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Markov‐switching model for nonstationary runoff conditioned on El Niño information
Author(s) -
Gelati E.,
Madsen H.,
Rosbjerg D.
Publication year - 2010
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 - 10.1029/2009wr007736
Subject(s) - surface runoff , autoregressive model , inflow , markov chain , environmental science , series (stratigraphy) , climatology , index (typography) , climate change , term (time) , econometrics , meteorology , mathematics , geology , statistics , computer science , geography , physics , quantum mechanics , ecology , paleontology , oceanography , world wide web , biology
We define a Markov‐modulated autoregressive model with exogenous input (MARX) to generate runoff scenarios using climatic information. Runoff parameterization is assumed to be conditioned on a hidden climate state following a Markov chain, where state transition probabilities are functions of the climatic input. MARX allows stochastic modeling of nonstationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We apply MARX to inflow time series of the Daule Peripa reservoir (Ecuador). El Niño‐Southern Oscillation (ENSO) information is used to condition runoff parameterization. Among the investigated ENSO indexes, the NINO 1+2 sea surface temperature anomalies and the trans‐Niño index perform best as predictors. In the perspective of reservoir optimization at various time scales, MARX produces realistic long‐term scenarios and short‐term forecasts, especially when intense El Niño events occur. Low predictive ability is found for negative runoff anomalies, as no climatic index correlating properly with negative inflow anomalies has yet been identified.