Africa Center of Excellence for Water Management

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Browsing Africa Center of Excellence for Water Management by Subject "Akaki Catchment"

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    Evaluating Methods and Polarizations of S-1 Sar for Time-Series Flood Hazard Mapping Akaki Catchment
    (Addis Ababa University, 2021-06-06) Worku, Tilaye; Tamiru, Alemseged (PhD)
    Lack or absence of data is the main limiting factor for studying flood hazard and risk in many basins across the globe. The Google Earth Engine (GEE) has a potential to narrow data gaps by providing ease of access to remote sensing data and enabling automatic and rapid generation of flood inundation map for Near Real-Time monitoring. In this study, GEE was used to assess and analyze the Sentinel-1 (S-1) Synthetic Aperture Radar (SAR) dataset for flood inundation mapping in the Akaki catchment which hosts Addis Ababa city in the central part of Ethiopia. Ground Control Points were collected at the time of satellite overpass for evaluating the accuracy of the generated flood inundation maps. Change detection and Histogram thresholding methods were compared using co-polarized (VV) and cross-polarized (VH) images. A new method which is Root of Normalized Image Difference (RNID) was developed for change detection. Major flood affected roads in Addis Ababa city and Land Use Land Cover (LULC) classes were detected from April to November of 2017 to 2020. The result shows the RNID method performed better than the histogram thresholding for flood inundation mapping in the study area. The VH polarization performed better than the VV polarization to detect the lower signal backscatter intensity generated from the flooded surface. An overall accuracy of 95% and kappa coefficient 0.86 was obtained when applying the RNID method and VH polarization. In the Akaki catchment, the remote sensing mapping showed that the flood commonly starts in May and recedes in November, but flood was frequent and widespread from June to September. At the downstream of the new expressway, the riverine and pluvial flood frequently occurred in the past four years. The flood inundation map also showed that several major roads of Addis Ababa are affected by flooding. The irrigated and built-up area were the most affected land use classes with an inundation extent of 1057.05 ha (21.28% of the total irrigated land) and 544 ha (1.44% of the total urban area) respectively. The S-1 SAR was found useful for time series flood inundation mapping and the new change detection method (RNID) performed better in urban and peri-urban flood mapping, but the accuracy of the flood map varies with the flood detection method and the image polarization.
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    Evaluating the Effect of DEM and Boundary Condition Data for Hydrodynamic Flood Modeling in a Data Scarce Area Akaki Catchment
    (Addis Ababa University, 2021-07-08) Negussie, Abel; Tamiru, Alemseged (PhD)
    Accurate flood inundations mapping is challenged by data availability for model calibration and validation. In this regard, evaluating the effect of input data for improving flood inundation mapping is necessary. In this study, a high-resolution digital elevation model (DEM) of 5m × 5m was obtained from the Ethiopia Geospatial information institute. However, the high-resolution DEM (5m) was found to have some limitations in capturing the river channel geometry of Akaki. As a result, field-measured (fifteen cross-sections) data was merged with DEM to improve the accuracy of the DEM. To fill the gap of boundary condition data, water depths of a flood event were measured at upstream (for simulating the model) and middle (for evaluated simulated water levels) parts of the model domain. For the tributary river, a stage hydrograph was developed based on community consultation and channel characteristics. HEC-RAS was used in this study to perform one-dimensional (1D) flood modeling of the Akaki floodplain and HEC-GeoRAS 10.4 was used for the processing of geospatial data and analysis of water surface profile results. The limitation of the DEM to capture the channel geometry was significantly improved by using field-collected cross-sections. The type of downstream boundary condition is found significant error source in modeling the flood of Akaki. Error statistics for model simulations show that the mean absolute error of water level is 1.65m when using the uncorrected DEM as model input. However, this was reduced by half as a result of correcting the DEM. The model results show that the two tributaries have a large contribution to the flood inundation of the study area. Overall, this study demonstrates how input data source and associated errors significantly affect the accuracy of flood characteristics that are simulated by a hydrodynamic flood model. As a result, researchers and concerned institutions should develop strategies to develop data gaps for enhanced understanding of flood hazard in the Akaki catchment.