Coupling duo-assimilation to hydrological model to enhance flood forecasting

References

• Abbaszadeh P, Moradkhani H, Daescu DN. 2019. The quest for model uncertainty quantification: a hybrid ensemble and variational data assimilation framework. Water Resource Res. 55(3):2407–2431. doi:10.1029/2018WR023629.
   PubMed Web of Science ®Google Scholar
• Abbaszadeh P, Moradkhani H, Yan H. 2018. Enhancing hydrologic data assimilation by evolutionary particle filter and Markov Chain Monte Carlo. Adv Water Resources. 111:192–204. doi:10.1016/j.advwatres.2017.11.011.
   Web of Science ®Google Scholar
• Ajami NK, Duan Q, Sorooshian S. 2007. An integrated hydrologic Bayesian multimodel combination framework: confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Res. 43:1. doi:10.1029/2005WR004745.
   PubMed Web of Science ®Google Scholar
• Arulampalam MS, Maskell S, Gordon N, Clapp T. 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans on Signal Process. 50(2):174–188. doi:10.1109/78.978374.
   Web of Science ®Google Scholar
• Beven K. 1989. Changing ideas in hydrology – the case of physically-based models. J Hydrol. 105(1–2):157–172. doi:10.1016/0022-1694(89)90101-7.
   Web of Science ®Google Scholar
• Beven K, Freer J. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol. 249(1-4):11–29. doi:10.1016/S0022-1694(01)00421-8.
   Web of Science ®Google Scholar
• Brier GW. 1950. Verification of forecasts expressed in terms of probability. Monthly Weather Rev. 78(1):1–3. doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.
   Google Scholar
• Burgers G, Leeuwen P, Evensen G. 1998. Analysis scheme in the ensemble Kalman filter. Monthly Weather Rev. 126(6):1719–1724. doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.
   Web of Science ®Google Scholar
• Chen H, Yang D, Hong Y, Gourley JJ, Zhang Y. 2013. Hydrological data assimilation with the ensemble square-root-filter: use of streamflow observations to update model states for real-time flash flood forecasting. Adv Water Resources. 59:209–220. doi:10.1016/j.advwatres.2013.06.010.
   Web of Science ®Google Scholar
• Clark MP, Rupp DE, Woods RA, Zheng X, Ibbitt RP, Slater AG, Schmidt J, Uddstrom MJ. 2008. Hydrological data assimilation with the ensemble Kalman filter: use of streamflow observations to update states in a distributed hydrological model. Adv Water Resources. 31(10):1309–1324. doi:10.1016/j.advwatres.2008.06.005.
   Web of Science ®Google Scholar
• DeChant CM, Moradkhani H. 2012. Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting. Water Resources Res. 48(4). doi:10.1029/2011WR011011.
   Google Scholar
• DeChant CM, Moradkhani H. 2014. Toward a reliable prediction of seasonal forecast uncertainty: addressing model and initial condition uncertainty with ensemble data assimilation and sequential Bayesian combination. J Hydrol. 519:2967–2977. doi:10.1016/j.jhydrol.2014.05.045.
   Web of Science ®Google Scholar
• Duan Q, Sorooshian S, Gupta V. 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Res. 28(4):1015–1031. doi:10.1029/91WR02985.
   Web of Science ®Google Scholar
• Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res. 99(C5):10143–10162. doi:10.1029/94JC00572.
   Web of Science ®Google Scholar
• Fan YR, Huang GH, Baetz BW, Li YP, Huang K, Li Z, Chen X, Xiong LH. 2016. Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: a hybrid sequential data assimilation and probabilistic collocation method. Environmental Modelling & Software. 86:30–49. doi:10.1016/j.envsoft.2016.09.012.
   Google Scholar
• Fortin V, Abaza M, Anctil F, Turcotte R. 2014. Why should ensemble spread match the RMSE of the ensemble mean? J Hydrometeorol. 15(4):1708–1713. doi:10.1175/JHM-D-14-0008.1.
   Web of Science ®Google Scholar
• Gharamti MEL, Hoteit I, Valstar J. 2013. Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering. Adv Water Resources. 60:75–88. doi:10.1016/j.advwatres.2013.07.011.
   Web of Science ®Google Scholar
• He M, Hogue TS, Margulis SA, Franz KJ. 2012. An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions. Hydrol Earth System Sci. 16:815–831. doi:10.5194/hess-16-815-2012.
   Web of Science ®Google Scholar
• Kieu QC. 2011. ây dựng hệ thống đồng hóa lỌc Kalman tổ hợp địa phương cho mô hình dự báo thời tiết WRF [Construction of a local integrated Kalman filter assimilation system for WRF weather forecast model]. VNU J Sci, Natural Sci Technol. 27(No.1S):17–28. Vietnamese.
   Google Scholar
• Lai RX, Fang HW, He GJ, Yu X, Yang M, Wang M. 2013. Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter. Journal of Hydrodynamics. 25(4):564–571. doi:10.1016/S1001-6058(11)60397-2.
   Web of Science ®Google Scholar
• Li Y, Chen C, Zhou J, Zhang G, Chen X. 2016. Dual state-parameter simultaneous estimation using localised ensemble Kalman filter and application in environmental model. Int J Embedded Systems. 8(1):93–103. doi:10.1504/IJES.2016.073743.
   Google Scholar
• Li Y, Ryu D, Western AW, Wang Q, Robertson DE, Crow WT. 2014. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting. J Hydrol. 519:2722–2736.
   Web of Science ®Google Scholar
• Liu H, Thiboult A, Tolson B, Anctil F, Mai J. 2019. Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset. J Hydrol. 568:985–996.
   Web of Science ®Google Scholar
• Liu Y, Weerts AH, Clark M, Hendricks Franssen HJ, Kumar S, Moradkhani H, Seo DJ, Schwanenberg D, Smith P, van Dijk AIJM, et al. 2012. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol Earth System Sci. 16(10):3863–3887. doi:10.5194/hess-16-3863-2012.
   Web of Science ®Google Scholar
• Madsen H, Skotner C. 2005. Adaptive state updating in real-time river flow forecasting – a combined filtering and error forecasting procedure. J Hydrol. 308(1-4):302–312. doi:10.1016/j.jhydrol.2004.10.030.
   Web of Science ®Google Scholar
• Mendoza PA, McPhee J, Vargas X. 2012. Uncertainty in flood forecasting: a distributed modeling approach in a sparse data catchment. Water Resources Res. 48(9). doi:10.1029/2011WR011089.
   Google Scholar
• Mockler EM, Chun KP, Sapriza-Azuri G, Bruen M, Wheater HS. 2016. Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations. Adv Water Resources. 97:299–313. doi:10.1016/j.advwatres.2016.10.008.
   Web of Science ®Google Scholar
• Moore RJ. 1985. The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J. 30(2):273–297. doi:10.1080/02626668509490989.
   Web of Science ®Google Scholar
• Moradkhani H. 2008. Hydrologic remote sensing and land surface data assimilation. Sensors. 8(5):2986–3004. doi:10.3390/s8052986.
   PubMed Web of Science ®Google Scholar
• Moradkhani H, DeChant CM, Sorooshian S. 2012. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method. Water Resources Res. 48:12. doi:10.1029/2012WR012144.
   Web of Science ®Google Scholar
• Moradkhani H, Hsu K-L, Gupta H, Sorooshian S. 2005. Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resources Res. 41(5). doi:10.1029/2004WR003604.
   PubMed Web of Science ®Google Scholar
• Moradkhani H, Sorooshian S, Gupta HV, Houser PR. 2005. Dual state–parameter estimation of hydrological models using ensemble Kalman filter. Adv Water Resources. 28(2):135–147. doi:10.1016/j.advwatres.2004.09.002.
   Web of Science ®Google Scholar
• Ngo DT. 2011. Nghiên cứu xây dựng hệ thống đồng hóa tổ hợp cho mô hình thời tiết và hệ thống tổ hợp cho một số mô hình khí hậu khu vực nhằm dự báo và dự tính các hiện tượng thời tiết, khí hậu cực đoan. [Studying to develop an integrated assimilation system for weather models and a combined system for some regional climate models in order to forecast and estimate extreme weather and climatic events]. Project ĐTNCCB-ĐHUD2011-G/10. Vietnamese.
   Google Scholar
• Thiboult A, Anctil F, Boucher MA. 2016. Accounting for three sources of uncertainty in ensemble hydrological forecasting. Hydrol Earth System Sci. 20(5):1809–1825. doi:10.5194/hess-20-1809-2016.
   Web of Science ®Google Scholar
• Tran TT. 2015. Xây dựng quy trình dự báo quỹ đạo và cường độ bão Tây Thái Bình Dương và Biển Đông hạn 5 ngày [Development of a technological process to forecast the trajectory and intensity of storms in the Pacific Northwest region and the East coastal area for 5 days]. Project KC0811/11-15. Vietnamese.
   Google Scholar
• Tran VN, Dwelle MC, Sargsyan K, Ivanov VY, Kim J. 2020. A novel modeling framework for computationally efficient and accurate real-time ensemble flood forecasting with uncertainty quantification. Water Resources Res. 56(3). doi:10.1029/2019WR025727.
   Web of Science ®Google Scholar
• Tran VN, Kim J. 2019. Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations. Stochastic Environ Res Risk Assessment. 33:1453–1476. doi:10.1007/s00477-019-01703-0.
   Web of Science ®Google Scholar
• Vrugt JA, Diks CGH, Gupta HV, Bouten W, Verstraten JM. 2005. Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation. Water Resources Res. 41(1):W01017. doi:10.1029/2004WR003059.
   Web of Science ®Google Scholar
• Wang S, Ancell BC, Huang GH, Baetz BW. 2018. Improving robustness of hydrologic ensemble predictions through probabilistic pre- and post-processing in sequential data assimilation. Water Resources Res. 54(3):2129–2151. doi:10.1002/2018WR022546.
   Web of Science ®Google Scholar
• Wang S, Huang GH, Baetz BW, Ancell BC. 2017. Towards robust quantification and reduction of uncertainty in hydrologic predictions: integration of particle Markov chain Monte Carlo and factorial polynomial chaos expansion. J Hydrol. 548:484–497. doi:10.1016/j.jhydrol.2017.03.027.
   Web of Science ®Google Scholar
• Weerts AH, El Serafy GY. 2006. Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resources Res. 42(9). doi:10.1029/2005WR004093.
   Web of Science ®Google Scholar
• Whitaker JS. 2012. Developments in ensemble data assimilation. Proceedings of the ECMWF seminar on data assimilation for atmosphere and ocean; Sep 6–9; USA. p. 17–30.
   Google Scholar
• Whitaker JS, Hamill TM. 2002. Ensemble data assimilation without perturbed observations. Monthly Weather Rev. 130(7):1913–1924. doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.
   Web of Science ®Google Scholar
• Xie X, Zhang D. 2013. A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter. Water Resources Res. 49(11):7350–7365. doi:10.1002/2012WR012853.
   Web of Science ®Google Scholar
• Zhang H, Hendricks Franssen H-J, Han X, Vrugt JA, Vereecken H. 2017. State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter. Hydrol Earth System Sci. 21(9):4927–4958. doi:10.5194/hess-21-4927-2017.
   Web of Science ®Google Scholar