Flash flood at the Mediterranean coast


In September 2023, storm Daniel caused a severe flooding crisis in the region around Derna, a coastal city in Libya, North Africa. Satellite images captured the extent of the disaster, showcasing large areas submerged under water, the change in the vegetation and the increased sediment load of the sea water after the event.

The heavy rainfall associated with this event was unprecedented, with rainfall levels surpassing 200 millimeters in just 48 hours. This led to the collapse of two dams regulating the water flow in the upstream part of the valley. The city’s inadequate infrastructure and urban planning exacerbated the situation, making it more susceptible to flooding.

The flooding in Derna was particularly devastating, with more than 30% of the city inundated by floodwaters and many buildings destroyed. This equated to approximately 15 square kilometers of land affected. Several thousand casualties were the consequence.

While it is not possible to attribute the event to climate change directly, it has played an important role in this flooding event. Rising global temperatures have led to more extreme weather patterns, increasing the frequency and intensity of heavy rainfall in many areas, including Derna.

Flood damage assessment, red: destroyed, orange/yellow: damaged, blue: flooded
(European Union, Copernicus Emergency Management Service data)


  • Satellite Map:
    • Look at the satellite image maps and click on the layer selector in the upper right.
    • Switch the satellite image from 2023-09-12 on and off and compare it with the image acquired five days before on 2023-09-07, before the flood. Which changes can you detect?
    • Zoom to Derna, the town at the coast, and look at the changes in detail (coastline, vegetation, roads near the coast).
    • Compare your findings with the flood damage assessment map from the Copernicus Emergency Management Service.
    • Deselect the layer group “true colour” and select the group false-colour infrared (FIR). Do the same analysis as above. Take into account, that in this representation vegetation appears in bright red.
    • Deselect the layer group “FIR” and select the group “Moisture Index“. Repeat your analysis (note that there is no absolute scaling of the moisture index; red is dry, blue is wet).
  • EO Browser:
    • Open the EO Browser.
    • Find the most recent Sentinel-2 dataset covering the area displayed in the satellite map.
    • Select a true colour visualisation.
    • Can you identify additional, recent changes in the area?
    • Select the false colour infrared representation. Can you identify the land-use of the most intensely vegetated areas (represented by bright red colours)?


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