# Integrated assessment of hydroclimatic extremes, land cover exposure, and vegetation responses in sub-humid and semi-arid regions in Brazil

Beatriz M. Funatsu, Pedro R. Mutti, Vincent Dubreuil, Damien Arvor, Hugo

Romero, Pablo Sarricolea

► To cite this version:

Beatriz M. Funatsu, Pedro R. Mutti, Vincent Dubreuil, Damien Arvor, Hugo Romero, et al. Integrated assessment of hydroclimatic extremes, land cover exposure, and vegetation responses in sub-humid and semi-arid regions in Brazil. Remote Sensing Applications: Society and Environment, 2025, 38, pp.101595. 10.1016/j.rsase.2025.101595. hal-05076113

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Integrated assessment of hydroclimatic extremes, land cover
exposure, and vegetation responses in sub-humid and semi-arid
regions in Brazil

Beatriz M. Funatsu<sup>a,*</sup>, Pedro R. Mutti<sup>b</sup>, Vincent Dubreuil<sup>a</sup>, Damien Arvor<sup>a</sup>,
Hugo Romero<sup>c</sup>, Pablo Sarricolea<sup>c</sup>

ARTICLE INFO

Keywords:

Tropical environment

Hydroclimatic hazards

Natural vegetation and agrosystems exposure

Biophysical vulnerability

Trends

A B S T R A C T

The São Francisco (SF) River crosses large swaths of the Brazilian semi-arid territory, and has a strategic role in the socio-economic development of northeastern Brazil. Hydroclimatic extremes (HCE) have the potential to inflict considerable social and ecological perturbations, thus a fine-scale spatial analysis of HCE and their trends is essential for decision-making concerning adaptive measures in the region. Trend analysis indicate that rainfall intensity increased/decreased by 10 % in 28 %/14 % of the basin during the period of 1981–2021. Parts of northern SF shows both rainfall intensification and longer dry periods, but dry conditions prevail in most of the basin. In particular, 52 % of the basin presents extended consecutive dry days. Vegetation responses to HCEs were assessed with the Normalized Difference Vegetation Index (NDVI) for the period 2001–2021, considering two contrasting land cover types: “natural vegetation” (NatVeg) and “croplands” (Crops). Correlations between NDVI and HCE indices revealed positive and significant values maximized with a lag between –1 and –3 months for both NatVeg and Crops. However, vegetation responses in singular dry and wet years showed contrasts only for the lower range of NDVI. Most of the basin show negative mean annual NDVI trends even in areas with rainfall intensification. Since NDVI showed a stronger decrease in dry episodes than increase in wet ones, it is inferred that even highly adapted vegetation such as those in the semi-arid portions of the basin face mounting challenges to recover from extended droughts exacerbated by drier year-to-year observed conditions.

1. Introduction

Hydroclimatic extremes (HCE) have become globally more frequent and/or persistent in the last decade, in accordance with
projections of global warming (Gordon et al., 1992; O'Gorman, 2015; Du et al., 2019; Mirdashtvan and Malekian, 2020; Seneviratne
et al., 2021; Rodell and Li, 2023; Zaitchik et al., 2023; Ballarin et al., 2024). Although the paradigm of "dry gets drier, wet gets wetter"
seems to hold in many parts of the world, Feng and Zhang (2016) and Zaitchik et al. (2023) argue that regional and methodological

<footnote><sup>a</sup> CNRS, UMR6554 LETG, Nantes Université, Université Rennes 2, Chemin de la Censive du Tertre, cedex 3, BP 81227, 44312, Nantes, France</footnote>

<footnote><sup>b</sup> Departamento de Ciências Atmosféricas e Climáticas, Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Brazil</footnote>

<footnote><sup>c</sup> University of Chile, Institute of Architecture and Urbanism, Department of Geography, Chile</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: beatriz.funatsu@univ-nantes.fr (B.M. Funatsu).</footnote>
nuances must be accounted for. Intensity and duration of extreme rainfall add to the nuances of interpretation (Du et al., 2019), depending on dataset and/or location. The implications of changes in HCE on the agro-ecosystems can be perceived through accrued seasonal or annual changes in soil moisture leading to increased sensible heat flux and temperature (Rahimabadi et al., 2024), and subsequently, potential evapotranspiration (Feng and Zhang, 2016). These effects can be particularly critical for vegetation in arid and semi-arid regions, in parts of the globe in which water supply is scarce (Zhang et al., 2018; Zarei et al., 2020), including parts of the Northeast Brazil (Marques et al., 2020; Mendes et al., 2020; Mutti et al., 2022; Costa et al., 2022).

Several studies concerning different facets of droughts have been performed for the Northeast Brazil, for example, meteorological droughts, regarding negative rainfall anomalies and increasing potential evapotranspiration (Kayano and Andreoli, 2006; Paredes Trejo et al., 2016; Marengo et al., 2017, 2018; Mutti et al., 2022, and references therein), and hydrological droughts, characterized by persistent negative anomalies in soil moisture content and water mass storage (Paredes-Trejo et al., 2021). Drought events affecting the Northeast Brazil have been reported since the 16th century (e.g., Marengo et al., 2018), and these episodes are likely to increase in intensity in duration under all Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios (de Medeiros et al., 2022b; Ballarin et al., 2024).

In this work, we focus on HCE changes and impacts on vegetation in the São Francisco (SF) watershed. The SF River is the main water body crossing large swaths of the Brazilian semi-arid territory, and has a strategic role in the socio-economic development of northeastern Brazil. Due to the latitudinal extent of the SF watershed (SFW), different climate mechanisms affect rainfall occurrence and its interannual variability. For example, the upper SF is under the influence of the South America Monsoon System (SAMS) and of the South Atlantic Convergence Zone (SACZ; Marengo et al., 2012; Ferreira and Reboita, 2022), whereas the semi-arid portion and estuary zones in the lower parts of the basin are sensitive to fluctuations in the position and intensity of the Intertropical Convergence Zone. While certain intense drought episodes have been linked to El Niño, the Tropical Atlantic sea surface temperature (SST) anomalies and the Pacific Decadal Oscillation also plays an important role on drought frequency and persistence, particularly in the MLSF and LSF (Kayano and Andreoli, 2006; Paredes Trejo et al., 2016, 2021; Marengo et al., 2017; Jimenez et al., 2021; Mutti et al., 2022; Reboita et al., 2021). The most recent drought episode affecting the Northeast Brazil, including the SF basin, consisted of an extended period of rainfall deficit between 2012 and 2018 (Fig. S2d; Marengo et al., 2018; Freitas et al., 2022). This drought was associated with the presence of an anticyclonic system near the southeast coast of Brazil, which in turn, acted to inhibit cloud formation over the São Francisco river's source region during the rainy season, leading to precipitation deficits throughout the period (de Medeiros et al., 2020; Freitas et al., 2022).

Although studies on droughts in the Brazilian Northeast region have been extensively performed, other features of hydroclimatic extremes such as the possibility of rainfall intensification have received less attention; vegetation responses to wet and dry rainfall extremes particularly regarding semi-arid vegetation of the Caatinga are also lacking. Analysis of trends in precipitation extremes using ground-based data for the past 40–50 years have shown that the Northeast Brazil presented a significant decrease in the frequency of intense rainfall, and in their intensity, as well as in the persistence of dry spells (de Oliveira et al., 2014; Oliveira et al., 2017; Bezerra

<figure><img src="image_3.png" /><figcaption>Fig. 1. São Francisco watershed geographical location, elevation data, main river course, and sub-basin limits.</figcaption></figure>
et al., 2019; Dubreuil et al., 2019; Regoto et al., 2021), and these trends have been recently confirmed for the Brazilian semi-arid region with satellite data covering the past 20 years (dos Santos et al., 2024). Increase in hot extremes particularly in spring and summer, and decrease of cold extremes in winter were also observed (dos Reis et al., 2020; Costa et al., 2020; Regoto et al., 2021). These variations in precipitation and temperature extremes have the potential to increase the stress on the vegetation capacity to regulate activity, and may be exacerbated by man-driven changes in land cover and use (Zarei et al., 2020; Gatti et al., 2021; Saatchi et al., 2021). For example, semi-arid dry forests such as the Caatinga are capable to continue storing carbon even during prolonged drought periods, highlighting the importance of this type of vegetation to regional and global carbon cycles (Moura et al., 2013; Mendes et al., 2020). However, the basin is covered by different types of vegetation that may respond differently to precipitation fluctuations (Liu et al., 2013; Vicente-Serrano et al., 2012), and rainfed crops may be particularly susceptible to changes in rainfall patterns (e.g., duration of rainy periods, rainfall intensity, intermittency, temporality; dos Reis et al., 2020; Reis et al., 2020; de Oliveira Aparecido et al., 2023; Rahimabadi et al., 2024). Thus, the objectives of our study are, (i) to provide a detailed spatio-temporal characterization of HCE and vegetation activity in the SF basin over the past decades, (ii) to provide an analysis of the impacts of dry and wet HCE on vegetation activity, and (iii) to assess the modulation of vegetation responses by land use and land cover (LULC) types in the SF basin. We assess a key vulnerability parameter (that is, exposure to HCE) and cross-analyze this parameter with LULC types, in order to interpret variations in vegetation activity.

The paper is structured as follows: after describing the study area (Section 2), data and methods are presented in Sections 3 and 4. We characterize the frequency, intensity and persistence of both dry and wet HCE that affected the SFW in the last 4 decades, based on fine-scale precipitation data, and estimate their trends (Section 5.1-5.3), followed by the analysis of variations in vegetation responses according to LULC, and finally, we address seasonal aspects (Section 5.4). Lastly, we cross-analyze HCE and NDVI according to LULC (Section 5.5). Discussion is presented in Section 6, followed by conclusions and perspectives (Section 7).

## 2. Study area

The SFW is the fourth largest in South America, covering 631,133 km², and encompassing 7.4 % of Brazilian territory running entirely within its limits (Fig. 1). The SF runs northeasterly for 2814 km, with 1371 km of navigable waters; it is important for fishery, irrigated agriculture, hydroelectric power generation and the integration of several states in the Northeast Brazil through a water transfer scheme (Nascimento and Becker, 2010; Godinho and Godinho, 2018; Paredes-Trejo et al., 2021; de Brito and de Azevedo, 2022; de Medeiros et al., 2022a; Mutti et al., 2022). The mean annual precipitation exhibits large spatial variability across the basin (Fig. 2a). The highest values are observed in the upper part of the SFW, gradually decreasing over the semi-arid portions in the mid-low SF, and increasing again in proximity to the coastal areas and a similar spatial pattern is found for the mean 99th percentile of daily precipitation (P99c; Fig. 2b). Three different biomes are present in the basin: Cerrado (savanna) in the upper (USF) and middle (MSF) SF; Caatinga (semi-arid tropical dry forest) in the MSF, mid-low (MLSF), and low (LSF) SF, and the Atlantic Forest in the USF region and in the coastal areas of the LSF. These biomes have distinct land cover (Fig. 3): The USF and MSF are dominated by savanna-like natural vegetation and cropland, which comprise ~80 % of the land cover class in those sub-basins; in the MLSF the limited availability of rainfall is reflected on the dominance of semi-arid tropical dry forests and sparse vegetation (68 % land cover), whereas in the LSF grassland predominates (~40 %).

<figure><img src="image_4.png" /><figcaption>Fig. 2. (a) Mean annual precipitation (1981–2010), (b) climatological P99c value.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. ESA-CCI majority class land cover in the SFW in 2020. At the right-hand side, zoom-in at the limit of the MSF and MLSF (corresponding to the red rectangle in the left panel) that illustrates the resolution of ESA-CCI land cover classification after aggregation. The relatively large water body near [9.3°S, 41°W] at the border of MSF and MLSF corresponds to the Sobradinho Dam.</figcaption></figure>

On the backdrop of this remarkable diversity of natural ecosystems, approximately 42% of the SFW area has been converted to non-natural uses (Fernandes et al., 2021; MapBiomas, 2024). In the semi-arid lands of the MLSF and northern MSF, the Caatinga vegetation coexists with numerous family farms, largely dependent on subsistence rainfed cropping (Almeida et al., 2020). Furthermore, the SF River allowed the development of key irrigation districts along its semi-arid portions, with diverse fruit farming and important local irrigated sugarcane farming (de Brito and de Azevedo, 2022). The western MSF is part of the well-studied "MATOPIBA" region, a recent agricultural frontier in Brazil comprising the states of Maranhão (MA), Tocantins (TO), Piauí (PI) and Bahia (BA), and characterized by the rapid suppression of the savanna-type Cerrado vegetation for the expansion of large agriculture lands, mainly soybean (da Silva et al., 2023; de Oliveira Aparecido et al., 2023). Finally, the higher-altitude and wetter lands of the USF have experienced local expansion of Eucalyptus and Pinus plantations as an effort to expand agro-silvopastoral systems instead of traditional land exploitation techniques (de Freitas et al., 2022; Barbosa et al., 2023). It is clear that, given the intricate and diverse natural and non-natural land uses of the SFW, water is a crucial resource and HCE such as droughts play an important role in the basin's economy and biodiversity.

## 3. Data

### 3.1. CHIRPS v2

We used the version 2 of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS; Funk et al., 2015) daily precipitation estimates, from 1981 to 2021, at spatial resolution of 0.05° (approximately 5.5 km in the Tropical region). CHIRPS incorporates 0.05° resolution satellite imagery and in-situ station data that are suitable for trend analysis and seasonal drought monitoring. At monthly scale, this dataset tends to overestimate low and underestimate high rainfall values (>100 mm/month) compared to rain gauge data, and has overall negative bias (−4.60%) over the Northeast Brazil (Paredes-Trejo et al., 2017). While this important caveat may lead to slight uncertainties on the estimation of extreme events if compared to ground measurements, this dataset is interesting as it provides a long-time, fine-scale daily series covering the entire area of the SFW, and allows an intercomparison with results obtained from rain gauge data presented in previous works (e.g., Bezerra et al., 2019).

### 3.2. Land use and cover

Annual land cover data was obtained from the European Satellite Agency (ESA) Climate Change Initiative (CCI) Program Land
cover Project (Copernicus Climate Change Service, 2019, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2PUGv2_0.pdf) for the period 1992–2015 (version 1.7) and 2016–2020 (version 2.0), directly from the ESA CCI Land cover website (http://maps.elie.ucl.ac.be/CCI/viewer/download.php). The land cover map provided by ESA followed the requirements of the Committee on Earth Observation Satellites (CEOS) for stage-3 validation of thematic maps. The validation plan is carefully described in the aforementioned ESA-CCI website. Additionally, this land cover product was also validated and compared to other available maps in specific studies in Brazil, which concluded that it can accurately represent LULC dynamics in the country (Rosan et al., 2021).

Land cover data, originally at a spatial resolution of 300 m (processed from Envisat - Environmental Satellite, SPOT - Satellite Pour l'Observation de la Terre, PROBA-V - Project for On-Board Autonomy-Vegetation, and NOAA - National Oceanic and Atmospheric Administration satellite data), were re-sampled to a 0.05° grid using the LC-CCI user tool 4.3 (available at ESA CCI Land cover website), in which the majority land cover type is retained for each grid point. ESA maps for the SFW are shown in Fig. 3: Two broad classes are clearly and accurately distinguishable, namely, natural vegetation (which encompasses semi-arid dry forests and savannas) and agricultural areas (including a mix of cropland, pastures and a mosaic of non-natural uses). For the purposes of the present work, this resolution is sufficient to perform a comparative analysis based on "natural vegetation" and "croplands" broad classes, at the scale of the São Francisco basin.

In the USF, the natural vegetation class includes a mixture of grasslands, shrubs and woodlands but also vegetation on rocky formations to the east, whereas in the LSF, natural vegetation is dominated by grasslands. The MSF presents a highly heterogeneous cropland surface, with extensive soybean cultivation at the west, and mixed family farming and pastures at the east. Finally, the MLSF is dominated by shrubs typical of the Caatinga biome, and agricultural activity is tailored to the scarcity of water resources.

### 3.3. Vegetation activity

Vegetation activity was assessed based on the Normalized Difference Vegetation Index (NDVI) obtained from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13C1) Version 6.1 product (Didan, 2021). NDVI data are provided in a climate modeling grid of 0.05°, compatible with both CHIRPS and ESA LULC re-sampled data. We summarized the 16-day data into monthly composites for the period 2001–2021, considering only good quality data according to the quality assessment bands of the MOD13C1 product, keeping the same horizontal resolution of 0.05°. It is worth mentioning that version 6 of the MODIS collections corrected previously reported trend biases originating from sensor degradation (Wang et al., 2012).

While other vegetation indices could have been used in this study (see, e.g., Karakani et al., 2021), we opted for the NDVI due to its well-known applicability for monitoring vegetation. Previous studies showed that the NDVI performed slightly better than other common indices (such as the EVI - Enhanced Vegetation Index and the SAVI - Soil-adjusted Vegetation Index) when representing natural vegetation in drylands and savannas (Ferreira and Huete, 2004; Costa et al., 2022; Fadl et al., 2024).

Fig. 4 shows the mean and maximum NDVI averaged over the 2001–2021 period. The NDVI ranges from 0 to 1, indicating vegetation greenness, while negative values usually portray water bodies (Tucker, 1979). The maximum NDVI corresponds to the maximum value registered in a given year. By comparing Figs. 3 and 4 one can notice the prevalence of higher NDVI values over croplands, mosaic lands and woody vegetation while lower NDVI values prevail over shrublands and sparse vegetation. Max NDVI indicates, for unchanged land cover throughout the period, the optimal condition that vegetation can reach within a given season

<figure><img src="image_6.png" /><figcaption>Fig. 4. (a) Mean, and (b) maximum annual NDVI averaged over the period 2001–2021. Color scale is the same for both panels.</figcaption></figure>
(Barbosa et al., 2006; Yan et al., 2022).

# 4. Methods

## 4.1. Hydroclimatic extremes

Hydroclimatic extremes were assessed through indices defined by the World Climate Research Program (WCRP) Expert Team of Climate Change Detection and Indices (ETCDDI; https://www.wcrp-climate.org/etccdi; see also Alexander et al., 2006; Sillmann et al., 2013) to characterize precipitation extremes. The ETCCDI indices describe moderate climate extremes with re-occurrence times of a year or shorter. HCE indices comprise: (1) absolute indices, which describe the annual maximum 1-day or (accumulated) 5-day precipitation (RR) amounts, (2) heavy precipitation threshold indices, which count the number of days that a particular daily precipitation Table 1 value is attained or exceeded, (3) duration indices, which describe the longest length of wet or dry spells, and (4) percentile-based threshold indices, which describe the occurrence of precipitation exceeding a high percentile threshold. In this work, we consider the annual maximum 1-day precipitation (Rx1), the heavy precipitation index (R10, with RR ≥ 10 mm), extremely wet days (RR ≥ 99th percentile), and consecutive dry (CDD) and wet (CWD) days. We also examine annual single daily rainfall intensity (ASDII) and the number of wet days (R1; RR ≥ 1 mm). The list and definition of indices used in this study are presented in Table 2. All indices were computed at grid-point level (that is, with keeping the 0.05° spatial resolution), using Fortran 90 codes developed in Funatsu et al. (2021); alternatively, software to process and analyze precipitation extremes are freely available by the ETCCDI team, at http://etccdi.pacificclimate.org/software.shtml.

## 4.2. Trend and correlation analyses

Trends for each of the HCE indices listed in Table 1, and for mean and maximum annual NDVI were assessed for each grid point in the domain using a simple, linear regression model estimated through the least-squares method (e.g., Schneider et al., 2010). Robustness of trends was assessed by estimating the 95 % confidence interval of the slope coefficient through the least-squares method (Student's t-test at the 5 % significance level). The linear approach is arguable as a choice to evaluate trends in systems with high temporal variability, but, as in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, it is used here to describe and assess recent changes. Furthermore, previous studies showed that parametric approaches (e.g., least-squares regression) do not differ significantly from non-parametric approaches (e.g., Mann-Kendall and median of pairwise slopes) when dealing with annual climate date, since these samples usually meet the main assumptions for the use of parametric tests (Huth and Pokorná, 2004; Pal and Al-Tabbaa, 2011).

At monthly scale, Mann-Kendall's non-parametric test was used to assess trend signal, with Sen's slope (median of the slopes calculated between each pair of points in the series) indicating the magnitude of the trend (at the 95 % confidence level). Additionally, Spearman's rank correlation coefficient (also at the 95 % confidence level) was used to examine possible links between different parameters, as well as between monthly HCE and NDVI trends. Hereafter, “anomalies” in HCE indices refer to the 1981–2010 baseline period. For the comparison between distributions of mean and maximum NDVI trends in each sub-basin, we used the Kolmogorov-Smirnov and Wilcoxon non-parametric tests to determine whether the shape and mean of the distributions were similar

<table><caption>**Table 1**
List of extreme precipitation indices, definitions, and units (Sillmann et al., 2013).</caption><thead><tr><th>Index Label</th><th>Index name</th><th>Definition</th><th>Units</th></tr></thead><tbody><tr><td>RR</td><td>Precipitation</td><td>Daily convective rainfall amount</td><td>mm</td></tr><tr><td>R1</td><td>Wet days</td><td>Number of days where RR ≥ 1 mm</td><td>days</td></tr><tr><td>ASDII or MSDII</td><td>Annual or Monthly simple daily intensity</td><td>Let RR<sub>wj</sub> be the daily precipitation amount on wet days w in period j (in this case, one year or 1 month). If R1<sub>j</sub> represents the number of wet days in j, then: SDII<sub>j</sub> = (Σ<sup>R1<sub>j</sub></sup><sub>1</sub> RR<sub>wj</sub>) / R1<sub>j</sub></td><td>mm, day<sup>-1</sup></td></tr><tr><td>Rx1</td><td>Max 1-day precipi-tation</td><td>Let RR<sub>ij</sub> be the daily precipitation amount on day i in period j. The maximum 1-day value for period j is then: Rx1<sub>j</sub> = max (RR<sub>ij</sub>)</td><td>mm</td></tr><tr><td>P99</td><td>Very wet days pre-cipitation</td><td>Let RR<sub>wj</sub> be the daily precipitation amount on a wet day w in period j and let P99c be the 99th percentile of precipitation on wet days in the 1981–2010 period. If R1 represents the number of wet days in the period, then P99t<sub>j</sub> = Σ<sup>R1mm<sub>j</sub></sup><sub>1</sub> RR<sub>wj</sub> where RR<sub>wj</sub> ≥ P99c</td><td>mm</td></tr><tr><td>P10</td><td>Total heavy precipitation amount</td><td>Let RR<sub>wj</sub> be the daily precipitation amount on a wet day w in period j. Then P10<sub>j</sub> = Σ<sup>R1mm<sub>j</sub></sup><sub>1</sub> RR<sub>wj</sub> where RR<sub>wj</sub> ≥ 10 mm</td><td>mm</td></tr><tr><td>R99</td><td>Very wet days</td><td>Let RR<sub>wj</sub> be the daily precipitation amount on a wet day w in period j and let P99c be the 99th percentile of precipitation on wet days in the 1981–2010 period. Count the number of days where RR<sub>ij</sub> ≥ P99c</td><td>days</td></tr><tr><td>R10</td><td>Heavy precipitation days</td><td>Let RR<sub>ij</sub> be the daily precipitation amount on day i in period j. Count the number of days where RR<sub>ij</sub> ≥ 10 mm</td><td>days</td></tr><tr><td>CDD</td><td>Maximum number of consecutive dry days (RR &lt; 1 mm)</td><td>Let RR<sub>ij</sub> be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where RR<sub>ij</sub> &lt; 1 mm</td><td>days</td></tr><tr><td>CWD</td><td>Maximum number of consecutive wet days (RR ≥ 1 mm)</td><td>Let RR<sub>ij</sub> be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where RR<sub>ij</sub> ≥ 1 mm</td><td>days</td></tr></tbody></table>
<caption>**Table 2**</caption>

Percentage of the basin in which positive or negative trends were found for (a) TAP (total annual precipitation), (b) Rx1, (c) ASDII, and (d) P99t. Values in brackets correspond to the % area in which trends are above 95 % confidence interval. The first (leftmost) column represents the threshold value considered for each line in the table and indicates the trend value relative to the mean value.

<table><thead><tr><th rowspan="2">Intensity/41 yrs</th><th colspan="2">(a) TAP</th><th colspan="2">(b) Rx1</th><th colspan="2">(c) ASDII</th><th colspan="2">(d) P99t</th></tr><tr><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th></tr></thead><tbody><tr><td>-</td><td>17.5<br>(0.02)</td><td>82.5<br>(6.3)</td><td>55.9<br>(7.2)</td><td>44.1<br>(5.3)</td><td>55.6<br>(14.0)</td><td>44.4<br>(5.2)</td><td>44.5<br>(5.1)</td><td>55.5<br>(8.4)</td></tr><tr><td>5 %</td><td>8.6<br>(0.02)</td><td>68.1<br>(6.3)</td><td>45.7<br>(7.2)</td><td>34.5<br>(5.3)</td><td>39.9<br>(14.0)</td><td>28.2<br>(5.2)</td><td>4.03<br>(5.1)</td><td>51.0<br>(8.4)</td></tr><tr><td>10 %</td><td>27.6<br>(14.0)</td><td>14.0<br>(5.2)</td><td>35.4<br>(7.2)</td><td>25.9<br>(5.3)</td><td>3.4<br>(0.02)</td><td>46.8<br>(4.9)</td><td>42.9<br>(5.1)</td><td>53.9<br>(8.4)</td></tr><tr><td>20 %</td><td>9.9<br>(9.1)</td><td>0.7<br>(0.5)</td><td>18.6<br>(7.2)</td><td>12.9<br>(5.3)</td><td>0.06<br>(0.02)</td><td>12.2<br>(4.9)</td><td>41.3<br>(5.1)</td><td>52.2<br>(8.4)</td></tr><tr><td>30 %</td><td>1.8<br>(1.8)</td><td>-</td><td>7.4<br>(5.8)</td><td>6.1<br>(4.4)</td><td>-</td><td>0.6<br>(0.1)</td><td>39.8<br>(5.1)</td><td>50.4<br>(8.4)</td></tr><tr><td>50 %</td><td>-</td><td>-</td><td>0.6<br>(0.6)</td><td>1.0<br>(0.8)</td><td>-</td><td>-</td><td>36.6<br>(5.1)</td><td>14.9<br>(8.4)</td></tr></tbody></table>

(distributions were considered statistically different when *p*-value <0.05 for the test statistics).

Mean NDVI time series, distributions and trends for each sub-basin were processed and extracted using Google Earth Engine, and all the aforementioned statistical analysis were conducted either with the R software (version 4.3.3) or IDL 9.0 statistical functions (REGRESS, R_CORRELATE).

### 4.3. Combined HCE-NDVI-LULC analysis

In a first step, we considered the dominant ESA LULC classes in the basin in 2020 (rainfed cropland and shrubland/grassland, according to Fig. 3, hereafter referred to as Cropland and NatVeg, respectively) that remained as the dominant class within a re-sampled pixel since 1992. Then, for each of these classes we performed correlation analysis to assess seasonal and interannual effects of HCE on vegetation activity. Two approaches were deployed: first, monthly lag-correlations between NDVI and HCE indices were estimated, based on the common available period (2001–2021). We averaged HCE and NDVI for NatVeg and Cropland pixels in each sub-basin prior to the correlation estimation; we did not account for spatial autocorrelation of HCE before performing the

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Flow chart summarizing the variables considered, data sources, spatial resolution and time range, methods, and analysis results.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Mean (1981–2021) values of (a) ASDII (mm.day⁻¹, (b) Rx1 (mm), (c) P99t (mm), (d) R1 (days), (e) R10 (days), (f) standard deviation of R99 (days), (g) CDD (days), and (h) CWD (days).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Linear trend coefficients for (a) ASDII (mm.day⁻¹.yr⁻¹, (b) Rx1 (mm.yr⁻¹), (c) P99t (mm.yr⁻¹), and total variation of number of days in the 41-year period for (d) R1, (e) R10, (f) R99, (g) CDD, and (h) CWD.</figcaption></figure>
averages based on the following assumptions: (1) pixels with the same LULC class (either NatVeg or Crops) are patchy, not always continuous, (2) sub-basins are subject to distinct climatic variability forcing (as described in the Introduction), (3) wet precipitation extremes do not have smooth spatial fluctuations. Second, we contrasted the NDVI responses of each vegetation class for a dry year (2012) and a wet year (2008), which were selected based on previous long-term climatological studies in the SFW (see e.g., Fig. 2 of Mutti et al., 2022), by evaluating the NDVI distribution and their inter-year and inter-class differences.

A work flow condensing the data, analysis steps, and objectives is presented in Fig. 5.

# 5. Results

## 5.1. HCE climatological features

The mean spatio-temporal distribution of HCE in the SF basin for the period 1981–2021 is shown in Fig. 6. ASDII (Fig. 6a) and P99t (that is, the total accumulated precipitation above the climatological P99c in a year; Fig. 6c) mean spatial distribution follows that of the mean total annual rainfall, whereas Rx1 presents a somewhat smoother spatial gradient except in the LSF, where a sharp R1 gradient (60–110 mm within 250 km) is observed (Fig. 6b). R1 and R10 (Fig. 6d and e) show a spatial distribution that is qualitatively similar to the mean precipitation distribution (Fig. 2a). Fig. 6f shows the standard deviation of the mean R99; R99 represents occurrences of rainfall exceeding the P99c value, thus by definition, it should occur 3 to 4 times a year. According to Fig. 6f, there is a 66 % probability of P99 occurrences between 1 and 7 days a year. These results are qualitatively close to those recently presented for the Brazilian Northeast semi-arid regions based on satellite data, for the period of 2001–2020 (dos Santos et al., 2024).

The duration of consecutive dry days (CDD; Fig. 6g) is long in the northern half of MSF, in the semi-arid portion of the basin, where CDD may last 140–150 days. Shorter dry windows are observed near the source (USF) and in most of the LSF. An interesting and rather surprising feature that is captured here and in the work of dos Santos et al. (2024) is the westward gradient of CDD at the heart of the semi-arid portions of the SFW. This gradient may be explained by the intermittent rainfall associated to coastal dynamics with the seasonal migration of the ITCZ in the northern parts of the basin, and the SACZ affecting the USF. On the other hand, consecutive wet days (CWD; Fig. 6h) show a rapid decrease from the coast towards the interior, but it has also a very large standard deviation (not shown) reflecting the high interannual variability and very sporadic nature of rainfall throughout the basin.

## 5.2. Trends

The variability of the HCE in the past 40 years is represented as trends in Fig. 7. Despite fine-scale spatial heterogeneity in the trends, there is a general signal towards drier conditions, with reduced R1 (Fig. 7d), longer CDD (Fig. 7g) and shorter CWD (Fig. 7h) all over the basin. At the same time, intensification of rainfall is also observed in large portions of the basin, most notably at the northwest of the MSF (Fig. 7a), accompanied by similar changes in P99t (Fig. 7c) and R99 (Fig. 7f), and to some extent also by R10 (Fig. 7e).

Tables 2 and 3 summarize the observed changes by quantifying the area in which positive or negative trends are observed for each parameter. 82.5 % of the basin present a general decreasing trend in total precipitation (TAP; trends significant for 6.3 % of the basin), against 17.5 % in which precipitation tend to increase (<0.1 % with significant trends). For 68/47/12 % of the basin, the rate of decrease over the 41-year period represented 5/10/20 % of the total rainfall.

Statistically significant increases in 10 % of the mean intensity of ASDII, Rx1 and P99 were observed in, respectively, 14 %, 7 % and 5 % of the basin, where a decrease of equal proportion was found between 5 and 8 % of the basin (Table 2). Concerning the parameters that reflect the yearly frequency of HCE, the most notable results are the increase in CDD (88 %), and decrease in CWD (80 %), R1 (92 %), and R10 (80 %) in large areas of the basin (Table 3). Though not statistically significant, it points to a general tendency of drying

**Table 3**

Percentage of the basin in which positive or negative trends were found for (a) CDD, (b) CWD, (c) R1, (d) R99, and (e) R10. Values in brackets correspond to the % area in which trends are above 95 % confidence interval. The first (leftmost) column represents the threshold value considered for each line in the table.

<table><thead><tr><th rowspan="2">Days/41 yrs</th><th colspan="2">(a) CDD</th><th colspan="3">(b) CWD</th><th colspan="2">(c) R1</th><th colspan="2">(d) R99</th><th colspan="2">(e) R10</th></tr><tr><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th><th>Pos</th><th>Neg</th></tr></thead><tbody><tr><td>-</td><td>87.9<br>(29.5)</td><td>12.0<br>(0.5)</td><td>20.0<br>(0.2)</td><td>79.9<br>(6.2)</td><td>8.4<br>(0.1)</td><td>91.6<br>(10.0)</td><td>41.9<br>(3.7)</td><td>58.0<br>(7.7)</td><td>20.1<br>(0.4)</td><td>79.9<br>(5.5)</td></tr><tr><td>1</td><td>86.1<br>(29.5)</td><td>10.2<br>(0.5)</td><td>6.3<br>(0.2)</td><td>50.8<br>(6.2)</td><td>6.4<br>(0.1)</td><td>89.3<br>(10.0)</td><td>23.5<br>(3.7)</td><td>36.6<br>(7.7)</td><td>13.5<br>(0.4)</td><td>71.2<br>(5.5)</td></tr><tr><td>3</td><td>81.7<br>(29.5)</td><td>7.4<br>(0.5)</td><td>0.2<br>(0.1)</td><td>6.2<br>(3.8)</td><td>3.3<br>(0.1)</td><td>83.4<br>(10.0)</td><td>3.5<br>(2.9)</td><td>5.2<br>(4.8)</td><td>5.4<br>(0.4)</td><td>48.8<br>(5.5)</td></tr><tr><td>5</td><td>77.4<br>(29.5)</td><td>5.4<br>(0.5)</td><td>-</td><td>0.3<br>(0.3)</td><td>1.5<br>(0.1)</td><td>73.4<br>(10.0)</td><td>0.07<br>(0.07)</td><td>0.02<br>(0.02)</td><td>2.3<br>(0.4)</td><td>28.4<br>(5.5)</td></tr><tr><td>10</td><td>66.4<br>(29.5)</td><td>2.9<br>(0.4)</td><td>-</td><td>-</td><td>0.15<br>(0.07)</td><td>27.6<br>(9.9)</td><td>-</td><td>-</td><td>0.4<br>(0.4)</td><td>5.1<br>(4.4)</td></tr><tr><td>15</td><td>53.7<br>(28.0)</td><td>1.7<br>(0.4)</td><td>-</td><td>-</td><td>0.04<br>(0.04)</td><td>1.15<br>(1.15)</td><td>-</td><td>-</td><td>0.03<br>(0.03)</td><td>0.3<br>(0.3)</td></tr><tr><td>30</td><td>19.8<br>(18.3)</td><td>0.25<br>(0.18)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
and reduced frequency of heavy rainfall. Notably, a significant increase of 15 days in CDD was found in 28 % of the basin, and 18 % of the basin has had a significant CDD increase of 30 days.

Fig. S1 allows to examine the degree of co-variability between the spatially averaged indices at annual scale. ASDII is strongly correlated with R99 ($\rho$ = 0.88), R10 ($\rho$ = 0.71), Rx1 ($\rho$ = 0.77) and to a lesser extent with CWD ($\rho$ = 0.54). Relationships between CDD and the other HCE indices are weak ($\rho$ < 0.16) and not statistically significant. R99 is also strongly correlated with R10 ($\rho$ = 0.84) and Rx1 ($\rho$ = 0.90), indicating that the strongest one-day rainfall is often above the P99c threshold, and also that often P99c is above 10 mm. CWD is strongly correlated with R10 ($\rho$ = 0.84) and R99 ($\rho$ = 0.76), suggesting that CWD in the SFW is linked to continuous episodes of heavy rainfall and/or extremely wet days.

## 5.3. Seasonal aspects

### 5.3.1. HCEs

The analysis presented in the previous sections showed the climatological HCE spatiotemporal distribution focusing on annual time-scales. However, it did not consider the temporality of these climate extremes within a hydrological year. This consideration is important in the analysis of relationships between rainfall, HCE and vegetation responses, as the latter is modulated by seasonal water availability. Thus, we examined the frequency of the HCE by month and by sub-basin as the rainy season is shifted between the southern and northern part of the basin, notably with an “inversed” rainy-dry season between LSF and USF (Fig. 8[I], grey bars); these regions are subject to distinct large-scale atmospheric dynamics that control the main features of rainfall frequency (see Introduction).

The analysis of the timing of HCE with respect to the seasonality of rainfall for each sub-basin is presented in Fig. 8I. For the USF and MSF basin, and to some extent the MLSF, the occurrences of R10, R99, and Rx1 closely follow the mean rainfall and R1 distributions (Fig. 8[I]a-c), with maximum values concentrated at the peak of the rainy season (between November and March). In the LSF, though precipitation extremes are also confined within the rainy season, the peaks of R10, R99 and Rx1 are not coincident as in the other parts of the basin: R10 is more frequent in the early part of the rainy season (March–April) and in July, whereas Rx1 and R99 are most frequently found in May.

Monthly mean trends (Fig. 8[II]) show that in the USF and MSF, dry months (Jun-Jul-Aug) have become drier, as well as in the beginning of the wet season (Sep-Oct) (Fig. 8[II]a,b). The MLSF shows mixed trends (Fig. 8[II]c), but drier conditions at the height of the wet season (Mar), a wetter end of wet season (May-Jun), and less extreme rainfall during the dry season (JJAS). In the LSF (Fig. 8[II]d), the number of rainy days (R1) tend to increase from May to October (rainy season) without increase in the total precipitation; in addition, extreme rainfall (R99 and Rx1) tends to decrease, suggesting that rainfall intensity decreased in the LSF sub-basin.

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** [I] Monthly distribution of CHIRPS-based mean precipitation (mm; grey bars), and the relative monthly frequency (with respect to the yearly total) R1, R10, R99 and Rx1. [II] monthly trends given as Kendall-$\tau$ coefficient for the same variables with significant trends marked with a yellow dot.</figcaption></figure>
### 5.3.2. Monthly vegetation greenness trends by LULC class

Before assessing the impact of HCE changes on vegetation activity, we examined the monthly mean NDVI evolution, focusing on the differences between NatVeg and Cropland responses to seasonal water availability (Fig. 9[I], grey bars).

In the higher, wetter portions of the basin (USF and somewhat MSF), NDVI reaches its lowest values in the end of the dry season (Sep), sharply increasing with the onset of the wet season and peaking on Jan–Feb, 3–4 months into the wet season. In the USF, Cropland presented higher maximum NDVI values (0.67 against 0.63 for NatVeg), while in the MSF both Cropland and NatVeg peaked at similar NDVI values (0,65). In both regions, NatVeg minimum NDVI was slightly higher than Cropland (0.380.42 against 0.28–0.35). In the MLSF, both land covers present similar minimum NDVI (0.28 in Sep), with NatVeg peaking at 0.59 and Cropland, which are mainly rainfed in this sub-basin, peaking at 0.49 after the short wet-season from Jan through Apr. In the LSF, NatVeg NDVI is consistently higher than Cropland throughout the year, while both respond similar to rainfall seasonality.

Trends were analyzed (Fig. 9[II]) considering only pixels with unchanged land cover in the 2001–2021 period, minimizing the influence of non-climatic factors on monthly long term NDVI behavior. In the MSF and MLSF, negative trends are observed throughout the entire year and for both natural vegetation and croplands. In these regions, December presented a non-significant positive trend in NDVI in contrast with the negative trends starting in Jan. The USF is the only sub-basin that, for both NatVeg and Cropland, showed positive trends in all months except for September. In the LSF, no particular seasonal behavior was observed, with mostly low-magnitude trends. The mean and max NDVI distributions for every sub-basin were considered significantly different (p-value<0.001 in all cases for both the Kolmogorov-Smirnov and Wilcoxon tests).

In addition to this seasonal long-term analysis, it is also important to assess overall changes in mean and maximum NDVI in the SFW (Fig. 10). While mean NDVI better portrays the averaged seasonal behavior of vegetation, max NDVI provides a better picture of the behavior of mature vegetation. In the USF, both max and mean NDVI trends were mostly positive, indicating more greenness, particularly in its central portion. The most negative trends in the SFW were observed in the MSF and MLSF, especially in the semi-arid MLSF (Fig. 10c). However, there is a clear spatial distinction in the spatial trends' distribution, with increase in max and mean NDVI at the westernmost border of the MSF (MATOPIBA agricultural expansion area), even in the southern part, in which negative HCE trends dominate (that is, drier conditions; Fig. 7). Negative NDVI trends dominate in the eastern part of the MSF (mix of pastures and natural vegetation) and well within the MLSF, with strong negative NDVI in the deep semi-arid, Caatinga portion of the basin, including around the Sobradinho dam. In the LSF, max NDVI presented mostly positive trends, while mean NDVI presented mostly negative trends together with a small dry HCE prevalence in the center of the subbasin.

## 5.4. Combined HCE-NDVI-LULC analysis

### 5.4.1. Lag correlations

We performed simple lag-correlations (0–11 months) between the monthly mean values of NDVI and HCE normalized anomalies

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** [I] Monthly distribution of precipitation (mm; 2001–2021; grey bars), and mean NDVI for natural vegetation (green) and cropland (yellow), for each sub-basin. [II] monthly trends given as Kendall-r coefficient for the same variables with significant trends marked with a yellow dot.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Spatial distribution of linear trends coefficients for (a) mean NDVI, and (b) max NDVI; trends based on the period 2001–2021. (c) Distribution of trend coefficients mean (dashed) and maximum (solid) NDVI, averaged over each sub-basin and the whole basin (SFW). Color scale for (a) and (b) correspond to the abscissa in (c).</figcaption></figure>

relative to the climatological period, averaged per basin. The normalization of HCE was done so that the NDVI responses to different HCE could be inter-compared. For this analysis, we chose the following HCE variables: MSDII (monthly SDII), P99, P10, Rx1m (maximum one-day precipitation in the month); CDD and CWD were not considered as they are typically assessed in yearly basis.

For both Cropland and NatVeg, the maximum positive correlation was found for a lag of -1 to -2 months (not shown), that is, NDVI will have a maximized positive response up to 3 months after the (wet) extreme event, and the correlations become negative between 5- and 9-months lag (maximum negative correlation with a 7-month lag). In the USF and MSF, correlations do not present notable differences between different LULC classes, but for the MLSF and LSF, the NDVI-HCE correlations are slightly weaker for Cropland than for NatVeg. We further analyzed the relationships between NDVI-HCE by applying a cross-lagged correlation, that is, each monthly NDVI series was correlated with all 12 monthly 21-year series of HCE indices. Fig. 11 shows the cross-lagged correlation results for NDVI and Rx1m (the analysis was performed for the same variables mentioned above; not shown). Notice that the maximum positive correlation of 1–3 months lags is discernible in Fig. 11.

In the LSF (Fig. 11a), Rx1m elicit positive correlations at the start and the end of the rainy season. For Cropland, the positive correlations were present for a shorter period of time. The correlations at any lag were weakest in June and August. One possible explanation would be the fact that vegetation already peaked at previous months, and therefore rainfall variability and even extremes would not affect it at these months. In general, both for Cropland and NatVeg, rainfall at the peak of the rainy season (Apr-May, see Fig. 8[I]d) seems to influence the response of NDVI up to 5 months (until Sep). The main differences between Cropland and NatVeg reside on the fact that statistically significant correlations are more frequent and more robust for the former. This could be seen as an expected result, as seeding dates in croplands are fixed (linked to the start of the rainy season), and thus the NDVI cycle has a higher correlation with rainfall patterns marked by a dry season; nevertheless, this still constitutes a relevant, novel result as here we are specifically considering HCE parameters.

The general analysis for the MLSF showed that NDVI is much less influenced by HCEs, in particular, P10 and P99. Still, a 1-month lag influence is observed with Rx1m and MSDII, mainly for Croplands (Fig. 11[I]b). We found no notable differences between NDVI responses for Cropland or NatVeg vegetation, or between NDVI-HCE indices, except Rx1m (Fig. 11c). In all cases the most significant correlations occurred with a 1-month lag, and HCE's at the end of the wet season (Mar-Apr) seemed to influence NDVI during its decreasing phase. NatVeg NDVI (Fig. 11[II]c) seems to respond better with a 1-month lag throughout the entire peak vegetation season, which is not observed for Cropland (Fig. 11[I]c).

Finally, no generalizable relationship between NDVI and HCEs were noticeable for the USF, for either type of LULC. As in the MLSF, correlations were strong and significant only between NDVI and monthly mean precipitation anomalies (not shown). Still, some significant, positive NDVI-Rx1m relationships can be noticed for Cropland (Fig. 11[I]d), particularly during and at the end of the dry season (Jun-Sep).

### 5.4.2. NDVI responses in a dry and a wet year

To further compare NDVI responses of Cropland and NatVeg for different types of HCEs, we analyzed the mean NDVI-HCE relationships for a dry (2012) and a wet (2008) year, as shown for example, for NDVI and P99 normalized anomalies (P99') in Fig. 12.

First, we investigated how different are the NDVI responses of NatVeg and Cropland vegetation in dry and wet years. We observed that the NDVI density responses in contrasted wet/dry years vary between basins for the same type of LULC. In the LSF (Fig. 12a), the peak NDVI density for NatVeg was around 0.50–0.70 in 2008, compared to a 0.20–0.70 NDVI spread without a clear peak in 2012. In comparison, the shape of the NDVI distribution for Cropland remains the same, with only a displacement of NDVI value peak frequency from 0.50 in 2008 to 0.38 in 2012. This indicates that NatVeg in LSF has heterogeneous responses to water stress, with notable differences of distributions between the dry and wet year, whereas for Cropland, vegetation activity has a more homogeneous behavior. In the MLSF (Fig. 12b), both NatVeg and Cropland NDVI density distribution shapes are broader in 2008 (wet year) than in 2012, and
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Cross-lagged correlation coefficients between monthly series of NDVI and Rx1m normalized anomalies, for [I] cropland, and [II] natural vegetation. Rows correspond to sub-basins: (a) LSF, (b) MLSF, (c) MSF, and (d) USF. Correlations significant at $p \le 0.05$ are marked with a yellow circle.</figcaption></figure>

the decrease of peak NDVI density is of about 0.12 for both LULC. Finally, NDVI distributions in wet and dry years are strongly overlapped, for both LULC, in the MSF (Fig. 12c) and USF (Fig. 12d).

Regarding how different are the responses of NatVeg and Cropland within either a wet or dry year, for all sub-basins, the NDVI density spread is larger and reaches higher values for NatVeg than for Cropland, in agreement with the seasonal average NDVI shown in Fig. 9. A notable exception was found for the strong positive anomalies of P99t (P99') in the USF (Fig. 12d), where Cropland NDVI maximum density is higher than that of NatVeg. Finally, concerning the behavior of NDVI for the different types of HCE, there was no regular pattern that could be observed regarding the NDVI-HCE responses (not shown), except for P99' and CWD', for which the scatterplot distributions were very similar in all sub-basins. As presented in Section 5.2, P99t and CWD have high trends correlation, implying that CWDs are frequently tied to periods of consecutive very wet days (P99).
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Observed distributions of NDVI and P99 normalized anomalies (P99') in singular wet (2008) and dry (2012) years, for Cropland (yellow tones) and NatVeg (green tones).</figcaption></figure>

# 6. Discussion

The spatial distribution of HCE trends based on CHIRPS data suggests that periods of dry days are becoming longer and more frequent (longer CDD, shorter CWD, and fewer wet days), in line with previous studies based on shorter periods (dos Santos et al., 2024), or with sparse ground-based data (Costa et al., 2020; Regoto et al., 2021; Bezerra et al., 2019). Beyond this general agreement, our analysis also show distinctive and spatially coherent areas of statistically significant rainfall intensification in several parts of the MSF that are sometimes coincident with those with lengthening or increased frequency of dry periods; these increases in both wet and dry extremes are aligned with mid- and long-term CMIP6 climate projections of precipitation extremes for the Northeast Brazil (de Medeiros et al., 2022).

Fig. 13 summarizes the conjugated “wet” and “dry” HCE trends observed in the SF basin. Indeed, whereas most of the USF, MLSF and LWF are dominated by at least one HCE towards drier conditions (increase in CDD, decrease in ASDII, CWD or R1), the northern half of the MSF has a mix of “drier” and “wetter” (increase in P99, R10, ASDII and/or Rx1) conditions. This apparent contradiction of intensification of both strong and fewer days/less rainfall can be conciliated by analyzing the timing (or, seasonality) of HCE occurrences: intense rainfall (R99, R10 and Rx1) generally occur during the rainy season, which, in turn gets shorter, with increased CDD, and both decreased CWD and R1. These gradual shifts in the already short wet season in the MLSF and MSF have been previously hinted in other studies focusing on monthly rainfall and potential evapotranspiration (Mutti et al., 2022). However, the rainfall intensification in large swaths, conjugated with drying trends, is a particularly novel result that has not been shown before.

In seasonal timescales, the majority of HCE indices and NDVI over cultivated areas show strong negative trends roughly from July
<figure><img src="image_15.png" /><figcaption>**Fig. 13.** Conjugated “wet” and “dry” HCE trends affecting (a) NatVeg, and (b) Croplands: only grid points in which there was at least one detected trend larger than 1 standard deviation for at least one of the variables indicated are shown. Dry and wet events comparisons (magenta, blue, and green) regard the net balance in detected HCE trends.</figcaption></figure>

to October in all but the LSF (Fig. 9), but these results should be considered with care since vegetation is mostly dormant this period and the large pixel size of MODIS NDVI product makes it difficult to further assess specific impacts. Nevertheless, it still indicates increased stress and a longer dry period that may negatively affect production later on, particularly when crops reach the flowering or grain filling stage (de Oliveira Aparecido et al., 2023). Positive NDVI trends in Cropland and NatVeg areas were found in December (which follows positive trends in RR, R99 and Rx1 in November), suggesting a shift towards earlier peaks of vegetation. Negative NDVI trends are observed throughout the rainy season in most of the basin (except for native vegetation in the USF), supporting the findings of previous studies (Vicente-Serrano et al., 2012; Liu et al., 2013) that showed that even highly adapted vegetation in water-limited semi-arid and semi-humid regions are vulnerable to prolonged droughts. It also suggests that rainfall intensification in some parts of the basin do not offset the accrued water stress during the prolonged drought conditions.

The overall seasonality of natural and non-natural vegetation types throughout the different sub-basins of the SFW (Fig. 9) can be explained by the different vegetation physiognomies of the Brazilian savannas and dry forests. In the MLSF, the Caatinga is mostly characterized by thorny deciduous and semi-deciduous small trees and brushes, which rapidly respond to water stress at the end of the wet season due to the low water storage capability of rocky and shallow soils (Moro et al., 2016; Fernandes et al., 2022). In the MSF, however, the presence of sedimentary mixed with karst topographies allowed the development of a mixed vegetation with characteristics of both dry forests and the Cerrado savanna (Moro et al., 2016; Fernandes et al., 2020). This natural vegetation has deeper roots and is adapted to sandy soils, allowing a smoother response to the end of the wet season while a few herbaceous species still thrive even during the dry season (Fernandes et al., 2020). Despite the well-known heterogeneity of the Caatinga vegetation, the mean NDVI behavior portrayed in Fig. 9 agrees with the general vegetation physiognomies behavior expected in each sub-basin. Croplands seasonal NDVI behavior, on the other hand, is highly driven by the prevalence of rainfed agriculture in these areas, with an adherence to the seasonal precipitation regime.

The comparisons of the relationships between NDVI responses to HCEs both in the cross-lagged correlation analysis (Fig. 11), and the analysis of NDVI-HCEs in a singular dry and wet years (Fig. 12), highlight important considerations that are linked to the specificities of Cropland and NatVeg in each sub-basin, previously described. First, the occurrence of an extreme precipitation event or a longer dry spell within one year do not elicit a comparable effect (linear or otherwise) on NDVI, in either LULC class. However, we observe that dry spells have a stronger (negative) effect on NDVI compared to wet events, for which only the lower NDVI bound is increased. Second, in the regions that have abundance of rain (USF) or water scarcity (MLSF), the correlations between NDVI and HCEs are weaker. We interpret these results in the following manner: in the USF, which is within the area affected by the SAMS and with relatively high total annual precipitation (even in years with below-average precipitation), factors such as sunlight availability changes may have a strong influence as an enhancing factor for vegetation activity (see, e.g., Hilker et al., 2014; Jones et al., 2014). This could explain the fact that in the USF NDVI trends are mostly positive (Figs. 9 and 10) whereas HCE trends are mostly negative (Figs. 7 and 13): less rain would be associated with less cloud cover, which would allow more downward shortwave radiation, thus more sunlight for photosynthetic activity. On the other hand, in the MLSF both NatVeg and Croplands (mainly rainfed familiar agriculture) are highly dependent on rainfall, thus higher positive correlations occur with monthly mean precipitation. Still, correlations with HCE (and in particular Rx1m) were found to be robust with precipitation at the beginning (Oct–Nov) and peak of the rainy season (Feb–Apr).

Despite the limited responses of vegetation activity to HCE anomalies in a particular year, the cumulative effect of rainfall anomalies (that is, the persistence effect), such as the prolonged dry period in the 2010 decade, affected vegetation at basin scale
(Fig. S2). Such NDVI shifts (and trends) may hardly be linked to LULC changes, as converted areas during the 2000–2021 period are sparse and cannot explain the bigger picture of the observed basin-scale vegetation dynamics (Fig. S3). Even though semi-arid and humid biomes have mechanisms for adaptation to long-term drought conditions (e.g., Vicente-Serrano et al., 2012), the period of nearly one decade of negative rainfall anomalies (Fig. S2d), increase in CDD and decrease in R1 were persistent enough for the vegetation to show signs of decrease in mean and max NDVI relative to the pre-drought period (Fig. S2e), and for which recovery was not attained in the two post-drought years (Fig. S2f). These large-scale spatial results confirm site-specific and local results found by Rezende et al. (2023), who reported a reduction in leaf cover and high mortality of plant individuals in several Caatinga sites within the MLSF sub-basin during the 2010’s drought. It is also coherent with the results of Liu et al. (2013), who showed that vegetation in water-limited semi-arid and semi-humid regions is highly sensitive to vegetation extreme events under more severe precipitation extremes.

Locally, apparent relationships between HCE, NDVI and LULC changes were visible in parts of the domain. In the USF, areas of expansion of forest plantations (mainly Pinus and eucalyptus) within pastoral areas (de Freitas et al., 2022; Barbosa et al., 2023) were found to be coincident with those in which the mean and max NDVI show positive trends, despite the predominance of dry HCE trends. Whether this is due to a better tree adaptation (e.g., deeper roots that reach water tables), to potential use of irrigation, or to the sustainable nature of agro-silvopastoral systems is a matter of further investigation. In addition, in several parts of the MATOPIBA region, areas with increase in maximum NDVI (Fig. 10) coincide with those with increased wet HCE (Fig. 13); as the rainfall intensification happens during the rainy season, it may help crops achieve a better production. However, longer periods of CDD may compromise the adoption of intensive agricultural practices such as double cropping (Arvor et al., 2014; Brumatti et al., 2020).

The observed negative trends in mean and max NDVI throughout most of the MLSF and MSF are consistent with the observed shifts in the duration of the wet and dry seasons, as well as the impacts of the prolonged 2012–2018 drought. According to de Medeiros et al. (2022), climate model simulations project increases in both dry spells and heavy precipitation for the Northeast Brazil for the medium (2046–2065) and far future (2081–2100). The magnitude of the projected changes is much higher than those recorded for the recent past here presented, highlighting the increasingly critical role of access to water in the semi-arid lands of Brazil. Even moderate emission scenarios have the potential to induce vegetation shifts from savannas, grasslands and forests to arid-associate shrubland (Parracciani et al., 2023). The few small areas that featured positive NDVI trends in MLSF and MSF regions (Fig. 10) were irrigation districts that developed near the SF River. As pointed out by de Carvalho et al. (2015); Hagel et al. (2019); de Oliveira Aparecido et al. (2023), climatic changes will lead to an indisputable need for adaptation to the majority of rainfed farmers that occupy these lands, while those few that have access to water are more prone to resist in a drier environment with even more concentrated rainfall.

## 7. Conclusions

Dry conditions that are climatologically prevalent in the northern, semi-arid parts of the basin, have become more widespread in the São Francisco basin, notably with the statistically robust increase of 15 days in CDD in over 25 % of the basin in the last 41 years. However, precipitation has also intensified in large areas of the basin, noticeably in the northern half of the MSF, in tandem with an increase in R99, Rx1 and also to some extent with R10 and CWD. The predominantly native vegetation (semi-arid tropical dry forests and shrubs) and croplands have suffered increased challenges linked to both wet and dry HCE in the basin. The increase in ASDII, Rx1, and R99 did not offset the effects of reduced precipitation (frequency and amounts) found in this period, and point to the vulnerability that both native vegetation and agricultural areas face in terms of the changes in precipitation patterns and persistent exposure to HCE. The effects of prolonged dry conditions have large impacts concerning the increase in plant activity stress, as exemplified by the droughts of 2012–2018 with an important decrease in the NDVI. With a dataset of 40 years, it is still not possible to say conclusively that the SFW is suffering a generalized process of aridification and/or desertification, but our results support the seasonal interpretation of "dry gets drier" and "wet gets wetter" that is, the dry season gets longer, and the wet season gets more often more intense precipitation - at least in large portions of the MSF. These trends are also consistent with most projections of increase in dry spells and extreme precipitation for all RCP scenarios (de Medeiros et al., 2022b) for the Northeast Brazil, including the SF basin. Besides the potential effects of prolonged droughts and rainfall intensification on agriculture, the integrated analysis provided here also has important implications in the context of the resilience of seasonally dry tropical forests - although they are highly adapted to semi-arid conditions, the increase in the intensity and duration of dry spells may push their resilience to the limits (Liu et al., 2013). Adaptive measures that consider the potential of biodiversity to improve environmental conditions in arid zones could include the incorporation of nurse plants (Moura et al., 2013; Danet et al., 2024), increasing functional diversity and functional redundancy (Aguirre-Gutiérrez et al., 2022), which favor a higher survival rate and more biomass production during droughts for the entire plant community as long as species are varied.

The present study focuses on the exposure of land cover to HCE events, covering hazard and vulnerability of the vegetation for two broad land cover types to HCE. This basin-scale approach provided a wide picture of the relationships of HCEs and NDVI, and opens the door to explore a finer, territorial scale approach focusing on key areas of NDVI, LULC and HCE changes, in order to achieve a better understanding of specific plant types (e.g., by functional groups within a same LULC class) responses to extreme climatic conditions, and to examine rainfall-vegetation relations in terms of phenology and biomass responses. In addition, further studies analyzing temperature changes coupled to the hydrological cycle are necessary and currently being performed to complete a cartography of vulnerability and risks linked to extreme events in the SF basin.
**CRediT authorship contribution statement**

**Beatriz M. Funatsu:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Pedro R. Mutti:** Writing – review & editing, Writing – original draft, Visualization, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Vincent Dubreuil:** Writing – review & editing. **Damien Arvor:** Writing – review & editing. **Hugo Romero:** Writing – review & editing, Funding acquisition. **Pablo Sarricolea:** Writing – review & editing.

**Ethics in publishing statement**

This research presents an accurate account of the work performed, all data presented are accurate and methodologies detailed enough to permit others to replicate the work.

This manuscript represents entirely original works and or if work and/or words of others have been used, that this has been appropriately cited or quoted and permission has been obtained where necessary.

This material has not been published in whole or in part elsewhere.

The manuscript is not currently being considered for publication in another journal.

That generative AI and AI-assisted technologies have not been utilized in the writing process or if used, disclosed in the manuscript the use of AI and AI-assisted technologies and a statement will appear in the published work.

That generative AI and AI-assisted technologies have not been used to create or alter images unless specifically used as part of the research design where such use must be described in a reproducible manner in the methods section.

All authors have been personally and actively involved in substantive work leading to the manuscript and will hold themselves jointly and individually responsible for its content.

**Funding statement**

This research was initiated with the support of the CLIMAT AmSud PRELASA Project Ref. 21-CLIMAT-12. P.M. also acknowledges the financial support by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (refs. 403279/2023-2 and 41315/2023-2).

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgements**

The authors thank the University of California Santa Barbara Climate Hazards Center, the US National Oceanic and Atmospheric Administration, the Climate Observatory and partners, ESA-CCI and COPERNICUS data service for freely providing CHIRPS v2, MODIS, and land cover classification datasets used in this study.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rsase.2025.101595.

**Data availability**

All source data is freely available at the time of submission at the websites indicated in the manuscript.

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