Abstract
Baseflows are one of the important components of streamflows and the influences of climate change and variability on changes in baseflows in space and time can aid in the management of low flows in watersheds. This study focuses on influences of climate variability on baseflows manifested through individual and coupled oceanic-atmospheric oscillations (viz., Atlantic multidecadal oscillation (AMO), El Niño-southern oscillation (ENSO), Pacific decadal oscillation (PDO), and North Atlantic oscillation (NAO)). Statistically significant differences in monthly baseflows in temporal windows that coincide with two phases (i.e., cool or warm; El Niño or La Niña) of oscillations at 574 gauging across least anthropogenically influenced across the continental the U.S. are evaluated in this study. Nonparametric statistical hypothesis tests are used to analyze these changes. Results from the analyses indicate that the influences of PDO and AMO on baseflows are the largest than other oscillations, and baseflows at only 12.2% of all sites were impacted by ENSO. The New England (01), Mid Atlantic (02), and Souris-Red-Rainy (09) regions displayed statistically significant differences in baseflow in two phases and with dominant influence attributed to the PDO cool phase. A total of 143 stations with higher baseflow median values during NAO warm/cool phase and El Niño. It is noted that the NAO cool phase/El Niño influences regional baseflows in the central U.S. and the NAO warm phase/ La Niña impacts baseflows in the southeastern U.S. The number of sites at which the baseflows were impacted by coupled oscillations was larger than those influenced by one oscillation.
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Data Availability
All data and materials are available from the corresponding author by request.
Abbreviations
- AMO:
-
Atlantic Multidecadal Oscillation
- HYSEP:
-
HYdrograph SEPeration
- ENSO:
-
El Nin ̃o-Southern Oscillation
- KDE:
-
Kernel Density Estimates
- HCDN:
-
Hydro-Climatic Data Network
- NAO:
-
North Atlantic Oscillation
- HUC:
-
Hydrologic Unit Code
- PDO:
-
Pacific Decadal Oscillation
- HUC2:
-
Hydrologic Unit Code two-digit
- SST:
-
Sea Surface Temperature
- WRS:
-
Wilcoxon rank-sum
- USGS:
-
U. S. Geological Survey
References
Abebel A, Foerch G (2006) Catchment characteristics as predictors of baseflow index (BFI) in Wabi Shebele river basin, East Africa. Proceedings of the Conference International Agricultural Research for Development. Tropentag 2006, University of Bonn, 11–13 October 2006. Bonn, Germany. http://www.tropentag.de/2006/abstracts/full/401.pdf
Ahiablame L, Sheshukov AY, Rahmani V, Moriasi D (2017) Annual baseflow variations as influenced by climate variability and agricultural land use change in the Missouri River Basin. J Hydrol 551:188–202. https://doi.org/10.1016/j.jhydrol.2017.05.055
Aiqin Z, Wenke W, Lei D, Jia J, Xiaoyan Z, Peng D, Feng Y (2011) The trend of base flow in Wei River Basin based on R/S and Mann-Kendall method. In 2011 International Symposium on Water Resource and Environmental Protection IEEE 2:987–989. https://doi.org/10.1109/ISWREP.2011.5893177
Bastola S, Seong Y, Lee D, Youn I, Oh S, Jung Y, Choi G, Jang D (2018) Contribution of baseflow to river streamflow: study on nepal’s bagmati and koshi basins. KSCE J Civ Eng 22(11):4710–4718. https://doi.org/10.1007/s12205-018-0149-9
Beebee R, Manga M (2004) Variation in the relationship between snowmelt runoff in oregon and enso and pdo. J Am Water Resour Assoc 40:1011–1024. https://doi.org/10.1111/j.1752-1688.2004.tb01063.x
Birk K, Lupo AR, Guinan P, Barbieri CE (2010) The interannual variability of Midwestern temperatures and precipitation as related to the ENSO and PDO. Atmósfera 23:95–128
Brandes D, Cavallo GJ, Nilson ML (2005) Base flow trends in urbanizing watersheds of the Delaware River basin. J Am Water Resour Assoc 41(6):1377–1391. https://doi.org/10.1111/j.1752-1688.2005.tb03806.x
Campozano L, Robaina L, Samaniego E (2020) The Pacific decadal oscillation modulates the relation of enso with the rainfall variability in coast of ecuador. Int J Climatol 40(13):5801–5812. https://doi.org/10.1002/joc.6525
Chen H, Teegavarapu RSV (2020) Comparative analysis of four baseflow separation methods in the south atlantic-gulf region of the u.s. Water 12:120. https://doi.org/10.3390/w12010120
Choubin B, Khalighi-Sigaroodi S, Malekian A, Ahmad S, Attarod P (2014) Drought forecasting in a semi-arid watershed using climate signals: a neuro-fuzzy modeling approach. J Mt Sci 11(6):1593–1605. https://doi.org/10.1007/s11629-014-3020-6
Corder GW, Foreman DI (2009) Nonparametric statistics for non-statisticians. A Step-By-Step Approach, New Jersey
Dai ZJ, Chu A, Du JZ, Stive M, Hong Y (2010) Assessment of extreme drought and human interference on baseflow of the Yangtze River. Hydrol Process 24(6):749–757. https://doi.org/10.1002/hyp.7505
Eckhardt K (2008) A comparison of baseflow indices, which were calculated with seven different baseflow separation methods. J Hydrol 352:168–173. https://doi.org/10.1016/j.jhydrol.2008.01.005
Esralew RA, Lewis JM (2010) Trends in base flow, total flow, and base-flow index of selected streams in and near Oklahoma through 2008 (No. 2010–5104). US Geological Survey (USGS)
Ficklin DL, Robeson SM, Knouft JH (2016) Impacts of recent climate change on trends in baseflow and stormflow in United States watersheds. Geophys Res Lett 43:5079–5088. https://doi.org/10.1002/2016GL069121
Goswami D, Kalita PK, Mehnert E (2010) Modeling and simulation of baseflow to drainage ditches during low-flow periods Water Resour Manage 24:173. https://doi.org/10.1007/s11269-009-9443-0
Hao Y, Zhang J, Wang J, Li R, Hao P, Zhan H (2016) How does the anthropogenic activity affect the spring discharge? J Hydrol 540:1053–1065. https://doi.org/10.1016/j.jhydrol.2016.07.024
Helsel DR, Hirsch RM (2002) Statistical methods in water resources. Techniques of water resources investigations, Book 4 Chapter A3. United States Geological Survey, Washington
Hodgkins GA, Dudley RW (2011) Historical summer base flow and stormflow trends for New England rivers. Water Resour Res 47(7). https://doi.org/10.1029/2010WR009109
Hunter T, Tootle G, Piechota T (2006) Oceanic-atmospheric variability and western U.S. snowfall. Geophys Res Lett 33(13):1–5. https://doi.org/10.1029/2006GL026600
Jhong BC, Huang J, Tung CP (2019) Spatial assessment of climate risk for investigating climate adaptation strategies by evaluating spatial-temporal variability of extreme precipitation. Water Resour Manage 33:3377–3400. https://doi.org/10.1007/s11269-019-02306-8
Johnson NT, Martinez CJ, Kiker GA, Leitman S (2013) Pacific and Atlantic sea surface temperature influences on streamflow in the Apalachicola–Chattahoochee–Flint river basin. J Hydrol 489:160–179. https://doi.org/10.1016/j.jhydrol.2013.03.005
Kelly MH, Gore JA (2008) Florida river flow patterns and the Atlantic multidecadal oscillation. River Res Appl 24:598–616. https://doi.org/10.1002/rra.1139
Koltun GF (1995) Determination of base-flow characteristics at selected streamflow-gaging stations on the Mad River, Ohio (Vol. 94, No. 4037). US Department of the Interior, US Geological Survey (USGS)
Li Z, Xu X, Xu C, Liu M, Wang K (2018) Dam construction impacts on multiscale characterization of sediment discharge in two typical karst watersheds of southwest China. J Hydrol 558:42–54. https://doi.org/10.1016/j.jhydrol.2018.01.034
Lins H, Slack J (1999) Streamflow trends in the United States. Geophys Res Lett 26(2):227–230. https://doi.org/10.1029/1998GL900291
Linsley RK, Kohler MA, Paulhus JLH (1988) Hydrology for engineers, International. McGraw-Hill, Singapore
Lott DA, Stewart MT (2016) Base flow separation: a comparison of analytical and mass balance methods. J Hydrol 535:525–533. https://doi.org/10.1016/j.jhydrol.2016.01.063
Mahanama S, Livneh B, Koster R, Lettenmaier D, Reichle R (2012) Soil moisture, snow, and seasonal streamflow forecasts in the United States. J Hydrometeorol 13(1):189–203. https://doi.org/10.1175/JHM-D-11-046.1
Mantua NJ, Hare SR (2002) The Pacific Decadal Oscillation J Oceanogr 58:35–44. https://doi.org/10.1023/A:1015820616384
Marx A, Backes C, Meese E, Lenhof HP, Keller A (2016) EDISON-WMW: exact dynamic programming solution of the Wilcoxon–Mann–Whitney test. Genomics Proteomics Bioinform 14:55–61. https://doi.org/10.1016/j.gpb.2015.11.004
Meyer SC (2005) Analysis of base flow trends in urban streams, northeastern Illinois, USA. Hydrogeol J 13(5–6):871–885. https://doi.org/10.1007/s10040-004-0383-8
Mohammed R, Scholz M (2019) Climate variability impact on the spatiotemporal characteristics of drought and aridityin arid and semi-arid regions. Water Resour Manage 33:5015–5033. https://doi.org/10.1007/s11269-019-02397-3
Nalley D, Adamowski BA, Gharabaghi B, Hu W (2019) A multiscale and multivariate analysis of precipitation and streamflow variability in relation to ENSO, NAO and PDO. J Hydrol 574:288–307. https://doi.org/10.1016/j.jhydrol.2019.04.024
Nathan RJ, McMahon TA (1990) Evaluation of automated techniques for base flow and recession analysis. Water Resour Res 26(7):1465–1473. https://doi.org/10.1029/WR026i007p01465
Parzen E (1962) On the estimation of probability density function. Ann Math Statist 33:1065–1076. https://www.jstor.org/stable/2240905
Rumsey CA, Miller MP, Susong DD, Tillman FD, Anning DW (2015) Regional scale estimates of baseflow and factors influencing baseflow in the Upper Colorado River Basin. J Hydrol Reg Stud 4:91–107. https://doi.org/10.1016/j.ejrh.2015.04.008
Seaber PR, Kapinos PF, Knapp GL (1987) Hydrologic unit maps. Water-Supply Paper 2294. USGS, Washington, DC
Scott DW, Factor LE (1981) Monte Carlo study of three data based nonparametric density estimators. J Am Stat Assoc 76:9–15
Sharma KP, Vorosmarty CJ, Moore B III (2000) Sensitivity of the Himalayan hydrology to land-use and climate changes. Clim Change 47:117–139. https://doi.org/10.1023/A:1005668724203
Singh S, Srivastava P, Abebe A, Mitra S (2015) Baseflow response to climate variability induced droughts in the Apalachicola–Chattahoochee–Flint River Basin, USA. J Hydrol 528:550–561. https://doi.org/10.1016/j.jhydrol.2015.06.068
Slack JR, Landwehr JM (1992) Hydro-Climatic Data Network: A U.S. Geological Survey streamflow data set for the United States for the study of climate variations, 1874–1998. U.S. Geological Survey Open-File Report 92–129. Available at https://pubs.usgs.gov/of/1992/ofr92-192/. Accessed 28 Aug 2015
Sloto RA, Couse MY (1996) HYSEP: a computer program for streamflow hydrograph separation and analysis. In: US Geological Survey, water resources investigation report 96–4040. USGS, Lemonyne
Stadnyk T, Gibson J, Longstaffe FJ (2014) Basin-scale assessment of operation base flow separation methods. J Hydrol Eng 20(5). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001089
Stewart M, Cimino J, Ross M (2007) Calibration of base flow separation methods with streamflow conductivity. Groundwater 45:17–27. https://doi.org/10.1111/j.1745-6584.2006.00263.x
Suman M, Maity R (2019) Assessment of streamflow variability with upgraded hydroclimatic conceptual streamflow model. Water Resour Manage 33:1367–1382. https://doi.org/10.1007/s11269-019-2185-8
Sutton RT, Hodson DLR (2005) Atlantic Ocean Forcing of North American and European Summer Climate. Science 290:2133–2137. https://doi.org/10.1126/science.1109496
Tamaddun KA, Kalra A, Ahmad S (2019) Spatiotemporal variation in the continental us streamflow in association with large-scale climate signals across multiple spectral bands. Water Resour Manage 33:1947–1968. https://doi.org/10.1007/s11269-019-02217-8
Teegavarapu RSV (2012) Floods in a changing climate: Extreme precipitation. Cambridge University Press, International Hydrology Series, pp p131-134
Teegavarapu RSV, Goly A, Obeysekera J (2013) Influences of Atlantic multidecadal oscillation phases on spatial and temporal variability of regional precipitation extremes. J Hydrol 495:74–93. https://doi.org/10.1016/j.jhydrol.2013.05.003
Tootle GA, Piechota TC, Singh A (2005) Coupled oceanic-atmospheric variability and U.S. streamflow. Water Resour Res 41:W12408. https://doi.org/10.1029/2005WR004381
van Kirk RW, Naman SW (2008) Relative effects of climate and water use on base-flow trends in the Lower Klamath Basin. Am Water Res Assoc 44(4):1032–1052. https://doi.org/10.1111/j.1752-1688.2008.00212.x
Visbeck MH, Hurrell JW, Polvani L, Cullen HM (2001) The North Atlantic Oscillation: past, present, and future. Proc Nat Acad Sci USA 98:12876–12877. https://doi.org/10.1073/pnas.231391598
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(4):80–83. https://www.jstor.org/stable/3001968
Wu J, Chiyuan M, Duan Q, Lei X, Li X, Li H (2019) Dynamics and attributions of baseflow in the semiarid loess plateau. J Geophys Res Atmos 124:10. https://doi.org/10.1029/2018JD029775
Xu ZX, Takeuchi K, Ishidaira H (2004) Correlation between El Niño-Southern Oscillation (ENSO) and precipitation in South-east Asia and the Pacific region. Hydrol Process 18(1):107–123. https://doi.org/10.1002/hyp.1315
Acknowledgements
The authors thank the United States Geological Survey (USGS) for providing, via their website, the daily streamflow data, that were used in the analysis reported in this study.
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Hao Chen: data analysis, original draft preparation; Ramesh S. V. Teegavarapu: investigation, methodology, writing review and editing; Yue-Ping Xu: supervision, formal analysis.
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Chen, H., Teegavarapu, R.S.V. & Xu, YP. Oceanic-Atmospheric Variability Influences on Baseflows in the Continental United States. Water Resour Manage 35, 3005–3022 (2021). https://doi.org/10.1007/s11269-021-02884-6
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DOI: https://doi.org/10.1007/s11269-021-02884-6