Preprints
https://doi.org/10.5194/hess-2022-72
https://doi.org/10.5194/hess-2022-72
 
04 Mar 2022
04 Mar 2022
Status: this preprint is currently under review for the journal HESS.

Development of a national 7-day ensemble streamflow forecasting service for Australia

Hapu Hapuarachchi1, Mohammed Bari2, Aynul Kabir1, Mohammad Hasan3, Fitsum Woldemeskel1, Nilantha Gamage1, Patrick Sunter1, Xiaoyong Zhang1, David Robertson4, James Bennett4, and Paul Feikema1 Hapu Hapuarachchi et al.
  • 1Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, Australia
  • 2Bureau of Meteorology, 1 Ord Street, West Perth, WA 6005, Australia
  • 3Bureau of Meteorology, The Treasury Building, Parkes Place West, Canberra, ACT 2600, Australia
  • 4Commonwealth Scientific and Industrial Research Organization, Research Way, Clayton, VIC 3168, Australia

Abstract. Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7-day ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual hourly rainfall-runoff model, GR4H (hourly) and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical Catchment Hydrologic Pre-Processor (CHyPP) is used for calibrating rainfall forecasts, and the Error Reduction and Representation In Stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation a bootstrapping block size of one month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 281 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days with daily updates.

Hapu Hapuarachchi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-72', Anonymous Referee #1, 29 Mar 2022
    • AC1: 'Reply on RC1', Mohammed Bari, 25 May 2022
  • RC2: 'Comment on hess-2022-72', Anonymous Referee #2, 20 Apr 2022
    • AC2: 'Reply on RC2', Mohammed Bari, 25 May 2022

Hapu Hapuarachchi et al.

Hapu Hapuarachchi et al.

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Short summary
We present the development of an operational 7-day ensemble streamflow forecasting service for Australia. We use a rainfall-runoff model (GR4H), Catchment Hydrologic Pre-Processor (CHyPP) for calibrating NWP rainfall forecasts, and the Error Reduction and Representation In Stages (ERRIS) for reducing errors and quantify uncertainty. Use of CHyPP and ERRIS significantly improves skill. From 2019, the operational forecasts are available at 209 locations covering all hydroclimatic regions.