Jens de Bruijn
Guest Research Scholar
Water Security Research Group
Biodiversity and Natural Resources Program
Biography
Jens de Bruijn joined the IIASA Water Security (WAT) Research Group in February 2020. He works on developing a large-scale agent-based model of farmer behaviour in the hydrological model CWatM, to which he is also a contributor. Furthermore, he works as an associate editor of ISIpedia, a science communication project.He also works as a post-doctoral researcher at VU-IVM where he similarly works on large-scale agent-based modeling of farmer behaviour, leads the model development of the COASTMOVE project and supervises several PhD students.
Dr. de Bruijn holds a doctoral degree from the Institute for Environmental Studies (IVM) at the VU University Amsterdam on the analysis of disaster-related social media, flood detection and disaster response. In this role, he also developed the Global Flood Monitor. Previously, he also worked as a data scientist at FloodTags and as a consultant for the World Bank to support rapid damage assessments (GRADE).
In 2015, in the course pursuing his MSc studies, he participated in the IIASA Young Scientists Summer Program (YSSP), working on a global assessment of non-renewable groundwater use for different crop types.
Last update: 22 APR 2021
Publications
Kalthof, M.W.M.L., de Bruijn, J. , de Moel, H., Kreibich, H., & Aerts, J.C.J.H. (2025). Adaptive behavior of farmers under consecutive droughts results in more vulnerable farmers: a large-scale agent-based modeling analysis in the Bhima basin, India. Natural Hazards and Earth System Sciences 25 (3) 1013-1035. 10.5194/nhess-25-1013-2025.
Veigel, N., Kreibich, H., de Bruijn, J. , Aerts, J.C.J.H., & Cominola, A. (2025). Content analysis of multi-annual time series of flood-related Twitter (X) data. Natural Hazards and Earth System Sciences 25 (2) 879-891. 10.5194/nhess-25-879-2025.
Ton, M.J., de Moel, H., de Bruijn, J. , Reimann, L., Botzen, W.J.W., & Aerts, J.C.J.H. (2024). Economic damage from natural hazards and internal migration in the United States. Natural Hazards 10.1007/s11069-024-06987-2.
Ton, M.J., Ingels, M.W., de Bruijn, J.A , de Moel, H., Reimann, L., Botzen, W.J.W., & Aerts, J.C.J.H. (2024). A global dataset of 7 billion individuals with socio-economic characteristics. Scientific Data 11 e1096. 10.1038/s41597-024-03864-2.
Feng, D., Beck, H., de Bruijn, J. , Sahu, R.K. , Satoh, Y., Wada, Y., Liu, J., Pan, M., Lawson, K., & Shen, C. (2024). Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL). Geoscientific Model Development 17 (18) 7181-7198. 10.5194/gmd-17-7181-2024.