Improving global and catchment estimates of runoff through computationally-intelligent ensemble approaches Applications of intelligent multi-model combination, cross-scale model comparisons, ensemble analyses, and new model parameterisations

Improving global and catchment estimates of runoff through computationally-intelligent ensemble approaches Applications of intelligent multi-model combination, cross-scale model comparisons, ensemble analyses, and new model parameterisations

Water related problems (scarcity, availability and hazards) together form one of the three major crises (the two other are food and energy) for today and the future across the globe (World Economic Forum, 2016, Schewe et al., 2014a, Hanasaki et al., 2013, Rockstrom et al., 2009). Water crises are wi...

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Bibliographic Details
Main Author: Zaherpour, Jamal
Format: Thesis (University of Nottingham only)
Language:English
Published: 2018
Subjects:
Global hydrological models; Catchment hydrological models; Multi-model combination (MMC); Cross-scale model comparisons; Ensemble analyses; Model evaluation; Human impact parametrisation; Machine learning; Model weighting
Online Access:https://eprints.nottingham.ac.uk/55340/

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