Cristina Poindexter

The contribution of an overlooked transport process to a wetland's methane emissions

Wetland methane transport processes affect what portion of methane produced in wetlands reaches the atmosphere. We model what has been perceived to be the least important of these transport processes: hydrodynamic transport of methane through wetland surface water and show that its contribution to total methane emissions from a temperate freshwater marsh is surprisingly large. In our 1 year study, hydrodynamic transport comprised more than half of nighttime methane fluxes and was driven primarily by water column thermal convection occurring overnight as the water surface cooled. Overall, hydrodynamic transport was responsible for 32% of annual methane emissions. Many methane models have overlooked this process, but our results show that wetland methane fluxes cannot always be accurately described using only other transport processes (plant-mediated transport and ebullition). Modifying models to include hydrodynamic transport and the mechanisms that drive it, particularly convection, could help improve predictions of future wetland methane emissions.

Observational Data Patterns for Time Series Data Quality Assessment

Observational data are fundamental for scientific research in almost any domain. Recent advances in sensor and data management technologies are enabling unprecedented amounts of observational data to be collected and analyzed. However, an essential part of using observational data is not currently as scalable as data collection and analysis methods: data quality assurance and control. While specialized tools for very narrow domains do exist, general methods are harder to create. This paper explores the identification of data issues that lead to the creation of data tests and tools to perform data quality control activities. Developing this identification step in a systematic manner allows for better and more general quality control tools. As our case study, we use carbon, water, and energy fluxes as well as micro-meteorological data collected at field sites that are part of FLUXNET, a network of over 400 ecosystem-level monitoring stations. In an effort toward the release of a new global data set of fluxes, we are doing data quality control for these data. The experience from this work led to the creation of a catalog of issues identified in the data. This paper presents this catalog and its generalization into a set of patterns of data quality issues that can be detected in observational data.

Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands

Wetlands and flooded peatlands can sequester large amounts of carbon (C) and have high greenhouse gas mitigation potential. There is growing interest in financing wetland restoration using C markets; however, this requires careful accounting of both CO2 and CH4 exchange at the ecosystem scale. Here we present a new model, the PEPRMT model (Peatland Ecosystem Photosynthesis Respiration and Methane Transport), which consists of a hierarchy of biogeochemical models designed to estimate CO2 and CH4 exchange in restored managed wetlands. Empirical models using temperature and/or photosynthesis to predict respiration and CH4 production were contrasted with a more process-based model that simulated substrate-limited respiration and CH4 production using multiple carbon pools. Models were parameterized by using a model-data fusion approach with multiple years of eddy covariance data collected in a recently restored wetland and a mature restored wetland. A third recently restored wetland site was used for model validation. During model validation, the process-based model explained 70% of the variance in net ecosystem exchange of CO2 (NEE) and 50% of the variance in CH4 exchange. Not accounting for high respiration following restoration led to empirical models overestimating annual NEE by 33–51%. By employing a model-data fusion approach we provide rigorous estimates of uncertainty in model predictions, accounting for uncertainty in data, model parameters, and model structure. The PEPRMT model is a valuable tool for understanding carbon cycling in restored wetlands and for application in carbon market-funded wetland restoration, thereby advancing opportunity to counteract the vast degradation of wetlands and flooded peatlands.

Hunting Data Rogues at Scale: Data Quality Control for Observational Data in Research Infrastructures

Data quality control is one of the most time consuming activities within Research Infrastructures (RIs), especially when involving observational data and multiple data providers. In this work we report on our ongoing development of data rogues, a scalable approach to manage data quality issues for observational data within RIs. The motivation for this work started with the creation of the FLUXNET2015 dataset, which includes carbon, water, and energy fluxes plus micrometeorological and ancillary data measured in over 200 sites around the world. To create an uniform dataset, including derived data products, extensive work on data quality control was needed. The unpredictable nature of observational data quality issues makes the automation of data quality control inherently difficult. Developed based on this experience, the data rogues methodology allows for increased automation of quality control activities by systematically identifying, cataloging, and documenting implementations of solutions to data issues. We believe this methodology can be extended and applied to others domains and types of data, making the automation of data quality control a more tractable problem.