DOI: 10.5194/hess-20-3059-2016
Scopus记录号: 2-s2.0-84980320154
论文题名: Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations
作者: López López P ; , Wanders N ; , Schellekens J ; , Renzullo L ; J ; , Sutanudjaja E ; H ; , Bierkens M ; F ; P
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 7 起始页码: 3059
结束页码: 3076
语种: 英语
Scopus关键词: Climate models
; Digital storage
; Hydrology
; Image resolution
; Meteorology
; Moisture
; Rivers
; Satellites
; Soil moisture
; Soils
; Stream flow
; Water resources
; Watersheds
; Coarse-resolution model
; Ensemble Kalman Filter
; High spatial resolution
; Meteorological observation
; Remotely-sensed observations
; Satellite soil moisture
; Spaceborne soil moisture
; Water resources management
; Soil surveys
; AMSR-E
; data assimilation
; data set
; downscaling
; gauge
; hydrological modeling
; remote sensing
; resource management
; river basin
; satellite
; soil moisture
; spatial resolution
; streamflow
; water budget
; water resource
; Australia
; Murrumbidgee Basin
; New South Wales
英文摘要: The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from ∼0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research. © Author(s) 2016. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78774
Appears in Collections: 气候变化事实与影响
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作者单位: Deltares, Delft, Netherlands; Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States; CSIRO Land and Water, Canberra, ACT, Australia; Unit Soil and Groundwater Systems, Deltares, Utrecht, Netherlands
Recommended Citation:
López López P,, Wanders N,, Schellekens J,et al. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations[J]. Hydrology and Earth System Sciences,2016-01-01,20(7)