Precipitation regionalization of the Brazilian Amazon

(wileyonlinelibrary.com) DOI: 10.1002/asl2.535

Precipitation regionalization of the Brazilian Amazon

Programa de Pós-graduação em Ciências Climáticas, Universidade Federal do Rio Grande do Norte, 59078-970 Natal, Brazil

*Correspondence to: E. B. Santos, Programa de Pós-graduação em Ciências Climáticas, Universidade Federal do Rio Grande do Norte, Campus Universitário Lagoa Nova, Caixa Postal 1524, 59078-970 Natal, RN, Brazil. E-mail: elianbs@gmail.com Received: 12 February 2014 Revised: 15 July 2014 Accepted: 20 August 2014 Abstract

The Brazilian Amazon is a large territory, where different weather systems act, contributing to non-homogeneity of the rainfall seasonal distribution in the region. The aim of this study is to determine sub-regions of homogeneous precipitation in the Amazon, linking them to the main atmospheric systems that affect the rainfall in the region. For this, hierarchical cluster analysis was applied on a data set composed by 305 rain gauges. The results suggest that the Brazilian Amazon has six pluvial homogeneous regions.

Keywords: climatological normal; cluster analysis; Silhouette Index

1. Introduction

The Brazilian Amazon is located in the equatorial region between 5∘N–18∘S and 42∘W–74∘W, and it is characterized by having a moist atmosphere with large and intense convective activity due to the dia-batic heating from the solar energy throughout the year in association with mechanisms, such as the Intertropical Convergence Zone (ITCZ) migration, Coastal Squall Lines (CSL) propagation, and others. The climate of this region is determined by a combi-nation of various physical and dynamical processes of large-scale, as well as local features, which are responsible for temporal and spatial distribution of precipitation.

Among the synoptic scale weather systems that affect the rainfall in the Amazon, the main ones are: (1) ITCZ, responsible for the maximum rainfall during the austral autumn (De Souza et al., 2005; De Souza and Rocha, 2006), (2) the South Atlantic Convergence Zone (SACZ), acting mainly in the south and southwestern Amazon region, responsible for the maximum rainfall in late spring and austral summer (Carvalho et al., 2004; Grimm, 2011; De Oliveira Vieira et al., 2013), and (3) the Bolivian High (BH), which also contributes for the precipitation during the austral summer (Figueroa et al., 1995; Lenters and Cook, 1997). During the austral win-ter, the migration of the ITCZ to the Northern Hemi-sphere and the weakening of BH change the intensity and distribution of precipitation in the Amazon. Dur-ing the austral winter, surges of cold high-latitude air, known locally as ‘friagens’, move across southeastern Brazil and Amazonia from the south, greatly modify-ing the atmospheric structure and climatic conditions. The characteristics of this phenomenon are more easily detected at stations southwest of the Amazon (Marengo

et al., 1997, Longo et al., 2004).

The main mesoscale system that acts in the region is the CSL (Cohen et al., 1995), which are formed along the northern coast of South America, associated with sea breeze circulation, more frequent between April and June and less frequent between October and November (Alcantara et al., 2011). The propagation of CSL modulates the diurnal cycle of precipitation, which is characterized by the strongest rainfall rate between 1400 and 1800 HL (Santos e Silva et al., 2012). Furthermore, the local wind mechanisms, such as river breeze, are also important to the diurnal cycle and the intensity of the rainfall in this region (Oliveira and Fitzjarrald, 1993; Silva Dias et al., 2004).

These weather systems operate in different localities in the Amazon, so the rainfall in this region is not homogeneous, showing great variability in time and space. Nevertheless, the question remains: how many sub-regions are sufficient to represent the rainfall vari-ability in the Amazon? In this context, the objective of this study is identifying homogeneous regions of precipitation, based on the rainfall climatology. Thus, contribute to the analysis of climate in the Brazilian Amazon.

2. Materials and methods

2.1. Data sets

Daily rainfall data set was obtained from National Water Agency – Agência Nacional de Água and Meteoro-logical Database for Education and Research – Banco

de Dados Meteorológicos para Ensino e Pesquisa

of the National Institute of Meteorology – Instituto

Nacional de Meteorologia. The total monthly and

cli-matology of precipitation were calculated following the recommendations of the World Meteorological Orga-nization (WMO), established in Technical Document

186 E. B. Santos, P. S. Lucio and C. M. Santos e Silva

WMO-TD/No. 341, for the period from 1983 to 2012. In this document, it is recommended: (1) to discard the month that has any missing daily value and (2) to exclude the climatological normal, the monthly data that present three or more consecutive gaps or more than five alternate months missing. The initial set consisted of 1129 rain gauges, but to implement WMO, 305 remained.

2.2. Methods

The climatological normal of the 305 stations were used as attributes to characterize the homogeneous regions by means of cluster analysis, a multivariate technique that searches for data elements based on the similar-ity between them. The clusters are determined so as to obtain homogeneity within them and heterogeneity between them.

The first stage of the clustering process is the estimation of a measure of similarity (or dissim-ilarity). In this work, the Euclidean distance was used, which according Mimmack et al. (2001) is one of the measures listed for regionalization of climate data. The Euclidean distance between two ele-ments X = [X1, X2, … , Xn] and Y = [Y1, Y2, … , Yn] is defined by d_xy=√(X₁− Y₁)2+(X₂− Y₂)2+ … +(X_n− Y_n)2 = √ √ √ √∑n i=1 ( Xi− Yi )2 (1) where Xiand Yiare the elements to be compared, which in this study are the climatological normal.

The second step is to define the method, which can be classified into agglomerative and divisive. We used the hierarchical agglomerative Ward method in the present paper, which identifies the lowest vari-ance between clusters, joining elements whose sum of squares between them is minimal or that the error of this sum is minimal (Hervada-Sala and Jarauta-Bragulat, 2004). Through prior knowledge about the data struc-ture, a distance cutoff was determined to define which clusters will be formed.

The quality of the formed clusters was assessed using the Silhouette Index (SI), developed by Rousseeuw (1987), which evaluates how an observation is similar to other observations inserted in its cluster, compared with inserted observations in other clusters. Each observation has an SI, and an overall average of all observations allows us to evaluate the overall perfor-mance of the cluster. The values of this index vary in the range from −1 to 1. The values close to 1 indicate that the object is in the correct cluster. The values close to −1 indicate that the observation was probably allocated to an inappropriate cluster. The values near zero indicate that the object is close to the boundary between two clusters and do not belong to one cluster or another. The SI(n) is calculated according to the

following equation (Rousseeuw, 1987): SI (n) = b (n) − a (n)

max {a (n), b (n)} (2)

with the observation being evaluated, a(n) is the mean distance of the nth observation to all others within the same cluster and b(n) is the average distance of the

nth observation to all other allocated in the closest

cluster. The overall quality of the cluster can be mea-sured by the average SI(n), according to the following equation:

SI = ∑N

n=1SIn

N (3)

where N is the total number of observations.

Confidence intervals (CI) to the 95% quantile of the climatological normal were constructed and applied. The objective of estimating quantile’s intervals is to build a CI for the parameter with a probability of 1 −𝛼 (confidence level) that the interval contains the true parameter. XFis the parameter of interest,𝜆i the lower limit, and 𝜆s the upper limit. The CI is given by the equation:

P(𝜆i< XF < 𝜆s )

= 1 −𝛼 (4)

Considering the 95% CI,𝛼 is 5%, which is the error that can occur when stated that 95% of the time interval (𝜆_i< X_F< 𝜆_s) contains X_F.

3. Results and discussion

In the formation of two sub-regions (Figure 1), the Brazilian Amazon was split into South (Region 1) and North (Region 2), which was expected, since the main rain-producing systems of the North and South of the Amazon are different. In the South, the main systems are SACZ and BH and in northern Amazon are ITCZ and CSL. Another important precipitation process in the Amazon, especially in the North, is the radiative surface heating, which can generate cells and convective clus-ters typical of tropical regions (Strong et al., 2005).

This subdivision is also consistent with the studies by Marengo (2004) and Marengo (2009), which subdivide the Amazon basin into North and South. According to the studies of the annual cycle of convective activity in the Amazon, the rainy period or strong convective activity in the region shows different patterns between northern and southern Amazonia (Nobre et al., 1991; Villar et al., 2009). However, as can be seen in Figure 1, the two sub-regions are not sufficient to define rainfall homogeneous regions in this region, because the region 2 (northern Amazon) exhibited a low SI (0.22), so the rainfall climatological normals in this region showed different patterns and some stations were outside the CI. As the length of the CI is associated with the accuracy, the smaller the length the more accurate is the average. Note that the length of the CI in region 2 is much larger than the region 1, confirming that region 2 does not represent a homogeneous rainfall region.

Figure 1. Analysis of grouping to two sub-regions: (a) spatial distribution of stations; (b) SI graph; and (c) and (d) precipitation climatological normals (grey lines) of the stations belonging to the regions 1 and 2, respectively. The dotted blue lines represent the CI.

Agreeing with some studies (Rao and Hada, 1990; Liebmann and Marengo, 2001; Marengo and Nobre, 2009; Reboita et al., 2010), which analyzed the mean annual cycle of rainfall in Amazonia, more than two sub-regions are required to represent the patterns of pre-cipitation in this region. According to Liebmann and Marengo (2001), the annual mean precipitation in the Brazilian Amazon varies by more than 50% within Brazilian Amazonia, ranging from less than 2000 mm in the south, east, and extreme north, to more than 3000 mm in the northwest, where orographic uplift begins to operate. A secondary maximum was also observed near the mouth of the Amazon River, which is associated with nighttime convergence of the east-erly trades with the land breeze. Whereas the three sub-regions (Figure 2), the northern Amazon region that was divided into two (coastal zone and northwest Ama-zon) and the South, remained the same. The subdivision in the northern Amazon is consistent with the systems operating in the region. In the coastal area of North Amazon, the precipitation is associated with sea breeze, and local convection. In addition, the trade winds can intensify the sea breeze and contribute to CSL forma-tion that can propagate ∼2000 km into the Amazon basin (Cohen et al., 1995; Santos e Silva, 2013).

The sub-regions of the North, coastal zone (region 2 in Figure 2) and northwestern Amazon (region 3 in

Figure 2), show different patterns, so they do not belong to the same .The coastal area of the Amazon has maxi-mum rainfall in the first half of the year and a dry period in the second half, while the northwestern Amazon has annual maximum in austral winter and a reduction in the austral summer, but does not have a well-defined dry season. In Figure 2(b), it is noticed that the formation of the SI in the three sub-regions showed better results where the lowest SI was 0.3. In the SI, it can also be seen a better result, because the lengths of the CI were lower and there was a decrease in the amount of stations outside the SI.

Dividing the Amazon into four sub-regions, it is noticed that the sub-regions of the North and South remain the same and the South Amazon is divided into two (regions 1 and 2 in Figure 3), but these sub-regions have similar patterns separated due to the intensity of precipitation. Precipitation of region 2 (Figure 3(d)) is less intense compared with region 1 (Figure 3(c)). The intensity of precipitation of these sub-regions may be related to the kinds of vegetations, as well as deforesta-tion areas (Durieux et al., 2003).

Amazon deforestation does not occur homoge-neously, as well as the Southern Amazon was not separated into homogeneous sub-regions. Deforestation forms a band extending from Maranhão to Rondônia, passing through eastern Pará and by the states of

188 E. B. Santos, P. S. Lucio and C. M. Santos e Silva

Figure 2. Analysis of clustering to three sub-regions: (a) spatial distribution of stations; (b) SI graph; and (c), (d), and (e) precipitation climatological normals (grey lines) of the stations belonging to the regions 1, 2, and 3, respectively. The dotted blue lines represent the CI.

Tocantins and Mato Grosso, forming the so-called arc of deforestation. In regions with contrasting vegeta-tion such as the arc of deforestavegeta-tion, there are direct thermal circulations that contribute to the formation of convective precipitation (Silva Dias et al., 2002). Saad

et al. (2010) showed that both the area and the shape

(with respect to wind incidence) of deforestation and the soil moisture status contributed to the state of the atmosphere during the time scale of several weeks, with distinguishable patterns of temperature, humidity, and rainfall. With regard to the types of vegetation, it is noticed that most of the region 2 in Figure 3 (driest region of the Brazilian Amazon) have stations that are in the cerrado, such as southern Maranhão. In the area of native forest (region 1 in Figure 3), it is noticed that the rainy season starts earlier and lasts longer than in the transition (cerrado) region, which is characterized by the region 2 in Figure 3.

These results are consistent with some studies that use climate models to simulate climate change caused by deforestation of the Amazon, which found that the average rainfall decreases with increasing deforesta-tion, and the distribution of rainfall is affected by the type of surface coverage as well as by the topography. While in the deforested areas of the region, an important decrease in precipitation occurs, the areas around these and the higher regions receive more rainfall (Ramos da Silva and Avissar, 2006; Ramos da Silva et al., 2008). However, we did not perform statistic tests to verify the direct effect between deforestation and rainfall over these regions to the studied period (1983–2012). In this sense, the precipitation variability in the sub-regions 1 and 2 can be attributed also to natural factor such as topography.

Similar to the formation of the four sub-regions, the next sub-regions have been separated due to the

Figure 3. Analysis of clustering to four sub-regions: (a) spatial distribution of stations; (b) SI graph; and (c), (d), (e), and (f) precipitation climatological normals (grey lines) of the stations belonging to the regions 1, 2, 3, and 4, respectively. The dotted blue lines represent the CI.

intensity of precipitation. For five sub-regions, the region that was subdivided was the coastal of the Amazon (forming regions 3 and 4 in Figure 4). These sub-regions showed the same patterns, but with dif-ferent intensities. Region 3 (Figure 4(e)) has stations nearer to the coast and with that in these regions the rainfall is higher due to the influence of CSL that are formed along the coastline. The stations in region 4 (Figure 4(f)) are furthest from the coastline, and as not all CSL propagate towards the inside of the Amazon, precipitation in this region will be of lower intensity compared with region 3 (Figure 4(e)).

In the formation of six sub-regions, the region that was subdivided was the northwestern Amazon (forming regions 5 and 6 of Figure 5). The region 6 consists of stations in the state of Roraima, all from the northern hemisphere. This region has climatic characteristics of the northern hemisphere, with annual maximum in

austral winter, as observed by Rao and Hada (1990) and Reboita et al. (2010). In turn, the region 5 (region of northwest and north-northwest of the state of Ama-zonas) has high precipitation throughout the year, show-ing no drought.

These results are in agreement with Rao and Hada (1990), by studying the annual cycle of precipitation, which infers that the tropical convection migrates from the central and southern portion of the Amazon basin in the austral summer to the northwestern sector of South America in the austral winter. Thus, the annual march of deep tropical convection seems to be a key factor in the annual rainfall cycle in this region. Keller Filho et al. (2005) delimited homogeneous regions in Brazil, with the temporal distribution of droughts and frequency distributions rainfall as variables using the hierarchical cluster analysis. Analyzing the regions that are part of the Amazon, more than six homogenous zones were

190 E. B. Santos, P. S. Lucio and C. M. Santos e Silva

Figure 4. Analysis of clustering to five sub-regions: (a) spatial distribution of stations; (b) SI graph; and (c), (d), (e), (f), and (g) precipitation climatological normals (grey lines) of the stations belonging to the regions 1, 2, 3, 4, and 5, respectively. The dotted blue lines represent the CI.

identified. However, as can be seen in Figure 5, six sub-regions are sufficient to represent the patterns of Amazon rainfall.

The best SI (0.45) was found in three sub-regions; however, we observed that to characterize the overall rainfall variability in Amazon basin six sub-regions are necessary. In addition with six sub-regions, both profile and intensity of rainfall are distinguished.

In the SI, it is noticed that as the number of sub-regions increased, fewer stations are outside the SI, and the lengths of these intervals decreased, thereby increasing the accuracy of the measurements.

4. Conclusions

The large territory and varied landforms of the Amazon allow the development and performance of different weather systems that contribute to the existence of at least three rainfall homogeneous sub-regions, associ-ated systems: ITCZ, SACZ, BH, and CSL.

These results suggest that three sub-regions are suf-ficient to separate the Brazilian Amazon into different patterns of precipitation, but in more detailed studies, the ideal is to use the six sub-regions, which are also separated considering rainfall intensity.

Figure 5. Analysis of clustering to six sub-regions: (a) spatial distribution of stations; (b) SI graph; and (c), (d), (e), (f), (g), and (h) precipitation climatological normals (grey lines) of the stations belonging to the regions 1, 2, 3, 4, 5, and 6, respectively. The dotted blue lines represent the CI.

All sub-regions formed by agglomerative hierarchical Ward method are consistent with the performance of the main weather systems of precipitation generators and/or local conditions in the region. Local conditions contribute mainly in separating sub-regions considering the intensity of precipitation.

The results may serve to help in the analysis of weather forecasts and validation of the annual cycle of climate models. In addition, it may also be useful in the planning of human activities, such as the activities of the productive sector – particularly those related to agriculture, hydropower generation, and distribution of energy, industry, etc.

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