Daily night-time lights reveal prolonging global electric power system recovery times following tropical cyclone damage

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However, systematically collected data on power supply disruptions are available for only a limited number of countries, leaving global patterns largely unexplored. In this study, we conducted the first global analysis of electric power system recovery times after 407 storms across 65 countries from 2012 to 2021, using satellite-based daily nighttime lights (NTL) observations to detect blackouts following storms. The median duration blackouts detected worldwide was 3 days, with a 5th-95th percentile range of 1 to 11 days. We found that high density urban areas had significant (P < 0.05) longer blackout events than low density urban areas and rural areas, which was driven by an upper tail of the events (95 quantiles of, respectively, 16, 12, and 11 days). Blackout durations have significantly increased over the study period (P < 0.05), with the rate of increase in rural areas (1.7 day/decade) nearly double that observed in urban areas (1 day/decade). The temporal variations in blackout duration in rural areas were significantly correlated with both storm attributes and pre-storm NTL brightness, whereas those in low-density urban areas were only correlated with storm attributes (P < 0.05 in all cases). These findings highlight the pressing need to strengthen the resilience of electric power systems to storms, particularly as global reliance on electricity grows and storm activity patterns shift in response to climate change. tropical cyclone power outage night-time light resilience remote sensing Figures Figure 1 Figure 2 Figure 3 Introduction The resilience of a system incorporates its capacity to resist, cope with and recover from external disturbances [ 1 ]. There has been extensive analysis of the reliability of electric power systems, i.e. the annual probability of failure of supply to customers in different locations [ 2 , 3 ]. The duration of power failure, or in other words the recovery time following failure, is also of great importance. Studies indicate that the economic consequences of power failure increase non-linearly with the duration of failure [ 4 ]. Whilst people and businesses can cope with short failures, or use back-ups for safety-critical systems, prolonged failures are more impactful and can lead to permanent damage, e.g. due to cold-chain breakdowns. Accordingly, power system regulators often set targets network operation targets for the duration of electricity supply disruptions. For example, in the UK the energy regulator Ofgem stipulates that electricity companies have a 12 hour time limit for supply restoration in normal weather; this is extended to 24 hours for severe weather conditions, or incidents affecting more than 5,000 customers; and it is further extended to 48 hours for very severe incidents and those affecting more than 13,000 customers [ 5 ]. Notwithstanding the existence of targets like these, national inter-comparisons of the resilience of electric power systems are vague [ 6 ] and are difficult to compare because of different reporting standards. In most countries worldwide there is no systematic reporting of the duration of power failures. Whilst simulation modelling of power systems can provide useful insights into system reliability [ 7 , 8 ] and even the feasibility of system ‘black starts’ [ 9 ], understanding the process of system repair and recovery remains essentially an empirical problem requiring observations of actual failures. System recovery usually involves mobilizing teams of engineers to repair or replace equipment and then restarting systems in a critical order. Some interesting in-depth analysis of infrastructure system recovery following major disturbance exist, notably following the Christchurch earthquake in New Zealand in 2010-11 [ 10 , 11 ]. These have elucidated the challenges facing utility operators to repair or replace damaged assets, and have provided important evidence for subsequent system resilience studies and simulations [ 12 ]. However, the scope for this type of forensic post-event reconstruction is inevitably limited. For large-scale cross-country comparisons, a different approach is required. Satellite-based nighttime lights (NTL) observations have become an invaluable tool for analyzing a wide range of socio-economic phenomena, including mapping urbanization processes, monitoring economic growth, and estimating impacts of nature and human-caused disasters (see Levin et al., 2020 [ 13 ] for a detailed review). Long-term trends in NTL have been routinely applied for global developmental products such as the European Commission’s Global Human Settlement Layer (GHSL)[ 14 ] and the World Bank’s Night Light Development Index [ 15 ]. More recently, the daily NTL product, i.e., the Black Marble (VNP46A2), has demonstrated its utility in assessing the impacts from short-term disasters events including tropical cyclones (TCs). For example, Montoya-Rincon et al. (2022) examined the severity of power loss due to Hurricanes Maria and Irma in Puerto Rico with blackout in NTL[ 16 ]. Chakraborty and Stokes (2023) examined multiple blackout evets in in Mozambique (following Cyclone Idai) and Puerto Rico (following Hurricane Maria) with an adaptable (to different reginal conditions) modelling approach [ 17 ]. Given that TCs are a major cause for large blackout events—causing nine out of ten of the largest blackouts in the USA and three of the largest blackouts ever [ 18 ]—the analysis of NTL blackouts after storms offers a promising tool to provide independent, open, and publicly available evaluation [ 19 ] for power system resilience on a global scale. Here, we integrated satellite-based daily NTL observation with spatially explicit historical storm records to measure blackout events worldwide from 2012 to 2022. Furthermore, we assessed whether blackout durations varied by levels of urbanization and examined how these durations evolved over the study period. Our analysis establishes the first consistent empirical record of electric power network recovery times following storms at a multi-country scale, including regions where such data were previously unavailable. This provides critical insights into power network resilience and supports prioritizing efforts to enhance emergency repair and recovery capacity. Method Storm records and socio-economic datasets Historical storms that occurred worldwide from 2012–2022 were identified from the International Best Track Archive for Climate Stewardship (IBTrACS [ 20 ]; Table S1). A factor of 0.88 was used to convert wind speeds measured in 1 minute or 3-minute averaging periods to the World Meteorological Organization standard 10-minute average [ 21 ]. Urban clusters were identified based on the GHSL [ 22 ], which were classified into three groups: High Density Clusters (HDC, cities), Low Density Clusters (LDC, towns and cities), and Rural Cluster (RUR). The income levels of counties were defined based on the World Bank Classification [ 23 ]. NTL blackout detection The NTL blackout was estimated using NASA’s Black Marble global daily nocturnal visible light data (500m; VNP46A2 [ 24 ]). For each storm, an area of interest was defined as a curved strip centering on the storm track and encompassing areas up to 200 km away from the track (400 km wide in total). All urban clusters within the strip were identified based on the GHSL data. The NTL blackout analysis was performed based on the (spatial) mean NTL brightness of each cluster, and only high-quality measurements with more than half of the pixels of a cluster showing persistent nighttime light (as defined by the quality layer of the Black Marble data) were used in the subsequent analysis. The blackout was detected with a dynamic baseline approach: for each cluster, NTL brightness was measured from 3 months before the storm to one month after, with the NTL brightness measured 3 months prior to the storm defining the baseline conditions. NTL blackouts were defined as measurements significantly lower than the baseline values (one-tailed test at P = 0.05). The blackout duration was computed as the time (in days) from the storm passage until two consecutive days of (back to) baseline conditions were observed. For computation efficiency, the analysis is performed at a 5 km resolution. There are two major sources of uncertainty that came with the Black Marble data: the geometric error, which resulted from the spatial observational coverage mismatch of the sensor, and the angular error, which stemmed from variation in the view angle of the satellite [ 25 ]. In this study, by working with the spatial average of NTL over a cluster we can reduce the geometric error [ 26 , 27 ]. The 3-month timeframe for the baseline provides a way to account for the periodic changes in view angle of the satellite [ 28 ] which is one of the main sources of the angular error. These baselines also contribute to account for some other noise in the data due to, for example, weekly and seasonal activities [ 25 , 29 ]. Another challenge in utilizing daily NTL data is the large amount of missing data caused by cloud coverage [ 30 ]. To mitigate this, we used a "next-available" gap-filling approach, whereby missing data for a specific day was filled by the next available high-quality measurement from subsequent days. If the next available data point is a blackout point, the missing data point is defined as part of the blackout. If the next available data point is a normal point, half of the duration of the missing data period is considered blackouts, and the period with missing data is designated as an uncertainty interval due to missing data. Given the relatively low signal-to-noise ratio and frequent cloud coverage, our analysis here is inherently conservative. Nevertheless, it remains an effective method for large-scale and long-term analyses. Validation with POUS data The accuracy of our NTL blackout measurement was assessed by comparison with PowerOutage.US data (POUS) [ 31 ]. POUS is utility records of power cuts in the USA from 2017–2022. Here we processed the POUS data using a similar method to Shah (2023) [ 32 ] who demonstrated that NTL observations and POUS records were well correlated in the 2021 Texas winter storm Uri, though they did not specifically examine the duration of the blackouts. The POUS data provides the number of customers tracked, \(\:{p}_{track}\) and the number reported as disconnected, \(\:{p}_{disc}\) , at the company level and an hourly time step. We aggregated the records into county level and discarded data for counties with less than 5% of the total county population tracked. The electricity supply fraction was computed as: \(\:{f}_{sup}=1-\left(\frac{{p}_{disc}}{{p}_{track}}\right)\) . An outage event was defined with a when the \(\:{f}_{sup}\) drops below 0.95, and is considered finished at the point when the supply has climbed above 0.95 for least 48 hours. Given the daily temporal resolution of NTL data, we only focused on POUS outages of 1 day or longer. The NTL blackouts were aggregated (from the urban cluster level) to the county level as spatial means to algin with the POUS outage records. In total, there were 134 events (storm-county pairs) overlapping between the POUS and NTL data, which are associated with 19 storms between 2017–2021 (Fig. 2 a). Both the POUS outage and NTL blackout data followed the Beta distribution (with, respectively, α = 0.93, β = 170, and Kolmogorov–Smirnov test P = 0.07; and α = 2, β = 160, and P = 0.49). Their relationship was evaluated using a linear regression with log transformation and weighted by the number of customers tracked within the county in the POUS data, yielding an r 2 = 0.75 (Fig. 3 ). This is a strong relationship for NTL-derived phenomena, building some confidence in our method. The lower bias for blackout estimated from the NTL data is likely to reflect the conservative test we used for detecting blackouts in NTL. Other potential explanations for the residual discrepancy include the possibility that lights are not switched on immediately even if power has been restored due to, for example, people have been evacuated and are unable to return home. On the other hand, POUS records do not include buildings with emergency power supplies (e.g. from private backup generators or off-grid dwellings) that could be visible in the NTL data. Nevertheless, our results suggest that NTL can be a powerful tool for measuring blackout duration at a large scale, particularly for regions lacking reliable utility data. Temporal trend analysis As the Black Marble record for 2022 was incomplete at the time of analysis, temporal trends were examined using data from 2012–2021. Blackout frequencies were analyzed using a General Linear Model (GLM) with a Poisson distribution, appropriate for count data. Blackout durations were analyzed using an Analysis of Covariance (ANCOVA), with urban level (categorized as HDC, LDC, and RUR) as a categorical factor and year as a covariate. To investigate the drivers of temporal trends, we extracted the maximum wind speed, storm forward speed, and pre-storm NTL values for each blackout event. Annual means of these predictors and blackout durations were computed, and correlations among the series of temporal variations were examined to identify potential relationships between the variables. Results During our study period, over 22,000 NTL blackout events were detected from 407 storms hitting 65 countries (Fig. 1 a & Table S2). The number of blackout-associated storms experienced by each country varied, with China, Japan, Mexico, and the USA experiencing the highest frequency, each with over 40 storms. Across all countries, the blackout detected had a median duration of 3 days with a 25–75 percentile range of 2 to 5 days and a 5–95 percentile range of 1 to 11 days (Fig. 3 and Table S3). We find that HDC tends to have slightly but significant longer blackouts compared to LDC and RUR, which was driven by an upper tail of blackouts (95 quantiles of, respectively, 16, 12, and 11 days). We explain this by the density of electricity assets in urban areas: when tropical cyclones impact these regions, multiple assets were damage, leading to longer repair times—particularly for the most severe 5% of events. Between 2012 and 2021, the number of blackouts detected showed a mild but significant increase (Fig. 5a). We also found that the number of storms with blackouts detected have significantly increased, whereas the total number of storms recorded in IBTrACS did not change significantly ( P = 0.04 and P = 0.38, respectively; Table S3). This indicates that the rise in blackout frequency is more likely to reflect changes in storm attributes and power system’s vulnerability, rather than an increase in storm occurrence. The blackout durations also significantly increased over the study period, with rates varied among urbanization levels (Fig. 5b; Table S3). The increase was faster for RUR, at 1.7 ± 0.1 day/decade, than HDC and LDC, at 1.0 ± 0.2 day/ decade. The temporal variations in blackout durations for LDC and RUR were significantly negatively correlated with the temporal variations in TC forward speed (P < 0.05 in both cases; Table 1 ). The temporal variations in blackout duration in RUR also positively correlated with storm maximum wind speed and pre-storm NTL brightness (P < 0.05 in both cases). These findings suggest that changes in storm attributes, particularly reduced forward speed, likely played an important role in prolonging blackouts during the study period. Additionally, increased electricity assets in rural areas (as inferred by pre-storm NTL brightness) may also contribute to the prolonging blackout durations in these areas. Similar analysis was also performed at the country level, but significant trends were only detected in limited countries (Table S4-S7). This is likely due to the smaller sample sizes available for each country. Table 1. Spearman correlation coefficients (r) between the temporal variations in blackout durations for High Density Clusters (HDC), Low Density Clusters (LDC), and Rural (RUR) and the temporal variations of storm wind maximum wind speed, storm forward velocity, and pre-storm nighttime light brightness (NTL). Correlations with P values smaller than 0.05 are highlighted in bold. Negative correlations with P < 0.1 were highlighted in blue, and positive correlations in orange. Discussion and conclusion Using NTL data, we reported the median power outage duration following a TC across 65 countries is 3 days. Previous studies for storm-induced power outage have been primarily focus on individual events [ 16 , 17 , 19 , 33 – 36 ], while broader analyses considering multiple causes have predominantly cantered on North America, particularly the USA [ 37 ]. Our analysis provides data that have hitherto been unavailable on multiple countries around the world. Compared to previous studies that measured the frequency of outage events (e.g., number of events longer than 1 and 8 hours [ 37 ]), our study provides a different measure of resilience by explicitly studying event duration, which could more directly link to economic impacts (as opposed to the direct asset damage) of power failure [ 38 , 39 ]. Risk analysis, including climate risk analysis of the impacts of extreme events, crucially requires information on the likely duration of blackouts and speed of recovery. Our analysis should also provide a stimulus for improved modelling of the losses associated with electric power supply failures, for example using agent-based models of the recovery process [ 40 ], which need to be calibrated against observed recovery times. The number of storms causing power sector disruptions and the duration of blackouts has increased significantly in the period 2012 to 2021. This observation aligns with reports of more frequent and prolonged outages in the USA for the period 2000–2021, 83% of which is associated with extreme weather [ 41 ]. Similar data have not hitherto been available for many other countries, so here we have demonstrated that this increasing occurrence and duration of disruption is observable across countries and habitation densities. This provides additional evidence about the increasing scale of weather-related disruptions to power networks. Moreover, negative correlations were found between the temporal trends of NTL blackout duration and storm forward speed for all urban levels with P < 0.1. During storms, power outages are commonly caused by tree damage to networks and flooding [ 42 ]. Slower-moving storms may result in prolonged exposure to wind hazards and heavy rainfall, leading to more destructive hazards as well as delays in repair efforts. These results underscore the potential risks posed by changes in storm characteristics, in addition to the increase in storm frequency [ 43 ], on infrastructure resilience. During the period 2012 to 2021, 1.1 billion million more people were connected to electricity supplies globally[ 44 ] and global electricity consumption increased from 20.0TWh/year to 25.9TWh/year[ 45 ]. This represents significant progress in achieving SDG7.1, and specifically Indicator 7.1.1 which measures the proportion of population with access to electricity. The overall increase in electricity supply reflects increasing demand for electricity services, driven in part by increasing electrification and a growing proportion of renewable energy (SDG7.2). These trends reflect growing dependence on electric power systems. However, these systems are vulnerable to a variety of forms of damage and disruption, including storms which cause the most severe acute damage to power supplies. Our analysis revealed that the increase in blackout duration was faster in rural areas compared to urban areas, with its temporal variations significantly correlated with increases in pre-storm NTL brightness. This may indicate a lag between new development and the corresponding resilience planning required to mitigate these risks. It is important to note that, by using the daily NTL data, our analysis is not likely to detect blackouts shorter than one day (or one evening). The cloud coverage of the NTL data and our conservative test are also likely to contribute to missing shorter events. Therefore, this study should not be considered an extensive survey of storm-induced blackouts globally, but rather an effective method for examining regional and temporal variations of large events. The omission of shorter events are likely results in an underestimation of the total number of blackouts and an overestimation of the average blackout duration. Future studies could incorporate more sophisticated corrections for uncertainties in Black Marble data to enhance detection power[ 46 ], which may enable the detection for more short-term events—albeit still measured in days—as well as significant trends at the country level. Nighttime light blackouts following storms can arise from various complex factors, such as intentional power cuts to prevent cascading failures[ 47 ], which might overestimate power network recovery times. Such events would require utility records for a more detailed study. While this study's validation relied on POUS records of power outages in the USA, future efforts should explore validation using other national and regional ground-based electricity outage records. Despite these limitations, our study has demonstrated a way of using satellite-based measurements that offer global and consistent coverage, making it possible to assess blackouts in remote, isolated areas that are otherwise hard to monitor with utility records, as well as their temporal changes. This broad scope is a distinct advantage of satellite data. Future research could extend this approach to other extreme weather events, such as heavy precipitation [ 48 ], anomalous heat [ 37 ], and compound events [ 49 ]. Additionally, a more compressive examination of the socio-economic status of affected areas could shed light on potential disparities in vulnerability across different population groups [ 32 , 37 , 50 ]. Declarations Acknowledgements This paper has been written with the support of the Climate Compatible Growth Programme of the UK's Foreign, Commonwealth and Development Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies. The work was also partially funded by the UK Centre for Greening Finance and Investment under grant NE/V017756/1. This work was also funded by the Irish Research Council under grant number GOIPD/2022/519 to YM. References United Nations Office for Disaster Risk Reduction (2016) Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. United Nations General Assembly, New York, NY, USA Kovalev GF, Lebedeva LM (2019) Reliability of power systems, vol 1. Springer Billinton R, Allan RN (2003) Reliability of electric power systems: An overview. 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Nat Energy 9(5):526–535 Moftakhari H, AghaKouchak A (2019) Increasing exposure of energy infrastructure to compound hazards: cascading wildfires and extreme rainfall. Environ Res Lett, 14(10) Feng K, Ouyang M, Lin N (2022) Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate. Nat Commun 13(1):4421 Dugan J, Byles D, Mohagheghi S (2023) Social vulnerability to long-duration power outages. Int J Disaster Risk Reduct, 85 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6030545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415832106,"identity":"7f33c843-767b-49c4-9f16-4c42cfe9cf0d","order_by":0,"name":"Yu Mo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYNCCCoYEBgk4j40YLWdI1sLYRooWgxsJrBs+zrPL45/dwPiYh8FOnkEiLYGQFrabM7clF0vcOcBsOIMh2bBBIu0AQS23ebcdSNwgkcAm8YGBGejC9AYitMyBaklgqCdWSwPclsNALQQcJnnmYdvNGceSE2fcSGw2nGFw3LCN51kCXi18x5OP3fhQY5fYPyP54GOeimp5fvY0A7xaFA4wwlwOYhgQEZHyDYRUjIJRMApGwSgAACGgRA82IaJPAAAAAElFTkSuQmCC","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Mo","suffix":""},{"id":415832107,"identity":"bb9e2b0f-194d-4b2f-ac76-4368b80960bc","order_by":1,"name":"Fred Thomas","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Fred","middleName":"","lastName":"Thomas","suffix":""},{"id":415832108,"identity":"9bca45c7-3c1f-444b-9606-dd41d3dc3c67","order_by":2,"name":"Jianan Rui","email":"","orcid":"","institution":"University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Jianan","middleName":"","lastName":"Rui","suffix":""},{"id":415832109,"identity":"90af6c69-dc1b-4c15-9d03-7a64d0c22322","order_by":3,"name":"Jim W Hall","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Jim","middleName":"W","lastName":"Hall","suffix":""}],"badges":[],"createdAt":"2025-02-14 12:21:47","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6030545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6030545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76581264,"identity":"dcc5843f-bd88-4a6a-bccb-eb0caf2c5978","added_by":"auto","created_at":"2025-02-18 15:04:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":461396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTropical cyclone tracks for the period 2012–2022 and count of blackout-associated storms in each country. A full list of the countries and associated storm count is shown in Table S2\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6030545/v1/0330326365183d0d390153ff.png"},{"id":76581263,"identity":"d55d1db7-9dc0-4899-988a-c2af9134d292","added_by":"auto","created_at":"2025-02-18 15:04:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154674,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of NTL blackouts with POUS power outage data. (a) Storm tracks that caused blackouts in the USA (2017-2021) with events detected with both NTL and POUS data. (b) Regression of the durations of electricity outage estimated from the NTL and POUS data for counties that experienced power outages in the USA between 2017-2021. Note that the axes are on a log10 scale. The area of the circles is proportional to the number of customers tracked in the POUS data. The grey lines indicate the uncertain caused in missing NTL data.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6030545/v1/1a3810c505c6179a37d63f6c.png"},{"id":76581262,"identity":"2b940fe6-eb8f-4b2c-b053-5529a841166f","added_by":"auto","created_at":"2025-02-18 15:04:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59195,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of durations of NTL blackouts across all countries. The black rugs indicate 25, 50, and 75 percentiles. The grey rugs indicate 5 and 95 percentiles. The boxes on the top show the blackout duration for high density urban clusters (HDC), low density clusters (LDC) and rural areas (RUR). The dots, boxes, and error bars indicate median, 25–75, and 5–95 percentiles, respectively. Lower case letters next to the boxes indicate significant differences based on an analysis of variance analysis (P \u0026lt; 0.05; see Table S3).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6030545/v1/ad9d64bc3690ccaa895c4223.png"},{"id":76584315,"identity":"6ae8992b-484d-43f2-9066-5aa2fd1a61ef","added_by":"auto","created_at":"2025-02-18 15:28:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1068149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6030545/v1/1e820fb7-a7f7-4d19-8911-0da5600cf7e6.pdf"},{"id":76581268,"identity":"30eb8a52-358a-4cdb-a7a2-a4c31e7b832c","added_by":"auto","created_at":"2025-02-18 15:04:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":88228,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6030545/v1/d901558c73370904944ab8b8.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDaily night-time lights reveal prolonging global electric power system recovery times following tropical cyclone damage\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe resilience of a system incorporates its capacity to resist, cope with and recover from external disturbances [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There has been extensive analysis of the reliability of electric power systems, i.e. the annual probability of failure of supply to customers in different locations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The duration of power failure, or in other words the recovery time following failure, is also of great importance. Studies indicate that the economic consequences of power failure increase non-linearly with the duration of failure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Whilst people and businesses can cope with short failures, or use back-ups for safety-critical systems, prolonged failures are more impactful and can lead to permanent damage, e.g. due to cold-chain breakdowns. Accordingly, power system regulators often set targets network operation targets for the duration of electricity supply disruptions. For example, in the UK the energy regulator Ofgem stipulates that electricity companies have a 12 hour time limit for supply restoration in normal weather; this is extended to 24 hours for severe weather conditions, or incidents affecting more than 5,000 customers; and it is further extended to 48 hours for very severe incidents and those affecting more than 13,000 customers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notwithstanding the existence of targets like these, national inter-comparisons of the resilience of electric power systems are vague [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and are difficult to compare because of different reporting standards. In most countries worldwide there is no systematic reporting of the duration of power failures.\u003c/p\u003e \u003cp\u003eWhilst simulation modelling of power systems can provide useful insights into system reliability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and even the feasibility of system \u0026lsquo;black starts\u0026rsquo; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], understanding the process of system repair and recovery remains essentially an empirical problem requiring observations of actual failures. System recovery usually involves mobilizing teams of engineers to repair or replace equipment and then restarting systems in a critical order. Some interesting in-depth analysis of infrastructure system recovery following major disturbance exist, notably following the Christchurch earthquake in New Zealand in 2010-11 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These have elucidated the challenges facing utility operators to repair or replace damaged assets, and have provided important evidence for subsequent system resilience studies and simulations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the scope for this type of forensic post-event reconstruction is inevitably limited. For large-scale cross-country comparisons, a different approach is required.\u003c/p\u003e \u003cp\u003eSatellite-based nighttime lights (NTL) observations have become an invaluable tool for analyzing a wide range of socio-economic phenomena, including mapping urbanization processes, monitoring economic growth, and estimating impacts of nature and human-caused disasters (see Levin et al., 2020 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] for a detailed review). Long-term trends in NTL have been routinely applied for global developmental products such as the European Commission\u0026rsquo;s Global Human Settlement Layer (GHSL)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and the World Bank\u0026rsquo;s Night Light Development Index [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. More recently, the daily NTL product, i.e., the Black Marble (VNP46A2), has demonstrated its utility in assessing the impacts from short-term disasters events including tropical cyclones (TCs). For example, Montoya-Rincon et al. (2022) examined the severity of power loss due to Hurricanes Maria and Irma in Puerto Rico with blackout in NTL[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Chakraborty and Stokes (2023) examined multiple blackout evets in in Mozambique (following Cyclone Idai) and Puerto Rico (following Hurricane Maria) with an adaptable (to different reginal conditions) modelling approach [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given that TCs are a major cause for large blackout events\u0026mdash;causing nine out of ten of the largest blackouts in the USA and three of the largest blackouts ever [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u0026mdash;the analysis of NTL blackouts after storms offers a promising tool to provide independent, open, and publicly available evaluation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] for power system resilience on a global scale.\u003c/p\u003e \u003cp\u003eHere, we integrated satellite-based daily NTL observation with spatially explicit historical storm records to measure blackout events worldwide from 2012 to 2022. Furthermore, we assessed whether blackout durations varied by levels of urbanization and examined how these durations evolved over the study period. Our analysis establishes the first consistent empirical record of electric power network recovery times following storms at a multi-country scale, including regions where such data were previously unavailable. This provides critical insights into power network resilience and supports prioritizing efforts to enhance emergency repair and recovery capacity.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStorm records and socio-economic datasets\u003c/h2\u003e \u003cp\u003eHistorical storms that occurred worldwide from 2012\u0026ndash;2022 were identified from the International Best Track Archive for Climate Stewardship (IBTrACS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; Table S1). A factor of 0.88 was used to convert wind speeds measured in 1 minute or 3-minute averaging periods to the World Meteorological Organization standard 10-minute average [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Urban clusters were identified based on the GHSL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which were classified into three groups: High Density Clusters (HDC, cities), Low Density Clusters (LDC, towns and cities), and Rural Cluster (RUR). The income levels of counties were defined based on the World Bank Classification [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNTL blackout detection\u003c/h3\u003e\n\u003cp\u003eThe NTL blackout was estimated using NASA\u0026rsquo;s Black Marble global daily nocturnal visible light data (500m; VNP46A2 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]). For each storm, an area of interest was defined as a curved strip centering on the storm track and encompassing areas up to 200 km away from the track (400 km wide in total). All urban clusters within the strip were identified based on the GHSL data. The NTL blackout analysis was performed based on the (spatial) mean NTL brightness of each cluster, and only high-quality measurements with more than half of the pixels of a cluster showing persistent nighttime light (as defined by the quality layer of the Black Marble data) were used in the subsequent analysis. The blackout was detected with a dynamic baseline approach: for each cluster, NTL brightness was measured from 3 months before the storm to one month after, with the NTL brightness measured 3 months prior to the storm defining the baseline conditions. NTL blackouts were defined as measurements significantly lower than the baseline values (one-tailed test at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). The blackout duration was computed as the time (in days) from the storm passage until two consecutive days of (back to) baseline conditions were observed. For computation efficiency, the analysis is performed at a 5 km resolution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere are two major sources of uncertainty that came with the Black Marble data: the geometric error, which resulted from the spatial observational coverage mismatch of the sensor, and the angular error, which stemmed from variation in the view angle of the satellite [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, by working with the spatial average of NTL over a cluster we can reduce the geometric error [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The 3-month timeframe for the baseline provides a way to account for the periodic changes in view angle of the satellite [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] which is one of the main sources of the angular error. These baselines also contribute to account for some other noise in the data due to, for example, weekly and seasonal activities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Another challenge in utilizing daily NTL data is the large amount of missing data caused by cloud coverage [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To mitigate this, we used a \"next-available\" gap-filling approach, whereby missing data for a specific day was filled by the next available high-quality measurement from subsequent days. If the next available data point is a blackout point, the missing data point is defined as part of the blackout. If the next available data point is a normal point, half of the duration of the missing data period is considered blackouts, and the period with missing data is designated as an uncertainty interval due to missing data. Given the relatively low signal-to-noise ratio and frequent cloud coverage, our analysis here is inherently conservative. Nevertheless, it remains an effective method for large-scale and long-term analyses.\u003c/p\u003e\n\u003ch3\u003eValidation with POUS data\u003c/h3\u003e\n\u003cp\u003eThe accuracy of our NTL blackout measurement was assessed by comparison with PowerOutage.US data (POUS) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. POUS is utility records of power cuts in the USA from 2017\u0026ndash;2022. Here we processed the POUS data using a similar method to Shah (2023) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] who demonstrated that NTL observations and POUS records were well correlated in the 2021 Texas winter storm Uri, though they did not specifically examine the duration of the blackouts. The POUS data provides the number of customers tracked, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{track}\\)\u003c/span\u003e\u003c/span\u003e and the number reported as disconnected, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{disc}\\)\u003c/span\u003e\u003c/span\u003e, at the company level and an hourly time step. We aggregated the records into county level and discarded data for counties with less than 5% of the total county population tracked. The electricity supply fraction was computed as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{sup}=1-\\left(\\frac{{p}_{disc}}{{p}_{track}}\\right)\\)\u003c/span\u003e\u003c/span\u003e. An outage event was defined with a when the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{sup}\\)\u003c/span\u003e\u003c/span\u003e drops below 0.95, and is considered finished at the point when the supply has climbed above 0.95 for least 48 hours. Given the daily temporal resolution of NTL data, we only focused on POUS outages of 1 day or longer. The NTL blackouts were aggregated (from the urban cluster level) to the county level as spatial means to algin with the POUS outage records.\u003c/p\u003e \u003cp\u003eIn total, there were 134 events (storm-county pairs) overlapping between the POUS and NTL data, which are associated with 19 storms between 2017\u0026ndash;2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Both the POUS outage and NTL blackout data followed the Beta distribution (with, respectively, \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;170, and Kolmogorov\u0026ndash;Smirnov test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07; and \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;160, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49). Their relationship was evaluated using a linear regression with log transformation and weighted by the number of customers tracked within the county in the POUS data, yielding an \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This is a strong relationship for NTL-derived phenomena, building some confidence in our method. The lower bias for blackout estimated from the NTL data is likely to reflect the conservative test we used for detecting blackouts in NTL. Other potential explanations for the residual discrepancy include the possibility that lights are not switched on immediately even if power has been restored due to, for example, people have been evacuated and are unable to return home. On the other hand, POUS records do not include buildings with emergency power supplies (e.g. from private backup generators or off-grid dwellings) that could be visible in the NTL data. Nevertheless, our results suggest that NTL can be a powerful tool for measuring blackout duration at a large scale, particularly for regions lacking reliable utility data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTemporal trend analysis\u003c/h3\u003e\n\u003cp\u003eAs the Black Marble record for 2022 was incomplete at the time of analysis, temporal trends were examined using data from 2012\u0026ndash;2021. Blackout frequencies were analyzed using a General Linear Model (GLM) with a Poisson distribution, appropriate for count data. Blackout durations were analyzed using an Analysis of Covariance (ANCOVA), with urban level (categorized as HDC, LDC, and RUR) as a categorical factor and year as a covariate. To investigate the drivers of temporal trends, we extracted the maximum wind speed, storm forward speed, and pre-storm NTL values for each blackout event. Annual means of these predictors and blackout durations were computed, and correlations among the series of temporal variations were examined to identify potential relationships between the variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring our study period, over 22,000 NTL blackout events were detected from 407 storms hitting 65 countries (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea \u0026amp; Table S2). The number of blackout-associated storms experienced by each country varied, with China, Japan, Mexico, and the USA experiencing the highest frequency, each with over 40 storms. Across all countries, the blackout detected had a median duration of 3 days with a 25\u0026ndash;75 percentile range of 2 to 5 days and a 5\u0026ndash;95 percentile range of 1 to 11 days (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S3). We find that HDC tends to have slightly but significant longer blackouts compared to LDC and RUR, which was driven by an upper tail of blackouts (95 quantiles of, respectively, 16, 12, and 11 days). We explain this by the density of electricity assets in urban areas: when tropical cyclones impact these regions, multiple assets were damage, leading to longer repair times\u0026mdash;particularly for the most severe 5% of events.\u003c/p\u003e\n\u003cp\u003eBetween 2012 and 2021, the number of blackouts detected showed a mild but significant increase (Fig.\u0026nbsp;5a). We also found that the number of storms with blackouts detected have significantly increased, whereas the total number of storms recorded in IBTrACS did not change significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38, respectively; Table S3). This indicates that the rise in blackout frequency is more likely to reflect changes in storm attributes and power system\u0026rsquo;s vulnerability, rather than an increase in storm occurrence. The blackout durations also significantly increased over the study period, with rates varied among urbanization levels (Fig. 5b; Table S3). The increase was faster for RUR, at 1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 day/decade, than HDC and LDC, at 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 day/ decade. The temporal variations in blackout durations for LDC and RUR were significantly negatively correlated with the temporal variations in TC forward speed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in both cases; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The temporal variations in blackout duration in RUR also positively correlated with storm maximum wind speed and pre-storm NTL brightness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in both cases). These findings suggest that changes in storm attributes, particularly reduced forward speed, likely played an important role in prolonging blackouts during the study period. Additionally, increased electricity assets in rural areas (as inferred by pre-storm NTL brightness) may also contribute to the prolonging blackout durations in these areas. Similar analysis was also performed at the country level, but significant trends were only detected in limited countries (Table S4-S7). This is likely due to the smaller sample sizes available for each country.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Spearman correlation coefficients (r) between the temporal variations in blackout durations for High Density Clusters \u0026nbsp; (HDC), Low Density Clusters (LDC), and Rural (RUR) and the temporal variations of storm wind maximum wind speed, storm forward velocity, and pre-storm nighttime light brightness (NTL). Correlations with P values smaller than 0.05 are highlighted in bold. Negative correlations with P \u0026lt; 0.1 were highlighted in blue, and positive correlations in orange.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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wg6i4ece3nsh2nGOjnSlKfD1+W14fISfVbbff5QK0f459P2HNQTh99y9e3eXKqa+Dp6u9uo1RseeL+v32qNHD5cqeVO76O8k2pQuGR2fvsmev6qcrXXr1rlUyXvAZPNbCAtcOn/6jraadI7yFzrymWdUNtGLQd26ddsbNGiuADDTwFUtIfxnqOM+GsQl478vHwjui9/ovpDJ71R9rcK+TfvTAw88YH9vFToQK4pOC56G9CoqBOwd+qooEywxJJ8fBs0/rvQWNxyjFw5Rpyn6eEj7D/ejye9fj0VF962p6AddYlnbhIoy6BLb+iHKNPfrw6HOUPH47zhu6DYdl/77TUXHt//ONYXHio69ZI/FrffCY9IfR1oX/k78lIrem7bR78//ZvxvMfqc4T71PP5zCX+74e9WwiFUo1P0cy06cSTdTr+lkF6n/270OvT56nnD35yfxkSG9atMwmNAk8/P0tHQkv5/9B2E+a++Ay2Hos+TyXP4/4keQ+FvIfo8Eh5LftLrjea3ccOTakr1+sJjTMeMXoM/H4RT9JiL0uN6TXptEuYF4fP731e4Xz2u5/W/VX8Mh8ryW4hO0WM//JxyyTP2JX0u0WNCy8mOnShtE/5fsnO7Fz2G/KTPNpPn8vQ/+u583ubP91oX0ncTPo+m8Jgur9+o1ofb6f+88JgLX2+m+w5pG+1Dx1Y0n4/Se9HnnO43p31Gj9V0+Xv0c47+PkXL4TFWkREwBHRg6UcdHrj+S9bBpC8/WiCRZD8+TakOJv0goz8Qn/lH6YAOfzQ6uPwJIpzC59Pr9euV1r7DE0SYOaBi0neu7yoVHTdhppuMPw7CScdEtDDhp0yPZy37Y0r70zGpY9P/VnSMpcvc9Rr0+jX3x6z2GX0uib4W/Z8vrPjnT0b7DjNk/96j9DsL35P2reXoyVH8ew230z71fSgP0Yk62XNUBnqv/rNMNunzTSf6nfjvPPpZxz2X1qfi88dkx5+eV1My+r/wmPbfofJPfc/+u/ayfX16fzo+/HGrtNbpOfWa9H96zmTHXEivTe9N2/t96f+jvwHtK3xd+k2Gn7t//iit074z+S3odYTvSf8Tdy4L9+l/s3o9SuuxTPKMfUWvx39OyaZ0x3my/9EUJ1om0Gea7Weh//ffh/ah7yTuO/ZlCv/dRuX7Nxo9FsPt9FlG1/tjIrpek/9dJpPsf9J9V/44jBPdX3TK9L2mm/QZV2RV9KfohaKSUTWyHwav6MdSpiproCLRaCBeUQad8j4NABJNWsJhhIsKMgd823MA+0dB92EAAAAAkBoBAwAAAIBYBAyVkB/1wAvTwIEoOoZ4JiPJAIUuem8ERu4CUFb0YaiEwrbeIdqv4kAUbYcdIvsCktNwsBpKOIo+bQDKgoABAAAAQCyaJAEAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYVfYUqVKlilsEAAAAcKApKtK7VP7ZgMGlAQCV1Lzl210KKAznn17bpQDkiiZJAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYjFKEoAyWbdunVmxYoX5+OOPzXe/+11Tr14907FjR1OrVi23RXo7duww8+fPt/v49ttvzWGHHWYaNmxozj//fLdFSdluj2KMkpS7D95fb56b+rBZuuhFc1i16qbrxVebHr0HmWrVa7gtsrdh7Urz+sLZZs2KN8znn24z/YeMMD37Xu8eLbb9sw/Nqy9Nt9sMHzXRrUUqjJKUvXzk61KW/WzdutWsWrXKfPXVV2bAgAFuLSoKAgYAWVOmvnDhQnPsscearl27ml27dpmXXnrJPta3b9+MTi4q/E+aNMnuQwX/L7/80p5cpGXLlqWCgGy3R0kEDLlRwX7UsCvNGZ26m37XDjdff/0vM2bEIPvYqAenZx007N6104y//1YbfDRvfZbpeH4vc1KjZqb20ce5LRIUKCz70x/N89MeNl/t3mW31fMhPQKG7OQjX5ds96NAYe3atWbjxo32QtCRRx5pBg8e7B5FRfGdkUVcGgDSUsH9ueees1eNlKkfeuihpkaNGuY73/mOee+998zu3bvNySef7LaOpxNI+/btTadOnWwtQfPmzYsKYV+bzz77zPzrX/8ybdq0cVsmZLs9Str8yVcuhWypcH/zwAtMte/VML/53QzzXwcfYg6r9n3zw+NPMs8++TvznYMOMk1atHNbp6f93XFjH7Nm5Rvm+mFjzcCfjzLH1W1g9xmldY1OaWWOr9/QvP7qbHPUMcebs7tc4h5FKicde5hLIZ185etl2Y8eV54uH3zwgb0gpItAqFjowwAgK0uWLLFXgXwG7ymDV0avq0S6YpRO48aNS504fC2B9h+V7fZAvrz60jP26v7FVwxxaxIaNWlljqpzvHnuyYdsTUAmfLCwedNaM2zUBHNe937ukdSOOa6+SwH5l698PZf91KlTx6VQEREwAMjKli1b7PyII46w85CqkkUnhXSSXa3S1SlRe9eobLcH8uWVF5+y8+NO+JGdh5q1PtPO16xcYufpqBmSggX1VTijY3e3Nr1oUyUgn/KVr+eyn7p167oUKiICBgAZ05UhdUiTZJm7riDJ9u1lay+vDs2qnlbnuExkuz2QLdUcqKOxHHv8iXYeOrL2sXb+3jvL7DwV9YPwHabPvfBytxbYv/KVr5f3+QH7FwEDgIz5K/qSqgNctk2Eli5daiZMmGCvTnXo0CFt57pstwfK6su/f+FSJmXH5q9373KpeBoNSZq1Psu8+dofzY0/6WIuaHOU6d25gRl/3y22uRKwr+UrXy+v8wMqBgIGAHm3c2fmBZ85c+aYxYsXmy++SBTMZs6caRYtWmTTyWS7PbAv/G3DGpeKt2ThC3au4ELNmx78wyvm8Zlv234Q82c/afs2ABVVNvl6KvnaD/YtAgYAeacRMjLVo0cPM3ToUNOuXbu9VdZvvvmmHZovmWy3B/YFjaCUiu7hoI7T0v/6EbbDtKhvgoZJVTMl9W14+YVpdj1Q0WSTr6eSr/1g3yJgAJB3VatWdanMqPpa/RD69+9v+yTIpk2b7DyZbLcHypuGO03l319/7VLGnFC/sUslqKmTbgIny9+Yb+dARZNtvh4nX/vBvkXAAGAvXaXXrVmik/oLyCGHHGLn6ZT1CpICAX8/Bd95LpVstwei1BFZ/Qiik28edEiGhZuq1aq7VNk0OoVx57F/5CtfL+/zA/YvAgYAeynD19B30almzZr2cQ1t6jN73fo/Sndfltq1y36HVX/DnkxPKtluD4QUEOjuydGpfoMm9nHVBqi5kKhZUdT7f/uLnZ/SvK2dx/FNkCTZPRsOPbSaSwH7Vr7y9X1xfsD+Q8AAYC9l+Lo7Z3Tq1auX26L4ngcffli60OM7s5122ml2novjjstu3PlstwdEAYH6EESnKwfd4rYwe5sLbVr3jp2H3t+41s7bnX2BnafSvuNFdp6sg/Q33+y289PP7GrnwL6Ur3x9X50fsO8RMADIivoO6CrSRx995NYkqDmTmgXpDp/hkHoaak8jG2ko1EzMmzfP9kto1qyZW5NattsD2erRe5CtZYj2L1BzJt2j4bKrbigx5KqGR5311DizevlitybhsiuH2vm0SWPsPLT45eftc8QFHpneSRooi3zl69nuJ5TJnaSx/3xnpBooA0CGDj30UPM///M/9m6dX3/9tTnppJPsyeCNN96wndm6d+9ut/EWLFhg1q5daz744ANz1lln2XWqrh4/frxZvXq1+eabb0z16tXt/6jwr/2ee+655vjjizuRZrs9Stv8CX08yuq/Dj7EHHHUMWZmURBQo9aR5sQGTWyw8ODoG8xRdY4zQ26+227j/XHmH8zk8aPN66/ONpcPuNmtNaZGzSPMMcfVNwvmPm3+ufMfpkHjU823//PfdvsXn33MjH7oOft4MksXv2RWLVtoA5Rzzru0KLj4vnsEcU46NjGKGtLLR74u2e4n9NZbb5nPPvvM/t8Pf/jDvQNaoGKosqeISwNAxnQSUAavamYNb6qqaA15GqXtFi5caPtCDBgwwK7T1akXXnjBfPzxx3ZZ9LhOEqqujl6BynZ7lDZvOXdXzZWChMceusMOf6p7J3S5qK/p2fd692gxbTdq2JX2Bm3DR010a4up5uHpx+61+xE1VVLtQ3T0JNG+hg1ONGUKqZ+Fmk4h3vmn01Y+W7nk66FM9yPaVhd/orRvNYlFxUDAAAAFgIABhYaAAcgf+jAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDDHefPNNU6VKlaTT2LFj3VbGdOnSJek2mtI97qeWLVua3r17mxkzZpgdO3bY/0tHr2/IkCGmfv36JfZz++23m61bt5q1a9eWeJ0AAABAWRAwxGjXrp3Zs2ePGTNmjFtjbFrrhg0b5tYY88orr5gtW7aYXr16uTXGLF261G4n/vF69erZ5RYtWthlPa5J6ZtuusmsXr3a9OnTx5x44olm/vz5dttkFFAoCGnfvr1dnjJlyt59Pfvss6Z69er2OZo2bWofBwAAAHJBwJBGGBy0adPGpUqqW7euad68uU137tzZBhshPT5w4ECbrlWrll32lFbtwooVK8zgwYPNzp07zfnnn580aPDBwoIFC8z06dPN+PHjSzyX9qXXq+BDAcq2bdvcIwAAAEDZEDBUEAokFAAo4JC+ffuWap50xRVX2GBAgYWCjDgKHFTzoKZJAAAAQC4IGCqYESNG2LlqGp544gmbFvVZUM2C3HzzzXaeimoezjnnHLcEAAAAlA0BQwWjgr76IMijjz5q5/LII4/YuZoahU2aUgmbUwEAAABlQcBQAWm0I1GHaE8dmsUHEwAAAMC+QMCQBY1MFA6HGk7Dhw93W+Xu+OOPd6lEU6SQ71wNAAAA7AsEDFnww6Umm8LhVwEAAIDKgoChAgqHQ40O0bpr1y6XAgAAAMofAUMFtGrVKjsP+yv49J///Gc7BwAAAPYFAoYKRn0WdK8Fueaaa+xcLr30UjvX0KrR+zMAAAAA5YWAoYK58cYb7VzDp15yySU2LQMGDDA1atSw6QceeMDO07n99ttdCgAAACgbAoY09tXVfD3PkCFDbO2CAoNZs2bZuz97Ss+dO9em77rrLjNjxgybjqN9nXfeeW4JAAAAKBsChjTCuy0/88wzSQMIrXv++edtWk2GokOhbt26de9N2LStlj2lVfhv3bq1mTBhgu2r8Nprr5kmTZq4LYqpA7RGalJA0adPH9OlSxczf/78va9Jcy337t3bXHDBBaU6TAMAAADZqrJHY4KiFBX6dd+FZDSEqr+LsgrtChKS0Ueb6nFPQYJu1qZCfteuXd3aeAoMZs6caebMmVNi39qP+jqo+VJYOwEA85ZvdymgMJx/em2XApArAgYAKAAEDCg0BAxA/tAkCQAAAEAsAgYAAAAAsQgYAAAAAMQiYAAAAAAQi4ABAAAAQCwCBgAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABALAIGAAAAALGq7Cni0gAquQkTJpgvvvjCLZX03e9+1xx55JGmdevW5uSTT3Zr82/OnDlmy5Yt5quvvjKHHXaYfb727du7RzOzY8cOM3/+fPPxxx+bb7/91hx77LGme/fuplatWm6L0u677z77nKF69eqZfv36uaXKbd7y7S5VWO64sY95Z8Xrbqmkw6pVN81an2Uuu3KoOaF+Y7c2/2Y9Nc688uJT5vNPt5mj6hxvBv1itGlxegf3aOZy2c/q5YvNymULzeYN75pq1WuYUQ9Od49UXuefXtulCktlzedr1KhhOnToEPu6s90e2fnOyCIuDaCSa9mypalatarZvHnz3uWBAweas846yxx88MHmvffeM3/9619tRnvEEUfYbfJp2rRp9iRyzjnnmCuuuMJ888035o033jA/+MEPsnq+6dOnm8aNG5vevXubr7/+2mzcuNF8+OGH9v0ks2rVKrN+/Xq3VKx58+bmuOOOc0uV2+ZPSgZLheLsLpeYpi3OMAvnzbDL1w8ba+68d6q5fMDNptEprcyzUx40i+c/Z049/WxTo2b+j/nx991iXp4z1dzym0fNDbc/aL5f8wfmt7cOMKefeV5Wz1fW/WxYu9LcNvQS89nHW805511qfvLTO+xnUghOOvYwlyoslSGfX7dunXnmmWfM//t//8/07NnT9OjRw74f5eV16tSxrz2U7fbIHk2SgAJTt25dlzLmtNNOc6nESaVJkyb2yszSpUvd2vzxV5w6deq0t2DfsWNHm5GvWbPGLmdi0aJF9mqV38f5559vr5jFXVGTTZs2mXbt2hldHwmnbK944cB07PEnupQx7c6+wKWMadSklblq8O3mq927zORxo93a/FGNwPzZT5o7xk61zyVndOxuTmzQxLz8wjS7nImy7mfqpHvMsMEXmX6DhtsahbLUauDAdCDn86opmDt3rk337dt373tRbbBqSJYtW2aXvWy3R9kQMAAFZsOGDXauZjzRJjz+6k+qwndZ6OrP2rVrbcHen0Q8ZejZPJ9OPtlUMW/dutXuX/+HwvTXDe/YefuOF9nmOKHjTviRncc1WyqrD95fbyaPH22atz5rbyHf02tYs+INt5RaWfaze9dOc+NPupjnnnzIjLz/KRtcoLAcyPm8mhUpoGnYsGGp166aEzU5CmW7PcqGgAEoMNu2bbPz2rVLt+/dtWuXnStzzyd/JSuuoB/tW5ANXV3auXOnPUkloxOYTiZPPPGErZ1A4VHbfTmleVs7D/3j75/Zufoz5JOvseh22TV2HqV+CJkoy37uHTnEbN601gwbNYFahQJ1oObzys9VQyFnnHGGnUcpP/ey3R5lR6dnoMDcddddNgO98sorS1Rbi297GnYGVhWzCt3ZCLtG6Qr/1KlT7cnptttuc2uL+W3D/8mU9v3SSy/Z9IUXXljq/Yh/v54Ci8GDB7ulwlGonZ6ld+cGttnR4zPfNrWPLtlnRX0D1Nyn68VXmSE332PXqQmQrupn449vfe5Sxmz/7ENzzSWn2SBkxoJNbm2xC9ocZefh/yRTlv34167/OalRM1tzog7SF18xxJzXvTA6+HuF2ulZDtR8Xn0O5s2bZ5sw3XDDDW5tseh+st0eZUfAABQQXQFavHhx0szVZ7zK8NWZOFnhuyyeffZZ2yk52YhE/iQjmWbouqKk6nZdQVNVs6qhdWUpboQkv/3f/va3vVXTasOrTnGFpFADBo0ONPKmvra9/4N/eMWtTVCHYLXxV+H6d5MXlAomykp9B9QcKAxCPB8ESLqAIdv9hOtUu6CmSFo3ZsQgW+PQf8gI07Pv9fbxQlCoAcOBnM/7wCXZhR01eZo5c2aJx7LdHmVHkySggPhq6po1a9qCtCgzV6a7cOFCexLp1q1b3k4isn17oqB61FGJq6EhPbcoQ8/Uww8/bE+Gvhpa4oIF0WPq3DxgwAAbXIhObCgMvjnSiY2a2rb9okK0rsSPGnalDRbuHjcrb8GCrHH9IU5uWtzZ1PvbhkTHT/VJSCfb/cx+ZoKdK8Dw/Rb0voaPnmTTz0972M5RuR3I+bxeWxw/0p3el5ft9ig7AgaggISdvyZPnmyv9ujKz0cffWQL04MGDcr7mNXqXyDNmjWz89DnnyeujGaToes1a2xtnXxU5a6rSzoRZqJXr172BEOb1sKxZOELLmXML67papvx6Cr8m396yVzab6h57Lnleb8Hg67mS7OWZ9p5aN27b9t5/QZN7DyVbPfj32u06ZEPhtQsS52oUbkdyPm8H+Zaw2WHwr4KYYfqbLdH2dEkCSgQvipaN9G5+eab3dr0fJVvNny1c7rn9DdTUwBQliFO1ZFZJ8ds3pO/qVG6qvHKphCbJPkmR2rD/9jzy93a9HLpw5DuOa+99HTbUVmjF6XqkJztfvz2yZpeie/vMHbCi6VGW6qsCrFJUmXI530ereZNXbt2tes0EpICAI36pNriULbbo2wIGIAC4duY6gqTrrTvC/5Ekqp9qa74J+skl4ls2sZ6BAyFw/cBuOyqG8yVg25xa8uXL7irqVD0bsq6uj/0yo6xnZhD2e5nyaIXzNg7Bid9r2qK1ee8Rjadrt9EZVKIAUNlyOdVO+AL/KL9Ks/WPpL1u8h2e5QNTZKAAuGrqXWH5H0t2dB+/iY+vl9BWfgTga5sZUNXolD5LV30op2373Chne9LTU5t51LF/E3W1BQqU5nu5+/bP7Xz4+s1sPPQmlWJezWo9gGVW2XI59XvTB2ndVEnvLDTqlWrpIX/bLdH2RAwAAVAV3lUJayCdb7brqaSauQindh0BShu7OxMaD+SaQCg7dXWNlk7W1QuugqvJjtq0pPvPgqp1Dw8ecdOXeVXHwPVCpx74eVubbxs93OcCxR+cPjRdh5aNO9ZO+9x+XV2jsqpMubzGgJWtQW6U38mN9/MdntkjoABKAD+Ko/ac+5Lurqjk1e0k7G/M6fas8adbDKxZMkSu//oyUgnKj8yR0g3btOQqvvyZIr9w1+F192d9yV1MFaQ8vXXu92ahGmPjbGdjm8eOa7U3aaTyXY/P2rU3M43/mWVnXtqqqR7MahpE3d8rtwqUz6vPFyFfwUc6vuQrvCf7fbIHgEDUMmpfalv26krPfuaqqL1/Lr6JWrrqmVdAYp2gHvooYdsBzll/iGt142IdELw+9H70lB+Xbp0KXUy0sgg6tug9rzalz+ZaLvzzz/fbYXKSu3/dTM2qVq1mp3vS7pJ2vzZU/aOSKTgRa8n2Z2Xb/xJF9uB2Q/5GspmPwoe1H9BQ6fq/YuChfH33mKbIv1y5Hi7DpVTZcjnRe9D+bbycFEfhFQdpbPdHmVHp2egklLGrc5myQwdOjSnK/vZ0ggc6oinq026mVCbNm2SDnWnE8n//u//mv79+5d4ffp/nXxU3S7q1HbiiSfGXkXSyUojfuj5dOVL27dt27ag27MWQqdn3xk4mekvb8joyn6+aKQlFd5VG6AC+7U3jEo6OpECBm3zwOPzk76+TPfjhdurhqLLRX0L6mZtoULo9FyZ8nn1P1BereFX9X/p8utst0duCBgAoAAU6p2eUbgK9U7PQHmgSRIAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgVpU9RVwaAAAAAEogYACAAjBv+XaXAgpDh4OWuRRQGA5u2dOl8o8mSQAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIxShKAvFq3bp1ZsWKF+fjjj813v/tdU69ePdOxY0dTq1Ytt0Xmtm7dalatWmW++uorM2DAALcWZcEoSblbvXyxefqxe83mTWvNYdWqmzM6dTf9rh1uqlWv4bZITf8/8qa+bine4zPfNrWPPs6md+/aaaY9NsYsWfiC+Wr3LnNUneNNl4v6mp59r7ePIx6jJGXvvc3bzL1T5piZC5eZ71eragZe0tnccPmFpmb1am6LzHX72Wiz8O133VKC9vn54ifdkjFf7tpt6nTq75aSG9izs3lo+LVuCakwShKAA4IK93PnzrWBwtChQ03v3r3N9u3bzVNPPWV27NjhtkpPgcKcOXPMjBkzzMaNG823337rHgH2j6mT7rGF/RMbNTV/fOtzc/e4WWb+7CfNHTf2sYX6TGx4b5VLxTuxQZO9wcL2zz401152un0eBQvy+afbzOTxo+3zAvn01rubTOfr7jQ1v3eY+XThZPPW1LFm0dtrzYVFBX8V7LOhfUWDBfnl1T1cKmHxivdcKt557U51KexPBAwA8kIBwcKFC22w0K9fP1ujULduXdOmTRuzc+dOs2jRIrdlevq/Hj16mPbt27s1wP6zYe1K89yTD9n0xZcPtvMT6jc27TteZGsbxt9/q12XzpoVr7tUvHO7XeFSxoy/95a9gULUO0X7WrLoBbcE5EYBQc+b7rY1Cbqar/kJdY40997Y37yzcYt56JmX3JaZuecPM83iR0ebf6+cWWL6Rb/ubouEaX98zUwdfWOp7TRpvWokurRt7rbG/kTAACAvlixZYmsCGjZs6NYktGzZ0hx22GG2pkA1B9moU6eOSwH7z0uz/uBSZu/Vf6l/0o/tfOmiF21tQDqqHRg2aoJtcqRainBS8CHNWp1h5x+8v958/sk2M3bCi/Zx/Y/fxls071mXAnIzZe5i88/dX5sb+5Y8xto0bWDqHlPbjJ0823zw6RdubWqqXRD9bypq/nTLTy4xl57b1q0pae4bK81l57ZzS9jfCBgA5MWWLVvs/IgjjrDz0JFHHmnnChqyoZoGYH9TQJDOmpVLXCo51VIM+eU95oyO3UsEHaImTXqOsDnSx9s2m9vuftw0atLKLmv98FETbR8GIN+emJOoAW5U91g7D3Vs3cTOM2k+JNeOesQ2R2p71XDz+OyFsc2ZTjnx+NigQv+jfhQ0R6o4CBgA5Ew1B+qYLMkK+aphEPVnAA4kKuhn4r13UnewVcFfwUIya1a9Yec9Lr/OzkXbqtlT1FHHFAcMTU7l6ityp5qDrZ8k8uYGJ5Su1T3u6MPt/I0/r7PzVJ5/ddnefakp09B7JpnGF//UBg7ZUHBCc6SKhYABQM7CDs2pRkOi8zIONN98k1lnz69j+hpkYvmSV+y8Wcsz7TxTbc+5wKWAsvv878Wd9lONhrRr99cuFU/Ni9T/QP0XHr5lkG3OpKZOChzunPCM2yo9miNVPAQMAPYZdX4GDiQfbkm0xy4vvjmS+idkMjyr+jXIZVfdUKppE1CeVq3f7FLpqanRNRd3MutnP2KG9b/YrlM/CN+/IRWaI1VMBAwA9hmNoASgmG+OdPoZXew8FTWPUsdp9WPo0XuQWwvsG2W5F4P8evDle4OGCc+/bOep0BypYiJgALDPVK1a1aWAA8Phtct3pK5smiPNmPKgvWGcOkNnerM4IF/qHVPbpbKnm78pCFC/hnRojlQxETAASEs3ZBs5cmSpacKECfbxQw45xM7ToYYBFc2sp8aZC9oclXSSHxx+tJ2nU7WoIJ+tbJojvfzCNHvvBd0wLllnaKCsDqt6sEulVr2owF9Wqp3oeFrTtDeAozlSxUXAACAtBQQaGjU61axZ0z5+8skn7w0G1q0rPZLGl19+aee1a5f9ChWwP/hhTdM5pXnyseRTybQ5kpoijRs7zN7DgWAB+abhTXX1X3RvhKg1mz6w8zNPPdnOy0p3kFbQkArNkSouAgYAaSkgGDx4cKmpV69ebgtj6tWrZ+cfflj6Bla+s/Npp51m50BF0bPv9aVuouYnL3rDtGT8DdeykUlzJN0QbtSwK22wEB2WVbUOmoBcDbyks52v/Evpjs2+GdHFHXLLvxetWGuu6Jq66Z2aI/nXgoqFgAFAXnTs2NHWMnz00UduTYKaM+keDboDdDjkqoZinTNnjlm6dKlbU1q2d4YGysOFPX/iUok7MHvv/+0vdt714qtKjFg0ddI9tknTtZeeXmL7UCbNkbTNmBGDzFe7d5mxdwwu1WRKtQ4NTuZKLHLn+xjMfX2FW5OgUY10XwV1Wg47Pavp0APTXjCvLHvHrUnQfRiSjYSk+zA0b1gvZc2Bb47Us2MbtwYVCQEDgLxQMNCqVSvzxRdfmHnz5tl1ChbeeOMNU6NGDRtQhJYsWWLWrl1rFi9e7NaU5u8MrX0SPGB/UbMkXeEXf0VfgcCaFa/buzP3u3a4XSdqPvTckw/ZtEY0mjxutE1HZdIc6d6RQ8zmTWvdUmnqAE0TJeSDgoHfDx9o79Dsb7Kmgr/u2qyCvgKK0JS5i83tDz9letx4l1uTcOWIB02HgSNMv9sftMGEJqV107eHhl3jtkpOzZF03wY1kULF852R6rkIAHmguzxrJCQFAq+88or5/PPPbVOlq6++2hx66KFuqwTVOqj50tFHH22aNy951UmBxmOPPWY+++wzt8bYfSqAaNmypVuDbGz+JHEnbpTNcXUbmJMaNTOvL5htHhnzS1s70PXiq82Qm+8uUUOgUZX+85//mPVrV9jhT392632mRs0j3KPFZkz5nfny79vNwJ//xvzXwaUHDRh/3y1mSdFzpNK6KNhodzY3b4tT9/987FLIRON6PzTntDzFjJ0y2/z07knmzTUbzIAeHc3jdw41hxz8X26rhD17jK2NuODMVubiDqe7tcb86Lg6Zu3ftpll7240C956x3z97/8xgy89z9xwxYWl9hE1cNS4ov21NGe3/LFbg2wdVKeRS+VflT1FXBoAUEnNW77dpYDC0OGgZS4FFIaDW/Z0qfyjSRIAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAkEaXLl1MlSpVkk5jx451W5X25ptvJv2fcKpZs6bd/+23327Wrl3r/jO9GTNmmN69e9v/9/vSfiZNmmR27Nhh53p+AAAAIFcEDGm88sor5h//+Idp0aKFW2PM9OnTzZ49e8ywYcPcmtLatWtntxkzZoxbY8zEiRPtOj/NnTvX1K1b19x1112madOmNghQgT+Ogor69eubIUOGmObNm5vXXntt7770PHr8Bz/4gbnuuuvcfwAAAAC5qVJU2Nzj0khBtQnDhw83nTt3tkFENlQDIEuXLrWBRJRqA7p162Z27txpA5NVq1a5R4opGDj77LNtWoFCkyZNbDpK+2rfvr0NahSAAIDMW77dpYDC0OGgZS4FFIaDW/Z0qfyjhqECUBCh2gZZvXp1qaZOqnVQsKCAYvz48bHBgmhfqm346KOP3BoAAACg7AgYKggV9FV7Iffcc0+JpklPPPGEDRbq1auXUa3BgAEDTPXq1d0SAAAAUHYEDBXIz372MztXcLBw4UKbFgUQcu6559p5OrVq1TKDBg1ySwAAAEDZETBUIA0aNHAps7dJkfokKICQVE2RAAAAgPJAwFCBaMQk709/+pNLFWvcuLFLAQAAAPsGAQMAAACAWAQMFcjWrVtdyphzzjnHpYr961//cikAAABg3yBgqEA2bdrkUsacfPLJdt6wYUM7l3Xr1rkUAAAAsG8QMFQgv//97+28Ro0apmvXrjatEY/8cKvPP/+8nQMAAAD7CgFDOZg0aZJLZW7+/PlmwYIFNn333XfbuTdixAg7103dtF06uodD9OZvAAAAQFkQMOTZjBkzsr5pmoZO7du3r00PHjy41D0U/N2bRdutXbvWppNRsHDFFVfYm7cBAAAAuSJgyIAK4b450MqVK0vchdlTh2Vd1e/Tp4/p1KmTW2tKXOlfv369SyUoUBgyZIhp3769vdeCgoLx48e7R0saNmyYmThxot2uadOm9v/0/56eX8HK9ddfb/ejpkwAAABArqrsKeLSSKJLly57mwplolevXrbgrsK8AoFU1FehVatWpkePHjbICO/DEEeBwRNPPGFeffVV20TJUz+Hq6++2vTu3dutAYBi85ZvdymgMHQ4aJlLAYXh4JY9XSr/CBgAoAAQMKDQEDCg0JRnwECTJAAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABALAIGAAAAALEIGAAAAADEImAAAAAAEIuAAQAAAEAsAgYAAAAAsQgYAAAAAMSqsqeISwOo5CZMmGC++OILt1TSd7/7XXPkkUea1q1bm5NPPtmtzb85c+aYLVu2mK+++socdthh9vnat2/vHk1P/7927Vq3VFq9evVMv3793FL221dW85Zvd6nCM/6+W8z82U+6pdKOqnO8qd+wibmw509Moyat3Nrc7d6100x7bIxZsvAF89XuXaZ567NM/+tHmBPqN3ZbZCbb/Xzw/nqz4+/bzYdbNpn3//YXs/2TbXb9g394xc4LRYeDlrlUYen2s9Fm4dvvuqWSvl+tqul4WlPzy6t7mFNOPN6tzb8Hpr1gnpizyGz9ZLupe0xtc/9NPzFd2jZ3j2Yu0/1ou9sffsotlTawZ2fz0PBr3VLldXDLni6Vf9QwAAVk8ODB5vzzz3dLxrRs2dKMHDnSTp06dbLBxNy5c826devcFvk1bdo0s3HjRnPmmWfa52zatKlZvHhxVs+nYCOOgp62bdu6pYRst0flM+Tme8zDUxe5pYT2HS8yf3zrc3P9sLHm80+3maWLXjTDBl9kVi9f7LbIjQr5d9zYxwYqN48cZ6a/vMF8XlRwv+vWa+xjmcp2P7OeGmeGXtnRjLypr5k8frR9X5s3rTXndrvCbYHKbu7vR5jFj452S8Y8fMsg8++VM+006/5bzaKiYKLzdXea9zYnAsl8u2HMY+beKXPMY3f81D7nyOv6mB433pX182WzHwUVcRQk/bxvN7eEsiJgAApM3bp1XcqY0047zaUSwUOTJk3Mt99+a5YuXerW5o+vWVBgoueSjh07mho1apg1a9bY5XQWLVpka0GuvPLKvYGOn/TaGzZsWOL9Zbs9Kq8fHH60SyWcfkYXOz+vez97xd6b9MAIl8qNCvkqqHe9+CrT4vQOplr1GuaoY463wcmaVW+4rdLLdj89+15vA6GR95e82tqs1RkuhULQ4IQ6LmXMxR2K8/k2TRuY31zf1/xz99dmRIor8mWlK/2PzlpgAxM9l1x6blvTvGE988TshXY5E9ns5/lXl5mOrZuYDXPG7Q2M/PTboX1tbcoJdY50W6OsCBiAArNhwwY7P/bYY02tWrVs2jviiCPsPK7ZUlmpBkHNglR498GCp6v8mT7fjh07bPOhaCF/69at5qOPPjJnnFGyUJTt9qi8/rrhHZdKaNbyTJcypn6DJi5lbEE8V7rKr0K+tGrbyc5D695926VSy9d+Tix6f7WPPs4toRCsXLfZzi/p1NbUrF7Npr1GdY+187hmS2WlK/9qFtTptKZ7C/leraLXsGhFfNPQULb7WbflQ9vcKBoUfPDpF2bWouXm6m4d3BrkgoABKDDbtiUKRLVr17bz0K5du+xchfh88jUWcX0j1J8hE7169XKpkpYtW2YaN25cKgDKdntUXiuXFV+VVHMkXan33neF8nzY/tmHtimQHFatuq0V8N5Z8bqdb//0QztPJdf9hO+X5kiF5+U3/2znZ55aOs/99P/usHM11cknX2MxpFdXO49SP4RMZLufXw++3KVK+t1Tc83Pr+hWKmBC2RAwAAXm448/tnM1x4n6/PPP7Vy1D56aEkWb86SbQrqarxoEBSHJOjfnWpuxatUqG3CoeVMmst0elYM6DHunNC/ut6J+AL4ALmqedEGbo7KevNnPTHApY87o1N2ljNmwdqVLGVO1KABIJ9f9rFlR3FyJ5kiF57lX37TzDq1PsfPQm2sStcyXndvOzkVNgA5pdUlWU0hX81VjoSAkWafkTGsz8rWft97dZPelZkzIDwIGoIDoSr/6KKjfQLSZjgrS6mMQ7Qjco0ePpEFBqimk/UoYhHgKJnK1evVq06JFC7eUXrbb48CnjswaXcgLC9CvvvSMSyVo9KGyUvARBiZhM6LVb//JpYypf9KPXSq5XPejUZJ80yqaIxWeV5a9Y/soqL1/tJmOCtLqGxDtCPyLft1Ltf9PN4WmzE0MFhAGIZ4K7pnK137u+cNMc8tPSgY1yA0BA1BAfHOkmjVr2vb9okK7ahEWLlxog4Vu3brltSPw9u2J6uOjjiq+Cuv5gEF9G8pCnZo1NGu0X0ScbLdH5bDhvUTQKr4ArUK5+giEzX7UUVhDlarTcLaTqJ+ED0yizYg0WpF3eO3iDqnJ5LqfpYtfcilj2p1zoUuhUPjmSC0a1Tdf7tpt0ypsqxah501322BhwcRf57Uj8KK3E8362jVrZOeh1evft3P1SUgnH/tRJ2i9t2j/B+SGgAEoIL45kkyePNnWBkydOtV2AFYTpUGDBuX9Hgw7dyaGfmzWrJmdh3wTKAUw2VLAs379etO1a/J2rlHZbo/KIyxkqyCuJkR9zmtkgwUFEP2HjDCPPbe8RMG8LMLAJGxGFF7xl5Malf4thHLdT/h+m59WPAIUCoNvjiTt+99qmw816nG97QCsEYPWz34k7/dgeGdjYvjqVE2gmjVMfyEq1/0oQPrd03PNndf1dmuQL9y4DSgQaho0b948e4X95ptvdmvTS3fjs2R8s6R0z3nffffZ/gQdOnTI6uZtotclajKViWy3r2wK9cZtKmTrvgSe7seQ7sZpYZ+ETKmWQUOg+v4Qqq3wAcjUSfeY5558yKYVoKS7gVou+wnfr25I99jzy226EBXijdvU5KjDwBH2JmcKDDKV7sZnyfhmSemes/HFP7Udlec8eFvKm7flYz96H6ImVoWIG7cByJlv/pOsL0EqufZhkKpVS4/GoaFWFSzEdYZORe9FtSKZFv6z3R6VR9g8RwXobO+ynI2/bSi+n8iPGhUXaMIr/j0uv86l4uWyn/D9ajQoFJYFyxPDB1/SqY2dZyrXPgxSr6igH6UhUlXIj+vEnExZ96NmV6pFKdRgobwRMAAFwjdH0nCi+1qyIVz9zdqSjdaUjoZFbd48s5OPZLs9Ko+wkN2sdfG9F1KJ9k/IZBLf70AjLflhW8NmROqPEN7/IU4u+1kTjPjUvgP9FwrNzIVv2XnPjtkFDPlwVsvSzVn9TdbUFCpTZd2PhlH91cDkQ2kjdwQMQAHwV/PVNCjffRRSibvPgfoTKIBR7UK2N0/zw6JmWiuR7faoPHQvg7DNf7Kbn+WTajCkyanFI7yEV/yH/PKeEvd/iFPW/ej9+hu9lXdtCioefxVeTXry3UchlaMOT35Mqz+B+lOoViCTm6flsh8/jGqmtRjIHgEDUAD81fxsmyPlSqMtKUjRUK6h+fPn23UqxGd78zQNi5pN4T/b7VF5LPvTH12q9GhD5SFZDYav4VDzoDM6JppKqGCvfgrqK6G5RmwKZbqfqPD90hyp8Pir8Nk2R8qVRiRSkLLrq2/cmoRfT5xhh3edPOqGjG6elst+NIzq6KF93RLKAwEDUMnpCrvuryC6or+vqcmRnl+1HKJO0Fpu165dqYL8Qw89ZDtC+yFfo/ywqJnWkmS7PSoP3eDs+WkPuyVjqn2vhi2ol6fzuvez8y+2J5r/adhW1XCog/KQm+6260QFe9+pWfOPt222aS/T/YR0r4nw/X61+58uhULg768g1Q871M73pRv7XmQenbnA1nLI40XBi17P1NE3lrrq3/aq4bYDsx/yNZTNfjw/jOq+rFUpRIySBFRSKqDPnFm6Y5oMHTo06yv7udAIRRs3btx707g2bdokvReCAob//d//Nf3790/6+iZMmGAuu+yyjF97tttXZoUySpKu1mvI1DjqFzDqweluKf9UcJ/0wAhbwFezoC4X9TU9+17vHk1Q4DL+3ltssKDX88uR40s1McpkPxLeSyIZDRmb7P8KQSGMkqSCdesrko969+nCyRld2c8XjVB075Q5e28ad++N/ZPeC0EBg7ZZOvnupK8v0/143X422kz5TWa1GJVdeY6SRMAAAAWgUIdVReEqxGFVUdgYVhUAAADAfkHAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACAWAQMAAACAWAQMAAAAAGIRMAAAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYlXZU8SlgYLx36tmuRRQGBb/p61LAYWhw0HLXAooDAe37OlS+UcNAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFiMkoSCxChJ2Xtl2TvmN48+a97ZuMV8v1pVc9m57cyd1/U2NatXc1tk773N28ysRW+ZNRu3moVvv2vX/XvlTDuXL3ftNlPmLjZPzFlktn6y3a67pFNbM/jS80ybpg3sMjLDKEm5W718sXn6sXvN5k1rzWHVqpszOnU3/a4dbqpVr+G2yN4H7683Sxe/ZN4v2uc7K1636/741ud2Lrt37TSvvvSMeeXFp8znn27L2/MWAkZJyl6u+bz+v8eNd7mleBvmjDMn1DnSLRlzw5jHzKOzFrilkpo3rGeWPTnGLSEVRkkCsF/dOeEZexJo0ai+LdAvmPhrm7lf+LPRtlCfrQ8+/cJ0K/rf1lfcbMZOnm2qF52Ypo6+0Xy6cLLbIhFMNL74p+b2h5/aGyzIzIXLTIeBI+yJCdhXpk66x4y8qa85sVFTW6C/e9wsM3/2k+aOG/vYQn22tn/2of3foVd2NM89+ZCpWhQIDBs1wUx/eYPbIhEsaJvJ40eb9h0vss+rYEHPe+1lp9tgA8iXfOTzy9/b5FLxFACEwYLOB3HBgvz8im4uhf2JgAFASm+9u8kW6uXnfRMZ9yknHm+v9Osq1A1jH7frMqWCfpsrh9kaBZ04Vjx9n5n22xvNpee23XsVSyenEUWBwuRRN9gTl6aHbxlkH/PGPzvfpYDytWHtSluol4svH2znJ9RvbAvxqm0Yf/+tdl2mVFPx8/6dbY3CiQ2amIenLjLDR000Z3TsXqLWYNpjY+z+pX2HC+1cNQvy1e5d5vd33WTTQK7ylc8vejtxvKbS/6KOLpUw50/LXao01XJ0aH2KW8L+RMAAIKUJz7/sUkWFpOCqULMGJ9i5rvjrClEmdFLSFax/7v7aBgsv/X6EPSlFKXCY8psbTJe2zd0aY665uJPpdFpTt1TytQDl6aVZf3ApY2offZxLGVP/pB/b+dJFL9oag0wo+FBNhQr8ChZGPTjdBh9Rql1QTYLnt1FA0bz1WTatYGLJohdsGshFvvJ51QartlhNjvzFHj8p+JBoAHDvlDm2djm6vabPFz+ZU7NX5A8BA4CUdKJIZ/GK91wqnmoNet50t1sy5qm7fpHyRJDssS2uaZKuOvmrYEB5U0CQzpqVS1wqnoKAUcOudEvGDB89KbYfwsfbNrtUabXrFActy5e84lJA2eUjn9cFod8PH2hri6MXdJT/6zmizZGef3WZvYBUp1N/0/aq4bYvg9ah4iFgABBLJ4BMvPHndS4VT52XdWKQgT07Z1xDoBONmjGpz4OuXtU9prZ5a+pYahiwT6hGIBPvvZO+kKPOy6pZkK4XX1WitiKdsAbjyNrHupQxX7v9AWWVr3xeA1EoWEjGBxvR/gi/e3quSxnb9El9Ga4c8aA5qsNV9FOrYAgYAMT619ffuFRqu1wgkIqqnT1VbatD8yGtLrEnBl1ViutUd/WvHrLNmPwoSgoa1AdCnaKB8vbNN5l19syk4P78tIddqigA+PRDc+2lp5sL2hxlenduYMbfd0vKztNf/j2zZn9AtvKZz8eZ+0Yi8A6bIylQUZCQjC4uKd+ntqHiIGAAEGvD1o9cKjcq3PvaBRnSq6tZP/sRW9Og9X4kjmTUl0FtYlWV7el/Ol93Z2yQAeTLh1syu/qajkY08rUL0u2ya8xjzy+3NQ1a70dc8ho1aWWHUPU+/OCvLgXkV77y+Ti+OZL6MIRNTVUj4fsqaPCLaD4vqm3g4lDFQMAAoNz99YNPXSrBd2Z+aPi1tj+C6ErT47MX2nRIJxhVc2scbgUYnoKGTPpOABVBtE9Ci9M72PmQm+/ZGxioE/PLL0yzabl55DiXMubJCb+1QYdqIb7Y/rFbW7I/A1AR+Xy625mt7DwZDX7h83mNiOfPC/JEkvMC9j0CBgCxjj3ycJfKzcdf/N2lSmvZ+ESXMmbu6ytcKjkFGOFISan2C+TD4bXruFRu/r69ZNAcOqlRM5cyZvkbxcMFK6gYO+FFO5qSaiF0zwYN4bpkYfHISCc3Pc2lgLLJVz4fJ1lzpFQ0It6sYKjiTEfhQ/kiYAAQq84RtVwqNd14LZXvVY1/PNvOy2rOBOTLrKfG2X4EySb5weFH23k6uvFaKoce9j2XKi1VLYGaJj34h1fsTds0XXXdrXubNh1V53h77wYgF/nK55OJa46UjporhTXK2P8IGADEUqadiTNPPdmlkmtUt3hUl6jjjs7u6tb3qh7qUtrvD10KKB8qsGfilObJR4fxjjvhRy5VWjjqUTqzn5ngUsb0G5S4iRuQi3zl88lk0hwpznntTrXzsgQqyD8CBgAp+ZvtpJKuqlknJA2H6sUN49esYV07V18GjaB054RnSnVs9iN6qI1rq5OLmzMBZdGz7/V7r95HJ093dE6nWaszXCo5BR6qEfDihmut36CJS5Wmm7T5m7mpszS1C8iXfOTzyWTbHCnkLw6VJdhA/hEwAEhp8KXnuVRitCNvzaYP7Dx6TwUV8lXY17Cp4fY39i0udC1YXjy+tt+PXN0t0RHU92UYO3m2ad//1r3jcast628efdamJ4+6IasqbqCsLuz5E5dKjHbkvf+3v9h59J4KUyfdY5s0adjUcPuLrxjiUsasfvtPLlW8Hzn3wuKRkkJqOjX2jsE2redTZ2kgX/KVz4fSNUfyw2traO0HppW+Y/mn/3eHHTUp7t4O2LcIGACkpNoBDXcnfrQKnSAWvf2uzczvvK63XSeqOVAhX3S/hBEPP2XToo5svk3qozMX2H34/Yiew5+Qwipo7Ufjcevk1KjH9XY0JW3rR1oCyptqB4aNSjQF8qMYKRBYs+J12yG537XFTYNUc/Dckw/Z9OefbjOTxxUPF3xe9362sC/zZ0+x+/D7ET1HGHjoMQUfCjwmjx9tayhG3v8UwQLyLl/5fChdc6Td3/zb/r9GvLu9aB+607OvfdZ82h9fMxNGJIJk7H9V9hRxaaBg/PeqWS6FTOkqv67uq8Cu5kADL+lsbrj8wlJXjnTlSScTNUGaPuZmO1xeSDfi0d09/Q17dPVJV7fCdrS6MvXriTPMohVr7QlFdNLqeFoTWwvBXZ6zt/g/XKXL1erli83Tj91rhz/VUKhdL77a9Og9yFSrXsNtkaBCvoIGFfBvu/txc0L9xu6RBDUtmvPMRLsfUZMn1WKE/SVUo6AgoXnrs2yn6FZtO+0dihWZ6XAQN/3KVr7yeel3+4M24NA9d+Jqg/V845+dv/fGnKKR8PpdcDY1C2VwcMueLpV/BAwoSAQMKDQEDCg0BAwoNOUZMNAkCQAAAEAsAgYAAAAAsQgYAAAAAMQiYAAAAAAQi4ABAAAAQCwCBgAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABALAIGAAAAALEIGAAAAADEImBIYezYsaZKlSqxU82aNU2XLl3MjBkz3H+UlOx//BQV91xvvvlm0vXhpNeg/9+xY4fbGwAAAJAfBAwpDBs2zPzjH/8wvXr1cmuM2bNnj520/u677zYrV640ffr0MUOGDHFbFNN2W7ZsMfXq1bPLNWrUMO+++65dHxV9rqVLl9rt2rVrZ+djxoyx68U/pmnevHnm/fffN8OHD7eBA0EDAAAA8omAIY1atWqZn/70p26pmNYPGjTIjB8/3i5PmDAhaU1D3bp19wYBrVq1Mk2aNLHpZPxztWjRwgYKIQUUyXTt2tW8+uqrNr169Wrzq1/9yqYBAACAfCBgyFHv3r1tzYFMmTLFzqOqV6/uUplR4JCNMChR4AIAAADkCwFDHqjmYH9r3ry5SwEAAAD5Q8CQI/UZWLBggU1///vft/P94fnnn7dzX9sBAAAA5AMBQ46eeOIJlzJJ+zrsCxpJSf0X5JZbbrFzAAAAIB8IGMpIhXT1X9DoRDJ9+vRSHZXL29q1a+1wqu3bt7fLgwcPju0cDQAAAJQFAUOW/L0PVEh/9tln7XCnGjpVwcO+oufWa2jatKkNWBQoaKhVP2ITAAAAkC8EDFnSvQ90vwTfV2Dbtm12lKJ9yd+HoXPnznZ51apVpmHDhjYNAAAA5BMBQxlo2NO5c+fadNz9F/aFp59+2gYu3H8BAAAA5YWAoYzUX2HixIk2rTs9q09DPqxfvz7j0ZYUuLz22ms2rcBF/RkAAACAfCJgyIHu9OxvmNatWzfbCTmZNm3a2LmGX9UwrKloH2effbZbSk93jvaBi/oz7K/aDgAAAFROBAw5GjdunKlXr57ZuXOnueaaa5IGBKqN0DYyc+ZMO09m/vz5tsDfqVMntyYzYeAyZMiQ2MAFAAAAyBYBQxoKAB555BG3lCjUh9QsaNasWTatvgQnnniiLfRv3brVrvO0jfobXHfddbbpUPi4Cvgq6Pft29f2jUjWiTpsbvTyyy+7VLEwcNHoSdqewAEAAAC5qrJHw+0gKRW6/X0WojRSUXjfBQUJ6ssQim6j4EM3etNdmf2N1qRFixbm3HPPNQMGDCgVLKhvhL/PQpSGdA3vu+CbMylo8KLbIOG/VyWCPKBQLP5PW5cCCkOHg5a5FFAYDm7Z06Xyj4ABBYmAAYWGgAGFhoABhaY8AwaaJAEAAACIRcAAAAAAIBYBAwAAAIBYBAwAAAAAYhEwAAAAAIhFwAAAAAAgFgEDAAAAgFgEDAAAAABiETAAAAAAiEXAAAAAACBWlT1FXBooGP+9apZLFZ4bxjxmHp21wC2VVveY2qZ5w3pm8KXnmTZNG7i1ufty127z64kzzHOvvmn+uftr0+m0pmb00L7mlBOPd1tkJtf9vPXuJtNh4Ajz8C2DzDUXd3JrK7/F/2nrUoVn/H23mPmzn3RLpR1V53hTv2ETc2HPn5hGTVq5tbnbvWunmfbYGLNk4Qvmq927TPPWZ5n+148wJ9Rv7LbITDb7iW6r99blor6mZ9/r3RaFo8NBy1yq8BRaPv/e5m3ms//7pdmw9SOzZtMHZusn2+36ZU+OsfNCcXDLni6VfwQMKEiFHDCIMtfWV9zsloy5pFNbM+23N5rHZy80Q++Z5NYaM+fB20yXts3dUtkp87/wZ6PNOxu32H22OvlE077/rfaxpZPvNjWrV7PpdHLdj9535+vutCegFU/fl/VJ7EBWyAGDfPD+ejP0yo5uyZj2HS8yw0dNNC+/MM2MGzvMrTVm5P1PmRand3BLZaeC+x039jGbN621+/xRo+bmF9d0tY898Ph8U616DZtOJ5v9bP/sQ/Pz/p1toBDV9eKrzJCb73FLhaGQAwYplHz+gWkvmNsffsotFSu0i0JSngEDTZKAAnTMEbVcKqHbmYmrqspcdSXHu+n+P7hUbnzmP7BnZ3tiUoZf75ja9irQ4hXvua3Sy2U/Ogn1GX6fDRZ0da2QggUY84PDj3aphNPP6GLn53XvZ6/Ye5MeGOFSufGFfBXUFYCoYH/UMcebzz/dZtasesNtlV42+xl/7y3m0n5DzfSXN5g/vvW5uX7YWPeIsTUsCppQOAoln/9Fv+7m3ytn2uAi1KH1KS6FfCBgAArQynWbXSohzFibNazrUsZm0LnS1R9l/nJeu1PtPPTmmg0ulVou+/FXrPz76di6iZ2jcPx1wzsuldCs5ZkuZUz9BsXHgwriuZr11DhbyJdWbUtf4Vz37tsulVo2+1FNRMfze9mmR77WIRoM/fvrr10KhaDQ8vmQmludUOdIt4R8IGAACtDLb/7ZpRLV1GEV75qNW10qdx98+sXequLvV6taotp74dvv2rm2SSfX/agtbMfTiguFyU5EqNxWLlvoUonmSGFTnvddoTwf1Cxo8vjRNn1Yteolmje9s+J1O9/+6Yd2nkq2+9H7OaNjd7dUrMmp7VzKmEOqVnUpFIJCy+fD99v/ouLmh8gPAgagAKkjmXfmqSe7VOJKvM+YRdXWh7S6JOvJ+91Tc13KmMvOLS64qOOxV73oxJBOLvtR579endubrZ8Un2jCExEKgzoBe6c0L+7PoSvzvgAuuiJ/QZujsp682c9McCljzuhUXIDfsHalSxlTtSgASCdf+3n/b3+xc3V+zrazNQ5shZTPy6IVxYE/zZHyj4ABKDCvLHvHtuP3wox1ytzFLpWgUSnKSiel8IQVXtVfsLy4eUizBie4VHK57Eed+9o1a2RHAVnkTpC60obCsnr54hIdgZu1OsOljHn1pWdcKkGjD5WVgo8wMAmbEa1++08uZUz9k37sUsnlcz9rXDA06BeJ2goUhkLK50UdvH3TKpojlQ8CBqDALH+v+GqNz1iVWYcjTag6WB3I1DFYncmynUTtZ/0JK1q9PHPhWy5lzLFHHu5SyZV1P8+/usxUP+xQc+m5bUucPMMrbSgMG95b5VLGnNigial99HG2MK0+AmGzH41CpKvw6jCc7STqJ+EDk2gzoqWLXnQpYw6vXcelksvXfhQMaT++wzQKR6Hk896sRcXb9Ox4ukshnwgYgAITZr7KoFW1XKdTf3sS0Ynlt0P7mvWzH8m52U54wgqrl8MrQdKicX2XSq4s+9Fj67Z8aIMFCfdBVXXhCQvZKkCrCVGf8xrZYEEBRP8hI8xjzy3PuVAdBiZhMyKNThR2pj6pUTOXSi4f+1HTJf/++l073K1FoSiEfD4Uvt+OwQhQyB8CBqCARDPf6WNuLnHFSDe50RB1Yee4sM1qppOEnerC6uXwSlAmVcfZ7kfvUY/9evDldln8ySST50PlEi1k33b34yVqBh78wyslRhaSaP+ETCYJO0+HzYiWLn7JpYprOFLJdT96z6OGXWn7Y4x6cHqJ94bKrxDy+VD4fhkyu/wQMAAFJMx8yztjXbW+eEg/3XjHC68E/fyKbi4VL5v9qMr9idkLSwQLGlXDn0zCkZJQGMJCdnl3/P3bhjUuZewN1rywhqPH5de5VLxc9qNg4dbre9qaCYKFwlTZ8/mo8P1e0qmNSyHfCBiAAhJmvpneiyC8MpXpJL49qkbg8FeywitBaqeaSfOgbPajznyPzlpQ4ipYox7X28dk7OTZdp0CCxSGsJDdrHXxvRdSCWsgMp3E9zvQlX1fUA9rONQfIbz/Q5yy7scHC7p5W3hX56mTCusOz4WusufzUYveLq6R69mRgKG8EDAABSK80i5htW950JUtOatlcSfj8ErQ74cPLFElHieb/cxatNzOUwlPSKjcdC+DsDlSspuf5ZNqMCS890FYwzHkl/dkdMW/LPtRsHDXrdfYx9TEStSx++UXphUFIP+0y6j8CiGfD+n9+hu9lXdtSqEjYAAKxJw/FRemo6NQlIdkV7b8lS8Nbeo7JCvD7/az0fbKv+bRq/+Z7kfUNjfZVTAZ2LOzXZ77+7IPm4kDy7I//dGlSo82VB6S1WD4Gg7dLM7fWE2BzB039rF9HzRXwT6U6X48X7Og4GjsHYP39qtQx+5xY4fZOz6jMBRCPh8K3y/NkcoXAQNQAHTjm3unzHFLxl6pUQZengZcnLia++Fnf7dzDeenK1/quPbQsGvsOlGG728ipPmmDz61aS/T/SSj4VQ93Y8BhUOjBD0/7WG3ZEy179WwBfXy5AvmX2z/2M41bKsK8eqgPOSmu+06USDjbxan+cfbittvS6b7ER8s+GZMUdywrXAUWj6v/D18v//8V6JZE8pHlT1FXBooGP+9apZLVW66iqOh9OKoeU55XnFXhn7T/X+wGb+qiwf06GhH5wjphKa7Meskotcz5Tc3lKp6zmQ/yWi/6tMgny6cnFHVeGW1+D/Jr9BVNrparyvrcfzIQeVFN4mb9MAIW8BXYb3LRX33NhHyFLiMv/cWGyzo9fxy5PhSTYwy3c/P+3eODRbk+mFjC7aGocNBy1yqciu0fD68l0QyGjI2k/NDZXRwy54ulX8EDChIhRIwAF6hBAyAVygBA+CVZ8BAkyQAAAAAsQgYAAAAAMQiYAAAAAAQi4ABAAAAQCwCBgAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABALAIGAAAAALEIGAAAAADEImAAAAAAEIuAAQAAAEAsAgYAAAAAsQgYAAAAAMQiYAAAAAAQi4ABAAAAQCwCBgAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABALAIGAAAAALEIGAAAAADEImAAAAAAEIuAAQAAAEAsAgYAAAAAsQgYAAAAAMQiYAAAAAAQi4ABAAAAQCwCBgAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABArCp7ilSpUsUtAgAAADjQFBXpXSr/bMDg0gAAAABQAk2SAAAAAMQiYAAAAAAQi4ABAAAAQCwCBgAAAACxCBgAAAAAxCJgAAAAABCLgAEAAABALAIGAAAAADGM+f9oTN7AO6rmEwAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e"},{"header":"Discussion and conclusion","content":"\u003cp\u003eUsing NTL data, we reported the median power outage duration following a TC across 65 countries is 3 days. Previous studies for storm-induced power outage have been primarily focus on individual events [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], while broader analyses considering multiple causes have predominantly cantered on North America, particularly the USA [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our analysis provides data that have hitherto been unavailable on multiple countries around the world. Compared to previous studies that measured the frequency of outage events (e.g., number of events longer than 1 and 8 hours [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]), our study provides a different measure of resilience by explicitly studying event duration, which could more directly link to economic impacts (as opposed to the direct asset damage) of power failure [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Risk analysis, including climate risk analysis of the impacts of extreme events, crucially requires information on the likely duration of blackouts and speed of recovery. Our analysis should also provide a stimulus for improved modelling of the losses associated with electric power supply failures, for example using agent-based models of the recovery process [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which need to be calibrated against observed recovery times.\u003c/p\u003e \u003cp\u003eThe number of storms causing power sector disruptions and the duration of blackouts has increased significantly in the period 2012 to 2021. This observation aligns with reports of more frequent and prolonged outages in the USA for the period 2000\u0026ndash;2021, 83% of which is associated with extreme weather [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Similar data have not hitherto been available for many other countries, so here we have demonstrated that this increasing occurrence and duration of disruption is observable across countries and habitation densities. This provides additional evidence about the increasing scale of weather-related disruptions to power networks. Moreover, negative correlations were found between the temporal trends of NTL blackout duration and storm forward speed for all urban levels with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1. During storms, power outages are commonly caused by tree damage to networks and flooding [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Slower-moving storms may result in prolonged exposure to wind hazards and heavy rainfall, leading to more destructive hazards as well as delays in repair efforts. These results underscore the potential risks posed by changes in storm characteristics, in addition to the increase in storm frequency [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], on infrastructure resilience.\u003c/p\u003e \u003cp\u003eDuring the period 2012 to 2021, 1.1\u0026nbsp;billion million more people were connected to electricity supplies globally[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and global electricity consumption increased from 20.0TWh/year to 25.9TWh/year[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This represents significant progress in achieving SDG7.1, and specifically Indicator 7.1.1 which measures the proportion of population with access to electricity. The overall increase in electricity supply reflects increasing demand for electricity services, driven in part by increasing electrification and a growing proportion of renewable energy (SDG7.2). These trends reflect growing dependence on electric power systems. However, these systems are vulnerable to a variety of forms of damage and disruption, including storms which cause the most severe acute damage to power supplies. Our analysis revealed that the increase in blackout duration was faster in rural areas compared to urban areas, with its temporal variations significantly correlated with increases in pre-storm NTL brightness. This may indicate a lag between new development and the corresponding resilience planning required to mitigate these risks.\u003c/p\u003e \u003cp\u003eIt is important to note that, by using the daily NTL data, our analysis is not likely to detect blackouts shorter than one day (or one evening). The cloud coverage of the NTL data and our conservative test are also likely to contribute to missing shorter events. Therefore, this study should not be considered an extensive survey of storm-induced blackouts globally, but rather an effective method for examining regional and temporal variations of large events. The omission of shorter events are likely results in an underestimation of the total number of blackouts and an overestimation of the average blackout duration. Future studies could incorporate more sophisticated corrections for uncertainties in Black Marble data to enhance detection power[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which may enable the detection for more short-term events\u0026mdash;albeit still measured in days\u0026mdash;as well as significant trends at the country level. Nighttime light blackouts following storms can arise from various complex factors, such as intentional power cuts to prevent cascading failures[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which might overestimate power network recovery times. Such events would require utility records for a more detailed study. While this study's validation relied on POUS records of power outages in the USA, future efforts should explore validation using other national and regional ground-based electricity outage records. Despite these limitations, our study has demonstrated a way of using satellite-based measurements that offer global and consistent coverage, making it possible to assess blackouts in remote, isolated areas that are otherwise hard to monitor with utility records, as well as their temporal changes. This broad scope is a distinct advantage of satellite data. Future research could extend this approach to other extreme weather events, such as heavy precipitation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], anomalous heat [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and compound events [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, a more compressive examination of the socio-economic status of affected areas could shed light on potential disparities in vulnerability across different population groups [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis paper has been written with the support of the Climate Compatible Growth Programme of the UK's Foreign, Commonwealth and Development Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies. The work was also partially funded by the UK Centre for Greening Finance and Investment under grant NE/V017756/1. This work was also funded by the Irish Research Council under grant number GOIPD/2022/519 to YM.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Office for Disaster Risk Reduction (2016) Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction. United Nations General Assembly, New York, NY, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovalev GF, Lebedeva LM (2019) Reliability of power systems, vol 1. Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBillinton R, Allan RN (2003) Reliability of electric power systems: An overview. Handb Reliab Eng, : p. 511\u0026ndash;528\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacmillan M et al (2023) Shedding light on the economic costs of long-duration power outages: A review of resilience assessment methods and strategies. 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Int J Disaster Risk Reduct, 85\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tropical cyclone, power outage, night-time light, resilience, remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-6030545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6030545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTropical cyclones are a leading cause of electric power outages, and the time required for power system recovery after storm damage is a critical measure of system resilience. However, systematically collected data on power supply disruptions are available for only a limited number of countries, leaving global patterns largely unexplored. In this study, we conducted the first global analysis of electric power system recovery times after 407 storms across 65 countries from 2012 to 2021, using satellite-based daily nighttime lights (NTL) observations to detect blackouts following storms. The median duration blackouts detected worldwide was 3 days, with a 5th-95th percentile range of 1 to 11 days. We found that high density urban areas had significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) longer blackout events than low density urban areas and rural areas, which was driven by an upper tail of the events (95 quantiles of, respectively, 16, 12, and 11 days). Blackout durations have significantly increased over the study period (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the rate of increase in rural areas (1.7 day/decade) nearly double that observed in urban areas (1 day/decade). The temporal variations in blackout duration in rural areas were significantly correlated with both storm attributes and pre-storm NTL brightness, whereas those in low-density urban areas were only correlated with storm attributes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in all cases). These findings highlight the pressing need to strengthen the resilience of electric power systems to storms, particularly as global reliance on electricity grows and storm activity patterns shift in response to climate change.\u003c/p\u003e","manuscriptTitle":"Daily night-time lights reveal prolonging global electric power system recovery times following tropical cyclone damage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-18 15:04:13","doi":"10.21203/rs.3.rs-6030545/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"453d1da5-2691-4326-953b-976e45069a73","owner":[],"postedDate":"February 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-18T15:04:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-18 15:04:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6030545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6030545","identity":"rs-6030545","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}