# Monitoring bacterial contamination of West African surface waters using Earth observation data and machine learning methods

Marc-Antoine Mant, Elodie Robert, Hedwige Nikiema, Moussa

Boubacar-Moussa, Amadou Abdourhamane-Toure, Beatriz M Funatsu, Manuela Grippa, Laurent Kergoat, Javier Perez-Saez, Hugo Pradalier, et al.

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To cite this version:

Marc-Antoine Mant, Elodie Robert, Hedwige Nikiema, Moussa Boubacar-Moussa, Amadou Abdourhamane-Toure, et al. Monitoring bacterial contamination of West African surface waters using Earth observation data and machine learning methods. Environmental Advances, 2025, 22, pp.100678. 10.1016/j.envadv.2025.100678. hal-05392839

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# Monitoring bacterial contamination of West African surface waters using Earth observation data and machine learning methods

Marc-Antoine Mant<sup>a,*</sup>, Elodie Robert<sup>a</sup>, Hedwige Nikiema<sup>b</sup>, Moussa Boubacar-Moussa<sup>a</sup>, Amadou Abdourhamane-Toure<sup>c</sup>, Beatriz M. Funatsu<sup>a</sup>, Manuela Grippa<sup>d</sup>, Laurent Kergoat<sup>d</sup>, Javier Perez-Saez<sup>e</sup>, Hugo Pradalier<sup>d</sup>, Emma Rochelle-Newall<sup>f</sup>, Marc Robin<sup>a</sup>

## ARTICLE INFO

**Keywords:**

*E. coli*

Remote sensing

Water quality

Modeling

Machine learning

## ABSTRACT

In 2021, diarrheal diseases caused approximately 434,000 deaths in sub-Saharan Africa, mainly due to water contamination by fecal pathogens such as *Escherichia coli*. While environmental conditions and human activities are known to influence bacterial contamination in surface waters, their respective impacts remain poorly quantified, complicating efforts to model this health risk. In this context, developing approaches to monitor contamination without relying solely on field-based analyses has become increasingly important. This study explores the potential of Earth observation (EO) data to monitor bacterial contamination in surface waters in West Africa. It investigates the relationship between *E. coli* concentrations and various water quality parameters, some measured in situ (water quality parameters, suspended particulate matter (SPM), etc.), and others derived from EO data (rainfall, specific humidity, etc.). These variables are integrated into eight machine learning models capturing the temporal dynamics of *E. coli* concentrations in two sites: the Bagré reservoir (Burkina Faso) and Kongou Lake (Niger). Using all available variables, ensemble tree-based models provided the best predictive performance for both sites. When using only EO variables, these models maintained good performance, achieving R² values of approximately 0.7 for Kaporé and 0.65 for Kongou. For both sites, the variables playing the strongest role are SPM (or NIR band) and rainfall, to which are added, for Kaporé, air humidity and NDVI. These results demonstrate the feasibility of using EO data alone to monitor *E. coli* contamination in West African surface waters. This approach can enable remote monitoring of microbial water quality.

## 1. Introduction

In 2021, diarrheal diseases were responsible for around 1.17 million deaths worldwide, including 340,000 children under 5 and 498,000 elderly people (Kyu et al., 2025). Despite a 60 % reduction in mortality since 1990, particularly among children (–80 %) due to improved sanitation, drinking water, healthcare, and vaccination (Kasumba et al., 2023; Kyu et al., 2025), developing rural regions in sub-Saharan Africa and South-East Asia are the areas most affected by these diseases. In 2021, these regions accounted for 86 % of global diarrheal deaths

(Muhsen et al., 2025), with sub-Saharan Africa alone recording 434,000 fatalities, including 246,000 children under five (Kyu et al., 2025). This persistent high mortality is driven by widespread bacteriological contamination of surface waters by pathogenic micro-organisms responsible for diarrheal diseases (*Enterotoxigenic Escherichia coli*, *Salmonella spp.*, *Shigella spp.*, Cryptosporidium spp., etc.), exacerbated by inadequate sanitation infrastructure (WHO and UNICEF, 2023) and high surface runoff during rainfall episodes allowing the transport of fecal bacteria and suspended particulate matter (SPM) from the soil surface to downstream surface waters (Jamieson et al., 2005; Moussa Boubacar

<footnote><sup>a</sup> LETG-Nantes (UMR 6554), CNRS, Nantes University, 44312 Cedex 3 BP 81227, France</footnote>

<footnote><sup>b</sup> LERMIT, University Joseph Ki-Zerbo, Ouagadougou, Burkina Faso</footnote>

<footnote><sup>c</sup> Abdou Moumouni University, Faculty of Science and Technology (FAST), Department of Geology, Niamey PB 10662, Niger</footnote>

<footnote><sup>d</sup> Géosciences Environnement Toulouse (GET, UMR 5563), Paul Sabatier University Toulouse III, CNRS, IRD, CNES, France</footnote>

<footnote><sup>e</sup> Population Epidemiology Unit (UEP), University Hospitals of Geneva (HUG), Geneva, Switzerland</footnote>

<footnote><sup>f</sup> Sorbonne University, Univ Paris Est Creteil, IRD, CNRS, INRAE, Institute of Ecology and Environmental Sciences of Paris (iEES-Paris), 4 Place Jussieu, Paris 75005, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: marc-antoine.mant@etu.univ-nantes.fr (M.-A. Mant), Elodie.Robert@univ-nantes.fr (E. Robert).</footnote>
et al., 2022; Nakhle et al., 2021). In addition to facilitating transport, SPM can contribute to the survival and growth of pathogens in water, providing protection from predators and the sun's UV rays (Mezrioui & Baleux, 1992; Petersen et al., 2020; Rochelle-Newall et al., 2016). Additionally, high reliance on untreated surface water, used by 73.11 million people in sub-Saharan Africa in 2022, poor hygiene practices (WHO and UNICEF, 2023), limited healthcare access, and socio-political instability limiting access to drinking water, contribute to the risk of diarrheal disease (Dos Santos et al., 2017).

Hydro-climatic factors such as temperature, surface runoff, humidity, precipitation, soil moisture, and solar radiation significantly influence bacterial dynamics (Chao et al., 2019; Colston et al., 2022; Dongzagla et al., 2021; Islam et al., 2017; Lemaitre et al., 2019; Robert et al., 2021) and diarrheal disease incidence (Carlton et al., 2014, 2016; Chua et al., 2024). The extent of these influences depends, however, on the pathogen types and the presence or absence of adequate water and sanitation infrastructures. In addition, climate change is expected to degrade water quality, promoting pathogen spread and increasing diarrheal disease risk, with a projected rise of 22 % to 29 % in tropical and subtropical regions by 2070-2099 (Kolstad et al., 2011).

Escherichia coli (E. coli) is often used as a fecal indicator bacteria (FIB) due to its close association with fecal contamination in temperate regions (Edberg et al., 2000; Jang et al., 2017). Its monitoring is recommended by the World Health Organisation (WHO) to assess water quality and the risk of diarrheal diseases (Odonkor et al., 2020). Nevertheless, the relationship between E. coli and pathogens is not always clear. Studies have shown that no single indicator can predict the presence of all pathogens in water (Wu et al., 2011). However, the detection of E. coli generally indicates fecal contamination and therefore a potential health risk. In Burkina Faso, Robert et al. (2021) found a significant correlation between E. coli, intestinal enterococci and cases of diarrhea. E. coli therefore also appears to be useful as FIB in West Africa, and a relevant parameter for predicting diarrheal disease.

A few studies have focused on the links between E. coli and the environment in tropical zones (Boithias et al., 2022; Nakhle et al., 2021; Robert et al., 2021; Rochelle-Newall et al., 2016; Moussa Boubacar et al. 2025). Moussa Boubacar et al. (2025) demonstrated that in Niamey, E. coli concentrations peak early in the rainy season, preceding the maximum SPM values, reflecting distinct sources: urban areas along the Niger River contribute most to bacterial contamination, while SPM is mainly supplied by upstream tributaries. Moreover, Nakhle et al. (2021) reported that in the Mekong River and its tributaries in Laos, E. coli concentrations are higher during the rainy season, coinciding with increased turbidity and SPM concentrations. The correlations between E. coli, turbidity and SPM concentrations highlight the role of sediment transport in the dispersal of fecal bacteria, particularly in areas subject to soil erosion, and underline the impact of land use on FIB dynamics (Boithias et al., 2022; Rochelle Newall et al., 2016).

Besides, some studies have explored traditional approaches based on classical regression methods to model E. coli concentration in tropical waters from independent variables (Wang et al., 2018), but their effectiveness is limited by the complexity of microbiological-environmental interactions. Machine learning (ML) models offer a more advanced approach, particularly in temperate zones (Nafsin et al., 2023; Sokolova et al., 2022; Stocker et al., 2022; Tselemponis et al., 2023) because they can analyze complex, non-linear data structure. However their application in tropical zones remains marginal (Mallik et al., 2025). In addition, most studies using these models are based solely on in-situ data, which limits their spatio-temporal scope. Given the scarcity of in situ data in sub-Saharan Africa, tele-epidemiology, which combines Earth observation (EO) data (satellite observations, reanalyzes, measuring stations, etc.) and epidemiological data, makes it possible to analyze climate-environment-health interactions and monitor pathogen distribution without the need for field measurements. Some studies have demonstrated the contribution of remote sensing to the study of microbial contaminants (Hong et al., 2024; Robert et al., 2021). Robert

et al. (2021), for example, developed a partial least squares (PLS) regression model to model E. coli concentration in the Bagré reservoir (Burkina Faso) based on precipitation estimated by IMERG algorithm, normalized difference vegetation index (NDVI), and Sentinel-2 reflectance (NIR band). Despite these advances, few studies have used remote sensing data, and even fewer have integrated ML to model the spatio-temporal dynamics of pathogens in tropical environments. Yet the synergistic use of ML and remote sensing would represent a powerful asset for improving the modeling of microbiological contamination, refining forecasts and improve water management policies, that are particularly crucial in sub-Saharan regions.

In this context, the present study has three main objectives, each focusing on a comparative analysis of two lakes located in different climate zones: one in a sub-humid zone (Bagré reservoir in Burkina Faso) and one in a semi-arid zone (Kongou Lake in Niger).

1. After selecting the most appropriate satellite-based precipitation estimation product, identify and compare the key environmental parameters influencing E. coli dynamics in both lakes.

2. Model E. coli dynamics using machine learning methods based on in-situ and Earth observation data, while assessing differences between the two sites.

3. Evaluate the potential of an approach relying solely on Earth observation data for health hazard monitoring, comparing its effectiveness across both study sites in the absence of in-situ data

## 2. Material and methods

### 2.1. Study sites

Bagré is located in the Boulgou Province, in Centre-East region in Burkina Faso (Fig. 1A). It is home to the Bagré reservoir, located on the Nakambé River, which drains a catchment area of 40,836 km² (Fig. 1B). The Bagré reservoir is a hydroelectric dam used by the local rural population for food and cash crops, including market gardening and irrigation, as well as livestock farming (Robert, 2011).

The climate of the Bagré region is tropical north Sudanian (Aw according to the Köppen classification, Kottek et al., 2006). It is thus characterized by two distinct seasons. The dry season for 7 months (mid-October to mid-may), is marked by the presence of the Harmattan (warm north-easterly wind). During this period, the quantity of aerosols (mineral dust) in the atmosphere is very high, while the atmospheric water vapor load is very low. The rainy season for 5 months (from mid-May to mid-October), is characterized by heavy convective precipitation, resulting from the major squall lines of the West African monsoon (Dunning et al., 2016). Vegetation cover, consisting mainly of low grass and shrubs (Figure 1B, Zanaga et al. 2022), is generally sparse, particularly at the start of the rainy season, making soils prone to erosion by water runoff. Water quality measurements were carried out in the northern part of the reservoir, more precisely in Kaporé (Fig. 1C). This rural area has a low population density (87 inhabitants/km² in 2019, INSD, 2020) with predominantly agricultural and secondly livestock activities.

The Kongou site is located in south-western Niger, near the capital Niamey, in Tillabéri region (Fig. 1A). It includes a mostly fossil tributary of the Niger River, which no longer flows permanently but remains an important area for livestock grazing and agriculture by the local population. The site, located in Niamey (Niger), experiences a hot semi-arid climate (Bsh according to the Köppen classification, Kottek et al., 2006) marked by a long dry season (8 months) extending from October to late May, and a short but intense rainy season from June to late September. Rainfall during the wet season is often driven by convective storms associated with the West African monsoon. Vegetation cover is very sparse (Fig. 1B, Zanaga et al. 2022), even during the rainy season, which promotes intense surface runoff. Water quality measurements were conducted at this rural site (Fig. 1C).
<figure><img src="image_4.png" /><figcaption>Fig. 1. Study site locations: (A) location at the national scale, (B) dominant land cover types, and (C) positions of water sampling stations.</figcaption></figure>

## 2.2. Data used in the study

### 2.2.1. *In-situ water quality data*

At Kaporé (Bagré reservoir), in situ water quality measurements began in March 2018 for *E. coli* and SPM concentrations, in July 2020 for pH, dissolved oxygen, conductivity and water temperature, in April 2022 for particulate organic carbon (POC), and in May 2022 for dissolved organic carbon (DOC) (Table S1, DOI associated). The frequency of measurements has evolved over the years: weekly between March 2018 and May 2020, to daily during two months of the rainy season (mid-June to mid-August 2020-2024). Between May 2020 and April 2024, readings were taken every 5 days at the beginning and end of the rainy season, and every 10 days in the dry season. This variability in temporal sampling is due to the high reactivity of the system, particularly at the start of the rainy season, when *E. coli* and SPM concentrations can fluctuate rapidly from one day to the next (Robert et al., 2021).

At Kongou, measurements began in June 2022 for all parameters, with a similar frequency: daily monitoring during two months of the rainy season, every 5 days at the beginning and end of the rainy season, and every 10 days in the dry season. However, between late July and mid-October 2023, no water quality measurements were carried out due

to political insecurity (Table S2, DOI associated).

At both sites, water quality parameters (conductivity, pH, temperature and dissolved oxygen) were measured directly *in situ* using a multi-parameter instrument (WTW Multi-parameter portable meter Multi-Line® Multi 3620 IDS SET F). The other measurements (SPM, *E. coli*, DOC and POC concentrations) were determined from a sample collected just below the water surface using a 500 mL plastic bottle. The samples were placed in an isothermal bag in the field, then taken to a laboratory for sample processing within 1 hour.

*E. coli* concentrations were determined using the standardized microplate method (NF EN ISO 9308-3) (Jarvis et al., 2010). Each sample was incubated at four dilution rates (i.e. 1:2, 1:20, 1:200 and 1:2000 with a volume of 150 µl per well in Kaporé and 100 µl per well in Kongou) in a microplate with 96 wells (MUG/EC, BIOKAR DIGNOSTICS) and incubated for 48 h at 44°C. Ringer's lactate solution was used for dilutions and one plate was used per sample. The number of positive wells for each microplate was noted and the most probable number (MPN) was determined using the Poisson distribution. MPN CALCULATOR software was used to convert between the number of wells detected and the MPN/100 ml *E. coli* values. Due to differences in dilution volumes, the detection limit is 50 MPN/100 ml for the Burkina
Faso site and 75 MPN/100 ml for the Niger site. Values below these limits were assigned a value of 25 MPN/100 ml and 37.5 MPN/100 ml respectively. This method has already been used successfully in the study region (Robert et al., 2021; Moussa Boubacar et al., 2025) and in other tropical environments (Boithias et al., 2022; Nakhle et al., 2021).

SPM concentrations were measured by water filtration through glass fiber filters (Whatman GF/F 0.7 µm nominal pore size (Moussa et al., 2022; Robert et al., 2021). Prior to filtration, filters were pre-weighed after oven drying at 105°C for 1 h 30 min. After sample filtration, the filters were dried and reweighed under the same conditions. SPM concentrations were calculated as the difference between the dry weight after and before filtration, divided by the volume of filtered water (50 or 100 ml depending on the time of year).

POC was measured by combustion at 950°C of a SPM filter under neutral gas flow (helium) with a FlashSmart ThermoScientific. Furthermore, the filtrate (from filtration for SPM) was poured into an amber bottle to which a drop of phosphoric acid ($H_3PO_4$) was added to remove the inorganic carbon. The bottle was then wrapped in aluminum foil and stored at 4°C. DOC was then analyzed using Shimadzu TOC-VCSN.

### 2.2.2. Multi-satellite data and derived products

Surface reflectance data measured by Sentinel-2 MSI were retrieved from March 13, 2018 to April 14, 2024 for Kaporé, and from June 19, 2022 to September 9, 2024 for Kongou, via Theia (https://theia.cnes.fr/atdistrib/rocket/#/home) at L2A level, i.e., already corrected for atmospheric effects by the Maccs-Atcor Joint Algorithm (MAJA) (Hagolle et al., 2017). The corresponding tile for Kaporé is T30PYT, and for Kongou it is T31PDR. We used data from bands B3, B4, and B8 at 10 m resolution, and from bands B11 and B12 at 20 m resolution, with a temporal resolution of 5 days.

Satellite precipitation estimation products use observations from several meteorological satellites to calculate cumulative precipitation over a given area, using specific algorithms. Among these products, Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) Final Run (Huffman et al., 2023 – GPM_3IMERGDF v07), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS-2.0) (Funk et al., 2015), Global Satellite Mapping of Precipitation Near-Real-Time product (GSMAp_NRT) (Kubota et al., 2020), CMORPH Climate Data Record (CRD), and Global Land Data Assimilation System (GLDAS_NOAH025_3H_V2.1 – Total precipitation rate) provide rainfall estimates. For this study, daily precipitation data were extracted from these products. Their spatial resolutions are approximately 5 km for CHIRPS, 10 km for IMERG and GSMAp, 8 km for CMORPH, and 27 km for GLDAS (Table S3). These products were compared with each other in sub-humid and semi-arid zones to identify the most suitable dataset for each climatic zone (as shown in Section 2.3.1).

The Global Land Data Assimilation System is a set of historical daily estimates of hydrometeorological variables derived from satellite observations, model-based reanalyses and in-situ data (Rodell et al., 2004). In addition to precipitation, seven daily variables from GLDAS (spatial resolution ~27 km), aggregated over the catchment area, were retrieved from Giovanni (https://giovanni.gsfc.nasa.gov/giovanni/) covering the period from March 2018 to April 2024 for Kaporé and from June 2022 to September 2024 for Kongou (Table S3). These variables are: specific air humidity (kg water/kg air, 3 h), surface air pressure (Pa, 3 h), mean surface wind (m/s, 3 h), surface runoff (kg/m²/s, 1d), net shortwave radiation (W/m², 1d), surface soil moisture (kg water/m², 1d), air temperature (Kelvin, 3 h).

## 2.3. Methodology

Sentinel-2 images corresponding to the water sampling dates, with a tolerance of ±2 days, were retrieved. Images showing clouds over the sampling area were discarded. A sub-catchment encompassing the water

sampling area was then selected (Fig. 1C) to investigate the influence of environmental factors, such as precipitation, vegetation cover, and humidity, on water quality. This sub-catchment was derived from the HydroBASINS Level 11 dataset (Version 1.c) (Lehner et al., 2013).

### 2.3.1. Precipitation estimation product selection for the study

Rainfall can be estimated using satellite-based algorithms, which differ in methodology and accuracy. Given its importance in the transport and dissemination of pathogens in surface waters, the most relevant precipitation estimation product for our study area needs to be selected.

In this context, a study was carried out to compare the suitability of several rainfall estimation algorithms in Burkina Faso and Niger (presented in section 2.2.2). Each algorithm was evaluated by comparing it with measurements from rain gauges located close to the study sites. Daily rainfall totals derived from satellite data were compared with daily rain gauge measurements, and a monthly analysis was carried out over the entire available period to assess the performance of the various products.

In Burkina Faso, satellite estimates were compared with data from a rain gauge located in Ouagadougou, around 150 km from the study site. Data were retrieved from the Trans-African Hydro-Meteorological Observatory (TAHMO, Van de Giesen et al., 2014) platform (https://tahmo.org/) over the period from April 29, 2017 to August 30, 2023. For the IMERG Final Run, GSMAp_NRT and CHIRPS-2.0 products, the rain gauge was located at the intersection of four pixels, and these pixels were averaged.

In Niger, satellite estimates were compared with data from two rain gauges located in Wankama, around 30 km from the Kongou site and 3 km apart. The presence of two rain gauges in close proximity ensures continuity of data collection, even in the event of failure of one of the instruments. These data were retrieved via the AMMA-CATCH observatory (Analyse Multidisciplinaire de la Mousson Africaine - Couplage de l'Atmosphère Tropicale et du Cycle Hydrologique, https://amma-catch.osug.fr/, Galle et al., 2018) for the period April 9, 2013 to December 31, 2022. For each day, the average of the two rain gauges was used. For the CMORPH CDR and CHIRPS-2.0 products, as the rain gauge is located at the intersection of two pixels, these pixels were averaged.

To assess the performance of the various satellite products to estimate daily precipitation, several indicators were calculated over the entire study period. These include false positive rate, probability of detection, root mean square error (RMSE), and mean absolute error (MAE) for each month of the year (Table S4). To select the best product, we took all these parameters into account, with particular emphasis on the rainy season, as this is a critical period for *E. coli* contamination.

### 2.3.2. Correlations between environmental variables and *E. coli* concentration by site

As meteorological conditions have a delayed effect on the spatiotemporal dynamics of pathogens (Colston et al., 2022; Wang et al., 2018), hydrometeorological variables were aggregated over different moving time windows, ranging from 1 to 8 days prior to the water sampling date. Finally, a time window of 5 days (5d) was selected for Kaporé and 8 days (8d) for Kongou, as these durations presented the best correlations between precipitation and *E. coli* concentration. Precipitation and surface runoff were considered as total accumulated amounts averaged over the watershed, whereas other GLDAS variables were used as 5-day (Kaporé) or 8-day (Kongou) moving averages, also averaged across the watershed area. This approach makes it possible to analyze the temporal relationships between *E. coli* concentration at a given time T and the hydrometeorological conditions of the preceding days (T - 5 days or T - 8 days).

Vegetation cover present in the catchment area was estimated by remote sensing using the NDVI index (Huang et al., 2021) based on Sentinel-2 data. For each date on which a Sentinel-2 image was available, the mean NDVI index was calculated within the selected
sub-watershed, after masking out water pixels (MNDWI > 0; Du et al., 2016).

Correlations between *E. coli* concentration and environmental and water quality parameters were calculated using Spearman's rank correlation method. This method was chosen because the relationships between variables are not necessarily linear, and Spearman's method allows us to capture monotonic relationships, providing a better understanding of which parameters may have a causal or non-causal effect on *E. coli* concentrations. Only statistically significant correlations (p-value ≤ 0.05) after application of the Bonferroni (Heumann, 2015) correction are presented. This correction ensures that reported p-values are less likely to reflect false positives, when dealing with a large number of correlations.

### 2.3.3. Modeling *E. coli* concentration using machine learning methods by site

To model the temporal evolution of *E. coli* concentrations, several machine learning algorithms were explored to capture non-linear relationships between environmental variables and microbial contamination (Table S5). The models tested included tree-based regression methods such as Decision Trees, Random Forests, and Extremely Randomized Trees (Breiman, 2001; Geurts et al., 2006; Quinlan, 1992), as well as boosting algorithms including Gradient Boosting, XGBoost and AdaBoost (Chen et al., 2016; Freund et al., 1997; Friedman, 2001). In addition, non-tree-based approaches such as Support Vector Regression (SVR) (Smola and Schölkopf, 2004) and k-Nearest Neighbors (KNN) (Altman, 1992) were also evaluated. The *E. coli* estimation models were trained using the same set of explanatory variables at both study sites (Kaporé and Kongou), enabling a comparison of variable importance between the different hydrosystems and climate.

The first model combined in-situ measurements and Earth observation data. In-situ variables included SPM, water temperature, pH, dissolved oxygen, and water conductivity. POC and DOC were excluded for the models, as their measurements only became available from April or May 2022 at Kaporé. Earth observation variables included Sentinel-2 reflectance in the near-infrared (Band 8-8A) bands, usually used to estimate SPM in turbid waters (Papa et al., 2023), as well as NDVI. Other predictors were meteorological and hydrological variables from the GLDAS dataset or IMERG product (section 3.1): atmospheric pressure, precipitation (IMERG), specific humidity, surface runoff, shortwave radiation, soil moisture, air temperature and wind speed. The second model relied solely on Earth observation data to assess their potential for estimating *E. coli*.

Cloud cover during the rainy season often prevented Sentinel-2 acquisitions, resulting in significant data gaps: over 70 % of observations were missing for band 8-8A and NDVI at both sites. This high rate of missing data is also due to the fact that Sentinel-2 has a revisit time of 5 days, whereas *E. coli* measurements were collected daily during the rainy season. For in-situ data, there were few missing values in Kaporé, but a few more in Kongou, where political instability prevented data collection, resulting in up to 20 % missing values. To fill these gaps, linear interpolation was applied (Table S6). According to Zhong et al. (2023), linear interpolation is an effective method in machine learning frameworks, particularly for reconstructing missing values without actually distorting the data structure. In our case, despite the importance of missing data, this method was deemed appropriate for band 8-8A, as these bands are essential for estimating SPM in turbid waters, an important factor in *E. coli* dynamics (Figures S7 and S8). The same interpolation was carried out for NDVI, which tends to vary in a relatively linear fashion and allows us to characterize vegetation and land surface conditions that influence water quality.

At Kaporé, the full model (in-situ + EO data) was trained on data from July 20, 2020 (start of measurements of water quality parameters) to August 4, 2023 (end of these measures), while the Earth observation-only model extends from March 13, 2018 to April 14, 2024. In Kongou, the full model was trained on data from June 19, 2022 to July 23, 2024

(end of these measures), while the Earth observation-only model extends from June 19, 2022 to September 14, 2024.

To evaluate algorithm performance, 10-fold cross-validation (Stone, 1974) was used. The dataset was first randomly shuffled, then divided into ten equally sized subsets for 10-fold cross-validation. In each iteration, nine of the ten subsets (or 90 % of the data) were used to train the model, while the tenth (the remaining 10 %) was used to validate it. This process was repeated ten times, each time changing the subset used for validation, so that each observation was used exactly once for validation and nine times for training. In addition, the explanatory variables were standardized (centered and reduced) using the Python module StandardScaler, to ensure comparable scaling between them and to improve the performance of scale-sensitive algorithms. The GridSearchCV technique (https://scikit-learn.org/stable/modules/generated/sklearn.grid_selection GridSearchCV.html) was applied to find optimal hyperparameters for each machine learning models, through an exhaustive search of a specified parameter grid. From the validation data, three metric scores were calculated to assess the models' performance in modeling *E. coli* (expressed as log₁₀ MPN): root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination R² (Table S9). In addition, we performed a Friedman test (Friedman, 1940) on all models for the three evaluation measures (R², RMSE, MAPE). The Friedman test was chosen because it is a non-parametric test specifically designed to compare multiple algorithms evaluated on the same data partitions, making it more appropriate than multiple independent pairwise tests (Trawiński et al., 2012). Since the Friedman test only indicates whether there is a significant difference between the models, but does not identify which models differ, we performed a post-hoc Nemenyi test (Nemenyi, 1963). This allowed us to determine which pairs of models had statistically significant differences in performance.

All data were processed in Python 3.13 under the Spyder 6.0.5 development environment (IDE).

## 3. Results

### 3.1. Precipitation estimation product selection for the study

For RMSE and MAE, which measure the difference between observed and estimated data, IMERG outperforms the other products at both sites (Figures S10, S11). In terms of detection probability, IMERG maintains a high level, exceeding 75 % over the whole year, reflecting its ability to correctly detect precipitation every month of the year. This performance is similar to that of other products. IMERG also has one of the lowest false positive rates, although CMORPH achieved better results for this measure. GSMAP has a significantly higher false positive rate in Burkina Faso. IMERG was therefore selected for this study.

### 3.2. Correlations between environmental variables and *E. coli* concentration by site

In Kaporé, *E. coli* concentration shows strong correlations with SPM (Spearman rs = 0.81), POC (rs = 0.74), and band B8A (rs = 0.61) (Fig. 2A). Similar patterns are observed in Kongou (Fig. 2B), with correlations of 0.73 (SPM), 0.71 (POC), and 0.71 (band B8). Hydrometeorological variables, such as cumulative rainfall and air humidity, are positively correlated with *E. coli* at both sites (cumulative rainfall: 0.64 in Kaporé, 0.70 in Kongou; humidity: 0.53 and 0.43 respectively). Conversely, on the one hand, dissolved oxygen is negatively correlated with *E. coli* at both sites (-0.45 at Kaporé, -0.44 at Kongou), and on the other hand, radiation is negatively correlated at Kaporé (rs = -0.44), and weakly at Kongou (rs = -0.29). Furthermore, soil moisture and NDVI show moderate correlations only at Kaporé (rs = 0.46 and 0.47). Water conductivity presents a strong negative correlation at Kaporé (rs = -0.59), but is not significant at Kongou. No strong correlations are found with atmospheric pressure, wind speed, water temperature, pH, or mean
<figure><img src="image_5.png" /><figcaption>Fig. 2. Significant ($p < 0.05$) Spearman correlations between parameters in Kaporé (Burkina Faso) and Kongou (Niger).</figcaption></figure>

air and surface temperatures. Spearman correlations also reveal multi-collinearity among some variables.

During the dry season, *E. coli*, SPM, and POC concentrations remain low and stable, showing little variation (Fig. 3B; Fig. 4B). Moreover, net solar radiation is high and constant (Fig. 3A; Fig. 4A). However, *E. coli* concentrations are higher at Kapore (average: 476 MPN/100 ml, max: 44 000 MPN/100 ml, median: 50 MPN/100 ml) than at Kongou (average: 397 MPN/100 ml, max: 1 100 MPN/100 ml, median: 330 MPN/100 ml), but SPM are higher at Kongou (average: 0.9 g/L for Kongou and 0.13 g/L for Kaporé), as are POC concentrations (average: 1.02 mg/L for Kongou and 0.32 mg/L for Kaporé). It is interesting to note that during the 2023-2024 dry season, SPM concentrations in Kongou water gradually increased, matching the levels recorded during the previous rainy season, despite the absence of precipitation. This increase was accompanied by a rise in *E. coli* concentrations. This change observed during the 2024 dry season follows a year 2023 marked by a rainfall deficit. In addition, water conductivity is higher at Kongou (average: 7.9 µSiemens/cm) than at Kaporé (average: 6.8 µSiemens/cm) (Fig. 3B; Fig. 4B).

At the start of the rainy season (blue background Fig. 3; Fig. 4), SPM and *E. coli* concentrations rise sharply, sometimes reaching very high levels, such as on May 19, 2023, with 1.582 g/L and 9 500 MPN/100 ml respectively in Kaporé, and on June 12, 2023, with 1.473 g/L and 4 400 MPN/100 ml respectively in Kongou. This period marks the transition to the rainy season, characterized by increasing humidity and the first convective rains of the West African monsoon.

During the rainy season, SPM and *E. coli* concentrations fluctuate

significantly, often reaching extreme values. Furthermore, dissolved oxygen gradually decreases at both sites (Fig. 3C; Fig. 4C). Net solar radiation becomes more variable, and water conductivity drops sharply, contrasting with the dry season. Unlike in the dry season, *E. coli* levels are higher at Kaporé (average: 10 601 MPN/100 ml, max: 380 000 MPN/100 ml, median: 2 200 MPN/100 ml) than at Kongou (average: 1 840 MPN/100 ml, max: 17 000 MPN/100 ml, median: 1 000 MPN/100 ml) during the rainy season, as are POC (average: 6.3 mg/L for Kaporé and 2.5 mg/L for Kongou) concentrations. Furthermore, SPM remain higher in Kongou (average: 1.37 g/L for Kongou and 1.04 g/L for Kaporé). Additionally, precipitation and conductivity remain lower at Kongou compared to Kaporé. At the end of the rainy season, however, *E. coli* and SPM concentrations decrease. Finally, the NDVI shows contrasting dynamics between the sites. At Kongou, NDVI remains low throughout the year (between 0.06 and 0.19, Fig. 4), whereas at Kaporé, it increases progressively throughout the rainy season (between 0.13 and 0.6, Fig. 3), continuing until the end of the monsoon (October).

### 3.3. Performance of the 8 machine learning models tested for modeling *E. coli* concentrations

For Kaporé, when all variables, combining in situ measurements and EO data were included in the models, ensemble tree-based algorithms such as Random Forest, Extra Trees, Gradient Boosting and XGBoost provided the best predictive performance, with an average $R^2$ of around 0.75 (Fig. 5A). These results were obtained using the best hyper-parameters optimized for each algorithm. In contrast, the Decision Tree
<figure><img src="image_6.png" /><figcaption>Fig. 3. Temporal dynamics of *E. coli* concentrations and selected hydroclimatic (J-5 to J-1) and water quality parameters at Kaporé.</figcaption></figure>

model performed notably worse. The Friedman test confirmed significant differences between models for all performance indicators (p-value < 0.05), and the Nemenyi post-hoc test showed that the Decision Tree was significantly less effective than Random Forest, Gradient Boosting and Extra Trees (Figure S12).

When the models were trained using only EO data, the Random Forest and Extra Trees models again outperformed the others, achieving an average R² of around 0.7 (Fig. 5B). Conversely, the SVR and Decision Tree models showed lower predictive accuracy. The Friedman test revealed significant differences between models (p-value < 0.05), and the Nemenyi post-hoc test confirmed that the Decision Tree performed significantly worse than all models (R² sometimes negative) except SVR and XGBoost, while SVR performed significantly worse than Random

Forest and Extra Trees (Figure S12).

For Kongou, when all variables were used, ensemble tree-based models, namely Random Forest, Extra Trees, Gradient Boosting and AdaBoost, achieved the best results, with an average R² of approximately 0.65 (Fig. 5C). The Decision Tree model showed considerably poorer performance. The Friedman test confirmed significant differences between models for all indicators (p-value < 0.05), and the Nemenyi post-hoc test indicated that both Decision Tree and SVR were significantly less effective than AdaBoost and Extra Trees (p-value < 0.05) (Figure S12).

When only Earth observation data were used, the results were similar (Fig. 5D). The Friedman test again revealed significant differences between the models (p-value < 0.05), and the Nemenyi post-hoc test
<figure><img src="image_7.png" /><figcaption>Fig. 4. Temporal dynamics of *E. coli* concentrations and selected hydroclimatic (J-8 to J-1) and water quality parameters at Kongou.</figcaption></figure>

confirmed that the Decision Tree performed significantly worse than Random Forest, Extra Trees, Gradient Boosting and AdaBoost (figure S12). However, due to the high variability observed in Decision Tree performance across validation folds, this model cannot be considered reliable (Figure S12).

### 3.4. Temporal modeling and importance of variables for the Extra Trees model

For Kaporé, the two Extra Trees models successfully reproduced the temporal dynamics of *E. coli* concentrations (R² = 0.77 for all data, and 0.69 for EO data only), capturing both peaks and troughs with satisfactory accuracy. While some extreme values were not perfectly

modeled, the overall trends were well represented (Fig 6A and 6B) particularly during the 2024 rainy season which saw the highest measured *E. coli* concentrations. Moreover, when all variables were considered, the analysis of variable importance (Extra Trees model) identified band B8A, SPM, and the average specific humidity over the previous five days as the most influential predictors of *E. coli* concentrations (Fig 7A). When only EO data were used, band B8A and the average specific humidity remained the most relevant variables (Fig 7B). The model successfully reproduced the temporal dynamics of *E. coli* concentrations, capturing both peaks and troughs with satisfactory accuracy. While some extreme values were not perfectly modeled, the overall trends were well represented (Fig 6A and 6B).

For Kongou, the two models effectively reproduced the temporal
<figure><img src="image_8.png" /><figcaption>Fig. 5. Performance of models for modeling *E. coli* in Kaporé and Kongou using all types of data and then using EO data only.</figcaption></figure>

variability of *E. coli* concentrations (R²=0.63 for all data, and 0.65 for EO data only), including major fluctuations, with generally good agreement between predicted and observed values. Both models accurately reproduce the specific dynamics observed during the dry year of 2024 (January-May). Although some extreme events were less well represented, the main temporal trends were simulated well (Fig 6C and 6D). Moreover, when all variables were included, the total precipitation accumulated over the previous eight days emerged as the dominant predictor, followed by SPM (Fig 7C). When restricted to EO data, total precipitation and band B8 were identified as the key influencing factors (Fig 7D). The model effectively reproduced the temporal variability of *E. coli* concentrations, including major fluctuations, with generally good agreement between predicted and observed values. Although some extreme events were less well represented, the main temporal trends were simulated well (Fig 6C and 6D).

These results demonstrate that ML and EO data can be used to monitor the dynamics of *E. coli* in surface waters in sub-humid and Sahelian zones.

# 4. Discussion

## 4.1. Summary of approaches and main results

This study combines in situ water quality measurements, EO data and machine learning models to estimate *E. coli* concentrations in two West African water bodies in Burkina Faso and Niger. The results show that bacterial contamination can be correctly estimated from environmental and EO variables. Most methods based on regression trees and boosting showed strong predictive performance, while the decision tree model performed significantly worse. The SVR and KNN models showed intermediate predictive performance. The importance of individual predictors varied across models. However, SPM, precipitation (for Kongou), and specific humidity (for Kaporé) consistently emerged as the most influential variables in predicting *E. coli* concentrations.

## 4.2. Performance of satellite precipitation products

Rainfall plays an important role in the transport of *E. coli* to surface waters by promoting runoff and soil erosion (Abia et al., 2015; Islam et al., 2017; Robert et al., 2021). In regions such as the Sahel, where in-situ measurements are limited, satellite rainfall data offer a valuable alternative, although their accuracy can vary significantly. Several studies have highlighted inconsistencies in satellite rainfall estimates. For example, Roca et al. (2010) reported that GSMaP tended to underestimate heavy rainfall in Niamey and the Ouémé basin. In contrast, Gosset et al. (2013), over a longer observation period, found an overestimation in these same regions. These contrasting results are partly due to differences in the time scales considered (Le Coz & van de Giesen, 2019). More recent work has shown that IMERG outperforms GSMaP, notably in Benin (Bodjrenou et al., 2025), which corresponds to our own observations in Niger and Burkina Faso. More generally, Ayugi et al. (2024) have shown that IMERG generally outperforms GSMaP, CHIRPS and CMORPH across the Sahel.

Beyond its overall accuracy, IMERG is more effective at capturing extreme rainfall events, which are important for modelling microbial contamination risks (Ayugi et al., 2024). However, uncertainties remain at sub-daily time scales and during convective storms in the Sahel, where satellites may struggle to accurately estimate localized precipitation (Zhuang et al., 2023).

These results highlight the need to validate satellite precipitation data in water quality studies, as they can strongly influence model results. Future work should aim to combine satellite data with ground-based observations (rain gauges, disdrometers) to reduce uncertainties and improve predictive models of microbial water quality in regions where data are scarce.

## 4.3. Importance of hydroclimatic and water quality parameters on *E. coli* concentrations

In addition to precipitation, this study highlights correlations between *E. coli* concentrations and various environmental variables, based on both in situ measurements and Earth observation data. However, the significance of these variables differs between sites:

* Kongou: *E. coli* concentrations are primarily correlated with SPM (Sentinel-2 Band B8 reflectance), POC, cumulative rainfall, air humidity, and anti-correlated with dissolved oxygen.

* Kaporé: In addition to these factors, surface runoff, NDVI, conductivity, solar radiation, and soil moisture also appear relevant.

These differences reflect the specific characteristics of each site, notably vegetation cover and hydrological dynamics. At Kaporé, sparse vegetation at the start of the rainy season allows rapid surface runoff, increasing SPM and *E. coli* mobilization. As vegetation density rises, contaminant transport is likely reduced, probably by trapping bacteria
<figure><img src="image_9.png" /><figcaption>Fig. 6. Temporal modeling of *E. coli* in Kaporé and Kongou using all types of data, then using only Earth observation data (Extra Trees model).</figcaption></figure>

during runoff events (Figure S13). In contrast, Kongou's sparse year-round vegetation has a limited role in pathogen filtration.

The results complement previous studies. For example, Robert et al. (2021) identified strong correlations between *E. coli* and SPM (Spearman's rs = 0.66), weekly rainfall estimated from IMERG (rs = 0.8), and water reflectance in Sentinel-2 Band B8 (rs = 0.72) at Kaporé. These findings highlighted the close relationship between SPM and

precipitation, as rainfall events strongly influence particle resuspension and runoff processes. However, Robert et al.'s analysis was based on weekly data and a smaller dataset. By using daily observations across two sites, we refined these relationships and identified new associations with POC, air and soil moisture, UV radiation, dissolved oxygen, and conductivity.

Moreover, SPM was identified as a significant predictor of *E. coli*
<figure><img src="image_10.png" /><figcaption>Fig. 7. Importance of variables in the Extra Trees model.</figcaption></figure>

concentrations. Its role in bacterial contamination is well established, but the underlying processes are complex (Robert et al., 2021; Boithias et al., 2022; Moussa et al., 2025). Increases in SPM may result from soil surface runoff, which transports particles and associated bacteria to surface waters, or from the resuspension of bottom sediments, where bacteria can persist. These processes are influenced by seasonal factors such as rainfall intensity and streamflow, as well as land use, including

agriculture and urbanization, which affect both the quantity and composition of SPM (Robert et al., 2016, 2021).

At Kaporé, *E. coli* concentrations were positively correlated with SPM and runoff, showing the strong influence of rainfall-driven transport. However, at Kongou, *E. coli* concentrations were primarily correlated with precipitation and SPM, and less so with runoff. Furthermore, in 2024, in Kongou, following a dry year in 2023, SPM and *E. coli*
concentrations increased before rainfall events, likely due to concentration effects caused by the greater reduction in water surface area during the dry season. In this specific case, the increase in SPM seems more related to hydrological concentration effects than to transport processes.

Correlation analyses also revealed differences between sites in terms of determinant environmental factors. In Kongou, cumulative precipitation over 8 days was consistently the main predictor of *E. coli* concentrations, ahead of even SPM, confirming the key role of precipitation in the movement of sediments and pathogens. In Kaporé, specific humidity ranked just behind SPM and even ahead of precipitation, suggesting that in more humid or vegetated areas, atmospheric humidity has a stronger influence on *E. coli*.

POC was also linked to *E. coli* dynamics due to the fact that it is transported together with SPM. The negative correlation between *E. coli* and dissolved oxygen (DO) may reflect oxygen consumption through bacterial activity, while the higher DO levels observed during cooler periods (December - February at Kaporé and Kongou) likely result from the greater solubility of oxygen in colder water. Air humidity can further influence *E. coli* by affecting bacterial survival in soil or vegetation before transport into water (Stine et al., 2005).

Solar radiation (UV) is an important regulator of bacterial persistence, as it can directly inactivate *E. coli* at the water surface, reducing their concentrations (Jang et al., 2017). Finally, some correlations may be indirect. For instance, the negative correlation between conductivity and *E. coli* may reflect the dilution of ions by precipitation rather than a direct effect on bacteria (McEgan et al., 2013). Overall, these results demonstrate that the dynamics of *E. coli* result from multiple environmental and hydrological processes that interact with each other.

## 4.4. Performance of machine learning models on *E. coli* modelling

Machine learning models performed very well in this study, demonstrating their potential for predicting bacterial water quality. These findings align with recent studies (Hong et al., 2024; Nafsin et al., 2023; Sokolova et al., 2022), which emphasize the advantages of machine learning over traditional regression techniques (Hamilton et al., 2009; Robert et al., 2021), particularly when high-resolution remote sensing data are available. By capturing non-linear relationships and complex interactions among variables, machine learning is particularly effective for modeling *E. coli* contamination.

Among the models tested, ensemble and boosting methods yielded the best results. They are effective because they can handle complex, non-linear relationships between environmental variables and reduce overfitting by combining multiple trees. In contrast, the simple decision tree model performed significantly worse due to its limited ability to generalize and its high sensitivity to overfitting (Halabaku and Bytyçi, 2024). The SVR and KNN models performed moderately.

The most important predictors differed from one model to another, reflecting both local environmental conditions and the way each algorithm determines the importance of variables. Tree-based models focus on predictors that reduce variance the most, SVR relies on support vectors close to the regression boundary, and KNN depends on the structure of nearby data points, leading to a different ranking of important variables.

Models using only Earth observation data performed well, demonstrating that remote sensing can be a valuable tool for monitoring microbial water quality in regions where field measurements are scarce.

## 4.5. Methodological limitations

Despite these promising results, a number of limitations need to be highlighted, justifying a cautious interpretation of the results.

Firstly, certain predictive variables, such as rainfall and humidity, are often closely related. It is therefore difficult to determine the extent to which each of them individually influences *E. coli* concentrations.

Although models such as Extra Trees can manage these relationships fairly well, highly correlated data can nevertheless make it difficult to identify the factors that actually influence *E. coli* dynamics. To better understand these effects, controlled laboratory experiments are needed, in which each environmental factor can be tested separately to determine its influence on the survival and growth of *E. coli*.

Secondly, the models are site-specific and do not necessarily transfer well to other locations. For instance, the model developed for Kaporé performed poorly when applied to Kongou, indicating that local conditions, such as climate, vegetation, and hydrology, strongly influence *E. coli* dynamics. These site-specific differences highlight the difficulty of applying a single model universally. More extensive testing within each type of environment may be necessary, as it could be feasible to develop separate models for sub-humid rural areas versus Sahelian rural zones, improving the reliability of surface water quality predictions.

Finally, some data were missing due to technical problems or access restrictions (political instability in Niger), and the measurements always involve a certain margin of uncertainty. In this study, missing values were filled in using linear interpolation to create continuous time series. However, this method assumes that changes occur at a constant rate between two dates, which can simplify natural variations and introduce biases, particularly during extreme events such as heavy convective precipitation or sudden changes in temperature.

## 5. Conclusion

This study, conducted at two distinct sites in West Africa - Kaporé (Burkina Faso) and Kongou (Niger) - demonstrated the feasibility of monitoring and estimating *E. coli* concentrations over time using Earth observation data and machine learning methods. The results highlighted the key role of environmental variables such as SPM, cumulative rainfall (estimated by IMERG), and specific air humidity (from GLDAS), which showed strong correlations with bacterial concentrations. Among the tested models, the regression tree and boosting-based approaches provided the best performance, even when relying solely on satellite-derived data, with slightly better results at Kaporé, likely due to a larger and more complete dataset. These findings support growing evidence that machine learning methods outperform traditional regression models in modeling microbial water quality, despite remaining challenges in interpretability. Thanks to Earth observation data and advanced ML algorithms, this approach offers significant potential for monitoring bacteriological water quality and assessing health risks. In the future, it would be interesting to develop short-term predictive models to anticipate fecal contamination events and to assess the potential impacts of climate change and land use dynamics on bacterial contamination of surface waters. Although not predictive, the methodology used here could be applied to future climate and land use scenarios, enabling projections to be made of trends in *E. coli* concentrations in West African surface waters up to the year 2100.

### Data availability

All the data used in this study are available from the corresponding author. Moreover, in-situ collected data used for this article are available at:

Robert, E. Nikiema D. E., Grippa M. 2025a, "*E. coli*, Suspended Particulate Matter and physicochemical parameters in the Bagre Lake (Kapore, Burkina Faso)", https://doi.org/10.48579/PRO/M9YZDB, data.InDoRES, provisional version

Robert, E., Abdourhamane Touré A., Boubacara Moussa M. 2025b, "*E. coli*, Suspended Particulate Matter and physicochemical parameters in the Kongou Lake (Niger)", https://doi.org/10.48579/PRO/RENEGI, data.InDoRES, provisional version
## Funding

This work was supported by the French Centre National d'Etudes Spatiales (CNES) through TELESSAO and TELESSAO-2 projects (APR TOSCA Santé, https://cnes.fr/en), by ANR through MAMIWATA project (grant number ANR-21-CE35-0014) and by CNRS through SANTELLITE project (MITI program).

## CRediT authorship contribution statement

**Marc-Antoine Mant:** Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Conceptualization. **Elodie Robert:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **Hedwige Nikiema:** Investigation. **Moussa Boubacar-Moussa:** Investigation. **Amadou Abdourhamane-Toure:** Investigation. **Beatriz M. Funatsu:** Writing – review & editing, Formal analysis. **Manuela Grippa:** Writing – review & editing, Validation, Supervision. **Laurent Kergoat:** Writing – review & editing, Validation, Supervision. **Javier Perez-Saez:** Writing – review & editing, Validation, Supervision. **Hugo Pradalier:** Investigation. **Emma Rochelle-Newall:** Writing – review & editing, Supervision. **Marc Robin:** Supervision.

## Declaration of competing interest

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

## Acknowledgments

M-A MANT warmly thanks Marielle Gosset and Romulo Juca Oliveira for their invaluable methodological support in selecting the most suitable precipitation estimation product. He also expresses his gratitude to Ali Niaone, Halarou Noma, Moussa Ousseini Boureima and Hamidou Koudougou for their contribution to the field measurement campaigns in Burkina Faso and Niger. Sincere thanks also go to Nafisatou Elhadji Abdou and Charifatou Aliou Hassane for their commitment during their Master's internship, particularly in data collection efforts. The first author would also like to warmly thank Carole Causserand for her help with dissolved organic carbon (DOC) measurements. Their contributions were essential to the success of this study. Finally, the author would like to thank the TAHMO and AMMA-CATCH observatories for making the rainfall data available in Burkina Faso and Niger, and in particular Geremy Panthou and Ibrahim Boubacar for providing the AMMA-CATCH (AMMA-CATCH, 1990) data.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.envadv.2025.100678.

## Data availability

Data will be made available on request.

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