Dynamic modeling of nitrogen losses in river networks unravels the coupled effects of hydrological and biogeochemical processes

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

A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

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 as ε[subscript max] (LUE), V[subscript cmax] (unstressed Rubisco catalytic capacity) or J[subscript max] (the maximum electron transport rate)

Nutrient management in African sorghum cropping systems: applying meta-analysis to assess yield and profitability

International audienceAbstractDeclining soil fertility and limited farmer access to inorganic fertilizer frequently cause sub-optimal grain yields throughout sub-Saharan Africa. Farm productivity is also at risk from extreme weather and future climate change. Significant uncertainty remains in predicting climate in Africa, increasing the challenge of planning for climate change adaptation. Sorghum is adapted to African climate patterns and is predicted to maintain widespread suitability across different African climatic zones under climate change. Sorghum’s drought tolerance and ability to withstand water logging make it an important crop for maintaining productive agroecosystems under a changing climate. Due to its status as a staple grain, improved sorghum management can provide smallholder farmers with stability in their household nutritional needs. We reviewed sorghum (Sorghum bicolor) yield trends across nutrient management scenarios using meta-analysis. We compared yield across eight nutrient management practices: (i) N-only, (ii) P-only, (iii) N and P, (iv) N and P microdose, (v) legume management, (vi) manure addition, (vii) organic matter (OM) amendment, and (viii) mixed amendment. Our review demonstrated (1) yield improvement considering all scenarios averaged 66 % relative to no nutrient inputs, (2) yield under chemical fertilizer amendment increased by 47–98 % of control yield, (3) yield under organic nutrient amendment increased by 43–87 % of control yield, and (4) the profitability of a management scenario was not solely determined by the magnitude of yield increase. For example, due to the high cost of fertilizer, addition of nitrogen (N) and phosphorus (P) generated the largest yield increase, but the lowest profit, in two of three countries analyzed. In contrast, an edible legume in rotation averaged 43 % yield improvement relative to no nutrient inputs and a net profit of US $146 to$263 per hectare. Facilitating access to both fertilizer inputs and diversified rotations has the greatest potential to increase grain yield in Africa

Engaging Researchers and Stakeholders in Improving New York’s Water Management

CaRDI Research & Policy Brief Issue 6

Modeling N 2 O flux from an Illinois agroecosystem using Monte Carlo sampling of field observations

Abstract We modeled the expected range of seasonal and annual N 2 O flux from temperate, grain agroecosystems using Monte Carlo sampling of N 2 O flux field observations. This analysis is complimentary to mechanistic biogeochemical model outcomes and provides an alternative method of estimating N 2 O flux. Our analysis produced a range of annual N 2 O gas flux estimates with mean values overlapping with results from an intermodel comparison of mechanistic models. Mean seasonal N 2 O flux was 1-4% of available N, while median seasonal N 2 O flux was less than 2% of available N across corn, soybean, wheat, ryegrass, legume, and bare fallow systems. The 25th-75th percentile values for simulated average annualized N 2 O flux rates ranged from 1 to 12.2 kg N ha -1 in the conventional system, from 1.3 to 8.8 kg N ha -1 in the cover crop rotation, and from 0.8 to 9.3 kg N ha -1 in the legume rotation. Although these modeling techniques lack the seasonal resolution of mechanistic models, model outcomes are based on measured field observations. Given the large variation in seasonal N gas flux predictions resulting from the application of mechanistic simulation models, this data-derived approach is a complimentary benchmark for assessing the impact of agricultural policy on greenhouse gas emissions

The effect of nitrogen addition on soil organic matter dynamics: a model analysis of the Harvard Forest Chronic Nitrogen Amendment Study and soil carbon response to anthropogenic N deposition

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

Challenges to incorporating spatially and temporally explicit phenomena (hotspots and hot moments) in denitrification models

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. © 2009 Springer Science+Business Media B.V