Assessment of an ASTER-generated DEM for 2D hydrodynamic flood modeling

Tarekegn, Tesfaye Haimanot; Haile, Alemseged Tamiru; Rientjes, T.H.M.; Reggiani, Paolo; Alkema, D.

doi:10.1016/j.jag.2010.05.007

Cited by 83 publications

(43 citation statements)

References 17 publications

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“…Furthermore, the model performed quite well in simulating the peak flow, which is important in flood modelling. The study by Tarekegn et al (2010) carried out on a study area in Ethiopia used a DEM which was generated from ASTER image. Integration between remote sensing and GIS technique were needed to construct the floodplain terrain and channel bathymetry.…”

Section: A MD Ali Et Al: 1-d Hydraulic Modelling Of Floodsmentioning

confidence: 99%

“…Despite having the lowest vertical accuracies, the ASTER and contour DEMs are still widely used in the field of hydraulic flood research as they are globally available and free (e.g. Tarekegn et al, 2010;Wang et al, 2011;Gichamo et al, 2012). The differences in the vertical accuracies may be partly due to the lack of information in topographical flats areas such as floodplains.…”

Section: Quality Of Dems Compared With the Reference Pointsmentioning

confidence: 99%

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Assessing the impact of different sources of topographic data on 1-D hydraulic modelling of floods

Ali

Solomatine

Baldassarre

2015

Hydrol. Earth Syst. Sci.

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Abstract. Topographic data, such as digital elevation models (DEMs), are essential input in flood inundation modelling. DEMs can be derived from several sources either through remote sensing techniques (spaceborne or airborne imagery) or from traditional methods (ground survey). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), the light detection and ranging (lidar), and topographic contour maps are some of the most commonly used sources of data for DEMs. These DEMs are characterized by different precision and accuracy. On the one hand, the spatial resolution of low-cost DEMs from satellite imagery, such as ASTER and SRTM, is rather coarse (around 30 to 90 m). On the other hand, the lidar technique is able to produce high-resolution DEMs (at around 1 m), but at a much higher cost. Lastly, contour mapping based on ground survey is time consuming, particularly for higher scales, and may not be possible for some remote areas. The use of these different sources of DEM obviously affects the results of flood inundation models. This paper shows and compares a number of 1-D hydraulic models developed using HEC-RAS as model code and the aforementioned sources of DEM as geometric input. To test model selection, the outcomes of the 1-D models were also compared, in terms of flood water levels, to the results of 2-D models (LISFLOOD-FP). The study was carried out on a reach of the Johor River, in Malaysia. The effect of the different sources of DEMs (and different resolutions) was investigated by considering the performance of the hydraulic models in simulating flood water levels as well as inundation maps. The outcomes of our study show that the use of different DEMs has serious implications to the results of hydraulic models. The outcomes also indicate that the loss of model accuracy due to re-sampling the highest resolution DEM (i.e. lidar 1 m) to lower resolution is much less than the loss of model accuracy due to the use of lowcost DEM that have not only a lower resolution, but also a lower quality. Lastly, to better explore the sensitivity of the 1-D hydraulic models to different DEMs, we performed an uncertainty analysis based on the GLUE methodology.

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Section: A MD Ali Et Al: 1-d Hydraulic Modelling Of Floodsmentioning

confidence: 99%

Section: Quality Of Dems Compared With the Reference Pointsmentioning

confidence: 99%

Assessing the impact of different sources of topographic data on 1-D hydraulic modelling of floods

Ali

Solomatine

Baldassarre

2015

Hydrol. Earth Syst. Sci.

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“…The modeling of 2D hydrodynamic is supported by the topographic data with the high resolution, but now it is very difficult to obtain detailed supporting data for modeling. As a solution we used a Digital Elevation Model (DEM) which was generated from the ASTER data [11].…”

Section: Methodologiesmentioning

confidence: 99%

Accuracy analysis of SRTM usage for upper Citarum River flood modeling

Siregar

2017

MATEC Web Conf.

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Abstract. Natural channel cross section is generally very irregular and varied, for complex cross-sections and natural cross sections no specific formula presents to express the elements of the geometric cross section of the channel. The study uses HEC-Geo RAS with Arc GIS 10 and HEC-RAS 4.1.0 for modeling and 1D hydrodynamic. The surveyed cross sections modeling is calibrated and validated with observed daily discharge. Then the surveyed data cross sections are replaced by the cross sections extracted from SRTM. Accuracy is suitability between the surveyed data cross sections with data cross sections which extracted from SRTM. Accuracy is expressed by the magnitude of the error. The ratio of simulated discharges between two models by surveyed data cross sections and SRTM data cross sections have index of performance indicator for Nash-Sutcliffe Index (NI) is 0.95 and index of Root Mean Square Error (RMSE) is 0.41. Index of Performance indicator for comparison of simulated discharges between models with Nash-Sutcliffe Index (NI) is 0.95 and index of Root Mean Square Error (RMSE) is 0.41. The results show that cross sections from SRTM can be an alternative to extract data cross sections for Upper Citarum River flood modeling.

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“…The relative roughness was used to aid the spatial parameterization of the Manning's roughness within the vegetation classes. Numerous researchers give guidance on selection of Manning's values for various vegetation classes [6,8,12,15,65]. After a careful review and investigation of Manning's values by comparing the study site vegetation characteristics and those given in the literature, the value ranges shown in Table 3 were adopted for the study area.…”

Section: Methodsmentioning

confidence: 99%

“…The development of an accurate and reliable hydraulic model that well describes surface water flow across a large wetland floodplain depends on topographic data and hydraulic roughness, viz. Manning's roughness of the floodplain [11,12]. Manning's roughness is one of the key variables of a hydraulic model; vegetation component plays a crucial role in the total resulting roughness, especially in vegetated floodplains.…”

Section: Introductionmentioning

confidence: 99%

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

et al. 2015

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Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (Ks) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning's roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning's coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water OPEN ACCESSRemote Sens. 2015, 7 837 levels with optimum floodplain roughness (np) with channel roughness nc = 0.03 m −1/3 /s; SWL2 is simulated water levels with calibrated values for both floodplain roughness np = 0.65 m −1/3 /s and channel roughness nc = 0.021 m −1/3 /s; and SWL3 is simulated water levels with calibrated channel roughness nc and spatial Manning's coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R 2 ), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R 2 = 0.95, which was improved in SWL2 to E = 0.95 and R 2 = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R 2 = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models.

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Assessment of an ASTER-generated DEM for 2D hydrodynamic flood modeling

Cited by 83 publications

References 17 publications

Assessing the impact of different sources of topographic data on 1-D hydraulic modelling of floods

Assessing the impact of different sources of topographic data on 1-D hydraulic modelling of floods

Accuracy analysis of SRTM usage for upper Citarum River flood modeling

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

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