T14 – Modelling Farmer Adaptation to Changing Flood Risk

Short description

Climate change intensifies the frequency and severity of extreme weather events, such as floods and Tropical Cyclones, posing significant threats to agricultural communities, particularly in developing Countries in sub-Saharan Africa and Asia. Farmers in these contexts often lack the financial and technological infrastructure to manage these risks effectively, leaving them highly vulnerable to crop losses and income disruptions. Farmers’ decisions regarding adaptation strategies have cascading consequences on their livelihoods, agricultural systems, and, ultimately, food security. Understanding the interplay between these decisions and various socio-economic factors is crucial for designing effective climate risk-reduction policies and building long-term resilience in agricultural systems under a changing climate. Existing research often focuses on individual adaptation options in isolation, overlooking the intricate web of factors and their long-term effects.

This research thesis aims to address this gap by developing a comprehensive framework that will:

  • Model farmer adaptation decisions: an existing agent-based model (ABM) will be adapted to represent individual farmers and their decision-making processes based on perceived risk, socioeconomic factors, and policy context.
  • Incorporate various adaptation options: different alternatives will be considered, including risk transfer (parametric insurance), risk reduction (e.g., changing crops, adapting planting/harvesting schedule, infrastructure investments), and risk avoidance (migration), evaluating their effectiveness under different historical and future scenarios.
  • Include a calibrated crop-damage function: coastal and river flood severity will be linked to crop yield losses, enabling the quantification of adaptation benefits and trade-offs.
  • Simulate historical and future scenarios: diverse climate simulations (reanalysis and projections) and policy settings will be used to simulate and explore farmers’ behaviour, insurance-policy effectiveness, and system resilience under historical and future stressful conditions of flood risk. In fact, while insurance and other adaptation options might mitigate immediate losses, persistent flood risks could still push farmers to migrate, leading to social and economic disruptions.

To achieve these goals, we expect the student to carry out the following activities: 

  • Explore the literature for impact-based crop yield models, based on either salinity (coastal flood) and/or water levels for floodplain crops, including an analysis of existing studies on crop-damage functions and datasets;
  • Look into global datasets (like FAO) for farmers’ geospatial data (exposure and vulnerability) for a possible case study area (Mozambique and/or other African regions) with key characteristics (type of crop, size, etc.);
  • Explore flood characteristics over historical and future scenarios, using state-of-the-art reanalysis (GloFAS-ERA5; Harrigan et al., 2020) for river floods and simulating future scenarios using a local coastal flood model (SFINCS; Leijnse et al. 2021), using projections of sea level rise and storm surge as inputs;
  • Use historical crop impact datasets (crop yield) for floods and analyse the relationship with flood occurrence and other flood characteristics, evaluating different calibrated crop-damage functions (e.g., Shrestha et al. 2021);
  • Modify an existing Agent-Based Model (coded in Python) to adapt decision logic to the case study; 
  • Simulate and analyse relevant historical and future scenarios to evaluate impacts, trade-offs, macro-scale emergent phenomena and adaptation policies efficacy.

References

Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data, 12, 2043–2060, https://doi.org/10.5194/essd-12-2043-2020 , 2020.

Huber, R., Xiong, H., Keller, K., & Finger, R. (2022). Bridging behavioural factors and standard bio-economic modelling in an agent-based modelling framework. Journal of Agricultural Economics, 73(1), 35–63. https://doi.org/10.1111/1477-9552.12447

Pandey, K., de Bruijn, J. A., de Moel, H., Botzen, W., and Aerts, J. C. J. H.: Simulating the effects of sea level rise and soil salinization on adaptation and migration decisions in Mozambique, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-17, 2024.

Leijnse, T., van Ormondt, M., Nederhoff, K., & van Dongeren, A. (2021). Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes. Coastal Engineering, 163, 103796. https://doi.org/10.1016/j.coastaleng.2020.103796

Nie, X., Zhou, J., Cheng, P., & Wang, H. (2021). Exploring the differences between coastal farmers’ subjective and objective risk preferences in China using an agent-based model. Journal of Rural Studies, 82, 417–429. https://doi.org/10.1016/j.jrurstud.2021.01.037 

Shrestha, B. B., Kawasaki, A., & Zin, W. W. (2021). Development of flood damage functions for agricultural crops and their applicability in regions of Asia. Journal of Hydrology: Regional Studies, 36, 100872. https://doi.org/10.1016/j.ejrh.2021.100872

Wens, M., Veldkamp, T. I. E., Mwangi, M., Johnson, J. M., Lasage, R., Haer, T., & Aerts, J. C. J. H. (2020). Simulating Small-Scale Agricultural Adaptation Decisions in Response to Drought Risk: An Empirical Agent-Based Model for Semi-Arid Kenya. Frontiers in Water, 2. https://www.frontiersin.org/articles/10.3389/frwa.2020.00015 

Relevant courses and knowledge

Natural Resources Management

Number of students

1

Requisites

The student should be comfortable with data handling and analysis. Coding skills in at least one programming language are essential (e.g. Python, R, or Matlab). Knowledge of Python and/or object-oriented programming are recommended.