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

Abstract

Land surface processes are an important part of the Earth’s mass and energy cycles. The application of a land surface process model for farmland in the low-hilly red soil region of southern China continues to draw research attention. Conventional model does not perform well in the simulation of irrigated farmland, because the influence of land surface water is not considered. In this study, an off-line version of the Simple Biosphere model 2 (SiB2) was locally parameterized in a typical farmland of the low-hilly red soil region using field observations and remote sensing data. The performance of SiB2 was then evaluated through comparison to Bowen-ratio direct measurements in a second growing period of rice in 2015 (late rice from 23 July to 31 October). The results show that SiB2 underestimated latent heat flux (LE) by 16.0% and overestimated sensible heat flux (H) by 16.7%, but net radiation flux (Rn) and soil heat flux were reasonably simulated. The single factor sensitivity analysis of Rn, H, and LE modeled in SiB2 indicated that downward shortwave radiation (DSR) and downward longwave radiation (DLR) had a significant effect on Rn simulation. In driving data, DSR, DLR and wind speed (u) were the main factors that could cause a distinct change in sensible heat flux. An irrigation module was added to the original SiB2 model to simulate the influence of irrigated paddy fields according to the sensitivity analysis results of the parameters (C1, bulk boundary-layer resistance coefficient; C2, ground to canopy air-space resistance coefficient; and Ws, volumetric water content at soil surface layer). The results indicate that application of the parameterized SiB2 with irrigation module could be better in southern Chinese farmland.

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

“…The SiB2 model is a widely used and biophysics-based land surface model developed by Sellers et al [20,21]. SiB2 contains a set of physics-based equations that couple the water balance, energy balance, and vegetation biochemical processes to simulate the exchanges of water, carbon, momentum, and energy fluxes among the atmosphere, a single canopy layer, and three soil layers (surface soil layer, root zone layer, and deep soil layer) [2,12,21,55]. As a parameterization scheme describing the processes of exchange between land and atmosphere, it simulates a more realistic vegetation physiological process owing to the incorporation of a canopy photosynthesis conductance submodel [3,30].…”

“…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].…”

“…To deepen our understanding of the surface-atmosphere exchanges of water, surface energy, and CO 2 fluxes, numerous measurement methods [e.g., the Bowen ratio-energy balance method, the eddy covariance (EC) method, and the scintillometer method] have been applied [2]. Among them, the EC technique is considered to be the most direct and trustworthy method to obtain data on soil-plant-atmosphere carbon, water, and energy fluxes [9,10].…”

“…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.…”

Impact Analysis of Univariate and Multivariate Bias Correction on Rice Irrigation Water Needs in Jiangxi Province, China

“…Regional climate models (RCMs) are one valid approach to derive climate information at a higher resolution (typically 10-50 km) through dynamical downscaling driven by lateral boundary conditions from GCM [9][10][11][12]. This resolution offers a more detailed representation of topography and on a time basis, and the rainy season (April to June) accounts for about 50% of the annual precipitation [43]. Agriculture is one of the leading sectors in Jiangxi province.…”