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Cited by 102 publications
(55 citation statements)
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“…Another solution, as mentioned before, is to reduce uncertainties in radar QPE by incorporating rain gauge records [37,[40][41][42][43][44][45]. The radar-rain gauge merged QPEs can then be used for streamflow/flood modelling, and it has been found that these combined products generally result in an optimal streamflow prediction compared to the use of a single product [61,68,69,72].…”
Section: Uncertainties In Qpesmentioning
confidence: 99%
See 2 more Smart Citations
“…Another solution, as mentioned before, is to reduce uncertainties in radar QPE by incorporating rain gauge records [37,[40][41][42][43][44][45]. The radar-rain gauge merged QPEs can then be used for streamflow/flood modelling, and it has been found that these combined products generally result in an optimal streamflow prediction compared to the use of a single product [61,68,69,72].…”
Section: Uncertainties In Qpesmentioning
confidence: 99%
“…It has been demonstrated that radar QPE can be a useful and reliable forcing for flood/streamflow forecasting in poorly gauged or ungauged regions [61,65]. For gauged areas, radar precipitation can be merged with gauged precipitation to generate a more accurate QPE product with a good spatiotemporal coverage and resolution, so as to benefit rainfall-runoff modelling and flood simulation [61,[67][68][69][70][71].…”
Section: Implementation Of Weather Radar Precipitationmentioning
confidence: 99%
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“…Model output can be in the form of area-wide, gridded time series of river flows, runoff and soilmoisture, or time series of river flows at gauged or ungauged locations. Applications of the model include both continuous simulation of river flows in a changing climate (Bell et al, , 2016 and real-time flood forecasting (Moore et al, 2006;Cole and Moore, 2009).…”
Section: Hydrological Initial Conditionmentioning
confidence: 99%
“…Currently, most operational hydrological forecasting systems employ lumped hydrological models (with deterministic or manual state updating), but there is a clear tendency to move towards distributed models combined with hydrological ensemble forecasts, (e.g. Koren et al, 2004;Cole and Moore, 2009;Weerts et al, 2012). The main advantage of spatially distributed models is the possibility to force them with spatially measured data, which nowadays become more readily available due to rapid developments in telemetry.…”
Section: Introductionmentioning
confidence: 99%