Spatial Downscaling of the Tropical Rainfall Measuring Mission Precipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China
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摘要
Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.
Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.
Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.
引文
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Chen F R,Liu Y,Liu Q et al.,2014.Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity.International Journal of Remote Sensing,35(9):3074-3093.doi:10.1080/01431161.2014.902550
Chen Sujing,Li Lijuan,Li Jiuyi et al.,2017.Analysis of the temporal and spatial variation characteristics of precipitation in the Lancang River Basin over the past 55 years.Journal of Geo-information Science,19(3):365-373.(in Chinese)
Derin Y,Anagnostou E,Berne A et al.,2016.Multiregional satellite precipitation products evaluation over complex terrain.Journal of Hydrometeorology,17(6):1817-1836.doi:10.1175/jhm-d-15-0197.1
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Fotheringham A S,Brunsdon C,Charlton M,2002.Geographically Weighted Regression:The Analysis of Spatially Varying Relationships.New York:Wiley.
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Goovaerts P,2000.Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall.Journal of Hydrology,228(1-2):113-129.doi:10.1016/S0022-1694(00)00144-X
Guan H D,Wilson J L,Xie H J,2009.A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain.Journal of Hydrology,375(3-4):578-588.doi:10.1016/j.jhydrol.2009.07.007
Harris P,Fotheringham A S,Crespo R et al.,2010.The use of geographically weighted regression for spatial prediction:an evaluation of models using simulated data sets.Mathematical Geosciences,42(6):657-680.doi:10.1007/s11004-010-9284-7
He Daming,Wu Shaohong,Peng Hua et al.,2005.A study of ecosystem changes in Longitudinal Range-Gorge Region and transboundary eco-security in Southwest China.Advances in Earth Science,20(3):338-344.(in Chinese)
He Daming,Liu Jiang,Hu Jinming et al.,2007a.Transboundary eco-security and its regulation system in the Longitudinal Range-Gorge Region.Chinese Science Bulletin,52(S2):1-9.doi:10.1007/s11434-007-7001-9
He Daming,Li Shaojuan,Zhang Yiping,2007b.The variation and regional differences of precipitation in the Longitudinal Range-Gorge the Region.Chinese Science Bulletin,52(S2):59-73.doi:10.1007/s11434-007-7007-3
He Daming,Feng Yan,Hu Jingming,2007c.Utilization of Water Resources and Environmental Conservation in the Internation Rivers,Southwest China.Beijing:Science Press.(in Chinese)
He Yunling,Zhang Yiping,2004.The climate characteristics and change trends on basins of Lancangjiang valley in Yunnan Province.Journal of Mountain Science,22(5):539-548.(in Chinese)
He Z H,Yang L,Tian F Q et al.,2017.Intercomparisons of rainfall estimates from TRMM and GPM multisatellite products over the Upper Mekong River Basin.Journal of Hydrometeorology,18(2):413-430.doi:10.1175/jhm-d-16-0198.1
Hou A Y,Kakar R K,Neeck S et al.,2014.The global precipitation measurement mission.Bulletin of the American Meteorological Society,95(5):701-722.doi:10.1175/bams-d-13-00164.1
Huffman G J,Adler R F,Arkin P et al.,1997.The Global Precipitation Climatology Project(GPCP)combined precipitation dataset.Bulletin of the American Meteorological Society,78(1):5-20.doi:10.1175/1520-0477(1997)078<0005:tgpcpg>2.0.co;2
Huffman G J,Bolvin D T,Nelkin E J et al.,2007.The TRMMMultisatellite Precipitation Analysis(TMPA):quasi-global,multiyear,combined-sensor precipitation estimates at fine scales.Journal of Hydrometeorology,8(1):38-55.doi:10.1175/jhm560.1
Immerzeel W W,Rutten M M,Droogers P,2009.Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula.Remote Sensing of Environment,113(2):362-370.doi:10.1016/j.rse.2008.10.004
Imran M,Stein A,Zurita-Milla R,2015.Using geographically weighted regression kriging for crop yield mapping in West Africa.International Journal of Geographical Information Science,29(2):234-257.doi:10.1080/13658816.2014.959522
Ishizawa H,Stevens G,2007.Non-english language neighborhoods in Chicago,Illinois:2000.Social Science Research,36(3):1042-1064.doi:10.1016/j.ssresearch.2006.06.005
Jia S F,Zhu W B,L?A F et al.,2011.A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China.Remote Sensing of Environment,115(12):3069-3079.doi:10.1016/j.rse.2011.06.009
Jing W L,Yang Y P,Yue X F et al.,2016.A comparison of different regression algorithms for downscaling monthly satellite-based precipitation over North China.Remote Sensing,8(10):835.doi:10.3390/rs8100835
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