As China has approximately 22% of the world's rice paddies, the regional quantification of CH₄ emissions from these paddies is important in determining their contribution to the global greenhouse gas effect. This paper reports the use of a biogeochemical model (DeNitrification and DeComposition or DNDC) for quantifying CH₄ emissions from rice fields in the Taihu Lake region of China. For this application, the DNDC model was linked to a 1:50 000 soil database derived from 1107 paddy soil profiles compiled during the Second National Soil Survey of China in the 1980s–1990s. The simulated results showed that the 2.3 Mha of paddy rice fields in the Taihu Lake region emitted the equivalent of 5.7 Tg C from 1982–2000, with the average CH₄ flux ranging from 114 to 138 kg C ha⁻¹ y⁻¹. As for soil subgroups, the highest emission rate (660 kg C ha⁻¹ y⁻¹) was linked to gleyed paddy soils accounting for about 4.4% of the total area of paddy soils. The lowest emission rate (91 kg C ha⁻¹ y⁻¹) was associated with degleyed paddy soils accounting for about 18% of the total area of paddy soils. The most common soil in the area was hydromorphic paddy soils, which accounted for about 53% of the total area of paddy soils with a CH₄ flux of 106 kg C ha⁻¹ y⁻¹. On a regional basis, the annual averaged CH₄ flux in the Taihu Lake plain soil region and alluvial plain soil region were higher than that in the low mountainous and hilly soil region and the polder soil region. The model simulation was conducted with two databases using polygons or counties as the basic units. The county-based database contained soil information coarser than the polygon system built based on the 1:50 000 soil database. The modeled results with the two databases found similar spatial patterns of CH₄ emissions in the Taihu Lake region. However, discrepancies exist between the results from the two methods. The total CH₄ emissions generated from the polygon-based database is 2.6 times the minimum CH₄ emissions generated from the county-based database, and is 0.98 times the maximum CH₄ emissions generated from the county-based database. The average value of the relative deviation ranged from −20% to 98% for most counties, which indicates that a more precise soil database is necessary to better simulate CH₄ emissions from rice fields in the Taihu Lake region using the DNDC model.

Soil organic carbon (SOC) models were often applied to regions with high heterogeneity, but limited spatially differentiated soil information and simulation unit resolution. This study, carried out in the Tai-Lake region of China, defined the uncertainty derived from application of the DeNitrification-DeComposition (DNDC) biogeochemical model in an area with heterogeneous soil properties and different simulation units. Three different resolution soil attribute databases, a polygonal capture of mapping units at 1∶50,000 (P5), a county-based database of 1∶50,000 (C5) and county-based database of 1∶14,000,000 (C14), were used as inputs for regional DNDC simulation. The P5 and C5 databases were combined with the 1∶50,000 digital soil map, which is the most detailed soil database for the Tai-Lake region. The C14 database was combined with 1∶14,000,000 digital soil map, which is a coarse database and is often used for modeling at a national or regional scale in China. The soil polygons of P5 database and county boundaries of C5 and C14 databases were used as basic simulation units. Results project that from 1982 to 2000, total SOC change in the top layer (0–30 cm) of the 2.3 M ha of paddy soil in the Tai-Lake region was +1.48 Tg C, −3.99 Tg C and −15.38 Tg C based on P5, C5 and C14 databases, respectively. With the total SOC change as modeled with P5 inputs as the baseline, which is the advantages of using detailed, polygon-based soil dataset, the relative deviation of C5 and C14 were 368% and 1126%, respectively. The comparison illustrates that DNDC simulation is strongly influenced by choice of fundamental geographic resolution as well as input soil attribute detail. The results also indicate that improving the framework of DNDC is essential in creating accurate models of the soil carbon cycle.

Agricultural land use has a remarkable influence on the stock and distribution of soil organic carbon (SOC). However, both regional soil sampling and process-based ecosystem models for SOC estimation at the regional scale have limitations when applied in areas with a large land use change. In the present study, a framework (CMCS) combining CENTURY modeling (CM) and chronosequences sampling (CS) was established, and a case study was conducted in Cangshan County, where vegetable cultivation conversion from grain production was significant in recent decades. The SOC stock (SOCS) of the non-vegetable area estimated by CM was comparable to that estimated by regional soil sampling in 2008. This result confirmed that CM was reliable in modeling SOC dynamics in a non-vegetable area without land use change. However, when applied to the overall cropland of Cangshan County, the CM, without considering the land use change, underestimated the SOCS by 0.23 Tg (6%), compared with the observed measurements (3.58 and 3.81 Tg, respectively). Using the CMCS framework of our study, the underestimation of CM was offset by the SOC sequestration estimated by CS. The SOCS estimated by the CMCS framework ranged from 3.72 to 4.02 Tg, demonstrating that this framework is reliable for the regional SOC estimation of large-area land use change. In addition, annual SOCS dynamics were obtained by this framework. The CMCS framework provides a low-cost and practicable method for the estimation of the regional SOC dynamic, which can further support the strategy of carbon peaking and carbon neutrality in China.