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Precipitation, moderated by spring temperature and vegetation, drives runoff efficiency in the Upper Colorado River Basin, USA

2025-12-29
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David Palumbo, Subhrendu Gangopadhyay, Upmanu Lall

Abstract

Streamflow in the Upper Colorado River Basin, USA has decreased proportionally more than precipitation in the recent multi-decadal drought. The causes are debated. Understanding how precipitation, and seasonal temperature, vegetation, and evapotranspiration dynamics affect streamflow is essential. Here we use causal inference with historical data to identify surface runoff efficiency drivers. Runoff efficiency increases in years with higher precipitation and snow accumulation accompanied by cooler spring temperatures and delayed vegetation phenology, which generally attenuates biomass accumulation. Conversely, runoff efficiency decreases in years with lower precipitation and snow accumulation, or warmer springs, when vegetation activity and productivity are accelerated or amplified. Summer temperature, often identified as a driver of higher evaporation and aridity, does not emerge as statistically significant. Years with extreme phases of winter-spring precipitation have distinct atmospheric circulation patterns and associated sea surface temperatures, indicating the influence of larger-scale climate drivers on the Basin’s precipitation and runoff efficiency dynamics.

Data availability

All processed hydroclimatic datasets supporting the analyses in this study are available at Zenodo (https://doi.org/10.5281/zenodo.17843029)79. These processed files were derived from publicly accessible datasets obtained from the sources referenced throughout the manuscript.

Code availability

All analyses were conducted using exiting R packages referenced throughout the manuscript.

References

  1. Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M. & Engel, R. Dramatic declines in snowpack in the western US. npj Clim. Atmos. Sci. 1, 2 (2018).

    Google Scholar 

  2. Knowles, N., Dettinger, M. D. & Cayan, D. R. Trends in snowfall versus rainfall in the western United States. J. Clim. 19, 4545–4559 (2006).

    Google Scholar 

  3. Cook, B. I. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s. Future 8, e2019EF001461 (2020).

    Google Scholar 

  4. White, D. D. et al. Chapter 28: Southwest. Fifth National Climate Assessment. https://nca2023.globalchange.gov/chapter/28 (2023).

  5. Hoerling, M. et al. Causes for the century-long decline in colorado river flow. J. Clim. 32, 8181–8203 (2019).

    Google Scholar 

  6. Seager, R. et al. Ocean-forcing of cool season precipitation drives ongoing and future decadal drought in southwestern North America. npj Clim. Atmos. Sci. 6, 141 (2023).

    Google Scholar 

  7. Schmidt, J. C., Yackulic, C. B. & Kuhn, E. The Colorado River water crisis: its origin and the future. WIREs Water 10, e1672 (2023).

    Google Scholar 

  8. Gangopadhyay, S., Woodhouse, C. A., McCabe, G. J., Routson, C. C. & Meko, D. M. Tree rings reveal unmatched 2nd century drought in the Colorado River Basin. Geophys. Res. Lett. 49, e2022GL098781 (2022).

    Google Scholar 

  9. Wheeler, K. G. et al. What will it take to stabilize the Colorado River?. Science 377, 373–375 (2022).

    Google Scholar 

  10. Overpeck, J. T. & Udall, B. Climate change and the aridification of North America. Proc. Natl. Acad. Sci. USA 117, 11856–11858 (2020).

    Google Scholar 

  11. Tillman, F., Gangopadhyay, S. & Pruitt, T. Scientific Investigations Report. 24 https://doi.org/10.3133/sir20205107 (2020).

  12. Lukas, J. & Harding, B. Current Understanding of Colorado River Basin Climate and Hydrology.” Chap. 2 in Colorado River Basin Climate and Hydrology: State of the Science. in Western Water Assessment (eds Lukas, J. & Payton, E.) 42–81 (University of Colorado Boulder, 2020).

  13. McCabe, G. J. & Wolock, D. M. Warming may create substantial water supply shortages in the Colorado River basin. Geophys. Res. Lett. 34, 2007GL031764 (2007).

    Google Scholar 

  14. U.S. Department of the Interior, Bureau of Reclamation. Final Supplemental Environmental Impact Statement for Near-Term Colorado River Operations. 478 https://www.usbr.gov/ColoradoRiverBasin/documents/NearTermColoradoRiverOperations/20240300-Near-termColoradoRiverOperations-FinalSEIS-508.pdf (2024).

  15. Whitney, K. M. et al. Spatial attribution of declining Colorado River streamflow under future warming. J. Hydrol. 617, 129125 (2023).

    Google Scholar 

  16. Wang, Z., Vivoni, E. R., Whitney, K. M., Xiao, M. & Mascaro, G. On the sensitivity of future hydrology in the Colorado River to the selection of the precipitation partitioning method. Water Resour. Res. 60, e2023WR035801 (2024).

    Google Scholar 

  17. Xiao, M., Udall, B. & Lettenmaier, D. P. On the causes of declining Colorado river streamflows. Water Resour. Res. 54, 6739–6756 (2018).

    Google Scholar 

  18. Udall, B. & Overpeck, J. The twenty-first century Colorado River hot drought and implications for the future. Water Resour. Res. 53, 2404–2418 (2017).

    Google Scholar 

  19. Nowak, K., Hoerling, M., Rajagopalan, B. & Zagona, E. Colorado river basin hydroclimatic variability. J. Clim. 25, 4389–4403 (2012).

    Google Scholar 

  20. Woodhouse, C. A., Pederson, G. T., Morino, K., McAfee, S. A. & McCabe, G. J. Increasing influence of air temperature on upper Colorado River streamflow. Geophys. Res. Lett. 43, 2174–2181 (2016).

    Google Scholar 

  21. Woodhouse, C. A. & Pederson, G. T. Investigating runoff efficiency in upper colorado river streamflow over past centuries. Water Resour. Res. 54, 286–300 (2018).

    Google Scholar 

  22. Hamlet, A. F., Mote, P. W., Clark, M. P. & Lettenmaier, D. P. Effects of temperature and precipitation variability on snowpack trends in the western United States*. J. Clim. 18, 4545–4561 (2005).

    Google Scholar 

  23. McCabe, G. J., Wolock, D. M., Pederson, G. T., Woodhouse, C. A. & McAfee, S. Evidence that recent warming is reducing upper colorado river flows. Earth Interact. 21, 1–14 (2017).

    Google Scholar 

  24. Bass, B., Goldenson, N., Rahimi, S. & Hall, A. Aridification of Colorado river basin’s snowpack regions has driven water losses despite ameliorating effects of vegetation. Water Resour. Res. 59, e2022WR033454 (2023).

    Google Scholar 

  25. U.S. Department of Agriculture, Natural Resources Conservation Service. Upper Colorado Region precipitation basin plots. https://nwcc-apps.sc.egov.usda.gov/awdb/basin-plots/POR/PREC/assocHUC2/14_Upper_Colorado_Region.html (accessed 22 September 2024).

  26. U.S. Department of the Interior, Bureau of Reclamation. Draft Annual Operating Plan for Colorado River Reservoirs 2022. https://www.usbr.gov/lc/region/g4000/aop/AOP22.pdf (2021).

  27. U.S. Department of the Interior, Bureau of Reclamation. Draft Annual Operating Plan for Colorado River Reservoirs 2023. https://www.usbr.gov/lc/region/g4000/aop/AOP22.pdf (2022).

  28. U.S. Department of the Interior, Bureau of Reclamation. Draft Annual Operating Plan for Colorado River Reservoirs 2024. https://www.usbr.gov/lc/region/g4000/AOP2024/AOP24_draft.pdf (2024).

  29. McCabe, G. J., Wolock, D. M. & Valentin, M. Warming is driving decreases in snow fractions while runoff efficiency remains mostly unchanged in snow-covered areas of the western United States. J. Hydrometeorol. 19, 803–814 (2018).

    Google Scholar 

  30. Milly, P. C. D. & Dunne, K. A. Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation. Science 367, 1252–1255 (2020).

    Google Scholar 

  31. Hogan, D. & Lundquist, J. D. Recent Upper Colorado river streamflow declines driven by loss of spring precipitation. Geophys. Res. Lett. 51, e2024GL109826 (2024).

    Google Scholar 

  32. Rumsey, C. A., Miller, M. P., Susong, D. D., Tillman, F. D. & Anning, D. W. Regional scale estimates of baseflow and factors influencing baseflow in the Upper Colorado River Basin. J. Hydrol. Regional Stud. 4, 91–107 (2015).

    Google Scholar 

  33. Li, D., Wrzesien, M. L., Durand, M., Adam, J. & Lettenmaier, D. P. How much runoff originates as snow in the western United States, and how will that change in the future?. Geophys. Res. Lett. 44, 6163–6172 (2017).

    Google Scholar 

  34. Miller, M. P., Buto, S. G., Susong, D. D. & Rumsey, C. A. The importance of base flow in sustaining surface water flow in the Upper Colorado River Basin. Water Resour. Res. 52, 3547–3562 (2016).

    Google Scholar 

  35. Marvel, K. et al. Chapter 2: Climate Trends. Fifth National Climate Assessment. https://nca2023.globalchange.gov/chapter/2 (2023).

  36. Bolinger, R., Lukas, J., Schumacher, R. & Gpble, P. Climate Change in Colorado. https://mountainscholar.org/items/99896af1-0564-4531-9628-be1e13dbc4cd (2023).

  37. Intergovernmental Panel On Climate Change (IPCC). Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2023).

  38. Zhao, S. & Zhang, J. Causal effect of the tropical Pacific sea surface temperature on the Upper Colorado River Basin spring precipitation. Clim. Dyn. 58, 941–959 (2022).

    Google Scholar 

  39. Tillman, F. D. et al. A review of current capabilities and science gaps in water supply data, modeling, and trends for water availability assessments in the upper Colorado river basin. Water 14, 3813 (2022).

    Google Scholar 

  40. Christensen, N. S. & Lettenmaier, D. P. A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin. Hydrol. Earth Syst. Sci. 11, 1417–1434 (2007).

    Google Scholar 

  41. Barnett, T. P. et al. Human-induced changes in the hydrology of the western United States. Science 319, 1080–1083 (2008).

    Google Scholar 

  42. Pierce, D. W. et al. Attribution of declining western U.S. snowpack to human effects. J. Clim. 21, 6425–6444 (2008).

    Google Scholar 

  43. U.S. Department of the Interior, Bureau of Reclamation. West-Wide Climate Risk Assessments: Bias-Corrected and Spatially Downscaled Surface Water Projections. 122 https://usbr.gov/climate/secure/docs/2011secure/west-wide-climate-risk-assessments.pdf (2011).

  44. U.S. Department of the Interior, Bureau of Reclamation. Colorado River Basin Water Supply and Demand Study: Study Report. https://usbr.gov/lc/region/programs/crbstudy/finalreport/Study%20Report/StudyReport_FINAL_Dec2012.pdf. (2012).

  45. U.S. Department of the Interior, Bureau of Reclamation. Water Reliability in the West − 2021 SECURE Water Act Report. 60 https://www.usbr.gov/climate/secure/2021secure.html (2021).

  46. Colorado River Basin Climate and Hydrology: State of the Science (University of Colorado Boulder, 2020).

  47. Currier, W. R. et al. Vegetation representation influences projected streamflow changes in the Colorado river basin. J. Hydrometeorol. 24, 1291–1310 (2023).

    Google Scholar 

  48. Pearl, J. From Bayesian Networks to Causal Networks. in Mathematical Models for Handling Partial Knowledge in Artificial Intelligence (eds Coletti, G., Dubois, D. & Scozzafava, R.) 157–182 (Springer US, Boston, MA, 1995). https://doi.org/10.1007/978-1-4899-1424-8_9.

  49. Neapolitan, R. E. Learning bayesian networks: Pearson Prentice Hall Upper Saddle River. NJ, 674 (2004).

  50. Ebert-Uphoff, I. & Deng, Y. Causal discovery for climate research using graphical models. J. Clim. 25, 5648–5665 (2012).

    Google Scholar 

  51. Ragno, E., Hrachowitz, M. & Morales-Nápoles, O. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges. Hydrol. Earth Syst. Sci. 26, 1695–1711 (2022).

    Google Scholar 

  52. Madadgar, S. & Moradkhani, H. Spatio-temporal drought forecasting within Bayesian networks. J. Hydrol. 512, 134–146 (2014).

    Google Scholar 

  53. Das, P. & Chanda, K. A Bayesian network approach for understanding the role of large-scale and local hydro-meteorological variables as drivers of basin-scale rainfall and streamflow. Stoch. Environ. Res Risk Assess. 37, 1535–1556 (2023).

    Google Scholar 

  54. Zaerpour, M. et al. Climate shapes baseflows, influencing drought severity. Environ. Res. Lett. 20, 014035 (2025).

    Google Scholar 

  55. Zaerpour, M. et al. Agriculture’s impact on water–energy balance varies across climates. Proc. Natl. Acad. Sci. USA 122, e2410521122 (2025).

    Google Scholar 

  56. Zaerpour, M. et al. Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain. Commun. Earth Environ. 5, 733 (2024).

    Google Scholar 

  57. Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019).

    Google Scholar 

  58. Runge, J. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proc. 36th Conf. Uncertain. Artif. Intell. (UAI), PMLR 124, 1388–1397 (2020). 124:1388-1397, 2020.

    Google Scholar 

  59. Liang, H. et al. Inferring causal associations in hydrological systems: a comparison of methods. Stoch. Environ. Res Risk Assess. 39, 2427–2448 (2025).

    Google Scholar 

  60. Crous, K. Y. Plant responses to climate warming: physiological adjustments and implications for plant functioning in a future, warmer world. Am. J. Bot. 106, 1049–1051 (2019).

    Google Scholar 

  61. Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).

    Google Scholar 

  62. Zhang, Y. & Hepner, G. F. Short-term phenological predictions of vegetation abundance using multivariate adaptive regression splines in the upper colorado river basin. Earth Interact. 21, 1–26 (2017).

    Google Scholar 

  63. Körner, C. Significance of Temperature in Plant Life. in Plant Growth and Climate Change (eds Morison, J. I. L. & Morecroft, M. D.) 48–69 (Wiley, 2006).

  64. Yildiz, O. & Barros, A. P. Elucidating vegetation controls on the hydroclimatology of a mid-latitude basin. J. Hydrol. 333, 431–448 (2007).

    Google Scholar 

  65. R Core Team. R A language and environment for statistical computing, R Foundation for Statistical. Computing R Foundation for Statistical Computing (2023).

  66. Scutari, M. Learning Bayesian Networks with the bnlearn R Package. J. Stat. Soft. 35, 1–22 (2010).

  67. Hotelling, H. New Light on the Correlation Coefficient and its Transforms. J. R. Stat. Soc. Ser. B Stat. Methodol. 15, 193–225 (1953).

    Google Scholar 

  68. Helsel, D., Hirsch, R., Ryberg, K., Archfield, S. A. & Gilroy, E. J. Statistical Methods in Water Resources. https://doi.org/10.3133/tm4a3 (2020).

  69. Gouhier, T., Grinstead, A. & Simko, V. R package biwavelet: Conduct univariate and bivariate wavelet analyses (version 0.20.21). (2021).

  70. U.S. Department of the Interior, Bureau of Reclamation. Current natural flow data 1906–‍2020, Updated 15 December 2022. https://www.usbr.gov/lc/region/g4000/NaturalFlow/current.html (accessed 18 September 2023).

  71. Prairie, J. R. & Callejo, R. D. Natural Flow and Salt Computation Methods, Calendar Years 1971–1995. 112 https://www.usbr.gov/lc/region/g4000/NaturalFlow/Final-MethodsCmptgNatFlow.pdf (2005).

  72. Vose, R. et al. NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). NOAA National Centers for Environmental Information https://doi.org/10.7289/V5SX6B56 (2015).

  73. Durre, I. et al. NOAA nClimGrid-Daily Version 1 – Daily gridded temperature and precipitation for the Contiguous United States since 1951. NOAA National Centers for Environmental Information https://doi.org/10.25921/C4GT-R169 (2022).

  74. Pinzon, J. E. et al. Vegetation Collection Global Vegetation Greenness (NDVI) from AVHRR GIMMS-3G + , 1981–‍2022. ORNL Distributed Active Archive Center https://doi.org/10.3334/ORNLDAAC/2187 (2023).

  75. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).

    Google Scholar 

  76. National Oceanic and Atmospheric Administration Physical Sciences Laboratory. NOAA PSL Climate Composites Tool. https://psl.noaa.gov/cgi-bin/data/composites/printpage.pl (accessed 22 June 2025 & 12 July 2025).

  77. Huang, B. et al. Extended reconstructed sea surface temperature, Version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Google Scholar 

  78. Broxton, P., Zeng, X. & Dawson, N. Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. NASA National Snow and Ice Data Center Distributed Active Archive Center https://doi.org/10.5067/0GGPB220EX6A.

  79. Palumbo, D. Dataset supporting ‘Precipitation, moderated by spring temperature and vegetation, drives runoff efficiency in the Upper Colorado River Basin, USA’. Zenodo https://doi.org/10.5281/zenodo.17843029 (2025).

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Acknowledgements

This research was partially funded by the Bureau of Reclamation. The views expressed in this paper are those of the authors and do not reflect the views or endorsements by the Bureau of Reclamation. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We thank reviewers Gregory Pederson and Olivier Champagne, the anonymous reviewer(s), and the editors for their constructive comments and suggestions, which improved the clarity and quality of this manuscript.

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Authors and Affiliations

  1. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    David Palumbo & Upmanu Lall

  2. Bureau of Reclamation, Washington, DC, USA

    David Palumbo

  3. Bureau of Reclamation, Denver, CO, USA

    Subhrendu Gangopadhyay

  4. Water Institute, Julie Ann Wrigley Global Futures Laboratory, Arizona State University, Tempe, AZ, USA

    Upmanu Lall

Authors
  1. David Palumbo
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  2. Subhrendu Gangopadhyay
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  3. Upmanu Lall
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Contributions

David Palumbo conceived and refined the study hypothesis, designed and conducted the research, performed the analyses, and prepared the manuscript. Subhrendu Gangopadhyay refined the study hypothesis, designed and conducted the research, performed the analyses, and prepared the manuscript. Upmanu Lall refined the study hypothesis, designed and conducted the research, performed the analyses, and prepared the manuscript.

Corresponding author

Correspondence to David Palumbo.

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Communications Earth and Environment thanks Gregory Pederson, Olivier Champagne and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Rahim Barzegar, Somaparna Ghosh, and Aliénor Lavergne [A peer review file is available].

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Palumbo, D., Gangopadhyay, S. & Lall, U. Precipitation, moderated by spring temperature and vegetation, drives runoff efficiency in the Upper Colorado River Basin, USA. Commun Earth Environ (2025). https://doi.org/10.1038/s43247-025-03136-w

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  • Received: 11 February 2025

  • Accepted: 12 December 2025

  • Published: 29 December 2025

  • DOI: https://doi.org/10.1038/s43247-025-03136-w

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  • Atmospheric dynamics
  • Climate sciences
  • Hydrology

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