About

Ryan Calder is an assistant professor of environmental health and policy in the Department of Population Health Sciences at the Virginia-Maryland College of Veterinary Medicine, Virginia Tech. His expertise and interests lie at the interface of ecological modeling, human health risk assessment, and decision science. He is focused on improving the predictive ability for the interacting environmental and health impacts of civil infrastructure projects linked to energy transitions and supports the development of science-based policy. Dr. Calder holds a Doctor of Science in Environmental Health from Harvard T.H. Chan School of Public Health, obtained in 2017, a Master of Applied Science in Civil Engineering from Concordia University in 2012, and a Bachelor of Engineering in Civil Engineering from Concordia University in 2010. He is a licensed professional engineer in the State of Nevada and the Province of Quebec, with additional professional licensure in Canada and Nevada. His professional experience includes roles as an engineer and analyst, and he has held postdoctoral positions at Harvard University and Duke University. Calder is a member of the Scholars Strategy Network and the International Environmental Modelling and Software Society.

Research topics

• Political Science
• Business
• Environmental resource management
• Environmental science
• Psychology
• Geography
• Ecology
• Medicine
• Development economics
• Economics
• Public relations
• Environmental health
• Environmental planning

Selected publications

• A Coastal Flood Mortality Risk Model with Projected Changes under Sea Level Rise

  2025-11-06

  articleOpen access1st authorCorresponding

  Coastal flooding, caused by sea level rise (SLR), storm surge, and tropical cyclones, is a growing threat. Previous studies have documented mortality associated with historical coastal flooding and modeled mortality risk as a function of SLR and human development. This study updates those estimates and provides new forecasts by including global mortality data from events between 1990 and 2024 and leverages an improved method for estimating historical and future coastal populations exposed to coastal flooding events. Primary data sources include the Emergency Events Database (EM-DAT) and the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) model. Trend analyses indicate increasing coastal flooding events and decreasing deaths per event over the 35-year period. On average, countries and territories with lower Human Development Index have higher coastal flooding event mortality rates. A mixed effect regression model suggests that mortality risk per event averages 0.55% of exposed individuals across countries, ranging from 0.00004% (Netherlands) to 16% (Peru), and totaling ~33,435 deaths per year. Applying projected 2050 and 2100 SLR and population changes, per-event mortality increases by 37–69% by 2050 and 43–250% in 2100 globally, depending on the socioeconomic and climate scenario. These increases result from projected increases in exposed populations (from demographic changes and sea level rise) and do not reflect likely changes in event frequency or magnitude. The country-specific and regional mortality forecasts presented here allow prioritization of investments and improved budgeting for coastal resilience and climate adaptation policies.

  PublisherOA PDFDOI

• Vertical distribution of Methylmercury in the Central Arctic Ocean explained by In Situ Methylation and Demethylation

  Research Square · 2025-02-04 · 1 citations

  preprintOpen access
  PublisherOA PDFDOI

• Local land‐use decisions drive losses in river biological integrity to 2099: Using machine learning to disentangle interacting drivers of ecological change in policy forecasts

  Meteorological Applications · 2025-01-01 · 4 citations

  articleOpen access

  Abstract Climate and land‐use/land‐cover (LULC) change each threaten the health of rivers. Rising temperatures, changes in rainfall and runoff, and other perturbations, will all impact rivers' physical, biological, and chemical characteristics over the next century. While scientists and policymakers have increasing access to climate and LULC forecasts, the implications of each for outcomes of interest have been difficult to quantify. This is partially because climate and LULC perturb ecological outcomes via incompletely understood site‐specific, interacting, and nonlinear mechanisms that are not well suited to analysis using classical statistical methods. This creates uncertainties over the benefits of local‐level interventions such as green infrastructure investments and urban densification, and limits how forecasts can be used to inform decision‐making. Here, we demonstrate how machine learning can be used to quantify the relative contributions of LULC and climate drivers to impacts on riverine health as measured by taxonomic richness of the macroinvertebrate orders Ephemeroptera , Plecoptera , and Trichoptera (EPT). We develop a cross‐validated Random Forest (RF) model to link EPT taxa richness to meteorological, water quality, hydrologic, and LULC variables in watersheds in New Hampshire and Vermont, USA. Prospective climate and LULC scenarios are used to generate predictions of these variables and of EPT taxa richness trends through the year 2099. The model structure is mechanistically interpretable and performs well on test data ( R 2 ~ 0.4). Impacts on EPT taxa richness are driven by local LULC policy such as increased suburbanization. Future trends are likely to be exacerbated by climate change, although warming conditions suggest possible increases in springtime EPT taxa richness. Overall, this analysis highlights (1) the impact of local LULC decisions on riverine health in the context of a changing climate, and (2) the role machine learning methods can play in developing models that disentangle interacting physical mechanisms to advance decision support.

  PublisherDOI

• Hotspots of Bacterial Pathogen Abundance and Exposure Risk in Soils of the Contiguous United States

  GeoHealth · 2025-12-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  Soils are reservoirs of pathogenic bacteria that cause human illness, particularly after mobilizing events such as extreme rain. Land-use patterns (e.g., proximity to agriculture) and soil properties (e.g., moisture) are associated with abundance of individual pathogenic bacteria. However, there are major uncertainties in (a) the importance of local/regional land-use decisions relative to overall natural variability of pathogenicity and (b) the correlations among pathogen abundance, climate-linked physical processes increasing pathogen mobility, and the vulnerability of human receptors. This impairs identification of priority areas for outbreak surveillance, which has traditionally focused on food and water distribution networks, and the development of process-based risk screening models. Here, we analyze a novel data set of 622 soil samples covering 42 of the 48 contiguous United States. We describe (a) the relationship between putative pathogenicity and natural and land-use drivers and (b) how hotspots of putative pathogen abundance intersect with climate-linked hazard of mobilization via fire, floods, wind, and fluvial transport, and the social vulnerability of local human populations. Variability in putative pathogenicity can be partially explained by known drivers, with natural variables having greater explanatory power than land-use variables. Relative abundance of putative pathogens is generally higher in forested ecoregions, notably in the eastern and southeastern United States and in proximity to surface waters. Higher relative abundance of putative pathogens, climate risks promoting pathogen mobility, and a relatively vulnerable rural population intersect in the southeastern United States. Integrated sampling and modeling are needed to monitor and forecast health risks from soilborne pathogens.

  PublisherDOI

• Trends and disparities in motor vehicle collision injuries in Washington, DC

  Accident Analysis & Prevention · 2025-09-26

  article1st authorCorresponding
  PublisherDOI

• Correlated uncertainty propagation enables multi-impact decision support for electrical system decarbonization

  2025-06-09

  preprintOpen accessSenior author

  Decarbonization planning requires comparing diverse pathways across economic, ecological, and health dimensions under uncertainty. Capacity expansion models generally treat pathway uncertainties as independent, overestimating uncertainty around inter-scenario differences, which drive decisions. U.S.–Canada trade tensions and abrupt federal termination of offshore wind permits threaten key planks of regional decarbonization plans and illustrate the need for models spanning a wider pathway space. We present PHASED (Probabilistic Hourly Assessment of Scenarios for Electrical Decarbonization), propagating correlated uncertainties across prescribed pathways through hourly dispatch over a 26-year horizon and generating joint posterior distributions across modeled outcomes. Applied to eight New England pathways, correlated uncertainty tracking yields >90% confidence in pairwise cost differences despite overlapping absolute cost intervals. Pathways with similar monetized impacts (roughly $470–477 billion by 2050) diverge on land use, avian mortality, and air quality. Rural areas receive greater relative air quality benefits than urban areas, cutting against assumptions that shape siting politics.

  PublisherOA PDFDOI

• A framework for developing a real-time lake phytoplankton forecasting system to support water quality management in the face of global change

  AMBIO · 2024-09-20 · 4 citations

  articleOpen access

  Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.

  PublisherOA PDFDOI

• Causal inference to scope environmental impact assessment of renewable energy projects and test competing mental models of decarbonization

  Environmental Research Infrastructure and Sustainability · 2024-11-07 · 2 citations

  articleOpen accessSenior author

  Abstract Environmental impact assessment (EIA), life cycle analysis (LCA), and cost benefit analysis (CBA) embed crucial but subjective judgments over the extent of system boundaries and the range of impacts to consider as causally connected to an intervention, decision, or technology of interest. EIA is increasingly the site of legal, political, and social challenges to renewable energy projects proposed by utilities, developers, and governments, which, cumulatively, are slowing decarbonization. Environmental advocates in the United States have claimed that new electrical interties with Canada increase development of Canadian hydroelectric resources, leading to environmental and health impacts associated with new reservoirs. Assertions of such second-order impacts of two recently proposed 9.5 TWh yr −1 transborder transmission projects played a role in their cancellation. We recast these debates as conflicting mental models of decarbonization, in which values, beliefs, and interests lead different parties to hypothesize causal connections between interrelated processes (in this case, generation, transmission, and associated impacts). We demonstrate via Bayesian network modeling that development of Canadian hydroelectric resources is stimulated by price signals and domestic demand rather than increased export capacity per se. However, hydropower exports are increasingly arranged via long-term power purchase agreements that may promote new generation in a way that is not easily modeled with publicly available data. We demonstrate the utility of causal inference for structured analysis of sociotechnical systems featuring phenomena that are not easily modeled mechanistically. In the setting of decarbonization, such analysis can fill a gap in available energy systems models that focus on long-term optimum portfolios and do not generally represent questions of incremental causality of interest to stakeholders at the local level. More broadly, these tools can increase the evidentiary support required for consequentialist (as opposed to attributional) LCA and CBA, for example, in calculating indirect emissions of renewable energy projects.

  PublisherOA PDFDOI

• Deep autoregressive modeling for land use land cover

  arXiv (Cornell University) · 2024-01-02

  preprintOpen accessSenior author

  Land use / land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related to topography, ecology, and human development. We identify a close connection between modeling of spatial patterns of land use and the task of image inpainting from computer vision and conduct a study of a modified PixelCNN architecture with approximately 19 million parameters for modeling LULC. In comparison with a benchmark spatial statistical model, we find that the former is capable of capturing much richer spatial correlation patterns such as roads and water bodies but does not produce a calibrated predictive distribution, suggesting the need for additional tuning. We find evidence of predictive underdispersion with regard to important ecologically-relevant land use statistics such as patch count and adjacency which can be ameliorated to some extent by manipulating sampling variability.

  PublisherOA PDFDOI

• Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

  Environmental Research Infrastructure and Sustainability · 2024-09-01 · 4 citations

  articleOpen access1st authorCorresponding

  Abstract The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

  PublisherDOI

Frequent coauthors

• Marc Jeuland

  Duke University

  11 shared

• Mark E. Borsuk

  9 shared

• Amina T. Schartup

  University of California, San Diego

  6 shared

• Elsie M. Sunderland

  Harvard University

  5 shared

• Ann Bostrom

  University of Washington

  5 shared

• Mary Kay Gugerty

  University of Washington

  5 shared

• Alison C. Cullen

  University of Washington

  5 shared

• Ketra Schmitt

  Concordia University

  4 shared

Education

• Other

  Virginia-Maryland College of Veterinary Medicine

• Other

  Virginia-Maryland College of Veterinary Medicine

• Other

  Virginia-Maryland College of Veterinary Medicine