%0 Journal Article %1 Schepen2016Optimising %A Schepen, Andrew %A Zhao, Tongtiegang %A Wang, Q. J. %A Zhou, Senlin %A Feikema, Paul %D 2016 %J Hydrology and Earth System Sciences Discussions %K verification skill seasonal hydrology reliability rivers %P 1--19 %R 10.5194/hess-2016-199 %T Optimising Seasonal Streamflow Forecast Lead Time for Operational Decision Making in Australia %U http://dx.doi.org/10.5194/hess-2016-199 %X Statistical seasonal forecasts of three-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the Bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The Bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have N-days lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-days sliding windows. Forecasts are produced for 23 forecast locations with 0–14 and 21-days lead time. The forecasts are assessed in terms of CRPS skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-days lead time are reduced on average by 4 percentage points with differences largely contained within +5 to −15 percentage points. A flexible N-days lead time forecasting system could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.

@article{Schepen2016Optimising, abstract = {Statistical seasonal forecasts of three-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the Bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The Bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have N-days lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-days sliding windows. Forecasts are produced for 23 forecast locations with 0–14 and 21-days lead time. The forecasts are assessed in terms of CRPS skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-days lead time are reduced on average by 4 percentage points with differences largely contained within +5 to −15 percentage points. A flexible N-days lead time forecasting system could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Schepen, Andrew and Zhao, Tongtiegang and Wang, Q. J. and Zhou, Senlin and Feikema, Paul}, biburl = {https://www.bibsonomy.org/bibtex/2ba0476ff1e6dbd438e3158def8412f56/pbett}, citeulike-article-id = {14030441}, citeulike-linkout-0 = {http://dx.doi.org/10.5194/hess-2016-199}, day = 09, doi = {10.5194/hess-2016-199}, interhash = {127723fd5aa2284304089c8c42a1b9a9}, intrahash = {ba0476ff1e6dbd438e3158def8412f56}, issn = {1812-2116}, journal = {Hydrology and Earth System Sciences Discussions}, keywords = {verification skill seasonal hydrology reliability rivers}, month = may, pages = {1--19}, posted-at = {2016-05-09 09:54:28}, priority = {2}, timestamp = {2018-06-22T18:36:26.000+0200}, title = {Optimising Seasonal Streamflow Forecast Lead Time for Operational Decision Making in Australia}, url = {http://dx.doi.org/10.5194/hess-2016-199}, year = 2016 }