| 海涂围垦区土壤盐分空间变异性随机模拟与不确定性评价 |
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| 引用本文： | 姚荣江,杨劲松,韩建均.海涂围垦区土壤盐分空间变异性随机模拟与不确定性评价[J].中国生态农业学报,2011,19(3):485-490. |
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| 作者姓名： | 姚荣江 杨劲松 韩建均 |
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| 作者单位： | 中国科学院南京土壤研究所,南京,210008 |
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| 基金项目： | 公益性行业(农业)科研专项经费项目(200903001)、江苏省企业院士工作站项目(BM2009622)、江苏省科技支撑计划项目(BE2010313)、江苏省自然科学基金面上项目(BK2009337)和海洋公益性行业科研专项经费项目(201105020)资助 |
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| 摘 要： | 以苏北海涂围垦区典型地块为例, 把随机模拟技术引入土壤盐分空间变异性研究中, 利用普通克里格法和序贯高斯模拟方法对土壤盐分的空间分布进行估值和模拟, 将随机模拟值与克里格插值及实测值进行对比分析, 并采用序贯指示模拟对土壤盐分空间分布的不确定性进行评价。结果表明: 由普通克里格法得到的土壤盐分空间分布整体比较连续, 具有明显的平滑效应, 减小了数据间的空间差异性, 改变了数据的空间结构; 序贯高斯模拟结果整体分布相对离散, 突出了原始数据分布的波动性。对非盐化土、轻度盐化土、中度盐化土和重度盐化土的空间不确定性进行的序贯指示模拟结果显示, 围垦后研究区耕层土壤盐渍化的发生概率已显著降低。轻度盐化土的高概率区是改良利用的主要区域, 宜采用农业生物改良措施, 对中度盐化土高概率区应通过完善田间灌排设施以加强改良治理, 客土法是重度盐化土高概率区较为高效的改良治理途径。
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| 关 键 词： | 海涂 围垦区 土壤盐渍化 空间结构 平滑效应 随机模拟 不确定性 客土法 |
| 收稿时间： | 2010/10/21 0:00:00 |
| 修稿时间： | 2011/1/19 0:00:00 |
Stochastic simulation and uncertainty assessment of spatial variation in soil salinity in coastal reclamation regions |
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| YAO Rong-Jiang,YANG Jin-Song and HAN Jian-Jun.Stochastic simulation and uncertainty assessment of spatial variation in soil salinity in coastal reclamation regions[J].Chinese Journal of Eco-Agriculture,2011,19(3):485-490. |
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| Authors: | YAO Rong-Jiang YANG Jin-Song and HAN Jian-Jun |
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| Institution: | Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China |
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| Abstract: | A stochastic simulation technique was introduced for mapping spatial variation in soil salinity in typical coastal reclaimed regions in north Jiangsu Province. Ordinary Kriging and sequential Gaussian simulation (SGS) were used to simulate spatial distributions of soil salinity. Then the stochastic simulation result was compared with that of Kriging. Uncertainty in the spatial distribution of soil salinity was also assessed using sequential indicator simulation (SIS). The results revealed that ordinary Kriging yielded a continuous and smooth spatial distribution of soil salinity. The quality of spatial variability from Kriging reduced with change in spatial structure. The SGS technique gave a relatively discrete and fluctuating spatial distribution of soil salinity. Based on SIS analysis, uncertainties in non-saline soils, light-saline soils, medium-saline soils and heavy-saline soils suggested that the probability of soil salinization decreased significantly after reclamation. In high probability regions of light-saline soils, agro-biological amelioration was urgently needed. In the high probability regions of medium-saline soils, field irrigation and drainage installations should be improved. Furthermore, soil borrowing seemed to efficiently harness high probability regions of heavy-saline soils. |
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| Keywords: | Coastal region Reclamation region Soil salinization Spatial structure Smoothing effect Stochastic simulation Uncertainty Soil borrowing |
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