Exploring spatiotemporal dynamics of soil moisture: three model conceptualizations in a subarctic catchment

Surface soil parameters, especially soil moisture has a key role in soil nutrient cycling, greenhouse gas emissions, vegetation water use as well as energy and water exchanges between land and the atmosphere. In this study, we model soil moisture with three different model conceptualizations developed in Spatial Forest Hydrology model (SpaFHy) in a subarctic Pallas catchment in Northern Finland, covered by coniferous forests and peatlands. The model versions differ in how the groundwater flow is treated, which is shown to have a clear impact on the spatiotemporal soil moisture dynamics within the catchment. The conceptualizations range from i) neglecting groundwater storage, to ii) TOPMODEL approach, and to iii) spatially distributed groundwater flow model. By comparing these scenarios, we are able to assess when and where solving the 2D ground water flow is prerequisite for accurate predictions of soil moisture, and in which conditions soil moisture variability is driven more by local processes. The model results are compared against continuous point-scale measurements, and spatially against distributed measurement campaigns conducted in the study area. In addition, we compare the spatiotemporal soil moisture simulations with novel SAR-based soil moisture maps. SAR signal is well suited to estimate topsoil moisture thanks to its high sensitivity to water. However, different topographic and vegetation settings create challenges for SAR signals to capture the properties of soil, and thus, SAR soil moisture estimates have not been as widely used in forested areas. Remote sensing products such as SAR-based soil moisture maps possess a major potential to further develop spatially distributed land surface and hydrological models.