Reliable long‐range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model. Issue 10 (27th October 2016)

• Record Type: Journal Article
• Authors: Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew
• Is Part Of: Water resources research. Volume 52:Issue 10(2016:Oct.)
• Page Start: 8238
• Page End: 8259
• Publication Date: 2016-10-27
• Subjects: seasonal streamflow forecasting -- ensemble prediction -- CGCM -- error modeling -- hydrological uncertainty; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR019193
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean‐atmosphere general circulation model (CGCM). The CGCM forecasts are post‐processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post‐processed CGCM forecasts are used to force the Wapaba monthly rainfall‐runoff model. Uncertainty in the hydrological modeling is accounted for with a three‐stage error model. Stage 1 applies the log‐sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias‐correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead‐times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead‐times. Forecast skill with respect to climatology is evident to lead‐times of about 3 months. At longer lead‐times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead‐times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers. Key Points: A new method to produce statistically reliable ensemble forecasts of streamflow to 12 months ahead Forecasts are designed to be skillful alternatives to stochastic scenarios Forecasts of accumulated volumes (e.g., 6 month total flow) can be skillful to long time horizons