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Peer-Review Record

On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling

Reviewer 1: Anonymous
Reviewer 2: Anonymous
Received: 30 December 2020 / Revised: 12 February 2021 / Accepted: 20 February 2021 / Published: 24 February 2021

Round 1

Reviewer 1 Report

Comments to manuscript

This study investigates the importance of the choice of metric used during the hyper-parameter calibration phase and its impact on discharge forecasts. The types of metrics used each focus on discharge accuracy, ensemble spread or observation-minus-background statistics. The calibration is performed for the ensemble square root Kalman filter over two catchments in Canada using two different hydrologic models per catchment.

The topic and the contents are relevant and it may be of interest for some of hydrologist and water managers. However, some results and limitations are needed to describe and discuss in detail. I would suggest a moderate revision for this paper to meet the journal standard of "Hydrology".

Main Comments:


  1. In abstract “This influence is reduced as the forecast lead time increases, but can remain considerable for a few days up to multiple weeks depending on the catchment and the model”. Please explain and discussed in detail, why is reduced as the forecast lead time increases?


  1. In text “Using the climatological precipitation for a lead time of 1-20 days appears to be insufficient to converge back to the climatological snow water equivalent and therefore insufficient to converge back to the climatological discharge volume”. How about for rainfall prediction, lead time of 10-20 days is too long for these two catchments? Please explain the length of lead time in practice.


  1. This article explains in detail that the different metrics will affect the selection of Ensemble Kalman filter hyper-parameters, but the full text does not give reasonable decision-making suggestions. How to choose metrics? Which metrics can achieve the best results under different circumstances?


  1. Please add the empirical value range of each parameter of the models in the Table 1 and Table 2. Most of the parameters of GR4J are out of the 80% confidence interval rang, please explain or analyze the reason?


  1. At the end of the section 3.1, the author chooses 4 sets of solutions. The scenario find four coordinates to be the hyper-parameter values. Please explain why these four coordinates were chosen. Based on which metric the author finds these four coordinate values. Hyper-parameters cannot be seen only from Figures 4 and 5.


  1. The title “On the choice of metric to calibrate time-invariant Ensemble Kalman filter hyper-parameters for discharge data assimilation and its impact on discharge forecast modelling” is too long.


  1. The references listed in this study are out-of-date. There are several new approaches for ensemble forecasting based on post-proceeding methods, which are much simple in application.

Author Response

Please see attached file.

Author Response File: Author Response.pdf

Reviewer 2 Report

Review attached

Comments for author File: Comments.pdf

Author Response

Please see attached file.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Accept in present form

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