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.

Regional climate models (RCMs) provide an improved representation of climate information as compared to global climate models (GCMs). However, in climate-agricultural impact studies, accurate and interdependent local-scale climate variables are preferable, but both RCMs and GCMs are still subjected to bias. This study compares univariate bias correction (UBC) and multivariate bias correction (MBC) method to simulate rice irrigation water needs (IWNs) in Jiangxi Province, China. This research uses the daily output of Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) forced with ERAINT (ECMWF ERA Interim) data and 13 Jiangxi ground-based observations, and the observation data are reference data with 1989–2005 defined as a calibration period and 2006–2007 as a validation period. The result shows that UBC and MBC methods favorably bias-corrected all climate variables during the calibration period, but still no significant difference is noted between the two methods. However, the UBC ignores the relationship between climate variables, while MBC preserves the climate variables’ interdependence which affect subsequent analyses. In rice IWNs simulation analysis, MBC has better skill at correcting bias compare to UBC in ETo (evapotranspiration) and Peff (effective rainfall) components. Nonetheless, both methods have a low ability to correct extreme values bias. Overall, both techniques successfully reduce bias, even though they are still less effective for precipitation compared to maximum and minimum temperature, relative humidity and windspeed.