A dynamic-statistical approach for probabilistic forecasting of daily soil moisture in the United States

Soil moisture forecasting is important for informing agricultural and environmental management. However, due to the strong interactions between climate, soils and vegetation, even small errors in the weather-related forcing commonly have remarkable impacts on the soil moisture, making soil moisture forecasting especially challenging. Therefore, it is necessary to develop a probabilistic forecasting strategy that accounts for the uncertainty of inputs such as rainfall and evapotranspiration. Here we develop a hybrid dynamic-statistical framework that combines statistical downscaled forecasts of precipitation and reference evapotranspiration from numerical weather predictions (NWP) with a probabilistic water balance model to produce probabilistic forecasts for daily soil moisture at site scale. Forecasts are initialized using in situ measurements over represented locations from the National Soil Moisture Network. We found that the skill of the soil moisture forecasts more relies on the skill of the precipitation forecasts than reference evapotranspiration forecasts. The forecasts are invariably highly skillful over the first 2-3 days, while the skill rapidly decreases over the following days. The soil moisture forecasts based on the statistically post-processed NWP forecasts show higher skill than persistence-based forecasts, climatology forecasts, or forecasts directly retrieved from NWP.