Abstract
Frequent floodings in Semarang City have generated increasing damages and losses in property and life quality. The cause of flooding is related to the coupled impacts of land subsidence, hydraulics hazards along with poor drainage and water retention systems. This paper studies the most recent flooding hazards caused by hydrological origins (i.e., river discharge, tidal) and land subsidence. In the study, riverine origin of flooding is simulated with the help of HEC-RAS 2D, while the tidal origin is simulated to high highest water level. However, due to the absence of the most recent topographic data, the role of land subsidence is measured by estimating the vertical changes of digital elevation model taken from Sentinel 1A. Flooding extent, in terms of depth and coverage, is verified based on satellite imagery Sentinel-2 which is cloud-processed using Google Earth Engine (GEE) and field survey. Fluvial flood is simulated with several boundary condition scenarios using combinations of 5-, 25-, or 50-year return periods of flood which is integrated with mean sea level (MSL) or high highest water level (HHWL) tides. Those boundary conditions are then incorporated into different terrains, namely LiDAR, DEMNAS, and TerraSAR DEM, to see how different digital elevation models (DEMs) can impact model sensitivity. By overlaying model outputs and land cover map, it can be concluded that settlements and water bodies are among the most potentially affected areas, covering up to 17 km2. This study is expected to help policymakers make a primary assessment of combined tidal and fluvial flood hazard through mitigation and adaptation measures.
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Acknowledgements
This research is funded by BUDI DN Doctoral Scholarship from the Lembaga Pengelola Dana Penelitian (LPDP), Ministry of Finance and Ministry of Research, Technology and Higher Education, Republic of Indonesia, and Research Doctoral Dissertation grand number FITB.PN-1-03-2021 from Ministry of Research and Technology/BRIN.
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Appendix 1: Flow accumulation from different DEMs
Appendix 1: Flow accumulation from different DEMs
Flow accumulation is an important factor in understanding and forecasting flood-prone locations. It is the amount of flow that builds in each raster pixel based on the cumulative weights of the pixels before it (Negese et al. 2022). Researchers can identify flood-prone places by examining flow accumulation patterns (Yunus 2021; Negese et al. 2022).
As shown in Fig.
Flow accumulation value and distribution differences Between TerraSAR, DEMNAS, and LiDAR DEM
15 and Table
10 the flow accumulation determined from different DEM resolutions statistically shows that when the resolution goes from fine to coarse, the maximum, mean, and standard deviation flow accumulations drop dramatically. When the DEM resolution is reduced from 1 to 9 m, from LiDAR to TerraSAR, the mean flow accumulation is reduced by one-third, while the maximum and standard deviation of flow accumulation are reduced by one-fourth.
A high DEM resolution enables the collection of more values. In other words, the greater the value of max flow accumulation, the greater the number of rivers or water bodies derived from the DEM, and the greater the potential of inundation area as related to flood extent findings as mentioned in Table 5. A more accurate representation of complicated topography will be possible with smaller grid cell sizes, and thus, high-resolution DEMs are better equipped to refine complex topographic characteristics (Wechsler 2006). If the topography is complex, larger differences between grid cells can be predicted. The surface would appear very varied over short distances, but observed slopes would be relatively consistent regardless of where they were tested over longer distances (Warren et al. 2004).
This explains how the slope becomes comparable or a smoothing effect develops in the observed slopes at low DEM resolutions. As a result, most low DEM resolution values are smoothed according to the major flow accumulation value, resulting in a drop in flow accumulation mean and standard deviation concerning its limited measured slope variability. This consideration is highly relevant to the visualized digital elevation model depicted in Fig.
Elevation values and distribution differences between TerraSAR, DEMNAS, and LiDAR DEM
16, where the topography is becoming very complex in the LiDAR DEM with clearly-bordered boxy pattern relevant to the Semarang’s urban coastal landscape, thus resulting in higher accuracy of modelled flood extent and depth, as opposed to the smoothed elevation differences found in DEMNAS and TerraSAR that produce a less satisfying result.
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Yuwono, B.D., Abidin, H.Z., Poerbandono et al. Mapping of flood hazard induced by land subsidence in Semarang City, Indonesia, using hydraulic and spatial models. Nat Hazards 120, 5333–5368 (2024). https://doi.org/10.1007/s11069-023-06398-9
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DOI: https://doi.org/10.1007/s11069-023-06398-9