Groundwater Data Requirement and Analysis

1
Groundwater Data Requirement and Analysis
C. P. Kumar
Scientist F

• National Institute of Hydrology
• Roorkee 247667 (India)

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Outline

• Introduction
• Data Requirement for Groundwater Studies
• Groundwater Data Acquisition
• Processing of Groundwater Data
• Interpolation of Hydrological Variables
• Geostatistical Analysis using ArcGIS
• Groundwater Data Management and Analysis Tools

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Introduction
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• Why should we devote resources for assessing
  groundwater conditions?
• Groundwater is a vital natural resource for our
  country
• A major source of drinking water and irrigation
  water supply
• Groundwater baseflow sustains streamflow during
  low flow periods
• Dependence on groundwater is rapidly increasing
• Theres a lot of stress on groundwater resource
  contamination, over-pumping

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• Groundwater is an important, but often overlooked
  component of the hydrologic cycle
• Groundwater and surface water are in reality an
  interconnected resource.
• Water management decisions that ignore the
  contributions of, or impacts to, groundwater are
  not sustainable in the long run.

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• Accurate and reliable groundwater resource
  information (including quality) is critical to
  planners and decision-makers.
• Huge investment in the areas of ground water
  exploration, development and management at state
  and national levels aims to meet the groundwater
  requirement for drinking and irrigation and
  generates enormous amount of data.
• We need to focus on improved data management,
  precise analysis and effective dissemination of
  data.

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Data Requirement for Groundwater Studies
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ALL GROUND-WATER HYDROLOGY WORK IS MODELING A
Model is a representation of a system.
Modeling begins when one formulates a concept
of a hydrologic system, continues with
application of, for example, Darcy's Law or the
Theis equation to the problem, and may culminate
in a complex numerical simulation.
9

• The success of any groundwater study, to a large
  measure, depends upon the availability and
  accuracy of measured/recorded data required for
  that study.
• Therefore, identifying the data needs and
  collection/monitoring of required data form an
  integral part of any groundwater exercise.
• The first phase of any groundwater study consists
  of collecting all existing geological and
  hydrological data on the groundwater basin in
  question.
• Any groundwater balance or numerical model
  requires a set of quantitative hydrogeological
  data that fall into two categories
• Data that define the physical framework of the
  groundwater basin
• Data that describe its hydrological framework

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Physical Framework 1. Topography 2. Geology
3. Types of aquifers 4. Aquifer thickness
and lateral extent 5. Aquifer boundaries 6.
Lithological variations within the aquifer 7.
Aquifer characteristics
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Hydrological Framework 1. Water table
elevation 2. Type and extent of recharge areas 3.
Rate of recharge 4. Type and extent of discharge
areas 5. Rate of discharge
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• The data required for a groundwater flow
  modelling study under physical framework are
• Geologic map and cross section or fence diagram
  showing the areal and vertical extent and
  boundaries of the system.
• Topographic map at a suitable scale showing all
  surface water bodies and divides. Details of
  surface drainage system, springs, wetlands and
  swamps should also be available on map.
• Land use maps showing agricultural areas.
• Contour maps showing the elevation of the base
  of the aquifers and confining beds.
• Isopach maps showing the thickness of aquifers
  and confining beds.
• Maps showing the extent and thickness of stream
  and lake sediments.
• These data are used for defining the geometry of
  the groundwater domain under investigation,
  including the thickness and areal extent of each
  hydrostratigraphic unit.

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• Under the hydrogeologic framework, the data
  requirements for a groundwater flow modelling
  study are
• Water table and potentiometric maps for all
  aquifers.
• Hydrographs of groundwater head and surface
  water levels.
• Maps and cross sections showing the hydraulic
  conductivity and/or transmissivity distribution.
• Maps and cross sections showing the storage
  properties of the aquifers and confining beds.
• Spatial and temporal distribution of rates of
  evaporation, groundwater recharge, groundwater
  pumping etc.

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Groundwater Data Acquisition
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• Some data may be obtained from existing reports
  of various agencies/departments, but in most
  cases additional field work is required.
• The observed raw data obtained from the field may
  contain inconsistencies and errors. Before
  proceeding with data processing, it is essential
  to carry out data validation in order to correct
  errors in recorded data and assess the
  reliability of a record.
• Amongst the hydrologic stresses including
  groundwater pumping, evapotranspiration and
  recharge, groundwater pumpage is the easiest to
  estimate.
• Field information for estimating
  evapotranspiration is likely to be sparse and can
  be estimated from information about the land use
  and potential evapotranspiration values.
• Recharge is one of the most difficult parameters
  to estimate.

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• Values of transmissivity and storage coefficient
  are usually obtained from data generated during
  pumping tests and subsequent data processing.
• For modelling at a local scale, values of
  hydraulic conductivity may be determined by
  pumping tests if volume-averaged values are
  required.
• In the field, in-situ hydraulic conductivity may
  be measured by Guelph Permeameter.
• For unconsolidated sand-size sediment, hydraulic
  conductivity may be obtained from laboratory
  permeability tests using permeameters.
• Laboratory analyses of core samples tend to give
  lower values of hydraulic conductivity than are
  measured in the field.

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• Monitoring of Groundwater Levels
• A network of observation wells and/or piezometers
  are established to obtain data on the -
• Depth and configuration of the water table
• Direction of groundwater movement
• Location of recharge and discharge areas
• In any drainage investigation, the highest and
  the lowest water table positions, as well as the
  mean water table during a hydrological year are
  important.
• For this reason, water level measurements should
  be made at frequent intervals. The interval
  between readings should preferably not exceed one
  month.
• All measurements in a study area should, as far
  as possible, be made on the same day because this
  gives a complete picture of the water table.

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• Monitoring of Groundwater Quality
• The objectives of the water quality monitoring
  network are to
• Detect water quality changes with time
• Identify potential areas that show rising trend
• Detect potential pollution sources
• Study the impact of land use and
  industrialization on groundwater quality
• Substantial costs are incurred to obtain and
  analyze samples. Field costs for drilling,
  installing, and sampling monitoring wells and
  laboratory costs for analyzing samples are not
  trivial.
• Comprehensive data analysis and evaluation by a
  knowledgeable professional should be the final
  quality assurance step .

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• The frequency of sampling required in a
  ground-water-quality monitoring program is
  dictated by the expected rate of change in the
  concentrations of chemical constituents.
• For monitoring concentrations of major ions and
  nutrients, and values of physical properties of
  ground water, twice yearly sampling should be
  sufficient.
• More frequent sampling should be considered if
  the types and conditions of any upgradient
  sources of these compounds are changing.
• Monitoring of ground-water quality should be a
  long-term activity.

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Processing of Groundwater Data
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Processing of Groundwater Data

• Before any conclusions can be drawn about the
  cause, extent, and severity of an areas
  groundwater related problems, the raw groundwater
  data on water levels and water quality have to be
  processed.
• This data then have to be related to the geology
  and hydrogeology of the area. The results,
  presented in graphs, maps, and cross-sections,
  will enable a diagnosis of the problems.
• The following graphs and maps have to be prepared
  that are discussed hereunder
• Groundwater hydrographs
• Water table-contour map
• Depth-to-water table map
• Water table-fluctuation map
• Head-differences map
• Groundwater-quality map
• A proper interpretation of groundwater data,
  hydrographs, and maps requires a coordinate study
  of a regions geology, soils, topography,
  climate, hydrology, land use, and vegetation.

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Groundwater Hydrographs

• When the amount of groundwater in storage
  increases, the water table rises when it
  decreases, the water table falls. This response
  of the water table to changes in storage can be
  plotted in a hydrograph.
• Groundwater hydrographs show the water-level
  readings, converted to water levels below ground
  surface, against their corresponding time.
• A hydrograph should be plotted for each
  observation well or piezometer. It is important
  to know the rate of rise of the water table, and
  even more important, that of its fall.

July Aug Sept Oct Nov Dec Jan
Feb Mar Apr May June
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• Groundwater hydrographs also offer a means of
  estimating the annual groundwater recharge from
  rainfall. This, however, requires several years
  of records on rainfall and water tables.
• An average relationship between the two can be
  established by plotting the annual rise in water
  table against the annual rainfall.
• Extending the straight line until it intersects
  the abscissa gives the amount of rainfall below
  which there is no recharge of the groundwater.
  Any quantity less then this amount is lost by
  surface runoff and evapotranspiration.

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Water Table Contour Map

• A water table - contour map shows the elevation
  and configuration of the water table on a certain
  date.
• To draw the water table-contour lines, we have to
  interpolate the water levels between the
  observation points, using the linear
  interpolation method.
• A proper contour interval should be chosen,
  depending on the slope of the water table. For a
  flat water table, 0.25 to 0.50 m may suit in
  steep water table areas, intervals of 1 to 5 m or
  even more may be needed to avoid overcrowding the
  map with contour lines.
• A water table-contour map is an important tool in
  groundwater investigations because, from it, one
  can derive the gradient of the water table
  (dh/dx) and the direction of groundwater flow,
  which is perpendicular to the water table-contour
  lines.

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• The topographic base map should contain contour
  lines of the land surface and should show all
  natural drainage channels and open water bodies.
• For the given date, the water levels of these
  surface waters should also be plotted on the map.
  Only with these data and data on the land surface
  elevation can water table contour lines be drawn
  correctly.

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• For a proper interpretation of a water
  table-contour map, one has to consider not only
  the topography, natural drainage pattern, and
  local recharge and discharge patterns, but also
  the subsurface geology.
• More specifically, one should know the spatial
  distribution of permeable and less permeable
  layers below the water table.
• For instance, a clay lens impedes the downward
  flow of excess irrigation water or, if the area
  is not irrigated, the downward flow of excess
  rainfall. A groundwater mound will form above
  such a horizontal barrier.

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Depth-to-Water Table Map

• A depth-to-water table map shows the spatial
  distribution of the depth of the water table
  below the land surface. A suitable contour
  interval may be 50 cm.
• The regions of map where the groundwater level is
  between 0-2 m depicts the area having drainage
  problems.
• Based on measurement results for a year, the map
  drawn using the lowest water table levels
  indicates to which extent the groundwater falls
  in a year.
• The section where the water table level is
  between 0-1 m determines the areas in which
  groundwater exists in the root-zone throughout a
  year.
• The depth and shape of the first impermeable
  layer below the water table strongly affect the
  height of the water table.

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Water Table - Fluctuation Map

• A water table - fluctuation map is a map that
  shows the magnitude and spatial distribution of
  the change in water table over a period (e.g. a
  season or a whole hydrological year).
• A water table-fluctuation map is a useful tool in
  the interpretation of drainage problems in areas
  with large water table fluctuations.
• The change in water table in fine-textured soils
  will differ from that in coarse-textured soils,
  for the same recharge or discharge.

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Head - Differences Map

• A head-differences map is a map that shows the
  magnitude and spatial distribution of the
  differences in hydraulic head between two
  different soil layers.
• We calculate the difference in water level
  between the two piezometers, and plot the result
  on a map. After choosing a proper contour
  interval (e.g. 0.10 or 0.20 m), we draw lines of
  equal head difference.
• The map is a useful tool in estimating upward or
  downward seepage.

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Groundwater - Quality Maps

• A groundwater-quality map (for example,
  electrical-conductivity map) is a map that shows
  the magnitude and spatial variation in the
  salinity of the groundwater.
• The EC values of all representative wells (or
  piezometers) are used for this purpose.
• Groundwater salinity varies not only horizontally
  but also vertically. It is therefore advisable to
  prepare an electrical-conductivity map not only
  for the shallow groundwater but also for the deep
  groundwater.
• In electrical conductivity maps, critical
  groundwater salinity is taken as 5000
  micromhos/cm, although it changes according to
  species of the crop to be grown.

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• By plotting all the EC values on a map, lines of
  equal electrical conductivity (equal salinity)
  can be drawn. Preferably the following limits
  should be taken less than 100 micromhos/cm, 100
  to 250 250-750 750 to 2500 2500 to 5000 and
  more than 5000. Other limits may, of course, be
  chosen, depending on the salinity found in the
  waters.
• Other types of groundwater-quality maps can be
  prepared by plotting different quality parameters
  (e.g. Sodium Adsorption Ratio (SAR) values).
• The groundwater in the lower portions of coastal
  and delta plains may be brackish to extremely
  salty, because of sea-water encroachment.
• In the arid and semi-arid zones, shallow water
  table areas may contain very salty groundwater
  because of high rates of evaporation. Irrigation
  in such areas may contribute to the salinity of
  the shallow groundwater through the dissolution
  of salts accumulated in the soil layers.

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Interpretation of Hydraulic Head and Groundwater
Conditions
Measurements of hydraulic head, normally achieved
by the installation of a piezometer or well
point, are useful for determining the directions
of groundwater flow in an aquifer system.
In the above figure, three piezometers installed
to the same depth enable the determination of the
direction of groundwater flow and, with the
application of Darcys law, the calculation of
the horizontal component of flow.
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In the above figure, two examples of piezometer
nests are shown that allow the measurement of
hydraulic head and the direction of groundwater
flow in the vertical direction to be determined
either at different levels in the same aquifer
formation or in different formations.
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Interpolation of Hydrological Variables
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• A fundamental problem of Hydrology is that our
  models of hydrological variables assume
  continuity in space (and time), while
  observations are done at points.
• The elementary task is to estimate a value at a
  given location, using the existing observations.

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• Hydrological data have variability in space and
  time.
• Spatial variability is observed by a sufficient
  number of stations
• Time variability is observed by recording time
  series
• Spatial variability can be in different range of
  values or in different temporal behaviour
• A continuous field v v(x,y,z,t) is to be
  estimated from discrete values vi v(xi,yi,zi,ti)

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• Global estimation characteristic value for area
• Point estimation estimation at a point P
  P(x,y)
• We need data AND a conceptual model, how these
  data are related, (i.e. a conceptual model of the
  process)
• If the process is well defined, only few data are
  needed to construct the model

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Example

• A groundwater table in a confined, homogeneous,
  isotropic aquifer under steady state discharge
  from a well is described by the Thiem well
  formula.
• Theoretically, the observation of two groundwater
  heads at different distances from the well is
  sufficient to reconstruct the complete
  groundwater surface.

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• Hydrological variables are random and uncertain ?
  Geostatistical Methods
• Mostly 2D consideration ? v v(x,y,t)

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Regionalisation and Interpolation

• Regionalisation Identification of the spatial
  distribution of a function g, depending on local
  information as well as by transfer of information
  from other regions by transfer functions.
• Regionalisation therefore means to describe
  spatial variability (or homogeneity) of
• Model parameters
• Input variables
• Boundary conditions and coefficients

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• Regionalisation includes the following tasks
• Representation of fields of hydrological
  parameters and data (contour maps)
• Smoothing spatial fields
• Identification of homogeneous zones
• Interpolation from point data
• Transfer of point information from one region to
  others
• Adaptation of model parameters for the transfer
  from point to area

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Interpolation

• Given z z(x,y) at some points we want to
  estimate z0 at (x0, y0)

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• Weighted linear combination -
• The methods differ in the way how they establish
  the weights.

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Global and Local Interpolation

• An interpolation method is working globally, if
  all data points are evaluated in the
  interpolation.
• Local interpolation techniques use only data
  points in a certain neighbourhood of the
  estimated point.

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Deterministic or Statistical Interpolation

• Deterministic methods attempt to fit a surface of
  given or assumed type to the given data points
• Exact
• Smoothing
• Statistical (stochastic) methods

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Choice of Interpolation Method

• Depends primarily on the nature of the variable
  and its spatial variation.
• Examples Rainfall, groundwater, soil physical
  properties, topography

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Example Groundwater Data

• Groundwater tables have smooth surface, but
  trend!
• Hydrogeological information is highly random, has
  faults, few points with good data

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Deterministic Interpolation Methods

• Polynomials
• Spatial join (point in polygon)
• Thiessen polygons
• TIN and linear interpolation
• Spline
• Inverse Distance Weighting (IDW)

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Polynomials

• General
• Plane
• Second Order
• Number of coefficients
• Over- and undershoots

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Spatial Join (point in polygon)

• Assign spatial properties by spatial join

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Thiessen Polygons

• Thiessen polygons
• A point in the domain receives the value of the
  closest data point
• Step-wise function

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Interpolation of elevation surface using Thiessen
Polygons
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TIN and Linear Interpolation

• Surface is approximated by facets of plane
  triangles
• Continuous surface, but discontinuous 1st
  derivative

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Splines

• Spline estimates values using a mathematical
  function that minimizes overall surface
  curvature, resulting in a smooth surface that
  passes exactly through the input points.
• Conceptually, it is like bending a sheet of
  rubber to pass through the points while
  minimizing the total curvature of the surface.

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Inverse Distance Weighting (IDW)

• Default method in many software packages b 2
• Controlled by exponent b

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Stochastic (Geostatistical) Interpolation

• Analysis of the spatial correlation in the random
  component of a variable
• Optimum determination of weights for interpolation

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Semi-variance

• Regionalized variable theory uses a related
  property called the semi-variance to express the
  degree of relationship between points on a
  surface.
• The semi-variance is simply half the variance of
  the differences between all possible points
  spaced a constant distance apart.

(Semi-variance is a measure of the degree of
spatial dependence between samples(
58

• Semi-variance The magnitude of the semi-variance
  between points depends on the distance between
  the points. A smaller distance yields a smaller
  semi-variance and a larger distance results in a
  larger semi-variance.

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Calculating the Semi-variance (Regularly Spaced
Points(

• Consider regularly spaced points distance (d)
  apart, the semi-variance can be estimated for
  distances that are multiple of (d) (Simple form)

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Semi-variance

• Zi is the measurement of a regionalized variable
  taken at location i ,
• Zih is another measurement taken h intervals
  away
• Nh is number of points

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Semi-variogram

• The plot of the semi-variances as a function of
  distance from a point is referred to as a
  semi-variogram or variogram.

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• Sill The point at which the semi-variance
  approaches a flat region. Sill defines the level
  of maximum variability.
• Range The range or span defines a neighborhood
  within which all data points are related to one
  another.

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Semi-variogram

• The semi-variance at a distance d 0 should be
  zero, because there are no differences between
  points that are compared to themselves.
• However, as points are compared to increasingly
  distant points, the semi-variance increases.

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Semi-variogram

• The range is the greatest distance over which the
  value at a point on the surface is related to the
  value at another point.
• The range defines the maximum neighborhood over
  which control points should be selected to
  estimate a grid node.

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Characteristics of the Semi-variogram (and
therefore of the data)
Nugget variance at zero distance which should
be zero but isnt Range Distance at which max.
variance is reached (data considered
decorrelated) Sill Level of max.
variability Anisotropy might thus be manifested
by varying range with direction (constant sill
so-called geometric anisotropy). This is observed
with the elevation data.
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• Experimental semi-variogram
• Things nearby tend to be more similar than things
  that are farther apart

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• Theoretical semi-variogram fit function through
  empirical semi-variogram

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Variogram - Spherical

• It is a model semi-variogram and is usually
  called the spherical model.
• a is called the range of influence of a sample.
• C is called the sill of the semi-variogram.

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Variogram - Exponential
Spherical and Exponential with the same range and
sill
Spherical and Exponential with the same sill and
the same initial slope
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Semi-variogram from ArcGIS
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Interpolation by Kriging

• Kriging is named after the South African
  engineer, D. G. Krige, who first developed the
  method.
• Kriging uses the semi-variogram, in calculating
  estimates of the surface at the grid nodes.

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Interpolation by Kriging

• Kriging goes through a two-step process
• Variograms and covariance functions are created
  to estimate the statistical dependence (called
  spatial autocorrelation) values, which depends on
  the model of auto-correlation (fitting a model),
• Prediction of unknown values

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• Kriging yields the estimated value AND the
  estimation variance

Standard deviation of estimated conductivity
Estimated conductivity
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Interpolation of elevation surface using Kriging
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Geostatistical Analysis using ArcGIS
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• A linkage between GIS and spatial data analysis
  is considered to be an important aspect
  to explore and analyze spatial relationships. The
  GIS methodology for the spatial analysis of
  the groundwater levels involves the following
  steps
• (a) Exploratory spatial data analysis (ESDA)
  using ArcGIS software for the data (e.g.
  groundwater level) to study the following
• Data distribution
• Global and local outliers
• Trend analysis
• (b) Spatial interpolation for data using ArcGIS
  software, while kriging is applied by involving
  the following procedures
• Semivariogram and covariance modelling
• Model validation using cross validation
• Surfaces generation of the groundwater level data

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Flow Chart of the Geostatistical Analysis Steps
78
Groundwater Data Management and Analysis Tools
79
Following are some of the software packages used
for groundwater data management and
analysis. AquaChem AquaChem is an integrated
software package developed specifically for
graphical and numerical analysis of geochemical
data sets. AquaChem also includes a direct link
to the popular PHREEQC program for geochemical
modeling. AquiferTest Pro Graphical analysis and
reporting of pumping test and slug test
data. EnviroInsite EnviroInsite is a desktop,
groundwater visualization package for analysis
and communication of spatial and temporal trends
in multi-analyte, environmental groundwater data.
GW Contour Data interpolation and contouring
program for groundwater professionals that also
incorporates mapping velocity vectors and
particle tracks. HydroGeo Analyst Groundwater
and borehole data management and visualization
technology. RockWorks Geological data
management, analysis visualization.
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Thank You !!!