Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model. Issue 11 (8th November 2021) - McInerney, David; Thyer, Mark; Kavetski, Dmitri; Laugesen, Richard; Woldemeskel, Fitsum; Tuteja, Narendra; Kuczera, George
Abstract: Sub‐seasonal streamflow forecasts are important for a range of water resource management applications, with a distinct practical interest in forecasts of high flows (e.g., for managing flood events) and low flows (e.g., for managing environmental flows). Despite this interest, differences in forecast performance for high and low flow events are not routinely investigated. Our study reveals that while forecasts evaluated over the full flow range can appear reliable, stratification into high/low flow ranges highlights significant under/over‐estimation of forecast uncertainty, respectively. We overcome this challenge by introducing a flow‐dependent (FD) nonparametric component into a post‐processing model of hydrological forecasting errors, the Multi‐Temporal Hydrological Residual Error (MuTHRE) model, yielding the MuTHRE‐FD model. The MuTHRE and MuTHRE‐FD models are compared in a case study with 11 Australian catchments, the GR4J rainfall‐runoff model and post‐processed rainfall forecasts from ACCESS‐S. Through its improved treatment of flow‐dependence, the MuTHRE‐FD model achieves practically significant improvements over the original MuTHRE model in the reliability of forecasted cumulative volumes for: (a) high flows out to 7 days; (b) low flows out to 2 days; and (c) mid flows for majority of lead times. The new MuTHRE‐FD model provides seamless sub‐seasonal forecasts with high quality performance for both high and low flows over a range of lead times. This improvement provides forecast users with increased confidence in using sub‐seasonal forecasts across a wide range of applications. Plain Language Summary: In a world where flows range from low to high, sub‐seasonal streamflow forecasts can inform water management applications ranging from the operation of flood storages to the sustenance of environmental flows vital for local vegetation and wildlife. However, this study reveals that forecast uncertainty tends to be over‐estimated for low flows and under‐estimated for high flows. Using these insights, we propose an innovative (in more ways than one) way to characterize forecast uncertainty, making direct use of observed flow data with minimal use of a priori assumptions. The new model is tested in a case study based on 11 catchments in the Murray‐Darling Basin in Australia, and is shown to improve the reliability of forecast flow volumes, especially at lead times of 1–8 days. The new approach paves the way to a greater confidence in streamflow forecasts for a broader range of applications. Key Points: Sub‐seasonal streamflow forecasts under/over‐estimate uncertainty in high/low flows, despite appearing reliable overall New nonparametric representation of flow dependency incorporated into the residual error model High quality "seamless" probabilistic streamflow forecasts achieved for a range of lead times and cumulative flow ranges