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\begin{document}

\title{Monitoring the impact of the 2022 Indus River flood using NASA's
Black Marble Nighttime Lights.}



\author[1]{Ekta Aggarwal}%
\author[1]{Sanjeev Gupta}%
\author[1]{ Alexander C. Whittaker}%
\author[1]{Philippa J Mason}%
\author[1]{Kartikeya S. Sangwan}%
\author[1]{Fritz Schlunegger}%
\affil[1]{Affiliation not available}%


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  \date{May 18, 2024}


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Ekta Aggarwal\textsuperscript{a}, Sanjeev Gupta\textsuperscript{a},
Alexander C. Whittaker\textsuperscript{a}, Philippa
Mason\textsuperscript{a}, Kartikeya S. Sangwan\textsuperscript{a}, Fritz
Schlunegger\textsuperscript{b}

\textsuperscript{a} \emph{Imperial College London,}
\textsuperscript{b~}\emph{University of Bern}

Major flood events cause adverse impacts on human populations,
infrastructure, and resources. The occurrence of flood events has led to
much research on flood vulnerability, exposure, and post-disaster
assessment. However, there has been a limited focus on studying the
recovery of societies and how this varies among different socio-economic
communities. Satellite datasets are an effective and cost-efficient way
to analyze the spatial extent of flooding and its impact on
anthropogenic activity in floodplains. Here we present an analysis of
NASA Black Marble nighttime lights (NTL) data for the 2022 Indus River
flood in Pakistan as it provides information on lit infrastructure which
is a useful proxy for real-time monitoring of human presence. We
investigate the impact of the flooding on the variance in NTL radiance
during and after the event, to investigate human exposure and response
to floods.

The Black Marble daily NTL VNP46A2 data product is the daily
moonlight-adjusted NTL. Here, we utilize this dataset to assess the
spatial impact of the 2022 Indus River flooding, which was one of the
biggest flood events in recent history affecting 33 million people. We
focus on the downstream floodplains of the Indus basin because frequent
flooding over this area is historically documented. To determine the
spatial extent of flooding we use ESA Sentinel SAR data to map the
spatial and temporal evolution of flood extent between June-September
2022. In tandem, we analyze NTL data to explore the variation in NTL
radiance values over a similar timescale. In particular, we use the NTL
to estimate the variation in its radiance for areas of high flood
exposure. We find that the NTL radiance in the flooded areas was
affected for approximately 13 weeks. It took approximately 10 weeks
post-flooding for the area's radiance to recover up to pre-flooding
level. The mean radiance from pre-flood to during the main flood period
shows a decline by a factor of 5. Additionally, there is varying
exposure and recovery for different socio-economic communities in the
area. Our findings have the potential to improve our understanding of
human response to floods and vulnerability at the lowest administrative
level in a fast, safe, and cost-effective manner. This may also provide
a framework for policymakers to assess flood vulnerability and impact at
a basin scale.

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