Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5065-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5065-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Enhancing the performance of 1D–2D flood models using satellite laser altimetry and multi-mission surface water extent maps from Earth observation (EO) data
Theerapol Charoensuk
CORRESPONDING AUTHOR
Department of Environmental and Resource Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
DHI A/S, 2970 Hørsholm, Denmark
Hydro-informatics Institute, 10900 Bangkok, Thailand
Claudia Katrine Corvenius Lorentzen
Department of Environmental and Resource Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Anne Beukel Bak
Department of Environmental and Resource Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Jakob Luchner
DHI A/S, 2970 Hørsholm, Denmark
Christian Tøttrup
DHI A/S, 2970 Hørsholm, Denmark
Peter Bauer-Gottwein
Department of Environmental and Resource Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
Department of Geosciences and Natural Resources Management, University of Copenhagen, 1350 Copenhagen, Denmark
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Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, https://doi.org/10.5194/hess-27-1011-2023, 2023
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This paper uses remote sensing data from ICESat-2 to calibrate a 1D hydraulic model. With the model, we can make estimations of discharge and water surface elevation, which are important indicators in flooding risk assessment. ICESat-2 data give an added value, thanks to the 0.7 m resolution, which allows the measurement of narrow river streams. In addition, ICESat-2 provides measurements on the river dry portion geometry that can be included in the model.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
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A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Liguang Jiang, Silja Westphal Christensen, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 6359–6379, https://doi.org/10.5194/hess-25-6359-2021, https://doi.org/10.5194/hess-25-6359-2021, 2021
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River roughness and geometry are essential to hydraulic river models. However, measurements of these quantities are not available in most rivers globally. Nevertheless, simultaneous calibration of channel geometric parameters and roughness is difficult as they compensate for each other. This study introduces an alternative approach of parameterization and calibration that reduces parameter correlations by combining cross-section geometry and roughness into a conveyance parameter.
Cecile M. M. Kittel, Liguang Jiang, Christian Tøttrup, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 333–357, https://doi.org/10.5194/hess-25-333-2021, https://doi.org/10.5194/hess-25-333-2021, 2021
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In poorly instrumented catchments, satellite altimetry offers a unique possibility to obtain water level observations. Improvements in instrument design have increased the capabilities of altimeters to observe inland water bodies, including rivers. In this study, we demonstrate how a dense Sentinel-3 water surface elevation monitoring network can be established at catchment scale using publicly accessible processing platforms. The network can serve as a useful supplement to ground observations.
Cited articles
Abrams, M., Crippen, R., and Fujisada, H.: ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD), Remote Sens., 12, 1–12, https://doi.org/10.3390/rs12071156, 2020.
AbuBaker, A., Qahwaji, R., Ipson, S., and Saleh, M.: One Scan Connected Component Labeling Technique, in: 2007 IEEE International Conference on Signal Processing and Communications, 24–27 November 2007, Dubai, United Arab Emirates, 1283–1286, https://doi.org/10.1109/ICSPC.2007.4728561, 2007.
Anon: Glossary of Terms, Mach. Learn., 30, 271–274, https://doi.org/10.1023/A:1017181826899, 1998.
Argall, P. S. and Sica, R. J.: LIDAR|Atmospheric Sounding Introduction, edited by: Holton, J. R., Academic Press, Oxford, 1169–1176, https://doi.org/10.1016/B0-12-227090-8/00203-7, 2003.
Auynirundronkool, K., Chen, N., Peng, C., Yang, C., Gong, J., and Silapathong, C.: Flood detection and mapping of the Thailand Central plain using RADARSAT and MODIS under a sensor web environment, Int. J. Appl. Earth Obs. Geoinf., 14, 245–255, https://doi.org/10.1016/j.jag.2011.09.017, 2012.
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT Mission and Its Capabilities for Land Hydrology, Surv. Geophys., 37, 307–337, https://doi.org/10.1007/s10712-015-9346-y, 2016.
Carabajal, C. C. and Boy, J.-P.: ICESAT-2 altimetry as geodetic control, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1299–1306, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1299-2020, 2020.
CDSE: Copernicus DEM – Global and European Digital Elevation Model, CDSE [data set], https://doi.org/10.5270/ESA-c5d3d65, 2022.
Chai, T. and Draxler, R. R.: Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geosci. Model Dev., 7, 1247–1250, https://doi.org/10.5194/gmd-7-1247-2014, 2014.
Channumsin, S., Sreesawet, S., Saroj, T., Saingyen, P., Puttasuwan, K., Udomthanatheera, P., and Jaturut, S.: Collision avoidance strategies and conjunction risk assessment analysis tool at GISTDA, J. Space Safe. Eng., 7, 268–273, https://doi.org/10.1016/j.jsse.2020.07.019, 2020.
Charoensuk, T., Lolupiman, T., Chantip, S., and Sisomphon, P.: Modeling dike breaching in The Chao Phraya River Basin using high resolution elevation data (Lidar), in: 13th International Conference on Hydroscience & Engineering, Advancement of hydro-engineering for sustainable development, Chongqing, China, 18–22 June 2018.
Charoensuk, T., Luchner, J., Balbarini, N., Sisomphon, P., and Bauer-Gottwein, P.: Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts, J. Hydrol. Reg. Stud., 52, 101737, https://doi.org/10.1016/j.ejrh.2024.101737, 2024.
Coppo Frias, M., Liu, S., Mo, X., Nielsen, K., Ranndal, H., Jiang, L., Ma, J., and Bauer-Gottwein, P.: River hydraulic modeling with ICESat-2 land and water surface elevation, Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, 2023.
Coppo Frias, M., Liu, S., Mo, X., Druce, D., Yamazaki, D., Folkmann Musaeus, A., Nielsen, K., and Bauer-Gottwein, P.: Improving 2D hydraulic modeling in floodplain areas with ICESat-2 data: A case study in Upstream Yellow River, EGU General Assembly 2024, 14–19 April 2024, Vienna, Austria, EGU24-14669, https://doi.org/10.5194/egusphere-egu24-14669, 2024.
Dandabathula, G. and Srinivasa Rao, S.: Validation of ICESat-2 Surface Water Level Product ATL13 with Near Real Time Gauge Data, Hydrology, 8, 19, https://doi.org/10.11648/j.hyd.20200802.11, 2020.
Dandabathula, G., Hari, R., Ghosh, K., Bera, A. K., and Srivastav, S. K.: Accuracy assessment of digital bare-earth model using ICESat-2 photons: analysis of the FABDEM, Model. Earth Syst. Environ., 9, 2677–2694, https://doi.org/10.1007/s40808-022-01648-4, 2023.
Danish Hydraulic Insitute: MIKE 21 Flow Model & MIKE21 Flood Screening Tool – Hydrodynamic Module – Scientific Documentation, 53 pp., http://manuals.mikepoweredbydhi.help/2017/Coast_and_Sea/M21HDFST_Scientific_Doc.pdf (last access: 10 February 2023), 2016.
Darnell, A. R., Tate, N. J., and Brunsdon, C.: Improving user assessment of error implications in digital elevation models, Comput. Environ. Urban Syst., 32, 268–277, https://doi.org/10.1016/j.compenvurbsys.2008.02.003, 2008.
DHI: MIKE 21 Flow Model FM, Reference mannual, 55 pp., http://icoe.org.vn/upload/2009/06/10/MIKE21_HD_Step_By_Step.pdf (last access: 7 June 2023), 2018.
DHI Water and Environment: MIKE FLOOD Reference Manual, 81–88, https://www.scribd.com/document/660190956/MIKE-FLOOD-UserManual-1-152-3 (last access: 10 February 2023), 2019.
DHI Water and Environment: MIKE 11 Reference Manual, https://www.scribd.com/document/94010463/Mike-11-Reference-Manual (last access: 9 February 2023), 2021.
Dumrongchai, P., Srimanee, C., Duangdee, N., and Bairaksa, J.: The determination of Thailand Geoid Model 2017 (TGM2017) from airborne and terrestrial gravimetry, Terr. Atmos. Ocean. Sci., 32, 859–874, https://doi.org/10.3319/TAO.2021.08.23.01, 2021.
ESA – European Space Agency: Sentinel-2 user handbook, https://sentinels.copernicus.eu/documents/247904/685211/Sentinel-2_User_Handbook (last access: 20 February 2024), 2015.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, https://doi.org/10.1029/2005RG000183, 2007.
Finn, H., Storm, B., Richaud, B., Klinting, A., and Gasc, A.: Flood Forecasting and Water Management System for Thailand, in: Advances in Hydroinformatics, Springer, Singapore, 541–557, https://doi.org/10.1007/978-981-10-7218-5_38, 2018.
Forecast, B.: Chapter 7 Forecast verification, Int. Geophys., 59, 233–283, https://doi.org/10.1016/S0074-6142(06)80043-4, 1995.
Hanson, F.: Final report: Improving the Efficiency of the CPY Flood Modelling System, Hydro and Agro Informatics Institute (HAII), DHI, Hørsholm, Denmark, 2017.
Hao, T., Cui, H., Hai, G., Qiao, G., Li, H., He, Y., and Li, R.: Impact of Slopes on ICESat-2 Elevation Accuracy Along the CHINARE Route in East Antarctica, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 15, 5636–5643, https://doi.org/10.1109/JSTARS.2022.3189042, 2022.
Jasinski, M., Gsfc, N., Stoll, J., Hancock, D., Robbins, J., Nattala, J., Morison, J., Jones, B., Ondrusek, M., Parrish, C., Ssai, C. C., Jasinski, M., Stoll, J., Hancock, D., Robbins, J., Nattala, J., Morison, J., Jones, B., Ondrusek, M., Pavelsky, T., Parrish, C., and Carabajal, C.: ICESat-2 Algorithm Theoretical Basis Document (ATBD) for Along Track Inland Surface Water Data, ATL13, Version 6, NASA, https://doi.org/10.5067/03JYGZ0758UL, 2023.
JICA – Japan International Cooperation Agency: Project for comprehensive flood management plan for the chao phraya river basin (sub-component 1–1 aerial survey by lidar), https://openjicareport.jica.go.jp/pdf/12127205.pdf (last access: 21 September 2023), 2012.
JICA: Data Collection Survey on the Outer Ring Road Diversion Channel in the Comprehensive Flood Management Plan for the Chao Phraya River Basin in the Kingdom of Thailand, https://openjicareport.jica.go.jp/pdf/12308631_01.pdf (last access: 21 September 2023), 2018.
Kittel, C. M. M., Hatchard, S., Neal, J. C., Nielsen, K., Bates, P. D., and Bauer-Gottwein, P.: Hydraulic Model Calibration Using CryoSat-2 Observations in the Zambezi Catchment, Water Resour. Res., 57, e2020WR029261, https://doi.org/10.1029/2020WR029261, 2021.
Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M., Younis, M., and Zink, M.: TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry, IEEE T. Geosci. Remote, 45, 3317–3341, https://doi.org/10.1109/TGRS.2007.900693, 2007.
Lachaise, M. and Schweißhelm, B.: TanDEM-X 30m DEM Change Maps Product Description, Issue Public Document TD-GS-PS-0216 Issue 1.0, 12 October 2023 [data set], https://download.geoservice.dlr.de/TDM30_EDEM/#details (last access: 20 May 2024), 2023.
Lamichhane, N. and Sharma, S.: Effect of input data in hydraulic modeling for flood warning systems, Hydrolog. Sci. J., 63, 938–956, https://doi.org/10.1080/02626667.2018.1464166, 2018.
Lemoine, F., Kenyon, S. C., Factor, J., Trimmer, R., Pavlis, N., Chinn, D., Cox, C., Klosko, S., Luthcke, S., Torrence, M., Wang, Y., Williamson, R., Pavlis, E., Rapp, R., and Olson, T.: The development of the joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) geopotential model EGM96, https://ntrs.nasa.gov/api/citations/19980218814/downloads/19980218814.pdf (last access: 12 February 2024), 1998.
Liu, Z., Zhu, J., Fu, H., Zhou, C., and Zuo, T.: Evaluation of the vertical accuracy of open global dems over steep terrain regions using icesat data: A case study over hunan province, china, Sensors, 20, 1–16, https://doi.org/10.3390/s20174865, 2020.
Lv, X., Liu, R., Liu, J., and Song, X.: Monitoring flood using multi-temporal ENVISAT ASAR data, in: International Geoscience and Remote Sensing Symposium (IGARSS), 29–29 July 2005, Seoul, South Korea,3627–3629, https://doi.org/10.1109/IGARSS.2005.1526633, 2005.
Martinis, S., Twele, A., Strobl, C., Kersten, J., and Stein, E.: A multi-scale flood monitoring system based on fully automatic MODIS and terraSAR-X processing chains, Remote Sens., 5, 5598–5619, https://doi.org/10.3390/rs5115598, 2013.
Martinis, S., Groth, S., Wieland, M., Knopp, L., and Rättich, M.: Towards a global seasonal and permanent reference water product from Sentinel-1/2 data for improved flood mapping, Remote Sens. Environ., 278, 113077, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2022.113077, 2022.
McClean, F., Dawson, R., and Kilsby, C.: Implications of Using Global Digital Elevation Models for Flood Risk Analysis in Cities, Water Resour. Res., 56, e2020WR028241, https://doi.org/10.1029/2020WR028241, 2020.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Morrison, D., Beevers, L., Wright, G., and Stewart, M. D.: The impact of data spatial resolution on flood vulnerability assessment, Environ. Hazards, 21, 77–98, https://doi.org/10.1080/17477891.2021.1912694, 2022.
Nandam, V. and Patel, P. L.: A framework to assess suitability of global digital elevation models for hydrodynamic modelling in data scarce regions, J. Hydrol., 630, 130654, https://doi.org/10.1016/j.jhydrol.2024.130654, 2024.
Neal, J. and Hawker, L.: FABDEM V1-2 [data set], https://doi.org/10.5523/bris.s5hqmjcdj8yo2ibzi9b4ew3sn, 2023.
Neuenschwander, A. L., Pitts, K. L., Jelley, B. P., Robbins, J., Klotz, B., Popescu, S. C., Nelson, R. F., Harding, D., Pederson, D., and Sheridan, R.: ATLAS/ICESat-2 L3A Land and Vegetation Height, (ATL08, Version 5), NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado, USA, https://doi.org/10.5067/ATLAS/ATL08.005, 2001.
Neuenschwander, A., Pitts, K., Jelley, B., Robbins, J., Markel, J., Popescu, S., Nelson, R., Harding, D., Klotz, B., Sheridan, R., and Neuenschwander, A.: Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) Algorithm Theoretical Basis Document (ATBD) for Land – Vegetation Along-Track Products (ATL08), https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl08_atbd_r005.pdf (last access: 2 December 2024), 2022.
Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M. R., Brenner, A. C., Brunt, K. M., Cavanaugh, J., Fernandes, S. T., Hancock, D. W., Harbeck, K., Lee, J., Kurtz, N. T., Luers, P. J., Luthcke, S. B., Magruder, L., Pennington, T. A., Ramos-Izquierdo, L., Rebold, T., Skoog, J., and Thomas, T. C.: The Ice, Cloud, and Land Elevation Satellite – 2 mission: A global geolocated photon product derived from the Aadvanced Ttopographic Llaser Aaltimeter Ssystem, Remote Sens. Environ., 233, 111325, https://doi.org/10.1016/j.rse.2019.111325, 2019.
Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Saba, J., Harbeck, K., Gibbons, A., Lee, J., Luthcke, S. B., Rebold, T., et al.: ATLAS/ICESat-2 L2A Global Geolocated Photon Data, ATL03, Version 5 [data set], NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/ATLAS/ATL03.005, 2021.
Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Gibbons, A., Lee, J., Harbeck, K., Saba, J., Luthcke, S., and Rebold, T.: Ice, Cloud, and Land Elevation Satellite (ICESat-2) Project Algorithm Theoretical Basis Document (ATBD) for Global Geolocated Photons ATL03, Version 6, ICESat-2 Project [data set], https://doi.org/10.5067/GA5KCLJT7LOT, 2022.
Nied, M., Pardowitz, T., Nissen, K., Ulbrich, U., Hundecha, Y., and Merz, B.: On the relationship between hydro-meteorological patterns and flood types, J. Hydrol., 519, 3249–3262, https://doi.org/10.1016/j.jhydrol.2014.09.089, 2014.
Nithirochananont, U., Chivapreecha, S., Peanvijarnpong, C., and Dejhan, K.: GISTDA EOC synthetic aperture radar data processing system, in: Proceedings – CSPA 2010: 2010 6th International Colloquium on Signal Processing and Its Applications, 21–23 May 2010, Malacca, Malaysia, 327–332, https://doi.org/10.1109/CSPA.2010.5545261, 2010.
Okeowo, M. A., Lee, H., Hossain, F., and Getirana, A.: Automated Generation of Lakes and Reservoirs Water Elevation Changes from Satellite Radar Altimetry, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 3465–3481, https://doi.org/10.1109/JSTARS.2017.2684081, 2017.
Paengwangthong, W. and Sarapirome, S.: DEM data assessment for hydrologic applications: A case study in Nam Khek Watershed, Thailand, in: 33rd Asian Conference on Remote Sensing 2012, ACRS 2012, 26–30 November 2012, 336–342, 2012.
Pavlis, N. K., Holmes, S. A., Kenyon, S. C., and Factor, J. K.: The development and evaluation of the Earth Gravitational Model 2008 (EGM2008), J. Geophys. Res.-Solid, 117, B04406, https://doi.org/10.1029/2011JB008916, 2012.
Perera, G. S. N. and Nalani, H. A.: UAVS for a complete topographic survey, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 441–447, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-441-2022, 2022.
Pulvirenti, L., Boni, G., Pierdicca, N., Fiorini, M., and Rudari, R.: Combined use of multi-temporal COSMO-SkyMed data and a hydrodynamic model to monitor flood dynamics, in: International Geoscience and Remote Sensing Symposium (IGARSS), 13–18 July 2014, Quebec City, QC, Canada, 3346–3349, https://doi.org/10.1109/IGARSS.2014.6947197, 2014.
Raj, T., Hashim, F. H., Huddin, A. B., Ibrahim, M. F., and Hussain, A.: A survey on LiDAR scanning mechanisms, Electronics, 9, 741, https://doi.org/10.3390/electronics9050741, 2020.
Raney, R. K., Luscombe, A. P., Langham, E. J., and Ahmed, S.: RADARSAT (SAR imaging), Proc. IEEE, 79, 839–849, https://doi.org/10.1109/5.90162, 1991.
Rao, P., Jiang, W., Hou, Y., Chen, Z., and Jia, K.: Dynamic change analysis of surface water in the Yangtze river basin based on MODIS products, Remote Sens., 10, 1–20, https://doi.org/10.3390/rs10071025, 2018.
Rosenfeld, A. and Pfaltz, J. L.: Sequential Operations in Digital Picture Processing, J. ACM, 13, 471–494, https://doi.org/10.1145/321356.321357, 1966.
Saksena, S. and Merwade, V.: Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping, J. Hydrol., 530, 180–194, https://doi.org/10.1016/j.jhydrol.2015.09.069, 2015.
Samantaray, S. and Sahoo, A.: Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation, Groundw. Sustain. Dev., 26, 101178, https://doi.org/10.1016/j.gsd.2024.101178, 2024.
Schwarz, K. P. and El-Sheimy, N.: Mobile mapping systems–state of the art and future trends, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35, Part B, https://isprs.org/proceedings/XXXV/congress/comm5/papers/652.pdf (last access: 2 December 2024), 2007.
Shen, J. and Tan, F.: 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, Springer Netherlands, 927–957, https://doi.org/10.1007/s11069-020-04198-z, 2020.
Shen, Y., Liu, D., Jiang, L., Yin, J., Nielsen, K., Bauer-Gottwein, P., Guo, S., and Wang, J.: On the contribution of satellite altimetry-derived water surface elevation to hydrodynamic model calibration in the Han river, Remote Sens., 12, 1–18, https://doi.org/10.3390/rs12244087, 2020.
Sholarin, E. A. and Awange, J. L.: Photogrammetry, in: Environmental Project Management. Environmental Science and Engineering, Springer, Cham., 213–230, https://doi.org/10.1007/978-3-319-27651-9_10, 2015.
Sisomphon, P., Boonya-aroonnet, S., Chonwattana, S., and Hansen, F.: Towards the development of a decision support system for flood management in Chao Phraya River Basin, Thailand, in: International Conference on Flood Resilience (ICFR), 5–7 September 2013, Exeter, UK, 2013.
Soille, P.: Morphological Image Analysis: Principles and Applications, in: 2nd Edn., Springer-Verlag, Berlin, Heidelberg, ISBN 3540429883, 2003.
Stein, L., Pianosi, F., and Woods, R.: Hydrological Processes – 2019 – Stein – Event-based classification for global study of river flood generating processes, Hydrological Processes, 34, 1514–1529, https://doi.org/10.1002/hyp.13678, 2019.
Tadono, T., Takaku, J., Tsutsui, K., Oda, F., and Nagai, H.: Status of “ALOS World 3D (AW3D)” global DSM generation, in: International Geoscience and Remote Sensing Symposium (IGARSS), 6–31 July 2015, Milan, Italy, 3822–3825, https://doi.org/10.1109/IGARSS.2015.7326657, 2015.
Technical University of Denmark: Enhancing the performance of 1D–2D flood models using satellite laser altimetry and multi-mission surface water extent maps from Earth observation (EO) data, Zenodo [code], https://doi.org/10.5281/zenodo.17070190, 2025.
Thanathanphon, W., Chanthip, S., and Sisomphon, P.: Development of an operational real time monitoring system for flood risk assessment, forecasting and management of mun and Chi River Basins, Thailand, 19th IAHR-APD Congress 2014, Hanoi, Vietnam, ISBN 978604821338-1, 2014.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B. Ö., Floury, N., Brown, M., Traver, I. N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R., Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission, Remote Sens. Environ., 120, 9–24, https://doi.org/10.1016/j.rse.2011.05.028, 2012.
Tottrup, C., Druce, D., Meyer, R. P., Christensen, M., Riffler, M., Dulleck, B., Rastner, P., Jupova, K., Sokoup, T., Haag, A., Cordeiro, M. C. R., Martinez, J. M., Franke, J., Schwarz, M., Vanthof, V., Liu, S., Zhou, H., Marzi, D., Rudiyanto, R., Thompson, M., Hiestermann, J., Alemohammad, H., Masse, A., Sannier, C., Wangchuk, S., Schumann, G., Giustarini, L., Hallowes, J., Markert, K., and Paganini, M.: Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection, Remote Sens., 14, 2410, https://doi.org/10.3390/rs14102410, 2022.
Turkington, T., Breinl, K., Ettema, J., Alkema, D., and Jetten, V.: A new flood type classification method for use in climate change impact studies, Weather Clim. Extrem., 14, 1–16, https://doi.org/10.1016/j.wace.2016.10.001, 2016.
Visessri, S. and Ekkawatpanit, C.: Flood management in the context of climate and land-use changes and adaptation within the chao phraya river basin, J. Disast. Res., 15, 579–587, https://doi.org/10.20965/jdr.2020.p0579, 2020.
Van Der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T.: Scikit-image: Image processing in python, Pee J., 2014, 1–18, https://doi.org/10.7717/peerj.453, 2014.
Wang, C., Zhu, X., Nie, S., Xi, X., Li, D., Zheng, W., and Chen, S.: Ground elevation accuracy verification of ICESat-2 data: a case study in Alaska, USA, Opt. Express, 27, 38168, https://doi.org/10.1364/oe.27.038168, 2019.
Wang, X. and Liang, X.: Accuracy evaluation of ICESAT-2 ATL08 in Finland, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W2-2023, 1817–1822, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1817-2023, 2023.
Weifeng, X., Jun, L., Dailiang, P., Jinge, J., Hongxuan, X., Hongyue, Y., and Jun, Y.: Multi-source DEM accuracy evaluation based on ICESat-2 in Qinghai-Tibet Plateau, China, Int. J. Digit. Earth, 17, 1–24, https://doi.org/10.1080/17538947.2023.2297843, 2024.
Werner, M.: Shuttle Radar Topography Mission (SRTM) mission overview, Frequenz, 55, 75–79, https://doi.org/10.1515/FREQ.2001.55.3-4.75, 2001.
Wessel, B.: TanDEM-X Ground Segment DEM Products Specification Document, Public Document TD-GS-PS-0021, 46 pp., https://tandemx-science.dlr.de/pdfs/TD-GS-PS-0021_DEM-Product-Specification_v3.2.pdf, 2016.
Willmott, C. J.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30, 79–82, 2005.
Wu, S., Li, J., and Huang, G. H.: Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation, Environ. Model. Software, 22, 1250–1260, https://doi.org/10.1016/j.envsoft.2006.08.001, 2007.
Yamazaki, D., Trigg, M. A., and Ikeshima, D.: Development of a global ∼90 m water body map using multi-temporal Landsat images, Remote Sens. Environ., 171, 337–351, https://doi.org/10.1016/j.rse.2015.10.014, 2015.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Yan, L., Zhang, L., Xiong, L., Yan, P., Jiang, C., Xu, W., Xiong, B., Yu, K., Ma, Q., and Xu, C. Y.: Flood Frequency Analysis Using Mixture Distributions in Light of Prior Flood Type Classification in Norway, Remote Sens., 15, 401, https://doi.org/10.3390/rs15020401, 2023.
Zhu, J., Yang, P.-F., Li, Y., Xie, Y.-Z., and Fu, H.-Q.: Accuracy assessment of ICESat-2 ATL08 terrain estimates: A case study in Spain, J. Cent. South Univers., 29, 226–238, https://doi.org/10.1007/s11771-022-4896-x, 2022.
Short summary
The objective of this study is to enhance the performance of 1D–2D flood models using satellite Earth observation data. The main factor influencing a 1D–2D flood model is the accuracy of the digital elevation model (DEM). Two workflows are introduced to improve the 1D–2D flood model: (1) in the DEM analysis workflow, 10 DEM products are evaluated using the ICESat-2 ATL08 benchmark, while (2) in the flood map analysis workflow, flood extent maps derived from multi-mission satellite datasets are compared with simulated flood maps.
The objective of this study is to enhance the performance of 1D–2D flood models using satellite...