PDF Download

[1]FAN Dong,XUE Huazhu,DONG Guotao,et al.Downscaling Study on TRMM 3B43 Data of the Heihe River Basin Based on Quadratic Polynomial Regression Model[J].Research of Soil and Water Conservation,2017,24(02):146-151.

Copy

Downscaling Study on TRMM 3B43 Data of the Heihe River Basin Based on Quadratic Polynomial Regression Model

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 24 Number of periods: 2017 02 Page number: 146-151 Column: Public date: 2017-04-28

Title:: Downscaling Study on TRMM 3B43 Data of the Heihe River Basin Based on Quadratic Polynomial Regression Model

Author(s):: FAN Dong^1,2, XUE Huazhu¹, DONG Guotao², JIANG Xiaohui², ZHANG Wenge², YIN Huijuan², GUO Xinwei²; 1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, He’nan 454000, China;
2. Key Laboratory of Soil and Water Loss Process and Control on the Loess Plateau, Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China

Keywords:: downscaling of TRMM data; quadratic polynomial regression model; DEM; NDVI; Heihe River Basin

CLC:: P332.1

DOI:: -

Abstract:: Precipitation data with high accuracy and high spatial resolution are important to ecology, hydrology and meteorology. In this study, we established a Quadratic Polynomial Regression Model (QPRM) between TRMM 3B43 precipitation, Digital Elevation Model (DEM) data and Normalized Difference Vegetation Index (NDVI) on four different scales (0.25°, 0.50°, 0.75° and 1.00°), and the TRMM 3 B430.25°×0.25° precipitation fields were downscaled to 1 km×1 km for each year from 2001 to 2013. The downscaled precipitation estimates were subsequently validated against the in-situ observation data obtained from nine rain gauge stations in the period of 13 years in the Heihe River Basin. The results showed that both spatial resolution of data and the data quality were significantly improved. Compared with multiple linear regression model downscaling method, the downscaled result obtained by QPRM is more accurate and closer to the measurements from rain gauges. The modeling scale has a great influence on the accuracy of the downscaled results, and 0.50° is the optimal scale to obtain high spatial resolution precipitation by downscaling TRMM 3B43 products using DEM and NDVI data.

References:: [1] 吕洋,杨胜天,蔡明勇,等. TRMM卫星降水数据在雅鲁藏布江流域的适用性分析[J].自然资源学报,2013,28(8):1414-1425.
[2] Xu G, Xu X, Liu M, et al. Spatial downscaling of TRMM precipitation product using a combined multifractal and regression approach:demonstration for south China[J]. Water, 2015,7(6):3083-3102.
[3] Goodrich D C, Faurès J M, Woolhiser D A, et al. Measurement and analysis of small-scale convective storm rainfall variability[J]. Journal of Hydrology, 1995,173(1):283-308.
[4] 孙乐强,郝振纯,王加虎,等. TMPA卫星降水数据的评估与校正[J].水利学报,2014,46(10):1135-1146.
[5] Wilheit T T. Some comments on passive microwave measurement of rain[J]. Bulletin of the American Meteorological Society, 1986,67(10):1226-1271.
[6] Fang J, Du J, Xu W, et al. Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area[J]. Advances in Water Resources, 2013,61(1):42-50.
[7] 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, 2007,8(1):38-55.
[8] Su F, Hong Y, Lettenmaier D P. Evaluation of TRMM Multisatellite Precipitation Analysis(TMPA)and its utility in hydrologic prediction in the La Plata Basin[J]. Journal of Hydrometeorology, 2007,9(4):622-640.
[9] 刘元波,傅巧妮,宋平,等.卫星遥感反演降水研究综述[J].地球科学进展,2011(11):1162-1172.
[10] 范丽军.统计降尺度方法的研究及其对中国未来区域气候情景的预估[D].北京:中国科学院研究生院,2006.
[11] Park N. Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables[J]. Advances in Meteorology, 2013,13(11):187-190.
[12] Guan H, Wilson J L, Xie H. A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain[J]. Journal of Hydrology, 2009,375(3):578-588.
[13] 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.
[14] Jia S, Zhu W, Lv 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.
[15] 王超,赵传燕. TRMM多卫星资料在黑河上游降水时空特征研究中的应用[J].自然资源学报,2013,28(5):862-872.
[16] 康尔泗.寒区和干旱区水文研究的回顾和展望[J].冰川冻土,1998,20(3):238-242.
[17] 程国栋,赵传燕.干旱区内陆河流域生态水文综合集成研究[J].地球科学进展,2008,23(10):1005-1012.
[18] 侯兰功,肖洪浪,邹松兵,等.黑河流域水循环特征研究[J].水土保持研究,2010,17(3):254-258.
[19] Liu J, Zhu A, Duan Z. Evaluation of TRMM3 B42 precipitation product using rain gauge data in Meichuan Watershed, Poyang Lake Basin, China[J]. Journal of Resources and Ecology, 2012,3(4):359-366.
[20] Li Z, Yang D, Hong Y. Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River[J]. Journal of Hydrology, 2013,500(14):157-169.
[21] Peng B, Shi J, Ni-Meister W, et al. Evaluation of TRMM Multisatellite Precipitation Analysis(TMPA)products and their potential hydrological application at an arid and semiarid basin in China[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(9):3915-3930.
[22] Zhang X, Hu Y, Zhuang D, et al. NDVI spatial pattern and its differentiation on the Mongolian Plateau[J]. Journal of Geographical Sciences, 2009,19(4):403-415.
[23] Spak S, Holloway T, Lynn B, et al. A comparison of statistical and dynamical downscaling for surface temperature in North America[J]. Journal of Geophysical Research:Atmospheres, 2007,112(8):271-283.

Similar References:

Memo

Last Update: 1900-01-01