Predicting rut depth with soil moisture estimates from ERA5-Land and in-situ measurements

Abstract. Spatiotemporal modelling is an innovative way of predicting soil moisture and has promising applications in supporting sustainable forest operations. One such application is the prediction of rutting, since rutting can cause severe damage to forest soils and ecological functions.

In this work, we used ERA5-Land soil moisture retrievals and several topographic indices to model the response variable, in-situ soil water content, by means of a random forest model. We then correlated the predicted soil moisture with rut depth from different trials.

Our spatiotemporal modelling approach successfully predicted soil moisture with a Kendall’s rank correlation coefficient of 0.62 (R² of 64 %). The final model included the topographic depth-to-water index, slope, stream power index, topographic wetness index, as well as temporal components such as numeric variables derived from date and ERA5-Land soil moisture retrievals. These retrievals showed to be the most important predictor in the model, indicating a large temporal variation. The prediction of rut depth was also successful, resulting in a Kendall’s correlation coefficient of 0.63.

Our results demonstrate that by using data from several sources, including ERA5-Land retrievals, topographic indices and in-situ soil moisture measurements, we can accurately predict soil moisture and use this information to predict rut depth. This has practical applications in reducing the impact of heavy machinery on forest soils and avoiding wet areas during forest operations.