A number of process-based models have been developed for quantifying carbon(C)sequestration in agro-ecosystems.The DeNitrification-DeComposition(DNDC)model was used to simulate and quantify long-term(1980-2008)soil or...A number of process-based models have been developed for quantifying carbon(C)sequestration in agro-ecosystems.The DeNitrification-DeComposition(DNDC)model was used to simulate and quantify long-term(1980-2008)soil organic carbon(SOC)dynamics in the important rice-producing province,Jiangsu,China.Changes in SOC storages were estimated from two soil databases differing in spatial resolution:a county database consisting of 68 polygons and a soil patch-based database of 701 polygons for all 3.7 Mha of rice fields in Jiangsu.The simulated SOC storage with the coarse resolution county database ranged between 131.0-320.6 Tg C in 1980 and 170.3-305.1 Tg C in 2008,respectively,while that estimated with the fine resolution database was 201.6 and 216.2 Tg C in 1980 and 2008,respectively.The results modeled with the soil databases differing in spatial resolution indicated that using the soil input data with higher resolution substantially increased the accuracy of the modeled results;and when lacking detailed soil datasets,the DNDC model,parameterized with the most sensitive factor(MSF) method to cope with attribute uncertainty,could still produce acceptable results although with deviations of up to 60% for the case study reported in this paper.展开更多
Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of...Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.展开更多
Reporting modeling results with uncertainty information can benefit decision making by decreasing the extent that variability exerts a disproportionate influence on the options selected. For making decisions with more...Reporting modeling results with uncertainty information can benefit decision making by decreasing the extent that variability exerts a disproportionate influence on the options selected. For making decisions with more confidence, the uncertainty interval should be as narrow as possible. Here, the soil organic carbon （SOC） dynamics of the major paddy soil subgroup from 4 different paddy field regions of China （located in 4 counties under different climate-soil-management combinations） were modeled using the DeNitrification- DeComposition （DNDC） model for the period from 1980 to 2008. Uncertainty intervals associated with the SOC dynamics for these 4 subgroups were estimated by a long-term global sensitivity and uncertainty analysis （i. e., the Sobolt method）, and their sensitivities to 7 influential factors were quantified using the total effect sensitivity index. The results, modeled with high confidence, indicated that in the past 29 years, the studied paddy soils in Xinxing, Yixing, and Zhongjiang counties were carbon （C） sinks, while the paddy soil in Helong County was a C source. The 3 C sinks sequestered 12.2 （5.4, 19.6）, 17.1 （8.9, 25.0）, and 16.9 （-1.2, 33.6） t C ha-1 （values in the parentheses are the 5th and 95th percentiles, respectively）. Conversely, the C source had a loss of -5.4 （-14.2, 0.06） t C ha-1 in the past 29 years. The 7 factors, which changed with the climate-soil-management context, exhibited variable influences on modeled SOC. Measures with potential to conserve or sequestrate more C into paddy soils, such as incorporating more crop residues into soils and reducing chemical fertilizer application rates, were recommended for specific soils based on the sensitivity analysis results.展开更多
基金Supported by the Knowledge Innovation Program of Chinese Academy of Sciences(Nos.KZCX2-YW-Q1-07 and KZCX2-YW-Q1-15)the National Basic Research Program(973 Program)of China(No.2010CB950702)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA05050509)
文摘A number of process-based models have been developed for quantifying carbon(C)sequestration in agro-ecosystems.The DeNitrification-DeComposition(DNDC)model was used to simulate and quantify long-term(1980-2008)soil organic carbon(SOC)dynamics in the important rice-producing province,Jiangsu,China.Changes in SOC storages were estimated from two soil databases differing in spatial resolution:a county database consisting of 68 polygons and a soil patch-based database of 701 polygons for all 3.7 Mha of rice fields in Jiangsu.The simulated SOC storage with the coarse resolution county database ranged between 131.0-320.6 Tg C in 1980 and 170.3-305.1 Tg C in 2008,respectively,while that estimated with the fine resolution database was 201.6 and 216.2 Tg C in 1980 and 2008,respectively.The results modeled with the soil databases differing in spatial resolution indicated that using the soil input data with higher resolution substantially increased the accuracy of the modeled results;and when lacking detailed soil datasets,the DNDC model,parameterized with the most sensitive factor(MSF) method to cope with attribute uncertainty,could still produce acceptable results although with deviations of up to 60% for the case study reported in this paper.
基金Under the auspices of Special Project of National Key Research and Development Program(No.2016YFD0200301)National Natural Science Foundation of China(No.41571206)Special Project of National Science and Technology Basic Work(No.2015FY110700-S2)
文摘Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.
基金supported by the National Natural Science Foundation of China (No.41471177)the Knowledge Innovation Program of Chinese Academy of Sciences (No.KZCX2-EW-QN404)the Strategic Priority Research Program of Chinese Academy of Sciences (No.XDA05050509)
文摘Reporting modeling results with uncertainty information can benefit decision making by decreasing the extent that variability exerts a disproportionate influence on the options selected. For making decisions with more confidence, the uncertainty interval should be as narrow as possible. Here, the soil organic carbon （SOC） dynamics of the major paddy soil subgroup from 4 different paddy field regions of China （located in 4 counties under different climate-soil-management combinations） were modeled using the DeNitrification- DeComposition （DNDC） model for the period from 1980 to 2008. Uncertainty intervals associated with the SOC dynamics for these 4 subgroups were estimated by a long-term global sensitivity and uncertainty analysis （i. e., the Sobolt method）, and their sensitivities to 7 influential factors were quantified using the total effect sensitivity index. The results, modeled with high confidence, indicated that in the past 29 years, the studied paddy soils in Xinxing, Yixing, and Zhongjiang counties were carbon （C） sinks, while the paddy soil in Helong County was a C source. The 3 C sinks sequestered 12.2 （5.4, 19.6）, 17.1 （8.9, 25.0）, and 16.9 （-1.2, 33.6） t C ha-1 （values in the parentheses are the 5th and 95th percentiles, respectively）. Conversely, the C source had a loss of -5.4 （-14.2, 0.06） t C ha-1 in the past 29 years. The 7 factors, which changed with the climate-soil-management context, exhibited variable influences on modeled SOC. Measures with potential to conserve or sequestrate more C into paddy soils, such as incorporating more crop residues into soils and reducing chemical fertilizer application rates, were recommended for specific soils based on the sensitivity analysis results.
