基于<sup>137</sup>Cs本底值计算模型和地理加权回归克里格对<sup>137</sup>Cs本底值空间分布的预测

doi:10.16843/j.sswc.2018.05.008

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Abstract [Background] Use of the fallout radionuclide ¹³⁷Cs as a tracer recently has been widely employed for the assessment of soil erosion. In the ¹³⁷Cs method, the magnitude of the rate of soil erosion is estimated by comparing the ¹³⁷Cs inventory of the sampling points with the local reference inventory, therefore, it is of extreme importance to determine the ¹³⁷Cs reference inventory obtained from a local stable site neither erosion nor deposition occurred while studying soil erosion using this tracing technique.[Methods] Based on the measured data from 147 meteorology stations and the ¹³⁷Cs fallout model derived by Walling & He, a case study on the application of geographically weighted regression Kriging interpolation (GWRK) combined with principal component analysis (PCA) for the assessment of the spatial distribution of the ¹³⁷Cs reference inventories was undertaken at Sichuan province, Southwest China.[Results] 1) Using PCA, the transformation process to turn the influence factors of longitude, latitude and precipitation into 2 principal component variables were educed. This measure not only maintained the 90% information of original data, but also decreased the multicollinearity among the variables significantly. It was the basis for the further interpolation of ¹³⁷Cs reference inventory. 2) With the principal components as input variables, the spatial distribution of the ¹³⁷Cs reference inventories at Sichuan province were obtained by 3 different methods:the GWRK method, the ordinary Kriging (OK) method and global regression Kriging method (GRK). In the meantime, some indicators such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) were calculated for evaluating the accuracy of different interpolation results. In terms of the interpolation results, taking into consideration of various influence factors and their spatial non-stationarity, the GWRK method had better accuracy compared with the other methods. Furthermore, this simulation result is in exactly agreement with those of the measurement.[Conclusions] This study reveals the regulars of space distribution of ¹³⁷Cs reference inventory and the effect of different factors. Additionally, the results of this study also demonstrate that it is obviously an approach available for obtaining the spatial distribution of ¹³⁷Cs reference inventory with high resolution and accuracy to adopt the GIS interpolation on the basis of the measured data, which is referential for the study of soil erosion using ¹³⁷Cs tracing technique.

Key words： ¹³⁷Cs reference inventory geographically weighted regression Kriging ¹³⁷Cs reference inventory mathematical model Sichuan province

Received: 25 January 2018

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	LI Hao
	WEN Anbang
	LIU Tao
	LI Ting

Cite this article:

LI Hao,WEN Anbang,LIU Tao, et al. Spatial distribution of ¹³⁷Cs reference inventory in Sichuan province using geographically weighted regression Kriging combined with ¹³⁷Cs reference inventory mathematical model[J]. SSWC, 2018, 16(5): 57-66.

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http://journal12.magtechjournal.com/Jweb_stbc/EN/10.16843/j.sswc.2018.05.008 OR http://journal12.magtechjournal.com/Jweb_stbc/EN/Y2018/V16/I5/57

[1]	MABIT L, BENMANSOUR M, WALLING D E. Comparative advantages and limitations of the fallout radionuclides 137Cs, 210Pbex and 7Be for assessing soil erosion and sedimentation[J]. Journal of Environmental Radioactivity, 2008, 99(12):1799.
[2]	刘宇, 吕一河, 傅伯杰, 等. 137Cs示踪法土壤侵蚀量估算的本底值问题[J]. 地理研究, 2010, 29(7):1171. LIU Yu, LÜ Yihe, FU Bojie, et al. Reference value of 137Cs tracing technique in soil loss estimation:A spatial variation analysis[J]. Geographical Research, 2010, 29(7):1171.
[3]	胡云锋, 刘纪远, 巴图娜存, 等. 内蒙古中东部地区137Cs背景值的确定[J]. 地理学报, 2014, 69(3):343. HU Yufeng, LIU Jiyuan, BATU Nacun, et al. Determination of 137Cs reference inventory in the large scale region:A case study in the central and eastern Inner Mongolia Plateau[J]. Acta Geographica Sinica, 2014, 69(3):343.
[4]	张威, 潘少明, 张克新, 等. 中国大陆Cs-137背景值研究[J]. 地理学报, 2015, 70(9):1477. ZHANG Wei, PAN Shaoming, ZHANG Kexin, et al. Study of the Cesium-137 reference inventory in the Mainland of China[J]. Acta Geographica Sinica, 2015, 70(9):1477.
[5]	四川省水利厅, 四川省统计局. 四川省第一次全国水利普查公报[J]. 四川水利, 2013, S1:4. Sichuan Provincial Water Resources Department, Sichuan Provincial Bureau of Statistics. Bulletin of first census for water of Sichuan province[J]. Sichuan Water Conservancy, 2013, S1:4.
[6]	WALLING D E, HE Q. The global distribution of bomb-derived 137Cs reference inventories[A]. Final Report on IAEA Technical Contract 10361/RO-R1[C]. Exeter:University of Exeter Press, 2000:1.
[7]	齐永青, 张信宝, 贺秀斌. 中国137Cs本底值区域分布研究[J]. 核技术, 2006, 29(1):42. QI Yongqin, ZHANG Xinbao, HE Xiubin. 137Cs reference inventories distribution pattern in China[J]. Nuclear Techniques, 2006, 29(1):42.
[8]	余锦华, 杨维权. 多元统计分析与应用[M]. 广州:中山大学出版社, 2005:38. YU Jinhua, YANG Weiquan. Multivariate statistical analysis and application[M]. Guangzhou:Sun Yat-sen University Press, 2005:38.
[9]	WU J, NORVELL W A, HOPLINS D G, et al. Improved prediction and mapping of soil copper by Kriging with auxiliary data for cation-exchange capacity[J]. Soil Science Society of America Journal, 2003, 67(3):917.
[10]	BAXTER S J, OLIVER M A. The spatial prediction of soil mineral N and potentially available N using elevation[J]. Geoderma, 2005, 128(3/4):325.
[11]	连纲, 郭旭东, 傅伯杰, 等. 黄土高原县域土壤养分空间变异特征及预测:以陕西省横山县为例[J]. 土壤学报, 2008, 45(4):577. LIAN Gang, GUO Xudong, FU Bojie, et al. Spatial variability and prediction of soil nutrients on a county scale on the Loess Plateau:A case study of Hengshan County, Shaanxi Province[J]. Acta Pedologica Sinica, 2008, 45(4):577.
[12]	FOTHERINGHAM A S, BRUNSDON C, CHARLTON, M E. Geographically Weighted Regression:The analysis of spatially varying relationships[M]. Chichester:John Wiley & Sons Ltd., 2002:16.
[13]	杨顺华, 张海涛, 郭龙, 等. 基于回归和地理加权回归Kriging的土壤有机质空间插值[J]. 应用生态学报, 2015, 26(6):1649. YANG Shunhua, ZHANG Haitao, GUO Long, et al. Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging[J]. Chinese Journal of Applied Ecology, 2015, 26(6):1649.
[14]	李青云, 蒋顺清, 孙厚才. 长江上游紫色土丘陵区小流域地面侵蚀量的测定[J]. 长江科学院院报, 1995, 12(1):51. LI Qingyun, JIANG Shunqing, SUN Houcai. Determination of surface erosion of the small watersheds in the hilly area of purple soils in the upper reaches of the Yangtze River[J]. Journal of Yangtze River Scientific Research Institute, 1995, 12(1):51.
[15]	QUINE T A, WALLING D E, ZHANG X, et al. Investigation of soil erosion on terraced fields near Yangting, Sichuan province, China, using caesium-137[A]. Walling D E. Erosion, Debris Flows and Environment in Mountain Regions[C]. Chengdu:IAHS Publications, 1992, 209:155.
[16]	文安邦, 张信宝, 王玉宽, 等. 长江上游紫色土坡耕地土壤侵蚀137Cs示踪法研究[J]. 山地学报, 2001, 19(增刊1):56. WEN Anbang, ZHANG Xinbao, WANG Yukuan, et al. A study on soil erosion rates of the purple slope cultivated land using caesium-137 technique in the upper of the Yangtze River[J]. Mountain Research, 2001, 19(S1):56.
[17]	侯建才, 李占斌, 李勉. 紫色丘陵区小流域土壤侵蚀产沙空间分布的137Cs法初步研究[J]. 农业工程学报, 2007, 23(3):46. HOU Jiancai, LI Zhanbin, LI Mian. Preliminary study on spatial distribution of soil erosion in a small watershed in purple hilly area using 137Cs tracer[J]. Transactions of the CSAE, 2007, 23(3):46.
[18]	郑进军, 张信宝, 贺秀斌. 川中丘陵区坡耕地侵蚀空间分布的WEPP模型和137Cs法初步研究[J]. 水土保持学报, 2007, 21(2):19. ZHENG Jinjun, ZHANG Xinbao, HE Xiubin. Combing WEPP model with 137Cs to study spatial pattern of soil redistribution on purple slope-land in Sichuan Hilly Basin[J]. Journal of Soil and Water Conservation, 2007, 21(2):19.
[19]	庄平, 高贤明. 华西雨屏带及其对我国生物多样性保育的意义[J]. 生物多样性, 2002, 10(3):339. ZHUANG Ping, GAO Xianming. The concept of the Rainy Zone of West China and its significance to the biodiversity conservation in China[J]. Biodiversity Science, 2002, 10(3):339.