Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-179-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-179-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Uncovering the veil of night light changes in times of catastrophe
Utrecht University School of Economics (U.S.E.), Utrecht University, Kriekenpitplein 21–22, 3584EC Utrecht, the Netherlands
Wouter Botzen
Utrecht University School of Economics (U.S.E.), Utrecht University, Kriekenpitplein 21–22, 3584EC Utrecht, the Netherlands
Institute for Environmental Studies (IVM), VU Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, the Netherlands
Related authors
No articles found.
Vylon Ooms, Thijs Endendijk, Jeroen C. J. H. Aerts, W. J. Wouter Botzen, and Peter Robinson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1882, https://doi.org/10.5194/egusphere-2025-1882, 2025
Short summary
Short summary
Intense rainfall events cause increasingly severe damages to urban areas globally. We use unique insurance claims data to study the effect of nature-based and other adaptation measures on damage. We compare an area in Amsterdam where measures have been implemented to a similar, adjacent area without measures using an innovative method. We find a significant reduction of damage where the adaptation measures were implemented. Urban areas can reduce rain damage by implementing adaptation measures.
Kushagra Pandey, Jens A. de Bruijn, Hans de Moel, W. J. Wouter Botzen, and Jeroen C. J. H. Aerts
Nat. Hazards Earth Syst. Sci., 24, 4409–4429, https://doi.org/10.5194/nhess-24-4409-2024, https://doi.org/10.5194/nhess-24-4409-2024, 2024
Short summary
Short summary
As sea levels rise, coastal areas will experience more frequent flooding, and salt water will start seeping into the soil, which is a serious issue for farmers who rely on good soil quality for their crops. Here, we studied coastal Mozambique to understand the risks from sea level rise and flooding by looking at how salt intrusion affects farming and how floods damage buildings. We find that 15 %–21 % of coastal households will adapt and 13 %–20 % will migrate to inland areas in the future.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
Short summary
Short summary
Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Laurine A. de Wolf, Peter J. Robinson, W. J. Wouter Botzen, Toon Haer, Jantsje M. Mol, and Jeffrey Czajkowski
Nat. Hazards Earth Syst. Sci., 24, 1303–1318, https://doi.org/10.5194/nhess-24-1303-2024, https://doi.org/10.5194/nhess-24-1303-2024, 2024
Short summary
Short summary
An understanding of flood risk perceptions may aid in improving flood risk communication. We conducted a survey among 871 coastal residents in Florida who were threatened to be flooded by Hurricane Dorian. Part of the original sample was resurveyed after Dorian failed to make landfall to investigate changes in risk perception. We find a strong influence of previous flood experience and social norms on flood risk perceptions. Furthermore, flood risk perceptions declined after the near-miss event.
Samuel Rufat, Mariana Madruga de Brito, Alexander Fekete, Emeline Comby, Peter J. Robinson, Iuliana Armaş, W. J. Wouter Botzen, and Christian Kuhlicke
Nat. Hazards Earth Syst. Sci., 22, 2655–2672, https://doi.org/10.5194/nhess-22-2655-2022, https://doi.org/10.5194/nhess-22-2655-2022, 2022
Short summary
Short summary
It remains unclear why people fail to act adaptively to reduce future losses, even when there is ever-richer information available. To improve the ability of researchers to build cumulative knowledge, we conducted an international survey – the Risk Perception and Behaviour Survey of Surveyors (Risk-SoS). We find that most studies are exploratory and often overlook theoretical efforts that would enable the accumulation of evidence. We offer several recommendations for future studies.
Cited articles
Aerts, J. C., Botzen, W. W., Emanuel, K., Lin, N., De Moel, H., and Michel-Kerjan, E. O.: Evaluating flood resilience strategies for coastal megacities, Science, 344, 473–475, 2014. a
Berlemann, M. and Wenzel, D.: Hurricanes, economic growth and transmission
channels: Empirical evidence for countries on differing levels of
development, World Dev., 105, 231–247, 2018. a
Bluhm, R. and Krause, M.: Top lights-bright cities and their contribution to
economic development, CESifo Working Paper No. 7411, https://EconPapers.repec.org/RePEc:ces:ceswps:_7411 (last access: 3 December 2019), 2018. a
Cavallo, E. and Noy, I.: Natural disasters and the economy – A survey, International Review of Environmental and Resource Economics, 5, 63–102, https://doi.org/10.1561/101.00000039, 2011. a
Cavallo, E., Galiani, S., Noy, I., and Pantano, J.: Catastrophic Natural
Disasters and Economic Growth, Rev. Econ. Stat., 95,
1549–1561, 2013. a
Ceola, S., Laio, F., and Montanari, A.: Satellite nighttime lights reveal increasing human exposure to floods worldwide, Geophys. Res. Lett.,
41, 7184–7190, 2014. a
Chen, X. and Nordhaus, W.: A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa, Remote Sens., 7,
4937–4947, https://doi.org/10.3390/rs70404937, 2015. a
Dartmouth Flood Observatory: DFO Event 2005-114 – Hurricane Katrina: New Orleans area – Rapid Response Inundation Map 1, https://floodobservatory.colorado.edu/2005114.html (last access: 21 June 2021), 2005. a
Del Valle, A., Elliott, R. J., Strobl, E., and Tong, M.: The short-term
economic impact of tropical cyclones: Satellite evidence from Guangdong
province, Economics of Disasters and Climate Change, 2, 225–235, 2018. a
de Ruig, L. T., Barnard, P. L., Botzen, W. W., Grifman, P., Hart, J. F.,
de Moel, H., Sadrpour, N., and Aerts, J. C.: An economic evaluation of
adaptation pathways in coastal mega cities: An illustration for Los
Angeles, Sci. Total Environ., 678, 647–659, 2019. a
Donaldson, D. and Storeygard, A.: The View from Above: Applications of Satellite Data in Economics, J. Econ. Perspect., 30, 171–98,
2016. a
Earth Observation Group, Payne Institute
for Public Policy, Colorado School of Mines: https://eogdata.mines.edu/products/dmsp, last access: 1 February 2021. a
Ebener, S., Murray, C., Tandon, A., and Elvidge, C. C.: From Wealth to Health: Modelling the Distribution of Income Per Capita at the Sub-National Level Using Night-Time Light Imagery, Int. J. Health Geogr., 4, 5, https://doi.org/10.1186/1476-072X-4-5, 2005. a
Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., and
Davis, C. W.: Relation Between Satellite Observed Visible-Near Infrared
Emissions, Population, Economic Activity and Electric Power Consumption,
Int. J. Remote Sens., 18, 1373–1379, 1997. a
Elvidge, C. D., Ziskin, D., Baugh, K. E., Tuttle, B. T., Ghosh, T., Pack,
D. W., Erwin, E. H., and Zhizhin, M.: A Fifteen Year Record of Global
Natural Gas Flaring Derived from Satellite Data, Energies, 2, 595–622,
2009b. a
Elvidge, C. D., Baugh, K. E., Zhizhin, M., and Hsu, F.-C.: Why VIIRS data are superior to DMSP for mapping nighttime lights, Proceedings of the Asia-Pacific Advanced Network, 35, 5860–5879, https://doi.org/10.1080/01431161.2017.1342050, 2013. a, b
Fan, X., Nie, G., Deng, Y., An, J., Zhou, J., and Li, H.: Rapid detection of
earthquake damage areas using VIIRS nearly constant contrast night-time
light data, Int. J. Remote Sens., 40, 2386–2409, 2019. a
Felbermayr, G. and Gröschl, J.: Naturally Negative: The Growth Effects of Natural Disasters, J. Dev. Econ., 111, 92–106, 2014. a
Fomby, T., Ikeda, Y., and Loayza, N. V.: The Growth Aftermath of Natural
Disasters, J. Appl. Econom., 28, 412–434, 2013. a
Gao, S., Chen, Y., Liang, L., and Gong, A.: Post-earthquake night-time light
piecewise (PNLP) pattern based on NPP/VIIRS night-time light data: A case
study of the 2015 Nepal earthquake, Remote Sens., 12, 2009, https://doi.org/10.3390/rs12122009, 2020. a
Ghosh, T., L Powell, R., D Elvidge, C., E Baugh, K., C Sutton, P., and Anderson, S.: Shedding light on the global distribution of economic activity, Open Geography Journal, 3, 147–160, 2010. a
Ghosh, T., Anderson, S. J., Elvidge, C. D., and Sutton, P. C.: Using nighttime satellite imagery as a proxy measure of human well-being, Sustainability, 5, 4988–5019, 2013. a
Gibson, J., Olivia, S., and Boe-Gibson, G.: Night lights in economics: sources and uses, J. Econ. Surv., 34, 955–980, 2020. a
Gibson, J., Olivia, S., Boe-Gibson, G., and Li, C.: Which night lights data
should we use in economics, and where?, J. Dev. Econ.,
149, 102602, https://doi.org/10.1016/j.jdeveco.2020.102602, 2021. a, b
Hallegatte, S., Green, C., Nicholls, R. J., and Corfee-Morlot, J.: Future flood losses in major coastal cities, Na. Clim. Change, 3, 802–806, 2013. a
Heger, M. P. and Neumayer, E.: The impact of the Indian Ocean tsunami on
Aceh's long-term economic growth, J. Dev. Econ., 141, 102365, https://doi.org/10.1016/j.jdeveco.2019.06.008, 2019. a
Henderson, M., Yeh, E. T., Gong, P., Elvidge, C., and Baugh, K.: Validation of urban boundaries derived from global night-time satellite imagery,
Int. J. Remote Sens., 24, 595–609, 2003. a
Hornbeck, R. and Naidu, S.: When the levee breaks: black migration and
economic development in the American South, Am. Econ. Rev., 104,
963–90, 2014. a
Hsiang, S. M. and Jina, A. S.: The causal effect of environmental catastrophe on long-run economic growth: Evidence from 6700 cyclones, Working Paper 20352, National Bureau of Economic Research, https://doi.org/10.3386/w20352, 2014. a
IPCC: Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and Meyer, L. A., IPCC, Geneva, Switzerland, p. 151, ISBN 978-92-9169-143-2, 2014. a, b
Ishizawa, O. A., Miranda, J. J., and Strobl, E.: The Impact of Hurricane Strikes on Short-Term Local Economic Activity: Evidence from Nightlight Images in the Dominican Republic, Int. J. Disast. Risk Sc., 10, 362–370, 2019. a
Jarmin, R. S. and Miranda, J.: The impact of Hurricanes Katrina, Rita and Wilma on business establishments, Journal of Business Valuation and Economic Loss Analysis, 4, https://doi.org/10.2202/1932-9156.1040, 2009. a, b
Keola, S., Andersson, M., and Hall, O.: Monitoring Economic Development from
Space: Using Night-Time Light and Land Cover Data to Measure Economic
Growth, World Dev., 66, 322–334, 2015. a
Klomp, J. and Valckx, K.: Natural disasters and economic growth: A
meta-analysis, Global Environ. Chang., 26, 183–195, 2014. a
Kohiyama, M., Hayashi, H., Maki, N., Higashida, M., Kroehl, H., Elvidge, C.,
and Hobson, V.: Early damaged area estimation system using DMSP-OLS
night-time imagery, Int. J. Remote Sens., 25, 2015–2036,
2004. a
Li, X. and Li, D.: Can night-time light images play a role in evaluating the Syrian Crisis?, Int. J. Remote Sens., 35, 6648–6661, 2014. a
Li, X., Chen, X., Zhao, Y., Xu, J., Chen, F., and Li, H.: Automatic intercalibration of night-time light imagery using robust regression, Remote
Sens. Lett., 4, 45–54, 2013. a
Li, X., Zhang, R., Huang, C., and Li, D.: Detecting 2014 Northern Iraq Insurgency using night-time light imagery, Int. J. Remote Sens., 36, 3446–3458, 2015. a
Ma, T., Zhou, C., Pei, T., Haynie, S., and Fan, J.: Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities, Remote Sens. Environ., 124, 99–107, 2012. a
Mård, J., Di Baldassarre, G., and Mazzoleni, M.: Nighttime light data
reveal how flood protection shapes human proximity to rivers, Sci. Adv., 4, eaar5779, https://doi.org/10.1126/sciadv.aar5779, 2018. a
Miranda, J. J., Ishizawa, O. A., and Zhang, H.: Understanding the Impact
Dynamics of Windstorms on Short-Term Economic Activity from Night Lights in
Central America, Economics of Disasters and Climate Change, 4, 657–698,
2020. a
Mohan, P. and Strobl, E.: The short-term economic impact of tropical Cyclone Pam: an analysis using VIIRS nightlight satellite imagery, Int. J. Remote Sens., 38, 5992–6006, https://doi.org/10.1080/01431161.2017.1323288, 2017. a
National Hurricane Center: Costliest U.S. tropical cyclones tables updated,
Tech. rep., National Hurricane Center, https://www.nhc.noaa.gov/news/UpdatedCostliest.pdf (last access: 30 October 2022), 2018. a
Nguyen, C. N. and Noy, I.: Measuring the impact of insurance on urban earthquake recovery using nightlights, J. Econ. Geogr., 20, 857–877, 2020. a
Noy, I.: The Macroeconomic Consequences of Disasters, J. Dev. Econ., 88, 221–231, 2009. a
Pistrika, A. K. and Jonkman, S. N.: Damage to residential buildings due to
flooding of New Orleans after hurricane Katrina, Nat. Hazards, 54,
413–434, 2010. a
Skoufias, E., Strobl, E., and Breivik Tveit, T.: Flood and Tsunami Damage Indices Based on Remotely Sensed Data: An Application to Indonesia, Nat. Hazards Rev., 21, 04020042, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000325, 2020. a
Skoufias, E., Strobl, E., and Tveit, T.: Can we rely on VIIRS nightlights to estimate the short-term impacts of natural hazards? Evidence from five South East Asian countries, Geomat. Nat. Haz. Risk, 12, 381–404,
2021. a
Small, C., Pozzi, F., and Elvidge, C. D.: Spatial analysis of global urban
extent from DMSP-OLS night lights, Remote Sens. Environ., 96,
277–291, 2005. a
Storeygard, A.: Farther on Down the Road: Transport Costs, Trade and Urban
Growth in Sub-Saharan Africa, Rev. Econ. Stud., 83, 1263–1295,
2016. a
Strobl, E.: The economic growth impact of natural disasters in developing
countries: Evidence from hurricane strikes in the Central American and
Caribbean regions, J. Dev. Econ., 97, 130–141, 2012. a
Sutton, P. C. and Costanza, R.: Global Estimates of Market and Non-Market
Values Derived from Night-Time Satellite Imagery, Land Cover, and Ecosystem
Service Valuation, Ecol. Econ., 41, 509–527, 2002. a
Sutton, P. C., Elvidge, C. D., and Ghosh, T.: Estimation of Gross Domestic
Product at Sub-National Scales Using Night-Time Satellite Imagery,
International Journal of Ecological Economics and Statistics, 8, 5–21,
2007. a
U.S. Burea of Econonomic Analysis: Regional Economic Accounts, https://www.bea.gov/data/economic-accounts/regional (last access: 18 February 2020), 2020. a
Wu, J., Wang, Z., Li, W., and Peng, J.: Exploring Factors Affecting the
Relationship Between Light Consumption and GDP Based on DMSP/OLS Night-Time
Satellite Imagery, Remote Sens. Environ., 134, 111–119, 2013. a
Zhang, Q. and Seto, K. C.: Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data, Remote Sens. Environ., 115, 2320–2329, 2011. a
Zhao, X., Yu, B., Liu, Y., Yao, S., Lian, T., Chen, L., Yang, C., Chen, Z., and Wu, J.: NPP-VIIRS DNB daily data in natural disaster assessment:
evidence from selected case studies, Remote Sens., 10, 1526, https://doi.org/10.3390/rs10101526, 2018. a, b
Short summary
Researchers studying economic impacts of natural disasters increasingly use night light as a proxy for local economic activity, when socioeconomic data are unavailable. But often it is unclear what changes in light intensity represent in the context of disasters. We study this in detail for Hurricane Katrina and find a strong correlation with building damage and changes in population and employment. We conclude that night light data are useful to study local impacts of natural disasters.
Researchers studying economic impacts of natural disasters increasingly use night light as a...
Altmetrics
Final-revised paper
Preprint