Effects of DEM resolution and resampling technique on building treatment for urban inundation modeling: a case study for the 2016 flooding of the HUST campus in Wuhan

Shen, Ji; Tan, Fangbi

doi:10.1007/s11069-020-04198-z

Cited by 22 publications

(9 citation statements)

References 47 publications

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“…(Yu & Lane, 2006). Recent evidence (Shen & Tan, 2020) shows that urban inundation modelling performance can be improved using better buildings representation due to higher DEM resolution generated by a resampling method. Therefore, the DEM was resampled using natural neighbour interpolation on the 1 m × 1 m horizontal grid.…”

Section: Methodsmentioning

confidence: 99%

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Investigating sea‐state effects on flash flood hydrograph and inundation forecasting

Papaioannou

Varlas

Λουκάς

et al. 2021

Hydrological Processes

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A common source of uncertainty in flood inundation forecasting is the hydrograph used.Given the role of sea-air-hydro-land chain processes on the water cycle, flood hydrographs in coastal areas can be indirectly affected by sea state. This study investigates sea-state effects on precipitation, discharge, and flood inundation forecasting implementing atmospheric, ocean wave, hydrological, and hydraulic-hydrodynamic coupled models. The Chemical Hydrological Atmospheric Ocean wave System (CHAOS) was used for coupled hydro-meteorological-wave simulations 'accounting' or 'not accounting' the impact of sea state on precipitation and, subsequently, on flood hydrograph. CHAOS includes the WRF-Hydro hydrological model and the WRF-ARW meteorological model two-way coupled with the WAM wave model through the OASIS3-MCT coupler. Subsequently, the 2D HEC-RAS hydraulic-hydrodynamic model was forced by the flood hydrographs and map the inundated areas. A flash flood event occurred on 15 November 2017 in Mandra, Attica, Greece, causing 24 fatalities, and damages was selected as case study. The calibration of models was performed exploiting historical flood records and previous studies. Human interventions such as hydraulic works and the urban areas were included in the hydraulic modelling geometry domain.The representation of the resistance caused by buildings was based on Unmanned Aerial System (UAS) data while the local elevation rise method was used in the urban-flood simulation. The flood extent results were assessed using the Critical Success Index (CSI), and CSI penalize. Integrating sea-state affected the forecast of precipitation and discharge peaks, causing up to +24% and from −8% to +36% differences, respectively, improving inundation forecast by 4.5% and flooding additional approximately 70 building blocks. The precipitation forcing time step was also highlighted as significant factor in such a small-scale flash flood. The integrated multidisciplinary methodological approach could be adopted in operational forecasting for civil protection applications facilitating the protection of socio-economic activities and human lives during similar future events.

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Section: Methodsmentioning

confidence: 99%

“…Recent evidence (Shen & Tan, 2020) shows that urban inundation modelling performance can be improved using better buildings representation due to higher DEM resolution generated by a resampling method.…”

mentioning

confidence: 99%

Investigating sea‐state effects on flash flood hydrograph and inundation forecasting

Papaioannou

Varlas

Λουκάς

et al. 2021

Hydrological Processes

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“…The accuracy of a DEM is greatly influenced by DEM resampling techniques such as nearest neighbour, bilinear, and bicubic. Previous findings [41] show that resampling tech-niques have a significant impact on urban inundation modelling at a local scale, especially when spatial variability is high. On the other hand, they are insignificant and insensitive to the extraction of topographic information, hydrological components, elevation, and slope gradient, but significant to lateral flow [42,43].…”

Section: Vertical Accuracy Of Demsmentioning

confidence: 98%

Accuracy Assessment, Comparative Performance, and Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CARTOSAT 30 m, SRTM 90 m, MERIT 90 m, and TanDEM-X 90 m) Using DGPS

2022

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Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) is examined in detail. The study area is a hard rock terrain that overall is flat-like with undulating and uneven surfaces (IIT (ISM) Campus and its environs) where the statistical analysis (corrected and uncorrected DEMs), correlation statistics and statistical tests (for elevation and slope), the impact of resampling methods, and the optimum number of GCPs for reduction of error in order to use it in further applications have been presented in detail. As the application of DGPS data at GCPs helps in the substantial reduction of bias by the removal of systematic error, it is recommended that DEMs may be corrected using DGPS before being used in any scientific studies.

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“…Theoretically, the interpolation procedure could introduce some artifacts and errors in the resampled DEM. However, the amount and magnitude of the interpolation artifacts are negligible for Frontiers in Remote Sensing frontiersin.org low-resolution DEM (GSD > 10 m), such as SRTM DEM (Muthusamy et al, 2021), (Jung and Jasinski, 2015), (Shen and Tan, 2020). We pre-processed Sentinel-1 dataset before feeding it to the proposed network.…”

Section: Datasetmentioning

confidence: 99%

Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data

Yadav

Nascetti²,

Ban³

2022

Front. Remote Sens.

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Floods are occurring across the globe, and due to climate change, flood events are expected to increase in the coming years. Current situations urge more focus on efficient monitoring of floods and detecting impacted areas. In this study, we propose two segmentation networks for flood detection on uni-temporal Sentinel-1 Synthetic Aperture Radar data. The first network is “Attentive U-Net”. It takes VV, VH, and the ratio VV/VH as input. The network uses spatial and channel-wise attention to enhance feature maps which help in learning better segmentation. “Attentive U-Net” yields 67% Intersection Over Union (IoU) on the Sen1Floods11 dataset, which is 3% better than the benchmark IoU. The second proposed network is a dual-stream “Fusion network”, where we fuse global low-resolution elevation data and permanent water masks with Sentinel-1 (VV, VH) data. Compared to the previous benchmark on the Sen1Floods11 dataset, our fusion network gave a 4.5% better IoU score. Quantitatively, the performance improvement of both proposed methods is considerable. The quantitative comparison with the benchmark method demonstrates the potential of our proposed flood detection networks. The results are further validated by qualitative analysis, in which we demonstrate that the addition of a low-resolution elevation and a permanent water mask enhances the flood detection results. Through ablation experiments and analysis we also demonstrate the effectiveness of various design choices in proposed networks. Our code is available on Github at https://github.com/RituYadav92/UNI_TEMP_FLOOD_DETECTION for reuse.

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Effects of DEM resolution and resampling technique on building treatment for urban inundation modeling: a case study for the 2016 flooding of the HUST campus in Wuhan

Cited by 22 publications

References 47 publications

Investigating sea‐state effects on flash flood hydrograph and inundation forecasting

Investigating sea‐state effects on flash flood hydrograph and inundation forecasting

Accuracy Assessment, Comparative Performance, and Enhancement of Public Domain Digital Elevation Models (ASTER 30 m, SRTM 30 m, CARTOSAT 30 m, SRTM 90 m, MERIT 90 m, and TanDEM-X 90 m) Using DGPS

Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data

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