AFAR-WQS: A Quick and Simple Toolbox for Water Quality Simulation

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The topic of manuscript is important and interesting. Therefore, the importance of study should be given in briefly in the Abtract section. More results should be included in the Abstract section.

The literature review was given in the Introduction section. Please add the gap analysis. what is the gap in the literature? An explanation should be added. The novelty of study is not clear. Please explain the novelty of study. Model development section is well designed. How will readers use this model in different basins? Is cooperation with stakeholders required within the scope of water management in the basin? These issues should be explained clearly.

 

Author Response

Comment 1: The importance of study should be given in briefly in the Abstract section. More results should be included in the Abstract section.

Response 1: Thank you very much for your valuable comment. We agree with your observation and have revised the Abstract to better highlight the importance of our research and include additional key results. Specifically, we have:

1. Added a statement emphasizing the practical relevance of AFAR-WQS in addressing the challenge of "paralysis by analysis" in water quality modeling, especially in decision making processes.
2. Included specific computational performance metrics (e.g., 30,000 segments simulated in 163 seconds) to demonstrate the efficiency of the toolbox.
3. Expanded on the real-world applications of AFAR-WQS, such as its use in prioritizing sanitation investments and modeling water quality determinants transport at a national scale.

These changes can be found in the Abstract section (Page 1, Lines 8–22) of the revised manuscript. The updated text now reads:
"Water quality management in large basins demands tools that balance scientific rigor with computational efficiency to avoid 'paralysis by analysis.' [...] AFAR-WQS resolves cumulative processes in networks of up to 30,000 segments in just 163 seconds on standard hardware, enabling real-time scenario evaluations. [...] Case studies demonstrate its utility in prioritizing sanitation investments, assessing water quality at national scale, and fostering stakeholder collaboration through participatory workshops."

We believe these revisions provide a clearer and more comprehensive overview of the study’s significance and outcomes. Thank you again for your insightful feedback, which has greatly improved the manuscript.

Comment 2: The literature review was given in the Introduction section. Please add the gap analysis. what is the gap in the literature? An explanation should be added. The novelty of study is not clear. Please explain the novelty of study

Response 2: Thank you very much for your valuable comment. We appreciate your observation and have expanded the Introduction section to explicitly address the gap in the literature that AFAR-WQS aims to fill. Specifically, we have:

1. Highlighted the computational limitations of existing water quality models, particularly their prolonged execution times when applied to large-scale basins, as noted in studies such as [13, 14].
2. Emphasized the need for rapid, scalable tools that can support decision-making and planning processes, especially in contexts where timely responses are critical (e.g., regulatory compliance, climate change adaptation). As highlighted by [11], there is a pressing need for tools that execute quickly to facilitate real-time decision-making processes.
3. Introduced the novelty of AFAR-WQS as a solution to this gap, leveraging graph theory and a Depth-First Search (DFS) algorithm to achieve computational efficiency without sacrificing scientific rigor. Furthermore, as discussed in [13], fast-executing models at large scales enable uncertainty and risk analyses, which are essential for robust decision-making in water resource management.

These changes can be found in the Introduction section (Page 2, Lines 52–68 and 86–96) of the revised manuscript. The updated text now reads:
"However, many of the aforementioned models exhibit significant computational complexity and extensive data requirements, which pose challenges for their implementation and calibration in large basins [12]. In most cases, prolonged execution times represent a major constraint, particularly in contexts requiring rapid decision-making and exploration of multiple scenarios [13,14]. As highlighted by [11], there is a pressing need for tools that execute quickly to facilitate real-time decision-making processes. Moreover, [13] underscores that fast-executing models at large scales enable uncertainty and risk analyses, which are essential for robust decision-making in water resource management. [...] AFAR-WQS directly addresses this challenge by offering a simplified yet robust modeling approach that enables efficient simulations of water quality determinants, making it a valuable tool for regulatory risk assessment and integrated water resources management."

We believe these revisions provide a clearer explanation of the literature gap and how AFAR-WQS addresses it. Thank you again for your valuable feedback, which has significantly strengthened the manuscript.

Comment 3: How will readers use this model in different basins? Is cooperation with stakeholders required within the scope of water management in the basin? These issues should be explained clearly.

Response 3: Thank you very much for your valuable comment. We appreciate your observation and have addressed it by adding a dedicated section titled "Application in the Decision-Making Process" to the manuscript. This section provides a detailed explanation of how AFAR-WQS can be applied in diverse basins and contexts, as well as the role of stakeholder cooperation in water management. Specifically, we have:

1. Illustrated the practical applications of AFAR-WQS in different geographic and institutional contexts, such as prioritizing sanitation investments, modeling a national scale, and designing climate-resilient water management plans.
2. Highlighted the flexibility of the toolbox to adapt to various basin characteristics, including urban drainage systems, transboundary rivers, and regions with limited data availability.
3. Emphasized the importance of stakeholder collaboration in water management, showcasing how AFAR-WQS facilitates participatory decision-making through interactive workshops and real-time scenario evaluations.

These changes can be found in the new section "Application in the Decision-Making Process" (Page 11-12, Lines 275–339) of the revised manuscript. The updated text now reads:
"The concept of assimilation factors, applied to topological networks via graph theory and solved using a depth-first search algorithm, provides AFAR-WQS with the ability to run multiple simulations of the same water quality determinant in macro-basins within a matter of minutes. [...] This collaborative approach not only enhances transparency in decision-making but also increases the likelihood of successfully implementing water management strategies."

We believe these revisions provide a clearer explanation of how AFAR-WQS can be used in different basins and the critical role of stakeholder cooperation. Thank you again for your insightful feedback, which has greatly improved the manuscript.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

can the aithors explain the novelty of their research? I remember studies carried out by ECETOC more than thirty years ago about Fate and Exposure Models of chemicals in water bodies (and later on alson in marine environments). Eqally, studies using the QSAR approach (Quantitative Structure Activity Relationships) have been carried out. The starting element of the relationship is defined as a  physico-chemical property of a substance and is labelled a desciptor. The Model is used to assess any physcio-chemical properrty (trough biological effects in water bodies) of a measured end-point.

Susch studies have been carried out manifold in order to assess the ecotoxicity of chemical compunds (to be ) brought into circulation by manufacturers and traders within the European Union as a guarantee that they have a so-called Predicted No-Effect Concentration or a No observed effect level or no observed effect concentration or meet a required rate of bio degradation.

All this took and take place within the realm of chemicals regulations within the EU. So what is new in this paper?  

https://www.ecetoc.org/publication/tr-074-qsars-in-the-assessment-of-the-environmental-fate-and-effects-of-chemicals/

https://www.ecetoc.org/wp-content/uploads/2021/10/ECETOC-TR-050.pdf

 

 

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Author Response

Comment 1: can the aithors explain the novelty of their research? So, what is new in this paper?

Response 1: Thank you very much for your valuable comment. We appreciate your observation and have expanded the Introduction section to explicitly address the novelty of our research and how AFAR-WQS differs from traditional Fate and Exposure Models or QSAR approaches. Specifically, we have:

1. Highlighted the Gap in the Literature: We have emphasized the computational limitations of existing water quality models, particularly their prolonged execution times when applied to large-scale basins, as noted in studies such as [13, 14]. As highlighted by [11], there is a pressing need for tools that execute quickly to facilitate real-time decision-making processes. Furthermore, [13] underscores that fast-executing models at large scales enable uncertainty and risk analyses, which are essential for robust decision-making in water resource management.
2. Explained the Novelty of AFAR-WQS: Unlike traditional Fate and Exposure Models or QSAR approaches, which focus on the behavior of specific substances (e.g., chemical compounds), AFAR-WQS is designed for integrated water quality management at the basin scale. It leverages graph theory and a Depth-First Search (DFS) algorithm to efficiently resolve cumulative processes in complex topological networks, enabling rapid simulations of 13 water quality determinants (e.g., pathogens, nutrients, mercury) across macro-basins. This approach is particularly novel because it balances scientific rigor with computational efficiency, avoiding the "paralysis by analysis" that often hinders timely decision-making.
3. Added Real-World Applications: We have included case studies demonstrating AFAR-WQS’s utility in prioritizing sanitation investments, assessing mercury transport at a national scale, and fostering stakeholder collaboration through participatory workshops. These applications highlight its practical relevance beyond theoretical or regulatory frameworks.
4. Expanded Results and Discussions: In response to your comment, we have also expanded the Results and Discussion section to provide more detailed insights into AFAR-WQS’s performance, scalability, and adaptability to diverse basins.
5. Enhanced Conclusions: We have revised the Conclusions section to better articulate how AFAR-WQS addresses the gap in the literature and its potential for future applications.

These changes can be found in the following sections of the revised manuscript:

• Introduction (Page 2, Lines 52–68 and 86–96): Explanation of the literature gap and novelty of AFAR-WQS.
• Results and Discussion (Page 11-12, Lines 275–339): Expanded discussion of AFAR-WQS’s performance and applications.
• Conclusions (Page 12-13, Lines 341–394): Enhanced conclusions highlighting the tool’s impact and future directions.

The updated text in the Introduction now reads:
"However, many of the aforementioned models exhibit significant computational complexity and extensive data requirements, which pose challenges for their implementation and calibration in large basins [12]. In most cases, prolonged execution times represent a major constraint, particularly in contexts requiring rapid decision-making and exploration of multiple scenarios [13,14]. As highlighted by [11], there is a pressing need for tools that execute quickly to facilitate real-time decision-making processes. Moreover, [13] underscores that fast-executing models at large scales enable uncertainty and risk analyses, which are essential for robust decision-making in water resource management. [...] AFAR-WQS directly addresses this challenge by offering a simplified yet robust modeling approach that enables efficient simulations of water quality determinants, making it a valuable tool for regulatory risk assessment and integrated water resources management."

We believe these revisions provide a clearer explanation of the literature gap, the novelty of AFAR-WQS, and how it addresses these challenges. Thank you again for your valuable feedback, which has significantly strengthened the manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

In my first review, I made a remark about the novelty of the paper,  since, at leat within the European Union, a similar approach is  used since decades and it even has a specific status, because it  serves as a tool to provide evidence of compliance with EU law on compatibility of substances, compounds and and products with EU water quality regulations. I even included a few literature  references.

I had expected that the authors should pay some attention to this  and point out to what extent their approach is novel. On pp 11 and  12 of their second version, they write a lot about their views  related to the novelty of their approach. They comment that it applies to "pollutants" which is a very non-scientifc wording, but I can still accept it, as being used in common language. In essence, however and by way of example, itrogen is a chemical element which may only be referred to as a pollutant under specific conditions, e.g, in an aquatic body. In that respect, I see no novelty in the approach.
The authors mention a lot of reasons why their approach is novel,  none of them are convincing to me. Either there is no novelty, or  else, the argument seems to be unjustified and non-substantiated.

Here is an example (lines 299 ff.) : 
CITATION This feature provides more robust support for selecting  which interventions to implement, reducing the likelihood of suboptimal decisions. For instance, in climate change scenarios,  where hydrological uncertainty is high, AFAR-WQS 301 can be used to assess the resilience of different management strategies in the face of extreme events such as droughts or floods. END OF CITATION

Honestly, I see no way in which the modelling of the socalled "fate"  of elements or compounds in an  aquatic body helps to assess any  management strategy. Its outcome is a list of concentrations of  these "fully diluted" elements or compounds and nothing more.  Whether or not these concentrations are acceptable or not should  result from a comparison with standard concentrations considered to be acceptable, legally prescribed or/and withouth risk to whatever organic and inorganic (living) bodies which are taken into consideration.

Author Response

Comment 1: To what extent is the AFAR-WQS approach novel, given that similar methods (such as chemical fate and exposure models) have already been used for decades in the EU to meet water quality regulations?

Response 1: Thank you very much for your valuable comments. We appreciate your observation regarding the use of the term "pollutant" and agree that its connotation depends largely on anthropogenic use or the impact it may have on organisms or ecosystems. In response to your feedback, we have replaced "pollutant" with the more precise term "water quality determinant" throughout the manuscript. This change better reflects the scientific nature of our work and avoids the potential misinterpretation of substances as inherently harmful without considering their context.

We also appreciate your observation regarding the similarity between AFAR-WQS and approaches like QSAR or ECETOC models used in the European Union. We would like to clarify that AFAR-WQS is not intended to replace or compete with these established methods, but rather to complement them by addressing a different need: providing computational capacity for large-scale water quality modeling in macro-basins.

As highlighted in the manuscript, the computational cost of water quality models is a well-recognized issue in the literature, as discussed by authors such as Darji et al. (2022), Hofstra et al. (2019), and Keupers (2017). In decision-making processes, water managers often need to quickly evaluate interventions and analyze the consequences of potential scenarios. However, in large basins or at the country level, this is not feasible with detailed models, as their configuration requires significant time, resources, and specialized personnel.

AFAR-WQS addresses this challenge by offering a simplified approach that allows managers to rapidly assess the potential impacts of interventions. The algorithm we use, combined with the way we have configured the estimation of assimilation factors for water quality determinants, enables AFAR-WQS to run simulations in a matter of minutes. This provides decision-makers with timely insights to guide their actions.

Furthermore, as mentioned in the manuscript, AFAR-WQS is particularly useful for on-the-ground implementation activities, especially when involving local communities. Tools like AFAR-WQS enable communities to better understand the mitigation of impacts, particularly when the benefits are not immediately visible in their location but occur downstream. By allowing stakeholders to quickly visualize how water quality conditions change in a downstream river section as a result of actions taken in their area, AFAR-WQS fosters greater community engagement and ensures that interventions are more sustainable in the long term.

For Decision Support Systems (DSS), tools like AFAR-WQS are especially valuable due to their rapid execution capabilities. In terms of usability and user experience, extended computation times are not an option; these platforms typically require very fast execution to support timely decision-making. AFAR-WQS meets this need by delivering results in minutes, making it an ideal tool for DSS applications where dynamic and efficient scenario evaluation is critical.

While the foundations of QSAR and ECETOC may be similar to those used in AFAR-WQS, our contribution lies in developing a computational tool that enables rapid simulations of multiple water quality determinants across complex topological networks. The key innovation of AFAR-WQS is its algorithmic efficiency, achieved through the use of graph theory and a Depth-First Search (DFS) algorithm, which resolves cumulative processes in large basins in minutes.

In contrast to ECETOC approaches, which have primarily been applied in small areas or specific river reaches, AFAR-WQS excels in large-scale applications, such as modeling the Amazon Basin or other macro-basins, where traditional methods would require prohibitive computational resources and time. This scalability makes AFAR-WQS particularly useful for:

• Evaluating the effects of nature-based solutions (NBS) on water quality at the basin scale.
• Rapid scenario evaluation in participatory workshops with stakeholders.
• Uncertainty and risk analyses in contexts where hydrological variability and anthropogenic pressures demand swift responses.

We recognize that the equations and conceptual foundations used in AFAR-WQS are not new; they are based on well-established principles, such as assimilation factors (Chapra, 1997, 2008) and ADZ-QUASAR models. However, our contribution lies in making these approaches computationally efficient and scalable for large basins, enabling applications that were previously impractical due to time and resource constraints.

We have expanded the Application in the Decision-Making Process section of the manuscript to better articulate these points and demonstrate how AFAR-WQS complements existing approaches like QSAR and ECETOC. These changes can be found on Page 2 and 11, Lines 47, 304–328.

Thank you again for your insightful feedback, which has helped us clarify the unique contributions of AFAR-WQS and its role as a computational tool for large-scale water quality management.

Author Response File: Author Response.pdf