Monthly Relative Humidity Dataset with
1-km Resolution in Nine Provinces in the Yellow River Basin (2000–2015)
Cai, H. Y.1* Jiang, X.1,2 Yang, X. H.1,2
1. State Key Laboratory of Resources and Environmental
Information System, Institute of Geographic Sciences and Natural Resources
Research, Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese
Academy of Sciences, Beijing 100049, China
Abstract: Relative
humidity is closely related to the diffusion, migration, and transformation of
air pollutants and is an important index for regional environmental quality
assessment. Based on the observation data from 940 meteorological stations in
nine provinces and their surrounding areas in the Yellow River Basin, we
generated the relative humidity dataset with 1-km resolution for nine provinces
in the Yellow River Basin (2000-2015), using
the ANUSPLIN software platform. The cubic spline function was applied for
interpolation, with elevation as an independent covariate. The temporal
resolution of the data was month, the spatial resolution was 1 km, and the
projection was based on Albers Conical Equal Area with the coordinate system of
WGS-84. The data are provided in the .tif format.
Keywords: Yellow River Basin; relative humidity;
2000–2015
DOI: https://doi.org/10.3974/geodp.2021.02.09
CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2021.02.09
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.2021.03.12.V1 or https://cstr.escience.org.cn/CSTR:20146.11.2021.03.12.V1.
1
Introduction
Relative humidity is jointly determined by the water vapor
content in the atmosphere and the air temperature; it is an important index to
measure the degree of regional dryness and wetness[1–3]. These two
parameters are directly related to the diffusion, migration, and transformation
of pollutants in the atmosphere[4–6] and closely related to human
health and comfort. In this sense, relative humidity is also an important
indicator for the evaluation of regional environmental quality and urban
livability.
The
Yellow River Basin spans over three major regions in China, covering nine
provinces. It is a key area of ecological protection and economic development,
leading to significant ecological issues. The Yellow River Basin is one of the
typical climate-sensitive areas in China, most of which are located in arid and
semi-arid areas, with an uneven distribution of water resources and a fragile
ecological environment. In the last 20 years, due to the unbalanced use of
water resources, the lake area has shrunk, and floods and droughts have
occurred frequently[7,8]. The high level of industrialization in the
area has led to serious air pollution[9]. In September 2019,
Chairman Xi Jinping stated that the ecological protection of the Yellow River
Basin should be upgraded to a major national strategy[10].
In
this context, temporal and spatial distribution data of relative humidity in
the Yellow River basin can help to deepen our understanding of the dry and wet
changes in this region, provide scientific
support for regional ecological environment quality assessment and manage-
ment, and have important significance for implementing ecological protection
strategies.
2 Metadata of the
Dataset
The metadata of the 1 km-monthly humidity dataset in Yellow
River Basin covering nine provinces of China (2000-2015)[11] is summarized in Table 1. It includes
the dataset full name, short name, authors, year of the dataset, temporal
resolution, spatial resolution, data format, data size, data files, data
publisher, and data sharing policy, etc.
Table 1 Metadata summary of the 1
km-monthly humidity dataset in Yellow River Basin covering nine provinces of
China (2000‒2015)
|
Items
|
Description
|
|
Dataset full name
|
1 km-monthly humidity dataset
in Yellow River Basin covering nine provinces of China (2000‒2015)
|
|
Dataset short name
|
RHU_9PYRB
|
|
Authors
|
Cai, H.Y. Y-8555-2019, State
Key Laboratory of Resources and Environmental Information System, Institute
of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, caihy@igsnrr.ac.cn
Jiang, X. AAE-1541-2021, State
Key Laboratory of Resources and Environmental Information System, Institute
of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences; University of Chinese Academy of Sciences, jiangx.20b@igsnrr.ac.cn
Yang, X. H. AAC-8887-2021, State
Key Laboratory of Resources and Environmental Information System, Institute
of Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences; University of Chinese Academy of Sciences, yangxh@lreis.ac.cn
|
|
Geographical region
|
Nine provinces
|
|
|
|
Year
|
2000‒2015
|
|
|
|
Temporal resolution
|
Monthly
|
|
Spatial resolution
|
1 km
|
|
Data format
|
.tif
|
|
|
|
Data size
|
1.45 GB in compression
|
|
Data files
|
(1) Folder ‘Studyarea’ is the
boundary data in .shp format, (2) Folder ‘9PYRB_RHU’ includes monthly spatial
and temporal distribution data of relative humidity from 2000 to 2015 in .tif
format. The Data name contains its phase information, such as “9PYRB_
Rhu20001.tif ” is the relative humidity data of nine provinces in the Yellow
River Basin in January 2000
|
|
Foundation
|
Ministry of Science and
Technology of P. R. China (2017YFC0503803)
|
|
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[12]
|
|
Communication and searchable
system
|
DOI, CSTR, Crossref, DCI, CSCD,
CNKI, SciEngine, WDS/ISC, GEOSS
|
|
|
|
|
|
|
3
Methods
3.1 Algorithm Principle
The ANUSPLIN package is a tool based on ordinary-thin disk
and local-thin disk spline function for multivariate interpolation and suitable
for interpolation of time-series meteorological data. Local thin-disk smooth
spline is an extension of the prototype of thin-disk smooth spline, which
allows the introduction of a covariant quantum model in addition to ordinary
spline-independent variables. The output statistics are best interpreted in
relation to the local-thin disk spline model for N observed data values zi
given by:
(1)
where xi is a d-dimensional vector
of spline independent variable; f is an unknown smooth function of the xi;
yi is a p-dimensional vector of independent
covariates; b is an unknown p-dimensional vector of coefficient
of yi; ei is the random error[13].
The
function f and coefficient b are determined by Equation (2),
representing the least square method:
(2)
where Jm(f) is a measure of
the complexity of function f(xi), the “roughness
penalty” defined in terms of mth order partial derivatives of f
and ρ is a smooth parameter, balancing data fidelity and surface
roughness; wi is termed the known relative error.
In
ANUSPLIN interpolation, the optimal model is judged by generalised cross
validation (GCV), generalised maximum likelihood (GML), mean square error
(MSE), and signal. When GCV is smaller and signal is less than half of the
number of sites, it is assumed that the best smoothing parameter has been found
in the fitting process. The selected model is suitable for interpolation, and
the effect of surface fitting is better.
3.2 Technical Route
The main process of
dataset production included the following (Figure 1):
(1) Data preprocessing of
the relative humidity records from meteorological stations. First, strict
quality inspection was carried out on the original relative humidity data (.
txt format), with monthly measurements from 2000 to 2015; the data were
obtained from the National Meteorological Information Center of China. In
general, the quality of the observation data in the nine provinces of the
Yellow River Basin was good, and the data were relatively complete. Here, we
detected and processed data outliers and eliminated the meteorological stations
without measurements. The preprocessed data were stored in ASCII format to
prepare for interpolation;
(2) Optimization of the
interpolation scheme. Four groups of interpolation schemes were designed, as
shown in Table 2, and the schemes were tested with the relative humidity in
2015. According to the GCV value, the interpolation scheme was best when the
independent variables were longitude, latitude, and elevation, the independent
covariate was elevation, and the number of splines was three;
(3) Data interpolation.
The monthly relative humidity records from meteorological stations during 2000
to 2015 was interpolated by the optimal scheme;
(4) Accuracy
verification of simulation results. The deviation and relative error index
values were used to verify the results by comparing the observed values with
the simulated ones.
(5) The
monthly relative humidity dataset with 1-km resolution in nine provinces in the
Yellow River Basin from 2000 to 2015 was generated.
Figure
1 Flow chart of the data processing
Table 2 ANUSPLIN interpolation model schemes and
GCVs
|
Number
|
Independent
variable
|
Independent
covariates
|
Spline
number
|
GCV
|
|
1
|
Longitude,
latitude
|
‒
|
2
|
13.5
|
|
2
|
Longitude,
latitude, elevation
|
Elevation
|
2
|
13.4
|
|
3
|
Longitude,
latitude
|
Elevation
|
3
|
12.2
|
|
4
|
Longitude,
latitude, elevation
|
Elevation
|
3
|
12.1
|
4 Data Results and Validation
4.1
Data Composition
The dataset includes two folders, one is for the boundary
data, and the other is for monthly spatial and temporal distribution data of
relative humidity.
4.2
Spatial Distribution Data
Taking March, June, September, and December 2015 as
examples, the spatial distribution characteristics of the relative humidity in
the nine provinces are shown in Figure 2. The relative humidity in summer and
autumn (June and September) was higher than that in spring and winter (March
and December); in winter, humidity was slightly higher than that in spring. The
variation of the relative humidity in different months was closely related to
the regional climate conditions, positively related to precipitation, and
negatively related to temperature[14]. Regarding spatial
distribution, the relative humidity in the southeast was higher than that in
the northwest in these 4 months, which is consistent with the spatial
distribution of rainfall in the basin[15].
4.3
Data Validation
Based on the measured relative humidity of the nine
provinces and surrounding meteorological stations in the Yellow River Basin
from January to December 2015, as well as on interpolation results, the
deviation MBE and relative error Er were selected for
verification, as follows:
(3)
(4)
where Pi is the interpolation result and Oi
is the observation data of meteorological stations. The closer |MBE| and
|Er| are to 0, the closer the interpolation is to the observed value,
and the more reliable the interpolation results are.
As
shown in Figure 3, taking the year of verification as an example, the |MBE| value
is between 0.00 and 0.10 and the |Er| value between 3.6% and 5.6%,
indicating that the interpolation result accuracy is high.
Figure 2 Spatial distribution of relative
humidity in nine provinces of the Yellow River Basin in different months of
2015
Figure 3 Validation of interpolation accuracy of
relative humidity in nine provinces of the Yellow River Basin in 2015
5
Discussion and Conclusion
Based on the relative humidity records from meteorological
stations in nine provinces and surrounding areas in the Yellow River Basin,
this study generated monthly relative humidity dataset with 1-km resolution
from 2000 to 2015. The cubic spline function was selected, with elevation as
the independent covariate for spatial interpolation. According to the accuracy
verification, the interpolation results are reliable. This dataset revealed the
spatial and temporal characteristics of relative humidity changes in nine
provinces in the Yellow River Basin from 2000 to 2015, providing reliable data
for the assessment and management of the ecological environment in the region.
Our
results showed that the relative humidity in summer and autumn was higher than
that in winter and spring, which was consistent with the temporal pattern of
regional rainfall. In terms of spatial distribution, the relative humidity of
the entire region was high in the south and low in the north, as well as high
in the east and low in the west. In this study, four interpolation schemes were
tested for the data interpolation, and the scheme with elevation, longitude,
and latitude as independent variables and elevation as independent covariate
delivered the best results. However, it should be noted that the optimal scheme
may vary with different meteorological indicators or regions. In addition, the
dataset has the disadvantage that when elevation is used as an independent
covariate, the interpolation range is limited by the elevation range. Because
of the lack of elevation data in the northern coastal area of Shandong
province, this dataset was missing in the corresponding area. Nevertheless,
this is negligible and does not affect the use of the whole-area data.
Author Contributions
Cai, H. Y. carried out the
overall design, data analysis, and paper writing for dataset production; Jiang,
X. is responsible for the paper writing as well as the processing of relative
humidity data; Yang, X. H. is responsible for the paper improvement.
Conflicts of Interest
The authors report no
conflicts of interest.
References
[1]
Liu, M. C.,
Yang, X. L., Yin, Y. C., et al. Climate characteristics and forecast of
relative humidity in Wuwei city [J]. Arid Zone Research, 2012, 29(4):
654-659.
[2]
Du, J.,
Cooper, F., Fueglistaler, S. Statistical analysis of global variations of
atmospheric relative humidity as observed by AIRS [J]. Journal of
Geophysical Research Atmospheres, 2012, 117: D12315.
[3]
Liu, Y. Y.,
Li, Y. F., Xie, J. F., et al. Climate change characteristics of free
atmospheric humidity and its relationship with temperature and precipitation in
Northeast China [J]. Scientia Geographica Sinica, 2016, 36(4): 628-636.
[4]
Eck, T. F.,
Holben, B. N., Kim, J., et al. Influence of cloud, fog, and high
relative humidity during pollution transport events in South Korea: aerosol
properties and PM2.5 variability [J]. Atmospheric Environment,
2020, 232: 1-16.
[5]
Zhang, Z.,
Wang, W. H., Wang, H. M., et al. Effects on humidity on absorbing
aerosol index [J]. Journal of Remote Sensing, 2019, 23(6): 1177-1185.
[6]
Suonan, K.
Z., Ren, G. Y., Jia, W. Q., et al. Climatological characteristics and
long-term trend of relative humidity in Wuhan [J]. Climatic and
Environmental Research, 2018, 23(6): 715-724.
[7]
Huang, J.
P., Zhang, G. L., Yu, H. P., et al. Characteristic of climate change in
the Yellow River basin during recent 40 years [J]. Journal of Hydraulic
Engineering, 2020, 51(9): 1048-1058.
[8]
Li, Y. Y.
Research on the drought assessment-propagation-driving-prediction under the
climate and land use land cover change scenarios [D]. Xi’An: Xi’an University
of Technology, 2018.
[9]
Wu, D. Hazy
weather research in China in the last decade: a review [J]. Acta Scientiae
Circumstantiae, 2012, 32(2): 257-269.
[10]
Xi, J. P.
Speech at the symposium on ecological protection and high-quality development
of the Yellow River Basin [J]. Qiushi, 2019, 20: 1-5.
[11]
Cai, H. Y.,
Jiang, X., Yang, X. H. 1 km-monthly humidity dataset in Yellow River Basin covering nine provinces of China (2000‒2015) [J/DB/OL]. Digital Journal of Global Change
Data Repository, 2021. https://doi.org/10.3974/geodb.2021.03.12.V1. https://cstr.escience.org.cn/CSTR:20146.11.2021.03.12.V1.
[12]
GCdataPR
Editorial Office. GCdataPR data sharing policy [OL].
https://doi.org/10.3974/dp.policy.2014.05 (Update 2017).
[13]
Liu, Z. H.,
Li, L. T., McVicar, T. R., et al. Introduction of professional
interpolation software for meteorology data: ANUSPLIN [J]. Meteorological
Monthly, 2008, 34(2): 92-100.
[14]
Xu, R. L.,
Li, B. F., Lian, L. S. Quantitative relationship between the spatiotemporal
change of relative humidity and climatic factors in the arid region of
Northwest China from 1960 to 2015 [J]. Research of Soil and Water
Conservation, 2020, 27(6): 233-239, 246.
[15]
Shao, X.
M., Yan, C. R., Wei H. B. Spatial and temporal structure of precipitation in
the Yellow River Basin based on Kriging method [J]. Chinese Journal of
Agrometeorology, 2006, 27(2): 65-69.