Soil Moisture Prediction in the Soil, Vegetation and Snow (SVS) Scheme

A new land surface scheme, soil, vegetation and snow scheme (SVS), has been developed at Environment of Canada to provide surface fluxes of momentum, heat and moisture for the Global Environmental Multi-Scale (GEM) model. In this study, the performance of SVS in estimating surface and root zone soil moisture is evaluated against the ISBA (Interactions between Surface, biosphere, and Atmosphere) scheme currently used as an operational scheme in numerical weather prediction system. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ soil moisture networks as well as Soil Moisture and Ocean Salinity (SMOS) brightness temperature data over North America domain. The results indicate that SVS more accurately estimates the time evolution of soil moisture and results in higher correlation coefficients and smaller errors as compared with ISBA. The sensitivity tests revealed that SVS soil moisture results are not affected significantly with the soil texture data. However, the vegetation data has a major impact on soil moisture results by SVS and accurate specification of these data is crucial for accurate soil moisture simulation.