eEARTH: Early action and risk tracking map

Filter the map to visualize conflict and climate impacts around the world.

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Security Threats

Conflict Dynamics

All Conflict Events

Resource-related Conflict Events

All Resource-related Events

Livestock-related

Farming-related

Fishing-related

Forestry-related

Land-related

Water-related

Agriculture & Climate

Water & Weather Conditions

Flooding

Precipitation

Soil Moisture

Temperature

Vegetation & Land Use

Maize Crop Conditions

Sorghum Crop Conditions

Wheat Crop Conditions

Vegetation

Seasonal Climate Forecasts

Precipitation Forecast (High)

Precipitation Forecast (Low)

Temperature Forecast (Hot)

Temperature Forecast (Cold)

Humanitarian Impacts

Internal Displacement (Current)

Food Insecurity (Current)

Food Insecurity (Projected)

Data Sets: Add a Layer

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Security Threats

Conflict Dynamics

All Conflict Events

Resource-related Conflict Events

All Resource-related Events

Livestock-related

Farming-related

Fishing-related

Forestry-related

Land-related

Water-related

Agriculture & Climate

Water & Weather Conditions

Flooding

Precipitation

Soil Moisture

Temperature

Vegetation & Land Use

Maize Crop Conditions

Sorghum Crop Conditions

Wheat Crop Conditions

Vegetation

Seasonal Climate Forecasts

Precipitation Forecast (High)

Precipitation Forecast (Low)

Temperature Forecast (Hot)

Temperature Forecast (Cold)

Humanitarian Impacts

Internal Displacement (Current)

Food Insecurity (Current)

Food Insecurity (Projected)

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Precipitation above normal

Past 3 months
3 month forecast
Overlap past and forecast

Precipitation anomaly

+3 Above average

0

−3 Below average
Missing data

Crop conditions

Exceptional
Favourable
Watch
Poor
Failure
Out of season
No data

Vegetation anomaly

+2 Above average

0

−2 Below average
Missing data

Acute food insecurity classification

1: Minimal
2: Stressed
3: Crisis
4: Emergency
5: Famine

Temperature above normal

Past 3 months
3 month forecast
Overlap past and forecast

Number of fatalities

0
1-3
4-10
11-20
>20

Temperature below normal

Past 3 months
3 month forecast
Overlap past and forecast

Number of fatalities

0
1-3
4-10
11-20
>20

Crop conditions

Exceptional
Favourable
Watch
Poor
Failure
Out of season
No data

Number of fatalities

0
1-3
4-10
11-20
>20

Acute food insecurity classification

1: Minimal
2: Stressed
3: Crisis
4: Emergency
5: Famine

Number of fatalities

0
1-3
4-10
11-20
>20

Flooded area

Flood extent, no wetlands
Flood extent, wetlands
ESA wetlands (2020)

Crop conditions

Exceptional
Favourable
Watch
Poor
Failure
Out of season
No data

Newly displaced people (monthly)

Disaster
Conflict

Crop conditions

Exceptional
Favourable
Watch
Poor
Failure
Out of season
No data

Precipitation below normal

Past 3 months
3 month forecast
Overlap past and forecast

Number of fatalities

0
1-3
4-10
11-20
>20

Number of fatalities

0
1-3
4-10
11-20
>20

Soil moisture anomaly

+2 Above average

0

−2 Below average
Missing data

Crop conditions

Exceptional
Favourable
Watch
Poor
Failure
Out of season
No data

Drought category

Alert
Warning
Watch

Temperature anomaly

+3 Above average

0

−3 Below average
Missing data

Crop conditions

Exceptional
Favourable
Watch
Poor
Failure
Out of season
No data

Number of fatalities

0
1-3
4-10
11-20
>20

The data you are looking for is not yet available. Please select a different date.

Note that some weather data providers may delay uploads by up to one month.

Prospective weather information encompasses various predictive statements about future climate and weather conditions. These include forecasts, outlooks, projections and scenarios, each describing expected or possible future meteorological trends. Ideally, such statements should include uncertainty levels or confidence assessments. If absent, users should consider questioning providers about uncertainty, confidence and skill metrics. This approach ensures a more comprehensive understanding of the predictive information's reliability and applicability.

With appropriately co-developed and quality-controlled data and associated early warning early action systems, opportunities exist to leverage the skill inherent in forecasts. These opportunities may increase during periods of enhanced predictability, such as certain ENSO events. Such periods offer potential for more accurate forecasting and timely action, improving early warning early action system effectiveness.

Precipitation patterns vary across different areas, and their response to climate variability and change can differ. The effects of precipitation can also change significantly depending on local factors like land use, soil type and vegetation. In regions like South Sudan, where seasonal flooding and waterlogged areas are common, interpreting precipitation data requires extra care, as these areas may not follow typical land-water patterns.

Precipitation variability poses significant challenges to hazard assessment in several ways:

Intense short-term rainfall can trigger flash floods, particularly in low-lying or topographically complex areas, even when overall annual or seasonal precipitation is below average.
Spatially variable rainfall may cause local flooding, while nearby areas remain abnormally dry, complicating anticipatory planning. Therefore, it is possible to have both flood and drought conditions in a certain area of interest, leading to both indirect and direct livelihood impacts.
Rainfall seasonality plays a critical role in agricultural cycles, particularly in areas reliant on rain-fed agriculture, influencing food security and livelihoods in countries like South Sudan.

Crop condition data provides monthly global assessments of key crops, classified into six categories relative to five-year average yields. While a robust high-level indicator, it relies on expert consensus and remote sensing, which introduces potential subjectivity, sensitivity to cloud cover and resolution limits, and limited ground validation in some regions. These classifications capture broad trends but may not fully reflect local factors such as pests, soil fertility or socio-economic stressors. Users should therefore treat the data as indicative of regional conditions rather than precise yield forecasts, and interpret it alongside complementary sources where possible.

Various vegetation indices exist, but care must be taken in order to assess appropriateness depending on the context of interest. The Enhanced Vegetation Index (EVI) is a satellite-derived data product. EVI provides a representation of quantified “greenness” and is thus an indicator of biomass over a certain area at a particular point in time or time range. EVI is similar to another satellite data-derived vegetation index, the Normalized Difference Vegetation Index (NDVI), but EVI is designed to address noise in the NDVI data from certain atmospheric and land cover conditions. Compared to NDVI, EVI is noted to be more sensitive in areas with dense vegetation, as it does not saturate as easily. Given the accessibility challenges in certain fragile and conflict settings, validation of EVI (or any satellite derived dataset) with in situ data may be impossible at worst and challenging at best. Therefore, additional care must be taken in exploring and assessing the appropriateness for use in such places as additional uncertainty and limitations are likely to exist. As with any ratio based index, the inherent nonlinearity should be acknowledged, particularly in semi-arid areas whereby a lower range of values is likely.

Since acute food insecurity can index a wide range of challenges in food availability, access and utilisation, it serves as a proxy for the impact of hazards on livelihoods. For select countries, FEWS NET offers periodic snapshots of food security status and projections of likely developments over defined outlook periods. These analyses, which are updated periodically, often align with local seasonal trends and livelihood patterns. Current analyses are used when available; otherwise, projections are presented in chart form.

Temperature forecasts provide either a range or a specific value for expected temperatures at a specific time or over a future period. Temperature forecasts typically represent near-surface conditions, usually at 2m above surface level. These predictions inherently account for elevation, as temperatures generally decrease with increasing altitude. Seasonal forecasts often describe the likelihood of temperature deviations from average over extended periods. Though rarely designed to predict heat waves or extreme temperatures, these forecasts indicate the likelihood of general temperature trends for the weeks or months ahead. Importantly, even during periods forecasted to be generally cooler (warmer) than average, individual days of extreme heat (cold) can still occur.

Caution is necessary when interpreting event data, such as conflict data, due to potential underreporting, inconsistent coverage and biases in source selection, among other reasons. Conflict data often relies on media reports, which can result in uneven coverage across regions, event types and actors. For instance, conflict events in rural or inaccessible areas, such as those controlled by insurgents, may be underreported. Conflict data is an essential tool for conflict monitoring and research, but like all data, it requires responsible use, including consulting multiple sources, performing benchmarks and cross-checking source selections. To uncover potential biases and underreporting, Crisis Group conducted thorough benchmarking exercises and performed statistical analysis for each country. The risk rating here is designed to capture the most severe conflict incidents, which tend to be better covered by the media and are therefore less prone to underreporting. Please note that the numbers shown may change as events are verified and more information becomes available.

“Resource-related conflict events” refer to conflict incidents qualified in data sources by keywords indicating specific economic activities (such as raising livestock, farming, fishing, and forestry) or concerning access to and control over land or water-related production factors. Event counts should be considered a lower bound, as there may be underreporting due to missing context or information, especially for minor incidents.

There are various flood types, including riverine, flash, coastal and glacial lake outburst. Flood typology can vary by region; for instance, rapid local inundation might be termed "waterlogging" in some areas and "flash flooding" in others. When seeking flood-related data (risk, monitoring, forecast or historical), specificity regarding flood type is crucial. Data labelled simply as "flood" may oversimplify diverse flood types and their impact. Compound flood events, such as coastal and riverine floods interacting, can occur. Their effects may not be linear or additive, a complexity not always captured in historical data or forecasts. Best practice involves inquiring about represented flood types and whether impact profiles reflect baseline or deviated risk levels. One should also consider how flood definitions may vary from place to place.

Internal displacement data plays a crucial role in addressing humanitarian needs and informing policy decisions, but it also has limitations that require responsible use. Challenges include inconsistent data collection methods, underreporting in conflict zones and the difficulty of tracking protracted or secondary displacement. Data accuracy may be influenced by access issues, the dynamic nature of crises and the vulnerability of displaced populations, which can lead to gaps in understanding. To ensure responsible use, analysts must consider these limitations, check findings against other sources and protect the privacy of displaced individuals. Please note that the numbers shown are preliminary estimates, which may change as events are verified and more information becomes available.

Soil moisture refers to the water, including water vapour, within unsaturated soil pores. It is a key variable when assessing the extent to which water exists within the pores of soil. Soil moisture is not a relevant variable when considering saturated environments such as rivers, lakes or groundwater. It can be measured at various depths, and depending on the use case, certain depths will be more or less relevant. Surface soil moisture typically refers to moisture within the top 10cm, but this value might be misleading depending on the question being asked. For example, when assessing plant-available moisture, measurements up to 200cm deep are generally more representative. Soil moisture is influenced by a variety of factors, including precipitation, temperature and soil characteristics like texture. Fluctuations in soil moisture are normal in certain climatic zones on both seasonal and inter-annual timescales.

Drought data provides monthly assessments of drought severity, classified into Watch, Warning, and Alert categories, by integrating precipitation deficits, soil moisture anomalies, and vegetation stress through the Combined Drought Indicator (CDI). This composite approach captures both meteorological and agricultural drought impacts, offering a robust high-level overview. However, the indicator is sensitive to uncertainties in its inputs—rainfall data gaps, model assumptions in soil moisture estimates, and vegetation changes unrelated to drought (e.g. pests, land use, flooding). In addition, the FAPAR anomaly used to track vegetation stress can be unreliable under cloudy conditions, as low cloud cover may go undetected and distort values. As a result, classifications highlight regional drought conditions and trends rather than precise local impacts. Users should therefore treat the data as an early warning signal and interpret it alongside complementary sources and contextual analysis.

These charts are not tailored to different land cover types. Temperature behaves differently across various types of land cover and land use regimes, which evolve at potentially different temporal scales. For instance, daily temperature patterns differ significantly on water and land. Particular care is necessary when interpreting temperature data for cases like South Sudan, as much of the country cannot be readily classified as either land or open water.

Regardless of land cover type, temperature is an important factor in determining the severity of other hazards, such as droughts and floods, and in driving their socio-economic impact in various ways, such as:

Changing the evapotranspiration rates at the land surface, potentially increasing the risk for meteorological drought; and
Influencing anticipatory actions based on co-occurring heat and flood/drought events.

Data source: CHC CHIRPS, ICPAC forecasts | Analysis: Crisis Group

Data source: CHC CHIRPS | Analysis: Crisis Group

Data source: GEOGLAM Crop Monitor | Analysis: Crisis Group

Data source: GDO VIIRS FAPAR | Analysis: Crisis Group

Data source: FEWS NET, IPC | Analysis: Crisis Group

Data source: NASA MODIS LST, ICPAC forecasts | Analysis: Crisis Group