Protecting the Sultanate of Oman from floods

Protecting populations, properties and infrastructure from extreme rainfall events

After being severely affecting in 2021 by Cyclone Shaheen, which caused extreme rainfalls, the Sultanate has made the prevention of flooding one of its national priorities. 


It has awarded a consortium led by Artelia a major assignment to assess the risk of flooding and study the protective measures to be implemented in the Al Batinah region, one of the areas worst impacted by the cyclone (risk mapping, feasibility studies and detailed design of dams, canals and rainwater networks).

CONTEXT & ISSUES

In September 2021, Cyclone Shaheen affected several countries in the Middle East, particularly the Sultanate of Oman. This was a highly unusual event of rare intensity. This is the first time that a cyclone has entered the Gulf of Oman at this time of year and progressed so far north and west. The torrential rain it produced in 24 hours, particularly in the semi-desert regions to the north, was equivalent to around two years’ normal rainfall. It caused several deaths and extensive material damage. The Sultanate has therefore launched a programme to better protect the country against these events, and has appointed Artelia to study its implementation.

Our Group has been operating in Oman for several decades, particularly in the flood protection sector. Following cyclone Gonu, which affected the country in 2007, our teams had already supported the Omani government and studied the implementation of flood control dams in several of the country’s catchment areas. Artelia is now leading a new programme focusing on the Al Batinah region in northern Oman. It involves producing a master plan for flood protection, including a study of numerous dams (feasibility of 36 sites, 15 detailed studies of priority facilities) and associated stormwater drainage infrastructures (flood evacuation channels, primary and secondary networks on the coastal fringe). Our teams will also be mapping flood-prone areas, based on risk modelling.