spatial variation studies of soil hydraulic properties in

SPATIAL VARIATION STUDIES OF SOIL HYDRAULIC PROPERTIES IN A PART OF PAVANJE RIVER BASIN USING ORDINARY KRIGING METHOD By HANZEL.H.FERNANDEZ Register No: 11WR04F

THESIS SUBMITTED FOR THE AWARD OF THE DEGREE OF MASTER OF TECHNOLOGY in WATER RESOURCES ENGINEERING & MANAGEMENT

DEPARTMENT OF APPLIED MECHANICS AND HYDRAULICS NATIONAL INSTITUTE OF TECHNOLOGY KARNATAKA SURATHKAL, P.O. SRINIVASNAGAR - 575 025 MANGALORE, INDIA JULY 2013

SPATIAL VARIATION STUDIES OF SOIL HYDRAULIC PROPERTIES IN A PART OF PAVANJE RIVER BASIN USING ORDINARY KRIGING METHOD A thesis report submitted to NATIONAL INSTITUTE OF TECHNOLOGY KARNATAKA, SURATHKAL in partial fulfillment of the requirements for the degree of MASTER OF TECHNOLOGY in Water Resources Engineering and Management By HANZEL.H.FERNANDEZ Register No.: 11WR04F

Under the guidance of Dr. VARIJA.K Associate Professor Department of Applied Mechanics and Hydraulics

DEPARTMENT OF APPLIED MECHANICS AND HYDRAULICS NATIONAL INSTITUTE OF TECHNOLOGY KARNATAKA SURATHKAL, P.O. SRINIVASNAGAR - 575 025 MANGALORE, INDIA JULY 2013

National Institute of Technology Karnataka Surathkal

DECLARATION I hereby declare that the Report of the P.G. Project Work entitled “SPATIAL VARIATION STUDIES OF SOIL HYDRAULIC PROPERTIES IN A PART OF PAVANJE RIVER BASIN USING ORDINARY KRIGING METHOD” which is being submitted to the National Institute of Technology Karnataka Surathkal, in partial fulfilment of the requirements for the award of Degree of Master of Technology in Water Resources Engineering and Management, in the Department of Applied Mechanics and Hydraulics, is a bonafide report of the work carried out by me. The material contained in this Report has not been submitted to any University or Institution for the award of any degree.

11WR04F, HANZEL.H.FERNANDEZ Department of Applied Mechanics and Hydraulics

Place: NITK, SURATHKAL Date:

National Institute of Technology Karnataka Surathkal

CERTIFICATE This is to certify that the P.G. Project Work Report entitled “SPATIAL VARIATION STUDIES OF SOIL HYDRAULIC PROPERTIES IN A PART OF PAVANJE RIVER BASIN USING ORDINARY KRIGING METHOD” submitted by HANZEL.H.FERNANDEZ (Register Number: 11WR04F) as the record of the work carried out by him, is accepted as the P.G. Project Work Report Submission in partial fulfilment of the requirements for the award of degree of Master of Technology in Water Resources Engineering and Mangement, in the Department of Applied Mechanics and Hydraulics.

Dr. VARIJA.K Associate Professor Department of Applied Mechanics and Hydraulics NITK Surathkal (Guide)

Chairman DPGC (Signature with Date and Seal)

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

iii

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

iv

TABLE OF CONTENTS

Declaration Certificate Acknowledgement ......................................................................................... i Abstract ........................................................................................................ii Contents ..................................................................................................... iii List of Figures ............................................................................................ vii List of Tables ............................................................................................... ix

1.

INTRODUCTION...................................................................................... 1

1.1.

GENERAL ............................................................................................. 1

1.2.

NEED FOR RESEARCH....................................................................... 2

1.3.

OBJECTIVES AND SCOPE OF STUDY ............................................. 4

1.4.

ORGANIZATION OF THESIS ............................................................. 4

2.

LITERATURE REVIEW ......................................................................... 5

2.1.

GENERAL ............................................................................................. 5

2.2.

DETERMINATION OF SOIL BASIC AND HYDRAULIC PROPERTIES ........................................................................................ 5

2.3.

PEDO-TRANSFER FUNCTIONS ........................................................ 8

2.4.

SPATIAL VARIATION OF SOIL PROPERTIES ................................ 9

2.5.

GEOSTATISTICS ............................................................................... 10

2.6.

ORDINARY KRIGING ....................................................................... 11

2.7.

INFERENCE ........................................................................................ 12 iii

3.

EXPERIMENTAL SETUP AND METHODOLOGY ......................... 13

3.1.

GENERAL ........................................................................................... 13

3.2.

STUDY AREA ..................................................................................... 13

3.3.

SOIL SAMPLE COLLECTION .......................................................... 15

3.4.

SAMPLE PREPARATION.................................................................. 17

3.5.

EXPERIMENTAL ANALYSIS .......................................................... 17

3.5.1.

WATER CONTENT DETERMINATION ................................... 17

3.5.2.

BULK DENSITY .......................................................................... 20

3.5.3.

GRAIN SIZE ANALYSIS (Dry Mechanical Sieve Analysis Only) ....................................................................................................... 22

3.5.4.

SATURATED HYDRAULIC CONDUCTIVITY (Falling Head Method) ......................................................................................... 23

3.5.5. 3.6.

ORGANIC MATTER CONTENT (Walkley and Black Method) 25 ESTIMATION OF RETENTION PARAMETERS USING PEDOTRANSFERFUNCTION ..................................................................... 26

3.7.

PREDICTION OF SOIL HYDRAULIC PROPERTIES AT UNSAMPLED LOCATIONS .............................................................. 29

3.7.1.

Interpolating a Surface from Sampled Point Data ......................... 29

3.7.2.

Exploratory Analysis of Data ........................................................ 29

3.7.3.

Semivariogram ............................................................................... 30

3.7.4.

Trend .............................................................................................. 30

3.7.5.

Modeling the Semivariogram ........................................................ 31

3.8. 3.8.1.

SOFTWARE USED ............................................................................. 32 STEPS IN CREATION OF SURFACE MAPS OF SOIL PROPERTIES ............................................................................... 33

3.9.

METHODOLOGY ............................................................................... 35

iv

4.

RESULTS AND DISCUSSION .............................................................. 37

4.1.

GENERAL ........................................................................................... 37

4.2.

RESULTS............................................................................................. 37

4.3.

SOIL BASIC PROPERTIES ................................................................ 38

4.3.1.

WATER CONTENT...................................................................... 38

4.3.2.

BULK DENSITY .......................................................................... 40

4.3.3.

ORGANIC MATTER CONTENT ................................................ 42

4.4.

GRAIN SIZE DISTRIBUTION ........................................................... 43

4.5.

SOIL HYDRAULIC PROPERTIES .................................................... 44

4.5.1.

SATURATED HYDRAULIC CONDUCTIVITY ........................ 44

4.5.2.

WATER RETENTION CURVE ................................................... 44

4.6.

KRIGING PROCEDURE .................................................................... 45

4.6.1.

Input to the kriging procedure ....................................................... 45

4.6.2.

Checking deviation from Normality .............................................. 45

4.6.3.

Checking trend patterns in the data................................................ 49

4.6.4.

Calculation of semivariogram parameters ..................................... 51

4.6.5.

Modelling the semivariogram ........................................................ 51

4.6.6.

Creation of surface maps of soil hydraulic properties ................... 54

4.7.

COMPARISON OF KRIGING METHOD TO OTHER INTERPOLATION METHODS .......................................................... 60

4.8.

DISCUSSION ...................................................................................... 62

4.8.1.

Descriptive Statistics...................................................................... 62

4.8.2.

SPATIAL VARIATION FROM KRIGED MAPS OF SOIL HYDRAULIC PROPERTIES ....................................................... 64

4.8.3.

ACCURACY OF PREDICTION BY KRIGING METHOD ........ 66

v

5.

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ......... 68

5.1.

SUMMARY ......................................................................................... 68

5.2.

CONCLUSIONS .................................................................................. 70

5.3.

SCOPE OF FUTURE WORK.............................................................. 71

5.4.

RECOMMENDATION ....................................................................... 71 References

72

Appendix I

74

Appendix II

77

vi

LIST OF FIGURES Figure No.

Description

Page No.

3.1

Map showing grid points generated using Google Earth

14

3.2

Map showing actual sampled locations at study area

14

3.3

A typical sampling location

15

3.4

Rammer, Dolly and Core Cutter used sample collection

16

3.5

Containers with soil samples kept in Oven for 24 hours at 105°C

20

3.6

Sieve sets and weighing balance used for Grain size analysis

23

3.7

Sill, Nugget and Range of a semivariogram

30

3.8

Flowchart of methodology

36

4.1

Variation of water content along depth at pits 1,2,3,4 and 5

38

4.2


38

4.3


39

4.4


39

4.5

Variation of Bulk density along depth at pits 1, 2, 3, 4 and 5

40

4.6

Variation of Bulk density along depth at pits 6, 7, 8, 9 and10

40

4.7


41

4.8


41

4.9

Variation of Organic matter content along depth at pits 1 to 10

42

4.10

Variation of Organic matter content along depth at pits 11 to 20

42

4.11

Variation of percentage of sand along depth at pits 1 to 10

43

4.12

Variation of percentage of sand along depth at pits 11 to 20

43

4.13

Histograms of Available Water Content data at 0-15cm, 30-45cm, 60-

46

75 cm 4.14

QQ plots of Available water content data at 0-15cm, 30-45cm and 60-

47

75 cm 4.15

Histograms of Saturated hydraulic conductivity data at 15-30cm and

48

45-60cm 4.16

QQ plots of saturated hydraulic conductivity data at 15-30cm and 45-

49

60cm vii

4.17

Classed Post maps of Available water content at depths (a) 0-15cm (b)

50

30-45 cm (c) 60-75cm and saturated hydraulic conductivity at (d) 1530cm and (e) 45-60cm 4.18

Semivariogram cloud and the fitted Hole-effect model for available

52

water content at 0-15cm depth 4.19


52

water content at 30-45cm depth. 4.20


53

water content at 60-75cm depth. 4.21

Semivariogram cloud and the fitted J-Bessel model for saturated

53

hydraulic conductivity at 15-30cm depth.

4.22

Semivariogram cloud and the fitted J-Bessel model for saturated

54

hydraulic conductivity at 45-60cm depth. 4.23

Spatial variation of saturated hydraulic conductivity at 15-30 cm depth

55

4.24

Spatial variation of saturated hydraulic conductivity at 45-60 cm depth

56

4.25

Spatial variation of available water content at 0-15 cm depth

57

4.26


58

4.27


59

4.28

The plots of Observed v/s Predicted values showing R2 value

61

viii

LIST OF TABLES Table No.

Description

Page No.

3.1

Guide lines for Soil quantity selection

19

3.2

Coefficients of pedotransfer function.

28

4.1

Input data for the kriging procedure

45

4.2

Evaluation of performance of kriged maps of soil hydraulic properties

61

through cross validation with natural neighbour and inverse distance weighting method 4.3

Descriptive statistics of soil hydraulic properties

67

ix

Introduction

CHAPTER 1 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 atmospherelithosphere 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

Introduction

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.


2

Introduction

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.


3

Introduction

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.


4

Literature review

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

5

Literature review

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

6

Literature review

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

7

Literature review

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 bulk density (ρb) 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 Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

8

Literature review

methodology used in continuous pedotransfer functions. Two types of fitting, 4parameters 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. θh = b0 + b1Clay + b2ρb + b3OM 2. θh = b0 + b1Sand + b2ρb + b3OM Where θh is the soil water content at different soil matric potentials (cm3/cm3), clay and sand in percentages, ρb 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 Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

9

Literature review

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 potentials≥−0.6 kPa, the position relative to crop rows 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 potentials≥−0.3 kPa. 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


10

Literature review

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

11

Literature review

(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.


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.


13


Fig 3.1: Map showing grid points generated using Google Earth

Fig 3.2: Map showing actual sampled locations at study area. Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

14


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.

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

15


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.

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

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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

content (θ) or gravimetric water content (w). The volumetric water content is the water content expressed in terms of volume and gravimetric water content is that Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

17


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 water content, θ, is cm3/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:


18


θ

3 = Wρ2 −W V

Eq. 3.1

b. b

where θ = Volumetric water content W2 = mass of container with lid with wet soil in g, W3 = mass of container with lid with dry soil in g, and ρb = Bulk density of soil Vb = volume of the container Table 3.1 Guide lines for Soil quantity selection

Size of Particles

Minimum Quantity of Soil Specimen to be Taken for Test

(More Than 90 Percent Passing)

(Weight in g)

425μm IS Sieve

25

2-mm IS Sieve

50

4.75-mm IS Sieve

200

9.50-mm IS Sieve

300

19-mm IS Sieve

500

37.5-mm IS Sieve

1000


19


Fig.3.5: Containers with soil samples kept in Oven for 24 hours at 105°C

3.5.2.

BULK DENSITY The bulk density of a soil sample of known volume is the mass (or weight) of

that sample divided by the bulk volume. The "ideal" soil would hold sufficient air and water to meet the needs of plants with enough pore space for easy root penetration, while the mineral soil particles would provide physical support and plant essential nutrients. Soil bulk density is a basic soil property influenced by some soil physical and chemical properties (Pravin R. Chaudhari, 2013) Soil bulk density is a measure of soil compaction and strength. A normal range of bulk densities for clay is 1.0 to 1.6 g/cm3 and a normal range for sand is 1.2 to 1.8 g/cm3. It is calculated as the dry weight of soil divided by its volume. This volume includes the volume of soil particles and the volume of pores among soil particles. Bulk density is typically expressed in g/cm3 3.5.2.1. 1

Apparatus Core cutters containing the soil samples.


20


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 3.5.2.2.

Desiccator, A desiccator with any suitable desiccating agent. Procedure

1

IS 2720 – Part 29 -1975 was referred.

2

Main apparatus include Core cutter with a sharp edge, dolly or collar and a rammer.

3

This method is applicable for soil that sticks to the surface of cutter (Clayey soil) and that is not very stiff (where cutter can be penetrated in to ground by ramming).

4

Sampling is done vertically by ramming downwards.

5

The inner surfaces of core cutter and dolly are greased.

6

The ground surface is levelled after removing the top soil.

7

Core cutter with collar on top and sharp edge at bottom is placed on the ground.

8

It is then driven in to the ground using the rammer till the soil collects up to the collar.

9

It is carefully taken out by loosening from outside such that the soil inside remains intact.

10

Dolly is carefully removed.

11 The soil surface in the core cutter is trimmed from both the ends. 3.5.2.3.

Calculation ρb (g/cm3) =

W 2−W 1

W2 = weight of core cutter with soil in g

𝑉𝑉

Eq. 3.2

W1 = weight of empty core cutter


21


V = volume of soil in the core cutter 3.5.3.

GRAIN SIZE ANALYSIS (Dry Mechanical Sieve Analysis Only)

Soil grain size distribution is a geotechnical process that allows civil engineers and geoscientists to classify soils by determining the different percentages of aggregate diameters in the sample. Knowing the grain size distribution or soil classification is necessary for any engineering project dealing with soil, because it has to be assured that your base, soil, has enough sustainability. IS: 2720 (Part 4) -198 clearly describe the method to be followed to classify the soil based on grain sizes. In this particular study, dry mechanical sieve analysis was conducted to find out the percentage of sand, i.e. the percentage of soil particles with 4.75mm and 0.075mm size. Further this value was used in the pedotransfer function to estimate the soil water retention parameters.

3.5.3.1.

Apparatus

1 Balance 2 I.S sieves 3 Sieve Brush 4 Rubber pestle and mortar 5 Mechanical Sieve Shaker 6 Soil Pan 3.5.3.2.

Procedure

Soil passing 4.75mm I.S. Sieve and retained on 75micron I.S. Sieve contains no fines. 1

500gm of the soil sample was taken after taking representative sample by quartering.

2

I.S sieves were selected and arranged in the order as below. 4.75mm, 2.36mm, 1.18mm, 0.6mm, 0.3mm, 0.15mm and 0.075mm.


22


3

The sieving was done by mechanical sieve shaker for 10 minutes.

4

The weight the material retained on each sieve is recorded.

5

The percentage retained on each sieve is calculated on the basis of the total weight of the soil sample taken.

6

From these results the percentage passing through each of the sieves is calculated

7

Make a semi logarithmic plot of Grain size versus Percentage finer.

Fig 3.6: Sieve sets and weighing balance used for Grain size analysis 3.5.4.

SATURATED HYDRAULIC CONDUCTIVITY (Falling Head Method)

According to United States Department of Agriculture (USDA) Soil Hydraulic Conductivity is a quantitative measure of a soil's ability to transmit water when subjected to a hydraulic gradient. It can be thought of as the ease with which pores of a soil permit water movement. Saturated hydraulic conductivity, Ksat, describes water movement through saturated media.


23


The falling head test is a common laboratory testing method used to determine the saturated hydraulic conductivity of fine as well as coarse grained soils. The soil sample is first saturated under a specific head condition. The water is then allowed to flow through the soil without maintaining a constant pressure head. Before starting the flow measurements, the soil sample is saturated and the standpipes are filled with de-aired water to a given level. The test then starts by allowing water to flow through the sample until the water in the standpipe reaches a given lower limit. The time required for the water in the standpipe to drop from the upper to the lower level is recorderd. Often, the standpipe is refilled and the test is repeated for couple of times. The recorded time should be the same for each tests within an allowable variation of about 10% otherwise the test is failed. On the basis of the test results, the permeability of the sample can be calculated as K=

2.3𝑎𝑎𝑎𝑎

Where

𝐴𝐴𝐴𝐴

ℎ

log⁡ ( 1) ℎ2

Eq. 3.3

K = Saturated Hydraulic Conductivity a = Cross sectional area of the stand pipe L = Height of the soil sample column A = Cross sectional area of sample t = Recorded time for the water column to flow though the sample h1 and h2 = the upper and lower water level in the standpipe measured using the same water head reference The permeameter used in this study for measuring hydraulic conductivity was Jodhpur permeameter. Due to lack of time the hydraulic conductivity experiment was limited to estimation of Ksat values at soil depth 15cm-30cm and 45cm-60cm only. Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

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3.5.5.

ORGANIC MATTER CONTENT (Walkley and Black Method)

Natural Resources Conservation Service (NRCS) defines Soil Organic matters as the total organic matter in the soil which can be divided into three general pools: living biomass of microorganisms, fresh and partially decomposed residues (the active fraction), and the well-decomposed and highly stable organic material. Surface litter is generally not included as part of soil organic matter. This test (Walkley and Black Method) is performed as per IS: 2720 (Part XXII) 1972. 3.5.5.1.

Reagents Used

1

Potassium dichromate, 1N.

2

Dissolve 49.040 g of K2 Cr2O 7 (A.R.) in distilled water to prepare 1 liter of solution. Ferrous ammonium sulphate, 1Normal. Dissolve 393.130g of Fe(NH4)2(SO4)26H2O in distilled water adding simultaneously 5 ml of concentrated H2SO4 to prevent the hydrolysis of the salt. Dilute to 1 liter by distilled water and standardize by titration with K2 Cr2O 7 solution.

3

Sulphuric acid, concentrated. Take H2SO4 (sp.gr.1.84) and add 1.25g silver sulphate to each 100ml of acid.

4

Diphenylamine indicator Dissolve 0.5g diphenylamine in a mixture of 100ml conc. H2SO4 and 20 ml distilled water.

5 3.5.5.2.

Phosphoric acid, H3 PO4, 85% (sp.gr.1.71). Procedure

Weigh suitable quantity of 0.5 mm perfectly dried soil not exceeding 10g (containing about 10-25 mg carbon) and transfer it to a dried 500ml conical flask. Add 10 ml of 1Normal, K2Cr2O7 and 20 ml Concentrated sulphuric acid having silver sulphate dissolved in it, and mix by gentle swirling. Allow the flask to rest for about 30 minutes, and after when reaction is over, dilute the contents by 200ml of distilled water. Add 10ml of phosphoric acid and 1ml diphenylamine indicator. The colour will Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

25


change to bluish purple. Titrate the contents with ferrous ammonium sulphate, carefully until the blue colour changes to brilliant green. The end point is very sharp in this titration. If more than 8ml of the 10ml added K2 Cr2O7 is consumed. (Titration value < 2ml) repeat the procedure with less quantity of the soil. If before the titration only the colour becomes green, repeat with smaller quantity of soil. 3.5.5.3.

Calculation % Carbon (Cox %) =

% Organic matter = Cox %

V1-V2 W

× 0.003 × 100

Eq. 3.4

1.724

Where V1=Volume of K2 Cr2O 7 (10ml) V2=Volume of ferrous ammonium sulphate. W= Weight of the soil taken Depending on the soil, only 60-90% of the total organic matter is recovered in this method. No recovery factor has been taken into account in the formula given. Therefore the results should be accompanied by with a mention “by Walkley and Black method”. 3.6. ESTIMATION

OF

RETENTION

PARAMETERS

USING

PEDO-

TRANSFERFUNCTION Water retention curve is the relationship between the water content, θ, and the soil water potential, ψ. This curve is characteristic for different types of soil, and is also called the soil moisture characteristic curve. It is used to predict the soil water storage, water supply to the plants (field capacity) and soil aggregate stability. Due to the hysteretic effect of water filling and draining the pores, different wetting and drying curves may be distinguished. Pedotransfer functions for predicting the water retention curve can be divided into 3 types: Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

26


(1) Point Estimation This type of PTF predicts water content (θ) at predefined soil water suction (h). The most frequently estimated θ are at 33 kPa (corresponding to field capacity) and at 1500 kPa (corresponding to permanent wilting point), which are needed to determine soil water capacity. (2) Parametric Estimation Parametric PTFs are based on the assumption that the soil water retention function can be described by a closed form equation with a certain number of parameters such as Brooks and Corey (1964), and van Genuchten. The parametric approach is usually preferred as it yields a continuous function of θ (h) relationship. Water retention at any potential can be estimated, and it also ensures that the water content predicted at lower potential will be smaller than the one at higher potential. The estimated parameters can be used to predict the unsaturated hydraulic conductivity based on hydraulic models. Soil water transport models usually only require the parameters of the hydraulic functions, thus the predicted parameters can be used directly in the models. (3) Physico-empirical Model In this approach, the soil water retention characteristics are derived from physical attributes. Arya and Paris (1981) translated the particle-size distribution into a soil water retention curve by converting solid mass fractions to water, and pore size distribution into soil water potential by means of a capillary equation. The method is difficult to apply as it requires information on the packing of soil particles The pedotransfer function used to determine the water retention parameters at different matric potentials at the sample points is given below: θh = b0 + b1Sand + b2ρb + b3OM


Eq. 3.6

27


Where θh is the soil water content at different soil matric potentials (cm3/cm3), clay and sand in percentages, ρb is the soil bulk density (g/cm3), and OM is the organic matter content percentage. Table 3.2: Coefficients of pedotransfer function. Ψ (kPa)

b0

b1

b2

b3

5

0.478

-0.0005

-0.13

0.0117

10

0.422

-0.0006

-0.0917

0.0112

20

0.374

-0.002

-0.0548

0.0195

33

0.304

-0.0016

-0.0462

0.0201

60

0.246

-0.0012

-0.0387

0.0187

100

0.207

-0.001

-0.034

0.0169

200

0.166

-0.0008

-0.027

0.016

1000

0.0844

-0.0002

-0.0124

0.0112

1500

0.0685

-0.0002

-0.0084

0.0103

This pedotransfer function was developed and validated by Swetha.P and Varija.K (2011) and was proposed to be used for point estimation of SWR curve for soils in the coastal area of Karnataka state by using the values of, clay or sand fraction of the soil, bulk density and organic matter content. By substituting the value of percentage of sand, soil bulk density and organic matter content percentage the water content present at different matric potentials, i.e., 5kpa, 10kpa, 20kpa, 33kpa, 60kpa, 100kpa, 200kpa, 1000kpa and 1500kpa was found out. The field capacity of soil is determined by the water content at 33kpa and wilting point is determined by the water content at 1500kpa. The available water content is then found out by calculating the difference between the water content present in between field capacity and wilting point. Water retention curve is plotted with the values of water content against matric potentials.


28


3.7. PREDICTION OF SOIL HYDRAULIC PROPERTIES AT UNSAMPLED LOCATIONS Geostatistics provides a set of statistical tools for incorporating the spatial coordinates of soil observations in data processing, allowing for description and modeling of spatial patterns, prediction at unsampled locations, and assessment of the uncertainty attached to these predictions. (P.Gooverts.1998) To get the data at unsampled locations two methods of prediction is possible, interpolation and extrapolation. Interpolation is nothing but estimating the attribute values of locations that are within the range of available data using known data values. Extrapolation on the other hand estimates the attribute values of locations outside the range of available data using known data values. All the values of soil hydraulic properties estimated are saved as georeferenced data into MS Excel® worksheets and used as input for the geostatistical methods. 3.7.1.

Interpolating a Surface from Sampled Point Data Interpolation can be global or local. Local interpolation takes into account a

neighborhood of sample points to estimate a value at an unsampled location, whereas the global interpolation uses all the known sample points to estimate the value at an unsampled location. Kriging is a global interpolation method. 3.7.2.

Exploratory Analysis of Data Exploratory analysis of the data is done in order to identify the deviation of data

set from normality and to recognize any visible trends. Normality is checked by observing the histograms and QQ plots where as the trend is identified by the posting the data values on to maps. If the data sets don’t fit to normal distribution then the basic transformations like log transformation can be done to fit the data to normal distribution curve. If any trend is observed then detrending must be done before the data values are inputted. The histograms and the QQ plots are generated using the geostatistical tools of geostatistical wizard of ArcMap® Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method

29


3.7.3.

Semivariogram Semivariance is a measure of the dissimilarity. It increases with increasing

separation distance. The semivariance versus lag plot is the semivariogram. The various characteristics of the semi variogram is given in the example below.

Fig 3.7: Sill, Nugget and Range of a semivariogram Sill: The semivariance value at which t he variogram levels off. It is also used to refer the “amplitude” of a certain component of the semivariogram. Range: The lag distance at which the semivariogram (or semivariogram component) reaches the sill value. Presumably, autocorrelation is essentially zero beyond the range. Nugget: In theory the semivariogram value at the origin (0 lag) should be zero. If it is significantly different from zero for lags very close to zero, then this semivariogram value is referred to as the nugget. The nugget represents variability at distances smaller than the typical sample spacing, including measurement error. 3.7.4.

Trend If the empirical semivariogram continues climbing steadily beyond the global

variance value, this is often indicative of a significant spatial trend in the variable,


30


resulting in a negative correlation between variable values separated by large lags. Three options for dealing with lag include: 1) Fit a trend surface and work with residuals from the trend 2) Try to find a “trend-free” direction and use the variogram in that direction as the variogram for the “random” component of the variable. 3) Ignore the problem and use a linear or power variogram To check for existing trends in a spatial data classed post maps were created. 3.7.5.

Modeling the Semivariogram For the sake of kriging (or stochastic simulation), we need to replace the

empirical semivariogram with an acceptable semivariogram model. Part of the reason for this is that the kriging algorithm will need access to semivariogram values for lag distances other than those used in the empirical semivariogram. More importantly, the semivariogram models used in the kriging process need to obey certain numerical properties in order for the kriging equations to be solvable. Using h to represent lag distance, a, to represent (practical) range, and c to represent sill, the five most frequently used models are: Nugget: g(h) = �

0, 𝑖𝑖𝑖𝑖 ℎ = 0 𝑐𝑐, 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

Spherical g(h) = �

ℎ

Eq. 3.7 ℎ 3

𝑐𝑐. �1.5 �𝑎𝑎 � − 0.5 �𝑎𝑎 � � ,

𝑐𝑐, 𝑜𝑜𝑜𝑜ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

−3ℎ

Exponential g(h) = c.(1-exp(

𝑎𝑎

−3ℎ 2

Gaussian g(h) = = c.(1-exp(

𝑎𝑎 2

Power g(h) = c.hω with 0