Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

Advances in Water Resources

Volumn 83, Issue , 2015, Pages 323-339

  Mazzoleni, Maurizio ^a   Alfonso, Leonardo ^a   Chacon Hurtado, Juan ^a   Solomatine, Dimitri ^a,b  

^a UNESCO IHE INSTITUTE FOR WATER EDUCATION   (Netherlands)

^b DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Data assimilation; Dynamic observations; Hydrological modeling; Intermittent observations; Uncertain observation

Indexed keywords

FLOOD CONTROL; FORECASTING; HYDROLOGY; MOBILE TELECOMMUNICATION SYSTEMS; MODEL STRUCTURES; REMOTE SENSING; RISK ASSESSMENT; STREAM FLOW; WEATHER FORECASTING;

DATA ASSIMILATION; DYNAMIC OBSERVATION; HYDROLOGICAL MODELING; INTERMITTENT OBSERVATION; UNCERTAIN OBSERVATIONS;

FLOODS;

DATA ASSIMILATION; FLOOD FORECASTING; HYDROLOGICAL MODELING; STREAMFLOW; UNCERTAINTY ANALYSIS;

BRUE BASIN; ENGLAND; SOMERSET; UNITED KINGDOM;

EID: 84937821619     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2015.07.004     Document Type: Article

References (66)

1. European Environment Agency, Vulnerability and adaptation to climate change, Scoping Report. Copenhagen, Denmark, 2005.
2. Wilby R., Beven K., Reynard N. Climate change and fluvial flood risk in the UK: more of the same?. Hydrol Process 2008, 22:2511-2523.
3. Solomatine D.P., Wagener T. Hydrological modeling. Treatise on Water Science 2011, 435-457. Elsevier, Editor-in-Chief. E.I.C.P. Wilderer (Ed.).
4. Todini E., Alberoni P., Butts M., Collier C., Khatibi R. ACTIF Best practice paper - Understanding and reducing uncertainty in flood forecasting. Proceeding of ACTIF Conference, ACTIF best practice paper number 1 2005, 43.
5. McLaughlin D. An integrated approach to hydrologic data assimilation: interpolation, smoothing and filtering. Adv Water Resour 2002, 25:1275-1286.
6. Pappenberg F., Matgen P., Beven K.J., Henry J.B., Pfister L., de Fraipont P. Influence of uncertain boundary conditions and model structureon flood inundation predictions. Adv Water Resour 2006, 29(10):1430-1449.
7. Goetzinger J., Bardossy A. Generic error model for calibration and uncertainty estimation of hydrological models. Water Resour Res 2008, 44:W00B07. 10.1029/2007WR006691.
8. World Meteorological Organization (WMO) Simulated real-time intercomparison of hydrological models. Oper Hydrol Rep 1992, 38. Geneva.
9. Refsgaard J.C. Validation and intercomparison of different updating procedures for real-time forecasting. Nordic Hydro 1997, 28:65-84.
10. Babovic V., Canizares R., Jensen H.R., Klinting A. Neural networks as routine for error updating of numerical models. J Hydraul Eng ASCE 2001, 127(3):181-193.
11. Abebe A.J., Price R.K. Information theory and neural networks for mapping model uncertainty in flood routing. J Comput Civil Eng ASCE 2004, 18(4):373-380.
12. Robinson A.R., Lermusiaux P.F.J., Sloan N.Q. III. Data Assimilation, In Brink and Robinson 1998; chapter 20, 541-593.
13. Moradkhani H., Hsu K., Gupta H.V., Sorooshian S. Uncertainty asssessment of hydrologic model states and parameters: Sequential data assimilation using particle filter. Water Resour Res 2005, 41:W05012. 10.1029/2004WR003604.
14. Liu Y.Q., Gupta H.V. Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resour Res 2007, 43(7):1944-7973.
15. Walker J.P., Houser P.R. Hydrologic data assimilation. Advances in water science methodologies 2005, 230. A.A. Balkema, The Netherlands. A. Aswathanarayana (Ed.).
16. Reichle R.H. Data assimilation methods in the Earth sciences. Adv Water Res 2008, 31(11):1411-1418.
17. Kalman R.E. A new approach to linear filtering and prediction problems. J Basic Eng 1960, 82(1):35-45.
18. Madsen H., Canizares R. Comparison of extended and ensemble Kalman filters for data assimilation in coastal area modeling. Int J Numer Methods Fluids 1999, 31:961-981.
19. Reichle R.H. Variational assimilation of remote sensing data for land surface hydrologic applications, Ph. D. dissertation, Mass. Inst. Technol., Dept. Civil Environ. Eng., Cambridge, 2000.
20. Evensen G. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 2003, 53:343-367.
21. Liu Y.Q., Weerts A.H., Clark M., Hendricks Franssen H.J., Kumar S., Moradkhani H., Seo D.J., Schwanenberg D., Smith P., van Dijk A.I.J.M., van Velzen N., He M., Lee H., Noh S.J., Rakovec O., Restrepo P. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol Earth Syst Sci 2012, 16. 10.5194/hess-16-3863-2012.
22. Li F., Crow W.T., Kustas W.P. Towards the estimation of root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Adv Water Res 2010, 33:201-214.
23. Brocca L., Melone F., Moramarco T., Wagner W., Naeimi V., Bartalis Z., Hasenauer S. Improving runoffprediction through the assimilation of the ASCAT soil moisture product. Hydrol Earth Syst Sci 2010, 14:1881-1893.
24. Chen F., Crow W.T., Starks P.J., Moriasi D.N. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture. Adv Water Resour 2011, 34(4):526-536.
25. Pauwels V.R.N., De Lannoy G.J.M. Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge. J Hydrometeorol 2006, 458-477.
26. Weerts A.H., El Serafy G.Y.H. Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoffmodels. Water Resour Res 2006, 42:1-17. 10.1029/2005WR004093.
27. Schuurmans J.M., Troch P.A., Veldhuizen A.A., Bastiaanssen W.G.M., Bierkens M.F.P. Assimilation of remotely sensed latent heat flux in a distributed hydrological model. Adv Water Res 2003, 26:151-159.
28. Neal J., Schumann G., Bates P., Buytaert W., Matgen P., Pappenberger F. A data assimilation approach to discharge estimation from space. Hydrol Process 2009, 23:3641-3649.
29. Yarvis M.D., Kushalnagar N., Singh H., Rangarajam A., Liu Y., Singh S.S. Exploiting heterogeneity in sensor networks, INFOCOM 2005. Proceedings IEEE, 24th Annual Joint Conference of the IEEE Computer and Communications Societies 2005, 2:878-890.
30. Solomatine D.P. AMODEU. Adaptive modelling in heterogeneous time-varying (dynamic) data environments under uncertainty: research directions. Web publication, 2012.
31. Clark M.P., Rupp D.E., Woods R.A., Zheng X., Ibbit R.P., Slater A.G., Schmidt J., Uddstrom M.L. Hydrological data assimilation with the ensemble Kalman filter: use of streamflow observation to update states in a distributed hydrological model. Adv Water Resour 2008, 31:1309-1324.
32. Xie X.H., Zhang D.X. Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter. Adv Water Res 2010, 33:678-690.
33. Chen J., Zhang W., Gao J., Cao K. Assimilating multi-site measurements for semi-distributed hydrological model updating. Quat Int 2012, 282(19):122-129.
34. Rakovec O., Weerts A.H., Hazenberg P., Torfs P.J.J.F., Uijlenhoet R. State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy. Hydrol Earth Syst Sci 2012, 16(9):3435-3449.
35. Karssenberg D., Schmitz O., Salamon P., De Jong C., Bierkens M.F.P. A software framework for construction of process-based stochastic spatio-temporal models and data assimilation. Environ Model Softw 2009, 25:1-14.
36. Lee H., Seo D.J., Liu Y., Koren V., McKee P., Corby R. Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment. Hydrol Earth Syst Sci 2012, 16:2233-2251.
37. McMillan H.K., Hreinsson E.Ö., Clark M.P., Singh S.K., Zammit C., Uddstrom M.J. Operational hydrological data assimilation with the recursive ensemble Kalman filter. Hydrol Earth Syst Sci 2013, 17:21-38.
38. Matgen P., Schumann G., Pappenberger F., Pfister L. Sequential assimilation of remotely sensed water stages in flood inundation models. symposium HS3007 at IUGG2007: remote sensing for environmental monitoring and change detection 2007, 316:78-88. IAHS Publ.
39. Schumann G., Bates P.D., Horritt M.S., Matgen P., Pappenberger F. Progress in integration of remote sensing-derived flood extent and stage data and hydraulic models. Rev Geophys 2009, 47:RG4001. 10.1029/2008RG000274.
40. Huang B., Kinter J.L., Schopf P.S. Ocean data assimilation using intermittent analyses and continuous model error correction. Adv Atmos Sci 2002, 19(6):965-992.
41. MacPherson B. Dynamic initialization by repeated insertion of data. Q J R Meteorol Soc 1991, 117:965-991.
42. Sinopoli B., Schenato L., Franceschetti M., Poolla K., Jordan M.I., Sastry S.S. Kalman filtering with intermittent observations. IEEE Trans Autom Control 2004, 49(9):1453-1464.
43. Cipra T., Romera R. Kalman filter with outliers and missing observations. Soc Estad Investig Oper Test 1997, 6(2):379-395.
44. Moore R.J., Jones D.A., Cox D.R., Isham V.S. Design of the HYREX raingauge network. Hydrol Earth Syst Sci 2000, 4(4):521-530.
45. Westerberg I.K., Guerrero J.L., Younger P.M., Beven K.J., Seibert J., Halldin S., Freer J.E., Xu C.Y. Calibration of hydrological models using flow-duration curves. Hydrol Earth Syst Sci 2011, 15:2205-2227.
46. Strahler A.N. Quantitative analysis of watershed geomorphology. Trans Am Geophys Union 1957, 38(6):913-920.
47. Journel A.G., Huijbregts C.J. Mining Geostatistics 1978, 600. Academic Press, London.
48. Matheron G. Principles of geostatistics. Econ Geol 1963, 58:1246-1266.
49. Savelieva E., Demyanov V., Maignan M., Kanevski M. Geostatistics: spatial predictions and simulations. Advanced mapping of environmental data 2008, ISTE and John Wiley and sons.
50. Eckhardt K. How to construct recursive digital filters for baseflow separation. Hydrol Process 2005, 19:507-515.
51. Szilagyi J., Szollosi-Nagy A. Recurvive streamflow forecasing: a state space approach 2010, CRC Press, London, UK.
52. Giandotti M. Previsione delle piene e delle magre dei corsi d'acqua. Ministero LL.PP., Memorie e studi idrografici 1934, 8(2). Servizio Idrografico Italiano, Rome (in Italian).
53. Cunge J.A. On the subject of a flood propagation computation method (Muskingum Method). J Hydraul Res 1969, 7(2):205-230.
54. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models part I - a discussion of principles. J Hydrol 1970, 10(3):282-290.
55. Reichle R.H., McLaughlin D.B., Entekhabi D. Hydrologic data assimilation with the ensemble kalman filter. Am Meteorol Soc 2002, 130:103-114.
56. Neal J., Atkinson P.M., Hutton C.W. Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements. J Hydrol 2007, 336(3-4):401-415. ISSN0022-1694 http://dx.doi.org/10.1016/j.jhydrol.2007.01.012.
57. Murphy J.M. The impact of ensemble forecast on predictability. Q J R Meteorol Soc 1988, 114:89-125.
58. Anderson J.L. An ensemble adjustment filter for data assimilation. Mon Weather Rev 2001, 129:2884-2903.
59. Pauwels V.R.N., De Lannoy G.J.M. Ensemble-based assimilation of discharge into rainfall-runoffmodels: a comparison of approaches to mapping observational information to state space. Water Resour Res 2009, 45:1-17.
60. De Lannoy G.J.M., Houser P.R., Pauwels V.R.N., Verhoest N.E.C. Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour Res 2007, 43:W09410. 10.1029/2006WR005449.
61. Moradkhani H., Sorooshian S., Gupta H.V., Houser P.R. Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv Water Resour 2005, 28:135-147.
62. Arnold C.P., Dey C.H. Observing-systems simulation experiments: past, present and future. Bull Am Meteorol Soc 1986, 67:687-695.
63. Errico R.M., Yang R., Privé N.C., Tai K.S., Todling R., Sienkiewicz M.E., Guo J. Development and validation of observing-system simulation experiments at NASA's global modeling and assimilation office. Q J R Meteorol Soc 2013, 139:1162-1178.
64. Errico R.M., Privé N.C. An estimate of some analysis-error statistics using the global modeling and assimilation office observing-system simulation framework. Q J R Meteorol Soc 2014, 140:1005-1012.
65. Huwald H, Barrenetxea G, de Jong S, Ferri M, Carvalho R, Lanfranchi V, McCarthy S, Glorioso G, Prior S, Solà E, Gil-Roldàn E, Alfonso L, Wehn de Montalvo U, Onencan A, Solomatine D, Lobbrecht A. D1.11 Sensor technology requirement analysis, Confidential Deliverable, The WeSenseIt Project (FP7/2007-2013 grant agreement no 308429), 2013.
66. Mazzoleni M., Alfonso Segura L., Solomatine D. Effect of different hydrological model structures on the assimilation of distributed uncertain observations of discharge. Hydroinformatics Conference 2014.