- University: AgroParisTech
- Place of defence: Montpellier
- Hosting institution(s): International Livestock Research Institute & Agroscope
Abstract
Rainfall intensification is reshaping carbon-water dynamics in tropical drylands, yet the mechanisms governing respiration responses to rainfall pulses remain poorly quantified. This study investigates the ecophysiological drivers of Birch-pulses in an East African semi-arid savanna using the eddy-covariance technique between 2018–2020. High-frequency fluxes were segmented into rainfall-pulse sequences and analysed through empirical and data-driven modelling frameworks, including a moisture corrected method (Rmoisture), an event segmented Peak–Tail (RPT) model, and an explainable machine-learning model known as XGBoost. The RPT formulation reproduced respiration dynamics with higher accuracy (R² = 0.70; RMSE = 1.56 μmol m⁻² s⁻¹) than temperature-only and Rmoisture, successfully capturing short-term flux peaks and decay trajectories. SHAP-based feature importance revealed that hydrological rate and antecedent dryness metrics dominated variability, while temperature exerted a secondary, modulating influence. These results confirm that respiration pulses arise primarily from complex abrupt dry–wet transitions rather than rainfall magnitude or mean soil moisture. Collectively, the findings advance the understanding of how antecedent hydrology, rewetting intensity, and thermal context interact to shape respiration in drylands. By integrating empirical and machine-learning approaches, this work provides a mechanistic framework for scaling and applying pulse-focused respiration partitioning to dryland sites across the region and beyond.
