Access to water in West African countries
KABORE Arnaud 20230241 KONFE Abdoul Fata 20210272 OUIYA Wendyam Faozia 20210239 MILLOGO Stanislas Hervé 20240100 Groupe 02 Master 1, Filière (GEAAH) Génie de l’eau de l’Assainissement et de l’Aménagement Hydro-Agricole Institut de l’Ingénierie de l’Eau et de l’Environnement Burkina- Faso.
Dr. Maïmouna Bologo/Traoré, Dr. Malicki Zorom, Dr. Yohan Richardson and Mr. Sina Thiam
Summarize :
Faced with growing challenges of water scarcity, pollution, and usage conflicts, the RTI project aims to establish equitable, sustainable, and collaborative water governance. Using a participatory methodology that combines field surveys, resource mapping, policy analysis, and stakeholder consultations, it pursues five specific objectives: identifying water uses, assessing resource conditions, analyzing conflicts, raising community awareness, and strengthening stakeholder capacities. By bringing together a diverse partnership (research institutions, government agencies, local authorities, and civil society), the project works to reduce water-related tensions and promote integrated water resource management, thereby contributing to climate-resilient territorial development in the face of environmental challenges.
Introduction
Access to water is a major issue for human, economic, and environmental development. As a vital resource, water remains unequally available and unevenly distributed across the world, particularly in developing countries where climatic, demographic, and socio-economic constraints exacerbate vulnerabilities. While water demand continues to rise due to urban growth, agricultural expansion, and industrial needs, pressures on water resources are intensifying, making sustainable management of this resource increasingly complex. In this context, analyzing the conditions of water access, its determinants, and its impacts on populations is essential for understanding local dynamics and contributing to the development of effective public policies. This article aims to examine the challenges related to water access through an analytical and multidimensional approach, drawing on recent data and recognized conceptual frameworks.
2 ZONE D’ETUDE
The project’s study area encompasses West Africa, a region characterized by significant climatic and hydrological diversity. It includes both Sahelian countries such as Niger, Mali, and Burkina Faso—characterized by arid to semi-arid climates—and more humid coastal nations like Guinea and Côte d’Ivoire. This climatic gradient results in a highly uneven distribution of water resources. Sahelian nations experience low rainfall and severe water stress, while coastal countries, despite benefiting from abundant precipitation, face significant challenges related to water management and pollution. Furthermore, the region is defined by major transboundary river basins, including the Niger, Senegal, and Volta. These shared waterways necessitate robust inter-state coordination to ensure equitable and sustainable resource management. Compounding these geographical and political complexities, West Africa faces mounting pressures from rapid demographic growth, escalating agricultural and industrial demands, and the accelerating impacts of climate change. These factors collectively intensify existing water scarcities and management challenges, making integrated water governance a critical priority for regional development.
# 3 PROBLEMATIQUE :
“While West Africa has significant surface and groundwater resources, why does a significant portion of its population still lack access to safe and affordable water?”
4 Objectif général :
To contribute to sustainable socio-economic development and improved living conditions for populations in West Africa by ensuring equitable, safe, and resilient access to water and sanitation. Using Information Research and Processing tools, this study aims to: -Improve Access to Sustainable Safe Water and Sanitation Services. -Strengthen Community Resilience to Climate Challenges and Conflicts. -Promote Local Governance and Sustainable Management of Water Services. -Support Local Economic Development through Water Security. - Preserve Water Resources and Associated Ecosystems
5 Revues documentaires
West Africa presents a critical paradox: it is endowed with significant water resources (e.g., the Senegal, Niger, and Volta rivers) yet has some of the world’s lowest rates of access to safe drinking water and sanitation. This literature review explores the multiple, interconnected relationships between water access and development dynamics in the region. The prevailing body of research demonstrates that water is not merely a technical issue but a central, multidimensional, and cross-cutting challenge that underpins the achievement of sustainable development goals.
5.1 Conceptual Framework: The Water-Development Nexus
The literature conceptualizes the water-development nexus around several key axes:
Human Capital: Access to safe water and sanitation is a key determinant of health (reducing diarrheal diseases, cholera, etc.). Work by the WHO (2021) emphasizes the impact of the waterborne disease burden on economic productivity and education, particularly for girls, who are often responsible for water collection.
Economic Productivity: The World Bank (2020) underscores that investments in the water sector are catalysts for economic growth, especially for agriculture, which employs over 60% of the West African population and relies heavily on rainfall and informal irrigation.
Inequality Reduction: Water access is a matter of social and gender justice. Studies from UN-Water (2022) show that lack of access disproportionately affects women, rural populations, and the poor, thereby exacerbating existing inequalities.
5.2 Major Challenges Highlighted in the Literature
5.2.1 Physical and Economic Water Scarcity
Despite apparent abundance, water resources are unevenly distributed and subject to high climate variability. Reports from the IPCC (2022) and OECD/ECOWAS (2020) warn of increasing droughts and floods, threatening water security. “Economic water scarcity”—the lack of investment in infrastructure—is as critical as physical scarcity.
5.2.2 Fragile Water Governance and Management
Many scholars (e.g., Moss, T., & al.) emphasize that weak institutions and fragmented management frameworks are a major obstacle. While Integrated Water Resources Management (IWRM) is widely promoted in policy documents (ECOWAS, 2020), its implementation remains partial, hampered by limited technical and financial capacities at the local level.
5.2.4 The Rural and Peri-Urban Water Challenge
Documents from the African Development Bank (2021) highlight the disparity between investments in major cities and the relative neglect of rural areas. Water supply systems there are often dilapidated or non-existent, and the maintenance of water points is a recurring problem.
5.3 Development Axes Linked to Water
5.3.1 Food Security and Climate-Resilient Agriculture
Water is central to food security. FAO (2022) studies promote small-scale irrigation and climate-smart agricultural practices (conservation agriculture, rainwater harvesting) to enhance the resilience of smallholder farmers
5.3.3 Public Health and Pandemic Resilience
The COVID-19 pandemic underscored the crucial role of Water, Sanitation, and Hygiene (WASH) in disease prevention. WHO and UNICEF (2022) note that investments in WASH are a prerequisite for strengthening public health systems
6. Environmental protection
Water use must respect the need to preserve aquatic ecosystems and maintain sustainable ecological balance.
7. Polluter-pays and user-pays principles
Users are expected to contribute financially to water management, depending on their usage, and polluters must bear the cost of the pollution they cause.
b) Current IWRM Principles in West Africa:
In West Africa, the principles of Integrated Water Resources Management (IWRM) are broadly aligned with international principles, while also taking into account the region’s socio-economic, cultural, and environmental realities. These principles are supported by regional institutions such as ECOWAS, UEMOA, and the West Africa Water Partnership (GWP-WA). The main IWRM principles in West Africa are:
2. Inclusive participation of stakeholders
All relevant actors – governments, local authorities, users, civil society, private sector, etc. – must actively participate in water management, based on the principle of participatory governance.
3. River basin approach
Water resources are managed at the level of watersheds, including transboundary basins (such as the Niger or Volta Rivers), taking into account interdependence between countries.
4. Decentralized Management
Water management should take place as close as possible to the users, through local structures (such as basin committees, local water committees, etc.), in support of national and regional policies.
5. Environmental Sustainability
Aquatic ecosystems must be protected and used in ways that preserve their capacity to regenerate, in line with the principles of sustainable development.
6. User-Pays and Polluter-Pays Principles
Water users must contribute to management costs, and those who pollute must bear the costs of their pollution. This approach serves as an incentive for responsible resource management.
7. Cross-Sectoral Integration
Water policies must be consistent with those of agriculture, environment, health, energy, etc., to avoid conflicts over usage. (Taken from the ECOWAS Regional Water Charter, IWRM Regional Action Plan)
8 Study Methodology
8.1. Study Parameters: The Countries of West Africa
8.1.1 Geographical Location
We selected the West African countries due to their high exposure to water stress and their increased vulnerability to the effects of climate change on water resources. Indeed, climate change represents a major challenge that considerably undermines the mechanisms of Integrated Water Resources Management (IWRM) in states already facing water tensions. The study thus focuses on the following countries: Benin, Burkina Faso, Côte d’Ivoire, The Gambia, Guinea, Guinea-Bissau, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo. Cape Verde was excluded from the analysis due to its insular geographic location, which distinguishes it from the African continent.
8.1.2. Sampling
For our study on integrated water resources management in the face of climate change in West Africa, we chose to use stratified sampling, a method that is particularly relevant for our study. This method allowed us to divide the population into homogeneous subgroups or “strata” based on certain characteristics, such as geographic region, climate type, water supply sources (groundwater, surface water), and the level of vulnerability to climate change.
Steps of Stratified Sampling:
Stratification: Divide the geographical areas of West Africa into different strata, for example: arid zones, semi-arid zones, humid zones, urban and rural areas.
Selection within Each Stratum: Choose a simple random sample from each stratum (geographic area).
Data Collection: Gather information on water management in each geographic area, taking into account local specifics related to climate change.
7.2. Data Collection Tools
Data collection allowed us to obtain the information necessary for the smooth progress of our study. The information was collected from various online sources such as: https://ourworldindata.org, ……, …….., …….
To also carry out field data collection, we used two main tools:
Questionnaire: Designed to be administered to households, this questionnaire aims to collect quantitative data on water access, challenges, and adaptation strategies.
Interview Guide: Intended for government officials, NGOs, environmental and climate experts, and managers of dams, boreholes, and water networks, it helps to deepen the qualitative aspects, notably the strategies implemented by experts and institutions.
8.3 Household Questionnaire
Survey on Access to Water, the Effects of Climate Change, and
Adaptation Strategies: Public Perceptions and Practices. 
8.4. Interview Guide for Institutions and Water Management Experts
Access to water in West African countries
Questionnaire link : https://kf.kobotoolbox.org/#/forms/aDordBdH3kcgnLKjSv8AMf Interview Guide link: https://kf.kobotoolbox.org/#/forms/aPvcc5t64aPj83PitA8NJ5
8.5 Description of the Methodology and Methodological Tools
8.5.1. Methodology
For our study, we used scientific tools to qualitatively assess the relationship between the indicators of integrated water resources management and the impacts of climate change.
8.5.2. Materials and Data Collection Tools
Excel and Word Software:
These software programs were necessary for data entry, organization, and graphical representation of our research results, observations, analyses, and conclusions. Their versatility allowed us to interpret and understand the results obtained more easily.
KoboToolbox:
This is an open-source suite of tools designed for field data collection, management, and analysis. It allows for:
Creating digital questionnaires in various formats (multiple-choice questions, free text, images, GPS, etc.).
Collecting data on smartphones or tablets via the KoboCollect application (based on ODK).
Storing and analyzing data in real time on a secure cloud platform.
Exporting data in different formats (Excel, CSV, SPSS) for in-depth analysis.
KoboCollect:
This tool was invaluable for collecting the data necessary for our analysis. This intuitive and flexible application facilitated the creation and administration of questionnaires and interview guides, providing an efficient method for field data collection.
R Studio:
This data analysis software was essential for performing our statistical analyses. Thanks to its advanced features, we were able to carry out factorial analyses, clustering methods, cross-tabulations, etc. Its accessibility and versatility make it an indispensable tool for researchers working with complex data.
QGIS:
This software proved to be a powerful tool for the visualization and spatial analysis of our data. As a Geographic Information System (GIS), it allowed us to create custom maps and explore the geographic relationships between the different elements of our study. Its advanced features helped us better understand the issues addressed in our project by highlighting the spatial and geographic aspects of the data.
Zotero:
Zotero is a comprehensive, free, and open-source bibliographic management software, valued for its ease of use. It allows, among other things:
Capturing and managing references from various sources, as well as associated files (PDFs and others);
Inserting references into a text document;
Producing bibliographies according to a specific bibliographic style;
Sharing references;
9 PRESENTATION OF THE VARIABLES:
9.1 Definition and importance of variables:
Integrated Water Resources Management (IWRM) is based on a comprehensive approach aimed at balancing resource availability, user needs and environmental sustainability. The choice of variables in this study was guided by the need to cover the main issues of water management in West Africa. Thus, the selected variables make it possible to analyse the availability of water resources, the pressures exerted on these resources, the sectoral uses and the means put in place to ensure sustainable management.
9.1.1 Disponibilité des ressources en eau :
The assessment of available resources is an essential step in IWRM analysis, as it determines the capacity of countries to meet water needs. For this, the following variables were retained :
Precipitation (mm/year): Key factor influencing groundwater and river recharge. It makes it possible to differentiate between arid countries (e.g. Burkina Faso) and wetter countries (e.g. Guinea).
Temperature (°C): Impacts water evaporation and water demand, especially in agriculture. Rising temperatures, due to climate change, are increasing water stress. These variables thus make it possible to understand the climatic disparities between countries and their implications for water management.
9.1.2 Pressure on the resource:
The effectiveness of water management depends largely on the level of pressure exerted on the available resources. Three major variables were chosen :
Annual amount of water withdrawn (x10⁶ m³): Overall indicator of the pressure on water resources. Excessive consumption can lead to water deficits and ecosystem degradation.
Water Stress (%): Assesses the intensity of water use in relation to its availability. High water stress indicates a critical situation requiring rigorous management policies.
Number of water infrastructures: Measures the capacity of countries to mobilize and manage water. Insufficient infrastructure can limit access to water, even in well-resourced areas. These variables help identify countries under high pressure on water, requiring more efficient management and infrastructure investment.
9.1.3 Sectoral distribution and uses of water:
The different economic sectors have specific water needs. The analysis of their consumption makes it possible to assess priorities and conflicts of use :
Amount of water withdrawn for agriculture (%): Agriculture is the largest consumer of water in West Africa. Inefficient use can increase water scarcity and soil degradation.
Amount of water withdrawn for industry (%): Increasing industrialization implies higher demand for water and increased risk of pollution.
Quantity of water withdrawn for municipal needs (%): Key indicator to assess people’s access to drinking water and basic services. By integrating these variables, the study sheds light on sectoral priorities and their impact on sustainable resource management.
9.1.4 Access to water and financial management:
Sustainable water management must also ensure equitable access to populations and effective mobilization of financing. Two variables are essential for this dimension:
Population with access to an improved source of drinking water (%) : A key human development indicator, revealing inequalities in access to water between countries.
Total official financial flows for water supply and sanitation: Reflects the commitment of governments and donors to improving access to water and sanitation. The choice of variables is based on a holistic approach, allowing the different dimensions of water management to be examined. By combining the availability of resources, pressures, sectoral practices and financial management, this study offers an in-depth analysis of the challenges and strategies adopted by the different countries. The Principal Component Analysis (PCA) will thus make it possible to classify the countries according to their similarities and to identify the key factors influencing integrated water resources management in West Africa.
#commende
library(ggrepel)## Warning: le package 'ggrepel' a été compilé avec la version R 4.4.3## Le chargement a nécessité le package : ggplot2## Warning: le package 'ggplot2' a été compilé avec la version R 4.4.3library(FactoMineR)## Warning: le package 'FactoMineR' a été compilé avec la version R 4.4.3library(Factoshiny)## Warning: le package 'Factoshiny' a été compilé avec la version R 4.4.3## Le chargement a nécessité le package : shiny## Warning: le package 'shiny' a été compilé avec la version R 4.4.3## Le chargement a nécessité le package : FactoInvestigate## Warning: le package 'FactoInvestigate' a été compilé avec la version R 4.4.3library(prettyR)
library(FactoInvestigate)
library(factoextra)## Warning: le package 'factoextra' a été compilé avec la version R 4.4.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(GPArotation)
library(rsconnect)## Warning: le package 'rsconnect' a été compilé avec la version R 4.4.3##
## Attachement du package : 'rsconnect'## L'objet suivant est masqué depuis 'package:shiny':
##
## serverInfolibrary(ggplot2)
library(corrplot)## Warning: le package 'corrplot' a été compilé avec la version R 4.4.3## corrplot 0.95 loadedRTI<-read.csv(file="C:/Users/hp/Desktop/Diagrammes RTI/Carte_projet_RTI/ACCESS_2.csv", header = TRUE, sep = ";",
dec = ",", row.names=1)
RTI## VMED AEP PHMEP Tur Nbre.PF PPDRD Cmepj PEUDA PEUI PEUM TPEDR PEC MEDDI
## BF 3.2 50 50 20 60 30 40 85 5 10 35 70 60
## NIGER 1.3 56 46 25 55 35 35 90 3 7 40 65 55
## TOGO 6.0 68 50 15 65 50 55 75 10 15 30 75 65
## MALI 4.0 77 49 20 58 40 40 85 5 10 38 68 58
## BENIN 7.5 70 50 15 62 45 50 80 7 13 32 72 62
## GUINEE 24.0 67 53 25 60 25 40 70 10 20 35 65 60
## SENEGAL 6.6 82 54 15 70 55 55 75 10 15 25 78 70
## CIV 14.6 73 50 15 68 50 55 65 20 15 28 75 68
## GAMBIE 4.8 84 53 20 55 35 40 85 5 10 40 60 55
## CAPVERT 1.7 87 53 10 75 65 60 60 20 20 30 80 75
## SIERRA L 17.3 58 50 25 55 30 35 75 10 15 38 58 55RTI_scaled <- scale(RTI)
res.pca <- PCA(RTI_scaled, graph = FALSE)10 CARTOGRAPHIC REPRESENTATION AND INTERPRETATION:
The maps were generated on the QGIS software using Shapefiles (SHP) spatial data that were uploaded to BNDT/IGB. Each map represents the spatial distribution of a specific variable over the study area (West African countries). The colors on the maps were put in order to differentiate the countries from each other. Some colours have been gradually changed so that countries with a high value (dark colour) can be distinguished from countries with a low value (light colour).
10.1 Average volume of water available/ inhabitants
On this map we can see that Nigeria, Mali, Senegal and Niger have a very high annual amount of water compared to the other country.
10.2 Access to drinking water
Water stress is the ratio between the amount of water used and the
amount of water available. This rate is used to assess the risk of water
scarcity in each country; the higher the rate, the more water shortages
are faced. 
We notice on the map that Mauritania and Senegal have a considerable stress rate, as well as Mali, Burkina Faso, Niger and Nigeria, which implies that these countries are facing water shortage problems.
10.3 Average pH of drinking water
This map shows us the rate of population having access to an improved
water source (tap, standpipe, etc.). It is noticeable that almost all
West African countries have a low rate. 
10.4 Turbidity
In this part, we evaluate the amount of rainwater that has fallen.
The map shows us high rainfall in coastal countries and low rainfall in
Sahelian countries. 
#
10.5 Number of wells and boreholes
According to the map, Nigeria is the only country that has a very
high number of water infrastructures. Then we have Mauritania, Mali,
Burkina Faso and Ghana which have a lot of infrastructure. Other
countries do not have enough water infrastructure. 
10.6 Proportion of the population served by a water distribution network
We can see on the map that Nigeria has a very high financial flow. Nigeria, Senegal, Mali, Burkina Faso and Ghana have a remarkable financial flow. The other countries have a low financial flow
10.7 Average water consumption/ person/ day
The annual rate of water withdrawn for 03 sectors of activity
(agriculture, industry and municipal needs) was evaluated. The annual
water rate of one sector is found according to the other sectors. 
10.8 Share of water used in agriculture
#
9.9.Share of water used in industry
9.10.Share of water used in the household
According to the 03 maps; It is noticeable that countries use more water in agriculture than in the other 02 sectors (industry and municipal needs)
10.11.Water loss rate in the network
The map shows low temperature in coastal countries and high
temperature in Sahelian countries 
10.12.Proportion of water in accordance
#
9.13.Maintenance and sustainability of hydraulic infrastructure 
11 Principal component analysis:
library(corrplot)
library(prettyR)
describe(RTI)## Description of RTI##
## Numeric
## mean median var sd valid.n
## VMED 8.27 6 52.53 7.25 11
## AEP 70.18 70 143.96 12.00 11
## PHMEP 50.73 50 5.42 2.33 11
## Tur 18.64 20 25.45 5.05 11
## Nbre.PF 62.09 60 44.89 6.70 11
## PPDRD 41.82 40 151.36 12.30 11
## Cmepj 45.91 40 84.09 9.17 11
## PEUDA 76.82 75 86.36 9.29 11
## PEUI 9.55 10 33.07 5.75 11
## PEUM 13.64 15 17.25 4.15 11
## TPEDR 33.73 35 25.82 5.08 11
## PEC 69.64 70 51.45 7.17 11
## MEDDI 62.09 60 44.89 6.70 11describe(RTI,num.desc=c("mean","median","var","sd","valid.n","min","max"))## Description of RTI##
## Numeric
## mean median var sd valid.n min max
## VMED 8.27 6 52.53 7.25 11 1.3 24
## AEP 70.18 70 143.96 12.00 11 50.0 87
## PHMEP 50.73 50 5.42 2.33 11 46.0 54
## Tur 18.64 20 25.45 5.05 11 10.0 25
## Nbre.PF 62.09 60 44.89 6.70 11 55.0 75
## PPDRD 41.82 40 151.36 12.30 11 25.0 65
## Cmepj 45.91 40 84.09 9.17 11 35.0 60
## PEUDA 76.82 75 86.36 9.29 11 60.0 90
## PEUI 9.55 10 33.07 5.75 11 3.0 20
## PEUM 13.64 15 17.25 4.15 11 7.0 20
## TPEDR 33.73 35 25.82 5.08 11 25.0 40
## PEC 69.64 70 51.45 7.17 11 58.0 80
## MEDDI 62.09 60 44.89 6.70 11 55.0 75#La matrice de correlation
mat_cor<-cor(RTI[,1:10],y = NULL)
corrplot(
mat_cor,
method ="color",
type = "upper",
addCoef.col ="black")
11.1 Inertia distribution:
. Inertia distribution The inertia of the first dimensions shows if there are strong relationships between variables and suggests the number of dimensions that should be studied. The first two dimensions of analyse express 75.41% of the total dataset inertia ; that means that 75.41% of the individuals (or variables) cloud total variability is explained by the plane. This percentage is high and thus the first plane represents an important part of the data variability. This value is strongly greater than the reference value that equals 54.52%, the variability explained by this plane is thus highly significant (the reference value is the 0.95-quantile of the inertia percentages distribution obtained by simulating 5738 data tables of equivalent size on the basis of a normal distribution). From these observations, it is probably not useful to interpret the next dimensions.
fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 50), title = "Variance expliquée par chaque axe")
Figure 2 - Decomposition of the total inertia The first factor is major: it expresses itself 65.58% of the data variability. Note that in such a case, the variability related to the other components might be meaningless, despite of a high percentage. An estimation of the right number of axis to interpret suggests to restrict the analysis to the description of the first 1 axis. These axis present an amount of inertia greater than those obtained by the 0.95-quantile of random distributions (65.58% against 31.92%). This observation suggests that only this axis is carrying a real information. As a consequence, the description will stand to these axis.
11.2 Dimension Description 1
The labeled individuals are those with the greatest contribution to the construction of the plan.
fviz_pca_var(res.pca, col.var = "cos2", repel = TRUE, title = "Corrélations des variables")
fviz_pca_biplot(res.pca, repel = TRUE, col.var = "green", col.ind = "red", title = "Plan Factoriel ACP")
The dimension 1 opposes individuals such as mali, mauritanie and senegal (to the right of the graph, characterized by a strongly positive coordinate on the axis) to individuals such as serra lionne and liberia (to the left of the graph, characterized by a strongly negative coordinate on the axis). The group in which the individuals mali, mauritanie and senegal stand (characterized by a positive coordinate on the axis) is sharing :
high values for the variables stress_H, qtt_an_eau, t., flux_fin_x10.6 and X.agri (variables are sorted from the strongest).
low values for the variables X.municip and précipit (variables are sorted from the weakest). The group in which the individuals serra lionne and liberia stand (characterized by a negative coordinate on the axis) is sharing :
high values for the variables X.ind and précipit (variables are sorted from the strongest).
-low values for the variable X.agri.
Figure- Variables factor map (PCA) The labeled variables are those the best shown on the plane.
dimdesc(res, axes = 1:1) $Dim.1
$Dim.1
Link between the variable and the continuous variables (R-square)
correlation p.value
X.agri 0.8795507 7.404536e-05 stress_H 0.8721416 1.013098e-04 qtt_an_eau 0.8559704 1.888069e-04 flux_fin_x10.6 0.8361276 3.688748e-04 N_d.infr 0.7419688 3.686384e-03 X.ind -0.7486458 3.236617e-03 X.municip -0.7953256 1.152514e-03 précipit -0.8223995 5.583339e-04 Figure 5 - List of variables characterizing the dimensions of the analysis. res.hcpc$desc.var0.9237087 6.555946e-06
Link between the cluster variable and the quantitative variables
Eta2 P-value
flux_fin_x10.6 0.7903516 0.0004050028 précipit 0.7728139 0.0006052139 X.ind 0.7590818 0.0008116117 stress_H 0.7276476 0.0014984962 X.agri 0.6613494 0.0044540952 N_d.infr 0.6084108 0.0092077547 qtt_an_eau 0.4961570 0.03246953280.8001538 0.0003187716
Description of each cluster by quantitative variables
$1 v.test Mean in category Overall mean sd in category
Overall sd p.value X.ind 2.700802 31.375 11.13308 5.225 11.07053
0.006917255 précipit 2.540191 2385.500 1175.61538 257.500 703.53684
0.011079191 X.agri -2.155921 14.950 55.91538 6.550 28.06684
0.031089847
$2 v.test Mean in category Overall mean sd in category
Overall sd p.value N_d.infr -2.049270 1969.00000 2539.76923 499.78062
893.26128 0.04043568 flux_fin_x10.6 -2.552973 13.79833 46.68923 13.84761
41.31871 0.01068078
$3 v.test Mean in category Overall mean sd in category
Overall sd p.value flux_fin_x10.6 3.042180 92.588 46.689231 26.386445
41.318709 0.002348713 t. 2.924978 32.310 29.523077 1.749606 2.609354
0.003444807 stress_H 2.913426 10.332 5.150769 3.789941 4.870343
0.003574861 N_d.infr 2.699889 3420.400 2539.769231 711.580382 893.261281
0.006936253 qtt_an_eau 2.436877 2373.600 1093.395385 1595.239117
1438.720317 0.014814710 X.agri 2.433963 80.860 55.915385 16.866132
28.066839 0.014934529 X.municip -2.142545 16.840 32.953846 15.734751
20.596811 0.032149691 X.ind -2.178683 2.326 11.133077 2.352434 11.070528
0.029355210 précipit -2.437629 549.400 1175.615385 338.585646 703.536838
0.014783958
#Contribution des variables aux axes
In a PCA, each axis is built from a combination of variables. Variables with high contributions are key in defining the axis. Variables with low contributions have little influence on the axis. The correlation circle shows which variables explain each axis: • Variables close to the edge of the circle → well represented. • Variables aligned with an axis → important for that axis. Understanding the contributions helps to properly interpret each axis and to identify the most influential variables.
fviz_pca_contrib(res.pca, choice = "var", axes = 1:2, title = "Contribution des variables aux axes")## Warning in fviz_pca_contrib(res.pca, choice = "var", axes = 1:2, title =
## "Contribution des variables aux axes"): The function fviz_pca_contrib() is
## deprecated. Please use the function fviz_contrib() which can handle outputs of
## PCA, CA and MCA functions.
fviz_contrib(res.pca, choice = "var", axes = 1, top = 12, title = "Contribution des variables - Axe 1")
fviz_contrib(res.pca, choice = "var", axes = 2, top = 12, title = "Contribution des variables - Axe 2")
Contributions des individus à la Dimension 1
base <- read.csv(file="C:/Users/hp/Desktop/Diagrammes RTI/Carte_projet_RTI/ACCESS_2.csv", header = TRUE, sep = ";",
dec = ",", row.names =1)
acp_result <- PCA(base, scale.unit = TRUE, ncp = 5, graph = FALSE)
# 4.3 Contributions des individus à la Dimension 1
fviz_contrib(acp_result, choice = "ind", axes = 1, top = 20)
Contributions des individus à la Dimension 2
# 4.4 Contributions des individus à la Dimension 2
fviz_contrib(acp_result, choice = "ind", axes = 2, top = 20)
a. Quality of the PCA
The first two principal components explain a large proportion of the total variance of the data.
The cumulative percentage of variance captured by Dim1 and Dim2 shows that the PCA faithfully summarizes the information contained in the original data set.
b. Interpretation of Axis 1 (Dim1)
The most contributing variables to Axis 1 are those that differentiate certain countries or observations.
This axis could represent a contrast between (for example) countries with better water access and those with greater difficulties.
c. Interpretation of Axis 2 (Dim2)
Axis 2 is shaped by different variables (such as food security, urbanization rate, etc.).
This axis appears to separate countries based on another major factor (such as level of agricultural development or resource management).
d. Position of variables on the correlation circle
Variables located close to the edge of the circle are very well represented: they explain much of the variability on the two principal axes.
Variables strongly aligned with an axis → key variables for that axis.
e. Distribution of individuals (countries)
Countries or observations placed in the same area of the factor map share similar characteristics.
Countries far from the center are very specific compared to the studied variables.
f. Contribution of variables
Variables with a high contribution (above average) are essential for building the axes.
These variables must be highlighted in the analysis and thematic interpretation.
12 HIERARCHICAL CLASSIFICATION:
The classification was carried out by the Ascending Hierarchical Classification algorithm and produced the following figures. Dendrogram: The dendrogram below shows that 3 main classes are created based on similarities between individuals.
# Réaliser l'ACP sans graphique (base de la classification)
res.pca <- PCA(base, graph = FALSE)
# Extraire les coordonnées des individus
ind_coord <- res.pca$ind$coord
# Calculer la Classification Hiérarchique Ascendante (CHA)
res.hcpc <- HCPC(res.pca, graph = FALSE)
# Afficher le dendrogramme
fviz_dend(res.hcpc, rect = TRUE, show_labels = TRUE, main = "Dendrogramme de la classification")## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# 4.2 Graphique des individus
fviz_pca_ind(acp_result,
col.ind = "cos2", # Colorer selon le cos²
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE)
Figure - Ascending Hierarchical Classification of the individuals. The classification made on individuals reveals 3 clusters. The cluster 1 is made of individuals such as liberia and serra lionne. This group is characterized by :
high values for the variables X.ind and précipit (variables are sorted from the strongest).
low values for the variable X.agri. The cluster 2 is made of individuals such as benin, gambie and togo. This group is characterized by :
low values for the variables flux_fin_x10.6 and N_d.infr (variables are sorted from the weakest). The cluster 3 is made of individuals such as burkina faso, ghana, mali, mauritanie and senegal. This group is characterized by :
high values for the variables flux_fin_x10.6, t., stress_H, N_d.infr, qtt_an_eau and X.agri (variables are sorted from the strongest).
low values for the variables précipit, X.ind and X.municip (variables are sorted from the weakest).
The hierarchical tree can be plotted on the factor map, with individuals colored according to their clusters.
12.1 Interpretation :
a. Objective of HCA
The Hierarchical Clustering Analysis (HCA) aims to group countries or observations based on their similarity across the analyzed variables.
It complements the PCA by forming homogeneous clusters of individuals (countries) with similar profiles.
b. Reading the dendrogram
The dendrogram is a tree-shaped graph showing how observations are progressively grouped together.
Two observations that merge early (at a low height) are very similar.
Conversely, those that merge later (at a higher height) are more different.
c. Number of clusters
By cutting the dendrogram at an appropriate level (often where there is a large jump in fusion heights), we can define 3 to 5 clusters (or more depending on your results).
Each cluster brings together countries with similar profiles based on the variables studied in the PCA.
d. Description of the clusters
Cluster 1: Countries with high values on certain variables (e.g., better access to drinking water, high urbanization rate, etc.).
Cluster 2: Countries with greater challenges on several indicators (e.g., low water access, low food security…).
Cluster 3: Intermediate countries, showing average levels across the studied variables.
(The exact interpretation depends on your specific dendrogram and country groupings.)
e. Link with PCA
The clusters identified through HCA correspond well to the patterns seen in the PCA plot.
This confirms the consistency of the analysis: → Countries close together on the factor map often belong to the same cluster in the HCA.
12.2 Hierarchical classification of countries:
After the analysis of the data, it was possible to group the
countries according to the criteria. 
# 12 Recommandations GIRE
Strategic Recommendation for IWRM (Integrated Water Resources Management) Based on Your PCA Your PCA and hierarchical classification show that the countries studied are divided into several distinct groups according to their performance or challenges related to water. Based on this, here is an adapted recommendation:
a. Strengthen capacities for countries facing difficulties
In your analysis, countries like Gambia, Benin, Togo, Sierra Leone, and Liberia are positioned far from the best performers on the main axes.
Recommended actions:
Train local stakeholders in sustainable water resource management.
Strengthen hydraulic infrastructures (wells, boreholes, irrigation networks, dams).
Implement water quality monitoring systems.
Support local governance to develop stronger water management plans.
b. Capitalize on leading countries
Countries like Mali, Senegal, Burkina Faso, and Mauritania demonstrate better performances in water use and management.
Recommended actions:
Share best practices between countries (South-South cooperation).
Strengthen cross-border projects (shared rivers, common aquifers).
Support existing initiatives in irrigation and water mobilization.
c. Adapt management strategies according to group profiles
Thanks to the classification, you identified homogeneous groups: different strategies are needed based on local realities.
Recommended actions:
Tailored approaches: a one-size-fits-all program will not be effective.
Prioritize areas with high water vulnerability for quick interventions.
Integrate social and economic dimensions into local water management projects.
d. Use PCA results to target key areas for improvement
Your PCA highlighted the main discriminant variables.
Recommended actions:
Focus actions on the variables that contributed the most to the axes (e.g., access to drinking water, sanitation coverage, irrigation control…).
Implement specific policies based on these key variables.
e. Strengthen resilience to climate change
Countries must integrate climate adaptation into their water management plans.
Recommended actions:
Promote artificial aquifer recharge.
Support water-resilient agriculture.
Develop early warning systems for droughts and floods.
13 Analysis and interpretation of the regression line:
# Installer et charger les packages nécessaires
if (!require(ggplot2)) install.packages("ggplot2")
if (!require(ggrepel)) install.packages("ggrepel")
library(ggplot2)
library(ggrepel)
# Lire les données
data <- read.csv2("ACCESS_2.csv", sep = ";")
# Nettoyer les noms de colonnes
colnames(data) <- gsub("//.", "", colnames(data))
# Convertir les variables numériques
data$VMED <- as.numeric(gsub(",", ".", data$VMED))
data$PEUDA <- as.numeric(gsub(",", ".", data$PEUDA))
# Régression linéaire simple (stress hydrique en fonction de VMED)
model <- lm(PEUDA ~ VMED, data = data)
summary(model) # Affiche les coefficients et R²##
## Call:
## lm(formula = PEUDA ~ VMED, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.321 -2.869 2.770 5.691 9.466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 81.2271 4.1903 19.385 1.2e-08 ***
## VMED -0.5329 0.3887 -1.371 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.91 on 9 degrees of freedom
## Multiple R-squared: 0.1728, Adjusted R-squared: 0.08084
## F-statistic: 1.88 on 1 and 9 DF, p-value: 0.2036# Extraire les coefficients pour l'équation
intercept <- coef(model)[1]
slope <- coef(model)[2]
r_squared <- summary(model)$r.squared
# Créer l'équation de la droite
equation <- paste0("y = ", round(slope, 2), "x + ", round(intercept, 2))
r2_label <- paste0("R² = ", round(r_squared, 3))
# Visualisation avec régression et équation
ggplot(data, aes(x = VMED, y = PEUDA)) +
geom_point(color = "steelblue", size = 3) + # Points pour chaque pays
geom_smooth(method = "lm", se = FALSE, color = "red") + # Ligne de régression
geom_text_repel(aes(label = Pays), box.padding = 0.5, size = 3) + # Labels des pays
annotate("text", x = min(data$VMED), y = max(data$PEUDA),
label = paste(equation, "/n", r2_label),
hjust = 0, vjust = 1, color = "darkred", size = 5) +
labs(
x = "Variable médiane",
y = "Water stress",
title = "Linear regression of water stress"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
axis.title = element_text(face = "bold"),
legend.position = "none"
)## `geom_smooth()` using formula = 'y ~ x'
This graph plots a linear regression model analyzing the relationship between an independent variable (x) and a country’s level of “water stress” (y). The negative slope of the line indicates an inverse relationship: as the x-variable increases, the predicted level of water stress tends to decrease.
13.1 Interpretation of the Graph:
Regression Equation: y = − 0.53 x + 81.23 y=−0.53x+81.23
This means for every one-unit increase in the x-variable, the model predicts a decrease of 0.53 units in the water stress index.
The starting point (intercept) is 81.23, which would be the predicted water stress level if the x-variable were zero.
R-squared Value: R 2 = 0.173 R 2 =0.173
This is a crucial statistic. It indicates that only 17.3% of the variation in water stress levels between the countries on the graph can be explained by the x-variable used in this model.
This is a relatively low value, suggesting that the model has weak explanatory power. The chosen x-variable is not a strong predictor of water stress on its own; other important factors (e.g., governance, infrastructure, economic investment) are not captured in this model and likely play a larger role.
Regression Line Equation:
The general equation of a regression line is:
Water Stress=a+b×Temperature where:
a = intercept
b = slope of the line
14 Conclusion
The analysis of indicators related to water access highlights the interdependence between resource availability, sectoral uses, quality, network performance, and the resilience of water infrastructure. The results underscore that access to water depends not only on the physical presence of the resource but also on the capacity of technical and institutional systems to mobilize, distribute, and manage it sustainably. The unequal distribution of usage, network losses, variable water quality, and the degradation of infrastructure are all factors that limit equitable access to water and exacerbate local vulnerabilities.
These findings demonstrate the importance of an integrated and anticipatory resource management approach, based on reliable data and strengthened governance. Improving water access requires coordinated efforts : investments in infrastructure, strengthening of local capacities, water planning adapted to climatic pressures, and the optimization of usage across all sectors. By providing a comprehensive analysis of the key variables, this study offers a relevant scientific basis to guide public policies and contribute to guaranteeing equitable, secure, and sustainable access to water for all populations in the long term.
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