Impact of global change on coastal oxygen dynamics and risk of hypoxia

Climate change and changing nutrient loadings are the two main aspects of global change that are linked to the increase in the prevalence of coastal hypoxia - the depletion of oxygen in the bottom waters of coastal areas. However, it remains uncertain how strongly these two drivers will each increase the risk of hypoxia over the next decades. Through model simulations we have investigated the relative influence of climate change and nutrient run-off on the bottom water oxygen dynamics in the Oyster Grounds, an area in the central North Sea experiencing summer stratification. Simulations were performed with a one-dimensional ecosystem model that couples hydrodynamics, pelagic biogeochemistry and sediment diagenesis. Climatological conditions for the North Sea over the next 100 yr were derived from a global-scale climate model. Our results indicate that changing climatological conditions will increase the risk of hypoxia. The bottom water oxygen concentration in late summer is predicted to decrease by 24 mu M or 11.5% in the year 2100. More intense stratification is the dominant factor responsible for this decrease (58 %), followed by the reduced solubility of oxygen at higher water temperature (27 %), while the remaining part could be attributed to enhanced metabolic rates in warmer bottom waters (15 %). Relative to these climate change effects, changes in nutrient runoff are also important and may even have a stronger impact on the bottom water oxygenation. Decreased nutrient loadings strongly decrease the probability of hypoxic events. This stresses the importance of continued eutrophication management in coastal areas, which could function as a mitigation tool to counteract the effects of rising temperatures

Assessing Short‐Term Impacts of Management Practices on N2O Emissions From Diverse Mediterranean Agricultural Ecosystems Using a Biogeochemical Model

Croplands are important sources of nitrous oxide (N2O) emissions. The lack of both long‐term field measurements and reliable methods for extrapolating these measurements has resulted in a large uncertainty in quantifying and mitigating N2O emissions from croplands. This is especially relevant in regions where cropping systems and farming management practices (FMPs) are diverse. In this study, a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC), was tested against N2O measurements from five cropping systems (alfalfa, wheat, lettuce, vineyards, and almond orchards) representing diverse environmental conditions and FMPs. The model tests indicated that DNDC was capable of predicting seasonal and annual total N2O emissions from these cropping systems, and the model\u27s performance was better than the Intergovernmental Panel on Climate Change emission factor approach. DNDC also captured the impacts on N2O emissions of nitrogen fertilization for wheat and lettuce, of stand age for alfalfa, as well as the spatial variability of N2O fluxes in vineyards and orchards. DNDC overestimated N2O fluxes following some heavy rainfall events. To reduce the biases of simulating N2O fluxes following heavy rainfall, studies should focus on clarifying mechanisms controlling impacts of environmental factors on denitrification. DNDC was then applied to assess the impacts on N2O emissions of FMPs, including tillage, fertilization, irrigation, and management of cover crops. The practices that can mitigate N2O emissions include reduced or no tillage, reduced N application rates, low‐volume irrigation, and cultivation of nonleguminous cover crops. This study demonstrates the necessity and potential of utilizing process‐based models to quantify N2O emissions from regions with highly diverse cropping systems

Parameter-induced uncertainty quantification of soil N 2 O, NO and CO 2 emission from Höglwald spruce forest (Germany) using the LandscapeDNDC model

Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i) uncertainty of information used to initialise and drive the model, (ii) uncertainty of model parameters describing specific ecosystem processes, (iii) uncertainty of the model structure, and (iv) accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange) which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC) method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O), nitric oxide (NO) and carbon dioxide (CO2) as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values), an objective criteria for chain convergence developed by Gelman et al. (2003) could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain the parameter-induced uncertainty of simulated N2O, NO and CO2 emissions. These were compared to observational data of an calibration set (6 yr) and an independent validation set of 4 yr. The comparison showed that most of the annual observed trace gas emissions were in the range of simulated values and were predicted with a high certainty (Root-mean-squared error (RMSE) NO: 2.4 to 18.95 g N ha−1 d−1, N2O: 0.14 to 21.12 g N ha−1 d−1, CO2: 5.4 to 11.9 kg C ha−1 d−1). However, LandscapeDNDC simulations were sometimes still limited to accurately predict observed seasonal variations in fluxes

A coupled hydrology-biogeochemistry model to simulate dissolved organic carbon exports from a permafrost influenced catchment

Funded by Natural Environment Research Council. Grant Number: NE/K000268/1 Natural Sciences and Engineering Research Council of CanadaPeer reviewedPublisher PD

Modeling ammonia emissions from dairy production systems in the United States

Dairy production systems are hot spots of ammonia (NH3) emission. However, there remains large uncertainty in quantifying and mitigating NH3 emissions from dairy farms due to the lack of both long-term field measurements and reliable methods for extrapolating these measurements. In this study, a process-based biogeochemical model, Manure-DNDC, was tested against measurements of NH3 fluxes from five barns and one lagoon in four dairy farms over a range of environmental conditions and management practices in the United States. Results from the validation tests indicate that the magnitudes and seasonal patterns of NH3 fluxes simulated by Manure-DNDC were in agreement with the observations across the sites. The model was then applied to assess impacts of alternative management practices on NH3 emissions at the farm scale. The alternatives included reduction of crude protein content in feed, replacement of scraping with flushing for removal of manure from barn, lagoon coverage, increase in frequency for removal of slurry from lagoon, and replacement of surface spreading with incorporation for manure land application. The simulations demonstrate that: (a) all the tested alternative management practices decreased the NH3 emissions although the efficiency of mitigation varied; (b) a change of management in an upstream facility affected the NH3 emissions from all downstream facilities; and (c) an optimized strategy by combining the alternative practices on feed, manure removal, manure storage, and land application could reduce the farm-scale NH3 emission by up to 50%. The results from this study may provide useful information for mitigating NH3 emissions from dairy production systems and emphasize the necessity of whole-farm perspectives on the assessment of potential technical options for NH3 mitigation. This study also demonstrates the potential of utilizing process-based models, such as Manure-DNDC, to quantify and mitigate NH3 emissions from dairy farms

Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling

Shelf and coastal sea processes extend from the atmosphere through the water column and into the sea bed. These processes are driven by physical, chemical, and biological interactions at local scales, and they are influenced by transport and cross strong spatial gradients. The linkages between domains and many different processes are not adequately described in current model systems. Their limited integration level in part reflects lacking modularity and flexibility; this shortcoming hinders the exchange of data and model components and has historically imposed supremacy of specific physical driver models. We here present the Modular System for Shelves and Coasts (MOSSCO, http://www.mossco.de), a novel domain and process coupling system tailored---but not limited--- to the coupling challenges of and applications in the coastal ocean. MOSSCO builds on the existing coupling technology Earth System Modeling Framework and on the Framework for Aquatic Biogeochemical Models, thereby creating a unique level of modularity in both domain and process coupling; the new framework adds rich metadata, flexible scheduling, configurations that allow several tens of models to be coupled, and tested setups for coastal coupled applications. That way, MOSSCO addresses the technology needs of a growing marine coastal Earth System community that encompasses very different disciplines, numerical tools, and research questions.Comment: 30 pages, 6 figures, submitted to Geoscientific Model Development Discussion

Predicting the large-scale consequences of offshore wind turbine array development on a North Sea ecosystem

Three models were applied to obtaina first assessment of some of the potential impacts of large-scale operational wind turbine arrays on the marine ecosystem in a well-mixed area in a shelf sea: a biogeochemical model,a wave propagation model and an a coustic energy flux model.The results of the models are discussed separately and together to elucidate the combined effects. Overall,all three models suggested relatively weak environmental changes for the mechanisms included in this study, however these are only a subset of all the potential impacts,and a number of assumptions had to be made. Further work is required to address these assumptions and additional mechanisms. All three models suggested most of the changes with in the wind turbine array,and small changes up to several tens of km outside the array. Within the array, the acoustic model indicated the most concentrated, spatially repetitive changes to the environment,followed by the SWAN wave model,and the biogeochemical model being the most diffuse. Because of the different spatial scales of the response of the three models,the combined results suggested a spectrum of combinations of environmental changes with in the wind turbine array that marine organism smight respond to. The SWAN wave model and the acoustic model suggested a reduction in changes with increasing distance between turbines. The SWAN wave model suggested that the biogeochemical model, because of the in ability of its simple wave model to simulate wave propagation,over-estimated the biogeochemical changes by a factor of 2 or more. The biogeochemical model suggested that the benthic system was more sensitive to the environmental changes than the pelagic system

Rapid coastal deoxygenation due to ocean circulation shift in the NW Atlantic.

Global observations show that the ocean lost approximately 2% of its oxygen inventory over the last five decades 1-3, with important implications for marine ecosystems 4, 5. The rate of change varies with northwest Atlantic coastal waters showing a long-term drop 6, 7 that vastly outpaces the global and North Atlantic basin mean deoxygenation rates 5, 8. However, past work has been unable to resolve mechanisms of large-scale climate forcing from local processes. Here, we use hydrographic evidence to show a Labrador Current retreat is playing a key role in the deoxygenation on the northwest Atlantic shelf. A high-resolution global coupled climate-biogeochemistry model 9 reproduces the observed decline of saturation oxygen concentrations in the region, driven by a retreat of the equatorward-flowing Labrador Current and an associated shift toward more oxygen-poor subtropical waters on the shelf. The dynamical changes underlying the shift in shelf water properties are correlated with a slowdown in the simulated Atlantic Meridional Overturning Circulation 10. Our results provide strong evidence that a major, centennial-scale change of the Labrador Current is underway, and highlight the potential for ocean dynamics to impact coastal deoxygenation over the coming century