Controls on spatial and temporal variability of soil moisture across a heterogeneous boreal forest landscape

Abstract. In the light of climate change and biodiversity loss, modeling and mapping soil moisture at high spatiotemporal resolution is increasingly crucial for a wide range of applications in Earth and environmental sciences, particularly in areas like boreal forests where comprehensive soil moisture datasets are scarce. Soil moisture, though a small fraction of Earth’s water, plays a fundamental role in terrestrial ecosystem dynamics, influencing meteorological processes, plant health, soil biogeochemistry, groundwater fluctuations, and nutrient exchanges at the land-atmosphere interface. However, understanding and modeling soil moisture dynamics is extremely complex due to the non-linear interplay of numerous physical and biological processes, the large number of drivers involved, and the wide range of spatial and temporal scales at play. Here, we focused on a boreal forest landscape in northern Sweden, where we monitored surface soil moisture with dataloggers at 82 locations during the 2022 vegetation period. We described spatial patterns and temporal fluctuations of soil moisture, we explored the relationships between the observed variations in soil moisture and a vast array of environmental and meteorological factors from multiple sources at varying spatial resolutions and temporal scales, and we tested how these relationships changed over time. Soil properties, topographical features, vegetation characteristics, and land use/land cover were all important contributors of spatial variations in soil moisture, suggesting that current soil moisture maps primarily relying on terrain indices could benefit from integrating this diverse range of information. Moreover, different spatial resolutions and user-defined thresholds of these indices largely affected the performance of the predictions, indicating that topographic proxies for soil moisture should be evaluated for the specific area of interest. Hydrological and meteorological conditions over five to seven days preceding soil moisture measurements were essential in explaining daily soil moisture fluctuations, and influenced the predominant mechanisms governing the spatial distribution of soil moisture. Our findings contribute to advancing physically based land surface and hydrological models, developing machine learning models for predicting spatiotemporal variability in soil moisture, and ultimately generating digital dynamic soil moisture maps for forest management and nature conservation.