A 500-year runoff reconstruction for European catchments
- 1Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic
- 2UFZ-Helmholtz Centre for Environmental Research, Leipzig 04318, Germany
- 1Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic
- 2UFZ-Helmholtz Centre for Environmental Research, Leipzig 04318, Germany
Abstract. Since the beginning of this century, Europe has been experiencing severe drought events (2003, 2007, 2010, 2018, and 2019) which have had adverse impacts on various sectors, such as agriculture, forestry, water management, health, and ecosystems. During the last few decades, projections of the impact of climate change on hydroclimatic extremes were often capable of reproducing changes in the characteristics of these extremes. Recently, the research interest has been extended to include reconstructions of hydro-climatic conditions to provide historical context for present and future extremes. While there are available reconstructions of temperature, precipitation, drought indicators, or the 20th century runoff for Europe, long-term runoff reconstructions are still lacking (e.g, monthly or daily runoff series for short periods are commonly available). Therefore, we considered reconstructed precipitation and temperature fields for the period between 1500 and 2000 together with reconstructed scPDSI, natural proxy data, and observed runoff over 14~European catchments to calibrate and validate the semi-empirical hydrological model GR1A and two data-driven models (Bayesian recurrent and long short-term memory neural network). The validation of input precipitation fields revealed an underestimation of the variance across most of Europe. On the other hand, the data-driven models have been proven to correct this bias in many cases, unlike the semi-empirical hydrological model GR1A. The comparison to observed historical runoff data has shown a good match between the reconstructed and observed runoff and between the runoff characteristics, particularly deficit volumes. The reconstructed runoff is available via figshare, an open source scientific data repository under the DOI https://doi.org/10.6084/m9.figshare.15178107, (Sadaf et al., 2021).
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Sadaf Nasreen et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2021-282', Gionata Ghiggi, 26 Oct 2021
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AC1: 'Reply on RC1', Sadaf Nasreen, 12 Jan 2022
Dear Dr. Ghiggi,
We'd like to express our gratitude to you for your thoughtful comments on our article, "A 500-year annual runoff reconstruction for 14 chosen European catchments". Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We carefully reviewed the comments and made changes that we hope will be accepted.
Kind regards,
On behalf of all coauthors,
Sadaf Nasreen
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AC1: 'Reply on RC1', Sadaf Nasreen, 12 Jan 2022
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RC2: 'Comment on essd-2021-282', Anonymous Referee #2, 01 Dec 2021
“A 500-year runoff reconstruction for European catchments” by Sadaf Nasreen et al.
The manuscript “A 500-year runoff reconstruction for European catchments” by Sadaf Nasreen et al. shows the work and effort that has been done to create a new dataset of long-term runoff reconstruction for various European catchments. While reconstructions of meteorological variables such as temperature and precipitation were already available, this study closes the gap by providing open source runoff reconstructions. This is valuable information as it can provide historical context for upcoming studies, which are interested in assessing present and future extremes such as droughts. To create the runoff reconstruction, a semi-empirical hydrological model (GR1A) as well as two data-driven model (LSTM and BRNN) were used and tested for their suitability. An extensive data collection including precipitation, temperature, drought indices, natural proxy data and runoff observations over the period 1500 to 2000 were used to calibrate and validate the models. The data-driven models showed the most promising results, being able to correct for biases in the input data compared to semi-hydrological model. Furthermore, the separate analysis focussing on droughts showed that the reconstructed timeseries of these models correlated well with the historical documented droughts.
The paper was well written and included extensive information on the approach and validation of reconstructed runoff timeseries, especially regarding drought events. The main points for improvement are mainly focussing on additional clarifications regarding certain aspects of the methods. Therefore, I would like to recommend publication after minor revisions. The comments for improvement can be found below.
Major comments:
Section 3.3 Data-driven models: While there is an extensive general explanation on the LSTM model, the BRNN description falls short. More importantly, information necessary to be able to follow as a reader on how the data driven models were trained and the final model parameters are not given or not fully clear. Aspects on the training/testing data, like the type of splitting or how much was withheld for training/testing purposes are not clear. Furthermore, aspects on the input data (for example the gridded+proxy, gridded+PDSI, and gridded+lag) and its preparation (e.g. type of normalization, handling with outliers, etc) would be of interest as well. An additional table (could be in the Appendix) with the input parameters as they are used in the ML models would support the readers understanding. While the Appendix covers the LSTM model structure, similar information on the BRNN is missing. Additional suggestion is to add the final model parameters (e.g. amount of neurons) into the schematization (Fig A.1) as well.
Overall I think adding more specific information on the ML models will only improve the readers understanding and as the data-driven models show the most promising results highlighting and clarifying their use is important.
Section 3 Methods: A schematic overview of the data-preprocessing (3.1), the incorporation of all the different datatypes and sources in the different model types (3.2 and 3.3) as well as the postprocessing steps (3.4 and 3.5) would be a nice addition to this section to not only visualize the general approach of the study but also support the subchapters and the readers understanding.
Minor comments:
line 43: As an example: Hansson et al…. remove “of”
Fig 1 (and also Fig 5 and Fig 6): think about changing red or green to a different colour to ensure that colorblind people can follow your figures
Line 75: sentence not flowing, for example move comma in front of reference and remove ‘was done’
Fig 2 and Fig 3 same range and colorbar per evaluation metric (makes it easier to compare), list min and max values of scale bar for readability
Section 3.4: possibly some lines on the pros and cons of the GR1A model
Section 4.1 (and throughout the rest of the manuscript): be consistent in addressing GOF (now a mix between GOF and gof)
Section 4.2 the information on calibration and validation should be part of the Methods and the models. Furthermore, it would be nice to move a figure with the timeseries of one station as seen in Fig S1 from the supplementary to that paragraph to highlight calibration and validation periods.
Line 255: ‘greatly increased the performance (NSE from 0.2 to 0.62).’ Compared to the values mentioned in prior example of Basel Reinhalle, 0.62 is not listed Table 3 for BRNN(Gridded+PDSI) but 0.57
Table 3: highlighting the different performances is a nice feature and helps spotting important trends, however the darkest colours make it hard to read the values (same for tables in supplementary). Maybe also add a note in the table description what the colour indication means.
Table 3: both stations at Basel show higher correlation scores for validation than calibration. Ideas why this is the case?
Figure 7: Whitespace around the figure seems to be cut too narrow as the max value for station BaselRheinhalle-Rhine is cut off (130 instead of 1300)
Line 347 and Table 5: listing of years does not include 1724, which is also indicated in bold in Table 5.
Section 6: ’using the data set below’ move below
Appendix: add references to equations and in text (easier to follow in case chapter layout changes)
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AC2: 'Reply on RC2', Sadaf Nasreen, 12 Jan 2022
Respected Reviewer,
We greatly appreciate your efforts to evaluate our manuscript. Your constructive comments helped us to take a look at the manuscript from different perspective and to improve unclear parts in the revised manuscript. We carefully considered the comments and made changes that we hope will be accepted for publication.
Kind regards,
On behalf of all co-authors,
Sadaf Nasreen
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AC2: 'Reply on RC2', Sadaf Nasreen, 12 Jan 2022
Sadaf Nasreen et al.
Data sets
A 500-year runoff reconstruction for European catchments Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel https://doi.org/10.6084/m9.figshare.15178107
Sadaf Nasreen et al.
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