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REVIEW 4 major objections 6 minor 33 references

Impact of Storm Surge and Power Peaking on Tidal-Fluvial Dynamics in Microtidal Neretva River Estuary

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A microtidal-specific non-stationary harmonic model reproduces Neretva estuary water levels to within 2–3% residual variance and attributes an upstream S1 tidal signal to hydropower peaking.

desk verdict Sensible extension of NS_Tide with a useful S1 diagnostic, but the validation split is stated inconsistently and the 2–3% out-of-sample claim needs verification before it can be trusted. read the letter →

arxiv 2411.13391 v1 pith:LKZMIA44 submitted 2024-11-20 physics.ao-ph

Nino Krvavica , Marta Marija Gržić , Silvia Innocenti , Pascal Matte This is my paper · ORCID
classification physics.ao-ph
keywords tidaldynamicsmicrotidalestuaryhydropowerpeakingstormsurgetide-surge-riverinteractionnon-stationaryharmonicanalysisNS_TideNeretvaRiver
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Microtidal estuaries are hard to forecast because storm surges can rival or exceed the tides. The paper tries to establish that a modified non-stationary harmonic model, muNS_Tide, which uses a storm-surge time series and quadratic-plus-linear river discharge as predictors, reproduces measured water levels along the Neretva estuary with residual variance around 2–3% at five stations, from the river mouth to the tidal-river limit. It also argues that the unusually strong S1 (24-hour) tidal-constituent signal upstream is not astronomical but is pumped into the river by hydropower peaking, using before-and-after filtering experiments with the STREAM numerical model. If true, the model gives a practical, site-flexible tool for decomposing water levels in microtidal estuaries and for attributing non-tidal variability to dam operations.

What carries the argument

The carrying object is the muNS_Tide non-stationary harmonic model: it splits water level into a subtidal stage term S(t) and a tidal-fluvial term F(t), and lets both depend on user-chosen predictors. In this paper the stage uses $Q$ and $Q^2$ plus storm surge $SS(t)$, while each constituent's cosine and sine coefficients vary linearly with $Q$ and $SS(t)$. The identification of power peaking rests on the STREAM numerical model, a one-dimensional two-layer shallow-water model, whose paired simulations with full versus low-pass-filtered inflow isolate the effect of high-frequency dam operations.

What would settle it

Run the same paired STREAM simulations during a period when upstream hydropower stations switch between peaking and steady operation with equal total daily volume; if the S1 amplitude at Gabela does not drop when the 24-hour discharge pulse is removed, the attribution fails. Equivalently, a second independent hydrodynamic model producing no S1 suppression under filtered inflow would also break the claim.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the original NS_Tide functional forms—built on river discharge to the 2/3 power and on coastal tidal range—fail when tidal range is smaller than storm-surge amplitude, leaving residual variance above 40% at downstream stations. Replacing the tidal-range predictor with a lagged storm-surge series, and using a quadratic discharge law in the stage term (Eq. 8) with linear discharge and surge modulations of each tidal constituent's amplitude and phase (Eq. 9), reduces validation residual variance to 2–3% at every station. The paper then uses the STREAM two-layer model to run paired simulations, one with measured discharge and one with only low-pass-filtered discharge, and finds that the S1 constituent's upstream growth, visible in observed data and in the unfiltered simulation, disappears when high-frequency discharge fluctuations are removed. That is the evidence behind the claim that power peaking amplifies S1 and modulates other constituents such as K2 and S2 in the tidal river.

Load-bearing premise

The power-peaking conclusion rests on the assumption that the numerical experiment that removes rapid discharge fluctuations strips out only the dam-operation signal, and that the model otherwise captures real tide-flow interactions, so the disappearance of the S1 peak can be credited solely to peaking.

Editorial extensions

If this is right

  • The muNS_Tide formulation can be applied to other microtidal estuaries where surge dominates tidal range, with GAM-based checks guiding the choice of predictors.
  • Tide-river interaction becomes larger than tide-surge interaction in the Neretva and peaks at the most upstream station, so predictions there need accurate discharge rather than just sea-level forcing.
  • Constituent-by-constituent decomposition allows significance testing of tide-river and tide-surge interactions; the paper finds tide-surge interaction significant for up to 10 constituents even though its amplitude contribution is small.
  • Removing the tidal-range term from NS_Tide prevents spurious oscillations and overfitting, improving predictability and AIC.
  • The significance tests and flexible predictor definitions make the method usable as a routine diagnostic for whether a diurnal signal in a regulated estuary is astronomical or operational.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: in a regulated microtidal river, stationary harmonic analysis will misclassify dam-induced S1 energy as a real tidal constituent, so discharge filtering or non-stationary predictors should be standard before interpreting diurnal constituents there.
  • Related consequence: the success of a quadratic discharge law in the Neretva suggests that the classical 2/3 discharge exponent should not be assumed in other microtidal estuaries; exploratory GAM-style fits can pick the stage–discharge relationship before fitting the harmonic model.
  • Testable extension: if the S1 attribution holds, hydropower peaking should also raise sub-daily variance of water levels upstream of the salt wedge, potentially affecting salt-wedge intrusion forecasts; this can be checked by correlating peaking schedules with salinity records.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a modification of the non-stationary harmonic analysis tool NS_Tide, called μNS_Tide, for microtidal estuaries. The stage term is expressed with quadratic river discharge and linear storm surge covariates, and the tidal-fluvial term with linear discharge and linear storm surge covariates (Eqs. 8–9). The model is applied to hourly water levels at five stations along the Neretva River estuary (Croatia) covering June 2015–December 2021, with a training/validation split and comparisons against the original NS_Tide, a quadratic-discharge variant (qNS_Tide), and a storm-surge variant (sNS_Tide). The authors report that μNS_Tide achieves about 2–3% residual variance during validation at all stations, outperforming the other formulations, and they use the STREAM numerical model in a two-simulation experiment to attribute the upstream amplification of the S1 tidal constituent to high-frequency discharge fluctuations from hydropower peaking.

Significance. If the validation design is sound, the paper offers a practical extension of NS_Tide that is better suited to microtidal environments, where storm surge rather than tidal range drives subtidal and tidal variability. The flexible user-defined predictor framework and the incorporation of recent uncertainty estimates are useful contributions. The power-peaking attribution for S1 is plausible and consistent with observations in other regulated estuaries. However, the primary quantitative claim—the 2–3% out-of-sample residual variance and the ranking of the four models—is compromised by an inconsistency in the stated training period between Sections 3.1 and 3.3, and by a potential circularity at the Usce station where the storm surge covariate is derived from the same water level series being modeled. These issues must be resolved before the central claims can be accepted.

major comments (4)
  1. [§3.1 and §3.3] The training period is stated as January 2017 to December 2021 in Section 3.1, but Section 3.3 states that the model parameters were determined using the training set January 2016 to December 2021. These two statements are mutually inconsistent. If the Section 3.3 split was actually used, the validation period (June 2015 to December 2016) overlaps with the training period for all of 2016, so the residual variances and model comparisons in Fig. 4 are not out-of-sample measures and are biased toward models that fit the validation data. If the Section 3.1 split was used, then Section 3.3 contains a factual error. Please clarify which split produced Fig. 4 and the other reported statistics, and confirm that all model choices (covariate lags, tidal constituent retention via the SNR criterion, and functional forms) were made using only the training period.
  2. [§3.2] The functional forms of the stage and tidal-fluvial terms are selected on the basis of GAM reconstructions, but the manuscript does not state whether the GAM was fitted to the training period only or to the full record. The supplementary figures (e.g., Fig. A.3 and B.2) show distributions of the full observation period, which suggests the full record may have been used. If the validation period informed the choice of quadratic versus linear forms, the subsequent validation is not independent. The authors should state explicitly that the GAM selection was performed on the training subset only, or rerun the selection on the training data and show that the chosen forms are stable across the two periods.
  3. [§3.2 and §4.5] At the Usce station (0 rkm), the storm surge covariate SS(t) is defined as a low-passed residual of the stationary harmonic analysis of the same observed water level series that the model then reconstructs. Because the stage term in μNS_Tide (Eq. 8) contains a linear term in SS, the model at Usce is partly using a filtered version of the target variable as a predictor, which can artificially inflate the fit and reduce the residual variance below a true out-of-sample value. The reported 2–3% residual variance at Usce is therefore not a genuine predictive metric. The authors should either compute SS from an independent coastal station (e.g., Ploce) and apply it at Usce, or quantify the fit at Usce with SS omitted from the stage term to assess the circularity.
  4. [§5.1] The attribution of the upstream S1 amplification to power peaking relies on the difference between STREAM simulation A (measured discharge) and simulation B (low-pass filtered discharge). This assumes that the low-pass filter removes only the hydropower peaking signal and no other physically relevant discharge variability, and that the STREAM model faithfully represents the tidal-fluvial interactions. The paper does not provide a sensitivity analysis with respect to the filter cutoff (0.03 cph) nor a quantitative validation of simulation A against the observed water levels over the study period (the cited calibration is from an earlier study). Please add such a validation and a discussion of how sensitive the S1 and other constituent differences are to the filtering choice, to strengthen the causal claim.
minor comments (6)
  1. [§4.3] There is a typo in the text: 'esides' should read 'Besides'.
  2. [§4.2] There is a typo: 'emodel' should read 'The model'.
  3. [§3.2] The SNR criterion for retaining tidal constituents is described as 'SNR greater than 2 for at least 50% of the reconstructed time steps,' but it is not stated whether the SNR is computed over the training period only or over the full record. This should be clarified to ensure the constituent selection does not use validation-period information.
  4. [§4.5] The sentence 'we used μNS_Tide, but only the storm surge term, SS(t), was included as a predictor in Eqs. 8 and 9' should specify whether the discharge terms are omitted entirely or set to zero, and how this affects interpretation of the Usce reconstruction.
  5. [Table E.1] The S1 amplitude at Gabela has a mean amplitude of 9.33 cm but an amplitude standard error of 7.32 cm, indicating substantial uncertainty. This should be acknowledged in the discussion of the S1 amplification, as the effect at that station is not tightly constrained.
  6. [Data and code availability] The statement that the NS_Tide code and data are 'available upon request' is weaker than the standard for reproducibility. Consider depositing the code in a permanent repository (e.g., Zenodo) and providing a clear data-access statement.

Circularity Check

1 steps flagged · score 6.0 of 10

Usce validation is partly circular because the storm-surge predictor is a filtered residual of the same water-level series, but the upstream validations and STREAM power-peaking experiment are independent.

  1. self definitional [Section 3.2 (SS definition, Eqs. 6-9); Section 4.5; Fig. 4 validation statistics.]
    "SS(t) is computed as a low-passed residual of the stationary harmonic tidal analysis at a coastal tidal station unaffected by fluvial influences ... we used μNS_Tide, but only the storm surge term, SS(t), was included as a predictor in Eqs. 8 and 9, as the effects of river discharges at the coast are negligible."

    At Usce, the target η_obs(t) is the same record from which SS(t) is built: SS = low-pass(η_obs − stationary tidal fit). The μNS_Tide stage at Usce is S(t)=a0+a3·SS(t), and the tidal-fluvial term uses SS(t) modulations (c_k,2, s_k,2). The subtidal variance of the observed series is therefore regressed on a filtered copy of itself, so the ~2-3% residual variance and the model ranking reported for Usce in Fig. 4 are not an out-of-sample predictive test; the 'storm surge' contribution at the coast is the low-frequency part of the reconstructed signal by construction. Upstream stations are not affected because SS is then an external coastal predictor.

full rationale

The only reduction-by-construction I can exhibit is at the Usce station, where the SS covariate is derived from the very water-level series the model reconstructs; this makes the coastal validation and the comparison of SS-based models vs. NS_Tide at that station partly tautological. At Opuzen, Norin, Metkovic, and Gabela, SS is an external coastal record and Q is measured upstream, so the μNS_Tide regression is a genuine out-of-sample fit and the headline 2-3% residual variance there has independent content. The STREAM-based S1 attribution is a controlled numerical experiment with a model calibrated in prior work (Krvavica et al., 2021), not a fitted parameter renamed as a prediction; its credibility rests on model fidelity, not on circularity. Separately, the paper contains a factual inconsistency in the training split: §3.1 states training is January 2017-December 2021 while §3.3 states January 2016-December 2021, so the validation period of June 2015-December 2016 may overlap with training; and the GAM-based choice of linear/quadratic forms is not explicitly restricted to the training period. These are correctness/leakage risks rather than definitional circularity, but they reinforce that the validation statistics should be treated cautiously. Overall the central claim is not wholly circular, but one of the five stations' headline validation numbers is partially forced by construction.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities, fields, or forces. Its central claims depend on free regression parameters, data-driven model-form selection, and domain assumptions about the representativeness of the storm surge series and the fidelity of the STREAM numerical model. The S1 constituent is a known radiational/solar harmonic, not an invented entity, although the paper reinterprets its upstream amplification as a power-peaking signature.

free parameters (5)
  • Stage regression coefficients a0, a1, a2, a3 (muNS_Tide, Eq. 8) = not reported; fitted per station by IRLS
    These coefficients determine the stage reconstruction, which explains 55-95% of the variance depending on station.
  • Tidal-fluvial regression coefficients c_k,0..2 and s_k,0..2 (Eq. 9) = not reported; implied by reported amplitudes and phases
    These coefficients control the tidal constituents and their modulation by discharge and storm surge, fitted for each retained constituent.
  • Covariate lags tau_Q and tau_SS = not reported
    Lags are chosen by maximizing cross-correlation with observed water levels, a data-fitting step that affects all predictions.
  • Retained tidal constituent set and low-pass cutoff (0.03 cph) = 45 to 54 constituents depending on station; cutoff 0.03 cph
    The cutoff is chosen by hand and constituents are retained using an SNR greater than 2 for at least 50% of time steps, a selection rule that shapes the model.
  • GAM-selected functional forms (quadratic Q, linear SS) = quadratic and linear forms chosen from GAM smooths
    The model structure is selected from the data at hand before fitting, which is a form of parameterization informed by the target time series.
assumptions (5)
  • domain assumption In microtidal estuaries, storm surge amplitude dominates tidal range, so replacing the coastal tidal range R with the storm surge series SS is more suitable.
    This hypothesis is stated in Section 3.2 and motivates sNS_Tide and muNS_Tide; it is not proven for all stations and may fail where tidal range remains relevant.
  • domain assumption The storm surge series SS, computed as a low-passed residual of stationary harmonic analysis at the coastal Usce station, is a valid exogenous predictor at all river stations with a single lag.
    Equations 6-9 apply the same Usce-derived SS to interior stations; at Usce itself this is partly circular, and other low-frequency coastal processes could contaminate SS.
  • ad hoc to paper The GAM-based selections of quadratic discharge and linear storm surge forms describe the true physical response and transfer to the validation period.
    Sections 3.2 and Figures B.1-B.2 show that model forms are chosen from the data rather than derived from theory, so the validation may be optimistic if the GAM used the validation period.
  • domain assumption The one-dimensional two-layer STREAM model faithfully simulates water levels in the Neretva, so the difference between simulation A (measured Q) and simulation B (low-pass Q) isolates the effect of power peaking.
    Section 5.1 relies on the numerical experiment; the calibration is not shown in this paper but is attributed to Krvavica et al. 2021.
  • standard math Classical harmonic analysis, IRLS, and Butterworth low-pass filtering are valid standard treatments for estimating tidal parameters and their uncertainty.
    These are standard methods from the cited literature, applied in Sections 3.2 and 3.3 without new mathematical claims.

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Cite this review

Pith. "Pith review of Impact of Storm Surge and Power Peaking on Tidal-Fluvial Dynamics in Microtidal Neretva River Estuary." pith.science (2026). https://pith.science/paper/LKZMIA44

@misc{pith2026241113391,
  author       = {Pith},
  title        = {Pith review of: Impact of Storm Surge and Power Peaking on Tidal-Fluvial Dynamics in Microtidal Neretva River Estuary},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LKZMIA44}},
  note         = {Machine review of arXiv:2411.13391}
}
abstract

This study investigates the interactions between tides, storm surge, river flow, and power peaking in the microtidal Neretva River estuary, Croatia. Based on the existing NS_Tide tool, the study proposes a new non-stationary harmonic model adapted for microtidal conditions, which incorporates linear storm surge, as well as linear and quadratic river discharge terms. This model enhances the NS_Tide's ability to accurately predict water levels from tide-dominated sections downstream to discharge-dominated areas upstream. River discharge was identified as the dominant factor for predicting stage levels at most stations, while the influence of storm surge, though consistent, decreased upstream. Strong tide-river interactions were observed throughout the study domain, with the stationary tidal component consistently contributing to water level fluctuations at all locations, and minimal influence from the tide-surge interaction component. Simulations using the STREAM numerical model were also used to isolate the variability in water levels caused by power peaking. These simulations demonstrated that high-frequency discharge fluctuations due to hydropower plant operations amplify the $S_1$ constituent in upstream river sections and modulate the amplitudes of other tidal constituents in the estuarine and tidal river sections. The proposed method proved highly effective in the microtidal context of the Neretva River and shows potential for adaptation to mesotidal and macrotidal systems.

Figures

Figures reproduced from arXiv: 2411.13391 by the authors.

Figure 1
Figure 1. The Neretva River estuary map with tidal, water level and discharge stations and their distance from the river mouth (rkm) (a) and longitudinal channel bottom profile (b). extends to about 33 kilometers upstream, where the influence of tides gradually decreases. Beyond this point, the river transitions into a typical riverine regime predominantly governed by fluvial processes. Due to the microtidal environment and w… view at source ↗
Figure 2
Figure 2. Water level and discharge time series at different stations measured in the Neretva River (2015-2021). average from the hourly time series to estimate short-term fluctuations. During low flow, uniform high-frequency patterns in residual water levels are observed at all stations. In contrast, high flow, especially in winter, changes both the water level and the residual amplitude at the upstream stations, where the r… view at source ↗
Figure 3
Figure 3. Power spectrum of water levels at different stations with details of the power spectrum for the main diurnal and semi-diurnal tidal harmonics. downstream to upstream stations. The 𝑆1 also shows a gradual increase in power upstream, with a particularly strong peak at Gabela. We argue that this amplification of the 𝑆1 constituent is more likely due to variations in upstream discharge (due to power peaking) rather than… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Statistics on water level reconstructions using the four definitions of non-stationary model for the validation period (June 2015 to December 2016): residual variance, RMSE, AIC, and number of selected tidal constituents. Station locations: Usce (0 rkm), Opuzen (11.9 r…
Figure 5
Figure 5. Figure 5: Reconstruction of the water levels using 𝜇NS_Tide with scatter plots at different stations in the Neretva River for the period June 2015 to December 2021. Krvavica et al.: Preprint submitted to Elsevier Page 16 of 28 [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: Relative and total contribution of partial reconstructions by 𝜇NS_TIDE model: a) Proportion of variance explained by the stage, tidal-fluvial, and residual terms at different stations, and b) total water level contributions of river 𝑆𝑄, storm surge 𝑆𝑆𝑆, tides 𝐹𝑇 , tide…
Figure 7
Figure 7. Figure 7: presents the amplitudes and phases for the main diurnal (𝐾1 , 𝑃1 , 𝑂1 , 𝑆1 ) and semi-diurnal (𝑀2 , 𝑆2 , 𝐾2 , 𝑁2 ) tidal constituents as a function of the distance from the river mouth. Each figure compares the mean tidal parameters for three characteristic regimes: av…
Figure 8
Figure 8. Figure 8: Changes across the stations of the 𝜇NS_Tide amplitudes for main diurnal and semi-diurnal constituents, during average flow conditions, high-flow conditions (river discharge above 75th-percentile), and low-flow conditions (river discharge below 25th-percentile) obtained…

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Reviewed August 12, 2026 · model on record in the stance chip above.