Forty Years of Water Research at the Institute of Geophysics, Polish Academy of Sciences

  • Robert J. Bialik
  • Jarosław J. Napiórkowski
  • Paweł M. Rowiński
  • Witold G. Strupczewski
Chapter

Abstract

The history of research on hydrological and hydrodynamic processes carried out at the Department of Hydrology and Hydrodynamics, Institute of Geophysics, Polish Academy of Sciences is discussed. The genesis and development of the Department are briefly presented. The chapter focuses on the structure of the Department as well as the people associated with it at different stages of its history. The main research and organisational achievements of the Department team are summarised and supported by selected references.

Keywords

Floods Optimization algorithms Pollutant transport Rivers Statistical hydrology Turbulent open-channel flow Water research 

References

  1. Bialik RJ (2011) Particle-particle collision in lagrangian modeling of saltating grains. J Hydraul Res 49(1):23–31CrossRefGoogle Scholar
  2. Bialik RJ, Nikora VI, Rowiński PM (2012) 3D Lagrangian modeling of saltating particles diffusion in turbulent water flow. Acta Geophys 60(6):1639–1660CrossRefGoogle Scholar
  3. Bialik RJ, Czernuszenko W (2013) On the numerical analysis of bed-load transport of saltating grains. Int J Sediment Res 28(3):413, 420Google Scholar
  4. Bialik RJ, Karpiński M, Rajwa A (2014) Discharge measurements in lowland rivers: field comparison between an electromagnetic open channel flow meter (EOCFM) and an acoustic doppler current profiler (ADCP). GeoPlanet: Earth and Planetary Sciences: Achievements, History and Challenges in Geophysics: 60th Anniversary of the Institute of Geophysics, Polish Academy of Sciences. Bialik et al. (eds.) 213–222 (in this issue)Google Scholar
  5. Czernuszenko W (1997) Drift velocity concept for sediment-laden flows. J Hydraul Eng 124(10):1026–1033CrossRefGoogle Scholar
  6. Czernuszenko W, Rowiński PM (1997) Properties of the dead-zone model of longitudinal dispersion in rivers. J Hydraul Res 35(4):491–504CrossRefGoogle Scholar
  7. Czernuszenko W, Rowiński PM, Sukhodolov A (1998) Experimental and numerical validation of the dead-zone model. J Hydraul Res 36(2):269–280CrossRefGoogle Scholar
  8. Czernuszenko W, Rylov A (2000) A generalization of prandtl’s model for 3D open channel flows. J Hydraul Res 38(2):133–139CrossRefGoogle Scholar
  9. Czernuszenko W (2002) Transport processes in river systems, in fresh surface waters. In: Dooge JCI (ed) In encyclopaedia of life support systems (EOLSS), developed under the auspices of the UNESCO, Eolss Publishers Co., Oxford. (http://www.eolss.net)
  10. Czernuszenko W, Rylov A (2002) Modeling of three-dimensional velocity field in open channel flows. J Hydraul Res 40(2):135–143CrossRefGoogle Scholar
  11. Dooge JCI, Strupczewski WG, Napiórkowski JJ (1982) Hydrodynamic derivation of storage parameters of the muskingum model. J Hydrol 54:371–387CrossRefGoogle Scholar
  12. Dooge JCI, Kundzewicz ZW, Napiórkowski JJ (1983) On backwater effects in linear diffusion flood routing. Hydrol Sci J 28(3):391–402CrossRefGoogle Scholar
  13. Dooge JCI, Napiórkowski JJ (1987) The effect of the downstream boundary conditions in the linearized St. Venant equations. Q J Mech Appl Math 40:245–256CrossRefGoogle Scholar
  14. Dooge JCI, Napiórkowski JJ, Strupczewski WG (1988) The linear downstream response of a generalized uniform channel. Acta Geophys Pol 35(3):277–291Google Scholar
  15. Dooge JCI, Napiórkowski JJ (1993) Study of open-channel dynamics as controlled process. J Hydraul Eng ASCE 119(4):542–543CrossRefGoogle Scholar
  16. Kaczmarek Z (1993) Water balance model for climate impact analysis. Acta Geophys Pol 41(4):423–437Google Scholar
  17. Kaczmarek Z, Napiórkowski JJ (1996) Water resources adaptation strategy in an uncertain environment. Adapting in climate change: 211–224, Springer, BerlinGoogle Scholar
  18. Kaczmarek Z, Napiórkowski JJ, Strzepek KM (1996) Climate change impact on the water supply system in the warta river catchment, Poland. Water Resour Develop 12(2):165–180CrossRefGoogle Scholar
  19. Kaczmarek Z, Napiórkowski JJ, Rowiński, PM (1998) Conceptual catchment water balance model. In: Babovic V, Larsen LC (eds.) Hydroinformatics, A.A. Balkema/Rotterdam/ Brookfield, p 143–148Google Scholar
  20. Kalinowska MB, Rowiński PM (2012) Uncertainty in computations of the spread of warm water in a river—lessons from environmental impact assessment. Hydrol Earth Syst Sci 16:4177–4190CrossRefGoogle Scholar
  21. Kalinowska MB, Rowiński PM, Kubrak J, Mirosław-Świątek D (2012) Scenarios of the spread of a waste heat discharge in a river vistula river case study. Acta Geophys 60(1):214–231CrossRefGoogle Scholar
  22. Kiczko A, Romanowicz RJ, Osuch M (2011) Impact of water management policy on flow conditions in wetland areas. Phys Chem Earth 36(13):638–645CrossRefGoogle Scholar
  23. Kochanek K, Strupczewski WG, Singh VP, Weglarczyk S (2008) The PWM large quantile estimates of heavy tailed distributions from samples deprived of their largest element. Hydrol Sci J 53(2):367–386CrossRefGoogle Scholar
  24. Kochanek K, Strupczewski WG, Bogdanowicz E (2012) On seasonal approach to flood frequency modeling Part II flood frequency analysis of polish rivers. Hydrol Process 26(5):717–730CrossRefGoogle Scholar
  25. Kundzewicz ZW (1980) Approximate flood routing modeling methods: a review-discussion. J Hydraul Div ASCE 106:2072–2075Google Scholar
  26. Kundzewicz ZW, Strupczewski WG (1982) Approximate translation in the muskingum model. Hydrol Sci J 27:19–27CrossRefGoogle Scholar
  27. Kundzewicz ZW, Dooge JCI (1985) Unified structural approach to linear flood routing. Adv Water Resour 8(1):37–43CrossRefGoogle Scholar
  28. Kundzewicz ZW, Kaczmarek Z (2000) Coping with hydrological extremes. Water Int 25(1):66–75CrossRefGoogle Scholar
  29. Markiewicz I, Strupczewski WG, Kochanek K, Singh VP (2006) Relationships between three dispersion measures used in flood frequency analysis. Stoch Env Res Risk A 20(6):391–405CrossRefGoogle Scholar
  30. Markiewicz I, Strupczewski WG (2009) Dispersion measures for flood frequency analysis. Phys Chem Earth 34:670–678CrossRefGoogle Scholar
  31. Markiewicz I, Strupczewski WG, Kochanek K (2010) On accuracy of upper quantiles estimation. Hydrol Earth Syst Sc 14(11):2167–2175CrossRefGoogle Scholar
  32. Mitosek HT (1995) Climate variability and change within the discharge time series: a statistical approach. Clim Chang 29(1):101–116CrossRefGoogle Scholar
  33. Napiórkowski JJ, Strupczewski WG (1979) The analytical determination of the kernels of the volterra series describing the cascade of nonlinear reservoirs. J Hydrol Sci 6:121–142Google Scholar
  34. Napiórkowski JJ, Strupczewski WG (1981) The properties of the kernels of the volterra series describing flow deviation from a steady state in an open channel. J Hydrol 52:185–198CrossRefGoogle Scholar
  35. Napiórkowski JJ, Dooge JCI (1988) Analytical solution of channel flow model with downstream control. Hydrol Sci J 33(3):269–287CrossRefGoogle Scholar
  36. Nikora VI, Rowiński PM, Sukhodolov A, Krasuski D (1994) Structure of river turbulence behind warm water discharge. J Hydraul Eng-ASCE 120(2):191–208CrossRefGoogle Scholar
  37. Nikora VI, Sukhodolov A, Rowiński PM (1997) Statistical sand waves dynamics in one-directional water flows. J Fluid Mech 351:17–39CrossRefGoogle Scholar
  38. Piotrowski A, Napiórkowski JJ, Rowiński PM (2006) Flash-flood forecasting by means of neural networks and nearest neighbour approach—a comparative study. Nonlinear Proc Geoph 13:443–448CrossRefGoogle Scholar
  39. Piotrowski AP, Napiórkowski JJ (2011) Optimizing neural networks for river flow forecasting—evolutionary computation methods versus levenberg—marquardt approach. J Hydrol 407:12–27CrossRefGoogle Scholar
  40. Piotrowski AP, Napiórkowski JJ (2012) Product-units neural networks for catchment runoff forecasting. Adv Water Resour 47:97–113CrossRefGoogle Scholar
  41. Piotrowski A, Rowiński PM, Napiórkowski JJ (2012) Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers. Expert Syst Appl 39(1):1354–1361CrossRefGoogle Scholar
  42. Piotrowski AP, Napiórkowski JJ (2013) A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modeling. J Hydrol 476:97–111CrossRefGoogle Scholar
  43. Piotrowski AP, Osuch M, Napiórkowski MJ, Rowiński PM, Napiórkowski JJ (2014) Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river. Comput Geosci 64:136–151CrossRefGoogle Scholar
  44. Rajwa A, Bialik RJ, Karpiński M, Luks B (2014) Dissolved oxygen in rivers: concepts and measuring techniques. GeoPlanet: Earth and Planetary Sciences: Achievements, History and Challenges in Geophysics: 60th Anniversary of the Institute of Geophysics, Polish Academy of Sciences. Bialik et al. (eds.) 337–350 (in this issue)Google Scholar
  45. Romanowicz RJ, Dooge JCI, Kundzewicz ZW (1988) Moments and cumulants of linearized St. Venant equation. Adv Water Resour 11(2):92–100CrossRefGoogle Scholar
  46. Romanowicz RJ, Kiczko A, Napiórkowski JJ (2010) Stochastic transfer function model applied to combined reservoir management and flow routing. Hydrol Sci J 55(1):27–40CrossRefGoogle Scholar
  47. Romanowicz RJ, Osuch M (2011) Assessment of land use and water management induced changes in flow regime of the upper narew. Phys Chem Earth 36(13):662–672CrossRefGoogle Scholar
  48. Romanowicz RJ, Osuch M, Wallis S (2013) Modeling of solute transport in rivers under different flow rates: a case study without transient storage. Acta Geophys 61(1):98–125CrossRefGoogle Scholar
  49. Rowiński PM, Czernuszenko W, Pretre JM (2000) Time-dependent shear velocities in channel routing. Hydrol Sci J 45(6):881–895CrossRefGoogle Scholar
  50. Rowiński PM (2002) Constituent transport in fresh surface waters. In: Dooge JCI (ed) In encyclopaedia of life support systems (EOLSS), developed under the auspices of the UNESCO. Eolss Publishers Co., Oxford. (http://www.eolss.net)
  51. Rowiński PM, Kubrak J (2002) A mixing-length model for predicting vertical velocity distribution in flows through emergent vegetation. Hydrol Sci J 46(6):893–904CrossRefGoogle Scholar
  52. Rowiński PM, Napiórkowski JJ, Szkutnicki J (2003) Transport of passive admixture in a multi-channel river system—the Upper Narew case study. Part 1. Hydrol Surv Ecohydrol Hydrobiol 3(4):371–379Google Scholar
  53. Rowiński PM, Piotrowski A, Napiórkowski JJ (2005) Are artificial neural networks techniques relevant for the estimates of longitudinal dispersion coefficient in rivers? Hydrol Sci J 50(1):175–187Google Scholar
  54. Rowiński PM, Guymer I, Kwiatkowski K (2008) Response to the slug injection of a tracer—large scale experiment in a natural river. Hydrol Sci J 53(6):1300–1309CrossRefGoogle Scholar
  55. Sukhodolov AN, Nikora VI, Rowiński PM, Czernuszenko W (1997) A case study of longitudinal dispersion in small lowland rivers. Water Environ Res 69(7):1246–1253CrossRefGoogle Scholar
  56. Strupczewski WG, Romanowicz RJ, Budzianowski R (1977) Markovian programming for flow regulation. J Hydrol Sci 4:1–16Google Scholar
  57. Strupczewski WG, Kundzewicz ZW (1980) Translatory characteristics of the muskingum method of flood routing—a comment. J Hydrol 48:363–372CrossRefGoogle Scholar
  58. Strupczewski WG, Kundzewicz ZW (1981) Linear reservoirs and numerical diffusion—discussion. J Hydraul Div ASCE 107:251–253Google Scholar
  59. Strupczewski WG, Budzianowski RJ (1983) Identification of the input-output stochastic model with correlated errors. J Hydrol 67:13–23CrossRefGoogle Scholar
  60. Strupczewski WG, Napiórkowski JJ (1990) Linear flood routing model for rapid flow. Hydrol Sci J 35:49–64CrossRefGoogle Scholar
  61. Strupczewski WG, Dooge JCI (1995) Relationships between higher cumulants of channel response I Properties of the linear channel response. Hydrol Sci J 40(6):675–687CrossRefGoogle Scholar
  62. Strupczewski WG, Dooge JCI (1996) Relationships between higher cumulants of channel response II Accuracy of linear interpolation. Hydrol Sci J 41(1):61–73CrossRefGoogle Scholar
  63. Strupczewski WG, Kaczmarek Z (2001) Non-stationary approach to at-site flood-frequency modeling Part II Weighted least squares estimation. J Hydrol 248:143–151CrossRefGoogle Scholar
  64. Strupczewski WG, Singh VP, Feluch W (2001a) Non-stationary approach to at-site flood-frequency modeling Part I Maximum likelihood estimation. J Hydrol 248:123–142CrossRefGoogle Scholar
  65. Strupczewski WG, Singh VP, Mitosek HT (2001b) Non-stationary approach to at-site flood-frequency modeling Part III Flood analysis of polish rivers. J Hydrol 248:152–167CrossRefGoogle Scholar
  66. Strupczewski WG, Kochanek K, Weglarczyk S, Singh VP (2007) On robustness of large quantile estimates to largest elements of the observation series. Hydrol Process 21(10):1328–1344CrossRefGoogle Scholar
  67. Strupczewski WG, Kochanek K, Markiewicz I, Bogdanowicz E, Singh VP (2011) On the tails of distributions of annual peak flow. Hydrol Res. 42(2–3):171–192CrossRefGoogle Scholar
  68. Strupczewski WG, Kochanek K, Bogdanowicz E, Markiewicz I (2012) On seasonal approach to flood frequency modeling Part I Two-component distribution revisited. Hydrol Process 26(5):705–716CrossRefGoogle Scholar
  69. Strupczewski WG, Kochanek K, Bogdanowicz E, Markiewicz I (2013) Inundation risk of embanked rivers. Hydrol Earth Syst Sci 10:2987–3025CrossRefGoogle Scholar
  70. Strzepek K, McCluskey A, Boehlert B, Jacobsen M, Fant IV C (2011) Climate variability and change: a basin scale indicator approach to understanding the risk to water resources development and management: in water papers. World Bank, WashingtonGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Robert J. Bialik
    • 1
  • Jarosław J. Napiórkowski
    • 1
  • Paweł M. Rowiński
    • 1
  • Witold G. Strupczewski
    • 1
  1. 1.Institute of Geophysics, Polish Academy of SciencesWarsawPoland

Personalised recommendations