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(00453)How to identify a representative subset of hydro-climatic simulations for impact modelling studies?

机译:(00453)如何识别水力气候模拟的代表性型措施,用于影响建模研究?

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Uncertainties in hydro-climatic projections are (in part)related to different components of the modeling chain. Although a combination of numerous projections (ensemble)would be needed to characterize the overall uncertainty, in practice a small set of scenario combinations are constructed to provide users with a subset that is manageable for decision-making. The approach is based on a framework, rooted in the information theoretic Maximum Information Minimum Redundancy (MIMR)concept, for identifying a representative subset from an available large 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 simulate river discharge until the mid of the 21st century. Representative subsets are judged in terms of different statistical characteristics for precipitation, temperature and discharge and the sensitivity of the identified subset is assessed for different seasons and future periods. Results indicate that a 20-35% subset of the available set of projections can represent a large fraction (more than 80%)of the ensemble range of hydro-climatic changes. We find that the identified representative subsets are sensitive to the regional hydro-climatic characteristics and the choice of variables, seasons and future periods.
机译:水力气候投影中的不确定性是与建模链的不同组分有关的(部分)。尽管需要许多投影(集合)的组合来表征整体不确定性,但在实践中,构造了一小组场景组合,以向用户提供可管理决策的子集。该方法基于框架,源于信息理论最大信息最小冗余(MIMR)概念,用于识别来自水力气候投影的可用大型集合的代表子集。我们分析了瑞典的16种降水和温度投影的集合,并使用这些作为对HBV水文模型的输入来模拟河流排放到21世纪中叶。代表子集根据不同的统计特征来判断沉淀,温度和放电以及所识别的子集的敏感性被评估为不同的季节和未来时期。结果表明,20-35%的可用投影的子集可以代表水力气候变化的集合范围的大部分(超过80%)。我们发现所确定的代表子集对区域水力气候特征和变量,季节和未来时期的选择敏感。

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