Abstract
Land surface soil moisture (SSM) is crucial in research and applications in hydrology, ecology, and meteorology. A novel SSM retrieval model, based on the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR), has recently been reported. It suggests a promising avenue for the retrieval of regional SSM using LST and NSSR derived from geostationary satellites in a future development. As part of a further improvement of previous work, effects of soil layer classification in the Common Land Model (CoLM) on modeled LST, NSSR and the associated SSM retrieval model in particular, have been evaluated. To address this issue, the soil profile has been divided in to three layers, named upper layer (0–0.05 m), root layer (0.05–1.30 m) and bottom layer (1.30–2.50 m). By varying the number of soil layers with the three layer zones, nine different soil layer classifications have been performed in the CoLM to produce simulated data. Results indicate that (1) modeled SSM is less sensitive to soil layer classification while modeled LST and NSSR are sensitive, especially under wet conditions and (2) the simulated data based SSM retrieval model is stable for a fixed upper layer with varying classifications of root and bottom layers. It also concludes an optimal soil layer classification for the CoLM while producing simulated data to develop the SSM retrieval model.
Highlights
With the presentation of the physical processes, initialization of underlying surface conditions and driven by atmospheric forcing data, the land surface model (LSM) is capable of describing the water budget, energy exchange and even the carbon or nitrogen cycle across the land-atmosphere interface by simulation [1,2,3,4]
Common Land Model (CoLM) has been developed based on the best features of three existing LSMs, including the Biosphere-Atmosphere Transfer Scheme (BATS) [22], the LSM developed by Bonan [23], and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94) [24]
Since LSM is capable of simulating the components of the water budget, energy exchange and other surface fluxes with acceptable accuracy, it plays an increasingly important role in obtaining surface variables at various scales
Summary
With the presentation of the physical processes, initialization of underlying surface conditions and driven by atmospheric forcing data, the land surface model (LSM) is capable of describing the water budget, energy exchange and even the carbon or nitrogen cycle across the land-atmosphere interface by simulation [1,2,3,4]. Models for land surface variable retrieval are developed [7,8,9] In these situations, the initial datasets of underlying surfaces, including the surface soil moisture (SSM), soil type and land cover type, etc., are used to generate simulated data on several discrete cloud-free days under given atmospheric conditions [7]. As only the atmospheric conditions of several individual cloud-free days are available in these cases, the spin-up process is usually not used to make the model state variables (e.g., soil moisture, surface temperature, latent heat, and net radiation) approach their equilibriums, the simulated data are dependent on the representation of physical processes in the LSM, and greatly affected by the soil layer classification in which the soil properties are specified [10]
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