1. Introduction
The Cananéia–Iguape coastal system, located on the southern coast of the state of São Paulo, Brazil, stands out for its size and preservation stage. It has a complex diversity while also exhibiting examples of human interference. Important environmental changes have occurred in approximately the last 150 years because of the opening of an artificial channel, the Valo Grande, connecting the Ribeira de Iguape River to a lagoonal system (Mahiques et al. 2009). The environmental changes led to significant modifications in salinity; in changes of the depositional patterns of sediments and foraminiferal assemblages (including periods of defaunation); and, more drastically, in the input of heavy metals to the coastal environment (Mahiques et al. 2009). This region is notable for its diversity and productivity. It is rich in aquatic species of high economic value and has extensive areas of mangroves, salt marshes, and Atlantic forest.
Mangroves are characterized by their salinized environment and constant tidal flooding. The great biodiversity that is characteristic of the mangrove has suffered significantly because of human actions (see Valiela et al. 2001; Duke et al. 2007). Studies by Alongi (Alongi 2002) and Schaeffer-Novelli et al. (Schaeffer-Novelli et al. 2002) indicate that the mangrove ecosystem is a biological indicator of climatic variations and increases in relative sea level.
Mangroves represent plant communities that are geographically distributed between the intertropical latitudes. The climate attributes control the vegetation in a limiting way. According to Schaeffer-Novelli (Schaeffer-Novelli 1995), the highest degree of mangrove development would require an average air temperature in the coldest month above 20°C and an annual air temperature range of at least 5°C. Blasco (Blasco 1984) states that species disappear when the coldest monthly average air temperature is less than 16°C.
According to Silva and Herz (Silva and Herz 1987), the mangrove acts as a thermal regulator because of the accumulation of solar radiation in the substrate, which has a high water content, is constantly renewed by the tides, and is always available for use by plants in the evaporation process. Therefore, data on the partition of radiant energy in the marsh are critical for understanding the processes that control the microclimate of the environment. According to Ribeiro et al. (Ribeiro et al. 2010), the structure and functionality of the mangrove depend on the stability of the physical environment. However, the physical environment is under pressure caused by anthropic action, including microclimatic changes. This fact has raised concerns about the possible irreversibility of the local environmental impact and its influence on the micrometeorological regime. Additionally, mangroves are important for coastal protection from the winds and tropical storm waves.
According to Alongi (Alongi 2002) mangrove forests and shrubland, or mangroves, form important intertidal ecosystems that link terrestrial and marine systems and provide valuable ecosystem goods and services. The continued decline of the forests is caused by conversion to agriculture, aquaculture, tourism, urban development, and overexploitation (Alongi 2002; Giri et al. 2008). The forests have been declining at a faster rate than inland tropical forests and coral reefs. Predictions suggest that 30%–40% of coastal wetlands and 100% of mangrove forests could be lost in the next 100 years if the present rate of loss continues. As a consequence, important ecosystem goods and services (e.g., natural barrier, carbon sequestration, biodiversity) provided by mangrove forests will be diminished or lost (Duke et al. 2007).
The coverage provided by the canopy controls the quantity, quality, and spatiotemporal distribution of solar radiation, which results in different levels of humidity, temperature, and soil moisture. Moreover, the canopy promotes the interception of rainfall and influences the permeability of incoming and outgoing solar radiation in the environment. This interaction between the climatic attributes and the vegetation cover depends on the characteristics (size, texture, thickness, and orientation of leaves and twigs) and structure (tree height, canopy continuity, density of individuals, and foliage density) of the vegetation, which is expressed by the leaf area index (LAI). The LAI was defined by Watson (Watson 1947) as the integrated leaf area of the canopy per surface unit projected on the ground (m2 m−2), and it is computed using the surface of only one side of the leaves. The determination of this index is important for vegetation structure studies because it is associated with physical processes such as evapotranspiration, CO2 flows, interception of solar radiation, and rainfall.
The aim of this study is to analyze the variations of climate attributes (air temperature and relative humidity, solar radiation, wind speed and direction, and rainfall) in the mangrove forest located in Barra do Ribeira, Iguape, São Paulo, Brazil, by testing whether variations are related to the main features of the canopy in the environment. The transmissivity of global solar radiation in the environment was also tested, with a focus on its temporal variability and vertical attenuation. Variation in the canopy density throughout the year was analyzed by obtaining the LAI and by quantifying rainfall interception by the mangrove canopy.
2. Materials and methods
The study area, which is located on the southern coast of São Paulo, Brazil, is formed by the northeast sector of the Cananéia–Iguape coastal system and is drained by the lower Ribeira de Iguape River. The Cananéia–Iguape system, southeast Brazil, consists of a complex of lagoonal channels, located in a United Nations Educational, Scientific and Cultural Organization (UNESCO) biosphere reserve. Nevertheless, important environmental changes have occurred in approximately the last 150 years because of the opening of an artificial channel, the Valo Grande, connecting the Ribeira de Iguape River to a lagoonal system (Mahiques et al. 2009). This system can be divided in two sectors, northern and southern, based on geomorphology and environmental conditions. In the northern sector, important environmental changes result of the influence an artificial channel Valo Grande. However, the southern sector, which is less influenced by the low salinity of the artificial channel, is considered the best conserved mangrove area along the coast of the state of São Paulo (Cunha-Lignon et al. 2011).
A meteorological tower was installed to obtain a vertical analysis of the variation in climate attributes. The tower was placed at the geographic coordinates of 24°38′01.4″S, 47°25′31.9″W and contained two meteorological stations: one set above the canopy on the edge of the tower at a height of 10 m and the other set beneath the canopy at a height of 2 m. Data collection occurred from 6 February to 31 December 2008. The sensors were programmed to record data every 10 min. For the analysis of rainfall interception, 16 totalizer rain gauges were spread over an area measuring 400 m2, which was subdivided into four portions. The following sensors were used: two CS215 sensors (Campbell Scientific) for air temperature and relative humidity; two TE525MM-L15 sensors (Texas Instruments) and 16 totalizer manual rain gauges for rain; one CNR1 balance radiometer (Kipp & Zonen) and one pyranometer (Kipp & Zonen) for solar radiation; and two 03001-LS15-LD15 sensors (Campbell Scientific) for wind speed and direction.
The LAI and canopy opening data were obtained by hemispherical photography using a Nikon model F-501 camera coupled to a Nikkor 8-mm fish-eye lens with a viewing angle of 180°. The photographs were processed using the software Gap Light Analyzer (GLA) version 2. A total of 64 photographs were obtained, and the four selected days for taking photographs were 22 March (early fall), 11 July (winter), 20 September (early spring and the rainy season), and 30 October 2008 (spring). Figure 1 shows the spatial distribution of the points obtained from the hemispherical photographs.
Boundary diagram for the 400-m2 plot used to obtain the hemispherical photographs.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Boundary diagram for the 400-m2 plot used to obtain the hemispherical photographs.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Boundary diagram for the 400-m2 plot used to obtain the hemispherical photographs.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Additionally, linear regression equations were obtained for the air temperature, rainfall, and solar radiation data. The climate attributes were analyzed monthly, daily, and hourly [0630–1800 local time (LT)]. However, to identify the role of the land/sea breeze for the hourly analysis of wind direction, 1000–2150 LT was considered daytime and 2200–0950 LT was considered nighttime.
3. Results and discussion
3.1. Leaf area index
The canopy opening averages were 35.2%, 41.3%, 42.2%, and 40.3%, and the LAI averages were 1.18, 0.96, 0.93, and 0.96 for plots 1, 2, 3, and 4 on 22 March 2008, 11 July 2008, 20 September 2008, and 30 October 2009, respectively. The canopy opening average was lower on 22 March 2008 (35.2%). On 11 July 2008, the opening was 41.3%, representing an increase of 2.2% during July and 20% compared to March. On 30 October 2008, there was a decrease in the canopy opening (40.3%), representing a 4.5% reduction in the canopy opening relative to September. These data are shown in Table 1.
Leaf area index and canopy opening for 22 Mar, 11 Jul, 20 Sep, and 30 Oct 2008 obtained by processing hemispherical photographs in the GLA software for the Barra do Ribeira mangrove, Iguape, São Paulo, Brazil.
The highest values for the LAI were recorded on 22 March 2008, demonstrating that the leaf production during the summer season is still noticeable despite the early fall in this hemisphere. The increase in the rainy season and the reduced interstitial salinity favor the formation of new leaves. Leaf production decreased in the less rainy season, which is clearly shown by the results obtained in late winter on 11 July 2008, with an LAI of 0.96, and in early spring on 20 September 2008, with an LAI of 0.93. On 30 October 2008, there was an increase in the LAI relative to September. There was an 18.5% reduction in the LAI from March to September and a 3.2% increase from September to October. This value favors a greater input of solar radiation, which tends to influence the amount of life present even in the substrate environment.
3.2. Solar radiation
During the study period, the daily average of global solar radiation for the sensor located at 10 m was 14.1 MJ m−2; the average was 4.0 MJ m−2 below the canopy. The maximum daily global solar radiation occurred on 5 December 2008, when the sensors recorded 30.4 MJ m−2 at 10 m and 12.6 MJ m−2 at 2 m. The minimum values occurred on 23 June 2008 and were recorded as 1.84 MJ m−2 at 10 m and 0.48 MJ m−2 at 2 m. Despite the greater intensity of solar radiation at 10 m than under the vegetation, the variation curves are similar, as shown in Figure 2. At this time scale, the variation in solar declination throughout the year contributes to the amount of solar radiation in the environment.
Total daily global solar radiation above and beneath the mangrove canopy, Barra do Ribeira, Iguape, São Paulo, Brazil, for 2008.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Total daily global solar radiation above and beneath the mangrove canopy, Barra do Ribeira, Iguape, São Paulo, Brazil, for 2008.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Total daily global solar radiation above and beneath the mangrove canopy, Barra do Ribeira, Iguape, São Paulo, Brazil, for 2008.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Because of the variation in the solar radiation caused by the presence of the canopy and cloud cover, a linear regression was established, which correlated the global solar radiation data obtained above and beneath the mangrove canopy (coefficient of determination R2 = 0.8497). The regression analysis revealed that overcast days exhibited the highest correlation between the data (Figure 3).
Relationship between the global solar radiation obtained above (RG10) and below (RG2) the mangrove canopy, Ilha dos Papagaios, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Relationship between the global solar radiation obtained above (RG10) and below (RG2) the mangrove canopy, Ilha dos Papagaios, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Relationship between the global solar radiation obtained above (RG10) and below (RG2) the mangrove canopy, Ilha dos Papagaios, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
The days classified as having high atmospheric transmissivity [i.e., clear-sky (τ > 0.66) and partially cloudy days (0.31 > τ < 0.65)] exhibited higher dispersion among the data than other classifications, as observed in the red circle in Figure 3. Meanwhile, cloudy days (τ < 0.30) exhibited less dispersion than other classifications and therefore higher correlations between the data.
Furthermore, the tide influences the amount of solar radiation that effectively reaches and remains in the subsoil, but this study did not quantify this influence. The average albedo α for the mangrove in 2008 was 7.5%. There was a high correlation between the global solar radiation and the reflected component. The albedo is important because it represents the amount of solar radiation that is not absorbed and therefore is unused by mangrove vegetation.
Canopy transmissivity
The average canopy transmissivity τd was 26.8% and varied between a minimum of 16.5% and a maximum of 44.2%. Figure 4 shows the average daily values for τd and solar declination. Canopy transmissivity in the environment exhibited a variation cycle that decreased and increased throughout the year. The τd had similar values on 1 January and 31 December 2008, thus closing an annual cycle of variation. This cycle occurs primarily because of the variation in solar declination and not the mangrove LAI.
Variation in the canopy transmissivity 
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Variation in the canopy transmissivity
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Variation in the canopy transmissivity
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
3.3. Air temperature and relative humidity
The analysis of the mean air temperature in the mangrove environment yielded results of 21.4°C above the mangrove canopy and 21.1°C beneath the mangrove canopy. The maximum and minimum temperatures were 36.7° and 5.2°C at 10 m and 35.5° and 5.6°C at 2 m from the surface, respectively; the maximum value occurred on 11 January 2008. The absolute maximum at 10 m was, on average, 1°C higher than at 2 m, with differences of up to 2.4°C. The difference in the absolute minimum between the sensors was between 0.6° and −0.4°C.
Figure 5 indicates that the sensor located in the mangrove is more protected from losses and the arrival of radiation into the environment. Therefore, the canopy has an attenuating effect on air temperature. This phenomenon is related to the barrier provided by the canopy of trees that blocks the entry of a portion of the solar radiation into the forest during the day. This smaller amount of solar radiation results in less heating of the soil and, consequently, reduced emissions of longwave radiation and less heating of the air between the ground and the canopy. The air temperature ranges were higher for the sensor at 10 m, with a maximum daily value of 20.4°C on 3 September 2008 and an annual value of 31.5°C. Conversely, the sensor at 2 m recorded an annual value of 29.9°C and a maximum daily value of 19.3°C on 3 September 2008.
Absolute maximum daily air temperatures of the mangrove forest, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Absolute maximum daily air temperatures of the mangrove forest, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Absolute maximum daily air temperatures of the mangrove forest, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
The lowest reading for relative humidity occurred in May at the 10-m sensor, with a value of 79.3%. The lowest reading at the 2-m sensor was 80.8%, which occurred in January. The highest values were recorded in October, with values greater than 84% at both sensors. The average relative humidity values were 82% at 10 m and 82.7% at 2 m. This difference may be related to the accuracy of the sensors, in which case the values would be equal at both measuring heights. The maximum relative humidity (100%) occurred at 2 m on 16 June 2008. At 10 m on the same day, a value of 96.5% was recorded. The minimum absolute values occurred on 22 September 2008, with values of 28.8% and 33.1% at 10 and 2 m, respectively. The highest relative humidity values occurred at 2 m because of the canopy effect, which contributes to retaining moisture within the internal air volume of the mangrove. The canopy also reduces the entry of solar radiation, thereby limiting the heating of the soil and the subsequent heating of the air. Moreover, the tidal variation contributes to increased humidity in the environment.
3.4. Rainfall
The total rainfall recorded at 10 m was 1981.5 mm in 2008. January stood out as the wettest month, with 509.3 mm, while July, with 19.6 mm, was the least rainy month. In the summer, 868.6 mm of rain was recorded, which is equivalent to 43.8% of the precipitation in 2008. In the fall, 438.2 mm of rainfall was recorded (22.1% of the total 2008 precipitation). The lowest amount of rainfall was measured in the winter (308.5 mm, or 15.6% of the total 2008 precipitation), which represented the dry season. Spring was the second least rainy season, with 366.2 mm, corresponding to 18.5% of the total precipitation. The total precipitation (or precipitation above the canopy P10) and the internal precipitation (or precipitation beneath the canopy P02) are highly correlated, as indicated by the linear regression model that has a coefficient of determination R2 of 0.9807 (Figure 6).
Coefficient of determination between the total precipitation and the internal precipitation for the mangrove, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Coefficient of determination between the total precipitation and the internal precipitation for the mangrove, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Coefficient of determination between the total precipitation and the internal precipitation for the mangrove, Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
The period of analysis for determining the interception of precipitation is shown in Table 2. A total precipitation of 1299.6 mm was recorded during the analyzed period. There were 145 rain events, which exhibited a high level of variation with values between 1.0 and 140.2 mm. A lower limit of 1 mm was adopted because events lower than this value can be associated with the occurrence of fog, which represents air saturation in low levels and not actual precipitation.
Period of analysis, number of rainy days, number of events, P10 (mm), P02 (mm), concentration C (mm), loss by interception I (mm), and the percentage of loss by interception (%). Here, concentration = (P10 − P02) < 0 = C; interception I = (P10 − P02) > 0 = I; and analysis valid for rain on a 10-min scale.
Table 2 shows the total precipitation at 10 m (P10); the precipitation recorded by the sensor at 2 m (P02); the amount of precipitation that reached the 2-m sensor coming from other catchment areas following concentration by leaves and branches (C); and the precipitation intercepted by the mangrove canopy, which did not reach the substrate/soil of the mangrove (I).
The average rainfall interception for the mangrove was 19.6%. Previous studies, including Arcova et al. (Arcova et al. 2003) and Oliveira Junior and Dias (Oliveira Junior and Dias 2005), analyzed the interception by vegetation for other forest types. The 2-m sensor recorded precipitation coming from other catchment areas following the concentration of rain by leaves and branches, which resulted from the canopy architecture and the leaf shape of mangrove and, in many instances, this concentration resulted in P02 being greater than P10. On average, this concentration by leaves and branches accounted for 9.4% of the total recorded at 2 m.
By comparing the interceptions that occurred in January and April with the same number of rainfall events, it was determined that interception was 23.1% for April. The interception for January was 15.3% of the monthly total for April, indicating that the type of rain influences interception. In January, high-intensity convective rains are predominant. In April, there is a higher probability of moderate- to low-intensity frontal rains. The lowest records of rainfall events occurred in the period from 16 to 22 February 2008, with two precipitation events corresponding to 16.5% of the interception. December stood out among the spring months (October–December) and had 23 events and a maximum interception of 25.8%. October and November had 10 precipitation events each, with interceptions of 18.7% and 20.6%, respectively.
There was a high correlation between the data from the two sensors in January, with a determination coefficient R2 of 0.9439. In January, which was considered to be a rainy month, the precipitation at the 2-m sensor was at times greater than that at the 10-m sensor. This phenomenon could be explained by the structural characteristics of the mangrove vegetation, which redistributes precipitation along the leaves and branches and concentrates it on the 2-m sensor. This trend occurred throughout the observation period. Thus, interception in the mangrove environment varies according to the structural features of the vegetation and the dominant precipitation regime. The amount of precipitation that effectively reaches the soil and its redistribution within the environment depends on the canopy density and its branch and stem ramifications. This process is very important for the mangroves because the amount of rainfall that actually reaches the soil reduces the salinity in the environment and determines the predominant species in the mangrove.
Furthermore, the precipitation is differentially distributed as it passes through the mangrove canopy as a function of the canopy architecture and density. The installation locations of the totalizer rain gauges accumulated a considerable amount of precipitation concentrated by leaves and branches. The four plots marked for installation of rain gauges had different records of precipitation: plot 1 = 1910 mm, plot 2 = 1543 mm, plot 3 = 1713 mm, and plot 4 = 1878 mm. Plot 1 recorded the highest internal precipitation value. Plots 3 and 4 also recorded high values of precipitation. However, despite the difference between the rain gauges and their locations, the mangrove vegetation, specifically the species R. mangle, contributed to the concentration of rainwater at the collection points.
3.5. Wind
At the 10-m sensor, the maximum gust recorded was 13 m s−1 on 12 November 2008. At the 2-m sensor, the maximum gust on the same day was 4.7 m s−1, a decrease of 63.7%. Figure 7 shows the maximum gusts recorded in the mangrove forest during the analysis period. However, there were technical sensor malfunctions during the period from 24 June to 20 September 2008. For the average maximum gust, the 10-m sensor recorded a value of 5.6 m s−1, while the average was 2.9 m s−1 at 2 m, a decrease of 48.8%. The average speed was 0.65 m s−1 at 10 m and 0.24 m s−1 at 2 m, corresponding to a decrease of 63.6% at 2 m.
Maximum gusts recorded at 10 and 2 m in the mangrove of Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Maximum gusts recorded at 10 and 2 m in the mangrove of Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Maximum gusts recorded at 10 and 2 m in the mangrove of Barra do Ribeira, Iguape, São Paulo, Brazil.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Several authors have studied wind speed reduction inside forests. In these studies, when compared to external environments, wind speed measurements inside forests revealed a dampening effect of 70%–85% (Cestaro 1988; Chen et al. 1993; Hawke and Wedderburn 1994; Morecroft et al. 1998) (as cited in Hernandes et al. 2002), which is very close to the results recorded in the mangrove environment. The predominant wind direction recorded at the 10-m sensor was predominantly from the east, accounting for 20% of the observations. A still-air situation predominated, accounting for 22.6% of the observations. At 2 m, the predominant direction was also from the east, accounting for 18% of the observations; however, there was a higher incidence of still air (24.5% of the observations). This effect was associated with the presence of vegetation, which tends to diminish the intensity of the wind, thereby minimizing its effects. Figure 8 shows the predominant wind directions in the mangrove forest at both levels. The east and southeast directions prevailed at 10 m; the east and west directions prevailed at 2 m.
Predominant wind direction in the mangrove of Ilha dos Papagaios, Barra do Ribeira, Iguape, São Paulo, Brazil, recorded (a) above the canopy (D10) and (b) beneath the canopy (D2) in 2008.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Predominant wind direction in the mangrove of Ilha dos Papagaios, Barra do Ribeira, Iguape, São Paulo, Brazil, recorded (a) above the canopy (D10) and (b) beneath the canopy (D2) in 2008.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Predominant wind direction in the mangrove of Ilha dos Papagaios, Barra do Ribeira, Iguape, São Paulo, Brazil, recorded (a) above the canopy (D10) and (b) beneath the canopy (D2) in 2008.
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
For the data analyzed on an hourly scale, we obtained the prevailing wind directions during the daytime (1000–2150 LT) and nighttime (2200–0950 LT). A change was noted in the wind direction caused by the land breeze (nighttime) and the sea breeze (daytime). During the daytime, wind coming from the east predominated at both 10 and 2 m, accounting for 28.2% and 27.3% of the observations, respectively. However, at nighttime, both sensors showed changes in the predominant wind direction to the west at 2 m (23.1%) and to the northwest at 10 m (15.8%). However, still-air situations were noted at both sensors, especially during the nighttime. At 10 m, still air was recorded in 15.2% of the observations during the day and in 30.1% of the observations at night. At 2 m, still air was recorded in 16.9% of the observations during the day and in 32% of the observations at night. This trend is caused by the effects of the land breeze because there is no heating of the air produced by radiative cooling at night, resulting in a less intense breeze. The 2-m sensor revealed more occurrences of still air because of the presence of vegetation. Figure 9 shows a summary of the data obtained above and below the canopy at 10 and 2 m, respectively.
Summary of the data obtained above and beneath the mangrove canopy, Barra do Ribeira, Iguape, São Paulo, Brazil. Legend: P10 = total precipitation at 10 m; P02 = total precipitation at 2 m; I = interception; UR10max = maximum relative humidity at 10 m; UR10med = mean relative humidity at 10 m; UR10min = minimum relative humidity at 10 m; UR2max = maximum relative humidity at 2 m; UR2med = mean relative humidity at 2 m; UR2min = minimum relative humidity at 2 m; RG10 = global solar radiation at 10 m; RG2 = global solar radiation at 2 m; T10max = maximum air temperature at 10 m; T10med = mean air temperature at 10 m; T10min = minimum air temperature at 10 m; T2max = maximum air temperature at 2 m; T2med = mean air temperature at 2 m; T2min = minimum air temperature at 2 m; V10max = maximum gust at 10 m; V2max = maximum gust at 2 m; D10 = predominant wind direction at 10 m; and D02 = predominant wind direction at 2 m
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Summary of the data obtained above and beneath the mangrove canopy, Barra do Ribeira, Iguape, São Paulo, Brazil. Legend: P10 = total precipitation at 10 m; P02 = total precipitation at 2 m; I = interception; UR10max = maximum relative humidity at 10 m; UR10med = mean relative humidity at 10 m; UR10min = minimum relative humidity at 10 m; UR2max = maximum relative humidity at 2 m; UR2med = mean relative humidity at 2 m; UR2min = minimum relative humidity at 2 m; RG10 = global solar radiation at 10 m; RG2 = global solar radiation at 2 m; T10max = maximum air temperature at 10 m; T10med = mean air temperature at 10 m; T10min = minimum air temperature at 10 m; T2max = maximum air temperature at 2 m; T2med = mean air temperature at 2 m; T2min = minimum air temperature at 2 m; V10max = maximum gust at 10 m; V2max = maximum gust at 2 m; D10 = predominant wind direction at 10 m; and D02 = predominant wind direction at 2 m
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
Summary of the data obtained above and beneath the mangrove canopy, Barra do Ribeira, Iguape, São Paulo, Brazil. Legend: P10 = total precipitation at 10 m; P02 = total precipitation at 2 m; I = interception; UR10max = maximum relative humidity at 10 m; UR10med = mean relative humidity at 10 m; UR10min = minimum relative humidity at 10 m; UR2max = maximum relative humidity at 2 m; UR2med = mean relative humidity at 2 m; UR2min = minimum relative humidity at 2 m; RG10 = global solar radiation at 10 m; RG2 = global solar radiation at 2 m; T10max = maximum air temperature at 10 m; T10med = mean air temperature at 10 m; T10min = minimum air temperature at 10 m; T2max = maximum air temperature at 2 m; T2med = mean air temperature at 2 m; T2min = minimum air temperature at 2 m; V10max = maximum gust at 10 m; V2max = maximum gust at 2 m; D10 = predominant wind direction at 10 m; and D02 = predominant wind direction at 2 m
Citation: Earth Interactions 17, 2; 10.1175/2012EI000464.1
4. Discussion and conclusions
The study area was dominated by the typical mangrove vegetation R. mangle. The mangrove canopy exhibited variations in the amount of leaves and the canopy opening, demonstrating that the leaf production characteristics during the summer were noticeable. Leaf production increased and interstitial salinity decreased during the rainy season, favoring the formation of new leaves, and there was a lower-level leaf production during drier seasons.
The physiognomic structure of the mangrove canopy had a direct influence on the variation of climate attributes. On average, the global solar radiation interacting with the mangrove canopy had a transmissivity of 26.8%. Furthermore, the atmospheric transmissivity τ influenced the canopy transmissivity τd. On cloudy days, the amount of solar radiation beneath the canopy was less than that on clear days. The transmissivity of the mangrove canopy showed a cycle of variation throughout the year, producing similar values at the beginning and end of the year. Regardless of the season, the presence of fog contributed to a reduction of the energy available in the environment; on clear days, the incidence of solar radiation was directly influenced by the sunlight incidence angle.
The air temperature was higher at the 10-m sensor than at the 2-m sensor. The canopy had an attenuating effect on the air temperature, mainly during the daytime. The lowest values for relative humidity observed throughout the day were recorded at 10 m, reflecting the absence of the canopy, as the canopy contributes to maintaining humidity in the internal air volume of the mangrove. The average rain interception for the mangrove environment was 19.6%. The amount of precipitation that actually hits the ground and its redistribution within the environment depends on the canopy density and its branch and stem architecture. For the mangrove, this process is very important because the amount of rainfall that actually reaches the soil contributes to the reduction of the salinity present in the environment and determines the predominant species in the mangrove.
There were 63.7% reductions at the 2-m sensor for both the maximum gust and average wind speed. However, despite the still-air situation highlighted at both levels, the reduction was higher at the 2-m sensor than at the 10-m sensor. This higher reduction at the 2-m sensor was associated with the presence of vegetation that tends to diminish the wind intensity on the lower level, minimizing its effects. East and southeast wind directions prevailed at the 10-m sensor, while east and west wind directions prevailed at the 2-m sensor. Therefore, the directions of the north–south and south–north quadrants were reduced, indicating the action of the sea and land breezes.
Microclimatic studies are important as they contribute to a better understanding of the characteristics of the mangrove ecosystem. This approach reflects the environmental conditions in which the forest is exposed. Microclimatic changes can interfere with both the growth of mangrove forest and in its ecological function. Moreover, such experiments highlight the importance of mangrove conservation as protector of the coastline, in cases of storms and/or extreme events. However, further research is important for understanding climate changes in the mangrove worldwide.
Acknowledgments
The authors appreciate the financial support made available by the National Council on Scientific and Technological Development (CNPq; Process Numbers 479219/2011-5, 305866/2009-5, and 470434/2006-6).
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