Vanessa Genovese

Net primary production of terrestrial ecosystems from 2000 to 2009

The CASA (Carnegie-Ames-Stanford) ecosystem model has been used to estimate monthly carbon fluxes in terrestrial ecosystems from 2000 to 2009, with global data inputs from NASA’s Terra Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation cover mapping. Net primary production (NPP) flux for atmospheric carbon dioxide has varied slightly from year-to-year, but was predicted to have increased over short multi-year periods in the regions of the high-latitude Northern Hemisphere, South Asia, Central Africa, and the western Amazon since the year 2000. These CASA results for global NPP were found to be in contrast to other recently published modeling trends for terrestrial NPP with high sensitivity to regional drying patterns. Nonetheless, periodic declines in regional NPP were predicted by CASA for the southern and western Untied States, the southern Amazon, and southern and eastern Africa. NPP in tropical forest zones was examined in greater detail to discover lower annual production values than previously reported in many global models across the tropical rainforest zones, likely due to the enhanced detection of lower production ecosystems replacing primary rainforest.

Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region

Previous field measurements have implied that undisturbed Amazon forests may represent a substantial terrestrial sink for atmospheric carbon dioxide. We investigated this hypothesis using a regional ecosystem model for net primary production (NPP) and soil biogeochemical cycling. Seasonal and interannual controls on net ecosystem production (NEP) were studied with integration of high-resolution (8-km) multiyear satellite data to characterize Amazon land surface properties over time. Background analysis of temporal and spatial relationships between regional rainfall patterns and satellite observations (for vegetation land cover, fire counts, and smoke aerosol effects) reveals several notable patterns in the model driver data. Autocorrelation analysis for monthly vegetation “greenness” index (normalized difference vegetation index, NDVI) from the advanced very high resolution radiometer (AVHRR) and monthly rainfall indicates a significant lag time correlation of up to 12 months. At lag times approaching 36 months, autocorrelation function (ACF) values did not exceed the 95% confidence interval at locations west of about 47°W, which is near the transition zone of seasonal tropical forest and other (nonforest) vegetation types. Even at lag times of 12 months or less, the location near Manaus (approximately 60°W) represents the farthest western point in the Amazon region where seasonality of rainfall accounts significantly for monthly variations in forest phenology, as observed using NDVI. Comparisons of NDVI seasonal profiles in areas of the eastern Amazon widely affected by fires (as observed from satellite) suggest that our adjusted AVHRR-NDVI captures year-to-year variation in land cover greenness with minimal interference from small fires and smoke aerosols. Ecosystem model results using this newly generated combination of regional forcing data from satellite suggest that undisturbed Amazon forests can be strong net sinks for atmospheric carbon dioxide, particularly during wet (non El Nino) years. However, drought effects during El Nino years can reduce NPP in primary forests of the eastern Amazon by 10–20%, compared to long-term average estimates of regional productivity. Annual NEP for the region is predicted to range from −0.4 Pg C yr−1 (net CO2 source) to 0.5 Pg C yr−1 (net CO2 sink), with large interannual variability over the states of Para, Maranhao, and Amazonas. As in the case of predicted NPP, it appears that periods of relatively high solar surface irradiance combined with several months of adequate rainfall are required to sustain the forest carbon sink for positive yearly NEP estimates.

Recent History of Large-Scale Ecosystem Disturbances in North America Derived from the AVHRR Satellite Record

Ecosystem structure and function are strongly affected by disturbance events, many of which in North America are associated with seasonal temperature extremes, wildfires, and tropical storms. This study was conducted to evaluate patterns in a 19-year record of global satellite observations of vegetation phenology from the advanced very high resolution radiometer (AVHRR) as a means to characterize major ecosystem disturbance events and regimes. The fraction absorbed of photosynthetically active radiation (FPAR) by vegetation canopies worldwide has been computed at a monthly time interval from 1982 to 2000 and gridded at a spatial resolution of 8–km globally. Potential disturbance events were identified in the FPAR time series by locating anomalously low values (FPAR-LO) that lasted longer than 12 consecutive months at any 8-km pixel. We can find verifiable evidence of numerous disturbance types across North America, including major regional patterns of cold and heat waves, forest fires, tropical storms, and large-scale forest logging. Summed over 19 years, areas potentially influenced by major ecosystem disturbances (one FPAR-LO event over the period 1982–2000) total to more than 766,000 km2. The periods of highest detection frequency were 1987–1989, 1995–1997, and 1999. Sub-continental regions of the Pacific Northwest, Alaska, and Central Canada had the highest proportion (>90%) of FPAR-LO pixels detected in forests, tundra shrublands, and wetland areas. The Great Lakes region showed the highest proportion (39%) of FPAR-LO pixels detected in cropland areas, whereas the western United States showed the highest proportion (16%) of FPAR-LO pixels detected in grassland areas. Based on this analysis, an historical picture is emerging of periodic droughts and heat waves, possibly coupled with herbivorous insect outbreaks, as among the most important causes of ecosystem disturbance in North America.

Changes in the Carbon Cycle of Amazon Ecosystems During the 2010 Drought

Satellite remote sensing was combined with the NASA-CASA (Carnegie Ames Stanford Approach) carbon cycle simulation model to evaluate the impact of the 2010 drought (July through September) throughout tropical South America. Results indicated that net primary production in Amazon forest areas declined by an average of 7% in 2010 compared to 2008. This represented a loss of vegetation CO2 uptake and potential Amazon rainforest growth of nearly 0.5 Pg C in 2010. The largest overall decline in ecosystem carbon gains by land cover type was predicted for closed broadleaf forest areas of the Amazon river basin, including a large fraction of regularly flooded forest areas. Model results support the hypothesis that soil and dead wood carbon decomposition fluxes of CO2 to the atmosphere were elevated during the drought period of 2010 in periodically flooded forest areas, compared to those for forests outside the main river floodplains.

Methane Emissions from Natural Wetlands in the United States: Satellite-Derived Estimation Based on Ecosystem Carbon Cycling

Abstract Wetlands are an important natural source of methane to the atmosphere. The amounts of methane emitted from inundated ecosystems in the United States can vary greatly from area to area. Seasonal temperature, water table dynamics, and carbon content of soils are principal controlling factors. To calculate the effect of wetlands (and their potential conversion to other land uses) on global greenhouse gas emissions, information on area covered by various wetland types is needed, along with verified projections of spatial variation in net methane emissions. Both of these variables are poorly known, and estimates are largely unavailable at the country level. Nationwide satellite datasets for the coterminous United States (excluding Alaska) have been combined with ecosystem model predictions of monthly net carbon exchange with the atmosphere to produce the first detailed mapping of methane fluxes from natural wetlands on a monthly and annual basis. The Carnegie–Ames–Stanford Approach (CASA) model’s pred...

Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data

BackgroundA simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate net primary productivity (NPP) of forest stands at the Bartlett Experiment Forest (BEF) in the White Mountains of New Hampshire.ResultsNet primary production (NPP) predicted from the NASA-CASA model using 30-meter resolution Landsat inputs showed variations related to both vegetation cover type and elevational effects on mean air temperatures. Overall, the highest predicted NPP from the NASA-CASA model was for deciduous forest cover at low to mid-elevation locations over the landscape. Comparison of the model-predicted annual NPP to the plot-estimated values showed a significant correlation of R2 = 0.5. Stepwise addition of 30-meter resolution elevation data values explained no more than 20% of the residual variation in measured NPP patterns at BEF. Both the Landsat 7 and the 250-meter resolution MODIS derived mean annual NPP predictions for the BEF plot locations were within ± 2.5% of the mean of plot estimates for annual NPP.ConclusionAlthough MODIS imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations as well as Landsat, the MODIS satellite data as inputs to the NASA-CASA model does accurately predict the average annual productivity of a site like the BEF.

Estimating carbon budgets for U.S. ecosystems

On a global basis, plants and soils may hold more than twice the amount of carbon present in the atmosphere [Geider et al., 2001]. Under increasing atmospheric carbon dioxide (CO2) concentrations and subsequently warming temperatures, these large biogenic pools may change in size [Cox et al., 2000]. Due to a lack of long-term field studies, there is uncertainty as to whether vegetation and soils will act as a net sink or a source of atmospheric CO2 in coming years. It is certain, however, that no retrospective analysis of the U.S. carbon balance will be possible without a comprehensive historical baseline of the sizes of various ecosystem carbon pools and the variability in their net annual increments.

Revealing land cover change in California with satellite data

The conversion of natural land cover into human-dominated cover types continues to be a change of global proportions with many unknown environmental consequences. Noteworthy conversions of this type include tree stand harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. Determining where, when, and why natural ecosystem conversions occur is a crucial scientific concern [Foley et al., 2005]. Characteristics of the land cover can have important impacts on local climate, radiation balance, biogeochemistry, hydrology and the diversity and abundance of terrestrial species [Randerson et al., 2006]. Consequently, understanding trends in land cover conversion at local scales is a requirement for making useful numerical predictions about other regional and global changes. It is urgent that accurate, timely, and economical tools be made available to document these conversions and aid in the management of their impacts.

Understanding Controls on Historical River Discharge in the World’s Largest Drainage Basins

Abstract Long-term (20 yr) river discharge records from 30 of the world’s largest river basins have been used to characterize surface hydrologic flows in relation to net precipitation inputs, ocean climate teleconnections, and human land/water use patterns. This groundwork study is presented as a precedent to distributed simulation modeling of surface hydrologic flows in large river basins. Correlation analysis is used as a screening method to classify river basins into categories based on major controls on discharge, for example, climate, land use, and dams. Comparisons of paired station records at upstream and downstream discharge locations within each major river basin suggest that the discharge signals represented in upstream discharge records are sustained in the downstream station records for nearly two-thirds of the drainage basins selected. River basins that showed the strongest localized climate control over historical discharge records, in terms of correlations with net basinwide precipitation r...

Satellite data help predict terrestrial carbon sinks

Accurate estimates of how much CO2 ecosystems can absorb will be fundamental to successful systems of international carbon accounting. NASAs Terra satellite platform, with the moderate resolution imaging spectro-radiometer (MODIS) instrument on board, provides a new era of observations for carbon cycle assessments. Direct input of satellite vegetation index “greenness” data from the MODIS sensor into ecosystem simulation models can be used to estimate spatial variability in monthly net primary production (NPP), biomass accumulation, and litter fall inputs to soil carbon pools. Global NPP of vegetation can be predicted using the relationship between leaf reflectance properties and the fraction of absorption of photosynthetically active radiation (FPAR) [Knyazikhin et al., 1999].