Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection
2.2.1. Observed Data
2.2.2. Coupled Model Intercomparison Project Phase-5 (CMIP5) Scenarios
2.3. Methodology
2.3.1. Extreme Learning Machine
2.3.2. Support Vector Regression
2.3.3. Model Development and Validation
3. Results
3.1. Climatic–Hydrological Relationship Identification
3.2. Future Temperature and Precipitation Forecasting
3.2.1. Future Temperature Forecasting
3.2.2. Future Precipitation Forecasting
3.3. Model Simulation Calibration and Comparison
3.4. Future Streamflow Forecasting
3.5. Future Climate Change Impact on the Water Yield
4. Discussion and Summary
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Station | Gauging Subject | Latitude/° | Longitude/° | Elevation/m | Time Span |
---|---|---|---|---|---|
Zhangye | Meteorology | 38.9 | 100.4 | 1482.7 | 1961–2013 |
Sunan | Meteorology | 38.8 | 99.6 | 2312 | 1995–2013 |
Minle | Meteorology | 38.5 | 100.8 | 2271 | 1995–2013 |
Tuole | Meteorology | 38.8 | 98.4 | 3367 | 1961–2013 |
Yeniugou | Meteorology | 38.4 | 99.6 | 3320 | 1961–2013 |
Qilian | Meteorology | 38.2 | 100.3 | 2787.4 | 1961–2013 |
Yingluoxia | Hydrology | 38.8 | 100.18 | 1674 | 1961–2013 |
No. | Model | Modeling Centre | Spatial Resolution | Data Length | ||
---|---|---|---|---|---|---|
Historical | RCP 4.5 | RCP 8.5 | ||||
1 | ACCESS1.0 | CSIRO-BOM (Australia) | 1.875° × 1.25° | 1948–2005 | 2006–2100 | 2006–2100 |
2 | ACCESS1.3 | CSIRO-BOM (Australia) | 1.875° × 1.25° | 1948–2005 | 2006–2100 | 2006–2100 |
3 | BCC-CSM1.1(m) | BCC (China) | 1.125° × 1.125° | 1948–2005 | 2006–2100 | 2006–2100 |
4 | CNRM-CM5 | CNRM-CERFACS (France) | 1.400° × 1.400° | 1948–2005 | 2006–2100 | 2006–2100 |
5 | HadGEM2-CC | MOHC (UK) | 1.875° × 1.25° | 1948–2005 | 2006–2100 | 2006–2100 |
6 | HadGEM2-ES | MOHC (UK) | 1.875° × 1.25° | 1948–2005 | 2006–2100 | 2006–2100 |
7 | MIROC5 | CCSR (Japan) | 1.400° × 1.400° | 1948–2005 | 2006–2100 | 2006–2100 |
8 | MRI-CGCM3 | MRI (Japan) | 1.125° × 1.125° | 1948–2005 | 2006–2100 | 2006–2100 |
Streamflow Changes | RCP 4.5 | RCP 8.5 | ||
---|---|---|---|---|
2020–2050 | 2060–2090 | 2020–2050 | 2060–2090 | |
Spring (MAM) | 0.13 ** | 0.08 * | 0.1 ** | 0.11 * |
Summer (JJA) | 0.08 | 0.02 | 0.16 ** | 0.13 * |
Autumn (SON) | 0.03 | 0.03 | 0.15 ** | 0.1 * |
Winter (DJF) | 0.04 ** | 0.03 | 0.01 | 0.02 |
Annual | 0.29 ** | 0.14 * | 0.30 ** | 0.43 ** |
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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. https://doi.org/10.3390/w11081588
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(8):1588. https://doi.org/10.3390/w11081588
Chicago/Turabian StyleZhu, Rui, Linshan Yang, Tao Liu, Xiaohu Wen, Liming Zhang, and Yabin Chang. 2019. "Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models" Water 11, no. 8: 1588. https://doi.org/10.3390/w11081588