Located in the mountainous area of southwest China, the Chengdu–Chongqing urban agglomeration (CCUA) has been rapidly urbanized in the last 4 decades, which has led to a 3-fold urban area expansion, thereby affecting the weather and climate. To investigate the urbanization effects on the thermal environment in the CCUA under complex terrain, we conducted simulations using the advanced Weather Research and Forecasting (WRF V4.1.5) model together with combined land use scenarios and terrain conditions. We observed that the WRF model reproduces the general synoptic summer weather pattern, particularly for the thermal environment. It was shown that the expansion of the urban area changed the underlying surface's thermal properties, leading to the urban heat island effect, enhanced further by the complex terrain. The simulation with the future scenario shows that the implementation of idealized measures including returning farmland to forests and expanding rivers and lakes can reduce the urban heat island effect and regulate the urban ecosystem. Therefore, urban planning policy has the potential to provide feasible suggestions to best manage the thermal environment of the future city toward improving the livelihood of the people in the environment.
With urban area expansion, the lower surface of the Chengdu–Chongqing urban agglomeration (CCUA) has changed compared with the natural land surface, leading to the heat island effect, which is also an important factor in global warming (Kalnay and Cai, 2003; Kawashima, 1975; Ning et al., 2019). As one of the most dramatic land use changes, urbanization alters land surface physical properties, including albedo, emissivity, heat conductivity and morphology, making urban areas exhibit greater heat capacity, Bowen ratio and roughness (Robaa, 2011). The impermeability of urban land surface reduces water vapor evaporation and increases sensible surface heat. The multiple reflection and absorption of radiation in the urban canopy make the energy absorbed by a city in the daytime more difficult to dissipate in the form of long-wave radiation at night. These changes in land surface characteristics significantly affect the surface energy budget, planetary boundary layer height (PBLH), thermal structure, and local/regional atmospheric circulation (Kawashima, 1975; Oke, 1995; Berling-Wolff et al., 2004; Hamdi et al., 2010). Therefore, it is imperative to assess the changes in urbanization and develop adaptation strategies.
Numerical simulation has been used to investigate the urban heat island effects on cities, such as Tokyo, Phoenix Metropolitan, Beijing and Hangzhou (Berling-Wolff et al., 2004; Chen et al., 2014; Saitoh et al., 1996; Wang et al., 2020). Urbanization also changes a city's precipitation by enhancing the spatial heterogeneity of the rainfall or making it extreme (Yang et al., 2019). Urbanization of cities in the arid and semi-arid areas can cause pronounced urban drying (Robaa, 2011). For cities under complex terrain conditions, their weather and climate are often exacerbated by the interaction of complex terrain with urbanization (Ning et al., 2018; Yang et al., 2019). On the other hand, cities will face severe water and heat stress (Zhao et al., 2021). Therefore, the demand for a suitable plan to alleviate stress is exigent.
In this study, we investigated the interaction of complex terrain and urbanization on the thermal environment of the urban agglomeration for CCUA, located in the mountainous area of southwest China. We further researched the effects of land use planning policies on the heat stress of the urban area. This research (1) clarifies the urban warming pattern caused by the urban expansion of the CCUA in the past, (2) measures the combined impact of complex terrain and urbanization on the summer urban thermal environment, and (3) reveals the potential of implementing the measures of returning farmland to forest and grassland and expanding the area of rivers and lakes to alleviate heat stress in the CCUA.
We used a numerical model WRF-ARW v4.1.5 (Skamarock and Klemp, 2008), coupled
with a single-layer urban canopy model (SLUCM) and the Noah land surface
model (Noah LSM, Niu et al., 2011; Yang et al., 2011), to study the
impact of urbanization on the regional thermal environment. We chose CCUA as
the study area, set up three one-way nested domains in the horizontal
direction (Fig. 1a), with resolutions of 1, 5 and 25 km, respectively, and
divided the atmosphere into 32 vertical layers. July, the hottest month in
2018, was selected as the simulation period, and the first 48 h of the
simulation results was discarded as the spin-up time of the model. The
forced initial field data simulated in the model were the re-analyzed data of
operational global analysis and forecast data, which are on
The urban environmental conditions of CCUA are similar, depending on the
same comprehensive transportation network, with two megacities (Chengdu (CD)
and Chongqing (CQ)) as the core city and the other 14 smaller cities
distributed between them, thus forming a large urban agglomeration (Wang et
al., 2015). The CCUA is located in the Sichuan
Basin in the central southern Asian continent (between latitudes 28
The underlying surface influences factors such as soil thermal conductivity, vegetation impedance, reflectivity, roughness and thermal inertia. The thermal inertia, in turn, affects the boundary layer structure and the land surface process. Therefore, more refined underlying surface information will improve the model simulation effect significantly.
WRF has two default land use dataset types: the Advanced Very
High-Resolution Radiometers from the US Geological Survey (USGS) and the
Moderate-Resolution Imaging Spectroradiometer (MODIS). The acquisition time
of USGS data was from April 1992 to March 1993, while MODIS's latest
land cover data were from 2010. The default data accuracy is low, and the
timeliness is not enough, restricting the simulation accuracy of the model.
Therefore, we replaced the WRF default data with the more accurate land use
data. We get the 30 m spatial resolution fusion land cover data for the
two periods (viz. 1980, the “historical scenario” in Fig. 2a, and 2018, the
“urban scenario” in Fig. 2b) from the Institute of Geographic Sciences and
Natural Resources of the CAS (Resource and Environment Science and Data
Center
Rules for converting the classification standard of LUCs from IGSNRR to USGS.
Land use/land cover in CCUA:
After statistically calculating the land cover, it is found that from 1980
to 2018, the growth of urban land use types in the CCUA increased to nearly
9000 km
To explore the impact of urbanization on the thermal environment of the CCUA based on the 2018 land use and land cover dataset, we replaced the urban land type with the nearest natural land use type of the area. Here, we term the land dataset without city type the “non-urban scenario (Fig. 2c)”.
In addition to the three land use scenario data, we also planned and designed a “future scenario” land use of the urban agglomeration landscape to explore the mitigation effect of landscape planning on the thermal environment stress of urban agglomeration. Urban agglomerations are brand-new regional units that have emerged from industrialization and urbanization to a higher stage (Bruinsma and Rietveld, 1993; Kawashima, 1975). The CCUA is the most dynamic region with a high potential for economic development in southwest China (Wang et al., 2015). However, the urban agglomeration is an extremely sensitive area where a series of ecological and environmental problems are highly concentrated and intensified (Bruinsma and Rietveld, 1993). For such ecological environment pressure, we can design and plan ecological corridors and ecological barriers in landscape ecology according to the natural geography, vegetation ecology, water system and topography of the urban agglomeration. It is expected that these ecological corridors and barriers can alleviate the urban heat stress caused by urbanization, meeting the growing cultural demands of people. Based on the land use dataset of the urban scenario (Fig. 2b), we designed the ideal land use and land cover scenario in the future: the future scenario (Fig. 2d). The specific method is to return farmland to forest and grassland in the five ecological protection areas around the urban agglomeration. Eight land ecological corridors and seven water system corridors were designed according to the hills, mountains and water systems in the urban agglomeration. In the corridor, the farmland should be returned to grassland to expand the river lake wetland. We plan the land use of the future urban agglomeration according to the government's planning documents (see Sect. 2.3 for details).
All the four land use datasets with a 30 m resolution were resampled for 1 km as the underlying surface data of the model in the simulated area.
In this subsection, we will present how to plan and design the future scenario of CCUA in Fig. 2d mentioned above. We first divided the scope of
ecological protection areas and ecological protection barriers in future
land use planning (Fig. 3). The designing and planning of the future scenario is
based on two planning documents issued by the government: the “Chengdu
Chongqing Urban Agglomeration Development Plan, 2014–2020”, which was
issued by the Development and Reform Commission of The People's Republic of
China (PRC), Ministry of Housing and Urban-Rural Development of the PRC
(
Scope of future land use scenarios planned through government policy documents.
According to the “Chengdu Chongqing Urban Agglomeration Development Plan, 2014–2020”, we define the location of the ecological protection areas of the urban agglomeration according to the guidance of the government, including five nature reserves (the Sichuan–Yunnan Forest Reserve, the Qinba Biodiversity Ecological Function Zone, the Da–Xiao–Liang Mountain Water and Soil Conservation Ecological Function Zone, the Wuling Mountain Ecological Diversity and Soil and Water Conservation Ecological Function Zone, and the Three Gorges Reservoir Water and Soil Conservation Ecological Function Zone) around the urban agglomeration and the land ecological corridor and water ecological corridor within the urban agglomeration. According to the document “Guidelines for Ecological Protection and Restoration of Mountains, Rivers, Forests, Fields, Lakes and Grasses, 2020 (trial version)”, we replace 75 % of farmland in five nature reserves with mixed grassland/shrubland. In the land ecological corridor, we replace 60 % of the farmland with a cropland/woodland mosaic, and in the water ecological corridor, we replace the farmland within 1 km along the river with wetland.
WRF experimental design; exp.: experiment.
Urban effect: exp. 3
By comparing the future scenario's land use and the urban scenario's land use, we expect that the planned landscapes will improve the thermal environment of urban agglomerations in the summer, enhancing the living comfort in the urban agglomerations.
To study the impact of urbanization on the surrounding environmental and meteorological elements under complex terrain, seven experiments were designed as described in Table 2: five types of land use scenarios (“WRF default scenario”, historical scenario, urban scenario, non-urban scenario and future scenario) and two types of terrain conditions (“Topography” (Fig. 1b), the current situation of the original complex terrain of the CCUA; “no-topography” (Fig. 1c), which is smoothing the mountainous terrain around the CCUA). The high-altitude mountains around CCUA have been removed through multiplying the altitude of the high-altitude area in the study area by a certain proportion of the scaling factor which makes the terrain of the whole simulation area smoother. The physical parameterization schemes and simulation time periods, as well as the study area, were the same as those mentioned in the previous section, except for the underlying surface land use datasets and terrain changes. Figure 1d is the distribution of more than 2000 meteorological observations stations from the China Meteorological Administration.
First, to verify the results of urban scenario and topography (exp. 5), the land-use-data-driven model can reproduce the weather conditions in July 2018 more accurately than the WRF default scenario (2010 MODIS) vs. topography (exp. 1). We compared the experimental results of exp. 5 and exp. 1 with the observation data of the National Meteorological Information Center of China Meteorological Administration. Secondly, by subtracting the results of the non-urban scenario and no-topography (exp. 4) from the urban scenario and no-topography (exp. 3), we compared the influence of the single urbanization factor on the thermal environment of urban agglomeration. Then, the result of exp. 5 minus exp. 3 was taken as a single terrain factor affecting the thermal environment.
Topography directly affects the local atmospheric circulation. Here is our
research question: will topography interact with urbanization to jointly
affect the thermal environment of urban agglomerations? We refer to Yang's
method to explore the interaction between this terrain and urbanization
before we quantified the impact of urbanization on the thermal environment
of urban agglomeration under complex terrain; i.e., [exp. 5 – (exp. 3
To verify the model performance, we compared the simulated monthly and daily
mean spatial temperature models in July 2018 with the WRF default land use
dataset and with observations of the meteorological stations (Fig. 4a, c and e). The correlation coefficient matrix of the 2018 land use simulation
results has a high spatial correlation coefficient (
The 2 m temperature space distribution of
Pearson correlation coefficient between 10 variables of simulation
results by WRF with 2018 land use dataset in CCUA. The asterisk indicates the variables that were judged to be significant after inspection (
Furthermore, the model exhibited a large negative deviation in the
mountainous area around the CCUA (Fig. 4b) due to the systematic error of
underestimating wind speed and temperature in the area. The
root mean square error between the observed and exp. 5 (urban scenario and
Topography) simulation results was about
Effects of urbanization on 2 m temperature:
To explore the impact of terrain on the thermal environment of urban
agglomerations in the summer, we made the land use of the CCUA constant
(non-urban scenario), compared the results under the two different terrain
scenarios: original complex terrain (topography) and smoothed topography
(no-topography). Comparing Fig. 6a with Fig. 6b, we can see that the
influence of terrain on the temperature pattern is basically consistent with
the current distribution pattern of summer monthly average temperature, and
terrain is the main factor determining the temperature pattern. In Fig. 6b,
the temperature inside the CCUA (compared with flat terrain) increases by
about 10
Influence of urbanization on urban heat flux and PBLH:
Sichuan Basin, where the CCUA is located, has a concave landform. The closed topography leads to low wind speed in Sichuan Basin, making the heat in the basin difficult to dissipate. Therefore, the thermal environment here is more severe than that in the flat terrain. In Fig. 7c, g and k, we observed that the complex terrain would increase the HFX and LH of the urban agglomeration. At the same time, due to the high altitude around the basin itself, it will significantly raise the atmospheric boundary layer of the urban agglomeration. These are the crucial attributions for the temperature rise caused by the complex terrain.
The many rivers in Sichuan Basin make the southeast monsoon convey large quantities of water vapor, blocked by the mountains around Sichuan Basin. The southeast of the mountainous area is low, which is favorable for receiving water vapor. On the contrary, the northwest mountainous area is of relatively high altitude, thereby conducive to water vapor loss, causing increased air humidity. Therefore, the topography is pertinent to forming a humid and hot climate in summer in the CCUA.
To determine the summer warming caused by a single urban land expansion
factor, we conducted two groups of experiments: exp. 3 and exp. 4. Both
groups of experiments smoothed the terrain to eliminate the influence of
complex terrain. We observed that the urban expansion would cause the
temperature of the whole urban agglomeration region to increase by
Urbanization will significantly change the surface albedo, heat capacity and thermal conductivity of the underlying urban surface. The change in
surface heat flow caused by urbanization is shown in Fig. 7c. The urban impervious surface absorbs more downward shortwave radiation, and the GRDFLX
is stored more in the daytime and released more at night. Daytime surface
temperature is mainly due to the increase in urban surface HFX, with a
maximum increase of 90 W m
The spatial distribution of 2 m temperature rise due to the
urbanization:
By comparing the results of exp. 3, exp. 4, exp. 5 and exp. 6, we conclude that terrain is the most crucial factor in forming local weather and climate
patterns. In the case of complex terrain and urbanization, the terrain would
affect the weather and climate simultaneously, causing climate change (Figs. 6d, 7d, h and i). Compared with the urban warming effect
(caused by a single urbanization factor), the warming effect of the urban
core area is more evident after the complex terrain is added. The warming
areas are more concentrated in the urban core area. The average temperature
in the core areas of CD and CQ increases by more than 1.5
Due to the joint influence of topography and urban expansion, the HFX
increased by about 30 W m
Considering the heat flux, temperature, boundary layer and other factors mentioned above, we think that the topography further enhances the heat island effect in the CCUA and the urban core area of CD and CQ.
The spatial distribution of 2 m temperature cooling because of the
future planning scenario:
Above (Sect. 3.2), we quantitatively studied the influence of different factors on the urban thermal environment, such as a single urban factor, a single topography factor, and the combined influence of urban and topography factors. The results reveal in detail the mechanism of how these factors affect the urban thermal environment.
To determine the summer warming caused by historical urban land expansion,
we calculated the simulated 2 m air temperature difference between the
urbanized land use in 2018 and the historical land use before urbanization
in 1980 (Fig. 8a–c) by the exp. 1 and exp. 5. The whole
region experienced some warming, with notable ones occurring in areas
consistent with the location of the urban grid in 2018. At the same time,
the northeast part of the urban agglomerations was warmed, probably
resulting from the urban agglomerations effect or terrain hindering heat
dissipation. The monthly average temperature of 2 m air in July of 2018 was
0.75
Figure 10a, b and c show the diurnal variation in surface temperature, 2 m air temperature and PBLH in the summer of the CCUA, CD and CQ in 1980 and 2018, respectively. Here, regardless of the CCUA, CD or CQ, the daily average surface temperature and the temperature of 2 m air simulated by the 2018 urbanization scenario were significantly higher than those of the 1980 historical scenario, and the daily average atmospheric boundary layer represented by the histogram is also increased by 50–100 m.
Compared with Fig. 11a and b, the change in surface radiation balance caused by urbanization was evident. The impervious surface layer of the CCUA absorbed more downward shortwave radiation, and the GRDFLX storage was larger during the day, while the GRDFLX released was larger at night. The increase in HFX has become the main component of surface energy flux, heating the air temperature of the city and promoting the formation of the urban heat island. The HFX reduction directly elevates near-surface temperature. Due to the decrease in soil evapotranspiration, the LH decreased significantly. From 1980 to 2018, large cities exhibited reduced soil moisture and near-surface wind speed, resulting in a lowered evaporation. The higher the surface temperature, the more intense was the long-wave radiation and the higher the net radiation energy lost in the daytime. Similar scenarios ensued for the main urban areas of CD and CQ because the proportion of impervious surface was higher. Changes in the surface heat flux became more apparent (Fig. 11c, d, e and f).
The diurnal variations in the changes in surface energy budget over
the land use grids of 1980, 2018 and future runs of the Chengdu–Chongqing urban agglomeration:
The heat flux distribution changes in surface heat flux over the
land use grids of
The two columns on the left side of Fig. 12a–b, d–e and g–h show the components of surface
heat flux during the simulation period caused by urbanization. The upper and
lower endpoints of the box graph represent the maximum and minimum values of
the heat flux, while the middle, upper and lower side represent the
average and the first and third quantiles, respectively. The four boxes from left to
right in each panel represent the ground heat flux, HFX, LH and
Therefore, we suggest that urbanization will inevitably produce a heat island effect in urban agglomerations, especially under a complex terrain. Consequently, some effective and ideal measures could be adopted to alleviate the heat island effect caused by urbanization.
Due to the significant urban heat island effect associated with
urbanization, to explore reasonable measures to alleviate the urban heat
island effect and improve the living comfort of urban residents, we designed
the future land use scenarios. To explore the extent to which the urban heat
island effect can be alleviated by returning farmland to forest and
grassland and expanding the river lake wetland area of urban agglomeration,
we compared the future scenario with the current urban scenario (Fig. 9). In
the central part of the CCUA, the average temperature dropped by about 0.5
Comparing the future scenario (Fig. 10) with the 2018 urban scenario, it was
obvious that planning measures could reduce the air temperature and ground
temperature 2 m above the CCUA area, CQ area and CD area. After the
planning, the overall average temperature dropped by 0.2–0.67
Comparing the two columns on the right side of Fig. 12b, c, e, f, h and i, the city's heat flux
changed after the urban agglomeration planning. With the urbanization
scenario in 2018, the average GRDFLX decreased by about 30 W m
There is a significant difference between the urban heat budget and the natural underlay surface. From the results, we observed that the planning measures have significantly improved the thermal environment of the city because the planned land use increases the natural underlay surface of the vegetation and water system. The urban impervious surface and building surface evinced a higher surface temperature, which was decisive in the HFX, whereas the natural underlay surface dominated the LH, caused by transpiration and evaporation of vegetation and wetland. Because the surface temperature was lower than the impervious surface of the city, planning policy can reduce the urban heat island effect and improve the comfort of human settlements.
In this study, we investigated the CCUA summer urban warming effect and its
adaptation strategies under the complex terrain in southwest China through
conducting seven simulations using the WRF/SLUCM model with the combined five land use scenarios, including WRF default, historical, urban, non-urban and future planning scenarios, and two kinds of terrain (original complex terrain and smoothed
terrain). It was found that urban land use types of the CCUA increased to
nearly 9000 km
The simulation for the future planning scenario shows that the
implementation of idealized measures (such as returning farmland to forest
and river lake expansion) can reduce the urban heat island effect. Likewise,
it can regulate the urban ecosystem; for example, the average 2 m temperature in summer of an urban agglomeration decreased by
This study focuses on exploring the impact of urbanization in a complex terrain environment on local geothermal environment using the WRF model with the USGS data of WRF's default data and the Institute of Geographic Sciences and Natural Resources Research (IGSNRR) land use data that are more timely and more suitable for CCUA in the local research area. We may also discuss simulation uncertainties from other land use and land cover data in future.
Most of the statistical treatments were done using the Origin software (
The initial and boundary field data in this work are available for download via the Research Data Archive of NCAR:
The scientific framing of this paper was developed by SC, ZX, BJ and PQ. The WRF model was initiated by SC and BL. The WRF model runs were set up, performed and extracted through a joint effort by the team of SC, BL and JX. Analyses and scientific post-processing were performed by LW, YW and RL. All authors discussed the results and contributed to the writing of the paper.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number: XDA23090102), the National Natural Science Foundation of China (NSFC) project (grant number: 41830967), and the National Meteorological Information Center, China Meteorological Administration for data support. We also thank the editor, Gabriele Messori, and the reviewer, Hideki Takebayashi, and the three anonymous reviewers for their kind comments on the paper.
This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA23090102) and the National Natural Science Foundation of China (NSFC) project (grant no. 41830967).
This paper was edited by Gabriele Messori and reviewed by Hideki Takebayashi and three anonymous referees.