Flood-prone districts in Ghana: where river flood risk concentrates
Thirteen of Ghana’s 260 districts stay in the national top 20 for modelled river flood damage under all nine combinations of assumptions I tested. Most sit on the lower Volta and the edge of Greater Accra. Ningo/Prampram, Keta, South Tongu, Ketu South, Weija Gbawe and Ada East lead the list.
- 13 Act
- 13 Investigate
- 234 Monitor
How to read this: orange districts are in the top 20 for modelled damage in all nine tests, amber in some, grey in none. Grey does not mean safe. River flooding only; not an official priority list.
- districts screened for river flood damage to homes and for people exposed
- 260
- districts in the national top 20 for damage under all nine combinations of assumptions
- 13
- of modelled annual damage to homes falls in just 20 districts
- 86.8%
- people live inside the modelled 1-in-100-year flood, about 421,700 of them children
- 1.18million
Which districts in Ghana are most at risk of river flooding?
Thirteen districts stay in Ghana’s top 20 for modelled river flood damage to homes in all nine combinations of assumptions I tested. In order of damage: Ningo/Prampram, Keta, South Tongu, Ketu South, Weija Gbawe, Ada East, Anloga, North Tongu, Ada West, Central Tongu, Shai Osudoku, Shama and Asuogyaman.
I call these the Act districts. Act does not mean stop building or move people. It means look here first: a local flood study, conversations with the communities affected, and a check on how ready the district is for the next flood.
Ningo/Prampram, east of Tema, comes first, with a median of GHS 631 million a year of modelled damage to homes. Keta, on the Keta lagoon just east of the Volta mouth, has the most people inside the 1-in-100-year flood: about 112,300.
Weija Gbawe, on the Densu west of Accra, shows why the test matters. Its rank swings from 1st to 11th as the assumptions change, yet it never leaves the top 20. A single model run would have hidden that swing.
The 13 Act districts cluster on the lower Volta and around Accra
How to read this: switch between priority class, damage and people; tap a district, or choose one from the list, to read its figures. “Accra to Keta” zooms in on the cluster.
Modelled river flooding for all 260 districts. Damage is to homes and their contents; people are those living inside the modelled 1-in-100-year flood.
Sources
Independent consultancy (2025–26), also my Oxford MSc dissertation. Hazard: JRC global river flood hazard maps v2 (CC BY 4.0). Buildings: Google Open Buildings v3. People: WorldPop (CC BY 4.0). Boundaries: OCHA/HDX COD-AB (Ghana Statistical Service).
| District | Region | Damage to homes a year, GHS m (median) | People in the 1-in-100-year flood extent | National rank across 9 combinations | Class |
|---|---|---|---|---|---|
| Ningo/Prampram | Greater Accra | 631.0 | 47,692 | 1st–3rd | Act |
| Keta Municipal | Volta | 598.6 | 112,294 | 1st–3rd | Act |
| South Tongu | Volta | 476.4 | 89,113 | 2nd–4th | Act |
| Ketu South | Volta | 421.3 | 101,768 | 4th–6th | Act |
| Weija Gbawe Municipal | Greater Accra | 414.4 | 51,164 | 1st–11th | Act |
| Ada East | Greater Accra | 371.6 | 63,149 | 5th–6th | Act |
| Anloga | Volta | 369.6 | 63,481 | 4th–7th | Act |
| North Tongu | Volta | 307.4 | 66,672 | 7th–8th | Act |
| Ada West | Greater Accra | 130.6 | 42,492 | 8th–12th | Act |
| Central Tongu | Volta | 107.8 | 27,705 | 9th–17th | Act |
| Shai Osudoku | Greater Accra | 101.3 | 17,392 | 10th–15th | Act |
| Shama | Western | 97.9 | 23,030 | 9th–20th | Act |
| Asuogyaman | Eastern | 70.9 | 15,552 | 13th–19th | Act |
Damage: median expected annual damage to homes and contents, floods from 1 in 10 to 1 in 500 years, flood extent as mapped, JRC Africa damage curve. People: median in the modelled 1-in-100-year flood extent. Rank: best and worst national rank for damage across the nine combinations.
Which districts should be investigated next?
Another 13 districts reach the top 20 in some of the nine combinations but not all. They are Aowin, Wassa Amenfi West, Kpone Katamanso, Twifo Atti-Morkwa, Upper Denkyira East, Kwaebibirem, Birim Central, Ga West, Lower Manya, Ablekuma West, Ketu North, Central Gonja and Akatsi South.
For these Investigate districts the first job is to find out why the result moves. Usually it is the flood edge: whether the river spreads one map cell further, about 90 m, decides whether a town is in or out.
Two examples show the range. Kpone Katamanso, next to Tema, has high modelled damage but only about 3,800 people in the flood. Central Gonja, in Savannah Region, is the reverse: 14th in Ghana for people in the 1-in-100-year flood, 30th for damage.
| District | Region | Damage to homes a year, GHS m (median) | People in the 1-in-100-year flood extent | National rank across 9 combinations | Class |
|---|---|---|---|---|---|
| Aowin | Western North | 71.3 | 17,178 | 11th–25th | Investigate |
| Wassa Amenfi West | Western | 63.5 | 16,552 | 12th–22nd | Investigate |
| Kpone Katamanso | Greater Accra | 62.0 | 3,805 | 13th–21st | Investigate |
| Twifo Atti-Morkwa | Central | 61.8 | 21,271 | 10th–23rd | Investigate |
| Upper Denkyira East Municipal | Central | 54.5 | 21,700 | 16th–21st | Investigate |
| Kwaebibirem | Eastern | 49.0 | 12,533 | 17th–31st | Investigate |
| Birim Central Municipal | Eastern | 38.1 | 6,351 | 13th–28th | Investigate |
| Ga West Municipal | Greater Accra | 33.9 | 3,910 | 19th–31st | Investigate |
| Lower Manya | Eastern | 32.3 | 9,365 | 14th–31st | Investigate |
| Ablekuma West Municipal | Greater Accra | 31.3 | 6,856 | 16th–59th | Investigate |
| Ketu North | Volta | 23.2 | 9,702 | 15th–42nd | Investigate |
| Central Gonja | Savannah | 22.8 | 20,625 | 17th–39th | Investigate |
| Akatsi South | Volta | 15.2 | 5,723 | 17th–49th | Investigate |
Which region of Ghana is most affected by river flooding?
In the model, the Volta Region: about 45% of Ghana’s modelled annual flood damage to homes and 41% of the people inside the 1-in-100-year flood. Greater Accra follows with 35% of the damage and 21% of the people. Together the two hold four-fifths of the damage. derived from the model
These regional shares are my own sums of the district results, so they carry the same limits. Ashanti’s 43 districts together hold about 1% of the damage, and none is Act or Investigate.
The five northern regions show the gap between counting people and counting money. About 76,700 people there live inside the 1-in-100-year flood, around 34,000 of them children, yet their share of damage is small. The model values homes from building size and local night-time light, so the same depth of water costs less to repair where night-time light, and usually income, is lower. That is why a ranking by people belongs beside a ranking by damage.
Volta and Greater Accra hold most of the modelled damage; the people are spread wider
How to read this: each bar is a region’s share of the national total. Switch between damage and people and watch Greater Accra fall and the Western and Central regions rise.
Derived from the model: sums of district medians, modelled river flooding only.
Show all 16 regions as a table
| Region | Share of modelled damage to homes | Share of people in the 1-in-100-year extent | Districts | Act | Investigate |
|---|---|---|---|---|---|
| Volta | 45.1% | 41.2% | 18 | 6 | 2 |
| Greater Accra | 35.1% | 20.5% | 29 | 5 | 3 |
| Western | 6.7% | 10.5% | 14 | 1 | 1 |
| Eastern | 4.5% | 5.5% | 33 | 1 | 3 |
| Central | 4.2% | 7.8% | 22 | 0 | 2 |
| Western North | 1.7% | 2.4% | 9 | 0 | 1 |
| Ashanti | 1.0% | 2.9% | 43 | 0 | 0 |
| Savannah | 0.6% | 2.6% | 7 | 0 | 1 |
| Northern | 0.3% | 1.9% | 16 | 0 | 0 |
| Oti | 0.3% | 1.2% | 8 | 0 | 0 |
| Bono East | 0.1% | 0.9% | 11 | 0 | 0 |
| Bono | 0.1% | 0.4% | 12 | 0 | 0 |
| Ahafo | 0.1% | 0.3% | 6 | 0 | 0 |
| North East | 0.1% | 1.3% | 6 | 0 | 0 |
| Upper East | 0.1% | 0.4% | 15 | 0 | 0 |
| Upper West | 0.0% | 0.3% | 11 | 0 | 0 |
How was the ranking built?
In five steps, from a national flood map to a short list that holds whichever assumptions you prefer.
Fig. 3 · Step by step
From 260 districts to 13 that hold under every assumption
Scroll through the five steps; the map changes with each.Read the five steps in turn; the map shows each result.
1 of 5 · The map
Every district on one national flood map
I started from open global flood maps for five flood sizes, from 1 in 10 to 1 in 500 years, on a 90 m grid. Then I counted the homes and the people inside each flood, district by district.
2 of 5 · Damage
Damage concentrates in a few districts
Damage is the cost of repairing homes and contents for the water depth in each flood, averaged over the years. Twenty districts hold most of it, and the top five almost half.
3 of 5 · Nine tests
Test the answer nine ways
Nobody knows exactly where a global map’s flood edge sits, or how much a given depth costs a Ghanaian home. So I ran three flood edges with three damage curves, each with 1,000 Monte Carlo samples. The shading counts how often each district made the top 20.
4 of 5 · The short list
Thirteen stay at the top every time
Thirteen districts made the top 20 in all nine tests. That is a short list a ministry can start on without first settling an argument about assumptions.
5 of 5 · People
Counting people changes the order
Ranked by people, Keta comes first. The two rankings share the same top ten, but further down they part: Mamprugu Moagduri in North East Region is 32nd for people in the 1-in-100-year flood and 94th for damage in that same flood.
Independent consultancy (2025–26), also my Oxford MSc dissertation; presented for discussion with the World Bank. Modelled river flooding; not an official priority list.
How much damage do river floods do to homes in Ghana?
In the model, about GHS 5.19 billion a year on average, roughly US$413 million, counting floods from 1 in 10 to 1 in 500 years. If every river reached its 1-in-100-year level at once, the damage to homes and contents would be about GHS 67 billion. Where the flood edge sits changes these numbers more than anything else.
The annual figure is a long-run average, not a forecast for next year. Most of it comes from the frequent floods: the 1-in-10 to 1-in-50-year range carries about three-quarters.
Moving the flood edge in by two map cells cuts the annual figure to GHS 2.93 billion; moving it out by one cell raises it to GHS 7.33 billion. That spread is why I ranked districts under every combination instead of trusting one run.
Bigger, rarer floods do more damage, and the flood edge matters most
How to read this: left to right, floods get rarer and bigger. The line is the median damage, the band its P5–P95 range; the dashed lines are the other two flood edges. Tap a flood size to read it.
Damage to homes and contents from one river flood across all 260 districts, JRC Africa damage curve.
Show the data as a table
| Flood | Narrower flood edge, GHS bn (P5–P95) | Flood edge as mapped, GHS bn (P5–P95) | Wider flood edge, GHS bn (P5–P95) |
|---|---|---|---|
| 1 in 10 years (10% a year) | 19.5 (16.1–23.4) | 38.5 (31.8–46.1) | 58.5 (48.3–70.1) |
| 1 in 50 years (2% a year) | 35.5 (29.3–42.6) | 60.6 (50.1–72.6) | 83.5 (69.0–100.1) |
| 1 in 100 years (1% a year) | 40.5 (33.4–48.5) | 67.2 (55.5–80.5) | 90.7 (74.9–108.7) |
| 1 in 200 years (0.5% a year) | 44.4 (36.7–53.2) | 73.1 (60.4–87.6) | 97.5 (80.5–116.8) |
| 1 in 500 years (0.2% a year) | 49.5 (40.9–59.4) | 80.8 (66.7–96.8) | 106.2 (87.7–127.3) |
| Annual average (1 in 10 to 1 in 500) | 2.93 a year (2.42–3.52) | 5.19 a year (4.28–6.21) | 7.33 a year (6.05–8.78) |
Does the model match a real flood?
Where I could test it, yes, reasonably well. I compared the modelled flood with the satellite map of the September 2020 White Volta flood in five 20 km squares. The average agreement score was 0.58. A published test of four global flood models’ 1-in-100-year maps against eight observed floods reported averages of 0.18 to 0.34; the tests differ, so that is context, not a ranking.
The score, the critical success index, is the overlap between the modelled and observed flood divided by everything either one calls flooded. A score of 1 would be a perfect match.
I also checked the people layer. The population data put about 6,370 people inside the satellite flood footprint. NADMO reported 7,561 people affected, but that count is for the Upper East Region and my test areas lie further downstream, so the two do not cover the same ground. I read them only as a sign the population layer is in a plausible range, not as a match.
White Volta, September 2020: model against satellite in five test areas
How to read this: longer bars mean closer agreement between the model and the satellite flood. Tap a bar to see how far the misses were from the flood edge.
Modelled 1-in-10-year flood extent against the Sentinel-1 flood map (Copernicus Global Flood Monitoring), 20 m data aggregated to the 90 m grid.
Show the data as a table
| Test area (20 × 20 km) | Agreement (CSI) | Missed flooding: 9 in 10 within | Extra flooding: 9 in 10 within |
|---|---|---|---|
| Area 1 | 0.57 | 2.79 km | 0.51 km |
| Area 2 | 0.66 | 0.13 km | 0.45 km |
| Area 3 | 0.54 | 0.13 km | 0.37 km |
| Area 4 | 0.71 | 0.28 km | 0.33 km |
| Area 5 | 0.43 | 0.37 km | 0.37 km |
| Mean of the five | 0.58 | – | – |
What does this ranking not show?
A national screen answers one question well: where to look first for river flood risk to homes. It is not the whole picture.
River flooding onlyRain-fed flooding in towns, blocked drains and the sea are not in this ranking. Dam releases from Akosombo, Bagre or Weija are not modelled either.
A zero is not “no risk”37 districts show no people in the modelled flood, Kumasi Metropolitan among them, and Accra Metropolis shows only 66. Most flooding in central Accra is drainage flooding, which this screen does not model.
Homes onlyDamage counts residential buildings and their contents. Roads, farms, businesses and lives are not in the money figures.
Screening, not street levelThe 90 m grid is right for comparing districts, not for saying whether one building floods.
The top 20 is a choiceA top-10 cut gives 7 Act and 6 Investigate districts; a top-30 cut gives 20 and 19. I used 20.
Not officialIndependent consultancy, also my Oxford MSc dissertation, presented for discussion with the World Bank. Not an official priority list of the Government of Ghana or the World Bank.
Questions people ask
Is this an official list of flood-prone districts?
No. It is my independent analysis, also my Oxford MSc dissertation, and it was presented for discussion with the World Bank. It is not an official priority list of the Government of Ghana, NADMO or the World Bank.
My district is “Monitor”. Does that mean it is safe?
No. Monitor only means the district never reached the national top 20 for modelled river flood damage to homes. Local flooding can still be serious, especially drainage flooding in towns, which this screen does not model.
Why is Accra not near the top?
Because the model covers river flooding, and most flooding in central Accra comes from heavy rain overwhelming drains. Accra Metropolis shows only 66 people in the modelled river flood. The Accra districts that do rank, such as Weija Gbawe and Ningo/Prampram, sit beside rivers and lagoons.
How many people in Ghana live where river floods can reach?
About 1.18 million people live inside the modelled 1-in-100-year river flood, including about 421,700 children. Keta, Ketu South and South Tongu have the most. People at risk of flooding in Ghana, by district.
What does a 1-in-100-year flood mean?
A flood with a 1% chance of happening in any year. It can happen two years running; over a 30-year mortgage the chance of at least one is about one in four.
Can the same method be used in another country?
Yes. Every input is open global data: JRC flood maps, Google Open Buildings, WorldPop and night-time lights. The numbers do not transfer between countries, but the method does, including the nine-way test. Flood risk assessment for Ghana and West Africa.
Sources
- Nuhu, H. (2026). National fluvial flood-risk assessment and priority stability under uncertainty in Ghana. MSc dissertation, University of Oxford. Independent consultancy, presented for discussion with the World Bank. Method and results.
- European Commission Joint Research Centre. Global river flood hazard maps, version 2 (CC BY 4.0). Dataset description.
- Google Research. Open Buildings v3 building footprints. Dataset description.
- WorldPop, University of Southampton. Gridded population, 2026 (CC BY 4.0). worldpop.org.
- Earth Observation Group. VIIRS night-time lights, used to value homes. eogdata.mines.edu.
- Ghana Statistical Service / OCHA. Ghana subnational administrative boundaries (COD-AB). Humanitarian Data Exchange.
- Huizinga, J., de Moel, H. and Szewczyk, W. (2017). Global flood depth-damage functions. European Commission Joint Research Centre.
- Copernicus Emergency Management Service. Global Flood Monitoring, Sentinel-1 flood extent, September 2020. GloFAS.
- Risling, A., Lindersson, S. and Brandimarte, L. (2024). A comparison of global flood models using Sentinel-1 and a change detection approach. Natural Hazards 120, 11133–11152. doi:10.1007/s11069-024-06629-7.
- Bernhofen, M. V. et al. (2018). A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique. Environmental Research Letters. Record.
Work with me
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