Estimation of Economic Losses from Tropical Enawo1 March 21, 2017 - World Bank Group

1. The work presented in this brief report, estimates the losses related to to be over USD 400 million, corresponding to about 4% of annual GDP. With this amount of losses, the Government of is likely to need increased post-disaster financing from development partners to cover some of its reconstruction and recovery needs.

2. (TC) Enawo made landfall in the north-east of Madagascar on March 7, 2017 as a category 4 cyclone, and then moved southward as a tropical depression before exiting the country on March 10. While the north-east regions suffered from wind damage and widespread flooding, central and southeastern parts of the country were affected by heavy rains and flooding. TC Enawo is the strongest cyclone to strike Madagascar since 2004, with maximum wind speeds of 230 km/h at landfall, and up to 220mm of rain in 24hrs recorded in .

3. As of March 20, 2017, preliminary field assessments conducted by the Government and partners estimate that close to 434,000 people have been affected. In addition, 81 deaths and 250 injuries have also been reported. Reported damages to infrastructures are high, with more than 40,000 houses, 3,300 classrooms and 100 health centers damaged.

1 This note was developed with modelling inputs from AIR Worldwide, the African Risk Capacity, and the World Bank Group’s Disaster-Resilience Analytics and Solutions (D-RAS) team, and include findings from a probabilistic risk assessment that was undertaken in the South West and funded by the EU-funded ACP-EU Africa Disaster Risk Financing Program managed by the Global Facility for Disaster Reduction and Recovery. It was prepared by Emma Phillips, Michel Matera, Julie Dana, Oscar Ishizawa, James Daniell and Rashmin Gunasekera (World Bank Group), Aaron Michel and Anna Trevino (AIR Worldwide), and Erin Tressler, David Maslo and Federico Doehnert (African Risk Capacity).

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4. By using quantitative risk modelling approach2, it was possible to estimate losses resulting from direct damage to buildings and infrastructure, which are estimated to be around USD 208 million (2015 currency) with a standard deviation of +/- 69 million, and corresponds to a mean return period of 11 years +5/-3 years. This is comparable to in 2004, which caused serious damage, in excess of USD 250 million, to both agricultural production and capital stock of Madagascar.

5. As indicated in the map below, the most affected region of the country is Sava, in the North East of Madagascar, where the tropical cyclone made landfall. The north-east regions of Analanjirofo and Sofia suffered from wind damage and widespread flooding, while central and southeastern parts of the country were affected by heavy rains and flooding. The sector that has been most exposed to the tropical cyclone is the residential sector, with 74.1% of the total loss related to damaged houses.

6. In addition, an agriculture sector model was developed to assess agricultural losses, which were estimated at approximately USD 207 million, dominated by the impact to the plantations, amounting to losses estimated at USD 164 million, in Sava and Diana regions.

7. This post-event loss calculation effort provides a useful initial estimate of the damages that can complement possible additional damage and loss assessments involving on the ground evaluation. The modelled loss approach presented here offers an early estimate of the economic impact of the cyclone, which the Government can use to start the recovery planning process.

2 The risks models and methodological approach used for this post-event loss calculation are detailed in the enclosed technical annex.

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Technical Annex - Using risk models to estimate post-disaster economic losses

Objective The objective of this technical annex is to explain how existing risk models, which were developed to help inform risk management decisions and investments, can also have utility as the basis for rapid post-event loss estimation as stakeholders attempt to rapidly understand the extent of economic losses from tropical cyclone Enawo and the associated flood impact, in order to begin rapid mobilization of response. This annex provides a: 1. Description of tropical cyclone Enawo; 2. Description of the 3 risk models and the methodology used by each for post-even loss calculation; 3. Overview of the post-event loss analysis drawn from each of the 3 risk models, which includes: (i) estimated economic losses; (ii) analysis of how this event compares with historical events; (iii) spatial distribution of the damage; and (iv) breakdown of modelled loss estimates; 4. Observations on the implications of this analysis.

1. Description of Tropical Cyclone Enawo Intense Tropical Cyclone Enawo, which reached Category 4 status on the Saffir-Simpson scale, made landfall in north-eastern Madagascar on March 7, 2017 at around 12:00 UTC as a category 4 cyclone, and then moved southward as a tropical depression to pass west of before exiting the island west of Taolagnaro on March 10, 2017 between 18:00 and 24:00 (UTC). While the north-east regions suffered from wind damage and widespread flooding, central and southeastern parts of the country were affected by heavy rains and flooding. However, waters receded quickly in many areas.

Cyclone Enawo is the strongest cyclone to strike Madagascar since 2004, with maximum wind speeds (1- minute sustained) of 230 km/h at landfall. By March 8, maximum winds decreased to 112 km/h and reduced further by the center of the country to non-damaging speeds.

In addition to high wind speeds and heavy rainfall, occurrence of flooding and landslides have caused damage to housing and agriculture sectors mainly in the northern and eastern regions Diana, Sofia, Sava, Analanjirofo, Aloatra-Mangoro, Atsinanana, Itasy, Bongolava, Analamanga, Vakinankaratra and Betsiboka.

From 8-9 March, 2017, approximately 220mm of rain in 24hrs have been recorded in Sambava, 189 mm of rain in 24h in Mananjary, and 153 mm of rain in 24 h recorded in Taolagnaro according to local measurements. Flooding and power outages are thought to be widespread in affected areas.

Below are a series of event intensity footprints of wind speed and flood depth for tropical cyclone Enawo:

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Figure 1: Accumulated Flood Depth (AIR Figure 2: Maximum 1-min Sustained Wind Worldwide - mean loss) Speed (AIR Worldwide - mean loss)

2. Description of risk models and the methodology used for post-even loss calculation AIR Worldwide: AIR is a scientific leader and provider of catastrophe risk modeling software and consulting services. In 2016, the firm was hired by the World Bank Group to develop a probabilistic disaster risk model under SWIO-RAFI. The hazard, exposure and risk data created were used in the development of risk-related data and risk profiles for Madagascar, , Comoros, and Zanzibar.

AIR’s work resulted in the development of a risk profile for Madagascar, which showed that direct aggregate losses for tropical for a 10 year return period3 are estimated at approximately USD 224 million and annual average losses for tropical cyclones estimated at USD 87 million. Following discussions about how the model might be useful for better understanding of Enawo, the AIR team kindly agreed to support the process of a post-event loss calculation on a pro bono basis.

A description of each of the key components of the risk model and its role in the post-event loss calculation follows below:

Hazard: The AIR catastrophe risk model was developed for the following hazards: tropical cyclone hazards (including wind, storm surge, and hazards); non-tropical cyclone flooding hazards; and earthquake hazards (including ground-shaking and earthquake-generate tsunami hazards).

3 The probability that catastrophic events will occur is often expressed as a return period, which gives the estimated time interval between events of similar size or intensity. The theoretical return period is the inverse of the probability that an event will be exceeded in any given year. An event with a 10-year return period has a 10% chance of occurring or being exceeded in any given year.

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The post-event loss calculation for tropical cyclone Enawo used 25 simulations of different combinations of event parameters (e.g., central pressure, radius of maximum winds) along Enawo’s central track. The track file for Intense Tropical Cyclone Enawo was generated based on latitude and longitude points provided by the JTWC and Météo-France La Reunion, the local RSMC of the SWIO. Radius of maximum winds (Rmax) data was taken from the Regional and Mesoscale Meteorology Branch (RAMMB) of NOAA/NESDIS. Both of these datasets were available at 6 hour intervals, and interpolated to create an hourly track file. Precipitation data, retrieved from NASA’s Giovanni GPM product, was based on rain rate and accumulated rainfall over the course of Enawo’s passing over northern Madagascar. Central pressure and Rmax from the time of landfall were perturbed to create 25 scenarios that represent a range of possible intensities from this event. The perturbations were based on uncertainty of the landfall parameters and the rate of decay of Enawo after landfall. Using station wind speed and rainfall measurements provided by Météo Madagascar following the event, 5 of the 25 events were selected as final candidates for assessing Enawo’s economic impact. Importantly, these results consider wind and precipitation flooding, but not storm surge, since the AIR team was not able to run this component of the model under the short time constraints.

The final 5 simulations of different combinations of event parameters were used to account for uncertainty in the actual measurements of these parameters for this specific event. As more definitive meteorological information becomes available, the post-even loss calculation process can be re-run with a higher degree of certainty.

Exposure: The considered exposure at risk consists of residential and non-residential buildings (i.e. primary structures) and infrastructure (e.g., roads, bridges). The exposure database is intended be used as an input to catastrophe risk models that estimate the economic losses sustained due to physical damage to buildings from natural hazards. In addition, this database can be used as a proxy to estimate the potential impacts on other sectors, such as affected population, and for calculating post-disaster emergency losses.

Following the construction of the gridded population database, the exposure database of buildings is developed from the bottom-up. AIR leverages information and data from sources such as official censuses, local agencies, publically available reports, and academic papers to derive risk counts (e.g., number of dwellings) and associated statistics (e.g. percentage of dwellings by occupancy and construction type). Additional information obtained from census and ancillary datasets pertaining to the physical characteristics of the risks is used in conjunction with construction cost estimates to derive the replacement values. The availability of high-quality exposure data throughout the SWIO region is quite limited.

Vulnerability: The damage module relates event intensities to building and infrastructure damage and was developed using research of local building practices, applicable building codes, engineering analysis, historical damage reports, and expert engineering judgement. Damage functions have been developed for the perils of tropical cyclone wind, flooding, and storm surge, non-tropical cyclone flooding, and earthquake ground shaking in terms of the intensity values of wind speed, flood depth, and shaking acceleration.

The African Risk Capacity (ARC): Established by a Heads of State decision at the African Union summit in July 2012, ARC is a comprehensive and integrated solution which transfers weather risk away from governments and the vulnerable households which they protect, to ARC. The objective is to help

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governments to build resilience and to better plan, prepare for and respond to extreme weather events and other natural disasters.

In 2014, ARC launched its first risk insurance product, covering drought risk, for Member States through its financial affiliate ARC Insurance Company Limited (ARC Ltd) and has since developed cyclone coverage and is working to develop flood coverage which will be available to Member States in 2018. Index-based insurance payouts, based on Africa RiskView – ARC’s software which determines losses from events of various magnitudes, are triggered and paid quickly after the need arises.

To complement their drought insurance product, and upon request from its Member States, ARC in collaboration with KAC4 developed a tropical cyclone model for the SWIO region. Based on historical data from over 30 years, and simulated data over 1,500 years, risk profiles have been produced for Comoros, Madagascar, Mauritius, Mozambique, Seychelles and Zanzibar.

In Madagascar, the average annual loss associated with tropical cyclones calculated by the KAC model is around USD 20 million; a 1-in-10 year event is associated with a loss of around USD 65 million5. ARC’s model estimates the economic loss generated by Enawo between USD 50-60 million, which is equivalent to an event between 1-in-8.5 years and 1-in-10 years.

Hazard: ARC’s cyclone model, developed by KAC, covers the hazards of wind, storm surge and wave damage; damage from excess rainfall in cyclones and associated floods is not covered. While developed by KAC, this model will be available to countries through ARC’s software Africa RiskView. Currently, the model uses cyclone track information produced by the US Joint Typhoon Warning Center (JTWC), but ARC is exploring the integration of data from RSMC La Réunion. Available information include cyclone track, locations, wind speed and storm surge depth.

Exposure and Vulnerability: ARC’s cyclone model takes into account three dimensions of exposure. Gridded population data from LandScan is combined with pixel-level information on infrastructure and economic activity to estimate the impact of a cyclone on the ground. This is then layered with macro- economic data such as GDP to calibrate the modelled losses.

Disaster-Resilience Analytics and Solutions (D-RAS) team: The D-RAS team is a World Bank internal team of technical experts who perform advisory and analytical services including developing custom-built tools and solutions related to disaster risk management. Using the team’s expertise in disaster risk modelling, compilation of historical loss data and development of novel methodologies, the team estimates physical damage and consequent economic losses for sectoral impacts in post-disaster situations.

Considering that the AIR model does not include agricultural losses, the team used an approach to rapidly estimate agricultural losses due to Cyclone Enawo. The cyclone model covers direct agricultural production losses over the period of 1-year estimated in 17 production components.

4 KAC developed the Caribbean Catastrophe Risk Insurance Facility (CCRIF)’s hurricane and earthquake parametric models and uses modelling technology which is strongly supported by the global reinsurance and capital markets. KAC also has a Real-time Forecast System which provides the early warning component which complements ARC’s parametric insurance programme as a benefit to assist country planning and awareness. 5 As indicated in the overview, ARC’s estimates are lower than the losses calculated by AIR, given that the impact of precipitation is not taken into account – however, the two models are proportionally in line, as losses calculated by ARC are generally around 25% of AIR’s outputs.

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Datasets used in the analysis include data on crop cycles (and seasonality) for each of the regions in Madagascar which are published by the government. For example, the team examined which crops are likely between the planting and the harvest periods such as the 2-season rice cycle in Madagascar. However, the main focus was the impact on the vanilla plantations due to high value associated with the crop for the coming harvest. Although, Livestock data, was collated from the Madagascan Government agricultural censuses (2005, 2010) and surveys (ENSOMD 2012-2013), they did not form part of the agricultural sector loss analysis. The analysis also show 78% of enterprises in Diana and Sava are agricultural.

A description of each of the key components of the risk model and its role in the post-event loss calculation follows below:

Hazard: The D-RAS team conducted a reanalysis of the cyclone track of Enawo and associated wind speeds and precipitation with over 25 weather stations of 3-6 hourly data from the Madagascar weather network. The model also assessed historic cyclone tracks from IBTRACS, Méteo-France and analysis of GPM/TRMM satellite derived precipitation.

Exposure: As an integral part of the analysis, an agriculture exposure database at FKT-level was developed including subnational census data (MICS 2016), GDP, capital stock and poverty datasets. Specifically, along with FKT-level population data from micro-census and survey data, agricultural production census data from 2005 and 2010 was used and digitized into a GIS system. This agriculture exposure database was produced at spatial resolution of district level for 15 sectors. To determine the replacement value of the stock, current prices for each of the sectoral components was used. The prices were adapted from the district level data for production, export and market price per kg in FMA/FMG via this census data. Rice, Corn, Beans, Peas, Cassava, Sweet Potato and Potato, Peanuts, Sugarcane, Coffee, Pepper, Cloves, Cocoa, Lychees and Vanilla were analysed as the key sectors via the Madagascar Govt. previous censuses and surveys.

Vulnerability: Historic disaster loss data from Méteo-France, BNGRC, CATDAT, FAO and Desinventar were examined for losses in the agricultural sector. For example, after Cyclone Gretelle (1997) Cloves suffered 85% destruction http://www.fao.org/docrep/004/w5026e/w5026e00.htm); Cyclone season of 2000 (Cyclones Eline, Gloria and Hudah) severely impacted vanilla, coffee and cloves production in Analalava (http://www.fao.org/docrep/004/x7379e/x7379e00.htm); and Cyclone Gafilo (2004) also caused major damage to vanilla plantations. Using these historical datasets, the D-RAS team calibrated and refined the relationships between the recorded wind speeds of these events and observed damage to agriculture. Combining the developed information on hazard, exposure and vulnerability relationships along with the team’s expert judgement, the D-RAS team estimated the economic impact on the agriculture sector from cyclone Enawo.

3. Overview of the post-event loss analysis drawn from the risk models Modelled Losses Resulting from Direct Damage to Buildings and Infrastructure Assets At the national level, the mean ground-up loss, which includes damages to infrastructure but does do not take into account agricultural losses or business interruptions, is estimated by the AIR model at USD 208 million (2015 currency) with standard deviations of +/- 69 million.

This loss corresponds to a mean return period of 11 years +5/-3 years. The large standard deviations are a result of the uncertainty in the aforementioned event parameters. This uncertainty was reduced

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significantly by selecting only 5 of the original 25 event simulations based on the weather station data provided by Méteo Madagascar.

This is comparable to Cyclone Gafilo in 2004, which killed more than 200 people and caused serious damage, in excess of USD 250 million, to both agricultural production and capital stock of Madagascar.

The ARC model similarly shows based on simulated cyclone tracks, which in turn are based on the available historical data, the damage associated with Enawo makes it the third or fourth worst cyclone to hit Madagascar in the last 30 years. This corresponds to a 1-in-8.5 to 1-in-10 year event6.

The African Risk Capacity (ARC) has also developed a cyclone model in collaboration with KAC7 to underpin underwriting of sovereign cyclone insurance contracts. ARC’s model estimates losses based on damage related to wind, storm surge and waves and through this process damage associated with Enawo is estimated at USD 50-60 million. ARC’s estimates are lower than the losses calculated by AIR, given that the impact of precipitation is not taken into account and differences in methodology, however, the two models are proportionally in line, as losses calculated by ARC are generally around 25% of AIR’s outputs8. There is a high consistency between the risk profiles produced by the two models and the loss estimates for Enawo.

Breakdown of Loss Estimate Summary Losses and their associated ranges split by region

The most affected region of the country is Sava, in the North East of Madagascar, where the tropical cyclone made landfall. The north-east districts Analanjirofo and Sofia suffered from wind damage and widespread flooding, while central and southeastern parts of the country were affected by heavy rains and flooding. REGION MEAN GUP LOSS ($M) National 208.1 ± 68.9 Sava 168.1 ± 46.1 Analanjirofo 30 ± 16.2 Sofia 9.6 ± 6.6 Betsiboka 0.2 ± 0.1 Diana ≤ 0.1 Alaotra-Mangoro ≤ 0.1 Boeny ≤ 0.1 Analamanga ≤ 0.1 Atsinanana ≤ 0.1 Bongolava ≤ 0.1

6 Based on 1,500 years of simulated tropical cyclone tracks 7 Kinetic Analysis Corporation. 8 Annual Average Loss (AAL) calculated by ARC for Madagascar is USD 20 million while AIR calculated USD 87 million.

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Figure 3: Spatial Distribution of Losses

The percentage of the total losses by sector

The sector that has been most exposed to the tropical cyclone is the residential sector, with 74.1% of the total loss in this sector.

SECTOR % TOTAL LOSS Residential 74.1% ± 4% Commercial 7.8% ± 0.6% Infrastructure 1.4% ± 0.2% Industrial 5.3% ± 0.4% Public 6.2% ± 0.4%

Agricultural Losses The agricultural loss model estimates USD 207 million in losses, dominated by losses associated with the potential impact to the vanilla plantations in Sava and Diana. The modelled direct damage to Vanilla crop and its associated loss of productivity is estimated at USD 164 million.

Vanilla beans are very vulnerable to cyclones (as are other plants such as Cloves). Cyclones in particular in 1997, 2000 and 2004 caused major losses to vanilla plantations in Madagascar. From a hazard standpoint, wind speeds of over 100km/hr are considered to be damaging for vanilla plantations. It is noteworthy that approximately 64% of the vanilla plantations (totaling USD 1 billion exports per year) have been exposed to winds above 100 km/h (by comparison only 15% of rice is in this zone).

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The average price of Madagascar vanilla beans is approximately USD320/kg (2-year average). However, in 2016, short fall in vanilla production caused vanilla prices to go up globally to USD 490/kg. In addition to supply and demand, the price is also expected to dependent on many other factors, such as difficulty of sourcing vanilla globally given the long curation times (4-8 years for plants), and quality of beans. However, the potential impacts on loss of productivity of vanilla in Madagascar could be significant as approximately 15% of GDP of Madagascar is related to production of vanilla beans.

Figure 4: Economic loss estimation and distribution for agricultural crop/productivity loss 4. Implications of this Analysis

This analysis is presented in order to demonstrate that risk models can be used as the basis for rapid post- event loss estimation and thereby be useful to stakeholders’ efforts to rapidly understand the extent of economic losses from tropical cyclone Enawo and the associated flood impact. Observations on potential implications of this analysis include: - The existence of state-of-the art catastrophe risk models for cyclone risk for Madagascar marks an important step forward in the country’s ability to understand risk and potential impact of severe events. As agreed by stakeholders in the recent meetings of the ISLANDS Financial Protection Program (Mauritius, January, 2017), the investment in development of risk models is a welcome contribution to the region’s ability to improve not only risk management but also post- disaster response. - The AIR and ARC models have some differences but can be seen as complementary, as they were developed for different purposes and present different views of risk. The collaborative approach used by Bank and ARC teams in preparation of this note is in response to a request from the Government of Madagascar (expressed at the ISLANDS Financial Protection meeting) to work together to improve the information (and speed of information) available to stakeholders, and strengthen government’s capacity to use this information. - The risk models also have utility in strengthening preparedness. If a storm is approaching and the approximate track is known, risk models can inform preventative measures to protect the

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population from the impacts of the storm. From a financial point of view, risk models can be used to inform policy decisions and investment decisions related to financial protection strategies using contingency funds, contingent loans/grants, and insurance. Should the Government so chose it could also use the risk model from the ARC team to underwrite insurance for events like Enawo in the future. - While post-event loss calculation using catastrophe models cannot replace post-disaster needs assessments, such approaches can be used to improve the speed and efficiency associated with efforts to estimate the impact of severe events, thereby hopefully improving the speed and efficiency of response. The technical teams are of the view that the post-event loss calculation efforts provide a useful initial estimate of the damages. Considering the likely high impact of Enawo, there will be a need for post- disaster financing to complement the UN Flash Appeal9, since Government will likely not have sufficient resources to allocate and/or reallocate to respond the reconstruction and recovery needs. The World Bank Group is ready to respond to the needs, and will continue to advocate for investment in disaster risk reduction and financial protection solutions to strengthen resilience in the country.

Disclaimer Technical Papers are published to communicate the results of The World Bank's work to the development community with the least possible delay. The typescript manuscript of this paper therefore has not been prepared in accordance with the procedures appropriate to formally edited texts. Some sources cited in the paper may be informal documents that are not readily available.

The findings, interpretations, and conclusions expressed herein are those of the author(s), and do not necessarily reflect the views of the International Bank for Reconstruction and Development / The World Bank and its affiliated organizations, or those of the Executive Directors of The World Bank or the governments they represent.

The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

9 The UN launched a Flash Appeal requesting USD 20.74 million to reach 250,000 people with life-saving assistance and protection for a 3-month period.

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