A Markov Chain Flow Model for flood forecasting

Patrice Yapo, Soroosh Sorooshian, Vijai Gupta

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This paper presents a new approach to streamflow forecasting, based on a Markov chain model for estimating the probabilities that the one‐step ahead streamflow forecast will be within specified flow ranges. With the new approach, flood forecasting is possible by focusing on a preselected range of streamflows. In addition, the approach introduces a multiobjective (two‐criterion) function for the assessment of model performance. The two criteria are (1) the probability of issuing a false alarm and (2) the probability of failing to predict a flood event. The goal is to minimize both criteria simultaneously. Three versions of the model are presented: a first‐order Markov chain model, a second‐order Markov chain model, and a first‐order Markov chain with rainfall as exogenous input model. These models compared favorably to time series models, using data from two watersheds (a semiarid watershed and a temperate watershed), when evaluated in terms of the multiobjective performance criterion.

Original languageEnglish (US)
Pages (from-to)2427-2436
Number of pages10
JournalWater Resources Research
Issue number7
StatePublished - Jul 1993

ASJC Scopus subject areas

  • Water Science and Technology


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