Open matamadio opened 3 years ago
Myanmar
Fluvial and coastal flooding: WRI aqueduct Cyclones: STORM IBTrACS model Road links: OpenStreetMap + OSRM Railways tracks: OpenStreetMap Electricity transmission lines: Gridfinder Fragility: Koks et al. 2019; Miyamoto et al. 2019; Habermann and Hedel 2018 Socio-economic impacts: Grided Population Density/Count (WorldPop Population Data); Gridded GDP per capita (DRYAD Gridded GDP) Costs: Koks et al. (2019), World Bank ROCKS database; World Bank PPI database.
Data in 4 folders:
The columns listed below (and described above, in Risks) are also aggregated to the Admin1 level for 4. Summary. Depending on the type of data, the outputs may be per RP, epoch and scenario OR Annual (please see Risks, above, for details):
The full sets of hazards addresses:
Hazard | Probabilities | Intensities and spatial extents | Climate scenario information |
---|---|---|---|
WRI Aqueduct flood hazard
|
1/2, 1/5, 1/10, 1/25, 1/50, 1/100, 1/250, 1/500, and 1/1000 | Flood depths in meters over 30 arc second grid squares. |
|
Cyclones from STORM IBTrACS model | 28 different probabilities from 1/10 to 1/10000 | 3-hour time step wind gust speeds in m/s at 0.1-degree grid squares. | None |
Generalised asset costs information are applied to estimate exposed value, as described in Table 3-3 of report. In some instances, they assumed a ±25% uncertainty in our cost estimations in line with the assumptions from Koks et al. (2019).
Figure 3-1 shows direct damage (fragility) curves for assets from different studies. Since having one fragility curve is not ideal for such a generalised context, Koks et al. (2019) suggested adding uncertainty to the fragility information and used five curves (derived from the original) to test the sensitivity of damage estimates to different fragility values
Figure 3-1: Generalised direct damage (fragility) curves vs flood depths for different types of infrastructure assets. (a) paved roads (from Koks et al. 20195); (b) unpaved road (from Koks et al. 20195); (c) railway lines (from Koks et al. 20195); and (d) power plants (from Miyamoto et al. 201943); (e) airports (from Habermann and Hedel 201844), (f) ports (based on expert judgment). The fragility curve for airports mainly represents flood damage to runways. The fragility curve for ports is based on expert input from a large port authority, details of which we cannot disclose. The boldest lines (State 1) are used in the original studies while the other curves (State 2 – State 5) are derived from the original curve by multiplying by 2, 3, 4, 5.
Figure 3-2: Generalised damage probability (fragility) curves vs wind speeds for different types of assets.
The main outputs are presented as three metrics:
Important attributes:
There are a number of alternative options that reflect different a) curation effort, b) friction for the user, c) compatibility with online tool and d) storage size criteria. A lot depends on the requirements of the analytics webtool, and if it will be discontinued. Please also note that the webtool does not use/show all the attributes in the datasets, only a few.
Option | Curation effort | Friction for user | Compatibility with Webtool | Storage size |
---|---|---|---|---|
Option 1 | Low | Worst | Best | Bad |
Option 2 | High | Best | Best | Worst |
Option 3 | High + Dev | Best | Best | Best |
Depends on the chosen curation/storage option. The metadata aligment step requires the same time for all three, quantified in 4-5 full days. The data curation effort varies a lot.
So the final size of optimised data could be around 1/5 of original data, relevant for Option 2 and 3.
As Pierre confirmed that the online tool will be discontinued, we just focus our effort on using the data for RDL services, without need to link them to the current web application. Then the option is simply:
This should be done by mid June, but depends on how fast they provide the rest of the data, and how fast we solve any arising conversion issues.
Following up conversation with @stufraser1 and @jeanpommier:
Review of SEA DRIF datasets and assessment of procedure for inclusion in RDL
Web tool at https://tool.oi-analytics.com
Metrics presented
Overview
Suggested procedure for RDL ingestion (draft)
Outputs:
Team:
Estimated number of days and budget: