NTSG Projects

Ecosystem Modeling

Download Biome-BGC 4.2

This project page serves as a central place for model information and source code. Biome-BGC is an ecosystem process model that estimates storage and flux of carbon, nitrogen and water. Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems.

RHESSys = Regional Hydrological and Ecological Simulation System

RHESSys combines the terrestrial ecosystem process model Biome-BGC with spatially explicit meteorological information from MTCLIM or Daymet and the TOPMODEL hydrologic routing model to let us to make spatial and temporal predictions of carbon, water, and nitrogen dynamics over landscapes.

Global Land Datasets

SMAP is one of four first-tier missions recommended by the National Research Council's Committee on Earth Science and Applications from Space (Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, Space Studies Board, National Academies Press, 2007). SMAP data have both high science value and high applications value.

This global database contains a satellite passive microwave remote sensing based land parameter retrievals generated from the Advanced Microwave Scanning Radiometer on EOS Aqua (AMSR-E), with funding from the NASA. The daily land parameter retrievals extend from 2002 and include daily air temperature minima and maxima (ta; ~2 m height), fractional cover of open water on land (fw), vegetation canopy microwave transmittance (tc), surface soil moisture (mv; ≤ 2 cm soil depth) and integrated water vapor content of the intervening atmosphere (V; total column).

The FT-ESDR is a NASA MEaSUREs (Making Earth System Data Records for Use in Research Environments) funded effort to provide a consistent long-term global data record of land surface freeze/thaw (FT) state dynamics for all vegetated regions where low temperatures are a major constraint to ecosystem processes.

Wetlands exert major impacts on global biogeochemistry, hydrology, and biological diversity. The extent and seasonal, interannual, and decadal variation of inundated wetland areas play key roles in ecosystem dynamics.

Seasonal vegetation dynamics significantly impact the carbon cycle and weather (surface energy balance, transpiration vapor fluxes). These impacts are related to growing season length for evergreen ecosystems, to timing of leaf flush and senescence for drought- or cold-deciduous systems, and to seasonal and annual variability in canopy biomass. Spaceborne remote sensing is the only practical tool for monitoring seasonal vegetation dynamics globally with high temporal repeat and moderate spatial resolution.

Approximately 68-80 percent of the North American region experiences seasonal freezing and thawing with the relative influence of these processes on terrestrial carbon budgets generally increasing at higher latitudes and elevations. The timing and duration of surface and soil freeze-thaw state is closely linked to vegetation phenology and growing season dynamics in northern temperate, sub-alpine, boreal and arctic biomes.

We are developing a new satellite-based approach for regional assessment and monitoring of terrestrial net carbon exchange (NEE) for the pan-Arctic; NEE quantifies the magnitude and direction of land-atmosphere net CO2 exchange and is a fundamental measure of the balance between carbon uptake by vegetation net primary production (NPP) and carbon loss through soil heterotrophic respiration (Rh).  We are using satellite microwave remote sensing to extract surface soil wetness and temperature information with existing satellite-based measurements of vegetation structure (L

Approximately 68-80 percent of the North American region experiences seasonal freezing and thawing with the relative influence of these processes on terrestrial carbon budgets generally increasing at higher latitudes and elevations. The timing and duration of surface and soil freeze-thaw state is closely linked to vegetation phenology and growing season dynamics in northern temperate, sub-alpine, boreal and arctic biomes.

Seasonal vegetation dynamics significantly impact the carbon cycle and weather (surface energy balance, transpiration vapor fluxes). These impacts are related to growing season length for evergreen ecosystems, to timing of leaf flush and senescence for drought- or cold-deciduous systems, and to seasonal and annual variability in canopy biomass. Spaceborne remote sensing is the only practical tool for monitoring seasonal vegetation dynamics globally with high temporal repeat and moderate spatial resolution.

Historic

In response to the need for improved regional assessment of biospheric responses to increasing atmospheric CO2 concentrations worldwide, eddy covariance flux tower researchers (AmeriFlux) and ecological modelers (Biome-BGC, LoTEC, and PnET-DAY) began a collaborative effort to provide a structure for the continuous monitoring of the terrestrial biosphere [Running et al., 1999]. This activity, known as the real time modeling effort, was initiated In October 2000.

This study will determine how mountain protected areas along a transect from western Washington to western Montana are affected by climatic variability .We will examine ecological responses to climatic variability within and between three mountain-dominated bioregions—the Olympic, North Cascades, and Northern Rocky Mountains—along a gradient from marine to continental climate.

This project is examining how biological and physical processes interact to control carbon uptake, storage and release in Arctic tundra ecosystems and how the self-organizing nature of these interactions varies across multiple spatial and temporal scales. Approximately 25% of the world’s soil organic carbon reservoir is stored at high northern latitudes in permafrost and seasonally-thawed soils in the Arctic, a region that is currently undergoing unprecedented warming and drying, as well as dramatic changes in human land use.

In the process of urbanization, land formerly occupied by crops, grasslands or forest becomes permanently paved for buildings, parking lots and transportation. While urban areas have generally a lower photosynthetic capacity than the surrounding rural environments, intensively irrigated and fertilized lawns and trees often counterbalance the decline in net primary productivity (NPP) due to the replacement of vegetated surface with constructed materials.

For decades scientists have sought to develop regionally applicable estimators of crop yield using models formulated from remote sensing data. With a few exceptions, most broad scale models, based on remote sensing, have used the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) to derive retrospective, empirical relationships between NDVI and yield. However, while retrospective analyses provide insight into past performance, they do little to satisfy the need for near real time yield information.

Until recently, the Advanced Very High Resolution Radiometer (AVHRR) was the only broad scale, globally applicable satellite that provided direct spectral data suited for continual monitoring of vegetation. As such, many studies have successfully used AVHRR normalized difference vegetation index (NDVI) to infer photosynthetic monitor growing season phenology and estimate vegetation. On 18 December, 1999 the first Moderate Resolution Imaging Spectroradiometer (MODIS) was launched on the Terra platform of the Earth Observing System (EOS).

Using MODIS and eddy-flux estimates of Gross Primary Production (GPP) data from several climate regimes, we are analyzing the accuracy of the MODIS GPP (MOD17A2) algorithm. The standard MODIS Output uses coarse 1º X 1.25º resolution daily minimum air temperature and humidity from the NASA Data Assimilation Office (DAO) as a control on the photosynthetic assimilation of atmospheric CO2.

The geography and dynamics of water across this pan-Arctic region are important elements of the larger Earth System especially given growing evidence of the vulnerability of the Arctic climate and terrestrial biosphere to global change.  The scope of this multidisciplinary project is develop online, near-real time capabilities for rapid assessment and monitoring pan- Arctic water budgets and river discharge to the Arctic Ocean.

This project is part of a larger NSF initiative called the Study of the Northern Alaska Coastal System (SNACS). Most regional observation networks indicate that dramatic changes have occurred across the Arctic in recent decades, but comparatively little work has been done to assess atmospheric and oceanic responses to the dramatic observed terrestrial changes. Both increases in surface air temperature and a shift in arctic air circulation patterns are likely to contribute to changes in ice distribution.

The lack of available water constrains hydrologic and ecological processes for two-thirds of the Earth’s land surface. We are working with colleagues at the NASA Jet Propulsion Laboratory to develop new satellite microwave remote sensing algorithms for detecting and monitoring land-atmosphere water and energy exchange over North America.

Flathead Lake, located in northwest Montana, is one of the 300 largest natural freshwater lakes in the world, covering an area of 480 km2 with a maximum depth of 113 m. The Lake is oligotrophic, yet experienced an increase in eutrophication from 1977 to 2001, and two lakewide blooms of macroalgae in 1984 and 1994 that represented anomalous declines in water quality likely due to increasing nutrient inputs from anthropogenic sources.

The Western Arctic Linkage Experiment (WALE) was initiated to investigate the role of northern terrestrial ecosystems in the larger Arctic system response to global change through model and satellite remote sensing analyses of regional carbon, water and energy cycles (McGuire et al. overview paper below). The NTSG portion of this investigation focused on assessing annual variability and regional trends in vegetation productivity for the WALE domain of Alaska and NW Canada, and the primary mechanisms driving observed changes over the 19-year (1982 - 2000) study period.

Hydrology

The Riverscape Analysis Project (RAP) is a web-based GIS Decision Support System that will assist in salmon conservation across the North Pacific Rim (NPR), based upon a robust classification (typology) of rivers and river habitats which is aimed at conserving the existing potential production of salmon in the context of the ocean domains influencing the rivers and salmon that spawn and rear in them. Funding for this project was provided by the Gordon and Betty Moore Foundation. 

We developed a hierarchical Dominant River Tracing (DRT) algorithm for automated extraction and spatial upscaling of basin flow directions and river networks using fine scale hydrography inputs (e.g. flow direction, river networks and flow accumulation). In contrast with previous upscaling methods, the DRT algorithm utilizes information on global and local drainage patterns from baseline fine scale hydrography to determine upscaled flow directions and other critical variables including upscaled basin area, basin shape and river lengths.

The objective of this study was to generate a global long-term (1983-2006) daily ET record with well quantified accuracy for studies on regional/global water balances changes. We applied a satellite remote sensing based evapotranspiration (ET) algorithm to assess global terrestrial ET. The algorithm quantifies canopy transpiration and soil evaporation using a modified Penman-Monteith approach with biome-specific canopy conductance determined from the NDVI, and quantifies open water evaporation using a Priestley-Taylor approach.

MODIS

The NASA Earth Observing System is a $7.3 billion program planning satellite-based earth monitoring for 15 years, and is the heart of global change science for the United States.

The goal of the MOD17 MODIS project is to provide continuous estimates of Gross/Net Primary Production (GPP/NPP) across Earth’s entire vegetated land surface. MOD17 GPP/NPP outputs are useful for natural resource and land management, global carbon cycle analysis, ecosystem status assessment, and environmental change monitoring. MOD17 is part of the NASA Earth Observation System (EOS) program and is the first satellite-driven dataset to monitor vegetation productivity on a global scale.

This project is part of NASA/EOS project to estimate global terrestrial evapotranspiration from earth land surface by using satellite remote sensing data. MOD16 global evapotranspiration product can be used to calculate regional water and energy balance, soil water status; hence, it provides key information for water resource management.  With long-term ET data, the effects of changes in climate, land use, and ecosystems disturbances (e.g. wildfires and insect outbreaks) on regional water resources and land surface energy change can be quantified.

Our goals are to operationally detect all major terrestrial ecosystem disturbances by using satellite remote sensing. These disturbances can be induced by different causes, such as wildfires, hurricanes, insect outbreaks, heatwaves, wind, ice storms, and deforestation. Quantification of the time, extent and severity of disturbances and the following recovery is critical to expedite our understanding of how climate change and human activity affect the dynamics of ecosystems.

As part of the MODIS global terrestrial evapotranspiration project, the goal of the Drought Severity Index (DSI) is to use satellite remote sensing data to monitor and detect drought on Earth’s land surface. The DSI enhances near-real-time drought monitoring capabilities that can assist decision makers in regional drought assessment and mitigation efforts, but without many of the constraints of more traditional drought monitoring methods. 

Spatial Climatology

The TopoWx ("Topography Weather") gridded dataset contains historical 30-arcsec resolution (~800-m) interpolations of minimum and maximum topoclimatic air temperature for the conterminous U.S. Using both DEM-based variables and MODIS land skin temperature as predictors of air temperature, interpolation procedures include moving window regression kriging and geographically weighted regression.

As a premiere ecosystem in North America (Hauer et al. 2007) and one of the world’s most intact temperate ecosystems (Pederson et al. 2010), the Crown of the Continent Ecosystem (CCE) straddles the Rocky Mountains from southern Alberta and British Columbia to northern Montana.

Meteorology at the land surface affects many processes in the terrestrial biogeochemical system. Measurements of near-surface meteorological conditions are made at many locations, but we are often faced with having to perform ecosystem process simulations in areas where no meteorological measurements have been taken. In some cases it is possible to install new instrumentation for a particular study, but there are many situations where this is not a feasible solution.