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Water Resource
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Flood disaster prediction model using Remote Sensing data and geographic information system
- Runoff Ratio
Many examples about the runoff couldn't be found, Table-1 shows a example in which three categories re considered - the land cover, the geology and the slope gradient. The land cover is classified to four classes - dense forest, sparse forest or arable land and barren land. The penetration represents the geology in this example. Slope gradient is roughly classified to three classes.
On the other hand, the land cover image can be generated by level-slicing of NVI (Normalized vegetation Index) which is computed with the following formula.
NVI = (I.R - R) / (1.R + R)
Fog 3 shows the land cover image in 1984 on the test area using LANDSAT - MSS data and table-2 shows the relationship between land cover and NVI. From the correspondence between table 1 and table 2, the runoff ration (=a) is assumed by the following function which contains the slope gradient (=b) and NVI (=g).
a = 0.01b- 0.37g+ 0.648
Fig. 4 shows the relationship of NVI between 1984's MSS data and 1988's TM data. As can be seen in Fig. 4 it si possible to assume the runoff ratio in 988 with considering the difference between the two sensors. So the following function gives the runoff ration in 1988.
a = 0.01 b- 0.26 g+ 0.629
Table 1 Example on runoff ratio
Penetration slope
Land cover |
Good |
Medium |
Bad |
| S |
G |
P |
S |
G |
P |
S |
G |
P |
| Dense forest |
.65 |
.55 |
.45. |
.55 |
.45 |
.35 |
.45 |
.45 |
.25 |
| Spare forest |
.75 |
.65 |
.55 |
.65 |
.55 |
.45 |
.55 |
.45 |
.35 |
| Glass land |
.85 |
.75 |
.65 |
.75 |
.65 |
.55 |
.65 |
.55 |
.45 |
| Barren forest |
.90 |
.80 |
.70 |
.80 |
.70 |
.35 |
.45 |
.45 |
.25 |
Table 2 Relationship between NVI and landuse
| NVI |
Land cover |
| ~ - .07 |
Road, Baresoil |
| -.07 ~ .07 |
Glass land, paddy field |
| .07 ~ .45 |
Spare forest |
| .45 ~ |
Dence forest |
- Arrival Distance and Arrival Time
The velocity in the actual flood flow changes depending on the quantity of discharge. The manning formula gives the velocity of the flow as follows
v = (R 2/3X I 1/2)/N
R : Hydraulics radius
I : Bed gradient
N : Roughness factor
Fig.5 The supposed sectional shape
When the sectional shape of the stream is supposed as Fig 6 the velocity increases in proportion to the third power of the quantity of the flow
u µ Q1/3
Q : Quantity of the flow
The arrival distance is defined in this study as the distance measured along the streamline starting from each pixel to the observation point. Fig6 shows how to compute the arrival distance.
Fig.7 Arrival time
Fig 6 arrival distance
The total arrival time
is given by accumulating subcomponents of the arrival time from a pixel
to the neighbor which is derive from division of the arrival distance by
the velocity. Fig - 7 shows the difference of the arrival time depending
on the strength of the precipitation. When the rainfall have a unit of
mm per hour, it tales 180 unit times for the water in the farthest pixel
to arrive at the observation point in the example shown in Fig 7 when
the rainfall has five mm per hour, the arrival time shortens to only 100 unit times.
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