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# Monitoring Extreme Floods and Droughts in the Amazon Basin with Surface-Water-Based Indices

Marie Parrens, Ahmad Al-Bitar, Ayan Santos Fleischmann

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► To cite this version:

Marie Parrens, Ahmad Al-Bitar, Ayan Santos Fleischmann. Monitoring Extreme Floods and Droughts in the Amazon Basin with Surface-Water-Based Indices. Journal of Hydrometeorology, 2023, 24 (9), pp.1417-1436. 10.1175/JHM-D-22-0170.1. hal-04244290

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https://hal.inrae.fr/hal-04244290v1

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# Monitoring Extreme Floods and Droughts in the Amazon Basin with Surface-Water-Based Indices

MARIE PARRENS<sup>a,b</sup> AHMAD AL-BITAR,<sup>b</sup> AND AYAN SANTOS FLEISCHMANN<sup>c</sup>

<sup>a</sup> Dynafor, Université de Toulouse, INRAE, INPT, INP-PURPAN, Castanet-Tolosan, France

<sup>b</sup> Centre d'Etudes Spatiales de la Biosphère (CESBIO), Toulouse University (CNES, CNRS, INRAe, IRD, UPS), Toulouse, France

<sup>c</sup> Mamirauá Institute for Sustainable Development, Tefé, Amazonas, Brazil

(Manuscript received 27 September 2022, in final form 16 June 2023, accepted 21 June 2023)

**ABSTRACT:** Extreme droughts and floods in the Amazon have great implications for ecosystems and societies. Over the last decade, the region has undergone major extreme events with no equivalent in the previous 100 years. Wetlands have been greatly impacted by these events. This study aims at presenting new indicators for wetlands based on water surface extent (WSE): duration of the flooded and nonflooded season, number of days of extreme events, delay of the start of the flooded season, and severity for each season. These indicators are more adapted for monitoring of wetlands than those based on precipitation, discharge, or groundwater information. They are computed for seven major Amazon sub-basins for flooded and nonflooded seasons. These indicators improve our knowledge of the temporal behavior of the water surface during different extreme events, such as the 2015/16 drought and the 2014 flood that occurred in the Madeira basin. For the Negro basin and from the point of view of wetlands, the 2015 nonflooded season was 55% more severe than the average of the nonflooded season during the 2011–18 period. For the Paru, Trombetas, Negro, and Solimões basins, we found that a delay in the arrival of the flooded season led to a weak flood season in terms of severity. No correlation between the onset of the flooded season and its severity was found for the Madeira, Xingu, and Tapajós basins. Future hydro-meteorological monitoring systems would benefit from including, in addition to variables such as river discharge and water elevation, precipitation, and vegetation dynamics, a severity index based on water surfaces as proposed in this study.

**KEYWORDS:** Extreme events; Flood events; Hydrologic cycle; Seasonal variability

## 1. Introduction

The freshwater systems of the Amazon basin, including a massive network of rivers and wetlands, play an important role in Earth's hydrological and biogeochemical cycles (Salati and Marques 1984; Guilhen et al. 2020; Fassoni-Andrade et al. 2021; Fleischmann et al. 2022). Many species in the region are adapted to and thus depend on wetlands (Melack and Coe 2021; Junk 1997). The Amazon basin also contains some of the largest areas of tropical rain forest left on Earth, hosting various ecosystems sensitive to droughts (Asner et al. 2010; Saleska et al. 2007) and thus at high risk of forest fires (Zeng et al. 2008), including black-water floodplain forests (Flores et al. 2017). Moreover, the risks of extreme floods and droughts create significant challenges for natural and human exploitation of the floodplain resources (Langill and Abizaid 2020; Goulding et al. 1996). Ecosystem functioning, forest structure, food security, and biodiversity are not only sensitive to extreme events but also to the amplitude, duration, and onset of the wet season (Melack and Coe 2021).

In past decades, the Amazon basin has undergone several intense climatic extremes, with no equivalent events in the last 100 years (Marengo and Espinoza 2016; Barichivich et al. 2018). On the one hand, the last Sciences Panel for the Amazon report noted that “the Amazon is one of the world’s most at-risk regions, with a possibility that over 90% of species could be exposed to unprecedented temperatures by 2100” (Artaxo et al. 2021). On the other hand, since the last century, flood risk to communities

living within the Amazonian floodplain has increased due to the population growth, urbanization, and a possible water cycle change and intensification (Gloor et al. 2013; Fleischmann et al. 2023). Moreover, floods have important consequences on fisheries and domestic agriculture due to the inundation of small farms and impacts on river-floodplain connectivity (Marengo et al. 2011, 2013; Satyamurty et al. 2013; Duponchelle et al. 2021; Arantes et al. 2013).

The water cycle intensification (or acceleration) has been associated with a joint increase of floods and droughts in a certain region (Chagas et al. 2022). Although not all parts of the Amazon have simultaneously faced an increase in both floods and droughts, it is clear that major (and sometimes contrasting) changes are taking place. Espinoza Villar et al. (2009) found that, across the Amazon basin, the intensification has been related to an increase in precipitation over northern parts of the basin, and a decrease in southern areas. Improving the knowledge of multiple components of the water cycle across different Amazon basins is thus fundamental to understanding the basin's hydrological intensification.

Many studies have addressed the Amazon extreme events, their causes, and impacts on water related social-ecological systems and services (Frappart et al. 2013; Marengo et al. 2008; Zeng et al. 2008; Phillips et al. 2009; Espinoza et al. 2011, 2013; Lewis et al. 2011; Marengo et al. 2011, 2012; Frappart et al. 2012; Asner et al. 2010; Saleska et al. 2007; Fleischmann et al. 2023, among others). Table 1 presents major extreme events over the last decade. The severe 2010 drought was stronger in central and eastern Amazon mainly during summer and autumn (Marengo and Espinoza 2016). The

Corresponding author: Marie Parrens, marie.parrens@purpan.fr
<table><caption>TABLE 1. History of major droughts and floods during the last decade over the Amazon basin (Marengo and Espinoza 2016). Nonexhaustive references list.</caption><thead><tr><td>Year</td><td>Extreme event</td><td>References</td></tr></thead><tbody><tr><td>2010</td><td>Drought</td><td>Lewis et al. (2011), Marengo et al. (2011), Espinoza et al. (2011)</td></tr><tr><td>2011</td><td>Flood</td><td>Espinoza et al. (2012), Boening et al. (2012)</td></tr><tr><td>2012</td><td>Flood</td><td>Marengo et al. (2013), Espinoza et al. (2013), Satyamurty et al. (2013)</td></tr><tr><td>2014</td><td>Flood</td><td>Espinoza et al. (2014)</td></tr><tr><td>2015</td><td>Drought</td><td>Jiménez-Muñoz et al. (2016), Barichivich et al. (2018)</td></tr><tr><td></td><td>Flood</td><td>Marengo and Espinoza (2016)</td></tr><tr><td>2016</td><td>Drought</td><td>Jiménez-Muñoz et al. (2016)</td></tr><tr><td>2021</td><td>Flood</td><td>Espinoza et al. (2022)</td></tr></tbody></table>

impact of the dry season drought was particularly observed in river streamflow and water levels. The 2015/16 drought is still to be fully studied, yet Jiménez-Muñoz et al. (2016) reported that the rain forest was largely affected by the 2015/16 extreme event during February–March 2016. The 2010 and 2015/16 droughts were essentially caused by El Niño. In contrast, the Amazon experienced one of the worst flooding events in the past decade in 2011/12, essentially due to La Niña. The Peruvian Amazon basin was particularly affected in 2011 and the Amazon, Solimões, and Rio Negro Rivers had high levels and floods in 2012 (Marengo and Espinoza 2016). More recently, the 2021 flood was the largest ever recorded in Manaus since records started in 1903 (Espinoza et al. 2022). The flood event was essentially located in the western and central Amazon during the summer. Important floods also occurred in 2014 in the southern Amazon during the summer. Abnormally wet conditions were reported with rainfall about 100% above the long-term mean.

Owing to the lack of continuous and long-term rainfall records in the Amazon, satellite rainfall data have been used instead, and the Amazon water cycle has been mainly analyzed through the assessment of satellite rainfall and in situ discharge (Callède et al. 2004; Labat et al. 2005; Marengo et al. 2008, 2013; Gloor et al. 2013), although simulated discharges have also been used (Wongchuig et al. 2019). However, it is necessary to move beyond rainfall and discharge data to understand extreme floods and droughts, e.g., by assessing water surface extent (WSE) and storage (Schumann et al. 2016; Lopez et al. 2020). Some studies focused on the variation of terrestrial water storage with Gravity Recovery and Climate Experiment (GRACE) mission (Feng et al. 2012; Chen et al. 2010; Pokhrel et al. 2013; Tourian et al. 2018; Fleischmann et al. 2023) but fewer studies focused on variation of WSE. This is important to improve our understanding of the impacts of such events, because of the interconnections between different geophysical variables (Langill and Abizaid 2020). Wongchuig et al. (2022) also found a clear link between hydrological extreme events and deforestation at regional scale.

Since the 1990s, the spatiotemporal behavior of WSE has been estimated by several remote sensing techniques across the Amazon (Fleischmann et al. 2022). Passive microwave observations have demonstrated their usefulness for observing this variable (Giddings and Choudhury 1989; Kandus et al. 2018; Fassoni-Andrade et al. 2021). Brightness temperature in the microwave domain is sensitive to the presence of surface water with a decrease in emissivity in horizontal and vertical polarizations, and an increase for the difference in polarization,

especially at low frequency (Choudhury 1991). In the past, Giddings and Choudhury (1989), Sippel et al. (1994, 1998), and Hamilton et al. (2002) used the Scanning Multichannel Microwave Radiometer (SMMR, 1979–87) to derive water surface extent over the Amazon, while Brakenridge et al. (2007) retrieved the variable from 2002 to 2011 using Advanced Microwave Scanning Radiometer (AMSR-E) data. More recently, Parrens et al. (2017) created a new WSE product over the Amazon basin at daily time step from 2010 to present by using brightness temperature from the Soil Moisture Ocean Salinity (SMOS) satellite (Kerr et al. 2001). Contrary to previous products, this new one benefits from the L-band frequency and a high temporal resolution. At this frequency, the signal is less impacted by the vegetation than at higher frequencies used by the two previous products. The use of WSE-based indexes could provide an interesting basis to monitor droughts and floods and the associated impacts on human activities and ecosystems, especially over wetlands and floodable environments.

In this study, we present a set of indicators based on WSE retrieved from L-band satellite data to monitor the dynamics of wetlands across the Amazon. The indicators are computed and analyzed over 2011–18. This recent period is particularly interesting because the basin has been under pressure by both human activities and climate change. Our study has three major components. First, we analyze the WSE response to precipitation in seven major Amazon subbasins at daily time step. Second, we compute and analyze the WSE annual cycle by defining the duration and the onset of the flooded and nonflooded seasons from the point of view of flooded surfaces. Impacts of major extreme events (i.e., floods and droughts) on water surface seasonality are also quantified. Third, we create a new indicator called the “severity index” to quantify the flooded and nonflooded season from the surface water perspective, aiming at comparing the seasonal severity between themselves. At the end of the paper, a detailed analysis of the water surface seasonality of Negro and Madeira subbasins is performed. The Madeira basin and the Negro basin have contrasting hydrometeorological processes. The Negro basin is located in the Northern Hemisphere whereas the Madeira one is located in the Southern Hemisphere. The timing of the rainy season differs between the two basins: the rainy season occurs from April to October in the Negro basin whereas it occurs from December to May in the Madeira basin. Moreover, wetlands of these two basins have been affected by contrasting hydrological extremes: the Madeira basin has faced
<figure><img src="image_4.png" /><figcaption>FIG. 1. (a) Delineation of the seven major Amazonian basins with a focus on (b) the Madeira and (c) Negro basins. Colors indicate the wetland type [data provided from Hess et al. (2015)], and the latitudes and longitudes are expressed in degrees.</figcaption></figure>

intense floods in 2014 and the Negro basin suffered from an extreme drought in 2015/16.

The manuscript is organized as follows: the Amazon basin and the datasets are presented in the following section, and the formulation of the extreme events indices and the results are presented in sections 3 and 4, respectively. The study finishes with a discussion (section 5) and a conclusion (section 6).

## 2. The Amazon basin

The Amazon River basin is the largest basin on Earth. Its drainage basin covers an area of about 6000000 km² (Richey et al. 1989). The Amazon River contributes up to 15% of the global river discharge to the ocean (approximately 209000 m³ s⁻¹ discharge; Molinier et al. 1996). The Amazon is highly interconnected by floodplain channels, resulting in complex flow patterns (Fleischmann et al. 2022; Alsdorf et al. 2007; Trigg et al. 2012). The Amazon basin covers seven countries: Brazil (63%), Peru (16%), Bolivia (12%), Colombia (6%), Ecuador (2%), Venezuela (1%), and Guyana (1%) (Espinoza Villar et al. 2009). Because it extends into two hemispheres, the Amazon region is characterized by contrasting rainfall regimes. Rainfall shows opposing phases between the northern and the southern tropics with a rainy season in austral winter in the north and summer in

the south. For the east and central part of the basin, the rainy season occurs from March to May and the dry season prevails from September to November. For the northern regions, low rainfall seasonality is observed in the upper Negro River whereas a very marked seasonality is noticed in the Branco basin (e.g., in Boa Vista city). The Andes's climate conditions vary along the eastern and western gradient: over the western part, relatively cold and arid conditions prevail along the Pacific coast and over the eastern part, warm, moist, and rainy conditions prevail (Garreaud 2009). Between the eastern and western part, the transition zone shares the same wet season as the eastern part of the Andes and dry months are observed during February-April (Segura et al. 2019). This area includes the rainiest regions of the Amazon (Espinoza et al. 2015; Chavez and Takahashi 2017). For more information on the Amazon hydrological regime, see Salati et al. (1978), Marengo (1992), Marengo et al. (2001), Espinoza Villar et al. (2009), Fassoni-Andrade et al. (2021), etc.

In this study, the Amazon basin is divided into seven major basins (Fig. 1). The area of each basin is presented in Table 2 and varies from 201599 km² for the smallest basin (Paru basin) to 2216665 km² for the largest basin (Solimões basin). The delineation of the Amazon in basins and the topography provided from the Shuttle Radar Topography Mission (SRTM) (Jarvis et al. 2004) are based on the HydroSHEDS products
<table><caption>TABLE 2. Area in square kilometers of each major basin.</caption><thead><tr><td>Basin</td><td>Area (km²)</td></tr></thead><tbody><tr><td>Paru basin</td><td>201 599</td></tr><tr><td>Xingu basin</td><td>512 026</td></tr><tr><td>Trombetas basin</td><td>387 463</td></tr><tr><td>Tapajós basin</td><td>492 312</td></tr><tr><td>Madeira basin</td><td>1 368 850</td></tr><tr><td>Solimões basin</td><td>2 216 665</td></tr><tr><td>Negro basin</td><td>714 738</td></tr></tbody></table>

(Lehner et al. 2008). Based on these products, watersheds were delineated in a consistent manner at different scales by using the concept of the Pfafstetter coding system (Verdin and Verdin 1999). For more information on the watershed boundaries, see Lehner and Grill (2013), and data can be downloaded at https://www.hydrosheds.org/.

In this study, a focus at finer spatial resolution is performed over the Madeira and Negro basins, which have differences in rainfall and runoff seasonality. Figure 1 shows the nine Madeira and Negro basins associated with their major floodplains. This finer watershed delineation is included in the HydroSHEDS product. The Negro basin is the northernmost basin of the Amazon and is located both in the Northern and Southern Hemisphere. It represents approximately 14% of the Amazon basin with an elevation lower than 100 m. The average precipitation is 2542 ± 317 mm yr⁻¹ for the Negro basin (Getirana et al. 2011). The timing of the rainy season differs widely along a south/north gradient: in the south, the rainy season occurs from December to May whereas it goes from March/April to October in the north (Marengo et al. 2001). The Negro River meets the Solimões River near Manaus, forming the Amazon River, and drains an area of around 700 000 km² (Frappart et al. 2008). During the 2011–18 period assessed in this study, the Negro basin has been affected by a severe drought in 2015/16 (Jiménez-Muñoz et al. 2016).

The Madeira basin represents around 23% of the Amazon surface and it is characterized by a high range of altitudes, varying from 50 to 6450 m. It has four main tributaries: the Madre de Dios River from the Peruvian Andes, the Beni River and Mamoré River from the central Bolivian Andes, and the Guarapé River from the southeastern part of the basin (Espinoza et al. 2019). The Madeira basin is characterized by a large wetland called Llanos de Mojos with a floodable surface estimated as up to 150 000 km² (Parrens et al. 2019; Ovando et al. 2016). The Madeira rainfall regime is heterogeneous: over the upper Madeira basin, the average precipitation is around 1800 mm yr⁻¹ and between 4000 and 6000 mm yr⁻¹ over “rainfall hotspots” located in lowlands (Espinoza et al. 2019). The rainy season occurs between December and March. More details about the Madeira’s hydrology from 1982 to 2017 can be found in Espinoza et al. (2019). The Madeira basin has been affected by severe floods in 2014 (Espinoza et al. 2014).

# 3. Spatial datasets over the Amazon basin

## a. Water surface extent

The Global Surface Water Fraction (GSWAF) WSE product (Parrens et al. 2017; Al Bitar et al. 2020) is based on L-band

microwave remote sensing from the SMOS satellite (Kerr et al. 2001). GSWAF is derived by an inversion process based on the daily brightness temperature product from SMOS (Al Bitar et al. 2017). It consists of a 3-day binary product over the Equal-Area Scalable Earth (EASE) 25 km grid of WSE from 2011 to 2019. In this study GSWAF has been interpolated each day (Fatras et al. 2021). The data express a percentage of flooded surface. Contrary to other existing products such as Global Inundation Extent from Multisatellites (GIEMS) (Prigent et al. 2001) and Surface Water Microwave Product Series (SWAMPS) (Schroeder et al. 2015), this product is more sensitive to the standing water above the ground and is less impacted by the vegetation. These advantages come from the use of low microwave frequency data (L-band, 1.4 GHz). GSWAF is based on multiangle and polarization retrievals (Al Bitar et al. 2020). Parrens et al. (2017) have already validated GSWAF product against precipitation, discharge (from in situ measurements and altimetry) and other WSE data over the Amazon during the 2010–15 period.

Figure 2 shows the daily WSE dynamics from 2011 to 2019 averaged over seven major Amazon basins (orange line). The high variability over Xingu and Tapajós basins is due to mountain areas in these basins: varying slopes/topography induce a multireflection and thus noise in the L-band microwave signal. In GSWAF data, over the Amazon basin, areas with an altitude higher than 500 m are masked out to solve this issue (Parrens et al. 2017). Indeed, higher than 500 m, slopes and topography are severe and cannot be ignored. However, in Xingu and Tapajós basins, slopes are moderate with an altitude lower than 500 m. So, these subset data were not filtered out. But some minor noise induced by moderate slopes can persist in the signal. Figure 2 shows that the maximum flooded surface is observed during March for the Xingu, Tapajós, and Madeira basins and 1–2 months later for the Paru, Trombetas, and Solimões basins. The Negro basin, partially located in the Northern Hemisphere, reaches the maximum flooded surface in July. During the wet season (November–January), the Madeira basin is the most flooded basin with 4.6% of its surface inundated on average during the 2011–19 period. In contrast, the Solimões basin is the least flooded basin with only 1.4% in average of its surface inundated during the wet period (February–April). During the dry season, the Paru and Trombetas basins are the most flooded basins with 2.25% of its surface inundated. GSWAF time series for the Xingu and Tapajós basins had more variability during the wet season than the other basins.

## b. Precipitation

The Frequent Rainfall Observations on Grids (FROGS) database (Roca et al. 2019) is used in this study. These daily data are gridded on a 1° × 1° EASE grid. The database includes satellite, ground-based, and reanalysis products. The 3B42 v7.0 (Huffman et al. 2007) product from the Tropical Rainfall Measuring Mission (TRMM) was extracted from FROGS, which is a reference product over tropical areas (Maggioni et al. 2016). TRMM is a joint mission between the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration (JAXA) Agency. The 3B42 product uses microwave data to calibrate the infrared-derived
<figure><img src="image_5.png" /><figcaption>FIG. 2. Daily precipitation (mm day⁻¹; blue line) and GSWAF WSE (% day⁻¹; orange line) time series from 2011 to 2019 for seven major Amazonian basins. For WSE, the percent represents the percentage of flooded surface.</figcaption></figure>

estimates and creates estimates that contain microwave-derived rainfall estimates when and where microwave data are available and the calibrated infrared estimates where microwave data are not available.

Figure 2 (blue line) shows precipitation time series from FROGS for seven major Amazonian basins. The data are averaged each day over the entire basin and are presented in millimeters. The maximum precipitation is observed in Febru-ary for the Paru, Trombetas, and Tapajós basins and one month before for the Xingu and Madeira basins and one month later for the Solimões basin. The peak of precipitation is observed in May for the Negro basin. On average, during the 2011–19 period, between 11 and 15 mm day⁻¹ occurs dur-ing the wet season for all the basins. During the dry season, Xingu and Tapajós basins are the driest basins whereas the Negro basin is the wettest one with at least 4.6 mm day⁻¹.

## 4. Indicators to monitor surface water dynamics

We compute a set of indices to characterize the extreme flood and drought events including a new severity index based on the duration and intensity of the floods. The first step is the computation of anomalies, followed by the computation of duration, number of events, and an integrated intensity-duration index named the severity index (SI).

### a. Anomalies and deciles

The derived indices are all based on the analysis of flood extents and precipitation anomalies. Anomalies are computed to compare datasets with different units, by normalizing them and removing the annual cycle. By considering the time series y with a time step i, the nonseasonal standard anomalies (ano) are computed with the following equation:

$$ \text{ano} = \frac{y_i - \bar{y}}{\text{std}(y)}, \qquad (1) $$

with std being the standard deviation of the time series y and ȳ being the mean of the time series during the whole assessed period. The computation of anomalies is applied to WSE and precipitation time series prior to other analyses.

Based on the anomalies, the flooded season is characterized with positive anomalies and the nonflooded season with negative ones. Statistical thresholds are considered for dry and wet seasons and are applied for each basin. The thresholds are based on the decile of two fitted empirical cumulative distribution functions (CDF) for the positive and negative values of anomalies (i.e., based on the CDF of the observed anomalies):

$$ D1_{\text{wet}} = P(0 < \text{ano} < 0.1) \quad \text{for } \text{ano} > 0.0, \qquad (2) $$

$$ D9_{\text{wet}} = P(0.9 < \text{ano} < 1.0) \quad \text{for } \text{ano} > 0.0, \qquad (3) $$

$$ D1_{\text{dry}} = P(0 < |\text{ano}| < 0.1) \quad \text{for } \text{ano} < 0.0, \qquad (4) $$

$$ D9_{\text{dry}} = P(0.9 < |\text{ano}| < 1.0) \quad \text{for } \text{ano} < 0.0, \qquad (5) $$

where |ano| corresponds to the absolute value of ano.

### b. Duration, onset, and number of extreme conditions

The dynamics of WSE indices are based on three metrics: 1) the duration of the flooded (or wet) and nonflooded (or dry) season, 2) the number of cumulative days of extreme conditions (droughts, floods), and 3) the day of the year (DoY) of the beginning of the flooded season. For a better understanding, an illustration is provided in Fig. 3 showing anomalies in time of the WSE over the Trombetas basin, and the applied thresholds to compute indices. We consider the duration of the flood season (DFS) as the number of days where the WSE anomalies are higher than the first decile (D1wet) of the wet season:

$$ \text{DFS}_{\text{year}} = \text{number of days, for ano} > \text{D1}_{\text{wet}}. \qquad (6) $$
<figure><img src="image_6.png" /><figcaption>FIG 3. Illustration of the metrics used to estimate the duration of the flooded and nonflooded seasons, the number of days of extreme events (floods and droughts) and the beginning of the flooded season. Example with WSE time series over the Trombetas basin.</figcaption></figure>

In the same way, the duration of the dry season (DDS) is considered as the number of days during which the WSE anomalies are lower than the first decile (D1<sub>dry</sub>) of the dry season:

$$DDS_{year} = \text{number of days, for ano} < D1_{dry}. \quad (7)$$

Major floods are identified when WSE anomalies are higher than the last decile of the flooded season (D9<sub>wet</sub>). Similarly, major wetland droughts are defined when WSE anomalies are lower than the last decile of the nonflooded season (D9<sub>dry</sub>). For each season, the number of major events is counted when the WSE anomalies is higher (lower) than D9<sub>wet</sub> (D9<sub>dry</sub>):

$$NbF_{year} = \text{number of days, for ano} > D9_{wet}, \quad (8)$$

$$NbD_{year} = \text{number of days, for ano} < D9_{dry}. \quad (9)$$

The DoY of the beginning of the flood season (Str<sub>F</sub>) is the DoY corresponding to the first occurrence of WSE anomalies equal to D1<sub>wet</sub> for a given year:

$$Str_{Fyear} = \text{DoY, for ano} = D1_{wet}. \quad (10)$$

### c. Severity index

The severity of the flooded or nonflooded season is the product of its duration and intensity (Sheffield and Wood 2012). We define two severity indices: 1) SI<sub>wet</sub> deals with the severity of the flooded season. It is calculated for each flooded season as the integration of the time series of WSE or precipitation anomalies above the D1<sub>wet</sub> threshold. Consequently, the SI<sub>wet</sub> index is always positive. The higher the SI<sub>wet</sub> index is, the more severe the flooded season from the point of view of wetlands. 2) SI<sub>dry</sub> concerns the severity of the nonflooded season, and it is calculated for each nonflooded season as the integration of the absolute time series of WSE or precipitation anomalies below the D1<sub>dry</sub> threshold. The SI<sub>dry</sub> index is always positive. A high value of the SI<sub>dry</sub> index corresponds to a very severe nonflooded season for wetland. A severe

nonflooded season experiments severe drought both by its intensity and duration:

$$SI_{wet,year} = \sum_{i}^{n} \text{ano, for ano} > D1_{wet}, \quad (11)$$

where i and n are the indices of the first and last occurrence for ano > D1<sub>wet</sub> over a given year, respectively, and

$$SI_{dry,year} = \sum_{i}^{n} |ano|, \text{ for ano} < D1_{dry}, \quad (12)$$

where i and n are the indices of the first and last occurrence for ano < D1<sub>dry</sub> over a given year, respectively.

### d. Cross correlation

The cross correlation measures the similarity of two time series (x and y) as a function of the displacement of one relative to the other. Here, the displacement corresponds to time (in days). Therefore, the cross-correlation value is the highest correlation value obtained if the x time series is moved by m days with respect to the y time series.

Associated with the r, the p value is also computed for the null hypothesis, considering a p value higher than 0.05 as nonsignificant.

## 5. Results

### a. Water surface response to precipitation for Amazon basins

Figure 2 shows the annual dynamics of the precipitation and WSE anomalies during 2011–18 for each Amazon basin, as well as the delay between the precipitation and WSE peaks. The delay differs for each basin (Table 3). The highest lag between precipitation and WSE is observed for Trombetas and Solimões basins with a lag greater than two months. In contrast, the lowest lag is observed for Tapajos and Xingu basins with a lag less than a week. The difference of lag results from a combination of physical and topographic processes which drive the hydrological
<table><caption>TABLE 3. Time lag in days between precipitation and WSE over seven major Amazon basins during the 2011–19 period. The minimum and maximum of flooded surface (%) for each basin are also noted.</caption><thead><tr><th>Basin</th><th>Correlation (R)</th><th>Lag (in days)</th><th>Minimum of flooded surface (%)</th><th>Maximum of flooded surface (%)</th></tr></thead><tbody><tr><td>Purus basin</td><td>0.76</td><td>44</td><td>1.71</td><td>4.43</td></tr><tr><td>Xingu basin</td><td>0.94</td><td>4</td><td>0.62</td><td>1.94</td></tr><tr><td>Trombetas basin</td><td>0.80</td><td>71</td><td>1.75</td><td>4.12</td></tr><tr><td>Tapajós basin</td><td>0.95</td><td>0</td><td>0.54</td><td>2.63</td></tr><tr><td>Madeira basin</td><td>0.92</td><td>38</td><td>1.02</td><td>6.29</td></tr><tr><td>Solimões basin</td><td>0.86</td><td>62</td><td>0.78</td><td>1.51</td></tr><tr><td>Negro basin</td><td>0.83</td><td>39</td><td>0.63</td><td>2.58</td></tr></tbody></table>

cycle of the Amazon's subbasins. Features to be considered are river and terrain slopes, river channel and floodplain characteristics that drive flood wave celerity (Fleischmann et al. 2016) and thus water routing, backwater effects along rivers (Meade et al. 1991), hydrological processes related to conversion from rainfall into runoff, and the topological location along the drainage network (i.e., upstream versus downstream reaches). For instance, the Tapajós and Xingu basins have higher slopes and low lag (Meade et al. 1991). Along most of the Madeira and Solimões subbasins there are large flat (low-slope) areas. Finally, the Trombetas and Paru basin have a relatively high topography. Parrens et al. (2017) found a time lag of two months between precipitation and inundation for the entire basin. Figure 4 represents the hydrological regime of the seven basins. It is characterized by a strong hysteresis for five of them: Paru, Trombetas, Madeira, Solimões, and Negro. A similar cycle is noticed in Trombetas and Paru (northeast basins) with a plateau during the nonflooded

season during which inundated surfaces dry up. In contrast, a plateau on surface extent is observed at the end of the flooded season with the decrease in precipitation. For the Negro and Madeira basins, a lag between precipitation and inundation is observed without saturation. A more important time lag between these two variables is observed in the Solimões basin according to Table 3. Fluctuations in the cycle correspond to the inundation of different tributaries of the Solimões River. A linear relationship between precipitation and WSE dynamics is observed for the Xingu and Tapajós basins (southwest basins). The major 2014 floods in the Madeira basin and the 2015/16 drought in the Negro basin are well illustrated in this figure.

b. Indicators to monitor dynamics of water surfaces

The seven indicators described in this section aim to better characterize dynamics of water surfaces at the scale of major Amazon basins.

<figure><img src="image_7.png" /><figcaption>FIG. 4. Precipitation anomalies with respect to water surface extent anomalies for the seven basins. Colors represent years from 2011 to 2018.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>FIG. 5. Yearly duration (in days) of the (top) dry and (bottom) wet season for the seven Amazon basins. Colors represent the seven basins.</figcaption></figure>

### 1) DURATION OF FLOODED AND NONFLOODED SEASONS

Figure 5 shows the duration of the flooded and nonflooded seasons from 2011 to 2018 for the seven major basin of the Amazon. A high variability on the nonflooded season duration during the period 2011–18 is observed for all basins. In January 2011, the nonflooded season over Negro was already started (see Fig. 2), which implies that the nonflooded season duration has not been computed for this year. The year 2014 is marked by a decrease of the nonflooded season duration for all the basins except for the Negro and Tapajós ones. In contrast, 2015 is the longest nonflooded season duration for all the basins except for the Madeira and Solimões basins. The shorter nonflooded season is observed in 2014 over the Paru basin and lasted 143 days and the longest nonflooded season is noticed in 2016 over the Madeira basin with a duration of 272 days.

As for the nonflooded season duration, a high variability of the flooded season is observed. All the basins have been affected by a decrease of the flooded season duration in 2016 except for the Negro basin which reached its minimum in 2015 with a flooded season duration of 106 days. In 2015/16, the Amazon was affected by an El Niño–Southern Oscillation (ENSO) event leading to an extreme drought (Jiménez-Muñoz et al. 2016). The higher flooded season duration is noticed for the Negro basin in 2011 with 246 days whereas the lower wet season duration is observed in 2016 for the Madeira basin with 86 days.

### 2) DURATION OF CUMULATIVE DAYS OF EXTREME FLOODS AND DROUGHTS

Figure 6 shows the number of major floods estimated during 2011–18. A long period (2 months) of major floods has been detected in 2018 over the Negro basin, in 2014 and 2015 over the Trombetas and Paru basins, in 2014 over the Madeira

basin and, in 2015 over the Solimões basin. Important floods are also noticed over the Solimões basin in 2012 and 2017. This results are in accordance with Marengo et al. (2013), Espinoza et al. (2013), and Satyamurty et al. (2013). In 2016, no major flood was observed for all the Amazon basins.

Figure 7 shows the number of dry days related to a critically dry period. In 2013, no extreme dry days were recorded. In contrast, the most important extreme dry period is observed over the Negro basin in 2015 with more than 2 months of drought during an El Niño event. Two years later, the entire Amazon basin suffered a drought from 30 to 40 days, except the Negro basin.

### 3) START OF THE FLOODED SEASON

Figure 8 presents the number of days of delay or advance of the wet season in respect to the 2011–18 mean for each basin. The start of the flooded season varied from year to year within the same basin. This difference can reach more than one month. For example, over the Paru basin, the flooded season started with 36 days of delay in 2016 whereas it came in advance for 11 and 17 days in 2015 and 2017, respectively. The 2015 year marked the more important delay of the wet season for Xingu, Tapajos, Madeira, and Negro basins with a delay of 59, 51, 36, and 28 days, respectively. For the Paru, Trombetas, Madeira, and Solimões this record is reached in 2016 with 36, 40, 31, and 30 days of delay, respectively. The Madeira basin is marked by two consecutive years of important delay (one month) of the start of the flooded season in 2015 and 2016. In 2012 (La Niña year), the wet season arrived in advance of 54 days in the Negro basin. In 2017, the flooded season occurred in advance for all basins. A high variability of the start of the flooded season is noticed over the Solimões basin: a difference of 32 days is noticed between the start of the flood season in 2011 and 2012. This observation is also
<figure><img src="image_9.png" /><figcaption>FIG. 6. Yearly cumulative days of extreme floods during the 2011–18 period.</figcaption></figure>

valid for years 2015 and 2016 (difference of 56 days), for years 2016 and 2017 (difference of 51 days), and for years 2017 and 2018 (difference of 44 days).

4) SEVERITY INDEX OF THE FLOODED AND NONFLOODED SEASONS

Four SI indicators are computed: the severity (intensity and duration) of the water surface wet season is computed with

both precipitation data (SI<sub>PP</sub>) and WSE (SI<sub>WSE</sub>). Similarly, the same computation is done for the dry water surface season. The higher the SI value, the more severe the season. The four SI indicators are presented in Fig. 9 for the seven major Amazon basins during the 2011–18 period. A satisfactory agreement between SI<sub>PP</sub> and SI<sub>WSE</sub> during wet and dry season is observed for all basins but some differences can be noticed. During the dry season, SI<sub>PP</sub> is significantly lower than

<figure><img src="image_10.png" /><figcaption>FIG. 7. Yearly cumulative days of extreme droughts during the 2011–19 period.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>FIG. 8. Number of days of delay (red) or advance (blue) of the wet season for each major basin in respect to the 2011–18 average.</figcaption></figure>

SI<sub>WSE</sub>, indicating a very severe nonflooding season in terms of wetland droughts. This is the case over the Negro basin in 2015, over the Solimões basin in 2011, 2012, and 2016, over the Madeira basin in 2016 and, over the Trombetas and Paru basin in 2016. During the wet season in the Madeira basin, SI<sub>WSE</sub> reveals the important 2014 floods since SI<sub>PP</sub> is just slightly over the 2011–18 average. Espinoza et al. (2014) reported that very intense precipitation has been observed at the end of January and beginning of February 2014 on the eastern flank of the Andes and in the lowlands east of the Andes. This area corresponds to the Andes–Amazon transition zone over the upper Madeira basin. SI indicator is not constructed to take into account local and intense phenomena over a large basin. It is constructed to take into account phenomena at large temporal and spatial scales. During the wet season, over the Negro basin, SI<sub>WSE</sub> and SI<sub>PP</sub> are almost equal except in 2018, for which only the WSE data showed important floods (Espinoza et al. 2022). In 2015, SI<sub>PP</sub> and SI<sub>WSE</sub> are the lowest value calculated during the whole 2011–18 period, highlighting a very dry flooded season. Over the Xingu basin, 2017 wet season is the year with the highest SI<sub>PP</sub> value computed with precipitation whereas it does not occur with SI<sub>WSE</sub>. The important 2017 rainy season did not lead to extreme floods. The reverse is observed in 2018.

Figure 10 shows the difference of severity between precipitation and WSE (SI<sub>PP</sub> – SI<sub>WSE</sub>) over the dry and wet seasons. As described in the methodology, the two indexes are integrated over different periods depending on the anomaly of the corresponding variable (i.e., precipitation or WSE). The amplitude of the difference is higher (from –128 to 115) during the wet season compared to the dry season (from –110 to 67) when considering all years and all basins. This can be explained by the more direct link between precipitation and WSE during the dry season. Differences in 2014 and 2015 wet season show the systematic underestimation of the severity from the SI<sub>PP</sub> compared to the SI<sub>WSE</sub>. The contrary can be

observed in 2011 and 2016, where strong precipitations did not induce large flooded areas. It is clear from these values that an anomaly based precipitation indicator is a poor proxy for the description of the anomaly in wetlands extent. The difference of indices over the Negro basin has a singular behavior when compared to the other basins, in coherency with the previous results in the paper.

5) IS THERE A RELATIONSHIP BETWEEN THE DIFFERENT INDICATORS?

Table 4 presents the correlation value (r) between flooded season indices for each of the assessed years: (i) the onset of the flood season, (ii) the duration of the flood season, (iii) the cumulative days of extreme floods during the flood season, and (iv) the severity index during the flood season (SI<sub>wet</sub>). The r value is computed for p values lower than 0.1. SI<sub>wet</sub> is highly correlated with the duration of the flood season and the number of cumulative days of extreme floods for all the basins. However, SI<sub>wet</sub> is more correlated with the duration of the flood season than the cumulative days of extreme floods. Table 4 shows that onset and duration are highly anticorrelated for all basins except the Xingu, Tapajós, and Madeira ones. For these basins, if the flooded season began earlier than usual, it will last longer than usual. Similarly, the more the flooded season is late, the more the severity of the flood season is important for the Paru, Trombetas, and Solimões basins, and to a lesser extent in the Negro basin. For the Xingu and Tapajós basins, no link between indices has been found except for the link between SI<sub>wet</sub> and the duration and the cumulative days of extreme floods.

c. Basin-scale analysis over the Negro and Madeira basins

The analyses presented in the previous sections were applied, in this section, to basins of the Madeira and Negro basins (SSB), for two particularly extreme events: the 2014
Flooded season

<figure><img src="image_12.png" /></figure>

Non-flooded season

<figure><img src="image_13.png" /><figcaption>FIG. 9. Radar plots of severity index: (top) severity index (SI) during the wet season computed with (left) WSE (SI<sub>WSE</sub>) and (right) precipitation data (SI<sub>PP</sub>) and (bottom) SI during dry season computed with (left) SWE and (right) precipitation data for each Amazon basin.</figcaption></figure>

floods in the Madeira (Espinoza et al. 2014) and the 2016 drought in the Negro basin (Jiménez-Muñoz et al. 2016).

1) MADEIRA SUBBASIN

Figure 11 shows the duration of the flooded and nonflooded seasons for the nine subbasins of the Madeira subbasin. Duration of the seasons are consistent with previous results (Fig. 5). Generally, all the subbasins follow the same variability in terms of season duration except for the subbasin (colored in green in Fig. 11) during 2011 and 2012. For the four

most southern subbasins, 2016 was the longest nonflooded season and 2014 the shortest nonflooded season. In 2016 and 2018, the nonflooded season duration highly differed following the nine subbasins and reached a difference of more than 50 days. The year 2016 was marked by a fall of the flooded season duration for all subbasins. Figure 11c shows the SI<sub>wet</sub> computed in 2014 compared to the mean of SI<sub>wet</sub> during the 2011–18 period for each Madeira subbasin. Positive results for all Madeira subbasins reveal the severity of the 2014 flood season. This season was particularly severe for the four southern basins containing the large Llanos de Moxos wetlands. In
<figure><img src="image_14.png" /><figcaption>FIG. 10. SI<sub>PP</sub> – SI<sub>WSE</sub> computed for the (left) dry and (right) wet season for the seven major basins of the Amazon.</figcaption></figure>

terms of severity, the 2014 flooded season was close to the average of the 2011–18 mean for the five northern subbasins located downstream of Llanos de Moxos.

## 2) NEGRO SUBBASIN

Figure 12 presents the flooded and nonflooded season duration for the nine subbasins of the Negro basin. Over the Negro basin, the nonflooded season occurs from November to February. The year indicated on Fig. 12 is relative to the beginning of the nonflooded season, i.e., to November. The longest nonflooded season was observed for the 2015/16 summer with almost 250 days of record for three subbasins (colored in red, yellow, and cyan in the figure). In general, the season duration followed the same trend for the nine subbasins but some exceptions can be noted: (i) in 2016, the number of days of the nonflooded season were smaller than the 2011–18 average except for the two northwest subbasins (basins colored in black and gray in Fig. 12), (ii) in 2017, the number of days of the flooded season highly increased compared to the previous year except for the two northwest subbasins. Over the Negro basin, a high variability of the flooded and nonflooded season duration was observed. For example, a difference of 100 days in the flooded season duration can be noticed in 2011 between the basin colored in yellow and those in gray (Fig. 12a). The same occurs in 2017 between the red and black basins. The years 2011 and 2016 have two seasons with huge differences of nonflooded season duration following the Negro basin. Figure 12 shows the severity of the 2015/16 nonflooded season (SI<sub>dry</sub>) in respect with the mean of the nonflooded season severity during the 2011–18 period. This figure evidences the four subbasins particularly affected by the 2015/16 drought related to the El Niño event. These basins are located at the confluence between the Negro and Branco Rivers. The two northwest subbasins were not affected by this extreme event as the severity of the nonflooded season was close to the 2011–18 mean severity.

# 6. Discussion

## a. Comparison with literature

Over the Amazon, Towner et al. (2020) reported four major floods in the Amazon basin during 2011–15. The first flood

occurred in February 2011 over the Beni River in the Madeira basin. This extreme event was associated with La Niña. Figure 4 shows an excess of precipitation (anomalies higher than 1.5) at the beginning of 2011, impacting WSE one month later. This event is also visible in Fig. 6 with a moderate intensity.

The second flood reported in Towner et al. (2020) occurred over the Solimões River in April 2012 and over the Amazon River in July 2012 (Espinoza et al. 2013; Satyamurty et al. 2013). Towner et al. (2020) reported that, during this event, 70 days over the 90th percentile of flow have been recorded over the Solimões River. These extreme events are clearly represented in Fig. 6 with 45 accumulated days of extreme floods and the flooded season came 20 days earlier in 2012 than the mean of the 2011–18 period (Fig. 8). However, duration of the flooded season in 2012 over the Solimões basin is representative of the 2011–18 period (Fig. 5).

The third flood reported in Towner et al. (2020) occurred in February 2014 over the Beni River and in April 2014 over the Mamoré River (Espinoza et al. 2014; Ovando et al. 2016). The two rivers are located in the Madeira basin. Figure 6 shows more than 60 days of extreme floods in the Madeira basin. This figure shows also that the four eastern basins (Trombetas, Paru, Tapajós, and Xingu basins) had been affected by extreme flood. This observation could be explained by the fact that the Madeira River is the most important southern tributary of the Amazon River. So, the 2014 extreme floods in the Madeira basin affected the Amazon River, affecting in turn the downstream reaches of the Amazon river (i.e., Trombetas, Paru, Tapajós, and Xingu basins).

Towner et al. (2020) also mentioned a flood event over the Solimões River (Solimões basin) in March 2014. However, no extreme floods were detected over the Solimões basin in 2014 with the methodology developed in this study. The severity in both, WSE and precipitation, over the Solimões basin in 2014 was closed to the 2011–18 mean (Fig. 9). The spatial resolution of the method in this study is not adapted for studying the Solimões flood that occurred in 2014.

The last flood reported in Towner et al. (2020) affected the Solimões basin in April 2015. This extreme event has been well detected with our method (Fig. 6). Figure 9 shows that the flood season severity of the Solimões basin in 2015 is the
<table><caption>TABLE 4. Correlation between the flood season onset, the flood season duration, the cumulative days of extreme floods, and the flood season severity. Correlation value (r) is reported in this table for *p* value lower than 0.1. NS means nonsignificant correlation with *p* value higher than 0.1.</caption><thead><tr><th></th><th>Onset</th><th>Duration</th><th>Extreme</th><th>SI</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center;">Paru basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>-0.91</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>NS</td><td>0.66</td><td>1</td><td></td></tr><tr><td>SI</td><td>-0.92</td><td>0.94</td><td>0.72</td><td>1</td></tr><tr><td colspan="5" style="text-align: center;">Trombetas basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>-0.93</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>NS</td><td>0.63</td><td>1</td><td></td></tr><tr><td>SI</td><td>-0.86</td><td>0.92</td><td>0.81</td><td>1</td></tr><tr><td colspan="5" style="text-align: center;">Madeira basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>NS</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>NS</td><td>0.63</td><td>1</td><td></td></tr><tr><td>SI</td><td>NS</td><td>0.90</td><td>0.87</td><td>1</td></tr><tr><td colspan="5" style="text-align: center;">Negro basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>-0.84</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>NS</td><td>NS</td><td>1</td><td></td></tr><tr><td>SI</td><td>-0.65</td><td>0.83</td><td>0.70</td><td>1</td></tr><tr><td colspan="5" style="text-align: center;">Xingu basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>NS</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>NS</td><td>NS</td><td>1</td><td></td></tr><tr><td>SI</td><td>NS</td><td>0.94</td><td>0.64</td><td>1</td></tr><tr><td colspan="5" style="text-align: center;">Tapajós basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>NS</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>NS</td><td>NS</td><td>1</td><td></td></tr><tr><td>SI</td><td>NS</td><td>0.82</td><td>0.67</td><td>1</td></tr><tr><td colspan="5" style="text-align: center;">Solimões basin</td></tr><tr><td>Onset</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Duration</td><td>-0.95</td><td>1</td><td></td><td></td></tr><tr><td>Extreme</td><td>-0.70</td><td>0.77</td><td>1</td><td></td></tr><tr><td>SI</td><td>-0.92</td><td>0.98</td><td>0.86</td><td>1</td></tr></tbody></table>

highest of all the 2011–18 period. In the same year, the Paru and Trombetas basins were also affected by floods. Explanations are the same as previously.

Our results also show floods in 2017 in the Solimões basin with an intensity similar to those of 2012. In 2018, the Negro basin suffered from more than 60 days of floods and the Madeira, Tapajós and Xingu also suffered of floods but with a lower intensity. However, these last two events are not yet referenced in the scientific literature. Barichivich et al. (2018) noticed 14 floods in 113 years (1903–2015 period) over the Amazon basin. In their paper, they mentioned the intensification of the water cycle in Amazon with more frequent floods causing major human suffering and disturbance to the rain forest ecosystems.

The Amazon basin suffered from an extreme drought in 2015 and 2016 (Jiménez-Muñoz et al. 2016). This event was well observed here (Figs. 7 and 9) with more than 60 days of drought in 2015 in the Negro, Paru, and Trombetas basins. The nonflooded season arrived 110 days late in 2015 over the Negro basin. The highest delay observed during the 2011–18 period (Fig. 8). In 2016, a moderate drought (40 days of consecutive drought were observed) occurred in the Madeira basin. In 2017, a drought with homogeneous intensity affected the entire basin except for the northern portions. Barichivich et al. (2018) noted for the Negro River at Manaus (in the Negro–Amazon confluence) that the frequency of severe hydrological droughts does not show any significant long-term change over the 1903–2015 period.

## b. Usefulness of these indicators in respect with others

Indicators that characterize extreme events in terms of water surface extent are scarce in literature, and here we introduce several metrics that accomplish this important aspect of hydrological monitoring. The application of the proposed severity index to WSE data is complementary to using only precipitation data to monitor the hydrology of wetlands. The flood propagation along floodplains, due to hydrodynamic processes such as floodplain attenuation and backwater effects, largely alters the timing of the flooding process in comparison to the precipitation that originated that flooding. For instance, in Table 3 it was showed that the Solimões and Purus basins present large WSE-precipitation delays, which could be associated with the large river-floodplain interactions that exist along these rivers (De Paiva et al. 2013; Alsdorf et al. 2007; Fleischmann et al. 2016, 2022). Regarding other indicators, many indices have been proposed to characterize and quantify floods and droughts based on other hydrological variables, such as the Palmer drought severity index (PDSI; Alley 1984), the standardized precipitation index (SPI; McKee et al. 1993), and the effective drought index (EDI; Byun and Wilhite 1999). Examples of two indicators, the standardized precipitation evaporation index (SPEI; Vicente-Serrano et al. 2010) and the self-calibrated PDSI (scPDSI; Van der Schrier et al. 2006), over the seven Amazon subbasins studied here, are presented in Fig. 13. The temporal resolution of the global-scale SPEI data (available at https://spei.csic.es/database.html is one month. Positive SPEI values indicate wet events whereas negative SPEI values indicate dry events. According to the classification of McKee et al. (1993) and Paulo et al. (2012), a moderate drought is characterized by SPEI values between -1.5 and -1, a severe one with values between -2 and -1.5, and an extreme one by SPEI lower than 2. In Fig. 13, we can see that no basin faced extreme droughts during 2011–18 based on SPEI values, although the 2015/16 event has been classified as a “one in a century” event (Jiménez-Muñoz et al. 2016). The scPDSI data (available at https://crudata.uea.ac.uk/cru/data/drought/) are available at global scale and at monthly time step. As for PDSI, positive scPDSI values indicate wet conditions and negative scPDSI values indicate dry conditions. For scPDSI values larger than 4, conditions are extremely wet, and for values lower than -4,
<figure><img src="image_15.png" /><figcaption>FIG. 11. Duration of (a) the nonflooded and (b) flooded season for the nine basins of the Madeira basin. Each Ma-
deira basin is associated with a color as indicated in (a). (c) SI$_{wet}$ in 2014 compared to the mean of SI$_{wet}$ during the
2011–19 period.</figcaption></figure>

conditions are extremely dry. We can see in Fig. 13 that, al-
though there is a good agreement between both indicators,
they are not able to characterize dry and wet seasons of water
surfaces (length of seasons, onset of flood season, number of
days of extreme floods or drought, severity of seasons). For
example, the Xingu and Tapajos basins had always wet condi-
tions during the 2011–18 period based on the SPEI and
scPDSI indices (Fig. 13). In a recent paper, Papastefanou et al.
(2022) compared three drought indices [scPDSI, the maxi-
mum cumulative water deficit (MCWD; Aragão et al. 2007),
and the rainfall anomaly index (RAI; Van Rooy 1965)] over
the Amazon basin and concluded that spatial extent and in-
tensity of a drought event highly depend on drought indica-
tors. Finally, to our knowledge, no indicator exists to quantify
flood severity based on WSE, except to characterize flash
flood impacts, highlighting the contributions of our study
(Schroeder et al. 2016).

c. Usefulness of these indicators for ecological and
social studies

The usual monitoring of flood and drought events over tropi-
cal regions use hydrological and biophysical variables such as
rainfall, river water levels, and vegetation biomass. While the
wetland ecosystem conditions can be monitored through these
variables, they do not capture a fundamental variable for wet-
land assessment which is the water surface extent. In this study
we presented new indices for extreme events based on WSE
monitoring. The proposed indices have a direct use for under-
standing the impact on social-ecological systems and ecosystem
services. Several recent studies have highlighted the necessity to
look at indices that are useful for understanding their ultimate
impact. For instance, Langill and Abizaid (2020) assessed how
different flood signatures based on magnitude, duration, and
timing can impact the livelihoods of riparian communities in the
Peruvian Amazon. An important signature, not assessed in this

study, is how the predictability of floods has changed over time
(including the occurrence of the so-called “repiquetes” events),
which largely affects the local communities’ resilience to ex-
treme events (da Cunha Ávila et al. 2021; Coomes et al. 2016;
Figueroa et al. 2020). Another important variable to be assessed
in future studies is the surface connectivity between rivers and
floodplains, which has major implications for fisheries (Arantes
et al. 2013) and human transportation, for instance. A recent
study has shown that river-floodplain connectivity has increased
in last decades along the Amazon floodplain due to increased
precipitation and water levels (Fleischmann et al. 2023). This
latter study has assessed only open water areas in floodplains
due to the nature of the sensor used (optical imagery). Future
studies could further address changes in WSE by using new,
high-spatial resolution WSE based, for instance, on L-band mi-
crowave data, which have the capability to detect water under
dense vegetation data to monitor connectivity between rivers
(Fleischmann et al. 2022). The importance of using WSE-based
indices is also highlighted by our results, for instance by showing
major differences in timing and duration between precipitation
and WSE (Fig. 4). This dynamics also differs from one basin to
another, suggesting that a direct monitoring of WSE is needed.
Our results show that future monitoring systems should include,
in addition to river discharge, river water levels and vegetation
dynamics, a severity index of WSE as the one suggested in this
study. Indeed, the use of only one variable is not sufficient and a
holistic view of extreme events should be provided by a multi-
variable monitoring system.

d. Study limitations

The severity index proposed here is suitable for hydrologi-
cal studies (based on WSE) but less useful for precipitation
assessments. For example, this indicator based on precipita-
tion does not capture the intense 2014 floods over the Ma-
deira basin that was mainly related to intense rainfall over the
<figure><img src="image_16.png" /><figcaption>FIG. 12. Duration of (a) the nonflooded (year represents the beginning of the nonflooded season) and (b) flooded season for the nine basins of the Negro basin. Each Negro basin is associated with a color indicated in (a). (c) SI<sub>dry</sub> in 2016 compared to the mean of SI<sub>dry</sub> during the 2011–19 period.</figcaption></figure>

Amazon-Andes transition region. This indicator integrates a too large area during the entire season to be useful for precipitation. On the other hand, the index rationale is interesting for WSE. For instance, WSE monitoring is based on frequent passive L-band microwave acquisition that has the advantage of a better surface sensing in the presence of vegetation but that are also vulnerable to the presence of radio frequency interference (RFI), closeness of the coast and high topographic slopes (Parrens et al. 2017). In the current study, RFI is taken into consideration in the data preprocessing, but these elements can degrade WSE estimation. The computation of anomalies are based on 10 years of data which needs to be extended to

present. The alternation of record droughts and floods conditions in the last decade along the Amazon reduced this effect. Finally, some nonlinear effects exist between L-band signal and WSE. For instance, deforestation reduces the total forest surface while it is not taken into account in the land cover maps, so the distribution of the percentage of WSE, forest, and ground components can be impacted and bias the WSE retrievals.

The indicators analyzed here were applied to a short period of eight years because of data availability. This period is long enough to demonstrate the usefulness of the proposed indicators, but cannot be used to analyze hydrological trends. In future studies, the methodology could be applied with new
<figure><img src="image_17.png" /><figcaption>FIG. 13. SPEI and scPDSI time series for the seven Amazon basin during the 2011–18 period.</figcaption></figure>

<figure><img src="image_18.png" /></figure>

SMOS or/and Soil Moisture Active and Passive (SMAP; O'Neill et al. 2020) data, or by using other inundation datasets such as Global Inundation Estimate from Multiple Satellites (GIEMS) Prigent et al. (2020).

In scientific literature, the use of indicators has been criti-
cized by some studies. For example, Zang et al. (2020) criti-
cized the use of SPEI to characterize droughts for ecological
applications, because it could lead to a misrepresentation of
actual water supply (especially for moist climates). The indi-
cators created in this study added knowledge on temporal sur-
face water dynamics over the Amazon basin but their use and
interpretation should be performed carefully.

7. Conclusions

This study provides a new perspective on extreme hydrome-
teorological events in the Amazon River basin, based on analy-
ses of surface water dynamics over the last decade. Based on
water surface extent (WSE) time series anomalies over the
2011–18 period, flooded and nonflooded seasons were identified
(see section 4) over seven major Amazonian basins. Four indi-
cators were created: (i) duration in days of the flooded and non-
flooded season computed as the number of days with absolute
WSE anomalies higher than the first decile of the distribution
for each season; (ii) number of cumulative days of extreme
floods or droughts characterized with the number of days with
absolute WSE anomalies higher than the last decile of the distri-
bution for each season; (iii) severity of the flooded or non-
flooded season computed as the integration of WSE anomalies
time series above the first decile of the distribution for each sea-
son; and (iv) onset of the flooded season determined as the day
of year of the beginning of the flooded season.

We found that the duration of the flooded and nonflooded
season differs for each year and the less intense flood season
duration over the 2011–18 period was reached during the

2015/16 El Niño event for all the basins. The number of cumu-
lative days of extreme floods and droughts computed in this
study are in accordance with scientific literature for the
2011–15 period. Results reveal also an intense flood over
the Negro basin in 2018 and moderate drought in 2017 for the
entire Amazon basin except the northern part. The severity in-
dex (SI) indicator permitted the quantification of the severity of
the 2015/16 season. The severity of the 2015 flooded season was
particularly weak for all the basins except the Negro one. It was
below the average of all the 2011–18 flooded seasons of 49%,
65%, 63%, 59%, 68%, and 58% for the Paru, Xingu, Trombe-
tas, Tapajós, Madeira, and Solimões basins, respectively.

This study puts forward a connection between the onset of
the flood season and the season severity for three basins, the
Paru, Trombetas, Solimões basins, and to a lesser extent for
the Negro basin. A late onset of the flood season leads to a
less intense flood season in term of severity. This relation has
not been found for the other Amazon basins.

This framework can be applied to better characterize hy-
drological conditions and their corresponding socioeconomic
impacts toward the development of drought and flood mitiga-
tion measures, and facilitate a relatively easy transition of the
local population through future extreme events. It is also a
relevant tool to analyze the intensification of the water cycle
over the Amazon. Despite some limitations, this study signifi-
cantly advances the understanding of the Amazonian hydrol-
ogy over the last decade, and our results have important
implications for the monitoring of extreme events, since the
overall framework can be applied to other WSE datasets, and
other wetland regions undergoing similar pressures.

*Acknowledgments.* This work was funded by the program Terre Océan Surfaces Continentales et Atmosphere (TOSCA, France) and by the SWOT-Downstream programme from
CNES. We thank the Centre Aval de Traitement des Données SMOS (CATDS) for providing the SMOS L3 TB dataset.

*Data availability statement.* Datasets for this research are included in Parrens et al. (2017).

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