forecasting future land use for watershed assessment

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONVOL. 35, NO.3 AMERICAN WATER RESOURCES ASSOCIATION JUNE 1999

FORECASTING FUTURE LAND USE FORWATERSHED ASSESSMENT'

Jonathan B. Butcher2

ABSTRACT: Protecting surface water quality in watersheds under-going demographic change requires both the management of exist-ing threats and planning to address potential future stressesarising from changing land use. Many reservoirs and threatenedwaterbodies are located in areas undergoing rapid populationgrowth, and increases in density of residential and commercial landuse, accompanied by increased amount of impervious surface area,can result in increased pollutant loading and degradation of waterquality. Effective planning to address potential threats, includingzoning and growth management, requires analytical tools to predictand compare the impacts of different management options. Thefocus of this paper is not on developing demographic projections,but rather the translation of such projections into changes in landuse which form the basis for assessment of future watershed loads.Land use change can be forecast at a variety of spatial and tempo-ral scales. A semi-lumped, GIS-based, transition matrix approach isrecommended as consistent with the level of complexity achievablein most watershed models. Practical aspects of forecasting futureland use for watershed assessment are discussed. Several recentreservoir water supply projection studies are used to demonstrate ageneral framework for simulating changes in land use and result-ing impacts on water quality. In addition to providing a technicalbasis for selecting optimal management alternatives, such a tool isinvaluable for demonstrating to different stakeholder groups thetrade-offs among management alternatives, both in terms of waterquality and future land use patterns within the watershed.(KEY TERMS: land use change; watershed assessment; land useplanning; source water protection; watershed management; model-ing.)

INTRODUCTION

A comprehensive watershed protection plan shouldaddress both existing and potential future conditionsin the watershed. In urbanizing areas the impacts ofdevelopment on water quality are of particular con-cern. Planning and selection of water supply protec-tion ordinances requires an estimate of future land

use and its effect on water quality. An example of thetype of question a manager may wish to answer is:Given the expected rate of population growth anddevelopment within a watershed, and accompanyingconversion of land use and land cover, what do weexpect to happen to environmental resources underexisting management measures and regulatory pro-grams? Are these protections adequate, or are addi-tional management strategies needed?

To answer these types of questions, one can eitherconsult a clairvoyant or use a model that developsrational projections concerning land use and popula-tion change and converts these projections into a pre-diction of watershed conditions and waterbodyresponse. This paper focuses on techniques to forecastland use changes in watersheds at a practical level ofcomplexity appropriate to our ability to model water-shed loading processes.

Types of Modeling Approaches

Given an estimate of land use, watershed pollutantloading and associated impacts can be modeled at var-ious levels of complexity: At the simplest level, loadscan be estimated by a spatially and temporallylumped approach through simple loading factorswhich assign average pounds-per-acre loads to thetotal watershed area in each use class. At the otherextreme of complexity are spatially and temporallydistributed models which account for variability withtime and specific geographic location of land uses,simulate associated runoff and load generation pro-cesses, and convert the geographic distribution ofland uses and meteorological time series into loading

1Paper No. 98133 of the Journal of the American Water Resources Association. Discussions are open until February 1,2000.2Principal Hydrologist, Tetra Tech, Inc., P.O. Box 14409, Research Triangle Park, North Carolina 27709 (E-Mail: [email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES AssocIATioN 555 JAWRA

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series. Intermediate complexity models are most oftenthe method of choice for water quality modeling at thewatershed scale, combining spatial lumping of landuses at the sub-watershed scale with temporally dis-tributed meteorology at the hourly or daily scale.

Projecting future land use may also be done at var-ious levels of complexity. The simplest approach is toassume eventual buildout of the watershed at zoneddensity, which is a spatially and temporally lumpedapproach. At the other extreme one could attempt topredict the timing of conversion of individual parcels— a fully distributed approach. As with watershedmodeling, an intermediate level is often more feasible,combining spatial lumping (estimating the total dis-tribution of land use at the sub-watershed scale) witha representation of the degree of development at dif-ferent time intervals (e.g., 25 years hence).

Two stages may be distinguished in the develop-ment of a land use forecast: a demographic projection(the number and location of people, houses and jobs)and a land use interpretation of the demographics.Demographic projections are available for manywatersheds from local planning agencies, and willoften form the basis for watershed land use forecasts.These also may range in complexity, from simple pop-ulation trend projections to detailed spatial analysesof housing and employment.

Sophisticated approaches to spatial land use pro-jection have been developed in the field of transporta-tion planning, for which the timing and location ofdevelopment are both important. Locational models ofurban form, referred to as Lowry models, relate resi-dential distribution to availability of jobs and ameni-ties (Lowry, 1964; Goldner, 1971). A strong interactionbetween transportation and development pressurehas long been recognized (Mitchell and Rapkin, 1954).The models of Putman (1971, 1983) build on theLowry models by integrating land use and transporta-tion demand models. A number of operational landuse-transportation models have been built on thisfoundation. Recent advances in metropolitan trans-portation planning models (e.g., Woolley and Young,1994) have added a focus on the environmentalimpacts of development.

When available, transportation and metropolitanplanning studies can form the basis of watershed landuse forecasts, but do not usually provide the completeinformation or appropriate spatial and temporal reso-lution necessary for watershed assessment. The basicchallenge is to convert projections of household andemployment locations in transportation zones to esti-mates of resulting land use and land cover in water-sheds.

Models that forecast land use are also a specializedclass of models of landscape change, a variety ofwhich have been developed in the field of landscape

ecology. In a review of such models, Baker (1989) dis-tinguishes between distributional models, in whichthe distribution of a variable in some landscape isrepresented, and spatial models, where the configura-tion of individual subareas of the landscape is repre-sented. Both types may be applied at a variety ofscales, ranging all the way down to the distribution ofindividual organisms in the landscape.

Various researchers have recently applied tech-niques of spatial landscape models to watershed landuse change (Sklar and Costanza, 1991). The advan-tage of true spatial models is that they can take intoaccount neighbor effects; e.g., a specific parcel is morelikely to develop when road access is provided andneighboring parcels are developed. Clarke et al.(1997) developed a cellular automaton model of theurbanization process in the San Francisco Bay area.Pijanowski et al. (in press) developed a spatial landtransformation model, applied to the Saginaw Baywatershed, based on transformation rules includingboth anthropogenic factors (grouped into categories of"Management Authority" and "Socioeconomics") andenvironmental factors.

Turner (1987) developed a spatial simulation oflandscape change in Oglethorpe County, Georgia,based on a transition probability matrix thatdescribes the expected rate of change from one landuse type to another, and found it necessary to employa spatial model in which the transition probability fora landscape patch is influenced by the state of neigh-boring patches. Parks (1991) extended this work toinclude economic influences on transition probabili-ties. Lee et al. (1992) summarized socioeconomic fac-tors that influence landscape change and proposed amodel with transition probabilities conditional onsocioeconomic factors. Riebsame et al. (1994) providefurther insights into the interplay of land value andbehavioral, cultural, and political factors as applied tothe history of land use change in the U.S. GreatPlains.

The California Urban Futures Model (Landis,1993, 1995) is an example of a functioning synthesisof metropolitan transportation forecasting and land-scape change models. Unlike traditional transporta-tion models, this model incorporates a variety ofvariables in addition to spatial accessibility to deter-mine location and density of new development, while,like spatial landscape models, it allocates growthfrom the "bottom up" based on potential profitabilityof individual developable land units. The GIS-basedanalysis of factors influencing site development in theapproaches of Landis and Pijanowski have a numberof important features in common with the approachproposed in this paper, but represent a level of com-plexity in analysis which is neither necessary oraffordable for most watershed assessment studies.

JAWRA 556 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

Forecasting Future Land Use for Watershed Assessment

In sum, recent work has established that land usechange on a tract or patch basis can be predictedthrough a transition model approach incorporatingsocioeconomic factors. Use of a fully-distributedapproach which addresses individual tracts, however,introduces considerable complexity which is oftenbeyond both the scope and appropriate resolution ofwatershed assessments. Achieving stakeholder buy-inis often the key to successful watershed management(U.S. EPA, 1995), and overly detailed projections candistract the attention of stakeholders, who may focuson what will happen to "their" tract rather than onwatershed-scale implications. Further, a distributedapproach is not needed to support watershed modelswhich are lumped at the sub-watershed scale, and forwhich only lumped land use forecasts are needed; e.g.,the total amount of land area in a sub-watershedwhich converts from forest to low-density residentialuse, and not the location of individual tracts in thesub-watershed. Overly complex approaches to landuse forecasts are rarely justified for watershed assess-ments because of the high level of uncertainty in anyprojection of the future. Simpler approaches are alsoneeded to allow evaluation of multiple managementscenarios within the typical time frame of a water-shed management study. On the other hand, modelsat the simplest level may produce unrealistic esti-mates of future land use (and a fully-lumped water-shed model cannot readily be calibrated to currentsite observations), and thus are less defensible forsupporting difficult policy choices. The following sec-tion presents some key practical issues for estimatingfuture land use for watershed assessment. This is fol-lowed by a more formal presentation of intermediatecOmplexity approaches to future land use simulation.

FORECASTING FUTURE LAND USE FORWATERSHED ASSESSMENT: KEY PRINCIPLES

Time Frames for Evaluation

Water supply reservoirs typically have an expectedoperational life of a century or more. Impacts of newdevelopment on watershed health may be permanent.Yet, it is difficult to develop realistic land use projec-tions for a period of more than 20 to 30 years into thefuture. One option is to assess impacts of full buildout(i.e., the maximum amount of new residential, com-mercial, and industrial development allowed by regu-lation, available land, and soil/slope suitability) —butthis may be unlikely within the foreseeable future inmany watersheds. Another option is to use popula-tion/development projections for a specific point in

time (e.g., 25 years out). For many watersheds, localland use plans, regional transportation studies, orother planning studies provide consensus estimates ofresidential and commercial growth patterns over thenext 25 or so years. A 25-year projection leaves indoubt the long term (e.g., 100-year) future of theresource, but projected rates of development beyond afew decades are highly uncertain at best, and usuallysubject to a wide range of interpretation among differ-ent stakeholders. A practical compromise approach isto evaluate implications of management scenarios attwo levels of watershed development, such as com-plete buildout and 25-year projections (or, in theabsence of growth projections, 25 percent of buildout).These two points in time provide snapshots of theeffectiveness of management scenarios as land usechanges in the watershed, regardless of the exact rateof growth. Complete buildout represents the ultimateimplications of a watershed management plan. Com-plete buildout will often not occur for many years, ormay never be fully realized, but analyzing this sce-nario provides a picture of watershed conditions thatcould eventually occur under a given managementplan or development policies.

Since full buildout may be many decades away, con-ditions at other, intermediate stages of developmentare generally of more immediate interest to stake-holders, such as 25 percent of buildout. Under theseconditions, a significant amount of development mayoccur in the watershed, but a significant portion ofexisting rural land uses still remains, perhaps includ-ing sources of nutrients and other pollutants fromagriculture. Depending on assumptions and restric-tions for development, water quality conditions atpartial buildout may be better or worse than waterquality conditions at full buildout. Both complete andpartial buildout implications should be considered.The relative weighting of these conditions is an issuefor stakeholder discussion.

Interpreting Zoning: Realized Versus NominalDensity

Zoning establishes a maximum potential density ofdevelopment, and can be used to evaluate a worst-case condition for water supply protection. However,assuming that all land area develops to zoned densitycan lead to erroneous or overly-conservative conclu-sions. First, an assumption of full buildout may not beappropriate, as described above. Second, zoned densi-ty should be interpreted as a not-to-be-exceeded maxi-mum, which is likely to differ from realized density.One of the most obvious limitations on realized densi-ty is potential lack of suitable soils for onsite waste

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 557 JAWRA

disposal in areas without sewers. Parts of a tractmust also be set aside for subdivision roads and otherutilities not already identified in regional land useplans. More generally, the amount of developmentthat can occur is ultimately limited by the character-istics of individual tracts. Calculating this limit iscomplicated by the fact that fine-scale limiting physi-cal features, such as stream buffer zones, may notimpose a direct limitation on the number of lots of agiven size that will fit into a landscape. For instance,a 2-acre lot could consist of 1 acre (or less) of "build-able" land, containing the house site and septic field,and 1 acre of "unbuildable" riparian buffer. Similarly,an area with only 10 percent of soils suitable for sep-tic drain fields may still have enough suitable sites toallow nearly full development if the septic-suitablesoil is sufficiently dispersed through the area.

Such fine-scale limitations can be accounted for byestimating an average effective or realized lot size,which is the lot size resulting from a subdivisionwhich works around buffer zones, unsuitable slopes,and other site or regulatory limitations. It is best toobtain estimates of realized lot size appropriate tolocal topography through examination of recent devel-opment in the area. For instance, based on densityachieved in recent major subdivisions in a Piedmont,North Carolina, county, the effective lot size for nomi-nal 2-acre zoning was estimated as 2.36 acres, result-ing in 15 percent fewer houses than the maximumimplied by the zoned density.

Role of Waste Disposal and Water Supply

Sewer extensions and public water supply are keygrowth-shaping factors. Availability of sewer and pub-lic water supply leads to greater development pres-sure, and enables greater density of development, aslot unsuitability for septic systems or wells isremoved as a constraint. Defining the extent of futuresewered area should be a part of any water supplyprotection program, and is a key component of thefuture land use analysis. Where a specific policy onsewerage expansion is not already in place, a numberof scenarios should be tested addressing differentassumptions for future sewered area.

Incorporating BMPs

In addition to water supply ordinance restrictionson density and types of land use, most water supplyprotection plans will also include enhanced use of bestmanagement practices (BMPs) to help control loadingfrom a variety of land use types. Use of BMPs should

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be incorporated into simulations of future conditions.Usually this is done through specification of assump-tions regarding percent of land in a given use catego-ry implementing BMPs. The effectiveness of specificBMPs can be evaluated with detailed modeling andthe results incorporated into simpler lumped model-ing suitable for rapid analysis of management alter-natives. This means "what if' scenarios can be runusing different management strategies in targetedareas of the watershed instead of trying to account foreach tract or simply applying management strategiesin a broad brush fashion to an entire watershed.

Reconciling Projected and Existing Land Uses

A spatially-lumped analysis of future land use willgenerate an estimate of use by categories within asub-watershed, based on relevant growth shapingcharacteristics and constraints. Such a lumped projec-tion may not, however, take into account existing,non-complying land uses. For instance, an area zonedfor minimum 1-acre lots may already hold a numberof existing 1/4-acre lots. Such non-complying uses arelocation-specific, and can present some problems for aspatially-lumped analysis. It is important to accountfor any existing high-intensity land uses before mak-ing future projections. Making this adjustment mayrequire decisions as to which types (or what percent-age) of existing land uses are potentially subject toconversion to more dense development.

INTERMEDIATE-COMPLEXITYSIMULATION OF FUTURE LAND USE

A forecast of future land use begins with the exist-ing distribution of land uses and characteristics, thenprojects this into the future based on stated assump-tions. The key step is to identify and tabulate homo-geneous analysis units within each sub-watershed,which are land areas possessing similar growth-controlling factors, including:

• environmental characteristics which may limitgrowth, such as poor soils or high slopes;

• infrastucture characteristics, such as futureavailability of sewer and water;

• jurisdiction, such as location within a watershedprotected area controlled by City A;

• development demand or expected rate (perhapsestimated from a transportation planning study); and

• existing land use.



Together, these factors determine the likely futureuse of a parcel. For instance, an existing wooded tractwith septic-suitable soils, no planned sewer, 1-acreproposed zoning, and high projected demand for resi-dential development has a high probability of conver-sion to a residential development at nominal 1-acrezoning. Overlaying these factors is readily accom-plished using a GIS.

A forecast of land uses within a discrete land area,such as a watershed, is usually accomplished in aspreadsheet or GIS database. The operations involvedin a forecast for specific future time may be summa-rized in matrix notation as

T.h=u

where T is an m x n transition matrix, h (n x 1) is atabulation of acreage in each of n possible combina-tions of growth shaping factors, u (m x 1) is the pre-dicted acreage in each of m land use categories, m isthe number of land use categories, and n is the num-ber of possible combinations of growth determiningfactors.

For instance, predictions of the likely developmentfate of individual tracts might be based on five fac-tors: political jurisdiction, applicable zoning withinthe jurisdiction, soil suitability for septic systems,presence of sewer service, and projected developmentpotential of tracts in a given area based on demo-graphic projections. If there were three politicaljurisdictions, each having four relevant zoning classi-fications, three ratings for soil septic suitability (high,medium, low), presence or absence of sewer service,and four development potential classes (e.g., strongresidential growth, moderate residential growth, lowresidential growth, and commercial/industrialgrowth), the number of combinations, n, would be3 x 4 x 3 x 2 x 4 = 288, and there would be 288 rows inthe vector h. The acreage entries in this vector wouldbe determined by overlaying geographic coverages ofeach of the relevant factors and summing the area, byfactor combination, in each of the resulting homoge-neous analysis units.

The transition matrix, T, has n columns, corre-sponding to the combinations of factors. Each of them rows in T corresponds to a future land use category.Thus, a column of the T matrix would contain entriesshowing the fraction of a given homogeneous analysisclass which is expected in future to be in each of them land uses. For example, a column in T mightindicate that a tract in jurisdiction A, zoned for 2-acreresidential development, with high septic suitabilityand high expected growth potential, and with existingforested land use, is estimated to have an 85 percentprobability by a specified future time of beingdeveloped in 2-3 acre residential lots, a 5 percent

probability of being developed in 5-10 acre lots, and a10 percent probability of remaining forest. Each of thecolumns of T represents a projection of the likelydevelopment course of lots with specified characteris-tics. Projections for multiple discrete future times areachieved by adding a third (time) dimension to T.

Because of its potentially large size, it is advanta-geous to generate the entries in T automatically. Thisis accomplished through rules summarizing the effectof growth factor combinations. For instance, all areaswith strong residential growth and sewer servicemight be assumed to result in a specified fraction ofland in residential lots at zoned density. Such rulescan be developed through a combination of analysis ofregulations, examination of development patterns insimilar areas, and experience and judgement of localplanners, elected officials, and real estate profession-als. There are several advantages to working in tran-sition matrix form:

• Alternate scenarios are readily tested by chang-ing the generative rules or manually updating a limit-ed portion of the transition matrix.

• The forecast is integrated with the GIS, and canautomatically update with the GIS.

• Specification of the matrix (or its underlyingrules) forces consideration and documentation ofassumptions for development patterns and the effectsof management options on land use.

• A forecast is readily replicated.

The transition matrix relationship may also beused to evaluate the required starting point for thedistribution of constraints in terms of a desired futureland use distribution. We would wish to find a set ofexisting growth shaping factors, h, which result in atarget predicted land use distribution, u, where

h = T-1 . u

where T-1 is the inverse of the T matrix. In most realcases, the matrix T will not be positive definite, andwill not have a defined, unique inverse. The goal isthen to find a best fit, i.e.

min(T.h-u)

where only those components of h subject to planning(e.g., zoning, extent of sewer area extensions) areallowed to vary in the optimization. Such an analysiscan provide a useful tool for evaluating watershedmanagement plans.

Of course, any projection of the future is subject toconsiderable uncertainty. Uncertainty in the forecastof future land uses is only one among many sources ofuncertainty in predictions of a watershed model,


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along with model uncertainty and natural variabilityin response to a given land use configuration. Evalua-tion of future conditions introduces the additionalproblem that models cannot be (immediately) cali-brated to future events, they can only be extrapolated.Nonetheless, an analysis of model accuracy and relia-bility is essential for any model prediction that is tobe used for decision making. Evaluating uncertaintyin the prediction of future land use is an integral partof the general task of establishing the reliability ofmodel watershed predictions.

At a minimum, the expected uncertainty in futureland use forecasts, and resulting impacts on waterquality model predictions, should be subjected to asensitivity analysis and the results presented to deci-sion makers. More formally, uncertainty due to landuse forecasts may be incorporated into a Monte Carloanalysis of the distribution of water quality predic-tions. Use of the transition matrix approach facili-tates this task.

In sum, for predicting future land use on a lumped(sub-watershed) basis, the required input is a tabula-tion of area in each homogenous analysis unit. Thetabulated areas can be converted to a prediction offuture land use through the application of a transitionmatrix that describes the percent of land area with agiven set of characteristics expected to result in eachtype of future land use under a given set of manage-ment options. Working in a lumped context alsomakes it easy to investigate effects of different man-agement strategies through modification of the transi-tion matrix.

EXAMPLES

Brief examples of key points are drawn from recentwater supply protection studies conducted for tworeservoirs in the Piedmont section of North Carolina.

Cane Creek Reservoir

The Cane Creek study elucidates development of afuture land conversion algorithm for evaluating man-agement options. Cane Creek Reservoir serves as awater supply for the City of Chapel Hill, North Caroli-na, and has a watershed area of 32 mi2. Land use inthe watershed is primarily forest and agricultural,but substantial development pressure is expected inthe near future as the adjacent metropolitan areaexpands. Most commercial and industrial uses areprecluded by existing ordinances. For future waterssupply protection planning, the key issues are the

amount and type of residential development expectedin the watershed under a candidate watershed protec-tion zoning scenario, and the water quality impactsassociated with that development.

Future residential land use may be limited by bothzoning ordinances and suitability for on-site wastedisposal. Potential future land use may be separatedinto areas which are limited and not limited in termsof achieving buildout to densities specified in zoningordinances, due to factors such as soils unsuitable toseptic tanks and steep slopes. Major institutional andenvironmental constraints on potential future landuse are shown in Figure 1.

As forest is converted to residential land use, nutri-ent loading is expected to increase, resulting in poten-tial degradation of water quality. A variety of meansto reduce future residential density or control itsimpacts were investigated, including down -zoning,purchase of land by the utility, open space require-ments for developments, and development-scale andregional stormwater detention. Given an estimate offuture land use conditional on the management sce-nario, watershed loading and reservoir response mod-els can be used to predict water quality impact. Theproject required rapid and efficient evaluation of mul-tiple management scenarios developed in coordinationwith a broad-based stakeholder group. Initially,detailed dynamic models of watershed loading weredeveloped for key land areas [e.g., the USDA AGNPSmodel (Young et al., 1986) was applied to dairy opera-tions]. These models were too complex for rapid evalu-ation of management scenarios, but instead were usedto develop a summary steady-state representation ofwatershed response conditional on land use. Thissummary watershed model was incorporated into aspreadsheet application and linked directly to thetransition-matrix forecast of future land use distribu-tion, enabling rapid evaluation of a large number ofcandidate management scenarios.

GIS cross-tabulation of all relevant environmentalfactors yields homogeneous analysis areas. Thesewere converted into an estimate of future land use viaa transition matrix based on a set of rules, partiallysummarized in Figure 2. The left side of the figurerepresents selected growth-shaping factors, while theright side diagrams conversion of a particular analy-sis class ("fully buildable land") at full buildout. Theproduct of the fractional rates assigned to each pathin the right hand diagram yields an entry in the tran-sition matrix for the column corresponding to thisanalysis class. Future land use was adjusted to per-cent of buildout conversion to provide a surrogatetime point for analysis. The spreadsheet model pro-vided a basis for relative ranking of efficacy of differ-ent management combinations in protecting waterquality, and was used by the stakeholder group to



Figure 1. Environmental Constraints on Development in the Cane Creek Watershed.

develop a consensus management plan. Quantitativeestimates of uncertainty in model predictions werenot developed as part of this study; instead, qualita-tive estimates were supplied for both the future landuse forecasts and watershed response models. Thestakeholder group used these qualitative uncertaintyestimates as one of several ranking criteria for select-ing among management scenarios.

Randleman Lake

Randleman Lake is a proposed reservoir to servethe needs of the Greensboro-High Point area of NorthCarolina. Much of the 171 mi2 watershed is in theHigh Point urban growth area, raising concerns overfuture water quality and the effectiveness of a

proposed nutrient reduction strategy requires esti-mates of future land use distribution. A transporta-tion study for 2025 conditions conducted by the localcouncil of governments identifies areas of the water-shed where intensive residential andcommercial/office/industrial land use is expected(Figure 3), showing that development pressures areexpected to be focused on the northern part of thewatershed. The transition matrix for future land useprojections was developed based on the assumptionthat 90 percent conversion from rural to urban landuses will occur in the projected high-growth areas bythis date, while only a 20 percent increase in popula-tion would occur outside the high-growth areas, con-sistent with county-wide population projections.

Similar to the Cane Creek study, the watershedwas subdivided into nine sub-watersheds, for each ofwhich a pollutant loading function simulation model


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Figure 2. Portion of Future Land Use Estimation Rule Structure for the Cane Creek Watershed.

was developed (Haith et al., 1992). Model representa-tions of average, high, and low flow years were inte-grated into linked spreadsheet models which combinethe watershed load predictions conditional on landuse with forecasts of future land use. The transitionmatrix for forecasting land use was based on homoge-nous analysis areas (determined by environmentalcharacteristics, infrastructure characteristics such aspresence of sewer, jurisdiction, and developmentdemand) and specification of water supply ordinanceswithin each jurisdiction to produce projected land useestimates on a lumped basis by sub-watershed. Thespreadsheets automatically reconcile forecasts withexisting land use distribution (to allow the continuedpresence of non-conforming, high intensity uses).

The nutrient reduction strategy proposed by localgovernments includes water supply protection ordi-nances which limit minimum residential lot size andpercent impervious surface cover, along with a num-ber of structural (stormwater detention, constructed

wetlands) and non-structural (buffers, use of agricul-tural BMPs) nutrient reduction options. Simulationof future land use with and without water supplyordinances shows little difference in the projectedtransition of land to residential uses, although per-centages of retained open space and forest are higherwith ordinances specifying maximum impervious sur-face requirements (Figure 4). Within the new residen-tial areas, however, the ordinances result in a lowerhousing density, which is projected to result in areduction in phosphorus loads within those sub-watersheds in which the ordinances are applied.

CONCLUSIONS

The transition matrix approach to forecastingfuture land use conditions can add considerable valueto water supply protection planning and watershed



County BoundariesWatershedsProjected Waiting AreasProjected Uving Areas

1 0 1 2 3 4 5MIes

Figure 3. Projected Growth Areas for Randleman Lake Watershed.

JOURNAL OF THE AMERICAN WATER RESOURCES AssoCIATIoN 563 JAWRA

A

Legend

—- j

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Existing ConditionsCommercial/Industrial (7%)

Residential (16%)

Forest (54%)Agriculture (21%)

Open (2%)

Future, with Ordinances Future, without OrdinancesCommercial!—' Forest (24%) Commercial! Forest (21%)Industrial (8%); Industrial (1 "'

Open (5%)N. Open (11 %) Agriculture (6%)

Agriculture (6%)Residential (51%)

Residential (52%)

Future Annual Phosphorus Loading12000

10000

>8000

6000

4000a-

2000

0

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1'

High Point Deep 2 Deep 3B Mud 2Sub-watershed

Figure4. Future Land Use and Phosphorus Load Predictions for Randleman Lake Watershed.

564 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION


modeling. Most importantly, the approach allowsrapid testing, tailoring, and targeting of managementstrategies which are appropriate to the land featuresof the watershed. Using an approach which is morerealistic than simply tiling the watershed withresidential lots at the zoned density increases thecredibility of projections with stakeholders. The tran-sition matrix approach is more cost effective and lessburdensome than attempting to predict the fate ofindividual land parcels, and allows rapid evaluationof multiple management scenarios. Because it isbased on a geographic analysis of existing land uses,model predictions can be readily updated in futureiterations of the watershed planning process.

ACKNOWLEDGMENTS

Funding for much of the work described in this paper was pro-vided by the Orange Water and Sewer Authority, Carrboro, NorthCarolina, and the Piedmont Triad Regional Water Authority,Greensboro, North Carolina. An earlier version of portions of thispaper was presented as Butcher and Brewer (1998).

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