Socio-hydrology in the floodplain

Socio-hydrology in the floodplain: Investigating patterns of human response to riverine floods by quantifying the human footprint in flood prone areas using nighttime light data

Verschuren, Lisa (TU Delft Civil Engineering and Geosciences)

Pande, S. (mentor)
Bricker, J.D. (graduation committee)
Hoes, O.A.C. (graduation committee)

Delft University of Technology

Water Management

2019-10-11

Flooding is globally one of the most damaging natural hazard. Flood risk will most likely increase in the near future due to increases in flood frequency attributed to climate change and growth in population and wealth in flood prone areas. This growth in wealth and population is increasingly considered as a major driver for the increase in flood losses in the last decades. Floodplains are susceptible to floods, but historically people have always been settling in floodplains. The growth of population in floodplains, which is a substantial cause for increased flood risk, is essential to consider for decision making in floodplain development, as improper development increases flood exposure and aggravates flood risk. The science of socio-hydrology tries to capture the interaction between humans and floods in the floodplain. But, it is necessary to identify these mechanisms on a broader scale. A way of doing this, is to look at the development of floodplain population density over the years, but population data is not available on a long temporal scale. Therefore, Nighttime light data was used to model the gaps in the availability of population data. Nighttime light data data captures the illumination on earth, and is available on a large temporal and spatial scale, and has a high correlation with population data. However, the relationship between Nighttime light data and population data is not straightforward. This study tries to model a population proxy through the use of Nighttime light data data and explains when and why it does or does not work. Validation of the model shows that in some regions the predicted data is relatively precise, but ultimately, due to the lack of data, the accuracy is unknown. This study shows that understanding the behaviour of NTL is valuable, because it has the potential to map Socio-Economic variables in data-scarce areas.

Socio-hydrology
Floodplains
Nighttime Light Data

http://resolver.tudelft.nl/uuid:5744a4b3-ca1e-4691-a35b-a7d2a47a1d64

Student theses

master thesis

© 2019 Lisa Verschuren