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Applied Geography 53 (2014) 402e416

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Applied Geography

journal homepage: www.elsevier .com/locate/apgeog

Assessment of freshwater ecosystem services in the Tualatin andYamhill basins under climate change and urbanization

Robert Hoyer a, *, Heejun Chang a, 1

a Department of Geography, PO Box 751 e GEOG, Portland State University, Portland, OR 97207 e 0751, USA

Keywords:Freshwater ecosystem servicesInVESTScenario analysisClimate changeUrbanizationRiparian restoration

* Corresponding author. Permanent address: 532997211, USA. Tel.: þ1 937 475 3354.

E-mail addresses: r.wesley.hoyer@gmail.com, r(R. Hoyer), changh@pdx.edu (H. Chang).

1 Tel.: þ1 503 725 3162.

http://dx.doi.org/10.1016/j.apgeog.2014.06.0230143-6228/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

We estimate and map the provision of freshwater ecosystem services (ES) for the Tualatin and Yamhillbasins of northwestern Oregon under a series of urbanization and climate change scenarios centered onthe year 2050 using the Integrated Valuation of Environmental Services and Tradeoffs (InVEST) modelingtoolset. Results for the study area suggest that water yield estimates are highly sensitive to climate,especially in the lowlands, while nutrient export and retention estimates are overwhelmingly driven byland cover. Sediment exports and retention are projected to increase throughout the study area due tohigher erosivity from increasing winter rainfall. When the ES estimates are summarized as bundles, thespatial patterns of the levels of ES provision are largely consistent regardless of climate and urbanizationscenarios. InVEST has utility as a landscape planning tool, and the presented analysis supports aconclusion that restoration efforts in the Yamhill basin would have a greater effect than those in theTualatin in improving water quality downstream. The estimated relative changes in ES provision underdifferent climate and urbanization scenarios are valuable for land management decisions because theyshow potential tradeoffs between provisioning and regulating ES.

© 2014 Elsevier Ltd. All rights reserved.

Introduction assessment tools will facilitate addressing the problem of estab-

A number of earth's ecosystem functions can be categorized asecosystem services (ES) that are necessary to support life andprovide benefits to humanity (MA, 2005). It is often argued they arenot fully considered or altogether missing from decision-making onnatural resource use (Costanza et al., 1997; Daily, 1997). Solvingreal-world conservation problems necessitates the formulation ofstandardized ES assessment methods (de Groot, Wilson, &Boumans, 2002). Standardization is important because it providesthe transparency, repeatability, and ultimately the credibility forintegration in the institutional decision-making structures forconservation and natural resource management (Crossman et al.,2013; Martínez-Harms & Balvanera, 2012). So even as the theo-retical underpinnings of ES are still debated, a key challenge re-mains in the development of assessment tools based on soundinterdisciplinary scientific knowledge (Daily et al., 2009; Portman,2013; Postschin & Haines-Young, 2011). Institutional trust in

NE, 15th Ave., Portland, OR

_wesley_hoyer@hotmail.com

lishing a relationship between incentivized land uses and bio-physical outputs that produce the desired ES. This is essential fordefining the conditionality necessary for participants in market-based instruments like ES credit exchanges or payments forecosystem services (PES) schemes (Engel, Pagiola,&Wunder, 2008;Jack, Kousky, & Sims, 2008; Zheng et al., 2013).

The costs and benefits from ecosystems are not distributedevenly and must be quantified in a spatially explicit manner (Eade& Moran, 1996; Naidoo & Ricketts, 2006; Troy & Wilson, 2006).Additionally, scientifically sound techniques for quantification andmapping of ES are essential components of an ES assessment(Burkhard et al., 2012; Burkhard, Crossman, Nedkov, Petz, &Alkemade, 2013). Researchers have presented several methods todo this (Chan, Shaw, Cameron, Underwood, & Daily, 2006; Egohet al., 2008; Estoque & Murayama, 2012; Raudsepp-Hearne,Peterson, & Bennet, 2010; Sch€agner, Brander, Mae, & Hartje,2013; Su, Xiao, Jiang, & Zhang, 2012), but they can vary widely. Thespatially explicit ES modeling tool, Integrated Valuation of Envi-ronmental Services and Tradeoffs (InVEST), developed by the Nat-ural Capital Project (www.naturalcapitalproject.org) (Tallis et al.,2013), offers a standardized approach to evaluate scenarios basedon simple ecological production functions parameterized on LULC.InVEST has been used to assess ES in a variety of conservationsettings around the globe (Ruckelshaus et al., 2013).

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416 403

LULC is a dominant factor in driving the heterogeneity of ES onthe landscape and tradeoffs among different types of ES (Bennett,Peterson, & Gordon, 2009; Qiu & Turner, 2013). Shifts in thepattern and makeup of LULC over time have consequences to thefuture amount and location of ES on the landscape (Lautenbach,Kegel, Lausch, & Seppelt, 2011). Future urban growth in the faceof increased population will likely influence the amount and thespatial patterns of supplies and demand of various ES types(Eigenbrod et al., 2011). Relevant ES assessments need to considerfuture changes in climate as well as LULC change especially whenconsidering water-related ES. Climate change will shift the amountand timing of water movement through the landscape, which alterthe transport dynamics of nutrients and sediments. Identifyingprojected changes in freshwater ES is essential for new adaptivemanagement strategies. Scenario analysis is seen as a method ofevaluating possible futures, and is now advocated as an importantresearch and planning tool in environmental studies (Peterson,Cumming, & Carpenter, 2003; Thompson et al., 2012). Involvingstakeholders in the scenario development is often perceived as animportant component, adding legitimacy to the process (Patel, Kok,& Rothman, 2007). Thus far, scenario analyses with InVEST haveused LULC as the ES change driver (Bagstad, Semmens,&Winthrop,2013; Goldstein et al., 2012; Nelson et al., 2009, 2010; Polasky,Nelson, Pennington, & Johnson, 2011). Fewer studies assessingfuture ES have incorporated climate (Bateman et al., 2013), and onlyone so far investigating climate impacts with InVEST (Terrado,Acu~na, Ennaanay, Tallis, & Sabater, 2014).

This research investigates the potential freshwater ecosystemservices response to the impacts of both climate change and landcover change in the form of increased urbanization and riparianbuffer restoration. Our approach simplifies a complex system byimplicitly assuming that changes to the landscape and to climateare independent for the study period we investigated. The studyarea is two river basins in northwest Oregon with a legacy of waterquality issues and a precedent of active management aimed at ESenhancement. Using InVEST along with urbanization scenariosdeveloped with stakeholder involvement (Hoyer & Chang, 2014),we investigate the following questions relevant to our study area.

(1) What are the estimated changes in freshwater ES in thefuture relative to current estimates under the two mainchange drivers, namely climate and LULC? Which driver ismore influential in determining future shifts in each ES?

(2) What is the spatial distribution of freshwater ES currentlyand how might it change under the above mentioneddrivers?

(3) Finally, what are the usefulness and limitations of InVEST'sfreshwater components? Does it provide useful informationto the ecologically-based management of the study area?

Methods

Study area

The Tualatin, Yamhill and adjacent Chehalem river basins innorthwestern Oregon serve as our study area (Fig. 1). These basinswere chosen since they are one of the fastest growing metropolitanareas in Oregon (Hoyer & Chang 2014), and future climates areprojected to be hotter and drier in summer (Chang & Jung 2010),which will have large implications for the provision of freshwaterES. The Yamhill basin is historically wetter than the Tualatin. Thesouthern fork of the Yamhill, at approximately three-quarters(1350 km2) the area of the whole Tualatin (1844 km2), yielded982 mm of water annually on average than the Tualatin's 659 mm

from 2001 to 2010 (USGS, 2013b). The adjacent Chehalem Creekbasin is also included due to its similarities to the larger basins. Thestudy area falls into three broad categories of upland forests (~53%),valley floors dominated by agriculture (~30%), and urban land(~14%) based on USGS National Land Cover Dataset (NLCD) 2006(Fry et al., 2011). Both river systems have reaches defined asimpaired and subject to total maximum daily loads (TMDLs) forseveral water quality indicators under the Clean Water Act (ODEQ,2012). Water quality issues in these basins contribute to theproblems facing the greater Willamette River Basin (ODEQ, 2006).Upgrades to treatment plants successfully reduced loading overtime, but the TMDL was recently amended to allow all facilities inthe Tualatin to contribute loadings during low-flow summermonths because of anticipated population growth (ODEQ, 2012).More developed land cover from increased population coupledwith climate change has the potential for deleterious effects on thebiological integrity of these river systems, as increasing tempera-ture and nutrient loads further degrade dissolved oxygen levelsnecessary for a healthy aquatic system (Chang & Lawler, 2011;Praskievicz & Chang, 2011).

Though facing similar problems, these basins have key differ-ences affecting their provision of water-related ES. The Tualatinbasin contains a much larger urban land cover base whereas theYamhill basin contains more agricultural lands and contributesmore water to the Willamette River (Fig. 1). However, in both ba-sins, ES like water purification and sediment retention have thepotential to mitigate some of the land uses and activities negativelyimpacting water quality as well as reduce the burden of existingand new infrastructure. There is already a precedent in the TualatinRiver basin where a riparian restoration incentive programenhanced the ES of thermal shading (Cochran & Logue, 2011) andwater purification (Singh & Chang 2014). In addition to shade,natural vegetation has the capacity to filter contaminants, excessnutrients, and mobilized sediments (Brauman, Daily, Duarte, &Mooney, 2007), and increase the sale price of near stream proper-ties (Netusil, Kincaid, & Chang, 2014).

Spatial data

We manipulated and processed spatially explicit land cover andclimate data (Table 1) as well as ran most InVEST models in ArcGIS10.1 (ESRI, 2012). Each model requires its own set of variables, butseveral are common among the three. We chose the sub-watershedspatial unit to provide a level of detail allowing for differentiation ofES characteristics in the study area. The scenario LULC data werederived from NLCD 2006 (Fry et al., 2011) using a simple landchange model based on the criteria identified at a stakeholderworkshop. We interviewed several stakeholders who representedfederal, state, and county perspectives regarding land use. For ourstudy area, urban conversion of agricultural and forest lands wasidentified as the major change factor in the coming decades withagricultural conversion of natural vegetation being negligible underthe assumption of all suitable lands already exploited. Specifics ofthe process are presented in Hoyer& Chang (2014). This model onlyfocused on urban growth, and produced a low and a high scenario.The difference between them is the areal amount of urban growthoccurring along the current urban/rural fringe. In order to test In-VEST's response to a landscape scale change in vegetation man-agement, we simulated a thirty meter total buffer strip of riparianvegetation directly adjacent to both sides of streams in the studyarea that meet two criteria: They are on privately held land andthey are not in already developed areas. We chose a 30 mwidth torepresent the smallest vegetation change possible with NLCD as aproject partner and stakeholders communicated anything larger forthe study area would be unrealistic (Cochran, personal

Fig. 1. Study area e The Tualatin and Yamhill River basins and the interstitial Chehalem Creek basin.

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416404

communication). Although we do anticipate restoration in currenturban lands, we assume that these will be targeted and will notalways register relative to NLCD's resolution (30 m).

Climate data were produced by combining historic data ofvarying spatial and temporal resolutions (Abatzoglou, 2013). Futurescenarios used three global climate models (GCMs) from theClimate Model Intercomparison Project five (CMIP5) (Taylor,Stouffer, & Meehl, 2012), and were downscaled with the multi-variate adapted constructed analogs (MACA) method (Abatzoglou& Brown, 2012). They were chosen to provide a low, medium,and high range of potential climate paths for our study area (Fig. 2).With no readily available future erosivity data, spatially explicitestimates were derived from climate model precipitation estimates(Nearing, 2001). The annual erosivity estimate is a function of amodified Fournier coefficient. An index was calculated usingaverage monthly and average annual precipitation (Renard &Freimund, 1994). The future period was set at 2036e2065 in or-der to center the scenario at 2050. InVEST modeling took place atboth historical and future periods with each model in order tocalculate a percent change in ES provision.

InVEST freshwater models

Integrated Valuation of Environmental Service and Tradeoffs(InVEST) is an ES modeling toolset implemented in a GIS

environment developed by the Natural Capital Project (Kareiva,Tallis, Ricketts, Daily, & Polasky, 2011; Tallis et al., 2013). We pro-vide short descriptions of the three freshwater models used. Wechose the freshwater set for their relevancy to the current conser-vation issues facing the study area and to keep the scope of theassessment manageable. Although valuation functions are avail-able, we chose to keep the assessment in biophysical productionunits. We felt they either did not apply (i.e. value of hydropower) orrequired accurate treatment cost data that are not currently avail-able to the researchers.

InVEST water yieldInVESTwater yield estimates an annual averagewater yield over

a long term (>10 years) based on equations developed by Zhang,Dawes, and Walker (2001).

Yxj ¼�1� AETxj

Px

�(1)

AETxjPx

¼ 1þ uxRxj1þ uxRxj þ 1

Rxj

(2)

ux ¼ ZAWCxPx

(3)

Table 1Data requirements and sources for the InVEST freshwater models. A e Water yield, B e Water purification, C e Sediment retention.

Data type InVESTmodel

Source Description

Annual averageprecipitation

A University of Idaho Gridded SurfaceMeteorological Data (METDATA)

Thirty years of daily downscaled data summed to the annual scaleand then averaged over the time periods 1981e2010 and 2036e2065.Resolution is 0.041667 decimal degrees (4 km � 4 km).

Potential or referenceevapotranspiration

A University of Idaho METDATA See above. Daily data for minimum/maximum temperature and solarradiation were averaged to the monthly scalefor calculation of reference evapotranspirationusing the Hargreaves method (Beguería &Vicente-Serrano, 2013; R Core Team, 2013).

Soil depth A USDA Natural Resource Conservation Service(NRCS) State Soil Geographic Database(STATSGO)

Maximum soil depth was set to 7000 mm. Polygons converted to500 m � 500 m raster dataset.

Plant availablewater content

A USDA NRCS STATSGO The fraction of water in soil that is available to plants. Polygonsconverted to 500 m � 500 m raster dataset.

Land use/land cover A, B, C USGS National Land Cover Database (NLCD)2006

Standard national land cover product for the contiguous United States.Study areas contain 15 land cover categories.

Watershedpolygons

A, B, C Derived from DEM Calibration polygons delineated using Arc Hydro (ESRI, 2012) fromNational Elevation Dataset (NED) 30 m DEM. Scenario and calibrationfor sediment retention polygons delineated in Soil and Water AssessmentTool (SWAT) (Arnold et al., 2012)

Water yield B InVEST Water Yield Model Non-aggregated raster of water yield (mm).Digital elevation

model (DEM)B, C NED 2004 DEM Hydrologically conditioned using National Hydrography Dataset (NHD)

Plus version 1 stream layer. 30 m resolution.Rainfall erosivity C USDA Isoerodent Map (Renard, Foster, Weesies,

McCool, & Yoder, 1997) of the US (digitized byNatural Capital)

Erosion potential due to kinetic energy of rainfall. (megajoules mm/hahour year)

Soil erodibility C USDA NRCS STATSGO K-factor is soil's susceptibility to detachment and transport by rainfall.(metric tons ha hour/ha megajoules mm)

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416 405

Rxj ¼kxj$ETox

P(4)

x

where Yxj is water yield at pixel x of LULC j, AETxj is actualevapotranspiration at pixel x of LULC j, Px is annual averageprecipitation at pixel x, ux is a ratio of a soil's plant availablewater capacity to Px, Rxj is the Budyko dryness index (Budyko,1974) at pixel x of LULC j, Z is the Zhang coefficient, an integerfrom 1 to 10 that summarizes the area's annual precipitationdistribution, AWCx is volume of water held in soil available toplants at pixel x, kxj is evapotranspiration coefficient at pixel x ofLULC (vegetation) j, and ETox is reference evapotranspiration atpixel x. Soil and/or root depth of vegetation also help determineAWCx. The model assumes no change in groundwater storage overthe long term.

InVEST water purificationThe InVEST water purification model produces an annual export

(kg ha�1 yr�1) by streamflow estimate for total phosphorus (TP) etotal nitrogen (TN) can also be estimated e using the land covertype export coefficient method (Reckhow, Beaulac, & Simpson,1980). The model adjusts the export values based on the hydro-logic sensitivity score, the relative dryness or wetness of a pixelcompared to the watershed's average water yield.

ALVx ¼ HSSx$polx (5)

HSSx ¼ lx

lW(6)

lx ¼ log

XU

YU

!(7)

where ALVx is the adjusted loading value at pixel x, HSSx is thehydrologic sensitivity score at pixel x, polx is the export coefficient

at pixel x, lx is runoff index at pixel x, lW is the average runoff indexin thewatershed of interest, and

PUYU is thewater yield above and

including pixel x. It then employs a percent retention parameter fora land cover type to calculate the ES of nutrient retention(kg ha�1 yr�1). It then tracks the nutrient load as it moves down-slope while accounting for each subsequent pixel's loading andretention until it reaches a streamwhere both are aggregated to theoutlet (Tallis et al., 2013). A stream is set at the user-defined flowthreshold, which we set to match closely to the USGS NationalHydrography Dataset stream layer (USGS, 2013a). InVEST does notaccount for uptake limits or in-stream processes that can affectnutrient loadings.

InVEST sediment retentionInVEST sediment retention, like water purification, estimates

streamflow export (metric ton ha�1 yr�1) and the ES of sedimentretention (metric ton ha�1 yr�1). It scales up the well-known field-level developed Universal Soil Loss Equation (USLE) (Wischmeier &Smith, 1978). Users can adjust the USLE terms C (Cover) and P(Practice) factors that account for vegetation (land cover) type andmanagement practices' effects on soil mobilization. InVEST appliesthe equation to eachmap pixel. As with water purification, it adds aretention rate (Tallis et al., 2013). It is limited in the same way thewater purification model is with no uptake limit or in-streamprocesses.

InVEST calibration

InVEST models were calibrated to empirically derived estimatesof water yield along with nutrient and sediment loads. Calibrationswere performed at nine locations at different time intervals (10, 15,30 years) depending on data availability within the full historicalstudy period of 1981e2010. Using the USGS estimator programLOADEST (Runkel, Crawford, & Cohn, 2004), streamflow and con-centration samples of total nitrogen, total phosphorus, and total

Fig. 2. Plots of mean differences for the whole study area from historical (1981e2010) to future (2036e2065) periods for daily maximum temperature averaged by month andaverage monthly precipitation for the climate datasets in the scenario analysis for assessing freshwater ecosystem services in the Tualatin and Yamhill basins. The downscaled GFDL-ESM2M, MIROC5, and HadGEM2-ES general climate models represent the low, medium, and high climate change scenarios, respectively.

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416406

suspended sediments were used to estimate annual average ex-ports. Samples were taken at gauges on a weekly schedule withsome gaps. With available data, we subtracted wastewater treat-ment plant contribution estimates from the final load estimate fornutrients in order to focus on landscape effects since InVESTcannotaccount for point sources.

Sensitivity analysis

We tested the sensitivity of InVEST freshwater tools using theTualatin River West Linn gauge near its mouth. There is no rec-ommended parameter range associated with InVEST tools, so theprocedure used the calibrated values (see Supplementalinformation) as a starting point. A single parameter type under-went a series of positive and negative adjustments. In some cases, aland cover's parameter reached the maximum or minimumallowable value before the full adjustment. For proportional pa-rameters, we used 0.001 as the minimum, and 1.0 as the maximum.Effective retention in both water purification and sediment reten-tion were set from 0 to 100%. Root depth was set to a minimum of10 cm to the dataset maximum of 7000 cm. A minimum annualexport coefficient was set at 1 kg per hectare and nomaximumwasset.

Mapping and statistical analysis

For the scenario analysis we summarize results per sub-watershed normalized by area. It is important to understand ifstream exports and retention of nutrient and sediments vary acrossthe landscape in a similar way. We use Spearman's rho to assess therelationships among each scenario output. For sediment results, wealso tested for spatial autocorrelation with Moran's I. Rook's con-tinuity was used for creating spatial weight metrics. We summarizeall results by presenting them as an ES bundle in a single map. First,the sub-watershed estimations are normalized from 0.1 to 0.9. Aweighted average is then taken where water yield is given fortypercent of the weight and TN retention, TP retention, and sedimentretention each represent twenty percent, based on initial stake-holder suggestions.

Results

Calibration

InVEST freshwater models produced estimates comparable tothe empirical observations. For water yield, these ranged fromapproximately ±10% for water yield, 8% to 18% for nutrient exports,

Table 3Average sub-watershed percent change for each scenario's InVEST freshwater modeloutput for the whole study area

Scenario Wateryield

TPexport

TPretention

Sedimentexport

Sedimentretention

Medium historic climateLow urban growth 0% 1% 0% 0% 0%High urban growth 0% 3% 1% 2% 0%High urban growthwith riparian buffers

0% �7% 5% �17% 0%

Low future climateHistoric urban growth 8% 0% 0% 12% 12%Low urban growth 8% 1% 0% 12% 12%High urban growth 8% 3% 1% 14% 13%High urban growthwith riparian buffers

8% �7% 5% �7% 13%

Medium future climateHistoric urban growth 15% 0% 0% 40% 41%Low urban growth 15% 1% 0% 41% 41%High urban growth 15% 3% 1% 44% 41%High urban growthwith riparian buffers

15% �7% 5% 17% 41%

High future climateHistoric urban growth �6% 0% 0% 18% 18%Low urban growth �6% 1% 0% 18% 18%High urban growth �6% 3% 1% 21% 18%High urban growthwith riparian buffers

�6% �7% 6% �1% 18%

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416 407

and 1% to 6% for sediments exports. The LOADEST results used forcalibration suggest that both flow and time have a relationship withload. They additionally revealed spatial variation in the distributionof nutrient and sediment sources. The Yamhill basin showed higherloading estimates than the Tualatin for both nutrients and sedi-ments. Accordingly, we assigned higher export coefficients toagricultural land in the Yamhill basin. For the water purificationmodel, our results show the hydrologic sensitivity score does notlead to a response from the export estimate in our study area(Table 2).

Scenario analysis

The changes in InVEST outputs for each scenario are summa-rized for the study area in Table 3. The results concisely showwhether climate or urbanization is the main change driver in eachfreshwater model.

Water yieldInVEST water yield model suggests that long term changes in

water supply are more sensitive to climate change than themodeled increase in urban land cover or the simulated increase inriparian vegetation. The land cover change effect relative to thewhole study area is small (from 2.5% to 7% depending on the sce-nario). This leads to stable sub-watershed patterns of water yieldamong the three climate change scenarios (Fig. 3). For the low andmedium climate scenarios, wetter climates are projected in thefuture. The projected increases in precipitation in these scenariosmore than compensate for increased temperature, resulting in anincrease in annual water yield. The high climate scenarios showmore seasonal variability in precipitation with increases duringearly winter (November and December) offset by losses duringmid-late winter (January and February) (Fig. 2). Couple this tohigher mean annual temperature leads to an increase in evapo-transpiration and thus a small decrease in annual water yield. Thespatial pattern suggests sub-watersheds lower in the basins willexperience larger positive (under the medium climate scenario) ornegative (under the high climate scenario) changes in water yield.The maps reveal subtle differences in yield change for some low-land sub-watersheds among the scenarios. These changes areattributed to either the new urbanization or riparian bufferinstallation effect on evapotranspiration.

Water purificationHigher TP exports occur only in the urbanizing watersheds of

the Tualatin basin and in some scenarios, the interstitial ChehalemCreek basin as well. The scenarios support the evidence from

Table 2Output of InVEST water purification for total phosphorus at two locations on theTualatin River at two different time periods. Parameter values are the same in allcases with only the average water yield differing. The drier 2000s period is overpredicted in both cases compared to the wetter 1990s, the stream loadings of whichare more in line with the annual average for the entire study period (1981e2010)used in the analysis.

Location:gauge andstation

Timeperiod

InVEST wateryield estimate(empiricalobservation)

Empiricalestimateof ave. ann.stream load(kg/yr.)

InVESTave. ann.streamload(kg/yr.)

Percentdifference

14207500WestLinn

1991e2000 874 (838) 173,541 166,958 �3.79%2001e2010 707 (659) 98,719 166,919 69.08%

14203500Dilley

1991e2000 1103 (1172) 31,804 33,154 4.24%2001e2010 869 (967) 20,517 33,240 62.01%

calibration that TP exports are land cover driven. Maps of thepercent differences from baseline to future scenarios reveal thepatterns of change (Fig. 4). There are clear differences in relativechanges in the Yamhill and the Tualatin basins. Yamhill exportsmuch more TP than the Tualatin. Most of this is attributed toagriculture thus a change to urban land cover types has a reductioneffect. Riparian buffers decrease stream exports or mitigate theeffects of increased urbanization.

Nutrient retention results show a similar pattern to nutrientexports (Fig. 5). Correlations between retention and export supportthis especially for TP (r¼ 0.92� 0.94). Nutrient retention estimatesare tied to the percent effective retention parameter in InVEST. Forinstance, decreases in TP exports and retention estimates areobserved in the Yamhill basin the sub-watershed where we expectthe most urbanization. There is no uptake limit so where there ismore upslope nutrient mobilization, there is also more retention inthe downslope pixels and vice versa. Widespread installation ofriparian buffer strips has the potential to increase retention ofnutrients in the agriculture dominated sub-watersheds. This isbased on the assumption that it can retain a high percentage ofnutrients that were exported upslope of them. Their placementnear streams results in highest effectiveness since all mobilizednutrients must pass through a riparian pixel prior to export to astream.

Sediment retentionSediment export maps suggest that there are influences from

both land cover and climate (Fig. 6). Spatial autocorrelation variesgreatly for projected differences among the scenarios(I ¼ 0.07 � 0.83, p < 0.05). High urbanization with riparian buffers(managed high in the map) produces the same values (I ¼ 0.34)regardless of climate scenario. Climate scenarios without new ur-ban land cover show variable influence depending on the climatescenario (low: I ¼ 0.46, medium: I ¼ 0.67, high: I ¼ 0.83). Theseexamples show how the interplay between the two variables canlead to either localized ormore global effects. Projected increases insoil mobilization are largest in the medium climate scenarios. TheMiroc5 climate model displays as much as a 60 mm average

Fig. 3. Scenario maps depicting percent change in water yield estimates modeled by InVEST's water yield model from a historic baseline (1981e2012) to potential future(2036e2065) conditions. The left column depicts the baseline in absolute terms the current land cover and modeled historic thirty year average climate. The middle two columnsshow change under the two urbanization scenarios and modeled future climates. The right column displays a “managed high” scenario where the 30 m riparian buffer strip is alsopresent along with high urbanization and modeled future climates.

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416408

increase in monthly winter precipitation from the historic to futuretime period (Fig. 2), leading to the highest projected erosivity rate.The same explanation can be applied to the other climate scenariosbut to a lesser extent as there is less increase or even decreases inprecipitation in some months. As with the nutrient modeling, ri-parian buffer strips reduce sediment exports. For the mediumclimate scenario, soil loss potential through mobilization is greatenough that the buffers can only mitigate the effects of a wetterwinter in the future.

Sediment retention shows a different pattern than exports(Fig. 7, upper left corner). This is supported by a weak negativecorrelation between retention and exports (r ¼ �0.50 � �0.41,p < 0.001). Spatial autocorrelation in projected differences aremoreclearly driven by the future climate scenario (low and medium:I¼ 0.54� 0.66, high: I¼ 0.79� 0.82, p < 0.001, polygon contiguity).Those using historic climate show weaker autocorrelation(I ¼ 0.13 � 0.22, p < 0.001). The maps for export and retentionsuggest a broad trend of increases for both except for exports whenriparian buffers are simulated. The autocorrelation evidence sug-gests that underlying this is a small but significant spatial diver-gence based on climate, which can be related to the spatial patternsof water yield maps under different scenarios (Fig. 3). So as seen inthe export results, the temporal distribution of rainfall under eachclimate scenario affects erosive potential, and the spatial

distribution of erosion potential influences where those changes inretention are slightly more pronounced.

Ecosystem bundlingWhen the sub-watershed estimates are normalized and

bundled, there are small changes in the ranking of each per sce-nario (Fig. 8). The Yamhill basin contains the majority of sub-watersheds providing the highest levels of bundled services un-der the weights used. Slight shifts do occur where a few of thewatersheds in Yamhill increase in bundled ES with installation ofriparian buffers. The upland portions of the study area have ahigher bundled value particularly the two sub-watersheds in thesouthwest of the Yamhill basin due to high water yield estimatesthat are also weighted at double the other freshwater ES.

InVEST sensitivity

All available parameters in InVEST display some degree ofsensitivity. For water yield, most parameters are set to relativelyhigh values. This is supported by the sensitivity result showingfurther reduction in all three parameters e the evapotranspirationcoefficient, root depth, and the Zhang seasonality coefficient e allincrease water yield (Table 4). In a system where peak rainfall andpeak potential evapotranspiration are largely out of phase like in

Fig. 4. Scenario maps depicting change in total phosphorus (TP) export from runoff estimates modeled by InVEST's water purification model from a historic baseline (1981e2010) topotential future (2035e2065) conditions. The left column depicts the baseline in absolute terms the current land cover and modeled historic thirty year average climate. The middletwo columns show change under the two urbanization scenarios and modeled future average climate. The right column displays a “managed high” scenario where the 30 m riparianbuffer strip is also present along with high urbanization and modeled future average climate.

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416 409

our study area, it suggests water storage in soil and vegetationwitha relatively high transpiration rates help explain long term annualaverage water yield.

The water purification model sensitivity tests support theobserved scenario map patterns. As exports coefficients arereduced both estimated exports by streamflow and retention arereduced and vice versa (Table 5). An export coefficient needs to beincreased a substantial amount in order to see a much smallerresponse in export estimates. The effective retention percentagehas a symmetrical effect on exports and retention rates. Manipu-lating the flow threshold effectively works the same way as theeffective retention parameter. This is inadvisable as it should reflectthe stream network in the study area.

The sediment retention estimates are more sensitive than theexport estimates (Table 6). C and P factor show a great degree ofsensitivity. Their behavior depends on the starting calibrationparameter value. C factor displays the largest fluctuationbecause of this. The effective retention parameter exhibitssimilar behavior to the water purification model. Its relativelysmall effect on retention rates suggests that it is in fact lessimportant than the parameters that are terms in USLE. Thelength slope factor determines when one of two equations inthe model is used for determining slope factor in USLE (Talliset al, 2013). Tests suggest that it is a major determinant inretention estimates.

Discussion

Mapping freshwater ES with InVEST

Our scenario maps offer a potential tool for determining whatareas in the study area are most sensitive to potential LULC andclimate change. It is clear climate drives the annual average supplyof water, a result echoing those of studies using process-basedhydrologic models in our study area and elsewhere (Castillo,Güneralp, & Güneralp, 2014; Franzcyk & Chang, 2009; Praskievicz& Chang, 2011; Tong, Sun, Ranatunga, He, & Yang, 2012). Nutrientexport and water purification service changes are driven by LULCchange (Chang, 2004). Another InVEST freshwater study did displaysome small adjustments to nutrient exports and retention for dryand wet years (Terrado et al., 2014). We hypothesize that differ-ences among the characteristics of each climate scenario were notsufficient to alter the hydrologic sensitivity score for each scenariorun of the water purification tool. New urban land cover is typicallywithin sub-watersheds that are already urbanized to a large extent.This explains the little to no locational shift in the sub-watershedsproviding the most freshwater ES embodied by the stabilityobserved in the normalized and bundled scenario maps (Fig. 8). Thechange maps also suggest a relatively stable landscape in terms ofES provision with deviations typically less than a third of currentestimates. An exception occurs at the mouth of the Tualatin basin.

Fig. 5. Scenario maps depicting change in total phosphorus (TP) retention by land cover estimates modeled by InVEST's water purification model from a historic baseline(1981e2010) to potential future (2035e2065) conditions. The left column depicts the baseline in absolute terms the current land cover and modeled historic thirty year averageclimate. The middle two columns show change under the two urbanization scenarios and modeled future average climate. The right column displays a “managed high” scenariowhere the 30 m riparian buffer strip is also present along with high urbanization and modeled future average climate.

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416410

However, when projected rainfall substantially increased over asingle season like in the Miroc5 medium scenario, large changes dooccur for sediment export estimates where certain sub-watershedsare projected to see a doubling or more.

In InVEST, the actual ecosystem service result of nutrient orsediment retention also appears to be highly contingent on howmuch exports are coming from upslope. For our study area, thisrelationship between export and retention estimates leads to theresult where sub-watersheds with the highest exports also containthe highest amounts of ecosystem services most of which arelocated in the Yamhill basin (Fig. 8). InVEST characterizes theseservices as retention of previously mobilized nutrients and sedi-ments. It does not explicitly consider a land cover type's ability tohold them in situ as a service. It is therefore recommended to useboth export and retention maps in tandem since they representtotal nutrient/sediment production when interpreting results. Thissuggestion is also furnished by Terrado et al. (2014).

InVEST utility and implications for land management

The spatially-explicit outputs produced by InVEST offer manyuseful applications. The modeling framework allows for a level ofvalidation in terms of ES mapping. More akin to calibration unlikeprocess-based hydrologic modeling, the values created in theInVEST modeling effort are adjusted to reflect field observations.

This is a critique in the majority of ES mapping projects that lackfield observations (Martínez-Harms & Balvanera, 2012; Sch€agneret al., 2013; Seppelt, Dormann, Eppink, Lautenbach, & Schmidt,2011). Although fine scale location of management priorities isoutside InVEST's scope, overall patterns of ES on the landscape canbe revealed, and thus aid in allocation of conservation resources.Using the bundle maps as evidence, the Yamhill basin provides agreater level of freshwater ES than the Tualatin (Fig. 8). This wouldincrease with riparian buffer installation. Therefore, InVEST canserve as a scoping tool and first assessment of an area's ES profile.Sub-watersheds can be prioritized for further study or restoration.In this case, we suggest more examination of the potential forrestoration in the Yamhill basin as it may have more impact onWillamette River downstream water quality issues. The largeexport increase projected at the Tualatin's mouth also warrantsfurther study considering its potential for increased urban landcover (Hoyer & Chang, 2014).

InVEST is a landscape planning tool and decision support sys-tem. It allows for the assessment of trade-offs among ES, and offersmethods to capture a portion of their monetary value. From thisperspective, it can offermuch to those facedwith land use decisions(Goldstein et al., 2012). Our findings suggest that riparian vegeta-tion improves water quality or mitigates potential declines causedby increased urbanization. Although not necessarily surprising, wenow have a spatially explicit estimate of the areal increase needed

Fig. 6. Scenario maps depicting percent change in sediment export from runoff estimates modeled by InVEST's sediment retention model from a historic baseline (1981e2010) topotential future (2036e2065) conditions. The left column depicts the baseline in absolute terms the current land cover and modeled historic thirty year average climate. The middletwo columns show change under the two urbanization scenarios and modeled future average climate. The right column displays a “managed high” scenario where the 30 m riparianbuffer strip is also present along with high urbanization and modeled future average climate.

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416 411

to achieve the corresponding social benefit of improved or main-tained water quality. Future research can proceed with spatiallyexplicit estimation of the value of the ES benefits versus the costsassociated with giving up that land's agricultural and residentialvalue. Johnson, Polasky, Nelson, and Pennington (2012) showedthat the benefits from increases in riparian vegetation in the Min-nesota River basin outweighed the losses incurred to returns fromagricultural land. They highlighted that uncertainties in the value ofES and of commodity prices can lead to a change in the balanceamong tradeoffs. This is an analytical nuancewhere InVEST's abilityto produce spatially explicit provision of ES estimates is extremelyuseful. Economic returns from land are also spatially variable. Thiswill allow for a spatial targeting of where investment in enhancingES provision is most cost effective. In our case, the result of theindividual ES maps along with all three bundled suggest targetingsub-watersheds in the Yamhill basin for riparian restoration and ESenhancement would lead to the most gains in water quality inrelation to the entire Willamette Basin. Will targeting these areasalso be the most cost effective areas as well? An assessment toollike InVEST can potentially help address the problem of deter-mining if the conditions of a PES scheme are being met, and tran-sition from being input-based to outcome-based, a commoncharacteristic of current schemes focused on freshwater ES(Martin-Ortega, Ojea, & Roux, 2013). It can also aid in locatingwhere benefits to sellers most outweigh opportunity costs.

Potential sources of uncertainty in ES mapping and modeling

Spatially explicit ES assessments contain uncertainty. The factthat ES are a product of a complex system evaluated with imperfectdata and imperfect tools must be acknowledged and clearlycommunicated by the producer of an assessment to the user of theinformation. Hou, Burkhard, and Müller (2013) point to originalinput data as the major source of uncertainty in ES assessments.Additionally, error is present in the assessmentmodel because of itsexplicit assumptions and incomplete knowledge of the system itsimulates. In light of the concerns associated with ES assessment,we discuss the three potential sources of ES assessment error e

input data used, model parameters, and model structure.InVEST requires two main input data e climate and land cover,

which are subject to error. First, land use and land cover (LULC)data are not perfectly accurate. Classification of landscapes con-stitutes a major source of uncertainty in any ecological assess-ment (Hou et al., 2013). While the general accuracy of NLCD ishigh, it contains error especially in grass dominated categories,and accuracy assessment procedures are still developing(Wickham et al., 2013). Second, the calibration climate datasetexhibits high correlation with field observations and minimalbias, but local scale effects still lead to error in certain climaticvariables (Abatzoglou, 2013), which could affect yield estimatesfor some sub-watersheds. While InVEST water models have only

Fig. 7. Scenario maps depicting percent change in sediment retention by land cover estimates modeled by InVEST's sediment retention model from a historic baseline (1981e2010)to potential future (2036e2065) conditions. The left column depicts the baseline in absolute terms the current land cover and modeled historic thirty year average climate. Themiddle two columns show change under the two urbanization scenarios and modeled future average climate. The right column displays a “managed high” scenario where the 30 mriparian buffer strip is also present along with high urbanization and modeled future average climate.

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a few parameters, as shown in sensitivity analysis, the modeloutputs are sensitive to all of them. Considering the lack ofknowledge or a support system to access parameter values,parameterization is necessarily user-defined and a potentialsource of error (Bagstad, Semmens, Waage, & Winthrop, 2013).For example, in the sediment retention model, the erosivityvariable does allow for climatic influence, which in our analysisled to increases in exports and retention. The sensitivity analysisreveals very large responses in the retention estimate to smallchanges in several of the parameters, making its final estimateuncertain. This illustrates the problem of transferring the USLEmethod at the landscape scale. Currently, there is no InVESToutput such as an uncertainty bound for estimates, which wouldbe a useful feature in future versions. A quantitative evaluation ofuncertainty would aid in a check of the reliability of the results topotential users of the assessment. One study developed a quan-titative sensitivity procedure for the water yield model. Itsapproach shows that the yield estimate's precision will be highlycontingent on the accuracy of the precipitation and potentialevapotranspiration data (S�anchez-Canales et al., 2012). Futureclimate uncertainty is one reason for our using several scenarios.There is yet to be published a procedure for assessing InVESToutput uncertainty caused by the user-defined parameters.

An ES assessment with InVEST is geared toward long termaverage conditions. The simplicity of its structure makes inter-

annual variability difficult to model, and is not equipped to pro-vide estimates at the seasonal, monthly, or daily time-step. Moretime scale sensitive ES like flood regulation are not addressed. Lowflow water quality is already an issue in our study area (Boeder &Chang, 2008; Chang & Lawler, 2011; Kelly, Lynch, & Rounds,1999), and InVEST cannot provide a seasonal or monthly distribu-tion of nutrient loads. This leads to a potential scale mismatchbetween management decisions and InVEST outputs. A moretemporally disaggregated modeling framework would necessitateincorporating the effects of the amount of runoff on water quality.In our case, results suggest a lack of sensitivity to climate input inthe water purification tool (Table 2), and portions of the study areawith no land cover changes shows little to no change in exports orloads (Figs. 4e7), so LULC being the major determinant of loadingsis a critical assumption. Although the coefficient modelingapproach proved valid previously, it required a high level of datacollection in specific basins for several LULC sub-categories (Johnes,1996). Doing similar work for an InVEST analysis would be a time-consuming process. So on one hand, the modeling framework itselfhas simplifications that make it difficult to claim the simulation ofimportant natural processes within the model captures the fullcomplexity inherent in the system. On the other hand, these sim-plifications make it easier to approach, and can be viewed as astrength when data availability is limited (Vigorstol & Aukema,2011).

Fig. 8. Scenario maps depicting bundled freshwater ecosystem services produced by InVEST's freshwater models from a historic baseline (1981e2010) to potential future(2036e2065) conditions representing combinations of varying climate change and low and high urbanization as well as high urbanization with a 30 m riparian buffer in largeportions of the study area. Each estimate is normalized from 0.1 to 0.9, and then a weighted average is taken. The bundled services include water yield (40%), phosphorus retention(20%), nitrogen retention (20%), and sediment retention (20%).

Table 4Sensitivity analysis of parameters in the InVEST water yield model. Calibrated modelfor the Tualatin River at the mouth was the test basin. Parameters were adjusted toamount shown until the minimum or maximum was reached.

Parameter Adjustment Response (mm)

Evapotranspiration coefficient (etk)Min. 0.001eMax. 1.0

Down 0.2 95.8Down 0.1 47.4Down 0.05 23.6Up 0.1 �45Up 0.05 �22.7

Root Depth in mmMin. 10eMax. 7000

Down 2000 32Down 1000 15.8Down 500 0Up 2000 �0.8Up 1000 �0.8Up 500 �0.8

Zhang coefficient (set at10 in calibration)

Min. 1eMax. 10

9 3.25 22.53 40.5

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Conclusions

Using the modeling toolset InVEST offers several insights intothe possible response of water-related ecosystem services in ur-banizing basins. Our scenario analysis is based on a few simpleassumptions, but it incorporates both land cover and climatechange into the assessment albeit as independent variables ratherthan being dynamically interrelated. Using both of these variablesis not common in the ES mapping literature. We were able to mapthe freshwater ES in the study area at the landscape scale and gaininsights into ES assessment research, and could inform land man-agement decisions in the study area.

Water yields are projected to modestly increase in the low andmedium climate scenarios and slightly in the high climate scenario.This is driven mainly by climate change with urbanization and ri-parian buffer installation playing a small role. Nutrient exports andretention respond almost exclusively to land cover with highsensitivity to the export coefficients explaining changes in thescenarios. Loss of natural vegetation increases exports moderatelyand reduces retention. Response to loss of agricultural land variesby location. Sediment exports and retention are influenced by landcover and climate. The projected increase in winter rainfall leads tohigher erosivity rates and is the main driver in a near across theboard increase for both. The simulated management strategy ofriparian buffer construction reduced exports and increased reten-tion rates in sub-watersheds where they are placed.

The spatial distribution of freshwater ES remains relativelystable at the sub-watershed scale. The lowland areas are projectedto have more change in water yield than the upland portions of thestudy area. Nutrient exports are projected to increase in response toincreased urban development in the Tualatin Basin. This is reversedin the Yamhill since urban land cover replaces agricultural landswith much higher nutrient exports. When the estimates are

Table 5Sensitivity analysis of parameters in the InVEST water purification model. Calibratedmodel for the Tualatin River at the mouth was the test basin. Parameters wereadjusted to amount shown until the minimum or maximum was reached.

Parameter Adjustment Exportresponse(kg/ha)

Retentionresponse(kg/ha)

Land cover exportcoefficient (kg/ha)

Min. 0eMax. None

Down 5000 �0.77 �1.65Down 2000 �0.52 �1.26Down 1000 �0.28 �0.71Up 5000 1.43 3.56Up 2000 0.57 1.43Up 1000 0.29 0.71

Effective retention (%)Min. 0eMax. 100

Down 25 1.45 �1.45Down 10 0.67 �0.67Down 5 0.50 �0.50Up 25 �0.53 0.54Up 10 �0.35 0.35Up 5 �0.23 0.23

Flow threshold (calibratedmodel set at 1300)

Min. 1eMax. None

Down 800 0.28 �0.28Up 800 �0.13 0.13

R. Hoyer, H. Chang / Applied Geography 53 (2014) 402e416414

bundled into a weighted average, there is very little change inspatial pattern of freshwater ES amongst the scenarios. It doesreveal the Yamhill basin providing more ES overall than the Tua-latin basin.

InVEST is useful for ES assessments but with some limitations.The ecosystem service retention estimates go up with the increasein exports but do not account for the limits on the system's uptakepotential. It contains a small number of parameters all of whichdisplay sensitivity, and have a large impact on the final ES estimate.With no recommended values, calibration can prove challengingand final estimates of ES contain uncertainty. However, it allowsresearchers to calibrate to data from the local area, a step uncom-mon in ES mapping projects. We argue this offers some credibility

Table 6Sensitivity analysis of parameters in the InVEST sediment retention model. Cali-bratedmodel for the Tualatin River at themouthwas the test basin. Parameters wereadjusted to amount shown until the minimum or maximum was reached.

Parameter Adjustment Exportresponse(MT/ha)

Retentionresponse(MT/ha)

Universal soil lossequation (USLE)

Crop (C) factor andmanagement (P) factor

Min. 0.001eMax. 1

C and P down 0.01 �0.086 4.29C and P up 0.01 0.122 �8.80C and P up 0.05 0.839 �42.33C down 0.01 �0.047 52.39C down 0.02 �0.053 53.62C up 0.01 0.038 38.29C up 0.02 0.163 28.88C up 0.05 0.402 �0.384P down 0.01 �0.038 �0.238P down 0.02 �0.073 �0.458P up 0.01 0.037 0.240P up 0.02 0.075 0.480P up 0.05 0.187 1.198

Effective retention (%)Min. 0eMax. 100

Down 25 0.317 �0.324Down 10 0.080 �0.081Down 5 0.036 �0.037Up 25 �0.061 0.063Up 10 �0.035 0.036Up 5 �0.021 0.021

Length slope factor(calibrated model setat default of 75)

Min. 1eMax. 100

Down 20 �0.012 �45.99Up 20 0.018 55.46

Flow threshold(calibrated modelset at 1300)

Min. 1eMax. None

Down 800 0.056 �12.76Up 800 �0.025 7.79

to the outputs especially in terms of the relative change exhibited infreshwater ES. Also being spatially explicit, InVEST can help man-agers gain a landscape scale picture of where in a management areaprovides the most ecosystem service benefit.

Our analysis suggests a further study of the less managedYamhill basin since targeting it for ES enhancement could poten-tially lead to the greatest gains in downstream water quality. Thisfinding is a starting point toward elucidating the tradeoffs amongregulating and provisioning ES in watersheds that have mixed landuses. Even with some assumptions, this analysis still can providedecision-relevant information and assist managers in understand-ing the potential patterns of freshwater ES of urbanizing basinsunder the dual pressures of climate change and land development.

Acknowledgments

This research was supported by a US National Science Founda-tion Grant #1226629 and by the Institute for Sustainable Solutionsat Portland State University. We appreciate Natural Capital Projectteam members for providing some biophysical data and technicalsupport for InVEST modeling. We would like to thank Clean WaterService Staff members for providing water quality data and stake-holder workshop participants for their invaluable comment. Ananonymous reviewer provided comments that greatly improvedthe paper. Views expressed are our own and do not necessarilyreflect those of sponsoring agencies.

Appendix A. Supplemental information

Supplemental information related to this article can be foundonline at http://dx.doi.org/10.1016/j.apgeog.2014.06.023.

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