Quantitative assessment of urban rain island effect and driving factors in Zhengzhou city

Currently, the whole world is in a period of rapid urbanization. According to the United Nations Population Division's projections, the urbanization rate in every developing country will exceed 50% by 2030, and two-thirds of people will live in urban areas by 2050 (Collier 2006). This is one of the inevitable important stages of human social development. To adapt to the growing population and the advancement of urbanization processes, the urban area is constantly expanding, and the underlying surface is dramatically evolving, leading to some differences in the land composition between urban and suburban areas (Harman et al. 2004; Carton et al. 2024). Meanwhile, with the frequent human activities, the rainfall conditions in urban areas are also changing. The phenomenon where rainfall is more in urban areas and downwind areas than suburbs is called the ‘urban rain island effect’ (Zhao et al. 2021). The URI tends to lead to the frequent occurrence of extreme events such as urban storm and waterlogging, causing damage to urban infrastructure and increasing human risks (Su et al. 2017). Therefore, emphasizing and strengthening research on the URI is an inevitable requirement under the global trend of urbanization.

The prior research has been conducted to explore the impact of urbanization on rainfall in urban and suburban areas. As early as the 20th century, some scholars conducted extensive studies on urban precipitation, finding that urban areas receive more rainfall than nearby suburbs, thus preliminarily confirming the existence of the URI (Changnon 1961; Goldreich & Manes 1979; Jauregui & Romales 1996). Changnon et al. made observations of rainfall in St. Louis for 5 consecutive years, further confirming that the increase in the extent and intensity of rainfall is related to the scale of the urban area (Changnon 1979). Liu and Niyogi's research has further confirmed that urbanization modifies rainfall, and rainfall increases downwind of both the city and over the city (Liu & Niyogi 2019). The study of the URI essentially involves an investigation into the rainfall patterns in urban areas, which can be approached from the perspectives of trend, periodicity, and driving factors (Liu et al. 2022).

Urbanization can alter the spatiotemporal patterns of rainfall by influencing the atmospheric and surface energy in urban areas (Oke 1982). Shimadera et al. found that the amount, intensity, and duration of rainfall increased in the urban area of Osaka and decreased in the surrounding suburbs with the urbanization, especially in summer (Shimadera et al. 2015). Luo et al. analyzed the spatial distribution of urban rainfall at a large scale, identifying the URI predominantly in the southeastern coastal and northwestern inland regions of China (Luo et al. 2022). By applying innovative trend analysis and the family of Mann–Kendall tests, Shahfahad et al. found that the rainfall in Delhi and Mumbai showed an increasing trend, but the growth rate is low and not monotonous (Shahfahad et al. 2022, 2023). These studies confirm a certain trend in the spatiotemporal distribution of the URI. However, they were conducted on the entire rainfall series without pinpointing any mutation points, thus failing to determine the specific onset of the urban rain island effect (URI) or compare rainfall trends before and after urbanization. Therefore, it is necessary to conduct mutation tests on the time series. Nowadays, the commonly used methods for mutation testing include statistical methods like the Standard Normal Homogeneity Test (SNHT) test and the Bayesian Online Change Point Detection (BOCD), as well as nonparametric rank-based tests like the M-K test and the Pettitt mutation test (Yacoub & Tayfur 2018). The Pettitt mutation test is widely used when only one mutation point in time series is detected and the point is in the center of the series, which makes it suitable for this study (Mallakpour & Villarini 2016).

Carton et al. analyzed the interannual and intraannual variability of rainfall in different Canadian cities and observed that the periodicity of rainfall has increased in the last few years (Carton et al. 2024). It is evident that the periodicity of rainfall in urban areas is also a crucial part in assessing the URI. Due to the ability of wavelet analysis to determine the periodicity and the phase of time series at different timescales, it has become a significant method in studying rainfall periodicity (Schulte et al. 2014). Alifujiang et al. used Morlet wavelet transform to investigate the primary periods and multi-timescale correlations of annual precipitation and runoff concentration index in the northern subbasin of Lake Issyk (Alifujiang et al. 2024). Zerouali et al. combined wavelet analysis with Bayesian estimation and Theil–Sen estimator to detect mutation points and trends in the time series of annual rainfall in northern Algeria (Zerouali et al. 2020).

Anthropogenic changes may lead to climate changes, which then exacerbate extreme rainfall, posing risks to humans, the environment, and urban systems (Singh et al. 2020). Therefore, identifying the driving factors of the URI is crucial for addressing the risks associated with this phenomenon. Sui et al. showed that cities with large population, obvious heat island effect, and high aerosol content were more prone to the URI under warm and humid climates (Sui et al. 2024). Sahoo et al. explored the impact of urbanization on heavy rainfall from the perspectives of land use changes and thermodynamics (Sahoo et al. 2020). Lu et al. found that the rainfall pattern is spatially clustered under high wind speed, while the maximum rainfall occurs at the city center under low background winds (Lu et al. 2024). Kamsali et al. suggested that atmospheric pollution can influence climate and alter rainfall patterns in cities (Kamsali et al. 2011). The aforementioned studies demonstrate that the URI is primarily influenced by climate factors and human activities. However, these studies have focused on the impact of a specific type of factors on the URI without comparing multiple driving factors to find the dominant factor. Therefore, this study utilized GRA to quantitatively analyze its driving factors. This method was widely applied in meteorology and hydrology. For instance, Ban et al. applied GRA to analyze the impact of urbanization factors such as vehicle emissions on air quality (Ban et al. 2023). Rehman et al. analyzed the underlying connection between urbanization and carbon emissions based on a GRA model in the most populated Asian countries from 2001 to 2014 (Rehman & Rehman 2022). Sun et al. combined GRA with an improved sequential Inverse Covariance Intersection (ICI) method to construct a gray spatiotemporal incidence model to analyze the spatiotemporal characteristics of multifactorial air pollution (Sun et al. 2024).

Current research on the URI has been conducted mainly within urban areas, focusing on specific research directions and a single method, and particularly lacks a typical study of the periodicity. These studies are insufficient to study the URI in a multifaceted way and to analyze the influence of multiple driving factors on the causes of URI. In fact, the formation of URI involves multiple factors, including urban buildings, climate, and pollutants. Therefore, more comprehensive studies are needed to quantitatively assess it.

The URI effect has led to an increasing number of urban flooding events, making it an important research topic (Hu et al. 2020). A single method can only study a particular aspect of the URI, which has some limitations. Therefore, this study integrates the Pettitt mutation test-Wavelet analysis-Gray relational analysis (PWG) method framework based on three methods, namely, Pettitt mutation test, wavelet analysis, and gray relational analysis, and takes Zhengzhou as an example to comprehensively and efficiently assess the trend, periodicity, and driving factors of the URI to provide a theoretical basis for mitigating a series of water problems that may result from the URI.

The wavelet variance plot reflects the distribution of the energy of the fluctuations with respect to the scale, and from the plot, it is possible to determine the main timescale in a time series, named the main period.

Chinese scientist Professor Deng Julong first proposed the gray system theory in 1990, which states that when known and unknown parameters coexist in a system, the system is a gray system (Julong 1990). Gray relation analysis is a statistical analysis method derived from this theory, and its basic thought is to describe the correlation between factors in terms of correlation degree based on the data series of the factors (Kaiquan 1990).

As shown in Figure 6(a), only seven of the years in the base period had rainfall enhancement coefficients greater than 1, and the years with URI account for 35% of the total years in the base period. The yearly growth rate of θ_year series is 0.0003, which increases with an extremely slow trend. During the development period, the urbanization process accelerated, 75% of the years have the URI phenomenon, and the yearly growth rate of θ_year series increases to 0.0059. From Figure 6(b), it can be seen that the number of years with URI phenomenon in the flood season before and after urbanization has increased from 7 to 18 years, and the yearly growth rate of θ_flood series has increased from 0.0006 in the base period to 0.0083 in the development period. The growth rate of rainfall enhancement coefficients in the development period is significantly larger than that in the base period, and the growth rate of θ_flood series is larger than that of θ_year series in both periods. Therefore, Zhengzhou has the obvious URI phenomenon, showing a tendency to increase with urbanization, and the URI is more serious during the flood season, in both the base period and the development period.

Gray relational analysis is carried out on the driving factors of the URI in the development period of Zhengzhou, and the correlation degree between each driving factor and θ_year is shown in Table 1. Among them, the atmospheric self-purification index is used to measure the ability of the atmosphere to remove atmospheric pollutants in the process of ventilation and dilution and wet scavenging; the larger the index, the stronger the ability of the atmosphere to remove pollutants; conversely, the weaker the ability of the atmosphere to remove pollutants (Liu et al. 2021).

The URI is of periodicity, the essence of which is that rainfall is of periodicity, which is manifested in intraannual and interannual periodicity. The intraannual periodicity is mainly reflected in that the URI in flood season is more serious than that in the whole year. The flood season in Zhengzhou generally occurs from June to August, which is during the summer months. During this time, heat emissions from human activities such as air-conditioning use, vehicle emissions and factory waste emissions increase. Anthropogenic heat sources in urban areas increase the impact of surface heat radiation, making the difference in heat between urban area and surrounding suburbs greater, which in turn makes it easier to create rainfall conditions (Li et al. 2024). Li et al. concluded that heavy rainfall in the urban agglomeration of East China is more frequent, durable, and intense from June to August each year, and urbanization leads to it to a certain extent, which is consistent with the results of this study (Li et al. 2023). The intraannual periodicity of the URI often results in the occurrence of extreme rainfall events, and the ‘7–20’ rainstorm in Zhengzhou in 2021 is a typical case. Due to the influence of typhoons in the western Pacific Ocean and the URI, short-duration heavy rainfall occurred in a number of centralized urban areas in Zhengzhou, such as Jinshui District and Erqi District. The inability of the internal runoff to be discharged quickly and the increase in the peak volume led to the generation of urban flooding, which brought serious losses to people's life and the property of Zhengzhou.

The interannual periodicity of the URI mainly comes from the periodicity of urban development and construction. Wang et al. found that impervious surfaces increase the surface sensible heat, while vegetation cover reduces the impervious area and has a strong ability to dissipate heat, which mitigates the heat island effect (UHI), and consequently the URI (Wang et al. 2019). In recent years, Zhengzhou has gradually emphasized the construction of greenspace, vegetation coverage has increased, and the subsurface conditions have been improved, which has suppressed the URI to a certain extent. In addition, buildings in urban areas are often regularly increased or decreased and remodeled, which also affects the URI by changing the wind-heat environment of the city. It has even been demonstrated that shading of high-rise buildings can reduce surface temperatures and create an urban cool island effect to mitigate the UHI, thereby promoting atmospheric stabilization and reducing precipitation (Kim et al. 2021). Thus, the complex ongoing process of urban construction is one of the key reasons for the periodicity of the URI.

Urbanization is manifested in the overbuilding of infrastructure such as concrete pavements and houses in cities, which has changed land use within cities and substantially increased impervious areas (Li et al. 2018). Changes in the underlying surface increase surface roughness. Rough hard surfaces and high-rise buildings release absorbed heat into the surrounding environment by means of heat radiation and heat convection, leading to higher urban temperatures and the UHI (Collier 2006). Meanwhile, human activities such as industry and commerce cause an increase in airborne pollutants, and particulate matter entering the atmosphere forms aerosols (Shastri et al. 2019). The atmosphere's self-purification ability is weakened, leading to the production of large numbers of condensation nucleus, which, when surrounded by sufficient water vapor, condense with water vapor to form water droplets (Doan et al. 2021). As a result, the combination of three types of factors – urbanization, climate, and environment – increases rainfall within the city and ultimately produces the URI.

In addition, Kaufmann et al. concluded that urbanization in the Pearl River Delta of China reduces local precipitation, and that this reduction may be caused by surface hydrological changes beyond the UHI and aerosol emissions (Kaufmann et al. 2007). Paul et al. suggest that rainfall is more intense in urban areas, but is only prominent in certain urban areas, implying that urbanization may also inhibit the production of the URI and that there is spatial heterogeneity in the URI (Paul et al. 2018). Oh et al. found that extreme rainfall intensity at relatively cold temperature is lower, and urban rainfall is weaker than rural rainfall (Oh et al. 2021). The aforementioned research shows that not every city will have the URI, and some may even have the opposite effect.

This study analyzes the trend and periodicity of the URI. The results are of great value as they can provide forward-looking guidance for the government in urban planning and management. During the high-incidence period of the URI, the government can plan the layout and renovation of the city in advance and make emergency preparations. Meanwhile, relevant departments can also formulate targeted policies based on the driving factors of the URI to effectively alleviate the adverse impacts of it. The study provides scientific guidance for the formulation of policies about urban construction, and it is conducive to creating a safer, more comfortable, and sustainable urban environment.

However, this study only uses the PWG method system to analyze Zhengzhou, a single research area, which has certain limitations. Spatial and temporal multiscale characteristics of the URI, the universality of this method framework, and the direct influence of urban topography and morphology on the URI are not fully considered. In view of this, future research should further improve the assessment system of the URI and deeply study the specific mechanisms of each driving factor on it, to more comprehensively reveal the essential characteristics of the URI and provide more scientific and effective theoretical support and practical guidance for urban construction.

Taking Zhengzhou as an example, this study proposes the PWG method framework based on the Pettitt mutation test, wavelet analysis, and gray relational analysis to provide a quantitative assessment of the trend, periodicity, and driving factors of the URI based on humanistic and meteorological data, with the following main findings.

The URI in Zhengzhou is closely related to the urbanization process. The growth rate of θ_year changes from 0.0003 before 2001 to 0.0059 after 2001, and the growth rate of θ_flood changes from 0.0006 to 0.0083. The growth rate of θ_flood is always larger than that of θ_year, which indicates that the URI is more obvious during the flood season. This is because the urbanization rate in Zhengzhou showed a linear increase during the 40 years from 1981 to 2020, significantly after 2001, which is consistent with the mutation time of the URI.

The URI is of periodicity. From 1981 to 2020, the main periods of θ_year and θ_flood are 10a and 16a, respectively, with the minor periods of 17a and 4a, respectively. The wavelet variance of θ_flood is generally larger than that of θ_year, further suggesting that the URI is more pronounced during the flood season.

The URI is influenced by urbanization, climate, and environment. The correlation degree of urbanization rate, temperature, wind speed, relative humidity, and sunshine duration with URI reaches more than 0.9. They are the most important driving factors of the URI in Zhengzhou. Among them, the urbanization rate has the highest correlation degree of 0.921, which fully indicates that the URI is closely related to urbanization. Temperature and atmospheric self-purification index were the most influential indicators of climatic and environmental factors on the URI, respectively, with correlation degrees of 0.920 and 0.848, respectively. Overall, climate change due to urbanization had the greatest impact on the URI.

Yunqiu Jiang: Conceptualization, methodology, software, and reviewing and editing. Caihong Hu: Conceptualization, methodology, software, funding, and reviewing and editing. Chengshuai Liu: Methodology and software. Wenzhong Li: Methodology and reviewing and editing. Tianning Xie: Methodology, software, and editing; Runxi Li: Software and reviewing and editing.

This work was funded by National Key Research Priorities Program of China (grant number 2023YFC3209303) and National Natural Science Foundation of China (grant number U2243219,51979250).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.