A 225-Year (1799&ndash;2024) Homogenized Daily Water Level Series of the Vistula River in Warsaw

Sobechowicz, Łukasz; Brykała, Dariusz; Kaznowska, Ewa; Wasilewicz, Michał; Wolski, Jacek; Noras, Marcin; Siwek, Wojciech Aleksander

doi:10.5194/essd-2025-538

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https://doi.org/10.5194/essd-2025-538

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04 Dec 2025

| 04 Dec 2025

Status: this preprint is currently under review for the journal ESSD.

A 225-Year (1799–2024) Homogenized Daily Water Level Series of the Vistula River in Warsaw

Łukasz Sobechowicz, Dariusz Brykała, Ewa Kaznowska, Michał Wasilewicz, Jacek Wolski, Marcin Noras, and Wojciech Aleksander Siwek

Abstract. We present a 225-year (1799–2024) homogenized daily water level series for the Vistula River in Warsaw, comprising 82,453 observations. The construction of this consistent dataset required adjustments for changes in gauge location, shifts in gauge zero, differences in historical measurement units, and calendar discrepancies between the Julian and Gregorian systems. A small number of missing observations were reconstructed using stage–stage relationships established between overlapping periods of observation at the Warsaw gauge and parallel measurements from downstream stations along the Vistula. The resulting dataset offers a robust foundation for long-term hydrological, climatic, and socio-environmental research. The dataset is openly available at Zenodo repository: https://doi.org/10.5281/zenodo.16919654 (Sobechowicz et al., 2025).

Received: 02 Sep 2025 – Discussion started: 04 Dec 2025

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Łukasz Sobechowicz, Dariusz Brykała, Ewa Kaznowska, Michał Wasilewicz, Jacek Wolski, Marcin Noras, and Wojciech Aleksander Siwek

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RC1:
'Comment on essd-2025-538', Anonymous Referee #1, 18 Jan 2026 reply
The authors have done an impressive job of creating a 225-year dataset of daily water levels in Warsaw. Such a long data record is crucial for climate research, hydrological modelling, and flood risk management. They have exhaustively included data from publications and yearbooks (somewhere in different units and different locations) prior to 1981. Nevertheless, there are some serious technical concerns that need to be addressed before publication. They are as follows,
A simple linear regression based on the neighbouring gauges has been used to fill the data. They have also selected specific highs and lows in the data for this purpose. The authors have observed a lag of up to 4 days for the waters to travel to the neighbouring gauges. Apart from this, the authors also use the maximum, minimum, and onset points of the flood data to fill in data that do not represent these conditions. In short, I find the gross assumption of an average time lag and the use of specific points overly simplistic, given the nonlinearity of the hydrodynamics of flows of varying intensity. More evidence of these kinds of assumptions would be useful for the readers to understand the reliability of the filled data. For instance, using cross-correlation between the time series of different gauges to show the time lag. The authors could have used robust techniques like Long Short-Term Memory (LSTM) neural networks for data filling, as they can capture non-linear temporal patterns while selectively retaining information relevant to the current output in long time series. The authors need to present the existing variations in time lag across different gauge locations and discuss the associated uncertainties. Please refer to the work done by Ren et al. (2022) in this regard.

The validation of the proposed linear-regression-based data filling is incomplete. A robust k-fold cross-validation is needed to assess the accuracy of the proposed data-filling methods. The data needs to be split iteratively into training and validation sets. Importantly, the error needs to be reported using RMSE, NSE, KGE, etc.

The authors themselves say that the bed level varied extensively due to gauge relocation and anthropogenic activities. They have taken these into consideration and modified the zero level. They need to comment on the reliability and accuracy of these adjustments.

Even high-resolution remote-sensing-based digital elevation models may not accurately represent riverbed topography. In addition, extensive cross-sectional surveys are needed to simulate the correct water levels that reflect the corresponding conditions. Therefore, the discharge time series is often highly useful to the hydrological community for validating hydraulic/hydrological models or for climate research. Can the authors provide a discharge time series or comment along these lines?

The minor comments are as follows,
Line 68, “km XXX of the Vistula River”, is difficult to understand. Please change similar lines in the manuscript.

Line 127, what do the authors mean by “km 421 + 600”? Please modify similar lines to provide greater clarity to readers.

In Table 4, what does the last column indicate?

Line 290, please use a clearer term than “early measurements”.

Lines 389 -417: the summary need not include the uses of the dataset.

Reference
Ren, H., Cromwell, E., Kravitz, B., and Chen, X.: Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks, Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, 2022.

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Citation: https://doi.org/10.5194/essd-2025-538-RC1

Łukasz Sobechowicz, Dariusz Brykała, Ewa Kaznowska, Michał Wasilewicz, Jacek Wolski, Marcin Noras, and Wojciech Aleksander Siwek

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https://doi.org/10.5194/essd-2025-538-supplement

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Daily Water Levels of the Vistula River at Warsaw, 1799–2024: A Complete and Homogenized Long-Term Record Ł. Sobechowicz et al. https://doi.org/10.5281/zenodo.16919654

Łukasz Sobechowicz, Dariusz Brykała, Ewa Kaznowska, Michał Wasilewicz, Jacek Wolski, Marcin Noras, and Wojciech Aleksander Siwek

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We present a 225-year (1799–2024) homogenized daily water level record for the Vistula River in Warsaw, comprising 82,453 observations. Adjustments were made for gauge relocations, zero shifts, unit differences, and Julian–Gregorian discrepancies. Missing data were reconstructed using stage–stage relationships with downstream stations. The dataset provides a robust basis for long-term hydrological, climatic, and socio-environmental research.


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