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Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty
Author(s) -
Kitanidis Peter K.,
Bras Rafael L.
Publication year - 1980
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr016i006p01025
Subject(s) - kalman filter , nonlinear system , linearization , hydrological modelling , computer science , state space , state space representation , watershed , econometrics , mathematical optimization , mathematics , statistics , algorithm , geology , artificial intelligence , quantum mechanics , climatology , machine learning , physics
The optimal control of watershed systems requires accurate real‐time short‐term forecasts of river flows. For the first time, this paper formulates a large, nonlinear conceptual model (the National Weather Service catchment model) in a mode amenable to analysis of uncertainty and the utilization of real‐time information (measurements, forecasts, guesses) to update system states and improve streamflow predictions. The proposed methodology is based on the state space formulation of the equations describing the hydrologic model and the assumption of sources of uncertainty in the data and in the model structure. The first two moments of random variables are estimated in a computationally efficient way using on‐line linear estimation techniques. Linearization of functional relationships is performed with the uncommon but powerful multiple‐input describing function technique for the most strongly nonlinear responses and Taylor expansion for the rest. The linear feedback rule developed is based on the Kalman filter.