1. Modeling the spatial and temporal dynamics of riparian vegetation induced by river flow fluctuation
- Author
-
Jingling Liu and Xiaoguang You
- Subjects
010504 meteorology & atmospheric sciences ,Stochastic modelling ,0208 environmental biotechnology ,Population ,Probability density function ,02 engineering and technology ,age structure ,Spatial distribution ,riparian vegetation distribution ,01 natural sciences ,species abundance ,population lifespan ,Carrying capacity ,education ,spatial temporal dynamics ,Ecology, Evolution, Behavior and Systematics ,stochastic model ,0105 earth and related environmental sciences ,Nature and Landscape Conservation ,Riparian zone ,Original Research ,geography ,education.field_of_study ,geography.geographical_feature_category ,Ecology ,nonstationarity ,020801 environmental engineering ,same‐aged population ,Environmental science ,Markov property ,Biological system ,Random variable ,river flow fluctuation - Abstract
River flow fluctuation has an important influence on riparian vegetation dynamics. A temporally segmented stochastic model focusing on a same‐aged population is developed for the purpose of describing both spatial and temporal dynamics of riparian vegetation. In the model, the growth rate of population, rather than carrying capacity, is modeled as the random variable. This model has explicit physical meaning. The model deduces a process‐based solution. From the solution process, the probability density of spatial distribution can be derived; therefore, the spatial distribution of population abundance can be described. The lifespan of a same‐aged population and the age structure of the species‐specific population can also be studied with the aid of this temporally segmented model. The influence of correlation time of river flow fluctuation is also quantified according to the model. The calibration of model parameters and model application are discussed. The model provides a computer‐aided method to simulate and predict vegetation dynamics during river flow disturbances. Meanwhile, the model is open and allows for more accurate and concrete modeling of growth rate. Because of the Markov property involved in the process‐based solution, the model also has the ability to deal with cases of nonstationary disturbances.
- Published
- 2018