Analysis of Local Water Humidity Effect Characteristics Based on Meteorological Data: A Case Study of Nanjing

Abstract

In order to explore the variation law and causes of the humidity effect of local water bodies, this paper selects the data of encrypted automatic weather stations (encrypted stations) and national conventional meteorological stations (conventional stations) in Nanjing from 2014 to 2020, and systematically studies the humidity effects and influencing factors of urban water bodies by constructing the humidity effect intensity (E) based on the conventional stations. The results show that the humidity effect of urban water has significant diurnal and monthly variation characteristics, and is extremely sensitive to temperature change, and compared to nighttime, the daytime period is generally more humid. The humidity effect is mostly normal in winter, while the humidification and humidity reduction effects in summer are particularly significant. There are also significant differences in the humidity effect between different typical water stations, which are mainly influenced by the background environment of urban and suburban areas, macro wind field, and local wind field configuration around the water body due to the dense building density in the main urban area, which is characterized by dry humidification, while the suburbs are characterized by humidity. When the water body is located on the side of a large water body (river or lake), the influence of local water–land wind field and macro wind field on the humidity effect is particularly significant, and the water wind will significantly enhance the humidity effect, while the land breeze will weaken the humidity effect. The research results can provide a reference for the urban planning and the design of the surrounding environment of water bodies in Nanjing.

1. Introduction

With the accelerating pace of urbanization, urban ecosystems are facing increasingly severe challenges, among which the deterioration of the urban climate is particularly significant [1]. The intensification of the urban heat island effect [2], air drying [3], and the continuous decline of air quality [4,5] have seriously affected the quality of life of residents and the sustainable development of cities [6,7]. In this context, the humidity effect of urban water bodies, as an indispensable component of urban systems, is becoming increasingly important. This effect has several positive aspects, including helping to regulate the urban microclimate [8], improving air quality [9], and profoundly impacting the health of residents [10]. However, it also carries certain risks, such as the possibility of inhibiting the dispersion of air pollutants in high-humidity environments, which can further deteriorate air quality [11].

In recent years, numerous researchers have conducted in-depth studies on the humidity effect of urban water bodies [8,9]. Based on computational fluid dynamics (CFD) simulations, Zhang Wei et al. [12] investigated the humidification effect of lakes in Hunan Martyrs’ Park on a localized area of the city and found that the lakes were able to significantly enhance the air humidity in the surrounding area. Similarly, by studying lakes and reservoirs in the city, it was found that these water bodies had a significant humidifying effect on the surrounding area during summer days [13,14,15]. In addition, the land–lake breeze, as an important climate regulation phenomenon of water bodies, has received extensive attention. Yang Wei et al. [16] analyzed the characteristics of the land–lake breeze in East Dongting Lake and found that it has a significant influence on regional humidity and precipitation distribution. Cao et al. [17] studied the characteristics and causes of the land–lake breeze in the Poyang Lake area, revealing the mechanism by which it regulates regional humidity and climate. These studies have shown that urban water bodies have significant potential in regulating local humidity and can create more comfortable microclimate environments within cities [18,19].

However, the effects of urban water bodies on urban systems are not unidirectional [20,21,22,23]. Various factors, such as urban building density [20] and layout [21], can influence the humidity effects of water bodies. For example, Song Xiaocheng’s [20] study on the effects on local thermal and humid climate of urban water bodies found that the presence of high-rise buildings blocks the transmission of water vapor and cuts off the connection between the water bodies and their surroundings, thus inhibiting the humidification effect. As another example, Liang Sheng et al. [21] studied the influence of building factors on the humidity effect of urban lakes based on CFD simulation and found that an effective building layout can enhance the humidity regulation function of lakes, while dense building clusters may weaken the humidification effect of lakes. In addition, the study by Dai Xi et al. [22] also found that the interaction between buildings and lakes directly affects the lakes’ ability to regulate the temperature and humidity in the surrounding area. These studies indicate that the humidity effect of urban water bodies is influenced by a combination of factors [23] and requires systematic research from various aspects.

Although existing studies have revealed the positive effects of urban water bodies on regulating humidity, there are still some urgent problems to be solved. First, most studies have focused on a single lake or a specific region, lacking a comprehensive comparative analysis of different types of urban water bodies (e.g., rivers, lakes, and reservoirs) [12,15,16,17,22,24]. In addition, uniform standards and methods for the quantitative assessment of humidity effects of urban water bodies are still lacking, making it difficult to directly compare and apply the results among different studies [13,21,25].

In this context, this study takes Nanjing as an example, proposes a unified index for assessing the humidity effect using the observation data of meteorological stations, and comparatively analyzes the humidity effect and its causes of different types of urban water bodies. This study aims to provide a scientific basis for urban planning and ecological design and to promote a greater role of urban water bodies in improving urban microclimates and enhancing the quality of life of residents.

2. Data and Methods

2.1. Introduction of the Study Area

Nanjing is located in the central part of the middle and lower reaches of the Yangtze River, in the southwest of Jiangsu Province. Nanjing lives across the Yangtze River, with a humid northern subtropical climate [26] possessing a rich and diverse geomorphology, with a variety of geomorphic units coexisting, such as the Yangtze River floodplain, water-rich soft soils, granite, and karst.

The city’s lakes and reservoirs are scattered, rivers are woven together, and the hydrology is complex. The water area takes up more than 11% of the city’s total area. There are the Qinhuai River, Xuanwu Lake, Gucheng Lake, Mochou Lake, hundred lakes, Shijiu Lake, Jinniu Lake, and other large and small rivers and lakes. Yangtze River crosses the territory of 120 large and small rivers. The river and lake water system mainly belongs to the Yangtze River system. The Yangtze River system includes the Qinhuai River system in the south of the river, the Chu River system in the north of the river, the riverine system formed by small rivers flowing into the river alone along both sides of the river, the two lakes system consisting of Shijiu Lake and Gucheng Lake, and the West Taihu Lake system in the east of Gaochun.

2.2. Data Used

2.2.1. Meteorological Data

The meteorological data used in this paper come from the official website of the China Meteorological Administration (https://www.cma.gov.cn/, data finally acquired on 1 July 2024).

As a national authoritative meteorological organization, China Meteorological Administration (CMA) strictly follows international standards and norms in its data collection and processing processes, with high data quality and reliability, making it an important data source in the field of meteorological research. The data involved in this study cover key meteorological elements such as hourly water vapor pressure, air temperature, wind direction, wind speed, and cloudiness at five conventional meteorological stations (conventional stations) and 104 encrypted automatic meteorological stations (encrypted stations) in Nanjing, spanning the period from 2014 to 2020.

The specific station location distribution is shown in Figure 1. The wide distribution and high-density layout of these stations further ensures the spatial representativeness of the data, which provides strong support for in-depth analysis of the meteorological characteristics of Nanjing.

2.2.2. China Land Cover Dataset

The remote sensing data used in this paper include land use data and satellite images. The land use data are China Land Cover Dataset (CLCD), and the data source is the high-quality data product developed by Prof. Xin Huang’s team at Wuhan University based on the Google Earth Engine platform (https://www.cnblogs.com/icydengyw/p/17184448.html, the data were finally acquired on 1 July 2024) [27]. The dataset utilizes 335,709 Landsat satellite images, and significantly improves the spatio-temporal consistency of the data by constructing spatio-temporal features and combining them with the Random Forest classifier for classification, and at the same time adopting the post-processing methods of spatio-temporal filtering and logical inference. Finally, based on the validation of 5463 visually interpreted samples, the overall accuracy of the CLCD data reaches 80%, which is outstanding among similar land cover data products, especially in terms of temporal resolution, providing continuous information on China’s land cover year by year from 1990 to 2020, which is extremely valuable for the study of long time series of land use changes.

In this study, based on the high-precision features of the CLCD dataset with a spatial resolution of 30 m, a detailed analysis of the land cover of Nanjing for the years 2014–2020 was conducted. Taking the stations as the center and 500 m as the radius [28], the local humidity effect under this range was discussed, and the sites with a water body area proportion of more than 50% and complete data without obvious missing measurements were screened out and defined as the typical sites of water body types, and the change in humidity data of these sites was used to represent the change in the humidity effect of the local environment in which they are located. This selection process makes full use of the advantages of high resolution and high accuracy of CLCD data to ensure the scientificity and reliability of site selection.

2.2.3. Satellite Images

Satellite images are from the Landsat8 OLI satellite sensor. Landsat-8 maintains the basic consistency in terms of spatial resolution and spectral characteristics as Landsat 1–7. The satellite has a total of 11 bands, bands 1–7, 9–11 spatial have a resolution of 30 m, and band 8 has the 15 m resolution of panchromatic bands. The satellite achieves global coverage every 16 days. The spatial resolution of the data used in this paper is 30 m, which is used to observe the distribution of the actual environment of the station, and the specific spatial distribution of the station and the corresponding satellite image are shown in Figure 2.

As can be seen from the distribution of typical water body stations in Figure 2 and the local satellite images of each station, stations M9236, M6707, and M9014 are located at the boundary between water bodies and land. The distribution of water and land around these stations shows a clear confrontation, with one side of the station being land and the other side being water. The water body surrounding Station M9236 is the Yangtze River, which flows from the southwest to the northeast relative to the station and is generally located on the northwest side of the station. Stations M6707 and M9014 are situated near Shijiu Lake in the southwest corner of Nanjing. However, their positions relative to the lake are different: M6707 is on the north side of the lake, while M9014 is on the south side. Station M3553 is located in the center of Xuanwu Lake in the urban area of Nanjing, surrounded by water on all sides. The area around Station M6708 is characterized by extensive farmland with a dense network of rivers crisscrossing the region. Station M6711 is located at Jinniu Lake in the suburban area of Nanjing. Jinniu Lake has an “L”-shaped distribution, and M6711 is situated at the corner of the lake, with large water bodies on both the southeast and northwest sides.

2.3. Methods

2.3.1. Data Preprocessing

The meteorological data from the encrypted stations and conventional stations were subjected to quality control using the Layida Criterion. The Layida Criterion [29,30,31] is a commonly used method for identifying and eliminating outliers and has been widely applied in various research fields. The specific formula is as follows:

$$\left|x-\mu \right|\ge 3\sigma$$

(1)

Here, x represents any type of meteorological data, μ is the mean of the corresponding meteorological data, and σ is the standard deviation of the corresponding meteorological data. In this context, x, μ, and σ all refer to the sample sequence of a single meteorological station. Data that satisfy this formula are considered outliers and are removed.

2.3.2. Humidity Effect Intensity Calculations

Conventional meteorological stations were set up in meteorological observation fields, which have strict specification requirements. The data they collected represent the baseline conditions of the meteorological background field and were the least affected by artificial environmental factors. The environments of the encrypted stations are complex and varied; these features reflect the meteorological characteristics of different local environments.

The conventional stations were used as the reference stations. The Thiessen polygons of the conventional stations (Figure 1) [32,33] were constructed to determine their encrypted stations. The water vapor pressure of the encrypted station was compared with that of the conventional station, so as to quantify the humidity effect on the local environment, and to define the intensity of the humidity effect, E, which was computed using the following formula:

$$E=e_{JM}-e_{JZ}$$

(2)

where E is the humidity effect intensity, $e_{JM}$ is the vapor pressure at the encrypted station, and $e_{JZ}$ is the vapor pressure at the corresponding conventional station (reference station). A positive E value indicates that the area has higher humidity compared to the reference environment, and a negative E value indicates that the area has lower humidity compared to the reference environment.

2.3.3. Random Forest

Random Forest [34,35,36] is a powerful integrated learning method, especially suitable for multi-categorization problems. It increases model diversity by constructing multiple decision trees, and each tree is trained using a randomly sampled subset of data and a subset of features. When categorizing, each tree makes independent predictions on the input samples and finally determines the categories through a majority voting mechanism. Random Forest has the advantages of high accuracy, robustness, and the ability to handle high-dimensional data, while providing a certain degree of interpretability through feature importance analysis, and is a commonly used algorithm in multi-classification tasks.

2.3.4. Confusion Matrix

Confusion Matrix [37,38,39] is an important tool for evaluating the performance of classification models, especially in model threshold adjustment and classification result analysis. It visualizes the performance of the model’s classification results under different thresholds by showing the comparison between the model’s predicted results and the real labels. The comparison results are shown in

Table 1. Confusion Matrix.

	True Positive (Positive)	True Negative (Negative)
Predicted Positive	True Positive (TP)	False Positive (FP)
Predicted Negative	False Negative (FN)	True Negative (TN)

.

Calculation of Performance Metrics:

1.

Accuracy: The proportion of samples correctly classified by the model out of the total number of samples. The higher the value, the better the classification result.

$$Accuracy={\displaystyle \frac{TP+TN}{TP+TN+FP+FN}}$$

(3)

2.

Precision: The proportion of samples actually positive among those predicted as positive. The higher the value, the better the classification result.

$$Precision={\displaystyle \frac{TP}{TP+FP}}$$

(4)

3.

Recall: The proportion of samples correctly predicted as positive among those that are actually positive. The higher the value, the better the classification result.

$$Recall={\displaystyle \frac{TP}{TP+FN}}$$

(5)

4.

F1 Score: The harmonic mean of precision and recall, used to comprehensively evaluate model performance. The higher the value, the better the classification result.

$$F1 Score=2\ast {\displaystyle \frac{Precision\ast Recall}{Precision+Recall}}$$

(6)

5.

AUC Value (Area Under the Curve): The AUC value refers to the area under the Receiver Operating Characteristic Curve (ROC Curve). It is an important metric for evaluating the performance of classification models, especially for binary classification problems. The AUC value ranges from 0 to 1, with higher values indicating better classification results.

$$TPR={\displaystyle \frac{TP}{TP+FN}}$$

(7)

$$FPR={\displaystyle \frac{TP}{FP+TN}}$$

(8)

The true rate is the proportion of positive classes correctly predicted by the model, while the false positive rate is the proportion of positive classes incorrectly predicted by the model. By varying the classification threshold, a series of TPR and FPR values can be obtained, and connecting these points forms the ROC curve. The AUC value is the area below the ROC curve. It measures the model’s ability to distinguish between positive and negative categories under all possible classification thresholds. The higher the AUC value, the better the model’s ability to distinguish between positive and negative categories under different thresholds.

3. Results

3.1. Qualitative Method Assessment of Humidity Effect

In order to thoroughly and accurately analyze the changing characteristics of the humidity effects at different typical water body sites, we classified the hourly humidity effect intensity into three categories: wet effect, normal effect, and dry effect. This categorization is based on Gu Li-Hua’s study of humidity in Nanjing [40], with ±0.5 hpa as the threshold value.

In order to verify the applicability of this categorization criterion, the Random Forest approach was used in this study [34,35,36]. The Confusion Matrix [37,38,39], which can comprehensively and intuitively reflect the performance of the classification model under different thresholds, was chosen as the method to evaluate the model classification results.

In order to comprehensively assess the model performance under different thresholds, this study carried out a nuanced comparison of different thresholds ranging from ±0.1 to ±1. Using the above methods, this study aimed to construct a more scientific and precise classification system for humidity effect intensity. This system lays a solid foundation for subsequent in-depth analysis of the environmental influence on humidity effects. The specific results are shown in

Table 2. Performance evaluation of different thresholds (according to Equations (3)–(8), the evaluation indexes of the classification results of the model under different thresholds are calculated respectively, the value range of each index is from 0 to 1, and the larger the value is, the better the classification results are under the changed evaluation indexes).

Threshold	Accuracy	Precision	Recall	F1 Score	AUC
±0.1	0.6692	0.7922	0.6692	0.6051	0.7957
±0.2	0.7508	0.8277	0.7508	0.7233	0.8461
±0.3	0.8272	0.868	0.8272	0.8173	0.8932
±0.4	0.8986	0.9142	0.8986	0.8961	0.9373
±0.5	0.9707	0.9739	0.9707	0.971	0.9823
±0.6	0.9473	0.9596	0.9273	0.9374	0.9708
±0.7	0.9149	0.9289	0.9149	0.9153	0.9243
±0.8	0.8776	0.9045	0.8776	0.8776	0.8908
±0.9	0.8338	0.8798	0.8338	0.8327	0.8476
±1	0.7934	0.8601	0.7934	0.7902	0.7265

.

The above table evaluates and scores the performance at different thresholds in terms of the accuracy, precision, recall, F1 score, and AUC value. The results show that all of the performance indicators (accuracy, precision, recall, F1 score, and AUC) reach their highest values when the threshold is ±0.5. This finding strongly suggests that ±0.5 is the optimal threshold for the model’s classification task, making it optimal for classifying the data.

Following this validation, this study qualitatively classified the calculated hourly humidity effect intensity values, E, as follows: E > 0.5 hPa is considered as “wet”, −0.5 ≤ E ≤ 0.5 hPa is considered as “normal”, and E < −0.5 hPa is considered as “dry”.

Given that existing studies on the humidity effect have revealed significant differences between daytime and nighttime hours [13,14,19,41], this study qualitatively categorized the intensity values of the hourly humidity effect at the encrypted stations for daytime and nighttime. Following the Chinese meteorological standard, each day was divided into daytime (8:00–20:00] and nighttime (20:00–8:00] time periods, each of which lasted 12 h, using Beijing time as the reference [30].

In the qualitative delineation of short-term climate effects, their duration is one of the core indicators of their intensity and impact [42]. Therefore, in this study, we defined the hourly humidity effect intensity of continuous wet effects as a humidification effect, continuous dry effects as a dehumidification effect, and continuous normal effects as a normal effect. Moreover, we conducted an exhaustive comparison and analysis of the categorization results of the hourly humidity effect intensity under different continuous change durations in each time period (

Table 3. Number of samples for each type of humidity effect at different continuous durations during daytime and nighttime hours.

Time Period	Duration (Hours)	Humidification Effect	Dehumidification Effect	Normal Effect	Atypical
Daytime	2 h	4438	10,941	23,037	197,288
	3 h	18,550	40,327	55,356	121,471
	4 h	23,100	44,996	62,356	105,252
	5 h	29,281	55,195	70,413	80,815
	6 h	29,372	56,966	69,538	79,828
Nighttime	2 h	2989	9723	21,728	201,264
	3 h	16,765	38,829	54,761	125,349
	4 h	22,526	44,450	61,628	107,100
	5 h	28,644	54,341	69,888	82,831
	6 h	28,595	56,399	68,915	81,795

) in order to select the most appropriate qualitative criteria.

As can be seen in the table above, when the duration of continuous variation in the characterization of humidity effects was set to 2 h, the number of samples that were successfully characterized as humidification, dehumidification, or normal effects during the diurnal time period was very small. This was due to the large number of samples with repeated characterizations that were ultimately categorized as atypical. However, when the duration was increased to 3 h, the number of samples in each category of humidity effects increased significantly, and the number of samples in the atypical category decreased dramatically, by approximately 75,000 for both the daytime and nighttime periods. However, when the duration was further set to 4 h or even longer, the changes in the data stabilized, and the number of samples in the atypical category did not decrease significantly.

It can be seen that the 3 h division criterion was highly sensitive to the qualitative classification of the data. Therefore, we determined that the hourly humidity effect type should last for a minimum of 3 h of consistent changes as the criterion for defining the humidity effect type for the diurnal period.

However, at the same time, it can also be seen in Table 3 that the atypical category still had the largest number of samples under each criterion. These samples did not undergo subsequent analysis, resulting in a waste of data. Therefore, we implemented a second classification criterion on the basis of the continuous 3 h classification criterion. That is, the longest cumulative duration during which the hourly humidity effect intensity was manifested as dry, wet, or normal in each time period was defined as the corresponding humidity effect, and the samples for which the humidity effect could not be defined were categorized as atypical. After synthesizing these two classification criteria, we qualitatively categorized the original samples in terms of diurnal and nocturnal time periods, and the results are shown in

Table 4. Qualitative categorization results of humidity effects in daytime and nighttime hours under the combined criteria, and the numbers in the table are the number of samples under the corresponding conditions.

Time Period	Humidification Effect	Dehumidification Effect	Normal Effect	Atypical
Day	66,129	82,285	85,624	6377
Night	81,529	67,284	79,079	12,103

.

Table 4 shows that after combining the two classification criteria, the number of samples in the atypical category was effectively reduced, while the number of samples in the humidification effect, dehumidification effect, and normal effect categories significantly increased. The raw data effectively reflect the changes in the humidity effects under the combined influence of these two classification criteria. Thus, this study ultimately determined the qualitative categorization criteria for humidity effects during daytime and nighttime hours, as shown in

Table 5. Qualitative type classification criteria of humidity effect during daytime or nighttime hours.

Qualitative Types of Humidity Effect	Classification Criterion 1	Classification Criterion 2
Dehumidification effect	Humidity effect intensity remains continuously dry for three hours or more.	Humidity effect intensity is dry with the longest cumulative duration.
Humidification effect	Humidity effect intensity remains continuously wet for three hours or more.	Humidity effect intensity is wet with the longest cumulative duration.
Normal effect	Humidity effect intensity is normal for three consecutive hours or more	Humidity effect intensity is normal with the longest cumulative duration.
Atypical		Humidity effect intensity is high, low, and normal with equal cumulative durations.

Note: Priority is given to classification based on Criterion 1. If any of the conditions in Criterion 1 are met and are unique, the classification is determined according to Criterion 1. Otherwise, the classification is determined according to Criterion 2.

.

3.2. Characteristics of Changes in Humidity Effects at Typical Water Body Sites

In order to deeply explore the dynamic change characteristics of the humidity effects of different types of water bodies within Nanjing, we adopted a multi-timescale comparative analysis method to comprehensively and meticulously examine the changes in the humidity effects at each typical site.

Figure 3 presents the hourly humidity effect intensities at six typical water body stations from 2014 to 2020 and provides the statistical average values of the hourly humidity effect intensity.

The hourly humidity effect intensity was calculated for the six typical water body stations from 2014 to 2020, and the average hourly humidity effect intensity was obtained and is shown in Figure 3. It can be seen that the hourly humidity effect intensity of the six stations generally exhibits three patterns:

• The humidity effect intensity at M9236 and M6708 is greater than 0 throughout the day, indicating a relatively humid state overall. In comparison, the humidification effect intensity at M6708 is particularly significant, and the site is generally more humid during the daytime than at night.
• Stations M3553 and M9014 have humidity effect intensities less than 0 throughout the day, indicating an overall dehumidification effect. However, the intensity of dehumidification is somewhat alleviated during the daytime.
• Stations M6707 and M6711 show significant differences in humidity effect intensity between day and night. The effect is dehumidification at night, but it shifts to a humidification effect during the day.

The average humidity effect intensity values of the six typical sites from 2014 to 2020 for both daytime and nighttime periods were calculated, and the results are shown in

Table 6. Average humidity effect intensity during daytime and nighttime at each typical water body station from 2014 to 2020.

	M3553	M6707	M6708	M6711	M9014	M9236
Day	−0.42	0.27	1.44	0.24	−0.34	0.39
Night	−0.73	−0.40	1.29	−0.18	−0.58	0.48

below.

Table 6 shows that the intensity value of the humidification effect at site M6708 was not only the high during the daytime hours but also during the nighttime hours, with little difference between day and night. The overall humidification effect was very significant. The average humidity effect intensity values of the M6707 and M6711 sites during the daytime and nighttime changed from positive values during the daytime to negative values at night, and the overall state changed from more humid during the daytime to a drier state at night. The humidity effect intensity values of the M3553 and M9014 sites during daytime and nighttime were both negative, and the humidity effect intensity values at night were all less than −0.5 hpa. The humidity effect intensity value of site M9236 was positive throughout the day, indicating a significant dehumidification state. There was no significant difference in the humidity effect intensity between day and night.

The number of days in each month that each typical site was characterized by different humidity effects was calculated, and the humidity effect type that accounted for the largest proportion of days in each month was selected as the dominant humidity effect type in that month. The statistical results are shown in Figure 4.

Figure 4 shows that the dominant humidity effect varied significantly from month to month, regardless of whether it was daytime or nighttime. The dominant humidity effect was mostly normal in the months of January, February, March, November, and December, when the temperature was relatively low. The humidification effect became increasingly significant in the months when the temperature was relatively high, which is in line with the research results of previous studies [13,14,15]. However, at the same time, the proportion of the dominant humidity effect showing the characteristic of humidification increased significantly in summer, when the temperature is relatively high. The higher temperatures in summer indicate that the characteristics of the humidity effect caused by the local environment are very sensitive to temperature changes, and the humidity effect is more obvious in months with higher temperatures.

By analyzing the monthly variation in the humidity effect intensity of each typical water body station in Figure 5, it can be seen that the M6708 and M9236 stations had the same day and night performance characteristics. The average humidity effect intensity was positive in each month of the year, with the humidity effect intensity in the summer half of the year being higher than that during winter. The M3553 and M9014 stations had the same day and night performance characteristics, and the dehumidification effect was significant in the summer months of June, July, and August, but not in other months. The day and night performance characteristics of stations M6707 and M6711 differed, with an obvious summer effect: daytime showed a humidification effect, while nighttime showed dehumidification.

Based on the monthly variation in the dominant humidity effect shown in Figure 4 and the monthly average humidity effect intensity values in Figure 5, it can be seen that the diurnal variation characteristics of each typical water body station align with the hourly variation characteristics shown in Figure 3. However, the humidity effect intensity at each typical water body site, despite being the same water body type, did not show a uniform pattern, with both dehumidification effect and humidification effect appearing.

3.3. Analysis of the Causes of Local Environmental Humidity Effects

From the perspective of the subsurface environment, the typical water body stations are close to the water sources, which provides obvious advantages regarding water vapor supply. However, the humidity effect characteristics of the six typical water body stations do not share a uniform pattern. It is obvious that the humidity effect of the local environment around a water body is more complex than traditionally understood. The urban/rural background environment in which the water body is situated, the relative position of the station and the water body, the macro wind field, the local water and overland winds caused by the diurnal temperature difference, and other factors all influence the characterization of humidity effects.

3.3.1. Differences Between Urban and Suburban Environments

Studies of urban dry and wet islands have shown that sites located in urban centers and urban fringes (suburbs), respectively, show significant differences in humidity [43]. The roughness of the subsurface in densely built-up urban center areas is much greater than that in the suburbs. Under the same meteorological conditions, the mechanical turbulence within a city is stronger than that in the suburbs. Together with the heat island effect of urban buildings, this contributes to the development of thermal turbulence, resulting in significantly lower humidity in the urban center than in the urban suburbs [44].

By combining building density data from satellite images and land use data, 21 typical urban stations and 43 suburban stations among 104 stations were selected to analyze the characteristics of the humidity effect intensity. The stations distribution are shown in Figure 6. Among them, one typical water body station (M3553) is located in the city center, and four (M6707, M6708, M6711, and M9236) are located in the suburbs of the city.

By calculating the statistical average of the humidity effect intensity between urban and suburban stations, Figure 7a shows that the humidity effect in the urban center of Nanjing is mainly characterized by the drying feature of dehumidification. The dehumidification effect intensity is more significant in the summer half of the year, and higher at night than in the daytime. This is highly consistent with the humidity effect intensity observed at the M3553 station located at Xuanwu Lake in the city center (Figure 3 and Figure 5). This indicates that, although the M3553 station is close to a water body, the general dehumidification effect of the background environment of the city center on station M3553 exceeds the humidification effect of the local water body on the station, resulting in the station’s overall dehumidification characteristic.

In other words, the center of the city is densely built and exhibits a dry, dehumidification-dominant feature. This urban background environment offsets the dehumidification effect of the local subsurface water environment, causing the humidity effect of the water body located in the densely built-up area of the city center to align with the dehumidification effect of the main urban area background [44,45].

Figure 7b shows the statistical average of the humidity effect in the suburbs of Nanjing. It can be seen that the humidity effect in the suburbs of Nanjing is mainly characterized by a humidification feature, which makes it much more humid overall compared with the urban area. This effect is more pronounced in the summer half of the year, and in terms of daily variation, the humidification effect intensity during the daytime is higher than that at night. By comparing the humidity effect intensity of the typical water body stations in the suburbs, it can be seen (Figure 3 and Figure 4) that the humidity effect at station M6711, located at the suburban Jinniu Lake, is very consistent with that of the entire suburb (Figure 7b), but the humidity effects of the remaining three suburban water body stations, namely M6707, M6708, and M9014, are quite different from that of the entire suburb.

It can be seen that even though these stations are located in the same suburban background environment, the differences in the local environment can still have a great impact on the humidity effect of the sites’ surroundings [13].

3.3.2. Influence of Wind Fields on the Humidity Effect in Water Bodies

The movement of wind at the surface usually carries water vapor and drives its transfer. The prevailing wind direction significantly affects the humidity effect in the environment surrounding a water body site [46,47]. In addition, due to differences in the thermal properties of land and water bodies, alternating diurnal and nocturnal wind directions can occur near water sites. During the daytime, land heats faster than lake/river surfaces (water surfaces) due to solar short-wave radiation. This causes hot air to rise, and low-level air moves from the water surface to the land, resulting in a land–lake breeze (water winds), which increases the humidification effect intensity. During the nighttime, the land cools faster than the water surface, and low-level air moves from the land to the water surface, resulting in land winds, which decrease humidification effect intensity [16,17].

• Wind field at selected meteorological stations;

The local wind field (LW) of a typical water body station was obtained by using the observation results of conventional station winds as the macroscopic wind field (MW) and the observation results of the typical water body station as the original local wind field (OLW), according to the following method:

The hourly wind speed and direction of typical water body stations and conventional stations were decomposed in the meridional and zonal directions. The wind speed in the meridional direction was subtracted from the wind speed in the zonal direction, and the wind speed in the meridional direction of each water body station was combined with the wind speed in the zonal direction of the corresponding conventional station to obtain the local wind speeds in the meridional and zonal directions at the station. Then, the wind speeds in the meridional and zonal directions were synthesized to obtain the hourly local wind speeds and direction at the water body station [46].

Following the above calculation, the influence of the macro wind field (MV) was eliminated, and further statistics on the frequency of local winds in each wind direction for each water body station during both daytime and nighttime from 2014 to 2020 were obtained. The results are shown in Figure 8. The variations in the hourly average wind speed and direction were calculated in the meridional and zonal directions at each water body station and the data were synthesized into graphs, as shown in Figure 9.

According to Figure 8, the prevailing wind direction at site M3553 was mainly westerly, showing more northwest winds during the daytime hours and changing to purely westerly winds during the night. Site M6707 showed a relatively balanced wind distribution during daytime hours, with similar frequencies of winds from all directions. However, during the night, winds coming from the north–northeast direction prevailed.

The wind distributions of sites M6708, M6711, and M9014 did not show significant differences between day and night. Specifically, the frequency of easterly winds was more prominent at site M6708, while site M6711 was dominated by northerly and north–northeast winds, and the frequency of southerly winds was more significant at site M9014.

It is worth noting that the diurnal wind direction at site M9236 was extremely pronounced, with the prevalent winds being completely opposite during the daytime. The prevalent winds were from the northwest direction during the daytime, while they shifted to the southeast direction at night.

Figure 9 shows that the changes in wind direction across the different stations exhibited both certain regularities and significant differences. The wind direction at station M6711 was relatively stable, showing northerly winds for most of the day, and the wind speed remained low, with no significant changes. Figure 2 indicates that station M6711 has a unique relationship with the nearby water body and is semi-surrounded by the water in an L-shape. Most of the prevalent winds during both day and night are northerly winds, but their speeds are weak, indicating they cannot have a substantial impact on the humidity effect of the site’s surroundings. Therefore, their humidity effect is mainly characterized by the typical humidity effect characteristics of suburban sites (Figure 7b).

The wind direction at site M6708 changed from a low easterly wind speed at night to a higher northerly wind speed during the day. Figure 2 shows that M6708 is located in the suburbs and surrounded by suburban rice fields. However, the local winds prevailing from all directions during both the day and night bring additional water vapor to the site, making it have a humidification effect throughout the day.

Site M3553, on the other hand, experienced a change in the wind direction from southwesterly at night to northwesterly during the day, although the change in wind speed during the day was not significant. According to the distribution of the M3553 site (Figure 2), it is characterized by dehumidification during the day because it is in an urban context, where the dehumidification effect of the urban buildings is stronger than the intensity of the humidification effect of the local water bodies. The prevailing westerly winds at M3553 also blow from the direction of the city. This further aggravates the dehumidification effect at the site due to the westerly winds arriving from the hot and dry urban area [46,47].

The changes in the wind directions at three sites, M9236, M9014, and M6707, were particularly complex and accompanied by relatively large wind speed fluctuations.

Specifically, the wind direction at M6707 alternated between southerly and northerly winds. Mainly northerly winds occurred from 0 a.m. to 9 a.m., with a gradual decrease in wind speed over time. Then, the direction changed to southerly winds at 10 a.m. with a gradual increase in wind speed, reaching a peaking at 13 p.m. Afterward, the wind speed gradually decreased, and the winds returned to the northerly direction at 17 p.m., with a renewed increase in wind speed. Based on the actual location shown in Figure 2, it is evident that the wind field at this site exhibits the characteristics of both land and water winds [48,49,50,51,52], with the winds mainly blowing in the direction of the lake in the daytime and mainly in the northerly direction in the nighttime (land winds).

Site M9014, on the other hand, experienced alternating northerly and southerly winds during the diurnal period, with predominantly southerly winds and strong wind speeds most of the time, and a brief period of weaker northerly winds between 10 a.m. and 13 p.m. The site was also characterized by a strong northerly wind (water wind) during the day. According to the analysis presented in Figure 2, it can be seen that the site was dominated by southerly winds (land winds) at night and exhibited weak northerly winds (water winds) around midday, i.e., it had a relatively weak water–land wind characteristic.

Site M9236 exhibited alternating northwesterly and southeasterly winds during the diurnal hours, with the southeasterly winds dominating. However, the winds shifted to the northwesterly direction during the period from 9 a.m. to 14 p.m. on a daily basis. Based on the location distribution shown in Figure 2, it is evident that the wind field at this site exhibits a changing pattern of water–land winds. During the daytime, mainly river winds occur from the northwest (water winds), but at night, mainly southeasterly winds occur (land winds).

2.

The Impact of Water–Land Breeze on Humidity Effects

Since this study focuses on stations surrounding water bodies in Nanjing, the local circulation primarily consists of land-water breezes induced by thermal contrasts between water and land surfaces [51,52,53,54]. Sites M6707, M9014, and M9236, where the phenomenon of water–land wind occurs, were further analyzed to observe changes in the local water–land wind (LW) and its corresponding macro wind (MW) during different seasons, as well as their influence on the humidity effect at the sites. The average temporal changes in the localized winds and their corresponding macro winds at the reference station in the zonal direction were calculated for stations M6707 and M9014 from 2014 to 2020 during different seasons (Figure 10 and Figure 11).

As can be seen in Figure 10, the local wind at station M6707 showed a significant pattern of change in latitude during the day, with the phenomenon of water–land wind being obvious. The pattern of changes in the humidity effect intensity at the station aligns with the temporal changes in the humidity effect intensity at the station. The daytime hours are affected by the lake wind from the south (water wind), and the humidity effect intensity shows that the humidity increases. At nighttime, the wind direction changes to the north (land wind), and the humidity effect intensity also changes and decreases accordingly. Seasonal changes are obvious. In summer, the south wind (water wind) lasts the longest during the day, and the wind speed is also the highest, starting at 9:00 a.m. and reaching its peak strength at 13:00 a.m. At 18:00 p.m., the wind direction changes from the south (water wind) to the land wind, which reaches its highest wind speed at 0:00 p.m. In winter, the water–land wind is the weakest, and the duration of the south wind (water wind) is very short, lasting less than 4 h. The macroscopic winds at station M6707 in the zonal upward direction did not show a clear pattern of daily changes. It is evident that the local water–land wind determines the change characteristics of the humidity effect at the station.

Table 7. Correlation changes of original local wind field, macro wind field, and local wind field with humidity effect intensity in different seasons at station M6707.

	Original Local Wind (OLW)	Macro Wind (MW)	Local Wind (LW)
Spring	0.87	−0.29	0.90
Summer	0.93	0.51	0.94
Autumn	0.92	0.56	0.97
Winter	0.64	−0.78	0.92

shows the correlation between the three attributes of wind at site M6707 and the humidity effect intensity during different seasons. It can be seen that the changes in OLW and LW at site M6707 have a strong correlation with the changes in the humidity effect intensity. The correlation between the humidity effect intensity and LW is slightly higher than that with OLW. The correlation with MW is relatively weaker, but it shows a strong negative correlation with the humidity effect intensity in winter.

This suggests that the local wind at station M6707 plays a dominate role in the variation in the humidity effect intensity at the station.

Figure 11 shows that the local wind speed in the zonal direction at station M9014 was consistently greater than 0 m/s, with the wind predominantly coming from the south (land wind). However, in the daytime, the influence of the wind from the lake in the north (water wind) creates a concave shape in the local wind curve. It can be seen that the degree of concavity of the local wind curve varies during different seasons; it is the largest in summer, but becomes weaker with a north wind (water wind) in the period from 10 to 15 o’clock. Seasonal variation clearly appeared in the macro winds at station M9014. The southerly winds in spring and summer combine with the local southerly winds at the station, strengthening the onshore winds. Meanwhile, the northerly winds in fall and winter weaken the local southerly winds, mitigating the corresponding reduction in the humidity intensity.

At station M9014, it is evident that the macro wind (land wind) and local wind (land wind) in the spring and winter seasons jointly affected the humidity, leading to its reduction. In the fall and winter, during the daytime, water winds from Shijiu Lake and the macro winds somewhat mitigated the humidity effect. However, the impact was not great, and the overall results still showed humidity characteristics.

Table 8. Correlation between the original local wind field, macro wind field and local wind field at site M9014 and the humidity effect intensity in different seasons.

	Original Local Wind (OLW)	Macro Wind (MW)	Local Wind (LW)
Spring	−0.52	−0.61	−0.32
Summer	−0.22	−0.89	0.23
Autumn	−0.29	−0.55	0.20
Winter	−0.57	−0.81	0.29

shows the correlation between the three wind attributes and the humidity effect intensity during different seasons at site 9014. The intensity of the humidity effect at the site was mostly negative, and the wind speed was greater than 0, manifested as southerly wind (land wind). The higher the wind speed, the lower the corresponding value of the humidity effect intensity, and the stronger the dehumidification effect. The change in the MW at site M9014 also shows a significant negative correlation with the change in the humidity effect intensity at the site. This correlation is the strongest among the three types of winds during all seasons. The correlation is relatively strong in summer and winter, reaching −0.89 and −0.81, respectively, which suggests that the macro winds at station M9014 play a dominate role in the variation in the humidity effect intensity at the station.

Station M9236 is located along the Yangtze River. The main body of the river is positioned in the northeast–southwest direction relative to the station. The axes of the decomposed wind direction were rotated clockwise by 45 degrees, to obtain u′ (northwest–southeast direction) and v′ (northeast–southwest direction). The average temporal changes in the local winds during different seasons at station M9236 and its corresponding macroscopic winds at the reference station in the northwest–southeast direction were calculated for the period between 2014 and 2020 (Figure 12).

Figure 12 shows that the local wind at station M9236 exhibited obvious diurnal variations in the northwest–southeast direction, and the land–lake breeze phenomenon was significant, with northwest wind (water wind) during the daytime and southeast wind (land wind) during the nighttime. The river wind (water wind) in the northwest direction lasted longer during the daytime in spring and summer, while the water wind was shorter during the daytime in fall and winter. The macro wind at station M9236 showed diurnal variations in the northwest direction in spring and summer, with southeast wind (land wind) during the day and northwest wind (water wind) during the night. Meanwhile, in fall and winter, northwest wind (water wind) occurred throughout the day.

Station M9236 is located along the bank of the Yangtze River, which is a rich source of water vapor. The site shows humidification throughout the year during the day, indicating that it is more strongly influenced by the northwesterly river winds (water winds), which bring water vapor to the station, than by winds from other directions.

The changes in the macro and local winds at the station indicate that the strength of the humidity effect at M9236 is jointly influenced by local and macro winds. During the daytime, when the macro winds gradually weaken, the local winds (water winds) at the site play a role in bringing abundant water vapor to the site. The macro winds and local winds complement each other, alternately bringing water vapor to the site, meaning that the environment around the site shows a humidification effect throughout the day.

Table 9. Correlation changes between the original local wind field, macro wind field, and local wind field at station M9236 with humidity effect intensity across different seasons.

	Original Local Wind (OLW)	Macro Wind (MW)	Local Wind (LW)
Spring	0.74	−0.04	0.51
Summer	0.67	−0.50	0.56
Autumn	0.64	−0.61	0.52
Winter	0.85	0.79	−0.72

shows the correlation of the three wind attributes with the humidity effect intensity during different seasons at site 9236. It can be seen that the correlation between different wind fields and the humidity varies significantly with the seasons. In spring, the original local wind field (OLW) shows a strong positive correlation with E (0.74), while the local wind field (LW) has a slightly weaker positive correlation with E (0.51), and the macroscopic wind field (MW) has almost no correlation (−0.04). In summer and fall, the correlation with the MW was enhanced, with correlations of −0.50 and −0.61, respectively. However, the OLW was the most correlated with changes in the humidity effect intensity. In winter, the correlation of the OLW with the humidity effect remained the strongest (0.85), and the correlation with the MW shifted from a negative to a positive correlation (0.79). However, the LW shifted from a positive correlation to the strongest negative correlation (−0.72). Overall, the OLW, compared to the MV and LW, was the most correlated with the change in the humidity effect intensity.

4. Discussion

4.1. Effect of Building Distribution on Humidity Effect in Urban Context

This study analyzed some of the causes of humidity effects at typical water body sites in Nanjing. Some existing studies on the factors influencing the humidity effect of water bodies have shown that the humidity effect of the urban background environment exhibits a dry-biased characteristic, which has an inhibitory effect on the humidification effect of the urban water bodies themselves [20,44,45,55,56,57]. This is consistent with the results of this study. However, it has also been shown that urban environments with different building densities have different effects on the humidification effect of water bodies [23,58,59]. Zhang Wei [23] and Ge Yaning [57] found that the humidification effect of water bodies in highly built-up areas was significantly weaker than that of water bodies in low built-up areas. This is also verified by the humidification effect exhibited at site M3553 in this study (Figure 3, Figure 5 and Figure 7a). However, the study by Zheng Zihao [58] found that the humidity effect of the water body in each direction was different in the urban built-up area, and Dai Xi found that [22] the heights and distribution of the buildings also had a significant effect on the humidity effect of the water body in the corresponding direction. The only site located in the urban area with dense buildings is M3553, which is situated at the lake-viewing pavilion in the middle of Xuanwu Lake (Figure 2). The relative spatial distribution of Xuanwu Lake and its surrounding buildings, as shown in Figure 1 and Figure 2, indicates that the lake is almost entirely surrounded by tall buildings, with the highest mountain in Nanjing—Zhongshan—located at the southeast corner. Therefore, it is difficult to discern the specific changes in humidity effects at the edges of Xuanwu Lake adjacent to buildings and mountains from the data of the M3553 site alone. Future research could involve increasing the number of encrypted stations around Xuanwu Lake to conduct a more comprehensive analysis of its humidity effects.

4.2. The Impact of Upwind and Downwind Directions of the Dominant Wind on Humidity Effect

Related studies have found that changes in wind have a significant impact on the presentation of the humidity effect. The up- and downwind directions of the prevailing wind [21,23,59], as well as lake–land wind [59,60], will have an impact on the humidity effect of the water body. For example, Liang Sheng [21], Zhang Wei [23], and Chen Zhiyin [52] have found that the humidity effect of a water body located in the upwind direction of the humidity is significantly lower than that in the downwind direction. In this study, the focus was on the influence of lake–land winds on the humidity effect at the water body site, and the results obtained are in agreement with those of Yang Wei [16], Qiu Yangyang [59], and Mai Zi [60]. However, there is a lack of research on the effect of the upwind and downwind directions of prevailing winds on the humidity effect of water bodies. This gap is also related to the selection of data. The number of meteorological stations near typical water bodies in Nanjing is relatively small, and most only have one station. Shijiu Lake has two stations—one located on the south side of the lake body and the other on the north side. However, due to the large overall area of Shijiu Lake, the distance between the stations is too far (Figure 2) [61], and the prevailing wind direction is different between them (Table 7 and Table 8), making it impossible to compare the upward and downward wind directions. However, this is a research angle that should not be ignored. The influence of upwind and downwind directions on the humidity effect of water bodies can be further investigated by increasing the number of stations, selecting water bodies of moderate size, and distributing the monitoring locations more widely.

4.3. Prospect of Humidity Effect of Complex Subsurface Nature

As an important part of the local environment, water bodies are inextricably linked to the local climate. Based on previous research [40], this study focused on the humidity effect on the local climate in the city. We proposed a quantitative assessment standard to measure the humidity effect (Equation (2)), qualitatively classified changes in the humidity effect over a short period of time, and used this method to describe the changes in the humidity effect on the local climate in a quantitative manner, achieving better results. And, the studies of humidity effect differ from direct studies on humidity, as it is more localized and accurate, providing a certain reference value for the numerical calculation of local humidity in practical applications.

However, this study has some limitations. This paper focuses solely on the humidity effects at typical water body stations in Nanjing, analyzing the actual environmental conditions at each site. However, due to the limitations of the types of local environments covered, the study does not encompass all possible scenarios of water body humidity effects. Further research is still needed on the combined synergistic effects of vegetation and water body humidity. Moreover, we also found that the differences among the humidity effects of different subsurface properties were also very significant [42,62]. However, the humidity effect of non-water body types of underlayment was not addressed in this study.

In subsequent studies, the concept of humidity effect intensity proposed in this paper can be extended to building-type and vegetation-type sites to analyze the changing characteristics of humidity effects across various types of subsurface [62]. Additionally, the quantitative method for assessing humidity effects can be used as a tool to compare and analyze the differences in the humidity effects among different subsurface properties. It is also possible to analyze the variation in humidity effects under different weather conditions by adding more meteorological factors and determining the influence of different types of subsurface on the humidity effects within a certain range. These methods can also be used to further verify the generalizability of the index proposed in this paper.

5. Conclusions

Taking the typical water body stations in Nanjing as the research object, this study explored the change characteristics of the humidity effect of different typical water body stations and analyzed possible reasons for the differences in the humidity effect. The main conclusions are as follows:

(1) The humidity effects of the typical water body stations show clear daily and monthly change characteristics. Overall, the daytime period exhibits greater humidification compared with the night. The intensity of the humidity effect is very sensitive to temperature changes. The lower temperatures during January, February, March, November, and December are mostly normal, with changes in the humidity effect becoming increasingly remarkable as the temperature increases and decreases. The highest temperatures occur during the summer, maximally increasing and decreasing the proportion and intensity of the humidity effect.

(2) There are remarkable differences in the humidity effects among different typical water body stations in Nanjing. The suburban background environment around the water body and the configuration of the macro wind field and local wind field are the main factors affecting its distribution.

(3) The economically developed main urban areas of the city, where high-rise buildings are densely distributed, are mainly characterized by a dry humidity reduction effect. In this environment, water bodies are affected by the urban humidity reduction effect, and their humidification effect is offset. The humidity effect tends to be the same as the humidity reduction effect of the main urban environment. The suburban humidity effect is mainly manifested as a humid characteristic. However, when the surrounding suburban environment of the water body cannot form effective water–land winds, the dominant wind around the water body and its ability to bring additional water vapor to the station become the main factors affecting the local humidity effect of the station.

(4) When a typical water body station is located next to a large water body (river or lake), the local water–land wind field and the macro wind field become important factors affecting the local humidity effect of the water body, with the water winds significantly enhancing the humidity effect and the land winds weakening the effect. When the local wind field and the macro wind field overlap, they complement each other, combining to enhance or weaken the humidity effect intensity.

The research in this paper focuses on the humidity effect of urban water bodies on the local environment, and the results can provide scientific references for urban planning and ecological design.