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

Abstract

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 d 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 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 1 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 283 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides daily updates of graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days.

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

“…Ensemble forecasting is considered more reliable and skilful, and it can greatly benefit water resource management by providing useful information about uncertainty [7]. In response to customer needs, the Bureau progressively developed the 7-day ensemble streamflow forecasting, and released it to the public in 2020 [8]. This is a comprehensive, nation-wide service for Australia, and covers most water resources catchments of high economic value and social significance [9].…”

“…Australia has a wide range of climate zones as defined by Köppen Climate Classification [10] -including tropical region in the north, temperate regions in the south, grassland and desert in the vast interior. Annual rainfall for each of the divisions vary from a 410 to 2800 mm [8]. The distribution of annual rainfall and Potential evapotranspiration (PET) varies significantly across the continent (http://www.bom.gov.au/jsp/ncc/climate_averages/rainfall/index.jsp, accessed 11 November 2023).…”

“…The Bureau's operational ensemble 7-day streamflow forecast service [8] now has 4 years of retrospective (archived) forecast data (January 2020 to December 2023). Performance of the overall end-to-end streamflow forecasting has not been analysed yet, and this paper presents the steps in filling this critical operational knowledge gap.…”

Machine learning for postprocessing ensemble streamflow forecasts

“…Reliable and skillful streamflow forecasts are crucial for informing decisions related to water resources management, water supply planning, and preparedness against extreme events. The ensemble prediction system is becoming increasingly popular for streamflow forecasting (Cloke & Pappenberger 2009;Demargne et al 2014;Troin et al 2021;Hapuarachchi et al 2022;Liu et al 2022), as they have demonstrated substantial improvements over the single-valued deterministic forecasts (Siddique & Mejia 2017). An ensemble prediction system can provide multiple realizations of possible streamflow conditions enabling decision-makers to have a better idea of the likelihood of a specific future event (e.g., the probability of exceeding a flood threshold).…”

“…Within an ensemble prediction system, hydrological models are generally forced with ensemble meteorological forecasts from weather prediction models (Demargne et al 2014;Pagano et al 2016;Alfieri et al 2017;Siddique & Mejia 2017;Zhang et al 2020;Hapuarachchi et al 2022;National Weather Service 2022) as opposed to streamflow simulations, which are often generated by hydrological models forced with meteorological observations (Kratzert et al 2018;Feng et al 2020;Konapala et al 2020). In the United States, the NOAA's National Weather Service River Forecast Centers are implementing the Hydrological Ensemble Forecast Service (HEFS) to incorporate meteorological ensembles into their flood forecasting operations (Brown et al 2014;Kim et al 2018;National Weather Service 2022).…”