DOI: 10.5194/hess-23-1973-2019
论文题名: Assessment of precipitation error propagation in multi-model global water resource reanalysis
作者: Bhuiyan M.A.E. ; Nikolopoulos E.I. ; Anagnostou E.N. ; Polcher J. ; Albergel C. ; Dutra E. ; Fink G. ; Martínez-De La Torre A. ; Munier S.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期: 4 起始页码: 1973
结束页码: 1994
语种: 英语
Scopus关键词: Evapotranspiration
; Hydrology
; Learning systems
; Model structures
; Neural networks
; Precipitation (meteorology)
; Runoff
; Weather forecasting
; Climate prediction centers
; European centre for medium-range weather forecasts
; Global water resources
; Ground-based observations
; Hydrological variables
; National Oceanic and Atmospheric Administration
; Precipitation characteristics
; Precipitation estimation from remotely sensed information
; Uncertainty analysis
; ensemble forecasting
; error analysis
; evapotranspiration
; global perspective
; hydrological modeling
; machine learning
; precipitation (climatology)
; resource assessment
; runoff
; uncertainty analysis
; water resource
; Iberian Peninsula
英文摘要: This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000-2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) - JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology - Tiled European Centre for Medium-Range Weather Forecasts - ECMWF - Scheme for Surface Exchanges over Land) - and one global hydrological model, WaterGAP3 (Water - a Global Assessment and Prognosis). Simulations were carried out for five precipitation products - CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model-forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications. © 2019 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162990
Appears in Collections: 气候变化与战略
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作者单位: Bhuiyan, M.A.E., Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States; Nikolopoulos, E.I., Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States; Anagnostou, E.N., Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States; Polcher, J., Laboratoire de Météorologie Dynamique du CNRS/IPSL, École Polytechnique, Paris, France; Albergel, C., CNRM-Université de Toulouse, Météo-France, CNRS, Toulouse, 31057, France; Dutra, E., Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; Fink, G., Landesanstalt für Umwelt Baden-Württemberg (LUBW), Karlsruhe, Germany; Martínez-De La Torre, A., Centre for Ecology and Hydrology, Wallingford, United Kingdom; Munier, S., CNRM-Université de Toulouse, Météo-France, CNRS, Toulouse, 31057, France
Recommended Citation:
Bhuiyan M.A.E.,Nikolopoulos E.I.,Anagnostou E.N.,et al. Assessment of precipitation error propagation in multi-model global water resource reanalysis[J]. Hydrology and Earth System Sciences,2019-01-01,23(4)