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Geophysical Research Letters
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Supporting Information for
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Microwave remote sensing of short-term droughts during crop growing seasons
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Xing Yuan1, Zhuguo Ma1, Ming Pan2, and Chunxiang Shi3
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RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Department of Civil and Environmental Engineering, Princeton University, Princeton, New
Jersey, USA
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National Meteorological Information Center, China Meteorological Administration, Beijing,
China
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S1. Sensitivity to temporal and spatial sampling
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In this study, the in-situ soil moisture data was collected every 10 days and aggregated into
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monthly, while the monthly ESA CCI satellite retrievals and reanalysis datasets were aggregated
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from daily and near continuous (e.g., 6 hourly) intervals respectively. The performance would be
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better if different datasets exactly match their scales. However, due to the data insufficiency,
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especially for the in-situ and satellite data (ESA CCI has many missing values at daily time
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Corresponding author address: Xing Yuan, RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China. E-mail: yuanxing@tea.ac.cn
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scale), doing the analysis at monthly scale is a tradeoff between short-term drought analysis and
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data adequacy. To evaluate the uncertainties in the monthly averages from just three in-situ
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values, we performed a sampling sensitivity test using the ERA Interim reanalysis data and the
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results showed no significant difference between drought indices calculated from three daily
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values in the month, where the average hit rate, false alarm rate and equitable threat score
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corresponding to Table 1 for ERA Interim are 0.36, 0.15, and 0.034, respectively. The sensitivity
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to the spatial sampling (resolution) was shown in Table S1.
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Table S1. The same as Table 1, but the datasets are firstly averaged into 1-degree grid cells,
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where the drought indices and the statistics are then calculated and averaged over all 1-degree
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grid cells with valid data.
Data
GLDASv1/CLM
GLDASv1/Mosaic
GLDASv1/Noah
GLDASv1/VIC
GLDASv2/Noah
ERA Interim
ESA CCI Merged
ESA CCI Passive
ESA CCI Active
ESA CCI Merged (raw data)
ESA CCI Passive (raw data)
ESA CCI Active (raw data)
H
0.34
0.34
0.35
0.33
0.42
0.38
0.36
0.35
0.39
0.36
0.40
0.51
F
0.15
0.16
0.15
0.15
0.16
0.15
0.16
0.14
0.15
0.18
0.26
0.32
ETS (10-2)
3.10
2.82
3.25
2.95
4.07
3.67
3.12
3.42
3.94
2.90
2.28
3.10
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S2. Definitions of hit rate (H), false alarm rate (F) and equitable threat score (ETS)
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Define a as the number of events when drought occurs in both the in-situ and reanalysis data,
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b for when drought occurs in the reanalysis but not in the in-situ observation, c for when drought
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occurs in the in-situ observation but not in the reanalysis, and d for when drought does not occur
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in either the reanalysis or in-situ observation. Then, the hit rate (H) is
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a
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ac
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(A1)
where it is also called the probability of detection. The false alarm rate (F) is
b
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bd
(A2)
where it is the probability of false detection. The ETS (Equitable Threat Score) is
a  aref
a  aref  b  c
where aref  (a  b)( a  c) /( a  b  c  d ) .
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3
,
(A3)
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Figure S1. Dominant MODIS land cover type over mainland China.
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Figure S2. ERA Interim monthly surface air temperature anomaly in July 2013.
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Figure S3. Mean annual precipitation (mm) averaged during 1961-2013 for 17 river basins in
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mainland China.
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