基于MGWR模型的黄河流域GPM卫星降水数据降尺度研究

doi:10.12118/j.issn.1000-6060.2022.475

干旱区地理 ›› 2023, Vol. 46 ›› Issue (7): 1052-1062.doi: 10.12118/j.issn.1000-6060.2022.475

基于MGWR模型的黄河流域GPM卫星降水数据降尺度研究

柏荷^1,²(),明義森^1,²,刘启航^1,²,黄昌^1,^2,³()

1.陕西省地表系统与环境承载力重点实验室，陕西西安 710127
2.西北大学城市与环境学院，陕西西安 710127
3.西北大学城市与环境学院地表系统与灾害研究院，陕西西安 710127

收稿日期:2022-09-21 修回日期:2022-11-17 出版日期:2023-07-25 发布日期:2023-08-03
通讯作者: 黄昌（1986-），男，博士，副教授，主要从事水文水资源遥感方面的研究. E-mail: changh@nwu.edu.cn
作者简介:柏荷（1996-），女，硕士研究生，主要从事水文水资源遥感方面的研究. E-mail: baihhhe@163.com
基金资助:
国家重点研发计划项目(2017YFC1502501);陕西省自然科学基金(2021JM314)

Downscaling of GPM satellite precipitation data in the Yellow River Basin based on MGWR model

BAI He^1,²(),MING Yisen^1,²,LIU Qihang^1,²,HUANG Chang^1,^2,³()

1. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Xi’an 710127, Shaanxi, China
2. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi, China
3. Institute of Earth Surface System and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, Shaanxi, China

Received:2022-09-21 Revised:2022-11-17 Online:2023-07-25 Published:2023-08-03

摘要/Abstract

摘要：

黄河流域地域广阔，但气象站点分布较少，导致气象资料短缺。卫星降水可以作为气象站点观测的重要补充，但其空间分辨率有限，导致其在区域研究中作用有限。以黄河流域作为研究区域，针对全球降水观测计划（GPM）卫星降水产品，以2002、2012年和2020年降水数据作为干旱年、标准年以及湿润年3个典型气候年份，在综合考虑归一化植被指数（NDVI）、数字高程模型（DEM）、坡度（Slope）、地表温度（LST）和风速（WDS）多种反映降水量空间分布特征的影响因子及其空间非平稳性特征的基础上，采用地理加权回归（GWR）模型、混合地理加权回归（MGWR）模型2种降尺度方法，得到了黄河流域1 km空间分辨率的降尺度降水数据，并进一步通过地面气象站点数据对降尺度结果进行验证。结果表明：（1）GPM年降水数据与地面气象站点观测数据在2002、2012年和2020年的黄河流域地区具有较高的相关性。（2）经MGWR模型降尺度的降水数据空间分辨率得到了显著提高，且在降水变化的空间细节表达方面较GWR模型更优。（3）在3个典型气候年份中，MGWR模型在降水量标准年中相对于GWR模型具有更高的准确性。研究结果能够为相关区域范围的降水降尺度研究提供宏观参考与借鉴，促进区域气候水文研究。

关键词: 混合地理加权回归模型（MGWR）, 地理加权回归模型（GWR）, 全球降水观测计划（GPM）, 黄河流域

Abstract:

Because the Yellow River Basin of China is a vast area with sparse meteorological stations, limited meteorological data are available. Satellite precipitation data are an alternative for precipitation observations. In this study, the precipitation data of the Yellow River Basin for 2002, 2012, and 2020 were considered representative of dry, standard, and wet years to downscale global precipitation measurement (GPM) precipitation data. The normalized difference vegetation index, digital elevation model, slope, land surface temperature, and wind speed that reflect the spatial distribution characteristics of precipitation and the characteristics of spatial nonstationarity were investigated and used in two downscaling methods, namely the geographically weighted regression model (GWR) and mixed geographically weighted regression model (MGWR) to obtain the downscaling precipitation data of 1-km spatial resolution in the Yellow River Basin. The downscaling results were verified by the ground meteorological station data. The results revealed that: (1) GPM annual precipitation data were highly correlated with ground meteorological station observation data in the Yellow River Basin in 2002, 2012, and 2020. (2) Downscaling with the MGWR model considerably improved the spatial resolution. In terms of the spatial details of precipitation, the downscaling results of the MGWR model were superior to those of the GWR model. (3) In the three typical climate years, the accuracy of MGWR downscaling data in the precipitation standard year was slightly higher than that of GWR downscaling data. The results of this study can provide a reference for precipitation downscaling research in related regions and promote regional climate and hydrological research.

Key words: mixed geographically weighted regression model (MGWR), geographically weighted regression model (GWR), global precipitation measurement (GPM), Yellow River Basin

柏荷, 明義森, 刘启航, 黄昌. 基于MGWR模型的黄河流域GPM卫星降水数据降尺度研究[J]. 干旱区地理, 2023, 46(7): 1052-1062.

BAI He, MING Yisen, LIU Qihang, HUANG Chang. Downscaling of GPM satellite precipitation data in the Yellow River Basin based on MGWR model[J]. Arid Land Geography, 2023, 46(7): 1052-1062.

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参考文献 35

[1]	Michaelides S, Levizzani V, Anagnostou E, et al. Precipitation: Measurement, remote sensing, climatology and modeling[J]. Atmospheric Research, 2009, 94(4): 512-533. doi: 10.1016/j.atmosres.2009.08.017
[2]	Pipunic R C, Ryu D, Costelloe J F, et al. An evaluation and regional error modeling methodology for near-real-time satellite rainfall data over Australia[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(20): 10767-10783. doi: 10.1002/2015JD023512
[3]	Kang E, Cheng G, Lan Y, et al. A model for simulating the response of runoff from the mountainous watersheds of inland river basins in the arid area of northwest China to climatic changes[J]. Science in China Series D: Earth Sciences, 1999, 42(1): 52-63.
[4]	唐国强, 万玮, 曾子悦, 等. 全球降水测量(GPM)计划及其最新进展综述[J]. 遥感技术与应用, 2015, 30(4): 607-615.
	[Tang Guoqiang, Wan Wei, Zeng Ziyue, et al. An overview of the global precipitation measurement (GPM) mission and it’s latest development[J]. Remote Sensing Technology and Application, 2015, 30(4): 607-615.]
[5]	田亚林, 李雪梅, 李珍, 等. 1980—2017年天山山区不同降水形态的时空变化[J]. 干旱区地理, 2020, 43(2): 308-318.
	[Tian Yalin, Li Xuemei, Li Zhen, et al. Spatial and temporal variations of different precipitation types in the Tianshan Mountains from 1980 to 2017[J]. Arid Land Geography, 2020, 43(2): 308-318.]
[6]	肖柳斯, 张阿思, 闵超, 等. GPM卫星降水产品在台风极端降水过程的误差评估[J]. 高原气象, 2019, 38(5): 993-1003. doi: 10.7522/j.issn.1000-0534.2018.00143
	[Xiao Liusi, Zhang Asi, Min Chao, et al. Evaluation of GPM satellite-based precipitation estimates during three tropical-related extreme rainfall events[J]. Plateau Meteorology, 2019, 38(5): 993-1003.] doi: 10.7522/j.issn.1000-0534.2018.00143
[7]	Hsu K, Gao X, Sorooshian S, et al. Precipitation estimation from remotely sensed information using artificial neural networks[J]. Journal of Applied Meteorology and Climatology, American Meteorological Society, 1997, 36(9): 1176-1190.
[8]	Hsu K, Gupta H V, Gao X, et al. Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation[J]. Water Resources Research, 1999, 35(5): 1605-1618. doi: 10.1029/1999WR900032
[9]	Sorooshian S, Hsu K, Gao X, et al. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall[J]. Bulletin of the American Meteorological Society, 2000, 81(9): 2035-2046. doi: 10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2
[10]	Huffman G J, Adler R F, Arkin P, et al. The global precipitation climatology project (GPCP) combined precipitation dataset[J]. Bulletin of the American Meteorological Society, 1997, 78(1): 5-20. doi: 10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2
[11]	Huffman G J, Adler R F, Bolvin D T, et al. Improving the global precipitation record: GPCP Version 2.1[J]. Geophysical Research Letters, 2009, 36(17): L17808, doi: 10.1029/2009GL040000. doi: 10.1029/2009GL040000
[12]	Huffman G J, Bolvin D T, Nelkin E J, et al. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales[J]. Journal of Hydrometeorology, American Meteorological Society, 2007, 8(1): 38-55.
[13]	Huffman G J, Bolvin D T, Braithwaite D, et al. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG)[R]. Greenbelt: National Aeronautics and Space Administration (NASA), 2014.
[14]	Anjum M N, Ding Y, Shangguan D, et al. Performance evaluation of latest integrated multi-satellite retrievals for global precipitation measurement (IMERG) over the northern highlands of Pakistan[J]. Atmospheric Research, 2018, 205: 134-146. doi: 10.1016/j.atmosres.2018.02.010
[15]	Tan M L, Duan Z. Assessment of GPM and TRMM precipitation products over Singapore[J]. Remote Sensing, Multidisciplinary Digital Publishing Institute, 2017, 9(7): 720, doi: 10.3390/rs9070720. doi: 10.3390/rs9070720
[16]	Chen F, Li X. Evaluation of IMERG and TRMM 3B43 monthly precipitation products over mainland China[J]. Remote Sensing, Multidisciplinary Digital Publishing Institute, 2016, 8(6): 472, doi: 10.3390/rs8060472. doi: 10.3390/rs8060472
[17]	Xu S G, Wu C Y, Wang L, et al. A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics[J]. Remote Sensing of Environment, 2015, 162: 119-140. doi: 10.1016/j.rse.2015.02.024
[18]	Duan Z, Bastiaanssen W G M. First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure[J]. Remote Sensing of Environment, 2013, 131: 1-13. doi: 10.1016/j.rse.2012.12.002
[19]	Wilby R L, Wigley T M L. Downscaling general circulation model output: A review of methods and limitations[J]. Progress in Physical Geography: Earth and Environment, 1997, 21(4): 530-548. doi: 10.1177/030913339702100403
[20]	Immerzeel W W, Rutten M M, Droogers P. Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula[J]. Remote Sensing of Environment, 2009, 113(2): 362-370. doi: 10.1016/j.rse.2008.10.004
[21]	Jia S, Zhu W, Lü A, et al. A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China[J]. Remote Sensing of Environment, 2011, 115(12): 3069-3079. doi: 10.1016/j.rse.2011.06.009
[22]	温伯清, 刘戎, 庞国伟, 等. GPM卫星降水数据的降尺度研究——以陕西省为例[J]. 干旱区地理, 2021, 44(3): 786-795.
	[Wen Boqing, Liu Rong, Pang Guowei, et al. Downscaling study of GPM satellite precipitation data: A case study of Shaanxi Province[J]. Arid Land Geography, 2021, 44(3): 786-795.]
[23]	曾昭昭, 王晓峰, 任亮. 基于GWR模型的陕西秦巴山区TRMM降水数据降尺度研究[J]. 干旱区地理, 2017, 40(1): 26-36.
	[Zeng Zhaozhao, Wang Xiaofeng, Ren Liang. Spatial downscaling of TRMM rainfall data based on GWR model for Qinling-Daba Mountains in Shaanxi Province[J]. Arid Land Geography, 2017, 40(1): 26-36.]
[24]	崔路明, 王思梦, 刘轶欣, 等. TRMM和GPM卫星降水数据在中国三大流域的降尺度对比研究[J]. 长江流域资源与环境, 2021, 30(6): 1317-1328.
	[Cui Luming, Wang Simeng, Liu Yixin, et al. Comparative study on downscaling of TRMM and GPM satellite precipitation data in three major river basins in China[J]. Resources and Environment in the Yangtze Basin, 2021, 30(6): 1317-1328.]
[25]	Bohnenstengel S I, Schlünzen K H, Beyrich F. Representativity of in situ precipitation measurements: A case study for the LITFASS area in north-eastern Germany[J]. Journal of Hydrology, 2011, 400(3): 387-395. doi: 10.1016/j.jhydrol.2011.01.052
[26]	Marzano F S, Cimini D, Montopoli M. Investigating precipitation microphysics using ground-based microwave remote sensors and disdrometer data[J]. Atmospheric Research, 2010, 97(4): 583-600. doi: 10.1016/j.atmosres.2010.03.019
[27]	Arshad A, Zhang W, Zhang Z, et al. Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of upper Indus Basin (UIB)[J]. Science of the Total Environment, 2021, 784: 147140, doi: 10.1016/j.scitotenv.2021.147140. doi: 10.1016/j.scitotenv.2021.147140
[28]	张小兵, 柳礼香. 1998—2018年黄河流域水资源变化特征研究[J]. 地下水, 2020, 42(5): 187-189, 291.
	[Zhang Xiaobing, Liu Lixiang. Study on the change characteristics of water resources in the Yellow River Basin from 1998 to 2018[J]. Ground Water, 2020, 42(5): 187-189, 291.]
[29]	王澄海, 杨金涛, 杨凯, 等. 过去近60 a黄河流域降水时空变化特征及未来30 a变化趋势[J]. 干旱区研究, 2022, 39(3): 708-722.
	[Wang Chenghai, Yang Jintao, Yang Kai, et al. Changing precipitation characteristics in the Yellow River Basin in the last 60 years and tendency prediction for next 30 years[J]. Arid Zone Research, 2022, 39(3): 708-722.]
[30]	杨飞, 张成业, 李军, 等. 基于GRACE的黄河流域陆地水储量时空变化研究[J]. 煤田地质与勘探, 2022, 52(4): 106-112.
	[Yang Fei, Zhang Chengye, Li Jun, et al. Temporal and spatial changes of terrestrial water storage in Yellow River Basin based on GRACE[J]. Coal Geology & Exploration, 2022, 52(4): 106-112.]
[31]	付含培, 王让虎, 王晓军. 1999—2018年黄河流域NDVI时空变化及驱动力分析[J]. 水土保持研究, 2022, 29(2): 145-153, 162.
	[Fu Hanpei, Wang Ranghu, Wang Xiaojun. Analysis of spatiotemporal variations and driving forces of NDVI in the Yellow River Basin during 1999—2018[J]. Research of Soil and Water Conservation, 2022, 29(2): 145-153, 162.]
[32]	Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression-modelling spatial non-stationarity[J]. Journal of the Royal Statistical Society. Series D (The Statistician), 1998, 47(3): 431-443.
[33]	Brunsdon C, Fotheringham A S, Charlton M. Some notes on parametric significance tests for geographically weighted regression[J]. Journal of Regional Science, 1999, 39(3): 497-524. doi: 10.1111/jors.1999.39.issue-3
[34]	Mei C L, He S Y, Fang K T. A note on the mixed geographically weighted regression model[J]. Journal of Regional Science, 2004, 44(1): 143-157. doi: 10.1111/j.1085-9489.2004.00331.x
[35]	郭妍. 陕西省TRMM降水数据反演精度的时空分布特征研究[D]. 咸阳: 西北农林科技大学, 2017.
	[Guo Yan. Spatlal & time distribution characteristics of retrieval accuracy on TRMM percipitation data in Shaanxi Province[D]. Xianyang: Northwest Agriculture & Forestry University, 2017.]

辅助因子	2002年	2012年	2020年
DEM	-13792.4780	111.5426	-38.1083
NDVI	-10307.8175	-14268.9533	-11486.5637
WDS	-13864.9890	-12589.8497	-11786.7153
Slope	283.4418	329.9034	374.2292
LST	291.0240	-6264.2374	227.1289

评价指标	年份	GPM	GWR	MGWR
R²	2002	0.700	0.670	0.598
	2012	0.786	0.773	0.690
	2020	0.736	0.683	0.688
Bias	2002	0.026	0.029	0.022
	2012	0.300	0.300	-0.092
	2020	0.311	0.309	0.154
RMSE	2002	62.144	65.482	71.950
	2012	72.644	73.940	92.269
	2020	115.091	134.041	134.229

基于MGWR模型的黄河流域GPM卫星降水数据降尺度研究

Downscaling of GPM satellite precipitation data in the Yellow River Basin based on MGWR model

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