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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (3) : 117-127     DOI: 10.6046/zrzyyg.2023097
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Construction and application of a comprehensive drought index based on Copula function on a kilometer scale: A case study of Chongqing, China
YANG Chenfei1(), WU Tianjun2(), WANG Changpeng1, YANG Lijuan1, LUO Jiancheng3,4, ZHANG Xin3,4
1. School of Sciences, Chang’an University, Xi’an 710064, China
2. School of Land Engineering, Chang’an University, Xi’an 710064, China
3. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Drought is identified as one type of the most serious natural disasters affecting agricultural production, and developing a comprehensive drought index holds practical significance for the assessment of drought in various districts and counties in Chongqing. By coupling the soil moisture and precipitation Z-index data downscaled using the XGBoost algorithm and using the Copula function, this study developed a gridded comprehensive drought index CSDIM-A on a kilometer scale with the boundaries of various districts and counties of Chongqing as the spatial division criteria and decades of a month as time units. Using this index, this study assessed the spatiotemporal characteristics of drought and performed an experimental demonstration of the index using Chongqing as the study area. The results indicate that the downscaling enhanced the spatial continuity of remote sensing-based products, thus providing support for the subsequent construction of a comprehensive drought index on a kilometer scale. The generalized extreme value distribution and the t location-scale distribution applied to the fitting of the data distributions of soil moisture and precipitation in most districts and counties of Chongqing, respectively, while the Frank-copula function suited for the fitting of the joint distribution of binary variables on a scale of a month decade. As validated based on soil moisture content, CSDIM-A can more effectively reflect drought than the precipitation Z-index, with its spatial distribution in various districts and counties consistent with the actual drought data. This indicates that the CSDIM-A can be used as a reference for drought assessment.

Keywords drought      soil moisture      precipitation Z-index      downscaling      comprehensive drought index     
ZTFLH:  TP753  
Issue Date: 03 September 2024
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Chenfei YANG
Tianjun WU
Changpeng WANG
Lijuan YANG
Jiancheng LUO
Xin ZHANG
Cite this article:   
Chenfei YANG,Tianjun WU,Changpeng WANG, et al. Construction and application of a comprehensive drought index based on Copula function on a kilometer scale: A case study of Chongqing, China[J]. Remote Sensing for Natural Resources, 2024, 36(3): 117-127.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023097     OR     https://www.gtzyyg.com/EN/Y2024/V36/I3/117
Fig.1  Scope and elevation of the study area
Fig.2  Research technology roadmap
Fig.3  Flow chart of downscaling remote sensing data
Copula类型 函数形式
Gaussian-copula p = C G a u s s i a n ( u , v ) = Φ 2 ( Φ - 1 ( u ) , Φ - 1 ( v ) )
t-copula p = C t ( u , v ) = T 2 ( T - 1 ( u ) , T - 1 ( v ) )
Gumbel-copula p = C G u m b e l ( u , v ) = e x p { - [ ( - l n u ) θ + ( - l n v ) θ ] 1 / θ }
Clayton-copula p = C C l a y t o n ( u , v ) = ( u - θ + v - θ - 1 ) - 1 / θ
Frank-copula p = C F r a n k ( u , v ) = - 1 θ l n 1 + ( e - θ u - 1 ) ( e - θ v - 1 ) e - θ - 1
Tab.1  Two-dimensional Copula function form
Fig.4  Comparison of spatial distribution before and after downscaling on January 1, 2022
Fig.5  Results of K-S test for marginal distribution of soil moisture variables
Fig.6  Results of K-S test for marginal distribution of precipitation variables
Fig.7  Applicability of Copula function in Chongqing City by districts and counties
Fig.8  Correlation of CSDIM-A and precipitation Z-index with soil moisture
Fig.9  Generalized extreme value distribution fitting effect
Fig.10  Binary Frank-copula distribution function
Fig.11  Spatial distribution of soil moisture, precipitation Z-index and CSDIM-A index in mid-July 2022 in Shizhu County, Chongqing
Fig.12  Spatial distribution of soil moisture, precipitation Z-index and CSDIM-A index in Chongqing
[1] 李忆平, 王劲松, 李耀辉. 2009/2010年中国西南区域性大旱的特征分析[J]. 干旱气象, 2015, 33(4):537-545.
doi: 10.11755/j.issn.1006-7639(2015)-04-0537
[1] Li Y P, Wang J S, Li Y H. Characteristics of a regional meteorological drought event in southwestern China during 2009—2010[J]. Journal of Arid Meteorology, 2015, 33(4):537-545.
[2] Wilhite D A, Glantz M H. Understanding the drought phenomenon:The role of definitions[J]. Planning for Drought:Toward a Reduction of Societal Vulnerability, 2019:11-27.
[3] 耿鸿江, 沈必成. 水文干旱的定义及其意义[J]. 干旱地区农业研究, 1992, 10(4):91-94.
[3] Geng H J, Shen B C. Definition and significance of hydrologic droughts[J]. Agricultural Research in the Arid Areas, 1992, 10(4):91-94.
[4] Jiménez-Donaire M D P, Tarquis A, Giráldez J V. Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain[J]. Natural Hazards and Earth System Sciences, 2020, 20(1):21-33.
[5] 朱悦璐, 张阳. 多变量综合干旱指数在渭河流域的应用[J]. 水资源与水工程学报, 2022, 33(1):31-38.
[5] Zhu Y L, Zhang Y. Application of multivariate comprehensive drought index in the Weihe River basin[J]. Journal of Water Resources and Water Engineering, 2022, 33(1):31-38.
[6] 玉院和. 基于多源遥感数据的云南省综合干旱监测模型研究[D]. 昆明: 云南师范大学, 2020.
[6] Yu Y H. Study of comprehensive drought monitoring model of Yunnan Province based on multi-source remote sensing data[D]. Kunming: Yunnan Normal University, 2020.
[7] 刘高鸣, 谢传节, 何天乐, 等. 基于多源数据的农业干旱监测模型构建[J]. 地球信息科学学报, 2019, 21(11):1811-1822.
doi: 10.12082/dqxxkx.2019.180666
[7] Liu G M, Xie C J, He T L, et al. Agricultural drought monitoring model constructing based on multi-source data[J]. Journal of Geo-Information Science, 2019, 21(11):1811-1822.
[8] 吴志勇, 程丹丹, 何海, 等. 综合干旱指数研究进展[J]. 水资源保护, 2021, 37(1):36-45.
[8] Wu Z Y, Cheng D D, He H, et al. Research progress of composite drought index[J]. Water Resources Protection, 2021, 37(1):36-45.
[9] Xu L, Chen N, Yang C, et al. A parametric multivariate drought index for drought monitoring and assessment under climate change[J]. Agricultural and Forest Meteorology, 2021, 310:108657.
[10] 李军, 吴旭树, 王兆礼, 等. 基于新型综合干旱指数的珠江流域未来干旱变化特征研究[J]. 水利学报, 2021, 52(4):486-497.
[10] Li J, Wu X S, Wang Z L, et al. Changes of drought characteristics in future in Pearl River basin describing by a new comprehensive standardized drought index[J]. Journal of Hydraulic Engineering, 2021, 52(4):486-497.
[11] 韩东, 王鹏新, 张悦, 等. 农业干旱卫星遥感监测与预测研究进展[J]. 智慧农业(中英文), 2021, 3(2):1-14.
[11] Han D, Wang P X, Zhang Y, et al. Progress of agricultural drought monitoring and forecasting using satellite remote sensing[J]. Smart Agriculture, 2021, 3(2):1-14.
doi: 10.12133/j.smartag.2021.3.2.202104-SA002
[12] 吴泽棉, 邱建秀, 刘苏峡, 等. 基于土壤水分的农业干旱监测研究进展[J]. 地理科学进展, 2020, 39(10):1758-1769.
doi: 10.18306/dlkxjz.2020.10.014
[12] Wu Z M, Qiu J X, Liu S X, et al. Advances in agricultural drought monitoring based on soil moisture[J]. Progress in Geography, 2020, 39(10):1758-1769.
doi: 10.18306/dlkxjz.2020.10.014
[13] 杜瑞麒, 张智韬, 巨娟丽, 等. 基于波文比和降水的综合干旱指数的构建及应用[J]. 节水灌溉, 2020(8):63-71.
[13] Du R Q, Zhang Z T, Ju J L, et al. Construction and application of comprehensive drought index based on Bowen ratio and precipitation[J]. Water Saving Irrigation, 2020(8):63-71.
[14] Sánchez N, González-Zamora A, Piles M, et al. A new soil moisture agricultural drought index (SMADI) integrating MODIS and SMOS products:A case of study over the Iberian peninsula[J]. Remote Sensing, 2016, 8(4):287.
[15] 吴喜之, 王兆军. 非参数统计方法[M]. 北京: 高等教育出版社, 1996.
[15] Wu X Z, Wang Z J. Nonparametric statistical method[M]. Beijing: Higher Education Press, 1996.
[16] 董留阳. 基于Copula函数模型和FDA方法的干旱指数的应用研究[D]. 重庆: 重庆大学, 2019.
[16] Dong L Y. Application of drought index based on Copula function model and FDA method[D]. Chongqing: Chongqing University, 2019.
[17] Nelsen R B. An introduction to copulas[M]. Springer Science and Business Media, 2007.
[18] 谢正丰, 王冬梅, 张鹏, 等. 黄土高寒区小流域土壤水分空间变异与环境影响因子[J]. 水土保持学报, 2022, 36(4):196-203.
[18] Xie Z F, Wang D M, Zhang P, et al. Spatial variation of soil moisture and its environmental influencing factors in A small watershed in the loess alpine region[J]. Journal of Soil and Water Conservation, 2022, 36(4):196-203.
[19] 朱阿兴, 闾国年, 周成虎, 等. 地理相似性:地理学的第三定律?[J]. 地球信息科学学报, 2020, 22(4):673-679.
doi: 10.12082/dqxxkx.2020.200069
[19] Zhu A X, Lyu G N, Zhou C H, et al. Geographic similarity:Third law of geography?[J]. Journal of Geo-Information Science, 2020, 22(4):673-679.
[20] 韩兰英, 张强, 马鹏里, 等. 中国西南地区农业干旱灾害风险空间特征[J]. 中国沙漠, 2015, 35(4):1015-1023.
doi: 10.7522/j.issn.1000-694X.2014.00104
[20] Han L Y, Zhang Q, Ma P L, et al. Spatial characteristics of comprehensive risk of agricultural drought disaster in southwestern China[J]. Journal of Desert Research, 2015, 35(4):1015-1023.
doi: 10.7522/j.issn.1000-694X.2014.00104
[21] 朱烨, 靳鑫桐, 刘懿, 等. 基于短时间尺度自适应帕尔默干旱指数的中国干旱演变特征分析[J]. 水资源保护, 2022, 38(4):124-130.
[21] Zhu Y, Jin X T, Liu Y, et al. Drought characteristics analysis in China based on self-calibrating Palmer drought severity index in a short time scale[J]. Water Resources Protection, 2022, 38(4):124-130.
[22] 江笑薇, 白建军, 刘宪峰. 基于多源信息的综合干旱监测研究进展与展望[J]. 地球科学进展, 2019, 34(3):275-287.
doi: 10.11867/j.issn.1001-8166.2019.03.0275
[22] Jiang X W, Bai J J, Liu X F. Research progress and prospect of integrated drought monitoring based on multi-source information[J]. Advances in Earth Science, 2019, 34(3):275-287.
doi: 10.11867/j.issn.1001-8166.2019.03.0275
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