Spatial Variation Studies of Soil Hydraulic Properties in a Part of Pavanje River Basin Using Ordinary Kriging Method

TABLE OF CONTENTS

Declaration

Certificate

Acknowledgement

Abstract

Contents

List of Figures

List of Tables

1. INTRODUCTION
1.1. GENERAL
1.2. NEED FOR RESEARCH
1.3. OBJECTIVES AND SCOPE OF STUDY
1.4. ORGANIZATION OF THESIS

2. LITERATURE REVIEW
2.1. GENERAL
2.2. DETERMINATION OF SOIL BASIC AND HYDRAULIC PROPERTIES
2.3. PEDO-TRANSFER FUNCTIONS
2.4. SPATIAL VARIATION OF SOIL PROPERTIES
2.5. GEOSTATISTICS
2.6. ORDINARY KRIGING
2.7. INFERENCE

3. EXPERIMENTAL SETUP AND METHODOLOGY
3.1. GENERAL
3.2. STUDY AREA
3.3. SOIL SAMPLE COLLECTION
3.4. SAMPLE PREPARATION
3.5. EXPERIMENTAL ANALYSIS
3.5.1. WATER CONTENT DETERMINATION
3.5.2. BULK DENSITY
3.5.3. GRAIN SIZE ANALYSIS (Dry Mechanical Sieve Analysis Only)
3.5.4. SATURATED HYDRAULIC CONDUCTIVITY (Falling Head Method)
3.5.5. ORGANIC MATTER CONTENT (Walkley and Black Method)
3.6. ESTIMATION OF RETENTION PARAMETERS USING PEDO- TRANSFERFUNCTION
3.7. PREDICTION OF SOIL HYDRAULIC PROPERTIES AT UNSAMPLED LOCATIONS
3.7.1. Interpolating a Surface from Sampled Point Data
3.7.2. Exploratory Analysis of Data
3.7.3. Semivariogram
3.7.4. Trend
3.7.5. Modeling the Semivariogram
3.8. SOFTWARE USED
3.8.1. STEPS IN CREATION OF SURFACE MAPS OF SOIL PROPERTIES
3.9. METHODOLOGY

4. RESULTS AND DISCUSSION
4.1. GENERAL
4.2. RESULTS
4.3. SOIL BASIC PROPERTIES
4.3.1. WATER CONTENT
4.3.2. BULK DENSITY
4.3.3. ORGANIC MATTER CONTENT
4.4. GRAIN SIZE DISTRIBUTION
4.5. SOIL HYDRAULIC PROPERTIES
4.5.1. SATURATED HYDRAULIC CONDUCTIVITY
4.5.2. WATER RETENTION CURVE
4.6. KRIGING PROCEDURE
4.6.1. Input to the kriging procedure
4.6.2. Checking deviation from Normality
4.6.3. Checking trend patterns in the data
4.6.4. Calculation of semivariogram parameters
4.6.5. Modelling the semivariogram
4.6.6. Creation of surface maps of soil hydraulic properties
4.7. COMPARISON OF KRIGING METHOD TO OTHER INTERPOLATION METHODS
4.8. DISCUSSION
4.8.1. Descriptive Statistics
4.8.2. SPATIAL VARIATION FROM KRIGED MAPS OF SOIL HYDRAULIC PROPERTIES
4.8.3. ACCURACY OF PREDICTION BY KRIGING METHOD

5. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1. SUMMARY
5.2. CONCLUSIONS
5.3. SCOPE OF FUTURE WORK
5.4. RECOMMENDATION

References

Appendix I

Appendix II

LIST OF FIGURES

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LIST OF TABLES

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ACKNOWLEDGEMENT

I am grateful to the Lord Almighty who is the “Source of Knowledge” and one who guided me in all aspects to bring out this project a successful one.

I am deeply indebted to my guide Dr. VARIJA.K, Associate Professor, Department of Applied Mechanics and Hydraulics, for her valuable guidance and constant encouragement rendered by her throughout this project work. I found every discussion held with her very inspiring and enlightening.

I express heartfelt thankfulness to Dr. SUBBA RAO, Professor and Head of Department of Applied Mechanics and Hydraulics for permitting me to carry out the work and for his valuable suggestions during the seminars.

I sincerely acknowledge the valuable help and support rendered by Shri.B.Jagadish, Shri.Gopal, Shri.Anand, Smt.Prathima and all other staff members during the course of this work.

I extend my sincere thanks to all my friends without their without their help, encouragement and active participation, this project would not have been a success.

Finally I would like to thank my parents and sister for their support. It would have been impossible for me to accomplish this study without their help and support.

HANZEL.H.FERNANDEZ

ABSTRACT

Soil hydraulic properties estimation has always been a challenging task for hydrologists and engineers as the methods to be implemented in the estimation are laborious, time consuming and costly. The two major hydraulic properties of soil that are required the most are soil water retention parameters and hydraulic conductivity. The main objective of this study is to find out the spatial structure of both these hydraulic properties and study their variation in a part of Pavanje river basin. This study presents a methodology that can be adopted in estimation of soil hydraulic properties over an entire region by using Geostatics particularly Kriging interpolation method. Soil basic properties are estimated at 5 different depths at 20 sites. Soil retention parameters are developed using a region specific pedotransfer function. Saturated hydraulic conductivity is estimated using permeameter. Semivariograms are developed to find out the dissimilarity between the pairs of soil hydraulic properties values in a 2 dimensional surface. Surface maps of soil hydraulic properties are developed using ArcGIS® software. The accuracy of prediction of Kriging interpolation method is found out by comparing with two other interpolation methods. The surface maps prepared can be used by hydrologists, engineers and agriculturists to improve their irrigation systems design, land scape modelling and precision farming etc.

Key words: Soil hydraulic properties, Geostatics, Kriging, Semivariogram, Surface maps

INTRODUCTION

1.1. GENERAL

Soil and water are the two main resources of our earth. Soil is a complex of mineral and organic substances. It is a product of development or evolution. It has evolved from a parent material, which is the mantle rock, by a slow process of physical and chemical weathering in addition to the influence of living organisms. The essential ingredients of soil are mineral substances, organic compounds, living organisms, water and air.

Physical properties of soils such as texture, structure, capacity to retain and transmit water are some of the properties which are important from a hydrologist’s point of view, apart from its chemical properties. The soil, located at the atmosphere- lithosphere interface, play an important role in determining the amount of precipitation that runs off the land and the amount that enters the soil for storage and future use. Soil plays a key role in water retention and storage. Water movement in soils occurs as a liquid flow in saturated soils, and as liquid and vapour flow in unsaturated soils.

Water existing in the soil strata is known as subsurface water and can be separated into soil water and groundwater. The soil water occurs in unsaturated zone and groundwater occurs in saturated zone. In the groundwater zone, all the soil or rock pores are completely filled by water and the upper limit of this zone is termed as water table. The soil water zone occupies the space above the water table which extends up to the soil surface. In this zone, some soil pores are filled with water, some are partially filled, and some are essentially empty, which are filled by air. The unsaturated zone is a transition zone, which supplies water for vegetation growth and through which water moves down to recharge groundwater. The water in this zone Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method 1 can remain in storage, move downwards by gravity to the water table and the groundwater, or move upwards through evaporation and transpiration.

Soils properties are characterized by high degree of spatial variability due to the combined effect of physical, chemical or biological processes that operate with different intensities and at different scales. Knowledge of the spatial variation of soil properties is important in several disciplines, including agricultural field research and precision farming. Reports have shown that there is large variability in hydraulic properties not only in large-sized fields, but also in small-sized fields.

Variation of soil physical properties have been extensively studied all over the world and the methods to determine their variation over a two dimensional surface is well established in literature. But to accurately determine the soil hydraulic properties extensive field study and complex methods should be implemented. Any under estimation of the properties will result in the failure of hydraulic structures, precision farming and landscape models that are dependent on these.

An appropriate understanding of spatial variation of soil properties is essential for modelling at landscape scale. The most important way to gather knowledge in this aspect is to prepare soil maps through spatial interpolation of point-based measurements of soil properties.

1.2. NEED FOR RESEARCH

Study of water in the unsaturated or vadose zone of the soil is important, since it is the direct source of moisture for vegetation and an integral part of hydrological cycle. Soil moisture movement studies provide potential information in the field of hydrology. These studies are important for understanding the mechanism of recharge through the soil and to provide soil moisture storage data for water balance. The surface runoff, soil moisture storage and deep percolation due to infiltration from a storm are influenced by the soil characteristics of the watershed.

Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method 2 Soil plays a key role in water retention and storage. Water movement in soils occurs as a liquid flow in saturated soils, and as a liquid and vapour flow in unsaturated soils. Soil hydraulic characteristics, especially hydraulic conductivity and soil water holding capacity, are important to the design and operation of irrigated agriculture systems. The performance of irrigation systems and practices depends highly on these soil hydraulic properties.

Information on soil hydraulic properties is also useful for irrigation scheduling and management including when, how much and at what rate water should be applied. In addition, soil hydraulic properties are often critical input parameters to irrigation and water management models, for scales ranging from plot, through paddock and farm, to catchment. Despite its importance, information on soil hydraulic properties is generally scarce. The reasons for the lack of such information include the high cost involved in collecting field data and the large spatial variability of soil hydraulic properties.

Determination of hydraulic properties of soil encompasses direct measurements and indirect estimation methods. Because of the shortcomings of direct measurement procedures, indirect estimation methods are gaining popularity. Computers offer the possibility to generate indirect estimates using regression or neural network algorithms. Various Pedo-transfer functions have been developed to determine these soil hydraulic properties which are otherwise difficult to determine.

Ongoing research at various institutes tries to develop Pedo-transfer functions for developing soil hydraulic properties from soil physical properties that are specific to their region which conforms to the soil group of that area. These PTFs can, therefore, be used in generating maps of required hydraulic parameters. Based on these surface maps of these hydraulic parameters, crops with specific water requirements may be selected for different locations in a farm, various irrigation developments and irrigation delivery infrastructure systems also can be planned.

Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method 3

1.3. OBJECTIVES AND SCOPE OF STUDY

The primary objective is to study the spatial variation of soil hydraulic properties, mainly soil water retention parameter and saturated hydraulic conductivity in a part of Pavanje river basin.

Other secondary objectives include:

To apply region specific Pedo-transfer functions and find the soil moisture retention values.

To map the interpolated soil hydraulic properties’ values at unsampled locations by using Ordinary Kriging spatial analyst tool of ArcGIS® software. To assess the quality of kriging method by measuring its prediction error and comparing with two other interpolation methods, Natural neighbour and Inverse distance weighting method

1.4. ORGANIZATION OF THESIS

Chapter 1 gives a brief introduction about the soil hydraulic properties estimation, the need for research in this topic and the objectives of this study.

Chapter 2 gives a review various literatures regarding the earlier studies conducted on estimation of the soil hydraulic as well as basic properties, spatial variation of hydraulic properties, geostatistical concepts, and kriging.

Chapter 3 gives description of the study area and outlines the experimental setup and describes the experimental methodology in detail with pictorial representations of the various steps undertaken in conducting the study.

Chapter 4 gives detailed discussion of the various results including the observations made from the created surface maps of hydraulic properties of soil.

Chapter 5 gives a summary of the whole study and the conclusions made from this study. It also gives a few recommendations as scope of future works.

CHAPTER 2 LITERATURE REVIEW

2.1. GENERAL

Soil hydraulic properties play a key part in designing hydraulic structures and managing agricultural lands in which the long-term fertility and productive capacity of the soil is maintained, or even improved. This understanding begins with knowledge of how the properties of soil are distributed in a given ecological region, and includes integration of all the components that contribute to the structure and function of the entire soil ecosystem.

There are a large number of literatures published on the soil hydraulic properties estimation and the study of its spatial variation. In this chapter, review of the relevant literature is presented. The chapter begins with the major studies conducted on estimation of soil hydraulic properties, its spatial as well as temporal variation, and at last gives a brief review of literatures dealing with the application of kriging in interpolating the data needed at unsampled locations which can be used for spatial variation studies and preparation of surface maps of soil hydraulic properties.

2.2. DETERMINATION OF SOIL BASIC AND HYDRAULIC PROPERTIES

Soil hydraulic properties reflect the ability of a soil to retain or transmit water and its dissolved constituents. For example, they affect the partitioning of rainfall and irrigation water into infiltration and runoff at the soil surface, the rate and amount of redistribution of water in a soil profile, available water in the soil root zone, and recharge to or capillary rise from the groundwater table. The hydraulic properties are also critical components of mathematical models for studying or predicting sitespecific water flow and solute transport processes in the subsurface.

For International Institute for Land Reclamation and Improvement (ILRI), R.J. Oosterbaan and H.J. Nijland (1994) conducted a study and brought out the variations of hydraulic conductivity within soil layers. Relationships between various drainage conditions and hydraulic conductivity were also studied. A review of various methods used for the determination of hydraulic conductivity was also done.

Murray D. Fredlund et.al (1997) presented a method of estimating the soil- water characteristic curve from the grain-size distribution curve and volume-mass properties. The grain-size distribution was divided into small groups of uniformly sized particles. A packing porosity and soil-water characteristic curve was assumed for each group of particles. The incremental soil-water characteristic curves were then summed to produce a final soil-water characteristic curve. The prediction of SWRC from GSD was found to be particularly accurate for silts. Clays. Tills and loams were more difficult to predict.

The report on ‘Estimation of hydrological properties of soil in Lokapavani area of KR Sagar command in Mandy district of Karnataka’ by NIH (2001) defines the grain size distribution as an attempt to determine the relative proportions of the different grain sizes that makes a given soil mass.

W.J. Rawls et.al (2003) used the U.S. National Soil Characterization database and the database from pilot studies on soil quality as affected by long-term management and studied the relationship between soil organic matter content and water retention curve. They found out that at low organic carbon contents, the sensitivity of the water retention to changes in organic matter content was highest in sandy soils. Increase in organic matter content led to increase of water retention in sandy soils, and to a decrease in fine-textured soils. At high organic carbon values, all soils showed an increase in water retention. The largest increase was in sandy and silty soils.

In Encyclopaedia of Hydrological Sciences, Wolfgang Durner and Kai Lipsius (2005) show us the need and the importance of measuring Soil hydraulic properties. They reviewed the common methods to estimate the hydraulic Pavanje river basin using ordinary kriging method 6 conductivity function from the water retention characteristic and various direct and indirect measurement techniques in the laboratory and the field. They conclude with an outlook on contemporary developments in measurement techniques, stressing the key role of inverse modelling of experiments to derive optimum hydraulic properties and the importance of a future combination of non-invasive measurement techniques with inverse modelling by stochastic data fusion.

Katie Price et.al (2010) characterized Soil physical properties under three land- uses. A total of 90 points were sampled (30 in each land-use class) throughout a 983 km² study area. Particle size distribution, in situ saturated hydraulic conductivity, bulk density, and volumetric moisture content at field capacity were measured at each point. The magnitudes of differences among soil physical properties under three land uses (forest, pasture, and managed lawn) and across two parent materials, alluvium (overbank fluvial sediment) and saprolite (heavily weathered bedrock), were determined. The study revealed that Particle size distributions did not significantly differ among land-use classes or parent materials, and the differences between the hydraulic properties of forest vs. nonforest soils were attributed to compaction associated with land management practices. The magnitudes of differences between forest and nonforest infiltration rates were explained by the widespread conversion of forest to other land uses in this region that was accompanied by decreased infiltration and increased overland flow, potentially significantly altering water budgets and leading to reduced baseflows and impaired water quality.

In a recent study conducted Runbin Duan et.al (2012) they identified that most of the models to estimate soil saturated hydraulic conductivity based on readily available soil survey data were studied on agricultural soils. Their main objective was to do a field study to investigate and compare the performance of three readily applied models, including the Campbell model, Smettem and Bristow model, and Saxton et al. model, in estimation of soil hydraulic conductivity from readily available soil data, in Texas soils with established grass from September 2009 to May 2010. They showed that two-parameter models, Campbell and Saxton et al. models had better performance than the one-parameter model, Smettem and Bristow model.

Pavanje river basin using ordinary kriging method 7 Pravin R Chaudhari et.al (2013) investigated the dependence of bulk density on texture, organic matter content and available nutrients (macro and micro nutrients) for soil of Coimbatore. They studied the relationships between some physical and chemical properties of soil such as, clay content (C), silt content (Si), sand content (S), CaCO3, organic matter content (OMC), total macro and micro nutrient content with soil bu b) for eight surface soil samples (0-15 cm). In their study Soil bulk density showed negative relationships with all soil properties (Si, C, CaCO3, OMC, total macro and total micro nutrient content) except with sand content (S). Besides texture and OMC, the nutrient concentration was also the most effective factor that affected the bulk density of soils.

2.3. PEDO-TRANSFER FUNCTIONS

Pedotransfer functions are used for estimating hydraulic properties from some basic soil properties. Several region-specific PTFs have been developed throughout the world for estimating hydraulic properties from basic soil properties. These PTFs can, therefore, be used in generating maps of required hydraulic parameters.

Kalman Rajkai et.al (2004) studied how successfully the soil water retention curve (SWRC) can be predicted with PTFs that were derived using conventional and modified statistical approaches. A three-parameter van Genuchten type model was used to describe the water retention curves of Hungarian soils. They used eight measured and transformed soil properties, as well as one measured retention point to construct PTF’s for the three retention parameters. The PTFs were calibrated using a large soil database containing measured soil water retention data, dry bulk density, sand, silt and clay percentages, and organic matter content. The PTFs derived in this paper is expected to provide improved relationships for estimating the soil water retention curve from soil texture and related properties.

Estimation of soil water retention curves using pedo-transfer functions was also studied by Svatopluk Matula et.al (2007). In this paper, Wosten’s continuous pedotransfer functions were applied to the data from a selected locality in the Czech Republic. Own continuous pedotransfer functions were derived, following the Pavanje river basin using ordinary kriging method 8 methodology used in continuous pedotransfer functions. Two types of fitting, 4- parameters and 3-parameters, were tested. Based on the results, it was concluded that the general equations of Wösten’s pedotransfer functions are not very suitable to estimate the soil water retention curves for the locality Tišice in the Czech Republic. They reported that it can be advantageous to estimate SWRC for a locality with no data available, using PTFs and the available basic soil properties and the estimates can be expressively improved if some retention curves are additionally measured.

K.Varija et.al (2011) developed and validated point PTFs for estimation of water retention curve from basic soil properties such as particle size distribution, bulk density and organic matter content using multiple linear regression technique. Fifty soil samples were collected from different locations at different depths in coastal area of Karnataka. PTFs were derived for point estimation of SMRC.

1. h = b0 + b1Clay + b2 b + b3OM
2. h = b0 + b1Sand + b2 b + b3OM

Where is the soil water content at different soil matric potentials (cm³ /cm³ ), clay b is the soil bulk density (g/cm3), and OM is the organic matter content. Based on statistics it was concluded equation using percentage of sand gave much better prediction than equation using percentage of clay.

2.4. SPATIAL VARIATION OF SOIL PROPERTIES

Priyabrata Santra et.al (2008) In his study spatial variation of bulk density, organic carbon, silt and clay contents for two soil depths (0-15 and 15-30 cm) in the agricultural farm of the Indian Agricultural Research Institute, New Delhi were quantified and the respective surface maps were prepared through ordinary kriging. Particle size distribution shows better spatial correlation structure than bulk density and organic carbon content. Gaussian model fits well with experimental semivariogram of bulk density, and silt and clay contents. Hole-effect model was found to be the best to fit the experimental semivariogram of organic carbon content. Spatial correlation structure for both surface (0-15 cm) and sub-surface (15-30 cm) soil layer remains the same, but the magnitude of spatial correlation differs. Cross Pavanje river basin using ordinary kriging method 9 validation of kriged map showed that spatial prediction of basic soil properties using semivariogram parameters is better than assuming mean of the observed value for any unsampled location.

Spatial and temporal variability of surface and subsurface (15 cm-depth), bulk density and near-saturated hydraulic conductivity of a loamy soil cultivated under conventional and conservation tillage were studied by Lionel Alletto and Yves Coquet (2009). For each tillage system, hydraulic conductivity measurements were done at different matric potentials, different dates during the maize growing season, and at different sites within the agricultural field according to soil texture and row/inter-row position. Time was found to be the most important source of bulk density variability for surface and subsurface. Whatever the tillage system, they observed an increase in bulk density during the growing season. For subsurface soil, the interaction between time and tillage system was also an important source of bulk density variability with a higher increase with time of bulk density values under conventional than under conservation tillage. Under both tillage systems, for matric ws was the main source of surface hydraulic conductivity variation with the lowest values measured in row positions. For subsurface soil, time and its interaction with tillage were the main sources of hydraulic conductivity variability for matric potential It was found that the effect of time on soil physical properties should be accounted for in transport models through soils, especially when contrasted tillage systems are compared.

2.5. GEOSTATISTICS

Main applications of Geostatistics to the description and modeling of the spatial variability of microbiological and physico-chemical soil properties were reviewed by P. Goovaerts (1998). Semivariogram were introduced to characterize the spatial variability of each attribute separately as well as their spatial interactions. Permissible models were fitted to experimental semivariogram values and finding the value of a soil property at unsampled locations using only observations of this particular property by ordinary kriging was explained. All the different tools were illustrated Pavanje river basin using ordinary kriging method 10 using two transects of 100 pH and electrical conductivity values measured in pasture and forest.

The determination of the spatial variability of field parameters is usually based on the concept that sampled values at nearby locations are more similar than those from further apart. Measurements from the field are usually gathered as point data, such as an individual plant. Geostatistical analysis methods can be used to interpolate the measurements to create a continuous surface map or to describe its spatial pattern. As a powerful tool in Geostatistics, variogram describes the spatial dependence of data and gives the range of spatial correlation, within which the values are correlated with each other and beyond which they become independent. The parameters of the best fitted model for a variogram can be used for Kriging.

R.M.Lark (2002) states that Geostatistics has been applied widely in soil science to solve the problem of estimating soil properties at unvisited sites from limited sample data. Central to any geostatistical analysis is the variogram, which describes the spatial dependence of a random function that is assumed to be realized in our soil variable. The variogram must be obtained from sample data. He shows that objective functions can readily be defined for estimation by the method of maximum likelihood. He describes the principles of the method, using Spatial Simulated Annealing for optimization, and applies optimized sample designs to simulated data. He concluded that for practical applications using this technique supplements simple systematic designs that provide an initial estimate of the variogram.

2.6. ORDINARY KRIGING

. Kriging has been recommended as the best method to interpolate point data since it minimizes the error variance using a weighted linear combination of the data. Therefore, it is very important to estimate variogram reliably from sufficient data and modelled properly.

Soil properties were predicted by using soil maps alone, kriging and a kriging- soil map combination, under the constraint of small data sets by Angel Utset et.al Pavanje river basin using ordinary kriging method 11 (2000). The effects of considering separated semivariograms for each particular soil type and a global semivariogram for the whole zone were also compared in Rhodic and Xhantic Ferralsols at the Havana-Matanzas plain. The results showed a considerable bias in the predictions made with the soil map, which is not found in kriging predictions. Generally, soil map predictions are also less accurate. However, the use of soil maps in Xhantic Ferralsols field-capacity predictions was the most reliable approach. The use of semivariograms for each soil type in kriging predictions only yields more accurate results for Rhodic Ferralsols, where enough data is available. The combined kriging-soil map procedure yields the smallest bias. Predictions of the combined procedure are more accurate, although accuracy differences found with the other two approaches were not very large. The combined kriging-soil map approach yields better predictions than the others for this case study D. P. Kalivas (2002) studied that whether the use of the coregionalization of the distance-to-river topographic variable with the soil properties topsoils clay and sand can improve their mapping. The interpolation techniques: ordinary kriging, kriging combined with regression (two models) and heterotrophic co-kriging were applied to sampling data.

2.7. INFERENCE

From the review of literature it is evident that several studies have been carried out to find the soil basic properties, soil hydraulic properties and its spatial and temporal variation across the globe. The methods used have been various, in the estimation of these properties. From all these, the most suitable methods and concepts applicable to this study have been adopted. Pavanje river basin using ordinary kriging method 12 Experimental Setup and Methodology

CHAPTER 3 EXPERIMENTAL SETUP AND METHODOLOGY

3.1. GENERAL

This chapter explains about the study area, methods adopted for sample collection, various accessories and equipments used in the experimental work, their setup and the methodology adopted to find the spatial variation of the soil hydraulic properties and generation of surface maps of hydraulic properties.

3.2. STUDY AREA

The study area investigated was the downstream catchment area of Pavanje River of Dakshina Kannada district, Karnataka as shown in Fig. 1. The area is surrounded by the Arabian Sea on the west and by the Pavanje River on the South and Pakshikere hamlet on North respectively. The areal extent of the region is about 37 km² spreading between 13°02'42”N and 13°02'06” N latitude and between 74°47'42” E and 74°48'18” E longitude.. In total, about 20 sampling points were considered from which 100 samples were collected for the investigation and their approximate locations are indicated in Fig. 2. The area experiences a hot, humid type of weather. The south-west monsoon (June-Sept.) is the principal rainy season for the region and the annual average rainfall is more than 2500 mm. The post-monsoon season (Oct.- Jan.) receives occasional rainfall due to the north-east monsoon. The pre-monsoon season (Feb.-May) is essentially the summer season with scanty pre-monsoon showers during April-May.

The region consists mainly of agricultural fields and some bare lands. Most part of the study area is residential area. NH66 runs through along the western boundary of the study area.

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Fig 3.1: Map showing grid points generated using Google Earth

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Fig 3.2: Map showing actual sampled locations at study area.

Pavanje river basin using ordinary kriging method 14

Experimental Setup and Methodology

3.3. SOIL SAMPLE COLLECTION

The area chosen for study was inspected and topographical condition, soil, vegetation and other ground realities were noted during the reconnaissance survey. After this an area of 35 hectares was chosen. Field sampling was carried during the period last week of January 2013 to Mid - March 2013. Sampling locations were adopted using a grid generated on a layer of aerial map exported from Google Earth. Then the actual sampling points were identified by visiting the site and choosing points as close to the generated grid points. Random sampling method was adopted for sample collection. Altogether, 100 geo-referenced soil samples were collected.

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Fig.3.3: A typical sampling location

From each site, disturbed and undisturbed soil samples were collected from five depths: 0-15, 15-30, 30-45, 45-60 and 60-75 cm. Core cutter method was used for undisturbed soil sample collection. The Apparatus used were:

1. Cylindrical core
2. Steel Rammer
3. Steel Dolly
4. Pick axe

A cylindrical core of inner diameter 10 cm and height 13 cm was used. The core was driven into ground using a rammer with a handle of 25 mm diameter and 900mm length and a weight at the end of 140mm diameter and 75 mm length. The adjacent soil was removed using a pick-axe to collect the core containing soil sample.

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Fig 3.4: Rammer, Dolly and Core Cutter used for sample collection

Each day before sampling was to be done, a visit was made to the site and the permission was obtained from the land owners to take the soil samples from there.

After the sample collection the cores were placed in plastic bags and disturbed samples were collected in polythene bags. They were brought to the laboratory for conducting further experiments. The bulk density, water content determination and mechanical sieve analysis were done at the Geotechnical laboratory of Dept. Of Applied Mechanics and Hydraulics of NITK Surathkal. The organic matter content test was conducted at Environmental Engineering laboratory of Civil Engineering department of NITK. The permeameter test to find out the saturated hydraulic conductivity was done at the Hydraulics Lab of NITK.

3.4. SAMPLE PREPARATION

For calculation of water content, disturbed sample directly brought from field was used. However for organic matter content determination and grain size analysis samples were air dried. About 1 kg of soil was taken for grain size analysis, and about 0.5 kg of soil was taken for organic matter content determination. After the grain size analysis the soil passing 2 mm sieve was used for saturated hydraulic conductivity experiment.

3.5. EXPERIMENTAL ANALYSIS

All the 100 soil samples were subjected to geochemical laboratory analysis. The analysis of the soil in laboratory included analysis of parameters like moisture content, bulk density, grain size analysis, saturated hydraulic conductivity and organic matter (OM). The procedures and experimental setup of all these experiments are explained below. Utmost care has been taken so that the methods adopted conform to the standard methods used for the determination of these properties.

3.5.1. WATER CONTENT DETERMINATION

The water content of the soil can be expressed as either volumetric water

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water content expressed in terms of volume and gravimetric water content is that expressed in terms of mass. In this study volumetric water content has been selected to express the water content of the collected soil samples. The unit of volumetric # "³ /cm³

3.5.1.1. Apparatus

1 Container: Any suitable non-corrodible air-tight container.
2 Balance, of sufficient sensitivity to weigh the soil samples to an accuracy of 0.04 percent of the weight of the soil taken for the test.
3 Oven, thermostatically controlled, with interior of non-corroding material to maintain the temperature at 110 ± 5°C.
4 Desiccator, A desiccator with any suitable desiccating agent.
5 Soil specimen The soil specimen taken shall be representative of the soil mass. The size of the specimen selected depends on the quantity required for good representation, which is influenced by the gradation and the maximum size of particles, and on the accuracy of weighing. The quantities mentioned in Table 3.1 are recommended for general laboratory use.

3.5.1.2. Procedure

Assuming the soil average size is of 2mm an amount of around 50gms of soil was taken. The container with lid was cleaned, dried and weighed (W1). The required quantity of the soil specimen was taken in the container and placed loosely, and weighed with lid (W2). Then kept it in an oven with the lid removed, and maintained the temperature of the oven at 110 ± 5°C. The specimen was dried in the oven for 24 hours. Every time the container was taken out for weighing the lid was replaced on the container and the container was cooled in a desiccator. The final mass (W3) of the container with lid with dried soil sample was recorded.

3.5.1.3. Calculation

The volumetric water content shall be calculated as follows:

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W2 = mass of container with lid with wet soil in g,

W3 = mass of container with lid with dry soil in g, and b= Bulk density of soil

Vb = volume of the container

Table 3.1 Guide lines for Soil quantity selection

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