Homogeneous groups
Cluster analysis identified 4 homogeneous groups regarding annual rainfall distribution associated to the risk of natural disasters, for stations located in MTJ and its surroundings (Fig. 3). Regionalization on the basis of the properties of hydro-meteorological data helps in identifying the regions reflecting the similar characteristics which could be useful in designing hydrological structures as well as planning and management of water resources of the region (Goyal et al., 2019). Clustering result showed relevance to the physiographic aspects that characterize the rainfall dynamics in CRJ. Local orography is a key modulator of the spatiotemporal connections and substantially enhances the probability of co-occurrence of extreme precipitation events even for distant locations (Mastrantonas et al. 2021). The dendrogram shows groups that were formed, where each group gathers rainfall stations that have the greatest similarity in the rainfall distribution behavior. The graphic representation of the dendrogram is characterized by the agglutination of stations that show similar rainfall distribution in CRJ, indicated by stations that make up each group. The dissimilarity obtained for clusters was small or close to zero, indicating that the groups and / or subgroups formed have similarities in the rainfall distribution behavior.
Proximity matrix of the Euclidean distance between the stations of each group can be seen in Table 2. The results found show that stations with shorter Euclidean distance show greater similarity, with dissimilarity being the degree of distance between the stations.
Table 2
Proximity matrix of the Euclidean distance between stations.
|
V
|
R
|
T
|
Sta T
|
C
|
G
|
M
|
P
|
J
|
J B
|
B I
|
C D
|
A B V
|
G
|
V
|
0.0
|
1.5
|
1.0
|
1.1
|
0.7
|
1.3
|
1.5
|
1.4
|
1.4
|
0.7
|
0.9
|
1.3
|
2.2
|
1.4
|
R
|
|
0.0
|
2.1
|
1.9
|
2.1
|
2.6
|
2.7
|
2.6
|
2.6
|
1.2
|
1.0
|
2.5
|
2.6
|
2.6
|
T
|
|
|
0.0
|
0.6
|
0.7
|
0.8
|
1.3
|
1.2
|
1.2
|
1.1
|
1.5
|
1.1
|
3.0
|
1.1
|
Sta T
|
|
|
|
0.0
|
0.9
|
1.2
|
1.3
|
1.3
|
1.2
|
1.1
|
1.2
|
1.1
|
3.1
|
1.2
|
C
|
|
|
|
|
0.0
|
0.9
|
1.1
|
1.0
|
1.0
|
1.1
|
1.4
|
0.8
|
2.7
|
1.0
|
G
|
|
|
|
|
|
0.0
|
0.9
|
0.8
|
0.9
|
1.6
|
1.8
|
0.7
|
2.8
|
0.7
|
M
|
|
|
|
|
|
|
0.0
|
0.3
|
0.3
|
1.8
|
1.9
|
0.4
|
3.0
|
0.5
|
P
|
|
|
|
|
|
|
|
0.0
|
0.4
|
1.7
|
1.8
|
0.4
|
2.9
|
0.3
|
J
|
|
|
|
|
|
|
|
|
0.0
|
1.7
|
1.8
|
0.3
|
2.9
|
0.4
|
J B
|
|
|
|
|
|
|
|
|
|
0.0
|
0.7
|
1.6
|
2.6
|
1.7
|
B I
|
|
|
|
|
|
|
|
|
|
|
0.0
|
1.7
|
2.4
|
1.8
|
C D
|
|
|
|
|
|
|
|
|
|
|
|
0.0
|
2.8
|
0.3
|
A B V
|
|
|
|
|
|
|
|
|
|
|
|
|
0.0
|
2.9
|
G M
|
|
|
|
|
|
|
|
|
|
|
|
|
|
0.0
|
wherein: V = Vidigal; R = Rocinha; T = Tijuca; Sta T = Santa Tereza; C = Copacabana; G = Grajaú; M = Madureira; P = Piedade; J = Jacaré; J B = Jardim Botânico; B I = Barra Itanhanga; C D = Cidade de Deus; A B V = Alto da Boa Vista and G M = Grande Meir.
Homogeneous rainfall distribution groups were characterized as follows: Group-I (light blue) composed of Vidigal, Rocinha, Jardim Botânico and Barra Itanhangá stations. Group-II (dark blue) composed of only one station, Alto da Boa Vista. Group-III (pink) represented by Tijuca, Santa Tereza, Copacabana and Grajaú stations. Group-IV (green), composed of Piedade, Grande Méier, Madureira, Cidade de Deus and Jacarepaguá stations. The results found show that stations with lower ED show greater similarity, and dissimilarity was the distancing degree between stations. Group 1 (light blue), from right to left on the dendrogram, is composed of Vidigal, Rocinha, Jardim Botânico and Barra Itanhangá stations. This result shows that the annual rainfall distribution behaved with greater similarity for these stations. Vidigal and Jardim Botânico stations were a subgroup due to the greater rainfall distribution similarity, which can be observed in Table 3.
Table 3
Statistics of the monthly rainfall (mm) of the stations the Groups.
Group
|
Station
|
Min
|
Max
|
Sum
|
Mean
|
Variance
|
SD
|
Median
|
CV
|
I
|
Vidigal
|
57.9
|
151.0
|
1284.0
|
107.0
|
739.6
|
27.2
|
102.5
|
25.4
|
Rocinha
|
85.5
|
185.8
|
1686.4
|
140.5
|
1016.4
|
31.9
|
144.8
|
22.7
|
Jard. Botânico
|
66.4
|
161.5
|
1430.5
|
119.2
|
878.5
|
29.6
|
119.6
|
24.9
|
Barra_Itanhaga
|
66.2
|
177.3
|
1562.6
|
130.2
|
1258.3
|
35.5
|
128.4
|
27.2
|
II
|
Alto da Boa Vista
|
97.5
|
257.2
|
2078.6
|
173.2
|
2518.6
|
50.2
|
173.5
|
29.0
|
III
|
Tijuca
|
53.7
|
191.6
|
1516.8
|
126.4
|
1982.2
|
44.5
|
119.0
|
35.2
|
Sta Tereza
|
48.4
|
165.6
|
1302.4
|
108.5
|
1393.9
|
37.3
|
104.2
|
34.4
|
Copacabana
|
41.4
|
143.0
|
1124.2
|
93.7
|
884.2
|
29.7
|
88.6
|
31.7
|
Grajau
|
38.4
|
178.0
|
1223.4
|
102.0
|
2137.9
|
46.2
|
96.2
|
45.4
|
IV
|
Madureira
|
25.9
|
184.3
|
1061.5
|
88.5
|
2380.0
|
48.8
|
82.7
|
55.2
|
Piedade
|
30.8
|
179.8
|
1079.1
|
89.9
|
2135.9
|
46.2
|
85.3
|
51.4
|
Jacarepaguá
|
32.8
|
174.6
|
1082.8
|
90.2
|
1994.2
|
44.7
|
82.4
|
49.5
|
Cidade_Deus
|
38.8
|
156.7
|
1078.9
|
89.9
|
1309.4
|
36.2
|
85.8
|
40.2
|
Grande_Meier
|
27.4
|
194.3
|
1139.5
|
95.0
|
2984.8
|
54.6
|
88.4
|
57.5
|
Min – minimum value, Max – maximum value, SD - standard deviation, CV- coeffient of variation.
The statistical analysis for stations that compose Group-I shows that Vidigal and Jardim Botânico stations present rainfall distribution similar to the other stations in the group. Thus, the lowest dissimilarity was observed for these two stations, with ED of 0.65, being the lowest trunk height of Group-I. This proximity can also be observed by statistical parameters available in Table 3, with maximum, total, average, variance, standard deviation, median and the closest coefficient of variation. The greatest dissimilarity presented by Group-I was observed for Rocinha station, which is a result of the higher rainfall values measured in that station. This dissimilarity is a result of the higher altitude of the station and its position to the windward side of MTJ, which makes this station to present the highest total rainfalls in the group. In this station, ED was 1.53 for Vidigal station, 1.18 for Jardim Botânico and 1.02 for Barra Itanhangá stations. Mastrantonas et al. (2021) indicate the strong influence of orography on precipitation intensity.In the case of Barra Itanhangá station, ED proximity value was smaller for subgroup composed of Jardim Botânico (0.73) and Vidigal (0.87) than with Rocinha (1.02) stations. For this reason, Barra_Itanhangá station directly connects to this subgroup composed of Jardim Botânico and Vidigal stations. For the Rocinha station, the ED proximity value (1.02) was lower with Barra_Itanhangá station, resulting in similarity in the annual rainfall behavior. Cluster composed of Group-I stations is due to the proximity of stations to the coast, making stations to be positioned to the windward of the Tijuca massif slope, facing the ocean. This slope is influenced by ocean currents, the effect of sea breeze, showing forest cover around it (Fig. 4). These characteristics make neighborhoods that compose Group-I to have great potential for the occurrence of natural disasters.
The physical and geographic locations of Group-I stations, mainly Rocinha and Vidigal stations, commonly present natural disasters due to rainfalls. Considering that the Rocinha station presented, in this analysis, the second highest total rainfall in the city of Rio de Janeiro. An example is the case of mass movement and deaths that occurred in February 2019, in Rocinha. In Vidigal, in the same rainfall event, more than 40 houses were interdicted for presenting risk of landslides. As expected in the case of rainfall, those classes with high precipitation were more susceptible. Water is one of the most important causative factors in landslide occurrence. The increasing of some variables, such as pore water pressure, swelling of some clay minerals, and increasing the weight of unstable earth mass, which can cause a landslide, depend on the infiltrated water. In addition, water is a lubricant factor on a sliding surface that facilitates landslide occurrence (Varnes, 1984).
In addition to the orographic effect that occurs in CRJ, rainfall distribution is influenced by meteorological factors of macro and mesoscales such as the passage of frontal systems (FS), the South Atlantic Convergence Zone (SACZ) and occurrence of the Mesoscale Convective Systems (MCS), producing spatial irregularities in rainfall, with modulation of the Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and El Nino Southern Oscillation (ENSO) on seasonal scales.
Figure 5 shows the behavior of the monthly rainfall distribution in Group-I, where it is observed that the stations of Vidigal and Jardim Botânico have the smallest amplitude of the monthly rainfall distribution. This result confirms the smallest Euclidean distance found between stations.
Group-II (dark blue in the dendrogram) is composed only of one cluster, consisting of the Alto da Boa Vista station. This station has the highest dissimilarity values compared to all other stations, with values ranging from (2.24 for Vidigal) to (3.08 for Santa Tereza) and close to Group-I due to their common geographical and rainfall characteristics. Alto da Boa station was grouped in this way due to the fact that it presents attributes such as proximity to the ocean, to the windward side of the slope facing the ocean, great availability of forest cover and, above all, higher altitude among analyzed stations, which favors the effect of orography on rainfall formation. These characteristics make this station to present the highest minimum, maximum, average and total values of the city. The altitude of this station is a very important factor for the higher total rainfall values observed. This factor allows the formation of orographic rains on a local scale, which combined with macro and mesoscale systems, such as the entry of FS from the polar region, and with spring and summer convective systems, provide rainfalls in the region under analysis.
The systems that cause rainfalls in CRJ, in general moving from South to North and the presence of the topography force humid air to rise to the windward side of massifs. The rising air cools and condenses, forming clouds and rain, which produce maximum rains to the windward side of slopes, as observed in stations such as Rocinha and Tijuca. After reducing humidity, air comes down in slopes, being compressed and heated, thus inhibiting the formation of clouds and consequently reducing rainfall to the leeward side of massifs, explaining the lower totals in Group-IV. The Alto da Boa Vista station, located on the margin of TNP, is the one that best represents the importance of the presence of an urban forest in the city of Rio de Janeiro (Kong et al. 2015). The Tijuca forest is rich in biodiversity, located in a densely populated area in CRJ, which plays an important role in the city's microclimate, as observed in this analysis, providing the TNP station with average annual total rainfall greater than 2,000 mm year− 1.
However, in the last decade, the urban forest of Tijuca has been suffering due to urban expansion, irregular occupation of forest areas and the cutting down of trees. This occupation makes society more vulnerable to natural disasters, which commonly occur in the study region, such as landslides and tree falls. The vulnerability is an important factor in determining overall vulnerability to flood hazards (Oulahen et al. 2015). Landslide is an important natural hazard, and therefore, recognition of both landslide-prone areas and landside susceptibility mapping is the interest of responsible organizations and researchers. The study of Opach et al. (2019) revealed clusters which allowed for a comprehensive interpretation of their community resilience through the unambiguous linking between well-delineated spatial areas and specific resilience signatures. The advantage of approach cluster is that both the magnitude and spatial structure of the precursors are utilized in generating the predictions of disasters (Totz et al. 2017).
One of these cases was the collapse of two buildings in Muzema, with twenty-two deaths due to heavy rainfalls in the region on April 2019. Of the 50 largest geological-geotechnical accidents in the city of Rio de Janeiro between 1966 and 2016, approximately 90% of them were in the region analyzed in this research (Wanderley and Bunhak, 2016). According to Bradford et al. (2012), the public perception of risk must be in the centre of attention, because the authorities’ lack of understanding the society is the reason for failure in the politics of flood risk management. Wachinger et al. (2012) review reveals that personal experience of a natural hazard and trust—or lack of trust in authorities and experts have the most substantial impact on risk perception. Cultural and individual factors such as media coverage, age, gender, education, income, social status, and others do not play such an important role but act as mediators or amplifiers of the main causal connections between experience, trust, perception, and preparedness to take protective actions. Knowledge of public risk perception is meant to assure an improvement in the effectiveness of flood risk management (Kellens et al. 2011).
In Fig. 6 it is noteworthy that between the months of April to June this was the only season among all to maintain high levels of rainfall and still obtain a slight increase in rainfall (173.5 mm to 181.7 mm), while the other stations obtained a decrease in the same period.
Group-III (purple color in the dendrogram) was composed of Tijuca, Santa Tereza, Copacabana and Grajaú stations. This cluster is formed by two subgroups, one consisting of Tijuca and Santa Tereza stations and the other consisting of Copacabana and Grajaú stations. Subgroup composed of Tijuca and Santa Tereza stations should be highlighted. Rainfall distribution in these stations showed close minimum, total and average values. Thus, the dissimilarity presented by these stations was the lowest in the group, showing greater correlation than the others in rainfall distribution similarity, with ED value of (0.63). The rainfall distribution characteristic in Tijuca and Santa Tereza stations is due to the fact that these stations are located in regions of adequate forest cover in their surroundings and altitude higher than 150 meters to the leeward side of MTJ. Associated with these characteristics, stations are located in the vicinity of the Guanabara Bay, which favors greater influence of humidity not only from the ocean. The statistics of stations that compose Group-III prove this classification and seems to be analogous to that presented (Ward, 1963; Serra, 1970; Machao et al. 2010).
The Tijuca and Santa Tereza neighborhoods stand out with disasters associated with rainfalls due to the irregular occupation of hills and slums. There is a strong correlation between the slope degree and the landslide occurrence so that the weights are increased with a greater degree of the slope apart from the slope above fifty degrees (Varnes, 1984). Thus, landslide deaths are common, such as the three deaths at Morro do Borel, Tijuca, and the ten deaths and ten missing at Morro dos Prazeres, Santa Tereza, on April 2010. Benz and Blum (2019) showed the most intense cluster, happened in Rio de Janeiro, Brazil, as well as neighboring cities Niterói and São Gonçalo in 2010. In an area of approximately 2800 km2, 111 landslide events were recorded within only 3 day, predominantly on 6 April 2010. This is almost 4 times as many landslide events in a single day than the second most intense clusters (IDs 1 and 3) located in Washington and Oregon, US. The improvement of residents’ knowledge about their environment and the residual risk seemed to be crucial to increase risk awareness, and the same was true for the strengthening of local support networks to foster preparedness (Scolobig et al. 2012).
Another very common problem observed in Tijuca is the frequent flooding, which led to the construction of three reservoirs with capacity of 119 million liters in the neighborhood. Heavy precipitation exerts strong societal and economic impacts, including flooding, and these precipitation events are projected to increase under anthropogenic warming. Zhang and Villarini (2017) find that the frequency of annual heavy precipitation at a global scale increases in both 1.5 and 2°C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative. Overall, the annual frequency of heavy precipitation across the globe is similar between 1.5 and 2°C. Increase of this stagnation in temperature was observedin the city of Rio de Janeiro by Wanderley et al. (2019). Climate projections for the rest of the century show continued intensification of daily precipitation extremes. Increases in total and extreme precipitation in dry regions are linearly related to the model-specific global temperature change, so that the spread in projected global warming partly explains the spread in precipitation intensification in these regions by the late twenty-first century. This intensification has implications for the risk of flooding as the climate warms (Donat et al. 2016).
Stations of the other subgroup-III, Copacabana and Grajaú, presented the lowest minimum, total and average rainfall values, with values close to one another. However, distant variance, standard deviation and coefficient of variation values were observed. Thus, ED value among stations was 0.884. Resulting from higher trunk height, it indicates lower similarity in rainfall distribution when compared to Tijuca and Santa Tereza stations. In the case of Copacabana and Grajaú stations, the rainfall distribution behavior is somehow different between stations. This is due to the fact that the Grajaú station is located in a region that still has a remaining forest cover, has forest reserve in balance with local constructions. In addition, its geographical position is closer to MTJ, the Grajaú station is thus influenced by the available humidity of the forest and the orographic factor in the formation of rains. The Copacabana station is the most distant from MTJ regarding stations that compose Group-III. Thus, the orographic effect on the rainfall distribution in that station is not observed. In addition, concrete buildings in the neighborhood are associated with one of the highest demographic density in the city of Rio de Janeiro. These characteristics make this station to present the lowest total rainfall of Group-III stations. However, flooding is often observed on the streets of the Copacabana neighborhood, as well as tree falls, with 5 events in February 2019. Floods represent about one-third of all natural disasters. Together with storms they comprise 77% of economic losses caused by extreme weather events in Europe (Lechowska, 2018).The city of São Paulo, home to 11 million people, suffers constantly the effects of flooding caused by extreme precipitation Haddad and Teixeira (2015) estimated that floods contributed to reduce city growth and residents' welfare, as well as hampering local competitiveness in both domestic and international markets. An intra-city total impact-damage ratio of 2.2 and an economy-wide total impact-damage ratio of 5.0 were found.
The behavior of these four seasons in the rainfall distribution, seen in Fig. 7, shows that for the Tijuca and Santa Tereza stations, the rainfall distribution on the annual scale was more similar, especially between the months of May and December, than in the other stations, composed by Grajaú and Copacabana.
Group-IV (green color in the dendrogram), was composed of Piedade, Grande Méier, Madureira, Cidade de Deus and Jacarepaguá stations. For these stations, it was verified that the subgroup composed of Madureira and Jacarepaguá stations had the lowest trunk height, with ED value of 0.26. This indicates the lowest dissimilarity or the greatest similarity among all stations included in all groups formed. All Group-IV subgroups obtained the lowest dissimilarity values, close to zero, with ED values below 0.48, which implies more HRRD for these locations, being therefore more similar than any other subgroup. This is because stations that compose Group-IV have similar characteristics in relation to their geographical positions in relation to MTJ. Some of the stations that compose this group are located to the leeward side of MTJ, thus receiving less or almost no influence from the relief due to the occurrence of orographic rains.
The topographic effect that causes orographic rains in Group-IV stations located to the windward side of the slope, has not been verified. Thus, rainfall distribution in these stations is due to macro and mesoscale synoptic systems, making maximum and minimum totals similar in these stations. This characteristic enables observing the lowest rainfall totals in stations under analysis. Group-IV stations are located in an area of great density, where there is practically no forest cover and greater distance from the Tijuca massif. These characteristics prevent the passage of humid winds from the Atlantic Ocean, causing low relative humidity and the appearance of heat islands. In addition, the location of stations away from the coast is a relevant attribute for the lower influence of the sea breeze. These characteristics confirm the lower annual rainfall totals observed in these stations. In these neighborhoods, the greatest problems caused by rainfalls are floods due to the overflow of rivers. The unplanned urbanization especially in developing countries and wide climate changes through global warming increase the risk of natural hazards. Landslide e Floods phenomenon are an important worldwide natural hazard.
The behavior of rainfall distributions for the stations that make up Group-IV behave very similarly and very close to each other throughout the year, being the most similar of all groups (Fig. 8).
The results of HRRD can be used by governments to identify critical areas of natural disasters, with the objective of developing actions that mitigate the impacts of the rainfall. These actions can be preventive measures with direct actions in neighborhoods such as: reforestation, infrastructure works, garbage collection, mapping of risk areas, rain forecasting and monitoring system, implementation of an operations center, audible warning sirens, weather radars. These actions can make CRJ more resilient, to heavy rainfall and natural disasters such as floods and landslides, mainly due to large daily rainfall totals.
Figure 9 shows the highest total rainfall values at Alto da Boa Vista stations during January and Rocinha in April (578.4 mm and 571 mm). Both stations belonging to Group I and Group II. The lowest pluiviometric totals were found in Madureira belonging to Group IV, during the months of August 2015 and July 2016, totaling only 0.6 mm.
The ten largest daily rains in CRJ show that all stations in Group I and Group II have maximum daily rainfall greater than 300 mm day− 1. The storm that occurred in the CRJ on April 8 and 9 was the day with the highest rainfall in 24 hours since Alert Rio began its measurement in 1997 (ALERTA RIO, 2021). Ten people died due to the rains at the CJR on April 8 and 9. The stations of Rocinha, Alto da Boa Vista, Barrinha, Jardim Botânico, Copacabana, Vidigal, Rio Centro and Cidade de Deus recorded the highest rainfall in an interval of 24 hours. At the Sumaré station 360.2 mm were measured on 04/04/2010. However, the station was closed in May 2010. Sumaré station is located next to Alto da Boa Vista station. At Tijuca station, 286 mm were measured on 04/26/2011.Annual maximum daily precipitation data represent one of the most important and readily available measures of extreme rainfall and are used frequently as inputs to assessments of flood risk (Westra et al. 2013). The warm/ wet extremes appear to increase in tropical and high-latitude regions like Africa, the eastern part of South America, the Middle East, East Asia (Wu et al. 2012). Studies have shown that extreme temperature and precipitation events increased in the second half of the 20th century (Donat et al. 2016; Zhou et al. 2016). the study of Raymond et al. (2020) found that there have been increasing occurrences of extremely humid and hot weather that had been rare or unprecedented in the past in Asia, Africa, Australia, South America, and North America. Climate projections for the rest of the century show continued intensification of daily precipitation extremes (Donat et al. 2016).
To Brazil precipitation extremes show heterogeneous signals for most of the country. In Northeast Brazil, there are changes towards a drier climate, especially in summer and autumn. In the Southern region, the climate is becoming wetter, with a reduction in consecutive dry days, especially in spring. For the other regions, there is no strong clear change sign, but both positive and negative precipitation extreme trends, without statistical significance, mostly in Southeast Region where is it located CRJ (Regoto et al. 2021).