Methodology for Climate Risk Mapping
77 climate hazard is given in Table 6.2. The adjusted matrix of climate risk is presented
in Table 6.3. Table 6.2: Weight and formula for calculating climate hazards index
Type of
hazard Weight
Formula
Flood 1.25
Probability of having monthly rainfall of more than 302 mm multiplied by average of area of Kelurahan being impacted by flood.
In order to get the index value of between 0 and 1, the calculated value is normalized by the maximum value
Drought 1.50
Probability of having dry month with length of more than 6 month multiplied by number of dry month above the 6 month DM
6+
. Dry month is defined as month with rainfall of less than 84 mm. If total
length of dry month is 8 month, the DM
6+
= 2 months. In order to get the index value of between 0 and 1, the calculated value is normalized
by the maximum value.
Land slide 0.75
Probability of having monthly rainfall of more than Q2 multiplied by slope indicator of the corresponding
Kelurahan. Kelurahan that has locations with slope of more than 45
o
, the indicator value will be equal to 1, otherwise zero.
Sea Level Rise
1.00 Fraction of
Kelurahan area being inundated by the sea level rise Max CCHI
4.50
Note: The weight is very subjective and determined based on Expert Judgement. Drought has the highest weight as its impact may be more severe than flood due its duration and extend of impacted
area. Impact of flood, land slide and sea level rise is more localized than that of drought.
Table 6.3:..Matrix of Climate Risk according the coping capacity index and composite climate hazard index
Coping Capacity Index
Composite Climate Hazard Index CCHI More than 3.5
Between 2.0 and 3.5 Less than 2.0
5 Very High
High Medium to High
4 High
Medium to High Medium
3 Medium to High
Medium Medium to Low
2 Medium
Medium to Low Low
1 Medium to Low
Low Very Low
Methodology for defining critical rainfall causing flood 302 mm and the one causing drought 84 mm was based on statistical distribution of the monthly rainfall
78 data from 27 stations 1989-2007 under hazards and without hazard condition. The
data of flood and drought hazards were taken from Bappeda 2007. From Box plot Figure 6.1, it was found that the monthly rainfall during flood years is relatively
larger than in no flood years. The average of rainfall amount is estimated around 324 mm when the flood occurred, and 205 mm when there was no flood. For this study,
we define the threshold of monthly rainfall associated with the flooding events as the 3
rd
quartile of monthly rainfall distribution, where in this case, equal to 302 mm. This threshold value means that if the monthly rainfall is more than 302 mm, the chance
of having flood disaster is large.
Figure 6.1:
Box plot of monthly rainfall in wet season during flood and no-flood years For drought, Box plot of monthly rainfall during dry season under drought years and
no drought years Figure 6.2 suggests that there is distinct different between distributions of monthly rainfall during drought and no drought year. Therefore, we
define that the critical threshold for monthly rainfall during dry season causing droughts is the 3
rd
quartile of monthly rainfall distribution in drought conditions, i.e. 84 mm.
Figure 6.2:Comparison between monthly rainfall during drought and no drought events.
Q3=84 Note :
Flood No Flood
Mean 324
205 Q1
210 78
Q2 307
181 Q3
428 302
Min 1
1 Max
987 790
Flood No Flood
79 Figure 6.3:Empirical Cumulative Distribution Functions eCDF and Scaled Density
Function of observed rainfall over Semarang and the threshold of having flood.
Figure 6.4 demonstrates a stem-leaf diagram of flood affected area in the Semarang city. Branches in the figure are equal to hundreds, while leaves are tens. It is found
that there were 21 months of flood events with an average of 1.2 months per year during the period of January 1989 to May 2007. Most of the inundated area were less
than 100 ha, with only two flood events affecting more than 100 ha, i.e. in January 2000 115 ha and February 1999 257 ha.
Figure 6.5 demonstrates that the flood inundated area over Semarang city will increase along with the increasing of rainfall amount. However, although there seems
to be a linear relationship between rainfall amounts and the inundated area, similar rainfall amount will have different effect on the same area. For example as shown in
Figure 6.4, a total rainfall amount of 375 mm may cause no effect to the flooding area equal to zero in one chance but could cause significant impact of disaster with
considerable inundated area in the city in another chance. This could be related to the dissimilarity in the occurrence of extreme rainfall defined by its frequency and
intensity. An extreme rainfall occurs intensely and continuously at a time more than a day, could create more catastrophic impact than several rainfall events that occur
discretely with long enough time interval, although the total of monthly rainfall are Figure 6.4:Distribution of
flood affected area.
Feb. 1999 Jan. 2000
302 0.58
0.2 0.4
0.6 0.8
1
200 400
600 800
Rainfall D
e n
s it
y
80 similar. Another possibility is that the spreading of flooding area could be also
caused by the rainfall event that occurred in the upstream area that brings water runoff into the region. However, such kind of event has a relatively small probability
if the rainfall in the region is low Figure 6.5.
Figure 6.5:Scatter plot of relationship between monthly rainfall and flood affected area.