325
Views
3
CrossRef citations to date
0
Altmetric
Research Article

The use of maximum entropy to increase the informational content of hydrological networks by additional gauges

ORCID Icon, ORCID Icon, & ORCID Icon
Pages 2274-2285 | Received 23 Jan 2020, Accepted 11 Jun 2020, Published online: 13 Aug 2020

References

  • Alfonso, L., Lobbrecht, A., and Price, R., 2010. Information theory–based approach for location of monitoring water level gauges in polders. Water Resources Research, 46, W12553. doi:10.1029/2009WR008101
  • Alizadeh, Z., Yazdi, J., and Moridi, A., 2018. Development of an entropy method for groundwater quality monitoring network design. Environmental Processes, 5, 769–788. doi:10.1007/s40710-018-0335-2
  • Banik, B.K., et al., 2017. Evaluation of different formulations to optimally locate pollution sensors in sewer systems. ASCE Journal of Water Resources Planning, 143 (7).
  • Blöschl, G. and Sivapalan, M., 1995. Scale issues in hydrological modelling: a review. Hydrological Processes, 9, 251–290. doi:10.1002/hyp.3360090305
  • Chacon-Hurtado, J.C., Alfonso, L., and Solomatine, D.P., 2017. Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework. Hydrology and Earth System Sciences, 21, 3071–3091. doi:10.5194/hess-21-3071-2017
  • Deleo, J.M., 1993. Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. In: Proceedings of the Second International Symposium on Uncertainty Modelling and Analysis. College Park, MD: IEEE Computer Society Press, 318–325.
  • Fielding, A.H. and Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24 (1), 38–49. doi:10.1017/S0376892997000088
  • Fuentes, M., Chaudhuri, A., and Holland, D.H., 2007. Bayesian entropy for spatial sampling design of environmental data. Environmental Ecology and Statistics, 14, 323–340. doi:10.1007/s10651-007-0017-0
  • Hijmans, R.J., et al., 2017. dismo: species distribution modeling. R package version 1.1-4. Available from: https://CRAN.R-project.org/package=dismo [accessed 12 January 2020].
  • Jaynes, E.T., 1957. Information theory and statistical mechanics, I. Physical Reviews, 106, 620–630. doi:10.1103/PhysRev.106.620
  • Joo, H., et al., 2019. Optimal stream gauge network design using entropy theory and importance of stream gauge stations. Entropy, 21, 991. doi:10.3390/e21100991
  • Karasev, I.F., 1968. On the principles of hydrological network design and prospective development. Trans. State Hydrol. Inst. Trudy GGI, 164, 3–36. (In Russian; English version in Soviet Hydrology, 6, 560–588, 1968).
  • Keum, J., et al., 2018. Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design. Journal of Hydrology, 561, 688–701. doi:10.1016/j.jhydrol.2018.04.037
  • Keum, J., et al., 2017. Entropy applications to water monitoring network design: a review. Entropy, 19 (11), 613. doi:10.3390/e19110613
  • Krstanovic, P.F. and Singh, V.P., 1992. Evaluation of rainfall networks using entropy: I. Theoretical development. Water Resources Management, 6, 279–293. doi:10.1007/BF00872281
  • Mishra, A.K. and Coulibaly, P., 2009. Developments in hydrometric network design. A review. Reviews of Geophysics, 47 (2), 1491. doi:10.1029/2007RG000243
  • Neumann, J., 2009. Flächendifferenzierte Grundwasserneubildung von Deutschland. Stuttgart, Germany: Schweizerbart.
  • Oudin, L., et al., 2005a. Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 2 – towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling. Journal of Hydrology, 303, 290–306. doi:10.1016/j.jhydrol.2004.08.026
  • Oudin, L., Michel, C., and Anctil, F., 2005b. Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 1 – can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs? Journal of Hydrology, 303, 275–289. doi:10.1016/j.jhydrol.2004.08.025
  • Phillips, S.J., Anderson, R.P., and Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259. doi:10.1016/j.ecolmodel.2005.03.026
  • Pyrce, R.S., 2004. Review and analysis of stream gauge networks for the Ontario stream gauge rehabilitation project. Peterborough, Ontario: Trent University, Watershed Science Centre. WSC Report No. 01-2004. Prepared for the Ontario Ministry of Natural Resources and Forestry. https://www.trentu.ca/iws/documents/Network_Design_Mar04.pdf.
  • R Core Team, 2018. R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing. Available from: https://www.R-project.org/ [accessed 12 January 2020].
  • Sawicz, K., et al., 2011. Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Hydrology and Earth System Sciences, 15, 2895–2911. doi:10.5194/hess-15-2895-2011
  • Schwarze, R., et al., 1991. Rechnergestützte Analyse von Abflusskomponenten und Verweilzeiten in kleinen Mittelgebirgseinzugsgebieten [Computer assisted analysis of runoff components and transit times in small catchments, in German]. Acta Hydrophysica, 35 (2), 143–184, Berlin.
  • Shannon, C.E., 1948. A mathematical theory of information. The Bell System Technical Journal, 27 (3), 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x
  • Singh, V.P., 1989. Hydrologic modelling using entropy. Journal of the Institute of Engineering, Civil Engineering Division, 70, 55–60.
  • Singh, V.P. and Fiorentino, M., 1992. A historical perspective of entropy applications in water resources’. In: V.P. Singh and M. Fiorentino, eds. Entropy and energy dissipation in water resources. Dordrecht, The Netherlands: Kluwer, 155–173.
  • Stutz, J. and Cheeseman, P., 1995. AutoClass – a Bayesian approach to classification. In: J. Skilling and S. Sibisi, eds.. Maximum entropy and bayesian methods, Cambridge 1994. Dordrecht, The Netherlands: Kluwer, 117–126.
  • Urbanek, S., 2019. rJava: low-level R to java interface. R package version 0.9-11. Available from: https://CRAN.R-project.org/package=rJava [accessed 12 January 2020].
  • Wang, W., et al., 2018. Optimization of rainfall networks using information entropy and temporal variability analysis. Journal of Hydrology, 559, 136–155. doi:10.1016/j.jhydrol.2018.02.010
  • WMO, 2008. Guide to hydrometeorological practices. 6st ed. Geneva, Switzerland: World Meteorological Organization, WMO No.168, TP 82, ISBN 978-92-63-10168–6.
  • Yang, Y. and Burn, D.H., 1994. An entropy approach to data collection network design. Journal of Hydrology, 157 (1–4), 307–324. doi:10.1016/0022-1694(94)90111-2
  • Yoo, C., Ku, H., and Kim, K., 2011. Use of a distance measure for the comparison of unit hydrographs. Application to the stream gauge network optimization. Journal of Hydrologic Engineering, 16 (11), 880–890. doi:10.1061/(ASCE)HE.1943-5584.0000393

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.