A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Effect of Available Topographic Maps on Flood Modeling Studies in Sri Lanka
The topography of a model will highly influence the uncertainty of hydraulic model predictions, thus making it a critical factor in urban flood modeling studies. Therefore, features of topographic maps, associated budget requirements, intended accuracy, and purpose of the flood modeling study have to be considered when selecting a dataset. In the Sri Lankan context, Digital Elevation Models (DEM) from topographic maps from the Survey Department of Sri Lanka and remote sensing methods such as Light Detection And Ranging (LiDAR) and Shuttle Radar Topographic Mission (SRTM) have been used for flood modeling. All these datasets possess distinct characteristics and an associated budget. This research is focused on the datasets of LiDAR, SRTM, and topographical maps from the Survey Department and measured the cross sections from a geographic survey. A methodology has been conducted to identify the effect of these datasets to identify relevant information. The study found that datasets are having mismatches in coordinates and could not capture some land features which were eventually rectified according to the study requirements. Furthermore, an accurate depiction of river bathymetry for a flood modeling study is essential. Hence, this paper provides knowledge on the available datasets and their depictions of river bathymetry following insights on selecting the most effective depending on the purpose and resources available. Hence, the findings would be beneficial for terrain map users to accomplish their tasks effectively.
Effect of Available Topographic Maps on Flood Modeling Studies in Sri Lanka
The topography of a model will highly influence the uncertainty of hydraulic model predictions, thus making it a critical factor in urban flood modeling studies. Therefore, features of topographic maps, associated budget requirements, intended accuracy, and purpose of the flood modeling study have to be considered when selecting a dataset. In the Sri Lankan context, Digital Elevation Models (DEM) from topographic maps from the Survey Department of Sri Lanka and remote sensing methods such as Light Detection And Ranging (LiDAR) and Shuttle Radar Topographic Mission (SRTM) have been used for flood modeling. All these datasets possess distinct characteristics and an associated budget. This research is focused on the datasets of LiDAR, SRTM, and topographical maps from the Survey Department and measured the cross sections from a geographic survey. A methodology has been conducted to identify the effect of these datasets to identify relevant information. The study found that datasets are having mismatches in coordinates and could not capture some land features which were eventually rectified according to the study requirements. Furthermore, an accurate depiction of river bathymetry for a flood modeling study is essential. Hence, this paper provides knowledge on the available datasets and their depictions of river bathymetry following insights on selecting the most effective depending on the purpose and resources available. Hence, the findings would be beneficial for terrain map users to accomplish their tasks effectively.
Effect of Available Topographic Maps on Flood Modeling Studies in Sri Lanka
Lecture Notes in Civil Engineering
Dissanayake, Ranjith (editor) / Mendis, Priyan (editor) / Weerasekera, Kolita (editor) / De Silva, Sudhira (editor) / Fernando, Shiromal (editor) / Konthesingha, Chaminda (editor) / Gajanayake, Pradeep (editor) / Peramuna, P. D. P. O. (author) / Neluwala, N. G. P. B. (author) / Wijesundara, K. K. (author)
International Conference on Sustainable Built Environment ; 2022 ; Yogyakarta, Indonesia
2023-08-10
17 pages
Article/Chapter (Book)
Electronic Resource
English
Using Digital Topographic Maps for Hydraulic Modeling
| British Library Conference Proceedings | 1998
Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference
| DOAJ | 2023
Topographic Influences on Flood Frequency Analyses
| Online Contents | 2000
Topographic maps in US libraries
| Elsevier | 1987
Scree Representation on Topographic Maps
| Online Contents | 2010