# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 2 Data and Method  
To obtain a list of typhoons that damaged rice plants in Vietnam and the Philippines, typhoons that passed through or passed close to each country (within 500 km from the coastline) from 1970 to 2018 were first identified from the Digital Typhoon database [20]. A 500 km buffer was chosen because rain within 500 km from the center of a typhoon can be considered typhoon rain [21], and we identified if the cyclone was within this 500 km limit by displaying the typhoon tracks on Google Earth and using the measurement tool to ensure the typhoon track was within the 500 km radius. Information such as the date of the event, the maximum wind speed, and the landfall wind speed were also noted. Landfall wind speed was determined by reviewing the typhoon track information on the Digital Typhoon database and finding the location at which the typhoon intersected with the coastline [22]. The recorded wind speed at the point nearest the coastline was taken to be the landfall wind speed. After this, we grouped the typhoons into six categories based on their landfall intensity to reflect each typhoon's potential to cause damage. Typhoons that did not make landfall were also included as a separate category, as rice crops in coastal areas can be damaged by strong winds and heavy rain from a passing typhoon. The six categories were No landfall (LF), tropical storm (TS), Typhoon Categories 1, 2, 3, and 4. Except for the No LF category, classification for the rest were based on the Saffir-Simpson Hurricane Wind Scale [23]. We used landfall wind speed instead of the maximum wind speed to determine the intensity of the typhoon, as the typhoon may have weakened by the time it reached the coastline of Vietnam and the Philippines [22]. In addition, if a typhoon had multiple landfall locations, the higher landfall wind speed was used for analysis. Next, information on damage locations and the amount of crop damage was compiled from newspaper reports, humanitarian relief reports, and other published online sources for each typhoon event. Non-English publications were translated with the help of Google Translate, and the information was included if relevant. These sources were obtained from a Google search with various permutations of the following keywords: \"typhoon/typhoons\", \"rice\", \"damage\", and the country name. Individual typhoon name(s) and the year of occurrence were also used. Information was also obtained by searching the regional ADHRC database, the national-level NDRRMC database for the Philippines, and ReliefWeb, a website for humanitarian information. To reflect the diversity of information reported, rice damage was classified according to (i) the size of the rice area damaged, (ii) the volume of rice production lost, and (iii) the value of the rice crop lost. The rice damage indicators we chose complemented each other as the production loss should be equal to the land affected multiplied by the yield per hectare, while the value of crop lost should be the yield lost multiplied by the price of rice. To facilitate comparison, the units of measurement were standardized to hectares (ha) for areal damages, metric tonnes for production losses, and United States dollar (US$) for monetary losses. As the monetary value of rice lost may be reported in the domestic currency, we used the prevailing yearly US$ exchange rate at the time of the report to convert all values to US$ to facilitate comparison. Rice damage data were sometimes amalgamated with damage data for other crops. In addition, the locations of rice damage were also noted. As the data for rice damage for each province was often aggregated, we focused on highlighting the frequency with which a province was affected by a passing typhoon. We included all agricultural loss data in our database, but we only analyzed data on rice damage data. Our database is found in Supplementary Material S1. A single typhoon event may have multiple values for each indicator of loss as we compiled damage data from multiple sources.