Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting. Issue 11 (6th November 2020)

• Record Type: Journal Article
• Authors: McInerney, David; Thyer, Mark; Kavetski, Dmitri; Laugesen, Richard; Tuteja, Narendra; Kuczera, George
• Is Part Of: Water resources research. Volume 56:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-06
• Subjects: subseasonal streamflow forecasts -- seamless forecasts -- probabilistic forecasts -- residual error model -- streamflow postprocessing; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026979
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Subseasonal streamflow forecasts, with lead times of 1–30 days, provide valuable information for operational water resource management. This paper introduces the multi‐temporal hydrological residual error (MuTHRE) model to address the challenge of obtaining "seamless" subseasonal forecasts — that is, daily forecasts with consistent high‐quality performance over multiple lead times (1–30 days) and aggregation scales (daily to monthly). The key advance of the MuTHRE model is combining the representation of three temporal characteristics of hydrological residual errors: seasonality, dynamic biases, and non‐Gaussian errors. The MuTHRE model is applied in 11 Australian catchments using the hydrological model GR4J and post processed rainfall forecasts from the numerical weather prediction model ACCESS‐S, and is evaluated against a baseline model that does not model these error characteristics. The MuTHRE model provides "high" improvements (practically significant in the majority of performance stratifications) in terms of reliability: (i) at short lead times (up to 10 days), due to representing non‐Gaussian errors, (ii) stratified by month, due to representing seasonality in hydrological errors, and (iii) in dry years, due to representing dynamic biases in hydrological errors. Forecast performance also improves in terms of sharpness, volumetric bias, and CRPS skill score; these improvements are statistically but not practically significant in the majority of stratifications. Importantly, improvements are consistent across multiple time scales (daily and monthly). This study highlights the benefits of modeling multiple temporal characteristics of hydrological errors and demonstrates the power of the MuTHRE model for producing seamless subseasonal streamflow forecasts that can be utilized for a wide range of applications. Key Points: A multi‐temporal hydrological residual error model is introduced that incorporates seasonality, dynamic biases, and non‐Gaussian errors Subseasonal streamflow forecasts show "seamless" performance at multiple lead times (1–30 days) and aggregation scales (daily‐monthly) Stratified performance shows large improvements in reliability for shorter lead times, for many months and dry years