Improving Access to MODIS Biophysical Science Products for NACP Investigators

MODIS 4 NACP is a NASA-funded project supporting the North American Carbon Program (NACP). The purpose of this Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) project is to provide researchers with Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical data products that are custom tailored for use in NACP model studies. Standard MODIS biophysical products provide used to improve our understanding on the climate and ecosystem changes. However, direct uses of the MODIS biophysical parameters are constrained by retrieval quality and cloud contamination. Another challenge that NACP users face is acquiring MODIS data in formats and at spatial-temporal resolutions consistent with other data sets they use. We have been working closely with key NACP users to tailor the MODIS products to fit their needs. First, we provide new temporally smoothed and spatially continuous MODIS biophysical data sets. Second, we are distributing MODIS data at suitable spatial-temporal resolutions and in formats consistent with other data integration into model studies

Terrestrial Biosphere Model Performance for Inter-Annual Variability of Land-Atmosphere CO2 Exchange

Interannual variability in biosphere-atmosphere exchange of CO2 is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter-annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long-term measurements of biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites in North America. Instead of focusing on model-data agreement we take a systematic, variability-oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.Organismic and Evolutionary Biolog

SATELLITE MICROWAVE MEASUREMENT OF LAND SURFACE PHENOLOGY: CLARIFYING VEGETATION PHENOLOGY RESPONSE TO CLIMATIC DRIVERS AND EXTREME EVENTS

The seasonality of terrestrial vegetation controls feedbacks to the climate system including land-atmosphere water, energy and carbon (CO2) exchanges with cascading effects on regional-to-global weather and circulation patterns. Proper characterization of vegetation phenology is necessary to understand and quantify changes in the earthÆs ecosystems and biogeochemical cycles and is a key component in tracking ecological species response to climate change. The response of both functional and structural vegetation phenology to climatic drivers on a global scale is still poorly understood however, which has hindered the development of robust vegetation phenology models. In this dissertation I use satellite microwave vegetation optical depth (VOD) in conjunction with an array of satellite measures, Global Positioning System (GPS) reflectometry, field observations and flux tower data to 1) clarify vegetation phenology response to water, temperature and solar irradiance constraints, 2) demonstrate the asynchrony between changes in vegetation water content and biomass and changes in greenness and leaf area in relation to land cover type and climate constraints, 3) provide enhanced assessment of seasonal recovery of vegetation biomass following wildfire and 4) present a method to more accurately model tropical vegetation phenology. This research will establish VOD as a useful and informative parameter for regional-to-global vegetation phenology modeling, more accurately define the drivers of both structural and functional vegetation phenology, and help minimize errors in phenology simulations within earth system models. This dissertation also includes the development of Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) vegetation health climate indicators as part of a NASA funded project entitled Development and Testing of Potential Indicators for the National Climate Assessment; Translating EOS datasets into National Ecosystem Biophysical Indicators

Climate-driven uncertainties in modeling terrestrial gross primary production: a site level to global-scale analysis

Abstract We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m À2 yr À1 across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study , we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework

Seasonal variation of source contributions to eddy-covariance CO 2 measurements in a mixed hardwood-conifer forest

Net ecosystem exchange (NEE) measurements using the eddy covariance technique have been widely used for calibration and evaluation of carbon flux estimates from terrestrial ecosystem models as well as for remote sensing-based estimates across various spatial and temporal scales. Therefore, it is vital to fully understand the land surface characteristics within the area contributing to these flux measurements (i.e. source area) when upscaling plot-scale tower measurements to regional-scale ecosystem estimates, especially in heterogeneous landscapes, such as mixed forests. We estimated the source area of a flux tower at a mixed forest (Harvard Forest in US) using a footprint model, and analyzed the spatial representativeness of the source area for the vegetation characteristics (density variation and magnitude) within the surrounding 1- and 1.5-km grid cells during two decades (1993–2011). Semi-variogram and window size analyses using 19 years of Landsat-retrieved enhanced vegetation index (EVI) confirmed that spatial heterogeneity within the 1-km grid cell has been gradually increasing for leaf-on periods. The overall prevailing source areas lay toward the southwest, yet there were considerable variations in the extents and the directions of the source areas. The source areas generally cover a large enough area to adequately represent the vegetation density magnitude and variation during both daytime and nighttime. We show that the variation in the daytime NEE during peak growing season should be more attributed to variations in the deciduous forest contribution within the source areas rather than the vegetation density. This study highlights the importance of taking account of the land cover variation within the source areas into gap-filling and upscaling procedures

Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman-Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that physically integrates Moderate Resolution Imaging Spectroradiometer (MODIS)-derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth-Wallace framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at thirteen core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of -4 %. These error statistics show higher accuracies than a widely-used SEB-based Surface Energy Balance System (SEBS) and PM-based MOD16 ET, which were found to overestimate (PBIAS = 28 %) and underestimate ET (PBIAS = -26 %), respectively. The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapour pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient points to its potential for near real time ET mapping from regional to continental scales.NASA Land-Cover Land-Use Change Grant (NNX17AH97G)NASA new investigator program award (NNX16AI19G)BIOTRANS (grant number, 00001145)CAOS-2 project grant (INTER/DFG/14/02)STEREOIII (INTER/STEREOIII/13/03/HiWET; CONTRACT NR SR/00/301)https://deepblue.lib.umich.edu/bitstream/2027.42/143157/1/hess-2017-535.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143157/4/hess-22-2311-2018.pdfDescription of hess-2017-535.pdf : SUPERSEDED: for historical purposes onl

Shedding Light on Photosynthesis: The Impacts of Atmospheric Conditions and Plant Canopy Structure on Ecosystem Carbon Uptake.

The Earth’s climate is influenced by complex interactions of physical, chemical, and biological processes that link terrestrial ecosystems and the atmosphere. One of these interactions involves the use of light in photosynthesis, which allows plants to remove CO2 from the atmosphere and slow the unprecedented rate of climate change the Earth is experiencing. However, modeling future climate remains challenging, in part because of limited knowledge of mechanisms controlling the effects of light on gross ecosystem CO2 uptake (conceptually, photosynthetic activity integrated across all leaves in a plant canopy). Unlike previous studies, this dissertation uses data from atmospheric science, ecosystem ecology, and plant physiology to provide evidence for mechanistic links between physical, biophysical, and ecological controls on the effects of light on processes tied to gross ecosystem CO2 uptake—specifically, ecosystem gross primary production (GPP) and leaf photosynthesis. First, this dissertation empirically demonstrates that the dominant effect of clouds is to reduce total light above canopies. However, optically thin clouds increase scattered, diffuse light, which canopies use more efficiently than they use direct light. This offsets reductions in total light and results in no net change in GPP under thin clouds, while GPP decreases under optically thick clouds because both diffuse and direct light decrease. Second, ground-based measurements indicate that the rate of increase in GPP with diffuse light changes throughout the day. The magnitude of increase depends on how canopies interact with the angle of incoming light to biophysically alter the distribution of light within canopies and thus, the proportions of leaves contributing to GPP. Third, the distribution of species and light within one forest canopy leads to differences in some of the rate-limiting biochemical reactions in leaf photosynthesis. These field-based data indicate which assumptions representing canopies in Earth system models may not have support in situ, and could be contributing to errors in model estimates of future climate. Overall, this dissertation identifies mechanisms through which clouds and plant canopy structure alter land-atmosphere CO2 fluxes and subsequently, Earth’s climate. It also provides an important interdisciplinary framework for testing assumptions about the feedbacks that living organisms form with their environment.PhDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133446/1/chengs_1.pd