Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Applying a Multi-Model Ensemble Method for Long-Term Runoff Prediction under Climate Change Scenarios for the Yellow River Basin, China

Zhang, Linus LU orcid and Yang, Xiaoliu (2018) In Water (Switzerland) 10(3).
Abstract
Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall–runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the imperfect character of individual models. In this paper, a genetic algorithm together with a Bayesian model averaging method are employed to provide a multi-model ensemble (MME) and combined runoff prediction under climate change scenarios produced from eight rainfall–runoff models for the Yellow River Basin. The results show that the multi-model ensemble method, especially the genetic algorithm method, can produce more reliable predictions than the other considered rainfall–runoff models.... (More)
Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall–runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the imperfect character of individual models. In this paper, a genetic algorithm together with a Bayesian model averaging method are employed to provide a multi-model ensemble (MME) and combined runoff prediction under climate change scenarios produced from eight rainfall–runoff models for the Yellow River Basin. The results show that the multi-model ensemble method, especially the genetic algorithm method, can produce more reliable predictions than the other considered rainfall–runoff models. These results show that it is possible to reduce the uncertainty and thus improve the accuracy for future projections using different models because an MME approach evens out the bias involved in the individual model. For the study area, the final combined predictions reveal that less runoff is expected under most climatic scenarios, which will threaten water security of the basin. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Water (Switzerland)
volume
10
issue
3
article number
301
publisher
MDPI AG
external identifiers
  • scopus:85043373084
ISSN
2073-4441
DOI
10.3390/w10030301
language
English
LU publication?
yes
id
d9a9fe0a-8c37-48a3-8074-37e373789e4e
date added to LUP
2018-03-13 11:24:01
date last changed
2021-10-06 02:34:44
@article{d9a9fe0a-8c37-48a3-8074-37e373789e4e,
  abstract     = {Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall–runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the imperfect character of individual models. In this paper, a genetic algorithm together with a Bayesian model averaging method are employed to provide a multi-model ensemble (MME) and combined runoff prediction under climate change scenarios produced from eight rainfall–runoff models for the Yellow River Basin. The results show that the multi-model ensemble method, especially the genetic algorithm method, can produce more reliable predictions than the other considered rainfall–runoff models. These results show that it is possible to reduce the uncertainty and thus improve the accuracy for future projections using different models because an MME approach evens out the bias involved in the individual model. For the study area, the final combined predictions reveal that less runoff is expected under most climatic scenarios, which will threaten water security of the basin.},
  author       = {Zhang, Linus and Yang, Xiaoliu},
  issn         = {2073-4441},
  language     = {eng},
  month        = {03},
  number       = {3},
  publisher    = {MDPI AG},
  series       = {Water (Switzerland)},
  title        = {Applying a Multi-Model Ensemble Method for Long-Term Runoff Prediction under Climate Change Scenarios for the Yellow River Basin, China},
  url          = {http://dx.doi.org/10.3390/w10030301},
  doi          = {10.3390/w10030301},
  volume       = {10},
  year         = {2018},
}