Next Article in Journal
Water Temperature, pH, and Road Salt Impacts on the Fluvial Erosion of Cohesive Streambanks
Previous Article in Journal
Trade-off Analysis of Ecosystem Services in a Mountainous Karst Area, China
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Water 2018, 10(3), 301; https://doi.org/10.3390/w10030301

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

1
Department of Water Resources Engineering, Lund University, Box-118, 221 00 Lund, Sweden
2
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Received: 3 January 2018 / Revised: 7 March 2018 / Accepted: 8 March 2018 / Published: 10 March 2018
Full-Text   |   PDF [3311 KB, uploaded 10 March 2018]   |  

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. View Full-Text
Keywords: water security; climate change; rainfall–runoff models; multi-model ensemble method; simulation; Yellow River Basin; genetic algorithms water security; climate change; rainfall–runoff models; multi-model ensemble method; simulation; Yellow River Basin; genetic algorithms
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, L.; Yang, X. Applying a Multi-Model Ensemble Method for Long-Term Runoff Prediction under Climate Change Scenarios for the Yellow River Basin, China. Water 2018, 10, 301.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top