Next Article in Journal
Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data
Previous Article in Journal
Application of Machine Learning to Tree Species Classification Using Active and Passive Remote Sensing: A Case Study of the Duraer Forestry Zone
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 15
Issue 10
10.3390/rs15102601
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Land Use/Cover and Meteorological Changes on Regional Climate under Different SSP-RCP Scenarios: A Case Study in Zhengzhou, China

by
Tianqi Bai
1,2,†,
Like Fan
3,†,
Genxin Song
1,4,*,
Hongquan Song
1,2,
Xutong Ru
1,4,
Yaobin Wang
1,4,
Haopeng Zhang
1,4,
Ruiqi Min
1,4 and
Weijiao Wang
1,4
1
Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China
2
Henan Key Laboratory of Integrated Air Pollution Control and Ecological Security, Henan University, Kaifeng 475004, China
3
School of Life Sciences, Henan University, Kaifeng 475004, China
4
Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2023, 15(10), 2601; https://doi.org/10.3390/rs15102601
Submission received: 11 April 2023 / Revised: 10 May 2023 / Accepted: 15 May 2023 / Published: 16 May 2023
Browse Figures
Versions Notes

Abstract

:
To better understand the possible role of projected land use and cover change (LUCC) in future regional climate projections, we explored the regional climate change response from land use/cover change under different climate scenarios. To do so, we propose a research framework based on different SSP-RCPs to simulate and explore the impacts of future land use/cover changes on the future climate of Zhengzhou City, China, using the Weather Research and Forecasting (WRF) model with land use/cover and meteorological data under different SSP-RCP scenarios based on CMIP6. Two scenarios, SSP2-4.5 and SSP5-8.5, were compared and analyzed by simulating changes in future climate factors of temperature at 2 m height above ground(T2) and precipitation. The results show that T2 is higher for all 4 months by the year 2060 compared to that in the year 2030. Furthermore, a comparison of the abovementioned years showed that the mean temperatures of January and July were higher than those of SSP2-4.5 under the SSP5-8.5 scenario in both years, but in 2030, the mean T2 of April and October were lower than those of SSP2-4.5 under the SSP5-8.5 scenario. In terms of precipitation, both scenarios have no significant precipitation in July in 2030 and 2060, but there is an unusual increase in January and October.

1. Introduction

Land use and land cover change (LUCC) is an essential theme in research on global climate and environmental change [1,2]. It is the most direct manifestation of the impact of human activities on the natural ecosystems of the earth’s surface, elucidating the impact of human economic and social activities on natural ecosystems [3]. LUCC activities, whether they transform natural vegetation into land for human utilization or change human-dominated land use, have changed a large portion of the Earth’s surface [4]. Anthropogenic LUCC, such as afforestation, deforestation, and urbanization, may significantly impact climate change. Moreover, human economic and social activities will continue to have an impact on LUCC in the future, continuing to accelerate urbanization and gradually becoming a key trigger for climate and environmental change [5].
To tackle these climate and environmental changes, various socioeconomic and emission pathways, shared socioeconomic pathways (SSPs), and representative concentration pathways (RCPs), as proposed in the Fifth Assessment Report of the IPCC [6,7,8], can provide important tools for climate change research to enhance the human capacity to address future climate challenges and adapt accordingly. The latest Coupled Model Comparison Project Phase 6 (CMIP6) model provides climate researchers with multiple future development scenarios under global climate change by coupling SSP and RCP [9,10], summarizing possible future scenarios regarding human economic and social activity, Earth’s natural environment conditions, and human greenhouse gas and aerosol emissions in several key areas. In particular, the model is applied to future climate change research, which is beneficial for assessing the contribution of human activities to future climate change and the future response of the Earth’s natural environment to human activities to facilitate new human responses to future climate change. The land use and cover change under different scenarios are very different, and coupled scenarios based on SSP and RCP are relevant to future world carbon emissions and for exploring the impact of land use and cover change in regional climate models based on SSP-RCP scenarios, as well as for exploring the regional climate.
Previous studies have indicated that anthropogenic LUCCs can alter the geographical characteristics of the land surface and gradually affect the regional climate [11,12,13,14,15,16]. Different land use/land cover over time and anthropogenic changes can impact regional climate (e.g., temperature and precipitation). For example, Cao et al. [12] used LULC data from 2001 to 2010 to study the regional climate of the agro-pastoral transition zone in northern China using the WRF model. The study showed that the vegetation distribution index and leaf area index increased in summer and surface albedo decreased in winter from 2001 to 2010. The WRF simulations showed that the differences in LULC lead to temperature changes in different seasons. Wang et al. [13] quantified the contribution of LUCC to climate change conditions in eastern China over the past 30 years. The results show that in northeast China, temperature decreases when forest land is converted to farmland due to increased surface albedo, and in southern China, deforestation usually leads to a decrease in temperature. Moreover, it is shown that in these areas there is little variation in summer temperatures due to changes in land use and cover. Li et al. [15] showed that urbanization causes a slight warming effect in the Lhasa River basin of the Tibetan Plateau in China. Concurrently, afforestation projects contribute to winter warming. Chu et al. [16] showed that the Songnen Plain in northeastern China has undergone dramatic changes in LULC due to the overuse and development of agriculture. The results of the study showed that there has been a significant warming trend in the region since 1980 and that the average annual precipitation has decreased at a rate of one unit per decade. Moreover, paddy fields, forestlands, and wetlands have a positive impact on precipitation, and among them, forestlands have the greatest impact on the increase in precipitation. In addition, the response of different land uses and land covers to increases in temperature varied considerably, with the highest response being for building land and the lowest response for forest land.
However, the above studies are based on assumptions about possible conversions of past land use and cover types or changes that have already occurred. Additionally, assumptions on how LUCC would affect climate under RCP or SSP-based future scenarios have also been reported. For example, Soulard and Rigge [17] studied and analyzed changes in the abundance and distribution of vegetation cover under future climate change scenarios, i.e., Business As Usual (BAU) and Representative Concentration Pathway (RCP) 8.5 scenarios. The results of the study showed that the area of bare ground will decrease at pixel sites with high bare ground cover, herbaceous cover will also decrease at pixel sites with moderate herbaceous cover, and shrub cover will increase at pixel sites with low shrub cover. Deng et al. [18] evaluated the RCP6.0 scenario for the period 2010–2050, in which possible future temperature changes could occur due to grassland degradation in Inner Mongolia. The analysis revealed an increase in cropland in Northeast China, deforestation in the Brazilian Amazon, and LUCCs in urban agglomerations in India and the U.S. The results suggest that LUCCs in all four of these regions lead to significant temperature increases in urban built-up areas. To be able to predict future regional climate change, the interaction between LULC and regional climate should be considered and assessed in the context of human socioeconomic activities [5], not only by incorporating representative future emission scenarios but also by analyzing a combination of socioeconomic and emission scenarios, so that relevant socioeconomic activities and climate policies should also be incorporated and explained.
Furthermore, most scholars have started to study the drivers for assessing the future impacts of global and regional climate change due to specific land use transitions under SSPs and RCPs scenarios, i.e., future LUCCs on climate under different SSP-RCP(SSPx-y) pathway scenarios. Bukovsky et al. [19] used Regional Climate Model (RCM) simulations to assess the combined impacts of greenhouse gases (GHG) forcing climate change and LUCCs in major regions of the United States under SSPs scenarios. The study shows that urbanization is an extreme way for humans to change land surface properties and affect the local climate in urban areas. Under the SSP scenario, LUCC significantly affects climate projections in different ways, with annual mean temperature increase decreasing with the expansion of large amounts of forested cropland. Dong et al. [5] assessed the climate impacts of land use/cover change changes in China under different SSP-RCPs land use datasets using the WRF model to compare regional meteorological changes due to historical and future land use. It was shown that urbanization-led near-surface temperatures show an increase in the three future scenarios and that precipitation decreases and the climate becomes drier with land use change, more extensive urbanization, and higher RCP effects. However, these studies have mainly focused on the national or global level and explored the future climate simulation results in combination with models and different SSP-RCPs, but have not conducted downscaling studies on the future climate of individual urban areas.
Flint et al. [20] used statistically downscaled future climate projections to downscale global climate projections to 100 m spatial resolution using the gradient-distance squared inverse method for spatial downscaling. They verified their accuracy with measured data and applied the results to hydrological models to illustrate the effects of future climate change on ecological processes. Sleeter et al. [21] used the Integrated Assessment Model, history of land use, and expert knowledge to downscale the projections of LUCC. This downscaling covered 16 sectors of LULC and multiple ecological regions and was linked to downscaled climate scenarios under shared socioeconomic assumptions. The results provided a trustworthy solution for localized land use and cover predictions. Kusaka et al. [22] used a regional climate model to quantify the uncertainty in the future climate of the city of Tokyo under the RCP4.5 scenario in a large-scale urban planning scenario and showed that the compact city scenario reduced the average temperature of urban residential areas, while the decentralized city scenario increased the temperature. Moreover, the effects of the differences between the urban scenario and the regional climate model are greater at night than during the day. However, future urban climate change under different SSP-RCPs scenarios has not been studied.
China’s rapid economic development and extensive land exploitation have further exacerbated LUCCs over the past few decades. As China has indicated that it will reach peak CO2 emissions by 2030 and the goal of carbon neutrality by 2060, there is an increasing interest in studying the relationship between LUCC and carbon emissions and sequestration [23]. Because of this, we used the WRF model to investigate the possible impacts of the combination of LUCC and future meteorological changes on urban regional climate under different future SSP-RCPs scenarios based on the CMIP6 and the climate patterns induced by different SSP-RCPs scenarios. As a new first-tier city in China, Zhengzhou has experienced dramatic LUCC under the influence of urbanization. Examining the impact of LUCCs under the future SSP-RCPs scenarios on the climate of Zhengzhou and their potential significance for urban carbon emissions is of utmost importance, given the scenario-based assumptions. In this study, we compared climate variability and extremes under different scenarios to enhance the ability of city managers in policy formation and emergency management when dealing with future climate uncertainties in cities. As a methodology for future climate change projections, this study can be used to provide technical tools and data support for urban decision makers to develop ecological and carbon sequestration measures to improve ecosystem services in cities based on different scenarios.

2. Materials and Methods

2.1. Study Area

The study area was set as Zhengzhou City, located in the central plain region of China (112°42′E–114°14′E, 34°16′N–34°58′N), with an area of about 7446.2 km2. Zhengzhou is currently the political, economic, technological, and educational center of the Henan Province, the most important mega-city in the Henan Province, an important transportation hub center in the central plain of China, and one of the eight ancient capitals with a resident population of over 12 million [24], including six municipal districts (Erqi, Zhongyuan, Guancheng, Jinshui, Huiji, Shangjie), five county-level cities (Xingyang, Gongyi, Dengfeng, Xinmi, and Xinzheng) and one county (Zhongmou) (Figure 1).

2.2. Scenario and Model Description

The climate change research community has developed a new set of scenarios to cope with future climate as well as social changes to study future climate impacts and human mitigation and adaptation to climate change, which reasonably describe different future changes in elements regarding socioeconomic, technological, and environmental conditions and greenhouse gas and aerosol emissions [25,26,27,28]. Among them, CMIP6 combines different future scenarios of SSPs and RCPs, emphasizing the driving role of different socio-economic development patterns on climate change, combining both SSPs and RCPs for different scenarios of LUCC under different SSP-RCP scenarios. For details of each specific scenario description, please refer to this study [9].
In this study, we chose two scenarios, SSP2-4.5 and SSP5-8.5, to study the impacts of future climate change on the Zhengzhou region. SSP2-4.5 integrates SSP2 and RCP4.5, with SSP2 assuming that future trends will continue along historical patterns without significant deviations, and the combination representing a scenario that combines medium societal fragility with moderate levels of forcing, representing a middle-of-the-road scenario of socioeconomic development and moderate greenhouse gas (GHG) emissions. Compared to other scenarios, SSP2-4.5 exhibits less extreme changes in LULC. SSP5-8.5 integrates SSP5 and RCP8.5, where SSP5 assumes an energy-intensive fossil-fuel-based economy, and SSP5-8.5 represents rapid and large-scale growth in fossil fuel use and high greenhouse gas emissions [2]. Moreover, it also suggests that, under this scenario, future land changes will be greatly influenced, leading to significant transformations.
This study uses “The Advanced Research WRF” (WRF–ARW) model [29]. This model has a wide range of applications at multiple spatial scales (from meters to thousands of kilometers), and its capabilities extend from regional to global climate simulations that simulate multi-scale climates in regions through physical and chemical morphological processes [30]. The WRF model version 3.8.1 was used for this experiment, and Table 1 shows the physical parameters used in this study [31,32,33,34,35,36].
Regarding the coupling with meteorological data and the linkage between climate factors in the SSP-RCP scenario, it is necessary to explore the linkage between climate factors and to compare different time periods. We chose two different SSP-RCP scenarios: SSP2-4.5 and SSP5-8.5. The SSP2-4.5 scenario assumes a reduction in GHG emissions and appropriate climate change mitigation measures in the future, while the SSP5-8.5 scenario assumes continued high GHG emissions in the future, leading to more severe climate change. These two scenarios represent different future development paths and possibilities. To further investigate future climate change under these scenarios, we chose 2030 and 2060 as two time points for climate simulations for one of the four seasons of each year (January, April, July, and October). These months represent the characteristics of different seasons due to the distinct seasonal variations in the study area. By comparing the results of climate simulations under these different scenarios and time points, the linkages between climate factors can be better understood and the likelihood of future climate change can be predicted and assessed. The climate validation for Zhengzhou City in 2020 was carried out before the experiment using the physical parameterization scheme in Table 1. The performance of the WRF model for this experiment was evaluated by comparing the simulation results with the observed temperature at 2 m height above ground (T2) and precipitation for January, April, July, and October 2020 in the nested domain 3, with the first five days of each month considered as the spin-up period and not being included in the analysis statistics. Table 1 shows that the center of the model domain is located at 34°N, 114°E, and domain 3 (containing Zhengzhou City) is the focus of this experimental study.
Table 2 shows the performance statistics for the validation of Mean Bias (MB), Normalized Mean Bias (NMB), Normalized Mean Error (NME), Root Mean Square Error (RMSE), and correlation coefficient (R) in 2020 regarding temperature (T2) and precipitation, which have similarities with the performance in other previous WRF model studies [37,38,39]. The high correlation coefficients between observed and simulated data for temperature (0.77 to 0.93) and precipitation (0.46 to 0.9) indicate that the WRF model configuration can simulate temperature and precipitation in the study area well. To assess climate change due to both scenarios, the same WRF configuration used was simulated for the above time period, with LULC consistent with the scenarios from meteorological data. The experiment allows us to understand not only the weather characteristics of the different seasons simulated by the model but also how the climate variables change under different future meteorological conditions of SSP-RCPs on LULC.

2.3. Data and Experimental Design

The land use/cover change data for this experiment were from the global land projection dataset produced by Li et al. using the latest IPCC-coupled socioeconomic and climate change scenarios at SSP-RCP 1 km resolution [40], which were generated by combining the top-down land demand constraints provided by the official CMIP6 dataset with bottom-up spatial simulations performed by meta-cellular automata. The dataset clearly shows the area changes of land use and cover under different scenarios, where the cultivated land changes the most under different scenarios and the area of other vegetation changes accordingly. In addition, the future meteorological data used for this experiment were extracted from a dataset produced by Xu et al., which is a global dataset based on CMIP6 [41]. This dataset has been bias-corrected to avoid some of the problems in dynamic downscaling and is useful for simulation prediction of the Earth’s future climate and atmospheric environment. Therefore, the evaluation by Xu et al. indicates that the bias-corrected meteorological data are of higher quality than the single CMIP6 models and are suitable for the present experiment. Please refer to the specific literature for accuracy validation of both datasets.
Based on our selected SSP-RCP scenarios, in this study, the comprehensive research framework proposed includes three main components (Figure 2). On the left is the workflow of Li et al., which was used to create a global 1 km resolution land dataset of future SSP-RCP scenarios (sketch), and on the right is the process for future climate scenario data, coupling the two in the WRF model for the same scenarios. First, scenarios were selected and land data for soil economic and climate change projections under different SSP-RCP scenarios were locked to the study area, simulation time periods were set, and the underlying geographic data in the WRF model were re-substituted with the land use and cover data under the SSP-RCP scenarios. Second, the climate scenario data corresponding to the land use data were put into the WRF model. Third, the internal physicalization parameter scheme and physical mechanisms of the WRF model were used to generate future climate data, followed by the extraction of climate variables, and finally, the analysis of the results. In the experiment, the regional climate response of the corresponding scenario is evaluated at a finer resolution through the WRF model. In this way, it is capable of studying the changes of climate elements in different urban areas with regards to future urbanization and capturing and assessing the changes of regional temperature, precipitation, and heat fluxes due to future LUCCs.

2.4. Area Variation for Different Scenarios

Based on the SSP-RCP 1 km resolution global land projection dataset by Li et al. for different scenarios, which uses IGBP’s global land cover classification, after processing this dataset, we estimated various LULC areas in Zhengzhou under SSP2-4.5 and SSP5-8.5 scenarios, as shown in Table 3.
Under the SSP2-4.5 scenario, comparing each LULC area in 2030 with that of 2060 (Figure 3) revealed that deciduous broadleaf forest, mixed forest, savanna, and grassland in Zhengzhou City would decrease by 2060, especially the grasslands, which are predicted to reduce by approximately 195 km2. In contrast, urban and built-up areas, cropland, and natural vegetation mosaic increased, with cropland increasing the most (by approximately 209 km2). Under the SSP5-8.5 scenario (Figure 4), the area of deciduous broadleaf forest, mixed forest, savanna, grassland, and cropland/natural vegetation mosaic in Zhengzhou in 2060 decreases relative to 2030, while mixed forest directly decreases to zero by 2060. Savanna and grassland decreased the most, by approximately 12 km2 and 23 km2, respectively, while the area of cropland and urban built-up areas both increased, expanding by approximately 41 km2 and 8 km2, respectively. The impact of LULC area change on urban areas is mainly reflected in the rapid urbanization phase, where accelerated urban expansion leads to the conversion of natural vegetation such as deciduous broadleaf forests, mixed forests, savannas, and grasslands into urban areas, which, in turn, has an impact on the climate of urban areas.
Comparing the LULC area under different scenarios in the same year, by 2030, the area of natural vegetation such as deciduous broadleaf forest, mixed forest, and savanna in the SSP5-8.5 scenario decreased compared with that in the SSP2-4.5 scenario, especially the area of deciduous broadleaf forest and savanna, which decreased by approximately 40 km2 and 219 km2, respectively. The area of urban and built-up areas also decreased, while the area of cropland increased by approximately 241 km2. In 2060, the area of mixed forests directly decreased to 0 in the SSP5-8.5 scenario, and the area of natural vegetation such as deciduous broad-leaved forests and savannas also decreased compared with that in the SSP2-4.5 scenario. Among them, the area of deciduous broad-leaved forest and grassland decreased by approximately 25.6 km2 and 46.6 km2, respectively, and the area of urban and built-up areas decreased by approximately 18.3 km2, while the area of cultivated land increased by approximately 73.7 km2. This suggests that, in the SSP5-8.5 scenario (high emissions and high socioeconomic development), the massive growth of Zhengzhou’s urban population will inevitably lead to an increase in agricultural land use, which will inevitably come at the expense of land cover types such as grasslands and savannas. In the above comparison of various LULC areas in Zhengzhou for two future scenarios, it is easy to see that the opposite of the decrease in natural vegetation such as deciduous broad-leaved forests is the increase in urban area and cropland area, forming a negative correlation. How the urban climate of Zhengzhou will change under this scenario is elaborated in Section 3.

3. Results

3.1. Temperature Change and Comparison

In this study, we used the T2 (the temperature at 2 m height above ground) difference between the SSP5-8.5 scenario and the SSP2-4.5 scenario (Figure 5 and Figure 6) to quantify the effect of the combination of LUCC and meteorological change on temperature change in Zhengzhou City amidst different scenarios. Figure 5 shows the difference in mean T2 between the two scenarios, SSP5-8.5 and SSP2-4.5, in January, April, July, and October of 2030 (SSP5-8.5–SSP2-4.5). The results indicate that the general trend of T2 variation around the main city of Zhengzhou is “increasing” in January and July, with a general increase of 1 to 2.6 °C, especially in the southwestern part of China during January. In April and October, the average T2 in the SSP5-8.5 scenario is significantly lower than that of the SSP2-4.5 scenario, especially in October, when the general area around most of Zhengzhou City decreases by more than 1.5 °C. Figure 6 shows the difference between the mean T2 in January, April, July, and October for the SSP5-8.5 and SSP2-4.5 scenarios in 2060. The results indicate that, accounting for urban expansion in different SSP-RCP scenarios and human economic and social impacts, the overall trend of the T2 change around Zhengzhou City in the SSP5-8.5 scenario is “increasing” in January and July, which is more notable than the warming of the SSP2-4.5 scenario.
Urban expansion would lead to a decrease in natural vegetation area in urban areas and would lead to an increase in urban built-up areas and cropland, resulting in a general increase in T2 of 3.2 to 4 °C in these areas. However, the average T2 in the SSP5-8.5 scenario was significantly lower than that in SSP2-4.5 in April and October, with a general area decrease of more than 1.5 °C in most areas of Zhengzhou in April. Therefore, this study shows that the warming effect of urban expansion under different SSP-RCP scenarios and urban areas under human economic and social influence is more pronounced in winter and summer, while the effect is weaker in spring and autumn.
In addition, the T2ave (average temperature of T2), T2min (minimum temperature of T2), and T2max (maximum temperature of T2) for January, April, July, and October in both the SSP2-4.5 and SSP5-8.5 scenarios extracted from the total LUCCs and meteorological changes under the same scenarios described above indicate the following (Figure 7): In 2030, the T2min of SSP5-8.5 in January is approximately 2.5 °C higher than that of SSP2-4.5, and the T2min of SSP5-8.5 in July is approximately 4.1 °C higher than that of SSP2-4.5; however, the T2min of SSP5-8.5 in April is approximately 0.1 °C lower than that of SSP2-4.5, and the T2min of SSP5-8.5 in October is approximately 0.75 °C lower than that of SSP2-4.5. The T2max of SSP5-8.5 in January is approximately 7.07 °C higher than that of SSP2-4.5, the T2max of SSP5-8.5 in April is approximately 0.7 °C lower than that of SSP2-4.5, the T2max of SSP5-8.5 in July is approximately 0.4 °C lower than that of SSP2-4.5, and the T2max of SSP5-8.5 in October is approximately 5.2 °C lower than that of SSP2-4.5. In 2060, T2min of SSP5-8.5 in January is approximately 2.5 °C higher than that of SSP2-4.5, and T2min of SSP5-8.5 in July is approximately 3.6 °C higher than that of SSP2-4; however, the T2min of SSP5-8.5 in April is approximately 3.3 °C lower than that of SSP2-4.5, and the T2min of SSP5-8.5 in October is approximately 5.2 °C lower than that of SSP2-4.5. The T2max of SSP5-8.5 in January is approximately 6.5 °C higher than that of SSP2-4.5, and T2max of SSP5-8.5 in July is approximately 6.9 °C higher than that of SSP2-4.5; however, the T2max of SSP5-8.5 in April is approximately 4.2 °C lower than that of SSP2-4.5, and the T2max of SSP5-8.5 in October is approximately 2.7 °C lower than that of SSP2-4.5. In this study, we use the WRF model; thus, we can quantify the urban temperature changes due to LUCCs for different SSP-RCP future scenarios.
In the above comparison, in 2030, the T2min and T2max in the SSP5-8.5 scenario are considerably higher in January and July compared to the SSP2-4.5 scenario; however, the temperature in April and October is relatively lower. In particular, the T2max in SSP5-8.5 is about 5.2 °C lower than that in SSP2-4.5, indicating that a decrease in temperature may occur in the future urban autumn in the SSP5-8.5 scenario. Furthermore, this phenomenon is also seen in the T2max comparison during October 2060. In 2060, the T2min and T2max in the SSP5-8.5 scenario are also higher in January and July compared to the SSP2-4.5 scenario, and even the T2max in July is approximately 7 °C higher than that in SSP2-4.5, reaching 46.3 °C. Therefore, it is important to take special precautions against extreme hot weather in cities to avoid heat stroke or even life-threatening health of the masses.

3.2. Precipitation Change and Comparison

Figure 8 shows the differences in the distribution of average precipitation in January, April, July, and October between the SSP5-8.5 and SSP2-4.5 scenarios for 2030 (SSP5-8.5–SSP2-4.5). First, in January and October, the overall trend of precipitation changes in the whole area of Zhengzhou in the SSP5-8.5 scenario as it is increasing, and the increase in precipitation is more significant in the southwestern region in January, mainly in the mountainous areas of Gongyi, Dengfeng, and Xinmi, probably due to the increased snowfall in the mountainous areas of Zhengzhou in the SSP2-4.5 scenario. Furthermore, the overall precipitation increases widely in October, generally >100 mm, especially in the southwestern region, which exceed 130 mm. However, in April and July, the average precipitation under the SSP5-8.5 scenario is considerably lower than that under SSP2-4.5, especially in July, when precipitation in the western part of Zhengzhou City decreases by more than 50 mm.
Figure 9 shows the differences in the distribution of mean precipitation between the SSP5-8.5 and SSP2-4.5 scenarios in January, April, July, and October of 2060. The results predicted that, in January, the average precipitation over the whole area of Zhengzhou in the SSP5-8.5 scenario was >50 mm and ≤50 mm in the SSP2-4.5 scenario. The urban expansion and high level of socioeconomic development would not bring more rainfall in winter or in April and October. The average precipitation in the SSP5-8.5 scenario was considerably higher than that in SSP2-4.5, and the average precipitation in most areas of Zhengzhou in the SSP5-8.5 scenario in April was 40–100 mm more than that in SSP2-4.5, with a notable increase in precipitation in the west. In contrast, the figure shows that precipitation in the SSP5-8.5 scenario in July increased less compared to that in the SSP2-4.5 scenario, and there was even a decrease in precipitation in urban centers. Therefore, the emission and radiative forcing levels in the SSP5-8.5 scenario do not directly increase precipitation in the winter and summer seasons in the future but instead may affect the summer rainy season, delaying it until autumn (October).
Overall, in 2030, in the SSP5-8.5 scenario, future precipitation in Zhengzhou is higher in January and October than in the SSP2-4.5 scenario, while the converse is true in April and July. In January, April, and October of 2060, the precipitation in the Zhengzhou area in the SSP5-8.5 scenario is higher than that in the SSP2-4.5 scenario, while the precipitation in July is lower than that in the SSP2-4.5 scenario, which may be due to some delay in the rainy season in the Zhengzhou area in this scenario.

4. Discussion

4.1. Climate Change in Zhengzhou under Different Scenarios

Most current studies have demonstrated the effects of LUCCs on temperature and precipitation and have taken future land cover and land change as a research direction; however, to the best of our knowledge, few studies have examined the relationship between different future SSP-RCP scenarios and the future climate of cities. In this study, we simulated two scenarios (SSP2-4.5 and SSP5-8.5) with the WRF model, which considered different socioeconomic development patterns and emission intensities, and compared the temperature and precipitation changes in Zhengzhou City in 2030 and 2060 amidst different socioeconomic and emission scenarios by combining future LUCCs with future meteorological changes.
First, we found that the T2max and T2ave increase the most in January and July. In the SSP2-4.5 and SSP5-8.5 scenarios, the T2ave was approximately 0.17 °C and 29.4 °C and 0.3 °C and 30.6 °C in January and July in 2030, respectively, while in 2060, this change was 2.77 °C and 30.4 °C and 4.27 °C and 33.8 °C, respectively. We found that the T2ave was approximately 2.6 °C higher in January than SSP2-4.5 for the SSP5-8.5 scenario, while the T2ave was approximately 3.2 °C higher in July than SSP2-4.5. Additionally, T2max was approximately 6.5 °C and 39.3 °C and 13.6 °C and 39.9 °C in January and July in 2030, respectively, while in 2060, this change was 8.0 °C and 39.4 °C and 14.6 °C and 46.4 °C, respectively. In particular, the T2max in July 2060 reached a staggering 46.4 °C. The city experienced extreme heat, which posed a threat to the lives of city residents. Due to such results, the government should initiate emergency management measures for the city’s hot weather.
In the SSP2-4.5 scenario, by 2060, there were considerable reductions in the land cover of deciduous broadleaf forests, mixed forests, savannas, and grasslands relative to 2030, and the values of T2min, T2max, and T2ave were higher in January and July in 2060 than that in 2030; however, the T2ave was lower in April and October than that in 2030. Moreover, in the SSP5-8.5 scenario, by 2060, the land cover of deciduous broadleaf forest, mixed forest, savanna, and grassland was reduced compared to that in 2030, and the T2min, T2max, and T2ave in January and July were estimated to be higher in 2060 than those in 2030; however, the T2min, T2max, and T2ave in April were estimated to be lower than those in 2030. Consequently, we found that the temperature of some months in 2060 was lower than that in 2030 under the gradual decrease in natural vegetation and the temperature change in urban areas, which did not maintain a negative correlation with the area of natural vegetation.
Second, the two future scenarios were influenced by LUCCs, extensive urbanization, higher emissions, and forcing radiation, while the present study focuses only on urban LUCC. The simulations show that the simulated projected precipitation changes in Zhengzhou tend to be in arid climates with a large temporal uncertainty in the rainy season, and the two scenarios for 2030 and 2060 show similar spatial patterns. In the context of socioeconomic development as well as climate forcing, considerably high greenhouse gas emissions and precipitation perturbations have a significant amplifying effect on the regional climate; therefore, the uncertainty of directed precipitation is substantially large.

4.2. Strengths and Uncertainties

In this study, changes in the T2max, T2min, T2ave, and total precipitation for different future months were analyzed on seasonal time scales using the WRF model to compare different climate responses under the two scenarios. The method facilitates the study of the coupling effect of land use and cover change data and meteorological data for the same scenarios in the model performance and whether it can be carried out smoothly in our research framework. Notably, the assessment of the regional climate response requires the use of finer and more precise surface attribute data and observations to help reveal the small-scale climate change.
Despite the high availability of data used in this study, climate simulations have inherent uncertainties about the future, and the types and patterns of projected changes may be the result of model parameterization processes [17]—detailed processes that need to be further explored by dynamically combining land and atmospheric modules at finer scales to better understand the relationship between the land surface and the atmosphere. In addition, there are unavoidable biases in the experimental design (e.g., boundary conditions, regional climate models, regions), and scenario assumptions can be adjusted and additional complexity considered. Therefore, these results do not guarantee that downscaled WRF is a better estimate of future climate factors for different future SSP-RCP scenarios [42].
Under this assumption, the future urban climate will roughly be based on the interaction between future global climate change and local urbanization effects, which is consistent with similar previous studies [43,44,45]. We can reasonably expect that the simulation results from the WRF model using LULC data from the SSP-RCP scenario and meteorological data from the same scenario can be used to study the response of future climate and land use changes to different cities. A prominent feature of the SSP-RCP scenario is that changes in urban areas are influenced by population and GDP levels in proportion to the expansion of urban areas. Urbanization is the most dominant land use and cover change in urban areas and is one of the most important human activities affecting the regional climate system. With the development of an increasing number of megacities (more than 10 million people), the climate impacts of this urban area expansion pattern cannot be ignored [45]. Other factors that were not considered in this study include sensible heat flux, latent heat flux, surface radiation, etc., which may play an important role in future climate change. In addition, this study sets up fewer control experiments and only explores the future climate change impacts in Zhengzhou City in two scenarios. The simulation time of the study was relatively short and cannot reflect the inter-annual climate change scenarios and future long-term climate effects caused by land use and vegetation changes. To better explore the future regional climate change in the SSP-RCP scenario, we need further simulations with longer time series, as well as simulation results from multiple models to compare our results.
This work suggests that, to fully explore the uncertainties in future regional climate changes and projections, not only land cover/use changes should be considered, but also meteorological elements under the same scenarios should be included. However, the future climate is fraught with uncertainty, as it depends not only on the dynamics of the Earth’s system but also on the economic development of human societies [46]. The approach we adopted can also be extended to climate simulations of other cities and scenarios with some generalizability and repeatability, but there are some limitations, such as uncertainties in model parameters and biases in simulation results. Moreover, future climate projections will further explore changes in urban LULC and, based on this, combine meteorological data to investigate different scenarios of future climate change.

5. Conclusions

In the two SSP-RCP scenarios examined in this study, the primary factor driving climate change in Zhengzhou was the temperature change, particularly the increase in average urban temperature. The near-surface temperature changes were evidenced by the significant increases observed in the T2min, T2max, and T2ave (Figure 7). Comparing the monthly mean temperatures in January and July, it can be observed that the mean temperature in the SSP5-8.5 scenario was higher than that in the SSP2-4.5 scenario. This indicates that urbanization and other socioeconomic developments have played crucial roles in the transformation and changes of land use and land cover, leading to a gradual warming of the mean urban temperature during both winter and summer seasons. In contrast, the simulated precipitation projections suggested that, in the future, Zhengzhou’s precipitation patterns will be more characteristic of an arid climate, with significant uncertainties during the rainy season. Both scenarios show no significant precipitation in July but exhibit anomalous increases in January and October, with similar spatial patterns projected for 2030 and 2060. In the context of continuous human socioeconomic development and climate forcing, fairly high anthropogenic greenhouse gas emissions and precipitation perturbations have a significant role in influencing the urban regional climate, and thus the uncertainty in precipitation is considerable.
Moreover, it is important to recognize that the changes in land use and land cover in urban areas, driven by China’s socioeconomic development and increased urban land development intensity, have significant implications for regional climate. The gradual expansion of urban and built-up areas, coupled with the reduction of natural vegetation such as grasslands and forests, has altered the surface characteristics and heat balance of urban environments. Understanding the impacts of land use and land cover changes on regional climate is crucial for urban decision-makers and policymakers to develop effective strategies for mitigating the potential adverse effects of climate change and fostering sustainable urban development.
In recent decades, as China’s socioeconomic development and urban land development intensity have increased, urban and built-up areas have gradually increased, while natural vegetation areas such as grasslands and forests have gradually decreased. The results of this study indicate that, under the influence of different future SSP-RCP scenarios, if the future follows this scenario and both scenarios become reality, future LUCCs in urban areas may lead to significant changes in regional climate and even extreme hot weather, increasing urban heat exposure and endangering the health of urban residents. Therefore, increasingly hot weather may require emergency managers to develop scenarios, and urban local governments must strengthen their emergency management capacity to cope with extreme weather in the face of possible future extremes to mitigate possible outcomes and develop city-wide mitigation and adaptation strategies. Moreover, temperature and precipitation simulations were conducted for the Zhengzhou region in different SSP-RCP scenarios for the years 2030 and 2060. These simulations provide valuable technical and data support for urban decision-makers in formulating socioeconomic development plans and carbon emission policies for the future region. Furthermore, we offer data support for urban governments to devise ecological strategies and carbon sequestration measures aimed at enhancing urban ecosystem services and addressing the challenges posed by uncertain future climate change.

Author Contributions

Conceptualization, G.S. and H.S.; methodology, H.S. and G.S.; software, T.B., Y.W. and H.Z.; validation, L.F. and X.R.; data curation, R.M. and W.W.; writing—original draft preparation, T.B.; writing—review and editing, G.S. and H.S.; visualization, T.B. and X.R.; supervision, G.S. and H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (32130066), the Training Plan for Young Backbone Teachers in Colleges and Universities in Henan Province, China (2021GGJS024), the 2022 Henan Provincial Government Decision-making Research Bidding Project (2022JC015) and the Youth Talent Program of Henan University, China.

Data Availability Statement

The datasets generated in current study are available from the corresponding authors upon reasonable request.

Acknowledgments

The authors thank the National Supercomputing Center in Zhengzhou for their computing support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sterling, S.M.; Ducharne, A.; Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Chang. 2013, 3, 385–390. [Google Scholar] [CrossRef]
  2. Meyer, W.B.; Turner, B.L. Land-use/land-cover change: Challenges for geographers. Geojournal 1996, 39, 237–240. [Google Scholar] [CrossRef]
  3. Mooney, H.A.; Duraiappah, A.; Larigauderie, A. Evolution of natural and social science interactions in global change research programs. Proc. Natl. Acad. Sci. USA 2013, 110 (Suppl. S1), 3665–3672. [Google Scholar] [CrossRef]
  4. Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K.; et al. Global consequences of land use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef]
  5. Dong, N.; Liu, Z.; Luo, M.; Fang, C.; Lin, H. The effects of anthropogenic land use changes on climate in china driven by global socioeconomic and emission scenarios. Earth’s Futur. 2019, 7, 784–804. [Google Scholar] [CrossRef]
  6. Van Vuuren, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.-F.; et al. The representative concentration pathways: An overview. Clim. Chang. 2011, 109, 5–31. [Google Scholar] [CrossRef]
  7. O’Neill, B.C.; Kriegler, E.; Ebi, K.L.; Kemp-Benedict, E.; Riahi, K.; Rothman, D.S.; van Ruijven, B.J.; van Vuuren, D.P.; Birkmann, J.; Kok, K.; et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Chang. 2017, 42, 169–180. [Google Scholar] [CrossRef]
  8. Riahi, K.; Van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Chang. 2017, 42, 153–168. [Google Scholar] [CrossRef]
  9. O’Neill, B.C.; Tebaldi, C.; van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Knutti, R.; Kriegler, E.; Lamarque, J.-F.; Lowe, J.; et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 2016, 9, 3461–3482. [Google Scholar] [CrossRef]
  10. Cook, B.I.; Mankin, J.S.; Marvel, K.; Williams, A.P.; Smerdon, J.E.; Anchukaitis, K.J. Twenty-First Century Drought Projections in the CMIP6 Forcing Scenarios. Earth’s Futur. 2020, 8, e2019EF001461. [Google Scholar] [CrossRef]
  11. Gao, X.; Zhang, N.; Chen, Z.; Pal, J.S.; Giorgi, F. Land use effects on climate in China as simulated by a regional climate model. Sci. China Ser. D Earth Sci. 2007, 50, 620–628. [Google Scholar] [CrossRef]
  12. Cao, Q.; Yu, D.; Georgescu, M.; Han, Z.; Wu, J. Impacts of land use and land cover change on regional climate: A case study in the agro-pastoral transitional zone of China. Environ. Res. Lett. 2015, 10, 124025. [Google Scholar] [CrossRef]
  13. Wang, M.; Xiong, Z.; Yan, X. Modeling the climatic effects of the land use/cover change in eastern China. Phys. Chem. Earth Parts A/B/C 2015, 87–88, 97–107. [Google Scholar] [CrossRef]
  14. Li, J.; Zheng, X.; Zhang, C.; Chen, Y. Impact of Land-Use and Land-Cover Change on Meteorology in the Beijing–Tianjin–Hebei Region from 1990 to 2010. Sustainability 2018, 10, 176. [Google Scholar] [CrossRef]
  15. Li, D.; Tian, P.; Luo, H.; Hu, T.; Dong, B.; Cui, Y.; Khan, S.; Luo, Y. Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau. Sci. Total Environ. 2020, 742, 140570. [Google Scholar] [CrossRef]
  16. Chu, X.-L.; Lu, Z.; Wei, D.; Lei, G.-P. Effects of land use/cover change (LUCC) on the spatiotemporal variability of precipitation and temperature in the Songnen Plain, China. J. Integr. Agric. 2022, 21, 235–248. [Google Scholar] [CrossRef]
  17. Soulard, C.E.; Rigge, M. Application of Empirical Land-Cover Changes to Construct Climate Change Scenarios in Federally Managed Lands. Remote Sens. 2020, 12, 2360. [Google Scholar] [CrossRef]
  18. Deng, X.; Zhao, C.; Lin, Y.; Zhang, T.; Qu, Y.; Zhang, F.; Wang, Z.; Wu, F. Downscaling the Impacts of Large-Scale LUCC on Surface Temperature along with IPCC RCPs: A Global Perspective. Energies 2014, 7, 2720–2739. [Google Scholar] [CrossRef]
  19. Bukovsky, M.S.; Gao, J.; Mearns, L.O.; O’Neill, B.C. SSP-Based Land-Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections. Earth’s Futur. 2021, 9, e2020EF001782. [Google Scholar] [CrossRef]
  20. Flint, L.E.; Flint, A.L. Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Ecol. Process. 2012, 1, 2. [Google Scholar] [CrossRef]
  21. Sleeter, B.M.; Sohl, T.L.; Bouchard, M.A.; Reker, R.R.; Soulard, C.E.; Acevedo, W.; Griffith, G.E.; Sleeter, R.R.; Auch, R.F.; Sayler, K.L.; et al. Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales. Glob. Environ. Chang. 2012, 22, 896–914. [Google Scholar] [CrossRef]
  22. Kusaka, H.; Suzuki-Parker, A.; Aoyagi, T.; Adachi, S.A.; Yamagata, Y. Assessment of RCM and urban scenarios uncertainties in the climate projections for August in the 2050s in Tokyo. Clim. Chang. 2016, 137, 427–438. [Google Scholar] [CrossRef]
  23. Wang, Z.; Li, X.; Mao, Y.; Li, L.; Wang, X.; Lin, Q. Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China. Ecol. Indic. 2022, 134, 108499. [Google Scholar] [CrossRef]
  24. Mu, B.; Mayer, A.L.; He, R.; Tian, G. Land use dynamics and policy implications in Central China: A case study of Zhengzhou. Cities 2016, 58, 39–49. [Google Scholar] [CrossRef]
  25. Alcamo, J.; Ribeiro, T. Scenarios as Tools for International Environmental Assessment; European Environment Agency: Copenhagen, Denmark, 2001; Volume 5. [Google Scholar]
  26. van Vuuren, D.P.; Riahi, K.; Moss, R.; Edmonds, J.; Thomson, A.; Nakicenovic, N.; Kram, T.; Berkhout, F.; Swart, R.; Janetos, A.; et al. A proposal for a new scenario framework to support research and assessment in different climate research communities. Glob. Environ. Chang. 2012, 22, 21–35. [Google Scholar] [CrossRef]
  27. van Vuuren, D.P.; Kriegler, E.; O’neill, B.C.; Ebi, K.L.; Riahi, K.; Carter, T.R.; Edmonds, J.; Hallegatte, S.; Kram, T.; Mathur, R.; et al. A new scenario framework for Climate Change Research: Scenario matrix architecture. Clim. Chang. 2013, 122, 373–386. [Google Scholar] [CrossRef]
  28. Yun, X.; Tang, Q.; Li, J.; Lu, H.; Zhang, L.; Chen, D. Can reservoir regulation mitigate future climate change induced hydrological extremes in the Lancang-Mekong River Basin? Sci. Total Environ. 2021, 785, 147322. [Google Scholar] [CrossRef]
  29. Skamarock, W.C.; Klemp, J.B.; Dudhia, J.; Gill, D.O.; Barker, D.M.; Duda, M.G.; Huang, X.Y. A Description of the WRF-ARW V3; National Center for Atmospheric Research: Boulder, CO, USA, 2008; pp. 1–4. [Google Scholar]
  30. Hernández-Ceballos, M.; Adame, J.; Bolívar, J.; De la Morena, B. A mesoscale simulation of coastal circulation in the Guadalquivir valley (southwestern Iberian Peninsula) using the WRF-ARW model. Atmos. Res. 2013, 124, 1–20. [Google Scholar] [CrossRef]
  31. Chen, F.; Dudhia, J. Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system.Part I: Model implementation and sensitivity. Mon. Weather. Rev. 2001, 129, 569–585. [Google Scholar] [CrossRef]
  32. Janjić, Z.I. The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Mon. Weather Rev. 1994, 122, 927–945. [Google Scholar] [CrossRef]
  33. Janjić, Z.I. Comments on “Development and Evaluation of a Convection Scheme for Use in Climate Models”. J. Atmos. Sci. 2000, 57, 3686. [Google Scholar] [CrossRef]
  34. Lin, L.J. Bulk Parameterization of the Snow Field in a Cloud Model. J. Appl. Meteorol. Clim. 1983, 22, 1065–1092. [Google Scholar] [CrossRef]
  35. Chou, M.D.; Suare, M.J. An Efficient Thermal Infrared Radiation Parameterization for Use in General Circulation Models; Citeseer: Princeton, NJ, USA, 1994. [Google Scholar]
  36. Chou, M.D.; Suare, M.J. A Solar Radiation Parameterization (CLIRAD-SW) for Atmospheric Studies; NASA Tech.: Washington, DC, USA, 1999.
  37. Hu, Y.; Zhang, X.-Z.; Mao, R.; Gong, D.-Y.; Liu, H.-B.; Yang, J. Modeled responses of summer climate to realistic land use/cover changes from the 1980s to the 2000s over eastern China. J. Geophys. Res. Atmos. 2015, 120, 167–179. [Google Scholar] [CrossRef]
  38. Politi, N.; Vlachogiannis, D.; Sfetsos, A.; Nastos, P.T. High-resolution dynamical downscaling of ERA-Interim temperature and precipitation using WRF model for Greece. Clim. Dyn. 2021, 57, 799–825. [Google Scholar] [CrossRef]
  39. Ru, X.; Song, H.; Xia, H.; Zhai, S.; Wang, Y.; Min, R.; Zhang, H.; Qiao, L. Effects of Land Use and Land Cover Change on Temperature in Summer over the Yellow River Basin, China. Remote Sens. 2022, 14, 4352. [Google Scholar] [CrossRef]
  40. Chen, G.; Li, X.; Liu, X. Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Sci. Data 2022, 9, 1–18. [Google Scholar] [CrossRef] [PubMed]
  41. Xu, Z.; Han, Y.; Tam, C.-Y.; Yang, Z.-L.; Fu, C. Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100). Sci. Data 2021, 8, 1–11. [Google Scholar] [CrossRef]
  42. Argüeso, D.; Evans, J.; Fita, L.; Bormann, K.J. Temperature response to future urbanization and climate change. Clim. Dyn. 2014, 42, 2183–2199. [Google Scholar] [CrossRef]
  43. Doan, Q.-V.; Kusaka, H.; Ho, Q.-B. Impact of future urbanization on temperature and thermal comfort index in a developing tropical city: Ho Chi Minh City. Urban Clim. 2016, 17, 20–31. [Google Scholar] [CrossRef]
  44. Lee, H.S.; Trihamdani, A.R.; Kubota, T.; Iizuka, S.; Phuong, T.T.T. Impacts of land use changes from the Hanoi Master Plan 2030 on urban heat islands: Part 2. Influence of global warming. Sustain. Cities Soc. 2016, 31, 95–108. [Google Scholar] [CrossRef]
  45. Krayenhoff, E.S.; Moustaoui, M.; Broadbent, A.M.; Gupta, V.; Georgescu, M. Diurnal interaction between urban expansion, climate change and adaptation in US cities. Nat. Clim. Chang. 2018, 8, 1097–1103. [Google Scholar] [CrossRef]
  46. Rohat, G.; Wilhelmi, O.; Flacke, J.; Monaghan, A.; Gao, J.; Dao, H.; van Maarseveen, M. Characterizing the role of socioeconomic pathways in shaping future urban heat-related challenges. Sci. Total Environ. 2019, 695, 133941. [Google Scholar] [CrossRef]
Remotesensing 15 02601 g001 550
Figure 1. Land use and land cover map of Zhengzhou City in 2020 (Source: Ministry of Natural Resources of the People’s Republic of China (30 m spatial resolution): http://www.globallandcover.com/, accessed on 5 May 2022).
Figure 1. Land use and land cover map of Zhengzhou City in 2020 (Source: Ministry of Natural Resources of the People’s Republic of China (30 m spatial resolution): http://www.globallandcover.com/, accessed on 5 May 2022).
Remotesensing 15 02601 g001
Remotesensing 15 02601 g002 550
Figure 2. Research framework.
Figure 2. Research framework.
Remotesensing 15 02601 g002
Remotesensing 15 02601 g003 550
Figure 3. Distribution of land use/cover area in Zhengzhou in 2030 (a) and 2060 (b) in the SSP2-4.5 scenario.
Figure 3. Distribution of land use/cover area in Zhengzhou in 2030 (a) and 2060 (b) in the SSP2-4.5 scenario.
Remotesensing 15 02601 g003
Remotesensing 15 02601 g004 550
Figure 4. Distribution of land use/cover area in Zhengzhou in 2030 (c) and 2060 (d) in the SSP5-8.5 scenario.
Figure 4. Distribution of land use/cover area in Zhengzhou in 2030 (c) and 2060 (d) in the SSP5-8.5 scenario.
Remotesensing 15 02601 g004
Remotesensing 15 02601 g005 550
Figure 5. Average T2 differences between two scenarios in 2030 (SSP5-8.5–SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: °C).
Figure 5. Average T2 differences between two scenarios in 2030 (SSP5-8.5–SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: °C).
Remotesensing 15 02601 g005
Remotesensing 15 02601 g006 550
Figure 6. Average T2 differences between two scenarios in 2060 (SSP5-8.5–SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: °C).
Figure 6. Average T2 differences between two scenarios in 2060 (SSP5-8.5–SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: °C).
Remotesensing 15 02601 g006
Remotesensing 15 02601 g007 550
Figure 7. Monthly mean, monthly maximum, and monthly minimum temperatures of T2 (the temperature at 2 m height above ground) for January, April, July, and October in the two scenarios of SSP2-4.5 and SSP5-8.5 in Zhengzhou (a) 2030; (b) 2060.
Figure 7. Monthly mean, monthly maximum, and monthly minimum temperatures of T2 (the temperature at 2 m height above ground) for January, April, July, and October in the two scenarios of SSP2-4.5 and SSP5-8.5 in Zhengzhou (a) 2030; (b) 2060.
Remotesensing 15 02601 g007
Remotesensing 15 02601 g008 550
Figure 8. Average precipitation differences between the two scenarios in 2030 (SSP5-8.5 − SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: mm).
Figure 8. Average precipitation differences between the two scenarios in 2030 (SSP5-8.5 − SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: mm).
Remotesensing 15 02601 g008
Remotesensing 15 02601 g009 550
Figure 9. Average precipitation differences between the two scenarios in 2060 (SSP5-8.5 − SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: mm).
Figure 9. Average precipitation differences between the two scenarios in 2060 (SSP5-8.5 − SSP2-4.5): (a) January; (b) April; (c) July; (d) October. (Unit: mm).
Remotesensing 15 02601 g009
Table
Table 1. Model configuration and physics schemes.
Table 1. Model configuration and physics schemes.
Physics ProcessPhysics SchemesResolutionNumber of Grid Points
Microphysics schemeLin et al. [34]25 km (domain d01),
5 km (domain d02),
1 km (domain d03)		61 × 51,
151 × 141,
201 × 136
Land-surface layer schemeUnified Noah land-surface model [31]
Radiation scheme (long wave)Goddard longwave [36]
Radiation scheme (short wave)Goddard short wave [35]
Boundary layer physicsMellor-Yamada-Janjic (MYJ) [32]
surface urban physicsSingle-layer, UCM [33]
Table
Table 2. MB, NMB, NME, RMSE, and R performance statistics for January, April, July, and October in 2020.
Table 2. MB, NMB, NME, RMSE, and R performance statistics for January, April, July, and October in 2020.
T2MB (°C)NMB (%)NME (%)RMSE (°C)R
January1.180.540.842.370.77
April0.290.020.112.170.93
July1.160.060.082.450.9
October1.710.110.132.510.88
PrecipitationMB (mm)NMB (%)NME (%)RMSE (mm)R
January−1.09−0.80.884.590.72
April−1.65−0.90.96.820.54
July−4−0.940.948.440.49
October−1.23−0.760.763.920.9
Table
Table 3. Area of land use and cover in Zhengzhou in 2030 and 2060 in both SSP2-4.5 and SSP5-8.5 scenarios (km2).
Table 3. Area of land use and cover in Zhengzhou in 2030 and 2060 in both SSP2-4.5 and SSP5-8.5 scenarios (km2).
ScenarioYearDeciduous Broadleaf ForestMixed ForestSavannasGrasslandsCroplandsUrban and Built-UpCropland/Natural Vegetation MosaicWater Bodies
SSP2-4.5203076.377.1554.64242.766288.98896.101.4111.25
206060.295.4228.8047.336497.57915.0110.6311.25
SSP5-8.5203035.730.7139.3223.946530.16888.2646.6711.25
206034.68027.120.716571.23896.7236.6311.25
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bai, T.; Fan, L.; Song, G.; Song, H.; Ru, X.; Wang, Y.; Zhang, H.; Min, R.; Wang, W. Effects of Land Use/Cover and Meteorological Changes on Regional Climate under Different SSP-RCP Scenarios: A Case Study in Zhengzhou, China. Remote Sens. 2023, 15, 2601. https://doi.org/10.3390/rs15102601

AMA Style

Bai T, Fan L, Song G, Song H, Ru X, Wang Y, Zhang H, Min R, Wang W. Effects of Land Use/Cover and Meteorological Changes on Regional Climate under Different SSP-RCP Scenarios: A Case Study in Zhengzhou, China. Remote Sensing. 2023; 15(10):2601. https://doi.org/10.3390/rs15102601

Chicago/Turabian Style

Bai, Tianqi, Like Fan, Genxin Song, Hongquan Song, Xutong Ru, Yaobin Wang, Haopeng Zhang, Ruiqi Min, and Weijiao Wang. 2023. "Effects of Land Use/Cover and Meteorological Changes on Regional Climate under Different SSP-RCP Scenarios: A Case Study in Zhengzhou, China" Remote Sensing 15, no. 10: 2601. https://doi.org/10.3390/rs15102601

APA Style

Bai, T., Fan, L., Song, G., Song, H., Ru, X., Wang, Y., Zhang, H., Min, R., & Wang, W. (2023). Effects of Land Use/Cover and Meteorological Changes on Regional Climate under Different SSP-RCP Scenarios: A Case Study in Zhengzhou, China. Remote Sensing, 15(10), 2601. https://doi.org/10.3390/rs15102601

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop