News

An informal view of my research

The Sound of the Carbon Cycle

The sound you are listening to is hourly NEE (µmol CO2 m-2 sec-1) measurements recorded at Harvard Forest EMS flux tower from 1991 to 2015. This is the world's longest continuous measurement of carbon exchange between a forest and the atmosphere. Hourly measurements were converted to 32-bit PCM audio with a sampling rate of 8760 Hz, or one year elapsed per second of audio. A small loss in fidelity is due to thresholding when doubling the original (quiet) volume. Audio conversion performed by Dr. Adam Erickson.

Hybrid Modeling

Overcoming limits to computation and process understanding through the use of machine/deep learning for pattern-based emulators of detailed processes, including spatiotemporal models.

Ecology and Evolution

Improving representation of the dynamic nature of plants in land models in terms of *omics and biophysics, and implications for succession and evolution per computational experiments.

Machine Learning

More than improving remote sensing retrievals and process representation, part of a broader hardware/software engineering trend toward intelligent systems for deep automation, extending to robotic measurement systems.

Earth Observation

Ingesting new passive and active sensing data streams into next-generation land models through improved process representation, statistical learning, and software engineering. Models and observations together as one.

Hyperon. Hypersurface Observation Network.

In 2021, I led the call for a new generation of intelligent, robotic, hyper-temporal, -spatial, - spectral, and -angular (hypersensing) surface networks to learn inversions of remote observations by resolving radiative-transfer uncertainties along the surface-atmosphere continuum. Leveraging commodity sensors and edge inference, the approach is intended to surpass the capabilities of existing networks such as FluxNet and PhenoCam. Hyperon is uniquely designed for the observation and simulation of surface-atmosphere exchanges as well as ecological and evolutionary dynamics, also benefitting the computer graphics industry.


Synthetic Spectranomics. Inferential surface 3-D geometry and hyperspectral reflectance.

In 2020, I proposed a new approach to realizing spaceborne spectranomics — long a dream of the Earth observation community — utilizing existing air- and space-borne observations. Inspired by self-driving cars, the methodology is based on the application of deep learning to multi-spectral Earth observation records for inferential sensing of surface 3-D geometry and hyperspectral reflectance.


SORTIE-NG. Next-generation land models.

In 2018, we proposed a new breed of hybrid statistical-physical land model built with the latest computational technologies, including modern C++ build systems, deep learning, and GPU acceleration. Innovation drives the SORTIE-NG model concept, which blends elements from classical gap, landscape, and terrestrial biosphere models in powerful new land models.


Earth-systems Research and Development Enviroment (ERDE). Generic API and toolkit.

ERDE (German for Earth) is a universal interface and toolkit for component models of the Earth system, focusing on terrestrial biosphere models. ERDE provides wrappers and methods enabling deterministic models to run probablistic Monte Carlo simulations with empirical priors. This allows for realistic trait variation and optimization of any model parameter.


DeepLand. AI for Earth system models.

Based on my PhD demonstration, the SORTIE-NG concept, and popular deep learning frameworks, DeepLand showcases the computational tooling needed to create next-generation Earth system models powered by deep learning. This includes providing a user-friendly Python interface on top of high-performance modern C++ code across a wide variety of architectures.

Selected Works

Overview of recent publications

Hypersurface Observation Network (Hyperon) – What it is and why we need it
Erickson et al.

AGU Fall Meeting, 2021, B15G-1499; https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/976063

                                        
@inproceedings{erickson2021b,
    author       = {Adam Erickson and Sujay V. Kumar and Derek L. Hudson and Snorre Stamnes and Eetu Puttonen and Samuli Junttila and S\"{o}ren Pirk and Bernhard H\"{o}fle and Lukas Chrostowski and Jan Eitel and Kim Calders},
    year         = 2021,
    title        = {{Hypersurface Observation Network (Hyperon)} --- What it is and why we need it},
    booktitle    = {AGU Fall Meeting 2021},
    series       = {AGU 2021},
    month        = 12,
    day          = 13,
    organization = {American Geophysical Union},
    address      = {New Orleans, Louisiana, USA},
    eid          = {B15G-1499},
    url          = {https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/976063}
}
                                    
In the terrestrial domain, large biogeochemical and energetic uncertainties surround the soil-plant-atmosphere continuum of forests, leading to wide disagreement in the projected land carbon sink and global carbon balance. This is largely due to an absence of global observation networks providing coincident information on the structure, composition, and function of forests and adjacent planetary boundary layer (PBL) over space and time. Such observations are critical to learning new models of biospheric processes and improving our understanding and predictions of land-atmosphere exchange. Simultaneously, a new generation of hyper-temporal, -spatial, - spectral, and -angular (hypersensing) surface reference networks are needed to learn inversions of air- and space-borne measurements that resolve radiative-transfer uncertainties related to these land-atmosphere exchanges.

Existing terrestrial networks such as FluxNet, SpecNet, PhenoCam, AeroNet, and ForestGeo remain limited to single measurement points and domains. For example, FluxNet typically records CO2 and H2O exchanges at a single point in space using domain-specific instruments, making the measurements ungeneralizable, unrepresentative, and thus unsuited to up-scaling to Earth observation records or diagnosing Earth system models. Furthermore, adding new measurements to these systems often requires costly new instruments. Absent are generalizable surface-atmosphere observation systems able to retrieve a growing variety of observables over space and time.

Toward addressing these needs in unified Earth observation and system modeling (EOSM), we propose the Hypersurface Observation Network (Hyperon) — formerly 5DNet — a modular, intelligent, robotic, and coincident surface-atmosphere observation system. Initially focusing on forests, Hyperon is intended to cover a variety of surface domains. While previously impractical, powerful new low-cost instruments and embedded computers designed for edge inference provide an exciting opportunity to realize this goal. We provide an early conceptual overview of Hyperon, reaching out to the community to develop standards for instrument and site configuration options and a decentralized governance model for ensuring free and open science.
Union Symposium 4: Towards evolvable physics-based plants and landscape processes in terrestrial biosphere models
Erickson et al.

Geophysical Research Abstracts, 2021, Vol. 23, EGU2021-US4; https://meetingorganizer.copernicus.org/EGU21/session/39990

                                        
@inproceedings{erickson2021a,
    author       = {Adam Erickson and Rico Fischer and Sujay Kumar and Annikki M\"{a}kel\"{a} and Nikolay Strigul},
    year         = 2021,
    title        = {{Union Symposium 4}: Towards evolvable physics-based plants and landscape processes in terrestrial biosphere models},
    booktitle    = {EGU General Assembly},
    series       = {EGU 2021},
    organization = {European Geophysical Union},
    address      = {virtual},
    eid          = {US4},
    url          = {https://meetingorganizer.copernicus.org/EGU21/session/39990},
    eprint       = {https://meetingorganizer.copernicus.org/EGU21/sessionAssets/39990/summary.pdf}
}
                                    
The terrestrial biosphere exerts disproportionate influence on Earth's climate, making improvements in its representation key to reducing climate uncertainty. After 50 years of development, land surface models contain detailed processes of energy fluxes, photosynthesis, hydrology, C-N-P cycles, and land-use within coarse non-interacting grid cells. Remaining discrepancies in fidelity to observed carbon and water cycles appear primarily related to deficiencies in the representation of forests and human activity. These include the omission of spatial processes of disturbance, migration, adaptation, and management. Also missing is the generative process of life, evolution, which gives rise to life history strategies, trophic-metabolic networks, leaf economics, local adaptation (i.e., optimality, acclimation), and plant behaviour. Despite improvements in representing vegetation demography by utilizing emergent properties of allometric scaling, canopy geometric realism remains low. This may bias carbon and water cycles per radiative transfer and coupled processes of photosynthesis, regeneration, evapotranspiration, heterotrophic respiration, and disturbance.

We believe that physics-based botanical models, forest landscape models, and terrestrial biosphere models may soon merge into new multi-scale models. While low-dimensional representations of forests are often used to improve computational efficiency and cope with a dearth of 4-D forest observatories, deep learning may be combined with new autonomous scanning systems - proximal and/or remote - such as our proposed global tower-based '5DNet' to infer evolvable 4-D physics-based models. This includes learning multi-generation tree models with 4-D traits from image and/or laser scanning time-series. To date, 4-D ontogeny has been inferred from individual scans of mature trees, multi-plant phenological events have been tracked in real-time, and the self-similar and -organizing nature of plants has been used to efficiently compress tree models down to their generating parameters. Achieving leaf-to-global scaling may require co-processor acceleration and fusing deep learning with 3-D radiative transfer modeling to infer global surface properties. An additional focus on evolution and human activity comes as 21st century land surface models mature into general simulations of life on Earth.

This Union Symposium presents exciting work toward achieving this moonshot in Earth observation and systems modeling.
NASA’s Surface Biology and Geology Designated Observable: A perspective on surface imaging algorithms
K. Cawse-Nicholson, P. A. Townsend, D. Schimel, et al.

Remote Sensing of Environment, 2021, Vol. 257; https://doi.org/10.1016/j.rse.2021.112349

                                        
@article{cawsenicholson2021,
    author    = {Kerry Cawse-Nicholson and Philip A. Townsend and David Schimel and Ali M. Assiri and Pamela L. Blake and Maria Fabrizia Buongiorno and Petya Campbell and Nimrod Carmon and Kimberly A. Casey and Rosa Elvira Correa-Pabón and Kyla M. Dahlin and Hamid Dashti and Philip E. Dennison and Heidi Dierssen and Adam Erickson and Joshua B. Fisher and Robert Frouin and Charles K. Gatebe and Hamed Gholizadeh and Michelle Gierach and Nancy F. Glenn and James A. Goodman and Daniel M. Griffith and Liane Guild and Christopher R. Hakkenberg and Eric J. Hochberg and Thomas R.H. Holmes and Chuanmin Hu and Glynn Hulley and Karl F. Huemmrich and Raphael M. Kudela and Raymond F. Kokaly and Christine M. Lee and Roberta Martin and Charles E. Miller and Wesley J. Moses and Frank E. Muller-Karger and Joseph D. Ortiz and Daniel B. Otis and Nima Pahlevan and Thomas H. Painter and Ryan Pavlick and Ben Poulter and Yi Qi and Vincent J. Realmuto and Dar Roberts and Michael E. Schaepman and Fabian D. Schneider and Florian M. Schwandner and Shawn P. Serbin and Alexey N. Shiklomanov and E. Natasha Stavros and David R. Thompson and Juan L. Torres-Perez and Kevin R. Turpie and Maria Tzortziou and Susan Ustin and Qian Yu and Yusri Yusup and Qingyuan Zhang},
    year      = 2021,
    title     = {{NASA's Surface Biology and Geology Designated Observable}: A perspective on surface imaging algorithms},
    journal   = {Remote Sensing of Environment},
    volume    = 257,
    pages     = {112349},
    publisher = {Elsevier},
    address   = {Amsterdam, North Holland, Netherlands},
    issn      = {0034-4257},
    doi       = {https://doi.org/10.1016/j.rse.2021.112349},
    url       = {https://www.sciencedirect.com/science/article/pii/S0034425721000675}
}
                                    
The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.
Synthetic Spectranomics: Deep learning of surface 3-D geometry, chemistry, and hyperspectra to inform next-generation land models
Erickson and Kumar

AGU Fall Meeting, 2020, B031-0004; https://ui.adsabs.harvard.edu/abs/2020AGUFMB031.0004E/abstract

                                        
@inproceedings{erickson2020poster2,
    author       = {Adam Erickson and Sujay Kumar},
    year         = 2020,
    title        = {Synthetic spectranomics: deep learning of surface {3-D} geometry, chemistry, and hyperspectra to inform next-generation land models},
    booktitle    = {AGU Fall Meeting 2020},
    series       = {AGU 2021},
    month        = 12,
    day          = 9,
    organization = {American Geophysical Union},
    address      = {virtual},
    eid          = {B031-0004},
    url          = {https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/710593}
}
                                    
Machine learning, and in particular deep learning, has transformed our approach to Earth observation and systems modeling, or EOSM. Trained on near-to-remote sensing observations, physical models, or hybrids of both, machine learning may improve existing model formulations while creating new classes of models. Potential applications include data geolocalization and assimilation, model optimization, model emulation/surrogates, upsampling/downscaling, land-surface reconstruction for physics-based 4-D models, learning new governing equations for latent or difficult-to-simulate dynamics, simulating observing systems with realistic noise (hybrid network + GAN), inverse modeling, causal inference, and model code generation, among others. Full utilization of protected global inventory datasets is possible with privacy preserving networks, while biases and adversarial robustness must be addressed. One important application is observation synthesis, allowing land models to benefit from the increased spatial, temporal, and spectral/polarimetric resolution proposed for future observing systems. This provides the continuous historical record needed to constrain, calibrate/validate, and develop models. Despite the many datasets released for remote sensing computer vision challenges to date, large labeled datasets remain scarce for a number of tasks relevant to Earth systems and planetary science generally. To address this limitation, we leverage complementary coincident observations from air- and space-borne platforms to generate labeled data relevant to modeling tasks. We focus on the information-theoretic task of maximizing mutual information between predictors X and targets Y, or feature extraction. First, we will utilize airborne spectranomics observations from NASA G-LiHT, NEON AOP, and/or ASU GAO to learn canopy geometry, chemistry, and hyperspectral reflectances from pseudo-multispectral and/or RGB imagery. Second, we aim to utilize NASA EO-1 Hyperion and ALI observations to learn the mapping from multi- to hyper-spectral. The trained model may then be applied to generate synthetic hyperspectral imagery using Landsat-8 OLI observations, for which ALI was a demonstrator. Subsequent work may extend this approach to commercial satellite constellations.
A software framework for optimizing the design of spaceborne hyperspectral imager architectures
Erickson et al.

Geophysical Research Abstracts, 2020, Vol. 22, EGU2020-19665; https://doi.org/10.5194/egusphere-egu2020-19665

                                        
@inproceedings{Erickson2020_02,
    author = {Adam Erickson and Benjamin Poulter and David Thompson and Gregory Okin and Shawn Serbin and Weile Wang and David Schimel},
    title = {A software framework for optimizing the design of spaceborne hyperspectral imager architectures},
    booktitle = {EGU General Assembly Conference Abstracts},
    organization = {European Geophysical Union},
    year = 2020,
    month = may,
    eid = {19665},
    url = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-19665.html},
}
                                    
Quantifying the capacity, and uncertainty, of proposed spaceborne hyperspectral imagers to retrieve atmospheric and surface state information is necessary to optimize future satellite architectures for their science value. Given the vast potential joint trade-and-environment-space, modeling key ‘globally representative’ points in this n-dimensional space is a practical solution for improving computational tractability. Given guidance from policy instruments such as the NASA Decadal Survey and the recommended Designated Target Observables, or DOs, the downselect process can be viewed as a constrained multi-objective optimization. The need to simulate imager architecture performance to achieve downselect goals has motivated the development of new mathematical models for estimating radiometric and retrieval uncertainties provided conditions analogous to real-world environments. The goals can be met with recent advances that integrate mature atmospheric inversion approaches such as Optimal Estimation (OE) that includes joint atmospheric-surface state estimation (Thompson et al. 2018) and the EnMAP end-to-end simulation tool, EeteS (Segl et al. 2012), which utilizes OE for inversions. While surface-reflectance and retrieval simulation models are normally run in isolation on local computing environments, we extend tools to enable uncertainty quantification into new representative environments and thereby increase robustness of the downselect process by providing an advanced simulation model to the broader hyperspectral imaging community in software-as-a-service (SaaS). Here, we describe and demonstrate our instrument modeling web service and corresponding hyperspectral traceability analysis (HyperTrace) library for Python. The modeling service and underlying HyperTrace OE library are deployed on the NASA DISCOVER high-performance computing (HPC) infrastructure. An intermediate HTTP server communicates between FTP and HTTP servers, providing persistent archival of model inputs and outputs for subsequent meta-analyses. To facilitate enhanced community participation, users may simply transfer a folder containing ENVI format hyperspectral imagery and a corresponding JSON metadata file to the FTP server, from which it is pulled to a NASA DISCOVER server for processing, with statistical, graphical, and ENVI-formatted results subsequently returned to the FTP server where it is available for users to download. This activity provides an expanded capability for estimating the various science values of architectures under consideration for NASA’s Surface Biology and Geology Designated Observable.
Machine-learning emulation of a forest biogeochemistry model for efficient biosphere optimization
Strigul and Erickson

Geophysical Research Abstracts, 2020, Vol. 22, EGU2020-19665; https://doi.org/10.5194/egusphere-egu2020-19744

                                        
@inproceedings{Erickson2020_03,
    author = {Nikolay Strigul and Adam Erickson},
    title = {Machine-learning emulation of a forest biogeochemistry model for efficient biosphere optimization},
    booktitle = {EGU General Assembly Conference Abstracts},
    organization = {European Geophysical Union},
    year = 2020,
    month = may,
    eid = {19744},
    url = {https://meetingorganizer.copernicus.org/EGU2020/EGU2020-19744.html},
}
                                    
Management controls the spatial configuration of a number of landscapes globally, from forests to rangelands. The majority of landcover change and all land-use change is the result of human decision-making. As human populations and global temperatures continue to increase, an engineering approach is needed to ensure the persistence of biological diversity and natural capital critical to human well-being. Such an approach may be based on manipulating ecosystems to achieve desired future states, informed by the latest simulation models. Models of the land surface are now being used to inform policy in the form of planning and management practices. This often involves the application of models that include spatial dynamics and operate at a landscape scale. The strong correspondence between the resolution and extent of modeling and management activities at this scale, and ability to efficiently simulate the decadal-to-centennial time-scales of interest, provide managers with a credible scientific tool for anticipating future land states under different scenarios. The importance of such tools to managers has grown dramatically with the challenges posed by anthropogenic climate change. As ecosystem simulation models continually improve in precision, accuracy, and robustness, we posit that models may be mathematically optimized as a basis for optimizing the management of real-world systems. Since current ecosystem simulation models are coarse approximations of highly complex and dynamic real-world systems, such optimizations should ideally account for uncertainty and physical or biochemical constraints, thereby improving the tractability of the optimization problem. In this work, we demonstrate the emulation and optimization of a forest biogeochemistry model from the SORTIE-PPA family of models. In doing so, we provide the first demonstration of the concept of biosphere optimization (Erickson 2015), which may one day be extended to include computational genetic manipulation experiments. To perform this work, we utilize the open-source Earth-systems Research and Development Environment (ERDE) library, which contains built-in functions for performing these and other analyses with land models, with a particular focus on forests.
Implementation of the perfect plasticity approximation with biogeochemical compartments in R
Erickson and Strigul

Ecography, 2020, online; https://doi.org/10.1111/ecog.04756

                                        
@article{Erickson2020_01,
    author = {Erickson, Adam and Strigul, Nikolay},
    title = {Implementation of the perfect plasticity approximation with biogeochemical compartments in R},
    journal = {Ecography},
    year = {2020},
    volume = {n/a},
    number = {n/a},
    pages = {},
    kerywords = {forest ecosystems, ecological modeling, biogeochemistry, software, R},
    doi = {10.1111/ecog.04756},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.04756},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.04756}
}
                                    
Modeling forest ecosystems is a landmark challenge in science, due to the complexity of the processes involved and their importance in predicting future planetary conditions. While there are a number of open‐source forest biogeochemistry models, few papers exist detailing the software development approach used to develop these models. This has left many forest biogeochemistry models large, opaque and/or difficult to use, typically implemented in compiled languages for speed. Here, we present a forest biogeochemistry model from the SORTIE‐PPA class of models, PPA‐SiBGC. Our model is based on the perfect plasticity approximation with simple biogeochemistry compartments and uses empirical vegetation dynamics rather than detailed prognostic processes to drive the estimation of carbon and nitrogen fluxes. This allows our model to be used with traditional forest inventory data, making it widely applicable and simple to parameterize. We detail the conceptual design of the model as well as the software implementation in the R language for statistical computing. Our aim is to provide a useful tool for the biogeochemistry modeling community that demonstrates the importance of vegetation dynamics in biogeochemical models.
A software framework for rapid prototyping of artificial intelligence in Earth system models
Erickson and Strigul

AGU Fall Meeting, 2019; https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/634946

                                        
@inproceedings{Erickson2019_03,
    title = {A software framework for rapid prototyping of artificial intelligence in Earth system models},
    author = {Erickson, Adam and Strigul, Nick},
    booktitle = {AGU Fall Meeting 2019},
    year = {2019},
    organization = {American Geophysical Union},
    url = {https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/634946}
}
                                    
Machine or deep learning represents a promising new way to represent processes in Earth system models. These models are fundamentally data-driven or pattern-based, whereby models are learned from data. This fills a critical gap in our current modeling abilities given the absence of process understanding to produce mechanistic or physical models. While machine/deep learning practitioners commonly use Python frameworks such as Scikit-Learn, TensorFlow, and PyTorch, component models of Earth systems are commonly implemented in low-level FORTRAN and may move toward C++ for its numerous industry-standard libraries. This divide between user-friendly high-level languages and high-performance low-level languages is known as the two-language problem. We address this problem by proposing the generation of a Python interface to C++ code using CMake and pybind11. This makes high-performance C++ classes and methods accessible to Python deep learning frameworks, allowing rapid prototyping of machine/deep learning process models.
Validation of a simple biogeochemistry variant of SORTIE-PPA in two temperate forests using the ERDE modeling framework
Erickson and Strigul

Geophysical Research Abstracts, 2019, Vol. 21, EGU2019-11708-1; https://meetingorganizer.copernicus.org/EGU2019/EGU2019-11708-1.pdf

                                        
@inproceedings{Erickson2019_02,
    author = {{Erickson}, Adam and {Strigul}, Nikolay},
    title = {Validation of a simple biogeochemistry variant of SORTIE-PPA in two temperate forests using the Erde modeling framework},
    booktitle = {EGU General Assembly Conference Abstracts},
    organization = {European Geophysical Union},
    year = 2019,
    month = apr,
    eid = {11708},
    url = {https://ui.adsabs.harvard.edu/abs/2019EGUGA..2111708E},
}
                                    
While vegetation model development has accelerated over the past three decades, lessprogress has been made in software interfaces that bind models and data. Such interfacesbecome necessary as model complexity increases, making models more cumbersome for newusers to parameterize, run, and analyze. Furthermore, model wrappers may provide newmodeling capabilities, such as Bayesian optimization. Toward this end, we have developed asimple geoscientific simulation model API and toolkit in R and Python known as theEarth-systems Research and Development Environment (Erde). While Erde is primarilyintended for vegetation models, its data structures and algorithms are applicable across arange of geoscientific modeling domains. We demonstrate an application of Erde with asimple biogeochemistry variant of SORTIE-PPA known as PPA-SiBGC. The PPA-SiBGCmodel combines the Perfect Plasticity Approximation with explicit above-and-below-groundbiogeochemical pools and simple flux models. We parameterized, ran, and validatedPPA-SiBGC at two research forests in the Eastern United States: (1) Harvard Forest,Massachusetts (HF-EMS) and (2) Jones Ecological Research Center, Georgia (JERC-RD).We assessed model fitness using these temporal metrics: net ecosystem exchange,aboveground net primary production, aboveground biomass, C, and N, belowgroundbiomass, C, and N, soil respiration, and, species total biomass and relative abundance.Without applying any optimization, we found that a simple biogeochemistry variant ofSORTIE-PPA was able to outperform an established forest landscape model (LANDIS-IINECN) across the metrics tested. While LANDIS-II NECN showed better NEE fit,PPA-SiBGC demonstrated better overall correspondence to field data for both sites(HF-EMS: R2=0.73, +0.07, RMSE=4.84, −10.02; JERC-RD: R2=0.76, +0.04,RMSE=2.69, −1.86)
A forest model intercomparison framework and application at two temperate forests along the East Coast of the United States
Erickson and Strigul

Forests, 2019, 10(2), p. 180; https://doi.org/10.3390/f10020180

                                        
@article{Erickson2019_01,
    author = {Erickson, Adam and Strigul, Nikolay},
    title = {A Forest Model Intercomparison Framework and Application at Two Temperate  Forests Along the East Coast of the United States},
    journal = {Forests},
    year = {2019},
    volume = {10},
    number = {2},
    pages = {180},
    url = {https://www.mdpi.com/1999-4907/10/2/180},
    issn = {1999-4907},
    doi = {10.3390/f10020180}
}
                                    
State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested. To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Experience

The long and winding road to innovation.

  • 2002 – 2004

    University of Puget Sound

    Bachelor of Arts (BA) student in International Political Economy (IPE) with a focus on International Economics in Tacoma, Washington, USA

  • 2009 – 2011

    University of Oregon

    Master of Community and Regional Planning (MCRP) student in the Department of Community and Regional Planning in Eugene, Oregon, USA

  • 2012 – 2015

    University of British Columbia

    PhD student in the Integrated Remote Sensing Studio and UBC Unmanned Aircraft Systems (UAS) team Photogrammetrist in Vancouver, British Columbia, Canada

  • 2016 – 2017

    Max Planck Institute for Biogeochemistry

    Postdoctoral Researcher in the Model-Data Integration (MDI) group of the Department of Biogeochemical Integration in Jena, Thüringen, Germany

  • 2017 – 2019

    Washington State University

    Postdoctoral Researcher in the Department of Mathematics and Statistics in Vancouver, Washington, USA

  • 2019 – 2022

    USRA / NASA

    NASA Postdoctoral Program (NPP) Fellow in the Earth Sciences Division at NASA Goddard Space Flight Center (GSFC) in Greenbelt, Maryland, USA

Skills

Programming Languages & Tools

Applications

  • Architecting efficient models of the terrestrial biosphere
  • Machine/deep learning for next-generation hybrid modeling
  • Plant evolution, ecology, and physiology modeling
  • Spatiotemporal deep learning methods in remote sensing
  • LiDAR, structure-from-motion, and SLAM point-cloud modeling
  • Embedded systems and mobile robotics
  • Server, database, and network administration

Contact