the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Apr 2026
HESS Opinions: Operationalizing sociohydrology from systems thinking to systems doing for sustainable and resilient water management
Abstract. Sociohydrology has advanced explanations of coupled human-water systems, but its translation into decision support remains uneven. We argue that a practical bridge from systems thinking to systems doing can be built around two complementary abstractions: (i) canonical feedback structures that make cases comparable without erasing context, and (ii) critical pathways that trace how interventions propagate through behavior, exposure, and outcomes. Canonical forms help organize recurring emergent phenomena such as the levee effect and reservoir effect, while critical pathways turn these insights into a repeatable workflow for intervention design, monitoring, and adaptive learning. We propose a minimal "systems doing loop" that links feedback mapping, pathway tracing, indicator selection, and iteration, with equity and legitimacy treated as explicit constraints on what counts as useful knowledge and acceptable action. This framing complements integrated water resources management by making unintended consequences operational and by clarifying what to monitor and revisit when system behavior changes.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.-
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Operationalising socio-hydrology is a timely and fascinating theme', Alberto Montanari, 12 May 2026
- AC1: 'Reply on RC1', Fuqiang Tian, 23 Sep 2026
- AC3: 'Reply on RC1', Fuqiang Tian, 23 Sep 2026
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RC2: 'Comment on egusphere-2026-1727', Ursula McKnight, 18 Aug 2026
General comments:
This opinion paper comes at an excellent time. I agree that those working in water resources management, from academics to practitioners, could greatly benefit from complementing predominantly predictive modelling approaches with fit-for-purpose tools that can help translate scientific knowledge and innovation into decisions at the science-society interface. The paper’s central proposition is clear: sociohydrology needs to move from explanation towards decision support, through canonical feedback structures, critical pathways, and a five-step “systems-doing loop”. Importantly, the authors position this not as another call for “holistic” approaches, but as a practical bridge from insight to action. I find this a timely and potentially valuable contribution.
Overall, the paper is well written and well supported. I nevertheless see several areas where the argument and accessibility could be strengthened, as detailed below.
Specific comments:
Comment 1. Abstract, line 20: term “abstractions”
I understand “abstractions” here to refer to simplified representations of complex system behavior, but I wonder whether this terminology will be immediately clear to readers outside the systems-thinking/modelling community. Given the paper’s explicit ambition to bridge science and practice, could the authors either briefly clarify what is meant by “abstractions” here or consider a more accessible term?
Comment 2. Abstract (line 26) and elsewhere: “making unintended consequences operational”
I find the term “operational” somewhat unclear in this context. Presumably, the objective is not to operationalize the consequences themselves, but to make potential unintended consequences visible, traceable, and actionable within the decision process. Such consequences may of course be positive or negative, and making them explicit should help identify and support pathways towards more desirable and sustainable outcomes, while avoiding or mitigating undesirable ones. I understand the value of the proposed approach as making explicit how interventions and decisions propagate along critical pathways, thereby improving understanding of possible consequences and supporting better decision-making under uncertainty. I suggest revising the terminology to make this distinction clearer.
Comment 3. Section 2: systems thinking and predictive modelling
I wonder whether readers unfamiliar with systems thinking and system dynamics tools (e.g. causal loop diagrams and related quantitative approaches) face too steep a learning curve here. Given the paper’s ambition to bridge science and practice, some additional explanation to orient readers unfamiliar with these approaches would greatly strengthen this section.
I also think there is an opportunity to place the argument in a broader modelling context. Systems thinking and system dynamics approaches have a long history, yet predictive simulation approaches have arguably remained much more prominent within fields such as hydrology and water resources management. This tension becomes particularly relevant with the increasing use of ML- and AI-based predictive tools. While these offer substantial capabilities, prediction alone does not necessarily help us understand how interventions propagate through coupled systems or identify the critical pathways and feedbacks that the authors emphasize here. Could the authors briefly address this distinction? It would help clarify why the proposed systems-doing approach is needed alongside, rather than instead of, predictive modelling.
Relatedly, the critical pathways are described as “simplified causal sequences” and later as providing pathway variables and “testable hypotheses”. It may therefore be useful to clarify that these pathways represent hypothesized causal relationships to be investigated and tested, rather than implying that these are established causal structures.
Comment 4. Section 3: comparative synthesis and place-based investigation
I particularly like the discussion of deliberately coupling comparative synthesis and place-based investigation. I suggest making the reciprocal learning process even more explicit. Comparative synthesis can help individual cases learn from experience elsewhere, potentially accelerating the uptake of systemic understanding rather than requiring each case to arrive at similar insights independently through e.g., trial and error. Conversely, place-based investigation can continually refine the comparative understanding of mechanisms, pathways, and boundary conditions.
This also reinforces an important point that could perhaps be stated more explicitly: traditional predictive modelling remains necessary, but should increasingly be coupled with systems-thinking approaches if we want to support decision-makers in understanding not only what may happen, but also why, through which pathways, and with what potential unintended consequences.
Comment 5. Section 4 (p. 5, line 115): participatory modelling
“Participatory modelling can support shared problem framing and learning…” I agree, and appreciate that the authors already acknowledge some of the challenges associated with participation and legitimacy. I wonder whether the discussion could go one step further and acknowledge the implementation capacity required for participatory modelling. Moving from systems thinking to systems doing requires not only appropriate methods, but sufficient time (including economic resources), facilitation skills, sustained stakeholder engagement, and institutional capacity to support these processes. Recognizing these practical requirements would further strengthen the paper’s emphasis on moving from “systems thinking” to “systems doing”.
Comment 6. Section 2 (line 50) and Section 4: floods, droughts, and hazard framing
The levee and reservoir effects are presented as examples involving different “hazards”, while Section 4 later refers to multiple hazards and compound extremes. While floods and droughts can of course be treated as distinct hazards, I wonder whether this framing risks reinforcing the sectoral separation that a systems approach should help us overcome. Floods and droughts are different manifestations of coupled hydrological systems, and their risks, management responses, and unintended consequences may themselves be interconnected.
I therefore suggest making this systemic connection more explicit. One of the strengths of the proposed approach should arguably be its ability to move beyond treating floods, droughts, and other extremes as separate management problems, and instead examine the feedbacks and critical pathways connecting them. The following paper may also be relevant in this context: https://doi.org/10.1038/s44221-026-00650-9.
Comment 7. Section 5: evaluating “systems doing”
The conclusion states that sociohydrology should be judged by whether it improves intervention choice, monitoring, and learning in real decision settings. I agree, but this also raises an important question: how do we know whether the proposed "systems-doing" loop has actually improved decision-making? Could the authors say a little more about what evidence or criteria might demonstrate this? This seems particularly important given the paper’s central ambition to move beyond systems thinking towards systems doing.
Citation: https://doi.org/10.5194/egusphere-2026-1727-RC2 - AC2: 'Reply on RC2', Fuqiang Tian, 23 Sep 2026
- AC4: 'Reply on RC2', Fuqiang Tian, 23 Sep 2026
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Operationalising socio-hydrology is a timely and fascinating theme', Alberto Montanari, 12 May 2026
Operationalising socio-hydrology is a timely and fascinating theme! Socio-hydrology has the potential to play a fundamental role in overcoming the lack of public—and local administrations—consensus on solutions aimed at improving sustainability and mitigating environmental and climate-related risks. To translate the concepts and methods of socio-hydrology into operational advice is indeed a challenge, also for the complications posed by so-called “institutional barriers”, that more and more represent the real obstacle to change adaptation rather than purely technical barriers (see, for instance, Oberlack, 2017).
The contribution herein presented is timely and highly significant. I enjoyed reading it as it has the potential to clarify the technical advance proposed by socio-hydrology. I believe this piece may be interesting not only to scientists working in the specific field, but also to the general public. To maximise the potential of reaching a large audience I am providing here below some minor suggestions in order to make the contributions more widely accessible. Quotes from the manuscript are copied in italic font.
- Line 20: “(i) canonical feedback structures that make cases comparable without erasing context….”. The sentence is clear to me, but I suggest in the abstract to use a less technical language.
- Line 34: I think clarity would benefit by explaining why so far (apparently) case representation has implied erasing the local context. An example would be highly beneficial, as the authors apparently recognise such “erase” as a limitation of socio-hydrological studies so far.
- Section 2: I am missing a definition of what is meant by “canonical forms”. I think details should be given, for the sake of more clarity with respect to non-specialist readers.
- Section 2: the examples of the levee effect and reservoir effect are meaningful. I suggest to organise the text by taking into account the emerging criticism that levee-effect narratives can sometimes be mobilized as critiques of engineering-dominant paradigms. The criticism is based on the claim that several studies do not sufficiently distinguish between structural-only approaches and hybrid resilience strategies. I think an excellent reference is the recent paper by Catalano and Hall (2026).
- Line 73: I suggest including a definition of leverage point. It would also be useful to provide an example of what can be considered a leverage point and how the underlying dynamics can be modelled and parametrised. Moving from identifying leverage points to the formalisation of how interventions propagate through feedback, delays, and adaptive responses is the critical step for ensuring that leverage points become efficient as predictive tools.
- Line 104: I agree that participation is essential for moving from concepts to operational practice. However, very often participation paradoxically emerge as a barrier to action when it produces delay, tokenism, conflict escalation, participation fatigue, or procedural overload. See, for instance, Bruley et al. (2026) and Wamsler et al. (2020). Revisiting the strategy and organisation of participation is a key subject in the scientific debate.
This is a significant and timely contribution that has the potential to make an excellent opinion piece.
Alberto MontanariReferences
Bruley, E., Scolobig, A., Ellena, M., Pickard, S., Englund, M., Baulenas, E., ... & Stoffel, M. (2026). Advancing stakeholder engagement in climate adaptation: a systematic review of structural barriers and operational enablers. Sustainability Science, 1-19.
Catalano, A. J., & Hall, D. M. (2026). Reviewing the Levee Effect: From Theory to Practice. Journal of Flood Risk Management, 19(2), e70211.
Oberlack, C. (2017). Diagnosing institutional barriers and opportunities for adaptation to climate change. Mitigation and adaptation strategies for global change, 22(5), 805-838.
Wamsler, C., Alkan-Olsson, J., Björn, H., Falck, H., Hanson, H., Oskarsson, T., ... & Zelmerlow, F. (2020). Beyond participation: when citizen engagement leads to undesirable outcomes for nature-based solutions and climate change adaptation. Climatic Change, 158(2), 235-254.Citation: https://doi.org/10.5194/egusphere-2026-1727-RC1 - AC1: 'Reply on RC1', Fuqiang Tian, 23 Sep 2026
- AC3: 'Reply on RC1', Fuqiang Tian, 23 Sep 2026
-
RC2: 'Comment on egusphere-2026-1727', Ursula McKnight, 18 Aug 2026
General comments:
This opinion paper comes at an excellent time. I agree that those working in water resources management, from academics to practitioners, could greatly benefit from complementing predominantly predictive modelling approaches with fit-for-purpose tools that can help translate scientific knowledge and innovation into decisions at the science-society interface. The paper’s central proposition is clear: sociohydrology needs to move from explanation towards decision support, through canonical feedback structures, critical pathways, and a five-step “systems-doing loop”. Importantly, the authors position this not as another call for “holistic” approaches, but as a practical bridge from insight to action. I find this a timely and potentially valuable contribution.
Overall, the paper is well written and well supported. I nevertheless see several areas where the argument and accessibility could be strengthened, as detailed below.
Specific comments:
Comment 1. Abstract, line 20: term “abstractions”
I understand “abstractions” here to refer to simplified representations of complex system behavior, but I wonder whether this terminology will be immediately clear to readers outside the systems-thinking/modelling community. Given the paper’s explicit ambition to bridge science and practice, could the authors either briefly clarify what is meant by “abstractions” here or consider a more accessible term?
Comment 2. Abstract (line 26) and elsewhere: “making unintended consequences operational”
I find the term “operational” somewhat unclear in this context. Presumably, the objective is not to operationalize the consequences themselves, but to make potential unintended consequences visible, traceable, and actionable within the decision process. Such consequences may of course be positive or negative, and making them explicit should help identify and support pathways towards more desirable and sustainable outcomes, while avoiding or mitigating undesirable ones. I understand the value of the proposed approach as making explicit how interventions and decisions propagate along critical pathways, thereby improving understanding of possible consequences and supporting better decision-making under uncertainty. I suggest revising the terminology to make this distinction clearer.
Comment 3. Section 2: systems thinking and predictive modelling
I wonder whether readers unfamiliar with systems thinking and system dynamics tools (e.g. causal loop diagrams and related quantitative approaches) face too steep a learning curve here. Given the paper’s ambition to bridge science and practice, some additional explanation to orient readers unfamiliar with these approaches would greatly strengthen this section.
I also think there is an opportunity to place the argument in a broader modelling context. Systems thinking and system dynamics approaches have a long history, yet predictive simulation approaches have arguably remained much more prominent within fields such as hydrology and water resources management. This tension becomes particularly relevant with the increasing use of ML- and AI-based predictive tools. While these offer substantial capabilities, prediction alone does not necessarily help us understand how interventions propagate through coupled systems or identify the critical pathways and feedbacks that the authors emphasize here. Could the authors briefly address this distinction? It would help clarify why the proposed systems-doing approach is needed alongside, rather than instead of, predictive modelling.
Relatedly, the critical pathways are described as “simplified causal sequences” and later as providing pathway variables and “testable hypotheses”. It may therefore be useful to clarify that these pathways represent hypothesized causal relationships to be investigated and tested, rather than implying that these are established causal structures.
Comment 4. Section 3: comparative synthesis and place-based investigation
I particularly like the discussion of deliberately coupling comparative synthesis and place-based investigation. I suggest making the reciprocal learning process even more explicit. Comparative synthesis can help individual cases learn from experience elsewhere, potentially accelerating the uptake of systemic understanding rather than requiring each case to arrive at similar insights independently through e.g., trial and error. Conversely, place-based investigation can continually refine the comparative understanding of mechanisms, pathways, and boundary conditions.
This also reinforces an important point that could perhaps be stated more explicitly: traditional predictive modelling remains necessary, but should increasingly be coupled with systems-thinking approaches if we want to support decision-makers in understanding not only what may happen, but also why, through which pathways, and with what potential unintended consequences.
Comment 5. Section 4 (p. 5, line 115): participatory modelling
“Participatory modelling can support shared problem framing and learning…” I agree, and appreciate that the authors already acknowledge some of the challenges associated with participation and legitimacy. I wonder whether the discussion could go one step further and acknowledge the implementation capacity required for participatory modelling. Moving from systems thinking to systems doing requires not only appropriate methods, but sufficient time (including economic resources), facilitation skills, sustained stakeholder engagement, and institutional capacity to support these processes. Recognizing these practical requirements would further strengthen the paper’s emphasis on moving from “systems thinking” to “systems doing”.
Comment 6. Section 2 (line 50) and Section 4: floods, droughts, and hazard framing
The levee and reservoir effects are presented as examples involving different “hazards”, while Section 4 later refers to multiple hazards and compound extremes. While floods and droughts can of course be treated as distinct hazards, I wonder whether this framing risks reinforcing the sectoral separation that a systems approach should help us overcome. Floods and droughts are different manifestations of coupled hydrological systems, and their risks, management responses, and unintended consequences may themselves be interconnected.
I therefore suggest making this systemic connection more explicit. One of the strengths of the proposed approach should arguably be its ability to move beyond treating floods, droughts, and other extremes as separate management problems, and instead examine the feedbacks and critical pathways connecting them. The following paper may also be relevant in this context: https://doi.org/10.1038/s44221-026-00650-9.
Comment 7. Section 5: evaluating “systems doing”
The conclusion states that sociohydrology should be judged by whether it improves intervention choice, monitoring, and learning in real decision settings. I agree, but this also raises an important question: how do we know whether the proposed "systems-doing" loop has actually improved decision-making? Could the authors say a little more about what evidence or criteria might demonstrate this? This seems particularly important given the paper’s central ambition to move beyond systems thinking towards systems doing.
Citation: https://doi.org/10.5194/egusphere-2026-1727-RC2 - AC2: 'Reply on RC2', Fuqiang Tian, 23 Sep 2026
- AC4: 'Reply on RC2', Fuqiang Tian, 23 Sep 2026
Peer review completion
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(437 KB) - Metadata XML
Operationalising socio-hydrology is a timely and fascinating theme! Socio-hydrology has the potential to play a fundamental role in overcoming the lack of public—and local administrations—consensus on solutions aimed at improving sustainability and mitigating environmental and climate-related risks. To translate the concepts and methods of socio-hydrology into operational advice is indeed a challenge, also for the complications posed by so-called “institutional barriers”, that more and more represent the real obstacle to change adaptation rather than purely technical barriers (see, for instance, Oberlack, 2017).
The contribution herein presented is timely and highly significant. I enjoyed reading it as it has the potential to clarify the technical advance proposed by socio-hydrology. I believe this piece may be interesting not only to scientists working in the specific field, but also to the general public. To maximise the potential of reaching a large audience I am providing here below some minor suggestions in order to make the contributions more widely accessible. Quotes from the manuscript are copied in italic font.
This is a significant and timely contribution that has the potential to make an excellent opinion piece.
Alberto Montanari
References
Bruley, E., Scolobig, A., Ellena, M., Pickard, S., Englund, M., Baulenas, E., ... & Stoffel, M. (2026). Advancing stakeholder engagement in climate adaptation: a systematic review of structural barriers and operational enablers. Sustainability Science, 1-19.
Catalano, A. J., & Hall, D. M. (2026). Reviewing the Levee Effect: From Theory to Practice. Journal of Flood Risk Management, 19(2), e70211.
Oberlack, C. (2017). Diagnosing institutional barriers and opportunities for adaptation to climate change. Mitigation and adaptation strategies for global change, 22(5), 805-838.
Wamsler, C., Alkan-Olsson, J., Björn, H., Falck, H., Hanson, H., Oskarsson, T., ... & Zelmerlow, F. (2020). Beyond participation: when citizen engagement leads to undesirable outcomes for nature-based solutions and climate change adaptation. Climatic Change, 158(2), 235-254.