Geology topics

Andrew E. Suyker

Publications and source records attributed to Andrew E. Suyker.

5 recordsLinked to original sources

USGS2014

Productivity and carbon dioxide exchange of leguminous crops: estimates from flux tower measurements

Net CO 2 exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic light-response function method. The analyses produced net CO 2 exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO 2 exchange rates and ecophysiological parameters of legumes were lower than those of maize ( Zea mays L.) but higher than for wheat ( Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO 2 m –2 yr –1 to sources of –2100 g CO 2 m –2 yr –1 , with an average of –330 g CO 2 m –2 yr –1 , indicating overall moderate CO 2 –source activity related to a shorter period of photosynthetic uptake and metabolic costs of N 2 fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO 2 , with an average net ecosystem production of 980 (range 550–1200) g CO 2 m –2 yr –1 .

Agronomy Journal↗

USGS2012

The General Ensemble Biogeochemical Modeling System (GEMS) and its applications to agricultural systems in the United States: Chapter 18

The General Ensemble Biogeochemical Modeling System (GEMS) was es in individual models, it uses multiple site-scale biogeochemical models to perform model simulations. Second, it adopts Monte Carlo ensemble simulations of each simulation unit (one site/pixel or group of sites/pixels with similar biophysical conditions) to incorporate uncertainties and variability (as measured by variances and covariance) of input variables into model simulations. In this chapter, we illustrate the applications of GEMS at the site and regional scales with an emphasis on incorporating agricultural practices. Challenges in modeling soil carbon dynamics and greenhouse emissions are also discussed.

Book chapter↗

USGS2012

Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States

Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation ( R 2 =0.94, N =24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement ( R 2 =0.98, N =24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C 3 versus C 4 plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C 3 versus C 4 based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.

Journal of Applied Remote Sensing↗

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↗

USGS2011

Estimating seasonal evapotranspiration from temporal satellite images

Estimating seasonal evapotranspiration (ET) has many applications in water resources planning and management, including hydrological and ecological modeling. Availability of satellite remote sensing images is limited due to repeat cycle of satellite or cloud cover. This study was conducted to determine the suitability of different methods namely cubic spline, fixed, and linear for estimating seasonal ET from temporal remotely sensed images. Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model in conjunction with the wet METRIC (wMETRIC), a modified version of the METRIC model, was used to estimate ET on the days of satellite overpass using eight Landsat images during the 2001 crop growing season in Midwest USA. The model-estimated daily ET was in good agreement ( R 2 = 0.91) with the eddy covariance tower-measured daily ET. The standard error of daily ET was 0.6 mm (20%) at three validation sites in Nebraska, USA. There was no statistically significant difference ( P > 0.05) among the cubic spline, fixed, and linear methods for computing seasonal (July–December) ET from temporal ET estimates. Overall, the cubic spline resulted in the lowest standard error of 6 mm (1.67%) for seasonal ET. However, further testing of this method for multiple years is necessary to determine its suitability.

Irrigation Science↗