the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DynQual v1.0: A high-resolution global surface water quality model
Marc F. P. Bierkens
Niko Wanders
Edwin H. Sutanudjaja
Ludovicus P. H. Beek
Michelle T. H. Vliet
Abstract. Maintaining good surface water quality is crucial to protect ecosystem health and for safeguarding human water use activities. Yet, our quantitative understanding of surface water quality is mostly predicated upon observations at monitoring stations that are highly limited in space and fragmented across time. Physically-based models, based upon pollutant emissions and subsequent routing through the hydrological network, provide opportunities to overcome these shortcomings. To this end, we have developed the dynamical surface water quality model (DynQual) for simulating water temperature (Tw) and concentrations of total dissolved solids (TDS), biological oxygen demand (BOD) and fecal coliform (FC) with a daily timestep and at 5 arc-minute (~10 km) spatial resolution. Here, we describe the main components of this new global surface water quality model and evaluate model performance against in-situ water quality observations. Furthermore, we describe both the spatial patterns and temporal trends in TDS, BOD and FC concentrations for the period 1980–2019, also attributing the dominant contributing sectors. The model code is available open-source (https://github.com/UU-Hydro/DYNQUAL) and we provide global datasets of simulated hydrology, Tw, TDS, BOD and FC at 5 arc-minute resolution with a monthly timestep (https://doi.org/10.5281/zenodo.7139222). This data has potential to inform assessments in a broad range of fields, including ecological, human health and water scarcity studies.
- Preprint
(12577 KB) -
Supplement
(3139 KB) - BibTeX
- EndNote
Edward R. Jones et al.
Status: open (until 05 Apr 2023)
-
CC1: 'Comment on gmd-2022-222', Jason Ke, 28 Dec 2022
reply
This manuscript developed a global water quality model DynQual V1.0 and interpreted its results for TDS, BOD, and FC. Overall this manuscript is well-written with good-quality figures. Model results regarding the spatial patterns of concentration and temporal trends by region and economic development are interesting. However, there are some concerns about the model evaluation. Â
(1) there seems no description of model calibration. How was the calibration done for the global water quality model? Is it a simultaneous calibration for both hydrology (discharge) and water quality (Tw, TDS, BOD, FC), or a two-step calibration strategy with discharge calibrated first followed by water quality calibration? Since the author mentioned that discharge was very important for model results (Supplement, Line 295), I would assume the discharge has to be well-calibrated before modeling water quality.
(2) The model evaluation that is very important to the model development paper seems underdeveloped. It is essential to evaluate the model performance before the model result interpretation. For example, it is ideal to evaluate model performance whenever data are available. For example, there are 27,238 stations with TDS data in the Supplement. Perhaps the author could do the following evaluation regarding 1) spatial pattern of mean concentration (e.g., model mean vs. data mean from the station with high data availability); 2) temporal dynamics regarding seasonal fluctuations and long-term trends (e.g., Fig 11, add data points to the temporal trend plots to evaluate if the model could reproduce the long-term trends)
(3) what is a good nRMSE value? It would be beneficiary to add the Nash–Sutcliffe model efficiency coefficient (NSE) which is a widely used dimensionless metric in hydrology and water quality literature.Â
(4) this manuscript in general lack literature discussion or comparison in terms of model performance (e.g., Figure 3), for example, what is other water quality model performance in terms of nRMSE? There might be few global scale water quality models. But I guess it could be useful to add a few comparisons with other watershed-scale water quality models.Â
(5) Line 200, can the decay coefficient be specified by the user?
(6) Line 220, is it a constant background concentration or a time-varying background concentration through each timestep?
(7) what was the computational time to run for 1-year simulation?
(8) Supplement Line 295, does it mean reaction is underestimated compared to discharge (dilution)?
(9) Supplement Line 300, what is high data availability, and how many data points during 1980-2019?
(10) Supplement Line 305, Figure S3 (b, c) what are the nRMSE and NSE values for these two rivers? It seems that the model overestimated a lot for peaksCitation: https://doi.org/10.5194/gmd-2022-222-CC1 -
AC1: 'Reply on CC1', Edward R. Jones, 26 Jan 2023
reply
Many thanks for your detailed review of our manuscript – we appreciate the time and effort you have put in to critically evaluating our work. We are pleased that you find the results from this study to be of interest, and that you consider the manuscript to be well-written and the figures to be of good quality.
Your comments and concerns are important ones, many of which are interconnected. Please see attach a PDF of our detailed point-by-point response, where we also indicate changes/alterations that we intend to make in the manuscript (in purple).
-
AC1: 'Reply on CC1', Edward R. Jones, 26 Jan 2023
reply
-
RC1: 'Comment on gmd-2022-222', Anonymous Referee #1, 14 Mar 2023
reply
The manuscript provides a new global model for water quality assessment (DynQual). The model runs at a daily time step for the grid cell of approximately 10km by 10 km at the equator. The authors made a major effort to develop such a comprehensive dynamic model on a global scale. One of the model's strengths (compared to other existing large-scale water quality models) is the sector contributions (e.g., livestock, irrigation, manufacturing) to surface water pollution per grid and day while considering dynamics in pollutant routing through the river network. The model is process-based and largely uncalibrated. It makes the model more flexible to represent the processes based on characteristics (e.g., livestock number, people, runoff) and less dependent on observations (which are often scarce). In this way, there is an opportunity to apply the model for the future while considering the future characteristics of the areas. Validating the global model is not easy. The authors did a great job here and managed to validate in a good way. The manuscript is well-written, with beautiful maps and graphs. It is easy to follow the description of the model and results.
I suggest a minor revision. Below, I provide suggestions that can be helpful to improve the manuscript:
- Abstract: It is a nice abstract, but does not provide any insights that we learn from the model application. Please add the main messages (2-3 sentences) that reflect the two main objectives of the model application:
- Pattern and trends
- Sector contributions
- Methods: They are described rather concisely. Details are provided in the supplementary materials, which is nice. Nevertheless, I have four suggestions to elaborate on:
- Figure 1: please add the legend. The description of what colors and different arrows (dashed and solid) mean is not clear;
- Pollutant loadings to streams: this is well described in the supplementary information, but very concisely described in the main methods. Please add a few more sentences to tell the reader how pollutant loadings are calculated (e.g., the summary of the description from the SI). You can elaborate on the text where you mention the mass-balance approach. Here, you can briefly tell that pollutant loadings from livestock activities are simulated as a function of livestock number, excretion rates of pollutants per animal and day, and removal of pollutants during waste management practices while considering runoff from land to streams (see SI Section 1.4 for details). A similar description can be given for other sectors.
- Sector contribution: you do mention sectors in the methods, but briefly. Please elaborate more on what sectors exactly include and how. For example, the irrigation sector: what does it include? Which crops? rainfed irrigation? Livestock sector: which animals? I do know that some details are in the SI. But I do feel a need to give a bit more description of the main methods of the manuscript. Â
- Downscaling: for example, some of the input data (e.g., livestock numbers) is regional, but DynQual requires the grid cell data. How did the authors go from regional to the grid cell, but also from annual to daily levels? Which model inputs require scaling (e.g., annual->daily; regional -> grid cell)? and which did not. This is not well elaborated. I suggest adding a few sentences on this in the main methods and giving more details in the SI. I suggest adding an overview table showing the list of model inputs and indicating which ones were aggregated from region to grid and from annual to daily.
- Discussion: It is very concise and to the point. Some aspects can be expanded and a few aspects can be added:
- Comparison with other studies: the authors do this for the pollutants that they consider. I also think that the manuscript will benefit if the authors add comparisons in terms of modeling approaches, pollution hotspots, and sector contributions (what new aspects are added in this DynQual model and what new aspects we learn from the model application compared to other models). The authors may consider expanding the discussion (a few sentences) on comparing their pollution hotspots not only for TDS, BOD, and FC but also for other pollutants as well because pollution hotpots often match between pollutants.
- Implications of the limitations: any models have limitations. DynQual has as well Examples are livestock numbers in extensive and intensive production systems that do not vary among days, excretions rates of pollutants in manure, and human waste that are constant across the days and within the regions. I understand that sources such as open defecation, and direct discharges of manure to rivers are not considered. It is fine, but this needs to be discussed. It is important to give examples of the main limitations and reflect critically on their implications on the main conclusions of the manuscript.
- The usefulness of the model: DynQual has many useful applications (e.g., trends, patterns, future analyses, etc). The authors briefly mention this in paragraph 545. I think the authors can better emphasize how useful their model is compared to other models. For example: which scientific questions we can answer with this model that we could not answer with the previous models? The authors could add a few sentences on this in paragraph 545.
- Supplementary information: It is well written. I have three suggestions:
- Units: they are missed in some equations. For example, units are not included for the following variables: Rdom,i,n and Popn (population per km2? Total population?) in equation [1], Lman,i,n and Rman,i,n in equation [2], Lurb,i,n and RUSR,i,n in equation [3]. Please also check the variables in equations [4] and [5] and add units for every variable. This will avoid misinterpretation.
- Livestock activities: is the number of livestock the same per day? Is this number per km2 or ha? Did you consider soil processes and associated retentions of the pollutant in soil when you calculate loadings into the streams? All this was not very clear to me. Please clarify the description of equation 4.
- Scaling: please elaborate on which input data required to be scaled from region to grid and from annual to daily, and how this was done.
Citation: https://doi.org/10.5194/gmd-2022-222-RC1 - Abstract: It is a nice abstract, but does not provide any insights that we learn from the model application. Please add the main messages (2-3 sentences) that reflect the two main objectives of the model application:
Edward R. Jones et al.
Data sets
Global monthly hydrology and water quality datasets, derived from the dynamical surface water quality model (DynQual) at 10 km spatial resolution Jones, Edward R.; Bierkens, Marc F. P.; Wanders, Niko; Sutanudjaja, Edwin H.; van Beek, Ludovicus P. H.; van Vliet, Michelle T. H. https://doi.org/10.5281/zenodo.7139222
Model code and software
DynQual Model https://github.com/UU-Hydro/DYNQUAL https://github.com/UU-Hydro/DYNQUAL
Edward R. Jones et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 730 | 177 | 14 | 921 | 49 | 5 | 7 |
- HTML: 730
- PDF: 177
- XML: 14
- Total: 921
- Supplement: 49
- BibTeX: 5
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1