DynQual v1.0: A high-resolution global surface water quality model

Jones, Edward R.; Bierkens, Marc F. P.; Wanders, Niko; Sutanudjaja, Edwin H.; van Beek, Ludovicus P. H.; van Vliet, Michelle T. H.

doi:https://doi.org/10.5194/gmd-2022-222

Preprints

https://doi.org/10.5194/gmd-2022-222

Preprints

Submitted as: model description paper

16 Dec 2022

Submitted as: model description paper | 16 Dec 2022

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

DynQual v1.0: A high-resolution global surface water quality model

Edward R. Jones¹, Marc F. P. Bierkens^1,2, Niko Wanders¹, Edwin H. Sutanudjaja¹, Ludovicus P. H. van Beek¹, and Michelle T. H. van Vliet¹ Edward R. Jones et al.

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¹Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
²Deltares, Unit Soil and Groundwater Systems, Utrecht, The Netherlands

Received: 11 Oct 2022 – Discussion started: 16 Dec 2022

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.

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Edward R. Jones et al.

Status: open (until 26 Feb 2023)

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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.
(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.
(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.
(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 peaks

Reply

Citation: 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).
  
  Reply
  
  Citation: https://doi.org/10.5194/gmd-2022-222-AC1

Edward R. Jones et al.

Supplement

https://doi.org/10.5194/gmd-2022-222-supplement

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.

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Short summary

DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.


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