Geology topics

John M. Frank

Publications and source records attributed to John M. Frank.

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

Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data

Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We found that Landsat Enhanced Vegetation Index (EVI), growing season AI, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r2 of 0.45 and root mean squared error (RMSE) of roughly one-quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005 and 2006 (150-d averages of 542.8, 544.3, and 536.5 g C m-2, respectively). Yet, we observed substantial variability in spatial patterns of soil respiration predictions that varied between years, suggesting that our method is sensitive to changes in respiration drivers. We compared our estimates to MODIS GPP and nocturnal net ecosystem exchange (NEE) derived from eddy covariance towers as a proxy for ecosystem respiration. Averaged across the predictive region, mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal NEE from eddy covariance towers. This study demonstrated that geospatial and remotely-sensed datasets can be used in a statistical modeling framework to estimate soil respiration at landscape scales.

Colorado, Wyoming↗

USGS2018

Using a Bayesian framework to account for advection in seven years of snowpack CO2 fluxes in a mortality-impacted subalpine forest

In subalpine forests where snowpacks can persist for 50% of the year or longer, wintertime subnivean soil respiration constitutes a significant, but poorly understood, portion of the carbon (C) cycle. The temporal and seasonal dynamics of this flux are difficult to measure compared to snow-free respiration. This makes it challenging to fully address key knowledge gaps in high-elevation forests, including effects of tree mortality on ecosystem C fluxes. Our paper attempts to overcome these challenges by developing a new method for estimating wintertime CO 2 flux from soil underneath winter snowpack based on CO 2 profiles in the snow and using this to examine potential effects of a tree mortality event on wintertime respiration. The chief problem for estimates based on snow CO 2 measurements is that during strong winds, vertical CO 2 profiles can depart significantly from linearity, resulting in CO 2 fluxes that are not steady-state diffusive fluxes. Consequently, these fluxes include an advective component induced by wind and atmospheric pressure, resulting in above-snowpack fluxes that may be temporally decoupled from biological flux production in the soil. These advective fluxes lead to errors in estimates of the total amount of CO 2 produced from the soil surface during the winter, and may complicate the detection of significant seasonal and interannual trends in snowpack CO 2 fluxes. Our goal was twofold: first, to improve methods for estimating wintertime subnivean soil respiration; and second, to use this new method to examine potential effects of forest mortality on respiration. We develop a Bayesian-based model of CO 2 snowpack profiles and accounts for CO 2 storage flux within the snowpack to calculate diffusive and advective fluxes from both the bottom and the top of the snowpack. We apply this model to hourly snowpack CO 2 profile data collected over 7 years at an AmeriFlux eddy covariance (EC) site in a Wyoming subalpine forest that had recently experienced a mass tree mortality event. Hourly advective fluxes from the snowpack surface are less variable over time than hourly EC fluxes. Nonetheless, on a daily time scale, the two methods agree well with each other. We found that soil biological CO 2 production entering the bottom of snowpack peaked 3 years after the onset of the bark beetle outbreak and 1-year after dead trees dropped their needles, presenting the first evidence of a short-term increase in respiration following bark beetle mortality. Our findings emphasize the importance of accounting for the role of wind-driven advection in wintertime respiration and suggest that tree mortality may nearly double wintertime respiration in the first few years.

Wyoming↗

USGS2014

Forest Ecosystem respiration estimated from eddy covariance and chamber measurements under high turbulence and substantial tree mortality from bark beetles

Eddy covariance nighttime fluxes are uncertain due to potential measurement biases. Many studies report eddy covariance nighttime flux lower than flux from extrapolated chamber measurements, despite corrections for low turbulence. We compared eddy covariance and chamber estimates of ecosystem respiration at the GLEES Ameriflux site over seven growing seasons under high turbulence (summer night mean friction velocity (u*) = 0.7 m s −1 ), during which bark beetles killed or infested 85% of the aboveground respiring biomass. Chamber-based estimates of ecosystem respiration during the growth season, developed from foliage, wood and soil CO 2 efflux measurements, declined 35% after 85% of the forest basal area had been killed or impaired by bark beetles (from 7.1 ±0.22 μmol m −2 s −1 in 2005 to 4.6 ±0.16 μmol m −2 s −1 in 2011). Soil efflux remained at ~3.3 μmol m −2 s −1 throughout the mortality, while the loss of live wood and foliage and their respiration drove the decline of the chamber estimate. Eddy covariance estimates of fluxes at night remained constant over the same period, ~3.0 μmol m −2 s −1 for both 2005 (intact forest) and 2011 (85% basal area killed or impaired). Eddy covariance fluxes were lower than chamber estimates of ecosystem respiration (60% lower in 2005, and 32% in 2011), but the mean night estimates from the two techniques were correlated within a year (r 2 from 0.18-0.60). The difference between the two techniques was not the result of inadequate turbulence, because the results were robust to a u* filter of > 0.7 m s −1 . The decline in the average seasonal difference between the two techniques was strongly correlated with overstory leaf area (r 2 =0.92). The discrepancy between methods of respiration estimation should be resolved to have confidence in ecosystem carbon flux estimates.

Wyoming↗