A Study on Spatial Variation of Summer Precipitation overHuaihe River BasinZhou Wenyan1

Luo Yong2 and Guo Pinwen1

1 Nanjing University of Information Science & Technology, Nanjing 210044
2 Laboratory for Climate Studies, National Climate Center, China Meteorological
Administration, Beijing 100081
1.Introduction
In the surface hydrological pararmeterization of general circulation models (GCMs), it iscommonly assumed that the precipitation processes are homogeneous over a GCM grid square andthe the Precipitation intensity is uniformly distributed. A few researchers have explored thatt theprecipitation is exponentially distributed. The analysis of historical precipitation over Huaihe river
basin explored that neither the uniform nor the exponential distribution assumption may beproperly at the grid scale and, that instead, the spatial variability in precipitation is characterized
by statistical patterns that are inhomogeneous. A stochastic precipitation disaggregation
scheme(proposed by Gao and Sorooshi) is utilized to statistical and generate spatial distribution of
the precipitation.
2..Data
By use of intensify hourly data over Huaihe basin during 1998-2003 summer, a analysis ismade to study areal spatial distribution of precipitation over this area.
The square is located in the Huaihe river basin with a scope of 31゜-36゜N，112゜-121
゜E. The paper use 32 gauge site observation. FIG.1 show the gauge scope and location.

FIG.1 Location of the selected grid.

3. Stochastic precipitation disaggregation scheme
Gao and Sorooshi(1994) proposed a stochastic precipitation disaggregation scheme for GCM
applications.

FIG.2. conceptual representation of hourly rainfall records on the time and gauge
identification number plane.
The hourly precipitation data are distributed in a time-space plane as illustrated
in FIG..2. The vertical axis represents time with an increment of one hour. Thehorizontal axis represents the raingage ID number. The whole dataset constitutes amatrix on the time-space plane. The nonzero elements in the matrix indicate
precipitation intensity I(t,i).
The intragrid spatial variability of precipitation is examined through thestatistical features of precipitation events. We divide the grid square into polygons.
The precipitation volume per-hour ,V, and its coverage area, S, can be represented as:n (t )n (t )n(t )i =1i =1i =1S (t ) = ∑ Si = A∑ Si / A = A∑ Win (t )n (t )

i =1i =1

V (t ) = ∑ [I (t , i ) Si ] = A∑ [I (t , i )Wi ]

Where
N(t)
number of gauges receiving precipitation at events tA
the area of the grid square,
Wi(=Si/A) the areal correction weight for gauge i.
We define two variables to characterize a precipitation event:

（1）

（2）

n (t )N (t ) = ∑ Wi = S (t ) / A（3）i =1n (t )Z (t ) = ∑ [I (t , i )Wi ]

（4）

i =1

We can get（5）

V(t)/S(t)=Z(t)/N(t) .

Therefor, the relationship between V and S is similar to the relationship between Z and N. Afterthe relationship between Z and N is extracted from the available dataset, it will be used todetermine S form the V predicted by a GCM.
4. Statistics over precipitation events
To get the statistical relationship between Z and N, a large number of events is required. By
use of the 32 gauge site data in Huai river basin. We get some relationship like that therelationship between mean Z and N and rainfall event with different N(FIG. miss). All the lines areclose to each other in the two pictures. So we can get the a set of probability distribution of N
according to the subranges of Z by Stochasticing the data.(only show a part of FIG..)45
40

Z=0.0-0.1 mm/hr
Total Events:2796

35
30
25
20
15
10
5
0
3.5

0.1

0.2

0.3

0.4

30.5

0.6

0.7

0.8

0.9

1

0.8

0.9

1

Z=0.1-0.2 mm/hrTotal Events:520

2.5
2
1.5
1
0.5
00.1

0.2

0.3

0.4

0.5

0.6

0.7

3.5

3

Z=0.2-0.4 mm/hr
Total Events:525

2.5

21.5

1

0.5

00.1

FIG.3

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

probability distribution functions of N

5.Simulations and summary
To stochastically determine where the hourly precipitation volume will distribute inside thesquare. According to the value of V, find the PDF curve(FIG.3). Input the curve into a random
number generator to determine the corresponding coverage area S. According to the normalized
predipitation depth map, the random generator will determine on which subarea the precipitation
will fall.
The scheme was tested using data over Huaihe river basin. The simulations will show at FIG.(onlyshow the 1998’s simulation)
31.8

31.7

31.6

31.5

31.4

31.3

31.2

31.1
115.4

115.5

115.6

115.7

115.8

115.9

115.7

115.8

115.9

(a)

31.8

31.7

31.6

31.5

31.4

31.3

31.2

31.1
115.4

115.5

115.6

(b)
FIG.4

precipitation spatial distribution test, (a)pattern in 1998, (b)simulated pattern in 1998.

Results show that the precipitation spatial distribution is heterogeneous with a close
relation between the average precipitation intensity and its coverage area. Statistical evidence
suggests that PDF of coverage area and occurrence frequency probably proposed stable mode withproperly interval mean rain intensity. The scheme can be used successfully to realize theprecipitation disaggregation.
Acknowledgements: This research was jointly supported by NFSC Grant 40375026 and40233034.
Reference:
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