Accuracy assessment of RS-based DEMs in flood inundation mapping of different morphological types of rivers

Document Type : Research Article


1 MSc. Student of Hydraulic Structures Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran

2 Associate Professor, Department of Irrigation & Reclamation Engineering, University of Tehran, IRAN

3 Assistant Professor of Water Engineering, Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran


Flood is one of the most devastating natural hazards that causes significant losses and damages. Flood inundation maps are initial requirements for river floodplain management and eliminating flood risk in integrated flood management plans.
Surveyed topography datasets, together with bathymetry data, were utilized to construct the river terrain for flood inundation mapping, however, such data is rare. Therefore, for tackling this challenging issue, DEMs as an accelerator and free of charge datasets, are widely used in flood modelling. The recent advancement of GIS and RS technologies enabled scientists to benchmark high-accuracy DEMs, such as LIDAR (Light Detection and Ranging), which perform as excellent as driven DEMs from surveyed topographies, for flood modelling. Despite the reliability of these finer datasets, the limited coverage of world and the exorbitant price of provision has forced experts to utilize coarser-resolution DEMs rather than these higher-resolution DEM sources. As a result, the accuracy of open-access and free of charge DEMs used in flood modelling in the data-sparse rivers should be investigated broadly, unless the lower-resolution DEMs hinder flood modelling results by incorrectly reproducing river terrain. In this study, three sets of widely used open-access DEMs’ performance in flood inundation mapping and estimating hydraulic parameters of four various rivers are assessed thoroughly.

In this paper, three sets of free and accessible 30m resolution DEM resources (ALOS, SRTM, ASTER) of four various sorts of rivers in Iran will be utilized as HEC-RAS geometry input file. In short, this study consists of 5 main steps:

1) Creating surveyed topography maps with 1:1000 or 1:2000 scales
2) Generating GDEM by interpolating topography maps obtained from the prior step
3) Determining channel geometry and extraction of cross-sections by operating GIS-based DEMs and GDEM in HEC-GeoRAS extension on ArcGIS
4) Flood modelling of the rivers by geometry input files produced from step 3, plus US and DS boundary conditions and design flow of the rivers
5) Assessment of DEMs’ performance in estimating hydraulic parameters based on efficiency measures, such as Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), Mean Absolute Percentage Error (MAPE), Relative Error (RE) and F-statistics.
6) Investigating the relation among the accuracy of DEMs and morphology characteristics of various Iran rivers, namely Sojasrud in Zanjan province, Taleghanrud in Alborz province, Gorganrud in Gorgan province and Sarbaz in Sistan-Baluchistan province of Iran.

Results and discussion
The river geometry derived from ALOS DEMs were more identical to surveyed cross-sections. According to previous studies, ALOS DEMs captures the more accurate depicting river bathymetry data, the higher performance of this dataset in flood modelling is expected. However, all of these GIS-based DEMs were unable to present the Gorganrud river geometry data.
Apart from the better representation of river geometry, the ALOS DEMs dataset is superior to ASTER and SRTM DEMs datasets in terms of fairy prediction of hydraulic parameters (water extents and water surface elevation). For instance, ALOS had the least RMSE (2.3-8.6 m) in predicting flood extent compared to the rest. Moreover, the higher value of F statistics (approximately 80%) proves that this model presented the flood inundation map with the highest agreement. Reversely, the RMSE of ASTER, the least accurate model, in flood extent estimation was from 2.8 to 15m. Identically, the predicted flood pattern map of these rivers using ASTER leads to the more significant discrepancy with the F-statistics of 68% at most. The SRTM performance in generating flood inundation map was better than ASTER and less accurate compared to ALOS (maximum value of 78% in F-statistic was recorded).
The accuracy of DEMs in stimulating WSE of the mountainous rivers was similar to each other, and it was higher than predicted WSE of lower-slope rivers. Moreover, the estimated WSE using ALOS led to less disagreement with the benchmark values, whereas the operation of ASTER and SRTM for this purpose resulted in overestimating. Although the Relative Error of these DEMs in predicting Gorganrud river flood WSE was almost 5%, the MAPE of whole DEMs was 1% within all cross-sections in the rest of the study rivers.

The accuracy of flood inundation mapping is highly dependent upon its geometry input file. The high-resolution DEMs, including LIDAR dataset, are often used due to their accuracy, still in most of flood inundation mappings projects, coarser-resolution DEMs are operated owing to their accessibility and free of charge datasets. It is critical to evaluate the influence of these DEMs on hydraulic outputs. In this research, various open-access DEMs configurations were operated in four various rivers located in different regions of Iran. The results reveal that the ALOS DEM dataset is superior to ASTER and SRTM DEM datasets in terms of a better representation of river geometry and fairy prediction of hydraulic parameters (water extents and water surface elevation). For instance, the higher percentage of F statistics (approximately 80%) proves that this model presented the flood inundation map with the highest agreement. However, the maximum values of F-statistics of SRTM and ASTER were nearly 78% and 68%, respectively, showing the flaws of these models in flood extents mapping.
The efficiency of all DEMs datasets in estimating WSE of the rivers was excellent, but Gorganrud river is an exemption. The Mean Absolute Percentage Error of these DEMs for estimating WSE, which was under 1% within the majority of cross-sections, was not meaningful.
Consequently, ALOS is particularly potent in accurately hydraulic modelling. Additionally, the remote-sensing based DEMs are more applicable in wide and (or) straight river reaches than narrow and meandering rivers. It would be better if DEMs are modified in advance to reduce the level of disparities in flood modelling. In this line, there are several modification methods led to lowering this predictable errors, such as using GCPs (Ground Control Points) or merging cross-sections with DEMs to create an integrated surface.


Adams, T., Chen, S., Davis, R., Schade, T., and Lee, D. (2010). The Ohio River Community HEC-RAS Model. World Environmental and Water Resources Congress 2010. doi:10.1061/41114(371)160
Anees, M.T., Abdullah, K., Nawawi, M.N.M., Ab Rahman, N.N.N., Piah, A.R.M., Zakaria, N.A., and Mohd Omar, A.K. (2016). Numerical modeling techniques for flood analysis. Journal of African Earth Sciences, 124, 478–486. doi: 10.1016/j.jafrearsci.2016.10.001
Arash, A.M., Yasi, M., Azizian, A. and Farhoudi, J. (2019). Studying Adequacy of ALOS, ASTER and SRTM DEMs for Hydraulic Modelling and Inundation Mapping in Areas with Data Scarcity. 7th Comprehensive Conference on Flood Engineering and Management., Tehran, Iran. (In Persian)
Azizian, A. (2019). The Effects of Topographic Map Scale and Costs of Land Surveying on Geometric Model and Flood Inundation Mapping. Water resources management, 33, 1315-1333. doi: 10.1007/s11269-019-2202-y.
Bagheri, A. and Torkaman Zadeh, M.H. (2018). Flood Hazard Mapping in Gabrik Watershed. J. Water Engeering. 6(4), 249-256. (In Persian)
Bates, P.D., Horritt, M.S., Aronica, G. and Beven, K. (2004). Bayesian updating of flood inundation likelihoods conditioned on flood extent data. Hydrological Processes, 18(17), 3347–3370. doi:10.1002/hyp.1499
Bates, P.D., Wilson, M. D., Horritt, M. S., Mason, D. C., Holden, N., & Currie, A. (2006). Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery: Data analysis and modelling. Journal of Hydrology, 328(1-2), 306–318. doi: 10.1016/j.jhydrol.2005.12. 028
Burdziakowski, P. (2018). UAV in todays photogrammetry - application areas and challenges. International Multidisciplinary Scientific GeoConference: SGEM, 18(2.3), 241-248.
Chen, H., Liang, Q., Liu, Y. and Xie, S. (2018). Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling. Journal of Hydrology, 559, 56–70. doi: 10.1016 /j.jhydrol.2018.01.056
Cook, A. and Merwade, V. (2009). Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. Journal of Hydrology, 377(1-2), 131–142. doi: 10.1016/j.jhydrol.2009.08.015 
Costabile, P. and Macchione, F. (2015). Enhancing river model set-up for 2-D dynamic flood modelling. Environmental Modelling & Software, 67, 89–107. doi: 10.1016/j.envsoft.2015.01.009
CRED & UNISDR (2015). The human costs of weather-related disasters.
Grimaldi, S., Li, Y., Pauwels, V.R.N. and Walker, J. P. (2016). Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges. Surveys in Geophysics, 37(5), 977–1034. doi:10.1007/s10712-016-9378-y
Grimaldi, S., Li, Y., Walker, J.P. and Pauwels, V.R. N. (2018). Effective Representation of River Geometry in Hydraulic Flood Forecast Models. Water Resources Research, 54(2), 1031–1057. doi:10.1002/2017wr021765
Haile, A. and Rientjes, T. (2005). Effects of LiDAR DEM resolution in flood modelling: A model sensitivity study for the city of Tegucigalpa, Honduras. In Proceedings of the ISPRS WG III/3, III/4, V/3 Laser Scanning Workshop (pp. 168–173). Enschede, the Netherlands: ISPRS.
Heidari, K., Momeni Goldiani, M. and Mardokh Por, A.R. (2019). Flood Hazard Mapping Using HEC-RAS (Case study: Emam Zadeh Ebrahim River, Gilan Province). 6th National Conference on Applied research in Civil Eng., Architecture and Urban Management., Tehran, Iran.
Horritt, M.S. and Bates, P.D. (2002). Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology, 268(1-4), 87–99. doi:10.1016/s0022-1694(02)00121-x
Jamali, B., Löwe, R., Bach, P.M., Urich, C., Arnbjerg-Nielsen, K. and Deletic, A. (2018). A rapid urban flood inundation and damage assessment model. Journal of Hydrology, 564, 1085–1098. doi: 10.1016/j.jhydrol.2018.07.064
Jarihani, A.A., Callow, J.N., McVicar, T.R., Van Niel, T.G. and Larsen, J.R. (2015). Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments. Journal of Hydrology, 524, 489–506. doi: 10.1016/j.jhydrol.2015.02.049
Jarihani, A.A., Callow, N., Johansen, K. and Gouweleeuw, B. (2013). Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods. Journal of Hydrology, 505, 78-90. doi: 10.1016/j.jhydrol.2013.09.010
Jung, Y. and Merwade, V. (2012). Uncertainty Quantification in Flood Inundation Mapping Using Generalized Likelihood Uncertainty Estimate and Sensitivity Analysis. Journal of Hydrologic Engineering, 17(4), 507–520. doi:10.1061/(asce)he. 1943-5584.0000476
Jung, Y. and Merwade, V. (2014). Estimation of uncertainty propagation in flood inundation mapping using a 1-D hydraulic model. Hydrological Processes, 29(4), 624–640. doi:10.1002/hyp.10185
Khanna, R.K., Agrawal, C.K. and Kumar, P., (2018). Remote sensing and GIS applications in flood management. Central Water Commission New Delhi, India.
Kwak, Y. (2017). Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data. ISPRS International Journal of Geo-Information, 6(7), 203. doi:10.3390/ijgi6070203
Kumar, A., Dasgupta, A., Lokhande, S. and Ramsankaran, R. (2019). Benchmarking the Indian National CartoDEM against SRTM for 1D Hydraulic Modelling. International Journal of River Basin Management, 1–39. doi:10.1080/15715124. 2019.1606816
Mersel, M.K., Smith, L.C., Andreadis, K.M. and Durand, M.T. (2013). Estimation of river depth from remotely sensed hydraulic relationships. Water Resources Research, 49(6), 3165–3179. doi:10.1002 /wrcr.20176
Merwade, V., Cook, A. and Coonrod, J. (2008b). GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping. Environmental Modelling & Software, 23(10-11), 1300–1311. doi: 10.1016/j.envsoft.2008 .03.005
Merwade, V., Olivera, F., Arabi, M. and Edleman, S. (2008a). Uncertainty in Flood Inundation Mapping: Current Issues and Future Directions. Journal of Hydrologic Engineering, 13(7), 608–620. doi:10.1061/(asce)1084-0699(2008)13:7(608)
Papaioannou, G., Loukas, A., Vasiliades, L. and Aronica, G. T. (2016). Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Natural Hazards, 83(S1), 117–132. doi:10.1007/s11069-016-2382-1
Puno, G.R., Amper, R.A.L., Opiso, E.M. and Cipriano, J.A.B. (2019). Mapping and analysis of flood scenarios using numerical models and GIS techniques. Spatial Information Research. doi: 10.1007/s41324-019-00280-2
Saadatseresht, M., Hashempour, A.H., and Hasanlou, M. (2015). UAV photogrammetry: a practical solution for challenging mapping projects. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 619.
Saksena, S. and Merwade, V. (2015). Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology, 530, 180–194. doi: 10.1016/j.jhydrol. 2015.09.069
Sanders, B.F. (2007). Evaluation of on-line DEMs for flood inundation modeling. Advances in Water Resources, 30(8), 1831–1843. doi: 10.1016/j. advwatres.2007.02.005
Shahbazi, Ch. (2016). Comparison Evaluation of DTM Production Using LiDAR Aerial and Satellite Image Data, M.Sc Thesis, Islamic Azad University Shahrood Branch, Shahrood, 107p. (In Persian)
Sistani Badouie, M., Negaresh H. and Fotouhi, S. (2017).       Investigating the Effect of Construction Structures on River Flood Extent Using HEC-RAS and ArcGIS Softwares. Case study: Babolrood River, Mazandaran. J. Geography and Environmental Hazards. 6(22), 163-182. (In Persian)
Special Reporting Committee on Iran Floods (2019). Report of River Engineering and Hydraulic Structures Working Group. University of Tehran. (In Persian)
Teng, J., Vaze, J., Dutta, D. and Marvanek, S. (2015). Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM. Water Resources Management, 29(8), 2619–2636. doi:10.1007/ s11269-015-0960-8
World Meteorological Organization. (2014). Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970-2012). World Meteorological Organization.
Yasi, M. and Nasiri Soltan Ahmadi, L. (2017). Simulation and Evaluation of Perennial Rivers Flows with HEC-RAS and RubarBE Models. J. Water and Soil Science-University of Tabriz. 27(2), 225-236. (In Persian).