Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High Frequency Data

1. Introduction

The Brazilian Atlantic Forest, once sprawling across 150 million hectares and dominating the country’s coastal regions, has undergone significant transformations over the centuries. Originally contiguous, the forest has evolved into fragmented patches, especially along the Brazilian coast, due to historical land use changes and urban expansion [13]. This fragmentation, particularly noticeable in smaller fragments of less than 50 hectares, has placed a substantial portion of the forest’s diverse biodiversity at risk of extinction [14,15,16]. Fragmented landscapes often experience edge effects driven by abiotic factors like water, wind, and temperature, pushing plant communities toward early successional stages [17,18,19,20].

As one of the planet’s most biodiverse ecosystems, the Brazilian Atlantic Forest faces escalating threats, with climate change posing a formidable challenge. The biome’s intricate web of life and ecological significance is now confronted with shifting aridity patterns, exacerbated by habitat fragmentation and ecological succession processes [1]. Recent ecological shifts in the Atlantic Forest have intensified, contributing to increased aridity and prompting concerns about the separation of Amazonian and Atlantic Forest lineages in Brazil [2].

Amidst these ecological transformations, environmental variables, particularly precipitation, play a pivotal role in shaping the vitality of the Atlantic Forest. Anthropogenic pressures and climate change effects loom large, and long-term forecasts signal a concerning decline in precipitation levels [3]. Such changes could significantly impact the delicate balance of this vulnerable ecosystem [4,5]. This study, conducted between 2000 and 2022, seeks to delve into the repercussions of evolving aridity patterns on the Brazilian Atlantic Forest.

This study focuses on meteorological indicators to unveil nuanced trends and pinpoint regions undergoing substantial alterations in aridity conditions. By scrutinizing the impact of changing climate patterns on the Atlantic Forest, this research aims to contribute valuable insights into the ecological dynamics of this biome. Subsequent sections provide a description of the methodology, data, and outcomes, shedding light on the complex relationship between climate variability and the status of the Brazilian Atlantic Forest.

2. Materials and Methods

We adopted the boundaries of the Brazilian Atlantic forest to be congruent with those delineated for the Atlantic forest biome by the Instituto Brasileiro de Geografia e Estatística (IBGE). The definition of the Atlantic forest biome by IBGE was based on an extensive process involving literature review, interinstitutional validation, and field surveys to verify the presence of the physical-biotic environment and historical evidence of the Atlantic forest within the demarcated boundaries [6]. These boundaries were sourced from TerraBrasilis, a platform managed by the Instituto Nacional de Pesquisas Espaciais (INPE), which facilitates data organization, accessibility, and utilization for Brazil’s environmental monitoring efforts.

To assess the exposure of plant species to extreme drought events in the Brazilian Atlantic forest, we relied on meteorological indicators. These indicators were derived from hourly data of precipitation, maximum and minimum air temperature recorded at 205 automatic weather stations, accessible from the Instituto Nacional de Meteorologia (INMET) database [https://bdmep.inmet.gov.br, accessed in August 2023]. An overview of these datasets, including station codes, sample sizes, monitoring periods, and the percentage of missing data, is provided in the supplementary materials. The geographic distribution of the 205 automatic weather stations as well as the boundaries for the Atlantic forest used in this study are presented in Figure 1. This dataset stands as an extensive and robust compilation, with a total of 10,299,236 data points. Spanning a broad temporal window, from distinct station-specific start dates to a standardized endpoint on December 31, 2022, the dataset exhibits a relatively modest percentage of missing data.

Past, current, and future drought conditions were quantified using several meteorological indicators, including the number of consecutive hours without precipitation, the number of dry and wet days per month, the Aridity Index, and the Mann-Kendall test statistic (Z) for trend analysis in evapotranspiration (ET) and maximum air temperature ($T_{max}$). The choice of these indicators was motivated by their relevance to recent severe drought events [11]. We opted against employing imputation methods to replace missing data, as this approach can introduce systematic bias into the meteorological indicators [7,8]. Consequently, all missing data were excluded from the analysis, and the accumulated or averaged values resulting from any missing hourly data from the 205 automatic weather stations were disregarded.

The Aridity Index was computed as the ratio of monthly precipitation to evapotranspiration. Monthly precipitation values were determined by aggregating the available hourly precipitation data, while monthly evapotranspiration values were calculated by aggregating daily reference crop evapotranspiration estimates. These daily estimates were generated using the Hargreaves and Samani equation [9], with input data encompassing measured hourly maximum and minimum air temperatures, elevation, and latitude.

The classification of wet and dry days per month was performed using a precipitation threshold of 0 mm per weather station. Days with precisely 0 mm of precipitation were categorized as dry, while those with precipitation exceeding 0 mm were classified as wet. Subsequently, the median values for the number of wet and dry days per month were employed as meteorological indicators to gauge drought exposure.

To determine the duration of consecutive hours without precipitation, we analyzed sequences of hours with no recorded precipitation. In instances of missing data within a sequence, we adopted a conservative approach by considering two distinct sequences. This approach was necessary as missing data could inadvertently elongate the periods to be without precipitation.

Mann-Kendall (MK) trend analysis was employed to examine the daily evapotranspiration estimates and maximum air temperature data at each weather station. A trend was considered statistically significant when the associated p-value was less than 0.05. The MK test statistics, Z and S, were used to assess both the presence and the strength of a monotonic upward or downward trend in daily evapotranspiration and maximum air temperature. The MK test statistic Z was further utilized as an indicator for evaluating potential future drought exposure.

Lastly, we conducted a hazard assessment by ranking the meteorological indicators calculated for each weather station, which allowed us to identify the regions most exposed to drought events. This hazard assessment was developed, taking into consideration environmental factors that influence tree growth dynamics in the Brazilian Atlantic forest under drought conditions [10], as well as the amount of data available after the data filtering process for missing data.

3. Results and Discussion

We used boxplots to visualize the distribution of accumulated monthly precipitation for all 205 automatic weather stations in the Brazilian Atlantic forest. They provide range, median, and distribution of values at each weather station during their monitoring period. The boxplots are presented in Figure 2. Figure 2 reveals the presence of positive outliers across most of the weather stations, which indicates instances of extreme rainfall events. These events are characterized by precipitation values significantly higher than the majority of the data points. The consistent occurrence of positive outliers across various weather stations implies that extreme rainfall events are a recurring phenomenon in the Brazilian Atlantic forest.

The Mann-Kendall (MK) test assessed the presence and strength of monotonic trends in the time series data. We assumed that trends were statistically significant when the p-value was less than 0.05. The results of the MK trend analysis are summarized in Figure 3. A substantial majority of the 205 weather stations exhibited a statistically significant upward trend in daily maximum temperature and number of hours without precipitation. In contrast, the majority of daily precipitation exhibited a statistically significant downward trend. Unlike the unidirectional trend observed in daily maximum temperature or daily precipitation, daily minimum temperature and daily evapotranspiration presented a spectrum of behaviors, including instances of no discernible, upward and downward trends. Despite the varied outcomes in the analysis of daily minimum temperature and daily evapotranspiration, the overarching findings of the MK test indicated an overall reduction in daily precipitation and an increase in both maximum daily temperature and number of consecutive hours without precipitation across the Brazilian Atlantic forest. This suggests a scenario in which the ecosystem may encounter drier conditions in the future.

It is noteworthy that aridity conditions, reflective of short-term extremes, could not be adequately captured through statistics based on monthly values of precipitation, evapotranspiration or even AI. As we evaluated hourly data for maximum temperature and consecutive hours without precipitation reveals that a significant portion of the Brazilian Atlantic forest is already undergoing dry conditions.

In an attempt of establishing a vulnerability ranking, the outcomes of the meteorological indicators were synthesized and organized in Table 1. The ranking reflects the ascending order of vulnerability, delineating the weather stations in terms of their susceptibility to drought events within the Brazilian Atlantic Forest. There is no significant correlation between the assigned rank numbers and the latitudinal positions and elevation of the weather stations (Figure 4. This observation suggests that neither geographical location, as represented by latitude, nor elevation play a significant role in influencing the trends of dryness observed at each weather station.

4. Conclusions

The findings of this study provide significant insights into the spatial-temporal dynamics of drought in the Brazilian Atlantic forest ecosystem. The consistent occurrence of extreme monthly precipitation values across various weather stations suggests that they are a recurring phenomenon in the Brazilian Atlantic forest. However, in spite of the regular incidence of extreme monthly precipitation, the findings from the Mann-Kendall (MK) test demonstrate a prevailing decrease in daily precipitation alongside an elevation in both maximum daily temperature and the duration of consecutive hours without precipitation throughout the Brazilian Atlantic Forest. Additionally, the vulnerability ranking synthesized from the outcomes of the meteorological indicators does not show a significant correlation with the latitudinal positions and elevation of the weather stations, which implies that geographical location and elevation do not significantly influence the trends of dryness observed at each weather station. This observed trend hints at a prospective scenario wherein the ecosystem is likely to face drier conditions, all the while maintaining a consistent range in monthly precipitation values. It aligns with the research conducted by [12], which also highlighted the vulnerabilities of the Brazilian Atlantic forest to climate change.