EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Flood forecasting and alerts in West Africa − experiences from co-developing a pre-operational system at regional scale

Jafet Andersson1, Abdou Ali2, Berit Arheimer1, Louise Crochemore1, Bode Gbobaniyi1, David Gustafsson1, Mohamed Hamatan2, Martijn Kuller3, Judit Lienert3, Melissande Machefer4, Umar Magashi5, Emmanuel Mathot6, Bernard Minoungou2, Aytor Naranjo4, Tharcisse Ndayizigiye1, Fabrizio Pacini6, Francisco Silva Pinto3, Léonard Santos1, and Addi Shuaib5
Jafet Andersson et al.
  • 1Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
  • 2AGRHYMET Regional Center, Niamey, Niger
  • 3Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland
  • 4isardSAT, Barcelona, Catalonia
  • 5Nigeria Hydrological Services Agency (NIHSA), Abuja, Nigeria
  • 6Terradue, Rome, Italy

Flooding is a rapidly growing concern in West Africa. Several floods have occurred in recent years with severe consequences including loss of lives and damaged infrastructure. Flooding is also projected to increase with climate change. Access to operational forecasts is a critical component in addressing these challenges. This study presents results from our joint efforts to co-design, co-adapt, and co-operate a short- and medium-term operational hydrological forecasting and alert pilot system for West Africa, within the FANFAR project (

The system has been co-developed through a cycle of workshops, training sessions, and expert exchanges involving representatives from hydrological services, emergency management agencies, river basin organisations, and expert agencies in 17 countries in West and Central Africa. Multi-criteria decision analysis was employed to clarify and prioritize system objectives and configurations. We found that the most highly prioritized objectives were: high accuracy, clear flood risk information, reliable access, and timely production and distribution of the information. Our agile development approach also provided ample opportunities to focus development efforts on the most highly prioritized components, and incorporate stakeholder feedback in the development process.

The system is built on an ICT cloud platform that employs a daily forecasting chain including meteorological reanalysis and forecasting, data assimilation of gauge observations and satellite altimetry, hydrological initialisation and forecasting, flood alert derivation, and distribution through e-mail, SMS, web visualisation and API. The system is designed to enable multiple configurations and integration of several information sources (e.g. different hydrological models, observations, flood hazard thresholds etc.). We present the system configurations, stakeholder-driven adaptations, challenges, and current forecast performance. To our knowledge, the FANFAR system constitutes a significant advancement toward the vision of achieving efficient flood management in West Africa.

How to cite: Andersson, J., Ali, A., Arheimer, B., Crochemore, L., Gbobaniyi, B., Gustafsson, D., Hamatan, M., Kuller, M., Lienert, J., Machefer, M., Magashi, U., Mathot, E., Minoungou, B., Naranjo, A., Ndayizigiye, T., Pacini, F., Silva Pinto, F., Santos, L., and Shuaib, A.: Flood forecasting and alerts in West Africa − experiences from co-developing a pre-operational system at regional scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7660,, 2020.

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  • CC1: Comment on EGU2020-7660, Manuel Figueroa, 08 May 2020

    Hello, great job! I was wondering about the process of identifying the threshold, how difficult was it?. In addition, what is the accuracy and temporal resolution of altimetry data?, some measurements over Amazonas River vary between cms to meters depending on the width of the river, the season, and other factors. Thanks in advance for answering.

    • AC1: Reply to CC1: thresholds & altimetry data, Jafet Andersson, 08 May 2020

      Hello Manuela! Thank you! 

      First regarding the thresholds. So far we use a standard approach based on extreme-value analysis to generate threshold levels corresponding to different return periods. It works ok for perennial rivers, but not as good in ephemeral rivers (). However, it is not always easy to communicate. Therefore we have a ongoing effort to try to integrate locally determined thresholds at specific gauging locations (which we obtained from national hydrological services), and to bias-adjust model outputs to be able to use those levels instead (bottom of slide 12). It is a work in progress though, so we don't know how well it will work. 

      Regarding the altimetry data. The data we use so far has a temporal resolution of about 1month. We are just exploring the accuracy right now and for sure those factors you mention are important. Our colleagues have done an accuracy assessment here: , but work is ongoing to look into this more. It is still an open question for us whether this data adds value, and if so in what way, which is something we are trying to understand better know.

      Best regards,


    • AC2: Reply to CC1, Jafet Andersson, 08 May 2020

      Dear Manuela,

      The system just removed one of my links about the perennial vs ephemeral streams & GEV models. See Andersson et al 2017 Providing peak river flow statistics and forecasting in the Niger River basin. DOI: 10.1016/j.pce.2017.02.010