DOI: 10.5194/hess-19-1727-2015
Scopus记录号: 2-s2.0-84928017257
论文题名: Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data
作者: Silvestro F ; , Gabellani S ; , Rudari R ; , Delogu F ; , Laiolo P ; , Boni G
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 4 起始页码: 1727
结束页码: 1751
语种: 英语
Scopus关键词: Atmospheric temperature
; Calibration
; Catchments
; Climate models
; Equipment testing
; Geomorphology
; Hydrology
; Remote sensing
; Runoff
; Satellites
; Soil moisture
; Stream flow
; Testing
; Weather satellites
; Digital elevation model
; Discharge observations
; Distributed hydrological model
; Land surface temperature
; Meteosat second generations
; Parameter calibration
; Satellite observations
; Surface soil moisture
; Parameter estimation
; calibration
; digital elevation model
; estimation method
; forecasting method
; geomorphology
; hydrological cycle
; hydrological modeling
; parameterization
; remote sensing
; satellite data
; Shuttle Radar Topography Mission
; soil moisture
; streamflow
; uncertainty analysis
; Europe
英文摘要: During the last decade the opportunity and usefulness of using remote-sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite-based products often allow for the advantage of observing hydrologic variables in a distributed way, offering a different view with respect to traditional observations that can help with understanding and modeling the hydrological cycle. Moreover, remote-sensing data are fundamental in scarce data environments. The use of satellite-derived digital elevation models (DEMs), which are now globally available at 30 m resolution (e.g., from Shuttle Radar Topographic Mission, SRTM), have become standard practice in hydrologic model implementation, but other types of satellite-derived data are still underutilized. As a consequence there is the need for developing and testing techniques that allow the opportunities given by remote-sensing data to be exploited, parameterizing hydrological models and improving their calibration. In this work, Meteosat Second Generation land-surface temperature (LST) estimates and surface soil moisture (SSM), available from European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) H-SAF, are used together with streamflow observations (S. N.) to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. The first part of the work aims at proving that satellite observations can be exploited to reduce uncertainties in parameter calibration by reducing the parameter equifinality that can become an issue in forecast mode. In the second part, four parameter estimation strategies are implemented and tested in a comparative mode: (i) a multi-objective approach that includes both satellite and ground observations which is an attempt to use different sources of data to add constraints to the parameters; (ii and iii) two approaches solely based on remotely sensed data that reproduce the case of a scarce data environment where streamflow observation are not available; (iv) a standard calibration based on streamflow observations used as a benchmark for the others. Two Italian catchments are used as a test bed to verify the model capability in reproducing long-term (multi-year) simulations. The results of the analysis evidence that, as a result of the model structure and the nature itself of the catchment hydrologic processes, some model parameters are only weakly dependent on discharge observations, and prove the usefulness of using data from both ground stations and satellites to additionally constrain the parameters in the calibration process and reduce the number of equifinal solutions. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78553
Appears in Collections: 气候变化事实与影响
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作者单位: CIMA Research Foundation, Savona, Italy; DIBRIS, University of Genova, Genova, Italy
Recommended Citation:
Silvestro F,, Gabellani S,, Rudari R,et al. Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data[J]. Hydrology and Earth System Sciences,2015-01-01,19(4)