Adaptive space-time sampling with wireless sensor nodes for flood forecasting

Journal of Hydrology

Volumn 414-415, Issue , 2012, Pages 136-147

  Neal, Jeffrey C ^a   Atkinson, Peter M ^b   Hutton, Craig W ^b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

EnKF; ETKF; Flood forecasting; Wireless sensors

Indexed keywords

ENKF; ENSEMBLE KALMAN FILTER; ERROR VARIANCE; ETKF; FLEXIBLE SENSOR; FLOOD FORECASTING; FORECAST ERROR VARIANCE; FORECASTING MODELS; ONE-DIMENSIONAL HYDRODYNAMIC MODEL; REAL-TIME DESIGN; SAMPLING STRATEGIES; SPACE AND TIME; SYSTEMATIC SAMPLING; WIRELESS SENSOR; WIRELESS SENSOR NODE; WIRELESS SENSORS;

ELECTRIC NETWORK SYNTHESIS; FLOOD CONTROL; FLOODS; KALMAN FILTERS; SENSOR NETWORKS; SENSOR NODES; WATER LEVELS;

WEATHER FORECASTING;

COMMUNICATION NETWORK; ENSEMBLE FORECASTING; ERROR ANALYSIS; FLOOD FORECASTING; HYDRODYNAMICS; HYDROLOGICAL MODELING; KALMAN FILTER; ONE-DIMENSIONAL MODELING; PROBABILITY; REAL TIME; SAMPLING; SENSOR; SPATIOTEMPORAL ANALYSIS; TRANSFORM; WATER LEVEL;

EID: 84855190111     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2011.10.021     Document Type: Article

References (37)

1. Andreadis K.M., Clark E.A., Lettenmaier D.P., Alsdorf D.E. Prospects for river discharge and depth estimation through assimilation of swath altimetry into a raster-based hydrodynamics model. Geophys. Res. Lett. 2007, 34. 10.1029/2007GL02972.
2. Aubert D., Loumagne C., Oudin L. Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model. J. Hydrol. 2003, 280:145-161.
3. Bishop C.H., Etherton B.J., Majumdar S.J. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Month. Weather Rev. 2001, 129(3):420-436.
4. Bishop C.H., Etherton B.J., Majumdar S.J. Verification region selection and data assimilation for adaptive sampling. Quarterly Journal of the Royal Meteorological Society 2006, 132:915-933.
5. Butts, M.B., Falk, A.K., Hartnack, J., Madsen, H., Klinting, A., Kalken, T. van, Cadman, D., Price, D. 2005. Ensemble-based methods for data assimilation and uncertainty estimation in the FLOOD RELIEF project. In: Proceedings of the International Conference " Innovation, Advances and Implementation of Flood Forecasting Technology, Tromsø, Norway, 17-19 October, 2005.
6. Carsell K.M., Pingel N.D., Ford D.T. Quantifying the Benefit of a Flood Warning System. Naval Research Review 2004, 5:131-140.
7. De Roure D. FloodNet: a new flood warning system. Roy. Acad. Eng. 2005, 23:49-51.
8. De Roo A.P.J., Gouweleeuw B., Thielen J., Bartholmes J., Bongioannini P., Todini E., Bates P.D., Horritt M.S., Hunter N., Beven K.J., Pappenberger F., Heise E., Rivin G., Hils M., Hollingsworth A., Holst B., Kwadijk J., Reggiani P., Van Dijk M., Sattler K., Sprokkereef E. Development of a European flood forecasting system. Int. J. River Basin Manage. 2003, 1(1):49-59.
9. Durand M.D., et al. Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model. Geophys. Res. Lett. 2008, 35:L20401. 10.1029/2008GL034150.
10. GE Druck. 1830/1840 Series High Performance Level Pressure Sensors. (accessed May 2011). http://www.ge-mcs.com/en/pressure-and-level/depth-and-level.html.
11. Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 1994, 99(C5):10143-10162.
12. Evensen G. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynam. 2003, 53:343-367.
13. Halcrow, 2011. ISIS Flow. (accessed May 2011). http://www.halcrow.com/isis/.
14. Kalman R.E. A new approach to linear filtering and prediction problems. Transaction of the ASME-Journal of Basic Engineering March 1960, 35-45.
15. Kaun, E.L., 2005. A Study into the Practical Issues Related to a Deployed Ad Hoc Wireless Sensor Network. New Orleans, LA, USA.
16. Leedal D., Neal J., Beven K., Young P., Bates P. Visualization approaches for communicating real-time flood forecasting level and inundation information. Journal of Flood Risk Management 2010, 3(2):140-150.
17. Livings, D., 2005. Aspects if the Ensemble Kalman Filter. Unpublished Master's Dissertation, Department of Mathematics, University of Reading.
18. Madsen H., Canizares R. Comparison of extended and ensemble Kalman filter for data assimilation in coastal area modelling. Int. J. Numer. Meth. Fluids 1999, 31:961-981.
19. Madsen H., Skotner C. Adaptive state updating in real-time river flow forecasting - a combined filtering and error forecasting procedure. J. Hydrol. 2005, 308:302-312.
20. Majumdar S.J., Bishop C.H., Etherton B.J., Toth Z. Adaptive sampling with the ensemble transform Kalman filter. Part II: field program implementation. Month. Weather Rev. 2002, 130(5):1356-1369.
21. Maybeck P.S. Stochastic Models, Estimation and Control 1979, Academic Press, New York.
22. McBratney A.B., Webster R. The design of optimal sampling schemes for local estimation and mapping of regionalised variables. II. Program and examples. Comput. Geosci. 1981, 7:335-365.
23. McLaughlin D.B. An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering. Adv. Water Resour. 2002, 25:1275-1286.
24. Neal J.C., Atkinson P.M., Hutton C.W. Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements. J. Hydrol. 2007, 336:401-415.
25. Neal, J., Schumann, G., Bates, P., Buytaert, W., Matgen, P., Pappenberger, F., 2009a. A data assimilation approach to discharge estimation from space. Hydrological Processes, 23, 3641-3649. doi:10.1002/hyp.7518.
26. Neal, J., Atkinson, P., Hutton, C., 2009b. Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model. J. Hydrol. 375, 589-600. doi:10.1016/j.jhydrol.2009.07.008.
27. Refsgaard J.C. Validation and intercomparison of different updating procedures for real-time forecasting. Nordic Hydrol. 1997, 28:65-84.
28. Reichle R. Data assimilation methods in the Earth sciences. Adv. Water Resour. 2008, 31:1411-1418. 10.1016/j.advwatres.2008.01.001.
29. Schreider S.Y., Young P.C., Jakeman A.J. An application of the Kalman filtering technique for streamflow forecasting in the upper Murray basin. Math. Comput. Modell. 2001, 33:733-743.
30. Shiiba M., Laurenson X., Tachikawa Y. Real-time stage discharge estimation by a stochastic-dynamic flood routing model. Hydrol. Process. 2000, 14:481-495.
31. Sprokkereef, E., 2001. Extension of the Flood Forecasting Model FloRIJN. NRC Publication 12-2001, ISSN no. 1568234X.
32. Troch P.A., Paniconi C., McLaughlin D. Catchment-scale hydrological modelling and data assimilation. Adv. Water Resour. 2003, 26(2):131-135.
33. Wang X.G., Bishop C.H. A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. Journal of the Atmospheric Sciences 2003, 60:1140-1158.
34. Werner M., Reggiani P., De Roo A.P.J., Bates P.D., Sprokkereef E. Flood forecasting and warning at the river basin and the European scale. Nat. Hazards 2005, 36:25-42.
35. WMO, 1992. Simulated Real-Time Intercomparison of Hydrological Models, WMO Operational Hydrology Report No 38, WMO No. 779, World Meteorological Organisation, Geneva.
36. Young P.C. Advances in real-time flood forecasting. Philos. Trans. Roy. Soc. Lond. Ser A - Math. Phys. Eng. Sci. 2002, 360(1796):1433-1450.
37. Zhou J., De Roure D. FloodNet: coupling adaptive sampling with energy aware routing in a flood warning system. J. Comput. Sci. Technol. 2007, 22(1):121-130.