I need to know why I should use daily or monthly data.
Why some use daily rainfall instead of monthly rainfall?
Why others use monthly rainfall instead of daily rainfall?
From year to year daily rainfall can be highly variable, and you would need to
keep track of 365 data points.
Weekly rainfall would require you to keep track of 52 data points.
Monthly rainfall would require you to only keep track of 12 data points.
A group of thirty points added together ( 30 days ) produces a useful
"group " of data points that are lumped together to create a total amount
of rainfall in one specific month. This can be tracked year after year, after year
to indicate a pattern on increasing rainfall, or decreasing rainfall.
You would actually need centuries of data to determine a real pattern,
provided the data is taken from the same location over that time period.
The records of the British Admiralty might help.
hi,
thanks for your answer.
It seems that you are advised me to use daily rainfall instead of monthly rainfall ?
Most of the impact models that predict weather effects on crop growth and productivity run on a daily basis. This is the resolution that you would need to capture effects of drought and extreme heat stress. Those models are based on biophysical processes in the crop-soil-atmosphere system, fairly well calibrated and most of them ready to use after a further local calibration. However, there are also models around that use monthly or even yearly data. You may even be able to build a statistical model to use monthly or yearly data, especially when working at the regional scale. However, to do this you will need to have quite a good data base on yield and weather data to construct a sensitive model.
Climate change impact on crops is a really interesting topic, but as Claas Nendel said, models run on a daily basis and future climate data on a daily basys is really complex process. In this way, I like developing monthly-yearly data-model better than weekly-daily data-model.
In this way, first try developing an actual climate database, using statistical procedures if you want or if you don't have data enough. Later build the a new future climate database and compare your new reults. You can compare using easy climate indices (summarize, average, median...).
Best regards and good luck!
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