Environmental Disasters in Northeast Brazil: Hydrometeorological, Social, and Sanitary Factors

Marcos Samuel Matias Ribeiro aClimate Sciences Postgraduate Program, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
bParagominas Campus, Federal Rural University of Amazonia (UFRA), Paragominas, Brazil

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Lara de Melo Barbosa Andrade aClimate Sciences Postgraduate Program, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil

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Maria Helena Constantino Spyrides aClimate Sciences Postgraduate Program, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil

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Kellen Carla Lima aClimate Sciences Postgraduate Program, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil

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Pollyane Evangelista da Silva aClimate Sciences Postgraduate Program, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil

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Douglas Toledo Batista cStatistics and Agronomic Experimentation Postgraduate Program, São Paulo University (USP), Piracicaba, Brazil

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Idemauro Antônio Rodrigues de Lara cStatistics and Agronomic Experimentation Postgraduate Program, São Paulo University (USP), Piracicaba, Brazil

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Abstract

The occurrence of environmental disasters affects different social segments, impacting health, education, housing, economy, and the provision of basic services. Thus, the objective of this study was to estimate the relationship between the occurrence of disasters and extreme climate, sociosanitary, and demographic conditions in the Northeast region of Brazil (NEB) during the period from 1993 to 2013. Initially, we analyzed the spatial pattern of the incidence of events; subsequently, generalized additive models for location, scale, and shape were used to identify and estimate the magnitude of associations between factors. Results showed that droughts are the predominant disasters in NEB representing 81.1% of the cases, followed by events triggered by excessive rainfall such as flash floods (11.1%) and floods (7.8%). Climate conditions presented statistically significant associations with the analyzed disasters, in which indicators of excess rainfall positively contributed to the occurrence of flash floods and floods but negatively contributed to the occurrence of drought. Sociosanitary factors, such as percentage of households with inadequate sewage, waste collection, and water supply, were also positively associated with the model’s estimations, that is, contributing to an increase in the occurrence of events, with the exception of floods, which were not significantly influenced by sociosanitary parameters. A decrease of 19% in the risk of drought occurrence was estimated, on average. On the other hand, events caused by excessive rainfall increased by 40% and 57%, in the cases of flash floods and floods, respectively.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Marcos Samuel Matias Ribeiro, msamuel19@outlook.com

Abstract

The occurrence of environmental disasters affects different social segments, impacting health, education, housing, economy, and the provision of basic services. Thus, the objective of this study was to estimate the relationship between the occurrence of disasters and extreme climate, sociosanitary, and demographic conditions in the Northeast region of Brazil (NEB) during the period from 1993 to 2013. Initially, we analyzed the spatial pattern of the incidence of events; subsequently, generalized additive models for location, scale, and shape were used to identify and estimate the magnitude of associations between factors. Results showed that droughts are the predominant disasters in NEB representing 81.1% of the cases, followed by events triggered by excessive rainfall such as flash floods (11.1%) and floods (7.8%). Climate conditions presented statistically significant associations with the analyzed disasters, in which indicators of excess rainfall positively contributed to the occurrence of flash floods and floods but negatively contributed to the occurrence of drought. Sociosanitary factors, such as percentage of households with inadequate sewage, waste collection, and water supply, were also positively associated with the model’s estimations, that is, contributing to an increase in the occurrence of events, with the exception of floods, which were not significantly influenced by sociosanitary parameters. A decrease of 19% in the risk of drought occurrence was estimated, on average. On the other hand, events caused by excessive rainfall increased by 40% and 57%, in the cases of flash floods and floods, respectively.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Marcos Samuel Matias Ribeiro, msamuel19@outlook.com

1. Introduction

Disasters result from the combination of natural hazards and exposed population, comprising social, environmental, geological, physical, and economic vulnerability and insufficient response capacity (Freitas et al. 2012; Chmutina and von Meding 2019; United Nations Office for Disaster Risk Reduction 2020). These events engage both natural and social processes, impacting society according to the pattern of interaction between the natural event and the social organization (Mattedi and Butzke 2001). The impacts of disasters such as droughts and floods on the environment can compromise sanitation, water, soil, and food quality, as well as change disease vector cycles, leading to major economic losses (Freitas and Ximenes 2012; Patz et al. 2012; Freitas et al. 2020) and negatively influencing the provision of essential services.

Since the beginning of this century, extreme weather events have become more frequent in Brazil, with alternating periods of intense rainfall and water shortage (Marengo et al. 2009; Cavalcanti 2012). These extreme natural events potentialize the occurrence of disasters, causing impacts of different magnitudes on society, depending on their intensity, and the social and environmental conditions of exposed populations. There are several risks of disasters associated with climate extremes, such as flash floods, windstorms, hail, waterlogging, and lightning storms (Hartmann et al. 2013; Ning et al. 2016), in addition to droughts, which usually have lower immediate destructive power if compared to landslides and floods despite the larger number of affected populations.

Droughts are characterized by the persistence of periods with low pluviosity or its absence, when the decrease in soil moisture is higher than its recharge, which drastically reduces water storages and impacts the hydrological system (Coimbra de Castro 2003; Grigoletto et al. 2016). Floods occur due to the inundation of areas beyond the limits of a water body natural floodplain. The overflow is usually gradual, caused by the persistence of prolonged rainfall in the water basin, where discharge rates are exceeded by total system inputs. Flash floods, however, are characterized by a fast, highly energetic flow originating from intense rainfall events usually occurring over lands with a high natural slope. Waterlogging is characteristic of plain areas where flowing capacity is exceeded, thus generating an elevation of the water table [National Center for Risk and Disaster Management (Centro Nacional de Gerenciamento de Riscos e Desastres 2013)]. The latter having lowest record in the region under study and out of scope of our work.

According to the Center for Studies and Research in Engineering and Civil Defense [Centro Universitário de Estudos e Pesquisas sobre Desastres/Universidade Federal de Santa Catarina (CEPED/UFSC 2012)], the most recurrent environmental disasters in Brazil in the period from 1991 to 2011 originated from climatic events, with drought accounting for 54% of total registered episodes, followed by hydrological disasters (flash floods, floods, and waterlogging) corresponding to 33% of total events. Disasters of storm-related (cyclones, windstorms, storms) represent only 7% of total events while geological events, which comprise landslides and mass movements, correspond to 6%.

Northeast Brazil (NEB) is characterized by the occurrence of extreme drought events (Moura and Shukla 1981; Groisman et al. 2005; Marengo et al. 2016; de Medeiros et al. 2020). Annually, heavy rainfall events are also registered in the region (Calheiros et al. 2006; Oliveira et al. 2013b; Rodrigues et al. 2020b; Oliveira and Lima 2019), usually concentrated in short periods of time as a result of the simultaneous interaction between topography, geographical location, surface characteristics, and the acting of different meteorological systems (Kayano et al. 2009).

Climatic extremes events revealed that water supply systems and the rainfed agricultural sector in NEB are highly sensitive to drought conditions (Marengo et al. 2018; Pereira and Cuellar 2015) and to heavy rainfall, despite the lower frequency of occurrence of the latter (Liebmann et al. 2011; Kouadio et al. 2012). Consequently, rainfall measurement and monitoring play an important role in managing precipitation-related risks.

NEB is known to be a highly vulnerable area to climate factors, particularly its semiarid region (Silva et al. 2013), which is characterized by a warmer and drier climate, and the more frequent occurrence of drought-type disasters. This is one of the most vulnerable regions in the country (de Almeida et al. 2016, 2020) due to its specific characteristics of water availability, aridity, vegetation cover, land use, and social aspects. The main impacts caused by drought in the region are malnutrition, water supply deficiency, agricultural and livestock losses, human migration, forest fires, water quality degradation, and health and poverty problems (Stanke et al. 2013).

Intense rainfall events have sporadic periodicity and can cause hydrological and meteorological disasters with damages comparable to those generated by drought disasters (Camarinha and Debortoli 2015; Moura et al. 2016; Debortoli et al. 2017), especially when occurring in densely populated cities that usually present areas with inadequate housing conditions and higher social vulnerability. Hydrometeorological disasters triggered by heavy rainfall events (rainfall equal to or greater than 60.0 mm day−1) and extreme rainfall events (rainfall equal to or greater than 100.0 mm day−1) are registered more frequently in the coastal cities of the northern NEB region, such as Fortaleza (Zanella et al. 2009; Olímpio et al. 2013), as well as in coastal cities of the eastern sector of NEB, such as João Pessoa, Recife, Olinda, Maceió, and Natal (Souza et al. 2012; Nóbrega et al. 2014). In these cities, extreme rainfall events are known to have caused geological disasters such as landslides, associated with irregular urban occupation, mainly precarious housing located on slopes and hills.

The impacts of environmental disasters on populations show that communities are affected unequally and in different ways, directly and indirectly, with effects that vary from short to long term, depending on the characteristic of the event and the socioeconomic and environmental vulnerability of the territory (Alderman et al. 2012). Thus, preventive measures can reduce damage caused by these phenomena or at least minimize them, given that planning, prior studies, and risk assessment are carried out. In this case, prevention starts by understanding the factors and processes that trigger natural phenomena. Similarly, prevention can provide adequate conditions for the improvement of societies’ resilience with regard to such events (Marcelino 2008).

In this context, the objective of this study was to estimate the relationship between natural disasters and climate extreme events, sociosanitary and demographic conditions in the Northeast region of Brazil during the period from 1993 to 2013. To this end we considered only the most frequent events in NEB, which are droughts, flash floods, and floods.

2. Material and methods

a. Study area

NEB is located at the tropical zone between 1°N and 18°S and 34.5° and 48.5°W, in the extreme northeast of South America. It is contiguous in the north and east bordering the Atlantic Ocean. It has an area of approximately 1 558 196 km2, corresponding to 21.25% of the national territory, and a population of 53 081 950 inhabitants (Instituto Brasileiro de Geografia 2010). The region encompasses the states of Maranhão (MA), Piauí (PI), Ceará (CE), Rio Grande do Norte (RN), Paraíba (PB), Pernambuco (PE), Alagoas (AL), Sergipe (SE), and Bahia (BA), geographically divided into 42 mesoregions (Fig. 1) that are the observational units in this study.

Fig. 1.
Fig. 1.

Geographical division of Northeast Brazil into mesoregions.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0132.1

Mesoregions were grouped according to environmental disasters, climate extremes, sociosanitary and demographic characteristics, in order to highlight common features among them. Thus, three distinct profiles of vulnerability to disasters, as defined by the grade of membership (GoM) fuzzy technique. The classification of the mesoregions according to these profiles was carried out according to the degree of belonging, considering the following predominant characteristics in each of the clusters formed.

There are three predominant types of climate in the region: humid coastal climate (from the coast of Bahia to Rio Grande do Norte); tropical climate (in part of the states of Bahia, Ceará, Maranhão, and Piauí); and semiarid tropical climate (throughout the northeastern inlands). The onset and duration of the wet season differs according to the different subregions: in the eastern part of NEB, it takes place between May and August; in the southern part of the region maximum rainfall occurs in November–December; and in the northern semiarid part of NEB the wet season occurs between February and May (Uvo et al. 1998; Kucharski et al. 2008). The intertropical convergence zone (ITCZ) is the meteorological system that most influences the rainfall pattern in the region, producing rainfall in almost the entire NEB when it reaches its southernmost seasonal position (Oyama and Nobre 2004; Hastenrath 2006). Upper-tropospheric cyclonic vortices, easterly waves disturbances and squall lines may also cause heavy rainfall at any time of the year (Mishra et al. 2001; Gomes et al. 2015; Pereira de Oliveira and Oyama 2015). Furthermore, the southern state of Bahia is also affected by the propagation of frontal systems and the South Atlantic convergence zone (SACZ) (Tomaziello et al. 2015; Paredes-Trejo et al. 2017).

The region is characterized by a remarkable interannual and seasonal variability in rainfall (Zhou and Lau 2001; Kayano and Andreoli 2004; Barbosa et al. 2006). This characteristic favors the occurrence of prolonged droughts that cause economic impacts on the population (Coêlho et al. 2004; Blunden and Arndt 2016; Paredes-Trejo et al. 2015). The average annual rainfall from the coast of Bahia to Rio Grande do Norte is 2000 mm; in part of the states of Bahia, Ceará, Maranhão, and Piauí, it ranges between 1000 and 1200 mm; and in the entire semiarid region it is usually less than 500 mm on average (Da Silva 2004; Alvares et al. 2013; Oliveira et al. 2013a; Rao et al. 2016; Oliveira et al. 2017). Furthermore, the region is characterized by high reference evapotranspiration rates, which intensifies the occurrence of soil water deficits and, consequently, drought, as previously described by Cabral Júnior and Bezerra (2018).

b. Data

Data on disaster occurrences in the Northeast region of Brazil were obtained from CEPED of the Federal University of Santa Catarina, published in the Brazilian Atlas of Natural Disasters for each federation unit for the period from 1991 to 2012. Data for subsequent years were obtained from the Integrated Disaster Information System (S2ID), managed by the National Secretariat for Civil Protection and Defense (SEDEC). These datasets were obtained at the municipal level and then aggregated into mesoregions.

The meteorological data used in this study were retrieved from the joint project between the University of Texas (United States) and the Federal University of Espírito Santo (Brazil). The method used to produce the database was described by Xavier et al. (2016). This dataset is arranged in a regular grid of 0.25° × 0.25° and cover the entire Brazilian territory. Among the available variables, only precipitation (mm) data were used. We initially aimed to select one grid point to represent each mesoregion. However, given their dimensions and different meteorological conditions, one point was not sufficient to adequately describe their characteristics. Thus, in order to provide a more comprehensive characterization of each mesoregion, we selected one grid point in each municipality composing each analyzed unit. Then, a synthetic time series was created for each mesoregion based on the daily mean values between these selected grid points. These synthetic time series were then used in the calculation of the extreme indices for each mesoregion.

Rainfall extreme indices were obtained from the Climdex, developed by Zhang and Yang (2004). For this purpose, we used the methodology proposed by Zhang et al. (2005) and Haylock et al. (2006) to assist in the monitoring and detection of climate change in local studies, which was developed for the computational language of the R software and is freely available for download. Thus, we analyzed six rainfall extreme indices: maximum number of consecutive days with precipitation below 1 mm [consecutive dry days (CDD)], number of consecutive days with precipitation greater than or equal to 1 mm [consecutive wet days (CWD)], maximum amount of precipitation on 1 day (RX1day), maximum amount of precipitation on five consecutive days (RX5day), annual precipitation exceeding the 95th percentile (R95p), and total annual precipitation on wet days (PRECPTOT).

Data on sociosanitary and urban infrastructure conditions were obtained from the 2010 Demographic Survey, conducted by the Brazilian Institute of Geography and Statistics [Instituto Brasileiro de Geografia (IBGE)], and contained the percentage of households with inadequate water supply, sewage, and waste collection services.

Because of the limitations inherent to the different datasets that were used, we considered the period from 1993 to 2013, assuring the compatibility of meteorological and disaster datasets and the use of the proposed statistical methods.

c. Method

1) Principal components analysis

The principal components analysis (PCA) technique is a statistical tool usually applied to fit models in which explanatory variables tend to be highly correlated. In these cases, the methodological procedure consists of an orthogonal transformation (Johnson and Wichern 2002; Mingoti 2005), in which the possibly correlated variables are converted into a new set of linearly independent variables, called principal components (PC). PC are linear combinations of the original variables and can be interpreted as such. The number of retained components is less than or equal to the number of original variables, where the first PC explains most of total data variance. Thus, this technique was used to eliminate correlations between explanatory variables of the used dataset, avoiding data multicollinearity and improving the adjustment and interpretation of the proposed statistical models.

The principal component analysis was applied to the dataset by considering the triannual mean values of the meteorological variables and the sociosanitary and demographic variables provided by the Demographic Census of 2010.

To detect potential multicollinearity in the analyzed data, the correlation matrix was observed and the linear dependence between independent variables was verified. For this purpose, we calculated the condition number K as the ratio between the highest and lowest eigenvalues of the correlation matrix of the independent variables. Multicollinearity was assessed as follows: K < 100 (no multicollinearity), 100 < K < 1000 (moderate–strong multicollinearity), and K > 1000 (severe multicollinearity) (Montgomery et al. 2012).

2) Generalized additive models for location, scale, and shape

The response variables for the events of drought, flash floods and floods are integer and positive numbers. Poisson is the standard distribution for a discrete count variable. The assumptions for this probability distribution are independence and constant average rate of occurrence, which result in a condition of variance equal to the mean (equidispersion). Thus, the use of Poisson regression models is very restrictive, since equidispersion rarely occurs in practical terms, with overdispersion (variance greater than the mean) usually occurring. In these cases, the literature suggests more comprehensive classes of distributions, which present additional parameters and deal with excessive variability more efficiently, such as COM-Poisson, Poisson-generalized, double-Poisson, gamma-count, and the negative binominal type I and II (Winkelmann 1995, 2008). In the present study, because of the presence of overdispersion in data and considering the parsimony and uniformity principles, the negative binomial distribution type II (NB) was adopted for all the response variables. Without loss of generality, the probability function is given by (Rigby and Stasinopoulos 2008)
P(Y=y|λ,ϕ)=Γ(y+λ/ϕ)ϕyΓ(λ+ϕ)Γ(y+1)(1+ϕ)y+λ/ϕy=0,1,,λ>0andϕ>0,
in which λ and ϕ are the mean and the shape parameters, respectively; and Γ() is a gamma function, where E(Y) = λ and Var(Y) = (1 + ϕ)λ, with the shape parameter controlling overdispersion. More details for the application of this NB class are found in Stasinopoulos et al. (2017) and Rigby and Stasinopoulos (2008).

On the other hand, disaster counts for the events of drought, flash floods and floods imply different hierarchical levels (mesoregion, triennium, and profile), which are correlated with each other. In addition, since this study comprises longitudinal data, mixed-effect models emerge as an option that aims to describe the dependency structures of correlated data by including relevant random effects (Diggle et al. 2002). In this context, the correlation between repeated measures is the result of an unmeasurable heterogeneity but is described by the variability of random effects. The inclusion of random effects in count data models also accommodates data overdispersion.

To establish an initial notation, we consider each response variable as Yitj, with a type-II NB distribution in which i = 1, 2, 3, …, 42 represents the ith mesoregion, t = 1, 2, …, 7 represents the tth repeated measure in time, and j = 1, 2, …, 4 represents the jth profile. The count of occurrences, that is, the achievements of the random variable Yitj, are associated with the explanatory variables defined as the five PC previously obtained when reducing the collinearity between the original explanatory variables of the study.

Thus, the generalized linear mixed regression model for correlated data is characterized by a hierarchical structure in which bj ~ N(0, σ2) and Yitj|bj ~ BN(λit, ϕ), where
E(Yitj|bj)=log(λitj)=g(μitj)=xitjTβ+zitjTbj,
and xitj is the ith line of the matrix associated with the known fixed effects, β is the vector of p regression parameters of the unknown fixed effects, zitj is the ith line of the incidence matrix of known random effects, BN indicates the negative binomial distribution, and bj is the random effect vector. The objective of the analysis is to estimate the fixed effects parameters and σ2. In the present study, we included the random effect for each mesoregion with the following functional structure for the mean of occurrences:
μitj=exp[(β0+bj)+β1Comp1+β2Comp2+β3Comp3+β4Comp4+β5Comp5],
where bj represents the random effect vector of profiles and CompX are the main originated components. The choice of profiles as random effects is justified by the heterogeneity between mesoregions with regard to the profiles of vulnerability to disasters. For each βji coefficient of the model, its contribution to the response variable is assessed and interpreted according to a linkage function exp(βji), which corresponds to the risk of occurrence of each analyzed event. The selection of models was carried out considering the Akaike information criterion (Stasinopoulos et al. 2017). Statistical analysis was performed using the GAMLSS package (Rigby and Stasinopoulos 2005) of the R software.

3. Results and discussion

NEB accounted for a total of 14 762 drought, flash flood, and flood events from 1993 to 2013. Drought events corresponded to 81.1% of the occurrences, followed by flash floods (11.1%) and floods (7.8%). It is worth mentioning that events of other natures also occurred less frequently and were not considered in the study, such as waterlogging, mass movements, windstorms, forest fires, and erosion.

With regard to the distribution of the most frequent events in the nine NEB states (Fig. 2), Bahia comprised the mesoregions with the highest number of records, accounting for 3051 events (20.7%), followed by Paraíba with 2342 (15.9%) and Piauí with 2047 (13.9%), respectively. The fewest number of events were registered in the states of Maranhão 3.4% (505 events), Sergipe 4.2% (617 events), and Alagoas 5.3% (787 events). Studies developed by de Almeida et al. (2016, 2020) that aimed at the construction of indicators of risk to natural disasters at the national level, reported that NEB presents an expressive number of municipalities (52%) where there is a very high risk to disasters.

Fig. 2.
Fig. 2.

Register of occurrence of the most frequent environmental disasters in NEB in the period 1993–2013.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0132.1

Figures 3a–g shows the occurrence of disasters classified as drought in NEB in each triennium comprised in the period from 1993 to 2013. It can be seen that the mesoregions with the most frequent occurrences of drought are located in the center of the region, thus corresponding to the delimitations of the Brazilian semiarid region. This is the region that is most affected by events caused by lack of rainfall because of its climate and environmental characteristics (Marengo et al. 2016, 2018).

Fig. 3.
Fig. 3.

(a)–(g) Triennial and (h) annual occurrence of drought in the mesoregions of NEB in the period 1993–2013.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0132.1

The fewest drought registers are found in mesoregions comprised by the state of Maranhão and those located in the eastern NEB, which correspond to the most humid areas. In studies developed by Reboita et al. (2010), rainfall rates larger than 2000 mm yr−1 are observed in the northern part of NEB and rates of approximately 1500 mm yr−1 are observed in the east. As previously mentioned, the ITCZ is the main atmospheric system associated with precipitation in the region, while the occurrence of squall lines, mesoscale convective complexes, easterly waves disturbances, and upper-tropospheric cyclonic vortices should also be highlighted. It is worth noting that the Maranhão state is characterized by a warm, humid climate mainly due to its geographical location and the acting of atmospheric systems such as the ITCZ (Nascimento et al. 2017).

A low frequency of drought events was observed in the southern part of the state of Ceará during the analyzed trienniums, although it is located in the semiarid region and presents dry climate conditions. This different behavior is possibly associated with the number of reservoirs found in the region (Agência Nacional de Águas 2017), which provides greater resilience and capacity to cope with events caused by the irregularity of rainfall, thus leading to fewer drought disasters despite the semiarid conditions.

Figure 3h shows the annual frequency of drought events in NEB comprising the 21 years of analysis. A considerable number of drought events can be observed in NEB region. The years with the most registers of drought episodes were 1993, 1998, 2002, 2003, 2004, 2005, 2006, and 2007, which coincides with years when El Niño–Southern Oscillation acted. Grimm (2003), Rodrigues et al. (2011), and Cai et al. (2020) also observed that episodes of El Niño associated with sea surface temperature (SST) anomalies in the Pacific Ocean alter the ocean–atmosphere circulation patterns, inhibiting convective activity over NEB through the anomalous circulation pattern of the descending branch of the Walker cell.

El Niño is not the only oceanic phenomenon affecting drought conditions in NEB. Differences in SST anomalies in the North and South Atlantic Oceans are also worth mentioning, which is known as the Atlantic dipole (Moura and Shukla 1981; Amorim et al. 2014). The positive phase of the Atlantic dipole influences the northward displacement of the ITCZ, which directly interferes with NEB rainfall regime through changes in circulation patterns (Kayano et al. 2013).

The peak of the drought occurrence time series is observed in 2012, with an extreme situation that is known to have lasted until 2016 and was associated with different oceanic conditions that changed the patterns of atmospheric circulation, thus causing strong anomalies in rainfall rates (Marengo et al. 2017; de Medeiros et al. 2020). The extreme conditions significantly impacted economic activities and the living conditions of the population (Brito et al. 2018). The event was considered the most severe drought to have occurred in the region in recent years (Alvalá et al. 2019; Cunha et al. 2018).

Flash floods are more occasional events, triggered by torrential and/or concentrated rain events, thus occurring at shorter time scales but with a highly destructive power when interacting with the population of locations with precarious infrastructure. This fact aggravates the vulnerability of the population exposed to this type of hazard.

Figures 4a–g shows the occurrence of flash flood events in NEB, considering each triennium comprised in the period from 1993 to 2013. The period from 2002 to 2004 presented the most significant registers (Fig. 3d), with a high frequency of events in 2004 (more than 400), followed by the period from 2008 to 2010 (Fig. 3f), with 2009 standing out. Note that a considerable number of mesoregions did not register flash flood events in the trienniums of 1999–2001 and 2005–07, which were the years with the lowest number of cases in the analyzed period.

Fig. 4.
Fig. 4.

As in Fig. 3, but for flash floods.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0132.1

The mesoregions located at the states of Bahia, Pernambuco, and Paraíba stood out with higher occurrences of flash flood events, which further increased in the anomalous trienniums in the time series. de Abreu et al. (2020) reported that these regions have higher altitude and altimetry, which favors the occurrence of flash floods. Souza and Azevedo (2012) and Silva (2014) observed higher registers of intense rainfall (rainfall equal to or greater than 60 mm day−1) and extreme rainfall (rainfall equal to or greater than 100 mm day−1) events in these regions, which potentialize the occurrence of flash floods. Regions to the south of the state of Bahia, where flash floods occur in practically all the analyzed trienniums, are influenced by the climate conditions of the southeast region of the country, in addition to the direct influence of atmospheric systems such as frontal systems and the SACZ (Grimm 2011; de Carvalho and Cavalcanti 2016).

In Fig. 4h one can observe the annual frequency of flash flood events in NEB. It can be seen that these events occur throughout all the data series, with the exception of 1993, which was influenced by the weather conditions associated with the extreme drought established since 1991 due to a strong El Niño event (Rao et al. 1995; Oliveira et al. 2010). When compared to the occurrence of drought events, flash flood occurrence is much less frequent in absolute terms since the overall climate conditions in NEB favor rainfall deficit (Cabral Júnior and Bezerra 2018). The years considered atypical throughout the series are 2004, 2009, and 2010, which are associated with macro-, meso-, and microscale atmospheric systems (Orlanski 1975) that favor the occurrence of rainfall in NEB region at different time scales.

In Figs. 5a–g, the spatial–temporal distribution of flood events in the Northeast region from 1993 to 2013 can be observed. It is worth mentioning that floods differ from flash floods as they are influenced by the gradual persistence of above-average rainfall. The first three analyzed trienniums (Figs. 4a–c) exhibited the lowest overall frequencies of these events, with isolated events occurring in certain mesoregions.

Fig. 5.
Fig. 5.

As in Fig. 3, but for floods.

Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0132.1

The first triennium with high records of flood events was 2002–04 (Fig. 4d), with 2004 accounting for the largest number of cases throughout the entire extension of NEB, with particularly high frequencies in the mesoregions of the Rio Grande do Norte state. The triennium 2008–09 also stood out (Fig. 4f) with many flood registers throughout all states. However, the highest concentration of events in this triennium was observed in mesoregions located in the northern part of NEB region, particularly in the states of Maranhão, Piauí, Ceará, Rio Grande do Norte, and Paraíba. In a study developed by Rodrigues et al. (2020a), which assessed daily rainfall in NEB, the northern portion of the region presented the highest monthly rainfall averages between January and May, with a peak in March of approximately 200 mm, in addition to larger annual accumulated rainfall, which was influenced by the positioning of the ITCZ and mesoscale systems (Palharini and Vila 2017).

Figure 5h shows the annual distribution of flood events in NEB. The anomalous years were the same as in the flash floods time series, with 2004, 2008, and 2009 standing out with the highest records. As previously discussed, the excessive rainfall that potentialized the occurrence of these types of events in the mentioned years originated from specific atmospheric systems that acted over NEB and caused high amounts of rainfall.

To estimate the risk of occurrence of events associated with climate and sociosanitary variables, the generalized additive model for location, scale, and shape was used. We verified that the explanatory variables of this study showed signs of moderate to strong multicollinearity (K = 152.66), which would compromise the estimation of the model parameters. Thus, PCA was performed and retrieved five new variables (PC) that are not correlated with each other (Table 1). These components explain 94% of the accumulated variance of the 10 climate, sociosanitary, and demographic explanatory variables.

Table 1.

Principal components analysis of the climate, sociosanitary, and demographic variables according to the mesoregions of NEB. Values in boldface type are the highest percentages of factor loading contributions to the respective variables in the construction of the principal components.

Table 1.

The five components have the following characteristics: PC1 is characterized by excessive rainfall, since it comprises R95p, RX1day, and RX5day; PC2 is characterized by highly urbanized regions with inadequate sewage and waste collection; PC3 is characterized by wet conditions, since it comprises CWD and PRECPTOT; PC4 corresponds to inadequacy in water supply; PC5 represents the maximum number of CDD.

Subsequently, generalized mixed linear models were adjusted in order to identify the relationship between the principal components and the number of environmental disaster events in NEB. Thus, four distinct distributions were tested for the response variables: Poisson, double Poisson, negative binominal (type I), and negative binomial (type II). The best distribution for the response variables was selected based on the residual analysis criteria for each of the proposed models. The models adjusted with the NB type-II distribution were the ones that best fitted the data. Therefore, Table 2 shows the parameters estimated with the generalized mixed linear model using the type-II NB distribution for the different analyzed disaster types.

Table 2.

Parameters estimation for the generalized additive model for location, scale, and shape of drought, flash flood, and flood events in NEB. Asterisks indicate the probability of the distribution in evidencing the association of natural disasters with the predictor variables, that is, the principal components analyzed.

Table 2.

With regard to drought events, the estimated model detected statistically significant associations with the components PC1, PC2, PC3, and PC4. PC1 and PC3 were negatively associated with drought, that is, the higher the rainfall extremes, the more consecutive wet days, and the higher annual rainfall for the region, the lowest the number of drought events. Each increase in PC1 is associated with s a reduction in drought events of approximately 28.5%, while the increase in PC3, which expresses the volume of accumulated rainfall in wet days, leads to a decrease of 20.4% in drought occurrence.

PC2 and PC4 positively contribute to the increase in the number of drought registers, that is, the greater the number of housings with inadequate sewage, waste collection and water supply, and the greater the percentage of urban population, the more drought disasters are notified. Each unit added in the inadequacies of the sanitary conditions and urban population of NEB corresponds to an average increase of 18.5% and 14.9% in the notification of drought-related disasters, respectively. In general, the simultaneous unit increase in all covariables results in a 19% decrease in drought events in the region.

With the exception of PC5, all other components positively contribute to the number of flash flood events. That is, the greater the rainfall extremes, number of domiciles with inadequate sewage and waste collection, percentage of urban population, consecutive wet days, and annual rainfall, the higher the number of flash flood cases in NEB. The contribution of PC1 to the increase in flash flood occurrence is 29.2%; for PC2 and PC3 it is 23.2% and 42.8%, respectively. The number of consecutive dry days (CP5) negatively contributes to the occurrence of flash floods, which is expected since a decrease of 73.3% in the occurrence of flash floods was observed with a larger number of consecutive dry days. The combined increase in the analyzed explanatory variables lead to an increase of 40% in the occurrence of flash floods in NEB.

For disasters classified as flood, only the components representing climate variables were significant for the model. CP1 and CP3 positively contribute to the number of flood events, that is, wet consecutive days and annual accumulated rainfall tend to increase the frequency of flood disasters, contributing individually with an increase of 69.7% and 37.0%, respectively. In contrast, PC5 contributes negatively by reducing in 0.3% the occurrence of floods. The combined increase in covariables results in a 57% increase in the number of flood events in NEB region. Last, note that the results described in the previous paragraphs represent an average assessment of the model’s behavior. More specific results for each mesoregion can be interpreted by analyzing the associated random effect of each model. In all adjusted mixed models, the random effect variance is not negligible. The estimation of the variance of the random effects represents the differences between NEB mesoregions.

Excessive rainfall in NEB is extremely important for the region’s development, since it suffers from critical rainfall deficits that significantly affect the population that heavily depends on subsistence agriculture. However, the social and environmental conditions of the cities can also potentialize the occurrence of hydrometeorological disasters associated with excessive rainfall. In a study developed by Oliveira and Lima (2019) that analyzed the period of return of extreme events in the capital cities of NEB, the authors observed the occurrence of more frequent and more intense events in the wet season. However, these locations present a different behavior whether they are situated in the northern (São Luiz, Teresina, and Fortaleza) or coastal (Natal, João Pessoa, Recife, Maceió, Aracaju, and Salvador) portions of NEB.

The inadequate sewage, waste collection and water supply conditions of households significantly contribute to enhance the exposure to the occurrence of droughts and flash floods. The precarious sanitary conditions of the population contribute to increase the risks associated with the episodes of disasters in NEB, which is further influenced by high percentages of urban population. Silva (2009) reported that the urbanization process is one of the main aspects that influence disasters resulting from excessive rainfall in urban areas, often caused by the occupation of risk areas by the population, which is associated with increased surface runoff, floodplain occupation and social vulnerability. In general, these areas are not covered by impact prevention or mitigation plans and therefore are severely exposed to these risks. Xavier et al. (2014) emphasized the importance of the combination of social and economic processes to the occurrence of disasters.

Rodrigues and Gouveia (2013) reported that mass movements, flash floods, water resources pollution and soil contamination are the main environmental impacts resulting from the urbanization process. In addition, flash flood events pose a series of threats such as the contamination of water sources and decreased agricultural productivity, especially when referring to vulnerable regions such as NEB, which usually struggle to face and recover from the adverse effects of these events (Silva et al. 2013).

By analyzing the mean estimates of the proposed models for each of the analyzed disaster classification (Table 3), one can observe a certain variability between the profiles considered in the study. The mesoregions classified as having an amorphous profile stood out with a very specific characteristic for the three types of studied disasters. For the other analyzed profiles, one can identify that drought events in profile 1 mesoregions presented an average count of 60 events (59.96). Projections developed by Spinoni et al. (2014) and the analysis of extreme events carried out by Da Silva et al. (2019a) indicated an increase in temperature and reduction in precipitation with an increasing trend for the number of consecutive dry days in the region.

Table 3.

Mean estimates of the profiles of vulnerability to environmental disasters in NEB, retrieved by the generalized additive model for location, scale, and shape of drought, flash flood, and flood events. Here, LT and UT respectively indicate the lower and upper limits of the 95% confidence interval.

Table 3.

With regard to flash floods and floods, profile 2 showed higher averages with approximately nine and five cases (9.13 and 4.62), respectively. Studies developed by Da Silva et al. (2019b) and Rodrigues et al. (2020b) indicated a growing trend in extreme rainfall events in the coastal portion of NEB. These authors reported that these types of events are becoming more frequent and more intense, with 178 mm day−1 mean accumulated values in a 2-yr return period. Note that all estimates from the model are within the 95% confidence interval (CI95%).

The mesoregions classified as profile 2 and profile 3 presented greater risk associated with the events triggered by excessive rainfall, which can be observed by the estimations retrieved with the models for the flash floods and floods events. The eastern coast and northern portion of NEB, covering the state of Maranhão, are areas that present higher rainfall amounts and, consequently, more frequent episodes of rainfall extremes (Oliveira et al. 2013b, 2017; Rodrigues et al. 2020b). Profile 2 and profile 3 differ with regard to sociosanitary aspects, since profile 2 has the best sanitary conditions, which leads to better capacity to cope with the events. Furthermore, profile 2 mesoregions encompass the largest urban agglomerations in NEB, including eight capital cities.

4. Conclusions

NEB is predominantly characterized by the frequent occurrence of droughts, which affect most part of the population although it is not an environmental disaster with highly destructive power. Events caused by excessive rainfall such as flash floods and floods are also recurrent, although much less common than droughts. The remarkable climate variability in NEB potentialize the impacts of these events due to extreme climate and weather conditions. However, climate extreme events in their own are not sufficient to characterize a disaster. Thus, the social and environmental conditions in which the populations are inserted also influence the occurrence of disasters.

Generalized additive models of location, scale, and shape proved to be effective in capturing the relationship between the occurrence of disasters and the climate, sociosanitary, and demographic conditions of NEB. Its applicability seems advantageous for taking into consideration the overdispersion and the nature of the probability distribution of the count of drought, flash flood, and flood events (dependent variables). This type of modeling allows the inclusion of random effects, which take into account the specific variability of each of the profiles formed on the basis of the disasters, sociosanitary, and climate characteristics of the mesoregions. Thus, these types of model emerge as an innovative method to estimate the risk of occurrence of events, considering the associated factors.

Through the proposed models, statistically significant associations were found between disasters registers and climate, sociosanitary, and demographic conditions. Conditions that favor the occurrence of excessive rainfall were significantly associated with all analyzed disaster types. They negatively contributed to the occurrence of water shortage events (droughts) and positively contributed to the occurrence of flash floods and floods, which are triggered by excessive rainfall. Sociosanitary conditions were also important parameters in the analyzed models, with nonsignificant results only for the flood disaster model.

Therefore, we expect that the results of this research can elucidate issues that will help in the development of public policies with regard to the prevention, mitigation, preparedness, response, and recovery from impacts of environmental disasters on the population of NEB.

Data availability statement

All data on the occurrences of natural disasters used during this study are openly available on the Integrated Disaster Information System (https://s2id.mi.gov.br/). Precipitation information comes from Xavier et al. (2016), and the sociohealth conditions microdata are from the Brazilian Institute of Geography and Statistics (IBGE).

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