A Thesis

entitled

Applicability of Soil Moisture Sensors in Determination of Infiltration Rate

by

Milan K C

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Science Degree in Civil Engineering

______Dr. Cyndee L. Gruden, Committee Chair

______Dr. Ashok Kumar, Committee Member

______Dr. Liangbo Hu, Committee Member

______Dr. Amanda Bryant-Friedrich, Dean College of Graduate Studies

The University of Toledo

December 2017

Copyright 2017, Milan K C

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of

Applicability of Soil Moisture Sensors in Determination of Infiltration Rate

by

Milan K C

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Civil Engineering

The University of Toledo

December 2017

The need for stormwater management has been heightened in urban areas due to the increase in impermeable surfaces, causing flooding, erosion and pollution of water and soil. This problem can be mitigated with a scientifically designed stormwater management system, which requires field data including soil infiltration rate.

Conventional approaches to measuring infiltration are tedious, time-consuming, and do not address the need for extensive, real-time data. The main objective of this research is to develop a technique that incorporates readily available real-time soil sensor data into the Green and Ampt Infiltration Model. Initial laboratory experiments confirmed that estimates of infiltration using Green and Ampt Infiltration Model with parameters found in the literature compared well to actual laboratory measurements of infiltration. A sensitivity analysis was then performed on the Green and Ampt Infiltration Model parameters of which saturated hydraulic conductivity (K) showed largest influence and porosity (η) showed negligible influence on the calculated soil infiltration rate while average capillary suction at the wetting front (ψ) showed high influence only on initial

iii values of calculated soil infiltration rate. The method was then validated to measure infiltration rate and cumulative infiltration of sand (with varying initial moisture content) and samples including sand and an equal volume of organic matter. Oven dried sand had average initial infiltration rate of 4.37 cm/min, 18.11% higher than that of sand, which was 3.7 cm/min. It also showed that sand mixed with equal volume of organic matter had average initial infiltration rate of 6.2 cm/min, 41.88% higher than that of oven dried sand.

Saturated hydraulic conductivity (K) of oven dried sand and sand were 0.30 cm/min and

0.22 cm/min respectively, within normal range (0.19 cm/min to 0.40 cm/min). The value of saturated hydraulic conductivity (K) after mixing sand with organic matter was 0.51 cm/min, 27.5% more than that of oven dried sand. Future research will involve testing this approach in field studies. The resulting method would provide a more economical and useful approach for measuring soil infiltration rate in stormwater applications.

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Dedicated to my lovely wife, mom and dad.

Acknowledgements

I am especially thankful to my advisor Dr. Cyndee L. Gruden for all her support and guidance. I am thankful to my wife, family and friends for their constant support. I would also like to thank Dr. Ashok Kumar and Liangbo Hu for taking the time to be a part of my thesis committee.

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Table of Contents

Abstract ...... iii

Acknowledgements ...... v

Table of Contents ...... vi

List of Tables ...... viii

List of Figures ...... ix

1 Literature Review...... 1

1.1 Background ...... 1

1.2 Soil and its phases ...... 3

1.3 Soil Moisture ...... 5

1.3.1 Measurement of Soil Moisture...... 6

1.3.2 Soil Moisture Sensor Applications ...... 9

1.4 Hydraulic Conductivity ...... 11

1.5 Soil Infiltration ...... 15

1.5.1 Infiltration Process in Soil ...... 16

1.5.2 Factors Affecting Infiltration Rate of Soil ...... 17

1.5.3 Soil Infiltration Measurements ...... 18

1.5.4 Soil Infiltration Models ...... 19

1.6 Summary ...... 24

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2 Objective ...... 27

2.1 Study Objective ...... 27

3 Methodology ...... 29

3.1 Overview ...... 29

3.2 Soil Moisture Sensor ...... 30

3.3 Soil Moisture Installation ...... 31

3.4 Comparison of Directly Measured Infiltration Rate with Green and Ampt

Infiltration Rate ...... 33

3.5 Sensitivity Analysis of Green and Ampt Equation Parameters ...... 36

3.6 Determination of Soil Infiltration Rate Using Two Soil Moisture Sensors ....38

4 Results ...... 42

4.1 Comparison of Directly Measured Infiltration Rate with Green and Ampt

Infiltration Rate ...... 42

4.2 Sensitivity Analysis of Green and Ampt Equation Parameters ...... 44

4.3 Determination of Soil Infiltration Rate Using Two Soil Moisture Sensors ....47

5 Discussion ...... 61

6 Conclusion ...... 65

References ...... 67

viii

List of Tables

1.1 Comparison of different soil moisture sensors ...... 9

3.1 Green and Ampt infiltration parameters for various soil classes ...... 35

3.2 Values of Green and Ampt infiltration parameters for sand ...... 36

3.3 Values of Green and Ampt infiltration parameters used for sensitivity

analysis ...... 37

4.1 Data points for directly measured infiltration rate and infiltration rate obtained

using Green and Ampt infiltration model ...... 44

4.2 Saturated hydraulic conductivity and initial infiltration rate for sand and its

organic matter mixture samples ...... 60

ix

List of Figures

1-1 United States Department of Agriculture (USDA) soil classification textural

triangle ...... 4

1-2 Procedure, equation and sample calculation for gravimetric method ...... 7

1-3 Simplified figure for Darcy’s law ...... 12

1-4 Hydraulic conductivity versus water content graph for loam, clay and loamy

sand ...... 13

1-5 Infiltration rate versus time ...... 17

1-6 Water infiltration process showing saturated, transmission and oven

dried soil zones ...... 21

1-7 Layers of soils showing uniform water entry assumption with clearly shown

wetting front as described in Green and Ampt infiltration equation ...... 22

3-1 Volumetric water content versus time graph ...... 30

3-2 Horizontal installation of 10HS soil moisture sensor ...... 31

3-3 Vertical and inclined installation of 10HS soil moisture sensor ...... 32

3-4 Graphical illustration of apparatus for comparison of directly measured

infiltration rate with Green and Ampt infiltration rate ...... 34

3-5 Two soil moisture sensor based soil infiltration rate measuring apparatus ...... 39

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4-1 Directly measured infiltration rate versus time and Green and Ampt based

infiltration rate versus time graph ...... 43

4-2 Sensitivity analysis for infiltration rate by changing hydraulic

conductivity (K) ...... 45

4-3 Sensitivity analysis for infiltration rate by changing porosity (η) at

K= 1.2 cm/min ...... 46

4-4 Sensitivity analysis for infiltration rate by changing average capillary

suction (Ψ) ...... 46

4-5 Directly measured initial infiltration rate versus volumetric water content

graph for sand sample-1 ...... 48

4-6 Cumulative infiltration and infiltration rate versus time graph for upper

sensor in sand sample-1 ...... 49

4-7 Cumulative infiltration and infiltration rate versus time graph for lower

sensor in sand sample-1 ...... 50

4-8 Directly measured initial infiltration rate versus volumetric water content

graph for oven dried sand sample-1 ...... 51

4-9 Cumulative infiltration and infiltration rate versus time graph for upper

sensor in oven dried sand sample-1 ...... 52

4-10 Cumulative infiltration and infiltration rate versus time graph for lower

sensor in oven dried sand sample-1 ...... 53

4-11 Directly measured initial infiltration rate versus volumetric water content

graph for bulked sand sample-1 ...... 54

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4-12 Cumulative infiltration and infiltration rate versus time graph for upper

sensor in bulked sand sample-1 ...... 55

4-13 Cumulative infiltration and infiltration rate versus time graph for lower

sensor in bulked sand sample-1 ...... 56

4-14 Directly measured initial infiltration rate versus volumetric water content

graph for organic mixed sand sample-1 ...... 57

4-15 Cumulative infiltration and infiltration rate versus time graph for upper

sensor in organic mixed sand sample-1 ...... 58

4-16 Cumulative infiltration and infiltration rate versus time graph for lower

sensor in organic mixed sand sample-1 ...... 59

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Chapter 1

Literature Review

1.1. Background

Soil infiltration is a very important process in geotechnical, environmental and agricultural engineering because it provides the information necessary to determine runoff and recharge at a site (Lowery, Hickey, Arshad, & Lal, 1996). Soil properties are heterogeneous and therefore change in both horizontal and vertical directions, making it extremely difficult to acquire accurate measurements (Dingman, 1994). The spatial heterogeneity of soil affects infiltration. Infiltration is further influenced by extrinsic factors like land cover, surface slope and climate which governs evapotranspiration.

There are several methods to measure infiltration rate, saturated hydraulic conductivity, cumulative infiltration and other infiltration characteristics of soil with high precision.

The most common of these methods is the infiltrometer, which is very useful for measuring infiltration rate at a specific location or point during a particular time frame

(Youngs, Elrick, & Reynolds, 1993). Since infiltration properties vary with time, position and moisture condition, accurate measurements of infiltration rate over the period of a

1

few hours and in a relatively small area is a tedious and time-consuming job requiring extensive labor and resources. In these circumstances, there is a need for continuous observation of soil properties, soil moisture and infiltration processes in micro scale as these processes are complex and of significant importance (Houser, 2005). These data are important in situations like flood prevention agricultural applications and stormwater management.

Given accurate and real-time soil infiltration properties, plant characteristics and soil moisture content would allow for effective irrigation scheduling to provide sufficient water and reduce water loss through runoff (Munoz-Carpena & Dukes, 2005).

Application of soil moisture sensor based system helps to increase productivity in agriculture (Munoz-Carpena & Dukes, 2005) and to obtain design parameters in modeling of stormwater system with soil information increases productivity up to 100%

(Clark, Acomb, & Philpot, 2008). Modern real-time field sensors, including soil moisture sensors, are cheap, accurate and can provide data at intervals of seconds. Previous research has shown that water use is reduced by up to 51% by application of automatic soil moisture sensor-based irrigation as compared to traditional timer-based irrigation

(Haley & Dukes, 2007; Clark et al., 2008). The system needs only weekly maintenance once it is set up and verified (Dukes, Simonne, Davis, Studstill, & Hochmuth, 2003). This research targets the shortcomings of conventional infiltration rate measuring devices by implementing real-time soil moisture sensors to determine soil infiltration rate. The proposed method does not claim to deliver better accuracy than conventional infiltration measurement methods. Rather, the goal of this research is to find a fast, economic, easy to use, continuous and reliable method for determining soil infiltration, which could be

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more effective in field of stormwater management and irrigation engineering than conventional techniques.

1.2 Soil and its Phases

Soil, unlike other civil engineering materials such as concrete and steel, is a multi- phase and particulate material that usually consists of mingled solids, water and air. Or more appropriately, soil is weathered layer on earth’s surface and has water and air in the porous spaces and voids between mineral grains, organic materials and rock fragments

(Ramann, 1905; Joffe, 1949). Soil is composed of primary and secondary minerals

(DeGomez, Kolb, & Kleinman, 2015). Primary minerals are soil materials similar to the parent materials they are formed of; whereas secondary minerals are stable mineral forms such as silicate clay obtained by weathering of primary minerals (Karathanasis, 2006).

Mineral particles present in soil can be categorized as sand, silt or clay. Sand is the largest of the three mineral particles, with large pore spaces around it causing higher porosity and improved aeration (Karathanasis, 2006). Silt is medium sized and larger than clay. Hydraulic conductivity is very high in sand and least for clay (Svensson, 2014).

Size of sand, silt and clay are 0.05 to 2 mm, 0.002 to 0.05 mm and less than 0.002 mm respectively (Whiting, Card, Wilson, & Reeder, 2011). Texture of soil and its properties depend upon its percentage of sand, silt and clay composition, demonstrated in the heuristic model in figure 1-1.

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Figure 1-1: United States Department of Agriculture (USDA) soil classification textural triangle (Mayo, 1982)

According to Idowu and Angadi (2003), organic matter takes up to 5% of soil volume and has high water holding capacity, which helps plant growth, and in turn affects soil moisture holding levels. The average water content in soil is around 25% and is vital for plant growth and biological organisms present in soil. Mineral materials occupy 45% of soil volume while air occupies around 25% of soil volume and provides oxygen for roots and microbial respiration for plant growth (Idowu & Angadi, 2013).

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1.3 Soil Moisture

Soil moisture content is very important in hydraulic conductivity and infiltration characteristics study as initial moisture content of soil affects hydraulic conductivity and infiltration rate significantly. Soil composition has a profound influence on moisture bearing capacity. Clay has higher surface area than silt and sand, resulting in greater water holding capacity and higher water holding capacity inversely influences soil infiltration (Acharya, Rauchecker, & Wu, 2014). It makes consideration of clay especially important for understanding soil chemistry, infiltration and water-holding capacities. Clays, as a mineral matter, are present in vast surface coverage areas

(DeGomez et al., 2015). Soils that are very stiff like stone have very low water content and is said to be in solid state and soils with very high water content can almost become liquid (Verruijt & Van Baars 2007). Reciprocally, soil moisture is responsible for change in physical, chemical and biological properties of soils (Edwards & Cresser, 1992). Soil moisture content or water content (w) can simply be defined as the amount or quantity of water the soil or soil subsurface contains with units as cm3 cm-3 (volumetric) and gm/gm

(gravimetric) and represented as

w = (volume of water / volume of oven dried soil) (in volumetric form) (1.1)

Even though the volume of moisture in soil is insignificant compared to large volumes of water within other components of the hydrologic cycle, it has significant impact on many hydrological, biogeochemical and biological processes (Li, Wang,

Kaseke, Li, & Seely, 2016). Furthermore, soil moisture affects the amount of runoff from precipitation into nearby rivers and streams. Replenishment of soil moisture happens from precipitation, irrigation, overland and subsurface flow of water and capillary rise

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from the water table (Hain, Crow, Anderson, & Yilmaz, 2015). Percolation is a factor for soil water loss to lower unsaturated layers when a precipitation event is higher than infiltration rate, then overland water flow happens, which does not replenish the soil

(Phillips, 2014). Simply, infiltration is water entering soil surface, increasing soil moisture content. Percolation involves water passing through soil layer into ground water, decreasing soil water content.

Water will move from higher to lower potential, for example, water at higher elevation flowing to lower elevation (Liu, Li, & Alva, 2012). Attraction of soil surfaces for water also causes soil water movement by capillary action (Karkare & Fort, 1993).

Further, within saturated soil, there might exist a potential energy in the form of external pressure such as that provided by a flooded area (Morgan, 2017).

1.3.1 Measurements of Soil Moisture

There are a variety of instruments and methods available to determine soil moisture content in this section, the soil moisture sensor method details are highlighted and compared.

(a) Gravimetric Method

With the gravimetric method, soil samples are first collected from the field and weighed. Then the soil is oven dried at a temperature range of 100-110 ºC for 24 hours.

Recording the change in weight of soil per unit weight of oven dried soil gives water content of the soil. This process is accurate but is time consuming and not practical for continuous measurement. This gravimetric water content (푤푐푚) can be converted into

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volumetric water content by multiplying by bulk density of soil. Figure 1-2 shows procedure, equation and sample calculation for gravimetric method.

Figure 1-2: Procedure, equation and sample calculation for gravimetric method (“Water

in road materials and subgrade soils, terminology”, n.d.)

(b) Soil Moisture Sensors

There are several types of soil moisture sensors used for in-situ tests and these have their own advantages and disadvantages for different purposes. Selection of appropriate soil moisture sensor depends upon the efficiency, cost, applicability, durability and accuracy of the sensor. Several types of soil moisture sensors are neutron soil moisture sensor, coaxial impedance dielectric reflectometry sensors, time domain reflectometry (TDR) and capacitance based sensors. The soil moisture sensors (e.g.,

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Decagon models EC-5 and 10HS) use capacitance/frequency domain technology to measure dielectric constant of the soil and have been frequently used in soil science based researches by Mittelbach, Lehner and Seneviratne (2012) and Dursun and Ozden (2011).

These devices measure volumetric water content of soil by gauging the dielectric constant of soil. They have a strong capability of measuring soil moisture content but only has approximately ± 3-4 % accuracy (Cobos & Chambers, 2010). Increments up to ± 1-2% are achieved when soil specific calibration is done. This calibration can be applied to all sensors of that type for a set kind of soil (Cobos & Chambers, 2010). After comparing all types of sensors based on cost of purchase and maintenance, response time, calibration requirement, maintenance requirement and setup requirement. Time domain reflectometry (TDR) was selected after considering all the factors and the comparison is shown in table 1-1.

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Table 1-1: Comparison of different soil moisture sensors (Muñoz-Carpena, Li & Olczyk,

2002)

Tensiometer GMS Dielectric TDR

Reading Direct-suction Indirect-suction Indirect-water Indirect-water

(electrical content content

resistance) (Dielectric- (Dielectric-

voltage) time)

Cost $70-110 Probe $30 Probe $100 Probe $260

Reader $250 Reader $375 Reader $325

Set-up Involved Minor Minimal Minimal

Maintenance Yes-important No No No

Response Fast Differs from Instantaneous Instantaneous

tensiometer at

high suction

Calibration No (only Yes Yes No (only for

adjustment) research)

1.3.2 Soil Moisture Sensor Applications

Soil moisture content of an agricultural field is necessary to determine irrigation scheduling. Using some handheld soil moisture sensors or regularly conducting field and lab tests to find soil moisture content will be technically, logistically and economically unviable and quite labor intensive (Phillips, 2014; Phillips, Newlands, Liang, & Ellert,

2014). Therefore, a permanently placed soil moisture sensor with remote access to real

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time data is necessary to get crucial information on intensity, duration and timing of moisture stress under rival cropping systems and identify crops exhibiting higher moisture stress tolerance or escaping detrimental moisture stress (Phillips, 2014).

Two types of methods are utilized in sensor based soil analysis: reactive (real- time) sensing and predictive (map-based) sensing (Phillips, 2014). Phillips (2014) explains that reactive sensing provides a decision support system based on local situations at that time; whereas a predictive system only generates soil information after off-site processing and interpretation of data (Adamchuk, Rossel, Sudduth, & Lammers,

2011; Mahmood, Hoogmoed, & Henten, 2012). Both sensor types have potential to reduce the need of manual soil sampling in the field to monitor soil moisture content over short or prolonged periods (Phillips, 2014). “Potentially, a trade-off between spatial and temporal coverage may arise when considering whether to use manual collection, in-situ sensor networks or remote sensing for soil moisture monitoring” (Phillips, 2014). There is a need for an integrated sensing system for soil moisture studies. Water input

(precipitation) and output (evapotranspiration) are continuously monitored from nearby weather stations to understand soil water response to climate and distributed sensor source nodes with connected soil sensors are used to continuously monitor in-field soil water content. Periodic remote sensing imagery of soil should be monitored to provide local water condition. Collectively, these data can be used to get an accurate condition of soil water in the field. This integrated sensing technique gives enhanced data over time and space (Phillips, 2014).

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1.4 Hydraulic Conductivity

Hydraulic conductivity is the measure of ease for water to pass through a medium or soil. Hydraulic conductivity (K) of a soil is considered the most important soil property, which affects the design of water-table management systems (Doty, Evans,

Hinson, Gibson, & Williams, 1986). Hydraulic conductivity is required to find out and describe infiltration and water movement through the soil profile (Dorsey, 1990). It is a necessary parameter in determination of cumulative infiltration and infiltration rate using different infiltration models. Hydraulic conductivity is further understood as the ability of a porous medium to allow water to flow through it due to pressure gradient (Hillel, 1982).

According to Darcy’s law (Darcy, 1856),

푑ℎ 푄 = −퐾퐴( ) (1.2) 푑푙

Where, dh = change in total hydraulic head (m or ft.) or = h1-h2

dl = length of column along the direction of flow (m or ft.) or = L

Q = volumetric flow rate of water (m3/sec or ft3/sec or m3/day)

A = cross-sectional Area (m2)

K = constant of proportionality called Hydraulic conductivity and its value depends upon properties of the porous medium like soil and properties of soil water.

(m3/sec or ft3/sec or m3/day)

The negative sign indicates that the direction of flow is toward lower hydraulic head. This equation is known as Darcy’s equation and the value dh/dl represents hydraulic gradient(s), which is the difference of head (dh) over a length of L (dl) as shown in figure 1-3.

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Figure 1-3: Simplified figure for Darcy’s law (Wilson, n.d.)

Darcy’s law can also be represented in terms of apparent velocity (V) by replacing (Q/A) in equation 1.2.

푑ℎ 푉 = −퐾( ) (1.3) 푑푙

V=K*s (since s=dh/dl) (1.4)

K=V/s (1.5)

So, hydraulic conductivity (K) can also be defined as apparent velocity (m/day) of groundwater flow when there is unit hydraulic gradient (s=1) (Oosterbaan & Nijland,

1994). K-value of a saturated soil depends mainly upon size, shape and distribution of pores and is also affected by soil temperature, viscosity and density of water (Oosterbaan

& Nijland, 1994). Soil texture, proportion of different components like sand, silt and clay in the soil affects the hydraulic conductivity (Turner, 2006). “Soils with higher sand percentages have larger sized particles, larger pores, lower water holding capacity and higher hydraulic conductivity, diffusivity and infiltration rates than clay soils which have

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smaller micro pores and bind water molecules more tightly” (Turner, 2006, p 9). Figure

1-4 shows relationship between hydraulic conductivity and water content for different soil types.

Figure 1-4: Hydraulic conductivity versus water content graph for loam, clay and loamy

sand (Ochsner, n.d.)

Hydraulic conductivity is a function of soil moisture conditions and soil suction

(Dorsey, 1990). According to Childs (1969), if all pores of soil are filled with water then soil is said to be saturated and the entire pore space of soil will be able to conduct water.

Soil water flow is mainly under influence of gravity in saturated soil and pressure gradient depending upon the amount of soil moisture in unsaturated soil. Soil suction becomes more important as soil moisture content decreases (Dorsey, 1990). Knowing saturated hydraulic conductivity is important as it is useful in drainage design and in computation of velocity with which water can move towards and into drain lines below the water table (Amoozegar & Wilson, 1999). “Unsaturated hydraulic conductivity is

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difficult to quantify because it depends on the potential gradient at any point in the system which, in turn, depends on the moisture content” (Dorsey, 1990, p 2). For practical approach, the region below the water table is considered saturated but complete saturation rarely exists even at several meters below the water table (Dorsey, 1990).

Hydraulic conductivity is highly important for infiltration rate since it mainly determines how easily water can flow through the soil and it is a measure of soil’s resistance to flow

(Turner, 2006). Unsaturated hydraulic conductivity mainly depends upon pressure head

(Serrano, 1997) and distribution of water in a soil matrix (Turner, 2006). Saturated hydraulic conductivity is used as a parameter in many infiltration equations as it is easier to determine than the unsaturated hydraulic conductivity (Serrano, 1997). Saturated hydraulic conductivity measuring methods include piezometer, auger hole, double tube, laboratory core methods, two and four wells method, infiltrometers, permeameters and shallow well pump-in (Bouwer & Jackson, 1974). These methods of hydraulic conductivity involve field or laboratory experiments with instruments and is not very efficient in large land area. Laboratory methods like permeameter is not useful as this research focuses on field based technique. Most of these methods have limitations of being only applicable to soil of certain properties and cannot be used for measurement deeper inside soil. This research is trying to overcome those limitations and soil moisture sensors are used for this task.

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1.5 Soil Infiltration

Soil infiltration is defined as the ability of soil to absorb water. Cumulative or total infiltration can be measured in terms of depth and it is depth of water infiltrated into and through the soil layers. Hydrologists use the infiltration approach to predict surface runoff from watersheds. The soil infiltration rate is the speed at which water passes through the soil per unit time and is measured in inches per hour (in/hr) or millimeters per hour (mm/hr). The difference between hydraulic conductivity and infiltration rate is that hydraulic conductivity is an intrinsic soil property while infiltration (rate) depends mainly upon potential difference, hydraulic conductivity, diffusivity, porosity, initial moisture content and water holding capacity of soil (Turner, 2006). Arable soil in good condition has a stable structure with continuous pores, resulting in good infiltration of water into and throughout the soil. Low infiltration rate is observed due to soil sealing and soil crusting (“Soil Quality Indicators: Infiltration”, 1998). Further, infiltration is caused by gravitational force and capillary action where gravitational force acts in the direction of gravity while capillary action is pulling movement of water through very small pores and is caused by surface tension force of water (Turner, 2006). Capillary action could be in the same or opposite direction of gravitational force.

The effects of infiltration rate are notable. According to “Soil quality indicators: infiltration” (1998), a low infiltration rate results in reduced base flow and increased storm flow (Walsh, Fletcher, & Burns, 2012), erosion of surface soil (Tsihrintzis &

Hamid, 1997) inadequate moisture for crop production (Letey, 1985), ponding on level fields, and runoff of surface applied fertilizers and pesticides on sloping landscapes.

Substances carried by runoff can in turn pollute water bodies and sources (Tsihrintzis &

15

Hamid, 1997). Resulting in multifarious, unwanted chemicals and materials entering the water system, and is the one of the main sources of water pollution throughout the U.S.

Without regulation or water quality controls, stormwater runoff leads to harmful effects on wildlife, aquatic life and human health (Tsihrintzis & Hamid, 1997). Therefore, infiltration is one of the prime factors in stormwater management. Additionally, with a high infiltration rate, fertilizer and pesticide leaching into groundwater might happen and necessitating proper chemical management to protect groundwater (Tsihrintzis & Hamid,

1997).

1.5.1 Soil Infiltration

According to Williams, Ouyang, Chen and Ravi (1998), when the rate at which water is applied is more than the soil infiltration rate, ponding happens. When the water application rate is lower than the soil’s infiltrability, water completely infiltrates through soil and when water application rate is higher than the soil’s infiltrability then ponding happens over soil surface (Williams et al., 1998). Therefore, infiltration depends upon the water application rate. Williams et al. (1998) further explain that a supply controlled infiltration process occurs when the water application rate is lower than soil infiltrability.

When water application rate is higher than soil infiltrability, soil infiltrability determines the actual infiltration rate and the process becomes soil profile-controlled (Williams et al.,

1998).

Figure 1-5 shows at typical soil infiltration curve. At the beginning of the infiltration process, infiltration rate is very high and decreases rapidly and eventually attains a stable rate almost equal to saturated hydraulic conductivity of soil (Turner,

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2006). For initially oven dried soil, infiltration rate takes some time to attain stable value.

For wet soil, a stable infiltration rate will be more quickly attained.

Figure 1-5: Infiltration rate versus time

1.5.2 Factors Affecting Infiltration Rate of Soil

Texture, compaction, surface crust, aggregation, water content, hydraulic conductivity, organic matter, pore types and frozen surface condition affect the soil infiltration rate (Soil Quality Indicators: Infiltration, 1998; Mangala, Toppo, & Ghoshal,

2016). Sandy soils with larger pores have a higher infiltration rate than silty and clay soils. Soil compaction reduces porosity and infiltrability of soil while soil surface crust also decreases infiltration rate by sealing pores. Soil with high initial moisture content, strong granular soil and high organic matter has a higher infiltration rate. Connected pore spaces and non-frozen soil cause a higher infiltration rate. Hydraulic conductivity is the

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most influential parameter, which directly influences infiltration rate. Water quality also affect soil infiltration rate (Edwards & Larson, 1969). Besides these soil parameters, there are several other extrinsic factors like land cover, slope of land surface and evapotranspiration, affecting infiltration rate and are not included in this research

(Mangala et al., 2016).

1.5.3 Soil Infiltration Measurement

There are various methods of infiltration rate measurement like ring infiltrometer, mariotte-double ring method, disc permeameter, small pilot infiltration test (PIT), boring test method, etc. (Lili, Bralts, Yinghua, Han, & Tingwu, 2008). Lili et al. (2008) explains that single and double ring methods are most popular one and uses measurement of volume of water supplied for a given period of time to determine infiltration rate. But, these two methods cannot measure initial infiltration rate, only works for high water inflow rate or precipitation, disturbs soil structure and cannot be applied to sloping soil surface (Lili et al., 2008). Also, it cannot do real-time measurement for a long period of time in varying depths and is expensive, tedious and manpower demanding for using in large fields. Similarly, other methods like disc permeameter and mariotte-double ring permeameter are not perfect for application in large fields and have many technical weaknesses.

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1.5.4 Soil Infiltration Models

There are quite a few infiltration models describing how the infiltration process happens. These models are of three types and called physical based models, approximate models and empirical models. According to Turner (2006), physical based models approaching Richard’s equation describes “water flow in soils in terms of the hydraulic conductivity and the soil water pressure as functions of soil water content, for specified boundary conditions” (p. 199). Turner further explains physical based models are highly dependent on soil properties like saturated hydraulic conductivity, soil moisture gradient and suction at the wetting front. Empirical models may give better results when its parameters are calibrated separately for different conditions since its parameters are determined by curve fitting or other means of estimation and empirical models like

Kostiakov, Horton and Holtan models (Turner, 2006). Below is an outline of the types of models, foundational to this research.

(1) Physical Based Equations/ Richard’s Equation

It uses two equations based on Darcy’s law and conservation of mass describing soil properties like hydraulic conductivity K(h), soil diffusivity D(θ) and water holding capacity C(h) on which soil infiltration depends (Turner, 2006).

This equation is written as:

휕ℎ 휕 휕ℎ 휕퐾(ℎ) 퐶(ℎ) = [퐾(ℎ) ]+ (h-based Richards equation) (1.6) 휕푡 휕푧 휕푧 휕푧

휕휃 휕 휕휃 휕퐾(휃) = [퐷(휃) ] + (θ-based equation) (1.7) 휕푡 휕푧 휕푧 휕푧

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where, C(h) is water holding capacity, h is capillary pressure head [L], H= h+z =

∂퐻 total hydraulic head, is hydraulic gradient in vertical (z) direction, 휃 is soil water ∂푧

∂휃 content, is change in water content with respect to time, D(θ) is Soil Diffusivity and ∂푡

K(h) is Hydraulic conductivity.

H-based Richard’s equation may be used for unsaturated or saturated conditions but θ-based is good for completely unsaturated flow. Turner (2006) explains that

Richard’s equation precisely gives vertical percolation when initial and boundary conditions are given but this equation requires numerous measurements to be made for change in soil properties from point to point in the field, making it impractical for this research.

(2) Approximate Models

It includes Philip, Smith & Parlange and Green and Ampt Infiltration Model and apply physical principles governing infiltration to simplify boundary and initial condition for determination of infiltration rate (Turner, 2006). These models assume ponded surface condition from the beginning time zero (Hillel, 1998) and water moves uniformly from surface to downward through deep homogeneous soil with well-defined wetting front, which holds more valid for sandy soil than clayey soil (Haverkamp, Rendon, &

Vachaud, 1987). These physical based approximation models do not require field measurements to get parameters like the physical based models but use parameters which could be obtained from soil water properties and makes it economical than empirical equations (Turner, 2006).

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(a) Green and Ampt Equation

The Green and Ampt Equation is based on Darcy’s law and this model assumes a

wetting front between the saturated soil and oven dried soil where saturated zone is

formed by matric suction pull by oven dried soil of the infiltrating water and wetting

front moves toward oven dried soil as shown in figure 1-6. Turner (2006) describes

transmission zone as region between wet and oven dried soil with wetting front as lower

boundary and have almost constant water content and its depth lengthens as wetting front

shifts downward toward oven dried soil zone. As most infiltration methods have

difficulty in determining the infiltration parameters or these parameters should be

determined empirically, Green and Ampt method is an equation whose parameters can be

determined easily (Stahr, Eisenhauer, Helmers, Dosskey, & Franti, 2004). This model

assumes that the soil considered has homogeneous water retention and transmission

properties with clearly defined wetting front having constant matric suction.

Figure 1-6: - Water infiltration process showing saturated, transmission and oven

dried soil zones (Adapted from Green and Ampt, 1911; Turner, 2006)

21

Let us consider a vertical column of soil as shown in figure 1-7 to observe the

Green and Ampt infiltration parameters.

Figure 1-7: - Layers of soils showing uniform water entry assumption with clearly

shown wetting front as described in Green and Ampt Infiltration Equation

Cumulative infiltration (F) for Green and Ampt infiltration equation is given as:

퐹 퐹 = 퐾푡 + 휓훥휃 푙푛 (1 + ) (1.8) 휓훥휃

where, K=saturated hydraulic conductivity of soil, t = time from the beginning of infiltration, 휓 = average capillary suction at the wetting front, 훥휃 = η-θi = increase in soil moisture content as wetting front passes through oven dried soil, η = porosity of soil and θi = initial soil moisture content.

휓훥휃 퐼푛푓𝑖푙푡푟푎푡𝑖표푛 푟푎푡푒 (푓) = 퐾( + 1) (1.9) 퐹

22

As described in Tindall, Kunkel and Anderson (1999), porosity (η) can be calculated from geotechnical tests or from bulk density of soil and for our simplicity it is assumed from table 3-1 as this parameter has little influence in soil infiltration rate.

Saturated hydraulic conductivity of soil (K), suction at the wetting front (휓) and initial soil moisture content (θi) can be determined by laboratory tests but in our research initial soil moisture content is going to be measured using soil moisture sensor and suction at the wetting front & saturated hydraulic conductivity is first assumed from table 3-1 and after that correct value is obtained by trial and error using two soil moisture sensors as explained in chapter 3.6. This method gives satisfactory result for both ponded and non- ponded surface condition (Turner, 2006).

(3) Empirical Methods

Empirical methods do not require soil surface and soil profile conditions related assumptions, resulting in less restriction on water application mode (Turner, 2006).

Similar condition specific calibration makes this model better than physical based model when soil is heterogenous and entrapped air and macro pore flow makes infiltration process complicated (Turner, 2006).

(a) Horton Equation

This model is based on idea that infiltration capacity (fp) decreases rapidly after infiltration starts until a steady minimum rate (fc) has been obtained (Turner, 2006). It assumes that this decrement in infiltration rate is due to soil surface operation instead of flow processes within the soil (Turner, 2006). Horton’s equation is written as:

23

−푘푡 푓푝 = 푓푐 + (푓0 − 푓푐)푒 (1.10) where, -1 fp =Infiltration capacity or rate at any time t from the beginning of infiltration[Lt ]

-1 fc=Final steady Infiltration Rate[Lt ]

-1 f0=Initial infiltration capacity at the beginning time zero (i.e. t=0) [Lt ]

k=a constant [t-1] depending upon initial moisture content of soil and water

application rate and it controls rate of decrease in Infiltration Rate (Turner, 2006).

Value of parameters fc, k and f0 are required to be measured experimentally and it’s against the purpose of this research and making it not a good model for this research.

As mentioned in Turner (2006), value of k, fc and f0 reflects soil physics laws as these are measured experimentally instead of laboratory measurement. Main disadvantage of this model is that it is only applicable when rainfall intensity is higher than final steady infiltration rate and makes it difficult job to determine three constants and has to be determined experimentally (Turner, 2006). Hydraulic conductivity also does not depend upon soil water content (Novotny & Olem, 1994).

1.6 Summary

Finding infiltration rate and cumulative infiltration in soil without using conventional approaches was the principal objective of this research. Of all the available infiltration models, Green and Ampt infiltration model was selected for application in this research because it requires fewer parameters and gives very realistic predictions. Green and Ampt Infiltration Model is also broadly applicable to wide range of soil profile

24

conditions. The infiltration parameters for Green and Ampt Infiltration Model include soil moisture content, saturated hydraulic conductivity (K), average capillary suction at the wetting front (휓) and porosity (η). These parameters are few in number and relatively easy to determine. The type of soil can be determined either by remote sensing data, web soil survey data (USDA NRCS) or by sieve analysis. Knowing the type of soil helps to find tentative infiltration parameters like saturated hydraulic conductivity, average capillary suction at the wetting front (휓) and porosity (η). The different types of soils are shown in figure 1-1 in United States Department of Agriculture (USDA) soil classification textural triangles. Also, soil aggregation, compaction, frozen surface condition, organic matter presence and various other factors affect soil infiltration rate and these infiltration parameters. Value of these parameters affect soil infiltration properties and changes with type of soil are available (Table 3-1, Rawls, Brakensiek, &

Miller, 1983; Maidment, 1993). Soil moisture content or volumetric water content

(VWC) is conventionally determined by gravimetric or volumetric methods but the soil moisture sensors described present an opportunity for an improved method giving real- time, accurate data at a low cost (financial and labor). Soil moisture sensors provide

VWC of soil at interval of few seconds with accuracy of around ± 3% to 4% (Cobos &

Chamber, 2010). Most of the infiltration models available and explained earlier have many drawbacks like high model complexity, needing many experiments, time consuming, unrealistic results and not applicable to all types of soil. In field of stormwater management and agriculture, there is need to know soil infiltration properties in real-time. These methods in their present form cannot serve this purpose. But, Green and Ampt infiltration model is the most suitable, realistic and appropriate model with

25

possibility to be modified to overcome all its drawbacks to give real-time infiltration information of soil. This research plans to exploit Green and Ampt method to develop a real-time infiltration information determination technology with help of some experiments and soil moisture sensor.

26

Chapter 2

Objective

2.1 Study Objective

The main objective of this study is to find a reliable, economical, and real-time method to determine in situ soil infiltration rate through the application of soil moisture sensors. In the method, the initial soil infiltration rate obtained from the implementation of two soil moisture sensors was used to estimate the soil saturated hydraulic conductivity. That value and other literature values were used in the Green and Ampt

Equation to determine soil infiltration and infiltration depth. This in-situ and real-time method to measure infiltration rate could be used for large areas with fewer resources in applications like stormwater management and agricultural engineering. The tasks for this research project included:

Task 1: Confirm applicability of Green and Ampt infiltration model to these

studies by comparing results calculated from the equation to laboratory

measurements of infiltration.

27

Task 2: Complete a sensitivity analysis of Green and Ampt Equation parameters

including saturated hydraulic conductivity (K), porosity (η) and average capillary

suction at the wetting front (Ψ) to determine the most influential parameter.

Task 3: Determine initial infiltration rate using two soil moisture sensors for an

accurate estimate of soil hydraulic conductivity. The soil hydraulic conductivity

along with other estimated soil parameters were applied to the Green and Ampt

Equation to determine soil infiltration rate and cumulative infiltration as a

function of time. In an effort to check this method on various sample types, the

experiment was repeated for various sample types including sand, sand and

organic matter, dried sand, and bulked sand.

The result of this research is expected to be a broadly applicable method for measuring in situ soil infiltration rates to help in the estimation of site runoff and precipitation infiltration for various applications.

28

Chapter 3

Methodology

3.1 Overview

This research focuses on the development of a technique for estimating infiltration by incorporating readily available real-time soil moisture sensor data into the

Green and Ampt infiltration model. Initial laboratory experiments confirmed that estimates of infiltration using Green and Ampt Infiltration Model with parameters found in the literature compared well to actual laboratory measurements of infiltration. A sensitivity analysis was then performed on the Green and Ampt infiltration equation parameters including saturated hydraulic conductivity (K), porosity (η), soil moisture content (θi) and average capillary suction at the wetting front (휓) to determine which had the largest influence on the calculated soil infiltration rate. This approach was then validated by measuring the infiltration rate and cumulative infiltration of sand (with varying initial moisture content) and sands that included an equal volume of organic matter.

29

3.2 Soil Moisture Sensor

Dielectric based 10HS soil moisture sensor was selected for this research because it has a large volume of influence and accuracy and lower cost in its category. As dielectric constant of water is much higher than that of air or soil minerals, it sensitively measures volumetric water content of soil (“10HS large soil moisture sensor,” 2015).

Sensors are connected to data loggers recording the data over a period.

Like other soil moisture sensors, the 10HS soil moisture sensors are also pre- calibrated and have an accuracy ranging from 3-5% and the accuracy can be checked

(“10HS large soil moisture sensor,” 2015) (Cobos & Chambers, 2010). The sensor accuracy test should be performed for each new condition (e.g., type of soil used) as per

Cobos and Chambers (2010). The sensor precision (repeatability) was measured at 0.05% and a sample precision check is shown in figure 3-1.

Volumetric water content vs time

0.1353

) 3

0.1352

/m 3 0.1351 0.135 0.1349 0.1348 0.1347 0.1346 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

Volumetric Volumetric water content (m time (min)

Figure 3-1: - Volumetric water content versus time graph

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3.3 Soil Moisture Sensor Installation

There are two types of installation of soil moisture sensors including trench

(horizontal) installation and downhole auger (vertical) installation. Horizontal installation involves inserting the soil moisture probes horizontal to the surface as shown in figure 3-

2. Horizontal installation does not disturb soil above, has discrete depth and have clear confirmation of quality installation. Its disadvantages include output being affected by water pooled over the sensor, soil disturbance is in large scale for deep installation and needs much effort for large excavation (“10HS large soil moisture sensor,” 2015).

Horizontal installation technique was not used for this research as experiments were done in cylindrical tube.

Figure 3-2: - Horizontal installation of 10HS soil moisture sensor (“10HS large soil

moisture sensor,” 2015)

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Vertical installation can have minimal disturbance on soil when deep installation is needed but it needs some special equipment to install deep inside and is impossible to assess quality of that deep installation (“10HS large soil moisture sensor,” 2015). It disturbs the normal condition of soil above it but could be avoided by using 45° inclined installation of sensor as shown in figure 3-3.

Figure 3-3: - Vertical and inclined installation of 10HS soil moisture sensor (Adapted

from “10HS large soil moisture sensor,” 2015)

Soil around the soil moisture sensor should avoid air gaps or excessive soil compaction, particularly around and between the sensor prongs as it affects the sensor reading (“10HS large soil moisture sensor,” 2015). The sensor should not be installed adjacent to large metal objects such as metal poles or stakes as it can weaken sensor’s electromagnetic field and adversely affect the sensor reading and sensor should not have any materials stuck between the sensor prongs as it will also adversely affect the sensor

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reading and should be inserted cautiously in dense soil as sensor prongs are easily breakable (“10HS large soil moisture sensor,” 2015).

3.4 Comparison of Directly Measured Infiltration Rate with

Green and Ampt Infiltration Rate

The purpose of this experiment was to compare directly measured infiltration rate with infiltration rate obtained using Green and Ampt Equation. This comparison was necessary to have independent check of whether infiltration rate obtained using Green and Ampt infiltration model gives correct result or not. An experimental setup of an open-ended cylindrical container of 11 cm diameter and 21 cm length was used for the experiment. It had mesh sieve screen on bottom side to contain sand over small aggregates and allowed water to flow through it. It had aggregate over the mesh sieve screen to prevent possible flow of sand with water. Then, sand was filled inside container leaving 1.5 cm deep space on top of the sand and sand was compacted slightly. The graphical illustration of this experiment is shown in figure 3-4. The room temperature during experiment was 24.6 °C and water temperature was 21.4 °C.

In the experimental setup, first water was added at a steady rate over the sand surface. As water infiltrated through the sand, it was measured in the graduated cylinder at fixed intervals of time to determine directly measured volumetric infiltration rate in cm3/min. This volumetric infiltration rate was converted into infiltration rate in terms of depth (cm/min) by dividing volumetric infiltration rate by cross sectional area of the container.

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As the soil used was sand, values of Green and Ampt infiltration parameters were taken from table 3-1 for sand and is presented in table 3-2. As K, Ψ and η values are now known, an equation solving calculation was performed on electronic spreadsheet or calculator using equation 3.1 and 3.2 to get infiltration rate at various time and was plotted with respect to time. As F and f are the only unknowns in equation 3.1 and 3.2, F and f could be determined by solving these two equations using electronic spreadsheet or calculator. Infiltration rate (f) was determined with respect to time for sand and plotted in graph. This plotted infiltration rate versus time graph was compared with directly measured infiltration rate versus time graph and checked how analogous these two graphs are with each other.

Figure 3-4: - Graphical illustration of apparatus for comparison of directly measured

infiltration rate with Green and Ampt infiltration rate

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Table 3-1: Green and Ampt infiltration parameters for various soil classes (Source:

Rawls, Brakensiek & Miller, 1983; Maidment, 1993)

Saturated Wetting front soil Porosity (η) hydraulic Soil Class suction head (ψ) (m3 m-3) conductivity (K) (cm) (mm/hr)

Sand 0.437 (0.374-0.500) 117.8-235.6 4.95 (0.97-25.36)

Loamy 0.437 (0.363-0.506) 59.8 6.13 (1.35-27.94) Sand

Sandy loam 0.453 (0.351-0.555) 21.8 11.01 (2.67-45.47)

Loam 0.463 (0.375-0.551) 13.2 8.89 (1.33-59.38)

Silt loam 0.501 (0.420-0.582) 6.8 16.68 (2.92-95.39)

Sandy clay 0.398 (0.332-0.464) 3.0 21.85 (4.42-108.0) loam

Clay loam 0.464 (0.409-0.519) 2.0 20.88 (4.79-91.10)

Silty clay 0.471 (0.418-0.524) 2.0 27.30 (5.67-131.50) loam

Sandy clay 0.430 (0.370-0.490) 1.2 23.90 (4.08-140.2)

Silty clay 0.479 (0.425-0.533) 1.0 29.22 (6.13-139.4)

Clay 0.475 (0.427-0.523) 0.6 31.63 (6.39-156.5)

35

Table 3-2: Values of Green and Ampt infiltration parameters for sand

Average capillary suction at the wetting front (Ψ) 4.95 cm = 1.95 in

Saturated hydraulic conductivity (K) 1.7 cm/min = 40.16 in/hr

Porosity (η) (m3 m-3) 0.437

And, the equations used to determine cumulative infiltration (F) and infiltration rate (f) based on Green and Ampt infiltration model are: -

퐹 퐹 = 퐾푡 + 휓훥휃 푙푛 (1 + ) (3.1) 휓훥휃

휓훥휃 푓 = 퐾( + 1) (3.2) 퐹

3.5 Sensitivity Analysis of Green and Ampt Equation

Parameters

The purpose of this experiment was to find most influential Green and Ampt infiltration parameters among saturated hydraulic conductivity (K), average capillary suction at the wetting front (Ψ) and porosity (η). This identification was very important as more priority could be given to determine highly influential parameters with higher accuracy, so that infiltration rate could be determined with higher accuracy. The sensitivity analysis was performed using Green and Ampt Infiltration Model to determine infiltration rate with various values of infiltration parameters. The whole analysis was done for one common soil sample. Out of Green and Ampt infiltration parameters among saturated hydraulic conductivity (K), average capillary suction at the wetting front (Ψ)

36

and porosity (η), each infiltration parameter was changed to many values while keeping other parameters constant and infiltration rate was determined by solving equation 3.3 and 3.4 in electronic spreadsheet or calculator. For example, one case had value of porosity (η) and average capillary suction at the wetting front (Ψ) kept constant at 0.437 m3 m-3 and 4.95 cm respectively while values for saturated hydraulic conductivity (K) were trialed as 0.6 cm/min, 1.2 cm/min, 1.7 cm/min, 2.1 cm/min and 3 cm/min. Then 4 curves were plotted for infiltration rate with respect to time and compared with each other for level of influence on infiltration rate. This sensitivity analysis was repeated for each parameter while keeping other parameters constant and the values of the parameters used in sensitivity analysis are shown in table 3-3. Values used for sensitivity analysis were selected in such a way that this sensitivity analysis works for a wide array of soils.

Graphs of infiltration rate with respect to time was plotted for each case and checked to find level of influence of infiltration parameters on infiltration rate. And, the equations used to determine cumulative infiltration (F) and infiltration rate (f) are: -

퐹 퐹 = 퐾푡 + 휓훥휃 푙푛 (1 + ) (3.3) 휓훥휃

휓훥휃 푓 = 퐾( + 1) (3.4) 퐹

Table 3-3: Values of Green and Ampt infiltration parameters used for sensitivity analysis

Parameters Values used for sensitivity analysis

Saturated hydraulic conductivity (K) (cm/min) 0.6, 1.2, 1.7, 2.1 and 3

Average capillary suction at the wetting front 4.95, 10, 15, 20 and 25.36

(Ψ) (cm)

Porosity (η) (m3 m-3) 0.374, 0.437 and 0.5

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3.6 Determination of Soil Infiltration Rate Using Two Soil

Moisture Sensors

This experiment was used to determine cumulative infiltration and infiltration rate of soil with respect to time. As infiltration rate measurement method using soil moisture sensor has already been verified in experiment from chapter 3.6, this method also should be applicable for any type of soil. This experiment was check for specific applicability of this method in determination of infiltration rate and cumulative infiltration of sand with varying initial soil moisture content and sand that included equal volume of organic matter. Comparison and analysis of infiltration rate for these various forms of sand sample was also done. In this process, experiment as well as calculation for solving

Green and Ampt infiltration equation in electronic spreadsheet or calculator was performed. This set of experiments involved the use of sand (Play Sand by Quikrete), which is widely available and specially graded washed sand that has been dried and screened. Organic matter (Topsoil by Scotts), which is mixture of organic materials and peat moss, was used to vary organic content of the samples.

The purpose of this experiment was to determine infiltration rate (f) and cumulative infiltration (F) of soil using Green and Ampt infiltration method. Using this experiment and calculation, infiltration rate and cumulative infiltration can be measured quickly, economically and effectively without many field visits of using conventional infiltration rate measuring devices like infiltrometers and this was the main goal of this entire research work.

38

In this experiment, a graduated cylinder as shown in figure 3-5 was used. Two launched soil moisture sensors separated by 10.16 cm distance were placed in the same vertical plane inside the sand filled cylinder. Then water was slowly added inside the cylinder at a steady rate until it reached bottom of the cylinder. Volumetric water content values at different time for both upper and lower soil moisture sensors were recorded using data logger. Directly measured initial soil infiltration rate is given by distance between sensors divided by time needed for a specific moisture content for both sensors.

This was a direct measurement of initial infiltration rate. Directly measured initial infiltration rate for various soil moisture content values are also determined and plotted in graph, along with determination of its average value.

Figure 3-5: - Two soil moisture sensor based soil infiltration rate measuring apparatus

39

Directly measured initial infiltration rate was obtained and used in the Green and

Ampt Equation (Equations 3.5 and 3.6). The value of porosity (η) and average capillary suction at the wetting front (Ψ) was taken from table 3-1 based on the sample type. Initial value of saturated hydraulic conductivity was selected from table 3-1 so that the initial value of infiltration rate calculated using Green and Ampt Equation was equal to directly measured initial infiltration rate and final steady infiltration rate value was equal to saturated hydraulic conductivity (K) of the soil. Using this solution, two sets of infiltration rate versus time and cumulative infiltration versus time graphs were plotted for volumetric water content data from each sensor.

This experiment was repeated for sand, oven dried sand, bulked sand and sand mixed with equal volume of organic matter. Reason for choosing sand and its mixture with organic matter is because this technique is mostly focused on application in tree filter and tree filter mostly contains sand mixed with organic matters. For sand also soil of varying moisture content is needed as its infiltration characteristics should be known for all weather. A description of each type of soil sample used is shown below.

(1) Sand and Oven dried Sand

Sand means a sand exposed to normal room temperature, pressure and humidity for more than 3 days to have same condition as the room. Oven dried sand was obtained by oven drying sand in oven for more than 24 hours. The room temperature during this experiment set was 24.1°C and water temperature was 20.9°C. Value of average capillary suction at the wetting front was 4.95 cm and porosity was taken 0.395 m3 m-3. The experiment was repeated for 4 times for this soil.

40

(2) Bulked Sand

Bulking of sand is increase in volume of sand owing to increase in its moisture content. This increase in volume is due to formation of film around sand particle due to surface tension force and causes 20% to 40% increase in volume. The room temperature during this experiment set was 23.3 °C and water temperature was 21.3 °C. Bulked sand was obtained by mixing 5% by volume of water in oven dried sand. Value of average capillary suction at the wetting front was 4.95 cm and porosity was taken 0.490 m3 m-3.

The experiment was repeated for 3 times for this soil.

(3) Mixture of Sand and Organic Matter

The soil used for this experiment was composed of 50% fine sand and 50% organic matter. The room temperature during this experiment was 23.3 °C and water temperature was 21.3 °C. The hypothesis was that addition of organic matter to soil keeps soil intact when rain impacts it, causing fewer clogged soil pores. It causes increase in saturated hydraulic conductivity (K) by 14.7% to 29.2% and also increases soil infiltration rate (Eusufzai and Fujii, 2012). Value of average capillary suction at the wetting front was 4.95 cm and porosity was taken 0.437 m3 m-3. The experiment was repeated for 3 times for this soil.

퐹 퐹 = 퐾푡 + 휓훥휃 푙푛 (1 + ) (3.5) 휓훥휃

휓훥휃 푓 = 퐾( + 1) (3.6) 퐹

41

Chapter 4

Results

4.1 Comparison of Directly Measured Infiltration Rate with

Green and Ampt Infiltration Rate

Directly measured infiltration rate (n=7) was compared with the infiltration rate obtained using the Green and Ampt Equation. The experiment and process is outlined in

Section 3.4. Figure 4-1 demonstrates that the results were in close agreement. The two curves show the most variation at the end of the experiment.

42

Infiltration rate versus time 2.5

2

1.5

1

0.5

Infiltrationrate (cm/min) 0 0 5 10 15 20 25 Time (min)

Directly measured infiltration rate Greeen-Ampt based infiltration rate

Figure 4-1: Directly measured infiltration rate versus time and Green and Ampt based

infiltration rate versus time graph

For comparison, directly measured infiltration rate and Green and Ampt based infiltration rate values at three minutes were 2.06 in/hr and 2.02 in/hr respectively. This is a difference of 1.98%. After three minutes, the difference between the results decreased until after 11 minutes when the difference slowly increased to a maximum of 6.67% at 20 minutes. This result shows that the Green and Ampt Equation gives similar output as directly measured infiltration and confirms that the Green and Ampt Equation would be useful for this research.

43

Table 4-1: Data points for directly measured infiltration rate and infiltration rate obtained

using the Green and Ampt Equation

Time from beginning of Directly Green and Ampt model based infiltration (min) measured infiltration rate (cm/min) infiltration rate (cm/min) 3 2.06 2.02 4 1.90 1.95 5 1.85 1.91 6 1.82 1.88 10 1.78 1.82 15 1.70 1.78 20 1.65 1.76

4.2 Sensitivity Analysis of Green and Ampt Equation

Parameters

A sensitivity analysis was performed with experimental setup outlined in section

3.5 to determine level of influence of Green and Ampt infiltration equation parameters on infiltration rate of soil. For example, infiltration rate was plotted as a function of time

(Figure 4-2) using Green and Ampt Equation for a range of values for saturated hydraulic conductivity (K) while also changing values of porosity and average capillary suction at wetting front (Appendix 1). The curves were compared with each other to check level of influence of saturated hydraulic conductivity (K) on variations in calculated infiltration rate. Similarly, this process was repeated for average capillary suction at the wetting front

(Ψ) and porosity (η). Values used for sensitivity analysis were selected in such a way that a wide range of soils (sand to clay) was considered. The values used in the sensitivity

44

analysis are provided in sand section of Table 3-1. The ranges of all the infiltration parameters for sand were used for sensitivity analysis. The sensitivity analysis results are shown in Figures 4-2 through 4-4. Each graph has the assumed parameter values below the title. Example figures were included for the sensitivity analyses of hydraulic conductivity, porosity and capillary suction at the wetting front because the trends were similar in all figures.

Infiltration rate vs time Porosity(η)=0.437;Suction(Ψ)=4.95cm; K(cm/min)=1.2(Ser1),3(Ser2),2.1(Ser3),0.6(Ser4),1.7(Ser5) 100 90 80 70 60 50 40 30

20 Infiltrationrate (in/hr) 10

0

3 1 5 7 9

15 25 35 45 55 65 80

100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 Time (min) K=1.2cm/min K=3cm/min K=2.1cm/min K=0.6cm/min K=1.7cm/min

Figure 4-2: Sensitivity analysis for infiltration rate by changing hydraulic conductivity

45

Infiltration rate vs time K=1.2cm/min; Suction(Ψ)=4.95cm; Porosity(η) =0.437(Ser1);0.5(Ser2)and 0.374(Ser3) 50 45 40 35 30 25 20 15

Infiltration Infiltration rate (in/hr) 10 5

0

1 3 5 7 9

80 15 25 35 45 55 65

300 100 120 140 160 180 200 220 240 260 280 320 340 360 380 400 420 440 Time (min) η=0.437 η=0.5 η=0.374

Figure 4-3: Sensitivity analysis for infiltration rate by changing porosity (η) at K= 1.2

cm/min

Infiltration rate vs time K=1.2cm/min;Porosity=0.437; Suction (Ψ) (cm)=25.36(Ser1);20(Ser2);15(Ser3);10(Ser4);4.95(Ser5); 80 70 60 50 40 30

20 Infiltration Infiltration rate (in/hr) 10

0

1 3 5 7 9

15 25 35 45 55 65 80

320 440 100 120 140 160 180 200 220 240 260 280 300 340 360 380 400 420 Time (min)

Ψ=25.36 Ψ=20 Ψ=15 Ψ=10 Ψ=4.9

Figure 4-4: Sensitivity analysis for infiltration rate by changing average capillary suction

(Ψ)

46

From Figure 4-2, it was observed that for every 1 cm/min increase in saturated hydraulic conductivity (K), the infiltration rate increased by an average of 25.35 cm/min, regardless of the time and other parameters. From Figures 4-3, the sensitivity curves for porosity indicated an impact in infiltration rate up to 20 minutes. Similarly, the difference between infiltration rate for different values of average capillary suction at the wetting front (Ψ) was noticeable and gradually reduced for all infiltration curves to converge

(Figure 4-4). At 1 minute from beginning, the difference between infiltration rate was as high as 1.30 cm/min for every 1 cm difference in Ψ. It gradually decreased to 0.54 cm/min at 6 minutes and 0.12 cm/min at 25 minutes. After that, the infiltration curves started to merge with each other. Average capillary suction only showed influence for first 40 to 50 minutes of the analysis.

4.3 Determination of Soil Infiltration Rate Using Two Soil

Moisture Sensors Test

The experimental design used two soil moisture sensors as explained in section

3.6. The experiments were performed on various sample types including sand, dried sand, sand plus equal volume organic matter, and bulked sand. Infiltration rate was measured as a function of time by determining the time required for the moisture reading on both sensors (upper and lower) to be equal. The very first reading is the initial infiltration rate, which was used to determine the saturated hydraulic conductivity (K) given literature values of average capillary suction at the wetting front (Ψ) and porosity

(η) for the soils used. General applicability of this test method was verified from earlier

47

experiments and involved determining infiltration rate and cumulative infiltration using

the Green and Ampt Equation and plotting with respect to time. The experiment was

repeated in triplicate for each sample type.

(A) Sample Results for Sand

The first set of experiments was performed on sand still containing some ambient

moisture content (1%). The laboratory experiment allowed the measure of infiltration rate

as a function of time as shown in figure 4-6 and 4-7. Also, initial infiltration rate was

measured directly as a function of water content is shown in figure 4-5.

Initial infiltration rate versus volumetric water 3.9 content 3.8

3.7

3.6

3.5

3.4

3.3 Initial infiltration Initial infiltration rate (cm/min) 3.2 0 0.05 0.1 0.15 0.2 0.25 0.3 Volumetric water content (m3/m3)

Figure 4-5: Directly measured initial infiltration rate versus volumetric water content

graph for sand sample-1

The initial infiltration rate measured was 3.8 cm/min. This allowed the

determination of the hydraulic conductivity (K) from the Green and Ampt Equation based

on approximating the literature values for average capillary suction at the wetting front

(Ψ) (4.95 cm) and porosity (η) 0.395 (Table 3-1) to achieve the initial infiltration rate

48

measured in the experiment (3.8 cm/min). The value of saturated hydraulic conductivity

(K) was determined to be 0.23 cm/min. The infiltration rates (and cumulative infiltration) as a function of time for the upper and lower soil moisture sensors are shown in Figure 4-

6 and 4-7 respectively. Both graphs have initial infiltration rate of 3.82 cm/min and are almost identical, which complies well with direct initial infiltration rate obtained.

Percentage difference between directly measured and average calculated initial infiltration rate is 0.52%. Value of hydraulic conductivity (K) obtained also complies with value range given in table 3-1. So, infiltration rate (and cumulative infiltration) as shown in Figure 4-6 and 4-7 determined by Green and Ampt Equation should be a good estimate. Triplicate sample comparison showed that it had standard deviation value of

0.36 and 0.04 for initial infiltration rate and saturated hydraulic conductivity. So, all these samples compare good with each other.

Cumulative infiltration and infiltration rate versus time 7

6

5

4

3

2

rate (cm/min) 1

0 0 2 4 6 8 10 12 14 16 18

time (min) Cumulative Cumulative infiltration (cm) infiltration / Total Infiltration Infiltration rate

Figure 4-6: Cumulative infiltration and infiltration rate versus time graph for upper sensor

in sand sample-1

49

Cumulative infiltration and infiltration rate versus time 4.5 4 3.5 3 2.5 2 1.5

1 (cm/min) 0.5 0 0 1 2 3 4 5 6 7 8 9 time (min)

Cumulative Cumulative infiltration (cm) infiltration / rate Total Infiltration Infiltration Rate

` Figure 4-7: Cumulative infiltration and infiltration rate versus time graph for lower

sensor in sand sample-1

(B) Sample Results for Oven Dried Sand

These samples were oven dried to remove any moisture. Otherwise, the same protocol was followed. Infiltration rate was measured as a function of time using the two-sensor method (Figure 4-9 and 4-10). Also, initial infiltration rate was measured directly as a function of water content is shown in figure 4-8.

50

Initial infiltration rate versus volumetric water content 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 Initialinfiltration rate (cm/min) 0 0 0.05 0.1 0.15 0.2 0.25 Volumetric water content (m3/m3)

Figure 4-8: Directly measured initial infiltration rate versus volumetric water content

graph for oven dried sand sample-1

The initial infiltration rate measured was 4.37 cm/min. This was 15% greater than the sand. This allowed the determination of the hydraulic conductivity (K) from the

Green and Ampt Equation based on the literature values for average capillary suction at the wetting front (Ψ) (4.95 cm) and porosity (η) 0.395 (Table 3-1) to achieve the initial infiltration rate measured in the experiment (4.37 cm/min). The value of saturated hydraulic conductivity (K) was determined to be 0.30 cm/min. The infiltration rate (and cumulative infiltration) as a function of time for the upper and lower soil moisture sensors are shown in Figure 4-9 and 4-10 respectively. Both graphs have initial infiltration rate of 4.37 cm/min and are almost identical, which compares well with direct initial infiltration rate obtained. There is no difference in value of directly measured and average calculated initial infiltration rate. Value of hydraulic conductivity (K) obtained also complies with value range given in table 3-1. So, infiltration rate (and cumulative

51

infiltration) as a function of time as shown in figure 4-9 and 4-10 can be considered to be a good estimation.

Cumulative infiltration and infiltration rate versus time 8 7 6 5 4 3

2 rate(cm/min) 1 0 0 2 4 6 8 10 12 14

Time (min) Cumulative Cumulative infiltration (cm) infiltration /

Total infiltration Infiltration rate

Figure 4-9: Cumulative infiltration and infiltration rate versus time for upper sensor in

oven dried sand sample-1

52

Cumulative infiltration and infiltration rate versus time 5

4

3

2

1

0

0 1 2 3 4 5 6 7 infiltrationrate (cm/min)

Cumulative Cumulative infiltration (cm) / time (min)

Total infiltration Infiltration rate

Figure 4-10: Cumulative infiltration and infiltration rate versus time for lower sensor in

oven dried sand sample-1

Average value of saturated hydraulic conductivity (K) and initial infiltration rate for room-exposed sand was 0.22 cm/min and 3.7 cm/min respectively. Average value of saturated hydraulic conductivity (K) and initial infiltration rate for oven dried sand was

0.33 cm/min and 4.63 cm/min respectively. Saturated hydraulic conductivity (K) of oven dried sand was 50% greater than that of sand while initial infiltration rate of oven dried sand was 25.14% greater than that of sand. Also, initial infiltration rate was measured directly as a function of water content in figure 4-8. Triplicate sample comparison showed that it had standard deviation value of 0.74 and 0.06 for initial infiltration rate and saturated hydraulic conductivity. So, all these samples compare really good with each other. Value of initial infiltration rate and hydraulic conductivity was same for two samples and 20% and 23.33% respectively more for the third sample.

53

(C) Sample Results for Bulked Sand

A set of experiments was carried out on bulked sand because this is what is expected under field conditions during rainfall. The sand had moisture content of 5%.

These experiments followed the same protocol as other experiments, and laboratory measurements of infiltration rate were taken as a function of time using the two-sensor approach. Also, initial infiltration rate was measured directly as a function of water content is shown in figure 4-15.

Initial infiltration rate versus volumetric water

content 16 14 12 10 8 6 4 2 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 3 3 Initialinfiltration rate (cm/min) Volumetric water content(m /m )

Figure 4-11: Directly measured initial infiltration rate versus volumetric water content

graph for bulked sand sample-1

The initial infiltration rate measured was 15.0 cm/min. This was 332.43% and

266.13% greater than that of sand and oven dried sand, respectively. This allowed the determination of the hydraulic conductivity (K) from the Green and Ampt Equation based on the literature values for average capillary suction at the wetting front (Ψ) (4.95 cm) and porosity (η) 0.437 (Table 3-1) to achieve the initial infiltration rate measured in the experiment (15.0 cm/min). The value of saturated hydraulic conductivity (K) was

54

determined to be 3.0 cm/min, which is 9.1 times greater than oven dried sand and 13.6 times greater than sand. The infiltration rate (and cumulative infiltration) as a function of time for the upper and lower soil moisture sensors are shown in Figure 4-16 and 4-17 respectively. The upper and lower sensor graphs have initial infiltration rate of 16.0 cm/min and 16.4 cm/min respectively and are almost identical and complies well with direct initial infiltration rate obtained. Percentage difference between directly measured and average calculated initial infiltration rate is 8.0%. Compliance of value of saturated hydraulic conductivity (K) with table 3-1 could not be performed as no literature values for saturated hydraulic conductivity (K) of bulked sand could be obtained for comparison.

Cumulative infiltration and infiltration rate versus time 45

40

infiltrationrate 35 30 25

(cm/min) 20 15 10 5 0

Cumulative infiltration (cm) / 0 2 4 6 8 10 12 time (min)

Series1 Series2

Figure 4-12: Cumulative infiltration and infiltration rate versus time graph for upper

sensor in bulked sand sample-1

55

Cumulative infiltration and infiltration rate versus time 50

40

infiltration 30

20

10

rate(cm/min) 0 0 2 4 6 8 10 12 14 time (min)

Cumulative Cumulative infiltration (cm) / Series1 Series2

Figure 4-13: Cumulative infiltration and infiltration rate versus time graph for lower

sensor in bulked sand sample-1

Value of initial infiltration rate and hydraulic conductivity was same for two samples and 20% and 23.33% respectively more for the third sample. Triplicate sample comparison showed that it had standard deviation value of 1.73 and 0.41 for initial infiltration rate and saturated hydraulic conductivity. So, all these samples compare favorably with each other.

(D) Equal Volume Mixture of Sand and Organic Matter

The soil used for this experiment was composed of 50% fine sand and 50% organic matter. The room temperature during this experiment was 23.3 °C and water temperature was 21.3 °C. Organic matter samples were introduced since most natural samples will contain organic matter and are expected to have a different infiltration rate

56

than sand only samples. It should result in increase in infiltration rate and hydraulic conductivity of soil. This increase should be 14.7% to 29.2% on an average (Eusufzai and

Fujii, 2012). Also, initial infiltration rate was measured directly as a function of water content in figure 4-9.

Initial infiltration rate versus volumetric water content 8 7 6 5 4 3 2 1 0

Initialinfiltration (cm/min) rate 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Volumetric Water Content (m3/m3)

Figure 4-14: Directly measured initial infiltration rate versus volumetric water content

graph for organic mixed sand sample-1

The initial infiltration rate measured was 6.2 cm/min. This was 67.6% and 33.9% greater than the sand and oven dried sand respectively. Bulked sand had 158% greater initial infiltration rate than that of this mixture. This allowed the determination of the hydraulic conductivity (K) from the Green and Ampt Equation based on the literature values for average capillary suction at the wetting front (Ψ) (4.95 cm) and porosity (η)

0.490 (Table 3-1) to achieve the initial infiltration rate measured in the experiment (6.2 cm/min). The value of saturated hydraulic conductivity (K) was determined to be 0.51 cm/min, which is 57.58% and 136.36% greater than oven dried sand and sand

57

respectively and 83.90% lesser than that of bulked sand. Initial infiltration rate of this mixture was 67.57% and 33.91% greater than that of sand and oven dried sand respectively and 61.25% lesser than that of bulked sand. This increase happens due to high absorption of water by organic matter, increasing the saturated hydraulic conductivity (K) of soil (Rawls, Nemes & Pachepsky, 2004) (Eusufzai and Fujii, 2012).

Value of hydraulic conductivity (K) obtained also complies with value range given in table 3-1. The infiltration rate (and cumulative infiltration) as a function of time for the upper and lower soil moisture sensors are shown in Figure 4-19 and 4-20. Value of initial infiltration rate was same for all three samples and had only difference of 0.01 in hydraulic conductivity value. Triplicate sample comparison showed that it had standard deviation value of 0 and 0.01 for initial infiltration rate and saturated hydraulic conductivity.

Cumulative infiltration and infiltration rate versus time 8

6

4

2

0 0 1 2 3 4 5 6 7 8 9

infiltrationrate (cm/min) Time (min) Cumulative Cumulative Infiltration (cm) and Total Infiltration (cm) Infiltration Rate (cm/min)

Figure 4-15: Cumulative infiltration and infiltration rate versus time graph for upper

sensor in organic mixed sand sample-1

58

Cumulative infiltration and infiltration rate versus time 7 6 5 4 3 2

1 rate(cm/min) 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Time (min) Cumulative Cumulative infiltration (cm) infiltration / Total Infiltration Infiltration Rate

Figure 4-16: Cumulative infiltration and infiltration rate versus time graph for lower

sensor in organic mixed sand sample-1

A summary of all the findings of these experiments has been provided in Table 4-2.

59

Table 4-2: Saturated hydraulic conductivity and initial infiltration rate for various

samples

Soil sample Initial infiltration rate Hydraulic conductivity (cm/min) (cm/min) Sand sample-1 3.80 0.23 Sand sample-2 3.30 0.18 Sand sample-3 4.00 0.26 Average 3.70 0.22 Standard Deviation 0.36 0.04 Oven dried sand sample-1 4.37 0.30 Oven dried sand sample-2 4.06 0.29 Oven dried sand sample-3 5.47 0.39 Average 4.63 0.33 Standard Deviation 0.74 0.06 Bulked sand sample-1 15.00 3.00 Bulked sand sample-2 15.00 3.00 Bulked sand sample-3 18.00 3.70 Average 16.00 3.23 Standard Deviation 1.73 0.41 Organic mixed sand sample-1 6.20 0.51 Organic mixed sand sample-2 6.20 0.52 Organic mixed sand sample-3 6.20 0.51 Average 6.20 0.51 Standard Deviation 0.00 0.01

60

Chapter 5

Discussion

Previous research indicates that the Green and Ampt Infiltration Model is highly realistic as compared to most other infiltration models because it is valid in a wide range of conditions (Turner, 2006). It is most applicable in this research because its parameters can be determined relatively easily through field measurement. The Green and Ampt infiltration model was shown to be a good infiltration model for application in the sand experiments carried out in this work. The two directly measured infiltration rate and

Green and Ampt based infiltration rate curves are almost overlapping with only slight differences in values (maximum percent difference in values was <7%).

In order to use the Green and Ampt Infiltration Model, it was necessary to carry out a sensitivity analysis to determine which parameter was most influential. In the sensitivity analysis, the value of each parameter (hydraulic conductivity, porosity, and capillary suction at the wetting front) was changed while the others were systematically varied along a wide range of values representing all soil types (Saltelli, Chan, & Scott,

2000). Our analysis indicated that saturated hydraulic conductivity (K) has almost uniform influence, irrespective of time (and thus soil moisture). Porosity had a negligible effect particularly after soils became moist. It was also shown that average capillary

61

suction at the wetting front (Ψ) decreases with increase in time because capillary effect decreases with increase total infiltration volume. So, the sensitivity analysis showed that hydraulic conductivity (K) is the most the influential Green and Ampt Infiltration

Equation parameter while porosity (η) has the least and negligible influence on soil infiltration. This finding was in accordance with previous studies (Turner, 2006; Chen et al., 2015). The local sensitivity analysis technique used in this study involves random value selection for the variable and calculates the output of model by performing partial derivatives at a specific point of the input variable space (Iooss & Saltelli, 2017).

Actually, change in value of one parameter affects other parameters. So, it would be better to apply use proprietary software packages to perform a global sensitivity analysis by studying the effect of small variations in input around a given range into output (Iooss

& Saltelli, 2017).

Given the results of the sensitivity analysis, experiments were designed to provide a good estimate of saturated hydraulic conductivity for input into the Green and Ampt

Infiltration Equation. These experiments were carried out by measuring soil moisture using two soil moisture sensors (upper and lower) on samples of oven dried sand, moist sand, bulked sand and a sample of sand and organic matter (equal volumes). In accordance with previous research, our experiments showed that soil with higher initial moisture content had a lower hydraulic conductivity and infiltration rate (Hu, She, Shao,

Chun, & Si, 2015; Gray & Norum, 1967). Soils with greater initial moisture content experience lesser downward pull, resulting in a lower infiltration rate. The values of hydraulic conductivity (K) and infiltration rate calculated in this study for oven dried and slightly moist sand were within an acceptable average range of 0.196 cm/min to 0.393

62

cm/min (Eusufzai & Fujii, 2012). In the samples containing organic matter, we observed a significant increase in infiltration. This increase happens due to high absorption of water by organic matter, increasing the saturated hydraulic conductivity (K) of soil

(Rawls, Nemes & Pachepsky, 2004; Eusufzai & Fujii, 2012).

Bulk sand samples were tested in this research because it is expected to most closely represent a field experiment during or following rainfall. The saturated hydraulic conductivity (K) of bulked sand was approximately 10 times greater than oven dried sand samples. As bulked sand has highly scarce distribution of sand particles in comparison to oven dried or sand an increase in soil hydraulic conductivity and infiltration rate was anticipated (Allred, 2008). This approach to determine soil infiltration rate could be applicable for bulked sand but needs to have applicability check with standard reference data. Also, during this experiment precautions should be made to avoid effects from settling of bulked soil, which can occur if the weight force exceeds the surface tension force.

These results could be improved if porosity (η) and average capillary suction at the wetting front (Ψ) values used in calculation were changed during the simulation at defined time steps, instead of keeping these parameters constant from the beginning. It was observed from sensitivity analysis that the effect of saturated hydraulic conductivity

(K) on infiltration remains constant with respect to time. So, value of saturated hydraulic conductivity (K) can be kept constant throughout the process while values of porosity (η) and average capillary suction at the wetting front (Ψ) could be changed during the simulation as per analysis presented in sensitivity analysis to provide more accurate model results.

63

Although these findings were collected from laboratory studies, it is anticipated that this method would be broadly applicable to field studies. However, if a site has soil with higher moisture content below the surface soil (e.g., due to capillary action or other mechanisms) then the actual hydraulic conductivity and infiltration rate might not be as high as predicted by this experiment (Gray & Norum, 1967). The oven dried soil condition mimicked in this experiment, where moisture moves down from top to bottom as a front through the soil, is useful in stormwater management for pre-rainfall conditions, particularly following multiple antecedent oven dried days. In this study,

Green and Ampt Infiltration Equation results varied slightly as did soil moisture for the upper and lower sensors. For stormwater related research, the results from both soil moisture sensors provide useful information since the surface infiltration condition is significantly affected by deeper infiltration activities (Gregory, Cunningham, Schmidt, &

Mack, 1999). However, if the objective of the research is to determine surface infiltration and loss of runoff due to infiltration during a typical rain event (90% event), then the

Green and Ampt Infiltration Equation results from the top sensor would be most applicable.

64

Chapter 6

Conclusion

The main objective of this research was not to find a replacement for the many good methods that already exist for taking single field measurements of infiltration including single ring infiltrometer, double ring infiltrometer, tension infiltrometer, guelph permeameter and Philip-Dunne permeameter. All of these methods could be applied for determining infiltration in a rain garden (Asleson, 2007). However, none of these methods provide real time soil infiltration rate or infiltration measurements that adequately describe large areas. Since soil properties are incredibly heterogeneous, using traditional infiltration devices to measure soil infiltration rate for large area will be quite expensive, time consuming, tedious and not viable. This proposed method attempts to fill those gaps.

This research incorporated field measurements of soil moisture to determine hydraulic conductivity and eventually infiltration rate and volume using the Green and

Ampt infiltration equation. The method, employing two soil moisture sensors deployed a vertical distance apart, was tested for applicability on a variety of sample types including oven dried sand, moist sand, bulked sand, and sand and organic matter mixed. Results achieved compared well to literature values indicating applicability in laboratory studies

65

that may be more broadly applicable to field studies. This approach is expected to be preferable to traditional point measurements in field studies where soil characteristics are heterogeneous and many soil moisture sensors could be deployed. The data could be collected with data loggers, and the infiltration rates and volumes calculated using web- hosted servers in real-time as observed in many other field applications. The infrastructure needed for field deployment including sensors, controllers, and remote data loggers are relatively inexpensive and widely available for such applications.

66

References

10HS large soil moisture sensor. (2015). Decagon Devices. Retrieved July 25, 2017 from http://www.decagon.com/en/soils/volumetric-water-content-sensors/10hs-large- volume-vwc/.

Acharya, M. S., Rauchecker, M., & Wu, W. (2014). Determination of water absorption and water holding capacities of different soil mixtures with minidrain system to enhance the plant growth. In Geophysical Research Abstracts (Vol. 16).

Adamchuk, V. I., Rossel, R. A. V., Sudduth, K. A., and Lammers, P. S. (2011). Sensor fusion for precision agriculture. In Sensor fusion - foundation and applications. InTech.

Allred, B. J. (2008, December). Laboratory Testing of Founoven dried Sands as Bulking Agents for Porous Media Filters Used to Treat Agricultural Drainage Waters. In AGU Fall Meeting Abstracts.

Amoozegar, A., & Wilson, G. V. (1999). Methods for measuring hydraulic conductivity and drainable porosity. Agricultural drainage, (agriculturaldra), 1149-1205.

Asleson, B. (2007). Assessment of the performance of Stormwater Best Management Practices (BMPs). Department of Civil Engineering Seminar. Powerpoint Presentation. Retrieved from http://stormwater.safl.umn.edu/sites/stormwater/ files/020207asleson.pdf

Bouwer, H. and Jackson, R. D. (1974). Determining soil properties. J. van Schilfgaarde (ed.), Drainage for Agriculture. American Society of Agronomy, Madison, WI. p6ll- 674.

Chen, L., Xiang, L., Young, M. H., Yin, J., Yu, Z., & Genuchten, M. T. (2015). Optimal parameters for the Green-Ampt infiltration model under rainfall conditions. Journal of Hydrology and Hydromechanics, 63(2), 93-101.

Childs, E. C. (1969). An introduction to the physical basis of soil water phenomena. J Wiley & Sons, New York.

67

Clark, M., Acomb, G., and Philpot, B. (2008). Florida field guide to low impact development. Produced for The Elizabeth Ordway Dunn Foundation by the Program for resource efficient communities. University of Florida.

Clemmens, A.J. (1983). Infiltration equations for border irrigation models. P266-274. In: Advances in infiltration. Proc. Nat. Conf. on Advances in Infiltration. Dec. 12-13, 1983. Chicago, Ill. ASAE Pub. 11-83. St. Joseph, Mo.

Cobos, D. R., & Chambers, C. (2010). Calibrating ECH2O soil moisture sensors. Application Note, Decagon Devices, Pullman, WA.

Darcy, H., (1856). The public fountains of the city of Dijon. Dalmont, Paris, 647.

DeGomez, T., Kolb, P., & Kleinman, S. (2015). Basic Soil Components. URL: http://www. extension. org/pages/54401/basic-soilcomponents#. VFnsksm-VVI [cited d. 11/11-14].

Dingman, S.L. (1994). Physical hydrology. Prentice-Hall. Prentice Hall.

Dorsey, J.D., Ward, A.D., Fausey, N.R. & Bair, E.S. (1990). A comparison of four field methods for measuring saturated hydraulic conductivity. Transactions of the ASAE, 33(6), 1925-1931.

Doty, C. W., Evans, R. O., Hinson, R. D., Gibson, H. J., and Williams, W. B. (1986). Agricultural water table management: A guide for eastern North Carolina. U.S. Department of Agriculture, Soil Conservation Service and Agricultural Research Service, North Carolina Agricultural Research Service, and North Carolina Agricultural Extension Service, Raleigh.

Dukes, M. D., Simonne, E. H., Davis, W. E., Studstill, D. W., & Hochmuth, R. (2003, May). Effect of sensor-based high frequency irrigation on bell pepper yield and water use. In Proceedings of 2nd International Conference on Irrigation and Drainage, May (pp. 12-15).

Dursun, M., & Ozden, S. (2011). A wireless application of drip irrigation automation supported by soil moisture sensors. Scientific Research and Essays, 6(7), 1573- 1582.

Edwards, A. C., & Cresser, M. S. (1992). Freezing and its effect on chemical and biological properties of soil. Advances in soil science, 18, 59-79.

Edwards, W. M., & Larson, W.E. (1969). Infiltration of water into soils as influenced by surface seal development. Transactions of the ASAE, 12(4), 463-0465.

68

Eusufzai, M. K., & Fujii, K. (2012). Effect of organic matter amendment on hydraulic and pore characteristics of a clay loam soil. Open Journal of Soil Science, 2(04), 372.

Gray, D. M., & Norum, D. I. (1967, November). The effect of soil moisture on infiltration as related to runoff and recharge. In Proceedings of hydrology symposium (No. 6).

Green, R.E. (1962). ‘Infiltration of water into soils as influenced by antecedent moisture’. (Doctoral Dissertation). Iowa State University, Iowa.

Green, W.H. & Ampt, G.A. (1911). Studies of Soil physics, I. Flow of air and water through soils. The Journal of Agricultural Science 4, no. 1 (1911):1-24.

Gregory, M. A., Cunningham, B. A., Schmidt, M. F., Mack, B. W., & Dresser, C. (1999). Estimating soil storage capacity of stormwater modeling applications. In Proceedings of the 6th Biennial Conference on Stormwater Research and Watershed Management, Southwest Florida, FL, USA (Vol. 1417).

Hain, C. R., Crow, W. T., Anderson, M. C., & Yilmaz, M. T. (2015). Diagnosing Neglected Soil Moisture Source–Sink Processes via a Thermal Infrared–Based Two-Source Energy Balance Model. Journal of Hydrometeorology, 16(3), 1070- 1086.

Haley, M. and Dukes, M. D. (2007). Evaluation of sensor-based residential irrigation water application. In 2007 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Haverkamp, R., Rendon, L. & Vachaud, G. (1987). Infiltration equations and their applicability for predictive use. Infiltration Development and Application. Honolulu, Hawaii. 142-152

Hillel, D. (1982). Introduction to Soil Physics. Academic Press, Orlando, FL.

Hillel, D. (1998). Environmental Soil Physics: Fundamentals, applications, and environmental considerations. Academic Press. San Diego, CA.

Houser, P.R. (2005). Infiltration and Soil Moisture Processes. Water Encyclopedia. 4:484-489

Hu, W., She, D., Shao, M. A., Chun, K. P., & Si, B. (2015). Effects of initial soil water content and saturated hydraulic conductivity variability on small watershed runoff simulation using LISEM. Hydrological Sciences Journal, 60(6), 1137-1154.

Idowu, J., & Angadi, S. (2013). Understanding and Managing Soil Compaction in Agricultural Fields. NM State University, Cooperative Extension Service. 69

Iooss, B., and Saltelli, A. Introduction to Sensitivity Analysis. Handbook of Uncertainty Quantification (2016). 1-20.

Joffe, J. S. (1949). Pedology (Vol. 68, No. 4, p. 346). LWW.

Jones, S.B., Wraith, J.M. & Or, D. (2002). Time domain reflectometry measurement principles and applications. Hydrological processes, 16(1), 141-153.

Karathanasis, A.D. (2006). Soil Mineralogy. In Land Use and Land Cover, from Encyclopedia of Life Support Systems (EOLSS). Oxford: UNESCOEolss Publishers.

Karkare, M., & Fort, T. (1993). Water movement in unsaturated porous media due to pore size and surface tension induced capillary pressure gradients. Langmuir, 9(9), 2398-2403.

Kopec, D.M. (1995). Soil characteristics and how they affect soil moisture. University of Arizona Cooperative Extension, 2(10).

Letey, J. (1985). Relationship between soil physical properties and crop production. JP Quirk, 277.

Li, B., Wang, L., Kaseke, K. F., Li, L., & Seely, M. K. (2016). The impact of rainfall on soil moisture dynamics in a foggy desert. PloS one, 11(10), e0164982.

Lili, M., Bralts, V. F., Yinghua, P., Han, L., & Tingwu, L. (2008). Methods for measuring soil infiltration: State of the art. International Journal of Agricultural and Biological Engineering, 1(1), 22-30.

Liu, G., Li, Y., & Alva, A. K. (2012). Water potential vs. pressure in relation to water movement and transpiration in plants. International Journal of Agronomy and Plant Production, 3(10), 369-373.

Lowery, B., Hickey, W.J., Arshad, M.A. and Lal, R. (1996). Soil water parameters and soil quality. Methods for assessing soil quality, (methodsforasses), 143-155

Mahmood, H., Hoogmoed, W., and Henten, E. (2012). Sensor data fusion to predict multiple soil properties. Precision Agriculture, 13(6):628-645.

Maidment, D. R. (1993). Handbook of hydrology (Vol. 1). New York: McGraw-Hill.

Mangala, O.S., Toppo, P., & Ghoshal, S. (2016). Study of Infiltration Capacity of Different Soils. Int. J. of Trend in Research and Development, 3(2), 388-390.

Mayo, J. 1982. Understanding Soils. The Kansas School Naturalist. 28(4)

70

Mittelbach, H., Lehner, I., & Seneviratne, S. I. (2012). Comparison of four soil moisture sensor types under field conditions in Switzerland. Journal of Hydrology, 430, 39- 49.

Morgan, D. (2017). Soil and Site Considerations With Drip Dispersal. GeoFlow Subsurface Drip.

Muñoz-Carpena, R., & Dukes, M.D. (2005). Automatic irrigation based on soil moisture for vegetable crops. Fact Sheet ABE, 356.

Muñoz-Carpena, R., Li, Y. & Olczyk, T. 2002. Alternatives for low cost soil moisture monitoring devices for vegetable production in South Miami-Dade County. Bul. ABE 333. Inst. Food Agr. Sci., University of Florida, Gainesville.

Naeth, M.A., Chanasyk, D.S. & Bailey, A.W. (1991). Applicability of the Kostiakov equation to mixed prairie and fescue grasslands of the Alberta. Journal of Range Management, 18-21.

Nciizah, A.D., & Wakindiki, I.I. (2015). Soil sealing and crusting effects on infiltration rate: a critical review of shortfalls in prediction models and solutions. Archives of Agronomy and Soil Science, 61(9), 1211-1230.

Novotny, V., and Olem, H. (1994). Water Quality; Prevention, Identification, and Management of Diffuse Pollution. Van Nostrand Reinhold Publishers., New York, NY.

Ochsner, T. (n.d.). Water Movement in Soils. Oklahoma State University. Retrieved July 24, 2017 from http://soilphysics.okstate.edu/software/water/conductivity.html.

Oosterbaan, R.J. & Nijland, H.J. (1994). Determining the saturated hydraulic conductivity, in Drainage Principles and Applications. Edited by Ritzema, H.P., Chap. 12, Publication 16, revised edition, Int. Ins. For Land Reclam. And Impr., Wageningen, Netherlands.

Phillips, A.J. (2014). Integrated Sensing of Soil Moisture at the Field-Scale: Measuring, Modeling and Sharing for Improved Agricultural Decision (Doctoral dissertation), University of Calgary, Calgary.

Phillips, A. J., Newlands, N. K., Liang, S. H., & Ellert, B. H. (2014). Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support. Computers and electronics in agriculture, 107, 73- 88.

Ramann, E. (1905). Bodenkunde, Julius Springer. Berlin, ed, 2.

71

Rawls, W. J., Ahuja, L., Brakensiek, D. L., & Shirmohammadi, A. (1993). Infiltration and soil water movement. In Handbook of Hydrology. McGraw-Hill, New York, 5-12.

Rawls, W.J., Brakensiek, D.L. & Miller, N. (1983). Green-Ampt infiltration parameters from soils data. Journal of hydraulic engineering, 109(1), 62-70.

Rawls, W. J., Nemes, A., & Parches, Y. A. (2004). Effect of soil organic carbon on soil hydraulic properties. Developments in soil Science, 30, 95-114.

Saltelli, A., Chan, K., & Scott, E. M. (Eds.). (2000). Sensitivity analysis (Vol. 1). New York: Wiley.

Serrano, S. E. (1997). Hydrology for engineers, geologists and environmental professionals. Hydroscience.

Soil Quality Indicators: Infiltration [PDF]. (1998, January). USDA Natural Resources Conservation Service. Retrieved July 24, 2017 from https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_052487.pdf

Stahr, L. M., Eisenhauer, D. E., Helmers, M. J., Dosskey, M. G., & Franti, T. G. (2004). Green-Ampt infiltration parameters in riparian buffers. In 2004 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological Engineers.

Svensson, A. (2014). Estimation of Hydraulic Conductivity from Grain Size Analyses. Chalmers University of Technology: Gothenburg, Sweden.

Tindall, J.A., Kunkel, J.R. & Anderson, D.E. (1999) Unsaturated zone hydrology for scientists and engineers (No. 04; GB1197. 7, T5.). Prentice Hall.

Tsihrintzis, V.A., & Hamid, R. (1997). Modeling and management of urban stormwater runoff quality: a review. Water Resources Management, 11(2), 136-164.

Turner, E.R. (2006). Comparison of infiltration equations and their field validation with rainfall simulation. (Doctoral dissertation). University of Maryland, College Park.

Verruijt, A., & Van Baars, S. (2007). Soil mechanics (pp. 19-25). Delft, the Netherlands: VSSD.

Visvalingam, M. and Tandy, J. D. (1972). The neutron method for measuring soil moisture contents- A review. European Journal of Soil Science, 23(4), 499-511.

Walsh, C.J., Fletcher, T.D., & Burns, M.J. (2012). Urban stormwater runoff: a new class of environmental flow problem. PLoS One, 7(9). E45814.

72

Wang, L., & Qu, J. J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2), 237- 247.

Water in road materials and subgrade soils, terminology. (n.d.). Retrieved July 24, 2017, from http://www.roadex.org/e-learning/lessons/drainage-of-low-volume-roads/ water-in-road-materials-and-subgrade-soils-terminology/.

Whiting, D., Card, A., Wilson, C., & Reeder, J. (2011). Estimating Soil Texture: Sand, Silt or Clayey? Colorado Master Gardener CMG GardenNotes #214. Retrieved July 27, 2017 from http://culter.colorado.edu/~kittel/SoilChar(526RibbonTest)_ handout.pdf.

Williams, J.R., Ouyang, Y. & Chen, J.S., & Ravi, V., (1998). Estimation of Infiltration Rate in vadose zone: application of selected mathematical models. Report of Environmental Protection Agency, Volume II. EPA/600/R-97/128b.

Wilson, E. (n.d.). Steady-flow Equations. Retrieved July 24, 2017, from http://jan.ucc.nau.edu/~doetqp-p/courses/env302/lec16/LEC16.html

Youngs, E. G., Elrick, D. E., & Reynolds, W. D. (1993). Comparison of steady flows from infiltration rings in Green and Ampt and Gardner soils. Water resources research, 29(6), 1647-1650.

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