Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics

Rice, Joshua S.; Emanuel, Ryan E.; Vose, James M.; Nelson, Stacy A. C.

doi:10.1002/2014wr016367

Cited by 95Hydrology and Watershed Management StudiesHydrology and Drought AnalysisClimate variability and models

Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics

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Rice, Joshua S., Emanuel, Ryan E., Vose, James M., Nelson, Stacy A. C.·2015·Water Resources Research

DOI 10.1002/2014wr016367

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Abstract

Changes in streamflow are an important area of ongoing research in the hydrologic sciences. To better understand spatial patterns in past changes in streamflow, we examined relationships between watershed‐scale spatial characteristics and trends in streamflow. Trends in streamflow were identified by analyzing mean daily flow observations between 1940 and 2009 from 967 U.S. Geological Survey stream gages. Results indicated that streamflow across the continental U.S., as a whole, increased while becoming less extreme between 1940 and 2009. However, substantial departures from the continental U.S. (CONUS) scale pattern occurred at the regional scale, including increased annual maxima, decreased annual minima, overall drying trends, and changes in streamflow variability. A subset of watersheds belonging to a reference data set exhibited significantly smaller trend magnitudes than those observed in nonreference watersheds. Boosted regression tree models were applied to examine the influence of watershed characteristics on streamflow trend magnitudes at both the CONUS and regional scale. Geographic location was found to be of particular importance at the CONUS scale while local variability in hydroclimate and topography tended to have a strong influence on regional‐scale patterns in streamflow trends. This methodology facilitates detailed, data‐driven analyses of how the characteristics of individual watersheds interact with large‐scale hydroclimate forces to influence how changes in streamflow manifest.

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The stochastic gradient boosting machine increases robustness by selecting samples randomly and has previously been employed in studies on river flow trend analysis and prediction.
“This GBM increases robustness by selecting samples randomly, and has previously been employed in studies on Q trend analysis and prediction53,54.”
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Low streamflow trends at human-impacted and reference basins in the United States
Dudley, R. W., Hirsch, R. M., Archfield, S. A., Blum, A. G., & Renard, B. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124254 · Cited by 106 · see citing works
Water Resources Assessment of China's Transboundary River Basins Using a Machine Learning Approach
Yan, J., Jia, S., Lv, A., & Zhu, W. (2018) · Water Resources Research · doi:10.1029/2018wr023044 · Cited by 94 · see citing works
“Few studies have focused on regional or continental scales; however, Rice et al. (2015) examined stream ﬂ ow trends in the continental United States using RF and determined the relation between stream ﬂ ow trend and water characteristics.”
Natural and Human Influences on the Link Between Meteorological and Hydrological Drought Indices for a Large Set of Catchments in the Contiguous United States
Tijdeman, E., Barker, L., Svoboda, M. D., & Stahl, K. (2018) · Water Resources Research · doi:10.1029/2017wr022412 · Cited by 86 · see citing works
“Different studies investigate the absolute and temporal changes in mean, minimum, or maximum flow and relate these changes to various human activities (e.g., Carlisle et al., 2011; Rice et al., 2015, 2016; Sadri et al., 2016).”
Background
Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North
Atashi, V., Gorji, H. T., Shahabi, S. M., Kardan, R., & Lim, Y. H. (2022) · Water · doi:10.3390/w14121971 · Cited by 78 · see citing works
Climate Elasticity of Low Flows in the Maritime Western U.S. Mountains
Cooper, M. G., Schaperow, J. R., Cooley, S., Alam, S., Smith, L. C., & Lettenmaier, D. P. (2018) · Water Resources Research · doi:10.1029/2018wr022816 · Cited by 75 · see citing works
“…in the Pacific Northwest and the Sierra Nevada mountains of California, coincident with a period of declining low- and middle-elevation mountain snowpack and annual precipitation (Jung et al., 2013; Kormos et al., 2016; Lins & Slack, 1999; Luce et al., 2013; Mote et al., 2018; Rice et al., 2015).”
Background
Which Potential Evapotranspiration Formula to Use in Hydrological Modeling World‐Wide?
Pimentel, R., Arheimer, B., Crochemore, L., Andersson, J., Pechlivanidis, I. G., & Gustafsson, D. (2023) · Water Resources Research · doi:10.1029/2022wr033447 · Cited by 74 · see citing works
Wavelet analyses of western US streamflow with ENSO and PDO
Tamaddun, K. A., Kalra, A., & Ahmad, S. (2016) · Journal of Water and Climate Change · doi:10.2166/wcc.2016.162 · Cited by 63 · see citing works
Forest Management Challenges for Sustaining Water Resources in the Anthropocene
Sun, G., & Vose, J. M. (2016) · Forests · doi:10.3390/f7030068 · Cited by 64 · see citing works
“Finally, improvements in data collection, storage, and access systems over the past few decades has created massive amounts of readily available data that has facilitated large scale “big data” analyses of hydrologic trends [81].”
BackgroundSelf-citation
Identification of Streamflow Changes across the Continental United States Using Variable Record Lengths
Tamaddun, K. A., Kalra, A., & Ahmad, S. (2016) · Hydrology · doi:10.3390/hydrology3020024 · Cited by 59 · see citing works
“Streamflow, which measures the amount of discharge in natural streams, plays an important role in the hydrosphere because streams are responsible for the transportation of mass and energy through watersheds [1].”
Background
The human factor in seasonal streamflows across natural and managed watersheds of North America
Singh, N. K., & Basu, N. B. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00848-1 · Cited by 56 · see citing works
Can precipitation extremes explain variability in runoff and sediment yield across heterogeneous karst watersheds?
Li, Z., Xu, X., Zhu, J., Zhong, F., Xu, C., & Wang, K. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125698 · Cited by 54 · see citing works
Parsing Weather Variability and Wildfire Effects on the Post‐Fire Changes in Daily Stream Flows: A Quantile‐Based Statistical Approach and Its Application
Beyene, M. T., Leibowitz, S. G., & Pennino, M. J. (2021) · Water Resources Research · doi:10.1029/2020wr028029 · Cited by 53 · see citing works
Assessment of hydrologic vulnerability to urbanization and climate change in a rapidly changing watershed in the Southeast U.S.
Suttles, K. M., Singh, N. K., Vose, J. M., Martin, K. L., Emanuel, R. E., Coulston, J. W., Saia, S. M., & Crump, M. T. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.06.287 · Cited by 53 · see citing works
Self-citation
Significant Baseflow Reduction in the Sao Francisco River Basin
Lucas, M. C., Kublik, N., Rodrigues, D. B. B., Neto, A. A. M., Almagro, A., Melo, D. D. C. D., Zipper, S. C., & de Oliveira, P. T. S. (2020) · Water · doi:10.3390/w13010002 · Cited by 53 · see citing works
Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives
Shiri, J., Keshavarzi, A., Kişi, Ö., Karimi, S., & Iturrarán‐Viveros, U. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.04.035 · Cited by 52 · see citing works
Do the Mann-Kendall test and Theil-Sen slope fail to inform trend significance and magnitude in hydrology?
Zhou, J., Deitch, M. J., Grunwald, S., & Screaton, E. J. (2023) · Hydrological Sciences Journal · doi:10.1080/02626667.2023.2212166 · Cited by 48 · see citing works
Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis
Liu, S., Ryu, D., Webb, J. A., Lintern, A. M., Waters, D., Guo, D., & Western, A. W. (2018) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2018.10.019 · Cited by 47 · see citing works
The Role of Land Cover in the Propagation of Rainfall Into Streamflow Trends
Chagas, V. B. P., & Chaffe, P. L. B. (2018) · Water Resources Research · doi:10.1029/2018wr022947 · Cited by 46 · see citing works
“…changes in streamflow regime (e.g., Ahiablame et al., 2017; Bard et al., 2015; Bennett et al., 2015; Chen et al., 2014; Do et al., 2017; Foufoula-Georgiou et al., 2015; Gao et al., 2015; Genta et al., 1998; Kormann et al., 2015; Li et al., 2017; Lorenzo-Lacruz et al., 2012; Rice et al., 2015).”
Background
HESS Opinions: Beyond the long-term water balance: evolving Budyko's supply–demand framework for the Anthropocene towards a global synthesis of land-surface fluxes under natural and human-altered watersheds
Sankarasubramanian, A., Wang, D., Archfield, S. A., Reitz, M. D., Vogel, R. M., Mazrooei, A., & Mukhopadhyay, S. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-1975-2020 · Cited by 41 · see citing works
“For instance, both annual precipitation and streamflow have increased during the period of 1948–1997 across the eastern United States, and those trends appear to arise primarily from increases in autumn precipitation (Small et al., 2006; Rice et al., 2015).”
Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches
Anderson, B. J., Slater, L. J., Dadson, S., Blum, A. G., & Prosdocimi, I. (2022) · Water Resources Research · doi:10.1029/2021wr030742 · Cited by 40 · see citing works
“…trends have been detected in historical streamflow records across the United States (e.g., Archfield et al., 2016; Douglas et al., 2000; Lins & Slack, 1999, 2005; Rice et al., 2015; Sadri et al., 2016; Slater & Villarini, 2016; Tamaddun et al., 2016; Y.-K. Zhang & Schilling, 2006).”
Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short‐Term Memory (LSTM) Networks
Xie, J., Liu, X., Tian, W., Wang, K., Bai, P., & Liu, C. (2022) · Water Resources Research · doi:10.1029/2021wr031663 · Cited by 38 · see citing works
Regional significance of historical trends and step changes in Australian streamflow
Amirthanathan, G. E., Bari, M., Woldemeskel, F., Tuteja, N. K., & Feikema, P. (2023) · Hydrology and earth system sciences · doi:10.5194/hess-27-229-2023 · Cited by 36 · see citing works
“In the continental United States, streamflow analyses from 1940 to 2009 for 967 gauging stations, show overall decreasing trends, including higher annual maxima, and lower annual minima (Rice et al., 2015).”
Background
Recent Acceleration of the Terrestrial Hydrologic Cycle in the U.S. Midwest
Yeh, P. J. ‐., & Wu, C. (2018) · Journal of Geophysical Research Atmospheres · doi:10.1002/2017jd027706 · Cited by 36 · see citing works
“The M-K trend test is a rank-based method widely applied in previous hydrologic literature for identifying the monotonic trends of hydroclimatic variables such as T, P, and R (Burn, 2008; Chen et al., 2007; Kustu et al., 2011; Rice et al., 2015; Stahl et al., 2010; Whitfield, 2001).”
Method
Disentangling the Contributions of Climate and Basin Characteristics to Water Yield Across Spatial and Temporal Scales in the Yangtze River Basin: A Combined Hydrological Model and Boosted Regression Approach
Sun, F., Mejía, A., & Che, Y. (2019) · Water Resources Management · doi:10.1007/s11269-019-02310-y · Cited by 36 · see citing works
“…major environmental factors such as climate conditions and basin physical characteristics (Feng et al. 2012; Liu et al. 2011), resulting in highly variable and contradictory results in previous studies (Berg et al. 2016; Pessacg et al. 2015; Rice et al. 2015; Sun et al. 2006; Zhou et al. 2015).”
Result
Heterogeneity in Spatiotemporal Variability of High Mountain Asia's Runoff and Its Underlying Mechanisms
Zhu, Y., Sang, Y., Wang, B., Lutz, A., Hu, S., Chen, D., & Singh, V. P. (2023) · Water Resources Research · doi:10.1029/2022wr032721 · Cited by 34 · see citing works
“Furthermore, catchment properties play an indirect but important role in determining how runoff in the basins responds to changes in climatic conditions (Liu et al., 2019; Rice et al., 2015).”
Background
An Analysis of Streamflow Trends in the Southern and Southeastern US from 1950–2015
Rodgers, K., Roland, V. L., Hoos, A. B., Crowley-Ornelas, E. R., & Knight, R. R. (2020) · Water · doi:10.3390/w12123345 · Cited by 34 · see citing works
“[6] identified a spatial correlation of trends in watersheds of the U.”
Background
The influence of watershed characteristics on spatial patterns of trends in annual scale streamflow variability in the continental U.S.
Rice, J. S., Emanuel, R. E., & Vose, J. M. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.07.006 · Cited by 32 · see citing works
“Basin morphology was found to have a somewhat weak relationship with trends in streamflow magnitude (Rice et al., 2015).”
InfluentialSelf-citation
Monthly Streamflow Prediction by Metaheuristic Regression Approaches Considering Satellite Precipitation Data
Mehraein, M., Mohanavelu, A., Naganna, S. R., Külls, C., & Kişi, Ö. (2022) · Water · doi:10.3390/w14223636 · Cited by 32 · see citing works
Investigating spatial heterogeneity of the controls of surface water balance in the contiguous United States by considering anthropogenic factors
Li, Z., & Quiring, S. M. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126621 · Cited by 30 · see citing works
How are streamflow responses to the E l N ino S outhern O scillation affected by watershed characteristics?
Rice, J. S., & Emanuel, R. E. (2017) · Water Resources Research · doi:10.1002/2016wr020097 · Cited by 29 · see citing works
“Watersheds can be conceptualized as spatiotemporal filters that translate input precipitation signals into output streamflow signals [e.g., Weiler et al., 2003; Nippgen et al., 2011; Rice et al., 2015].”
BackgroundSelf-citation
Trend assessment of annual instantaneous maximum flows in Turkey
Yılmaz, M., & Tosunoğlu, F. (2019) · Hydrological Sciences Journal · doi:10.1080/02626667.2019.1608996 · Cited by 29 · see citing works
“To avoid all the restrictive requirements (such as serial correlation, length, distribution of data) for application of the classical MK and SR trend methods, an innovative method has been developed by Sen (2012). Sen’s trend method (STM) is a modern, simple, easy to interpret, and effective trend analysis procedure t…”
Method
Evaluating low flow patterns, drivers and trends in the Delaware River Basin
Hammond, J. C., & Fleming, B. J. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126246 · Cited by 29 · see citing works
Gaps in Water Quality Modeling of Hydrologic Systems
Lucas, L. V., Brown, C. J., Robertson, D. M., Baker, N. T., Johnson, Z. C., Green, C. T., Cho, S. J., Erickson, M. L., Gellis, A. C., Jasmann, J. R., et al. (2025) · Water · doi:10.3390/w17081200 · Cited by 32 · see citing works
Modified Kumaraswamy distributions for double bounded hydro-environmental data
Pereira, M. S., Guerra, R. R., & Bayer, F. M. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.127021 · Cited by 27 · see citing works
The Role of Climate in Monthly Baseflow Changes across the Continental United States
Ayers, J. R., Villarini, G., Schilling, K. E., Jones, C. S., Brookfield, A. E., Zipper, S. C., & Farmer, W. H. (2022) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0002170 · Cited by 27 · see citing works
“Previous studies examining streamflow trends across the United States have tended to focus on extreme events and high flows that cause costly floods (e.g., Archfield et al. 2016; Douglas et al. 2000; Hirsch and Ryberg 2012; Hodgkins and Dudley 2011; Rice et al. 2015; Slater and Villarini 2016).”
Background
Forecasting Snowmelt-Induced Flooding Using GRACE Satellite Data: A Case Study for the Red River Watershed
Wang, S., & Russell, H. A. J. (2016) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2016.1171134 · Cited by 26 · see citing works
“Recent studies by Hirsch and Ryberg (2012) and Rice et al. (2015) show that the Red River region is experiencing significant increase in flood frequency.”
Background
v‐plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions
Blumenschein, M., Debbeler, L. J., Lages, N. C., Renner, B., Keim, D. A., & El‐Assady, M. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14002 · Cited by 23 · see citing works
Projection of Streamflow Change Using a Time‐Varying Budyko Framework in the Contiguous United States
Li, Z., & Quiring, S. M. (2022) · Water Resources Research · doi:10.1029/2022wr033016 · Cited by 22 · see citing works
“GB has previously been used to examine the influence of watershed spatial characteristics on continental streamflow trends (Rice et al., 2015).”
Method
Climate Change in the Lumbee River Watershed and Potential Impacts on the Lumbee Tribe of North Carolina
Emanuel, R. E. (2018) · Journal of Contemporary Water Research & Education · doi:10.1111/j.1936-704x.2018.03271.x · Cited by 22 · see citing works
“A recent study of nearly 1000 long-term, USGS stream gages by Rice et al. (2015) found no significant trends in mean annual streamflow amount or intra-annual variance for the Lumbee River between the 1940s and 2000s.”
Self-citation
Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards
Allen-Dumas, M. R., Xu, H., Kurte, K. R., & Rastogi, D. (2021) · Frontiers in Water · doi:10.3389/frwa.2020.562304 · Cited by 21 · see citing works
Drivers of long-term precipitation and runoff variability in the southeastern USA
Engström, J., & Waylen, P. R. (2017) · Theoretical and Applied Climatology · doi:10.1007/s00704-016-2030-4 · Cited by 21 · see citing works
“(1993), but as long-term time series analysis is not part of this paper, removal of basins displaying a changing hydrologic behavior leads to an incomplete depiction of the actual water balance (Rice et al. 2015) as there is widespread human influence in the region.”
Background
Landscape and climate conditions influence the hydrological sensitivity to climate change in eastern Canada
Aygün, O., Kinnard, C., Campeau, S., & Pomeroy, J. W. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128595 · Cited by 20 · see citing works
Geospatial patterns in runoff projections using random forest based forecasting of time-series data for the mid-Atlantic region of the United States
Gaertner, B. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169211 · Cited by 19 · see citing works
Trend Detection in Annual Streamflow Extremes in Brazil
de Souza, S. A., & Junior, D. S. R. (2022) · Water · doi:10.3390/w14111805 · Cited by 16 · see citing works
Enhanced flood prediction using LSTM and climate parameters: multi-station analysis of snowmelt-induced flooding in the Red River of the North
Atashi, V., & Gorji, H. T. (2025) · Journal of Hydroinformatics · doi:10.2166/hydro.2025.236 · Cited by 16 · see citing works
Riverine heat waves on the rise, outpacing air heat waves
Sadayappan, K., & Li, L. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2503160122 · Cited by 16 · see citing works
“Similarly, low streamflow reduces ther mal capacity of running water, elongates water residence times ( 49 ), and exacerbate riverine heat waves ( 50 , 51 ).”
New method for scaling nonpoint source pollution by integrating the SWAT model and IHA-based indicators
Chen, L., Xu, Y., Li, S., Wang, W., Liu, G., Wang, M., & Shen, Z. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116491 · Cited by 15 · see citing works
The color of environmental noise in river networks
Tu, T., Comte, L., & Ruhí, A. (2023) · Nature Communications · doi:10.1038/s41467-023-37062-2 · Cited by 15 · see citing works
Nonlinear trends in signatures characterizing non-perennial US streams
Kar, K. K., Roy, T., Zipper, S. C., & Godsey, S. E. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131131 · Cited by 14 · see citing works
Low-flow period seasonality, trends, and climate linkages across the United States
Simeone, C. E., McCabe, G. J., Hecht, J. S., Hammond, J. C., Hodgkins, G. A., OLSON, C. G., Wieczorek, M., & Wolock, D. M. (2024) · Hydrological Sciences Journal · doi:10.1080/02626667.2024.2369639 · Cited by 13 · see citing works
“2020), 10th percentile streamflows (Rice et al .”
Understanding the effects of afforestation on water quantity and quality at watershed scale by considering the influences of tree species and local moisture recycling
Haas, H., Kalin, L., Sun, G., & Kumar, S. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131739 · Cited by 13 · see citing works
“…trends of streamflow and water quality deterioration being reported across the SE-US (Dai, 2016; Engstr ¨ om et al., 2021; Lins and Slack, 2005; Rice et al., 2015; Rodgers et al., 2020; Tamaddun et al., 2016), our findings suggest that forest conversion and/or restoration may be a sustainable…”
Background
A data-driven analysis of frequent patterns and variable importance for streamflow trend attribution
Zeng, X. H., Schnier, S., & Cai, X. (2020) · Advances in Water Resources · doi:10.1016/j.advwatres.2020.103799 · Cited by 13 · see citing works
Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement
Stackpoole, S., Oelsner, G., Stets, E., Hecht, J., Johnson, Z. C., Tesoriero, A., Walvoord, M., Chanat, J. G., Dunne, K., Goodling, P. J., et al. (2023) · JAWRA Journal of the American Water Resources Association · doi:10.1111/1752-1688.13137 · Cited by 11 · see citing works
“Using modeled streamflow estimates, Rice et al. (2015) found positive trends in average conditions (e.g., annual mean and variance) in the Northeast and Southeast conterminous United States (CONUS).”
BackgroundResultInfluential
Comparison of Simulated Spatial Patterns Using Rain Gauge and Polarimetric-Radar-Based Precipitation Data in Catchment Hydrological Modeling
He, X. L., Koch, J., Zheng, C., Bøvith, T., & Jensen, K. H. (2018) · Journal of Hydrometeorology · doi:10.1175/jhm-d-17-0235.1 · Cited by 12 · see citing works
Historical changes in New York State streamflow: Attribution of temporal shifts and spatial patterns from 1961 to 2016
Glas, R. L., Burns, D. A., & Lautz, L. K. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.060 · Cited by 12 · see citing works
Wildland fire impacts on water yield across the contiguous United States
Hallema, D. W., Sun, G., Caldwell, P. V., Robinne, F., Bladon, K. D., Norman, S. P., Liu, Y., Cohen, E. C., & McNulty, S. G. (2019) · doi:10.2737/srs-gtr-238 · Cited by 12 · see citing works
Nonlinear relationships of runoff and sediment yield with natural and anthropogenic factors at event scale
Huang, X., & She, D. (2022) · Hydrological Processes · doi:10.1002/hyp.14715 · Cited by 10 · see citing works
“The turning point and broken lines are usually drawn by eye, which cannot capture the complex relationships between the predictive variables and response variables (Bhatt et al., 2013; Rice et al., 2015).”
Background
Tendency of Runoff and Sediment Variety and Multiple Time Scale Wavelet Analysis in Hongze Lake during 1975–2015
Duan, Y., Xu, G., Liu, Y., Liu, Y., Zhao, S., & Fan, X. (2020) · Water · doi:10.3390/w12040999 · Cited by 10 · see citing works
“It is of great significance to explore the changing trend of water flow in the field of water resources [1].”
Background
Understanding regional streamflow trend magnitudes in the Southern Murray-Darling Basin, Australia
Gao, Z., Guo, D., Peel, M., & Stewardson, M. J. (2022) · Australasian Journal of Water Resources · doi:10.1080/13241583.2022.2074942 · Cited by 8 · see citing works
Gradation of the significance level of trends in precipitation over China
Xie, P., Zhao, Y., Sang, Y., Gu, H., Wu, Z., & Singh, V. P. (2018) · Hydrology research · doi:10.2166/nh.2018.187 · Cited by 7 · see citing works
Spatiotemporal variations in the elasticity of runoff to climate change and catchment characteristics with multi-timescales across the contiguous United States
Gong, X., Xu, A., Du, S., & Zhou, Y. (2022) · Journal of Water and Climate Change · doi:10.2166/wcc.2022.242 · Cited by 7 · see citing works
Sensitivity of streamflow simulation in the Delaware River Basin to forecasted land‐cover change for 2030 and 2060
Williamson, T. N., & Claggett, P. R. (2018) · Hydrological Processes · doi:10.1002/hyp.13315 · Cited by 7 · see citing works
“Specifically, the distribution of less frequent events relative to the typical environment, as characterized by the skewness, kurtosis, and tails of the Q distribution, changes as a result of disturbance (Rice et al., 2015).”
BackgroundInfluential
Effect of land use and land cover change on spatial and temporal variation of hydrological parameters of Godavari river Basin, India
Aher, M. C., & Yadav, S. M. (2025) · Theoretical and Applied Climatology · doi:10.1007/s00704-024-05288-9 · Cited by 6 · see citing works
Spatial Variability of Long‐Term Streamflow Trends in the Southeastern United States
Wilbanks, K. A., Jackson, C. R., & Batzer, D. P. (2025) · River Research and Applications · doi:10.1002/rra.4449 · Cited by 5 · see citing works
Seasonal influence of tropical Pacific and Atlantic sea surface temperature on streamflow variability in the patia river basin
Canchala, T., Escobar, Y. C., Alfonso-Morales, W., Torres-López, W. A., Sánchez, L. D., & Cerón, W. L. (2024) · Theoretical and Applied Climatology · doi:10.1007/s00704-024-04934-6 · Cited by 4 · see citing works
“The streamflow is a relevant part of the hydrologic cycle since it relates to the amount of discharge in natural streams, allows for the transportation of mass and energy through watersheds, and maintains the mass balance of water in the natural system (Rice et al. 2015; Tamaddun et al. 2016).”
Changes in Magnitude and Shifts in Timing of Australian Flood Peaks
Bari, M., Amirthanathan, G. E., Woldemeskel, F., & Feikema, P. (2023) · Water · doi:10.3390/w15203665 · Cited by 4 · see citing works
Annual discharge trends for Canadian rivers
Hulley, M., Watt, E., & Clarke, C. (2018) · International Journal of River Basin Management · doi:10.1080/15715124.2018.1508025 · Cited by 4 · see citing works
“Additional studies have investigated trends in annual discharge in US rivers that span ecoregions in Canada and include work by Kumar et al. (2009), USGS (2011), Anderson et al. (2013) and Rice et al. (2015).”
Background
Improved Accuracy of Watershed-Scale General Circulation Model Runoff Using Deep Neural NetworksPreprint
Rice, J. S., Saia, S. M., & Emanuel, R. E. (2019) · doi:10.31223/osf.io/awqjg · Cited by 4 · see citing works
“…discussed in Section 1, DNN structure—including input, hidden, and 659 output layers (Figure S1b)—conserves the conceptualization of watersheds as spatio-temporal 660 filters (e.g., Weiler et al., 2003; Nippgen et al., 2011; Emanuel et al., 2014; Rice et al., 2015; 661 Rice & Emanuel, 2019).”
BackgroundSelf-citation
Analysis of short- and long-term controls on the variability of event-based runoff coefficient
Xu, T., Li, P.-C., & Merwade, V. (2024) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2024.101993 · Cited by 3 · see citing works
Baseflow Index Trends in Iowa Rivers and the Relationships to Other Hydrologic Metrics
Anderson, E. S., & Schilling, K. E. (2025) · Hydrology · doi:10.3390/hydrology12050116 · Cited by 3 · see citing works
“As hydrologic processes are increasingly being recognized as nonstationary [8], examining historical streamflow records has been utilized to identify and quantify changes in hydrologic behavior [9–11].”
Peak streamflow trends in Michigan and their relation to changes in climate, water years 1921–2020
Levin, S. B. (2024) · Scientific investigations report · doi:10.3133/sir20235064d · Cited by 3 · see citing works
Streamflow
Allan, J. D., Castillo, M. M., & Capps, K. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-61286-3_2 · Cited by 1 · see citing works
Incorporating Ecoregions and Freeze–Thaw Effects Effectively Improves the Accuracy of Machine Learning Models for Baseflow Prediction in Seasonally Frozen Regions
Deng, T., Lyu, H., Zhong, R., Zou, Y., Wang, J., Li, Y., & Yue, G. (2026) · Water Resources Management · doi:10.1007/s11269-025-04482-2 · Cited by 1 · see citing works
“The relative contributions of these factors vary among ecoregions, reflecting differences in geography, climate, and vegetation character - istics (Rice et al. 2015).”
Spatiotemporal Variations in Climatic Factors, Catchment Characteristic Induced Runoff Changes with Multi-Time Scales across the Contiguous United States
Gong, X., Du, S.-M., Li, F., & Ding, Y. (2025) · Journal of Earth Science · doi:10.1007/s12583-021-1542-8 · Cited by 1 · see citing works
“The common practice of detecting only the monotonic trend can-153 not reflect the long-term variability and change in stream-flow; for instance, Rice et al. (2015) proved that flow tended to be increasing across the continental U. S., and trends in streamflow were identified by analyzing mean daily…”
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“The trend magnitude was estimated using the Thiel‐Sen slope, a non‐ parametric technique widely used in hydrologic studies (Murray et al., 2023; Rice et al., 2015; Tillman et al., 2022).”
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Berzonsky, M. P., Smykalov, V. D., Sadayappan, K., & Li, L. (2026) · Geophysical Research Letters · doi:10.1029/2026gl122253
“In the continental United States, for example, 54% of sites in the Caravan data set exhibit increasing river discharge, comparable to the 63% reported previously (Rice et al., 2015).”
Predicting streamflow drought in the conterminous United States using machine learning and a donor-gage approach, 1982–2020
Heldmyer, A. J., Sando, R., Simeone, C. E., Wieczorek, M., Hamshaw, S. D., Goodling, P., McShane, R. R., Diaz, J., Watkins, D., Pulver, B. A., et al. (2026) · Hydrology and earth system sciences · doi:10.5194/hess-30-5925-2026
“These variables were derived from a prior study (Konapala and Mishra, 2020), and selected based on their relatively high influence over streamflow drought (Addor et al., 2018; Rice et al., 2015; Stoelzle et al., 2014) with a short description.”

Authors: Rice, J. S., Emanuel, R. E., Vose, J. M., & Nelson, S. A. C.
Affiliations: North Carolina State University (US)
US Forest Service (US)
Corresponding author: Rice, Joshua S.
ORCID: Rice, Joshua S.
Emanuel, Ryan E.
Vose, James M.
Nelson, Stacy A. C.
Venue: Water Resources Research
Publisher: American Geophysical Union (AGU)
Published: 2015-08
Volume: 51
Issue: 8
Pages: 6262-6275
ISSN: 0043-1397 (ISSN-L)
1944-7973
DOI: 10.1002/2014wr016367
OpenAlex: W1955666802
Cited by: 95
References: 86
Citations by year: 2026: 5 · 2025: 10 · 2024: 7 · 2023: 8 · 2022: 17 · 2021: 6 · 2020: 10 · 2019: 8 · 2018: 13 · 2017: 5 · 2016: 6
FWCI: 5.30
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 7 d)
Locations: Water Resources Research (publishedVersion)
Funders: National Science Foundation (DGE-1252376)
Department of Forestry and Environmental Resources at North Carolina State University
Topics: Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Keywords: streamflow trends, continental United States, streamflow variability, streamflow extremes, hydroclimate, topography, boosted regression trees

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Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics

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