Ensemble evaluation of hydrological model hypotheses

It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error

Session HS38 "Hydroinformatics: computational intelligence and technological developments in water science applications" General Assembly 2006 European Geosciences Union (EGU) Vienna, Austria, 02 – 07 April 2006

Hydrological Sciences Programme - Session HS38 "Hydroinformatics: computational intelligence and technological developments in water science applications" Hydroinformatics has emerged over the last decade to become a recognised and established field of independent research within the hydrological sciences. Hydroinformatics is concerned with the development and hydrological application of mathematical modelling, information technology, systems science and computational intelligence tools. It provides the computer-based decision-support systems that are now entering more and more into the offices of consulting engineers, water authorities and government agencies. The aim of this session is to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent computational technologies in a hydrological modelling context. Topics of interest are expected to cover a broad spectrum of theoretical and practical activities that would be of interest to hydro-scientists and water-engineers. The main topics will address the following classes of methods and technologies: \u2022 Predictive models based on the methods of computational intelligence: neural networks, fuzzy systems, support vector machines, genetic programming, cellular automata, chaos theory, etc. \u2022 Methods for the analysis of complex data sets: principal and independent component analysis, feature extraction, data-infilling, information theory, etc. \u2022 Optimization methods associated with heuristic search procedures: various types of genetic and evolutionary algorithms, randomized and adaptive search, ant colony and particle swarm optimization, etc. \u2022 Hybrid modelling involving different types of models \u2013 both process-based and data-driven; \u2022 Novel methods of analysing model uncertainty; \u2022 Appropriate software architectures for linking different types of model. Applications could belong to any area of hydrology or water resources: rainfall-runoff modelling, flow forecasting, sedimentation modelling, analysis of meteorological and hydrologic data sets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimization of water resources, etc

Hydroinformatics: computational intelligence and systems analysis EGU General Assembly 2010 - Vienna, Austria, 2-7 May 2010

Session HS8.1 Hydroinformatics: computational intelligence and systems analysis Hydroinformatics has emerged over the last decade to become a recognised and established field of independent research within the hydrological sciences. Hydroinformatics is concerned with the development and hydrological application of mathematical modelling, information technology, systems science and computational intelligence tools. It provides the computer-based decision-support systems that are now entering more and more into the offices of consulting engineers, water authorities and government agencies. The aim of this session is to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent computational technologies in a hydrological modelling context. Topics of interest are expected to cover a broad spectrum of theoretical and practical activities that would be of interest to hydro-scientists and water-engineers. The main topics will address the following classes of methods and technologies: * Predictive models based on the methods of computational intelligence: neural networks, fuzzy systems, support vector machines, genetic programming, cellular automata, chaos theory, etc. * Methods for the analysis of complex data sets: principal and independent component analysis, feature extraction, data-infilling, information theory, etc. * Optimization methods associated with heuristic search procedures: various types of genetic and evolutionary algorithms, randomized and adaptive search, ant colony and particle swarm optimization, etc. * Hybrid modelling involving different types of models both process-based and data-driven. * Novel methods of analysing model uncertainty. * Appropriate software architectures for linking different types of models. Applications could belong to any area of hydrology or water resources: rainfall-runoff modelling, flow forecasting, sedimentation modelling, analysis of meteorological and hydrologic data sets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimization of water resources, etc. This is the sixth consecutive annual session on Hydroinformatics at EGU Assemblies. Each past event was a major success. On the basis of the best papers received an edited volume and two special issues of peer-reviewed journals were initiated

Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed "river forecasting." The field is now firmly established and the research community involved has much to offer hydrological science. First, however, it will be necessary to converge on more objective and consistent protocols for: selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies. It is also clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design, ensemble experiments, and hybridization with conventional hydrological models are yielding new tools for decision-making. The full potential for modelling complex hydrological systems, and for characterizing uncertainty, has yet to be realized. Further gains could also emerge from the provision of an agreed set of benchmark data sets and associated development of superior diagnostics for more rigorous intermodel evaluation. To achieve these goals will require a paradigm shift, such that the mass of individual isolated activities, focused on incremental technical refinement, is replaced by a more coordinated, problem-solving international research body