Process-based modeling of soil hydrology and its verification at the Biosphere 2 Landscape Evolution Observatory

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Soil water movement is a key process in several provisioning and regulating ecosystem services. However, it’s accurate prediction through mathematical models remains a challenge, because of large uncertainties in all model components, even when using process-based descriptions like the Richards equation.Data assimilation methods offer the possibility to combine information from uncertain models and uncertain measurements into an improved consistent description of the state, provided the uncertainties can be quantified correctly. The largest uncertainties are typically associated with the hydraulic properties of the soil. Including the pertinent hydraulic parameters into an augmented state allows data assimilation methods to estimate them. This can even be achieved in the presence of model errors like preferential flow, if these errors are considered appropriately. So far, such consistent descriptions have only been demonstrated on smaller scales up to one-dimensional soil profiles. On larger scales detailed process-based descriptions have not yet been accomplished. This is due to lack of information on the heterogeneous soil hydraulic properties in combination with the highly nonlinear and interacting processes.A unique research infrastructure for the experimental study of soil hydrology on hillslopes is the Landscape Evolution Observatory (LEO) at Biosphere 2. It consists of three artificial hillslopes with an extensive sensor and sampler network.To improve our understanding at this larger hillslope scale, the objective of this project is the consistent and process-based description of soil water movement at LEO including the representation of heterogeneity and evolution of soil hydraulic parameters. This will be addressed along the following lines: (i) deduce the evolution of soil hydraulic parameters in time through data assimilation at selected profiles in the hillslopes, (ii) determine the heterogeneity of soil hydraulic properties and their effects through hydraulic experiments and forward simulations, and (iii) gain and verify a consistent description of parts of the hillslope through data assimilation methods. This project will answer if current monitoring techniques suffice to obtain a consistent and usefully accurate representation of hillslope hydrology and if so, how the description is achieved and with what uncertainty. Beyond this, I expect quantitative insight into the formation of heterogeneity at LEO.