Interpretable rainfall modelling reveals rapid reorganisation of Amazonian rainfall under vegetation loss
Understanding how vegetation loss alters rainfall remains a major challenge in climate and hydrological science, as deforestation modifies precipitation through heterogeneous, seasonal and nonlinear land-atmosphere feedbacks. Existing models struggle to capture these dynamics: convection is parameterised at coarse scales, tipping behaviour is poorly constrained, and rainfall-deforestation analyses are limited to multi-decadal timescales. Therefore, many approaches resolve correlations rather than causal effects, limiting our ability to anticipate hydrological disruption. Using a neural-network model for hourly rainfall prediction, combined with pathway diagnostics and sensitivity analyses...
| Authors | Lilly Horvath-Makkos, Fayyaz Minhas |
|---|---|
| Primary category | physics.ao-ph |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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