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Denitrification, the anaerobic reduction of nitrogen oxides to nitrogenous gases, is an extremely challenging process to measure and model. Much of this challenge arises from the fact that small areas (hotspots) and brief periods (hot moments) frequently account for a high percentage of the denitrification activity that occurs in both terrestrial and aquatic ecosystems. In this paper, we describe the prospects for incorporating hotspot and hot moment phenomena into denitrification models in terrestrial soils, the interface between terrestrial and aquatic ecosystems, and in aquatic ecosystems. Our analysis suggests that while our data needs are strongest for hot moments, the greatest modeling challenges are for hotspots. Given the increasing availability of high temporal frequency climate data, models are promising tools for evaluating the importance of hot moments such as freeze-thaw cycles and drying/rewetting events. Spatial hotspots are less tractable due to our inability to get high resolution spatial approximations of denitrification drivers such as carbon substrate. Investigators need to consider the types of hotspots and hot moments that might be occurring at small, medium, and large spatial scales in the particular ecosystem type they are working in before starting a study or developing a new model. New experimental design and heterogeneity quantification tools can then be applied from the outset and will result in better quantification and more robust and widely applicable denitrification models.

Farmers, food supply-chain entities, and policymakers need a simple but robust indicator to demonstrate progress toward reducing nitrogen pollution associated with food production. We show that nitrogen balance—the difference between nitrogen inputs and nitrogen outputs in an agricultural production system—is a robust measure of nitrogen losses that is simple to calculate, easily understood, and based on readily available farm data. Nitrogen balance provides farmers with a means of demonstrating to an increasingly concerned public that they are succeeding in reducing nitrogen losses while also improving the overall sustainability of their farming operation. Likewise, supply-chain companies and policymakers can use nitrogen balance to track progress toward sustainability goals. We describe the value of nitrogen balance in translating environmental targets into actionable goals for farmers and illustrate the potential roles of science, policy, and agricultural support networks in helping farmers achieve them.

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low‐temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf‐to‐canopy scaling and better values of model parameters that control the maximum potential GPP, such asεmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate). Key Points Gross primary productivity (GPP) from 26 models tested at 39 flux tower sites Simulated light use efficiency controls model performance Models overpredict GPP under dry conditions

The importance of lotic systems as sinks for nitrogen inputs is well recognized. A fraction of nitrogen in streamflow is removed to the atmosphere via denitrification with the remainder exported in streamflow as nitrogen loads. At the watershed scale, there is a keen interest in understanding the factors that control the fate of nitrogen throughout the stream channel network, with particular attention to the processes that deliver large nitrogen loads to sensitive coastal ecosystems. We use a dynamic stream transport model to assess biogeochemical (nitrate loadings, concentration, temperature) and hydrological (discharge, depth, velocity) effects on reach-scale denitrification and nitrate removal in the river networks of two watersheds having widely differing levels of nitrate enrichment but nearly identical discharges. Stream denitrification is estimated by regression as a nonlinear function of nitrate concentration, streamflow, and temperature, using more than 300 published measurements from a variety of US streams. These relations are used in the stream transport model to characterize nitrate dynamics related to denitrification at a monthly time scale in the stream reaches of the two watersheds. Results indicate that the nitrate removal efficiency of streams, as measured by the percentage of the stream nitrate flux removed via denitrification per unit length of channel, is appreciably reduced during months with high discharge and nitrate flux and increases during months of low-discharge and flux. Biogeochemical factors, including land use, nitrate inputs, and stream concentrations, are a major control on reach-scale denitrification, evidenced by the disproportionately lower nitrate removal efficiency in streams of the highly nitrate-enriched watershed as compared with that in similarly sized streams in the less nitrate-enriched watershed. Sensitivity analyses reveal that these important biogeochemical factors and physical hydrological factors contribute nearly equally to seasonal and stream-size related variations in the percentage of the stream nitrate flux removed in each watershed.

Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site‐level intercomparison. This study expands upon previous single‐site and single‐model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate‐scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model‐by‐band effect but also a nonsignificant model‐by‐site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales. Key Points Twenty‐one ecosystem models were tested in the frequency domain at nine flux towers Model error is greatest at the annual and growing‐season diurnal timescales There are large event‐driven errors and model differences at the synoptic scale

Recent observations indicate that long-term N additions can slow decomposition, leading to C accumulation in soils, but this process has received limited consideration by models. To address this, we developed a model of soil organic matter (SOM) dynamics to be used with the PnET model and applied it to simulate N addition effects on soil organic carbon (SOC) stocks. We developed the model’s SOC turnover times and responses to experimental N additions using measurements from the Harvard Forest, Massachusetts. We compared model outcomes to SOC stocks measured during the 20th year of the Harvard Forest Chronic Nitrogen Amendment Study, which includes control, low (5 g N m⁻² yr⁻¹) and high (15 g N m⁻² yr⁻¹) N addition to hardwood and red pine stands. For unfertilized stands, simulated SOC stocks were within 10 % of measurements. Simulations that used measured changes in decomposition rates in response to N accurately captured SOC stocks in the hardwood low N and pine high N treatment, but greatly underestimated SOC stocks in the hardwood high N and the pine low N treatments. Simulated total SOC response to experimental N addition resulted in accumulation of 5.3–7.9 kg C per kg N following N addition at 5 g N m⁻² yr⁻¹ and 4.1–5.3 kg C per kg N following N addition at 15 g N m⁻² yr⁻¹. Model simulations suggested that ambient atmospheric N deposition at the Harvard Forest (currently 0.8 g N m⁻² yr⁻¹) has led to an increase in cumulative O, A, and B horizons C stocks of 211 g C m⁻² (3.9 kg C per kg N) and 114 g C m⁻² (2.1 kg C per kg N) for hardwood and pine stands, respectively. Simulated SOC accumulation is primarily driven by the modeled decrease in SOM decomposition in the Oa horizon.

We applied the Denitrification-Decomposition (DNDC) model to a typical corn-soybean rotation on silty clay loams with tile-drainage in east-central Illinois (IL). Model outcomes are compared to 10 years of observed drainage and nitrate leaching data aggregated across the Embarras River watershed. We found that accurate simulation of NO₃-N leaching and drainage dynamics required significant changes to key soil physical and chemical parameters relative to their default values. Overall, our calibration of DNDC resulted in a good statistical fit between model output and IL data for crop yield, NO₃-N leaching, and drainage. Our modifications to DNDC reduced the RMSE from 9.4 to a range of 1.3-2.9 for NO₃-N leaching and from 51.2 to a range of 13-23.6 for drainage. Modeling efficiency ranged from 0.25 to 0.85 in comparison with measured drainage and leachate values and from 0.65 to 1 in comparison with crop yield data. However, analysis of simulation results at a monthly time step indicated that DNDC consistently underpredicted peak drainage events. Underprediction ranged from 50 to 100 mm month-¹ following three extreme precipitation events, a flux equivalent to 0.25-0.5 of the total measured monthly flux. Our simulations demonstrated high interannual variation in nitrate leaching with average annual NO₃-N loss of 24 kg N ha-¹, peak annual NO₃-N loss of 58 kg N ha-¹ and low annual NO₃-N loss of 1-5 kg N ha-¹.

Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed N mass balances, determine greenhouse gas emissions (N₂O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained Mollisol from five models (DAYCENT, SWAT, EPIC, DRAINMOD-N II and two versions of DNDC, 82a and 82h), after first calibrating each model to crop yields, water flux, and nitrate leaching. Known annual crop yields and daily flux values (water, nitrate-N) for 1993-2006 were provided, along with daily environmental variables (air temperature, precipitation) and soil characteristics. Measured denitrification fluxes were not available. Model output for 1997-2006 was then compared for a range of annual, monthly and daily fluxes. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating riverine water and nitrate-N fluxes (1997-2006 mean measured nitrate-N loss 28 kg N ha⁻¹ year⁻¹, model range 21-28 kg N ha⁻¹ year⁻¹). Monthly patterns in observed riverine nitrate-N flux were generally reflected in model output (r ² values ranged from 0.51 to 0.76). Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, including 10-year average denitrification estimates, ranging from 3.8 to 21 kg N ha⁻¹ year⁻¹ and substantial variability in simulated soybean N₂ fixation, N harvest, and the change in soil organic N pools. DNDC82a and DAYCENT gave comparatively low estimates of total denitrification flux (3.8 and 5.6 kg N ha⁻¹ year⁻¹, respectively) with similar patterns controlled primarily by moisture. DNDC82h predicted similar fluxes until 2003, when estimates were abruptly much greater. SWAT and DRAINMOD predicted larger denitrification fluxes (about 17-18 kg N ha⁻¹ year⁻¹) with monthly values that were similar. EPIC denitrification was intermediate between all models (11 kg N ha⁻¹ year⁻¹). Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle (e.g., N₂ fixation, N harvest, change in soil N storage) no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes (without known values) from the dominant agroecosystem in the midwestern Illinois is quite uncertain at this time.

Ecosystem models are increasingly used to guide natural resource management policy decisions. In this study, we build on available agroecosystem policy modeling tools by testing two methodologies for applying the Denitrification-Decomposition (DNDC) model to naturally-drained, temperate grain cropping systems. We used long-term observations from the Rodale Institute Farming Systems Trial (FST) to validate the DNDC model for application to grain cropping systems on silty clay loam soils typical of mid-Atlantic farmlands. Based on modeling efficiency (EF), Theil's Inequality (U ²), and correlation coefficient (r) metrics, the DNDC model showed moderate fit between observations and simulations at annual time scales for drainage (EF = 0.34, U ² = 0.12, r = 0.74) and nitrate leaching (EF = −0.05, U ² = 0.4, r = 0.86). Replication of observed seasonal water flux and nitrate leaching trends were difficult to capture in model simulations, resulting in a weak fit between observations and simulations for drainage (EF = −1.2, U ² = 0.89, r = 0.28) and nitrate leaching (EF = −2.5, U ² = 2.1, r = 0.3). Our comparison of observations and model outcomes highlights the challenge of scaling up belowground fluxes to farm or watershed scales. Ecosystem model representation of water transport generally assumes highly homogeneous soil conditions. In contrast, data from lysimeter sampling represents a small percentage of the total study area and is unlikely to capture average soil field properties. Additionally, our Rodale work highlights the limitation of biogeochemistry models which use vertical mass movement to describe water drainage and nitrate leaching. The application of the DNDC model to the Rodale FST demonstrates that model studies are not a simple substitute for field observation. The predictive utility of model outcomes can only be broadened through rigorous testing against long-term field observations.