EGU21-3714, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-3714
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Do you want Seamless Subseasonal Streamflow Forecasts?    Ask MuTHRE!

Mark Thyer1, David McInerney1, Dmitri Kavetski1, Richard Laugesen2, Narendra Tuteja2, and George Kuczera3
Mark Thyer et al.
  • 1University of Adelaide, School of Civil, Env and Mining Engineering, Adelaide, Australia (mark.thyer@adelaide.edu.au)
  • 2Bureau of Meteorology, Canberra, ACT, Australia
  • 3School of Engineering, University of Newcastle, Callaghan, NSW, Australia

Sub-seasonal streamflow forecasts (with lead times of 1-30 days) provide valuable information for many consequential water resource management decisions, including reservoir operation to meet environmental flow and irrigation demands, issuance of early flood warnings, and others. A key aim is to produce “seamless” forecasts, with high quality performance across the full range of lead times and time scales.  

This presentation introduces the Multi-Temporal Hydrological Residual Error model (MuTHRE) to address the challenge of obtaining “seamless” sub-seasonal forecasts, i.e., daily forecasts with consistent high-quality performance over multiple lead times (1-30 days) and aggregation scales (daily to monthly).

The model is designed to overcome common errors in streamflow forecasts:

  • Seasonality
  • Dynamic biases due to hydrological non-stationarity
  • Extreme errors poorly represented by the common Gaussian distribution.

The model is evaluated comprehensively over 11 catchments in the Murray-Darling Basin, Australia, using multiple performance metrics to scrutinize forecast reliability, sharpness and bias, across a range of lead times, months and years, at daily and monthly time scales.

The MuTHRE model provides ”high” improvements, in terms of reliability for

  • Short lead times (up to 10 days), due to representing non-Gaussian errors
  • Stratified by month, due to representing seasonality in hydrological errors
  • Dry years, due to representing dynamic biases in hydrological errors.

Forecast performance also improved in terms of sharpness, volumetric bias and CRPS skill score; Importantly, improvements are consistent across multiple time scales (daily and monthly).

This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.

 

 

 

How to cite: Thyer, M., McInerney, D., Kavetski, D., Laugesen, R., Tuteja, N., and Kuczera, G.: Do you want Seamless Subseasonal Streamflow Forecasts?    Ask MuTHRE!, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3714, https://doi.org/10.5194/egusphere-egu21-3714, 2021.

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