Research on Construction and Application of Water Processes Based on Knowledge Graph: Analysis of Dynamic Paths and Impact Factors

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article describes the approach to water bodies and processes in an original form. Apparently, this is useful in solving some consumer tasks using artificial intelligence. Perhaps at some point in solving these problems, it will be possible to accurately determine the nature of the water consumed. The article can be recommended for publication if some comments are removed. First. Water has an original property, such that its smallest volume cannot be considered in three states (liquid, ice, steam) as a volume having the same physical and chemical properties. Second. Water enters the river not only in the form of snow, hail, rain, and surface runoff, but also as an underground inflow. The third. For deep machine learning, you can't limit yourself to just 25 elements. At the initial stage it is possible, but in the end it is impossible. The authors themselves point this out in the conclusion.

Author Response

Comments 1: The article describes the approach to water bodies and processes in an original form. Apparently, this is useful in solving some consumer tasks using artificial intelligence. Perhaps at some point in solving these problems, it will be possible to accurately determine the nature of the water consumed. The article can be recommended for publication if some comments are removed. First. Water has an original property, such that its smallest volume cannot be considered in three states (liquid, ice, steam) as a volume having the same physical and chemical properties. Second. Water enters the river not only in the form of snow, hail, rain, and surface runoff, but also as an underground inflow. The third. For deep machine learning, you can't limit yourself to just 25 elements. At the initial stage it is possible, but in the end it is impossible. The authors themselves point this out in the conclusion.

Response 1: We sincerely appreciate your recognition of our original approach to water process modeling and its AI applicability. According to the Reviewer's good instruction, we have addressed all three comments through targeted revisions:

For Comment 1: The original definition – 'Unit water refers to the smallest volume of water that undergoes three-state transitions while maintaining invariant physicochemical properties throughout hydrological cycling' – has been deleted. This is replaced with “In addition, due to the different boundaries of the carrying water bodies, various water bodies such as rivers and lakes have been formed. This will prepare for the subsequent division of the water body.” (See lines 116-118 on page 3 of the revised manuscript).

For Comment 2: References to surface runoff as a water body are removed, as rain/snow/hail constitute distinct water bodies, while surface runoff is reclassified as inter-body linkages in our framework (See lines 233-234 on page 9 of the revised manuscript).

For Comment 3: The contested limitation statement is deleted per the reviewer's suggestion.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors investigate the construction and application of a top-down water-process knowledge graph to elucidate the dynamic behavior of water movement and its driving factors. The manuscript is generally well prepared, the abstract is informative, and the literature has been adequately reviewed. Overall, the manuscript  is a valuable contribution that can guide researchers in the field of water-resources management. After the following minor comments are addressed, the manuscript could be suitable for publication.

1. Please specify which software or computational platforms were used for the analysis. In addition, indicate the amount of data collected during the data-acquisition phase.
2. All figures should be regenerated or reformatted to improve readability and visual clarity.
3. Did the analysis reveal any previously unrecognized processes beyond the well-established physical mechanisms? If not, please discuss whether such discoveries are feasible with the proposed framework.
4. Are there comparable studies in the literature that analyze similar physical processes? If so, a comparative discussion of similarities and differences would enrich the manuscript’s discussion section.

Author Response

Comments 1：Please specify which software or computational platforms were used for the analysis. In addition, indicate the amount of data collected during the data-acquisition phase.

Response1: Thank you for highlighting the importance of these details for reproducibility. According to the Reviewer's good instruction, this study employed graph database technologies using Neo4j (version 5.15, https://neo4j.com/) and Python 3.12 (with Scrapy and Py2neo) as the computational platform. The hardware environment consisted of a Windows 11 work-station equipped with an AMD Ryzen™ 9 7945HX processor (5.4 GHz boost) and 32 GB RAM. In addition, during the data-acquisition phase, the data collected in this study primarily draws from Handbook of Hydrology. Through systematic knowledge integration, we have constructed an extensible framework. (See lines 189-195 on page 5 of the revised manuscript)

Comments 2: All figures should be regenerated or reformatted to improve readability and visual clarity.

Response 2: We sincerely appreciate this suggestion. All figures have been regenerated at 300 dpi with improved readability and visual clarity. (See revised Figs 1–9 in the revised manuscript)

Comments 3: Did the analysis reveal any previously unrecognized processes beyond the well-established physical mechanisms? If not, please discuss whether such discoveries are feasible with the proposed framework.

Response 3: While our analysis did not uncover fundamentally new physical processes, the knowledge graph framework revealed critical interaction-level insights beyond conventional modeling approaches. Specifically, it identified:

1. Cross-state factor intersections: e.g., "water area" simultaneously affects evaporation (process) and river volume (entity) (Shown in Fig. 6 in the revised manuscript), implying coupled dynamics previously underexplored.
2. Path-dependent contamination: Paths like Agricultural water → Soil water → River highlight non-point pollution sources (See Section 3.2 in the revised manuscript).

In addition, we have added comparative analysis (See the first paragraph of the Discussion in the revised manuscript):

We have constructed a knowledge graph of water processes and analyzed the paths between any two water bodies and the elements driving the movement and change of water bodies. Several studies in the literature analyze related physical water processes using knowledge graphs [21-23]. Their core similarity is that they both study water by using the method of knowledge graphs, but differ in scope: Literatures [21-22] mainly study the construction of the knowledge graph of water conservancy information; Literature [23] primarily studies water quality through knowledge graphs, which is a relatively specific water process. This paper investigates the dynamic processes of water bodies across different states, including: (1) transition paths between any two water bodies; (2) the elements that can drive the movement and change of water bodies based on these paths.

 

[21]. Feng J, Xu X, Lu J. Construction and Application of Water Conservancy Information Knowledge Graph. Computer and Modernization. 2019; (9): 35-40. https://doi:10.3969/j.Issn.

[22]. Duan H, Han K, Zhao H, Jiang Y, Li H, Mao W. Research on water conservancy comprehensive knowledge graph construction. Journal of Hydraulic Engineering.2021; 52: 948-958. https://doi:10.13243/j.cnki.slxb.20200924.

[23]. Rondon Diaz JD, Vilches-Blazquez LM. Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin. Journal of Hydroinformatics. 2022; 24(2): 295-314. https://doi:10.2166/hydro.2022.070.

Comments 4: Are there comparable studies in the literature that analyze similar physical processes? If so, a comparative discussion of similarities and differences would enrich the manuscript’s discussion section.

Response 4: We appreciate the reviewer's constructive suggestion. There are indeed comparable studies in the literature that analyze similar physical processes. Accordingly, we have added a dedicated comparative discussion in Section Discussion highlighting key similarities and differences. (See the first paragraph of the Discussion in the revised manuscript)

We have constructed a knowledge graph of water processes and analyzed the paths between any two water bodies and the elements driving the movement and change of water bodies. Several studies in the literature analyze related physical water processes using knowledge graphs [21-23]. Their core similarity is that they both study water by using the method of knowledge graphs, but differ in scope: Literatures [21-22] mainly study the construction of the knowledge graph of water conservancy information; Literature [23] primarily studies water quality through knowledge graphs, which is a relatively specific water process. This paper investigates the dynamic processes of water bodies across different states, including: (1) transition paths between any two water bodies; (2) the elements that can drive the movement and change of water bodies based on these paths.

 

[21]. Feng J, Xu X, Lu J. Construction and Application of Water Conservancy Information Knowledge Graph. Computer and Modernization. 2019; (9): 35-40. https://doi:10.3969/j.Issn.

[22]. Duan H, Han K, Zhao H, Jiang Y, Li H, Mao W. Research on water conservancy comprehensive knowledge graph construction. Journal of Hydraulic Engineering.2021; 52: 948-958. https://doi:10.13243/j.cnki.slxb.20200924.

[23]. Rondon Diaz JD, Vilches-Blazquez LM. Characterizing water quality datasets through multi-dimensional knowledge graphs: a case study of the Bogota river basin. Journal of Hydroinformatics. 2022; 24(2): 295-314. https://doi:10.2166/hydro.2022.070.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

 

Thank you very much for inviting me to examine the manuscript titled "Research on construction and application of water processes based on knowledge graph: analysis of dynamic paths and impact factors". I read it and have some recommendations, which I hope will assist the study team improve the work and publish it in the Water.

Congratulations to the study team on developing an excellent research idea of developing a Knowledge graph for the hydrological cycle (particularly hydrological processes) using documents, data, and books. I realize that the manuscript's goal is to give information on the water process so that readers may comprehend where water comes from and how it is transported.

However, the research findings are limited to the general level of water processes, providing only general and basic information to readers who are unfamiliar with hydrology. Should it be possible to add quantification of those processes as well as scenarios (for example, how will the hydrological process take place if the air temperature rises?) in order for all researchers to be able to apply the results of this study? Or are these hydrological processes different at different latitudes, basins, and so on. If the author can develop a knowledge graph for a given basin, it will be more useful for researchers in that basin.

Some suggestions on the form and content of the manuscript:
- The text in the Figures is too small to read and the resolution of the Figures in the manuscript should be increased
- The abstract and research methods are well written
- Further discussion is encouraged
- What are the next steps after this study? What can be done to address the study's limitations?Some suggestions on the form and content of the manuscript:
- The text in the Figures is too small to read and the resolution of the Figures in the manuscript should be increased
- The abstract and research methods are well written
- Further discussion is encouraged
- What are the next steps after this study? What can be done to address the study's limitations?

 

Best regards,

 

 

Author Response

Comments 1: However, the research findings are limited to the general level of water processes, providing only general and basic information to readers who are unfamiliar with hydrology. Should it be possible to add quantification of those processes as well as scenarios (for example, how will the hydrological process take place if the air temperature rises?) in order for all researchers to be able to apply the results of this study? Or are these hydrological processes different at different latitudes, basins, and so on. If the author can develop a knowledge graph for a given basin, it will be more useful for researchers in that basin.

Response 1: We agree with the Reviewer's good advice. Yes, we acknowledge that certain water processes were not explicitly considered in this study, such as those processes at different latitudes, basins, and a given basin. Moreover, quantification of those processes as well as scenarios (for example, how will the water process take place if the air temperature rises?) also were not taken into consideration. To address these limitations, future work will: (1) Establish composite entity concepts (e.g., watershed entities encapsulating vegetation, river, soil) and con-struct their knowledge graphs; (2) Implement spatiotemporal graph neural networks (STGNNs) to quantify and analyze process dynamics based on spatial topologies and attribute evolution. (See the last paragraph of the Discussion in the revised manuscript)

In addition, since the basin is a compliant entity, we will proceed in the next step. To demonstrate its applicability, we provide a knowledge graph construction case study for Qinghai Lake (See the two to end paragraphs of the Conclusions in the revised manuscript) as follows:

More critically, to demonstrate the practical applicability of our framework to water processes, we apply the concept-instance relational principle from Reference [21]- which states that relations between conceptual entities imply analogous instance-level linkages. Using the Cypher query:

MATCH Paths= (p1: Entity)-[r*..]-> (p2: Entity {name: 'Lake'})

RETURN Paths,

we extracted pathways to the conceptual water body “Lake” (id=9). Fig 7 shows a positive correlation between path length (1-7 steps) and the number of paths. To illustrate, we take Qinghai Lake as a concrete example. Due to space constraints, we focus on paths of length 1, which yield five distinct pathways (shown schematically in Fig 8):

Specifically, the conceptual pathway (River—>Lake) manifests physically as inflow relationships from seven rivers into Qinghai Lake: Buha River, Shaliu River, Quanji River, Haergai River, Ganzi River, Reverse Flow River, and Heima River [28]. Fig 9 visualizes this instance-level mapping. As can be seen from Fig 9, the instantiated graph will become more complex.

Comments 2: The text in the Figures is too small to read and the resolution of the Figures in the manuscript should be increased.

Response 2: We sincerely apologize for the suboptimal figure quality. All figures in the manuscript have now been revised as follows:

1. Font scaling: Text elements enlarged by 150% (minimum font size: 9pt).

2.Resolution enhancement: Increased to 300 dpi (TIFF format) to ensure printing clarity.

In addition, each modified picture is as follows:

Comments 3: The abstract and research methods are well written.

Response 3: We deeply appreciate the reviewer's recognition of the abstract clarity and methodological rigor. This positive feedback reinforces our commitment to maintaining high standards in scholarly communication.

 

Comments 4: Further discussion is encouraged.

Response 4: We thank the reviewer for suggesting deeper discussion. The following enhancements have been made in Discussion (See the Discussion in the revised manuscript):

We have constructed a knowledge graph of water processes and analyzed the paths between any two water bodies and the elements driving the movement and change of water bodies. Several studies in the literature analyze related physical water processes using knowledge graphs [21-23]. Their core similarity is that they both study water by using the method of knowledge graphs, but differ in scope: Literatures [21-22] mainly study the construction of the knowledge graph of water conservancy information; Literature [23] primarily studies water quality through knowledge graphs, which is a relatively specific water process. This paper investigates the dynamic processes of water bodies across different states, including: (1) transition paths between any two water bodies; (2) the elements that can drive the movement and change of water bodies based on these paths.

More critically, to demonstrate the practical applicability of our framework to water processes, we apply the concept-instance relational principle from Reference [21]- which states that relations between conceptual entities imply analogous instance-level linkages. Using the Cypher query:

MATCH Paths= (p1: Entity)-[r*..]-> (p2: Entity {name: 'Lake'})

RETURN Paths,

we extracted pathways to the conceptual water body “Lake” (id=9). Fig 7 shows a positive correlation between path length (1-7 steps) and the number of paths. To illustrate, we take Qinghai Lake as a concrete example. Due to space constraints, we focus on paths of length 1, which yield five distinct pathways (shown schematically in Fig 8):

Specifically, the conceptual pathway (River—>Lake) manifests physically as inflow relationships from seven rivers into Qinghai Lake: Buha River, Shaliu River, Quanji River, Haergai River, Ganzi River, Reverse Flow River, and Heima River [28]. Fig 9 visualizes this instance-level mapping. As can be seen from Fig 9, the instantiated graph will become more complex.

Comments 5: What are the next steps after this study? What can be done to address the study's limitations?

Response 5: We appreciate the Reviewer's insightful questions regarding future work and limitations. We acknowledge that certain water processes were not explicitly considered in this study, such as those processes at different latitudes, basins, and a given basin. Moreover, quantification of those processes as well as scenarios (for example, how will the water process take place if the air temperature rises?) also were not taken into consideration. To address these limitations, future work will: (1) Establish composite entity concepts (e.g., watershed entities encapsulating vegetation, river, soil) and con-struct their knowledge graphs; (2) Implement spatiotemporal graph neural networks (STGNNs) to quantify and analyze process dynamics based on spatial topologies and attribute evolution. (See the last paragraph of the Conclusions in the revised manuscript)

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The article has been revised. You can publish it in the presented form.

Author Response

Comments 1: The article has been revised. You can publish it in the presented form.

Response 1: Thank you for your positive evaluation of our revised manuscript. We sincerely appreciate your time and expertise in reviewing our work. We are pleased to know the current version meets the publication standard and will proceed accordingly. Should there be any additional minor revisions or formalities required prior to final publication, please do not hesitate to let us know. We remain fully available to address any last-minute requirements.

Reviewer 3 Report

Comments and Suggestions for Authors

It is necessary to build a knowledge graph for the water cycle for a specific basin in this manuscript.

Author Response

Comments 1: It is necessary to build a knowledge graph for the water cycle for a specific basin in this manuscript.

Response 1: We sincerely appreciate the reviewer's insightful suggestion regarding the basin-specific knowledge graph implementation. In direct response to this recommendation, we have now integrated a complete water processes knowledge graph case study for the Qinghai Lake Basin into the revised manuscript (See lines 296-318 in the revised manuscript).

Focusing on the Qinghai Lake Basin as a specific watershed, we construct a knowledge graph for the Qinghai Lake Basin. The Qinghai Lake Basin, also known as the Qinghai Lake Basin, is an independent and complete physical geography unit. Its geographical coordinates range from 97°48’55.70’’E to 101°11’24.88’’E and 36°17’43.58’’N to 38°19’16.20’’N, with elevations spanning 3,195 to 5,291 meters [29].

Firstly, within the Qinghai Lake Basin, conceptual entities were mapped to instance entities. Due to space limitations, the instance entities for the Qinghai Lake Basin were primarily referenced from Literature [29], with the mapping results presented in Table 4. As shown in Table 4, marine entities (id=12) are not involved here, as there are no oceans within the Qinghai Lake Basin.

Table 4. The Mapping Relationship between Conceptual Entities and Instance Entities within the Qinghai Lake Basin.

Secondly, based on the conceptual entity relationships in Table 2 and the instance entities in Table 4, we constructed the Qinghai Lake Basin knowledge graph using Neo4j and Python. The visualization employs a color-coded schema where each conceptual entity category shares a unified hue. As shown in Figure 7: all lake instances (Qinghai Lake, Erhai Lake, and Gahai Lake) are rendered in #0000FF (blue), while wetland instances use #FF00FF (magenta). This approach enables intuitive recognition of the 22 entity types within the knowledge graph.

Author Response File: Author Response.pdf