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

Nilsson, Patrik LU ; Bertacchi Uvo, Cintia LU and Berndtsson, Ronny LU (2006) In Journal of Hydrology 321(1-4). p.344-363
Abstract
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 =... (More)
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). (Less)
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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
monthly runoff, combination, modelling, conceptual, hydrological modelling, artificial neural networks
in
Journal of Hydrology
volume
321
issue
1-4
pages
344 - 363
publisher
Elsevier
external identifiers
  • wos:000237099500023
  • scopus:33645973241
ISSN
0022-1694
DOI
10.1016/j.jhydrol.2005.08.007
language
English
LU publication?
yes
id
85536480-d55e-486a-8092-5594f1211424 (old id 410725)
date added to LUP
2016-04-01 16:11:31
date last changed
2020-03-24 04:32:24
@article{85536480-d55e-486a-8092-5594f1211424,
  abstract     = {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).},
  author       = {Nilsson, Patrik and Bertacchi Uvo, Cintia and Berndtsson, Ronny},
  issn         = {0022-1694},
  language     = {eng},
  number       = {1-4},
  pages        = {344--363},
  publisher    = {Elsevier},
  series       = {Journal of Hydrology},
  title        = {Monthly runoff simulation: Comparing and combining conceptual and neural network models},
  url          = {http://dx.doi.org/10.1016/j.jhydrol.2005.08.007},
  doi          = {10.1016/j.jhydrol.2005.08.007},
  volume       = {321},
  year         = {2006},
}