Hydrological modelling and flood risk in a data scarce country: Matola, Mozambique

Abstract: Flooding is a frequent natural hazard globally that is capable of major damage to society. The hazard is especially prevalent in Mozambique, in which many flood events with negative effects have occurred. Disaster risk management and research is therefore important in the country. However, as a developing country, it is subject to data scarcity. Matola is one of the most populous cities in Mozambique, and is located between two rivers (the Matola and Infulene) and the coastline of Maputo bay. Precipitation events frequently result in pluvial flooding in the city. This study aims to produce flood hazard maps (looking both at water depth and velocity) and disaster risk maps for Matola for the years 2000, 2020 and 2040 based on changes or projections in precipitation and urbanisation via the use of the hydrological model (TFM-DYN), its input data and population data. The input data required by the TFM-DYN includes land cover, a digital elevation model, precipitation, infiltration, and surface roughness. Land cover and digital elevation GIS data was gathered from the National Cartography and Remote Sensing Centre (CENACARTA). Precipitation was based on daily measurements in the catchments where a storm hyetograph was derived from an intensity-duration-frequency curve and the alternating block method. Infiltration and surface roughness are based on literature. Risk maps were produced as a combination of population density per neighbourhood and maximum water depth. Water velocity and depth maximums increase each year. Flood hazard and flood risk over the area did not differ by very much from each other, it is assumed this is because the flood risk only took two factors into account. The flood hazard maps can be a useful tool in risk assessment and disaster management in Matola. The flood risk maps show the possible changes in risk between 2000, 2020 and 2040, although the study recommends an improvement in data quality and the inclusivity of more factors in flood risk.