Global RApid Post-Disaster Damage Estimation (GRADE) Report Mw 6.3 Herat Earthquake Sequence in Afghanistan, October 2023 Disclaimer © 2023 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) with external contributions. The ndings, analysis and conclusions expressed in this document do not necessarily re ect the views of any individual partner organization of The World Bank, its Board of Directors, or the governments they represent. Although the World Bank and GFDRR make reasonable efforts to ensure all the information presented in this document is correct, its accuracy and integrity cannot be guaranteed. 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Global RApid Post-Disaster Damage Estimation (GRADE) Report Mw 6.3 Herat Earthquake Sequence in Afghanistan, October 2023 Acknowledgements This report was prepared by a World Bank team led by Rashmin Gunasekera (Senior Disaster Risk Management Specialist, Global Facility for Disaster Reduction and Recovery - GFDRR) and Oscar Anil Ishizawa Escudero (Senior Disaster Risk Management Specialist, GFDRR). The team is comprised of James Daniell, Antonios Pomonis, Johannes Brand, Andreas Schaefer, B an Khazai, Roberth Romero, Marie Johanna Guth, Diana Cubas and Kerri Cox of the GFDRR's Global Program for Disaster Risk Analytics and the World Bank's Disaster Resilience Analytics and Solutions (D-RAS) team. Editorial support was provided by L. Lau. The assessment received nancial support from GFDRR, Afghanistan Resilience Trust Fund (ARTF), and the Ministry of Finance of Japan through the Japan-World Bank Program for Mainstreaming Disaster Risk Management in Developing Countries. We also thank the peer reviewers from the World Bank Philipp Peterman (Senior Disaster Risk Management Specialist, SMNUR) and Jian Vun (Senior Disaster Risk Management Specialist, SEAU1). The team gratefully acknowledges the guidance and contribution of the following World Bank colleagues: Melinda Good (Country Director, SACKB); Abhas Jha (Practice Manager, SSACD); Niels B. Holm-Nielsen (Practice Manager, GFDRR); Abedalrazq F. Khalil (Practice Manager, SSAU1); Olivier Lavinal (Program Leader for Sustainable Development, SSADR); Yunziyi (Lisa) Lang (Climate Change Specialist, SSACD); Elif Ayhan (Senior Disaster Risk Management Specialist, SSACD); Tahir Akbar (Senior Urban Specialist, SACU1); Iguniwari Thomas Ekeu-Wei (Climate Change Specialist, SSACD); Carina Fonseca Ferreira (Disaster Risk Management Specialist, SSACD); Efrem Ferrari (Consultant, SSACD); Zoe Elena Trohanis (Lead Disaster Risk Management Specialist, GFDRR); Mirtha Liliana Escobar (Senior Disaster Risk Management Specialist, GFDRR); Mary Elinor Boyer (Disaster Risk Management Specialist, GFDRR); Lara Sophie Maha Loussert (Consultant, GFDRR); Pol Nadal Cros (Consultant; GFDRR); Keiko Saito (Senior Disaster Risk Management Specialist, GFDRR); Mika Iwasaki (Senior Program O cer, GFDRR); Yoko Kobayashi (Senior External Affairs O cer, GFDRR), Patricia Anne Janer (Consultant, GCSIMA); Elena Karaban (Manager, ECRSA); Diana Ya-Wai Chung (Senior External Affairs O cer, ECRSA); and Abdullah Yadgare (External Affairs O cer, ECRSA). Abbreviations Contents 1.0 Executive Summary 1 1.1 Key Highlights 1 2.0 Introduction 4 2.1 Event Characteristics and Descriptions 4 2.2 Human and Sectoral Impact of Earthquake in Afghanistan 5 2.3 Fragility, Con ict, and Violence (FCV) Context 8 2.4 Socio-economic Vulnerability and Considerations 9 3.0 Post-Disaster Rapid Damage Estimation Methodology 12 3.1 Methodology 12 3.2 Building Back Better Considerations 15 4.0 Results and Interpretation 16 4.1 Summary of Results 16 4.2 Interpretation of Results and Impacts 19 4.3 Brief Comparison to the GFDRR Multi-Hazard Risk Assessment (MHRA) Pro le 20 5.0 Conclusions 22 5.1 Event Summary 22 5.2 Damage Costs 22 References 23 Annex A: Development of Social Vulnerability Index 24 Annex B: Data Sources 28 Annex C: Afghanistan Exposure Model 30 Annex D: Historical Earthqaukes in Afghanistan 31 Abbreviations CSO: Central Statistics O ce D-RAS: Disaster-Resilience Analytics & Solutions Team, GPURL, World Bank EMS: European Macroseismic Intensity Scale EMSC: European-Mediterranean Seismological Centre FAO: Food and Agriculture Organization GFDRR: Global Facility for Disaster Reduction and Recovery GPURL: Urban, Disaster Risk Management, Resilience and Land Global Practice GRADE: Global RApid post-disaster Damage Estimation ICT: Information and Communication Technology IDP: Internally Displaced Person ITA: Interim Taliban Administration MHRA: Multi-Hazard Risk Assessment Mw: Moment Magnitude MMI: Modi ed Mercalli Intensity NGOs: Non-Governmental Organizations NSIA: National Statistics and Information Agency OSM: Open Street Map PDNA: Post-Disaster Needs Assessment SoVI: Social Vulnerability Index TEV: Total Exposure Value UNOCHA: United Nations O ce for the Coordination of Humanitarian Affairs UNOSAT: United Nations Satellite Centre USGS: United States Geological Survey US$: United States Dollars WASH: Water, Sanitation, and Hygiene WFP: World Food Programme WHO: World Health Organization i 1.0 Executive Summary Following the Herat province (Western Afghanistan) earthquake sequence of October 7 to 15, 2023, the World Bank carried out a remote desk-based assessment of the physical damages using the Global RApid post-disaster Damage Estimation (GRADE) methodology.1 The objective of the assessment is to develop a model-based estimate of the direct physical (economic) damages2 to residential buildings (houses), non- residential buildings (e.g., education, health, worship, commercial, industrial assets) and infrastructure (e.g., transport, power, water, telecommunications), and to evaluate the spatial distribution of damages in order to support the development of a roadmap for recovery and reconstruction. This report summarizes the key ndings of the assessment. Impacts of concurrent or secondary hazards such as landslides were small and thus not evaluated in this report. The assessment bene tted from damage data and reports from public sources, such as the United Nations O ce for the Coordination of Humanitarian Affairs (UNOCHA), World Health Organization (WHO), United Nations Satellite Centre (UNOSAT), and Copernicus Satellite Derived Damage Assessments, as well as information from international and local Non-Governmental Organizations (NGOs), and Miyamoto International. This assessment could potentially inform the envisioned post-disaster damage and needs assessment (PDNA) for this event. 1.1 Key Highlights Ÿ The October 7 to 15, 2023 earthquake sequence of four events of the same magnitude (Mw 6.3) affected 11 districts including Zindajan, Gulran, Injel, Kushk (Rabat-e-Sangai), Guzara, Ghoryan, Karukh, Kushk- e-Kuhna, Herat, Pashtonzarghon, and Kohsan; all of which belong to the Herat province. Districts of Herat and Injel alone account for half of the affected residents. Ÿ The combined earthquakes from October 7 to 15 resulted in 1,482 fatalities and 2,100 injuries, directly affecting 154,000 people (UNOCHA October 20 update), up from an earlier estimate of 43,400 people, which had also mentioned that nearly 23% of these were children under the age of ve. Around 114,000 individuals require urgent humanitarian assistance. Ÿ The total estimated cost of direct physical damages to buildings (residential and non-residential) and infrastructure and livestock/agriculture is approximately US$314 million (equivalent to approximately 2.2% of the Afghanistan GDP in 2021) (Table 1). Economic losses are reported in terms of capital damages, estimated via the GRADE methodology. It is important to note that this estimate covers only the replacement cost of building structures. It does not account for costs of replacement of contents in both residential and non-residential structures. Additionally, it excludes costs related to reconstruction or building back better, or further development in the area. Moreover, it does not take into account factors like production interruption, income loss, temporary housing/relocation expenses, and demolition costs. Lastly, the impact of concurrent/secondary hazards such as rainfall/landslides were negligible and were not factored in. 1 Global RApid post-disaster Damage Estimation (GRADE) approach developed at the World Bank and conducted by the Global Practice for Urban, Disaster Risk Management, Resilience and Land (GPURL) Disaster-Resilience Analytics & Solutions (D-RAS) Knowledge Silo Breaker (KSB). The methodology aims to address speci c damage information needs in the rst few weeks after a major disaster See https://www.gfdrr.org/sites/default/ les/publication/DRAS_web_04172018.pdf for details of the methodology. Up to present, over 40 different GRADE assessments have been conducted and experience so far has shown that, on average, the estimated damages are above 80 percent accurate relative to detailed on ground assessments that follow in the weeks and months after a disaster. 2 Direct physical damage is quanti ed using the gross capital stock, which is the replacement cost of an asset, if newly rebuilt, based on current unit costs and construction practices in the affected region. Replacement cost does not include xed/mobile industry capital, transport equipment, household contents, commercial stock contents etc. 1 Ÿ The GRADE assessment shows that the median estimated damages calculated for both residential and non-residential building stock are nearly equal, each amounting to approximately US$125 million. Following closely are damages to infrastructure and agriculture, which totaled US$64.4 million. A high level of vulnerability of the residential building stock was also observed, with very high damage ratios for residential buildings (highest ratio in Zindajan at 37.9%).3 In contrast, the damage ratio for non- residential buildings is notably lower due to better construction quality (13.7% in Zindajan). Similarly, estimated damages to infrastructure are also lower and reach approximately 9.5% of the baseline infrastructure exposure in the Zindajan district. Table 1: Direct physical economic damage by sector and district/province in absolute values (in US$ millions). District/Province Residential Non-Residential Infrastructure/ Total Median Agriculture Injel (Herat) 37.3 38.0 19.2 94.4 Herat (Herat) 29.1 32.9 11.6 73.6 Zindajan (Herat) 9.1 9.1 15.1 33.2 Kushk (Herat) 9.3 10.7 5.3 25.3 Ghoryan (Herat) 7.4 8.5 3.9 19.8 Guzara (Herat) 9.1 7.6 2.5 19.2 Gulran (Herat) 6.7 5.9 2.9 15.4 Karukh (Herat) 3.0 3.2 1.5 7.7 Pashtonzarghon (Herat) 3.4 2.6 0.8 6.7 Kushk-e-Kuhna (Herat) 2.1 1.7 0.5 4.4 Kohsan (Herat) 1.8 1.3 0.4 3.6 Other Districts (Herat) 3.4 2.7 0.6 6.6 Badghis Province 2.8 1.1 0.3 4 Farah Province <0.1 <0.1 <0.1 <0.1 Ghor Province <0.1 <0.1 <0.1 <0.1 Total 124.5 125.1 64.4 314.0 Ÿ Housing in earthquake-affected districts suffered severe damage due to predominantly non- resilient construction, with 90% using vulnerable paksha and 10% khama-khashta construction. Lack of stone foundations and plinth courses exacerbated vulnerability to seismic forces. Structural collapse was often instantaneous, particularly impacting women, children, and the elderly as the rst earthquake struck at 11:11 am when most men were outdoors. 3 In the assessment, the "Residential" sector encompasses housing, grain/fodder storage, and, in some cases, livestock shelters and household infrastructure/connections. The "Non-Residential" sector includes a wide range of structures such as private, public, and commercial buildings, schools, hospitals, clinics/health centers, public administration buildings, mosques, agricultural warehouses/larger shelters, and industrial buildings or warehouses. It also encompasses mixed-use facilities where both commercial and residential activities occur. Lastly, the "Infrastructure" category covers transport infrastructure (e.g., roads, railways, bridges), critical power and water infrastructure (power plants, pipelines, power networks, transmission lines, water structures), and ICT infrastructure (communications, cables, towers, TV, radio, etc.). Additionally, this category includes agricultural components such as the stocks of fodder, livestock, and machinery. 2 Map1: Spatial distribution of total direct damages at district level. 3 2.0 Introduction 2.1 Event Characteristics and Description The Herat Province in Western Afghanistan was signi cantly affected by a series of four shallow earthquakes, all with the same moment magnitude (Mw) 6.3, accompanied by recurring aftershocks since October 7, 2023. The initial two Mw 6.3 earthquakes struck around 40 km northwest of the city of Herat on October 7 at 11:11 and 11:42 am local time, followed by numerous aftershocks. Two more Mw 6.3 earthquakes occurred on October 11 and 15, further impacting the same areas. All four events were shallow with focal depths of 6 to 14 km, according to the United States Geological Survey (USGS). The epicenters of the four earthquakes were located within the Herat region, the third-highest seismic hazard zone in Afghanistan (Waseem et al., 2019), which had remained relatively unaffected by strong earthquakes for an extended period. Tectonically, the Herat region is dominated by the 730-km-long Hari Rud fault. This fault stretches from its intersection with the Chaman fault north of Kabul and extends westward across the Ghor and Herat provinces to the Iranian border. The October 2023 earthquake events were the deadliest in Afghanistan since the May 30, 1998 earthquake that impacted districts in Badakhshan and Takhar provinces (killing around 4,600 people and injuring 12,000). Since 1920, seven other earthquakes of magnitude 6 or larger (and up to magnitude 7.3) occurred within 250 km of the affected region, all within Iran. Historic events causing damage in the Herat region, and possibly centered within the province, include those in 849, 1102 and 1364 AD. Probabilistic seismic hazard assessments for the area had been primarily in uenced by substantial earthquakes occurring across the Iranian border. Examples include the devastating Mw 7.3 Ardekul earthquake on May 10, 1997, that resulted in at least 1,567 fatalities, including ve people in Afghanistan, and the Mw 6.6 and 7.1 Ghaenat (Khorasan) earthquakes on November 14 and 27, 1979 that caused a minimum of 300 casualties in Iran. For the purposes of this GRADE report, a custom ShakeMap was developed to better illustrate the reported impacts of the four earthquakes. Figure 1  depicts the composite event ShakeMap, complete with a color- coded scale indicating the severity of shaking in Modi ed Mercalli Intensity (MMI) scale zones, ranging from "violent/extremely damaging" to "severe/very damaging" and "very strong/damaging" ground motion, corresponding to macroseismic intensities IX, VIII, and VII on the MMI scale, respectively. The epicenters of the four Mw 6.3 earthquakes are also shown. 4 Figure 1: Recreated composite MMI (Modied Mercalli Intensity) Shakemap of the October 5 to 17, 2023 earthquake sequence in Herat province (Western Afghanistan). It shows the seismic shaking's spatial distribution. The brown/dark orange locations correspond to severe ground shaking intensities VII and VIII. The epicenters are shown as red bullets with the occurrence dates in day/month format. 2.2 Human And Sectoral Impact of the Earthquake in Afghanistan The combined earthquakes from October 7 to 15 have had devastating effects, resulting in a total of 1,482 fatalities and 2,100 injuries, and impacting 154,000 people.4 Around a quarter of those affected were children under the age of ve, based on earlier reports. Additionally, 114,000 individuals are now in dire need of humanitarian assistance, particularly those residing within the high-intensity impact areas (MMI ≥ VI). The initial event on October 7 caused most of the casualties, but the subsequent earthquakes on October 11 and 15 collectively also resulted in seven deaths and 331 injuries. Simultaneously, a remote sensing assessment by the Global Shelter Cluster reported that around 21,600 buildings had been damaged. The UNOCHA update on October 26th reported that, based on damage assessments so far, around 154,000 individuals have been affected, living in more than 25,500 homes, across Herat Province; with 8,429 homes completely destroyed and 17,088 severely damaged, predominantly in the districts of Herat, Injel and Zindajan. Across the region, damage to buildings and infrastructure/agriculture occurred in the 11 districts of Zindajan, Gulran, Injel, Kushk (Rabat-e-Sangai), Guzara, Ghoryan, Karukh, Kushk-e-Kuhna, Herat, Pashtonzarghon, and Kohsan; all of which belong to the Herat province. To provide context, the combined population of these 11 districts, based on 2021-2022 data from Afghanistan's National Statistic and Information Authority (NSIA), is approximately 1.769 million people. This population represents nearly 81% of the total population of Herat province, and nearly 5.3% of the country's total population estimated by the 4 As of October 20, 2023 (UNOCHA) 5 https://reliefweb.int/report/afghanistan/Hirat-earthquakes- ash-update-7-earthquakes-Hirat-province-western-region-afghanistan-20-october-2023 5 NSIA. Given the large population of Herat province, Afghanistan's second most populous province, 8.7% of the population in the 11 most affected districts was directly impacted by the earthquakes. However, this ratio is much higher in the districts closer to the earthquake epicenters such as Zindajan, Injel and Kushk. Ÿ Human casualties: According to a WHO report on October 19, and information from the ITA Ministry of Public Health, there have been 1,482 reported deaths and 2,100 injuries, including seven fatalities and 331 injuries, due to the October 11 and October 15 earthquakes. Of these injuries, 876 were treated at the Herat Regional Hospital, with 38 still hospitalized as of October 17. The majority of casualties have been reported in the Ghoryan, Gulran, Injel, Kohsan, Kushk, and Zindajan districts. Most of the casualties were women and children, as many men were working outdoors at the time. Ÿ Ÿ Population affected and in need of humanitarian assistance: As of UNOCHA Update 75 on October 20, approximately 154,000 individuals in 25,500 homes either completely destroyed or severely damaged by the earthquakes, have been directly affected. Injel is the hardest-hit district (23,053 people), followed by Kushk/Rabat-e-Sangai (8,541), Zindajan (7,523), Gulran (3,428), Herat (717), and Kohsan (133). Injel represents 53% of those directly impacted, while Zindajan suffered signi cant damage (2,137 severely damaged homes and 1,697 moderately damaged). The analysis of the October 15 earthquake is ongoing and not included in these gures. A rapid assessment indicates that 114,000 people need humanitarian assistance, especially in water, sanitation, and hygiene (WASH), and many households lost winter livestock supplies due to the earthquakes. Ÿ Ÿ Villages and buildings: Impact assessments based on satellite imagery analysis have identi ed 289 villages that were signi cantly affected, falling into categories of very high (11), high (110), and moderate (168) impact. As of October 14, assessments have been concluded for the 11 villages with very high impact and 70 villages with high or moderate impact. Additionally, following the Mw 6.3 earthquake on October 11, remote sensing assessments by UNOSAT covering a 3,150 km2 area across four districts (Ghoryan, Zindajan, Injel, and Kushk) identi ed 148 villages, among which 21 were "almost destroyed," 10 were "severely affected," 92 were "moderately affected," and 25 showed "no visible damage." Notably, the "almost destroyed" villages were primarily located between the epicenters of the October 7 and October 11 earthquakes. An estimated 30 additional villages were impacted by the earthquake on October 15, with ongoing assessments in progress for these areas. Ÿ Ÿ Housing sector: As assessments across the impacted villages are continuing, information on the number of damaged houses is uctuating, incomplete and not detailed (e.g., by location, typology, and level of damage). According to the latest UNOCHA update, 8,429 houses have been completely destroyed and 17,088 severely damaged, affecting around 154,000 people (UNOCHA, October 20, Update 7). Thousands more have been damaged across the affected districts, leaving at least 56,000 people in need of urgent shelter assistance6 but information as to their number is still uctuating. Ÿ Ÿ Education sector: On October 18, the Provincial Education Department reported that a total of 125 active schools had been affected by the earthquakes, with 86 of them classi ed as severely damaged, spread across various districts: Herat (30), Kohsan (14), Injel (13), Kushk/Rabat-e-Sangai (10), Zindajan (7), Ghoryan (6) and Gulran (6). Ÿ Ÿ Health sector: Rapid and initial assessments found damage to 40 health facilities. These affected facilities are distributed across multiple areas, including Herat City and 13 surrounding districts, such as Injel, Guzara, Zindajan, Gulran, Kushk/Rabat-e-Sangai, Ghoryan, Karukh, Kohsan, Obi, Pashtun Zarghun, Kushk-e-Kuhna, and Farsi. Among these health facilities, 35 are primary healthcare facilities, and ve are hospitals of critical importance: Herat Regional Hospital, Herat Maternity Hospital, Sakena Yacobi Hospital, Ghoryan District Hospital, and Gulran District Hospital. The damage assessment indicates a range of severity among these health facilities. While the Karnil BHC in Zindajan district has become entirely non-functional, healthcare services are being provided in temporary tents established outside the damaged facility. For the remaining 39 facilities, detailed damage 5 https://reliefweb.int/report/afghanistan/Hirat-earthquakes- ash-update-7-earthquakes-Hirat-province-western-region-afghanistan-20-october-2023 6 A strong dust storm on October 12 destroyed several hundred tents. 6 reports are still pending, but initial ndings suggest varying levels of impact. Some facilities sustained light to moderate damage, characterized by partial structural damage and cracks on the walls. Notably, both the Herat Regional Hospital and Sakina Yakoubi Maternity Hospital experienced moderate damage, with visible cracks observed in different sections of their buildings. Spatially, damage was reported in 21 of the 40 facilities, with multiple districts affected, including Injel (4), Guzara (3), Herat City (3), Gulran (3), Zindajan (2), Ghoryan (2), Kushk/Rabat-e-Sangai (2), Karukh (1), and Pashtonzarghon (1). Ÿ Infrastructure: The earthquakes' impact has been particularly severe to the water supply infrastructure, leading to substantial damage to essential water points and sanitation facilities in the affected districts, increasing the potential for disease outbreaks. Ÿ Ÿ Cultural heritage: Herat is renowned for its rich cultural heritage, featuring over 850 cultural monuments and historical sites. Notable historical sites damaged in the earthquakes include Qila Ikhtiarudin (Herat citadel), the largest mosque Masjid Jami Herat, and the Minaret of Jam. A summary of the main impacts of the October 2023 earthquakes is shown in Table 2 ; while a comparison with the main impacts of the June 2022 Paktika Afghanistan earthquake (for which a GRADE assessment was also carried-out) is shown in Table 3. The gures for the Herat earthquakes are as of information on October 26 and it is understood that damage statistics to housing are not complete. The comparison served as one more benchmark for the validation of the October 7 to 15, 2023 Herat earthquakes GRADE assessment. Human casualties were comparatively more severe in the 2022 Paktika earthquake because this event occurred in the night (01:24 am local time) as opposed to midday (11:11 am) for the rst and most lethal earthquake in Herat province. Relative damage to housing in also comparatively higher in the 2022 Paktika earthquake which suggests that the reported housing damage data for the October 7 to 15, 2023 Herat earthquake sequence are incomplete. Table 2: Summary of key impacts of the October 7 to 15, 2023 earthquakes in Herat province (Source UN OCHA, WHO, Reliefweb) Impact Summary Affected Region Rural areas in the 11 most impacted districts of Hirat Province: Zindajan, Gulran, Injel, Kushk/Rabat-e-Sangai, Guzara, Ghoryan, Karukh, Kushk-e-Kuhna, Hirat, Pashtonzarghon, and Kohsan. Affected Population Approximately 157,000 people directly affected; and 114,000 individuals in need of humanitarian assistance.7 Impacted Villages 289 villages damaged: 11 categorized as very high impact, 110 as high impact, and 168 as moderate impact. Casualties 1,482 fatalities; 2,100 injured, of which 876 were admitted to hospital. Damaged Houses 8,429 homes completely destroyed, and 17,088 severely damaged. Information on damaged houses is uctuating and incomplete as assessments continue. Shelter 56K people in need of urgent shelter assistance, with info. on exact numbers still changing. Impact on Non- Schools: 86 severely damaged and 39 moderately damaged, including 70 community-based residential Buildings education facilities. Health facilities: 53 damaged (35 primary health care facilities and 5 hospitals in Hirat City, and 13 in surrounding districts). WASH 114,000 people in need of WASH support Infrastructure Severe impact on water supply infrastructure. Substantial damage to water points and sanitation facilities. Cultural Heritage Notable historical sites damaged, including Hirat citadel and Minaret of Jam. 7 UN OCHA Multisectoral Herat Earthquake Response Plan (October 16) https://reliefweb.int/report/afghanistan/afghanistan-Hirat-earthquake-response-plan-october-2023-march-2024 The number of affected people is expected to rise as damage assessments continue (assessments were completed in 81 of the 289 affected villages by October 14). 7 Table 3: Comparison of the main consequences of the June 2022 Paktika earthquake and the October 2023 Herat earthquakes in Afghanistan. Indicator June 21, 2022 October 7-15, Paktika Earthquake 2023 Hirat Earthquakes Moment Magnitude (Mw) 5.9 6.3 (4 earthquakes of the same magnitude) Occurrence Date June 21 October 7 to 15 Occurrence Time (local) 1:24 AM 11:11 AM Number of Worst Affected Districts 6 11 Population ('000s) of Affected 228 1,769 Districts (NSIA 2021-22) Number of Villages Severely >25 121 Damaged People Killed 1,031 1,482 People Injured 2,949 2,100 Destroyed Houses 4,873 8,429 Damaged Houses 8,417 17,088 Destroyed/Damaged Schools 9 schools, 50 madrasas 125 affected (86 severely) Destroyed/Damaged Health 26 village clinics 53 (40 in Hirat City, 13 in surrounding Facilities districts) including 5 hospitals and 35 primary health care facilities 2.3 Fragility, Conict, and Violence (FCV) Context The earthquake is exacerbating an already dire situation, as Afghanistan is suffering from decades of con ict, successive droughts, a shrinking economy, rising poverty, food insecurity and malnutrition. Over 90% of the population lives below the poverty line and around 17 million people are experiencing acute food insecurity8 (approximately 50% of the population, based on the NSIA estimate for 2021-2022). In the early stages of the disaster, it was estimated that the earthquake affected 1,400 internally displaced persons (IDPs);9 a number that may rise as assessments progress. This crisis adds to the already dire refugee crisis in the country, including 52,000 refugees, 13,500 Afghan returnees and 3.2 million IDPs who had already been displaced because of previous disasters and con icts. These compounding factors further strain response and recovery efforts, particularly as the winter season approaches, emphasizing the critical need for adequate shelter.10 Notably, Herat province, which hosted the second-highest number of displaced people in Afghanistan between 2021–2022, receiving 250,000 IDPs over this period, faced signi cant hurdles even before the earthquake. IDPs in Herat struggled to access livelihood opportunities as they did not own land or livestock and were in urgent need of humanitarian assistance. Furthermore, Herat province alone received over 8 European Commission (2023). EU approves €3.5 million humanitarian aid package in response to the earthquake in Afghanistan. Available from: https://civil-protection-humanitarian-aid.ec.europa.eu/news-stories/news/eu-approves-eu35-million-humanitarian-aid-package-response-earthquake -afghanistan-2023-10-10_en [accessed 10/20/2023] 9 UN news (2023). Earthquake kills at least 100 in Herat, Afghanistan. Available from: https://news.un.org/en/story/2023/10/1142027 [accessed 10/20/2023] 10 UNHCR (2023). Afghanistan Earthquake Response: Emergency Appeal October 2023 – March 2024. Available from: https://reliefweb.int/report/afghanistan/unhcr-afghanistan-earthquake-response-emergency-appeal-october-2023-march-2024 [accessed 10/19/2023] 8 500,000 IDPs between 2012 and 2022, underscoring the magnitude of the displacement crisis in the region.11 Since the establishment of the Interim Taliban Administration (ITA) in August 2021, Afghanistan has seen a shortfall in international aid, with many major aid groups and NGOs pulling out and crucial aid programs halted – crippling an economy already heavily dependent on aid. While international players have acknowledged the urgency of the situation, and several donors have announced new contributions to the Afghanistan Humanitarian Fund totaling around US$25.5 million, the administrative procedures for providing aid remain unresolved and many governments are reluctant to deal directly with the ITA. As a result, the 2023 Humanitarian Response Plan remains critically underfunded, with only 34% of requirements received as of October 16. 2.4 Socio-economic Vulnerability and Considerations As efforts to provide relief and support are mobilized, the following socio-economic conditions and vulnerabilities may exacerbate the impacts of the earthquake events: Ÿ Gender: Natural hazards do not discriminate by gender, but their effects do. Men and women, boys and girls are affected differently from disasters, even if they live in the same household.12 In 2023, Afghanistan was ranked at the bottom of the Global Gender Gap Index, emphasizing its continued challenges in narrowing gender disparities across sectors.13 The rst earthquake struck at 11:11 am when most men were outdoors, predominantly impacting women, children, and the elderly. As of October 17, 2023, 58% of adult persons who lost their lives due to the earthquakes were women. Women also represented 60% of injured persons and 61% of missing persons, as women are more likely to stay inside the house, due to cultural norms.14 These existing inequalities also are hindering access to essential services, such as shelter, water and sanitation as well as vital relief items.15 Ÿ Ÿ Children: Children under the age of 14 comprise 46% of Herat province's total population, and those under nine years old constitute 32% of the province's population. WHO reported on October 19 that 23% of the affected population were children under ve. Tragically, 245 children are unaccompanied, facing heightened child protection risks. Over 19,000 individuals, especially children under ve, have increased nutrition needs, while 13,000 children require urgent educational assistance. Disrupted health services and psychosocial risks further threaten nutrition. Ÿ Ÿ Rural population: Herat province is characterized by a predominantly rural demographic, with 69% residing in rural areas and 31% in urban areas, according to the 2021-22 NSIA statistics. The rural population faces heightened vulnerabilities, with strained pre-earthquake education and healthcare services impacting timely medical care for the injured. Earthquake impacts on rural agriculture disrupted livelihoods and exacerbated economic vulnerability in these communities. Ÿ Weather and environmental conditions: The timing of the earthquakes coincides with Afghanistan's harsh winter. Many affected villages are at high altitudes, requiring immediate assistance like transitional shelter, warm clothing, and heating materials. Dust storms have further disrupted relief efforts and damaged makeshift shelters. 11 ACAPS Brie ng Note - Afghanistan: Earthquakes in Herat Province (October 12, 2023) https://reliefweb.int/report/afghanistan/acaps-brie ng-note-afghanistan-earthquakes-Hirat-province-12-october-2023 12 Erman, A. et al. 2021. Gender Dimensions of Disaster Risk and Resilience: Existing Evidence. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/35202 13 World Economic Forum. (2023). Global Gender Gap Report 2023. https://www.weforum.org/reports/global-gender-gap-report-2023/ 14 UN OCHA. (2023). Afghanistan Gender Update #2 - Earthquake in Herat Province, 19 October 2023. ReliefWeb. [https://reliefweb.int/report/afghanistan/afghanistan-gender-update-2-earthquake-Hirat-province-19-october-2023][1].. 15 Gender in Humanitarian Action Afghanistan. (n.d.). Gender Update #2: Earthquake in Herat Province. Developed by the Gender in Humanitarian Action Group (GiHA) in Afghanistan. 9 A Social Vulnerability Index (SoVI) was developed as part of this assessment to illustrate how socio- economic factors could amplify earthquake impacts and highlight district-level variations in terms of social and economic vulnerability ( ). The Social Vulnerability Index (SoVI) identi es districts at most risk from the earthquake's impacts. When combined with the physical damage estimates from the GRADE assessment, this approach provides a more comprehensive view of the disaster's effects, including potential indirect and long-term socioeconomic consequences. The indicators in the SoVI cover essential areas, including limited market access, inadequate healthcare services, rural population percentages, and prevalence of women-headed households, chosen for their established signi cance in driving vulnerability. For instance, restricted market access hampers resource and emergency supply accessibility post-disaster, increasing vulnerability. Similarly, insu cient healthcare services lead to higher casualties and prolonged recovery. Rural areas, often facing infrastructure challenges, have distinct earthquake vulnerabilities. Likewise, several districts, including Gulran, Ghoryan and Kushk, which have higher number women-headed households and are isolated from Herat city, and exhibit a higher vulnerability due to disparities in access to resources and decision-making power.16 Additionally, con ict severity, lack of development, and the absence of livestock were considered. Con ict- affected regions are known to experience increased vulnerabilities as con ict disrupts community structures, resources, and access to services. A proxy for development was constructed based on residential, non-residential and infrastructure capital intensity normalized by population. Livestock ownership, vital for economic stability and food security in rural areas, was evaluated per household in districts. The absence of livestock assets can contribute to vulnerability. It is important to note that the SoVI, based on these indicators and weights, serves as an initial proxy for social vulnerability and may not comprehensively encompass all aspects of vulnerability. Therefore, it should complement qualitative data from interviews and post-disaster needs assessments to offer a holistic view of vulnerability in the earthquake-affected areas. More details on the considerations and methodology, including the selection of indicators and weights, are presented in Annex A. 16 UN OCHA. (2023). Afghanistan Gender Update #2 - Earthquake in Herat Province, 19 October 2023. ReliefWeb. [https://reliefweb.int/report/afghanistan/afghanistan-gender-update-2-earthquake-Hirat-province-19-october-2023][1].. 10 Figure 2: District-Level Social Vulnerability Index (SoVI) - analysis for Herat Province, highlighting impact of vulnerability factors, including limited market access and healthcare services, gender disparities, rural population, conict severity, lack of development and livestock assets. Districts exhibiting higher social vulnerability compared to others are represented in deeper shades of orange and red, denoting values exceeding 0.5. 11 3.0 Post-Disaster Rapid Damage Estimation Methodology 3.1 Methodology Over the past eight years, the World Bank Disaster-Resilience Analytics & Solutions (D-RAS) team has produced 12 earthquake-related GRADE assessments (Gunasekera et al., 2023). The most recent was for the September 8, 2023 Mw 6.8 earthquake in Al Haouz, Morocco. This assessment included modelling of earthquake ground-shaking and associated impacts. Much calibration is generally needed, including comparison of past risk studies, collection of damage data statistics, comparison with past events, and comparison with asset values. The D-RAS team had developed an earthquake risk model for GFDRR in 2015, including baseline exposure for residential and non-residential buildings by vulnerability class and associated ground shaking vulnerability curves; however, this was signi cantly updated. The GRADE assessment provides an estimation of the direct damage caused by the earthquakes in Afghanistan, through a remote-based methodology that utilizes a mix of earthquake damage modelling, and incremental loading due to four events of similar nature; and in addition, an assessment of capital stock value of different assets and sectors. As part of this assessment, several tasks were undertaken to estimate the physical damage caused by the earthquake. These include: a) Rapid collection and analysis of satellite imagery, ITA damage assessment reports, public data such as newspaper articles, local newspapers, situation reports, UNOCHA and WHO Situation Reports, UNOSAT Satellite Derived Damage Assessments and information from local NGOs. Further information is detailed in Annex B. b) Recreation of earthquake ground motion through hazard modelling and close examination of the strong ground motion recordings, including directivity, across the affected region, in addition to intensity and damage data across Afghanistan. The intensities were calibrated, and multiple maps created for each event. c) Development of a full buildings and infrastructure exposure database via capital stock and construction typology information; and analysis of current unit costs of construction in Afghanistan and projected to 2023. d) Vulnerability and Fragility Modelling checks of Afghanistan Building and Infrastructure typologies including modelling of agriculture/livestock damages. Using this model, along with ShakeMap modelling of the multiple events (Figure 1), a modelled estimate of the cost of direct damages to buildings has been produced, including cross validation with damage data that has fed through in this time period. Due to the large number of remote exposure products available globally, a selection process was needed to choose the baseline information to be used for the nal exposure model. The Google building footprints was combined with GHS BUILT-C MSZ to characterize the building heights and sizes; census data also gave checks across Herat and the typologies were split into residential and non-residential (where mixes of industrial, commercial, public, and other stock are contained). 12 In building the exposure model, the value per storey in a building was determined by multiplying the number of households by the number of storeys of a building. This approach allowed the team to aggregate data per storey and per structural vulnerability class for each district in Afghanistan, taking into account population and building statistics. As a result, it was found that, for instance, 53% of households reside in single-storey buildings, while as an example, just 7% inhabit three-storey structures. The exposure model was constructed by analyzing extensive datasets, enabling the team to derive construction costs and oor areas per person. For non-residential assets, the team consulted comprehensive databases encompassing schools, health facilities, xed assets, and establishment surveys to assess capital stock across different locations. The estimated replacement value for the entire Afghanistan, for residential and non-residential building stock, amounts to US$48.1 billion, which is nearly 3.4 times Afghanistan's 2021 GDP. It is important to clarify that exposure data is generated at the province level and then downscaled using various data sources. However, it is worth noting that, due to the nature of the capital stock estimation process, neighboring districts such as Injel and Herat may inherently have some exposure (and damage) that could be attributed to one district but actually occurred in the adjacent district. For infrastructure/agriculture, the databases of Open Street Map (OSM), Central Statistics O ce (CSO) reports, UNOCHA, ITA data and global datasets were used to create a roads, energy, water, and Information and Communication Technology (ICT) database to be distributed in the 2015 study. This was updated using the newest OSM data. Additionally, livestock and agriculture data from Global Products, Food and Agriculture Organization (FAO) were used to derive agriculture exposure in terms of food production. The team evaluated replacement cost as opposed to reconstruction cost of buildings in the affected region. Unit costs of construction appropriate for the area were derived from local information, but use was also made of media feeds on the costs of existing housing projects in the area, construction statistics, as well as on the costs proposed after the earthquake. Unit costs of construction for the replacement of destroyed, or the repair of damaged buildings (depending on the damage level), were thus obtained. To verify the economic exposure values, disaggregated macro-economic (capital stock) information was also used and corroborated with detailed estimates. In terms of the baseline exposure values, the total built capital stock (in buildings and infrastructure) estimate in Afghanistan is shown in Table 4 below. The spatial distribution of the baseline exposure is also provided in Figure 3 and in Annex C. This exposure model was then combined into the process. It is important to note that the GRADE analysis focuses on the costs of direct damages but not on the losses, and assesses key sectors such as residential, non-residential buildings and infrastructure. The damage estimate is produced for the repair and reconstruction of the affected buildings exposure based on “as was” capital stock replacement values. In addition, for the destroyed housing stock, “build back better” considerations highlight additional costs, which are discussed in the next Section. 13 Table 4: Table of capital stock estimates (median, in US$ millions) of Afghanistan. Note this does not include productive capital, equipment or other building contents-based exposure. District/Province Residential Non-Residential Infrastructure/ Total Value Agriculture Injel (Herat) 283 550 307 1140 Herat (Herat) 525 814 494 1833 Zindajan (Herat) 24 66 33 123 Kushk (Herat) 68 156 83 306 Ghoryan (Herat) 75 143 80 298 Guzara (Herat) 57 158 79 294 Gulran (Herat) 36 98 50 184 Karukh (Herat) 33 78 41 152 Pashtonzarghon (Herat) 39 107 54 200 Kushk-e-Kuhna (Herat) 18 49 25 92 Kohsan (Herat) 22 61 31 114 Other Districts (Herat) 151 401 204 756 Badghes Province 207 258 172 637 Farah Province 309 369 250 928 Ghor Province 321 392 263 976 Rest of Afghanistan 20128 22111 15590 57829 Total 22296 25812 17755 65864 Figure 3: Spatial distribution of baseline exposure dataset used in this GRADE analysis zoomed into Herat Province. 14 3.2 Building Back Better Considerations This GRADE assessment used capital replacement costs, which is the estimated cost to bring back an asset to its pre-damage condition without rebuilding to a different typology. Reconstruction costs, especially with 'build back better' considerations, exceed direct replacement costs due to a number of factors, including the use of superior more resilient materials, advanced design and engineering through updated building codes, heightened post-disaster demand causing supply constraints, the necessity for interim infrastructure like temporary housing, and other factors enhancing the earthquake resilience of the building stock. It should also be differentiated from the actual cash value of the property, which is either de ned as the book value, or net capital stock, which includes depreciation of the asset. Generally, replacement costs are more than book value, and less than reconstruction costs. When upgrading the rural stock in the affected region to a more resilient stock, an increased “build back better” cost of 2.5 times the replacement cost is needed (upwards of ~US$180 million extra for the over 25,000 destroyed and severely damaged houses reported so far). The 2.5 ratio is a rule of thumb and can potentially change due to a) updated local damage reports, b) local engineering information/practices including building regulations and seismic resistance codes, c) socio-economic variables and d) demand surge factors. Appropriate and cost-e cient build back better guidelines exist in the literature, such as in the book by Coburn et al. (1995). 15 4.0 Results and Interpretation 4.1 Summary of Results Following the methodology described above, Table 5  and  Table 6 show the breakdown of the best estimate of direct physical economic damages in Afghanistan by sector and by district in absolute numbers (millions US$), and relative to baseline capital stock exposure (percentage) respectively. Figures 5 to 7 show the spatial distribution of the estimated damages by district. Table 5: Direct physical economic damage by sector anddistrict in absolute values (in US$ millions). District/Province Residential Non-Residential Infrastructure/ Total Median Agriculture Injel (Herat) 37.3 38.0 19.2 94.4 Herat (Herat) 29.1 32.9 11.6 73.6 Zindajan (Herat) 9.1 9.1 15.1 33.2 Kushk (Herat) 9.3 10.7 5.3 25.3 Ghoryan (Herat) 7.4 8.5 3.9 19.8 Guzara (Herat) 9.1 7.6 2.5 19.2 Gulran (Herat) 6.7 5.9 2.9 15.4 Karukh (Herat) 3.0 3.2 1.5 7.7 Pashtonzarghon (Herat) 3.4 2.6 0.8 6.7 Kushk-e-Kuhna (Herat) 2.1 1.7 0.5 4.4 Kohsan (Herat) 1.8 1.3 0.4 3.6 Other Districts (Herat) 3.4 2.7 0.6 6.6 Badghis Province 2.8 1.1 0.3 4 Farah Province <0.1 <0.1 <0.1 <0.1 Ghor Province <0.1 <0.1 <0.1 <0.1 Total 124.5 125.1 64.4 314.0 Table 6: Direct physical economic damage by sector and district in relative terms (in % of the exposures). District/Province Residential Non-Residential Infrastructure/ Total Agriculture Injel (Herat) 13.2% 6.9% 4.3% 7.8% Herat (Herat) 5.5% 4.0% 2.3% 4.0% Zindajan (Herat) 37.9% 13.7% 9.5% 17.3% Kushk (Herat) 13.7% 6.9% 4.3% 7.7% Ghoryan (Herat) 9.9% 5.9% 3.6% 6.3% Guzara (Herat) 15.9% 4.8% 2.8% 6.4% Gulran (Herat) 18.7% 6.0% 3.7% 7.8% Karukh (Herat) 9.1% 4.1% 2.4% 4.7% Pashtonzarghon (Herat) 8.7% 2.4% 1.3% 3.3% Kushk-e-Kuhna (Herat) 12.0% 3.4% 1.9% 4.7% Kohsan (Herat) 8.1% 2.2% 1.1% 3.1% Other Districts (Herat) 2.2% 0.7% 0.3% 0.9% Badghis Province 1.4% 0.4% 0.2% 0.7% Other Provinces 0.0% 0.0% 0.0% 0.0% Total 0.6% 0.5% 0.4% 0.5% 16 Figure 4: Spatial distribution of total direct damages at district level. Figure 5: Spatial distribution of total direct damages as percentage of total exposure value (TEV) at district level. 17 Figure 6: Spatial distribution of residential and non-residential damages at district level. 18 Figure 7: Spatial distribution of infrastructure/agriculture damages at district level. 4.2 Summary of Results In the districts near the earthquake epicenters, the damage to the housing stock was severe. This was largely due to the fact that most houses in this remote and economically disadvantaged rural area lacked the resilience to withstand the strong shaking generated by the earthquakes. In the affected villages, families typically reside within compound walls, usually with four families of around six members each making up a compound. Multi-generational extended families often share the same roof. Surveys conducted on-site in affected villages, such as Zindajan and neighbouring districts, revealed that 90% of the compounds were constructed using paksha techniques, involving traditional unreinforced construction methods using raw earth and straw for walls and roofs, with wooden support poles and beams. The remaining 10% utilized khama-khashta construction involving adobe blocks. These types of construction are highly vulnerable to strong seismic forces. Furthermore, both compound and house/room walls lacked stone foundation and plinth courses (the lowest part of the wall that appears above ground level, usually formed of a course of stones or bricks). This further exposed these structures to moisture in ltration, further compromising their structural integrity (Miyamoto International, 2023). In many cases, structural collapse was nearly instantaneous due to the strong ground shaking experienced in the epicentral region. As a result, the earthquake sequence resulted in severe human casualties, particularly in the villages close to the initial earthquake's epicenter. The rst earthquake occurred around midday (11:11 am local time) when most men were outdoors, leaving mostly women, children and the elderly exposed to the risk of house collapse. 19 Impact on cultural and public buildings: Notably, religious and public buildings, often ornate and symbolic structures, were also affected. Some of these structures were damaged even at great distances from the epicentral areas. This brings into focus the need for a careful approach to rebuilding but also cultural heritage considerations. This is the case in many of the districts with signi cant damage to minarets, parts of the Great Mosque at Herat, Qala Iktyaruddin and other landmarks. Damage to rural houses, agriculture, and businesses in mountainous regions: In rural and mountainous areas, the damage was particularly devastating, with entire villages in need of signi cant rebuilding. The building styles in these regions, often made of local materials and using traditional techniques, proved particularly vulnerable to the strong seismic forces. The districts of Zindajan, Gulran, and parts of Injel were most heavily impacted, showcasing the vulnerabilities of rural construction methods combined with the high seismic activity. In addition, livestock suffered losses, and many of their shelters were destroyed. Coupled with damaged irrigation systems, these factors threaten food security in the affected region, especially since the region had already been grappling with drought, previous cold wave and other food insecurity challenges. Widespread minor to moderate urban damage: In contrast, urban areas like Herat experienced widespread but generally less severe damage, as these were situated 30 to 40 km away from the earthquake epicenters. Most structures sustained cracks and minor structural defects, and there were some instances of moderate and severe damage. The district of Herat and eastern parts of Injel were mostly affected in this manner. Interestingly, the urban buildings, which are generally more expensive per square meter, had lower overall damage ratios due to better construction standards and also the height and structure of the buildings, as well as the distance from the epicenters of the earthquakes. Infrastructure damage in mountainous areas and challenges in remote mountainous villages: The region's infrastructure was signi cantly damaged. Roads were impacted by landslides and severe shaking, while critical systems like water supply, dams, and power components were damaged by the ground movement. Remote villages in mountainous regions faced unique challenges, such as road closures due to falling boulders. These obstacles signi cantly hampered rescue efforts and made access to these areas di cult, exacerbating the crisis. Many social media photos have shown residents removing boulders from highways and roads. 4.3 Brief Comparison to the GFDRR Multi-Hazard Risk Assessment (MHRA) Prole In terms of seismic risk, Herat province experiences comparatively reduced seismic hazard levels in contrast to other Afghan regions in the East (See Annex D for details of historical earthquakes in Herat province and wider Afghanistan). Notably, the primary contributors to economic damages from disasters within the province are ood events, accounting for approximately US$54 million annually, and droughts, estimated at US$280 million per annum (GFDRR, Afghanistan Disaster Risk Pro le, 2017). The GFDRR Risk Pro le produced via the MHRA17 developed stochastic event sets for earthquake shaking and earthquake-induced landslides among other hazards, with event damage tables and return periods for these events. By modifying the event damage tables, using the capital stock adjustment to 2023, the approximate return period of this event can be examined. In terms of fatalities, the October 7 to 15 earthquake sequence is around a 10-year return period event, which means that an event with over 1,000 fatalities (such as the October 7 to 15, 2023, earthquakes), could be expected to occur once every 10 years, anywhere in Afghanistan (Figure 8). This style of event incorporating four earthquakes in short timeframe is not generally well-accounted for in Probable Maximum Deaths 17 GFDRR's Afghanistan Disaster Risk Pro le: https://documents1.worldbank.org/curated/en/284301491559464423/pdf/114097-WP-P155025-PUBLIC-afghanistan-low.pdf 20 curves, however, the earthquake damage return period for this type of event is between 8 and 12 years. In terms of economic damage, earthquakes causing economic damage similar to the damage seen from the October 7 to 15, 2023 earthquakes, are expected to occur every 10 years around Afghanistan, but potentially not as a sequence of four similar magnitude earthquakes in short timeframe. The October 7 to 15, 2023 earthquakes occurred in a rural area, with vulnerable housing stock which is less costly than the average of the Afghan housing stock. Figure 6: Probable Maximum Damage and Fatality curves for Afghanistan from the MHRA prole modied to 2023 capital stock used in this estimate. The blue dot represents the economic damage estimate from the 2023 earthquakes (lefthand column); and the red dot represents the number of fatalities (righthand column). PML and PMD curve for Afghanistan 3000 25000 20000 Probable Maximum Deaths (people) Probably Maximum Loss (Million USD) 2000 15000 10000 1000 5000 RP: 10.0 years RP: 10.0 years 0 0 0 100 200 300 400 500 Return Period (years) 21 5.0 Conclusions 5.1 Event Summary 1. The earthquake sequence of four earthquakes of the same moment magnitude (Mw 6.3) occurred 30 to 45 km northwest of Herat City in Western Afghanistan. 2. All four earthquakes were shallow focused and occurred in a region where there had not been damaging earthquakes since 1364 AD. 3. The affected region is rural and mountainous, with non-resilient housing with low unit costs of construction. However, with a high production per capita, Herat Province is one of the largest producing provinces in Afghanistan. 4. Weak residential housing construction in these rural areas led to high casualties (1,482 killed and 2,100 injured). This event was the deadliest in Afghanistan since the May 30, 1998, earthquake that affected districts in Badakhshan and Takhar provinces (around 4,600 killed and 12,000 injured). 5. As of October 20, 2023, there are 8,429 destroyed and 17,088 damaged houses reported (Source: UN OCHA). These gures are expected to increase as damage assessments in some affected villages are still in progress. 5.2 Damage Costs 1. The total cost of direct economic damages to buildings (residential and non-residential) and infrastructure is approximately US$314 million (equivalent to approximately 2.2% of the 2021 GDP). 2. The Build Back Better cost factor for the destroyed houses is about 2.5 times the replacement value (potentially upwards of ~US$180 million extra may be needed). 3. Nearly 40% of the estimated damage was incurred by the housing sector. 4. Approximately 40% of the total damage was attributed to non-residential buildings. It is worth noting that that the baseline exposure of non-residential buildings in the worst affected areas is lower than in major cities. However, better-constructed non-residential buildings and religious buildings often incur many times the cost of residential buildings when damaged. Consequently, even though they may be less vulnerable for the same hazard intensities, the absolute damages in terms of costs incurred are higher. 5. Infrastructure and agricultural damage accounted for around 20% of the total damage. This damage primarily pertained to the effects on the road and water supply networks. The agricultural impact, especially to livestock, is expected to be signi cant. 22 References Boyd, O.S., Mueller, C.S. and Rukstales, K.S. (2007). Preliminary Earthquake Hazard Map of Afghanistan. USGS Open-File Report 2007–1137, 29 pp. Coburn, A., Hughes, R., Pomonis, A. and Spence, R. (1995). Technical Principles of Building for Safety. Intermediate Technology Publications, 74 pp. Daniell, J.E., Pomonis, A., Tsang, H-H, Wenzel, F., Gunasekera, R. and Schaefer, A. (2018). The Top 100 Fatal Earthquakes: Examining Fatality Risk Reduction Globally with Respect to Seismic Code Implementation. 16th European Conference on Earthquake Engineering, 18-21 June 2018, Thessaloniki, Greece. Erman, A., De Vries Robbé, S.A., Thies, S.F., Kabir, K. and Maruo, M. (2021). Gender Dimensions of Disaster Risk and Resilience: Existing Evidence. GFDRR-World Bank, Washington, DC., 68 pp. Grünthal G. (1998). European Macroseismic Scale 1998. Cahiers du Centre Européen de Géodynamique et de Séismologie, vol. 15, Centre Européen de Géodynamique et de Séismologie, Luxembourg, 99 pp. Gunasekera, R., Ishizawa, O., Daniell, J., Macabuag, J., Pomonis, A., Toyos, G. and Cox, K. (2023). The GRADE Methodology: New Frontier in Rapid Post-Disaster Damage Estimation for the Developing Countries. SECED 2023 Conference, Earthquake Engineering and Dynamics for a Sustainable Future, September 14-15, 2023, Cambridge, UK. Jaiswal, K., Wald, D. and Porter, K. (2010). A Global Building Inventory for Earthquake Loss Estimation and Risk Management. Earthquake Spectra, Vol. 26, No. 3, 731–748, August 2010. Karnik, V., Schenkova, Z. and Schenk, V. (1984). Vulnerability and the MSK Scale. Engineering Geology, Vol. 20, 161-168, 1984. Malan, L.G. (2010). Sustainable construction in Afghanistan. Thesis for the Master of Arts in Security Studies (Stabilization and Reconstruction), Monterey, California. Naval Postgraduate School, 148 pp. Maqsood, S.T. and Schwarz, J. (2008). Seismic Vulnerability of Existing Building Stock in Pakistan. 14th World Conference on Earthquake Engineering, October 12-17, 2008, Be ing, China. Maqsood, S.T. and Schwarz, J. (2011). Development of a Building Inventory and Vulnerability Database for Pakistan. Australian Earthquake Engineering Society 2011 Conference, November 18-20, Barossa Valley, South Australia. Miyamoto International (2023). First Assessment of Herat Afghanistan Earthquake: Preliminary Shelter and Housing Response in Zindajan District, West of Herat. October 13, 2023, 16pp. Spence, R.J.S., Coburn, A.W., Sakai, S. and Pomonis, A. (1991). A parameterless seismic intensity scale for use in seismic risk analysis and vulnerability assessment. In Earthquake, Blast and Impact, 534 pp. Szabo, A. and Bar eld, T.J. (1991). Afghanistan: An Atlas of Indigenous Domestic Architecture. 264 pp. Waseem, M, Lateef, A., Ahmad, I., Khana, S. and Ahmed, W. (2019). Seismic hazard assessment of Afghanistan. Journal of Seismology, 2019, https://doi.org/10.1007/s10950-018-9802-5. World Economic Forum (2023). Global Gender Gap Report 2023: Insight Report. June 2023, 382 pp. 23 Annex A: Development of Social Vulnerability Index This Annex provides insight into the methodology and explanations of indicators for the current Social Vulnerability Index (SoVI) analysis as presented in this GRADE assessment. The Social Vulnerability Index (SoVI) identi es districts at most risk from the earthquake's impacts. When combined with the physical damage estimates from the GRADE assessment, this approach provides a more comprehensive view of the disaster's effects, including potential indirect and long-term socioeconomic consequences. The SoVI results can also be used to inform the Post-Disaster Needs Assessment (PDNA). The following is an explanation of the indicators selected in the construction of the SoVI, re ecting social and economic vulnerability of the communities affected by the October 2023 earthquakes in Afghanistan: Ÿ Women Headed Households: Gender dynamics signi cantly in uenced the outcomes of the October 2023 earthquakes in Afghanistan. The initial quake at 11:11 am primarily affected women, children, and the elderly, as most men were away from home. As of October 17, 2023, women accounted for 58% of the deaths, 60% of the injured, and 61% of the missing. Disparities in access to services like shelter and sanitation further exacerbate the impact of the earthquake for women. The SoVI incorporated preliminary data from the Multi-Sectoral Rapid Assessment Form by UN-OCHA, which indicates that overall 5% of households in the earthquake impacted districts are Women-Headed Households (WHH). However, this analysis also incorporated data on districts with the highest women-headed household rates: Kushk (9%), Ghoryan (8%), and Gulran (8%) as reported by UN Women (2023, Update 2). For consistency across districts where only average women-headed household values were available, the NSIA 2021-2022 dataset's normalized female percentages were multiplied by the respective women- headed household percentages for each district. This approach yielded a composite value re ecting both the proportion of the female population and the percentage of women-headed households. This important to ensure coherent integration of gender data into the overall vulnerability assessment. The full WHH dataset has been requested from UN-OCHA and could potentially inform the upcoming PDNA. Ÿ Ÿ Children: Children under the age of 14 in Herat province constitute 46% of the demographic pro le, with 70% of under ve years old who are particularly susceptible to the detrimental aftermath of earthquakes. The interruption of essential services, such as healthcare and education, and psychological risks pose a long-term vulnerability to children in the affected districts. The 2021-2022 NSIA data provides only province-level data, hence, a district-level indicator could not be developed for use in the SoVI. Ÿ Ÿ Rural Population: The rural population makes up 69% of the Herat province demographic, according to the 2021-22 NSIA statistics. The earthquake has exacerbated the challenges faced by these rural communities. Even before the disaster, access to essential services such as education and healthcare was limited in rural areas. The rural economy's heavy reliance on agriculture means that the earthquake's disruptions to agriculture have dire implications for their nancial stability and recovery. Moreover, pre- existing infrastructural weaknesses in rural areas amplify the earthquake's impact. These factors collectively underscore the vulnerability of the rural population. NSIA 2021-2022 data was used to obtain a percentage of rural population in each district. In addition to the vulnerable population groups, considered above, the following factors drive the capacity of the affected communities to cope with the impacts of the earthquake, and were used in the construction of SoVI: Ÿ Lack of Market Access: Limited market access in the aftermath of an earthquake can severely hinder the recovery process for the affected populations. The inaccessibility of resources and emergency supplies exacerbates the vulnerability, particularly in remote areas where markets are crucial for the provision of basic needs and reconstruction materials. District-level market access data by the World Bank (2016) was used to construct a lack of market access indicator by using an inverse normalized value. 24 Ÿ Lack of Healthcare Services: The state of healthcare services is a determinant of a population's resilience in the face of natural disasters. 53 healthcare facilities were damaged by the earthquake (35 primary health care facilities and 5 hospitals in Herat City, and 13 in surrounding districts). The number of hospitals and primary health care centers in each of the districts were normalized and inversed to construct a lack of healthcare services indicator for each district. Ÿ Ÿ Lack of Livestock: Livestock ownership is a cornerstone of economic stability and food security in the rural areas of Herat, with many households depending on it for their livelihood. The earthquake has led to livestock loss, further exacerbating the fragile economic fabric of these communities, and leaving them without a critical source of income and sustenance. The livestock and agriculture data from Global Products, Food and Agriculture Organization (FAO) were used to extract the number of cattle, sheep and goat for each district. Weights were calculated corresponding to the economic value of each type of livestock, and then a weighted sum was obtained of livestock intensity for each district. These values were normalized and inverted to obtain the lack of livestock indicator. Ÿ Ÿ Lack of Development: An indicator for lack of development was developed utilizing the exposure dataset constructed for the GRADE analysis. This follows a multi-faceted approach, as detailed in the GRADE report, which is validated against macro-economic data and local construction costs. It offers a comprehensive measure of capital intensity within residential and non-residential building stock, as well as infrastructure components such as roads, utilities, and agricultural capital intensity values for assessing development across the districts of Herat. Ÿ Ÿ Con ict Severity Index: This indicator utilized from the World Bank Afghanistan District Dashhoard was created from (i) security incidents, (ii) civilian causalities, and (iii) con ict induced displacement. It is important to note that the Con ict Severity Index is based on OCHA data from 2016-2018 and does not include important data since the Taliban's takeover in August 2021, which added to the already dire refugee crisis, with Herat province particularly impacted, hosting 250,000 IDPs recently. Methodology To construct the SoVI, each indicator as listed above is normalized to ensure comparability by converting all indicator values to a common scale from 0 to 1, where 0 represents the least vulnerable score and 1 represents the most vulnerable score. Next, weights are assigned to each indicator to re ect their relative importance. The weight matrix consists of three scenarios: - Scenario 1: equal weights for all indicators - Scenario 2: weights variation with more emphasis on gender and rural population, and - Scenario 3: weights variation based on more emphasis on agriculture/development SoVI is calculated using the following expression: Where, represent each of the social vulnerability indicators, and the respective weight for the respective of the social vulnerability indicators, according to each of the three weight scenarios. The table below shows the weights used for the 7 indicators for each of the three scenarios: Women- Lack of Lack of Lack of Lack of Conict Percent Severity headed Market Health Houesholds Rural Access Access Livestock Development Index Scenario 1 0.143 0.143 0.143 0.143 0.143 0.143 0.143 Scenario 2 0.200 0.200 0.100 0.100 0.100 0.100 0.200 Scenario 3 0.100 0.100 0.100 0.100 0.200 0.200 0.200 25 For each district, the SoVI is calculated by multiplying the normalized value of each indicator by its assigned weight and summing the results. This is done for each weighting scenario, resulting in three different SoVI values per district, shown below: Woman Lack of Lack of Lack of Conict SoVI SoVI SoVI Percent Market Health Lack of -headed households Rural Access Access Livestock Develop Severity -ment Index Scenario Scenario 2 Scenario 3 As a nal step, each district is ranked based on its index value for each of the three weight scenarios. The change in ranking is determined by comparing the ranks across the three weight scenarios. A greater rank change indicates higher sensitivity to the weight assignment based on the three scenarios used. The highest rank in each of the scenarios, indicates highest vulnerability and the lowest rank (16) is the lowest vulnerability. Change District RANK 1 RANK 2 RANK 3 in Ranking Gulran 8 5 11 5 Adraskan 3 3 4 0 Kushk-e-Kuhna 1 1 2 1 Ghoryan 13 12 15 6 Obi 10 10 12 1 Kohsan 11 14 9 5 Shindand 4 4 3 0 Farsi 2 2 1 1 Chisht-e-Sharef 5 6 7 2 The SoVI depicted in Figure 2 of the GRADE report is based on the weighting scheme of Scenario 3. This scheme was selected after careful consideration of its relevance and impact on the overall index. 26 Limitations and future development for the Social Vulnerability Index (SoVI) Utilization of Factor Analysis: In a future development of a SoVI, it is recommended to employ factor analysis. The use of factor analysis is a robust method to help in understanding the latent structures behind the observed variables and in identifying the most signi cant indicators contributing to social vulnerability. Incorporation of Additional Data and Stakeholder Input: The current iteration of the SoVI would bene t from the inclusion of more comprehensive data. Speci cally: - Ÿ Children under 14: The inclusion of metrics related to children can provide a deeper understanding of the vulnerability of this demographic, which is crucial for comprehensive social vulnerability assessment. Ÿ - Ÿ Women-headed Households: Complete data regarding women-headed households at the district level, rather than using extreme values and average as used in the current methodology. A request to UN- OCHA for the complete Women-headed Household data for the Herat province districts has been made. Ÿ - Ÿ Other Indicators and Weights: Based on the ndings of the Post-Disaster Needs Assessment (PDNA), it may be necessary to adjust the indicators and their corresponding weights. This iterative process will re ne the SoVI, making it more representative of the on-ground realities. It is important to note that the current SoVI is an initial indicative index. While it provides valuable insights, it is expected to evolve as more data becomes available and as methodologies are re ned. The iterative process will ensure that the index remains relevant and continues to accurately re ect the social vulnerability of the populations being assessed. Engagement with stakeholders, including PDNA team, ITA entities, and aid organizations, will be crucial in understanding the local context and deriving representative weights based on expert input. This input will provide the ground truth necessary to validate and enhance the SoVI. 27 Annex B: Data Sources The main data-sources used in this GRADE Assessment are summarized below. Hazard: Ÿ Parameterless Seismic Intensity (PSI) method (Spence et al., 1991) and European macroseismic intensity scale (EMS) (Grünthal 1998). Ÿ Seismic Hazard Asssment of Afghanistan (Waseem et al., 2019). Ÿ USGS ShakeMaps, European-Mediterranean Seismological Centre (EMSC) ShakeMaps, Erdbebennews data on intensities, and evaluation of Shakemap results Ÿ Fault Rupture data from Sentinel-2, Sentinel-1, InSAR. Ÿ CATDAT (historical info) Ÿ EMSC data Exposure: Ÿ ITA NSIA, Estimated Population of Afghanistan (2017-18, 2018-19, 2019-20, 2020-21 and 2021-22). Ÿ Socio Demographic and Economic surveys, Integrated Business Enterprise Surveys and other Statistical datasets in Afghanistan including Provincial GDP, and checks against CATDAT data, investment data, building types, religious structures from CSO. 2019 District Data Dashboard Ÿ ITA ministry websites and social media reporting Ÿ USAID reports on telecoms, internet; informal economies and roads Ÿ Livestock data from Global Products, FAO Agriculture Exposure in terms of food production. Ÿ Afghanistan Settlement Layers – World Food Programme (WFP). Ÿ CSO Afghanistan and various other descriptions of Afghanistan architecture (Szabo et al., 1991). Ÿ FAO Reports on the Cold Wave of 2022-23 in Afghanistan; and the 2022 Paktika Earthquake effects on Livestock and Agriculture. Ÿ Google Buildings Dataset Ÿ Infrastructure: OSM data from 2023, including HOTOSM updates, for roads, bridges, and other elements, augmented via various ITA datasets, Geonodes and annual yearbook statistics Ÿ Population and Building Footprints: GHSL 2015 250m Adjusted to present day using multiple Census datasets, GHS-BUILT V, BUILT S, BUILT C MSZ at 100m and 10m (GHS BUILT Products (Volume, Surface, Population, Height, building characterization) Ÿ WSF3D at 90m from DLR. Ÿ Capital Stock Modelling (Daniell; GAR 2015; IMF WEO; World Bank) Ÿ Global Program for Safer Schools study, Planopolis, ITA data (incl. health statistics and other energy statistics) Ÿ Meta Relative Wealth Index, HRSL, WorldPop for population checks Ÿ Grid nder Power data Ÿ UN OCHA administrative boundaries and other World Bank boundaries 28 Vulnerability/Risk: Ÿ GFDRR Risk Pro le 2015-16 on Afghanistan and the Disaster Risk Geonode Ÿ Demographic Health Survey data Ÿ GRADE Afghanistan in 2022 Ÿ Social Media data (Twitter, Telegram, Facebook sites) Ÿ ReliefWeb and news reports from various NGOs and other entities on the ground. Ÿ Virtual On-Site Operations Coordination Centre, Humanitarian Response Data Ÿ UN OCHA and WHO Situation Reports. Ÿ UNOSAT Satellite Derived Damage Assessments. Ÿ COPERNICUS Satellite Derived Damage Assessment (EMSR-700) Ÿ Malan (2010) also provided signi cant input into local typologies and SeisVARA (Haldar et al. (2013) including additional Empirical vulnerability functions, semi-analytical fragility functions, Moroccan risk studies via the MHRA study. Ÿ Historical event data (DaLA, PDNA, CATDAT) from many past Afghanistan earthquakes Ÿ Global Seismic Code Index and Building Practice Factor Ÿ Miyamoto International Ground Surveys 29 Annex C: Afghanistan Exposure Model Figure 9: Spatial distribution of baseline exposure dataset used in this GRADE analysis (updated from GFDRR Country Risk Prole exposure, derived by Deltares and World Bank analyses). 30 Annex D: Historical Earthquakes in Afghanistan A catalogue of historical earthquakes that are known to have caused loss of life in Afghanistan is shown in the Table below (Daniell et al., 2018), for the period 1998-2023. The time of occurrence is Greenwich Mean Time (GMT) and the number of fatalities per event is a best estimate from many different academic publication sources. Considering a longer time period from 1900 onwards, this suggests over 81% of this loss of life occurring in the last 25 years (1998-2023). The earthquakes of February and May 1998 in the provinces of Takhar and Badakhshan were responsible for the loss of more than 7,000 lives. Table 7: List of fatal earthquakes in Afghanistan for the period 1998 to October 2023. Events causing more than 100 fatalities are highlighted. Using the CATDAT Damaging Earthquakes Database, an important secondary effect of earthquakes are landslides, which are related to magnitude of the shaking, prior rainfall, topography, and other factors. Some events have signi cant landslide-related death tolls, whereas others have no landslide deaths. A total of approximately 2,700 landslide deaths versus around 10,400 shaking deaths have occurred from earthquakes since 1950 in Afghanistan, showing the impact of landslides. 31