A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-10, Vol.13 (19), p.3806
• Main Authors: Cotugno, Angela, Smith, Virginia, Baker, Tracy, Srinivasan, Raghavan
• Format: Article
• Language: English
• Subjects: Approximation; Case studies; Catastrophic events; Cloud cover; Developed countries; Digital Elevation Models; Discharge; Emergency preparedness; Extreme values; Flood mapping; Flood peak; Floodplains; Floods; HEC-RAS; Historic floods; Human populations; Hydraulics; Hydrologic data; Hydrologic models; Hydrology; Infrastructure; Land cover; Land use; Mapping; Modelling; Neural networks; Normalized difference vegetative index; peak discharge; Precipitation; Rain; Remote sensing; remotely sensed precipitation; River banks; River flow; Rivers; Runoff; Simulation; Soil properties; Soils; Surface water; Topography; Urban areas; Urban development; urban flood mapping; Urban runoff; Urban watersheds; Urbanization; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13193806

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work