Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks

Johnson, Dennis L.; Smith, Michael; Koren, Victor; Finnerty, Bryce

doi:10.1061/(asce)1084-0699(1999)4:2(117)

Cited by 87 publications

(49 citation statements)

References 6 publications

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“…1b). Several studies have focused on the Baron Fork due to its unregulated nature, high stream gauge density and long time series of radar rainfall data (e.g., Johnson et al, 1999;Smith et al, 2004a).…”

Section: Study Catchmentsmentioning

confidence: 99%

Controls on runoff generation and scale-dependence in a distributed hydrologic model

Vivoni

Entekhabi

Bras

et al. 2007

Hydrol. Earth Syst. Sci.

135

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Abstract. Hydrologic response in natural catchments is controlled by a set of complex interactions between storm properties, basin characteristics and antecedent wetness conditions. This study investigates the transient runoff response to spatially-uniform storms of varying properties using a distributed model of the coupled surface-subsurface system, which treats heterogeneities in topography, soils and vegetation. We demonstrate the control that the partitioning into multiple runoff mechanisms (infiltration-excess, saturationexcess, perched return flow and groundwater exfiltration) has on nonlinearities in the rainfall-runoff transformation and its scale-dependence. Antecedent wetness imposed through a distributed water table position is varied to illustrate its effect on runoff generation. Results indicate that transitions observed in basin flood response and its nonlinear and scaledependent behavior can be explained by shifts in the surfacesubsurface partitioning. An analysis of the spatial organization of runoff production also shows that multiple mechanisms have specific catchment niches and can occur simultaneously in the basin. In addition, catchment scale plays an important role in the distribution of runoff production as basin characteristics (soils, vegetation, topography and initial wetness) are varied with basin area. For example, we illustrate how storm characteristics and antecedent wetness play an important role in the scaling properties of the catchment runoff ratio.

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Section: Study Catchmentsmentioning

confidence: 99%

Controls on runoff generation and scale-dependence in a distributed hydrologic model

Vivoni

Entekhabi

Bras

et al. 2007

Hydrol. Earth Syst. Sci.

135

View full text Add to dashboard Cite

show abstract

“…Radar rainfall estimates have been widely used in hydrologic modeling ͑Pessoa et al 1993;Mimikou and Baltas 1996;Peters and Easton 1996;Kull and Feldman 1998;Vieux and Bedient 1998;Winchell et al 1998;Johnson et al 1999;Koren et al 1999;Smith et al 1999a,b;Bedient et al 2000;Carpenter and Georgakakos 2000;Yates et al 2000͒.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations

et al. 2004

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Currently, the river forecasting system deployed in each of 13 River Forecast Centers of the National Weather Service primarily uses lumped parameter models to generate hydrologic simulations. With the deployment of the weather surveillance radar 1988 Doppler radars, more and more precipitation data with high spatial and temporal resolution have become available for hydrologic modeling. Hydrologists inside and outside the National Weather Service are now investigating how to effectively use these data to enhance river-forecasting capabilities. In this paper, six years of continuously simulated hydrographs from an eight-subbasin model are compared to those from a single-basin ͑or lumped͒ model, both applied to the Blue River basin ͑1,232 km 2 ͒ in Oklahoma. The Sacramento soil moisture accounting model is used to generate runoff in all cases. Synthetic unit hydrographs for each subbasin convey the water to the outlet of the basin without explicit flow routing. Subdividing the basin into eight subbasins captures spatially variable rainfall reflected in the next generation weather radar products and produces improved results without greatly increasing the computational and data requirements. Strategies for calibrating the hydrologic model parameters for multiple subbasins are explored.

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“…Sun et al (2000) indicated that, without the use of optimal estimation methods, radar data could result in errors greater than those associated with gauge observations. Preliminary analysis by Johnson et al (1999) showed comparable performance of radar and gauge with some instances of erroneous results in the case of radar-based simulation. Different results were reported in Obled et al (1994), which showed that using distributed rainfall information did not lead to improved hydrograph simulations.…”

Section: Introductionmentioning

confidence: 95%

Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty

Habib

Aduvala

Meselhe

2008

Hydrological Sciences Journal

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Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks

Cited by 87 publications

References 6 publications

Controls on runoff generation and scale-dependence in a distributed hydrologic model

Controls on runoff generation and scale-dependence in a distributed hydrologic model

Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations

Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty

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