Thomas Wöhling
Environmental Engineering · 2008–2019
A researcher in the Science corpus: 5 papers; trusts 24 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 46 citations into it; #1,249 of 5,479 by trust here, #3,093 by citations
- Global and Planetary Change — 33 citations into it
- Civil and Structural Engineering — 31 citations into it; #1,473 of 6,160 by trust here, #3,212 by citations
- Water Science and Technology — 31 citations into it; #368 of 1,711 by trust here, #1,136 by citations
- Artificial Intelligence — 13 citations into it
- Computational Theory and Mathematics — 11 citations into it
- Ocean Engineering — 11 citations into it
- Statistics and Probability — 9 citations into it
Whom Thomas Wöhling trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jasper A. Vrugt
- Henrik Madsen
- Kalyanmoy Deb
- C. T. ter Braak
- Shlomo P. Neuman
- Qingyun Duan
- Keith Beven
- Matthias Ehrgott
- Soroosh Sorooshian
- Andrew Gelman
Who trusts Thomas Wöhling overall
- Jasper A. Vrugt
- H. Dai
- E. Diamantopoulos
- Jirka Šimůnek
- G. Flerchinger
- Ming Ye
- Harry Vereecken
- S. Iden
- M. T. V. Genuchten
- Todd G. Caldwell
Papers from Thomas Wöhling's perspective
Ordered by how strongly the researchers Thomas Wöhling trusts endorse each paper. Same corpus, a different order for every researcher.
- The future of distributed models: model calibration and uncertainty prediction.
- Effective and efficient global optimization for conceptual rainfall‐runoff models
- Inference from Iterative Simulation Using Multiple Sequences
- Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
- A manifesto for the equifinality thesis
- An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction
- Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling
- Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
- A Simplex Method for Function Minimization
- Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods
Their papers
- How well do crop modeling groups predict wheat phenology, given calibration data from the target population? (2019) — 51 citations
- Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016) — 696 citations
- Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data (2011) — 106 citations
- Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models (2008) — 81 citations
- Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties (2008) — 139 citations