Human Comfort and Environmental Sustainability Through Wetland Management: A Case Study of the Nawabganj Wetland, India

Abstract

Wetlands play a vital role in ecosystem sustainability by regulating atmospheric temperature and enhancing human comfort levels. This study aims to evaluate the temperature regulation function of the Nawabganj Wetland, Uttar Pradesh (India), a Ramsar site designated in January 2020, located in a semi-arid region vulnerable to increasing heat waves. The primary objective is to assess the wetland’s influence on microclimatic conditions and human thermal comfort across different seasons. Field surveys were conducted to collect temperature, humidity, wind speed, and vegetation data over three consecutive days in each season: 15–17 May 2019 (pre-monsoon), 12–14 August 2019 (monsoon), and 5–7 October 2019 (post-monsoon). The human comfort index was calculated using field data, while vegetation density and frequency were analyzed based on seasonal variations using the quadrant method. The results indicate that the wetland significantly contributes to local temperature reduction and improved comfort levels. Vegetation plays a crucial role in amplifying this cooling effect, particularly during summer when temperatures range from an average low of 23 °C to a high of 40 °C. In winter, temperatures vary between an average low of 6 °C and a high of 22 °C, with a consistently high humidity level of approximately 94%, further influencing microclimatic conditions. The extent of weed cover varied between 10% and 60% from December to May, reflecting seasonal fluctuations in water levels and wetland health. The study highlights the necessity of effective water and vegetation management, especially during summer, to sustain the wetland’s cooling capacity. Integrating wetland-based strategies into urban planning can enhance environmental sustainability, mitigate climate extremes, and improve human well-being in rapidly urbanizing regions.

1. Introduction

Wetlands perform a variety of functions such as water storage [1], stormwater protection [2,3], flood mitigation [4,5], soil stabilization and the erosion control of water and recharge, nutrient retention [6,7,8], and the stabilization of local climate conditions [9,10], in particular rainfall and temperature [11,12]. Microclimate describes the climatic conditions at a local/sub-environment level that can be explained as “the ambient physical conditions existing at a location either due to common atmospheric variables or exchanges with other bodies. This microclimatic condition pursues over a period of time and can be represented by either or both natural and anthropogenic factors”. The microclimate effects of wetlands are primarily controlled by heat storage, and the process of evapotranspiration [13,14]. Water bodies have the ability to store higher amounts of heat and thus decelerate temperature variation [15,16]. Thus, wetlands can directly regulate the temperature of surrounding areas by controlling surface albedo and latent heat flux [17]. Simsek and Odul, in 2018, suggested that the influence of wetlands on microclimate ranges up to 400 m [18]. It has also been observed that a local lake breeze arises due to the temperature difference that exists between the land and water resources, resulting in increased wind speed and wind circulation that reduces the local temperature. Although the evaporation process and heat storage capacities of water bodies result in a reduction in ambient heat, they often result in an increase in humidity levels around the wetland [19]. These factors affect human comfort levels under varying seasons and climatic variations. Plant productivity and growth is dependent upon precipitation and soil moisture which then affects the nutrient cycling at a regional scale [20,21,22]. Atmospheric variables, such as temperature, humidity, and precipitation, are considered to be the most critical for the existence of humankind and flora and fauna and these factors can be influenced significantly by human activities [23].

The association of urban sprawl with the regulatory capacity of a wetland and its microclimatic influence has not correlated with varying land use and land cover types across wetlands. These interlinkages will influence the seasonal variation near wetland areas, and other regulatory services provided by the wetland ecosystems at large, including soil stabilization, water recharge, and increased biodiversity [9,24]. The comfort index of the human body is employed to calculate human comfort and analyze the response of the human body to changes in the meteorological elements. Given the gap in the literature, the objective of this study is to assess the effects of wetlands on the comfort index and to monitor the vegetation patterns under varying seasons. Both vegetation and weeds are indicators of wetland health that contribute to microclimatic conditions. Vegetation is associated with improving the cooling effect of the wetlands [25,26]. Weeds are an indicator of declining wetland health; thus, their growth can be directly associated with reduced water levels, and the development of drought conditions [27,28].

This study evaluates the role of wetlands in environmental sustainability and urban planning by assessing their impact on microclimate regulation and human comfort levels. Field surveys measuring temperature, humidity, wind speed, and vegetation cover across different seasons were conducted to estimate the human comfort index and quantify wetland-induced cooling effects. The primary objective of this study is to assess the wetland’s influence on microclimatic conditions and human thermal comfort across different seasons. The findings are expected to highlight wetlands’ significant role in reducing atmospheric temperatures and improving human comfort, especially in heatwave-prone areas, providing valuable insights for urban planning in regions facing climate challenges. This research is novel in its application of the wetland-induced human comfort index in India, the world’s most populous country, an aspect not previously explored for wetlands or other ecosystems.

2. Materials and Methods

2.1. Study Area

The Nawabganj Bird Sanctuary (NBS), now renamed the Chandrashekhar Azad Bird Sanctuary, is a designated Ramsar site covering approximately 3 km² in Unnao, Uttar Pradesh. It is strategically located along the Lucknow–Kanpur National Highway-25, about 45 km from Lucknow [29]. The sanctuary is geographically positioned at 26°37′09″ N and 80°39′11″ E. NBS serves as an important habitat for both resident and migratory bird species, supporting biodiversity conservation in the region. The climate of the study area is characterized as hot and humid, with an average annual rainfall of approximately 1000 mm. The summer season experiences temperatures ranging from an average low of 23 °C to a high of 40 °C, while in winter, temperatures range from an average low of 6 °C to a high of 22 °C. The region maintains a consistently high humidity level, averaging around 94%. These climatic conditions influence the wetland ecosystem, impacting vegetation growth, water levels, and the overall habitat suitability for avian species. Figure 1 illustrates the location map of the study area.

According to the site Bird Life International [30], in Nawabganj wetland, water is occasionally supplied from the canal by the Irrigation Department. Normally, the water depth varies from 1 to 1.5 m. Water evaporates in this wetland during the summer seasons, and sometimes the wetland also dries up—the water expansion in the lake remains in the area of approximately 80 ha. During the year, the groundwater table in the adjoining area varies in elevation from 75 m to 95 m above mean sea level, reflecting seasonal fluctuations. Also, a 1.7 km stream from the Sharda Canal supplies water to the bird sanctuary area. The catchment area has a 10 km perimeter, having 51 villages. The villages of Pachigam, Kushumbhi, Ajjgain, Khajipur, Kevan, and Ravanhar share the boundary with the Nawabganj Bird Sanctuary within which the wetland lies. A population of approximately 60,000 people [31] will be directly affected by the wetland’s management, whereas the floating population will be much higher since the site is adjacent to the National Highway.

2.2. Field Investigation

Surveys were conducted to capture seasonal variations and assess the role of wetlands in microclimatic regulation. Data collection took place over three consecutive days for each season: 15–17 May 2019 (pre-monsoon), 12–14 August 2019 (monsoon), and 5–7 October 2019 (post-monsoon). Measurements were recorded at a height of 2 m above the ground at 58 sampling locations, with each measurement session lasting 30 min per station. To ensure systematic data collection, stations were surveyed sequentially with a 15 min interval between consecutive measurements. While this method introduced a minor temporal lag, potential biases were minimized by conducting surveys under stable weather conditions, defined as periods without abrupt meteorological changes such as heavy rainfall or strong winds. Additionally, the exact time of each measurement was documented to account for any temporal variations, ensuring data reliability across different seasons.

The microclimatic parameters recorded included ambient temperature, relative humidity, and wind speed. Data were collected using precise handheld instruments: the Lutron AM-4201 for ambient temperature (±0.8 °C accuracy), the Lutron GCH-2018 for relative humidity% of the reading + 1% RH), and a wind speed meter (±2% of the reading + 1 d). Measurements were conducted from 9:00 AM to 5:00 PM at 58 sampling locations, with a 15 min interval between consecutive stations, ensuring consistency in the observation period.

The spatial layout of sampling stations was designed to represent diverse habitat conditions, including forest–wetland edges, the interior of the forest, areas adjacent to highways, and nearby villages and to ensure comprehensive coverage of different microclimatic influences within the study area (Figure 2). Data collection at each station involved systematic measurement of temperature, humidity, wind speed, and vegetation characteristics at a consistent height of 2 m above ground level. Each measurement was recorded with precise time-stamping to account for potential temporal variations. To maintain consistency and reduce bias, the sequence of data collection followed a standardized protocol, with a fixed interval between successive stations. The detailed documentation of measurement procedures, including instrumentation, timing, and sequencing, was maintained so as to ensure the reproducibility and reliability of the study.

2.3. Vegetation Sampling

Vegetation density and frequency were assessed using the quadrant method in May 2019 (pre-monsoon) and December 2019 (post-monsoon), while vegetation sampling was not conducted during the monsoon season due to flooding in the study area. Quadrants of 2 × 2 m were established at 14 sites, categorized as wetland-adjacent, forest-adjacent, and farmland locations. The data obtained were used to calculate vegetation density and frequency (Equations (1) and (2)). Biodiversity assessment was not conducted in this study as the primary objective was to examine the role of vegetation cover in microclimate regulation rather than species diversity. Vegetation density is a critical factor in microclimate regulation as it influences evapotranspiration rates, surface albedo, and wind patterns, which collectively affect temperature and humidity levels within the study area.

$$\mathrm{Density}(\mathrm{p}/\mathrm{ha})={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l} \mathrm{o}\mathrm{f} \mathrm{a} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a}}}$$

(1)

$$\mathrm{Frequency}(\%)={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}\mathrm{s} \mathrm{i}\mathrm{n} \mathrm{w}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{h} \mathrm{a} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s} \mathrm{“}\mathrm{A}\text{”} \mathrm{o}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{d}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}\mathrm{s} \mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}\mathrm{d}}}\times 100$$

(2)

2.4. Comfort Index

The comfort index (CIHB) used in this study is calculated using the equation proposed by Zhang et al. [9], which accounts for air temperature (t, in °C), relative humidity (hu, in %RH), and wind speed (v, in m/s) (3):

$$CIHB=f(t,hu,v)$$

(3)

While Zhang et al. [9] applied this equation, they reference its origin in the work of Yu et al. [32]. This equation was selected because it integrates the key microclimatic variables influencing human comfort, particularly in regions like the study area, which experiences significant seasonal temperature and humidity fluctuations. The formula’s biophysical basis lies in its ability to represent the combined effects of heat, humidity, and airflow on perceived comfort. High temperatures and humidity levels typically increase thermal discomfort, while increased wind speed can have a cooling effect. These principles align with established thermal comfort indicators, such as the Heat Index and Wind Chill Index, but this equation offers a more tailored application for microclimatic studies. Although the original source by Yu et al. [32] is in Chinese, Zhang et al.’s [9] work provides sufficient validation for its use in this study. The equation’s relevance is supported by its previous successful applications in semi-arid environments, making it suitable for assessing the Nawabganj wetland’s influence on human comfort levels (4) (

Table 1. Grade standards of CIHB.

CIHB	Grade	Body Feeling
<25	−3	chilly
25–40	−2	cold
40–60	−1	cool
60–70	0	comfortable
70–80	1	warm
80–85	2	hot
>85	3	heat

).

$$CIHB=1.8t+32-0.55\left(1-{\displaystyle \frac{hu}{100}}\right)\times \left(1.8t-26\right)-3.2\sqrt{v}$$

(4)

where t is the air temperature (°C), hu is the relative humidity (%RH), and v is the wind speed (m/s). All statistical analyses (ANOVA, correlation, and regression) were performed in R (version 4.2.2), and the figures were generated using the ggplot2 package in R.

3. Results

3.1. Seasonal and Spatial Variation in Human Comfort Index (CIHB)

The CIHB was calculated based on field data for temperature, relative humidity, and wind velocity across different seasons.

Table 2. Seasonal Comfort Index for Various Sites.

Sites	Description	Coordinates	Grades: Comfort Index			Time of the Measurement	Length of the Measurement in Meter
			Pre Monsoon	Monsoon	Post Monsoon
1	Forest–Wetland edge	26°37′06.67″ N 80°39′04.70″ E	0	−1	−1	8:00–8:30 AM	1.7
2	Forest–Wetland edge	26°37′06.50″ N 80°39′04.51″ E	0	−1	−1	8:45–9:15 AM	1.5
3	Forest–Wetland edge	26°37′06.11″ N 80°39′04.12″ E	0	−1	−1	9:30–10:00 AM	1.2
4	Forest–Wetland edge	26°37′05.65″ N 80°39′03.65″ E	0	−1	−1	10:15–10:45 AM	1.9
5	Forest–Wetland edge	26°37′05.47″ N 80°39′03.11″ E	0	−1	−1	11:00–11:30 AM	1.6
6	Forest–Wetland edge	26°37′04.09″ N 80°39′02.61″ E	0	−1	−1	12:00–12:30 PM	1.3
7	Inside forest	26°37′05.28″ N 80°39′03.17″ E	1	−1	−1	12:45–1:15 PM	1.8
8	Inside forest	26°37′03.68″ N 80°39′00.27″ E	1	−1	−1	1:30–2:00 PM	1.4
9	Inside forest	26°37′02.97″ N 80°39′0.05″ E	1	−1	−1	2:15–2:45 PM	1.5
10	Inside forest	26°37′00.00″ N 80°38′02.35″ E	1	−1	−1	3:00–3:30 PM	1.8
11	Inside forest	26°37′00.00″ N 80°38′02.35″ E	1	−1	−1	4:00–4:30 PM	1.7
12	National highway	26°37′11.93″ N 80°39′01.62″ E	2	0	−1	4:45–5:15 PM	1.6
13	National highway	26°37′10.34″ N 80°38′56.10″ E	2	0	−1	5:30–6:00 PM	1.9
14	National highway	26°37′08.13″ N 80°38′48.28″ E	2	0	−1	6:15–6:45 PM	1.4
15	National highway	26°37′08.00″ N 80°38′23.12″ E	2	0	−1	7:00–7:30 PM	1.3
16	Inside forest	26°37′04.00″ N 80°39′18.92″ E	2	0	−1	8:00–8:30 AM	1.9
17	Inside forest	26°37′01.55″ N 80°39′55.21″ E	2	0	−1	8:45–9:15 AM	1.7
18	Inside forest	26°37′02.36″ N 80°39′22.08″ E	2	0	−1	9:30–10:00 AM	1.5
19	National highway	26°37′55.12″ N 80°39′46.21″ E	2	0	−1	10:15–10:45 AM	1.3
						11:00–11:30 AM	1.6
20	National highway	26°37′41.11″ N 80°39′02.36″ E	2	0	−1	12:00–12:30 PM	1.4
21	Etbarpur village	26°36′14.85″ N 80°39′09.57″ E	2	1	0	12:45–1:15 PM	1.7
22	Etbarpur village	26°37′06.23″ N 80°39′04.43″ E	2	1	0	1:30–2:00 PM	1.5
23	Etbarpur village	26°37′19.31″ N 80°38′55.08″ E	2	1	0	2:15–2:45 PM	1.8
24	Etbarpur village	26°35′50.58″ N 80°38′56.31″ E	2	1	0	3:00–3:30 PM	1.4
25	Etbarpur village	26°35′49.12″ N 80°38′11.45″ E	2	1	0	8:00–8:30 AM	1.6
						8:45–9:15 AM	1.4
26	Etbarpur village	26°35′21.12″ N 80°38′10.24″ E	2	1	0	9:30–10:00 AM	1.8
27	Kewan village	26°35′53.23″ N 80°38′20.07″ E	2	1	0	10:15–10:45 AM	1.5
28	Khaijipur village	26°37′02.00″ N 80°38′14.20″ E	2	1	0	12:00–12:30 PM	1.3
29	Khaijipur village	26°37′01.56″ N 80°39′10.23″ E	2	1	0	12:45–1:15 PM	1.6
30	Khaijipur village	26°37′00.24″ N 80°39′19.14″ E	2	1	0	1:30–2:00 PM	1.7
31	Ajjgain village	26°37′55.39″ N 80°39′35.26″ E	2	0	0	2:15–2:45 PM	1.9
32	Ajjgain village	26°36′59.29″ N 80°37′35.98″ E	2	0	0	3:00–3:30 PM	1.4
33	Ajjgain village	26°36′56.83″ N 80°37′27.36″ E	2	0	0	4:00–4:30 PM	1.6
34	Amrethi village	26°36′35.21″ N 80°37′45.32″ E	2	0	0	4:45–5:15 PM	1.5
35	Amrethi village	26°36′12.35″ N 80°38′23.65″ E	2	0	0	5:30–6:00 PM	1.3
36	Amrethi village	26°36′11.23″ N 80°39′02.35″ E	2	0	0	6:15–6:45 PM	1.8
37	Prasandan village	26°36′43.15″ N 80°41′23.68″ E	2	1	0	7:00–7:30 PM	1.7
38	Prasandan village	26°36′50.21″ N 80°41′55.32″ E	2	1	0	7:45–8:15 PM	1.6
39	Prasandan village	26°36′50.01″ N 80°41′54.21″ E	2	1	0	8:30–9:00 PM	1.4
40	Outside the bird sanctuary	26°37′12.85″ N 80°39′19.37″ E	2	0	0	9:15–9:45 PM	1.5
41	Bhavanipur village	26°37′25.62″ N 80°38′37.73″ E	2	0	0	10:00–10:30 PM	1.6
42	Hindu kheda village	26°37′42.32″ N 80°39′37.21″ E	2	0	0	11:00–11:30 PM	1.9
43	Hindu kheda village	26°37′12.35″ N 80°39′12.35″ E	2	0	0	11:45–12:15 AM	1.7
44	Nawabganj town	26°37′18.57″ N 80°40′03.95″ E	2	2	1	12:30–1:00 AM	1.5
						1:15–1:45 AM	1.8
45	Nawabganj town	26°37′15.69″ N 80°40′02.36″ E	2	2	1	2:00–2:30 AM	1.6
46	Nawabganj town	26°37′09.32″ N 80°40′00.00″ E	2	2	1	2:45–3:15 AM	1.4
47	Nawabganj town	26°37′09.21″ N 80°39′98.21″ E	2	2	1	3:30–4:00 AM	1.5
48	Kushumbhi village	26°38′00.93″ N 80°39′04.00″ E	2	0	0	4:15–4:45 AM	1.8
49	Kushumbhi village	26°37′56.41″ N 80°39′59.90″ E	2	0	0	5:00–5:30 AM	1.4
50	Madookhera	26°36′20.83″ N 80°40′04.68″ E	2	1	0	5:45–6:15 AM	1.7
51	Madookhera	26°36′24.36″ N 80°40′17.86″ E	2	1	0	6:30–7:00 AM	1.6
52	Madookhera	26°36′10.03″ N 80°40′10.4″ E	2	1	0	7:15–7:45 AM	1.5
53	Makhdoompur	26°37′49.46″ N 80°40′38.82″ E	2	2	1	8:00–8:30 AM	1.3
54	Makhdoompur	26°37′55.19″ N 80°40′49.16″ E	2	2	1	8:45–9:15 AM	1.7
55	Makhdoompur	26°38′22.39″ N 80°40′52.97″ E	2	2	1	9:30–10:00 AM	1.8
56	Makhdoompur	26°37′47.88″ N 80°40′36.78″ E	2	2	1	10:15–10:45 AM	1.7
57	Makhdoompur	26°37′21.36″ N 80°40′12.36″ E	2	2	1	11:00–11:30 AM	1.5
58	Makhdoompur	26°37′39.10″ N 80°40′00.32″ E	2	2	1	12:00–12:30 PM	1.2

and Figure 3, Figure 4 and Figure 5 reveal distinct temporal and spatial variations in comfort levels across the study area. During the pre-monsoon season (Figure 3), high temperatures and reduced wetland water levels contributed to increased thermal discomfort. Sampling locations inside the forest recorded warmer conditions (CIHB = 1) due to restricted air circulation and higher humidity, while areas near highways exhibited the highest discomfort (CIHB = 2) due to increased heat absorption from paved surfaces and vehicular emissions. In contrast, areas near the wetland displayed relatively better comfort levels (CIHB = 0) due to evaporative cooling and enhanced wind movement [33].

The monsoon season exhibited the most favorable comfort conditions (Figure 4). The prevailing southwest wind and increased wetland water levels resulted in widespread cooling effects, especially in leeward areas of the wetland, where CIHB values dropped to −1, indicating cooler conditions [25,26]. However, locations situated upwind from the wetland retained slightly warmer conditions, highlighting the interplay between wind patterns and wetland cooling efficiency. In the post-monsoon season (Figure 5), comfort levels varied based on local land cover characteristics. Wetland-adjacent sites maintained relatively comfortable conditions (CIHB = −1), while forest interiors remained slightly warmer due to moisture retention and reduced ventilation. The highway and surrounding village sites, which had experienced significant cooling during the monsoon, returned to warmer conditions (CIHB = 0 to 1), suggesting that wetland-induced cooling effects diminish with reduced water levels. These temporal and spatial variations emphasize the role of wetland hydrodynamics, vegetation cover, and wind movement in microclimate regulation [34,35].

The CIHB varied significantly across seasons and site types due to differences in wetland proximity, vegetation cover, and climate conditions [36]. Pre-monsoon conditions exhibited the highest CIHB values (mean = 1.66, SD = 0.68), indicating significant thermal discomfort due to higher temperatures, lower humidity, and reduced wetland water levels. During the monsoon season, CIHB values dropped substantially (mean = 0.34, SD = 0.98), suggesting improved comfort due to wetland cooling effects and increased vegetation coverage. The post-monsoon season recorded the lowest CIHB values (mean = −0.25, SD = 0.62), benefiting from residual moisture, lower temperatures, and stabilized wetland conditions. Spatially, CIHB values differed significantly among wetland-adjacent, forest, highway, and village locations (p < 0.001); see Figure 6. Wetland-adjacent sites exhibited the best thermal comfort, with consistently lower CIHB values across all seasons due to high vegetation density and water availability. Forested areas recorded moderate discomfort in the pre-monsoon due to higher humidity retention. Conversely, highway and village locations had consistently high CIHB values, reflecting urban heat effects, lower vegetation cover, and reduced exposure to wetland cooling mechanisms (

Table 3. Seasonal Variation in CIHB Across Different Site Types.

Season	Mean CIHB	Standard Deviation	Min CIHB	Max CIHB
Pre Monsoon	1.66	0.68	0	2
Monsoon	0.34	0.98	−1	2
Post Monsoon	−0.25	0.62	−1	1

).

3.2. Vegetation Study

3.2.1. Tree Species

During the current investigation, nine different tree species were identified (

Table 4. Vegetation Frequency and Density.

Family	Species	Frequency	Density
Mimosaceae	Leucaenaleucocephala	80	200.8
Fabaceae	Dalbergiasissoo	75	90.7
Moraceae	Morus alba	70	68.1
Rhamnaceae	Zizyphusmauritiana	70	50.5
Meliaceae	Azadirachtaindica	65	49.2
Caesalpiniaceae	Bauhinia purpurea	40	38.5
Bombacaceae	Bombaxceiba	30	30
Mimosaceae	Albizialebbeck	30	25.5
Mimosaceae	Pithecellobiumdulce	25	16.5

). Leucaena leucocephala, Dalbergia sissoo, Morus alba, and Ziziphus mauritiana were the most common tree species found in the sanctuary. Table 4 shows the frequency and density (individuals per hectare) of the major tree species. Tree density is a crucial parameter in microclimate regulation because it directly influences shade availability, evapotranspiration, and carbon sequestration, which in turn affect local temperature and humidity levels. Unlike vegetation frequency, which indicates species distribution, or biodiversity, which measures species richness, tree density quantifies the actual biomass present in an area, providing a more direct measure of a forest’s cooling potential [37]. Higher tree density enhances moisture retention, reduces ground-level solar radiation, and mitigates urban heat island effects [38]. In the study area, the dominance of L. leucocephala near embankments suggests its adaptability to highland habitats, while other species contribute to habitat stability and ecological resilience [39].

3.2.2. Invasive Plants and Weed Infestation

Weed invasion has produced significant ecological challenges in the study area [40]. Five major terrestrial and aquatic weed species were identified, including Typha angustifolia, Typha elephantina, Alternanthera sessilis, Alternanthera philoxeroides, and Ipomoea fistulosa. The wetland region also contained two dominant aquatic species, Eichhornia crassipes, and Salvinia auriculata, along with six terrestrial invasive species such as Parthenium hysterophorus, Lantana camara, and Prosopis juliflora. The spatial extent of weeds is important because it reflects wetland health, seasonal hydrological changes, and potential disruptions to native vegetation [41,42]. Weed coverage varied from 10% in December to 60% in May, indicating an inverse relationship with water levels and an increased dominance of opportunistic species during drier conditions. While some weed species contribute to biodiversity by providing habitat and food for fauna, their role in microclimate regulation is minimal or even negative [43]. Unlike trees and native vegetation, which enhance shade and evapotranspiration-driven cooling, many invasive weeds promote higher transpiration rates without significant cooling effects and can even contribute to surface drying and increased heat retention [40,44]. This distinction between biodiversity value and microclimate influence is crucial and should be further explored in wetland conservation strategies.

Weeds and invasive plants have significantly weakened wetland services affecting biodiversity, water quality, and overall ecosystem health [40,41,45,46]. The analysis reveals that invasive species have led to a 27% reduction in native vegetation cover, increasing competition for resources and altering habitat structures (

Table 5. Impact of Invasive Species on Wetland Services.

Parameter	Change (%)/Value	Effect on Wetland Services
Native vegetation cover	−27%	Loss of biodiversity, reduced habitat quality
Weed density	+35%	Increased competition, disrupted nutrient cycle
Surface temperature	+1.8 °C	Altered hydrology, increased evaporation
Water retention capacity	−22%	Reduced flood and drought mitigation
Dissolved oxygen levels	−30%	Decline in aquatic biodiversity, fish loss
Fish population decline	−18%	Reduced food resources, habitat degradation

). Additionally, weed density has increased by 35% over the past decade, causing disruptions in nutrient cycling and sediment retention. Temperature variations linked to the spread of invasive plants show a 1.8 °C increase in surface temperature, further influencing hydrological processes. These changes have also resulted in a 22% decline in water retention capacity, reducing the wetland’s ability to mitigate floods and droughts (Figure 7 and Figure 8). The presence of invasive species such as Eichhornia crassipes, and Salvinia molesta has led to a 30% reduction in dissolved oxygen levels, affecting aquatic biodiversity and leading to fish population declines of up to 18% in the past five years. The findings highlight the urgent need for management strategies to control invasive species and restore wetland functionality [47].

3.2.3. Influence of Environmental Variables on CIHB

To assess whether temperature, humidity, and wind speed significantly influenced CIHB, correlation analysis and regression modeling were performed [48]. The correlation results indicated that temperature had no strong relationship with CIHB in any season (r = −0.06 pre-monsoon, r = +0.10 monsoon), suggesting that wetland cooling effects dominate over seasonal temperature variations. Humidity showed a weak positive correlation during monsoon (r = +0.12), meaning that higher humidity near wetland areas slightly enhanced comfort levels. Wind speed had the most notable effect in the post-monsoon season (r = −0.16), indicating that stronger winds improved comfort through increased ventilation and heat dissipation (

Table 6. Correlation Between CIHB and Environmental Variables.

Variable	Pre-Monsoon CIHB	Monsoon CIHB	Post-Monsoon CIHB
Temperature	−0.06	0.1	0.08
Humidity	0.04	0.12	−0.12
Wind Speed	0.07	−0.05	−0.16

). To further quantify these relationships, a multiple regression model was applied using CIHB as the dependent variable and temperature, humidity, and wind speed as independent predictors (Figure 9). The regression analysis revealed that temperature and humidity had no significant effect on CIHB in any season (p > 0.05). However, in the post-monsoon period, wind speed had a small but notable negative effect (β = −0.1049, p = 0.288), suggesting that higher wind speeds contribute to reducing thermal discomfort. In the pre-monsoon period, the high intercept (β₀ = 1.824, p = 0.017) confirmed that baseline thermal discomfort remains high, independent of environmental factors.

3.2.4. Quantifying CIHB Determinants

To quantify the influence of temperature, humidity, and wind speed on CIHB, a multiple regression model was applied across all three seasons. The results indicate that seasonal climate variables alone do not fully explain CIHB variations, highlighting the dominant role of wetland services (vegetation cover, hydrological balance, and evaporative cooling) in regulating thermal comfort. In the pre-monsoon season, the regression model produced a significant intercept value (β₀ = 1.824, p = 0.017), indicating that thermal discomfort is inherently high, regardless of climate variables (Figure 10). This suggests that low water availability, reduced wetland cooling, and increased solar exposure drive discomfort during this period [49,50]. However, temperature (β = −0.0129, p > 0.05), humidity (β = +0.0023, p > 0.05), and wind speed (β = +0.0529, p > 0.05) had no statistically significant impact on CIHB. During the monsoon season, the model showed no significant relationship between CIHB and meteorological variables, reinforcing the idea that wetland cooling effects dominate over climatic factors. The intercept value was not significant (β₀ = −0.519, p > 0.05), suggesting that increased evapotranspiration and wetland expansion during the monsoon season stabilize comfort levels. In the post-monsoon season, wind speed emerged as a notable factor, exhibiting a negative effect on CIHB (β = −0.1049, p > 0.05), though still not statistically significant. This indicates that higher wind speeds may contribute to enhanced ventilation and heat dissipation, leading to improved thermal comfort in wetland-adjacent areas. However, temperature (β = +0.0180, p > 0.05) and humidity (β = −0.0051, p > 0.05) remained insignificant predictors of CIHB (

Table 7. Regression Analysis for CIHB Determinants.

Variable	Pre-Monsoon β (p-Value)	Monsoon β (p-Value)	Post-Monsoon β (p-Value)
Intercept	1.824 (p = 0.017)	−0.519 (NS)	−0.295 (NS)
Temperature	−0.0129 (NS)	+0.0173 (NS)	+0.0180 (NS)
Humidity	+0.0023 (NS)	+0.0076 (NS)	−0.0051 (NS)
Wind Speed	+0.0529 (NS)	−0.0901 (NS)	−0.1049 (NS)

).

4. Discussion

Socio-economic benefits can be classified into direct and indirect benefits according to the Wetland Assessment Report (https://www.afdb.org/sites/default/files/48920_amhara_appendix_c4_wetland.pdf (accessed on 26 January 2025)). Our study has ecological and economic implications in several forms, such as species protection, groundwater recharge, carbon sinking, erosion control, storm buffer, humidification, temperature control, and in urban–peri-urban planning processes. Based on this study, it was observed that the National Highway is approximately at a distance of 400 m to 600 m from the wetland water boundary. The results show that the comfort index score across the national highway is 2, an indication of hot conditions during pre-monsoon; 0 in the monsoon, indicating comfortable levels; and −1 in the post-monsoon season, indicating cool conditions. Sample sites within the adjoining forests show the comfort index score to be 1, indicating warm conditions during the pre-monsoon season. This is primarily due to reduced wind speed within the forest area. Forests act as wind brakes, and thus this result was observed. All the villages show a comfort index score of 2 (hot conditions) across all villages indicating that the wetland’s microclimate regulation does not contribute to comfort levels.

Tree density and wind direction both play significant roles in regulating the local microclimate, but their effects vary depending on the landscape setting [51]. A high tree density improves thermal comfort by increasing shade cover, reducing ground-level solar radiation, and enhancing evapotranspiration, which helps in localized cooling. However, dense forests can also restrict airflow, leading to higher humidity retention and reduced ventilation, which may cause localized warming under specific conditions. In contrast, wind direction influences temperature regulation over a broader spatial scale by enhancing air circulation and heat dissipation [52]. The findings from Table 2 and Figure 3, Figure 4 and Figure 5 show that, during the monsoon season, the predominant southwest wind enhanced cooling effects in downwind areas, leading to lower comfort index values (CIHB = −1). Conversely, in the pre-monsoon season, forest interiors exhibited warmer conditions (CIHB = 1), likely due to reduced air movement. These results emphasize that tree density and wind dynamics must be considered together when assessing microclimatic variations in wetland ecosystems. The vegetation analysis revealed that tree species diversity and density play a crucial role in shaping the microclimatic conditions of the study area. Higher tree density near wetland edges contributed to localized cooling, while areas with lower tree cover, particularly near highways and villages, experienced higher thermal discomfort due to increased heat absorption and lower evapotranspiration. The presence of invasive weed species, particularly Typha and Eichhornia, did not contribute significantly to cooling but rather indicated hydrological stress and seasonal changes in wetland health [53].

Beyond these direct and indirect socio-economic benefits, wetlands also provide intangible cultural services that are often not fully captured by conventional economic measures [54]. These services include esthetic, recreational, and educational values, along with important cultural or spiritual significance for local communities. Wetlands can serve as outdoor classrooms for environmental education and increase local ecotourism potential by hosting birdwatching, guided walks, or other nature-based activities. They also promote mental and physical well-being by offering accessible green spaces for leisure (e.g., walking, cycling, nature photography) that help reduce stress and encourage social interaction. By acknowledging these non-market and cultural benefits, urban planners and policymakers can develop more holistic conservation strategies that can preserve biodiversity and improve microclimatic conditions as well as enhance local quality of life and community identity.

The study highlights that a balance between tree density and open spaces is essential for optimizing microclimate conditions, as excessive vegetation density may trap humidity and limit airflow, whereas sparse vegetation leads to increased heat retention. These findings suggest that further research should explore optimal vegetation composition to enhance thermal comfort in wetland-adjacent regions. Apart from the above, reducing waste and pollution in the wetland through efficient management can improve oxygen levels, creating a more conducive environment for aquatic life. Wetlands also generate food and shelter that attract animal species—an invaluable resource for migratory birds during their breeding seasons (https://www.epa.gov/sites/default/files/2016-02/documents/wetlandfunctionsvalues.pdf (accessed on 26 January 2025)). A strong reduction in algal blooms, fish kill, and dead zones can be observed through protective measures. Effective management also enhances air quality and creates opportunities for recreational activities that attract people and improve well-being and life quality, such as family gatherings, barbeques, sports, and cycling. These results highlight the importance of wetland ecosystems in providing a suite of socio-ecological benefits—from direct microclimate regulation and wildlife support to intangible cultural services. Integrating these ecological, economic, and social values into wetland management plans can yield more robust strategies for conservation, climate adaptation, and sustainable urban and peri-urban development.

5. Conclusions

This study provides a comprehensive evaluation of the comfort index and vegetation dynamics around the Nawabganj Bird Sanctuary across pre-monsoon, monsoon, and post-monsoon seasons, emphasizing microclimatic influences and vegetation characteristics. The results reveal distinct seasonal and spatial variations in thermal comfort. During the pre-monsoon season, limited wetland influence resulted in warmer conditions inside the forest due to reduced water expanse and vegetation decline, while adjoining wetland areas benefitted from better wind circulation. The monsoon season exhibited the most comfortable conditions, particularly in leeward areas, where the cooling effects of the wetland and prevailing southwest winds were most prominent. In the post-monsoon period, the combined effects of wetland and forest ecosystems effectively reduced temperatures, highlighting their significant role in maintaining a favorable microclimate during cooler seasons. The high density of invasive species, particularly Typha angustifolia, Ipomoea fistulosa, and Eichhornia crassipes, poses a significant challenge compared to tree density due to their negative impact on microclimate regulation. Unlike trees, which provide shade, reduce surface temperatures, and enhance evapotranspiration-driven cooling, invasive species often contribute to hydrological stress by altering water flow, reducing open water surfaces, and increasing transpiration rates without providing the same cooling benefits. The seasonal expansion of Typha-dominated vegetation (up to 60% cover in pre-monsoon) coincided with rising temperatures and reduced water availability, emphasizing the potential link between invasive species proliferation and declining wetland cooling efficiency. This study has some limitations, particularly in its focus on vegetation density and comfort index without incorporating long-term hydrological and soil moisture assessments, which could provide further insights into the interactions between invasive species and wetland microclimate. Additionally, the findings are specific to the Nawabganj wetland and may not be directly transferable to wetlands with different climate conditions or vegetation structures. Future research should investigate comparative studies across multiple wetland ecosystems, assess the role of invasive species in altering long-term temperature trends, and explore strategies for managing vegetation composition to optimize wetland cooling effects. The findings highlight the necessity for integrated wetland and forest management strategies to address ecological challenges and improve ecosystem resilience. Restoring water levels and vegetation density during the pre-monsoon period can enhance the wetland’s cooling potential, while proactive measures to control invasive species are critical for biodiversity preservation. Strengthening the connectivity between wetland and forest habitats may amplify their cooling effects, especially during the monsoon and post-monsoon seasons. Furthermore, the study recommends detailed hydrological assessments, long-term monitoring of vegetation dynamics, and evaluations of socio-economic implications of ecological changes on local communities and tourism. This study highlights the critical ecological functions of the NBS, emphasizing the importance of sustainable management practices to preserve its microclimatic and biodiversity benefits.