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

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation, maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from imputation methods due to potential bias. Drought quantification involved the computation of the Aridity Index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann-Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting geographical location and elevation does not strongly influence observed dryness trends.

Keywords:

Subject: Environmental and Earth Sciences - Environmental Science

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 due to historical land use changes and urban expansion, especially along the Brazilian coast[36].This fragmentation has placed a substantial portion of the forest’s diverse biodiversity at risk of extinction, particularly in smaller fragments of less than 50 hectares [37,38,39] as fragmented forests often experience edge effects driven by abiotic factors (e.g. water, wind, and temperature) [26], pushing plant communities toward early successional stages, which invariably leads to a reduction in the forest’s overall biodiversity [40,41,42,43]. Besides the historical land use changes, driven by urban expansion, the Brazilian Atlantic Forest faces escalating threats due to abiotic factors, with climate change posing an additional challenge, as the biome’s intricate web of life and ecological significance is confronted with shifting aridity patterns [24,25].

Many researchers have extensively studied aridity patterns using air temperature and precipitation data. For instance, a study conducted in Iran investigated time series variations in air temperature indices, De Martonne aridity index (IDM), and total precipitation using long-term meteorological data from 1960 to 2019. The study revealed that the climate in Iran has generally become warmer and drier over the past 60 years [44]. Similarly, a comparative study in Mongolia explored spatiotemporal variations of aridity using four different equations: de Martonne aridity index (IDM), Thornthwaite aridity index (AI), moisture coefficient by V. I. Mezentsev (MI), and Hydrothermal coefficient by Selyaninov (HTC) [45]. The study found that the total area of drylands determined by IDM, MI, AI, and HTC is approximately 64.1%, 70.7%, 85%, and 98%, respectively. Time series analysis of AI and MI both showed a decreasing tendency for the period of 1961–2015.

The response of forests to shifts in aridity patterns has also been the subject of extensive research. A study carried out in the Forest Reserve of Fina, Mali, it used remotely sensed data to reveal significant vegetation response to fluctuations in precipitation and air temperature. There has been observed a consistent rise in NDVI levels occurring between 15 to 30 days after rainfall following a period of drought [23]. Another study underscored the intricate interplay between precipitation, air temperature, and forests. It demonstrates, for example, how the deep roots of tropical trees can tap into water reserves, thereby sustaining their cooling role within the ecosystem, even in regions experiencing lower rainfall [22]. These studies provide an understanding of how changes in precipitation and air temperature patterns affect forests, although the specific impacts can vary depending on the geographical location, type of forest, and other environmental factors. Long-term forecasts signal a decline in precipitation and an increase in air temperature levels [21]. Such changes could significantly impact the balance of the Brazilian Atlantic Forest [27,28]. Aridity indices, commonly used to assess the dryness of a region, are typically derived from daily [12] and/or aggregated monthly data [11,13]. These statistics, due to their longer time scale, tend not to exhibit significant changes over short periods [14,15]. However, this research overlooks more immediate fluctuations in aridity that can have substantial impacts on terrestrial ecosystems as we offer a perspective on the aridity status of the Atlantic Forest by utilizing hourly data. This higher resolution data provides a more sensitive measure of short-term changes, offering a more nuanced understanding of the shifts in aridity. To do that, we use meteorological indicators derived from high resolution data, to explore the emerging trends and identify specific regions experiencing shifts in aridity conditions. Then, we established a vulnerability ranking to pinpoint regions undergoing immediate alterations in aridity conditions.

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 [29]. 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 Table 1. 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 entries. 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_{m a x}

). The choice of these indicators was motivated by their relevance to recent severe drought events [34].

We opted against employing imputation methods to replace missing data, as this approach can introduce systematic bias into the meteorological indicators [30,31]. While excluding missing data rather than employing imputation methods can have implications for the results of the analysis by introducing systematic bias from imputation methods, we exclude missing data entirely from the analysis to maintain data integrity, transparency, mitigate the risk of bias and because of the importance of spatial density. We acknowledge that this approach reduces the sample size and potentially limits the scope of analysis. However, by including all available stations, it ensures that crucial spatial coverage isn’t sacrificed as each meteorological station provides valuable data points that contribute to capturing the intricate local variations in weather patterns. This is particularly significant considering the need to investigate edge effects due to droughts in the Brazilian Atlantic Forest [10]. Thus, 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 evapotranspiration estimates for a reference crop. These daily estimates were generated using the Hargreaves and Samani equation [32], which considers measured hourly maximum and minimum air temperatures, elevation, and latitude to compute the reference crop evapotranspiration rates under standard meteorological conditions, as follows:

E T_{0} = 0.0023 \times (T_{m a x} + T_{m i n}) \times \sqrt{T_{m a x} - T_{m i n}} \times (T_{a v g} + 17.8)

(1)

, in which

E T_{0}

represents the reference crop evapotranspiration (ET) in millimeters per day,

T_{m a x}

is the daily maximum temperature in Celsius,

T_{m i n}

is the daily minimum temperature in Celsius and

T_{a v g}

is the daily average temperature in Celsius. We considered this approach for estimating evapotranspiration (ET) appropriate for drought analysis in the Brazilian Atlantic forest, where the ecosystem is known to be especially vulnerable to droughts along its edges due to the prevalence of C3 and C4 grass-like vegetation, which shares functional characteristics with Samani’s reference crop. Moreover, the Hargreaves and Samani equation is particularly advantageous as it incorporates ET estimation based on both maximum and minimum hourly temperature data. This approach enables the capture of immediate fluctuations in aridity.

The choice of the Aridity Index for drought analysis instead of other standardized indices was deliberate, considering that standardized indices primarily focus on analyzing the cumulative probability of annual and monthly data. Typically, these datasets follow a gamma distribution [9], which may not be suitable for hourly datasets, such as the one utilized here, due to their distinct probability distributions.

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. This threshold is very close to the one established by [20], who assumed a threshold of 1 mm for dry days. The rationale behind our choice stems from the unique climatic characteristics of the Brazilian Atlantic Forest, which extends along Brazil’s Atlantic coastline, encompassing regions with varying levels of aridity. By setting a threshold of 0 mm, we aimed to capture the slightest presence or absence of rainfall, recognizing that even minimal precipitation, or its absence, can yield significant ecological implications. 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 [33], 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 important to note that while metorological indicators derived from annual and monthly values of precipitation or evapotranspiration provide valuable insights [16,17,18,19], they may not sufficiently capture short-term aridity extremes. This examination of hourly data for maximum temperature and consecutive hours without precipitation reveals that a significant portion of the Brazilian Atlantic Forest is already experiencing dry conditions.

In an attempt of establishing a vulnerability ranking, the outcomes of the meteorological indicators were synthesized and organized in Table 2. 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. While the elevation can exert an overall significant influence on the characteristics and impacts of droughts. It is imperative to contextualize this within the specific geographical and climatic conditions of the Brazilian Atlantic Forest. The range of elevation within this biome is not as pronounced as those observed in regions with more mountainous topography. Furthermore, the data utilized in our study was sourced from meteorological stations that may not have been optimally positioned to accurately record the effects of elevation on drought trends. These stations are predominantly located at altitudes less than 1000 meters above sea level, which may limit the scope of elevation-related data captured.

4. Conclusions

The findings of this study provide significant insights into the dynamics of drought in the Brazilian Atlantic forest. 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 [35], which also highlighted the vulnerabilities of the Brazilian Atlantic forest to climate change.

Additionally, we underscored the importance of recognizing short-term aridity extremes, which are often overlooked when relying solely on monthly precipitation, evapotranspiration, or Aridity Index (AI) values. By analyzing hourly data, including maximum temperature and consecutive hours without precipitation, we discovered that a significant portion of the Brazilian Atlantic Forest is already experiencing dry conditions. Identifying these short-term extremes is crucial as they can serve as an early warning system for potential long-term drought conditions, enabling farmers, researchers, and policymakers to prepare for potential impacts, such as crop failure or water shortages [8]. They also provide insights into plant survival strategies during short-term drought, such as physiological or behavioral changes like stomatal closure to reduce water loss [7]. Short-term extremes can be used to calibrate and validate drought impact models, providing a range of conditions against which these models can be tested [5,6]; for the development of mitigation strategies, such as irrigation plans to protect crops known to be susceptible to damage under short-term drought conditions [4]; and to have significant ecological impacts, affecting plant-pollinator interactions, soil moisture levels, and even increasing susceptibility to pests and diseases [1,2,3].

Author Contributions

Conceptualization, Chaves, M.B., Rivera, C. and Farias Pereira, F.; methodology, Rivera, C. and Farias Pereira, F.; formal analysis, Chaves, M.B., Cavalcante, N. and Farias Pereira, F.; investigation, Chaves, M.B., Cavalcante, N. and Farias Pereira, F.; writing—original draft preparation, Chaves, M.B. and Farias Pereira, F.; writing—review and editing, Chaves, M.B., Rivera, C. and Farias Pereira, F.; visualization, Chaves, M.B.; funding acquisition, Farias Pereira, F. All authors have read and agreed to the published version of the manuscript.

Funding

We are grateful to Fundação de Amparo à Pesquisa do Estado de Alagoas (FAPEAL) for the travel grant offered to the authors through ERC – Fapeal/Confap/CNPq 2022 Call – Research opportunities in Europe for active PhD researchers from Brazil, which could enrich the work by presenting its bottom line and discussing its outcomes with researchers and peers from all over the world in seminars at Lund University, Sweden.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Weather stations localization

Figure 2. Boxplots of accumulated monthly precipitation for all 205 automatic weather stations in the Brazilian Atlantic forest.

Figure 3. Spatial distribution of detected trend in minimum air temperature, maximum air temperature, aridity index, evapotranspiration, precipitation, and consecutive hours without precipitation by weather station.

Figure 4. Scatter plots illustrating the relationship between latitude and elevation against the order of the weather station in the ranking of vulnerability to drought events. The black solid line represents the linear regression model fitted to the data.

Table 1. This table presents key information on meteorological stations utilized in the study, including station codes, sample sizes, monitoring periods, and the percentage of missing data for precipitation, maximum temperature, and minimum temperature. The dataset comprises a total of 205 automatic weather stations, providing a robust compilation of 10,299,236 data points. Spanning a broad temporal window from station-specific start dates to a standardized endpoint on December 31, 2022.

Station	Sample	Start	Precipitation	Maximum T	Minimum T
	size		Missing data (%)	Missing data (%)	Missing data (%)
A011	144360	2006-07-14	15.90	11.37	11.37
A035	132984	2007-10-31	6.11	5.62	5.62
A301	158040	2004-12-21	13.58	13.44	13.44
A303	174024	2003-02-24	18.98	14.50	14.50
A304	174048	2003-02-23	15.53	13.47	13.47
A320	135456	2007-07-20	21.20	18.88	18.88
A322	135768	2007-07-07	9.26	9.28	9.28
A344	128448	2008-05-07	39.23	37.74	37.74
A352	128544	2008-05-03	34.10	21.23	21.23
A355	125544	2008-09-05	34.61	14.79	14.79
A356	125496	2008-09-07	17.27	14.39	14.39
A357	125592	2008-09-03	28.84	11.88	11.87
A401	198456	2000-05-12	11.43	10.58	10.56
A405	175632	2002-12-19	37.88	24.68	24.68
A406	174912	2003-01-18	16.67	14.69	14.69
A407	174408	2003-02-08	7.70	7.55	7.55
A409	174336	2003-02-11	16.94	13.82	12.41
A410	174768	2003-01-24	34.05	23.81	23.81
A413	136800	2007-05-25	10.96	10.69	10.69
A414	136656	2007-05-31	9.37	9.81	9.81
A417	136872	2007-05-22	10.15	10.27	10.27
A421	126768	2008-07-16	27.17	22.21	22.21
A427	126936	2008-07-09	13.87	12.72	12.72
A431	129192	2008-04-06	20.19	18.07	18.07
A434	126912	2008-07-10	5.15	3.20	3.20
A437	127296	2008-06-24	24.64	11.70	11.70
A438	127368	2008-06-21	12.28	12.28	12.28
A444	118440	2009-06-28	22.13	11.71	11.71
A445	118416	2009-06-29	22.43	16.84	16.84
A446	118536	2009-06-24	17.64	9.07	9.07
A447	118368	2009-07-01	34.09	17.87	17.87
A451	49896	2017-04-23	49.66	49.66	49.66
A455	57840	2016-05-27	32.72	32.81	32.81
A456	42216	2018-03-09	10.59	10.61	10.61
A502	175992	2002-12-04	6.39	6.55	6.55
A508	175752	2002-12-14	11.99	10.64	10.64
A509	158112	2004-12-18	5.82	2.08	2.08
A510	151632	2005-09-14	3.90	2.74	2.74
A511	147792	2006-02-21	5.48	4.59	4.59
A513	144048	2006-07-27	6.84	2.53	2.53
A514	145224	2006-06-08	4.96	5.14	5.14
A515	144408	2006-07-12	6.62	2.86	2.86
A517	143256	2006-08-29	6.72	3.73	3.73
A518	136800	2007-05-25	1.86	1.61	1.61
A521	142272	2006-10-09	0.61	0.63	0.63
A522	143472	2006-08-20	8.53	6.12	6.12
A524	143568	2006-08-16	3.63	2.49	2.49
A527	143376	2006-08-24	9.07	8.38	8.38
A529	136704	2007-05-29	6.69	6.11	6.11
A530	141096	2006-11-27	3.38	1.15	1.15
A531	141024	2006-11-30	1.90	1.90	1.90
A532	136776	2007-05-26	6.09	5.32	5.32
A533	136632	2007-06-01	2.89	1.28	1.28
A534	135096	2007-08-04	15.07	13.35	13.35
A540	135192	2007-07-31	4.99	2.26	2.26
A549	134256	2007-09-08	6.39	6.47	6.47
A550	134352	2007-09-04	5.94	2.66	2.66
A552	134136	2007-09-13	4.93	4.79	4.79
A554	136848	2007-05-23	7.13	5.28	5.28
A555	127752	2008-06-05	0.36	0.36	0.36
A556	107568	2010-09-24	15.56	9.13	9.13
A557	89496	2012-10-16	2.13	2.12	2.12
A566	49296	2017-05-18	4.11	4.37	4.37
A567	47784	2017-07-20	3.85	3.98	3.98
A570	45624	2017-10-18	3.77	4.02	4.02
A601	198192	2000-05-23	18.42	5.63	5.63
A602	176640	2002-11-07	8.98	9.57	9.57
A603	177072	2002-10-20	13.57	11.41	11.41
A604	176352	2002-11-19	12.81	11.65	11.65
A606	142704	2006-09-21	5.61	3.29	3.29
A607	142632	2006-09-24	3.05	2.41	2.41
A608	142704	2006-09-21	4.36	4.41	4.41
A609	142536	2006-09-28	6.97	4.98	4.98
A610	141984	2006-10-21	18.98	19.00	19.00
A611	142584	2006-09-26	7.53	5.54	5.54
A612	141768	2006-10-30	4.21	4.19	4.19
A613	135000	2007-08-08	11.70	4.97	4.97
A614	141864	2006-10-26	3.43	3.42	3.42
A615	141696	2006-11-02	11.85	4.54	4.54
A616	141912	2006-10-24	5.75	2.13	2.13
A617	141912	2006-10-24	7.01	5.42	5.42
A618	141744	2006-10-31	1.38	1.58	1.58
A619	141312	2006-11-18	8.68	6.62	6.62
A620	127584	2008-06-12	8.64	5.94	5.94
A621	137832	2007-04-12	3.97	3.46	3.46
A622	127464	2008-06-17	10.18	9.19	9.19
A623	127368	2008-06-21	9.95	7.79	7.79
A624	107736	2010-09-17	5.76	2.05	2.05
A625	57576	2016-06-07	2.91	2.95	2.94
A626	57696	2016-06-02	1.79	1.93	1.93
A627	39216	2018-07-12	2.99	3.13	3.13
A628	46944	2017-08-24	13.28	13.77	13.77
A629	37056	2018-10-10	0.54	0.66	0.66
A630	36936	2018-10-15	7.61	7.30	7.30
A631	50808	2017-03-16	20.42	19.59	19.59
A632	50688	2017-03-21	6.88	7.16	7.16
A633	51648	2017-02-09	6.63	5.07	5.07
A634	51528	2017-02-14	7.49	7.63	7.63
A636	47304	2017-08-09	1.59	1.62	1.62
A637	1272	2022-11-09	2.83	2.83	2.83
A652	136992	2007-05-17	1.27	1.13	1.13
A657	98832	2011-09-23	12.83	11.45	11.45
A659	64416	2015-08-27	1.27	1.34	1.34
A667	64296	2015-09-01	6.02	5.67	5.67
A701	144120	2006-07-24	0.47	0.53	0.49
A704	186984	2001-09-02	29.59	21.96	21.95
A706	182400	2002-03-12	26.18	22.25	22.25
A707	174528	2003-02-03	10.48	10.12	10.12
A709	174432	2003-02-07	30.10	26.37	26.37
A712	144240	2006-07-19	12.93	11.84	11.84
A713	143448	2006-08-21	8.04	7.57	7.57
A715	143616	2006-08-14	14.25	11.29	11.29
A716	143280	2006-08-28	21.46	21.01	21.00
A721	142080	2006-10-17	31.91	20.54	20.54
A726	142608	2006-09-25	13.79	14.07	14.07
A727	142752	2006-09-19	12.46	6.57	6.57
A728	140568	2006-12-19	12.75	12.86	12.86
A733	134688	2007-08-21	11.95	12.69	12.69
A734	134496	2007-08-29	9.48	9.00	9.00
A735	134400	2007-09-02	15.21	15.26	15.26
A736	132696	2007-11-12	21.74	17.28	17.28
A740	132984	2007-10-31	16.97	8.02	8.02
A741	128784	2008-04-23	22.70	10.09	10.09
A743	127272	2008-06-25	6.96	0.80	0.80
A744	44136	2017-12-19	10.57	10.71	10.71
A746	127104	2008-07-02	34.91	25.77	25.77
A749	127512	2008-06-15	20.51	16.11	16.11
A750	127632	2008-06-10	24.05	18.50	18.50
A751	127824	2008-06-02	25.06	17.26	17.26
A752	127968	2008-05-27	18.12	22.47	22.88
A755	103128	2011-03-28	17.51	3.45	3.45
A762	54024	2016-11-02	3.91	4.48	4.48
A763	49392	2017-05-14	2.64	2.71	2.71
A765	51864	2017-01-31	33.61	30.28	30.28
A766	51672	2017-02-08	35.35	32.13	32.13
A768	49464	2017-05-11	4.25	4.28	4.28
A769	45600	2017-10-19	25.31	25.32	25.31
A771	42120	2018-03-13	1.31	0.17	0.17
A805	184752	2001-12-04	14.55	14.32	14.32
A806	174840	2003-01-21	7.32	6.93	6.93
A807	174696	2003-01-27	18.58	18.72	18.72
A810	141408	2006-11-14	14.45	11.22	11.22
A814	127944	2008-05-28	9.42	8.76	8.76
A815	129072	2008-04-11	2.45	1.72	1.72
A816	125208	2008-09-19	20.54	17.88	17.88
A817	144672	2006-07-01	11.91	11.96	11.96
A818	144456	2006-07-10	18.21	5.58	5.58
A819	144504	2006-07-08	20.26	13.59	13.59
A820	141336	2006-11-17	5.13	4.22	4.22
A821	141216	2006-11-22	9.66	8.35	8.35
A823	142128	2006-10-15	7.12	7.49	7.49
A824	141384	2006-11-15	23.62	22.31	22.31
A825	142056	2006-10-18	39.40	30.58	30.58
A828	141168	2006-11-24	12.19	8.28	8.28
A829	141888	2006-10-25	6.01	4.47	4.46
A835	141264	2006-11-20	7.43	6.41	6.41
A837	130080	2008-02-29	4.81	4.87	4.87
A839	141120	2006-11-26	1.24	1.26	1.26
A840	141024	2006-11-30	2.98	3.03	3.03
A841	133992	2007-09-19	5.07	5.08	5.08
A842	139368	2007-02-07	21.42	20.50	20.50
A843	138192	2007-03-28	21.21	18.32	18.32
A844	138840	2007-03-01	3.31	2.83	2.83
A845	136272	2007-06-16	21.59	8.43	8.44
A846	130440	2008-02-14	25.13	18.76	18.76
A848	127896	2008-05-30	13.05	9.76	9.77
A849	129960	2008-03-05	26.51	14.28	14.28
A850	130008	2008-03-03	20.99	18.22	18.22
A851	136536	2007-06-05	17.24	11.75	11.75
A853	136680	2007-05-30	2.43	2.25	2.25
A854	131952	2007-12-13	2.09	2.12	2.12
A855	132840	2007-11-06	21.74	18.31	18.31
A856	130176	2008-02-25	15.64	6.46	6.41
A857	129816	2008-03-11	21.45	7.58	7.58
A858	129744	2008-03-14	7.23	7.28	7.31
A859	129648	2008-03-18	12.52	2.13	2.13
A860	130128	2008-02-27	7.61	7.05	7.05
A861	129864	2008-03-09	18.66	17.55	17.67
A862	129576	2008-03-21	8.25	6.82	6.82
A863	130008	2008-03-03	6.03	6.15	6.15
A864	121800	2009-02-08	28.78	2.50	2.50
A865	71496	2014-11-05	21.30	18.84	18.84
A866	127872	2008-05-31	16.18	15.67	15.67
A867	125016	2008-09-27	6.47	4.71	4.71
A868	109800	2010-06-23	7.48	7.49	7.49
A869	129840	2008-03-10	17.04	17.26	17.26
A870	57768	2016-05-30	11.57	10.92	10.92
A871	130104	2008-02-28	10.26	10.13	10.13
A873	129792	2008-03-12	6.44	6.28	6.28
A874	102672	2011-04-16	8.47	8.59	8.59
A875	128640	2008-04-29	14.12	7.35	7.35
A876	128280	2008-05-14	16.03	5.15	5.15
A879	125880	2008-08-22	4.73	4.17	4.17
A880	128736	2008-04-25	3.16	3.20	3.20
A882	89808	2012-10-03	16.49	3.00	3.00
A883	88128	2012-12-12	4.13	4.14	4.14
A884	79704	2013-11-28	0.36	0.37	0.37
A894	59208	2016-03-31	1.32	1.34	1.34
A895	33912	2019-02-18	1.93	1.97	1.97
A897	53520	2016-11-23	2.26	2.30	2.30
A898	34008	2019-02-14	1.11	1.14	1.14
B803	56208	2016-08-03	46.66	44.16	44.16
B804	56136	2016-08-06	21.96	22.05	22.05
B806	57744	2016-05-31	4.09	4.14	4.11
F501	79032	2013-12-26	1.25	1.44	1.44

Table 2. A vulnerability ranking for weather stations presented in ascending order according to their susceptibility to drought events within the Brazilian Atlantic Forest.

Station	NDD	NWD	AI	Trend in ET	Trend in Tmax	Trend in NCDH	Rank
A554	22	8	0.31	9.784	21.125	0.492	1
A752	27	3	0.40	3.016	3.613	0.616	2
A533	22	9	0.72	6.601	10.564	0.561	3
A455	19	11	0.28	8.297	6.572	0.517	4
A874	21	10	0.78	6.372	6.132	0.770	5
A555	21	9	0.19	2.827	3.102	0.565	6
A531	19	11	0.37	6.581	6.699	0.487	7
A529	19	11	0.72	12.163	12.430	0.496	8
A704	21	9	0.44	9.543	10.455	-0.227	9
A876	20	10	0.69	6.708	6.506	0.519	10
A631	18	13	0.57	9.400	5.845	0.587	11
A816	21	10	0.64	6.783	5.626	0.453	12
A527	20	10	0.54	4.666	4.580	0.488	13
A621	22	8	0.39	3.003	4.029	0.413	14
A734	19	11	0.59	6.771	8.050	0.426	15
A632	18	12	0.59	5.988	3.571	0.595	16
A634	26	4	0.04	-0.064	0.347	0.599	17
A735	20	11	0.55	3.143	6.924	0.454	18
A557	17	13	0.53	1.860	13.693	0.590	19
A615	22	9	0.68	3.725	2.800	0.481	20
A444	18	13	0.28	6.937	3.985	0.479	21
A727	19	12	0.50	3.146	6.724	0.455	22
A622	17	13	0.55	4.953	4.225	0.605	23
A835	21	9	0.83	2.541	3.327	0.567	24
F501	24	7	0.35	1.055	-0.468	0.563	25
A552	18	12	0.34	1.336	5.535	0.561	26
A513	23	8	0.49	1.652	2.766	0.458	27
A617	18	13	0.63	5.477	6.011	0.499	28
A633	18	12	0.56	3.300	0.589	0.622	29
A733	25	6	0.16	1.172	2.341	0.360	30
A344	20	10	0.42	1.625	7.621	0.193	31
A818	20	11	0.78	5.122	8.088	0.295	32
A716	21	9	0.73	2.819	5.790	0.307	33
B803	16	15	0.85	8.013	10.117	0.580	34
A304	22	9	0.19	0.593	4.076	0.256	35
A858	18	13	0.96	6.904	7.064	0.492	36
A884	21	10	0.77	1.413	-1.292	0.677	37
A743	19	12	0.59	1.562	1.022	0.610	38
A530	17	13	0.63	8.100	7.903	0.316	39
A657	24	6	0.15	-0.743	-2.005	0.491	40
A509	19	12	0.61	5.506	6.322	0.159	41
A864	16	14	0.77	6.019	5.586	0.572	42
A434	21	9	0.30	-0.043	-0.243	0.457	43
A521	18	12	0.81	2.845	5.508	0.497	44
A514	16	14	0.72	5.565	6.682	0.515	45
A011	18	11	0.60	1.972	6.513	0.342	46
A859	17	13	1.23	6.082	6.210	0.579	47
A846	17	14	1.29	9.931	8.324	0.534	48
A869	16	15	0.80	6.755	7.496	0.517	49
A768	19	12	0.42	-0.903	-0.851	0.610	50
A875	18	13	1.33	6.840	6.071	0.519	51
A502	18	13	0.67	6.714	6.462	0.195	52
A556	19	12	0.45	-2.268	1.676	0.572	53
A636	20	10	0.35	-2.310	-3.657	0.564	54
A845	20	10	1.00	2.049	3.050	0.467	55
A410	15	16	0.73	6.889	13.642	0.465	56
A627	22	9	0.55	-0.827	-3.505	0.528	57
A407	16	15	0.48	4.552	7.147	0.351	58
A709	22	8	0.32	-1.305	1.845	0.240	59
A518	20	10	0.45	0.886	0.189	0.386	60
A612	21	9	0.86	1.791	2.235	0.385	61
A630	20	10	0.43	-2.644	-4.861	0.566	62
A862	17	13	1.12	4.609	4.799	0.557	63
A614	20	10	0.43	-1.344	1.068	0.429	64
A880	18	12	1.07	3.388	3.295	0.515	65
A620	18	12	0.51	1.760	0.960	0.465	66
A613	20	11	0.41	-2.102	-1.265	0.497	67
A749	18	12	0.68	-0.742	1.263	0.602	68
A652	19	11	0.57	1.117	2.219	0.403	69
A755	18	12	1.29	2.837	2.914	0.581	70
A659	22	9	0.71	-2.382	-4.458	0.579	71
A602	19	12	0.72	3.200	4.103	0.224	72
A623	19	11	0.70	-0.788	-0.448	0.583	73
A819	18	12	0.76	2.361	4.858	0.315	74
A625	20	11	0.75	0.139	-1.269	0.558	75
A610	18	12	0.87	3.443	4.687	0.310	76
A866	15	15	1.04	1.839	7.543	0.650	77
A667	20	10	0.64	-4.085	-5.136	0.607	78
A763	16	15	0.78	3.434	1.801	0.610	79
A894	15	15	1.05	4.625	3.680	0.646	80
A626	20	11	0.76	0.130	-1.532	0.558	81
A823	17	13	1.43	3.169	3.622	0.588	82
A706	20	11	0.66	0.708	2.212	0.323	83
A301	16	14	0.51	1.334	12.855	0.303	84
A861	18	12	1.16	3.617	2.196	0.494	85
A618	20	10	0.46	-0.096	-0.981	0.338	86
A628	21	10	0.65	-2.927	-6.258	0.552	87
A355	17	14	0.43	2.012	4.467	0.274	88
A601	19	11	0.55	2.219	0.827	-0.096	89
A616	20	11	0.66	-1.960	2.470	0.396	90
A824	20	10	0.64	0.198	1.629	0.291	91
B806	17	14	0.88	2.114	1.102	0.605	92
A817	17	13	1.37	2.904	4.448	0.543	93
A841	14	17	1.01	8.740	12.219	0.469	94
A740	18	12	1.01	2.118	3.124	0.450	95
A837	20	11	1.20	1.579	2.699	0.457	96
A515	18	13	0.79	2.730	4.585	0.264	97
A637	21	10	0.60	0.195	-1.316	0.295	98
A414	15	15	0.48	3.229	3.956	0.330	99
A451	14	16	0.54	3.213	4.812	0.389	100
A532	17	14	0.46	0.419	0.848	0.503	101
A851	17	13	1.15	1.618	2.798	0.585	102
A417	14	17	0.77	6.709	9.262	0.355	103
A741	16	14	1.04	3.862	5.197	0.442	104
A035	16	15	0.64	2.800	6.656	0.248	105
A550	16	15	0.86	1.649	4.998	0.531	106
A701	18	12	0.81	0.644	3.687	0.362	107
A873	19	11	0.98	-4.112	-2.063	0.622	108
A566	14	16	0.77	3.761	-0.169	0.587	109
B804	17	14	1.14	1.723	1.482	0.613	110
A438	16	14	0.52	-0.188	5.500	0.373	111
A860	15	16	1.19	6.704	6.617	0.451	112
A721	18	13	0.53	1.664	0.847	0.250	113
A603	20	11	0.60	1.386	-1.701	0.012	114
A524	15	16	0.46	1.506	3.518	0.421	115
A744	17	13	0.44	-6.271	-4.955	0.587	116
A849	9	21	1.01	7.824	8.805	0.512	117
A870	17	14	0.89	1.550	-1.348	0.591	118
A511	12	18	0.84	4.703	7.364	0.444	119
A850	12	18	1.45	5.874	8.729	0.529	120
A517	17	13	0.77	0.216	2.103	0.457	121
A303	14	16	0.35	2.866	3.502	0.153	122
A510	13	17	1.06	5.528	8.611	0.380	123
A865	14	16	1.00	1.937	1.806	0.640	124
A619	19	12	0.99	2.564	0.592	0.233	125
A522	17	13	0.57	-1.720	-1.237	0.491	126
A863	12	19	1.01	3.441	2.705	0.625	127
A728	19	11	0.86	-2.993	-2.883	0.497	128
A750	8	23	0.97	4.312	4.020	0.647	129
A765	18	12	0.91	-1.363	-6.509	0.584	130
A540	18	12	0.75	-1.258	-1.049	0.448	131
A867	14	15	1.07	0.270	2.669	0.667	132
A855	14	17	1.27	7.278	6.364	0.400	133
A843	17	13	1.24	1.805	1.277	0.494	134
A769	18	12	0.71	-3.805	-4.196	0.503	135
A624	13	18	0.58	0.710	1.238	0.587	136
A707	15	15	1.26	6.620	8.099	0.089	137
A406	16	15	0.52	2.129	1.537	0.235	138
A352	10	20	0.91	1.349	8.241	0.576	139
A570	17	14	0.69	-1.242	-3.115	0.565	140
A456	NA	NA	NA	2.593	1.548	0.541	141
A712	19	12	1.00	-3.146	1.676	0.414	142
A840	19	12	1.24	-0.178	-0.172	0.474	143
A814	13	17	1.60	2.773	2.852	0.627	144
A606	12	18	0.66	1.205	12.149	0.342	145
A821	14	16	0.80	1.816	3.084	0.442	146
A431	13	18	0.60	8.213	-1.750	0.378	147
A607	15	15	0.91	1.247	4.111	0.421	148
A446	8	22	1.08	4.987	5.122	0.530	149
A810	20	10	0.82	-4.139	-5.149	0.344	150
A815	8	22	0.84	1.737	2.952	0.625	151
A409	14	17	0.75	0.919	11.779	0.247	152
A413	14	17	0.51	1.654	2.066	0.298	153
A609	15	15	0.89	0.522	1.894	0.482	154
A713	13	18	1.15	3.582	4.669	0.429	155
A322	16	14	0.37	-4.627	-0.834	0.388	156
A883	16	14	1.18	-0.549	-1.582	0.621	157
A882	13	17	1.08	1.119	-0.040	0.676	158
A751	11	19	1.40	5.840	4.555	0.487	159
A447	11	20	0.59	1.967	2.493	0.433	160
A897	18	13	1.52	-2.877	-4.832	0.681	161
A549	8	22	1.11	3.178	3.598	0.567	162
A356	18	12	0.55	-5.689	-5.749	0.272	163
A771	15	15	1.15	2.087	-2.456	0.550	164
A357	14	16	0.63	-5.420	-2.979	0.591	165
A868	16	14	1.32	-2.160	-2.248	0.712	166
A844	10	20	1.27	3.615	3.351	0.494	167
A839	13	18	0.79	2.736	2.453	0.308	168
A895	16	14	1.30	0.451	-1.966	0.528	169
A611	17	14	0.91	-2.020	-0.016	0.393	170
A421	12	18	0.76	-0.781	7.679	0.306	171
A857	9	22	1.54	3.271	4.571	0.501	172
A736	9	22	1.55	7.815	9.211	0.246	173
A871	13	18	1.10	1.922	1.729	0.481	174
A762	10	20	1.04	0.951	0.296	0.635	175
A807	10	20	1.26	5.506	4.792	0.343	176
A405	10	20	1.09	2.692	6.280	0.344	177
A806	13	17	0.89	2.105	2.600	0.265	178
A879	16	14	1.03	-4.895	-2.844	0.532	179
A820	14	16	1.08	1.605	1.769	0.377	180
A856	10	20	1.50	3.838	2.936	0.442	181
A508	12	18	0.72	-0.098	4.674	0.183	182
A848	5	25	2.69	2.799	3.804	0.526	183
A726	14	16	0.76	-0.170	0.800	0.269	184
A829	13	17	0.99	-1.408	-0.260	0.515	185
A320	12	18	0.87	-6.175	15.821	0.221	186
A437	10	21	1.25	2.669	5.656	0.306	187
A604	15	16	0.69	-1.921	-0.227	0.224	188
A766	13	17	1.11	-1.592	-4.499	0.587	189
A828	14	16	1.20	1.277	1.037	0.259	190
A853	14	16	1.37	-0.127	-0.433	0.410	191
A825	17	13	1.64	-4.363	-4.945	0.352	192
A854	10	21	0.99	0.373	0.637	0.439	193
A534	11	19	0.72	-5.127	-4.445	0.484	194
A898	11	20	1.53	0.650	-2.597	0.528	195
A608	12	19	0.95	-2.382	-1.017	0.431	196
A567	12	18	2.14	-3.901	-3.983	0.560	197
A746	13	18	1.53	-2.048	-0.585	0.412	198
A445	10	21	1.09	-0.760	-0.074	0.366	199
A842	12	19	1.97	-0.617	-1.314	0.453	200
A805	12	19	1.14	1.027	-0.813	0.080	201
A427	9	22	0.88	-3.188	-2.396	0.442	202
A629	9	22	1.17	-3.593	-6.473	0.553	203
A715	12	18	1.08	-4.048	-3.087	0.270	204
A401	6	24	0.99	-2.531	0.712	0.072	205

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