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Emilio Mayorga

Senior Oceanographer






B.S. Environmental Engineering Science, Massachusetts Institute of Technology, 1992

Ph.D. Chemical Oceanography, University of Washington, 2004


GeoHackWeek: Workshop on Geospatial Data Science

APL-UW researchers teamed with University and industry partners to explore open source geospatial software development during a workshop held 14–18 November.

14 Nov 2016

BiGCZ: Cyberinfrastructure for Bio and Geoscience processes in the Critical Zone

The goal of this project is to co-develop with the "Critical Zone" science community a high-performance web-based integration and visualization environment for joint analysis of cross-scale Bio and Geoscience processes in the Critical Zone (BiGCZ), spanning experimental and observational designs.

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1 Dec 2013

The Critical Zone (CZ) is Earth's permeable near-surface layer -- from the atmosphere at the vegetation's canopy to the lower boundary of actively circulating groundwaters. The BiGCZ system will be an open-source software system leveraging the ODM2 information model and specifically designed to address the challenges of managing, sharing, analyzing and integrating diverse data from the multiple disciplines encompassing CZ science.

ODM2: Observations Data Model 2

ODM2 is a community information model aimed at extending interoperability of feature-based earth observations derived from sensors and samples and improve the capture, sharing, and archival of these data. ODM2 has been designed from a general perspective, with extensibility for achieving interoperability across multiple disciplines and systems that support publication of earth observations.

1 Aug 2012

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2000-present and while at APL-UW

Spatially explicit fate factors of waterborne nitrogen emissions at the global scale

Cosme, N., E. Mayorga, and M.Z. Hauschild, "Spatially explicit fate factors of waterborne nitrogen emissions at the global scale," Int. J. Life Cycle Assess., 23, 1286-1296, doi:10.1007/s11367-017-1349-0, 2017.

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1 Jun 2018


Marine eutrophication impacts due to waterborne nitrogen (N) emissions may vary significantly with their type and location. The environmental fate of dissolved inorganic nitrogen (DIN) forms is essential to understand the impacts they may trigger in receiving coastal waters. Current life cycle impact assessment (LCIA) methods apply fate factors (FFs) with limited specificity of DIN emission routes, and often lack spatial differentiation and global applicability. This paper describes a newly developed method to estimate spatially explicit FFs for marine eutrophication at a global scale and river basin resolution.


The FF modelling work includes DIN removal processes in both inland (soil and river) and marine compartments. Model input parameters are the removal coefficients extracted from the Global NEWS 2-DIN model and residence time of receiving coastal waters. The resulting FFs express the persistence of the fraction of the original DIN emission in the receiving coastal large marine ecosystems (LMEs). The method further discriminates three DIN emission routes, i.e., diffuse emission from soils, and direct point emissions to freshwater or marine water. Based on modelling of individual river basins, regionally aggregated FFs are calculated as emission-weighted averages.

Results and discussion

Among 5772 river basins of the world, the calculated FFs show 5 orders of magnitude variation for the soil-related emission route, 3 for the river-related, and 2 for emissions to marine water. Spatial aggregation of the FFs at the continental level decreases this variation to 1 order of magnitude or less for all routes. Coastal water residence time was found to show inconsistency and scarcity of literature sources. Improvement of data quality for this parameter is suggested.


With the proposed method and factors, spatial information of DIN emissions can be used to improve the environmental relevance and the discriminatory power of the assessment of marine eutrophication impacts in a geographically differentiated characterization model at a global scale.

Modeling sources of nutrients in rivers draining into the Bay of Bengal — A scenario analysis

Pedde, S., C. Kroeze, E. Mayorga, and S.P. Seitzinger, "Modeling sources of nutrients in rivers draining into the Bay of Bengal — A scenario analysis," Reg. Environ. Change, 17, 2495-2506, doi:10.1007/s10113-017-1176-7, 2017.

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1 Dec 2017

We model future trends in river export of nutrients to the Bay of Bengal, and the sources of this pollution. We focus on total nitrogen (TN), total phosphorus (TP), and dissolved silica (DSi) inputs to the Bay of Bengal Large Marine Ecosystem (BOB LME) in the years 2000, 2030, and 2050. In 2000, rivers exported 7.1 Tg N and 1.5 Tg P to the BOB LME. Three rivers (Ganges, Godavari, Irrawaddy) account for 75–80% of the total river export of N and P. For 2050, we calculate an increase in river export of N to 8.6 Tg, while P export stabilizes at the 2000 level. Future trends are the net effect of increasing river export of dissolved N (by 40%) and P (by 80%), and decreasing river export of particulate N and P. The increases in dissolved N and P loads are associated primarily with increased N and P losses from agriculture and sewage systems. The decreasing export of particulate N and P is associated with damming of rivers and increased human water consumption. There are large differences in nutrient export among rivers. Rivers draining into the western BOB LME generally export more N and P than eastern BOB LME rivers. Most N and P in western BOB LME rivers are from anthropogenic sources. Future increases in dissolved inorganic N and P (DIN and DIP) export can be large for individual rivers: up to more than a factor of five for DIP and more than a doubling for DIN. The calculated nutrient export ratios (N and P relative to DSi) indicate an increasing risk for blooms of non-siliceous algal species, which can potentially produce toxins and otherwise disrupt coastal ecosystems. Our results indicate that basin-specific management may be the most effective approach towards reducing the risk of coastal eutrophication in the BOB LME.

Enhancing interoperability and capabilities of earth science data using the Observations Data Model 2 (ODM2)

Hsu, L., and 5 others including E. Mayorga, "Enhancing interoperability and capabilities of earth science data using the Observations Data Model 2 (ODM2)," Data Sci. J., 16, doi:10.5334/dsj-2017-004, 2017.

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6 Feb 2017

Earth Science researchers require access to integrated, cross-disciplinary data in order to answer critical research questions. Partially due to these science drivers, it is common for disciplinary data systems to expand from their original scope in order to accommodate collaborative research. The result is multiple disparate databases with overlapping but incompatible data. In order to enable more complete data integration and analysis, the Observations Data Model Version 2 (ODM2) was developed to be a general information model, with one of its major goals to integrate data collected by in situ sensors with those by ex-situ analyses of field specimens. Four use cases with different science drivers and disciplines have adopted ODM2 because of benefits to their users. The disciplines behind the four cases are diverse — hydrology, rock geochemistry, soil geochemistry, and biogeochemistry. For each case, we outline the benefits, challenges, and rationale for adopting ODM2. In each case, the decision to implement ODM2 was made to increase interoperability and expand data and metadata capabilities. One of the common benefits was the ability to use the flexible handling and comprehensive description of specimens and data collection sites in ODM2's sampling feature concept. We also summarize best practices for implementing ODM2 based on the experience of these initial adopters. The descriptions here should help other potential adopters of ODM2 implement their own instances or to modify ODM2 to suit their needs.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center