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Hangzhou, China

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Hangzhou, China
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Zhang J.,Chongqing Institute of Meteorological science | Liu Z.,Zhejiang Geographic Information Center | Wang J.,China Agricultural University | He Y.,Chongqing Institute of Meteorological science | Luo H.,Zhejiang Geographic Information Center
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2017

Under the context of more frequent global extreme weather events, accurately monitoring the impact of drought on crop growth in Southwest China has important practical significance for the sustainable development of regional agriculture. Firstly, the study selected 3 types of meteorological drought monitoring models including the percentage of precipitation anomaly(Pa), the standardized precipitation index(SPI), and the relative moisture index (MI) and 2 types of remote sensing drought monitoring models including the vegetation supply water index(VSWI) and the normalized differential vegetation index(NDVI). Secondly, the correlation analysis between 3 meteorological drought monitoring indices, 2 remote sensing monitoring indices and soil relative moisture data was made by using a pixel-to-station paired correlation approach. Thirdly, MI and NDVI, which had the highest correlation coefficients with soil relative moisture, were selected to develop a comprehensive drought index(DI) finally. The new comprehensive DI makes full use of the complementary advantage of ground meteorological site data and remote sensing spatial data, and is suitable to the condition of complex underlying surface. The independent soil moisture data and observed actual drought disaster were used to test the reliability of model. The study result showed that in a month time scale, MI had a highest correlation coefficient of 0.477 with soil relative humidity among all the meteorological drought indices while NDVI had a higher correlation coefficient of 0.416 with soil relative humidity than VSWI. In addition, the correlation of the same type of drought monitoring indices was higher than the different type of drought monitoring indices. This demonstrated that different types of drought indices were complementary because they represented different drought information. Estimated DI had a good correlation with measured soil moisture with the r of 0.816 and the estimated average accuracy reached 88.38%, which was a high accuracy for drought monitoring in southwest area. Furthermore, DI performed better than the single index MI or NDVI in all altitudes, which suggested that DI based on multiple data sources was better than the index based on single data source in different altitudes. The spatial-temporal distribution of drought in 2009-2010 over the southwest region was analyzed according to the DI. The results of drought monitoring showed that the drought disaster area was mainly concentrated in Panxi region in southern Sichuan Province, most part of Yunnan Province and western Guizhou Province. The drought emerged from September 2009, increased gradually from October 2009 to February 2010, relieved gradually from March to May 2010 and terminated in June 2010. The temporal and spatial distribution of drought based on the drought monitoring model was consistent with the actual observed data, which showed DI had a good reliability to monitor the drought process in Southwest China. DI integrated the information of vegetation, rainfall, temperature and evapotranspiration and reduced the uncertainty of the single index inmonitoring drought. Therefore, DI could monitor drought more stably, continuously and truly compared to other drought monitoring indices. This work provides a new approach to monitor drought in Southwest China. © 2017, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.

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