tracking acid mine-drainage in southeast arizona using gis and sediment delivery … · 2014. 9....

Tracking acid mine-drainage in Southeast Arizonausing GIS and sediment delivery models

Laura M. Norman & Floyd Gray &

D. Phillip Guertin & Craig Wissler & James D. Bliss

Received: 12 February 2007 /Accepted: 30 October 2007 /Published online: 11 December 2007# Springer Science + Business Media B.V. 2007

Abstract This study investigates the application ofmodels traditionally used to estimate erosion andsediment deposition to assess the potential risk ofwater quality impairment resulting from metal-bearingmaterials related to mining and mineralization. Anintegrated watershed analysis using Geographic Infor-mation Systems (GIS) based tools was undertaken toexamine erosion and sediment transport characteristicswithin the watersheds. Estimates of stream deposits ofsediment from mine tailings were related to thechemistry of surface water to assess the effectivenessof the methodology to assess the risk of acid mine-drainage being dispersed downstream of abandonedtailings and waste rock piles. A watershed analysis waspreformed in the Patagonia Mountains in southeasternArizona which has seen substantial mining and whererecent water quality samples have reported acidicsurface waters. This research demonstrates an improve-ment of the ability to predict streams that are likely tohave severely degraded water quality as a result of pastmining activities.

Keywords Erosion . Geospatial analysis . GIS .

Nonpoint source pollution . Sediment . Surface water .

Water quality .Watershed management

Introduction

Increasingly, water quality professionals are beingasked to assess the potential risk of impairment to oursurface waters. The risk assessments are used toidentify monitoring sites, identify potential sources ofpollutants, and identify priority areas for fundingwater-quality improvement projects. For many water-sheds there is often a lack of water quality monitoringdata to base the assessments, so secondary data sourcesneed to be used in the assessment process. Techniquesneed to be developed that can identify stream reachesat risk for different pollutants from various sources thatare based on readily available data.

In the western United States abandoned mines andtheir tailings have been identified as an importantsource of water-quality impairment. Water qualityconcerns that have been identified to abandonedmines include acidity and heavy metals such ascopper, lead, mercury and arsenic depending on themining operation (Hem 1985; Dean and Fogel 1982;Segnupta 1993; Gray et al. 2000; Welch et al. 2000).

This study will develop a methodology forperforming a watershed analysis using a GeographicInformation Systems (GIS)-based platform. The study

Environ Monit Assess (2008) 145:145–157DOI 10.1007/s10661-007-0024-5

L. M. Norman (*) : F. Gray : J. D. BlissU.S. Geological Survey, 520 N. Park Avenue,Suite #355, Tucson, AZ 85719-5035, USAe-mail: [email protected]

D. P. Guertin :C. WisslerSchool of Natural Resources, The University of Arizona,Tucson, AZ 85719, USA

will apply the Universal Soil Loss Equation (USLE;Wischmeier and Smith 1978) across the study water-shed to estimate potential soil loss from a hillslope andthen routes the sediment to the stream channel usingSpatially Explicit Delivery MODel (SEDMOD; Fraser1999; Brady 2000; Brady et al. 2001). The analysiscan be applied so that the sources of sediment deliveredto the stream channel can be tracked. In this study theamount of the sediment derived from mine areas wastracked and compared to the total sediment yield to astream segment. The sediment yield results wherecompared to surface water-quality samples (Dean1982; Dean and Fogel 1982; Gray et al. 2000) andrelationships developed between different water qualityconstituents and ratio of mined-derived sediment yieldand total sediment yield. One of the goals of this projectwas to identify stream reaches with potential impair-ment from non-point source (NPS) pollutants and theirsources within the watershed. NPS pollution is definedas pollution that comes from many sources throughouta system rather than from one identifiable point(Stringer and Perkins 1997). The incorporation of thesources in this study was to determine if poor waterquality came from redistributed sediment originating atlocal mines. The hypothesis is that the historical minewaste is a primary source of this NPS pollution.

Water yield in a semi-arid environment is stronglyaffected by the interrelation of erosion and sedimentmovement and is of great importance to downstreamwater supply (Renard 1970). This analysis is designedto characterize the contribution of point and NPSmaterials found in the drainage systems to provideinformation useful for defining sources of pollutantsand identify potential areas for remediation. Severalstudies in the Patagonia and southern Santa RitaMountains study area have been conducted in anattempt to determine which effects are related to pasthuman activity and which are an expression of thenatural mineral deposit environment (Chaffee et al.1981; Dean 1982; Dean and Fogel 1982; Gray et al.2000). Previous studies show that transport of sedi-ment by overland flow from tailing piles and wastedumps can be predicted with watershed models andrelated to other water quality constituents (Brady 2000;Brady et al. 2001). Fraser et al. (1998) used similarprocedures to relate fecal coliform levels to sedimentyield from farmlands. This study will correlate water-chemistry samples with predicted sediment depositionoriginating from abandoned mines using watershed

models to determine if the procedures can be used todetect stream reaches that are at risk to impairment.

The characteristics of acid mine-drainage havebeen described extensively in the study area (Chaffeeet al. 1981; Dean 1982; Chatman 1994; Hyde 1995;Gray et al. 2000). However, this study is the first totest the correlation between mine-derived sedimentloads and acidity in the water.

Study area

The Patagonia Mountains, located in Santa CruzCounty in southeast Arizona (Fig. 1), were minedintermittently from the 1600s to the mid-1960s primar-ily for silver, lead, copper, zinc, and gold (Dean 1982).The watershed study area covers approximately112 km2 in the highlands above the town of Patagonia,Arizona, inside the Coronado National Forest, whereseveral ‘ghost town’ mining communities were oncelocated. The watersheds are predominantly managedby the U.S. Forest Service and are geographically partof the Patagonia Mountains – Canelo Hills Unit withinthe Basin and Range Province (Chatman 1994).

Mean annual precipitation in the Patagonia water-shed study area is 43.3 cm and most occurs in latesummer in the form of localized afternoon convectivethunderstorms. Milder and more dispersed frontalstorms occur in the winter, which typically last longeryet are less intense. Sometimes this precipitation is inthe form of snow; precipitation at other times of theyear is rare. Flow in the streams of the PatagoniaMountains is greatest during summer rains. Theelevation of the study area varies between 1,120 and2,201 m. All stream drainage from this area flowsdirectly into the Sonoita Creek (Fig. 1), which is atributary of the Santa Cruz River.

The last known mining operation in the PatagoniaMountains ended in 1964; however, extensivereserves (primarily copper) have been identified. Theremains of past mining activity are numerous andscattered throughout the area. The landscape appearsonly slightly altered by mining activity because therehas been no mining for a period of 40 to 100 years.Despite this long gap in mining activity, poor waterquality can still be identified in some streams (Hyde1995). This is in part due to the extremely slownatural recovery rate caused by the dry climate. Pastmining activity is the major source of surface-watercontamination and the potential for renewed mining

146 Environ Monit Assess (2008) 145:145–157

operations with concomitant release of pollutants ishigh. Several companies own claims and patents inthe area and are still interested in the mountain’ssilver, copper, and manganese deposits. Today’seconomy has simply delayed the pursuit of thesemineral resources for the time being but mining willmost likely begin again (Dean and Fogel 1982).

The condition of degraded water in springs, seeps,and ephemeral reaches may be related to historicallymined areas (Chaffee et al. 1981; Dean 1982; Deanand Fogel 1982; Gray et al. 2000). Mine waters areoften “acid” because of the common association of

the sulfide pyrite with most metal ores and many solidfuels. Pyrite, as well as a number of other orematerials, rapidly decomposes when broken andexposed to moisture and air, eventually producingsulfuric acid. This chemical reaction occurs sponta-neously and the acid mine water then has the ability toleach other pollutants in the solution (Hem 1985).

The Patagonia watershed area contains manyknown mines and prospects. Many of these oldmining sites continue to be environmental problemsby contributing acid runoff into streams, lakes, andunderground water supplies (Fig. 2a). These sites also

Fig. 1 Site map depictingPatagonia Mountains studyarea in southeast Arizona

Environ Monit Assess (2008) 145:145–157 147

produce sediment, and visually mar otherwise sceniclandscapes (Fig. 2b) (Harwood 1978).

This area is of great concern due to the presence ofconsiderable environmental degradation. A Prelimi-nary Assessment – Site Inspection study, as requiredby Comprehensive Environmental liability Act (CER-CLA), is ongoing in the Patagonia Mountains. Thisinvolves the discovery, assessment, evaluation, andeventual remediation of point and NPS contaminantsfound in the watersheds of old mining sites. Themanner in which contaminants are transported awayfrom these waste piles has not yet been identified;however, mining activity was predominantly locatedin and along streams (Fig. 2c). Low pH (acidic)measurements detected in stream flows are indicatorsof high metal concentrations, thought to be toxic.Examples of this include readings as low 2 and 3,which is very acidic. Previous research shows thatincreased acidity can be in direct correlation withdeposit workings and tailings in Harshaw Creek(Dean 1982; Dean and Fogel 1982; Gray et al. 2000).

Several of the mines located near to the study area,in the Mansfield Canyon watershed (Fig. 1), wererecently identified by CERCLA in a list of highpriority environmental degradation sites within theCoronado National Forest. The Mansfield watershedis approximately 6 km long and drains the central partof the Wrightson Mining district (Corn 2000; DuHamel

2000). It is located approximately 10 km from thePatagonia study area. In the United States, this site isamong approximately 50 mining sites subject toSuperfund (CERCLA) action.

Evaluation of mining sites is difficult because littleto no data is available describing background con-centrations of metals prior to mining (Runnells et al.1992). Mineral deposits located near or at the surfacecan result in elevated concentrations of heavy metalsin surface and ground water even prior to humanmining activities. The current study is a test case foranalyzing a large number of watersheds in anefficient, cost-effective manner and to provide guide-lines for determining levels of NPS pollution.

Previous studies

Historical accounts suggest that near-surface mineraldeposits containing base and precious metals andassociated trace elements which adversely affectecosystems in the Patagonia Mountains. The land-scape has been greatly altered and there is insufficienthistorical documentation to reconstruct its state priorto mining activities. However, names supplied byearly settlers, like Red Mountain (Fig. 2d) and AztecGulch, suggest that sulfide mineral deposits naturallynegatively affected the water, soils and vegetation ofthe area.

Fig. 2 Photographs of thePatagonia watershed studyarea: a Adit and acid drain-age; b World’s Fair Mineruins of millsite and wastepile; c Worlds Fair Minearea, below waste pile onwest bank of stream drain-age; d Red Mountain,Arizona


Water quality and sediment sampling have beencompleted and the stream systems in the study areahave proven to be highly contaminated. Dean (1982)analyzed the water chemistry of the Harshaw Creekwatershed and Chaffee et al. (1981) did extensivesediment sampling and analysis in many of thePatagonia watersheds. The findings of these studiesresulted in the U.S. Forest Service designation ofsome areas under CERCLA.

Water samples have been recorded to quantifychemicals at different locations in the watersheds anddemonstrate significant differences in pH (Dean 1982;Dean and Fogel 1982; Gray et al. 2000). The pH is ameasurement of positive hydrogen ions describingalkaline conditions (measuring >7) or acid conditions(measuring <7) in water. Drainage from acid-minescan substantially lower the pH of a stream system.There seems to be no single contributor to thepersistently low pH found in the water; observationsin surface-water and ground-water have found anaverage pH reading for the disturbed areas of 3.5(Dean 1982; Dean and Fogel 1982; Gray et al. 2000),whereas in general, normal pH ranges between 6.5and 8.5. Water samples collected by Gray et al. (2000)were acidified to pH <2 with ultra-pure nitric acid(HNO3) and delivered to laboratories for chemicalanalysis. Most are from surface water and a few areground-water samples from residents’ wells. Thenatural concentrations of metals far exceed EPAstandards for drinking water as well as the EPAwater-quality criteria for the protection of aquatic life(Runnells et al. 1992). Natural conditions downstreamin the Patagonia watershed study area suggest dilutionbecause most contaminants decrease as water movesthrough the basin, while pH increases to neutral(measuring ~7). Both carbonate buffering and settlingof sediment contributes to improved water quality(Dean 1982).

Excessive erosion and elevated sediment-transferrates prove to be a persistent source of contamination.Steep slopes, acidity, and absence of topsoil areproblems in some areas susceptible to erosion andmay contribute sediment and other pollutants down-stream (USDA 1973). Average slope in the study areais 16.5%; however, maximum slopes are as steep as53%. Erosion and subsequent dispersion of sedimentalong stream channels is relatively rapid due to thishigh relief. As a result of the rapid rate of erosion, fresh

pyrite is exposed in some of the canyons within thiswatershed system (Chaffee et al. 1981).

Erosion of soil and sediment deposition in streamchannels are problems in the watershed because manytailings and mine roads remain unstabilized andcontinue to contribute sediment to the channel system(Dean 1982). The primary problem is the dissolutionof toxic minerals and sediment erosion from the wastepiles and tailings. Similar to other contaminatedwatersheds, some of the NPS sources are small mineswithin and outside the study area, as well as exposedsulfide mine, private roads, grazing areas, andresidences (Verma and Thames 1977).

Past research suggests a need for revegetation oftailings that would reduce the surface runoff, erosion,and chemical leaching. Research needed includesadditional soil analysis of tailings and field plotstudies (Chaffee et al. 1981; Dean 1982). Reclamationof tailings and waste piles would require particularlyconscientious water and erosion control due to thefact that many channels cut through mined sites (Dean1982).

Application development

The USLE is an empirical formula used to predictaverage annual soil loss from hillslope elements intons per acre per year (Wischmeier and Smith 1978).This model has been used to predict potential soilerosion in many studies (Eli 1981; Eli and Paulin1983; Eli et al. 1980; DeVantier and Feldman 1993),and was adapted by Brady (2000) and Brady et al.(2001) in a GIS and account for mine and tailingsdimensions in the Patagonia watershed study area.The USLE was used to predict the average annual soilloss caused by sheet and rill erosion in a GIS, using 30-mgrid cells. During rainstorms, in watersheds this large,most sediment remains in the watershed while only afraction of soil that is eroded reaches the stream systemvia the watershed outlet. The actual measured sedimentyield is much less than USLE estimates of gross erosionin watershed scale due to hill-slope deposition. Becauseof this, an additional model, SEDMOD, was applied tocalculate deposition.

SEDMOD was developed by Fraser (1999) andapplied by Brady (2000) and Brady et al. (2001) tocalculate the amount of sediment delivered to the


channel across the Patagonia watersheds. In order tobetter identify NPS pollution sources, a sedimentdelivery ratio (SDR) is calculated by SEDMOD basedon the watershed area total and adjusted properties ofeach cell’s slope gradient and shape, surface rough-ness, proximity to streams, soil texture and overlandflow; this improves the estimate of downstreamsediment rates (Fraser 1999; Fraser et al. 1998). TheSDR depicts the percent of that eroded material to beyielded from each cell. The higher SDR occur in andadjacent to streams as expected, as they are anobvious transporter of sediment. Lower SDRs corre-spond to areas of low relief that have little or nomoving water on the surface. SDR was multiplied bythe USLE annual soil loss in SEDMOD to predict netsediment or NPS pollution delivery. This computation

gives the total net sediment yielded by the watershedon a cell-by-cell basis. SEDMOD also allows for acalculation of the total potential gross erosion of thewatershed, an estimate of sediment delivered to thestreams, and estimated total delivery to the watershedoutlet (Brady 2000; Brady et al. 2001).

Methods

In addition to modeling, mines were identified in aGIS to determine the amount of contributing sourcesediment each was likely to supply to the streamsystem. It was assumed in this study that all sedimentyielded by mines would reach stream channels overlong time periods due to the volume and history of

Fig. 3 Water sample points,mines, and roads in thePatagonia watershed studyarea


mine activity, and by the proximity to streams. Therelative size of each mine site (operation, facilities,tailings, etc.) was averaged to a 30-m GRID cell inorder to calculate sediment erosion within a GIS(Brady 2000; Brady et al. 2001). Where no tailings orsite-related waste materials exist, the sediment yieldwas based on soil type and relief at the site.

Water samples were collected from Alum Fluxand Harshaw watersheds during the period 1980–1998. Twenty four samples from the dataset wereselected that resulted from runoff to be included inthis study to map the chemical effects of overlandflow in and around abandoned mines sites. Theserunoff samples represent conditions in which rain-water rapidly interacts with many of the watershedcomponents including natural slopes, tailings, etc.Acidity, conductivity, total dissolved solids (TDS),and temperature were measured and recorded atthese sample sites and sent to labs for chemicalanalysis. Water sample locations were converted into

a GIS point feature class for purposes of analysis andwatersheds were delineated using spatial analysistechniques (Fig. 3).

Each sample point was assigned an ID number,which became the primary key in the databaseanalysis. The database was populated with the resultsof sampling and chemical analyses for each samplelocation (Table 1).

The sample points were used as either input tostreams or as watershed outlets in a watersheddelineation script developed in ESRI ArcView 3.1(Brady et al. 2002). The script was used to delineate“nested watersheds” by calculating total contributingdrainage area at each water sampling point that werethen used in water-quality comparisons. Each nestedwatershed was analyzed to identify the historical minesites located within and their potential contribution tothe water chemistry at that sample site. In addition,the amount of sediment contributed by each site inSEDMOD was tabulated to create relationships

Table 1 Water quality chemistry at sample points

ID Date Temperature pH Fe (mg/L) Cu (mg/L) Mn (mg/L) Zn (mg/L) SO4 (mg/L)

1 09/07/80 18.0 7.10 1.29 0.14 15.70 1.05 65.002 02/09/81 4.0 7.10 39.00 0.19 18.00 3.90 376.003 09/07/80 18.0 5.70 15.90 0.50 37.90 2.91 108.004 09/07/80 11.0 2.90 1,860.00 5.50 58.00 1,800.00 391.005 09/07/80 6.70 5.20 0.06 2.30 0.32 45.006 09/07/80 4.60 183.00 0.05 37.60 14.00 196.007 09/07/80 6.60 8.10 0.05 3.17 0.30 288 09/07/80 17.0 4.10 27.80 0.35 8.90 2.16 3949 09/07/80 18.0 7.30 2.00 10.80 0.05 0.30 20210 03/05/81 12.0 6.80 0.05 0.05 0.05 0.05 27.0011 04/15/98 15.1 3.87 0.97 1.71 33.00 23.00 1,600.0012 02/24/98 12.2 3.91 1.21 1.72 35.52 24.37 419.0013 03/04/98 15.5 8.12 1.04 0.57377 0.07 0.01 277.0014 03/04/98 10.7 4.36 1.98 1.442602 4.45 2.55 260.0015 03/04/98 17.9 8.01 1.00 0.051699 0.09 0.03 275.0016 05/13/98 22.8 7.65 0.78 0.0022 0.01 0.02 388.0017 04/07/98 19.0 8.16 0.92 0.0099 0.01 0.12 169.0018 04/07/98 20.7 3.24 20.07 0.34 9.49 4.91 2,200.0019 04/21/98 27.2 8.11 1.09 0.0015 0.01 0.00 328.0020 02/24/98 20.0 7.65 0.68 0.0073 0.81 0.10 224.0021 02/24/98 20.2 7.86 0.23 0.0007 0.01 0.00 28.9022 04/24/97 27.4 3.59 0.58 2.4 58.00 26.00 1,300.0023 06/02/98 27.3 3.18 5.19 1.907 123.89 50.02 3,000.0024 06/11/97 32.8 2.66 41.00 0.065 96.00 25.00 990.00


between total mine site contribution per nestedwatershed and water quality.

Results and discussion

Each water sampling site (nested watershed outlet)potentially contains sediments from mines in thatnested watershed. Some nested watersheds have noevidence that mining has contributed sediment to theiroutlet. Table 2 lists the number of mines identified tobe contributing to each water sample point, based onresults of the nested watershed analysis.

At sample point #9, a pH of 7.3 is reported. Thisneutral reading suggests that no mines contributeacid-drainage to this point or it has been neutralizedand the nested watershed map depicts this (Fig. 4).However, the pH reading at Sample #10 is slightlymore acidic (6.8), which could represent more acidicbackground conditions upslope (Fig. 4).

Nested watersheds found upstream from water-sample points 4 and 24 (Fig. 5) exhibit the highestlevel of acidity based on the chemical analysis(Table 1). Both nested watersheds exist in the head-waters portion of their respective watersheds or sub-watershed units (Upper Harshaw and Alum Gulch-FluxCanyon watersheds respectively) and have dischargesdownstream from minesites. There may be somemixing between water draining from the mines anddiluting surface runoff from nearby unaffected areas.

The amount of sediment delivered to the riparianzone, as calculated by SEDMOD (Brady 2000; Bradyet al. 2001), was determined at each water samplepoint (Table 3).

Acid-mine drainage is usually associated with alow pH, along with elevated levels of copper (Cu),iron (Fe), manganese (Mn), sulfate (SO4), and zinc(Zn) (Hem 1985). In this study, the percentage ofsediment contributed by mines was derived fromSEDMOD output of net sediment delivery, by nested

Samplepoint

Numberof mines

Samplepoint

Numberof mines

Samplepoint

Numberof mines

Samplepoint

Numberof mines

1 16 7 6 13 16 19 142 14 8 2 14 16 20 143 14 9 0 15 16 21 124 7 10 0 16 1 22 35 7 11 21 17 1 23 66 7 12 21 18 1 24 7

Table 2 Number of minescontributing to water qualitysample points

Fig. 4 Map showing nestedwatershed #9 and samplingpoints 9 and 10. SamplePoint # 9 exhibits neutralwaters without any influ-ence from mining opera-tions upstream or incontributing area


watersheds of sampling points (Table 3) and com-pared to each of the above mentioned measuredchemicals (Table 1).

Those water quality measurements used in this studyare the product of two sources – Dean (1982) andunpublished data given in this report (Gray, personalcommunication). Only a few background samples arepresent and are unsuitable for making generalizationsabout mine effects on water chemistry because theywere taken above waste/tailings impoundments or indiscrete canyons unaffected by anthropologic activi-ties. In addition, the watersheds in this study can besubdivided into three subwatersheds each of which,Upper Harshaw Creek (UHC), Lower Harshaw Creek(LHC), and Alum-Flux Creek (ALC), have very dif-ferent water chemistry (Gray, personal communica-tion). The LHC subwatershed drains the highlydisseminated sulfate pyrite on naturally occurringgypsum salts of Red Mountain and is a major tributaryto Harshaw Creek. Unfortunately, LHC contains onlythree water sample points. Combining the small datasets from the three watersheds was not done forpurposes of exploring the possible impact of minewaste on water chemistry. Temporal variations, in-

cluding mine remediation, between the two samplingstudies have occurred. The studies used differentsampling protocol (including sample in tributaries offthe main stem stream) and had differing study goals.

Data grouped by three basins and by two studieshas resulted in small data sets that are difficult to useto show significant relationships between mine wasteand water chemistry. Among the three watersheds,UHC has the largest data set (n=16), five samples areunpublished data provided for this study while therest are from Dean (1982).

Sediments delivered to point (SDP) is the totalamount of sediment (tons/hectare/year) reaching thesample point from all sources within the watershed. Forthose basins containing mine tailings and pyretic wasterock, the model provides an estimate of the amount ofmaterial delivered as remobilized mine materials (tons/acre/year) or mine contribution (MC) and can be usedto estimate the percent of total sediments delivered topoint (i.e., MC/SPD). Mine contribution (MC) data(tons/hectare/year) was also used directly in theanalysis. The higher the percentage of MC/SDP, thehigher content the mine materials to total sedimentsdelivered to a point and it is assumed in this study,

Fig. 5 Location map of thetwo nested watersheds inwhich water at their outletsare most acidic and themines within


where tailing sediments have a greater opportunity toinfluence water chemistry at the sample point.

Figure 6 gives an overview of the data where allbasins within the Patagonia Mountains are shownusing different symbols for basins and differentcolors for data sources. For the three water samples(shown as stars) from Lower Harshaw Creek, the pHvaries from 3 to greater than 8 while the contributionfrom mine tailings at all the sample points are all lessthan 5%. The difference here is that the low pH valuecomes from natural surface material from the RedMountain porphyry copper deposit. These materialsinclude disseminated pyrite and gypsum salts, ex-posed in the bedrock and soils due to naturalweathering. At the other extreme is Alum Flux Creekwere water samples have a pH of 4 or less and withmore tailings than any other basin ranging from 20%to more than 30% (Fig. 6). Upper Harshaw Creek isintermediate to the other basins with water sample

pH from 3 to 8 and percent tailings from zero tonearly 25%. The unpublished data set provided inthis study for Upper Harshaw Creek are considerablyhigher in pH (Fig. 6) than found in the wider spreadin values from Dean (1982). As can be seen, each ofthe basins and data sets available are distinctive andshould not be combined for statistical analysis ordone so with care.

The correlations between percent contribution ofmine tailings (MC/SDP) at sample points and samplewater chemistry were poor for the LHC and AFCwatersheds. It is felt this is partly do to the samplesample sizes in these watersheds.

The larger data set for Upper Harshaw Creekprovided more promising results. Initial data analysissuggests that there are several associations betweenmetals in water samples (for example, between Mn,Zn, Fe), and metals (Fe, Zn.) and pH using waterchemistry collected by Dean (1982). Few associationsare suggested that involve water chemistry (Table 1)and either MC (Table 3) or MC/SDP using data foundin Table 3. One additional complication is that onesample site (No. 4) is affected by tailings transportedacross the watershed divide from then AFC watershedin the UHC watershed during disposal operationduring mining up to 1974 (Dean 1982). These tailingshave chemical and physical characteristics atypical ofother mine-related tailings and waste found elsewherein the UHC watershed. Unlike other waste material

Fig. 6 Plot of pH and MC/SDP (in percent). Points are forunpublished data provide for this study when black and redwhen taken from Dean (1982). Upper Harshaw Creek data isgiven as solid points. Stars are from Lower Harshaw Creek andopen circles are from Alum Flux Creek

Table 3 Modeled nested watershed net sediment delivery,mine contribution to sample point nested watershed, andsediment delivered to each sample locality point outlet

Samplepoint

Net sedimentdelivery

Minecontribution

Sedimentdeliveredto point

1 140,252 3,773 31,2542 85,487 3,732 19,1803 85,306 3,732 19,1404 65,563 2,127 14,5425 65,332 2,127 14,4906 53,285 2,127 11,5207 44,318 1,988 9,7818 13,804 800 3,2849 7,651 0 1,773

10 4,660 0 1,08311 144,287 7,088 31,83012 138,226 7,088 30,48013 142,828 3,773 31,82114 140,032 3,773 31,20415 134,818 3,773 30,07516 10,339 34 2,41017 8,635 34 2,01318 8,492 34 1,98219 90,029 3,732 20,19320 85,642 3,732 19,21621 77,368 3,497 17,30122 32,072 1,350 6,94523 35,950 2,493 7,72024 42,877 2,769 9,314

All values are reported in metric tons/hectare/year


addressed in this study, these tailings are the endproduct of metal extraction after milling. Data for thissite is designated as outlier “a” when shown on scatterplots and is not used during data analysis for sites inthe UHC watershed.

Analysis of the data from the UHC watershed onthe 10 remaining sample from Dean (1982) suggestthat both Fe and Mn concentrations are correlated tothe percent of total sediments delivered to samplepoints as express by MC/SDP.

As the amount of mine waste relative to othersediments increases so does the Fe content at watersample sites along Upper Harshaw Creek (Fig. 7.)One interpretation of the plot is that as the amount ofmine waste increases so does the amount of Fe. TheR2 of the Fe (in logarithms base 10) regression onMC/SDP is 0.64 and is significant at an alpha equal0.01. This suggests that 64% of the variability in theFe ratio can be explained by knowledge of the MC/SDP ratio. Another interpretation of the Fe concen-trations in water-sample sites is that it is an expressionof water transport directly from the mine andproximity to the mine waste sources as is also theMC/SDP ratio and that there is no direct relationshipbetween the two at all.

As the amount of mine waste relative to othersediments increases, so does the Zn content at water-sample sites along Upper Harshaw Creek (Fig. 8.)One interpretation of the plot is that as the amount ofmine waste increases so do the amount of Zn. The R2

of the Zn (in logarithms base 10) regression on MC/

SDP is 0.43 and is significant at an alpha equal 0.05.This suggests that 43% of the variability in the Zn canbe explained by knowledge of the MC/SDP ratio.Like the relationship described previously, anotherinterpretation of the Zn concentrations in watersample sites is that it is an expression of watertransport and proximity to the mine waste sources asis also the MC/SDP ratio and that there is no directrelationship between the two at all.

Conclusions

The Patagonia watershed study area has been minedfor metallic mineral deposits. The surface-waterquality is severely degraded. Acid mine-drainageappears to occur in the study area due to the proximityof mines to the surface-flow discharge. It appears thatsediment in streambeds contributed from mined areasis linked to poor water quality, as expressed by lowpH and elevated concentrations of iron and zinc. Thisstudy also indicates that the altered acidity andelevated metals within the surface-stream systemsoccur even above historically mined sites. Thissuggests that in some cases, poor water quality occursnaturally and is an expression of oxidation of metalsin near-surface ore deposits (Schrader 1915; Chaffeeet al. 1981; Dean 1982; Gray et al. 2000).

This study focuses on mining areas that areassociated with degraded water in streams. Mines inthe study area that are thought to be contributing acid

Fig. 7 Scatter plot of MC/SDP and Fe that is scaled inlogarithms base 10 using data from 10 sample sites from Dean(1982). The regression line is shown in red. See text fordiscussion of outlier designated “a” that is excluded fromanalysis

Fig. 8 Scatter plot of MC/SDP and Zn that is scaled inlogarithms base 10 using data from 10 sample sites from Dean(1982). The regression line is shown in red. See text fordiscussion of outlier designated “a” that is excluded fromanalysis


mine-drainage, whose sample point outlet registers avery acidic or low pH (<3.0) reading, can be recognizedusing nested watershed delineation (Table 3). Thisanalysis may help guide in the designation of thesesites needing Superfund action. Streams in the water-sheds are mainly received water from small discharg-ing springs with limited flow. These springs areperennial but discharges at various points occur somedistance along the stream with intervening dry areas,making the streams intermittent. Some flow as aresponse to storm/precipitation events and extenddown-gradient drainage flows within the watersheds.Given the periodicity of flow and sediment trans-ported, the modeled sediment values are thus unlikelyto relate directly to water-quality constituents at thetime of measurement.

Historic mine waste was hypothesized to be theprimary source of NPS pollution. Poor correlationswere found between the MC/SDP from the LHC andAFC watersheds. Mine waste and tailings apparentlyremain significant components contributing to de-graded water quality. However, several competingprocesses in the arid and semi-arid watersheds of thesouthwestern United States are responsible for thegeneration and release of metals and acidic water thatoften overshadow the effects of mine waste rock/tailing dispersion. A significant component of metaldispersion in these watersheds is the circulation andeventual surface discharge of waters that haveabsorbed metals and acidity via passage throughnetworks of underground mine tunnels. This processgenerates degraded water by reaction with sulfideminerals and salts in the walls and fractures withinsubsurface mine workings and has significant impactson the water chemistry in upper Alum Gulch. Theseimpacted waters may discharge at the mine site orsome distance down-gradient of the site and providesthe source waters that form salts when conditionsbecome appropriate. In addition, the seasonal accu-mulation and remobilization of evaporative salts is aprocess that has recently been recognized within thewatersheds that also have a direct contribution to themetals concentration and acidity of waters and canovershadow the effect of the MC component. Evap-orative salts are the main release and dispersion agentin Alum Gulch. The salts cannot be measured insediment models. Finally, the widely disseminatedpyrite and gypsum in surface outcrops of porphyrycopper deposit, such as those found in lower Harshaw

near Red Mountain, creates widespread acidic, metal-laden storm runoff and therefore camouflages theeffects of any anthropogenic surface disturbances.

The study did show that the application of USLEand SEDMOD to model the sediment derived frommine waste could be used to assess the potential riskof NPS impairment due to runoff from mine sites. Therelationships from the UHC watersheds between Fe(Fig. 7) and Zn (Fig. 8) and the ratio MC/SDP wererelatively strong. This methodology can assist withthe differentiation of background and mine-relatedwater-quality problems. However, the results alsoindicate that geology can influence the local waterchemistry and make developing robust regionalrelationships difficult without additional data.

Recommendations for future work include a need foradditional water sampling locations in order to bettercharacterize the entire watershed so that differentgeologic areas are adequately represented. A moresystematic approach to sampling distribution wouldalso aid in this effort. Further fieldwork, to aid in modelvalidation is also required. This would involve furthersedimentation and erosion quantification at the sampledsites. In addition, high-resolution aerial photos couldhelp to better identify vegetative conditions, whichwould help to constrain USLE and SEDMOD modeloutputs. The use of extreme events such as 100-yearfloods, or droughts, or the input of managementpractices targeting erosion (vegetation, contouring,etc.) into these models would allow management totest outcomes and solutions to a wide variety ofpotentially damaging conditions with the goal ofpreventing costly loss or injury to lives or properties.

Acknowledgements The authors would like to thank theCoronado National Forest for funding this research. Addition-ally, we thank James Callegary and Steve Weile, at the USGS,for their reviews of this material.

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