ESWC SS 2012 - Tuesday Tutorial Dan Brickley and Denny Vrandecic: Linked Open Data
REU sponsor: Dr. Baxter Vieux Dr. Fekadu Moreda Gary Brickley
description
Transcript of REU sponsor: Dr. Baxter Vieux Dr. Fekadu Moreda Gary Brickley
Distributed Hydrologic Modeling--Jodi Eshelman
Analysis of the Number of Rain Gages Required to Calibrate Radar Rainfall for the Illinois
River Basin
REU sponsor: Dr. Baxter Vieux
Dr. Fekadu Moreda
Gary Brickley
Jodi Eshleman
Distributed Hydrologic Modeling--Jodi Eshelman
Introduction• Radar rainfall estimates are an important supplement to
rain gage accumulations for modeling river basins.
• Radar estimates can be biased or in error and must be corrected.
Questions:
1. How many rain gages are necessary to correct the radar?
2. What degree of accuracy can be achieved?
3. How do different correction methods compare?
Distributed Hydrologic Modeling--Jodi Eshelman
Background
• Are 10 gages adequate to calibrate the radar for the Illinois River Basin?
• Radar error in estimating rainfall – overshoot cloud tops– Z/R relationship transforms reflectance to rain rate
• Correction of radar by applying some correction based on rain gage accumulation
• Correcting radar estimates provides more accurate spatial estimates of rainfall for river basin simulation.
Distributed Hydrologic Modeling--Jodi Eshelman
WSR-88D or NEXRAD
• Weather Surveillance Radar-1988 Doppler
• Prototyped in Norman at NSSL
• Scans Every 5 or 6 minutes during precipitation
• 150+ installed in US and abroad
0.5°
1.5°2.5°
Distributed Hydrologic Modeling--Jodi Eshelman
Location of Gages
Distributed Hydrologic Modeling--Jodi Eshelman
Presentation Outline
• Test 4 different correction factors– Mean field bias
– Probability density function
– <1mm
– Weighted
• Gage density study• Size and time progression analysis
Distributed Hydrologic Modeling--Jodi Eshelman
Correction Factor Comparison
• Adjustment to rain gage mean
• Average difference after correction
• Simulated discharge volume
Distributed Hydrologic Modeling--Jodi Eshelman
MEAN VALUESStorm Event MFB PDF <1mm MFB <1mm PDF Weighted Raw Mesonet
Jan-95 0.5277 0.4950 0.5992 0.4058 0.4648 0.4302 0.4950Mar-95 0.2004 0.1964 0.1944 0.1933 0.2374 0.1832 0.1964Jun-95 0.6481 0.6413 0.6530 0.6773 0.6688 0.7317 0.6550Nov-96 0.4191 0.4256 0.4218 0.4335 0.4081 0.4312 0.4191
NovDec96 0.1889 0.1746 0.1682 0.1503 0.1700 0.2091 0.1746Apr-96 0.7290 0.7155 0.7289 0.7483 0.7322 0.8370 0.7155
May-96 0.1712 0.1729 0.1622 0.2031 0.1662 0.1956 0.1729Feb-97 0.4472 0.4507 0.4472 0.4581 0.4660 0.4582 0.4507
AVERAGE DIFFERENCE AFTER CORRECTIONStorm Event MFB PDF <1mm MFB <1mm PDF Weighted Raw
Jan-95 17% 16% 25% 21% 16% 18%Mar-95 36% 35% 34% 34% 46% 32%Jun-95 6% 6% 6% 8% 7% 12%Nov-96 10% 10% 10% 10% 10% 11%
NovDec96 27% 24% 24% 23% 24% 32%Apr-96 16% 15% 16% 17% 16% 23%
May-96 10% 11% 10% 23% 9% 20%Feb-97 11% 11% 11% 12% 12% 12%
PDF is closest to Mesonet
Distributed Hydrologic Modeling--Jodi Eshelman
Volume Comparison
Novdec96
Feb97
Jan95
Nov96
May96
Jun95
Apr96
Mar95
Nov96
Jun95
NovDec96Apr96
May96
Mar95
Feb97
Jan95
NOVDEC96FEB97JUN95
APR96
NOV96
JAN95
MAY96
MAR95
0.0E+00
2.0E+07
4.0E+07
6.0E+07
8.0E+07
1.0E+08
1.2E+08
0.0E+00 1.0E+07 2.0E+07 3.0E+07 4.0E+07 5.0E+07 6.0E+07 7.0E+07 8.0E+07 9.0E+07 1.0E+08 1.1E+08 1.2E+08 1.3E+08
Observed Vol (m3)
Mod
el V
ol (
m3)
MFB
RAW
Weighted
Linear (MFB)
Linear (PDF)
Linear (RAW)
Linear(Weighted)
Distributed Hydrologic Modeling--Jodi Eshelman
Presentation Outline
• Test 4 different correction factors– Mean field bias
– Probability density function
– <1mm
– Weighted
• Gage density study• Size and time progression analysis
Distributed Hydrologic Modeling--Jodi Eshelman
Gage Density
2
22
d
stn
Statistically estimating the mean with prescribed accuracy
Where:
n=number of gages requireds2=Varianced=Allowable margin of error (5-30% mean)=% Confidence (60-90%)
Distributed Hydrologic Modeling--Jodi Eshelman
Calibration Comparison
555 55530
18
69
69
4
20 8076 50 2829
5
0.5
0
5
10
15
20
25
30
35
0 50 100 150 200 250 300 350Number of Gages Needed to Calibrate
Allo
wab
le M
arg
in o
f Err
or
(% m
ean
)
49.545
20.321
0.499
29.915
68.781
80.343
28.172
4.264
68.921
17.789
29.23
5.259
76.008
Total Mean Depth (mm)
Distributed Hydrologic Modeling--Jodi Eshelman
Standard Error Approach
# gauges Variance S.E.(X) S.E.(X)% Avg. Difference X
10 1085.99 10.42 28.5 32%9 1080.25 10.39 29.1 32%8 989.36 9.95 27.6 38%7 1042.69 10.21 27.2 45%6 1045.36 10.22 27.6 48%5 1087.81 10.43 26.6 58%4 1110.16 10.54 27.0 59%3 1086.94 10.43 25.4 54%2 978.62 9.89 25.0 49%
Distributed Hydrologic Modeling--Jodi Eshelman
Presentation Outline
• Test 4 different correction factors– Mean field bias
– Probability density function
– <1mm
– Weighted
• Gage density study• Size and time progression analysis
Distributed Hydrologic Modeling--Jodi Eshelman
Mean Total Accumulation
May96
Mar95
NovDec96
Jun95
Jan95
Apr96Feb97
Nov96
y = 1.0368x
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
Mesonet (mm)
Un
co
rre
cte
d R
ad
ar
(mm
)
Distributed Hydrologic Modeling--Jodi Eshelman
Time Progression
First 6-Hour Period
miam
cook
jayx
tahl
pryo
w estw ebb
y = 1.5943x
0
5
10
15
20
25
30
35
40
0 10 20 30 40
Mesonet (mm)
Ra
da
r (m
m)
06/08 18-23
Second 6-Hour Period
sall
w ebb
tahl cooktull
w est
y = 0.487x
0
24
68
10
1214
1618
20
0 5 10 15 20
Mesonet (mm)
Ra
da
r (m
m)
06/08 23-09 05
Seventh 6-Hour Period
miam
jayx
pryo
tahl
tull
y = 1.0587x
0
10
20
30
40
50
60
70
0 20 40 60
Mesonet (mm)
Ra
da
r (m
m)
06/10 05-11
Final 6-Hour Period
cook
tahl
west
webbsall
tulljayx
wist
miampryo
y = 0.5172x
0
1
2
3
4
5
6
0 2 4 6
Mesonet (mm)
Ra
da
r (m
m)
06/11 11-17
Distributed Hydrologic Modeling--Jodi Eshelman
Conclusions
• PDF correction factor is most effective– Mean adjustment is closer to Mesonet– Average difference is less than MFB
• Weighted PDF – – Weighting gages close to the basin improve discharge
volume simulations
• Gage density study– 10 gages are sufficient for 30% of the mean and 90%
confidence– Due to large variance, smallest storms are negligible– little consideration for flooding