tempy

BW
['1348458', '1351266', '1351816', '1352180', 
'1353126', '1353308', '1353330', '1353332', 
'1353340', '1353340', '1353346', '1353350', 
'1348526', '1351816', '1350718', '1350718', 
'1350658', '1406166', '1406166', '1406166', 
'1406166', '1410410', '1413900', '1420666', 
'1434353', '1351816', '1439896', '1439896', 
'1406166']


HB
['1394540', '1394540']

NRW
['1351288', '1350604', '1350656', '1350564',
'1383102', '1404498', '1406268', '1313838',
'1350686', '1418048', '1420444', '1429842',
'1429882', '1437666']


Hi Christian,

I’m having trouble getting data to be any more informative that from what I showed you today based on some of the limitations of the data:
1.	I only have the conversion equation of Voltage to Volumetric Water Content (VWC)—water/soil ratio (m3/m3)
a.	With this I am able to calculate the rate of change in this ratio (dVWC/dTime) which is dependent Evapotranspiration
b.	However when dVWC is plotted a semi-constant curve results about Zero (the Orange line in the example dVWC_PDF attached)
i.	I plotted the cumulative sum of the rate of change to see how much evaporation occurred, but as expected it is just the inverse of the VWC data, see CSUM_PDF)
ii.	I plotted the dVWC at different intervals (2Hrs, 4Hrs), but the results were very much alike
2.	We have no data regarding the soil which we could use to translate the ratio into a specific volume of water for that given soil
a.	Data such as the Bulk Density of the soil is typically used to convert such ratios into volume of water in the soil 
b.	However, we can estimate volume of water indirectly by assuming, for example, that if  VWC = .175, then there is 175 mm of water per m3 of soil... Decagon says however that these values should be within +/- 3% so perhaps this is a non-issue
3.	Data is highly variable from sensor to sensor (pre-programmed open field irrigation, drip irrigation, farmer-decision) have distinctive curves, though without more information on which is which it is difficult to determine which is which with 100% certainty.
a.	 The open-field systems will be the one which will benefit from a predictive system. Drip irrigation systems have a very-steady nearly constant VWC 
b.	With the variability within each sensor’s Data it is difficult to compute a precise ‘Evaporation rate’ (dVWC) with a naïve algorithm (something that takes sums and averages over time), thus I need more time to formulate something which can handle the variability
i.	If the goal is to use AN Evaporation rate to predict future irrigation requirements, its not as simple as that. A model needs to be written, and the above mentioned variabilities   
4.	Without the rain data and knowing which fields are open, it’ll be hard to create the sort of predictive model I’d like to implement.
a.	If I know how much it rained, or how much the farmer irrigates 

SH
['1350714', '1412190', '1412866', '1423540', '1432129']