\usepackage{hyperref}

Renewable Water Resources in Eastern Africa

Author

Benton Otieno

Published

February 16, 2024

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Introduction

Water security mainly refers to issues pertaining to quality, reliability, quantity, environmental provisioning, and equitable access to water supplies. According to the working definition of UN Water, water security can be defined as: “The capacity of a population to safeguard sustainable access to adequate quantities and acceptable quality of water for sustaining livelihoods, human well-being, and socio-economic development, for ensuring protection against water-borne pollution and water related disasters, and for preserving ecosystems in a climate of peace and political stability” (Gunda et al. 2019).

Africa has a combined population of over 1 billion people and represents about 17% of the global population. Its water resources are estimated to be nearly 9% of the world’s freshwater resources (Haile et al. 2020). However, these resources are unevenly distributed, with the most water-rich countries in Central and Western Africa holding 54% of the continent’s total resources and the 27 most water-poor countries holding only 7%. In the Eastern Africa region, countries are characterised by varying degrees of water scarcity as given by Mapani et al. (2019).

In 2004, the African Union (AU) launched the Africa Water Vision for 2025, which seeks to prevent the threats posed to water security in Africa and create a future where there is an equitable and sustainable use and management of water resources for poverty alleviation, socio-economic development, regional cooperation, and the environment. This will be achieved by sustainably managing Africa’s waters, rich biodiversity, land, forests, and putting in place adaptive measures for mitigating climate change (Mutschinski and Coles 2021) .The current study sought to analyse the renewable water resources and water withdrawals and access in the Eastern Africa region.

Methods

Data was collected from AQUASTAT database of the World Food and Agriculture Organisation (FAO), stored on an excel workbook, then imported to R for cleaning and analysis to determine the renewable water resources in the region, water withdrawals, and access. The FAO AQUASTAT is a global information system on water resources and serves as a comprehensive platform that disseminates data for water resource management across the world.

Data importation and cleaning

Code 
#load packages for data cleaning 
library(readxl) 
library(dplyr) 
library(tidyr) 
library(lubridate) 
library(readxl) 
library(readr) 
library(janitor) 
library(tidyverse)
library(markdown)
Code 
#Import data and save as a new data frame
data_water <-  read_excel(here::here("data/raw/water_resources_east_africa_Bentono.xlsx"))
Code 
#Select columns "1" to "20" and rows "1" to "15" 
data_water_subset <- data_water[1:15, 1:20]  
  

#Change column infromation and leave NAs as blank 
data_water_subset <- data_water_subset %>%   mutate(across(c(int_renew_wat, tot_renew_wat, depend_ratio, tot_renew_inhab, tot_dam_cap, irrig_withd, pop_safe_wat, rural_pop_safe_wat, urban_pop_safe_wat, latest_year),                  ~ifelse(is.na(.), "", .)))
Code 
#Save tidy data in the processed folder 
write_csv(data_water_subset, here::here("data/processed/final_capstone_data.csv"))

Results

Freshwater sustainability is the most crucial sustainable development challenge since it deals with most needed and finite resource on earth. Availability of freshwater resources plays an important role in determining the socio-economic development of a country or region. Table 1 gives the renewable water resources in the region by average and country. As of 2015, Kenya’s and South Sudan’s renewable water resources per capita were rated between 500 to 1,000 m3 (water scarcity). Tanzania and Uganda were rated between 1,700 to 2,500 m3 (vulnerable) the remaining countries in the east African region were rated between 1,000 to 1,700 m3 (water stressed) as depicted in Figure 1.

Code 
#Loading packages for data analysis

library(tidyverse)
library(readxl)
library(janitor)
library(rmarkdown)
library(ggplot2)
library(gt)
library(scales)
library(readr)
library(here)
library(knitr)
library(kableExtra)
library(magrittr)
library(htmltools)
library(dplyr)
library(stringr)
library(tidyr)
library(plotrix)
library(ggrepel)
library(cowplot)
library(bibtex)
Code 
#Printing Table 1 with a new row showing averages

subset <- data_water_subset[, 1:4]


coerce_numeric <- function(x) {
  as.numeric(as.character(x))
}

# Convert columns 2, 3, and 4 to numeric
subset[, 2:4] <- lapply(subset[, 2:4], coerce_numeric)

# Calculate average for columns 2, 3, and 4 excluding NA values
averages <- colMeans(subset[, 2:4], na.rm = TRUE)

# Create a new row with the averages
average_row <- c("Average", round(averages, 3))

# Add the new row to the data frame
subset <- rbind(subset, average_row)

#Delete extra rows
subset <- subset[-c(17), ]

# Define the column widths
column_widths <- c(80, 100, 150, 150)


# Define the new column names
new_column_names <- c("Country", "Precipitation (mm/yr)", "Internal renewable water (million m3)", "Total renewable water (million m3)")

# Replace the existing column names with the new ones
colnames(subset) <- new_column_names

# Print the plot of Table 1
knitr::kable(subset, "html") %>%  # Render the table using knitr
  kableExtra::add_header_above(c("Table 1: Renewable water Resources in East Africa (2013-2017)" = 4)) %>%  # Add the header with the specified title
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE,  # Apply styling to the table
                            latex_options = c("label = tab:water_resources",  # Set the label for cross-referencing
                                              "caption = {\\label{tab:water_resources} Table 1: Renewable water Resources in East Africa (2013-2017)}")) %>%  # Set the caption with the label
  kableExtra::column_spec(seq_len(ncol(subset)), width = c(80, 100, 150, 150))  # Specify column widths
Table 1: Renewable water Resources in East Africa (2013-2017)
Country Precipitation (mm/yr) Internal renewable water (million m3) Total renewable water (million m3)
Burundi 1274 10.06 12.54
Comoros 900 1.2 1.2
Djibouti 220 0.3 0.3
Eritrea 384 2.8 7.315
Ethiopia 848 122 122
Kenya 630 20.7 30.7
Madagascar 1513 337 337
Mauritius 2041 2.751 2.751
Rwanda 1212 9.5 13.3
Seychelles 2330 NA NA
Somalia 282 6 14.7
South Sudan 900 26 49.5
Sudan 250 4 37.8
Uganda 1180 39 60.1
Tanzania 1071 84 96.27
Average 1002.333 47.522 56.105
Code 
#Plotting Figure 1 (Bar graph of Country vs Totat renewable water resources/inhabitant)

# Copy the original data frame and update the column name
renamed_df <- data_water_subset %>%
  select(Country, tot_renew_inhab) %>%  
  rename(`Total renewable water resources per inhabitant` = tot_renew_inhab) %>%  
  mutate(`Total renewable water resources per inhabitant` = as.character(`Total renewable water resources per inhabitant`),  
         `Total renewable water resources per inhabitant` = if_else(is.na(`Total renewable water resources per inhabitant`), "0", `Total renewable water resources per inhabitant`),  # Replace NA with "0" as character
         `Total renewable water resources per inhabitant` = as.numeric(`Total renewable water resources per inhabitant`))  # Convert to numeric

plot <- ggplot(data = renamed_df, aes(x = reorder(Country, `Total renewable water resources per inhabitant`), 
                                      y = `Total renewable water resources per inhabitant`, 
                                      fill = Country)) +
  geom_bar(stat = "identity") +
  labs(x = "Country", y = "Total renewable water resources per inhabitant (m3)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) + 
  scale_fill_discrete(guide = "none") + 
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10), 
                   breaks = function(x) x[seq(1, length(x), by = 1)]) + 
  scale_y_continuous(breaks = seq(0, 14000, by = 2000)) + 
  coord_cartesian(ylim = c(0, 14000)) +
  labs(caption = "Figure 1: Renewable water resources in East Africa (2013-2017)", 
     label = "fig:plot") +  # Set label for cross-reference
  theme(plot.caption = element_text(hjust = 0))  # Left-align caption

# Print the plot
print(plot)

Of the Eastern Africa countries, Eritrea and Djibouti faced the highest water stress index levels with a ratio of water withdrawals to supply more than 80%. The remaining countries had low water stress (less than 10%) except Tanzania with a low to medium stress (10 – 20%). The water stress index was developed for countries, governments, and companies to identify the possible risk of water supply interruptions to businesses, agricultural activities, operations, domestic and industrial use, and other water related activities. Owing to population growth, climate change, expanded irrigation, improved living standards, and increased industrial activities, the gap between water demand and supply has been growing resulting in reduced freshwater availability. Rapid urbanization and expanded irrigation farming (industrial and commercial) are threatening the limited water resources, including freshwater sources undefined. Agriculture (Figure 2) being the main economic activity in the region accounts for up to 87% use of the available wateresources as compared to municipal (Figure 3) and industrial (Figure 4) uses. The values given for the withdrawals are varyring estimates between 1999 and 2017.

Code 
#Plotting Figures 2, 3, and 4 showing renewable water withdrawals by different ssectors per country
# Convert relevant columns to numeric
data_water_subset[, c("agric_withd", "ind_withd", "munic_withd")] <- 
  lapply(data_water_subset[, c("agric_withd", "ind_withd", "munic_withd")], as.numeric)

# Selecting required columns
subset_data <- data_water_subset[, c("Country", "agric_withd", "ind_withd", "munic_withd")]
       

# Create the bubble chart with country names and color coding for Agricultural withdrwal (Figure 2) 
ggplot(subset_data, aes(x = Country, y = agric_withd, size = agric_withd, color = agric_withd)) +
  geom_point() +
  geom_text(aes(label = Country), nudge_y = 3, size = 1.9) + # Add country names over bubbles
  scale_size_continuous(range = c(3, 10), guide = "none") + # Remove size legend
  scale_color_gradient(low = "blue", high = "red") + # Color gradient based on values
  labs(x = "Country",
       y = "Agricultural water withdrawal",
       color = NULL, # Remove color legend title
       size = NULL, # Remove size legend title
       caption = paste0("Figure 2: Agricultural water withdrawal in Eastern Africa")) + # Add figure label
  theme(axis.text.x = element_blank(), # Remove X-axis text
        axis.ticks.x = element_blank(),  # Remove X-axis ticks
        plot.caption = element_text(hjust = 0)) # Align caption to the left

Code 
# Create the bubble chart with country names and color coding for Municipal withdrawal (Figure 3)
ggplot(subset_data, aes(x = Country, y = munic_withd, size = munic_withd, color = munic_withd)) +
  geom_point() +
  geom_text(aes(label = Country), nudge_y = 0.1, size = 2.1) + # Add country names over bubbles
  scale_size_continuous(range = c(3, 10), guide = "none") + # Remove size legend
  scale_color_gradient(low = "blue", high = "red") + # Color gradient based on values
  labs(x = "Country",
       y = "Municipal water withdrawal",
       color = NULL, # Remove color legend title
       size = NULL,
       caption = paste0("Figure 3: Municipal water withdrawal in Eastern Africa")) + # Add figure label
  theme(axis.text.x = element_blank(), # Remove X-axis text
        axis.ticks.x = element_blank(), # Remove X-axis ticks
        plot.caption = element_text(hjust = 0)) # Align caption to the left

Code 
# Create the bubble chart with country names and color coding for Industrial withdrwal (Figure 4)
ggplot(subset_data, aes(x = Country, y = ind_withd, size = ind_withd, color = ind_withd)) +
  geom_point() +
  geom_text(aes(label = Country), nudge_y = 0.03, size = 2.3) + # Add country names over bubbles
  scale_size_continuous(range = c(3, 10), guide = "none") + # Remove size legend
  scale_color_gradient(low = "blue", high = "red") + # Color gradient based on values
  labs(x = "Country",
       y = "Industrial water withdrawal",
       color = NULL, # Remove color legend title
       size = NULL,
       caption = paste0("Figure 4: Industrial water withdrawal in Eastern Africa")) + # Add figure label
  theme(axis.text.x = element_blank(), # Remove X-axis text
        axis.ticks.x = element_blank(), # Remove X-axis ticks
        plot.caption = element_text(hjust = 0)) # Align caption to the left

There has been an improvement in access to sanitation and clean drinking water (Table 2), though the trend will decline given the rapid urbanization and increased degradation of the environment (Sherry et al. 2019). Countries such as Comoros, Mauritius, Seychelles, and Djibouti have the highest access to clean drinking water with more than 90% of the population having access. In Kenya, Burundi, Rwanda, and Uganda, between 60 to 80% of the population can access clean drinking water. Ethiopia, Eritrea, Madagascar, Sudan, South Sudan, and Tanzania all have between 50 to 60% of their population accessing clean drinking water. Somalia has the lowest access in the region a

Code 
# Printing Table 2; Access to safe drinking water in Eatern Africa
# Selecting specific columns
water_access <- data_water_subset[c("Country", "pop_safe_wat", "rural_pop_safe_wat", "urban_pop_safe_wat")]

# Renaming the columns
colnames(water_access) <- c("Country", "Total", "Rural", "Urban")

# Omitting rows with NA values
water_access <- na.omit(water_access)

# Rounding the values to one decimal place
water_access[, 2:4] <- lapply(water_access[, 2:4], function(x) round(as.numeric(x), 1))

knitr::kable(water_access, "html") %>%
  kableExtra::add_header_above(c("Table 2: Access (population percent) to safe drinking water in Eastern Africa (2015)" = 4)) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE, latex_options = "label = tab:water_resources") %>%
  kableExtra::column_spec(seq_len(ncol(subset)), width = c(80, 100, 150, 150))
Table 2: Access (population percent) to safe drinking water in Eastern Africa (2015)
Country Total Rural Urban
Burundi 75.9 73.8 91.1
Comoros 90.1 89.1 92.6
Djibouti 90.0 64.7 97.4
Eritrea 57.8 53.3 73.2
Ethiopia 57.3 48.6 93.1
Kenya 63.2 56.8 81.6
Madagascar 51.5 35.3 81.6
Mauritius 99.9 99.8 99.9
Rwanda 76.1 71.9 86.6
Seychelles 95.7 95.7 95.7
Somalia NA NA NA
South Sudan 58.7 56.9 66.7
Sudan 55.5 50.2 66.0
Uganda 79.0 75.8 95.5
Tanzania 55.6 45.5 77.2

Conclusion

  • The Eastern Africa region has limited water resources representing about 6.5% of Africa’s internal resources.

  • Water resource distribution within the region varies significantly with four aridity zones including moist sub-humid, dry sub-humid, semi-arid, and arid.

  • The western part of Eastern Africa has surplus rainfall, while the northern areas have large water deficits.

Acknowledgement

This work was developed following the successful completion of the data science for open wash data course. The author is grateful to Lars Schöbitz of the Global Health Engineering group at ETH Zurich for the training.

References

Gunda, Thushara, David Hess, George M. Hornberger, and Scott Worland. 2019. “Water Security in Practice: The Quantity-Quality-Society Nexus.” Water Security 6 (March): 100022. https://doi.org/10.1016/J.WASEC.2018.100022.
Haile, Gebremedhin Gebremeskel, Qiuhong Tang, Seyed Mohammad Hosseini-Moghari, Xingcai Liu, T. G. Gebremicael, Guoyong Leng, Asfaw Kebede, Ximeng Xu, and Xiaobo Yun. 2020. “Projected Impacts of Climate Change on Drought Patterns over East Africa.” Earth’s Future 8: 1–23. https://doi.org/10.1029/2020EF001502.
Mapani, Benjamin, Hodson Makurira, Lapologang Magole, Maideyi Meck, Theresa Mkandawire, Marloes Mul, and Cosmo Ngongondo. 2019. “Integrated Water Resources Development and Management:innovative Technological Advances for Water Security in Eastern and Southern Africa.” Physics and Chemistry of the Earth 112: 1–2. https://doi.org/10.1016/j.pce.2019.08.004.
Mutschinski, Kristin, and Neil A. Coles. 2021. “The African Water Vision 2025: Its Influence on Water Governance in the Development of Africa s Water Sector, with an Emphasis on Rural Communities in Kenya: A Review.” Water Policy. IWA Publishing. https://doi.org/10.2166/wp.2021.032.
Sherry, Julia, Luke Juran, Korine N. Kolivras, Leigh Anne H. Krometis, and Erin J. Ling. 2019. “Perceptions of Water Services and Innovations to Improve Water Services in Tanzania.” Public Works Management and Policy 24: 260–83. https://doi.org/10.1177/1087724X18815486.