Erandathie Lokupitiya

Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape.

An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2 -C sink estimates were generally slightly larger, 8-20% for PSU, 10-20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region.

Assessing the impact of crops on regional CO2 fluxes and atmospheric concentrations.

Human conversion of natural ecosystems to croplands modifies not only the exchange of water and energy between the surface and the atmosphere, but also carbon fluxes. To investigate the impacts of crops on carbon fluxes and resulting atmospheric CO2 concentrations in the mid-continent region of the United States, we coupled a crop-specific phenology and physiology scheme for corn, soybean and wheat to the coupled ecosystem–atmosphere model SiB3–RAMS. Using SiBcrop–RAMS improved carbon fluxes at the local scale and had regional impacts, decreasing the spring uptake and increasing the summer uptake over the mid-continent. The altered fluxes changed the mid-continent atmospheric CO2 concentration field at 120 m compared to simulations without crops: concentrations increased in May and decreased >20 ppm during July and August, summer diurnal cycle amplitudes increased, synoptic variability correlations improved and the gradient across the mid-continent region increased. These effects combined to reduce the squared differences between the model and high-precision tower CO2 concentrations by 20%. Synoptic transport of the large-scale N–S gradient caused significant day-to-day variability in concentration differences measured between the towers. This simulation study shows that carbon exchange between crops and the atmosphere significantly impacts regional CO2 fluxes and concentrations.

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2 seasonal uptake over agricultural regions.

Use of AVHRR NDVI time series and ground-based surveys for estimating county-level crop biomass

Crop biomass and residue production are major components of cropland carbon dynamics that can be estimated using yield data from ground-based surveys. In the USA, surveyed yield data are available at county level and have been widely used for various research, economic and policy purposes, in addition to biomass estimation. However, survey data may be unavailable for certain times and/or locations and thus biomass estimates using remotely sensed data might be used to fill in any missing biomass data for estimating residue production and carbon dynamics in croplands. Compared to ground-based surveys, remotely sensed data are collected on a regular schedule and may also provide more spatially resolved data. We analysed composite biweekly Normalized Difference Vegetation Index (NDVI) data obtained using the Advanced Very High Resolution Radiometer (AVHRR) sensor and crop aboveground biomass (AGBM) estimated from available county-level yield data reported by the National Agricultural Statistics Service (NASS) for three crops (corn, soybean and oats) during 1992, 1997 and 2002. The aim of the study was to explore the relationships between NDVI and crop biomass to complete the missing biomass data in counties where no NASS-reported yields are available for biomass estimation. AGBM was estimated from Pathfinder biweekly NDVI, using canonical correlation analysis (CCA) and best subset multiple regressions incorporating canonical variates from NDVI time series. Cross-validation of model estimates was performed by randomly splitting the dataset into training and application subsets, simulating a 10–40% range of missing values. NDVI and crop biomass in Iowa during a given year were well correlated, with coefficient of determination (R 2) values > 0.8 in most cases. Using the available (training) data from a single year or a combination of years to derive models for filling the missing (validation) data within the same time period yielded a mean estimated biomass with < 1% relative error and bias. However, models applied to out-of-sample years had lower (< 0.4) R 2 values for the relationships between biomass and NDVI, although the mean residuals were low.