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

Towards understanding predictability in ecology: a forest gap model case study

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Reanalysis in Earth System Science: Towards Terrestrial Ecosystem Reanalysis

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics

Forest responses to last‐millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity

Forecasts of future forest change are governed by ecosystem sensitivity to climate change, but ecosystem model projections are under‐constrained by data at multidecadal and longer timescales. Here, we quantify ecosystem sensitivity to centennial‐scale hydroclimate variability, by comparing dendroclimatic and pollen‐inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was higher in models than observations and highest in simpler models. Model‐data comparisons suggest that interactions among biodiversity, demography and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. Integrating ecosystem models with observations from timescales extending beyond the instrumental record can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world

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

Forecasting the effects of fertility control on overabundant ungulates

2014 Summer.Includes bibliographical references.Overabundant populations of native vertebrates can cause environmental degradation and loss of biological diversity. Culling or regulated harvest is often used to control over- abundant species. These methods become infeasible in residential areas and national parks. White-tailed deer populations on the eastern coast of the United States have grown ex- potentially during the urbanization of the 20th century causing severe environmental and economic damage. Managers of National Parks in the Washington, D. C. area seek to reduce densities of white-tailed deer from the current average (50 deer per km2). It has been shown theoretically that fertility control is not an effective way to reduce an overabundant populations, but these conclusions have not be verified with empirical models. Here, we present a Bayesian hierarchical model using 13 years of distance sampling data from 10 National Parks in the National Capital Region Network to forecast the effects of fertility control on overabundant ungulates. We estimated a survival probability for adult female deer that was the same as what we found in previous literature (adult female = 0.74). However, our estimation of adult male and juvenile probabilities were different than what has been found in past studies (adult male = 0.39, juvenile = 0.67). This may be because of the high densities of white-tailed deer in our study area. Our posterior predictive checks show that our model does adequately represent the data (β = 0.419). Our model experiments found that fertility control is not capable of rapidly reducing deer abundance unless a high relative effort over no action is feasible. However, it can be combined with culling to maintain a population below carrying capacity with a high probability of success. This gives managers confronted with problematic overabundance a framework for implementing management actions with a realistic assessment of uncertainty

Forecasting the Effects of Fertility Control on Overabundant Ungulates: White-Tailed Deer in the National Capital Region - Fig 2

<p>A. The vertical line indicates a manager’s objective for the population. The area that is shaded red indicates the probability that an objective will be met given no action. B. The Posterior distribution conditional on a management action, for example, culling or delivering contraceptives. The blue shaded area under the curve is the probability that a manager will reach the objective given this action. C. The net effect of management is the ratio of the blue shaded area to the red shaded area.</p