Establishment of comprehensive drought monitoring model based on downscaling TRMM and MODIS data

Hao-zhe YU; Li-juan LI; Jiu-yi LI

doi:10.31497/zrzyxb.20201019

Journals >Journal of Natural Resources >Volume 35 >Issue 10 >Page 2553 > Article

Journal of Natural Resources
Vol. 35, Issue 10, 2553 (2020)

Establishment of comprehensive drought monitoring model based on downscaling TRMM and MODIS data

Hao-zhe YU^1、2、3, Li-juan LI^2、*, and Jiu-yi LI²

Author Affiliations

¹School of History Culture and Tourism, Shaanxi University of Technology, Hanzhong 723000, Shaanxi, China

²Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

³College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

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DOI: 10.31497/zrzyxb.20201019 Cite this Article

Hao-zhe YU, Li-juan LI, Jiu-yi LI. Establishment of comprehensive drought monitoring model based on downscaling TRMM and MODIS data[J]. Journal of Natural Resources, 2020, 35(10): 2553 Copy Citation Text

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Fig. 1. Geographical location, elevation and distribution of meteorological stations in the Beijing-Tianjin-Hebei region

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Fig. 2. Flow charts of comprehensive drought monitoring model

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Fig. 3. R² and Bias variations after downscaling of TRMM annual scale

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Fig. 4. Observed precipitation vs. downscaling TRMM precipitation (monthly scale)

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Fig. 5. R² and Bias variations after downscaling of TRMM monthly scale

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Fig. 6. Monthly precipitation in the Beijing-Tianjin-Hebei region during 2007-2016

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Fig. 7. Scatter plots and correlation coefficient of R values between SPI-3 and CDI index in each month from 2007 to 2016

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Fig. 8. Scatter plots and correlation coefficient of R values between drought-affected crop area and CDI, VCI, TCI from 2007 to 2016

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Fig. 9. Scatter plots and correlation coefficient of R values between accumulative CDI and standardized unit yield of crop in the growing period (March-October) from 2007 to 2016

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Fig. 10. Scatter plots and goodness-of-fit between CDI and CI in growing period of main crops (March-October) from 2007 to 2016

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Fig. 11. Drought map of Beijing-Tianjin-Hebei region in 2016 based on monitored CDI index

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Fig. 12. Histogram plot of CDI of the Jing-Jin-Ji region in crop (winter wheat and maize) growing period (March-October) from 2007 to 2016

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月份	拟合方程	R²
3	$CDI = - 3.42 + 8.79 \times (PCI) - 11.75 \times {(PCI)}^{2} - 2.83 \times (VCI) - 13.42 \times (TCI) + 7.52 \times {(TCI)}^{2}$	0.522
4	$CDI = - 1.61 + 7.15 \times (PCI) - 2.64 \times {(PCI)}^{2} - 0.73 \times (VCI) - 2.14 \times (TCI) + 4.26 \times {(TCI)}^{2}$	0.738
5	$CDI = - 1.97 + 10.02 \times (PCI) - 4.24 \times {(PCI)}^{2} - 0.15 \times (VCI) - 0.05 \times (TCI) + 0.21 \times {(TCI)}^{2}$	0.732
6	$CDI = - 2.37 + 9.92 \times (PCI) - 4.33 \times {(PCI)}^{2} - 0.19 \times (VCI) - 0.71 \times (TCI) - 0.13 \times {(TCI)}^{2}$	0.728
7	$CDI = - 1.75 + 9.26 \times (PCI) + 1.11 \times {(PCI)}^{2} - 0.59 \times (VCI) + 1.01 \times (TCI) - 0.27 \times {(TCI)}^{2}$	0.744
8	$CDI = - 1.69 + 9.27 \times (PCI) + 1.14 \times {(PCI)}^{2} - 0.58 \times (VCI) + 0.29 \times (TCI) + 1.16 \times {(TCI)}^{2}$	0.762
9	$CDI = - 1.68 + 5.39 \times (PCI) - 0.561 \times {(PCI)}^{2} + 0.03 \times (VCI) - 0.13 \times (TCI) + 0.41 \times {(TCI)}^{2}$	0.703
10	$CDI = - 1.02 + 8.43 \times (PCI) - 0.71 \times {(PCI)}^{2} - 0.23 \times (VCI) - 3.38 \times (TCI) + 3.70 \times {(TCI)}^{2}$	0.820

Table 1. Monthly scale regression equation from March to October in 2007-2016

干旱等级	特旱	重旱	中旱	轻旱	无旱
CDI	<-2	[-2, -1)	[-1, 0)	[0, 1)	≥1

Table 2. Classification of drought grades

季节	K值	CDI趋势	干旱趋势	面积/10³ km²	比例/%
春	<0	↓	↑	199.27	93.12
	0	-	-	4.58	2.14
	>0	↑	↓	10.14	4.74
夏	<0	↓	↑	33.06	15.45
	0	-	-	4.39	2.05
	>0	↑	↓	176.54	82.5
秋	<0	↓	↑	102.16	47.74
	0	-	-	4.52	2.11
	>0	↑	↓	107.31	50.15
冬	<0	↓	↑	175.81	82.16
	0	-	-	4.54	2.12
	>0	↑	↓	33.64	15.72

Table 3. Statistics of CDI index changes in the four seasons of 2007-2016

Hao-zhe YU, Li-juan LI, Jiu-yi LI. Establishment of comprehensive drought monitoring model based on downscaling TRMM and MODIS data[J]. Journal of Natural Resources, 2020, 35(10): 2553

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