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Information - HS34 Calibration, data assimilation, and uncertainty estimation of spatially distributed and integrated catchment models
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Event Information |
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In the last few decennia hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. Before meaningful predictions can be made with these models, however some form of model calibration and state updating is needed. While much progress has been made in the development of efficient optimization and state estimation methods for low dimensional, lumped hydrologic models, it is not particularly clear how best to adapt these methods to spatially distributed hydrologic models, which potentially contain a large number of model parameters and states, and for which different measurement data are available for calibration and state estimation purposes. This session aims to integrate contributions from the various disciplinary areas within hydrology focusing on the calibration and uncertainty estimation of spatially distributed hydrologic models. Particular interests lie in contributions that are related to the following themes, (1) calibration of spatially distributed models, (2) filtering methods and applications for distributed state estimation, (3) characterization of simulation uncertainty through ensemble methods, such as multi-model averaging and other Bayesian or pseudo-Bayesian methods, and (4) incorporating observational errors in hydrological uncertainty
analysis. It is our intent to invite researchers and practitioners from groundwater, vadose zone, surface hydrology and hydrometeorology to present their latest work on model calibration, data assimilation and uncertainty estimation in distributed and integrated watershed and water quality models.
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Preliminary List of Solicited Speakers |
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 Back to Session Programme
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