This study investigates the potential of freely available Sentinel-1 imagery coupled with Google Earth Engine (GEE) for mapping and monitoring flooding in Dar es Salaam. Sentinel-1 images (n = 55) available during the rainy season (March–May) since 2016 were used and processed in GEE. For separating water and land surfaces, we used a histogram-based automatic thresholding method. The binarization accuracy was assessed using confusion matrix based on 1064 randomly generated points in GEE. Overall accuracy of 95% (Kappa = 0.90) were achieved. Dar es Salaam has experienced flood inundation per flood event on average over an area of 50 km² in March 2019 and 2021. Territories located along the Ocean and inland water shores, built and bare ground were subject to flooding compared to other land cover types. Flooding inundations have been difficult to detect in the city center. With the current temporal and spatial resolution of Sentinel-1, flood detection in city centers remains a challenge yet. However, Sentinel-1 images, coupled with GEE cloud computing simplified flood mapping and monitoring in a large urban region and this approach can be applied in other large cities and their surroundings for countries where data gap and lack of processing tools are critical challenges.

Even though flooding is a threat to rural communities in plains drained by ephemeral rivers, the magnitude of its impact, coping/prevention mechanisms, and the implications for river management are not well known. In this study, data were collected using a phenomenological‐based research design. Field observations, a questionnaire (n = 440), key informant interviews (10), and one focus group discussion (with seven discussants) were used to collect the desired data from flood‐prone farmers. As the results show, 42% of the respondents believe that flooding has become stronger over the past 20 years, whereas 38% believe there has been no change. As mitigation measures, 52% of the respondents suggested evacuating to neighbouring villages as the best option. Most of the damage was experienced in farmlands (including crops, either harvested or standing) and settlements. As coping mechanisms, 31% of the respondents were displaced, whereas an additional 40% were forced to construct new houses for their settlement. Chi‐square testing showed that the farmers’ responses statistically varied among different groups (p < .001). In the meantime, community participation in flood control and catchment management is important. Improving flood management knowledge and the skills of different groups in a community is equally important. In this case, a qualitative inquiry approach was a good option to assess the hydrological conditions of rivers.