Advertisement

Environmental Earth Sciences

, Volume 69, Issue 2, pp 469–477 | Cite as

A data exploration framework for validation and setup of hydrological models

  • Karsten RinkEmail author
  • Thomas Fischer
  • Benny Selle
  • Olaf Kolditz
Special Issue

Abstract

Over the course of hydrological research projects often a large number of heterogeneous data sets are acquired from sources as diverse as boreholes, gauging stations or satellite imagery. This data then need to be integrated into models for the simulation of hydrological processes. We propose a framework for exploration of geoscientific data and visually guided preparation of such models. Data sets from a large number of sources can be imported, combined and validated to avoid potential problems due to artefacts or inconsistencies between data sets in a subsequent simulation. Boundary conditions and domain discretisations for surface and subsurface models can be created and tested regarding criteria indicating possible numerical instabilities. All data sets including simulation results can be integrated into a user-controlled 3D scene and aspects of the data can be enhanced using a number of established visualisation techniques including thresholding and user-defined transfer functions. We present the application of this framework for the preparation of a model for simulation of groundwater flow in a river catchment in southwest Germany investigated in the scope of the WESS project.

Keywords

Data exploration Hydrology Simulation Visualisation OpenGeoSys 

Notes

Acknowledgements

WESS is supported by a grant from the Ministry of Science, Research and Arts of Baden-Württemberg (AZ Zu 33-721.3-2) and the Helmholtz Center for Environmental Research, Leipzig (UFZ).

References

  1. Alcaraz S, Lane R et al (2011) 3d geological modelling using new Leapfrog Geothermal software. In: Proceedings of 36th Workshop on Geothermal Reservoir Engineering, Stanford University, USAGoogle Scholar
  2. Brauchler R, Cheng JT, Dietrich P, Everett M, Johnson B, Liedl R, Sauter M (2007) An inversion strategy for hydraulic tomography: Coupling travel time and amplitude inversion. J Hydrol 345(3-4): 184–198CrossRefGoogle Scholar
  3. Brunner P, Simmons CT (2012) HydroGeoSphere: A Fully Integrated, Physically Based Hydrological Model. Ground Water 50(2):170–176CrossRefGoogle Scholar
  4. Cheng WC, Putti M, Kendall DR, Yeh WWG (2011) A real-time groundwater management model using data assimilation. Water Resour Res 47(W06528)Google Scholar
  5. Engelhardt I, Rausch R et al (2012) Impact of shifts in climate during the Mid- and Late Holocene on groundwater resources on the Arabian Peninsula. Environ Earth Sci (submitted, this issue)Google Scholar
  6. Geuzaine C., Remacle JF (2009) Gmsh: a three-dimensional finite element mesh generator with built-in pre- and post-processing facilities. Int J Numer Meth Eng 79(11):1309–1331CrossRefGoogle Scholar
  7. Gräbe A, Rödinger T et al (2012) Numerical analysis of the groundwater regime in the western Dead Sea Escarpment, Israel + West Bank. Environ Earth Sci. doi: 10.1007/s12665-012-1795-8
  8. Grathwohl P et al (2012) Catchments as reactors: a comprehensive approach for water fluxes and solute turn-over. Environ Earth Sci (submitted, this issue)Google Scholar
  9. Hötzl H, Möller P, Rosenthal E (2009) The water of the Jordan Valley—scarcity and deterioration of groundwater and its impact on the regional development. Springer Verlag, BerlinGoogle Scholar
  10. Jones RR, McCaffrey KJW et al (2009) Integration of regional to outcrop digital data: 3d visualization of multi-scale geological models. Comput Geosci 35(1):4–18CrossRefGoogle Scholar
  11. Kalbacher T, Mettier R et al (2007) Geometric modelling and object-oriented software concepts applied to a heterogeneous fractured network from the grimsel rock laboratory. Comput Geosci 11(1):9–26CrossRefGoogle Scholar
  12. Knupp PM (2000) Achieving finite element mesh quality via optimization of the Jacobian matrix norm and associated quantities. Part I–a framework for surface mesh optimization. Int J Numer Meth Eng 48:401–420CrossRefGoogle Scholar
  13. Kolditz O, Bauer S et al (2012) OpenGeoSys: an open source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media. Environ Earth Sci 67(2):589–599Google Scholar
  14. Kolditz O, Diersch HJ (1993) Quasi-steady-state strategy for numerical simulation of geothermal circulation in hot dry rock fractures. Int J Non Lin Mech 28(4):467–481CrossRefGoogle Scholar
  15. Luo J, Diersch HJ, Monninkhoff B (2012) 3D Modeling of saline groundwater flow and transport in a flooded salt mine in Stassfurt, Germany. Mine Water Environ 31(2):104–111CrossRefGoogle Scholar
  16. Maxwell RM, Lundquist JK, Mirocha JD, Smith SG, Woodward CS, Tompson AFB (2011) Development of a coupled groundwater-atmosphere model. Mon Weather Rev 139(1):96–116CrossRefGoogle Scholar
  17. Rink K, Fischer T, Kolditz O (2011) Data visualisation and validation for hydrological models. In: Proceedings of international conference on computer graphics, visualization, computer vision and image processing, pp 169–176. ISBN:978-989-8533-00-5Google Scholar
  18. Rink K, Kalbacher T, Kolditz O (2012) Visual data exploration for hydrological analysis. Environ Earth Sci 65(5):1395–1403CrossRefGoogle Scholar
  19. Schroeder W, Martin K, Lorensen B (2006) Visualization Toolkit: an object-oriented approach to 3D graphics, 4th edn. Kitware, Inc., USAGoogle Scholar
  20. Selle B, Rink K et al (2012) Recharge and discharge controls on groundwater travel times and flow paths to production wells for the Ammer catchment in SW Germany. Environ Earth Sci (submitted, this issue)Google Scholar
  21. Shewchuk JR (2002) What is a good linear finite element? Interpolation, conditioning, anisotropy, and quality measures. Tech rep, Department of Electrical Engineering and Computer Science, University of Berkeley, USAGoogle Scholar
  22. Si H (2010) Constrained Delaunay tetrahedral mesh generation and refinement. Finite Elem Anal Des 46(1):33–46CrossRefGoogle Scholar
  23. Stein ML (1999) Statistical interpolation of spatial data: some theory for kriging. Springer, New YorkCrossRefGoogle Scholar
  24. Sun F, Shao H, et al (2012) Groundwater deterioration in Nankou—a suburban area of Beijing: data assessment and remediation scenarios. Environ Earth Sci. doi: 10.1007/s12665-012-1600-8
  25. Taron J, Elsworth D (2009) Thermal-hydrologic-mechanical-chemical processes in the evolution of engineered geothermal reservoirs. Int J Rock Mech Min Sci 46(5): 855–864CrossRefGoogle Scholar
  26. Uhlenküken C, Schmidt B, Streit U (2000) Visual exploration of high-dimensional spatial data: requirements and deficits. Comput Geosci 26(1):77–85CrossRefGoogle Scholar
  27. Xie M, Agus SS, Schanz T, Kolditz O (2004) An upscaling method and a numerical analysis of swelling/shrinking processes in a compacted bentonite/sand mixture. Int J Numer Anal Meth Geomech 28(15):1479–1502CrossRefGoogle Scholar
  28. Zehner B (2010) Mixing Virtual reality and 2D visualization—using virtual environments as visual 3D information systems for discussion of data from geo- and environmental sciences. In: Proceedings of international conference on computer graphics and applications, pp 364–369Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Karsten Rink
    • 1
    Email author
  • Thomas Fischer
    • 1
  • Benny Selle
    • 3
  • Olaf Kolditz
    • 1
    • 2
  1. 1.Department of Environmental InformaticsHelmholtz Centre for Environmental ResearchLeipzigGermany
  2. 2.Applied Environmental System AnalysisDresden University of TechnologyDresdenGermany
  3. 3.WESS, Water & Earth System ScienceUniversity of TübingenTübingenGermany

Personalised recommendations