Based on each sector’s damage maximum height relationship (3 damage sectors) and the research about hydrologic risk (flood level frequency and exceedance probability analysis), damage probability curves were developed for the three sectors. Therefore, the change in annual average damage value or the risk of 1% flood frequency damage to infrastructure accounting for 42%, housing 39%, and agriculture 19%. Corresponding to 2% flood frequency, damage to infrastructure is accounted for 43%, housing 41% and agriculture 16%. Corresponding to 4% flood frequency (occurred in 2000s), damage to infrastructure is accounted for 42%, housing 43% and agriculture 15%. Corresponding to 10% flood frequency, damage to infrastructure is accounted for 40%, housing 54% and agriculture 6%. Corresponding to 50% flood frequency, only damage to housing.
Table 2 provides total estimated damage in 2030, 2060, 2090 based on the medium and high climate change scenarios. Damage in 2030 and 2060 are similar however, in 2090, total damages increase significantly.
Table 2.Estimated Damage at 2030, 2060, 2090 according to climate change scenarios for 1% and 10% AEP floods
|
Scenarios |
P (%) |
Damage (1000 USD) |
||
|
2030 |
2060 |
2090 |
||
|
Medium |
1 |
56 |
56 |
1,055 |
|
High |
863 |
863 |
1,243 |
|
|
Medium |
10 |
21 |
21 |
863 |
|
High |
375 |
375 |
863 |
|
3.2 Assessment of Social Vulnerability
3.2.1 Analyzing the Exposure by flood due to climate change
The risk assessment of flooding was undertaken using the model developed by FMMP/ MRC for the Tam Nong District. Specifically, the research team used the SWAT model, combining ISIS and IQQM to simulate historical floods to calibrate and test the model and thereby simulate the flood frequency of 1%, 4%, 10%, 20%, and 50%. Based on the method of stacking depth of inundation maps, flood velocity, time submerged (outputs of the model ISIS) weighted maps of flood frequency corresponding to 1%, 4%, 10%, 20%, 50% exceedance probabilities were prepared. This modelling represented the base case.
Flood risk flood frequency corresponding to 1% is calculated by 4 climate change scenarios; IPSL-RCP 8.5, IPSL-RCP 6.0, GFDL-RCP 6.0 and GISS-RCP 6.0. The base water level with probability 1% is 4.5m in Tam Nong District. For the IPSL-RCP 8.5 and IPSL-RCP 6.0 scenarios the base water level in 2030s, 2060s and 2090s are all higher than the baseline maximum water level. In contrast, scenario GFDL-RCP 6.0 and scenario GISS-RCP 6.0, maximum water level in 2030s, 2060s and 2090s is generally lower than baseline water level with the exception of GFDL-RCP 6.0 which is higher for the 2090 projection.
|
Level |
Water level |
Peak flow velocity |
||
|
H (m) |
Weight |
V (m/s) |
Weight |
|
|
1 |
<0.6 |
0.2431 |
0 - 0.2 |
0.2511 |
|
2 |
0.6-1.5 |
0.3429 |
0.2 - 0.6 |
0.699 |
|
3 |
1.5-2.9 |
0.2312 |
0.6 - 0.9 |
0.0497 |
|
4 |
2.9-4.3 |
0.1828 |
> 0.9 |
0.0002 |
|
Flood warning level 3 |
≥ 3.5 |
0.06 |
||
Based on flood duration and flood depth map of Tam Nong district and results from map of exposure, flood risk of Phu Thanh A Commune is at a low level in comparison with other communes in Tam Nong District.
3.2.2 Analyzing the Sensitivity and Adaptive Capacity by flood and climate change
Calculation of Sensitivity based on factors as population conditions, livelihood, infrastructure and environment. Choose factors corresponding to conditions of Long An A and Long Phu A Villages, specifically:
- Population factors such as population density, rate of literacy, poverty rate, etc.
- Livelihood factors such as main occupation (paddy), average earnings …
- Infrastructure factors such as kind of housing, warning system, transportation system, locations for evacuation …
- Environmental factors such as state of river, canal; rate of standard restroom (toilet with septic tanks); rate of tap water using …
3.2.3 Analyzing flood and climate change vulnerability
To analyze adaptive capacities, we chose adaptation factors in each village including flood prevention condition, flood prevention experience, supporting from local authorities and recovery. These factors are described in turn;
Flood prevention condition factor: storing food after flood and preparing ship/boat.
Flood prevention experience factor: flood prevention training, supporting from local authority and supporting from local people.
Recovery factor: cleaning after flood, funding and human resources to repair housing and funding to recovery produce.
|
ADAPTATIVE CAPACITY |
IMPACTS |
||||||
|
Population |
Livelihood |
||||||
|
Factors |
Variables |
Density |
Literacy rate |
Poverty rate |
Main occupation |
Average earning |
|
|
Medium |
Low |
Low |
Medium |
Low |
|||
|
Coping condition |
Storing flood before flood (%) |
Low |
Medium |
Medium |
Medium |
Medium |
Medium |
|
Preparing ship/boat (%) |
Low |
Medium |
Medium |
Medium |
Medium |
Medium |
|
|
Experience |
Experience in flood prevention (%) |
Very high |
Low |
Low |
Low |
Low |
Low |
|
Know the way to coping with flood (%) |
Very high |
Low |
Low |
Low |
Low |
Low |
|
|
Supporting |
Flood prevention training (%) |
Very low |
High |
Medium |
Medium |
High |
Medium |
|
Supporting from local authorities (%) |
Low |
Medium |
Medium |
Medium |
Medium |
Medium |
|
|
Supporting from local people (%) |
Very high |
Low |
Low |
Low |
Low |
Low |
|
|
Recovery |
Cleaning after flood(%) |
Very low |
High |
Medium |
Medium |
High |
Medium |
|
Funding and human resources to repair housing after flood (%) |
Very low |
High |
Medium |
Medium |
High |
Medium |
|
|
Funding to recover production (%) |
Very low |
High |
Medium |
Medium |
High |
Medium |
|
Figure 16: Flood vulnerability in Population factor and Livelihood factors at Long An A Village
|
ADAPTATIVE CAPACITY |
IMPACTS |
||||||||
|
Infrastructure |
Environment |
||||||||
|
Factors |
Variables |
Rate of house (temporary and semi solid) (%) |
Warning system (%) |
Transportation system (km/km2) |
Community house for flood avoid and childcare (%) |
State of river, canal (km/km2) |
Rate of standard toilet (%) |
Rate of tap water using (%) |
|
|
Medium |
Low |
Low |
Low |
Low |
Low |
Low |
|||
|
Coping condition |
Storing flood before flood (%) |
Low |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
|
Preparing ship/boat (%) |
Low |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
|
|
Experience |
Experience in flood prevention (%) |
Very high |
Low |
Low |
Low |
Low |
Low |
Low |
Low |
|
Know the way to coping with flood (%) |
Rất cao |
Low |
Low |
Low |
Low |
Low |
Low |
Low |
|
|
Supporting |
Flood prevention training (%) |
Very high |
High |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
|
Supporting from local authorities (%) |
Low |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
|
|
Supporting from local people (%) |
Very high |
Low |
Low |
Low |
Low |
Low |
Low |
Low |
|
|
Recovery |
Cleaning after flood (%) |
Very low |
High |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
|
Funding and human resources to repair housing after flood (%) |
Very low |
High |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium |
|
|
Funding to recover produce |
Very low |
High |
Medium |
Medium |
Medium |
Medium |
Medium |
Medium
|
|
