This dataset contains groundwater monitoring data collected from monitoring wells across 10 districts in Malawi between 2024 and 2025. The data were captured using automated data loggers installed in monitoring wells and were collected and managed by BASEflow.
The dataset provides time-series measurements of key groundwater parameters, enabling detailed analysis of aquifer behavior across multiple geographic locations.
Variables Included
-
Date – Date of measurement
-
Waterpoint Name – Name of the monitoring site
-
District – Administrative district where the monitoring well is located
-
Latitude & Longitude – Geographic coordinates of the monitoring well
-
Source – Data collection method (automated data logger)
-
Water Level – Groundwater level measurement (typically meters below ground level, depending on installation reference)
-
Temperature – Groundwater temperature (°C)
-
Conductivity – Electrical conductivity (µS/cm), indicating dissolved ion concentration and groundwater quality characteristics
The use of automated loggers ensures high-frequency, consistent, and reliable measurements suitable for time-series analysis and hydrogeological assessment.
- Purpose and Use Cases 1. Groundwater Resource Monitoring
-
Tracking spatial and temporal groundwater level variations across districts
-
Assessing seasonal recharge and depletion patterns
-
Identifying long-term aquifer trends
- Water Quality Surveillance
-
Monitoring conductivity trends as a proxy for salinity and mineralization
-
Detecting potential contamination or quality shifts
- Climate and Drought Analysis
-
Supporting drought early warning systems
-
Evaluating groundwater resilience to climate variability
- Infrastructure Management
-
Informing borehole design and pump installation depths
-
Supporting preventive maintenance planning
-
Assessing borehole performance over time
- Hydrogeological Research and Modelling
-
Input data for groundwater flow and recharge models
-
Calibration of aquifer simulations
-
Comparative inter-district hydrogeological analysis
- Policy, Regulation, and Planning
-
Evidence base for district-level and national water resource planning
-
Supporting groundwater abstraction regulation
-
Informing investment decisions in rural and urban water supply
Potential Users
-
Ministry responsible for Water and district water offices
-
Hydrologists and hydrogeologists
-
WASH sector NGOs and implementing partners
-
Academic and research institutions
-
Climate and environmental analysts
-
Development partners supporting water security and resilience programs
This dataset provides a structured, multi-district groundwater evidence base to support sustainable groundwater management and water security planning in Malawi.
You can install the development version of mwgroundwaterdata from GitHub with:
# install.packages("devtools")
devtools::install_github("openwashdata/mwgroundwaterdata")## 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)Alternatively, you can download the individual datasets as a CSV or XLSX file from the table below.
- Click Download CSV. A window opens that displays the CSV in your browser.
- Right-click anywhere inside the window and select “Save Page As…”.
- Save the file in a folder of your choice.
| dataset | CSV | XLSX |
|---|---|---|
| mwgroundwatta.rda | Download CSV | Download XLSX |
The package provides access to This dataset contains groundwater monitoring data collected from monitoring wells across 10 districts in Malawi between 2024 and 2025. The data were captured using automated data loggers installed in monitoring wells and were collected and managed by BASEflow.
library(mwgroundwaterdata)The dataset mwgroundwaterdata contains 1415 observations and 9
variables
mwgroundwaterdata |>
head(3) |>
gt::gt() |>
gt::as_raw_html()| date | waterpoint_name | district | latitude | longitude | source | water_level | temperature | conductivity |
|---|---|---|---|---|---|---|---|---|
| 31/12/2024 | Balaka Water Office | Balaka | -14.9923 | 34.95948 | Logger | 3.1000 | 27.415 | 896.8 |
| 31/12/2024 | Balaka Water Office | Balaka | -14.9923 | 34.95948 | Logger | 2.7012 | 27.415 | 895.8 |
| 1/1/2025 | Balaka Water Office | Balaka | -14.9923 | 34.95948 | Logger | 3.3367 | 27.416 | 895.7 |
For an overview of the variable names, see the following table.
|
variable_name |
variable_type |
description |
|---|---|---|
|
date |
character |
Date when data was captured |
|
waterpoint_name |
character |
The name of the water point |
|
district |
character |
Administrative district the water point is located |
|
latitude |
numeric |
GPS latitude coordinate |
|
longitude |
numeric |
GPS longitude coordinate |
|
source |
character |
The device that captured the information |
|
water_level |
numeric |
Water level of the water |
|
temperature |
numeric |
Temperature of the data |
|
conductivity |
numeric |
Electical conductivity of the water |
library(mwgroundwaterdata)
# Visualization: Geospatial Map
# Import the libraries to be used
library(tidyverse)
library(lubridate)
library(leaflet)
# Create summary dataset INSIDE the README
well_summary <- mwgroundwaterdata %>%
group_by(waterpoint_name, latitude, longitude, district) %>%
summarise(
avg_water_level = mean(water_level, na.rm = TRUE),
avg_conductivity = mean(conductivity, na.rm = TRUE),
.groups = "drop"
)
leaflet(well_summary) %>%
addTiles() %>% # OpenStreetMap tiles
addCircleMarkers(~longitude, ~latitude,
radius = ~avg_conductivity/400,
color = ~colorNumeric("plasma", avg_water_level)(avg_water_level),
popup = ~paste0(waterpoint_name, "<br>Avg Water Level: ", round(avg_water_level,2),
"<br>Avg Conductivity: ", round(avg_conductivity,1))) %>%
addLegend("bottomright",
pal = colorNumeric("plasma", well_summary$avg_water_level),
values = well_summary$avg_water_level,
title = "Avg Water Level (m)")Avg Water Level: 2.86
Avg Conductivity: 893.3","Chidoole Primary School
Avg Water Level: 16.07
Avg Conductivity: 225.4","Chikwawa Post Office
Avg Water Level: 34.17
Avg Conductivity: 5686.2","Chilayeni Primary School
Avg Water Level: 9
Avg Conductivity: 338.7","Chilomoni Police Station
Avg Water Level: 44.03
Avg Conductivity: 459.3","Kawiya Ccap
Avg Water Level: 6.2
Avg Conductivity: 302.5","Khwalala Primary School
Avg Water Level: 15.23
Avg Conductivity: 1365.1","Malaka Primary School
Avg Water Level: 7.21
Avg Conductivity: 760.6","Mikalati Primary School
Avg Water Level: 5.89
Avg Conductivity: 330.3","Mmanga Cdss
Avg Water Level: 10.68
Avg Conductivity: 916.6","Mokhoto Primary School
Avg Water Level: 22
Avg Conductivity: 1204.1","Mpatseabwire
Avg Water Level: 24.48
Avg Conductivity: 912.1","Mulanje Water Office
Avg Water Level: 12.17
Avg Conductivity: 121.9","Mwanza Prison
Avg Water Level: 5.99
Avg Conductivity: 332.1","Nansomba Lea
Avg Water Level: 4.91
Avg Conductivity: 939.8","Ngabu Water Office
Avg Water Level: 8.45
Avg Conductivity: 2144","Nsambangombe
Avg Water Level: 22.64
Avg Conductivity: 349.3","Nsanje Water Office
Avg Water Level: 12.84
Avg Conductivity: 420.6","Ntaja Water Office
Avg Water Level: 11.52
Avg Conductivity: 159.3"],null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addLegend","args":[{"colors":["#0D0887 , #2C0594 5.19498421325033%, #6001A6 17.3411621089675%, #8E0CA4 29.4873400046848%, #B52F8C 41.633517900402%, #D45270 53.7796957961192%, #EB7655 65.9258736918364%, #FB9F3A 78.0720515875536%, #FCCE25 90.2182294832708%, #F0F921 "],"labels":["5","10","15","20","25","30","35","40"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"bottomright","type":"numeric","title":"Avg Water Level (m)","extra":{"p_1":0.05194984213250334,"p_n":0.9021822948327082},"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[-16.92180746,-14.8676492],"lng":[34.4642941,35.60694735]}},"evals":[],"jsHooks":[]}</script>
Data are available as CC-BY.
Please cite this package using:
citation("mwgroundwaterdata")
#> To cite package 'mwgroundwaterdata' in publications use:
#>
#> Mhango E (2026). _mwgroundwaterdata: Malawi Groundwater Monitoring
#> Time-Series Dataset (2024–2025)_. R package version 0.0.0.9000,
#> <https://github.com/openwashdata/mwgroundwaterdata>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {mwgroundwaterdata: Malawi Groundwater Monitoring Time-Series Dataset (2024–2025)},
#> author = {Emmanuel Mhango},
#> year = {2026},
#> note = {R package version 0.0.0.9000},
#> url = {https://github.com/openwashdata/mwgroundwaterdata},
#> }