Modeling denitrication in a changing climateJanet R. Barclaya,b,, M. Todd Waltera,aDepartment of Biological and Environmental Engineering, Riley Robb Hall, Cornell University, Ithaca, NY 14853, United StatesbDepartment of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, United Statesarti cle i nfoArticle history:Received 3 September 2014Received in revised form 18 December 2014Accepted 19 December 2014Available online 14 January 2015Keywords:DenitricationClimate changeModelingEcosystem servicesNitrogenabstractAlthoughuncertaintyexistsinthespecicsofclimatechange, sufcientconsensushasemergedregardingthescaleanddirectionofchangesthatitisimportantformanagerstoconsidertheimplicationsof thesechangesforessential ecosystemservicessuchasdenitrication. ForthenortheasternUnitedStates, itisexpectedthatbytheendofthe21st century, annual precipitation will increase by 10% and annual temperatures by 3 C.Because denitrication rates are highly inuenced by soil moisture and soil temperature,we expect the projected changes in temperature and precipitation to alter the rates andpatternsof denitrication. WedevelopedthreefutureweatherscenariosbasedontheB1, A2, and A1F1 emission scenarios and utilized them as inputs to a coupled hydrolog-ic-denitrication model to analyze the effects of changing temperature and soil moistureon denitrication rates, assuming nitrate availability remains unchanged. Our results sug-gest denitrication rates will increase by 5.111.8 kg N ha1yr1across the watershed. Thegreatest projected increases are in the areas and seasons with the highest baseline rates,with smaller increases in those with lower baseline rates. We found that changing tem-perature is likely to be a much stronger driver of change to denitrication rates(7.7 kg N ha1yr1) than changing precipitation (1.4 kg N ha yr1). This study utilizes sce-nario modeling and a eld validated model to quantify the future benets of the ecosystemservicedenitrication, addressingalackofecosystemservicequanticationstudiesandproviding a model for quantication studies. 2015 Elsevier B.V. All rights reserved.1. IntroductionMuchuncertaintyexistsinseveral ofthespecicsofhowglobal climateischanging, howeversomeconsensushasemerged. Forthenortheastern USA, it isexpectedthat by theend ofthe21stcentury annual precipitation will increaseon the order of 10%, with larger increases in the winter and slight decreases in the summer months (Hayhoe et al., 2007).Annual temperatures for the same period are projected to increase approximately 35 C, with greater increases in the sum-mer months and lesser increases in the winter months (Hayhoe et al., 2007). Although the uncertainty in these estimations islarge, particularly with regard to the precipitation (Hayhoe et al., 2007), the implications of changes of this magnitude arelikely signicant and it is important that we consider the effects of projected changes in precipitation and temperature.One important biogeochemical ecosystem service that is likely to be affected is denitrication, the microbially mediatedreduction of nitrate (NO3) to dinitrogen (N2), nitric oxide (NO), or nitrous oxide (N2O) gas. Denitrication plays an importanthttp://dx.doi.org/10.1016/j.swaqe.2014.12.0062212-6139/ 2015 Elsevier B.V. All rights reserved.Tel.: +1 802 310 2685 (J.R. Barclay). Address: Department of Biological and Environmental Engineering, Riley Robb Hall, Cornell University, Ithaca, NY14853, United States. Tel./fax: +1 607 255 2488 (M.T. Walter).E-mail addresses: jrb474@cornell.edu (J.R. Barclay), mtw5@cornell.edu (M.T. Walter).Sustainability of Water Quality and Ecology 5 (2015) 6476ContentslistsavailableatScienceDirectSustainability of Water Quality and Ecologyj our nal homepage: www. el sevi er. com/ l ocat e/ swaqerole in reducing nitrogen (N) loads to streams, estuaries and coastal waters, and changes to denitrication rates, and there-fore stream N loads, will have environmental, economic and human health effects (EPA, 2011).Denitrication is one of many essential and benecial services provided to humans by our ecosystems (Daily et al., 1997;Sandhu and Wratten, 2013). As with other ecosystem services, it can be difcult to incorporate the benets of denitricationinto management decisions because those services provide natural, rather than nancial, capital (Nelson and Daily, 2010). Anecosystem services approach accounts for the services provided by an ecosystem, beginning with a quantication of thosebenets. Becauseweexpect thebenets of denitricationtochangewithchanges intemperatureandprecipitation,exploratory modeling to consider the ability of a landscape to provide denitrication services in a changing climate is essen-tial to sound management (Seppelt et al., 2011; Volk, 2013).Theimplicationsof changingclimatefor denitricationratesarecomplexduetothecomplexityof theprocessesinvolved. At least twomechanisms of ratechanges arepossible: changes inresidencetimeandchanges inreactionconditions.Some have suggested that climate change will reduce the residence time of soluble nitrate, thereby reducing the amountthat is denitried and increasing the nitrate load to streams (Boyacioglu et al., 2012; Howarth et al., 2006). With this changemechanism,nitrate-rich water ows more quickly through the landscape,reducing the time spent in areas conducive todenitrication and thereby reducing the amount of nitrate that is denitried. Others have noted the potential complicationsof disconnectingzonesof nitrateloadingfromzonesof denitrication(Butterbach-Bahl andDannenmann, 2011). Thischange mechanism would decrease the residence time to zero, reducing denitrication rates to near zero.In terms of reaction conditions, soil temperature and degree of saturation have been shown to be important controls ondenitrication rates and changes in these conditions are expected to have a direct effect on denitrication rates (Heinen,2006;HenaultandGermon, 2000;Singhetal., 2010). Theincreasedtemperaturesoftheclimateprojectionswouldbeexpected to have a rate-increasing effect on denitrication. Increased precipitation would be expected to increase the degreeof soil saturation, however, increasedevapotranspirationmightcounteractthiseffect(Huntington, 2006;Walteretal.,2004). Weacknowledgethat manyotherfactors, includingcarbonandnitrate availability, inuencedenitricationratesand may be affected by climate change (Barnard and Leadley, 2005; Brown et al., 2012; Butterbach-Bahl andDannenmann 2011); however, as an initial step and for the purpose of this study we are focusing on the direct effects ofchangesinsoilmoistureandtemperature. ThisisconsistentwithworkbyPinayetal. (2007)identifyingsoilmoistureand temperature as the most important factors in denitrication rates.The objective of this study is to explore how the benets provided by the ecosystem service of denitrication may beaffected by changes in temperature and precipitation. Specically, we seek to answer three questions: (1) How might themagnitude and temporal patterns of the ecosystem service of denitrication change?, (2) What are the relative strengthsof precipitation and temperature as drivers of these changes?, and (3) How might changes in the patterns of precipitationand temperature affect denitrication rates? Answers to these questions will allow the ecosystem service of denitricationto be factored into future management plans. Additionally, it will contribute to addressing the lack of ecosystem servicequantication studies and provide a model that subsequent quantication studies can follow.2. Methods2.1. Site descriptionTo address these questions, we applied a simple denitrication model to the southern watershed draining the CornellUniversity Teaching and Research Farm in Harford, NY, USA and surrounding hillslopes (Fig. 1A). This 1550 ha watershedconsists of highlands to the northeast and southwest and a central valley that drains into the East Branch of Owego Creek.The elevation ranges from 360 to 613 m. In the valley bottom, the primary soil type is Howard gravelly loam and on the adja-cent hillslopes the primary soil types are Langford channery silt loam and Volusia channery silt loam (USDA NRCS). The pri-mary land uses are forest and agriculture (Fig. 1B) and the primary N input to the soil is manure fertilizer. Anderson et al.(2014a) gives a detailed analysis of N inputs and exports for the farm. Seventy-eight percent of the farm is in the watershedof this study, accounting for fty-one percent of the total watershed area.2.2. Model overviewWe used the simple denitrication model coupled with a semi-distributed hydrologic model described in Barclay et al.(2015). Briey, this model contains a semi-distributed lumped hydrologic module based on a ThornthwaiteMather waterbalance (Thornthwaite and Mather, 1955) and a simple denitrication module of the type described by Heinen (2006). Thishydrologic model describes upland soil moisture well, which is essential for spatially distributed denitrication models dueto the importance of soil saturation to denitrication rates (Heinen, 2006; Tague, 2009; Whelan and Gandol, 2002). Weselected this denitrication model because of its simple form and easily measured inputs.The hydrologic model tracks daily soil water in 10 wetness classes from driest (wetness class 10) to wettest (wetnessclass 1). Soil water above the available water capacity is routed to groundwater, lateral ow (to the next wettest class) orto overland ow. The main inputs to the hydrologic model are daily precipitation and air temperature and the primary para-J.R. Barclay, M.T. Walter / Sustainability of Water Quality and Ecology 5 (2015) 6476 65meters concern the available water capacity and ow routing. We mapped the wetness classes onto the watershed using theSoil Topographic Index (STI) (Lyon et al., 2004), which had been binned into 10 quantiles (Fig. 1C). The highest STI valuescorrespond to wetness class 1 (the wettest class) and the lowest to wetness class 10. In wetness class 1, the shallow ground-waterinterceptsthesoilwater, creatinghighlevelsofsoilwaterthatmakesthisclassofparticularinterestforbiogeo-chemical processing. Thehydrologicmodel wascalibratedusingstreamdischargeandsoil moisturetomaximizetheoverall NashSutcliffe efciency (NSE) (Nash and Sutcliffe, 1970). The optimization was done using the DEoptim packagein R (Ardia et al., 2012) and resulted in an overall NSE of 0.72. Details of the hydrologic model are given in Barclay et al.(2015).The denitrication model is a reduction function in which a potential denitrication rate is modied by scalars related tocarbon and nitrate availability, soil moisture, and soil temperature. This model was parameterized on the study watershedusing in situ pushpull denitrication measurements (Addy et al., 2002; Anderson et al., 2014b) using the DEoptim packagein R (Ardia et al., 2012) and a leave-one-out cross-validation process (Arlot and Celisse, 2010). The calibrated model com-pared well with the observed values (R2= 0.77, RMSE = 272 kg N ha1yr1,NRMSE = 0.77) (Barclay et al., 2015).We overlaid the 10 wetness classes from the hydrologic model with the three primary land uses (wetlands, cropped agri-culture, and forests/pastures/meadows) to create 30 overall classes (Fig. 1D). The coupled model predicts daily denitrica-tion rates for each overall class, based on inputs of Julian Day, nitrate (NO3N), dissolved organic matter (DOC), soil organicmatter (OM), air temperature, and precipitation. OM is based on the wetness class and NO3N and DOC vary according toFig. 1. Maps of the study watershed from Barclay et al. (2015) (A) The watersheds location within the Finger Lake region of NewYork State; (B) The primaryland uses in the catchment; (C) The Soil Topographic Index (STI), binned by quantile into 10 equal area wetness classes; (D) The 30 unique combinations ofwetness class and land use which formed the calculation basis for the denitrication model.66 J.R. Barclay, M.T. Walter / Sustainability of Water Quality and Ecology 5 (2015) 6476land use and Julian Day. Although agricultural crop models exist to model NO3N on a daily time-step using agriculturalmanagementdata(seeFerrantetal. (2011)foracomparisonofTNT2andSWAT, twosuchmodels), forsimplicityweassumed that agricultural management and current seasonal patterns of NO3N would remain unchanged. The details of the-se inputs and the calibration process are given in Barclay et al. (Barclay et al., 2015) Air temperature and precipitation inputsare from the weather series described below.2.3. Baseline Climate SeriesFollowing the example of Hayhoe et al. (2007), we used a baseline period of 19611990, however the on-site weatherstation at our study site was not established until 2004 so we generated our baseline data series using historic weather datafrom the NOAA National Climate Data Center, Station GHCND: USC00304174 (ITHACA CORNELL UNIVERSITY) (NCDC), whichis in Ithaca, NY, 21 km to the west of our study site. A comparison of the concurrent weather data from Ithaca and Harford(20052012) indicated that Harford is approximately 0.9 C (0.1 C) colder than Ithaca, and annually receives 3 cm (6 cm)more rain and has 18 (14) more days of rain. Over the eight year comparison period, Harford experienced 3 more rainydays month1inthewettermonthsofJanuaryApril, and0.5morerainydays month1inthemonthsofMaythroughDecember. We assumed this same relationship held in the baseline period of 19611990. To create a historic baseline forHarford, NY (19611990) from the historic Ithaca weather, we decreased the daily minimum, maximum, and average tem-peratures by 0.9 C, increased the daily precipitation depth by 5%, and randomly added 0.5 days of rain month1for MayDecember and 3 days rain month1for JanuaryApril.2.4. Future climate series generationWeusedthreefutureemissionsscenarios(B1, A2, andA1F1)developedbytheIntergovernmental Panel onClimateChange (IPCC, 2001) as the basis for generating our future climate series. Briey, A1F1 is a scenario of rapid economic growthwith an emphasis on fossil fuels, A2 is a scenario of slower, more fragmented economic growth focused at the regional andlocal scale, and the B1 scenario involves a rapid shift in economic structures such that material resource use decreases andefciency increases (IPCC, 2001).Projectionsforannual, summerandwintershiftstomeantemperatureandprecipitation(Table1)aremulti-modelensemble averages taken from Hayhoe et al. (2007). We modied the daily baseline summer and winter temperature andprecipitation values by the projected shifts to the summer and winter mean values. Then, we adjusted the spring and fallvalues until the new annual mean matched the projected annual mean values. This method, known as the delta method,has the advantage of simplicity and has been shown to be relatively successful at modeling the observed climate. The draw-back of the delta method is that it can mis-predict extreme events (Anandhi et al.,2011; Gleick,1986; Hay et al.,2000;Hayhoe, 2010).To address the question regarding the relative strengths of projected changes in temperature and precipitation as driversof changes in denitrication rates, we generated two hypothetical climate series. The Precipitation Only and TemperatureOnly series were generated by using the precipitation (or temperature) series from the A1F1 scenario and the temperature(or precipitation) series from the baseline data (last two rows of Table 1).One drawback of the delta method of downscaling is that it assumes the future weather patterns will be the same as thecurrentweatherpatterns. Toexploretheinuenceofchangingweatherpatternsondenitrication, wegeneratedthreehypothetical weather series with the same annual and seasonal means as the A1F1 series. In the Random series, precipitationamounts randomly sampled from a distribution of double the A1F1 seasonal values occur on randomly selected days fromthe season in question. Average daily temperature values for this series were generated randomly using the monthly meanand standard deviation. Daily maximum and minimum temperatures were generated from daily temperature ranges, deter-mined randomly using the monthly mean daily temperature range. In the Mean series, daily precipitation and temperaturevalues were taken fromthe mean daily values from the A1F1 scenario. This creates a scenario in which precipitation is a dailyoccurrence. In the Cyclic series, temperature and precipitation patterns occur in 5 day increments each month. Again, basedon the A1F1 series, each months precipitation is uniformly divided among ve consecutive days in the month (all other daysTable 1Projected temperature and precipitation changes for the northeastern United States for 20702099 relative to a baseline period of 19611990 (Hayhoe et al.,2007).Series Annual temp (C) Summer temp (C) Winter temp (C) Annual precip. (%) Summer precip. (%) Winter precip. (%)Baseline B1 2.9 2.4 1.7 7 (1) 12A2 4.5 4.3 3.7 9 (2) 14A1F1 5.3 5.9 5.4 14 0 30A1F1 Temp. 5.3 5.9 5.4 A1F1 Precip. 14 0 30J.R. Barclay, M.T. Walter / Sustainability of Water Quality and Ecology 5 (2015) 6476 67have no precipitation) and daily temperatures are selected as in the Random series, except that the daily minimum, maxi-mum, and average temperatures remain constant for ve days before changing.2.5. Statistical analysisFor statistical analysis of the model outputs, we used the DunnettTukeyKramer method of multi-level pairwise com-parisons (Dunnett, 1980; Lau, 2013). The advantage of this method is that it does not require that the variances are equal.3. Results and discussion3.1. Future temperature and precipitation seriesWe compared the annual and seasonal precipitation and soil temperature series with the climate projections to verify theweather series generation process (using the delta method) produced the anticipated results. Both the annual and seasonalprecipitation and temperature changes matched the expectations (Fig. 2). The number of low ow days increased (days inwhichthemodeleddischargewasbelowthe18%thresholdinthebaselinescenario)andtheannual streamdischargedecreased in the A2 scenario. The summer decrease in soil moisture came earlier and in wetness class 1 was larger (see Sup-porting Information for details).3.2. Comparison of baseline climate and B1, A2, and A1F1 scenariosWith all future scenarios, our model predicts an increase in the ecosystem service of denitrication relative to the base-line conditions. The greatest increase is with scenario A1F1 (11.8 kg N ha1yr1, 41% increase) and the smallest increase iswith scenario B1 (5.1 kg N ha1yr1, 23% increase); however, all three future scenarios are statistically different than thebaseline (p < 0.05) (Fig. 3). In all four weather scenarios, the highest watershed-wide denitrication rates occur during thespring season. Our model predicts increased seasonal denitrication rates for all three scenarios (Fig. 4). There is a slight shiftin the seasonal pattern of denitrication, with an increase in the spring fraction for the A2 scenario, a decrease in the summerFig. 2. Annual(A)andSeasonal(B)MeanPrecipitationandAnnual(C)andSeasonal(D)MeanSoilTemperaturewiththefourclimatescenarios. Thisconrms that our weather series generation process duplicated the projected changes.68 J.R. Barclay, M.T. Walter / Sustainability of Water Quality and Ecology 5 (2015) 6476fraction for the B1 and A2 scenarios, an increase in the fall fraction for the A2 scenario, and an increase in the winter fractionfor the A2 and A1F1 scenarios (Fig. 4).3.3. Wetness class 1 denitrication ratesIn contrast to the watershed-wide rates, the denitrication rates in the wettest areas (wetness class 1) are high in thesummer season, as well as during the spring. During the summer, this wettest class remains sufciently wet to maintainanaerobic conditions, while the rest of the watershed dries substantially (Fig. 5A). The locations where wetness class 1 over-laps with wetland vegetation and soils create unique and important biogeochemical hotspots. In the baseline scenario, 64%of the annual denitrication from wetness class 1 occurs during the summer, with an additional 29% occurring during thespring. With the A1 and B2 scenarios, the summer fraction of annual wetness class 1 denitrication decreases and the springfraction increases, and in the A2 and A1F1 the winter fraction increases (Fig. 5B). During the spring, when much of the water-shed is wet enough to create anaerobic conditions, wetness class 1 contributes 6471% of the seasonal denitrication fromFig. 3. Annual modeled denitrication rates. For this and all subsequent boxplots, boxes extend from the rst to the third quartiles of annual rate (n = 30),the center line indicates the median rate, and the whiskers extend to 150% of the interquartile range. Means with different letters are signicantly different(DunnettTukeyKramer test, p < 0.05).Fig. 4. Seasonal rates and seasonal fraction of annual rates. Means from the same season with different letters are signicantly different (DunnettTukeyKramer test, p < 0.05).J.R. Barclay, M.T. Walter / Sustainability of Water Quality and Ecology 5 (2015) 6476 69the watershed, with a lower fraction in the baseline weather conditions and the highest fraction associated with the highestemissions, i.e., the A1F1 scenario. As the rest of the watershed dries in the summer season, the wetness class 1 contributionincreases to 8190% of the summer denitrication across the watershed, with the higher class 1 fractions from the futureclimate scenarios (Fig. 5C). The higher summer contributions in the future scenarios are due to decreased soil moisture inthe rest of the watershed, resulting from higher temperatures. Annually, the wettest class contributes 7481% of the annualdenitrication in all scenarios (Fig. 5D).3.4. Patterns of changesThe spatial pattern of changes in annual denitrication rates follows the same pattern as the baseline denitrication rates(Fig. 6andFig. 7AC);specically, areaswithlowerbaselinerateshavesmallincreasesandareaswithhigherbaselinedenitricationrateshavelargerincreases. Thesechangesrangefrom1to3 kg N ha1yr1inmuchofthewatershedto150 kg N ha1yr1inthewettestwetlandswiththeA1F1scenario. Asapercentageof thebaselinerates, thegreatestincreasesaretheinareaswithloworhighbaselinerates;moderatebaselinerateshadthelowestpercentageincreases(Fig. 7FH).Temporally, the watershed-wide rates of denitrication follow the same basic pattern with all climate scenarios, thoughthe future scenarios have higher rates, especially during the spring season (Fig. 8). Our model suggests that most denitri-cation rates will scale upwards due to increased temperature, but that some will decrease due to decreased soil moisture.3.5. Slight increase in days without denitricationIn all but the wettest and driest areas of the watershed, we project an increase in the number of days annually on whichsoil conditionsaresufcientlyunfavorablethatnodenitricationispredictedtooccur(SeeSupportingInformationforFig. 5. Denitrication in wetness class 1 (A) Seasonal denitrication rates, (B) Seasonal fraction of annual class 1 denitrication, (C) Class 1 fraction ofwatershed-wide seasonal rate, (D) Class 1 fraction of annual watershed-wide rate. Means in the same season with different letters are signicantly different(DunnettTukeyKramer test, p < 0.05).70 J.R. Barclay, M.T. Walter / Sustainability of Water Quality and Ecology 5 (2015) 6476details). With the baseline weather, in most of the watershed, the model predicts denitrication rates of 0 kg N ha1yr1on110200 days yr1. This increases to 119220 days yr1in the future scenarios, with slightly greater increases in the A2 sce-nario. This increase is due to a decrease in anaerobic conditions resulting from drier soils.3.6. Relative inuence of changes in precipitation and temperatureTo examine the relative inuence of projected changes in precipitation and temperature, we created a future weather ser-ies that combined the precipitation from the A1F1 scenario with the temperature from the baseline series, and another thatcombined the temperature from the A1F1 scenario with the precipitation from the baseline series. Though the median annu-al denitrication rate increased with both new scenarios, only the increases in the A1F1 (11.8 kg N ha1yr1) and the tem-perature series (7.7 kg N ha1yr1) are signicant at the 95% condence level (Fig. 9). Brown et al. (2012) found a biggereffect from elevated precipitation, though this may be due to a low temperature increase (