Development of a national 7-day ensemble streamflow forecasting service for Australia
<p>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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-09-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/26/4801/2022/hess-26-4801-2022.pdf |
| _version_ | 1811208201880731648 |
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| author | H. A. P. Hapuarachchi M. A. Bari A. Kabir M. M. Hasan F. M. Woldemeskel N. Gamage P. D. Sunter X. S. Zhang D. E. Robertson J. C. Bennett P. M. Feikema |
| author_facet | H. A. P. Hapuarachchi M. A. Bari A. Kabir M. M. Hasan F. M. Woldemeskel N. Gamage P. D. Sunter X. S. Zhang D. E. Robertson J. C. Bennett P. M. Feikema |
| author_sort | H. A. P. Hapuarachchi |
| collection | DOAJ |
| description | <p>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.</p> |
| first_indexed | 2024-04-12T04:18:12Z |
| format | Article |
| id | doaj.art-c32afe9ac7914895882fc8c1154bb1d9 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-04-12T04:18:12Z |
| publishDate | 2022-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-c32afe9ac7914895882fc8c1154bb1d92022-12-22T03:48:20ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382022-09-01264801482110.5194/hess-26-4801-2022Development of a national 7-day ensemble streamflow forecasting service for AustraliaH. A. P. Hapuarachchi0M. A. Bari1A. Kabir2M. M. Hasan3F. M. Woldemeskel4N. Gamage5P. D. Sunter6X. S. Zhang7D. E. Robertson8J. C. Bennett9P. M. Feikema10Bureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, AustraliaBureau of Meteorology, 1 Ord Street, West Perth, WA 6005, AustraliaBureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, AustraliaBureau of Meteorology, The Treasury Building, Parkes Place West, Canberra, ACT 2600, AustraliaBureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, AustraliaBureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, AustraliaBureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, AustraliaBureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, AustraliaCommonwealth Scientific and Industrial Research Organization, Research Way, Clayton, VIC 3168, AustraliaCommonwealth Scientific and Industrial Research Organization, Research Way, Clayton, VIC 3168, AustraliaBureau of Meteorology, 700 Collins Street, Docklands, VIC 3008, Australia<p>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.</p>https://hess.copernicus.org/articles/26/4801/2022/hess-26-4801-2022.pdf |
| spellingShingle | H. A. P. Hapuarachchi M. A. Bari A. Kabir M. M. Hasan F. M. Woldemeskel N. Gamage P. D. Sunter X. S. Zhang D. E. Robertson J. C. Bennett P. M. Feikema Development of a national 7-day ensemble streamflow forecasting service for Australia Hydrology and Earth System Sciences |
| title | Development of a national 7-day ensemble streamflow forecasting service for Australia |
| title_full | Development of a national 7-day ensemble streamflow forecasting service for Australia |
| title_fullStr | Development of a national 7-day ensemble streamflow forecasting service for Australia |
| title_full_unstemmed | Development of a national 7-day ensemble streamflow forecasting service for Australia |
| title_short | Development of a national 7-day ensemble streamflow forecasting service for Australia |
| title_sort | development of a national 7 day ensemble streamflow forecasting service for australia |
| url | https://hess.copernicus.org/articles/26/4801/2022/hess-26-4801-2022.pdf |
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