气候变化领域集成服务门户(CCPortal): Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

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DOI	10.5194/hess-22-6257-2018
	Evaluating post-processing approaches for monthly and seasonal streamflow forecasts
	Woldemeskel F.; McInerney D.; Lerat J.; Thyer M.; Kavetski D.; Shin D.; Tuteja N.; Kuczera G.
发表日期	2018
ISSN	1027-5606
起始页码	6257
结束页码	6278
卷号	22 期号:12
英文摘要	Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations - logarithmic (Log), log-sinh (Log-Sinh), and Box-Cox with (BC0.2) - and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.
语种	英语
scopus关键词	Reliability; Runoff; Stream flow; Water management; Bureau of meteorologies; Comprehensive analysis; Forecast verifications; Hydrological modeling; Post-processing scheme; Probabilistic properties; Ranked probability skill scores; Streamflow forecasting; Weather forecasting; catchment; forecasting method; numerical method; rainfall; seasonal variation; streamflow; water resource; Australia
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159836
作者单位	Woldemeskel, F., Bureau of Meteorology, VIC, Melbourne, Australia; McInerney, D., School of Civil, Environmental and Mining Engineering, University of AdelaideSA, Australia; Lerat, J., Bureau of Meteorology, Canberra, ACT, Australia; Thyer, M., School of Civil, Environmental and Mining Engineering, University of AdelaideSA, Australia; Kavetski, D., School of Civil, Environmental and Mining Engineering, University of AdelaideSA, Australia, School of Engineering, University of Newcastle, Callaghan, NSW, Australia; Shin, D., Bureau of Meteorology, VIC, Melbourne, Australia; Tuteja, N., Bureau of Meteorology, Canberra, ACT, Australia; Kuczera, G., School of Engineering, University of Newcastle, Callaghan, NSW, Australia
推荐引用方式 GB/T 7714	Woldemeskel F.,McInerney D.,Lerat J.,et al. Evaluating post-processing approaches for monthly and seasonal streamflow forecasts[J],2018,22(12).
APA	Woldemeskel F..,McInerney D..,Lerat J..,Thyer M..,Kavetski D..,...&Kuczera G..(2018).Evaluating post-processing approaches for monthly and seasonal streamflow forecasts.Hydrology and Earth System Sciences,22(12).
MLA	Woldemeskel F.,et al."Evaluating post-processing approaches for monthly and seasonal streamflow forecasts".Hydrology and Earth System Sciences 22.12(2018).