USING CLIMATE MODELS TO PREDICT
EXTREME RAINFALL TREND IN YANHE
RIVERBASIN, CHINA
Saiyu Yang*
College of Humanities and music, Hunan Vocational College of Science And
Technology, Changsha, Hunan, 410013, China
glorious_cs1@163.com
Peng Dai
Brilliance Technology Co., Ltd, Chengdu Branch, Chengdu, Sichuan, 610213, China
Reception: 03/11/2022 Acceptance: 26/12/2022 Publication: 17/01/2023
Suggested citation:
Y., Saiyu and D., Peng (2023). Using climate models to predict extreme
rainfall trend in Yanhe riverbasin, China. 3C Tecnología. Glosas de
innovación aplicada a la pyme, 12(1), 15-31. https://doi.org/
10.17993/3ctecno.2023.v12n1e43.15-31
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ABSTRACT
Climate model is an effective medium to study climate system and climate change. Its
simulation results are essentially a crucial data basis for climate prediction and climate
change risk assessment. With the acceleration of global warming, the surface
ecological environment, hydrological dynamic cycle process, social and economic
development are all affected thereupon, resulting in certain influence on the
production and life of human beings. In this regard, this paper conducts a study on
extreme precipitation events of different climate models with Yanhe River Basin as the
study area. The results show that: 1. Yanhe River Basin is a sensitive area to climate
change. In the future, the precipitation in this area, for a long time will not increase
obviously, but fluctuate greatly; 2. The temporal and spatial difference of extreme
precipitation events in the study area is significant. From 2000 to 2050, the
interdecadal fluctuation of extreme precipitation events in the study area is significant.
In the future, the area with the largest volume of precipitation above 12mm will be
concentrated in the southeast part of the study area, followed by the western
boundary area; 3. There are few areas with precipitation above 50mm in the Yanhe
River Basin, and the occurrence frequency has decreased significantly; 4. The
simulation results of different climate models are different. Alao, pursuant to the data
analysis results, different models have certain differences in the spatial simulation of
extreme precipitation. It is speculated that the terrain factors and Monsoon Simulation
factors may affect the simulation results of extreme precipitation events.
KEYWORDS
Climate model; Statistical downscaling; Future climate change; Space-time difference;
Extreme precipitation
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PAPER INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. CASE STUDIES AND DATA SOURCES
2.1. Regional overview and site
2.2. Data sources
3. METHODOLOGY
3.1. Anusplin meteorological interpolation
3.2. Statistical downscaling method
4. 4 RESULT ANALYSIS
4.1. Analysis on overall change trend of precipitation
4.2. Spatial and temporal pattern and analysis of extreme precipitation index
4.3. Comparison of different GCM models of extreme precipitation
5. DISCUSSION
6. CONCLUSION
7. DATA AVAILABILITY STATEMENT
REFERENCES
CONFLICT OF INTEREST
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1. INTRODUCTION
Most cities in my country are located on the banks of rivers, lakes and seas, and
are threatened by river floods to varying degrees. In the context of global warming,
with the acceleration of urbanization, urban torrential rain events frequently occur,
extreme flooding events increase, and the resulting disaster losses are also
increasing. However, at present, the capacity of urban flood control in my country is
generally low, and urban drainage standards are backward. In the event of upstream
floods and urban torrential rains, supported by the long-term high water level of
external river floods, it is difficult to discharge or even limit the discharge of internal
waterlogging, which can easily cause serious flood disasters to the city.
The increase of extreme weather caused by climate change has had an important
impact on the ecological environment, economic development and personal and
property safety of countries all over the world [1], especially in the Loess Plateau of
China, where geological disasters are seriously developed, the use of climate models
to study future climate change can effectively deal with the risk of geological disasters
caused by climate change. Climate model is a set of mathematical and physical
equations that describe the behavior of climate system based on basic physical and
chemical laws [2]. The data involved in this program are widely used to predict the
characteristics of future climate change and analyze the trend of climate change. The
research results of climate prediction based on climate models are an important basis
for the government's Special Committee on Climate Change (IPCC) to assess future
climate change [3]. At the same time, Representative Concentration Pathways (RCPs)
scenarios fully consider the impact of future greenhouse gas emissions on climate
change. Among them, RCP 2.6 scenario refers to that the radiation forcing reaches
the peak before 2100 and drops to 2.6w/m2 by 2100, and the global average
temperature rise is limited to 2.0 ℃
[4-5]. It can well simulate the average
characteristics of large-scale and seasonal climate, but the PCPs spatial resolution
(100~500km) is difficult to directly respond to the model to assess the impact of
climate change or site scale environmental factors [6]. Therefore, improving the
reliability of climate prediction is one of the important issues in the study of Watershed
climate models [7].
In order to effectively improve the reliability of climate prediction, this paper
introduces the climate model into the analysis of extreme rainfall trends along the river
basin. The correction value and the daily precipitation data of future climate change
are calculated, and the analysis of the extreme rainfall trend along the river basin in
the future is finally completed.
2. CASE STUDIES AND DATA SOURCES
2.1. REGIONAL OVERVIEW AND SITE
Yanhe River Basin is the first-class tributary of the middle reaches of the Yellow
River, between 36 ° 23 ′~37 ° 17 ′ N and 108 ° 45 ′~110 ° 28 ′ E. Yanhe River Basin is
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one of the serious soil and water loss areas in the Loess Hilly and gully region. It
belongs to the warm temperate continental semi-arid climate zone [8-9]. The north is
Qingjian River Basin, the southwest is Beiluo River Basin, and the south is connected
with Yunyan River Basin [10]. The geographical location of the Yanhe River Basin is
shown in Fig 1.
Figure.1. Geographical location of Yanhe River Basin
As shown in Figure 1,the main tributaries of Yanhe River include Xichuan, Xingzi
River, Nanchuan and Panlong River, with a total length of 286.9 kilometers, an altitude
of 958-1731m and an area of about 7725km2. The basin covers 6 counties and
districts including Baota, Ansai and Yanchang, a total of 53 towns and townships,
about 1027 administrative villages, with a total population of 990000 [11-12].
2.2. DATA SOURCES
Historical observational meteorological data of Yanhe River Basin is derived from
the data of 154 surface meteorological stations covering Shaanxi Province from 1981
to 2013 and interpolated to 0.25 ° by thin disk spline ×
0.25 ° horizontal resolution
grid, generating multi-year daily precipitation data from China National Meteorological
Information Center [13-14]. The global climate model is derived from the coupled
multimodal global climate model prediction data set cmip5 in the nex-gddp project in
the United States [15]. This data set is the main tool used by IPCC to predict future
climate change by simulating earth systems such as atmosphere, ocean, land surface
and vegetation, sea ice, etc [16]. Some studies show that cmip5 model has greatly
improved the spatial resolution compared with the previous model, and significantly
improved the simulation effect of extreme precipitation. Three GCMS in cmip5 are
selected to predict future extreme precipitation changes, including the climate system
model BCC of Beijing Climate Center_ CCSM, the multidisciplinary climate research
model miroc5 jointly developed by the climate system research center of the
University of Tokyo, Japan, the Japan Institute of environment and the Japan earth
environment research center. It summarizes the earth system, spatial resolution and
other parameters of the three climate model data. The model has been used to predict
climate change and related extreme events in the Yangtze River Basin, and the model
simulation results are good and close to the observed data [17-18]. Spatial resolution
of mode data 0.25 ° ×
0.25 °, the time resolution is day by day, and the time span is
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1981-2060. The typical concentration emission scenario rcp4.5, that is, the emission
scenario is set to maintain the current level of population, economic and technological
development. By 2100, the radiation forcing will be stable at 4.5w/m2, and the change
of greenhouse gas emissions will increase first and then tend to be stable [19].
3. METHODOLOGY
3.1. ANUSPLIN METEOROLOGICAL INTERPOLATION
Meteorological element data is the basis of a variety of geoscience models and
climatology models. Accurate climate element data can be obtained by establishing
high-density meteorological observation sites. However, due to the limitations of
economic level, technical means and terrain conditions, meteorological data in many
places It is more difficult to obtain. In order to obtain meteorological data in areas
outside meteorological observation sites, researchers usually combine statistical
methods with geographic information systems to estimate based on the observed
values of existing meteorological observation sites, that is, spatial interpolation of
meteorological element data.
ANUSPLIN software is a classic meteorological interpolation software, based on
thin disk spline function, suitable for interpolation of various natural station data. The
accuracy is high, and the elevation can be considered as a covariate for difference
[20-21]. The model formula is as follows:
Where, Z is the dependent variable at point I, f (XI) is the unknown smooth function
to be estimated about Xi, Xi is the d-dimensional independent variable, BT is the p-
dimensional coefficient about Yi, Yi is the p-dimensional independent covariate, EI is
the random error, and N is the number of observations [22].
Where: function f and coefficient b are determined by least square estimation:
Where JM (f) is the roughness measure function of function f (x), which is defined
as the m-order partial derivative of function f; is a positive smoothing parameter.
3.2. STATISTICAL DOWNSCALING METHOD
The regional climate model is the result of the comprehensive action of the driving
forces of the multi-scale general circulation model, such as latitude, sea land
distribution, terrain and underlying surface conditions [23]. The assumptions for using
statistical downscaling include: the climate state at different scales is stable and the
statistical relationship is significant, and the large-scale climate model simulation is
effective and the statistical relationship established is effective. Considering the
zi= f(xi)+ bTyi+ ei(i = 1,⋯, N)（3.AUTONUM\*Arabic）
∑
N
i=1
[
zi−f
(
xi
)
−bTyi
wi
]2
+ρJm(f)（3.AUTONUM\*Arabic
）
ρ
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complex terrain of the study area, in order to reduce the boundary impact, the north of
Hengduan Mountain where the study area is located is taken as the prediction area.
Statistical downscaling adopts a numerical deviation correction method combining
statistics and dynamics [24]. The method considers that the climate model of any
scale is composed of stable long-term climate state and short-term weather
fluctuation. The specific expression is as follows:
Where, represents the meteorological condition at any time, represents the
climate state corresponding to the scale where the meteorological condition is located,
represents the climate anomaly of the scale relative to the secondary scale, and
indicates the average climate state [25]. The observation data and climate
reanalysis data are divided in this way. For climate models with different spatial scales
(such as global and regional), the expression of the linear correction value of the
climate anomaly model of the cumulative distribution function is:
Where, K is a constant, is the horizontal and vertical correlation distance, is
the climate state corresponding to the ground observation data at this spatial scale,
and is the climate anomaly of the spatial scale corresponding to the global
climate model relative to the spatial scale corresponding to the ground observation
data. The equation is also applicable to global and regional climate models [26-27].
The equation is valid only when n>1 and . Therefore, the deviation correction
expression of daily precipitation data of future climate change is as follows:
In the formula, represents the daily precipitation value after correction, D is
1-365, and are the average value of historical daily climate data and
climate change precipitation estimates. represents the estimated precipitation of
climate change [28]. The prediction of extreme rainfall trends along the river basin is
realized by the revised daily precipitation value, the average value of historical daily
climate data, the average value of climate change precipitation estimates, and the
estimated precipitation amount of climate change.
4. 4 RESULT ANALYSIS
4.1. ANALYSIS ON OVERALL CHANGE TREND OF
PRECIPITATION
Under the rcp4.5 emission scenario, during the 50 years from 2006 to 2056, the
regional average precipitation of extreme precipitation in the study area is shown in
Fig 2.
α(t)=¯
α+α′
(t)(3.AUTONUM\*Arabic)
α(t)
α
α′
(t)
¯
α
∂α
LC
∂c−∂¯
α
RA
∂c=∂α′
GCM
∂c= k(¯
βRA +β′
GCM)= kβLC(3.AUTONUM\*Arabic)
∂
¯
βRA
β′
GCM
¯
αn=¯
αn
Pr
d
=¯
Mstd+¯
Mprjd−Mstd(3.AUTONUM\*Arabic)
Prd
¯
Mst_d
¯
Mprj_d
Mst_d
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(a) Regional average precipitation.
(b) Precipitation Mann-Kendall test.
(c) Precipitation anomaly.
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Figure 2. Inter-annual departure of extreme precipitation in study area.
Fig 2(a) shows that the annual average daily precipitation in the study area
fluctuates around 2.5 mm, with no obvious increase or decrease. Based on the Mann
Kendall method, this paper conducted a test and analysis of the precipitation in the
study area from 2000 to 2050, and found that the precipitation in the study area
fluctuated greatly in the past 50 years. In Fig 2(b), the interannual fluctuations are
relatively large around 2036, and the main mutation year in this region is around 2016.
During the 50 years from 2000 to 2050, there were more abrupt changes, indicating
the possibility of extreme climate events. In addition to the Mann Kendall rainfall test,
precipitation anomalies are also analyzed in Fig2(c). The results show that the
precipitation anomaly analysis results show a similar trend to the Mann Kendall test
results, which more strongly indicate that under the background of future climate
change, the rainfall in the study area will change greatly, and extreme precipitation
events are more likely to occur [29].
4.2. SPATIAL AND TEMPORAL PATTERN AND ANALYSIS OF
EXTREME PRECIPITATION INDEX
In this paper, absolute quantity index, intensity index and frequency index are
selected to analyze the spatial and temporal distribution characteristics of extreme
precipitation. The results show that there are significant differences in the frequency
and distribution pattern of extreme precipitation in the study area. The absolute
quantity index analysis results show that in the next 50 years, the precipitation in most
parts of the study area may exceed 12mm, especially in the eastern and western
parts of the study area. The spatial distribution of absolute indicators is shown in Fig
3.
Figure 3. Spatial distribution of otherthe absolute indices: (a) Daily precipitation over 12
(mm); (b) Daily precipitation over 50 (mm); (c) Daily precipitation over 100 (mm).
In a of Fig 3, the number of days with precipitation exceeding 12 mm in some areas
may exceed 1000 days, that is, more than 20 times a year. In the future, the areas
with the most precipitation above 12 mm will be concentrated in the southeast of the
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study area, followed by the western border area. These areas will receive more than
21 times of precipitation above 12mm per year. The central region has the least
precipitation, at least 19 times a year. Compared with the areas with precipitation
greater than 12mm, there are fewer areas with precipitation greater than 50mm in b of
Fig 3, and the frequency of occurrence is significantly lower. The high-value areas are
distributed in the southeast border area and the northwest border area. Fig 3 c. For
precipitation above 100mm, the study area will not appear. From the temporal and
spatial distribution pattern of absolute precipitation index, it can be seen that the
erosive precipitation in the study area is widely distributed, and the distribution of
heavy rain precipitation is obviously localized, mainly in the junction of the northeast
and southwest of the study area [30].
For the case where the daily precipitation of c in Fig 3 exceeds 100mm, the spatial
distribution of the intensity index is analyzed, as shown in Fig 4.
Figure 4. Spatial distribution of the intensity indices: (a) Daily precipitation; (b) Cumulative
precipitation in 3 days; (c) Cumulative precipitation in 5 days.
Fig 4 shows that the analysis results of the intensity index show that single
precipitation or continuous precipitation of more than 50 mm may occur in most areas,
and more precautions should be taken against the risk of extreme rainstorms. In Fig
4a, it can be seen that the areas with daily precipitation rx1 higher than 30mm are
only distributed in the southeast and northwest border areas of the study area[31-32].
The terrain in the southeast is lower. The daily precipitation in most areas of the study
area is between 25 and 30 mm;
From (b) and (c) in Fig 4, we can see from the spatial distribution of RX3 and rx5
that the areas with precipitation exceeding 55mm for three consecutive days are
mainly distributed in the southeast and northwest of the study area with precipitation
exceeding 55mm for five consecutive days of the entire study area. In terms of
precipitation, the accumulated precipitation in most parts of the study area may
exceed 55mm, while the precipitation in the northwest and southeast of the study area
may exceed 70mm.
The comparative analysis of the intensity indicators of different indicators shows
that under the background of future climate change, extreme short-term sustained
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high-intensity precipitation is very likely to occur in most areas of the study area, and
the risk of mountain disasters cannot be reduced.
In order to reduce the uncertainty of climate change prediction and the possible
model error of predicting a single precipitation, the frequency of extreme precipitation
in the study area was analyzed for multiple consecutive days.
Figure 5. Spatial distribution of the duration indices: (a) The number of over 50 (mm)
cumulative precipitation in 3 days; (b) The number of over 100 (mm) cumulative precipitation
in 3 days; (c) The number of over 50 (mm) cumulative precipitation in 5 days; (d) The number
of over 100 (mm) cumulative precipitation in 5 days.
From (a) and (b) in Fig 5, we can see that precipitation events may occur
continuously for 3 days and 5 days in the study area, and the frequency of occurrence
is higher in the southeast and northwest of the study area, and the prediction results
of the spatial distribution of the intensity index. In addition, it can be seen from c in Fig
5 that due to being in a semi-arid area, the study area did not have extreme
precipitation events with precipitation exceeding 50 mm and 100 mm for three
consecutive days and five consecutive days, respectively.
4.3. COMPARISON OF DIFFERENT GCM MODELS OF EXTREME
PRECIPITATION
Prediction of extreme precipitation events from a single climate model is uncertain.
Therefore, this paper selects several representative extreme precipitation indicators
for model comparison to reduce the uncertainty of a single climate model. The
distribution of extreme precipitation under future climate change is shown in Fig 6.
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Figure 6. The distributions of extreme precipitation under climate change in the future
According to the data analysis results in Fig 6, there are certain differences in
different spatial simulations of extreme precipitation. It is speculated that topographic
factors and monsoon simulation factors may affect the simulation results of extreme
precipitation events. According to the analysis of extreme precipitation index under
different models, as shown in a and b in Fig. 6, the simulation results of absolute
quantity index are not very different, while in the BCC_CS model and the miroc5
model, the two models are generally spatially consistent. The number of annual
occurrences of absolute precipitation exceeding 12mm is higher than 21. In the
BCC_CS mode, the annual occurrence of absolute precipitation exceeding 12mm can
reach 28 times. The areas with more occurrences are mainly located in the
southeastern part of the study, and the annual occurrence of absolute precipitation
exceeding 12 mm in the western part of the study area is less frequent; as shown in c
and d in Fig. 6, the precipitation is predicted and evaluated for three consecutive days
under the BCC_CS model and the miroc5 model. . There is a certain difference in the
amount of precipitation for three consecutive days under these two modes. The
maximum precipitation for three consecutive days in the miroc5 mode can reach 130
mm, which is located in the southeastern part of the study area; the frequencies of
precipitation events in the BCC_CS mode and the miroc5 mode for 5 consecutive
days are shown in e and f in Fig 6. From the results, the precipitation frequency index
shows that the average frequency of precipitation events for 5 consecutive days in the
study area can reach 22 per year. The simulation results of these two modes also
have certain continuity and spatial consistency.
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5. DISCUSSION
(1) The climate model with statistical downscaling can effectively reduce the model
uncertainty. The terrain of the study area is complex, so it is more practical to select
rcp4.5 to maintain the current greenhouse gas emission scenario. First, the simulation
effect of different models of IPCC cmip5 on Precipitation in China decreases from the
southeast coast to the northwest inland (Sun Jian, 2016). In terms of the simulation
effect of average daily precipitation, the simulation effect may be better in some areas,
but the overall simulation ability is weak. Therefore, based on the rcp4.5 emission
scenario with high stability, statistical downscaling using the meteorological data of
regional ground stations can eliminate the uncertainty of the climate model to a certain
extent. In small areas with complex terrain, statistical downscaling technology is better
than dynamic downscaling in rainfall prediction, because in small areas with complex
terrain, extreme precipitation is not only affected by large-scale circulation factors
such as monsoon and terrain, but also affected by small-scale climate and weather
systems such as surface radiation and cloud, and is easily disturbed by human
activities. The numerical deviation correction method combining statistics and
dynamics can simulate single day precipitation to a certain extent and improve the
accuracy of GCM data, but its long-term trend and extreme precipitation simulation
effect need to be further tested in combination with ground data.
(2) The above results show that the frequency and distribution pattern of extreme
precipitation in the Yanhe River Basin will be significantly different in the future. In
terms of absolute quantity index, most areas in the study area may have more than
12mm of precipitation, and the number of 12mm of daily precipitation in some areas
may exceed 20 times a year, mainly concentrated in the southeast, followed by the
western region, and less in the central region. The results of intensity index show that
most areas in the study area may have single precipitation or continuous precipitation
with a daily precipitation of more than 50mm. Areas with a daily precipitation of more
than 55mm for three consecutive days are mainly distributed in the southeast and
northwest of the study area. Areas with a daily precipitation of more than 55mm for
five consecutive days basically cover the whole study area. Precipitation events are
likely to occur in the study area for 3 and 5 consecutive days, with more occurrences
in the southeast and northwest regions, which is consistent with the predicted results
of the spatial distribution of the intensity index. The comparison and analysis of
different indexes show that the spatial and temporal distribution of extreme
precipitation in the study area is closely related to the terrain, which may be the result
of the joint action of regional terrain and climate change.
(3) Rainstorm and continuous precipitation are the main factors inducing geological
disasters, and the disaster risk in the study area may be intensified in the
future.Geological disasters in mountainous areas are the result of the joint action of
extreme weather and disaster pregnant environment. The above analysis shows that
under the background of future climate change, the intensity of extreme precipitation
in most regions in the study area will increase, the number of precipitation days will
increase, and the continuous precipitation for 3 or 5 consecutive days may be large,
and the cumulative precipitation can reach 80 mm. The occurrence of continuous
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precipitation may not only reduce the threshold of extreme precipitation, but also
accelerate the development of disaster pregnant environment such as geological
disasters. From the perspective of terrain, steep terrain areas are often prone to
geological disasters. Especially in the southeast region where there may be multiple
high-intensity extreme precipitation, it is necessary to formulate targeted geological
disaster response strategies to adapt to climate change.
6. CONCLUSION
1. Yanhe River Basin is a sensitive area to climate change. In the future, the
precipitation in this area will not increase significantly for a long time, but it will
fluctuate greatly.
2. From 2000 to 2050, the interdecadal fluctuation of extreme precipitation events in
the study area is significant. In the future, the area with the most precipitation above
12 mm will be concentrated in the southeast of the study area, followed by the
western boundary area. Compared with the area with precipitation above 12 mm,
the area with precipitation above 50 mm will be less, and the occurrence frequency
will decrease significantly. According to the results of intensity index, single
precipitation or continuous precipitation of more than 50mm may occur in most
areas.
3. Different climate models have different simulation effects. According to the data
analysis results, different models have certain differences in the spatial simulation
of extreme precipitation. It is speculated that the terrain factors and Monsoon
Simulation factors may affect the simulation results of extreme precipitation events.
When simulating absolute index and continuous precipitation index, BCC_ CSM
simulation results are on the high side; When simulating the intensity index for three
consecutive days, the simulation result of ccsm4 is higher and is similar to that of
BCC_ CSM simulation results have significant differences.
7. DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/
supplementary material, further in quiries can be directed to the corresponding author.
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CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential conflict of
interest.
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