USGS ScienceSearch

USGS · 70156761

Simulation of nitrous oxide and nitric oxide emissions from tropical primary forests in the Costa Rican Atlantic Zone

Abstract

Nitrous oxide (N 2 O) and nitric oxide (NO) are important atmospheric trace gases participating in the regulation of global climate and environment. Predictive models on the emissions of N 2 O and NO emissions from soil into the atmosphere are required. We modified the CENTURY model (Soil Sci. Soc. Am. J., 51 (1987) 1173) to simulate the emissions of N 2 O and NO from tropical primary forests in the Atlantic Zone of Costa Rica at a monthly time step. Combined fluxes of N 2 O and NO were simulated as a function of gross N mineralization and water-filled pore space (WFPS). The coefficients for partitioning N 2 O from NO were derived from field measurements (Global Biogeochem. Cycles, 8 (1994) 399). The modified CENTURY was calibrated against observations of carbon stocks in various pools of forest ecosystems of the region, and measured WFPS and emission rates of N 2 O and NO from soil to the atmosphere. WFPS is an important factor regulating nutrient cycling and emissions of N2O and NO from soils making the accuracy of the WFPS prediction central to the modeling process. To do this, we modified the hydrologic submodel and developed a new method for the prediction of WFPS at the monthly scale from daily rainfall information. The new method is based on: (1) the relationship between monthly rainfall and the number of rainfall events, and (2) the relative cumulative frequency distribution of ranked daily rainfall events. The method is generic and should be applicable to other areas. Simulated monthly average WFPS was 0.68±0.02 — identical with the field measurement average of 0.68±0.02 from the annual cycle observed by Keller and Reiners (Global Biogeochem. Cycles, 8 (1994) 399). Simulated fluxes of N 2 O and NO were 52.0±9.4 mg-N m −2 month −1 and 6.5±0.7 mg-N m −2 month −1 , respectively, compared with measured averages of 48.2±11.0 mg-N m −2 month −1 and 7.1±1.1 mg-N m −2 month −1 . The simulated N 2 O/NO ratio was 11.2±1.9 compared with the measured value of 10.9±4.7. WFPS is the dominant determinant of the fraction of gross N mineralization that is emitted from the soil as N 2 O and NO. If WFPS were not limiting during part of the year, this fraction would be 4.2%. With some periods of lower WFPS, the realized fraction is 2.2%. Because of the strong relationships between N 2 O and NO emission rates and rainfall and its derivative, WFPS, these moisture variables can be used to scale up nitrogen trace gas fluxes from sites to larger spatial scales.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shu-Guang Liu, William A. Reiners, Michael Keller, Davis S. Schimel. 2000. Simulation of nitrous oxide and nitric oxide emissions from tropical primary forests in the Costa Rican Atlantic Zone. https://doi.org/10.1016/s1364-8152(00)00030-x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

A catalogue of Do's and Don'ts in the modeling of environmental systems

Modeling plays a vital role in understanding and managing complex environmental systems, but its credibility and quality depend heavily on a comprehensive set of defensible model activities and practices, especially when the system of interest is plagued with uncertainties and conflicting stakeholder perspectives. This paper proposes a catalogue of Do's and Don'ts to guide modelers in addressing the many pertinent considerations through the whole modeling cycle. This practical tool provides advice on approaching modeling effectively through adhering to good modeling practice. It emphasizes model choices that align with the model purpose and context, and the justification and documentation of modeling decisions and assumptions. Managing uncertainty is a core consideration. The identification, assessment and reporting of these uncertainties is important across the entire modeling process, which spans problem framing, technical design, implementation and application phases. Such good practices are critical for transparency and reliability of the modeling.

Environmental Modelling and Software

Automating physics-based models to estimate thermoelectric-power water use

Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).

Environmental Modelling and Software

Transfer learning with convolutional neural networks for hydrological streamline delineation

Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline delineation. We evaluate the performance of eleven image-based pre-trained models and a baseline model using datasets from Rowan County, North Carolina, and Covington River, Virginia in the USA. Our results demonstrate that when models are adapted to a new area, the fine-tuned ImageNet pre-trained model exhibits superior predictive accuracy, markedly higher than the models trained from scratch or those only fine-tuned on the same area. Moreover, the pre-trained model achieves better smoothness and connectivity between classified streamline channels. These findings underline the effectiveness of transfer learning in enhancing the delineation of hydrological streamlines across varied geographies, offering a scalable solution for accurate and efficient environmental modelling.

North Carolina, Virginia