Articles | Volume 30, issue 18
https://doi.org/10.5194/hess-30-5925-2026
Copyright waived. This work has been dedicated to the public domain (Creative Commons Public Domain Dedication).
https://doi.org/10.5194/hess-30-5925-2026
Copyright waived. This work has been dedicated to the public domain (Creative Commons Public Domain Dedication).
Research article
| 
23 Sep 2026
Research article |
| 23 Sep 2026
Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020
U.S. Geological Survey, Wyoming-Montana Water Science Center, Cheyenne, WY, 82007, USA
Roy Sando
U.S. Geological Survey, Wyoming-Montana Water Science Center, Helena, MT, 59601, USA
Caelan Simeone
U.S. Geological Survey, Oregon Water Science Center, Portland, OR, 97204, USA
Michael Wieczorek
U.S. Geological Survey, Maryland-Delaware-D.C. Water Science Center, Catonsville, MD, 21228, USA
Scott Hamshaw
U.S. Geological Survey, Water Mission Area, Catonsville, MD, 21228, USA
Phillip Goodling
U.S. Geological Survey, Water Mission Area, Catonsville, MD, 21228, USA
Ryan McShane
U.S. Geological Survey, Wyoming-Montana Water Science Center, Cheyenne, WY, 82007, USA
Jeremy Diaz
U.S. Geological Survey, Water Mission Area, Catonsville, MD, 21228, USA
David Watkins
U.S. Geological Survey, Water Mission Area, Catonsville, MD, 21228, USA
Bryce Pulver
U.S. Geological Survey, Utah Water Science Center, Salt Lake City, UT, 84119, USA
Apoorva Shastry
Dept. of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
Konrad Hafen
U.S. Geological Survey, Idaho Water Science Center, Boise, ID, 83702, USA
John Hammond
U.S. Geological Survey, Maryland-Delaware-D.C. Water Science Center, Catonsville, MD, 21228, USA
Related authors
Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, and Toby Minear
Hydrol. Earth Syst. Sci., 26, 6121–6136, https://doi.org/10.5194/hess-26-6121-2022, https://doi.org/10.5194/hess-26-6121-2022, 2022
Short summary
Short summary
Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM) but are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished, dataset of approximately 48 000 gauges. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.
Ryoko Araki, Anne Holt, John C. Hammond, Admin Husic, Gemma Coxon, and Hilary K. McMillan
Hydrol. Earth Syst. Sci., 30, 3647–3673, https://doi.org/10.5194/hess-30-3647-2026, https://doi.org/10.5194/hess-30-3647-2026, 2026
Short summary
Short summary
We mapped dominant hydrologic processes across the United States by analyzing observed streamflow dynamics. Using random forest models and interpretable machine learning techniques, we predicted processes in data-scarce regions and identified key drivers such as climate, soil and geology, land cover, topography, and human influence. The resulting maps of dominant processes and their drivers reveal strong regional patterns that guide hydrologic model selection and water resource management.
Caelan E. Simeone and John C. Hammond
EGUsphere, https://doi.org/10.5194/egusphere-2026-2731, https://doi.org/10.5194/egusphere-2026-2731, 2026
Short summary
Short summary
This study examines how quickly rivers across the U.S. shift between very dry and very wet conditions, an emerging challenge for water managers because rapid changes compress decision windows and create operational stress. Drought-to-flood transitions occur more quickly and more often than flood-to-drought transitions, with short events concentrated in certain regions. Flood-to-drought transitions are less common but can involve large and rapid declines in flow.
Phillip J. Goodling, Jennifer H. Fair, Amrita Gupta, Jeffrey D. Walker, Todd Dubreuil, Michael Hayden, and Benjamin H. Letcher
Hydrol. Earth Syst. Sci., 29, 6445–6460, https://doi.org/10.5194/hess-29-6445-2025, https://doi.org/10.5194/hess-29-6445-2025, 2025
Short summary
Short summary
We describe a stream monitoring method using low-cost cameras and a deep learning model. It produces a relative hydrograph (0–100%). We applied the method to 11 cameras at 8 sites and found model performance sufficient to describe floods and droughts. The models were trained on image pairs annotated by people. We examined how well people performed annotations and how many annotations were needed. We concluded this method can be used to gain new insights into under-monitored small streams.
Admin Husic, John Hammond, Adam N. Price, and Joshua K. Roundy
Hydrol. Earth Syst. Sci., 29, 4457–4472, https://doi.org/10.5194/hess-29-4457-2025, https://doi.org/10.5194/hess-29-4457-2025, 2025
Short summary
Short summary
We used interpretable machine learning to evaluate the accuracy of two continental-scale hydrologic models. We analyzed a suite of catchment attributes and found that soil water content had the biggest impact on model performance, especially in dry areas. Key thresholds for variables like precipitation and road density were identified, which could guide future improvements in these models. Our findings highlight the potential of data-driven methods to inform process-based models.
Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, and Toby Minear
Hydrol. Earth Syst. Sci., 26, 6121–6136, https://doi.org/10.5194/hess-26-6121-2022, https://doi.org/10.5194/hess-26-6121-2022, 2022
Short summary
Short summary
Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM) but are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished, dataset of approximately 48 000 gauges. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.
Martin A. Briggs, Phillip Goodling, Zachary C. Johnson, Karli M. Rogers, Nathaniel P. Hitt, Jennifer B. Fair, and Craig D. Snyder
Hydrol. Earth Syst. Sci., 26, 3989–4011, https://doi.org/10.5194/hess-26-3989-2022, https://doi.org/10.5194/hess-26-3989-2022, 2022
Short summary
Short summary
The geologic structure of mountain watersheds may control how groundwater and streamwater exchange, influencing where streams dry. We measured bedrock depth at 191 locations along eight headwater streams paired with stream temperature records, baseflow separation and observations of channel dewatering. The data indicated a prevalence of shallow bedrock generally less than 3 m depth, and local variation in that depth can drive stream dewatering but also influence stream baseflow supply.
Edward Le, Ali Ameli, Joseph Janssen, and John Hammond
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-106, https://doi.org/10.5194/hess-2022-106, 2022
Preprint withdrawn
Short summary
Short summary
We used a statistical method to analyze whether snow persistence, defined as the duration of time that snow remains on the ground, explains the variability of streamflow at low and high flow conditions. Results show that as persistence of snow increases, the magnitude of low flow increases and the variability of low flow decreases, regardless of climatic aridity and seasonality. Snow persistence affects stream high flow variability at a narrow range of climatic aridity and seasonality.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013.
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018.
Archer, K. J. and Kimes, R. V.: Empirical characterization of random forest variable importance measures, Comput. Stat. Data Anal., 52, 2249–2260, https://doi.org/10.1016/j.csda.2007.08.015, 2008.
Baek, S. H., Steiger, N. J., Smerdon, J. E., and Seager, R.: Oceanic Drivers of Widespread Summer Droughts in the United States Over the Common Era, Geophys. Res. Lett., 46, 8271–8280, https://doi.org/10.1029/2019GL082838, 2019.
Barnhart, T. B., Molotch, N. P., Livneh, B., Harpold, A. A., Knowles, J. F., and Schneider, D.: Snowmelt rate dictates streamflow, Geophys. Res. Lett., 43, 8006–8016, https://doi.org/10.1002/2016GL069690, 2016.
Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Weather Rev., 115, 1083–1126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2, 1987.
Basara, J. B., Maybourn, J. N., Peirano, C. M., Tate, J. E., Brown, P. J., Hoey, J. D., and Smith, B. R.: Drought and Associated Impacts in the Great Plains of the United States – A Review, Int. J. Geosci., 4, https://doi.org/10.4236/ijg.2013.46A2009, 2013.
Beven*, K.: How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5, 1–12, https://doi.org/10.5194/hess-5-1-2001, 2001.
Biau, G. and Scornet, E.: A random forest guided tour, TEST, 25, 197–227, https://doi.org/10.1007/s11749-016-0481-7, 2016.
Bjerknes, J.: ATMOSPHERIC TELECONNECTIONS FROM THE EQUATORIAL PACIFIC, Mon. Weather Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2, 1969.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Broxton, P., Zeng, X., and Dawson, N.: Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US (1), National Snow and Ice Data Center, https://doi.org/10.5067/0GGPB220EX6A, 2019.
Calle, M. L. and Urrea, V.: Letter to the Editor: Stability of Random Forest importance measures, Brief. Bioinform., 12, 86–89, https://doi.org/10.1093/bib/bbq011, 2011.
Carpenter, D. H. and Hayes, D. C.: Low-flow Characteristics of Streams in Maryland and Delaware, U.S. Geological Survey, 120 pp., https://doi.org/10.3133/wri944020, 1996.
Clette, F., Cliver, E. W., Lefèvre, L., Svalgaard, L., and Vaquero, J. M.: Revision of the Sunspot Number(s), Space Weather, 13, 529–530, https://doi.org/10.1002/2015SW001264, 2015.
Cohen, J.: A Coefficient of Agreement for Nominal Scales, Educ. Psychol. Meas., 20, 37–46, 1960.
Cook, B. I., Smerdon, J. E., Seager, R., and Cook, E. R.: Pan-Continental Droughts in North America over the Last Millennium, J. Climate, 27, 383–397, https://doi.org/10.1175/JCLI-D-13-00100.1, 2014.
Deweber, J. T., Tsang, Y.-P., Krueger, D. M., Whittier, J. B., Wagner, T., Infante, D. M., and Whelan, G.: Importance of Understanding Landscape Biases in USGS Gage Locations: Implications and Solutions for Managers, Fisheries, 39, 155–163, https://doi.org/10.1080/03632415.2014.891503, 2014.
Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J.: The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S., Geophys. Res. Lett., 28, 2077–2080, https://doi.org/10.1029/2000GL012745, 2001.
Falcone, J. A.: GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow, U.S. Geological Survey, Reston, VA, http://pubs.er.usgs.gov/publication/70046617 (last access: 25 March 2019), 2011.
Feaster, T. D. and Lee, K. G.: Low-flow frequency and flow-duration characteristics of selected streams in Alabama through March 2014, Scientific Investigations Report, U.S. Geological Survey, 2017–5083, https://doi.org/10.3133/sir20175083, 2017.
Fleiss, J. L., Levin, B., and Paik, M. C.: Statistical Methods for Rates and Proportions, 1st edn., Wiley, https://doi.org/10.1002/0471445428, 2003.
Floriancic, M. G., Berghuijs, W. R., Molnar, P., and Kirchner, J. W.: Seasonality and Drivers of Low Flows Across Europe and the United States, Water Resour. Res., 57, e2019WR026928, https://doi.org/10.1029/2019WR026928, 2021.
Frost, H. R., Li, Z., and Moore, J. H.: Principal component gene set enrichment (PCGSE), BioData Min., 8, 25, https://doi.org/10.1186/s13040-015-0059-z, 2015.
Gbedawo, V., Owusu Agyeman, G., Ankah, C., and Daabo, M.: An Overview of Computer Memory Systems and Emerging Trends, Am. J. Electr. Comput. Eng., 7, 19–26, https://doi.org/10.11648/j.ajece.20230702.11, 2023.
Gershunov, A. and Barnett, T.: Interdecadal modulation of ENSO teleconnections, B. Am. Meteorol. Soc., 79, 2715–2725, https://doi.org/10.1175/1520-0477(1998)079<2715:IMOET>2.0.CO;2, 1998.
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, http://www.deeplearningbook.org (last access: 1 October 2025), 2016.
Gorski, G., Stets, E. G., Scholl, M. A., Degnan, J. R., Mullaney, J. R., Galanter, A. E., Martinez, A. J., Padilla, J., LaFontaine, J. H., Corson-Dosch, H. R., and Shapiro, A.: Water supply in the conterminous United States, Alaska, Hawaii, and Puerto Rico, water years 2010–20, Professional Paper, 1894-B, U.S. Geological Survey, https://doi.org/10.3133/pp1894B, 2025.
Griffin, D. and Anchukaitis, K. J.: How unusual is the 2012–2014 California drought?, Geophys. Res. Lett., 41, 9017–9023, https://doi.org/10.1002/2014GL062433, 2014.
Hammond, J. C., Simeone, C., Hecht, J. S., Hodgkins, G. A., Lombard, M., McCabe, G., Wolock, D., Wieczorek, M., Olson, C., Caldwell, T., Dudley, R., and Price, A. N.: Going Beyond Low Flows: Streamflow Drought Deficit and Duration Illuminate Distinct Spatiotemporal Drought Patterns and Trends in the U.S. During the Last Century, Water Resour. Res., 58, e2022WR031930, https://doi.org/10.1029/2022WR031930, 2022.
Hamshaw, S., Goodling, P., Hafen, K., Hammond, J., McShane, R., Sando, R., Shastry, A., Simeone, C., Watkins, D., White, E., and Wieczorek, M.: Regional Streamflow Drought Forecasting in the Colorado River Basin using Deep Neural Network Models, in: Proc. 2023 SedHyd Conf. St Louis MO, https://pubs.usgs.gov/publication/70247427 (last access: 1 October 2025), 2023.
Heldmyer, A. and Sando, R.: Random forest and donor-based simulated streamflow drought for 3,198 gages across the CONUS, 1982–2020, USGS [data set], https://doi.org/10.5066/P1FSRPMU, 2025.
Hotelling, H.: Analysis of a complex of statistical variables into principal components, J. Educ. Psychol., 24, 417–441, https://doi.org/10.1037/h0071325, 1933.
Hu, Z.-Z. and Huang, B.: Interferential Impact of ENSO and PDO on Dry and Wet Conditions in the U.S. Great Plains, J. Climate, 22, 6047–6065, https://doi.org/10.1175/2009JCLI2798.1, 2009.
Huang, S., Leng, G., Huang, Q., Xie, Y., Liu, S., Meng, E., and Li, P.: The asymmetric impact of global warming on US drought types and distributions in a large ensemble of 97 hydro-climatic simulations, Sci. Rep., 7, 5891, https://doi.org/10.1038/s41598-017-06302-z, 2017.
Jiang, S., Zheng, Y., Wang, C., and Babovic, V.: Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments, Water Resour. Res., 58, e2021WR030185, https://doi.org/10.1029/2021WR030185, 2022.
Jolliffe, I. T. (Ed.): Principal Component Analysis for Special Types of Data, in: Principal Component Analysis, Springer, New York, NY, 338–372, https://doi.org/10.1007/0-387-22440-8_13, 2002.
Kiang, J., Stewart, D., Archfield, S., Osborne, E., and Eng, K.: A National Streamflow Network Gap Analysis, U.S. Geological Survey Scientific Investigations Report, 2013–5013, https://pubs.usgs.gov/sir/2013/5013/ (last access: 7 March 2024), 2013.
Kim, J. E., Yu, J., Ryu, J.-H., Lee, J.-H., and Kim, T.-W.: Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model, Nat. Hazards, 109, 707–724, https://doi.org/10.1007/s11069-021-04854-y, 2021.
Konapala, G. and Mishra, A.: Quantifying Climate and Catchment Control on Hydrological Drought in the Continental United States, Water Resour. Res., 56, e2018WR024620, https://doi.org/10.1029/2018WR024620, 2020.
Krabbenholft, C., Allen, G., Lin, P., Godsey, S., Allen, D., Burrows, R., DelVecchia, A., Fritz, K., Shanafield, M., Burgin, A., Zimmer, M., Datry, T., Dodds, W., Jones, N., Mimms, M., Franklin, C., Hammond, J., Zipper, S., Ward, A., Costigan, K., Beck, H., and Olden, J.: Assessing placement bias of the global river gauge network, Nat. Publ., 5, 586–592, https://doi.org/10.1038/s41893-022-00873-0, 2022.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., and Hunt, T.: caret: Classification and Regression Training (6.0-94), https://cran.r-project.org/web/packages/caret/index.html (last access: 6 February 2024), 2023.
Landis, J. R. and Koch, G. G.: The measurement of observer agreement for categorical data, Biometrics, 33, 159–174, 1977.
Li, X., Khandelwal, A., Jia, X., Cutler, K., Ghosh, R., Renganathan, A., Xu, S., Tayal, K., Nieber, J., Duffy, C., Steinbach, M., and Kumar, V.: Regionalization in a Global Hydrologic Deep Learning Model: From Physical Descriptors to Random Vectors, Water Resour. Res., 58, e2021WR031794, https://doi.org/10.1029/2021WR031794, 2022.
Livneh, B. and Badger, A. M.: Drought less predictable under declining future snowpack, Nat. Clim. Change, 10, 452–458, https://doi.org/10.1038/s41558-020-0754-8, 2020.
Mainali, J. and Pricope, N. G.: High-resolution spatial assessment of population vulnerability to climate change in Nepal, Appl. Geogr., 82, 66–82, https://doi.org/10.1016/j.apgeog.2017.03.008, 2017.
Mallya, G., Zhao, L., Song, X. C., Niyogi, D., and Govindaraju, R. S.: 2012 Midwest Drought in the United States, J. Hydrol. Eng., 18, 737–745, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000786, 2013.
Mantua, N.: The Pacific Decadal Oscillation. A brief overview for non-specialists, Encycl. Environ. Change, J. Oceanogr., 58, 35–44, https://doi.org/10.1023/A:1015820616384, 1999.
Manuel, J.: Drought in the Southeast: Lessons for Water Management, Environ. Health Perspect., 116, A168–A171, https://doi.org/10.1289/ehp.116-a168, 2008.
McCabe, G. J., Palecki, M. A., and Betancourt, J. L.: Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States, P. Natl. Acad. Sci. USA, 101, 4136–4141, https://doi.org/10.1073/pnas.0306738101, 2004.
McCabe, G. J., Wolock, D. M., Lombard, M., Dudley, R. W., Hammond, J. C., Hecht, J. S., Hodgkins, G. A., Olson, C., Sando, R., Simeone, C., and Wieczorek, M.: A hydrologic perspective of major U.S. droughts, Int. J. Climatol., 43, 1234–1250, https://doi.org/10.1002/joc.7904, 2023.
McDonald, R.: gdptools (0.0.1), https://gdptools.readthedocs.io/en/latest/ (last access: 1 October 2025), 2022.
McIntyre, N., Lee, H., Wheater, H., Young, A., and Wagener, T.: Ensemble predictions of runoff in ungauged catchments, Water Resour. Res., 41, https://doi.org/10.1029/2005WR004289, 2005.
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, https://doi.org/10.1016/j.jhydrol.2010.07.012, 2010.
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H., Entin, J. K., Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B. H., and Bailey, A. A.: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res.-Atmos., 109, https://doi.org/10.1029/2003JD003823, 2004.
Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R. A., Carrao, H., Spinoni, J., Vogt, J., and Feyen, L.: Global Changes in Drought Conditions Under Different Levels of Warming, Geophys. Res. Lett., 45, 3285–3296, https://doi.org/10.1002/2017GL076521, 2018.
Nguyen, P.-L., Min, S.-K., and Kim, Y.-H.: Combined impacts of the El Niño-Southern Oscillation and Pacific Decadal Oscillation on global droughts assessed using the standardized precipitation evapotranspiration index, Int. J. Climatol., 41, E1645–E1662, https://doi.org/10.1002/joc.6796, 2021.
Nunes Carvalho, T. M., Lima Neto, I. E., and Souza Filho, F. de A.: Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning, Environ. Sci. Pollut. Res., 29, 74967–74982, https://doi.org/10.1007/s11356-022-21168-z, 2022.
Pagliero, L., Bouraoui, F., Diels, J., Willems, P., and McIntyre, N.: Investigating regionalization techniques for large-scale hydrological modelling, J. Hydrol., 570, 220–235, https://doi.org/10.1016/j.jhydrol.2018.12.071, 2019.
Peña-Gallardo, M., Vicente-Serrano, S. M., Hannaford, J., Lorenzo-Lacruz, J., Svoboda, M., Domínguez-Castro, F., Maneta, M., Tomas-Burguera, M., and Kenawy, A. E.: Complex influences of meteorological drought time-scales on hydrological droughts in natural basins of the contiguous Unites States, J. Hydrol., 568, 611–625, https://doi.org/10.1016/j.jhydrol.2018.11.026, 2019.
Piechota, T. C. and Dracup, J. A.: Drought and Regional Hydrologic Variation in the United States: Associations with the El Niño-Southern Oscillation, Water Resour. Res., 32, 1359–1373, https://doi.org/10.1029/96WR00353, 1996.
Pournasiri Poshtiri, M., Pal, I., Lall, U., Naveau, P., and Towler, E.: Variability patterns of the annual frequency and timing of low streamflow days across the United States and their linkage to regional and large-scale climate, Hydrol. Process., 33, 1569–1578, https://doi.org/10.1002/hyp.13422, 2019.
R Core Team: R: A language and environment for statistical computing, https://www.R-project.org/ (last access: 1 October 2025), 2021.
Rao, J., Ren, R., Xia, X., Shi, C., and Guo, D.: Combined Impact of El Niño–Southern Oscillation and Pacific Decadal Oscillation on the Northern Winter Stratosphere, Atmosphere, 10, 211, https://doi.org/10.3390/atmos10040211, 2019.
Rice, J. S., Emanuel, R. E., Vose, J. M., and Nelson, S. A. C.: Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics, Water Resour. Res., 51, 6262–6275, https://doi.org/10.1002/2014WR016367, 2015.
Rudin, C.: Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead, Nat. Mach. Intell., 1, 206–215, https://doi.org/10.1038/s42256-019-0048-x, 2019.
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., and Zhong, C.: Interpretable machine learning: Fundamental principles and 10 grand challenges, Stat. Surv., 16, 1–85, https://doi.org/10.1214/21-SS133, 2022.
Schlögl, M. and Laaha, G.: Extreme weather exposure identification for road networks – a comparative assessment of statistical methods, Nat. Hazards Earth Syst. Sci., 17, 515–531, https://doi.org/10.5194/nhess-17-515-2017, 2017.
Seneviratne, S. I.: Historical drought trends revisited, Nature, 491, 338–339, https://doi.org/10.1038/491338a, 2012.
Shen, C., Chen, X., and Laloy, E.: Editorial: Broadening the Use of Machine Learning in Hydrology, Front. Water, 3, https://doi.org/10.3389/frwa.2021.681023, 2021.
Simeone, C., Foks, S., Towler, E., Hodson, T., and Over, T.: Evaluating Hydrologic Model Performance for Characterizing Streamflow Drought in the Conterminous United States, Water, 16, 2996, https://doi.org/10.3390/w16202996, 2024.
Simeone, C. E.: Streamflow Drought Metrics for Select United States Geological Survey Streamgages for Three Different Time Periods from 1921–2020, USGS [data set], https://doi.org/10.5066/P92FAASD, 2022.
Singh, S., Abebe, A., Srivastava, P., and Chaubey, I.: Effect of ENSO modulation by decadal and multi-decadal climatic oscillations on contiguous United States streamflows, J. Hydrol. Reg. Stud., 36, 100876, https://doi.org/10.1016/j.ejrh.2021.100876, 2021.
Stoelzle, M., Stahl, K., Morhard, A., and Weiler, M.: Streamflow sensitivity to drought scenarios in catchments with different geology, Geophys. Res. Lett., 41, 6174–6183, https://doi.org/10.1002/2014GL061344, 2014.
Tahmasebi, P., Kamrava, S., Bai, T., and Sahimi, M.: Machine learning in geo- and environmental sciences: From small to large scale, Adv. Water Resour., 142, 103619, https://doi.org/10.1016/j.advwatres.2020.103619, 2020.
Tyralis, H., Papacharalampous, G., and Langousis, A.: A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources, Water, 11, 910, https://doi.org/10.3390/w11050910, 2019.
U.S. Geological Survey: USGS 01440400 Brodhead Creek near Analomink Pennsylvania, USGS, https://doi.org/10.5066/F7P55KJN, 2024a.
U.S. Geological Survey: USGS 09152500 Gunnison River near Grand Junction Colorado, USGS, https://doi.org/10.5066/F7P55KJN, 2024b.
van Huijgevoort, M. H. J., Hazenberg, P., van Lanen, H. A. J., and Uijlenhoet, R.: A generic method for hydrological drought identification across different climate regions, Hydrol. Earth Syst. Sci., 16, 2437–2451, https://doi.org/10.5194/hess-16-2437-2012, 2012.
Wang, S., Huang, J., He, Y., and Guan, Y.: Combined effects of the Pacific Decadal Oscillation and El Niño-Southern Oscillation on Global Land Dry–Wet Changes, Sci. Rep., 4, 6651, https://doi.org/10.1038/srep06651, 2014.
Welchowski, T., Maloney, K. O., Mitchell, R., and Schmid, M.: Techniques to Improve Ecological Interpretability of Black-Box Machine Learning Models, J. Agric. Biol. Environ. Stat., 27, 175–197, https://doi.org/10.1007/s13253-021-00479-7, 2022.
Williams, A. P., Seager, R., Abatzoglou, J. T., Cook, B. I., Smerdon, J. E., and Cook, E. R.: Contribution of anthropogenic warming to California drought during 2012–2014, Geophys. Res. Lett., 42, 6819–6828, https://doi.org/10.1002/2015GL064924, 2015.
Wlostowski, A. N., Jennings, K. S., Bash, R. E., Burkhardt, J., Wobus, C. W., and Aggett, G.: Dry landscapes and parched economies: A review of how drought impacts nonagricultural socioeconomic sectors in the US Intermountain West, WIREs Water, 9, e1571, https://doi.org/10.1002/wat2.1571, 2022.
Woodhouse, C. A., Pederson, G. T., Morino, K., McAfee, S. A., and McCabe, G. J.: Increasing influence of air temperature on upper Colorado River streamflow, Geophys. Res. Lett., 43, 2174–2181, https://doi.org/10.1002/2015GL067613, 2016.
Yang, R. and Xing, B.: Possible Linkages of Hydrological Variables to Ocean–Atmosphere Signals and Sunspot Activity in the Upstream Yangtze River Basin, Atmosphere, 12, 1361, https://doi.org/10.3390/atmos12101361, 2021.
Yeh, S.-W., Cai, W., Min, S.-K., McPhaden, M. J., Dommenget, D., Dewitte, B., Collins, M., Ashok, K., An, S.-I., Yim, B.-Y., and Kug, J.-S.: ENSO Atmospheric Teleconnections and Their Response to Greenhouse Gas Forcing, Rev. Geophys., 56, 185–206, https://doi.org/10.1002/2017RG000568, 2018.
Zhang, F., Biederman, J. A., Dannenberg, M. P., Yan, D., Reed, S. C., and Smith, W. K.: Five Decades of Observed Daily Precipitation Reveal Longer and More Variable Drought Events Across Much of the Western United States, Geophys. Res. Lett., 48, e2020GL092293, https://doi.org/10.1029/2020GL092293, 2021.
Zhu, P., Abramoff, R., Makowski, D., and Ciais, P.: Uncovering the Past and Future Climate Drivers of Wheat Yield Shocks in Europe With Machine Learning, Earths Future, 9, e2020EF001815, https://doi.org/10.1029/2020EF001815, 2021.
Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R. B., Menarguez, M. A., Zhang, G., and Wang, J.: Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016, P. Natl. Acad. Sci. USA, 115, 3810–3815, https://doi.org/10.1073/pnas.1719275115, 2018.
Short summary
We used machine learning to explore what causes streamflow droughts across the U.S. We found that different regions are influenced by different factors like temperature, snow, and rainfall. Our new method can also predict droughts in areas without streamflow data, helping improve water resource planning.
We used machine learning to explore what causes streamflow droughts across the U.S. We found...
