Geology topics

Lawrence B. Flanagan

Publications and source records attributed to Lawrence B. Flanagan.

4 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↗

USGS2018

Grasslands

Key findings: Total grassland carbon stocks in the conterminous United States, estimated to be about 7.4 petagrams of carbon (Pg C) in 2005, are projected to increase to about 8.2 Pg C by 2050. Although U.S. grasslands are expected to remain carbon sinks over this period, the uptake rate is projected to decline by about half. In the U.S. Great Plains, land-use and land-cover changes are expected to cause much of the change in carbon cycling as grasslands are converted to agricultural lands or to woody biomes (medium confidence). Increasing temperatures and rising atmospheric carbon dioxide (CO2) concentrations interact to increase productivity in northern North American grasslands, but this productivity response will be mediated by variable precipitation, soil moisture, and nutrient availability (high confidence, very likely). Soil carbon in grasslands is likely to be moderately responsive to changes in climate over the next several decades. Field experiments in grasslands suggest that altered precipitation can increase soil carbon, while warming and elevated CO2 may have only minimal effects despite altered productivity (medium confidence, likely). Carbon stocks and net carbon uptake in grasslands can be maintained with appropriate land management including moderate levels of grazing. Fire suppression can lead to encroachment of woody vegetation and increasing carbon storage in mesic regions, at the expense of grassland vegetation (high confidence, likely).

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↗

USGS2005

Integration of CO2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Aim&ensp; Extrapolation of tower CO 2 fluxes will be greatly facilitated if robust relationships between flux components and remotely sensed factors are established. Long-term measurements at five Northern Great Plains locations were used to obtain relationships between CO 2 fluxes and photosynthetically active radiation ( Q ), other on-site factors, and Normalized Difference Vegetation Index ( NDVI ) from the SPOT VEGETATION data set. Location&ensp; CO 2 flux data from the following stations and years were analysed: Lethbridge, Alberta 1998–2001; Fort Peck, MT 2000, 2002; Miles City, MT 2000–01; Mandan, ND 1999–2001; and Cheyenne, WY 1997–98. Results&ensp; Analyses based on light-response functions allowed partitioning net CO 2 flux ( F ) into gross primary productivity ( P g ) and ecosystem respiration ( R e ). Weekly averages of daytime respiration, γ day , estimated from light responses were closely correlated with weekly averages of measured night-time respiration, γ night ( R 2 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of γ day on γ night for all sites were different from 1 : 1 relationships. Over 13 site-years, gross primary production varied from 459 to 2491 g CO 2 m −2 year −1 , ecosystem respiration from 996 to 1881 g CO 2 m −2 year −1 , and net ecosystem exchange from −537 (source) to +610 g CO 2 m −2 year −1 (sink). Maximum daily ecological light-use efficiencies, ɛ d , max = P g /Q , were in the range 0.014 to 0.032 mol CO 2 (mol incident quanta) −1 . Main conclusions&ensp; Ten-day average P g was significantly more highly correlated with NDVI than 10-day average daytime flux, P d ( R 2 = 0.46 to 0.77 for P g -NDVI and 0.05 to 0.58 for P d -NDVI relationships). Ten-day average R e was also positively correlated with NDVI , with R 2 values from 0.57 to 0.77. Patterns of the relationships of P g and R e with NDVI and other factors indicate possibilities for establishing multivariate functions allowing scaling-up local fluxes to larger areas using GIS data, temporal NDVI, and other factors.

Global Ecology and Biogeography↗