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TT-162 Environmental

PROBABILISTIC DESIGN OF MINE-SITE PONDS USING A DYNAMIC WATERMANAGEMENT MODEL

byDr. Ing. James R. Kunkel, Knight Pisold and Co., Senior Associate, 1580 LincolnStreet, Suite 1000, Denver, Colorado USA, Tel: +1 303 867 2286, Fax: +1 303 6298789,jkunkel@knightpiesold.com

andIng. Victor Lishnevsky, Knight Pisold and Co., Associate, 1580 Lincoln Street, Suite1000, Denver, Colorado USA, Tel: +1 303 867 2244, Fax: +1 303 629 8789,vlishnevsky@knightpiesold.com

SUMMARY

Water management at a mine site is a critical component for the design of ponds for

mine facility operation and mine facility closure. A comparison of pond sizes designed

using both a probabilistic and deterministic dynamic water balance model is made.

The two models both have the capability of using actual operational data and climate

inputs to give storage requirements of the ponds. Both short-term (60 days) and long-

term (many years) designs incorporating a user-selected storm event and a

probabilistic or deterministic analysis may be utilized for calculating storage volumes

for various mine facilities. Based on these, operational decisions based on actual

values or probabilities over time can be made. Typically, the final design of the ponds

is based on the average volume from the deterministic model plus the volume from the

100-year return period storm. An example of such a design is presented in the paper.

OBJECTIVES

The overall objective of pond volume designs for mine facility operation and mine

facility closure is to provide a specific set of operating rules and facility sizes for

managing water over the life of the mine. The principal goals of the design are to:

determine the range of normal operating volumes for the ponds so that the

pond size and configuration can be approximately defined for the life of the

mine in order to provide an efficient storage system,

determine the maximum potential pond volume under conditions of extreme

precipitation in order to provide the necessary pond freeboard on the dam at all

times, and

determine the water flow between a given pond and various other mine

components, and to size required pipes and pumps between them.

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DATA GATHERING

An important input component is climatological data which consist of monthly (or daily)

precipitation, evaporation and runoff. The second input is the operation of the project

and development of the facilities. How the mine site development and closure occurs

over time is more important to the sizing of ponds for water storage than the

climatological data. It also is important to note that if the development process

changes, the pond design must be updated to assure that adequate storage is always

available. Thus, inputs from both the mine owner related to mine development, long

term climatological data from government agencies, and various water sources and

sinks must be obtained and analyzed. This often is done in a separate climatology and

water supply report.

For the design example presented in this paper, the key climatological conditions at the

site can be summarized as follows:

the average annual precipitation is approximately 1,172 millimeters (mm),

the average maximum temperature varies from 13 to 16 degrees Celsius and

average minimum temperature varies from 1 to 12 degrees Celsius throughout

the year,

the average annual potential evaporation was estimated to be 1,685 mm,

the average annual evaporation from a pond surface was estimated to be 1,180

mm,

the design 100-year/24-hour storm event produces 92 mm of precipitation,

the mine life was 28 years, and

the 45 years of detailed climatological data used in the design and analyses were

defined in a separate climatological report.

APPLICATIONThe pond sizing begins with a traditional operational water balance model (Kunkel,

2001). The key component in the model is the pond storage which serves as a large

reservoir for the process water supply. The model operates on a monthly basis by

quantifying the monthly changes to the one or more storage ponds from inflows,

outflows, evaporation, and absorption. The model uses the following governing

equation:

V = I Q E,

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where V = change in pond volume, I = inflows to the pond from direct precipitation on

the pond, runoff and other water inflows, Q = outflows from the pond consisting of

reclaim water to the process plant and excess water discharges to treatment or

discharge, and E = evaporation.

The procedure used in the operational water balance to perform pond design is the

robust water balance previously described by Kunkel (2001) and Kunkel and

Lishnevsky (2002). The traditional approach to an operational water balance is to use

the systematic climatological time series data as average monthly values and once-

through operation. This approach uses only a 12-season model with each season

represented by a month. An alternative approach for operational water balance

modeling is to use the complete monthly time series for as many years of data as are

available, or to use a stochastically generated monthly time series. Modern stochastic

climate data generating techniques are easily applied to obtain data in-fill and/or

extension of the historical climatological data. Typical stochastic data generation

models include WGEN (Richardson and Wright, 1984) and ClimGen (Stckle and

others, 1998; 1999). Similar stochastic models also are available within the EPIC

computer program (Sharpley and Williams, 1990) which is extensively used by Knight

Pisold and Co.

The robust operational water balance approach (Kunkel and Lishnevsky, 2002) uses

the systematic climatological record or a synthetically generated monthly time series.

Figure 1 shows a typical monthly climatological time series of 33 years and a mine life

of 6 years for a Per mine site, which indicates how the robust operational water

balance operates. The first operational water balance model run is placed at the start

of the mine life in the first year of the 33 years of precipitation data, while the second

model run is placed in the second year of the data, and so on. Each model run is

considered to be independent of the previous run. Therefore, each of the 33 runs

produces 33 equally-likely, independent outcomes which can be analyzed to calculate

the probability of occurrence of each of the runs. The reason for performing 33 equally

likely and independent water balance runs is because it is unknown, a priori,when the

mine project will come on line or what the climate conditions will be when the project

comes on line.

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EXAMPLE: 6 YEAR MINE LIFE, 33 YEAR CLIMATE RECORD

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EXAMPLE: 6 YEAR MINE LIFE, 33 YEAR CLIMATE RECORD

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Figure 1. Operational Water Balance Procedure for 6-Year Mine Life and 33 Years of

Climatological Data

For the current case of a 28-year mine life and 45 years of climatological data, the

model computes the volume in a given storage pond, together with certain other

outputs (discharge rates, etc.) for each month during the mine life. The operational

mine life was estimated by the mine owner to be 28 years (November 2011 through

November 2038) or 336 months (28 years times 12 months per year). One run of the

model has a duration of 336 months, and for 45-years of precipitation data, a total of 45

independent model runs were computed. Each model run produced 336 months of

output data (outcomes) covering the period of mine operations, and when all 45 of the

runs were complete, each of the 12 months was represented by 45 values of output

data (pond volumes, discharge rates, etc.). Monthly maximum, minimum and various

other probabilistic values were then determined for the output data (outcomes) for each

month of each of the 28 years of the mine life.

DEVELOPMENT

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The pond design volume is calculated as the larger of the storm volume pond modeling

or the probabilistic pond modeling defined as follows.

Storm Volume Pond Modeling

The average and maximum monthly sizes of pond volumes throughout the mine life

from each run was determined by:

Average Monthly Volume. This volume is calculated as the average of the 45 pond

volumes predicted from each month the facility is in operation (336 months) by

stepping the model through 45 years of data. The model is run for 28 years at a time

(336 months), beginning in each of the 45 years of available data. The result is 45

equally likely sets of outcomes of 28 years each. These 45 equally likely outcome

sets are used to calculate probabilities of occurrence (see Probabilitstic Pond

Modeling below), and

Design Storm Event. This volume considers the runoff from the 100-year storm event

occurring in 24 hours from the upstream catchment reporting to the pond plus 100

percent of the precipitation falling within the limits of the pond.

The sum of the average monthly and design storm event volumes was used to predict

the maximum potential pond volume for every month the facility is in operation (336

months). The largest monthly volume from this sum was selected as the design pond

volume.

Probabilistic Pond Modeling

From the 45 equally likely average, maximum, and minimum pond volumes determined

for each of the 336 months the facility will be in operation, a frequency analysis was

performed to determine the 1 percent (represents a 100-year return period), 10 percent

(10-year return period) and 50 percent (2-year return period) chance of exceedance

pond volumes for each of the 336 months. The largest monthly volume was selected as

the required probability analysis pond volume. The percent chance of exceedance was

calculated using an Extremal Type I Distribution (Kite, 1977).

Pond Volume Design

Selection of the design pond volume is based on the monthly volume from either (1)

storm volume pond modeling, or (2) the probabilistic pond modeling, whichever is

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larger. In some cases, freeboard was added to the pond design volume to determine

the final size of the pond.

For the current case of a 28-year mine life and 45 years of climatological data, the

average monthly results of the design pond volume with the 100-year, 24-hour storm

event are shown on Figure 2.

Figure 2. Operational Pond Volume with Storm Event (Storm Volume Modeling)

The probabilistic pond modeling results are presented on Figure 3.

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Figure 3. Probabilistic Pond Volume Modeling

Analyses of the results shown in Figures 2 and 3 indicate that the average pond

volume from the storm volume modeling (Figure 2) is approximately the same as the

50 percent probability volume (Figure 3), as would be expected. In this case if the

decision to design the pond to store approximately 4,500,000 m 3was taken, the mine

owner may feel comfortable that sufficient storage was available to reduce the

likelihood of overtopping on average (the 1 in 2 year probability). Normally, the mine

owner would never know that there is a risk of having to store larger volumes unless

the probability analyses were performed as shown on Figure 3.

If less risk is desired, a design pond volume of approximately 6,000,000 m3(the 1 in 10

year probability) may be appropriate, or even a design pond volume of 8,000,000 m 3

(the 1 in 100 year probability). Typically, the traditional operational water balance

would provide a pond volume with the 100-year, 24-hour storm event as shown on

Figure 2; whereas, the probabilistic design pond volume with less risk would be as

shown on Figure 3.

CONCLUSIONS

Our conclusions related to operational water balance modeling for pond storage at

mine sites include the following:

The type of operational water balance utilized for design of pond storage

volumes at mine sites can result in startlingly different results which may

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impose excessive risk of either overtopping or a water shortage during

operation.

The robust water balance method should be utilized for operational volumes

and flows of all mine facilities for both surface- and ground-water management.

The probabilistic results should be presented so that mine owners and

managers can assess their risk related to design storage volumes or design

flow rates for various facilities.

Water management components other than ponds, such as heap leach pads,

tailing storage facilities, processing facilities, hydroelectric facilities, water

treatment plants, land application facilities, and other mine-related facilities can

be analyzed using a similar technique.

This modeling approach can be made available as a web-based model which

can be shared within a given mine project.

REFERENCES

Kite, G.W., 1977, Frequency and Risk Analysis in Hydrology, Fort Collins: WaterResources. Publications, 224 pp., http://www.wrpllc.com/links.html

Kunkel, J.R. and V. Lishnevsky, 2002, A Robust Water-Balance Method for Sizing

Heap Leach Solution Ponds, Proceedings of the SME Annual Meeting,Phoenix, Arizona, February 25-27, 2002, (only available on compact disk),Preprint 02-049, 5p.

Kunkel, J.R. 2001,A Robust Water-Balance Method for Sizing Heap Leach SolutionPonds and Reservoirs, Proceedings of the XXV Convention of Peruvian MiningEngineers, Arequipa, Peru, September 10-14, 2001 (only available on compactdisk), 11p.

Richardson, C.W. and D.A. Wright, 1984, WGEN: A Model for Generating DailyWeather Variables, U.S. Department of Agriculture, Agricultural ResearchService, ARS-8, August, http://soilphysics.okstate.edu/software/cmls/WGEN.pdf

Sharpley, A.N., and J.R. Williams, 1990, EPIC-Erosion/Productivity Impact Calculator:1. Model Documentation, US Department of Agriculture Technical Bulletin No.1768, 235 p., http://www.epa.gov/nrmrl/pubs/600r05149/600r05149epic.pdf

Stckle, C.O., P. Steduto, and R.G. Allen, 1998, Estimating Daily and Daytime MeanVPD from Daily Maximum VPD. 5th Congress of the European Society ofAgronomy, Nitra, The Slovak Republic.

Stckle, C.O., G.S. Campbell, and R. Nelson, 1999, ClimGen Manual. BiologicalSystems Engineering Department, Washington State University, Pullman, WA,

28 p., http://bsyse.wsu.edu/climgen/

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