Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting

Vivoni, Enrique R.; Entekhabi, Dara; Bras, Rafael L.; Ivanov, V. Y.; Horne, Matthew P. Van; Grassotti, Christopher; Hoffman, Ross N.

doi:10.1175/jhm514.1

Cited by 77 publications

(65 citation statements)

References 42 publications

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“…Figure 5 presents the scaledependence of the mean depth to groundwater, µ [N wt ], for the three initial states (wet, medium, dry). Catchment scale (A) variation is captured by sampling fifteen subbasins ranging in area from 0.78 to 808 km 2 (see Vivoni et al, 2006, becomes progressively shallower (closer to the land surface, z=0) as more low lying areas near the stream network are sampled. Since lowland regions have less effective drainage in natural settings, inclusion of these areas in the basin average reduces µ [N wt ].…”

Section: Role Of Antecedent Groundwater Conditions In Nonlinearity Anmentioning

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: Role Of Antecedent Groundwater Conditions In Nonlinearity Anmentioning

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

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“…Providing precipitation-runoff model and hydrograph of flood created by forecasted and observed precipitation -Routing forecasted flood in different points and determining the aspects and size of flood Precipitation forecasting is one of the most difficult phenomena of hydrologic cycle because of high changes in time and location scale [10] and [16]. Forecasting precipitation is expected in different scales of high short time (up to 12 hours), short time (12 hours to 4 days), middle time (4 days up to one month) and ling time (seasonal, annual and climate periods).…”

Section: Predicting Precipitation and Floodmentioning

confidence: 99%

“…In flood warning system forecasting QPF very short time or generally short time in the scale of a catchment is required. Using QPF in forecasting the flow of rivers lets the time of forecasting and the efficiency of forecasting and warning flood to be increased [16]. Forecasting very short time precipitation about 0-6 hours and with high transparency in place and time is often described as Nowcasting [17].…”

Section: Predicting Precipitation and Floodmentioning

confidence: 99%

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Flood Management, Flood Forecasting and Warning System

Hajibabaei¹,

Ghasemi²

2017

IJSEA

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Flood phenomenon annually causes many human and financial losses so that flood has even had many losses in countries which are properly developed in river bank protection and construction of reservoir dams aiming to control flood. Today, controlling flood through its management is discussed and one of efficient and effective tools in this field is non-structural flood management as the complementary of structural ones that can have a very important role in controlling flood and consequently reducing the losses derived by that. Flood forecasting and warning systems are considered as one of the non-structural management tools which has given high importance within recent decades and providing information about flood and fast and easy reaction after receiving mentioned information to have minimum losses in flood areas as well as providing some information to manage water resources are of the aims of this system.

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“…The spatial distribution of precipitation fields over the basin has been obtained by the next-generation weather radar (NEX-RAD) system, available for the study area (Vivoni et al 2006) in the form of hourly NEXRAD 4-km gridded estimates. These precipitation fields have been used both to directly force the hydrological model and to provide precipitation data sampled by fictitious rain gauge networks as described in "Assumptions and Simulations.…”

Section: Precipitationmentioning

confidence: 99%

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

2014

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Retrieving precipitation data from a rain gauge network is a classical and common practice in hydrology and climatology. These data represent the key input in hydrological modeling to reproduce, for example, the characteristics of a flood phenomenon. The accuracy of the model results is strongly dependent on the consistency of the monitoring network in terms of spatial scale, i.e., network density and location of the rain gauges and time resolution. In this context, several studies have been carried out to analyze how the rainfall sampling influences the estimation of total runoff volume. The aim of this paper is to use a physically based and distributed-parameter hydrologic model to investigate how the number and the spatial distribution of a rain gauge network influence the estimation of the hydrograph and its characteristics in conjunction with different spatial and temporal characteristics of rainfall forcing and different soil-type characteristics. The TIN-based real-time integrated basin simulator (tRIBS) hydrologic model was used to simulate hydrologic response at Baron Fork Basin, Oklahoma. Downscaled next-generation radar (NEXRAD) measurements were assumed to represent the true precipitation distribution over the basin. Additional precipitation fields have been derived from the interpolation of eight fictitious rain gauges randomly placed in the area. The hydrological response from tRIBS that is driven by these precipitation fields has been compared with the response of the model forced with NEXRAD precipitation. The analysis has been carried out assuming first simplified spatial distributions of soil characteristics and then the real soil-type distribution. Results have shown the dependence of the best rain gauges configuration for the estimation of runoff on the spatiotemporal characteristics of storm events and the soil-type distribution.

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Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting

Cited by 77 publications

References 42 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

Flood Management, Flood Forecasting and Warning System

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

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