John L. Sullivan, Jr.

tobascothwackUrban and Civil

Nov 15, 2013 (3 years and 9 months ago)

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Dept. of Meteorology


John L. Sullivan, Jr.

M.S. Candidate


18 March 2008

Dept. of Meteorology


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

Dept. of Meteorology


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


s
ave lives, save property, save
money, cleaner waters

sun
showers
associated
with the
sea
-
breeze

Dept. of Meteorology


Typically 8 in. diameter


Placement is commonly based
on human needs

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



Dept. of Meteorology

from
Quina

2003

1996
-
2001 South Florida

Dept. of Meteorology


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


NOAA/NWS Ruskin


Dept. of Meteorology


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

Rain 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


Dept. of Meteorology


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

Dept. of Meteorology


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



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


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

Dept. of Meteorology


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

Dept. of Meteorology


VanCleve

and
Fuelberg

(2007) examined 5
basins, including Suwannee River


Suwannee most dynamic with gauges


More elevation
changes in
North Florida


R
ainfall varies
during

distinct

seasons

7

13

10

17

13

23

29

49


WAM set up for Florida
portion of Suwannee
only


First magnitude springs


Focus on Upper Santa Fe
River region



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

Dept. of Meteorology


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

Dept. of Meteorology


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)

Dept. of Meteorology


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


0
1
2
3
4
5
6
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Year
Rain Gauges
Dept. of Meteorology


FSU QC gauge dataset


Number of gauges
increases with time


Hourly values in
-
filled
with nearest available
neighbor (SWET)


Dynamic scheme


Summed to daily


converted to WAM input
files


ESRI
ArcMap

9.2 used to create 100
×

100 m
raster coverage

o
Linked to rainfall

files
using

gauge identifier

Dept. of Meteorology


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)

Dept. of Meteorology


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 (R
2
)


Volume: Accumulation charts, Mass Balance
Error (MBE)


Predictive Skill: Nash
-
Sutcliffe efficiency (E
NS
)


Dept. of Meteorology

Nash
-
Sutcliffe Efficiency (E
NS
)



Ranges
from
-
∞ to
1



Indicates
how well plot of observed versus
predicted
values fits
the 1:1
line



E
NS

= 1


perfect
fit



E
NS

< 0


model predictions no better than average
of observed
data

Mass Balance Error (MBE)



Ranges
from
-
∞ to




0
% ideal

Dept. of Meteorology

Six
-
Year Composite

Annual

Seasonal

Dept. of Meteorology


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



Standard
Deviation of
Differences

Mean
Difference
(Bias)

R
2

E
NS

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%













Dept. of Meteorology



Standard
Deviation of
Differences

Mean
Difference
(Bias)

R
2

E
NS

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, E
NS
, MBE
of
FSU MPE better than
Thiessen


R
2

of
Thiessen (0.61) ≈ FSU MPE (0.58)



Dept. of Meteorology


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

0
500
1,000
1,500
2,000
2,500
3,000
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Millions
Cumulative Flow (m
3
)
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
Millions
Cumulative Flow Difference from Observed (m
3
)
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 m
illion m
3

overestimate

0
50
100
150
200
250
300
350
400
450
0
50
100
150
200
250
300
350
400
450
Measured (m
3
/s)
FSU MPE Simulated (m
3
/s)
0
50
100
150
200
250
300
350
400
450
0
50
100
150
200
250
300
350
400
450
Measured (m
3
/s)
Thiessen Simulated (m
3
/s)

Daily values (365 days
×

6 yrs)


Trend below 1:1 line,
esp. for measured
values greater than 50
m
3
s
-
1

o
underestimate


For less than 50 m
3
s
-
1

measured streamflow

o
Thiessen input
leads to greater
overestimates?

o
MPE input closer to
observed or
possibly greater
underestimates?

R
2

= 0.609

R
2

= 0.579

Dept. of Meteorology



Standard
Deviation of
Differences

Mean
Difference
(Bias)

R
2

E
NS

MBE

High (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 25
th

percentile of
observed conditions


Drought years when streamflow reached zero


WAM ability to forecast extreme dry events?


Both input results bad


high

flow days


ab
ove the 75
th

percentile


Was WAM developed to perform best during high
-
flow
conditions when using point
-
based rain gauge data?


MPE Pros +

MPE Cons
-


Captures spatial
variability


Accumulation & Mass
Balance Error


Improved Bias & E
NS


Did not perform better
in smaller New River
AOI


R
2

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?

Dept. of Meteorology

Six
-
Year Composite

Annual

Seasonal

Dept. of Meteorology


Drought during
first 3 yrs


Average to above
average last 3 yrs


MPE better for
2000
-
2002 & 2004


Thiessen better in
2003 & 2005

0
100,000
200,000
300,000
400,000
500,000
600,000
2000
2001
2002
2003
2004
2005
SepOct2004
Rest2004
acre-feet
Thiessen
FSU MPE
Measured
Dept. of Meteorology


Overall R
2

improves in
later years (exc. Thiessen
2000 anomaly)


SUW gauges added


More rainfall


E
NS

improves
substantially in 2003


E
NS

2005 equal

R
2

E
NS

FSU MPE Best

Thiessen Best


Bias: 2000
-
2002 & 2004


MBE: 2000
-
2002 & 2004


E
NS
: 2000
-
2002 & 2004


R
2
: 2001
-
2003 & 2005


Bias: 2003 & 2005


MBE: 2003 & 2005


E
NS
: 2003


R
2
: 2000 & 2004



E
NS

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

Dept. of Meteorology


Rest2004:
MPE better than
Thiessen


SepOct2004:
MPE better than
Thiessen


and… Rest2004 ~
2000
-
2002


More peak
streamflow events
in 2003 & 2005

0
100,000
200,000
300,000
400,000
500,000
600,000
2000
2001
2002
2003
2004
2005
SepOct2004
Rest2004
acre-feet
Thiessen
FSU MPE
Measured

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
Flow (m
3
/s)
Thiessen
FSU MPE
Measured
-5
0
5
10
15
20
25
30
35
40
Jan-00
Feb-00
Mar-00
Apr-00
May-00
Jun-00
Jul-00
Aug-00
Sep-00
Oct-00
Nov-00
Dec-00
Flow Difference (m
3
/s)
Thiessen
MPE
0
20
40
60
80
100
120
140
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Flow (m
3
/s)
Thiessen
FSU MPE
Measured
-65
-45
-25
-5
15
35
55
75
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05
Flow Difference (m
3
/s)
Thiessen
MPE
2000

2005

MPE Pros +

MPE Cons
-


Accumulations and
MBE during dry years
and tropical rainfall


E
NS

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?

Dept. of Meteorology

Six
-
Year Composite

Annual

Seasonal

Dept. of Meteorology


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

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-feet
Thiessen
FSU MPE
Measured
Dept. of Meteorology

MPE weaknesses


E
NS


Jan


Mar


stratiform
dominant


R
2


Jul


Sep


Convection/
sea
breeze

dominant


Key Seasonal

Differences

R
2

E
NS

FSU MPE Best

Thiessen Best


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


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


E
NS
: Apr
-
Jun, Jul
-
Sep,
Oct
-
Dec


R
2
: Jan
-
Mar, Apr
-
Jun,
Oct
-
Dec


Bias: Jan
-
Mar


MBE: Jan
-
Mar


E
NS
: Jan
-
Mar


R
2
: Jul
-
Sep



R
2

actually better for MPE during Jan
-
Mar?


MPE displayed better MBE, bias, E
NS

during Jul
-
Sep,
but not R
2
? Adds some uncertainty

MPE 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


R
2

during
summer/convective

events

Dept. of Meteorology

the end is near…

Dept. of Meteorology


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?




Dept. of Meteorology


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 common

Must remember:


Personal judgment key to any modeling study


Results are based on this configuration of
WAM for this basin only



Dept. of Meteorology


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

Dept. of Meteorology


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

Questions?

THE
END

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, M. L., and J. A. Smith, Rainfall estimation by the WSR
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, M., Accuracy of radar rainfall estimates for stream flow simulation,
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, J. P., and J. S.
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88 radar rainfall on short time scales to hydrological modeling o
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