Advertisement

Surveys in Geophysics

, Volume 37, Issue 5, pp 977–1034 | Cite as

Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

  • Stefania GrimaldiEmail author
  • Yuan Li
  • Valentijn R. N. Pauwels
  • Jeffrey P. Walker
Article

Abstract

Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.

Keywords

Hydraulic modelling of floods Remote sensing Flood extent and level Data assimilation Real-time forecast 

Notes

Acknowledgments

This study is financially supported by the Bushfires and Natural Hazards CRC project—Improving flood forecast skill using remote sensing data. Valentijn Pauwels is funded by ARC Future Fellow grant FT130100545. The authors would like to acknowledge the Australian Bureau of Meteorology and Geoscience Australia for their valuable comments and support.

References

  1. Adhikari P, Hong Y, Douglas K, Kirschbaum D, Gourley J, Adler R, Robert Brakenridge G (2010) A digitized global flood inventory (1998–2008): compilation and preliminary results. Nat Hazards 55:405–422. doi: 10.1007/s11069-010-9537-2 CrossRefGoogle Scholar
  2. Alsdorf DE (2002) Interferometric SAR observations of water level changes: potential targets for future repeat-pass AIRSAR missions. In: Paper presented at the AIRSAR earth science and application workshop, Pasadena, California, March 4–6 2002Google Scholar
  3. Alsdorf DE, Melack JM, Dunne T, Mertes LAK, Hess LL, Smith LC (2000) Interferometric radar measurements of water level changes on the Amazon flood plain. Nature 404:174–177CrossRefGoogle Scholar
  4. Alsdorf DE, Smith LC, Melack JM (2001) Amazon floodplain water level changes measured with interferometric SIR-C radar. Geosci Remote Sens IEEE Trans 39:423–431. doi: 10.1109/36.905250 CrossRefGoogle Scholar
  5. Alsdorf D, Dunne T, Melack J, Smith L, Hess L (2005) Diffusion modeling of recessional flow on central Amazonian floodplains. Geophys Res Lett 32:L21405. doi: 10.1029/2005GL024412 CrossRefGoogle Scholar
  6. Alsdorf D, Bates P, Melack J, Wilson M, Dunne T (2007) Spatial and temporal complexity of the Amazon flood measured from space. Geophys Res Lett 34:L08402. doi: 10.1029/2007GL029447 CrossRefGoogle Scholar
  7. Andreadis KM, Schumann GJP (2014) Estimating the impact of satellite observations on the predictability of large-scale hydraulic models. Adv Water Resour 73:44–54. doi: 10.1016/j.advwatres.2014.06.006 CrossRefGoogle Scholar
  8. Andreadis KM, Clark EA, Lettenmaier DP, Alsdorf DE (2007) Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophys Res Lett 34:L10403. doi: 10.1029/2007GL029721 CrossRefGoogle Scholar
  9. Aronica G, Hankin B, Beven K (1998) Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data. Adv Water Resour 22:349–365. doi: 10.1016/S0309-1708(98)00017-7 CrossRefGoogle Scholar
  10. Aronica G, Bates PD, Horritt MS (2002) Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE. Hydrol Process 16:2001–2016. doi: 10.1002/hyp.398 CrossRefGoogle Scholar
  11. Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Prob 14:R1CrossRefGoogle Scholar
  12. Bates PD (2004) Remote sensing and flood inundation modelling. Hydrol Process 18:2593–2597. doi: 10.1002/hyp.5649 CrossRefGoogle Scholar
  13. Bates PD, Horritt MS, Smith CN, Mason D (1997) Integrating remote sensing observations of flood hydrology and hydraulic modelling. Hydrol Process 11:1777–1795. doi: 10.1002/(SICI)1099-1085(199711)11:14<1777:AID-HYP543>3.0.CO;2-E CrossRefGoogle Scholar
  14. Bates PD, Stewart MD, Siggers GB, Smith AM, Hervouet JM, Sellin RHJ (1998) Internal and external validation of a two-dimensional finite element code for river flood simulations. Proc Inst Civ Eng Water Marit Energy 130:127–141. doi: 10.1680/iwtme.1998.30972 CrossRefGoogle Scholar
  15. Bates PD, Horritt MS, Aronica G, Beven K (2004) Bayesian updating of flood inundation likelihoods conditioned on flood extent data. Hydrol Process 18:3347–3370. doi: 10.1002/hyp.1499 CrossRefGoogle Scholar
  16. Bates PD, Wilson MD, Horritt MS, Mason DC, Holden N, Currie A (2006) Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery: data analysis and modelling. J Hydrol 328:306–318. doi: 10.1016/j.jhydrol.2005.12.028 CrossRefGoogle Scholar
  17. Bates P, Neal J, Alsdorf D, Schumann GP (2014a) Observing global surface water flood dynamics. Surv Geophys 35:839–852. doi: 10.1007/s10712-013-9269-4 CrossRefGoogle Scholar
  18. Bates PD, Pappenberger F, Romanowicz RJ (2014b) Uncertainty in flood inundation modelling. In: Applied uncertainty analysis for flood risk management. Imperial College Press, London (UK), pp 232–269. doi: 10.1142/9781848162716_0010
  19. Bazi Y, Bruzzone L, Melgani F (2005) An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. Geosci Remote Sens IEEE Trans 43:874–887. doi: 10.1109/TGRS.2004.842441 CrossRefGoogle Scholar
  20. Bercher N, Kosuth P (2012) Monitoring river water levels from space: quality assessment of 20 years of satellite altimetry data. In: Paper presented at the 20 years of progress in radar altimetry, Venice (Italy), 24–29 September 2012Google Scholar
  21. Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36. doi: 10.1016/j.jhydrol.2005.07.007 CrossRefGoogle Scholar
  22. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298. doi: 10.1002/hyp.3360060305 CrossRefGoogle Scholar
  23. Biancamaria S, Bates PD, Boone A, Mognard NM (2009) Large-scale coupled hydrologic and hydraulic modelling of the Ob river in Siberia. J Hydrol 379:136–150. doi: 10.1016/j.jhydrol.2009.09.054 CrossRefGoogle Scholar
  24. Biancamaria S et al (2011) Assimilation of virtual wide swath altimetry to improve Arctic river modeling. Remote Sens Environ 115:373–381. doi: 10.1016/j.rse.2010.09.008 CrossRefGoogle Scholar
  25. Biancamaria S, Lettenmaier DP, Pavelsky TM (2015) The SWOT mission and its capabilities for land hydrology. Surv Geophys. doi: 10.1007/s10712-015-9346-y Google Scholar
  26. Birkett CM, Mertes LAK, Dunne T, Costa MH, Jasinski MJ (2002) Surface water dynamics in the Amazon Basin: application of satellite radar altimetry. J Geophys Res Atmos 107:LBA 26-21. doi: 10.1029/2001JD000609 CrossRefGoogle Scholar
  27. Blyth KEN (1997) Floodnet: a telenetwork for acquisition, processing and dissemination of earth observation data for monitoring and emergency management of floods. Hydrol Process 11:1359–1375. doi: 10.1002/(SICI)1099-1085(199708)11:10<1359:AID-HYP529>3.0.CO;2-6 CrossRefGoogle Scholar
  28. Brandimarte L, Brath A, Castellarin A, Baldassarre GD (2009) Isla Hispaniola: a trans-boundary flood risk mitigation plan. Phys Chem Earth A/B/C 34:209–218. doi: 10.1016/j.pce.2008.03.002 CrossRefGoogle Scholar
  29. Brisco B, Kapfer M, Hirose T, Tedford B, Liu J (2011) Evaluation of C-band polarization diversity and polarimetry for wetland mapping. Can J Remote Sens 37:82–92. doi: 10.5589/m11-017 CrossRefGoogle Scholar
  30. Brivio PA, Colombo R, Maggi M, Tomasoni R (2002) Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int J Remote Sens 23:429–441. doi: 10.1080/01431160010014729 CrossRefGoogle Scholar
  31. Calabresi G (1995) The use of ERS SAR for flood monitoring: an overall assessment. In: Paper presented at the 2nd ERS applications workshop, London, UK, 6–8 DecemberGoogle Scholar
  32. Charney J, Halem M, Jastrow R (1969) Use of incomplete historical data to infer the present state of the atmosphere. J Atmos Sci 26:1160–1163. doi: 10.1175/1520-0469(1969)026<1160:UOIHDT>2.0.CO;2 CrossRefGoogle Scholar
  33. Choudhury BJ (1989) Monitoring global land surface using Nimbus-7 37 GHz data theory and examples. Int J Remote Sens 10:1579–1605. doi: 10.1080/01431168908903993 CrossRefGoogle Scholar
  34. Coxon G, Freer J, Westerberg IK, Wagener T, Woods R, Smith PJ (2015) A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations. Water Resour Res 51:5531–5546. doi: 10.1002/2014WR016532 CrossRefGoogle Scholar
  35. De Groeve T (2010) Flood monitoring and mapping using passive microwave remote sensing in Namibia. Geomat Nat Hazards Risk 1:19–35. doi: 10.1080/19475701003648085 CrossRefGoogle Scholar
  36. De Roo A, Van Der Knijff J, Horritt MS, Schmuck G, De Jong S (1999) Assessing flood damages of the 1997 Oder flood and the 1995 Meuse flood. In: Paper presented at the 2nd international ITC symposium on operationalization of remote sensing, Enschede, The NetherlandsGoogle Scholar
  37. Delmeire S (1997) Use of ERS-1 data for the extraction of flooded areas. Hydrol Process 11:1393–1396. doi: 10.1002/(SICI)1099-1085(199708)11:10<1393:AID-HYP528>3.0.CO;2-N CrossRefGoogle Scholar
  38. Di Baldassarre G, Montanari A (2009) Uncertainty in river discharge observations: a quantitative analysis. Hydrol Earth Syst Sci 13:913–921. doi: 10.5194/hess-13-913-2009 CrossRefGoogle Scholar
  39. Di Baldassarre G, Uhlenbrook S (2012) Is the current flood of data enough? A treatise on research needs for the improvement of flood modelling. Hydrol Process 26:153–158. doi: 10.1002/hyp.8226 CrossRefGoogle Scholar
  40. Di Baldassarre G, Schumann G, Bates P (2009a) Near real time satellite imagery to support and verify timely flood modelling. Hydrol Process 23:799–803. doi: 10.1002/hyp.7229 CrossRefGoogle Scholar
  41. Di Baldassarre G, Schumann G, Bates PD (2009b) A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent. J Hydrol 367:276–282. doi: 10.1016/j.jhydrol.2009.01.020 CrossRefGoogle Scholar
  42. Di Baldassarre G, Schumann G, Brandimarte L, Bates P (2011) Timely low resolution SAR imagery to support floodplain modelling: a case study review. Surv Geophys 32:255–269. doi: 10.1007/s10712-011-9111-9 CrossRefGoogle Scholar
  43. Domeneghetti A, Castellarin A, Brath A (2012) Assessing rating-curve uncertainty and its effects on hydraulic model calibration. Hydrol Earth Syst Sci 16:1191–1202. doi: 10.5194/hess-16-1191-2012 CrossRefGoogle Scholar
  44. Domeneghetti A, Tarpanelli A, Brocca L, Barbetta S, Moramarco T, Castellarin A, Brath A (2014) The use of remote sensing-derived water surface data for hydraulic model calibration. Remote Sens Environ 149:130–141. doi: 10.1016/j.rse.2014.04.007 CrossRefGoogle Scholar
  45. Domeneghetti A, Castellarin A, Tarpanelli A, Moramarco T (2015) Investigating the uncertainty of satellite altimetry products for hydrodynamic modelling. Hydrol Process 29:4908–4918. doi: 10.1002/hyp.10507 CrossRefGoogle Scholar
  46. Dottori F, Di Baldassarre G, Todini E (2013) Detailed data is welcome, but with a pinch of salt: accuracy, precision, and uncertainty in flood inundation modeling. Water Resour Res 49:6079–6085. doi: 10.1002/wrcr.20406 CrossRefGoogle Scholar
  47. Dung NV, Merz B, Bárdossy A, Thang TD, Apel H (2011) Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data. Hydrol Earth Syst Sci 15:1339–1354. doi: 10.5194/hess-15-1339-2011 CrossRefGoogle Scholar
  48. Dunne S, Entekhabi D (2005) An ensemble-based reanalysis approach to land data assimilation. Water Resour Res 41:W02013. doi: 10.1029/2004WR003449 CrossRefGoogle Scholar
  49. Durand JM, Gimonet BJ, Perbos JR (1987) SAR data filtering for classification. Geosci Remote Sens IEEE Trans 25:629–637. doi: 10.1109/TGRS.1987.289842 CrossRefGoogle Scholar
  50. Evans TL, Costa M, Telmer K, Silva TSF (2010) Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. Sel Top Appl Earth Obs Remote Sens IEEE J 3:560–575. doi: 10.1109/JSTARS.2010.2089042 CrossRefGoogle Scholar
  51. Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res Oceans 99:10143–10162. doi: 10.1029/94JC00572 CrossRefGoogle Scholar
  52. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367. doi: 10.1007/s10236-003-0036-9 CrossRefGoogle Scholar
  53. Evensen G (2004) Sampling strategies and square root analysis schemes for the EnKF. Ocean Dyn 54:539–560. doi: 10.1007/s10236-004-0099-2 CrossRefGoogle Scholar
  54. Faruolo M, Coviello I, Lacava T, Pergola N, Tramutoli V (2009) Real time monitoring of flooded areas by a multi-temporal analysis of optical satellite data. In: Geoscience and remote sensing symposium, 2009 IEEE international, IGARSS 2009, 12–17 July 2009. pp IV-192–IV-195. doi: 10.1109/IGARSS.2009.5417339
  55. Franceschetti G, Iodice A, Riccio D (2002) A canonical problem in electromagnetic backscattering from buildings. IEEE Trans Geosci Remote Sens 40:1787–1801. doi: 10.1109/TGRS.2002.802459 CrossRefGoogle Scholar
  56. Frappart F, Calmant S, Cauhopé M, Seyler F, Cazenave A (2006) Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin. Remote Sens Environ 100:252–264. doi: 10.1016/j.rse.2005.10.027 CrossRefGoogle Scholar
  57. Frost VS, Stiles JA, Shanmugam KS, Holtzman JC, Smith SA (1981) An adaptive filter for smoothing noisy radar images. Proc IEEE 69:133–135. doi: 10.1109/PROC.1981.11935 CrossRefGoogle Scholar
  58. Frost VS, Stiles JA, Shanmugan KS, Holtzman J (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. Pattern Anal Mach Intell IEEE Trans 4:157–166. doi: 10.1109/TPAMI.1982.4767223 CrossRefGoogle Scholar
  59. Fu L-L (2001) Chapter 3.3 Ocean circulation and variability from satellite altimetry. In: Gerold Siedler JC, John G (eds) International geophysics, vol 77. Academic Press, Cambridge, Massachusetts (USA), pp 141-XXVIII. doi: 10.1016/S0074-6142(01)80116-9
  60. Furrer R, Bengtsson T (2007) Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J Multivar Anal 98:227–255. doi: 10.1016/j.jmva.2006.08.003 CrossRefGoogle Scholar
  61. García-Pintado J, Neal JC, Mason DC, Dance SL, Bates PD (2013) Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling. J Hydrol 495:252–266. doi: 10.1016/j.jhydrol.2013.03.050 CrossRefGoogle Scholar
  62. García-Pintado J, Mason DC, Dance SL, Cloke HL, Neal JC, Freer J, Bates PD (2015) Satellite-supported flood forecasting in river networks: a real case study. J Hydrol 523:706–724. doi: 10.1016/j.jhydrol.2015.01.084 CrossRefGoogle Scholar
  63. Giustarini L et al (2011) Assimilating SAR-derived water level data into a hydraulic model: a case study. Hydrol Earth Syst Sci Discuss 8:2103–2144. doi: 10.5194/hessd-8-2103-2011 CrossRefGoogle Scholar
  64. Giustarini L, Matgen P, Hostache R, Dostert J (2012) From SAR-derived flood mapping to water level data assimilation into hydraulic models. In: Neale CMU, Maltese A (eds) Remote sensing for agriculture, ecosystems, and hydrology XIV, Edinburgh (UK), 24–26 September. Proceedings of SPIE, pp 85310U–85310U–85312. doi: 10.1117/12.974655
  65. Giustarini L, Hostache R, Matgen P, Schumann GJP, Bates PD, Mason DC (2013) A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens 51:2417–2430. doi: 10.1109/tgrs.2012.2210901 CrossRefGoogle Scholar
  66. Giustarini L et al (2015) Accounting for image uncertainty in SAR-based flood mapping. Int J Appl Earth Obs Geoinf 34:70–77. doi: 10.1016/j.jag.2014.06.017 CrossRefGoogle Scholar
  67. Grayson R, Blöschl G (2001) Spatial patterns in catchment hydrology: observations and modelling. Cambridge University Press, CambridgeGoogle Scholar
  68. Gupta HV, Sorooshian S, Yapo PO (1998) Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resour Res 34:751–763. doi: 10.1029/97WR03495 CrossRefGoogle Scholar
  69. Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geogr Inf Sci 17:235–249. doi: 10.1080/13658810210157822 CrossRefGoogle Scholar
  70. Hall J, Tarantola S, Bates P, Horritt M (2005) Distributed sensitivity analysis of flood inundation model calibration. J Hydraul Eng 131:117–126. doi: 10.1061/(ASCE)0733-9429(2005)131:2(117) CrossRefGoogle Scholar
  71. Hall JW, Manning LJ, Hankin RKS (2011) Bayesian calibration of a flood inundation model using spatial data. Water Resour Res 47:W05529. doi: 10.1029/2009WR008541 CrossRefGoogle Scholar
  72. Hall AC, Schumann GJP, Bamber JL, Bates PD, Trigg MA (2012) Geodetic corrections to Amazon River water level gauges using ICESat altimetry. Water Resour Res 48:W06602. doi: 10.1029/2011WR010895 CrossRefGoogle Scholar
  73. Hamilton SK, Sippel SJ, Melack JM (1996) Inundation patterns in the Pantanal wetland of South America determined from passive microwave remote sensing. Arch Hydrobiol 137:1–23Google Scholar
  74. Hartmann HC, Pagano TC, Sorooshian S, Bales R (2002) Confidence builders: evaluating seasonal climate forecasts from user perspectives. Bull Am Meteorol Soc 83:683–698. doi: 10.1175/1520-0477(2002)083<0683:CBESCF>2.3.CO;2 CrossRefGoogle Scholar
  75. Henderson FM, Lewis AJ (2008) Radar detection of wetland ecosystems: a review. Int J Remote Sens 29:5809–5835. doi: 10.1080/01431160801958405 CrossRefGoogle Scholar
  76. Henry JB, Chastanet P, Fellah K, Desnos YL (2006) Envisat multi-polarized ASAR data for flood mapping. Int J Remote Sens 27:1921–1929. doi: 10.1080/01431160500486724 CrossRefGoogle Scholar
  77. Hess LL, Melack JM, Simonett DS (1990) Radar detection of flooding beneath the forest canopy: a review. Int J Remote Sens 11:1313–1325. doi: 10.1080/01431169008955095 CrossRefGoogle Scholar
  78. Hong S-H, Wdowinski S, Sang-Wan K (2010) Evaluation of TerraSAR-X observations for wetland InSAR application. Geosci Remote Sens IEEE Trans 48:864–873. doi: 10.1109/TGRS.2009.2026895 CrossRefGoogle Scholar
  79. Horritt M (1999) A statistical active contour model for SAR image segmentation. Image Vis Comput 17:213–224. doi: 10.1016/S0262-8856(98)00101-2 CrossRefGoogle Scholar
  80. Horritt MS (2000) Calibration of a two-dimensional finite element flood flow model using satellite radar imagery. Water Resour Res 36:3279–3291. doi: 10.1029/2000WR900206 CrossRefGoogle Scholar
  81. Horritt MS (2006) A methodology for the validation of uncertain flood inundation models. J Hydrol 326:153–165. doi: 10.1016/j.jhydrol.2005.10.027 CrossRefGoogle Scholar
  82. Horritt MS, Bates PD (2002) Evaluation of 1D and 2D numerical models for predicting river flood inundation. J Hydrol 268:87–99. doi: 10.1016/S0022-1694(02)00121-X CrossRefGoogle Scholar
  83. Horritt MS, Mason DC, Cobby DM, Davenport IJ, Bates PD (2003) Waterline mapping in flooded vegetation from airborne SAR imagery. Remote Sens Environ 85:271–281. doi: 10.1016/S0034-4257(03)00006-3 CrossRefGoogle Scholar
  84. Horritt MS, Di Baldassarre G, Bates PD, Brath A (2007) Comparing the performance of a 2-D finite element and a 2-D finite volume model of floodplain inundation using airborne SAR imagery. Hydrol Process 21:2745–2759. doi: 10.1002/hyp.6486 CrossRefGoogle Scholar
  85. Hostache R, Matgen P, Schumann G, Puech C, Hoffmann L, Pfister L (2009) Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods. Geosci Remote Sens IEEE Trans 47:431–441. doi: 10.1109/TGRS.2008.2008718 CrossRefGoogle Scholar
  86. Hostache R, Lai X, Monnier J, Puech C (2010) Assimilation of spatially distributed water levels into a shallow-water flood model. Part II: use of a remote sensing image of Mosel River. J Hydrol 390:257–268. doi: 10.1016/j.jhydrol.2010.07.003 CrossRefGoogle Scholar
  87. Houser P, De Lannoy GM, Walker J (2010) Land surface data assimilation. In: Lahoz W, Khattatov B, Menard R (eds) Data assimilation. Springer, Berlin, pp 549–597. doi: 10.1007/978-3-540-74703-1_21
  88. Houtekamer PL, Mitchell HL (2005) Ensemble Kalman filtering. Q J R Meteorol Soc 131:3269–3289. doi: 10.1256/qj.05.135 CrossRefGoogle Scholar
  89. Hunt BR, Kostelich EJ, Szunyogh I (2007) Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Phys D 230:112–126. doi: 10.1016/j.physd.2006.11.008 CrossRefGoogle Scholar
  90. Hunter NM, Bates PD, Horritt MS, De Roo APJ, Werner MGF (2005) Utility of different data types for calibrating flood inundation models within a GLUE framework. Hydrol Earth Syst Sci 9:412–430. doi: 10.5194/hess-9-412-2005 CrossRefGoogle Scholar
  91. Hunter NM, Bates PD, Horritt MS, Wilson MD (2006) Improved simulation of flood flows using storage cell models. Proc Inst Civil Eng Water Manag 159:9–18. doi: 10.1680/wama.2006.159.1.9 CrossRefGoogle Scholar
  92. Jin YQ (1999) A flooding index and its regional threshold value for monitoring floods in China from SSM/I data. Int J Remote Sens 20:1025–1030. doi: 10.1080/014311699213064 CrossRefGoogle Scholar
  93. Jung HC et al (2010) 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. doi: 10.1002/esp.1914 CrossRefGoogle Scholar
  94. Jung HC et al (2012) Calibration of two-dimensional floodplain modeling in the central Atchafalaya Basin Floodway System using SAR interferometry. Water Resour Res 48:W07511. doi: 10.1029/2012WR011951 CrossRefGoogle Scholar
  95. Karim F, Petheram C, Marvanek C, Ticehurst C, Wallace J, Gouweleeuw B (2011) The use of hydrodynamic modelling and remote sensing to estimate floodplain inundation and flood discharge in a large tropical catchment. In: Paper presented at the 19th international congress on modelling and simulation, 12–16 December, Perth (Australia), pp 3796–3802Google Scholar
  96. Kirchgessner P, Nerger L, Bunse-Gerstner A (2014) On the choice of an optimal localization radius in ensemble Kalman filter methods. Mon Weather Rev 142:2165–2175. doi: 10.1175/MWR-D-13-00246.1 CrossRefGoogle Scholar
  97. Kouraev AV, Zakharova EA, Samain O, Mognard NM, Cazenave A (2004) Ob’river discharge from TOPEX/Poseidon satellite altimetry (1992–2002). Remote Sens Environ 93:238–245. doi: 10.1016/j.rse.2004.07.007 CrossRefGoogle Scholar
  98. Kugler Z, De Groeve T (2007) The global flood detection system. JRC scientific and technical reports, pp 45. EUR 23303 EN, ISSN 1018-5593Google Scholar
  99. Lacomme P, Hardange JP, Marchais JC, Normant E (2001) Air and spaceborne radar systems—an introduction, vol Spie Press Monograph (Book 108). William Andrew Publishing/Noyes, NY (USA), pp 524Google Scholar
  100. Lai X, Monnier J (2009) Assimilation of spatially distributed water levels into a shallow-water flood model. Part I: mathematical method and test case. J Hydrol 377:1–11. doi: 10.1016/j.jhydrol.2009.07.058 CrossRefGoogle Scholar
  101. Lai X, Liang Q, Yesou H, Daillet S (2014) Variational assimilation of remotely sensed flood extents using a 2-D flood model. Hydrol Earth Syst Sci 18:4325–4339. doi: 10.5194/hess-18-4325-2014 CrossRefGoogle Scholar
  102. Lang MW, Kasischke ES (2008) Using C-band synthetic aperture radar data to monitor forested wetland hydrology in Maryland’s Coastal Plain, USA. Geosci Remote Sens IEEE Trans 46:535–546. doi: 10.1109/TGRS.2007.909950 CrossRefGoogle Scholar
  103. Lee J (1983) A simple speckle smoothing algorithm for synthetic aperture radar images. Syst Man Cybern IEEE Trans 13:85–89. doi: 10.1109/TSMC.1983.6313036 CrossRefGoogle Scholar
  104. Lee J, Jen-Hung W, Ainsworth TL, Kun-Shan C, Chen AJ (2009) Improved sigma filter for speckle filtering of SAR imagery. Geosci Remote Sens IEEE Trans 47:202–213. doi: 10.1109/TGRS.2008.2002881 CrossRefGoogle Scholar
  105. Lee H et al (2011) Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry. Remote Sens Environ 115:3530–3538. doi: 10.1016/j.rse.2011.08.015 CrossRefGoogle Scholar
  106. Li Y, Grimaldi S, Walker J, Pauwels V (2016) Application of remote sensing data to constrain operational rainfall-driven flood forecasting: a review. Remote Sens 8:456. doi: 10.3390/rs8060456 CrossRefGoogle Scholar
  107. Liu Y et al (2012) Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol Earth Syst Sci 16:3863–3887. doi: 10.5194/hess-16-3863-2012 CrossRefGoogle Scholar
  108. MacIntosh H, Profeti G (1995) The use of ERS SAR data to manage flood emergencies at the smaller scale. In: Paper presented at the 2nd ERS Applications Workshop, London, UKGoogle Scholar
  109. Madsen H, Skotner C (2005) Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure. J Hydrol 308:302–312. doi: 10.1016/j.jhydrol.2004.10.030 CrossRefGoogle Scholar
  110. Marcus WA, Fonstad MA (2008) Optical remote mapping of rivers at sub-meter resolutions and watershed extents. Earth Surf Proc Land 33:4–24. doi: 10.1002/esp.1637 CrossRefGoogle Scholar
  111. Martinis S, Twele A, Voigt S (2009) Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat Hazards Earth Syst Sci 9:303–314CrossRefGoogle Scholar
  112. Martinis S, Twele A, Voigt S (2011) Unsupervised extraction of flood-induced backscatter changes in SAR data using markov image modeling on irregular graphs. IEEE Trans Geosci Remote Sens 49:251–263. doi: 10.1109/TGRS.2010.2052816 CrossRefGoogle Scholar
  113. Martinis S, Kersten J, Twele A (2015) A fully automated TerraSAR-X based flood service. ISPRS J Photogramm Remote Sens 104:203–212. doi: 10.1016/j.isprsjprs.2014.07.014 CrossRefGoogle Scholar
  114. Mason DC, Horritt MS, Dall’Amico JT, Scott TR, Bates PD (2007) Improving river flood extent delineation from synthetic aperture radar using airborne laser altimetry. Geosci Remote Sens IEEE Trans 45:3932–3943. doi: 10.1109/TGRS.2007.901032 CrossRefGoogle Scholar
  115. Mason DC, Bates PD, Dall’Amico JT (2009) Calibration of uncertain flood inundation models using remotely sensed water levels. J Hydrol 368:224–236. doi: 10.1016/j.jhydrol.2009.02.034 CrossRefGoogle Scholar
  116. Mason DC, J-p. Schumann G, Bates PD (2010) Data utilization in flood inundation modelling. In: Flood risk science and management. Wiley, New York, pp 209–233. doi: 10.1002/9781444324846.ch11
  117. Mason DC, Davenport IJ, Neal JC, Schumann GJP, Bates PD (2012a) Near real-time flood detection in urban and rural areas using high-resolution synthetic aperture radar images. IEEE Trans Geosci Remote Sens 50:3041–3052. doi: 10.1109/tgrs.2011.2178030 CrossRefGoogle Scholar
  118. Mason DC, Schumann GJP, Neal JC, Garcia-Pintado J, Bates PD (2012b) Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: a case study. Remote Sens Environ 124:705–716. doi: 10.1016/j.rse.2012.06.017 CrossRefGoogle Scholar
  119. Mason DC, Giustarini L, Garcia-Pintado J, Cloke HL (2014) Detection of flooded urban areas in high resolution synthetic aperture radar images using double scattering. Int J Appl Earth Obs Geoinf 28:150–159. doi: 10.1016/j.jag.2013.12.002 CrossRefGoogle Scholar
  120. Massonnet D, Rossi M, Carmona C, Adragna F, Peltzer G, Feigl K, Rabaute T (1993) The displacement field of the Landers earthquake mapped by radar interferometry. Nature 364:138–142CrossRefGoogle Scholar
  121. Matgen P, Henry JB, Pappenberger F, Fraipont PD, Hoffmann L, Pfister L (2004) Uncertainty in calibrating flood propagation models with flood boundaries derived from synthetic aperture radar imagery. In: Proceedings of the 20th congress of the International Society of Photogrammetry and Remote Sensing, 12–23 July, Instanbul, Turkey, pp 352–358Google Scholar
  122. Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007a) Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int J Appl Earth Obs Geoinf 9:247–263. doi: 10.1016/j.jag.2006.03.003 CrossRefGoogle Scholar
  123. Matgen P, Schumann G, Pappenberger F, Pfister L (2007b) Sequential assimilation of remotely sensed water stages in flood inundation models. In: Paper presented at the IAHS symposium on remote sensing for environmental monitoring and change detection—24th General Assembly of the International Union of Geodesy and Geophysics (IUGG), Perugia, Italy, 2–13 JulyGoogle Scholar
  124. Matgen P et al (2010) 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. doi: 10.5194/hess-14-1773-2010 CrossRefGoogle Scholar
  125. Matgen P, Hostache R, Schumann G, Pfister L, Hoffmann L, Savenije HHG (2011) Towards an automated SAR-based flood monitoring system: lessons learned from two case studies. Phys Chem Earth A/B/C 36:241–252. doi: 10.1016/j.pce.2010.12.009 CrossRefGoogle Scholar
  126. Mitchell AL, Milne AK, Tapley I (2015) Towards an operational SAR monitoring system for monitoring environmental flows in the Macquarie Marshes. Wetlands Ecol Manag 23:61–77. doi: 10.1007/s11273-014-9358-2 CrossRefGoogle Scholar
  127. Moradkhani H (2008) Hydrologic remote sensing and land surface data assimilation. Sensors (Basel, Switzerland) 8:2986–3004. doi: 10.3390/s8052986 CrossRefGoogle Scholar
  128. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900CrossRefGoogle Scholar
  129. Mueller N et al (2016) Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens Environ 174:341–352. doi: 10.1016/j.rse.2015.11.003 CrossRefGoogle Scholar
  130. Musa ZN, Popescu I, Mynett A (2015) A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation. Hydrol Earth Syst Sci 19:3755–3769. doi: 10.5194/hess-19-3755-2015 CrossRefGoogle Scholar
  131. Neal JC, Atkinson PM, Hutton CW (2007) Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements. J Hydrol 336:401–415. doi: 10.1016/j.jhydrol.2007.01.012 CrossRefGoogle Scholar
  132. Neal J, Schumann G, Bates P, Buytaert W, Matgen P, Pappenberger F (2009) A data assimilation approach to discharge estimation from space. Hydrol Process 23:3641–3649. doi: 10.1002/hyp.7518 CrossRefGoogle Scholar
  133. Neal J, Schumann G, Bates P (2012) A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas. Water Resour Res 48:W11506. doi: 10.1029/2012WR012514 CrossRefGoogle Scholar
  134. Nerger L, Gregg WW (2007) Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK filter. J Mar Syst 68:237–254. doi: 10.1016/j.jmarsys.2006.11.009 CrossRefGoogle Scholar
  135. Oberstadler R, HÖNsch H, Huth D (1997) Assessment of the mapping capabilities of ERS-1 SAR data for flood mapping: a case study in Germany. Hydrol Process 11:1415–1425. doi: 10.1002/(SICI)1099-1085(199708)11:10<1415:AID-HYP532>3.0.CO;2-2 CrossRefGoogle Scholar
  136. O’Grady D, Leblanc M (2014) Radar mapping of broad-scale inundation: challenges and opportunities in Australia. Stoch Environ Res Risk Assess 28:29–38. doi: 10.1007/s00477-013-0712-3 CrossRefGoogle Scholar
  137. Oliver C, Quegan S (2004) Understanding Synthetic Aperture Radar Images. SciTech Publishing, London (UK), pp 479Google Scholar
  138. O’Loughlin F, Trigg MA, Schumann GJP, Bates PD (2013) Hydraulic characterization of the middle reach of the Congo River. Water Resour Res 49:5059–5070. doi: 10.1002/wrcr.20398 CrossRefGoogle Scholar
  139. Papa F, Prigent C, Rossow WB, Legresy B, Remy F (2006) Inundated wetland dynamics over boreal regions from remote sensing: the use of Topex-Poseidon dual-frequency radar altimeter observations. Int J Remote Sens 27:4847–4866. doi: 10.1080/01431160600675887 CrossRefGoogle Scholar
  140. Pappenberger F, Beven K, Horritt M, Blazkova S (2005) Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations. J Hydrol 302:46–69. doi: 10.1016/j.jhydrol.2004.06.036 CrossRefGoogle Scholar
  141. Pappenberger F, Frodsham K, Beven K, Romanowicz R, Matgen P (2007) Fuzzy set approach to calibrating distributed flood inundation models using remote sensing observations. Hydrol Earth Syst Sci 11:739–752. doi: 10.5194/hess-11-739-2007 CrossRefGoogle Scholar
  142. Parker WV (2012) Discover the benefits of radar imaging. Earth Imaging J Remote Sens Satell Images Satell Imag. http://eijournal.com/2012/discover-the-benefits-of-radar-imaging
  143. Pavelsky TM, Smith LC (2008) RivWidth: a software tool for the calculation of river widths from remotely sensed imagery. Geosci Remote Sens Lett IEEE 5:70–73. doi: 10.1109/LGRS.2007.908305 CrossRefGoogle Scholar
  144. Prestininzi P, Di Baldassarre G, Schumann G, Bates PD (2011) Selecting the appropriate hydraulic model structure using low-resolution satellite imagery. Adv Water Resour 34:38–46. doi: 10.1016/j.advwatres.2010.09.016 CrossRefGoogle Scholar
  145. Proud SR, Fensholt R, Rasmussen LV, Sandholt I (2011) Rapid response flood detection using the MSG geostationary satellite. Int J Appl Earth Obs Geoinf 13:536–544. doi: 10.1016/j.jag.2011.02.002 CrossRefGoogle Scholar
  146. Pulvirenti L, Chini M, Pierdicca N, Guerriero L, Ferrazzoli P (2011a) Flood monitoring using multi-temporal COSMO-SkyMed data: image segmentation and signature interpretation. Remote Sens Environ 115:990–1002. doi: 10.1016/j.rse.2010.12.002 CrossRefGoogle Scholar
  147. Pulvirenti L, Pierdicca N, Chini M, Guerriero L (2011b) An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic. Nat Hazards Earth Syst Sci 11:529CrossRefGoogle Scholar
  148. Raclot D (2006) Remote sensing of water levels on floodplains: a spatial approach guided by hydraulic functioning. Int J Remote Sens 27:2553–2574. doi: 10.1080/01431160600554397 CrossRefGoogle Scholar
  149. Raclot D, Puech C (2003) What does ai contribute to hydrology? aerial photos and flood levels. Appl Artif Intell 17:71–86. doi: 10.1080/713827055 CrossRefGoogle Scholar
  150. Rees WG (2012) Physical principles of remote sensing. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  151. Revilla-Romero B, Hirpa F, Pozo J, Salamon P, Brakenridge R, Pappenberger F, De Groeve T (2015) On the use of global flood forecasts and satellite-derived inundation maps for flood monitoring in data-sparse regions. Remote Sens 7:15702CrossRefGoogle Scholar
  152. Rodriguez E (2015) Surface water and ocean topography mission (SWOT), Science Requirements Document, JPL document D-61923. https://swot.jpl.nasa.gov/files/swot/SRD_021215.pdf
  153. Rodriguez E, Moller D (2004) Measuring surface water from space. In: Paper presented at the AGU San Francisco (USA)Google Scholar
  154. Romanowicz R, Beven K (1998) Dynamic real-time prediction of flood inundation probabilities. Hydrol Sci J 43:181–196. doi: 10.1080/02626669809492117 CrossRefGoogle Scholar
  155. Romanowicz R, Beven K (2003) Estimation of flood inundation probabilities as conditioned on event inundation maps. Water Resour Res 39:1073. doi: 10.1029/2001WR001056 CrossRefGoogle Scholar
  156. Romanowicz RJ, Beven KJ, Tawn J (1996) Bayesian calibration of flood inundation models. Floodplain processes. Wiley, Chichester, pp 333–360Google Scholar
  157. Schlaffer S, Matgen P, Hollaus M, Wagner W (2015) Flood detection from multi-temporal SAR data using harmonic analysis and change detection. Int J Appl Earth Obs Geoinf 38:15–24. doi: 10.1016/j.jag.2014.12.001 CrossRefGoogle Scholar
  158. Schmugge T (1987) Remote sensing applications in hydrology. Rev Geophys 25:148–152. doi: 10.1029/RG025i002p00148 CrossRefGoogle Scholar
  159. Schumann GJP, Moller DK (2015) Microwave remote sensing of flood inundation. Phys Chem Earth A/B/C 83–84:84–95. doi: 10.1016/j.pce.2015.05.002 CrossRefGoogle Scholar
  160. Schumann G, Henry J-B, Hoffmann L, Pfister L, Pappenberger F, Matgen P (2005) Demonstrating the high potential of remote sensing in hydraulic modelling and flood risk management. In: Paper presented at the annual conference of the remote sensing and photogrammetry society with the NERC earth observation conference, Portsmouth, UK, 6–9 September 2005Google Scholar
  161. Schumann G, Hostache R, Puech C, Hoffmann L, Matgen P, Pappenberger F, Pfister L (2007a) High-resolution 3-D flood information from radar imagery for flood hazard management. Geosci Remote Sens IEEE Trans 45:1715–1725. doi: 10.1109/TGRS.2006.888103 CrossRefGoogle Scholar
  162. Schumann G, Matgen P, Hoffmann L, Hostache R, Pappenberger F, Pfister L (2007b) Deriving distributed roughness values from satellite radar data for flood inundation modelling. J Hydrol 344:96–111. doi: 10.1016/j.jhydrol.2007.06.024 CrossRefGoogle Scholar
  163. Schumann G, Cutler M, Black A, Matgen P, Pfister L, Hoffmann L, Pappenberger F (2008a) Evaluating uncertain flood inundation predictions with uncertain remotely sensed water stages. Int J River Basin Manag 6:187–199. doi: 10.1080/15715124.2008.9635347 CrossRefGoogle Scholar
  164. Schumann G, Matgen P, Cutler MEJ, Black A, Hoffmann L, Pfister L (2008b) Comparison of remotely sensed water stages from LiDAR, topographic contours and SRTM. ISPRS J Photogramm Remote Sens 63:283–296. doi: 10.1016/j.isprsjprs.2007.09.004 CrossRefGoogle Scholar
  165. Schumann G, Matgen P, Pappenberger F (2008c) Conditioning water stages from satellite imagery on uncertain data points. Geosci Remote Sens Lett IEEE 5:810–813. doi: 10.1109/LGRS.2008.2005646 CrossRefGoogle Scholar
  166. Schumann G, Pappenberger F, Matgen P (2008d) Estimating uncertainty associated with water stages from a single SAR image. Adv Water Resour 31:1038–1047. doi: 10.1016/j.advwatres.2008.04.008 CrossRefGoogle Scholar
  167. Schumann G, Bates PD, Horritt MS, Matgen P, Pappenberger F (2009a) Progress in integration of remote sensing-derived flood extent and stage data and hydraulic models. Rev Geophys. doi: 10.1029/2008RG000274 Google Scholar
  168. Schumann G, Di Baldassarre G, Bates PD (2009b) The utility of spaceborne radar to render flood inundation maps based on multialgorithm ensembles. Geosci Remote Sens IEEE Trans 47:2801–2807. doi: 10.1109/TGRS.2009.2017937 CrossRefGoogle Scholar
  169. Schumann G, Di Baldassarre G, Alsdorf D, Bates PD (2010) Near real-time flood wave approximation on large rivers from space: Application to the River Po, Italy. Water Resour Res 46:W05601. doi: 10.1029/2008WR007672 CrossRefGoogle Scholar
  170. Schumann GJP, Neal JC, Mason DC, Bates PD (2011) The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods. Remote Sens Environ 115:2536–2546. doi: 10.1016/j.rse.2011.04.039 CrossRefGoogle Scholar
  171. Schumann GJP, Bates PD, Di Baldassarre G, Mason DC (2012) The use of radar imagery in riverine flood inundation studies. In: Fluvial remote sensing for science and management. Wiley, pp 115–140. doi: 10.1002/9781119940791.ch6
  172. Schumann GJP et al (2013) A first large-scale flood inundation forecasting model. Water Resour Res 49:6248–6257. doi: 10.1002/wrcr.20521 CrossRefGoogle Scholar
  173. Schumann GJP, Vernieuwe H, De Baets B, Verhoest NEC (2014) ROC-based calibration of flood inundation models. Hydrol Process 28:5495–5502. doi: 10.1002/hyp.10019 CrossRefGoogle Scholar
  174. Schumann GJP, Bates PD, Neal JC, Andreadis KM (2015) Chapter 2—Measuring and mapping flood processes. In: Baldassarre JFSPD (ed) Hydro-meteorological hazards, risks and disasters. Elsevier, Boston, pp 35–64. doi:http://dx.doi.org/10.1016/B978-0-12-394846-5.00002-3
  175. Shiiba M, Laurenson X, Tachikawa Y (2000) Real-time stage and discharge estimation by a stochastic-dynamic flood routing model. Hydrol Process 14:481–495. doi: 10.1002/(SICI)1099-1085(20000228)14:3<481:AID-HYP950>3.0.CO;2-F CrossRefGoogle Scholar
  176. Siddique-E-Akbor AHM, Hossain F, Lee H, Shum CK (2011) Inter-comparison study of water level estimates derived from hydrodynamic–hydrologic model and satellite altimetry for a complex deltaic environment. Remote Sens Environ 115:1522–1531. doi: 10.1016/j.rse.2011.02.011 CrossRefGoogle Scholar
  177. Sippel SJ, Hamilton SK, Melack JM, Novo EMM (1998) Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain. Int J Remote Sens 19:3055–3074CrossRefGoogle Scholar
  178. Sivapalan M et al (2003) IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrol Sci J 48:857–880. doi: 10.1623/hysj.48.6.857.51421 CrossRefGoogle Scholar
  179. Smith LC (1997) Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol Process 11:1427–1439. doi: 10.1002/(SICI)1099-1085(199708)11:10<1427:AID-HYP473>3.0.CO;2-S CrossRefGoogle Scholar
  180. Snyder C, Bengtsson T, Bickel P, Anderson J (2008) Obstacles to high-dimensional particle filtering. Mon Weather Rev 136:4629–4640. doi: 10.1175/2008MWR2529.1 CrossRefGoogle Scholar
  181. Speck R, Turchi P, Süß H (2007) An end-to-end simulator for high-resolution spaceborne SAR systems. In: Algorithms for synthetic aperture radar imagery XIV, Orlando, Florida, USA, 7 May. Proceedings of SPIE. doi: 10.1117/12.717222
  182. Stephens EM, Bates PD, Freer JE, Mason DC (2012) The impact of uncertainty in satellite data on the assessment of flood inundation models. J Hydrol 414–415:162–173. doi: 10.1016/j.jhydrol.2011.10.040 CrossRefGoogle Scholar
  183. Stephens E, Schumann G, Bates P (2014) Problems with binary pattern measures for flood model evaluation. Hydrol Process 28:4928–4937. doi: 10.1002/hyp.9979 CrossRefGoogle Scholar
  184. Stokstad E (1999) Scarcity of rain, stream gages threatens forecasts. Science 285:1199–1200. doi: 10.1126/science.285.5431.1199 CrossRefGoogle Scholar
  185. Tarpanelli A, Barbetta S, Brocca L, Moramarco T (2013a) River discharge estimation by using altimetry data and simplified flood routing modeling. Remote Sens 5:4145CrossRefGoogle Scholar
  186. Tarpanelli A, Brocca L, Melone F, Moramarco T (2013b) Hydraulic modelling calibration in small rivers by using coarse resolution synthetic aperture radar imagery. Hydrol Process 27:1321–1330. doi: 10.1002/hyp.9550 CrossRefGoogle Scholar
  187. Ticehurst C, Chen Y, Karim F, Dushmanta D (2013) Using MODIS for mapping flood events for use in hydrological and hydrodynamic models: experiences so far. In: Paper presented at the 20th international congress on modelling and simulation—MODSIM 2013, Adelaide (Australia)Google Scholar
  188. Ticehurst C, Guerschman J, Chen Y (2014) The strengths and limitations in using the daily MODIS open water likelihood algorithm for identifying flood events. Remote Sens 6:11791CrossRefGoogle Scholar
  189. Ticehurst C, Dutta D, Karim F, Petheram C, Guerschman J (2015) Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling. Nat Hazards 78:803–820. doi: 10.1007/s11069-015-1743-5 CrossRefGoogle Scholar
  190. Tomkins KM (2014) Uncertainty in streamflow rating curves: methods, controls and consequences. Hydrol Process 28:464–481. doi: 10.1002/hyp.9567 CrossRefGoogle Scholar
  191. Townsend PA (2002) Estimating forest structure in wetlands using multitemporal SAR. Remote Sens Environ 79:288–304. doi: 10.1016/S0034-4257(01)00280-2 CrossRefGoogle Scholar
  192. Wang Y (2004) Using Landsat 7 TM data acquired days after a flood event to delineate the maximum flood extent on a coastal floodplain. Int J Remote Sens 25:959–974. doi: 10.1080/0143116031000150022 CrossRefGoogle Scholar
  193. Werner M, Blazkova S, Petr J (2005) Spatially distributed observations in constraining inundation modelling uncertainties. Hydrol Process 19:3081–3096. doi: 10.1002/hyp.5833 CrossRefGoogle Scholar
  194. Westerhoff RS, Kleuskens MPH, Winsemius HC, Huizinga HJ, Brakenridge GR, Bishop C (2013) Automated global water mapping based on wide-swath orbital synthetic-aperture radar. Hydrol Earth Syst Sci 17:651–663. doi: 10.5194/hess-17-651-2013 CrossRefGoogle Scholar
  195. Whitcomb J, Moghaddam M, McDonald K, Kellndorfer J, Podest E (2009) Mapping vegetated wetlands of Alaska using L-band radar satellite imagery. Can J Remote Sens 35:54–72. doi: 10.5589/m08-080 CrossRefGoogle Scholar
  196. Wilson M et al (2007) Modeling large-scale inundation of Amazonian seasonally flooded wetlands. Geophys Res Lett 34:n/a–n/a. doi: 10.1029/2007GL030156 CrossRefGoogle Scholar
  197. Woodhouse IH (2005) Introduction to microwave remote sensing. CRC Press, Florida, pp 400Google Scholar
  198. Wright N, Villanueva I, Bates P, Mason DC, Wilson M, Pender G, Neelz S (2008) Case study of the use of remotely sensed data for modeling flood inundation on the river Severn, UK. J Hydraul Eng 134:533–540. doi: 10.1061/(ASCE)0733-9429(2008)134:5(533) CrossRefGoogle Scholar
  199. Yan K, Di Baldassarre G, Solomatine DP, Schumann GJP (2015) A review of low-cost space-borne data for flood modelling: topography, flood extent and water level. Hydrol Process. doi: 10.1002/hyp.10449 Google Scholar
  200. Yu D, Lane SN (2006) Urban fluvial flood modelling using a two-dimensional diffusion-wave treatment, part 1: mesh resolution effects. Hydrol Process 20:1541–1565. doi: 10.1002/hyp.5935 CrossRefGoogle Scholar
  201. Zwally HJ et al (2002) ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J Geodyn 34:405–445. doi: 10.1016/S0264-3707(02)00042-X CrossRefGoogle Scholar
  202. Zwenzner H, Voigt S (2009) Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrol Earth Syst Sci 13:567–576. doi: 10.5194/hess-13-567-2009 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Civil EngineeringMonash UniversityClaytonAustralia

Personalised recommendations