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Results: 83
Number of items: 83
  • Bouten, W., van Belle, J., van Gasteren, J. R., Vrugt, J. A., & Shamoun-Baranes, J. (2005). Predicting Bird migration: data-driven versus concept-driven models. In A. Anagnostopoulos (Ed.), Bird Strike Committee Europ Proc. and Working Papers (Vol. 1, pp. 193-193). Athens International Airport.
  • Jansen, B., Nierop, K. G. J., Vrugt, J. A., & Verstraten, J. M. (2004). (Un)certainty of overall binding constants of Al with dissolved organic matter determined by the Scatchard approach. Water Research, 38, 1270-1280. https://doi.org/10.1016/j.watres.2003.11.017
  • Huisman, J. A., Bouten, W., Vrugt, J. A., & Ferré, P. A. (2004). Accuracy of frequency domain analysis scenarios for the determination of complex dielectric permittivity. Water Resources Research.
  • Vrugt, J. A., Schoups, G., Hopmans, J. W., Young, C., Wallender, W. W., Harter, T., & Bouten, W. (2004). Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization. Water Resources Research, 40(6), W06503.
  • Vrugt, J. A., Bouten, W., Gupta, H. V., & Hopmans, J. W. (2004). Toward improved indentifiability of soil hydraulic parameters: On the selection of a suitable parametric model. Vadose Zone Journal, 2, 98-113. https://doi.org/10.2136/vzj2003.9800
  • Dekker, S. C., Bouten, W., & Vrugt, J. A. (2004). Identification of rainfall interception model parameters from throughfall and canopy storage measurements. Geophysical Research Abstracts, 1541-1541.
  • Bouten, W., & Vrugt, J. A. (2004). Distributed modeling of catchments: Balancing modeling objectives, model complexity and data availability. In Geophysical Research Abstracts (Vol. 6, pp. 7799-7799). EGU.
  • Vrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2004). A shuffled Complex Evolution Metropolis algorithm for confronting parameter uncertainty in hydrologic modeling. Geophysical Research Abstracts, 5359-5359.
  • Vrugt, J. A. (2004). Merging the strengths of global optimization and data-assimilation to simultaneously estimate parameters and state variables in hydrologic models. In Proceedings of the ESF/LESC Exploratory Workshop on Hydrological risk: recent advances in peak river flow modeling, prediction and real time forecasting, Assessment of the impacts of land-use and climate change
  • Heimovaara, T. J., Huisman, J. A., Vrugt, J. A., & Bouten, W. (2004). Obtaining the spatial distribution of water content along a TDR probe using the SCEM-UA Bayesian inverse modeling scheme. Vadose Zone Journal, 3, 1128-1145.
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