Examination of Physical and Regulatory Variables Leading to Small Dam Removal in Wisconsin

Examination of Physical and Regulatory VariablesLeading to Small Dam Removal in WisconsinCAILIN H. ORR*BRIAN M. ROTH*KENNETH J. FORSHAYCenter for LimnologyUniversity of Wisconsin680 North Park StreetMadison, Wisconsin, USA

JAMES D. GONZALESDepartment of Rural SociologyUniversity of Wisconsin430 Agricultural HallMadison, Wisconsin, USA

MICHAEL M. PAPENFUSDepartment of Agricultural & Applied EconomicsUniversity of Wisconsin312 Taylor HallMadison, Wisconsin, USA

REBECCA D.G. WASSELLUSDA Forest ServiceOkanogan and Wenatchee National ForestsCle Elum Ranger District803 W. 2nd StreetCle Elum, Washington, USA

ABSTRACT / The decision to remove or repair a dam de-pends on multiple variables, many of which encompassboth physical and social factors. In Wisconsin, the Depart-ment of Natural Resources is mandated to inspect smalldams every ten years. A safety inspection often acts as atrigger event to a dam removal or repair decision. Althoughthe issues surrounding a dam removal decision are oftencouched as ecological, these decisions are influenced bytheir social and regulatory context. In this work, we exam-ine descriptive variables of Wisconsin dams that were in-spected and consequently removed or maintained between1985 and 1990. We hypothesize that geographic location,height of dam, size of impoundment, age of dam, and typeof ownership determine the likelihood of a safety inspec-tion, and the subsequent likelihood of removal. Using a lo-gistic model, we find that publicly owned dams had thegreatest probability of inspection after 1985. Of thesedams, older dams and those with smaller impoundmentswere most likely to be removed. We were unable to build astrong predictive model for dam removal with our suite ofvariables, suggesting that a community’s decision to re-move or maintain a dam is complex and heterogeneous.

Dam removal has gained national attention and isincreasingly being discussed as a viable solution to ag-ing dams that have become safety hazards. The focus ofrecent study has been on small dams where there hasbeen an increase in the body of literature on the eco-logical impacts of dams (see review by Bednarek 2001)and emerging work on the ecological changes follow-ing dam removal (Bednarek 2001, Doyle and others2000, see also the special edition of BioScience, August2002). There is also growing evidence that, in somesituations, dam removal may have real ecological ben-efits such as fish passage (American Rivers 2000), and

water quality improvements (Doyle and others 2000).An aging dam could be viewed as an environmentalopportunity. However, even in conditions wheredams no longer fulfill their original purpose and damremoval may have significant ecological benefits tothe river, aging dams are often repaired. In a study ofsmall dams in Wisconsin, Born and others (1998)found that for 14 dams that were removed, damrepair was considerably more expensive than removaland that the availability of matching grants was afactor in decision-making. Case studies of small damremoval in other parts of the country found removalcosts to be smaller than repairs when comparingchanges to the physical structures alone (AmericanRivers and others 1999). We explore how physicalattributes of a dam and particular regulatory actionsinfluence removal or repair decisions to determinehow removed dams differ from those that are main-tained and to identify the most influential factors inremoval or repair outcomes.

KEY WORDS: Dam removal; Place attachment; River management

Cailin H. Orr and Brian M. Roth share primary authorship.

Published online February 24, 2004.

*Authors to whom correspondence should be addressed, email:[email protected] and [email protected]

DOI: 10.1007/s00267-003-2941-6

Environmental Management Vol. 33, No. 1, pp. 99–109 © 2004 Springer-Verlag New York Inc.

History

Dams are a prevalent feature on the landscape. In1996, there were approximately 75,000 large dams onrecord, distributed across the United States (US ArmyCorps of Engineers 1996). Currently there is no na-tional record of smaller structures; however, it is clearthat their overall number is much higher than that oflarge dams. In Wisconsin alone, there are more than3800 dams in the state inventory (WDNR 2002a). Manyof these dams were built in the mid-1800s after theWisconsin Mill Dam Act of 1840 (Figure 1). The gov-ernment promoted and supported dam constructionduring this period to support emerging logging, paper,and grain mill industries. These dams and resultingreservoirs played a vital role in the development of theearly Wisconsin economy by aiding timber transport aswell as providing power for mills. With the develop-ment of hydroelectric power in the early 1900s, therewas continued incentive to construct and convert damsto generate electricity. Additionally, dams were con-structed for flood control, recreation, residential devel-opment, and irrigation. Timber and grain mills wereonce important reasons for dam closure because theyno longer depended on small dams for powering theiroperations.

The declining condition of some aging dams hascaused them to fall within Wisconsin State Statute31.19, which charges the WDNR with the removal of“unsafe structures” (WDNR 2002b). Exactly how manydams have exceeded their functional residency is diffi-cult to determine due to their large structural andfunctional diversity (Poff and Hart, 2002). Reservoirsbehind aging dams accumulate silt through time, which

decreases the storage capacity and blocks water flow forhydroelectric power generation.

Regulation of these structures falls to the WisconsinDepartment of Natural Resources (WDNR 2002b) ex-cept for the 5% of dams that are both large and pro-duce hydroelectric power, which are under the jurisdic-tion of the Federal Energy Regulatory Commission.Many of these dams are proximal to residential areas,which creates a physical hazard for those individualswho may use the dam for recreation. Potential damfailure, another danger of aging dams, can cause orexacerbate property-damaging floods and release largequantities of accumulated sediments downstream.

While regulatory agencies can determine that someaction needs to be taken to ameliorate the problems ofa damaged dam, individual owners are responsible forrepair versus removal decisions and liability and accessto funding may impact an owner’s decision. Approxi-mately 50% of the dams in Wisconsin are owned byprivate individuals, 19% by the State of Wisconsin, 16%by municipalities such as townships or county govern-ments, and 15% by other ownership types (WDNR,2002b). Property owners who wish to preserve a dam-aged dam may opt to repair it at their own expense. Insome cases, owners may transfer ownership to a munic-ipality that will repair and maintain the dam however,local governments are often wary of accepting respon-sibility for a potential safety hazard. Wisconsin statestatute Section 31.385 created a grant program for themaintenance, repair, modification, abandonment,and/or removal of municipal dams. Previously,through $11.5 million allocated for bonding, theWDNR was able to provide municipalities with up to$200,000 matching funds for dam removal or repair(WDNR 2002b). Currently all of the funds allocated tothis program have been distributed, and no additionalfunds are available (Associated Press 2002).

Methods

Patterns of Removal

We used the legacy of dam removal in Wisconsin toexplore the question of why some dams are removedwhile many more are repaired. We were interested inwhether removed dams differ from the remaining ones.In addition, our goal was to determine the factors thatinfluence a repair versus removal decision. Initially weused interviews with dam regulators to identify poten-tial drivers of dam removal patterns based on individ-uals’ experience with previous removals. Once thesepotentially important variables were identified, we useda dam database to evaluate these variables. Finally, we

Figure 1. Distribution of dam ages, with most dams beingbuilt in the mid-1900s. There are 1370 dams on record asbeing built before the year 1950 and 3500 by 1980 (WDNR2002a).

100 C. H. Orr and others

used follow up interviews to verify and explain ourdatabase findings.

Interviews

We identified factors that may act as trigger events byconducting informal interviews with WDNR staff.WDNR is the regulatory agency involved in assessmentof small dams in Wisconsin; however, their role inremoval and repair decisions is not apparent. The goalof the interviews was to discover what WDNR staff mem-bers believe drives small dam removal in Wisconsin andpotential predictors of dam removal. The staff mem-bers we interviewed described factors that they consid-ered likely determinants of dam removal and explainedwhy each factor was important. Following the databaseanalysis, we used additional interviews to elucidate re-sults from the statistical analyses on the dam database,and we describe important factors for analysis in themethods section.

Database Information

The Wisconsin Department of Natural Resourcesmaintains an electronic database of more than 3800dams (both existing and removed) in the State of Wis-consin, available at http://www.dnr.state.wi.us (WDNR2002a). Information available from this database in-cludes physical, geographical, and management data.We used this information to evaluate the variables de-termined to be possible drivers in the pattern of re-moval across the state and through time. For example,we hypothesize that older dams and those with smallimpoundments would be the most likely to be removedbecause they were the largest liability and provided thesmallest return in terms of impoundment area. Thisphysical information is the basis for economic costestimates of dam removal versus repair and thereforehas a large influence on repair versus remove decisions.From our initial interview process we generated thehypothesis that dam removals result from a cascade ofevents that begins with an initial opportunity or “trig-ger” event.

We used the interview information as a primary stepto narrow the database by removing all dams withoutdata on factors that WDNR staff suggested may beimportant in order to evaluate the influence of thesefactors in predicting dam removal. To ensure we werecomparing only dams subject to the same set of regu-lations and same available funding resources, we re-moved any dam listed as “large” because these alsocome under federal regulation. Consequently, we re-stricted our analysis to “small” dams, or those less than6 ft (2 m) in height or those with impoundments lessthan 150 acre-feet. There was still ample size variation

in the small dams to test our hypothesis about dam size.Any dams were eliminated from analysis if data weremissing from these fields (both removals and existingdams). We eliminated approximately 1300 dams priorto analysis, leaving 2533 dams with complete data foranalysis.

Important Factors

WDNR staff described four factors that are relevantto repair versus removal decisions: dam inspections, thedams’ geographic region, the dam physical character-istics, and dam ownership. Individual categories withinthese main factors are listed in Table 1.

We analyzed inspections in two ways. First, we usedthe date of inspection as a continuous variable. Second,we grouped inspections into two overlapping catego-ries: Whether the dams were inspected at all (inspect-ed), and a subset determined by whether the dam wasinspected between 1985 and 2001 (inspected after1985). The 1985 division represents the beginning ofthe municipal grant program that made funds availablefor either removal or repair of municipally owneddams.

The geographic region variables were based on theWDNR’s current geographic management units(GMUs) (Figure 2); however, these units were instatedonly 5 years ago. As a consequence, any dams removedprior to 1997 may have been in a different WDNRmanagement region than they are currently listed.Therefore, we used the current GMUs as a proxy forgeographic regions in our models. The counties ineach geographic region are listed in Table 1.

Ownership variables were grouped into five catego-ries: private/utilities, municipalities, state, federal, andother. A comprehensive list of dam owners under thesecategory headings are listed in Table 1. Lastly, we de-fined physical variables as those that described size orage. These variables were limited to dam height (feet),dam impoundment size (acre-feet), and dam age(years).

Data Analysis

We used logistic regression to build statistical modelsthat can determine which of several physical, geograph-ical, and social (ownership) variables can best describewhich dams are removed. Logistic regression uses ei-ther continuous (e.g., time) or categorical (e.g.,whether a dam was inspected or not) data to build astatistical model that best fits a binary response (eithera dam was removed or not). Logistic regression modelsare fitted using the maximum likelihood method(Chatterjee and others 2000). For more on uses oflogistic regression in ecology, see Manel and others

Social and Physical Variables in Dam Removal 101

Figure 2. Geographic managementunit divisions in Wisconsin.

Table 1. Variables used in logistic regressiona

Physicalvariables(units) Dam size (ft) Dam age (yr)

Impoundment size(acre-ft)

Ownership Private/Utility Municipal State Federal OtherPrivate, Cranberry

Bog, Utility, LakeAssociation

County, City, Township,Village

Department ofNatural Resources,Department ofTransportation

Forest Service,Department ofDefense,Department of theInterior

Core of Engineers,County LandConseervationDistrict, StatutoryDrainage District

Geographicregion

South-central southeast West-central Northeast North

[Counties(N)]

Crawford, Richland,Columbia,Jefferson, Dane,Rock, Green,Lafayette, Grant,Iowa, Sauk,Dodge (12)

Sheboygan, Washington,Waukesha, Walworth,Kenosha, Racine,Milwaukee, Ozaukee(8)

St. Croix, Dunn,Chippewa, Clark,Pierce, Pepin, EauClaire, Buffalo,Trempealeau,Jackson, Marathon,Portage, Wood,Adams, Juneau,Monroe,

Marinette, Oconto,Menominee,Shawano, Door,Kewaunee,Waupaca,Outagamie, Brown,Manitowoc,Calumet,

Bayfield, Douglas,Ashland, Iron,Vilas, Forest,Florence, Oneida,Langlade,Lincoln, Taylor,Price, Rusk,Sawyer, Washbum,

Lacrosse, Vernon(18)

Winnebago, Foud duLac, GreenLake,Marquette,Waushara (16)

Barron, Polk,Burnett (18)

Inspectionschedule

Inspected? Inspected after 1985? Inspected after 1990?

aOnly physical variables are continuous; all others are dichotomous.


(2001). The logistic regression function in Systat (Systat9.0, SPSS, Inc., Chicago, Illinois, USA) provides theodds ratio as well as a prediction table. The odds ratiorepresents how much the logit (or ratio of positiveresponses to negative responses, or in our case, damsremoved to dams not removed) responds to incremen-tal changes in a given parameter. For a continuousindependent variable, an odds ratio of 1.05 for a pa-rameter estimate means that for 1 unit change in theparameter, while all others are constant, the logit willincrease by 5%, or, as applies to our work, the odds thata dam will be removed increases by 5%. For dichoto-mous variables, the odds variable has a slightly differentinterpretation. If a dichotomous parameter estimatehas an odds ratio of 1.05, this means that a dam with thecharacteristic described by the parameter has a 5%greater chance of being removed than a dam withoutthe characteristic.

Prediction tables use the model estimates to createmetadata from which to compare to actual data. Pre-diction tables are therefore able to give an estimate ofhow well the model fits, in percent of correct predic-tions. In the literature, this is known as sensitivity. Forease of interpretation and to avoid confusion with sen-sitivity analysis (which we do not perform), we will callthe quantity described above “percent correct.” We usepercent correct as the main estimator of model perfor-mance, while we use the odds ratio of individual param-eters to estimate their influence on the model perfor-

mance. We used t-tests on specific variables that wefound to be important variables in logistic regressionanalysis. Further explanations of both odds ratios andprediction tables can be found in Hosmer and Leme-show (1989) and Chatterjee and others (2000).

Results

All Dams: Which Factors Contribute Most to DamRemoval?

When all 2533 dams were included in analysis, yearof dam inspection was a significant predictor of damremoval (P � 0.011), but the model was only able toaccurately predict 3.4% of dam removals. When bothdam inspected and dams inspected after 1985 wereinput as independent variables, only dams inspectedafter 1985 significantly predicted dam removal (P �0.003) (Table 2). When both dam inspection categori-cal variables are included in analysis, the model accu-rately predicts 3.3% of the removals; when only damsinspected after 1985 is included, the model accuratelypredicts 3% of the removals (Table 2). The odds ratioof this parameter, when both inspection variables wereincluded, indicates that dams inspected after 1985 weremore than twice as likely to be removed than thoseinspected prior to 1985. Dam age was the only signifi-cant predictor of dam removal when only physical vari-ables were included in analysis (P � 0.01). However, all

Table 2. Models describing inspection after 1985a

Model Parameter name Parameter estimate P Odds ratio Percent correct

Ownership Private/Utilities (0) �0.496 0.055 0.609 35Municipal (0) �1.694 0.000 0.184State (0) �0.988 0.000 0.372Federal (0) �1.432 0.000 0.239Other (0) alone 0.902 0.000 2.464 20.4

Geography North (0) 0.262 0.003 1.299 27west-Central (0) 1.032 0.000 2.807Northeast (0) 0.055 NS 1.056South-central (0) 0.607 0.000 1.835South-east (0) alone �0.546 0.000 0.579 21.5

Physical characteristics Age �0.027 0.000 0.973 28.8Height 0.042 0.000 1.042Impoundment-size 0.000 0.000 1.000

Best Municipal (0) �1.114 0.000 0.328 45.5State (0) �0.675 0.000 0.509Federal (0) �1.312 0.000 0.269West-central (0) 0.960 0.000 2.613South-central (0) 0.436 0.000 1.546Age �0.023 0.000 0.977Height 0.115 0.000 1.122

aNumbers after the parameter name indicate that the model tests for the significance of the parameter by analyzing the dams that do not havethe parameter characteristic (1 for dams with the characteristic, 0 for dams without). Parameter names that are followed by “alone” indicate thatthe parameter failed tolerance checks when grouped with other variables, and as a result were analyzed alone.


physical variables allowed the model to successfully pre-dict 4.9% of the removals (Table 2). When removals arepredicted by geographic region or by dam owner, novariables were significant (all P � 0.13). However, thesouth-central region did contribute to the model thatbest describes dam removal with all dams included inanalysis. This model includes dam age, and inspectedafter 1985, and south-central region variables, and pre-dicts 6.8% of the removals (Table 2).

Factors Leading to Dams Inspected After 1985

Following the above analysis, we sought to predictwhich factors led to inspections after 1985. We foundthat physical attributes, geographic region, and damownership all influence dams inspected after 1985 (Ta-ble 3). When the seven most influential of these vari-ables are used as predictors, the best model accuratelypredicts 45.5% of dams inspected after 1985, higherthan all models for each individual variable group (Ta-ble 3). Dams that are owned by municipal and federalgovernments are especially likely to be inspected, being2.6 and 5 times, respectively, more likely to be in-spected than dams that are not owned by municipalitiesor the federal government (odds ratios � 0.269 and0.328 for dams that are not owned by municipal andfederal governments, respectively) (Table 3).

Dams Inspected After 1985: Which FactorsContribute Most to Dam Removal?

Of dams that were inspected after 1985, the chancethat a dam gets removed increases as the dam gets older

and as the impoundment size gets smaller (Table 4).For every year the dam ages, the odds it gets removedincrease by 2.2% when all other physical variables areheld constant. Likewise, for every unit decrease in im-poundment size, the odds the dam is removed decreaseby 4.5%.1 The model can only predict 8.1% of removalswhen only physical variables are included in analysis.The south-central region of the state is the only signif-icant geographic predictor (P � 0.02, all others: P �0.15). Dams in this region are � 3 times more likely tobe removed than dams in other geographic regions.However, the model only accurately predicted 8% of allremovals when all geographic regions are included.Dam ownership does not significantly contribute todam removals (all P � 0.2).

The best model for dam removal includes the southcentral geographic region, dam age, and impound sizevariables (Table 4). This model only accurately predicts12.6% of all removals. However, dams in the south-central region are 2.2 times as likely to be removed thansimilar dams in other regions when dam age and im-pound size variables are held constant (odds ratio fordams not in south-central region � 0.446).

1To account for both nonnormality of reservoir size and dams with noreservoirs, impoundment size was transformed as such: In (impound-ment size � 1). Therefore, a 1-unit decrease as stated in the text isequal to a one full-integer decrease in (impoundment size � 1).

Table 3. Models describing dam removals with all dams included in analysisa


Ownership Private/Utilities (0) �5.709 NS 0.003 2.3Municipal (0) �6.503 NS 0.001State (0) �5.930 NS 0.003Federal (0) �5.789 NS 0.003Other (0) alone 5.973 NS 392.606 1.4

Geography North (0) 0.223 NS 1.250 1.7West-central (0) 0.350 NS 1.420North-east (0) �0.145 NS 0.865South-central (0) �0.218 NS 0.840South-east (0) alone 0.914 NS 0.914 1.3

Physical characteristics Age �0.035 0.000 0.965 5.0Height 0.027 NS 1.027Ln (Impoundment size � 1) �0.044 NS 0.957

Inspection Inspected (0) �0.505 0.097 0.604 3.3Inspected after 1985 (0) �0.751 0.003 0.472Inspected (continuous) alone 0.044 0.011 1.045 3.4

Best South-central (0) �0.436 0.029 0.647 6.8Age �0.031 0.000 0.068Inspected after 1985 (0) �0.706 0.000 0.494

aSee Table 2 for notes on the numbers after the parameter name


Importance of Individual Variables

The south-central region variable appears consis-tently in models that best describe inspections after1985, removal of all dams, and removal of dams in-spected after 1985. More dams have been removed inthis region than all others (Figure 3). However, thesouth-central region has the lowest proportion of damsinspected (22%), and the second lowest proportion ofdams inspected after 1985 (18%), relative to the totalnumber of dams in the region (Figure 4). A largeproportion (79%) of dam inspections in the south-central region have occurred after 1985 compared toonly 43% in the west-central region (Figure 5). Thesouth-central region has the highest ratio of inspectionsafter 1985 to removals (7.3:1) while the lowest ratiooccurs in the northern region (32.3:1) (Figure 6).

Logistic regression analysis also indicates that olderdams and those with smaller impoundments are morelikely to be removed than newer ones and those withlarger impoundments. The average age of removed

dams is significantly older than dams not removed(paired t test, t � 9.066, df � 32, P � 0.001). In contrast,the average size of dam impoundments (log trans-formed) behind intact dams is significantly larger thanimpoundments behind removed dams (paired t test, df� 32, P � 0.001), which corresponds well with ourregression analysis which states that when the dam isinspected after 1985, larger impoundments are lesslikely to be removed.

Discussion

Role of Regulatory Agency

We found in follow-up interviews with WDNR staffthat the history and structure of the regulatory agencyhelped to clarify our results. The WDNR is the primarystate authority responsible for regulating dams underState Statute 31.19 and the Water Power Law created in1917. The legislature requires the WDNR, through the

Figure 3. Number of dams removed by geo-graphic region.

Table 4. Models predicting dam removal of dams inspected after 1985


Ownership Private/utilities (0) 0.188 NS 1.201 6.0Municipal (0) �0.371 NS 0.690State (0) �0.398 NS 0.671Federal (0) �0.517 NS 0.596Other (0) 6.227 NS 532.077

Geography North (0) �0.142 NS 0.867 8.0West-central (0) �0.693 NS 0.500North-east (0) �0.649 NS 0.522South-central (0) �1.216 0.022 0.296South-east (0) alone 0.693 NS 2.000 5.0

Physical characteristics Age �0.025 0.001 0.975 8.1Height �0.008 NS 0.992Ln (impoundment size � 1) �0.510 0.002 0.600

Best South-central (0) �0.793 0.001 0.453 11.3Age �0.022 0.004 0.979Ln (impoundment size � 1) �0.577 0.004 0.562


Dam Safety Program, to inspect dams on navigablewaterways once every 10 years, except for dams regu-lated by a federal agency. These laws make the WDNRthe major instigator for change in the status of dammanagement through inspection and ability to man-date management actions. How and when inspections

are conducted and followed through are importantdeterminants of an owner’s decision to remove or re-pair a structure.

A required portion of each inspector’s report pro-vides information on the possibility of dam failure,potential liability that dam owners may face, and steps

Figure 4. Proportions of all dams inspectedand those inspected after 1985 by geo-graphic region.

Figure 5. The proportion of all inspectionsconducted after 1985.

Figure 6. Ratio of dams inspected to damsremoved by region. Higher ratios indicatefewer removals for a given number of in-spections.


that should be taken to amend the problem. Individualdam inspectors may also include information on thepossible benefits of choosing dam removal as a reme-diation option such as: reduced liability; repair andremoval cost estimates; habitat restoration; water qual-ity; and improved recreational opportunities. The qual-ity and type of information on potential costs and ben-efits of dam removals are not uniform for all inspectionreports. The dam inspection report may be the mainsource of information available to a dam owner at thetime the decision to repair or remove a dam is made.Dam inspections may trigger the dam removal processby providing a source of information regarding thepotential costs and benefits of dam removal and possi-ble financial assistance. If this is the case, dam removalis more likely in situations where more informationabout the ecological impacts of dams or the potentialfunding sources available for removal were included inan inspection report.

The history of WDNR regulation changes certainlyinfluenced patterns of inspection and may have alsoinfluenced patterns of removal. River regulation andriver ecology have historically come under the purviewof disparate regulatory agencies. Inspection engineershave not been assigned to work with ecologists and fishbiologists in state or federal agencies, and the result hasbeen a predictably varied management regime. Untilrecently, the dam safety inspector’s appointed job wasspecifically to ensure the safety and structural integrityof dams, and not to include consideration of biologicand ecologic aspects of river regulation in their inspec-tions. Prior to 1986, inspection fees, based on the size ofthe dam, were paid by the dam owner and funded thedam inspection program. Because of this funding ar-rangement, the WDNR often prioritized inspection oflarge dams to fund the program, while preserving someinspections to fund future years. This pattern changedwith the inception of a municipal dam grants programthat paid for the inspection of dams of any size ownedby municipalities. There are currently 13–14 dam in-spectors assigned to various areas of the state. Concom-itant with the agency reorganization in 1997, someinspectors began to work in interdisciplinary teamswhere they have had the opportunity to invite inputfrom personnel with varied areas of expertise to pro-vide detailed accounts of potential costs and benefits ofa particular project. Inspectors in more isolated officesor with higher inspection quotas may not include thisinformation in their reports. This variation in the qual-ity and type of information in inspection reports islikely to be biased such that inspectors working inregions close to the central bureau in the state capitalmay be more likely to include more information and

consequently present a larger opportunity for dam re-moval.

Comparing Predictive Models to Interviews

Old dams with small impoundments that have beenrecently inspected by an engineer working in the south-central region of the state are more likely to be re-moved. Even under these conditions, only a small per-centage of dams are removed compared to the numberrepaired. Inspection may be an opportunity for a re-moval process to be triggered, but as inspection alone isnot a predictor of removal, other factors must contrib-ute to the ultimate decision to breach a dam. The bestmodel we can develop from data available on all thedams and removals in the state is still a fairly poorpredictor of dam removal. This strongly suggests thatthe physical, geographic, and ownership attributes weassessed are not the only major factors determiningwhether a dam is retained or removed. Additional con-siderations such as individual and organizational in-volvement influence the decision-making process.

Local residents can greatly influence the dam re-moval decision. Communities are often strongly op-posed to dam removal, especially in cases where theremoval is perceived as a threat from an entity outsideof the local community (American Rivers and others1999). One of the more common arguments for keep-ing a dam concerns the affinity that people develop fortheir local environment (Johnson and Graber 2002).People who grew up in a community surrounding thereservoir may get upset at the prospect of losing whathas always been a prominent feature of the local land-scape. While the man-made reservoir may constitute a“degraded” environment from an ecological perspec-tive, it constitutes the “natural” environment from asocial perspective—particularly in cases where the damhas been in place for generations.

The controversy surrounding dam removal is in factnot altogether different from the controversy that at-tends dam construction. Dam construction creates con-siderable disruption at the geographic, economic, andcultural levels. Similarly, when a dam is removed, dis-ruption may occur at the geographic (loss of “natural”environment), economic (decline in lakeshore real es-tate values, businesses catering to lake-specific recre-ational activities), and cultural (loss of familiar land-scape) levels. Through time, the environment createdby a dam may come to have symbolic relevance tosurrounding communities. Consequently, just as af-fected populations resist dam construction projects, sotoo do affected populations resist dam removalprojects. Natural resource managers have increasedtheir understanding of peoples’ relationship to their


natural environment in an effort to understand theaffinity that people develop for particular natural areas(Cantrill 1998). Termed place attachment, this rela-tionship is variously defined as an emotional bond be-tween people and their environment (Altman and Low1992), the emotional linkage of individuals to a partic-ular environment (Hunter 1978), and emotional invest-ment in place (Hummon 1992). Evaluation of howplace attachment influences dam removal decisionswould be useful in framing decision-making processesbut to date, no work of this type has been done.

In addition to WDNR inspection reports providingtechnical, educational, and financial assistance to damowners, the involvement and advocacy of nongovern-mental organizations may complement or substitute forthe WDNR inspection as a triggering process for damremoval. In particular, these organizations may providecritical resources for private small dam owners whoconstitute the largest percentage of dam owners in thestate. Although the state has allocated funds for a pro-gram targeted specifically at these small private owners,these funds have not been made available to date.

Recently, nonprofit organizations such as AmericanRivers, The River Alliance of Wisconsin, and TroutUnlimited have launched campaigns to either advocatedam removal or help inform dam owners about thepros and cons of removal. Nonprofit environmentalorganizations were probably not influential in deter-mining trigger events to dam removal, such as an in-spection, or targeting for environmental concerns inthe removals we studied because they typically becomeinvolved later in the decision-making process. However,these private organizations do influence the processonce a dam has had a trigger event such as a failedinspection and becomes a potential candidate for re-moval. They perform community education and out-reach by holding public talks, act as experts in publichearings, comment on administrative documents, andprovide fact sheets outlining the economic and eco-logic benefits to removal, (Helen Sarakinos, SmallDams Manager for The River Alliance of Wisconsin,personal communication). This input is likely to influ-ence rates of dam removal. Although these organiza-tions do not directly determine inspections or causedam removals, they may have an influence on the atti-tude of the people involved in the process and in thatway change the final decision to repair or remove asmall dam on a case-by-case basis. Growth of theseorganizations may influence future removals but theireffects on the numbers of removals we observed in thisstudy are likely to be only modest as these programs arerelatively new.

Conclusion

Dam removal has had an unusually long history inWisconsin.The state has more removals than any otherin the country. Growing ecological literature says thatin some situations dam removal is a powerful restora-tion tool if it is implemented in a deliberate and calcu-lated way. Yet removals here are occurring independentof the environmental, engineering, and regulatory fea-tures we observed. Decisions about river restoration aremade on a case-by-case basis as the result of decision-making processes that may not take into account of allthe options and consequences of each choice. Therehave been attempts made to develop guidelines forcomprehensive consideration of stakeholders duringdam removal decision-making (Heinz Center 2002),but there is no evidence that these guidelines are beingemployed in a consistent way to actual removals. If damremoval is going to be evaluated as a real solution to theproblem of aging dams, there are three entities thatneed to exist in every decision-making process. The firstis a regulatory agency that can, and is required to,uniformly assess ecological impact of dams along withtheir assessment of dam safety and structural integrity.The second is a funding source available to dam own-ers, including private owners, that can be used forremoval or repair. The third is an interested partyavailable to collect and collate relevant informationabout different remediation options and make themavailable in a comprehensible form to decision makersand all relevant stakeholders.

If our regression model cannot predict dam re-moval, a more accurate conceptual model of the damremoval process, as described to us by a regional DNRdam inspection engineer, might be this:

Dam Removal � O � E � F � P1 � P2

where O is the Opportunity or impetus for change, E isthe Economics, F is the Fortitude, the time investmentby an interested party, P1 is the Place attachment andcommunity pressure, and P2 is the Political involve-ment.

Opportunity could be an inspection, a structuralfailure or any event that questions the status quo of aparticular structure. Economics mostly comes into playin the form of funding availability and total cost ofremoval. Fortitude is equivalent to leadership by aninterested individual or organization who can gatherinformation and track the project to completion. Pub-lic and political involvements are the input given bystakeholders and outside parties to the decision-makingprocess that could sway the decision makers to take aparticular action.


We found it difficult to predict dam removal usingpast removals as a guide and that no particular featureof an individual dam makes it a likely candidate forremoval. However, there is opportunity for the growingbody of ecological information to guide decisions aboutwhere future removals should occur. Removals can beexpensive in terms of both money and time invested byall parties involved. To ensure that the limited re-sources allocated to dam removal are bringing the mostrestoration benefit, efforts should be targeted towardsrivers that have the best chance of recovering ecologicalfunction after a dam removal.

Organizations or individuals interested in imple-menting coordinated dam removal as a river restora-tion technique need to acknowledge the details of thedecision-making process and of the complexity of fac-tors beyond safety, cost, and ecology that influence theeventual state of the river in question. A good exampleof a focused restoration effort is the four consecutiveremovals of aging dams on the Baraboo River in Cen-tral Wisconsin. The final removal in 2002 connectedapproximately 120 miles of main stem river and theeffort is expected to improve fish habitat and swift-water recreation opportunities on the river (AmericanRivers 2000, WDNR 2002b). This example could beused as a model for other river systems. Other modelshave begun to emerge to guide stakeholders throughdam repair versus removal decisions (Heinz Center2002) and further effort needs to be made to incorpo-rate these multidimensional models into all decision-making processes.

AcknowledgementsPortions of this work has been supported by the NSF

IGERT grant 9870703, Human Dimensions of Socialand Aquatic System Interactions. The authors are in-debted to WDNR Dam Safety Engineers Meg Gallowayand Sue Josheff, and River Alliance Small Dams Man-ager Helen Sarakinos for the information and sugges-tions they provided. We also appreciate the valuableinput given by Emily H. Stanley, Peter Nowak, Stanley I.Dodson, Marieke Heemskerk, and two anonymous re-viewers.

References

Altman, I., Low, S.M. 1992. Place attachment. Plenum Press,New York, 314 pp.

American Rivers. May 10. 2000. Fish come back to the Ken-nebec. http://www.amrivers.org/damremovaltoolkit/ed-wardsdam.htm. May 10. Accessed 15 April 2001.

American Rivers, Friends of the Earth, and Trout Unlimited.1999. Dam removal success stories: restoring rivers throughselective removal of dams that don’t make sense. AmericanRivers, Friends of the Earth, and Trout Unlimited, Wash-ington, DC, 115 pp.

Associated Press. 2002. Small towns deal with expense ofreplacing or fixing dams. In International Rivers Network(eds.), River Revival Bulletin No. 36, 18 March.

Bednarek, A. T. 2001. Undamming rivers: a review of theecological impacts of dam removal. Environmental Manage-ment 27:803–814.

Born, S. M., K. D. Genskow, T. L. Filbert, N. Hernandez-Mora,M. L. Keefer, and K. A. White. 1998. Socioeconomic andinstitutional dimensions of dam removals: the Wisconsinexperience. Environmental Management 22:359–370.

Cantrill, J. G. 1998. The environmental self and a sense ofplace: communication foundations for regional ecosystemmanagement. Journal of Applied Communication Research26:301–318.

Chatterjee, S., A. S. Hadi, and B. Price. 2000. RegressionAnalysis By Example. Wiley-Interscience, New York.

Doyle, M. W., E. H. Stanley, M. A., Luebke, and J. M. Harbor.2000. Dam removal: Physical, biological, and societal con-siderations. Proceedings of the 2000 Joint Conference onWater Resource Engineering and Water Resource Planningand Management. American Society of Civil Engineers,New York.

Heinz Center 2002. Dam removal: science and decision-mak-ing. H. J. Heinz Center for Science, Economics and theEnvironment. H. J., Washington, DC.

Hosmer, D. W., and S. Lemeshow. 1989. Applied LogisticRegression. Wiley-Interscience, New York.

Hummon D. M. 1992, Community attachment: local senti-ment and sense of place. In I. Altman and S. M. Low (eds.).Place Attachment. Plenum Press, 314 p.

Hunter, A. 1978. Persistence of local sentiments in mass soci-ety. D. Street (ed.), Handbook of Contemporary UrbanLife. Jossye-Bass, San Francisco.741 p.

Johnson, S. E., and B. E. Graber. 2002. Enlisting the socialsciences in decisions about dam removal. BioScience52:731–738.

Manel, S., H. C. Williams, and S. J. Ormerod. 2001. Evaluatingpresence-absence models in ecology: the need to accountfor prevalence. Journal of Applied Ecology 38:921–931.

Poff, N. L., and D. D. Hart. 2002. How dams vary and why itmatters for the emerging science of dam removal. BioScience52:659–668.

Shuman, J. R. 1995. Environmental considerations for assess-ing dam removal alternatives for river restoration. RegulatedRivers: Research and Management 11:249–261.

Wisconsin Department of Natural Resources. 2002a. Statewidedams database. Available online. Accessed 11 February2002. http://www.dnr.state.wi.us/org/water/wm/dsfm/dams/datacentral.html

Wisconsin Department of Natural Resources. 2002b. http://www.dnr.state.wi.us/org/water/wm/dsfm/dams/index.htmlAccessed 11 April 2002.


Examination of Physical and Regulatory Variables Leading to Small Dam Removal in Wisconsin

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