Skip to main content
Log in

Complexity as a streamflow metric of hydrologic alteration

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

We explore the potential of using a complexity measure from statistical physics as a streamflow metric of basin-scale hydrologic alteration. The complexity measure that we employ is a non-trivial function of entropy. To determine entropy, we use the so-called permutation entropy (PE) approach. The PE approach is desirable in this case since it accounts for temporal streamflow information and it only requires a weak form of stationarity to be satisfied. To compute the complexity measure and assess hydrologic alteration, we employ daily streamflow records from 22 urban basins, located in the metropolitan areas of the cities of Baltimore, Philadelphia, and Washington DC, in the United States. We use urbanization to represent hydrologic alteration since urban basins are characterized by varied and often pronounced human impacts. Based on our application of the complexity measure to urban basins, we find that complexity tends to decline with increasing hydrologic alteration while entropy rises. According to this evidence, heavily urbanized basins tend to be temporally less complex (less ordered or structured) and more random than basins with low urbanization. This complexity loss may have important implications for stream ecosystems whose ability to provide ecosystem services depend on the flow regime. We also find that the complexity measure performs better in detecting alteration to the streamflow than more conventional metrics (e.g., variance and median of streamflow). We conclude that complexity is a useful streamflow metric for assessing basin-scale hydrologic alteration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Arnold CL, Gibbons CJ (1996) Impervious surface coverage—the emergence of a key environmental indicator. J Am Plan Assoc 62(2):243–258. doi:10.1080/01944369608975688

    Article  Google Scholar 

  • Ayyub BM, McCuen RH (2011) Probability, statistics, and reliability for engineers and scientists, 3rd edn. CRC Press

  • Baker DB, Richards RP, Loftus TT, Kramer JW (2004) A new flashiness index: characteristics and applications to midwestern rivers and streams. J Am Water Resour Assoc 40(2):503–522. doi:10.1111/j.1752-1688.2004.tb01046.x

    Article  Google Scholar 

  • Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102. doi:10.1103/PhysRevLett.88.174102

    Article  Google Scholar 

  • Bandt C, Shiha F (2007) Order patterns in time series. J Time Ser Anal 28(5):646–665. doi:10.1111/j.1467-9892.2007.00528.x

    Article  Google Scholar 

  • Basu NB, Rao PSC, Winzeler HE, Kumar S, Owens P, Merwade V (2010) Parsimonious modeling of hydrologic responses in engineered watersheds: structural heterogeneity versus functional homogeneity. Water Resour Res 46:W04501. doi:10.1029/2009WR007803

    Article  Google Scholar 

  • Basu NB, Thompson SE, Rao PSC (2011) Hydrologic and biogeochemical functioning of intensively managed catchments: a synthesis of top-down analyses. Water Resour Res 47, W00J15. doi:10.1029/2011WR010800

  • Beighley RE (2001) GIS adjustment of measured streamflow data from urbanized watersheds. University of Maryland, Ph.D. dissertation, p 262

  • Brandes D, Cavallo GJ, Nilson ML (2005) Base flow trends in urbanizing watersheds of the Delaware River basin. J Am Water Resour Assoc 41:1377–1391. doi:10.1111/j.1752-1688.2005.tb03806.x

    Article  Google Scholar 

  • Brown LR, Cuffney TF, Coles JF, Fitzpatrick F, McMahon G, Steuer J, Bell AH, May JT (2009) Urban streams across the USA: lessons learned from studies in 9 metropolitan areas. J North Am Benthol Soc 28(4):1051–1069. doi:10.1899/08-153.1

    Article  Google Scholar 

  • Chou C-M (2014) Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stoch Env Res Risk Assess 28:1401–1408. doi:10.1007/s00477-014-0859-6

    Article  Google Scholar 

  • Dooge JCI (1986) Looking for hydrologic laws. Water Resour Res 22(9):S46–S58. doi:10.1029/WR022i09Sp0046S

    Article  Google Scholar 

  • Eliazar I, Klafter J (2010) Universal generation of 1/f noises. Phys Rev E 82:021109

    Article  Google Scholar 

  • Feldman DP, Crutchfield JP (1998) Measures of statistical complexity: why? Phys Lett A 238:244–252. doi:10.1016/S0375-9601(97)00855-4

    Article  CAS  Google Scholar 

  • Fleming SW (2007) Quantifying urbanization-associated changes in terrestrial hydrologic system memory. Acta Geophys 55(3):359–368. doi:10.2478/s11600-007-0016-4

    Article  Google Scholar 

  • Folke C, Carpenter S, Walker B, Scheffer M, Elmqvist T, Gunderson L, Holling CS (2004) Regime shifts, resilience, and biodiversity in ecosystem management. Annu Rev Ecol Evol Syst 35:557–581. doi:10.1146/annurev.ecolsys.35.021103.105711

    Article  Google Scholar 

  • Gall H, Park J, Harman CJ, Jawitz JW, Rao PSC (2013) Landscape filtering of hydrologic and biogeochemical responses in managed catchments. Landscape Ecol 28(4):651–664. doi:10.1007/s10980-012-9829-x

    Article  Google Scholar 

  • Grassberger P (1986) Toward a quantitative theory of self-generated complexity. Int J Theor Phys 25(9):907–938. doi:10.1007/BF00668821

    Article  Google Scholar 

  • Hopkins KG, Morse NB, Bain DJ, Bettez ND, Grimm NB, Morse JL, Palta MM, Shuster WD, Bratt AR, Suchy AK (2015) Assessment of regional variation in streamflow responses to urbanization and the persistence of physiography. Environ Sci Technol 49(5):2724–2732. doi:10.1021/es505389y

    Article  CAS  Google Scholar 

  • Hurst HE (1951) Long-term storage capacity of reservoirs. Trans Am Soc Civ Eng 116:770–799

    Google Scholar 

  • Jovanovic T, Mejía A, Gall H, Gironás J (2016) Effect of urbanization on the long-term persistence of streamflow records. Phys A 447:208–221. doi:10.1016/j.physa.2015.12.024

    Article  Google Scholar 

  • Kantelhardt JW, Koscielny-Bunde E, Rybski D, Braun P, Bunde A, Havlin S (2006) Long-term persistence and multifractality of precipitation and river runoff records. J Geophys Res Atmos 111(D1):D01106. doi:10.1029/2005JD005881

    Article  Google Scholar 

  • Konrad C, Booth D (2005) Hydrologic changes in urban streams and their ecological significance. In: Brown LR et al (eds) Effects of urbanization on stream ecosystems. Am. Fish. Soc., Symposium 47, Bethesda, MD, pp 157–177

  • Kowalski AM, Martin MT, Plastino A, Rosso OA, Casas M (2011) Distances in probability space and the statistical complexity setup. Entropy 13(6):1055–1075. doi:10.3390/e13061055

    Article  Google Scholar 

  • Lamberti PW, Martin MT, Plastino A, Rosso OA (2004) Intensive entropic non-triviality measure. Phys A 334(1–2):119–131. doi:10.1016/j.physa.2003.11.005

    Article  Google Scholar 

  • Lange H, Rosso OA, Hauhs M (2013) Ordinal pattern and statistical complexity analysis of daily stream flow time series. Eur Phys J 222(2):535–552. doi:10.1140/epjst/e2013-01858-3

    Google Scholar 

  • Li Z, Zhang Y-K (2008) Multi-scale entropy analysis of Mississippi River flow. Stoch Env Res Risk Assess 22:507–512. doi:10.1007/s00477-007-0161-y

    Article  Google Scholar 

  • Lopez-Ruiz R, Mancini HL, Calbet X (1995) A statistical measure of complexity. Phys Lett A 209(5–6):321–326. doi:10.1016/0375-9601(95)00867-5

    Article  CAS  Google Scholar 

  • Mejía A, Daly E, Rossel F, Jovanovic T, Gironas J (2014) A stochastic model of streamflow for urbanized basins. Water Resour Res 50(3):1984–2001. doi:10.1002/2013WR014834

    Article  Google Scholar 

  • Mejía A, Rossel F, Gironás J, Jovanovic T (2015) Anthropogenic controls from urban growth on flow regimes. Adv Water Resour 84:125–135. doi:10.1016/j.advwatres.2015.08.010

    Article  Google Scholar 

  • Mihailović D, Mimić G, Drešković N, Arsenić I (2015) Kolmogorov complexity based information measures applied to the analysis of different river flow regimes. Entropy 17:2973

    Article  Google Scholar 

  • Morley SA, Karr JR (2002) Assessing and restoring the health of urban streams in the Puget Sound basin. Conserv Biol 16(6):1498–1509. doi:10.1046/j.1523-1739.2002.01067.x

    Article  Google Scholar 

  • NOAA (2015) National climatic data center, quality controlled local climatological data. http://cdo.ncdc.noaa.gov/qclcd/QCLCD?prior=N. Accessed on January 2015

  • Olden JD, Poff NL (2003) Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Res Appl 19:101–121. doi:10.1002/rra.700

    Article  Google Scholar 

  • Poff NL, Richter BD, Arthington AH, Bunn SE, Naiman RJ, Kendy E, Acreman M, Apse C, Bledsoe BP, Freeman MC, Henriksen J, Jacobson RB, Kennen JG, Merritt DM, O’Keeffe JH, Olden JD, Rogers K, Tharme RE, Warner A (2010) The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshw Biol 55:147–170. doi:10.1111/j.1365-2427.2009.02204.x

    Article  Google Scholar 

  • Postel S, Richter B (2003) Rivers for life: managing water for people and nature. Island Press

  • Ravirajan K (2007) Development and application of a stream flashiness index based on imperviousness and climate using GIS. University of Maryland, M.S. thesis, p 275

  • Ribeiro HV, Zunino L, Mendes RS, Lenzi EK (2012) Complexity–entropy causality plane: a useful approach for distinguishing songs. Phys A 391:2421–2428. doi:10.1016/j.physa.2011.12.009

    Article  Google Scholar 

  • Richter BD, Baumgartner JV, Powell J, Braun DP (1996) A method for assessing hydrologic alteration within ecosystems. Conserv Biol 10:1163–1174. doi:10.1046/j.1523-1739.1996.10041163.x

    Article  Google Scholar 

  • Riedl M, Muller A, Wessel N (2013) Practical considerations of permutation entropy. Eur Phys J 222(2):249–262. doi:10.1140/epjst/e2013-01862-7

    Google Scholar 

  • Rodríguez-Iturbe I, Rinaldo A (2001) Fractal river basins: chance and self-organization. Cambridge Univ. Press, New York. 564 pp

  • Rosso OA, Larrondo HA, Martin MT, Plastino A, Fuentes MA (2007a) Distinguishing noise from chaos. Phys Rev Lett 99(15), 154102 1–4. doi:10.1103/PhysRevLett.99.154102

  • Rosso OA, Zunino L, Perez DG, Figliola A, Larrondo HA, Garavaglia M, Martin MT, Plastino A (2007b) Extracting features of Gaussian self-similar stochastic processes via the Bandt-Pompe approach. Phys Rev E 76(6), 061114 1–6. doi:10.1103/PhysRevE.76.061114

  • Salas JD (1993) Analysis and modeling of hydrologic time series. In: Maidment DR (ed) Handbook of hydrology. McGraw-Hill, New York, pp 19.5–19.9

  • Sen AK (2008) Complexity analysis of riverflow time series. Stoch Env Res Risk Assess 23:361–366. doi:10.1007/s00477-008-0222-x

    Article  Google Scholar 

  • Serinaldi F, Zunino L, Rosso OA (2014) Complexity-entropy analysis of daily stream flow time series in the continental United States. Stoch Env Res Risk Assess 28(7):1685–1708. doi:10.1007/s00477-013-0825-8

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell System Technol. J 27(3):379–423

    Article  Google Scholar 

  • Singh VP (1997) The use of entropy in hydrology and water resources. Hydrol Process 11:587–626. doi:10.1002/(SICI)1099-1085(199705)11:6<587:AID-HYP479>3.0.CO;2-P

    Article  Google Scholar 

  • Singh V (2011) Hydrologic synthesis using entropy theory: review. J Hydrol Eng 16:421–433. doi:10.1061/(ASCE)HE.1943-5584.0000332

    Article  Google Scholar 

  • Sivakumar B (2008) Dominant processes concept, model simplification and classification framework in catchment hydrology. Stoch Env Res Risk Assess 22:737–748. doi:10.1007/s00477-007-0183-5

    Article  Google Scholar 

  • USGS (2015) National water information system: Web Interface, http://waterdata.usgs.gov/nwis. Accessed on January 2015

  • Walsh CJ, Fletcher TD, Burns MJ (2012) Urban stormwater runoff: a new class of environmental flow problem. PLoS ONE 7(9):1–10. doi:10.1371/journal.pone.0045814

    Article  Google Scholar 

  • Wenger SJ et al (2009) Twenty-six key research questions in urban stream ecology: an assessment of the state of the science. J North Am Benthol Soc 28(4):1080–1098. doi:10.1899/08-186.1

    Article  Google Scholar 

  • Yang GX, Bowling LC (2014) Detection of changes in hydrologic system memory associated with urbanization in the Great Lakes region. Water Resour Res 50(5):3750–3763. doi:10.1002/2014WR015339

    Article  Google Scholar 

  • Zanin M, Zunino L, Rosso OA, Papo D (2012) Permutation entropy and its main biomedical and econophysics applications: a review. Entropy 14(8):1553–1577. doi:10.3390/e14081553

    Article  Google Scholar 

  • Zunino L, Pérez DG, Martín MT, Garavaglia M, Plastino A, Rosso OA (2008) Permutation entropy of fractional Brownian motion and fractional Gaussian noise. Phys Lett A 372:4768–4774. doi:10.1016/j.physleta.2008.05.026

    Article  CAS  Google Scholar 

  • Zunino L, Zanin M, Tabak BM, Pérez DG, Rosso OA (2010) Complexity-entropy causality plane: a useful approach to quantify the stock market inefficiency. Phys A 389(9):1891–1901. doi:10.1016/j.physa.2010.01.007

    Article  Google Scholar 

  • Zunino L, Tabak BM, Serinaldi F, Zanin M, Pérez DG, Rosso OA (2011) Commodity predictability analysis with a permutation information theory approach. Phys A 390(5):876–890. doi:10.1016/j.physa.2010.11.020

    Article  Google Scholar 

  • Zunino L, Fernández Bariviera A, Guercio MB, Martinez LB, Rosso OA (2012a) On the efficiency of sovereign bond markets. Phys A 391(18):4342–4349. doi:10.1016/j.physa.2012.04.009

    Article  Google Scholar 

  • Zunino L, Soriano MC, Rosso OA (2012b) Distinguishing chaotic and stochastic dynamics from time series by using a multiscale symbolic approach. Phys Rev E 86(046210):1–10. doi:10.1103/PhysRevE.86.046210

    Google Scholar 

Download references

Acknowledgments

We acknowledge the criticisms and suggestions, which helped improve the overall quality of the manuscript, made by the four anonymous reviewers. The first and last authors gratefully acknowledge the funding support provided by the Department of Civil and Environmental Engineering at the Pennsylvania State University. The third author is supported, in part, by the Penn State Institutes of Energy and the Environment. The present work was partially developed within the framework of the Panta Rhei Research Initiative of the International Association of Hydrological Sciences.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alfonso Mejía.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jovanovic, T., García, S., Gall, H. et al. Complexity as a streamflow metric of hydrologic alteration. Stoch Environ Res Risk Assess 31, 2107–2119 (2017). https://doi.org/10.1007/s00477-016-1315-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-016-1315-6

Keywords

Navigation