In 2020, about 5.4 million people in the Philippines had been affected by storm that occurred in the country. Super Typhoon Goni was the strongest typhoon to have occurred in that year, affecting over two million people. The number of affected people by storms were highest in 2013 at 17.94 million.

Tropical cyclones (TCs) pose a major risk to societies worldwide. While data on observed cyclones tracks (location of the center) and wind speeds is publicly available these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots. Based on available spatially-explicit data on population densities and Gross Domestic Product (GDP) we estimate 1) the number of people and 2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated country-event level exposure data (TCE-DAT) covers the period 1950 to 2015. It is considered key information to 1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We validate the adequateness of our methodology by comparing our exposure estimate to estimated exposure obtained from reported wind fields available since 1988 for the United States. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders.

Files included in the data set: (1) TCE-DAT_historic-exposure_1950-2015.csv: Exposed population and assets by event and country using historical socio-economic exposure estimates.(2) TCE-DAT_2015-exposure_1950-2015.csv: Exposed population and assets by event and country using fixed socio-economic exposure at 2015 values.(3) Data-description_TCE-DAT_2017.005.pdf: full description of the data set including information on data sources and the description of variables/ data columns

In 2019, about 0.05 million people in the Philippines had been affected by floods that occurred in the country. The number of people impacted by floods in the Philippines was highest in 2012 at about 4.61 million people.

Blog post about this prediction can be found here: http://bit.ly/2fWF2jq

The predicted priority index of Typhoon Haima is produced by a machine learning algorithm that was trained on four past typhoons: Haiyan, Melor, Hagupit and Rammasun. It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on people affected and houses damaged.

First run The Priority Index is a 1-5 classification that can be used to identify the worst hit areas: those that need to be visited for further assessments or support first.

Second run The model now predicts two things:

• a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy.
• the precentage of total damage (damaged houses versus all houses)

The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing.

Data sources:

• Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX)
• Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX)
• Avg. wind speed (km/h) - University College London
• Typhoon path - University College London
• Houses damaged - NDRRMC
• Rainfall - GPM
• Poverty - Pantawid pamilyang pilipino program (aggregated)

For the second run of the algorithm we also included:

• Roof and wall materials
• New geographical features

The result of different models can be found in the file 'Typhoon Haima - performance of different models - second run.csv' A note on how to interpret this.

• date running date
• alg_date same
• alg_model name of the algorithm used
• alg_predict_on name of the learning variable
• alg_use_log i s the learning variable transformed in log
• code_version version of the learn.py code

All the columns with feat_ indicates the importance of that feature, if not present that feature was not used.

• learn_matrix name of the learning matrix with the 5 typhoons
• run_name unique run name (pickle files and csv files have this name for this model)
• typhoon_to_predict name of a new typhoon to predict
• val_accuracy accuracy based on 10 categories of damage 0% 10% 20% …
• val_perc_down perc of underpredicted categories
• val_perc_up perc of overpredicted categories
• Val_best_score best r2 score
• Val_stdev_best_score error on best score based on the CV
• Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit
• Val_mean_error_num_houses average error on the number of houses
• val_median_error_num_houses median
• val_std_error_num_houses std deviation of the errors (lower is better)

Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.

Severe tropical storm Paeng was the costliest major extreme natural events and disasters in the Philippines, with damages amounting to 13.1 billion Philippine pesos. The tropical storm occurred in October 26 to 31, 2022 and affected nearly all regions in the country.

In this analysis we have combined several data sources around the floods in Bangladesh in August 2017.

Visualization

• See attached map for a map visualization of this analysis.
• See http://bit.ly/2uFezkY for a more interactive visualization in Carto.

Situation

Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action.

Indicators and sources

In the attached map, we combined several sources:

• Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20)
• Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.)
• Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis.

Detailed methodology Vulnerability

• The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop).
• The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas.
• Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).

In 2023, roughly 12.06 million people were affected by major natural events and disasters in the Philippines. The peak number of people affected due to natural calamities were reported in 2013 when Typhoon Haiyan hit various regions in the Visayas and Mindanao.

In 2022, about 1.12 million people were injured due to major natural events and disasters in the Philippines. The peak number of injured people caused by natural disasters was reported in 2013 when Typhoon Haiyan hit various regions in the Visayas and Mindanao.

The Philippine Institute of Volcanology and Seismology (PHIVOLCS) and Geoscience Australia (GA) have developed a long-term partnership in order to better understand and reduce the risks associated with earthquake hazards in the Philippines. The Project discussed herein was supported by the Australian Agency for International Development (AusAID). Specifically, this partnership was designed to enhance the exposure and damage estimation capabilities of the Rapid Earthquake Damage Assessment System (REDAS), which has been designed and built by PHIVOLCS. Prior to the commencement of this Project, REDAS had the capability to model a range of potential earthquake hazards including ground shaking, tsunami inundation, liquefaction and landslides, as well as providing information about elements at risk (e.g., schools, bridges, etc.) from the aforementioned hazards. The current Project enhances the exposure and vulnerability modules in REDAS and enable it to estimate building damage and fatalities resulting from scenario earthquakes, and to provide critical information to first-responders on the likely impacts of an earthquake in near real-time. To investigate this emergent capability within PHIVOLCS, we have chosen the pilot community of Iloilo City, Western Visayas.

A large component of this project has been the compilation of datasets to develop building exposure models, and subsequently, developing methodologies to make these datasets useful for natural hazard impact assessments. Collection of the exposure data was undertaken at two levels: national and local. The national exposure dataset was gathered from the Philippines National Statistics Office (NSO) and comprises basic information on wall type, roof type, and floor area for residential buildings. The NSO census dataset also comprises crucial information on the population distribution throughout the Philippines. The local exposure dataset gathered from the Iloilo City Assessors Office includes slightly more detailed information on the building type for all buildings (residential, commercial, government, etc.) and appears to provide more accurate information on the floor area. However, the local Iloilo City dataset does not provide any information on the number of people that occupy these buildings. Consequently, in order for the local data to be useful for our purposes, we must merge the population data from the NSO with the local Assessors Office data. Subsequent validation if the Iloilo City exposure database has been conducted through targeted foot-based building inventory surveys and has allowed us to generate statistical models to approximate the distribution of engineering structural systems aggregated at a barangay level using simple wall and roof-type information from the NSO census data.

We present a comparison of the national and local exposure data and discuss how information assembled from the Iloilo City pilot study - and future study areas where detailed exposure assessments are conducted - could be extended to describe the distribution of building stock in other regions of the Philippines using only the first-order national-scale NSO data. We present exposure information gathered for Iloilo City at barangay level in a format that can be readily imported to REDAS for estimating earthquake impact.

In 2023, 20 people in the Philippines went missing due to major natural events and disasters. The peak number of missing people was reported in 2013 when Typhoon Haiyan hit various regions in the Visayas and Mindanao.

In 2023, over three million families were affected by major natural events and disasters in the Philippines. The peak number of reported families affected by minor and major natural extreme events and disasters was reported in 2013 when Typhoon Haiyan hit various regions in the Visayas and Mindanao.

In 2023, 100 people died due to major natural events disasters in the Philippines. The peak number of deaths due to natural calamities was reported in 2013 when Typhoon Haiyan hit various regions in the Visayas and Mindanao.

In 2023, six climate-related disasters due to floods occurred in the Philippines, indicating an increase from the previous year. During the observed period, the number fluctuated and peaked in 2011 with 15 flooding incidents.

In 2022, the national government of the Philippines spent about 24 billion Philippines for environmental protection. The government expenditure for protecting the environment considerably improved in 2018 from 8.6 billion Philippine pesos in 2017.

In 2023, the total value of damaged agricultural properties during natural calamities in the Philippines amounted to around 10 billion Philippine pesos. Due to its geographical location, the country is prone to natural disasters such as tropical cyclones and earthquakes.

In 2023, roughly 8,600 houses were totally damaged due to major natural events and disasters in the Philippines, indicating a significant decline from the previous year. The peak number of damaged houses due to natural calamities was reported in 2013 when Typhoon Haiyan hit various regions in the Visayas and Mindanao.

As of January 2024, there were 33 volcanoes within the Luzon Island of the Philippines. The Taal Volcano located on the Island of Luzon, erupted on January 12, 2020 causing several hazards across the provinces of Batangas and Cavite.

In 2023, about 1.5 million people in Afghanistan were internally displaced due to natural disasters - the most out of any country. Pakistan followed in second with 1.2 million internally displaced, while Ethiopia, China, and South Sudan rounded out the top five.

During the 2023 fiscal year, government expenditures on climate change in the Philippines amounted to approximately 464.5 billion Philippine pesos, reflecting an increase from the previous year. This includes the adaptation and mitigation of climate change in the Philippines.

In 2021, two 7.1 magnitude earthquakes occurred in the Philippines. One was in Davao Occidental on January 21 at around 8:23 in the evening, and the second occurred in Davao Oriental on Aug 12 at around 1:46 in the morning. Overall, there were over 12 thousand earthquake events that happened in the Philippines in that year.