DOI: 10.5194/hess-21-1077-2017
Scopus记录号: 2-s2.0-85013420472
论文题名: Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models
作者: Muthusamy M ; , Schellart A ; , Tait S ; , Heuvelink G ; B ; M
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 2 起始页码: 1077
结束页码: 1091
语种: 英语
Scopus关键词: Catchments
; Errors
; Forecasting
; Gages
; Mathematical transformations
; Measurement errors
; Metadata
; Precipitation (meteorology)
; Rain
; Rain gages
; Runoff
; Stochastic models
; Stochastic systems
; Geostatistical approach
; Geostatistical models
; Hydrodynamic modelling
; Prediction uncertainty
; Rainfall estimations
; Rainfall measurements
; Spatial correlations
; Spatial stochastic simulations
; Uncertainty analysis
; catchment
; hydrodynamics
; hydrological modeling
; numerical model
; precipitation intensity
; rainfall
; raingauge
; uncertainty analysis
; upscaling
英文摘要: In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 × 200m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher uncertainty at lower intensities. Results from this study can be used for uncertainty analyses of hydrologic and hydrodynamic modelling of similar-sized urban catchments as it provides information on uncertainty associated with rainfall estimation, which is arguably the most important input in these models. This will help to better interpret model results and avoid false calibration and force-fitting of model parameters. © 2017 The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79251
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Civil and Structural Engineering, University of Sheffield, Sheffield, United Kingdom; Soil Geography and Landscape Group, Wageningen University, Wageningen, Netherlands
Recommended Citation:
Muthusamy M,, Schellart A,, Tait S,et al. Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models[J]. Hydrology and Earth System Sciences,2017-01-01,21(2)