Journal Articles

Yoon, J., Wan, H., Daniel, B., Srikrishnan, V., & Judi, D. (2023). Structural model choices regularly overshadow parametric uncertainty in agent-based simulations of household flood risk outcomes. Comput. Environ. Urban Syst., 103, 101979.
Wan, H., Yoon, J., Srikrishnan, V., Daniel, B., & Judi, D. (2023). Landscape Metrics Regularly Outperform Other Traditionally-Used Ancillary Datasets in Dasymetric Mapping of Population. Computers, Environment and Urban Systems, 99, 101899.
Huang, X., Srikrishnan, V., Lamontagne, J., Keller, K., & Peng, W. (2023). Effects of global climate mitigation on regional air quality and health. Nature Sustainability.
Reed, P. M., Hadjimichael, A., Moss, R. H., Brelsford, C., Burleyson, C. D., Cohen, S., et al. (2022). Multisector Dynamics: Advancing the Science of Complex Adaptive Human-Earth Systems. Earth’s Future, 10(3), e2021EF002621.
Srikrishnan, V., Lafferty, D. C., Wong, T. E., Lamontagne, J. R., Quinn, J. D., Sharma, S., et al. (2022). Uncertainty Analysis in Multi-Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems. Earth’s Future, 10(8), e2021EF002644.
Srikrishnan, V., Guan, Y., Tol, R. S. J., & Keller, K. (2022). Probabilistic projections of baseline 21st century CO_2 emissions using a simple calibrated integrated assessment model. Climatic Change, 170, 37.
Wong, T. E., Rennels, L., Errickson, F., Srikrishnan, V., Bakker, A., Keller, K., & Anthoff, D. (2022). MimiBRICK.jl: A Julia package for the BRICK model for sea-level change in the Mimi integrated modeling framework. Journal of Open Source Software, 7(76), 2556.
Wan, H., Yoon, J., Srikrishnan, V., Daniel, B., & Judi, D. (2021). Population Downscaling Using High-Resolution, Temporally-Rich U.S. Property Data. Cartography and Geographic Information Science, 1–14.
Keller, K., Helgeson, C., & Srikrishnan, V. (2021). Climate Risk Management. Annu. Rev. Earth Planet. Sci., 49(1), 95–116.
Errickson, F. C., Keller, K., Collins, W. D., Srikrishnan, V., & Anthoff, D. (2021). Equity is more important for the social cost of methane than climate uncertainty. Nature, 592(7855), 564–570.
Helgeson, C., Srikrishnan, V., Keller, K., & Tuana, N. (2021). Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions. Philos. Sci., 88(2), 213–233.
Srikrishnan, V., & Keller, K. (2021). Small increases in agent-based model complexity can result in large increases in required calibration data. Environmental Modelling & Software, 138, 104978.
Zarekarizi, M., Srikrishnan, V., & Keller, K. (2020). Neglecting uncertainties biases house-elevation decisions to manage riverine flood risks. Nature Communications.
Srikrishnan, V., Alley, R., & Keller, K. (2019). Investing in science to improve climate risk management. Eos, 100.
Ruckert, K. L., Srikrishnan, V., & Keller, K. (2019). Characterizing the deep uncertainties surrounding coastal flood hazard projections: A case study for Norfolk, VA. Sci. Rep., 9(1), 11373.
Wong, T. E., Klufas, A., Srikrishnan, V., & Keller, K. (2018). Neglecting model structural uncertainty underestimates upper tails of flood hazard. Environmental Research Letters, 13(7), 074019.
Morris, J., Srikrishnan, V., Webster, M., & Reilly, J. (2018). Hedging strategies: Electricity investment decisions under policy uncertainty. The Energy Journal, 39(1).
Oddo, P. C., Lee, B. S., Garner, G. G., Srikrishnan, V., Reed, P. M., Forest, C. E., & Keller, K. (2017). Deep uncertainties in sea-level rise and storm surge projections: Implications for coastal flood risk management. Risk Analysis.
Wong, T. E., Srikrishnan, V., Hadka, D., & Keller, K. (2017). A multi-objective decision-making approach to the journal submission problem. PLoS One, 12(6), e0178874.
Srikrishnan, V., Young, G. S., & Brownson, J. R. S. (2017). Skill and skill prediction of cloud-track advection-only forecasting under a cumulus-dominated regime. Journal of Applied Meteorology and Climatology, 56(3), JAMC–D–16–0224.1.
Srikrishnan, V., Young, G. S., Witmer, L. T., & Brownson, J. R. S. (2015). Using multi-pyranometer arrays and neural networks to estimate direct normal irradiance. Solar Energy, 119, 531–542.

Papers In Review or In Press

Ye, H., Nicholas, R. E., Srikrishnan, V., & Keller, K. (2023). Emulation Methods and Adaptive Sampling Increase the Efficiency of Sensitivity Analysis for Computationally Expensive Models. arXiv.
Kabir, E., Srikrishnan, V., Liu, M. V., Steinschneider, S., & Anderson, C. L. (2023). Quantifying the multi-scale and multi-resource impacts of large-scale adoption of renewable energy source. submitted.
Liu, M. V., Srikrishnan, V., Doering, K., Steinschneider, S., & Anderson, C. L. (2023). Heterogeneous Vulnerability of Zero-Carbon Power Grids under Climate-Technological Changes. submitted.
Wan, H., Daniel, B., Srikrishnan, V., Yoon, J., Judi, D., Roy Chowdury, P. K., & Bhaduri, P. (2023). Using interpretable machine learning to identify key patterns underlying home price dynamics. submitted.
Maragoni, G., Mauri, M., Srikrishnan, V., Keller, K., & Tavoni, M. (2023). Robust Solar Radiation Management Strategies for Well-below 2\textdegreeC Strategies. In revision.
Helgeson, C., Keller, K., Nicholas, R. E., Srikrishnan, V., Cooper, C., Smithwick, E. A. H., & Tuana, N. Integrating values to improve the relevance and inclusiveness of climate-risk research. In revision.
Lucash, M., Williams, N. G., Srikrishnan, V., Keller, K., Scheller, R. M., Helgeson, C., et al. Balancing multiple forest management objectives under climate change in central Wisconsin, U.S.A. submitted.


Reed, P. M., Hadjimichael, A., Moss, R. H., Monier, E., Alba, S., Brelsford, C., et al. (2022). MultiSector Dynamics: Scientific Challenges and a Research Vision for 2030. A Community of Practice Supported by the United States Department of Energy’s Office of Science.


Reed, P. M., Hadjimichael, A., Malek, K., Karimi, T., Vernon, C. R., Srikrishnan, V., et al. (2022). Addressing Uncertainty in Multisector Dynamics Research. Zenodo.

Conference Papers

Srikrishnan, V., Brownson, J. R. S., & Young, G. S. (2014). The All-Seeing Eye: Using Multi-Pyranometer Arrays and Neural Networks to Estimate Direct Normal Irradiance. In 43rd ASES National Solar Conference 2014, SOLAR 2014, Including the 39th National Passive Solar Conference and the 2nd Meeting of Young and Emerging Professionals in Renewable Energy (pp. 511–518). American Solar Energy Society.