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Monthly runoff simulation: Comparing and combining conceptual and neural network models

In English

Författare

Summary, in English

Runoff estimation is of high importance for many practical engineering applications so that, e.g. power production, dam safety and water supply can be ensured. The methods and time step relevant for runoff simulations vary depending on the location and the application. Long-term runoff simulation for Scandinavia is of high importance as its hydropower production is affected by climate variability, which strongly influences winter temperature and precipitation. This work investigates the possibility of modelling monthly runoff for two Norwegian river basins. Two methodologies-artificial neural networks (NN) and conceptual runoff modelling (CM)-are compared and NN offer the best estimations of monthly runoff for both tested basins with R-2 = 0.82 and 0.71, respectively. The combination of NN and CM by using snow accumulation and the soil moisture calculated by the CM as input to the NN proved to be an excellent alternative to perform high quality monthly runoff simulations and improved the simulations skill for both basins (R-2=0.86 and 0.75, respectively).

Publiceringsår

2006

Språk

Engelska

Sidor

344-363

Publikation/Tidskrift/Serie

Journal of Hydrology

Volym

321

Issue

1-4

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Water Engineering

Nyckelord

  • monthly runoff
  • combination
  • modelling
  • conceptual
  • hydrological modelling
  • artificial neural networks

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0022-1694