Pre-Launch Tasks Proposed in our Contract of December 1991

We propose, during the pre-EOS phase to: (1) develop, with other MODIS Team Members, a means of discriminating different major biome types with NDVI and other AVHRR-based data; (2) develop a simple ecosystem process model for each of these biomes, BIOME-BGC; (3) relate the seasonal trend of weekly composite NDVI to vegetation phenology and temperature limits to develop a satellite defined growing season for vegetation; and (4) define physiologically based energy to mass conversion factors for carbon and water for each biome. Our final core at-launch product will be simplified, completely satellite driven biome specific models for net primary production. We will build these biome specific satellite driven algorithms using a family of simple ecosystem process models as calibration models, collectively called BIOME-BGC, and establish coordination with an existing network of ecological study sites in order to test and validate these products. Field datasets will then be available for both BIOME-BGC development and testing, use for algorithm developments of other MODIS Team Members, and ultimately be our first test point for MODIS land vegetation products upon launch. We will use field sites from the National Science Foundation Long-Term Ecological Research network, and develop Glacier National Park as a major site for intensive validation

A bottom-up evolution of terrestrial ecosystem modeling theory, and ideas toward global vegetation modeling

A primary purpose of this review is to convey lessons learned in the development of a forest ecosystem modeling approach, from it origins in 1973 as a single-tree water balance model to the current regional applications. The second intent is to use this accumulated experience to offer ideas of how terrestrial ecosystem modeling can be taken to the global scale: earth systems modeling. A logic is suggested where mechanistic ecosystem models are not themselves operated globally, but rather are used to 'calibrate' much simplified models, primarily driven by remote sensing, that could be implemented in a semiautomated way globally, and in principle could interface with atmospheric general circulation models (GCM's)

Future potential net primary production trends of contiguous United States rangelands

Rangelands are an important ecosystem covering nearly 24% of the earth’s terrestrial vegetation. Climate change is predicted to affect many of the factors that influence the production of rangeland vegetation. Understanding future trends and patterns in net primary production (NPP) requires projected potential NPP to better understand how rangelands will be affected by a changing climate. Here, I used climate data projected from a global climate model (GCM) to drive the biogeochemical model (Biome-BGC) in an attempt to simulate future potential NPP trends in rangelands of the contiguous United States from 2001-2100 on a 100 km2 scale. In response to the simulated climate projections, I found an overall slight increase in potential NPP throughout time. However, these increases were not spatially consistent; in some areas, NPP decreased substantially. Biome-BGC found three distinct zones that have similar potential NPP trends and primary correlating climatic factors that drove these trends. The south western portion of the United States may see a decrease in NPP driven mostly by a decrease in moisture. This simulation indicates a rise in NPP in the Great Plains mostly from c4 grasses driven primarily by an increase in temperature. Furthermore, it projects little to no change in The Great Basin driven by a combination of a slight increase in precipitation and maximum temperature

Pre-Launch Tasks Proposed in our Contract of December 1991

We propose, during the pre-EOS phase to: (1) develop, with other MODIS Team Members, a means of discriminating different major biome types with NDVI and other AVHRR-based data. (2) develop a simple ecosystem process model for each of these biomes, BIOME-BGC (3) relate the seasonal trend of weekly composite NDVI to vegetation phenology and temperature limits to develop a satellite defined growing season for vegetation; and (4) define physiologically based energy to mass conversion factors for carbon and water for each biome. Our final core at-launch product will be simplified, completely satellite driven biome specific models for net primary production. We will build these biome specific satellite driven algorithms using a family of simple ecosystem process models as calibration models, collectively called BIOME-BGC, and establish coordination with an existing network of ecological study sites in order to test and validate these products. Field datasets will then be available for both BIOME-BGC development and testing, use for algorithm developments of other MODIS Team Members, and ultimately be our first test point for MODIS land vegetation products upon launch. We will use field sites from the National Science Foundation Long-Term Ecological Research network, and develop Glacier National Park as a major site for intensive validation

Land Ecosystems and Hydrology

The terrestrial biosphere is an integral component of the Earth Observing System (EOS) science objectives concerning climate change, hydrologic cycle change, and changes in terrestrial productivity. The fluxes o f CO2 and other greenhouse gases from the land surface influence the global circulation models directly, and changes in land cover change the land surface biophysical properties o f energy and mass exchange. Hydrologic cycle perturbations result from terrestrially-induced climate changes, and more directly from changes in land cover acting on surface hydrologic balances. Finally, both climate and hydrology jointly control biospheric productivity, the source o f food, fuel, and fiber for humankind. The role of the land system in each of these three topics is somewhat different, so this chapter is organized into the subtopics of Land-Climate, Land-Hydrology, and Land-Vegetation interactions (Figures 5.1, 5.2, and 5.3)

ASSESSING FOREST RESPONSES TO CLIMATE CHANGE AND RESOLVING PRODUCTIVITY MEASUREMENTS ACROSS SPATIAL SCALES

Informed decisions regarding forest and carbon resources require knowledge of the impacts of environmental changes on forest productivity. We also need to reconcile the diverging productivity estimates that are presently available. This dissertation assembles two publications addressing the impacts of climate change on forest productivity and one exploring the relationship between three estimates of forest productivity. In the first chapter, I evaluated whether forests have responded to recent changes in climatic conditions. Through combining published evidence I show that forests have responded to changes in the patterns of light, water, and temperatures over the last half of the 20th century. Most published studies showed a positive growth trend. Negative growth trends were found for drier study areas. Conclusions on the greening of the world\u27s forests, are difficult due to poor geographical coverage and measurement method disparity. In the second chapter, I compared three productivity estimation methods (two ground-based and one satellite-based) using 166 sites in Austria. Results of disturbance-free projections show the relevance of each method to actual site productivity and their combined usefulness in identifying the most appropriate scale for monitoring climate forcings. Each estimation method provides information on a portion of the underlying actual NPP. In the last chapter, I explore the effect of three IPCC climate change scenarios on forests of the US Northern Rockies. Results show an increase in growing season length and in water stress, and a decrease in snow quantities and in number of days with ground snow for all forests by 2089. Under the driest and warmest scenario, the majority of the sites became carbon sources, and I identify a water/temperature tipping point, past which system stored carbon drastically declines. For these disturbance-free projections, water availability drove the system. In this dissertation, I resolve a otential source of conflict among forest productivity estimates; combined, these estimates lead to a broader understanding of productivity. I also present evidence that forests are already responding to climate change, and that more drastic changes are likely in the future

Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics

Effects of land markets and land management on ecosystem function: A framework for modelling exurban land-change

This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux. To demonstrate the framework, we implement a simple integration of a new agent-based model of exurban residential development and land-management decisions with the ecosystem process model BIOME-BGC. Using a stylized scenario, we evaluate the influence of different exurban residential-land-management strategies on carbon storage at the parcel level over a 48-year period from 1958 to 2005, simulating stocks of carbon in soil, litter, vegetation, and net primary productivity. Results show 1) residential parcels with management practices that only provided additions in the form of fertilizer and irrigation to turfgrass stored slightly more carbon than parcels that did not include management practices, 2) conducting no land-management strategy stored more carbon than implementing a strategy that included removals in the form of removing coarse woody debris from dense tree cover and litter from turfgrass, and 3) the removal practices modelled had a larger impact on total parcel carbon storage than our modelled additions. The degree of variation within the evaluated land-management practices was approximately 42,104 kg C storage on a 1.62 ha plot after 48 years, demonstrating the substantial effect that residential land-management practices can have on carbon storag

Estimation of Incident Photosynthetically Active Radiation From Moderate Resolution Imaging Spectrometer Data

Incident photosynthetically active radiation (PAR) is a key variable needed by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and various applications. In this study, the authors develop a new method based on the look-up table approach for estimating instantaneous incident PAR from the polar-orbiting Moderate Resolution Imaging Spectrometer (MODIS) data. Since the top-of-atmosphere (TOA) radiance depends on both surface reflectance and atmospheric properties that largely determine the incident PAR, our first step is to estimate surface reflectance. The approach assumes known aerosol properties for the observations with minimum blue reflectance from a temporal window of each pixel. Their inverted surface reflectance is then interpolated to determine the surface reflectance of other observations. The second step is to calculate PAR by matching the computed TOA reflectance from the look-up table with the TOA values of the satellite observations. Both the direct and diffuse PAR components, as well as the total shortwave radiation, are determined in exactly the same fashion. The calculation of a daily average PAR value from one or two instantaneous PAR values is also explored. Ground measurements from seven FLUXNET sites are used for validating the algorithm. The results indicate that this approach can produce reasonable PAR product at 1 km resolution and is suitable for global applications, although more quantitative validation activities are still needed

Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid