The design of an early warning system for floods in Dar Es Salaam, Tanzania

Abstract

With climate change increasing its mark on all aspects of the hydrological cycle, societies all over the world living in flood-prone areas are increasingly exposed to flood hazards. In many parts of the world, especially in less developed areas, societies lack knowledge and data to predict future flood events. By predicting a future flood event, an organization creates a time frame in which it can implement a mitigating action that reduces the financial damage inflicted. In recent years, development in new measuring techniques has significantly lowered the cost of collecting data and information on different aspects of the hydrological cycle. These developments enable organizations in regions restrained of knowledge and data to establish methods to analyze aspects of the hydrological cycle and thereby predicting the probability of a flood hazard several hours or days in advance. This thesis explores various possibilities of designing and implementing an c{EWS} for the c{BRT} in Dar es Salaam. The EWS design is based on the forecasting requirements, investigated with the BRT-system. Several operational forecasting methods are available. The EWS designed in this thesis makes use of rainfall data obtained from rainfall stations located in the Dar es Salaam region, installed and managed by the c{TAHMO}. This forecasting data is chosen because it provides the needed lead-time with the lowest margin of error. This forecasting data is processed and analyzed by the designed EWS and subsequently produces a probability level on a flood event. It thereby provides an advice on if the BRT-system should implement a mitigating action based on the principle of pursuing an optimal economic outcome. The designed EWS produces the flood probability in real-time, updated every hour with a lead time of one hour. This time frame enables the BRT-system to implement a mitigating action, thereby reducing the inflicted cost.

The probability level of a flood event is determined by training the EWS with historic flood and rainfall data. In addition, the implementation of both a hydrological and relational model in the EWS was tested. The results show that the hydrological model is the better option. The results also show that the implementation of an EWS ensures a decrease in financial damage endured by the BRT-system. The produced outcome of the EWS was validated by a 'leave one out' method. This validation was done by consecutively leaving one flood event out of the historical data frame and analyzing the variability of the resulting outcome. Finally, the designed EWS is best implemented in the BRT-system alongside the EWS-systems currently in place.