State-space simulation of soil surface water content in grassland of northern Loess Plateau

In order to understand the spatial distribution of soil surface water content and its relations with environmental factors in grassland of the northern Loess Plateau, the autoregressive state-space models and classical linear regression models were used, to simulate the spatial distribution of soil surface water content in a grassland of the northern Loess Plateau, based on the saturated soil hydraulic conductivity (Ks), soil surface temperature (T), elevation (E) and litter mass (L). The results showed that state-space models could be applied to the wind and water erosion transitional area of the Loess Plateau where landscape factors varied greatly and the state-space models were consistently more effective than linear regression models. Among the mono-variable state-space models, Ks based models showed the best simulation result (R2 = 0.936). Among the multi-variable state-space models, Ks, E and L included model showed the best simulation result (R2 = 0.976), and the combination of such variables based models provided the best approach to explain the spatial variation of soil surface water content. State-space models are recommended for studying spatial relations between soil surface water content and other variables in the wind and water erosion transitional area of the Loess Plateau.