USAID Flood Response – Post Intervention Survey (Mulanje, 2019–2020)

This dataset contains detailed post-intervention monitoring data for rural water points in the Mulanje district of Malawi, collected as part of the USAID Flood Response program during 2019 and 2020. Using the mWater mobile data collection platform, enumerators conducted on-site assessments of water point conditions following flood recovery efforts.

The data captures a comprehensive range of water point characteristics, including physical condition through photographs, operational performance of pumps, and hydraulic measurements such as time and effort required to pump a standard volume of water. Additionally, water quality parameters were rigorously tested—covering chemical contaminants like arsenic, ammonia, fluoride, nitrate, free chlorine, and total dissolved solids, as well as physical indicators such as pH, temperature, and turbidity.

Microbiological quality was assessed via E. coli concentrations, including counts per 100 milliliters, confidence intervals, and risk classifications, supported by photographic documentation of test results. These indicators provide critical insight into the safety and usability of water sources after flood-related disruptions.

Use Cases

This dataset serves multiple practical purposes for water management and public health:

• Evaluating the effectiveness of flood recovery interventions on water infrastructure.

• Monitoring water quality trends to identify ongoing or emerging contamination risks.

• Informing maintenance and rehabilitation priorities based on pump performance and structural assessments.

• Supporting public health risk assessments through microbial contamination data.

• Providing evidence for community-level decision making and donor reporting.

• Guiding future emergency preparedness and response planning for water systems in flood-prone areas.

Potential Users

The dataset is highly valuable to a range of stakeholders including:

1. Government agencies responsible for water supply and sanitation, particularly at the district and national levels.

2. International donor organizations and development partners managing WASH and disaster recovery programs.

3. Field engineers and technical teams engaged in infrastructure repair and monitoring.

4. Public health officials tracking waterborne disease risks.

5. Researchers studying environmental health, water security, and climate resilience.

6. NGOs and civil society organizations supporting community water management and advocacy.

Installation

You can install the development version of postfloodintervention from GitHub with:

# install.packages("devtools")
devtools::install_github("openwashdata/postfloodintervention")

## Run the following code in console if you don't have the packages
## install.packages(c("dplyr", "knitr", "readr", "stringr", "gt", "kableExtra"))
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
library(postfloodintervention)
data(postfloodintervention)

Alternatively, you can download the individual datasets as a CSV or XLSX file from the table below.

1. Click Download CSV. A window opens that displays the CSV in your browser.
2. Right-click anywhere inside the window and select “Save Page As…”.
3. Save the file in a folder of your choice.

dataset	CSV	XLSX
postfloodintervention	Download CSV	Download XLSX

Data

The package provides access to post-intervention monitoring data for rural water points in the Mulanje district of Malawi, collected as part of the USAID Flood Response program during 2019 and 2020.

postfloodintervention

The dataset postfloodintervention contains 257 observations and 28 variables

postfloodintervention |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()

date_of_sample	waterpoint_id	latitude	longitude	operational_feel_of_pump	time_to_pump_20_litres	number_of_strokes_to_yield_water	sediment_presence	electrical_conductivity_magnitude	electrical_conductivity_units	arsenic_magnitude	arsenic_units	ammonia_mg_per_l	fluoride_ppm	nitrate_mg_per_l	total_dissolved_solids_ppt	free_chlorine_mg_per_l	ph	temperature_magnitude	temperature_units	turbidity_tube_magnitude	turbidity_tube_units	comments	type_of_sample	ecoli_mpn_per_100ml	ecoli_upper_95ci_per_100ml	ecoli_health_risk_category	ecoli_image
17/06/2019	79477116	-16.00085	35.43693	Normal/satisfactory	39.941	1	Absent	176.4	μS / cm	5.0	ppb	0.15	0.2	0.5	0.80	0	6.40	24.6	C	5	NTU	NA	Point of collection	0	2.87	safe	https://api.mwater.co/v3/images/7fbb039ed2324a6da2a27168b59bcaa0
17/06/2019	12060128	-15.99983	35.44297	Normal/satisfactory	37.851	1	Absent	215.7	μS / cm	0.5	ppb	2.40	0.2	0.5	0.10	0	6.20	25.0	C	5	NTU	NA	Point of collection	0	2.87	safe	https://api.mwater.co/v3/images/f9d763bd60374df48e921d0a77f595a9
17/06/2019	12079117	-15.99827	35.43772	Normal/satisfactory	42.281	1	Absent	4630.0	μS / cm	10.0	ppb	0.15	2.0	0.5	0.22	0	6.18	25.1	C	5	NTU	The borehole is now ina very good condition and the borehole committee members are very happy with the performance.	Point of collection	0	2.87	safe	https://api.mwater.co/v3/images/fdaacc5054aa4bd6a90701d54e112196

For an overview of the variable names, see the following table.

variable_name	variable_type	description
date_of_sample	character	Date when the water sample was collected
waterpoint_id	numeric	Geographic latitude coordinate of the water point
latitude	numeric	Geographic longitude coordinate of the water point
longitude	numeric	File names or URLs of photos illustrating the current condition of the water point
operational_feel_of_pump	character	Qualitative assessment of how the pump feels during operation
time_to_pump_20_litres	numeric	Time taken (in seconds or minutes) to pump 20 liters of water
number_of_strokes_to_yield_water	numeric	Number of pump strokes needed to produce water
sediment_presence	character	Presence or absence of sediment in the water
electrical_conductivity_magnitude	numeric	Measured magnitude of electrical conductivity in the water sample
electrical_conductivity_units	character	Units of electrical conductivity measurement (e.g., μS/cm)
arsenic_magnitude	numeric	Concentration of arsenic detected in the water sample
arsenic_units	character	Units used to measure arsenic concentration (e.g., μg/L)
ammonia_mg_per_l	numeric	Ammonia concentration in mg per liter
fluoride_ppm	numeric	Fluoride concentration in parts per million
nitrate_mg_per_l	numeric	Nitrate concentration in mg per liter
total_dissolved_solids_ppt	numeric	Total dissolved solids in parts per thousand
free_chlorine_mg_per_l	numeric	Concentration of free chlorine in mg per liter
ph	numeric	pH level of the water sample (acidity/alkalinity)
temperature_magnitude	numeric	Temperature value of the water sample
temperature_units	character	Units for temperature measurement (degree celsius)
turbidity_tube_magnitude	numeric	Measured turbidity of water using a turbidity tube
turbidity_tube_units	character	Units for turbidity measurement (e.g., NTU)
comments	character	Additional notes or observations related to the water point or sample
type_of_sample	character	Type or source of the water sample (e.g., well, tap, river)
ecoli_mpn_per_100ml	numeric	Most probable number (MPN) of E. coli bacteria per 100 milliliters
ecoli_upper_95ci_per_100ml	numeric	Upper limit of the 95 percent confidence interval for E. coli MPN per 100 ml
ecoli_health_risk_category	character	Health risk classification based on E. coli levels (e.g., low, medium, high)
ecoli_image	character	File name or URL of image showing E. coli test result or sample compartment color change

Example

## Run the following code in console if you don't have the packages
## install.packages(c("postfloodintervention", "tidyverse"))
library(postfloodintervention)

# Water Quality Parameters
# Purpose: Multi-panel boxplots for chemical indicators (arsenic, fluoride, nitrate, ammonia, free chlorine, pH) to detect outliers or contamination patterns.

# Load libraries
library(tidyverse)

# Select relevant chemical columns and pivot longer for plotting
chemicals_long <- postfloodintervention %>%
  select(arsenic_magnitude, fluoride_ppm, nitrate_mg_per_l, ammonia_mg_per_l, free_chlorine_mg_per_l, ph) %>%
  pivot_longer(
    cols = everything(),
    names_to = "chemical",
    values_to = "value"
  ) %>%
  filter(!is.na(value))  # Remove missing values

# Plot multi-panel boxplots
ggplot(chemicals_long, aes(x = chemical, y = value)) +
  geom_boxplot(fill = "#4a90e2", outlier.color = "red") +
  facet_wrap(~ chemical, scales = "free") +   # Free y-scale per chemical
  labs(
    title = "Water Quality Parameters: Chemical Indicators",
    x = NULL,
    y = "Concentration"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_blank(),   # Hide x labels since facets show names
        axis.ticks.x = element_blank())

License

Data are available as CC-BY.

Citation

Please cite this package using:

citation("postfloodintervention")
#> To cite package 'postfloodintervention' in publications use:
#> 
#>   Mhango E (2025). "postfloodintervention: USAID Flood Response Post
#>   Intervention Survey Data." doi:10.5281/zenodo.15837460
#>   <https://doi.org/10.5281/zenodo.15837460>,
#>   <https://github.com/openwashdata/postfloodintervention>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{mhango:2025,
#>     title = {postfloodintervention: USAID Flood Response Post Intervention Survey Data},
#>     author = {Emmanuel Mhango},
#>     year = {2025},
#>     doi = {10.5281/zenodo.15837460},
#>     url = {https://github.com/openwashdata/postfloodintervention},
#>     abstract = {Post-intervention monitoring data for rural water points in the Mulanje district of Malawi, collected as part of the USAID Flood Response program during 2019-2020. The dataset includes comprehensive water point assessments covering physical condition, operational performance, hydraulic measurements, water quality parameters, and microbiological quality assessments.},
#>     keywords = {open data,washdata,water points,water quality,boreholes,rehabilitation,flood response,E. coli,Malawi},
#>     version = {0.1.2},
#>   }