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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 209-218     DOI: 10.6046/zrzyyg.2020395
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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
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Abstract  

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.
Keywords TRMM      spatial downscaling      spatiotemporal variation      Xiangjiang River basin     
ZTFLH:  TP79P339  
Corresponding Authors: ZHANG Xiang     E-mail: 1723257974@qq.com;zhangxiang@whu.edu.cn
Issue Date: 23 December 2021
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Tianyi FAN
Xiang ZHANG
Bing HUANG
Zhan QIAN
Heng JIANG
Cite this article:   
Tianyi FAN,Xiang ZHANG,Bing HUANG, et al. Downscaling of TRMM precipitation products and its application in Xiangjiang River basin[J]. Remote Sensing for Natural Resources, 2021, 33(4): 209-218.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020395     OR     https://www.gtzyyg.com/EN/Y2021/V33/I4/209
Fig.1  Digital elevation and river network map of the Xiangjiang River basin
数据类型 数据名称 空间分
辨率		 时间分
辨率		 数据来源
遥感数据 TRMM 3B42 0.25° 日 美国国家航空航天局
NDVI 1 km 月
DEM 90 m — 中国科学院计算机网络信息中心地理空间数据云平台
气象数据 降水 — 日 中国气象数据网
Tab.1  Research data and sources
Fig.2  Spatial distribution map of TRMM multi-year average precipitation in the Xiangjiang River basin from 2006 to 2017
方法 全局/局
部回归		 参数估计方法 计算量 优势
MLR 全局 普通最小二乘法 低 理论完善
GWR 局部 加权最小二乘法 高 动态建模, 逐点赋权, 减少“关系微弱”数据的干扰
Tab.2  Comparison of downscaling methods
Fig.3  Spatial distribution map of data points before and after downscaling in the Xiangjiang River basin
精度评
价指标		 公式① 最优值
R2 R 2 = ∑ j = 1 n ( x j - x - ) ( y j - y - ) ∑ j = 1 n ( x j - x - ) 2 ∑ j = 1 n ( y j - y - ) 2 2 1
RMSE/mm RMSE = ∑ j = 1 n ( x j - y j ) 2 n 0
ARE/% ARE = ∑ j = 1 n x j - y - n × y - 0
Tab.3  Accuracy evaluation index
Fig.4  Spatial distribution map of TRMM monthly average precipitation before and after downscaling
Fig.5  Verification of the results of the downscaling models predicting monthly precipitation
月份 R2 RMSE/mm ARE/%
TRMM MLR GWR TRMM MLR GWR TRMM MLR GWR
1月 0.56 0.58 0.84 35.50 10.41 5.91 61.91 13.51 6.71
2月 0.38 0.87 0.84 42.15 13.64 7.75 46.51 13.30 7.08
3月 0.63 0.94 0.94 35.99 16.13 4.53 25.51 7.99 2.31
4月 0.72 0.94 0.95 51.76 21.51 7.62 37.45 8.95 3.02
5月 0.44 0.84 0.91 80.32 36.35 11.62 29.23 14.01 3.99
6月 0.54 0.84 0.91 76.22 40.40 12.83 29.14 13.40 3.75
7月 0.70 0.90 0.94 63.05 23.57 7.01 49.58 13.07 3.80
8月 0.52 0.84 0.80 65.13 38.61 10.57 34.16 16.26 5.04
9月 0.38 0.82 0.93 55.04 27.58 8.08 37.79 22.13 6.98
10月 0.61 0.90 0.96 31.40 13.84 4.03 63.02 12.42 4.80
11月 0.82 0.89 0.96 39.19 17.40 7.60 50.92 12.60 4.73
12月 0.60 0.88 0.94 40.90 16.15 9.58 62.34 21.26 10.74
平均值 0.58 0.85 0.91 51.39 22.97 8.09 43.96 14.08 5.25
Tab.4  Statistics of TRMM monthly precipitation accuracy evaluation results before and after downscaling
Fig.6  Spatial distribution map of multi-year average monthly precipitation in Xiangjiang River basin
Fig.7  Distribution map of temporal and spatial variation trend of precipitation in the Xiangjiang River basin
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