Articles | Volume 19, issue 9
https://doi.org/10.5194/hess-19-3755-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-3755-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
I. Popescu
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
A. Mynett
UNESCO-IHE Institute for Water Education, Delft, the Netherlands
Department of Civil Engineering, Technical University Delft, Delft, the Netherlands
Related authors
No articles found.
Mostafa Saberian, Vidya Samadi, and Ioana Popescu
Hydrol. Earth Syst. Sci., 30, 371–399, https://doi.org/10.5194/hess-30-371-2026, https://doi.org/10.5194/hess-30-371-2026, 2026
Short summary
Short summary
Recent progress in NN (neural network) accelerated improvements in the performance of catchment modeling. Yet flood modeling remains a very difficult task. Focusing on two headwater streams, we developed N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting) and N-BEATS (Network-Based Expansion Analysis for Interpretable Time Series Forecasting) models and benchmarked them with LSTM (long short-term memory) to predict flooding. N-HiTS and N-BEATS outperformed LSTM for flood predictions. We demonstrated how the proposed models can be augmented with an uncertainty approach to predict flooding that is interpretable without considerable loss in accuracy.
This article is included in the Encyclopedia of Geosciences
Faisal Sardar, Muhammad Haris Ali, Ioana Popescu, Andreja Jonoski, Schalk Jan van Andel, and Claudia Bertini
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-276, https://doi.org/10.5194/hess-2023-276, 2023
Manuscript not accepted for further review
Short summary
Short summary
This article analyzes surface and groundwater interactions in a small transboundary lowland catchment. The study also investigates the influence of rainfall representation in model on surface subsurface hydrological simulations. Emphasizing the significance of these interactions, the research highlighted the role of subsurface baseflow in contributing to river discharge. Despite minimal impact on streamflow, spatial variability in rainfall can cause localized fluctuations in groundwater levels.
This article is included in the Encyclopedia of Geosciences
Betina I. Guido, Ioana Popescu, Vidya Samadi, and Biswa Bhattacharya
Nat. Hazards Earth Syst. Sci., 23, 2663–2681, https://doi.org/10.5194/nhess-23-2663-2023, https://doi.org/10.5194/nhess-23-2663-2023, 2023
Short summary
Short summary
We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
This article is included in the Encyclopedia of Geosciences
Cited articles
Barneveld, H., Silander, J., Sane, M., and Malnes, E.: Application of satellite data for improved flood forecasting and mapping, 4th International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability, 6–8 May 2008 Toronto, Canada, retrieved from: http://www.hkv.nl/site/hkv/upload/publication/Application_of satellite_data_for improved flood_forecasting HJB.pdf (last access: 1 June 2015), 2008.
Belaud, G., Cassan, L., and Bader, J. C.: Calibration of a propagation model in large river using satellite, 6th International Symposium on Environmental Hydraulics, Athens, Greece, 869–874, 2010.
Bercher, N., Calmant, S., Picot, N., Seyler, F., and Fleury, S.: Evaluation of Cryosat-2 measurements for the monitoring of large river water levels, retrieved from: Along-Track.com: http://chronos.altihydrolab.fr/2012-09-23_Venice_ESA_20_years_of_progress_in_altimetry/Bercher.2012b_(Venice_Paper)_CryoSat-2_hydro.pdf (last access 25 June 2015), 2014.
Chen, H., Yang, D., Hong, Y., Gourley, J., and Zhang, Y.: Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting, Adv. Water Resour., 59, 209–220, https://doi.org/10.1016/j.advwatres.2013.06.010, 2013.
Crétaux, J. F., Stéphane, C., Romanovski, V., Shabunin, A., Lyard, F., Bergé Nguyen, M., Cazenave, A., Fabrice, H., and Perosanz, F.: An absolute calibration site for radar altimeters in the continental domain: lake Issykkul in Central Asia, J. Geodesy, 83, 723–735, 2009.
Crétaux, J. F., Bergé-Nguyen, M., Leblanc, M., Del Rio, R. A., Delclaux, F., Mognard, N., Lion, C., Pandey, R. K., Tweed, S., Calmant, S., and Maisongrande, P.: Flood mapping inferred from remote sensing data, Fifteenth International Water Technology Conference, 28–30 May 2011, Alexandria, Egypt, 2011.
CSA – Canadian Space Agency:RADARSAT Constellation, retrieved from: RADARSAT constellation: http://www.asc-csa.gc.ca/eng/satellites/radarsat/, last access: 23 June 2015.
Dellepiane, S., de Laurentiis, R., and Giordano, F.: Coastline Extraction from SAR Images and a Method for the Evaluation of the Coastline Precision, Pattern Recog. Lett., 25, 1461–1470, 2004.
Di Baldassarre, G., Schumann, G., and Bates, P. D.: A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent, J. Hydrol., 367, 276–282, https://doi.org/10.1016/j.jhydrol.2009.01.020, 2009.
DLR: Earth Observation: TanDEM-X – the Earth in three dimensions, retrieved from: http://www.dlr.de/dlr/en/desktopdefault.aspx/tabid-10378/566_read-426/#/gallery/345, last access: 1 June 2015.
Duan, Z. and Bastiaanssen, W.: Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data, Remote Sens. Environ., 134, 403–416, https://doi.org/10.1016/j.rse.2013.03.010, 2013.
ESA: Sentinel 3, retrieved from Copernicus, observing the Earth: http://www.esa.int/Our Activities/Observing_the Earth/Copernicus/Sentinel-3, last access 15 June 2015.
Ezer, T. and Liu, H.: On the dynamics and morphology of extensive tidal mudflats: Integrating remote sensing data with an inundation model of Cook Inlet, Alaska, Ocean Dynam., 60, 1307–1318, https://doi.org/10.1007/s10236-010-0319-x, 2010.
Fraser, C. S. and Ravanbakhsh, M.: Performance of DEM generation technologies in coastal environments, 7th International Symposium on Digital Earth, 23–25 August 2011, Perth, Australia, 40–49, 2011.
García-Pintado, J., Mason, D., Dance, S., Cloke, H., Neal, J., Freer, J., and Bates, P.: Satellite-supported flood forecasting in river networks: A real case study, J. Hydrol., 523, 706–724, https://doi.org/10.1016/j.jhydrol.2015.01.084, 2015.
Getirana, A. C., Bonnet, M.-P., Calmant, S., Roux, E., Rotunno Filho, O. C., and Mansur, W. J.: Hydrological monitoring of poorly gauged basins based on rainfall–runoff, J. Hydrol., 379, 205–219, https://doi.org/10.1016/j.jhydrol.2009.09.049, 2009.
Giustarini, L., Matgen, P., Hostache, R., Montanari, M., Plaza, D., Pauwels, V. R. N., De Lannoy, G. J. M., De Keyser, R., Pfister, L., Hoffmann, L., and Savenije, H. H. G.: Assimilating SAR-derived water level data into a hydraulic model: a case study, Hydrol. Earth Syst. Sci., 15, 2349–2365, https://doi.org/10.5194/hess-15-2349-2011, 2011.
Gorokhovich, Y. and Voustianiouk, A.: Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics, Remote Sens. Environ., 104, 409–415, https://doi.org/10.1016/j.rse.2006.05.012, 2006.
Horritt, M. S.: A methodology for the validation of uncertain flood inundation models, J. Hydrol., 326, 153–165, https://doi.org/10.1016/j.jhydrol.2005.10.027, 2006.
Islam, R. Z., Begum, S. F., Yamaguchi, Y., and Ogawa, K.: Distribution of suspended sediment in the coastal sea off the Ganges–Brahmaputra River mouth: observation from TM data, Marine Syst., 32, 307–321, https://doi.org/10.1016/S0924-7963(02)00045-3, 2002.
Jarihani, A., Callow, J., Johansen, K., and Gouweleeuw, B.: Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods, J. Hydrol., 505, 78–90, https://doi.org/10.1016/j.jhydrol.2013.09.010, 2013.
Jarihani, A., McVicar, T., Van Niel, T., Emelyanova, I., Callow, J., and Johansen, K.: Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of "Index-then-Blend" and "Blend-then-Index" Approaches, Remote Sensing, 6, 9213–9238, https://doi.org/10.3390/rs6109213, 2014.
Jarihani, A., Callow, J., McVicar, T., Van Niel, T., and Larsen, J.: Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments, J. Hydrol., 524, 489–506, https://doi.org/10.1016/j.jhydrol.2015.02.049, 2015.
Jung, C. H., Hamski, J., Durand, M., Alsdorf, D., Hossain, F., Lee, H., and Hasan, K.: Characterization of complex fluvial systems using remote sensing of spatial and temporal water level variations in the Amazon, Congo, and Brahmaputra Rivers, Earth Surf. Proc. Land., 35, 294–304, https://doi.org/10.1002/esp.1914, 2010.
Karlsson, J. M. and Arnberg, W.: Quality analysis of SRTM and HYDRO1K: a case study of flood inundation in Mozambique, Int. J. Remote Sens., 32, 267–285, https://doi.org/10.1080/01431160903464112, 2011.
Khan, S. I., Hong, Y., Vergara, H. J., Gourley, J. J., Brakenridge, G. R., De Groeve, T., and Yong, B.: Microwave Satllite Data for Hydrologic Modelling in Ungauged Basins, IEEE Geosci. Remote Sens. Lett., 9, 663–667, https://doi.org/10.1109/LGRS.2011.2177807, 2012.
Kim, J., Lu, Z., Lee, H., Shum, C. K., Swarzenski, C. M., Doyle, T. W., and Baek, S.-H.: Integrated analysis of PALSAR/Radarsat-1 InSAR and ENVISAT altimeter data for mapping of absolute water level changes in Louisiana wetlands, Remote Sens. Environ., 113, 2356–2365, 2009.
Kumar, A., Narayana, A., and Jayappa, K.: Shoreline changes and morphology of spits along southern Karnataka, west coast of India: A remote sensing and statistics-based approach, Geomorphology, 120, 133–152, https://doi.org/10.1016/j.geomorph.2010.02.023, 2010.
León, J. G., Calmant, S., Seyler, F., Bonnet, M.-P., Cauhopé, M., Frappart, F., and Fraizy, P.: Rating curves and estimation of average water depth at the upper Negro, J. Hydrol., 328, 481–496, https://doi.org/10.1016/J.JHYDROL.2005.12.006, 2006.
Li, S., Sun, D., Goldberg, M., and Stefanidis, A.: Derivation of 30-m-resolution water maps from TERRA/MODIS and SRTM, Remote Sens. Environ., 134, 417–430, https://doi.org/10.1016/j.rse.2013.03.015, 2013.
Liew, S. C., Gupta, A., Wong, P. P., and Kwoha, L. K.: Recovery from a large tsunami mapped over time: The Aceh coast, Sumatra, Geology, 114, 520–529, https://doi.org/10.1016/j.geomorph.2009.08.010, 2010.
Lillesand, T., Kiefer, R. W., and Chapman, J. W.: Remote sensing and image interpretation, 5th Edn., John Wiley and sons, Hobone, NJ, USA, 2004.
Long, S., Fatoyinbo, T., and Policelli, F.: Flood extent mapping for Namibia using change detection and thresholding with SAR, Environ. Res. Lett., 9, 035002, https://doi.org/10.1088/1748-9326/9/3/035002, 2014.
Mason, D. C., Horritt, M. S., Dall'Amico, J. T., and Scott, T. R.: Improving River Flood Extent Delineation From Synthetic Aperture Radar Using Airborne Laser Altimetry, IEEE T. Geosci. Remote, 45, 3932–3943, https://doi.org/10.1109/TGRS.2007.901032, 2007.
Mason, D. C., Scott, T. R., and Dance, S. L.: Remote sensing of intertidal morphological change in Morcambe Bay, U,K., between 1991 and 2007, Estuar. Coast. Shelf Sci., 87, 487–496, https://doi.org/10.1016/j.ecss.2010.01.015, 2010.
Matgen, P., Montanari, M., Hostache, R., Pfister, L., Hoffmann, L., Plaza, D., and Savenije, H.: Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept, Hydrol. Earth Syst. Sci., 14, 1773–1785, https://doi.org/10.5194/hess-14-1773-2010, 2010.
Mcmillan, H., Hreinsson, E., Clark, M., Singh, S., Zammit, C., and Uddstrom, M.: Operational hydrological data assimilation with the recursive ensemble Kalman filter, Hydrol. Earth Syst. Sci., 17, 21–38, https://doi.org/10.5194/hess-17-21-2013, 2013.
Meijerink, A. M., Bannert, D., Batelaan, O., Lubczynski, M. W., and Pointet, T.: Remote sensing applictions to groundwater, Series on Groundwater, UNESCO, Paris, France, 2007.
NOAA, S. I.: Jason3, retrieved from: http://www.nesdis.noaa.gov/jason-3/?CFID=731ecb89-8379-48fc-ad50-9546e71739&CFTOKEN=0, last access: 29 May 2015.
Noh, S., Tachikawa, Y., Shiiba, M., and Kim, S.: Applying sequential Monte Carlo methods into a distributedhydrologic model: lagged particle filtering approach with regularization, Hydrol. Earth Syst. Sci., 15, 3237–3251, https://doi.org/10.5194/hess-15-3237-2011, 2011.
Owe, M., Brubaker, K., Ritchie, J., and Albert, R.: Remote sensing and Hydrology, 2000, IAHS, Wallingford, OX, UK, 2001.
Papa, F., Bala, S. K., Pandey, R. K., Durand, F., Gopalakrishna, V. V., Rahman, A., and Rossow, W. B.: Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal, J. Geophys. Res., 117, C11021, https://doi.org/10.1029/2012JC008158, 2012.
Pavelsky, T., Morrow, R., Peterson, C., Andral, A., Bronner, E., and Srinivasan, M.: SWOT 101: A Quatum Improvement of Oceanography and Hydrology from the Next Generation Altimeter Mission, retrieved from: Surface Water and Ocean Topography: https://swot.jpl.nasa.gov/files/swot/SWOT-101 Jan2015.pdf, last access: 29 May 2015.
Penton, D. J. and Overton, I. C.: Spatial modelling of floodplain inundation combining satellite imagery and elevation models, MODSIM 2007 International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia and New Zealand CSIRO, Clayton south, Vic, Australia, 2007.
Pereira-Cardenal, S. J., Riegels, N. D., Berry, P. A. M., Smith, R. G., Yakovlev, A., Siegfried, T. U., and Bauer-Gottwein, P.: Real-time remote sensing driven river basin modeling using radar altimetry, Hydrol. Earth Syst. Sci., 15, 241–254, https://doi.org/10.5194/hess-15-241-2011, 2011.
Quinn, P. F., Hewett, C. J. M., Muste, M., and Popescu, I.: Towards new types of water-centric collaboration, Proceedings of the Institution of Civil Engineers: Water Management, 163, 39–51, https://doi.org/10.1680/wama.2010.163.1.39, 2010.
Ričko, M., Birkett, C., Carton, J., and Crétauxc, J.-F.: Intercomparison and validation of continental water, J. Appl. Remote Sens., 6, 061710, https://doi.org/10.1117/1.JRS.6.061710, 2012.
Santos da Silva, J., Roux, E., Filho, O., Bonne, M. P., Seyler, F., and Calmant, S.: 3D Selection of Envisat Data for Improved Water Stage Times Series on the Rio Negro and Adjacent Wetlands (Amazon Basin), 2nd Hydrospace Workshop, 07_06, 12–14 November 2007, ESA, Geneva, 2007.
Santos da Silver, J., Calmant, S., Seyler, F., Lee, H., and Shum, C.: Mapping of the extreme stage variations using ENVISAT altimetry in the Amazon basin rivers, Int. Water Technol. J., 2, 14–25, 2012.
Sarhadi, A., Soltani, S., and Modarres, R.: Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis, J. Hydrol., 458–459, 68–86, https://doi.org/10.1016/j.jhydrol.2012.06.039, 2012.
Schumann, G., Matgen, P., Pappenberger, F., Black, A., Cutler, M., Hoffmann, L., and Pfister, L.: The Refix Model: Remote Sensing Based Flood Modelling, ISPRS Commission VII Mid-term Symposium "Remote Sensing: From Pixels to Processes", Enschede, the Netherlands, 2006.
Schumann, G., Matgen, P., Hoffmann, L., Hostache, R., Pappenberger, F., and Pfister, L.: Deriving distributed roughness values from satellite radar data for flood inundation modelling, J. Hydrol., 344, 96–111, https://doi.org/10.1016/j.jhydrol.2007.06.024, 2007.
Schumann, G., Matgen, P., Cutler, M., Black, A., Hoffmann, L., and Pfister, L.: Comparison of remotely sensed water stages from LiDAR,topographic contours and SRTM, Photogram. Remote Sens., 63, 283–296, https://doi.org/10.1016/j.isprsjprs.2007.09.004, 2008.
Schumann, G. J.-P., Neal, J., Voisin, N., Andreadis, K., Pappenberger, F., Phanthuwongpakdee, N., and Bates, P.: A first large-scale flood inundation forecasting model, Water Resour. Res., 49, 6248–6257, https://doi.org/10.1002/wrcr.20521, 2013.
Schumann, G. J.-P., Bates, P., Neal, J., and Andreadis, K.: Measuring and Mapping Flood processes, in: Hydro-Meteorological Hazards, Risks, and Disasters edited by: Paron, P. and Di Baldassare, G., Elsevier, Amsterdam, the Netherlands, 35–64, 2015.
Seyler, F., Calmant, S., Santos da Silva, J., León, G. J., Frappart, F., Bonnet, M.-P., Filizola, N., Roux, E., Cochonneau, G., Zoppas Costi, A.-C., De, O. E., Guyot, J.-L., and Seyler, P.: New perspectives in monitoring water resources in large tropical transboundary basins based on the combined used of remote sensing and radar altimetry, in: Improving Integrated Surface and Groundwater Resources Management in a Vulnerable and Changing World, IAHS Publication, Wallingford OX, UK, 282–288, https://doi.org/10.13140/2.1.5101.0569, 2009.
Siddique-E-Akbor, A. H., Hossain, F., Lee, H., and Shum, C. K.: Inter-comparison study of water level estimates derived from, Remote Sens. Environ., 115, 1522–1531, 2011.
Skakun, S.: A neural network approach to flood mapping using satellite imagery, Comput. Inform., 29, 1013–1024, 2010.
Smith, L. C.: Satellite Remote Sensing of River Innundation Area, Stage and Discharge: A review, Hydrol. Process., 11, 1427–1439, 1997.
Stephens, E., Bates, P., Freer, J., and Mason, D.: The impact of uncertainty in satellite data on the assessment of flood inundation models, J. Hydrol., 414–415, 162–173, https://doi.org/10.1016/j.jhydrol.2011.10.040, 2012.
Sun, W., Ishidaira, H., and Bastola, S.: Estimating discharge by calibrating hydrological model against water surface width measured from satellites in large ungauged basins, Annu. J. Hydraul. Eng., 53, 49–54, 2009.
Sun, W., Ishidaira, H., and Bastola, S.: Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet. Hydrol. Earth Syst. Sci., 14, 2011–2022, https://doi.org/10.5194/hess-14-2011-2010, 2010.
Syvitski, J. P., Overeem, I., Brakenridge, R. G., and Hannon, M.: Floods, floodplains, delta plains – A satellite imaging approach, Sediment. Geol., 267–268, 1–14, https://doi.org/10.1016/j.sedgeo.2012.05.014, 2012.
Tarpanelli, A., Barbetta, S., Brocca, L., and Moramarco, T.: River Discharge Estimation by Using Altimetry Data and Simplified Flood Routing Modeling, Remote Sensing, 5, 4145–4162, https://doi.org/10.3390/rs5094145, 2013.
Vermeulen, C. J., Barneveld, H. J., Huizinga, H. J., and Havinga, F. J.: Data-assimilation in flood forecasting using time series and satellite data, International conference on innovation advances and implementation of flood forcasting technology, ACTIF/Floodman/FloodRelief, Tromso, 2005.
Villladsen, H., Andersen, O., and Stenseng, L.: Annual cycle in lakes and rivers from cryosat-2 altimetry – the brahmaputra river, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 13–18 July 2014, Quebec, Canada, 894–897, https://doi.org/10.1109/IGARSS.2014.6946569, 2014.
Westahoff, R., Huizinga, J., Kleuskens, M., Burren, R., and Casey, S.: ESA Living Planet Symposium, 686, ESA Communications, Bergen, Norway, 2010.
Woldemicheal, A., Degu, A., Siddique-E-Akbor, A., and Hossain, F.: Role of Land–Water Classification and Manning's Roughness Parameter in Space-Borne Estimation of Discharge for Braided Rivers: A Case Study of the Brahmaputra River in Bangladesh, IEEE J. Select. Top. Appl. Earth Obs. Rem. S., 1939–1404, 395–403, https://doi.org/10.1109/JSTARS.2010.2050579, 2010.
Yan, K., Di Baldassarre, G., Solomatine, D., and Schumann, G.: A review of low-cost space-borne data for flood modelling: topography, flood extent and water level, Hydrol. Process., 29, 3368–3387, https://doi.org/10.1002/hyp.10449, 2015.
Yang, C. and Ouchic, K.: Analysis of bar morphology using multi-temporal and multi-sensor satellite images: Example from the Han Estuary, Korea, Mar. Geol., 311-314, 17–31, https://doi.org/10.1016/j.margeo.2012.04.004, 2012.
Zhang, J., Xu, K., Yang, Y., Qi, L., Hayashi, S., and Watanabe, M.: Measuring water storage fluctuations in Lake Dongting, China, by Topex/Poseidon satellite altimetry, Environ. Monit. Assess., 115, 23–37, https://doi.org/10.1007/s10661-006-5233-9, 2006.
Short summary
Hydrological data collection is a challenge for the scientific community, especially as some events e.g. floods occur in un-gauged rivers or infrequently.
Some such events are however recorded by satellites.
Using satellite remote sensing in estimating surface water parameters has its limitations, but recent improvements in sensor specifications, expansion in research methods and knowledge of satellite data have increased its utilization.
The review is on modelling and mapping with RS.
Hydrological data collection is a challenge for the scientific community, especially as some...