Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model

Abstract

AbstractSub‐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.