Downscaling of TRMM precipitation products and its application in Xiangjiang River basin
FAN Tianyi1,2(), ZHANG Xiang2(), HUANG Bing1, QIAN Zhan1, JIANG Heng1
1. Research Center of Dongting Lake, Hunan Hydro & Power Design Institute, Changsha 410007, China 2. State Key Lab of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
To meet the demand of various industries for high-resolution and high-precision precipitation data, this study establishes the downscaling models of the TRMM precipitation data of the Xiangjiang River basin based on the methods of multivariate linear regression (MLR) and geographically weighted regression (GWR). The leave-one-out cross-validation method was adopted to select the optimal model, and a satellite-ground fusion precipitation product with a resolution of 0.05° was obtained through inversion. On this basis, the spatial-temporal change characteristics of the precipitation in the Xiangjiang River basin were analyzed. The results are as follows. The spatial resolution of the TRMM precipitation data was greatly improved after downscaling. As verified using the precipitation observed at meteorological stations, the coefficient of determination of the TRMM precipitation data increased by more than 0.27, and the root mean square error and average relative error of the TRMM precipitation data decreased by more than 28.42 mm and 29.88 percentage points, respectively on average after downscaling. All these indicate that the regression downscaling model that takes account of vegetation, terrain, and geographic elements can accurately describe the spatial distribution characteristics of precipitation. According to the verification using the precipitation observed at meteorological stations, the coefficient of determination of the GWR downscaling model increased by 0.06 compared to the MLR downscaling model. Meanwhile, the root mean square error and average relative error of the precipitation data obtained using the GWR downscaling model decreased by 14.88 mm and 8.83 percentage points, respectively on average compared to precipitation data obtained using the MLR downscaling model. These indicate better effects of the GWR downscaling model. The spatial-temporal change characteristics of the precipitation in the Xiangjiang River basin during 2006—2017 are greatly different on different time scales, which is reflected in the changing trend and its significance and the locations and area of corresponding zones.
Yuan F, Zhao J J, Ren L L, et al. Streamflow simulation in the Upper Ganjiang River Basin using the TRMM multi-satellite precipitation data[J]. Journal of Tianjin University(Science and Technology), 2013,46(7):611-616.
[3]
李哲. 多源降雨观测与融合及其在长江流域的水文应用[D]. 北京:清华大学, 2015.
Li Z. Multi-source precipitation observations and fusion for hydrological applications in the Yangtze River Basin[D]. Beijing:Tsinghua University, 2015.
Wang C G, Hong Y. Review on inversion,verification and application of satellite remote sensing of precipitation[J]. Water Resources and Hydropower Engineering, 2018,49(8):4-12.
[5]
Hamid E Y, Kawasaki Z, Mardiana R. Impact of the 1997—98 El Niño Event on lightning activity over Indonesia[J]. Geophysical Research Letters, 2001,28(1):147-150.
doi: 10.1029/2000GL011374
[6]
Chen Y, Genio A D, Chen J. The tropical atmospheric El Nino signal in satellite precipitation data and a global climate model[J]. Journal of Climate, 2007,20(14):3580-3601.
doi: 10.1175/JCLI4208.1
[7]
Li X H, Zhang Q, Xu C Y. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment,Poyang Lake Basin[J]. Journal of hydrology, 2012, 426-427:28-38.
doi: 10.1016/j.jhydrol.2012.01.013
Chen C, Zhao S H. Drought monitoring and analysis of Huanghuai Hai plain based on TRMM precipitation data[J]. Remote Sensing for Land and Resources, 2016,28(1):122-129.doi: 10.6046/gtzyyg.2016.01.18.
doi: 10.6046/gtzyyg.2016.01.18
Wei Z M, Yue G Y, Li J, et al. Comparison study on accuracies of precipitation data using GPM and TRMM product in Haihe River Basin[J]. Bulletin of Soil and Water Conservation, 2017,37(2):171-176.
Gan F W, Yu S N, Huang Y M, et al. Accuracy evaluation of TRMM multi-satellite precipitation analysis over Nanliu River Basin[J]. Water Power, 2018,44(11):21-25,43.
Li M, Qin T L, Liu S H, et al. Spatial-temporal validation of TRMM 3B42V7 precipitation products and analysis of precipitation characteristics in the upper reaches of Nujiang River[J]. Plateau Meteorology, 2017,36(4):950-959.
Li Y L, Xiong L H, Yan L. A geographically weighted regression Kriging approach for TRMM-rain gauge data merging and its application in hydrological forecasting[J]. Resources and Environment in the Yangtze Basin, 2017,26(9):1359-1368.
Wang X J. Downscaling method and spatial variability of precipitation in Tianshan Mountain based on the TRMM date[D]. Shihezi:Shihezi University, 2013.
[14]
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
[15]
Duan Z, Bastiaanssen W. 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
Xiong J N, Li W, Liu Z Q, et al. Research on downscaling of TRMM data in the Tibetan Plateau based on GWR model[J]. Remote Sensing for Land and Resources, 2019,31(4):88-95.doi: 10.6046/gtzyyg.2019.04.12.
doi: 10.6046/gtzyyg.2019.04.12
Zeng Z Z, Wang X F, Ren L. 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.
[18]
Zeng Q, Chen H, Xu C Y, et al. Feasibility and uncertainty of using conceptual rainfall-runoff models in design flood estimation[J]. Hydrology Research, 2016,47(4):701-717.
doi: 10.2166/nh.2015.069
Du J K, Jia Y W, Li X X, et al. Study on the spatial-temporal distribution pattern of precipitation in the Taihang Mountain region using TRMM data[J]. Advances in Water Science, 2019,30(1):1-13.
He Q Q, Shi L, Tan L L, et al. Downscaling research and temporal and spatial characteristics of TRMM precipitation products in the central and eastern regions of China[J]. Meteorological Science, 2019,39(3):312-321.
[23]
Chen C, Zhao S, Duan Z, et al. An improved spatial downscaling procedure for TRMM 3B43 precipitation product using geographically weighted regression[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015,8(9):4592-4604.
doi: 10.1109/JSTARS.4609443
[24]
范永东. 模型选择中的交叉验证方法综述[D]. 太原:山西大学, 2013.
Fan Y D. A summary of cross-validation in model selection[D]. Taiyuan:Shanxi University, 2013.
Jiang Y, Xu Z X, Wang J. Comparison among five methods of trend detection for annual runoff series[J]. Journal of Hydraulic Engineering, 2020,51(7):845-857.
Liu X C, Zhang H Y, Zhao J J, et al. Spatial downscaling of TRMM precipitation data based on GWR model in Northeast China[J]. Journal of Geo-Information Science, 2015,17(09):1055-1062.
Fan T Y, Zhang X. Analysis of the spatial-temporal characteristics of water use efficiency of vegetation in the main grain production area of China from 2000 to 2014[J]. Journal of Irrigation and Drainage, 2019,38(3):99-107.
Ning S, Zhang Z Y, Liu L, et al. Adaptability of precipitation estimation method based on TRMM data combined with partial least squares downscaling in different landforms of Xinjiang, China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020,36(12):99-109.
2021, Vol. 33 
), 张翔2(
