weather & sensor based irrigation controllers

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WEATHER & SENSOR BASED IRRIGATION CONTROLLERS

Contact Information

Charles Swanson979-845-5614

[email protected]

Dr. Guy [email protected]

Agenda

ET Concepts Landscape Water Requirements Calculations Irrigation Scheduling Calculations ET Calculation Methods ET Controller Technologies Bench Testing Protocols Controller Demonstrations Discussion WHAT IS “ET”?

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Evapotranspiration, ET

Measurement of the total requirements of plants and crops

The word evapotranspiration is a combination of the words “evaporation” and “transpiration”

Very difficult to measure directly May be calculated using weather data

ET Theory and Current Practice

Penman 1949 first proposed the “energy balance method” for determining plant water requirements

This method required daily or hourly weather data: solar radiation, temperature, wind, and relative humidity

ET is calculated for a single plant/crop which is used as a reference for determining the water requirements of all other plants/crop

Reference Evapotranspiration, ETo

Alfalfa was the first reference crop used A cool season grass is now the standard reference

plant The reference cool season grass is similar to a

fescue, except that it is growing under idea conditions

Reference Evapotranspiration, ETo

Also is called the “Potential ET (PET) Used as a reference from which the water

requirements of all other plants can be determined Note: ETo = PET ETo is the potential evapotranspiration (PET) of a

cool season reference grass growing 4-inches tall under well watered conditions

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USDA ARS Lab, Bushland Texas

• Considered the leading ET research facility in the US• Use lysimeters & weather data to verify ET equation

Crop Coefficient (Kc)

Crop coefficients (Kc) are used to relate ETo to the

water requirements of specific plants and crops

Percentage of plant water use of ETo

Sometimes referred to as the plant coefficient, turf

coefficient, etc.

Crop Coefficient (Kc)

Kc varies depending on the type of plant/crop and growth stage

Kc may also be adjusted for such factors as:

Plant density

Desired plant quality

Level of stress

Site conditions

Micro-climates

etc.

Turf Coefficient, Tc

A factor used to relate ETo to the actual water use by a specific type of turf

Reflects the percentage of ETo that a specific turf type requires for maximum growth

Turf CoefficientsWarm Season 0.6Cool Season 0.8Sports Turf 0.8

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Allowable Stress

A factor reflecting an “acceptable” turf quality when water supply is reduced

Research shows that turf water supply can be reduced by 40% or more and still maintain an acceptable appearance

Adjustment Factor, Af

A modification to the crop coefficient

Used to reduce water application for allowable stress

Plant Quality Adjustment Factor, AfPlant Quality

Af

Maximum 1.0High 0.8Normal 0.6Low 0.5Minimum 0.4

WATER BALANCE CALCULATIONS

Water Requirements

WR = ETo x kc x AfWhere: WR = Water Requirement in inches ETo = Daily, Weekly Reference Evapotranspiration Kc = “Crop” Coefficient Af = Adjustment Factor for desired plant quality

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Equation for Calculating Water Requirements (WR)

WR = ETo x Kc x Af Example:

ETo = 1.59 inches (1st week of August 2007 in Dallas) Kc = 0.6 (warm season turf)Af = 0.5 (“Low plant quality” adjustment)

WR = 1.59 x 0.6 x 0.5 WR = 0.48 inches (1st week in Aug)

Effective Rainfall(Rainf )

The portion of rainfall that does not runoff, but becomes available for plant water use

A simplified method to account for the complex relationships between infiltration and runoff during rain events.

Includes the effects of slope, soil type, surface roughness (i.e., “depressional storage”) and other factors

Does not consider how wet or dry the soil is


Should be estimated based on actual site conditions When using long-term averages (monthly or yearly

average rainfall data), can assume Rainf is about 2/3 (67%) of normal rainfall

DO NOT use 2/3 for individual storm events or daily/weekly actual rainfall data


For landscape irrigation scheduling, some use a surface storage approach based on soil type (see SWAT handout to be discussed later in class)

Most landscape (home sites, parks, commercial properties) have very shallow root zone depths.

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For daily or weekly totals of actual irrigation and in absence of site specific data for homes and commercial sites:

0 – 0.10 inches Rainf = o0.10 – 1.0 inches Rainf = “full amount”1.0 – 2.0 Rainf = 0.67 or 67%> 2.0 inches Rainf = 0


Example: during the 1st week in Aug 2007, Dallas received 0.04 inches of rainfall

Since the total rainfall is less than 0.1 inches, we assume no rainfall fell

Water Requirements with Rainfall

Problem: during the 1st week in Aug 2007, Dallas received 0.04 inches of rainfall, what is our total WR (previous example)

Since the total rainfall is less than 0.1 inches, we use a RAINf = 0

WR = (ETo x Kc x Af ) – RAINf

WR = (0.48 inches) – 0 WR = 0.48 inches

Irrigation System Efficiency

Sometimes water requirements (WR) is adjusted for irrigation system efficiency Application efficiency (AE) spray/evaporative losses before the water reaches the

ground

Distribution efficiency (also known as the distribution uniformity -DU) how even the water is applied over the area

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Irrigation System Efficiency

Spray/evaporative losses typically range from 20-30% -(application efficiency of 70-80%)

Distribution efficiency (or distribution uniformity) varies widely (40-90+%)

My recommendations are: Generally, do not adjust WR for DU Use professional judgment for adjustments by the

application efficiency (AE) general not when producing irrigation schedules may be necessary in some water budgeting applications

IF Adjusting for Irrigation Efficiency

WRa = WR/AE Example: our irrigation system in Dallas has an

estimated AE of 80% WRa = 0.48/0.8 WRa = 0.6 inches for the first week in Aug

IRRIGATION SCHEDULING CALCULATIONS

Irrigation Frequency

The soil and root zone depth determines the frequency of irrigation

The concept is to: wait to irrigate until the plants have depleted the

water in the root zone Run the irrigation system just long enough to fill back up

the root zone

Usually, these calculations are done on a weekly basis

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The process is to: first calculate the Plant Available Water (PAW) Then calculate the Irrigation Frequency (I) and station

runtime (RT)

Plant Available Water

PAW = D x SWHC x MAD PAW Plant Available Water in root zone D Effective root zone depth SWHC Soil Water Holding Capacity MAD Managed Allowable Depletion

Definitions

Plant Available Water (PAW) The amount of water in the effective root zone available for

plant uptake Effective root zone (D)

The depth of the root zone that contains about 80% of the total root mass

Soil Water Holding Capacity (SWHC) The amount of water that can be held or stored in the soil

Managed Allowable depletion (MAD) How dry the soil is allowed to become between irrigations

(50% for most plants)

Effective Root Zone

The depth containing about 80% of the total root mass

Excludes “tap” and “feeder” roots Easily measured with a soil probe

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Plant Available Water

The amount of water that can be held or stored per foot of soil depth

Soil Water Holding Capacity

• The amount of water in the effective root zone “available” for plant uptake

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Soils

Typical Water Holding Capacity (inches of water per foot of soil)

Soil Texture

At Field Capacity

At Permanent Wilting Point

Soil Water Holding Capacity

Plant Available Water (@ MAD = 50%)

Sand 1.0-1.4 0.2-0.4 0.8-1.0 0.45

Sandy Loam 1.9-2.3 0.6-0.8 1.3-1.5 0.70

Loam 2.5-2.9 0.9-1.1 1.6-1.8 0.85

Silt Loam 2.7-3.1 1.0-1.2 1.7-1.9 0.90

Clay Loam 3.0-3.4 1.1-1.3 1.9-2.1 1.00

Clay 3.5-3.9 1.5-1.7 2.0-2.2 1.05

Plant Available Water for Three Root Zone Depths at 50% MAD

Soil Texture

Soil Water Holding Capacityinches of water per foot of soil

Available Water @ 50% MAD

(inches of water per foot of

soil)

Available Water @ 50% MAD

(inches of water per inch of

soil)

Total PlantAvailable Water (inches water)

2” Root Zone

4” Root Zone

6” Root Zone

Sand 0.90 0.45 0.038 0.08 0.15 0.23

Loam 1.70 0.85 0.71 0.14 0.28 0.48

Clay 2.10 1.05 0.088 0.18 0.35 0.53

Plant Available WaterDallas Example

Effective root zone depth is 5 inchesThe soil is a loam. Step One: check units for root depth and SWHC Convert units of rooting depth if necessary SWHC = 1.8 inches/ft Root zone depth = 5 inches = 5/12 ft = 0.42 ft

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Plant Available WaterDallas Example

PAW = D x SWHC x MAD D (Effective root zone depth) = 0.42 ft SWHC (Soil Water Holding Capacity) = 1.8 in/ft MAD (Managed Allowable Depletion) = 0.5

PAW = 0.42 x 1.8 x 0.5 PAW = 0.38 inches


Station Runtime

Precipitation Rate - measurement in inches per hour of how fast an irrigation system applies water to a landscape

Station – sprinkler group on a common valve that may be pat of an automated irrigation system that operates at the same time

Runtime – how long a station is operated during an irrigation event

Station Runtime

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Station RuntimeDallas Example

METHODS FOR CALCULATING ETO

Evapotranspiration, ET

Many methods have been proposed to calculate ET from weather data

Methods that use solar radiation have proven to be the most accurate

Solar Radiation

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Most Common Methods Used to Calculate ETo from Weather Data Penman-Montieth

Sensors: Solar Radiation, Temperature, Relative Humidity & Wind Speed

Hargreaves Temperature Based

Blainey-Criddle Temperature & Latitude

Penman-Monteith

The standardized Penman-Monteith Equation is considered the most accurate

Requires hourly or daily data on solar radiation, temperature, relative humidity and wind speed

Penman-Monteith Equation Penman-Monteith Equation

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Penman-Monteith Equation Hargreaves Equation

ET = 0.0023Ra (Tmean+17.8) (∆T).5

Ra = extraterrestrial (exoatmospheric) radiation for the location

Tmean = Average Daily Temperature oC ∆T = difference between maximum temperature and

minimum temperature [° C] (i.e. the difference between the maximum and minimum temperature for the given month, averaged over several years)

Ra Reference Table - FAO Hargreaves

One of the few valid temperature-based estimates of potential evaporation, though it was designed for estimating potential evapotransporation for agricultural systems.

Estimates potential ET in (mm d-1) Can be averaged to obtain a monthly value

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Blaney-Criddle Method (FAO)

ETo = p (0.46 Tmean +8) p = Mean Daily Percentage of Daylight Hours Tmean = Mean Daily Temperature ( oC )

Daylight hours used to approximate effects of solar radiation

Use latitude to look up typical daylight hours charts

Blaney-Criddle Method

Percentage of Annual Daytime Hours based on Latitude

Blaney-Criddle

One of several methods that is based on temperature

For the humid Southeast, the Thornthwaite method more commonly used

Blaney-Criddle (SCS Version)

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ET and SMART CONTROLLERS

ET (Smart) Controller

What is an ET (Smart) Controller? Irrigation Controller that determines runtime for

individual stations based on historic or real time ETo and additional site specific data.

IA’s Definition of Smart Controllers

Estimate or measure depletion of available plant soil moisture in order to operate an irrigation system, replenishing water as needed while minimizing excess water use.

A properly programmed smart controller requires initial site specific set-up and will make irrigation schedule adjustments, including run times and required cycles throughout the irrigation season without human intervention. (SWAT, 2007)

WaterSense: Weather Based Irrigation Controller An irrigation controller that creates or modifies

irrigation schedules based on ET principles by: Storing historical crop ET (ETc) data characteristics of

the site and modifying the data with an onsite sensor Using onsite weather sensors as a basis for calculating

real time ETc Using a central weather station as a basis for ETc

calculations and transmitting the data to individual users from remote sites; or

Using Onsite weather sensors

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Controller Programming

Irrigation Days Runtime Precipitation Rate

(Needed to determine Runtime)

Start Time(s)

Precipitation Rate Sprinkler Type Irrigation Days Soil Type Plant Type Slope Adjustment Factors Other Site Information

Typical (Conventional) Controller Smart Controller

Smart Controller Types

Type of Controller Description

Historic ETo Uses historical ET data stored in the controller

Sensor-Based Uses one or more sensors (usually temperature and/or solar radiation to adjust or to calculate ETo using an approximate method

ET Controller Real-Time ETo is transmitted to the controller daily. Alternatively, the runtimes are calculated centrally based on ETo and then transmitted to the controller

On-Site Weather Station(Central Control)

A controller or a computer which is connected to an on-site weather station for use in calculating ETo with a form of the Penman Equations

Controller Communication Methods

Type of Controller Communications Used

Historical ETc None

Sensor-Based None

ET Controller Pager / Cellular / Phone LineInternet / Ethernet / WIFI

Centralized Weather Station Direct ConnectShort Haul ModemsRadio

Climatologically Based Controllers

“ET Controller” Includes the following types of controllers:

Historical ETc

On-site sensor Centralized weather station as a basis for ETc and

transmit the data to homeowner from remote sites

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CONTROLLER PRODUCTS

ET Controller Products*Controller Communication Sensors

Toro Intelli-Sense Paging Rain

Weathermatic SmartLine None Temperature & Rain, Including Freeze

Rainbird ET Manager Paging Rain Bucket

ET Water Paging Rain

Accurate WeatherSet None Solar Radiation & Rain

Rainbird ESP-SMT None Rain Bucket, Temperature

Hunter ET System None Solar Radiation, Temperature, Relative Humidity, Rain Bucket & Wind

Hunter Solar Sync None Solar Radiation, Temperature & Rain/Freeze

Irritrol Climate Logic None Temperature, Rain

*Major Products Marketed In Texas ~2010-2015

ET Controller Products*

Controller Communication Sensors

ET Water Paging Rain

Weathermatic SmartLine None/Cellular Temperature & Rain, Including Freeze

Hunter Solar Sync None Solar Radiation, Temperature & Rain/Freeze

Irritrol Climate Logic None Temperature, Rain

Toro Evolution/ET Weather None Temperature, Rain

Hydropoint WeatherTRAKLC+

Cellular/Internet Rain

Calsense CS3000 Cellular, Internet, Radio ET Gage, Tipping Bucket, Wind

*Major Products Marketed In Texas

WIFI Based ET Controller Products

Controller Communication Sensors

Rachio* WiFi Rain Shutoff

Rain Machine* WiFi Rain Shutoff

Hunter-Hydrawise* WiFi Rain Shutoff

Skydrop* WiFi Rain Shutoff

Rainbird LNK WiFi Module* WiFi Rain Shutoff

Cyber Rain WiFi Rain Shutoff

Blue Spray WiFi Rain Shutoff

Orbit Bhyve* WiFI Rain Shutoff

Sprinkl Conserve* WIFI Rain Shutoff

*Products Seen Marketed In Texas

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SOIL MOISTURE SENSORS

Soil Moisture Sensor-Based Controllers

Soil Moisture Sensor-Based Controllers function in 2 ways: Provide Closed Loop Feedback Similar to a home AC thermostatMost Common Method

Give Feedback to Weather-Based System Controller

Soil Moisture Based Systems

2 Major Categories of Soil Moisture Sensors Soil Water Content (Volumetric) A sensor that measures volumetric content of water in a

volume of soil, %

Soil Water Tension A Sensor that measures the matric potential of water held in

the soil Sometimes referred to as Soil Water Potential or Soil Matric

Potential The force with which water is held by the soil matrix (soil particles

and pore space)

Soil Moisture Sensor Operation

Typically take the place of Rain Sensors on Controllers Use of Sensor Ports or in Series with Common Wire/Port

Operate by opening the circuit until the soil moisture content reaches a programmed deficit at which point the circuit is closed and the controller can begin its scheduled irrigation until the circuit is opened again.

Most require setting irrigation runtime, frequency, ect.

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Using Soil Moisture Sensors

Installing Multiple Sensors at Multiple Depths improves accuracy.

Depth of Placement should be representative of the effective root zone.

Difficult to obtain accurate readings in the top 2 inches of soil

Can be expensive and challenging to use in large or elaborate landscapes Finding a representative install location in the

landscape Often different plant materials will require their own

sensors Changes in Soil type Different root zone depth

Using Soil Moisture Sensors

Types of Soil Moisture Sensors

Granular Matrix Sensor Gypsum Blocks Tensiometer Capacitance Probe Time Domain Transmissometry, TDT Time Domain Reflectometry, TDR Frequency Domain Reflectrometry, FDR

Soil Moisture Sensor Technologies

Commonly Used in Landscape Irrigation Granular Matrix Capacitance TDR/TDT

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Granular Matrix Sensors

Contain a set of electrodes in a granular matrix material (combination of quartz & gypsum)

Changes in soil electrical conductivity (resistance) are correlate to soil matric potential i.e. the suction head as the soil wets and dries

Reading Water Potential

Available Water varies inthe soil based on the matricpotential (soil suction)

Graph shows typical relationship of soil suction toavailable water depletion

Most SMS products will simplify the range a sensor reads for irrigation mgmt. Such as 1-10 threshold scale

Capacitance Sensors

Capacitance: the ability to hold an electric charge –of the surrounding soil in order to obtain the dielectric permittivity of the soil

Sensor determines the dielectric constant (Ka) by measuring the charge time of the capacitor, using the soil as the dielectric medium Since Ka of Air = 1 and Water = 80, the capacitor

uses a linear function to determine the dielectric permittivity of the soil

TDR/TDT

TDR & TDT are similar in operation Operate using an electromagnetic wave passed

through the soil via parallel rods from a transmission line. With TDR, the speed and strength of the wave after it

travels from one rod to another is directly related to the dielectric properties (soil moisture content) of the soil

With TDT, the rod is connected to the electrical source at the beginning and end of the rod to measure the travel time of the wave between rods

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Landscape Sensor Overview

SensorSensor Type

Sensor Reading

Costs (Comparably)

Sensitive to

Salinity

Affected by Temperature

Sensor Response

Granular Matrix

Matric Potential

cBars LowGenerally

NoNo Slow

CapacitanceVolumetric

Water Content

% Moderate Yes NoModerate-

High

TDR/TDTVolumetric

Water Content

%Moderate-

HighGenerally

NoNo

Moderate-High

Sensor Calibration

All sensors require calibration based on sensor type Most manufacturers have simplified calibration for

sensors used with landscape irrigation controllersCalibration by: Soil Type, User Defined Threshold, or Timed Calibration by irrigating the landscape to field

capacity/saturation

Major Soil Moisture Products

Manufacturer Model Sensor Category Sensor Type

Baseline, Inc WaterTec S100 Soil Water Content TDT

The IrrometerCompany

WaterSwitch/Watermark Battery

Soil Water Potential Granular Matrix

Toro Precision Soil Sensor Soil Water Content Capacitance

Dynamax Moisture Clik Soil Water Content Capacitance

Acclima SC Series Soil Water Content TDT

Rainbird SMRT-Y SMS Soil Water Content TDT

Hunter Soil Click Soil Water Potential Granular Matrix

UgMO UgMO Soil Sensor Soil Water Content TDT

Toro (Sentinel) Pro Series Soil Water Content Capacitance

Calsense CS-2W-MOIST Soil Water Content Capacitance

Common Landscape Irrigation Senors

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Rain Sensors

Also called Rain Shut-off Device or Rain Switch Designed to interrupt a scheduled cycle of an automatic

irrigation controller (timer device) when a certain amount of rainfall has occurred.

3 Models: Utilize a receptacle to weigh the amount of water

Tipping Buckets Utilize a receptacle to detect the water level Use of a hygroscopic expanding material to sense the

amount of rainfall Most widely used method

Rain Sensors

Rain Sensor Tipping Bucket

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Rain Sensors

Delay irrigation until the sensor “drys out” Some controllers can have a programmed timed delay

(such as 48 hours) once a sensor is triggered to avoid the sensor re-activating too soon Ex. Weathermatic & Hunter

Can contain an internal tipping bucket that measures the amount of rainfall to adjust the water balance Ex. Irrisoft, Hunter & Rainbird Sensor

Summary of Rain Sensor Project

How do Rain Sensors Operate? How long will they prevent operation of the controller?

How does Rain Sensor Performance effect weekly irrigation scheduling? Should irrigation professionals create irrigation

schedules that assume (average) rainfall?

Questions about Rain Sensors Rain Sensor Study

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Hunter Mini-Click RFC* RainClick

Orbit 57069N Weathermatic 420GLS Toro TRS Rainbird

RSD-BEX WR2-RFC*

Rain Sensors

Sensors installed October 2018 Datalogger recorded timestamp when sensor

triggered and “resumed irrigation” Sensors installed for minimum

threshold, (1/8”) To Date (9/30/19)

43 Rain Sensor Triggering Events Total Rainfall: 47.79 inches

Initial Study Period

48 Hours, 8 events

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What effects Sensors “off-time”?? Total Rainfall Rainfall Period Time from first rain to last rain recorded

Total Rain Time Data logged hours that had rainfall (Actual Rain Time)

Rainfall Intensity Average Total Rainfall / Total Rain Time

Analysis Breakdown

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On Average, “Actual Rainfall Time” had the strongest correlation to sensor triggered period R2 = 0.7352

On Average, Total Rainfall had the weakest correlation to sensor triggered period R2 = 0.3853

Average Off Time Summary

10/19/2018 - 3/17/2020 60 Rainfall Events (+17 events) 57.73 inches of Rainfall ( +9.94” Rainfall )

Project Update

R2 Analysis Initial Updated

Total Rainfall, Inches 0.385 0.211

Rainfall Period, Hours 0.683 0.743

Actual Rainfall Time 0.735 0.711

Rainfall Intensity 0.464 0.193

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24 Hours 1 Inch Rainfall

The amount of rain has little effect on duration a rain sensor is active

Analysis suggest irrigation professionals (and homeowners) should anticipate the effects of rainfall when programming controllers Maximize the use of controllers with programmable

sensor delay

There is a need for better rain sensor technology that not only detects rain but also takes credit for rain

Rain Sensor Summary

Controller Rain Gage-Sensors that have been discontinued by Manufacturers

REVIEW & DISCUSSION OF PERFORMANCE AND PROTOCOLS

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SWAT Program

Smart Water Application Technology Is a national partnership initiative of water

purveyors and irrigation industry representatives created to promote landscape water use efficiency through the application of state-of-the-art irrigation technologies.

A program of the Irrigation Association, IA

SWAT ET Controller Testing

Defines 2 Criteria: How the test is to be conducted and what data will be

recorded SWAT Testing Protocol, does not result in a Pass or Fail

Defines performance limits that must be met to quantify the capabilities of the product. Performance standards are established by related

considerations and organization

SWAT Testing

Measures Irrigation Adequacy & Irrigation Excess Adequacy

Reflects how well irrigation met the consumptive use of the vegetation

Did the controller wait too long to irrigate?

Excess Water applied in excess of consumptive use of the

vegetation Measure controllers cycle/soak ability plus surpluses, a

function of irrigation scheduling efficiency

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SWAT Testing Results

Controller Irrigation Adequacy,Average of 6 Test Zones

Irrigation Excess,Average of 6 Test Zones

Hunter ET System 100% .5%

Rain Bird ET Manager 100% 0%

Toro Intelli-Sense 100% 0%

Irritrol Smart Dial 100% 0%

Weathermatic 100% .4%

WeatherTRAK* 100% 0%

Hunter Solar Sync 100% 7.55%

Rainbird ESP-SMT 100% 1.5%

ET Water 100% 1.5%

* WeatherTRAK ET Everywhere Network includes Toro Intelli-Sense, Irritrol Smart Dial & Hydropoint Systems

WaterSense Certification Program

WaterSense is a program of the EPA designed to encourage water efficiency in the US through the use of a special label on professional programs and consumer products

November 2011, EPA released testing specifications for Weather Based Irrigation Controllers Adopted a Modified SWAT Protocol

WaterSense Certification Program

To Receive the WaterSense Label, a controller must: Irrigation Adequacy equal or greater than 80%, For each zone

Irrigation Excess less than or equal to 10% For Each zone Average for all six zones less than or equal to 5%

Must be tested for 30 daysMust have 4 rainfall days of 0.10” or moreMinimum 2.50” Total ETo

EPA WaterSense Program

WaterSense labeled products are required or highly recommended for use in some sites

WaterSense label does not guarantee the device will save water but rather meets minimum criteria in laboratory testing

In 2012 EPA started labeling Smart Irrigation Controller-Weather Based Controllers (ET Based) Currently 788 weather based irrigation controller

product configurations carry the WaterSense label

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Labeled Weather-Based Irrigation Controllers27 Manufacturers have 788 Weather Based Irrigation Controllers Labeled*

Hydropoint Hydro-Rain Irritrol Netafim USA Netro Nxeco Orbit Rachio Rainbird RainMachine

Aeon Marix Baseline Blossom Calsense Cyber-Rain DIG Corp ET Water Green IQ H2O Pro Hunter

RainMaster Scotts Smart Rain Spruce Toro Tucor Weathermatic

*As of 6/1/2020

HISTORY OF TAMU TESTING OF “SMART CONTROLLERS”

https://itc.tamu.edu/projects/smart-controller-evaluation/

Testing History

In 2005, San Antonio Water System & BexarMetWater District initiated a pilot program to provide smart controllers to high water use customers with landscape irrigation systems. 4 different brand controllers to 19 customers

Agrilife Extension conducted an end user satisfaction survey of participants in 2006 & 2007 Results shows many users experience problems Communication difficulties Software failure Controller failed to operate on a particular day Inability/Difficulty in adjusting for operation under water restrictions

Testing History

Due to problems experienced during the SAWS/BexarMet program a testing facility was developed in 2008 at TAMU to evaluate controller performance

Formalized Program evaluated most commonly marketed controllers in Texas Controllers programmed side by side for similar

landscape conditions Analyzed for their seasonal and annual performance

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Testing History

Initial TAMU Testing, 2009-2013 showed inconsistent performance of products Controller would do well for a single seasonal period

then perform poorly the following season and apply more irrigation than needed

Early indications showed the inconsistent performance could be attributed to: Poor or inaccurate methods for determining local ET Insufficient accounting for rainfall throughout the year

In 2017 WiFi based controllers were incorporated into the testing program

Year 2013 Testing Analysis

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Comparison Points

Irrigation Depth, calculated from total runtime and precipitation rate was compared against Irrigation Requirements derived from a daily water balance

ETo ETc (ETo x Kc)

2013 Testing Period

Controllers operated for 196 days Seasonal Breakdowns:

Spring: March 4-May 11 (77 days) Summer: July 29-September (49 days) Fall: September 16-December 1 (70 days)

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2013 Testing Period Climate

Spring ETo = 14.14 inches Rain = 8.58 inches

Summer ETo = 13.20 inches Rain = 0.86 inches

Fall ETo = 10.06 inches Rain = 18.71 inches

Average Conditions

Wet Conditions

Dry Conditions

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Irrigation Adequacy

Adequacy Analysis

Extreme Upper Limit ETo x Kc

Adequacy Upper Limit ETo x Kc x Af

Adequacy Lower Limit ETo x Kc – Net (80%) Rainfall

Extreme Lower Limit ETo x Kc – Total Rainfall

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Adequacy Statistical Analysis

Average Conditions

Dry-Drought Conditions

Wet-Rainy Conditions

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Conclusions

Over irrigation continues to be a problem Influencing Factors: Improper ETo calculation/acquisition

Texas has no state funded ET Network only scattered research and private ET Stations

Insufficient accounting for rainfall? Only 3 controllers come standard with a tipping bucket type

rain gauge

Conclusions

Controllers are not allowed access to the weather station used to calculate reference ETo to simulate “real world” operations

6 Controller evaluated currently have the EPA WaterSense Label

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5 Year Summary Analysis

200-2014

5 Year Analysis

Controller performance was quantified from 2000-2014

Performance was normalized to evaluate amount of Irrigation applied vs required irrigation

A +/- 20% of required irrigation was created since controllers use various methods for calculation of ETo

5 Year Analysis

Controller performance was normalized to evaluate amount of irrigation applied vs required irrigation

Generally controllers appeared consistent Observed controllers had the greatest difficulty

with deeper root zone (greater plant available water) Station 6 stands out… Clay Soil, 20 inch deep roots Largest Plant Available Water Storage

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5 Year Analysis

However, 3 controllers showed no signs of excessive irrigating Controller B Controller C Controller E

Question: What makes these controllers different?

Controller Comparison

These are the only 3 controller that have “tipping bucket” style rain sensors!

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Summary

Over irrigation continues to be a problem Influencing Factors: Improper ETo calculation/acquisition

Texas has no state funded ET Network only scattered research and private ET Stations

Insufficient accounting for rainfall Only 3 controllers come standard with a tipping bucket type

rain gauge

Results suggest that controllers which measure rainfall onsite have the better long term performance and water conservation potential

2017 to Present

WiFi based Controller Testing

Smart Controller Testing

Due to increasing popularity-deployment, the testing program was started in summer 2017 to evaluate Internet Based “WIFI” Irrigation Controllers

Controllers were selected based on: Irrigator Input-Use Costs <$300 No Subscription Costs WaterSense Labeled

Wifi Controller Evaluation

Currently Evaluating 6 WiFi based Irrigation Controllers Controllers:

Rainbird LNK-Wifi RainMachine Skydrop* Hunter Hydrawise-HC Orbit B-hyve Rachio Sprinkl**

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Texas Regulations, Chapter344.62:

“other technology” as controllers monitor climate conditions, including rain, state regulations allow no sensor to be installed However cities may still require

No sensor was connected to any controller during testing

WiFi Controller Evaluation-Setup

Table 1. The virtual landscapes as defined by Representative Texas Landscapes

Station 1 Station 2 Station 3 Station 4 Station 5 Station 6

Plant Type Flowers Turf Turf GroundcoverSmallShrubs

LargeShrubs

PlantCoefficient (Kc)

.8 .6 .6 .5 .5 .3

Root ZoneDepth (in) 3 4 4 6 12 20

Soil Type Sand Loam Clay Sand Loam Clay

MAD (%) 50 50 50 50 50 50

AdjustmentFactor (Af) 1 .8 .6 .5 .7 .5

PrecipitationRate (in/hr) .2 .85 1.4 .5 .35 1.25

Slope (%) 0‐1 0‐1 0‐1 0‐1 0‐1 0‐1

Landscape Description - Virtual


Controller Programmable Parameters

Controller Runtime ‐Frequency

Sprinkler/ PR (in/hr)

Plant Type, kc

Root Zone

Soil Type

Sun/ Shade MAD Other

Advanced*

Hunter X

Rainbird X

Rachio X X X X X X X

Skydrop X X X X

RainMachine X X

Orbit X X XX X X X X XX

*Other Advanced includes settings such as Available Water, Field Capacity, Wilting Point, Efficiency, etc.


Controller Programmable Parameters

Controller Runtime ‐Frequency

Sprinkler/ PR (in/hr)

Plant Type, kc

Root Zone

Soil Type

Sun/ Shade MAD Other

Advanced*

Hunter X

Rainbird X

Rachio X X X X X X X

Skydrop X X X X

RainMachine X X

Orbit X X XX X X X X XX

Sprinkl X X X X X

*Other Advanced includes settings such as Available Water, Field Capacity, Wilting Point, Efficiency, etc.

Runtime Required for Majority of Controllers

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Table 2. Texas Landscapes, Peak Watering Schedule for College StationStation 1 Station 2 Station 3 Station 4 Station 5 Station 6

Plant Type Flowers Turf Turf GroundcoverSmallShrubs

LargeShrubs

Peak Month (July) ETo, in

7.10 7.10 7.10 7.10 7.10 7.10

Weekly ETc, in 1.28 .77 .58 .40 .56 .25

Plant Available Water, in

.23 .57 .70 .45 1.70 3.50

# Of Irrigation Days Per Week

7 2 2 2 1 1

Days Every Day Mon/Thurs Mon/Thurs Mon/Thurs Mon Mon

Total Runtime Per Day

54 28 12 24 96 12

Cycles Per Day 2 2 2 2 2 2

Runtime Per Cycle

27 24 6 12 48 6

Schedule Programming

Controller Manufacturer ModelA Hunter Hydrawise‐HC

B Rainbird ESP‐TM2 LNK Wifi

C Rachio Generation 2

D Skydrop Halo Controller

E RainMachine Touch HDF Orbit B‐HYVEG Sprinkl Control

Controller Models

Seasonal Performance of Controllers

Daily Runtimes from each controller were charted to evaluate how each controller scheduled irrigation throughout the testing period.

Stations 1, 3, & 6 were selected based on their variability in plant type, plant available water/root zone depth Calculated Irrigation Frequency

Seasonal Performance

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Flowers

Turfgrass

Large Shrubs

Flowers

Turfgrass

Large Shrubs

Flowers

Turfgrass

Large Shrubs

Flowers

Turfgrass

Large Shrubs

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Flowers

Turfgrass

Large Shrubs

Flowers

Turfgrass

Large Shrubs

Analysis Breakdown…

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Controller Station 1 Station 2 Station 3 Station 4 Station 5 Station 6Rainbird 3.10 3.29 1.73 1.07 1.24 0.75Hydrawise 4.95 1.84 1.96 1.00 1.12 0.50Rachio 2.52 0.66 0.50 0.35 0.00 0.00Rainmachine 0.45 0.22 0.16 0.09 0.53 0.23Orbit 1.85 1.02 0.28 0.94 0.10 0.00Sprinkl 4.68 2.78 1.96 1.40 0.70 0.50TexasET 0.00 0.00 0.00 0.00 0.00 0.00Moisture Balance 3.22 1.19 0.41 0.24 0.00 0.00ETo 5.15 5.15 5.15 5.15 5.15 5.15Rainfall 6.44 6.44 6.44 6.44 6.44 6.44

WiFi Analysis January‐February 2019 (Winter)


WiFi Analysis March‐April 2019 (Spring)

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WiFi Analysis May‐September 2019 (Summer)Controller Station 1 Station 2 Station 3 Station 4 Station 5 Station 6Rainbird 22.39 23.60 10.45 7.30 9.63 4.92Hydrawise 26.26 10.77 10.62 6.98 8.96 3.99Rachio 30.49 19.35 13.75 9.91 9.70 4.28Rainmachine 8.43 5.75 4.03 2.82 4.54 2.01Orbit 11.77 45.84 38.92 13.78 9.62 2.40Sprinkl 20.54 12.30 8.68 6.20 8.96 4.00TexasET 17.56 9.48 6.54 3.99 6.31 1.97Moisture Balance 22.34 12.40 8.28 5.29 6.23 0.00ETo 31.98 31.98 31.98 31.98 31.98 31.98Rainfall 16.29 16.29 16.29 16.29 16.29 16.29

WiFi Analysis May‐September 2019 (Summer)

Zone 6 Summer Analysis

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WiFi Analysis October‐November 2019 (Fall)


WiFi Analysis December 2019 (Winter)

Summary

Performance Varied for each controller No 2 controller are alike Some controllers had “excessive” zones during some

seasons

All controller showed some type of change in seasonal scheduling

All controller showed a response to rainfall events Future evaluation could include the addition of rain

shut off sensors

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Non-WiFi ET Controller Update June 2019 New “Stand Alone” ET Controllers were installed Toro Evolution w/Weather Sensor Hunter Solar Sync Weathermatic SL1600

Existing Controllers Rainbird ESP-SMT Irritrol Climate Logic

ET Controller Update

Controller Station 1 Station 2 Station 3 Station 4 Station 5 Station 6Rainbird LNK 14.24 15.34 6.63 4.72 6.14 3.21Hydrawise 16.19 7.01 6.71 4.59 6.16 2.74Rachio 15.74 11.25 7.90 5.82 5.23 2.28Rainmachine 6.38 4.42 3.10 2.20 3.39 1.50Orbit 8.28 34.31 28.28 10.40 7.95 1.92Sprinkl 15.14 9.52 6.72 4.80 7.28 3.25Toro 11.67 7.60 5.47 3.91 5.75 2.56Hunter SlrSnc 9.28 5.44 3.78 2.82 4.78 1.92RainBird ESP SMT 21.54 13.21 9.98 7.66 9.92 3.50Weathermatic 11.88 6.12 4.59 1.55 3.96 1.61Irritrol 3.87 2.50 1.79 1.32 1.85 0.80TexasET 14.23 7.74 5.32 3.24 5.13 1.58Moisture Balance 14.77 8.22 6.04 3.54 5.32 0.00EToRainfall

WiFi Analysis July‐September 2019 (Summer)

20.513.62

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Controller Station 1 Station 2 Station 3 Station 4 Station 5 Station 6Rainbird LNK 4.55 4.53 2.33 1.47 1.85 1.00Hydrawise 6.47 2.18 2.24 1.20 1.12 0.50Rachio 4.96 2.47 1.83 1.25 0.53 0.24Rainmachine 0.96 0.56 0.39 0.26 0.50 0.22Orbit 4.09 6.78 5.60 4.03 4.09 0.00Sprinkl 7.03 4.36 3.08 2.04 2.80 1.25Toro 2.57 1.53 6.23 9.40 0.89 0.30Hunter SlrSnc 2.73 1.79 1.05 0.85 1.27 0.44RainBird ESP SMT 7.10 3.42 2.36 1.71 1.03 0.00Weathermatic 2.41 1.14 0.85 0.47 0.45 0.19Irritrol 2.46 1.70 1.18 0.89 1.19 0.50TexasET 2.07 0.79 0.42 0.18 0.39 0.00Moisture Balance 4.79 1.63 0.77 0.45 0.00 0.00EToRainfall 4.21

WiFi Analysis October‐November 2019 (Fall)

7.25

Controller Station 1 Station 2 Station 3 Station 4 Station 5 Station 6Rainbird LNK 1.69 1.98 0.98 0.62 1.00 0.54Hydrawise 2.52 1.39 1.40 0.70 1.12 0.50Rachio 1.99 0.79 0.56 0.39 0.54 0.24Rainmachine 0.59 0.23 0.16 0.11 0.36 0.16Orbit 2.04 0.00 0.00 2.70 3.78 0.00Sprinkl 2.64 1.59 1.12 0.80 1.12 0.50Toro 0.68 0.45 12.54 1.58 0.26 0.12Hunter SlrSnc 0.87 0.43 0.23 0.20 0.27 0.08RainBird ESP SMT 2.87 1.48 1.43 0.79 1.07 0.00Weathermatic 1.36 0.33 0.25 0.17 0.24 0.00Irritrol 0.64 2.72 4.48 1.60 1.12 3.98TexasET 0.00 0.00 0.00 0.00 0.00 0.00Moisture Balance 1.91 0.92 0.72 0.47 0.00 0.00EToRainfall

WiFi Analysis December 2019 (Winter)

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Soil Moisture Sensor Based Irrigation Controllers Testing

In the Pipeline….

May 2013 WaterSense Released a Notice of Intent to Develop a Draft Specification for Soil Moisture based Control Technologies Working with the American Society of Agricultural and

Biological Engineers (ASABE) - Standard S633 Committee: Testing of Soil Moisture Sensor for Landscape Irrigation

EPA closed public comment February 2020 Focus more on precision than accuracy

EPA Final Specification expected Late 2020

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Soil moisture sensors are an alternative to ET Controllers

Most controllers are compatible with soil moisture sensors Can connect in place of a rain sensor

Help prevent over-irrigating by only allowing the controller to turn on once the soil has dried out

2 Basic Types of Sensors: Water Potential Volumetric Water Content

Testing Soil Moisture Sensors Soil Moisture Sensors

Soil Water Potential

Volumetric Water Content

Protocol Development

Understanding SMS Operation & Testing

Testing of Soil Moisture Sensors

Development of a testing protocol evaluatedmultiple means of testing sensors that evaluated soilwater potential and soil water content type sensors similarly

Test Methods A wetting to dry down approach Requires extended time period per cycle (Weeks)

A fixed calculated moisture content approach Provides results within days

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Testing of Soil Moisture Sensors Testing of Soil Moisture Sensors

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CONTROLLER DEMONSTRATIONS

HANDS ON PRACTICE WITH CONTROLLERS SURVEY

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weather & sensor based irrigation controllers

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