the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Jan 2025
Evaluating the performance of CE-QUAL-W2 version 4.5 sediment diagenesis model
Abstract. This study set out to assess the performance of the state-of-the-art CE-QUAL-W2 v4.5 sediment diagenesis model. The model was applied to a reservoir in Portugal using observed sediment particulate organic carbon values corresponding to a six-year period (2016–2021). The model was calibrated by comparing its results with 35 observed dissolved oxygen and water temperature profiles, as well as annual total nitrogen, total phosphorus, biochemical oxygen demand, and chlorophyll-a measurements corresponding to three different depths. In addition to model calibration, a sensitivity analysis was also conducted by varying the input particulate organic carbon values and applying a user-specified sediment oxygen model (zero-order model). The results demonstrated the overall effectiveness of the sediment diagenesis model, which accurately simulated dissolved oxygen profiles, nutrient concentrations, and organic matter levels (Dissolved oxygen profiles: NSE = 0.41 ± 0.67; RMSE = 1.73 mg/L ± 0.69), highlighting its potential as an effective tool for simulating lakes and reservoirs and supporting water management processes. The study further suggests that the zero-order model is able to serve as an effective starting point for implementing the sediment diagenesis model, providing an initial estimate for mean reservoir sediment oxygen demand (SOD) values.
- Preprint
(2004 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on gmd-2024-202', Anonymous Referee #1, 27 May 2025
The authors have written an interesting, well-developed methods paper where they compare CE-QUAL-W2's zero-order sediment model against the full sediment diagenesis (SD) model introduced in V4 of the CE-QUAL-W2 water-quality model. I believe many water-quality modellers using CE-QUAL-W2 are reluctant to try the new SD model due to the sheer number of coefficients in the compartment, so it is interesting that the authors were able to model their waterbody mostly using the default parameters of the diagenesis model. While the authors primarily discussed the results for DO, there does seem to be value in collecting a few sediment samples where possible, based on the better results for TP, TN and (potentially) Chl-a with the SD model.
The introduction was good and provided sufficient context for why the authors thought the work was of interest to the water-quality modelling community.
The methods were sufficient although the information regarding the configuration and calibration of the main water quality model could be more in-depth (e.g., appendix table of most the important coefficients) rather than leaving the reader to have to search through the CE-QUAL-W2 user manual. I found one or two sections needed rereading several times to fully understand the objectives of the study and the model setup. Section 2.2 combines the model configuration (e.g., bathymetry, algal groups), a summary of the following method section, and a summary of the overall modelling approach, and I believe this could be better structured by separating the model set-up. Note that machine learning is not my area of expertise and so I am unable to comment on the derivation of the forcing datasets for water-quality.
The results were well presented visually and with plenty of discussion provided by the authors. However, I was unable to follow what was being discussed and shown regarding TOC and POC in Section 3.3 (lines 310 to 325). It was not clear to me if the black line and circles show TOC or POC as the legend (TOC) and y-axis/caption (POC) are for different variables, nor could I follow how it was concluded that the particulate fraction of organic carbon constituted 40% of the TOC. Lines 310 to 320 and Figure 4 should be clarified.
Furthermore, while this paper is of interest for those of us using the CE-QUAL-W2 model, and could be cross-transferred to other waterbodies using the CE-QUAL-W2 model, the authors did not attempt to place their findings in the context of the broader water-quality modelling science, and how this work may contribute. I think this should be added to the discussion to strengthen this submission.
Finally, there were numerous editorial errors throughout the manuscript that need addressing; a few examples below, although there are more:
1) Discrepancies in the citations and the bibliography. Examples include:
Line 54: Should be just ‘Zoubabi-Aloui’
Line 73: I believe this should be ‘Wells 2021’
Line 139: ‘Adelena et al. 2015’, does not appear in the bibliography
Line: 142: Should be ‘Berger and Wells 2014’
Etc.
2) Also seems to be some discrepancies in the Section number cross-refs (for example Lines 106 and 109, refer to Section 1.2.3 and 1.2.4, respectively, with other instances throughout the document).
3) Line 285 .. for DO, “…the W2_zero-order model performed slightly better according to all metrics, with the exception of PBIAS”. I am wondering if the authors mean R2 (which is marginally worse than the SD model)? Perhaps it is me that is mistaken, but for PBIAS it seems the zero-order model performs better for DO than the SD model, with the assumption the goal is a low-bias model. This should be clarified.
4) Line 312: It should read Fig4b after NSE.
Citation: https://doi.org/10.5194/gmd-2024-202-RC1 -
RC2: 'Comment on gmd-2024-202', Anonymous Referee #2, 28 May 2025
Comments on Evaluating the performance of CE-QUAL-W2 version 4.5 sediment diagenesis model Manuel Almeida, Pedro Coelho
Overall, this is a useful evaluation of the sediment diagenesis model in CE-QUAL-W2 model. The next logical step would be to compare first order and zero order model with sediment diagenesis. The MAE for temperature simulations seems high compared to other systems and this can drastically affect dissolved oxygen profiles. This may be the result of inflow temperatures as well as outflow dynamics. It would be useful to work on improving temperature predictions (if there is a path forward) and to see how that affects the results in this study. The dissolved oxygen profiles are very complex in this reservoir and often the model reproduced the correct shape of the profiles. There were a few comments on the text which are summarized below:
Line 42-43: “if the SOD is not accurately computed the waterbody phosphorous balance will, in turn, be incorrect.” This expression needs further explanation. If the zero order SOD model is used, then the anoxic release of PO4 is a linear function of the SOD in the CE-QUAL-W2 model, in other words SOD[g O2/m2/day]*PO4release rate [g P/g O2]. If one uses a predictive model, like sediment diagenesis, then the SOD and P release from the sediments will be a function of the organic and nutrient loading of particulate matter from the water column.
Line 48: “In other words, the modeling uncertainty may diminish but will persist without observed POC, PON and POP” – it is unclear, is this a discussion about water column POC, PON and POP or sediment POC, PON, and POP?
Line 73: “dissolved oxygen uptake rates in the water column (Wells, 2011).” – reference to Wells, 2011 not found in references
Line 114-115: “This is not, however, a predictive approach, as, other than variations resulting from the temperature dependence of the decay rate, the rates remain constant over time (Wells, 2021).” – Note that also when there is anoxia in the water column SOD is turned OFF.
Line 142: “model has been elaborated in works by Prakash et al. (2014), Berg and Wells (2014), and Vandenberg et al. (2015)” – change ‘Berg’ to ‘Berger’. Also, the V4.5 model had many enhancements to the sediment diagenesis model as outlined in the User Manual. The initial V4 model is much different and limited compared to the V4.5 model.
Line 157-158: “The meteorological data used to drive the model, including hourly air temperature, dew point, solar radiation, cloud cover, and wind characteristics, were sourced from ERA5-Land…” – Was there an effort to ground-truth this ERA5-Land dataset with on-site meteorological measurements in the area as a check?
Line 235: “six state variables was evaluated with five different metrics (vide section 1.2.6).” – note sure what ‘(vide section’ means – typo?
Line 245: “two parameters retained their default values shown in Table 1.” – I think Table 1 is an incorrect table reference.
Line 305: Figure 3 is very hard to see data and model. Figure needs to be broken up or redone to allow others to see model vs data clearly.
Line 391: “W2_zero-order model (2.50 gO₂/m²/day) was significantly higher than the mean SOD computed with the best W2_SD model (Run 2) (0.810 gO₂/m²/day).” – This is not a correct comparison since the Zero order SOD was at 20oC (or at its maximum) and the SD model result is actual SOD at the temperature at the bottom of each segment. Looking at the temperature near the bottom in Fig 3 a year-round average is probably around 10-12oC – hence much lower year-round than the 20oC maximum rate.
Line 392: “This can be explained by the fact that the W2_zero-order model SOD represents all of the reservoir’s DO uptake rate in the water column and not just the sediment uptake.” – See comment above – it is related to the temperature. The zero order model only is for sediment demand, not water column demand.
Line 400: “The zero-order model employs a constant SOD value that only varies with water temperature and does not account for organic matter decay or its impact on SOD values.” – Why did you not use the zero order model with the first order model as reported in your introduction?
Citation: https://doi.org/10.5194/gmd-2024-202-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 128 | 51 | 17 | 196 | 13 | 20 |
- HTML: 128
- PDF: 51
- XML: 17
- Total: 196
- BibTeX: 13
- EndNote: 20
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1