lecture 3 - 1 ers 482/682 (fall 2002) information sources ers 482/682 small watershed hydrology
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![Page 1: Lecture 3 - 1 ERS 482/682 (Fall 2002) Information sources ERS 482/682 Small Watershed Hydrology](https://reader030.vdocuments.mx/reader030/viewer/2022032704/56649d555503460f94a336f5/html5/thumbnails/1.jpg)
ERS 482/682 (Fall 2002) Lecture 3 - 1
Information sources
ERS 482/682Small Watershed Hydrology
![Page 2: Lecture 3 - 1 ERS 482/682 (Fall 2002) Information sources ERS 482/682 Small Watershed Hydrology](https://reader030.vdocuments.mx/reader030/viewer/2022032704/56649d555503460f94a336f5/html5/thumbnails/2.jpg)
ERS 482/682 (Fall 2002) Lecture 3 - 2
Issues
• Model errors– Assumptions– Function
QPET
Water balance
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ERS 482/682 (Fall 2002) Lecture 3 - 3
Issues
• Model errors• Measurement errors
Figure 2 (Sullivan et al. 1996)
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ERS 482/682 (Fall 2002) Lecture 3 - 4
Issues
• Model errors• Measurement errors
• Spatial variability• Temporal variability
Figure 2 (Sullivan et al. 1996)
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ERS 482/682 (Fall 2002) Lecture 3 - 5
Model errors
• Check and be aware of assumptions• Calibration• Validation• Verification
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ERS 482/682 (Fall 2002) Lecture 3 - 6
Measurement errors
• Estimate the error– Instrument error– Data error
• Standard deviation of normal distribution 95% probability that an error will be between ±1.96 SD of the true value
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ERS 482/682 (Fall 2002) Lecture 3 - 7
0
500
1000
1500
2000
2500
3000
1915 1935 1955 1975 1995
year
dis
char
ge (
cfs)
0
5
10
15
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
Mor
e
discharge (cfs)
f(x)
frequency
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ERS 482/682 (Fall 2002) Lecture 3 - 8
RELATIVE frequency
0
0.05
0.1
0.15
0.2
0
200
400
600
800
1000
1200
1400
1600
1800
2000
discharge (cfs)
f(x)
n ns,observatio # total
frequency
0
5
10
15
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
Mor
e
discharge (cfs)
f(x)
![Page 9: Lecture 3 - 1 ERS 482/682 (Fall 2002) Information sources ERS 482/682 Small Watershed Hydrology](https://reader030.vdocuments.mx/reader030/viewer/2022032704/56649d555503460f94a336f5/html5/thumbnails/9.jpg)
ERS 482/682 (Fall 2002) Lecture 3 - 9
Normal distribution
0
0.05
0.1
0.15
0.2
0.25
- 20 - 10 0 10 20
x
f(x)
2
2
1
2
1
x
exfKurtosis: flat vs. peaked
standard deviation
mean
particular error
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ERS 482/682 (Fall 2002) Lecture 3 - 10
0
0.05
0.1
0.15
0.2
0.25
- 20 - 10 0 10 20
x
f(x)
Descriptive statistics
Mean,x
n
xx
n
ii
1
For errors, we hope this is 0!For errors, we hope this is 0!
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ERS 482/682 (Fall 2002) Lecture 3 - 11
Measures of central tendency
• Mean• Center of gravity
• Median• Half the x-values are smaller and half are larger
• Mode• Value of x with the largest frequency
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ERS 482/682 (Fall 2002) Lecture 3 - 12
0
0.05
0.1
0.15
0.2
0.25
- 20 - 10 0 10 20
x
f(x)
Descriptive statistics
Mean,x
2xx
n
ii xx
nss
1
22
1
1
WHY?
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ERS 482/682 (Fall 2002) Lecture 3 - 13
0
0.05
0.1
0.15
0.2
0.25
- 20 - 10 0 10 20
x
f(x)
Descriptive statistics
Mean,x
s s
sx 96.1 sx 96.1p = .95
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ERS 482/682 (Fall 2002) Lecture 3 - 14
Measurement errors
• Estimating missing data (Sec. 4.2.3)– Station-average method– Normal-ratio method– Inverse-distance weighting– Regression
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ERS 482/682 (Fall 2002) Lecture 3 - 15
Station-average method
G
ggp
Gp
10
1ˆ
0p̂
p1
p2
p3
p4 p5
p6
p7G = # of gages with data
Use when gage values are similar
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ERS 482/682 (Fall 2002) Lecture 3 - 16
Normal-ratio method
G
gg
g
pP
P
Gp
1
00
1ˆ
0p̂
p1
p2
p3
p4 p5
p6
p7
G = # of gages with dataP0 = average annual precip at gage 0Pg = average annual precip at gage g
Use when gage values are not similar
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ERS 482/682 (Fall 2002) Lecture 3 - 17
Inverse-distance weighting
G
gg
bg pd
Dp
10
1ˆ
0p̂
p1
p2
p3
p4 p5
p6
p7
G = # of gages with datadg = distance of gage g from gage 0b = 1 or 2
G
g
bgdD
1
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ERS 482/682 (Fall 2002) Lecture 3 - 18
Regression
GG pbpbpbbp 221100ˆ
0p̂
p1
p2
p3
p4 p5
p6
p7
G = # of gages with databg = regression coefficient for gage g
Caution: Series and data mustbe independent
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ERS 482/682 (Fall 2002) Lecture 3 - 19
Spatial variability
A
dxdyyxpA
P ,1
P = total precipitation on the watershedA = area of watershed
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ERS 482/682 (Fall 2002) Lecture 3 - 20
Weighted averages
G
ggg pwP
1
ˆ
wg = weight of gage g
G
ggw
11 10 gw
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ERS 482/682 (Fall 2002) Lecture 3 - 21
Weighted averages
• Thiessen polygons
G
ggg pa
AP
1
1ˆ
A
aw g
g ag = area of subregion for gage g
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ERS 482/682 (Fall 2002) Lecture 3 - 22
Weighted averages
• Isohyetal methods– isohyet: contour of equal precipitation
I
iii pa
AP
1ˆ
1ˆ
2
ˆ ii
ipp
p
ai = area of subregion between pi- and pi+ isohyets p0.5
p1.0
p1.5
p2.0
a2
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ERS 482/682 (Fall 2002) Lecture 3 - 23
Temporal variability
• Exceedence probability or return period
Stochastic hydrology (GEOL 702J)
– PDF = probability distribution function• f(x) = p (X = x)
– 1 – CDF• 1-F(x) = p(X > x) exceedence probabilityexceedence probability
probabilityprobability
non-exceedence probabilitynon-exceedence probability
– CDF = cumulative distribution function• F(x) = p (X x)
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ERS 482/682 (Fall 2002) Lecture 3 - 24
Displaying cumulative frequency
f(x)
Discharge (cfs)
1 2 3 4 5 6 More
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
+
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ERS 482/682 (Fall 2002) Lecture 3 - 25
Displaying cumulative frequency
f(x)
Discharge (cfs)
1 2 3 4 5 6 More
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
+
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ERS 482/682 (Fall 2002) Lecture 3 - 26
<12
34
56
More
0
0.2
0.4
0.6
0.8
1f(
x)
discharge (cfs)
Displaying cumulative frequency
F(x
)
0
1
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ERS 482/682 (Fall 2002) Lecture 3 - 27
Normal distribution
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40
x
f(x)
and F
(x)
CDF: Given x Find P(Xx)
CDFCDF
PDPDFF
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ERS 482/682 (Fall 2002) Lecture 3 - 28
Normal distribution
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40
x
F(x
)
Non-exceedenceprobability: P (Xx)
Exceedenceprobability: P (X>x)
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ERS 482/682 (Fall 2002) Lecture 3 - 29
Return period
xXP
y,probabilit Exceedence
1 periodReturn
10-year design = 10.0
1
xXP or 1 – P(Xx)
50-year design = 02.0
1
xXP
01.0
1
xXP100-year design =
Does the 1-yearstorm occur
every year???
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ERS 482/682 (Fall 2002) Lecture 3 - 30
Normal distribution
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40
x
F(x
)
10-year design = 10.0
1
xXP
Given F(x)=P(Xx)What is x?
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ERS 482/682 (Fall 2002) Lecture 3 - 31
Using the normal distribution as a model
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40
x
f(x)
and F
(x)
70% non-exceedence probability
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ERS 482/682 (Fall 2002) Lecture 3 - 32
Using the normal distribution as a model
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40
x
f(x)
and F
(x)
+ +++
+
++
++
++++
30% exceedence probability
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ERS 482/682 (Fall 2002) Lecture 3 - 33
Figures 2-7, 2-8, 2-9 (Dunne & Leopold 1978)