TY - JOUR
T1 - An Information Theory Approach to Identifying a Representative Subset of Hydro-Climatic Simulations for Impact Modeling Studies
AU - Pechlivanidis, I. G.
AU - Gupta, H.
AU - Bosshard, T.
N1 - Funding Information:
This study was partially funded by the EU FP7-funded project SWITCH-ON (grant agreement 603587), which explores the untapped potential of Open Data to tackle changes in the Hydrosphere. This work was also partially funded by the project AQUACLEW, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Commission (grant agreement 690462). Funding was also received from the Swedish Environmental Protection Agency (Naturvårdsverket) through the Climate Change and Environmental Objectives (CLEO) project (contract no. 802-0115-09). The investigation was performed at the SMHI Hydrological Research unit, where much work benefits from joint efforts in developing models and concepts by the whole team. Thanks go to Dr C. Li for sharing the MIMR scripts. The scientific findings will contribute to the decadal research initiative ‘‘Panta Rhei -changes in hydrology and society’’ by the International Association of Hydrological Sciences (IAHS). The authors would finally like to express their sincere gratitude to Dr Martyn Clark, Dr Ole Ro€ssler and an anonymous reviewer for their constructive comments. The data that support the findings of this study are available in Zenodo (http://doi.org/10. 5281/zenodo.1239857). I.G.P. contributed with the basic idea, the overall study design, result analysis, figures and writing the manuscript; H.G. contributed with complementary ideas, interpretation of results, and commenting on the manuscript; T.B. contributed with the basic idea, compilation of model outputs, supporting information figures, and commenting on the manuscript.
Funding Information:
This study was partially funded by the EU FP7-funded project SWITCH-ON (grant agreement 603587), which explores the untapped potential of Open Data to tackle changes in the Hydrosphere. This work was also partially funded by the project AQUACLEW, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Commission (grant agreement 690462). Funding was also received from the Swedish Environmental Protection Agency (Naturv?rdsverket) through the Climate Change and Environmental Objectives (CLEO) project (contract no. 802-0115-09). The investigation was performed at the SMHI Hydrological Research unit, where much work benefits from joint efforts in developing models and concepts by the whole team. Thanks go to Dr C. Li for sharing the MIMR scripts. The scientific findings will contribute to the decadal research initiative ?Panta Rhei - changes in hydrology and society? by the International Association of Hydrological Sciences (IAHS). The authors would finally like to express their sincere gratitude to Dr Martyn Clark, Dr Ole R?ssler and an anonymous reviewer for their constructive comments. The data that support the findings of this study are available in Zenodo (http://doi.org/10.5281/zenodo.1239857). I.G.P. contributed with the basic idea, the overall study design, result analysis, figures and writing the manuscript; H.G. contributed with complementary ideas, interpretation of results, and commenting on the manuscript; T.B. contributed with the basic idea, compilation of model outputs, supporting information figures, and commenting on the manuscript.
Publisher Copyright:
©2018. The Authors.
PY - 2018/8
Y1 - 2018/8
N2 - 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. We analyze an ensemble of 16 precipitation and temperature projections for Sweden, and use these as inputs to the HBV hydrological model to project river discharge until the mid of this century. Representative subsets are judged in terms of different statistical properties of three essential climate variables (precipitation, temperature and discharge), whilst we further assess the sensitivity of the optimized subset for different seasons and future periods. Our results indicate that a quarter to a third of the available set of projections can represent more than 80% of the total information of hydro-climatic changes. We find that the representative subsets are sensitive to the regional hydro-climatic characteristics and the choice of variables, seasons and periods of interest. Therefore we recommend that any selection process should not be solely driven by climatic variables but, rather, should also consider variables of the impact model.
AB - 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. We analyze an ensemble of 16 precipitation and temperature projections for Sweden, and use these as inputs to the HBV hydrological model to project river discharge until the mid of this century. Representative subsets are judged in terms of different statistical properties of three essential climate variables (precipitation, temperature and discharge), whilst we further assess the sensitivity of the optimized subset for different seasons and future periods. Our results indicate that a quarter to a third of the available set of projections can represent more than 80% of the total information of hydro-climatic changes. We find that the representative subsets are sensitive to the regional hydro-climatic characteristics and the choice of variables, seasons and periods of interest. Therefore we recommend that any selection process should not be solely driven by climatic variables but, rather, should also consider variables of the impact model.
KW - climate models
KW - impact studies
KW - information theory
KW - maximum information minimum redundancy
KW - representative subset
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U2 - 10.1029/2017WR022035
DO - 10.1029/2017WR022035
M3 - Article
AN - SCOPUS:85052366174
VL - 54
SP - 5422
EP - 5435
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 8
ER -