Land surface process modeling of high and cold area with vegetation cover has not yielded satisfactory results in previous applications. In this study, land surface energy budget is simulated using a land surface mode...Land surface process modeling of high and cold area with vegetation cover has not yielded satisfactory results in previous applications. In this study, land surface energy budget is simulated using a land surface model for the A'rou meadow in the upper-reach area of the Heihe River Basin in the eastern Tibetan Plateau. The model performance is evaluated using the in-situ observations and remotely sensed data. Sensible and soil heat fluxes are overestimated while latent heat flux is underestimated when the default parameter setting is used. By analyzing physical and physiological processes and the sensitivities of key parameters, the inappropriate default setting of optimum growth and inhibition temperatures is identified as an important reason for the bias. The average daytime temperature during the period of fastest vegetation growth(June and July) is adopted as the optimum growth temperature, and the inhibition temperatures were adjusted using the same increment as the optimum temperature based on the temperature acclimation. These adjustments significantly reduced the biases in sensible, latent, and soil heat fluxes.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.91125002,40971221)FP7 CEOP-AEGI(Coordinated Asia European Long-Term Observing System of the Qinhai Tibet Plateau Hydro-meteorological Processes and the Asian Monsoon System with Ground Satellite Image data and numerical simulation)project
文摘Land surface process modeling of high and cold area with vegetation cover has not yielded satisfactory results in previous applications. In this study, land surface energy budget is simulated using a land surface model for the A'rou meadow in the upper-reach area of the Heihe River Basin in the eastern Tibetan Plateau. The model performance is evaluated using the in-situ observations and remotely sensed data. Sensible and soil heat fluxes are overestimated while latent heat flux is underestimated when the default parameter setting is used. By analyzing physical and physiological processes and the sensitivities of key parameters, the inappropriate default setting of optimum growth and inhibition temperatures is identified as an important reason for the bias. The average daytime temperature during the period of fastest vegetation growth(June and July) is adopted as the optimum growth temperature, and the inhibition temperatures were adjusted using the same increment as the optimum temperature based on the temperature acclimation. These adjustments significantly reduced the biases in sensible, latent, and soil heat fluxes.
