DOI: 10.5194/hess-18-2343-2014
Scopus记录号: 2-s2.0-84903280934
论文题名: The suitability of remotely sensed soil moisture for improving operational flood forecasting
作者: Wanders N ; , Karssenberg D ; , De Roo A ; , De Jong S ; M ; , Bierkens M ; F ; P
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2014
卷: 18, 期: 6 起始页码: 2343
结束页码: 2357
语种: 英语
Scopus关键词: Catchments
; Climate models
; Errors
; Flood control
; Forecasting
; Meteorological instruments
; Remote sensing
; Satellites
; Soil moisture
; Soil surveys
; Advanced microwave scanning radiometer
; Continuous ranked probability scores
; Discharge observations
; Distributed hydrological model
; Earth observing systems
; Ensemble Kalman Filter
; Remotely sensed soil moisture
; Soil Moisture and Ocean Salinity (SMOS)
; Floods
; AMSR-E
; ASCAT
; baseflow
; catchment
; data assimilation
; ensemble forecasting
; flood control
; flood forecasting
; hydrological modeling
; Kalman filter
; low flow
; remote sensing
; river discharge
; satellite data
; SMOS
; soil moisture
; Danube Basin
英文摘要: We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments. © Author(s) 2014. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78211
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Physical Geography, Utrecht University, Utrecht, Netherlands; European Commission, Join Research Centre, Ispra, Italy; Deltares, Utrecht, Netherlands
Recommended Citation:
Wanders N,, Karssenberg D,, De Roo A,et al. The suitability of remotely sensed soil moisture for improving operational flood forecasting[J]. Hydrology and Earth System Sciences,2014-01-01,18(6)