Data consistency and homogeneity #11
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For a meaningful study, it was important to fill in the missing data and assess the quality of the hydro-meteorological data after gathering the necessary data. In various studies, several data quality approaches have been utilized, with some of them being used to assess the quality of the obtained data. After filling the missed values we should check the quality of our data by using consistency and homogeneity because during measurement and filling missing data different errors are produced. So we should check the quality of our data using this two methods since the data should be homogeneous and independent. Homogeneity and consistency assures that the observations are from the same population. Several methods are used to check the quality of the data, but the double mass curve method (DMC) is the most effective. It is a plot of the accumulated rainfall data at a station in question against the mean of the cumulative rainfall for a group of surrounding stations. This method is used after the data at all stations were filled. |
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So, if I understand correctly, for precipitation at least the statement is that DMC is relevant. Bahafta has worked on code for DMC (see here), please check it and comment / recommend any changes or clarifications. Also, can someone please post the equations (a structured description) of DMC? Then later on we can add that in the documentation, and based on that improve the code as started by Bahafta or propose additions. For homogeneity, if I understand correctly, that is simply a matter of plotting multiple lines in the same graph in order to compare them, correct? For the rest, I read 'several methods are used to check the quality' - @BeyeneSe (and others): could you list (besides DMC) a couple of the ones commonly used within AWTI studies? |
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Multiple participants have mentioned the wish to work on code for testing data consistency and homogeneity. Can you explain better what testing consistency and homogeneity implies?
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