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ENVI 412 Hydrologic Losses and Radar Measurement Dr. Philip B. Bedient Rice University
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Q e = energy used for evaporation Q h = sensible heat Q = stored energy Q v = advected energy Q N = net radiation absorbed by water body Lake Energy Budget
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Function of wind speed, T, and humidity gradient Energy source - solar energy Mass transfer, energy budget, and pan evaporation Penman’s combined (1948) Lake Evaporation
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E = e s - e a (a + bu) Where E = evaporation (cm/day) e s = Sat vapor pressure (T) e a = Vapor pres at fixed z u = wind speed in m/sec a,b = constants Mass Transfer
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Shallow Lake Evap (Kohler, 1955)
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Evaporation Pans Anemometer - wind Rain Gage - precip. Pan for water - evap Level measured daily Refilled as necessary
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Soil Moisture Cycle Autumn - rainfall recharge Winter - max soil storage Spring - some evap loss Summer - most depleted
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Surface Flow Distribution
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Horton’s Infiltration Concept f(t) = Rate of water loss into soil f = f c + (f o - f c ) exp (-kt) f c = final rate value f o = initial rate value K = decay rate Can integrate to get F(t) = Vol of infiltration
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Horton’s Eqn
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index Method Assumes constant rate over time of rainfall Volume above line is DRO Volume below line is F(t) Trial and error computed
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Example of Index DRO VOL Infiltration F(t)
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Example of Index Assume 4.9 in of DRO from a 560 acre Basin index Set up a general Eqn for index 2(1.4 - +3(0.7- Find by trial and error by assuming a value and solving - try = 1.5 in/hr And it only accounts for 2.4 in of DRO 0.5 in/hr yields 9.0 in of DRO - too much DRO Try 1.0 in/hr or 2(.4) +3(1.3)+2(.1) = 4.9 inches
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Brays Bayou at Main St Bridge
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Measure v at 0.2 and 0.8 of depth Average v and multiply by W*D Sum up across stream to get total Q Stream Cross-Section for Q
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Plot of z vs. Q Determined from stream measurements of V Unique for each stream Changes with development Available for all USGS gages Typical Rating Curve for Stream
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Standard Flood Alert System Use measured rainfall Predict hydrologic Response in x,y, and t Alert various agencies and emergency mgrs Save lives and damages
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Use of NEXRAD Rainfall for Hydrologic Prediction Dr. Baxter Vieux, University of Oklahoma National Severe Storm Laboratory
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Recent Innovation Uses radar - NWS DPA every 5 minutes Accurate to 230 km Provides better spatial detail than gages NEXRAD Radar Data
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Radar Provides Visual Effects Midnight1 a.m.
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Brays Bayou Sims Bayou Radar–Gage Calibration October 17, 1994 Total Rainfall Radar (in.) Total Rainfall measured at the Gage (in.)
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Cumulative Rainfall (in.) October, 1994 Calibration
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Weather Radar Systems Recently deployed weather radar systems such as NEXRAD offer accurate and reliable precipitation estimation Increased sensitivity coupled with improved processing provides high-resolution radar data sets for a variety of applications. Provides another source of rainfall information in addition to rain gauges
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WSR-88D - NEXRAD The first operational WSR-88D was installed in May 1990 at Twin Lakes, OK 160 + deployed nationwide and overseas. Is now being used for much more than weather forecasts. Most significant advancement in hydrology in last 20 years!
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Users of Radar and Meteorological Data Real-time access to radar and other meteorological data is now provided to users outside of the NWS users outside of the NWS Nexrad has spawned a private sector meteorological services industry Now other users are beginning to experience the benefits within the hydrologic community
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Low Precision 16-level Image
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16-level precision image vs. 256-level data
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FAS2 will add 482 radar rain gauges over Brays
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June 8-9, 2001 T.S. Allison Storm Total June 8-9, 2001 26.6 in
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Prospects for Flood Modeling in Real-Time Forecasting urban streams that respond rapidly to heavy rainfall is difficult. Such forecasts can easily underpredict the river stage with little or no lead time Why have hydrologic models lagged the development of radar technology and meteorological science? How can we improve current hydrologic practice in order to forecast flood levels in real-time?
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