Publications

Journal Articles

Hosseini-Shakib, I., Alipour, A., Seiyon Lee, B., Srikrishnan, V., Nicholas, R. E., Keller, K., & Sharma, S. (2024). What drives uncertainty surrounding riverine flood risks? J. Hydrol. (Amst.), 634(131055), 131055. https://doi.org/10.1016/j.jhydrol.2024.131055

Kabir, E., Srikrishnan, V., Liu, M. V., Steinschneider, S., & Anderson, C. L. (2024). Quantifying the impact of multi-scale climate variability on electricity prices in a renewable-dominated power grid. Renew. Energy, 223(120013), 120013. https://doi.org/10.1016/j.renene.2024.120013

Lucash, M. S., Williams, N. G., Srikrishnan, V., Keller, K., Scheller, R. M., Hegelson, C., et al. (2023). Balancing multiple forest management objectives under climate change in central Wisconsin, U.S.A. Trees, Forests and People, 14, 100460. https://doi.org/10.1016/j.tfp.2023.100460

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. https://doi.org/10.1016/j.compenvurbsys.2023.101979

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

Huang, X., Srikrishnan, V., Lamontagne, J., Keller, K., & Peng, W. (2023). Effects of global climate mitigation on regional air quality and health. Nature Sustainability. https://doi.org/10.1038/s41893-023-01133-5

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

Pollack, A., Campbell, J., Condon, M., Cooper, C., Coronese, M., Doss-Gollin, J., et al. (2024). Peer-reviewed climate change research has a transparency problem. The scientific community needs to do better. Nature Climate Change. In review.

Liu, M. V., Srikrishnan, V., Doering, K., Steinschneider, S., & Anderson, C. L. (2024). Heterogeneous Vulnerability of Zero-Carbon Power Grids under Climate-Technological Changes. Joule. submitted.

Darnell, C., Rennels, L., Errickson, F., Wong, T., & Srikrishnan, V. (2023, December 13). Impacts of emissions uncertainty on Antarctic instability and sea-level rise. Nature Climate Change. In revision.

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. Environ. Model Softw. arXiv.

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. in revision.

Helgeson, C., Keller, K., Nicholas, R. E., Srikrishnan, V., Cooper, C., Smithwick, E. A. H., & Tuana, N. (2024). Integrating values to improve the relevance and inclusiveness of climate-risk research. Earth’s Future. In revision.

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