The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia
Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km<sup>2</sup>) with little or no seasonal snowmelt, where real-time wa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-02-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/14/219/2014/nhess-14-219-2014.pdf |
| _version_ | 1818365504059867136 |
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| author | J. C. Bennett Q. J. Wang P. Pokhrel D. E. Robertson |
| author_facet | J. C. Bennett Q. J. Wang P. Pokhrel D. E. Robertson |
| author_sort | J. C. Bennett |
| collection | DOAJ |
| description | Skilful forecasts of high streamflows a month or more in advance are likely
to be of considerable benefit to emergency services and the broader
community. This is particularly true for mesoscale catchments
(< 2000 km<sup>2</sup>) with little or no seasonal
snowmelt, where real-time warning systems are only able to
give short notice of impending floods. In this study, we generate forecasts
of high streamflows for the coming 1-month and coming 3-month periods using
large-scale ocean–atmosphere climate indices and catchment wetness as
predictors. Forecasts are generated with a combination of Bayesian joint
probability modelling and Bayesian model averaging. High streamflows are
defined as maximum single-day streamflows and maximum 5-day streamflows that
occur during each 1-month or 3-month forecast period. Skill is clearly
evident in the 1-month forecasts of high streamflows. Surprisingly, in
several catchments positive skill is also evident in forecasts of large
threshold events (exceedance probabilities of 25%) over the next month.
Little skill is evident in forecasts of high streamflows for the 3-month
period. We show that including lagged climate indices as predictors adds
little skill to the forecasts, and thus catchment wetness is by far the most
important predictor. Accordingly, we recommend that forecasts may be improved
by using accurate estimates of catchment wetness. |
| first_indexed | 2024-12-13T22:21:19Z |
| format | Article |
| id | doaj.art-313b058a243f44f387c06139aacef369 |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-12-13T22:21:19Z |
| publishDate | 2014-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-313b058a243f44f387c06139aacef3692022-12-21T23:29:22ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812014-02-0114221923310.5194/nhess-14-219-2014The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast AustraliaJ. C. Bennett0Q. J. Wang1P. Pokhrel2D. E. Robertson3CSIRO Land and Water, Graham Road, Highett, Victoria 3190, AustraliaCSIRO Land and Water, Graham Road, Highett, Victoria 3190, AustraliaCSIRO Land and Water, Graham Road, Highett, Victoria 3190, AustraliaCSIRO Land and Water, Graham Road, Highett, Victoria 3190, AustraliaSkilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km<sup>2</sup>) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean–atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.http://www.nat-hazards-earth-syst-sci.net/14/219/2014/nhess-14-219-2014.pdf |
| spellingShingle | J. C. Bennett Q. J. Wang P. Pokhrel D. E. Robertson The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia Natural Hazards and Earth System Sciences |
| title | The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia |
| title_full | The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia |
| title_fullStr | The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia |
| title_full_unstemmed | The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia |
| title_short | The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia |
| title_sort | challenge of forecasting high streamflows 1 ndash 3 months in advance with lagged climate indices in southeast australia |
| url | http://www.nat-hazards-earth-syst-sci.net/14/219/2014/nhess-14-219-2014.pdf |
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