On the optimal level of complexity for the representation of groundwater-dependent wetland systems in land surface models

Abstract

Wetland systems are among the largest stores of carbon on the planet, the most biologically diverse of all ecosystems, and dominant controls on the hydrologic cycle. However, their representation in land surface models (LSMs), which are the terrestrial lower boundary of Earth system models (ESMs) that inform climate actions, is limited. Here, we explore different possible parameterizations to represent wetland–groundwater–upland interactions with varying levels of system and computational complexity. We perform a series of numerical experiments informed by field observations from a particular type of wetland, called a fen, at the well-instrumented White Gull Creek in Saskatchewan, in the boreal region of North America. In this study, we focus on how modifying the modelling connection between the upland and the wetland affects the system's outcome. We demonstrate that the typical representation of groundwater-dependent wetlands in LSMs, which ignores interactions with groundwater and uplands, can be inadequate. We show that the optimal level of model complexity depends on the land cover, soil type, and the ultimate modelling purpose, being nowcasting and prediction, scenario analysis, or diagnostic learning.