# Lake Chad vegetation cover and surface water variations in response to rainfall fluctuations under recent climate conditions (2000-2020) br

Paul Gerard Gbetkom, Jean-Francois Cretaux, Michel Tchilibou, Alice Carret, Manon Delhoume, Muriel Berge-Nguyen, Florence Sylvestre

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Paul Gerard Gbetkom, Jean-Francois Cretaux, Michel Tchilibou, Alice Carret, Manon Delhoume, et al.. Lake Chad vegetation cover and surface water variations in response to rainfall fluctuations under recent climate conditions (2000-2020) br. Science of the Total Environment, 2023, 7 (7), 159302 [12 p.]. 10.1016/j.scitotenv.2022.159302. hal-04720473

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Distributed under a Creative Commons Attribution 4.0 International License
1 Lake Chad vegetation cover and surface water variations in
2 response to rainfall fluctuations under recent climate conditions
3 (2000–2020).

4 Paul Gérard Gbetkom¹*, Jean-François Crétaux¹*, Michel Tchilibou¹, Alice Carret¹, Manon Delhoume¹,

5 Muriel Bergé-Nguyen¹ and Florence Sylvestre²*

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1. LEGOS, Université de Toulouse, IRD, CNES, CNRS, UPS, Toulouse

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2. Aix-Marseille Université, CNRS, IRD, Collège de France, INRAE, CEREGE, Europôle de l'Arbois, Aix-en-Provence, France, *Present address University of N'Djamena, Department of Geology, N'Djamena, Chad

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* Correspondence: paul.gerard.gbetkom@legosobs-mip.fr; jean-francois.cretaux@legosobs-mip.fr

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12 Abstract

13 Monitoring the evolution of the Sahelian environment is a major challenge because the great Sahelian
14 droughts, marked by significant environmental consequences and social impacts, contributed, for
15 example, to the drying up of Lake Chad. We combined remote sensing images with a water level
16 database from the Hydroweb project to determine the response of Lake Chad vegetation cover and
17 surface water variations to rainfall fluctuations in the Lake Chad watershed under recent climate
18 conditions. The variance in lake surface water levels was determined by computing the monthly
19 anomaly time series of surface water height and area from the Hydroweb datasets. The spatiotemporal
20 variability of watershed rainfall and vegetation cover of Lake Chad were highlighted through
21 multivariate statistical analysis. The spatial distribution of correlations between watershed rainfall and
22 Lake Chad vegetation cover was investigated. The results show an increase in watershed rainfall,
23 vegetation cover, and surface water area and height, as their slopes were all positive i.e., 5.1 10<sup>-4</sup>
24 (mm/day); 4.26 10<sup>-6</sup> (ndvi unit/day); 1.2 10<sup>-3</sup> (km<sup>2</sup>/day) and 6 10<sup>-5</sup> (m/day), respectively. The rainfall
25 variations in the watershed drive those of Lake Chad vegetation cover and surface water, as the rainfall
26 trend was strongly and positively correlated with those of vegetation cover (0.79), surface water height
27 (0.57), and area (0.53). The time lag between the watershed rainfall fluctuations and lake surface water
variations corresponded to approximately ~ 112 days. Between rainfall variations and vegetation cover changes, the spatial distribution of the time lag showed a response time of less than 16 days in the western shores of the lake and on both sides of the great barrier, about 16 days in the bare soils of the northern basin and the eastern part of the south basin, and greater than 64 days in the marshlands of the southern basin. For the analysis of lakes around the world, this research provides a robust method that computes the spatiotemporal variances of their trends and seasonality and correlates these with the spatiotemporal variances of climate changes. The correlations obtained have strong potential for predicting future changes in lake surface water worldwide.

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**Keywords:** Sahel, Lake Chad, Rainfall, Surface water levels, Vegetation cover, Spatiotemporal changes.

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# 1 Introduction

In the early 1970s, the Sahelian region experienced a significant drop in rainfall over several decades (Vischel et al., 2015), with a particular intensity that raised the hypothesis of a rapid and irreversible progression of the Sahara desert towards its southern borders (Tucker et al., 1991). In the Sahel, this period has been characterized by repeated droughts, desertification, the spread of the agricultural front, changes in groundwater levels, and the perturbation of rivers regimes (Marega, 2016). Following these crises, the Sahel became an observation and experimentation laboratory for scientific research (Fall, 2014). This is the context in which Barbé and Lebel, (1997), Lamb, (1982) Nicholson, (1980) focused on rainfall and found that this is the most important variable in the Sahel for analyzing and characterizing climate change. Three main phases in the recent evolution of the climate in the Sahel were thus established: the wet periods of the 1950s–1970s, the Sahelian great droughts of 1970s–1990s, and the resurgence of humidity marked by the appearance of rainy years during the 1990s (Albergel and Valentin, 1989; Ali and Lebel, 2009; Descroix et al., 2016; Ickowicz et al., 2014; L’Hôte et al., 2002; Ozer et al., 2003; Panthou et al., 2014; Sanogo et al., 2015). Research has also demonstrated that land use land cover (LULC) state in this environment is closely related to the
quantity of rainfall and surface water (Cornet and Rambal, 1981; Pierre et al., 2011). The relationship between vegetation and rainfall was then carefully studied in order to analyze the environmental changes in the Sahel (Cissé, 2016; Herrmann et al., 2005; Tucker et al., 1985). With the advent of satellite images in the 1970s (e.g. Landsat MSS in 1972), several studies conducted at the Sahel scale have highlighted that the humidity recovery during the 1990s was in fact the beginning of the Sahel's re-greening (Anyamba and Tucker, 2005; Eklundh and Olsson, 2003; Olsson et al., 2005; Tucker and Nicholson, 1999).

Lake Chad is one of the main symbols of current climate changes in the Sahel. Great droughts caused lake shrinkage from approximately ~ 22,000 km² in 1963 to less than 2,500 km² in 1987; consequentially, drying up of the northern basin, the appearance of permanent or seasonal swamps, leading the lake into a state previously observed during its history: the “Little Chad” (Lemoalle and Magrin, 2014; Nagarajan et al., 2018). To understand these changes, Olivry et al. (1996) analyzed the hydrology of Lake Chad and showed that the lake’s water supply is closely linked to the inflow from the Chari-Logone rivers, whose delta is located in the southern part of the Lake Chad Basin. Coe and Foley, (2001) then studied the climate variability and water use practices and found that the observed decrease in lake area since the 1960s and 1970s was influenced for 50% by human water use. However, when Lemoalle (2014) focused on rainfall variations in the Chari-Logone watershed, the decrease in the volume and surface of Lake Chad was explained by variations in the inflow from the Chari-Logone rivers to the lake. Indeed, the active watershed of the Lake Chad (different from the conventional watershed), which collects most of the rainfall contributing to the filling of the lake, is mainly made up of the Chari-Logone watershed (~ 80% of the inflow) and the Komadougou Yobé watershed (less than 1% of the inflow) (Magrin et al., 2015). Nour et al. (2021) recently studied the hydrological functioning of the Chari-Logone watershed during the period 1960–2015 (based on the available hydroclimatic data) and reported that the flow of the Chari-Logone rivers is highly sensitive to rainfall variations. These results are consistent with those of previous studies revealing that rainfall fluctuations over the Lake Chad watershed or the Chari-Logone watershed are the main drivers of the lake water balance (Babama'aji, 2013; Lemoalle and Magrin, 2014).
The annual time series of average rainfall over the lake watershed showed a rate of increase of ~ 4.84 mm/year for the period 1985-2015 (Zhu et al., 2017). In response to this slight increase in rainfall, the surface water extent in the southern basin of the lake has remained stable (with a slightly increasing trend since the 2000s) (Pham et al., 2020; Zhu et al., 2017); however, the surface water extent of the entire Lake Chad slightly decreased because of the reduction in the surface water extent in the northern basin caused by the increase in vegetation cover and evapotranspiration (Pham et al., 2020).

In fact, as described by Gbetkom (2020), vegetation cover shows high variability because of lake shrinkage, from total loss of vegetation in the bare soils during the great droughts to progressive re-greening from the beginning of the 2000s. Mayomi and Mukhtar (2018) reported an increase in vegetation cover from 24.95% of total lake area to 46.24% from 1999 to 2017, and Pham et al. (2020) reported an increase of ~ 30% in permanent vegetation cover within the Lake Chad since the 2000s.

The impact of rainfall variations in the watershed on the vegetation cover and water balance of the lake was then clearly established. However, the time delay of the lake water level and vegetation cover in response to rainfall variations in watersheds has not been investigated.

The time lag between rainfall and LULC in the lake is by far a key issue. As a result of the great droughts and the decrease in water levels, the lake has become an important area of agricultural production due to the appearance of vast flooded lands that are used as pastures or farmland according to the seasons (Raimond et al., 2014). Thanks to agriculture, livestock and fishing, the lake provides food security for a hinterland of ~ 13 millions of people concentrated in the big cities of Maiduguri and N'Djamena (Pesche et al., 2016). For example, the “Little Chad” allows for the production of between 50,000 and 100,000 tons of smoked and dried fish and an estimated 600,000 to 900,000 tons of maize, contributing to 80% of the maize needs of the cities of Maiduguri and N'Djamena (Lemoalle and Magrin, 2014). This production is closely linked to the calendar of floods and water recession and requires good anticipation of rainfall for agricultural planning and land use management.

Pham et al. (2020) found a 1-month delay between the peak of the Chari-Logone Rivers at the N'Djamena gauge station and the peak of the lake surface water storage, and partly answered this
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question. However, the time lag between rainfall in the watershed and lake surface water has not yet been studied. With respect to the vegetation, if it is established that the increase in rainfall since the 2000s has improved the vegetation cover in the lake, no research has explored the sensitivity of the lake's vegetation cover according to the time lag to rainfall variations. This information has potentially valuable applications in terms of water management in lakes for several societal purposes.

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This study focused on the Lake Chad vegetation cover and surface water level variations (height and area) in response to rainfall fluctuations in the Lake Chad watershed under recent climate conditions (2000–2020). Compared to Coe and Birkett (2004), the approach that predicted the lake height based on the Chari discharge measured at the Chari/Ouham confluence, our method allows us to determine how the lake surface level and extent are influenced by watershed rainfall. We also investigated the spatial distribution of the correlations and delays between watershed rainfall and the vegetation cover of Lake Chad. To this end, satellite images (MOD13Q1 and IMMERG) were combined with a water levels database (derived from altimetry measurements) to monitor recent changes in watershed rainfall, Lake Chad vegetation cover, and surface water levels. The benefit of this approach is that it highlights the spatiotemporal variability of the rainfall and vegetation cover.

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# 2 Data and methodology

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## 2.1 The study region

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Located in the Sahelian region, Lake Chad is a transboundary basin shared by four countries: Cameroon, Chad, Niger, and Nigeria (figure 1). Since the shrinking of Lake Chad open water during the 1980s—following the Sahelian droughts between 1970 and 1990—this lake is made up of two basins, the southern and northern pools, separated by an area of shoals, named the “Great Barrier” (Olivry et al., 1996). It is a shallow, endorheic water body with an open water area whose variations over the period 2000–2020 shows estimated maxima of 5800 km² (in 2001–2002) and minima of ~ 1800 km² (2010) (Pham et al., 2020). The lake’s water supply is mainly due to its tributaries (Chari, Logone, Komadougou Yobé and El Beid), which account for most of the water supply (more than 80%), the remainder is supplied by direct rains over the lake (Olivry et al., 1996). These supplies are

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balanced by the extensive evapotranspiration characteristics of arid regions and are estimated to be >
2000 mm/year (Bouchez et al., 2016). Similar to the Sahelian region, the lake has a very high seasonal
variability. The dry season from October or November to May, marked by high temperatures of up to
30°C, follows a short rainy season from June to September when most of the rainfall is concentrated
(Rim, 2008; Vischel et al., 2015).

<figure><img src="image_2.png" /><figcaption>Figure 1: (a) Limits of the conventional basin of the Lake Chad (the basemap is the Google satellite image generated through the Quantum GIS plug-in), (b) The Great barrier and the both pools (generated with Landsat-8 image, 08-15/04/2020, path and row: 184051, 185050, 185051, 186050), (c) The monthly mean of precipitation and temperature in the N'Djamena weather station from 2000 to 2015 (data are provided by the Agence Nationale de la Météorologie (ANAM) and the Direction des Ressources en Eau (DRE) from Ministère de l'Hydraulique Rurale et Urbaine.</figcaption></figure>

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## 2.2 Data description

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### 2.2.1 The HYDROWEB water level data

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To monitor temporal variations in open water in the southern basin of Lake Chad, we used water levels databases from the Hydroweb project (https://hydroweb.theia-land.fr/). It is a database produced by LEGOS (Laboratoire d'Etudes en Géophysique et Océanographie Spatiales) and CLS (Collecte Localisation par Satellite), made up of times series of water levels of great lakes, reservoirs, and rivers (Crétaux et al., 2011). Data were obtained after corrections (geophysical, environmental, and instrumental) of the altimetry measurements recorded by the Topex/Poseidon, ERS-2, GFO, Jason-1, Envisat, Jason-2, Jason-3, Cryosat-2, Saral/Altika, Sentinelle-3A, and Sentinelle-3B satellites
(Rosmorduc et al., 2021). Data are available since 1992 and are updated in near real time (data latency is approximately 1.5 d) for most lakes and rivers. The orbital cycles of each mission, which drive the time resolution of the measurements over a given lake or river, varies from 10 to 35 days (Rosmorduc et al., 2021). Therefore, we used this database for Lake Chad monitoring because of the three characteristics mentioned above: data availability, temporal resolution, and data pre-processing. For Lake Chad, this database provides information about the water level, surfaces, and volume variations of the southern basin (Crétaux et al., 2016). In this study, we focused on water levels and surfaces from 2000 to 2020.

## 2.2.2 Vegetation cover data

The vegetation spectral index (NDVI) was used to analyze changes in the vegetation cover of the Lake Chad from 2000 to 2020. Through reflectance values in red (0.6–0.7 µm) and near infrared (0.7–1.1 µm), the NDVI quantifies the intensity of photosynthetic activity of plants at a given time (Rouse et al., 1973). For a time-series analysis of vegetation cover, it is necessary to acquire NDVI values under several climatic conditions (dry and rainy seasons). The data used for this purpose were MODIS MOD13Q1 images from the MODIS TERRA sensor proposed by the LP DAAC operator in charge of the MODIS sensor (Giglio et al., 2018). MOD13Q1 images have 12 bands, and the first two are spectral indices of vegetation (Table S1) (NDVI for chlorophyll activity of plants and EVI for the quantity of biomass) (Solano et al., 2010). These data correspond to a 16-day period summary of the best available pixel value in terms of low clouds, low viewing angle, and the highest NDVI/EVI value (Didan et al., 2015). MODIS NDVI data are mainly used for such analyses (Bergé-Nguyen and Crétaux, 2015; Horion et al., 2014; Zoungrana et al., 2018) because of their accessibility, temporal resolution, and width of ground coverage, (which allows the coverage of vast territories. One file/granule (of coordinate h19v07) was sufficient to cover Lake Chad, and 481 was thus necessary to study the dynamics of the lake’s vegetation cover from 2000 to 2020.

## 2.2.3 Rainfall data

Rainfall information was obtained from the most recent product (version 6) of the Integrated Multi-satellite Retrievals (IMERG) for the GPM data set (Tan et al., 2019). IMERG is an algorithm whose
objective is to estimate precipitation over the majority of Earth's surface. All satellite microwave precipitation estimates were intercalibrated, merged, and interpolated using microwave-calibrated infrared (IR) satellite estimates and precipitation gauge analyses. The product (IMERG V06) is a global, gauge-based, and gridded data set of precipitation amount at half-hourly time intervals with spatial resolution of 0.1° × 0.1° and a temporal coverage starting at 2000-06-01 (Tan et al., 2019). These data are valuable for global-level studies, time-series analyses, or when working on the Earth's surface region where precipitation data are not available because of the lack of precipitation-measuring instruments on the ground. Applications such as flood warming (Ma et al., 2020; Saouabe et al., 2020), storm detection and tracking (Takahashi et al., 2021), and fire weather (Gowan and Horel, 2020) are based on the IMERG data.

The IMERG products are available on the NASA website (https://gpm.nasa.gov/data) and are organized into three different levels: IMERG-E (Early), IMERG-L (Late) and IMERG-F (Final). The Early and Late products were generated in near-real-time with a 4h and 12h time-lag respectively. The Final product takes two months because more sensor data sources are needed (Tan et al., 2019). For this study, we chose the IMERG-F product, which has a high accuracy (Ma et al., 2020) because it is first calibrated with ground rainfall stations before being made available. Daily data from 2000-06-01 to 2020-12-31 were downloaded and then cumulated over the Lake Chad watershed on a 16-day time scale corresponding to that used with MODIS data in this study.

## 2.3 Methodology

### 2.3.1 Trend analyses of surface water levels

According to the strong seasonality of lake water level variations and rainfall in the watershed, anomaly time series were calculated using monthly values, to determine their variance. Thus, each time series was standardized by subtracting the median of the same month in the time series from the raw data and then dividing by the maximum deviation of the same month in the time series. The result is a time series of lake water level variations, and rainfall anomalies, covering the period of 2000–2020.
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$$y'(t) = \frac{y(t) - \text{median}}{\in \max}$$

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y' = anomaly, y = raw value, t = time, and $\in \max$ = maximum deviation from the median

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## 2.3.2 Spatiotemporal analyses of rainfall and vegetation cover

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The MODIS and IMERG raw data were first decomposed using the Hanning filter (Hattermann et al., 2021) to differentiate the low-frequency signal (with periods > 23×16-days), representing the interannual variability of vegetation cover and rainfall, from the high-frequency signal (with periods ≤ 23×16-days), which contains seasonal and intra-seasonal variability. To determine spatiotemporal variability in vegetation cover and precipitation for the period 2000–2020, we used multivariate statistical analysis by empirical orthogonal function (EOF). In remote sensing techniques, EOF is very useful when processing a large multi-spectral or time-series data set (Li et al., 2013). This method was used to identify coherent spatiotemporal variability. This approach merges coherent variations of the original information into a small number of new components (independent variables), which reduces redundancy (National Center for Atmospheric Research Staff, 2013). The newly created components are statistically uncorrelated and project the original information on an orthogonal basis. To obtain the orthogonal basis, the eigenvectors of a spatially weighted anomaly covariance matrix were computed with the corresponding eigenvalues, providing a measure of the percent variances explained by each pattern (Zhao et al., 2020). The importance of each new component is indicated by the magnitudes of the eigenvalues. According to the large number of MODIS and IMERG images (481), the EOF method was suitable for this spatiotemporal analysis. We computed the EOF individually for the low- and high-frequency signals. The results highlight the interannual and seasonal spatial patterns of vegetation and rainfall through eigenvectors (Li et al., 2013; Toumazou and Crétaux, 2001) and how they change with time using the eigenvalues.

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## 2.3.3 Times-series correlations analyses

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The high-frequency time-series of IMERG precipitation was correlated to the high frequency time-series of NDVI and water levels based on cross-correlation analyses, which allowed us to determine the relationship between precipitation and Lake Chad LULC variability (vegetation and open water).
Cross-correlation examines the correlation between two time series variables to determine if these variables are related to each other and if the values of one time series are predictive of the future values of another time series (Begum et al., 2021). This results in correlation values ranging from -1 to +1, where a perfect linear correlation is close to (+/-) 1 and 0 for no correlation (Polanco-Martínez et al., 2020). We also computed the lag of these correlations to assess the time delay of the vegetation cover or water levels in response to rainfall.

The correlation between vegetation and precipitation was determined using a pixel-based approach. For each 16-day period of IMERG data, the mean value of precipitation across the watershed was deduced. In the NDVI image corresponding to the same 16-day period, this value was then correlated with each NDVI pixel in the lake. For each pixel, we find the maximum value of this correlation to highlight where the vegetation in Lake Chad reacts sensitively with rainfall variations across the watershed. Thus, for each pixel, we also deduced the delay corresponding to the maximum correlation.

For the water level and precipitation correlation, the mean values of both variables were used. 16-day mean values of the height and area levels were computed from the time series of water levels and correlated with the corresponding mean value of precipitation across the watershed. The maximum value of this correlation was then used to obtain the maximum delay in the water levels in response to rainfall.

# 3 Results

## 3.1 Spatiotemporal dynamics of rainfall over the Lake Chad watershed.

Rainfall was recorded over the entire watershed between April and November (figure S1), with annual totals ranging from 407.84 mm (2012) to 266.14 mm (2000) (figure S4), and a total annual average of 341.39 mm during the observed period. The rain did not reach the lake until the 10th of June and completely withdrew by the 1st of November. The lake is watered over its entire surface from mid-July to the end of September, for approximately two and a half months.
<figure><img src="image_3.png" /></figure>

Figure 2: First (top left) and second (bottom left) mode of the spatiotemporal variability of seasonal and intra-seasonal signal of rainfall in the lake watershed. The right panel is the first mode of the spatiotemporal variability of Interannual signal of rainfall in the lake watershed. For each mode, the EOF (map) correspond to the weight of the PC (curve) and both components represent the variance. The PC is normalized by the sum of amplitude. For the both left panel, the PCs representing the seasonal mean of rainfall variance, are obtained by averaging each 16-day's period between 2000 and 2021.

### 3.1.1 Seasonal and intra-seasonal variability of rainfall.

The first two orthogonal modes from the high frequency signal explained 96% of the seasonal and intra-seasonal variability of rainfall in the watershed (figure 2).

The first mode provided 90% of the high frequency variability and corresponded to a unimodal seasonal cycle of the rainfall (PC1, figure 2). This seasonal variability (same on the whole watershed since all variances explained by the EOF1 are positive) was very significant between 10° and 15° north latitude (EOF1 > 0.96), where the variations of rainfall throughout the year were the most important, and then became less important below 8°N towards the more humid equatorial zones, and above 16°N in the arid regions of Sahara Desert (EOF1 < 0.93).

The time series of PC2 was dipolar, with positive and negative variances (figure 2). Between 12° and 14° north latitude (–0.15 < EOF2 < 0.15), the amplitude of variances was very weak. Above 14°N, variances were important and negative, the cycle was inverted and became unimodal being the maximum in mid-August; however, in this area, the rainfall was low. Therefore, this mode could only be significant for positive variances below 12°N, where precipitation was important and the rainfall
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cycle was bimodal with an attenuation of values in August. However, this second mode only represented 6% of the high frequency variability compared with the 90% of the first mode.

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### 3.1.2 Interannual variability of rainfall

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The interannual variability was analyzed based on the principal modes obtained from the processing of the low-frequency signal of rainfall. Thus, these components help to describe the main changes in rainfall in the watershed from 2000 to 2020. Unlike the seasonal signal, the different interannual variabilities were more scattered across the principal modes obtained. Indeed, we can notice that the EOF based on the low-frequency rainfall signal does not show a high preponderance of the first mode (Table S2) and that it is difficult to separate the modes of spatiotemporal variability. The 5 first mode represented only 60% of the total variance of the signal. However, because the objective was to determine rainfall trends, only the first component was of particular interest.

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The first component, which appears to be more or less uniform over the entire watershed, explains 35% of the interannual variability in rainfall and shows a general trend of rainfall increasing from 2000 to 2020 (figure 2). There was a significant increase in rainfall between 2009 and 2012 and a gradual increase in rainfall amounts that restarted approximately in 2014.

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### 3.1.3 Rainfall dynamic for each year in Lake Chad watershed

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The raw monthly rainfall values (figure 3) show the evolution of the rainy season for each year in terms of the start, duration, end, and maxima. Over the entire basin, most of the rainfall occurred between June and October. These raw values indicate that the rainfall recorded in August is an important climate indicator. Over the observed period, they represent an average of 28% of the annual sum of rainfall for each year and vary between 130 and 210 mm according to the curve of the total rainfall in August (figure 3). Rainfall in August was the lowest (< 150mm) in 2000, 2002, 2004, and 2009. These years were identified as the least rainy years (figure 2). In contrast, 2012 and 2019 were the rainiest years and the rainfall in August were the most significant (>200 mm).

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The rainfall dynamics for each year are also highlighted by the rainfall anomaly map (figure 3). For example, in the most deficient years in terms of rainfall, negative anomalies were recorded between

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August and September of 2000, April–August of 2002, June–September of 2004, and May–September of 2009. For positive anomalies, the longest period in any year was recorded in 2012 (February, May–August, and December), which explains why this year had the highest positive rainfall variance (figure 2). From 2016 to 2019, May, June, and July were marked by strong positive anomalies, while the negative values in August 2017 explained the decrease in variance in 2017 (figure 2), and the positive values recorded in August and October 2019 and September 2020 justified the peaks of positive variances observed in those years.

Furthermore, September rainfall appears to describe a 5-year cycle, based on the positive September anomalies recorded in 2001, 2006, 2011, 2015, and 2020.

<figure><img src="image_4.png" /><figcaption>Figure 3: Rainfall dynamic for each year in the lake watershed. The top left panel is the raw monthly rainfall values, the top right panel is the monthly rainfall anomalies, and the bottom panel corresponds to the total rainfall in August.</figcaption></figure>

## 3.2 Spatiotemporal dynamics of vegetation cover over Lake Chad.

During the observed period, the vegetation cover occupied an average annual area of ~ 7300 km². In the southern basin of the lake, which receives the main tributaries and contains most of the open water, vegetation cover remains important throughout the year (>0.5) and shows an improvement between the end of July and beginning of December (figure S2). In the northern basin, where moisture mainly depends on water from the southern basin, bare soils are more important, and the vegetation cover only increases between mid-July and the end of September (figure S2).
<figure><img src="image_5.png" /></figure>

Figure 4: First (top left) and second (bottom left) mode of the spatial and temporal variability of seasonal and intra-seasonal signal of the Lake Chad vegetation cover. The right panel is the first mode of the spatiotemporal variability of Interannual signal of the Lake Chad vegetation cover. For each mode, the EOF (map) correspond to the weight of the PC (curve) and both components represent the variance. The PC is normalized by the sum of amplitude. For the both left panel, the PCs representing the seasonal mean of vegetation cover variance are obtained by averaging each 16-day's period between 2000 and 2021. The white color in the middle of the maps corresponds to the fill values representing permanent open water.

### 3.2.1 Seasonal and intra-seasonal variability of NDVI.

The first two orthogonal modes from the high frequency signal explained 93% of the seasonal and intra-seasonal variability of the Lake Chad vegetation cover (Figure 4).

The first mode contained 84% of the high frequency variability, and the PC1 plotted a unimodale seasonal cycle of vegetation cover over the entire lake (figure 4). Vegetative activity begins in June with rains resumption and ends in October at the beginning of the dry season, with the maximum occurring in August. This seasonal variability is very significant for emerged spaces and bare soils, particularly where the weight of EOF1 is more important (EOF1 > 0.966), and then becomes weak in marshlands with EOF1 < 0.9. The correlation computed between the seasonal rainfall (from IMERG) and the vegetation cover (from the NDVI) was very high and positive at 0.88 (Table S5).

The second mode accounted for 6% of the high frequency variability. For positive variances covering the irregularly flooded marshlands of the northern basin (EOF2, figure 4), the vegetation cover was more important between February and August, gradually decreased from mid-September, and reached a minimum in November. In contrast, for the almost permanent marshlands bordering the open water
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of the southern basin (where the EOF2 variances were negative), this seasonal cycle was inverted and
the most important vegetation cover was recorded between July and December, with a maximum in
November.

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3.2.2 Interannual variability of NDVI

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Changes in the Lake Chad vegetation cover during the 2000–2020 period are highlighted by the
interannual variability derived from the results of the EOF analysis applied to the low-frequency
NDVI signal. As with rainfall, the different interannual variability of vegetation cover was contained
in several modes (Table S3). Therefore, only the first component was of particular interest for
determining vegetation cover trends.

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The first mode, which explained 31% of the interannual variability of the Lake Chad vegetation cover, contained positive and negative variances. Therefore, the time series reported by PC1 was a dipolar signal (figure 4). EOF1 shows that the lake was mostly dominated by positive variances, thus there was an overall increase in vegetation cover across the entire lake. The negative variances located in some marshlands of the two basins showed an opposite trend.

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3.3 Open water variations in Lake Chad.

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From 2000 to 2020, the annual averages of water height and area have respectively varied from a minimum of 279.74 m and 1270.28 km² in 2004 to a maximum of 280.58 m and 1294.36 km² in 2020 (figure S4). During this period, the total annual average of water level is 280.23 mm and 1286 km² for area. During a given year, the lake's water levels (height and area) between the minima recorded from May to July and maxima recorded from October to December are quite important (figure S3). The recorded data showed that there were significant changes in the lake water levels between 2000 and 2020 (figure S3). These data were irregular until 2007 and became more regular from 2008 onwards (figure S3) as the launch of the Jason-2 (2008) and the sentinel-3A (2016) missions increased the temporal resolution of the altimetry measurements (from 35 days before 2008 to ~5 days after). In the period 2000–2007, the maximum heights did not exceed 281 m and the minimum heights were regularly less than 279.5 m. From 2007, the minimum heights stabilized at ~ 279.5m and the

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maximum levels recorded were sometimes higher than 281 m, especially in 2010, 2012 and 2017 to 369 370 The 2016–2020 interval was marked by a gradual increase in the minimum heights of the lake. 371 Regarding the lake water area, the data were more irregular until 2007. The maximum values saturated 372 between 1302 and 1303 km² over the entire observed period. The most important variations were 373 observed with the minimum values, which are decreased from 2008 to 2011, stabilized during 2011– 374 2015, and increased from 2016 (similar to the water heights).

The anomaly curves show that for the area, the highest negative values (–0.75 to –1) were obtained 375 between 2000 and 2011 (i.e., 2000, 2001, 2002, 2007, and 2011). During this period, positive 376 anomalies were rarely greater than 0.5. The area anomaly curve is similar to that of height over the 377 period 2000–2011, with the most negative values (–0.75 to –1) recorded in 2000, 2005 and 2011, and 378 positive values regularly below to 0.5. A trend was hardly visible between 2000 and 2011 for both 379 lake height and area (figure S3). From 2010, the data records were more regular, and the curves clearly 380 illustrate the increase in the lake’s water levels, showing that this increase seems to have started in 381 2012 and was confirmed from 2016 onwards. Indeed, from 2012, the negative anomalies for both 382 curves were lower, except in 2013 for height and 2016 for area when these values ranged from –0.75 383 to –1. The positive anomalies, were increasingly high, particularly from 2017 onwards, and saturated 384 at ~ 1 in 2019 and 2020. 385

According to figure 5, anomalies were particularly negative between June and September over the 386 2000–2011 period for water heights and the 2000–2015 period for areas. They were less negative from 387 August 2012 (for height and area), and from July 2016, they were positive and regular, with maxima 388 obtained in 2020. Over the last two years of the period, the positive anomalies were marked all over 389 the year for the lake water level, and from December to the August for water extent. 390

Figure 5 also shows the impacts of rainfall (visible in figure 3) on the dynamics of the lake water 391 height and area. For example, the rainfall deficit in 2004 explains the negative anomalies in height and 392 area observed between August 2004 and February 2005. The positive anomalies for height and area 393 that lasted from September 2012 to March 2013 could be explained by the rainfall in 2012. The 394
<figure><img src="image_6.png" /></figure>

positive anomalies for height and area detected from July 2016 onwards can be explained by the
positive rainfall anomalies recorded from May 2016.

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>Figure 5: Monthly anomalies of the Lake Chad’s surface water levels. The left panel is height and the right panel corresponds to area.</figcaption></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

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

## 3.4 Correlations

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

The comparison of the low- and high-frequency signals for the four studied parameters allows cross-checking of their evolution during the observation period (figure 6). In this graph, the values of each curve were normalized by the sum of the amplitudes to overlay trends (low frequency) and then averaged to obtain the seasonality (high frequency).

<figure><img src="image_17.png" /></figure>

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

However, to compute all the correlations shown in Tables 4 and 5, we used the raw values of low and high frequency (not normalized and not averaged).

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /><figcaption>Figure 6: Trends (a) and seasonality (b) of rainfall, vegetation cover, and the surface water levels</figcaption></figure>
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All the trends increased with positive slopes (Table S4). This supports the results shown in figure 6 regarding the increase in rainfall in the watershed, vegetation cover enhancement, and the increase in the lake surface water area and height between 2000 and 2020.

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The trend diagrams also show the degree of similarity in the evolution of the four parameters. This was the case for the strong fluctuations in 2004 and 2012, where variations in rainfall always occurred before those in vegetation cover or water levels. This is also reflected in the increase observed from 2016 onwards and is shared by all trend curves. The correlation coefficients between these trends supported this observation. Indeed, the rainfall trend correlated at 0.79 with vegetation cover, at 0.57 with surface water height, and at 0.53 with surface water area (Table S4). Therefore the trend line of rainfall was strongly and positively correlated with vegetation cover, surface water height, and area. This highlights the important linear relationship between rainfall availability in the watershed and the lake LULC (vegetation cover and open water).

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The correlations and cross-correlations, computed from the seasonal signals (Table S5), allowed us to evaluate the influence of rainfall on the vegetation cover and water levels of the lake, and to estimate their time delay with rainfall variations in the watershed (Table S5). At lag0, the correlation rates between precipitation and water levels were negative, that is, -0.5 and -0.51 for area and height, respectively. Indeed, the seasonal profiles show that rainfall was inversely correlated with lake water level variations (figure 6). On an average, rainfall was highest in August, and the highest water levels were recorded in November. Maximum correlations (0.75 for area and 0.82 for height) were obtained at lag7 corresponding to 112 days, implying that lake area and height vary almost similarly to watershed rainfall changes with a 112-day time lag.

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The seasonality of rainfall and vegetation cover followed the same pattern (figure 6). Therefore, vegetation is strongly and positively correlated with precipitation at lag0 with a correlation coefficient of 0.86 (Table S5). The maximum correlation (0.88) was obtained at lag1 for a time lag of 16 days.

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The above results are the average values of the correlations and the time lag. However, the correlation between the rainfall and NDVI images allowed us to obtain their spatial distribution (figure 7). The

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correlation map shows that the highest correlations (greater than 0.75) were found in the southern basin and south of the northern basin. Those ranging from 0.6-0.75 were contained in the bare soils of the northern basin, and correlations < 0.4 corresponded to the permanent vegetation covers of the southern basin and the great barrier.

The time lag was 0 on the western shore of the lake and on both sides of the great barrier, where the influence of precipitation on vegetation cover was immediate (on a 16-day period). However, in the bare soils of the northern basin and the eastern part of the south basin, the time lag was equal to 1, that is, the vegetation cover reacted 16 days after the rainfall. For marshlands in the southern basin, the time lag was more important (> 4), and the impact of rainfall occurred after a minimum of 64 days.

<figure><img src="image_22.png" /><figcaption>Figure 7: Spatial distribution of correlations and time lag between of vegetation to rainfall</figcaption></figure>

## 4 Discussion

### 4.1 Accuracy assessment of the different trends observed

We ran a Mann-Kendall correlation test on the LULC time series used in this study (annual sum of rainfall, annual means of NDVI, water height, and area) to assess the different trends observed above.

The Mann-Kendall correlation is robust, non-parametric (Yue and Pilon, 2004) and is usually used (Andrieu, 2018; Edea et al., 2016; Tra Bi et al., 2013). It determines the trend in time and value over time series based on Kendall's "tau," producing a trend based on the calculation of the relative frequency of decreases minus the relative frequency of increases (Genesis et al., 2016; Gressin, 2014).

All the correlations, shown in Table S6, were computed with an alpha (P-value) of 0.05.
For rainfall, vegetation cover, and surface water height, the Mann-Kendall correlation supported an increasing trend from 2000 to 2020 (Table S6). The slopes were positive, the P-values were less than 0.05, and the correlations detected significant trends in the annual sum of rainfall, annual means of NDVI, and annual means of surface water height. The time series of the annual sum of rainfall clearly shows peaks in 2012, as well as an increase from 2014 onwards (figure S4), observed on the first component of the interannual variability of rainfall. The variations in the curve of the annual means of NDVI across Lake Chad were similar to those of the first component of the interannual variability of vegetation cover. The annual mean surface water height confirmed that their increase began in 2012. However, with respect to surface water area, the Mann-Kendall correlation did not detect any trend due to a P-value greater than 0.05. However, the slope was positive, and the trend line increased.

The LULC trends in Lake Chad and its watershed have been widely reported by several studies conducted across the Sahel, in the Lake Chad watershed, and in part of the lake itself. Table 1 summarizes these studies and allows us to compare our results with those of the previous studies.

<table><caption>Table 1. Comparison of LULC trends results found in previous studies.</caption><thead><tr><th></th><th>Author</th><th>Study area</th><th>Time period</th><th>Trend</th></tr></thead><tbody><tr><td rowspan="3">Rainfall</td><td>(Vischel et al., 2015)</td><td>Sahelian region</td><td>1950-2010</td><td>Increasing</td></tr><tr><td>(Sanogo et al., 2015)</td><td>West Sahel</td><td>1980-2010</td><td>Increasing</td></tr><tr><td>Our study</td><td>Lake watershed</td><td>2000-2020</td><td>Increasing</td></tr><tr><td rowspan="5">vegetation cover</td><td rowspan="3">(Dardel et al., 2014)</td><td>Sahelian region</td><td>1981-2011</td><td>Increasing</td></tr><tr><td>Faraka (Niger)</td><td>1994-2011</td><td>Decreasing</td></tr><tr><td>Gourma (Mali)</td><td>1984-2011</td><td>Increasing</td></tr><tr><td>(Gbetkom, 2020)</td><td>Cameroonian part of Lake Chad</td><td>1973-2018</td><td>Increasing</td></tr><tr><td>Our study</td><td>Lake Chad</td><td>2000-2020</td><td>Increasing</td></tr><tr><td rowspan="4">surface water levels</td><td>(Buma et al., 2018)</td><td>Lake Chad (Surface water area)</td><td>2003-2016</td><td>Increasing</td></tr><tr><td rowspan="3">(Pham et al., 2020)</td><td>Lake Chad (Surface water area)</td><td></td><td>Decreasing</td></tr><tr><td>Southern basin (Surface water area)</td><td>2001-2018</td><td>Stable</td></tr><tr><td>Northern basin (Surface water area)</td><td></td><td>Decreasing</td></tr><tr><td>Our study</td><td>Open water of the southern basin (height and)</td><td>2000-2020</td><td>Increasing</td></tr></tbody></table>
<table><tr><td></td><td></td><td>area)</td><td></td><td></td></tr></table>

This comparison with our results highlights several peculiarities. For vegetation cover, for example, Dardel et al. (2014) combined the GIMMS-3g NDVI dataset and field observations and observed various trends, with an increasing trend in the Sahel between 1981 and 2011 and in the Gourma region (Mali) (between 1984 and 2011), and a decreasing trend in the Faraka region (Niger) (between 1994 and 2011). Gbetkom, (2020) demonstrated an increase in vegetation cover in the Cameroonian part of Lake Chad (between 1973 and 2018) through the supervised classification of Landsat images. This study focused on the MODIS time series covering Lake Chad. Concerning the water levels, the difference in the observed trends was mainly related to the types of data used and the extraction method of the surface water area. Buma et al. (2018) applied spectral indices to Landsat images and Pham et al. (2020) classified MODIS images. They have estimated water surface area ranging from 1242–22,301 km² and from 1800–5800 km², respectively, higher than those we calculated between 1250 and 1300 km², based on altimetry measurements and limited to the permanent open water in the central part of the southern basin (archipelago were excluded from our calculation of the lake area). Furthermore, while this study analyzed trends for open water from the southern basin, Buma et al. (2018) focused on the entire lake, and Pham et al. (2020) differentiated trends for the whole lake and the northern and southern pools. These results demonstrate that the observed LULC trends are strongly linked to the scale of the study area, the images used, and their spatiotemporal resolutions.

Several previous studies for other lakes worldwide also located in arid/semi-arid areas with strong influence of rainfall on water surface and vegetation cover such as the Lake Mead in USA (Murray and Khaki, 2020), Lake Popoo in Bolivia, (Lima-Quispe et al. 2021), Lake Urmia in Iran (Nhu et al. 2020), and Lake Ebinur in China (Maihemuti et al. 2020), have pointed out that rainfalls were among the key influencing factors of changes. Over the Tibetan Plateau, studies have shown that the lakes water level and extent have strongly increased over the last 20 years with predominant direct correlation with precipitation changes (Zhang et al., 2021). These studies confirm our results by showing that for both natural lakes (Lake Ebinur) and reservoirs (Lake Mead), surface water trends
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can be directly linked to rainfall trends. However, our study is more global by weighting trend and seasonality signals of each parameter studied and describing their spatial variability (rainfall and vegetation cover).

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## 4.2 Time lag between rainfall and surface water levels variations

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Our results showed a time lag of 112 days in the response of Lake Chad levels to watershed rainfall variations. One of the best approaches to evaluate these results is to examine the relationship between rainfall variations, lake water levels, and variations in the discharge and height of the Chari-Logone rivers (the main tributaries of Lake Chad), as explored previously (Coe and Birkett, 2004; Pham et al., 2020; Zhu et al., 2017). Zhu et al. (2017) indicated a time lag of approximately one to two months between the direct rainfall over Lake Chad (less important than the rainfall over the entire watershed that we used) and the lake water variation. Coe and Birkett (2004) showed that the time lag between altimetric measurements of the Chari River water height at the Chari/Ouham confluence is about 10 days with discharge and height measured at N'Djamena (500 km downstream), and 39 and 66 days with open water height of the lake south basin and western marshland, respectively (both located more than 600 km downstream). The resulting time lag from discharge at N'Djamena and the open water of Lake Chad was 29 days in their study. We correlated the rainfall watershed with the Chari-Logone discharge, measured at N'Djamena (more than 100 km upstream from the Chari mouth) from 2000 to 2016, and found a time lag of approximately two months in the response of Chari/Logone discharge to the rainfall watershed (figure S5). Considering these values and the results from Coe and Birkett (2004), this indicates that the average time lag between rainfall in the watershed and the lake surface level variations would be ~ 89 days instead of 112 days, as observed in our study. The difference of 23 days between our results and previous results may have several potential explanations: changing the precipitation regime and discharge magnitude between N'Djamena and the lake compared to the study by Coe and Birkett (2004), some underground infiltrations, and remaining measurement errors.
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## 4.3 Implications of the results and their limitations

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The multivariate statistical analysis by EOF used in this study allowed us to identify coherent and independent spatiotemporal variability on the NDVI and IMERG. For seasonal and interannual signals, the physical phenomena explained by each component were therefore separated and described by their corresponding weights and percentages of variance. This was particularly necessary for the interannual signal which was more scattered across the principal modes obtained. If these components were not independent, then they would show a mixture of several trends of rainfall and vegetation cover in the study area. It would therefore be impossible to obtain the weight, spatial distribution, and amplitude of the vegetation cover and rainfall trends.

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As current climate changes are bringing serious menaces and uncertainties to global water management, these outcomes are particularly valuable for predicting changes in lakes' surface water. Indeed, the results of the methodology of this study have shown how to link trends and seasonality of lake surface water levels (height and area) and vegetation cover, to those of the environmental variables (particularly climatic) that determine their dynamics. For challenges that need to be addressed by futures satellites like SWOT or Copernicus Sentinel, regarding the measurement and the monitoring of several environmental parameters (LULC, vegetation cover, water level, air temperature, rainfall, etc.), these results are then particularly interesting especially for the arid and semi-arid regions of the world (Central Asia, Caucasus, Sahel, Australia, Northeast Brazil, etc.). The correlation values obtained between lake surface water levels (height and area) or vegetation cover and key climatic parameters (watershed rainfall in this case) represent significant information regarding the degree of predictability of lake surface water levels or vegetation cover. In addition, as Global Climate Model (GCMs) are now increasingly regionalized, this study can help to improve the spatiotemporal quality of climate projections.

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As a limit of this research, the time period of this study was limited to the period 2000 to 2020. Therefore our results do not allowed investigating the important climatic changes that occurred in the Sahel during the great droughts, since satellite altimetry measurements for Lake Chad are available from 1992, MODIS data started in February 2000 and IMERG images in June 2000. For the study of

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different lakes around the world, the period could be longer depending on the availability of data, and
several other environmental variables (evaporation, temperature, human uses, river discharge, etc.)
could be integrated depending on the study case. Moreover, the current and future constellation of
satellite instruments (thanks to the Copernicus program in particular) will open opportunities to
perform long term survey of Lake Chad basin (and other basin as well). Another limitation of our
study is the lack of discharge measurements at the mouth of the Chari. It has led us to use the
discharge in N'Djamena and previous outcome (Coe and Birkett 2004) to determine the time lag
between the precipitation in the watershed and the water level changes on the Lake Chad. With the
future altimetry mission SWOT (Surface Water Ocean Topography) we aim to improve this estimation
since SWOT will provide worldwide river discharge and lake water storage changes (Rodriguez
2015).

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558 **5 Conclusion**

The vegetation cover changes of Lake Chad in this study were based on the vegetation spectral index of NDVI extracted from 481 MOD13Q1 satellite images from the MODIS TERRA sensor. For rainfall variations in the Lake Chad watershed, daily IMERG products from June 1, 2000, to December 31, 2020, were used and cumulated on 16-day timescales corresponding to the timescale of MOD13Q1. The monitoring of open water changes in Lake Chad was carried out using the Hydroweb databases, providing height and area values. We then determined the variance of lake surface water levels by computing the monthly anomaly time series of the surface water height and area. Using the Hanning temporal filter on MOD13Q1, IMERG, and Hydroweb data, the low-frequency signal representing the interannual variability of vegetation cover, rainfall, and surfaces water levels, was differentiated from the high-frequency signal containing seasonal and intra-seasonal variability. Through multivariate statistical analysis by EOF applied to MOD13Q1 and IMERG low/high-frequency signals, spatiotemporal variabilities at interannual and seasonal scales for vegetation cover and rainfall were then derived. To determine the relationship between rainfall and Lake Chad LULC variability, the
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seasonal signal of IMERG was correlated to that of NDVI and surface water levels based on a cross-correlation approach providing the lag of each correlation.

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The study outcomes showed a general trend of rainfall increasing from 2000 to 2020 over the entire Lake Chad watershed, explained by 35% of the interannual variability of rainfall. During the same period, there was also an overall increase in vegetation cover across the entire lake, explained by 31% of the interannual variability in NDVI of the lake. The low-frequency signals for the four studied parameters support their increasing trends because all slopes were positive i.e., 5.1 10⁻⁴ (mm/day); 1.2 10⁻³ (km²/day); 6 10⁻⁵ (m/day) and 4.26 10⁻⁶ (ndvi unit/day) for precipitation, surface water area, surface water height, and vegetation cover, respectively. The overlay of the trend curves clearly shows that rainfall variations in the watershed drive those of the LULC in the lake. Indeed, the rainfall trend was strongly and positively correlated with those of vegetation cover (0.79), surface water height (0.57), and area (0.53). Consequently, the increase in rainfall observed in the Sahel region since the 2000s has also occurred in the Lake Chad watershed, leading to an increase in water levels and an improvement in the vegetation cover of the lake. These results also demonstrated the resilience of this environment to climate variability.

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At seasonal scales, rainfall was inversely correlated with lake water levels variations, that is, -0.5 and -0.51 for area and height, respectively, and the maximum correlations (0.75 for area and 0.82 for height) were obtained with a response time of 112 days. The seasonality of rainfall and vegetation cover followed the same pattern, with a mean maximum correlation of 0.88 for a time lag of 16 days. In response to rainfall variations, the vegetation cover of the western shores of the lake and on both sides of the great barrier reacted with a time lag of less than 16 days. However, the vegetation cover in the bare soils of the northern basin and the eastern part of the south basin reacted with a 16-day delay. Concerning the marshlands of the southern basin, the reaction occurred for a minimum of 64 days. These results make it possible to anticipate vegetative growth with ~ 16 days in advance in emerging areas dominated by annual plants, (where pluvial agriculture is carried out) and with ~ 64 days in marshlands (where fishing and flood recession agriculture is practiced).

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599 Acknowledgments:

600 The project was funded by the ESA through the CCI program for the lake ECVs project. The authors
601 are grateful to the National Aeronautics and Space Administration (NASA) for the MODIS and
602 IMERG satellite images for direct access through the Land Processes Distributed Active Archive
603 Center (LP DAAC) and the Global Precipitations Measurements (GPM) website. The authors also
604 thank LEGOS and CLS for providing the altimetry database in a standard and suitable form via the
605 Hydroweb project. Thanks are given to the Direction des Ressources en Eau Ministère de
606 l'Hydraulique Rurale et Urbaine du Tchad for providing Chari-Logone discharge data.

607

608 Author Contributions:

609 Paul Gérard Gbetkom, Jean-François Crétaux, and Michel Tchilibou designed the study. Jean-François
610 Crétaux and Florence Sylvestre supervised the scientific development of this project. Paul Gérard
611 Gbetkom, Michel Tchilibou, Alice Carret, Manon Delhoume, and Muriel Bergé-Nguyen contributed
612 to data processing. Paul Gérard Gbetkom, Jean-François Crétaux, and Florence Sylvestre wrote the
613 manuscript, and all authors participated in the discussion of the results.

614 References

615

616 Albergel, J., Valentin, C., 1989. « Sahelisation » d'un petit bassin versant soudanien : kognere-boulsa
617 au Burkina-Faso, in: Bret, B. (Ed.), Les Hommes face aux sécheresses. Éditions de l'IHEAL,
618 pp. 179-190. https://doi.org/10.4000/books.iheal.1256

619 Ali, A., Lebel, T., 2009. The Sahelian standardized rainfall index revisited: THE SAHELIAN
620 STANDARDIZED RAINFALL INDEX REVISITED. Int. J. Climatol. 29, 1705-1714.
621 https://doi.org/10.1002/joc.1832
622 Andrieu, J., 2018. Analyse de la cinématique de la végétation en Côte d'Ivoire pendant les conflits, à travers une metrique phénologique et la correlation de Kendall de deux series temporelles de NDVI. Tropicultura 13.

625 Anyamba, A., Tucker, C.J., 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J. Arid Environ. 63, 596–614.
626 https://doi.org/10.1016/j.jaridenv.2005.03.007

628 Babama'aji, R.A., 2013. Impacts of Precipitation, Land use Land cover and Soil type on the Water Balance of Lake Chad Basin (Thesis).

630 Barbé, L.L., Lebel, T., 1997. Rainfall climatology of the HAPEX-Sahel region during the years 1950–1990. J. Hydrol. 188–189, 43–73. https://doi.org/10.1016/S0022-1694(96)03154-X

632 Begum, B., Tajbar, S., Khan, B., Rafiq, L., 2021. Identification of relationships between climate indices and precipitation fluctuation in Peshawar City-Pakistan 15.

634 Bergé-Nguyen, M., Crétaux, J.-F., 2015. Inundations in the Inner Niger Delta: Monitoring and Analysis Using MODIS and Global Precipitation Datasets. Remote Sens. 7, 2127–2151. https://doi.org/10.3390/rs70202127

637 Bouchez, C., Goncalves, J., Deschamps, P., Vallet-Coulomb, C., Hamelin, B., Doumnang, J.-C., Sylvestre, F., 2016. Hydrological, chemical, and isotopic budgets of Lake Chad: a quantitative assessment of evaporation, transpiration and infiltration fluxes. Hydrol. Earth Syst. Sci. 20, 1599–1619. https://doi.org/10.5194/hess-20-1599-2016

641 Buma, W., Lee, S.-I., Seo, J., 2018. Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE. Sensors 18, 2082. https://doi.org/10.3390/s18072082

643 Cissé, S., 2016. Etude de la variabilité intra saisonnière des précipitations au Sahel : impacts sur la végétation (cas du Ferlo au Sénégal) (phdthesis). Université Pierre et Marie Curie - Paris VI ; Université Cheikh Anta Diop de Dakar.

646 Coe, M.T., Birkett, C.M., 2004. Calculation of river discharge and prediction of lake height from satellite radar altimetry: Example for the Lake Chad basin. Water Resour. Res. 40. https://doi.org/10.1029/2003WR002543
649 Coe, M.T., Foley, J.A., 2001. Human and natural impacts on the water resources of the Lake Chad basin. J. Geophys. Res. Atmospheres 106, 3349–3356. https://doi.org/10.1029/2000JD900587

651 Cornet, A., Rambal, S., 1981. Simulation de l'utilisation de l'eau par une phytocénose de la zone sahélienne au Sénégal : test de deux modèles, in: Hydrologie, cycle de l'eau et informatique. Informatique et Biosphère, Paris, pp. 21–23; 156–178.

654 Crétaux, J., Jeliński, W., Calmant, S., Kouraev, A., Vuglinski, V., Bergé-Nguyen, M., Gennero, M., Niño, F., Rio, R.A., Cazenave, A., Maisongrande, P., 2011. SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data. https://doi.org/10.1016/J.ASR.2011.01.004

658 Crétaux, J.-F., Abarca-del-Río, R., Bergé-Nguyen, M., Arsen, A., Drolon, V., Clos, G., Maisongrande, P., 2016. Lake Volume Monitoring from Space. Surv. Geophys. 37, 269–305. https://doi.org/10.1007/s10712-016-9362-6

661 Dardel, C., Kergoat, L., Hiernaux, P., Mougin, E., Grippa, M., Tucker, C.J., 2014. Re-greening Sahel: 30years of remote sensing data and field observations (Mali, Niger). Remote Sens. Environ. 140, 350–364. https://doi.org/10.1016/j.rse.2013.09.011

664 Descroix, L., Diongue Niang, A., Panthou, G., Bodian, A., Sane, Y., Dacosta, H., Malam Abdou, M., Vandervaere, J.-P., Quantin, G., 2016. Évolution récente de la pluviométrie en Afrique de l'ouest à travers deux régions : la Sénégambie et le Bassin du Niger Moyen. Climatologie. https://doi.org/10.4267/climatologie.1105

668 Didan, K., Munoz, A.B., Huete, A., 2015. MODIS Vegetation Index User's Guide (MOD13 Series) 35.

670 Edea, E.O., Yabi, I., Orekan, V., BOKO, M., 2016. Variations saisonnière et interannuelle de NDVI mises en évidence à partir d'images MODIS 250 m dans le bassin versant de l'Ouémé Supérieur [ Seasonal and interannual variations of NDVI identified from MODIS 250 m in the catchment of Upper Ouémé ] 25, 52–66.

674 Eklundh, L., Olsson, L., 2003. Vegetation index trends for the African Sahel 1982–1999. Geophys. Res. Lett. 30. https://doi.org/10.1029/2002GL016772
676 Fall, A., 2014. Le Ferlo sénégalais : approche géographique de la vulnérabilité des anthroposystèmes sahéliens (Thèse de doctorat). Université Paris 13, France.

678 Gbetkom, P.G., 2020. Etudes des dynamiques spatiales d'évolution de l'occupation et de l'utilisation des sols dans la fenêtre lacustre camerounaise du lac Tchad et son arrière-pays à partir des grandes sécheresses sahéliennes de 1970. (Theses). Aix Marseille University.

681 Genesis, T.Y., Olsson, L., Tengberg, A., Mariano, G.-R., Zvoleff, A., 2016. Évaluer la dégradation des terres pour soutenir le développement durable en Afrique Contexte pour l'utilisation de l'outil de suivi de la dégradation des terres du FEM (FEM-LDMP).

684 Giglio, L., Schroeder, W., Hall, J.V., 2018. MODIS Collection 6 Active Fire Product User's Guide Revision B 64.

686 Gowan, T.A., Horel, J.D., 2020. Evaluation of IMERG-E Precipitation Estimates for Fire Weather Applications in Alaska. Weather Forecast. 35, 1831–1843. https://doi.org/10.1175/WAF-D-20-0023.1

689 Gressin, A., 2014. Mise à jour d'une base de données d'occupation du sol à grande échelle en milieux naturels à partir d'une image satellite THR (These de doctorat). Paris 5.

691 Guerrien, M., 2003. L'intérêt de l'analyse en composantes principales (ACP) pour la recherche en sciences sociales: Présentation à partir d'une étude sur le Mexique. Cah. Am. Lat. 181–192. https://doi.org/10.4000/cal.7364

694 Hattermann, T., Nicholls, K.W., Hellmer, H.H., Davis, P.E.D., Janout, M.A., Østerhus, S., Schlosser, E., Rohardt, G., Kanzow, T., 2021. Observed interannual changes beneath Filchner-Ronne Ice Shelf linked to large-scale atmospheric circulation. Nat. Commun. 12, 2961. https://doi.org/10.1038/s41467-021-23131-x

698 Herrmann, S.M., Anyamba, A., Tucker, C.J., 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate. Glob. Environ. Change 15, 394–404. https://doi.org/10.1016/j.gloenvcha.2005.08.004

701 Horion, S., Fensholt, R., Tagesson, T., Ehammer, A., 2014. Using earth observation-based dry season NDVI trends for assessment of changes in tree cover in the Sahel. Int. J. Remote Sens. 35, 2493–2515. https://doi.org/10.1080/01431161.2014.883104
704 Ickowicz, A., Garba, I., Toutain, B., Cesaro, J.-D., Gerber, P., Touré, I., 2014. Plaidoyer pour un

705 système d'information sur le pastoralisme au Sahel. Afr. Contemp. 249, 90.

706 https://doi.org/10.3917/afco.249.0090

707 Lamb, P.J., 1982. Persistence of Subsaharan drought. Nature 299, 46-48.

708 https://doi.org/10.1038/299046a0

709 Lemoalle, J., 2014. Le fonctionnement hydrologique du lac Tchad, in: Le développement du lac

710 Tchad: situation actuelle et futurs possibles, Expertise collégiale. IRD Éditions, Marseille, pp.

711 16-58 (sur la clé USB).

712 Lemoalle, J., Magrin, G. (Eds.), 2014. Le développement du lac Tchad: situation actuelle et futurs

713 possibles, Expertise collégiale. IRD éditions, Marseille.

714 L'Hôte, Y., Mahé, G., Somé, B., Triboulet, J.P., 2002. Analysis of a Sahelian annual rainfall index

715 from 1896 to 2000; the drought continues. Hydrol. Sci. J. 47, 563-572.

716 https://doi.org/10.1080/02626660209492960

717 Li, J., Carlson, B.E., Lacis, A.A., 2013. Application of spectral analysis techniques in the

718 intercomparison of aerosol data: 1. An EOF approach to analyze the spatial-temporal

719 variability of aerosol optical depth using multiple remote sensing data sets. J. Geophys. Res.

720 Atmospheres 118, 8640-8648. https://doi.org/10.1002/jgrd.50686

721 Lima-Quispe, N., Escobar, M., Wickel, A.J., von Kaenel, M., Purkey, D., 2021. Untangling the effects

722 of climate variability and irrigation management on water levels in Lakes Titicaca and Poopó.

723 J. Hydrol. Reg. Stud. 37, 100927. https://doi.org/10.1016/j.ejrh.2021.100927

724 Ma, M., Wang, H., Jia, P., Tang, G., Wang, D., Ma, Z., Yan, H., 2020. Application of the GPM-

725 IMERG Products in Flash Flood Warning: A Case Study in Yunnan, China. Remote Sens. 12,

726 1954. https://doi.org/10.3390/rs12121954

727 Magrin, G., Lemoalle, J., Pourtier, R., 2015. Atlas du lac Tchad. Passages.

728 Mahamat Nour, A., Vallet-Coulomb, C., Gonçalves, J., Sylvestre, F., Deschamps, P., 2021. Rainfall-

729 discharge relationship and water balance over the past 60 years within the Chari-Logone sub-

730 basins, Lake Chad basin. J. Hydrol. Reg. Stud. 35, 100824.

731 https://doi.org/10.1016/j.ejrh.2021.100824
732 Maihemuti, B., Aishan, T., Simayi, Z., Alifujiang, Y., Yang, S., 2020. Temporal Scaling of Water

733 Level Fluctuations in Shallow Lakes and Its Impacts on the Lake Eco-Environments.

734 Sustainability 12, 3541. https://doi.org/10.3390/su12093541

735 Marega, O., 2016. Changements socio-environnementaux et évolution des pratiques agropastorales en

736 Afrique sahélienne : étude comparée entre le Ferlo (Sénégal), le Gourma (Mali) et le Fakara

737 (Niger). (These de doctorat). Sorbonne Paris Cité.

738 Mayomi, I., Mukhtar, A., 2018. Geospatial Assessments of the Shrinking Lake Chad. ISSN 6, 17.

739 Murray, H., Khaki, M., 2020. Analysis of Surface Water Areal changes using Remote Sensing Data.

740 Adv. Environ. Eng. Res. 02, 1-1. https://doi.org/10.21926/aeer.2103019

741 Nagarajan, C., Pohl, B., Rüttinger, L., Sylvestre, F., Vivekananda, J., Wall, M., Wolfmaier, S., 2018.

742 Climate-Fragility Profile: Lake Chad Basin 32.

743 National Center for Atmospheric Research Staff, 2013. The Climate Data Guide: Empirical

744 Orthogonal Function (EOF) Analysis and Rotated EOF Analysis [WWW Document]. URL

745 https://climatedataguide.ucar.edu/climate-data-tools-and-analysis/empirical-orthogonal-

746 function-eof-analysis-and-rotated-eof-analysis (accessed 2.10.22).

747 Nhu, V.-H., Mohammadi, A., Shahabi, H., Shirzadi, A., Al-Ansari, N., Ahmad, B.B., Chen, W.,

748 Khodadadi, M., Ahmadi, M., Khosravi, K., Jaafari, A., Nguyen, H., 2020. Monitoring and

749 Assessment of Water Level Fluctuations of the Lake Urmia and Its Environmental

750 Consequences Using Multitemporal Landsat 7 ETM+ Images. Int. J. Environ. Res. Public.

751 Health 17, 4210. https://doi.org/10.3390/ijerph17124210

752 Nicholson, S.E., 1980. The Nature of Rainfall Fluctuations in Subtropical West Africa. Mon. Weather

753 Rev. 108, 473-487. https://doi.org/10.1175/1520-0493(1980)108<0473:TNORFI>2.0.CO;2

754 Olivry, J.-C., Chouret, A., Vuillaurrle, G., Lemoalle, J., Bricquet, J.-P., 1996. Hydrologie du lac

755 Tchad, Monographie hydrologique. Editions de l'ORSTOM, Paris.

756 Olsson, L., Eklundh, L., Ardö, J., 2005. A recent greening of the Sahel—trends, patterns and potential

757 causes. J. Arid Environ. 63, 556-566. https://doi.org/10.1016/j.jaridenv.2005.03.008
758

Ozer, P., Erpicum, M., Demarée, G., Vandiepenbeeck, M., 2003. The Sahelian drought may have ended during the 1990s. Hydrol. Sci. J. 48, 489–492.

759

760

https://doi.org/10.1623/hysj.48.3.489.45285

761

Panthou, G., Vischel, T., Lebel, T., 2014. Recent trends in the regime of extreme rainfall in the Central Sahel: RECENT TRENDS OF EXTREME RAINFALL IN THE WEST AFRICAN SAHEL.

762

763

Int. J. Climatol. 34, 3998–4006. https://doi.org/10.1002/joc.3984

764

Pesche, D., Losch, B., Imbernon, J., NEPAD Rural Futures Programme, CIRAD (Organization), 2016. Une nouvelle ruralité émergente: regards croisés sur les transformations rurales africaines.

765

766

Pham, B.-D., Sylvestre, F., Papa, F., Frappart, F., Bouchez, C., Crétaux, J.-F., 2020. The Lake Chad hydrology under current climate change. Sci. Rep. 10, 5498. https://doi.org/10.1038/s41598-020-62417-w

767

768

769

Pierre, C., Bergametti, G., Marticorena, B., Mougin, E., Lebel, T., Ali, A., 2011. Pluriannual comparisons of satellite-based rainfall products over the Sahelian belt for seasonal vegetation modeling. J. Geophys. Res. 116, D18201. https://doi.org/10.1029/2011JD016115

770

771

772

Polanco-Martínez, J.M., Fernández-Macho, J., Medina-Elizalde, M., 2020. Dynamic wavelet correlation analysis for multivariate climate time series. Sci. Rep. 10, 21277. https://doi.org/10.1038/s41598-020-77767-8

773

774

775

Raimond, C., Range, C., Guerin, H., 2014. La multi-activite et la multifonctionnalite, principes d'un développement durable pour le Lac ?, in: Le Développement Du Lac Tchad : Situation Actuelle et Futurs Possibles, Expertise Collégiale. IRD Éditions, Marseille, pp. 423–474.

776

777

778

Rim, Z., 2008. Etude géochimique et hydrodynamique de la nappe libre du bassin du lac Tchad dans les régions de Diffa (Niger oriental) et du Bornou (nord-est du Nigeria) (PhD Thesis). Université de Montpellier II.

779

780

781

Rosmorduc, Vayre, Taburet, 2021. Hydroweb Product User Manual 24.

782

Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS 309–317.

783

784

Sanogo, S., Fink, A.H., Omotosho, J.A., Ba, A., Redl, R., Ermert, V., 2015. Spatio-temporal characteristics of the recent rainfall recovery in West Africa: RECENT RAINFALL

785
786 RECOVERY IN WEST AFRICA. Int. J. Climatol. 35, 4589-4605.

787 https://doi.org/10.1002/joc.4309

788 Saouabe, T., El Khalki, E.M., Saidi, M.E.M., Najmi, A., Hadri, A., Rachidi, S., Jadoud, M., Tramblay,

789 Y., 2020. Evaluation of the GPM-IMERG Precipitation Product for Flood Modeling in a

790 Semi-Arid Mountainous Basin in Morocco. Water 12, 2516.

791 https://doi.org/10.3390/w12092516

792 Solano, R., Kamel, D., Andree, J., 2010. MODIS VI (MOD13) C5 User's Guide 42.

793 Takahashi, H., Lebsock, M., Luo, Z.J., Masunaga, H., Wang, C., 2021. Detection and Tracking of

794 Tropical Convective Storms Based on Globally Gridded Precipitation Measurements:

795 Algorithm and Survey over the Tropics. J. Appl. Meteorol. Climatol. 60, 403-421.

796 https://doi.org/10.1175/JAMC-D-20-0171.1

797 Tan, J., Huffman, G.J., Bolvin, D.T., Nelkin, E.J., 2019. IMERG V06: Changes to the Morphing

798 Algorithm. J. Atmospheric Ocean. Technol. 36, 2471-2482. https://doi.org/10.1175/JTECH-

799 D-19-0114.1

800 Toumazou, V., Crétaux, J.-F., 2001. Using a Lanczos Eigensolver in the Computation of Empirical

801 Orthogonal Functions. Mon. Weather Rev. 129, 1243-1250. https://doi.org/10.1175/1520-

802 0493(2001)129:1243:UALEIT>2.0.CO;2

803 Tra Bi, Z.A., Bi, Z.K., Yao Brou, T., Mahé, G., Emran, A., 2013. Télédetection et analyse statistique

804 de la dynamique de la végétation dans un contexte de variabilité climatique sur le bassin

805 versant du Bouregreg (Maroc), in: Boegh, E., Blyth, E., Hannah, D.M., Hisdal, H.,

806 Kunstmann, H., Su, B., Yilmaz, K.K. (Eds.), Climate and land surface changes in hydrology :

807 proceedings of H01, IAHS-IAPSO-IASPEI assembly, Publication - AISH. AISH,

808 Wallingford, pp. 403-410.

809 Tucker, C.J., Dregne, H.E., Newcomb, W.W., 1991. Expansion and contraction of the sahara desert

810 from 1980 to 1990. Science 253, 299-300. https://doi.org/10.1126/science.253.5017.299

811 Tucker, C.J., Nicholson, S.E., 1999. Variations in the Size of the Sahara Desert from 1980 to 1997.

812 Ambio 28, 587-591.
813 Tucker, C.J., Vanpraet, C.L., Sharman, M.J., Van Ittersum, G., 1985. Satellite remote sensing of total
814 herbaceous biomass production in the senegalese sahel: 1980–1984. Remote Sens. Environ.
815 17, 233–249. https://doi.org/10.1016/0034-4257(85)90097-5

816 Vischel, T., Lebel, T., Panthou, G., Quantin, G., Rossi, A., Martinet, M., 2015. Chapitre 2. Le retour
d'une période humide au Sahel ?, in: Sultan, B., Lalou, R., Amadou Sanni, M., Oumarou, A.,
817 Soumaré, M.A. (Eds.), Les sociétés rurales face aux changements climatiques et
818 environnementaux en Afrique de l'Ouest. IRD Éditions, pp. 43–60.
819
820 https://doi.org/10.4000/books.irdeditions.8937

821 Yue, S., Pilon, P., 2004. A comparison of the power of the t test, Mann-Kendall and bootstrap tests for
trend detection / Une comparaison de la puissance des tests t de Student, de Mann-Kendall et
du bootstrap pour la détection de tendance. Hydrol. Sci. J. 49, 21–37.
822
823
824 https://doi.org/10.1623/hysj.49.1.21.53996

825 Zhao, Y., Li, F., Yao, R., Jiao, W., Hill, R.L., 2020. An Empirical Orthogonal Function-Based
Approach for Spatially- and Temporally-Extensive Soil Moisture Data Combination. Water
826 12, 2919. https://doi.org/10.3390/w12102919
827

828 Zhu, W., Yan, J., Jia, S., 2017. Monitoring Recent Fluctuations of the Southern Pool of Lake Chad
Using Multiple Remote Sensing Data: Implications for Water Balance Analysis. Remote Sens.
829 9, 1032. https://doi.org/10.3390/rs9101032
830

831 Zoungrana, B.J.-B., Conrad, C., Thiel, M., Amekudzi, L.K., Da, E.D., 2018. MODIS NDVI trends and
fractional land cover change for improved assessments of vegetation degradation in Burkina
832 Faso, West Africa. J. Arid Environ. 153, 66–75. https://doi.org/10.1016/j.jaridenv.2018.01.005
833

834
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

### Trends

* Increase of the lake Chad surface water (height and area) and vegetation cover from 2000 to 2020

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

### Response time to rainfall changes in the watershed

* 112-day lag in Lake Chad surface water

* 16-day for plant growth in pluvial agriculture of the lake

* 64-day for plant growth in flood recession agriculture of the lake