Publications

Journal Articles

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. https://doi.org/10.1016/j.compenvurbsys.2022.101899

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. https://doi.org/10.1029/2021EF002621

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. https://doi.org/10.1029/2021EF002644

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. https://doi.org/10.1007/s10584-021-03279-7

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. https://doi.org/10.21105/joss.04556

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. https://doi.org/10.1080/15230406.2021.1991479

Keller, K., Helgeson, C., & Srikrishnan, V. (2021). Climate Risk Management. Annu. Rev. Earth Planet. Sci., 49(1), 95–116. https://doi.org/10.1146/annurev-earth-080320-055847

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. https://doi.org/10.1038/s41586-021-03386-6

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. https://doi.org/10.1086/711501

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. https://doi.org/10.1016/j.envsoft.2021.104978

Zarekarizi, M., Srikrishnan, V., & Keller, K. (2020). Neglecting uncertainties biases house-elevation decisions to manage riverine flood risks. Nature Communications. https://doi.org/10.1038/s41467-020-19188-9

Srikrishnan, V., Alley, R., & Keller, K. (2019). Investing in science to improve climate risk management. Eos, 100. https://doi.org/10.1029/2019eo131077

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. https://doi.org/10.1038/s41598-019-47587-6

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. https://doi.org/10.1088/1748-9326/aacb3d

Morris, J., Srikrishnan, V., Webster, M., & Reilly, J. (2018). Hedging strategies: Electricity investment decisions under policy uncertainty. The Energy Journal, 39(1). https://doi.org/10.5547/01956574.39.1.jmor

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. https://doi.org/10.1111/risa.12888

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. https://doi.org/10.1371/journal.pone.0178874

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. https://doi.org/10.1175/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. https://doi.org/10.1016/j.solener.2015.06.004

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.

Maragoni, G., Mauri, M., Srikrishnan, V., Keller, K., & Tavoni, M. (2023). Robust Solar Radiation Management Strategies for Well-below 2\textdegreeC Strategies. In review.

Huang, X., Srikrishnan, V., Lamontagne, J., Keller, K., & Peng, W. Pricing carbon emissions reduces health inequities from air pollution exposure. Resubmitted.

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 review.

Yoon, J., Wan, A., Daniel, B., Srikrishnan, V., & Judi, D. Structural model choices regularly overshadow parametric uncertainty in agent-based simulations of household flood risk outcomes. In revision.

Reports

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. https://doi.org/10.5281/zenodo.5825890

Books

Reed, P. M., Hadjimichael, A., Malek, K., Karimi, T., Vernon, C. R., Srikrishnan, V., et al. (2022). Addressing Uncertainty in Multisector Dynamics Research. Zenodo. https://doi.org/10.5281/zenodo.6110623

Conference Papers