the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Feb 2026
HESS Opinions: Applied hydrologic models in the era of machine learning – retain, revamp, reconcile, or replace?
Abstract. Despite advancements in the performance of machine learning (ML) based hydrologic models, some institutions are hesitant to pursue ML as a replacement for existing conceptual or process-based hydrologic models in many applications. In several of these circumstances, traditional hydrologic models continue to be favored due to their familiarity, reliability, interpretability, established performance benchmarks under varied settings, availability of detailed training modules and a trained workforce, as well as close integration with data, processing, and decision-making pipelines. Recognizing these advantages, this perspective argues for two pragmatic and institutionally compatible paths forward for integration of ML within applied models: (1) reconciling ML as a complementary option in applied hydrologic modeling workflows; and (2) revamping or upskilling hydrologic modeling workflows using ML. To support this perspective, we highlight key opportunities where ML can be used as a tool to enhance results across various stages of the model implementation and operational workflow including data pre-processing, parameter calibration, parameter transferability, data assimilation, solver enhancement, accelerating scenario simulations and post-processing. Each of these two integration strategies can be implemented into current applied model frameworks, thereby combining the strengths of both physical modeling and ML. These strategies can help overcome current bottlenecks and address institutional needs of continuity and compatibility, while also offering the potential to improve model performance with ML.
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Status: open (extended)
- CC1: 'Comment on egusphere-2026-583', Nima Zafarmomen, 25 Feb 2026 reply
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RC1: 'Comment on egusphere-2026-583', Anonymous Referee #1, 16 May 2026
reply
This manuscript presents a perspective on integrating machine learning (ML) into applied hydrologic modeling workflows, arguing for “reconciliation” or “revamping” rather than replacement of legacy models. The topic is timely and practically relevant, as many operational agencies are indeed grappling with how to incorporate ML advances. However, I have several concerns regarding the depth of the contribution, the clarity of the intended audience, and whether the proposed roadmap is actionable enough to move the field forward. My overall impression is that the manuscript largely synthesizes well-known challenges and opportunities without putting forward substantially new ideas or demonstrating that the proposed workflow would be effective in practice. The arguments would benefit from more specificity, more critical assessment of limitations, and clearer articulation of what this perspective adds beyond what the community already understands.
Major Comments
- Table 1: The purpose of Table 1 is unclear. Many widely used models such as VIC, SUMMA, and WRF-Hydro, which are mentioned in the text, are absent. In its current form, the table reads as an ad hoc selection of models without a clear rationale for inclusion or exclusion. I would suggest organizing models by category (e.g., lumped rainfall-runoff models, distributed watershed models, hydrodynamic models, integrated surface-subsurface models) and clearly stating the selection criteria. If the intent is simply to provide illustrative examples, the authors should state this explicitly and trim the table accordingly.
- Section 4.3 Step 3. Select an Appropriate ML Technique: This is proposed as a step in the road map. In practice, this is challenging and often cannot be resolved a priori. The best-performing ML approach varies depending on the use case, data availability, catchment characteristics, and the nature of the modeling deficiency being addressed. There is rarely a single "correct" technique, and the selection process typically requires iterative experimentation and benchmarking. As written, Step 3 risks giving practitioners an unrealistically linear impression of a complex and iterative process.
- Section 4: It is unclear who is the intended audience of the proposed road map. The framework is presented at a high level that may be too general for any specific audience (academia, agencies, or industry to find directly actionable. Each of these communities faces different constraints. For example, agencies may lack ML expertise and computational infrastructure; academics may lack access to operational systems and institutional buy-in. The manuscript would be strengthened by explicitly addressing the skills, resources, and institutional conditions required to implement the proposed workflow. I would encourage the authors to ground the framework with more concrete guidance, perhaps including rough resource estimates, example timelines, or case studies where similar integration has been attempted.
- As an Opinions paper, this manuscript is expected to offer a clear, forward-looking perspective that advances the community's thinking. While the framing around “retain, revamp, reconcile, or replace” is effective, the individual ideas presented (using ML for data gap-filling, calibration, surrogate modeling, etc.) are already well-established in the literature. I would encourage the authors to more clearly articulate what is new in their perspective.
Minor comments
- Table 1 lists “select municipal water treatment plants” as the typical users of MIKE SHE, which could be misleading. MIKE SHE is a fully integrated, physically-based hydrologic model whose user base is considerably broader than municipal water treatment.
- Lines 171–178 (Section 3.2, solver enhancement): The body of ML for accelerating ODE/PDE solutions is substantively different from the data-driven hydrologic modeling discussed elsewhere in the paper and falls more naturally under the umbrella of physics-informed methods, and the authors should use this term explicitly. This would help the reader connect this discussion to a rich and rapidly growing literature (e.g., physics-informed neural networks or PINN, neural operators)
- General proofreading: The manuscript would benefit from a careful proof reading. For example, missing period after outputs in line 141. The sentence starting line 140 largely repeats the point already made two lines above.
Citation: https://doi.org/10.5194/egusphere-2026-583-RC1
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- 1
This manuscript presents a timely and well-articulated opinion on the evolving role of applied hydrologic models in the era of ML. The paper is well structured, written in a clear and accessible style, and supported by illustrative examples spanning forecasting, planning, and decision-support contexts. The proposed roadmap for ML integration is particularly valuable, as it frames adoption not only as a technical evolution but also as an institutional and cultural transition. Overall, the manuscript makes a meaningful contribution to an important and ongoing discussion. However, several issues should be addressed to further strengthen the clarity, rigor, and practical impact of the paper.
Comments
1- The manuscript would benefit from a more explicit decision framework clarifying when users should retain, revamp, reconcile, or replace existing modeling approaches. While the conceptual distinctions are helpful, readers will seek clearer decision criteria or guiding principles. Incorporating a structured comparison (e.g., a decision matrix based on data availability, interpretability needs, regulatory constraints, computational cost, and operational risk) would substantially improve the manuscript’s applicability.
2- The paper surveys a wide range of models, tasks, and ML integration opportunities. Although informative, the breadth risks diluting the central message. The manuscript would be strengthened by prioritizing or highlighting the most impactful and realistic integration pathways (e.g., calibration acceleration, surrogate modeling, bias correction, forcing-data improvement). This would enhance focus and provide clearer guidance for applied users.
3- Sections discussing LLMs)introduce interesting perspectives but would benefit from clearer boundaries regarding current capabilities versus future potential. Framing LLM-related discussions explicitly as emerging prospects would improve precision and avoid overgeneralization. I do recommend to consider papers such as “Can large language models effectively reason about adverse weather conditions?”, which reflects an active and relevant research frontier. Additionally, other emerging computational paradigms could be briefly acknowledged to broaden the forward-looking perspective. For example, quantum computing is increasingly discussed in environmental modeling contexts. The authors may consider citing recent developments such as "HydroQuantum: A new quantum-driven Python package for hydrological simulation" as an example of exploratory directions that, while still nascent, may influence future modeling workflows.