Developing Strategies for Year-Round Spray Irrigation Of

Developing Strategies For Year-Round

Spray Irrigation of Wastewater

Effluent in Ohio

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Kpoti Mawutodzi Gunn, B.S.

Graduate Program in Food, Agricultural and Biological Engineering

The Ohio State University

2010

Master's Examination Committee:

Karen M. Mancl, Advisor

Norman R. Fausey

John J. Lenhart

Mike A. Rowan

Kpoti Mawutodzi Gunn

2010

Abstract

In the U.S. the national goal is to eliminate the discharge of pollutants into waterways.

Onsite soil based wastewater treatment and irrigation of treated wastewater are two ways to meet this goal. In Ohio, approximately 49 % of the total land area is too shallow, relative to limiting conditions, to provide complete sewage treatment, but is deep enough to accept reclaimed wastewater by spray irrigation. However, year-round dispersal of wastewater effluent is hindered by winter sub-freezing air temperatures that cause spray irrigation system to freeze and the accumulation of snow that may bury the irrigation system.

To investigate the freezing problem, three different models of revolving rotor sprinklers

(Rainbird 5000-s, Toro s-800 and Hunter-PGC) and one model of revolving impact sprinkler (Rainbird 2045-pj) were tested at -25°C in a laboratory setting, with water at

24°C. The sprinklers were drained at the end of each irrigation event. The heads sprayed water properly, but they exhibited rotational delay ranging from 4 to 10 minutes. Mann-

Whitney tests showed that the Rainbird sprinklers had shorter rotational delay. The rotational delay was not eliminated by the irrigation of water at temperature varying from

28 to 40°C. A Mann-Whitney test showed that the rotational delay of Rainbird 5000-s decreased by one minute when the temperature of water changed from 28 to 32°C. The rotational delay may cause overall low distribution uniformity.

The snow depth was investigated where Hi-pop up irrigation sprinklers may be used all year-round to disperse reclaimed wastewater in Ohio. Maximum depths of snow cover over thirty to forty eight years at two hundred and fifty three stations in Ohio and in adjacent states were used in a geostatistical interpolation operation. A triangulated irregular network interpolation method was used to predict and represent the maximum depth of the snow cover in the different regions of the state. The study revealed that snow cover may reach a minimum of 12 in. in all counties in Ohio during the months of

January and February, at least once every thirty to fifty years.

The drainage of sprinkler and riser at the end of irrigation events is a potential means to prevent systems from freezing; but it does not assure that the system will provide proper distribution uniformity, which may lead to reclaimed wastewater pounding and runoff.

Using Hi-pop up sprinklers for year-round application of reclaimed wastewater poses the risks of the system being covered by snow. An alternative would be to use shrub rotor irrigation sprinklers mounted on risers.

iii

To my family.

Acknowledgements

I hereby want to reflect on the contributions provided by:

- Dr. K. Mancl for her patience and resourcefulness

- C. Gecik and C. Cooper for the technical support

- B. Berghauer and the T Carmichael Inc (Powell, OH) team for helping with the field

experimentation setup.

I also express my gratitude to everyone that contributed directly or indirectly to the success of this work.

Vita

1999...... B.S. Agronomy, University of Lome

2005...... B.S. Environmental Science, Bellevue University

2007 to present ...... Graduate Teaching Associate, Department of Food, Agricultural and Biological Engineering, The Ohio State University

Fields of Study

Major Field: Food, Agricultural and Biological Engineering

Other: Water Management, Environmental Science

Table of Contents

Abstract ...... ii Acknowledgements ...... v Vita ...... vi List of Tables ...... x List of Figures ...... xi List of Abbreviations ...... xiii Chapter 1: Introduction and Research Objectives ...... 1 1.1. Introduction...... 1 1.2. Research Objectives...... 3 1.3. Importance of Wastewater Treatment and Reuse through Irrigation in Ohio...... 3 Chapter 2: Literature Review ...... 8 2.1. Irrigation in Cold Conditions: Performance of Irrigation Equipment at Low Temperature...... 8 2.2. Improvements to Winter Irrigation Systems...... 10 2.3. Environmental Impacts of Winter Irrigation Systems Design...... 12 Chapter 3: Methods ...... 15 3.1. Irrigation in Cold Conditions...... 15 3.1.1. Freezer Setup...... 15 3.1.2. Materials...... 17 3.1.3. Experimental Designs...... 19 3.1.3.1. Drainage Tests of Different Irrigation Sprinkler Models...... 19

vii

3.1.3.2. Drainage Test of Different Heated Irrigation Sprinkler Models...... 21 3.1.3.3. Evaluation of the Performance of the Rainbird Sprinklers with Water at Warmer Temperatures...... 21 3.1.4. Data Analysis...... 22 3.2. Depth of Snowpack in Ohio...... 23 3.2.1. Data Collection and Management...... 23 3.2.2. Data Treatment...... 24 3.2.2.1. Data Representation...... 24 3.2.2.2. Interpolation Methods...... 24 3.2.2.3. The Kriging Interpolation...... 24 3.2.2.4. Application of the Kriging Method...... 26 3.2.2.5. Triangulated Irregular Network (TIN) Interpolation...... 30 3.2.3. Estimated Values Generation and Map Design...... 30 Chapter 4: Results ...... 31 4.1. Irrigation in Cold Conditions...... 31 4.1.1. Impact of Water Application on Ambient Freezer Temperature...... 31 4.1.2. Head Drainage...... 33 4.1.3. Water Flow rate...... 33 4.1.4. Rotational Delay...... 35 4.1.5. Rotational Velocity...... 39 4.2. Evaluation of the Performance of the Rainbird Sprinklers Spraying Warmer Water...... 41 4.2.1. Performance of the Sprinkler Heads...... 41 4.2.2. Temperature in Freezer...... 44 4.3. Accumulation of Snow in Ohio...... 45 4.3.1. Weather Station Distribution...... 45 4.3.2. Data Exploration...... 47 4.3.2.1. Normal Distribution of the Data...... 47 4.3.2.2. Trend Surface Analysis...... 50 4.3.2.3. Spatial Autocorrelation...... 52

viii

4.3.3. Kriging Interpolation...... 55 4.3.3.1. Interpolation without neither Transformation nor Trend Removal...... 55 4.3.3.2. Interpolation with Trend Removal...... 60 4.3.3.3. Interpolation after Data Transformation...... 64 4.3.4. TIN Interpolation...... 68 Chapter 5: Discussion ...... 71 5.1. Simulation of Ohio Winter Weather Conditions ...... 71 5.2. Effects of Spray Irrigation on Ambient Temperature...... 72 5.3. Impacts of Cold Temperatures on the Water Flow Rate...... 73 5.4. Impact of Cold Temperatures on the Rotational Delay of the Heads...... 75 5.5. Impact of Water Temperature on the Rotational Delay of the Rainbird Spray Head Models...... 79 5.6. Comparison of Irrigation System Winterization...... 80 5.7. Snow Depth Interpolation Methods Comparison...... 81 5.8. Maximum Snow Depth in Ohio...... 85 5.9. Limitations...... 90 Chapter 6: Conclusion and Recommendations for Future Work ...... 91 6.1. Conclusion...... 91 6.2. Recommendations for Future Work...... 93 Bibliography ...... 95 Appendix A: Experimental Data ...... 100 Appendix B: Collected Snow Depth Data ...... 103 Appendix C: Field Automatic Sprinkler Drainage ...... 111 Appendix D: Experimental Water Mixing Device ...... 112

List of Tables

Table 1: List of irrigation sprinklers tested with their nozzles and corresponding water pressures...... 20 Table 2: Default settings for ordinary Kriging interpolation on original values...... 29 Table 3: Kriging interpolation on original values with trend removal...... 29 Table 4: Ambient freezer temperature variations during 10 minute water spray with unheated sprinkler heads in freezer...... 32 Table 5: Ambient freezer temperature variations during 10 minute water spray with heated sprinkler heads in freezer...... 33 Table 6: Water flow rate (gpm) during 10 minute spray testing of sprinkler heads at -25°C...... 35 Table 7: Individual rotational delay (minutes) for the unheated heads tested at -25°C...... 38 Table 8: Results of a Kruskal-Wallis test on the rotational delay of the spray heads tested at -25°C. (5% significance level)...... 39 Table 9: Rotational velocity of the sprinkler heads tested at -25°C...... 40 Table 10: Classification of the rotational delay of the sprinkler heads tested with water at different temperatures at -25°C...... 43 Table 11: Temperature changes in the freezer for 10 minutes water at different temperature spray test...... 44 Table 12: Ordinary Kriging cross validation results...... 59 Table 13: Kriging interpolation with trends removal cross validation results...... 63 Table 14: Kriging interpolation over transformed data, with trends removal cross validation results...... 67 Table 15: Cost-benefit comparison between the head drainage and the heat tape methods for year-round irrigation system winterization...... 81 Table 16: Comparison between interpolation methods...... 84 Table 17: Monthly maximum snow depth for Ohio counties (inches) according to 48 years data record...... 86 Table 18: Stopping positions data for the Rainbird heads after sixty 2-minutes application tests...... 100 Table 19: Performance of the Rainbird heads spraying water at different temperatures under freezing conditions...... 102 Table 20: Snow depth data at the weather stations...... 103

List of Figures

Figure 1: Land repartition for soil treatment of wastewater in Ohio...... 7 Figure 2: Sprinkler head/riser set sitting on the bottom panel of the freezer...... 16 Figure 3: Drain on the distribution line at the bottom of the riser...... 17 Figure 4: Foam panel installed in the freezer...... 17 Figure 5: Rotational velocity monitoring device...... 19 Figure 6: Graph of rotational delay of the different heads tested at -25 °C...... 38 Figure 7: Mean rotational delay of the Rainbird models 5000s and 2045-pj tested at -25°C with water at different temperatures...... 43 Figure 8: Location of weather stations collecting snow depth data in Ohio and adjacent states. .. 46 Figure 9: Descriptive statistics and histograms of the maximum snow depth at the weather stations...... 48 Figure 10: Normal probability plots of the maximum snow depth at the weather stations...... 49 Figure 11: Directional trends of the maximum snow depth at the weather stations...... 51 Figure 12: Semivariogram of the maximum snow depths at the weather stations for the months of October, November, December and January...... 53 Figure 13: Semivariogram of the maximum snow depths at the weather stations for the months of February, March, April and May...... 54 Figure 14: Ordinary Kriging interpolated snow depths versus measured snow depths (October, November, December and January)...... 57 Figure 15: Ordinary Kriging interpolated snow depths versus measured snow depths (February, March, April and May)...... 58 Figure 16: Kriging interpolated snow depths with trend removal versus measured snow depths (October, November, December and January)...... 61 Figure 17: Kriging interpolated snow depths with trend removal versus measured snow depths (February, March, April and May)...... 62 Figure 18: Kriging interpolation over transformed data results (October, November, December and January) ...... 65 Figure 19: Kriging interpolation over transformed data results (February, March, April and May) ...... 66 Figure 20: Maps derived from measured point values with TIN interpolation (October, November, December and January)...... 69 Figure 21: Maps derived from measured point values with TIN interpolation (February, March, April and May)...... 70 Figure 22: Flow rate versus inlet pressure for the sprinkler heads tested...... 75 xi

Figure 23: Repartition of the stopping positions of the Rainbird heads after sixty 2-minutes applications tests...... 78 Figure 24: Charts of the percentiles comparisons between the distribution of the stopping position test values and a generated set of uniformly distributed...... 79 Figure 25: Maximum snow depths in Ohio according to 48 years data record...... 89 Figure 26: Sprinkler drainage dispositive...... 111 Figure 27: Water mixing device used in the variable water temperature test ...... 112

xii

List of Abbreviations

AngVel: rotational velocity

ASE: average standard error

BOD: biochemical oxygen demand

COD: chemical oxygen demand

CSV: comma separated values

EMWT: extreme minimal winter temperature

GIS: geographic information system

MS: mean standardized n/a: data not recorded n/r: no rotation

NCDC: national climatic data center

NPDES: national pollutant discharge elimination system

OF: overland flow

PVC: polyvinyl chloride

RI: rapid infiltration

RMS: root mean square

RMSS: root mean square standardized

SR: slow rate

StVar; rotational velocity standard variation

TIN: triangulated irregular network

xiii

Chapter 1: Introduction and Research Objectives

1.1. Introduction.

“Wastewater may be defined as a combination of the liquid- or water-carried wastes removed from residences, institutions, and commercial and industrial establishments, together with such groundwater, surface water, and storm water as may be present”

(Tchobanoglous and Burton, 1991). Wastewater usually contains organic matter that produces foul odors while decomposing, nutrients (nitrogen, phosphorus) that fuel growth of aquatic vegetation, and pathogenic organisms harmful to humans and animals. An accumulation of organic matter in surface waters can harm the environment, by reducing oxygen availability for aquatic life.

In 1972, the United Stated Congress amended the law governing wastewater disposal and directed the development of discharge requirements by adopting the Water Pollution

Control Act as a response to extensive and ongoing pollution of U.S. surface waters. Now known as The Clean Water Act, its goal is to eliminate the discharge of pollutants into waters, and “restore and maintain the chemical, physical and biological integrity of the

1 nation‟s waters” (PL-92-500, 1972). That goal is still in place and prompts the necessity to treat wastewater thoroughly before discharge into the waters of the nation.

Wastewater reuse is now strongly promoted to lessen the demand for natural water resources. Not only does reuse of wastewater prevent the discharge of pollutants into waterways, it also reduces the demand for potable water. According to USEPA (2002), household activities that do not require drinkable water (toilet flushing, landscape irrigation) consume most of the water supplied by public utilities. As a result, reusing reclaimed wastewater as a source of water for such activities would be a more efficient way of using this valuable resource and reducing the demands for treated drinking water.

Different technologies have been studied and different scenarios have been proposed for the treatment and reuse of wastewater. In most urban settings, wastewater is collected via sewers, treated in municipal wastewater treatment plants and discharged to surface waters. According to a 1996 survey, 72 % of all the U.S. population is served by sixteen thousand and twenty four (16,024) publicly owned wastewater treatment facilities

(USEPA, 2006). The treatment in municipal plants has been popular since the last quarter of the 20th Century, because of the reduced treatment time and small space requirements.

But increased energy and natural resource costs has made the operation and maintenance of municipal wastewater treatment plants questionable as a sustainable way of dealing with wastewater. Elevated operation and maintenance costs keep some small communities and individual households from connecting to a conventional wastewater treatment plant, which also requires the construction of sewers and the acquisition and renewal of discharge permits. Sewer construction costs can amount to 33% of the budget 2 of municipal wastewater treatment infrastructure (EPA, 2008). With all the complexity, rising energy costs are another limitation for communities that depend on electricity for wastewater treatment.

1.2. Research Objectives.

Year-round disposal of wastewater by slow rate land infiltration is desired to minimize the need for many months of storage. However, irrigation systems used in Ohio during winter are exposed to sub-zero temperatures and accumulated snow. The objectives of this study were to:

- Develop wastewater irrigation strategies for year-round, including sub-freezing

application

- Identify winter weather conditions that could limit the application of wastewater

1.3. Importance of Wastewater Treatment and Reuse through Irrigation in Ohio.

Natural soil is the ideal media for sustainable treatment of wastewater. In soil, organic matter is incorporated into the soil matrix, water and nutrients are captured and used by growing plants and microorganisms, and pathogens are immobilized by adsorption onto soil surfaces and eventually die.

Several options are available to utilize the soil for wastewater treatment and disposal.

- Subsurface Soil Absorption Systems.

They are commonly referred to as septic tank leach field units, subsurface wastewater infiltration systems (Water Pollution Control Federation, 1990), infiltration trench or large soil absorption systems (Siegrist, et al., 1986). They are used for wastewater treatment and disposal in rural households, commercial buildings as well as in community facility settings as the permanent means of wastewater treatment. They mostly consist of long narrow trenches filled with porous media (gravel), in which the primary treated wastewater is dispersed via a perforated pipe. The water flows by gravity out of the pipe and infiltrates the soil profile, at an approximate loading rate of 0.4 to

1.8 gal ft-2 day-1 (EPA, 2002). The most adopted scenario is primary treatment in a septic tank followed by dispersal in the soil infiltration field. Secondary treatment, during which pollutants (BOD, COD, nitrogen, phosphorus and pathogens) are removed, consists of a diverse array of physical, chemical, and biochemical processes in the soil horizons below the infiltration trenches. As a requirement, the soil horizons below the trenches are important part of the system. To provide the expected functions, they must be naturally permeable and stay unsaturated during the life of the system. Loss of infiltrative capacity due to wastewater strength, application rate and frequency shall be accounted for during the system design (Hargett, et al., 1981)

- Mound Systems.

A layer of sand augments the treatment capacity of naturally shallow soils. The wastewater is distributed on top of the sand.

- Slow Rate Land Treatment.

Slow rate land treatment (SR) is defined as the controlled application of pre-treated wastewater onto vegetated land, for the purposes of treatment, reuse and disposal. A fraction of the water and nutrients applied is captured and utilized by the vegetation for growth, while the other fraction infiltrates the soil profile and goes through physical, chemical and biologic treatment. The efficiency of the system depends on the capacity of the soil to provide an acceptable level of treatment to the portion of the wastewater that percolates. As a requirement, conditions that may limit percolation must be deep enough and the soil must have an acceptable hydraulic capacity. The land treatment is done by furrow, micro or spray-irrigation, at an application rate of 0.11 to 0.38 gal ft-2 day-1

(Water Pollution Control Federation, 1990). Slow rate land treatment has been practiced in the U.S. for decades. In 1872, the technique was already in use in sewage farms for crops irrigation, in Augusta, Maine (Pound and Crites, 1973). Webster (1954) also noted that it was practiced in Seabrook Farms (New Jersey) in 1900. It is popular in the

Southwestern and Southeastern arid and semi-arid U.S. (California, Arizona, Nevada,

Florida, Hawaii) where the pre-treated wastewater is reused on agricultural and landscape lands (cemeteries, highway medians and shoulders, industrial areas, landscaping and golf courses) to compensate for water shortage and to reduce potable water consumption

(Asano, et al., 2007). It is slowly gaining popularity in temperate regions because of stringent surface water discharge rules imposed by the USEPA. Treated wastewater has been applied by spray irrigation on forested lands in Pennsylvania since the 1960‟s

(Pennypacker, et al., 1967).

- Rapid Infiltration Land Treatment.

Rapid Infiltration (RI) land treatment consists of the application of pre-treated wastewater to permeable soils, at an approximate high rate of 0.4 to 6.8 gal ft-2 day-1 (EPA, 2002).

Most RI land systems function on the basis of recurring sequences of flooding, infiltration and drying. They can be operated year-round, even in cold weather regions, without any need of a storage provision for winter months (Reed, et al., 1985). The treatment of the wastewater occurs by physical, chemical and biological processes in the vadose zone and a portion of the under lying aquifer. The vegetation does not play any important role in the treatment of the water, but rather helps reduce soil erosion and maintain a high level of permeability (Crites, 1984). The RI treatment is very efficient in removing pollutants. In general, a RI land system requires deep and sandy soils to perform satisfactorily.

- Overland Flow Treatment.

Overland Flow (OF) systems have been popular only for the past 30 years. The treatment technique involves the application at a very low rate of wastewater on top of a vegetated sloped land. The applied wastewater flows in a thin uniform sheet down the slope, at a very low rate and is treated by a series of physical, chemical and biological processes.

The vegetation plays an important role in the treatment by capturing Nitrogen and

Phosphorus. The technique is mostly practiced in areas with low-permeable and imperfectly drained soils, with slope as great as 2 to 8% (Crites, 1984). An NPDES permit is required in case the effluent is discharged into a nearby stream.

In Ohio, soil treatment using leach field and mound systems can be implemented approximately on 30% of the land area (Mancl and Slater, 2001). These areas are suited for soil treatment systems because limiting conditions (groundwater, bedrock, glacial till, fragipans and sand) are located at depth greater than 2 ft, and permeability is greater than

0.5 in. hr-1. In areas where soils are not suited for leach field and mound system implementation, wastewater can be treated and reused through irrigation to avoid discharging pollutants in surface waters. One foot of soil depth is necessary to accept the additional water. In Ohio, an additional 50% of the land area has soils deep enough to accept reclaimed wastewater (Figure 1).

Figure 1: Land repartition for soil treatment of wastewater in Ohio.

Chapter 2: Literature Review

2.1. Irrigation in Cold Conditions: Performance of Irrigation Equipment at Low Temperature.

Bounzoun (1979) investigated the problems related to wastewater spray application in freezing conditions. During a field experiment, he tested downward spraying Fulljet ¾

HH6W nozzles at 0°F to examine their capabilities to drain the low segments of the spray laterals at the end of water application. They did not perform satisfactorily, due to continuous dripping that caused ice formation at the tip of the nozzles resulting in clogging. He also tested an upward spraying Buckner Turf King rotary sprinkler in the same field conditions to assess their performance in cold condition. It also did not perform satisfactorily due to freezing damage, nozzle plugging, and excessive maintenance requirements.

During a cold weather field experiment, Bodman (1968) tested the Rainbird Models 20 and 20A (23° and 7° trajectory angles respectively) rotating impact-driver non-indexing commercial type sprinkler heads. His goal was to investigate their functionality, their

8 capability to provide satisfactory ice penetration and their distribution characteristics at approximately 22°F (-5.5°C). Both heads discharged reliably with satisfactory distribution, but the Model 20A failed to rotate properly and to provide reliable ice penetration. The malfunction was explained by ice build-up on the heads‟ driver and spring due to misting.

Irrigation sprinklers have been used in different situations. A field experiment used a spray irrigation system to dispose of wastewater at the Michigan State University

(Leland, et al., 1979). The irrigation system‟s pipes, valves and spray nozzles froze up, which resulted in multiple operational shutdowns and uneven distribution patterns.

During a sprinkler irrigation test for frost protection in an apple orchard, Koc and others

(2000) used the Rainbird model 30H impact sprinkler placed on risers in the design of irrigation system. Inadequate bud coverage was recorded and slow rotation of sprinkler heads was thought to be the cause. A fruit grower that used impact sprinkler system for orchard frost protection noticed that spray heads do not clog up during cold spells, but did stick in one direction when the air temperature reaches approximately 22°F, leading to spray under-coverage. This problem becomes more pronounced when the need to spray arises often during a long and harsh advective cold spell, leading to the need for increased human monitoring to keep the system from freezing and to ensure its continuous functioning. It also leads to an increased risk of irrigation system damage (Sullivan,

2008). The problem with irrigation system malfunction was also mentioned by Demchak

(2007) in his discussion on tips and techniques of frost protection of orchards.

2.2. Improvements to Winter Irrigation Systems.

In an attempt to develop an irrigation sprinkler head for winter distribution of treated sewage effluent, Givens (1965) designed four different types of sprinkler heads; a revolving head, two stationary manifold heads and a deflector type head. He tested them against a commercial revolving sprinkler head. The deflector stationary heads worked satisfactorily under freezing conditions, but did not provide adequate distribution. The stationary manifold head did not provide a satisfactory distribution of the sewage effluent, due to clogging. The commercial revolving heads were judged unfit for the distribution of sewage effluent under freezing conditions due to inconsistent, non- uniform distribution. The modified revolving head also did not have an assured rotation, so it was considered unfit for uniform distribution.

Bodman (1968) modified the Rainbird Model 35-PJ into indexed-position part circle sprinkler heads set at 120 and 240° and tested them in freezing conditions. The modified heads had an acceptable distribution and satisfactory ice penetration, but failed to rotate properly. He also developed a random indexing grooved deflector sprinkler but its distribution characteristics were not proven reliable. Bodman noted that nozzle plugging, misting, heterogeneous throw and improper lateral drainage caused the malfunction of the heads in freezing conditions. He suggested that distribution laterals and risers be drained quickly after application. The Rainbird models studied by Bodman are no longer on the market.

With the poor performance exhibited by the downward spraying Fulljet ¾ HH6W nozzles and the Buckner Turf King rotary sprinkler, Bouzoun (1979) modified the Fulljet 10

¾ HH6W nozzles by creating a second vane in the nozzle and incorporating a brass tube to provide an escape route for the draining water even if the other vane is clogged. The modified version of the nozzle was able to deliver the daily amount of effluent in a single cycle. He did not report on rotation or uniformity of distribution. His work led him to conclude that distribution laterals and risers should be buried deep enough to avoid freezing, and vertical risers should be insulated. He also suggested that the modified

Fulljet nozzle be used at the low points of laterals.

During a field experiment to assess the performance of Ohio‟s landscape plants under a year-round irrigation regime, Caldwell and others (2007) applied heating tape along distribution lines, risers and rotor heads to prevent them from freezing when the ambient air temperature falls. Some failures of head rotation were reported, but the heads did not fail to apply the expected water amount. The technique showed that it is possible to operate a rotor sprinkler irrigation system in winter, without risks of freezing and breaking. But the technique has been questioned because of the costs of construction and operation, and the supplemental use of electricity to power the heat tape. The authors also suggested the drainage of the heads and risers as a solution to the freezing problem.

A fruit producer in personal communication shared his method to prevent freezing in frost protection irrigation sprinklers. He shakes risers to loosen internal as well as external ice formations. The method, while effective, is labor intensive, and requires ongoing monitoring (Sullivan, 2008).

2.3. Environmental Impacts of Winter Irrigation Systems Design.

Two variables, Snow cover and air temperature, impact greatly the design of winter spray irrigation systems in Ohio.

Snow cover has been defined as “The depth of snow in inches measured at the reporting time” (NOAA, 2003). The reported value is an average, as the depth of the snow on the ground is not the same at all locations at the time of measurement, and the snow mass on the ground is subject to changes, depending on the air and ground temperatures. These changes can lead to variations of the depth at the same location overtime.

Snow cover impacts peoples‟ lives in different ways; hence it is the object of interest in different studies. It generally serves important roles in nature and the economy. In Ohio, permanent landscape irrigation pop-up rotor sprinkler systems have been identified as a potential means for treated wastewater effluent dispersal on landscaping in rural areas. A potential limitation to this method is the depth of the snow cover. The accumulated snow can exceed the height at which pop-up sprinklers rise to when operated, making it impossible to use this type of irrigation head in system design.

Hi-pop up sprinklers have been used in year-round application of wastewater. In a year- round field experiment for effluent discharge, Toro series 300-12-02 shrub hi-pop sprinklers were installed 20 cm below ground level in a PVC sleeve with a lid to keep them from freezing (Mote, et al., 1993). Hathaway and Mitchell (1985) used pop-up sprinklers in an all season‟s field experiment to dispose of sand filter effluent by irrigation.

Snowfall and snow depth data and statistics collected by weather stations around the U.S. are stored in the National Climatic Data Center databases. Many authors have used this data to evaluate and map the snow depth variations in temperate regions of North

America, using different methods. Dyer and Mote (2006) used snow cover depth data from 1960 to 2000 to assess patterns of snow cover depth over Northern America, by creating daily snow depth grids and calculating 5-year period statistics for each grid cell.

They found that snow depth, as well as snow cover extent is decreasing substantially over

Northern America.

Burakowski and others (2008) collected snow cover data over 1965 to 2000 from the national climatic data center and the United States Historical Climate Network and analyzed the data by computing temporal time-series for snow-covered days. They concluded an overall decrease in snow covered days, the northern regions having more snow covered days than the southern and coastal regions. However the literature does not indicate any narrower study that focused on mapping the maximum snow cover depth for the different regions in Ohio.

Schmidlin and others (1992) used 1948 to 1990 daily snow depth data in Ohio to estimate the 50-year return period for snow depths. Schmidlin (1989) compiled years of snowfall and snow cover data from the National Weather Service Cooperative Climatic Station at

Chardon (north-East Ohio) to find after analysis that snow depth in Chardon is the greatest in Ohio, with an average of 43.4 cm (17 in.) and maxima as high as 79 to 86 cm

(31 to 34 in.).

Edgell (1994) determined that the mean extreme minimum winter temperature over much of Ohio was between -22 and -24°C. He compiled minimum temperature data from

National Weather Service stations that have at least thirty years of EMWT values and conducted a statistical study to determine that the northern region recorded the lowest temperatures. Hickcox (1984) conducted a survey of the temperatures in Ohio and found that the coldest temperature in 1981 was -31°C at Wauseon. Several days below freezing is considered a cold spell. The average winter cold spell in Ohio lasts for five to ten days

(Schmidlin, 1996).

Chapter 3: Methods

3.1. Irrigation in Cold Conditions.

A series of experiments were conducted to assess the performance of spray irrigation equipment exposed to sub-zero temperatures. The spray irrigation equipment was operated in a chest freezer located in the wastewater treatment and reuse laboratory of the

Department of Food, Agricultural and Biological Engineering at The Ohio State

University.

3.1.1. Freezer Setup.

A spray irrigation sprinkler head was installed on a riser. The sprinkler head/riser set was installed on the bottom panel of the freezer, by way of a bulkhead fitting installed in a whole drilled in the middle of the mentioned panel (Figure 2). A water distribution pipe was connected to the fitting at the bottom of the freezer to supply water to the sprinkler head. A drain was installed on the distribution pipe at the bottom of the riser (Figure 3).

An electric valve, connected to a controller, and a flow meter were installed on the water distribution line to control and monitor the flow of water to the sprinkler. The flow meter

15 was installed upstream of the valve. A pressure reducer was installed between the flow meter and the valve to regulate the pressure down to 50 psi, appropriate for the valve.

In the freezer, an insulating foam panel was installed in horizontal position at 4 in. below the top (Figure 4). The foam panel was dimensioned to fit snuggly inside the perimeter of the freezer, to keep the cool air inside when the door was open. Seal tape was installed along the perimeter to create a watertight connection between the foam board and the side panels of the freezer. A cut made in the middle of the foam panel allowed the installation of a rotational velocity monitoring device. Two other cuts in the panel allowed the insertion of a thermometer probe to monitor the temperature inside the created cold box, and the observation of the head in the freezer. The freezer was elevated by 7.5 in. to allow the supply pipe to be affixed underneath.

Figure 2: Sprinkler head/riser set sitting on the bottom panel of the freezer.

Figure 3: Drain on the distribution line at the bottom of the riser.

Figure 4: Foam panel installed in the freezer.

3.1.2. Materials.

- Freezer: a household grade 24.9 in3 chest freezer (model GLFC2528FW from

Frigidaire) with a defrost drain was used to provide a permanent cold environment in

which the sprinkler heads were tested. The defrost drain allowed the freezer to empty

after each irrigation event

- Valve: the electric anti-siphon model 311A-75 from Irritrol in conjunction with the

pressure regulator model OMR-100 from the same manufacturer was used. The

purpose of the pressure regulator was to adjust the pressure to the requirements of the

sprinkler head tested. The valve was controlled by a solenoid connected to an

irrigation controller.

- Flow meter: the Universal flow monitor model MN-BSB10GM-6-32V1.0-RT0XR

was used to monitor the water flow rate. It has a maximum operating pressure of 300

psig and temperature of 200°F (93°C), and a range of 0 to 10 gpm.

- The water distribution line and riser was constructed using ¾ in. PVC schedule forty

pipe, with a pressure rating of 480 psi.

- King Drain: attached to the water supply line, it is activated by a downward

hydrostatic pressure force when the water pressure drops at the end of the irrigation

event.

- Rotational velocity measurement device (figure 5): a shaft with an aluminum wire

was affixed to the rotation part of the sprinkler head and extended vertically through

an aluminum tube out of a perforation made in the foam board that was installed in

the freezer. The shaft rotated in concert with the sprinkler head. A piece of foam was

installed in the tube to guide the wire vertically. A piece of horizontal wire was

attached to the vertical wire, and clearly marked. A circular 30° division graduated

sheet attached to the tube indicated the position of the horizontal wire at all time. An

electronic stop watch was used to time the rotational velocity of the horizontal wire.

Figure 5: Rotational velocity monitoring device.

3.1.3. Experimental Designs.

3.1.3.1. Drainage Tests of Different Irrigation Sprinkler Models.

Three rotor revolving sprinklers and an impact revolving sprinkler from different manufacturers (Table 1) were tested at -25°C with drainage.

All the heads were adjusted to full circle rotation, and their check valve was removed to allow low pressure head drainage at the end of each application. Each head was installed on the riser in the freezer and tested for seven consecutive days. The heads were tested on the first day at room temperature (+25°C). Each head type was tested twice, except the

Rainbird Maxibird 2045-pj which was tested three times.

Seven consecutive days was chosen for the duration of the test. The two Rainbird heads had the seven-day trial and an additional test lasting ten days.

Table 1: List of irrigation sprinklers tested with their nozzles and corresponding water pressures.

Experimental Heads tested Nozzle pressure (psi)

Rainbird 5000-s Plus Shrub 3 37

Rainbird Maxibird 2045-pj 8 25

Toro S800 Shrub 3 40

Hunter PGS-ARV 6 low angle 50

The irrigation events were conducted at intervals of twenty four hours, and each event lasted ten minutes. The following variables were observed during each event:

- Rotational delay: total duration between the inception of an irrigation event and the

start of the sprinkler head rotation (minutes).

- Rotational velocity: circular angle covered by the head in a period of one second (rad

sec-1)

- Ambient air temperature: the air temperature in the freezer was measured with a

thermometer at the beginning and at the end of irrigation events, and reported in

degrees Celsius. A Red-safety alcohol filled glass thermometer, which has a range of

-25 to 100°C, was used for that purpose

- Water flow rate: was recorded at the beginning and every 2.5 minutes during the

irrigation events, and reported in gallons per minute (gpm)

3.1.3.2. Drainage Test of Different Heated Irrigation Sprinkler Models.

Rotor heads were tested in this part of the study. In addition to draining, a heating tape,

(Model Guardian TM from Raychem that outputs 6 watt ft-1) was wrapped around the head. A 2 in. thick foam insulating sheet was used to concentrate the heat around the head. The heat was provided every day for twenty four hours. Each head was tested for seven consecutive days and the rotational delay, as well as the rotational velocity, the ambient air temperature and the water flow rate were recorded. This design was already proven successful at keeping irrigation heads functional during winter time (Caldwell, et al., 2007), and will serve as a control in this study.

3.1.3.3. Evaluation of the Performance of the Rainbird Sprinklers with Water at Warmer Temperatures.

The Rainbird heads were tested with warm water. Water at temperature varying from

28°C to 40°C from day to day, with 4°C increment, was provided to the distribution circuit. Each temperature level was repeated four times. The water temperature level was assigned to the days randomly, to minimize the effects of material fatigue on the results.

The total duration of the warm water test was sixteen days per head. The rotational delay, as well as the rotational velocity, the ambient air temperature and the water flow rate were recorded.

A device was designed to mix hot water to cold water. The two opposing ports of a tee pipe fitting were connected; one to a hot water supply pipe and the other one to a cold water supply pipe via rubber hoses. Hot water and cold water mixed in the tee fitting, and the resulting water exited, via the third port, into a three way diverting ball valve. One

21 exit port on the diverting ball valve was connected to the irrigation circuit and the other one to a hose out of which samples of water were taken for temperature measurement.

The water flow rate was adjusted using manual valves to set the water supplied to the irrigation system to the desired temperature. A thermometer was used to measure the temperature of sampled water. Five samples were taken to confirm the water temperature before initiation of the irrigation event.

3.1.4. Data Analysis.

The irrigation sprinkler heads were operated and drained between irrigation events to evaluate their performance in a cold environment. The data analysis focused on classifying the heads based on the rotational delay, the rotational velocity and the flow rate, and comparing these parameters to the ones yielded by the heated heads. The rotational delays were compared using Kruskal-Wallis and Mann-Whitney non- parametric tests, as the sizes of the samples were small. No statistical test was used to compare rotational velocity and flow rate; simple data exploration revealed whether the heads performed similarly or whether there were differences between them.

Water at different temperatures was fed to the Rainbird heads to evaluate the effect of water temperature on the rotational delay of the sprinkler heads. Kruskal-Wallis and

Mann-Whitney non-parametric tests were used to compare the rotational delay of each head at different water temperature, as well as the rotational delay of the two heads.

3.2. Depth of Snowpack in Ohio.

3.2.1. Data Collection and Management.

Maximum daily snow depth data for each month of the year were collected from the

National Climatic Data Center (NCDC). These data cover thirty to forty eight years of non-missing data (1948-1996), and were treated to remove uncertainties and reading accuracy errors (NCDC, 2007). Data from a total of two hundred and fifty three weather stations, including one hundred and fifty nine in Ohio, twenty two in Indiana, nine in

Kentucky, twelve in Michigan, thirty three in Pennsylvania and eighteen in West-

Virginia, were collected. Data from neighboring states were considered to account for the influence of snow accumulation in the bordering counties on the snow depth values in

Ohio. The geographic coordinates of the stations, were also recorded from the NCDC web-site. Snow depth values were recorded in inches. The data collection and organization were performed with Microsoft Excel 2007. The files were saved in Comma

Separated Values Files (CSV) format.

Maps of the state and counties were obtained from Census 2000 County Arc View Shape files for the States of Ohio, Indiana, Kentucky, Michigan, Pennsylvania and West-

Virginia.

The CSV file holding the snow depth data and the downloaded states shape files were imported into a personal geodatabase. They were referenced to the North American 1983

Geographic Coordinates System, and projected to the North American Datum 1983

Universal Transverse Mercator Zone 17 North.

3.2.2. Data Treatment.

3.2.2.1. Data Representation.

The weather stations and the states data were added to Arc Map as points and polygon feature Shape files, and represented on a map. A nearest neighbor analysis, based on the determination of Poisson probability distribution for mean nearest-neighbor distance, was conducted in Arc Map to identify station clustering patterns and mean separation distance between points.

3.2.2.2. Interpolation Methods.

The collected snow depths applied to specific coordinate points, and do not represent the depths at all locations in Ohio. To draw information about the maximum snow depth at all geographic points, two different types of interpolation methods, interpolation with

Triangulated Irregular Network and interpolation with Kriging, were conducted and the results were compared.

3.2.2.3. The Kriging Interpolation.

The Kriging method uses a surface-fitting algorithm to interpolate a grid from a set of measured data (randomly and regularly distributed). Formally developed as an interpolation tool for the mining industry, it has evolved as an important tool for Digital

Terrain Modeling, environmental pollution assessment and resource management.

Bridges (2008) used ordinary Kriging in a GIS environment to investigate forest site productivity in western Tennessee. Wang and others (2009) used mean Kriging in a GIS environment, in comparison with simple random sampling, spatial random sampling and ordinary Kriging, to study the surface temperature of the Shandong Province in eastern

China, and found that the model yields the highest accuracy. The Mean Kriging also outperformed the other methods in the estimation of the proportion of cultivated lands in the same province (Wang, et al., 2009).

The Kriging method is a local neighborhood interpolation operation that works basically on continuous variables. It assumes that the surface is homogenous and uses a semivariogram to establish regression relationship between pairs of measurements and their separation distances.

The semivariogram generates a family of least-squares values that are used to estimate the value of the studied variable at specific locations. Different variations are used for the method, which involves different concepts, based on the degree at which the conditions for simple Kriging are met or not.

- Simple Kriging: the estimated value of the variable is the mean of the considered

area. Based on that concept, the semivariogram (r) is estimated as: r =

d = distance between two sample points z (ui), i = 1,2,…,N represent the z data available over the study area

N = number of data points

- Ordinary Kriging: accounts for the local variation of the mean that is considered

constant but unknown. This concept uses weights based on distance between the

points

- Universal Kriging: considers that the mean varies within each local neighborhood. It

uses a polynomial function to model the trend over the studied region.

An advantage of the Kriging interpolation is that a standard deviation is estimated to provide a measure of the prediction accuracy. Another advantage of the method is that it assigns less weight to points in cluster, and treats clusters as single points. As a disadvantage, the Kriging method can produce estimated data that are higher or lower than the measured values, which results in sharply rising and falling values (spiky) in the map. This problem has been addressed by other variations of the Kriging concept.

A typical application of the Kriging interpolation assumes that the data field has wide- sense stationary character, meaning the data do not change over time and in space, and enough data is available to estimate the semivariogram. It also assumes that the data are normally distributed and the data points present no evidence of spatial clustering.

3.2.2.4. Application of the Kriging Method.

The Kriging function of the geostatistical analyst was used in a GIS environment to predict snow depths for non-surveyed geographic locations in Ohio. As a preliminary requirement to the use of the method, the quality of the data was evaluated using the geostatistical analyst capabilities. Normal distribution, trends and spatial autocorrelation were assessed.

- Normal distribution of the data: descriptive statistics, histograms and normal

probability plots (QQPlots) were used to analyze the data. Central tendencies were

evaluated based on the relation between the medians and the means. Standard

deviation and skewness were used to evaluate data distribution and symmetry. The

histograms and normal probability plots were used to verify the observations from the

descriptive statistics.

- Trends surface analysis: data points were projected onto perpendicular planes, and

best fit polynomial lines were drawn through the projected points to model the trends

in different directions. Short range variations were not captured by the interpolation if

a general trend exists. This operation was performed to discern broad local and

regional trend and to remove its influence before the actual Kriging interpolation

- Spatial autocorrelation: a semivariogram was used to investigate the similarities of

point values close to each other. The difference squared between the values of each

pair of points relative to the distance separating the points were represented on a

graph. The distance between the values of each pair was plotted on the x-axis and the

difference squared on the y-axis. Each dot on the plot represented a pair of points. In

general semivariogram values were lower when the distance between pairs of points

were close to null, and increased as the distance increased. Lower autocorrelation

were normally shown at greater separation distances.

Based on the conclusions of the data exploration, different models of the Kriging interpolation were performed:

- Kriging interpolation without trend removal: default settings (table 2) were conserved

and trends were not removed.

- Kriging interpolation with trends removal: in this model, trends observed during the

data exploration were removed before the interpolation operation. The trends were

reconsidered before the final surface output. Anisotropy was considered in this model

to account for directional influence on the semivariogram. Ten lags of 15000 m each

were also used as average distance between neighboring points (Table 3).

- Kriging interpolation on transformed data with trends removal: the transform

y= was performed to change non-normally distributed to normally distributed

data (Wang, et al., 2009). The variable x is the original snow depth value measured at

a weather station. Trends and anisotropy were considered again in this model. Ten

lags of 15000 m each were also used as average distance between neighboring points.

Table 2: Default settings for ordinary Kriging interpolation on original values.

Table 3: Kriging interpolation on original values with trend removal.

3.2.2.5. Triangulated Irregular Network (TIN) Interpolation.

The TIN interpolation uses measured elevation values without the assistance of a surface fitting algorithm to compute estimated values at non-surveyed locations. It is an exact local interpolation method that fits the estimated surface to original data points (Lo and

Yeung, 2007), and has been used in various situations. Gyozo (2007) used TIN interpolation of ridgeline elevations to devise a “smoothing” technique for Digital

Elevation Model and noted that the TIN method is adaptive to the nature of the data and simple to use. The TIN model assumes continuous terrain surfaces and uses triangular facets formed by the connection of neighboring sampled points to define the attributes of other points. The attributes of a point in a triangular facet is estimated by averaging the attributes of the vertices of the triangle.

In this study, the TIN function of 3D analyst was used in a GIS environment to interpolate the measured snow depth values over the considered surface area.

3.2.3. Estimated Values Generation and Map Design.

The resulting layers of the Kriging operations and the TIN operations were converted to raster files that were used to design maps showing the estimated depth of the snowpack for each month in Ohio. The maps were used to compare the interpolation methods, and a zonal statistic operation using the Ohio layer as feature zone and the county field as zone field was performed on the results of the chosen method to generate attribute values for each county and each month in Ohio. The maximum snow depth for each county was extracted and used to generate a map showing the yearly maximum snow depth in each county. 30

Chapter 4: Results

4.1. Irrigation in Cold Conditions.

4.1.1. Impact of Water Application on Ambient Freezer Temperature.

The ambient temperature in the freezer was measured at the beginning and end of irrigation applications throughout the experimentation. The goal of the measurement was to monitor the temperature and assure that the freezer was providing an environment with the air temperature as low as the extreme air temperature of Ohio winter. A second goal was to observe the effects of water application on the microclimate in the freezer.

The temperature measurement was not done for every single application. However, the record (Table 4) showed that the temperature dropped between irrigation events, staying at approximately -20 to -25°C at the beginning of water application. It rose to +18 to

+20°C at the end of irrigation events. The same observation applied to the heated head tests (Table 5).

Table 4: Ambient freezer temperature variations during 10 minute water spray with unheated sprinkler heads in freezer.

Rainbird 5000-s Rainbird 2045-pj Toro s-800 Hunter-PGC

Day of Day

application

at end (°C) end at (°C) end at (°C) end at (°C) end at

emperature emperature emperature emperature emperature emperature emperature emperature emperature

at start (°C) start at (°C) start at (°C) start at (°C) start at

T T T T T T T T

1 n/a n/a 25.0 25.5 20.0 20.0 25.0 22.0 2 -20.0 n/a -20.0 19.0 -20.0 20.0 -250 15.0 3 -20.0 23.0 -25.0 18.5 -25.0 20.0 -25.0 18.0 4 -25.0 20.0 -25.0 19.5 -25.0 20.0 -25.0 15.0 5 -25.0 18.0 -20.0 19.0 -25.0 20.0 -25.0 14.0 6 -25.0 20.0 -20.0 17.5 -25.0 20.0 -25.0 20.0 7 -25.0 20.0 -25.0 17.0 -25.0 n/a -25.0 18.0 8 -25.0 20.0 -25.0 18.0 n/a n/a n/a n/a 9 -25.0 20.0 n/a n/a n/a n/a n/a n/a 10 -25.0 20.0 n/a n/a n/a n/a n/a n/a n/a: data not recorded

Table 5: Ambient freezer temperature variations during 10 minute water spray with heated sprinkler heads in freezer.

Rainbird 5000s Toro s-800 Hunter-PGC

rature

of application of

at at end(°C) at end(°C) at end(°C)

at at start (°C) at start (°C) at start (°C)

Day

Temperature Temperature Temperature Temperature Tempe Temperature

1 n/a n/a n/a n/a n/a n/a 2 -20.0 20.0 -25.0 25.0 -20.0 20.0 3 -20.0 20.0 n/a n/a n/a n/a 4 -20.0 20.0 -25.0 20.0 -20.0 n/a 5 -20.0 20.0 n/a n/a n/a n/a 6 -20.0 n/a -22.0 n/a -20.0 n/a 7 n/a n/a n/a n/a -20.0 n/a n/a: data not recorded

4.1.2. Head Drainage.

The unheated as well as heated heads drained at the end of every irrigation event as expected. The drain did not exhibit any sign of clogging.

4.1.3. Water Flow rate.

During the experiment, none of the spray heads tested, whether heated or unheated, exhibited signs of clogging or failure to spray. Water flow was immediate for all the heads at inception of the irrigation applications, except for one of the unheated Rainbird

5000-s head on the second day of treatment: water flow started 42 seconds after the irrigation inception.

In general the water flow rate fluctuated greatly at the beginning of the application event, for both the heated and the unheated heads, but settled quickly toward the end of the first

30 second window, at approximately 90 to 95% of the final flow rate value for the unheated heads. The flow rate increased slowly to its final value by the time the head started rotating. The maximum flow rate was reached after the settling period of 30 seconds for the heads tested at room temperature and the heated heads.

Specific nozzles and experimental pressure settings were used to set the flow rate of all the heads at 2.8 gpm. The Rainbird 5000-s and Rainbird 2045-pj heads only reached 2 gpm when unheated. When heated, the Rainbird 5000-s head sprayed at a rate of 3 gpm.

The Toro s-800 and Hunter PGC heads sprayed approximately at a rate of 2.5 to 3.5 gpm, unheated or heated (Table 6).

Table 6: Water flow rate (gpm) during 10 minute spray testing of sprinkler heads at

-25°C.

Rainbird 5000-s Toro s-800 Hunter-PGC Rainbird 2045-pj

eated eated eated eated eated

head head head head head head head head

nheated nheated nheated nheated nheated

H H H H

Day of application of Day

U U U U

1 2.0 3.0 3.0 3.2 - 3.5 2.6 3.5 2.0 n/a 2 2.0 2.5 - 3 2.7 3 - 3.5 2-3 3.5 2.0 n/a 3 2.0 3.0 3.4 n/a 2-3 3.5 2.0 n/a 4 2.0 3.0 2.6 3 - 3.5 1-2.5 3.4 - 4 ≈2 n/a 5 2.0 3.0 3.2 3 - 3.5 2-3 n/a 2.0 n/a 6 2.0 3.0 2.7 3.5 2-3 ≈2 - 2.5 ≈2 n/a 7 2.0 3.0 3.0 n/a 2-3 2 - 3.5 2.0 n/a 8 2.0 n/a n/a n/a 2-3 n/a 2.0 n/a 9 2.0 n/a n/a n/a n/a n/a 2.0 n/a 10 2.0 n/a n/a n/a n/a n/a n/a n/a 11 n/a n/a n/a n/a n/a n/a n/a n/a 12 n/a n/a n/a n/a n/a n/a 2.0 n/a 13 n/a n/a n/a n/a n/a n/a 2.0 n/a 14 n/a n/a n/a n/a n/a n/a 2.0 n/a n/a: data not recorded

4.1.4. Rotational Delay.

A rotational delay of ten minutes was recorded when a head did not rotate at all during the scheduled irrigation time, for the convenience of comparison and representation.

The heated heads did not exhibit rotational delay. The unheated heads were tested at room temperature (25°C) on the first day and did not exhibit rotational delay. From the

35 second day in cold condition, they exhibited rotational delay, with delay ranging from approximately 3 to 10 minutes, depending on the head type (Figure 6).

From the second day in cold condition, the Toro-s800 head did not rotate at all for the scheduled irrigation time (10 minutes). A second trial confirmed this observation. The

Hunter-PGC head also did not rotate for the scheduled irrigation time from the fourth day during the first trial.

On the third, fifth and seventh days of its first trial, the Rainbird 5000-s head, after a rotational delay, operated back and forth four times at a 90° angle before resuming a 360° rotation. These events were counted as rotational delay, and were not observed during the second trial, which was extended to ten days of application.

During the first trial, the Rainbird 2045-pj head had fast counterclockwise rotations, without impact on days two, four and seven. The same scenario was repeated on all days during the second trial, except on day one. Sometimes the head did not rotate counterclockwise. During the third trial, the head did not complete total counterclockwise rotation, but stopped rotating shortly after starting counterclockwise rotation.

Figure 6 showed the average rotational delay of the spray heads tested as a function of the number of days in freezing condition. A large gap was shown between the Toro and

Hunter heads pair in one group and the Rainbird heads pair in the other, with the delay times of the first pair leading the ones of the latter. A data exploration of the values in each pair (Table 7) as considered did not provide enough information to discriminate the members of the pairs. 36

Due to the small sample size, it was not possible to conduct a parametric test to discriminate the day to day rotational delay of the different spray heads. A Kruskal-

Wallis non-parametric test did not allow any discrimination either, as the calculated p- values were too large and did not indicate any difference between the spray heads at a 5% significance level (Table 8).

A Kruskal-Wallis test on the combined rotational delay of the spray heads issued a null p- value, which indicated a net difference between the heads. A Mann-Whitney Test showed no significant difference, at a 5% significance level, between the Rainbird heads (p-value

= 0.09), but a significant difference between the Toro and the Hunter heads (p-value =

0.01). A Mann-Whitney test also showed a significant difference, at a 5% significance level, between the Rainbird Heads combined and the Hunter heads, and between the

Rainbird heads combined and the Toro heads.

These results supported the observation previously drawn from the graph (figure 6) and led to the following ranking for the rotational delay:

Rainbird heads < Hunter head < Toro head

A Kruskal-Wallis test also shows, at 5% significance level, there was no evidence of rotational delay increase during the length of the test (P-value > 0.05).

Table 7: Individual rotational delay (minutes) for the unheated heads tested at - 25°C.

Day Day Day Day Day Day Day Day Day Day Heads 1 2 3 4 5 6 7 8 9 10

0 2.86 3.66 3.34 2.98 3.34 5.29 4.27 3.50 3.75 Rainbird 5000s 0 4.21 4.31 4.49 4.51 4.55 4.66 n/a n/a n/a 0 7.42 10.0 10.00 10.00 10.00 10.00 n/a n/a n/a Toro 800s 0 10.00 n/a 10.00 10.00 10.00 10.00 n/a n/a n/a 0 3.46 5.70 4.00 n/a n/a 5.15 2.95 4.00 n/a Rainbird 0 4.37 4.40 4.93 3.82 3.00 4.14 4.56 n/a n/a 2045pj 0 4.73 4.75 5.84 4.69 4.49 5.12 n/a n/a n/a 0 9.62 9.50 7.95 10.00 10.00 10.00 10.00 n/a n/a Hunter- PGC 0 7.27 6.79 8.89 9.25 5.67 9.11 n/a n/a n/a n/a: data not recorded

12.00

10.00

8.00

6.00 Rainbird 5000-s Toro s-800 4.00 Hunter PGC

rotation delay delay rotation(minutes) 2.00 Rainbird 2045-pj

0.00 1 2 3 4 5 6 7 8 9 10 Day of application

Figure 6: Graph of rotational delay of the different heads tested at -25 °C.

Table 8: Results of a Kruskal-Wallis test on the rotational delay of the spray heads tested at -25°C. (5% significance level).

Days 1 2 3 4 5 6 7

P-value 1 0.087 0.09 0.073 0.129 0.129 0.1

4.1.5. Rotational Velocity.

All the spray heads tested, except the Rainbird 2045-pj, operated clockwise and counterclockwise alternatively, at 360°. The Rainbird 2045-pj head operated continuously clockwise, at a rate of approximately 0.5 to 0.7 rad sec-1 (Table 9). The rotational velocity measurement of the Rainbird 2045-pj head was not very accurate

(standard variation of 0.03 to 0.1 rad sec-1), because of the fast rate of rotation. However it provided enough information for the purpose of this study. The Rainbird 5000-s operated at 0.1 rad sec-1, with a standard variation of 0.01 rad sec-1. The Toro s-800 and the Hunter-PGC operated at a slower pace of 0.05 rad sec-1, with standard variation of

0.001 rad sec-1.

The heated heads rotated at the same rates as the unheated heads. Overall, the cold treatment did not affect the rotational velocity of any of the heads.

Table 9: Rotational velocity of the sprinkler heads tested at -25°C.

Rainbird 5000-s Toro s-800 Hunter PGC Rainbird 2045-pj unheated unheated unheated unheated

head heated head head heated head head heated head head heated head

) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

------

sec sec sec sec sec sec sec sec sec sec sec sec sec sec sec sec

StVar StVar StVar StVar StVar StVar StVar StVar

AngVel AngVel AngVel AngVel AngVel AngVel AngVel AngVel

Day of application Day

rad

(rad (rad (rad (rad (rad (rad (rad ( (rad (rad (rad (rad (rad (rad (rad (rad

1 0.10 0.01 0.10 0.01 0.05 0.00 0.05 0.00 0.05 0.00 0.05 0.00 0.74 0.07 n/a n/a 2 0.11 0.01 0.10 0.01 0.05 0.00 0.05 0.00 0.05 0.00 0.05 0.00 0.73 0.09 n/a n/a 3 0.11 0.00 0.10 0.01 n/r n/r n/a n/a 0.05 0.00 0.05 0.00 0.73 0.07 n/a n/a 4 0.11 0.01 0.10 0.01 n/r n/r 0.05 0.00 0.05 0.00 0.05 0.00 0.67 0.03 n/a n/a

40 5 0.11 0.01 0.10 0.00 n/r n/r 0.05 0.00 0.05 0.00 n/a n/a 0.73 0.07 n/a n/a

6 0.11 0.01 0.10 0.01 n/r n/r 0.05 0.00 0.05 0.00 0.06 0.00 0.68 0.13 n/a n/a 7 0.11 0.01 0.10 0.00 n/r n/r n/a n/a 0.06 0.00 0.05 0.00 0.68 0.05 n/a n/a 8 0.11 0.01 n/a n/a n/a n/a n/a n/a n/r n/r n/a n/a 0.62 0.05 n/a n/a 9 0.11 0.01 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.48 0.07 n/a n/a 10 0.11 0.01 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 11 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 12 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.54 0.08 n/a n/a 13 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.53 0.03 n/a n/a 14 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.49 0.06 n/a n/a AngVel: rotational velocity

n/a: data not recorded

n/r: no rotation

StVar; rotational velocity standard variation

4.2. Evaluation of the Performance of the Rainbird Sprinklers Spraying Warmer Water.

4.2.1. Performance of the Sprinkler Heads.

Rainbird Heads sprayed water at different temperatures. The objective was to evaluate the effects of changing water temperatures on the rotational delay of the heads operated in a freezing environment.

Despite the higher than normal temperature of the water, the heads did not rotate immediately at the beginning of the irrigation events. The 5000-s head‟s rotational delay ranged from 2.65 to 4.21 minutes. The 2045-pj head‟s rotational delay ranged from 3 to

4.64 minutes (Figure 7).

For the Rainbird 5000-s head, a data exploration showed that the lowest rotational delay value of the 28°C treatment was higher than the highest value of the 32°C and 36°C treatments, which suggested possible differences between the responses of the first treatment and the two latter. An exploration of the responses of the 32°C and the 36°C treatments did not indicate any difference. The 40°C treatment did not set itself apart from the 32°C and 36°C treatments.

Non-parametric tests were conducted to discriminate the responses of the treatments. A

Kruskal-Wallis test conducted on all samples indicated, at a significance level of 5%, that at least one treatment was different from the others (p-value = 0.013).

A Mann-Whitney test conducted to compare the 28°C treatment to the 32°C and 36°C treatments indicated, at a 5% significance level, that a difference existed between the

responses of the two compared groups (p-value = 0.03). This statement confirmed the observation of the data exploration and led to the conclusion that the 32°C and 36°C levels performed better than the 28°C level, in terms of rotational delay.

A Mann-Whitney test was not able to discriminate between the 32°C and the 36°C treatments (p-value = 0.6650), and between the 32°C and 36°C treatments on one hand and the 40°C treatment on the other (p = 0.0525), at a significance level of 5%, which confirmed the observation of the data exploration.

For the Rainbird 2045-pj, a data exploration did not indicate any difference between the treatments, as no treatment had values that were clearly higher than the values of any other treatment. In addition, a Kruskal-Wallis test indicated no difference between the groups, at a significance level of 5%.

A Mann-Whitney test to compare the delay times of the two heads for the same water temperatures showed no difference between the heads at any of the temperatures (0.11 < p-value < 0.67).

Overall the treatments can be ranked as in table 10.

4.50 4.00 3.50 3.00 Rainbird 5000-s 2.50

(mn) 2.00 Rainbird 2045-pj 1.50 1.00

Mean rotationMeantime delay 0.50 0.00 28 32 36 40 Water temperature (°C)

Figure 7: Mean rotational delay of the Rainbird models 5000s and 2045-pj tested at -25°C with water at different temperatures.

Table 10: Classification of the rotational delay of the sprinkler heads tested with water at different temperatures at -25°C.

Temperature (°C) Sprinkler heads 28 32 36 40

Rainbird 5000-s (a) 3.92 (b) 3.18 (b) 3.40 (b) 2.83

Rainbird 2045-pj (a) 4.06 (a) 3.68 (a) 3.70 (a) 3.50

The Rainbird 5000-s head displayed the same rotational velocity (0.1 rad sec-1 and a standard variation of 0.01 rad sec-1) at all water temperatures, and lower than the rotational velocity of the Rainbird 2045pj head (0.6 rad sec-1 and a standard deviation of

0.02 rad sec-1).

Both heads rotated at the same velocities as they did for water at 24°C, and for unheated heads. They sprayed at a rate of 2 gpm at all water temperatures.

4.2.2. Temperature in Freezer.

The ambient temperature in the freezer was not measured for every single application.

But the few measurements that were done indicated that the temperature dropped to -20 to -25°C between irrigation events. It rose at the end of irrigation events (Table 11).

Table 11: Temperature changes in the freezer for 10 minutes water at different temperature spray test.

Rainbird 5000-s Rainbird 2045-pj Water temperature Beginning End Beginning End (°C) temperature temperature temperature temperature (°C) (°C) (°C) (°C) -20.0 25.0 -20.0 26.0 -20.0 25.0 n/a 24.0 28 -20.0 25.0 n/a 26.0 n/a n/a n/a 25.0 -20.0 n/a -25.0 27.0 -20.0 n/a n/a 27.0 32 -20.0 25.0 n/a 27.0 -20.0 25.0 n/a 26.0 -20.0 25.0 -25.0 26.0 -20.0 23.0 n/a 30.0 36 n/a n/a n/a 29.0 n/a 29.0 n/a 30.0 -20.0 30.0 n/a 30.0 -20.0 28.0 n/a 28.0 40 -20.0 30.0 n/a 31.0 n/a n/a -20.0 33.0 n/a: data not recorded

4.3. Accumulation of Snow in Ohio.

4.3.1. Weather Station Distribution.

The data points used in this work were neither spatially clustered nor dispersed (Figure

8). This observation was confirmed by the ratio of observed mean distance to expected mean distance of 1.03, and meant that the points were equally distributed on the considered surface area. Consequently the values at these points can be valuably used in extrapolation operations.

The nearest neighbor observed mean distance was 16115.57 m.

Figure 8: Location of weather stations collecting snow depth data in Ohio and adjacent states.

4.3.2. Data Exploration.

4.3.2.1. Normal Distribution of the Data

Data from two hundred and fifty three weather stations were analyzed for each winter month. They were classified into ten classes of snow depth (in inches) shown on the x- axis (Figure 9). The y-axis shows the frequencies of the data values. The Data are unimodal in general, except the ones for the months of November and December. They range from 0 to 38 in., depending on the months, with the month of May having the smallest range and the month of February having the largest, and are centered approximately on the same values (mean and median approximately identical). The standard deviation varies in general, from four to seven, which indicates that the measured values are highly variable. Only the months of October and May have low standard deviation (1.8 and 0.6 respectively). The statistics show that the data are generally symmetrically distributed around the center, except for the months of October and May that have higher skewness values. These observations give preliminary pieces of evidence that the data may be normally distributed, in general, and that they are highly variable. The observation of general normal distribution, except for the months of

October, April and May was reinforced by the histograms (Figure 9).

The normal probability plots (Figure 10) is the plot of the quantiles of a normal distribution (straight line) versus the observed Data. The x-axis presents the normalized values of the Data. Data are aligned along the normal Plot. In general, the normal probability plots confirm the observations previously drawn from the histograms.

Figure 9: Descriptive statistics and histograms of the maximum snow depth at the weather stations.

Figure 10: Normal probability plots of the maximum snow depth at the weather stations.

4.3.2.2. Trend Surface Analysis.

Data points are projected onto perpendicular planes, and best fit polynomial lines are drawn through the projected points to model the trends in different directions (Figure 11).

The data in general show second order trends in the north and east direction (respectively

Y-direction and X-direction). The data also show slight increases in both directions as well, except for the month of November that shows a decrease after an increase. If the interpolation is performed on the base of these trends, short range variations will not be captured, and the whole operation will be influenced by the polynomial trends. The trends were removed before performing the Kriging interpolation.

October February

November March

December April

January May

Figure 11: Directional trends of the maximum snow depth at the weather stations.

4.3.2.3. Spatial Autocorrelation.

Except for the months of October and May, The semivariograms (Figures 12 and 13) in general show strong spatial autocorrelation between close data points, while far ones are not. The semivariograms are directionally influenced. The semivariograms give a piece of evidence that the data are accurate, and that anisotropy should be considered in the interpolation of the data values.

October

November

December

January Figure 12: Semivariogram of the maximum snow depths at the weather stations for the months of October, November, December and January. 53

February

March

April

May Figure 13: Semivariogram of the maximum snow depths at the weather stations for the months of February, March, April and May. 54

On the whole, the data exhibit characters of normality, except for the months of October,

April and May. Consequently, interpolation didn‟t require any normal transformation, except for the aforementioned months. A transformation y= was applied to data of the months that have high variability values, to reduce variability. The data exhibit increasing second-order trends in general in the southwest-northeast direction. The semivariogram indicates that the data are spatially autocorrelated, and that points that are located far away from each other have higher semivariogram values. Based on these observations, the surface interpolation was conducted with confidence using the Kriging method.

4.3.3. Kriging Interpolation.

4.3.3.1. Interpolation without neither Transformation nor Trend Removal.

The interpolation revealed a general increased trend in the maximum snow depth in the

Southwest-Northeast direction. For the months of February to May, the depth increases more in the South-North direction than in the Southwest-Northeast direction. The snow depth is high in some spots in the southern area, compared to the middle regions, for months like October, November, January, February and March, and detached from the northern regions (Figures 14 and 15).

The prediction variables (Table 12) show that the model used for interpolation did not yield highly accurate results. While the cross validation shows a mean error close to zero for all the months, the root mean square standardized is noticeably greater than 1 for the months of December, January, February and March. Only the interpolation of the months of October and May yield low root mean square and average standard error. An accurate

interpolation would have yielded a mean error, a root mean square and an average standard error close to zero, and a root mean square standardized close to one.

A comparison between point values on the maps (Figures 14 and 15) and the interpolated values shows that most measured high point values are located in lower interpolated values regions. The prediction error maps show that interpolation in the far northern and southern areas most likely contain significant errors.

Figure 14: Ordinary Kriging interpolated snow depths versus measured snow depths (October, November, December and January).

Figure 15: Ordinary Kriging interpolated snow depths versus measured snow depths (February, March, April and May).

Table 12: Ordinary Kriging cross validation results.

Prediction October November December January February March April May variables Mean -0.00646 0.03905 -0.005971 -0.04246 -0.02092 -0.02598 -0.002826 0.002198 RMS 1.379 6.414 4.036 5.062 4.991 4.323 3.745 0.6013 ASE 1.366 6.575 3.575 4.557 4.259 3.77 4.089 0.5819 MS -0.002185 0.005686 0.0006521 0.008389 -0.003326 -0.005139 -0.001219 0.003659 RMSS 1.002 0.9782 1.127 1.109 1.172 1.137 0.9211 1.033 Regression (0.398 X) (0.239 X) + (0.491 X) + (0.073 X) + (0.168 X) + (0.284 X) + (0.331 X) + (0.199 X) + Equation + 0.753 8.458 5.909 15.451 12.501 8.195 3.833 0.155 ASE: average standard error

59 MS: mean standardized

RMS: root mean square

RMSS: root mean square standardized

X: measured snow depth at the weather station

4.3.3.2. Interpolation with Trend Removal.

While a general increasing trend in the maximum snow depth in the Southwest-Northeast direction was shown by the interpolation results, the months of February to May seemed to record, more gain in the South-North direction than in the Southwest-Northeast direction. The snow depth is high in some spots in the southern area, compared to the middle regions, for months like October, November, March and May, and detached from the northern regions (Figures 16 and 17).

The prediction variables (Table 13) show that the model did not yield accurate results.

The cross validation shows a mean error close to zero for all the months but the root mean square standardized is greater than one for the months of March and May. Of all the months, only the months of October and May had low root mean square and average standard error.

A comparison between point values on the maps (Figure 16 and 17) and the interpolated values show that most measured high point values are located in lower interpolated values regions. In general, the prediction error maps show that interpolation in the far northern and southern areas most likely contain errors. For the months of January and

February, the prediction error maps show almost no error in the central region, while the month of May is shown as not predictable at all.

Figure 16: Kriging interpolated snow depths with trend removal versus measured snow depths (October, November, December and January).

Figure 17: Kriging interpolated snow depths with trend removal versus measured snow depths (February, March, April and May).

Table 13: Kriging interpolation with trends removal cross validation results.

Prediction October November December January February March April May variables Mean -0.002021 0.05417 -0.009453 -0.04872 -0.04728 0.005113 -0.01606 -0.002665 RMS 1.378 6.366 3.987 4.976 4.851 4.378 3.707 0.6405 ASE 1.265 6.655 3.724 4.77 4.509 3.929 4.009 0.5396 MS 0.0002624 0.00795 -0.002493 -0.009898 -0.01027 0.001662 -0.003783 -0.00008915 RMSS 1.079 0.9582 1.067 1.043 1.075 1.106 0.9286 1.155 Regression (0.449 X) + (0.261 X) + (0.503 X) + (0.106 X) + (0.220 X) + (0.272 X) + (0.370 X) + (0.177 X) + Equation 0.707 8.140 5.684 15.024 11.730 8.413 3.666 0.133 ASE: average standard error

MS: mean standardized

RMS: root mean square

RMSS: root mean square standardized

X: measured snow depth at the weather station

4.3.3.3. Interpolation after Data Transformation.

An analysis of the transformed data for all the months showed that non-normality and variability were mitigated by the method. The second order trend as well as the anisotropy quality of the data was conserved in all cases.

The interpolation over the transformed data revealed a general increasing trend in the southwest-northeast direction (Figures 18 and 19). The month of February particularly shows an increase in the south-north direction. The months of October and March show high values spots in the central and southern regions, discontinued from the northern and northeastern high values regions. The interpolation did not cover the whole Ohio territory because no snow depth was recorded in these areas originally, which caused the transformation to issue “null” values in these areas. The maps show general positive differences between the highest measured values and the interpolation values.

The prediction variables (Table 14) show that the model yielded accurate results. The cross validation shows mean error, root mean square and average standard error close to zero for all the months. The root mean square standardized close is also close to one for all the months.

The prediction error maps show in general that prediction errors are greater in the far northern and southern regions. They also show that lot of error was expected in the central regions while predicting for the months of October, December, March, April and

May.

Figure 18: Kriging interpolation over transformed data results (October, November, December and January)

Figure 19: Kriging interpolation over transformed data results (February, March, April and May)

Table 14: Kriging interpolation over transformed data, with trends removal cross validation results.

Prediction October November December January February March April May variables Mean 0.001903 0.01211 -0.00176 -0.005923 -0.005459 -0.008304 0.0009799 -0.003164 RMS 0.2667 0.9633 0.6056 0.7008 0.6738 0.6503 0.3275 0.2366 ASE 0.2644 1.006 0.5911 0.6824 0.6498 0.6191 0.3333 0.2259 MS 0.005405 0.0118 -0.002603 -0.008377 -0.008177 -0.01281 0.002662 -0.005669 RMSS 1.007 0.9591 1.023 1.027 1.037 1.047 0.983 1.035

Regression (0.156 X) + (0.228 X) + (0.479 X) + (0.099 X) + (0.244 X) + (0.291 X) + (0.286 X) + (0.014 X) + Equation 0.281 2.460 1.748 3.648 2.876 2.335 0.598 0.113 ASE: average standard error

MS: mean standardized

RMS: root mean square

RMSS: root mean square standardized

X: measured snow depth at the weather station

4.3.4. TIN Interpolation.

The TIN interpolation shows a generally scattered distribution of deep snow depth spots all over Ohio, with no apparent concentrations or trends (Figures 20 and 21). However, some trend in the Southwest-Northeast direction is noticeable for the months of October,

November, December, April and May.

The TIN interpolation placed higher measured values of snow depths in deeper snow accumulation regions and vice versa for the lower snow depth measurements.

Figure 20: Maps derived from measured point values with TIN interpolation (October, November, December and January).

Figure 21: Maps derived from measured point values with TIN interpolation (February, March, April and May).

Chapter 5: Discussion

5.1. Simulation of Ohio Winter Weather Conditions

To simulate Ohio‟s winter conditions, the experiment was conducted in a freezer. The measured ambient temperature in the freezer varied between -20 and -25oC at the beginning of irrigation events. The mean EMWT over Ohio was reported to be between

-22 and -24oC (Edgell, 1994). Even when heat tape was applied, the freezer temperature still dropped between -20 and -25oC. Its use did not affect the ambient temperature in the freezer.

Water spray raises humidity by misting (Kiu and Kang, 2006). Humidity affects the function of the spray heads in a cold environment, as condensing water vapor causes the formation of ice sheets inside and on the heads, and clogs the spray nozzles, or locks the rotation mechanism into a stationary position (Bodman, 1968). The humidity in the freezer was not monitored during the experiment. However it was likely higher in the freezer than on a spray field, as no air circulated in the freezer as it would have been in an outdoor setting (Kiu and Kang, 2006). 71

Another situation that can be expected in a field setting is the temperature variation. The laboratory experiment was conducted in an extreme environment, where the temperature of the air is kept low everyday for twenty four hours, except after an irrigation event. In a field setting, it is likely that the temperature will rise above freezing during the day and will drop at night. As a result, the irrigation system will not be exposed continuously to freezing conditions, as the probability of a seven day cold spell is low.

5.2. Effects of Spray Irrigation on Ambient Temperature.

The temperature in the freezer rose during irrigation events and dropped between events.

The same pattern was observed even when the heat tape was used. Water at mean room temperature has potential thermal energy. When sprayed, the water droplets cool as they release the heat to the surrounding air (Snyder and Paulo de Melo-Abreu, 2005). The ability of heat to be lost as heat flux explains the rise in temperature in the freezer during and immediately after irrigation events.

Anconelli and others (2002) found that below canopy irrigation raised the air temperature below canopy. That effect disappears as height increased. Kiu and Kang (2006) also observed an impact of water spray on field microclimate. Spray irrigation caused air temperature to rise up to a height of 4 m. That effect was mostly impressive at canopy level, where the temperature rise reached 15°C.

The rise in ambient temperature was an advantage for the heads operated at low temperature in a controlled environment. Before the surrounding air temperature dropped and caused remaining water in the head mechanisms to freeze, the heads would have

drained to the point where no ice formation clogs the water path as well as the spray nozzle. Unless the water is sprayed under canopy, and there is not much wind in the spray area, that effect cannot however be expected in a field setting. Even if the temperature changes, the rise will not be as high as it was observed in this experiment (40°C), but rather in the range of 10 to 15°C, as it was observed by Anconelli and others (2002) and by Kiu and Kang (2006).

5.3. Impacts of Cold Temperatures on the Water Flow Rate.

The spray heads tested in this study drained perfectly after every application, and did not fail to spray reliably at every trial. The cold environment did not impede the water flow, for any of the heads, as almost the same mean flow rate was observed for any considered head, whether the ambient temperature was low (-25°C) or high (+25°C), or whether the heads were heated or not. When the heads were drained before the temperature drops, no ice formed to occlude the passage of the water and to keep the heads from spraying at the next application. In his study, Bodman (1968) drained the irrigation systems‟ laterals at the end of application and found that the spray heads provided reliable flow at the next irrigation event. But Bounzoun (1979) used downward spraying heads, for which the water drained through the nozzle at the end of the irrigation events, in the design of an experimental irrigation system, and found that ice formations produced by dripping water clogged the nozzles.

The drainage of the heads was facilitated in this study by the removal of their check valve. These valves are installed on the head to avoid supply line drainage during low head and improve water conservation (Landscape and Irrigation, 2007). In the case of 73

treated wastewater disposal, draining will not affect the heads‟ function as long as water infiltrates the soil profile properly. However, emptying the riser/sprinkler sets after each irrigation event may likely increase the frequency at which water hammer occurs, at the beginning of irrigation events. A high frequency of water hammer occurrence may shorten the irrigation system‟s life. A suggested way to avoid water hammer impacts may be to use slow opening valves to supply water to the system at increasing operating pressure.

In this study, the drain used in the design was placed at the bottom of the riser, at room temperature. A total of 0.003 ft3 of water drained at the end of each irrigation event compared to 2.67 ft3 sprayed, a ratio of 1 to 893. In a field setting, the drain will be buried in a compartment filled with pea gravel located below the frost line (Appendix C), hence, protected against freezing when the ambient air temperature drops (Monnett, et al.,

1996).

The water flow rate experienced by the heads was either lower or higher than the desired flow rate. The differences between the observed and the expected flow rates can be explained by the setting of the pressure regulation at the valve. The divisions on the setting dial of the pressure regulator were in 5 psi increments for low values and 10 psi increments for high values, which made it impossible to fine tune the pressure setting. In addition, the relationship between pressure at the head and water flow rate was considered linear for a range of pressure of 25 to 50 psi, as published by the manufacturers (Figure 22), and pressure settings were inferred for a flow rate 2.8 gpm for each head. 74

Figure 22: Flow rate versus inlet pressure for the sprinkler heads tested.

5.4. Impact of Cold Temperatures on the Rotational Delay of the Heads.

All unheated heads, compared to heated heads, displayed rotational delays at low temperature. They did not display any rotational delay at room temperature (first day).

The delay could be explained by the formation of ice on the rotation mechanism of the heads, even after drainage of the unheated heads. To verify that point, dry heads were installed in the freezer at -25°C for a period of twenty four hours before any irrigation event was run. All the heads rotated at irrigation inception, after twenty four hours in the cold environment, but exhibited rotational delay on the following days.

The heat supplied by heat tape kept ice from forming on the rotation mechanism of the heated heads, which kept them from experiencing rotational delay. Caldwell and others

(2007) also found that heat tape was useful in keeping the sprinkler heads from freezing.

The rotational delay varied between the head models. The Toro s800 and the Hunter-PGC heads had the longest delays, with mean delays of most of the 10 minutes. The Rainbird

Heads had the shortest delays at 4 and 4.5 minutes mean delays respectively for the 5000- s and the 2045-pj models. None of the heads failed to spray at extreme low temperatures when drained. However rotation failure is of concern, as it can negatively affect water distribution characteristics.

Distribution characteristics will be negatively affected as rotational delay increases and irrigation time decreases, because most of the water sprayed during the irrigation event will be deposited in one direction. Givens (1965) found that rotation failures of revolving sprinklers caused low distribution uniformity, but Bodman (1968) reported that indexed position sprinklers as well as rotating impact-driver commercial type sprinklers still provided satisfactory distribution even with rotation failures. Bodman (1968) also reported that ice formed on the driving arm kept impact-driven sprinkler heads from rotating. Assuming that the irrigation time is estimated to be 20 minutes for a total of 0.2 in. application, a head that is discharging at a rate of 3 gpm and that is locked in one position for 9 minutes would deposit approximately 45% of its application in one direction, leaving only 55% to be distributed in all the directions. That would be the case of the Toro s800 and the Hunter-PGC models. That percentage will increase if a lower irrigation time is chosen. If one of the Rainbird models (2 gpm) of this study was used 76

instead of the Toro or the Hunter models for the same irrigation time, approximately 21% of the total amount of the water to be applied will be deposited in one direction, leaving

79% to be distributed in all the directions. That disparity makes the Rainbird heads more appropriate than the other two head models for the design of a system for winter irrigation. However, little information is available to determine whether an irrigation system designed with the Rainbird heads will achieve acceptable distribution uniformity.

An assumption that a satisfactory uniformity of distribution can be achieved by the

Rainbird models in the long run, after multiple irrigation events, was considered. A test was conducted in the laboratory setting to assess whether the heads stopped and locked randomly in any direction when the irrigation events end. Each one of the heads was run sixty times for 2 minutes, and the stopping angles at the end of each run, relative to 0°, were recorded. The distribution of the stopping angles were compared to a generated set of uniformly distributed values, using a Kolmogorov-Smirnov statistical test. Figure 23 shows the distributions of the stopping angle for both heads, and suggests that the stopping position of both heads were not uniformly distributed.

The Kolmogorov-Smirnov test yielded P-values of 0.78 and 0.24 respectively for the

5000-s and the 2045-pj heads, indicating, at 5% significance level, that there was not enough evidence to draw a conclusion about uniform distribution of the stopping points.

A comparison between the percentiles of the recorded values and a generated set of uniformly distributed values showed that the measured values departed from a uniform distribution (Figure 24).

The results of this test showed that the heads will most likely stop in the same direction every time the irrigation system is run. Consequently, almost the same direction will receive 20 to 40% of the total amount of water during successive irrigation events, leading to skewed distributions overtime. Hence, an irrigation system that is designed with either of these two irrigation head models will not provide a satisfactory distribution due to rotational delays. Same assessment applies to the Toro-s800 and the Hunter-PGC models.

° °

° ° ° °

° °

Figure 23: Repartition of the stopping positions of the Rainbird heads after sixty 2- minutes applications tests.

Measurement percentile Uniform set percentile

Figure 24: Charts of the percentiles comparisons between the distribution of the stopping position test values and a generated set of uniformly distributed.

5.5. Impact of Water Temperature on the Rotational Delay of the Rainbird Spray Head Models.

Spraying warmer than 24°C water did not help eliminate the rotational delay exhibited by the Rainbird heads. It made a difference in the rotational delay of the 5000-s model, from

28°C to 32°C, but did not affect the rotational delay of the 2045-pj model.

Impact sprinkler heads have open rotation mechanisms, and their rotation is caused by impact of a driver arm that is activated by the pressure of the water exiting a vane.

Therefore, the rotation mechanism is not bathed by the water that is being sprayed, and any ice formation that is holding the driving arm is not directly affected. Compared to the

impact sprinkler heads, the rotation mechanism of the rotor sprinkler heads is enclosed in a sleeve. The flowing water bathed the mechanism before exiting the spray nozzle and transferred heat by convection to the enclosed mechanism to accelerate the thaw of ice formations in the head. As a result, warmer water caused the head to rotate faster.

5.6. Comparison of Irrigation System Winterization.

Table 15 compares the head drainage method used in this study for irrigation system winterization to the heat tape method used by Caldwell and others (2007).

Table 15: Cost-benefit comparison between the head drainage and the heat tape methods for year-round irrigation system winterization.

Head drainage Heat tape

Installation - Total cost estimated at - Caldwell and others (2007) $0.16 ft-2 for worst case reported $1.79 ft-2. Current scenario, with current market prices will put the total market prices cost of winterization at - Easy approximately $2.48 ft-2 - Small time consumption - Time consuming Operation - Periodic check to assure that - yearly electricity consumption and the drains are not clogged estimated at 0.54 KWh ft-2, a Maintenance total of $0.053 ft-2 - periodic check to lower risks of electrical short Requirements - Distribution lines and drain - Easy access to electricity compartments installed below frost line - low water table - slow opening valve can help alleviate water hammer due to drainage in water conduction lines Functionality - High probability of poor - low probability of rotational distribution uniformity delay - low risks of flow failure - High probability of satisfactory distribution uniformity - low risks of flow failure

5.7. Snow Depth Interpolation Methods Comparison.

This work used four different methods to interpolate measured snow depth data from two hundred and fifty three weather stations in Ohio, Michigan, West-Virginia, Pennsylvania and Kentucky. The methods used were Ordinary Kriging without trend removal,

Ordinary Kriging with trend removal, Ordinary Kriging on transformed data and trend removal, and TIN Development.

A comparison (Table 16) showed that the Kriging methods revealed perfectly the general

Southwest-Northeast direction increasing trends observed previously during the data exploration. The TIN Methods did not reveal clearly that trend.

All the methods revealed that deep accumulation of snow can occur independently of the southwest-northeast trend in the central and southern regions, and during all the winter months, except the month of December and April.

The prediction parameters showed that the Kriging methods did not perform the interpolation of the snow depth data satisfactorily, even with trends removal. The interpolation with transformation and trend removal yielded better results, but differences between the measured values and the interpolated values are greatly noticeable. For example, point values in the south-east region are as high as 5 in. for the month of

October, but the highest interpolation values is 2.7 in. (0.8 versus 0.5 on the map in

Figure 18). In the north-east region, point values are as high as 34 in. for the month of

December, but the highest interpolation value is 24 in., a difference of approximately 9 in. (5.8 versus 4.9 on the map in Figure 18). The underperformance of the Kriging methods can be explained by the strong heterogeneity that characterizes the original snow depth data. In an attempt to create a smooth surface, the Kriging interpolation generally placed higher snow depth values in lower interpolated values areas, and vice-versa, and this resulted in the estimation of average values. The TIN interpolation performed

differently, placing the measured values exactly in like depth areas, but the results is a non-smooth surface, where extremes are emphasized. Also, the TIN interpolation did not issue any assessment means to allow evaluation of the method, which represents a disadvantage over the Kriging methods.

On one hand, the Kriging methods provided interpolated depth values that do not agree with the measured values, and the prediction parameters are not satisfying most of the time. On the other hand, the TIN interpolation, while issuing interpolated values that agree with the measured values, did not provide any means to assess the validity of the method. However, this method can be supported by the fact that the data used originated from spatially uniformly distributed weather stations. As the goal of this study was to reveal the maximum depths at which snow accumulates over the State of Ohio during the winter months, the TIN interpolation stood out to be the best method to apply.

Table 16: Comparison between interpolation methods.

Methods Generally Increasing High values Prediction Difference Interpolation increasing trends in the spots in the Parameters between covers the trends in the S-N direction center and interpolated whole Ohio SW-NE southern values and territory direction regions measured values Kriging Yes February to October, Not good Yes Yes Interpolation May November, and without trend January to removal nor March transformation

84 Kriging Yes February to October, Not good Yes Yes Interpolation May November, with trend March and May removal only Kriging Yes February October and Good Yes No Interpolation March with trend removal and transformation TIN Slight for 5 No n/a n/a No No Interpolation months only n/a: data not recorded or not applicable

5.8. Maximum Snow Depth in Ohio.

The maximum snow depth in Ohio, according to forty eight years of record, ranges from

16 to 34 in. with a median value of 21.5 in (Figure 25). The maximum depths occur generally in January and February, but some counties record their maximum in

November and December (Columbiana, Mahoning, and Trumbull). Deep snow covers are recorded mostly in Northeast Ohio (Ashtabula, Geauga and Lake), where some specific points peak at 34 in. (Chardon), due to Lake Erie effect. Schmidlin (1989) found that the snow depth in Chardon reaches 34 in. sometimes during winter. He also found that deep snow cover of about 20 in. and plus are rare events in Ohio, and occur about every thirty to fifty years. Southern and Western Ohio also receives high snow cover depths

(Highland and Mercer)

Traces of snow are observed in some counties in May and October. In most of Ohio, snow really starts accumulating in November.

Table 17: Monthly maximum snow depth for Ohio counties (inches) according to 48 years data record.

COUNTIES OCT NOV DEC JAN FEB MAR APR MAY Adams 2.1 14.6 12.3 23.7 17.6 11.4 6.9 0.0 Allen 2.2 15.2 15.7 19.8 18.0 15.3 6.8 0.0 Ashland 2.5 15.0 16.5 22.0 19.4 17.9 11.1 1.0 Ashtabula 8.0 25.0 30.5 25.6 29.6 25.6 16.5 1.7 Athens 1.3 17.2 9.9 20.8 16.9 10.5 16.2 0.0 Auglaize 3.6 18.8 13.5 19.8 16.9 10.7 6.2 0.0 Belmont 2.5 27.0 15.2 26.0 19.8 16.7 11.7 3.0 Brown 2.7 12.0 11.6 24.1 17.9 13.9 8.0 0.0 Butler 2.7 9.7 15.3 18.3 15.7 16.0 4.5 0.0 Carroll 2.3 21.4 19.8 20.3 16.9 14.0 13.9 2.5 Champaign 4.5 15.5 13.9 17.4 17.8 10.6 6.5 0.0 Clark 4.8 19.6 13.8 18.9 17.7 11.6 5.2 0.0 Clermont 2.4 8.3 10.5 20.7 16.3 11.9 5.0 0.0 Clinton 3.5 19.3 16.9 24.2 22.7 21.5 6.1 1.9 Columbiana 3.0 28.5 21.2 18.0 18.9 15.8 19.3 0.6 Coshocton 1.0 22.0 15.8 24.9 17.0 12.3 14.7 0.4 Crawford 1.4 18.7 18.2 20.8 20.0 17.8 8.9 0.6 Cuyahoga 7.8 19.7 20.3 20.6 17.5 15.4 10.2 0.9 Darke 2.5 21.6 17.7 19.1 18.7 13.6 6.0 0.0 Defiance 3.3 11.8 16.7 19.4 23.0 15.0 9.5 2.4 Delaware 2.5 14.8 16.1 23.7 20.1 12.0 11.5 1.0 Erie 3.1 12.8 17.9 18.3 20.3 19.4 10.0 1.4 Fairfield 2.6 13.2 13.4 20.6 16.5 10.6 12.6 0.9 Fayette 4.8 20.8 16.1 22.4 19.7 16.7 5.9 1.1 Franklin 2.7 16.3 13.8 17.4 17.6 10.7 5.6 1.3 Fulton 3.0 10.5 16.0 18.4 22.0 15.5 10.9 2.7 Gallia 0.8 19.4 8.4 21.4 15.2 20.0 2.8 0.0 Geauga 10.0 23.5 33.6 25.7 30.6 29.2 17.0 0.3 Greene 3.9 17.2 16.2 19.1 20.4 18.0 4.6 1.3 Guernsey 1.8 24.8 15.8 22.8 18.8 15.3 11.9 0.0 Hamilton 2.3 10.0 13.8 16.8 13.2 11.1 3.4 0.0 Hancock 1.0 12.8 15.8 21.4 23.3 14.6 8.4 1.3 Hardin 4.6 14.5 16.4 20.0 20.3 14.2 7.2 0.4 Harrison 2.0 23.6 14.1 23.7 17.9 13.7 11.8 4.9 Henry 2.6 12.8 15.1 18.2 22.5 15.0 9.3 3.9 Continued 86

Table 17 continued Highland 3.0 16.1 15.1 26.0 16.1 14.5 7.4 0.7 Hocking 3.0 10.8 11.7 22.9 15.9 12.0 18.7 0.0 Holmes 1.9 17.3 18.8 21.2 17.3 12.4 11.3 0.1 Huron 3.0 12.1 16.8 20.2 21.9 19.1 8.2 1.0 Jackson 2.5 21.9 9.0 19.8 16.9 17.7 7.2 0.0 Jefferson 1.6 32.6 14.4 21.9 25.7 14.3 7.8 3.2 Knox 1.6 17.4 13.5 21.9 18.3 15.8 11.9 0.9 Lake 9.5 23.6 31.8 23.8 23.9 24.5 14.9 1.0 Lawrence 1.0 14.9 8.0 21.5 16.0 15.1 3.3 0.0 Licking 1.0 18.6 12.2 20.9 19.8 13.0 14.8 1.9 Logan 3.9 14.8 13.6 19.9 15.2 11.1 5.0 0.0 Lorain 4.2 15.0 17.6 18.0 18.0 21.9 8.8 0.6 Lucas 2.6 11.0 16.3 17.4 20.9 15.7 8.9 3.0 Madison 5.0 17.8 14.9 18.8 17.8 11.9 5.9 0.0 Mahoning 3.0 28.4 18.8 16.8 18.1 15.4 12.9 0.5 Marion 4.8 14.3 16.8 22.5 20.8 14.8 7.5 0.4 Medina 3.3 15.0 20.0 17.6 18.7 19.8 8.0 1.0 Meigs 1.0 10.6 8.0 20.2 16.9 14.7 4.6 0.0 Mercer 3.1 19.9 17.0 25.9 26.9 19.9 6.6 0.0 Miami 3.6 18.9 14.1 18.7 14.9 10.7 6.9 0.0 Monroe 2.9 26.0 13.6 20.0 16.3 13.4 4.3 0.0 Montgomery 2.2 14.5 12.1 17.9 13.6 13.6 4.2 0.5 Morgan 0.8 21.1 12.8 20.6 22.2 11.8 12.4 0.0 Morrow 0.9 14.5 15.8 22.7 18.9 14.6 8.8 0.0 Muskingum 2.0 19.8 13.5 20.9 24.3 12.0 15.5 0.0 Noble 2.7 25.5 14.3 19.9 18.0 15.6 8.5 0.0 Ottawa 2.1 10.6 17.8 16.7 16.0 13.1 9.7 2.0 Paulding 4.7 11.1 16.4 16.7 17.7 14.3 8.0 0.3 Perry 1.9 19.7 17.8 21.0 17.4 11.2 14.8 0.0 Pickaway 4.1 15.3 14.1 18.8 14.3 10.6 17.8 0.0 Pike 2.0 20.7 11.3 20.7 16.3 13.4 4.3 0.0 Portage 9.8 22.3 21.9 22.5 20.9 22.1 16.7 0.2 Preble 1.9 11.4 15.4 17.9 16.4 14.9 4.9 0.0 Putnam 1.9 11.2 16.0 20.0 19.1 15.9 7.2 1.3 Richland 2.1 12.2 17.0 21.7 20.0 17.9 9.5 0.9 Ross 2.9 15.8 12.7 21.5 15.0 12.9 17.8 0.0 Sandusky 2.0 12.8 17.4 18.4 18.8 14.9 9.5 2.5 Scioto 1.2 16.2 9.8 21.7 18.8 13.3 4.8 0.0 Continued 87

Table 17 continued Seneca 1.7 16.4 17.6 21.8 22.5 17.4 8.2 2.0 Shelby 4.9 19.4 14.5 16.4 14.0 10.0 6.0 0.0 Stark 2.4 20.3 21.4 19.9 20.0 15.3 14.0 0.0 Summit 3.6 15.0 21.4 17.1 19.6 16.6 7.6 0.6 Trumbull 7.0 26.4 25.4 22.7 27.0 22.3 13.2 1.6 Tuscarawas 2.0 21.4 14.7 20.6 17.1 12.1 15.6 0.0 Union 4.4 15.8 17.0 20.1 18.4 11.5 8.6 0.0 Van Wert 2.5 15.4 15.1 22.5 22.4 17.4 7.0 0.0 Vinton 3.0 13.9 8.4 19.1 14.2 14.7 15.4 0.0 Warren 3.0 14.3 14.4 19.4 19.4 17.7 5.1 1.3 Washington 1.4 23.0 12.9 19.6 17.0 19.8 9.8 0.0 Wayne 2.9 18.0 19.8 18.9 19.9 16.7 9.6 1.0 Williams 3.3 12.5 18.5 21.5 24.4 19.0 12.5 1.5 Wood 2.3 12.8 13.3 18.5 20.9 14.8 8.3 3.5 Wyandot 3.5 16.0 19.9 22.9 23.9 15.8 9.0 1.0

Figure 25: Maximum snow depths in Ohio according to 48 years data record.

5.9. Limitations.

This project used data reported by NCDC that claims that they were assessed to remove inaccuracies and to compensate for missing data. However, NCDC advised that the data should be used carefully, as they are results of statistical manipulations.

The interpolation methods used in this analysis for data interpolation are not proven entirely flawless, and the estimated values may be different from the real values, which may cause field conditions to be quite different from the conclusions of this study.

Only few weather stations collecting snow depth data were located in the northern region

(Figure 8). Due to this, the interpolation results may have some disparities.

Based on these limitations, it would be advisable to use the results of this study with some reservations. The best course of action would be to study local conditions before implementing any project related to snow cover.

Chapter 6: Conclusion and Recommendations for Future Work

6.1. Conclusion.

Winter temperature and snow depth are challenges for year-round wastewater irrigation spray system design in Ohio.

Revolving sprinklers were tested in a commercial grade freezer, and drained at the end of each event. The freezer was successful at simulating an environmental condition similar to one that can be expected in Ohio during winter. The spray irrigation caused the rise of the ambient temperature in the freezer, which could have reduced ice formation in the irrigation heads. In a field setting, temperature variations will not necessarily be important. Consequently, no significant impact on sprinklers‟ function should be expected.

Sprinkler head drainage was facilitated by the use of king drain at the base of the riser and the removal of the sprinkler heads „check valve. Cold temperature did not impede water flow as long as the heads were drained after irrigation events.

Cold temperatures caused the delay of head rotation even when the heads were drained.

Rotational delay of the sprinkler head may lead to low distribution uniformity, in the short term as well as in the long run, which may increase the chance of reclaimed wastewater pounding and runoff.

The spray of water at warmer temperature promoted a small reduction of the rotational delay of the Rainbird rotor sprinkler head, but did not eliminate it. It did not affect the delay of the impact sprinkler. The difference is potentially due to “enclosure” of the rotor sprinkler head. Hence warming the treated wastewater before spraying may not help improve the distribution uniformity at cold temperature.

The choice between the irrigation head drainage method and the use of heat tape for winterization of irrigation system will depend on the available budget, and the level of distribution uniformity desired. The heat tape experiences more uniform distribution due to no rotational delay, but its installation and operation costs are 20 fold higher than that of the drainage method.

A study of the maximum snow depth in Ohio, based on 48 years of data record, revealed that no area may be suitable for the usage of hi-pop up sprinkler for the design of year- round spray irrigation system. All the counties may experience at least 12 in. snow depth during the months of January and February. Therefore, at least two months of storage may be necessary for wastewater spray irrigation systems in Ohio.

The alternative option would be to use shrub irrigation sprinkler placed on riser. A developed reference map suggests a riser height of 35 in. in the north-eastern snowbelt,

30 in. in the central region and 20 in. in the south-western region.

Overall, this study demonstrated that drainage of shrub sprinklers mounted on risers can potentially be used to operate spray irrigation sprinklers system year-round in Ohio.

6.2. Recommendations for Future Work.

Winter irrigation of treated wastewater in Ohio appears feasible; however more work is needed to optimize this technique:

- The laboratory findings need to be studied in field scale experiments, with wastewater

effluent and multiple replications for more statistical validation.

- The environmental impact of distribution uniformity should be measured.

- Other means to alleviate the rotational delay should be explored:

o Using the wedge drive sprinkler model from Rainbird advertised as having anti-

jamming mechanism.

o Coating the rotation mechanism parts with a hydrophobic material to expedite

the drainage of the head and prevent freezing water droplets from sticking to

gearing parts.

o Placing a miniature source of heat energized by solar power in the sprinkler‟s

compartment.

- The development of a stationary sprinkler that displays a satisfactory distribution

pattern in sub-freezing conditions may be explored.

- Ways to alleviate misting during spray should be studied.

Studies should also be conducted to investigate the temperature and rate of infiltration of water in Ohio‟s soils under sub-freezing temperatures.

Bibliography

Abernathy, A.R, Zirschky, J and Borup, B. 1985. Overland Flow Wastewater Treatment at Easley, S.C. Journal of the Water Pollution Control Federation. 1985, Vol. 57, 4, pp. 291-299.

Anconelli, S., et al. 2002. Micrometeorological test of microsprinklers for frost protection of fruit orchards in Northern Italy. Physics and Chemistry of the Earth. 2002, Vol. 27, pp. 1103-1107.

Arizona Cooperative Extension. 1999. irrigation system selection. AZ master gardener manual: irrigation. [Online] 1999. [Cited: 07 05, 2009.] http://ag.arizona.edu/pubs/garden/mg/irrigation/Selection.html.

Asano, T., et al. 2007. Water Reuse: Issues, Technologies, and Applications. 1st ed. s.l. : Mc GrawHill, 2007.

Belanger, G, et al. 2002. climate change and winter survival of perennial forrage crops in Eastern Canada. 2002, pp. 1120-1130.

Bodman, G.R. 1968. The Development of Indexed Position Sprinkler heads for Distribution of Sewage Effluent in Wooded Areas during Freezing Temperatures. The Pennsylvania State University. 1968. p. 12, Thesis.

Bounzoun, J. 1979. Freezing Problems associated with spay irrigation of wastewater during the winter; Special report. U.S. Army Cold Regions Research and Engineering Laboratory. Hanover, New Hampshire : s.n., 1979. 12 pages.

Bradley, B.D, et al. 2002. Evaluation of Onsite Wastewater Treatment Technologies Using Sustainable Development Criteria. Clean Technologies and Environmental Policy. 2002, Vol. 4, pp. 87 - 99.

Bridges, C. 2008. Estimating forest site productivity using spatial interpolation. Southern journal of Applied Forestry. 2008, Vol. 32, 4, pp. 187-189.

Burakowski, E, et al. 2008. Trends in wintertime climate in the northeastern United States: 1965-2005. 2008, p. D20114.

Burks, B and Minnis, M.M. 1994. Onsite Wastewater Treatment Systems. s.l. : Hogarth House Ltd, 1994. pp. 1-4.

Caldwell, H., Mancl, K. and Quigley, M.F. 2007. The effects of year-round irrigation on landscape plant quality and health in Ohio. The Ohio Journal of Science. 2007, Vol. 107, 4, pp. 76-81.

Crites, R.W. 1984. Land Use of Wastewater and Sludge. 1984, Vol. 18, 5, pp. 140A - 145A.

Demchak, K. 2007. Frost Protection: Tips and Techniques. New York Berry News. 04 23, 2007, Vol. 6, 4.

Dyer, J and Mote, T. 2006. spatial variability and trends in observed snow depth over North America. 2006, p. L16503.

Edgell, J.D. 1994. Extreme Minimum Winter Temperatures in Ohio. Ohio Journal of Science. 1994, Vol. 94, 3, pp. 41-54.

Elsasser, H and Burki, R. 2002. climate change as threat to tourism in the alps. 2002, pp. 253- 257.

EPA. 2008. Clean Watersheds Needs Survey 2004: Report to Congress. s.l. : EPA, 2008. p. 10.

—. 2002. Onsite Wastewater Treatment Systems Manual. Office of Water, US Environmental Protection Agency. 2002. Manual. http://www.epa.gov/nrmrl/pubs/625r00008/html/html/625R00008.htm. EPA 625/R-00/008.

Givens, F.B. 1965. The development of Irrigation sprinklers heads for winter distribution of treated sewage effluent in Wooded areas. Department of Agricultura Engineering, The Pennsylvania State University. 1965. pp. 1-42, Thesis.

Gyozo, J. 2007. Adaptive smoothing of valleys in DEMs using TIN interpolation. Computers & Geosciences. 2007, Vol. 33, pp. 573-585.

Hargett, D.L, Tyler, J.E and Siegrist, R.L. 1981. Soil Infiltration Capacity as Affected by Septic Tand Effluent Application Strategies. Small Scale Waste Management Publication. 1981, Vol. 8, 10, p. 13.

Hathaway, R.J. and Mitchell, D.T. 1985. Sand Filtration of Septic Tank Effluent for all Seasons Disposal by Irrigation. New Orleans : ASAE, 1985. pp. 343-350.

Henry, C.D, et al. 1954. Sewage Effluent Disposal Through Crop Irrigation. Sewage and Industrial wastes. Feb 1954, Vol. 26, 2, pp. 123-135.

Hickcox, D.H. 1984. Temperature extremes in Ohio during 1981. Ohio Journal of Science. 1984, Vol. 84, 1, pp. 11-15.

Hunter Industries. 1997. Irrigation System Freeze Protection. San Marcos, California : s.n., 1997. p. 1, Notes.

Kiu, H.J. and Kang, Y. 2006. Effect of sprinkler irrigation on microclimate in the winter wheat field in the north China Plain. Agricultural water management. 2006, Vol. 84, pp. 3-19.

Koc, A.B., et al. 2000. Automated cycled Sprinkler Irrigation System for Frost Protection of Appled Buds. Applied Engineering in Agriculture, ASAE. 2000, Vols. 0883-8542, 00, pp. 1603- 231.

Landscape and Irrigation. 2007. "Smart" products: harnessing the benefits of water-efficient technology. landscape and Irrigation. 2007, Vol. 31, 7, p. 20(7).

Leland, D.E., Wiggerst, D.C. and Burton, M. 1979. Winter Spray Irrigation of Secondary Municipal Effluent. Journal of Water Pollution Control Federation. 1979, Vol. 51, 7, pp. 1850- 1858.

Lo, C.P. and Yeung, A.K.W. 2007. Concepts and Techniques of Geographic information Systems. s.l. : Pearson Prentice Hall, 2007. p. 532.

Mancl, K, Rowan, M and Caldwell, H. 2004. On-site sprinkler irrigation on treated wastewater in Ohio. 2004, p. 3.

Mancl, K. Gustafson, R. 2005. Mound System: Pressure Distribution of Wastewater, Design and construction in Ohio. s.l. : The Ohio State University Extension, 2005. Vol. bulletin 829.

Mancl, K.M and Slater, B. 2001. Suitability assessment of Ohio's soils for soil-based wastewater treatment. Ohio Journal of Science. 2001, Vol. 101, 3/4, pp. 48-56.

Monnett, G.T, Reneau, Jr and Hagedorn, G. 1996. Evaluation of Spray irrigation for On-site Wastewater Treatment and Disposal on marginal soils. Water Environment Research. 1996, Vol. 68, 1, pp. 11-18. URL: http://www.jstor.org/stable/25044680.

Mote, C.R., Kleene, G.W. and Allison, J.S. 1993. Environmental Impact of Irrigation Lawns with Treated Domestic Wastewater. Journal of Environemtal Health. 1993, Vol. 55, 5, pp. 17-23.

NCDC, NOAA. 2007. US snow climatology background. project description. [Online] Decembre 3, 2007. [Cited: january 28, 2009.] http://www.ncdc.noaa.gov/ussc/USSCAppController?action=scproject#QC1.

NOAA. 2003. atmospheric variables. NOAA.GOV. [Online] december 11, 2003. [Cited: january 28, 2009.] http://www.srh.noaa.gov/ffc/html/gloss1.shtml.

Ojima, D, et al. 1999. Potential climate change impacts on water resources in the Great Plains. december 1999, Vol. 35, 6, pp. 1443-1454.

Pennypacker, S.P, Sopper, W.E and Kardos, L.T. 1967. Renovation of Wastewater Effluent by Irrigation of Forest Land. Journa of Water Pollution Control Federation. 1967, Vol. 39, 2, pp. 285-296.

PL-92-500. 1972. Federal Water Pollution Control Act. 1972. Public law. http://www.glin.gov/view.action?glinID=67980. .

Pound, C.E and Crites, R.W. 1973. U.S. EPA 660/2-73-006b. Washington, D.C. : USEPA, 1973.

Powell, A.A and Himelrick, D.G. unknown. Principles of Freeze Protection for Fruit Crops. The Alabama Cooperative Extension System. [Online] unknown. http://www.aces.edu/dept/peaches/frzactive.html.

Powell, M, Dallemand, B and A, Mayo. 1998. wastewater pond: Design and Construction. Agricultura Experiment Station and Cooperative Externsion Service, Kansas Stater University. 1998. factsheet.

Reed, S.C, Crites, R.W and Wallace, A.T. 1985. Problems with rapid infiltration - A post mortem analysis. 1985, Vol. 57, 8, pp. 854-858.

Rice, R.C and Bouwer, H. 1984. Soil-Aquifer Treatment Using Primary Effluent. Journal of the Water Pollution Control Federation. 1984, Vol. 56, 1, pp. 84-88.

Rochester, E. 1995. Landscape Irrigation Design. 8th. Auburn : ASAE, 1995.

Schmidlin, T. 1996. climate and weather. [book auth.] Peacefull L. A geography of Ohio. s.l. : Kent State University Press, 1996, chapter two, p. 17.

Schmidlin, T.W. 1989. climatic Summary of snowfall and Snow Depth in the Ohio Snowbelt at Chardon. 1989, Vol. 89, 4, pp. 101-108.

Schmidlin, T.W, Edgell, D.J and Delaney, M.A. 1992. Design ground snow loads for Ohio. 1992, pp. 622-627.

Siegrist, R, Andeson, D.L and Hargett, D. 1986. Large Soil Absorption Systems for Wastewaters from Multiple Home Developments. EPA. 1986. EPA/600/S2-86/023.

Siracusa, G. and La Rosa, A.D. 2006. design of a constructed wetland for wastewater treatment in a Sicilian town and environmental evaluation using the emergy analysis. 2006, Vol. 197, pp. 490-497.

Smith, B. 2008. Landscape Irrigation Management. Part 4: Winter Irrigation & Winterization. Clemson Extension, Clemson University. 2008. Fact Sheet. 98

Smith, R.G and Schroeder, D.E. 1985. field Studies of the Overland Flow Process for the Treatment of Raw and Primary Treated Municipal Wastewater. Journal of the Water Pollution Control Federation. 1985, Vol. 57, 7, pp. 785-794.

Snyder, R and Paulo de Melo-Abreu, J. 2005. Frost Protection: fundamentals, practice and economics - Volume 1. s.l. : FAO, 2005. pp. 162-163. Vol. 1.

Sullivan, M. 2008. personnal communication. [interv.] K.S. Gunn. 2008.

Tarjuelo, J. M., et al. 1999. Analysis of uniformity of sprinkler irrigation in a semi-arid area. Agricultural Water Management. 05 1999, Vol. 40, 2-3, pp. 315-331.

Tchobanoglous, G and Burton, L. 1991. wastewater engineering: treatment, disposal and reuse. 3rd. s.l. : McGraw hill, Inc, 1991.

USEPA. 2006. Facilities Database (needs survey) - Frequently asked questions. Clean Watersheds Needs Survey. [Online] USEPA, march 8, 2006. [Cited: 02 21, 2009.] http://www.epa.gov/OW-OWM.html/mtb/cwns/1996rtc/faqwfd.htm.

Wang, Jin-Feng, Li, Lian-Fa and Christakos, George. 2009. Sampling and Kriging Spatial Means: Efficiency and Conditions. Sensors. 2009, Vol. 9, pp. 5224-5240.

Water Pollution Control Federation. 1990. Natural Systems for Wastewater Treatment. [Manual]. 1990.

Webster, R.A. 1954. Discussion: Domestic and Industrial Waste Treatment Plants. Sewage and Industrial Wastes. 1954, Vol. 26, 2, pp. 123-135.

Zvomuya, F, Rosen, C.J. and Gupta, S. 2006. Nitrogen and Phosphorus leaching from growing season versus year-round application of wastewater on seasonally frozer lands. 2006, Vol. 35, pp. 324-333.

Appendix A: Experimental Data

Table 18: Stopping positions data for the Rainbird heads after sixty 2-minutes application tests.

Rainbird 5000-s Rainbird 5000-s Rainbird 2045-pj Rainbird 2045-pj stop stop stop stop points on points on points on points on graduated angle graduated angle graduated angle graduated angle sheet from 0° sheet from 0° sheet from 0° sheet from 0° 4.5 52.5 4 45 1 0 24.5 352.5 7 90 2 15 1.7 10.5 16.5 232.5 7.5 97.5 1.5 7.5 1 0 1 0 9.5 127.5 24.5 352.5 12 165 6.5 82.5 11 150 22 315 4.5 52.5 11 150 11.5 157.5 23 330 11.25 153.75 16.5 232.5 11.5 157.5 22.5 322.5 1.75 11.25 4 45 13.5 187.5 23.75 341.25 3.5 37.5 14 195 16 225 1 0 1.75 11.25 15 210 17 240 1 0 12 165 1 0 18.5 262.5 3.5 37.5 5.5 67.5 1.5 7.5 20.5 292.5 5 60 4.25 48.75 3.5 37.5 21 300 6 75 22 315 1.5 7.5 20.5 292.5 6.5 82.5 21.5 307.5 6.5 82.5 18.5 262.5 7 90 2 15 13 180 18 255 6.5 82.5 14 195 15 210 18 255 7 90 19.5 277.5 15 210 17 240 8 105 10 135 10 135 15.5 217.5 8.75 116.25 12 165 8.5 112.5 Continued

100

Table 18 continued 15 210 9.5 127.5 22.5 322.5 4 45 14.5 202.5 11 150 16 225 7.75 101.25 14 195 11.25 153.75 22.25 318.75 11.25 153.75 13.5 187.5 11.75 161.25 19.5 277.5 5.5 67.5 12.5 172.5 12 165 11.5 157.5 10.25 138.75 12.5 172.5 12 165 15 210 19.5 277.5 11 150 13 180 12.5 172.5 4 45 9.25 123.75 14.5 202.5 21 300 6.25 78.75 9 120 15.5 217.5 24.5 352.5 15.5 217.5 8 105 16 225 11.5 157.5 21 300 7 90 6.75 86.25 3.5 37.5 7 90

101

Table 19: Performance of the Rainbird heads spraying water at different

temperatures under freezing conditions.

Rainbird 5000s Rainbird 2045-pj

) ) ) )

al al

1 1 1 1

- - - -

rate

sec sec sec sec

gVel

Water Water (°C) temperature Rotation (mn) delay An (rad StVar (rad Flow (gpm) Rotation (mn) delay AngVel (rad StVar (rad Flow (gpm) 3.83 0.10 0.01 2 3.72 0.67 0.03 2 3.81 0.10 0.01 2 3.87 0.62 0.02 2 28 3.84 0.11 0.01 2 4.64 0.66 0.02 2 4.21 0.10 0.01 2 4.03 0.66 0.03 2 2.70 0.10 0.01 2 3.80 0.65 0.03 2 3.58 0.10 0.00 2 3.83 0.64 0.02 2 32 2.85 0.10 0.01 2 3.36 0.63 0.02 2 3.60 0.11 0.01 2 3.75 0.61 0.03 2 3.25 0.10 0.01 2 3.88 0.64 0.03 ≈2 3.19 0.11 0.01 2 3.54 0.64 0.03 2 36 3.43 0.10 0.01 2 3.74 0.60 0.02 2 3.72 0.10 0.02 2 3.63 0.66 0.21 2 2.65 0.11 0.01 2 3.02 0.64 0.04 2 2.69 0.11 0.01 2 3.79 0.62 0.02 2 40 3.15 0.10 0.01 2 3.29 0.63 0.03 2 n/a n/a n/a n/a 3.90 0.63 0.02 2 n/a: data not recorded

102

Appendix B: Collected Snow Depth Data

Table 20: Snow depth data at the weather stations.

Maximum recorded snow depth (inches) Stations Latitude Longitude Oct Nov Dec Jan Feb Mar Apr May Acmetonia Lock 3 40.5333 -79.8167 1 17 13 18 24 11 6 1 Adrian 2 NNE 41.9167 -84.0167 3 7 14 18 23 17 8 0 Albion 42.25 -84.7667 2 11 25 24 19 15 6 0 Alexandria 4 WSW 40.0833 -82.6833 1 19 10 20 20 12 6 2 Amesville 39.4 -81.9667 0 4 4 11 16 6 12 0 Angola 41.6333 -84.9833 4 15 20 23 22 16 14 2 Ann Arbor University of Michigan 42.3 -83.7167 1 10 16 20 19 20 6 2 Ashland 40.8667 -82.3 2 13 14 16 18 14 8 1 Athens 2 N 39.3427 -82.0965 0 18 10 21 16 10 17 0 Athens 5 NW 39.3833 -82.1833 0 5 8 6 6 5 0 0 Atwood lake 40.5167 -81.2833 0 22 11 20 16 12 12 0 Auburn 2 SSE 41.3333 -85.05 0 6 18 3 4 3 2 0 Bakerstown 3 WNW 40.65 -79.9833 1 25 12 30 19 11 4 0 Barnesville 39.9916 -81.185 2 6 15 19 20 17 12 0 Beach city lake 40.6333 -81.5667 0 18 12 17 14 11 3 0 Beaver falls 1 NE 40.7667 -80.3167 0 30 24 21 21 11 7 1 Bellefontaine 40.3533 -83.7747 1 8 10 14 10 10 2 0 Belleville Dam 20 39.15 -81.75 0 2 7 14 17 3 0 0 Belleville lock & dam 39.1161 -81.7439 0 0 0 0 0 0 0 0 Bens Run 1 SSE 39.4667 -81.1 0 4 3 18 10 10 2 0 Berne 40.6667 -84.9333 2 11 17 24 25 13 6 0 Beverly WTP 39.5469 -81.6286 0 23 13 11 10 6 3 0 Continued 103

Table 20 continued Bluffton 40.7833 -85.1333 1 12 9 11 12 10 5 0 Bluffton 1 N 40.75 -85.1833 1 6 15 14 28 9 7 0 Bolivar Dam 40.65 -81.4333 2 18 13 12 13 12 15 0 Bournville 1 SSW 39.2667 -83.1667 1 7 6 18 12 8 3 0 Bowling Green WWTP 41.3831 -83.6111 2 11 10 14 18 14 7 3 Braddock lock 2 40.4 -79.8667 0 4 6 17 14 9 1 0 Branchland 1 N 38.2333 -82.2 0 14 8 19 12 21 1 0 Brookville 39.4167 -85.0167 0 9 17 12 9 9 2 0 Buckeye lake 1 N 39.9519 -82.4817 0 4 7 21 17 9 8 0 Bucyrus 40.8128 -82.9694 1 19 17 19 14 16 5 0 Butler 40.7667 -79.9333 0 10 29 17 22 15 5 1 Butler 2 SW 40.85 -79.9167 1 6 12 22 17 15 5 0 Cadiz 40.2686 -80.9981 2 20 11 17 15 12 10 5 Cairo 3 S 39.1667 -81.1667 0 2 6 22 14 10 0 0 Caldwell 6 NW 39.8167 -81.6 0 7 9 16 16 13 1 0 Cambridge 40.0167 -81.5833 0 5 6 21 19 10 10 0 Cambridge City 3 N 39.8667 -85.1833 2 8 12 20 15 9 4 0 Canfield 1S 41.0167 -80.7667 3 27 10 12 15 9 8 0 Carpenter 2S 39.1469 -82.2194 0 3 7 20 12 11 0 0 Carrollton 3 NNE 40.6167 -81.0667 1 5 19 19 14 10 3 0 Celina 3 E 40.5694 -84.5364 2 14 17 26 27 20 5 0 Centerburg 40.3 -82.7 1 16 12 14 12 7 3 0 Centerburg 2 40.3 -82.7 1 7 7 17 8 10 3 0 Chalk Hill 2 ENE 39.85 -79.5833 5 22 24 30 38 29 12 0 Chardon 41.5833 -81.1833 10 22 34 26 31 30 17 0 Charles mill lake 40.74 -82.3569 1 12 14 19 18 13 3 1 Chelsea 42.3333 -84.0167 1 4 7 14 13 10 8 0 Cheviot 39.1547 -84.6233 0 10 10 13 12 10 2 0 Chillicothe 39.3333 -82.9667 2 6 8 8 11 10 1 0 Chillicothe mound city 39.3744 -83.0036 1 4 8 19 15 13 11 0 Chippewa Lake 41.0517 -81.9361 2 8 19 17 18 18 8 1 Cincinnati ABBE WSMO 39.15 -84.5167 1 9 12 14 9 9 2 0 Cincinnati fern bank 39.1169 -84.6961 0 9 6 8 11 9 2 0 Cincinnati fern bank 39.1169 -84.6961 0 4 12 11 13 11 2 0 Circleville 39.6106 -82.9547 3 6 8 18 12 8 2 0 Clarington lock 14 39.8 -80.8333 0 5 7 16 15 8 1 0 Continued 104

Table 20 continued Claysville 3 W 40.1167 -80.4667 3 8 18 16 19 15 6 1 Clendening Dam 40.2667 -81.2833 1 22 13 21 16 10 3 0 Coldwater State School 41.9667 -85 2 10 15 30 28 16 10 0 Columbus fly crossing 39.9047 -82.92 0 13 13 13 11 9 5 0 Confluence 1 NW 39.8333 -79.3667 4 8 11 20 25 17 3 1 Connellsville 39.8 -79.6667 0 16 12 15 16 21 2 0 Cooper dale 40.2211 -82.0667 0 7 7 20 13 7 11 0 Coraopolis Neville IS 40.5167 -80.1667 0 3 5 10 6 5 4 0 Corry 41.9167 -79.6333 7 20 28 26 32 34 14 0 Coshocton wpc plant 40.2403 -81.8711 1 17 11 25 17 11 15 0 Creston 38.9667 -81.2667 0 3 8 18 18 6 1 0 Danville 2 W 40.4397 -82.3039 1 7 12 22 15 13 5 1 Dayton Airport 39.9061 -84.2186 0 0 0 0 0 0 0 0 Dayton MCD 39.7633 -84.1911 0 11 10 16 13 7 3 0 Dearborn 42.3167 -83.23 1 7 13 17 16 9 5 0 Decatur 1 N 40.85 -84.9333 1 12 10 10 12 12 7 0 Defiance 41.2777 -84.3852 2 10 15 19 21 15 4 0 Delaware 40.3175 -83.0739 2 8 15 18 18 12 12 0 Delaware lake 40.3667 -83.0667 0 9 10 24 17 11 5 0 Donora 1 SW 40.1667 -79.8667 0 22 10 17 14 20 3 0 Dorset 41.6833 -80.6667 4 15 20 20 28 18 16 1 Dover Dam 40.5667 -81.4167 0 18 11 16 15 11 5 0 Eaton 39.7347 -84.6336 0 11 11 11 12 7 5 0 Elyria 3 E 41.3833 -82.05 4 15 16 18 16 15 8 0 Enterprise 39.5586 -82.4842 0 4 7 23 15 12 13 0 Falmouth 38.6833 -84.3333 0 7 6 8 15 9 1 0 Farmers 2 S 38.1167 -83.55 0 7 7 10 12 10 1 0 Farmland 5 NNW 40.25 -85.15 3 7 12 17 16 10 5 0 Farrel Sharon 41.2167 -80.5167 0 26 20 14 8 7 3 0 Findlay WPCC 41.0461 -83.6622 0 11 12 20 23 9 8 0 Flemingsburg 2 N 38.45 -83.7333 0 10 7 5 9 8 2 0 Fort Wayne Disposal Plant 41.1 -85.1167 6 5 14 17 12 11 7 0 Franklin 39.5536 -84.319 2 7 8 13 13 10 3 0 Franklin 41.4 -79.8333 2 21 19 32 20 16 6 0 Fredericktown sp 40.4167 -82.5333 1 5 12 19 18 16 1 0 Fremont 41.45 -82.7167 2 10 18 15 13 13 10 1 Continued

105

Table 20 continued Galion water works 40.7236 -82.8 0 7 16 19 19 13 9 0 Gallipolis 5 W 38.8333 -82.2833 0 18 8 21 12 20 1 0 Geneva 3 S 41.75 -80.95 6 24 30 16 13 17 12 0 Glenwillard Dash Dam 40.55 -80.2 0 8 13 13 20 15 1 0 Grayson 3 SW 38.3 -82.9667 0 9 6 16 16 6 0 0 Grayson Lake 38.25 -83 0 1 4 22 12 8 0 0 Greensboro Lock 7 39.7833 -79.9167 0 22 10 18 15 15 5 0 Greensville 2 NE 41.4167 -80.3667 3 25 15 18 17 13 8 0 Greensville water pl 40.1 -84.65 1 15 18 12 11 12 6 0 Greer 40.5167 -82.2 0 4 12 16 15 9 12 0 Gross Point farms 42.4 -82.8833 0 4 15 18 18 12 5 0 Hamilton 39.4 -84.5667 2 9 8 11 9 12 2 0 Hamilton water works 39.4167 -84.55 2 8 9 8 7 10 4 0 Hamlin 38.2833 -82.1167 0 8 8 19 13 16 13 0 Hartford City 4 ESE 40.4333 -85.2833 8 7 10 15 15 14 7 0 Hastings 39.55 -80.6667 0 30 16 11 11 9 1 0 Hillsboro 39.2 -83.6167 3 11 14 26 12 10 7 0 Hillsdale 41.9333 -84.6333 3 10 18 15 19 16 7 1 Hiram 41.3 -81.15 10 20 18 22 18 21 17 0 Hogsett Gallipolis Dam 38.6833 -82.1833 0 12 7 4 8 20 0 0 Hoytville 2 NE 41.2167 -83.7667 0 7 13 14 18 8 6 1 Hudson 3 E 41.85 -84.3 1 5 10 9 20 9 4 1 Huntington Sewage Plant 38.4 -82.5333 0 2 8 17 11 5 0 0 Ironton 38.5333 -82.6833 1 12 8 9 10 5 1 0 Irwin 40.1167 -83.4833 0 16 12 10 8 10 6 0 Jackson 3 NW 39.0775 -82.7053 2 22 9 12 15 15 1 0 Jamestown 2 NW 41.5 -80.4667 6 17 18 19 16 18 10 2 Kenton 2 W 40.65 -83.65 1 13 14 17 15 12 5 0 Kings Mills 39.3531 -84.2606 3 8 8 10 10 11 4 0 Knightstown 2 ENE 39.8 -85.4833 1 9 14 15 13 9 4 0 La Rue 40.5833 -83.3833 5 4 12 15 11 10 5 0 Lagrange Sewage Plant 41.65 -85.4333 4 8 14 21 18 21 5 1 Lakeview 3 NW 40.5167 -83.8833 0 15 12 20 15 10 5 0 Lancaster 2 NW 39.7333 -82.6333 0 4 7 15 12 7 3 0 Continued

106

Table 20 continued Laurelville 39.4667 -82.7333 3 4 7 14 12 10 19 0 Leesville lake 40.4667 -81.2 0 20 11 13 13 11 12 0 Liberty 3 SSE 39.5833 -84.9167 2 10 18 22 19 18 2 0 LIMA WWTP 40.7247 -84.1294 2 13 14 19 18 10 5 0 Linesville 5 WNW 41.6833 -80.5167 5 19 21 26 24 26 12 1 Lithopolis 39.7833 -82.8167 1 2 8 10 10 7 3 0 Lloyd Greenup Dam 38.65 -82.8667 0 3 6 9 13 3 0 0 London 4W 39.9 -83.5167 5 12 7 19 18 12 4 0 Louisville 40.8333 -81.25 2 17 21 20 20 15 6 0 Madison Sewage Plant 38.7333 -85.4 4 5 10 10 16 7 1 0 Mansfield 5W 40.7667 -82.6167 1 6 14 18 8 9 7 0 Marietta Lock 1 39.4108 -81.4536 0 7 8 14 8 5 0 0 Marietta WWTP 39.4089 -81.4331 0 3 5 19 16 20 10 0 Marion 2N 40.6167 -83.1333 0 11 15 18 18 14 2 0 Marshalville 1 SSW 40.8833 -81.7333 1 6 14 15 16 13 4 0 Marysville 40.2411 -83.3669 3 15 17 19 12 10 7 0 Maysville Sewage Plant 38.6833 -83.7833 0 12 5 15 9 6 2 0 Mc Connelsville lock 7 39.6539 -81.8569 0 7 9 20 20 10 4 0 Mc Mechen Dam 13 39.9833 -80.7333 0 23 8 14 12 6 1 0 McArthur 39.2503 -82.4822 3 4 8 16 12 12 12 0 Mckeesport 40.3333 -79.8667 0 28 16 15 14 11 1 0 Meadville 1 S 41.6333 -80.1667 6 21 24 19 21 17 7 3 Mercer 2 NNE 41.25 -80.2167 1 9 10 15 18 10 6 1 Miamisburg 39.6167 -84.2833 2 11 11 16 10 11 2 0 Middlebourn 3 ESE 39.4667 -80.8667 1 4 14 22 22 16 12 0 Middlebourne 40.05 -81.3333 1 25 16 16 9 11 2 0 Middletown 39.5167 -84.4167 2 7 8 17 8 7 3 0 Milford 39.1814 -84.2867 0 3 8 17 13 10 2 0 Millersburg 40.55 -81.9167 1 4 18 20 15 8 6 0 Millport 2 NW 40.7692 -80.8562 3 22 18 13 16 16 20 0 Mineral ridge water works 41.15 -80.7833 1 26 17 14 12 16 10 0 Mohawk dam 40.3486 -82.0908 0 23 13 21 17 12 5 0 Mohicanville dam 40.7333 -82.15 1 9 14 18 16 14 10 0 Monroe 41.9167 -83.4 0 10 15 18 13 9 6 0 Monroeville 1NW 41.9833 -84.8833 2 8 6 8 7 8 5 0 Continued

107

Table 20 continued Montpelier 41.5802 -84.6077 2 10 16 20 22 12 11 1 Morenci 41.7167 -84.2167 3 4 14 15 17 14 8 2 Mosquito creek lake 41.3 -80.7667 3 25 13 18 20 11 6 0 Moundsville 39.9 -80.75 1 4 8 28 16 8 1 0 Muncie Walnut St Bridge 40.2167 -85.3833 0 13 11 10 14 6 2 0 Napoleon 41.3939 -84.1144 0 13 14 16 19 15 6 4 Natrona Lock 4 40.6167 -79.7167 1 28 18 20 26 12 6 1 Nelsonville 39.5014 -82.2439 0 8 7 21 16 9 13 0 New Carlisle 39.9322 -84.0333 0 16 13 19 14 6 3 0 New Castle 1 N 40.0167 -80.3667 0 33 15 18 24 12 3 1 New Castle 4 N 39.8833 -85.3333 0 6 14 10 13 9 3 0 New Cumberland 40.5333 -80.6333 0 8 8 17 13 4 0 0 New Lexington 2 NW 39.7331 -82.2156 1 20 18 16 15 10 3 0 New Martinsville 4 NNE 39.6833 -80.85 0 26 14 15 12 8 2 0 New Philadelphia 40.4878 -81.4317 0 4 10 18 15 11 16 0 New Philadelphia 1 A 40.5 -81.45 0 15 12 20 11 10 12 0 New Straitsville 39.5833 -82.25 2 7 10 13 14 11 15 0 Newark water works 40.0875 -82.4131 0 13 8 15 14 13 15 0 Newcomerstown 40.2667 -81.6 0 16 7 7 13 7 12 0 Newell 40.0833 -79.9 0 5 10 10 14 12 0 0 North East 2 SE 42.2 -79.8167 5 27 27 22 25 15 11 1 Norwalk WWTP 41.2667 -82.6167 3 10 15 19 22 12 7 1 Norwich 1 E 39.9833 -81.7833 0 4 9 16 12 8 4 0 Oberlain 41.2667 -82.2167 3 8 15 15 16 22 7 0 Ottawa 41.0324 -84.0543 0 2 10 12 8 8 5 0 Painesville 4 NW 41.75 -81.3 9 22 21 21 13 12 11 1 Paulding 41.1244 -84.5922 4 9 15 12 11 13 7 0 Pendora 40.9541 -83.9616 0 11 16 20 18 16 7 0 Philo 39.8667 -81.9 2 20 9 13 10 9 8 0 Philo 3 SW 39.8386 -81.9166 1 18 14 20 25 10 15 0 Piedmont Lake 40.1833 -81.2167 0 7 8 24 16 8 1 0 Piketon aec pump station 39.0706 -83.0197 0 18 8 12 14 10 0 0 Piqua 40.1525 -84.2372 1 11 9 18 12 10 7 0 Pleasant Hill 40.0494 -84.3458 1 7 8 13 6 7 6 0 pleasant hill lake 40.6167 -82.3333 2 12 14 22 17 12 10 0 Plymouth 2 WSW 40.9833 -82.7 0 11 17 20 20 18 4 0 Continued 108

Table 20 continued Portland Water Works 40.4667 -84.9667 4 5 16 10 15 8 4 0 Portsmouth sciotoville 38.7569 -82.8872 0 4 7 22 19 8 5 0 Prospect 40.4833 -83.2 2 4 15 21 21 12 6 0 Ravenna 2 S 41.1333 -81.2833 2 15 22 16 20 15 7 0 Ravenswood Dam 22 38.95 -81.75 0 3 8 13 14 3 0 0 Richmond Waterworks 39.85 -84.85 0 10 8 15 9 11 2 0 Richmond Wtr Wks 39.85 -84.8833 2 5 7 21 14 5 5 0 Ripley 38.8167 -81.7167 0 5 9 18 20 7 9 0 Ripley exp farm 38.7872 -83.7972 1 3 6 22 18 14 8 0 Roseville 39.8164 -82.0722 0 2 7 21 14 8 16 0 Sedalia 39.7339 -83.4775 5 17 15 14 16 10 3 0 Senecaville lake 39.9222 -81.4347 0 8 14 20 12 9 4 0 Sidney 2 N 40.3167 -84.1667 5 8 8 10 11 10 4 0 Slippery Rock 1 SSW 41.05 -80.0667 1 25 24 19 20 15 5 0 Springfield 39.9167 -83.8167 0 20 12 10 9 10 4 0 St Marys 39.3833 -81.2 0 4 6 10 10 8 1 0 St Marys 3 W 40.5447 -84.4375 0 18 4 6 5 3 6 0 Steubenville 40.3765 -80.6283 1 13 13 15 13 14 2 0 Stryker 41.5042 -84.43 1 10 15 19 24 13 7 0 Summerfield 2 NE 39.8167 -81.3 3 26 10 11 14 12 0 0 Tappan Dam 40.3561 -81.2281 0 20 12 21 18 11 11 0 Tiffin 41.1167 -83.1667 0 15 16 21 20 16 7 1 Tipp City 39.9528 -84.1689 1 5 10 12 15 11 5 0 Titusville Water Works 41.6333 -79.7 4 12 18 21 24 28 8 2 Union City Filtration Plant 41.9 -79.8167 7 16 28 26 26 25 15 1 Uniontown 1 NE 39.9167 -79.7167 1 9 16 20 17 19 5 0 Upper Sandusky 40.8333 -83.2833 2 12 20 23 24 14 9 1 Urbana wwtp 40.1 -83.7833 4 11 14 15 18 8 6 0 Utica 40.2167 -82.4333 0 7 12 18 17 10 10 0 Van Wert 1S 40.8494 -84.5808 0 14 12 20 18 16 7 0 Vanceburg Dam 32 38.65 -83.35 0 12 9 12 17 4 0 0 Versailles 40.2197 -84.4822 0 22 16 15 14 10 4 0 Vevay 38.75 -85.0833 2 6 10 21 15 11 0 0 Warren 3 S 41.2 -80.8167 3 24 18 13 15 11 6 0 Continued

109

Table 20 continued Warren public service 41.2167 -80.8 3 26 24 14 10 10 4 0 Washington 2 SW 40.15 -80.2667 0 7 10 20 16 26 9 0 Washington court house 39.5269 -83.4281 2 21 15 19 15 11 4 0 Waterloo 2 NW 41.5 -85.05 2 8 18 22 28 29 8 0 Wauseon wwtp 41.55 -84.1333 3 9 16 17 15 12 11 2 Waverly 39.1114 -82.9797 0 19 9 16 11 9 4 0 Waynesburg 1 E 39.9 -80.1667 3 6 10 26 21 16 9 1 Wellsburg wtr plant 40.2833 -80.6167 0 33 8 22 26 14 4 0 West Manchester 3 SW 39.8731 -84.6556 2 4 9 18 10 7 3 0 West Union 2 39.2833 -80.7667 0 3 6 16 15 18 4 0 Westerville 40.1264 -82.9433 0 5 8 12 13 8 4 0 Willis 5 SSW 42.0833 -83.5833 0 9 18 32 33 15 3 0 Willoughby 3 NW 41.6667 -81.4333 6 5 8 13 10 9 8 0 Wills creek lake 40.15 -81.85 0 13 10 19 15 11 12 0 Wilmington 3 N 39.4833 -83.8167 3 17 17 22 23 22 5 2 Winchester airport 3 E 40.1667 -84.9167 2 18 14 21 15 11 5 0 Winfield Lock 38.5333 -81.9167 0 13 7 18 11 27 0 0 Wooster EXP STN 40.7833 -81.9167 3 18 20 18 20 13 8 0 Washington Dam 19 39.25 -81.7 0 2 5 13 17 5 0 0 Xenia 2 N 39.7167 -83.9333 2 7 8 9 10 11 4 0 Zanesville wwtp 39.9125 -82.0042 0 11 10 10 10 8 2 0

110

Appendix C: Field Automatic Sprinkler Drainage

Figure 26: Sprinkler drainage dispositive.

111

Appendix D: Experimental Water Mixing Device

Figure 27: Water mixing device used in the variable water temperature test

112