Vegetation physiological parameter setting in the Simple Biosphere model 2 (SiB2) for alpine meadows in the upper reaches of Heihe river

Abstract

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. SiB2, land surface process, alpine meadow, Heihe River Basin, Tibetan Plateau Citation:Li Y, Sun R, Liu S M. 2014. Vegetation physiological parameter setting in the Simple Biosphere model 2 (SiB2) for alpine meadows in the upper reaches of Heihe river. Science China: Earth Sciences,

Application of the Simple Biosphere Model 2 (SiB2) with Irrigation Module to a Typical Low-Hilly Red Soil Farmland and the Sensitivity Analysis of Modeled Energy Fluxes in Southern China

“…In the Global Energy and Water Cycle Experiment (GEWEX) Asian Monsoon Experiment (GAME)-Tibet project, Rn was underestimated by 11%, while H, LE, and G were overestimated by 8%, 3%, and 13%, respectively, using SiB2 [2]. Li et al [3] modeled energy components for alpine meadows in the upper reaches of the Heihe River, and their results showed that H was overestimated by 28% and LE was underestimated by 12%. After adjusting the optimum growth temperature to 288 K based on the average daily temperature during their study period (default value is 298 K), the biases in H, LE and G were reduced to a large extent (to less than 7% bias).…”

“…Land is the basis of human survival, and accounts for only 29.2% of the surface area of the Earth. Land surface processes impact the climate primarily through the exchange of energy, momentum, and matter, such as CO 2 or H 2 O, between the surface and the atmosphere, and across the atmospheric boundary layer [1][2][3][4]. Climate simulations are especially sensitive to diurnal variations in a surface partitioning of available energy (R n -G 0 ) into sensible (H) and latent (LE) heat fluxes [5,6].…”

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

“…In addition, surface conditions, such as vegetation cover and snow cover, act as important environmental controls on soil freeze/thaw dynamics and permafrost distribution. Vegetation cover affects the partition among the sensible, latent and soil heat fluxes, and therefore plays an important role in surface water and energy balances (Li et al, 2015a). Although most Tibetan Plateau permafrost region is dominated by alpine meadows or grasslands with sparse vegetation, Wang et al (2012a) still found that decrease of alpine meadow and alpine swamps in the Tibetan Plateau were related to the increasing sensitivity of soil to climate changes and the greater shifts in soil temperature and water dynamics.…”

Correction to a Simple Biosphere Model 2 (SiB2) Simulation of Energy and Carbon Dioxide Fluxes over a Wheat Cropland in East China Using the Random Forest Model

“…Land surface processes modulate the weather and climate primarily through the exchange of energy, momentum, water, and carbon dioxide (CO 2 ) across the atmospheric boundary layer [1][2][3][4][5]. Climate simulations are especially sensitive to the temporal characteristics in the energy partitioning of available energy into sensible heat (H) and latent heat (LE) fluxes [6][7][8].…”

“…Most such investigations found that the processes of dynamic vegetation changes could not be reflected. There has also been some research carried out into revising SiB2 by correcting its fixed parameters [2,3] and physical equations [31] to address biases that arise from the model's complexity and diversity of study area and vegetation. However, few studies have used machine learning algorithms to correct the outputs of SiB2.…”