LAUSR

Abstract The composition, abundance and age structure of groundwater-dependent vegetation (GDV) are tightly coupled with groundwater level variation. Modeling the space–time dynamics of GDV is valuable for ecosystem conservation and recovery. But there are few modeling frameworks on GDV space–time dynamics. In this study, we propose a stochastic process-based theoretical framework, and develop a Species-specific Theoretical Evolution Model (STEM) to analyze and simulate GDV dynamics. In the model, the groundwater level variation is described as stochastic processes. Based on this method, the temporal dimension of vegetation dynamics is involved. The model focuses on the life cycle of same-aged population and divides this life cycle into two evolution phases. Each phase is formulated with a stochastic differential equation respectively. The solution processes interpret the probability distributions of GDV, and the plot-wised model application in a certain geomorphology area derives different typical distribution patterns for the population abundance and lifespan. The results also reveal the positive effects of water regulations on vegetation restoration. This framework describes the spatial–temporal dynamics of GDV rather than providing a stationary solution. It can adapt to the non-stationarity of groundwater level variations and distinctive sensitivity during the plants life cycle because of the Markov property of stochastic equations. The model can also help to predict vegetation responses under the groundwater level variations.