气候变化领域集成服务门户(CCPortal): A virtual hydrological framework for evaluation of stochastic rainfall models

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DOI	10.5194/hess-23-4783-2019
	A virtual hydrological framework for evaluation of stochastic rainfall models
	Bennett B.; Thyer M.; Leonard M.; Lambert M.; Bates B.
发表日期	2019
ISSN	1027-5606
起始页码	4783
结束页码	4801
卷号	23 期号:11
英文摘要	Stochastic rainfall modelling is a commonly used technique for evaluating the impact of flooding, drought, or climate change in a catchment. While considerable attention has been given to the development of stochastic rainfall models (SRMs), significantly less attention has been paid to developing methods to evaluate their performance. Typical evaluation methods employ a wide range of rainfall statistics. However, they give limited understanding about which rainfall statistical characteristics are most important for reliable streamflow prediction. To address this issue a formal evaluation framework is introduced, with three key features: (i) streamflow-based, to give a direct evaluation of modelled streamflow performance, (ii) virtual, to avoid the issue of confounding errors in hydrological models or data, and (iii) targeted, to isolate the source of errors according to specific sites and seasons. The virtual hydrological evaluation framework uses two types of tests, integrated tests and unit tests, to attribute deficiencies that impact on streamflow to their original source in the SRM according to site and season. The framework is applied to a case study of 22 sites in South Australia with a strong seasonal cycle. In this case study, the framework demonstrated the surprising result that apparently "good" modelled rainfall can produce "poor" streamflow predictions, whilst "poor" modelled rainfall may lead to "good" streamflow predictions. This is due to the representation of highly seasonal catchment processes within the hydrological model that can dampen or amplify rainfall errors when converted to streamflow. The framework identified the importance of rainfall in the "wetting-up" months (months where the rainfall is high but streamflow low) of the annual hydrologic cycle (May and June in this case study) for providing reliable predictions of streamflow over the entire year despite their low monthly flow volume. This insight would not have been found using existing methods and highlights the importance of the virtual hydrological evaluation framework for SRM evaluation. © 2019 Author(s).
语种	英语
scopus关键词	Catchments; Climate change; Errors; Forecasting; Rain; Runoff; Stochastic systems; Stream flow; Virtual addresses; Evaluation framework; Evaluation methods; Hydrological modeling; Hydrological models; Rainfall statistics; Statistical characteristics; Stochastic rainfalls; Streamflow prediction; Stochastic models; catchment; climate change; drought stress; hydrological modeling; hydrological regime; rainfall; seasonality; stochasticity; streamflow; Australia; South 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/159560
作者单位	Bennett, B., School of Civil Environmental and Mining Engineering, University of Adelaide, North Terrace CampusSA 5005, Australia; Thyer, M., School of Civil Environmental and Mining Engineering, University of Adelaide, North Terrace CampusSA 5005, Australia; Leonard, M., School of Civil Environmental and Mining Engineering, University of Adelaide, North Terrace CampusSA 5005, Australia; Lambert, M., School of Civil Environmental and Mining Engineering, University of Adelaide, North Terrace CampusSA 5005, Australia; Bates, B., School of Agriculture and Environment, University of Western Australia, Crawley, WA 6009, Australia
推荐引用方式 GB/T 7714	Bennett B.,Thyer M.,Leonard M.,et al. A virtual hydrological framework for evaluation of stochastic rainfall models[J],2019,23(11).
APA	Bennett B.,Thyer M.,Leonard M.,Lambert M.,&Bates B..(2019).A virtual hydrological framework for evaluation of stochastic rainfall models.Hydrology and Earth System Sciences,23(11).
MLA	Bennett B.,et al."A virtual hydrological framework for evaluation of stochastic rainfall models".Hydrology and Earth System Sciences 23.11(2019).