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.
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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
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 2002
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 404:174–177
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
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
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
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
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
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
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
Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Prob 14:R1
Bates PD (2004) Remote sensing and flood inundation modelling. Hydrol Process 18:2593–2597. doi:10.1002/hyp.5649
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
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
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
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
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
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
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
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 2012
Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36. doi:10.1016/j.jhydrol.2005.07.007
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
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
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
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
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
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
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
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
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
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 December
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
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
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
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
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 Netherlands
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
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
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
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
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
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
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
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
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
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
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
Dunne S, Entekhabi D (2005) An ensemble-based reanalysis approach to land data assimilation. Water Resour Res 41:W02013. doi:10.1029/2004WR003449
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
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
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
Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367. doi:10.1007/s10236-003-0036-9
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Grayson R, Blöschl G (2001) Spatial patterns in catchment hydrology: observations and modelling. Cambridge University Press, Cambridge
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
Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geogr Inf Sci 17:235–249. doi:10.1080/13658810210157822
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)
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
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
Hamilton SK, Sippel SJ, Melack JM (1996) Inundation patterns in the Pantanal wetland of South America determined from passive microwave remote sensing. Arch Hydrobiol 137:1–23
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
Henderson FM, Lewis AJ (2008) Radar detection of wetland ecosystems: a review. Int J Remote Sens 29:5809–5835. doi:10.1080/01431160801958405
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
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
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
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
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
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
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
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
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
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
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
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
Houtekamer PL, Mitchell HL (2005) Ensemble Kalman filtering. Q J R Meteorol Soc 131:3269–3289. doi:10.1256/qj.05.135
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
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
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
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
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
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
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 3796–3802
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
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
Kugler Z, De Groeve T (2007) The global flood detection system. JRC scientific and technical reports, pp 45. EUR 23303 EN, ISSN 1018-5593
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 524
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
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
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
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
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
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
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
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
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, UK
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
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
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 9:303–314
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
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
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
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
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
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
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
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
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 364:138–142
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 352–358
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
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 July
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
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
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
Moradkhani H (2008) Hydrologic remote sensing and land surface data assimilation. Sensors (Basel, Switzerland) 8:2986–3004. doi:10.3390/s8052986
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 50:885–900
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
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
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
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
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
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
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
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
Oliver C, Quegan S (2004) Understanding Synthetic Aperture Radar Images. SciTech Publishing, London (UK), pp 479
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
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
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
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
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
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
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
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
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
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 11:529
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
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
Rees WG (2012) Physical principles of remote sensing. Cambridge University Press, Cambridge
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 7:15702
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
Rodriguez E, Moller D (2004) Measuring surface water from space. In: Paper presented at the AGU San Francisco (USA)
Romanowicz R, Beven K (1998) Dynamic real-time prediction of flood inundation probabilities. Hydrol Sci J 43:181–196. doi:10.1080/02626669809492117
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
Romanowicz RJ, Beven KJ, Tawn J (1996) Bayesian calibration of flood inundation models. Floodplain processes. Wiley, Chichester, pp 333–360
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
Schmugge T (1987) Remote sensing applications in hydrology. Rev Geophys 25:148–152. doi:10.1029/RG025i002p00148
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
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 2005
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
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
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
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
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
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
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
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
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
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
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
Schumann GJP et al (2013) A first large-scale flood inundation forecasting model. Water Resour Res 49:6248–6257. doi:10.1002/wrcr.20521
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
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
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
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
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 19:3055–3074
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
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
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
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
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
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
Stokstad E (1999) Scarcity of rain, stream gages threatens forecasts. Science 285:1199–1200. doi:10.1126/science.285.5431.1199
Tarpanelli A, Barbetta S, Brocca L, Moramarco T (2013a) River discharge estimation by using altimetry data and simplified flood routing modeling. Remote Sens 5:4145
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
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)
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 6:11791
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
Tomkins KM (2014) Uncertainty in streamflow rating curves: methods, controls and consequences. Hydrol Process 28:464–481. doi:10.1002/hyp.9567
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
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
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
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
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
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
Woodhouse IH (2005) Introduction to microwave remote sensing. CRC Press, Florida, pp 400
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)
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
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
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
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
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.
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Grimaldi, S., Li, Y., Pauwels, V.R.N. et al. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges. Surv Geophys 37, 977–1034 (2016). https://doi.org/10.1007/s10712-016-9378-y
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DOI: https://doi.org/10.1007/s10712-016-9378-y