Use of Time Series, Barometric and Tidal Analyses to Conceptualize and Model Flow in an Underground Mine: the Corning Mine Complex, Ohio

USE OF TIME SERIES, BAROMETRIC AND TIDAL ANALYSES TO CONCEPTUALIZE AND MODEL FLOW IN AN UNDERGROUND MINE: THE CORNING MINE COMPLEX, OHIO.

A thesis presented to the faculty of the College of Arts and Sciences of Ohio University

In partial fulfillment of the requirements for the degree Master of Science

Parameswar Sahu November 2004

This thesis entitled USE OF TIME SERIES, BAROMETRIC AND TIDAL ANALYSES TO CONCEPTUALIZE AND MODEL FLOW IN AN UNDERGROUND MINE: THE CORNING MINE COMPLEX, OHIO.

BY PARAMESWAR SAHU

has been approved for the Department of Geological Sciences and the College of Arts and Sciences by

Mary W. Stoertz

Associate Professor of Geological Sciences

Leslie A. Flemming

Dean, College of Arts and Sciences

Sahu, Parameswar. M.S. November 2004. Hydrogeology Use of Time Series, Barometric and Tidal Analyses to Conceptualize and Model Flow in an Underground Mine: The Corning Mine Complex, Ohio. (172 pp.) Director of Thesis: Mary W. Stoertz

Understanding flow-system dynamics of underground coal mine complexes, such as the

Corning mine complex that discharges acid mine drainage into Sunday Creek, is essential to designing in-situ remediation. Time series analysis is applied to the study of mine aquifers to characterize the systems between the input function (precipitation) and the output functions (discharge and head). Results are presented as correlograms, coherency diagrams, phase diagrams, and cross correlograms. The analysis of Corning discharge shows that the aquifer has a short response time and has a low storage capacity. A time lag of 3 to 4 days is found between precipitation and mine discharge, which corresponds to pressure pulse propagation and not to the actual advective flow of water. The results also demonstrate the important spatial heterogeneity of the aquifer and indicate that the mine does not behave as a single pool.

Assuming porous-medium confined flow, barometric pressure and tidal signals are analyzed to yield mine aquifer properties. The analysis yields estimates of hydraulic conductivity, storage, barometric efficiency, and strain sensitivity. Comparison of the results with the available literature values indicates that the aquifer properties determined in this study have reasonable values. Parameter estimates are used to develop a numerical model of the Corning complex, using MODFLOW. The model simulates observed

hydraulic head distributions and mine discharge rates with fair accuracy. The result of the

simulations shows that 45% of the inflow to the mine complex derives from the

surrounding coal strata, including detached mines. Travel time for water captured at subsidence captures is found to exceed 10 years, because of low gradients. It is finally concluded that a combination of time series analysis, barometric analysis and numerical modeling can provide useful information about the hydrogeology of a mine system prior to a large-scale and expensive in-situ remediation design.

Approved: Mary W. Stoertz Associate Professor of Geological Sciences

Acknowledgments

I sincerely wish to thank all those who provided guidance and support that resulted in the completion of this research. Particularly, to my advisor Dr. Mary Stoertz, for your guidance, support, confidence and trust in me to complete this project. Thank you for always having a few minutes to point me in the right direction.

I would also like to thank Dr. Dina Lopez for your invaluable suggestions regarding all aspects of this study. Your support and encouragement kept me going when I felt that there was no light at the end of tunnel. Thank you. Dr. Douglas Green, Dr. Damian

Nance and Dr. Greg Springer, members of the thesis committee, for your guidance and support of my research goals. Your time and thoughtful comments are very much appreciated. Also, to Ben McCament for your assistance in the compilation of important data necessary for conducting my research.

To Ohio University Department of Geological Sciences Faculty and Staff, thank you for providing me with a challenging and supporting atmosphere in which to learn. I would also like to thank all my graduate colleagues and a very special thank you to my friend

Peter Schillig. I have thoroughly enjoyed my time with you peoples at Ohio University.

Finally, I would like to extend special praise and gratitude to my family. This thesis would not have been possible without your love and support.

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Table of Contents

Abstract...... 3

Acknowledgments...... 5

Table of Contents...... 6

List of Tables ...... 8

List of Figures...... 9

Chapter 1 Introduction ...... 11

1.1 Introduction...... 11 1.2 Brief Overview of Problems ...... 14 1.3 Underground Mine Systems – Literature Review ...... 17 1.3.1 Hydrogeologic Behavior...... 17 1.3.2 Factors Controlling Mine Drainage ...... 19 1.3.3 Acid Mine Drainage...... 21 1.4 Aims of Project ...... 23 1.5 Structure of Thesis ...... 26

Chapter 2 Study Area...... 27

2.1 Sunday Creek Watershed...... 27 2.2 Geology...... 30 2.3 Hydrogeology ...... 31 2.4 Mining History...... 33 2.5 Previous Investigations ...... 34 2.6 Data Collection ...... 36

Chapter 3 Mine System Conceptualization via Time Series Analysis ...... 40

3.1 Introduction...... 40 3.2 Methods...... 42 3.2.1 Simple Correlation and Spectral Analysis ...... 43 3.2.2 Cross Correlation and Cross Spectral Analysis ...... 45 3.3 Results and Discussion ...... 49 3.3.1 Relationship between Precipitation and Mine Discharge ...... 49 3.3.2 Spatial Variability of Mine Aquifer Properties...... 59

Chapter 4 Mine System Parameterization via Barometric Analysis...... 63 7

4.1 Introduction...... 63 4.2 Theory...... 69 4.2.1 Water Level Frequency Response Theory...... 69 4.2.2 Atmospheric Loading Analysis Theory...... 71 4.2.3 Earth Tide Analysis Theory...... 73 4.3 Methods...... 77 4.3.1 Atmospheric Loading Analysis...... 77 4.3.2 Earth Tide Analysis...... 84 4.3.3 Poroelastic Parameters...... 88 4.3.4 Correlation Analysis ...... 92 4.4 Results and Discussion ...... 93 4.4.1 Observations ...... 93 4.4.2 Barometric Efficiency and Aquifer Storage Properties ...... 96 4.4.3 Transmissivity and Hydraulic Conductivity Estimation...... 99 4.4.4 Correlation Analysis ...... 100 4.4.5 Limitations of Aquifer Property Estimates...... 102

Chapter 5 Mine-Water Flow Prediction via Numerical Modeling ...... 104

5.1 Introduction...... 104 5.2 Model Program ...... 107 5.3 Modeling Methods...... 109 5.3.1 Conceptual Model...... 109 5.3.2 Numerical Flow Model Design...... 114 5.3.3 Model Simulation and Calibration...... 120 5.4 Results and Discussion ...... 121 5.4.1 Results...... 121 5.4.2 Model Limitations...... 130

Chapter 6 Conclusions and Recommendations...... 132

6.1 Time Series Analysis ...... 132 6.2 Barometric Pressure and Tidal Analysis...... 134 6.3 Flow Modeling...... 136 6.4 Strength and Weaknesses of Different Analysis...... 138 6.5 Recommendations...... 140

References...... 141

Appendices...... 149

Appendix I Corning Mine Complex Time Series Data ...... 150 Appendix II Water Levels and Observed Heads of Monitoring Wells...... 156 Appendix III FORTRAN Code...... 169 8

List of Tables

Table 2.1: Flow and chemical characteristics of Corning discharge ...... 29

Table 2.2: Specification of the Monitoring Wells ...... 37

Table 2.3: Data used for the analyses ...... 39

Table 3.1: Time-lag response of well heads and discharge to precipitation based on cross-spectrum analysis ...... 60

Table 4.1: Potential tidal amplitude ...... 76

Table 4.2: Assumed constants for barometric and tidal analysis...... 88

Table 4.3: Estimated poroelastic parameters from barometric and tidal analysis ...... 97

Table 4.4: Estimated transmissivity from type curve analysis ...... 97

Table 4.5: Correlation among aquifer properties determined by frequency response analysis...... 101

Table 4.6: Correlation among wells based on aquifer parameters using a Pearson-Product Correlation Coefficient ...... 101

Table 5.1: Summary of the input parameters for the calibrated model ...... 113

Table 5.2: Corning discharge flow calibration ...... 122

Table 5.3: Calibration of hydraulic conductivity (K) for Layer 1 (Overburden)...... 124

Table 5.4: Calibration of hydraulic conductivity (K) for Layer 2 (Overburden)...... 124

Table 5.5: Calibration of hydraulic conductivity (K) for Layer 3 (Mined Layer)...... 125

Table 5.6: Calibration of hydraulic conductivity (K) for Layer 4 (Clay Layer)...... 125 9

List of Figures

Figure 1.1: Location map showing Sunday Creek Watershed in Ohio...... 12

Figure 1.2: Map of Corning mine complex showing individual mines, Corning discharge, monitoring wells and subsidence captures...... 13

Figure 1.3: The Corning Discharge ...... 13

Figure 2.1: Generalized stratigraphic section of the study area...... 28

Figure 2.2: Overburden diagram of study area from borehole logs...... 31

Figure 2.3: Borehole installation log ...... 36

Figure 3.1: Variation of discharge with precipitation...... 49

Figure 3.2: Autocorrelation functions of precipitation and discharge time series...... 50

Figure 3.3: Spectral density function of precipitation ...... 51

Figure 3.4: Cross correlation of precipitation and discharge...... 53

Figure 3.5: Cross amplitude function for precipitation and discharge...... 55

Figure 3.6: Gain function for discharge relative to precipitation...... 56

Figure 3.7: Coherence between precipitation and discharge signals ...... 58

Figure 3.8: Delay or phase between precipitation and discharge signals ...... 58

Figure 3.9: Effects of precipitation on water levels in monitoring wells...... 61

Figure 4.1: Flow chart illustrating aquifer properties that can be determined from the atmospheric loading and Earth tide water level response data ...... 66

Figure 4.2: Barometric Efficiency (BE) schematic diagram ...... 71

Figure 4.3: Tidal cycles during the lunar month...... 74

Figure 4.4: Dilation and compression of saturated rock matrix due to Earth tide strain ...75

Figure 4.5: Flow chart illustrating major steps required in the atmospheric loading analysis...... 78 10

Figure 4.6: Response of water levels to the barometric pressure ...... 80

Figure 4.7: Water level and barometric pressure frequency spectra with applied filters ..81

Figure 4.8: Estimated Barometric Efficiency (BE) of monitoring wells...... 83

Figure 4.9: Type curve: Amplitude Ratio versus T’ on a semi-log Plot...... 84

Figure 4.10: Flow chart illustrating major steps required in the Earth tide analysis ...... 85

Figure 4.11: Estimated specific storage of monitoring wells ...... 87

Figure 4.12: Typical (a) barometric pressure and (b) Earth tide strain data...... 95

Figure 4.13: Autocorrelogram of the barometric pressure data...... 96

Figure 4.14: Correlation between porosity and specific storage...... 98

Figure 5.1: Flow chart representing the mine modeling, showing the role played by the time series and barometric pressure analyses ...... 106

Figure 5.2: Conceptual model of flow within the Corning mine complex ...... 110

Figure 5.3: Study area dimensions and surface topography elevations...... 111

Figure 5.4: Different flow boundaries of the Corning mine complex ...... 111

Figure 5.5: Horizontal grid design and dimensions of the flow model ...... 116

Figure 5.6: Vertical grid design and dimensions showing different layers ...... 116

Figure 5.7: Horizontal grid showing boundary conditions and hydrologic properties....118

Figure 5.8: Sensitivity analysis of Corning discharge to recharge ...... 123

Figure 5.9: Sensitivity analysis of Corning discharge to Corning head ...... 123

Figure 5.10: Flow model RMS error for the hydraulic conductivity of mined layer...... 126

Figure 5.11: Sensitivity analysis of hydraulic conductivity to different layers ...... 127

Figure 5.12: Sensitivity analysis of head of different wells to recharge...... 128

Figure 5.13: Head distribution and flow direction within the mined layer...... 129 11

Chapter 1: Introduction

1.1 Introduction

Southeastern Ohio, like much of Appalachia, has been extensively mined for coal during

the past two hundred years. Coal mining leads to the production of acid mine drainage

(AMD), which is formed by a series of complex geochemical reactions that occur when

water comes in contact with pyrite in coal, refuse or overburden of a mine operation

(Phipps et al., 1996) is the most pervasive water pollution problem in this region.

Watersheds that contained the coal that fostered this nation’s industrial growth are now impacted by AMD, which has polluted approximately 6,000 miles of streams in the

Appalachian Region and 1,750 miles of streams in Ohio alone (Appel, 1989).

Southeastern Ohio contains numerous watersheds that have been affected by AMD from abandoned underground coal mining. Among these watersheds is the Sunday Creek

Watershed (Fig. 1.1). The study area, informally named the “Corning Mine Complex”

(Fig. 1.2), is located in this watershed and is an interconnected 6-square-mile complex of ten smaller mines. Middle Kittanning No. 6 coal has been deep mined from this complex using the room-and-pillar technique since the early 1800’s, extensively modifying the groundwater flow regime of the area. The Corning discharge (Fig. 1.3) is one of the major causes of AMD degradation of Sunday Creek (McCament, 2004; Light, 2001). The discharge is located along upper Sunday Creek near Corning, Ohio (Fig. 1.2) and was caused by drilling into an abandoned underground high sulfur coal mine complex in the early 1970’s to release water from the mine to facilitate up-dip surface mining. The 12

Figure 1.1: Location map showing Sunday Creek Watershed in Ohio. 13

Figure 1.2: Map of Corning Mine Complex, showing individual mines, Corning discharge, monitoring wells and subsidence captures.

Figure 1.3: The Corning Discharge. 14

Sunday Creek draft AMD Abatement and Treatment plan (SCWG, 2003) considers it the

highest priority for remediation in order to restore the stream to warm-water habitat.

Recent discussions by the Sunday Creek Watershed Group have centered on the

possibility of treating the acidic and metal-laden water within the mine, prior to

discharging into Sunday Creek. While the methods under discussions are proprietary, the

general idea is to create an In-Situ Reactive Zone (Arcadis G&M, 2004) within the mine

by injecting CO2 and nutrients into the underground mine. The strategic introduction of both CO2 and nutrients need understanding of the plumbing of the mine, so that treatment

is effective. This study investigates the hydrologic behavior of the Corning mine complex

through the application of (a) time series analysis of well hydrographs, (b) barometric

pressure analysis of well hydrographs, and (c) numerical flow modeling, in order to

understand the system better to design effective remediation of the discharge.

Scientifically, the research provides insight into characteristics and processes controlling flow in abandoned underground mines, a poorly understood area of hydrogeology.

1.2 Brief Overview of Problems

Upon the transformation from an agricultural to an industrial economy during the mid-

1800, Ohio evolved into one of the largest coal producers in the United States. More than

three billions tons of coals were mined from 1800 to 1993 (Crowell, 1995). More than

70% of that coal originated from southeastern counties such as Belmont, Harrison,

Jefferson, Perry, Athens, Meigs, and Hocking (Crowell, 1995). Coal has been extracted

15 via a number of different surface and underground mining techniques. However, the oldest techniques include those utilized underground, some of which were popularized before any mining safety or reclamation laws were established (Crowell, 1995).

Underground mining remained the principal method of coal mining in Ohio until the surface mining industry took over in 1948. Conventional underground mining companies were unable to compete with the more cost efficient and highly productive surface mining operations. The reclamation of mined land during the early part of the twentieth century was considered irrational from a business standpoint and there were no existing laws outlining reclamation procedures. Consequently, most mining companies completely abandoned their mining sites, leaving behind wide open underground mining complexes and numerous piles of coal spoil materials (Overly, 1997).

The first surface law in Ohio was passed in 1947 and required the identification of land affected by surface mining operations. This law later served as a model for the 1997

Federal Surface Mining Control and Reclamation Act. Although much has been done with such regulations to prevent future degradation of watersheds, previous underground and surface mining has left many streams with poor water quality, due in part to numerous abandoned underground mines that are presently discharging acidic waters

(Stachler, 1997). Low pH, high specific conductance and high sulfate and iron concentrations result in environmental problems, killing aquatic life and vegetation, and contaminating both surface and subsurface water. The environmental problems in turn

cause local economic losses through loss of recreational uses of the streams and

decreased property values, in addition to such intangibles, as low morale among

residents. Pollution of a coal mine aquifer can also lead to the pollution of adjacent

aquifers and their loss as a water resource. One aspect of the serious nature of

groundwater pollution is that it is a long-term problem (Fetter, 1994).

Unregulated and abandoned underground coal mines impact the environment in two

specific ways, through the creation of acid mine drainage (AMD) and the development of

subsidence features due to collapse of overburden into mine voids (McCament, 2004).

Coal mining disturbs large volumes of geologic material and exposes them to air and

water, causing sulfide minerals (commonly associated with coal deposits) to be oxidized,

resulting in AMD (Skousen and Ziemkiewicz, 1996). Acid water causes environmental

problems because most organisms (other than certain algae and bacteria) are adapted to

waters buffered by the carbonate system and cannot tolerate strong acidity, and because many toxic trace elements are mobilized under strongly acidic conditions (Drever, 1988).

It is this highly toxic acid water that kills aquatic life and vegetation, and contaminates both surface and subsurface water. AMD is not only unsightly, but also extremely detrimental to the stream ecology. After a discharge has occurred, even if it possible to divert it, natural conditions can take a long time to return.

In addition to the AMD problem, areas with abandoned underground mines are at risk for subsidence. Subsidence generally occurs where overburden is thin, generally less than

30m (Light, 2001). Subsidence features affect both groundwater levels and surface structures, and are created when roof supports or pillars left during mining collapse through time, causing overburden fracturing and collapse. Collapse can extend up to 20 times the height of the original room and extend to the ground surface in shallow overburden (Bell and Genske, 2001). The collapse at the surface appears as a sinkhole and especially if located within a stream valley can capture large volumes of surface water into abandoned mine complexes, providing additional recharge for AMD production (McCament, 2004).

1.3 Underground Mine Systems – Literature Review

1.3.1 Hydrogeologic Behavior

The physical consequences of mining on the local geology can have a marked influence on the hydrogeology. Mining alters the hydrologic regime of the rock mass by changing the direction of flow, creating additional pathways through which water may flow, and generally allowing greater movement of water through the strata (Henton, 1979; Singh et al., 1986). The difficulties associated with predicting hydrogeologic behavior are compounded as early mine workings are often unknown and unmapped (Henton, 1979).

Random subsidence caused by roof collapse and the likelihood of unrecorded pillar and barrier robbing add to the difficulty. Aljoe and Hawkins (1994) have applied aquifer testing to underground mines that have experienced widespread roof collapse and conclude that both Darcian and pseudokarst type flow conditions can exist in different

places. The hydrogeology of such mines can be characterized as preferred flow pathways

within a general diffuse flow system.

One factor which is an indicator of the likely hydrogeologic conditions is the nature of

the roof of the mine (Sherwood and Younger, 1994). In seams with a competent roof

rock, collapse will have broken the rock, leaving voids and channels through which water

may flow freely. In seams with a plastic roof rock, it is possible that the roof rock may

settle and seal against seat earth clays giving low hydraulic conductivities (Sherwood,

1997). Study of mines in western Pennsylvania found velocities of 0.11 – 2.95 m/day

through mine voids, though higher velocities occurred where extensive workings were

close to the surface discharge point (Aljoe and Hawkins, 1994). Coal is itself a permeable

material and the nature of the hydrologic communication between worked and unworked

coal seams will vary according to local conditions. High inflows of water to longwall

mine faces have been reported by Singh et al. (1986) at rates of 8×10-2 - 2×10-3 m/sec.

A mine complex does not behave as a single hydrostatic unit; rather, it has several interconnected workings known as ‘ponds’ (Minett et al., 1987). Different ponds are not entirely isolated from each other. They exhibit close hydraulic connection, such that no barriers, or only barriers with high hydraulic connectivity, exist between individual mines in the complex (Donovan et al., 2000). Mine complexes with high-conductivity barriers have similar hydraulic heads while mine complexes with low conductivity barriers have variable hydraulic heads (Donovan et al., 2000; Aljoe and Hawkins, 1994).

For mines developed below regional drainage systems (i.e., at elevations below the

prevailing hydraulic head), the movement of significant volumes of water through a peripherial barrier from adjacent strata will cause an increase in groundwater availability resulting in what is commonly known as “groundwater rebound” (Altoona, 1998).

However, for mines developed below regional drainage systems but closely adjacent to other deep mine complexes with heads below regional levels, the movement of significant volumes of water through a peripheral barrier to the adjacent mine will cause an increase in groundwater availability resulting in either increased heads or discharge in the adjacent mines (Altoona, 1998).

The water table above underground mines is generally lowered by mining, most

drastically where subsidence has occurred and where overburden is thin. The subsidence cracks increase hydraulic conductivity and interconnections of water bearing rock units,

which in turn cause increased infiltration of precipitation and subsurface water, and higher base flows to the mines (Hobba, 1993). Fractures within the overburden caused by subsidence also act as reservoirs for groundwater storage, and conduits connecting water bearing rocks or a surface water body to the mine workings (Schmidt, 1985).

1.3.2 Factors Controlling Mine Drainage

As the level of groundwater in abandoned mines rises, it reaches a point where it can

discharge at the surface, which may be through old springs or the products of mining

such as shafts, adits, boreholes or points of weakness caused by subsidence (Schmidt,

1985). Although the quantity of water that is likely to discharge is ultimately controlled

by recharge, its estimation is often complicated by inadequate knowledge of local stream flow duration, porosity and permeability (Ngah et al., 1984). The factors on which the timing and quality of mine discharge depends are unique to the hydrology and geology of each area. They are therefore difficult to predict (Sherwood, 1997).

Recharge to mines can occur by diffuse flow through overlying strata, by fracture flow

(associated with mine collapse), or through surface capture (Moebs and Clar, 1990).

Pumping tests and pressure injection methods at numerous sites have shown that the mean diffuse flow through the overburden tends to decrease with depth, with the greatest flow occurring between a 50-ft depth and the surface (Moebs and Clar, 1990). The dominance of fracture flow mainly depends on the relative abundance of soft and plastic rocks (such as shale) and hard and brittle rocks (such as sandstone and limestone), with an increased probability of fracture flow where the latter are more abundant (Schmidt,

1985). When roof supports or coal pillars fail through time, overburden is fractured and may subside into the mine void. Surface sinkholes or fractures can capture streams and divert surface runoff into the mine as a new source of recharge (Hobba, 1993).

Abandoned mines that receive recharge from direct surface capture exhibit psudo-karst behavior, or relatively rapid response to recharge (Pigati, 1997; Stachler, 1997; Light,

2001). Mines without known direct surface capture are recharged mainly by slow groundwater seepage, and discharge responds much more slowly to precipitation events

(Shimala, 2000; López and Stoertz, 2001). Surface runoff and recharge are seasonal even

with uniform rainfall, because of soil moisture storage and evapotranspirative demands.

Most mines show seasonal variations in discharge, with the highest discharge occurring

in the spring months and the lowest during summer when evapotranspiration is highest.

During heavy rainfall, the mine inflow creates a hydraulic ram effect within the

submerged mine workings, very similar to observations in karst hydrology (Bögli, 1980).

The pressure wave from this hydraulic ram moves ahead of the mine inflow, causing a flood peak in the mine discharge before the actual inflow reaches the mine opening

(Pigati and López, 1997).

After meteoric water enters the open mine workings, the flow system is dominated by down-dip gravity drainage (Brant and Moulton, 1960). In the case of down-dip drift mines, water that enters the mine workings will freely gravity drain until the down-dip extent of the mine is reached. As the water cannot freely discharge, it will pool in the mine and may eventually flood the mine to the elevation of the mine opening, where it can then discharge into the local watershed (Brant and Moulton, 1960). It can take anytime between a few months to many years for groundwater level to reach a point where discharges arise at the surface.

1.3.3 Acid Mine Drainage

Acid Mine Drainage (AMD) is a common problem found in many watersheds with a mining history of coal or metalliferous deposits, and is typically associated with a

decrease in pH and increase in acidity, sulfate levels, and the solubility and

concentrations of dissolved metals (Brocksen et al., 1992). Acid generation from underground mines is a product of the biological and atmospheric oxidation of sulfide minerals (mainly pyrite) in contact with water. Thus the rate and/or extent of these particular processes are directly dependent on the quantity, types, texture, and morphologies of sulfide minerals present, the presence of an oxidant, the types of bacteria, and the amount of water or humidity surrounding these minerals (SRK Inc.,

1989).

AMD is caused by the following series of chemical reactions (Sobek et al., 1978):

2+ 2- + FeS2 (s) + 3.5 O2 (g) + H2O (l) = Fe (aq) + 2 SO4 (aq) + 2 H (aq) (Equation 1)

2+ + 3+ Fe (aq) + 0.25 O2 (g) + H (aq) = Fe + 0.5 H2O (Equation 2)

3+ + Fe (aq) + 3 H2O (l) = Fe(OH)3 (s) + 3H (aq) (Equation 3)

3+ 2+ 2- + FeS2 (s) + 14 Fe (aq) + 8 H2O (l) = 15 Fe (aq) + 2 SO4 (aq) + 16 H (aq) (Equation 4)

The oxidation of iron sulfide, usually pyrite or marcasite releases ferrous iron (Fe2+), sulfate, and acidity in the form of free hydrogen (Eq. 1). This ferrous iron then undergoes oxidation to become ferric iron (Fe3+) (Eq. 2). Ferrous iron oxidation occurs both abiotically (at pH above 5) and as a result of bacterial activity (at pH below 5). Iron

bacteria like Thiobacillus ferrooxidans and Ferrobacillus ferrooxidans (Wildeman et al.,

1993) act as a catalyst in the oxidation of sulfide minerals. Following this oxidation, the

ferric iron hydrolyzes to form ferric hydroxides (an orange deposit observed when AMD

pollutes surface waters, known as “Yellow Boy”) and acidity (Eq. 3). In low-oxygen

environments, ferric iron acts as an oxidizing agent, resulting in the generation of sulfate

and large quantities of acidity (Eq. 4).

Acid mine drainage impacts streams, rivers, lakes, and groundwater in several ways and

needs to be treated before entering into receiving waters. Through the oxidation of sulfide minerals contained in rocks associated with coal, sulfuric acid is produced and heavy metals are released into the drainage. Secondary reactions can dissolve other minerals found with coal deposits, including those containing manganese, aluminum, zinc, and trace elements (Williams et al., 1995). There are five main chemical effects of AMD: increase in acidity, lower pH, decreased effectiveness or obliteration of natural buffering system, increased soluble metal concentrations, and increased particulate metals (Gray,

1997). Acid mine drainage water can be detrimental to the number and diversity of organisms, increase the corrosiveness of the water, and limit the industrial, recreational, and aesthetic uses of the water.

1.4 Aims of Project

The proposed research focuses on an abandoned underground coal mine complex near

Corning, Ohio (Figs. 1.1 and 1.2). The research aims at developing new approaches to understand groundwater flow through abandoned mine workings by time series analysis, barometric pressure analysis and numerical modeling. These methods are efficient because they are based on mine-pool hydrographs, which are integrative in nature. The

methods make allowances for the accuracy of the available data. The methods presented

here provide insights into mine hydrology that are critical to in-situ remediation of the

acid mine drainage problems.

The Corning discharge (Fig. 1.3) is one of the two largest contributors of AMD in the

Sunday Creek Watershed, and is considered a high priority for remediation to restore the

Sunday Creek mainstream to warm-water habitat. The Corning discharge with its high flow, and high acidity and metal load, is not well suited for conventional active or passive treatment. Source control or in-situ remediation is preferred, but the sources and pathways of water are difficult to determine in this complex underground system. The difficulties associated with predicting hydrogeological behavior are also compounded by a long history of mining. Random roof collapse and the likelihood of unrecorded pillar robbing further complicate the behavior. However, source remediation of the discharge is possible only by an understanding of the water budget, flow paths, underground pool interconnections, and residence time of water in various parts of the mine (Hobba, 1993).

This project assembles information on these basic but elusive processes and properties

into a hydrologic model. The outcome of the experiment will be helpful in developing

remediation plans for Corning as well as other large acid mine drainage discharges in the region.

This thesis develops three approaches to understanding the mine hydrogeology:

1. Use of “Time Series Analysis” to conceptualize the underground mine aquifer:

The analysis of spatially distributed time series data such as discharge flow rates,

water levels and precipitation data provides valuable information on the

hydrodynamic characteristics of the mine aquifer such as spatial and temporal

variability of head and discharge, the delays between the input and output (i.e.

precipitation and discharge) and the specific storage of the mine aquifer.

2. Use of “Barometric Pressure and Tidal Loading Analysis” to parameterize the

underground mine aquifer: The quantitative study of water level fluctuations

inside a mine due to barometric and tidal loading yields important information

about mine properties such as barometric efficiency, specific storage, hydraulic

conductivity, porosity and matrix compressibility.

3. Use of “Flow Modeling Analysis” to simulate water flow through an underground

mine aquifer: The model reconstructs a known head distribution and identifies

which hydrogeologic parameters most influence the flow paths. The model is

calibrated using aquifer properties estimated from the barometric pressure and

tidal analysis, and is a valuable tool in making predictions about hydrogeologic

conditions.

Results of the modeling are compared with those produced by time series analysis and barometric pressure analysis. Comparison of different methods illustrates the strengths and weakness of the different methods.

1.5 Structure of Thesis

This thesis is divided into 6 chapters. Chapters 1 and 2 introduce the thesis and provide a brief description of the study area. Chapter 2 also describes previous work done in the study area and the data collection methodology, as distinct from the methodology for data analysis, described in subsequent chapters. The analysis methodology and results are put into three separate chapters according to the analysis type.

In Chapter 3, time series analysis is addressed, describing the methods and results. This analysis conceptualizes the underground mine system. Chapter 4 addresses the barometric and tidal analyses, describing the relevant theory, methodology and results.

This analysis parameterizes the flow system. In Chapter 5, mine water modeling is addressed, including development of both conceptual and numerical models. The conclusions of all three methods collectively are synthesized and summarized in Chapter

Chapter 2: Study Area

2.1 Sunday Creek Watershed

The Sunday Creek Watershed lies in the unglaciated hills of Southeastern Ohio (Fig. 1.1).

Sunday Creek is a 27 mile long stream with a watershed area of 139 mi2 (Sturgeon and

Associates, 1958). It originates in the southern portion of Perry County, and drains into the Hocking River in Athens County. The creek has two main branches: the East Branch

(15.5 miles) and the West Branch (14 miles), and a total of six other main tributaries. The steep-sided hills of the watershed are predominantly forested, whereas the flood plains are mainly used for agriculture.

The local geology is of the Pennsylvanian system with an age of 320-286 million years.

Economically recoverable beds of coal occur within the Allegheny Group (Fig. 2.1), consisting of the Kittanning and Freeport coal layers (Flint, 1951) and have been exploited throughout the watershed. Thirty-nine percent of the watershed contains underground and surface mines (SCWG, 2003). Although the specific date of the first coal mine in the watershed is not known, the first mines in Perry County started in 1816

(Crowell, 1995) and it is likely that mining in the watershed began during this time period. Although the level of mining activity has decreased in the watershed since the

1950’s, mining continues today with a currently operating coalmine just north of the village of Glouster (Fig. 1.1) (McCament, 2004).

Figure 2.1: Generalized stratigraphic section of the study area.

AMD impacts are widespread throughout the Sunday Creek Watershed and are the largest cause of water quality impairment (OEPA, 1991). Although AMD impacts are widespread in the watershed, two particular AMD contributors named the Corning and

Truetown discharge (Fig. 1.1) account for 90% of acid and metals loading into the mainstream. These discharges have a pronounced impact on the downstream section of

Sunday Creek. Both banks and substrate of Sunday Creek are coated with the bright orange iron-hydroxide precipitate regionally known as “yellow boy” (Light, 2001).

The Corning discharge (Fig. 1.3) is located in the upper portion of Sunday Creek in John

Altier Park in the village of Corning in southern Perry County, Ohio. The discharge was

caused by drilling of a borehole into the mine in the early 1970’s to release water from the mine to facilitate up-dip surface mining (McCament, 2004). Borehole depth is approximately 45 ft and was estimated to be four to six inches in diameter originally, but is currently three feet in diameter at the discharge elevation due to the erosive action of the discharging water.

The Corning discharge rate and chemical concentrations (Table 2.1) vary with recharge

(McCament, 2004). Discharge averages 2.61 cfs with an average pH of 5.3. The volume of AMD entering Sunday Creek from the Corning discharge is approximately 580 million gallons per year (SCWG, 2003). Loadings into Sunday Creek (Table 2.1) from this discharge accounts for nearly 100% of the total AMD load into the upper Sunday Creek

Flow Discharge pH Conductivity DO Net Iron Total Regime (cfs) (µS/cm) (mg/L) Acidity (kg/day) Metals (kg/day) (kg/day) High 5.45 5.55 1710 3.63 1304 489 521

Low 0.89 4.30 1220 1.86 145 89 94

Average 2.61 5.31 1415 2.53 593 257 284

Table 2.1: Flow and chemical characteristics of Corning discharge (McCament, 2004).

resulting in aquatic degradation of approximately 5 miles from the discharge point. With

the recent creation of the Sunday Creek Watershed Group, mining related problems

within the Sunday Creek Watershed are being researched for remediation and overall

improvement of water quality in the Sunday Creek Watershed.

2.2 Geology

The Sunday Creek Watershed lies in the Allegheny Plateau, characterized by dissected,

moderately high relief (300 feet). The pertinent geologic strata in the study area are the

Conemaugh and the Allegheny Series (Fig. 2.1) of the Pennsylvanian System (320 – 286 my) (Nadon et al., 1998). The bedrock consists primarily of sandstones, shale, coal and limestone, whereas the Sunday Creek valley floor is covered with Quaternary alluvium and Wisconsin Lake silts (Sturgeon and Associates, 1958). The bedrock dips approximately 30 feet per mile to the southeast (Sturgeon and Associates, 1958). The rock units were deposited during a period of fluctuating sea level on the shore of an epicontinental sea (Flint, 1951).

Coal underlying the Sunday Creek watershed is bituminous coal of the Allegheny and

Lower Conemaugh series. The Middle Kittanning (No. 6) coal was mined in the Corning mine complex and lies near the middle of the Allegheny series (Fig. 2.1) (Sturgeon and

Associates, 1958). The thickness of the seam ranges from 6 to 14 feet for the mines associated with the Corning discharge. The Middle Kittanning coal has a total sulfur

content of 1.4 – 1.7% with 89% - 100% of the total sulfur in the form of pyrite (FeS2)

(Botoman and Stith, 1981). The Upper Freeport (No. 7) coal seam exists in the region but

only on ridge tops at shallow depths. It was not mined extensively in the study area.

Stratigraphic units documented during the drilling and installations of monitoring wells in

2003 are depicted in Figure 2.2, with the wells extrapolated to a west-to-east line. Shift of the coal layer in Figure 2.2 resulted due to the extrapolation of different wells during plotting.

Figure 2.2: Overburden diagram of study area from borehole logs.

2.3 Hydrogeology

The hydrogeologic system of the Appalachian plateau is one of multiple perched water tables, overlying deeper regional aquifers. Regional groundwater flow continues in a longer, deeper path toward more deeply incised stream valleys. Underground mines act as

under-drains that greatly alter the natural flow systems that existed prior to mining

(Schmidt, 1985). In earlier studies (Flint, 1951) sandstones, coal seams, and some

siltstones are considered to be aquifers with shale, clay, and siltstones acting as aquitards.

Flow systems in Southeast Ohio tend to be localized due to the deeply dissected

topography that hydrologically segregates systems (ODNR, 1981). Recharge does not

come from distant areas, but occurs mainly through mine-associated pathways such as

poorly plugged shafts, boreholes or subsidence fractures. The presence of interconnected

fractures in overlying sandstone may provide pathways for water (Schmidt, 1985).

Precipitation supplies most of the water to the mines investigated, but some inter-basin

transfer of water may occur underground (Hobba, 1993). Average annual precipitation in the area is 40 in/yr, with 26 in/yr returning to the atmosphere as evapotranspiration before it can recharge the groundwater (Harstine, 1991).

Subsidence is likely to play a major hydrologic role in the Corning mine complex because it has been shown that older room and pillar mines with less than 300 feet of overburden undergo erratic and unpredictable subsidence (Moebs, 1982). Three subsidence capture features are present on unnamed tributaries, one in the Congo Run watershed to the west and two north of village of Rendville in the Sunday Creek mainstream watershed (Fig. 1.2). These subsidence holes occur in the streambed and essentially capture the entire drainage of their respective watersheds. The drainage area

for the Congo capture is 72 acres, and the Sunday Creek mainstream capture is 240 acres

(SCWG, 2003).

2.4 Mining History

In Perry County, coal mining began around 1816 and coal production saw steady growth

through the 1800’s and early 1900’s. The coal production reached a maximum during

World War I, after which it slowly declined. In the Sunday Creek Watershed, coal was mined through underground room and pillar technique. The amount of coal extracted varies with the type of room and pillar mining and ranges from 40 to 60% coal removal

(Illinois DNR, 2003).

The Corning Mine Complex, which exploited the Middle Kittanning (No. 6) coal seam, was mined starting in the late 1800’s. The mine complex encompasses approximately six square miles and consists of ten different coalmines (Fig. 1.2). The mines are cataloged by the state as follows: Py (Perry)-302, Py-261, Py-149, Py-66, Py-137, Py-132, Py-180,

Py-102 and portions of Py-146 and Py-262 (ODNR, 1981). The oldest mine in the

complex is Py-66, which closed in 1903. The most recent mine to close was Py-146,

which closed in 1954. The Corning discharge is located in a main tunnel connecting Py-

302 to the west and Py-102 to east of Sunday Creek (McCament, 2004). Py-302 was last operational in 1927.

2.5 Previous Investigations

AMD from both underground sources and surface spoil is well studied in the coal producing regions of Appalachia. However, every AMD site is unique with respect to the local geologic and hydrologic conditions, and the remediation of each site will depend upon those local conditions. Therefore, an understanding of those conditions and their relative importance is critical. A number of researchers have studied flow of water through mines, and their findings are important in understanding flow in the Corning mine. Several recent studies of mine discharges in the Monday Creek, Racoon Creek, and

Sunday Creek Watersheds in Southeastern Ohio have provided insights into the nature of

AMD characteristics from abandoned underground mines (Pigati, 1997; Stachler, 1997;

Shimala, 2000; Light, 2001; López and Stoertz, 2001; McCament, 2004).

Light (2001) and McCament (2004) studied the Truetown and Corning discharges of

Sunday Creek Watershed. Both studies focused mainly on the geochemical aspects of

AMD producing mine complexes, but neither attempted to fully characterize the mine- complex hydrogeology. All the studied abandoned mines in the region have shown seasonal variations with respect to discharge, with the highest discharge usually occurring in the spring months and the lowest occurring during summer when evapotranspiration is highest. Concentrations of acidity and metals also vary and tend to be highest in the spring when stored reactions products are “flushed” from the mine with increased recharge from precipitation (López and Stoertz, 2001).

McCament (2004) characterized the Corning Mine Complex and his findings are of particular interest for this research. The major findings of his research are summarized below:

a. Subsidence stream captures are a significant source of recharge to the Corning

mine complex. For a particular precipitation event stream captures contribute 50%

of the recharge to the mine complex; however a yearly water budget indicates that

recharge from subsidence flow is 12% of the total flow at the Corning discharge.

b. The mine is approximately 75% flooded, with the average mine-pool elevation

(hydraulic head) approximately 4 to 5 feet above the discharge elevation.

c. The degree of confinement for the mine aquifer is higher toward the discharge

point (southeast) and lowers toward the beach zone. This is because the coal seam

dips toward the discharge point.

d. The Corning discharge has variable flow from <1 to >5 cfs (Table 2.1), and high

acidity and iron loads averaging 593 kg/day acidity and 257 kg/day iron.

e. Water-quality data from monitoring wells is consistent with the hydraulic data

showing a chemically distinct “eastern mine complex”. Otherwise, the main pool

or “western mine complex” is chemically homogeneous. Consequently, the

eastern complex contributes less than 10% of the flow to the discharge but nearly

50% of the AMD metals.

2.6 Data Collection

Five monitoring wells were installed in the Corning mine complex (Fig. 1.2) for a related project (McCament, 2004) to study the mine pool hydrogeology. The Ohio Department of

Natural Resources, Division of Mineral Resources Management, drilled and installed the monitoring wells. Six-inch hollow-stem augers were used to drill the bedrock. A 3-inch rock bit with an air-rotary drill was used inside the hollow-stem augers for drilling to mine depth. Monitoring wells consisting of 2-inch PVC pipe, with a 10-foot screen of slotted PVC, were installed into the mine void (Fig. 2.3).

Figure 2.3: Borehole installation log.

Well 1 is the only well where drilling did not encounter a solid mine ceiling over a void,and the well is believed to have encountered a collapsed room or fractured pillar.

Monitoring well elevation was determined to a precision of 1.5 feet using a Global

Positioning System. Table 2.2 has the detail specification of all the wells.

Well Elevation of Depth of Well Mean Water Mean Static Number Well Site (meter) (meter) Depth (from Water Level top of well (meter) cover) (meter) Well 1 229.96 18.26 8.23 221.73

Well 2 233.10 21.34 11.39 221.71

Well 3 233.04 25.50 11.21 221.83

Well 4 261.93 44.51 40.12 221.81

Well 5 268.15 51.46 46.37 221.78

Table 2.2: Specification of the Monitoring Wells.

The wells were developed by pumping them. In-Situ Troll 4000 and 8000 mini-Troll probes were installed in monitoring wells and at the Corning discharge. These probes were programmed to monitor water-level elevations, temperature and pH in each well and the Corning discharge for a time period between 6/2/2003 and 10/24/2003. The data were recorded every six hours. Data were subsequently transferred to a Notebook computer for processing. Manual water-level measurements were also obtained from the

wells to verify probe readings. The manual measurements were made using an electronic

water-level indicator and then converted to water-level elevations using the surveyed

elevation of the measuring points.

Flow at the Corning discharge could not be directly measured at the discharge point

because of its location in the bank of Sunday Creek. Discharge was measured 200 ft

upstream and 100 ft downstream of the Corning site, by calculating the Corning

discharge as the difference between downstream and upstream flows. No other surface

inflows to the stream between the upstream and downstream sites interfere with the

calculations. Velocity was measured with two types of current meters, the Pygmy flow

meter and a Swoffer 2100 electronic flow meter. Using statistical regression, six-hourly

daily flow measurements were calculated from the measured discharge rates.

Precipitation and barometric pressure data were obtained from the Army Corps of

Engineers at Tom Jenkins Dam, located approximately 3 miles south of the village of

Corning. All the data used for the analyses (Table 2.3) were obtained on a six-hourly basis.

The analyses undertaken in the present study are aimed at fully characterizing the

hydrogeology of the Corning mine pool, using information contained in the hydrographs

collected by McCament (2004). Using time series analysis and theory of barometric

pressure loading and earth-tide strain developed for confined aquifers, information about

the hydrologic system of the mine aquifer can be extracted from the hydrographs.

Table 2.3: Data used for the analyses

Types of Data Locations Frequency Period

Precipitation All 6 hours 6/2/03 to 10/24/03 Atmospheric Pressure All 6 hours 6/2/03 to 10/24/03 Corning Discharge 6 hours 6/2/03 to 10/24/03 Discharge Well 1 6 hours 6/2/03 to 10/24/03

Well 2 6 hours 6/2/03 to 9/7/03 Head Well 3 6 hours 9/17/03 to 10/24/03

Well 4 6 hours 6/2/03 to 10/24/03

Well 5 6 hours 6/2/03 to 10/24/03

Table 2.3: Data used for the analyses

This information is then used to conceptualize a numerical model. Because the analytical methods are distinctly different, the next three chapters are devoted to each method, and contained within each chapter is the literature review, methods, results and discussion of the approach. The findings are then integrated in a final Conclusions chapter.

Chapter 3: Mine System Conceptualization via Time Series Analysis

3.1 Introduction

The regional study of an underground mine system is challenging primarily because of its highly heterogeneous characteristics. Most mines are expected to have both transmissive conduits and discontinuities together with blocks of low transmissivity, resulting in both diffuse and conduit types of flow. However, it is difficult to quantify the relative importance of these different flows, though such information would provide valuable insight into the system. Because of heterogeneity, quantitative data obtained from discrete points in the system, either by pumping or by using dye tracers, can rarely be extrapolated to estimate representative value for the system as a whole (Padilla et al.,

1994). In contrast, analysis of water-level and mine-discharge variations and related hydrologic controls (e.g., precipitation) can reflect the overall response of the aquifer and help in evaluating its storage, drainage potential, hydrogeologic boundaries etc., without imposing any external stresses on the system (Samani, 2001). Studies of water-level and mine discharge responses to precipitation inputs can provide a basic understanding of the mine aquifer preliminary to project specific investigations. In hydrology, this screening approach can be performed using time series analysis. A time series is a set of observations generated sequentially in time (Box and Jenkins, 1976), with pertinent examples being discharge and hydraulic head hydrographs.

Time series analysis, as developed principally by Jenkins and Watts (1968), and Box and

Jenkins (1976) have been applied in hydrology by Yevjevich (1972), Spolia et al. (1980) and others. These works have been oriented essentially towards forecasting, completion

of data and estimation of parameters of stochastic models. Methods for the description

and the functioning of karstic aquifers appear in Padilla and Pulido-Bosch (1995).

A conventional study of a time series by correlation and spectral methods of analysis uses

both univariate and cross analysis. Univariate analysis characterizes the individual

structure of the time series, the functions applied being autocorrelation (in the time

domain) and spectral density (in the frequency domain). Cross analysis characterizes the

transformation of an input function into an output function. The cross-correlation

function is analyzed in the time domain, and the cross amplitude, phase, coherence and

gain functions are analyzed in the frequency domain. The interpretation of the correlation

and spectral-analysis function is not difficult when the aquifer systems studied have

simple internal structures, such as homogeneous systems of continuous porous media. In

these cases, it is sufficient to follow the models and ranges of standardized validity found in the literature. However, the interpretation is difficult when dealing with heterogeneous and discontinuous aquifers.

Time series are especially useful for studying the underground systems because they use easily available data which are relatively inexpensive to collect (Padilla et al., 1994;

Clarke, 1996). Correlation and spectral analysis are forms of time series analysis that are usually easy to implement and often provide good insight into an aquifer. Correlation and spectral analysis are based on a system approach as they relate inputs to outputs through the use of statistical functions. The aquifer is considered a filter that transforms, retains, or eliminates the input signal in the creation of an output signal. The nature and degree of

transformation of the input signal therefore provides valuable information on the nature of flow in the system.

Objective

The objective of this study was to test whether correlation and spectral analyses are valuable tools when used with spatially distributed time series data of several types characterizing the hydrogeology of an underground mine system. The analysis of spatially distributed time series such as mine discharge hydrographs, piezometric levels and precipitation data provides valuable information on the hydrodynamic characteristics of the aquifer, including aquifer boundaries, spatial variability, transmissive properties

(e.g., the time lag and attenuation between input and output) and the specific storage and porosity of the aquifer.

The available data were the results of three months of monitoring water level and discharge in five monitoring wells and at the Corning discharge. For the study, the following three sets of data were analyzed: (a) Daily precipitation data monitored at the nearest weather station at Tom Jenkins Dam – resample into 6-hourly data by dividing daily precipitation by 4 ; (b) 6-hourly head data from five monitoring wells; (c) Corning discharge data.

3.2 Methods

The statistical methods for analyzing time series are well established. This study focuses on the methods of analyzing a single time series (univariate) as well as double time series

(bivariate), and describes their applicability to the present study. The expressions used to obtain the coefficients for correlation and cross-spectral analysis functions follow Jenkins and Watts (1968), Box and Jenkins (1976), Chow (1978) and Chatfield (1982).

3.2.1 Simple Correlation and Spectral Analysis

3.2.1.1 Simple Correlation

Simple correlation analysis quantifies the linear dependency of successive values over a time period. The correlogram is a graphical representation of correlation coefficient r(k)

as a function of lag k, where the values of r(k) are plotted as ordinates against their

respective values of k. If an event, such as seasonal recharge, has a long-term influence on the time series, the slope of the autocorrelation function, r(k), decreases slowly. The correlation coefficients of lag k are calculated by (Chatfield, 1982):

C (k ) r(k) = (3.1) C (0)

1 n − k C(k) = ∑ ( xt − x )( xt + k − x) , (3.2) n t =1 where C(0) is the variance of the series, k is the time lag (k = 0 to m), n is the length of the time series, x is a single event, x is the mean of the events and m is the cutting point.

The cutting point determines the interval in which the analysis is carried out and is usually chosen to circumscribe a given behavior like annual or long-term effects.

3.2.1.2 Simple Spectral Analyses

Simple spectral analysis is complementary to correlation analysis. The spectral density

function corresponds to a change from a time mode to a frequency mode through a

Fourier transformation of the auto-correlation function. The interpretation of the spectral

density function, S(f), through the identification of the different peaks representing

periodical phenomena, leads to the characterization of the system. In the case of an

infinite series, Jenkins and Watts (1968) show that the spectral density is:

⎡ m ⎤ S(f) = 2 ⎢1+ 2∑ D(k)r(k)cos(2πfk)⎥ , (3.3) ⎣ k =1 ⎦

⎛ k ⎞ ⎜1+ cosπ ⎟ m D(k) = ⎝ ⎠ , (3.4) 2

Where f = j/2m, j = 1 to m, f is the frequency in cycles per day and D(k) ensures that the

S(f) estimated values are not biased. If the time series, such as precipitation or discharge, contains distinct periodic terms, the spectral density of these terms will appear as high and sharp peaks in the estimated spectrum (Samani, 2001).

3.2.1.3 Regulation Time

The spectral density function also determines the regulation time, Treg:

S( f = 0) Treg = (3.5) 2

The regulation time is similar to the passing band in a signal treatment. It defines the duration of the influence of the input signal, such as precipitation, and it gives an indication of the length of the impulse response of the system.

3.2.2 Cross-Correlation and Cross-Spectral Analyses

The methods for examining the relationship between two time series are cross correlation

and cross spectrum functions in time and frequency domain.

3.2.2.1 Cross-Correlation

The cross-correlation analysis is used to establish a link between the input time series xt

(such as precipitation) and the output time series yt (such as discharge). The cross- correlation function obtained with two series is not symmetrical [rxy (k) ≠ ryx (k)]. If rxy

(k) > 0 for k > 0, the input influences the output, while if rxy (k) > 0 for k < 0, the output influences the input. A perfectly symmetrical cross-correlation function centered at k = 0 indicates that both the input and the output signals react at the same time to a third independent signal, such as temperature. The plot of rxy(k) against k is the cross correlogram. The maximum values of rxy(k) observed at a lag d indicates that one series is related to the other when delayed by time d. Usually, the shorter the delay, the faster the transfer. The cross-correlation function rxy(k) is defined as (Chatfield, 1982):

C xy (k) rxy(k) = (3.6) σ xσ y

1 n−k Cxy(k) = ∑(xt − x)(yt+k − y) (3.7) n t=1

Where Cxy(k) is the cross-correlogram, and σx and σy are the standard deviations of the time series.

3.2.2.2 Cross-Spectral Analyses

The cross-spectral density function, Sxy(f), corresponds to the Fourier transformation of

the cross-correlation function. It is expressed as a function of the cospectrum, hxy(f) and the quadrature spectrum, λxy(f). The asymmetry of the cross-correlation function makes it necessary to express the spectral-density function with a complex number:

Sxy(f) = hxy(f) - i λxy(f), (3.8)

⎡ m ⎤ hxy(f) = 2 ⎢rxy (0) + ∑(rxy (k) + ryx (k))D(k)cos(2πfk)⎥ , (3.9) ⎣ 1 ⎦

⎡ m ⎤ λxy(f) = 2 ⎢rxy (0) + ∑(rxy (k) + ryx (k))D(k)sin(2πfk)⎥ (3.10) ⎣ 1 ⎦

Various functions may be derived from the cross spectrum among which the amplitude, phase, coherence and gain functions were used for this study.

3.2.2.3 Amplitude and Phase

In polar coordinates, the cross-spectrum can also be expressed as a function of the

amplitude S xy ( f ) and phase θxy(f):

- iθxy(f) Sxy(f) = S xy ( f ) e (3.11)

From the standpoint of its application to hydrologic series in general and this study in

particular, the cross-amplitude function, S xy ( f ) , can be associated with the duration of the impulse response function, and indicates the filtering of the periodic components of the input signal, precipitation or recharge for example. So, it identifies the way in which

the input signal has been modified by the system to provide an output signal, hydraulic

head or discharge for example. This function characterizes the modulating effect of the

aquifer in the short, medium and long term, and with respect to the purpose of this study,

contains information about the aquifer properties. In the frequency domain, it represents

the input-output covariance:

2 2 S xy ( f ) = hxy ( f ) + λxy ( f ) (3.12)

The phase function, θxy(f), shows the delay between the precipitation and discharge for different frequencies. It ranges between –π/2 and +π/2. The plot of phase function against the frequency is the phase diagram. On the phase diagram, the frequency at which coherence maximum gives the phase between the two series. In turn, division of phase by angular frequency gives the lag (dephasing) between the two series. The phase function

θxy(f) is defined as (Box and Jenkins, 1976):

⎡λxy ( f )⎤ θxy(f) = arctan ⎢ ⎥ (3.13) ⎣⎢ hxy ( f ) ⎦⎥

In the time mode, the dephasing τ is:

θ ( f ) τ = xy (3.14) 2πf

3.2.2.4 Coherence and Gain Functions

The coherence function shows whether the input series contains the same type of periodic

components in the output series, and thereby indicates the correlation between the

periodic variables. It is expressed as:

S xy ( f ) COxy(f) = (3.15) S x ( f )S y ( f )

The plot of coherence function against frequency is the coherency diagram. The values of

COxy vary between unity and zero, where unity indicates that the two series are fully dependent and zero shows two independent series (Chow, 1978).

The gain function expresses an amplification (>1) or an attenuation (<1) of the output signal in comparison with the input signal. In a karstic environment, this phenomenon can be related to the storage of water during the high rainfall period and the release of water during the dry period:

S xy ( f ) gxy(f) = (3.16) S x ( f )

A FORTRAN program was written, with the help of Dr. Dina Lòpez (Associate

Professor, Ohio University), to calculate these statistical parameters. The program reads transient data recorded at regular time intervals, finds the auto-correlation function, the spectral density function, the regulation time, the cross-correlation and cross-spectral density functions (between pairs of variables), the amplitude function of the cross- spectral density, the coherence function, and the gain function.

3.3 Results and Discussion

3.3.1 Relationship between Precipitation and Mine Discharge

The relationship between precipitation (the system input) and mine discharge (the system output) (Fig. 3.1) is of great interest because precipitation is readily measured and mine

discharge is the variable of interest for treatment and stream vulnerability assessment. As

this section will show, the two time series contain much information about the mine

system itself, critical for in-situ remediation.

4 Discharge 3.5 3 ta 2.5 Da d

e 2 d n

e 1.5 tr Precipitation

De 1 0.5 0 0 102030405060708090 Time, days

Figure 3.1: Variation of discharge with precipitation. Data have a sampling interval of 0.25 days.

The autocorrelogram of the precipitation data (Fig 3.2) shows oscillation with rapid damping as time lag increases and little autocorrelation. This pattern is expected because precipitation in Ohio tends to be evenly distributed throughout the year; it does not show

50 the strong seasonality evident in the region. The autocorrelogram of the mine discharge

(Fig. 3.2) shows oscillation and a slow decay. Generally, if an event has a long-term influence on the system, the slope of the autocorrelation function decreases slowly, revealing the presence of periodic components.

1.2 1 0.8 Discharge k) ( r

, 0.6 n o i 0.4 12 days discharge cycle at el

r 0.2 Precipitation r o

C 0 -0.2 0 5 10 15 20 25 30 -0.4 Lag, days

Figure 3.2: Autocorrelation functions of precipitation and discharge time series.

The spectrum of the precipitation time series (Fig. 3.3) exhibits its highest peak at 0.017 cycles per day, equivalent to a 58-day periodicity. However, the cutting point for analysis is 30 days or 0.03 cycles per day, so this conclusion is not really valid. A longer period of data should be analyzed to get the actual periodicity in the time series data.

58-days period 14 12 (f)

S 10 y, t i s 8 n e

D 6 al

tr 4 ec p

S 2 0 0.017 0 0.1 0.2 0.3 0.4 0.5 Frequency, cycles/day

Figure 3.3: Spectral density function of precipitation. The sampling frequency is 4 cycles/day.

A simple correlation analysis of both precipitation and discharge time series quantifies the linear dependency of successive values over a time period. Their relationship shows that the mine has an intermediate to small storage capacity; recharge water is discharged within a relatively short period of time compared to porous-media systems. This conclusion, based on the autocorrelation of discharge, is also supported by a small regulation time (Treg = 9.4 days) for the mine aquifer. The Treg value of 14, 22.5 and 50 days are considered as having considerable storage capacity for different karst aquifers

(Obarti et al., 1988). Also the discharge has an important cycle shown by negative r(k) after 12 days (Fig. 3.2). This pattern can be explained by the presence of any recharge cycle; discharge of stored water from earlier recharge may still be declining even as the mine receives new water. The storage capacity of an aquifer is also reflected in the

discharge autocorrelagram. Water is stored in an aquifer during the recharge period and is gradually released during the subsequent dry period. This behavior would not be

observed in a highly mined aquifer that would simply not store as much water.

The cross-correlation function (CCF) of discharge with precipitation (Fig. 3.4) shows that the response time of the mine is very short, around 9 days. This response time is consistent with the regulation time (Treg) of 9.4 days calculated from the spectral density function. The response time between precipitation and discharge is generally 3 days, as evidenced by sharp peak and is the delay of the system. The delay is defined as the time lag between k = 0 and the maximum cross-correlation [rxy(k)]. Usually the shorter the delay, the faster is the transfer. The maximum rxy(k) is low (i.e. 0.3) for the discharge, indicating that the input signal from precipitation is significantly attenuated during its passage through the system. This finding is expected because the system includes the unsaturated zone and evapotranspirative processes.

The negative part of the abscissa axis has a different pattern than the positive side; the function is asymmetric. A perfectly symmetrical cross-correlation function centers at k=0

(i.e. at lag = 0). The asymmetric pattern of Fig. 3.4 may result from control exercised by the system on the input function. Also, a series of peaks in the CCF are observed after the first peak, which may be caused by the presence of different flow components within the same system. The geometry of the mine suggests that several flowpaths of different duration are likely.

0.4

0.3 3 days delay between precipitation & discharge n o

i 0.2 t a l e r r 0.1 Total response time 9 days Co s s

o 0

Cr -30-25-20-15-10-5 0 5 1015202530 -0.1

-0.2 Lag, days

Figure 3.4: Cross correlation of precipitation and discharge.

The response of mine hydraulic heads and mine discharge to precipitation can also indicate of the degree of roof collapse, the intensity of room-and-pillar mining, the flow regime, and the drainage and storage potential of the system. In a karst system, a short time response (less than 10 to 15 days), or a long time response (more than 2 months), corresponds to high and low degree of karstification respectively (Obarti et al., 1988). A high degree of karstification reflects development of speleological features like sinkholes, interconnected caves, and solution channels resulting in a high drainage potential, high transmissivity, low storage capacity and turbulent flow within the conduits (Padilla et al.,

1994).

As a karst system is an appropriate analog for an underground room-and-pillar mine system, the response time of 9 days can be considered analogous to an intermediate

karstified system. In this scenario, the system is expected to be dominated by large

scarcely permeable blocks (i.e. mine pillars or areas of roof collapse) that constitute the

matrix or body of the mine system with highly permeable features (i.e. mine tunnels).

The flow is predominately through wide conduits with scarcely any storage in the pores

of the matrix, resulting in a low storage potential for the mine system.

Delays between inputs and outputs are useful in the study of an underground mine aquifer

system because they give an estimate of variation of the pressure pulse transfer times and

of the particle travel times through the system. The delays constitute important

information for the management of the mine discharge, for vulnerability studies and

potentially for in-situ remediation. A long delay between input and output can be

attributed to the travel of the pressure pulse through the unsaturated zone, while a short

delay comes from the transmission of the pressure pulse through the saturated zone,

acting as a water hammer (Brown, 1973). Longer delays therefore correspond to the

precipitation input that travels mostly through the unsaturated zone, whereas the shorter

delays correspond to precipitation input that occurs through subsidence capture of runoff directly to the saturated zone. However, it is clear that this is a simplified representation of the aquifer and in reality, the time required for a pressure pulse to reach the resurgence is also influenced by both transmissivity and storativity.

The cross amplitude function (CAF) relating precipitation and discharge (Fig. 3.5) shows

a clear decrease in the middle and high frequencies in favor of the low ones. The system

notably filters the input signal (precipitation) at higher frequencies (above 0.2 or periods

of less than 5 days) and there is a significant increase in the signal at low frequencies.

Many times the function approaches zero, for frequencies greater than 0.2, but never

vanishes. When the amplitude function of a time series data becomes noticeable or

positive at higher frequencies, it indicates the presence of a quickflow component in the

system; whereas a zero amplitude function or one that approaches zero indicates a

baseflow component. This phenomenon indicates that there is a major quickflow

component inside the mine complex, although there is also a noticeable baseflow

component.

20 ude t i 15 pl m A

s 10 s o r Quickflow Baseflow C 5

0 0.00.10.20.30.40.5 Frequency, cycles/day

Figure 3.5: Cross amplitude function for precipitation and discharge. The sampling frequency is 4 cycles/day.

The strong filtering of the input signal can also be seen from the gain function (GAF)

(Fig. 3.6). The frequency corresponding to a gain of 1 represents the duration of baseflow

in the aquifer, and the frequency for which the gain begins to take values of less than 0.4

corresponds to duration of the quickflow (Padilla et al., 1994). Values between 1 and 0.4

are considered to be intermediate flow, i.e. having characteristics between baseflow and

quickflow.

2 1.8 1.6 1.4 1.2 Amplification Baseflow n i 1 Ga 0.8 Attenuation Intermediate 0.6 Flow 0.4 0.2 Quickflow 0 0.0 0.1 0.2 0.3 0.4 0.5 Frequency, cycles/day

Figure 3.6: Gain function for discharge relative to precipitation. The sampling frequency is 4 cycles/day.

The GAF shows strong attenuation for the input series at frequencies above 0.02 (period of 50 days). Most of the time the GAF is below 0.4, but there is a sharp increase in the input signal between frequency 0.2 and 0.3, indicating the presence of some intermediate flow. In a karst aquifer, the level of amplification of the input signal is related to the

delay. The signal is attenuated when the discharged water comes with a lower delay from

the release of storage water. The observed attenuation is another indication of the aquifer’s small to moderate storage capacity. The low storage of the system could be explained by the presence of many large tunnels which directly contribute to the mine discharge.

The coherence function (COF) (Fig. 3.7) shows less consistency between the input

(precipitation) and output (discharge) signals at frequencies between 0.1 to 0.2 and 0.3 to

0.5. Variations in the precipitation in these two frequency ranges continue to have a

corresponding response in the discharge, although it is very filtered and attenuated. Also,

the coherence observed at low frequencies is higher than that at higher frequencies. The

presence of different coherence at different frequencies is consistent with the existence of

both quickflow (high coherence) and baseflow (lower coherence) components in the

system. The high coherence at low frequencies is expected, as seasonally high rainfall is

likely to be associated with seasonally high discharge. The highest coherence function

observed between precipitation and discharge is 0.582.

The phase function (Fig. 3.8) shows the delay between the precipitation and discharge for

different frequencies. The frequency at which coherence is maximum (0.021) gives the

phase between the two series, ~0.512 radians in this case. In turn, the mean delay of the

mine system calculated by the dephasing equation (Eq. 3.14) is 3.8 days. This time lag is

comparable with that found from the cross-correlogram (Fig. 3.4).

0.7 0.582 0.6

0.5 e

nc 0.4 e r High Coherence

he 0.3 o

C 0.2

0.1 Low Coherence

0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Frequency, cycles/day

Figure 3.7: Coherence between precipitation and discharge signals. The sampling frequency is 4 cycles/day.

2 1.5

) 1 n

a 0.512 i 0.5 ad

R 0 ( -0.5 ase h

P -1 -1.5 -2 0 0.021 0.05 0.1 0.15 0.2 0.25 0.3 Frequency, cycles/day

Figure 3.8: Delay or phase between precipitation and discharge signals. The sampling frequency is 4 cycles day.

3.3.2 Spatial Variability of Mine Aquifer Properties

Physically, piezometric heads are neither inputs nor outputs of the mine system.

However, as they are influenced by both precipitation and mine discharge, they can serve

as indicators of the state of the system at different locations in the aquifer (Padilla and

Pulido-Bosch, 1995). They provide information on the dynamics of the water level changes in the aquifer and on the origin and dynamics of recharge. The head in any particular well can be influenced by the structure of the mine including the continuity of open tunnels, the integrity of pillars, existence of collapsed areas, and subsidence that captures surface water.

Cross-correlation and cross-spectral analysis were performed using precipitation as the

input series and hydraulic head as the output series. The coherence in an underground

mine system indicates qualitatively the importance of subsidence and deformation over

the aquifer. The study of this function at different locations provides insight into the

spatial variability of the mine and by extension, its hydrodynamic characteristics. The average COF varies spatially (Table 3.1) and is the lowest for Well 2 (0.22). The area around these wells reacts differently to distributed recharge through precipitation than the rest of the mine. Wells 4 and 5 have a particularly low COF (0.34 and 0.32) that could be related to the unconfined behavior in the beach area. The part of the mine around Wells 1 and 3 (COF of 0.51 and 0.44) transmits the precipitation into signals more linearly to the underground mine, and may be a result of more confinement in the fully flooded part of the mine.

Well No. Average Maximum Frequency Phase Lag Coherency Coherency (cycles/day) (radians) (days)

Well 1 0.51 0.877 0.05 1.036 3.3

Well 2 0.22 0.769 0.046 0.896 3.1

Well 3 0.44 0.843 0.05 1.086 3.4

Well 4 0.34 0.788 0.046 1.242 4.3

Well 5 0.32 0.773 0.042 1.108 4.2

Discharge ---- 0.582 0.021 0.512 3.8

Table 3.1: Time-lag response of well heads and discharge to precipitation based on cross-spectrum analysis.

The interconnectedness of the mine pool can also be assessed by evaluating the similarities or differences among the head responses to precipitation of different monitoring wells. The distinct differences in the coherence function among monitoring wells indicates that the mine pool is not functionally a single reservoir, but has a series of interconnected areas separated by some degree of hydraulic resistance. Analysis of monitoring wells hydrographs confirms this view (Fig. 3.9). The Well 1 hydrograph shows a strong distinct response to each precipitation event, whereas hydrographs for

Well 4 and Well 5 show weaker responses. The magnitude of hydraulic head increase is not identical in all monitoring wells. The variation in overall head increase and lag time is an indication that the mine has resistance to flow and pressure propagation within the pool. The magnitude of response and lag time before a response in hydraulic head is a direct function of the inter-connectedness of the mine pool (McCament, 2004).

15.5 2 Precip daily 15 well 1 1.8 well 2

1.6 ) 14.5 well 4 h c 5) well 5

1.4 n i

- 14 (

1 1.2 n l l

13.5 o i e

1 t w a (

13 it 0.8 ip 20 12.5 c e H

0.6 r t P F 12 0.4 11.5 0.2 11 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 / / / / / / / / / / / / / / / / / / / / 2 7 2 7 2 7 2 7 2 7 2 7 1 6 1 6 1 6 1 5 0 0 1 1 2 2 0 0 1 1 2 2 0 0 1 1 2 2 3 0 06/ 06/ 06/ 06/ 06/ 06/ 07/ 07/ 07/ 07/ 07/ 07/ 08/ 08/ 08/ 08/ 08/ 08/ 08/ 09/

Figure 3.9: Effects of precipitation on water levels in monitoring wells.

The lag time between precipitation and head increase in different wells is probably due to different path lengths of percolating water from capture points to the mine. The time lag for Well 2 is lowest, whereas it is highest for Well 4 (Table 3.1). According to Brown

(1973), the longer delays correspond to the precipitation input which travels mostly through the unsaturated zone, whereas the shorter delays correspond to flows that occur through the saturated zone. Accordingly, recharge near Wells 4 and 5 may traverse the thicker unsaturated zone, resulting in a longer delay.

Given that recharge is heterogeneous, because of varying thickness of the unsaturated zone and the existence of focused recharge via subsidence capture, the diffused portions

62 of the mine should show different response to recharge, unless they are connected to a single pool that integrates all the inputs. The fact that responses differ confirms the existence of separate, though interconnected, pools.

Chapter 4: Mine System Parameterization via Barometric Analysis

4.1 Introduction

Underground mines are emerging as a new category of aquifers that are attracting attention in the hydrogeology community. Many of these mines discharge acidic, metal rich waters, contaminating both surface and subsurface waters. A long history of mining,

random roof collapse and unrecorded pillar robbing complicate the hydrogeological

behavior of these underground systems. However, understanding the flow-system

dynamics of mine complexes, made possible with a hydrologic model supported by

appropriate data, can provide insight essential to developing remediation plans for deep

mine systems.

Conceptually, an aquifer is a three-phase system consisting of rock/sediment, water, and

air. The aquifer is relatively permeable and is surrounded by less permeable materials

(perching layers, confining layers or overburden). The mine system similarly consists of

three phases. The aquifer in this case is the coal layer, which is partially removed. The

typical extent of coal removal in a room-and-pillar mine ranges from 40 to 60% (Illinois

DNR, 2003), leaving another 60 to 40% coal inside the mine. Overburden subsidence,

pillar collapse, and storage of gob (waste) in rooms are common. In the mine aquifer, both Darcian and pseudokarst type flow conditions can exist in different places. The overall system is characterized as preferred flow pathways within a general diffuse flow system, so the mine is likely to experience both open-channel flow and porous-medium flow.

Given that a mine is partly open channel and partly coal and roof rubble or intact coal, the

question arises as to the validity of porous-medium treatment of a mine. The approach

will be valid if velocity potential is negligible (DeWiest, 1969; Freeze and Cherry, 1979), or more specifically when the Reynolds number is less than 10 (Fetter, 1994). It is expected that parts of any particular mine will exhibit sufficiently high velocities to invalidate Darcy’s law or the use of porous-media theory, at-least in those parts. In cases where a mine can be treated as a porous medium, then several powerful techniques are available to determine the mine aquifer properties. These techniques use the water-level hydrograph of an aquifer, as measured in wells. Fluctuations in water levels in response to driving forces provide insight into the aquifer’s permeability, storage and other poroelastic properties.

Among the forces driving well-water fluctuations, the changes in atmospheric/barometric and tidal pressures are continuous and universal, and the related data are readily attained at low cost. Thus, the influence of atmospheric pressure on water levels as measured in wells has a high potential to serve as an in-situ hydraulic test (Rojstaczer, 1988; Furbish,

1991; Ritzi et al., 1991; Mehnert et al., 1999). Recently, the topic of barometric pressure in the context of environmental issues has also focused on many practical aspects with important implications. Fluctuations in static water levels in wells due to changes in barometric pressure can lead to erroneous estimates of hydraulic gradient, and consequently, erroneous parameters for contaminant transport at sites with monitoring programs and remedial measures (Hare and Morse, 1997).

Changes in atmospheric/barometric pressure and Earth tides are known to cause natural

fluctuations of the groundwater level in boreholes penetrating confined aquifers (Todd,

1959; Freeze and Cherry, 1979). Atmospheric pressure is the stress applied to the Earth’s crust by the atmosphere, whereas Earth tides result from the combined gravitational attraction among Earth, Sun and Moon. Melchior (1983) reports on barometric readings of atmospheric pressure that show both diurnal and semi-diurnal fluctuations. Diurnal fluctuations show a single maximum reading during the coldest hours and a single minimum reading during the warmest hours in a day, whereas semi-diurnal fluctuations are characterized by two maxima and two minima per day. According to Davis and De

Wiest (1966), water-level fluctuations due to Earth tides are small (amplitude typically less than 30 mm) compared to those due to barometric changes, which may be up to 100 mm. Also, water-level changes caused by the solid Earth tide and changes caused by atmospheric pressure differ in two important ways, in the mechanics of how they cause a water level disturbance, and in their prevailing frequencies.

Barometric pressure changes and Earth tide strain are dominant influences on water-level

fluctuations. These stresses act over large areas and produce measurable water-level responses in boreholes. These natural stresses can be used to estimate, or place bounds on, several aquifer parameters (Fig. 4.1) that help define the mine-water flow system. The large-scale nature of the stress may provide information on a larger portion of the aquifersystem than is possible with traditional aquifer testing by pumping tests. If the aquifer system responds to atmospheric loading and Earth tide strain in a predictable

Figure 4.1: Flow chart illustrating aquifer properties that can be determined from the atmospheric loading and Earth-tide water level response data.

manner, then it is possible to estimate hydraulic conductivity and specific storage.

Analysis of water-level fluctuations may also provide estimates of barometric efficiency, porosity, matrix compressibility and strain sensitivity. Because of the valuable insight that can be obtained about the dynamic attributes of aquifer system, Narasimhan (1998) advocated the long term monitoring of wells that respond to Earth tide and atmospheric data.

For wells that respond to Earth tides or atmospheric tides, methods have also been

developed to estimate transmissivity (T) or hydraulic diffusivity for confined aquifers

(Hsieh et al., 1987; Ritzi et al., 1991; Furbish, 1991) and for partially confined and unconfined aquifers (Rojstaczer, 1988; Rojstaczer and Riley, 1990) by analyzing the water level and atmospheric pressure data. These methods were derived to be quite general and are suitable for analyzing water level and atmospheric pressure data that vary in a nonperiodic or even a nearly random fashion.

Water level time-series data contain three predictable components: (a) Low frequency

components include climatic trends, seasonal periodicity and recharge events. (b) Under

static confined conditions, pore pressure responds to intermediate frequency barometric

pressure fluctuations. (c) The high frequency signal typically is well defined and is

attributed to tidal strain. Water-level hydrographs from the Corning underground coal

mines (McCament, 2004) show all three components, suggesting that analysis of the mine hydrograph time series may elucidate mine properties.

Rojstaczer (1988) summarized, through frequency analysis, the general theory of

barometric pressure effects on various groundwater conditions. However, the relationship

between barometric pressure and water levels in a well for an underground mine system

has not been fully explored or understood. Therefore, the following assumptions were made prior to the analysis: (a) water is drainable in the system and vertical deformation occurs only due to changes in barometric pressure and Earth tide effect; (b) the air

movement by barometric pressure and head loss of barometric pressure in the unsaturated

zone are ignored; and (c) the seasonal fluctuation of the water table is ignored in describing barometric and Earth tide effects on wells over a short period of time.

Objectives

Estimate coal mine aquifer properties by frequency-response analysis.

(a) To estimate selected aquifer fluid-flow properties, including hydraulic

conductivity and specific storage, from water level, barometric pressure and Earth

tide time-series data collected June 2003-September 2003.

(b) To estimate selected aquifer material properties including matrix compressibility,

porosity, storativity, strain sensitivity, and barometric efficiency, from time series

data.

The frequency response analysis is based on the theoretical underpinnings of how the different forces are exerted on the mine-aquifer system. The next sections describe the theory for water level response to applied pressures and the mechanisms behind the application of atmospheric loading and Earth tide analysis to calculating aquifer parameters.

4.2 Theory

4.2.1 Water Level Frequency Response Theory

Atmospheric loading, earth tides, and earthquakes cause pore-pressure changes in the

saturated zone and presumably in mines that can be measured as water-level changes in

boreholes. Water level fluctuations inside the mine complex in response to barometric

pressure are due in large part to the different manner in which the pressure is propagated

through the water column in the well and the porous media outside the well. It is assumed

that the aquifer or mine system response to atmospheric loading and earth tide strain is

governed by the porous elastic properties of the rock-water medium and the hydraulic

properties of the vertical pressure diffusion in the rock-water, rock-gas media. The

aquifer system porous elastic response to changes in stress is governed by the principle of

effective stress (Melchior, 1983):

∆σT = ∆σe + ∆P (4.1)

where, ∆σT = change in the total stress

∆σe =change in the effective grain-to-grain stress of the aquifer skeleton and

∆P = change in the pore-fluid pressure.

A change in the total stress on the aquifer system is balanced in part by the skeleton and in part the pore fluid pressure. The balancing of the stress change between the pore fluid and the skeleton is determined by the compressibility of the skeleton and the degree of confinement or drainage of pore fluid pressure. When there is a hydraulic connection

between stressed portions and unstressed portions of the aquifer system, the fluid

pressure gradient may cause fluid to drain at a rate depending on the hydraulic

conductivity. As fluid pressure changes in response to the drainage of fluid, this stress

change is shifted to the aquifer skeleton. For example, if fluid pressure decreases in

response to drainage, more of the total stress becomes effective on the skeleton. The

drainage process introduces a time lag or time dependency to the porous-elastic response of the aquifer system. For stress changes caused by Earth tides or atmospheric pressure, which are periodic or nearly periodic, it is useful to examine the frequency response of the aquifer system to these stresses.

The water level response to a change in stress can be a function of the frequency of the applied stress. Estimates of saturated zone hydrologic properties can vary as the frequency of the applied stress varies. When the water level response to a change in atmospheric pressure is frequency dependent, the barometric efficiency of the well/aquifer, for example, changes as the frequency of the applied stress changes.

Barometric efficiency (Fig. 4.2) is the ratio of the water level fluctuations to the corresponding change in atmospheric pressure. Barometric efficiency in a confined aquifer may approach a maximum of about 90% (Davis and Dewiest, 1966), but are usually in the range of 20 to 75% (Freeze and Cherry, 1979).

Figure 4.2: Barometric Efficiency (BE) schematic diagram. Elliptical objects represent aquifer matrix material. Pb is a normalized applied barometric load.

4.2.2 Atmospheric Loading Analysis Theory

Atmospheric loading is the stress applied to the Earth’s crust by the atmosphere.

Barometric pressure at land surface is a measure of the atmospheric load. The response of an aquifer to atmospheric loading depends on specific hydrogeologic conditions.

Confined aquifers typically respond to changes in atmospheric pressure. Unconfined

aquifers may respond to atmospheric loading if the unsaturated zone is thick or has

relatively low permeability to air (Weeks, 1979).

The response of an aquifer to atmospheric loading depends on the elastic properties of the aquifer skeleton. Jacob (1940) proposed a model for barometric effects on confined aquifers that assumes a pressure change is transmitted instantaneously, without attenuation, through the confining bed to the interface between the confining bed and the

aquifer. In the aquifer the atmospheric load is carried by the aquifer rock structure and by

the pore fluid. In the borehole the atmospheric load is carried entirely by the fluid. Flow

occurs because of changes in pore fluid pressure caused by the compression of the aquifer

skeleton and the pore fluid. The atmospheric load also creates a pressure imbalance

between the aquifer and the borehole that results in a water level change in the borehole.

The water level response to atmospheric loading depends largely on the aquifer matrix

compressibility, with low compressibility favoring high sensitivity. In stiff (or low compressibility) aquifers an incremental change in atmospheric load is mostly supported by the skeleton and not by the fluid. The skeleton deforms only slightly and any change

in porosity that would affect fluid pressure is minimized. Therefore, there is a greater fluid pressure imbalance between the borehole and the aquifer, which causes a larger water level response. In soft (or highly compressible) aquifers some of the load is borne by the fluid as the skeleton compresses and expands. There is a small fluid pressure imbalance between the borehole and the aquifer, and the observed water level response is

smaller. High porosity tends to result in large water-level fluctuations because there are

relatively smaller pore volume strains in deformed high-porosity material (Rojstaczer and

Agnew, 1989); that is, the same volume strain constitutes a smaller percentage of the total fluid volume.

The analysis should be done for the borehole response to atmospheric loading only in the

frequency range of 0.08 to 1 cycles/day (cpd). Water-level fluctuations below 0.08 cpd

are not suitable for the analysis because they can be easily affected by long term trends

unrelated to Earth tides or atmospheric loading. Earth tide induced water-level

fluctuations are present at frequencies greater than 1 cpd and they generally contaminate the atmospheric loading signal above these frequencies. Digital filtering can be used to isolate the desired frequency range in the water-level and barometric pressure data.

4.2.3 Earth Tide Analysis Theory

Earth tides are caused by the same forces that generate ocean tides. The combined gravitational attraction between the Earth, Sun, and Moon causes periodic dilation and contraction of the Earth’s crust, termed Earth tides (Fig. 4.3). The greatest gravitational effect occurs during the syzygy when the Sun and the Moon are aligned with the Earth.

During quadrature, when the Sun and Moon are 90o out of phase, the gravitational effect, and thus the magnitude of crustal deformation, is minimized. The strength of the gravitational fields varies throughout the year because the distances between the Earth,

Moon, and Sun vary as the Earth orbits the Sun. The moon’s effect on Earth tides is about

Figure 4.3: Tidal cycles during the lunar month.

two times greater than the Sun’s because the force acts in inverse proportion to the distance squared and Moon is much closer than Sun.

Strain due to Earth tides causes a small volumetric change in aquifers. During the daily cycle the aquifer deformation cycles between dilation and compression (Fig. 4.4). When the rock dilates, pore-fluid pressure decreases causing the water level in boreholes to fall.

Conversely, when the aquifer compresses, hydrostatic pressure increases and water levels

rise in boreholes. The sensitivity of a borehole’s water-level response to Earth tides

depends on the aquifer matrix compressibility and porosity. Boreholes tapping low-

compressibility, low-porosity rocks are more likely to exhibit water-level fluctuations due

to Earth tides (Rojstaczer and Agnew, 1989).

Figure 4.4: Dilation and compression of saturated rock matrix due to Earth tide strain.

There are five principal Earth tides that account for about 95% of the tide-generating force (Table 4.1). The semi-diurnal tides are denoted S2, M2, and N2, and the diurnal tides are K1 and O1. The M2, N2, and O1 tides are lunar tides, S2 is a solar tide, and K1

76 is a luni-solar tide. The periods of these tides (Table 4.1) are precisely known from astronomical observations.

Tide Source Frequency Period Potential (cycles per (solar hours) Amplitude solar day) (cm2/s2) O1 Lunar 0.929525706 25.81934169 9,407 K1 Solar/Lunar 1.002737909 23.93446961 12,952 N2 Lunar 1.895981969 12.65834823 3.074 M2 Lunar 1.932273614 12.42060121 15 S2 Solar 2.000000000 12.00000000 6,143

Table 4.1: Potential tidal amplitude (Harrison, 1971)

Solar heating of the atmosphere causes semi-diurnal and diurnal fluctuations in barometric pressure that can contaminate the response of borehole water levels to Earth tides at periods of 12 and 24 hours. It is consequently impossible to distinguish the atmospheric loading and Earth tide strain effects at 1 and 2 cpd so, the Earth tide analysis will focus on the water-level response to the principal lunar tides, M2 and O1. The N2 lunar tide is usually too weak to generate a significant water-level response. The sensitivity of water level responses only in the frequency band 1 to 2 cpd to Earth tides should be analyzed for the study.

Earth tide strain can be measured directly with strain meters installed in boreholes.

Unfortunately, such a strain meter is not available in the study area. Earth-tide strain can

be calculated, however, from knowledge of the gravitational attraction, orbits and periodicity of the Earth, Moon, and Sun. Using this information the theoretical amplitude of Earth tide strain can be calculated for any location on the Earth, provided some assumptions are made concerning topography, geologic structure, and crustal rock properties. This research project utilizes Earth tide strain values calculated from a computer program ETGTAB 3.0. The program fit data to theoretical tides and additional parameters using the method of least squares adjustment. Although these Earth tide strains are theoretical, they are considered the actual tidal strain at the study area for

analysis. The actual strain can differ by as much as 50 percent from theoretical estimates

(Beaumont and Berger, 1975).

The fundamental quantity obtained from Earth-tide analysis is the areal strain sensitivity,

which is the ratio of water level change to applied tidal strain. The strain sensitivity is a

strong function of matrix compressibility and porosity; high sensitivity is favored by low

porosities and low matrix compressibility. The Earth tide strain response contrasts with

atmospheric loading responses where high sensitivity is favored by high porosity and

high matrix compressibility.

4.3 Methods

4.3.1 Atmospheric Loading Analysis

Six-hourly water level and barometric pressure data are processed to estimate the

barometric efficiency (BE). Type curves are used to estimate transmissivity. The

analytical procedures are completed on each well. The major steps required to determine

the poroelastic parameters from atmospheric loading analysis are illustrated in Figure 4.5.

Figure 4.5: Flow chart illustrating major steps required in the atmospheric loading analysis.

Step 1: Condition the data for transformation to the frequency domain. The raw time series data are conditioned to make them suitable for analysis. Depth-to-water data are converted to meters of absolute head with respect to men sea level. Barometric pressure

data are converted from Pascal to meters of water. Most data contain significant linear trends. The linear trends are quantified with regression analysis and subtracted from the raw data. Figure 4.6 shows water level and barometric pressure data for five wells installed in the Corning mine pool.

Step 2: Digitally filter water level and barometric pressure data. The water level and

barometric pressure data are digitally filtered to isolate fluctuations within the frequency

range of 0.08 to 1 cpd. Long term trends are removed by omitting frequencies less than

0.08cpd. Large, long-lasting weather fronts at frequencies less than 1 cpd produce

significant atmospheric loading effects on water levels. At frequencies higher than 1 cpd,

Earth-tide induced water level fluctuations contaminate the atmospheric loading pressure,

which makes these frequencies undesirable for analysis.

Step 3: Fast Fourier Transform of water-level and barometric pressure data. The atmospheric loading and water level time series data fluctuate in a periodic manner. To determine the effect barometric pressure has on the observed water levels, these data are analyzed in the frequency domain. A Fourier transform is used to transform the barometric pressure and water level data from the time domain to the frequency domain.

The Fourier transform produces a continuous periodic frequency function from the time series data. A 12th-order highpass Butterworth Filter (Oppenheim and Wilsky, 1983) with

a cutoff frequency of 0.08 cpd is applied to attenuate low frequencies. Then the filtered

data are run through a 12th-order lowpass Butterworth Filter with a cutoff frequency of 1

4.7 10.6 4.5 10.6 Well 1 10.5 4.4 Well 2 10.5 4.5 10.4 4.3 10.4 10.3 4.3 10.3 4.2 10.2 10.2 4.1 4.1 10.1 10.1 4 10 3.9 10 3.9 9.9 0 102030405060708090100110120130140 0 102030405060708090

12.7 10.5 4.1 10.6 Well 3 10.4 4 Well 4 10.5 12.65 10.3 3.9 10.4 10.3 12.6 10.2 3.8 10.2 10.1 3.7 12.55 10.1 10 3.6 10 12.5 9.9 3.5 9.9 0 5 10 15 20 25 30 35 0 10 20 30 40 50 60 70 80 90 100110120130140

4.1 10.6 Legend 4 Well 5 10.5 3.9 10.4 _____ Water Level 3.8 10.3 _____ Barometric Pressure 3.7 10.2 X axis – Days since 6/2/2003 3.6 10.1 Y axis – Water level in meter (Left side) 3.5 10 Y axis – Barometric pressure in meter of H2O (Right side) 3.4 9.9 0 102030405060708090100110120130140

Figure 4.6: Response of water levels to the barometric pressure.

cpd to remove tidal and high frequencies. Finally, an inverse transform is performed to

produce time series consisting of the intermediate bandpass frequencies. Figure 4.7

shows the frequency spectra of the water level and barometric pressure data with the

applied filters.

(a) Water level frequency spectra

0.8 , m 0

4 0.6 * e

d Diurnal u

lit 0.4 p m A 0.2

0 0 0.5 1 1.5 2 2.5 Frequency, cycles/day

(b) Barometric pressure frequency spectra

0.8 m , 0

4 0.6 * ude t i

pl 0.4 Semi-Diurnal m

A Diurnal 0.2

0 00.511.522.5 Frequency, cycles/day

Figure 4.7: Water level and barometric pressure frequency spectra with applied filters.

Step 4: Calculate power spectra and transfer functions. Spectral analysis describes the frequency content of the water-level and barometric-pressure signals. The water-level and atmospheric-loading power spectra, which are a measure of the signal energy at various frequencies, are compared. Transfer functions estimated at individual frequencies show how well the atmospheric loading and water level fluctuations are correlated. The transfer functions are screened to ensure that atmospheric loading is the dominant process causing the water level fluctuations. Water level data are regressed with barometric pressure data to estimate barometric efficiency of each well (Fig. 4.8).

Step 5: Use of Type Curves to estimate Transmissivity: For some known value of transmissivity and specific storage, Mehnert et al. (1999) have generated plots of amplitude ratio versus dimensionless transmissivity T’ as a type curve (Fig. 4.9). This type curve is based on a model for groundwater flow between the well and the aquifer developed by Cooper et al. (1965). To estimate the amplitude ratio and then T’ from field data, first the dominant frequencies of the atmospheric pressure data are estimated by spectral analysis (Fig. 4.7). The water level data are known to respond at the same frequency as the atmospheric data, but water level amplitude and/or phase may vary from the atmospheric pressure data. The amplitudes and phase angles of atmospheric data are estimated by nonlinear regression. The amplitude ratio is computed by dividing the amplitude of the water level data by the amplitude of the atmospheric data. Given the

221.65 221.6 y = 0.8243x + 211.9 Well 1 y = 0.2709x + 218.63 Well 2 221.6 221.55 221.5 221.5 221.4 221.45 221.4 221.3 221.35 221.3 221.2 10.25 10.3 10.35 10.4 10.45 10.25 10.3 10.35 10.4 10.45

221.4 224.3 y = -0.2224x + 223.57 Well 3 y = 0.4859x + 219 Well 4 221.35 224.1 221.3 223.9 221.25

221.2 223.7 10.2 10.25 10.3 10.35 10.4 10.45 10.5 10.25 10.3 10.35 10.4 10.45

222 .2 y = 0.4633x + 217. 26 We ll 5 Legend 222 .1 X axis – Barometric pressure in meters of H2O Y axis – Head in meters 222

221 .9

221 .8 10. 25 10.3 10.35 10.4 10.45

Figure 4.8: Estimated Barometric Efficiency (BE) of monitoring wells.

amplitude ratio and a value of specific storage, T’ is estimated using the type curve (Fig.

4.9). The well radius (rw) is known and is assumed to be equal to the radius of the casing.

2 Finally, T can be calculated from the definition of T’ (T’ = T/rw ω).

Figure 4.9: Type curve – Amplitude Ratio versus T’ on a semi-log Plot.

4.3.2 Earth Tide Analysis

Six- hourly water-level altitude and Earth-tide strain data series are analyzed to produce estimates of the borehole’s sensitivity to areal strain. The data series must span at least one complete tidal cycle (about 28 days) and longer time periods are helpful to improve

the parameter estimates. The major steps required to determine the poroelastic parameters

from atmospheric loading analysis are illustrated in Figure 4.10.

Figure 4.10: Flow chart illustrating major steps required in the Earth tide analysis.

Step 1: Condition the data for transformation to the frequency domain. See Step 1 in atmospheric loading analysis.

Step 2: Calculate theoretical tidal potential from the computer program ETGTAB. The

program ETGTAB is used for the computation of earth tides with a six-hour time interval for one specific station in order to generate a table of tidal potential. This program is written mainly in FORTRAN 90. The required input parameters to calculate tidal potential for any specific site are latitude and longitude of the station, starting time, time interval and the mean elevation of the area.

Step 3: Digitally filter water level and Earth tide data. The water level and Earth tide

data are digitally filtered to isolate fluctuations within the frequency range of 0.8 to 2

cpd. The M2 and O1 tidal frequencies are used in the analysis.

Step 4: Fast Fourier Transform of water-level and Earth tide data. See Step 3 in

atmospheric loading analysis, except 12th-order highpass cutoff frequency is 1 cpd and lowpass cutoff frequency is 2 cpd.

Step 5: Regress theoretical Earth tide strain versus filtered water level data and calculate specific storage. Frequency-weighted linear regressions of theoretical tidal

strains on observed water level amplitudes are performed to yield specific storage values

for portions of the mine complex associated with each well (Fig. 4.11).

Step 6: Calculate storativity, porosity and matrix compressibility from the specific

storage using standard equations.

3.0E-07 1.5E-06 y = 0.00000175x - 0.00000001 Well 1 y = 0.00000789x - 0.00000004 Well 2 2.0E-07 1.0E-06 1.0E-07 5.0E-07 0.0E+00 0.0E+00 -1.0E-07 -5.0E-07 -2.0E-07 -1.0E-06 -3.0E-07 -1.5E-06 -0.08 -0.03 0.02 0.07 -0.01 -0.005 0 0.005 0.01

1.2E-06 2.0E-07 Well 3 y = -0.00000442x - 0.00000000 Well 4 y = 0.0000107x - 0.0000000 7.0E-07 1.0E-07

2.0E-07 0.0E+00

-3.0E-07 -1.0E-07

-8.0E-07 -2.0E-07 -0.025 -0.015 -0.005 0.005 0.015 0.025 -0.01 -0.01 0.00 0.01 0.01

6.0E-07 Legend y = 0.00000454x - 0.00000003 Well 5 4.0E-07 X axis – Change in head, meters 2.0E-07 Y axis – Tidal strain 0.0E+00 -2.0E-07 -4.0E-07 -6.0E-07 -0.03 -0.02 -0.01 0 0.01 0.02 0.03

Figure 4.11: Estimated specific storage of monitoring wells.

4.3.3 Poroelastic Parameters

The atmospheric loading analysis can provide estimates of barometric efficiency, and the

Earth tide analysis provides estimates of areal strain sensitivity. These estimates are used

to calculate aquifer storage properties such as specific storage, storativity, porosity,

matrix compressibility, transmissivity, hydraulic conductivity and aquifer diffusivity. The assumed constants used during the analysis are presented in Table 4.2.

Parameters Values

Poisson’s Ratio, v 0.25 Love Numbers, h 0.603 Love Numbers, l 0.084 Density of Water 999 kg/m3 Gravitational constant, g 9.81 m/s2

Bulk modulus of water, Kw 2.15E09 Pascal

Table 4.2: Assumed constants for barometric and tidal analysis.

4.3.3.1 Barometric Efficiency (BE)

BE is the ratio of the static confined pore pressure response to an applied uniaxial load

(equation 4.2) (Domenico and Schwartz, 1990). Todd (1959) views BE as a measure of

the competence of overlying confining strata to resist pressure changes, equating thicker

strata with higher BE values.

dh BE = (4.2) dPb

where, dh = change in hydrostatic head

dPb = change in atmospheric pressure

4.3.3.2 Strain Sensitivity

Tidal strain wave amplitudes can be computed for each well. Theoretical tidal potential is related to tidal strain according to the following equation (Melchior, 1983):

⎛1− 2v ⎞ ⎛W2 g ⎞ et = ⎜ ⎟()2h − 6l ⎜ ⎟ (4.3) ⎝ 1− v ⎠ ⎝ r ⎠

where, et = Tidal strain (dilation)

v = Poisson ratio

h, l = Love numbers

W2 = Tidal potential

4.3.3.3 Specific Storage

The ratio of theoretical tidal strain to observed amplitude is the specific storage (Ss)

(equation 4.4) (Bredehoeft, 1967).

e − Ss = t (4.4) dh

where, dh = change in hydrostatic head

et = tidal strain (dilation)

Frequency-weighted linear regressions of theoretical tidal strains on observed water level

amplitudes are performed to yield specific storage values for portions of the mine

complex associated with each well.

4.3.3.4 Storativity

Storativity of a confined aquifer is defined as the product of specific storage and its

saturated thickness (equation 4.5). Value of storativity can be determined for any

assumed (or known) value of aquifer thickness and known specific storage value (Hobbs

and Fourie, 2000).

S = b × S s (4.5)

where, b = aquifer thickness

Ss = specific storage

4.3.3.5 Porosity

Jacob (1940) linked BE to specific storage, through the porosity η (equation 4.6), assuming isothermal conditions.

ρ gη BE = w (4.6) K w Ss

-3 where, ρ w = density of water (999 kg m )

g = gravitational constant (9.81 m s-2)

o Kw = bulk modulus of H2O (2.27e09 Pa at 25 C)

η = porosity

4.3.3.6 Matrix Compressibility

Matrix compressibility of the aquifer is calculated using equation 4.7 (Domenico and

Schwartz, 1990).

γSs β b = (4.7) ρ w g

-3 where, ρ w = density of water (999 kg m )

g = gravitational constant (9.81 m s-2)

Ss = specific storage

γ = loading efficiency (1 – BE)

4.3.3.7 Transmissivity

Transmissivity of a well can be calculated using a set of type curves and estimating the

ratio of the amplitudes of the well response over the atmospheric pressure. The type

curves are generated by Mehnert et al. (1999) based on a model for groundwater flow

between the well and the aquifer developed by Cooper et al. (1965). Transmissivity can

be calculated from the definition of dimensionless transmissivity T’ using equation 4.8

(Mehnert et al., 1999):

T T '= 2 (4.8) rw ω

where, T = Transmissivity

rw = Well radius

ω = frequency

4.3.3.8 Hydraulic Conductivity

It is a constant of proportionality defining the specific discharge of a porous medium

under a unit hydraulic gradient. The ratio of the transmissivity to the saturated thickness

of the aquifer is the hydraulic conductivity, K (equation 4.9) (Domenico and Schwartz,

1990).

T K = (4.9) b

where, T = Transmissivity

b = Saturated thickness of the aquifer

4.3.3.9 Hydraulic Diffusivity

The ratio of the transmissivity to the storativity is the hydraulic diffusivity (D) (equation

4.10) (Domenico and Schwartz, 1990).

T D = (4.10) S

where, T = Transmissivity

S = Storativity

4.3.4 Correlation Analysis

The strength of the association among aquifer properties and among wells can be determined using correlation analysis. The analysis provides information on what properties are related and how they vary relative to each other. Correlation between the wells indicates which intervals have aquifer properties that vary in a similar manner.

Correlation values (ρ) are dimensionless and scaled to lie in the range between -1 and 1.

When there is no correlation between two variables, ρ = 0. When one variable increases as the second increases the correlation is positive (ρ > 0). When the parameters vary in opposite directions the correlation is negative (ρ < 0).

4.4 Results and Discussion

Aquifer properties of five wells were estimated by analyzing the water level frequency response to atmospheric loading and earth tide strain. They are presented in Table 4.3.

The estimated aquifer properties appear to be reasonable given the hydrogeologic conditions. Several conclusions are formed based on the relationship between properties and the methods used to obtain the properties.

4.4.1 Observations

Water level data were collected using a pressure transducer for the period June 2003 to

September 2003. Different wells show different responses to the barometric pressure data

(Fig 4.6), but all hydrographs show a cyclic, semi-diurnal pattern. The record also shows a time lag between barometric and hydraulic data sets. The short-period semi-diurnal water level and barometric fluctuations are superimposed on longer-period fluctuations

(Figs. 4.7a and 4.7b) caused by changes in weather, seasonal recharge and Earth tides.

Except for Well 3, in all other wells water level fluctuates dramatically in response to changes in barometric pressure (Fig 4.6). The Well 3 hydrograph clearly fluctuates in

direct response to the changes in barometric pressure, but an increase in the barometric

pressure results in a corresponding decrease in the water level elevation in the well.

Similarly a decrease in the barometric pressure results in an increase in the water level

elevation. This type of response assumes that the barometric efficiency is a simple linear

function, i.e., water level change is directly proportional to the simultaneous barometric

pressure change. Whereas, damping effect and phase shift in water level relative to

atmospheric loading means that the observed water-level change can be responding to a

previous barometric pressure change and not necessarily the simultaneous pressure

change. Water level fluctuations inside the mine complex in response to barometric

pressure are due in large part to the different manner in which the pressure is propagated

through the water column in the well and the porous media outside the well.

The barometric data derive from a chart-recording barometer which forms part of the

climatic monitoring station located approximately 3 miles south of the study area.

Analysis of the barometric data (Fig 4.12a) gives a mean barometric value of 10.362 m of

H2O. The series is also periodic, having strong diurnal and semi-diurnal components, which is confirmed via spectral analysis (Fig. 4.7b & Fig. 4.13). The barometric data used in this study show maxima occurring in the periods 10:00 to 12:00 and 21:00 to

23:00 and minima in the periods 05:00 to 07:00 and 17:00 to 18:00 (Fig. 4.13). The Earth tide data (Fig. 4.12b) is similarly cyclic having a mean Earth tide strain value of

8.63×10-9.

Barometric Pressure Data 10.55 10.5

O 10.45 Mean Value = 10.362 H2

f 10.4 o 10.35 m ,

BP 10.3 10.25 10.2 0 1020304050607080 Julian Days

Earth-Tide Strain Data

2.5E-07 Mean Value = 8.63E-09 n

i 1.5E-07 a r t S

5.0E-08 de -5.0E-08 h Ti t r a

E -1.5E-07

-2.5E-07 0 1020304050607080 Julian Days

Figure 4.12: Typical (a) barometric pressure and (b) Earth-tide strain data.

0.4

0.3 r , t 0.2 Maximum BP

cien 10:00 – 12:00 & 21:00 – 23:00 i f 0.1 ef o 0 C

n 0 1224364860728496108120 o i -0.1 lat

e Minimum BP

r -0.2 r 05:00 – 07:00 & 17:00 – 19:00 o

C -0.3

-0.4 Lag, 6-hours

Figure 4.13: Autocorrelogram of the barometric pressure data.

4.4.2 Barometric Efficiency and Aquifer Storage Properties

An analysis of the hydrograph for the period June 2003 to September 2003, in conjunction with the barometric record for the same period, has permitted an assessment of the barometric efficiencies (Fig. 4.8) of the aquifer. The potentiometric responses to barometric pressure demonstrate a range of barometric efficiencies (Table 4.3) for the mine aquifer. The wide range of barometric efficiency values reflects the sensitivity of the calculation to small variations in either or both ‘dh’ and ‘dPa’ values. These values are in reasonable agreement with the 30 to 80% pore pressure coefficient suggested for karst aquifers (Palciauskas and Domenico, 1989).

Well BE Specific Storativity Porosity Matrix Conductivity Transmissivity Diffusivity Storage Compressibility K, m/sec T. m2/sec a, m2/sec (1/meter) (1/Pascal) Well 1 0.8243 0.00000175 0.000005334 0.3146 3.13744E-11 1.46059E-06 4.45188E-06 0.834623172

Well 2 0.2709 0.00000789 1.92358E-05 0.4689 5.86989E-10 1.36953E-05 3.33891E-05 1.735777315

Well 3 0.2224 0.0000107 2.60866E-05 0.524 8.48996E-10 2.43472E-06 5.93584E-06 0.227543643

Well 4 0.4859 0.00000442 2.15563E-05 0.4712 2.31865E-10 2.12994E-06 1.03877E-05 0.481886999

Well 5 0.4633 0.00000454 9.68836E-06 0.4614 2.4863E-10 4.86772E-06 1.03877E-05 1.072185592

Table 4.3: Estimated poroelastic parameters from barometric and tidal analysis.

Well Amplitude T' Transmissivity Thickness Conductivity Conductivity Ratio, AR T, m2/sec b, meter K, m/sec K, m/day Well 1 0.82 6 4.45188E-06 3.048 1.46059E-06 0.126195024

Well 2 0.96 45 3.33891E-05 2.438 1.36953E-05 1.183272453

Well 3 0.89 8 5.93584E-06 2.438 2.43472E-06 0.210359547

Well 4 0.93 14 1.03877E-05 4.877 2.12994E-06 0.184026862

Well 5 0.93 14 1.03877E-05 2.134 4.86772E-06 0.420571231

Table 4.4: Estimated Transmissivity from type curve analysis.

The mine aquifer storage estimates are representative of long period, low frequency

responses of the aquifer system to stresses. For uncemented granular material, the

barometric efficiency of an aquifer system is inversely proportional to the specific storage

(Jacob, 1940). So, the frequency response of barometric efficiency to atmospheric

loading can be used to elucidate the relationship between the elastic properties of the

aquifer and the frequency of the applied load.

The relationship between specific storage and barometric efficiency (Fig 4.14) can be

determined when a priori estimates of Poisson’s ratio, v, and the solid grain

compressibility of the aquifer, βs (Table 4.2) are known or estimated. Larger barometric efficiency values correspond with relatively small values of specific storage. For midrange values of specific storage and porosity, a barometric efficiency between 0.2 and

1 y

c 0.8 n e i c fi

f 0.6 E c i tr

e 0.4 m o r

a 0.2 B

0 0.0E+00 2.0E-06 4.0E-06 6.0E-06 8.0E-06 1.0E-05 1.2E-05 Specific Storage (1/meter)

Figure 4.14: Correlation between porosity and specific storage. The correlation between these two parameters is 0.94.

0.5 could be expected (Table 4.3). The small variation in specific storage among the

different boreholes may demonstrate the very small variation in the storativity of a

comparatively inelastic confined aquifer in the unstressed (natural) and stressed

(unnatural) states respectively.

4.4.3 Transmissivity and Hydraulic Conductivity Estimation

The water level fluctuations inside a well can also be analyzed to estimate transmissivity,

hydraulic conductivity and diffusivity (Table 4.3). Data analysis for this simplified

method involves using a set of type curves (Fig. 4.9) and estimating the ratio of the amplitudes of the well response over the atmospheric pressure. The amplitude ratio is the amplitude of the well response divided by the amplitude of the atmospheric pressure and can be considered normalized amplitude. The method is based on the principle that, (a) the amplitude ratio is a function of transmissivity, the well radius, and the frequency of the sinusoidal oscillation; and (b) the amplitude ratio is a weak function of storativity.

The amplitude ratio as a function of dimensionless transmissivity, T’ has an S-shape on the semi log plot (Fig 4.9). Based on the amplitude ratio, values of T’ from slightly greater than 0.01 to slightly greater than 30 can be determined from the type curve (Table

4.4). This range was called the “identifiability window” by Ritzi et al. (1991). The range of hydraulic conductivity values that can be computed using a typical well radius of

2.54cm and frequency of 1×10-5 sec-1 is between 1×10-5 m/sec to 1×10-10 m/sec.

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4.4.4 Correlation Analysis

The correlation between aquifer properties is presented in Table 4.5. In these correlations, the wells are observations and the aquifer properties are variables. The highest observed

positive correlation is 0.99 for specific storage and matrix compressibility. The strong

correlation between specific storage and matrix compressibility is an expected hydrologic

relationship: the higher the specific storage of the aquifer, the more compressible its solid

matrix. Also a strong negative correlation of -0.93 exists between barometric efficiency

and specific storage. Larger barometric efficiency is observed for relatively small values

of specific storage (Fig. 4.14).

The strength of the association between wells was also determined using correlation

analysis. The analysis provides information on what wells have aquifer properties that

vary in a similar manner. The five aquifer properties (barometric efficiency, specific storage, porosity, matrix compressibility and hydraulic conductivity) are used as

observations and the wells as variables. The correlation between wells is presented in

Table 4.6. The highest correlation is between Well 4 and Well 5. This high correlation

indicates that the two wells have similar surrounding aquifer properties and that the

properties between the wells tend to vary together. There is also a strong correlation

between Well 2 and Well 3. Well 1 has distinctly different behavior and is not highly

correlated with other wells.

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BE Specific Storativity Porosity Matrix Conductivity Transmissivity Diffusivity Storage Comp. BE 1

Specific Storage -0.931744 1

Storativity -0.827486 0.833064 1

Porosity -0.937164 0.830754 0.850720 1

Matrix Comp. -0.911691 0.998077 0.813820 0.794661 1

Conductivity -0.498613 0.341806 0.143558 0.244817 0.345053 1

Transmissivity -0.481349 0.303471 0.217758 0.252738 0.302349 0.979181 1

Diffusivity -0.060309 -0.11309 -0.33629 -0.17649 -0.10414 0.879624 0.839971 1

Table 4.5: Correlation among aquifer properties determined by frequency response analysis

Well 1 Well 2 Well 3 Well 4 Well 5

Well 1 1 Well 2 0.655011575 1

Well 3 0.536308194 0.989047174 1 Well 4 0.875615816 0.938508608 0.877269612 1

Well 5 0.867192363 0.944299043 0.885382902 0.999852546 1

Table 4.6: Correlation among wells based on aquifer parameters using a Pearson-Product Correlation Coefficient

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4.4.5 Limitations of Aquifer Property Estimates

There are several limitations in the computation of aquifer properties from atmospheric loading and Earth tide responses. The overall signal quality, validity of assumptions,

accuracy of the theoretical Earth tide strain computations, and validity of the assumed

constants all can affect the final parameter estimates. A strong water-level response to

atmospheric loading and Earth tide strain is necessary to confidently estimate aquifer

properties. Noise in the water-level data, due to measurement error or other hydrologic stresses, or non-ideal aquifer and well-bore conditions, may obscure the response. The accuracy of the resulting aquifer-property estimates reflects the uncertainty or noise in the original data.

A static-confined water level response occurs when the frequency of the imposed stress is high enough to cause the aquifer to respond in a confined manner (Rojstaczer and

Agnew, 1989). The static-confined response is the undrained response of the aquifer or the ideal confined-aquifer response. Water-table drainage and borehole storage are assumed to be negligible when the static-confined response is observed (Rojstaczer,

1988). Parameter estimates obtained from water-level fluctuations that represent the static-confined aquifer response should be constant for a range of frequencies. The extent to which they are not constant reflects drainage. However, when only the static-confined response is observed throughout the analyzed frequency range it is not possible to estimate all of the research-objective parameters. For example, when there is no fluid flow or drainage, the response is independent of hydraulic conductivity, and that

103

parameter cannot be estimated. Therefore, both frequency dependent and static-confined

water level responses to atmospheric loading and Earth tides should be considered to

estimate the aquifer parameters. The static-confined approach, however, makes the

assumption of no drainage and may yield parameter estimates that are in error due to

drainage.

Strain sensitivity estimates depend on the accuracy of the theoretical Earth tide strain

determined using the ETGTAB 3.0 computer program. Topography and geologic

structure can cause errors in the calculated strain, which assumes flat topography and a homogeneous subsurface.

Rock properties must be estimated to do the analysis. The validity of these estimates

affects the aquifer property estimates. For example, a Poisson’s ratio of 0.25 was used to

calculate matrix compressibility, specific storage, and porosity. If the actual Poisson’s

ratio differs significantly from 0.25, error will be introduced into the results.

104

Chapter 5: Mine-Water Flow Predictions via Numerical Modeling

5.1 Introduction

A numerical model, which is basically a sophisticated water budget, increases

understanding of the hydrogeology of a system. Using the model to reconstruct a known head distribution and known flow rates identifies which hydrogeologic parameters most influence the flow paths and is a first step in making predictions about hydrogeologic conditions.

Models of mine water can broadly be divided into flow and chemical transport models.

Mine flow models can be further categorized as those applied to active and those applied to inactive workings. Models of inflow to active deep mines are used as a tool for planning dewatering. It is only recently with the threat of acid mine drainage that modeling flow in inactive mines has been of interest (Sherwood and Younger, 1994).

Hydrogeologists, mining engineers, and regulatory officials who use numerical groundwater models in assessing the impacts of underground mining on groundwater resources commonly are interested in predicting three aspects of mine hydrogeology: dewatering rate, drawdown, and absolute hydraulic head. Toran and Bradbury (1988) found it necessary to procure field data before, during and after mining. If one or two of these data sets is unavailable, they predicted large uncertainties associated with modeling.

The literature on modeling mine water flow in mines is limited. Standard groundwater modeling approaches apply Darcy’s Law, which assumes a simple porous medium in

105 which flow is laminar. Flow through roadways in the mine-workings is likely to be turbulent, however bearing more similarity to channel flow than laminar groundwater flow. The challenge in dual-porosity media has been encountered not only in mined aquifers, but also in aquifers with dominant fracture flow. One approach used in this type of modeling is to represent the fractured strata as an Equivalent Porous Medium (EPM).

Effective parameters are assigned that produce a volumetric flow pattern similar to that in the fractured strata. However, due to the limitations inherent in assigning effective parameters, this type of model is able to reproduce only regional rather than local conditions.

Analytical models of water in mine-workings, such as Deep Mine Model (Ricca and

Hemmerich, 1978) require vast amounts of data. Rogoz (1994) has developed a model that represents the rise in water level in abandoned mine-workings without the need for excessive amounts of data. However, the model assumes that at least one mine in a coalfield is working and that once rebound reaches a point at which it flows into active workings, it is pumped away. The limitations of existing modeling techniques mean that some new approaches are required to gain an understanding of mine hydrology, especially the hydrology of abandoned mines.

Objective

The main objective of this study was to gain a deeper understanding of the hydrogeologic behavior of the Corning mine complex, in order to design an effective in-situ remediation

106

scheme. Numerical modeling using a commercial groundwater flow model MODFLOW

results in a detailed and self-consistent water budget for the mine, constrained by the available data. MODFLOW is not data intensive and makes allowances for the accuracy of the data which are available. The entire process can be represented by a flow chart

(Fig. 5.1), in which the conceptual model forms the basis of a numerical model constrained by available data, including result of the time series analysis and barometric pressure analysis.

Figure 5.1: Flow chart illustrating the mine modeling, showing the role played by the time series and barometric pressure analysis.

107

5.2 Model Program

MODFLOW is the world’s most widely used quasi-three dimensional, groundwater

modeling program (McDonald and Harbaugh, 1988; Anderson and Woessner, 1992). It

uses block-centered finite difference equations in a backward difference form. The block

centered model means that head values are calculated for the center of each cell or cell

node making up the model grid and the model is finite difference means that the head

value calculated at the center of a cell is representative of the head value for the entire

cell (McDonald and Harbaugh, 1988). It assumes that the strata in the coalfield can be

represented by an equivalent porous medium (EPM), through which flow is laminar.

Because of its ability to simulate a wide variety of systems, MODFLOW has become the

worldwide standard groundwater flow model. It is used to simulate systems for water

supply, contaminant remediation and mine dewatering.

Visual MODFLOW incorporates the numerical values representative of all the

hydrogeologic properties making up the area of investigation (hydraulic conductivity,

porosity, specific storage, etc.) in order to solve the groundwater flow equation in three

dimensions. The form of the groundwater flow governing equation used to calculate hydraulic head values throughout the modeled area as outlined by Anderson and

Woessner (1992) is as follows:

∂ ⎛ ∂h ⎞ ∂ ⎛ ∂h ⎞ ∂ ⎛ ∂h ⎞ ∂h ⎜ K xx ⎟ + ⎜ K yy ⎟ + ⎜ K zz ⎟ −W = S s (5.1) ∂x ⎝ ∂x ⎠ ∂y ⎝ ∂y ⎠ ∂z ⎝ ∂z ⎠ ∂t

where x, y and z = rectangular coordinates,

K = hydraulic conductivity,

108

h = head,

W = volumetric flux representing sources and sinks of water to the system,

Ss = storage coefficient

t = time

The modular structure of MODFLOW consists of a main program and a series of highly independent subroutines called modules. The modules are grouped in packages. Each package deals with a specific feature of the hydrologic system that is to be simulated, such as flow from rivers or flow into drains, or with a specific method of solving linear equations that describe the flow system. The division of MODFLOW into modules permits the user to examine specific hydrologic features of the model independently. This modular approach also facilitates development of additional capabilities because new modules or packages can be added to the program without modifying the existing ones.

The input/output system of MODFLOW was designed for optimal flexibility.

A large amount of information and a complete description of the flow system are required to make the most efficient use of MODFLOW. In situations where only rough estimates of the flow system are needed, the input requirements of MODFLOW may not justify its use. To use MODFLOW, the region to be simulated must be divided into cells with a rectilinear grid resulting in layers, rows and columns. Files must then be prepared that contain hydraulic parameters (hydraulic conductivity, transmissivity, specific yield, etc.),

109 boundary conditions (location of impermeable boundaries and constant heads), and stresses (pumping wells, recharge from precipitation, rivers, drains, etc.).

Particle tracking is used to visualize the groundwater flow field and to track contaminant pathways. The particle tracking program known as MODPATH (Pollock, 1989) is mostly used because of its availability within the Visual MODFLOW program. Particle tracking incorporates particles into the calculated flow regime and interprets the advective flow paths of the contaminant particles with respect to time (Anderson and Woessner, 1992).

5.3 Modeling Methods

5.3.1 Conceptual Model

A conceptual model is a simplified pictorial image of the area to be modeled (Anderson and Woessner, 1992). The conceptual model (Fig. 5.2) is constructed by defining the hydrostratigraphic units, preparing a water budget, and defining the flow regime of the area to be modeled.

5.3.1.1 Model Dimensions and Boundaries

The dimensions of the conceptual model for the present study are based on a regional scale so that all of the hydrogeologic influences acting on the underground mine are considered during the flow modeling. Using a coordinate system in meters in state plane coordinates and relative to sea level, the regional scale conceptual model is a rectangular volume with dimensions ranging from 403,410 to 409,060 m (Length = 5650 m) in the X

110

direction, from 4,383,240 to 4,386,530 m (Width = 3290 m) in the Y direction, and from

175 to 310 m (Depth = 135 m) in the Z direction (Fig. 5.3). The resulting study area has dimensions of 18.58 square km.

Figure 5.2: Conceptual model of flow within the Corning mine complex.

Boundaries were approximated through the interpretation of two USGS 7.5 topographic maps for the mine area as well as from a previous field investigation by McCament

(2004). There is a south-eastward flow trend within the mine complex, determined by the regional structural dip of 30 ft/mile (Sturgeon and Associates, 1958). South and southeast extent of mines (Fig. 5.4) were assumed as no flow boundaries because of its down-dip location. North and southwest extent of mines, assumed to be connected via intact coal have boundaries with significant inflow.

111

305 300 295 290 285 280 275 270 265 260 255 250 245 240 235 230 225 220

Figure 5.3: Study area dimensions and surface topography elevations (units in meters).

Figure 5.4: Different flow boundaries of the Corning mine complex.

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5.3.1.2 Hydrostratigraphic Units

Through the analysis of geologic maps, cross sections and well borings, the groundwater

system in the vicinity of Corning can be divided into two hydrostratigraphic units: (a) the shallow aquifer, consisting of unconsolidated sediments and underlying shallow fractured

bedrock; and (b) the lower hydrostratigraphic unit, 20 to 80m below the surface,

consisting of Pennsylvanian shale, sandstone and coal with few open fractures. The

mined horizon is located within the lower hydrostratigraphic unit. The mined horizon,

though contained within the lower hydrostratigraphic unit, is distinct because it has a very

high conductivity and porosity due to mine tunnels.

5.3.1.3 Hydrogeologic Properties

Information gathered through literature review and from previous studies (Table 5.1) is

used to characterize the upper hydrostratigraphic unit. The geometric mean of hydraulic

conductivity for the shallow aquifer is of the order of magnitude of 10-6 m/s.

Conductivity of the zone above the mined layer is expected to be one or two orders of magnitude greater than the upper layer due to the expected development of stress-relief fractures during mining and roof collapse. The hydraulic conductivity of the lower hydrostratigraphic unit, except the mined layer, decreases with depth. Vertical hydraulic conductivities are expected to be one or two orders of magnitude less than horizontal conductivities (Donovan et al., 2000). Horizontal and vertical hydraulic conductivity variations correspond primarily to preferential pathways of groundwater flow, including mine service connections, mine tunnels and capture zones. The hydrogeologic properties

113 calculated in Chapter 4 (Table 4.3) characterize the coal layer within the lower hydrostratigraphic unit, because that is where the wells are completed.

Parameter Overburden Coal Mine (Anderson and Woessner, 1992; (Table 4.3) SCWG, 2003) Alternating layers of sandstone Geologic Make-up Mine void and shale

Hydraulic Conductivity 5.084E-06 – 5.087E-09 m/sec 1.37E-05 – 4.86E-06 m/sec

Specific Storage 7.2E-06 – 7.2E-07 m-1 1.07E-05 – 7.89E-06 m-1

Specific Yield 0.01 – 0.30 0.20 – 0.35

Porosity 0.01 – 0.30 0.31 – 0.52

Uniform recharge 10 – 15 inch/year

6000 – 7000 inch/year (for 45 m2 Subsidence Capture drainage area)

Table 5.1: Summary of the input parameters for the calibrated model

5.3.1.4 Stressors

Inflows and outflows of water to and from the mine system include percolating recharge from precipitation, stream capture into subsided overburden, flow to and from unmined adjacent coal, inter-mine transfer of water, tunnels or shafts penetrating the mine and discharge from the mine. Inflow to the Corning mine complex is mainly through precipitation that infiltrates soils and percolates through overburden, and from three subsidences captures: the Congo capture and two Rendville captures. Extensive fractures

114 caused by mine subsidence are assumed to be the prevalent avenue of surface water inflow to the system. Total drainage area for the three stream captures is 312 acres, and these captures contribute nearly 12% of the water inflow into the mine (McCament,

2004). The remaining 88% of recharge is attributed to diffuse flow through overburden and through the inter-mine transfer of water. Adjacent mines, do not directly connect to the Corning complex, as far as is known, but coal itself is a very permeable unit. So flow is expected to enter via the unmined coal seam, especially if there are higher-head adjacent mine pools.

Stoertz et al. (2001) and McCament (2004) report the average annual recharge through porous overburden of the mine complex as 10 - 15 in/year. Nearby Sunday Creek is assumed to be isolated from the coal seam and therefore does not provide recharge.

Corning discharge is the only known outflow location, averaging 2.61 cfs in 2003. The various inflows and outflows are represented by arrows in the conceptual model (Fig.

5.2).

5.3.2 Numerical Flow Model Design

The development of a groundwater flow model begins with the transformation of the conceptual model into a grid design. Input parameters that were defined for the conceptual model on the basis of ranges of estimates (from barometric pressure response, for example) are constrained through a calibration process. Those values determined to provide results consistent with the conceptual model and with the lowest error between

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observed and modeled heads at fluxes are incorporated into the final groundwater flow

model.

5.3.2.1 Grid Design

The model area (5.65 by 3.29 km) was discretized into 125 columns, 73 rows and 4

layers (Figs. 5.5 and 5.6). This discretization created a total of 36,500 nodal points

throughout the model. A uniform 45m grid spacing was used for consistency with

available geological data. The grid spacing is necessarily small due to the complexity of

both mine void geometry and the target head distribution. Variations in topography were

accounted for by inserting a Surfer (Keckler, 1994) elevation grid (Fig. 5.2) into the flow

model grid. The Surfer grid map defines the upper surface topography of the model, and

the lower surface was assigned a constant elevation of 175m (Fig 5.6).

The flow model has 4 layers (Fig. 5.6). Layer 1 represents the shallow aquifer and was

assigned a thickness of 6 to 80m based on the topographic variation in the region. Layer 2

represents a sequence of fractured bedrock lying between the shallow aquifer and the mined horizon, which has a thickness of 15m. Layer 3 contains the mined horizon. Layer

3 was assigned a uniform thickness of 4m to represent the approximate screened interval of wells monitoring the mined horizon. As the coal slope is quite small, it is represented as a horizontal layer for modeling purposes. Layer 4 represents the saturated clay layer located below the mined horizon, and was assigned a thickness of 26m.

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A’ Figure 5.5: Horizontal grid design and dimensions of the flow model, units in meters. See Figure 5.6 for AA’ profile.

A’ A

Layer 1 Layer 2 Layer 3 Layer 4

Figure 5.6: Vertical grid design and dimensions showing different layers, units in meters. Section AA’ is a particular slice along a N-S transect of Figure 5.5.

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5.3.2.2 Model Boundary Conditions

Anderson and Woessner (1992) define three types of boundary conditions used for

simulating flow regimes in groundwater models. Type I boundary conditions are known

as specified head boundary conditions where the head value is known. Type II boundary conditions are known as specified flow boundary conditions where the value of head

(flux) across the boundary is known. A no-flow boundary is a special case of Type II boundary that results from specifying the flux to be zero. Type III boundary conditions are known as head dependent flow boundaries for which flux across the boundary is calculated given a boundary head value. This type of boundary condition is sometimes called a mixed boundary condition because it relates boundary heads to boundary flows.

A Type I boundary condition, or specified/constant head boundary condition (Fig. 5.4) is assigned to the Corning mine discharge. Water level measurements taken at wells located near that area provided high-confidence head elevations for the conceptual model. A

Type II or no-flow boundary condition is assigned to the southern and south-eastern margin of the flow model, due to its depth because of its down-dip location (Fig. 5.4) and

the implied presence of flow barriers. Type II boundary conditions with non-zero flux are

assigned to the north and south-western margins (Fig. 5.4) to represent influx of water

from adjacent unmined coal aquifers, and to avoid constraining head values within those

areas.

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5.3.2.3 Model Stressors

The upper surface of the model was simulated as a specified-flux boundary representing recharge to the mine from the infiltration of precipitation. It was assumed that recharge is equal to that portion of the infiltrating precipitation that contributes to the regional groundwater budget. Additional horizontal recharge due to inter-mine flow was treated as additional flux into the system.

The model contains four different zones of recharge (Fig 5.7). There is a uniform recharge of 10 to 15 in/year through the overburden or the entire upper surface (Zone 1).

Inter-mine Flow Recharge Zone 3

Uniform Recharge Zone 1 Subsidence Capture Zone 2

Constant Head Inter-mine Flow Boundary Recharge Zone 4 No Flow Boundary

Figure 5.7: Hydrologic grid showing boundary conditions and hydrologic properties

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The model includes three subsidence captures (Zone 2): 6911 in/year for the Congo capture and 6497 inch/year for two Rendville captures modeled as a single cell (SCWG,

2003). The northern (Zone 3) and south-western and north-eastern (Zone 4) borders of the mine complex were modeled as recharge boundaries due to presumed inter-mine flows of water. Zone 3 was assigned a higher recharge rate than Zone 4 due to its up-dip location and shorter distance between adjacent mines. Mine maps, the regional dip and topographic maps were used to estimate a recharge ratio of 5:1 between Zone 3 and Zone

4. The remaining boundaries were regional divides, modeled as no flow boundaries (Fig.

5.7). The Corning discharge was modeled as a constant-head boundary cell, with a fixed head of 221.2 m. The advantage of treating the Corning discharge as a constant head cell was being able to compare the modeled discharge to the measured discharge in the model calibration. The model’s mass balance subroutine allowed calculation of the flux to this cell for comparison with the measured Corning discharge.

5.3.2.3 Model Parameters

Those hydrogeologic parameters of the groundwater flow model included: values of hydraulic conductivity, storage, specific yield and effective and total porosity. The input parameters for Layers 1, 2 and 4 (Fig. 5.6) were collected from a literature search (Table

5.1), whereas those for the mine layer were derived from the barometric pressure analysis

(Table 4.3). Although these data are variable, the values obtained provided a starting point for calibration and in this case, provides the appropriate order of magnitude for hydraulic conductivity consistent with recharge. Head observation wells (Table 2.2) were

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also placed in the MODFLOW Program Well Package as defined by the conceptual

model.

5.3.3 Model Simulation and Calibration

The flow model assumed steady state rather than transient conditions. A model simulating steady state conditions assumes that groundwater flow conditions are not

changing with respect to time. For a flow model to be used in a predictive role, it must be able to successfully simulate observed aquifer behavior. An important part of modeling is the calibration process. Flow model calibration is a process wherein certain parameters of the model such as recharge and hydraulic conductivity are altered in a systematic fashion and the model is repeatedly run until the computed solution matches field-observed values within an acceptable level of accuracy (Anderson and Woessner, 1992).

Calibration targets included water levels in five monitoring wells, fluxes from mine inflow measurements and mine discharge.

MODFLOW simulations resulted in a head distribution and velocity field over all active wells and calculations of fluxes at constant head wells, specifically the Corning discharge. To evaluate the model “error”, observed head values in the five wells were compared to modeled heads at those same wells. MODFLOW reports a Root Mean

Squared (RMS) error for each model simulation, which is described as the square root of the difference between the calculated head values determined by the model and the observed head values collected in the field. A RMS error as close to 0 or 0.1 as possible

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is desirable, as an error this low would indicate that observed and calculated head values

are equal or very close. Sequential simulation using different stressors and parameters

gave different head distributions, which were then compared to field measured heads in

the RMS error analysis.

Insight into system behavior was also obtained by performing a particle tracking test

using MODPATH (Pollock, 1989). Three particles was released in each cell at the

subsidence point and tracked forward through time. Particle travel times were evaluated by computing the median value for the time taken for all particles released at the subsidence capture to exit through the discharge point

5.4 Results and Discussion

5.4.1 Results

The model was calibrated by varying hydraulic conductivity and recharge until simulated heads and simulated flows were consistent with field data. Overburden recharge and

subsidence captures alone were not able to reproduce the approximate Corning discharge.

It is concluded that there is significant inter-mine flow to the Corning mine complex. As

there is a south-eastward (down-dip) flow trend within the mine complex and because the

adjacent mines are nearer to the northwest, it is assumed that the recharge from Zone 3

(northwestern part of the mine) is greater than the recharge at other boundaries. Holding

constant the better constrained values of overburden recharge and subsidence capture, the

flow into the mine was simulated (Table 5.2) by varying the boundary inflow from the

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Parameters Held Constant Overburden Recharge (Zone 1) 14 inch/year Subsidence Capture (Zone 2) 6911 inch/year – Congo capture 6497 inch/year – Rendville Capture

Evaluation of Zone 3 and Zone 4 Recharge vs Corning Discharge in cfs Recharge inch/year Discharge cfs 284 and 68 2.595 289 and 69 2.616 294 and 70 2.636 299 and 71 2.658

Table 5.2: Corning discharge flow calibration.

adjacent mines. A recharge rate of 289 in/year from Zone 3 (implying 69 in/year from

Zone 4 by the 5:1 ratio) reproduces an observed discharge rate of 2.61cfs from the

Corning discharge. The inter-mine flow of water is very important: it contributes nearly

45% of the total recharge to the mine complex. The Corning discharge flow rate is found to be highly sensitive to the recharge rate (Fig. 5.8), but it is insensitive to the Corning head elevation (Fig. 5.9).

Initial models indicated that the values of porosity, specific yield, and specific storage for both the overburden and mine layer had little effect on the model outputs, and consequently these values were held constant at the approximate average of the suggested

range of parameter values (Table 5.1). Tables 5.3 to 5.6 show the calibrated hydraulic

conductivities value for each layer with respect to other model input parameters held

constant during calibration. These tables report only hydraulic conductivity values near the calibration point.

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2.7

2.68

s 2.66 f c

e, 2.64 g r a

h 2.62 sc i

D 2.6

2.58

2.56 66 67 68 69 70 71 72 73 Recharge Zone 4, inch/year

Figure 5.8: Sensitivity analysis of Corning discharge to recharge.

3 s f c ,

rge 2.5 ha c s i

ng D 2 i n r o C

1.5 220 220.5 221 221.5 222 Corning Head, m

Figure 5.9: Sensitivity analysis of Corning discharge to Corning head.

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Parameters Held Constant Hydraulic Conductivity (Kx,y,z) m/s Porosity Layer 2 0.00644 Layer 2 0.25 Layer 3 0.0737 Layer 3 0.4 Layer 4 8.60E-08 Layer 4 0.2

Specific Storage Specific Yield Layer 2 4.30E-06 Layer 2 0.2 Layer 3 4.50E-05 Layer 3 0.25 Layer 4 7.20E-06 Layer 4 0.15

Overburden Recharge = 14 inch/year Constant Head Elevation = 220.66 m Evaluation of Layer 1 K (m/s) vs. Root Means Square Error (RMS) Layer 1 Kx,y,z (m/s) RMS (m) 0.000816 0.1248 0.000489 0.0149 0.000149 0.1932

Table 5.3: Calibration of hydraulic conductivity (K) for Layer 1 (Overburden)

Parameters Held Constant Hydraulic Conductivity (Kx,y,z) m/s Porosity Layer 1 0.00049 Layer 1 0.15 Layer 3 0.0737 Layer 3 0.4 Layer 4 8.60E-08 Layer 4 0.2

Specific Storage Specific Yield Layer 1 7.20E-06 Layer 1 0.15 Layer 3 4.50E-05 Layer 3 0.25 Layer 4 7.20E-06 Layer 4 0.15

Overburden Recharge = 14 inch/year Constant Head Elevation = 220.66 m Evaluation of Layer 2 K (m/s) vs. Root Means Square Error (RMS) Layer 2 Kx,y,z (m/s) RMS (m) 0.00716 0.0297 0.00644 0.0149 0.00375 0.1323

Table 5.4: Calibration of hydraulic conductivity (K) for Layer 2 (Overburden)

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Parameters Held Constant Hydraulic Conductivity (Kx,y,z) m/s Porosity Layer 1 0.00049 Layer 1 0.15 Layer 2 0.00644 Layer 2 0.25 Layer 4 8.60E-08 Layer 4 0.2

Specific Storage Specific Yield Layer 1 7.20E-06 Layer 1 0.15 Layer 2 4.30E-06 Layer 2 0.2 Layer 4 7.20E-06 Layer 4 0.15

Overburden Recharge = 14 inch/year Constant Head Elevation = 220.66 m Evaluation of Layer 3 K (m/s) vs. Root Means Square Error (RMS) Layer 3 Kx,y,z (m/s) RMS (m) 0.0981 0.0429 0.0737 0.0149 0.0318 0.1686

Table 5.5: Calibration of hydraulic conductivity (K) for Layer 3 (Mined Layer)

Parameters Held Constant Hydraulic Conductivity (Kx,y,z) m/s Porosity Layer 1 0.00049 Layer 1 0.15 Layer 2 0.00644 Layer 2 0.25 Layer 3 0.0737 Layer 3 0.4

Specific Storage Specific Yield Layer 1 7.20E-06 Layer 1 0.15 Layer 2 4.30E-06 Layer 2 0.2 Layer 3 4.50E-05 Layer 3 0.25

Overburden Recharge = 14 inch/year Constant Head Elevation = 220.66 m Evaluation of Layer 4 K (m/s) vs. Root Means Square Error (RMS) Layer 2 Kx,y,z (m/s) RMS (m) 5.20E-09 0.0153 8.60E-08 0.0149 8.10E-07 0.0155

Table 5.6: Calibration of hydraulic conductivity (K) for Layer 4 (Clay Layer)

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The lowest RMS error of 0.0149 (Fig. 5.10) was obtained for the hydraulic conductivities

of 4.89×10-4 m/sec (Layer 1), 6.44×10-3 m/sec (Layer 2), 7.37×10-2 m/sec (Layer 3) and

8.6×10-8 m/sec (Layer 4). A hydraulic conductivity value of 7.37×10-2 m/sec is within the expected range of horizontal hydraulic conductivity for the mined layer, assuming the presence of open-channel flow inside the coal mine. Development of fractures during mining may have resulted in relatively high hydraulic conductivities (Table 5.4) in Layer

2, just above the mine void. Hydraulic conductivity of Layer 4 (Table 5.6) is in the range of that for clay, which is consistent with the underclay it represents.

Figure 5.10: Flow Model RMS error for the hydraulic conductivity of mined layer.

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An order-of-magnitude variation of hydraulic conductivity for Layer 4 produced the lowest RMS error (0.001 m), whereas an order of magnitude variation for Layer 2 produced the highest RMS error (0.9 m) (Fig. 5.11), indicating that the model head are quite sensitive to K in the layer above the coal. The presence of well head/water level in

Layer 2 makes it more sensitive to the hydraulic conductivity as compared to other layers

(Fig. 5.11). The RMS value is insensitive to vertical hydraulic conductivity. The horizontal and vertical hydraulic conductivity are therefore linked through the standard anisotropy ratio of 0.1.

1.2 Layer 1 Layer 2 Layer 3 Layer 4 1

0.8 y = -0.3683x - 0.3644

m 0.6 y = -0.5712x - 1.7958 , S 0.4 RM

0.2

0 y = 5E-05x + 0.0151 y = -1.0243x - 2.2736 -0.2 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 Log K, m/sec

Figure 5.11: Sensitivity analysis of hydraulic conductivity to different layers.

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221.85 W1 W2 W3 W4 W5

221.8

221.75 m 221.7 ad, e y = 0.005x + 221.46 H 221.65 y = 0.005x + 221.46 y = 0.0047x + 221.45

221.6 y = 0.0043x + 221.43 y = 0.0041x + 221.42 221.55 20 30 40 50 60 70 80 Recharge Zone 4, inch/year

Figure 5.12: Sensitivity analysis of head of different wells to recharge.

Heads in different wells are almost equally sensitive to the recharge (Fig. 5.12). Slopes of regressions however, indicate that the wells closer to the fixed-head discharge are somewhat less sensitive to recharge rates. Wells 4 and 3, which are farther from the discharge, show the least sensitivity to the discharge. This behavior can be explained through the hydraulic-ram analog: as flow focuses toward the discharge, the gradient steepens.

The head distribution in the mined layer (Fig 5.13) shows a general down-dip direction, with the slope of the head surface steepening toward the Corning discharge. The eastern complex appears to constitute relatively low flow, but as McCament (2004) points out,

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the loading from that area is disproportionate to flow. Captured surface water joins the

mine water flowing from up-dip areas and flows in a focused path toward the discharge.

It is important to emphasize, however, that the model assumes an equivalent porous

medium and does not explicitly model rooms and pillars. The actual flow paths may be

tortuous.

Figure 5.13: Head distribution and flow direction within the mined layer.

A particle tracking analysis of water flow inside the mine (Layer 3) indicates that water moves southeast-south from the Rendville captures to the discharge (Fig. 5.13), whereas the Congo capture has an east-southeast flow direction. Particles move relatively slowly

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inside the complex, with rapid movement close to the discharge point. The average

particle travel time to discharge from the Congo capture is greater than ten years, whereas

that from Rendville captures is greater than eleven years.

5.4.2 Model Limitations

A major problem with modeling the Corning Mine Complex using MODFLOW is the

lack of data, particularly for the spatial distribution of heads within the workings.

Uncertainty of parameter values and boundary conditions often results in predictions

from numerical models that are not unique, and it is difficult to evaluate how model

structure and averaging of hydraulic conductivity values affect these predictions. The complex geometry and high gradients of the strata that make up the aquifer, made retaining water at the edge of the aquifer difficult. The instability that resulted from these areas drying out prevented a good calibration from being achieved.

The main drawback of using MODFLOW is that standard groundwater modeling approaches are inappropriate for the coal mining environment. All such software uses

Darcy’s law which assumes a simple porous media in which laminar flow occurs. A coalfield consists of interconnected workings, areas in which the roof has collapsed and tracts of unworked coal which act as barriers. Flow in the unworked coal measures will be Darcian, but in the workings the flow is likely to be turbulent; comparable to stream flow rather than groundwater flow. This is particularly true when the coalfield covers a relatively small area and has steeply dipping geology. The assumptions necessary in the

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EPM conceptual model make the model result in a gross simplification. If further modeling with an off-the-shelf package is deemed necessary, then a discrete fracture or dual-porosity model would perhaps be more appropriate, although parameterization of

such a complex model would be virtually impossible for a mine complex.

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Chapter 6: Conclusions and Recommendations

With the presence of numerous abandoned coal mines, AMD is likely to be a continuing problem in the southeastern Ohio. Understanding the flow-system behavior of mine complexes is therefore critical to remediation of the AMD problem caused by those mines. The ability of existing hydrogeologic and hydrogeochemical studies to represent this complex environment using the sparse data sets that are available is limited.

Therefore some new approaches (time series analysis, barometric pressure analysis and numerical flow modeling) have been developed to predict the flow dynamics inside the underground mine systems. These methods are not data intensive and make allowances for the accuracy of the data that are available.

A combination of time series analysis/physical properties of mine complex and geologic evidence can provide useful information about the hydrogeology of a region prior to large-scale and expensive tests. This type of study has several advantages: (a) lack of disturbance to the groundwater system; (b) relative simplicity in terms of the statistical method; (c) low cost associated with analysis of data from existing wells and measurable discharges.

6.1 Time Series Analysis

Time series analysis, which uses the concept of correlation and spectral analysis,

considers an underground mine as a filter that transforms, directly transmits or attenuates

the input signal (precipitation) in the creation of an output signal (discharge and head).

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To understand the hydrodynamic characteristics of the Corning mine system, five monitoring-well hydrographs, the Corning mine-discharge hydrograph, and precipitation were analyzed by employing time series techniques in both time and frequency domains.

Results are presented as correlograms, variance spectra, phase diagrams, coherency diagrams and cross correlograms. The following conclusions summarize the major findings:

1. The response time of the mine is very short, around 9 days. The short response

time of the Corning mine complex indicates that the mine is an analogue to an

intermediate karstified system.

2. A time lag of 3 to 4 days is found between precipitation and the response of water

levels and mine discharge. Varying lags between precipitation and water levels in

different wells are attributed to different path lengths of percolating water from

capture points to the mine. The large time lag for Wells 4 and 5 indicates that

recharge to the western most portion of the mine traverses a thick unsaturated

zone, consistent with thicker overburden for Wells 4 and 5.

3. The mine has an intermediate to small storage capacity, such that water is

displaced within a short time period. The storage capacity of a typical aquifer

reflects the precipitation and discharge cycles. Water storage in an aquifer

increases during the recharge period, and decreases as the water is released during

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the dry season. This storage-and-release behavior would not be observed in a

highly mined system that would simply not store as much water. The intermediate

to small storage of the Corning system is attributed to the presence of multiple

tunnels directly linked to the mine discharge. The high variability of the Corning

discharge (0.89 to 5.45 cfs) is consistent with a low-storage system.

4. Flow within the mine system is dominated by a quickflow component, with the

presence of minor but important baseflow components. The baseflow components

are important because poor water quality released during periods of low stream-

flow in Sunday Creek can be devastating because of little dilution.

5. Differences in the coherence function among monitoring-well hydrographs

indicates that the mine pool is not a single reservoir, but is a series of

interconnected areas separated by some degree of hydraulic resistance.

Heterogeneous recharge is expected to result in heterogeneous water levels,

unless recharge replenishes a single pool. The differing responses in the wells

confirm the existence of separate, though interconnected pools.

6.2 Barometric Pressure and Tidal Loading Analysis

Water level of the mine has a significant response to barometric pressure and tidal loading. All of the hydrographs show a cyclic and semi-diurnal pattern. Short-period semi-diurnal water level and barometric fluctuations are superimposed on longer-period

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fluctuations caused by changes in weather, seasonal recharge and Earth tides. Variations

in water level fluctuations among wells are due in large part to the different manner in

which the pressure is propagated through the water column in the well and the porous media surrounding the well.

The quantitative study of water level fluctuations due to barometric and tidal loading

yields important information about aquifer poroelastic properties (Roeloffs, 1995), and by

extension, mine properties. Properties include barometric efficiency and specific storage

parameters and hydraulic conductivity. With a few assumptions, these parameters are

used to calculate aquifer storage properties such as specific storage, storativity, porosity

and matrix compressibility.

1. Aquifer properties for an underground mine aquifer system can be estimated by

analyzing the water level frequency response to atmospheric loading and Earth

tides. Comparison of the frequency response estimates with the available

literature values indicates that the aquifer properties determined in this study

(Table 4.3) have reasonable values. The agreement among parameters indicates

that the use of barometric pressure and tidal loading analyses is appropriate for a

mine system. Frequency response hydrograph analysis can be used to confirm

aquifer properties estimated by traditional methods, as an alternative if traditional

methods are cost prohibitive, or as a means to gain a general understanding of a

system prior to extensive monitoring for a project.

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2. The water level response to barometric pressure yields a range of barometric

efficiencies for the mine aquifer that is consistent with the 30 to 80% pore

pressure coefficient suggested for karst aquifers.

3. Barometric efficiency is highest for relatively small values of specific storage as

expected. The small variation in specific storage demonstrates the small variation

in the storativity of a comparatively inelastic mine aquifer in the unstressed

(natural) and stressed (unnatural) states respectively.

4. The correlation analysis (Table 4.6) indicates that the highest correlation is

between Wells 4 and 5, meaning that the regions around wells have similar

aquifer properties and that the properties between the wells tend to vary together.

There is also a strong correlation between Wells 2 and 3. Well 1 has distinctly

different properties and is not highly correlated with the other wells.

6.3 Flow Modeling

A regional-scale numerical flow model of the mine approximately represents reported field conditions for the Corning mine complex. Modeling was achieved using

MODFLOW, a quasi-three dimensional groundwater modeling program. The model was calibrated by varying model parameters and recharge within expected ranges based on the barometric and tidal analysis and field studies until simulated heads and simulated flows were in close agreement with the field data. On the regional scale of this model, the

137 assumption of equivalent porous media for fractured rock and mine workings was adequate for model calibration. The simulations and predictions of this model, while not absolutely accurate, should be of use to public managers and researchers who need to make decisions regarding water quality and remediation. The following conclusions summarize the main findings of the modeling:

1. Model calibrated water levels are acceptably close to calibration targets, given

fluctuations in measured water levels, generally within 0.5 to 1m. Modeled flows

are more than 90% of the measured flows, which are also acceptably close to the

calibration targets. Flow modeling simulation indicated that the values of

porosity, specific yield, and specific storage for both the overburden and mine

layer had a relatively small to no effect at all on the model calibration.

2. The overburden infiltration and stream capture can only account for about half of

the flow measured at Corning. The remainder, based on the heads measured in the

wells, appears to derive from the surrounding mines. It contributes 45% of the

total recharge water to the mine complex. The recharge rate of 289 in/year from

Zone 3 and that of 69 in/year from Zone 4 can reproduce an observed discharge

rate of 2.61 cfs from the Corning discharge.

3. Hydraulic conductivity values of 4.89×10-4 m/sec (Layer 1), 6.44×10-3 m/sec

(Layer 2), 7.37×10-2 m/sec (Layer 3) and 8.6×10-8 m/sec (Layer 4) reproduced the

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lowest RMS error of 0.0149 for the calibrated model. A hydraulic conductivity

value of 7.37×10-2 m/sec is within the excepted range of horizontal hydraulic

conductivity for the mined layer, assuming the presence of open channel flow

inside the coal mine. Development of fractures during the mining activities

resulted higher hydraulic conductivities for the layers just above the mine void.

This layer is also most sensitive to the change in hydraulic conductivity as

compared to other layers. Again, change in vertical hydraulic conductivities is not

that significant to the model calibration.

4. Particle tracking of water captured at the Rendville subsidences shows that the

travel time to the discharge is greater than eleven years. Travel time for water

captured at Congo capture is similarly greater than ten years. The implications for

in-situ treatment are that passive transport of injected reagents or nutrients will be

very slow. Therefore, injected substances will have to be introduced at multiple

points or under a strong gradient.

6.4 Strength and Weaknesses of Different Analysis:

Understanding flow systems dynamics of the mine complexes is possible with the time series analysis, water level frequency analysis and a hydrologic model. These methods provide insight essential to develop remediation plans for Corning as well as other large acid mine drainage discharges in the region. Scientifically, the research provides insight

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into characteristics and processes controlling flow in abandoned underground mines, a

poorly understood area of hydrogeology.

Time series analysis is helpful for characterizing the underground mine systems. The

method provides useful information about the hydrogeology of a region prior to large

scale and expensive tests. The barometric pressure and tidal analyses are possible only by

making numerous simplifying assumptions, the most important being that of confined conditions. The overall signal quality, validity of assumptions, accuracy of the theoretical earth tide strain computations, and validity of the assumed constants all can affect the final parameter estimates. A longer period of record and less interval measurement is required to establish conclusively the mutual exclusivity of these responses.

The availability and reliability of data will always be a limiting factor when modeling the

mine environment. On the regional scale of this model, the assumption of equivalent

porous media for fractured rock and mine workings is adequate for model calibration.

While the simulations and predictions of this model are not absolutely accurate, because

flow may cease to be laminar under higher-gradients, they may be accurate enough to be

of use to mine planners and regulatory officials who need to make decisions regarding

water quality and remediation.

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6.5 Recommendations

Accurate maps of mine-workings are essential to work of this type. Particularly important are the locations of major roadways and their condition. A detailed knowledge of the head distribution would enable more enlightened assessment of flow regimes and likely storage capacity. The storage coefficient is likely to be the most important parameter in any model of mine workings. Continuous data regarding changes in water level are therefore invaluable. Long-term monitoring of the well heads and flow rates may shed light on the seasonality of the Corning discharge.

A proper appreciation of surface hydrology would allow accurate estimates of recharge to the mine workings over time, as would detail data regarding inflows from the adjacent aquifers and the unworked strata. A very thorough hydrogeologic investigation involving a large number of boreholes and monitoring well measurements would likely be necessary to fully understand the hydrodynamics of the Corning mine complex. This understanding is necessary for successfully treating acidic mine water within the Corning mine complex.

Development of a more comprehensive water quality model would aid the planning of treatment techniques. In situations where more data are available, a good choice of existing models would be a model of the fracture flow genre. Development of a more complex minewater model should concentrate on representing flow in three differing environments: the rock matrix, the goaf and along roadways.

141

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149

APPENDICES

150

Appendix I – Corning Mine Complex Time Series Data 1 2 D a t e Tim e BP T idal Strain Daily pp t . Dis c h a rge D a t e Tim e BP Tidal Strain D a ily pp t . Dis c harge (m eter o f H2 O) ( inches) (cfs) ( m e ter o f H2 O) ( in c h e s) ( c fs) 6/ 2/ 2003 14: 00 10. 40648 1. 88723E- 0 7 0 .000 3.4390 6 /14/ 2003 8: 00 10. 33052 6 .59967E- 0 8 0 .010 3.064 4 6/ 2/ 2003 20: 00 10. 38231 -1 .05946E- 0 7 0 .000 3.4433 6/ 14/ 2003 14: 00 10. 34088 1 .69108E- 0 7 0 .010 3.084 1 6/ 3/ 2003 2: 00 10. 36159 -6 .51419E- 0 8 0 .260 3.4629 6/ 14/ 2003 20: 00 10. 33743 -1 .41216E- 0 7 0 .010 3.052 0 6/ 3/ 2003 8: 00 10. 33052 -5 .34902E- 0 8 0 .260 3.5219 6 /15/ 2003 2: 00 10. 36159 -9 .64982E- 0 8 0 .003 3.047 6 6/ 3/ 2003 14: 00 10. 29599 1. 91287E- 0 7 0 .260 3.5128 6 /15/ 2003 8: 00 10. 35469 2 .96918E- 0 9 0 .003 3.021 3 6/ 3/ 2003 20: 00 10. 28564 -8 .55662E- 0 8 0 .260 3.4888 6/ 15/ 2003 14: 00 10. 37541 2 .11531E- 0 7 0 .003 3.054 4 6/ 4/ 2003 2: 00 10. 29599 -6 .41972E- 0 8 0 .003 3.4974 6/ 15/ 2003 20: 00 10. 36505 -1 .35997E- 0 7 0 .003 3.023 2 6/ 4/ 2003 8: 00 10. 29945 -6 .88309E- 0 8 0 .003 3.4821 6 /16/ 2003 2: 00 10. 37541 -7 .76035E- 0 8 0 .085 3.017 0 6/ 4/ 2003 14: 00 10. 32016 1. 73652E- 0 7 0 .003 3.4744 6 /16/ 2003 8: 00 10. 37541 -4 .74618E- 0 8 0 .085 3.010 3 6/ 4/ 2003 20: 00 10. 32361 -5 .71341E- 0 8 0 .003 3.4323 6/ 16/ 2003 14: 00 10. 38922 2 .20754E- 0 7 0 .085 3.006 0 6/ 5/ 2003 2: 00 10. 35124 -6 .68964E- 0 8 0 .003 3.4299 6/ 16/ 2003 20: 00 10. 37886 -1 .14853E- 0 7 0 .085 2.995 9 6/ 5/ 2003 8: 00 10. 36505 -6 .82011E- 0 8 0 .003 3.4423 6 /17/ 2003 2: 00 10. 38922 -6 .35674E- 0 8 0 .063 3.010 3 6/ 5/ 2003 14: 00 10. 36850 1. 36672E- 0 7 0 .003 3.4347 6 /17/ 2003 8: 00 10. 37541 -7 .49042E- 0 8 0 .063 3.018 9 6/ 5/ 2003 20: 00 10. 38922 -2 .49231E- 0 8 0 .003 3.4500 6/ 17/ 2003 14: 00 10. 36159 1 .997E- 0 7 0 .063 3.047 6 6/ 6/ 2003 2: 00 10. 39957 -7 .36895E- 0 8 0 .005 3.4299 6/ 17/ 2003 20: 00 10. 34433 -8 .55213E- 0 8 0 .063 3.052 0 6/ 6/ 2003 8: 00 10. 41339 -4 .91714E- 0 8 0 .005 3.4917 6 /18/ 2003 2: 00 10. 34433 -5 .61444E- 0 8 0 .000 3.056 7 6/ 6/ 2003 14: 00 10. 39957 8. 46665E- 0 8 0 .005 3.5472 6 /18/ 2003 8: 00 10. 34433 -7 .71986E- 0 8 0 .000 3.054 4 6/ 6/ 2003 20: 00 10. 36850 5. 44349E- 0 9 0 .005 3.5238 6/ 18/ 2003 14: 00 10. 34088 1 .57906E- 0 7 0 .000 3.111 8 6/ 7/ 2003 2: 00 10. 36505 -8 .45765E- 0 8 0 .138 3.5760 6/ 18/ 2003 20: 00 10. 31326 -5 .56496E- 0 8 0 .000 3.104 2 6/ 7/ 2003 8: 00 10. 31671 -1 .22366E- 0 8 0 .138 3.5717 6 /19/ 2003 2: 00 10. 31326 -5 .51097E- 0 8 0 .000 3.122 9 6/ 7/ 2003 14: 00 10. 32361 2. 59578E- 0 8 0 .138 3.5904 6 /19/ 2003 8: 00 10. 29599 -5 .82588E- 0 8 0 .000 3.131 5 6/ 7/ 2003 20: 00 10. 32361 2. 75773E- 0 8 0 .138 3.5492 6/ 19/ 2003 14: 00 10. 30635 1 .06665E- 0 7 0 .000 3.109 4 6/ 8/ 2003 2: 00 10. 33743 -9 .88376E- 0 8 0 .013 3.5583 6/ 19/ 2003 20: 00 10. 32707 -3 .07265E- 0 8 0 .000 3.045 7 6/ 8/ 2003 8: 00 10. 33397 3. 83294E- 0 8 0 .013 3.5846 6 /20/ 2003 2: 00 10. 34433 -5 .92036E- 0 8 0 .000 3.025 6 6/ 8/ 2003 14: 00 10. 33052 -2 .77123E- 0 8 0 .013 3.4831 6 /20/ 2003 8: 00 10. 35469 -2 .5058E- 0 8 0 .000 3.032 3 6/ 8/ 2003 20: 00 10. 34088 3. 52702E- 0 8 0 .013 3.4289 6/ 20/ 2003 14: 00 10. 36505 5 .58295E- 0 8 0 .000 3.040 9 6/ 9/ 2003 2: 00 10. 34778 -1 .14673E- 0 7 0 .003 3.4299 6/ 20/ 2003 20: 00 10. 35469 -1 .36312E- 0 8 0 .000 3.007 9 6/ 9/ 2003 8: 00 10. 34778 9.3 754E- 0 8 0 .003 3.4313 6 /21/ 2003 2: 00 10. 35469 -6 .67165E- 0 8 0 .000 3.012 2 6/ 9/ 2003 14: 00 10. 36159 -6 .27576E- 0 8 0 .003 3.4179 6 /21/ 2003 8: 00 10. 35469 1 .51608E- 0 8 0 .000 3.039 0 6/ 9/ 2003 20: 00 10. 37541 2. 43832E- 0 8 0 .003 3.3571 6/ 21/ 2003 14: 00 10. 35469 1 .24166E- 0 8 0 .000 3.066 3 6/ 10/ 2003 2: 00 10. 39612 -1 .29249E- 0 7 0 .000 3.3307 6/ 21/ 2003 20: 00 10. 34433 -5 .3985E- 0 9 0 .000 3.029 9 6/ 10/ 2003 8: 00 10. 39612 1. 41351E- 0 7 0 .000 3.3849 6 /22/ 2003 2: 00 10. 34778 -7 .60739E- 0 8 0 .000 3.025 6 6/ 10/ 2003 14: 00 10. 38922 -6 .72563E- 0 8 0 .000 3.4289 6 /22/ 2003 8: 00 10. 35469 5 .60994E- 0 8 0 .000 3.057 7 6/ 10/ 2003 20: 00 10. 37886 -5 .0386E- 0 9 0 .000 3.4222 6/ 22/ 2003 14: 00 10. 36159 -1 .90747E- 0 8 0 .000 3.584 6 6/ 11/ 2003 2: 00 10. 36505 -1 .38876E- 0 7 0 .005 3.4390 6/ 22/ 2003 20: 00 10. 34088 -5 .66843E- 0 9 0 .000 3.536 2 6/ 11/ 2003 8: 00 10. 35814 1. 67354E- 0 7 0 .005 3.4390 6 /23/ 2003 2: 00 10. 35124 -8 .58812E- 0 8 0 .000 3.517 5 6/ 11/ 2003 14: 00 10. 37541 -3 .59E- 0 8 0 .005 3.1272 6 /23/ 2003 8: 00 10. 36159 9 .25843E- 0 8 0 .000 3.549 2 6/ 11/ 2003 20: 00 10. 31671 -4 .73269E- 0 8 0 .005 3.0951 6/ 23/ 2003 14: 00 10. 37541 -3 .617E- 0 8 0 .000 3.573 6 6/ 12/ 2003 2: 00 10. 32016 -1 .40406E- 0 7 0 .038 3.1161 6/ 23/ 2003 20: 00 10. 36505 -1 .34063E- 0 8 0 .000 3.517 5 6/ 12/ 2003 8: 00 10. 29945 1.6 2 E- 07 0. 038 3.1094 6 /24/ 2003 2: 00 10. 38231 -9 .50136E- 0 8 0 .000 3.493 1 6/ 12/ 2003 14: 00 10. 29599 2. 57329E- 0 8 0 .038 3.1794 6 /24/ 2003 8: 00 10. 39612 1 .20657E- 0 7 0 .000 3.519 5 6/ 12/ 2003 20: 00 10. 28564 -9 .16396E- 0 8 0 .038 3.1252 6/ 24/ 2003 14: 00 10. 40993 -3 .77895E- 0 8 0 .000 3.532 9 6/ 13/ 2003 2: 00 10. 29878 -1 .32398E- 0 7 0 .008 3.1085 6/ 24/ 2003 20: 00 10. 40303 -2 .71725E- 0 8 0 .000 3.486 4 6/ 13/ 2003 8: 00 10. 29945 1. 24795E- 0 7 0 .008 3.0975 6 /25/ 2003 2: 00 10. 40993 -1 .02482E- 0 7 0 .000 3.469 6 6/ 13/ 2003 14: 00 10. 32707 1. 01267E- 0 7 0 .008 3.1205 6 /25/ 2003 8: 00 10. 41684 1 .37167E- 0 7 0 .000 3.517 5 6/ 13/ 2003 20: 00 10. 30980 -1 .25785E- 0 7 0 .008 3.0951 6/ 25/ 2003 14: 00 10. 41684 -2 .41133E- 0 8 0 .000 3.555 9 6/ 14/ 2003 2: 00 10. 32707 -1 .16428E- 0 7 0 .010 3.0864 6/ 25/ 2003 20: 00 10. 38576 -4 .51675E- 0 8 0 .000 3.519 5

151

Appendix I – Corning Mine Complex Time Series Data 3 4 Da te Tim e BP Tida l Stra in Daily ppt. D ischar ge D a te T im e B P Ti da l Stra in Da ily ppt. Discha rge (m et er o f H 2 O) (inches) (cfs) ( m e ter o f H 2 O ) (inches) (cf s ) 6/2 6 /2 003 2 :00 10. 38 922 - 1 .072 95 E- 07 0. 000 3.5 3 7 2 7 /7/20 03 20 :00 1 0 .37 19 5 - 3 .03 66 6E- 0 8 0 .10 3 3 .54 05 6 /26 /2 0 0 3 8: 0 0 1 0 .3 7 1 9 5 1 .39 86 6E -0 7 0 .0 0 0 3 .59 37 7/ 8 /20 03 2: 0 0 1 0 .3 85 76 -7 .6 38 88 E - 0 8 0. 3 3 0 3 .5 47 2 6/26 /20 0 3 14 :00 10. 35 469 3.4 1 9 0 5 E - 0 9 0 .000 3.6 4 3 0 7 /8/2 0 0 3 8 :00 10 .36 8 5 0 1. 270 45 E- 07 0 .33 0 3 .53 9 6 6/26 /20 0 3 20 :00 10. 33 052 - 6 .532 19 E- 08 0. 000 3.6 0 3 3 7 /8/20 03 14 :00 1 0 .39 61 2 - 6 .20 37 8E- 0 8 0 .33 0 3 .58 27 6/2 7 /2 003 2 :00 10. 34 088 - 1 .08 5 1 E - 0 7 0 .000 3.5 7 1 7 7 /8/20 03 20 :00 1 0 .37 88 6 - 3 .58 10 1E- 0 8 0 .33 0 3 .53 72 6/2 7 /2 003 8 :00 10. 35 814 1. 276 3E- 0 7 0 .000 3.5 2 1 9 7 /9/2 0 0 3 2 :00 10 .39 2 6 7 - 1 .02 1 6 7 E- 07 0 .01 0 3 .57 3 6 6/27 /20 0 3 14 :00 10. 38 231 4.1 8 8 3 4 E - 0 8 0 .000 3.5 5 1 6 7 /9/2 0 0 3 8 :00 10 .35 8 1 4 1. 743 27 E- 07 0 .01 0 3 .57 3 6 6/27 /20 0 3 20 :00 10. 36 505 - 8 .534 13 E- 08 0. 000 3.5 1 7 5 7 /9/20 03 14 :00 1 0 .35 81 4 - 5 .99 23 4E- 0 8 0 .01 0 3 .61 24 6/2 8 /2 003 2 :00 10. 37 541 - 1 .054 51 E- 07 0. 000 3.5 0 8 4 7 /9/20 03 20 :00 1 0 .35 12 4 - 5 .18 25 6E- 0 8 0 .01 0 3 .59 37 6 /28 /2 0 0 3 8: 0 0 1 0 .3 7 8 8 6 1 .01 17 7E -0 7 0 .0 0 0 3 .53 29 7/ 1 0 /20 03 2: 0 0 1 0 .3 44 33 -1 .2 40 31 E - 0 7 0. 0 9 0 3 .6 19 1 6/28 /20 0 3 14 :00 10. 38 231 8.6 6 4 5 9 E - 0 8 0 .000 3.5 5 1 6 7 /10/2 0 0 3 8 :00 10 .32 3 6 1 1. 938 96 E- 07 0 .09 0 3 .65 7 4 6/28 /20 0 3 20 :00 10. 37 541 - 1 .027 06 E- 07 0. 000 3.5 0 4 1 7/1 0 /2 00 3 1 4 :0 0 1 0 .29 94 5 - 2 .61 37 7E- 0 8 0 .09 0 3 .68 85 6/2 9 /2 003 2 :00 10. 38 576 - 9 .784 78 E- 08 0. 000 3.4 9 5 0 7/1 0 /2 00 3 2 0 :0 0 1 0 .27 87 3 - 7 .49 49 2E- 0 8 0 .09 0 3 .65 74 6 /29 /2 0 0 3 8: 0 0 1 0 .3 9 6 1 2 6 .32 07 5E -0 8 0 .0 0 0 3 .49 98 7/ 1 1 /20 03 2: 0 0 1 0 .2 85 64 -1 .3 57 27 E - 0 7 0. 0 0 0 3 .6 86 2 6/29 /20 0 3 14 :00 10. 41 684 1.3 1 4 5 4 E - 0 7 0 .000 3.5 2 3 8 7 /11/2 0 0 3 8 :00 10 .28 5 6 4 1 .79 41 E- 0 7 0 .00 0 3 .64 3 0 6/29 /20 0 3 20 :00 10. 42 029 - 1 .150 33 E- 07 0. 000 3.4 8 6 4 7/1 1 /2 00 3 1 4 :0 0 1 0 .31 67 1 3 .099 64 E- 08 0 .00 0 3 .63 2 0 6/3 0 /2 003 2 :00 10. 42 374 - 8 .601 61 E- 08 0. 000 3.4 7 4 4 7/1 1 /2 00 3 2 0 :0 0 1 0 .31 32 6 - 9 .96 02 4E- 0 8 0 .00 0 3 .58 70 6 /30 /2 0 0 3 8: 0 0 1 0 .4 2 3 7 4 1 .87 59 8E -0 8 0 .0 0 0 3 .47 64 7/ 1 2 /20 03 2: 0 0 1 0 .3 30 52 -1 .3 32 53 E - 0 7 0. 0 4 5 3 .5 73 6 6/30 /20 0 3 14 :00 10. 43 755 1.6 8 8 8 3 E - 0 7 0 .000 3.5 1 7 5 7 /12/2 0 0 3 8 :00 10 .33 7 4 3 1. 342 88 E- 07 0 .04 5 3 .57 3 6 6/30 /20 0 3 20 :00 10. 41 339 - 1 .202 97 E- 07 0. 000 3.4 8 6 4 7/1 2 /2 00 3 1 4 :0 0 1 0 .35 12 4 9 .618 33 E- 08 0 .04 5 3 .58 2 7 7 /1/20 03 2:0 0 10. 42 374 - 7 .112 53 E- 08 0. 095 3.4 9 5 0 7/1 2 /2 00 3 2 0 :0 0 1 0 .34 77 8 - 1 .19 71 2E- 0 7 0 .04 5 3 .51 52 7 /1/20 03 8:0 0 10. 40 993 - 2 .496 81 E- 08 0. 095 3.5 3 4 8 7 /13/2 0 0 3 2 :00 10 .38 5 7 6 - 1 .16 7 8 8 E- 07 0 .00 0 3 .49 9 8 7/1 /20 03 14 :00 10. 39 957 1.9 1 7 3 7 E - 0 7 0 .095 3.5 5 8 3 7 /13/2 0 0 3 8 :00 10 .39 2 6 7 7. 094 53 E- 08 0 .00 0 3 .49 3 1 7/1 /20 03 20 :00 10. 37 195 - 1 .175 07 E- 07 0. 095 3.5 6 2 6 7/1 3 /2 00 3 1 4 :0 0 1 0 .41 33 9 1 .522 38 E- 07 0 .00 0 3 .52 6 2 7 /2/20 03 2:0 0 10. 36 159 - 5 .528 97 E- 08 0. 000 3.5 9 2 3 7/1 3 /2 00 3 2 0 :0 0 1 0 .39 26 7 - 1 .30 91 4E- 0 7 0 .00 0 3 .49 31 7 /2/20 03 8:0 0 10. 33 397 - 5 .987 84 E- 08 0. 000 3.6 2 5 8 7 /14/2 0 0 3 2 :00 10 .39 6 1 2 - 9 .04 2 4 9 E- 08 0 .00 0 3 .49 5 0 7/2 /20 03 14 :00 10. 33 397 1.9 4 3 0 1 E - 0 7 0 .000 3.6 5 7 4 7 /14/2 0 0 3 8 :00 10 .39 6 1 2 6 .11 83 E- 0 9 0 .00 0 3 .51 7 5 7/2 /20 03 20 :00 10. 31 671 - 1 .06 8 E- 0 7 0. 000 3.6 3 0 1 7/1 4 /2 00 3 1 4 :0 0 1 0 .40 99 3 1 .864 28 E- 07 0 .00 0 3 .53 4 8 7 /3/20 03 2:0 0 10. 32 361 - 4 .111 86 E- 08 0. 000 3.6 2 3 4 7/1 4 /2 00 3 2 0 :0 0 1 0 .39 95 7 - 1 .31 76 8E- 0 7 0 .00 0 3 .51 08 7 /3/20 03 8:0 0 10. 31 671 - 7 .818 83 E- 08 0. 000 3.6 4 1 1 7 /15/2 0 0 3 2 :00 10 .39 6 1 2 - 6 .06 8 8 2 E- 08 0 .01 0 3 .52 1 9 7/3 /20 03 14 :00 10. 33 052 1.7 4 0 1 2 E - 0 7 0 .000 3.6 7 0 8 7 /15/2 0 0 3 8 :00 10 .38 2 3 1 - 4 .52 5 7 4 E- 08 0 .01 0 3 .56 0 2 7/3 /20 03 20 :00 10. 32 016 - 8 .984 01 E- 08 0. 000 3.6 0 3 3 7/1 5 /2 00 3 1 4 :0 0 1 0 .37 54 1 1 .937 16 E- 07 0 .01 0 3 .61 6 7 7 /4/20 03 2:0 0 10. 34 088 - 3 .171 62 E- 08 0. 010 3.5 8 7 0 7/1 5 /2 00 3 2 0 :0 0 1 0 .34 08 8 - 1 .23 71 6E- 0 7 0 .01 0 3 .56 69 7 /4/20 03 8:0 0 10. 34 778 - 7 .422 94 E- 08 0. 010 3.6 1 0 0 7 /16/2 0 0 3 2 :00 10 .33 7 4 3 - 3 .40 1 0 6 E- 08 0 .00 0 3 .53 7 2 7/4 /20 03 14 :00 10. 35 814 1.3 2 7 1 3 E - 0 7 0 .010 3.6 1 9 1 7 /16/2 0 0 3 8 :00 10 .36 5 0 5 - 7 .43 1 9 4 E- 08 0 .00 0 3 .50 4 1 7/4 /20 03 20 :00 10. 35 469 - 6 .964 07 E- 08 0. 010 3.5 6 5 0 7/1 6 /2 00 3 1 4 :0 0 1 0 .39 61 2 1 .766 21 E- 07 0 .00 0 3 .48 5 5 7 /5/20 03 2:0 0 10. 34 433 - 2 .969 18 E- 08 0. 000 3.5 8 7 0 7/1 6 /2 00 3 2 0 :0 0 1 0 .39 26 7 - 1 .0 986 E- 07 0 .00 0 3 .43 6 6 7 /5/20 03 8:0 0 10. 35 469 - 4 .620 22 E- 08 0. 000 3.5 8 7 0 7 /17/2 0 0 3 2 :00 10 .40 9 9 3 - 1 .46 6 5 9 E- 08 0 .00 0 3 .42 8 9 7/5 /20 03 14 :00 10. 38 231 7.6 8 8 3 7 E - 0 8 0 .000 3.6 0 8 1 7 /17/2 0 0 3 8 :00 10 .41 3 3 9 - 7 .9 088 E- 08 0 .00 0 3 .44 0 9 7/5 /20 03 20 :00 10. 34 778 - 5 .020 61 E- 08 0. 000 3.5 8 8 9 7/1 7 /2 00 3 1 4 :0 0 1 0 .43 06 5 1 .422 51 E- 07 0 .00 0 3 .46 5 3 7 /6/20 03 2:0 0 10. 36 159 - 3 .679 98 E- 08 0. 013 3.5 6 6 9 7/1 7 /2 00 3 2 0 :0 0 1 0 .40 64 8 - 9 .3 754 E- 08 0 .00 0 3 .46 2 9 7 /6/20 03 8:0 0 10. 37 195 2.9 6 9 1 8 E - 0 9 0 .013 3.5 9 4 7 7 /18/2 0 0 3 2 :00 10 .39 9 5 7 - 4 .63 3 7 1 E- 09 0 .00 0 3 .48 7 4 7/6 /20 03 14 :00 10. 36 850 1.7 0 9 5 3 E - 0 8 0 .013 3.6 2 7 7 7 /18/2 0 0 3 8 :00 10 .37 8 8 6 - 6 .27 5 7 6 E- 08 0 .00 0 3 .51 9 5 7/6 /20 03 20 :00 10. 35 124 - 3 .58 5 5 E - 0 8 0 .013 3.5 6 5 0 7/1 8 /2 00 3 1 4 :0 0 1 0 .37 19 5 9 .906 25 E- 08 0 .00 0 3 .55 5 9 7 /7/20 03 2:0 0 10. 36 850 - 5 .308 53 E- 08 0. 103 3.5 7 7 9 7/1 8 /2 00 3 2 0 :0 0 1 0 .35 46 9 - 7 .82 33 3E- 0 8 0 .00 0 3 .49 98 7 /7/20 03 8:0 0 10. 37 195 6. 496 2E- 0 8 0 .103 3.5 7 3 6 7 /19/2 0 0 3 2 :00 10 .37 8 8 6 - 3 .82 3 9 4 E- 09 0 .00 0 3 .48 7 4 7/7 /20 03 14 :00 10. 36 850 - 3 .347 07 E- 08 0. 103 3.5 3 9 6 7 /19/2 0 0 3 8 :00 10 .37 5 4 1 - 3 .12 2 1 3 E- 08 0 .00 0 3 .49 3 1

152

Appendix I – Corning Mine Complex Time Series Data 5 6 Da te Tim e BP T ida l Stra in Da ily ppt . Discha rg e Da te Tim e BP T ida l Stra in Da ily ppt . D ischa r g e (m eter of H2O) (inc he s) ( c f s ) ( m e ter of H2O) (inc he s) ( c f s ) 7/ 19/ 2003 14 :00 10.3788 6 5 .47498E -0 8 0 .000 3. 5195 7/ 31/ 2003 8: 00 10.3615 9 -7.35996E -0 8 0 .003 3. 5482 7/ 19/ 2003 20 :00 10.3685 0 -6.52769E -0 8 0 .000 3. 4807 7/ 31/ 2003 14 :00 10.3650 5 1 .70863E -0 7 0 .003 3. 5693 7/ 20/ 2003 2: 00 10.3754 1 -1. 0932E- 0 8 0 .000 3. 4931 7/ 31/ 2003 20 :00 10.3546 9 -1. 2583E- 0 7 0 .003 3. 5626 7/ 20/ 2003 8: 00 10.3685 0 9 .13246E -0 9 0 .000 3. 5238 8/ 1/ 2003 2 :00 10.3512 4 8 .23271E -0 9 0 .000 3. 5717 7/ 20/ 2003 14 :00 10.3581 4 1 .51608E -0 8 0 .000 3. 5693 8/ 1/ 2003 8 :00 10.3512 4 -9.24943E -0 8 0 .000 3. 5372 7/ 20/ 2003 20 :00 10.3443 3 -5.60544E -0 8 0 .000 3. 5540 8/ 1/ 2003 14: 00 10.3719 5 1 .46209E -0 7 0 .000 3. 5889 7/ 21/ 2003 2: 00 10.3236 1 -2.41133E -0 8 0 .093 3. 5913 8/ 1/ 2003 20: 00 10.3443 3 -1.20746E -0 7 0 .000 3. 5693 7/ 21/ 2003 8: 00 10.2959 9 5 .21855E -0 8 0 .093 3. 6555 8/ 2/ 2003 2 :00 10.3477 8 3 .15812E -0 8 0 .060 3. 5760 7/ 21/ 2003 14 :00 10.2787 3 -1.55207E -0 8 0 .093 3. 6775 8/ 2/ 2003 8 :00 10.3546 9 -8.31369E -0 8 0 .060 3. 6023 7/ 21/ 2003 20 :00 10.2614 7 -5.10608E -0 8 0 .093 3. 6531 8/ 2/ 2003 14: 00 10.3512 4 1 .01582E -0 7 0 .060 3. 6057 7/ 22/ 2003 2: 00 10.2683 7 -4.13885E -0 8 0 .145 3. 6498 8/ 2/ 2003 20: 00 10.3477 8 -1.11164E -0 7 0 .060 3. 5736 7/ 22/ 2003 8: 00 10.2683 7 9 .27643E -0 8 0 .145 3. 6368 8/ 3/ 2003 2 :00 10.3581 4 4 .08037E -0 8 0 .085 3. 5803 7/ 22/ 2003 14 :00 10.2821 8 -3.44154E -0 8 0 .145 3. 6277 8/ 3/ 2003 8 :00 10.3512 4 -4.57973E -0 8 0 .085 3. 5779 7/ 22/ 2003 20 :00 10.2787 3 -5. 0386E- 0 8 0 .145 3. 6004 8/ 3/ 2003 14: 00 10.3408 8 4 .59772E -0 8 0 .085 3. 6023 7/ 23/ 2003 2: 00 10.2925 4 -2. 8747E- 0 8 0 .178 3. 5870 8/ 3/ 2003 20: 00 10.3236 1 -9.90625E -0 8 0 .085 3. 5889 7/ 23/ 2003 8: 00 10.3029 0 1 .19082E -0 7 0 .178 3. 5516 8/ 4/ 2003 2 :00 10.3443 3 3 .26609E -0 8 0 .028 3. 6004 7/ 23/ 2003 14 :00 10.3270 7 -5.84388E -0 8 0 .178 3. 5286 8/ 4/ 2003 8 :00 10.3270 7 1 .25965E -0 8 0 .028 3. 5827 7/ 23/ 2003 20 :00 10.3339 7 -5.81239E -0 8 0 .178 3. 5084 8/ 4/ 2003 14: 00 10.3305 2 -8.36768E -0 9 0 .028 3. 6368 7/ 24/ 2003 2: 00 10.3512 4 -7.97629E -0 8 0 .070 3. 4874 8/ 4/ 2003 20: 00 10.3167 1 -8.69609E -0 8 0 .028 3. 6124 7/ 24/ 2003 8: 00 10.3719 5 1 .48189E -0 7 0 .070 3. 4610 8/ 5/ 2003 2 :00 10.3236 1 7 .96279E -0 9 0 .000 3. 6167 7/ 24/ 2003 14 :00 10.3995 7 -3.07715E -0 8 0 .070 3. 4696 8/ 5/ 2003 8 :00 10.3063 5 7 .95379E -0 8 0 .000 3. 6100 7/ 24/ 2003 20 :00 10.3995 7 -6.10481E -0 8 0 .070 3. 4246 8/ 5/ 2003 14: 00 10.3201 6 -4.91714E -0 8 0 .000 3. 6387 7/ 25/ 2003 2: 00 10.4202 9 -9.60483E -0 8 0 .003 3. 4045 8/ 5/ 2003 20: 00 10.2994 5 -7.75135E -0 8 0 .000 3. 6191 7/ 25/ 2003 8: 00 10.4375 5 1 .55252E -0 7 0 .003 3. 4002 8/ 6/ 2003 2 :00 10.3201 6 -2.82971E -0 8 0 .008 3. 6234 7/ 25/ 2003 14 :00 10.4651 8 -7.73785E -0 9 0 .003 3. 4222 8/ 6/ 2003 8 :00 10.3201 6 1 .40136E -0 7 0 .008 3. 6191 7/ 25/ 2003 20 :00 10.4479 1 -7.13952E -0 8 0 .003 3. 3978 8/ 6/ 2003 14: 00 10.3201 6 -6.71214E -0 8 0 .008 3. 6320 7/ 26/ 2003 2: 00 10.4548 2 -1.06845E -0 7 0 .000 3. 4222 8/ 6/ 2003 20: 00 10.3132 6 -7.26548E -0 8 0 .008 3. 6148 7/ 26/ 2003 8: 00 10.4513 7 1 .44905E -0 7 0 .000 3. 4653 8/ 7/ 2003 2 :00 10.3236 1 -6.80211E -0 8 0 .058 3. 6277 7/ 26/ 2003 14 :00 10.4651 8 2 .72174E -0 8 0 .000 3. 4941 8/ 7/ 2003 8 :00 10.3098 0 1 .80355E -0 7 0 .058 3. 6258 7/ 26/ 2003 20 :00 10.4341 0 -8.39017E -0 8 0 .000 3. 5152 8/ 7/ 2003 14: 00 10.3063 5 -5.86187E -0 8 0 .058 3. 6320 7/ 27/ 2003 2: 00 10.4202 9 -1.09455E -0 7 0 .003 3. 5348 8/ 7/ 2003 20: 00 10.3167 1 -7.34196E -0 8 0 .058 3. 5980 7/ 27/ 2003 8: 00 10.4099 3 1 .16608E -0 7 0 .003 3. 5516 8/ 8/ 2003 2 :00 10.3236 1 -1.01537E -0 7 0 .013 3. 5219 7/ 27/ 2003 14 :00 10.3926 7 6 .99556E -0 8 0 .003 3. 6124 8/ 8/ 2003 8 :00 10.3236 1 1 .90927E -0 7 0 .013 3. 5128 7/ 27/ 2003 20 :00 10.3615 9 -9. 7263E- 0 8 0 .003 3. 5980 8/ 8/ 2003 14: 00 10.3305 2 -2.66776E -0 8 0 .013 3. 4888 7/ 28/ 2003 2: 00 10.3581 4 -1.01897E -0 7 0 .313 3. 6124 8/ 8/ 2003 20: 00 10.3270 7 -7. 9403E- 0 8 0 .013 3. 4974 7/ 28/ 2003 8: 00 10.3408 8 7 .28798E -0 8 0 .313 3. 6057 8/ 9/ 2003 2 :00 10.3374 3 -1.20612E -0 7 0 .013 3. 4821 7/ 28/ 2003 14 :00 10.3443 3 1 .13818E -0 7 0 .313 3. 6124 8/ 9/ 2003 8 :00 10.3305 2 1 .70053E -0 7 0 .013 3. 4744 7/ 28/ 2003 20 :00 10.3374 3 -1.09815E -0 7 0 .313 3. 5779 8/ 9/ 2003 14: 00 10.3477 8 1 .99745E -0 8 0 .013 3. 4323 7/ 29/ 2003 2: 00 10.3408 8 -8.35418E -0 8 0 .003 3. 5760 8/ 9/ 2003 20: 00 10.3408 8 -8.88503E -0 8 0 .013 3. 4299 7/ 29/ 2003 8: 00 10.3408 8 1 .98845E -0 8 0 .003 3. 5693 8/ 10/ 2003 2: 00 10.3477 8 -1.20567E -0 7 0 .048 3. 4423 7/ 29/ 2003 14 :00 10.3546 9 1 .50573E -0 7 0 .003 3. 5827 8/ 10/ 2003 8: 00 10.3581 4 1 .86878E -0 7 0 .048 3. 4347 7/ 29/ 2003 20 :00 10.3512 4 -1.19757E -0 7 0 .003 3. 5439 8/ 10/ 2003 14 :00 10.3685 0 -8.18773E -0 9 0 .048 3. 4500 7/ 30/ 2003 2: 00 10.3685 0 -5.59645E -0 8 0 .000 3. 5329 8/ 10/ 2003 20 :00 10.3477 8 -6.79761E -0 8 0 .048 3. 4299 7/ 30/ 2003 8: 00 10.3685 0 -3.27059E -0 8 0 .000 3. 5415 8/ 11/ 2003 2: 00 10.3512 4 -1.01807E -0 7 0 .125 3. 4917 7/ 30/ 2003 14 :00 10.3788 6 1 .71672E -0 7 0 .000 3. 5583 8/ 11/ 2003 8: 00 10.3408 8 6 .32524E -0 8 0 .125 3. 5472 7/ 30/ 2003 20 :00 10.3615 9 -1.25425E -0 7 0 .000 3. 5415 8/ 11/ 2003 14 :00 10.3581 4 1 .10219E -0 7 0 .125 3. 5238 7/ 31/ 2003 2: 00 10.3754 1 -2.33035E -0 8 0 .003 3. 5415 8/ 11/ 2003 20 :00 10.3305 2 -1.086E -0 7 0 .125 3. 5760

153

Appendix I – Corning Mine Complex Time Series Data 7 8 Date T im e B P T idal Stra in Da ily ppt. D is c h ar ge Date T im e B P T idal Stra in Da ily ppt. D is c h ar ge (m e t e r of H2O) (in c h e s ) (c fs) ( m e t e r of H2O) (in c h e s ) ( c fs) 8/ 1 2 /2 0 0 3 2 :00 10. 3 581 4 -6 .937 07E -0 8 0 .0 0 3 3. 571 7 8 /23/ 2 0 0 3 20: 0 0 10 .3 823 1 -6. 514 19 E- 0 8 0. 00 0 3 .2 8 7 6 8/ 1 2 /2 0 0 3 8 :00 10. 3 685 0 2 .8 7 92E -0 9 0 .0 0 3 3. 590 4 8 /2 4/ 2 003 2 :00 10 .3 961 2 -9. 708 31 E- 0 8 0. 00 0 3 .2 6 5 6 8 /12/ 2 0 0 3 14: 0 0 10. 3 961 2 1 .3 464 8E -0 7 0 .0 0 3 3. 549 2 8 /2 4/ 2 003 8 :00 10 .4 133 9 1 .5 097 8E -0 7 0 .0 0 0 3. 26 22 8 /12/ 2 0 0 3 20: 0 0 10. 4 030 3 -1 .145 83E -0 7 0 .0 0 3 3. 558 3 8 /24/ 2 0 0 3 14: 0 0 10 .4 202 9 -4 .3 188 E -09 0. 00 0 3 .3 1 6 4 8/ 1 3 /2 0 0 3 2 :00 10. 4 375 5 -3 .081 64E -0 8 0 .0 0 0 3. 584 6 8 /24/ 2 0 0 3 20: 0 0 10 .3 857 6 -6. 878 59 E- 0 8 0. 00 0 3 .3 1 4 9 8/ 1 3 /2 0 0 3 8 :00 10. 4 375 5 -4 .575 23E -0 8 0 .0 0 0 3. 483 1 8 /2 5/ 2 003 2 :00 10 .3 788 6 -1. 054 51 E- 0 7 0. 00 0 3 .3 1 7 3 8 /13/ 2 0 0 3 14: 0 0 10. 4 720 8 1 .3 991 1E -0 7 0 .0 0 0 3. 428 9 8 /2 5/ 2 003 8 :00 10 .3 823 1 1 .2 295 1E -0 7 0 .0 0 0 3. 32 93 8 /13/ 2 0 0 3 20: 0 0 10. 4 686 3 -1 .165 63E -0 7 0 .0 0 0 3. 429 9 8 /25/ 2 0 0 3 14: 0 0 10 .3 788 6 3 .1 131 4E -0 8 0 .0 0 0 3. 35 95 8/ 1 4 /2 0 0 3 2 :00 10. 4 962 5 6 .0 283 3E -0 9 0 .0 0 0 3. 431 3 8 /25/ 2 0 0 3 20: 0 0 10 .3 754 1 -7. 589 39 E- 0 8 0. 00 0 3 .3 3 7 4 8/ 1 4 /2 0 0 3 8 :00 10. 4 962 5 -7 .530 91E -0 8 0 .0 0 0 3. 417 9 8 /2 6/ 2 003 2 :00 10 .3 823 1 -9. 654 32 E- 0 8 0. 00 0 3 .3 5 3 7 8 /14/ 2 0 0 3 14: 0 0 10. 5 031 6 1 .2 7 63E -0 7 0 .0 0 0 3. 357 1 8 /2 6/ 2 003 8 :00 10 .3 650 5 7 .5 219 1E -0 8 0 .0 0 0 3. 35 47 8 /14/ 2 0 0 3 20: 0 0 10. 4 824 4 -1 .147 18E -0 7 0 .0 0 0 3. 361 4 8 /26/ 2 0 0 3 14: 0 0 10 .3 788 6 6 .8 3 3 6 E -0 8 0 .0 0 0 3. 40 88 8/ 1 5 /2 0 0 3 2 :00 10. 4 893 5 3 .5 180 2E -0 8 0 .1 4 0 3. 390 6 8 /26/ 2 0 0 3 20: 0 0 10 .3 443 3 -8 .6 331 E -08 0. 00 0 3 .3 8 0 1 8/ 1 5 /2 0 0 3 8 :00 10. 4 720 8 -8 .358 68E -0 8 0 .1 4 0 3. 394 4 8 /2 7/ 2 003 2 :00 10 .3 685 0 -6. 865 09 E- 0 8 0. 05 5 3 .3 9 6 8 8 /15/ 2 0 0 3 14: 0 0 10. 4 548 2 1 .0 234 7E -0 7 0 .1 4 0 3. 358 0 8 /2 7/ 2 003 8 :00 10 .3 581 4 1 .4 935 9E -0 8 0 .0 5 5 3. 38 68 8 /15/ 2 0 0 3 20: 0 0 10. 4 306 5 -1 .0 968 E -0 7 0. 14 0 3 .335 0 8 /27/ 2 0 0 3 14: 0 0 10 .3 581 4 9 .9 017 5E -0 8 0 .0 5 5 3. 38 68 8/ 1 6 /2 0 0 3 2 :00 10. 3 995 7 5 .3 130 3E -0 8 0 .0 1 3 3. 327 4 8 /27/ 2 0 0 3 20: 0 0 10 .3 581 4 -9. 888 26 E- 0 8 0. 05 5 3 .3 5 0 4 8/ 1 6 /2 0 0 3 8 :00 10. 3 581 4 -7 .2 205 E -0 8 0. 01 3 3 .346 1 8 /2 8/ 2 003 2 :00 10 .3 823 1 -2. 456 32 E- 0 8 0. 00 3 3 .3 2 5 0 8 /16/ 2 0 0 3 14: 0 0 10. 3 650 5 6 .9 550 7E -0 8 0 .0 1 3 3. 309 2 8 /2 8/ 2 003 8 :00 10 .3 857 6 -4. 534 74 E- 0 8 0. 00 3 3 .3 0 3 0 8 /16/ 2 0 0 3 20: 0 0 10. 3 305 2 -1 .026 17E -0 7 0 .0 1 3 3. 413 6 8 /28/ 2 0 0 3 14: 0 0 10 .3 926 7 1 .1 480 8E -0 7 0 .0 0 3 3. 34 94 8/ 1 7 /2 0 0 3 2 :00 10. 3 270 7 5 .8 798 7E -0 8 0 .0 0 0 3. 403 5 8 /28/ 2 0 0 3 20: 0 0 10 .3 754 1 -1. 116 14 E- 0 7 0. 00 3 3 .3 3 6 0 8/ 1 7 /2 0 0 3 8 :00 10. 3 339 7 -4 .530 24E -0 8 0 .0 0 0 3. 381 5 8 /2 9/ 2 003 2 :00 10 .3 823 1 2 .7 667 3E -0 8 0 .1 3 3 3. 34 61 8 /17/ 2 0 0 3 14: 0 0 10. 3 546 9 3 .4 595 4E -0 8 0 .0 0 0 3. 382 5 8 /2 9/ 2 003 8 :00 10 .3 788 6 -9. 055 99 E- 0 8 0. 13 3 3 .3 2 9 3 8 /17/ 2 0 0 3 20: 0 0 10. 3 374 3 -9 .447 38E -0 8 0 .0 0 0 3. 379 1 8 /29/ 2 0 0 3 14: 0 0 10 .3 823 1 1 .0 999 4E -0 7 0 .1 3 3 3. 36 14 8/ 1 8 /2 0 0 3 2 :00 10. 3 754 1 5 .2 770 4E -0 8 0 .0 0 0 3. 332 7 8 /29/ 2 0 0 3 20: 0 0 10 .3 754 1 -1. 219 61 E- 0 7 0. 13 3 3 .3 3 2 7 8/ 1 8 /2 0 0 3 8 :00 10. 3 788 6 -7 .827 83E -0 9 0 .0 0 0 3. 310 6 8 /3 0/ 2 003 2 :00 10 .3 823 1 7 .6 118 9E -0 8 0 .0 7 3 3. 32 93 8 /18/ 2 0 0 3 14: 0 0 10. 3 995 7 1 .8 444 9E -0 9 0 .0 0 0 3. 310 6 8 /3 0/ 2 003 8 :00 10 .3 892 2 -1 .0 815 E -07 0. 07 3 3 .2 5 8 9 8 /18/ 2 0 0 3 20: 0 0 10. 3 823 1 -8 .624 11E -0 8 0 .0 0 0 3. 292 9 8 /30/ 2 0 0 3 14: 0 0 10 .4 133 9 8 .4 261 6E -0 8 0 .0 7 3 3. 25 65 8/ 1 9 /2 0 0 3 2 :00 10. 3 926 7 3 .6 574 8E -0 8 0 .0 0 0 3. 284 3 8 /30/ 2 0 0 3 20: 0 0 10 .4 410 1 -1 .2 745 E -07 0. 07 3 3 .2 3 0 1 8/ 1 9 /2 0 0 3 8 :00 10. 4 133 9 3 .4 955 3E -0 8 0 .0 0 0 3. 269 9 8 /3 1/ 2 003 2 :00 10 .4 479 1 1 .086 E -07 0. 00 3 3 .2 2 7 8 8 /19/ 2 0 0 3 14: 0 0 10. 4 133 9 -2 .5 238 E -0 8 0. 00 0 3 .277 6 8 /3 1/ 2 003 8 :00 10 .4 444 6 -9. 253 93 E- 0 8 0. 00 3 3 .2 1 6 7 8 /19/ 2 0 0 3 20: 0 0 10. 4 030 3 -7 .854 82E -0 8 0 .0 0 0 3. 294 3 8 /31/ 2 0 0 3 14: 0 0 10 .4 375 5 4 .3 2 3 3 E -0 8 0 .0 0 3 3. 26 99 8/ 2 0 /2 0 0 3 2 :00 10. 4 064 8 1 .2 551 5E -0 8 0 .0 0 0 3. 284 3 8 /31/ 2 0 0 3 20: 0 0 10 .4 099 3 -1. 265 05 E- 0 7 0. 00 3 3 .2 6 9 9 8/ 2 0 /2 0 0 3 8 :00 10. 4 030 3 7 .8 008 3E -0 8 0 .0 0 0 3. 280 9 9 /1 /2 00 3 2: 00 10 .3 995 7 1 .1 674 3E -0 7 0 .4 6 5 3. 30 30 8 /20/ 2 0 0 3 14: 0 0 10. 4 030 3 -4 .386 28E -0 8 0 .0 0 0 3. 295 3 9 /1 /2 00 3 8: 00 10 .3 892 2 -4. 732 69 E- 0 8 0. 46 5 3 .2 9 5 3 8 /20/ 2 0 0 3 20: 0 0 10. 3 892 2 -7. 20 7E -0 8 0 .0 0 0 3. 325 0 9 /1 /2 0 03 14 :0 0 1 0 .3 961 2 -3. 284 09 E- 0 9 0. 46 5 3 .3 5 3 7 8/ 2 1 /2 0 0 3 2 :00 10. 3 961 2 -1 .660 04E -0 8 0 .0 0 0 3. 306 3 9 /1 /2 0 03 20 :0 0 1 0 .3 615 9 -1. 190 37 E- 0 7 0. 46 5 2 .7 0 8 5 8/ 2 1 /2 0 0 3 8 :00 10. 3 857 6 1 .1 615 8E -0 7 0 .0 0 0 3. 319 7 9 /2 /2 00 3 2: 00 10 .3 857 6 9 .8 702 6E -0 8 0 .1 3 8 3. 32 07 8 /21/ 2 0 0 3 14: 0 0 10. 3 823 1 -5 .200 56E -0 8 0 .0 0 0 3. 335 0 9 /2 /2 00 3 8: 00 10 .3 788 6 1 .6 015 6E -0 8 0 .1 3 8 3. 30 39 8 /21/ 2 0 0 3 20: 0 0 10. 3 581 4 -6 .730 13E -0 8 0 .0 0 0 3. 383 4 9 /2 /2 0 03 14 :0 0 1 0 .3 823 1 -4. 444 77 E- 0 8 0. 13 8 3 .2 8 9 5 8/ 2 2 /2 0 0 3 2 :00 10. 3 615 9 -4 .755 18E -0 8 0 .2 1 8 3. 400 2 9 /2 /2 0 03 20 :0 0 1 0 .3 685 0 -1 .0 653 E -07 0. 13 8 3 .2 8 0 0 8/ 2 2 /2 0 0 3 8 :00 10. 3 512 4 1 .4 4 32E -0 7 0 .2 1 8 3. 360 4 9 /3 /2 00 3 2: 00 10 .3 754 1 5 .9 293 5E -0 8 0 .0 1 5 3. 29 72 8 /22/ 2 0 0 3 14: 0 0 10. 3 650 5 -4 .813 66E -0 8 0 .2 1 8 3. 405 9 9 /3 /2 00 3 8: 00 10 .3 615 9 8 .2 102 2E -0 8 0 .0 1 5 3. 30 73 8 /22/ 2 0 0 3 20: 0 0 10. 3 408 8 -6 .4 827 E -0 8 0. 21 8 3 .358 0 9 /3 /2 0 03 14 :0 0 1 0 .3 581 4 -7. 153 01 E- 0 8 0. 01 5 3 .3 4 6 1 8/ 2 3 /2 0 0 3 2 :00 10. 3 581 4 -7 .602 89E -0 8 0 .0 0 0 3. 341 8 9 /3 /2 0 03 20 :0 0 1 0 .3 270 7 -9. 150 46 E- 0 8 0. 01 5 3 .3 4 3 7 8/ 2 3 /2 0 0 3 8 :00 10. 3 719 5 1 .5 736 6E -0 7 0 .0 0 0 3. 309 7 9 /4 /2 00 3 2: 00 10 .3 236 1 8 .4 126 6E -0 9 0 .0 0 3 3. 36 71 8 /23/ 2 0 0 3 14: 0 0 10. 3 926 7 -3 .189 61E -0 8 0 .0 0 0 3. 324 0 9 /4 /2 00 3 8: 00 10 .3 132 6 1 .3 608 7E -0 7 0 .0 0 3 3. 35 04

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Appendix I – Corning Mine Complex Time Series Data 9 10 Da te Ti m e B P T i d a l S t rain Daily p p t. Dis c h a rg e D at e T i m e BP T i d a l Strain Da i l y pp t . (m e t er of H2O) (i n c h e s ) (c fs ) ( m e t er of H2O) (i n c h e s ) 9/ 4/ 2 0 0 3 14 : 0 0 1 0. 32 01 6 - 8.0 1 2 2 8 E - 0 8 0 .0 03 3 .3 3 7 4 9 / 1 6 /2 0 0 3 8: 00 1 0 .4 06 48 - 2 .9 46 68 E- 08 0 .0 0 0 9/ 4/ 2 0 0 3 20 : 0 0 1 0. 33 74 3 - 7.6 7 4 8 7 E - 0 8 0 .0 03 3 .2 8 5 2 9 / 16 / 2 00 3 14 : 0 0 1 0.4 4 1 0 1 - 2.5 2 3 8 E - 0 8 0 .0 0 0 9/ 5/ 20 03 2: 00 1 0 .3 6 8 5 0 - 4 .2 15 33 E - 08 0.0 0 3 3 .2 62 2 9 / 1 6 / 2 0 0 3 20 : 0 0 1 0.4 0 9 9 3 - 8 .8 7 6 0 4 E - 0 8 0 .0 00 9/ 5/ 20 03 8: 00 1 0 .3 8 2 3 1 1 .5 7 4 1 1 E - 0 7 0 .0 03 3 .2 3 2 1 9 / 1 7 /2 0 0 3 2: 00 1 0 .4 16 84 9 .08 74 8 E - 0 8 0 .0 00 9/ 5/ 2 0 0 3 14 : 0 0 1 0. 40 99 3 - 1.0 0 3 2 2 E - 0 7 0 .0 03 3 .2 4 8 8 9 / 1 7 /2 0 0 3 8: 00 1 0 .4 30 65 1 .12 46 9 E - 0 8 0 .0 00 9/ 5/ 2 0 0 3 20 : 0 0 1 0. 39 26 7 - 5.8 3 9 3 8 E - 0 8 0 .0 03 3 .2 2 3 4 9 / 17 / 2 00 3 14 : 0 0 1 0.4 6 1 7 2 - 4 .6 3 3 7 1 E - 0 8 0 .0 00 9/ 6/ 20 03 2: 00 1 0 .4 0 9 9 3 - 8 .1 42 74 E - 08 0.0 0 0 3 .2 34 5 9 / 1 7 / 2 0 0 3 20 : 0 0 1 0.4 1 3 3 9 - 8 .2 4 6 2 1 E - 0 8 0 .0 00 9/ 6/ 20 03 8: 00 1 0 .3 9 9 5 7 1 .7 0 1 8 8 E - 0 7 0 .0 00 3 .2 1 9 1 9 / 1 8 /2 0 0 3 2: 00 1 0 .4 09 93 6 .24 42 7 E - 0 8 0 .1 28 9/ 6/ 2 0 0 3 14 : 0 0 1 0. 42 72 0 - 4.6 5 1 7 1 E - 0 8 0 .0 00 3 .2 5 2 2 9 / 1 8 /2 0 0 3 8: 00 1 0 .3 99 57 5 .47 94 8 E - 0 8 0 .1 28 9/ 6/ 2 0 0 3 20 : 0 0 1 0. 38 92 2 - 5.7 3 1 4 1 E - 0 8 0 .0 00 3 .2 4 4 5 9 / 18 / 2 00 3 14 : 0 0 1 0.4 0 3 0 3 - 6 .2 9 8 2 5 E - 0 8 0 .1 28 9/ 7/ 20 03 2: 00 1 0 .3 8 9 2 2 - 1 .0 18 52 E - 07 0.0 0 0 3 .2 53 1 9 / 1 8 / 2 0 0 3 20 : 0 0 1 0.3 3 7 4 3 - 7 .3 7 7 9 5 E - 0 8 0 .1 28 9/ 7/ 20 03 8: 00 1 0 .3 8 9 2 2 - 1 .0 18 52 E - 07 0.0 0 0 3 .2 53 1 9 / 1 9/ 20 03 2: 00 1 0 .2 44 20 2 .58 67 8 E - 0 8 0 .3 43 9/ 7/ 2 0 0 3 14 : 0 0 1 0. 40 64 8 - 1.4 9 8 0 8 E - 0 8 0 .0 00 3 .2 7 8 5 9 / 1 9 /2 0 0 3 8: 00 1 0 .2 20 03 9 .60 03 4 E - 0 8 0 .3 43 9/ 7/ 2 0 0 3 20 : 0 0 1 0. 37 88 6 - 5.4 9 7 4 7 E - 0 8 0 .0 00 3 .2 6 3 2 9 / 19 / 2 00 3 14 : 0 0 1 0.3 0 2 9 0 - 7 .3 2 3 9 7 E - 0 8 0 .3 43 9/ 8/ 20 03 2: 00 1 0 .3 7 8 8 6 - 1 .0 03 22 E - 07 0.0 0 0 3 .2 63 2 9 / 1 9 / 2 0 0 3 20 : 0 0 1 0.3 3 7 4 3 - 6 .3 1 6 2 5 E - 0 8 0 .3 43 9/ 8/ 20 03 8: 00 1 0 .3 8 5 7 6 1 .0 3 1 5 6 E - 0 7 0 .0 00 3 .2 5 2 2 9 / 2 0 /2 0 0 3 2: 00 1 0 .3 78 86 - 1 .4 71 09 E- 08 0 .0 0 0 9/ 8/ 2 0 0 3 14 : 0 0 1 0. 40 64 8 1 .6 87 03 E- 08 0.0 0 0 3 .4 51 0 9 / 2 0/ 20 03 8: 00 1 0 .4 09 93 1 .28 97 9 E - 0 7 0 .0 00 9/ 8/ 2 0 0 3 20 : 0 0 1 0. 38 92 2 - 5.7 3 1 4 1 E - 0 8 0 .0 00 3 .4 2 5 6 9 / 20 / 2 00 3 14 : 0 0 1 0.4 5 1 3 7 - 7.5 5 3 4 E - 0 8 0 .0 0 0 9/ 9/ 20 03 2: 00 1 0 .3 8 2 3 1 - 7 .8 59 32 E - 08 0.0 0 0 3 .4 14 6 9 / 2 0 / 2 0 0 3 20 : 0 0 1 0.4 1 3 3 9 - 5 .1 4 2 0 7 E - 0 8 0 .0 00 9/ 9/ 20 03 8: 00 1 0 .4 0 3 0 3 4 .9 3 0 6 3 E - 0 8 0 .0 00 3 .3 9 1 6 9 / 2 1 /2 0 0 3 2: 00 1 0 .4 23 74 - 5 .3 58 01 E- 08 0 .0 0 3 9/ 9/ 2 0 0 3 14 : 0 0 1 0. 44 10 1 4 .2 96 31 E- 08 0.0 0 0 3 .4 06 9 9 / 2 1/ 20 03 8: 00 1 0 .4 20 29 1 .47 24 4 E - 0 7 0 .0 03 9/ 9/ 2 0 0 3 20 : 0 0 1 0. 41 68 4 - 6.3 2 0 7 5 E - 0 8 0 .0 00 3 .3 8 4 9 9 / 21 / 2 00 3 14 : 0 0 1 0.4 3 0 6 5 - 6 .8 8 7 5 9 E - 0 8 0 .0 03 9/ 10 / 2 0 0 3 2 : 0 0 1 0 .4 0 6 4 8 - 4 .2 24 33 E - 08 0.0 0 0 3 .3 72 4 9 / 2 1 / 2 0 0 3 20 : 0 0 1 0.4 0 3 0 3 - 3 . 9 9 0 3 9 E - 0 8 0 .0 03 9/ 10 / 2 0 0 3 8 : 0 0 1 0 .4 2 7 2 0 - 4 .4 08 78 E - 09 0.0 0 0 3 .3 56 1 9 / 2 2/ 20 03 2: 00 1 0 .3 75 41 - 8 .3 54 18 E- 08 0 .2 5 5 9 / 1 0 /2 0 0 3 14 : 0 0 1 0. 46 17 2 5 .9 06 86 E- 08 0.0 0 0 3 .3 74 8 9 / 2 2/ 20 03 8: 00 1 0 .3 23 61 1 .4 4 7 7 E - 0 7 0 .2 5 5 9 / 1 0 /2 0 0 3 20 : 0 0 1 0. 43 06 5 - 7.1 0 3 5 3 E - 0 8 0 .0 00 3 .3 5 9 5 9 / 22 / 2 00 3 14 : 0 0 1 0.2 5 8 0 1 - 5 .3 4 0 0 2 E - 0 8 0 .2 55 9 / 1 1 /2 00 3 2:00 10 .4 20 29 1.03 47 1E- 0 9 0 .00 0 9/2 2 /20 0 3 2 0 :0 0 1 0 . 27 87 3 - 3.06 81 5E- 0 8 0 .25 5 9/ 11 / 2 0 0 3 8 : 0 0 1 0 .4 3 4 1 0 - 4 .8 94 64 E - 08 0.0 0 0 9 / 2 3/ 20 03 2: 00 1 0 .3 02 90 - 9 .6 40 82 E- 08 0 .0 0 5 9 / 1 1 /2 0 0 3 14 : 0 0 1 0. 46 17 2 6 .3 70 23 E- 08 0.0 0 0 9 / 2 3/ 20 03 8: 00 1 0 .3 09 80 1 .17 68 7 E - 0 7 0 .0 05 9 / 1 1 /2 0 0 3 20 : 0 0 1 0. 40 30 3 - 7. 90 88 E- 08 0.0 0 0 9 / 2 3 / 2 0 0 3 14 : 0 0 1 0.3 4 7 7 8 - 3 .0 9 5 1 4 E - 0 8 0 .0 05 9 / 1 2 /2 00 3 2:00 10 .3 92 67 4.35 47 9E- 0 8 0 .00 0 9/2 3 /20 0 3 2 0 :0 0 1 0 . 33 74 3 - 2.64 07 7E- 0 8 0 .00 5 9/ 12 / 2 0 0 3 8 : 0 0 1 0 .3 8 9 2 2 - 7 .8 36 82 E - 08 0.0 0 0 9 / 2 4/ 20 03 2: 00 1 0 .3 65 05 - 8 .5 07 14 E- 08 0 .0 0 0 9 / 1 2 /2 0 0 3 14 : 0 0 1 0. 41 68 4 5 .7 44 91 E- 08 0.0 0 0 9 / 2 4/ 20 03 8: 00 1 0 .3 75 41 6 .71 21 4 E - 0 8 0 .0 00 9 / 1 2 /2 0 0 3 20 : 0 0 1 0. 37 88 6 - 8.5 9 7 1 1 E - 0 8 0 .0 00 9 / 24 / 2 00 3 14 : 0 0 1 0.3 9 2 6 7 - 5 .8 4 8 3 8 E - 0 9 0 .0 00 9 / 1 3 /2 00 3 2:00 10 .3 85 76 7.88 18 1E- 0 8 0 .00 0 9/2 4 /20 0 3 2 0 :0 0 1 0 . 32 36 1 - 2.95 56 8E- 0 8 0 .00 0 9 / 1 3 /2 00 3 8:00 10 .3 82 31 - 8 .9 97 5E- 0 8 0 .00 0 9/25 / 2 00 3 2 : 0 0 10 .31 3 2 6 - 4 .67 8 7 E - 0 8 0 .00 3 9 / 1 3 /2 0 0 3 14 : 0 0 1 0. 40 99 3 4 .2 51 32 E- 08 0.0 0 0 9 / 2 5/ 20 03 8: 00 1 0 .3 23 61 1 .48 45 9 E - 0 9 0 .0 03 9 / 1 3 /2 0 0 3 20 : 0 0 1 0. 38 23 1 - 9.0 8 2 9 8 E - 0 8 0 .0 00 9 / 25 / 2 00 3 14 : 0 0 1 0.3 6 5 0 5 1 .56 5 5 7 E - 0 8 0 .0 03 9 / 1 4 /2 00 3 2:00 10 .3 71 95 1.02 43 7E- 0 7 0 .00 8 9/2 5 /20 0 3 2 0 :0 0 1 0 . 34 08 8 - 4.12 98 5E- 0 8 0 .00 3 9/ 14 / 2 0 0 3 8 : 0 0 1 0 .3 7 8 8 6 - 8 .3 94 67 E - 08 0.0 0 8 9 / 2 6/ 20 03 2: 00 1 0 .3 40 88 1 .34 06 3 E - 0 8 0 .1 25 9 / 1 4 /2 0 0 3 14 : 0 0 1 0. 39 95 7 2 .1 81 89 E- 08 0.0 0 8 9 / 2 6/ 20 03 8: 00 1 0 .3 30 52 - 6 .3 70 23 E- 08 0 .1 2 5 9 / 1 4 /2 0 0 3 20 : 0 0 1 0. 37 54 1 - 9.2 9 8 9 2 E - 0 8 0 .0 08 9 / 26 / 2 00 3 14 : 0 0 1 0.3 3 3 9 7 2 .69 0 2 5 E - 0 8 0 .1 25 9 / 1 5 /2 00 3 2:00 10 .3 61 59 1.12 37 9E- 0 7 0 .02 3 9/2 6 /20 0 3 2 0 :0 0 1 0 . 27 87 3 - 6.02 83 3E- 0 8 0 .12 5 9/ 15 / 2 0 0 3 8 : 0 0 1 0 .3 5 1 2 4 - 6 .2 57 76 E - 08 0.0 2 3 9 / 2 7/ 20 03 2: 00 1 0 .2 47 66 8 .22 37 2 E - 0 8 0 .0 10 9 / 1 5 /2 0 0 3 14 : 0 0 1 0. 39 26 7 - 1.7 0 9 5 3 E - 0 9 0 .0 23 9 / 2 7 /2 0 0 3 8: 00 1 0 .2 51 11 - 1 .1 01 74 E- 07 0 .0 1 0 9 / 1 5 /2 0 0 3 20 : 0 0 1 0. 37 88 6 - 9.2 2 6 9 4 E - 0 8 0 .0 23 9 / 27 / 2 00 3 14 : 0 0 1 0.2 7 1 8 2 2 .35 7 3 5 E - 0 8 0 .0 10 9 / 1 6 /2 00 3 2:00 10 .3 99 57 1 . 081 5 E- 0 7 0 . 00 0 9 /2 7/20 03 2 0 :0 0 1 0 . 27 87 3 - 8.21 47 2E- 0 8 0 .01 0

155

Appendix I – Corning Mine Complex Time Series Data 11 12 Da te Tim e B P Tid a l St ra in Da ily ppt. D a t e T i m e BP Tida l Stra in Da ily ppt. ( m eter of H2O ) (inches) (m eter of H2O) ( i nches ) 9/ 28/ 2003 2: 00 10.29254 1.41396E- 0 7 0 .000 10/ 9/2 003 20: 00 10.3754 1 - 3. 14913E- 0 9 0 .003 9/ 28/ 2003 8: 00 10.27528 - 1 .2403 1E- 0 7 0 .000 10/ 10/ 2003 2: 00 10.3615 9 - 3. 80144E- 0 8 0 .000 9/ 28/2003 14:00 10.29254 5. 7584E- 0 9 0 .000 10/ 10/ 2003 8: 00 10.3754 1 - 8. 09775E- 0 9 0 .000 9/ 28/2003 20:00 10.30290 - 1 .0090 7E- 0 7 0 .000 10/10/ 2003 14:00 10.3788 6 - 6. 28476E- 0 8 0 .000 9/ 29/ 2003 2: 00 10.32361 1.74776E- 0 7 0 .018 10/10/ 2003 20:00 10.3546 9 4 .87215 E- 08 0.000 9/ 29/ 2003 8: 00 10.34433 - 1 .0194 2E- 0 7 0 .018 10/ 11/ 2003 2: 00 10.3581 4 6 .90108 E- 08 0.000 9/ 29/2003 14:00 10.39957 - 2 .1774E- 0 8 0 .018 10/ 11/ 2003 8: 00 10.3615 9 - 8. 56562E- 0 8 0 .000 9/ 29/2003 20:00 10.40303 - 1 .1111 9E- 0 7 0 .018 10/11/ 2003 14:00 10.3857 6 - 1. 40361E- 0 8 0 .000 9/ 30/ 2003 2: 00 10.40993 1.74417E- 0 7 0 .038 10/11/ 2003 20:00 10.3443 3 - 3. 10864E- 0 8 0 .000 9/ 30/ 2003 8: 00 10.41684 - 5 .1735 6E- 0 8 0 .038 10/ 12/ 2003 2: 00 10.3339 7 1 .0734E- 0 7 0 .000 9/ 30/2003 14:00 10.44791 - 5 .1735 6E- 0 8 0 .038 10/ 12/ 2003 8: 00 10.3201 6 - 9. 88826E- 0 8 0 .000 9/ 30/2003 20:00 10.40648 - 1 .0994 9E- 0 7 0 .038 10/12/ 2003 14:00 10.3374 3 - 1. 87148E- 0 8 0 .000 10 /1/ 2003 2: 00 10.43065 1.42835E- 0 7 0 .000 10/12/ 2003 20:00 10.3339 7 - 4. 62922E- 0 8 0 .000 10 /1/ 2003 8: 00 10.42029 1.14718E- 0 8 0 .000 10/ 13/ 2003 2: 00 10.3443 3 1 .35008 E- 07 0.228 10/ 1/2003 14:00 10.43755 - 7 .7108 6E- 0 8 0 .000 10/ 13/ 2003 8: 00 10.3408 8 - 9. 69031E- 0 8 0 .228 10/ 1/2003 20:00 10.38576 - 9 .7892 8E- 0 8 0 .000 10/13/ 2003 14:00 10.3719 5 - 2. 75773E- 0 8 0 .228 10 /2/ 2003 2: 00 10.37886 9.02899E- 0 8 0 .028 10/13/ 2003 20:00 10.3098 0 - 5. 94285E- 0 8 0 .228 10 /2/ 2003 8: 00 10.40648 7.15751E- 0 8 0 .028 10/ 14/ 2003 2: 00 10.2821 8 1 .48999 E- 07 0.000 10/ 2/2003 14:00 10.45137 - 9 .3259 1E- 0 8 0 .028 10/ 14/ 2003 8: 00 10.2338 4 - 8. 06176E- 0 8 0 .000 10/ 2/2003 20:00 10.41339 - 7 .7828 4E- 0 8 0 .028 10/14/ 2003 14:00 10.1509 8 - 3.9499E - 0 8 0 .000 10 /3/ 2003 2: 00 10.38576 3.05015E- 0 8 0 .028 10/14/ 2003 20:00 10.1302 6 - 6. 88759E- 0 8 0 .000 10 /3/ 2003 8: 00 10.37541 1.16068E- 0 7 0 .028 10/ 15/ 2003 2: 00 10.1924 1 1 .47874 E- 07 0.000 10/ 3/2003 14:00 10.36505 - 9 .8432 7E- 0 8 0 .028 10/ 15/ 2003 8: 00 10.2614 7 - 5. 23655E- 0 8 0 .000 10/ 3/2003 20:00 10.27873 - 5 .3985E- 0 8 0 .028 10/15/ 2003 14:00 10.3443 3 - 5. 32202E- 0 8 0 .000 10 /4/ 2003 2: 00 10.25801 - 2 .3708 4E- 0 8 0 .000 10/15/ 2003 20:00 10.3305 2 - 7. 30597E- 0 8 0 .000 10 /4/ 2003 8: 00 10.24075 1.37887E- 0 7 0 .000 10/ 16/ 2003 2: 00 10.3374 3 1 .31633 E- 07 0.040 10/ 4/2003 14:00 10.28909 - 9 .3529E- 0 8 0 .000 10/ 16/ 2003 8: 00 10.3443 3 - 1. 54757E- 0 8 0 .040 10/ 4/2003 20:00 10.30290 - 3 .0636 5E- 0 8 0 .000 10/16/ 2003 14:00 10.3754 1 - 6. 75712E- 0 8 0 .040 10 /5/ 2003 2: 00 10.31326 - 6 .2667 6E- 0 8 0 .000 10/16/ 2003 20:00 10.3443 3 - 7. 10353E- 0 8 0 .040 10 /5/ 2003 8: 00 10.32016 1.35637E- 0 7 0 .000 10/ 17/ 2003 2: 00 10.3650 5 1 .01762 E- 07 0.000 10/ 5/2003 14:00 10.35814 - 8 .1067 5E- 0 8 0 .000 10/ 17/ 2003 8: 00 10.3685 0 2 .58678 E- 08 0.000 10/ 5/2003 20:00 10.34433 - 1 .1291 9E- 0 8 0 .000 10/17/ 2003 14:00 10.3857 6 - 8. 12474E- 0 8 0 .000 10 /6/ 2003 2: 00 10.34778 - 8 .1067 5E- 0 8 0 .000 10/17/ 2003 20:00 10.3823 1 - 6. 20378E- 0 8 0 .000 10 /6/ 2003 8: 00 10.35814 1.12244E- 0 7 0 .000 10/ 18/ 2003 2: 00 10.3857 6 6 .10481 E- 08 0.003 10/ 6/2003 14:00 10.38922 - 6 .4467 1E- 0 8 0 .000 10/ 18/ 2003 8: 00 10.3685 0 6 .64915 E- 08 0.003 10/ 6/2003 20:00 10.36505 1.66454E- 0 9 0 .000 10/18/ 2003 14:00 10.3754 1 - 9. 29892E- 0 8 0 .003 10 /7/ 2003 2: 00 10.36159 - 7 .7828 4E- 0 8 0 .000 10/18/ 2003 20:00 10.3408 8 - 4. 59772E- 0 8 0 .003 10 /7/ 2003 8: 00 10.36505 7.37345E- 0 8 0 .000 10/ 19/ 2003 2: 00 10.3201 6 1 .40811 E- 08 0.000 10/ 7/2003 14:00 10.41339 - 4 .7012E- 0 8 0 .000 10/ 19/ 2003 8: 00 10.3201 6 1 .00187 E- 07 0.000 10/ 7/2003 20:00 10.37886 7.10803E- 0 9 0 .000 10/19/ 2003 14:00 10.3788 6 - 1. 01537E- 0 7 0 .000 10 /8/ 2003 2: 00 10.38922 - 5 .5379 6E- 0 8 0 .003 10/19/ 2003 20:00 10.3650 5 - 2. 35285E- 0 8 0 .000 10 /8/ 2003 8: 00 10.39612 2.76223E- 0 8 0 .003 10/ 20/ 2003 2: 00 10.3650 5 - 3.2301E - 0 8 0 .000 10/ 8/2003 14:00 10.43755 - 3 .1626 2E- 0 8 0 .003 10/ 20/ 2003 8: 00 10.3685 0 1 .19892 E- 07 0.000 10/ 8/2003 20:00 10.39267 5.12858E- 0 9 0 .003 10/20/ 2003 14:00 10.3995 7 - 1. 05496E- 0 7 0 .000 10 /9/ 2003 2: 00 10.39612 - 1 .8939 7E- 0 8 0 .003 10/20/ 2003 20:00 10.3201 6 3 .05915 E- 09 0.000 10 /9/ 2003 8: 00 10.38922 - 1 .8489 9E- 0 8 0 .003 10/ 21/ 2003 2: 00 10.2649 2 - 6. 90108E- 0 8 0 .000 10/ 9/2003 14:00 10.41339 - 2 .0379 3E- 0 8 0 .003 10/ 21/ 2003 8: 00 10.2269 4 1 .18767 E- 07 0.000

156

Appendix II – Water Levels and Observed Heads of Monitoring Wells

W a ter L e vel (m eter of H2O) Ob s erved Head (m eter) Da te T i m e Well 1 W ell 2 W ell 3 W ell 4 W ell 5 Da te T i m e Well 1 W ell 2 W ell 3 W ell 4 W ell 5 6/ 2 / 20 03 1 4 : 0 0 4 .3 67 4. 21 1 3 . 8 4 5 3. 77 6 6 / 2 / 2 00 3 1 4 : 0 0 2 2 1 . 3 5 1 2 5 2 2 1 . 68 53 1 2 23 .8 75 23 2 2 1 . 9 1 4 8 9 6/ 2 / 20 03 2 0 : 0 0 4 .3 49 4. 21 4 3 . 8 4 7 3. 78 5 6 / 2 / 2 00 3 2 0 : 0 0 2 2 1 . 3 3 2 3 5 2 2 1 . 68 25 6 2 23 .8 76 64 2 2 1 . 9 2 3 3 3 6/3 / 20 03 2:00 4 . 34 5 4 .2 16 3 . 8 5 1 3 .7 59 6/3 / 20 03 2 : 00 2 2 1 . 3 2 8 6 9 2 2 1 .6 81 04 22 3.88 08 6 2 2 1 .89 7 3 1 6/3 / 20 03 8:00 4 . 31 7 4 .2 19 3 . 8 6 2 3 .7 55 6/3 / 20 03 8 : 00 2 2 1 . 3 0 0 6 5 2 2 1 .6 77 69 22 3.89 21 0 2 2 1 .89 3 8 0 6/ 3 / 20 03 1 4 : 0 0 4 .2 88 4. 22 6 3 . 8 6 6 3. 75 9 6 / 3 / 2 00 3 1 4 : 0 0 2 2 1 . 2 7 1 6 9 2 2 1 . 67 06 8 2 23 .8 95 62 2 2 1 . 8 9 7 3 1 6/ 3 / 20 03 2 0 : 0 0 4 .2 77 4. 23 0 3 . 8 7 1 3. 76 6 6 / 3 / 2 00 3 2 0 : 0 0 2 2 1 . 2 6 0 4 2 2 2 1 . 66 64 1 2 23 .9 01 24 2 2 1 . 9 0 5 0 5 6/4 / 20 03 2:00 4 . 28 7 4 .2 35 3 . 8 7 3 3 .7 71 6/4 / 20 03 2 : 00 2 2 1 . 2 7 1 0 8 2 2 1 .6 62 14 22 3.90 33 5 2 2 1 .90 9 9 7 6/4 / 20 03 8:00 4 . 29 6 4 .2 37 3 . 8 7 5 3 .7 97 6/4 / 20 03 8 : 00 2 2 1 . 2 7 9 6 2 2 2 1 .6 59 40 22 3.90 47 6 2 2 1 .93 5 9 8 6/ 4 / 20 03 1 4 : 0 0 4 .3 03 4. 23 9 3 . 8 7 5 3. 80 1 6 / 4 / 2 00 3 1 4 : 0 0 2 2 1 . 2 8 6 6 3 2 2 1 . 65 78 8 2 23 .9 04 76 2 2 1 . 9 3 9 5 0 6/ 4 / 20 03 2 0 : 0 0 4 .3 22 4. 24 0 3 . 8 7 7 3. 80 6 6 / 4 / 2 00 3 2 0 : 0 0 2 2 1 . 3 0 6 1 4 2 2 1 . 65 63 5 2 23 .9 06 87 2 2 1 . 9 4 4 4 2 6/5 / 20 03 2:00 4 . 35 0 4 .2 43 3 . 8 7 8 3 .8 09 6/5 / 20 03 2 : 00 2 2 1 . 3 3 3 5 7 2 2 1 .6 53 61 22 3.90 82 8 2 2 1 .94 7 2 3 6/5 / 20 03 8:00 4 . 37 3 4 .2 46 3 . 8 8 2 3 .8 12 6/5 / 20 03 8 : 00 2 2 1 . 3 5 6 7 3 2 2 1 .6 50 86 22 3.91 24 9 2 2 1 .95 0 7 5 6/ 5 / 20 03 1 4 : 0 0 4 .3 53 4. 25 0 3 . 8 7 8 3. 81 4 6 / 5 / 2 00 3 1 4 : 0 0 2 2 1 . 3 3 7 2 3 2 2 1 . 64 66 0 2 23 .9 08 28 2 2 1 . 9 5 2 1 5 6/ 5 / 20 03 2 0 : 0 0 4 .3 80 4. 24 9 3 . 8 8 2 3. 81 8 6 / 5 / 2 00 3 2 0 : 0 0 2 2 1 . 3 6 4 0 5 2 2 1 . 64 81 2 2 23 .9 12 49 2 2 1 . 9 5 7 0 8 6/6 / 20 03 2:00 4 . 41 0 4 .2 51 3 . 8 8 2 3 .8 22 6/6 / 20 03 2 : 00 2 2 1 . 3 9 4 2 2 2 2 1 .6 45 38 22 3.91 24 9 2 2 1 .96 0 5 9 6/6 / 20 03 8:00 4 . 43 0 4 .2 54 3 . 8 8 9 3 .8 25 6/6 / 20 03 8 : 00 2 2 1 . 4 1 3 7 3 2 2 1 .6 42 33 22 3.91 95 3 2 2 1 .96 3 4 0 6/ 6 / 20 03 1 4 : 0 0 4 .3 89 4. 25 9 3 . 8 9 2 3. 82 7 6 / 6 / 2 00 3 1 4 : 0 0 2 2 1 . 3 7 3 1 9 2 2 1 . 63 80 6 2 23 .9 21 63 2 2 1 . 9 6 5 5 1 6/ 6 / 20 03 2 0 : 0 0 4 .3 65 4. 26 2 3 . 8 9 4 3. 81 8 6 / 6 / 2 00 3 2 0 : 0 0 2 2 1 . 3 4 8 5 0 2 2 1 . 63 47 1 2 23 .9 23 74 2 2 1 . 9 5 7 0 8 6/7 / 20 03 2:00 4 . 37 0 4 .2 65 3 . 8 9 7 3 .8 02 6/7 / 20 03 2 : 00 2 2 1 . 3 5 3 3 8 2 2 1 .6 31 97 22 3.92 72 6 2 2 1 .94 0 9 1 6/7 / 20 03 8:00 4 . 34 8 4 .2 68 3 . 8 9 9 3 .8 04 6/7 / 20 03 8 : 00 2 2 1 . 3 3 1 4 3 2 2 1 .6 29 22 22 3.92 93 7 2 2 1 .94 2 3 1 6/ 7 / 20 03 1 4 : 0 0 4 .3 42 4. 26 9 3 . 8 9 9 3. 80 4 6 / 7 / 2 00 3 1 4 : 0 0 2 2 1 . 3 2 5 3 4 2 2 1 . 62 77 0 2 23 .9 29 37 2 2 1 . 9 4 2 3 1 6/ 7 / 20 03 2 0 : 0 0 4 .3 37 4. 27 3 3 . 9 0 1 3. 81 8 6 / 7 / 2 00 3 2 0 : 0 0 2 2 1 . 3 2 0 4 6 2 2 1 . 62 34 3 2 23 .9 30 77 2 2 1 . 9 5 7 0 8 6/8 / 20 03 2:00 4 . 35 8 4 .2 75 3 . 9 0 3 3 .8 18 6/8 / 20 03 2 : 00 2 2 1 . 3 4 1 4 9 2 2 1 .6 21 91 22 3.93 28 8 2 2 1 .95 7 0 8 6/8 / 20 03 8:00 4 . 35 8 4 .2 75 3 . 9 0 6 3 .8 09 6/8 / 20 03 8 : 00 2 2 1 . 3 4 2 1 0 2 2 1 .6 21 91 22 3.93 64 0 2 2 1 .94 7 2 3 6/ 8 / 20 03 1 4 : 0 0 4 .3 61 4. 28 0 3 . 9 0 5 3. 82 2 6 / 8 / 2 00 3 1 4 : 0 0 2 2 1 . 3 4 4 8 5 2 2 1 . 61 64 2 2 23 .9 34 99 2 2 1 . 9 6 0 5 9 6/ 8 / 20 03 2 0 : 0 0 4 .4 12 4. 27 8 3 . 9 1 4 3. 83 3 6 / 8 / 2 00 3 2 0 : 0 0 2 2 1 . 3 9 5 4 4 2 2 1 . 61 91 7 2 23 .9 44 13 2 2 1 . 9 7 1 8 4 6/9 / 20 03 2:00 4 . 44 2 4 .2 83 3 . 9 1 5 3 .8 46 6/9 / 20 03 2 : 00 2 2 1 . 4 2 5 9 2 2 2 1 .6 13 68 22 3.94 55 4 2 2 1 .98 4 5 0 6/9 / 20 03 8:00 4 . 45 8 4 .2 85 3 . 9 2 0 3 .8 54 6/9 / 20 03 8 : 00 2 2 1 . 4 4 2 0 8 2 2 1 .6 12 16 22 3.94 97 6 2 2 1 .99 2 9 3 6/ 9 / 20 03 1 4 : 0 0 4 .4 50 4. 28 7 3 . 9 2 0 3. 85 7 6 / 9 / 2 00 3 1 4 : 0 0 2 2 1 . 4 3 4 1 5 2 2 1 . 60 94 1 2 23 .9 49 76 2 2 1 . 9 9 5 7 5 6/ 9 / 20 03 2 0 : 0 0 4 .4 68 4. 28 9 3 . 9 2 3 3. 86 4 6 / 9 / 2 00 3 2 0 : 0 0 2 2 1 . 4 5 1 8 3 2 2 1 . 60 78 9 2 23 .9 53 27 2 2 2 . 0 0 2 7 8 6/ 1 0 / 2 00 3 2: 0 0 4.5 0 0 4 . 2 9 2 3. 92 3 3 . 8 6 6 6/ 1 0 / 2 0 0 3 2: 0 0 22 1. 48 41 4 2 21 .60 5 1 4 2 2 3 . 9 5 3 2 7 2 22 .0 04 18 6/ 1 0 / 2 00 3 8: 0 0 4.4 9 1 4 . 2 9 3 3. 92 7 3 . 8 6 7 6/ 1 0 / 2 0 0 3 8: 0 0 22 1. 47 43 9 2 21 .60 3 6 2 2 2 3 . 9 5 6 7 9 2 22 .0 05 59 6/ 1 0 / 2 00 3 1 4 : 0 0 4 .4 51 4. 29 9 3 . 9 3 1 3. 86 7 6 / 1 0/ 2 0 0 3 14 : 0 0 2 2 1 . 4 3 5 0 7 2 2 1 . 59 81 3 2 23 .9 61 01 2 2 2 . 0 0 5 5 9 6/ 1 0 / 2 00 3 2 0 : 0 0 4 .4 37 4. 30 4 3 . 9 3 1 3. 87 0 6 / 1 0/ 2 0 0 3 20 : 0 0 2 2 1 . 4 2 0 7 4 2 2 1 . 59 26 5 2 23 .9 61 01 2 2 2 . 0 0 9 1 0 6/ 1 1 / 2 00 3 2: 0 0 4.4 4 6 4 . 3 0 4 3. 93 2 3 . 8 6 6 6/ 1 1 / 2 0 0 3 2: 0 0 22 1. 42 92 8 2 21 .59 2 6 5 2 2 3 . 9 6 2 4 1 2 22 .0 04 18 6/ 1 1 / 2 00 3 8: 0 0 4.4 4 8 4 . 3 0 6 3. 93 4 3 . 8 7 3 6/ 1 1 / 2 0 0 3 8: 0 0 22 1. 43 14 1 2 21 .59 1 1 2 2 2 3 . 9 6 4 5 2 2 22 .0 11 92 6/ 1 1 / 2 00 3 1 4 : 0 0 4 .4 37 4. 30 8 3 . 9 3 7 3. 87 2 6 / 1 1/ 2 0 0 3 14 : 0 0 2 2 1 . 4 2 0 7 4 2 2 1 . 58 89 9 2 23 .9 66 63 2 2 2 . 0 1 0 5 1 6/ 1 1 / 2 00 3 2 0 : 0 0 4 .4 24 4. 30 9 3 . 9 4 0 3. 87 7 6 / 1 1/ 2 0 0 3 20 : 0 0 2 2 1 . 4 0 7 6 3 2 2 1 . 58 74 7 2 23 .9 70 15 2 2 2 . 0 1 5 4 3 6/ 1 2 / 2 00 3 2: 0 0 4.4 3 7 4 . 3 1 2 3. 93 8 3 . 8 6 3 6/ 1 2 / 2 0 0 3 2: 0 0 22 1. 42 07 4 2 21 .58 4 7 2 2 2 3 . 9 6 8 0 4 2 22 .0 02 07 6/ 1 2 / 2 00 3 8: 0 0 4.4 3 7 4 . 3 1 2 3. 94 4 3 . 8 5 9 6/ 1 2 / 2 0 0 3 8: 0 0 22 1. 42 07 4 2 21 .58 4 7 2 2 2 3 . 9 7 3 6 6 2 21 .9 97 86 6/ 1 2 / 2 00 3 1 4 : 0 0 4 .4 16 4. 31 5 3 . 9 4 4 3. 87 5 6 / 1 2/ 2 0 0 3 14 : 0 0 2 2 1 . 3 9 9 7 1 2 2 1 . 58 19 8 2 23 .9 73 66 2 2 2 . 0 1 4 0 3 6/ 1 2 / 2 00 3 2 0 : 0 0 4 .4 04 4. 31 6 3 . 9 4 4 3. 88 0 6 / 1 2/ 2 0 0 3 20 : 0 0 2 2 1 . 3 8 7 8 2 2 2 1 . 58 04 6 2 23 .9 73 66 2 2 2 . 0 1 8 9 5 6/ 1 3 / 2 00 3 2: 0 0 4.4 3 0 4 . 3 1 6 3. 94 4 3 . 8 8 2 6/ 1 3 / 2 0 0 3 2: 0 0 22 1. 41 37 3 2 21 .58 0 4 6 2 2 3 . 9 7 3 6 6 2 22 .0 20 35 6/ 1 3 / 2 00 3 8: 0 0 4.4 4 4 4 . 3 1 5 3. 94 6 3 . 8 8 5 6/ 1 3 / 2 0 0 3 8: 0 0 22 1. 42 77 5 2 21 .58 1 9 8 2 2 3 . 9 7 5 7 7 2 22 .0 23 17 6/ 1 3 / 2 00 3 1 4 : 0 0 4 .4 43 4. 31 6 3 . 9 4 8 3. 87 3 6 / 1 3/ 2 0 0 3 14 : 0 0 2 2 1 . 4 2 7 1 4 2 2 1 . 58 04 6 2 23 .9 77 88 2 2 2 . 0 1 1 9 2 6/ 1 3 / 2 00 3 2 0 : 0 0 4 .4 31 4. 31 7 3 . 9 4 8 3. 88 7 6 / 1 3/ 2 0 0 3 20 : 0 0 2 2 1 . 4 1 4 6 4 2 2 1 . 57 92 4 2 23 .9 77 88 2 2 2 . 0 2 5 2 8 6/ 1 4 / 2 00 3 2: 0 0 4.4 5 4 4 . 3 1 7 3. 94 6 3 . 8 9 9 6/ 1 4 / 2 0 0 3 2: 0 0 22 1. 43 78 1 2 21 .57 9 2 4 2 2 3 . 9 7 5 7 7 2 22 .0 37 93

157

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Wat er L e ve l ( m et er of H 2 O ) O b served H e ad (m et er) Da t e Tim e Well 1 W el l 2 W e ll 3 W ell 4 W ell 5 Da te Ti m e Well 1 W ell 2 W e ll 3 W ell 4 W ell 5 6 / 14 / 2 0 03 8: 00 4.47 0 4 .3 15 3.949 3.894 6/ 1 4 / 2 003 8: 0 0 221 .45 335 22 1.58 198 223 .979 29 222 .03 301 6 / 14 / 2 0 03 1 4 : 0 0 4 .46 4 4.3 1 7 3 .951 3.896 6/ 1 4 / 2 0 03 14:00 221 .44 817 22 1.57 924 223 .981 40 222 .03 512 6 / 14 / 2 0 03 2 0 : 0 0 4 .46 2 4.3 1 7 3 .951 3.906 6/ 1 4 / 2 0 03 20:00 221 .44 604 22 1.57 924 223 .981 40 222 .04 426 6 / 15 / 2 0 03 2: 00 4.48 6 4 .3 16 3.953 3.908 6/ 1 5 / 2 003 2: 0 0 221 .46 951 22 1.58 046 223 .983 51 222 .04 637 6 / 15 / 2 0 03 8: 00 4.49 1 4 .3 16 3.953 3.888 6/ 1 5 / 2 003 8: 0 0 221 .47 500 22 1.58 046 223 .983 51 222 .02 668 6 / 15 / 2 0 03 1 4 : 0 0 4 .49 6 4.3 1 7 3 .955 3.889 6/ 1 5 / 2 0 03 14:00 221 .47 987 22 1.57 924 223 .984 91 222 .02 809 6 / 15 / 2 0 03 2 0 : 0 0 4 .48 6 4.3 1 7 3 .955 3.889 6/ 1 5 / 2 0 03 20:00 221 .47 012 22 1.57 924 223 .984 91 222 .02 809 6 / 16 / 2 0 03 2: 00 4.50 5 4 .3 19 3.955 3.903 6/ 1 6 / 2 003 2: 0 0 221 .48 841 22 1.57 771 223 .984 91 222 .04 145 6 / 16 / 2 0 03 8: 00 4.51 5 4 .3 16 3.957 3.889 6/ 1 6 / 2 003 8: 0 0 221 .49 877 22 1.58 046 223 .987 02 222 .02 809 6 / 16 / 2 0 03 1 4 : 0 0 4 .51 2 4.3 1 7 3 .975 3.920 6/ 1 6 / 2 0 03 14:00 221 .49 542 22 1.57 924 224 .005 30 222 .05 902 6 / 16 / 2 0 03 2 0 : 0 0 4 .51 5 4.3 2 2 4 .012 3.930 6/ 1 6 / 2 0 03 20:00 221 .49 877 22 1.57 497 224 .042 56 222 .06 887 6 / 17 / 2 0 03 2: 00 4.53 2 4 .3 28 4.026 3.927 6/ 1 7 / 2 003 2: 0 0 221 .51 584 22 1.56 918 224 .055 92 222 .06 535 6 / 17 / 2 0 03 8: 00 4.53 1 4 .3 32 4.028 3.919 6/ 1 7 / 2 003 8: 0 0 221 .51 492 22 1.56 522 224 .058 03 222 .05 762 6 / 17 / 2 0 03 1 4 : 0 0 4 .52 1 4.3 4 0 4 .028 3.911 6/ 1 7 / 2 0 03 14:00 221 .50 517 22 1.55 668 224 .058 03 222 .04 918 6 / 17 / 2 0 03 2 0 : 0 0 4 .51 1 4.3 4 6 4 .029 3.914 6/ 1 7 / 2 0 03 20:00 221 .49 481 22 1.55 089 224 .059 44 222 .05 270 6 / 18 / 2 0 03 2: 00 4.51 5 4 .3 51 4.031 3.922 6/ 1 8 / 2 003 2: 0 0 221 .49 877 22 1.54 601 224 .061 55 222 .06 113 6 / 18 / 2 0 03 8: 00 4.52 6 4 .3 53 4.035 3.918 6/ 1 8 / 2 003 8: 0 0 221 .50 944 22 1.54 327 224 .065 06 222 .05 621 6 / 18 / 2 0 03 1 4 : 0 0 4 .50 7 4.3 6 0 4 .037 3.932 6/ 1 8 / 2 0 03 14:00 221 .49 115 22 1.53 626 224 .067 17 222 .07 027 6 / 18 / 2 0 03 2 0 : 0 0 4 .48 8 4.3 6 9 4 .039 3.937 6/ 1 8 / 2 0 03 20:00 221 .47 134 22 1.52 773 224 .069 28 222 .07 519 6 / 19 / 2 0 03 2: 00 4.49 9 4 .3 73 4.039 3.937 6/ 1 9 / 2 003 2: 0 0 221 .48 262 22 1.52 376 224 .068 58 222 .07 519 6 / 19 / 2 0 03 8: 00 4.49 8 4 .3 77 4.041 3.941 6/ 1 9 / 2 003 8: 0 0 221 .48 201 22 1.51 950 224 .070 69 222 .08 012 6 / 19 / 2 0 03 1 4 : 0 0 4 .49 3 4.3 8 0 4 .037 3.948 6/ 1 9 / 2 0 03 14:00 221 .47 652 22 1.51 645 224 .067 17 222 .08 644 6 / 19 / 2 0 03 2 0 : 0 0 4 .52 3 4.3 8 0 4 .037 3.958 6/ 1 9 / 2 0 03 20:00 221 .50 669 22 1.51 645 224 .067 17 222 .09 629 6 / 20 / 2 0 03 2: 00 4.56 0 4 .3 82 4.037 3.963 6/ 2 0 / 2 003 2: 0 0 221 .54 388 22 1.51 523 224 .067 17 222 .10 121 6 / 20 / 2 0 03 8: 00 4.58 3 4 .3 82 4.039 3.964 6/ 2 0 / 2 003 8: 0 0 221 .56 644 22 1.51 523 224 .068 58 222 .10 261 6 / 20 / 2 0 03 1 4 : 0 0 4 .57 4 4.3 8 7 4 .041 3.966 6/ 2 0 / 2 0 03 14:00 221 .55 790 22 1.50 944 224 .070 69 222 .10 472 6 / 20 / 2 0 03 2 0 : 0 0 4 .57 0 4.3 9 0 4 .043 3.967 6/ 2 0 / 2 0 03 20:00 221 .55 363 22 1.50 669 224 .072 80 222 .10 613 6 / 21 / 2 0 03 2: 00 4.58 5 4 .3 91 4.041 3.971 6/ 2 1 / 2 003 2: 0 0 221 .56 857 22 1.50 548 224 .070 69 222 .10 965 6 / 21 / 2 0 03 8: 00 4.58 7 4 .3 91 4.043 3.969 6/ 2 1 / 2 003 8: 0 0 221 .57 070 22 1.50 548 224 .072 80 222 .10 754 6 / 21 / 2 0 03 1 4 : 0 0 4 .55 3 4.3 9 6 4 .044 3.967 6/ 2 1 / 2 0 03 14:00 221 .53 687 22 1.50 029 224 .074 20 222 .10 613 6 / 21 / 2 0 03 2 0 : 0 0 4 .54 9 4.3 9 8 4 .044 3.972 6/ 2 1 / 2 0 03 20:00 221 .53 321 22 1.49 907 224 .074 20 222 .11 105 6 / 22 / 2 0 03 2: 00 4.57 1 4 .3 98 4.043 3.974 6/ 2 2 / 2 003 2: 0 0 221 .55 516 22 1.49 907 224 .072 80 222 .11 246 6 / 22 / 2 0 03 8: 00 4.58 8 4 .3 96 4.044 3.972 6/ 2 2 / 2 003 8: 0 0 221 .57 131 22 1.50 029 224 .074 20 222 .11 105 6 / 22 / 2 0 03 1 4 : 0 0 4 .61 1 4.4 0 3 4 .077 3.994 6/ 2 2 / 2 0 03 14:00 221 .59 509 22 1.50 730 224 .107 25 222 .13 214 6 / 22 / 2 0 03 2 0 : 0 0 4 .60 6 4.4 0 5 4 .076 3.994 6/ 2 2 / 2 0 03 20:00 221 .58 960 22 1.50 852 224 .105 84 222 .13 214 6 / 23 / 2 0 03 2: 00 4.60 2 4 .4 03 4.074 3.990 6/ 2 3 / 2 003 2: 0 0 221 .58 533 22 1.50 730 224 .103 73 222 .12 863 6 / 23 / 2 0 03 8: 00 4.60 2 4 .4 02 4.076 3.992 6/ 2 3 / 2 003 8: 0 0 221 .58 533 22 1.50 578 224 .105 84 222 .13 074 6 / 23 / 2 0 03 1 4 : 0 0 4 .57 2 4.4 0 5 4 .076 3.992 6/ 2 3 / 2 0 03 14:00 221 .55 577 22 1.50 852 224 .105 84 222 .13 074 6 / 23 / 2 0 03 2 0 : 0 0 4 .56 8 4.4 0 6 4 .074 3.992 6/ 2 3 / 2 0 03 20:00 221 .55 150 22 1.51 005 224 .103 73 222 .13 074 6 / 24 / 2 0 03 2: 00 4.60 1 4 .4 03 4.074 3.990 6/ 2 4 / 2 003 2: 0 0 221 .58 472 22 1.50 730 224 .103 73 222 .12 863 6 / 24 / 2 0 03 8: 00 4.60 6 4 .4 02 4.074 3.990 6/ 2 4 / 2 003 8: 0 0 221 .59 021 22 1.50 578 224 .103 73 222 .12 863 6 / 24 / 2 0 03 1 4 : 0 0 4 .56 9 4.4 0 3 4 .076 3.990 6/ 2 4 / 2 0 03 14:00 221 .55 241 22 1.50 730 224 .105 84 222 .12 863 6 / 24 / 2 0 03 2 0 : 0 0 4 .56 5 4.4 0 3 4 .075 3.992 6/ 2 4 / 2 0 03 20:00 221 .54 876 22 1.50 730 224 .105 14 222 .13 074 6 / 25 / 2 0 03 2: 00 4.57 9 4 .4 02 4.074 3.990 6/ 2 5 / 2 003 2: 0 0 221 .56 278 22 1.50 578 224 .103 73 222 .12 863 6 / 25 / 2 0 03 8: 00 4.60 4 4 .4 00 4.075 3.990 6/ 2 5 / 2 003 8: 0 0 221 .58 808 22 1.50 426 224 .105 14 222 .12 863 6 / 25 / 2 0 03 1 4 : 0 0 4 .56 5 4.4 0 2 4 .079 3.994 6/ 2 5 / 2 0 03 14:00 221 .54 876 22 1.50 578 224 .109 36 222 .13 214 6 / 25 / 2 0 03 2 0 : 0 0 4 .54 4 4.4 0 5 4 .077 3.994 6/ 2 5 / 2 0 03 20:00 221 .52 773 22 1.50 852 224 .107 25 222 .13 214

158

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e vel (m eter of H2O) Ob s e rve d Head (m eter) Da te T i m e Well 1 W ell 2 W ell 3 W ell 4 W ell 5 D a t e T i m e Well 1 W ell 2 W e l l 3 W e l l 4 W ell 5 6/ 26/ 20 03 2: 00 4.55 6 4 .403 4.077 3 . 992 6/ 26/ 2003 2: 00 221.53961 22 1.50730 224.1072 5 222.13074 6/ 26/ 20 03 8: 00 4.55 5 4 .403 4.079 3 . 994 6/ 26/ 2003 8: 00 221.53839 22 1.50730 224.1093 6 222.13214 6/ 26/ 200 3 14: 00 4.50 3 4 .406 4.079 3 . 994 6/ 26/ 2003 1 4 : 0 0 221.48688 22 1.51005 224.1093 6 222.13214 6/ 26/ 200 3 20: 00 4.48 3 4 .406 4.077 3 . 990 6/ 26/ 2003 2 0 : 0 0 221.46646 22 1.51005 224.1072 5 222.12863 6/ 27/ 20 03 2: 00 4.50 9 4 .402 4.074 3 . 987 6/ 27/ 2003 2: 00 221.49267 22 1.50578 224.1037 3 222.12582 6/ 27/ 20 03 8: 00 4.54 0 4 .399 4.075 3 . 987 6/ 27/ 2003 8: 00 221.52407 22 1.50304 224.1051 4 222.12582 6/ 27/ 200 3 14: 00 4.54 5 4 .398 4.075 3 . 987 6/ 27/ 2003 1 4 : 0 0 221.52925 22 1.50151 224.1051 4 222.12582 6/ 27/ 200 3 20: 00 4.53 3 4 .398 4.074 3 . 985 6/ 27/ 2003 2 0 : 0 0 221.51706 22 1.50151 224.1037 3 222.12371 6/ 28/ 20 03 2: 00 4.55 8 4 .397 4.072 3 . 984 6/ 28/ 2003 2: 00 221.54175 22 1.50090 224.1016 2 222.12230 6/ 28/ 20 03 8: 00 4.57 2 4 .393 4.072 3 . 984 6/ 28/ 2003 8: 00 221.55577 22 1.49664 224.1016 2 222.12230 6/ 28/ 200 3 14: 00 4.54 2 4 .394 4.069 3 . 980 6/ 28/ 2003 1 4 : 0 0 221.52559 22 1.49816 224.0995 1 222.11879 6/ 28/ 200 3 20: 00 4.53 9 4 .393 4.069 3 . 980 6/ 28/ 2003 2 0 : 0 0 221.52285 22 1.49664 224.0995 1 222.11879 6/ 29/ 20 03 2: 00 4.56 4 4 .390 4.068 3 . 977 6/ 29/ 2003 2: 00 221.54754 22 1.49389 224.0981 1 222.11597 6/ 29/ 20 03 8: 00 4.58 0 4 .387 4.068 3 . 974 6/ 29/ 2003 8: 00 221.56369 22 1.49115 224.0981 1 222.11246 6/ 29/ 200 3 14: 00 4.56 0 4 .386 4.066 3 . 972 6/ 29/ 2003 1 4 : 0 0 221.54327 22 1.48963 224.0960 0 222.11105 6/ 29/ 200 3 20: 00 4.55 7 4 .386 4.064 3 . 972 6/ 29/ 2003 2 0 : 0 0 221.54114 22 1.48963 224.0938 9 222.11105 6/ 30/ 20 03 2: 00 4.57 4 4 .383 4.062 3 . 969 6/ 30/ 2003 2: 00 221.55790 22 1.48688 224.0917 8 222.10754 6/ 30/ 20 03 8: 00 4.57 4 4 .380 4.062 3 . 967 6/ 30/ 2003 8: 00 221.55790 22 1.48414 224.0917 8 222.10613 6/ 30/ 200 3 14: 00 4.55 5 4 .379 4.062 3 . 966 6/ 30/ 2003 1 4 : 0 0 221.53839 22 1.48262 224.0924 8 222.10472 6/ 30/ 200 3 20: 00 4.53 6 4 .380 4.060 3 . 967 6/ 30/ 2003 2 0 : 0 0 221.52011 22 1.48414 224.0903 7 222.10613 7/ 1/ 200 3 2: 00 4.54 9 4 .377 4.058 3 . 964 7/ 1/ 2003 2: 00 221.53321 22 1.48109 224.0882 6 222.10261 7/ 1/ 200 3 8: 00 4.54 8 4 .374 4.060 3 . 966 7/ 1/ 2003 8: 00 221.53199 22 1.47835 224.0903 7 222.10472 7/ 1/ 200 3 14: 00 4.50 3 4 .377 4.060 3 . 966 7/ 1/ 2003 1 4 : 0 0 221.48688 22 1.48109 224.0903 7 222.10472 7/ 1/ 200 3 20: 00 4.48 7 4 .377 4.058 3 . 964 7/ 1/ 2003 2 0 : 0 0 221.47073 22 1.48109 224.0882 6 222.10261 7/ 2/ 200 3 2: 00 4.49 3 4 .376 4.056 3 . 964 7/ 2/ 2003 2: 00 221.47713 22 1.47987 224.0861 6 222.10261 7/ 2/ 200 3 8: 00 4.47 7 4 .376 4.056 3 . 963 7/ 2/ 2003 8: 00 221.46097 22 1.47987 224.0861 6 222.10121 7/ 2/ 200 3 14: 00 4.44 8 4 .374 4.056 3 . 961 7/ 2/ 2003 1 4 : 0 0 221.43202 22 1.47835 224.0861 6 222.09980 7/ 2/ 200 3 20: 00 4.45 5 4 .376 4.050 3 . 958 7/ 2/ 2003 2 0 : 0 0 221.43842 22 1.47987 224.0805 3 222.09629 7/ 3/ 200 3 2: 00 4.48 0 4 .370 4.049 3 . 958 7/ 3/ 2003 2: 00 221.46372 22 1.47408 224.0791 2 222.09629 7/ 3/ 200 3 8: 00 4.48 6 4 .369 4.049 3 . 956 7/ 3/ 2003 8: 00 221.46951 22 1.47286 224.0791 2 222.09488 7/ 3/ 200 3 14: 00 4.45 9 4 .369 4.047 3 . 955 7/ 3/ 2003 1 4 : 0 0 221.44269 22 1.47286 224.0770 2 222.09347 7/ 3/ 200 3 20: 00 4.45 2 4 .367 4.043 3 . 953 7/ 3/ 2003 2 0 : 0 0 221.43568 22 1.47134 224.0735 0 222.09137 7/ 4/ 200 3 2: 00 4.48 2 4 .365 4.040 3 . 950 7/ 4/ 2003 2: 00 221.46585 22 1.46859 224.0699 8 222.08855 7/ 4/ 200 3 8: 00 4.49 8 4 .360 4.040 3 . 948 7/ 4/ 2003 8: 00 221.48201 22 1.46433 224.0699 8 222.08644 7/ 4/ 200 3 14: 00 4.47 3 4 .359 4.039 3 . 946 7/ 4/ 2003 1 4 : 0 0 221.45671 22 1.46280 224.0692 8 222.08504 7/ 4/ 200 3 20: 00 4.46 9 4 .359 4.038 3 . 945 7/ 4/ 2003 2 0 : 0 0 221.45244 22 1.46280 224.0678 8 222.08363 7/ 5/ 200 3 2: 00 4.48 3 4 .357 4.036 3 . 944 7/ 5/ 2003 2: 00 221.46646 22 1.46067 224.0657 7 222.08223 7/ 5/ 200 3 8: 00 4.49 4 4 .354 4.032 3 . 940 7/ 5/ 2003 8: 00 221.47774 22 1.45793 224.0622 5 222.07871 7/ 5/ 200 3 14: 00 4.49 4 4 .353 4.032 3 . 940 7/ 5/ 2003 1 4 : 0 0 221.47774 22 1.45671 224.0622 5 222.07871 7/ 5/ 200 3 20: 00 4.46 8 4 .353 4.030 3 . 938 7/ 5/ 2003 2 0 : 0 0 221.45183 22 1.45671 224.0601 4 222.07660 7/ 6/ 200 3 2: 00 4.48 8 4 .350 4.027 3 . 935 7/ 6/ 2003 2: 00 221.47225 22 1.45366 224.0566 3 222.07379 7/ 6/ 200 3 8: 00 4.50 4 4 .346 4.027 3 . 935 7/ 6/ 2003 8: 00 221.48780 22 1.44970 224.0566 3 222.07379 7/ 6/ 200 3 14: 00 4.46 8 4 .347 4.021 3 . 929 7/ 6/ 2003 1 4 : 0 0 221.45183 22 1.45092 224.0510 0 222.06746 7/ 6/ 200 3 20: 00 4.46 8 4 .340 4.022 3 . 932 7/ 6/ 2003 2 0 : 0 0 221.45183 22 1.44391 224.0524 1 222.07027 7/ 7/ 200 3 2: 00 4.48 8 4 .342 4.021 3 . 929 7/ 7/ 2003 2: 00 221.47134 22 1.44543 224.0510 0 222.06746 7/ 7/ 200 3 8: 00 4.48 1 4 .346 4.021 3 . 927 7/ 7/ 2003 8: 00 221.46433 22 1.44970 224.0510 0 222.06535 7/ 7/ 200 3 14: 00 4.46 9 4 .340 4.017 3 . 925 7/ 7/ 2003 1 4 : 0 0 221.45244 22 1.44391 224.0467 8 222.06395

159

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Wa ter L e vel ( m et er o f H2 O ) Observ ed Hea d ( m et er) Date T i m e Wel l 1 W e l l 2 W el l 3 W ell 4 W el l 5 Date T i m e W e ll 1 W e l l 2 W ell 3 W el l 4 W ell 5 7/7/ 2003 20:00 4. 477 4.33 7 4 .017 3.924 7/ 7/ 2003 2 0 :00 221. 46036 221.441 16 224. 04678 222. 06254 7/ 8/200 3 2: 00 4. 495 4.33 6 4 .011 3.920 7/8 / 2003 2: 00 221. 47865 221.439 64 224. 04116 222. 05902 7/ 8/200 3 8: 00 4. 495 4.32 9 4 .011 3.920 7/8 / 2003 8: 00 221. 47865 221.432 63 224. 04116 222. 05902 7/8/ 2003 14:00 4. 482 4.33 3 4 .010 3.918 7/ 8/ 2003 1 4 :00 221. 46585 221.436 90 224. 03975 222. 05621 7/8/ 2003 20:00 4. 494 4.31 9 4 .019 3.922 7/ 8/ 2003 2 0 :00 221. 47774 221.422 87 224. 04889 222. 06043 7/ 9/200 3 2: 00 4. 489 4.33 3 4 .015 3.920 7/9 / 2003 2: 00 221. 47286 221.436 90 224. 04538 222. 05902 7/ 9/200 3 8: 00 4. 480 4.33 3 4 .015 3.922 7/9 / 2003 8: 00 221. 46372 221.436 90 224. 04538 222. 06043 7/9/ 2003 14:00 4. 462 4.33 3 4 .011 3.920 7/ 9/ 2003 1 4 :00 221. 44543 221.436 90 224. 04116 222. 05902 7/9/ 2003 20:00 4. 457 4.33 2 4 .013 3.922 7/ 9/ 2003 2 0 :00 221. 44116 221.435 37 224. 04327 222. 06043 7/10/ 2003 2:00 4. 453 4.33 5 4 .011 3.924 7/ 10/ 2003 2:00 221. 43690 221.438 42 224. 04116 222. 06254 7/10/ 2003 8:00 4. 443 4.33 5 4 .013 3.925 7/ 10/ 2003 8:00 221. 42653 221.438 42 224. 04327 222. 06395 7/ 10/20 03 14: 00 4. 402 4.33 7 4 .009 3.922 7/1 0 /2003 14: 00 221. 38569 221.441 16 224. 03905 222. 06043 7/ 10/20 03 20: 00 4. 414 4.33 6 4 .011 3.924 7/1 0 /2003 20: 00 221. 39758 221.439 64 224. 04116 222. 06254 7/11/ 2003 2:00 4. 420 4.33 7 4 .008 3.922 7/ 11/ 2003 2:00 221. 40337 221.441 16 224. 03764 222. 06043 7/11/ 2003 8:00 4. 427 4.33 6 4 .008 3.924 7/ 11/ 2003 8:00 221. 41038 221.439 64 224. 03764 222. 06254 7/ 11/20 03 14: 00 4. 441 4.33 3 4 .005 3.922 7/1 1 /2003 14: 00 221. 42501 221.436 90 224. 03553 222. 06043 7/ 11/20 03 20: 00 4. 446 4.33 3 4 .003 3.924 7/1 1 /2003 20: 00 221. 42988 221.436 90 224. 03342 222. 06254 7/12/ 2003 2:00 4. 473 4.33 2 4 .000 3.919 7/ 12/ 2003 2:00 221. 45671 221.435 37 224. 02991 222. 05762 7/12/ 2003 8:00 4. 490 4.33 0 4 .001 3.922 7/ 12/ 2003 8:00 221. 47347 221.434 15 224. 03132 222. 06043 7/ 12/20 03 14: 00 4. 468 4.33 2 3 .998 3.925 7/1 2 /2003 14: 00 221. 45183 221.435 37 224. 02780 222. 06395 7/ 12/20 03 20: 00 4. 484 4.32 9 3 .996 3.925 7/1 2 /2003 20: 00 221. 46798 221.432 63 224. 02569 222. 06395 7/13/ 2003 2:00 4. 524 4.32 8 3 .992 3.924 7/ 13/ 2003 2:00 221. 50791 221.431 41 224. 02218 222. 06254 7/13/ 2003 8:00 4. 540 4.32 9 3 .994 3.927 7/ 13/ 2003 8:00 221. 52407 221.432 63 224. 02428 222. 06535 7/ 13/20 03 14: 00 4. 530 4.32 9 3 .992 3.927 7/1 3 /2003 14: 00 221. 51370 221.432 63 224. 02218 222. 06535 7/ 13/20 03 20: 00 4. 522 4.33 0 3 .992 3.929 7/1 3 /2003 20: 00 221. 50608 221.434 15 224. 02218 222. 06746 7/14/ 2003 2:00 4. 521 4.33 0 3 .989 3.927 7/ 14/ 2003 2:00 221. 50456 221.434 15 224. 01866 222. 06535 7/14/ 2003 8:00 4. 521 4.32 9 3 .989 3.927 7/ 14/ 2003 8:00 221. 50517 221.432 63 224. 01866 222. 06535 7/ 14/20 03 14: 00 4. 491 4.33 0 3 .986 3.925 7/1 4 /2003 14: 00 221. 47500 221.434 15 224. 01655 222. 06395 7/ 14/20 03 20: 00 4. 487 4.32 9 3 .988 3.927 7/1 4 /2003 20: 00 221. 47073 221.432 63 224. 01796 222. 06535 7/15/ 2003 2:00 4. 498 4.32 9 3 .984 3.925 7/ 15/ 2003 2:00 221. 48140 221.432 63 224. 01444 222. 06395 7/15/ 2003 8:00 4. 499 4.32 6 3 .990 3.927 7/ 15/ 2003 8:00 221. 48262 221.429 88 224. 02007 222. 06535 7/ 15/20 03 14: 00 4. 442 4.33 2 3 .982 3.922 7/1 5 /2003 14: 00 221. 42592 221.435 37 224. 01233 222. 06043 7/ 15/20 03 20: 00 4. 420 4.33 0 3 .980 3.920 7/1 5 /2003 20: 00 221. 40337 221.434 15 224. 01022 222. 05902 7/16/ 2003 2:00 4. 434 4.32 5 3 .977 3.919 7/ 16/ 2003 2:00 221. 41739 221.428 36 224. 00671 222. 05762 7/16/ 2003 8:00 4. 462 4.32 2 3 .977 3.918 7/ 16/ 2003 8:00 221. 44543 221.425 62 224. 00671 222. 05621 7/ 16/20 03 14: 00 4. 464 4.31 7 3 .975 3.914 7/1 6 /2003 14: 00 221. 44756 221.421 35 224. 00460 222. 05270 7/ 16/20 03 20: 00 4. 475 4.31 7 3 .973 3.913 7/1 6 /2003 20: 00 221. 45884 221.420 74 224. 00319 222. 05129 7/17/ 2003 2:00 4. 505 4.31 4 3 .973 3.909 7/ 17/ 2003 2:00 221. 48841 221.418 00 224. 00319 222. 04777 7/17/ 2003 8:00 4. 500 4.31 0 3 .971 3.909 7/ 17/ 2003 8:00 221. 48353 221.413 73 224. 00108 222. 04777 7/ 17/20 03 14: 00 4. 471 4.31 1 3 .973 3.908 7/1 7 /2003 14: 00 221. 45457 221.415 25 224. 00319 222. 04637 7/ 17/20 03 20: 00 4. 457 4.31 1 3 .971 3.906 7/1 7 /2003 20: 00 221. 44055 221.415 25 224. 00108 222. 04426 7/18/ 2003 2:00 4. 457 4.30 8 3 .971 3.906 7/ 18/ 2003 2:00 221. 44116 221.412 21 224. 00108 222. 04426 7/18/ 2003 8:00 4. 447 4.30 7 3 .969 3.904 7/ 18/ 2003 8:00 221. 43080 221.410 99 223. 99897 222. 04285 7/ 18/20 03 14: 00 4. 408 4.30 7 3 .963 3.901 7/1 8 /2003 14: 00 221. 39209 221.410 99 223. 99335 222. 03934 7/ 18/20 03 20: 00 4. 402 4.30 3 3 .963 3.899 7/1 8 /2003 20: 00 221. 38569 221.406 72 223. 99335 222. 03793 7/19/ 2003 2:00 4. 429 4.30 0 3 .961 3.898 7/ 19/ 2003 2:00 221. 41251 221.403 98 223. 99124 222. 03652 7/19/ 2003 8:00 4. 447 4.29 6 3 .959 3.894 7/ 19/ 2003 8:00 221. 43080 221.399 71 223. 98913 222. 03301

160

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e ve l (m e t e r of H2O) Ob s e rved He ad (m e t er ) Da te T i m e Well 1 W el l 2 W el l 3 W el l 4 W ell 5 Da te T i m e Well 1 W ell 2 W ell 3 W ell 4 W ell 5 7 / 19 / 2 0 0 3 14 : 0 0 4 .42 0 4 . 29 7 3 .95 9 3 . 89 2 7 / 1 9/ 20 03 14 : 0 0 2 2 1 .4 03 98 221 .40 0 9 3 2 23. 989 13 22 2.0 302 0 7 / 19 / 2 0 0 3 20 : 0 0 4 .42 1 4 . 29 4 3 .95 9 3 . 89 3 7 / 1 9/ 20 03 20 : 0 0 2 2 1 .4 04 59 221 .39 8 1 9 2 23. 989 13 22 2.0 316 0 7/ 2 0 / 2 0 0 3 2: 0 0 4 . 43 4 4 .29 3 3 . 95 6 3 .88 9 7/ 2 0 / 2 003 2 : 00 22 1.4 1 8 0 0 221 .39 6 9 7 2 23. 985 61 22 2.0 280 9 7/ 2 0 / 2 0 0 3 8: 0 0 4 . 44 1 4 .28 9 3 . 97 0 3 .89 8 7/ 2 0 / 2 003 8 : 00 22 1.4 2 5 0 1 221 .39 2 7 0 2 24. 000 38 22 2.0 365 2 7 / 20 / 2 0 0 3 14 : 0 0 4 .47 7 4 . 29 8 3 .96 7 3 . 88 8 7 / 2 0/ 20 03 14 : 0 0 2 2 1 .4 60 97 221 .40 1 8 4 2 23. 996 86 22 2.0 266 8 7 / 20 / 2 0 0 3 20 : 0 0 4 .46 3 4 . 29 6 3 .96 8 3 . 88 5 7 / 2 0/ 20 03 20 : 0 0 2 2 1 .4 46 95 221 .39 9 7 1 2 23. 998 27 22 2.0 231 7 7/ 2 1 / 2 0 0 3 2: 0 0 4 . 45 3 4 .29 7 3 . 96 5 3 .87 8 7/ 2 1 / 2 003 2 : 00 22 1.4 3 6 2 9 221 .40 0 9 3 2 23. 994 75 22 2.0 168 4 7/ 2 1 / 2 0 0 3 8: 0 0 4 . 43 0 4 .29 6 3 . 96 5 3 .87 3 7/ 2 1 / 2 003 8 : 00 22 1.4 1 3 7 3 221 .39 9 7 1 2 23. 994 75 22 2.0 119 2 7 / 21 / 2 0 0 3 14 : 0 0 4 .40 1 4 . 29 4 3 .96 5 3 . 86 7 7 / 2 1/ 20 03 14 : 0 0 2 2 1 .3 85 08 221 .39 8 1 9 2 23. 994 75 22 2.0 055 9 7 / 21 / 2 0 0 3 20 : 0 0 4 .37 7 4 . 29 4 3 .96 1 3 . 82 8 7 / 2 1/ 20 03 20 : 0 0 2 2 1 .3 60 39 221 .39 8 1 9 2 23. 991 24 22 1.9 669 2 7/ 2 2 / 2 0 0 3 2: 0 0 4 . 38 9 4 .29 2 3 . 95 7 3 .83 1 7/ 2 2 / 2 003 2 : 00 22 1.3 7 2 2 8 221 .39 5 4 4 2 23. 987 02 22 1.9 697 3 7/ 2 2 / 2 0 0 3 8: 0 0 4 . 40 2 4 .28 6 3 . 95 5 3 .83 8 7/ 2 2 / 2 003 8 : 00 22 1.3 8 5 6 9 221 .38 9 6 5 2 23. 984 91 22 1.9 767 6 7 / 22 / 2 0 0 3 14 : 0 0 4 .39 3 4 . 28 6 3 .95 5 3 . 84 0 7 / 2 2/ 20 03 14 : 0 0 2 2 1 .3 77 15 221 .38 9 6 5 2 23. 984 91 22 1.9 781 7 7 / 22 / 2 0 0 3 20 : 0 0 4 .39 5 4 . 28 3 3 .95 1 3 . 85 2 7 / 2 2/ 20 03 20 : 0 0 2 2 1 .3 78 68 221 .38 6 9 1 2 23. 981 40 22 1.9 908 2 7/ 2 3 / 2 0 0 3 2: 0 0 4 . 41 6 4 .27 9 3 . 95 1 3 .86 3 7/ 2 3 / 2 003 2 : 00 22 1.3 9 9 7 1 221 .38 2 6 4 2 23. 981 40 22 2.0 020 7 7/ 2 3 / 2 0 0 3 8: 0 0 4 . 43 1 4 .27 7 3 . 94 8 3 .87 3 7/ 2 3 / 2 003 8 : 00 22 1.4 1 5 2 5 221 .38 0 5 1 2 23. 977 88 22 2.0 119 2 7 / 23 / 2 0 0 3 14 : 0 0 4 .44 1 4 . 27 3 3 .94 8 3 . 88 2 7 / 2 3/ 20 03 14 : 0 0 2 2 1 .4 25 01 221 .37 6 5 4 2 23. 977 88 22 2.0 203 5 7 / 23 / 2 0 0 3 20 : 0 0 4 .45 3 4 . 27 1 3 .94 4 3 . 89 1 7 / 2 3/ 20 03 20 : 0 0 2 2 1 .4 36 90 221 .37 5 0 2 2 23. 973 66 22 2.0 294 9 7/ 2 4 / 2 0 0 3 2: 0 0 4 . 46 9 4 .26 7 3 . 93 8 3 .90 8 7/ 2 4 / 2 003 2 : 00 22 1.4 5 2 4 4 221 .37 0 7 5 2 23. 968 04 22 2.0 463 7 7/ 2 4 / 2 0 0 3 8: 0 0 4 . 44 0 4 .26 3 3 . 94 0 3 .91 2 7/ 2 4 / 2 003 8 : 00 22 1.4 2 3 7 9 221 .36 6 4 9 2 23. 970 15 22 2.0 505 9 7 / 24 / 2 0 0 3 14 : 0 0 4 .42 0 4 . 26 0 3 .93 8 3 . 90 3 7 / 2 4/ 20 03 14 : 0 0 2 2 1 .4 03 37 221 .36 3 7 4 2 23. 968 04 22 2.0 414 5 7 / 24 / 2 0 0 3 20 : 0 0 4 .42 8 4 . 25 7 3 .93 6 3 . 91 1 7 / 2 4/ 20 03 20 : 0 0 2 2 1 .4 11 60 221 .36 1 0 0 2 23. 965 93 22 2.0 491 8 7/ 2 5 / 2 0 0 3 2: 0 0 4 . 43 6 4 .25 4 3 . 93 0 3 .91 1 7/ 2 5 / 2 003 2 : 00 22 1.4 2 0 1 3 221 .35 8 2 6 2 23. 960 30 22 2.0 491 8 7/ 2 5 / 2 0 0 3 8: 0 0 4 . 42 6 4 .25 0 3 . 93 0 3 .90 6 7/ 2 5 / 2 003 8 : 00 22 1.4 0 9 4 6 221 .35 3 9 9 2 23. 960 30 22 2.0 442 6 7 / 25 / 2 0 0 3 14 : 0 0 4 .40 4 4 . 24 9 3 .93 0 3 . 89 1 7 / 2 5/ 20 03 14 : 0 0 2 2 1 .3 87 82 221 .35 2 4 7 2 23. 960 30 22 2.0 294 9 7 / 25 / 2 0 0 3 20 : 0 0 4 .39 6 4 . 24 9 3 .92 7 3 . 89 1 7 / 2 5/ 20 03 20 : 0 0 2 2 1 .3 79 29 221 .35 2 4 7 2 23. 956 79 22 2.0 294 9 7/ 2 6 / 2 0 0 3 2: 0 0 4 . 41 4 4 .24 6 3 . 92 2 3 .88 9 7/ 2 6 / 2 003 2 : 00 22 1.3 9 7 5 8 221 .34 9 7 2 2 23. 952 57 22 2.0 280 9 7/ 2 6 / 2 0 0 3 8: 0 0 4 . 42 4 4 .24 2 3 . 92 2 3 .88 5 7/ 2 6 / 2 003 8 : 00 22 1.4 0 7 6 3 221 .34 5 4 6 2 23. 952 57 22 2.0 231 7 7 / 26 / 2 0 0 3 14 : 0 0 4 .39 1 4 . 24 2 3 .92 5 3 . 86 1 7 / 2 6/ 20 03 14 : 0 0 2 2 1 .3 75 02 221 .34 5 4 6 2 23. 954 68 22 1.9 992 6 7 / 26 / 2 0 0 3 20 : 0 0 4 .36 5 4 . 24 2 3 .92 1 3 . 85 1 7 / 2 6/ 20 03 20 : 0 0 2 2 1 .3 49 11 221 .34 5 4 6 2 23. 951 16 22 1.9 894 2 7/ 2 7 / 2 0 0 3 2: 0 0 4 . 36 3 4 .23 9 3 . 91 9 3 .84 2 7/ 2 7 / 2 003 2 : 00 22 1.3 4 6 9 8 221 .34 2 7 1 2 23. 949 05 22 1.9 809 8 7/ 2 7 / 2 0 0 3 8: 0 0 4 . 35 6 4 .23 7 3 . 92 1 3 .82 8 7/ 2 7 / 2 003 8 : 00 22 1.3 3 9 3 6 221 .34 1 1 9 2 23. 951 16 22 1.9 669 2 7 / 27 / 2 0 0 3 14 : 0 0 4 .31 4 4 . 23 7 3 .91 7 3 . 81 4 7 / 2 7/ 20 03 14 : 0 0 2 2 1 .2 97 91 221 .34 1 1 9 2 23. 946 95 22 1.9 521 5 7 / 27 / 2 0 0 3 20 : 0 0 4 .31 2 4 . 23 7 3 .91 5 3 . 80 9 7 / 2 7/ 20 03 20 : 0 0 2 2 1 .2 95 77 221 .34 0 5 8 2 23. 945 54 22 1.9 472 3 7/ 2 8 / 2 0 0 3 2: 0 0 4 . 31 7 4 .23 4 3 . 91 3 3 .80 0 7/ 2 8 / 2 003 2 : 00 22 1.3 0 1 2 6 221 .33 7 8 4 2 23. 943 43 22 1.9 388 0 7/ 2 8 / 2 0 0 3 8: 0 0 4 . 30 6 4 .23 4 3 . 91 1 3 .80 2 7/ 2 8 / 2 003 8 : 00 22 1.2 8 9 9 8 221 .33 7 8 4 2 23. 941 32 22 1.9 409 1 7 / 28 / 2 0 0 3 14 : 0 0 4 .30 2 4 . 23 1 3 .90 9 3 . 80 0 7 / 2 8/ 20 03 14 : 0 0 2 2 1 .2 86 02 221 .33 5 0 9 2 23. 939 21 22 1.9 388 0 7 / 28 / 2 0 0 3 20 : 0 0 4 .30 0 4 . 23 0 3 .90 8 3 . 80 5 7 / 2 8/ 20 03 20 : 0 0 2 2 1 .2 83 89 221 .33 3 5 7 2 23. 937 81 22 1.9 437 2 7/ 2 9 / 2 0 0 3 2: 0 0 4 . 31 3 4 .22 5 3 . 90 4 3 .81 7 7/ 2 9 / 2 003 2 : 00 22 1.2 9 6 9 9 221 .32 9 3 0 2 23. 933 59 22 1.9 556 7 7/ 2 9 / 2 0 0 3 8: 0 0 4 . 32 6 4 .22 3 3 . 90 2 3 .81 8 7/ 2 9 / 2 003 8 : 00 22 1.3 0 9 7 9 221 .32 6 5 6 2 23. 932 18 22 1.9 570 8 7 / 29 / 2 0 0 3 14 : 0 0 4 .30 9 4 . 22 0 3 .90 2 3 . 81 4 7 / 2 9/ 20 03 14 : 0 0 2 2 1 .2 93 03 221 .32 3 8 1 2 23. 932 18 22 1.9 521 5 7 / 29 / 2 0 0 3 20 : 0 0 4 .30 9 4 . 21 8 3 .89 8 3 . 82 3 7 / 2 9/ 20 03 20 : 0 0 2 2 1 .2 93 03 221 .32 2 2 9 2 23. 927 96 22 1.9 620 0 7/ 3 0 / 2 0 0 3 2: 0 0 4 . 33 5 4 .21 6 3 . 89 4 3 .82 8 7/ 3 0 / 2 003 2 : 00 22 1.3 1 8 9 4 221 .31 9 5 5 2 23. 924 45 22 1.9 669 2 7/ 3 0 / 2 0 0 3 8: 0 0 4 . 34 6 4 .21 1 3 . 89 4 3 .82 1 7/ 3 0 / 2 003 8 : 00 22 1.3 2 9 6 1 221 .31 5 2 8 2 23. 924 45 22 1.9 598 9 7 / 30 / 2 0 0 3 14 : 0 0 4 .32 0 4 . 21 0 3 .89 2 3 . 80 7 7 / 3 0/ 20 03 14 : 0 0 2 2 1 .3 03 39 221 .31 3 7 6 2 23. 922 34 22 1.9 458 3 7 / 30 / 2 0 0 3 20 : 0 0 4 .31 0 4 . 21 0 3 .89 0 3 . 81 4 7 / 3 0/ 20 03 20 : 0 0 2 2 1 .2 93 64 221 .31 3 7 6 2 23. 920 23 22 1.9 521 5 7/ 3 1 / 2 0 0 3 2: 0 0 4 . 32 8 4 .20 4 3 . 88 9 3 .81 0 7/ 3 1 / 2 003 2 : 00 22 1.3 1 1 3 2 221 .30 8 2 7 2 23. 918 82 22 1.9 486 4

161

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e ve l (m e t e r of H2O) Ob s e rved He ad (m e t er ) Da te T i m e Well 1 W el l 2 W el l 3 W el l 4 W ell 5 Da te T i m e Well 1 W ell 2 W ell 3 W ell 4 W ell 5 7/ 3 1 / 2 0 0 3 8: 0 0 4 . 32 4 4 .20 3 3 . 88 7 3 .80 7 7/ 3 1 / 2 003 8 : 00 22 1.3 0 7 6 6 221 .30 6 7 5 2 23. 916 71 22 1.9 458 3 7 / 31 / 2 0 0 3 14 : 0 0 4 .30 8 4 . 20 0 3 .88 5 3 . 79 7 7 / 3 1/ 20 03 14 : 0 0 2 2 1 .2 91 51 221 .30 4 0 0 2 23. 914 60 22 1.9 359 8 7 / 31 / 2 0 0 3 20 : 0 0 4 .29 5 4 . 20 0 3 .88 2 3 . 79 7 7 / 3 1/ 20 03 20 : 0 0 2 2 1 .2 78 70 221 .30 4 0 0 2 23. 912 49 22 1.9 359 8 8/ 1 / 20 03 2: 0 0 4 . 30 3 4 .19 7 3 . 88 1 3 .79 5 8/ 1/ 20 03 2: 0 0 22 1.2 8 6 6 3 221 .30 1 2 6 2 23. 911 09 22 1.9 338 7 8/ 1 / 20 03 8: 0 0 4 . 30 1 4 .19 5 3 . 87 9 3 .79 7 8/ 1/ 20 03 8: 0 0 22 1.2 8 4 5 0 221 .29 8 5 2 2 23. 908 98 22 1.9 359 8 8/ 1 / 200 3 14: 0 0 4 . 29 4 4 .19 3 3 . 87 7 3 .78 3 8/ 1 / 20 03 14 : 0 0 2 2 1 .2 77 48 221 .29 6 3 8 2 23. 906 87 22 1.9 212 2 8/ 1 / 200 3 20: 0 0 4 . 27 4 4 .19 3 3 . 87 5 3 .78 1 8/ 1 / 20 03 20 : 0 0 2 2 1 .2 57 67 221 .29 6 3 8 2 23. 905 46 22 1.9 198 1 8/ 2 / 20 03 2: 0 0 4 . 28 3 4 .18 9 3 . 87 0 3 .79 1 8/ 2/ 20 03 2: 0 0 22 1.2 6 6 8 2 221 .29 3 3 3 2 23. 899 84 22 1.9 296 6 8/ 2 / 20 03 8: 0 0 4 . 29 4 4 .18 4 3 . 87 0 3 .78 1 8/ 2/ 20 03 8: 0 0 22 1.2 7 8 0 9 221 .28 7 8 5 2 23. 899 84 22 1.9 198 1 8/ 2 / 200 3 14: 0 0 4 . 26 7 4 .18 4 3 . 86 8 3 .77 6 8/ 2 / 20 03 14 : 0 0 2 2 1 .2 50 66 221 .28 7 8 5 2 23. 897 73 22 1.9 148 9 8/ 2 / 200 3 20: 0 0 4 . 26 3 4 .18 2 3 . 86 6 3 .77 9 8/ 2 / 20 03 20 : 0 0 2 2 1 .2 46 40 221 .28 6 3 2 2 23. 895 62 22 1.9 177 0 8/ 3 / 20 03 2: 0 0 4 . 27 5 4 .18 0 3 . 86 3 3 .77 4 8/ 3/ 20 03 2: 0 0 22 1.2 5 9 2 0 221 .28 3 5 8 2 23. 893 51 22 1.9 127 8 8/ 3 / 20 03 8: 0 0 4 . 27 0 4 .17 7 3 . 86 3 3 .77 0 8/ 3/ 20 03 8: 0 0 22 1.2 5 3 4 1 221 .28 0 8 4 2 23. 893 51 22 1.9 085 6 8/ 3 / 200 3 14: 0 0 4 . 26 2 4 .17 5 3 . 86 2 3 .75 5 8/ 3 / 20 03 14 : 0 0 2 2 1 .2 45 79 221 .27 9 3 1 2 23. 892 10 22 1.8 938 0 8/ 3 / 200 3 20: 0 0 4 . 24 2 4 .17 4 3 . 86 0 3 .75 8 8/ 3 / 20 03 20 : 0 0 2 2 1 .2 25 36 221 .27 8 0 9 2 23. 890 00 22 1.8 966 1 8/ 4 / 20 03 2: 0 0 4 . 25 2 4 .17 3 3 . 85 6 3 .76 0 8/ 4/ 20 03 2: 0 0 22 1.2 3 6 0 3 221 .27 6 5 7 2 23. 886 48 22 1.8 987 2 8/ 4 / 20 03 8: 0 0 4 . 27 0 4 .16 7 3 . 85 4 3 .75 7 8/ 4/ 20 03 8: 0 0 22 1.2 5 3 4 1 221 .27 1 0 8 2 23. 884 37 22 1.8 952 1 8/ 4 / 200 3 14: 0 0 4 . 26 0 4 .16 7 3 . 85 4 3 .73 9 8/ 4 / 20 03 14 : 0 0 2 2 1 .2 44 26 221 .27 1 0 8 2 23. 884 37 22 1.8 776 3 8/ 4 / 200 3 20: 0 0 4 . 23 8 4 .16 6 3 . 85 2 3 .74 5 8/ 4 / 20 03 20 : 0 0 2 2 1 .2 22 01 221 .26 9 5 6 2 23. 882 26 22 1.8 839 6 8/ 5 / 20 03 2: 0 0 4 . 25 0 4 .16 4 3 . 84 9 3 .74 4 8/ 5/ 20 03 2: 0 0 22 1.2 3 3 2 9 221 .26 8 0 4 2 23. 878 75 22 1.8 825 5 8/ 5 / 20 03 8: 0 0 4 . 24 7 4 .16 0 3 . 84 7 3 .74 7 8/ 5/ 20 03 8: 0 0 22 1.2 3 1 1 6 221 .26 4 0 7 2 23. 876 64 22 1.8 853 6 8/ 5 / 200 3 14: 0 0 4 . 24 3 4 .15 9 3 . 84 7 3 .73 1 8/ 5 / 20 03 14 : 0 0 2 2 1 .2 26 89 221 .26 2 5 5 2 23. 876 64 22 1.8 691 9 8/ 5 / 200 3 20: 0 0 4 . 22 1 4 .15 9 3 . 84 4 3 .73 7 8/ 5 / 20 03 20 : 0 0 2 2 1 .2 04 94 221 .26 2 5 5 2 23. 874 53 22 1.8 755 2 8/ 6 / 20 03 2: 0 0 4 . 23 5 4 .15 4 3 . 83 9 3 .74 4 8/ 6/ 20 03 2: 0 0 22 1.2 1 8 9 6 221 .25 8 2 8 2 23. 868 90 22 1.8 825 5 8/ 6 / 20 03 8: 0 0 4 . 24 4 4 .15 2 3 . 83 9 3 .74 0 8/ 6/ 20 03 8: 0 0 22 1.2 2 7 5 0 221 .25 6 1 5 2 23. 868 90 22 1.8 790 3 8/ 6 / 200 3 14: 0 0 4 . 22 2 4 .15 1 3 . 83 7 3 .73 3 8/ 6 / 20 03 14 : 0 0 2 2 1 .2 05 86 221 .25 4 9 3 2 23. 866 79 22 1.8 713 0 8/ 6 / 200 3 20: 0 0 4 . 21 4 4 .15 0 3 . 83 3 3 .73 7 8/ 6 / 20 03 20 : 0 0 2 2 1 .1 97 93 221 .25 3 4 1 2 23. 863 28 22 1.8 755 2 8/ 7 / 20 03 2: 0 0 4 . 22 8 4 .14 7 3 . 83 1 3 .73 5 8/ 7/ 20 03 2: 0 0 22 1.2 1 1 9 5 221 .25 0 6 6 2 23. 861 17 22 1.8 741 1 8/ 7 / 20 03 8: 0 0 4 . 22 7 4 .14 4 3 . 83 0 3 .73 3 8/ 7/ 20 03 8: 0 0 22 1.2 1 0 7 3 221 .24 7 9 2 2 23. 859 76 22 1.8 713 0 8/ 7 / 200 3 14: 0 0 4 . 20 9 4 .14 0 3 . 82 8 3 .72 9 8/ 7 / 20 03 14 : 0 0 2 2 1 .1 92 45 221 .24 3 6 5 2 23. 857 65 22 1.8 677 9 8/ 7 / 200 3 20: 0 0 4 . 21 0 4 .14 0 3 . 82 6 3 .73 5 8/ 7 / 20 03 20 : 0 0 2 2 1 .1 93 66 221 .24 3 6 5 2 23. 856 25 22 1.8 741 1 8/ 8 / 20 03 2: 0 0 4 . 22 6 4 .13 6 3 . 82 2 3 .73 7 8/ 8/ 20 03 2: 0 0 22 1.2 0 9 8 2 221 .23 9 3 8 2 23. 852 03 22 1.8 755 2 8/ 8 / 20 03 8: 0 0 4 . 23 0 4 .13 3 3 . 82 2 3 .73 4 8/ 8/ 20 03 8: 0 0 22 1.2 1 4 0 9 221 .23 6 6 4 2 23. 852 03 22 1.8 727 1 8/ 8 / 200 3 14: 0 0 4 . 21 3 4 .13 3 3 . 81 8 3 .73 7 8/ 8 / 20 03 14 : 0 0 2 2 1 .1 96 71 221 .23 6 6 4 2 23. 848 51 22 1.8 755 2 8/ 8 / 200 3 20: 0 0 4 . 22 0 4 .12 9 3 . 81 8 3 .73 7 8/ 8 / 20 03 20 : 0 0 2 2 1 .2 03 72 221 .23 2 3 7 2 23. 848 51 22 1.8 755 2 8/ 9 / 20 03 2: 0 0 4 . 23 0 4 .12 7 3 . 81 4 3 .73 9 8/ 9/ 20 03 2: 0 0 22 1.2 1 3 4 8 221 .23 0 8 5 2 23. 844 30 22 1.8 776 3 8/ 9 / 20 03 8: 0 0 4 . 23 5 4 .12 3 3 . 81 4 3 .73 7 8/ 9/ 20 03 8: 0 0 22 1.2 1 8 9 6 221 .22 6 5 8 2 23. 844 30 22 1.8 755 2 8/ 9 / 200 3 14: 0 0 4 . 22 1 4 .12 3 3 . 81 2 3 .73 3 8/ 9 / 20 03 14 : 0 0 2 2 1 .2 04 94 221 .22 6 5 8 2 23. 842 19 22 1.8 713 0 8/ 9 / 200 3 20: 0 0 4 . 21 9 4 .12 0 3 . 81 2 3 .73 3 8/ 9 / 20 03 20 : 0 0 2 2 1 .2 02 81 221 .22 3 8 4 2 23. 842 19 22 1.8 713 0 8/ 1 0 / 2 0 0 3 2: 0 0 4 . 22 8 4 .11 7 3 . 80 9 3 .74 2 8/ 1 0 / 2 003 2 : 00 22 1.2 1 1 9 5 221 .22 1 1 0 2 23. 838 67 22 1.8 804 4 8/ 1 0 / 2 0 0 3 8: 0 0 4 . 24 2 4 .11 4 3 . 81 1 3 .71 4 8/ 1 0 / 2 003 8 : 00 22 1.2 2 5 9 7 221 .21 8 3 5 2 23. 840 78 22 1.8 530 2 8 / 10 / 2 0 0 3 14 : 0 0 4 .22 7 4 . 11 3 3 .80 9 3 . 71 3 8 / 1 0/ 20 03 14 : 0 0 2 2 1 .2 10 73 221 .21 6 8 3 2 23. 838 67 22 1.8 516 1 8 / 10 / 2 0 0 3 20 : 0 0 4 .21 4 4 . 11 3 3 .80 6 3 . 71 2 8 / 1 0/ 20 03 20 : 0 0 2 2 1 .1 97 93 221 .21 6 8 3 2 23. 836 56 22 1.8 502 1 8/ 1 1 / 2 0 0 3 2: 0 0 4 . 22 6 4 .11 1 3 . 80 3 3 .70 9 8/ 1 1 / 2 003 2 : 00 22 1.2 0 9 8 2 221 .21 4 7 0 2 23. 833 05 22 1.8 481 0 8/ 1 1 / 2 0 0 3 8: 0 0 4 . 22 6 4 .10 8 3 . 80 2 3 .70 7 8/ 1 1 / 2 003 8 : 00 22 1.2 0 9 8 2 221 .21 1 9 5 2 23. 831 64 22 1.8 452 9 8 / 11 / 2 0 0 3 14 : 0 0 4 .20 8 4 . 10 7 3 .80 1 3 . 70 5 8 / 1 1/ 20 03 14 : 0 0 2 2 1 .1 91 53 221 .21 0 4 3 2 23. 830 94 22 1.8 431 8 8 / 11 / 2 0 0 3 20 : 0 0 4 .20 7 4 . 10 4 3 .79 5 3 . 70 2 8 / 1 1/ 20 03 20 : 0 0 2 2 1 .1 90 31 221 .20 7 6 9 2 23. 825 31 22 1.8 403 6

162

Appendix II – Water Levels and Observed Heads of Monitoring Wells

W a t e r L e vel ( m et er of H2O) Ob s erved Head (m eter) Date T i m e W e ll 1 W ell 2 W el l 3 W ell 4 W ell 5 D a te Tim e W e ll 1 W ell 2 W ell 3 W ell 4 W el l 5 8/12/20 03 2:00 4.23 3 4 .1 01 3 . 7 9 2 3 .698 8 / 1 2 /2003 2:00 221.216 8 3 221 .2 0494 223 . 8 2180 2 2 1.8368 5 8/12/20 03 8:00 4.25 0 4 .0 97 3 . 7 9 0 3 .693 8 / 1 2 /2003 8:00 221.233 2 9 221 .2 0068 223 . 8 1969 2 2 1.8319 3 8 / 12 / 2 003 14 : 0 0 4 .23 7 4 . 0 9 3 3 .7 86 3.692 8/12/200 3 14:00 221.221 1 0 221 .1 9641 223 . 8 1617 2 2 1.8305 2 8 / 12 / 2 003 20 : 0 0 4 .24 5 4 . 0 9 0 3 .7 83 3.688 8/12/200 3 20:00 221.229 0 2 221 .1 9366 223 . 8 1266 2 2 1.8270 1 8/13/20 03 2:00 4.27 4 4 .0 87 3 . 7 7 8 3 .686 8 / 1 3 /2003 2:00 221.257 6 7 221 .1 9092 223 . 8 0844 2 2 1.8241 9 8/13/20 03 8:00 4.26 5 4 .0 83 3 . 7 7 8 3 .683 8 / 1 3 /2003 8:00 221.248 5 3 221 .1 8665 223 . 8 0844 2 2 1.8220 8 8 / 13 / 2 003 14 : 0 0 4 .24 6 4 . 0 8 1 3 .7 76 3.681 8/13/200 3 14:00 221.229 6 3 221 .1 8513 223 . 8 0633 2 2 1.8192 7 8 / 13 / 2 003 20 : 0 0 4 .24 0 4 . 0 8 0 3 .7 73 3.677 8/13/200 3 20:00 221.224 1 4 221 .1 8391 223 . 8 0281 2 2 1.8157 6 8/14/20 03 2:00 4.25 0 4 .0 76 3 . 7 6 9 3 .676 8 / 1 4 /2003 2:00 221.233 9 0 221 .1 7964 223 . 7 9930 2 2 1.8143 5 8/14/20 03 8:00 4.25 0 4 .0 72 3 . 7 6 9 3 .672 8 / 1 4 /2003 8:00 221.233 9 0 221 .1 7538 223 . 7 9930 2 2 1.8108 4 8 / 14 / 2 003 14 : 0 0 4 .22 5 4 . 0 7 2 3 .7 69 3.672 8/14/200 3 14:00 221.209 2 1 221 .1 7538 223 . 7 9930 2 2 1.8108 4 8 / 14 / 2 003 20 : 0 0 4 .20 4 4 . 0 7 2 3 .7 67 3.669 8/14/200 3 20:00 221.188 1 8 221 .1 7538 223 . 7 9719 2 2 1.8080 2 8/15/20 03 2:00 4.21 6 4 .0 69 3 . 7 6 4 3 .667 8 / 1 5 /2003 2:00 221.200 0 7 221 .1 7324 223 . 7 9367 2 2 1.8059 1 8/15/20 03 8:00 4.20 9 4 .0 68 3 . 7 6 6 3 .667 8 / 1 5 /2003 8:00 221.193 0 6 221 .1 7172 223 . 7 9578 2 2 1.8059 1 8 / 15 / 2 003 14 : 0 0 4 .16 6 4 . 0 6 8 3 .7 64 3.667 8/15/200 3 14:00 221.150 0 8 221 .1 7172 223 . 7 9367 2 2 1.8059 1 8 / 15 / 2 003 20 : 0 0 4 .14 3 4 . 0 6 9 3 .7 61 3.666 8/15/200 3 20:00 221.126 3 0 221 .1 7324 223 . 7 9156 2 2 1.8045 1 8/16/20 03 2:00 4.13 4 4 .0 68 3 . 7 5 9 3 .664 8 / 1 6 /2003 2:00 221.117 7 7 221 .1 7172 223 . 7 8945 2 2 1.8031 0 8/16/20 03 8:00 4.13 4 4 .0 65 3 . 7 5 9 3 .663 8 / 1 6 /2003 8:00 221.117 7 7 221 .1 6898 223 . 7 8945 2 2 1.8017 0 8 / 16 / 2 003 14 : 0 0 4 .12 4 4 . 0 6 5 3 .7 58 3.663 8/16/200 3 14:00 221.107 4 1 221 .1 6898 223 . 7 8805 2 2 1.8017 0 8 / 16 / 2 003 20 : 0 0 4 .13 7 4 . 0 6 5 3 .7 54 3.650 8/16/200 3 20:00 221.120 5 1 221 .1 6898 223 . 7 8383 2 2 1.7883 4 8/17/20 03 2:00 4.15 4 4 .0 62 3 . 7 4 7 3 .653 8 / 1 7 /2003 2:00 221.137 5 8 221 .1 6623 223 . 7 7680 2 2 1.7918 5 8/17/20 03 8:00 4.17 3 4 .0 57 3 . 7 4 8 3 .655 8 / 1 7 /2003 8:00 221.157 0 9 221 .1 6044 223 . 7 7821 2 2 1.7932 6 8 / 17 / 2 003 14 : 0 0 4 .16 6 4 . 0 5 5 3 .7 45 3.653 8/17/200 3 14:00 221.150 0 8 221 .1 5922 223 . 7 7469 2 2 1.7918 5 8 / 17 / 2 003 20 : 0 0 4 .16 8 4 . 0 5 3 3 .7 41 3.650 8/17/200 3 20:00 221.151 6 0 221 .1 5648 223 . 7 7117 2 2 1.7883 4 8/18/20 03 2:00 4.20 2 4 .0 48 3 . 7 3 7 3 .646 8 / 1 8 /2003 2:00 221.186 0 4 221 .1 5221 223 . 7 6696 2 2 1.7848 2 8/18/20 03 8:00 4.21 8 4 .0 46 3 . 7 3 7 3 .643 8 / 1 8 /2003 8:00 221.202 2 0 221 .1 4947 223 . 7 6696 2 2 1.7820 1 8 / 18 / 2 003 14 : 0 0 4 .20 3 4 . 0 4 3 3 .7 33 3.642 8/18/200 3 14:00 221.186 6 5 221 .1 4642 223 . 7 6344 2 2 1.7806 0 8 / 18 / 2 003 20 : 0 0 4 .20 4 4 . 0 4 1 3 .7 33 3.640 8/18/200 3 20:00 221.187 5 7 221 .1 4520 223 . 7 6344 2 2 1.7784 9 8/19/20 03 2:00 4.22 1 4 .0 39 3 . 7 2 8 3 .637 8 / 1 9 /2003 2:00 221.204 9 4 221 .1 4246 223 . 7 5782 2 2 1.7756 8 8/19/20 03 8:00 4.21 3 4 .0 33 3 . 7 2 6 3 .634 8 / 1 9 /2003 8:00 221.196 7 1 221 .1 3667 223 . 7 5571 2 2 1.7721 7 8 / 19 / 2 003 14 : 0 0 4 .18 6 4 . 0 3 3 3 .7 28 3.634 8/19/200 3 14:00 221.169 2 8 221 .1 3667 223 . 7 5782 2 2 1.7721 7 8 / 19 / 2 003 20 : 0 0 4 .17 9 4 . 0 3 1 3 .7 24 3.630 8/19/200 3 20:00 221.162 2 7 221 .1 3514 223 . 7 5430 2 2 1.7686 5 8/20/20 03 2:00 4.19 3 4 .0 29 3 . 7 2 0 3 .629 8 / 2 0 /2003 2:00 221.176 9 0 221 .1 3240 223 . 7 5008 2 2 1.7672 4 8/20/20 03 8:00 4.19 6 4 .0 26 3 . 7 2 0 3 .626 8 / 2 0 /2003 8:00 221.179 6 4 221 .1 3027 223 . 7 5008 2 2 1.7644 3 8 / 20 / 2 003 14 : 0 0 4 .16 0 4 . 0 2 5 3 .7 19 3.624 8/20/200 3 14:00 221.143 9 8 221 .1 2905 223 . 7 4868 2 2 1.7623 2 8 / 20 / 2 003 20 : 0 0 4 .15 0 4 . 0 2 4 3 .7 14 3.622 8/20/200 3 20:00 221.133 9 2 221 .1 2752 223 . 7 4446 2 2 1.7609 2 8/21/20 03 2:00 4.16 1 4 .0 22 3 . 7 1 2 3 .619 8 / 2 1 /2003 2:00 221.145 2 0 221 .1 2600 223 . 7 4235 2 2 1.7574 0 8/21/20 03 8:00 4.15 9 4 .0 21 3 . 7 1 2 3 .619 8 / 2 1 /2003 8:00 221.142 4 6 221 .1 2478 223 . 7 4235 2 2 1.7574 0 8 / 21 / 2 003 14 : 0 0 4 .12 2 4 . 0 1 8 3 .7 11 3.617 8/21/200 3 14:00 221.105 8 8 221 .1 2204 223 . 7 4094 2 2 1.7560 0 8 / 21 / 2 003 20 : 0 0 4 .10 0 4 . 0 1 8 3 .7 07 3.616 8/21/200 3 20:00 221.083 3 3 221 .1 2204 223 . 7 3672 2 2 1.7545 9 8/22/20 03 2:00 4.10 8 4 .0 15 3 . 7 0 3 3 .611 8 / 2 2 /2003 2:00 221.091 8 6 221 .1 1899 223 . 7 3321 2 2 1.7496 7 8/22/20 03 8:00 4.11 3 4 .0 10 3 . 7 1 2 3 .604 8 / 2 2 /2003 8:00 221.096 7 4 221 .1 1350 223 . 7 4235 2 2 1.7426 4 8 / 22 / 2 003 14 : 0 0 4 .10 9 4 . 0 1 2 3 .7 14 3.611 8/22/200 3 14:00 221.092 4 7 221 .1 1625 223 . 7 4446 2 2 1.7496 7 8 / 22 / 2 003 20 : 0 0 4 .09 0 4 . 0 1 4 3 .7 12 3.609 8/22/200 3 20:00 221.074 1 8 221 .1 1777 223 . 7 4235 2 2 1.7475 6 8/23/20 03 2:00 4.11 8 4 .0 10 3 . 7 0 5 3 .606 8 / 2 3 /2003 2:00 221.102 2 2 221 .1 1350 223 . 7 3532 2 2 1.7447 5 8/23/20 03 8:00 4.14 4 4 .0 05 3 . 7 0 3 3 .604 8 / 2 3 /2003 8:00 221.127 8 3 221 .1 0924 223 . 7 3321 2 2 1.7426 4 8 / 23 / 2 003 14 : 0 0 4 .13 8 4 . 0 0 4 3 .7 03 3.603 8/23/200 3 14:00 221.122 0 4 221 .1 0771 223 . 7 3321 2 2 1.7412 3

163

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e vel (m eter of H2O) Ob served Head (m eter) D a te Ti m e Well 1 W ell 2 W el l 3 W ell 4 W el l 5 Date T i m e Well 1 W ell 2 W ell 3 W el l 4 W el l 5 8/23/2003 20 : 0 0 4 .135 4.003 3. 70 0 3 .601 8/23/ 20 03 20:00 22 1. 1186 8 221.10649 223.72969 221.73982 8/ 24 / 2003 2:00 4 .160 4.000 3. 69 4 3 .598 8/ 24/2003 2:00 22 1. 1439 8 221.10375 223.72407 221.73631 8/ 24 / 2003 8:00 4 .168 3.997 3. 70 9 3 .595 8/ 24/2003 8:00 22 1. 1516 0 221.10070 223.73883 221.73350 8/24/2003 14 : 0 0 4 .204 4.003 3. 71 1 3 .591 8/24/ 20 03 14:00 22 1. 1875 7 221.10649 223.74094 221.72998 8/24/2003 20 : 0 0 4 .182 4.004 3. 70 5 3 .590 8/24/ 20 03 20:00 22 1. 1662 3 221.10771 223.73532 221.72857 8/ 25 / 2003 2:00 4 .193 4.001 3. 70 1 3 .586 8/ 25/2003 2:00 22 1. 1769 0 221.10497 223.73110 221.72506 8/ 25 / 2003 8:00 4 .195 3.998 3. 70 0 3 .585 8/ 25/2003 8:00 22 1. 1790 3 221.10222 223.72969 221.72365 8/25/2003 14 : 0 0 4 .161 3.998 3. 70 0 3 .583 8/25/ 20 03 14:00 22 1. 1452 0 221.10222 223.72969 221.72154 8/25/2003 20 : 0 0 4 .152 3.997 3. 69 5 3 .582 8/25/ 20 03 20:00 22 1. 1354 5 221.10070 223.72547 221.72014 8/ 26 / 2003 2:00 4 .166 3.994 3. 69 4 3 .580 8/ 26/2003 2:00 22 1. 1500 8 221.09796 223.72407 221.71873 8/ 26 / 2003 8:00 4 .161 3.993 3. 69 2 3 .577 8/ 26/2003 8:00 22 1. 1445 9 221.09674 223.72196 221.71522 8/26/2003 14 : 0 0 4 .136 3.991 3. 69 2 3 .579 8/26/ 20 03 14:00 22 1. 1192 9 221.09521 223.72196 221.71733 8/26/2003 20 : 0 0 4 .105 3.990 3. 68 8 3 .575 8/26/ 20 03 20:00 22 1. 0891 2 221.09369 223.71774 221.71381 8/ 27 / 2003 2:00 4 .131 3.987 3. 68 4 3 .572 8/ 27/2003 2:00 22 1. 1150 3 221.09095 223.71423 221.71029 8/ 27 / 2003 8:00 4 .133 3.984 3. 68 2 3 .570 8/ 27/2003 8:00 22 1. 1165 5 221.08759 223.71212 221.70889 8/27/2003 14 : 0 0 4 .108 3.984 3. 68 4 3 .570 8/27/ 20 03 14:00 22 1. 0918 6 221.08759 223.71423 221.70889 8/27/2003 20 : 0 0 4 .125 3.984 3. 68 1 3 .567 8/27/ 20 03 20:00 22 1. 1086 3 221.08759 223.71071 221.70537 8/ 28 / 2003 2:00 4 .157 3.979 3. 67 9 3 .565 8/ 28/2003 2:00 22 1. 1403 2 221.08333 223.70860 221.70397 8/ 28 / 2003 8:00 4 .173 3.977 3. 68 1 3 .565 8/ 28/2003 8:00 22 1. 1564 8 221.08058 223.71071 221.70397 8/28/2003 14 : 0 0 4 .153 3.978 3. 68 1 3 .567 8/28/ 20 03 14:00 22 1. 1366 7 221.08180 223.71071 221.70537 8/28/2003 20 : 0 0 4 .134 3.979 3. 67 6 3 .564 8/28/ 20 03 20:00 22 1. 1177 7 221.08333 223.70649 221.70256 8/ 29 / 2003 2:00 4 .147 3.977 3. 67 3 3 .563 8/ 29/2003 2:00 22 1. 1311 8 221.08058 223.70298 221.70115 8/ 29 / 2003 8:00 4 .149 3.974 3. 67 3 3 .560 8/ 29/2003 8:00 22 1. 1327 0 221.07754 223.70298 221.69905 8/29/2003 14 : 0 0 4 .129 3.974 3. 66 9 3 .559 8/29/ 20 03 14:00 22 1. 1122 8 221.07754 223.69946 221.69764 8/29/2003 20 : 0 0 4 .128 3.971 3. 67 1 3 .559 8/29/ 20 03 20:00 22 1. 1113 7 221.07479 223.70157 221.69764 8/ 30 / 2003 2:00 4 .138 3.971 3. 66 7 3 .555 8/ 30/2003 2:00 22 1. 1214 3 221.07479 223.69735 221.69412 8/ 30 / 2003 8:00 4 .151 3.968 3. 66 4 3 .554 8/ 30/2003 8:00 22 1. 1348 4 221.07205 223.69384 221.69272 8/30/2003 14 : 0 0 4 .161 3.965 3. 66 4 3 .551 8/30/ 20 03 14:00 22 1. 1445 9 221.06931 223.69384 221.68920 8/30/2003 20 : 0 0 4 .163 3.964 3. 66 0 3 .551 8/30/ 20 03 20:00 22 1. 1467 3 221.06778 223.69032 221.68920 8/ 31 / 2003 2:00 4 .168 3.961 3. 66 0 3 .549 8/ 31/2003 2:00 22 1. 1516 0 221.06504 223.68962 221.68780 8/ 31 / 2003 8:00 4 .171 3.961 3. 66 2 3 .549 8/ 31/2003 8:00 22 1. 1543 5 221.06504 223.69173 221.68780 8/31/2003 14 : 0 0 4 .152 3.960 3. 65 8 3 .549 8/31/ 20 03 14:00 22 1. 1360 6 221.06352 223.68821 221.68780 8/31/2003 20 : 0 0 4 .131 3.960 3. 66 0 3 .549 8/31/ 20 03 20:00 22 1. 1150 3 221.06352 223.68962 221.68780 9/1/2003 2:00 4 .124 3.960 3. 65 4 3 .548 9/1/ 20 03 2:00 22 1. 1074 1 221.06352 223.68399 221.68639 9/1/2003 8:00 4 .112 3.958 3. 65 6 3 .548 9/1/ 20 03 8:00 22 1. 0961 3 221.06230 223.68610 221.68639 9/ 1/2003 14:00 4 .095 3.958 3. 64 8 3 .544 9/ 1/2003 14:00 22 1. 0790 6 221.06230 223.67837 221.68287 9/ 1/2003 20:00 4 .071 3.955 3. 66 5 3 .551 9/ 1/2003 20:00 22 1. 0546 8 221.05925 223.69524 221.68920 9/2/2003 2:00 4 .104 3.947 3. 69 3 3 .554 9/2/ 20 03 2:00 22 1. 0875 9 221.05102 223.72337 221.69272 9/2/2003 8:00 4 .107 3.950 3. 70 7 3 .558 9/2/ 20 03 8:00 22 1. 0912 5 221.05376 223.73672 221.69623 9/ 2/2003 14:00 4 .106 3.948 3. 71 0 3 .563 9/ 2/2003 14:00 22 1. 0897 3 221.05224 223.74024 221.70115 9/ 2/2003 20:00 4 .100 3.957 3. 71 2 3 .567 9/ 2/2003 20:00 22 1. 0833 3 221.06077 223.74235 221.70537 9/3/2003 2:00 4 .110 3.965 3. 71 0 3 .570 9/3/ 20 03 2:00 22 1. 0940 0 221.06931 223.74024 221.70889 9/3/2003 8:00 4 .109 3.969 3. 71 0 3 .577 9/3/ 20 03 8:00 22 1. 0930 8 221.07327 223.74024 221.71522 9/ 3/2003 14:00 4 .090 3.985 3. 70 8 3 .580 9/ 3/2003 14:00 22 1. 0741 8 221.08881 223.73813 221.71873 9/ 3/2003 20:00 4 .076 3.978 3. 70 8 3 .585 9/ 3/2003 20:00 22 1. 0595 5 221.08180 223.73813 221.72365 9/4/2003 2:00 4 .081 3.997 3. 70 5 3 .586 9/4/ 20 03 2:00 22 1. 0644 3 221.10070 223.73461 221.72506 9/4/2003 8:00 4 .084 4.000 3. 70 2 3 .588 9/4/ 20 03 8:00 22 1. 0677 8 221.10375 223.73251 221.72647

164

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e vel (m eter of H2O) Ob served Head (m eter) D a te Ti m e Well 1 W ell 2 W el l 3 W ell 4 W el l 5 Date T i m e Well 1 W ell 2 W ell 3 W el l 4 W el l 5 9/ 4/2003 14:00 4 .084 4.001 3. 69 9 3 .590 9/ 4/2003 14:00 22 1. 0677 8 221.10497 223.72899 221.72857 9/ 4/2003 20:00 4 .104 4.001 3. 69 7 3 .591 9/ 4/2003 20:00 22 1. 0882 0 221.10497 223.72688 221.72998 9/5/2003 2:00 4 .145 4.001 3. 69 3 3 .591 9/5/ 20 03 2:00 22 1. 1284 4 221.10497 223.72337 221.72998 9/5/2003 8:00 4 .173 4.000 3. 69 3 3 .591 9/5/ 20 03 8:00 22 1. 1564 8 221.10375 223.72337 221.72998 9/ 5/2003 14:00 4 .164 4.001 3. 69 3 3 .595 9/ 5/2003 14:00 22 1. 1479 4 221.10497 223.72337 221.73350 9/ 5/2003 20:00 4 .161 4.004 3. 69 0 3 .595 9/ 5/2003 20:00 22 1. 1445 9 221.10771 223.71985 221.73350 9/6/2003 2:00 4 .188 4.003 3. 68 8 3 .595 9/6/ 20 03 2:00 22 1. 1720 2 221.10649 223.71774 221.73350 9/6/2003 8:00 4 .195 4.003 3. 69 0 3 .595 9/6/ 20 03 8:00 22 1. 1790 3 221.10649 223.71985 221.73350 9/ 6/2003 14:00 4 .170 4.005 3. 68 8 3 .596 9/ 6/2003 14:00 22 1. 1537 4 221.10924 223.71774 221.73490 9/ 6/2003 20:00 4 .161 4.007 3. 68 8 3 .596 9/ 6/2003 20:00 22 1. 1452 0 221.11076 223.71774 221.73490 9/7/2003 2:00 4 .171 4.007 3. 68 4 3 .596 9/7/ 20 03 2:00 22 1. 1543 5 221.11076 223.71423 221.73490 9/7/2003 8:00 4 .181 4.005 3. 68 4 3 .596 9/7/ 20 03 8:00 22 1. 1650 1 221.10924 223.71423 221.73490 9/ 7/2003 14:00 4 .153 4.007 3. 68 2 3 .596 9/ 7/2003 14:00 22 1. 1366 7 221.11076 223.71212 221.73490 9/ 7/2003 20:00 4 .145 3. 68 0 3 .596 9/ 7/2003 20:00 22 1. 1290 5 223.71001 221.73490 9/8/2003 2:00 4 .162 3. 67 9 3 .594 9/8/ 20 03 2:00 22 1. 1458 1 223.70860 221.73279 9/8/2003 8:00 4 .172 3. 67 9 3 .601 9/8/ 20 03 8:00 22 1. 1558 7 223.70860 221.73982 9/ 8/2003 14:00 4 .151 3. 67 6 3 .601 9/ 8/2003 14:00 22 1. 1348 4 223.70649 221.73982 9/ 8/2003 20:00 4 .146 3. 67 6 3 .601 9/ 8/2003 20:00 22 1. 1296 6 223.70649 221.73982 9/9/2003 2:00 4 .166 3. 67 1 3 .600 9/9/ 20 03 2:00 22 1. 1494 7 223.70087 221.73842 9/9/2003 8:00 4 .182 3. 67 1 3 .598 9/9/ 20 03 8:00 22 1. 1662 3 223.70087 221.73631 9/ 9/2003 14:00 4 .166 3. 67 1 3 .598 9/ 9/2003 14:00 22 1. 1494 7 223.70087 221.73631 9/ 9/2003 20:00 4 .167 3. 66 9 3 .598 9/ 9/2003 20:00 22 1. 1509 9 223.69876 221.73631 9/ 10 / 2003 2:00 4 .187 3. 66 5 3 .596 9/ 10/2003 2:00 22 1. 1705 0 223.69524 221.73490 9/ 10 / 2003 8:00 4 .204 3. 66 7 3 .596 9/ 10/2003 8:00 22 1. 1875 7 223.69735 221.73490 9/10/2003 14 : 0 0 4 .182 3. 66 5 3 .595 9/10/ 20 03 14:00 22 1. 1656 2 223.69524 221.73350 9/10/2003 20 : 0 0 4 .180 3. 66 5 3 .595 9/10/ 20 03 20:00 22 1. 1634 9 223.69524 221.73350 9/ 11 / 2003 2:00 4 .198 3. 66 3 3 .593 9/ 11/2003 2:00 22 1. 1817 8 223.69313 221.73139 9/ 11 / 2003 8:00 4 .192 3. 66 3 3 .593 9/ 11/2003 8:00 22 1. 1753 8 223.69313 221.73139 9/11/2003 14 : 0 0 4 .163 3. 66 5 3 .595 9/11/ 20 03 14:00 22 1. 1467 3 223.69524 221.73350 9/11/2003 20 : 0 0 4 .140 3. 66 2 3 .591 9/11/ 20 03 20:00 22 1. 1235 6 223.69173 221.72998 9/ 12 / 2003 2:00 4 .149 3. 66 2 3 .591 9/ 12/2003 2:00 22 1. 1327 0 223.69173 221.72998 9/ 12 / 2003 8:00 4 .150 3. 66 2 3 .591 9/ 12/2003 8:00 22 1. 1333 1 223.69173 221.72998 9/12/2003 14 : 0 0 4 .126 3. 66 0 3 .590 9/12/ 20 03 14:00 22 1. 1095 4 223.68962 221.72857 9/12/2003 20 : 0 0 4 .117 3. 66 0 3 .590 9/12/ 20 03 20:00 22 1. 1010 1 223.68962 221.72857 9/ 13 / 2003 2:00 4 .138 3. 65 6 3 .586 9/ 13/2003 2:00 22 1. 1214 3 223.68610 221.72506 9/ 13 / 2003 8:00 4 .145 3. 65 4 3 .588 9/ 13/2003 8:00 22 1. 1290 5 223.68399 221.72647 9/13/2003 14 : 0 0 4 .114 3. 65 4 3 .586 9/13/ 20 03 14:00 22 1. 0982 6 223.68399 221.72506 9/13/2003 20 : 0 0 4 .105 3. 65 2 3 .585 9/13/ 20 03 20:00 22 1. 0891 2 223.68188 221.72365 9/ 14 / 2003 2:00 4 .120 3. 65 0 3 .583 9/ 14/2003 2:00 22 1. 1037 5 223.68048 221.72154 9/ 14 / 2003 8:00 4 .123 3. 65 2 3 .583 9/ 14/2003 8:00 22 1. 1064 9 223.68188 221.72154 9/14/2003 14 : 0 0 4 .094 3. 65 0 3 .582 9/14/ 20 03 14:00 22 1. 0778 4 223.68048 221.72014 9/14/2003 20 : 0 0 4 .088 3. 65 0 3 .582 9/14/ 20 03 20:00 22 1. 0720 5 223.68048 221.72014 9/ 15 / 2003 2:00 4 .088 3. 64 7 3 .579 9/ 15/2003 2:00 22 1. 0720 5 223.67696 221.71733 9/ 15 / 2003 8:00 4 .089 3. 64 5 3 .579 9/ 15/2003 8:00 22 1. 0729 6 223.67485 221.71733 9/15/2003 14 : 0 0 4 .107 3. 64 3 3 .579 9/15/ 20 03 14:00 22 1. 0903 4 223.67274 221.71733 9/15/2003 20 : 0 0 4 .114 3. 64 3 3 .579 9/15/ 20 03 20:00 22 1. 0982 6 223.67274 221.71733 9/ 16 / 2003 2:00 4 .138 3. 63 9 3 .574 9/ 16/2003 2:00 22 1. 1220 4 223.66923 221.71240

165

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e vel (m eter of H2O) Ob served Head (m eter) D a te Ti m e Well 1 W ell 2 W el l 3 W ell 4 W el l 5 Date T i m e Well 1 W ell 2 W ell 3 W el l 4 W el l 5 9/ 16 / 2003 8:00 4 .157 3. 64 1 3 .574 9/ 16/2003 8:00 22 1. 1403 2 223.67134 221.71240 9/16/2003 14 : 0 0 4 .136 3. 64 1 3 .572 9/16/ 20 03 14:00 22 1. 1192 9 223.67134 221.71029 9/16/2003 20 : 0 0 4 .133 3. 63 7 3 .570 9/16/ 20 03 20:00 22 1. 1165 5 223.66712 221.70889 9/ 17 / 2003 2:00 4 .155 3. 63 5 3 .569 9/ 17/2003 2:00 22 1. 1388 0 223.66501 221.70748 9/ 17 / 2003 8:00 4 .166 3. 63 7 3 .567 9/ 17/2003 8:00 22 1. 1494 7 223.66712 221.70537 9/17/2003 14 : 0 0 4 .140 12. 5 9 6 3 .6 3 7 3.567 9/17/ 20 03 14:00 22 1. 1235 6 221. 251 5 8 223.66712 221.70537 9/17/2003 20 : 0 0 4 .127 12. 5 9 9 3 .6 3 5 3.565 9/17/ 20 03 20:00 22 1. 1107 6 221. 254 6 2 223.66501 221.70397 9/ 18 / 2003 2:00 4 .133 12. 5 9 9 3 .6 3 5 3.565 9/ 18/2003 2:00 22 1. 1171 6 221. 254 6 2 223.66501 221.70397 9/ 18 / 2003 8:00 4 .134 12. 5 9 6 3 .6 3 5 3.564 9/ 18/2003 8:00 22 1. 1177 7 221. 251 5 8 223.66501 221.70256 9/18/2003 14 : 0 0 4 .095 12. 6 0 2 3 .6 4 1 3.564 9/18/ 20 03 14:00 22 1. 0790 6 221. 257 3 7 223.67134 221.70256 9/18/2003 20 : 0 0 4 .055 12. 6 1 4 3 .6 5 2 3.555 9/18/ 20 03 20:00 22 1. 0385 2 221. 269 2 6 223.68188 221.69412 9/ 19 / 2003 2:00 4 .044 12. 6 4 2 3 .6 4 3 3.553 9/ 19/2003 2:00 22 1. 0278 5 221. 297 3 0 223.67274 221.69131 9/ 19 / 2003 8:00 4 .054 12. 6 3 5 3 .6 4 3 3.565 9/ 19/2003 8:00 22 1. 0376 1 221. 290 2 9 223.67274 221.70397 9/19/2003 14 : 0 0 4 .117 12. 6 1 4 3 .6 4 1 3.563 9/19/ 20 03 14:00 22 1. 1010 1 221. 269 2 6 223.67063 221.70115 9/19/2003 20 : 0 0 4 .157 12. 6 0 4 3 .6 3 9 3.564 9/19/ 20 03 20:00 22 1. 1403 2 221. 259 5 0 223.66923 221.70256 9/ 20 / 2003 2:00 4 .168 12. 5 9 5 3 .6 3 7 3.564 9/ 20/2003 2:00 22 1. 1516 0 221. 250 3 6 223.66712 221.70256 9/ 20 / 2003 8:00 4 .162 12. 5 8 9 3 .6 4 1 3.565 9/ 20/2003 8:00 22 1. 1458 1 221. 244 5 7 223.67134 221.70397 9/20/2003 14 : 0 0 4 .154 12. 5 9 3 3 .6 3 9 3.565 9/20/ 20 03 14:00 22 1. 1381 9 221. 248 8 3 223.66923 221.70397 9/20/2003 20 : 0 0 4 .156 12. 5 9 6 3 .6 3 7 3.565 9/20/ 20 03 20:00 22 1. 1397 2 221. 251 5 8 223.66712 221.70397 9/ 21 / 2003 2:00 4 .164 12. 5 9 5 3 .6 3 7 3.565 9/ 21/2003 2:00 22 1. 1473 4 221. 250 3 6 223.66712 221.70397 9/ 21 / 2003 8:00 4 .160 12. 5 9 3 3 .6 4 1 3.565 9/ 21/2003 8:00 22 1. 1439 8 221. 248 8 3 223.67063 221.70397 9/21/2003 14 : 0 0 4 .119 12. 6 0 3 3 .6 3 7 3.567 9/21/ 20 03 14:00 22 1. 1031 4 221. 257 9 8 223.66712 221.70537 9/21/2003 20 : 0 0 4 .102 12. 6 0 7 3 .6 4 1 3.569 9/21/ 20 03 20:00 22 1. 0860 7 221. 262 2 4 223.67063 221.70748 9/ 22 / 2003 2:00 4 .088 12. 6 1 4 3 .6 4 3 3.563 9/ 22/2003 2:00 22 1. 0714 4 221. 269 2 6 223.67274 221.70115 9/ 22 / 2003 8:00 4 .060 12. 6 2 2 3 .6 4 3 3.553 9/ 22/2003 8:00 22 1. 0434 0 221. 277 7 9 223.67274 221.69131 9/22/2003 14 : 0 0 4 .046 12. 6 2 8 3 .6 4 6 3.558 9/22/ 20 03 14:00 22 1. 0299 9 221. 283 2 8 223.67626 221.69623 9/22/2003 20 : 0 0 4 .067 12. 6 3 1 3 .6 6 2 3.564 9/22/ 20 03 20:00 22 1. 0510 2 221. 286 0 2 223.69173 221.70256 9/ 23 / 2003 2:00 4 .095 12. 6 3 2 3 .6 6 3 3.565 9/ 23/2003 2:00 22 1. 0784 5 221. 287 5 4 223.69313 221.70397 9/ 23 / 2003 8:00 4 .110 12. 6 3 1 3 .6 7 9 3.564 9/ 23/2003 8:00 22 1. 0940 0 221. 286 0 2 223.70860 221.70256 9/23/2003 14 : 0 0 4 .186 12. 6 2 9 3 .6 7 4 3.580 9/23/ 20 03 14:00 22 1. 1698 9 221. 284 8 0 223.70438 221.71873 9/23/2003 20 : 0 0 4 .189 12. 6 2 4 3 .6 7 3 3.598 9/23/ 20 03 20:00 22 1. 1732 4 221. 279 0 1 223.70298 221.73631 9/ 24 / 2003 2:00 4 .187 12. 6 1 9 3 .6 7 5 3.608 9/ 24/2003 2:00 22 1. 1705 0 221. 274 7 4 223.70509 221.74615 9/ 24 / 2003 8:00 4 .185 12. 6 1 9 3 .6 7 6 3.596 9/ 24/2003 8:00 22 1. 1683 7 221. 274 7 4 223.70649 221.73490 9/24/2003 14 : 0 0 4 .153 12. 6 3 1 3 .6 7 9 3.570 9/24/ 20 03 14:00 22 1. 1366 7 221. 286 0 2 223.70860 221.70889 9/24/2003 20 : 0 0 4 .116 12. 6 3 9 3 .6 7 6 3.567 9/24/ 20 03 20:00 22 1. 0994 8 221. 294 5 5 223.70649 221.70608 9/ 25 / 2003 2:00 4 .118 12. 6 4 1 3 .6 7 1 3.582 9/ 25/2003 2:00 22 1. 1016 2 221. 296 0 8 223.70087 221.72014 9/ 25 / 2003 8:00 4 .131 12. 6 3 2 3 .6 7 1 3.600 9/ 25/2003 8:00 22 1. 1144 2 221. 287 5 4 223.70087 221.73842 9/25/2003 14 : 0 0 4 .147 12. 6 3 1 3 .6 7 1 3.596 9/25/ 20 03 14:00 22 1. 1311 8 221. 286 0 2 223.70087 221.73490 9/25/2003 20 : 0 0 4 .147 12. 6 3 2 3 .6 7 3 3.598 9/25/ 20 03 20:00 22 1. 1311 8 221. 287 5 4 223.70298 221.73631 9/ 26 / 2003 2:00 4 .157 12. 6 3 4 3 .6 7 1 3.596 9/ 26/2003 2:00 22 1. 1403 2 221. 289 0 7 223.70087 221.73490 9/ 26 / 2003 8:00 4 .155 12. 6 3 2 3 .6 7 4 3.582 9/ 26/2003 8:00 22 1. 1388 0 221. 287 5 4 223.70438 221.72014 9/26/2003 14 : 0 0 4 .118 12. 6 4 2 3 .6 8 0 3.551 9/26/ 20 03 14:00 22 1. 1016 2 221. 297 3 0 223.71001 221.68991 9/26/2003 20 : 0 0 4 .094 12. 6 5 7 3 .6 7 8 3.537 9/26/ 20 03 20:00 22 1. 0778 4 221. 312 8 4 223.70790 221.67514 9/ 27 / 2003 2:00 4 .099 12. 6 5 7 3 .6 7 2 3.539 9/ 27/2003 2:00 22 1. 0827 2 221. 312 8 4 223.70227 221.67795 9/ 27 / 2003 8:00 4 .111 12. 6 4 9 3 .6 7 6 3.564 9/ 27/2003 8:00 22 1. 0952 1 221. 304 3 1 223.70649 221.70256 9/27/2003 14 : 0 0 4 .136 12. 6 3 9 3 .6 8 0 3.570 9/27/ 20 03 14:00 22 1. 1192 9 221. 294 5 5 223.71001 221.70889 9/27/2003 20 : 0 0 4 .140 12. 6 4 8 3 .6 8 6 3.582 9/27/ 20 03 20:00 22 1. 1235 6 221. 303 0 9 223.71563 221.72014

166

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L e vel (m eter of H2O) Ob served Head (m eter) D a te Ti m e Well 1 W ell 2 W el l 3 W ell 4 W el l 5 Date T i m e Well 1 W ell 2 W ell 3 W el l 4 W el l 5 9/ 28 / 2003 2:00 4 .162 12. 6 5 0 3 .6 8 6 3.585 9/ 28/2003 2:00 22 1. 1458 1 221. 305 8 3 223.71563 221.72365 9/ 28 / 2003 8:00 4 .165 12. 6 5 0 3 .6 8 6 3.593 9/ 28/2003 8:00 22 1. 1488 6 221. 305 8 3 223.71563 221.73139 9/28/2003 14 : 0 0 4 .168 12. 6 5 2 3 .6 8 3 3.608 9/28/ 20 03 14:00 22 1. 1522 1 221. 307 3 6 223.71352 221.74615 9/28/2003 20 : 0 0 4 .186 12. 6 4 8 3 .6 8 3 3.629 9/28/ 20 03 20:00 22 1. 1692 8 221. 303 0 9 223.71352 221.76724 9/ 29 / 2003 2:00 4 .217 12. 6 4 3 3 .6 8 2 3.650 9/ 29/2003 2:00 22 1. 2006 8 221. 298 8 2 223.71212 221.78834 9/ 29 / 2003 8:00 4 .246 12. 6 3 8 3 .6 8 2 3.669 9/ 29/2003 8:00 22 1. 2296 3 221. 293 0 3 223.71212 221.80802 9/29/2003 14 : 0 0 4 .257 12. 6 3 8 3 .6 8 2 3.677 9/29/ 20 03 14:00 22 1. 2403 0 221. 293 0 3 223.71212 221.81576 9/29/2003 20 : 0 0 4 .216 12. 6 3 6 3 .6 8 2 3.693 9/29/ 20 03 20:00 22 1. 2000 7 221. 291 8 1 223.71212 221.83193 9/ 30 / 2003 2:00 4 .211 12. 6 3 4 3 .6 8 0 3.700 9/ 30/2003 2:00 22 1. 1951 9 221. 289 0 7 223.71001 221.83896 9/ 30 / 2003 8:00 4 .207 12. 6 3 4 3 .6 8 6 3.681 9/ 30/2003 8:00 22 1. 1909 2 221. 289 0 7 223.71563 221.81927 9/30/2003 14 : 0 0 4 .179 12. 6 4 6 3 .6 8 3 3.669 9/30/ 20 03 14:00 22 1. 1628 8 221. 301 5 6 223.71352 221.80802 9/30/2003 20 : 0 0 4 .166 12. 6 4 6 3 .6 8 0 3.687 9/30/ 20 03 20:00 22 1. 1500 8 221. 301 5 6 223.71001 221.82560 10/ 1 / 2003 2:00 4 .197 12. 6 4 2 3 .6 8 3 3.682 10/ 1/2003 2:00 22 1. 1805 6 221. 297 3 0 223.71352 221.82068 10/ 1 / 2003 8:00 4 .201 12. 6 4 1 3 .6 8 8 3.664 10/ 1/2003 8:00 22 1. 1845 2 221. 296 0 8 223.71774 221.80310 10/1/2003 14 : 0 0 4 .177 12. 6 5 5 3 .6 8 7 3.645 10/1/ 20 03 14:00 22 1. 1607 5 221. 310 1 0 223.71704 221.78342 10/1/2003 20 : 0 0 4 .155 12. 6 5 7 3 .6 8 3 3.648 10/1/ 20 03 20:00 22 1. 1388 0 221. 312 8 4 223.71352 221.78693 10/ 2 / 2003 2:00 4 .164 12. 6 5 3 3 .6 8 1 3.667 10/ 2/2003 2:00 22 1. 1479 4 221. 308 5 7 223.71141 221.80591 10/ 2 / 2003 8:00 4 .198 12. 6 4 6 3 .6 8 3 3.672 10/ 2/2003 8:00 22 1. 1817 8 221. 301 5 6 223.71352 221.81084 10/2/2003 14 : 0 0 4 .200 12. 6 4 8 3 .6 8 6 3.661 10/2/ 20 03 14:00 22 1. 1833 0 221. 303 0 9 223.71563 221.79959 10/2/2003 20 : 0 0 4 .195 12. 6 4 9 3 .6 8 6 3.656 10/2/ 20 03 20:00 22 1. 1784 2 221. 304 3 1 223.71563 221.79466 10/ 3 / 2003 2:00 4 .200 12. 6 5 2 3 .6 8 7 3.642 10/ 3/2003 2:00 22 1. 1833 0 221. 307 3 6 223.71704 221.78060 10/ 3 / 2003 8:00 4 .188 12. 6 5 6 3 .6 9 7 3.596 10/ 3/2003 8:00 22 1. 1720 2 221. 311 3 2 223.72688 221.73490 10/3/2003 14 : 0 0 4 .123 12. 6 7 5 3 .6 9 5 3.554 10/3/ 20 03 14:00 22 1. 1064 9 221. 330 5 2 223.72477 221.69272 10/3/2003 20 : 0 0 4 .166 12. 6 8 2 3 .6 9 7 3.546 10/3/ 20 03 20:00 22 1. 1494 7 221. 337 5 3 223.72688 221.68498 10/ 4 / 2003 2:00 4 .152 12. 6 7 5 3 .6 9 1 3.543 10/ 4/2003 2:00 22 1. 1354 5 221. 330 5 2 223.72126 221.68147 10/ 4 / 2003 8:00 4 .138 12. 6 7 4 3 .6 8 9 3.572 10/ 4/2003 8:00 22 1. 1220 4 221. 329 0 0 223.71915 221.71100 10/4/2003 14 : 0 0 4 .164 12. 6 7 1 3 .6 8 6 3.591 10/4/ 20 03 14:00 22 1. 1473 4 221. 326 8 6 223.71563 221.72998 10/4/2003 20 : 0 0 4 .184 12. 6 6 2 3 .6 8 6 3.610 10/4/ 20 03 20:00 22 1. 1677 6 221. 317 1 1 223.71563 221.74826 10/ 5 / 2003 2:00 4 .212 12. 6 5 5 3 .6 8 6 3.626 10/ 5/2003 2:00 22 1. 1958 0 221. 310 1 0 223.71563 221.76443 10/ 5 / 2003 8:00 4 .232 12. 6 5 0 3 .6 8 9 3.622 10/ 5/2003 8:00 22 1. 2156 1 221. 305 8 3 223.71915 221.76092 10/5/2003 14 : 0 0 4 .221 12. 6 5 6 3 .6 8 7 3.621 10/5/ 20 03 14:00 22 1. 2043 3 221. 311 3 2 223.71704 221.75951 10/5/2003 20 : 0 0 4 .217 12. 6 5 2 3 .6 8 7 3.634 10/5/ 20 03 20:00 22 1. 2006 8 221. 307 3 6 223.71704 221.77217 10/ 6 / 2003 2:00 4 .197 12. 6 4 8 3 .6 8 7 3.643 10/ 6/2003 2:00 22 1. 1805 6 221. 303 0 9 223.71704 221.78201 10/ 6 / 2003 8:00 4 .159 12. 6 4 3 3 .6 8 9 3.635 10/ 6/2003 8:00 22 1. 1430 7 221. 298 8 2 223.71915 221.77357 10/6/2003 14 : 0 0 4 .143 12. 6 5 0 3 .6 8 7 3.632 10/6/ 20 03 14:00 22 1. 1269 1 221. 305 8 3 223.71704 221.77076 10/6/2003 20 : 0 0 4 .140 12. 6 4 8 3 .6 8 7 3.638 10/6/ 20 03 20:00 22 1. 1235 6 221. 303 0 9 223.71704 221.77709 10/ 7 / 2003 2:00 4 .155 12. 6 4 5 3 .6 8 6 3.645 10/ 7/2003 2:00 22 1. 1388 0 221. 300 0 4 223.71563 221.78342 10/ 7 / 2003 8:00 4 .170 12. 6 4 3 3 .6 8 7 3.638 10/ 7/2003 8:00 22 1. 1537 4 221. 298 8 2 223.71704 221.77709 10/7/2003 14 : 0 0 4 .154 12. 6 4 8 3 .6 8 7 3.634 10/7/ 20 03 14:00 22 1. 1375 8 221. 303 0 9 223.71704 221.77217 10/7/2003 20 : 0 0 4 .148 12. 6 4 5 3 .6 8 6 3.643 10/7/ 20 03 20:00 22 1. 1317 9 221. 300 0 4 223.71563 221.78201 10/ 8 / 2003 2:00 4 .168 12. 6 4 1 3 .6 8 3 3.655 10/ 8/2003 2:00 22 1. 1522 1 221. 296 0 8 223.71352 221.79326 10/ 8 / 2003 8:00 4 .188 12. 6 3 5 3 .6 8 6 3.646 10/ 8/2003 8:00 22 1. 1714 1 221. 290 2 9 223.71563 221.78482 10/8/2003 14 : 0 0 4 .171 12. 6 4 2 3 .6 8 7 3.630 10/8/ 20 03 14:00 22 1. 1543 5 221. 297 3 0 223.71704 221.76865 10/8/2003 20 : 0 0 4 .154 12. 6 4 3 3 .6 8 6 3.634 10/8/ 20 03 20:00 22 1. 1381 9 221. 298 8 2 223.71563 221.77217 10/ 9 / 2003 2:00 4 .164 12. 6 4 1 3 .6 8 5 3.635 10/ 9/2003 2:00 22 1. 1479 4 221. 296 0 8 223.71493 221.77357 10/ 9 / 2003 8:00 4 .170 12. 6 3 9 3 .6 8 7 3.624 10/ 9/2003 8:00 22 1. 1537 4 221. 294 5 5 223.71704 221.76232 10/9/2003 14 : 0 0 4 .145 12. 6 4 6 3 .6 8 5 3.616 10/9/ 20 03 14:00 22 1. 1284 4 221. 301 5 6 223.71493 221.75459

167

Appendix II – Water Levels and Observed Heads of Monitoring Wells

W a ter L e vel (m eter of H2O) Ob s e r v ed He ad (m eter ) Da te T i m e Well 1 W ell 2 W el l 3 W ell 4 W el l 5 Da te Tim e Wel l 1 W ell 2 W ell 3 W ell 4 W ell 5 1 0 / 9 / 2 003 20 : 0 0 4 .131 1 2 .64 3 3.6 8 7 3 .61 0 10/ 9 / 200 3 2 0 : 0 0 221 .11 442 221 .298 82 223 .717 04 2 21. 748 2 6 1 0 / 1 0 / 200 3 2 : 00 4. 126 1 2 .64 5 3.6 8 3 3 .61 2 10/ 1 0 / 2 0 03 2: 00 221 .11 015 221 .300 04 223 .713 52 2 21. 751 0 7 1 0 / 1 0 / 200 3 8 : 00 4. 130 1 2 .64 1 3.6 8 7 3 .60 6 10/ 1 0 / 2 0 03 8: 00 221 .11 350 221 .296 08 223 .717 04 2 21. 744 7 5 10 / 1 0 / 200 3 14 : 0 0 4 .114 1 2 .64 5 3.6 8 5 3 .59 8 10/ 1 0 / 2 0 03 1 4 : 0 0 221 .09 735 221 .300 04 223 .714 93 2 21. 736 3 1 10 / 1 0 / 200 3 20 : 0 0 4 .104 1 2 .64 3 3.6 8 3 3 .60 3 10/ 1 0 / 2 0 03 2 0 : 0 0 221 .08 759 221 .298 82 223 .713 52 2 21. 741 2 3 1 0 / 1 1 / 200 3 2 : 00 4. 117 1 2 .64 1 3.6 8 1 3 .61 1 10/ 1 1 / 2 0 03 2: 00 221 .10 040 221 .296 08 223 .711 41 2 21. 749 6 7 1 0 / 1 1 / 200 3 8 : 00 4. 130 1 2 .63 6 3.6 8 5 3 .59 8 10/ 1 1 / 2 0 03 8: 00 221 .11 350 221 .291 81 223 .714 93 2 21. 737 0 1 10 / 1 1 / 200 3 14 : 0 0 4 .108 1 2 .64 3 3.6 8 5 3 .58 6 10/ 1 1 / 2 0 03 1 4 : 0 0 221 .09 186 221 .298 82 223 .714 93 2 21. 725 0 6 10 / 1 1 / 200 3 20 : 0 0 4 .092 1 2 .64 3 3.6 8 5 3 .58 4 10/ 1 1 / 2 0 03 2 0 : 0 0 221 .07 571 221 .298 82 223 .714 93 2 21. 722 2 5 1 0 / 1 2 / 200 3 2 : 00 4. 091 1 2 .64 3 3.6 8 3 3 .57 9 10/ 1 2 / 2 0 03 2: 00 221 .07 510 221 .298 82 223 .713 52 2 21. 717 3 3 1 0 / 1 2 / 200 3 8 : 00 4. 095 1 2 .64 3 3.6 8 3 3 .57 7 10/ 1 2 / 2 0 03 8: 00 221 .07 845 221 .298 82 223 .713 52 2 21. 715 2 2 10 / 1 2 / 200 3 14 : 0 0 4 .083 1 2 .64 3 3.6 8 1 3 .59 0 10/ 1 2 / 2 0 03 1 4 : 0 0 221 .06 717 221 .298 82 223 .711 41 2 21. 728 5 7 10 / 1 2 / 200 3 20 : 0 0 4 .097 1 2 .63 6 3.6 8 0 3 .60 1 10/ 1 2 / 2 0 03 2 0 : 0 0 221 .08 119 221 .291 81 223 .710 01 2 21. 739 8 2 1 0 / 1 3 / 200 3 2 : 00 4. 121 1 2 .63 2 3.6 8 0 3 .61 0 10/ 1 3 / 2 0 03 2: 00 221 .10 436 221 .287 54 223 .710 01 2 21. 748 2 6 1 0 / 1 3 / 200 3 8 : 00 4. 133 1 2 .62 8 3.6 8 3 3 .59 1 10/ 1 3 / 2 0 03 8: 00 221 .11 655 221 .283 28 223 .713 52 2 21. 729 9 8 10 / 1 3 / 200 3 14 : 0 0 4 .109 1 2 .63 8 3.6 8 3 3 .57 4 10/ 1 3 / 2 0 03 1 4 : 0 0 221 .09 247 221 .293 03 223 .713 52 2 21. 712 4 0 10 / 1 3 / 200 3 20 : 0 0 4 .097 1 2 .63 9 3.6 8 7 3 .54 8 10/ 1 3 / 2 0 03 2 0 : 0 0 221 .08 058 221 .294 55 223 .717 04 2 21. 686 3 9 1 0 / 1 4 / 200 3 2 : 00 4. 092 1 2 .65 0 3.6 9 0 3 .51 7 10/ 1 4 / 2 0 03 2: 00 221 .07 571 221 .305 83 223 .720 55 2 21. 655 4 5 1 0 / 1 4 / 200 3 8 : 00 4. 097 1 2 .65 9 3.7 0 0 3 .44 9 10/ 1 4 / 2 0 03 8: 00 221 .08 119 221 .314 37 223 .730 40 2 21. 587 2 6 10 / 1 4 / 200 3 14 : 0 0 4 .090 1 2 .67 6 3.6 8 5 3 .46 3 10/ 1 4 / 2 0 03 1 4 : 0 0 221 .07 418 221 .331 74 223 .714 93 2 21. 602 0 2 10 / 1 4 / 200 3 20 : 0 0 4 .105 1 2 .66 7 3.6 7 9 3 .51 5 10/ 1 4 / 2 0 03 2 0 : 0 0 221 .08 912 221 .322 60 223 .709 30 2 21. 654 0 5 1 0 / 1 5 / 200 3 2 : 00 4. 164 1 2 .64 9 3.6 7 2 3 .57 4 10/ 1 5 / 2 0 03 2: 00 221 .14 734 221 .304 31 223 .702 27 2 21. 712 4 0 1 0 / 1 5 / 200 3 8 : 00 4. 235 1 2 .62 8 3.6 7 4 3 .60 5 10/ 1 5 / 2 0 03 8: 00 221 .21 896 221 .283 28 223 .703 68 2 21. 743 3 4 10 / 1 5 / 200 3 14 : 0 0 4 .179 1 2 .62 4 3.6 7 4 3 .61 6 10/ 1 5 / 2 0 03 1 4 : 0 0 221 .16 288 221 .279 01 223 .703 68 2 21. 754 5 9 10 / 1 5 / 200 3 20 : 0 0 4 .175 1 2 .61 8 3.6 7 4 3 .62 7 10/ 1 5 / 2 0 03 2 0 : 0 0 221 .15 922 221 .273 52 223 .703 68 2 21. 765 8 4 1 0 / 1 6 / 200 3 2 : 00 4. 164 1 2 .61 8 3.6 7 2 3 .63 2 10/ 1 6 / 2 0 03 2: 00 221 .14 734 221 .273 52 223 .702 27 2 21. 770 7 6 1 0 / 1 6 / 200 3 8 : 00 4. 175 1 2 .61 5 3.6 7 6 3 .62 9 10/ 1 6 / 2 0 03 8: 00 221 .15 922 221 .270 78 223 .705 79 2 21. 767 2 4 10 / 1 6 / 200 3 14 : 0 0 4 .161 1 2 .62 1 3.6 7 2 3 .62 6 10/ 1 6 / 2 0 03 1 4 : 0 0 221 .14 459 221 .276 27 223 .702 27 2 21. 764 4 3 10 / 1 6 / 200 3 20 : 0 0 4 .158 1 2 .61 9 3.6 7 0 3 .63 7 10/ 1 6 / 2 0 03 2 0 : 0 0 221 .14 185 221 .274 74 223 .700 16 2 21. 775 6 8 1 0 / 1 7 / 200 3 2 : 00 4. 176 1 2 .61 5 3.6 7 2 3 .63 8 10/ 1 7 / 2 0 03 2: 00 221 .16 014 221 .270 78 223 .702 27 2 21. 777 0 9 1 0 / 1 7 / 200 3 8 : 00 4. 183 1 2 .61 5 3.6 7 2 3 .64 3 10/ 1 7 / 2 0 03 8: 00 221 .16 715 221 .270 78 223 .702 27 2 21. 782 0 1 10 / 1 7 / 200 3 14 : 0 0 4 .170 1 2 .61 7 3.6 6 8 3 .64 2 10/ 1 7 / 2 0 03 1 4 : 0 0 221 .15 374 221 .272 00 223 .698 05 2 21. 780 6 0 10 / 1 7 / 200 3 20 : 0 0 4 .171 1 2 .61 4 3.6 7 0 3 .63 7 10/ 1 7 / 2 0 03 2 0 : 0 0 221 .15 496 221 .269 26 223 .700 16 2 21. 775 6 8 1 0 / 1 8 / 200 3 2 : 00 4. 175 1 2 .61 8 3.6 7 0 3 .63 2 10/ 1 8 / 2 0 03 2: 00 221 .15 922 221 .273 52 223 .700 16 2 21. 770 7 6 1 0 / 1 8 / 200 3 8 : 00 4. 173 1 2 .61 7 3.6 7 2 3 .61 7 10/ 1 8 / 2 0 03 8: 00 221 .15 648 221 .272 00 223 .702 27 2 21. 756 0 0 10 / 1 8 / 200 3 14 : 0 0 4 .152 1 2 .62 4 3.6 7 2 3 .59 0 10/ 1 8 / 2 0 03 1 4 : 0 0 221 .13 606 221 .279 01 223 .702 27 2 21. 728 5 7 10 / 1 8 / 200 3 20 : 0 0 4 .126 1 2 .62 9 3.6 7 2 3 .58 2 10/ 1 8 / 2 0 03 2 0 : 0 0 221 .10 954 221 .284 80 223 .702 27 2 21. 720 1 4 1 0 / 1 9 / 200 3 2 : 00 4. 121 1 2 .63 1 3.6 6 7 3 .59 5 10/ 1 9 / 2 0 03 2: 00 221 .10 436 221 .286 02 223 .696 65 2 21. 733 5 0 1 0 / 1 9 / 200 3 8 : 00 4. 136 1 2 .62 2 3.6 6 7 3 .60 0 10/ 1 9 / 2 0 03 8: 00 221 .11 990 221 .277 79 223 .696 65 2 21. 738 4 2 10 / 1 9 / 200 3 14 : 0 0 4 .131 1 2 .62 1 3.6 6 1 3 .60 8 10/ 1 9 / 2 0 03 1 4 : 0 0 221 .11 503 221 .276 27 223 .691 02 2 21. 746 1 5 10 / 1 9 / 200 3 20 : 0 0 4 .145 1 2 .61 5 3.6 6 4 3 .61 4 10/ 1 9 / 2 0 03 2 0 : 0 0 221 .12 844 221 .270 78 223 .694 54 2 21. 752 4 8 1 0 / 2 0 / 200 3 2 : 00 4. 161 1 2 .61 5 3.6 6 1 3 .62 4 10/ 2 0 / 2 0 03 2: 00 221 .14 459 221 .270 78 223 .691 02 2 21. 762 3 2 1 0 / 2 0 / 200 3 8 : 00 4. 127 1 2 .61 0 3.6 6 7 3 .60 0 10/ 2 0 / 2 0 03 8: 00 221 .11 076 221 .264 99 223 .696 65 2 21. 738 4 2 10 / 2 0 / 200 3 14 : 0 0 4 .086 1 2 .62 2 3.6 6 8 3 .55 8 10/ 2 0 / 2 0 03 1 4 : 0 0 221 .06 992 221 .277 79 223 .698 05 2 21. 696 2 3 10 / 2 0 / 200 3 20 : 0 0 4 .079 1 2 .63 4 3.6 7 4 3 .52 2 10/ 2 0 / 2 0 03 2 0 : 0 0 221 .06 291 221 .289 07 223 .703 68 2 21. 660 3 8 1 0 / 2 1 / 200 3 2 : 00 4. 086 1 2 .64 5 3.6 6 8 3 .50 7 10/ 2 1 / 2 0 03 2: 00 221 .06 931 221 .300 04 223 .698 05 2 21. 645 6 1 1 0 / 2 1 / 200 3 8 : 00 4. 086 1 2 .64 5 3.6 6 6 3 .50 6 10/ 2 1 / 2 0 03 8: 00 221 .06 931 221 .300 04 223 .695 94 2 21. 644 2 0

168

Appendix II – Water Levels and Observed Heads of Monitoring Wells

Water L evel (meter of H2O) Ob served Head (meter) Date Time Well 1 W ell 2 W ell 3 W ell 4 W ell 5 Date Time Well 1 W ell 2 W ell 3 W ell 4 W ell 5 10/ 21/2003 14:00 4.079 12. 645 3.660 3.525 10 /21/2003 14:00 22 1.0629 1 221. 300 04 223.69032 221.66389 10/ 21/2003 20:00 4.102 12. 632 3.662 3.530 10 /21/2003 20:00 22 1.0854 6 221. 287 54 223.69243 221.66881 10/22/2003 2 :00 4.110 12. 631 3.659 3.541 10/22/ 2003 2:00 22 1.0940 0 221. 286 02 223.68891 221.68006 10/22/2003 8 :00 4.126 12. 625 3.660 3.543 10/22/ 2003 8:00 22 1.1101 5 221. 280 53 223.69032 221.68147 10/ 22/2003 14:00 4.118 12. 625 3.657 3.555 10 /22/2003 14:00 22 1.1016 2 221. 280 53 223.68680 221.69412 10/ 22/2003 20:00 4.134 12. 618 3.660 3.551 10 /22/2003 20:00 22 1.1177 7 221. 273 52 223.69032 221.68991 10/23/2003 2 :00 4.131 12. 622 3.657 3.553 10/23/ 2003 2:00 22 1.1144 2 221. 277 79 223.68680 221.69131 10/23/2003 8 :00 4.130 12. 618 3.657 3.560 10/23/ 2003 8:00 22 1.1135 0 221. 273 52 223.68680 221.69905 10/ 23/2003 14:00 4.132 12. 617 3.651 3.586 10 /23/2003 14:00 22 1.1156 4 221. 272 00 223.68118 221.72506 10/ 23/2003 20:00 4.164 12. 604 3.650 3.611 10 /23/2003 20:00 22 1.1479 4 221. 259 50 223.67977 221.74967 10/24/2003 2 :00 4.147 12. 598 3.644 3.640 10/24/ 2003 2:00 22 1.1311 8 221. 253 10 223.67415 221.77849 10/24/2003 8 :00 4.107 12. 588 3.648 3.640 10/24/ 2003 8:00 22 1.0903 4 221. 243 35 223.67766 221.77849 10/ 24/2003 14:00 4.099 12. 595 3.648 3.626 10 /24/2003 14:00 22 1.0827 2 221. 250 36 223.67766 221.76443

169

Appendix III – FORTRAN Code c Program Spectral.for c ************************ c This program reads transient data recorded at regular time intervals, finds the auto-correlation c function, the spectral density function, the regulation time, the cross-correlation and c cross-spectral density functions (between pair of variables), the amplitude function of c the cross-spectral density, the coherence function, and the gain function. See reference: c Padilla and Pulido-Bosch, 1995, Study of hydrographs of karstic aquifers by means of correlation c and cross-spectral analysis, Journal of Hydrology, v. 168, p. 73-89 for definitions. c*************************** c Explanation of variables: c x(i) = value of the variable x at the i time step c y(i) = value of the variable y at the i time step C k = time lag (k =0, m) c m = is cutting point (interval in which the analysis is carried out). c r(k) = correlation function (auto or cross) c s(f) = spectral density function (auto or cross) c d(k) = Tukey filter c f(n)=requency = j/2m (j = 1, m) c treg = regulation time (length of the impulse response of the system) c h(n) = cospectrum (real part of cross-spectral correlation) c lamda(n) = quadrature spectrum (imaginary part of cross-spectral correlation) c xamp(n) = cross amplitude (input-output covariance) c xphase(n) = dephasing between the input and output functions c xphasetime(n)= dephasing in the time mode c cohere(n) = corence function c gain(n) = amplification (>1) or attenuation (<1) of the output signal c c integer*4 pnn parameter (pnn = 100000) integer*4 i, j, nn, k, m real*8 x(pnn), y(pnn), rx(pnn),ry(pnn),rxy(pnn),ryx(pnn), 1 tregx, tregy,f(pnn), sx(pnn), sy(pnn),sxy(pnn),hxy(pnn), 2 lamdaxy(pnn), dephaset(pnn),phase(pnn),coherexy(pnn),cyx(pnn), 3 gainxy(pnn), cx(pnn), cy(pnn),cxy(pnn),xaver,yaver,d(pnn),pi, 4 meancohere c open (unit = 8, file = 1 "spectra.in", 2 status = "old") open (unit = 9, file = 1 "spectra.out", 2 status = "unknown") write (6,3) 3 format (' What is the value of "m" for the cutting point? ')

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read (5,*) m write (6,5) 5 format (' How many data points do you have in your data set? ') read (5,*) nn c c read data c read (8,*) do 40 i = 1, nn read (8,*) x(i), y(i) 40 continue xaver =0.0d0 yaver = 0.0d0 do 60 i = 1, nn xaver = xaver + (x(i)/nn) yaver = yaver + (y(i)/nn) 60 continue write (9,64) 64 format ('average X average Y') write (9,70) xaver, yaver 70 format (2f10.4) cx(0) =0.0d0 cy(0) =0.0d0 cxy(0) =0.0d0 cyx(0) =0.0d0 rx(0) = 1.0d0 ry(0) =1.0d0 do 80 i = 1, nn cx(0) = cx(0) + (x(i) -xaver)**2.0d0 cy(0) = cy(0) + (y(i) -yaver)**2.0d0 cxy(0) = cxy(0) + (x(i) -xaver)*(y(i)-yaver) cyx(0) = cyx(0) + (y(i) -yaver)*(x(i)-xaver) 80 continue cx(0) =cx(0)/nn cy(0) =cy(0)/nn cxy(0) =cxy(0)/nn cyx(0) =cyx(0)/nn if (cx(0) .eq. 0.0d0) cx(0) = 0.1d-8 if (cy(0) .eq. 0.0d0) cy(0) = 0.1d-8 rxy(0) = cxy(0)/(cx(0)*cy(0))**0.5d0 ryx(0) = cyx(0)/(cx(0)*cy(0))**0.5d0 c c Calculate the autocorreclation and cross-correlation function c do 120 k = 1, m cx(k) =0.0d0 cy(k) =0.0d0 cxy(k) =0.0d0 cyx(k) =0.0d0 do 100 j = 1, nn-k cx(k) = cx(k) + (1.0d0/nn)*(x(j) -xaver)*(x(j+k)-xaver) cy(k) = cy(k) + (1.0d0/nn)*(y(j) -yaver)*(y(j+k)-yaver) cxy(k) = cxy(k) + (1.0d0/nn)*(x(j) -xaver)*(y(j+k)-yaver)

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cyx(k) = cyx(k) + (1.0d0/nn)*(y(j) -yaver)*(x(j+k)-xaver) 100 continue rx(k) = cx(k)/cx(0) ry(k) = cy(k)/cy(0) rxy(k) = cxy(k)/(cx(0)*cy(0))**0.5d0 ryx(k) = cyx(k)/(cx(0)*cy(0))**0.5d0 120 continue

do 160 j = 1, m k = -j rxy(k) = ryx(j) ryx(k) = rxy(j) 160 continue pi=3.1416d0 c c Calculte the cross-spectral density function. c do 280 k =1,m d(k) = (1.0d0 + dcos((pi*k)/m))/2.0d0 280 continue meancohere=0.0d0 do 320 j = 0,m f(j) = j/(2.0d0*m) sx(j) = 2.0d0 sy(j) = 2.0d0 hxy(j) = 2.0d0*rxy(0) lamdaxy(j) = 2.0d0*rxy(0) do 300 k = 1,m sx(j) = sx(j) + 4.0d0*d(k)*rx(k)*dcos(2.0d0*pi*f(j)*k) sy(j) = sy(j) + 4.0d0*d(k)*ry(k)*dcos(2.0d0*pi*f(j)*k) hxy(j) = hxy(j) + & 2.0d0*(rxy(k)+ ryx(k))*d(k)*dcos(2.0d0*pi*f(j)*k) lamdaxy(j) = lamdaxy(j) + & 2.0d0*(rxy(k)+ ryx(k))*d(k)*dsin(2.0d0*pi*f(j)*k) 300 continue if (f(j) .eq. 0.0d0) f(j) = 0.1d-8 if (hxy(j) .eq. 0.0d0) hxy(j) = 0.1d-8 if (sx(j) .eq. 0.0d0) sx(j) = 0.1d-5 if (sy(j) .eq. 0.0d0) sy(j) = 0.1d-5 if (lamdaxy(j) .eq. 0.0d0) lamdaxy(j)=0.1d-8 sxy(j) = (hxy(j)**2.0d0 + lamdaxy(j)**2.0d0)**0.5d0 phase(j) = datan(lamdaxy(j)/hxy(j)) dephaset(j) = phase(j)/(2.0d0*pi*f(j)) if (sx(j) .lt. 0.0d0)sx(j)=0.000001d0 if (sy(j) .lt. 0.0d0)sy(j)=0.000001d0 coherexy(j)= sxy(j)/(sx(j)*sy(j))**0.5d0 write(6,*)coherexy(j) gainxy(j) = sxy(j)/sx(j) if (sx(j) .gt. 0.01d0 .and. sx(j) .gt. 0.01d0) 1 meancohere= meancohere + coherexy(j)/m 320 continue tregx = sx(0)/2.0d0 tregy = sy(0)/2.0d0

172 c c Write cross-correlation values to output file c write (9,400) 400 format ('lag rx(k) ry(k) rxy(k) ryx(k) ') do 420 k = -m,m write (9,440) k, rx(k), ry(k), rxy(k), ryx(k) 420 continue 440 format (i8, 4f10.3)

c c Write cross-correlation values to output file write (9,500) 500 format ('f(j) sx(j) sy(j) sxy(j) phase(j) ') do 520 j = 0,m write (9,540) f(j), sx(j), sy(j),sxy(j), phase(j) 520 continue 540 format(5f10.3) write (9,560) 560 format ('f(j) dephaset(j) coherexy(j) gainxy(j)') do 570 j = 0,m write (9,580) f(j), dephaset(j), coherexy(j), gainxy(j) 570 continue 580 format (4f15.3) write (9,630) 630 format ('tregx tregy average coherence') write (9, 660) tregx, tregy, meancohere 660 format (3f10.4) close (unit = 8) close (unit = 9) end