Joseph C. Coughlan

A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes

An ecosystem process model is described that calculates the carbon, water and nitrogen cycles through a forest ecosystem. The model, FOREST-BGC, treats canopy interception and evaporation, transpiration, photosynthesis, growth and maintenance respiration, carbon allocation above and below-ground, litterfall, decomposition and nitrogen mineralization. The model uses leaf area index (lai) to quantify the forest structure important for energy and mass exchange, and this represents a key simplification for regional scale applications. FOREST-BGC requires daily incoming short-wave radiation, air temperature, dew point, and precipitation as driving variables. The model was used to simulate the annual hydrologic balance and net primary production of a hypothetical forest stand in seven contrasting environments across North America for the year 1984. Hydrologic partitioning ranged from 14/86/0% for evaporation, transpiration and outflow, respectively, in Fairbanks, AK (annual precipitation of 313 mm) to 10/27/66% in Jacksonville, FL (annual ppt of 1244 mm), and these balances changed as lai was increased from 3 to 9 in successive simulations. Net primary production (npp) ranged from 0.0 t C ha−1 year−1 at Tucson, AZ, to 14.1 t C ha−1 year−1 at Knoxville, TN and corresponded reasonably with observed values at each site. The sensitivity of ecosystem processes to varying lai in different climates was substantial, and underscores the utility of parameterizing this model at regional scales in the future with forest lai measurements derived from satellite imagery.

Forest ecosystem processes at the watershed scale: basis for distributed simulation

Abstract A framework is described to compute and map forest evapotranspiration and net primary productivity over complex mountainous terrain. The methodology is based on the interface of geographic information processing and remote sensing with FOREST-BGC, a nonlinear deterministic model designed to simulate carbon, water and nitrogen cycles in a forest ecosystem. The model as input the geographic patterns of leaf area index ( lai ), available soil water capacity ( swc ) and microclimatic parameters over the landscape. These patterns are represented with the use of a template consisting of the set of hillslopes, stream channels and subwatersheds that completely define the landscape. A geo-referenced database containing digital elevation data, remotely sensed information and other environmental data are stratified by this template. We have found that the stratification of the surface data sets by a hillslope or watershed template produces landscape units with low internal variance of the important model parameters but high between unit variance. By producing templates at different levels of resolution, we have the ability to reorganize the model parameter set to different levels of surface generalization. The model is directly parameterized for each of these surface units which can then be simulated in parallel, providing the ability to expand the simulation to large regions.

Regional ecosystem simulation: A general model for simulating snow accumulation and melt in mountainous terrain

A general snow accumulation and melt model was developed to (1)determine how accurately snow accumulation and ablation can bemodeled over heterogeneous landscapes with routinely available climatologic, topographic, and vegetation data, and (2) improveestimates of annual forest snow hydrology for point and regionalcalculations of annual forest productivity. The snow model wasdesigned to operate within the Regional HydroecologicalSimulation System (RHESSys), a GIS based modeling system tomanage spatial data for distributed computer simulations onwatershed scales. One feature of the RHESSys Snow Model (RSM) isit can use satellite derived forest leaf area index (LAI) torepresent catchment forest cover; difficult to obtain in adequatecover and resolution by any other means. The model was testedover 3 water years (October to September) with data recorded by10 snow telemetry stations (SNOTEL) in 5 states ranging inmeso-climate and elevations from a coastal Oregon site (1067 m)to a continental Colorado site (3261 m).Predictions for the 10 sites were made with identicalparameter values and only site climate varied for all sites. Theaverage difference between observed and predicted snow depletiondates for all sites and water years was 6.2 days and 8 of the 30simulations were within ± 2 days (R2 = 0.91). Radiation melt wasthe dominate snow ablation component at the Colorado site wheresublimation was 10% (LAI = 0) to 20% (LAI = 6) ofsnow loss while air temperature was the dominate component at theOregon site with sublimation reduced to 1% (LAI = 0) to6% (LAI = 6) of snow loss. LAI had a greater effectdetermining snow depletion than site aspect. Aspect increased inimportance if the snow depletion occurred during early springwhen solar insolation differences between hillslopes is greaterthan in the late spring.An accurate prediction of daily snowpack water equivalent(SWE) was not a strong determinant for making an accurateprediction of snowpack depletion date. Predicted snowpackdepletion dates were more sensitive to timing when the snowpackreached an isothermal condition. Daily estimates of SWE were mostsensitive to correctly estimating snowfall from SNOTEL data. Thismeans that for purposes of determining the snow depletion dateswhich are useful for forest ecosystem modeling, tracking SWE isless important then triggering snowmelt. Comparisons ofsimulations to published snow depletion dates show that RSMpredicted the relative ranking and magnitude of depletion fordifferent combinations of forest cover, elevation, and aspect.

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.

Ecohydrological Changes in the Murray-Darling Basin. III. A Simulation of Regional Hydrological Changes

Regional scale changes to the hydrological cycle of the Murray-Darling Basin (MDB) in Australia have occurred as a result of European settlement 200 years ago. Replacement of deep-rooted perennial plants (trees) by shallower rooting plants (pastures and cropping) is of particular significance in altering water-tables and causing waterlogging and secondary salinization. The purpose was to locate the areas at risk of waterlogging and salinization as a result of tree clearing. To achieve thais, present-day evaporation (ET) from 0.8% of the MDB (7750 km 2 ) is compared with ET from a reconstruction of the pre-European condition. The spatial geographical database for the 155 × 50 km study area consisted of vegetation, soils, climate and topographic information at 1.6 × 1.6 km cell resolution (3072 individual cells for each data layer) (...)

Modeling biogenic emissions of isoprene: exploration of model drivers, climate control algorithms, and use of global satellite observations

Abstract An improved global budget for isoprene emissions from terrestrial vegetation sources is fundamental to a better understanding of the oxidative capacity of the lower atmosphere and changes in the concentration of major greenhouse gases. In this study, we present a biosphere modeling analysis designed to ascertain the interactions of global data drivers for estimating biogenic isoprene emissions. We have integrated generalized isoprene emission algorithms into a process-based simulation model of ecosystem carbon fluxes, the NASA-CASA (Carnegie–Ames–Stanford Approach) model. This new modeling approach for predicting isoprene emissions operates on scales designed to directly link regional and global satellite data sets with estimates of ecosystem carbon cycling, hydrology, and related biogeochemistry. The NASA-CASA model results indicate that the annual isoprene flux from terrestrial plant sources is 559 Tg C . Three ecosystem types, broadleaf evergreen forest, dry tropical forest, and wooded grassland (savanna), account for approximately 80% of these global vegetation isoprene emissions. Based on analyses to improve understanding of the relative influence of climatic (e.g., light and temperature) versus biotic (NPP) controllers on predicted isoprene emission estimates, it appears that the largest portion of total biogenic flux to the global atmosphere is emitted from ecosystems that are mainly light-limited for isoprene emissions. These modeling results imply that, along with better process understanding of base emission factor controls for volatile organic compounds, improvements in global fields of solar surface radiation fluxes in warm climate zones will be needed to reduce major uncertainties in isoprene source fluxes.

Terrestrial Observation and Prediction System: integration of satellite and surface weather observations with ecosystem models

Satellite data are widely used in land surface models to compute carbon and water exchange processes. However, much of this work is retrospective in nature. To better represent current land surface conditions in weather/climate models or to provide timely information on ecosystem conditions for natural resource management, one must move from retrospective to real-time analysis. A number of advances allow us to develop a system that would allow such real-time assimilation. These include consistent and timely availability of land surface products from EOS/MODIS, and on-line availability of weather data from a number or surface weather stations. We have developed a data assimilation system, terrestrial observation and prediction system, that integrates satellite data, surface weather observations and weather/climate forecasts with a terrestrial ecosystem model. TOPS produces daily 1 km estimates of carbon and water fluxes using MODIS derived LAI, land cover and gridded meteorological data created using more than 2000 surface weather stations over the conterminous U.S. Daily outputs are expressed as anomalies from historical normals that were computed using 20 years (1982-2001) of satellite and surface weather data. TOPS is also capable of using short/mid-term weather/climate forecasts to produce forecasts of land surface conditions (snow pack, runoff, soil moisture and primary production) that are useful in resource management.

Investigations of BOREAS spatial data in support of regional ecosystem modeling

Simulation models are commonly used to scale up water, energy, and trace gas flux estimates from study plots to an entire region or biome, such as the North American boreal forest. As a means to validate ecosystem model predictions at the scale of the typical experimental area (100 m 2 to 1 km 2 ), it is necessary to quantify potential errors in spatial data layers used as model inputs, independently from prediction errors resulting from incorrect model design or flawed process algorithms. Our analysis of land cover, hydrology, and soil maps generated as part of the Boreal Ecosystem-Atmosphere Study (BOREAS) suggests that coverages for plant functional types, derived from a combination of Landsat Thematic Mapper and the advanced very high resolution radiometer (AVHRR) satellite images, provide the highest quality information to define spatial model parameters. This information is critical for boreal ecosystem simulations of small patch types (e.g., dry conifer, fen, and disturbed sites), which are frequently obscured at 1-km pixel resolution. We find that soil property information for the dominant class, as contained in the regional BOREAS soil maps, does not appear to be a highly consistent indicator of hydrologic dynamics for fen and other boreal wetlands at the study area level. It appears instead that accurate regional modeling analyses for methane and other biogenic trace gas fluxes will depend largely on relatively fine scale remote sensing to resolve the high level of undifferentiated pixel mixing seen among boreal forest types and fen/bog areas in aggregated 1-km AVHRR land cover data.

Mapping Regional Forest Evapotranspiration And Photosynthesis By Coupling Satellite Data With Ecosystem Simulation

Annual evapotranspiration (ET) and net photosynthesis (PSN) were estimated for a mountainous 28 x 55 km region of predominantly coniferous forests in western Montana. A simple geographic information system integrated topographic, soils, vegetation, and climate data at a 1.1—km scale defined by the satellite sensor pixel size. Leaf area index (LAI) of the forest was estimated with data from the NOAA (National Oceanic and Atmospheric Administration) Advanced Very High Resolution Radiometer (AVHRR). Daily microclimate of each cell was estimated from ground and satellite data and interpolated using MT—CLIM, a mountain microclimate simulator. A forest ecosystem simulation model, Forest—BGC, was used to calculate ET and PSN daily for each cell. Ranges of estimated LAI (4—15), ET (25—60 cm/yr), and PSN (9—20 Mg°ha—1°yr—1) across the landscape follow the trends expected in both magnitude and spatial pattern. These estimates compared well with field measurements of related variables, although absolute validation o...

Combining Remote Sensing and Forest Ecosystem Modeling: An Example Using the Regional HydroEcological Simulation System (RHESSys)

Images from airborne or satellite-based remote sensing systems are the only data available for regional and global productivity studies that do not require interpolation or extrapolation. Four categories of image use are identified: image classification, model initialization, model input and model verification. Model initialization using vegetation indices derived from images is discussed using a regional modeling framework, the Regional HydroEcological Simulation System (RHESSys). In this chapter, we illustrate RHESSys’ sensitivity to soil moisture and the interrelationships between the soil data theme and the vegetation and climate data themes. Improving image transfer functions can increase the quality of vegetation estimates; however, ancillary data (such as topography and soil data) are also needed at appropriate levels of accuracy and precision. An example simulation is provided, which uses vegetation data from two watersheds in western Montana. Results demonstrate the model’s sensitivity to soil data in a wet, dry climate, and indicate the importance of considering the data collection process as an integrated effort guided by modeling requirements and model sensitivity. Additional consideration must be made for validation and collection of independent data for these purposes.