Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2437-2023
https://doi.org/10.5194/gmd-16-2437-2023
Model description paper
 | 
09 May 2023
Model description paper |  | 09 May 2023

GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model

Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts

Related authors

The effects of upstream water abstraction for commercial export farming on drought risk and impact of agropastoral communities in the drylands of Kenya
Ileen N. Streefkerk, Jeroen C. J. H. Aerts, Jens de Bruijn, Khalid Hassaballah, Rhoda Odongo, Teun Schrieks, Oliver Wasonga, and Anne F. Van Loon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2382,https://doi.org/10.5194/egusphere-2024-2382, 2024
Short summary
Content Analysis of Multi-Annual Time Series of Flood-Related Twitter (X) Data
Nadja Veigel, Heidi Kreibich, Jens A. de Bruijn, Jeroen C. J. H. Aerts, and Andrea Cominola
EGUsphere, https://doi.org/10.5194/egusphere-2024-2556,https://doi.org/10.5194/egusphere-2024-2556, 2024
Short summary
Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India
Maurice W. M. L. Kalthof, Jens de Bruijn, Hans de Moel, Heidi Kreibich, and Jeroen C. J. H. Aerts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1588,https://doi.org/10.5194/egusphere-2024-1588, 2024
Short summary
Simulating the effects of sea level rise and soil salinization on adaptation and migration decisions in Mozambique
Kushagra Pandey, Jens A. de Bruijn, Hans de Moel, Wouter Botzen, and Jeroen C. J. H. Aerts
EGUsphere, https://doi.org/10.5194/egusphere-2024-17,https://doi.org/10.5194/egusphere-2024-17, 2024
Short summary
Using rapid damage observations from social media for Bayesian updating of hurricane vulnerability functions: A case study of Hurricane Dorian
Jens A. de Bruijn, James E. Daniell, Antonios Pomonis, Rashmin Gunasekera, Joshua Macabuag, Marleen C. de Ruiter, Siem Jan Koopman, Nadia Bloemendaal, Hans de Moel, and Jeroen C. J. H. Aerts
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-282,https://doi.org/10.5194/nhess-2020-282, 2020
Revised manuscript not accepted
Short summary

Related subject area

Hydrology
Generalised drought index: a novel multi-scale daily approach for drought assessment
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024,https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024,https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024,https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024,https://doi.org/10.5194/gmd-17-7083-2024, 2024
Short summary
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024,https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary

Cited articles

Ablo, A. D. and Yekple, E. E.: Urban water stress and poor sanitation in Ghana: perception and experiences of residents in the Ashaiman Municipality, GeoJournal, 83, 583–594, https://doi.org/10.1007/s10708-017-9787-6, 2018. 
Aerts, J. C. J. H., Botzen, W. J., Clarke, K. C., Cutter, S. L., Hall, J. W., Merz, B., Michel-Kerjan, E., Mysiak, J., Surminski, S., and Kunreuther, H.: Integrating human behaviour dynamics into flood disaster risk assessment, Nat. Clim. Change, 8, 193–199, https://doi.org/10.1038/s41558-018-0085-1, 2018. 
Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., and Siebert, S.: Development and testing of the WaterGAP 2 global model of water use and availability, Hydrolog. Sci. J., 48, 317–337, https://doi.org/10.1623/hysj.48.3.317.45290, 2003. 
Arnold, R. T., Troost, C., and Berger, T.: Quantifying the economic importance of irrigation water reuse in a Chilean watershed using an integrated agent-based model, Water Resour. Res., 51, 648–668, https://doi.org/10.1002/2014WR015382, 2015. 
Batchelor, C. H., Rama Mohan Rao, M. S., and Manohar Rao, S.: Watershed development: A solution to water shortages in semi-arid India or part of the problem?, Land Use and Water Resources Research, 3, 1–10, https://doi.org/10.22004/ag.econ.47866, 2003. 
Download
Short summary
We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.