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Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. 智库出版物
2019
作者:  Zaherpour J;  Mount N;  Gosling S;  Dankers R;  Eisner S;  Gerten D;  Liu X;  Masaki Y
收藏  |  浏览/下载：3/0  |  提交时间：2019/06/18
Machine learning  Model weighting  Gene expression programming  Global hydrological models  Optimisation  
Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. 智库出版物
2018
作者:  Scanlon BR;  Zhang Z;  Save H;  Sun AY;  Müller Schmied H;  van Beek LPH;  Wiese DN;  Wada Y
收藏  |  浏览/下载：3/0  |  提交时间：2019/06/18
global hydrological models, land surface models, GRACE satellites, terrestrial total water storage anomalies, global mean sea level  
Multi-model assessment of global hydropower and cooling water discharge potential under climate change. 智库出版物
2016
作者:  van Vliet M;  van Beek LPH;  Eisner S;  Flörke M;  Wada Y;  Bierkens MFP
收藏  |  浏览/下载：4/0  |  提交时间：2019/06/18
Water resources  Water temperature  Hydropower  Cooling water  Climate change  Global hydrological models