# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## Discussion
### Data Limitations and Uncertainty  
The data we compiled were fraught with uncertainties due to a lack of methodological standardization. For example, during data compilation, we found that damage locations were reported at a range of spatial scales ranging from region/division level (largest scale) to province/district level to municipalities/districts/sub-districts/townships (smallest scale). Non-standardization also meant that several indicators of rice damage were used. Some sources provided areal damages; others published production losses. For our database, units were standardized to hectares for areal damages, metric tonnes for production losses, and US dollars for monetary losses. Information on rice damage from multiple locations was sometimes amalgamated to give a single loss value, while location-specific figures were available in some sources. Ideally, a spatial analysis of rice damage for each typhoon event should be performed to give a more accurate overview of geographical vulnerability. However, this was not always possible when damage data from various locations were combined. In addition, the definition of \"damage\" also varied across news sources. Some news sources used the term \"damage\" to describe the impact on rice crops. Other news sources used terms such as \"affected\", \"ruined\", \"destroyed\", and \"submerged\". The terms \"affected\" and \"submerged\" may indicate that the crop was affected by the typhoon but not damaged. As a conservative measure, we considered all values as the amount of rice damaged by the typhoon.  
Ambiguous descriptions were another problem. Some sources provided detailed locations of rice damage, while others provided non-specific geographical information like \"Northern Vietnam\" [31] or \"South of Hai Phong\" [32]. There were also spelling errors, different spellings for a similar place, and changes to the names of locations, which added uncertainties to the database. Next, online news sources did not always provide information on how their data were collected. In our analysis, for each typhoon, we shortlisted data based on the minimum and maximum values reported and derived a midpoint as we found it challenging to evaluate which source had the \"most accurate\" rice damage value. While the most recent source may have been published when the event had concluded and there was arguably more time to collect and verify information, without any detailed methodological information, attempting to make a value judgment was difficult. In addition, rice damage data were sometimes combined with other crops to provide an aggregated value for crop loss. Tyhoon Haiyan in 2013 was one the strongest typhoon that was recorded in the Philippines [33]. However, no information on rice losses was available as the rice production loss was combined with corn, vegetables, and other high-value commercial crops [34]. If data on rice damage was combined with other crops, they were excluded from our analysis for consistency.  
Lastly, we acknowledged that loss and damage figures may be deliberately revised downwards to cover up inadequate disaster preparedness and response. In the chaos of a disaster, double counting and false reporting were also possible [35]. For Typhoon Ketsana in 2009, a comparison of central government data with provincial-level data for Quang Nam province in Vietnam showed a large difference in the quantity of paddy inundated. Specifically, the Central government had reported that 3930 ha of paddy were inundated, while provincial data records showed 22,523 ha. Our findings suggest that potential errors were introduced in the process of data collection [36]. These issues have been previously highlighted by reviewers of disaster data [11; 37; 38], but the problems persist. Therefore, users of disaster data should be aware of the limitations associated with the use of disaster data.