Groundwater Data Requirement and AnalysisGroundwater Data Requirement and Analysis

National Institute of HydrologyNational Institute of HydrologyRoorkeeRoorkee –– 247667 (India)247667 (India)

C. P. KumarScientist ‘F’

OutlineOutline

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

Introduction

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

• There’s a lot of stress on groundwater resource –contamination, over-pumping…

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, orimpacts to, groundwater are not sustainable in the long run.

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.

Data Requirement for

Groundwater Studies

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.

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

Physical Framework

1. Topography 2. Geology 3. Types of aquifers 4. Aquifer thickness and lateral extent5. Aquifer boundaries6. Lithological variations within the aquifer7. Aquifer characteristics

Hydrological Framework

1. Water table elevation2. Type and extent of recharge areas3. Rate of recharge4. Type and extent of discharge areas5. Rate of discharge

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 groundwaterdomain under investigation, including the thickness and areal extent of each hydrostratigraphic unit.

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.

Groundwater Data Acquisition

Some data may be obtained from existing reports of various agencies/departments, but in most cases additional field work isrequired.

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.

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 ofhydraulic conductivity than are measured in the field.

Monitoring of Groundwater LevelsA network of observation wells and/or piezometers are established to obtain data on the -

o Depth and configuration of the water table

o Direction of groundwater movement

o 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.

Monitoring of Groundwater Quality

The objectives of the water quality monitoring network are to:

o Detect water quality changes with timeo Identify potential areas that show rising trendo Detect potential pollution sourceso 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 assurancestep .

The frequency of sampling required in a ground-water-quality monitoring program is dictated by the expected rate of change inthe 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.

Processing of Groundwater Data

Before any conclusions can be drawn about the cause, extent, andseverity of an area’s 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:

o Groundwater hydrographso Water table-contour mapo Depth-to-water table mapo Water table-fluctuation mapo Head-differences mapo Groundwater-quality map

A proper interpretation of groundwater data, hydrographs, and maps requires a coordinate study of a region’s geology, soils, topography, climate, hydrology, land use, and vegetation.

Processing of Groundwater Data

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.

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Groundwater Hydrographs

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.

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 mmay 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.

Water Table – Contour Map

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.

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.

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.

Depth-to-Water Table Map

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.

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.

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.

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.

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 Darcy’s law, the calculation of the horizontal component of flow.

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.

Interpolation of Hydrological Variables

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.

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)

Global estimation: characteristic value for areaPoint 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

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)

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

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

Interpolation

Given z = z(x,y) at some points we want to estimate z0at (x0, y0)

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y

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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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x0

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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

Choice of Interpolation Method

Depends primarily on the nature of the variable and its spatial variation.

Examples: Rainfall, groundwater, soil physical properties, topography

Example: Groundwater Data

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

Deterministic Interpolation Methods

PolynomialsSpatial join (point in polygon)Thiessen polygonsTIN and linear interpolationSplineInverse Distance Weighting (IDW)

Polynomials

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•Plane:

•Second Order:

•Number of coefficients

•Over- and undershoots

Spatial Join (point in polygon)

Assign spatial properties by spatial join

Thiessen Polygons

Thiessen polygonsA point in the domain receives the value of the closest data pointStep-wise function

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Interpolation of elevation surface using Thiessen Polygons

Thiessen Polygons

TIN and Linear Interpolation

Surface is approximated by facets of plane trianglesContinuous 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.

Inverse Distance Weighting (IDW)

Default method in many software packages β = 2Controlled by exponent β

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Stochastic (Geostatistical) Interpolation

Analysis of the spatial correlation in the random component of a variableOptimum determination of weights for interpolation

Semi-varianceRegionalized variable theory uses a related property called the semisemi--variancevariance 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)

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.

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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+

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Semi-variance

Zi is the measurement of a regionalized variable taken at location i ,Zi+h is another measurement taken h intervals awayNh 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.

Semi-variogramThe 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.

Semi-variogramThe 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.

Characteristics of the Semi-variogram(and therefore of the data)

Nugget: variance at zero distance which should be zero but isn’t…

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.

Experimental semi-variogramThings nearby tend to be more similar than things that are farther apart

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0 200 400 600 800 1000 1200 1400 1600 1800Lag Distance

0

50

100

150

200

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

0 200 400 600 800 1000 1200 1400 1600 1800Lag Distance

0

50

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Var

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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

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Spherical and Exponential with the same range and sill

Spherical and Exponential with the same sill and the same initial slope

Semi-variogram from ArcGIS

This is an example ofa variogram producedusing ArcGIS'sGeostatistical Analyst.

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.

Kriging goes through a two-step process:

1. 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),

2. Prediction of unknown values

Interpolation by Kriging

Kriging yields the estimated value AND the estimation variance

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Estimated conductivityStandard deviation of estimated conductivity

Kriging

Interpolation of elevation surface using Kriging

Geostatistical Analysis using ArcGIS

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

Flow Chart of the Geostatistical Analysis Steps

Groundwater Data Management

and Analysis Tools

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.