| Technology Application |
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Drought monitoring and analysis of Huanghuai Hai plain based on TRMM precipitation data |
| CHEN Cheng1,2,3, ZHAO Shuhe1,2,3 |
1. Jiangsu Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;
2. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China;
3. Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China |
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Abstract TRMM (tropical rainfall measuring mission) precipitation data, covering a wide range with high temporal resolution, is an effective data source to monitor drought on a regional scale. The spatial resolution of 0.25° TRMM 3B43 data was processed using the downscaling method. The downscaling data with 0.05° spatial resolution were used to construct a percentage of monthly precipitation anomalies (Pa index) and Z index, and the two indices were used to monitor the temporal and spatial change of drought from the winter of 2010 to the spring of 2011 in the Huanghuai Hai plain. The standardized precipitation index (SPI) during the same period was also calculated to verify the results. The results showed that the downscaling results had higher reliability with the fitting result R2 higher than 0.76. Pa index that emphasizes gains and losses of precipitation can be used for drought monitoring on the regional scale, but it lacks the space distribution of drought; Z index fitting the precipitation based on the Person-Ⅲ distribution is ideal for monitoring the temporal and spatial distribution of drought, but the drought grade is difficult to divide. The drought grade of Pa index was used to correct the drought grade of Z index. Two indices and SPI had higher degree of correlation with R2 greater than 0.75, indicating that Pa and Z index is effective for drought monitoring. The results achieved by the authors could provide a practical means for monitoring drought on a regional scale.
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| Keywords
ASTER
lithologic index(LI)
principal component analysis
extraction of mineral and rock information
prospecting prediction
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Issue Date: 27 November 2015
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