Acknowledgements This work has been supported by an EPA STAR grant, “Developing regional-scale stressor models for managing eutrophication in coastal marine ecosystems, including interactions of nutrients, sediments, land-use change, and climate variability and change,” EPA Grant Number R830882, R.W. Howarth, P.I. * For more information, Aber, J. D., C. L. Goodale, S. V. Ollinger, M.-L. Smith, A. H. Magill, M. E. Martin, R. A. Hallett, and J. L. Stoddard Is Nitrogen Deposition Altering the Nitrogen Status of Northeastern Forests? Bioscience 54(4): Billen, G. and J. Garnier Nitrogen transfers through the Seine drainage network: a budget based on the application of the ‘Riverstrahler’ model. Hydrobiologia. 410: 139–150. Boyer, E.W., C. L. Goodale, N. A. Jaworski and R. W. Howarth Anthropogenic nitrogen sources and relationships to riverine nitrogen export in the northeastern U.S.A. Biogeochemistry 57/58: Howarth, R.W., D.P. Swaney, E.W. Boyer, R.M. Marino, N. Jaworski and C.L. Goodale. The influence of climate on average nitrogen export from large watersheds in the Northeastern United States. Accepted for publication in Biogeochemistry. Mayer, B., E.W. Boyer, C.L. Goodale, N.A. Jaworski, N. Van Breemen, R.W. Howarth, S.P. Seitzinger, G. Billen K. Lajtha, K.J. Nadelhoffer, D. Van Dam, L.J. Hetling, M. Nosal, and K. Paustian, Sources of nitrate in rivers draining sixteen watersheds in the northeastern U.S.: Isotopic constraints. Biogeochemistry 57/58: 171–197. Van Breemen, N., E.W. Boyer, C.L. Goodale, N.A. Jaworski, K. Paustian, S.P. Seitzinger, K. Lajtha, B. Mayer, D. Van Dam, R.W. Howarth, K.J. Nadelhoffer, M. Eve, and G. Billen Where did all the nitrogen go? Fate of nitrogen inputs to large watersheds in the northeastern U.S.A. Biogeochemistry 57/58: 267–293. ABSTRACT Nitrogen load to the coastal zone drives estuarine ecosystem dynamics, and, in cases of extreme loading, can result in ecosystem catastrophes like the anoxic zone in the Mississippi plume. Riverine nitrogen fluxes depend on interactions between input loading rates within the watershed, climate, and landscape processes. Direct relationships between average Net Anthropogenic Nitrogen Inputs (NANI) and riverine fluxes have been shown with a relatively simple nitrogen accounting methodology for 16 large watersheds in the northeastern US (Boyer et al., 2002). More recently, statistical models have shown that the average response of these watersheds per unit nitrogen load is strongly related to precipitation or hydrology (Howarth et al., in press). To examine the temporal response of these systems in response to climate and land use change, we are developing a simple Regional Nutrient Management model (ReNuMa) based on watershed-scale water balances and statistical relationships between N loads and responses. For the 6-year period examined so far, interannual discharge is well predicted over a range of watersheds. Nitrogen fluxes vary latitudinally across watersheds in response to the balance of anthropogenic sources, including atmospheric deposition and fertilizer use. D. P. Swaney 1 *, R. W. Howarth 1, A. E.. Galford 1, R.M. Marino 1,and E.W. Boyer 2 1 Cornell University, Ithaca, NY USA, 2 University of California at Berkeley, Berkeley, CA USA References The predecessor of ReNuMa is the Generalized Watershed Loading Function Model (GWLF): Haith, D. A., Shoemaker, L. L Generalized watershed loading functions for stream flow nutrients. Water Resources Bulletin 23(3): A spreadsheet-based version of the model can be found on the web at: Other references Ongoing work and future directions Our work to date has focused on developing parameterizations of biogeochemical responses related to landuse/landcover: Atmospheric deposition Landscape level N retention of agricultural N sources (fertilizer, manure, N-fixation) Nitrogen retention and losses (DIN) from forests In-stream and landscape denitrification We are currently working on refining the model parameters for individual watersheds to reduce bias at this scale, as well as developing alternative parameterizations of processes which incorporate results from smaller-scale models. Additional processes to be considered will include: Processing and transport of total N in addition to dissolved N Phosphorus losses from P-saturated soils Soil erosion and sediment transport Water & wetland Forest & shrubland Urban & barren ReNuMa: Nitrogen Dynamics Human Waste Direct addition (load) to streams In-stream denitrification Threshold response in DIN concentrations following Aber et al (2003) Direct addition (load) to streams Direct addition to streams Riverine DIN flux Threshold response in DIN concentrations similar to Billen &Garnier (2000) Onsite treatment Wastewater Treatment Plants Atmos Deposition Manure Fertilizer Agricultural N-Fixation Agricultural Landuses Landscape denitrification & other losses Annual DIN flux in 16 large Northeastern US watersheds ReNuMa: Hydrological Dynamics BarrenWetland Vineyards/ orchards Row cropsOpen WaterForestShrubland Unsaturated zone Saturated zone Evapotranspiration Snowpack Urban Daily Precipitation Daily Temperature Snowmelt Baseflow Shallow flow/runoff Streamflow 16 watersheds in the Northeastern USA in which Net Anthropogenic Nitrogen Inputs (NANI) have been related to average riverine N fluxes over the period (Boyer et al., 2002). We are extending the analysis to simulate seasonal and annual streamflow and nitrogen fluxes using the ReNuMa model. Annual streamflow in 16 large Northeastern US watersheds The 2928 weather stations in the National Climate Data Center network for Northeastern states were identified ( To select candidate stations for each watershed, Thiessen polygons for the network were generated using ArcView™ 3.2. Stations with polygons intersecting a watershed and with >95% complete records (daily temperature and precipitation) were averaged to obtain representative weather data for each watershed. Missing temperature data for each station were replaced with averages of the records preceding and following the missing interval; missing precipitation values were replaced with zero. Minor adjustments to evaporative cover factor for the Androscoggin and Susquehanna rivers were the only changes made to baseline parameter values for the watersheds. While individual watersheds may exhibit some bias above or below observations (ie USGS annual streamflows ), agreement over all years and across all watersheds was generally good. Examples of threshold responses in agricultural and forest systems in the literature. Left: response of [NO3] to fertilizer loads in agricultural leachate (Billen & Garnier, 2000); Right: response of [NO3] to N deposition in forest leachate (Aber, et al., 2003). We use a similar parameterization to estimate landscape response from these land cover types. SCS runoff equation for each cover type (1 st order linear reservoir) Human waste contributes N through sewers and septic system effluent. N deposition traverses lakes and wetlands without retention, but exhibits a threshold landscape response in forests. Agricultural N sources (fertilizer, manure and fixation) also exhibit a threshold response due to retention (ie landscape denitrification, etc). The proportions of in-river denitrification are based on estimates in Van Breemen et al., 2002) LATITUDINAL AND TEMPORAL CHANGES IN DISCHARGE AND NITROGEN FLUXES FROM LARGE WATERSHEDS IN THE NORTHEASTERN UNITED STATES: AN APPLICATION OF RENUMA Preliminary comparison of simulated vs observed annual DIN fluxes for the 12 watersheds with adequate observations of DIN ; E NS = Nash-Sutcliffe efficiency; n=6 years*12 watersheds =72 ) Run# Run description BiasR 2 E NS Baseline (all sources) Pt sources + agriculture N dep (no forest) 3Pt sources + agriculture Pt sources + agriculture without load response 5Pt sources only Across all watersheds, simulated annual DIN fluxes also match observed values reasonably well. Some individual watersheds exhibit significant bias above or below observed fluxes, but generally correlate well with annual variations over time. Simulated annual DIN fluxes improve as more sources are added. The table indicates the increase in several goodness-of-fit measures as more terms are included in the model. Point sources alone (#5) underpredict the DIN load, but still exhibit a significant R 2 (ie a linear relationship with observations). Adding a fixed contribution from agricultural lands greatly improves the agreement, and adding a load-dependent contribution improves it further. Direct N deposition and the response of forests also improves the agreement with observed fluxes across watersheds. This figure shows the variation in major sources of N inputs (bars) and N fluxes in rivers (areas) along a gradient from North to South. (Latitudes of the watershed centers are shown along the top of the figure.) Across all watersheds, total annual DIN and simulated annual DIN fluxes vary in response to the magnitude of N inputs. The magnitude of these inputs as well as their major components vary significantly from the deposition-driven watersheds of the north, to urban watersheds with large wastewater loads, to southern watersheds with large agricultural sources of N.