electricsheepafrica/africa-fao-eve-global-flood-monitoring-system

FAO EVE Global Flood Monitoring System

Publisher: Food and Agriculture Organization (FAO) of the United Nations · Source: HDX · License: cc-by · Updated: 2026-04-03

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Abstract

The DIEM Events Visualization in Emergencies (EVE) system provides resources to enhance the understanding of flood events and their impact on different land cover types, with a particular focus on agricultural areas. EVE provides a flood persistence analysis as well as an estimation of the population exposed to such events.

EVE utilizes satellite-derived data from the NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) at a 375-meter resolution, alongside land cover data from the European Space Agency’s WorldCover 10m 2021 dataset. Covering approximately 40 countries, the system offers daily and biweekly insights, providing a continuously updated view of flood dynamics and their effects. Access the FAO EVE user guide to learn more.

The platform presents results through interactive maps, charts, and tables, supporting decision-making in disaster management, agricultural planning, and environmental monitoring. Most resources are publicly accessible, though downloading aggregated data at the admin2 level requires a DIEM account.

EVE products are preliminary analyses and have not yet undergone field validation. Users are encouraged to provide ground feedback to the FAO Data in Emergencies (DIEM) team to enhance the accuracy and utility of the data.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the start_date, end_date column(s). Geographic scope: AFG, AGO, BGD, BFA, BDI, KHM, CMR, CAF, and 28 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Climate and environment
Unit of observation	Country-level aggregates
Rows (total)	194,508
Columns	19 (8 numeric, 9 categorical, 2 datetime)
Train split	155,606 rows
Test split	38,901 rows
Geographic scope	AFG, AGO, BGD, BFA, BDI, KHM, CMR, CAF, and 28 others
Publisher	Food and Agriculture Organization (FAO) of the United Nations
HDX last updated	2026-04-03

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Variables

Geographic — adm0_iso3 (THA, VNM, COL), admin_level (admin2), pop_exposed (range 0.0–1883766.0).

Temporal — period_number (range 13.0–54.0), start_date, end_date.

Outcome / Measurement — total_area_flooded_sq_km (range 0.01–5332.05), total_area_flooded_ha (range 1.0–533205.0), perc_total_area_flooded (range 0.0–99.3086).

Identifier / Metadata — adm0_name (Thailand, Viet Nam, Colombia), adm1_pcode (CO05, CO13, PK62022), adm1_name (Antioquia, Bolívar, Punjab), adm2_pcode (MMR001D001, CD7409, CD9202), adm2_name (Chau Thanh, Sucre, Bolívar) and 2 others.

Other — cropland_flooded_sq_km (range 0.0–2478.43), cropland_flooded_ha (range 0.0–247843.0), perc_cropland_flooded (range 0.0–100.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-fao-eve-global-flood-monitoring-system")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
adm0_iso3	object	0.0%	THA, VNM, COL
adm0_name	object	0.0%	Thailand, Viet Nam, Colombia
admin_level	object	0.0%	admin2
adm1_pcode	object	0.0%	CO05, CO13, PK62022
adm1_name	object	0.0%	Antioquia, Bolívar, Punjab
adm2_pcode	object	0.0%	MMR001D001, CD7409, CD9202
adm2_name	object	0.0%	Chau Thanh, Sucre, Bolívar
period_number	int64	0.0%	13.0 – 54.0 (mean 35.1181)
start_date	datetime64[ns]	0.0%
end_date	datetime64[ns]	0.0%
cropland_flooded_sq_km	float64	0.0%	0.0 – 2478.43 (mean 11.8787)
cropland_flooded_ha	int64	0.0%	0.0 – 247843.0 (mean 1187.8734)
total_area_flooded_sq_km	float64	0.0%	0.01 – 5332.05 (mean 54.5666)
total_area_flooded_ha	int64	0.0%	1.0 – 533205.0 (mean 5456.6583)
perc_cropland_flooded	float64	0.0%	0.0 – 100.0 (mean 3.7183)
perc_total_area_flooded	float64	0.0%	0.0 – 99.3086 (mean 3.3393)
pop_exposed	int64	0.0%	0.0 – 1883766.0 (mean 7721.7498)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
period_number	13.0	54.0	35.1181	37.0
cropland_flooded_sq_km	0.0	2478.43	11.8787	0.2
cropland_flooded_ha	0.0	247843.0	1187.8734	20.0
total_area_flooded_sq_km	0.01	5332.05	54.5666	8.17
total_area_flooded_ha	1.0	533205.0	5456.6583	817.0
perc_cropland_flooded	0.0	100.0	3.7183	0.2217
perc_total_area_flooded	0.0	99.3086	3.3393	0.7009
pop_exposed	0.0	1883766.0	7721.7498	602.0

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from Food and Agriculture Organization (FAO) of the United Nations and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 36 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_fao_eve_global_flood_monitoring_system,
  title     = {FAO EVE Global Flood Monitoring System},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/fao-eve-global-flood-monitoring-system},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.