Application of Some Entropic Measures in Hydrologic Data Infilling Procedures

Inadequacy of streamflow records, in many situations, is well recognized by water resources managers. Among other causes responsible for inadequacy of the streamflow records, is the existence of intermittent missing data gaps. This paper addresses this issue and proposes data infilling procedures based on pattern recognition techniques. The characteristics and relationships of distinct groups of data, rather than the entire time series as a whole, forms the basis of model development. Two types of models are proposed, including the models for infilling missing values based on the characteristics and relationships of only the streamflow time series with missing data values; and the models which also incorporate relevant information on the characteristics and relationships of the other time series of nearby rivers. As expected, the latter type of models are found to perform better. Further investigations into the relative efficacy of the proposed models with those existing in literature are continuing.

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