The Pdm Rainfall-runoff Model 483 the Pdm Rainfall-runoff Model

The Probability Distributed Model, or PDM, has evolved as a toolkit of model functions that together constitute a lumped rainfall–runoff model capable of representing a variety of catchment-scale hydrological behaviours. Runoff production is represented as a saturation excess runoff process controlled by the absorption capacity (of the canopy, surface and soil) whose variability within the catchment is characterised by a probability density function of chosen form. Soil drainage to groundwater is controlled by the water content in excess of a tension threshold, optionally inhibited by the water content of the receiving groundwater store. Alternatively, a proportional split of runoff to fast (surface storage) and slow (groundwater) pathways can be invoked with no explicit soil drainage function. Recursive solutions to the Horton-Izzard equation are provided for routing flows through these pathways, conveniently considered to yield the surface runoff and baseflow components of the total flow. An alternative routing function employs a transfer function that is discretely-coincident to a cascade of two linear reservoirs in series. For real-time flow forecasting applications, the PDM is complemented by updating methods based on error prediction and state-correction approaches. The PDM has been widely applied throughout the world, both for operational and design purposes. This experience has allowed the PDM to evolve to its current form as a practical toolkit for rainfall-runoff modelling and forecasting.

[1]  Kieran M. O'Connor,et al.  Derivation of discretely coincident forms of continuous linear time-invariant models using the transfer function approach , 1982 .

[2]  Jitendra R. Raol,et al.  Real-time parameter estimation , 2004 .

[3]  R. Moore,et al.  Forecasting for flood warning , 2005 .

[4]  Eric F. Wood,et al.  A land-surface hydrology parameterization with subgrid variability for general circulation models , 1992 .

[5]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[6]  Peter C Young,et al.  Advances in real–time flood forecasting , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  S. Sorooshian,et al.  Uniqueness and observability of conceptual rainfall‐runoff model parameters: The percolation process examined , 1983 .

[8]  R. Moore.  The probability-distributed principle and runoff production at point and basin scales , 1985 .

[9]  James C. I. Dooge,et al.  Linear Theory of Hydrologic Systems , 1973 .

[10]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[11]  R. J. Moore,et al.  Real-Time Flood Forecasting Systems: Perspectives and Prospects , 1999 .

[12]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[13]  Philip E. Gill,et al.  Practical optimization , 1981 .

[14]  R. Moore,et al.  A distribution function approach to rainfall runoff modeling , 1981 .

[15]  V. Bell,et al.  Incorporation of groundwater losses and well level data in rainfall-runoff models illustrated using the PDM , 2002 .

[16]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[17]  S. Sorooshian,et al.  Calibration of watershed models , 2003 .

[18]  Vijay P. Singh,et al.  Statistical analysis of rainfall and runoff , 1982 .

[19]  E. Todini.  The ARNO rainfall-runoff model , 1996 .