How to explain the variability in extreme precipitation indices like CDD, CWD, and R1mm between two close meteorological stations (stations separated only by a few kilometers)?
Given that I tested data homogeneity and performed several statistical analyses like Durbin–Watson, linear least squares, Mann-Kendall, and Locally weighted regression methods.
Dear Salah:
My answer is yes. It is possible but you need a profound statistical analysis. Linear trend means non-stationarity of your signal and non-stationarities like monotonous, periodic or step-like trends are often caused by external effects. For example, different atmospheric conditions, height above sea level, etc. However, I suggest you try to remove such trends and perform statistical tests.
I would like to cooperate with you in this topic.
Cheers Dr. Millan
Dear Humberto Millán ,
Many thanks.
Yes I tested data homogeneity and performed several statistical analysis like Durbin–Watson, linear least squares, Mann-Kendall, and Locally weighted regression methods.
Regards,
Yes, indeed, it can. This is termed as temporal and spatial variability. In terse, different meteorological processes and its interaction, in general, can arise such variability.
Best,
My answer is yes. Due to change in global climatic effects there may be variations in hydro-climatic characteristics pertaining to temporal and spatial variability in precipitation. However, the trend can be analysed through different statistical analysis.
Thanks
this is not possible because short area is not locked. Surrounding conditions are also taken into consideration.
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