Journal article 403 views
Statistical analysis of error propagation from radar rainfall to hydrological models
Hydrology and Earth System Sciences, Volume: 17, Issue: 4, Pages: 1445 - 1453
Swansea University Author: Ian Cluckie
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This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation un- certainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed t...
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This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation un- certainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combi- nations 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 hydro- graph 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 sim- ulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This prelimi- nary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.
The work was carried out in association with two major research grants. Initial support was from the EPSRC £20M+ Flood Risk Management Research Consortium (FRMRC) that was chaired by Cluckie. Additional funding came from the early phase of the NERC EPIRUS research project that formed part of the FREE program. This work focused on the development of new types of distributed hydrological model that could generate ensembles describing the uncertainty of model output (flow or level) when subjected to severe storms. The aim was to understand the propagation of uncertainty in complex fully distributed modeling systems. IF 3.15.
Faculty of Science and Engineering