Abstract
One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available.
Highlights
Introduction and GoalsThe magnitude of the surface energy fluxes including net radiation (NR), latent heat flux (LE), sensible heat flux (H), and ground heat flux (G) impact both atmospheric and land surface processes in relation to water, energy, and biogeochemical cycles [1,2,3,4].evapotranspiration, soil temperature, and moisture are key in triggering and maintaining drought and flooding conditions
Previous studies outlined the importance of these parameters during the water infiltration and shallow-soil energy exchange processes [93,94]
The parameter values found during the calibration process using the Shuffled Complex Evolution (SCE) algorithm are summarized in
Summary
Evapotranspiration, soil temperature, and moisture are key in triggering and maintaining drought and flooding conditions. In all these processes, vegetation cover, Remote Sens. 2021, 13, 1271 type, and activity are key factors that control, among others, transpiration rates, ground albedo, below-canopy soil temperature and moisture, and solar radiation sheltering [5,6,7,8]. Sudden natural or human-induced changes in land cover, vegetation type, and canopy biomass influence the SEB fluxes, including abrupt shifts in evapotranspiration (ET), soil moisture, and ground temperature that influence regional hydro-climatic patterns [13,14] and long-term water, carbon, and energy fluxes and stocks [15,16,17,18]. The inclusion of dynamically changing vegetation parameters like albedo, stomatal resistance, and leaf area index, among others, can significantly improve the accuracy of any land surface model
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