Modelling land surface–atmosphere interactions over the Australian continent with an emphasis on the role of soil moisture

Abstract

Soil moisture is a major natural state resistor variable in the global energy cycle as it influences the partitioning of both surface available energy into sensible and latent heat fluxes, and of precipitation into evapotranspiration and runoff. Consequently, physically based models of the biosphere need to simulate land surface conditions by including parameterisations for soil moisture. Soil moisture content is also important for determining the status of agricultural production since water in soil represents the major component of the hydrological cycle that is available to plants. Soil moisture is therefore important in ecological processes, and most biomass production models will include estimates of soil water availability. Given the identified importance of the soil moisture variable, it is perhaps surprising that there is a paucity of reliable long-term measurements, particularly over the major agricultural regions of Australia. Consequently, a diverse range of approaches, such as physically based models, stochastic modelling and remote sensing, have often been required to compensate for a dearth of actual measurements. This paper describes recent advances in soil water content simulation and prediction, utilising a numerical weather prediction model incorporating an improved land surface schema. This schema was developed in collaboration with the University of New South Wales and the Bureau of Resource Sciences. The land surface schema is essentially a surface hydrological model for prediction of evapotranspiration, surface and subsurface runoff and deep soil drainage, by parameterisation and solving the Richards' equation and the temperature diffusion equation for multi-soil layers. Soil moisture simulations obtained from this model for the Australian continent are presented. The model is shown to perform well, and further parameterisation work is progressing to improve the agreement between simulated and observed results.