Geology topics

Andrew D. Richardson

Publications and source records attributed to Andrew D. Richardson.

12 recordsLinked to original sources

USGS2025

Network of networks: Time series clustering of AmeriFlux sites

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

Agricultural and Forest Meteorology↗

USGS2025

Phenology in higher education

Phenological data collection and analysis are well-suited to higher education settings, providing valuable opportunities for hands-on data collection, manipulation, and interpretation. Few subjects are more conducive or accessible for engaging diverse learners in meaningful and impactful science at such large scales and minimal cost. In this chapter, we provide a range of examples of how instructors have incorporated observing and analysis of seasonal phenomena into their curricula. Many of these examples can be readily replicated and folded into new courses.

Book chapter↗

USGS2023

Atmospheric radiocarbon for the period 1910 to 2021 recorded by annual plants

We present a timeseries of 14 CO 2 for the period 1910–2021 recorded by annual plants collected in the southwestern United States, centered near Flagstaff, Arizona. This timeseries is dominated by five commonly occurring annual plant species in the region, which is considered broadly representative of the southern Colorado Plateau. Most samples (1910–2015) were previously archived herbarium specimens, with additional samples harvested from field experiments in 2015–2021. We used this novel timeseries to develop a smoothed local record with uncertainties for “bomb spike” 14 C dating of recent terrestrial organic matter. Our results highlight the potential importance of local records, as we document a delayed arrival of the 1963–1964 bomb spike peak, lower values in the 1980s, and elevated values in the last decade in comparison to the most current Northern Hemisphere Zone 2 record. It is impossible to retroactively collect atmospheric samples, but archived annual plants serve as faithful scribes: samples from herbaria around the Earth may be an under-utilized resource to improve understanding of the modern carbon cycle.

Radiocarbon↗

USGS2021

FLUXNET-CH4: A global, multi-ecosystem database and analysis of methane seasonality from freshwater wetlands

Methane (CH 4 ) emissions from natural landscapes constitute roughly half of global CH 4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH 4 flux are ideal for constraining ecosystem-scale CH 4 emissions due to quasi-continuous and high-temporal-resolution CH 4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH 4 Version 1.0, the first open-source global dataset of CH 4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/ , last access: 7 April 2021). FLUXNET-CH 4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH 4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH 4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH 4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH 4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH 4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH 4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20 ∘ S to 20 ∘ N) the spring onset of elevated CH 4 emissions starts 3 d earlier, and the CH 4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH 4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH 4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH 4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH 4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH 4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH 4 peak). FLUXNET-CH 4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH 4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH 4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

Earth System Science Data↗

USGS2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Agricultural and Forest Meteorology↗

USGS2021

PS3: The Pheno-Synthesis software suite for integration and analysis of multi-scale, multi-platform phenological data

Phenology is the study of recurring plant and animal life-cycle stages which can be observed across spatial and temporal scales that span orders of magnitude (e.g., organisms to landscapes). The variety of scales at which phenological processes operate is reflected in the range of methods for collecting phenologically relevant data, and the programs focused on these collections. Consideration of the scale at which phenological observations are made, and the platform used for observation, is critical for the interpretation of phenological data and the application of these data to both research questions and land management objectives. However, there is currently little capacity to facilitate access, integration and analysis of cross-scale, multi-platform phenological data. This paper reports on a new suite of software and analysis tools – the “Pheno-Synthesis Software Suite,” or PS3 – to facilitate integration and analysis of phenological and ancillary data, enabling investigation and interpretation of phenological processes at scales ranging from organisms to landscapes and from days to decades. We use PS3 to investigate phenological processes in a semi-aride, mixed shrub-grass ecosystem, and find that the apparent importance of seasonal precipitation to vegetation activity (i.e., “greenness”) is affected by the scale and platform of observation. We end by describing potential applications of PS3 to phenological modeling and forecasting, understanding patterns and drivers of phenological activity in real-world ecosystems, and supporting agricultural and natural resource management and decision-making.

Ecological Informatics↗

USGS2019

FLUXNET-CH4 synthesis activity: Objectives, observations, and future directions

This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH 4 ) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH 4 flux measurements globally, initial results comparing CH 4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH 4 fluxes across sites ranged from −0.2 ± 0.02 g C m –2 yr –1 for an upland forest site to 114.9 ± 13.4 g C m –2 yr –1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m –2 yr –1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH 4 flux across wetland sites globally. Water table position was positively correlated with annual CH 4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH 4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH 4 estimates due to gap-filling and random errors were on average ±1.6 g C m –2 yr –1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH 4 flux database, the controls on ecosystem CH 4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH 4 emissions.

Bulletin of the American Meteorological Society↗

USGS2017

Cross-scale phenological data integration to benefit resource management and monitoring

Climate change is presenting new challenges for natural resource managers charged with maintaining sustainable ecosystems and landscapes. Phenology, a branch of science dealing with seasonal natural phenomena (bird migration or plant flowering in response to weather changes, for example), bridges the gap between the biosphere and the climate system. Phenological processes operate across scales that span orders of magnitude—from leaf to globe and from days to seasons—making phenology ideally suited to multiscale, multiplatform data integration and delivery of information at spatial and temporal scales suitable to inform resource management decisions. A workshop report: Workshop held June 2016 to investigate opportunities and challenges facing multi-scale, multi-platform integration of phenological data to support natural resource management decision-making.

Conference Paper↗

USGS2014

Approaches for advancing scientific understanding of macrosystems

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them.

Frontiers in Ecology and the Environment↗

USGS2013

Phenology monitoring protocol: Northeast Temperate Network

Phenology is critical to many aspects of human life and nearly all ecological relationships and processes. Recent climate change has already led to widespread changes in phenological patterns across the globe, and more change is inevitable. This protocol has been developed to provide standardized methods for monitoring phenology within the National Park Service (NPS) Northeast Temperate Network (NETN), as part of the NPS Inventory and Monitoring Program (I&M). NETN encompasses the Appalachian National Scenic Trail (APPA), Acadia National Park (ACAD), the Boston Harbor Islands National Recreation Area (BOHA), and 10 national historical parks and national historic sites in the northeastern US. This protocol was developed in collaboration with and relies upon the procedures and infrastructure of the USA National Phenology Network (USA-NPN), including Nature’s Notebook , USA-NPN’s online plant and animal phenology observation program (www.nn.usanpn.org). Organized in 2007, USA-NPN is a nation-wide partnership among federal agencies, schools and universities, citizen volunteers, and others to monitor and understand the influence of seasonal cycles on the nation’s biological resources. The overall goal of NETN’s phenology monitoring program is to determine trends in the phenology of key species in order to assist park managers with the detection and mitigation of the effects of climate change on park resources. An additional programmatic goal is to interest and educate park visitors and staff, as well as a cadre of volunteer monitors.

National Park Service Natural Resource Report↗

USGS2011

Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site‐level synthesis

[1] 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.

Journal of Geophysical Research: Biogeosciences↗

USGS2009

Climate and hydrological changes in the northeastern United States: recent trends and implications for forested and aquatic ecosystems

We review twentieth century and projected twenty-first century changes in climatic and hydrologic conditions in the northeastern United States and the implications of these changes for forest ecosystems. Climate warming and increases in precipitation and associated changes in snow and hydrologic regimes have been observed over the last century, with the most pronounced changes occurring since 1970. Trends in specific climatic and hydrologic variables differ in their responses spatially (e.g., coastal vs. inland) and temporally (e.g., spring vs. summer). Trends can differ depending on the period of record analyzed, hinting at the role of decadal-scale climatic variation that is superimposed over the longer-term trend. Model predictions indicate that continued increases in temperature and precipitation across the northeastern United States can be expected over the next century. Ongoing increases in growing season length (earlier spring and later autumn) will most likely increase evapotranspiration and frequency of drought. In turn, an increase in the frequency of drought will likely increase the risk of fire and negatively impact forest productivity, maple syrup production, and the intensity of autumn foliage coloration. Climate and hydrologic changes could have profound effects on forest structure, composition, and ecological functioning in response to the changes discussed here and as described in related articles in this issue of the Journal.

Connecticut, Maine, Massachusetts, New Hampshire, ↗