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Original Articles

Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

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Pages 386-407 | Received 24 Apr 2017, Accepted 20 Dec 2017, Published online: 15 Feb 2018

References

  • Abaza, M., et al., 2014. Sequential streamflow assimilation for short-term hydrological ensemble forecasting. Journal of Hydrology, 519, 2692–2706. doi:10.1016/j.jhydrol.2014.08.038
  • Abaza, M., Garneau, C., and Anctil, F., 2015. Comparison of sequential and variational streamflow assimilation techniques for short-term hydrological forecasting. Journal of Hydrologic Engineering, 20 (2). doi:10.1061/(ASCE)HE.1943-5584.0001013
  • Anderson, J.L., 2001. An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 129, 2884–2903. doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2
  • Andreadis, K.M., et al., 2007. Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophysical Research Letters, 34 (10). doi:10.1029/2007GL029721
  • Andreadis, K.M. and Lettenmaier, D.P., 2006. Assimilating remotely sensed snow observations into a macroscale hydrology model. Advances in Water Resources, 29 (6), 872–886. doi:10.1016/j.advwatres.2005.08.004
  • Andreadis, K.M. and Schumann, G.J.-P., 2014. Estimating the impact of satellite observations on the predictability of large-scale hydraulic models. Advances in Water Resources, 73, 44–54. doi:10.1016/j.advwatres.2014.06.006
  • Barbetta, S., et al., 2011. Case study: improving real-time stage forecasting Muskingum model by incorporating the rating curve model. Journal of Hydrologic Engineering, 16 (6), 540–557. doi:10.1061/(ASCE)HE.1943-5584.0000345
  • Barth, B., Ringen, S., and Sallas, J., 2014. City of Dallas Floodway System (DFS) case study: 100-year levee remediation. In: Rocky Mountain Geo-Conference 2014. Reston, VA: American Society of Civil Engineers, 59–68. doi:10.1061/9780784413807.004
  • Barthelemy, S., et al., 2017. Ensemble-based data assimilation for operational flood forecasting - On the merits of state estimation for 1-D hydrodynamic forecasting through the example of the “Adour maritime” river. Journal of Hydrology, 552, 210–224. doi:10.1016/j.jhydrol.2017.06.017
  • Beven, K., 2016. Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrological Sciences Journal, 61, 1652–1665. doi:10.1080/02626667.2015.1031761
  • Biancamaria, S., et al., 2011. Assimilation of virtual wide swath altimetry to improve Arctic river modeling. Remote Sensing of Environment, 115, 373–381. doi:10.1016/j.rse.2010.09.008
  • Boku, L. and Xuewei, Q., 1987. Some problems with the Muskingum method. Hydrological Sciences Journal, 32, 485–496. doi:10.1080/02626668709491207
  • Brocca, L., et al., 2010. Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrology and Earth System Sciences, 14, 1881–1893. doi:10.5194/hess-14-1881-2010
  • Brocca, L., et al., 2012. Assimilation of surface- and root-zone ASCAT soil moisture products into rainfall-runoff modeling. IEEE Transactions on Geoscience and Remote Sensing, 50 (7 PART1), 2542–2555. doi:10.1109/TGRS.2011.2177468
  • Brochero, D., Anctil, F., and Gagné, C., 2011. Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – part 1: optimization criteria. Hydrology and Earth System Sciences, 15, 3307–3325. doi:10.5194/hess-15-3307-2011
  • Bröcker, J., 2012. Evaluating raw ensembles with the continuous ranked probability score. Quarterly Journal of Royal Meteorological Society, 138, 1611–1617. doi: 10.1002/qj.1891
  • Clark, M.P., et al., 2008. Hydrological data assimilation with the ensemble Kalman filter: use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources, 31, 1309–1324. doi:10.1016/j.advwatres.2008.06.005
  • Daley, R., 1991. Atmospheric data analysis. Cambridge, UK: Cambridge University Press, Cambridge Atmospheric and Space Science Series.
  • Deb, K., et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197. doi:10.1109/4235.996017
  • DeChant, C.M. and Moradkhani, H., 2012. Examining the effectiveness and robustness of data assimilation methods for calibration and quantification of uncertainty in hydrologic forecasting. Water Resources Research, 48, W04518. doi:10.1029/2011WR011011
  • Dee, D.P., 1995. On-line estimation of error covariance parameters for atmospheric data assimilation. Monthly Weather Review, 123(4), 1128–1145. doi:10.1175/1520-0493(1995)123<128:oleoec>2.0.CO;2
  • Earl, R.A. and Vaughan, J.W., 2015. Asymmetrical response to flood hazards in south central Texas. Papers in Applied Geography, 1, 404–412. doi:10.1080/23754931.2015.1095792
  • Evensen, G., 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367. doi:10.1007/s10236-003-0036-9
  • Evensen, G., 2009. Data assimilation: the ensemble Kalman filter. 2nd ed. Berlin: Springer.
  • Franz, K.J. and Hogue, T.S., 2011. Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community. Hydrology and Earth System Sciences, 15, 3367–3382. doi:10.5194/hess-15-3367-2011
  • García-Pintado, J., et al., 2013. Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling. Journal of Hydrology, 495, 252–266. doi:10.1016/j.jhydrol.2013.03.050
  • Georgakakos, A.P., Georgakakos, K.P., and Baltas, E.A., 1990. A state-space model for hydrologic river routing. Water Resources Research, 26, 827–838. doi:10.1029/WR026i005p00827
  • Giustarini, L., et al., 2011. Assimilating SAR-derived water level data into a hydraulic model: a case study. Hydrology and Earth System Sciences, 15, 2349–2365. doi:10.5194/hess-15-2349-2011
  • Gochis, D.J., Yu, W., and Yates, D.N., 2015. The WRF-Hydro model technical description and user’s guide, version 3.0. NCAR Technical Document; p. 123. [accessed 2018 Jan 2]. http://www.ral.ucar.edu/projects/wrf_hydro/.
  • Haddad, O.B., et al., 2015. Application of a hybrid optimization method in Muskingum parameter estimation. Journal of Irrigation and Drainage Engineering, 141, 4015026. doi:10.1061/(ASCE)IR.1943-4774.0000929
  • Hajian-Tilaki, K., 2013. Receiver Operating Characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine, 4 (2), 627–635.
  • Jean-Baptiste, N., et al., 2011. Data assimilation for real-time estimation of hydraulic states and unmeasured perturbations in a 1D hydrodynamic model. In: eds, B. Amaziane and D. Barrera. Mathematics and Computers in Simulation, MAMERN 2009: 3rd International Conference on Approximation Methods and Numerical Modeling in Environment and Natural Resources. Amsterdam: Elsevier, Vol. 81, 2201–2214. doi:10.1016/j.matcom.2010.12.021
  • Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45. doi:10.1115/1.3662552
  • Kim, Y., et al., 2013a. Estimating the 2011 largest flood discharge at the Kumano River using the 2d dynamic wave model and particle filters. Journal of Japan Society of Civil Engineers, Series B1 (Hydraulic Engineering), 69 (4), 163–168. doi:10.2208/jscejhe.69.I_163
  • Kim, Y., et al., 2013b. Simultaneous estimation of inflow and channel roughness using 2D hydraulic model and particle filters. Journal of Flood Risk Management, 6, 112–123. doi:10.1111/j.1753-318X.2012.01164.x
  • Kitanidis, P.K., and Bras, R. L., 1980. Real-time forecasting with a conceptual hydrologic model: 1. analysis of uncertainty. Water Resources Research, 16 (6), 1025–1033. doi:10.1029/WR016i006p01025
  • Lahoz, W.A., and Schneider, P., 2014. Data assimilation: making sense of Earth Observation. Frontiers In Environmental Science. 2. doi:10.3389/fenvs.2014.00016
  • Lee, H., et al., 2011. Variational assimilation of streamflow into three-parameter Muskingum routing model for improved operational river flow forecasting. In: The EGU General Assembly. Munich: EGU.
  • Liu, Y., et al., 2008. Ensemble data assimilation for channel flow routing to improve operational hydrologic forecasting. In: The AGU Fall Meeting. Washington, DC: AGU.
  • Liu, Y., et al., 2012. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrology and Earth System Sciences, 16, 3863–3887. doi:10.5194/hess-16-3863-2012
  • Liu, Y. and Gupta, H.V., 2007. Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resources Research, 43, 1–18. doi:10.1029/2006WR005756
  • Madsen, H., et al., 2003. Data assimilation in the MIKE 11 Flood Forecasting system using Kalman filtering. In: G. Blöschl, ed. Water Resources Systems— hydrological Risk, Management and Development (Proceedings of symposium IlS02b held during IUGG2003 al Sapporo. July 2003). Wallingford, UK: International Association of Hydrological Sciences, IAHS Publ. no. 281, 75–81.
  • Madsen, H. and Skotner, C., 2005. Adaptive state updating in real-time river flow forecasting - A combined filtering and error forecasting procedure. Journal of Hydrology, 308, 302–312. doi:10.1016/j.jhydrol.2004.10.030
  • Matgen, P., et al., 2010. Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept. Hydrology and Earth System Sciences, 14, 1773–1785. doi:10.5194/hess-14-1773-2010
  • Maybeck, P.S., 1982. Stochastic models, estimation, and control. New York, NY: Academic Press.
  • Mazzoleni, M., 2017. Improving flood prediction assimilating uncertain crowdsourced data into hydrological and hydraulic models. Thesis (PhD). UNESCO-IHE PhD Thesis Series, CRC Press, Leiden, The Netherlands.
  • McCarthy, G.T., 1938. The unit hydrograph and flood routing. Unp ublished manuscript presented at a conference of the North Atlantic Division. US Army, Corps of Engineers, 24 June 1938.
  • McLaughlin, D., 1995. Recent developments in hydrologic data assimilation. Reviews of Geophysics, 33, 977–984. doi:10.1029/95RG00740
  • McLaughlin, D., 2002. An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering. Advances in Water Resources, 25, 1275–1286. doi:10.1016/S0309-1708(02)00055-6
  • Moradkhani, H., et al., 2005a. Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resources Research, 41, W05012. doi:10.1029/2004WR003604
  • Moradkhani, H., et al., 2005b. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources, 28 (2), 135–147. doi:10.1016/j.advwatres.2004.09.002
  • Moradkhani, H., DeChant, C.M., and Sorooshian, S., 2012. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method. Water Resources Research, 48, W12520. doi:10.1029/2012WR012144
  • Murphy, A.H. and Winkler, R.L., 1987. A general framework for forecast verification. Monthly Weather Review, 115, 1330–1338. doi:10.1175/1520-0493(1987)115<1330:AGFFFV>2.0.CO;2
  • Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10, 282–290. doi:10.1016/0022-1694(70)90255-6
  • Neal, J., et al., 2009. A data assimilation approach to discharge estimation from space. Hydrological Processes, 23, 3641–3649. doi:10.1002/hyp.7518
  • Neal, J.C., Atkinson, P.M., and Hutton, C.W., 2007. Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements. Journal of Hydrology, 336, 401–415. doi:10.1016/j.jhydrol.2007.01.012
  • Neal, J.C., Atkinson, P.M., and Hutton, C.W., 2012. Adaptive space–time sampling with wireless sensor nodes for flood forecasting. Journal of Hydrology, 414–415, 136–147. doi:10.1016/j.jhydrol.2011.10.021
  • Noh, S.J., et al., 2013. Ensemble Kalman filtering and particle filtering in a lag-time window for short-term streamflow forecasting with a distributed hydrologic model. Journal of Hydrologic. Engineering, 18, 1684–1696. doi:10.1061/(ASCE)HE.1943-5584.0000751
  • O’Donnell, T., 1985. A direct three-parameter Muskingum procedure incorporating lateral inflow. Hydrological Sciences Journal, 30, 479–496. doi:10.1080/02626668509491013
  • Park, S.K. and Xu, L., 2013. Data assimilation for atmospheric, oceanic and hydrologic applications. Berlin: Springer Science & Business Media.
  • Pauwels, V.R.N. and De Lannoy, G.J.M., 2006. Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge. Journal of Hydrometeorology, 7 (3), 458–477. doi:10.1175/JHM490.1
  • Pauwels, V.R.N. and De Lannoy, G.J.M., 2009. Ensemble-based assimilation of discharge into rainfall-runoff models: A comparison of approaches to mapping observational information to state space. Water Resources Research, 45 (8), W08428. doi:10.1029/2008WR007590
  • Phillips, J.D., 2008. Geomorphic controls and transition zones in the lower Sabine River. Hydrological Processes, 22, 2424–2437. doi:10.1002/hyp.6835
  • Press, W.H., et al., 1992. Numerical recipes in FORTRAN. 2nd ed. Cambridge, UK: Cambridge University Press.
  • Puente, C.E. and Bras, R.L., 1987. Application of nonlinear filtering in the real time forecasting of river flows. Water Resources Research, 23, 675–682. doi:10.1029/WR023i004p00675
  • Rakovec, O., et al., 2012. State updating of a distributed hydrological model with ensemble Kalman filtering: effects of updating frequency and observation network density on forecast accuracy. Hydrology and Earth System Sciences, 16, 3435–3449. doi:10.5194/hess-16-3435-2012
  • Rakovec, O., et al., 2015. Operational aspects of asynchronous filtering for flood forecasting. Hydrology and Earth System Sciences, 19, 2911–2924. doi:10.5194/hess-19-2911-2015
  • Rakovec, O., et al., 2016. Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resources Research, 52, 7779–7792. doi:10.1002/2016WR019430
  • Refsgaard, J.C., 1997. Validation and intercomparison of different updating procedures for real-time forecasting. Nordic Hydrology, 28, 65–84. doi:10.2166/nh.1997.005
  • Reichle, R., McLaughlin, D.B., and Entekhabi, D., 2002. Hydrologic data assimilation with the ensemble Kalman filter. American Meteorological Society, 130, 103–114. doi:10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2
  • Reichle, R.H., Crow, W.T., and Keppenne, C.L., 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resources Research, 44, W03423. doi:10.1029/2007WR006357
  • Ricci, S., et al., 2011. Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting. Hydrology and Earth System Sciences, 15, 3555–3575. doi:10.5194/hess-15-3555-2011
  • Richardson, D.S., 2001. Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Quarterly Journal of the Royal Meteorological Society, 127, 2473–2489. doi:10.1002/qj.49712757715
  • Sakov, P., Evensen, G., and Bertino, L., 2010. Asynchronous data assimilation with the EnKF. Tellus A, 62, 24–29. doi:10.1111/j.1600-0870.2009.00417.x
  • Samani, H.M.V. and Jebelifard, S., 2003. Design of circular urban storm sewer systems using multilinear Muskingum flow routing method. Journal of Hydraulic Engineering, 129, 832–838. doi:10.1061/(ASCE)0733-9429(2003)129:11(832)
  • Schumann, G.J.-P., et al., 2016. Unlocking the full potential of Earth observation during the 2015 Texas flood disaster. Water Resources Research, 52, 3288–3293. doi:10.1002/2015WR018428
  • Seo, D.-J., Koren, V., and Reed, S., 2003. Improving a priori estimates of hydraulic parameters in a distributed routing model via variational assimilation of long-term streamflow data. IAHS Publications Series (Red Books), 282, 138–414.
  • Sun, L., et al., 2016. Review of the Kalman-type hydrological data assimilation. Hydrological Sciences Journal, 61 (13), 2348–2366. doi:10.1080/02626667.2015.1127376
  • Todini, E., 2007. A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach. Hydrology and Earth System Sciences, 11, 1645–1659. doi:10.5194/hess-11-1645-2007
  • Trambauer, P., et al., 2015. Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa. Hydrology and Earth System Sciences, 19, 1695–1711. doi:10.5194/hess-19-1695-2015
  • USGS (US Geological Survey), 2016. USGS discharge data for Riverside [WWW Document]. Available from: https://waterdata.usgs.gov/nwis/sw [Accessed 2 January 2018].
  • Verlaan, M., 1998. Efficient Kalman filtering algorithms for hydrodynamic models. Thesis (PhD). Delft University of Technology, Delft, The Netherlands
  • Walker, J.P. and Houser, P.R., 2005. Hydrologic data assimilation. In: J. Tiefenbacher, eds. Approaches to managing disaster – assessing Hazards, emergencies and disaster impacts. London: InTechOpen, 233–234. doi:10.5772/1112
  • Walker, J.P., Willgoose, G.R., and Kalma, J.D., 2001. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: a comparison of retrieval algorithms. Advances in Water Resources, 24 (6), 631–650. doi:10.1016/S0309-1708(00)00043-9
  • Weerts, A.H. and El Serafy, G.Y.H., 2006. Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall–runoff models. Water Resources Research, 42, 1–17. doi:10.1029/2005WR004093
  • Weigel, A.P., Liniger, M.A., and Appenzeller, C., 2007. The discrete brier and ranked probability skill scores. Monthly Weather Review, 135, 118–124. doi:10.1175/MWR3280.1
  • WMO (World Meteorological Organization), 1992. Simulated real-time intercomparison of hydrological models. Geneva, Switzerland: World Meteorological Organization, WMO Operational Hydrology Report.
  • Xu, D.-M., Qiu, L., and Chen, S.-Y., 2012. Estimation of nonlinear Muskingum model parameter using differential evolution. Journal of Hydrologic Engineering, 17, 348–353. doi:10.1061/(ASCE)HE.1943-5584.0000432
  • Yuan, X., et al., 2016. Parameter identification of nonlinear Muskingum model with backtracking search algorithm. Water Resources Management, 30, 2767–2783. doi:10.1007/s11269-016-1321-y

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