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Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data
Author(s) -
Wang Enli,
Zhang Yongqiang,
Luo Jiangmei,
Chiew Francis H. S.,
Wang Q. J.
Publication year - 2011
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr009922
Subject(s) - streamflow , environmental science , climatology , surface runoff , drainage basin , flood forecasting , water year , hydrology (agriculture) , meteorology , geography , geology , ecology , cartography , geotechnical engineering , biology
Well‐validated rainfall‐runoff models are able to capture the relationships between rainfall and streamflow and to reliably estimate initial catchment states. While future streamflows are mainly dependent on initial catchment states and future rainfall, use of the rainfall‐runoff models together with estimated future rainfall can produce skilful forecasts of future streamflows. This is the basis for the ensemble streamflow prediction system, but this approach has not been explored in Australia. In this paper, two conceptual rainfall‐runoff models, together with rainfall ensembles or analogues based on historical rainfall and the Southern Oscillation index (SOI), were used to forecast streamflows at monthly and 3‐monthly scales at two catchments in east Australia. The results showed that both models forecast monthly streamflow well when forecasts for all months were evaluated together, but their performance varied significantly from month to month. Best forecasting skills were obtained (both monthly and 3 monthly) when the models were coupled with ensemble forcings on the basis of long‐term historical rainfall. SOI‐based resampling of forcings from historical data led to improved forecasting skills only in the period from September to December at the catchment in Queensland. For 3 month streamflow forecasts, best skills were in the period from April to June at the catchment in Queensland and in the period from October to January for the catchment in New South Wales, both of which were the periods after the rainy season. The forecasting skills are indicatively comparable to the statistical forecasting skills using a Bayesian joint probability approach. The potential approaches for improved hydrologic modeling through conditional parameterization and for improved forecasting skills through advanced model updating and bias corrections are also discussed.