Flash Flood Forecasting for Small Urban Watersheds in the Baltimore Metropolitan Region

Abstract The utility of distributed hydrologic models in combination with high-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) rainfall estimates for flash flood forecasting in urban drainage basins is examined through model simulations of 10 flood events in the 14.3 km2 Dead Run watershed of Baltimore County, Maryland. The hydrologic model consists of a simple infiltration model and a geomorphological instantaneous unit hydrograph–based representation of hillslope and channel response. Analyses are based on high-resolution radar rainfall estimates from the Sterling, Virginia, WSR-88D and observations from a nested network of 6 stream gauges in the Dead Run watershed and a network of 17 rain gauge stations in Dead Run. For the three largest flood peaks in Dead Run, including the record flood on 7 July 2004, hydrologic model forecasts do not capture the pronounced attenuation of flood peaks. Hydraulic controls imposed by valley bottom constrictions associated with bridges and bridge abutments are a dominant element of the extreme flood response of small urban watersheds. Model analyses suggest that a major limitation on the accuracy of flash flood forecasting in urban watersheds is imposed by storm water management infrastructure. Model analyses also suggest that there is potential for improving model forecasts through the utilization of information on initial soil moisture storage. Errors in the rainfall field, especially those linked to bias correction, are the largest source of uncertainty in quantitative flash flood forecasting. Bias correction of radar rainfall estimates is an important element of flash flood forecasting systems.

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Radar rainfall estimation for flash flood forecasting in small urban watersheds

Advances in Water Resources ◽

10.1016/j.advwatres.2006.09.007 ◽

2007 ◽

Vol 30 (10) ◽

pp. 2087-2097 ◽

Cited By ~ 80

Author(s):

James A. Smith ◽

Mary Lynn Baeck ◽

Katherine L. Meierdiercks ◽

Andrew J. Miller ◽

Witold F. Krajewski

Keyword(s):

Flash Flood ◽

Flood Forecasting ◽

Urban Watersheds ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Flash Flood Forecasting

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Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

Journal of Hydrometeorology ◽

10.1175/2011jhm1292.1 ◽

2012 ◽

Vol 13 (1) ◽

pp. 338-350 ◽

Cited By ~ 96

Author(s):

Menberu M. Bitew ◽

Mekonnen Gebremichael ◽

Lula T. Ghebremichael ◽

Yared A. Bayissa

Keyword(s):

High Resolution ◽

Real Time ◽

Bias Correction ◽

Hydrological Modeling ◽

Rain Gauge ◽

Hydrologic Model ◽

Model Simulations ◽

Mountainous Watershed ◽

Satellite Rainfall ◽

Rainfall Estimates

Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

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Flash Flood Forecasting for Ungauged Locations with NEXRAD Precipitation Data, Threshold Frequencies, and a Distributed Hydrologic Model

World Environmental and Water Resources Congress 2009 ◽

10.1061/41036(342)622 ◽

2009 ◽

Cited By ~ 2

Author(s):

Brian A. Cosgrove ◽

Seann Reed ◽

Feng Ding ◽

Yu Zhang ◽

Zhengtao Cui ◽

...

Keyword(s):

Flash Flood ◽

Flood Forecasting ◽

Hydrologic Model ◽

Precipitation Data ◽

Distributed Hydrologic Model ◽

Flash Flood Forecasting

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Real-Time Flash Flood Forecasting

Oxford Research Encyclopedia of Natural Hazard Science ◽

10.1093/acrefore/9780199389407.013.298 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jonathan J. Gourley ◽

Robert A. Clark

Keyword(s):

United States ◽

Real Time ◽

Flash Flood ◽

Weather Prediction ◽

The United States ◽

Flood Forecasting ◽

Flash Floods ◽

Hydrologic Model ◽

The World ◽

Flash Flood Forecasting

Flash floods are one of the world’s deadliest and costliest weather-related natural hazards. In the United States alone, they account for an average of approximately 80 fatalities per year. Damages to crops and infrastructure are particularly costly. In 2015 alone, flash floods accounted for over $2 billion of losses; this was nearly half the total cost of damage caused by all weather hazards. Flash floods can be either pluvial or fluvial, but their occurrence is primarily driven by intense rainfall. Predicting the specific locations and times of flash floods requires a multidisciplinary approach because the severity of the impact depends on meteorological factors, surface hydrologic preconditions and controls, spatial patterns of sensitive infrastructure, and the dynamics describing how society is using or occupying the infrastructure. Real-time flash flood forecasting systems rely on the observations and/or forecasts of rainfall, preexisting soil moisture and river-stage states, and geomorphological characteristics of the land surface and subsurface. The design of the forecast systems varies across the world in terms of their forcing, methodology, forecast horizon, and temporal and spatial scales. Their diversity can be attributed at least partially to the availability of observing systems and numerical weather prediction models that provide information at relevant scales regarding the location, timing, and severity of impending flash floods. In the United States, the National Weather Service (NWS) has relied upon the flash flood guidance (FFG) approach for decades. This is an inverse method in which a hydrologic model is run under differing rainfall scenarios until flooding conditions are reached. Forecasters then monitor observations and forecasts of rainfall and issue warnings to the public and local emergency management communities when the rainfall amounts approach or exceed FFG thresholds. This technique has been expanded to other countries throughout the world. Another approach, used in Europe, relies on model forecasts of heavy rainfall, where anomalous conditions are identified through comparison of the forecast cumulative rainfall (in space and time) with a 20-year archive of prior forecasts. Finally, explicit forecasts of flash flooding are generated in real time across the United States based on estimates of rainfall from a national network of weather radar systems.

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A distributed hydrologic model and threshold frequency-based method for flash flood forecasting at ungauged locations

Journal of Hydrology ◽

10.1016/j.jhydrol.2007.02.015 ◽

2007 ◽

Vol 337 (3-4) ◽

pp. 402-420 ◽

Cited By ~ 146

Author(s):

Seann Reed ◽

John Schaake ◽

Ziya Zhang

Keyword(s):

Flash Flood ◽

Flood Forecasting ◽

Hydrologic Model ◽

Distributed Hydrologic Model ◽

Threshold Frequency ◽

Flash Flood Forecasting

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Flash flood forecasting within the PREVIEW project: value of high-resolution hydrometeorological coupled forecast

Meteorology and Atmospheric Physics ◽

10.1007/s00703-008-0315-6 ◽

2008 ◽

Vol 103 (1-4) ◽

pp. 115-125 ◽

Cited By ~ 10

Author(s):

B. Vincendon ◽

V. Ducrocq ◽

S. Dierer ◽

V. Kotroni ◽

M. Le Lay ◽

...

Keyword(s):

High Resolution ◽

Flash Flood ◽

Flood Forecasting ◽

Flash Flood Forecasting

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An assessment of distributed flash flood forecasting accuracy using radar and rain gauge input for a physics-based distributed hydrologic model

Journal of Hydrology ◽

10.1016/j.jhydrol.2011.05.046 ◽

2012 ◽

Vol 412-413 ◽

pp. 114-132 ◽

Cited By ~ 53

Author(s):

Jonathan P. Looper ◽

Baxter E. Vieux

Keyword(s):

Flash Flood ◽

Flood Forecasting ◽

Rain Gauge ◽

Hydrologic Model ◽

Forecasting Accuracy ◽

Distributed Hydrologic Model ◽

Flash Flood Forecasting

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An All-Season Flash Flood Forecasting System for Real-Time Operations

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-12-00212.1 ◽

2014 ◽

Vol 95 (3) ◽

pp. 399-407 ◽

Cited By ~ 6

Author(s):

Patrick Broxton ◽

Peter A. Troch ◽

Mike Schaffner ◽

Carl Unkrich ◽

David Goodrich

Keyword(s):

High Resolution ◽

Real Time ◽

Overland Flow ◽

Flash Flood ◽

Flood Forecasting ◽

Flash Floods ◽

Hydrological Models ◽

Flood Prediction ◽

Infiltration Excess ◽

Flash Flood Forecasting

Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorological observations and predictions, as well as calibrated hydrological models, which should effectively simulate how a catchment filters rainfall inputs into streamflow. Furthermore, because of the requirement of both hydrological and meteorological components in flash flood forecasting systems, there must be extensive data handling capabilities built in to force the hydrological model with a variety of available hydrometeorological data and predictions, as well as to test the model with hydrological observations. The authors have developed a working prototype of such a system, called KINEROS/hsB-SM, after the hydrological models that are used: the Kinematic Erosion and Runoff (KINEROS) and hillslope-storage Boussinesq Soil Moisture (hsB-SM) models. KINEROS is an event-based overland flow and channel routing model that is designed to simulate flash floods in semiarid regions where infiltration excess overland flow dominates, while hsB-SM is a continuous subsurface flow model, whose model physics are applicable in humid regions where saturation excess overland flow is most important. In addition, KINEROS/hsB-SM includes an energy balance snowmelt model, which gives it the ability to simulate flash floods that involve rain on snow. There are also extensive algorithms to incorporate high-resolution hydrometeorological data, including stage III radar data (5 min, 1° by 1 km), to assist in the calibration of the models, and to run the model in real time. The model is currently being used in an experimental fashion at the National Weather Service Binghamton, New York, Weather Forecast Office.

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Effect of Precipitation Sampling Error on Flash Flood Monitoring and Prediction: Anticipating Operational Rapid-Update Polarimetric Weather Radars

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0286.1 ◽

2021 ◽

Author(s):

Yixin Wen ◽

Terry Schuur ◽

Humberto Vergara ◽

Charles Kuster

Keyword(s):

Temporal Resolution ◽

Flash Flood ◽

Sampling Error ◽

Flood Forecasting ◽

Hydrologic Model ◽

Spatial Sampling ◽

Temporal And Spatial ◽

Precipitation Sampling ◽

Flood Warnings ◽

Flash Flood Forecasting

AbstractQuantitative precipitation estimates (QPE) at high spatiotemporal resolution are essential for flash flood forecasting, especially in urban environments and headwater areas. An accurate quantification of precipitation is directly related to the temporal and spatial sampling of the precipitation system. The advent of phased array radar (PAR) technology, a potential next-generation weather radar, can provide updates that are at least 4-5 times faster than the conventional WSR-88D scanning rate. In this study, data collected by the KOUN WSR-88D radar with ~1 minute temporal resolution is used as an approximation of data that a future PAR system could provide to force the Ensemble Framework for Flash Flood Forecasting (EF5) hydrologic model. To assess the effect of errors resulting from temporal and spatial sampling of precipitation on flash flood warnings, KOUN precipitation data (1-km/1-min) is used to generate precipitation products at other spatial/temporal resolutions commonly used in hydrologic models, such as those provided by conventional WSR-88D radar (1-km/5-min), spaced-based observations (10-km/30-min), and hourly rainfall products (1-km/60-min). The effect of precipitation sampling errors on flash flood warnings are then examined and quantified by using discharge simulated from KOUN (1-km/1-min) as truth to assess simulations conducted using other generated coarser spatial/temporal resolutions of other precipitation products. Our results show that: 1) observations with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and distribution of precipitation, 2) time series of precipitation products show that QPE peak values decrease as the temporal resolution gets coarser, and 3) the effect of precipitation sampling error on flash flood forecasting is large in headwater areas and decrease quickly as drainage area increases.

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