Modular Flood Early Warning System an Approach to Flood Adaptation

One major impact of climate change is the ever-increasing frequency of foods events as a result of deep convective storms exacerbated by the increase in global surface temperature. Therefore, flood early warning system (FEWS) is critical tool in safeguarding life and protection of property of the vulnerable communities. 

FEWS are used whenever there is a need to understand the potential impacts of flooding and to make informed decisions about flood risks and management and mitigation flood modelling can be used for purposes such as planning and design of sustainable drainage and storm water management systems or designing and planning flood protection systems or identifying areas at risk of flood and developing evacuation plans, assessing the impact of land use changes, identifying areas that are vulnerable to flood and prioritizing for flood mitigation. 

FEWS can be achieved through flood modelling; a process of simulating the behaviour of flood water using data and computer models to forecast future flood events. Modelling aims to predict the intricate process that control flood events such as precipitation run off and river flow patterns for different return periods depending on the location risk and detail of the modelling already available for the area of concern. This requires a robust modelling system and observational data to force the model.

Kenya Meteorological Department (KMD) through the TEMBO project, is implementing a modular FEWS for smaller cities with a population of less than one million inhabitants. The FEWS is being piloted in Kenya with Narok and Kisumu Counties used as testbed. The choice of the two counties is based on the episodic frequent flooding events that have been reported in the recent past. 

The modular FEWS is achieved by integrating three different models. The WRF model is used to forecast the expected weather parameters which are in turn used to force the hydrological model. To ensure high quality accurate weather forecasts, the surface observations from the TAHMO stations, precipitable water vapour estimates from the 6 GNSS stations in Kenya and precipitation measurements from the doppler radar to be installed in Western Kenya will be used in data assimilation in WRF.  

Precipitation and evapotranspiration outputs from the WRF model is then used to force the GR4J hydrological model to generate the expected river flow measurements.

The results from the GR4J model will then run through the SFINCS hydrodynamical model to output the risk of flooding. 

The wrf and GR4J models are already running for the Yala and Narok river basins. The results of which are shared to the stakeholders who include farmers disaster management authorities among other stakeholders to guide in their decision making process to safeguard property and save lives.