A decadal record of soil moisture space–time variability over a south-east Australian catchment

Semi-arid to temperate south-east Australian catchments with agricultural landscapes demonstrate unique hydro-climatic characteristics. Understanding the behaviour of soil moisture over such catchments and the influence of driving factors are crucial for hydrologic, climatic and agricultural applications. However, this is challenging due the complex, non-linear relationship between these factors and soil moisture, and the lack of long-term catchment scale data records. To address this, spatial and temporal patterns of soil moisture over two south-east Australian river catchments (i.e., Krui and Merriwa) and the influence of soil texture, topography, vegetation and rainfall on soil moisture variability were evaluated using a decadal in-situ dataset. This unique in-situ soil moisture monitoring network is established over a semi-arid to temperate catchment representing typical south-east Australian agricultural landscape and the data record has captured some major climatic events. Time stability of catchment-scale soil moisture and the potential of predicting catchment mean soil moisture content using one representative station were also examined using a linear regression model. Soil texture was found as the dominating factor driving the spatial variability of soil moisture in the area. The temporal patterns of soil moisture showed a positive agreement with vegetation dynamics and rainfall at topsoil layers (0–5 cm and 0–30 cm). A higher spatial variability of soil moisture was observed during dry catchment conditions compared to wet catchment conditions. The deeper soil layers (30–60 cm and 60–90 cm) showed highly stable soil moisture values, which might be the driving force of the agriculture in the area. A linear regression based prediction model demonstrated a good potential in estimating spatial mean soil moisture content from one representative station. The results are useful in parameterization of soil moisture variability in land surface, climatic and hydrologic models, agricultural applications and in remote sensing of soil moisture.