydrologic Model Calibration to Seasonal Streamflow Forecasting?

Abstract

Hydrologic model calibration is usually a central element of streamflow forecasting based on the ensemble streamflow prediction (ESP) method. Evaluation measures of forecast errors such as root-mean-square error (RMSE) are heavily influenced by bias, which in turn is readily reduced by calibration. On the other hand, bias can also be reduced by postprocessing (e.g., ?training? bias correction schemes based on retrospective simulation error statistics). This observation invites the question: How much is forecast error reduced by calibration, beyond what can be accomplished by postprocessing to remove bias? The authors address this question through retrospective evaluation of forecast errors at eight streamflow forecast locations distributed across the western United States. Forecast periods of length ranging from 1 to 6 months are investigated, for forecasts initiated from 1 December to 1 June, which span the period when most runoff occurs from snowmelt-dominated western U.S. rivers. ESP forecast errors are evaluated both for uncalibrated forecasts to which a percentile mapping bias correction approach is applied, and for forecasts from an objectively calibrated model without explicit bias correction. Using the coefficient of prediction (Cp), which essentially is a measure of the fraction of variance explained by the forecast, the authors find that the reduction in forecast error as measured by Cp that is achieved by bias correction alone is nearly as great as that resulting from hydrologic model calibration.