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    Here are some resources regarding soil water capacity that may be of help.

    Here is a resource explaining how to test the quality of your soil

    Most of these are geared toward agriculture, but still apply to turf.



    I’ve been using the ET checkbook water balance algorithm all summer and am happy with the results. My implementation is based on the same code as the stand-alone simulation found here:

    The code is not tightly integrated with the OSPI program; it’s mainly stand-alone Python programs that run as cron jobs. I have a database containing tables for weather data, sprinkler zone configuration, the irrigation checkbook, and the watering schedule. I modified the sprinklers_pi program to load its schedule from the database every day. (I used sprinklers_pi instead of Interval because I was familiar with its code and because its schedule model was an exact match to my data: a schedule is a simple list of watering events consisting of a start time, zone number and duration.) I configure zones by editing a text file (a nice UI would be great, but not worth the effort to me so far). I get an email every day telling me what watering took place the previous day and giving me a ten day weather forecast with the projected watering schedule for each day. Most of the experimenting I’ve done this summer has been in tweaking the projected schedule. The tricky part is forecasting future ET values.

    Here in Seattle the summer is mostly dry for long stretches with an occasional rainy day. Last night we got a lot of rain. There was no watering yesterday because there was rain in the forecast. Now that the rain has fallen the ground is saturated and the projection is for no watering for three days. At that point watering will resume first with my lawn zone and zones with shallow roots. A day or two later irrigation will resume for zones that have larger capacity to store water. My lawn in shallow clay soil gets watered every two to three days during dry stretches. Other zones containing shrubs in deeper loamy soil get watered less often. I found I had to tweak some of the shrub zone parameters because we planted flowers that need more frequent watering. My biggest irrigation issues are mixed plant types in zones and incomplete coverage by sprinkler head placement (e.g. water flow to some flower areas is partially blocked by shrubs). Therefore I do some additional hand watering during dry stretches. It would be much easier if I had big fields of soy beans instead of home landscaping!

    The ET approach to irrigation is nice because it’s so dynamic. When the weather is hot and dry, watering happens more often. Windy days mean more watering; overcast days mean less. If it rains (or if rain is in the immediate forecast), watering is suspended. When watering resumes depends on how much rain falls and how hot/dry/windy the following days are.

    If anyone is interested in trying this approach themselves, I’m happy to share my experience and code. Start by checking the link above. The primary reference I’ve used is this document from Irrisoft, which describes this approach and is pretty easy to read:



    This is cool news Dan and thanks for sharing. I knew you’d come back one day with an implementation :).

    I can’t wait to take a look at what you’ve done.




    Hi folks,

    I’m new to this forum, hope is still active even though the last post dates 2 years ago.

    To the point: I was experimenting with Dan Tripp’s code in Python and it works great for the beginning. A friend of mine has a handful of moisture sensors from Plantlink and I thought it would be much more accurate to use directly an average soil moisture value for adjusting the ET values calculated with the Penman-Monteith formula. Since the ET values are calculated based on the air humidity, I do not know yet how to integrate the known soil moisture in the entire picture.

    Any ideas?





    If you can measure soil moisture content directly, you can bypass the ET calculations completely since they are used to estimate soil moisture. The core of the algorithm is the “checkbook” balance calculation. Instead of calculating the balance each day based on rainfall, irrigation and evaporation (ET), you can just measure the balance directly. The various zone calculations using factors like soil type, plant types and root depth would still be useful to determine how long to water. These calculations do not involve ET or any weather data. Good luck, and let us know how it goes.


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