GNSS/GRACE/GRACE-FO/气象数据结合反演干旱指数

doi:10.11947/j.AGCS.2023.20220484

测绘学报 ›› 2023, Vol. 52 ›› Issue (11): 1883-1891.doi: 10.11947/j.AGCS.2023.20220484

GNSS/GRACE/GRACE-FO/气象数据结合反演干旱指数

姚朝龙^1,2,3, 陈涌鑫¹, 罗志才⁴, 李琼⁵, 叶雪淼¹, 温进杰¹

1. 华南农业大学资源环境学院, 广东广州 510642;
2. 自然资源部建设用地再开发重点实验室, 广东广州 510642;
3. 广东省土地信息工程技术研究中心, 广东广州 510642;
4. 华中科技大学物理学院地球物理研究所, 湖北武汉 430074;
5. 西南石油大学土木工程与测绘学院, 成都四川 610500

收稿日期:2022-08-02 修回日期:2023-07-03 发布日期:2023-12-15
作者简介:姚朝龙(1986-),男,博士,讲师,研究方向为GNSS、卫星重力数据处理及水文气象应用。E-mail:clyao@scau.edu.cn
基金资助:
国家自然科学基金(42004013);广东省自然科学基金面上项目(2022A1515010469);广州市科技计划项目基础研究计划-基础与应用基础研究项目(202102020526;202102020380)

A drought index derived from a combination of GNSS GRACE/GRACE-FO and meteorological data

YAO Chaolong^1,2,3, CHEN Yongxin¹, LUO Zhicai⁴, LI Qiong⁵, YE Xuemiao¹, WEN Jinjie¹

1. College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China;
2. Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation, South China Agricultural University, Guangzhou 510642, China;
3. Guangdong Province Land Information Engineering Technology Research Center, South China Agricultural University, Guangzhou 510642, China;
4. MOE Key Laboratory of Fundamental Physical Quantities Measurement, Institute of Geophysics and PGMF, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China;
5. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China

Received:2022-08-02 Revised:2023-07-03 Published:2023-12-15
Supported by:
The National Natural Science Foundation of China (No. 42004013);Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515010469);Guangzhou Science and Technology Project (Nos. 202102020526;202102020380)

摘要/Abstract

摘要： 构建融合多源数据的综合干旱指数对准确、客观、全面监测与评价干旱具有重要意义。本文利用主成分分析方法,将基于GNSS测站垂向形变和GRACE/GRACE Follow-On(GRACE-FO)重力卫星数据的单一水文干旱指数与我国常用的综合干旱指数(CI)进行融合,在西南地区构建反映水文气象要素的新型综合干旱指数(CDI)。结果表明,相比于CI和基于GNSS、GRACE/GRACE-FO数据的单一干旱指数,CDI指数与改进的帕默尔干旱指数(scPDSI)的相关性均有较大提升,最大相关系数达到0.84,相关系数提高的站点数占比分别达到100%、93%和67%,所有站点相关系数的平均值从0.48、0.34、0.57提高到0.64,表明本文构建的CDI指数能有效融合多源数据包含的干旱信息,提升区域干旱监测的效果。

关键词: GNSS, GRACE/GRACE Follow-On, 数据融合, 干旱指数, 主成分分析

Abstract: Developing the combined drought index (CDI) integrating multi-source data is of important significance for accurate, objective and comprehensive drought monitoring and assessment. In this study, we built a novel CDI reflecting hydro-meteorological variables in Southwest China based on the principle component analysis. The proposed CDI integrates drought indices derived from Global Navigation Satellite System (GNSS) vertical displacements and time-variable gravity fields from Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) as well as the commonly used meteorological composite index (CI) in China. The results show that compared to the individual CI index and the drought indices based on GNSS, GRACE/GRACE-FO data, correlation coefficients between the proposed CDI and self-calibrating palmer drought severity index (scPDSI) improved significantly, with the maximum value of 0.84; the number of sites with improved correlations for the individual drought indices account for 100%, 93%, and 67%, respectively;the averaged correlation coefficients of all studied sites increased from 0.48, 0.34, 0.57 to 0.64, respectively. The results indicate the effectiveness of integrating drought information containing in multi-source data in the proposed CDI index, which could help to improve regional drought monitoring.

Key words: GNSS, GRACE/GRACE Follow-On, data fusion, drought index, principle component analysis

中图分类号:

P227

姚朝龙, 陈涌鑫, 罗志才, 李琼, 叶雪淼, 温进杰. GNSS/GRACE/GRACE-FO/气象数据结合反演干旱指数[J]. 测绘学报, 2023, 52(11): 1883-1891.

YAO Chaolong, CHEN Yongxin, LUO Zhicai, LI Qiong, YE Xuemiao, WEN Jinjie. A drought index derived from a combination of GNSS GRACE/GRACE-FO and meteorological data[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(11): 1883-1891.

参考文献

[1] AGHAKOUCHAK A, FARAHMAND A, MELTON F S, et al. Remote sensing of drought:progress,challenges and opportunities[J].Reviews of Geophysics,2015,53(2):452-480.
[2] JIAO Wenzhe, WANG Lixin, MCCABE M F. Multi-sensor remote sensing for drought characterization:current status, opportunities and a roadmap for the future[J]. Remote Sensing of Environment, 2021, 256:112313.
[3] VAN LOON A F. Hydrological drought explained[J]. Wiley Interdisciplinary Reviews:Water, 2015, 2(4):359-392.
[4] 粟晓玲, 张更喜, 冯凯. 干旱指数研究进展与展望[J]. 水利与建筑工程学报, 2019, 17(5):9-18. SU Xiaoling, ZHANG Gengxi, FENG Kai. Progress and perspective of drought index[J]. Journal of Water Resources and Architectural Engineering, 2019, 17(5):9-18.
[5] 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. WU Zhiyong, CHENG Dandan, HE Hai, et al. Research progress of composite drought index[J]. Water Resources Protection, 2021, 37(1):36-45.
[6] ZARGAR A, SADIQ R, NASER B, et al. A review of drought indices[J]. Environmental Reviews, 2011, 19:333-349.
[7] 张强, 张良, 崔显成, 等. 干旱监测与评价技术的发展及其科学挑战[J]. 地球科学进展, 2011, 26(7):763-778. ZHANG Qiang, ZHANG Liang, CUI Xiancheng, et al. Progresses and challenges in drought assessment and monitoring[J]. Advances in Earth Science, 2011, 26(7):763-778.
[8] 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. JIANG Xiaowei, BAI Jianjun, LIU Xianfeng. Research progress and prospect of integrated drought monitoring based on multi-source information[J]. Advances in Earth Science, 2019, 34(3):275-287.
[9] YAO Yibin, YANG Yuanxi, SUN Heping, et al. Geodesy discipline:progress and perspective[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4):1-10.
[10] 成帅, 袁林果, 姜中山, 等. 应用GPS数据和Slepian基函数反演川云渝地区陆地水储量变化[J]. 地球物理学报, 2021, 64(4):1167-1180. CHENG Shuai, YUAN Linguo, JIANG Zhongshan, et al. Investigating terrestrial water storage change in Sichuan, Yunnan and Chongqing using Slepian basis functions[J]. Chinese Journal of Geophysics, 2021, 64(4):1167-1180.
[11] 何思源, 谷延超, 范东明, 等. 利用GPS垂直位移反演云南省陆地水储量变化[J]. 测绘学报, 2018, 47(3):332-340. DOI:10.11947/j.AGCS.2018.20170255. HE Siyuan, GU Yanchao, FAN Dongming, et al. Seasonal variation of terrestrial water storage in Yunnan Province inferred from GPS vertical observations[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(3):332-340. DOI:10.11947/j.AGCS.2018.20170255.
[12] JIANG Weiping, YUAN Peng, CHEN Hua, et al. Annual variations of monsoon and drought detected by GPS:a case study in Yunnan, China[J]. Scientific Reports, 2017, 7(1):5874.
[13] 姚朝龙, 罗志才, 胡月明, 等. 利用GPS垂向位移监测西南地区干旱事件[J]. 测绘学报, 2019, 48(5):547-554. DOI:10.11947/j.AGCS.2019.20180308. YAO Chaolong, LUO Zhicai, HU Yueming, et al. Detecting droughts in Southwest China from GPS vertical position displacements[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(5):547-554.DOI:10.11947/j.AGCS.2019.20180308.
[14] FERREIRA V G, MONTECINO H C, NDEHEDEHE C E, et al. Space-based observations of crustal deflections for drought characterization in Brazil[J]. Science of the Total Environment, 2018, 644:256-273.
[15] JIANG Zhongshan, HSU Y J, YUAN Linguo, et al. Hydrological drought characterization based on GNSS imaging of vertical crustal deformation across the contiguous United States[J]. Science of the Total Environment, 2022, 823:153663.
[16] 姚朝龙. 联合GRACE和水文气象数据研究自然与人为因素对区域水储量变化的影响[D]. 武汉:武汉大学, 2017. YAO Chaolong.Natural-and human-induced impacts on regional terrestrial water storage changes from GRACE and hydro-meteorological data[D]. Wuhan:Wuhan University, 2017.
[17] 冉艳红, 钟敏, 陈威, 等. 利用GRACE-FO重力卫星探测2019年长江中下游极端干旱[J]. 科学通报, 2021, 66(1):107-117. RAN Yanhong, ZHONG Min, CHEN Wei, et al. Monitoring the extreme drought in the middle and lower reaches of the Yangtze River in 2019 from GRACE-FO satellites[J]. Chinese Science Bulletin, 2021, 66(1):107-117.
[18] 瞿伟, 晋泽辉, 张勤, 等. GRACE与GRACE Follow-On重力卫星数据揭示出的黄河流域2002-2020年干旱特征[J]. 测绘学报, 2023, 52(5):714-724. DOI:10.11947/j.AGCS.2023.20210458. QU Wei, JIN Zehui, ZHANG Qin, et al. Drought characteristics of the Yellow River Basin from 2002 to 2020 revealed by GRACE and GRACE Follow-On data[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(5):714-724. DOI:10.11947/j.AGCS.2023.20210458.
[19] ZHAO M, GERUO A, VELICOGNA I, et al. A global gridded dataset of GRACE drought severity index for 2002-14:comparison with PDSI and SPEI and a case study of the Australia millennium drought[J].Journal of Hydrometeorology,2017, 18:2117-2129.
[20] ZHOU Hao,LUO Zhicai, TANGDAMRONGSUB N, et al. Characterizing drought and flood events over the Yangtze River Basin using the HUST-Grace2016 solution and ancillary data[J]. Remote Sensing, 2017, 9, 1100.
[21] 何秀凤, 高壮, 肖儒雅, 等. InSAR与北斗/GNSS综合方法监测地表形变研究现状与展望[J]. 测绘学报, 2022, 51(7):1338-1355. DOI:10.11947/j.AGCS.2022.20220148. HE Xiufeng, GAO Zhuang, XIAO Ruya, et al. Application and prospect of the integration of InSAR and BDS/GNSS for land surface deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7):1338-1355. DOI:10.11947/j.AGCS.2022.20220148.
[22] LI Bofeng, ZHANG Zhetao. Several kinematic data processing methods for time-correlated observations[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(4):1-9.
[23] GU Yanchao, YUAN Linguo, FAN Dongming, et al. Seasonal crustal vertical deformation induced by environmental mass loading in mainland China derived from GPS, GRACE and surface loading models[J]. Advances in Space Research, 2017, 59(1):88-102.
[24] 姜卫平, 王锴华, 邓连生, 等. 热膨胀效应对GNSS基准站垂向位移非线性变化的影响[J]. 测绘学报, 2015, 44(5):473-480. DOI:10.11947/j.AGCS.2015.20140296. JIANG Weiping, WANG Kaihua, DENG Liansheng, et al. Impact on nonlinear vertical variation of GNSS reference stations caused by thermal expansion[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(5):473-480. DOI:10.11947/j.AGCS.2015.20140296.
[25] YAO Chaolong, SHUMC K, WANG Chuzhu, et al. Human migration-induced impacts on noise in GNSS position time series[J].All Earth,2022,34(1):1-7.
[26] 明锋. GPS坐标时间序列分析研究[J]. 测绘学报, 2019, 48(10):1340. DOI:10.11947/j.AGCS.2019.20190006. MING Feng. Research on the GPS coordinate time series analysis[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10):1340. DOI:10.11947/j.AGCS.2019.20190006.
[27] 常文娟, 梁忠民, 马海波. 基于主成分分析的干旱综合指标构建及其应用[J]. 水文, 2017, 37(1):33-38, 82. CHANG Wenjuan, LIANG Zhongmin, MA Haibo. Construction of drought composite indicator based on principal component analysis and its application[J]. Journal of China Hydrology, 2017, 37(1):33-38, 82.
[28] PRAJAPATI V K, KHANNA M K, SINGH M, et al. PCA-based composite drought index for drought assessment in Marathwada region of Maharashtra state,India[J].Theoretical and Applied Climatology,2022,149:207-220.
[29] LIU Yi, ZHU Ye, REN Liliang, et al. On the mechanisms of two composite methods for construction of multivariate drought indices[J]. Science of the Total Environment, 2019, 647:981-991.
[30] RAJSEKHAR D, SINGH V P, MISHRA A K. Multivariate drought index:an information theory based approach for integrated drought assessment[J]. Journal of Hydrology, 2015, 526:164-182.

GNSS/GRACE/GRACE-FO/气象数据结合反演干旱指数

A drought index derived from a combination of GNSS GRACE/GRACE-FO and meteorological data

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	鲁铁定, 李祯, 贺小星, 周世健. 融合VMD和XGBoost算法的GNSS高程时间序列预测方法[J]. 测绘学报, 2023, 52(8): 1235-1244.
[2]	苏小宁, 石睿娟, 鲍庆华, 朱庆, 孟国杰, 闫浩文. GNSS连续观测站构造运动变化特征自适应提取方法[J]. 测绘学报, 2023, 52(8): 1245-1254.
[3]	何佳星, 郑南山, 丁锐, 张克非, 陈天悦. 粒子群优化卷积神经网络GNSS-IR土壤湿度反演方法[J]. 测绘学报, 2023, 52(8): 1286-1297.
[4]	王梓豪, 唐炉亮, 杨雪, 戴领, 李朝奎. 利用车载GNSS轨迹大数据的U-Turn道路结构信息获取方法[J]. 测绘学报, 2023, 52(8): 1330-1341.
[5]	张兵, 高连如, 李嘉鑫, 洪丹枫, 郑珂. 高/多光谱遥感图像超分辨率融合研究进展与展望[J]. 测绘学报, 2023, 52(7): 1074-1089.
[6]	宋敏峰, 何秀凤, 王笑蕾, 肖儒雅, 贾东振, 李伟强. 顾及地球曲率及椭球高的GNSS-R几何计算方法[J]. 测绘学报, 2023, 52(6): 884-894.
[7]	瞿伟, 晋泽辉, 张勤, 高源, 张普方. GRACE与GRACE Follow-On重力卫星数据揭示出的黄河流域2002—2020年干旱特征[J]. 测绘学报, 2023, 52(5): 714-724.
[8]	陈冠旭, 高柯夫, 赵建虎, 刘经南, 刘焱雄, 刘杨, 李梦昊. GNSS-声学位置服务中声速误差修正方法[J]. 测绘学报, 2023, 52(4): 536-549.
[9]	邝英才, 吕志平, 李林阳, 王方超, 许国昌. GNSS-A水下定位的动态非线性Gauss-Helmert模型及其抗差总体卡尔曼滤波算法[J]. 测绘学报, 2023, 52(4): 559-570.
[10]	赵春梅, 王磊, 何正斌. 卫星激光测距站分级与GNSS卫星轨道精度校核[J]. 测绘学报, 2023, 52(3): 357-366.
[11]	侯鹏宇, 张宝成, 刘腾, 查九平. CDMA+FDMA非差非组合区域PPP-RTK[J]. 测绘学报, 2023, 52(2): 183-194.
[12]	谢劭峰, 王义杰, 黄良珂, 彭华, 黎峻宇, 刘立龙. 中国区域大气加权平均温度垂直递减率格网模型[J]. 测绘学报, 2023, 52(2): 206-217.
[13]	孙根云, 王鑫, 安娜, 张爱竹. 基于多源遥感的大尺度高分辨率不透水面深度学习提取方法[J]. 测绘学报, 2023, 52(2): 272-282.
[14]	王浩岩, 刘远刚, 李少华, 梁博, 何宗宜. GNSS高采样率路径增量地图匹配方法[J]. 测绘学报, 2023, 52(2): 329-340.
[15]	黄观文, 陈孜, 徐永福. 膨胀土边坡GNSS实时监测技术[J]. 测绘学报, 2023, 52(11): 1873-1882.