Global
High–temperature Disaster Risk of Rice Dataset
Su, P.1,3 Wang, J. A.1,2* Zhang, A. Y.1 Wang, R.1
1. Faculty
of Geographical Science, Beijing Normal University, Beijing 100875, China;
2. Key
Laboratory of Tibetan Plateau Land Surface Processes and Ecological
Conservation (Ministry of Education), Xining 810008, China;
3. China
School of Geographic Science, Qinghai Normal University, Xining 810008, China
Abstract: Under
the background of climate change and population expansion, the food supply
pressure increased. Rice is a temperature–sensitive crop, thus its future yield
and growth environment will also undergo significant changes with climate
changes. This paper used the MaxEnt model and the redistribution method to
project the planting areas under rice. Through combining the planting area and extreme
high temperature disasters, we obtained the high–temperature exposure
to rice. The
EPIC model was used to generate the vulnerability curve
of rice yield loss responding to high–temperature stress. In addition, the
yield loss rate under different scenarios obtained by high–temperature
intensity and vulnerability curves. It was treated as the rice yield loss data.
The data set was divided into three parts, rice potential cultivation area
data, rice high–temperature exposure data, and rice
high–temperature yield loss data. The spatial
resolution of the rice potential cultivation area data and rice
high–temperature exposure data is 0.25°×0.25°
, and the spatial resolution of rice high–temperature yield loss data is 0.5°×0.5°.
The data set contented 21 files in total, and the data volume was about 46.8MB.
Keywords: global change; rice; crop potential distribution; exposure; vulnerability
DOI: https://doi.org/10.3974/geodp.2022.04.05
CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2022.04.05
Dataset Availability Statement:
The dataset supporting this paper
was published and is accessible through the Digital Journal of Global Change Data Repository
at: https://doi.org/10.3974/geodb.2022.06.04.V1 or https://cstr.escience.org.cn/CSTR:20146.112022.06.04.V1.
1 Introduction
The
latest IPCC special report pointed out that according to the current global
warming rate, the global temperature will rise by 1.5 ℃ by 2040 [1], which will change the growth environment of rice in the future.
Without considering the improvement of crops varieties, the cultivation area of
rice in the future should be adjusted to the environment. Similarly, the global
high–temperature disasters on rice will also undergo
spatial and temporal changes in the future, which necessitate crop
distribution, exposure and yield loss data –for rice. Therefore, we can
intuitively recognize the distribution of rice planting area and the yield loss
induced by extreme high–temperature under different scenarios, which helps to
formulate measures to reduce losses according to local conditions [2], and it is of great significance to world food security.
2 Metadata of the Dataset
The
data name, author, geographical region, year, temporal resolution, spatial
resolution, composition, publishing and sharing service platform, sharing
policy and other information of “Global Rice High–temperature Disaster Risk
Data Set” [3] are shown in Table 1.
Table
1 Metadata Summary of the “Global Rice High–temperature Disaster
Risk Data Set”
Items
|
Description
|
Dataset full name
|
Global Rice High–temperature Disaster Risk Data Set
|
Dataset short name
|
GlobalRice_Risk
|
Authors
|
Jing’ai Wang, AAA–6406–2022, Faculty of Geographical Science, Beijing
Normal University, and Key Laboratory of Tibetan Plateau Land Surface
Processes and Ecological Conservation (Ministry of Education), jwang@bnu.edu.cn
Peng Su, ABH–3399–2021, School of Geographic Science, Qinghai Normal
University, 201947331031@stu.qhnu.edu.cn
Anyu Zhang, AAA–6787–2022, Faculty of Geographical Science, Beijing
Normal University, zay@mail.bnu.edu.cn
Ran Wang, AAE–1120–2019, Faculty of Geographical Science, Beijing
Normal University, wangr0225@163.com
|
Geographical region
|
Global
|
Year
|
2000s, 2030s and 2050s
|
Temporal resolution
|
1970–2000(2000s), 2016–2035(2030s) and 2046–2065(2050s))
|
Spatial resolution
|
The spatial resolution of rice distribution and exposure data is
0.25°*0.25°, and the spatial resolution of rice vulnerability data is
0.5°*0.5°
|
Data format
|
.tif
|
|
|
Data size
|
46MB
|
|
|
Data files
|
rice cultivation area data, rice high–temperature exposure data, and
rice high–temperature vulnerability data
|
Foundation(s)
|
the National Key Research and Development Program of China (Grant
No.2016YFA0602402)
|
Data publisher
|
Global Change Research Data Publishing &
Repository, http://www.geodoi.ac.cn
|
Address
|
No. 11A, Datun Road, Chaoyang District, Beijing 100101, China
|
Data sharing policy
|
Data from the Global Change
Research Data Publishing & Repository includes metadata, datasets (in the Digital Journal of Global Change Data Repository), and
publications (in the Journal of Global Change Data & Discovery). Data sharing policy includes: (1) Data are openly
available and can be free downloaded via the Internet; (2) End users are
encouraged to use Data subject to citation; (3) Users, who are by definition
also value–added service providers, are welcome to redistribute Data
subject to written permission from the GCdataPR Editorial Office and the
issuance of a Data redistribution license; and (4) If Data are used to compile
new datasets, the ‘ten per cent principal’ should be followed such that Data
records utilized should not surpass 10% of the new dataset contents, while
sources should be clearly noted in suitable places in the new dataset[4]
|
Communication and searchable system
|
DOI,
CSTR, Crossref, DCI, CSCD, CNKI, SciEngine, WDS/ISC, GEOSS
|
3 Methods
3.1 Data Collection or Processing
The
original data used in this study, including environment data (topographic and
soil properties data), crop data (historical rice planting area data) and
scenarios data (climate data). This paper selected the global digital elevation model (DEM) from USGS and the global
surface slope data of GAEZ as topographic data. This paper selected global soil
properties data from ISRIC. The historical rice harvested area data came from
EARTHSTAT, SPAM 2005 v2.0, and MIRCA2000. The climate data in this paper were
from NASA, including precipitation, the highest temperature and the lowest
temperature. And the multi–mode data results are averaged to get the
comprehensive results. The detailed data list is shown in Table 2.
Table
2 Database
Category
|
Name
|
Year
|
Temporal Resolution
|
Sources
|
Environment data
|
Global Multi–resolution
Terrain Elevation Data (GMTED2010)
|
2010
|
1km×1km
|
United States Geological Survey
(USGS)
https://topotools.cr.usgs.gov/gmted_viewer/
|
WISE derived soil
properties on a 30 by 30 arc–seconds global grid
|
2012
|
30’’×30’’
|
International Soil
Reference and Information Centre (ISRIC)
http://www.isric.org
|
–Crop data
|
①Harvested Area and Yield for 175 Crops year 2000
②SPAM 2005 v2.0
③MIRCA2000
|
2000 or2005
|
5’×5’
|
①http://www.earthstat.org/harvested–area– yield–175–crops/
②http://mapspam.info/maps/
③http://www.uni–frankfurt.de/45218031/data_ download?
|
FAO rice production
statistics
|
1960~2015
|
Country unit and subnational
unit
|
http://www.fao.org/faostat/en/#data/QC;http://kids.fao.org/agromaps/
|
FAO rice demand statistics
|
1960~2015
|
Country unit
|
http://www.fao.org/faostat/en/#data/FBS
|
FAO demographic data
|
1960~2015
|
Country unit
|
http://www.fao.org/faostat/en/#data/OA
|
FAO GDP statistics
|
1960~2015
|
Country unit
|
http://www.fao.org/faostat/en/?#data/MK
|
Rice growth period
|
1961~1990
|
5’×5’
|
https://nelson.wisc.edu/sage/data–and–models/crop–calendar–dataset/index.php
|
Scenarios data
|
NASA’s Climate Data
Services
|
1960~2099
|
0.25°×0.25°
|
https://cds.nccs.nasa.gov/nex–gddp/
|
Global dataset of gridded
population and GDP scenarios
|
2005~2099
|
0.25°×0.25°
|
http://www.cger.nies.go.jp/gcp/population–and–gdp.html
|
Land–Use Harmonization 2 (LUH2)
|
1970~2100
|
0.25°×0.25°
|
http://luh.umd.edu/data.shtml
|
Historical wind speed
(WorldClim V1.4)
|
1970~2000
|
5’×5’
|
http://www.worldclim.com/version1
|
Wind speed under different
scenarios
|
1970~2099
|
0.5°×0.5°
|
https://www.isimip.org/gettingstarted/input–data–bias–correction/
|
3.2.1 Methodology
3.2.1 Estimated Rice
Cultivation Distribution
3.2.1.1
Estimated potential distribution of rice
This
data set used the MaxEnt model to estimate rice potential distributions. This
model is based on the maximum entropy method to simulate the species niche and
distribution. The input data of the model were the crop distribution samples
and the environmental variables. Refer to the previous work [5], the selection of samples and environmental variables in this paper
showed as follows.
First, we
calculated the ratio of historical crop cultivation area. Secondly, we divided
the ratio into four groups, and the fourth group (ratio≥66%) determined the number of
samples selected in the other three groups according to the proportion of the
cultivation area and the number of samples. Besides, we used the randperm
function in MATLAB to randomly select the specified number of samples in each
group. The number of samples was 559 (ratio <10%), 768
(10%≤ratio
<33%), 541(33%≤ratio
<66%) and 360 (66%≤ratio).
In order to
reduce the impact of random selection of samples, this paper chose 30 sets of samples. We calculated rice suitability
by these 30 sets of samples, and took the averaged results as the final rice
suitability.
This study
believed that terrain, climate [6–8] and soil [9–11] were the
factors that affected rice growth. Hence, we chose elevation, climate
indicators (22 indicators) and soil indicators (20 indicators) as candidate
indicators [12]. In order to extract indicators with a significant impact on the
growth range of rice, we screened the indicators three times. See Table 3 for the indicators used in the model.
Table
3 The indicators used in MaxEnt model
Indicators
|
Meaning of indicators
|
BIO1
|
Annual Mean Temperature
|
BIO2
|
Mean Diurnal Range
|
BIO3
|
Isothermality
|
BIO5
|
Max Temperature of Warmest Month
|
BIO8
|
Mean Temperature of Wettest Quarter
|
BIO12
|
Annual Precipitation
|
BIO18
|
Precipitation of Warmest Quarter
|
Solar
radiation
|
Solar radiation
|
Wind
speed
|
Wind speed
|
CECS
|
Cation exchange capacity
|
CFRAG
|
Coarse fragments % (> 2 mm)
|
CNrt
|
C/N ratio
|
GYPS
|
Gypsum content
|
ORGC
|
Organic carbon content
|
TAWC
|
Volumetric water content (–33 to –1500 kPa,
cm m–1)
|
TEB
|
Total exchangeable bases
|
DEM
|
digital elevation model (DEM)
|
3.2.1.2
Estimated rice yield redistribution
Rice yield estimation
The
relationship between per capita rice demand and per capita GDP was used to
calculate the amount of rice consumption under different SSP scenario. The
fitting function referred to previous research mainly including power function
linear model [13] and logarithmic linear model [14] (see Figure 1 for fitting examples). –An example of curve fitting
of per capita GDP–per capita rice demand was shown in the figure 1.

Figure 1 Curve fitting of
GDP per capita and per capita annual rice demand
Rice yield redistribution
The
overall idea of redistribution of rice yield can be expressed as a multi–objective
optimization model where equations 1–4 are established at the same time.
(1)
where, D, S and I
represent the country’s rice demand, supply and net import respectively.
(2)
where,
and
represent the
rice harvest area and c3 crop harvest area of the i–th grid, respectively.
(3)
Where, n
represents the number of iterations, n=1 represents the initial situation
inferred from the change in the suitable zone, and Su represents the suitability.
(4)
where,
represents the
national rice irrigation rate predicted by FAO, and
and
represent the
rice irrigation area and rice harvest area in grid i, respectively.
When adjusting
the rice harvest area, we followed the following three principles: 1. Make the
country’s rice supply and demand balance; 2. Make the country’s rice irrigation
ratio as close as possible to the FAO forecast value; 3. When increasing (or
decreasing) the planting area, start from the high suitable area (or start to
decrease from the low suitable area), until the rice is planted to all the cultivated
land in the suitable area (or there is no rice planted). Since it is impossible
to determine the irrigation ratio within the increase range, it is assumed that
the irrigation ratio for all the increased areas is the same. Figure 2 showed
the process in detail.
3.2.2 Exposure
and Vulnerability Estimation Methods
3.2.2.1
Calculation of hazard
The rice exposure to
high–temperature was calculated by the high temperature days and the
accumulated temperature exceeding the rice growth
threshold. The rice growth threshold was defined as the maximum
temperature suitable for rice growth during the rice growth period[15] (the growth threshold of rice is 38 ℃). We defined a single-day
average

Figure 2 The future
rice harvest area and yield estimation framework
temperature
exceeding 38 ℃ (≥38 ℃) as a high–temperature event for rice, and
used the cumulative stress value (GHTS) of the high–temperature event during
the growth period as the high–temperature intensity.
3.2.2.2
Calculation of rice exposure
The
exposure of rice is the spatial superposition of the rice potential
distribution area and the hazard area. The exposure indicators used in this
data set were the rice harvest area.
3.2.2.3
Calculation of rice vulnerability
The
vulnerability curve is established through binary relationship between the
intensity of the hazard and the loss of the crop. In this study, the EPIC model
was used to construct the –vulnerability curve of rice through the
high–temperature intensity and yield loss rate. According to the vulnerability
curve and the high–temperature intensity under different scenarios, the loss
rate of rice under each scenario was –obtained.
The EPIC model
is a dynamic model that integrates factors such as climate, soil, moisture, and
field management, and is often used for crop yield simulation. This paper used
the EPIC model to calculate the vulnerability curve between the
high–temperature intensity and the rice yield loss rate. The equations are as
follows.
The high
temperature disaster intensity index (HSI) in this study was defined as
(5)
where,
TSi represents the temperature stress
value on the i–th day, n represents the number of days during
the growth period, TS represents the
cumulative temperature stress during the growth period under a certain
scenario, and TSmax represents the
potential maximum value of the cumulative stress during the growth period.
The yield loss rate
(YL) in this study was defined as
(6)
where, Y represents the simulated output under
a certain scenario, and Ymax
represents the simulated output under the optimal scenario (TS=0).
According to the
above equations, the HSI and the corresponding YL were used to fit the
vulnerability curve through the logistics regression equation. The fitting
equation used in this study was:
(7)
where, a, b, c, d are curve function
parameters.
3.2.2.4
Calculation of rice yield loss
In
the exposed area, the probability density curve of the high-temperature
disaster intensity was calculated by using the information diffusion theory,
and combined it with the vulnerability curve, the
probability density curve of the rice yield loss rate was obtained. Then, we
calculated its loss expectation to reflect the average state of rice yield loss
induced by high temperature disasters.
The cumulative high-temperature
stress value in a certain period was simulated by EPIC, and the annual
high-temperature disaster intensity index (HSI) was calculated (Equation 5).
Taking this data as an information diffusion sample, the probability density
distribution was estimated by using the normal diffusion method. The specific
calculation process is as follows:
Let U={u1,
u2,…,un} be the discrete universe containing the possible
values of HSI, the value range of HSI is 0 to 1, and the resolution of the
universe is 0.0001, so U={0,0.0001 ,0.0002,…,1}. The information carried by the
HSI in each grid is diffused into each ui through the information
diffusion function (Equation 8).
(8)
where, k is the
code of each grid, h is the normal diffusion coefficient, which can be
calculated by Equation 9.
(9)
where a and b are
the minimum and maximum values of HSI, respectively, and m is the number of
samples. Then the information accumulation and normal information distribution
of the sample can be calculated by Equation 10 and Equation 11, respectively.
(10)
(11)
where, Ck
is the information accumulation of each kth sample, F(HSIk,
uj) is the normalized information distribution of HSI, for each
point uj, add all normalized information to get the HSI from the
given sample at uj information gain. The information gain is shown
in Equation 12.
(12)
The diffusion
information of the sample was obtained by summing q(ui)
(Equation 13):
(13)
Then calculated the probability density distribution of HSI
(Equation 14):
(14)
Defined the probability density distribution of HSI as the expected
value of the yield loss rate of rice in a certain period (Equation 15).
(15)
where, p(uj)
is the estimated probability value when the high-temperature disaster intensity
is uj.
3.3 Technical Route
The
data set was divided into three parts: rice potential cultivation area data,
rice high–temperature exposure data, and rice –yield loss data. Among them, the
rice potential cultivation area data was calculated under the natural and
socio–economic scenarios, the rice high–temperature exposure data was
calculated by the hazard and the rice planting area. The rice– yield loss data
was calculated from the rice vulnerability curve, which was simulated by the
EPIC model. The detailed process was shown in Figure 3.

Figure 3 Data producing technical
route
4 Results and Validation
4.1 Data Composition
The
data set periods were divided into 1970–2000 (historical), 2016–2035
(near–term), and 2046–2065 (mid–term). The scenarios were divided into three
combinations of rcp2.6–ssp1, rcp4.5–ssp2 and rcp8.5–ssp3.
Data spatial
resolution: The spatial resolution of rice distribution and exposure data is
0.25°´0.25°, and the spatial resolution of
rice vulnerability data is 0.5°*0.5°.
Data format:
GeoTIFF
4.2 Data Products
4.2.1 Display of Rice Cultivation Area Data
In
this paper, the MaxEnt model was used to simulate the natural suitable areas of
rice, and on this basis, the supply and demand allocation of national units was
carried out, and finally the rice planting area under different scenarios was
obtained and mapped (Figure 4). Compared with the historical period, under the
RCP8.5–SSP3 scenario, the planting area of rice in mid–term showed a decreasing
trend, with the most obvious decrease in planting area in South America and the
Indian peninsula.

Figure 4 Rice planting distribution.
The historical distribution (left) and the mid–term distribution
under the RCP8.5–SSP3 scenario (right)
4.2.2 Display of Rice High–Temperature Exposure Data
The rice planting area and
the extreme high–temperature hazard area were superimposed to obtain the rice
–exposure to high–temperature under each scenario, and mapped (Figure 5).

Figure 5 Rice exposure to
extreme high temperature hazard. The historical period (left) and the
mid–term under the RCP8.5–SSP3 scenario (right)
Compared with the historical
period, under the RCP8.5–SSP3 scenario, the area of rice
exposure to high temperature will
generally increase in the mid–term, with the most significantly in Africa and
southern my country.
4.2.3 Display of Rice High–Temperature Vulnerability Data
Combining
the rice vulnerability curve with the high–temperature hazard, the yield loss
ratio of rice caused by high–temperature under each scenario was obtained and
mapped (Figure 6). Compared with the historical period, under the RCP8.5–SSP3
scenario, the loss of rice in the mid–term increased significantly.

Figure 6 Rice yield loss
caused by high–temperature hazard. The historical (left) and the mid–term
under the RCP8.5–SSP3 scenario (right)
4.3 Data Validation
This
data set was calculated based on hazard (high temperature events) and hazard–bearing body (rice planting area).
The hazard was calculated from different climatic models from CMIP5. The
availability of these data has been widely proven [16]. The calculation processes of rice planting area were divided into rice
potential distribution and rice planting redistribution.
The process of redistribution was based on the historical rice harvest data, so
it is difficult to verify its accuracy. Therefore, the accuracy verification of
this dataset mainly focused on the verification of the rice potential
distribution. In the previous research, we verified the suitable area by remote
sensing image classification and other methods [6], which proved the rationality of the data. Here we would discuss
the random selection of samples in more details.
When the number of
random sample groups was more than 30, the uncertainty reduction was not
significant. Therefore, considering the reduction of uncertainty and
efficiency, we chose to set the number of sample groups to 30. The standard
deviations of 30 random sample groups were spatially mapped, as
shown in Figure 7.
The figure showed
that the areas with large standard deviations were mainly concentrated in the
Pamirs and Asia Minor Peninsula, indicating that the simulation results of rice
distribution in these regions were quite different among the sample groups. Due
to historical rice planting area in these two regions was relatively small, so
it had little effect on the overall simulation results.
5 Discussion and Conclusion
This data set used the
MaxEnt model and redistribution method to obtained the
distribution of rice planting. Through remote sensing classification, ROC curve
and other methods, this paper verified the validity of the rice distribution
prediction. By calculating the hazed intensity caused by future high
temperature event, and combined it with the crop distribution, we obtained rice
exposure to high–temperature data. Using the EPIC model to simulate the

Figure 7 Spatial distribution of standard
deviations (30 sample groups)
yield loss rate of rice under
different intensities of high temperature stress, we obtained the vulnerability
curve of rice facing high–temperature hazard, and then we obtained the rice
yield loss rate.
This data set
comprehensively considered the impacts of the natural and socio-economic
factors to estimate the future rice planting distribution. On this basis, we
calculated exposure and vulnerability with consideration the dual dynamic
changes of crop and hazard. On the one hand, this data set has significance for
future rice planting and food security research. On the other hand, it also has
important value for climate change impact assessment.
Author Contributions
Jing’ai
Wang designed the algorithms of dataset. Anyu Zhang and Ran Wang
contributed to the data processing and analysis. Peng Su, Anyu Zhang and Ran
Wang contributed to the measures. Peng Su contributed to the data verification.
Conflicts of Interest
The
authors declare no conflicts of interest.
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