Thomas Wöhling

Table of contents

      1. Research Interests
      2. Projects
      3. Teaching
      4. Education & Employment
      5. Publications
      6. Awards
Thomas Wöhling © M. Wagner

Senior Research Scientist

Name

Dr. Thomas Wöhling

Team leader Stochastic Modelling of Hydrosystems, Head of the Soil Hydrology Lab

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Organization Name

Chair of Hydrology

Chair of Hydrology

Address work

Visitor Address:

Neubau Chemische Institute, Room 360 Bergstr. 66

01069 Dresden

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work Tel.
+49 351 463-34326
fax Fax
+49 351 463-37162

Research Interests

  • Integrated modelling of coupled environmental systems, particularly
    • flow and contaminant transport in surface water – groundwater systems
    • water, nutrient and energy fluxes in soil-plant-atmosphere systems
    • distributed hydrological modelling
    • vadose zone flow and transport processes in heterogeneous media
  • Evaluation and optimisation of monitoring networks
  • Worth of data and observation types to inform environmental models
  • Inverse modelling, model calibration, and efficient parameter estimation methods
  • Multiobjective optimization techniques and Pareto analysis
  • Stochastic modelling and uncertainty analysis
  • Model selection techniques and Bayesian model averaging

Projects

Teaching

  • HSE - Fundamentals and Principles of Hydrology
  • BHYWI21 - Wasserhaushalt

Education & Employment

2010 - 2015 Senior Research Scientist at Water and Earth System Sciences Competence Cluster (WESS), Tübingen, Germany.
2008 - 2010 Senior Research Scientist at Lincoln Environmental Research, Lincoln Ventures Ltd, Hamilton, New Zealand.
2006 - 2008 Research Hydrologist / Modeller at Lincoln Environmental Research, Lincoln Ventures Ltd, Hamilton, New Zealand.
2005 PhD in Hydrology, Technische Universität Dresden, Germany.
1999 - 2005 Research Associate at Institute of Hydrology and Meteorology, Dresden University of Technology, Dresden, Germany.
1999 Dipl.-Hydrol., Technische Universität Dresden, Germany.

Publications

Full list of publications here.

  • Gosses, M. & Wöhling, T. (2019). Simplification error analysis for groundwater predictions with reduced order models. Advances in Water Research, 125, 41-56, doi: 10.1016/j.advwatres.2019.01.006.

  • Chow, R., Wu, H., Bennet, J., Dugge, J, Wöhling, T., Nowak, W. (2018). Sensitivity of simulated hyporheic exchange to river bathymetry: The Steinlach River Test Site. Groundwater, doi: 10.1111/gwat.12816 (in print).

  • Gosses, M., Nowak, W., Wöhling, T. (2018). Explicit treatment for Dirichlet, Neumann and Cauchy boundary conditions in POD-based reduction of groundwater models. Advances in Water Research, 115, 160-171, doi: 10.1016/j.advwatres.2018.03.011.

  • Höge, M., Wöhling, T., Nowak, W. (2018). A primer for model selection: The decisive role of model complexity. Water Resources Research, 54, doi: 10.1002/2017WR021902.

  • Loschko, M., Wöhling, T., Rudolph, D., Cirpka, O.A. (2017). Accounting for the decreasing reaction potential of heterogeneous aquifers in a stochastic framework of aquifer-scale reactive transport. Water Resources Research, 54, doi: 10.1002/2017WR021645.

  • Wöhling T., Gosses, M. Wilson, S., Davidson, P. (2017). Quantifying river-groundwater interactions of New Zealand's gravel-bed rivers: The Wairau Plain. Groundwater, doi:10.1111/gwat.12625, (in print).

  • Woodward, S.J.R., Wöhling Th., Rode, M., Stenger, R. (2017). Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference. Journal of Hydrology, 552(9), 684-703, doi: 10.1016/j.jhydrol.2017.07.021.

  • Loschko, M., Wöhling, Th., Rudolph, D., Cirpka, O.A. (2016). Cumulative relative reactivity: A concept for modeling aquifer-scale reactive transport. Water Resources Research, 52, doi:10.1002/2016WR019080.

  • von Gunten, D., Wöhling, Th., Haslauer, C. Merchán, D., Causape, J., Cirpka, O.A. (2016). Using an integrated hydrological model to estimate the usefulness of meteorological drought indices in a changing climate. Hydrology and Earth System Sciences, 20, 4159-4175, doi:10.5194/hess-20-4159-2016.

  • Vereecken H, Schnepf A, Hopmans JW, Javaux M, Or D, Roose T, Vanderborght J, Young M, Amelung W, Aitkenhead M, Allisson SD, Assouline S, Baveye P, Berli M, Brüggemann N, Finke P, Flury M, Gaiser T, Govers G, Ghezzehei T, Hallett P, Hendricks Franssen HJ, Heppel J, Horn R, Huisman JA, Jacques D, Jonard F, Kollet S, Lafolie F, Lamorski K, Leitner D, McBratney A, Minasny B, Montzka C, Nowak W, Pachepsky Y, Padarian J, Romano N, Roth K, Rothfuss Y, Rowe EC, Schwen A, Šimůnek J, Van Dam J, van der Zee SEATM, Vogel HJ, Vrugt JA, Wöhling T, Young IM (2016). Modelling soil processes: Key challenges and new perspectives, Vadose Zone Journal, 15(5), 57p, doi: 10.2136/vzj2015.09.0131. Open access here.
  • Hannes, M., Wollschläger, U. Wöhling, T., Vogel, H.-J. (2016). Revisiting hydraulic hysteresis based on long term monitoring of hydraulic states in lysimeters. Water Resources Research. (in print), doi: 10.1002/2015WR018319.
  • Wöhling, Th., Geiges, A., Nowak, W. (2016). Optimal design of multi-type groundwater monitoring networks using easily accessible tools. Groundwater, (in print), doi: 10.1111/gwat.12430.
  • Woodward, S.J.R., Wöhling Th., Stenger, R. (2016). Uncertainty in the modelling of spatial and temporal patterns of shallow groundwater flow paths: the role of geological and hydrological site information. Journal of Hydrology, 534, 680-694, doi: 10.1016/j.jhydrol.2016.01.045.
  • Schöniger, A., Wöhling, Th., Nowak, W. (2015). A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking. Water Resources Research, 51(7), 7524–7546, doi: 10.1002/2015WR016918.
  • von Gunten, D., Wöhling, Th., Haslauer, C. Merchán, D., Causape, J., Cirpka, O.A. (2015). Estimating climate-change effects on a Mediterranean catchment under various irrigation conditions. Journal of Hydrology Regional Studies, 4, 550-570, doi: 10.1016/j.ejrh.2015.08.001.
  • Schöniger, A., Illman, W., Wöhling, Th., Nowak, W. (2015). Finding the right balance between groundwater model complexity and experimental effort via Bayesian Model selection. Journal of Hydrology, 531(1), 96-110, doi: 10.1016/j.jhydrol.2015.07.047.
  • Wöhling, Th., Schöniger, A., Gayler, S., Nowak, W. (2015). Bayesian model averaging to explore the worth of data for maximum-confidence soil-plant model selection and prediction. Water Resources Research, 51, 2825-2846, doi:10.1002/2014WR016292.
  • Schöniger, A., Wöhling, Th., Samaniego, L., Nowak, W. (2014): Model selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidence. Water Resources Research. 50(12),9484-9513.
  • von Gunten, D., Wöhling, Th., Haslauer, C. Merchán, D., Causape, J., Cirpka, O.A. (2014): Efficient calibration of a distributed pde-based hydrological model using grid coarsening. Journal of Hydrology. 519, 3290-3304.
  • Lemke, D., González-Pinzón, R., Liao, Z., Wöhling, Th., Osenbrück, K., Haggerty, R., Cirpka, O.A. (2014): Sorption and transformation of the reactive tracers resazurin and resorufin in natural river sediments. Hydrology and Earth System Sciences, 18, 3151–3163.
  • Barkle, G.F., Stenger, R. and Wöhling, Th. (2014): Fate of urine nitrogen through a volcanic vadose zone and into schallow groundwater. Soil Research, 52 (7), 658-670.
  • Gayler, S., Wöhling, Th., Grzeschik, M., Ingwersen, J., Wizemann, H.-D., Högy, P., Attinger, S., Streck, T., Wulfmeyer, V. (2014): Incorporating dynamic root growth enhances the performance of Noah-MP ensemble simulations at two contrasting winter wheat field sites. Water Resources Research, 50(2), 1337-1356.
  • Gayler, S., Priesack, E., Ingwersen, J., Wizemann, H.-D., Högy, P., Cuntz, M., Attinger, S., Wulfmeyer, V., Streck, T. (2013): Multiresponse, multiobjective calibration as a diagnostic tool to compare accuracy and structural limitations of five coupled soil-plant models and CLM3.5. Water Resources Research, 49(12), 8200-8221.
  • Barkle, G.F., Wöhling, Th. and Stenger, R. (2013): Variability of unsaturated Bromide fluxes as measured through a layered volcanic vadose zone in New Zealand. Hydrological Processes, 28, 6080-6097.
  • Wöhling, Th., Geiges, A., Nowak, W., Gayler, S., Högy, P. and Wizemann, H.-D. (2013): Towards optimizing experiments for maximum-confidence model selection between different soil-plant models. Procedia Environmental Sciences, 19, 514-523, DOI: 10.1016/j.proenv.2013.06.058.
  • Lemke, D., Liao, Z., Wöhling, Th., Osenbrück, K., Cirpka, O.A. (2013): Concurrent conservative and reactive tracer tests in a stream undergoing hyporheic exchange. Water Resources Research. 49(5), 3024–3037.
  • Wöhling, Th., Samaniego, L., Kumar, R. (2013): Evaluating multiple performance criteria to calibrate the distributed hydrological model of the Upper Neckar catchment. Environ. Earth Sci. 69 (2).
  • Gayler, S. Ingwersen, J., Priesack, E., Wöhling, Th., Wulfmeyer, V., Streck, T. (2013): Assessing the relevance of sub surface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3.5: Comparison with field data and crop model simulations. Environmental Earth Sciences, 69 (2).
  • Grathwohl, P., Rügner, H., Wöhling, Th., et mult. (2013): Catchments as Reactors: A comprehensive approach for water fluxes and solute turn-over. Environ. Earth Sci. 69 (2) DOI: 10.1007/s12665-013-2281-7.
  • Osenbrück, K, Wöhling, Th., Lemke, D., et mult. (2013): Assessing hyporheic exchange and associated travel times by hydraulic, chemical, and isotopic monitoring at the Steinlach Test Site, Germany. Environmental Earth Sciences, 69 (2).
  • Cadwell, T.J, Wöhling, Th., Young, M.H., Boyle, D.P., McDonald, E.V. (2013): Characterizing disturbed desert soils using multi-objective inverse parameter optimization. Vadose Zone Journal, doi:10.2136/vzj2012.0083
  • Wöhling, Th., Bidwell, V.J., Barkle, G.F. (2012): Dual-tracer, non-equilibrium mixing cell modelling and uncertainty analysis for unsaturated bromide and chloride transport. Journal of Contaminant Hydrology. 140-141, 150-163
  • Philipp, A., Liedl, R., Wöhling, Th. (2012): An analytical model of surface flow on hillslopes based on the zero inertia equations. Journal of Hydraulic Engineering, 138 (5), 391-399
  • Barkle, G.F., Wöhling, Th., Stenger, R., Mertens, J., Moorhead, B., Wall, A. and Claque, J. (2011): Measuring water and contaminants fluxes throughout the vadose zone using Automated Equilibrium Tension Lysimeters (AETLs). Vadose Zone Journal, 10(2), 747-759.
  • Köhne, J.M., Wöhling, Th., Pot, V., Benoit, P., Leguédois, S., Le Bissonnais, Y. ,Simunek, J. (2011): Coupled simulation of surface runoff and soil water flow using multi-objective parameter estimation. Journal of Hydrology, 403, 141-156.
  • Wöhling, Th., Vrugt, J.A. (2011): Multi-response multi-layer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data. Water Resources Research, 47, W04510, doi:10.1029/2010WR009265.
  • Dann, R., Bidwell, V., Thomas, S., Wöhling, Th., Close, M. (2010): Modelling of Nonequilibrium Bromide Transport through Alluvial Gravel Vadose Zones. Vadose Zone Journal, 9, 731-746.
  • Moore, C., Wöhling, Th., Doherty, J. (2010): Efficient regularization and uncertainty-analysis using global optimization methodology. Water Resources Research, 46, W08527, doi:10.1029/2009WR008627.
  • Janssen, M., Lennartz, B. and Wöhling, Th. (2010): Percolation losses in paddy fields with a dynamic soil structure: Model development and applications. Hydrological Processes, 24, 813-824.
  • Wöhling, Th., Schütze, N., Heinrich, B., Simunek, J. and Barkle, G.F. (2009): Three-dimensional modeling of multiple Automated Equilibrium Tension Lysimeters to measure vadose zone fluxes. Vadose Zone Journal, 8(4), 1051-1063.
  • Wöhling, Th. (2009): Does vadose zone forecasting depend on the type of calibration data? In Anderssen, R.S., R.D. Braddock and L.T.H. Newham (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, pp. 2377-2383. ISBN: 978-0-9758400-7-8.
  • Wöhling, Th., Vrugt, J.A. (2008): Combining multi-objective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models. Water Resources Research, 44, W12432, doi:10.1029/2008WR007154.
  • Vrugt, J.A., Stauffer, P.H., Wöhling, Th., Robinson, B.A. and Vesselinov, V.V. (2008): Inverse modeling of subsurface flow and transport properties: A review with new developments. Vadose Zone Journal, 7(2), 843-864.
  • Wöhling, Th., Barkle, G.F., Vrugt, J.A. (2008): Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties. Soil Science Society of America Journal, 72(2), 305-319.
  • Wöhling, Th., Vrugt, J.A. (2008): Uncertainty of Vadose Zone Modelling using Model Ensembles and Bayesian Model Averaging. Proceedings of Water Down Under 2008 incorporating the 31^{st} Hydrology and Water Resources Symposium and the 4^{th} International Conference on Water Resources and Environmental Research. 14-17 April 2008, Adelaide, Australia.
  • Wöhling, Th., Schmitz, G.H. (2007): A Physically Based Coupled Model for Simulating 1D Surface - 2D Subsurface Flow and Plant Water Uptake in Irrigation Furrows. I: Model Development. Journal of Irrigation and Drainage Engineering. 133(6), 538-547.
  • Wöhling, Th., Mailhol, J.C. (2007): A Physically Based Coupled Model for Simulating 1D Surface - 2D Subsurface Flow and Plant Water Uptake in Irrigation Furrows. II: Model Test and Evaluation. Journal of Irrigation and Drainage Engineering. 133(6), 548-558.
  • Stenger, R., Wöhling, Th., Barkle, G. and Wall, A. (2007): Empirical and semi-empirical dielectric permittivity water content relationships for vadose zone materials of volcanic origin. Australian Journal of Soil Research. 45, 299-309.
  • Schmitz, G.H., Wöhling, Th., de Paly, M., and Schütze, N. (2007): GAIN-P: A New Strategy to increase furrow irrigation efficiency. The Arabian Journal for Science and Engineering. 32 (1C), 103-114.
  • Schmitz, G.H., Schütze, N. and Wöhling, Th. (2007): Irrigation control: towards a new solution of an old problem. Volume 5 of IHP/HWRP-Berichte, International Hydrological Programme (IHP) of UNESCO and The Hydrology and Water Resources Programme (HWRP) of WMO, Koblenz, Germany, 222 pp.
  • Vrugt, J.A., Wöhling, Th. (2007): Upscaling Soil Hydraulic Properties Using Field-Scale Inverse Modeling and Bayesian Model Averaging. Invited presentation at the AGU Fall Meeting. 10-14 December 2007, San Francisco, CA, USA.
  • Wöhling, Th., Vrugt, J.A. (2007): Multiobjective inverse parameter estimation for modelling vadose zone water movement. MODSIM07 - International Congress on Modelling and Simulation. Land, Water & Environmental Management: Integrated Systems for Sustainability. 10-13 December 2007, Christchurch, New Zealand.
  • Wöhling, Th., Fröhner, A., Schmitz, G.H. and Liedl, R. (2006): Efficient solution of interacting 1D surface - 2D subsurface flow during furrow irrigation advance. Journal of Irrigation and Drainage Engineering. 132(4), 380-388.
  • Wöhling, Th., Lennartz, F. , Zappa, M. (2006): Real-Time Updating Procedure for Flood Forecasting with conceptual HBV-Type Models. Technical Note, Hydrology and Earth System Sciences, Vol. 10, 7-6-2006, pp 783&788.
  • Wöhling, Th., Singh, R., & Schmitz, G.H. (2004): Physically based modeling of interacting surface-subsurface flow during furrow irrigation advance. Journal of Irrigation and Drainage Engineering, 130(5), 296-303.
  • Wöhling, Th., Schmitz, G.H., & Mailhol, J.C. (2004): Modeling 2D infiltration from irrigation furrows. Journal of Irrigation and Drainage Engineering, 130(4), 349-356.

Awards

  • STAHY Best Paper Award (2018)
  • ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018)
  • ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009)

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Michael Wagner
Last modified: Aug 18, 2020
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