Chapter2 Precipitation

7/28/2019 Chapter2 Precipitation

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Precipitation

CTP Review

June 23, 2012


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Precipitation

Single strongest variable driving

hydrologic processes

Formed water vapor in the atmosphere

As air cools its ability to hold water

decreases and some turns to liquid or ice-

i.e. glass condensation (Figure 2.1)


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Air Saturation


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Weather Patterns

Weather (day to day) vs. climate (years-

decades and patterns)

What are hydrologists most concerned

with?

Climate and geography result in biome

classification


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Figure 2.2

Biomes and Rainfall


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Causes of Precipitation


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Measurement of Precipitation

Terminology (2.3)

Types of devices (2.4.2)

Snowfall conversions (2.4.1)

Location of devices (2.4)

Interpretation of data (2.3.3, 2.6)


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Rainfall Terminology

Type-hail, rain, snow, sleet

Depth

Storm Duration

Average rate of precipitation-Intensity

Return Period or Recurrence Interval


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Types of Rain Gages


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Snow Measurement

Determine the water equivalent

5%-60% of snow depth may be water

equivalent-- density

Snow pillows use antifreeze solution and

pressure measurement to measure water

equivalent


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Location of Gages

Gauges measurepoint rainfall

True precipitation unaffected by

surroundings-winds, trees, buildings

Clearance distance 2 times height of object

For large areas multiple gauges are needed

for more accurate estimates


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Table 2.3

Size of Watershed Number of Gage SitesAcres

40 2

100 3

600 4

Square Miles

5 10

10 15

100 50

300 100


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Rain Gage Density & Error


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Interpretation of Data

Time distributions

Area distributions

Using point data to find average rainfall

Thiessen method


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Figure 2.14

Storm Patterns (Histograms)


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Thiessen Method for Average Rain

Step 1


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Step 2


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Step 3


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Step 4


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Prediction-Frequency

Distributions To plan and design projects must be able

to predict probability of rainfall events

Duration, Intensity, Return Period

Often must estimate Return Periods

UseHazen method to develop intensity-

duration-frequency curve (Example 2.5).


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Table 2.6Annual Precip. for LA, CA 1934-1953Year Depth (in) Rank Depth (in) Year1934 14.6 1 32.8 19411935 21.7 2 26.2 19521936 12.1 3 23.4 19381937 22.4 4 22.4 19371938 23.4 5 21.7 19351939 13.1 6 19.2 19401940 19.2 7 19.2 1944

1941 32.8 8 18.2 19431942 11.2 9 14.6 19341943 18.2 10 13.1 19391944 19.2 11 12.7 19471945 11.6 12 12.1 19361946 11.6 13 11.6 1945

1947 12.7 14 11.6 19461948 7.2 15 11.2 19421949 8 16 10.6 19501950 10.6 17 9.5 19531951 8.2 18 8.2 19511952 26.2 19 8 19491953 9.5 20 7.2 1948

You have determined that

more than 23.4 of annual

rainfall will result in a net

Economic loss for your

crop.

Now, you need to predictHow often this will occur.


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Table 2.6Annual Precip. for LA, CA 1934-1953Year Depth (in) Rank Depth (in) Year1934 14.6 1 32.8 19411935 21.7 2 26.2 19521936 12.1 3 23.4 19381937 22.4 4 22.4 19371938 23.4 5 21.7 19351939 13.1 6 19.2 19401940 19.2 7 19.2 1944

1941 32.8 8 18.2 19431942 11.2 9 14.6 19341943 18.2 10 13.1 19391944 19.2 11 12.7 19471945 11.6 12 12.1 19361946 11.6 13 11.6 1945

1947 12.7 14 11.6 19461948 7.2 15 11.2 19421949 8 16 10.6 19501950 10.6 17 9.5 19531951 8.2 18 8.2 19511952 26.2 19 8 19491953 9.5 20 7.2 1948


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Probability of Occurrence, Fa (%)=

100 (2n-1)

2y

Equation 2.2, page 46

n = the rank of each event

y = the total number of events

For Example:

Year 1938--23.4 in--Rank #3

100 (2*3-1)2*20

= 12.5


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Table 2.6Annual Precip. for LA, CA 1934-1953Year Depth (in) Rank Depth (in) Year Prob. F a1934 14.6 1 32.8 1941 2.51935 21.7 2 26.2 1952 7.5

1936 12.1 3 23.4 1938 12.51937 22.4 4 22.4 1937 17.51938 23.4 5 21.7 1935 22.51939 13.1 6 19.2 1940 27.51940 19.2 7 19.2 1944 32.51941 32.8 8 18.2 1943 37.51942 11.2 9 14.6 1934 42.51943 18.2 10 13.1 1939 47.51944 19.2 11 12.7 1947 52.51945 11.6 12 12.1 1936 57.51946 11.6 13 11.6 1945 62.51947 12.7 14 11.6 1946 67.51948 7.2 15 11.2 1942 72.51949 8 16 10.6 1950 77.51950 10.6 17 9.5 1953 82.51951 8.2 18 8.2 1951 87.51952 26.2 19 8 1949 92.5

1953 9.5 20 7.2 1948 97.5


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Return Period=100

Fa

For Example:

Year 1938-- Fa= 12.5

10012.5

= 8 yrs

Fa= probability of occurrence (%)


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Table 2.6Annual Precip. for LA, CA 1934-1953Year Depth (in) Rank Depth (in) Year Prob. F a Return Period1934 14.6 1 32.8 1941 2.5 40.01935 21.7 2 26.2 1952 7.5 13.31936 12.1 3 23.4 1938 12.5 8.01937 22.4 4 22.4 1937 17.5 5.71938 23.4 5 21.7 1935 22.5 4.41939 13.1 6 19.2 1940 27.5 3.61940 19.2 7 19.2 1944 32.5 3.1

1941 32.8 8 18.2 1943 37.5 2.71942 11.2 9 14.6 1934 42.5 2.41943 18.2 10 13.1 1939 47.5 2.11944 19.2 11 12.7 1947 52.5 1.91945 11.6 12 12.1 1936 57.5 1.71946 11.6 13 11.6 1945 62.5 1.6

1947 12.7 14 11.6 1946 67.5 1.51948 7.2 15 11.2 1942 72.5 1.41949 8 16 10.6 1950 77.5 1.31950 10.6 17 9.5 1953 82.5 1.21951 8.2 18 8.2 1951 87.5 1.11952 26.2 19 8 1949 92.5 1.1

1953 9.5 20 7.2 1948 97.5 1.0


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Frequency-Magnitude Graph


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Predict probability of a given return period

storm occurring within a given number of years

What is the probability that the 8-year event will

happen in LA within the next 5 years? Or, what

The probability of an economic loss due to reduced

Harvest in the next 5 years?

equation 2.4, page 47

P(T,n)= 1 - ( 1 - )n

1T

(49%) 0.49= 1 - ( 1 - )518


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Figure 2.19

Rainfall Rate-Duration-Frequency

R i f ll R t D ti F


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Figure 2.20

Rainfall Rate-Duration-Frequency

Prediction

by Month

Documents

Chapter2 Precipitation