A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn modeldata eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon 5 pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986- 2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional 10 workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of   500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement 15 with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn’s carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.Published versio

Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance

Ecosystem models are often calibrated and/or validated against derived remote sensing data products, such as MODIS leaf area index. However, these data products are generally based on their own models, whose assumptions may not be compatible with those of the ecosystem model in question, and whose uncertainties are usually not well quantified. Here, we develop an alternative approach whereby we modify an ecosystem model to predict full-range, high spectral resolution surface reflectance, which can then be compared directly against airborne and satellite data. Specifically, we coupled the two-stream representation of canopy radiative transfer in the Ecosystem Demography model (ED2) with a leaf radiative transfer model (PROSPECT 5) and a simple soil reflectance model. We then calibrated this model against reflectance observations from the NASA Airborne VIsible/InfraRed Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration successfully constrained the posterior distributions of model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. The calibrated model was able to accurately reproduce surface reflectance and leaf area index for sites with highly varied forest composition and structure, using a single common set of parameters across all sites. We conclude that having dynamic vegetation models directly predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.https://gmd.copernicus.org/preprints/gmd-2020-324/gmd-2020-324.pdfFirst author draf

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams

Integrating plant physiology into simulation of fire behavior and effects

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future

Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century

Assessing the Societal Burden of Glaucoma Patients With vs. Without Physical or Mental Comorbidities.

Thesis (Master's)--University of Washington, 2017-06Background: Glaucoma is a collection of eye diseases that damage the eye’s optic nerve resulting in vision loss and blindness. Treatment for glaucoma is primarily pharmacologic, however, adherence issues with topical medications are problematic. Patients with select comorbid conditions that impact physical or mental function might be most at risk for adherence issues. Objective: Characterize patients with vs. those without select physical or mental comorbidities and estimate differences in healthcare resource use (HCRU), healthcare expenditures and health related quality of life (HRQoL) between the two groups, using Medical Expenditure Panel Survey (MEPS) data. Methods: MEPS data collected between 2003-2014 was aggregated and viewed cross-sectionally using the first year of data for each patient during a two year panel survey. The subgrouping by physical or mental comorbid conditions was done using ICD-9 codes collected by MEPS. Between group comparisons in the outcomes of interest (HCRU, expenditure, HRQoL) were conducted using several different regression analyses. Results: We identified a total of 2,928 glaucoma patients during the 11 years of collected data, including 1,539 who had a comorbid condition labeled as physical or mental. Those with select physical or mental comorbidities had greater unadjusted HCRU and expenditures (all P<0.05), however after adjustment for many possible confounders significant associations did not persist for the majority of individual HCRU or expenditure outcomes. HRQoL as measured by the two component, physical and mental, SF-12 was lower in the with select physical and mental comorbidity subgroup both before and after adjustment for confounding (all P<0.05). Conclusion: Our study displays that glaucoma patients with a physical or mental comorbidity have increased healthcare resource use and expenditure with a lower health-related quality of life compared to those without one, however much, if not all, of this difference is attributable to different baseline characteristics between the two subgroups