Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

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.

Assessment of an ensemble seasonal streamflow forecasting system for Australia

“…Parameters for each stage are estimated sequentially using maximum likelihood estimation (MLE), as detailed by Bennett et al (2016). The data transformation (Stage 1) allows us to assume that residuals, ε, are normally distributed and homoscedastic (i.e.…”

“…To establish whether FoGSS is a system capable of being deployed across the Australian continent, we test FoGSS as it was described by Bennett et al (2016): that is, as described in Sect. 2, using the Wapaba rainfall-runoff model.…”

“…Each ensemble member in the forecast is a realistic 12-month hydrograph at a monthly time step. In a previous paper (Bennett et al, 2016), we described the theoretical underpinnings of FoGSS and showed that it performed well for two high-rainfall Australian catchments, producing skilful and reliable ensemble forecasts. We noted that the viability of FoGSS as a continent-wide forecasting system remained to be tested.…”

Skilful seasonal forecasts of streamflow over Europe?

“…While climate-model-based seasonal streamflow forecasting experiments are more common outside of Europe, for example for the United States (Wood et al, 2002(Wood et al, , 2005Mo and Lettenmaier, 2014), Australia (Bennett et al, 2016), or Africa , they remain limited in Europe, with a few examples in France (Céron et al, 2010;Singla et al, 2012;Crochemore et al, 2016), in central Europe (Demirel et al, 2015;Meißner et al, 2017), in the United Kingdom Prudhomme et al, 2017) and at the global scale (Yuan et al, 2015a;Candogan Yossef et al, 2017). This is because, although the quality of seasonal climate forecasts has increased over the past decades, there remains limited skill in seasonal climate forecasts for the extra-tropics, particularly for the variables of interest for hydrology, notably precipitation and temperature (Arribas et al, 2010;DoblasReyes et al, 2013).…”

Monthly streamflow forecasting at varying spatial scales in the Rhine basin

“…See e.g. Yuan et al (2015a) or Bennett et al (2016) for recent implementations of such a model chain.…”