Sciences and Exploration Directorate

J. Blake Clark


J. Blake Clark's Contact Card & Information.
Phone: 301.286.3621
Org Code: 616
Mail Code 616.2
Greenbelt, MD 20771

Research Interests

Coastal organic carbon cycling

Earth Science: Carbon Cycle

My research focuses on carbon cycling in the coastal ocean. I use process-based experiments to construct and inform biogeochemical models to investigate carbon cycling on multiple time and space skills. I have ongoing research in the Chesapeake Bay estimating organic matter cycling and fluxes in a tidal marsh influenced estuary, focusing on the export of dissolved organic matter from marshes and the role tidal marshes play in estuarine biogeochemistry. My current focus has shifted to the Arctic where I am taking my skills developed in Chesapeake Bay and applying them to study the organic carbon dynamics of the Yukon River plume.

Biogeochemical model development

Earth Science: Carbon Cycle

Incorporating new and important processes into biogeochemical models requires precise field and laboratory measurements to inform mathematical models. Part of my work focuses on identifying current gaps in the biogeochemical modeling frameworks we use. Once identified, targeted sampling and experiments can build data sets that can be used to build a mathematical model. These mathematical models can be implemented on multiple scales to quantify the importance of any given process to elemental cycling as a whole.

Regional to Global Organic and Inorganic Carbon Processes

Earth Science: Carbon Cycle


Postdoctoral Fellow

Universities Space Research Association - NASA Goddard Space Flight Center

March 2019 - April 2021

Associate Scientist

Universities Space Research Association - NASA Goddard Space Flight Center

April 2021 - November 2021

Assistant Research Scientist

University of Maryland Baltimore County - NASA Goddard Space Flight Center

December 2021 - Present

Current Projects

Remote Sensing of Environmental Change in Arctic Coastal Aquatic Ecosystems

Ocean Biochemistry


PI: Dr. Wes Moses -- NRL

We propose to develop a remote sensing capability to monitor ecosystem changes in coastal Arctic waters, in particular, changes in primary production, due to changing riverine fluxes caused by recent warming trends. We will focus on coastal waters around Colville, Kuparuk, and Sagavanirktok rivers - three of the largest rivers in the North Slope of Alaska. Changing riverine fluxes affect light and nutrient availability - two most critical factors affecting primary production - in a complex manner. Understanding how riverine materials transported into the coastal Arctic mix with ocean waters and affect primary production and phytoplankton community structure using a combination of in situ data, remote sensing measurements, and modeling is the overarching objective of this effort.


We will integrate existing data with limited new measurements to characterize ecosystem variability and changes in coastal Arctic waters. Past and ongoing efforts that will be leveraged include results from two NASA funded projects focused on characterizing material transport along Arctic rivers, data from the ongoing Beaufort Lagoon Ecosystem Long Term Ecological Research program, and data from a separate project funded by the Naval Research Laboratory (NRL) to quantify optical properties of coastal Arctic waters in the same study area.


We will use in situ measurements of constituent concentrations and composition, inherent optical properties, in situ radiometry and airborne and spaceborne hyperspectral and multispectral data to retrieve essential biogeochemical quantities from remote sensing data and use them within a modeling framework. Remote sensing assets that will be used include airborne hyperspectral data from the Airborne Visible / Infrared Imaging Spectrometer - Next Generation sensor and MicroSHINE, a custom-made sensor owned by NRL, high-resolution spaceborne data from WorldView-2/3, Landsat-8, and Sentinel- 2 satellites, and ocean color data from MODIS, VIIRS, and OLCI. The high-resolution airborne and spaceborne data will be used to assess spatial scales of mixing in the coastal ocean and investigate sub-pixel variability in current multi-spectral ocean color sensors and future high-spectral and high-spatial resolution sensors such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and Surface Biology and Geology (SBG) missions.

Arctic Deltas and Coastal Margins as Buffers and Transformers of Carbon Along a Rapidly Changing Land-Ocean Continuum

Carbon Cycle


PI: Dr. Maria Tzortziou--CCNY

Warming at twice the global rate is the new 'normal' for the Arctic. Its ice is melting, its permafrost is thawing, its ocean is acidifying. Freshwater and carbon cycles are intensifying, with direct impacts on ecosystems and resources. Changes on the Arctic land and in the Arctic Ocean are expected to continue in the future. Yet, estimates of the changing carbon fluxes and transformations across these inherently linked systems  the land, the ocean, and the rivers that connect them  are poorly constrained, increasing uncertainties in our understanding and modeling of their impacts on local and larger scale ocean community composition, acidification, and productivity.

Bringing together a diverse team of satellite researchers, experimentalists, and modelers this study will address this high priority research area. Focused on the Yukon River-Bering Sea continuum  one of the most productive areas for Alaska fisheries and simultaneously a ''ground zero'' for climate change  we will address three key science questions: (i) How do changes in hydrological forcing and terrestrial sources affect the transport and export of particulate and dissolved organic and inorganic carbon along this rapidly changing land-ocean continuum? (ii) What is the relative importance and interplay of physical and biogeochemical processes (flocculation, microbial, photochemistry) in transforming carbon as it moves from the Alaskan terrestrial landscape to the Arctic Ocean? (iii) How will changing environmental conditions and future pressures (e.g., increasing temperatures, shifting river flow, and increasing levels of atmospheric carbon dioxide) affect these processes and their impact on carbon fluxes and cycles under various scenarios?


The proposed effort uniquely integrates new and existing field datasets, process experiments, and satellite observations with a novel ecosystem model to improve quantitative and predictive understanding of the coupled physical-biogeochemical processes that transform organic and inorganic carbon as it moves from the Alaskan terrestrial landscape to the Yukon River, delta, plume and adjacent northern Bering Sea. Incorporating strong collaborations with indigenous Alaskan communities, Alaska Department of Fish and Game, and NOAA Alaska Fisheries Science Center, we will combine existing datasets with process experiments, ongoing oceanographic research surveys, and targeted new measurements encompassing aquatic, soil, sediment and terrestrial vegetation endmembers. Remote sensing and modeling efforts will inform the location and timing of new field measurements. Satellite and field data will improve model parameterizations and enable hindcasting, scenario testing, and scaling of processes in time and space, thus reducing uncertainty in model predictions of carbon cycles in a changing climate.


The proposed study responds to this solicitation's Sub-element 3.1 on: ''Carbon Fluxes between and within Land, Freshwater, and Marine Systems'', specifically addressing sub-topics 3.1.1. The Land-Ocean Continuum, and 3.1.3. Fluxes and Biogeochemistry of Carbon within Oceans. The proposed collection of new hyperspectral datasets and development of improved algorithms in high latitude environments will allow to constrain uncertainties in key carbon parameters and biogeochemical fluxes in the context of preparing for future sensors, including the high spatial resolution Surface Biology and Geology (SBG) Designated Observable and the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission that is expected to make unprecedented observations of the changing Arctic ocean ecology and biogeochemistry.

Integration of Observations and Models into Machine Learning for Coastal Water Quality

Novel Data Analysis Development


PI: Dr. Stephanie Schollaert Uz -- NASA GSFC

Coastal areas are impacted by population growth, development, aging infrastructure, and extreme weather events causing greater runoff from land. Monitoring water quality is an urgent societal need. A growing fleet of satellites at multiple resolutions provide the ability to monitor large coastal areas using big data analytics and machine learning. Within our AIST18 project, we started working closely with state agencies who manage water resources around the Chesapeake Bay. We propose to build upon these activities to improve the integration of assets to monitor water quality and ecosystem properties and how they change over time and space. Initially we are taking advantage of technologies and data collected in and around the Chesapeake Bay, with a plan to expand to other watersheds.

As the largest estuary in North America, the Chesapeake Bay receives runoff from approximately 100,000 tributaries, carrying sediment, fertilizer, and pollutants from farms, developed communities, urban areas, and forests. These constituents degrade water quality and contribute to its optical complexity. Resource managers tasked with enforcing pollution reduction goals for these point and non-point sources are also challenged by shrinking budgets with which to monitor multiple aspects of the ecosystems while the use of the Bay for recreation, fishing, and aquaculture is increasing. Of particular concern are the increasing number of harmful algal blooms (HABs) and septic tank leaks due to aging infrastructure and rising sea level [Wolny et al., 2020; Mitchell et al., 2021]. State agencies already work closely with NOAA and EPA and are looking to NASA to apply advanced technologies to further improve their natural resource management.

Our AIST18 project demonstrated promising results with multispectral optical, medium spatial resolution satellite data trained using geophysical model variables within a machine learning (ML) architecture by extracting multi-source feature maps. The nearshore environment demands finer spatial resolution than government assets alone can provide, thus we plan to build on this work by utilizing higher spatial resolution data from commercial satellites. Following our demonstration of feasibility using medium resolution satellite imagery from one sensor, we will now derive feature maps from many sensors of varying spatial, spectral, and temporal resolution. These can be effectively merged regardless of initial source resolution at progressively higher (hierarchical) contextual levels by fusing at multiple layers within the ML model. Heterogeneous feature maps can be adaptively scored and weighted, which influences their significance in the resulting predictions. We plan to analyze higher spectral information from in situ inherent optical property observations to determine the minimum set of requirements for remote sensing of water quality, e.g. water clarity, phytoplankton blooms, and the detection of pollutants. In situ observations will facilitate ML training using higher spectral and spatial resolution imagery from commercial satellites at the coast. We are also collaborating with community experts to evaluate the utility of hyperspectral remote sensing for detecting aquatic features not discernable through multispectral imaging, such as phytoplankton community structure and the likelihood of harmful blooms. In situ observations will facilitate ML training using hyperspectral and higher spatial resolution imagery from commercial satellites at the coastal margins and land-water interface. Finally, we aim to eventually integrate upstream assets of land cover classification, elevation, vertical land motion, and hydrology as inputs to the ML architecture, leveraging other projects that characterize the watershed and runoff of sediments and nutrients to coastal water bodies. Adapting our process to an open science framework will facilitate future integration of these data beyond the aquatic community.

Integrating lateral fluxes into CMS ocean carbon estimates

Carbon Cycle

Co-Investigator and CMS Science Team Member

PI: Dr. Cecile Rousseaux -- NASA GSFC

Climate, weather, and land characteristics directly affect the concentration and composition of organic and inorganic matter, including carbon, delivered to the rivers and ultimately to the oceans. Although the uptake of carbon dioxide by phytoplankton at the surface of the ocean and its recycling into dissolved organic carbon and nutrients are routinely represented in models, the lateral transfer of carbon from land to oceans is severely underrepresented or completely missing from current models. This is sorely needed for carbon accounting and particularly critical in the global assessment and estimates of carbon stocks. In this project we improve existing CMS products by adding this transfer and transformation of organic and inorganic matter as well as quantifying the effects of land use and changes on the resulting global ocean carbon flux. An existing terrestrial biosphere model (Ecosystem Demography model, ED) combined with the Land-Use Harmonization (LUH) dataset provide fluxes of carbon and nutrients from land to rivers under varying land use and land cover change scenarios. The River-Estuary model transports and transforms aqueous forms of carbon and nutrients to represent the lateral fluxes of carbon and nutrients from rivers to the NASA Ocean Biogeochemical Model (NOBM) currently used to produce the CMS global carbon fluxes. This project will add critical components and processes to the current CMS-flux products by adding the effects of land use and change on the transfer of carbon to rivers, the transport and transformation of organic and inorganic matter in rivers and the effects these processes have on the global ocean carbon budget. The modeling tools and output developed by this project will directly feed into the global carbon budget and be adopted by stakeholders in the ocean carbon sector, among others, who will provide feedback that will be used to codevelop monitoring tools and mitigation solutions.


Doctor of Philosophy. Marine Estuary Environmental Science: Focus in Biogeochemistry and Oceanography. Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD. September 2014 – May 2019. Advisor: Professor Raleigh Hood

Bachelor of Science, Aquatic Biology. University of California at Santa Barbara, Santa Barbara, CA. September 2008 - September 2012. Advisor: Professor Craig Carlson

Professional Societies

American Society of Limnology and Oceanography

2017 - Present

American Geophysical Union

2019 - Present

Professional Service

NASA Carbon Monitoring System Science Team Member, 2023-Present

NAS Arctic-COLORS Science Definition Team Member, 2023-Present

Chesapeake Bay Program Scientific and Technical Advisory Committee (STAC) “Assessing the environment in outcome units” workshop. Invited Participant Annapolis, MD. March 20-21, 2019.

Chesapeake Bay Program Scientific and Technical Advisory Committee (STAC) “Chesapeake Bay Program Climate Change Modeling 2.0 workshop.” Invited Participant Annapolis, MD. September 24-25, 2018


NASA Goddard Earth Science Peer Award - 2021

NASA Postdoctoral Fellowship - 2019

University of Maryland College of Computer, Mathematical and Natural Science Merit fellowship - 2017

Horn Point Laboratory Graduate Student Research Fellowship - 2017

University of Maryland Graduate School Dean’s Fellowship - 2018



2022. "The Transformation and Export of Organic Carbon Across an Arctic River‐Delta‐Ocean Continuum." Journal of Geophysical Research: Biogeosciences 127 (12): [10.1029/2022jg007139] [Journal Article/Letter]

2022. "Arctic biogeochemical and optical properties of dissolved organic matter across river to sea gradients." Frontiers in Marine Science 9 [10.3389/fmars.2022.949034] [Journal Article/Letter]

2022. "The Impacts of Freshwater Input and Surface Wind Velocity on the Strength and Extent of a Large High Latitude River Plume." Frontiers in Marine Science 8 [10.3389/fmars.2021.793217] [Journal Article/Letter]

2021. "Photochemical and Microbial Degradation of Chromophoric Dissolved Organic Matter Exported From Tidal Marshes." Journal of Geophysical Research: Biogeosciences 126 (4): [10.1029/2020jg005744] [Journal Article/Letter]

2021. "Preferential loss of Yukon River delta colored dissolved organic matter under nutrient replete conditions." Limnology and Oceanography lno.11706 [10.1002/lno.11706] [Journal Article/Letter]

2020. "A Comprehensive Estuarine Dissolved Organic Carbon Budget Using an Enhanced Biogeochemical Model." Journal of Geophysical Research: Biogeosciences 125 (5): [10.1029/2019jg005442] [Journal Article/Letter]

2019. "A mechanistic model of photochemical transformation and degradation of colored dissolved organic matter." Marine Chemistry 214 103666 [10.1016/j.marchem.2019.103666] [Journal Article/Letter]

2017. "Estuarine Sediment Dissolved Organic Matter Dynamics in an Enhanced Sediment Flux Model." Journal of Geophysical Research: Biogeosciences 122 (10): 2669-2682 [10.1002/2017jg003800] [Journal Article/Letter]

2017. "Wind-Driven Dissolved Organic Matter Dynamics in a Chesapeake Bay Tidal Marsh-Estuary System." Estuaries and Coasts 41 (3): 708-723 [10.1007/s12237-017-0295-1] [Journal Article/Letter]

2017. "Ecological Forecasting and the Science of Hypoxia in Chesapeake Bay." BioScience 67 (7): 614-626 [10.1093/biosci/bix048] [Journal Article/Letter]

Talks, Presentations and Posters


Modeling of Organic Carbon Export and Processing in Arctic Deltas, Plumes and Coastal


November 19, 2021

Arctic-COLORS Data Synthesis Meeting

Linking optics to biogeochemical models to better quantify marine carbon cycling

July 22, 2021

Ocean Carbon and Biogeochemistry Annual Workshop

Estimating the fluxes and controls of estuarine organic matter: a case study of the Rhode River, MD. Invited Seminar. GCReW Symposium. Smithsonian Environmental Research, Edgewater, MD.

April 28, 2019

Estimates of wetland-estuary organic matter cycling using a new biogeochemical modeling system. Invited Seminar. Virginia Institute of Marine Science, College of William and Mary, Gloucester, VA. 

September 22, 2018

Modeling organic carbon at the wetland estuary interface – A small model with large insights. Invited Seminar. Smithsonian Environmental Research Center, Edgewater, MD

11, 2017

Progress and challenges in up-scaling carbon modeling to a regionally significant wetland-estuary system. Lightning talk. Ocean Carbon and Biogeochemistry Workshop, Woods Hole, MA. 

July 24, 2017


Activities for Teaching Estuarine Ecosystem Simulation Modeling.

December 5, 2021

Coastal and

Estuarine Research Federation Biennial Meeting

Modeling of Organic Carbon Export and Processing in the Yukon River Delta and Coastal


June 4, 2021

Quarterly Changes in the Arctic and Boreal System meeting

Biodegradation of Yukon River delta dissolved organic matter is marginally

enhanced by nutrient enrichment.

March 19, 2020

Ocean Sciences Meeting

Modeling of complex flow patterns across a large wetland-estuarine complex in Southern Dorchester Co, MD. Oral Presentation. Chesapeake Research and Modeling Symposium. Annapolis, MD.

July 14, 2018

Modeling of complex flow patterns across a large wetland-estuarine complex in Southern Dorchester Co, MD. Oral Presentation. Atlantic Estuarine Research Society Biennial Meeting. Rehoboth, DE

May 6, 2018

Development and application of a mechanistic model of the photochemical degradation of colored dissolved organic matter. Oral presentation. Ocean Sciences Meeting, Portland, OR.

March 14, 2018

Modeling of photodegradation and biogeochemical cycling in a wetland-estuary system. Oral. Coastal and Estuarine Research Federation Biennial Conference, Providence, RI.

December 7, 2017

Insights gained from a wetland-estuary dissolved organic matter modeling system. Poster. Ocean Carbon and Biogeochemistry Workshop, Woods Hole, MA.

July 26, 2017

Modeling of estuarine sediment organic matter remineralization with a dissolved organic matter intermediate state variable. Oral. Aquatic Sciences Meeting, Honolulu, HI

April 1, 2017

Mass conservative modeling of dissolved organic matter photochemistry and biogeochemical cycling. Poster. Ocean Carbon and Biogeochemistry Workshop, Woods Hole, MA.

August 27, 2016

Modeling the marsh-estuary organic carbon cycle in the Rhode River, MD. Oral. Chesapeake Modeling Symposium, Williamsburg, VA. 

July 1, 2016

Atmospheric forcing and marsh dissolved organic matter fluxes: modeling and observations from a Chesapeake Bay tidal marsh-estuary ecosystem. Poster. Ocean Sciences Meeting, New Orleans, LA. 

March 25, 2016

Three-dimensional modeling of a Chesapeake Bay tidal marsh Ecosystem, Oral Finite Volume Community Ocean Model (FVCOM) User workshop. Bedford Institute of Oceanography, Halifax, NS, CAN. 

November 21, 2015

Three-dimensional modeling of a Chesapeake Bay tidal marsh Ecosystem. Poster Community Surface Dynamic Modeling Systems (CSDMS) Annual Meeting. National Center for Atmospheric Research, Boulder, CO.

June 27, 2015