On the parametric and NEXRAD-radar sensitivities of a distributed hydrologic model suitable for operational use

Carpenter, T. M.; Georgakakos, Konstantine P.; Sperfslagea, Jason A

doi:10.1016/s0022-1694(01)00476-0

Cited by 118 publications

(102 citation statements)

References 19 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of error propagation from radar rainfall to hydrological models

Zhu

Peng

Cluckie

2013

Hydrol. Earth Syst. Sci.

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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 semidistributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km 2 ) 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.

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Section: Introductionmentioning

confidence: 99%

Statistical analysis of error propagation from radar rainfall to hydrological models

Zhu

Peng

Cluckie

2013

Hydrol. Earth Syst. Sci.

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“…In pursuit of more realistic synthetic radar rainfall data, models evolved from being purely statistical, or on the basis of a conceptual understating of the uncertainty involved [e.g., Krajewski and Georgakakos, 1985;Carpenter et al, 2001;Georgakakos and Carpenter, 2003;Russo et al, 2006], to a hybrid approach that combined statistical descriptions of space-time rainfall with a physicsbased mechanism of radar observations [e.g., Krajewski et al, 1993Krajewski et al, , 1996Anagnostou and Krajewski, 1997;Borga et al, 1997;Jordan et al, 2003]. Further realism was attained by replacing statistical models of rainfall with high-resolution mesoscale numerical weather prediction models [e.g., Sharif et al, 2002Sharif et al, , 2004.…”

Section: Introductionmentioning

confidence: 99%

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

Villarini

Krajewski

Ciach

et al. 2009

Water Resources Research

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[1] The existence of large errors in precipitation products delivered by the network of Weather Surveillance Radar, 1988 Doppler (WSR-88D) radars is broadly recognized. However, their quantitative characteristics remain poorly understood. Recently, the authors developed a functional-statistical model that quantifies the relation between radar rainfall and the corresponding true rainfall in a way that is applicable to the probabilistic quantitative precipitation estimation planned for future use by the U.S. National Weather Service. The model consists of a deterministic distortion function and a random uncertainty factor, both conditioned on given radar rainfall values. It also accounts for the spatiotemporal correlations in the random uncertainty factor. The model components were estimated on the basis of a 6-year-long data sample that considers the effects of seasons, range from radar, and time scales. In this study, the authors present two different applications of the aforementioned uncertainty model: (1) the estimation of rainfall probability maps and (2) the generation of radar rainfall ensembles. In the former, maps of the rainfall exceedance probability for any threshold are produced, given a radar rainfall map. We also present the analytical derivation of the exceedance probability maps at coarser spatial scales. In the latter, the users can generate ensembles of probable true rainfall fields that are consistent with the observed radar rainfall and its error structure. Simulation of the random component is based on the Cholesky decomposition method. Finally, the authors discuss possible uses of these applications in hydrology and hydroclimatology.

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“…Real-time radar products are now readily available in many western countries in the world (e.g. Gekat et al, 2004;Krajewski and Smith, 2002;Carpenter et al, 2001). There is, however, only limited use of these products in operational hydrology.…”

Section: Introductionmentioning

confidence: 99%

Effect of spatial distribution of daily rainfall on interior catchment response of a distributed hydrological model

Schuurmans

Bierkens

2007

Hydrol. Earth Syst. Sci.

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Abstract.We investigate the effect of spatial variability of daily rainfall on soil moisture, groundwater level and discharge using a physically-based, fully-distributed hydrological model. This model is currently in use with the district water board and is considered to represent reality. We focus on the effect of rainfall spatial variability on day-to-day variability of the interior catchment response, as well as on its effect on the general hydrological behaviour of the catchment. The study is performed in a flat rural catchment (135 km 2 ) in the Netherlands, where the climate is semi-humid (average precipitation 800 mm/year, evapotranspiration 550 mm/year) and rainfall is predominantly stratiform (i.e. large scale). Both range-corrected radar data (resolution 2.5×2.5 km 2 ) as well as data from a dense network of 30 raingauges are used, observed for the period March-October 2004. Eight different rainfall scenarios, either spatially distributed or spatially uniform, are used as input for the hydrological model. The main conclusions from this study are: (i) using a single raingauge as rainfall input carries a great risk for the prediction of discharge, groundwater level and soil moisture, especially if the raingauge is situated outside the catchment; (ii) taking into account the spatial variability of rainfall instead of using areal average rainfall as input for the model is needed to get insight into the day-to-day spatial variability of discharge, groundwater level and soil moisture content; (iii) to get insight into the general behaviour of the hydrological system it is sufficient to use correct predictions of areal average rainfall over the catchment.

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On the parametric and NEXRAD-radar sensitivities of a distributed hydrologic model suitable for operational use

Cited by 118 publications

References 19 publications

Statistical analysis of error propagation from radar rainfall to hydrological models

Statistical analysis of error propagation from radar rainfall to hydrological models

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

Effect of spatial distribution of daily rainfall on interior catchment response of a distributed hydrological model

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