Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface- land surface processes model

Understanding key controls on hydrologic dynamics is important for effectively allocating resources for data collection, reducing model dimensionality, and making modeling decisions. This work seeks to elucidate the physical factors responsible for the observed hydrologic patterns of a watershed in Michigan using an integrated hydrologic model, Process-based Adaptive Watershed Simulator and Community Land Model (PAWS+CLM). The model is tested using observed data for streamflows, soil temperature, groundwater table depths, and satellite-based observations of evapotranspiration (ET) and leaf area index (LAI). Numerical experiments are carried out to lump the effects of key controls, including land use types, nitrogen levels, groundwater redistribution, and soil texture, into different process indices. Using analysis of variance (ANOVA), we quantitatively determine the strengths of these controls on ET, net primary production (NPP), and other important variables. Groundwater flow is found to be the major control on runoff and infiltration, with soil texture ranking next, while vegetation type and nitrogen levels are found to dominate NPP, top soil temperature, and transpiration. Soil texture and groundwater are found to have comparable influence on soil moisture, which is in agreement with analysis of field data in the literature. All controls are found to colimit ET, which serves as the nexus for ecosystem-hydrology interactions. From the simulation results, we find that nitrogen significantly controls transpiration, through which it influences other hydrologic fluxes. While there is room for improving descriptions of the nitrogen cycle in the current version of CLM, these novel results call for an understanding of the interplay between hydrology and biogeochemistry. Additional analysis shows that the relative strengths of the controls examined in this work are fairly robust with respect to changes in parameters and spatial resolution.