气候变化领域集成服务门户(CCPortal): Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed

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DOI	10.5194/hess-22-4685-2018
	Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed
	Casson D.R.; Werner M.; Weerts A.; Solomatine D.
发表日期	2018
ISSN	1027-5606
起始页码	4685
结束页码	4697
卷号	22 期号:9
英文摘要	Hydrological modelling in the Canadian sub-Arctic is hindered by sparse meteorological and snowpack data. The snow water equivalent (SWE) of the winter snowpack is a key predictor and driver of spring flow, but the use of SWE data in hydrological applications is limited due to high uncertainty. Global re-analysis datasets that provide gridded meteorological and SWE data may be well suited to improve hydrological assessment and snowpack simulation. To investigate representation of hydrological processes and SWE for application in hydropower operations, global re-analysis datasets covering 1979-2014 from the European Union FP7 eartH2Observe project are applied to global and local conceptual hydrological models. The recently developed Multi-Source Weighted-Ensemble Precipitation (MSWEP) and the WATCH Forcing Data applied to ERA-Interim data (WFDEI) are used to simulate snowpack accumulation, spring snowmelt volume and annual streamflow. The GlobSnow-2 SWE product funded by the European Space Agency with daily coverage from 1979 to 2014 is evaluated against in situ SWE measurement over the local watershed. Results demonstrate the successful application of global datasets for streamflow prediction, snowpack accumulation and snowmelt timing in a snowmelt-driven sub-Arctic watershed. The study was unable to demonstrate statistically significant correlations (p/0.05) among the measured snowpack, global hydrological model and GlobSnow-2 SWE compared to snowmelt runoff volume or peak discharge. The GlobSnow-2 product is found to under-predict late-season snowpacks over the study area and shows a premature decline of SWE prior to the true onset of the snowmelt. Of the datasets tested, the MSWEP precipitation results in annual SWE estimates that are better predictors of snowmelt volume and peak discharge than the WFDEI or GlobSnow-2. This study demonstrates the operational and scientific utility of the global re-analysis datasets in the sub-Arctic, although knowledge gaps remain in global satellite-based datasets for snowpack representation, for example the relationship between passive-microwave-measured SWE to snowmelt runoff volume. © 2018 Author(s).
语种	英语
scopus关键词	Runoff; Snow; Stream flow; Watersheds; European Space Agency; Hydrological assessment; Hydrological modeling; Hydrological modelling; Hydrological process; Snow water equivalent; Snowpack accumulation; Streamflow prediction; Snow melting systems; arctic environment; assessment method; climate forcing; data set; estimation method; European Union; global perspective; hydrological modeling; in situ measurement; peak discharge; satellite data; snow water equivalent; snowmelt; snowpack; streamflow; uncertainty analysis; watershed; winter; Centrostegia thurberi
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/159920
作者单位	Casson, D.R., IHE Delft Institute of Water Education, Hydroinformatics Depart Group, P.O. Box 3015, DA, Delft, 2601, Netherlands, Deltares Operational Water Management, P.O. Box 177, MH, Delft, 2600, Netherlands; Werner, M., IHE Delft Institute of Water Education, Hydroinformatics Depart Group, P.O. Box 3015, DA, Delft, 2601, Netherlands, Deltares Operational Water Management, P.O. Box 177, MH, Delft, 2600, Netherlands; Weerts, A., Deltares Operational Water Management, P.O. Box 177, MH, Delft, 2600, Netherlands, Wageningen University and Research, Hydrology and Quantitative Water Management Group, P.O. Box 47, AA, Wageningen, 6700, Netherlands; Solomatine, D., IHE Delft Institute of Water Education, Hydroinformatics Depart Group, P.O. Box 3015, DA, Delft, 2601, Netherlands, Delft University of Technology, Water Resources Section, P.O. Box 5048, GA, Delft, 2600, Netherlands
推荐引用方式 GB/T 7714	Casson D.R.,Werner M.,Weerts A.,et al. Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed[J],2018,22(9).
APA	Casson D.R.,Werner M.,Weerts A.,&Solomatine D..(2018).Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed.Hydrology and Earth System Sciences,22(9).
MLA	Casson D.R.,et al."Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed".Hydrology and Earth System Sciences 22.9(2018).