the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CoSWAT-WQ v1.0: a high-resolution community global SWAT+ water quality model
Abstract. Quantifying the global extent of anthropogenic impacts on freshwater quality remains challenging due to limited monitoring data, especially in low and middle-income regions. To address this gap and improve our understanding of surface water quality, we introduce CoSWAT-WQ, a large-scale water quality model developed to simulate river water quality constituents of Total Nitrogen (TN) and Total Phosphorus (TP), across global and regional freshwater systems. CoSWAT-WQ, an adaptation of the Soil and Water Assessment Tool (SWAT), is run at a global scale, providing high-resolution simulations that capture spatial and daily temporal dynamics of riverine nutrient loads. Here, we describe the model's inputs, setup structure, processes, and evaluate its performance by comparing model outputs to in-situ water quality observations and other global nutrient models. CoSWAT-WQ achieves comparable ranges of river nutrient loads in comparison to other global nutrient models. Additionally, a normalized root mean square error (nRMSE) < 1 was achieved with in-situ observations at more than 80 % of the gauging stations for TN and TP concentrations. However, there was a general weak underestimation of observed concentrations and variability as seen with low Kling–Gupta efficiency (KGE) values for selected stations. Despite its limitations, the model enables the simulation of river TN and TP constituents at a global scale while keeping local relevance. CoSWAT-WQ’s modular setup allows coupling with sectoral models addressing lake systems, agricultural runoff, and aquatic biodiversity, thereby broadening its applicability for cross-sectoral assessments. The model outputs offer valuable data that can inform ecological risk assessments, human health evaluations, and policy decisions on global freshwater quality management.
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CC1: 'How well can we predict water quality at the global scale?', Tobias Krueger, 05 Mar 2025
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Dear authors,
I would like to respectfully raise a number of concerns regarding our ability – as a hydrological modelling community – to predict water quality at the global scale. Some are generic; some are more specific to this manuscript.
Representing the relevant processes at the global scale and having data to parameterise them
Land management is known to be very important for modulating pollution transfers. But how to even define those sets of rules (decision tables) for scheduling management at the 2x2km grid scale or HRU scale (page 5)? How do decisions made at the farm scale or smallholder scale aggregate? How to represent heterogeneity?
What information exists to estimate tile drainage globally (figure 2)?
How do the uncertainties in global data (which are often generated themselves from models), e.g. on point sources, plant and harvest dates, and fertilizer and manure use rates, affect model predictions (page 6)?
Choosing an appropriate tool for what we want to predict
When it is said on page 13 that the primary objective of global water quality models is not to predict exact daily concentrations, then maybe a model that aims to represent processes at that scale is not appropriate. Monthly aggregated data are not predicted well by the daily model either, see below.
When the aim is instead to identify spatial and temporal pollution hotspots within global river networks (page 13), there may be other data-based methods that are better suited for this purpose.
Model calibration
The model in this study is said to be uncalibrated (page 6). How were parameter values determined instead?
The lack of sufficient observational data, strong spatial biases in available observations and uncertainties in input datasets (page 7) are put forward as obstacles to model calibration. What do these obstacles say about our ability to run a model at all?
It is argued that the model is theoretical applicable in regions with no calibration data without a significant loss in performance (page 7). However, the hydrological modelling literature has repeatedly shown that even physically well-founded models require calibration to make them work in particular places. Among other things, this is because the processes that are represented are “effective” processes at the scale of application, either because physical theory is moved between scales or scale-bound abstractions are used in the first place (the MUSLE is an obvious example in this study).
When it is said that uncalibrated physically-based models are often preferred for global change assessment on page 7, I am unclear whether this is because they are uncalibrated or because they are physically-based, and why exactly either of these features makes them preferrable. Keeping in mind the limits of physically-based models discussed above.
Model intercomparison
If all models in an intercomparison are affected by the aforementioned shortcomings in process representation and data, can a model intercomparison really enhance confidence in large-scale water quality models as argued on page 7?
In this study, the log-scaling of the predictions in figure 5 make the discrepancies between the two models appear smaller than they are on the original scale, which are often an order of magnitude. When recalculating the R2 statistic on the back-transformed data, R2 decreases from 0.71 to about 0.41 for TP, for example. I am unclear why the same data appear twice with two different symbols in the graph.
I suggest that a model intercomparison would benefit from a deeper exploration of the mechanics of the individual models. What might explain the differences or similarities in predictions?
Model comparison with observations
The model does not fit the monthly data well (as seen by the KGE values and figure 8). Data uncertainties are mobilised to explain mismatches but not instances of better fits (page 10). However, if the data are uncertain then the better fits might equally well be due to chance.
The recognition on page 13 that a comparison with observations and other global nutrient models can identify which parts of the model can be improved but that it is difficult to determine specific areas for improvement to me points to a promising research programme.
The recognition on page 13 that hydrology plays a particularly important role in model results whereas the discussion focuses on nutrient-related processes points into the same direction. The hydrological model setup is in a parallel discussion at EGUsphere.
I agree with the assessment on page 14 that model validation and calibration to improve accuracy and reliability are needed.
I hope these comments are helpful for improving global water quality assessments.
Kind regards,
Tobias Krueger
Citation: https://doi.org/10.5194/egusphere-2025-703-CC1
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