Dept. of Meteorology John L. Sullivan, Jr. M.S. Candidate 18 March 2008

Dept. of Meteorology

Modeling Streamflow Using Gauge-Only Versus

Multi-Sensor RainfallJohn L. Sullivan, Jr.

M.S. Candidate

18 March 2008


Introduction◦ Motivation◦ Precipitation Measurement Methods◦ Previous Research

Data & Methodology◦ Hydrologic Model◦ Study Area◦ Rainfall Input

Results◦ Multi-Year Composite, Annual, Seasonal◦ Basin Size & Rainfall Patterns

Conclusions

Outline


Florida rainfall is highly variable Need for most accurate rainfall

measurement as input to hydrologic models Rainfall drives model – better

measurements lead to better streamflow approximation?

Why is this important? Better streamflow forecasts save lives, save property, save money, cleaner waters

Motivation

sun showers

associated with the

sea-breeze


Typically 8 in. diameter Placement is commonly

based on human needs

Rain Gauges

NOAA/NWS Birmingham

Spacing varies depending on region Not Perfect Measurement:

Wind turbulence, evaporative losses, mechanical malfunctions, poor gauge placement, clogging, and other interferences

Spatial variability of rainfall problematic


from Quina 2003

1996-2001 South Florida


High spatial and temporal resolution Derived based on Z-R relationships

Not a Perfect Technique Z-R relationship issues

Varies from storm to storm Varies within the same storm

Calibration issues Low-level beam blockage Radar beams overshooting precipitation tops Outages due to storm events that they measure Other various technical issues

Radar: WSR-88D

NOAA/NWS Ruskin


Scheme developed by the National Weather Service (NWS) Hydrologic Research Lab (HRL)

Optimum combination of gauge and radar precipitation amounts◦ relative accuracy of gauge measurements ◦ high spatial resolution of radar data

Code ported from NWS to FSU computers◦ More gauges◦ Adjusted parameters

Mapped onto Hydrologic Rainfall Analysis Project (HRAP) grid ~ 4 × 4 km

FSU/NWS Multi-Sensor Precipitation Estimator Scheme

FSU MPE Data SourcesRain Gauges Radar

NCDC (DSI-3240) 5 Florida Water

Management Districts (WMDs)◦ Northwest, St. Johns River,

South, Southwest, Suwannee River

Quality controlled by FSU using objective scheme (Marzen and Fuelberg 2005)

ASCII text files

Digital Precipitation Arrays (DPAs) provided by NWS Southeast River Forecast Center (SERFC)

Quality controlled by SERFC

XMRG binary files


VanCleve and Fuelberg (2007) compared mean areal precipitation between MPE and rain gauges over several Florida basins

Determined significant findings regarding basin size, basin gauge density, and seasonal considerations…

However, there is still a need to understand the impact of the FSU multi-sensor rainfall on a streamflow model, which is where the data mostly are utilized

Background


Recent studies have utilized radar-derived precipitation data in models

Some studies used radar-derived data, others used multi-sensor schemes

Past studies have made it difficult to draw conclusions that would promote one precipitation data source over another (Kalin and Hantush 2006)

Quantification of multi-sensor impacts in hydrologic models limited

Neary et al. (2004) called for more studies to evaluate the newer NWS MPE products in a distributed hydrologic model

Related Literature

Developed by Soil and Water Engineering Technology, Inc. (SWET)

Fully-distributed, physically-based hydrologic model with water quality parameters

Used by FDEP, FDACS, WMDs; among others

Watershed Assessment Model (WAM)

Known to be particularly adept at handling Florida topography (or lack of elevation changes) and soil features (i.e., karst features)

Image courtesy UF Center for Invasive Plants

Image courtesy FDEP


Surface flow is identified at 100 × 100 m grid cells WAM routes individual flow values at the cells to

determine values throughout the watershed Attenuation is based on flow rate, characteristics of

the flow path, and the distance of travel (Jacobson et al. 1998)

Imbedded models drive water movement through the land◦ GLEAMS (Knisel 1993)◦ EAAMod (Bottcher et al. 1998; SWET 1999)◦ Two sub-models written specifically for WAM (SWET 2002)

ESRI ArcView 3.2a interface GIS-based coverages:

◦ Land use, soil, topography, hydrography, basin and sub-basin boundaries, point source, service area, and climate data

WAM Details


VanCleve and Fuelberg (2007) examined 5 basins, including Suwannee River

Suwannee most dynamic with gauges

Study Area

More elevation changes in North Florida

Rainfall varies during distinct seasons 7

13

10

1713

23

29

49

WAM set up for Florida portion of Suwannee only

First magnitude springs Focus on Upper Santa

Fe River region

Suwannee River Overview with WAM

Gauge spacing around AOI ~ 25 km

230 km radius

Closest available radar at lowest elevation angle used at each hour

2 radars fully cover AOI

2 other radars partially influence AOI


Understand the sensitivity of a hydrologic model (WAM) to gauge-only and multi-sensor input data◦ Compare differences in streamflow statistics

Understand the advantages and disadvantages of using higher-resolution FSU MPE data

Determine the ability of a fully-distributed hydrologic model (WAM) to incorporate the higher-resolution rainfall data

Goals


Two model runs with different rainfall input◦ Gauge-only = Thiessen polygons◦ Multi-sensor = HRAP grid cells

Input data span 1996-2005 Only 2000-2005 analyzed

◦ 3 year model spin-up◦ 1 year streamflow adjustment period

Previous setup - extended climate/boundary data through 2005

No need or method to calibrate for change in rainfall inputs since most of WAM’s parameters have physical meanings (SWET)

Model Runs


1) QC hourly gauge data (1996-2005)◦ More in later years with SUW WMD sites◦ Missing data at some gauges for extended periods◦ WAM rainfall coverage static

WAM cannot have missing data or there is no water to model!

2) MPE hourly product (1996-2005)◦ Always same number of cells◦ WAM not originally designed for large number of

unique rainfall values WAM can only accept daily data due to soil

sub-models

Inputting the Two Different Datasets into WAM

0

1

2

3

4

5

6

1996 1997 1998 1999 20002001 2002 2003 2004 2005

Year

Rain

Gau

ges


FSU QC gauge dataset

Number of gauges increases with time◦ Hourly values in-filled

with nearest available neighbor (SWET)

◦ Dynamic scheme

Gauge Input – Thiessen polygons

Summed to daily converted to WAM input files ESRI ArcMap 9.2 used to create 100 × 100 m

raster coverageo Linked to rainfall files using gauge identifier


FSU MPE dataset Consistent with Thiessen methods

◦ Missing hourly values in-filled with nearest available neighbor

◦ Less than 1% data missing◦ Benefit of higher-resolution MPE did not need in-fill

since results were very similar Summed to daily values converted to WAM input

files ESRI ArcMap 9.2 used to create 100 × 100 m

raster coverage ◦ Linked to input files using unique identifier

Close communication with SWET (not open source)

MPE Input – HRAP grid


Based on daily streamflow output from model runs

2 model runs compared to each other and to observed streamflow at USGS stream gauges for each AOI

Standard Statistics: Standard deviation of differences, mean difference (bias), coefficient of determination (R2)

Volume: Accumulation charts, Mass Balance Error (MBE)

Predictive Skill: Nash-Sutcliffe efficiency (ENS)

Comparisons


Nash-Sutcliffe Efficiency (ENS) Ranges from -∞ to 1 Indicates how well plot of observed versus predicted

values fits the 1:1 line ENS = 1 perfect fit ENS < 0 model predictions no better than average of

observed data

Mass Balance Error (MBE) Ranges from -∞ to ∞ 0% ideal


Six-Year CompositeAnnualSeasonal

Results


Std Dev of Diff (AOI size difference) Worthington Springs results better than New River New River AOI

• Higher gauge density than Worthington• Still too small to model efficiently, stats much lower

To compare only rainfall input differences, focus on larger Worthington Springs AOI

Basin Size (2000-2005)

Standard Deviation of Differences

Mean Difference

(Bias) R2 ENS MBE

Worthington Springs AOI

Thiessen 14.02 4.07 0.6090.48

5 47.74%

FSU MPE 13.54 -0.33 0.5790.55

7-3.86%

New River AOI

Thiessen 9.90 -1.40 0.3720.35

6 -36.13%

FSU MPE9.87 -2.09 0.396

0.344 -53.85%


Composite Streamflow Results (2000-2005)


Mean Difference

(Bias) R2 ENS MBE

Worthington Springs AOI

Thiessen 14.02 4.07 0.609 0.485 47.74% FSU MPE 13.54 -0.33 0.579 0.557 -3.86%

New River AOI Thiessen 9.90 -1.40 0.372 0.356 -36.13%

FSU MPE 9.87 -2.09 0.396 0.344 -53.85%

Bias, ENS, MBE of FSU MPE better than

Thiessen R2 of Thiessen (0.61) ≈ FSU MPE (0.58)


MPE totals: 255-360 in. Gauge totals: 285-380

in. 0 gauges positioned in

SE portion Rain gauges in areas

where rainfall is relatively large

Placement of rain gauges relative to the rainfall pattern would have to be analyzed on a case by case basis to determine its impact

Overall Spatial Distribution of Rainfall

0

500

1,000

1,500

2,000

2,500

3,000

Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05

Mill

ion

sC

um

ula

tive

Flo

w (

m3 )

Thiessen FSU MPE Measured

-150

-50

50

150

250

350

450

550

650

750

850

Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05

Mill

ion

sC

um

ula

tive

Flo

w D

iffe

ren

ce f

rom

Ob

serv

ed (

m3 )

Thiessen MPE

Accumulation MPE initially

overestimates ~ 2003 large

underestimates Then follows observed

closely

Thiessen always overestimates

Error compounded, not canceled

End of 6 yrs, MPE most accurate

800 million m3 overestimate

0

50

100

150

200

250

300

350

400

450

0 50 100 150 200 250 300 350 400 450

Measured (m3/s)

FS

U M

PE

Sim

ula

ted

(m

3 /s)

0

50

100

150

200

250

300

350

400

450

0 50 100 150 200 250 300 350 400 450

Measured (m3/s)

Th

iess

en S

imu

late

d (

m3 /s

)

Daily values (365 days × 6 yrs)

Trend below 1:1 line, esp. for measured values greater than 50 m3 s-1

o underestimate For less than 50 m3 s-1

measured streamflowo Thiessen input leads

to greater overestimates?

o MPE input closer to observed or possibly greater underestimates?

R2 = 0.609

R2 = 0.579

Scatter Plots


Low Flow vs. High Flow


Mean Difference

(Bias) R2 ENS MBEHigh (75th Percentile) Thiessen 25.24 2.11 0.524 0.391 7.07% FSU MPE 24.81 -7.82 0.504 0.357 -26.28%Low (25th Percentile) Thiessen 3.86 2.67 0.000 -1453.7 1533.8% FSU MPE 2.75 1.62 0.001 -669.0 928.0%

Decisions made when flow is low or high low flow days at or below the 25th percentile of observed

conditions◦ Drought years when streamflow reached zero◦ WAM ability to forecast extreme dry events?◦ Both input results bad

high flow days above the 75th percentile◦ Was WAM developed to perform best during high-flow

conditions when using point-based rain gauge data?

Composite Results Summary

MPE Pros + MPE Cons -

Captures spatial variability

Accumulation & Mass Balance Error

Improved Bias & ENS

Did not perform better in smaller New River AOI

R2 not improved

Below 1:1 on daily scatter plot, esp. at high values – underestimates (as did Thiessen)

Model Issues? Low-flow simulations poor for both inputs High flow not improved, rainfall measurement to

model physics relationship issue?



Results


Drought during first 3 yrs

Average to above average last 3 yrs

MPE better for 2000-2002 & 2004

Thiessen better in 2003 & 2005

Yearly Accumulation Results

0

100,000

200,000

300,000

400,000

500,000

600,000

2000 2001 2002 2003 2004 2005 SepOct2004 Rest2004

acre

-fe

et



Overall R2 improves in later years (exc. Thiessen 2000 anomaly)

SUW gauges added More rainfall

ENS improves substantially in 2003

ENS 2005 equal

R2

EN

S

Yearly Statistics Comparison

FSU MPE Best Thiessen Best

Bias: 2000-2002 & 2004 MBE: 2000-2002 & 2004 ENS: 2000-2002 & 2004 R2: 2001-2003 & 2005

Bias: 2003 & 2005 MBE: 2003 & 2005 ENS: 2003 R2: 2000 & 2004

ENS equal in 2005 Thiessen never performed better than FSU MPE

during one year in every statistical category MPE performed better than Thiessen in every

category during 2001 and 2002

2003 2004 2005

Example Hydrograph

Frances

Jeanne


Rest2004: MPE better than Thiessen

SepOct2004: MPE better than Thiessen

and… Rest2004 ~ 2000-2002

More peak streamflow events in 2003 & 2005

Was 2004 really “above average”?

0

100,000

200,000

300,000

400,000

500,000

600,000

2000 2001 2002 2003 2004 2005 SepOct2004 Rest2004

acre

-fee

t


Fewer events in 2000-2002 more likely to hit or miss rain gauges than more events in 2003 & 2005

Thiessen exhibits large spikes during 2000 – less underestimation

More underestimation during 2005

0

5

10

15

20

25

30

35

40

45

Jan-00 Feb-00 Mar-00 Apr-00 May-00 Jun-00 Jul-00 Aug-00 Sep-00 Oct-00 Nov-00 Dec-00

Flo

w (

m3 /s

)


-5

0

5

10

15

20

25

30

35

40


Flo

w D

iffe

ren

ce (

m3 /s

)

Thiessen MPE

0

20

40

60

80

100

120

140


Flo

w (

m3 /s

)


-65

-45

-25

-5

15

35

55

75


Flo

w D

iffe

ren

ce (

m3 /s

)

Thiessen MPE

2000 2005

Annual Results SummaryMPE Pros + MPE Cons -

Accumulations and MBE during dry years and tropical rainfall

ENS usually better (2003 exception)

Standard deviation of differences usually less (2004 exception)

More rainfall (streamflow) events lead to higher likelihood of FSU MPE to underestimate rainfall?



Results


Subjectively divided into 3-month periods

Oct-Mar: more stratiform than other months

Esp. Jan-Mar: beam overshooting issues? (closest radar ~ 75 km)

Jul-Sep: more summer convective scenarios

Seasonal Accumulations

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

acre

-fee

t



MPE weaknesses ENS

◦Jan – Mar◦stratiform dominant

R2

◦Jul – Sep◦Convection/seabreeze dominant

Key Seasonal Differences

R2

ENS

Seasonal Statistics Comparison

FSU MPE Best Thiessen Best

Bias: Apr-Jun, Jul-Sep, Oct-Dec

MBE: Apr-Jun, Jul-Sep, Oct-Dec

ENS: Apr-Jun, Jul-Sep, Oct-Dec

R2: Jan-Mar, Apr-Jun, Oct-Dec

Bias: Jan-Mar MBE: Jan-Mar ENS: Jan-Mar R2: Jul-Sep

R2 actually better for MPE during Jan-Mar? MPE displayed better MBE, bias, ENS during Jul-

Sep, but not R2? Adds some uncertainty

Seasonal Results SummaryMPE Pros + MPE Cons -

Accumulations during most of the year

Correlations/skill during most of the year

Transition and convective dominant seasons

Accumulations during early year stratiform events

R2 during summer/convective events


Summary by Conclusions

the end is near…


Fewer missing data issues with FSU MPE Smaller New River basin did not show positive

results, even with higher-resolution MPE Overall, FSU MPE-derived streamflow

accumulations much better than Thiessen, bias and skill slightly improved with MPE, correlations are inconclusive

Low flow – both inputs lead to poor comparison to observed, WAM improvement?

High flow – model physics issue with distributing less intense values over watershed?

Conclusions


FSU MPE accumulations much better than Thiessen in dry years and tropical events

Less peak events MPE better than Thiessen More peak events MPE underestimates,

Thiessen more accurate FSU MPE underestimation issues during periods

when stratiform is commonMust remember: Personal judgment key to any modeling study Results are based on this configuration of WAM

for this basin only

More Conclusions


Steve Martinaitis comparing FSU MPE dataset in another hydrologic model, MIKE SHE

WAM needs testing by developers (SWET) to ensure that MPE data used to best capability – make modifications?

NWS MPE data now available as well, make comparisons with FSU MPE to better understand the effects of the Florida WMD gauges and FSU parameters on MPE scheme

Future Work


Dr. Henry Fuelberg Dr. Paul Ruscher and Dr. Guosheng Liu Joel Lanier, National Weather Service

Tallahassee Judi Bradberry, Southeast River Forecast Center Barry Jacobson and Del Bottcher, SWET All of my friends in the meteorology

department, esp. the wonderful Fuelberg Lab My family; esp. Trey, parents, and siblings Project funded by the Florida Department of

Environmental Protection

Acknowledgements

Questions?

THE END

ReferencesAnagnostou, E. N., C. A. Morales, and T. Dinku, The use of TRMM precipitation radar observations in determining ground radar calibration biases, J. Atmos. Oceanic Technol., 18, 616-628, 2001. Austin, P. P., Relation between measured radar reflectivity and surface rainfall, Mon. Weather Rev., 115, 1053-1070, 1987. Baeck, M. L., and J. A. Smith, Rainfall estimation by the WSR-88D for heavy rainfall events, Wea. Forecasting, 13, 416-436, 1998. Bedient, P. B., B. C. Hoblit, D. C. Gladwell, and B. E. Vieux, NEXRAD radar for flood prediction in Houston, J. Hydrol. Eng., 5(3), 269-277, 2000. Bell, V. A., and R. J. Moore, A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies, Hydrol. Earth Syst. Sci., 2(2–3), 278–283, 1998. Borga, M., Accuracy of radar rainfall estimates for stream flow simulation, J. Hydrol., 267, 26-39, 2002. Borga, M., E. N. Anagnostou, and W. F. Krajewski, A simulation approach for validation of a bright band correction method, J. Appl. Meteorol., 36, 1507-1518, 1997. Borga, M., E. N. Anagnostou, and E. Frank, On the use of real-time rainfall estimates for flood prediction in mountainous basins, J. Geophys. Res., 105, 2269-2280, 2000. Bottcher, A.B, N.B. Pickering, and A.B. Cooper, EAAMOD-FIELD: A flow and phosphorous model for high water tables, Proceedings of the 7th Annual Drainage Symposium, St. Joseph, MI, Am. Soc. of Agr. Eng., 1998. Bradley, A. A., and A. Kruger, Recalibration of hydrologic models for use with WSR-88D precipitation estimates, Special Symposium on Hydrology, Phoenix, AZ, Amer. Meteor. Soc., 1998. Breidenbach, J. P., and J. S. Bradberry, Multisensor precipitation estimates produced by National Weather Service River Forecast Centers for hydrologic applications, Proceedings 2001 Georgia Water Res. Conf., Institute of Ecology, Univ. of Georgia, Athens, 1-4, 2001. Cluckie, I. D., K. A. Tilford, and G. W. Shepard, Radar signal quantization and its influence on rainfall runoff models, in Hydrological Applications of Weather Radar, edited by I. D. Cluckie and C. G. Collier, pp. 440-451, Ellis Horwood, Chichester, England, 1991. Collier, C. G., Applications of Weather Radar Systems: A Guide to Uses of Radar Data in Meteorology and Hydrology, pp. 41-74, John Wiley, New York, 1996. Cooper, H. J., H. E. Fuelberg, T. S. Wu, D. Gilbert, J. Mandrup-Poulsen, R. J. Lanier, A. I. Watson, and J. L. Sullivan, Applying WSR-88 radar rainfall on short time scales to hydrological modeling of a small basin in Florida, 20th Conf. on Hydrology, Atlanta, GA, 2006a. Cooper, H. J., R. J. Lanier, H. E. Fuelberg, and A. I. Watson, Comparing the operational capabilities of the site specific hydrologic predictor (SSHP) and a fully distributed hydrological model (MIKE SHE) using WSR-88 radar rainfall inputs over a small basin in Florida, 20th Conf. on Hydrology, Atlanta, GA, 2006b. EPA, Watershed Assessment Model (WAMView) tool info sheet, Watershed and Water Quality Modeling Technical Support Center, Ecosystems Research Division, United States Environmental Protection Agency, 2007. [Available online at www.epa.gov/athens/wwqtsc.] Essery, C. I., and D. N. Wilcock, Variation in rainfall catch from standard U.K. Meteorological Office raingages, Hydrol. Sci. J., 36(1), 23-24, 1990. Fabry, F., A. Bellon, M. R. Duncan, and G. L. Austin, High resolution rainfall measurements by radar for very small basins: The sampling problem re-examined, J. Hydrol., 116, 415-428, 1994. Fabry, F. A., and I. Zawadzki, Long-term observations of the melting layer of precipitation and their interpretation, J. Atmos. Sci., 52, 838-851, 1995. Fulton, R. A., Sensitivity of WSR-88D rainfall estimates to the rain-rate threshold and rain gauge adjustment: A flash flood case study, Wea. Forecasting, 14, 604-624, 1999. Fulton, R. A., J. P. Breidenbach, D. J. Seo, D. A. Miller, and T. O’Bannon, The WSR-88D rainfall algorithm, Wea. Forecasting, 13, 377-395, 1998. Greenhalgh, T., personal communications, 2007. Habib, E., W. F. Krajewski, and G. J. Ciach, Estimation of rainfall interstation correlation, Notes and Correspondence, J. of Hydromet., 2, 621-629, 2001. Harrison, D. L., S. J. Driscoll, and M. Kitchen, Improving precipitation estimates from weather radar using quality control and correction techniques, Meteorol. Appl., 6, 135-144, 2000. Harrold, T. W., E. J. English, and C. A. Nicholass, The accuracy of radar-derived rainfall measurements in hilly terrain, Q. J. R. Meteorol. Soc., 100, 331-350, 1974. Jacobson, B., personal communications, 2007. Jacobson, B. M., A. B. Bottcher, N. B. Pickering, and J. G. Hiscock, Unique routing algorithm for watershed assessment model, Am. Soc. of Agr. Eng., ASAE Paper No. 98-2237, 1998. Jayakrishnan R., R. Srinivasan, and J. G. Arnold, Comparison of raingage and WSR-88D stage III precipitation data over the Texas-Gulf basin, J. of Hydrol., 292, 135–152, 2004. Jayakrishnan, R., R. Srinivasan, C. Santhi, and J. G. Arnold, Advances in the application of the SWAT model for water resources management, Hydrolog. Process., 19, 749-762, 2005. Johnson, D., M. Smith, V. Koren, and B. Finnerty, Comparing mean areal precipitation estimates from NEXRAD and rain gauge networks, J. Hydrol. Engng., Am. Soc. Civ. Engrs., 4(2), 117–124, 1999. Jordan, P., A. Seed, and G. Austin, Sampling errors in radar estimates of rainfall, J. Geophys. Res., 105, 2247-2257, 2000. Joss, J., and A. Waldvogel, Precipitation measurement and hydrology, in Radar in Meteorology, edited by D. Atlas, pp. 577-606, Am. Meteorol. Soc., Boston, Mass., 1990. Kalin, L., and M. M. Hantush, Hydrologic modeling of an eastern Pennsylvania watershed with NEXRAD and rain gauge data, J. of Hydrol. Eng., 11(6), 555-569, 2006. Kalinga, O. A. and T. Y. Gan, Semi-distributed modeling of basin hydrology with radar and gauged precipitation, Hydrol. Process., 20, 3725-3746, 2006. Kitzmiller, D., personal communications, 2008. Knisel, W. G., GLEAMS: Groundwater Loading Effects of Agricultural Management Systems, UGA-CPES-BAED Publication no. 5, 1993. Legates, D. R. and T. L. DeLiberty, Precipitation measurement biases in the United States, Water Resour. Bull., 29(5), 855–861, 1993. Lericos, T. P., H. E. Fuelberg, A. I. Watson, and R. L. Holle, Warmn season lightning distributions over the Florida peninsula as related to synoptic patterns, Wea. Forecasting, 17, 83-98, 2002. Marzen, J. and H. E. Fuelberg, Developing a high resolution precipitation dataset for Florida hydrological studies, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 2005. Mroczka, B. A., The influence of the synoptic flow on warm season rainfall patterns over the Florida peninsula. M.Sc. Thesis, Dept. Meteorology. The Florida State University (unpublished), 2002. Nash, J. E., and J. V. Sutcliffe, River flow forecasting through conceptual models Part I: A discussion of principles, J. Hydrol., 10, 282-290, 1970. Neary, V. S., E. Habib, and M. FLemming, Hydrologic modeling with NEXRAD precipitation in middle Tennessee, J. Hydrol. Eng., 9(5), 339-349, 2004. Nelson, B. R., W.F. Krajewski, A. Kruger, J. A. Smith, and M. L. Baeck, Archival precipitation data set for the Mississippi River Basin: Algorithm development, J. Geophys. Res., 108, 18,1-18,14, 2003. Numec J., Water resources systems and climate change, Facets of Hydrol., Vol. II, pp. 131–152, John Wiley, New York, 1985. Ogden, F. L., H. O. Sharif, S. U. S. Senarath, J. A. Smith, M. L. Beck, and J. R. Richardson, Hydrologic analysis of the Fort Collins, Colorado, flash flood of 1997, J. Hydrol., 228, 82–100, 2000. Pereira Fo, A. J., K. Crawford, and C. Hartzell, Improving WSR-88D hourly rainfall estimates, Wea. Forecasting, 13, 1016-1028, 1998. Quina, G. S. III, Statistical and hydrological evaluations of rain gauge- and radar-derived precipitation for the Florida peninsula. M.Sc. Thesis, Dept. Meteorology. The Florida State University (unpublished), 2003. Reed, S., V. Koren, M. Smith, Z. Zhang, F. Moreda, D. J. Seo, and DMIP Participants, Overall distributed model inter-comparison project results, J. Hydrol., 298, 27-60, 2004. Seo, D. J., Real-time estimation of rainfall fields using rain gauge data under fractional coverage conditions, J. Hydro., 208, 25-36, 1998a. Seo, D. J., Real-time estimation of rainfall fields using rain gauge data under fractional coverage conditions, J. Hydro., 208, 37-52, 1998b. Sevruk, B., International workshop on precipitation measurement: Preface, Hydrol. Proc.,5, 229-232, 1991. Smith, C. J., The reduction of errors caused by bright bands in quantitative rainfall measurements made using radar, J. Atmos. Oceanic Technol., 3, 129-141, 1986. Smith, J.A. and W. F. Krajewski, Estimation of mean field bias of radar rainfall estimates, J. Appl. Meteorol., 30(4), 397–412, 1991. Smith, J. A., and W. F. Krajewski, A modeling study of rainfall rate: Reflectivity relationships, Water Resour. Res., 29, 2505-2514, 1993. Smith, J. A., D. J. Seo, M. L. Baeck, and M. D. Hudlow, An intercomparison study of NEXRAD precipitation estimates, Water Resour. Res., 32, 2035-2045, 1996. Smith, M. B., D. J. Seo, V. I. Koren, S. M. Reed, Z. Zhang, Q. Duan, F. Moreda, and S. Cong, The distributed model intercomparison project (DMIP): motivation and experiment design, J. Hydrol., 298, 4-26, 2004. Stellman, K. M., H. E. Fuelberg, R. Garza, and M. Mullusky, An examination of radar and rain gauge-derived mean areal precipitation over Georgia watersheds, Wea. Forecasting, 16, 133-144, 2001. SWET, EAAMOD technical and user manuals. Final Reports to the Everglades Research and Education Center, University of Florida, Belle Glade, FL, 1999. [Available online at http://www.swet.com.] SWET, WAMView training manual. Developed for EPA Region IV Training. Published by: Soil and Water Engineering Technology, Inc., Gainesville, FL, 2002. Ulbrich, C. W., and L. G. Lee, Rainfall measurement error by WSR-88D radars due to variations in Z-R law parameters and the radar constant, J. Atmos. Oceanic Technol., 16, 1017-1024, 1999. VanCleve, D. D. and H. E. Fuelberg, An intercomparison between mean areal precipitation from gauges and a multisensor procedure, 21st Conf. on Hydrology, San Antonio, TX, Amer. Meteor. Soc., 2007. Vieux, B. E., P. B. Bedient, Estimation of rainfall for flood prediction from WSR-88D reflectivity: A case study, 17–18 October, 1994. Wea. Forecasting, 13, 407–415, 1998. Verdi, R.J., S.A. Tomlinson, and R.L Marella, The drought of 1998-2002: Impacts on Florida’s hydrology and landscape, U.S. Geological Survey Circular 1295, 34 p., 2006. [Available online at http://pubs.usgs.gov/circ/2006/1295.] Wang, D., M. B. Smith, Z. Zhang, S. Reed, and V. I. Koren, Statistical comparison of mean areal precipitation estimates from WSR-88D, operational and historical gage networks, 15th Conf. on Hydrology, Long Beach, CA, Amer. Meteor. Soc., 2000. Wilson, J. W., and E. A. Brandes, Radar measurement of rainfall: A summary, Bull. Am. Meteorol. Soc., 60, 1048-1058, 1979. Winchell M, Gupta HV, and Sorooshian S., On the simulation of infiltration- and saturation-excess runoff using radar-based rainfall estimates: Effects of algorithm uncertainty and pixel aggregation, Water Resour. Res., 34(10), 2655–2670, 1998. Wu, T. S., G. S. Quina, D. Gilbert, and H. E. Fuelberg, Developing a procedure for a calibrated high-resolution precipitation database (CHPD) for Florida, TMDL Conference, Chicago, IL, 2003. Yoo, C., U. Kim, K. Kim, and K. Kim, Inter-station correlation and estimation errors of areal average rain rate, Stoch. Environ. Res. Risk Assess., 22, 143-157, 2008. Young, C. B., B. R. Nelson, A. A. Bradley, J. A. Smith, C. D. Peters-Lidard, A. Kruger, and M. L. Baeck, An evaluation of NEXRAD precipitation estimates in complex terrain, J. Geophys. Res., 104, 19,691-19,703, 1999.

Documents

Dept. of Meteorology John L. Sullivan, Jr. M.S. Candidate 18 March 2008