A novel method for downsizing the model parameter ensemble

Imagine you could use only three parameter sets of a hydrological model to represent the modelling uncertainty in the flood frequency analysis. Imagine how many more input scenarios could be then analysed at a lower computational cost. This study assists you in selecting such three parameter sets that could be assumed as representative for the full parameter ensemble.

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New session on multi-techniques at EGU21

Call for abstracts for a new session at the EGU General Assembly 2021!

Are you working with multi-dataset, multi-objective or multi-model techniques in hydrologic or water quality modelling?
Then our new HS2.3.7 session on Multi-dataset, multi-variable
and multi-objective techniques to improve prediction of hydrological
and water quality models organized together with David C. Finger & Alberto Montanari is just the right for you!

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New objective function to account for discharge data uncertainty

Calibration data for hydrological models are uncertain

Discharge data used to calibrate and evaluate hydrological models can be highly uncertain and this uncertainty affects the conclusions that we can draw from modelling results. In this work, we investigated the role of discharge data uncertainty in hydrological model calibration to give recommendations on methods to account for data uncertainty.

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The latest work is now in open discussion of NHESS

Representative ensemble for simulation of extreme floods

This work proposes methods for reducing the computational requirements of hydrological simulations for the estimation of very rare floods that occur on average less than once in 1000 years. These methods enable the analysis of long time streamflow series (here for example 10 000 years) at low computational costs with representing modelling uncertainty. They are to be used within continuous simulation frameworks with long input time series and are readily transferable to similar simulation tasks.

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