Abstract
Abstract. This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combinations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.
Highlights
IntroductionThe advances of radar rainfall estimates with high spatial and temporal resolution have demonstrated the prospect of improving the accuracy of rainfall inputs on which the accuracy of stream flow simulation and realtime flood forecasting through hydrological models depends.There is a wide range of studies which have focused on using weather radars for quantitative measurement of rainfall in various hydrological models in order to evaluate the radar performance in different hydrological applications, especially in flood forecasting (Collier and Knowles, 1986; Owens, 1986; Cluckie and Owens, 1987; Cluckie et al, 1989; Bell and Moore, 1998a, b; Borga, 2001; Carpenter et al, 2001; Tachikawa et al, 2002; Hossain et al, 2004; Reichel et al, 2008; Zhu and Cluckie, 2011); in particular, the value of radar-based data from the UK Nimrod system has been highlighted repeatedly, for example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events.the advantage of the weather radar rainfall estimates has been limited by a variety of sources of uncertainty in the radar reflectivity process, including random and systematic errors such as the hardware calibration, which acquires accurate measurements of transmitted power, bandwidth, antenna gain, wavelength and pulse width (ProbertJones, 1962; Battan, 1973), the deflection of the radar beam (anomalous propagation), non-meteorological echoes (clutter), signal attenuation, orographic enhancement, radar beam overshooting, variation of the vertical profile of reflectivity (VPR), extrapolation of the measurements to the ground, drop size distribution, Z-R relationship, sampling effects and bright band, all of which can be referred to in the numerous discussions on radar rainfall estimation errors (Harrold et al, 1974; Browning, 1978; Wilson and Brandes, 1979; Duncan et al, 1993; Fabry et al, 1992, 1994; Kitchen, 1997; Krajewski and Smith, 2002; Rico-Ramirez et al, 2007).D
A simplified statistical error model based on empirical random error distribution was constructed to define and quantitate the errors in the radar rainfall estimates through hydrological models with different rainfall-runoff mechanisms
The propagation of radar rainfall estimation errors was assessed through different hydrological models, ranging from fully distributed to semi-distributed to lumped models in the Upper Medway Catchment in Kent, United Kingdom
Summary
The advances of radar rainfall estimates with high spatial and temporal resolution have demonstrated the prospect of improving the accuracy of rainfall inputs on which the accuracy of stream flow simulation and realtime flood forecasting through hydrological models depends.There is a wide range of studies which have focused on using weather radars for quantitative measurement of rainfall in various hydrological models in order to evaluate the radar performance in different hydrological applications, especially in flood forecasting (Collier and Knowles, 1986; Owens, 1986; Cluckie and Owens, 1987; Cluckie et al, 1989; Bell and Moore, 1998a, b; Borga, 2001; Carpenter et al, 2001; Tachikawa et al, 2002; Hossain et al, 2004; Reichel et al, 2008; Zhu and Cluckie, 2011); in particular, the value of radar-based data from the UK Nimrod system has been highlighted repeatedly, for example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events.the advantage of the weather radar rainfall estimates has been limited by a variety of sources of uncertainty in the radar reflectivity process, including random and systematic errors such as the hardware calibration, which acquires accurate measurements of transmitted power, bandwidth, antenna gain, wavelength and pulse width (ProbertJones, 1962; Battan, 1973), the deflection of the radar beam (anomalous propagation), non-meteorological echoes (clutter), signal attenuation, orographic enhancement, radar beam overshooting, variation of the vertical profile of reflectivity (VPR), extrapolation of the measurements to the ground, drop size distribution, Z-R relationship, sampling effects and bright band, all of which can be referred to in the numerous discussions on radar rainfall estimation errors (Harrold et al, 1974; Browning, 1978; Wilson and Brandes, 1979; Duncan et al, 1993; Fabry et al, 1992, 1994; Kitchen, 1997; Krajewski and Smith, 2002; Rico-Ramirez et al, 2007).D. The advances of radar rainfall estimates with high spatial and temporal resolution have demonstrated the prospect of improving the accuracy of rainfall inputs on which the accuracy of stream flow simulation and realtime flood forecasting through hydrological models depends. There is a wide range of studies which have focused on using weather radars for quantitative measurement of rainfall in various hydrological models in order to evaluate the radar performance in different hydrological applications, especially in flood forecasting (Collier and Knowles, 1986; Owens, 1986; Cluckie and Owens, 1987; Cluckie et al, 1989; Bell and Moore, 1998a, b; Borga, 2001; Carpenter et al, 2001; Tachikawa et al, 2002; Hossain et al, 2004; Reichel et al, 2008; Zhu and Cluckie, 2011); in particular, the value of radar-based data from the UK Nimrod system has been highlighted repeatedly, for example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. Zhu et al.: Statistical analysis of error propagation from radar rainfall to hydrological models
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