{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["54(8)"],"submitter":["Pechlivanidis IG"],"funding":["Naturvårdsverket"],"pubmed_abstract":["Uncertainties in hydro-climatic projections are (in part) related to various components of the production chain. An ensemble of numerous projections is usually considered to characterize the overall uncertainty; however in practice a small set of scenario combinations are constructed to provide users with a subset that is manageable for decision-making. Since projections are unavoidably uncertain, and multiple projections are typically informationally redundant to a considerable extent, it would be helpful to identify an informationally representative subset in a large model ensemble. Here a framework rooted in the information theoretic Maximum Information Minimum Redundancy concept is proposed for identifying a representative subset from an available ensemble of hydro-climatic projections"],"journal":["Water resources research"],"pagination":["5422-5435"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6175403"],"repository":["biostudies-literature"],"pubmed_title":["An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact Modeling Studies."],"pmcid":["PMC6175403"],"pubmed_authors":["Pechlivanidis IG","Gupta H","Bosshard T"],"additional_accession":[]},"is_claimable":false,"name":"An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact Modeling Studies.","description":"Uncertainties in hydro-climatic projections are (in part) related to various components of the production chain. An ensemble of numerous projections is usually considered to characterize the overall uncertainty; however in practice a small set of scenario combinations are constructed to provide users with a subset that is manageable for decision-making. Since projections are unavoidably uncertain, and multiple projections are typically informationally redundant to a considerable extent, it would be helpful to identify an informationally representative subset in a large model ensemble. Here a framework rooted in the information theoretic Maximum Information Minimum Redundancy concept is proposed for identifying a representative subset from an available ensemble of hydro-climatic projections","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Aug","modification":"2026-05-01T21:09:26.816Z","creation":"2019-03-27T00:03:45Z"},"accession":"S-EPMC6175403","cross_references":{"pubmed":["30344354"],"doi":["10.1029/2017WR022035"]}}