Thomas Wöhling

© M. Wagner

Professor

Name

Prof. Dr. habil. Thomas Wöhling

Stochastische Hydrologie

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Organisationsname

Professur für Hydrologie

Professur für Hydrologie

Adresse work

Besuchsadresse:

Neubau Chemische Institute, Raum 360 Bergstr. 66

01069 Dresden

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work Tel.

Forschungsinteressen

• Integrierte Modellierung von gekoppelten hydrologischen Systemen, z.B.:
  • Wasser- und Schadstofftransport in Oberflächen-Grundwassersystemen, "Braided River" Systeme
  • Wasser-, Stoff- und Energieflüsse in Boden-Pflanze-Atmoshäre Systemen
  • hydrologische Modellierung auf verschiedenen Skalen
  • Fluss- und Transportprozesse in der ungesättigten Zone und in heterogenen Medien
• Evaluierung und Optimierung von Messnetzen
• Datenwertanalyse
• Inverse Modellierung, Kalibrierung, effiziente Methoden zur Parameterbestimmung
• Multikriterielle Optimierungstechniken und Pareto Analyse
• Stochastische Modellierung und Unsicherheitsanalyse
• Modellwahl- und -diskriminierung, Ensemblemodellierung, Bayes'sche Modellmittelung

Projekte

• KlimaKonform (2020 - 2026)

• Surface water groundwater exchange in New Zealand's braided rivers – part of Lincoln Agritech's Braided Rives programm (2019 - 2024)  
  Wairau River Story Map
  Isotope Analysis

• ISOSIM: groundwater age simulation tool for environmental tracer data

• Junior Research Group on DOC mobilisation in the Bavarian Forest National Park (2019 - 2022)
• VAMOS II: Monitoring and modelling of non-equilibrium soil water dynamics and lateral subsurface flow in hillslope soils (2019 - 2022)
• Combining novel monitoring techniques with modelling and optimal sensor placement for more reliable prediction of river - groundwater exchange fluxes (2013 - 2015)
• Efficient nonlinear model reduction for improved predictive uncertainty quantification and optimal design of monitoring networks in coupled groundwater-surface water systems (2015 - 2018)
• International Research Training Group "Integrated Hydrosystem Modelling" (2012 - 2021)

Lehre

• MHSE02 - Climatology and Hydrology: Fundamentals and Principles of Hydrology (HSE)
• UW-BHW-651 - Wasserhaushalt und -bewirtschaftung der Oberflächengewässer
• MHYD22 - Regionale Hydrologie
  Grosse Hydrologische Exkursion (mehr)
• MHYD23 - Vertiefungspraxis Hydrologie
• MHSE09 - Study Project

Ausbildung & berufliche Tätigkeit

Publikationen

© T. Wöhling

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Publikationen: Link zum Forschungsinformationssystem.
Wissenschaftliche Zeitschriftenbeiträge:

• Dudley BD, Hill AF, McKenzie A, ..., Wöhling T. (2026). Community participation as a pathway to strengthen national precipitation isotope products in New Zealand. Journal of the Royal Society of New Zealand. 56:e70061, doi.org/10.1002/snz2.70061.
• Rudolph M, Wöhling T, Wagner T, Hartmann A. (2025). The effect of available data on the worth of future observations for groundwater modelling. Water Resources Research, 62, e2025WR041972.
• Durney P, Di Ciacca, A, Wilson S, Wöhling T (2025). Multi-purpose [multi-response] data worth assessment of a coupled surface water-groundwater model. Groundwater,  63(4). https://doi.org/10.1111/gwat.13490.
• Wöhling T, Crespo Delgadillo AO, Kraft M, Guthke A (2025). Comparing physics-based, conceptual and machine-learning models to predict groundwater heads by BMA. Groundwater, 63(4), doi.org/10.1111/gwat.13487.
• Di Ciacca A, Knight LG, Brand M, Durney P, Wilson SR, Wöhling T (2025). Groundwater recharge from ephemeral rivers in the Canterbury Plains (New Zealand): historical reconstruction and environmental implications. Journal of Hydrology Regional Studies, 59, 102317.
• Zamboni P, Blümlein M, Lenz J, Gonçalves WN, Marcato J, Wöhling T, Eltner A. (2025). Measuring water ponding time, location and connectivity on soil surfaces using timelapse images and deep learning. Catena, 254, 108919.
• Rudolph M, Wöhling T, Wagner T, Hartmann A. (2024). Extending GLUE with Multilevel Methods to Accelerate Statistical Inversion of Hydrological Models, Water Resources Research, 60, e2024WR037735. 
• Di Ciacca A, Wilson S, Durney P, Stecca G, Wöhling T (2024). Model simplification to simulate groundwater recharge from a perched gravel-bed river. Journal of Hydrology, 643, 132016.
• Raut A, Ganguli P, Kumar R, Reddy NN, Das BS, Wöhling T (2024). Streamflow drought onset and severity explained by non-linear responses between climate-catchment and land surface processes. Hydrological Processes, 38:e15245.
• Wilson S, Hoyle J, Measures R, Di Ciacca A, Morgan LK, Banks EW, Robb L, Wöhling T (2024). Conceptualising surface water-groundwater exchange in braided river systems. HESS (accepted).
• Hsueh H-F, Guthke A, Wöhling T, Nowak W (2024). Optimized Predictive Coverage by Averaging Time-Windowed Bayesian Distributions. Water Resources Research, 60, e2022WR033280.
• Kuhnert L, Beudert B, Wöhling T. (2024). Response times as explanatory variable for export of dissolved organic carbon (DOC) from small forested catchment. Journal of Hydrology, 633, 130985 .
• Ejaz F, Guthke A, Wöhling T, Nowak W (2023). Comprehensive uncertainty analysis for surface water and groundwater forecasts under climate change based on a lumped geo-hydrological model. Journal of Hydrology, 626,130323.
• Wallach D, Palosuo T, Thorburn P, .. et mult. (2023). Proposal and extensive test of a calibration protocol for crop phenology models. Agronomy for Sustainable Development. (43) 46.
• Brunetti G, Simunek J, Wöhling T, Stumpp C (2023). An in-depth analysis of Markov-Chain Monte Carlo ensemble samplers for inverse vadose zone modeling. Journal of Hydrology, (624) 129822.
• Rudolph MG, Collenteur RA, Kavousi A, Giese M, Wöhling T, Birk S, Hartmann A, Reimann T. (2023). A data-driven approach for modelling Karst spring discharge using Transfer Function Noise Models. Environmental Earth Sciences. 82(339).
• Kavousi A, Reimann T, Wöhling T, Birk S, Luhmann AJ, Kordilla J, Noffz T, Sauter M, Liedl R (2023). Joint-Inversion of spring flow and transport signatures: A multi-purpose approach for characterization and forecast of a Karst system. Hydrogeology. 31, 1005–1030.
• Lazarovitch N, Kisekka I, Oker TE, Brunetti G, Wöhling T, Xianyue L, Yong L, Skaggs TH, Furman A, Sasidharan S, Raij-Hoffman I, Šimůnek J (2023). Modeling of Irrigation and Related Processes with HYDRUS. Advances in Agronomy. 181, 106 p., ISBN 0065-2113.
• Di Ciacca A, Wilson S, Kang J, Wöhling T (2023). Deriving transmission losses from ephemeral rivers using satellite imagery and machine learning. HESS, 27, 703–722.
• Vogel H-J, Gerke HH, Mietrach R, Zahl R, Wöhling T (2022). Soil hydraulic conductivity in the state of non-equilibrium. Vadose Zone Journal, e20238. doi: 10.1002/vzj2.20238.
• Ehrhardt A, Berger K, Filipović V, Wöhling T, Vogel HJ, Gerke HH (2022). Tracing lateral subsurface flow in layered soils by undisturbed monolith sampling, targeted laboratory experiments and model-based analysis. Vadose Zone Journal, e20206. doi.org/10.1002/vzj2.20206.
• Hsueh H-F, Guthke A, Wöhling T, Nowak W (2021). Diagnosis of model-structural errors with a sliding time-window Bayesian analysis, Water Resources Research, 58, e2021WR030590, doi: 10.1029/2021WR030590.
• Ejaz F, Wöhling T, Höge M, Nowak W (2021). Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion. Journal of Hydrology. 127347, doi.org/10.1016/j.jhydrol.2021.127347.
• Seidel S, Palosuo T, Thorburn, P. ... et. mult. (2021). The chaos in calibrating crop models. Environmental Modelling and Software, 145, 105206, 1364-8152, doi: 10.1016/j.envsoft.2021.105206.
• Jeannin, P-Y, ... et. mult. (2021). Karst modelling challenge 1: Results of hydrological modelling. Journal of Hydrology. 600, 126508, doi: 10.1016/j.jhydrol.2021.126508.
• Gosses M & Wöhling T (2021). Robust data worth analysis with surrogate models. Groundwater. doi: 10.1111/gwat.13098.
• Wallach D, Palosuo T, ... et. mult. (2021). How well do crop modeling groups predict wheat phenology, given calibration data from the target population? European Journal of Agronomy, 124, 126195, doi: 10.1016/j.eja.2020.126195.
• Wallach D, Palosuo T, Thorburn, P. ... et. mult. (2021). Multi-model evaluation of phenology prediction for wheat in Australia. Agricultural and Forest Meteorology. 298–299, 108289, doi: 10.1016/j.agrformet.2020.108289.

• Wöhling T, Burbery L (2020). Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession. Science of the Total Environment, 747, 141220, doi: 10.1016/j.scitotenv.2020.141220.

• Wöhling T, Wilson SR, Wadsworth V, Davidson P. (2020). Detecting the cause of change using uncertain data: Natural and anthropogenic factors contributing to declining groundwater levels and flows of the Wairau Plain Aquifer, New Zealand. Journal of Hydrology: Regional Studies, 31, 100715, doi: 10.1016/j.ejrh.2020.100715.

• Chow, R., Bennet, J., Dugge, J, Wöhling, T., Nowak, W. (2019). Evaluating subsurface parameterization to simulate hyporheic exchange: The Steinlach River Test Site. Groundwater, 58(1), 93-109, doi: 10.1111/gwat.12884.

• Wöhling, T. (2019). Natürliche und anthropogene Einflussfaktoren auf das hydrologische Regime des Wairau Plain Aquifer in Neuseeland. Hydrologie und Wasserbewirtschaftung. 63(3), 147-157, doi: 10.5675/HyWa_2019.3_2.

• M. Loschko, Wöhling, T., Rudolph, D.L., Cirpka, O.A. (2019). An electron-balance based approach to predict the decreasing denitrification potential of an aquifer. Groundwater, 57(6), 925-939, doi: 10.1111/gwat.12876.

• Gosses, M. & Wöhling, T. (2019). Simplification error analysis for groundwater predictions with reduced order models. Advances in Water Resources, 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 Resources, 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,

  56(4), 647-666, doi:10.1111/gwat.12625.

• 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.

Preise & Auszeichnungen

• Groundwater: Editor's Choice Award (2025)
• 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)