A Causal Inference Framework for Analysing Drought Drivers

The impact of droughts on vegetation is commonly assessed through correlational analysis of satellite-derived variables, such as NDVI, precipitation anomalies, soil moisture, and more (Hao & Singh 2015, Park et al. 2016, Joiner et al. 2018). However, these correlation-based approaches cannot disentangle the true causal drivers from their confounded associations (Zhang et al. 2022). This limits our ability to understand and attribute the scale of vegetation stress to specific drought mechanisms (e.g. soil moisture deficits versus irrigation resilience), and our ability to design effective interventions that address the primary drivers.

As such, we propose a novel causal inference framework to estimate the impact of drought on vegetation health using satellite time-series data, and demonstrate its application to the Iberian Peninsula. We firstly define a graphical causal model based on established eco-hydrological pathways, and then integrate multi-sensor remote sensing data (MODIS NDVI, SPEI, etc.) and climate reanalysis (ERA5). By extending traditional causal inference methods for georeferenced time-series raster data and controlling for well-established confounding variables (temperature, solar radiation, precipitation, soil moisture, land cover, and irrigation), we isolate the effect of drought severity on vegetation. We also implement novel visualisation methods to display these causal influence estimates.

While causal inference allows us to move beyond correlation and understand the impact on vegetation from each of these key variables, counterfactual intervention is also essential to understand how varying conditions would otherwise change the outcome (Schölkopf et al. 2021), i.e. the severity of the drought impact. Therefore, by leveraging these interventions, our results go from descriptive analytics to actionable insights on drought severity under the changing climate. This enables more effective drought impact assessment for scientists, policymakers, and industry experts.

References:
1. Hao, Z. and Singh, V.P., 2015. Drought characterization from a multivariate perspective: A review. Journal of Hydrology, 527, pp.668-678.
2. Park, S., Im, J., Jang, E. and Rhee, J., 2016. Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agricultural and forest meteorology, 216, pp.157-169.
3. Joiner, J., Yoshida, Y., Anderson, M., Holmes, T., Hain, C., Reichle, R., Koster, R., Middleton, E. and Zeng, F.W., 2018. Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales. Remote Sensing of Environment, 219, pp.339-352.
4. Zhang, X., Hao, Z., Singh, V.P., Zhang, Y., Feng, S., Xu, Y. and Hao, F., 2022. Drought propagation under global warming: Characteristics, approaches, processes, and controlling factors. Science of the Total Environment, 838, p.156021.
5. Schölkopf, B., Locatello, F., Bauer, S., Ke, N.R., Kalchbrenner, N., Goyal, A. and Bengio, Y., 2021. Toward causal representation learning. Proceedings of the IEEE, 109(5), pp.612-634.