Geomorphic and climate-driven vegetation dynamics in badlands – A case study from Laval catchment, Draix-Bléone critical zone observatory, SE France

Badlands are characterized by highly eroded, rugged landscapes with steep slopes, limited vegetation, and significant soil degradation. In badlands, vegetation plays a key role in erosion mitigation by intercepting runoff and acting as a significant factor in soil stability. However, vegetation dynamics in such an environment are determined by geomorphological factors like slope, erosion, sediment flux, and climatic conditions, characterized by temperature and precipitation patterns.

This study evaluates the significance of such drivers of vegetation transition within the badland systems using a State-and-Transition Model (STM) approach. This model predicts vegetation dynamics as a function of two basic processes: extinction (loss of vegetation) and colonization (vegetation growth over a barren patch of land). It is forced with vegetation states at four different time points (i.e., 1982, 1994, 2015, and 2021), while climate variables (e.g., temperature and precipitation), and sediment fluxes are averaged for the periods between these states. Geomorphological parameters (i.e., topographic elevation and slope) are assumed to be constant throughout the simulation period. It estimates vegetation transition probabilities using logistic regression. The model parameters are optimized through Bayesian methods (i.e., Markov Chain Monte Carlo algorithm) for climate conditions and geomorphology in the Laval catchment in the Draix-Bléone critical zone observatory, southeastern France. Model performance is quantified through repetitive training and testing to ensure the soundness of the predictions.

The results indicate that colonization is negatively impacted by higher slopes and annual sediment fluxes and is supported by increasing mean annual temperatures and summer precipitation. In contrast, vegetation extinction is driven mainly by geomorphic disturbances (e.g., slope and sediment fluxes during extreme events), while climatic factors seem to have little impact on vegetation extinction in this study area. Indeed, the forward prediction model, initiated using the 1982 vegetation state with best-fit parameters as forcing, resulted in a reasonably close match of the predicted states to the conditions observed, i.e., those of 1994, 2015, and 2021, which had an accuracy of ~0.8, with uncertainties of around ~0.35.

The present study integrates both geomorphological and climatic data to develop valid interpretations concerning environmental factors responsible for vegetation dynamics within badland topography, adding to an improved understanding of the ecosystem dynamics of these sensitive environments.