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Open AccessArticle

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

by Rui Zhu 1,2,3, Linshan Yang 4,*, Tao Liu 1,2,3, Xiaohu Wen 4, Liming Zhang 1,2,3 and Yabin Chang 1,2,3
1
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2
National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3
Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
4
Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1588; https://doi.org/10.3390/w11081588
Received: 9 July 2019 / Revised: 26 July 2019 / Accepted: 27 July 2019 / Published: 31 July 2019
(This article belongs to the Section Hydrology)
Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future hydrological responses. The ensemble of GCM outputs derived very close historical hydrological hindcasts but had great uncertainty in future hydrological projections. Using the variables of GCM outputs as inputs to SVR can reduce intermediate downscaling links between variables and decrease the cumulative effect of bias in projecting future hydrological responses. Future precipitation in the study area will increase in the future under both scenarios, and this increasing trend is more significant under RCP 8.5 than under scenario 4.5. The results also indicate the streamflow change will be more sensitive to temperature (precipitation) under the RCP 8.5 (4.5) scenario. The findings and approach have important implications for hydrological response studies and the evaluation of impacts on localized regions similar to the mountainous watershed in this study. View Full-Text
Keywords: global climate model; hydrological response; extreme learning machine; support vector regression; Heihe River global climate model; hydrological response; extreme learning machine; support vector regression; Heihe River
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Zhu, R.; Yang, L.; Liu, T.; Wen, X.; Zhang, L.; Chang, Y. Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models. Water 2019, 11, 1588.

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