Nonlinear multivariate modelling of wetland dynamics

Wetlands are very complex yet pivotal ecosystems on Earth. They serve as habitats for various flora and fauna. Alongside, wetlands are crucial for biogeochemical exchange between the Earth's surface and its atmosphere. A large proportion of organic carbon is sequestered in wetlands and plays a substantial role in the carbon cycle. The planning and management of wetlands depend a lot upon a reliable wetland model. The underlying complex dynamics of wetlands hinder the modelling of wetland extent. This study for the first time considers multivariate nonlinear dynamical system modelling using Nonlinear Autoregressive with Exogenous Inputs (NARX) model class. The data consists of weather variables and wetland fractions for two wetland sites falling under Asia and Africa. The model is simulated using fresh testing data and can predict wetland extent satisfactorily for both sample sites. The accuracy of the models is quantified using Root Mean square Error (RMSE) and Mean Absolute Error (MAE). A transparent NARX structure reveals the dynamical elements for the potential planning and management of wetlands.