The current intensification of the hydrologic cycle, in combination with expanding settlements in flood prone areas, makes an increasing share of the global population exposed to flood risks. Many parts of the world are, however, still lacking the data needed for flood risk management and risk reduction. The recent development of information and communication technologies has remarkably lowered the costs to collect data for flood resilience, which has accommodated the rise of community mapping projects to fill data gaps in resource-strained environments. This thesis utilises drainage data collected by the Ramani Huria community mapping project in Dar es Salaam, Tanzania, to investigate if community mapped drainage data can improve flood predictions on neighbourhood scale. A coupled 1D-2D hydrodynamic model is developed of Kijitonyama ward, and is run with and without Ramani Huria’s drainage data implemented in the 1D schematisation. The simulated flood depth for the scenarios is validated with citizen’s observations on flood depth during a rain event on 3 March, 2019. The developed model is then applied to investigate the impact of solid waste accumulation in the drainage system on floods, by closing the drainage segments that were recorded as blocked in Kijitonyama ward by Ramani Huria staff the morning after the simulated event. An experimental scenario is also run, to examine the impact of blocked culverts. The results show that community mapped drainage data indeed can enhance the performance of hydrodynamic models, as the model output corresponds better with the validation data when implementing Ramani Huria’s drainage data in the 1D schematisation, compared with a scenario run with only a 2D schematisation. The scenarios run with solid waste blockages do not influence the model output when comparing with citizen’s observations, but increase the water level in the drainage segments located upstream of the blockages.