Dynamic modelling

We develop different models as alternative explanations of the feedback mechanisms driving the mutual shaping of hydrological extremes and society. Mathematical equations are used to formalize competing hypotheses on the behaviour of river basins, floodplains and cities as human-water systems.
We build upon recent efforts to conceptualise either human-flood interactions (Di Baldassarre et al. 2013; Grames et al., 2016; Yu et al., 2017), water management rules (Mateo et al., 2015; Di Baldassarre et al., 2016), or human-drought interactions (Kuil et al., 2016). Yet, these models focus on either floods or droughts. This is a major limitation as humans respond to both hydrological extremes, and therefore a singular focus on one of them does not allow for explaining long-term phenomena, such as the aforementioned sequence effect.

For this reason, we propose competing hypotheses about the causal links between key variables and parameters underlying the mutual shaping of hydrological extremes and society. Causal links are then be expressed using a set of mathematical equations. Human responses to hydrological extremes are also formalized by building upon studies about primary mechanisms underlying human adaptation, such as social memory, changing social contracts or learning processes (Folke et al., 2005; Adger et al., 2013; Pahl-Wostl et al., 2013).
The hypothesised relationships are tested by using the observations analysed in six case studies, and the vast amount of data collected in numerous human-water systems around the world.
Explanatory models are then used to simulate the behaviour of human-water systems over long time scales (20-30 years), and explore the potential impacts of global change, as well as the long-term effects of different policies and strategies, in the six case studies


  • Adger, W.N., Quinn, T., Lorenzoni, I., Murphy, C., and Sweeney, J. (2013). Changing social contracts in climate-change adaptation. Nature Climate Change, 3(4), 330-333.
  • Di Baldassarre, G., Di Baldassarre, G., A. Viglione, G. Carr, L. Kuil, J. L. Salinas, and G. Blöschl (2013), Socio-hydrology: conceptualising human-flood interactions, Hydrology and Earth System Sciences, 17(8), 3295-3303.
  • Di Baldassarre, G., Martinez, F., Kalantari, Z., and Viglione, A. (2016). Drought and Flood in the Anthropocene: Feedback Mechanisms in Reservoir Operation, Earth System Dynamics Discussions, doi:10.5194/esd-2016-65
  • Folke, C., Hahn, T., Olsson, P., and J. Norberg (2005). Adaptive governance of social-ecological systems. Annu. Rev. Environ. Resour., 30, 441–73.
  • Grames, J., Prskawetz, A., Grass, D., Viglione, A., and G. Blöschl (2016). Modeling the interaction between flooding events and economic growth. Ecological Economics, 129, 193-209.
  • Kuil, L., Carr, G., Viglione, A., Prskawetz, A., and G. Blöschl (2016). Conceptualizing socio-hydrological drought processes: The case of the Maya collapse. Water Resources Research, 52(8), 6222-6242.
  • Mateo, C. M., N. Hanasaki, D. Komori, et al. (2014). Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models, Water Resour. Res., 50, 7245–7266.
  • Pahl-Wostl, C., Becker, G., Knieper, C. and Sendzimir, J. (2013). How multilevel societal learning processes facilitate transformative change: a comparative case study analysis on flood management. Ecology and Society, 18(4), 58.
  • Yu, D. J., Sangwan, N., Sung, K., Chen, X. and Merwade, V. (2017), Incorporating institutions and collective action into a socio-hydrological model of flood resilience. Water Resources Research. doi:10.1002/2016WR019746