Anomaly and
Mutation Dataset of Shallow Soil Temperature in Xi’an Region (1961–2017)
Liu, Y. G.* Su, Y.1 Fang, J. G.2
1. College of Geography and
Environment, Baoji University of Arts and Sciences, Baoji 721013, China;
2. Climate Center
of Shaanxi Province, Xi’an 710014, China
Abstract: In this study, based on existing
literature research and the monthly ground temperature observation data from
1961 to 2017 in Xi’an city, Hu county, Chang’an district, and Gaoling district,
the Anomaly and mutation dataset of soil shallow temperature
in Xi’an region (1961-2017) was developed
through processing methods such as climate diagnosis, unit conversion, error
correction, and distance equality data, and data analysis methods such as
wavelet analysis and Mann-Kendall non-parametric test. The dataset includes (1) Chronological
data, year-by-year flat data, average annual temperature distance data,
month-by-month flat data.(2) Periodic, mutation, and outlier
data. This dataset is stored in xlsx format and consists of 6 data files with a
data size of 546 KB.
Keywords: Xi’an region; shallow soil temperature; anomaly index; variation characteristics
DOI: https://doi.org/10.3974/geodp.2022.04.02
CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2022.04.02
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.2022.04.06.V1 or https://cstr.escience.org.cn/CSTR:20146.11.
2022.04.06.V1.
1 Introduction
Shallow soil temperature (shallow
ground temperature) refers to the temperature of different soil depths between
5 and 20 cm from the ground surface[1],
which is one of the important indicators reflecting the physical properties of
soil and has an important impact on crop growth, urban construction, and
geothermal resource development and utilization. With the intensification of
global warming and human activities[2], the
IPCC Fifth Assessment Report (AR5) stated that the global average surface temperature
increased by 0.85 °C between 1880 and 2012[3,4] and
that the increase in global surface temperatures will inevitably lead to
changes in soil temperatures. Therefore, many scholars have studied regional
shallow ground temperature variations and their effects from different
perspectives, indicating that shallow ground temperature variations in
different regions have regional differences and increasing trends[5–11].
At present, there are
few systematic studies on the shallow soil temperature change in the Xi’an
area. With the accelerated development and urbanization of the western region,
the land use mode of Xi’an has changed, which had a great impact on the
coordinated development of agricultural production, urbanization, and
ecological environment. Therefore, it is of great significance to study the
change law of shallow ground temperature and its influencing factors in Xi’an
under the background of climate warming for the assessment of the agricultural
development and climate change, the rational use of climate resources, and the
ecological civilization construction in Xi’an.
Due to the short
detection time scale and few ground temperature measurement stations in the
past, the systematic analysis of ground temperature cannot be well performed[12]. Therefore, based on the monthly data of shallow ground
temperature in the Xi’an area, the shallow soil temperature distance flat and
abrupt datasets in this area were prepared and verified by Pearson correlation
analysis to further improve the accuracy of the dataset, which can provide data
support to study the spatio-temporal variation of shallow ground temperature in
Xi’an and the analysis of the causes of changes.
2 Metadata of the Dataset
The
metadata summary of the Anomaly and mutation dataset of shallow soil temperature
in Xi’an region (1961-2017)[13] is summarized in Table 1. It includes the full
name of the
Table 1 Metadata summary of the Anomaly
and mutation dataset of shallow soil temperature in Xi’an region (1961-2017)
Items
|
Description
|
Dataset
full name
|
Anomaly and mutation dataset
of shallow soil temperature in Xi’an region (1961–2017)
|
Dataset
short name
|
SoilTempAnomalyXiAn_1961-2017
|
Authors
|
Liu, Y. G., College of
Geography and Environment, Baoji University of Arts and Sciences/Shaanxi Provincial
Key Laboratory of Disaster Monitoring and Mechanism Simulation,
yingeliu@163.com
Su, Y., College of Geography
and Environment, Baoji University of Arts and Sciences/Shaanxi Provincial Key
Laboratory of Disaster Monitoring and Mechanism Simulation, 1963582780@qq.com
Fang, J. G., Climate Center of
Shaanxi Province
|
Geographical area
|
Xi’an area (Xi’an city, Hu county,
Chang’an district, Gaoling district)
|
Data format
|
.xlsx Year
1961–2017
|
Data size
|
546 KB
|
Dataset files
|
(1) Chronological interval
data; (2) Year-on-year data; (3) Annual and seasonal average temperature
level data; (4) Month-by-month data; (5) Data on years of geothermal
anomalies; (6) data on abrupt changes in geothermal temperatures
|
Foundations
|
National Natural Science
Foundation of China project (41771048); Shaanxi Provincial Key R&D
Program Project (2022SF-364)
|
Data publisher
|
Global Change Science Research
Data Publishing System http://www.geodoi.ac.cn
|
Address
|
No. 11, 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 datase[14]
|
Communication
and searchable system
|
DOI, DCI, CSCD, WDS/ISC, GEOSS,
China GEOSS, Crossref
|
dataset, short name of the
dataset, authors, year of the dataset, temporal resolution, spatial resolution,
data format, data size, data files, data publisher, and data sharing policy.
3 Data Processing

Figure 1 Study area and urban distribution map
|
3.1 Data Sources and
Pre-Processing
The research
data in this paper were obtained from the mon- thly average temperature observation
data of the 5–20 cm soil layer in Xi’an city, Hu county, Chang’an district, and
Gaoling district from 1961 to 2017 (Gaoling Station provided data from 1970–2017
due to the unavailability of geothermal data measurement), and the
distribution of the study area and city is shown in Figure 1.
First, the
month-by-month ground temperature data was converted by units and the outliers
were corrected. Secondly, the 30-year average ground temperature from 1961 to
1990 was used as the standard to calculate the monthly, quarterly, and annual ground
temperature in different years. According to the seasons in China, spring is
March to May, summer is June to August, autumn is September to November, and
winter is December to February[15]. Seasonal ground temperature is the
average of the corresponding month, and the annual ground temperature is the
average temperature from January to December.

Figure 2 Technology route
for research and development of shallow soil temperature dataset in Xi’an
|
3.2 Technical Route
The dataset was organized using the monthly observed shallow soil temperature
data from 1961 to 2017 in Xi’an, and the technical route is shown in Figure 2.
The steps are as follows: (1) Data pre-processing and error correction were
carried out, the temperature spacing index of year, quarter, and month was
calculated, the datasets of the year, month, and season interval datasets were
established, and the spatio-temporal variation characteristics of shallow
ground temperature was analyzed. (2) The wavelet transform and Mann- Kendall
nonparametric test method were used to establish a mutation dataset, and the
geothermal index of four regions in Xi’an was analyzed by periodic and mutation
tests. According to the evaluation standard of the “National Climate Impact
Assessment” of the China Meteorological Administration, the ratio of the ground
temperature distance data to the standard deviation is used to determine
whether the ground temperature is abnormal. When the ratio is ≤ –2, the ground
temperature is abnormally low, and when the ratio is ≥ 2, the ground
temperature is abnormally high. Based on this, the abnormal dataset was
established. (3) The periodic characteristics, abnormal characteristics, and
mutation characteristics of ground temperature indices of different scales were
analyzed.
4 Data Results and Validation
4.1 Dataset Composition
The Dataset of shallow soil temperature
distance and abrupt changes in the Xi’an area (1961-2017)
consists of the following data:
(1)
Chronological data in Xi’an: The annual and seasonal geothermal intervals are flat, and the regional geothermal
epochs are flat (Tab 1); (2) Year-by-year flat data in the Xi’an area: The
annual season and regional ground temperature are flat year by year, and the
ground temperature of each layer and the whole shallow layer is flat year by
year (Tab 2); (3) Average annual temperature distance data in Xi’an (Tab 3);
(4) Month-by-month flat data in Xi’an: The monthly maximum temperature and
lowest temperature distance are flat, and the monthly average temperature
distance data is flat (Tab 4); (5) Data of abnormal years of
geothermal anomalies in Xi’an (Tab 5); (6) Geothermal mutation data in the
Xi’an area: including mutation year and M-K test hyperbola (Tab 6); The .xlsx
file in the dataset includes 6 data tables with a data size of 546 KB.
4.2 Data Results
4.2.1 Characteristics of Average Geothermal Variation in Years and Seasons
Both the annual average geothermal temperature and the
seasonal average geothermal temperature of 5–20 cm in Xi’an showed a
fluctuating upward trend, and there was a sharp upward trend after the 1990s.
Among the layers, the maximum trend rate was 0.38–0.46 ºC/10a in spring and the minimum trend rate was
0.07–0.12 ºC/10a in summer. The
increase was 0.14–0.19 ºC/10a in autumn.
and 0.12–0.15 ºC/10a in winter. The annual average
geothermal trend rate increased by 0.18–0.22 ºC/10a. The increase in geothermal growth since the 1990s
is related to the rapid development of urbanization and global warming in Xi’an
(Figure 3).
4.2.2 Chronological Variation of Geothermal
Temperatures in Various Regions
The geothermal intergenerational flatness change in various regions is
shown in Figure 4. It can be seen from the figure that the 5–20 cm soil layer
in the Xi’an area shows a positive and negative flattening change, among which
the 1960s to the end of the 1970s and after 2010 is positive and flat, and the
soil temperature increases. From 1980 to 2010, the soil temperature decreased,
and only the 5 cm and 20 cm soil layers of Gaoling district increased in the
1990s. Meanwhile, the highest value of the distance to the flat also appeared
in Gaoling district in the 1990s, and the lowest value appeared in Chang’an district
in the 1980s. In general, the interdecadal changes of geothermal temperature in
various regions showed the characteristics of alternating high and low, among
which the change in the distance from Chang’an district to parity is the most
obvious, showing a significant downward trend, followed by Gaoling district,
showing a clear upward trend. Hu county showed a weak
downward trend, and Xi’an city had the smallest change, showing a weak upward
trend.

Figure
3 Year-to-year variation of shallow
ground temperature in Xi’an region

Figure
4 Geothermal chronology of different soil
layers in the Xi’an area
4.2.3 Multiscale Characteristics of Ground
Temperature
In this
dataset, the anomalous years are summarized by the anomalous characteristics of
strata in Various Strata in Xi’an (Tab 5). From the table, the abnormally low
years mostly appear in 1960–1970 and 1980–1990, and the abnormally high years
appear in 1970–1980, 1990–2000, and after 2000. Overall, there were fewer years
with average annual temperature anomalies, occurring only in 15 cm and 20 cm
soil layers.
Then, the M-K
non-parameter test was performed on the flat data to obtain the mutation year
of shallow ground temperature in Xi’an (Tab 6). The mutation points of the four
soil layers occurred after the 21st century and there was no mutation in
summer.
The geothermal change
in Xi’an also shows obvious periodic characteristics. Figure 6 shows the cross
wavelet transformation energy spectrum of the average ground temperature of the
5-20 cm soil layer in Xi’an.
In the figure, the more yellow the color, the greater the energy spectral
density. Figure 6 shows that from 1975 to 2005, the average temperature of the 5–20 cm
soil layer in Xi’an has a significant short cycle of 2–6 a and a long cycle of
8–20 a.

Figure 5 Cross wavelet transform of average 5-20 cm ground temperature in the Xi’an area
4.3 Data Result Error and Verification
The data
error comes from the lack of measurement of the monthly ground temperature data
of the weather station, but the data quality has been tested in this dataset to
minimize the data error. Among them, the missing measurement data or extreme
outliers within 3 days are replaced by the average of the ground temperature of
the previous and subsequent 2 days, and the data years with more than one day
missing are not used. For example, the geothermal data of Xi’an city, Chang’an district,
and Hu county from 1951 to 1960 and the geothermal
data of Gaoling district from 1951 to 1969 have missing measurements in a
certain year.
The Pearson
correlation analysis was carried out using the seasonal distance and shallow
ground temperature in the study area of Xi’an and the average seasonal
temperature of the ground of Xi’an Meteorological Station every year to verify
the reliability of the data. As shown in Table 2, the correlation between
ground temperature and shallow ground temperature in the study area was high,
both reaching about 0.8, passing the significance test of 0.01 level. At the
same time, it can be seen that the correlation between ground temperature and
shallow ground temperature in spring and summer decreases with the deepening of
depth, indicating that the ground temperature has a strong influence on the
shallow soil temperature with solar radiation as the heat source. The correlation between ground temperature and shallow ground
temperature in winter increases with the deepening of the depth, indicating
that the ground temperature with soil as the heat source increases with the
increase of soil depth. This is consistent with previous conclusions
about the characteristics of vertical variations in ground temperature in the
study area[16]
and is sufficient to verify the reliability of the distance-level data.
Table 2 Pearson correlation analysis
between temperature and
shallow ground temperature
in Xi’an
Depth (cm)
|
Spring
|
Summer
|
Autumn
|
Winter
|
5
|
0.88
|
0.86
|
0.79
|
0.79
|
10
|
0.87
|
0.84
|
0.81
|
0.81
|
15
|
0.84
|
0.82
|
0.81
|
0.84
|
20
|
0.85
|
0.74
|
0.71
|
0.84
|
5 Discussion and Summary
The shallow
soil temperature pitch and abrupt change dataset in the Xi’an area (1961–2017)
is based on the shallow soil temperature data of four regions in Xi’an from
1961 to 2017. The original data were analyzed by temporal variation
characteristics of ground temperature seasonally and chronologically in the
context of significant global climate warming, as well as the variation
characteristics of the ground temperature with the depth of the soil layer.
Then, wavelet analysis and Mann-Kendall nonparametric test were used to analyze
the month-by-month flat data by periodic and mutation tests, and the outliers
were sorted out.
The main
conclusions are as follows:
(1) The
shallow ground temperature changes in Xi’an showed a fluctuating upward trend,
and it showed a sharp upward trend after the 1990s. Among the layers, the trend
rate is the largest in spring, the smallest in summer, and moderate in autumn
and winter. The annual average ground temperature trend rate increased by 0.18–0.22 ºC/10a.
(2) The
interdecadal changes of geothermal temperature in various regions in the soil
layer of 5–20 cm generally showed the stage characteristics of alternating cold
and warm. The change of distance and flat value in Chang’an district was the
most obvious, showing a significant downward trend, followed by Gaoling district,
showing a clear upward trend. Hu county showed a weak
downward trend, and Xi’an city had the smallest change, showing a weak upward
trend.
(3) The
multi-scale characteristics of the geothermal temperature in Xi’an are as
follows: the mutation points are all after the 21st century, and there is no
mutation in the summer; the geothermal variations also show abnormal features,
with fewer years of annual average geothermal anomalies occurring in soil
layers of 15 cm and 20 cm; there are significant 2–6 a and 8–20 a cycles in the
average temperature of the 5–20 cm soil layer.
The
month-by-month ground temperature data of the four regions in Xi’an can only
reflect the temporal variation trend of soil shallow temperature in Xi’an and
the characteristics of the ground temperature variation with soil depth.
However, it is difficult for these data to reflect the factors affecting the
ground temperature change and its connection with these factors. Therefore, the dataset and the
influence mechanism of ground temperature change need to be further studied.
Author Contributions
Liu, Y. G. designed the algorithms of dataset. Su,
Y. and Fang, J. G. contributed
to the data processing and analysis. Su, Y. wrote the data paper.
Conflicts of Interest
The
authors declare no conflicts of interest.
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