DOI: 10.5194/hess-21-2881-2017
Scopus记录号: 2-s2.0-85020695893
论文题名: Global evaluation of runoff from 10 state-of-the-art hydrological models
作者: Beck H ; E ; , Van Dijk A ; I ; J ; M ; , De Roo A ; , Dutra E ; , Fink G ; , Orth R ; , Schellekens J
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 6 起始页码: 2881
结束页码: 2903
语种: 英语
Scopus关键词: Catchments
; Climate change
; Hydrology
; Runoff
; Uncertainty analysis
; Ensemble performance
; Hydrological impacts
; Hydrological modeling
; Hydrological models
; Land surface models
; Performance metrics
; Spatial and temporal resolutions
; Temporal resolution
; Climate models
; climate change
; data set
; hydrological modeling
; land surface
; performance assessment
; runoff
; snowmelt
; spatiotemporal analysis
; streamflow
英文摘要: Observed streamflow data from 966 medium sized catchments (1000-5000 km2) around the globe were used to comprehensively evaluate the daily runoff estimates (1979-2012) of six global hydrological models (GHMs) and four land surface models (LSMs) produced as part of tier-1 of the eartH2Observe project. The models were all driven by the WATCH Forcing Data ERA-Interim (WFDEI) meteorological dataset, but used different datasets for non-meteorologic inputs and were run at various spatial and temporal resolutions, although all data were re-sampled to a common 0. 5° spatial and daily temporal resolution. For the evaluation, we used a broad range of performance metrics related to important aspects of the hydrograph. We found pronounced inter-model performance differences, underscoring the importance of hydrological model uncertainty in addition to climate input uncertainty, for example in studies assessing the hydrological impacts of climate change. The uncalibrated GHMs were found to perform, on average, better than the uncalibrated LSMs in snow-dominated regions, while the ensemble mean was found to perform only slightly worse than the best (calibrated) model. The inclusion of less-accurate models did not appreciably degrade the ensemble performance. Overall, we argue that more effort should be devoted on calibrating and regionalizing the parameters of macro-scale models. We further found that, despite adjustments using gauge observations, the WFDEI precipitation data still contain substantial biases that propagate into the simulated runoff. The early bias in the spring snowmelt peak exhibited by most models is probably primarily due to the widespread precipitation underestimation at high northern latitudes. © 2017 The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79153
Appears in Collections: 气候变化事实与影响
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作者单位: Dept. of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; Fenner School of Environment and Society, Australian National University (ANU), Canberra, Australia; European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, Ispra, VA, Italy; European Centre for Medium-Range Weather Forecasts (ECMWF), Redding, United Kingdom; Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; Center for Environmental Systems Research (CESR), University of Kassel, Kassel, Germany; Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland; Inland Water Systems Unit, Deltares, Delft, Netherlands
Recommended Citation:
Beck H,E,, Van Dijk A,et al. Global evaluation of runoff from 10 state-of-the-art hydrological models[J]. Hydrology and Earth System Sciences,2017-01-01,21(6)