Hydrogeology Journal

, Volume 21, Issue 1, pp 221–224 | Cite as

Modeling challenges for predicting hydrologic response to degrading permafrost

  • S. L. PainterEmail author
  • J. D. Moulton
  • C. J. Wilson

The fate of the approximately 1,700 billion metric tons of carbon (Tarnocai et al. 2009) currently frozen in permafrost affected regions of the Arctic and subarctic is highly uncertain (IPCC 2007), primarily because of the potential for topographic evolution and resulting drainage network reorganization as permafrost degrades and massive ground ice contained in ice-rich permafrost soils melts. Computer modeling is a key tool in untangling these complex feedbacks to understand the evolution of the Arctic and subarctic landscapes and the potential feedbacks with the global climate system. Some of the challenges associated with modeling the hydrologic system in and around degrading permafrost are discussed in this essay.

Modeling requirements depend very strongly on the spatial resolution of the model. Two different classes can be identified, depending on whether microtopography is explicitly resolved or incorporated into the model through a subgrid parameterization. The focus here is on...


Permafrost Subsidence Groundwater/surface-water relations Multiphase flow Numerical modeling 

Prévision de la réponse hydrologique à un permafrost se dégradant: les défis de la modélisation

Desafíos del modelado para la predicción de respuestas hidrológicas a la degradación del permafrost


Desafios da modelação na predição da resposta hidrológica à degradação do permafrost



This work was funded by Los Alamos National Laboratory Directed Project LDRD201200068DR and by the NGEE Arctic project. The Next-Generation Ecosystem Experiments (NGEE Arctic) project is supported by the Office of Biological and Environmental Research in the DOE Office of Science.


  1. Balay S, Brown, J, Smith B et al (2011) PETSc users manual, revision 3.2, ANL-95/11, Argonne National Laboratory, Lemont, ILGoogle Scholar
  2. Bartelt P, Lehning M (2002) A physical SNOWPACK model for the Swiss avalanche warning, part I: numerical model. Cold Reg Sci Technol 35(3):123–145CrossRefGoogle Scholar
  3. Benson DJ (1992) Computational methods in Lagrangian and Eulerian hydrocodes. Comput Methods Appl Mech Eng 99:235–394CrossRefGoogle Scholar
  4. Berndt M, Breil J, Galera S et al (2011) Two-step hybrid conservative remapping for multimaterial arbitrary Lagrangian-Eulerian methods. J Comp Phys 230:6664–6687CrossRefGoogle Scholar
  5. Chen Z, Huan G, Ma Y (2006) Computational methods for multiphase flows in porous media. SIAM Computational Science and Engineering Series, Philadelphia, PACrossRefGoogle Scholar
  6. Dall’Amico M, Endrizzi S, Gruber S, Rigon R (2011) A robust and energy-conserving model of freezing variably-saturated soil. Cryosphere 5:469–484CrossRefGoogle Scholar
  7. Heroux M A, Willenbring JM (2007) Trilinos users guide. SAND Report: SAND2003-2952, Sandia National Laboratories, Albuquerque, NMGoogle Scholar
  8. IPCC (2007) Climate change 2007: Synthesis report. In: Core Writing Team, Pachauri RK, Reisinger A (eds) Contribution of working groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, 104Google Scholar
  9. Jorgenson MT, Shur YL, Pullman ER (2006) Abrupt increase in permafrost degradation in Arctic Alaska. Geophys Res Lett 33:L02503. doi: 10.1029/2005GL024960 CrossRefGoogle Scholar
  10. Kelly CT (2003) Solving nonlinear Equations with Newton’s method. SIAM Fundamentals of Algorithms Series, Philadelphia, PACrossRefGoogle Scholar
  11. Liljedahl A, Hinzman L, Schulla J (2012) Ice-wedge polygon type controls low-gradient watershed scale hydrology. Proceedings Tenth International Conference on Permafrost, Salekhard, Yamal-Nenets, Russia, 25–29 June 2012Google Scholar
  12. Lipnikov K, Manzini G, Svyatskiy D(2011) Monotonicity conditions in the mimetic finite difference method. In: Fort J et al. (eds) Proc. Mathematics "Finite Volumes for Complex Applications VI Problems and Perspectives", vol 1, Springer, Heidelberg, Germany, pp 653–662 Google Scholar
  13. Painter SL (2011) Three-phase numerical model of water migration in partially frozen geological media: model formulation, validation, and applications. Comput Geosci 15(1):69–85CrossRefGoogle Scholar
  14. Panday S, Huyakorn PS (2004) A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow. Adv Water Resour 27(4):361–382CrossRefGoogle Scholar
  15. Spaans EJA, Baker JM (1996) The soil freezing characteristic: its measurement and similarity to the soil moisture characteristic. Soil Sci Soc Am J 60:13–19CrossRefGoogle Scholar
  16. Tarnocai C, Canadell JG, Schuur EAG et al (2009) Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochem Cycles 23:GB2023CrossRefGoogle Scholar
  17. Walker HF, Woodward CS, Yang UM (2010) An accelerated fixed-point iteration for solution of variably saturated flow. In: Carrera J (ed) Proc. XVIII International Conference on Water Resources, Barcelona, June 2010Google Scholar
  18. Wheeler MF, Xue G, Yotov I (2012) A multipoint flux mixed finite element method on distorted quadrilaterals and hexahedra. Numer Math 121:165–204CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2012

Authors and Affiliations

  1. 1.Computational Earth Sciences Group, Earth and Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Applied Mathematics, Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  3. 3.Earth Systems Observation Group, Earth and Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosUSA

Personalised recommendations