Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models Georgia Papacharalampous1,*, Demetris Koutsoyiannis2, and Alberto Montanari3 1 Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, 157 80 Zographou, Greece;
[email protected]; https://orcid.org/0000-0001-5446-954X 2 Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou 5, 157 80 Zographou, Greece;
[email protected]; https://orcid.org/0000-0002-6226-0241 3 Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, via del Risorgimento 2, 40136 Bologna, Italy;
[email protected]; https://orcid.org/0000-0001-7428-0410 * Correspondence:
[email protected], tel: +30 69474 98589 This is the accepted manuscript of an article published in Advances in Water Resources. Please cite the article as: Papacharalampous GA, Koutsoyiannis D, Montanari A (2020) Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: Methodology development and investigation using toy models. Advances in Water Resources 136:103471. https://doi.org/10.1016/j.advwatres.2019.103471 Abstract: We introduce an ensemble learning post-processing methodology for probabilistic hydrological modelling. This methodology generates numerous point predictions by applying a single hydrological model, yet with different parameter values drawn from the respective simulated posterior distribution. We call these predictions “sister predictions”. Each sister prediction extending in the period of interest is converted into a probabilistic prediction using information about the hydrological model’s errors.