Multi-dataset, multi-variable and multi-objective techniques combined with metaheuristic calibration techniques to improve prediction of hydrological, ecological, and water quality models
Convener:
Stefano BassoECSECS
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Co-conveners:
Berit Arheimer,
David C. Finger,
Alberto Montanari,
Anna Sikorska-SenonerECSECS
calibration techniques have proven to improve the performance of hydrologic and water quality
models by extracting complementary information from multiple data sources or multiple features of
modelled variables. This is useful if more than one variable (runoff and snow cover, sediment,
pollutant concentration, or stable isotope) or more than one characteristic of the same variable (e.g.,
flood peaks and recession curves) are of interest. Similarly, a multi-model approach can overcome
shortcomings of individual models, while testing a model at multiple scales using a large sample of
catchments helps to improve our understanding of the model functioning in relation to catchment
processes. The use of multiple data sources in data-driven approaches can help engineering data-
driven models with higher predictability skills. Finally, the quantification of multiple uncertainty
sources enables the identification of their contributions and this is critical for uncertainty reduction
and decision making under uncertainty.
This session welcomes contributions that apply one or more of the multi-aspects in hydrological,
ecological and water quality studies. In particular, we seek studies covering the following issues:
• Frameworks using multi-objectives or multi-variables to improve the identification (prediction) of
hydrological, ecological or water quality models;
• Studies using multi-model or multiple-data-driven approaches;
• Use of multiple scales, sites or large-sample studies to improve understanding of catchment
processes;
• Assimilation of remotely sensed data or use of multi-datasets to improve model identification;
• Hypothesis testing with one of the multi-aspects;
• Metaheuristics (e.g., Monte Carlo) or Bayesian approaches in combination with multi-aspects of
model identification;
• Techniques to optimize model calibration or uncertainty quantification via multi-aspect analyses;
• Studies handling multiple uncertainty sources in a modelling framework.
• Application of machine learning and data mining approaches to learn from large, multiple or high-
resolution data sets.