Environmental Engineering Reference
In-Depth Information
Romanowicz, R.J., Young, P.C. and Beven, K.J. (2006)
Data assimilation and adaptive forecasting of
water levels in the River Severn catchment. Water
Resources Research, 42, W06407;
doi: 10.1029/2005WR004373.
Saltelli, A., Chan, K. and Scott, M. (eds) (2000) Sensi-
tivity Analysis. John Wiley, Chichester, UK.
Schweppe, F. (1965) Evaluation of likelihood functions
for Gaussian signals. IEEE Transactions on Informa-
tion Theory, 11, 61-70.
Seo, D., Koren, V. and Cajina, N. (2003) Real-time
variational assimilation of hydrologic and hydrome-
teorological data into operational hydrologic forecast-
ing. Journal of Hydrometeorology, 4, 627-641.
Seo, D-J., Cajina, L., Corby, R. and Howieson, T. (2009)
Automatic state updating for operational streamflow
forecasting via variational data assimilation. Journal
of Hydrology, 367, 255-275.
Smith, P.J., Beven, K.J. andTawn, J.A. (2006) Detectionof
structural inadequacy in process-based hydrological
models: A particle-filtering approach. Water Re-
sources Research, 44, W01410;
doi: 10.1029/2006WR005205.
Stigter, J. and Beck, M. (2004) On the development and
application of a continuous-discrete recursive predic-
tion error algorithm. Mathematical Biosciences, 191,
143-158.
Thiemann, M., Trosset, M., Gupta, H. and Sarooshian, S.
(2001) Bayesian recursive parameter estimation for
hydrologic models. Water Resources Research, 37,
2521-2536.
Tian, X., Xie, Z. and Dai, A. (2008) A land surface soil
moisture data assimilation system based on the dual-
UKF method and the community land model. Journal
of Geophysical Research, 113, D14127; doi: 10.1029/
2007JD009650.
Todini, E. (1978) Mutually interactive state/parameter
estimation (MISP) in hydrological applications. In:
Vansteenkiste, G.C. (ed.) Modeling Identification and
Control in Environmental Systems. North Holland,
Amsterdam, pp. 699-713.
von Bertalanffy, K.L. (1968) General System Theory:
Foundations, Development, Applications. George
Braziller, New York.
Vrugt, J.A., Diks, C.G.H., Gupta, H.W., Bouten, W. and
Verstraten, J.M. (2005) Improved treatment of uncer-
tainty in hydrologic modeling: Combining the
strengths of global optimization and data assi-
milation. Water Resources Research, 41, W01017;
doi: 10.1029/2004WR003059.
Wagener, T., McIntyre, N., Lees, M.J., Wheater, H.S. and
Gupta, H. (2003) Towards reduced uncertainty in
conceptual rainfall-runoff modelling: Dynamic iden-
tifiability analysis. Hydrological Processes, 17,
455-476.
Weerts, A.H. and Serafy, G.Y.H.E. (2006) Particle filter-
ing and ensemble Kalman filtering for state updating
with hydrological conceptual rainfall-runoff models.
Water Resources Research, 42, W09403; doi: 10.1029/
2005WR004093.
Wheater, H.S., Jakeman, A.J. and Beven, K.J. (1993) Prog-
ress and directions in rainfall-runoff modelling. In:
Jakeman, A.J., Beck, M.B. and McAleer, M.J. (eds)
Modelling Change in Environmental Systems. Wiley,
Chichester, chapter 5.
Young, P.C. (1970) An instrumental variable method
for real-time identification of a noisy process.
Automatica, 6, 271-287.
Young, P.C. (1974) Recursive approaches to time-series
analysis. Bulletin of the Institute of Mathematics and
its Applications, 10, 209-224.
Young, P.C. (1976) Some observations on instrumental
variable methods of time-series analysis. Internation-
al Journal of Control, 23, 593-612.
Young, P.C. (1984) Recursive Estimation and Time-Se-
ries Analysis. Springer-Verlag, Berlin (revised and
enlarged edition to be published in 2011).
Young, P.C. (1993) Time variable and state dependent
modelling of nonstationary and nonlinear time
series. In: Subba Rao, T. (ed.) Developments in Time
Series Analysis. Chapman and Hall, London, pp.
374-413.
Young, P.C. (1999a) Data-based mechanistic modelling,
generalised sensitivity and dominant mode analysis.
Computer Physics Communications, 117, 113-129.
Young, P.C. (1999b) Nonstationary time series analysis
and forecasting. Progress in Environmental Science,1,
3-48.
Young, P.C. (2001a) Data-based mechanistic modelling
and validationof rainfall-flowprocesses. In: Anderson,
M.G. and Bates, P.D. (eds) Model Validation: Perspec-
tives inHydrological Science. JohnWiley, Chichester,
pp. 117-161.
Young, P.C. (2001b) The identification and estimation of
nonlinear stochastic systems. In: Mees, A.I. (ed.),
Nonlinear Dynamics and Statistics, Birkhauser, Bos-
ton, pp. 127-166.
Young, P.C. (2002) Advances in real-time flood forecast-
ing. Philosophical Transactions of the Royal Society,
Physical and Engineering Sciences, 360, 1433-1450.
