Enabling Distributed Hydrologic Prediction Through Radar Detection of Rainfall Baxter E

Engineering Conferences International ECI Digital Archives Fifty Years Of Watershed Modeling - Past, Present Proceedings And Future

2012 Enabling Distributed Hydrologic Prediction through Radar Detection of Rainfall Baxter E. Vieux University of Oklahoma, USA

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Recommended Citation Baxter E. Vieux, "Enabling Distributed Hydrologic Prediction through Radar Detection of Rainfall" in "Fifty Years Of Watershed Modeling - Past, Present And Future", A.S. Donigian, AQUA TERRA onC sultants; Richard Field, US EPA (retired); Michael Baker Jr., Inc. Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/watershed/13

This Article is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion in Fifty Years Of Watershed Modeling - Past, Present And Future by an authorized administrator of ECI Digital Archives. For more information, please contact [email protected]. Enabling Distributed Hydrologic Prediction through Radar Detection of Rainfall The future…? “Its already here, just not very evenly distributed.” –William Gibson

Baxter E. Vieux Ph.D., P.E. Professor, School of Civil Engineering and Environmental Science, University of Oklahoma

Vieux & Associates, Inc.

Fifty Years of Watershed Modeling - Past, Present and Future , September 24-26, 2012, Boulder, Colorado, USA 1 2 Presentation Overview

 Real-time hydrologic prediction of stormwater provides critical information for management actions  Two key technologies: 1. Accurate rainfall rates at high resolution over large areas in real- time, and 2. Predicted stage and flow rates at any location in the drainage network based on hydraulics 3 Outline

Action Level

Rainfall Site-specific Forecasts 5 Vflo™ Characteristics Requirements dictated by users needing real-time stormwater and flood stage at specific locations Solves conservation of mass/momentum on a gridded basis Numerical solution finite element/difference Designed to utilize rainfall rates found in radar Continuous soil moisture, Green and Ampt/Saturation Excess Uses geospatial data as input for parameterization 21st Century development  utilizes modern data (geospatial, radar, and xml, kml, Java) Hydraulic approach to Hydrology 6 Model Input/Parameters

Representative Rainfall . Gauge–adjusted Radar Rainfall input Physical Characteristics . Channel/overland Hydraulic routing in a drainage network composed of grids Rainfall . Soil Infiltration/Impervious Runon . Land Cover for hydraulic

Runon roughness . Continuous Soil Moisture Runoff . Reservoir Detention Runon . Recharge/channel losses Infiltration 7 Physics of Rainfall-runoff

What is the solution to the equation governing surface runoff depth, h, as a function of runoff rates, (R-I)? Rainfall  h S 1/ 2  h5/3 Rate -  o  R  I infiltration  t n  x How can it be the following?

Depth (NRCS) Model Equations ie=R-I

Q Q1 2 Conservation of Mass h Q0

Momentum f o  Manning, Qα ∙ hm  m=5/3, α κ ∙ S1/2/n Numerical Solution  Dispersion causes attenuation without solving diffusive wave equation Vieux, B.E., V.F. Bralts, L.J. Segerlind and R.B. Wallace, (1990), "Finite Element Watershed Modeling: One-Dimensional Elements," ASCE, J. of Water Resources Management Planning, Vol. 116, No. 6, November/December, pp. 803-819. Trial Watershed Tutorial 2 Kinematic and diffusive waves

Kinematic Attenuation translation (dispersion) No attenuation attenuation

L=100 m n=0.035, So=3% 11 Urban Watershed Setup LiDAR DEM (1m)  30 m Model

NOAA SARP Joes Creek, Dallas 12 Outline

What is Gauge Adjusted Radar Rainfall?

Radar Rain Gauge ?

Combination of gauge and radar produces better maps of rainfall than either system alone Z  0.080N D 7 o o 14 Radar Measurement Reflectivity depends on the number of Z=250 R1.2 drops and distribution of sizes called the Z=300 R1.4 drop size distribution (DSD). Z=ARb 7 Z  0.080No Do

Rain rates depend on time rate of Rainfall arrival of each drop Rate (R) and its size (DSD).

4.667 RADAR R  N o Do / 4026 GAUGE 15 Gauge–Adjustment of Radar

Bias correction factor, F

n 1 n Gi i1 F  n 1 n  Ri i1

Corrected Radar

Radj = F· Ri 16 Quantifying GARR Reliability

Bias correction and quality control is necessary before NEXRAD data can be used in hydrology, engineering, or modeling of public works projects Radar Data Source = KDIX LII # of gauges used = 11 of 11

Vieux, B.E., and J.E. Vieux, 2005. Mean Field Bias = 0.987 Statistical evaluation of a radar rainfall system for sewer system Ave Diff (%) = 14.5 management. J. of Atmospheric Cal Ave Diff (%)= 13.9 Research, 77 pp. 322– 336. Rel Dis (%) = 15.6 17 Bias Correction Factors, F

4.5 4 Radar to Stream Gauge Volume 3.5200 Mean field 180 Adjusted 3 y = 1.076x 160 bias factor, R2 = 0.9646 2.5) 140 F for each 2120 1.5100 6-hr moving 80

Mean Field Bias Mean Field 1 Unadjusted (+) window. 60 Radar Rainfall Volume (mm Radar Volume Rainfall y = 1.1003x 0.5 2 40 R = 0.2129 Gi 020 FMR  0 Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan R 0 50 100 150 200 i 2005Stream Gauge Volume2006 (mm) 2007 Fang, Z., A. Zimmer, P.B. Bedient, H. Robinson, J. Christian, and B.E. Vieux. Using a Distributed Hydrologic Model to Evaluate the Location of Urban Development and Flood Control Storage. J. Water Resour. Plng. and Mgmt. 17 136(5), pp. 597-601 (September/October 2010). 18 Outline

1. Physics– based approach 2. Accurate rainfall rates at high resolution over large areas in real-time  Gauge Adjusted Radar Rainfall (GARR)  Real-time rainfall systems 3. Distributed hydrologic modeling applications  Recent flood forecasting at gauged and ungauged locations 19 Vflo™ Distributed hydrologic modeling applications City of Austin – Flood Early Warning System (FEWS) Real-time operation for 2000+ km2 Forecast stage at 200+ locations for predictive inundation 21 Stream gauges Gauge-adjusted radar input Continuous Soil Moisture Rainfall-runoff modeling web services and desktop model Automated notifications Lake/Rattan Creek 33,398 cells at 75m XML/KML interface with Setup by FEWS using LiDAR DEM internal databases Vieux & Associates, Inc. TS Hermine (9-10-2010) Gauge Adjusted Radar Rainfall

Courtesy – City of Austin/FEWS Courtesy – City of Austin/FEWS Courtesy – City of Austin/FEWS Courtesy – City of Austin/FEWS Courtesy – City of Austin/FEWS First result with TS Hermine

Uncalibrated

Courtesy – City of Austin/FEWS Tropical Storm Hermine

07/07/10 – 07/09/10 00:00 Courtesy – City of Austin/FEWS   h s1/ 2  h5 / 3    R I  t n  x GA Calibration Hydraulic Roughness 29 TS Hermine Observations at 21 USGS Gauges

Peak stage forecasts with radar input: Average Difference (Obs/Sim) = 17.7% (radar/gauge Average Diff = 15.6%) 30 Streamflow Prediction Accuracy GARR versus Gauge-only

5.0 0.0 -5.0 -10.0 -15.0 GARR

-20.0 RGO of E.

-25.0 y

NSE -30.0 Baxter -35.0 Universit

-40.0 Median NSEu = 0.80 (radar input) , -45.0 Median NSEu = -0.31 (gauge only input) Copyright -50.0 Vieux 1.00 10.00 100.00 1000.00 Area per rain gauge (sq. km.) Looper, J.P. and B.E. Vieux, 2011. An assessment of distributed flash flood forecasting accuracy using radar and rain gauge input for a physics-based model. Journal of Hydrology. Fourmile Burn Area 7-30-2012 Field Observation of Peak Arrival Fourmile Canyon Creek @ Broadway, Boulder 33 Summary

Hydrographs produced from high-resolution rainfall combined with high-resolution geospatial data (LiDAR) Applications Understanding where runoff is occurring for water quality modeling even in ungauged areas Lead-time for emergency management actions Evaluation of drainage infrastructure performance Watershed models for management decisions and design calibrated to actual storm events 34 Documentation

Vflo online documentation (Wiki type) Desktop Help and Tutorials Publications- B.E. Vieux, Distributed Hydrologic Modeling Using GIS, Springer 2nd Edition (2004) Bedient, Huber and Vieux, Hydrology and Floodplain Analysis, 5th Edition (2013) Journal Articles (on request) Journal of Hydrology Journal of Hydrologic Engineering